From a02c78435c1f9a892513816cf3ad75d2f553d97f Mon Sep 17 00:00:00 2001 From: Dirk Roorda Date: Fri, 13 Jan 2017 14:10:25 +0100 Subject: [PATCH] code and docs update --- .../kings_ii-checkpoint.ipynb | 396 +- .../kings_ii_TF-checkpoint.ipynb | 196 +- .../parallels-checkpoint.ipynb | 6778 +- .../docs/tools/parallel/Isaiah-mt-1QIsaa.html | 4738 +- .../tools/parallel/Isaiah-mt-1QIsaa_37.html | 154 +- .../tools/parallel/Isaiah-mt-1QIsaa_38.html | 81 +- .../tools/parallel/Isaiah-mt-1QIsaa_39.html | 36 +- .../docs/tools/parallel/images/tf-small.png | Bin 0 -> 79182 bytes .../docs/tools/parallel/images/tf-xsmall.png | Bin 0 -> 22782 bytes static/docs/tools/parallel/kings_ii.html | 27909 +- static/docs/tools/parallel/kings_ii.ipynb | 397 +- ...{kings_ii_TF.html => kings_ii_legacy.html} | 186 +- ...ings_ii_TF.ipynb => kings_ii_legacy.ipynb} | 170 +- .../docs/tools/parallel/kings_parallels.pdf | Bin 25385 -> 23907 bytes .../tools/parallel/kings_parallels_h.html | 2006 +- .../tools/parallel/kings_parallels_p.html | 2006 +- .../tools/parallel/kings_similarities.pdf | Bin 13289 -> 13316 bytes .../tools/parallel/kings_similarities.tsv | 243784 +++++++-------- static/docs/tools/parallel/parallels.html | 19474 +- static/docs/tools/parallel/parallels.ipynb | 6778 +- static/docs/tools/parallel/parallels_TF.html | 19607 -- static/docs/tools/parallel/parallels_TF.ipynb | 7147 - .../docs/tools/parallel/parallels_legacy.html | 6244 + .../tools/parallel/parallels_legacy.ipynb | 5364 + 24 files changed, 180165 insertions(+), 173286 deletions(-) create mode 100644 static/docs/tools/parallel/images/tf-small.png create mode 100644 static/docs/tools/parallel/images/tf-xsmall.png rename static/docs/tools/parallel/{kings_ii_TF.html => kings_ii_legacy.html} (99%) rename static/docs/tools/parallel/{kings_ii_TF.ipynb => kings_ii_legacy.ipynb} (99%) delete mode 100644 static/docs/tools/parallel/parallels_TF.html delete mode 100644 static/docs/tools/parallel/parallels_TF.ipynb create mode 100644 static/docs/tools/parallel/parallels_legacy.html create mode 100644 static/docs/tools/parallel/parallels_legacy.ipynb diff --git a/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii-checkpoint.ipynb b/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii-checkpoint.ipynb index abfe1dcf..2d3f8c88 100644 --- a/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii-checkpoint.ipynb +++ b/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii-checkpoint.ipynb @@ -4,15 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "\n", + "\n", "# Kings and parallels\n", "\n", "# 0. Introduction\n", @@ -66,39 +59,29 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0.00s This is LAF-Fabric 4.5.19\n", - "API reference: http://laf-fabric.readthedocs.org/en/latest/texts/API-reference.html\n", - "Feature doc: https://shebanq.ancient-data.org/static/docs/featuredoc/texts/welcome.html\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "import sys,os, re, pickle\n", + "import sys, os, re, pickle\n", "import collections, difflib\n", "from Levenshtein import ratio\n", "\n", + "# (sudo -H) pip(3) install python-Levenshtein\n", + "# brew install freetype # on mac os x\n", + "# (sudo -H) pip(3) install matplotlib\n", + "\n", "from IPython.display import HTML, display_pretty, display_html\n", "from difflib import SequenceMatcher\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", - "import laf\n", - "from laf.fabric import LafFabric\n", - "from etcbc.preprocess import prepare\n", - "from etcbc.lib import Transcription\n", - "fabric = LafFabric()" + "from tf.fabric import Fabric\n", + "from tf.transcription import Transcription" ] }, { @@ -107,11 +90,16 @@ "source": [ "## 0.3 Data source\n", "\n", - "We use the ETCBC database in its version 4b, as archived at DANS, downloadable via DOI\n", + "We use the ETCBC database in its version 4c, downloadable from the GitHub repo\n", + "[text-fabric-data](https://github.com/ETCBC/text-fabric-data).\n", + "The format of the data obtained through Github is immediately ready to be used by Text-Fabric,\n", + "and hence by this notebook as well.\n", + "\n", + "A previous version of this notebook was based on version 4b,\n", + "as archived at DANS, downloadable via DOI\n", "[10.17026/dans-z6y-skyh](http://dx.doi.org/10.17026/dans-z6y-skyh).\n", - "It is also possible to get this data through Github:\n", - "[etcbc/laf-fabric-data](https://github.com/ETCBC/laf-fabric-data).\n", - "The format of the data obtained through Github is immediately ready to be used by LAF-Fabric, and hence by this notebook as well.\n", + "It is also possible to get this data through Github in TF format:\n", + "[etcbc/text-fabric-data-legacy](https://github.com/ETCBC/text-fabric-data-legacy).\n", "\n", "The transcription of 1QIsaa is in a file produced by the ETCBC. This file is included \n", "[here](https://shebanq.ancient-data.org/shebanq/static/docs/tools/parallel/1QIsaa_an.txt)\n", @@ -128,7 +116,7 @@ "outputs": [], "source": [ "source = 'etcbc'\n", - "version = '4b'\n", + "version = '4c'\n", "QISA_FILE = '1QIsaa_an.txt'" ] }, @@ -140,52 +128,62 @@ "\n", "We only use a few data features from the ETCBC database. You see them in the code below.\n", "Their documentation can be found through the SHEBANQ help function or via this direct link:\n", - "[Feature-doc](https://shebanq.ancient-data.org/shebanq/static/docs/featuredoc/features/comments/0_overview.html).\n", + "[Feature-doc](https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html).\n", "Here is the direct link to\n", - "[otype](https://shebanq.ancient-data.org/shebanq/static/docs/featuredoc/features/comments/otype.html)." + "[otype](https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/otype)." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": { - "collapsed": false, - "scrolled": true + "collapsed": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - " 0.00s LOADING API: please wait ... \n", - " 0.00s USING main DATA COMPILED AT: 2015-11-02T15-08-56\n", - " 0.00s USING annox DATA COMPILED AT: 2016-01-27T19-01-17\n", - " 3.04s LOGFILE=/Users/dirk/laf-fabric-output/etcbc4b/kings/__log__kings.txt\n", - " 3.04s INFO: LOADING PREPARED data: please wait ... \n", - " 3.04s prep prep: G.node_sort\n", - " 3.18s prep prep: G.node_sort_inv\n", - " 3.73s prep prep: L.node_up\n", - " 7.52s prep prep: L.node_down\n", - " 13s prep prep: V.verses\n", - " 13s prep prep: V.books_la\n", - " 13s ETCBC reference: http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html\n", - " 15s INFO: LOADED PREPARED data\n", - " 15s INFO: DATA LOADED FROM SOURCE etcbc4b AND ANNOX lexicon FOR TASK kings AT 2016-03-04T15-50-30\n" + "This is Text-Fabric 2.2.1\n", + "Api reference : https://github.com/ETCBC/text-fabric/wiki/Api\n", + "Tutorial : https://github.com/ETCBC/text-fabric/blob/master/docs/tutorial.ipynb\n", + "Data sources : https://github.com/ETCBC/text-fabric-data\n", + "Data docs : https://etcbc.github.io/text-fabric-data\n", + "Shebanq docs : https://shebanq.ancient-data.org/text\n", + "Slack team : https://shebanq.slack.com/signup\n", + "Questions? Ask shebanq@ancient-data.org for an invite to Slack\n", + "111 features found and 0 ignored\n" ] } ], "source": [ - "API = fabric.load(source+version, 'lexicon', 'kings', {\n", - " \"xmlids\": {\"node\": False, \"edge\": False},\n", - " \"features\": ('''\n", - " otype\n", - " language lex_utf8\n", - " book chapter verse\n", - " ''',''),\n", - " \"prepare\": prepare,\n", - " \"primary\": False,\n", - "}, verbose='NORMAL')\n", - "exec(fabric.localnames.format(var='fabric'))" + "ETCBC = 'hebrew/{}{}'.format(source, version)\n", + "PHONO = 'hebrew/phono'\n", + "TF = Fabric( modules=[ETCBC, PHONO] )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.00s loading features ...\n", + " | 0.00s Feature overview: 104 nodes; 5 edges; 2 configs; 7 computeds\n", + " 0.13s All features loaded/computed - for details use loadLog()\n" + ] + } + ], + "source": [ + "api = TF.load('''\n", + " language lex_utf8 verse\n", + "''')\n", + "api.makeAvailableIn(globals())" ] }, { @@ -208,40 +206,6 @@ "The following alternative languages are available:" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ar: arabic (aka العَرَبِية)\n", - "de: german (aka Deutsch)\n", - "el: greek (aka Ελληνικά)\n", - "en: english (aka English)\n", - "es: spanish (aka Español)\n", - "fr: french (aka François)\n", - "he: hebrew (aka עברית)\n", - "id: indonesian (aka Bahasa Indonesia)\n", - "ko: korean (aka 한국어)\n", - "la: latin (aka Latina)\n", - "nl: dutch (aka Nederlands)\n", - "ru: russian (aka Русский)\n", - "sw: swahili (aka Kiswahili)\n", - "tr: turkish (aka Türkçe)\n", - "zh: chinese (aka 中文)\n" - ] - } - ], - "source": [ - "for (ln, (en_name, own_name)) in sorted(T.langs.items()): \n", - " print('{}: {:<10} (aka {})'.format(ln, en_name, own_name))" - ] - }, { "cell_type": "code", "execution_count": 5, @@ -258,10 +222,11 @@ "REFBOOKS = {'2_Kings'}\n", "REFCHAPTERS = set(range(19,26))\n", "\n", + "TF_OUTPUT = os.path.expanduser('~/tf/text-fabric-output')\n", "# the results of the parallel notebook.\n", - "CROSSREF_APP = 'parallel'\n", + "CROSSREF_APP = 'parallels'\n", "# directory of computed intermediary results of parallel.\n", - "PRECOMP_DIR = '{}/{}{}/{}/{}'.format(API['output_dir'], source, version, CROSSREF_APP, 'stored')\n", + "PRECOMP_DIR = '{}/{}{}/{}/{}'.format(TF_OUTPUT, source, version, CROSSREF_APP, 'stored')\n", "# precomputed list of verse chunks\n", "CHUNK_GREP = '{}/chunks/chunk_{}_{}'.format(PRECOMP_DIR, 'O', 'verse')\n", "# precomputed matrix of similarities based on verse chunks and the SET method\n", @@ -290,15 +255,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "book_node = dict()\n", - "for b in T.book_nodes:\n", - " book_name = T.book_name(b, lang=LANG)\n", + "for b in F.otype.s('book'):\n", + " book_name = T.bookName(b, lang=LANG)\n", " book_node[book_name] = b\n", " if book_name == '2_Kings': \n", " book_node[book_name+'r'] = b\n", @@ -331,22 +296,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4m 22s 276 external crossrefs saved; 22 internal crossrefs skipped; from total 24832 crossrefs\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + " 15s 276 external crossrefs saved; 22 internal crossrefs skipped; from total 24792 crossrefs\n", "2_Kingsr\t19\t1\tIsaiah\t37\t1\t100\n", "2_Kingsr\t19\t2\tIsaiah\t37\t2\t100\n", "2_Kingsr\t19\t3\tIsaiah\t37\t3\t100\n", @@ -365,8 +324,9 @@ "with open(MATRIX_GREP, 'rb') as f: grep_dist = pickle.load(f)\n", "\n", "def get_verse_ref(chunk):\n", - " vn = L.u('verse', chunks[chunk][0])\n", - " return (vn, (T.book_name(L.u('book', vn), lang=LANG), int(F.chapter.v(vn)), int(F.verse.v(vn))))\n", + " sec = T.sectionFromNode(chunks[chunk][0], lang=LANG)\n", + " vn = T.nodeFromSection(sec, lang=LANG)\n", + " return (vn, sec)\n", "\n", "all_verse_nodes = set()\n", "n_internal = 0\n", @@ -398,7 +358,7 @@ " crossrefs.add(((bkx, chx, vsx), (bky, chy, vsy), r))\n", " all_verse_nodes |= {v1, v2}\n", "\n", - "msg('{} external crossrefs saved; {} internal crossrefs skipped; from total {} crossrefs'.format(\n", + "info('{} external crossrefs saved; {} internal crossrefs skipped; from total {} crossrefs'.format(\n", " len(crossrefs), n_internal, len(grep_dist),\n", "))\n", "\n", @@ -425,29 +385,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4m 24s Exporting graph info, assembling sets\n", - " 4m 24s 276 edges, 296 verses, 46 chapters, 10 books\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + " 17s Exporting graph info, assembling sets\n", + " 17s 276 edges, 296 verses, 46 chapters, 10 books\n", "1_Kings 2_Chronicles 2_Kings 2_Kingsr Deuteronomy Exodus Ezekiel Haggai Isaiah Jeremiah\n" ] } ], "source": [ - "msg('Exporting graph info, assembling sets')\n", + "info('Exporting graph info, assembling sets')\n", "ncolfile = open(NCOL_FILE, 'w')\n", "for (x, y, r) in sorted(crossrefs, key=lambda z: (\n", " book_node[z[0][0]], z[0][1], z[0][2], \n", @@ -459,7 +413,7 @@ "all_verses = {(x[0][0]+'r', x[0][1], x[0][2]) for x in crossrefs} | {x[1] for x in crossrefs}\n", "all_chapters = {(x[0], x[1]) for x in all_verses}\n", "all_books = {x[0] for x in all_chapters}\n", - "msg('{} edges, {} verses, {} chapters, {} books'.format(\n", + "info('{} edges, {} verses, {} chapters, {} books'.format(\n", " len(crossrefs), len(all_verses), len(all_chapters), len(all_books),\n", "))\n", "print(' '.join(sorted(all_books)))" @@ -490,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false @@ -498,9 +452,9 @@ "outputs": [ { "data": { - "image/png": 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X3/TMn8HVq1czatQo6tSpQ926dXniiSdITU0FLrUoeemll6hVqxZ16tThnXfe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9yWgCLQbXFtWK1WysrKAEhJSQkaLgDe4qftnXPXm0ajkRolQgghhBDXgKxgEEJ0Gc8nw+2tNeGp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\naGho4Ec/+hH79+/HbreTk5PDtm3bWLp06XCdtgBqamoCkwcyM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UWxc+dONmzYwJEjR4jL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87s/boSJ7iUWmknNTZCo5N0UmkyGPQlwGM2am0TeUrpnmK1ZcgL4FTXZ2dsrkCf1Kt97t0L8D\nQo/i0xMb/H4/bW1tScexWq1JxQf9w+VyXfBKbX19PQATJkwYsLgQiUSIxWLY7fZxv0jzer1GccFu\ntycUF6DvKrnL5Ur4WryBBk+Gw2HjPt3d3XR3dyd9v8ViSdn54HQ6h+zqvNfrpaamhlAohNlspry8\nnIkTJ172cYeToigZPydECCGEEGI0kg4GIUaZSCSC3+9PKD54vV7ja4P5vVIUxdhmoRcf4jsgOjs7\nqampQVEUlixZkjKJAfqKHD6fzzjeeG4v14sL4XAYu93OsmXL0hYSLkUsFhtw8OSlxm7qWy8Gs+A+\nf/48x48fR1VVnE4n8+bNG9LnKIQQQgghModskRBinNOjFeO7HuI/j0QiFzyGqqqcP38ei8XCtGnT\nmDVrVkIHRHwEpT7YcTjb8zNR/+LC0qVL0xZlrgR98GS6AsRg3vd0sZtOpxOr1UpjYyPNzc1AX1dL\neXn5uH7PhRBCCCHGOikwCDFExup+uHA4nLDdIr74EAgE0DSNrq4uent7MZvNTJs2LWnbgx676XQ6\nMZvN5OTkMGnSJLKyssZloUGfVREKhbDb7SxZsoSsrKwr9vMu5dyMRqNGp0O62M10YrEY7e3tRKNR\nrFYr06ZNo6SkJGH2w3jfGiM+MVb/dorRT85Nkank3BSZTGYwCCEGZLPZsNlsTJgwIek2VVWN9IPc\n3FwKCwuxWq1GESIWixn383g8dHR0oKpq0t5+ffBg/+QLt9uN3W4ftuc6HHw+n1FcsNlsV7y4cKks\nFkvamR+qqhqxm/0LEN3d3bS0tBCJRDCZTOTm5hKNRjl+/HjCMex2u7HVYrTHbgohhBBCiEsnHQxC\nCMPBgwfp6uoiLy+PxYsXJ9wWDAaNlIuenh66uroIh8NGa/5gWCyWhGGT/Wc/jKYr4X6/nz179hjF\nhaVLl2ZkceFSdXR0cOzYMcLhcELUZnwBQh84OhCz2Zww6yFTYzeFEEIIIUQi2SIhRBorVqygqqoK\nq9WKpmkUFRVRU1NDVVUVTzzxBNXV1VgsFpatWMa3kZEEswAAIABJREFUvv8tpk6ZSiGFZJN4xfep\np57imWeeweFwoGkaiqJw6NAhSkpKRuaJDaG2tjY+/vhjgAGvxKca7Ki34PeP3dQ/Lid2U//carUO\n6fO9HPHFBavVyrJly8ZMcUHTNE6fPk1TUxMAeXl5lJeXp3z9hyJ2Uy866P99ObGb4XCY+++/n127\ndtHV1UVZWRlbtmxh9erVNDY2UlpaSlZWlvG7+81vfpONGzdiNpsxm80pjxmJRFi0aBE+n894TYQQ\nQgghxgPZIiFEGoqi8PLLL3PPPfckfL2rq4uvf/3rLLx+IScsJ3hxw4s8fM/DfPnRL7NoxSIKKGAR\ni7DyyeJq3bp1vPrqq8P9FK6oWCxmtL0XFRUNuFgOh8NompYw7HGgFvyBYje9Xq8RvziY2M2srKyU\n2y+cTuewJVj4/X727t1rFBeGu3PhSu7VjMViHDt2jI6ODgBj3kK6LoOhiN0MBoNpO2AuNnYzGo0y\nc+ZMdu/ezYwZM9i5cydr167lyJEjQN/fgc7OTuNn698TjUYxmUwpj7l161YKCws5efLkIF5BIXuJ\nRaaSc1NkKjk3xVgjBQYxbqTqmFm9ejWttLKf/Sgo3PTATTy64lHj9jba2M9+lrFsOB/qsGtqajIW\nzAN1Y6iqSjgcxmKxDPrqst6Z4HQ6yc/PT7o9Eong8/mM7RfxRQi/32+8b5FIhK6uLrq6ulL+jIG2\nXgxV94NeXAgGg0ZxIVVRZTQKBALU1NTg9/sxmUyUlZVRWFh4WcfUZ37k5uYm3abHbvr9/qQuCD12\nMxqN4vF48Hg8Sd+fLnbz4Ycfxul0AvCFL3yB0tJSqqurueaaa4wtHqm6FfRujPjCWUNDA7/4xS/Y\ntm0bX/va1y7rtRBCCCGEGA+kwCDGjccee4yNGzdSXl7O5s2bWb58OQDH+WRg3eH3D1M8v5hFKxYB\n8N6O93jzuTfZc2APk5gEwG9+8xvy8/OZOnUqGzZsYP369cP/ZIZQIBAwWr/LysoGLBzoV5qHclij\n1WolLy+PvLy8pNtUVSUQCKRNvtDjFzVNw+v14vV6U/4Mu92esvigp2EMRiAQYN++fUZxYcmSJSNS\nXLgSVzm6urqora0lFoths9mYN2/eFX9uZrPZeA/602M34wsO/WM34+dBpGK1WvH7/dTV1ZGTk8P5\n8+dRFIV58+ahKAorV67kmWeeYdKkvt/rN954g23btvHRRx8ZvwPf+MY3ePbZZ3E4HFfuhRhj5Cqc\nyFRybopMJeemGGtkBoMYF/bu3UtlZSU2m40dO3bwwAMPcPDgQfJL8/mADwBoONTAoysfZdO/b8KS\nb0nYG15iLWGZaxknTpwgLy+PwsJCPvzwQ2655Ra+973vceutt47wM7x0hw8fpqOjg5ycHK6++uq0\nWw30OQs2my1j0iDC4TBerxe/3582dvNC4he68dsv9O0YZrOZQCDA3r17CQQCRnEhJydnGJ7hlXfm\nzBkaGxvRNI2cnBwqKioyPvlBPxdTFR/02M1YLMbGjRspKiri7/7u7wgGg3R1dVFeXk5vby//+I//\nSDAY5Le//W3CsU0mEw6Hg//4j//gn//5n9m5cyfvv/8+d911l8xgEEIIIcS4IkMehRikNWvWcOON\nN7Juwzr2speW4y18a8W3uHfrvSxevZidP9tJ8dXFxv2dXieTz0zG4XCQk5NjfLz22mvU1dXx2muv\nGQMPR5OOjg4OHz4MwLXXXpv2qrU+2BHA7XaPiuepqmpC50P/Dgg9dvNCLBYLzc3NxlDLpUuXMmXK\nlBGL3RyqvZr63A193sWUKVOYNWvWqE910Lc63HnnnfT29vLyyy8TDocJBoNYLBZj8GRHRwfXX389\nra2tCV0Uf/4fUhYvXsw777xDWVkZ7733HnfffbcUGAZB9hKLTCXnpshUcm6KTCZDHoUYJH0R4cBB\na2Mrm/5qE3d8+w5W3r6Srs4uY4J9NBoFwBzt27OtD6Q7f/480LdHu6WlhZ///OeYzWays7MTChA5\nOTnG8MOLnYh/pamqSn19PdA3zG+glnh9sONwDlO8XCaTKe3gSSBh8GT/4oO+FSQSiVBfX084HDbe\n35qaGmpqaoBPYjf1bofRErsZDAapra3F6/WiKAplZWVMmTJlpB/WkDCZTDzwwAP09vby9ttvG90Y\n+paKWCxGOBzGarWiKEpS0oXJZKK2tpbGxkY++9nPomka4XCYnp4epk2bxocffsjMmTNH4qkJIYQQ\nQmQ86WAQY15PTw9VVVUsX74ci8XCa6+9xvr16zlw4AAOh4NPLf8Ua+5fw5ce/lLS90YjUYLBILM7\nZqN1afz2t7+ltLQUVVU5fPgw//RP/8R/+2//jU996lMXfBwulyup+KAXIAY7B2AoNTY20tDQ0BfP\nuWxZ2rZ4vRNAn+g/HkSjUbq6uvjTn/5Ed3c30WiUGTNmAFx07Gb8vIdMiN3s6emhtraWSCSC1Wql\noqIi5RDG0Wr9+vUcOnSIXbt2JaRb7NmzB5fLRWlpKZ2dnTz88MO0t7ezc+fOhO+32WyYTCba29uN\nr33wwQc8+OCD7N+/n/z8/FFTZBNCCCGEuByyRUKIFNrb27nhhhuoq6vDbDZTUVHB5s2bWbVqFU8/\n/TRPPfUUDrcDDQ00QIFf9v4SgD/84g/88tlfUn+470r/7bffzrvvvks4HKaoqIivfvWr3HrrrfT2\n9uLxeOjt7TU+9O6HwbBarSm7H3JycsjKyhryK+HBYJA9e/agqipz585l2rRpae8bCASIRqO43e6M\nvSI/1EKhEHv27MHv92M2m1myZIkxhFKP3Yyf95AqdvNC9NjNVB0QV6pTpKWlhYaGBjRNIysri4qK\nijE1wLCpqYmSkhIcDoeRFKEoCq+88gqKorBp0yba2trIzs5m1apVbN68mcmTJwPw+uuv88ILL3D4\n8OGk115mMAghhBBiPJICgxCXqIMOjnIUH31zBg69d4jFKxZTRBEVVGDi4hfWgUAgoeAQX4Dw+/2D\nPo6iKGRlZaXdfnEpcwA+/vhj2trayMrK4tprrx1Vgx2vtFAoxN69e/H5fEnFhcHQYzdTbb2Ij90c\nSHzsZv8OiH379vG5z33uop6TqqqcOHGC1tZWACZPnkxZWVnKuMaxTlVVQqFQ0vtgNpux2WzSnXCZ\nZC+xyFRybopMJeemyGQyg0GISzSJSXyWz9JBBz58ePCwghXYuPRp+k6nE6fTSWFhYdJt0Wg0qeMh\n/t/xLfiapuHxePB4PLS0tCQdy263p+1+SDWQsauryxjsN2fOnLQLKj0qUFGUjE8VGCqhUIh9+/YZ\nxYVrr732oooLMPjYTT394kKxm3pRQHfkyBECgUDK4kOq2M1wOExNTQ0ejwdFUSgpKWH69OkX+cqM\nHSaTCafTSSwWQ1VVFEXBbDZLYUEIIYQQYghIB4MQGUZPbIgvOMR/rg8gHIz4QYd6x8Pp06dRFIXi\n4mIWLFiQ9nvD4TChUAiHwzFi8wKGk15c8Hq9RnFhwoQJw/oY9NhNvdshvvgw2K6X+NhNRVFobW3F\nZDKRlZXFokWLyM/Pv8LPQgghhBBCjAWyRUKIcSAcDqftfvB4PAO24OuFC5PJxKRJk9JuvcjKykJV\nVSwWS8KgvLEqHA6zd+9eo7hwzTXXMHHixJF+WAlisRh+v3/QsZu9vb1G2onNZmPKlCnYbDZcLldS\n4oX+MV62wQghhBBCiAuTAoMQQ2S07odTVRWv15uyANHV1UVLSwuappGdnY3b7U57nHA4jKIo5Ofn\nk5ubm3L2w1jZv9+/uHD11VczadKkkX5YaaU7N/XYTY/HQ319PWfOnCEYDGKxWMjLyxvUgE6r1Zq0\n5UL/yOTYTZE5RuvfTjH2ybkpMpWcmyKTyQwGIcY5k8lkFAH677OvqanhzJkzmEwmiouLE7oeent7\n8Xq9AMbedLPZTE9PDz09PSl/ltvtNooN/QsQoyWZIBwOG9siTCZTxhcXBqInJ7S0tOB0OpkzZw4z\nZ85kxowZRvdDquSL+NjNSCRCd3c33d3dScdXFMXofIgfQOlyuUY0dlMIIYQQQmQO6WAQYhzo7u7m\nwIEDACxevDjlAMJYLGa01ff29hKJRIzhkpcSu5mu+HAlYjcvRSQSYd++ffT29mIymbjmmmtGbXEB\nwOv1UlNTQygUwmw2M3fu3EE9n6GK3bTZbAkFh/6DJ2WIohBCCCHE6CJbJIQQSTRNM5IRJk+eTGVl\nZdr7DjTY0e/3Jw2c1D+/lNjNdAWI4Uis6F9cuPrqq0f18MO2tjbq6+tRVRWn08m8efOGbHZGfOxm\n/yLExcRupkq80AsSFos00wkhhBBCZBopMAiRxooVK6iqqsJqtaJpGkVFRdTU1FBVVcUTTzxBdXU1\nFouFJSuW8Mj3H6GptombVtzEJFJfAY5EIixatAifz0dTU9MwP5uL09zcTH19PSaTif/yX/5L2kF+\nqqoa8YwXuziNRqNJxYf4f8fHbl6I3W5PW3xIFbt5seKLC4qicPXVV1NQUHBZxxxO8Xs1NU2jsbGR\nM2fOADBhwgTKy8uHbcGuqip+vz/t9gs9dvNCHA5H2uJD/9jNgYTDYe6//3527dpFV1cXZWVlbNmy\nhdWrV9PY2EhpaSlZWVlomoaiKDzyyCNs3LgRs9mcFFX54osvsn37dtrb28nOzubWW2/lu9/9bkZ0\n32Qy2UssMpWcmyJTybkpMpnMYBAiDUVRePnll7nnnnsSvt7V1cXXv/51yq8v55TlFNs3bOfRex7l\ny49+mb3sJZdcruEa7CQuyrdu3UphYSEnT54czqdx0cLhsPEYS0pKBkwJ0FvhLyVJwGKxMHHixJTJ\nC3rsZroCRP/YzVAoRFtbG21tbUnH0mM3UxUgsrOzLzgHIBKJUF1dPWqLC/Gi0Sh1dXV0dXUBUFRU\nRHFx8bBuRdDjL7Oyspg8eXLS7aFQKGnLhZ6EEd/1EgwGCQaDdHR0JB1Dj91Mtf3C5XIlDBuNRqPM\nnDmT3bt3M2PGDHbu3MnatWs5cuQI0Pd3oKOjI6HwEYvFiMViKIqC3W43Cgg333wz//2//3cmTJhA\nd3c3t9xyCz/4wQ946KGHhuz1E0IIIYQYa6SDQYwLK1eu5K677uKrX/1q0m3NNHOYwwAc33+cR1c8\nyls9bxm355DDp/k0Cn0Lt4aGBm688Ua2bdvG1772tYzuYKirq+Ps2bM4nU6WLl2a9uqrPgTQarUO\n+4DGcDic1PGgf3i93kG14OucTmdCwSG+AGG1Wqmurqanp2fUFxf8fj81NTUEAgFMJhNz584ddVs8\n+sdu9u+A6B+7mU7/wZPxHRA2m42rrrqKJ598kmuuuYbS0lJ6enrSJqAoioLD4Ugq0nR0dLBu3TrK\ny8v54Q9/eNnPXQghhBBiNJAOBiEG8Nhjj7Fx40bKy8vZvHkzy5cvB+Akn3QhHH7/MMXzi41/v7fj\nPd587k3+34H/x2T6rtB+4xvf4Nlnn834pITe3l7Onj0LwJw5c9IWF/Qhf/oV3OFms9nIz89PuUDW\nYzdTFR/0QZTxAoEAgUCA1tbWhK/HYjGam5vRNA23281VV11Fa2srgUBg1MVudnR0cOzYMWKxGHa7\nnXnz5pGVlTXSD+uimc1msrOzyc7OTnl7IBBIu/UivutF36KRquPF5/NRW1trdHsoikJFRQWKorBy\n5Uq2bNliDMJ844032LZtG9XV1UYnzI4dO1i/fj0ej4eCggK2bdt2BV4JIYQQQoixQzoYxLiwd+9e\nKisrsdls7NixgwceeICDBw8yqXQSf+JPADQcauDRlY/y+FuP03qulYXXLcRms2Gz2ZhpnckS2xJ+\n/etf88///M/s3LmT999/n7vuuisjOxg0TeOjjz7C4/GQn5/PggUL0t53oMGOmS4YDKbtfvD5fEBf\ncaGpqQm/34+iKBQVFZGTk5N0LD12M1X3QyYUkzRN4/Tp0/zqV7/i6quvJjc3l4qKilH3ng2FaDSa\nMvFCL0homkYsFuOZZ55h6tSpfO1rXyMcDuP3+5kzZw49PT1s27aNUCjErl27Eo5tMpmS3u8TJ07w\n6quvsmHDhpRbQcQnZC+xyFRybopMJeemyGTSwSBEGkuXLjU+v/vuu9mxYwdvv/026zasA6DleAtP\n3PAE922/j9KrSzn55smEK6JtgTZOnznNQw89xPe+9z0OHjzI6dOnicVidHd343K5hiX9YLDOnTuH\nx+NBURTKysrS3k9VVcLhMCaTaVRO8nc4HDgcjpSLvlgsRldXFx988AEmk4lAIMCUKVMwm814PJ6k\n2E19oap3fcTTYzdTFSCGI3YzFotx7NgxY0bBtGnTKCkpGbcDBy0WC7m5ueTm5ibdpmkafr+fO+64\ng/z8fLZu3UogECAYDGKz2YjFYkyYMIH/8T/+B3/913+Nz+fD7XYnfH9/ZWVlVFZWct999/HWW28l\n3S6EEEIIIfqMvhWFEEPgz9U4nDhpbWxl019t4o5v38HK21cS8AdYtnoZ4XDY+LBFbbS0tNDa2sr6\n9evRNM24ilpWVsZ3v/tdpk2bZuwHd7lcxofb7cbhcAzbYjASiRiDHYuLiwecwh8Oh/teB6dzWIcD\nDpeTJ0/icDgoLS1l0aJFTJkyxbhNj91M1f0QCAQSjhOJROjo6Eg5hDA+djNV98PlFp4CgQA1NTVG\nB8batWspLCy8rGOOZYqi8OCDD+L1ennnnXeM11/TNAKBALFYjGAwSHNzM4qiJCWcpPs9jf+9EunJ\nVTiRqeTcFJlKzk0x1sgWCTHm9fT0UFVVxfLly7FYLLz22musX7+eAwcO4HA4+NTyT7Hm/jV86eEv\npT6ABtcErgEvnDlzBr/fTyAQoKqqihdffJEXX3yRnJycARfoJpMJp9NpFBycTqdRiHC73UPaPVBf\nX09zczMOh4OlS5emnS0wkoMdr7RYLMZHH31EZ2cniqIkFRcuRI/dTFWA8Hg8lxS7maoAcaHYza6u\nLurq6ohGo9hsNubNm5d2ZoHos379eg4dOsSuXbsS4lb37NmDy+WitLSUzs5OHn74Ydrb29m5c2fC\n99vtdsxmMz/5yU/44he/SEFBAUePHmXt2rWsWbOG7373u8P9lIQQQgghRsSlbJGQAoMY89rb27nh\nhhuoq6vDbDZTUVHB5s2bWbVqFU8//TRPPfUUDrcDDQ00QIEnf/0ki1Ys4g+/+AP/8ex/cOzwsaTj\n6jMYjh8/TiAQMKL34v87GAwOKgXBZrMlFBziCxCpptqn4/V62bdvHwALFixImyygX81VVRWXyzWm\nWu37FxcWLlzI1KlTh+z48bGbqbofQqHQoI8VH7vZvwDh8Xg4c+YMmqaRnZ3NvHnzsNlssldzAE1N\nTZSUlOBwOIzCmqIovPLKKyiKwqZNm2hrayM7O5tVq1axefNmY3vN66+/zgsvvGBEWn71q1/l7bff\nxufzUVBQwNq1a3n66aczaitUJpLzU2QqOTdFppJzU2QyKTAIcYl66aWGGrroAuDQe4dYsmIJxRRT\nRvoZBheiqqrR8ZCqADGYKD6TyZR264XL5UroUNi/fz89PT1MnDiRRYsWpT1mJBIhGAxit9vH1IIp\nFouxf/9+Ojo6rkhxYTDiYzf7FyAGE7upFzBCoRBWq5VJkyZRXFxMbm4u2dnZHDlyhNWrVydcnReD\np88d6d+FYrFYsFqtY3Kr0HCS/6MsMpWcmyJTybkpMpkUGIS4TN4//8eChQlMwMyVjS4MhUL4fL6U\nBYj4KL6BOBwOXC4XkUiE1tZW7HY7S5cuJT8/P2XspL6AVRQFl8s1ZhZU8cUFgIULFzJt2rQRflSJ\n4mM3UxUggsEgHo/HKDzp8ztSsVgsabsfRlPs5khRVRVVVVEUBZPJNGZ+D4QQQgghhooUGIQYQ/QZ\nCfpH/wJE/BVYVVU5c+YM0WiUvLw8Jk6cCIDZbE7YeqF3POhXxsdKxGEsFuPAgQO0t7cDfdtDpk+f\nPsKP6uL09PRw+PBhPB4PkUiEiRMnEo1GjQKEHrs5WFlZWQkFiPjPx9rMDSGEEEIIMfSkwCDEEMn0\ndjVN0wgGg0bxob6+njNnzqCqKoWFhUkRjDq9PdxsNmOz2XA4HGlnP4yWrROxWIyDBw8asaLz58+n\nqKhohB/VxTl79iwnT55E0zSysrKoqKhIKgLEYjE8Hg//+Z//yfz585O6Hwaz3UZns9nSdj8MR+ym\nGLsy/W+nGL/k3BSZSs5NkckupcAgMZVCjEKKouB0OnE6ncZAu5kzZ1JZWcnkyZOJRCIpZz90dXUR\njUaxWCzGoMf+kYw6q9Wa0PkQX4AYztjNgYz24oKqqpw8eZJz584BUFBQwOzZs1NubzCbzeTl5VFY\nWMiCBQsSbtPfy/itF/EFiP7vcTgcHjB2Mzs7O20BYrQUnoQQQgghxPCTDgYhRrkDBw7Q3d1NXl4e\nixcvTns/fbCj1WpFVdW0sx8ikcgFf2b/2M3+BYihjN1MR1VVDhw4MGqLC+FwmJqaGjweD4qiUFJS\ncsW2dUQikaS0C/3fFxu76XA4krZc6P++UOymEEIIIYQYPWSLhBDjTFtbGx9//DGKorBkyRLcbnfK\n+13MYMdwOGwUHPoXINJ1O/TXP3YzPv3iYmI301FVlYMHD3L+/HkAKisrmTFjxmUdczh5PB5qamqM\n7SoVFRVMmDBhRB5L/9jN/gWIi43dTFd8yMnJGZbCkxBCCCGEGBpSYBBiiIyG/XCxWIw9e/YQCoUo\nKipi9uzZae8bCoUIh8M4nc7LWuTpsZvpChCXErvZvwBxofQDVVU5dOgQra2tAMybN4+ZM2de8nMa\nbq2trZw4cQJVVXG5XMybNw+n0zno7x/uczMUCiV1P+gFiMHEbsZzuVxpCxASuzk2jIa/nWJ8knNT\nZCo5N0UmkxkMQqSxYsUKqqqqsFqtaJpGUVERNTU1VFVV8cQTT1BdXY3ZYubaFdfyd9//O05zmrnM\nZQpTMPHJrIEXX3yR7du3097eTnZ2Nrfeeivf/e53R2QeQVNTE6FQCKvVSklJSdr76YMdLRbLZV9B\nNplMZGVlkZWVlfJ2PXYzVfqFHrupRzV6vd6Ux9BjN1MVIKxW66gtLqiqyqlTp2hpaQFg0qRJzJ07\nN+PjJO12O3a7nfz8/KTbUsVuxnc/9N9uo58T+syJeBaLJWXihT4PYjCvUzgc5v7772fXrl10dXVR\nVlbGli1bWL16NY2NjZSWlpKVlYWmaSiKwiOPPMLGjRsxmUxYLJaEzprnn3+en/70pzQ2NlJQUMB9\n993HN7/5zUt4BYUQQgghxg/pYBDjwsqVK7n77ru55557Er7+29/+Fp/PR8n1JTRbmnlpw0t0tnTy\nnXe+A4ALF0tYgou+q6sNDQ3k5eUxYcIEuru7ueWWW7jpppt46KGHhvX5BAIB9uzZg6ZpVFRUMGXK\nlLT39fv9xGIx3G73iA5m1GM30xUgLjQHQNM0zp49SzAYxG63M2fOHMrKyozZDy6XKyMGT6YSiUSo\nra2lp6cHgJkzZzJjxowxP68gGAwmFR/0AsSlxG6m637QEzf8fj/PP/8899xzDzNmzGDnzp3cdttt\nHDlyBE3TmDVrFoFAIG3KSvzw0ueff56//Mu/ZNGiRRw/fpzPf/7zbN26lbVr117eiyKEEEIIMUpI\nB4MQA0hV0Fq9ejWNNFJDDTZs3PTATTy64lHjdj9+9rGP/8p/xYSJ0tJS47ZYLIbJZOL48ePD8vjj\n1dfXo2kaubm5AxYXIpEIsVgMm8024otvs9lsXI3uLz52M9XWi1AoREtLi7FAz83Nxe/3c/jwYeMY\niqIkxW7GD58cqfQDn89HTU0NwWAQs9nM3LlzmTRp0og8luHmcDhwOBxMnjw56bZoNJqy+0EvQPTf\nbjNQ14vNZjMKDtdffz1er5fm5mauu+46SktLqa6u5pprrkHTNEKhUNpuiGAwiNPpRFGUhG6FuXPn\ncvPNN/PBBx9IgUEIIYQQYgBSYBDjxmOPPcbGjRspLy9n8+bNLF++HA2NU5wy7nP4/cMUzy/m0HuH\nWLRiEe/teI83n3uTPx74I1OZCsCOHTtYv349Ho+HgoICtm3bNqzPo6Ojg87OTgDmzJmT9n76Yspk\nMmV8tGB87Gb/xbemaezfvx9VVZkwYQJTp05lwoQJRgEiEAigquolxW7qWy/0ReVQa29v59ixY6iq\nisPhoLKy8rJnDYyVvZoWi4W8vDzy8vKSbtM0Db/fn3b2Q6rYzfb2dtrb2xO+3tvbS01NDadOnaKn\npwdFUZg7dy4mk4nrrruOZ5991ih+vPHGG2zbto3q6mqsVmvSY9q9ezfr168fwldgbBor56cYe+Tc\nFJlKzk0x1kiBQYwLW7dupbKyEpvNxo4dO7jppps4ePAgE0onEKBvsdJwqIEd39nBxjc2cvToUbQ8\njalXT+XRXz1K9ZlqlliWkJOTw2233cZtt93GiRMnePXVVyksLBy256GqKvX19QBMmzYt7SwE6Ft0\naZo2JKkNI0XTNA4fPkxbWxtOp5OrrroqoYsE+l6TYDCYlHbRP3YzEonQ09NjdEHESxe7qRcgLnZ2\nhaZpNDU1cfr0aQDy8vIoLy9PuXAVyRRFwe1243a7U3bopIrd1D+8Xi+qqhKLxfiXf/kXPvOZz+B2\nu/H7/Xz/+99n1qxZ9Pb28tJLL/HlL3+Z999/H4C1a9eydu1aYrFY0vv07W9/G03TkrZYCSGEEEKI\nRDKDQYxLa9as4cYbb2TdhnXsZS8tx1v41opvce/We1n0+UUcPXo04f5Or5PJZ/qudFosFmMf+J49\ne/i///f/8pOf/MTYD34lh/Y1NjbS0NCA1Wpl2bJlaResqqri8/mwWCwXlVCQSTRN48iRI8ZQxDlz\n5jBr1qyLPk587Gb/2Q+Djd202+1GsaF/AcJutycUcKLRKHV1dXR1dQEwffp0SkpKRm2RZ7TRNA2P\nx8Ndd91FT08Pzz77LD6fD5/Ph9vtNuYvdHU5JeOIAAAgAElEQVR1cccdd9Da2poQ76p30+h++MMf\n8r3vfY8//vGPTJ06ddifjxBCCCHESJEZDEIM0p9/WXDj5nzjeTb91Sbu+PYdrLx9JZ5eD1OmTCEQ\nCBAMBvuSGkKfLOSj0ShdXV10dXXR0NBAXV0dv/3tb43b3W530hA6/XN9GN2lCAaDNDY2AlBaWjrg\n1fBQKAT0LYxHI03T+Pjjjy+7uAB9+/NtNlvKVvxYLEYgEEhbgNDnAIRCIUKhkFE0iBcfu2kymYyE\nBKfTSWVlpSxKh5miKDz00EP4fD7effddY3uQvoUmEokQCoVoampCUZSk4aLxs0r+5V/+ha1bt7J7\n9255H4UQQgghBkEKDGLM6+npoaqqiuXLl2OxWHjttdfYvXs327dvp6O5g8c/9zhffPCLrPnaGgCy\nc7Jp+KiBRSsWAaCpGot7FxPpjfBv//ZvXHvttVgsFo4ePcp//ud/Mn/+/ISfp18tPXv2bNJjsdls\nKafg5+TkkJWVNeAgxuPHj6OqKtnZ2QMudqLRKNFoNCMGO14KTdM4evQozc3NAMyePfuSiwsXYjab\nB4zd1AdPptp60T92s7W1ldbWVlRVxWKxMHXqVPbt25c2dtPtdl/SbAzZqzmw9evXU1tby65duxJe\n37179+JyuSgtLaW3t5fnnnuO6667LmnoqL4d5uc//zmPP/447733HsXFxcP6HEYzOT9FppJzU2Qq\nOTfFWCMFBjHmRSIR/v7v/566ujrMZjMVFRX86le/oqysjKeffpqWhhZ+/uTP+V9P/i/QAAWe/PWT\nAPzhF3/gfz/7v6k7XAd5cOrUKV5++WV8Ph8FBQXcddddbNy4MSGOL35veKphdB0dHXR0dCQ9TkVR\njJSF/gWIWCxmDLCbM2dO2nZ7fbCjoigZP9gxFb24cObMGQDKysooKysbscejpyBMnDgx6bb42M2T\nJ0/S29tLTk4OJpOJvLw8o7gTDAYJBoPGYM54Fosl7dYLp9M5KgtEI6mpqYkf/ehHOBwOYzaKoii8\n8sorKIrCpk2baGtrIzs7m1WrVvGv//qvxve+/vrrvPDCCxw5cgSAJ554gs7OTpYuXYqmaSiKwp13\n3snLL788Is9NCCGEEGI0kBkMQtAXR1lHHW20odLXMu3GTSmlFFF0ycdNNYxO/7fH40lqz05F0zQ6\nOjpQFIX8/HxmzZqV1P3gdrtRFIVQKEQ4HMbpdF70YMJMcPToUWMw4qxZswZMycgEsViM+vp6o/gz\ndepUSktLURQlIXazfwdEOBy+4LH1WQDpChAyMPLSaJpGOBxOiMFUFAWLxSKvqRBCCCFEnEuZwSAF\nBiHihAjhx48ZMznkXNGfpWkaPp8vZQxfb2+vMUfB5/Ph8XgwmUxMmjQp5RBJk8lEVlYWVquVvLw8\nJk+enFCAGA3FhtFWXAgGgxw9ehS/34+iKJSVlaVMPEglEokkbL2IL0D4/X4G87fRarUmJF3EFyBG\nc3LIcNE0DVVVURTF+BBCCCGEEJ+QAoMQQyQT9sOFQiE6Ojr48MMP8fl85OTkYDab8Xg8eL3epEVo\nOBxGVdWUsxdcLlfSwEn9c5fLNZxPK6WamhqampqAvgGWc+fOHeFHNLDu7m5qa2uNWRcVFRXk5AxN\nQUpVVaPQkGr45EcffcTChQsHPIYeu5mqAHEpsZtCDFYm/O0UIhU5N0WmknNTZDJJkRBiDLHb7XR3\ndzNx4kRmzJjBtddeaxQO9MGCetdDZ2cn7e3tBINBY1J+PH1xqiccxLNYLEnFB/3fVzp2ExKLCyUl\nJRlfXGhububUqVNomkZ2djbz5s0b0nkXJpMJt9udEJ0Yz2w2s2TJkpQFCH3mhx5T6vP5Uh5joNjN\ny0k6EUIIIYQQ45t0MAiRobq7uzlw4AAAixcvThmzCH2t3npbvT6LIX7oZP/tF+kWnelkZWWlLUBc\n7mK0rq6OU6dOAX3FhfLy8ss63pWkqirHjx/n/PnzABQWFlJWVpZRgxj12M34okP87If4uQPpmM1m\no/jQvwDhdDqveMFJCAHPPvssDQ0N/OhHPxrphyKEEGIcky0SQowRqqpSXV2Nz+dj8uTJVFZWpr1v\nOBwmFArhcDgGNaQuGo0mdD/0L0AMZhGqs9lsSVsu9M8vFLsZX1woLi6moqJi0D93uIVCIWpqavB6\nvSiKQmlpKdOmTRvph3XR+sduxhcg9NjNC3E4HGlnP4zG5BIhLlc4HOb+++9n165ddHV1UVZWxpYt\nW1i9enXa73n//fe58847jbkzkUiEW2+9lba2Nt5555200blCCCHEcJItEkIMkZHeD3f27Fl8Ph9m\ns3nAmEZVVQmFQpjN5kFPwLdYLOTl5aXsiNC7IfonX+jFh1Sxm+3t7UaKQjw9djNV98PZs2dpbm4G\nYObMmRldXOjp6aG2tpZIJILVaqW8vDxtN8lwuJxz80Kxm/07H+ILEHriiR67mSpqVY/dTFWAkNjN\n8WGk/3aOhGg0ysyZM9m9ezczZsxg586drF27liNHjjBz5sy036cPFg2Hw3zpS18iGAzyu9/9TrYp\nXSHj8dwUo4Ocm2KskQKDEBkmHA5z8uRJoG/bgN1uT3tfPWlioPtcDEVRjP3/qRIRUsVu6h9erzch\ndlPTNOO2eK2trbS3t2Oz2Zg5cyZWqxWv15tQgHC5XBkx1f/cuXOcOHHC2H4yb968Mft//s1ms/H6\n96dpGsFgMGUBwu/3G7Gb0Wg05XsOibGbqQoQEhEpRiuXy8U//MM/GP/+whe+QGlpKdXV1QMWGAAC\ngQA333wzVquVnTt3Gn/Ln3rqKY4fP87PfvYzGhsbKS0t5d/+7d944oknCAQCPPTQQ2zatAnoK/p9\n/etf5ze/+Q1Tp07lK1/5Cj/4wQ+M7ojnnnuO7du309vby/Tp03n55ZdZuXLlFXo1hBBCjHdSYBDj\nwooVK6iqqsJqtaJpGkVFRdTU1FBVVcUTTzxBdXU1ZouZq1dczYbvb2Dyisk00EARRVj5ZOHz/PPP\n89Of/pTGxkYKCgq47777+OY3vzmkj7WhoYFYLIbL5WL69Olp7xeLxYhGo1it1mHbF2+1Wpk4cWLK\nK+D6YMF0BYhwOGwUFwDcbjd2u53jx48nHctkMqXceqH/+0qnIKiqysmTJ42hmPn5+cyZMycj5g+M\nxFUOvTjgdDpT3q7HbqYqQAQCATRNM7pj/H5/yo6XoYjdHKhVXV+kZWVloWkaiqLw8MMPs3HjRsxm\nMxaLJeFnvPfeezz99NN89NFHTJw40Sj6iYHJVbi+Imp9fT3z588f8H7BYJA1a9aQl5fHm2++mVRk\n63/Of/DBB9TX11NbW8uyZcu45ZZbKC8v58knn6SpqYlTp07h9XpZs2aN8b3Hjh3jpZdeorq6msLC\nQpqami5qG9xYIuemyFRyboqxRgoMYlxQFIWXX36Ze+65J+HrXV1d/O3X/5ai64s4bznPSxte4jv3\nfIfvvPMduunmFKe4lmvJ4ZOruj/72c9YtGgRx48f5/Of/zwzZ85k7dq1Q/I4e3t7OXv2LACzZ89O\n21KuX1FWFGXIuhcul8lkMpInUs0n+Pjjjzly5AgzZswgOzub/Px8oxjh8/kSYjdVVaW7u5vu7u6U\nP0uP3UxVgEi3CB6scDhMbW2tcRW+uLiYGTNmXNYxxzqr1Upubi65ublJt8XHbqYqQESjUaCvSJHu\nPR8odtPtdhuFn4Fa1aHv70BbW1vCAktVVVRVJRKJYLfbjWO53W7uvfdebr/9drZs2TLkr5kYm6LR\nKHfeeSdf+cpXLpiI4/F4+PDDD9mxY8cFO3gUReHJJ5/EZrOxaNEirrrqKg4ePEh5eTlvvvkmr7zy\nivE38Bvf+AZPPfUU0NeZFA6HOXLkCJMmTbpgR4UQQghxuaTAIMaNVENFV69ezQlOUE89Nmzc9MBN\nPLriUQ69d4hFKxYRIkQ11VzHdZgxJ3QrzJ07l5tvvpkPPvhgSAoMmqZRX18P9F0xT9UloItEIqiq\nOugruyPtxIkTnDlzhry8PBYsWEBlZWXC41ZVFY/Hk7b7QV+E6gYTu5lu+ORAcwC8Xi9Hjx4lHA5j\nNpupqKhgwoQJQ/dCDIHRtlczPnazoKAg6fZwOJx29oM+eHKwsZtut5tbb70VgM7OTj73uc8ZrerX\nXHMNmqYZ720qoVAIp9OJoigsXbqUpUuX8vvf/36IXonxYbSdn0NJ0zTuvPNO7HY727dvv+D9CwoK\n+MEPfsBdd93FW2+9xec///kB719YWGh87nK58Hq9ALS0tFBUVGTcFl8QLSsr48UXX+TJJ5/k6NGj\nXH/99bzwwgtMnTr1Yp/eqDeez02R2eTcFGONFBjEuPHYY4+xceNGysvL2bx5M8uXL0dFpYkm4z6H\n3z9M8fxiNLWvGPHejvd487k3ef/A+xRRlHTM3bt3s379+iF5fGfPnsXj8WAymZg9e3ba+6mqaiyS\nrvRWgaFw8uRJYxvE9OnTk4oL0LcITXcFHPr2KeuDJvsXH/x+f8J9o9EonZ2ddHZ2pjyWHrvZv/gQ\nDAY5ffo0qqridDqprKy87G4IcWE2mw2bzZaykJMudlP/XO9ECIVChEIhurq6Er6/q6uL2tpafD4f\nBw4cQFEUysvLURSFFStWsGXLFqPo8cYbb7Bt2zaqq6tlHoS4JPfeey/t7e28/fbbg95O9dd//df8\n+Mc/5stf/jK/+tWvLmmRMXXqVM6cOWMMy21qakq4fd26daxbtw6v18vf/u3fsnHjRn76059e9M8R\nQgghBiPzVydCDIGtW7dSWVmJzWZjx44d3HTTTRw8eJC80jxC9A1KbDjUwI7v7ODh//Uw+xv307yz\nmcI5hTz0xkNUnagiYooYi1Gr1cq3v/1tNE1L2nZxKSKRCA0NDUBfqsJAgwTD4TCapmG32zO+e+Hk\nyZNGV8a0adOYP3/+JT1mff9//BU8XTQaNQoP/QsQqWI3vV4vXq+XlpYWoK9g09nZSXd3NxaLhcLC\nQioqKjh8+HBCAcLtdmdECsJ4usphNpvJyspKG9mnx26mK0Bs27aNz33uc+Tm5hIOh/nJT37CnDlz\n6Onp4fnnn2fdunVGh8LatWtZu3YtsVhMCgyXYTydn/HWr19PbW0tu3btuui41nXr1hEOh7n55pt5\n5513+MxnPpN0n4FivdeuXcuzzz7LkiVL8Pl8vPTSS8Ztx44do7m5mb/4i7/AZrPhdDoThvGOJ+P1\n3BSZT85NMdZIgUGMC0uXLjU+v/vuu9mxYwdvv/026zasA6DleAtP3PAE9/3gPvy2vqn4jY2NnD17\nFrfbzcnoSY63HMflcmG329m9eze/+93v2L59OwcPHkxYiF5KAkJDQwORSASHwzHgHtlYLGbEJWbC\nwMGBNDQ0GMWFqVOnsmDBgitSELFYLEyYMCHlFXB9sGC67gefz8e5c+eM+M3s7GwcDgeNjY00NjYm\nHMtkMiV0P/TvgJBF6fBLF7upaRrr1q1jypQp/PjHPyYYDBrvsd7988gjj3DTTTfh8/lwu90J3yvE\nxWhqauJHP/oRDofDKIIqisIrr7zCbbfdNqhj3H333YTDYW688UbefffdpNv7/+2M//c//MM/sH79\nekpLS5k2bRp33HEH//qv/wr0dfds3LiR2tparFYrn/nMZ/jRj350qU9VCCGEuCApMIhxSVEUNE0j\niyzaGtvY9FebuOPbd7DyjpXs/2g//mY/OcU5RCIRenp6CJ4JEumO4HQ6+fjjj/nTn/7Ehg0baGtr\nS2rLNpvNSfv+B0pA8Hg8xtX0OXPmjKrBjumcOnWKY8eOAX3FhYULF45It0V87Gb/Pcc+n49Dhw7R\n2dlJIBCgoKAAs9mcNnZTVdW0EYzQt9hNV3wYythN2at5Yffeey8dHR28/fbbxtVkTdOMIoOmaTQ3\nN6MoStLV3EzoUhnNxuP5OXPmzIvuCli+fHnSVoa/+Zu/4W/+5m8AWLJkifH14uLipE6s//N//o/x\nucvl4tVXXzX+/U//9E/GTIaFCxdSVVV1UY9trBqP56YYHeTcFGONFBjEmNfT00NVVRXLly/HYrHw\n2muvsXv3brZv3057czuPf+5xvvjgF1nztTUAVFZW0tPQQ0F5AefOncPr8WJrsKFYFOrq6ti9ezf3\n3XcfbrfbSHxwOBy4XC5jz/5gExBycnJoampCVVWKioqYNGlS2ucRjUZRVTXjt0acOnWKuro64Mp2\nLlyO9vZ2jh07hqqqFBYWMm/evISr2PDJYMH47Rb9YzfjBYNBgsEg58+fT/p5/YtO8QWI4YjdHE/S\ntarv3bsXl8tFaWkpnZ2dPP7441x33XVkZ2cnfL/eiaIPhAyHw6iqSigUwmQySaeKyDjnzp3j5MmT\nfPrTn+bYsWO88MILfOMb3xjphyWEEGKcUkaqHVRRFE1aUcVwaG9v54YbbqCurs5IBti8eTOrVq3i\n6aef5qmnnsLhdqChgQYo8OOTPyYWi7H/7f28tfktHn34UWPfdm9vr7HI0IfFfeUrXyEYDBIKhbBY\nLMb2hUgkMmDmeE9PD+fOnUNRFEpKSpKKD/oCVE8/MJvNQ3o1fKg1NjZSW1sLwJQpU1i4cGFGXRHW\nNI2mpiZOnz4NQF5eHuXl5Ze0aAyFQknzHuK7Hy5G//c9vhghgyYHr6mpiZKSEhwOh/E7qLeqK4rC\npk2baGtrIzs7m1WrVrF582YmT54MwOuvv84LL7xgRFq+//77rFy5MuF3bfny5QlXjoUYyLPPPsuW\nLVuS/l5/9rOfZefOnUP2c5qamvjCF77AqVOnyMvL47bbbmPLli1SuBRCCHHZ/tz1fVELDykwCAGE\nCHGCE7TQQpQofp8fpUeh0lnJ3Ly5nDlzhv3799Pc3ExXVxd2u51p06ZhtVrJzs7GZrPh8/mS9m/r\ntzscDmw2G5FIxFiEdnV18fHHHxOLxZg0aRL5+flpH58eSzlhwgTy8vJSLkRHettEfHGhsLCQRYsW\nZVRxIRqNcuzYMSNdYvr06RQXF1+RxxiLxfB6vWm7H/rHbg6kf+xm/+6HTHqNRwNN04hEIgnvgaIo\nWK1WWZAJIYQQQsSRAoMQlylGjBAh3v/9+8yeORuz2UxpaSmKohAOh6mrq6O+vp729nY6OjrIzc1l\nypQpRsEhNzcXn89HW1sbPp8v6fgul4v8/HwKCgro6emhpaWFaDTKnDlzjGGE/RMQ4mMpB7rSbrPZ\n0i5Er3QCQlNTEzU1NUBmFhcCgQBHjx4lEAgYMaD6leuRejzpuh/6x272V1dXR3l5OfDJjIl07/tI\nF50ymaZpRkEwk87V0U72EotMJeemyFRybopMdikFBrlcI0QcM2ZcuHCanWRnZ+PxePD5fGRlZWGz\n2Vi4cCEzZszg8OHDdHR00N7ezokTJygoKCAWi9Hc3My0adNYsmQJVquVtrY22traaG9vJxwO4/f7\naWpq4vjx45w6dQqXy8XixYvJycmhuLg4IRlCT0A4d+4cPT09RKPRhKviwWAw4bGHw2Ha29tpb29P\nel5XMgEhvrgwefLkjCsudHV1UVtbSywWw2azUVlZmTb2cLgMNnYzVQEinqZpSbGb8Uay6JTpFEXJ\n2K1GQgghhBCjlXQwCJFGIBDgzJkzOJ1OYyK3TtM0Y0tAKBSis7MTn89HQUEBLpcL6LuSP3v2bCZO\nnIimafT29hoFh4MHD+Lz+XA6ncyYMQPoGwSob5UoKCggJ6cvxSIYDGK325Oy1cPhsLHw7L8g7Z+A\ncCH9ExDiF6IDzXw4ffo0R48eBfqKC1dddVVGLVpPnz5txE3m5ORQUVFx0Rn1mUTTNHw+X8L7Hf95\n/6LTQFIVneLfdxlmKIQQQggxvskWCSGGWGNjI+FwmOLi4pQL03A4zNGjRzl9+jSaptHd3Y2qquTm\n5hr7uSdOnMjs2bONq9Xnz5/nyJEjeL1epk+fbnQl9Ge328nKyiI/P58ZM2Zc1LC/i01AGEiqBISc\nnBx6e3tpamrCbDZTUFDA4sWLM6a4EIvFjK0s0DdwctasWRnz+K6U+KJT//fe4/EkzQgZSKqik16A\nyORBo0IIIYQQYmhIgUGIIaLvh+vu7qatrY28vDwKCgrS3r+zs5PDhw8bhYJAIIDVasVsNhsLsZyc\nHEpLSzl9+jSRSISioiJmz54N9CUStLe3Gx0OwWCQaDRKNBrFZrNhMpnIzs6moKCA/Px88vPzE7ZT\nXKz4BIRU3Q8X0tXVZbTkFxQUsGDBgpTDJ0ciASEYDFJTU4PP50NRFMrKypgyZcqwP44r5VL3aqqq\nitfrTVuAGIqik554IsMSxy/ZSywylZybIlPJuSkymcxgEGKIZWdn09HRQW9vL5MmTUp7BXzixIl8\n9rOfNbZN6Atri8WCzWYzBjj+4Q9/IBAIMHXqVD796U8b32+325k+fTrTp08H+uIrT58+TXd3Nx6P\nx9iX7/F4OHnyJCaTiQkTJhgFh7y8vIu6omy32ykoKEhZNLlQAkJbW5tRXNA7LM6fP8/58+eTjqWn\naAwUvTmUuru7qaurIxKJYLVaqaioIDc3d0h/xmhlMpmM118/z+IFg8GU77fH40kqOsViMbq7u+nu\n7k75s1LFrervu8RuCiGEEEKMXdLBIMQFnD9/np6eHiZPnjyoxWowGOTo0aM0NzcDfZW//Px8wuEw\nBw4cQNM0Jk+eTH5+PqWlpRQXFyftd/f7/cRiMdxuN4DRSdHW1kZ3d3fKOEy9s6GgoMD4vqHW0tLC\nvn378Pv92Gw2ioqKEtIvLpSAEC9dAoK+EL3YBISWlhYaGhrQNI2srCzmzZsnKQpDJBaLGQWuVN0P\nFxO7ma7olJOTQ1ZW1pjfxiKEEEIIMVpckS0SiqL8BLgRaNU0bdGfvzYBeB0oBk4BazVN6/nzbY8B\nXwWiwN9pmvZumuNKgUEMmxUrVlBVVYXVakXTNIqKiqipqaGqqoonnniC6upq/j97bx7eVnmm/3+0\nr5YsL/G+O3ZWICShM0AnIV1ImbbQdigN5UebYUpYyhdoOy30KktSmhmWmZbJlLYwZUpm2IZCoVNS\n2gk0bC2QhQRndWQ7XuRVtixZ+3LO7w9zDpItO47jJE7yfrhyYUvykXT0WtZzv89z31q9lvNWnscN\nD99AYXEhRRRRSSXamJaOjg5MJhOtra1s2LCBXbt2kZeXR2tr64T36fV6aWpqUnd/+/v7sVqtGI1G\nzGazasJoMBiorq6mpqYGk8k0qbEjQCKRYHBwUBUcJorDTB+nmAljw56eHpqampBlmfz8fJYsWTJu\nTGNsAsLYr1Op1JTvz2QyTdj9YLfb1Y4NSZJoaWmhr68PGDWbrK+vF4XqSUQRmbIJEMcqOtnt9glf\n97GCUTwe56abbmLr1q34fD7q6urYuHEjq1evzrjdhg0buPfee9m6dSurVq1Sx48kSUKj0aDT6Xj4\n4YfZvHkz7e3tFBYWcuONN/Kd73xHPUZ7eztr167l3Xffpaqqik2bNvGJT3zi+E6cQCAQCAQCwSzm\nRAkMFwNBYHOawHA/MCjL8gMajeZ7gEuW5Ts0Gs0C4ElgOVAObAXmZlMShMAgOJlccsklXHvttaxd\nuzbj8ldeeYWR0AjFlxbj0/v46c0/Zah7iCu/dyXnrDwHAwaWsIRQZ4hoNEpfXx/t7e1EIhE2btw4\nqcAAo8Vva2sru3btorOzE4AlS5awePFi+vv7aW9vV4turVZLZWUlRUVF2Gy2KRvpRSKRcXGYY1E8\nJAoKCnC5XMfs3zAVceFoKLGbE8UvTicBwWKx4PP5ANSuhYaGhjM6AeF0m9VMJpMTjtuMjIwcU9rJ\nWNHJYDDw1FNPcd1119HY2MiWLVtYs2YNe/fupbKyEoDW1lauuOIKhoaGeOKJJ7j44ouzCl0/+clP\n+PSnP83555+P2+3m05/+NA888ABf/vKXAbjwwgu56KKLuO+++3j55Ze57rrrcLvd5Ofnz8yJOkM4\n3dan4OxBrE3BbEWsTcFs5oR4MMiy/JZGo6kac/HlwIoPv34C2AbcAXweeEaW5SRwRKPRHAYuAN49\nlgclEJwIsglaq1evpplmWmnFiJHPffNzfG/l99TrEyR4n/c5P/d8or1R6uvrufjii3n11VendJ9a\nrZba2lo8Hg8jIyMAhEIh3nvvPWpra7nkkkvo6Oigra2NRCKB2+3m0KFDVFdX09jYSE5OzlHvw2Kx\nUFlZSWVl5bg4zKGhISRJUuflDx8+rMZhKoKDw+GY9Pjp4kJeXt60xAX4aCTCZrNRUlIy7vpjSUCQ\nJIm+vj56e3tJpVJotVqKi4sZGhri7bffxmKxTNiGb7FYRALCSUSv15OXl0deXt6465TYzYnEh7Gi\nUywWUw1RFebOncsbb7zBW2+9RU5ODgUFBfz3f/83n/3sZ3E4HFx//fVs3LiRW265hVQqNWEXzW23\n3aZ+3dDQwOWXX87bb7/Nl7/8ZZqbm3n//ff5v//7P0wmE1/84hd5+OGHef7557n++utn6EwJBAKB\nQCAQnP5M1+RxjizLfQCyLPdqNJo5H15eBvwl7XaeDy8TCE45d955J3fccQeNjY3cd999rFixghQp\nOulUb9P0ehNVC6uYf+F8ALY9vY3n7n+O13a9hk6nIxgMHlObP0BHRweyLDNv3jyqq6s5dOgQoVAI\nt9uNx+Nh4cKFfPKTn+TIkSMcOHAAWZbp6emhp6eHoqIi5s6di8vlmtJ9aTQanE4nTqeT+vp6UqkU\nQ0NDquAQCARIpVIZpoxms1n1bigoKMBsNqvH6+3tVcUFl8vF+eeff1zpFZNhNBrJz8/PuiM8Nnaz\nra2N5uZm7HY7iURi3BhIJBIhEolkNZ6cKAFB8QCY7QkIZ9IuhzISYbfbs14fj8cnTTsZKzp1dnbS\n1dVFKpXinXfeYefOnQwODtLd3U0wGGT//v3qGv+///s/Hn30UbZv355xn0pyy5tvvsmNN94IwP79\n+6mtrc3wNjn33HPZt2/fCTgrpzdn0jpYZFYAACAASURBVPoUnFmItSmYrYi1KTjTmKlP0mLWQTCr\neeCBB1iwYAFGo5Gnn36az33uc+zZs4fcmlwSJABo+6CNp3/4NN995rvs2bOHeDyOo8HBzU/ezNuH\n3mZucC6SJGEymabc1h2JRGhvbwegpqaG0tJSiouLcbvduN1uIpEIO3bsUH0DPv7xj+P3+2lpaSEU\nCtHX10dfXx/5+fnMnTt30qjMbOh0uoy0iGxxmNFolK6uLrq6uoDROM2CggIAOjs70Wg05ObmsnTp\n0hMmLhwNJabTZrMRjUZxOBwsW7aMgoIC5s6dSyKRmLD74VgTEGw224QCRLr4IjjxGI1G1UdkLErs\npvJ6+3w+brnlFlatWkV5eTkjIyO8+OKL3H777erPRKNRVXRqbGxk06ZNWY97zz33IMsyX//61wEI\nBoPjDF4dDoeapiIQCAQCgUAgGGW6AkOfRqMpkmW5T6PRFAPKNqEHqEi7XfmHl2Xl3nvvVb9euXKl\nUPAEJ4zly5erX1977bU8/fTTo/PaN68BoNvdzV2X3cWNm26kbGEZ773yHjVLa4jFYgwNDREeCRPs\nCqLT6di7dy/79u0jGAzyu9/9bpwRncPhUM3oWlpakGWZnJwcdSxAp9PR2NhIeXk5+/btU1v9u7q6\naGxsZP78+VRWVtLT04Pb7cbv9zM4OMjg4KDamVBSUjKtNv+xcZjBYFAVGwYHB9V5eUVw0Gg0FBYW\nUlVVRSAQOOY4zJkkkUhw8OBB/H4/AFVVVVRUjL7d6HQ6zGbzhLGbYxMQ0r8em4AQCoUIhUL09vaO\nO5bBYMh4nU9FAoKY1RwlPXZTlmXWrFlDaWkpL730Ejqdjttuu421a9fyd3/3d4yMjKDX67HZbJhM\nJmKxGEDWyMyf/exn/Pd//zdvvfWW6uVht9sJBAIZt/P7/VMaYTrbEOtTMFsRa1MwWxFrUzCb2LZt\nG9u2bTuuY0xVYNB8+E/ht8DXgfuBrwEvpV3+pEaj+TGjoxH1wHsTHTRdYBAITiYfGpaQQw7edi/f\n/9T3+eo9X+WSqy8hHApjtVqx2WxYLBYSiQTmoBmtVossy2qBLcsy3d3dWXcxjUYjBoOBQCCA2Wzm\n3HPPpbu7G4fDgc1mQ6vVYrPZuOCCC+jp6WHnzp0kEgna2tro7+9n4cKFlJaWUlpaysDAAG63G6/X\ni9/vZ+fOndhsNurr6ykrKzuurgKlPb2mpkb1ajh06JDadWE2m8nLy+Pw4cMcPnxYjcNUxilOVBzm\nWILBIAcOHCAWi6kCTbaZ/mzodDpyc3PJzc3Nen04HJ6w+2FsAoKS4DE4ODjuOJMlIDgcjhlJ8hBk\n57rrrsPr9bJlyxb19+H111/H4/HwxBNPADAwMMD69eu5/fbbufXWW4lGo+M6kZ544gl+/OMf8+ab\nb2b4hCxcuJDW1lZCoZC65vfs2cM111xzkp6hQCAQCAQCwYln7Kb/+vXrj/kYU0mReApYCeQDfcA9\nwIvAc4x2K7QzGlM5/OHt7wSuAxKImErBLMDv9/Puu++yYsUK9Ho9zzzzDDfccAO7d+/GbDbz1yv+\nmtU3reaL3/pixs+lkilCoRCJWIIlgSWYtWbi8Tgej4cdO3bw4x//mE2bNjEyMjJuF1yWZbUjwGKx\nZLRXKwkI6W33BoOBYDDI4OCgKmAUFxezaNEidZfV5/PhdrszdtbNZjO1tbVUVVXNiHfAwMAA77//\nPrIsY7PZqKqqYnh4eMI4TJvNpgoO+fn5J6SI7u/vx+12I0kSFouF+fPnY7VaZ/x+sqF0dEwkQBxr\nAsJE3Q82m00YT06TG264gQ8++ICtW7dmrAufz0cikVC/X7ZsGQ899BCrVq3Kun6eeeYZvv/97/On\nP/2J+fPnj7v+wgsv5OKLL+aHP/whL7/8Mv/wD//A4cOHRYqEQCAQCASCM5YTElN5ohACg+Bk4fV6\nueyyyzh06BA6nY558+Zx3333sWrVKjZs2MD69esx28zIyKNuIhp4IfACANue2sZvNv6G1/7wGrFY\njHfeeYdrrrkmoxhcsWLFaNxlWhHa1tZGZ2cn8Xgcu90+YfEoSRLxeBytVovRaCQej+P3+9Vi2mq1\nqmMTTqcTq9VKMBikpaUFj8ejFrgGg4GamhpqamqmXeSniwtOp5OlS5dmxD2Gw+EM/4b04g1QvRoU\nwcHlch3XyIAsyxw5cgSPZ3TKyuVy0djYOGtMGCdKQFC+PtbYTUV4GCtA5OTknNGxm8dDR0cH1dXV\nmM1mtXNBo9Hwi1/8gjVr1mTctra2lscee4yLLroISZJ49tlneeihh1STx4ULF9Ld3Y3JZFI7la65\n5hoeeeQR9b6+9rWv8e6771JVVcUjjzzCJZdccnKfsEAgEAgEAsFJRAgMAsE0SZCggw466SRKlA+2\nfcCnVn6Kaqpx4UKSJHWOf2RkhFgshtPpJC8vD4vFklHUx2Ix3n33XSRJYu7cuZSUlBAMBrPugnu9\nXqLRKEajMaMYV65TEisUs7ucnJwMnwflmAaDQe2EqKyspK6uLut8+USkiwuKgeJkRe1EcZjp6HQ6\n1aCvsLDwmObVE4kEhw4dUo0YKyoqqKysPK12+ZUEhGyv+9jYzaNhsVhUseHw4cNccsklqgBxsro5\nzhRkWSaZTJJMJtXXQKvVYjAYTpmJ6ZmEmCUWzFbE2hTMVsTaFMxmpiMwzI6tQIHgFGPAQN2H/yVJ\nYsTIEpao12u1WrWDwGg00t3djd/vV7sODAaDKjS0tLQgSRI2m43S0lI0Go1aDCrmijDafh+JRJAk\niVgsNq4QnTNnDp2dnQwPDxOPx+nu7sZut1NQUJCRgKCkIvj9fvR6Pbt27cJqtVJeXs68efMoLi4m\nJydnQsHB6/Wye/fuKYsLMPU4TCUFA1BNGBXRYaJEhnA4zP79+4lGo2i1WhoaGrKmCMx2ppqAMPZ1\nDwQC47pDlNjNvr4+mpubM4QWvV4/afeDKJoz0Wg0GAwGDAaDKjCcTsKVQCAQCAQCwWxGdDAIBMeI\nLMt0dHQwPDyM3W7HZDKpu5/RaJTW1lYkSeK8886b0FhQlmXC4bDqdTBRgZNKpeju7mbXrl309vYS\njUZH4zMdDvR6vdrhoNw2EAgwPDyccbnNZsPlcmV0Pyj/kskkra2tmEwmcnNzpyQuTIVoNMrg4GBG\nHOZYlDhMxb9Bp9MxODhIc3MzqVQKs9nM/PnzT5qR5GwiGo1O2P0wNnbzaNhstnEpJyJ2UyAQCAQC\ngUBwNMSIhEBwkohEInR2dqLRaLDZbMiyjF6vp6Ojg3g8jtPppK6uDrPZnFU8iMfjxGIxdazhaMiy\nTFdXF/v37ycejwOjBXpdXR16vT6jEB0eHsbj8dDT05OxE26xWHC5XGpLfTAYpKOjA1mWsVgsLFy4\nEJfLdUISEJSRj4GBgYzRDwWNRoMkSUSjUZxOJyUlJcyfP194D2RBid2cSIAYazg6GUrsZjbx4WTF\nbgoEAoFAIBAIZidCYBAIZoipzMN1dHQQi8UoKSkhEokwMDBAf3+/GqOo1WrRaDRYLBbMZrNarEmS\nRCgUQqfTHfP8fDwe59ChQxw5ckS9rKKigvnz52MymTJum0gkOHz4MPv371e9HiKRCBqNhlQqxcDA\nALIsYzKZqK6untQ80WQyTRi/eKwJCJIk4fP5VMFhaGiIvr4+dWc+NzeX4uJi5syZo3Y4CJ+Bjzja\n2gyHw+MMJ5Wvx8ZuToYSuzmRACFiNwXZELPEgtmKWJuC2YpYm4LZjPBgEAhOIk6nk/7+fsLhME6n\nkwMHDgCQl5eHVqvFYrEQj8cJh8NEIhFMJpN6GTBOEJgKRqORxYsXU1FRQVNTE8PDw3R2dtLb28u8\nefOoqqpSi32DwcCCBQtYsGCBGvU4ODhIKBSio6OD0tJSCgsLOf/881VDQqUQHTvSEIvFiMVieL3e\ncY9JSUDIJkBkS0DQarXk5+eTn59PZWUlTU1NaDQaRkZGsFgs6PV6kskk3d3ddHd3A6Nt/un+DaKz\nYWKsVitWq5Xi4uJx1ymxmxN1P6QbdcqyrBpSKkke6Sixm9nEBxG7KRAIBAKBQHB2IjoYBIJpIkkS\nbW1twGgB3t/fj8ViYe7cuUSjUXV8wmg0EovFSCQSSJKELMvY7fbj9haQZZn29nYOHjyojkLk5uay\nePHiCb0f2tra+OMf/8jw8DAmk4mqqirsdjt1dXVUVVWphoBjBYf0QjQYDE47ASG9CJUkifb2dlKp\nFEajkfnz55OTk0M4HFZHKSaLw1QEh+ONwxSMkh67me11j8ViUz5Weuxmttd+tkSNCgQCgUAgEAgm\nRoxICAQnmf7+fnp7e+nr68NgMHDuueficrmIx+P4/X4SiQQ6nQ6Hw4FOp2N4eJhEIoHRaFQ7Go53\nNz4ej7N//346OzvVy6qqqpg3b15GG7vP52Pnzp2kUik1BUIZk4DR7oiamhqqq6snbX9PT0DIVoiO\nFQSyEYlECIfDaLVaHA4HlZWV4/wfcnJy0Gq1+P3+jHGKieIwFcHhWOIwBVMnXXQa+7pPV3TKJkCI\ncRiBQCAQCASC2YEQGASCo3D48GHOOeccrrzySjZv3sy7777LXXfdxc6dO9HoNZy78lyue/g6ug92\nc+nKS6miCgcO9ef9fj+33norv//979FoNHzjG99gxYoVxONxqqqqWLhwoXpbZUc4GAwiSRI6nQ6T\nyYTJZCKZTKo7wukRl8fD0NAQTU1NBAIBALUroKKiAr/fz44dO0ilUthsNpYvX47JZCIcDtPS0kJH\nR4dauOt0Oqqrq6mpqZkw2nIylASEibofgsFgxpjIZO30SgKCUoDabDZSqRSxWEw95limGod5ujIb\nZzVnQnRSSI/dHCs+jI3djMfj3HTTTWzduhWfz0ddXR0bN25k9erVGcfcsGED9957L1u3buWSSy4h\nmUySTCZVUUSn0/HWW2/xox/9iF27dpGXl0dra2vGMf785z9z++23c+DAAWpra/npT3/KRRdddBxn\n7cxkNq7P2cJPf/pTfvWrX9HU1MTVV1/N448/rl73P//zP9x77714PB4qKgr50Y/+Py6//GNADlAB\nlAGja/8nP/kJmzZtwuv1kpOTw1VXXcWDDz4oOrmOglibgtmKWJuC2YwQGASCo3DppZcSjUapqqpi\n8+bNvPLKKwRCAQouLSCoD/LTm3/KUPcQV37vSs5ZeQ5atJzLuRRRBMDatWsJh8Ns3ryZ3t5eVq5c\nyRe/+EU+8YlPcOGFF2YdTUilUgwPDxMIBNBqteTl5WG325EkiUgkovod6PV6VWiY7vy6MnZw8OBB\nNU1AGdEwmUxYrVYuuOCCcf4PsViM1tZW2tvb1WJQq9VSXl5OXV0ddrt9Wo8nnWg0qhpOxmIxnE4n\nBoPhuBIQLBaLaloZj8dV7wuTyaSeQ4fDoQoOShzm6czp+EEkXXQaK0CEQqFjOlZ67KbBYODZZ5/l\n61//OvPnz+fVV19lzZo17N27l8rKSgBaW1u54oorGBoa4oknnuCiiy4a1wUDsHPnTtra2ojH42zc\nuDFDYPD5fMydO5dHH32UL3zhCzz11FPccssttLW14XQ6j+/knGGcjuvzZPHiiy+i1Wr5wx/+QCQS\nUQWG7u5uampq+N///RWf/nQ+W7a8w5VXbqS9/QkKCpT1lQ+cD+hoa2sjNzcXl8vF8PAwX/rSl/jc\n5z7Hbbfddqqe2mmBWJuC2YpYm4LZjBAYBIJJeOaZZ3jxxRdZsGABbrebzZs3A7Cf/XTQAYD7fTff\nW/k9nvc/r/6cDh0rWYkBA4WFhbzyyissXbqUeDzOLbfcwl/+8hceffRRqqursxrrwWiBFQqFVB8G\nvV6Pw+HAbDZnCA2yLKPT6cYVyceKUsy73W7a29uRJIny8nIuv/zyScWCRCJBe3s7ra2tGTP3paWl\n1NfXT7uY8vv9qleEwWBg3rx5WY81UQJCIBAgEokc9X7STTUlSVLHUJT/W61WSkpKKC0tpaCgAKfT\nKcwITzHpsZvZXvuxkaaTYTQauffee/na177GZZddhsPh4KabbuKWW27h29/+Nr/4xS+4+OKLJz3G\n22+/zbp16zIEhpdffpnvfe977N27V72ssbGRO+64g7Vr1x77kxac1dx11114PB5VYHjvvff4/Oc/\nT2/vZmBU/Joz5yv87//ey8c+Ni/tJ2uBhoxjDQ4O8pWvfIXGxkb+/d///eQ8AYFAIBCcNYgUCYFg\nAgKBAPfccw9/+tOfeOyxx9TLEyToplv9vun1JqoWVqnfb3t6G8/d/xxbd2+lhhoAta36yJEjpFIp\n2tvbKSwsZGRkhMLCwnE75KlUikQigc1mw2QyqWMCQ0NDmM1mtfXfYrGoUZLBYJBwOKxGXB5rEWw2\nm6mrq6OjowODwYAkSVgsFv7yl7+wYMECysrKsv6cwWCgvr6empoaOjs7aWlpIRwOq4kOhYWF1NfX\nU1BQMOXH0t3dTVtbm2puOW/evAnHFqaagJAtglGSJIxGI0ajkdzcXGRZJhqNEg6H8fl8GckYSreI\nzWajqKiIsrIyKioqKCoqEgkIJxmdTkdubu6ExqTpotPY132s6OT1eunu7kav17Nnzx527tzJ8PAw\nXq+XkZERPvjgAxwOBxaLha1bt/L444+zffv2jGNMVdCQZTlDcBAIpsuyZcuYP7+a3/3uL1x22XJ+\n+9t3MJuNnHPO6N+cp5/exv33P8fu3Y8BdYCOp59+mhtuuEH9u/Ov//qvp/Q5CAQCgUCgIAQGwVnB\n3XffzTe+8Q1KS0szLh9hhCSjbfltH7Tx9A+f5t7f3svuV3dz3ifOY+WalaxcsxIfPmqoYfXq1dx/\n//3827/9G9u3b+f3v/898Xic3NxchoaG8Pv95OXlqcdXilyNRqN2JOTk5GCxWAgEAkSjUWKxGHa7\nHbvdjtVqzRAaQqFQhtAw1RnbQCDAjh07sFgsnHfeeRQWFtLe3k40GmXXrl10dHSwePHiCbsZFB+G\nyspKenp6cLvdBAIBBgYGGBgYwOVyUV9fT1FR0YTFuCRJtLS00NfXB8CcOXOoq6ub9oiCXq8nLy8v\n4/wqjE1AGFuIxmIxUqmUai4ZiUTUCMbe3l727NkDjAosSiRmSUkJLpdr1iUgnG2tlJOJTolEQn2d\nh4aGWLduHZ/+9KdpaGhgYGCAF198kdtvvx0YVeCTySTDw8MMDw+zcOFCHn74YVpbW7Fareq6HBwc\nJJlMZnQwlJSU4PF42LRpE5deeikvvfQSLS0t9Pb2jvNqONt55513+Ku/+qtT/TBmNT6fj5GRkYy1\n89nPLuMrX/knYrEEBoOejRu/DIyK2WvWrGTNmpVAHAgBDtasWcOaNWtoaWlh8+bNFBUVnYJncnpx\ntr13Ck4fxNoUnGkIgUFwxrN79262bt3K7t27x12nYbQ47nZ3c9dld3HjphupW1rHnj/tQZZkNFpN\nxu02bdrEN7/5TRYvXozD4eBzn/scr776Kg6HQxUYXC6XWnQnk0m1VT+9EFeKZWU2fWRkhEgkgtPp\nVNv5zWYzsVgsoyg2m82YzeZJi3RFXEgmk1gsFpYvX47FYqG2tpZ9+/bR09OD1+vl9ddfp7a2loaG\nhgmPp9VqKSsro6ysjL6+Pg4fPozP58Pn87F9+3ZycnKor6+ntLQ0Q/yIx+McOHCAkZERNBoN1dXV\nE3ZNzAQajUYVacaKSMrjGSs+eL1eenp6GBwcJBQKIUkSiURCLUAPHz6M2WxWRyuUThKRgDB7MBgM\n5OXl4XK5uOOOOygqKuKll15Cp9Px7W9/m3Xr1nHttdcSCAQwGAzk5uaSk5NDJBJRRSe/3088HldH\nabKRm5vLz3/+czZu3Mg999zDxz/+cS666KIJR6IEgsmQJIlkMonP5yMWi/HnP/+ZH/3oVzz++I1U\nV+exZ08b3//+r2loqOSyyz4+6bHq6upYsGABN954I88///yktxUIBAKB4GQgBAbBGc/rr79Oe3s7\nlZWVyLJMMBgklUqxf/9+3tnxDkPtQ3z/U9/nq/d8lUuuvgQpJbHobxYRjUZHUxQ04MIFjBYaDz30\nEP/wD/+ARqPh5Zdf5oILLlB3vpWOA5vNhizLxGIxtFrthFGUZrM5Y2xicHBQLWB1Op16fTweJxKJ\nqP+UwnesMKCIC4lEIkNcgNFowGXLltHf38/evXsJhUK43W48Hg8LFy6kpKRk0vNYVFREUVERQ0ND\nHD58mP7+fkZGRnj//fc5ePAgdXV1VFZWEg6HOXDgAPF4HL1eT2NjIy6XawZeyeljNBrVVImxSJLE\nyMgIHo+Hzs5Ouru78Xq9hMNhotEo0WgUn8+HVqvFbDZjtVrx+/1ZUz+OJQFhuohdjvFcd911eL1e\ntmzZop7j1157DY/Ho45EDQwM8IMf/IDbb7+d22+/nf7+frxeryr+abVaNBoNer0enU5HbW1txn3U\n1tZy1VVXAaNjFLW1tdx1113jbne2I85HJslkklAopP5tCIVCRKNRkskkqVQKvV5PW1sb55zTSGVl\nLolEgnPOqWbJklr27PFw2WXpRzMB47vOEomE6KSZAuK9UzBbEWtTcKYhBAbBGc+6detYs2aN+v2D\nDz5Ie3s7P//5z+nz9HHHJ+7g87d8ns984zMAaHVajEYj8XiceDyO1WSljNHd9+bmZlpaWjCZTLS3\nt/OrX/2KN954AwCn00koFMLv92Oz2YjFYsiyrCYdTET62ITf71cNH3NyclQ/ACXeMpFIZBS+RqMR\nq9WKXq9nZGREFRfMZjPLli3LGjM5Z84cVqxYgdvtxu12E4lE2LFjB3PmzGHRokXYbLZJz2deXh4f\n+9jH8Pv9uN1uenp6iEQi7N27l127dqlJGTk5OcyfP39aUZcnE61Wi9PpxOl0smDBAmC0gBwcHGRg\nYIDu7m4GBgbUsZVoNIrf7yeZTKLVatWxFr1er+5K+ny+rPeVnoAwVoA40+I0TxY33HADBw8eZOvW\nrRmiz2uvvZYRj7ls2TIeeughVq1aBYwWZUajkTlz5qgdDcroTCKR4MiRIxQUFKhjRLt372bRokWE\nw2HuvvtuKisr+dSnPnVyn6xgVqOICYqQEAqFMsxyU6mUGo+q1WrVqOC5c+fy5JNP0to6yJIltfT3\nR9i1q41vf/vKMfdQDmj55S9/yec//3kKCwvZv38///zP/8xnPvOZk/pcBQKBQCCYCJEiITjrWL9+\nvTq3umHDBtavX4/ZZkZGHh151cC9v72Xxr9q5PWnXuf3//p7Duw9AIyOSGzYsIFQKMS8efN44IEH\n+OQnPwmM+gAoMY8VFRVqYsKxFo6RSER1zzcYDDgcjnGt28lkknA4TDweV79XugbMZjPLly+fUrt+\nKBRi3759qk+CVqulvr6e+vr6Ke+2K50QTU1NBAIBAOx2O0uWLGHu3LlnROEcjUbxer0MDAzg9XpV\nw0hJktTCVOlU0el0SJKkdspMFaPROM7vQfnebrerIyhiVvMjOjo6qK6uzhgb0mg0/OIXv8gQFWF0\nZ/2xxx7j4osvJpVKsWnTJv7jP/6D999/n2QyySuvvMLVV1+dIQZecMEFvPDCC+Tl5bF27Vq2bNmC\nRqNh9erVbNq06ZjMTs8Wzpb1qbwHp4sJ6UayCooIabPZeOSRR3jwwQcz1thXv/pVLr/8crZt28Zv\nf/sbhod9FBQ4WLfuM3znO1ei1Wp46qk/8U//9DxNTc2Alr//+79ny5YthEIhCgsL+fKXv8yGDRuy\ndlUJPuJsWZuC0w+xNgWzGRFTKRBMkxQpuummk05ChNi7bS+f+ptP4RpxYZWsOBwOdacfYOHChRQW\nFo47js/nw+v1YrFYcDqd2Gy2KRszpqMUqKFQSO2CUMYmMh53KsXQ0BCHDh0ilUqh0+lUf4hjSULo\n7e1l7969qiu/zWZj4cKFUzIOSyQSHDx4EK/Xi9frJZVKYbVa1bbziooK6urqjtoZcToRCARUwWFw\ncHCckKDValWDSJPJhCzLqhmh8v+pxG4qKF0uOTk5tLS0sGLFigwxQhQWU0cZk3K73VgsFiorK9Hp\ndMRiMfr6+lTfkXg8rho+wqholp+fL871UTgTPyinUqkMIUHxxBmLIiYogoLNZssw5w0Gg3g8Hvx+\nP4lEAp/Ph9FoxOFwUFpaSnl5ORpNkGBwH3r9EBaLEchhtHOhFDj2vyWCjzgT16bgzECsTcFsRggM\nAsEMo0Qj6nQ62traGB4exuVyce6552a9fSqVwu12k0wmqaurO+7d+0QioZrQabVacnJy1OIdRj+w\nbt++fXSUw2qlvr5e3UVXTOumKjSkUimam5tpbW1Fkkaz2IuLi1m0aNGEYw7BYJADBw4Qi8XQ6XQ0\nNDSQk5NDe3s7ra2taoeFRqOhpKSE+vp6nE7ncZ2T2UYqlVKFpYGBAYaHh8fdRvGAKCwspLCwEIvF\nkpGAMNaAUondnCpms3nC7gcRuzkeJTq1uLiYuro69XLlddTpdFRUVKDX69WECkVoyMnJIS8vTwgN\nZyiKmJAuKGQTEzQaTYaQMFZMSCddWIDR6NVkMondbsdsNlNbW4vD4QA++pujGMsKBAKBQHAqEQKD\nQHACiEaj9PX10dnZSSqVYtmyZRPuxsuyzJEjRwiFQlRWVqofGo+XsWMTTqeTeDyuigsmk0k1dFS8\nApQ5XyWRYqpFZjAYpKmpCa/XC6AKB7W1tRkfngcGBjh8+DCSJGGxWJg/f37GWEYqlaKjo4OWlpaM\nD+hFRUXU19dnjZs8E4jH42o3x8DAAOFweNxtlCjMwsJC8vPzx5mAZovdTO9+SJ/rPhparXac30P6\n96c6dvNUcOjQIbxeLw0NDeM6kbxer7qzXF5ejk6nQ5ZlITScgSjRtWM7E8Z+NlHEhHRBwWKxHLU7\nLRQK4fF4MkRHRThUvGqqq6szfgcVI1+n0zkjprACgUAgEBwPQmAQCGaI9Ha1ZDLJzp07kSSJgoIC\n5s6dO+HPRaNRgsEgXq8Xm81Gr7ziPAAAIABJREFURUXFjD0mJe1AMXlsb28nFothMplYtmyZakYH\nowWqIjRIkqTGK060w5YNj8fD/v371bliu93OokWLKCgooL29na6uLgBcLheNjY0TFqqSJOHxeHC7\n3QSDQfXyvLw86uvrz/j89lAolOHfkG48CKNv3Lm5uargkJubO+lrtG3bNv76r/96wu6HYDA4rkCa\nDKvVmjXx4kyO3dy5cyfRaJTly5ePEwhkWaavr4+RkRHMZjNlZWXq6yHLMn6/H5/PJ4SGCZitrb6S\nJGWICUpnQjYxwWKxZHQmTEVMSCcUCtHd3a2avWq1Wux2O6FQSB1lq6qqyurh4ff7kWWZ3Nzc43vC\ngnHM1rUpEIi1KZjNTEdgOPu2rgSCY6Sjo4NIJILVaiUvL0/9gDiWVCpFIpHAZrOpIoAiAMwEStqB\nLMscOnSIZDKJ0WjMmvyQLigoJoTK7pwScXm0D8xlZWXMmTOH5uZm2traCAaD/PnPfyaVSpGbm6vu\n8FZVVU3aHaH4MJSXl9PX14fb7cbn8zE0NMR7772Hw+Ggvr6ekpKSaflVzHaUIqWqqgpZlhkeHlYF\nB5/PhyRJavJEc3Mzer2e/Px8VXBIF44UlFSRiWI3g8HghN0PYwUOpR28t7d33LH0ev2E3Q8zFbt5\nskkmk0SjUUwmU1ZRQKPRUFRUpLbK9/b2UlJSgkajUcUgh8OhdjQoIy0Oh4O8vLwJI2kFJ4+xYoKy\nxqfamTDddR0Oh/F4PBnCQmFhIalUSu0Is9vt1NbWZh1/kCSJVCo1Y38zBAKBQCA4FYgOBoFgEsLh\nMNu3b0eWZRobGzGZTOj1enJycsYV1eFwmFQqhc1mY2RkhP7+fpxOJ3PmzJmxxxMKhdi+fTuxWAyj\n0UhtbS0GgwGj0YjT6ZywuJFlmXg8TiQSIZlMqtGXU/0wHQgE2LFjh5pUodfrufDCC1myZMm0RAGv\n14vb7WZgYEC9zGq1UldXR0VFxWlZuE6HZDLJ4OCgKjiMjIyMu43ZbFbFhoKCguMuPqLR6DjxQREg\nQqHQMR3LbrdPKEDM1vnxwcFBDh48SF5eHvPnz5/wdpIk0dXVRSwWw+FwZO20kSRJFRoUo08hNJxc\nJEkiGo1mdCZkExOArJ0JM/FeEw6H6e7uZmhoCBgVFubMmUNubi4dHR2Ew2E0Gg2lpaWTCqmxWIxQ\nKITdbhcdMQKBQCCYFYgRCYFghtmzZw8+nw+n08mSJUvU+Viz2ZzRPp5IJDJ2RSVJoq2tDYDq6uoZ\n+xD73nvvEYvFMBgMXHDBBVitVgKBgPoB1mq1kpOTM2nRrwgNyk62IjRMNos/ODjIoUOH6O/vp7e3\nVy10HQ4Hixcvnrafgt/vx+12093drV5mMpmora2lqqrqrCvSotGoOkqRHoeZjsPhUAWHvLy8GRVj\nkslk1u4HRYA41tjNsSMXyr/ppqvMBG1tbXR3d1NVVUV5efmkt00mk3R1dZFIJHC5XBPGUkqSpI5O\nKOfI6XTicrnOujV8IhkrJiidCdkMUS0WS0ZngtVqnXHhciJhobi4GJ/PR2dnJ5IkqUaO2bqR0gkG\ngyQSCXJzc4Uxq0AgEAhmBUJgEAiOEwmJBAne2vYW5yw8h3379gGoHgdKxF0ikVB3mRRDPqXAVz4Y\nKokCylz98aB0UkSjUQwGA8uXLycnJ0e9Ph6Pq9FnOp1OTZuYjEQiQSQSUZMejEYjFosloyCSZZnO\nzk46OjqA0aKptraW1tZWjhw5ot6uoqKC+fPnT3t3PRgM0tLSQldXl1osGAwGqqqqqK2tPWtbhgOB\ngCo4KHGYTU1NLF68GPjIKE4RHI41nvRYkGWZcDic1fshEAhkFUMmQondnKj74UTu3jY1NREIBFi8\neLH6OzTZOYvH43R1dZFKpY76uzyR0JCXl3fWmGnO1Cyx4iMztjMhm5hgNpszhASr1XpCz3ckEqG7\nu5vBwUHgo1GIkpISYFTEUhIjCgsL1SjUyVDGp5QOOUgAEmAEhNgwE4g5d8FsRaxNwWxGCAwCwVE4\nfPgw55xzDldeeSWbN28G4NVXX+Xmb95MZ2cnjR9r5Lb/vI0edw9lpWXkDuXSWNioGjv6/X7+3//7\nf/z+979Ho9Fw4403cueddxKPx8d1AcTjcdrb2zEajVRVVU37MR9NXFBILwAlSTrq2IRCMpkkEomo\nyQQGg0FtHW5ublY/RJeWllJdXa3uPA8PD9PU1KQ6pBsMBubNm3dUT4bJiEQitLW1ceTIEbVA02q1\nVFZWUldXd8aaDk4FJQ5zy5Yt1NTUTDkO82QxUexmIBAgGAwec+zmRN0P6SJePB7npptuYuvWrfh8\nPurq6ti4cSOrV6/OON6GDRu49957+eMf/4jdblfXqnIcrVbL22+/zY9+9CN27dpFXl4era2t6s9H\no1G2bt3K+vXrOXz4MA6Hg+uvv54f/OAHWR9/NqEhNzcXl8t1xgsN0/mgPFZMUCIiJxITxnYmnKxz\nOlZY0Gg0zJkzh5KSEoxGIz6fjyNHjpBIJNDr9WzdupXnnnuOpqYmrr76ah5//HEAnnrqKdatW6eu\nPyXN4rXXXuOv/qoes7kHGPzwXk1s2+Zhw4ZfsmvX++PWZmdnJwsWLFCPpQje//Iv/8Ltt99+Us7L\n6YIo4gSzFbE2BbMZITAIBEfh0ksvJRqNUlVVxebNm/F6vdTX1/OPj/8j53z2HDb/YDN739zLD/73\nBwwODqLT6fhs9Wep0o8KBGvXriUcDvPLX/6SlpYWvvCFL/Dd736Xr33ta1mLua6uLiKRCOXl5dMq\n9iKRCO+9954qLixbtuyo0ZepVEpNm9BoNNhsNux2+1Fb0pUPubFYjHg8Tk9PjxovOVHagyzLtLe3\nc/DgQXXkIjc3l8WLFx9X10Y8HqetrY22tjb1uBqNhrKyMurq6mYs/vN0ZibiME8WkiQRCoUmFCCU\nLpqpoHToOBwOjEYjzz//PNdccw3z5s3jzTff5JprrmHv3r1UVlYC0NrayhVXXMHQ0BA///nPKSsr\nw2azjRuP2LlzJ62trSQSCTZu3JhRxAEsWLCAVatWceuttxKPx/nUpz7Fo48+ymc/+9lJn/fw8LBq\n5qnRaNTRiTNdaJgIWZZVr4F0QSHb+I3JZFJFBEVQOBXnLRqNqsKCLMtoNBq1Y8FkMpFKpejs7KS/\nvx8YHWOqra1ly5YtaLVa/vCHPxCJRFSBYSxPPPEEP/zhD9m58zfk5HSh1WZ+jtu+/RDNzUNEIqVs\n3Hj/uLWZzpEjR5g7dy6tra0zmmIkEAgEgrMTkSIhEEzCM888g8vlYsGCBbjdbgB+85vfULuolqVf\nXArAV+/9KlcVXMXBnQcprB411TukP0QxxZgw8bvf/Y5XXnkFu91OQ0MDX/nKV9i8eTPXX3991vvM\nzc0lEokwPDx8zAJDJBLJ6FyYirgAowVYbm4uVqsVv99PMBgkEongcDgmfQw6nQ673U4sFqOjowNZ\nljEajZSVleFwONQP1uloNBqqq6spLS1l//79dHZ2Mjw8zJtvvklVVRXz5s2bVru70WiksbGR+vp6\n2tvbaWlpIRqN0tXVRVdXF0VFRdTX10/b++FMwGg0UlpaSmlpKTBqAJru35BIJNQC7siRI8cchzmT\naLVaNXlCebzpxGKxjKSL9H+hUCjDsC+VSjE8PKx2cJx77rk0NTXR1NQEjP7O/exnP+PSSy8lJyeH\n22+/nTvvvJM777yTcDiMTqfLakC5dOlSli5dyttvv531OXR0dLB27VpgdBf9wgsvZN++fZMKDMoI\nS25urio0DA8P4/f7zxqhIZtnghLxmY7JZBrXmXCq/SuyCQtKx4IythUKhdT3J41GQ0VFBUVFRWg0\nGq644goAtm/fjsfjmfB+nnjiCa666osYDC1otePX5vLljSxfDq++2p3lp8cf62/+5m+EuCAQCASC\nU8aZ/clGIPiQQCDAPffcw5/+9Ccee+wx9fIP9n1A+bkf7WSarWbmVM1h58s7ueLbV/D+lvd57oHn\n+OPuP1JHHYBa7Oj1emRZ5uDBg6RSqazFms1mQ6fTEQwGSSaTUy4mFHEhEomg1+unLC6ko7TLh0Ih\ngsEgPp+PcDiM0+mc8HF4PB6OHDmCLMtqnFoymVQz481mM2azedxzNRqNnHfeeVRWVqoz7u3t7fT0\n9DB//nwqKiqmNTah0+mora2lurqarq4uWlpaCAaD9PX10dfXR35+PvX19TOa1DHbmaiVUinMqqur\n1RZ9RXCYbhzmycJkMqmPYyySJKlRkNm6H9KL1UAgQG9vL1arlebmZnbu3Kn+bDAY5MiRIxQWFhIO\nh3nmmWf4z//8T3bu3JlxfxMZWd522238+te/5lvf+ha7du3iz3/+M9/97nen9PwUocHpdKriiCI0\nKKMTZ0JySiwW4w9/+ANLlixRBYVsYoLRaBzXmXCqxYR0YrEY3d3deL3ejI6F0tJSVViQZZmenh48\nHg+yLGO1WqmtrT3mMa729nbefPNNHn74NpQl8PTT27j//ufYvfunY249OO7nx/Jf//Vf3HPPPcf0\nGM4WRBu6YLYi1qbgTEMIDIKzgrvvvptvfOMb43ZPh4PDWOd89IFQlmRMdhOJaIKioiIqr65k5dUr\n8TNq2LV69Wruv/9+Hn/8cVpbW3n22WfVvHWHwzGu8FZaooeGhggEAlPacY9Go8ctLqTfv91ux2Kx\nEAgEiEQiDAwMYLfbsdvtGTPALS0taotvUVERdXV1aLVataU5EokQDodVocFisYx7vnl5eXz84x9X\nxybi8Th79uyho6ODxYsX43Q6p/U8FB+GiooKent7OXz4MH6/n8HBQQYHB3E4HNTX11NaWirc1xk9\nXy6XC5fLRUNDgxqHqQgOIyMjJJNJVaiBUdf9dP+G2RKTp9VqcTqdE66dSCRCIBDA5/Px9a9/nc98\n5jOce+659PX18eKLL6pz6Mq6SCaTDA8Pc8455/DII4+MO95Eo3t/+7d/y7XXXstDDz2EJEncfPPN\nFBUVkUqlpiwO6HQ68vPz1Y6G9K6G001oUMYcFL+EUChEMpnE4/FQXFys3s5gMGREQ1qt1lmztsYy\nkbBQUlKS0fUSi8VobW1VY2WLioqoqKiYVkfQ5s2bueiii6iutqPTjY6krVmzki9/+W+QJHnMuESS\nUePH7Lz55pv09/fzpS996Zgfh0AgEAgEM4UQGARnPLt372br1q3s3r173HU2u43uwEdtpxqthlQ0\nxdJVSzGZP0ou0Hzo4r1p0ya++c1v0tDQQF5eHldffTXPPvusOmOeXrQrKAKD3+/H5XJNWgDHYrEM\ncWHp0qXTLsrT0el0uFwudWxiZGREHZsAOHjwIMFgEI1GQ21treqGDqOFmdlsxmQyqRGXkUhEjeUc\nmyWv1WqpqamhpKSE/fv34/F48Pl8vPnmm1RXV9PY2Djt3UqNRkNJSQklJSUMDAzgdrvxer0EAgF2\n7drFoUOHqK+vp6ys7LQp1I6V6exy6PV6ioqKVB+NSCSiejd4vV5VQOrs7KSzsxM4sXGYM4nFYsFs\nNnPrrbdSUFDACy+8gE6n4zvf+Q4333wz69atIxAIsH79erUDRzEVnOrYks/nY/Xq1TzyyCOsWbOG\nnp4eLr/8cgoKCli7di1lZWXHVFxOJDSkj07MpvMdj8fHeSYo3ijpGAwGVq1alSEozFYxIZ1YLEZP\nTw8DAwOqsFBQUEBpaem4cZrBwUHa29tJJpMYjUZqamqO6z36v/7rv/j2t78NaDJecyUxYzQZJv0n\nJv77sXnzZr70pS+d1Wa4kyF2iAWzFbE2BWcaQmAQnPG8/vrrtLe3U1lZqcZMSpLE/v37uf6G63n7\nVx/NXEdDUXpaeqg/vz7jGAUUAKPz3Zs3byYUCqHX67nvvvu44IILMJvNRKPRrEWLXq/HbrcTDAZV\nESIbsViM9957T50TX7p06XHHW45FaUUPBoMEg0E8Hg99fX2kUimMRiPz5s2b8MOyRqPBZDJlCA3K\nc1aEhvTRC7PZzPnnn6+OTQSDQdra2ujp6WHBggWUlZUd13NRil+fz4fb7aa3t5dQKMSePXs4dOgQ\ntbW1VFVVnfEz7tPBYrFQUVGhzmkrcZgDAwMMDQ2RSqXUEYSWlpaTGoc5Ha677jq8Xi9btmxRi7RX\nX30Vj8fDz372M2A0Nvaee+5h3bp13H333ciynHUcIptQ0Nrail6v56tf/SoAZWVlXHPNNWzZsoU1\na9bQ29tLSUnJMZ+TdKFBERiU/+fm5pKbm3vShQZFTEjvTMgmJuj1+nGdCadbnGw8Hqe7u3tKwkIy\nmaS9vV1NkHC5XFRXVx/XaMfbb79NT08Pl112GTrdCBrNaBywJI2uTYPBMEZcmDiyMhqN8txzz/HS\nSy9N+/EIBAKBQDATiE/egjOedevWsWbNGvX7Bx98kPb2dn7+858jSRL/+I//yNu/eZvlly3nyfVP\nUnteLUPdQ5Q3jHozGDFSyuhoRWtrKyaTCbvdzmuvvcZjjz3GG2+8gcViUeMe9Xr9uA+dTqeTYDCI\n3+/PKjAonQuKuLBs2bIZFxcUNBoNOTk5BAIBuru7kWUZs9l8TOkMRqMRo9FIIpFQkydisRhGoxGL\nxZLx/AsKClixYgWtra00NzcTjUbZtWuXOjZxvLP/LpeL5cuXEwwGcbvdeDweotEo+/fv5/Dhw9TU\n1FBTU3Na7KROhRMxq6lEQNbV1alxmEp3w/DwMJIkqeaRBw4cOKVxmGO54YYbOHjwIFu3bs14jV97\n7bWMwvi8887j1ltvVdvHNRpNhvgkyzLxeJxUKoUkScRiMbRaLQaDgYaGBmRZ5plnnuGqq66ir6+P\n//mf/2HVqlWYTCZCoRD9/f1Zk1amgk6no6CgAJfLpY5MDA0NqaMTJ0poSDcCVQSFbIkeer0+wy/B\nZrNNKCacDrPESkpOf3+/OhKTn59PaWlp1rUcCARoa2tT10RVVVVWv5CxpFIpEokEqVSKZDJJLBZD\nr9err+UTTzzBF77whQ+9elxAD5BQhS/ldqNrM0E8njdubSq88MIL5OXlsWLFiuM8O2cup8PaFJyd\niLUpONMQAoPgjEdpi1aw2+2YzWbVD+H555/nGzd/gweveZB5H5vHHc/cQW9rLwBvPPUGL/7Ti+xv\n2g/Ae++9x7e+9S0CgQANDQ089dRTzJs3Dxg12VNc78f6MSiO6OFwmHg8nlEIKeJCKBQ6YZ0L6UiS\nRGtrK729vWg0GvLz88nLyyMWizEwMIDT6ZzyTqTBYMBgMKjiihJxaTAYsFgs6vPUarXq6MK+ffvo\n6enB6/Xy+uuvU1tbS0NDw3EXUHa7nfPOO4/GxkZaWlro6OggkUjQ3NxMS0sLlZWV1NXVndJi+HRA\nKXYLCka7dpQ4TEVwUNZwd3c33d2j40V2u10VHE5mHGZHRwePPvooZrNZLe41Gg2/+MUvMkRFGF2D\nis8EwLPPPstDDz3E9u3bAXjrrbf4zGc+o3YhWK1WVqxYwWuvvUZOTg4vvPAC3/3ud7nxxhuxWCx8\n/vOf5wc/+AF6vZ6uri4CgYB67qbLiRQaEolERlfCVMQE5f/ZUjdORxRhYWBgAEka9TLIy8ujrKws\n6/uCJEl4PB56enqA0ff4urq6KZ+P++67j/Xr16tr6sknn+See+7h7rvvJhaL8etf/5onn3wSAIPB\nCpwHvM+TT/6RBx74NR98MNp988YbTVxyyR1Z16bC5s2bufbaa6d1XgQCgUAgmEk0ExlanfA71mjk\nU3XfAsFYZGT66aeLLoIE0aOniCIqqMDER67h4XAYWZax2WxZ26Hj8TjBYBCDwUBOTk7GdT6fD6/X\ni8vlUouQWCzGjh07CAaDqrjgcrlO2POMx+McPHiQQCCARqOhqqqK8vJydXQkGAwiyzIWiwWHw3HM\nxUwqlVKFBlmW0ev1qtCQfr76+/vZu3cvoVAIGG3ZX7hwYYb3w0w817a2Ntra2tSdbK1WS1lZGfX1\n9ac0NeF0RonDHBgYYHBwcFz7/KmMw5yIVCrF9u3b0Wg0LF++HFmWSSaTapGp0+nQ6/XTfpzxeJyu\nri5SqZT6nGeCZDKpjkzIsqyadx7tnCrJL+mdCbFYbNztdDpd1s6E2TT+MhMkEgm1YyFdWCgtLZ3Q\nryASidDa2kooFFK9X0pLS2d8LQ8PD6PVatO6x6IMDzchy33k5uag0eQAFcDROyYEAoFAIJhpNBoN\nsiwf0wcDITAIBFMkHo8Ti8Uwm82T7tCGw2Gi0ShWqzVjpyuVStHW1oZWq6W6uppkMsn27dtVceH8\n88+fUsrEdBkZGeHAgQPE43F0Oh3z5s0bJ2Ykk0n8fj+xWEwdpZhITJkMSZJUjwZZltHpdFgslozi\nJZVK4Xa7cbvd6of+OXPmsGjRImw228w8aT6anW5tbSUajaqXFxcXU19ff0IFnTOd9DjMgYEBdZwi\nndkQhxkIBGhqasLhcLB48eITch/RaBSPx4MkSRQXF48TGI+HyYQGSZIyOhOU95+xaLXacdGQZrP5\njBMT0skmLLhcLsrKyiY1Quzv76ejowNJkjCZTNTW1s7o66mQSqXw+/1YLBa1g0KWZXp7ezEajeTn\n58/4fQoEAoFAcCwIgUEgmCHGzsMpKRHKjt9kyLKsxgA6HI6MOe++vj41rlJJbtDpdCxZsuSEfpjs\n6+ujpaUFSZKwWq3Mnz9/0lEBJfpPMRpzOBzTMnCTJEk1gpQkSW1TTy9sQqEQ+/btU+MSlXGK+vr6\nGZ07T6VSeDwe3G632jkBox4R9fX1U5qpng3M5lnN9DjMgYEBgsHguNucijjMzs5OOjo6KCsro7q6\n+oTdTzgcxuPxABy1iJ0O0WiU3t5e+vv7iUQiGeNI6UKBVqsd15lwssSE2bA+E4kEvb299PX1ZQgL\npaWlk4qXiUSCI0eO4PP5gNH3hsrKyhNmFKsk8qT/nYjFYgwODpKTk3NCRI2zmdmwNgWCbIi1KZjN\nTEdgEB4MAsEUUNqLp1JkazQa1Y8hGAxm+DEokZW7du1SC+4TKS5IksSRI0fUWfn8/Pwp+R0o3QZK\n8sXg4OC0xiaUQsdisahCg7LLqggNNpuNCy64gN7eXvbu3UskEqG5uRmPx8PChQunbZw3Fp1OR2Vl\nJRUVFfT09HD48GECgYBqXpibm0t9fT3FxcVn9K7uiWQ6cZhOp1MVHE5UHObIyAjAlE1Mp4vVaqW4\nuJje3l56enooLy+fdrJCKpUa15kQiUSA0d9rxTQwHo8jSRKFhYXMmTOHnJwczGbzKR9LORUowkJ/\nf79qlJibm0tZWdlRu6KGh4fVcSq9Xk9VVdUJ7yBIJBJoNJnxlIovxpliSisQCASCsw/RwSAQHAXF\nwNBoNB5TsaD4MRiNRrUtPJFIsG3bNkKhEBaLhWXLlp2wD7GJRIJDhw4xPDwMoBbXx1o8JxIJ/H4/\n8XgcrVZLTk4OVqt1WkW44tQfDodJpVJq9KXFYkGn05FKpWhubqa1tVXdeSwuLmbRokUnxJyxv78f\nt9utRs/BqGFhXV0d5eXlZ2WRdqKQZVkVdNLjMNPRarXk5+ergsNMxGHKssyOHTtIJpMsXbr0pBRu\nit+KTqejoqLiqKaXindJugGjIiako9FoMjoTjEYj0WiUQCAAjIpoLpcLp9N5Vq3dZDKpdiwoa8rp\ndFJWVnbUkZxUKkVXV5faQZWTk0Ntbe0Jj9yUJInh4eGMvw8Ag4ODxONxIXQKBAKBYFYgRiQEghlm\nKsaOk6GYq1mtVnQ6HTt27GBoaIhkMsn8+fNpaGg4IY87FApx4MABotEoOp2OhoaG4xYyxo5NOJ3O\n4yrW4vE4kUhENQlUhAa9Xk8wGKSpqQmv1wugPofa2toTUjgNDQ3hdrvVIgNQoztPZIv02Ux6HObA\nwAB+v3/cbYxGI4WFhargMB2RKRKJ8P7772M2mzn//PNn4qFPCa/Xi8/nw2g0Ul5eru5SjxUTlM6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Ol0JJNJdu/ezcsvv0xHR8e8iB1ar+ahueGGGxgfH+df/uVfsNvtmM1mNm/ezIMPPkhjYyMn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\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L5cLpdMritVQqUSwWGRoaAlY6ARKJhCyyVVUlkUhI0QWej1AUO+wGg4FAIIDRaKRYLNLe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K/Xi2q1irm5uRWTKFQVST9HtY561JMMRHLQetI0DbOzswAWwh2j0SgrMDo6OviaybLM+Rm5XA6S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CtojlRIbGyp+T/AaxHx0OmM1m1cRgMBiora1l1qxZdHZ20tnZic/no7a2ltbWVvbs2UMsFmNo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DduHCtWrKCnp4d169ZRVFSE3W5PITDMZjMlJSXU1NTQ1dWFx+PB4XBgMpkoKCigtbWVmpoa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577zz2LRpE93d3VxxxRVMmzYNrVbLvHnzuOSSS3jppZf6DGJi0L/44ouZNWsWS5cu7XOs\nESNG8Pvf/56//OUv38q5966q7B30KAIbRY5CPB7H6/WSSCQIhUIYjUZyc3OlwkYQAYKQEDkJQl0A\nyAFVrNL3tmAYjUZCoVBGLoKod+zu7pbXvrgtEokQDofRaDSymnL8+PHo9XqsVqskLcSwLz6jBoMh\nwzaRSCRoamqirq4OnU6H0Whk7NixWCwWSRoIy8eWLVuIRqPY7XYqKipkaKJQKIjPb/q+BfGi1WqJ\nx+MYDAYMBgMqlQq32y3zLPY12FEg3R4Ri8Voa2sjEolgNptlTayoD12wYAFOp5PNmzej0Wj46U9/\nyoMPPsill15KKLQVi8VHKOSmoaGHK644lWnTJqLVapg3769ccskvWLUqRaCdeeaZXHzxxeTk5OB2\nuznnnHO4//77ufbaa7/Wc/gxQFkhVnCgQrk2FQw2KATDIEJt7W8zfn7qqUsyfr700mO59NJj5c9n\nnnkoZ5556ID2BVBU5GDZssv2eh46nYZVq67e4zY33ngKN954Sp/bnU4zTz/9y6yPufjio7j44qMy\nbhs7toRPP12YcduCBT+V30+YUM5nn92cdX+TJ4+Utg2AIUNyeOmluX22O/bY4T9qcgFg7NjdoZci\npb6mpkYSDPPmzePuu+9m7ty5UAuMy76fX//Xr1O6KTWMHDmSM888k/fee4+zzz6bL774gv/85z8s\nW7aMjo4ODjvsMIYPH87999/PGWecIYPwBgJBHGTLNki/fSB1cBqNhp6eHrl6C7vT/cX3wk+vUqkw\nm80EAgFCodDXzjsQMmqhSujq6iKZTOJwOOTQCKm8B+F3h9SKvs/nIxQK4XK5sNvtMoVfrVYPunR6\ns9nMokWL5M+nnXYaw4YNY926dZx11lkZ286fP58pU6b0ew1NnDiRiRMn8t5772W9f/bs2QA888wz\n38q5ZyMY0hsPhCVADOSBQIBAIEBPTw8ajQadTiffX6FyicfjqFQquaIuyC+TyURXV8oClpubK69L\nYY8QQ7wgqXq3R6S3QpjNZrq7u+XxYrEYWq0Wq9UqCQxBCArlRPq59K6HDIVC1NTU0NbWJlUZI0eO\nzMh/UKvVxONxqqurCYfDWCwWRo4cSSQSkfWQQhkhiAMBobwQGQhmszlDadDY2AhASUnJ1yIE0+0R\noVCItrY2qXgSKo5QKEQkEkGj0VBfX89NN92E0+nEZDIxbdo0vvjiC7TaCKNGJUkkXLS2tlJZaefw\nwydgsaSug/nzpzFlyo3yuMOGDZPfi9do+/bt+3z+ChQoUKBAwbcNxSKhQEEWVFdXf9+ncMBh3rx5\nWCwWxowZQ0lJCaeemmoUWbFiBUajkenTp6dUC7sr4Hl2zbOMv3I8PcEefH4fbo+bjpoOtv9nO5s2\nbeLVV1/FbDbz8ccfs2rVKoqKimhqaqK+vp7m5maGDh3Kl19+KVPeBXFgtVpxuVwUFRVRVlZGVVUV\nY8aMYfz48UyaNIkjjzySiRMncsghhzB69GgqKysZMmQIBQUFMqtgoF3zYmARQ7qo3ROheGJlVAw1\nGo0Gg8EgB4uvA6FeEIOjCKDLyclhzZo1kmDQ6XREo1HMZjM6nY62tjZpnbDZbBntEVarddCHmLa2\ntrJt2zbGjevLcK1Zs4YxY8YAqaFw2bJlHHFEX/tVeqDhd4lsVZXC3pBIJOQQLIgHcS2JsM68vDxp\naRChhoI8EcSBGKxtNhvd3d3EYjGGDEm1CvW2R4jbILM9QrShaDQaSVAJsk98BoxGI1qtlu7ubj79\n9FMZ9ihW7ROJhLQlmEwm+Vlpb29n48aNdHZ2otPpKCoqYsSIEfK1EQoOlUrFtm3bCAQCGAwGRo0a\nJVUL6daJ3qGR8Xgcj8dDPB7HYrFgs9kyMkiE0sBisXzt5gZB5gSDQVpaWmSAZn5+Pmq1Gp1Oh1ar\nJRwO09nZyWWXXcarr76Kz+dj586drFq1ipNPPhm9vpWCgvNpb08RSpFIhK1bt8rjvP32RsaNKydd\nGvbss8/icDjIz89nw4YNXHHFFV/rOfxYsGbNmu/7FBQoyArl2lQw2KAoGBQoUDAgPPTQQzz44IN8\n8MEHrFmzBoPBgN/vZ+HChbuzEnotDs+cMpOTxp1Ec3Nzxu2+XT4eeOkBEokEp5xyilxFtdvt5OTk\nSKXBkCFDaGtr45BDDpGKg/0dyCcaJdxuN8lkUq5UxuNxOZAJskLI1O12O36/H5/P1ycQciAQw6RY\nxRb5Cy6Xi5aWFkkaCCuIGDbb29sz8hdg8NojeiMWi3HBBRdw4YUXkpeXR3Nzs8xZ2LhxI4sXL+be\ne+9l06ZNRKNRxowZw9NPP91nPwO113xTiGtCVFX2bpKA3dYFEcIoVsvVajW5ubmS4BKDbDQaJRaL\nEQ6HZbWlxWIhEAhIIkCoF4Q9QqvVShtQJBJBq9XKIT0Wi8kGCofDIck1kbkghn9BYCQSCcxmM3q9\nXu4/XV1gsVhkxsSOHTtk+4ROp6OsrAyn05lBAAiSor6+Ho/Hg06nY/To0TIAE3ZbJ+LxeEZopLAV\nidrPdFsTpEiY1tZWAEm6fB1Eo1E8Ho+0pOTn52cEUkKKKPR6vfT09DB58mSee+45SktLSSQSXHDB\nBZx66qloNF/S3b0CgB076vjyyy+/qrWNs3lzA7ff/iwvv7wYCACpz/LMmTOZOXMmNTU1PPXUU1/b\n4qFAgQIFChR8m1AIBgX7Fb1tCQcqlAaJ7FCpVBx99NE8/fTTPPzww9TX13PRRRdRVlbW72OEHUGj\n0cjV2hXvreCtt97i9ddfp7y8HK1WS2trK88//7xcZYbUgJOXl/e9S/uF/UEMEWq1WsrKxc+A9MCL\nAc3r9eL3+/d5uE9XMEQiEfx+P2q1mpycHI4++mg+//xzOVgBOJ1OEokEbW1t+P1+cnJyBlX+gqhl\nFIGM6V9ikLztttsIBAJMmzaNd955Rz62ubmZW265hcsvv5yRI0fKwRfI+H5/Q1w3IngwkUjIXI30\noEchr08mk7jdbtngYDAYMuolRU5D71YIk8lEU1OqMUhYF6CvPUKtVksbgYDX65WkR3pjiQhp9Hq9\nsp1C2COEskkoQdLtPlqtFo/HQ01NjXxOLpcLl8uF0WjMWlm5a9cuPB4PGo2GUaNGyeE9PUDS5/NJ\nIlCoJcRxNRoNNputDzHZ1NQkLSPpz3lfIEgKQW4UFBRkVUZFo1GpRDn33HOZO3cub775Jt3d3cyb\nN4/Fixfzhz+cD4DHk8pQsdvtOBwO6upaOfXUW3jggbkcffRYsjVKVFVVMXbsWObOncuLL774tZ7L\njwGKz13BgQrl2lQw2KAQDAoUKNhnxGIxamtrefvtt9m1axcPPfQQkJI8n3fnefzm3N9ww89uACDH\nmUOOc3f96WOvPcbDKx7m3bXvUlFRIW8fN24ctbW1BAIB6QFfv349F1544X58ZtkhBjoxBOp0OkKh\nUAaZAEgCQqPRYDKZZEuByWQasCUjkUjIVVetVktHRwexWAyHw4FOp5N1lRaLRVol7Ha7bBno6emh\npKQEm80mKw3FEHigIZlMynPc01c4HN6jsuCBBx7A7XZz8803S8m8Wq3G4/GwZMkS5syZw8yZM6U1\nJv2rN/anjUTI/EWgoxjmhfVBqApEzoYgq0T4YXpOg7DphMPhjFYKobyx2+0Z2QMi20FcQwLisxeL\nxWS9qcvlktkrgUBAHisajeJwOIjFYtJGIYgtQQCEw2G0Wi1arZaGhgZJdpjNZqlWEuRD7wyExsZG\n2tvbMRgMjBgxIoNoTM9mSA/F9Pv9JJNJaf3QarV9FAV+v5/u7m40Gg3FxcVf670Lh8M0NTURiURw\nOp0UFBRkvXYSiQRut1u+17t27WLOnDlYLBYSiQQzZ87kd7/7HdHoFXR0fIHP5yMQCGCz2YjH9Zxy\nyk3ceusFzJp1AmAEspOt0WiU2trar/VcFChQoECBgm8TCsGg4DuFWv0rtm+/ncrK/P1yvNtue5nt\n29v7DYocKKqrqxUVw1dob2/nzTffZMaMGZhMJl5//XWWL1/O8uXLWbRoUcYq8OGHH869v7qX6WOn\nZ93X39/8OwufXsiatWsyyAVIpfQfeuih3Hbbbdx+++2sXLmSTZs2cc4553ynz29vELaHdMIg9cd/\nXCb2A1KuLsIfVSoVdrudrq4uvF4vLpdrQPaO3vWUIn/B6XQC8PrrrzNs2DB0Oh1erxeDwYDRaJRy\ncEjlLWg0GhnGZ7PZ9uvgLBoG9kYcCJJmXyCqOcXzvueee+jq6uK5556TK+EGg4GOjg4mT57MDTfc\nwHXXXQfsXkne0zkL64sY1AUJIVbExXAuSKBv8roKIiC9qlJcTyLbA8Dn80m7gEqlkgO/UDqIZgSh\nYvD7/RgMBiwWC9u3b0ej0VBSUpLRqAApdZHIFhEr7GIYb29vl/kNQmXh9/uJx+PY7XYZNmmxWDIy\nQt555x0mT56cMfirVCo2b94sq19LSkqwWCwyP0ScS/rno62tjYaGBjQaDZWVlfL6F++VIGDS81nS\n6ydFpacISk1/rKilLCoqGjDxlw6/309HR4ckWIqKivrd1uv1StuUxWJh6NChPPzww/zmN7/B7Xbz\n7LPPMmbMGDZu7MZmCxEIBLBarUQiKs4883auuuoMLr9cBCJXIBQMf/vb3zjjjDPIz89n8+bN/O53\nv+OUU/oGJyvYjTVr1igrxQoOSCjXpoLBBoVgGEQYOvQmgsEIdXVLMZlSQ87f/raWZ575iLfeun6v\njz/hhD8we/aR/PKX/ddC7uvjv4ldftiwm/jb3y7ixBNHZ73/7be3cuGFj7FzZ6blYn979Ac7VCoV\nf/7zn5k7dy6JRIKKigruu+8+TjvttD7barVanIc6MVvMEIRlby3jzufvZOOfNwJwy99vocvbxaRJ\nk+SwdOGFF/Lwww8DsHz5clm9VlFRwYsvvpgh6/4+kD6MJZNJgsGg9MWnD8fpnnYBnU4nWyWCweCA\nVATCHiEGPUESiBA6MTiJ/Afhqff5fHLoEgPVd2GP6E0cBINBqUJI/36gjR8CKpUKg8GAyWSS/6YT\nCeJnvV4vP+MNDQ28+OKLGI1GJk2aJPfzyCOPsG3bNnbs2MHixYtZvHixvN6EIuS5557jnnvu4ZNP\nPgFg7dq1Mg8EUivskydP5s033wTg8ssv58knn5T3L126lMcff5yLLrroa7+W/TVJCHWAxWKhra1N\nWkNEloAYisXgL8gIMXBHo1FMJhN+v59YLIbT6cTlcuF2uyWJIiw/ogEhEolgtVploKlQKuTk5EgF\niTgHjUZDIBBAr9djMBikqiY3N5eamhpZJyvsC7t27ZLHqaqqkscwm80yBDJ90He73XI1vry8nPz8\nTIJafCaF6kOoGYQaQgSsqtXqjMYKSBF2wWAQg8HQZ797QzKZpKurS2agOJ1OqdjIhmAwSE9PjyR7\nVCoVL7zwAtdccw1//OMf0Wg0TJo0idmzZxOJJBk/fgFPPHEVxxwzmkceWc2OHa0sXvwMixc/QzKp\nQqXSyM/0e++9x8KFCwkEAuTn53PeeeexZMmSfXo+ChQoUKBAwXcBhWAYRFCpIJFIcu+9b2RUQH6f\nA/d3mZeWGhi+m32PGjWKeDyBRvPdrfqKgedAR15e3oATjqVENwo0wSzHLGadOgtMQCnUNtTCHprg\nysvLeeutt77pKX+riMViqFQquXIeCoXk8CWGQhE6JwL70mGxWGQSv8Fg2GsVXnrAowjZE6vW8Xic\ngw46CNg9ZKWHOQrfPqQICDGMDKQuU9T5ZSMLxIArwgP3Fb0JA/F9+lfvesGBoLy8fI9ERnqFZTo0\nGg2zZs3i/PPPlz+ffPLJe9zX448/zuOPP75P57c3ZGuSEO+hqGAMh8NSOaPT6cjPz5cr9uL1EoSB\nyAZJJpOYTCbq6uqAlGJAEF+hUEiSOYKsEhD5Du3t7ahUKpxOp8x4iEQist1CtKgI8kwQGmazmSlT\npuDz+YjFYnR2dkqCLDc3l2HDhknLh16vx2w2EwwGM0gjv9/Ptm3biMfjFBQUZA1gFOqhYDAosySs\nVqskEwKBAMlkss/nLR6Py8DZIUOG7NP1Fo/HaW9vl5ajnJycjLaPbNt7PB7UajVOp1Me67DDDmP1\n6tV0dHTQ1dVFd3e3tFqtXfseOl0Cu13NrbcOZ8mSKwAbUAZkkiGPPfbYgM9dQQrKCrGCAxXKtalg\nsEEhGAYZbrhhKnff/Srz5k3Bbjf1uf/992u49trn2batjZEjC7j33vM46qgqbr75n7z77nY++mgH\n1177PL/4xVHcf//5bNnSwtVXL2fdugYKCmwsWXIG5547sc9++3s8wOuvf8kf/nA/HR1+Zs36CQ8+\nOBOA2tp2Lr/8adav34VarWbq1DE8/PAs7HYTF130OA0NXZx++kNoNGoWLTqNX/96qjxeT0+EU099\ngEgkjs12NSqViq1bU6s34XCUiy9+nJde+pyKChdPPnkJEyaUA9Dc7OGqq5bzzjvbsNkMXHvtSVx1\n1YlAyl6xaVMTRqOOl1/ewB//eC6XXHI0d931Kv/zP2vxeIKcdNJo/vKXC3A6+65Eu909zJ79GB99\nVEc8nuDooyv5y18uoLQ0lT9wwgl/4JhjqlizZiv/+c9ONm5cRF6elQULVrBq1SY0GjW/+MVRLFly\nxg+CeNgjdKTUvBV72/DAhrBHpMu3RSK8IBzEqnO6XSIdojLS7Xbj8/kypN7ZjidWXoV/XgxvRqNR\nJvqLwQ5SBINYWY3H4+Tm5qLT6Whvb5eryGI4EsOqIAvSvxep/PsCoS7Y05cILTyQoNFo9kr07K/z\nEP8Km4IIROzp6ZH2B6/XK3MK7HZ7HxWNGLI1Gg3hcFhmKyQSCaxWKzabTZInoVAIk8mEXq+ns7Mz\no+rSbDbT3d1NJBLBbDZ/JdWPyCBIQYKIa91sNstMBaGwEa0KHo8Hn8+HRqNh6NCh5OfnEw6H8fl8\nKaWT0yktPUK9EAwGqa6uJh6P43A4KC8vz3rtRKNRaa8RtbCCXIhEIjJ/QuShCDQ3N0u7wr6oeiKR\nCG1tbdIqlZ+fL4mabNeRCOQUlZXp2ySTSdrb22lubpatG06nk66uLnQ6HcXF5ahURtRqC9kCHRUo\nUKBAgYIDHQrBMMhw+OEVTJkyit///jVuv/3MjPu6uwPMmPEgDz44k/PPP5znn1/Haac9SE3NHdxx\nx3/x3ns1GRaHnp4IU6feyx13nMmrr17Dhg27OPnk+zj44FJGj870nGZ7vMDKlRtZt24hbncPEycu\n5YwzxjN16liSSbjpplOYPHkkHk+Qc855hMWLX+aPfzyPp566hHff3cZjj13MCSf0zUIwm/WsWnU1\ns2c/1qeV4uWXN/DSS3N54olfsHDhP5k3bxkffPD/SCaTnH76g5x11mE899zl7NzZxU9/ei+jRxdx\n8sljAfjXvzbwwgtzuPnmYxg6tJL773+Tf/1rPe++ewN5eVauvno5V165jGXLLutzTolEkl/+8hhe\neOEKYrEEv/zlk8yfv5yXXport3nmmY/597+vZuTIAhKJJOee+yjFxQ5qa3+L3x9mxowHKS93cfnl\nx+3Du67gu4JYrU8nDkR6v8hbiMVikhTob2gVg7YY5nvLtgXEKrGQU4vqP7EC6vf7WbduHVOmTCEa\njaLRpCTTbreb+vp6fD4fZrOZmpoaAoGArPbbuXPnPj1vITXf29eBRhz80JBukRCBhSLoUdgLNBqN\nDAoV2QtC4SBaJMTQ39PTI4fepqamrwbWYmlBUKlUhEIhGTAo1DeCUIjH4zL8UKheRMaByWSSxxSD\ntgjSFAqbaDTK888/z/jx44lEIthsNqqqqjAajcRiMTweD7A7T0SQd0J5sWXLFqLRKHa7nSFDhmQl\n7ERrSDwel9egUO0kk0kCgQAqlUqqQgQxGAwG6ejoQKVSUVpaOuD3KBAI0NHRIas6haojPW8l22PC\n4TAmkykjYDIUCsnPprCXFBQUUFtbSzwep7y8XGZp/OBJ5gMQis9dwYEK5dpUMNigEAyDELfddjrH\nHvt7rr32pIzbV67cyMiRhcya9RMAzj9/Evff/yYvv7yBiy46qs9+XnllA8OG5cn7xo8v4+yzD2PF\ninXccktf/31/uPHG6dhsRmw2IyecMJLPP9/J1KljqarKp6oqJfvMzbWyYMFJLFmyMuOxX6eT/thj\nhzNt2jgAZs8+kvvuS3moP/64jo6OAAsXpmrUhg7N47LLjmX58k8lwXDUUZWcfvp4qqurMRh0PPLI\nuzz00EyKi1M+20WLZlBRcSPPPJPoM1y5XBbOOuswAAwGuPHGUzjppD9lbPOLXxwlyZmODi+rVm3C\n47kXg0GH0ajj2mtP4tFH31UIhgME6fYIgfTUf9EaEQwGpX+9P9jtdjo6OvD5fBmS8HQEg0Ep/+7s\n7GTHjh34/X58Ph/r1q2joaGBhoYGPvnkE7kS3djYSDAYpK2tTZIQWq1WDmHpuQ86nU5mHIhcg2zf\nHwir+z8GiGtL/CuGfjGUer1eSXKJSsVEIiEVM+l2lUgkIoMXe3p6CIVCsq5UWCpEqKNWq5VZHwJm\nszkj2FGv1xONRolEIrLRRFybarUas9ksGyqcTiehUIi6ujqCwSCJRIKCggJKSkrk80pf0ddqtRnq\nhVgsRnV1NeFwGIvFQlVVlSTQBIQlQuRBCJIrEolIIkJYeIxGYx9FUWNjI8lkkoKCgj51mNkgVAiC\nQMnPz5cNG9nyVgSi0ahUaaTnM3R0dFBXVyfJmaKiInJycti8ebMMzrRYLLIBQ4ECBQoUKPihQiEY\nBiHGjSthxoyDufPOfzNmzG6lQVOTh4oKV8a2FRW5NDa6s+6nvr6TDz/cgcu1AEjlKcTjCWbPPmKf\nzqewcLcU1WzW4/en/rBsa/NyzTXP8+672/D7w8TjCVwuyz7tOxuKinb/UWc26wmFoiQSCRoaumhs\n7M54PolEguOPHyG3LytL2RlEg0R9fSdnnfVn1GqVfIxOp6G1soGa/gAAIABJREFU1SdJB4FgMMK1\n1z7Pq69uxu3u+aoyLZyRtSD2n9p3F9FonOLi/5b7TiaTlJdnvkcKvh9ks0cAUsIeiUSkL36g0Gq1\nUjoumgrSAxN9Pp+0NcDuJH9RT9nZ2cmwYcPkgCNWUAXRYbPZGDp0qFQtuFwuJkyYgMPhwGAw9OsX\nV/D9QVhYRAaFCGwUg6oICdTr9ZJ4ECv06aGQYtg3mUzU19ej1WopLS2VAz5kVnD2DgsVNaeCZNJq\ntbjdqf8bbDabtGwA8lyampqkgqK6uhq1Ws0RRxxBcXExOTk5kgjxeDzEYjFsNptUAAkCIWVv20og\nEMBgMGS094jzjcVikrhQq9VStSB+t2o0GqngEPaidOJB2JN0Ot0eGx8ERA5FMBhEo9FQWFiYoVYQ\nxGNvIk6QEpBSaYiWj7q6Ojo6OuTtRUVFGAwG2traiEQi6PV6iouLCYfDijLoO4SyQqzgQIVybSoY\nbFD+2hykWLz4dCZMuIPrrz9Z3lZS4uDFFzsztmto6OKUU1Kr/b1XVMvKXEyZMpJXX71mQMfcV0nn\nTTf9E7VaxRdfLMbhMPG///s5V121fMD721cFaVlZDpWV+VRX95+03fuY5eUuHnvsIo46qmqv+//D\nH15n27Y2PvnkRvLzbaxfv5MJE36bQTCk77+sLAejUUdn5x8VOewBCLE6nD5EpFdRqtVqenp6SCQS\nJBIJPB6PTK8XK63pGQdixVYMkmIlNh2COBAtAmL7/Px8tFotKpVKDiBarZYJEyZgtVr5+OOP2bVr\nF8OHD2fChAn4/X4pdS8sLFQUCQcw0oNCxfcqlUoGJQrFgVCiCLWDRqPJsDaIXBBRp2k2m7Hb7cRi\nMWmhEIOrCPTUaDSSRBNhjMKOI3I5TCYTOp2OUCgktxXqBp/PR1dXlzx/u91OUVGRJAIgFdoo7ALp\nCgBBnNXU1Egrz+jRo9Hr9bJuUlg6RGWoxWLB7/fLdgih2BCtFCJzIj30MpFI0NjYCEBxcfFePwvp\neQtGo5H8/Px+fwf0htfrJRqNSgWI1+ulpqZGnn95eTm5ubmEQiF6enpob29Hr9dTVlYGkKFIUaBA\ngQIFCn6oUGjyQYqqqnx+/vPDuf/+N+Vtp556MNu2tbF8+SfE4wmee+4TvvyymRkzDgFSSoPa2na5\n/YwZB7N1ayvPPPMhsVicaDTOp5/WsWVLS9Zj9n783uDzhbBaDdhsBhobu/n971/LuL+oyE5tbUe/\njy8stNPZGcDrDfa7DexusvjJT4Zisxm4++5XCYWixOMJvviiiU8/revzmOrqagCuuOI4brrpnzQ0\npGrY2tt9/Otf6/t9PiaTDrvdSFdXgMWLX9njeRUVOZg6dSwLFjyPz5cKLKutbeedd7bu8XEK9g8i\nkQjhcBiv10tzczM7duxg06ZNbNiwgU8//ZQPP/yQjRs38sEHH/Dxxx+zdetWPvvsM9avX8/WrVup\nr6+nra0Nr9cryQVArjrbbDZyc3MpLS2lqqqK0aNHU15ezpgxY/jpT3/KpEmTKC0tZezYsRx//PEM\nHz6ckpISGhsbcTqdFBYW4nQ6MyTZeXl5ANKGIQIpFRy46K9Jwu/3y2Faq9ViMpnkMC0ISTG4RqNR\naa3o6upCo9HITIR0FYBY/RfXRDrhIIIVReCkyDIQAZEi1FCcS21tLY2NjahUKkwmE8XFxVRWVvLO\nO+/I8wwGgwQCAXQ6nQxVFKGUarWaxsZGOjs70Wg0jBo1CpPJJFsrYHeWgU6nkxYkEeAoAi61Wi2J\nRIJgMIhWq0Wv10sVCCDDTs1mswyi7A89PT0yCNJms2Ul54SKo7caSLTLiHaMhoYGNm/eLG0fBx10\nEEVFRZIAEqRHWVmZzHUQz/3rWAMV7B0DbUNSoGB/Q7k2FQw2KAqGQYTeq+CLFs3gmWc+kiv9LpeF\nV16Zz9VXL2fu3GUMH57PypXzpS3hmmtO5OKLn+DPf36H2bOP4N57f85rr13DggUruO66F0gmk4wf\nP4Q//vHcrMfP9vg9LczfeusMLrrocZzOBQwfns/s2Ufypz+tlvf/v/83nauuWs5///eL3HzzqVx3\n3ckZjx81qoiZMydRWbmQRCLJ5s2L+3ldUv+q1WpeeWU+1123gmHDbiISiTNqVCF33HFm1selnlMq\nx2Lq1HtpbvZQUGDn5z+fyBlnjO+z7bXX/pRZs/6HvLzrKS11cv31J2eQEdlUCk89dQm/+c0/GDt2\nMX5/mMrKPH7zm2n9ns8PDiFSlZUGIHse2n6HGHCEuqC/L7EinD5IiLR+YYsQCfbp4YyRSET6w9Nz\nDdK/RO2gw+GQsncR6Od0OrHZbLLyMycnZavx+/3y/GF3PaXP5yMYDGIwGKTnW9QAGo1G3G53Rk2e\nghTEQAd8r5L03gSDqHEUxJHdbsdms0kFi1AHiIYJ2E00xONx2UxgtVoJBoPyOgmHw3Lw9/v9ModA\nqAkMBgNOp1O2RojrWa1WEwqFpKIGoKmpiW3btpFIJCguLqa0tBSHwyFf0/TnIa5pcf3FYjHZetLS\n0oJKpWLEiBFYrVYAmUMh9iMaLwCZG2EwGOSgL3IlRLuKICfEObS0pAjxPdVSChuHsDfk5eXJ8+mN\nbPYIoWAS9Z+bN2+WLS/FxcWUlZVlZGy0tbXJxpfc3FypLhHva0oREgPipDp+lT/VFChQoEDBDweq\n74spV6lUSYWl//p49NGFzJnzA+8AVLBf8eij9cyZ89uv9djZs2ezevVqgsEgRUVF3HDDDVx66aUZ\n2yxZsoTFixezevVqThx/ItQCwpGjAgpgTfMaltyzhM8++wyXyyWHaIH333+fBQsW8OWXX1JZWclD\nDz3EMcdktpL0B0Ec7Ik0EF97+90jBgGRcC8832JF12KxYDAYSCQSGAwGLBaLHEjESvPe9i882Xl5\neajVatrb23G73RQXF2M2m1m7di1er5cjjzySvLw8NmzYQCgUQqvVEovFGDVqFA6Hgy1btrBhwwby\n8vKkj/Ozzz4jkUhQVFREOBympKREytMHEyKRCFdeeSWrV6+mu7ubqqoqli5dyvTp0/noo4+45ZZb\nWLduHVqtlilTpnDfffdRWFgoVQICKpWK9957j6VLl/Z7bdbX13PJJZfw0UcfUVFRwQMPPMBJJ53U\n+5T2GbFYDLfbLVf0hXWhoaGB2tpacnNzqaioIJlMkpeXh06nw2Aw0NXVhd/vp6CggM7OTkl0hcNh\nCgsLMRqNWK1WCgsLCQQCUjXgdrtpb2+Xq/kejwebzUZRUZG0J4gcELvdjk6no6Ojg66uLhKJBD6f\nT1pwiouLGTt2LGazWYY1BoNB9Ho9Ho+HZDKJy+XKyCkR1oDm5mYAqqqqyM9Phf2K/QuSThAc4j6R\nM+FyuaQlwmQy4Xa70el0OBwOwuEwkUgEq9VKfX093d3duFwuKiqy/38pPovCblFQUNBvy0sikZCK\njPSgyK6uLmkhaW1tlRaKqqqqjKDH++67j8cee4wtW7YwY8YMVqxYIRs0hEIkFmtArW7gssuW8umn\n26ivb2PNmsc4/viZpJja1HV/9dVX889//pNYLMYxxxzDX/7yF4qLi7/mVahAgQIFChRkx1eKwX1a\npVJocQUKFOwVN954I3/9618xGo1s3bqVyZMnM2HCBA47LNWaUVtbywsvvEBJSQl0AeuARNoOkkAr\nWHZYuHTmpcyaNYulS5dmHKO7u5szzjiDRx99lLPOOotly5Zx+umns2PHDkwmUwZBIDIOen8vVi8H\nCrHi2LtNQagUcnJy5ApqJBIhGo3KCkmx0iv86unb7I1gEBYJEfjocDjk6qzRaJTScIPBgNVqlQOI\nCMdTq9WS0GhrawMgPz9fyt/F4JWbm0tTUxMej2dQEgyxWIzy8nLeffddysrKWLlyJeeddx6bNm2i\nu7ubK664gmnTpqHVapk3bx6XXHIJ//jHP/oQTGIF/+KLL856bQLMnDmTY445hlWrVrFy5Up+9rOf\nsX37dnJzc7/RcxDXivg3kUjg9Xrx+/1ycBctDYL0Eqv7arVaKluETcJiseByueQ1JKwEol0BkBkF\notZSKB4EEaFSqdDr9Wi1Wln96Ha7JfEQj8cpKiqiuLgYvV4vCQTRMiFCTJ1OZwa5EI/H6erqorGx\nUWYSCHJB5EKInIferSxCVSGeQywWQ6/Xy7BKcX3HYjE0Gg2BQIDu7m7UanXq91IWCDVBNBrNyDrp\nD+mqCYFAICCPJc4lNzeXYcOG9dmX3W7nsssuY+3atRlhjuIzbTY3EQptJRQKc/TRY1iw4CzOPXcp\n0A58BBwBGLj33nv56KOP2LRpE3a7ncsvv5yrrrqKF154od9zV6BAgQIFCvYXFIJBgYIsqK6uzkgz\n/7Fj7Nix8nsRWllTUyMJhnnz5nH33Xczd+5cqAMOyr6fSVWTmGSbxBuhN4DUH9aCNFi5ciUul4sR\nI0bw6aefUlZWhsViYdGiRZxwwgn7fM6CODAajXv8vrdsWgxUWq1WDjOiUULI1NNT8MXKcSgUwmAw\nEI1GM0Lu+oPJZJJWCYPBIL3mWq1WBvzZ7XYMBoMM4NNoNHzyySdMmTIFjUZDMBiU1goxqAl/vd1u\nlwNqIBCQw+lggtlsZtGiRfLn0047jWHDhrFu3TrOOuusjG3nz5/PlClT+lWvTJw4kYkTJ7J27do+\n923bto3//Oc/vP766xgMBs4++2zuu+8+XnzxRebMmfONnoMIIxShh6Ktwe/3YzAYyMnJkddVJBLB\nZDJlXF+iRtLtdqPVaikuLpYhkMJGIQZ/vV4vSbhQKITf78dut5Ofny9zFgTZInIOvF4vO3fulLkG\n+fn5Uq2Qk5OTsZIfj8d54403OOqoo7BarX3qILu7u9mxY4dscygpKSGZTBIKhTLsR71rXBOJhFRg\niGMLRCIR2ZAiAldFiwpAUVFR1us+GAzS3t4ugyFzc3P3aiPqbY+IxWK0tLTIthetVsvQoUPlZzEd\ngUCAI444glgsRkNDA52dnfI1SyQS6PUR1Opa+R7NmTMds9ksG4ygB9gGHERdXR3Tpk2TmSs///nP\nuf766/d47gpSPnclrV/BgQjl2lQw2KAQDAoUKBgQ5s2bxxNPPEEwGGTChAmceuqpAKxYsQKj0cj0\n6dNTluHdf/vz7JpnuXP5nay+fTWRSET6i9d3raenp4eVK1fKbbdv304oFKKmpkbeFo/HqauryziP\nbJkGvb8MBsPX9tULX3v66qMY0MRt4mdhoTAYDJJgAOSK6N5gt9vp7Oyko6ODZDIpBzLRSOFwOKRn\nHrLnL4RCIYxGo5Ri+3w+uY1KpcLhcNDR0YHH45EDyWBFa2sr27ZtY9y4cX3uW7NmDWPGjCGRSBCL\nxVi+fDkPPvggH3/8ccZ22ZL8v/jiCyorKzNUIOPHj+eLL774Vs5bkAGiGSEUCslWCYfDIYd9cd2J\na0+QXslkknA4jNlsJicnRzZPiKwRoVgQxwGkLUNUMApSy2g0otFo0Gq1tLa2Ul1dLWs0Kyoq0Gg0\ntLe343A4sNlscigXNZFCKdFbMdPT08OWLVtQqVTS9hGPx6XVQSghQqFQHwVQOBwmkUhkBGEC0lYg\nGjbEe+f1emXlZkFBQZ/X2+PxSNLO5XLJz9OeINojBFkRi8Worq6Wr6PdbqeqqqoPqSLOa+fOnSST\nyT6kjHg/iooqWbnyVo4+eqysIE3PvUihGRjFpZdeyjXXXENzczMOh4O///3v8vexAgUKFChQ8H1D\nIRgUKMgCRb3QFw899BAPPvggH3zwAWvWrMFgMOD3+1m4cCFvvJFSJNDLoTBzykyOqTpGJqYLxPwx\neuPggw/G7XazceNGpk2bxurVq2ltbcXhcHD88cdL8uC7DuTLFuLW+zZRJSh+NhqNsopSr9dL+fbe\nVkS1Wi0Wi4WmpqaM1en04Qd2BzwmEgkmTpwoB6L29nbi8Tg2mw2TyUQ8Hsfv98usCNhNYni9Xlwu\n1/caaPhdIhKJMHPmTM4//3ysVis1NTXSQrNp0yZuvfVW7rnnHj7//HMADjroIJ566qk++8mmcPD7\n/Rleeki9rk1NTd/KuYuhWVSfer1ebDabVAH0rphMV9IA0v/vcrnkNScIhp6eHhlWKtpM4vE44XCY\nvLw8XC6XHPTTFTm1tbV0dnbKysvCwkLy8/PZvHkzyWRSKiUERGjq8ccfL8ktgUgkwqZNm4jH4+Tl\n5VFZWZmROyCUNpFIRL4eAvF4XDZgiM9cMBiUto/0zBOh2BDBjqWlpX2UEJ2dnQQCAdRqNQUFBVkJ\ngWwQ5IVQBG3ZsoVQKIRaraasrIySkpJ+P1sNDQ2SQBoyZIi8PV0J1d39Oil/GRm1oL3OAggyYsQI\nysrKKC0tRavVcvDBB/PQQw8N6Hn8mKGsECs4UKFcmwoGGxSC4UeCJ5/8gP/5n7W8++4N39o+b7vt\nZbZvb+fpp3/Jzp1djBt3Gx7PvXsdqurrOxk2bCGx2MP7Zdix2a5m48ZFDB2affV22LCb+NvfLuLE\nE0f3uW/t2u1cfvnTfPnlbd/1af4goFKpOProo3n66ad5+OGHqa+v56KLLpI97mR564XiQKfTya8h\nliEYDAaOP/54eb9Go6GkpITrr7+e++67j2nTpnHyySczatSovdbLfVsQQ4tI2xe3CV+4SPEXK5li\nm3QVg16vl4ODSL/fEywWixz4RMheKBSS1X7xeJxAIJCxCi1Wh9vbU7WwYpXW5/ORTCaxWq1SbaHR\naLBarfh8PgKBgCQefihIJBKEw2H5uvRu/xA2k9/97neEQiGmT5/OBx98IB/f3NzMrbfeyqWXXpph\n9QGyDHDZ216sVmufuk8RjvhtQAzIfr+feDyOz+cjNzeXkpIStFptRu1iKBTCZDIRiURkraGwCYiV\n/HTyq6enB6vVikajkWSDsEKUlJRIhYxoYfD7/bS0tMhrTgRLms1mfD4fPT09UikhEI1G8Xg8qNVq\ncnJyMn6vx2IxtmzZQjAYxGq1MmLECDk8i8rL9CBHEaoqIEgH8ZyEDULkQYgWFvHZ7erqIh6PY7fb\nM0ihWCxGW1sbkUgEvV5PQUHBHvMWekOoCVpaWqRlRK/XM3r06D1eB+3t7Xg8HgwGQx/CI10JBWqS\nSWSOi7CD9IWaK6/8FeFwmO7ubsxmM3fddRfTp0/nww8/HPDzUaBAgQIFCr4rKATDjwjfRUud+GOp\nrMyF13vf93ou/cHnu19+f8klT1BW5mLJkjP2+BiRwXDsscMVciELYrEYtbW1vP322+zatUuunrW3\nt3Penefxm3N/ww0/S5FZhQWFFBYU7n6wGgrjqX753sTBcccdJyXr8XicysrK/eot7m2FgL6999mC\n3mC3ikF44kXmwUDqIcXqrEjwj0ajmM1mGfiYXqe4efNmDj/8cFmlmS1/offA43Q68fl8uN3uA4Zg\nEN773mRBbxIhHA7vtfXjoYcewufzcdNNN0lyyGAw4PF4WLp0KXPnzuWCCy7AbDbL8ELxb29kIz3H\njRtHbW0tgUBAkjvr16/nwgsv/FZeC0EGuN1uAoGAfA5FRUW43W5JaIl8D7Gqr1KpJFngdDozrlUR\ndhiJRNBqtajVaoLBoFTDaLVa2bogbAnt7e14vV7C4TAWi4W8vDxJPuj1elpaWqStQiAej8t2B7PZ\nzHvvvSdX4xKJBNu2bZO1qYJcEHagdJVPOrmXvm9B7omVfmGzAr7KKNhNToRCIbq6utBqtZSWlsr9\nhEIh2traZPVmbm7uPpHbwoLS2tpKd3c3iUQCm83GyJEj96iACAQCNDY2otFoyMvLk2SIgFBCpdQm\nOUSjDSQSCUmcaDS9z9EMWFm/fj1Lly6VBMpVV13FokWL6Orq2m9k7A8Ris9dwYEK5dpUMNigEAwK\nFCjYI9rb23nzzTeZMWMGJpOJ119/neXLl7N8+XIWLVqU4RM+/PDDuffKe5k+enrWfSWTSSJ5ESKt\nEbkynb5S9/nnn3PQQQfR09PDokWLKC8v5+STT94vzxP6t0eI0Lx0SXPvASWbiiF95bk/iPA60RbR\n0dFBLBbDZrOh1WrlQCggBlyPxyN99+mZDEAfT7nIpRAD+0DyIb4uRF3onhQHA60LzYb0kE6j0chd\nd92F2+3mpZdewuVyydubmpqYPHkyv/71r7nuuuuA1HspVsT7O28Rupd+bY4YMYJDDz2U2267jdtv\nv52VK1eyadMmzjnnnG/0WgkIIkAELxoMBlwuFyaTSdY9ijpDrVYrlQhiNV+r1WI2m+VnUYRGim3E\nzz09PbIG02azyWOKqsxwOIxWq6WoqIjc3Fz8fr/cfzKZxOPxoNfrZXOGuE3kLqQrdpLJJDU1NXg8\nHgCGDh0q2y+yVbkmEgl5v4CwdIjPmrBHRKNR+T4LxONxmpubUavV5Ofny/u8Xi9dXSnrQU5OTh+r\ny0DQ0dFBS0sLwWBQ7r+oqGiP5EIsFqOurk6SP1arVdbfxuNxotEogUAAg8HwVTWnBYNBh9GoSstg\niH31OkQJhyMYDCkFzqRJk3jqqaeYPHkyJpOJhx56iNLSUoVcUKBAgQIFBwQUgmEQ4a67/s1f/7qW\ntjYf5eUu7rjjTP7rvw7Nuu2CBc+zbNnHhEJRhg7N49lnL2Xs2BK83iDz5y/n3//+AotFz2WXHcvC\nhXsPj+ptezjhhD9w3HEjePPNLWzY0MjRR1eybNlluFx9q/JefPEzbrjhRV55ZR5VVflcdtnTrFq1\niXg8yciRBbzyynzy8zNXXZ944n3+8Y//8K9/zQNgxIhbmDChjOeeSyW6l5f/P155ZT6HHDIEtfpX\nbN9+O2+8sYW///1j1GoV9977BiecMIr//d8rAfjPf3ayYMEKGhq6mD59HE8++QsA3n57Kxde+Bg7\nd/4OSNkp5s8/gaee+jBjW71+8H6UVCoVf/7zn5k7dy6JRIKKigruu+8+TjvttD7barVanIc6MdvN\n4IVlby3jzufvZOOfNwLwTsM7nHDaCXLV0mw2M3nyZN58800A7r77bv7v//4PlUrF9OnTeemll/bb\n88xmjxCrxWJwypQ090W6ikF44PdGMIRCISA1/CQSCdra2mQYHOzOXxDnNn16irxpaWmR24kV3kAg\ngEqlkhWW6XA4HLS1teHxeLIG3w0E6cRBf4oD4Y/fV+j1elkT2t+/vcM7GxoaeOGFFzAajRx6aOp3\nnUql4pFHHmHbtm3s2LGDxYsXs3jxYtl+Igic5557jnvuuYdPPvkEgLVr13LKKaf0e20uX76ciy++\nmJycHCoqKnjxxRe/cUWlgKgXFSv2ZrNZthqIUMf01oj0vI54PE5ubq60DwgrjdgvpIbdZDIpq1Bt\nNhsGg0GGIYpgSYvFQlFRETk5OXg8HjkMiyFYq9Vit9vl58Hr9RKJRDCbzZKYE6tw9fX1dHZ2kkgk\nKC0tlQSE0WjMquoRGQe98xTSmy+EvUPkNqTvp6urSypMioqKSCaTdHZ2ykySgoKCPgqCvUGQBF6v\nF7Vajclkwul0Yjab91j7mkwmaWhoIBKJYLVaZVDnHXfcwW233SbPe9myZfz617/mhhtuYNiwEfzf\n/63guOMMQJhRoy6noSFlgZo+/RYAduzYQXk53HPPPVx99dWMGDGCaDTKQQcdtF9/V/5QoawQKzhQ\noVybCgYbBu9U9CPE8OEFvPfef1NYaGfFinVceOFj1NTcQWFh5mrma69tZu3a7Wzffgc2m5Hq6hac\nzpR3d/785fh8IerqltLe7mPq1PsoKXFwySXH7PX4vf9mfPbZT/j3v69myBAn06ffzz33vMbSpZnV\ncY8//h533vlv3nhjAcOG5fHoo+/g9YZobLwLvV7L55/vxGTqO8xNnjyS665bAUBzs4doNM4HH9QC\nUFvbTiAQ4ZBDhmSc1+WXH8f779dktUisWLGO1167BoNBy9FH380TT7zPnDnHZ31ee9p2MCIvL481\na9YMaNva2tR7QAJohVn5s5h11iwwAqUwefpkElck+n38smXLvvH5fl0M1B7RW+GQjnQVg06nkxWB\n/W0PyKHPYrEQDAYJh8OoVCrsdjvJZBK/3y892WKlGlKrqpCZvwCp4THb8Ww2Gx0dHdLfn75NNBrt\nQxZkIw/EoLcv0Ol0eyQNvkl4Z3l5+R7PKb3CMh1arZYLLriAmTNnAqmhdurUqXvcV3l5OW+99dY+\nn+NAoFKpcLvdeDwerFarfF3i8bhUH0DqtRSqAnFfIpHIeP1Eg4l4jEqlksSQsDqYzWbZbKDVatFq\ntbhcLvLz86USQrRtJBIJWZWq0WgkqRIIBGTFqslkymh/aGpqoqWlhVgsRnFxMVarFavVusea1EQi\nISs7AVlbqdfrZQBlOrmSrsKJx+M0NjaiVqtl9WVLS4usfi0oKNjnilafz8f27dsz2jlEuKbT6dyj\n9UkQeaKOU/zOuPXWW7n11luJRCK43W753lksFvn5hSjQxI4dL5MKdbQC5cDu/8ddLhfPPPPMPj0f\nBQoUKFCgYH9BIRgGEc45Z4L8/txzJ7J06So+/ngHp58+PmM7nU6Dzxdi8+ZmfvKToYwaVQSk/sB7\n7rlP2bDhFsxmPRUVuVx//U95+umPBkQw9MYllxxFVVXKG37eeYfz8ssb5H3JJPzpT2/w+OPv8/bb\nv6a42CHPrbPTz9atbRx8cCmHHVaedd/DhuVhsxn5/POdVFe3MG3aWNav38XWra28/34Nxx03PONY\ne8M115woiZjTTz+YNWs29Usa9N7288937f0APzaogeKvvn4gEGRCb3uEsEOIgWtvuQpGo5FQKCSt\nFOLf/iAUDEajEZ/PJyv7tFqtlK8LCbrdbuftt9/myCOPlKuqgmDIlr8QjUYzSIL29nY6Ozupq6tD\no9FI4iBbNePeoNPp+iUL0n/e03P/vqBWqwcUwLm/4Ha7CQaDuN1u7HY7OTk58n3X6XSEw2FptxFK\nm3A4jEajwWg0yoYCYeERzRNiYA8EArjdbhkQGo/HaW9tUu9qAAAgAElEQVRvR6VSkZeXR0FBgQwU\nFTYE2H1tilaD9NwGv98vf05vf/jnP/9JYWEhkUiE0tJSWWe5twE/nYgTz108J/GZEGqe3gqd1tZW\nIpEIDocDi8VCc3Mz8Xgcs9lMXl7ePpFXiUSCxsZGmpqaZGBqUVGR/Pw7nc49XtN+v1+2i1RUVABI\nYiKRSBAIBORnThAvmb9PdEDFV18Kvk0oPncFByqUa1PBYINCMAwiPPXUB/zpT29QV9cJQCAQpqPD\n32e7E04Yxfz5JzBv3jIaGro5++zDuOeec+jpiRCLxSkv3+3jrKjIpbHR/bXOp6hot9fVbNbj94cy\n7r/nntdYtGiGJBcALrroKHbtcnP++X/F4wly4YVH8Nvf/leWsKuUiuGtt6rZvr2NKVNGkpNjZs2a\naj74oJbJk0fu07mmqzzMZj09Pdl92tm2bW729rutgh8G0vMSxB/7wosvVkr7C3fsDbVaLUkGEbYn\nfPC9IQLrxAq0GKCE1UIMegIiW6GtrQ2fz4der6e7u5uWlhY2bdqE3++nu7ubTZs2Za25i8VidHV1\nZaxE94ZI9s9GFqTfvi8J/Ar2jObmZvx+v7x28vLyMtpKBHEgrqFQKCRl9yIbRNgpxHsuMg30ej2t\nra1yoHW73TKrQaPRMGTIEMxmswwnFTkBIpPCZDJJVY2w8Xg8HrmSn57z4PF42LVrF06nk5KSEvLz\n89FoNHslc3rnL4jj6XS6jGtYNK2k7y8ajcqBPjc3V1ZUOp1OHA7HgEJWBUKhENu3b5efw6KiIvLz\n8wkEAtK6siebRTQapa6uDoCSkhIMBoPMtQgGg/T09MjnaTAYsFgs+3R+ChQoUKBAwQ8Byl+IgwQN\nDV3MmfMMb711HUcdVQXAYYfd0e/q/fz5JzB//gl0dPg599xH+P3vX+PWW2eg1Wqor+9i9OiUqqG+\nvpPSUue3fr4qFbz22rVMm3YfhYU2zj47pb7QaNTccstp3HLLaTQ0dHHKKfczalRhVgXF8ceP4OWX\nN1BX18nChaficJj4+98/5sMPa7nqqhP7Oe7A/piz2/c9CEzBDxd7s0eIwS1buGM2ZFMxZAtWTFcv\nQMpHHgwGcTqd7Ny5k87OTmlrUKlUMgn/xRdfpK2tjdzcXD799FNisRg7d+5ErVb38aYLiNVrQZCU\nlZXhcDj6kAj7KiVX8M0Qj8dpaWmhu7sbk8lEXl4eRqMxg2CA3facZDKJ1+vF5XJhtVrp7OyUQaQi\nw0HkNgiIwVY0d4hgUKEEENeLUM0Ia04ikcDpdNLa2gqkckJEY4TT6eyjqNiyZQvjxo2jsLCQYcOG\nSavQ3n7vpucvCHuGwWBApVLJ+0SbSu/sg6amJknSBYNBqcrYU0ZCNrS1tVFfXy9zHyorK3E4HHR3\ndxMKhaSCqD8kk0nq6uqIRqPY7XYKCgrk6+7z+aRCw2KxSNJhf9Q0K9gNZYVYwYEK5dpUMNigEAyD\nBIFAGLVaRV5eKqn6ySc/YNOmxqzbfvppHYlEkgkTyjGZdBiNOtTqlJT2vPMmsnDhP3nyyV/Q2Rng\nT396g//+76kDOod9yXVLJmHcuGL+/e+rmT79fnQ6DaefPp41a6rJy7MydmwxVqsBna7/gW7y5BFc\nd90KiorslJQ4sdmMzJ79OPF4gsMOK8v6mMJCO7W17QM/UQU/CvS2RwhCQSga0r3oA4HIYhAydqEm\n6B2O2NLSQmdnp1xB3rZtG+FwWBIKXV1d0osu9ge7gx/tdrusuHQ4HOTk5DB8+PAMxUFv4sDn89HS\n0oLNZqOoqOjbfikV7CM6Ojrke15cXExpaSnhcFgGLArCSGQchMNhqRhwOp10dXVlWBtEE0Y6GREM\nBvH5fBQWFmIwGCgvL0ev1+Pz+SRBIEgAYXfo6emRQ3AkEsFoNBKJRGTDifgsCNn/tm3biMViFBQU\nMGrUKHmtDoSwEscXeRFCvQC7AypF7Wv6ZzAQCNDR0UEkEpEZCQUFBftkf4lGo+zYsUM2TbhcLoYN\nG4ZOpyMSieDz+dBoNFKt0R9aWlrw+/3o9XoqKiqIxWL09PRktHyYTKYBK6EUKFCgQIGCHyoUgmGQ\nYMyYYq6//mSOPPIuNBo1F110JMceOzzrtl5viAULVrBjRwdGo45p08Zyww3TAHjggfO56qrlVFbe\njMmkY86c4wacv5C+SLW3FStx9yGHDOHll+cxY8aDPPGElu7uHn71q7/T2OjGajVw/vmTmD37iKz7\nGDGiEJvNyPHHjwDAZjNSVZVPQYEt4/jpp3Lppcdw7rmP4nItYMqUkfzjH3OznqvX6+nnvBU562BD\nf/aIdEWDWD3ubQtIJBJZKxiFHLqzs5NoNCrVD70f39XVJfcdDAblirVer5ehjmq1GofDQWFhIVVV\nVXz88ceUlpai0+k444wzcDgc1NbW0tHRQXl5+V5JA6vVikajwefzkZeXp1gdvmc0Nzfj9XqxWq2y\nnlJkCiSTSSnJF2GDPT09mEwmdDodWq0WjUYjFQwiJ0BYIHw+Hx0dHTIQ0mAwkJOTQ25urhyeY7GY\nzBYR16pQT1itVlkzKQgGk8kk1QHJZBK32011dTXJZJL8/HxaW1sZM2aM3OdAVukFQSKOLZomRA2n\neC3SMz2SyST19fX4/X6pyCgsLNynzA+3201tbS2RSASNRkNFRUVGw4oIYszJydkjaeH1emlpaUGl\nUlFRUUE0GpWqBbPZLD9zkCI09hQUq+C7g+JzV3CgQrk2FQw2qL5Ondi3cmCVKvl9HXsw4NFHFzJn\njhIC9V2hurqaUaNGfd+n8a3i0UfrmTPnt9/3aRxwEMqC9FwBEcQmBn+xShyPxzOIBLFK2x/C4bBU\nQgCSODAajTKZ32w2U1lZSUdHB01NTeTl5fGTn/yEaDRKdXW1DP4bOXIkeXl5PPfcc1KxMG1aihj8\n/PPPiUQiHHTQQbJlYk/o7Oykq6uL3NxcXC7XXrdX8N0gEonw7rvvUltbS35+PqWlpVRWVtLV1UV7\nezvFxcWUlJSwZcsWOjo60Ov11NTU4HQ6qaqqIi8vj127dhEIBCgqKqKnpwedTkdOTg6hUIja2lop\n+RcVkwUFBZSWlhIIBGhra0Ov10urjM/nk+oJv9+Py+WiubmZSCRCSUmJbFMQ1gW/38+mTZsIh8Pk\n5eUxduxY1q5dy1FHHSXrK/c2SCcSCXp6etDr9VK9IFQbYlAXJJxo2ADYtWsXO3bsAGDo0KGUlpYO\n2HKQSCTYuXMnzc3NQIp0q6qqyshXCAaDtLS0YDAYKC4u7pdcjkQiVFdXE4vFKCwszGjhMJlMGbYK\nofbQ6/UDVkMp+PagDHEKDlQo16aCAxlfKSL3aYVVWbpSoCALBhu58L3BC0RI1VRa97LtfoDwoKd/\neb1e/H4/yWRSBit6vV6pOBCJ9nq9fo8KFmFjSA9CNBgMRKNRTCYTVqtV+t5VKhU9PT00NjZKq8L6\n9euxWCwUFhZitVppaGgAdhMgwi9fWVnJjh07yMvLA1I5DiKgb08BdOlwOBx0dXXh8XjkwPhjg2gn\nAKTNZH+jpaWFYDBILBbDYrFQVFQk1QhiwBaWB7VajdvtxmAwSEtNLBbDaDTi9/ulSiaRSOB2u2VW\ngiAFRA2i1WqVz1vYf8S/IhsgFApJa0Q0GkWr1WIwGHA4Utk04nNSU1NDJBLBarUyevRotFotkydP\nJhAIoNFoBrRKLzIWRNCjyF6A1LUfiURk64fIfOjq6qK+vp5kMsmQIUP2qSmip6eH7du309PTg0ql\noqSkpA85EY/H6erqQqVS4XK5+r020nMXjEYjer1eNr6kq08EhLJEsUd8P1AGOAUHKpRrU8Fgg0Iw\nKFCgYK+YPXs2q1evJhgMUlRUxA033MCll16asc2SJUtYvHgxq1ev5sSxJ8J2IL3ExAlr2taw5N4l\nfPbZZ7hcLmprazP2sX79eq666io2bNiA3W5nzpw53HzzzXs9P5F4n25RyGZdEAF2vR+nVqszPN+w\nO49BSLazBSL2/j7bIBIIBAiHwzLxX2wjAh5NJhORSIRAICCT+SGVsyC874LoAGhvT2WICCtEej3l\nQIdkrVaL1WrF7/cTCAT61P79UBGJRLjyyitZvXo13d3dVFVVsXTpUqZPn85HH33ELbfcwrp169Bq\ntRx33HHcfffd8nUUlgPxGno8Hq655hpWrVqFSqVi7ty53Hrrrd/q+TY3N0uFisPhwGq1SuWMCDwU\n7RKCALBarbI2UkjwYXe1o1DFRKNRWREpLD/CYiByDdLtFSJEUavVSkLN4/EQDAYpLS3F6XRKYiwa\njbJz5055XY8ePVpaCEQDxUBzEATBIM4jffgWLSpitV+tVtPW1iYrJPPy8sjPzx+QzSeZTNLS0sLO\nnTtlnsrw4cMzal0F3G43sVhM2lb6Q2NjoyRySktL0ev1WCwWSf6kn5fIkRhoUKwCBQoUKFDwQ4VC\nMChQkAWD0SLxTXDjjTfy17/+FaPRyNatW5k8eTITJkzgsMMOA6C2tpYXXnjh/7N33mFSVnf7/0yv\nOzPbd7YvKyJgV7AlEjVBFGMXAQELJqLGQmISkYSgMdiNJVjw1Sg/W9AkpmAJitiikpdEX1BZyi67\n6/YyvbffH5tzmN2dWXYpgutzXxcXuzPznKedB+Z7n/t735SWlkIX8EmGQdxgabQwf8Z8Zs+ezbJl\nywZ9ZPbs2VxwwQW8++671NfX861vfYuJEydy6qmnZvQ5SH9td1qudDodRqORnJwcLBaLXHE0Go2y\n71pInXd31VFETgojO1F4pCdI+Hw+qXQwmUxSSh0Oh8nJycFsNhMIBEgmk6xfv57DDjtM9ooLgmEo\nh/tMsNvt+P1+PB7PqCEY4vE4lZWVvPfee1RUVLB69WpmzJjBpk2bcLlc/OAHP+C5555Do9GwcOFC\nFixYwCuvvALQL70A4MYbbyQUCtHU1ER7ezunnXYa1dXVXHrppXvlWAOBAB6PB4/HQ1VVFaWlpTLp\nQahnYrEYHo8HrVYrVTb5+fly3icSCXm8IjpSKA8cDgc5OTmEw2GSyWQ/48T0lfR4PE4oFCISicj0\ning8LiNQ1Wp133MN8hi6urokIXbQQQf182R46623OPnkk4ft7SEIBkGACAhCRbQVibSNYDBIKBTC\nbDZTVlYGsEulRDQaZfv27dJPoqCggOrq6ozHKAhBQcJlI+16enqkimLMmDE4HA4MBoM0iB2YnpEp\nqUbBVwtFhq7gQIUyNxWMNij/0ylQoGCXmDBhgvw5lUqhUqnYvn27JBiuvfZa7r77bq6++mpoAg7L\nPM6ksZOYZJnEW9G3gL7iOJ0saGhoYMKECbz99tuEw2Fqamr44x//KCXdw4VoFxiYpDBQcRCLxYjF\nYjKPPr0fXK/XyyJuT4oCkXkvZOdiFTMUCknptyAYrFYrRqNRriaLYrOkpESa2qVSKex2OzqdTqYE\nwMgJBkGaBINBotHoiJz3D1SYzWaWLFkif58+fTo1NTVs2LCB8847TxI9AFdddRVnnHFGv+2Fz4ZG\no+Hvf/87r7/+OgaDgaqqKubPn89TTz211wiG1tZW/H6/nKsFBQUy1lCr1cp7A0ilgUajkcSQIEQA\nmTpSUFAg1Q+5ubnSrDGZTPYreEVsqtiHSIywWCx0d3ejUqlwuVwkEglZxAeDQdRqNV6vl56eHlQq\nFTU1NZjNZrkiL5QRw51LwsRRHF/6cyaSUkwmEz6fD7/fL58bo9FIUVGRfAaGUgT09vbS0NAgWz1q\namrIz8/P+NlYLCafJ6vVmvG5TyaTuFwuNm/eTDKZpKampp9HQ3q87cCxQWmPUKBAgQIFox8KwaBg\nSNxxx2s0NHSzYsXc/X0oXykU9cJgXHvttTz99NOEQiGOPvpozjzzTABeeukljEYj06ZNgwR9f/6L\nF9a9wLIXl/HqL16VxXwsFmN9z3oCgQBr1qzpt48zzzyTVatWMXPmTNrb29myZQvnnnuufD+dOBiq\nVWE4vd8DoyhhZxEger0zrUTuDtJVDGq1Wq4qCzM70UMvzqG9vV32pENfdF4wGKS7u5sJEyZI9UIw\nGCQej2MwGPqt/g4Hwiiyu7sbr9crPR1GEzo6Oti6dSsTJ06UaSEC77//PuPHj5e/r1q1ivvvv59/\n/etf/ZIKBJLJJJs2bdorxyXk+m63G7vdTlFRkSTuxLyAvpV3oWRQqVTYbDa0Wi3JZJJYLEYwGJRE\ngEajkakPyWQSh8NBa2urJMj0er2U6QNSqi9ey8nJkSkSqVQKj8cjDSMFAeX3+/nyyy+BPi8QMX/T\nn5+RqheEsiedlBC+IxqNBr/fT29vLwaDAYPBIJNWSkpKCIVCWQv2RCJBY2MjnZ2dQB8BV1tbm7Xl\nQSRipFIpLBZLRg8JESfa0NCASqWirKyM8vLyfp/JlBKR6d8aBV89lBViBQcqlLmpYLRBIRhGCaLR\nONdc8zxvvrkZlytAbW0hy5ady7Rphw653TvvbGHOnKdobr4TgFgswcUXr6Cry89rr13HokVnDLn9\nvsC6dXXcdttq/v3vJvLyLNTX908++Oc/t7Nw4Sq++KKdMWMKWL58FiedlDmSs6vLxw03/IF33tlK\nMBjl0ENLue++C5k8uQboO/9TT70fi8Ugv+AvXz6LuXOP3+fn+XXD8uXL+d3vfseHH37IunXrMBgM\n+P1+Fi9ezFtv9SkSGCA0mPWdWZw45kTpGyCgjvZfcdRqtZhMJk499VTuuOMO/va3v5FMJrnhhhu4\n/PLLJXGwN+XFooAXY4oiQBRe0WhUHtueIl3FkEgk5Mq00WgkHo8TCARIJBLY7XbZDy/k4WazGZ1O\nR05OjpR4C4JBGPeNVL0gYLPZ6Onpwev1kpeXN2p6w0UBfskllzB37lwqKioIBoNEIhFSqRSbNm3i\nzjvv5LnnnpPbzJgxgxkzZkhVwLRp07jrrrv4/e9/T3t7O7///e/lfdtTuFwu6X9RWlpKaWlpP3NH\nQQaINgHRPmC1WiXpEQgEcLlcMlkh3ZDUYrFIvxGhgjGbzSQSCamgEddJPAcmkwmPxyOLaJVKhcVi\nQa/XYzKZCAQC0jOlsrKSgoICqcCAnWRBuknjriCOVxy/QDAYJJFIEIlEJNFQUlLCjh07AHA6nfI6\nZHo+/X4/27Ztk34WFRUVQyZBAFJFZLFYZERsuiohEAgQi8Xo6OgAIDc3l+rq6n5jCMXIQENYRb2g\nQIECBQq+SRgd3yYVEI8nqazM4733bsLjeZBf//ocZsx4gqam3l1uK74HRaNxzjvvUbzeMGvW3IjV\nOrIV0b0Fi8XA/Pknce+9Fw56z+UKcPbZy/n5z0/H43mAn/50Kt///nI8nlDGsfz+CJMnV/Of/yym\nt/d+5s07nunTf0cwGJWfKStz4PU+iM/3EF7vg8ydezx1dXX77Py+zlCpVJx44ok0NzfzyCOPsHTp\nUubNm0dFRcV/PzB4G5Gg4HA4ZERedW01RqORqVOncvbZZ3POOecwadIkFi1axJ133kkkEqG5uZkP\nP/yQl19+OatceU8w0MxR9Einmz2q1eq9llcvTCCFv4Io6tL9F8TKtd/vl9GZgjwQpMPmzZv79d0D\nGY3qhgONRiNNAIUk/UCGUCHEYjFZfAaDQfx+P16vF4/Hg8vlwuVyMWvWLDQaDbfffrtswUkkEtTX\n1zNr1izuvPNOTjjhhEH7EIXhww8/jMFgYOzYsZx33nnMnj170Gr17qKtrQ2Px4PFYsFqtUriQKx8\npydceL1eEokEpaWlaDQaIpGIVK5Eo1Hy8vIoLi6WSgRhlOj1eiVhkUqlpM+GMDUVBISYkyJhRZil\n6nQ6CgoKsFqtRCIRtm7dSiqVoqSkhNLSUqkGEc+HIOQ++OCDYV8HEUuZriqIxWIEAgG8Xq9UcDid\nTvx+P5FIBJPJRH5+/qD9Q1+B/+WXX/LZZ58RDocxmUwceuihlJaWDkkuRCKRfu0qsFPFFAgEcLvd\nxGIxGU2r1+upqakZRMhlIxJEUofiv7B/sW7duv19CAoUZIQyNxWMNigEwyiB2axnyZKzqKjoy7Sf\nPv0wamoK2LChcVjbh0JRzjrrd6RSKVav/hFGY98XpFtv/Rtz5z4FQGNjD2r1Alau/JCqqkUUFd3E\nsmWvyjHC4RiXXvp78vIWMnHiUu655w0qKm6W79911+uUl/8cm+0Gxo//FW+/nbmInzSpmksuOY6a\nmsF9sv/8Zz0lJXbOP/9oVCoVl1xyHIWFOfzpT//OOFZNTQE33vhdiopsqFQqfvCDbxONxqmrax/W\ndVGQGfF4nPr6etauXctDDz2E0+nE6XTS3NbMjDtmcM/L98jPFhUWcfDYgxlTM4bysnKKncUUjCmQ\nxa34Ml5fX49Wq+WSSy6RxnIzZ87k1VdfzXYYu41MkmVRBGg0mn5963sLGo1GmkaGQiEZZ5dOMBiN\nRkKhkJSqa7VaSTC0tbXJVWVRXO+pggGQ0YOCrNgfSCcOotHokMSBx+PB5/MRCAQIBoOEw2HpRyCM\nDBcuXIjb7ebll1/G4XBgt9vJy8ujt7eXiy66iMWLFzNnzpyMXgGiYHU4HDz77LO0tbWxceNGEokE\nkydP3uNzTSQSdHR04PF4sNvtOJ1Ombwg1AtCtSBW8TUaDfn5+USjUdxut/QrMBgM6PV6qRpIJpNY\nLBaSySSRSETuU6fTyZhH0QKRnlahUqn6xbWK2MXS0lIikQh1dXUkEgny8/OpqqqS5yGulzCGHEk7\nUTweJxaLyXQVQEZQ9vT0AH0+CPn5+ajVatrb+/7NLi8vl2apA1NZvvjiC7788ktSqRTFxcUceuih\n0oAyG0Ssp0hwSU+1cLlckmwxGo3Se6KysjJjS5IgJQdGXoq2DgUKFChQoOCbAIVOH6Xo6PCydWsH\nEyeW7vKz4XCcM854GIfDxEsvXYVO13/FduAXxg8+2M7Wrb9m8+Z2Jk++gwsuOJpx40pYuvRvNDX1\nsmPHMvz+CGec8bBUR2zZ0sHy5evYsGExxcU2mpp6SSRGZtyXDX2S59ZhffaTT5qJxRIcdFCRfK2z\n04fT+VPMZj3nnHMEt99+ruLBkIauri7Wrl3LWWedhclkYs2aNbz44ou8+OKLLFmyRK7aARx77LE8\ncN0DTBs7LeNYqVSKaHGUaEtUFkFixfXggw8mlUrx4osvcvHFF9PR0cEf/vAHTjvttL1+TmLFNr2w\nSS+Q0r0Y9iZMJhOhUIhoNCqNJQXBkJubi9FolD300PfsCXWC6CWfOnUqKpWK7u5uGVO4JwaNRqNR\ntm9EIpEhY/lGCrESL/5O/5P+2lAJIKJg0+l0ckVevJb+u8CCBQvYtm0bb775plSEQF+k4PTp01mw\nYAGXX3551v2Je15fX4/D4cDhcPDGG2/wxBNP8O677+7xNens7JQmjVarVaoPxLnqdDq6urqkWkGQ\nBm1tbUSjfc+NMHCMxWKSDBNkmVarJRqNSjWOaM9Jf0+QFmJ/sViMzs5OGdsqTCcTiQSbN28mFotJ\nDwPx/4EgdIQaAkCv1w+7lzg9RUWgp6eHrq4u1Go1ubm5OBwOIpEIXV1dJBIJcnNzsVqtg9qburq6\n2LFjhyzkx4wZQ25u7rCOw+PxyPYkrVYrYzgjkQgqlUrGgtbV1ZFMJiksLMw4trgPA58foZRSCIb9\nD6XPXcGBCmVuKhhtUBQMoxDxeII5c57isstO5OCDi3f5eZ8vzEcf1XPppScMIhcGQqWCpUu/j16v\n5fDDyzniiHI+/bTP9OullzawePGZ2GwmSksdXH/9KXI7jUZNNJpg06YW4vEElZV51NSM3FTuhBPG\n0NbmYdWq/yUeT/DMMx+yfXtXv5aHbPB6Q8yb93uWLv0+OTl9X2rHjy/hk09+SVvbPaxd+2M2bGji\nJz95acTHNZqhUql49NFHqaioIC8vj5/97Gc8+OCDTJ8+ndzcXIqKiuQfrVaL4wgH5rK+wu75t5/n\nsKt3Rkq82/YupiNNnHXWWTQ3N2M2mzn99NOBPpn/n/70J+6//37y8vI4+uijOfzww1m8ePFeP6eB\nTu/pv+9LQzax2iuKo3A4TCAQIB6PY7VapZFeOBzGYDBgtVrlNmJVt7y8HLPZLFsa9kS9IOBwOIDh\nqxjE6rqQ6acrDnw+3yDFgVgdF4qDaDQqzf10Oh0GgwGTyYTFYiEnJwe73Y7D4SAvLw+Hw4HNZsNq\ntWI2mzGZTDIBQRTJAk1NTaxYsYJPPvmE4uJicnJysNlsvPDCCzz55JM0NDSwbNkySkpKKC4upqSk\nRG67atUqjjvuOHnPN2zYwGGHHYbNZmPx4sU8//zzHHLIIXt8rdva2qS5Y0FBgby/4lqkUilCoZBc\n4Y9EIvK+qFQqzGYzubm5qFQqGVcp1A+ipUfcm/Rx09t9ksmkbMUwGAxSMWM0GgmHw+h0OhwOB3V1\ndUQiESwWCwcffPCglXkxhkhoGK6HRzKZJBqNotFo5Dl3dXXR0dGBWq2mqKgIu90uPSgGxmWK5zWV\nSrF161a2b98uCYjDDz982OSCiLw0GAzymQoGgySTSRlVazQaaW5uJhKJ9IvGHIhMpKS4NgPnqQIF\nChQoUDCaoSgYRhlSqRRz5jyFwaDl4YdnDmubwkIrDz00k7lzn+KPfzQwdeqEIT9fXLyz39ts1uP3\n90lxW1s9lJfv/GIn2jUAamsLeeCBGSxd+nc+/7yN00+fwH33XYTTaR/J6ZGXZ+GVV67mJz95mWuu\neZ7TT5/I9743Xu730ENvpbGxT8b62mvXSfPHcDjG2Wc/wokn1vKzn50uxysqslFU1FegVVXlc/fd\nF/D97y/nxhuPVVQM/0VBQcGw+wOFCRwAPTC7fDazL54NRqAcptimkLwiu3LlO9/5DuvXr9+zA94F\n0s0c09sjRAH2VRiyidVkt9tNPB6XZo7QZzYXDoexWq1SveB2u2Xv9yeffMIpp5wiZfSit35PYLVa\n6erqwuv1yuI1k9JgJIoDUXAOVBqIn/eFm35lZRirwL4AACAASURBVOWQkaYiwjI9vhH6iJ9LL72U\nyy67TH72oosu4qKLLtqrxxcOh+V1rqysxOl0ymJZEAE9PT2kUimCwSA+n0+qUxwOB4FAgGg0Klsk\n4vE4Xq8Xi8WCTqeTbT7C2yCZTKLX6yWJAUjjR3EfRWuKuD9CrdDa2kogEMBgMDBu3Lh+hXO6Akh4\nL4jjHE6ee7pxZTwel6oOjUaD0+mU8yQajUrlTnFxsdyH8AwRqg6NRkNlZSXFxbsm1AXEtVOr1ZjN\nZvmMCfWEeP67urpwuVxoNBpqamoyztt0UnIgCZPu66Jg/2I4c1OBgv0BZW4qGG1QCIZRhvnzV9Ld\n7efVV69Doxn+ism55x7JE0/M5aKLHucvf7mG73xn5MW102nnyy9dHHJI36rgQIPJmTMnMXPmJPz+\nMD/84bPcfPOfeOaZ7FLlbPj2t8eyfv0iABKJJGPGLOYnP/keAJs2/WrQ56PROOee+wiVlXk89tgl\nuxx/qAJFwQiQ/98/BxgGyqvTnd8BaZS3t8wdB0IY1+n1elwul5Ski5Vk0aqQm5vbz38BkOZ26T32\nQxX7AulFZTbiQK1W4/F4aG1tzWgaKYiB/UEc7G0M7JP/qtDe3o7P50Or1ZKTk4PD4egnoRf+HIFA\ngM7OTunTUVRURH5+voxvhL6VcmFemZubi1qtlq0SwshReHYA0ntBtAAJgiBd2u/xeFCr1YRCIelB\ncsghhwxqwRE+BaI9IlOkYzYIQkOtVpNIJGhtbZUEX0FBAUajUbZ4uN1uadYoklNE/GRXVxc6nQ6L\nxcJBBx0kzRmHAzG2SL0IBAIAMjFDEALBYJCWlhYAqqqqsrYiiWs7sKVKEDeKuaMCBQoUKPgmQflf\nbxRhwYLn2Ly5nTffXIheP/JbO3PmJKLROOec8wivvXY9J55YO+gzQ9UyM2Ycwx13vMaxx1YRCERY\nvnydfG/Llg5aWtycdFIter0Wk0mftZDv6wOOE40mSCZTRCLCGb3vC+wnnzRz6KGlBINRliz5K5WV\neXzve5lVF/F4ggsueAyzWc/TT1866P116+oYM6aQyso8mpt7ufnmP3HuuUcq6oVRjKHaI7L1Ue8t\nJJNJWTBptVq8Xi/hcBi73Y7JZMLv98vWAY1GI9UJIuazqKiIcePGScm8yWSSrvtCap6NRMgGQRDY\n7Xa5Qm42mweRCF8H4uBAh0iPsNvtlJSUyHukVqvR6/X09vbS1dUlTQfz8/MpKChAq9VK2b7P55Mt\nDqK1Qa/Xo9PpZIymIBtEUkm6p0C60aMgCoxGI4lEAo/HQyAQkIak48aNy1i4i+3EPtIL712twgkf\nifTWB7PZjFarlQaVggTp7OxEo9FQVlYmiY+6ujo8Hg9arZbS0lLKy8tHTBb5fD78fr/czmAwYDAY\niEQi/VJkGhoapGGkMEPNhEwpEbtjfKlg30JZIVZwoEKZmwpGGxSCYZSgqamXFSvew2jUUlx8E9BX\nODz++CXMmjV85/N5804gGk1w1lm/4x//uGHQ+wO/J6V/cVqy5CwWLHiOmprFlJbaueSSyfz+9x8C\nEInEufnmP7F5czs6nYYTT6xlxYo5GY/h3Xe3csop98t9mc3XMWXKwaxd+2MA7r77DV59dRMqlYpp\n0yby5z9fnfV8/vnP7bz66iZMJj12+43ymEX7xH/+08ycOU/hdofIz7dw/vlHcfvt58jtr776OVQq\nFY88MnvXF0/B1wKZ2iNEj7RQBeyrFUcxvpC0i4JeFDg9PT2EQiGMRqM0s4tEIv1c7n0+Hz09PWg0\nGsxmM9FolO7ubqxWa7/nMb0nfyiDxPRtQqFQvx50BXsPXq+X3t5eAoGA9IBIV9N4vV6++OILAoGA\nTGwoLy+XKhfomzfC8BB2qldEK4Tf7ycUCvVriTAYDIRCoX6JEWJ7s9ksC2O/34/L5ZK/jx07Nmv7\njfBfGKnaR6gXfD4fwWAQo9FIfn6+9H3Q6/UEAgHUajUtLS3S0NLhcNDe3k5TU5NsEZkwYYL0DhkJ\nhDpEpVJht9uliWMo1Bd1LM5F7MtqteJ0OrOOl41IUMwdFShQoEDBNxUKwTBKUFmZRzL52Ii3mzLl\nYJqa7uz32pVXfosrr/wWAMceWy1fr6rKJ5Hovw9R9EOfH8PKlTtbHh577B3Ky/u+AB52WBkff7xo\n2Mc01Lk8//yVwxoH4OSTDx50zOlYuPC7LFz43UGv19XVMW7cOB59dNctFQq+PhA90YJASFcspJvV\n7asVx1AoJFd8hdGhVquVcnfRE26z2dDr9bIojcfjcpV33bp11NTUkEqlsNlscuVapVJhs9kkibA7\n52C32wkGg3g8nn4JDAr2HEK9YDKZyMvLk60AKpWKzs5OmpqaiMVixGIx8vPzKSkpwWQy4Xa7ZUuD\nyWRCpVIRDAZlW0I0GpUqhkQiIckEQRSkkxHpvgkiPSQUCmE2m/niiy9wuVxUV1czZsyYrMW7UMUI\nEkMkVAgM1UscDAbp7u6WpFppaals+TCbzZJwEaSZTqejuLiYuro6XC4X0DdHKysrh1QUZEIymcTv\n99PV1UUqlaKoqIicnBxUKtUgY9fOzk6pkqiurh7yWcpGJOzrVisFI4fS567gQIUyNxWMNigEg4K9\nhvZ2D/X13Zxwwhi2bOngvvve7JckoUDB/sZQ7RF7uuKYyddg4O9dXV0yptLlchGPx6UEXXgvCNd+\nu92O2WymsbGRZDIpUxXMZrMkFAoKClCr1ZIcydQHPhJYLBa5mi0KLgV7jmQyKQmGgoICnE6nTHoQ\nLRHxeByDwSALf5EgIopf6GtlSKVSeL1embyRSCRkXKmYB2JOQF9RL7wXDAZDv9/FZ8LhMJ2dnTIu\ntrCwMOu5CMIiPQVkOAgGg7S3t0uipLi4GJVKJc1LdTqdVGq0traSSqUwm81s2bJFEn+VlZWYTKYR\nxbKmUql+KSdqtZrCwsJ+6SvinMTcF74L1dXVuzy/TETCvm61UqBAgQIFCg5kKN8evwG4447XWLbs\ntUGrMN/+9kGsXn3dXttPNJrgqqueZceOHhwOM7NmTeLqq6fstfG/SigeDKMT6e0RA1cts6047oo0\nEK9lM1tMb0sQpnJ2u53u7m60Wi1ms1muAosWhcLCQvLy8lCpVPT29pmlioLsqKOOYtu2bbIAhb6o\nyt7eXrxer9xudyBUEL29vXg8HvLzD0CXzq8henp6JIlgs9nIz8+XUZ7hcBi1Wk1JSYn0VxDEkU6n\nk8WvUCmEQiEikQgOh4NgMCjjLIVJZCwWw2g09ksCEfGr0WhUfhb62gVisRhbtmwhlUpRUFBAeXn5\nkOciimeVSoVerx801zKtwvn9flpbW0kmk+Tl5UkjRRG1KowoRTqE2+3G6/UCfX4SOTk51NbWotFo\npKnkcBCLxaSvSSKRQK1WyyjUgZ+Dvmd9x44dADidzoxmp5muxUDCI1NkpYL9D2WFWMGBCmVuKhht\nUP73+wZg0aIzWLTojH2+n8rKPDZuHJzioOAbiiTQA8QAA/s9UWJge0Q8Hpe95MKNX6PR4Pf7+xEJ\nQxEHAz0NsvkcALJ3XBAK6TGTOp1OFptGo1FKt9PbJoSLvs/nA+i3AqvT6TCZTASDQSl5313Y7fa9\nQlYc6Eg3v0z35NgXaGtrw+12k5OTQ0FBAe3t7TKpwW63YzQapWrEYDBQWFgoVSqimE8mk/3UB2LV\nXxg9CsNC8bNer5fjibkkUg3sdruMQ+3u7sbn82E2m6msrJTjZ4MoqrVa7S5X91OpFC6XC5fLRSqV\noqSkRBpKCmLEaDRKpUYsFqOxsZG2tjZycnLQaDSUl5dTWlqKSqWS7RS7IhiSySSBQEAaWxoMBvx+\nPzqdTsawph+jIB5Fm4rNZhtW5GUm1VN6q9X+SCpRoECBAgUK9jeU//0UKMiAurq6/X0IBxTmzp2L\n0+nE4XBwyCGH8OSTTw76zG233YZarWbt2rXQBLwDbAD+D/gX8C6s+9M6Tj31VBwOB2PGjOm3fXNz\nMzk5OdhsNmw2Gzk5OajVan7729+O6FiFO348HicajUp5tNfrJRgMEggEcLlc0nAvEAjg8/mIRCJy\nG1HU6XQ6jEYjJpNJrnza7XYcDoc0n7PZbFitVsxms5S363S6QUWrMJEzGo2EQiG5Mm0wGNDr9QSD\nQYLBIAaDQZIHPT09xGIxLBaLNNx76623gP4EAyAVDWLFdneh1WqxWq3E43EZ3/d1QTQa5corr6S6\nuhq73c7RRx/N66+/DsDHH3/M1KlTyc/Pp7i4mBkzZkgjP9G2kk4mRaNRFixYQElJCQUFBZxzzjky\nLnQkiMViMp5SmBiGQiGpWigrK5P+AGq1GqfTKQtW4XUgyK729nZJUom4U5FMIvwVBIGmUqlkXKVQ\nLyQSCUwmEyaTiXA4zI4dO4hGo2g0GkpKSuS42SCK8VQqJY1DB2LdunVAHxHR0dEhlQhFRUXY7XYZ\niSoIFEGGJRIJtmzZQlNTE6lUivz8fCZOnEhZWdkgQ9ZsBEgqlSIUCuFyuWTrhd1ul/fWbrcPIifE\ns+J2u+U9qqqq2iXhJIgEYRAroJg7HrgQc1OBggMNytxUMNqgEAzfIFx++dMsWfJXAN55ZwsVFTcP\ne9uRfl4BqNULqK/v2q1ta2puYe3azRnf2x/3YtGiRTQ0NOB2u/nrX//KL37xC/7zn//I9+vr63n5\n5ZcpLS2FVuBzIDJgkCBYvrQw/4L53HvvvYP2UVFRgc/nw+v14vV62bhxIxqNhgsvvBDIThz4/X58\nPp+UnLtcLjweD16vF7/fTzAYlP4G0Ce51mq1aDQaLBYLFotFFvW5ubnk5eUNIg5MJlM/4mB3VibF\n6qvJZMLn85FIJCR5YTAYiMVihMPhfgRDR0cHAPn5+ajVaqLRKJFIREq90yGk5KJY3RMIAz0Rh/l1\nQTwep7Kykvfeew+Px8Ovf/1rSSS4XC5+8IMf8MUXX/DFF19gsVhYsGBBv22FqgTggQce4OOPP2bT\npk20trbicDi47rqRt5R1dnbS29tLOBwmFAqRk5OD0WiktLSUoqIiPB6PbHXQarUUFxfLIlgUyVqt\nFo/HQygUkkWyUN8IY8dIJILNZpPKDJFqIJ4XQUCItobGxkZJpuXl5VFQUCBJiWxIJpPD8l6IRqO0\ntbXJdIiCggIsFoskSkTLgslkkuktGzdupL6+nlQqRVVVFUcccUS/FIuBCqSBiMViMmZTGKXabDb5\n7AtiZSDi8TjhcJiOjg5UKhXV1dXDam0Qx5PJ3FEkdihQoECBAgXfRCgEwyjEd75zH3l5C4nFhl7F\nHGqBJlNxPEqV0hmxNzwY9uX1+qrvxYQJE2RsoZBQb9++Xb5/7bXXcvfdd6PT6voIhiyYdPAkLjn0\nEmqqa+RrouhIJw5CoRBPPPEEJ510EjabbUjiQKzMCtd8YX4nVv2FGsJisZCbm4vNZsNoNGI0GrFY\nLNKN32g07lNJc7qCwefzEYvFMJlMcr9Czi6OP5lM0t3dDSDbI7xeL8ccc4xUdwxEejKAIFR2B2az\nWaoqhio4DzSYzWaWLFlCRUUFANOnT6empoYNGzYwbdo0zj77bKxWK0ajkauuuoqPP/643/bpbRM7\nduzg9NNPp6CgAL1ez8UXX8xnn3024mNqaGigqakJlUpFUVGRLOZNJhORSIRoNIrP55PqhXQCKxaL\nyRV+j8dDMpmkqKiIWCwmCQZBmIn7JFQLInFCtGIIfxG1Wk1dXZ2U8Vssln4tOEPdb0EMCKItEyZN\nmkRbWxvxeFwqfkRUqijKBUlmMpno7u5m48aNfPnll7LAnzhx4qAiP92IMR2CUPN4PNLs0uFwoNfr\nZSSmRqPJmDohDCC7uvr+rystLR1E3GXDQMNYcSzi+ozW1qKvM5Q+dwUHKpS5qWC0QSEYRhkaG3t4\n//1tqNUq/vrXT3d7nK/7d6NEIrnfx8/Suv+1xbXXXovFYmH8+PGUlpZy5plnAvDSSy9hNBqZNm0a\nJOjzXvgvXlj3AkdecySxeIxYPEY0FiXiiRDq6Ct6BHEgTN0EcRAKhXjhhReYOXMmQFbiYGCrgiAS\nhCpArP6Klod0/wVRcKX/vK+QSCSIxWKyH97v9/cjGILBIJFIBKvVKldZ/X4/oVAIvV5PXl4egJSb\nDxXRJ/wbfD5fVv+I4eDrqmJIR0dHB1u3bmXixIlSAQN9heC7777LIYccIj+7atUqjj/+eFk4zp8/\nn/fff5+2tjaCwSDPPfecnPPDQSqVor6+ns8//1wqF4455hjsdrv0L8ikXkhHMpmUqSHxeFySAeK+\narVaHA6H9BERx56uMBAtEMIPpLm5md7eXvR6PYWFhSQSCaxWq7zfQxFTQuGRKR1B+C2IGMiCggJs\nNpv8vEqlks+BMEbcvn0727ZtIxgMkkwmKS8vZ8yYMRkVBPF4XPqbiP2Fw2HpXaLX63E4HJjNZmni\n6na7AcjNzc1IyMXjcbq7u4nFYjgcDkmy7ArZIm0Vc0cFChQoUKBAIRhGHVau/IgTThjDZZedyNNP\n/3O3xpgy5V5SKTj88F9js93ASy9tAPoK5vvvX0Nx8U2Ulf283/jRaJybbnqZqqpFOJ0/5ZprnicS\n6fuy1dPj5/vf/x25uQvJz/8xU6bslMe3tXm48MLHKSq6idraxTz88Nqsx/Xqqxs5+ujbsdtvoKpq\nEbfe+jf5XmNjD2r1Ap566gOqqhZx2ml9ffsffVTPSSfdTW7uQo466nbeeWdL1vFram7hzjtfZ+LE\npTgcNzB//kqi0b4v7KIt4e6738Dp/ClXXPEMAE888R5jx/6SgoIfc+65j9DW1r8YW716I7W1iykq\nuomf/eyP8vX6+i5OO+1+Cgp+TFHRTcyZ8yReb6jftuvX72DixKXk5/+437Gk4957/8GFFz7e77Xr\nr3+RhQtXZT3P3cXy5cvx+/28//77nH/++dI4bfHixTz00EN9HxpQz876ziz+ed8/JWkgFQfBnauR\ngjgwm82SONi4cSPd3d3MmzcPu92elThIN1EcCmK1V5jlJRIJSTyIn/clhHpB9L6LQkSv10uDP2FC\nZzQaicfj9Pb2Eo1G5XWBPoPHDRs2DOluv7c8FARR4fV65ar+gY5kMik9FVwuFzNnzmTmzJnY7Xba\n2tro6emhq6uL9957j7vuuotbbrlFbjtjxgw++ugjea5jx46loqKCsrIyHA4Hmzdv5pe//OWwjiMS\nifDFF1/w+eef4/f7sVqtUvIvWhUSiQThcFiusJeUlPSbhyKZRBSzgiATRbYwL9RoNNKwUUjz1Wq1\nJNWEukej0ch2jUQiQW1tLZFIRJoamkwmNBpNVgWD8HEQaoSB172zsxOPx8P69etxOp1YLBapVEgn\n9kQrypYtW+ju7pZpGYWFhRQXF8tne+C+05/TeDyOx+PB7/ejUqmkd0s6Sej1eqV/SbZYy/b2diKR\niIzAHC6GMncUiigFBx6UPncFByqUualgtEEhGEYZVq78iDlzjmP27Mm88cbndHX5RjzGO+/cBMDG\njUvweh/koouOAaC93YPPF6a19W7+53/mcu21L+Dx9BVOP//5n9i2rZP/+78lbNt2Oy0tbm67bTUA\n9923hoqKPHp67qOz816WLTsX6PtC9v3v/46jjqqgre1u3nprIQ8+uJY1az7PeFxWq4H/9/+uwON5\nkNWrf8Rjj707SKXx7rtb2bz5Vt5443paW92cddbvWLJkOi7Xb7n33gu44ILH6OnJ3p/+/PPrWbPm\nRtasuYK6unZuv/1V+V57uwe3O0hT052sWDGHtWs3c8str/Dyyz+kre0eKivzmDnziX7jvfLKJ/z7\n37/g3/9ezF/+8ilPPfXBf88dbrnlDNrb7+GLL5by5Zduli79W8Zj2b799kHHIjBnznG88cZnkpxI\nJJL84Q//y6WXnpD1HPcEKpWKE088kebmZh555BGWLl3KvHnzpCSdDLW+TqeT/gU6na7P/8CokUWP\nVqtFq9Wi1+tlcfHss89ywQUX7FEagkC66z30lzaLQn9fG7IJ/wXRHiF6t/V6vSwQg8GgJFwikYj0\nX8jLy0OtVstechFtORTMZjNarZZAICDPd6TQaDR7zdNhTyHiF0OhkIwx7OnpoaOjg9bWVpqbm2lo\naKChoYHm5mZaWlqYO3cuKpWKm2++WapBkskkTU1NXHHFFfzmN7/hxBNPHLQvQVhdc801RCIRXC4X\ngUCA8847r0+lswv09PSwceNG6SWiVqspKyujtLRUtioI9YJQHWi1WkpKSvqNI1qRxLU3mUxEo1EZ\nJarX66XBY7rHiFarlXM+fSy32y1X6wVpIswhHQ6HTJ4QPicDIZQHA9ULsViMtrY2QqEQBoOB/Px8\n6SmSSqWk54M41q6uLun/YDabKS8vl89/fn5+Ro8ToTxRq9Xy/sfjcUwmE7m5uYOOKRKJEAgE0Ov1\nWck4l8uF1+sllUpRU1MzIlIgk8/CrjwiFChQoECBgm8KFIJhFOH997fR1NTLjBnHcPTRlRx0UBHP\nP79+t8cbKK/W67X88pfT0WjUnHHGoVitBurq2gF44on3+e1vZ2C3m7BYDNx88+m88MK/ANDpNLS1\neWho6EGjUXPSSQcB8K9/7aC7O8DixWei0aipri7gyiu/xYsv/m/G4zn55IOZOLEUgEMPLWPmzEn9\nFAkqFdx66/cxmfQYDDqeffZjpk8/jNNPnwjAaaeN59hjq3j11U1Zz/m6606htNTBpElHsHjxmfIc\nADQaNbfeejY6nQaDQcfzz69n/vyTOOKICnQ6DXfccR4fflhPU1Ov3Obmm6dht5soL8/lxhtPk+PV\n1hZy2mnj0Wo15OdbWbjwNN55Z2vGY3E4zIOORaCkxM7JJ4+VKpPXXttEYWEORx5ZkfUc9wbi8Tj1\n9fWsXbuWhx56CKfTidPppLmtmRl3zOCel++Rn9VpdRgNRkzGvnYAg8WANr/vS7hYFQ2HwwQCAfx+\nP729vbz00kvMmTNnr6yci+JEFAPpTvSiuNvX/dLpCoZ0/wVRiHk8HlKpFCaTCb1ej8/no7e3V/bt\nw872iFNOOWWXxyuK0FQqJbfbHTgcDmDftUkI4iAcDss++p6eHjo7OwcRB01NTbS2ttLR0UFPTw9u\nt1sSB2Ll2Gg0YrVaWbJkCX6/n5deeomKigoqKiqorq4mHA4zd+5cfvGLX3DZZZdl7LcXBeKnn37K\n5Zdfjt1uR6fTcd1117F+/Xp6e3sHbQN982rbtm1s3bpVziuRtOBwOLDb7VK9kEqlZLJJJvWCmLOC\nNDCZTPK1eDyOTqeTaQ5CiRMMBtHr9RgMBsLhsFT4xONxXC6X9PMoLi4mJydHzkmLxSIJNlGoZ1Ix\niMjHdDVAMBiktbWVWCxGTk4OJSUlnHbaafK5Tlcv+Hw+Wlpa6O7uRqPR4HQ6mTBhAr29vSSTSUmw\nZCrQBekhlBo6nQ6Hw4HFYhn0LCSTSdxuNyqVCofDkfFZiUQifPnll0Cfv8lIiMx01VOm9gglPeLA\nhdLnruBAhTI3FYw2KFT7KMLKlR8ydep4cnP7vjTPmjWJZ575kBtuOG2vjJ+fb+m3smQ26/H7I3R1\n+QgGoxxzzG/ke8lkShIUP/3pVJYu/TtTpz6ASqXiBz/4Fj//+TQaG3tpaXGRl7cQ6FvVTyaTnHzy\n2Iz7X7++gZtv/jObNrUSjcaJRuNSXSFQXp4rf25s7GHVqg387W//J8ePxxOceuohZEP69lVVebS2\nuuXvhYU56HQ7V6xaWz0cc0yV/N1iMZCfb6GlxUVlZd6Q43V2ernhhlW8995W/P4IiUSSvLz+xc5Q\nx5KOefOO57HH3mX+/G/x3HPrmTv3uKzntzvo6upi7dq1nHXWWZhMJtasWcOLL77Iiy++yJIlS+QX\na4Bjjz2WB258gGljsqz0piBWFiPVmJLFkZB8iy/uf/nLX8jNzWXy5MkEAgH5vljZHGmKgyAU1Gq1\nXNk1GAz9CrR9iWQyKZUHWq0Wn89HPB7H4XBgMpnw+/0yPcLhcGA0GmlpaSEYDGI0GmWRL4iCgfGU\n2aDX6zGZTIRCIUKhUEYH/V3BYDBgNBrlKrkw+twVRJyhMAUUPw/8e1dxmmKVWCg9RPrHwL/TV5IX\nLFjAjh07ePPNN/sVji0tLUyfPp0FCxZw+eWXD7k/6DMrXLlyJVOmTMFkMrF8+XLKysqkH0Y6fD4f\n27Ztk0V4ZWUlLpcLv9+PxWKhvLwclUolvRG8Xq8kRoxG4yD1gjAuFSkSVqsVl8vVr1VArJgLdUAk\nEpHXQkRYRqNRqZQwGAzk5uZKQqGrq0smqQgI8iASifS7duKepcdgut1u6XGQn5/fTykg1AtGoxGV\nSkVnZycNDQ0ylvLggw+WbSvRaBSTySQjLAc+j/F4HK/XSyKRkNcikweEgNvtJpFISK+LgUgmkzQ0\nNAB95EphYWHWsTIhW3vEV0VWKlCgQIECBQc6FIJhlCAcjrFq1QaSyRRO508BiEYTuN1BNm5s4bDD\nyvbZvgsKrJjNej77bClO52DzOavVyL33Xsi9917I55+3csop9zN5cg0VFbmMGVNIXd1tw9rP7NlP\ncv31p/LGGzeg02lYuHAVPT39e8zTv9tVVOQxb97xPP74nGGfS3Nz3+pkXV0djY0xSksdGccGKC21\n09jYI38PBCL09AT6EQPNzS7Gj3cC0NjYK8dbtOgV1GoVn322FLvdxF/+8gnXXfdixmMZuO1AnHvu\nkVxzzQt89lkrf//7/3HPPRcM+3yHA5VKxaOPPsrVV19NMpmkqqqKBx98kOnTpw/6rFarxXGYA3OR\nGdrg+bef545Vd7Dx0Y2ggnc73+WU6TtX4M1mM1OmTGHt2p3eG3/4wx+YN2+eTFIQpED6qmo66SDI\ng0ykQzqhAP3bI8Lh8D43d4SdxnjCf0FEAwr/hc7OTlkA2mw2zGazNIG02Wwy3s/n62t3+uSTT5g6\ndeqw9p2Tk0MkEsHn82EwGHYrJcNutxMOdogJJQAAIABJREFUh/F4PBgMhqxEwcDXhoIo5I1Go7yH\n2ciDkaCpqYkVK1ZgNBqlYaJKpeLxxx9n69atNDQ0sGzZMpYtWyaL8/b2PhXWqlWruO+++9i4cSMA\n9957L9dffz1jx44lFotx6KGH8uc//7nf/pLJJC0tLbS2tpJKpTCbzRx00EEYDAY2bdqE3+/H6XRK\nM0WhnAkEAlK9UFxcPKgQjsVi0j/DYrHg8/kkkZBKpbBYLLKQFj4NIolFpDWoVCrZapOTk0NRUZH0\n94jH41JFI1otYCfBMFDBIOaswWAglUrR3d0t4yDFuAJvv/02kyZNkkqNLVu20NXVJb1Bxo4di06n\nk8eWSqVwOp3yfoh7LlQewuBSkBBDFfAiWUYkxGRCS0sLoVBIkgsjJQRisdigfzcU9cLXA+vWrVNW\nihUckFDmpoLRBoVgGCX485//g1ar5tNPf9lvlf2ii1awcuWH3HPPhSMar6TERn19N2PG7Hp1R6gS\nbrzxD/zud7MoLMyhpcXFZ5+1MXXqBFav3sghh5RQW1tITo4RrVaDWq1i8uRqcnIM3H33G1x//ano\ndBo2b24nFIpy7LHVg/bj90fIzTWj02lYv76B559fL9sfYHBqw5w5xzF58h1ccMHnfPe7hxCNJvj4\n4wbGji3KWqwvX/4O06cfhtsdYtmyNcyceWzW8541axKzZz/J7NmTGTeumFtueYXjj6+homLnCuc9\n9/yDyZOr8fnCPPTQWm666Xv/PZcwDoeZnBwDLS0u7rnnH1mPxWTSs2zZa1mPxWDQccEFRzF79pMc\nd1xNP4Jjb6CgoGDYBkT19fU7f6mF2bWzmT1vNhiBMphinEJy3tBtD6+//nrG18XqrSAdBq6Apxvc\npSsWAFnsiFVGMZboD9+XSPdfEP30Qr6u1+vxer1EIhFpZqnVaqV5XW5uLhqNhmAwSCwWkz4Vw4Va\nrSYnJ0ca4mVTP4jrkYkoEC0cvb290lNgKAgfiUwqg/Sf98V1r6ysHLKtZsmSJcDOFWehstJoNFx2\n2WVcdtll8rN5eXk8++yzWccKh8Ns27ZN3tOSkhIqKytRq9WyjUO0uJhMJln8+3w+qV4wGAw4nc5+\n4yaTyX4r9tFoFJ1Oh8VikT4Idrud5uZm+TwI08f0hIdQKERbWxuJRIKcnBxKS0tlHGMgECCVSmGz\n2SQRAcjnIZ1gEG0s4n61tbXJuVhUVJSRHEmlUoRCITZv3izVGCUlJZSVlckiXJAyDodDEhdCASA8\nFJLJpDSVFKaj2SCMHzUajVT9DERvb69s0RDxoyPBQMIyfd/pKTUKFChQoEDBNxnK/4ajBCtXfsQV\nV5xEWVn/4vJHPzqFG274A3fddf6Ixlu69Czmzfs94XCMFSvmUFg42Cgr/cvenXeex223reb44++k\npydAWZmDq6+ewtSpE9i6tYMf/egFurv95OaaufbaKUyZcjAAf//7j/jxj1+ipuYWotEE48YVc/vt\n52Q8pkcemcWPf/wyP/rRC0yZcjAXX3wsbvfO5IWB3z3Ly3P5y1+u4ac//SOzZv0PWq2ayZOrefTR\nS7Ke9+zZk5g69UHa2jyce+6RLF6cPZbutNPG8+tfn8355z+G2x3kxBNrefHFH/Q7nnPOOYJjjvkN\nXm+Yyy8/kSuuOAmAX/2q7/o6HAs56KBC5s49nt/+9s1+13Ykx3LppSfwP//zAU8/fWnWz3zlsAKZ\nu112C5m+wGciHdIN6sRqoyh6RHHwVcbJpfsvtLa2kkgkMBqNGI1GUqkUvb29stDS6XRyFVan0w3y\nUbDZbBx55JHD3rcw2YO+4kpcj0wtC0NBq9XKz1qt1iFbFb4OEvH0GMfdQWdnJ42NjZIEGDNmTL+i\ntq2tDY/HQ05ODmVlZVK9oFar8fl8shAe6L0ASD+KdNPF3NxcXC7XICWBIByEugH65lksFqOhoYFE\nIoHBYKCwsLAfoSKKd5vNJlU14rro9XoikYgkLqLRqHzOurq6UKlUWCwWCgoKBt3rVCrFcccdR0tL\ni2yfMBgMlJeXYzKZ5DX3+Xy43W40Go1UEaRSKdRqNR6PRxIaInVDRMlmgzCxTKVS2O32jJ8Nh8M0\nNzcD4HQ6peHsSJDp3w3x785IyQoFXz2UFWIFByqUualgtEG1Jznpe7RjlSq1v/Y9GrBixWJ++MOq\nXX9QwbBRU3MLTz45b0iPhgMVzc29jB+/lPb2u7FaM/fJr1jRyA9/+JuM740mJJNJKRcPBoOysBOF\nkvBf0Gq1MppvXxbF9fX1pFIpxowZw8aNG/F4PDgcDpxOJ0ajkQ8++IBgMMgxxxxDZWUlbW1trF+/\nHoPBwLhx43A6nTQ1NeF2uxkzZgwFBQUA/QiCoXwOBLEiilYReSmwqxYFYSLY2NiITqejurp6n12r\nAx2icBdGj3l5edTU1PQjK6LRKP/4xz+or6+nsrKSb3/72zJSMhqN0tHRQVdXF0ajkSOPPHJQL39j\nYyPhcFgW16lUiry8PLZv3044HKawsJD8/Hw2b95MLBbrt9pfWVlJbm4un3/+uWwDyMvLw263Y7PZ\nCIVCBINBAoEAJpOJgoIC4vE4yWSSsrIy9Ho9nZ2d+Hw+ysrKZBStMOAUKRHZlDAul4stW7ZIcqK0\ntBSz2UwikcBkMsm5t3nzZsLhMOXl5ZJoE/4V0Kf2MZvNsp1Eo9EM6SHi8/nw+XxYLBbs9sFteslk\nkrq6OsLhMPn5+eTl5e1yzIFIpVKyLSTdnyISiRCNRrFYLLvVgqRAgQIFChQcyPjvIsCIvigrCgYF\nCjKgrq6OcePG7e/DGBaSyST33dfXzpGNXPgmQfgxCJ8Ds9ksi2zRNy161IW6QLRXpLdY7A3SQcjD\nzWazTEsQq8qiZUL0jIuiraenh3A4jMViIZlM0tbWRmdnJ4lEgkAgwFtvvcWkSZMGpbwMhJCWC6LA\naDQSjUax2Wzk5ORI8mC452k2m2VP/N6ID/26we12U19fTzQaRaPRUFVVJRM+0tHe3o7L5UKn01FZ\nWSnnnFarpbu7G7fbjVarpbi4uB+5kEwm6e3tJRwOS/PFUCgk76MgjET6idlspqurSx4PIAkEYSpa\nXl4uSYhIJCJbNETrjPAGCQQChEKhfi04QtXgcrkIhUKo1WpKSkoyFuWpVIrW1la2b9/OJ598wskn\nn0xtba2MShXxs8LwUcx5h8MhnwHhYSEUMrAz+nEoL45oNIrf75eKn0xoamoiHA5jNptxOp0yZWMk\nEMcykBASCSZ7m1y44447aGhoYMWKFXt13G8ylD53BQcqlLmpYLRBIRgUKPgvvg7S7oEIBqMUF99E\nTU0Br7123f4+nAMK6ekRoggzm81EIhFMJpNcOU1vsUhvFRBExZ6QDoLAMBqNeDwe6QkhojlbWlqI\nRCIYDAYZJbh9+3ZZDIqCMRwOo9fr5WqzTqfL2qIgfh5Y8AhzvvTtRwK73U4wGMTj8XyjCIZkMklz\nczNtbW0AWK1Wamtrs65+t7S04PF4yM3NpaioSF5vkeYhkiPSvRdisZi8ttB3rcWqf7rPSHrBbTQa\nZSuD8BVpb2/HarVK9YBerycWi8nWCpEsYbVaZYSm2WwmEAgQDoex2+2SYAgGg3R3d0u1T2FhYcZz\njkajbN++nZ6eHqm2OOyww1CpVLhcrn5mrPF4XJpqOp1OGTuZSqWwWq3SEyL9fCF7K1MymcTlcgFk\njaTs7u7G5XKh0Wiorq7e5ZjZkK09QqhMrrzySt58801cLhe1tbUsW7aMadOypOkA77zzDnPmzJFt\nG7FYjIsvvpiuri5ee+01Fi1aNKLjU6BAgQIFCg4UKASDAgX/RX39zvaBr4t6wWzW4/M9tL8P44CD\naJMQxVJ6D7fol05fWR64XbqJ5K5Ih/RIxoEtC93d3fj9fuLxOL29vbLvXBSTPT19KSSicBM99Uaj\nkcLCQgoLC2WUn9PppKamhtra2t26JiqVCpvNhsvlwufzZTXCywaLxSINKEXROdoRDAbZtm0bwWAQ\nlUpFaWkpZWVlWVerA4EALS0tJJNJCgsLZZKBTqeTsY5arZaioiJ0Op00QxSpEfF4HIPBIFNSrFYr\nHo9HEmRCvQA7zRTFnAwEAsRiMen7IBQDGo1GEhHBYBBAtl9otVqZACGMHoVHSUdHh0w2sVgsGaMh\ne3t7qa+vl8X3QQcdxJQpU+S1EAoJoR5qbW2V6gthIKrVatHr9VgslkEEgXhus11vQdrZbLaMnhrB\nYJAvv/wSgKqqKvR6vWy5GAlZKJ5xnU7Xb7v0iN7Kykree+89KioqWL16NTNmzGDTpk1UVlZmHVeM\nFY1GOf/88wmHw6xZs2bYcbAKRgZlhVjBgQplbioYbVAaBhUoUDDqkB5HKUgDrVa7S3NHtVotUxCM\nRiMGgwGNRkMymSQSieD3++nt7aWjo4OWlhZ27NhBQ0MDzc3NtLa20tnZSU9PD263W/auJ5NJuRIt\nHO5FX7wooGpqaqipqZF93Ha7nbKyMgoLC1Gr1VL+vacybHFe4XBYplsMFyqVSva3i5X20YpUKkVb\nWxubNm0iGAxiMBiYMGECFRUVQ96D1tZWPB4PRqORqqoqqTYQPgnRaBStVktpaSnxeFzGkQqCQJgs\nCoNDMU/D4bA0ORXbiTmu0+lki4NarcbpdMoCHnYWsclkEo/Hg1qtloSWUBaIFgzhWyLICofDIedg\nuuIlkUiwfft2tmzZIgmD8ePHS1VGIpEgFAr1U8qEw2E6OjqkUkKtVmM0GtHr9RnjYoW6KJvSRihC\nDAZDxkjKRCJBQ0MDqVSK4uJiqQoZ2OYwHKT/eyKQHgtqsVhYsmQJFRUVAEyfPp2amho2bNiwy7FD\noRBnnXUWqVSK1atXS3Lh1ltvZe7cuQA0NjaiVqtZuXKlbM1ZtmyZHCMcDnPppZeSl5fHxIkTueee\ne+SxANx1112Ul5djs9kYP348b7/99ojOX4ECBQoUKBgJFILhG4A77niNH/7w/+3Wts8/v55p0x6U\nv6vVC6iv79pbh3bAoq6ubtBrp5xyH0899cF+OJr9j7lz5+J0OnE4HBxyyCE8+eSTgz5z2223oVar\nWbt2bd8LMaAFaADagERfn+Gpp56Kw+FgzJgxg8aorq7GbDZjs9mw2WxDSoyHQnp7hCAVhERbxFcK\nt36hJOjs7KS1tZXm5mYaGhpoaGigqamJtrY2uru7ZdSjcN1PJBLS6V+Y0gmZd15eHvn5+eTk5OB0\nOiktLZWfyc3NlUaN0WhU9qIDMilAjCUKNbGiDQw7MjQbRNyfz+cbMtIxE0SPu8fj2aUHxP5ANBrl\nyiuvpLq6GrvdztFHHy1jTz/++GOmTp1Kfn4+xcXFzJgxg+bmZqkaSR9j8+bNXHDBBZxyyil897vf\nZcqUKRQUFHDEEUdk3XcqlaKpqYlAIIDD4ZD3VKvVytQE0Wog/DSE74C4nkajEZVKJQtvcd+DwaCc\nZ9FoFJ/PB/TN6VAohM/nQ6VSUVxcLNtk0qNHVSoVwWBQRlaK5AkRbynUFO3t7fT09KDVauVcTo96\nhT5DxY0bN8pEiYqKin7k2Lp166RSQhAdarWa7du3EwqFyMvLw+FwYLVa5ZiZFAVDtTIkEglJlmRr\njWhsbJTtIIL4yEQUDAci1SKd7EgneAaio6ODrVu3MnHixEHvpSMcDnPGGWdgNpt55ZVXBqlEBp7X\nBx98wNatW3nzzTe57bbb5P9TS5cupampiR07drBmzRqeffZZue2WLVtYvnw5GzZswOv18sYbb3xj\njVr39N9OBQr2FZS5qWC0YfRrXBWwaNEZu73t7NmTmT17svz9a2hTcEDg1lv/xvbt3axcefn+PpTd\nwqJFi3jiiScwGo1s2bKFKVOmcPTRR3PUUUcBfUkJL7/8MqWlpX0bbAV2AIm0QfRgCVqYP38+s2fP\n7rcCJ6BSqVi9ejWnnHLKiI9RyJhjsZhMTBCrsaKgEqZ5Q61Ci0JCtFEMFcmYDtFaIf72+XyySOro\n6JAyd0FKCIO+nJwcrFYrkUhERvsJl3tReIoEgl3FSQ4HGo0Gq9WKz+cjEAiQkzM4gjYbROHp9/sJ\nBAKDEin2N+LxeFapusvl4qqrruK73/0uyWSSG2+8kfnz5/PKK69Ioz6/38+OHTuIxWI89NBD1NTU\nkJ+fDyDJhmxwuVx0dnaiUqkoLy+XK/NCcRCJRDAajdjtdmnCaDKZ8Pv9crVfqBTSPS5isRixWAyb\nzUZ3d7dUOohWBjFnCgsLsVqtsqgW+xZz3+PxoNfryc/PlzGowgxVkCChUEgSYKKtAvqUPalUipaW\nFlpaWiQxUltbK/cj2pESiQSRSEQ+P0L14/F4MJlMHHTQQWi12n7jZyr4xVzPpGxwuVwkk0lyc3Mz\nKhw6OzvxeDxotVqqq6tlDKYgHkfSHiGeZ0H4pN+XTMqLeDzOnDlzuOyyyzj44IOHHNvn8/HRRx/x\nwgsv7FJVoVKpWLp0KXq9nsMPP5wjjjiCTz/9lHHjxvHSSy/x+OOPS2L2+uuv59ZbbwX6rl80GmXT\npk3k5+cP2bKhQIECBQoU7A0oBIOCEWF3Fy0TiSQazb4TzKRSqb1q0vh18WD4qjBhwgT5s7jW27dv\nlwTDtddey913383VV18NzUA0wyBRmKSdxKQpk3ir7q2s+xq4Mi5WYrP5HKT/DTvjG/V6vXR51+l0\nciXVZDINGck4UvNDgYG94kJab7PZZKyhKOri8TiBQEAaAcbjcdxuN6FQCJPJJJUCXq8X2KkcCAQC\ne6VX02w2Ew6HCQaDGI3GEUnGhfO/2+0+4AgGs9nMkiVL5O/pUvXzzjuPZDIpW0Ouuuoqzjijj3xN\nJpN0dXXhdrtlMV9bWytXlHfs2MF7773HM888k3Xfra2tuN1uLBYLpaWl0gNEeC8ITwVh0KjX60kk\nEnR3dwPI+FTRPgB9xWo0GpX+GU1NTXIeB4PBfgaHhYWFRKNRmRwiyAOBQCCA0WgkJydHxrdC3yq6\nx+ORvhpFRUW43W6p1BHqhs8//xy/3w9ASUmJlOAHg0E5rwGOPfZYYrEYer0el8tFJBKhq6sLk8kk\nkyVgp0GiIFUGQrRHDPx3XbSamM3mjKaTfr+f1tZWoE8RJeZ2MpmUvhMjgVBApT8jgkgcqDhIpVLM\nmTMHg8HAww8/vMuxCwsLeeihh5g7dy5//OMfmTp16pCfLy4ulj+bzWZ5P1pbWykvL5fvpbdH1NbW\n8sADD7B06VI+//xzTj/9dO67775+JqPfFCh97goOVChzU8Fog9IiMYpw112vU17+c2y2Gxg//le8\n/XaffPLWW//G3LlPAdDY2INavYCnn/4nlZU3k5//Yx5//F3+9393cMQRvyYvbyHXXfeCHPOZZz7k\n/7N35lFyldX6fmqeu6ururt6TieRkEQjMyoIAdSABC4IJCRRgpfBoEG4qCABzS+KgheBK1cmoygK\nhtErgkoAhQQQDMMSDIYMpNNDep5qnoffH8X+UtVdnXRnIIPnWSsLUnXqnFOnTnV67+99333SST8u\nebw//3k9Rx/9A8rLr2bSpGV873tPq+fkOL/85d+YNGkZn/nM/4x6/cyZK/jzn9erv2cyWaqrv8Xb\nb+dTtf/+9xZOPPFWKiqu4aijfsDatZvVtqeeejvf+c6TfPrTt+JwfJ1t2wZ44IFXmTr1RsrKrmbq\n1Bt5+OHX1fa//OXfmDlzBV7vN/j85/+X9vYh9dw11zyGz/ctysuv5ogjbmLDhq4xr/H77/fxiU/c\nQnn51XzhC/fi9+elwGvXbqax8fqibSdPvoEXXtjIs8/+i5tvXs2jj76Jy3UVRx31gzH3fyCzdOlS\nHA4HM2bMoK6ujjPPPBOAxx9/HKvVmrcz5IDeHa95eM3DHLn0yOIdbQE+qHvS6bTypweDQbLZLAsX\nLqSqqopTTjmF1atX09LSQltbG52dnfT09KhU+GAwqEbyyYquw+HAZrOpwt3r9VJbW8ukSZOor6+n\nubmZhoYGampqqKqqoqKigrKyMux2u8pb2FuItcHlcqkVa7PZjMlkUsVXLpfD7XaTSCTo6+tTIX02\nm41kMqkaDBUVFZhMJiVz31OkYM3lcgSDwQnZHWw2G2azmVgspkYZHqiMlKoXhvK98sorzJgxQ03z\nePTRR7nwwgtpampixowZRcXjb37zG04++eQxV3/F759KpaisrFQKAlGyiIqmrq4Oh8OhVvsHBgbI\nZDI4nU7S6bTK/QBUKKNkBsRiMaU2iMVitLa2YjAYcLlceDweZYsoDH4UFUChPUI+a6PRSDQapaur\nS01xsFqtKngRUHaMLVu2qFGQ06dPp7m5Wa2MA0WjLUWZEA6H1fs3m82Ul5dTUVEB7GgaAiVVAJKb\nMvLxVCpFKBTCaDSWHEmZTqdpbW0ll8tRW1tbpM7ZVf7KWJQKmhxrX5deeikDAwP83//937h/lpx7\n7rn8/Oc/Z968ebstk66trVVhlpAfy1nIggULePnll2lrawPg+uuL/63S0NDQ0NDYm2gKhkOEzZt7\nufvuNbz11o34fGW0tw+RyexYvRq5CvT66628//4PeOmlLZx99t18/vMf5YUXriGRSHPUUT9g/vxj\nOemkwz54beljOp0WHnzwEj760TrefbeTz33uJxx1VBP/8R87fMovvbSFjRu/h14/eieLFh3HqlWv\nc+aZswBYvfpfVFU5OfLIRjo7hznrrLv47W8v5fTTP8pf//oe559/H5s2fR+vN79q+tBDr7N69VVM\nm1ZNOJzg6qsf5a23buQjH6mmtzfI0FAEgD/84W1+9KPV/PGPV/KRj1Txox+tZuHCX/C3v13Hc89t\n4JVX3uf993+Ay2Vl06Ye3G47mzZtKqliePDBdTz33NU0N3u56KJf8fWvP8KDD16y0+t0+ukf5YYb\nzjioLRIAd999N3fddRevvfYaa9aswWKxEA6HufHGG/nrXz9QJGRQzQOAhacs5LwTziMSjaiiIZvN\n0u/vJ51Oq194hdtuu00Vg7/61a9YvHgxa9euxe1279SqUBhkJ351Cb+TlcexVkr3BblcTjUVZLU5\nnU5js9mw2WzE43GGh4cxGo243W5sNhvRaFStzEozQQIi5X0mEgmeeeYZ5s6du8eKHZPJhN1uJxqN\nEovFJjR6sry8nP7+fgKBAFVVVXt0HvuKkVL1wqJ2/fr1/Pd//zcPPPCAWu0+55xzuPzyy7HZbKOu\n7YMPPlikjBhJX18fQ0ND6PV6Jck3Go0EAgH6+/OZNZJhIvuOxWIEg0Flm5FgR6BouoTZbFZqAqfT\nSSQSob29nWw2i8PhoLq6WhWzYmUotATI9zSbzapMhVwup/JEjEYjPp+PcDhcdC9ks1na2tqwWq2k\n02kqKiqYMmWK+j4VKoVELTE8PMyLL77IZz7zGTVWNRgMYjQaqa+vV9dLzqGUCkA+Oygu4MUaAXnF\nRqkxrGJvcblcRav9ss9d2aNGUkqpINdVvtvCFVdcwcaNG/nLX/6iGi7jZcGCBSSTSc455xyeeeYZ\nTjjhhFHb7KwJOH/+fG655RaOPfZYIpEId999t3pu8+bNdHZ2cuKJJ2I2m9VUj39H1qxZo60UaxyQ\naPemxqGGpmA4RDAY9CSTGd59t5N0OkNTk4fJkytLbqvTwfLlczGbjXz2szNwOMwsXHg8Xq+Tujo3\nJ510GP/4R8cuj3nyydP46EfznvuPfayeBQuOK1IZ6HTwve+djc1mxmIZLcFeuPB4nnrqn8Tj+dWg\nhx9+nYULjwPgt799nblzZ3H66fli8zOfmcGxx07iz39+V73+y1/+FNOn13yQ/G/AYNCzfn0n8XgK\nn6+MGTPyEtCf/exlli07g2nTfOj1eq6//gzefruDjo4hTCYDoVCcDRu6yeVyHH54DT7f6JUx4aKL\nPsGMGbXYbGZuuuk/ePzxtw7IsLt9hU6n44QTTqCjo4N77rmHFStWsHjx4h2S3BKXIplMqpT7RCKR\nD0xL5Yssu92Oy+VSvu8zzzyTKVOmMG3aNG699Va8Xi8tLS3U1tZSVVWFx+MpGpsno/iEwsKk0MMt\nHvs9ncIwXhKJhArtC4fDapqAWDSGhoZUw8HhcJBIJNSkAAm5zGazJJPJopGaer1eFabRaJR4PE4q\nlVIF20QRyb6sNo8Xl8uFXq9XqpMDjVJSdbk+W7du5bzzzuO2227jxBNPBPIWlPr6eiwWy6jr+Mor\nr9Db28v5558/5vG2b99OMBikoqICj8cD5O+BwcFBEokEdrtdNR7kXPr6+gBUmKfFYlGFbCKRUAWx\nZACIWqGrq0vdF6IkkGkpZrNZBZAKUiSLdSKZTKpsEpPJpBQXkgGRSqWIxWK0tbURieSbtM3NzRx+\n+OFFNgEZa2k2m0mlUgwMDBCLxZRaweVy0dPTA4DX6y1qYMk5ydSWkUgwZeH3NRgMkk6ncTqdJQv4\n3t5eQqEQJpOp6FrL/vbEHlH4unQ6PWoSRXt7OytXruTtt9/G5/PhcrkoKyvj4YcfHrXPsVi8eDG3\n3347Z511Fm+++eao50c2vQr/vnz5curr65k8eTJz5sxh3rx5RffS9ddfT1VVFXV1dfT393PLLbeM\n+7w0NDQ0NDQmiqZgOESYOrWKn/xkPitW/JENG7o5/fSZ3HHHPGpqyktuX129Qzpqs5nx+Qr/biIc\n3vUIu3XrtrFs2e95990uksk0yWSaefOOKdqmoaFip+c8c2YtTz/9T846axZPPfVPbrrpHCBvsXjs\nsbd4+ul/Avnsh3Q6w2c+M129vrFxx77tdjOPPno5P/7xc1xyyW/49Kencvvt85g2zUdb2yBXX/0Y\n3/zmE2pfOp2Ozk4/p556OFdeeSpLl66ivX2Y8847ittuO3/MDIbGRo/6/0mTvKRSGQYGwru8Voca\n6XSalpYW1q5dy/bt29WKWX9/P/Nvmc+3532bay+4FkB5/EVpYDAYqLRVYjQad+kDlpXYiZyXyJkT\niYQqUHZnNN2eEIvFgLydQBL/5fih2kVjAAAgAElEQVRWq1UVl16vF71ej9/vJ5FIYLPZiqY8iHze\nbrcr6fsJJ5xAJpNRYzcLZf/SxJD/ysr4WOj1elwulxqrKWMod4UUu4FAgFAoNO7XfViIVP3Pf/6z\nas7odDra29s5++yzueGGG7jwwguBvF+98N4Yeb1+85vfcN55542p8IjH42ocYm1tLVarlWw2SzAY\nJBQKYbPZqKmpKSqKZVqI1WpVn5NYdMQ2JBYGCRO0Wq1s27ZNNaJEESHTHMRSIM0JyRyQe1GyF8S3\nL8oFUTxIZklHR4cqyO12O7W1tVRXVxe9Z8k8MRqNqnkoIyPPPfdc9Hq9sjFZrdYd4a8Fr5f7eWTR\nXzieUj6LeDxOJBLBbDaXzP0IhUJ0d3ej0+lobm4etc/dsUeMpVQoNVGiqalpwo222bNnj7IyXHbZ\nZVx22WVAPstCmDRp0qgGoJrWQ75R+5vf/Eb9/b777lOZDLNmzWLdunUTOrdDFW2FWONARbs3NQ41\nNAXDIcSCBcfx8svX0taWT+f/9rf/b58e74tfvJ9zzz2Szs7/xu//CUuWnDwqBHJXKu4FC45l1arX\n+cMf3uGjH61VqovGRg+LF3+SoaH/YWjofxge/h9Cof/l2mtPL9h38c4/97mZPPfcf9HTcyuHH17D\n5Zc/+MG+KvjZz75YtK9w+H/55CfzYxKvvPJU3nzzRjZsWMGmTT38+MfPjXm+HR07shva2gYxmQxU\nVjpxOMxEozv86JlMlv7+HY2HvRlA+WHT39/Po48+qnzkzz77LI888gif/exn+etf/8q7777LO++8\nwzvvvENdbR0rr1rJ0rOWqtebjCasFismowmD3kCSJElrkmw2qxQNAB0dHbz66qukUikSiQQ//vGP\nGRwcVKvMu0LsF7KaKwVQOp0eVRDsa6Sos1qtqsFgNBpVsSJSbyncgsEg8Xgcq9U6ZsCjTqdT/v1U\nKoXNZlPNB4vFosL4UqmUCnAU2bsoHaToLER8/xPNVJCmQiAQ2N3LtE8QqfpTTz1VVNR3dXUxd+5c\nrrjiCv7zP3dYlUY2ngqL0Hg8zmOPPVa0/Uh6enrUhIa6ujqlKJFC32g0FhXYqVRKhX7KKrPJZMJk\nMimLjzQdotEoRqMRh8NBa2urymTwer0YDAal5AFUgSsBotI4iEajKqNkcHBQNa3Ky8sxGAzKqhCP\nx2lra6O3Nx+iUl9fT1VVVcmiXL63cm8BKvtEbBqSCVBXVzfK6lAYuDjyZ+PIMMZMJqNCMkuNpEwm\nk7S2tqpjlWpA7I49QpoghecuP1dE7XGg0NPTw6uvvkoul2PTpk3cfvvtnHfeefv7tDQ0NDQ0/k3R\nGgyHCJs39/Lii5tIJtOYzUZsNnPJ3APY/UkQIwmHE1RU2DGZDLz++jZWrXq96PnxHGfBguN47rkN\n3Hvv2qJxmF/60id4+ul/8txzGz745TfF2rWb6eryl9xPX1+Qp556h2g0iclkwOm0qPd/xRWzufnm\nZ1R4YyAQ44kn3gLgzTdbef31baTTGWw2E1arCb1ep+aLj+Shh9axcWMP0WiS//f/nmbevGPQ6XRM\nm+YjHk/zzDPvkk5n+MEP/kQyuWOkoM9XRmvrwEFpp9DpdNx77700Njbi8Xi47rrruPPOO5k7d64K\nU5Q/RpMR92Q3dmt+tXfVi6uY9dVZal8vrX8J25k2zjrnLDo6OrDb7Zx+er5pFAqF+OpXv4rH46Gh\noYHnnnuO1atXq2C4XVHKHiFBd6UKmX2JrDJLDoQE+FmtVqLRqEre93q9pNNpQqGQktLLqMFYLIZe\nr8fhcBTt+6233lKFaOFITavVisPh2GnTIRKJlGw6iGpiIoGPFosFq9Wq7B0HAjuTqt9///20trZy\n8803U1NTg8/no6amRr320Ucf5fjjjy9qRD355JNUVFQwe/bsMY+5bds2YrEYVVVVKi8jl8upMMLq\n6uoiD39/fz+5XA6Hw6FGlkpmQzSaD421Wq0MDQ2Ry+WoqKigv7+foaEhdDodXq9X3c+SNQKoUEmx\n0Ugugtx7yWSSVCqFw+Ggqqqq6PvQ1dXFhg0bSCaTGI1Gpk6dSmNjIzqdbtRo1Hg8TjAYVNMtJNDS\nbDZjsVhYs2YNAwMDRKNRbDbbqIwOsUeMlYkycjxlIBBQ+RGl1A6tra2k02ncbvcopYUcb3fsEdKY\nLHzdRJUQt9xyi7oHC//MnTt3QueyK5LJJEuWLKGsrIzPfvazfOELX8hP9NEoYndDNDU09jXavalx\nqKFZJA4REok011//f2zc2IPJZOCEE6aycuWXSm47ss4a/fexC7HCp+65ZyHf+MYTXHnlw8yePY0L\nLzwWvz825n5LUVNTzqc+NYWXX36fxx//inq8oaGCP/zha1x77e9YuPAXGI16jj++mXvv/WLJc8xm\nc9xxx1+4+OIH0OngyCMb1bbnnnskkUiCBQt+QXv7EOXlNj73uRlccMExBINxrrnmcbZtG8BqNXH6\n6TO59trT6ejYVuK967jook9y8cW/YtOmXk45ZRr33Zc/RlmZjXvuWcill/6GbDbHddfNKbKHzJt3\nDA89tA6v9xtMmVLJm2/euOuLc4BQWVk57n/8Wlpa8jkMG4EOWHTqIhaduij/pBFmz5tN9rrSUuKZ\nM2fyzjvv7PZ5FtojCv3rMHqVel8iHngJbhTpuownlMkBMi1ieHhYededTidGo5HBwUFgR9ZBISKl\nj0Qi2O32Uc9L06GwUJZVYynuMpnMKHuFyWRSxWOp45bC7XbT09OD3+8vKtb3F7uSqi9fvpxMJkMy\nmRzVSFm4cCFf/vKXi362LFiwgAULFoy5v2AwSG9vLxaLBZ/PpyZs9PX1qQK7UL0QDoeJRCIYjUZM\nJpNqPOh0OjV6VXI7UqkUdrsdv9+vRlnW19fT0dGhFAmSYyDnLGMlpamQSqXI5XJKyWE0GtWITJn4\n0NLSohQPXq+X8vJyZWmSfcKOAFVRrLjdbux2u3qt3W5X70MmUzQ0NIz6WS32jZ2Np5TvcSQSUcqe\nUhaVrq4uZZ0Ya8JHqcDIXSGNmZFKBclyGa8aatmyZSxbtmzcx91dmpqaWL9+/a431NDQ0NDQ+BDQ\n7a8VVZ1OlzsYV3MPFFauvJGvfGXS/j4NjYOIlSvb+MpXfvjhHTAB9ABJwAb4gH1U5xdOjzAYDKpg\nF/+6zWbbNwcugRSdHo+HRCLB9u3bMRqNuFwuGhoaePfdd9m2bRsf+chHOProo2ltbWXz5nw46vTp\n02lqamLbtm309/fT2NhYMqdCxnpK4OXuUNh0kD+hUEhNJxA7x8hch0Ky2Sytra1ks1kmT578odpQ\n9gTx+cu/QbsTAJrL5di4cSOvvfYaBoOBk046CbfbTSqVUtMM6urqmDIlb8WSqQypVIry8nI1QSQY\nDKLT6ZTiJZPJKCWJXq+nvb2dVCqVVwgZjbS2tqLT6WhoaKC8vJxQKKQmkIiSRD5DCROtrKxUzaXG\nxka8Xi/bt2+nt7dXBSpOmjSJ8vJyhoaGcDgcuFwupbaora1VeRC5XI7y8nJsNhvpdBq/34/JZFKW\nmY6ODnp7e7HZbEyfPn3UPRGJREgkEphMpqIxknJNZRym0Wikv78fvV5PVVXVqM8nEAjQ0tLygYJs\n2pgZGeFwGL1eP6EpKdJ8sdvt6vzT6bTKmZjolAgNDQ0NDY2DlQ/y0CYkA9YUDBoaGvsGC/Ah9cAK\nVyllVV4CIj9M9QIU5y/IyrOsnlosFvWYSMclf6GiokIVXCPzF0Zit9vVarisHE+UUkoHk8nE0NAQ\nqVRKrZAX5jJIsn9h06GsrEwF+o3XzrK/2dORpZlMhkgkQmdnJ/F4nMmTJyurQCAQKKleGBwcVJND\nDAaDmh4higuxSkQiETXNQaYwNDU1YTable1C8hXE0mAwGJRlQc7P7/crK0Z5eTkDAwMqV6C1tZXO\nzk71+dXV1eH1elVDLh6P43A4lFImEAhgNBoxm83KjgMoS4c0uWKxmLq/a2trS46S3JllodAeMTw8\nTC6XKzmSMpFIqBG3DQ0NYzYP9mR6hNzfhY/Bh6uG0tDQ0NDQOBjRMhg0NEowVgaDxoGJyKrFM240\nGtXK7Ie9qi6ryGazmXA4rEYESl5BJBLBYDBQXV1NPB5X4YpmsxmXy0U8HldTBEoVTmvWrFGBj5lM\nRjU09gYWiwW73a4S6yXTwWazKWtGLpdTq+Uy6lCn0xEIBFSmw6FKLpcjkUgQDocJhUJ0dnaSyWRo\nbGxUExz8fr/KXpBCPB6P4/f7lbVB8gpSqRTJZBKDwYDJZCIYDKoshY6O/KjgmpoaGhoaVGikvF4y\nFaTZI8V0Op0uyvAozC5IJpNs2bKFvr4+dQ9OnToVk8mE2WxWWQrpdJpoNKr2KdNGpMkgDZBkMqnG\nxUJ+XOebb76Jx+PBZrONanyNZzwloNQSTqezKL8CdqhmMpmMGm87Frtjj5BzHBlMuT+yXDT2LprP\nXeNARbs3NQ41NAWDhobGQY3I/M1msyqIpNj7sNPexd9vsVhUFoMUU1arlf7+frLZLOXl5VgsFvr6\n+kgkEiqg0Wg0qgkTZWVlOz13m81GJBIhEomULOZ2F5fLRSKRIBQKYbFYdjpKUK69xWIhkUgQCASw\nWCyqsTNyZObBTDabJRaLqUKzp6eHYDBIdXU15eXl6PV6gsGg+jxEvZDL5ejr61NqAlEvQN4uAMXT\nRvR6PS0tLRgMBrxeL5Mm5WVAyWRS3VtiaRGFjlxfaTYVjpGU5/x+P0NDQ3g8HsrLy2lublZBkDL5\nQbaVKRgWi0VlmySTSXUv5HI5pbSQJpiMOjUYDFRWVpZs7Ml5GQyGMRUMmUxGhaCOtFAAdHZ2Eo1G\nsVqtY+YuCIUNmPEiTYlCpUKpxzQ0NDQ0NDRKc3D/xqehsY84/PDD9/cpaIyTkdMjdDrdfgl3hB3q\nhcKCUfza0mAA8Hq9QN5HHo/Hsdlso+wRpYor2DEv22AwYLfbSafTe3WKg16vx+l0ks1mCYfDJbcR\ni4HZbMZms+HxeNTqvigdpNkSi8XU9IpYLKZW4g8mpUMymSQcDqsQRrPZzNatW1U+gahThoeHMRqN\nVFVVKfVCIBBQihSz2aymR8RiMVKpFFarlUgkova9ZcsWMpkMHo+HqVOnKrVAYTNHpoMAqjkgTQe9\nXq/k/NKMaG1txe/PT+ApLy/nsMMOw+VyFWVQyNhJ+VxkYoVOpyORSJDL5VTzKJlMqiBJCVPt7OwE\n4Oyzzx6zoSQKhVKNP3l/0rgoNZJyeHiYgYEB9Ho9kydP3mnjYHfsETJxZWRTIpVK7Rc1lMbeRX52\namgcaGj3psahhtZg0DggOfXU2/nlL/8GwK9//RonnfTjcb3uP//zAZYvf2pfnprGAcZY9ojdCe7b\nU2QF2WazqQaDFDiFmQzV1dVks1lCoZBqMEjewq7yFwqx2WwqbX9vhubKNAQJD9wVdrtdjVnU6XTY\nbDacTqeyV0j45q6aDgda8G8ulyMajRaNDBXlyfDwMFarFZ/Ph16vJxQKKctIfX09kG9+DQ4OqikL\nkl8ghbQoCKTR0NraSiwWw+VyMW3aNHX/SuEvKghpAuj1emW7kVwGaRaIrF+mlJhMJqqrq1X2hzTi\npGEwPDysLBgmk0lZe6TBUUq9IOGpfX19JJNJHA6Hum9HFuMymQFKWxbS6bRqaJWVlY1qDsbjcdrb\n2wFobGxUDZyx2F17xMjcFml8fNhqKA0NDQ0NjYMVrcGgAYBefwUtLf37+zTG5MP+vU7LYDg4kF/+\nRb0AOwqnPQnx211ESWCxWFSDQYIUc7mcmhjg8/mIRCJqZdpoNOJ0OpX3XJQBpSj0ahqNRmw2G6lU\naq+qGHQ6nbJohEKhXRb+Op1OTRGQBok8bjQasVgsqungcDjGbDqEw2HVdJBV8v3VdJCCVwIvxd4A\n8P7775NIJKipqcHlcqlpCmIPkOK3v79fKRNMJhMOh0M1LWQ1X0ZLdnV1EQqFMJvNzJgxo+j+lUaP\nwWBQygNRjAwPD6PX65WdJZFIqNeINcNut9PU1FTUnCi0I4j9we12Yzab1fep0Hok5xOPx8lms2pE\najKZpLe3F8gHLr744osYDIYJ5y9EIhGSyaSyCxWSzWbZtm0b2WyWyspKPB7PuD6/3bVHFJ6fFu54\n6KD53DUOVLR7U+NQQ2swaAAfbgGfyRw80miNPBdddBG1tbW43W6mT5/O/fffP2qb73//++j1el54\n4YX8AzGgBdgItAHJ/D+ip512Gm63W43vK6StrY3TTjsNh8PBzJkz+etf/7rT85IiTQoiWRHe0ykB\nu0MulyMej6tzkfOR/AUp9lwuFxaLRUnnrVYrdrtdhfzB+NQLgqgYotHoXi3GJWRSpPO7QhoMgUBg\np+ch12RXTYdEIjHhpkMymeSyyy6jubmZ8vJyjj76aFavXg3AunXrmDNnDl6vF5/Px/z582lvb1dZ\nGYJ8jt/97nfxer00NjZSVVVFeXk5ra2tRKNR2tvb0el01NXVqWaSjFcU9YIoNGRMqjQoYrGYKrZT\nqRRGo5HBwUH12U+dOrWowJYgT8lIkEBIycnQ6XRFmQd+vx+/308ymVTjKiV0USwAmUxGNU+kASH3\nYKGtQJQbMtJUxsEaDAbVROns7CSbzeL1etUxxrJHiLJopLohlUqpSRVut3vUazs6OpTSR67vzigV\n1Lgr5NoUBjmOZZnQ0NDQ0NDQGBvtX8xDiMmTb+D225/niCNuoqLiGhYu/AXJZFo9//Ofv8xhh32X\nyspvcO6599DTEwBg9uzbyOXg4x+/ibKyq3n88bdG7fvXv36NT3/6Vr7+9Ydxu/+LmTNX8MILG9Xz\nDzzwKjNnrqCs7Go+8pHvsHLlS+q5tWs309h4Pbfe+iy1tddyySW/xu+PcvbZd1Fd/S283m9w9tl3\n0dk5PK73uXFjD3Pm/ASv9xvMmPH/Sp4vwOBgmLPPvouKimvwer/B7Nm3jWv/oGUwjGTZsmVs27YN\nv9/PU089xXe+8x3+8Y9/qOdbWlp44okn8sF2WeBd4CVgM9AKvAesAcewg0svvZTbbiv9WSxcuJBj\njjmGoaEhfvCDH3DBBRcwODg45nlJEQ+o4mV/pb2LT91qtSqpt6x6SoMBduQvyHhKq9U67vGUMNqr\nKQ2MVCqlVq/3Fk6nE4PBoPIHdoasrkvxOhF21XQoDB6UpkMkEhnVdEin0zQ1NfHyyy8TCAS46aab\nVCNheHiYJUuW0NLSwnvvvYfNZuOyyy4jnU6TSCSIx+Ok02kikQiJRAKDwcCCBQsIBoOEQiGCwSDN\nzc10dnYSCATweDx4PB5lQzAYDFRVVWGz2chmsyrQs9AaIZMfRH1gNBoJBoMMDQ2h0+mYNGkSLper\n6N4Ve4Q0zeLxuAp8BHC73WrSQjgcVjkfVquV+vp6lcNgNBpV80uUMhaLBavVitVqVceQkFQ5duEE\nCml0yGjUcDislBu1tbVkMhlOOumkMQMeYbQSIJfLMTQ0pEZSjnztwMAAQ0NDGAyGXeYujDzWRBoM\npc6vlGVC4+BF87lrHKho96bGoYbWYDjEePzxt3juuavZtu2HvPPOdh544FUAXnhhIzfc8CRPPPEV\nurt/TFOThwsv/DkAa9d+C4D165cTDN7JvHnHlNz3unXbOOwwH4ODd7BixVmcd959+P35lU2fr4w/\n//lKgsE7+dWvLuaaax7n7bc71Gt7egL4/VHa23/EypVfIpvNccklJ9LRcQvt7bdgt5u58spHdvn+\notEkc+b8hC996RMMDNzOI49cxte+toqNG3tGbXv77c/T2OhhcPB2+vpu4+abz53YxdRQzJw5U61Y\nitd769at6vmlS5dy66235n8RbwO2AyMXmbNwnO04vnjCF5k8efKoY2zZsoV//OMfrFixAovFwnnn\nncfHP/5xfve735U8p1L2CGF/2CNK5S8UNhikUVJVVVVUyEqDQTIZYOyAx1KIF16n0+11FYNYJXK5\nnDq3nVGoYthTCpsOdrtdNR0kZFGn041qOuRyOa699lp8Ph/pdJozzzyTyZMn89Zbb3HGGWdw7rnn\nqpGhS5YsYd26dep4yWSSYDBIJpPBZrONWVRu3ryZbDaLz+fD6XQq9YLRaFSr68PDw2r0qMViwel0\nKiWCZGbIMfv7+9HpdEyePBmHw6FCQQWxMEh4Zm9vr1IJiHolHA4TCATw+/3kcjm8Xi91dXWYTCbV\n0JDJDWKvkMDJwikSYp+Q708ikVDnIyNVTSYTFouFXC7H9u3bgfwoTZPJpJpQY+UvlFIWhcPhokkq\nI9+7HGPSpEmjRlaOhTQeJ6I6KBXkKPaI/fHzRENDQ0ND42BFazAcYlx99Wn4fGW43XbOPnsWb7+d\n/+Vs1arXufTSEzniiEZMJgO33PIFXnuthfb2IfXaXRUmPl8ZV111GgaDnvnzj+Xww3386U/rAfj8\n5z9Gc3N+HvlJJx3GnDkzefnlLeq1BoOe733vPzCZDFgsJjweB1/4wlFYLCYcDgvLln2el17aUvK4\nhfzxj/9k8uRKFi/+FDqdjiOOaOT8848uqWIwmQx0dwfYtm0Qg0HPiSd+ZNcX8AO0DIbRLF26FIfD\nwYwZM6irq+PMM88E4PHHH8dqtXLGGWfkmwoDO17z8JqHOXLpkcU7agFKLIb/61//YsqUKUVFxhFH\nHMG//vWvkudTWMyI31oCH/dH2nupCRJSmBgMBlV0+3w+gsFgUVCfy+VSvnyr1brTQqqUV1OKvn2h\nYpBV7ng8vsucB5vNhsViUcGNexu9Xq/e63iaDi0tLWzZsoWpU6eSTCaVygTglVdeYcaMGWSzWRKJ\nBI888gizZ89WqgmAp59+msrKSmbNmsV9992H3++np6cHi8WCz+cjm82qDARRLySTSbUi73A41NQI\nCcCUQMtkMklPT74xOmXKFNUkKmxsSF6DTN2QaRMVFRWYzWZyuRz9/f20trYqJUZZWRk1NTVqRGXh\ne5YsEFEzyN/leVmxl+ucTqdVFojYZGQs5eDgILFYDIvFooIjs9ksr7zySsn8hcKRrYJM59DpdLhc\nrqKGQCaTYdu2beRyOTUKdDyIPWIiqoPCZqWcuzRFtHDHQwfN565xoKLdmxqHGlpb/hDD59shrbbb\nzXR35yXXXV0BjjlmknrO4bDg9Tro7BymqWnXgVkA9fXF3thJk7x0deVHnz3zzLt8//t/ZPPmPrLZ\nHLFYko9/fIdXtqrKhcm0o+iLxZL81389xrPPbsDvz6+6hsMJtTo+Fm1tg/z979vweK4BIJfLZzos\nXvzJUdtee+0cVqz4I3Pm/ASdTsfll3+ab3/7jHG9V43R3H333dx111289tprrFmzBovFQjgc5sYb\nb9yRlZChSLmw8JSFnDbzNN755ztF+3pr+C2i0ShPP/20euyll14im80WPdbb28vg4GDRY4IUsEaj\nUQXgSdr7/lhxFMn6+vXrGRrKN+7kfPR6Pe+99x5WqxWbzUYwGFR/Wltb1fscHBykvLyctra2MY+z\nfv36kmqCVCpFJBLBYDDgdDr3alGUyWSU31/CH3e2bSaT4d13391vK79SqC5fvpzPfe5zBAIB3njj\nDZxOJ5BvIN58883ccccd9Pf3U1ZWxoUXXsgXv/hF1fi58MILWbJkCT6fj7///e+cf/75BAIBbDYb\nDQ0NuN1ulbNgsViUeqGvr09ZI6Q5IzaHbDarGh39/f0YjUaamprwer34/X7VJBHEthGPx1V2QVlZ\nGW63m8HBQbq7u9XkB6PRiMfjUavw8rNUJqzIfSjNBJ1OV5Q1ACiVAewYXSnWo3g8jtfrVY2H7u5u\nIB/sKNYKaSKMRIr+wveXzWaV4sLhcIxqCEg+hsPhyFuvxsneskeMZenQ0NDQ0NDQ2Dlag+HfhLq6\nctradnjZI5EEg4MRGhoqxr2Pzk5/0d/b24c455wjSCbTXHDBz3jooUs455wj0Ov1fOEL91IoiBhZ\nj9x++/Ns2dLHG28so6rKxTvvdHD00T/cZYOhsdHDKadM49lnr97l+TqdVm677QJuu+0CNmzo4tRT\n7+D44ydz6qm7zlfQMhhKo9PpOOGEE3jwwQe55557aGtrY/HixTQ2NuY3KCGCicfjoyTzoeEQmUxG\nFSmyXTAYLHqsr68PvV5f9BigCjWRfEthk81msVgs+2XFUYo68fPncjlisRg2m41AIMDw8DBer5fu\n7m4ikYj6I9ehp6dHSeilyC1FZWXlqOsB+WsiAYI7k/jvLvK+hoaGdjkiUBQku8pt2Ffkcjl+8Ytf\nkEql+PznP897772HxWKhsbGR7u5urrnmGq655hpmzJihrAxyzlJsT58+Xe3vU5/6FF//+tf5/e9/\nz0UXXYTP58NsNtPT06PUC3a7nWAwqEIQJUsikUgU5S7E43G6urqwWCzU1NRQV1enlCEj7RGhUAi/\n368+07KyMsxms1JJxGIx7HY7FRUVSg0AqIBDWYWX1XlRlxTmLcCOz0lsAtIYK1StFGYR9PT0kE6n\nKS8vV3khso/TTjtt1Och+y28J4PBoFJIiJpC6Ovrw+/3YzQaaW5untD3eXftESOVT6Ue0zi40Xzu\nGgcq2r2pcaihNRj+TVi48DgWLbqfRYuO5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RKJ0NPTg8FgoKamhrq6fKqpvO90Ok13dzep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dY7oYoF15WZaZ0OD1evHxxx8DABRFQSKRGDKeMhAIjNp4yk8qRFTqhqDv9/U8BxIhnOMQqZtD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\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6aG9vF2X0Xq+X4uLicVsFRmOVGIhpnTBFJKvVit/vx+VyTZl1orW1lXfeeYfTp0+Lx6xW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\nXZhMJiZPnozD4SCVShEMBo3MiHQ6TSgUMjIR9DV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NZhRFIZlMZvR30DQNi8VivKYQQggh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d3icgIvKvKCsri5UrV1JUVITNZsPX19etPy8vj6EDB9K7e3dX24SkJHwNg3379//vwE8+4WRZ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\n", 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13i8o9ZB1bt0JoJeqni+A9Qal3QHoBpPCQ2B6Pz/qc4UmoDsO2aeGcT5v/eWnXLizf87Z\nL0D6G3T2WgB94bpd8vWCqtv6h8FcRIJVh/EBpCA6C3M31GAADWHqWwKmt34PmDtJ7EHsKgBWi0hC\n8GtPREQUfpg2hIiIKPi6Wbd1O4uDCUxd5TRNYVIn/O5vYSJSASYXdT1rOdFwDUw1c3reFCanri8K\nYLu3XqDZEZFYmDy9DWDSYZSG64Xw+k7Pm8L0eswvXZ2evxPgPM6BpbYAXsvhOp1zlt4kIrVzsx3z\nkT2I9Z6/QqqqIrILQLI1qQa8HzOLkBVMvRM+BmEEcJd90QBWWD2e3XVwej7PX/0s78GkYYiFCZ63\nArAhgPncrYO5M6ECgARfaTlEpDSygk1AcFKGACaA6zcnMQDnXq41fJRp5/Q8kLotQtZ+KRDWILPO\ng3aOUtVABtYLJnvgGjB3t9gHbcxPwWx3SfbiAFapalo2y9woIr/DBDN9BbCdz1vDReRjVQ3VQJ55\n8QpMr2P7wLjrRaSfqu50LmTli34Hpie88/uMKohKish1MJ8t9nUvVNX3/c2jqpdEpIqPwUSPwvTa\n/khEesDkQy8B05N8Olw/C4mIiC5LDF4TEREFX0vr4Y39B+1pAKNV1SPFgp2INIDJz9wJJtVHIMoF\nUGZbgMuy16MaTN7VW2DSIASrHnlxg9PzHiLSIoB5nAfJrJbTFarqQRH5BmZwvXgA20VkIUzahy+z\n6V1bkL4LoIxzvtRYH2XeATAV5vtiBxGpaPVgd+ec6sIjwG2leGnsNCnbdByqelFEvoZJ9wKYCzQ5\nDl6raqaIvAMT9AJMMNNbTulbYYJbCuBrK31JXimAPb5Syzix7wt7mhQXIlIdrrnT/+NexotAygSN\ndUfJ88g6v81S1QUFWQfLmzDnywSYC2tvish5VX3b/2xBEYx219Tp+dYA1/s1TPDal/dgesALzB0r\n34nIXAAfq2pBD2CYa6p6TEQGAXgf5vPwKphz8FaY1Dw2mNze18Ich0cAHEDWZ0W+5/EWkToAVsIE\nlwUmrUtA6bx8BK7dy6wSkbEwF/YAoLOINLJSYxEREV22GLwmIiIKPm+92tJgAhe7YHJcL1TVU74W\nICJdYW4TLoGsW4X9sfc2DOTW6IBTXlhB4U9gAi3BrkdeVHaqi8+UFj4IXAPZOTEEwHoA5WHe4/9Z\nj4sisgPAZzA9zjcEELTML6kBlLno9Ly4twKq+peIrIPp2RcBoD9cB8KDiNwAk74FMMfVOi+Liofr\nxZdAe+MedHqel4shi2CC1wKgn4g84JzX3eKcUsRXD/PcCMa+cL6L45SqZhuEU9VUEUmDuTsiX4lI\nB5hBNu13YXyArPzOBe1XmF7+n8FcoIqAGfTzfHa9X4Mg2Pv6Vy+ve+N3MFNV/V5EHoEZSBcwd8i8\nAOAFETkOczFpM8zgwOF0J4kHVV1p5X9fiKxzQivr4SgG4L8wecWdz1cuwWERaYXsUwnNV9WALvaK\nSBWY8195mHPNfwHcnA/51t+ESSdjv2BxMwK7cEJERFRoMec1ERFR8D2pqhFuj1hVraWqvVX1tWwC\n1xVggkH2254PwuQAbgOT/7KU87IBPOM0eyCf7QH1EBaRkjC99spY9TgK4EkA7WECQ9Fu9bgnh/XI\nC+c8rv4GuPL1CLQnuwtV/QGmF/F0mGCVfXnFYHrbPwgTwDgoIim5WUcQBDMlgHMgd6CX1+3TFMDb\nPgL27gFUb2lFvHEul+uLIVZ+6D3Wv1fAbbA3EamIrLQmGQD85k/P6eqDsAzn7ZeTQJjflBPBICLX\nw9x5YB808FMAd4QyLYWVqiQZwB/WpAgAi62UC/m66iAsIzf7Otv9rKr/hDnGNwLIRNZ5Kx5Ad5he\n83tFZK2I1Pe5oDCgqmthetaPgbmw+geA8zD5vL8AMArAtdaAxjWdZnW/GNAAWRcffT2yG0MCACAi\nV1h1qQETuP4VwE3WwJpBZZ1jnVNg1Qv2OoiIiMINe14TERGFn/tggnUKc9txko88wnb51cu5H4Dq\nVj1+BdBCVf0N+FVgA2LBBHaiYerWQFX3ZFM+aFT1KIDRIvIgzMCUba2/bZC1DaoCmCsiDVX1oYKq\nWz5YAXO7fQyAZiJSV1X/CwAiUgxmUDQ7X7mY3YNr0XDrBelDtNPzvN7yvxjAJOv5nQBWO702ACbA\naR/8M9wGtXPefjkZuDI6+yK5JyJNYHLx2tfzJYBbrMFbQ0pV94nITQA2weQ7LwHgXyLS2wp+hqvc\n7OuA9rOqbgaw2bo4eiPM+aotTKoN+8XGjgD+IyIdVLVAU8/khNWb+VXr4ZWIlINrDnlv78ffBYeA\nLkaISBmYwPU11qSjMIFrvz3i8+h/Ts/zO0UXERFRyLHnNRERUfhxHtxuUjaBa8D3IG95lez0fGoA\nQb38qoc3zrmXA+odF2yqelFVN6vqZFXtBtOrtytMEM8e+BhrBfkKJVU9B5O+xs6593UXmMCJAvjJ\nz+31JwA4p+moHuDqazo9z2tAeRGyBvPrKSLOAb/8ShkSLM7vPcYaXNIvK6CWbxeTRKQezB0GcTDb\n9FsA3fIhRUKuWRe0OiIrz3RJAMtEpH3oapUt531dNcB5Ai0HwFx8U9WlqjpGVVvA3M0zFsBxmDYS\nBWBmTpYZppw/v87CdWBUqOocL3dIOT+KZZcr3TqPfIysXOUnAHQMUs58f5zPX4HeyUJERFRoMXhN\nREQUfio7Pfeby1JEImB6/Ya0HpZ2+VQPb5wHM+tcgOv1SVUvWb06b0JWmgoAyO90BfltkdPzAU7P\nAwr6Wre573KalO3xKiLFYQbGtNue3Tz+WKkk7ANFRsHKky4iV8MMBgmY3t0r8rKe/GDV/YT1rwC4\nLoDZfA0Ym2ciUhump2k5qz4/AujsLxVSqFgD2XVCVj7qKAArRaRt6Grl1w6n59cHOE+e9rWq/qWq\n0wDcBrM/BUBjEclRUDwMDbL+KoB3g31HgIhEwtzBYc+3fRomx3VB5J++1un5Hz5LERERXSYYvCYi\nIgo/znmDs7t1vA/MAFH5kWM24HqIyHUwP6gLKtfth/ZVAxho5RwNC6p6Hia4Z1cxVHUJkg0wARIB\nkCAiN1i9f3tarysAvz0UrWXYpQSwzttgevUCptfkVwHX1jfnILy9B/ld1l8F8L6178LRZ07Psxtk\nDsh6X0ElItVgBiy9EuZ42AfT0/SY3xlDSFW/hblLwJ56JhrAh9Y5K9xssv4KgB5udwh4EJEkmJ7X\neT7vqupnAJwvQBTa85Z1ceJmp0lB7UluXVxbjqwLtmcB9CyIVCsi0gDmwoZ9n2/K73USERGFGoPX\nRERE4eeA0/OevgpZA829gKx0CKGqRykAb9r/zYd6ePMvAD9bz6MBLLICCtkSkdIiEpXTFYpInIgE\n+v6qOT0/mtN1hRNr8D3n4PSdMD2Xo2COvS+s3sH+vIms47SliAzxVVBEygJ4FlmDyr0VpHQU/wJw\nwapDBxG5EsAdTq8v8jpXeJhr/RUAd4lIc18FRaQlzD4K6oUkEakEE7iuBtdB6f7nd8YwoKpbAXRD\n1iCIZQCsEZFrfc8VEquRlRKpDICnfRW0znfP2//1Uy6gC3tWu3O+SFkoz1tWu56PrPPHwmAGla27\nnd6F6dEPmHPKrVZO8dwuM6D85la5+cjqIX8UrhdKiYiILksMXhMREYWfVU7PHxOR/u4FRKQFgM0w\nqT3yK+elcz2GicgDIuLy3UFErgLwKYDG8ByYL9+o6iUAI2B6hwtM6pDN2QT1mojIFAC/wDW4HKjb\nAPwkIn8TEa95m0WkhIg8AKC30+Q1uVhXuHEO7N4OwDn4nG2eaCsH7GzrXwEwQ0TudS9nHU+fwORP\nF5h0D5NzWWf3OpyAGWAQMN+BpwFIsP7/XVU3BmM9+eRDZPU+jwCw2lvuZhHpYJUVAEFLkyAi8TD7\npba17CMwgetfgrWO/Kaqn8Ok8DlnTYoHsE5EGoWuVq5UNQPAk9a/AjMw7NPuF+ZEpDxMipvmyHo/\nviwTkRUicouvi3ZWj/olAIpZk35U1V9z+z78yNPFTevcO1REYn283g3AFwBqWes6BJPPO5jmI+v8\nngFgQF4GAbWC4YdEZKKI1PVTLhHmHNAc5r0pgH+o6tncrpuIiKiwKJZ9ESIiIipg8wD8DSZQFAXg\nbRF5DMBOmF5eDWF+wCrMIFQbADwU7Eqo6hoR+QJAG5gfyy/BBFO2wdxeXhcmf7ENJiA8HcCUYNfD\nT/3Wicj9MEHICJjco1+LyF6Y7XICpidhJZgBtcrbZ83DauvA9HZ/QUQOweRy/hNm+1Sy6hDvtJ75\nqvpNHtYXFlR1l4h8B3PslQVwo/XSBQBLA1zMWJjUMs0BlIAJYD8K4N8wF2DqAEiE2Zf2Zaeo6m9B\neRPGImQFnm6z/iqAxUFcR9Cpqlq91b+A2f4VAKwXkR0w5wXAHONNYN7PczA5f6tYr2Uib+YAaICs\n3qzfw5wLApl3j6pOz+P6g0JVN4rILTApH0rCbMt1IpKkqj+FtnaGqr5hBWG7wpxX/gHgHhHZBHNO\nqwYgCUAkgL0wF2QesGb3tp9tMEH7HgAuiMj31nwnAcTCDKDaClmdmjIAjM7r+xCR3gCe8PGy/Rw8\nX0TcL76+r6q+epzXAzAM5tyxA8BPML3py8Gk0rAf7wrzmdReVU/m8i14EJFRcL2rYR+A9gEOAnpJ\nVcf4eO0KABMATBCR32E+V47CXJgoC5P/P8GpvAJ4WVXn5fxdEBERFT4MXhMREYUZVT0nIt1hghI1\nrcn1rQeQFUDaDDOA3v/lY3X6WPVoav2fAM8f0bsA9IMJPAYqKOlFrEDPfwHMgAl+AsBV1sOlKLIC\nDrtgAjc5rVsasnp6AyboU8PHejJhgvl/y+YtFCaLYYKizlaraqq3wu5U9YwV5JmHrMBxVbim7rBv\nv98BDFXVT/NWZQ8fwuz7OLfp+RG8DmoKHVX9SURuAvA+ss4LTZHVNu3b7nUAj8ME+ezyOpii/cKP\n/T0lW49AfArTFsKCqq4VkX4A3gNQHCa38wYRaaeq+0NbO4fbACyAOa8CJjjbx+l1hRlE91YAw52m\ne9vPp5F17isOM0BpM7cy9mPnCIBhQboL4QqYiyne2I+jOl5e+zqb5SrMb9iWcB2s0vkc/y8AY1T1\nCIKrgvXXXv9rrEcgMgD4Cl4DWXWvjKwgvLfXjwN4UFUXBrheIiKiQo/BayIiouAJWo5ZK1DVFMD9\nAG4BcDXM5/ZhmKDFYgDvWT0yA113juunqkdE5AaYAEl/mN6XUTBBjp8AvAPgbVU9b93WbF+Hv3UF\nq4y9jhtE5BqY7dQNphdhRZicsWdgttkeAF8CWKOq3+Vmvar6rohshMl12gYmMJOArEBoKoD/wvQk\nXhjEnpyBbgsNoExeyi+GSeFh76GpCCBliMsKVdMB9LOOqYEwPUivhDmm/oI5tlcBmKeq2aVDsNfB\n+W92678gIksB3O00eZeqfh/YOwh4nTmqV6DlVXWHiDSESZlzG8x5IQpmQM2tAGbZc++KiP24zAhS\nzvDcnt8KYhDXHK1DVVeJyACYVBkRMHdNrBeRG53yt4es3anqBQB3iMg8mIsQrWACpydgek2/DWCB\ndaGzrNOsHhflVLWrldu7A0zv5HowF42iAZyH6eG7Cybf9hKrjQZLbva9v3kmwNx90AEmVVVFmDtd\nTsBc8NoA4F1V3ZaL9QajfjmeT1UvWelCbrAeTWAuFl0BoDTMxYejALbBpO55N4wHliUiIsoXYsbg\nISIiIiKiy4F1MedHWCk+VNVXD1gq5ETkKwDXwezrFqr6bYirRERERBRUHLCRiIiIiOjy4jzIa3Zp\nGKiQEpFaMPmQAZMf2d9dJURERESFEoPXRERERESXCRGpA9fcum+Hqi6U76bB/J5TmDRSGSGuDxER\nEVHQMXhNRERERBTmRMQmImtEpJOIeP0OLyI9YAZyjbEmfaOqGwqskhQUIvK0iNzvls/a+fVaIrIK\nQFdrUgaAqQVWQSIiIqICxJzXRERERERhTkQiAFy0/j0BM4DbbwAuACgHMxhfFadZUgG0CuLAoVRA\nROQtAHfCBKV3wQyOewpmkMV6AK6F68Cpj6nqsyGoKhEREVG+KxTBaxG5AkBnAAdh8rkRERERERUl\nNgD/gQl9HMthAAAgAElEQVRWAoB4KWN/7SCA8QB+zv9q5UgsgHuDsJy3APwvCMsJV5MAdLGe+9vP\n5wG8BuCdgqgUERERUQ5EAqgJYK2qHsvLggpL8HoAgMWhrgcRERERERERERERBeROVc3TGCzFglWT\nfHYQABYtWoR69eqFuCpE5G7s2LF46aWXQl0NIvKBbZQofBWl9vnrr7/i1ltvzdMyRASzZs1CkyZN\nglQrIv+KUhslKmzYPonC1+7duzFw4EDAiunmRWEJXp8DgHr16qFZs2ahrgsRuYmNjWXbJApjbKNE\n4asotc9mzZrh0qVLoa4GUY4UpTZKVNiwfRIVCnlO/+x1pHIiIiIiIiIiIiIiolBi8JqI8uz48eOh\nrgIR+cE2ShS+2D6JwhvbKFH4YvskKhoYvCaiPNu3b1+oq0BEfrCNEoUvtk+i8MY2ShS+2D6JigYG\nr4koz5577rlQV4GI/GAbJQpfbJ9E4Y1tlCh8sX0SFQ2iqqGuQ7ZEpBmAbdu2bWMyfiIiIiIiIiIi\nIqIwtX37djRv3hwAmqvq9rwsiz2viYiIiIiIiIiIiCjsMHhNRERERERERERERGGHwWsiyrNx48aF\nugpE5AfbKFH4YvskCm9so0Thi+2TqGhg8JqI8qx69eqhrgIR+cE2ShS+2D6JwhvbKFH4YvskKho4\nYCMRERERERERERERBQUHbCQiIiIiIiIiIiKiyxqD10REREREREREREQUdhi8JqI827NnT6irQER+\nsI0ShS+2T6LwxjZKFL7YPomKBgaviSjPxo8fH+oqEJEfbKNE4Yvtkyi8sY0ShS+2T6KigcFrIsqz\n1157LdRVICI/2EaJwhfbJ1F4YxslCl9sn0RFA4PXRJRn1atXD3UViMgPtlGi8MX2SRTe2EaJwhfb\nJ1HRwOA1EREREREREREREYUdBq+JiIiIiIiIiIiIKOwweE1EeTZlypRQV4GI/GAbJQpfbJ9E4Y1t\nlCh8sX0SFQ0MXhNRnp05cybUVSAiP9hGicIX2ydReGMbJQpfbJ9ERYOoaqjrkC0RaQZg27Zt29Cs\nWbNQV4eIiIiIiIiIiIiIvNi+fTuaN28OAM1VdXtelsWe10REREREREREREQUdhi8JiIiIiIiIiIi\nIqKww+A1EeXZX3/9FeoqEJEfbKNE4Yvtkyi8sY0ShS+2T6KigcFrIsqzoUOHhroKROQH2yhR+GL7\nJApvbKNE4Yvtk6hoYPCaiPJs4sSJoa4CEfnBNkoUvtg+icIb2yhR+GL7JCoaGLwmojxr1qxZqKtA\nRH6wjRKFL7ZPovDGNkoUvtg+iYoGBq+JiIiIiIiIiIiIKOwweE1EREREREREREREYYfBayLKszlz\n5oS6CkTkB9soUfhi+yQKb2yjROGL7ZOoaGDwmojybPv27aGuAhH5wTZKFL7YPonCG9soUfhi+yQq\nGhi8JqI8mz59eo7nqVmzJoYOHZrj+TZv3gybzYZly5bleN7Lkc1mw+jRo0NdDQ+52b+HDh2CzWbD\nwoUL86lWRVd6ejoSEhJyNW+4HmNFxcSJE2Gz8eva5WrixImYMWNGqKtBQcL2ennKzfdcKpz4XbTw\nmT59umO/TZ061W9Z++/Izz77LMfrmT9/Pmw2G3755ZfcVpXCSEpKSq5/G1Fo8NsVUSG1YMEC2Gy2\nQnu12WazQURyNW9u58tP33zzDe6//340bNgQpUuXRo0aNXD77bdj7969uVregQMHcO+996J27dqI\niopCbGws2rZti2nTpuHcuXNBrn3w5WX/FlX52aZFhAGVIAhWO/e1r0+dOoWWLVuiVKlSWLduHQDu\nu4KUnp6OJ554AjfffDOuuOKKXAcwuH/DA9trwbFvI/sjKioKVapUQZcuXfDqq68iLS0t3+swY8YM\nLFiwIN/XQ8Hnfvw4PyIiIvCf//wn1FWkHArnfZqX359F9bdNdr9RkpKS0Lhx4wKuVd4U1c/rwqxY\nqCtARLlXmD9Af/rpp1x/YKhqkGuTd1OmTMGXX36Jvn37onHjxjh8+DBeffVVNGvWDFu3bkX9+vUD\nXtZHH32Evn37IjIyEoMGDULDhg1x4cIFfP755xg/fjx+/PFHvPHGG/n4bvIuL/u3KMuvNj179mxk\nZmbmy7KLkmC2c/d9ffr0aXTs2BE//PADli9fjk6dOgEAHn/8cTzyyCNBfR/k3V9//YVJkyahRo0a\naNq0KTZt2pTrZXH/hh7ba8ESEUyaNAk1a9bExYsXcfjwYWzatAljxozB1KlTsXLlSjRq1Cjf1v/6\n66+jfPnyGDx4cL6tg/KP8/Hjrk6dOgVfIcqzcNynN954I86ePYsSJUqEZP2Fmb/fKIUxJsHfRoUP\ng9dEhDNnzqBUqVIFus7ixYsX6Pry24MPPoglS5agWLGs02q/fv3QsGFDPPfccwH33jt48CD69++P\nhIQEbNiwARUqVHC8NmLECEyaNAmrV68OSp3zc79fbvu3sIuIiEBERESoq1HoBaudu0tLS0OnTp2w\na9cufPDBB45AGGDuYgjXH1nnz59HiRIlCuWPFm8qV66Mw4cPo0KFCti2bRtatmwZlOUW1v1b2LG9\nFrwuXbqgWbNmjv///ve/Y9OmTejWrRt69eqF3bt3o2TJkiGsYc5cunQJmZmZ/E5TQNyPHyr8wnGf\nFuVzNGXhb6PCh93iiC4jP/30E/r06YMrrrgCUVFRaNmyJVatWuVSxn7bz2effYaRI0eiYsWKqFat\nmuP1P/74A0OHDkWlSpUQGRmJhg0bYu7cuS7LsOcLW7p0KZ588klERUWhTJky6Nu3L06fPo0LFy5g\nzJgxqFixImJiYjB06FBcvHjRZRnuOZFPnDiBhx56CI0bN0ZMTAxiY2PRtWtX7Nq1y+N9iggyMzMx\nefJkVKtWDVFRUbjpppuwf//+YGzGXGnVqpXLD2TA9Cpo2LAhdu/eHfBypkyZgvT0dMyZM8clcG1X\nq1YtjBo1ymP6ihUr0KhRI8c+W7t2rcvr9jycu3fvxoABA1C2bFkkJiY6Xt+wYQMSExNRunRpxMfH\no3fv3tizZ4/XZezfvx8pKSmIj49HXFwchg4d6pHKxFvO69TUVIwdOxYJCQmIjIxEtWrVMHjwYBw/\nftzvNgnkuM7IyMCTTz6JunXrIioqCuXKlUNiYiLWr1/vd9nh7MiRIxgyZAiqVauGyMhIVK5cGb17\n93bJtbdy5Up0794dVapUQWRkJOrUqYOnn37aoydBtWrVPPK6vfDCC2jTpg3KlSuHUqVKoUWLFnj/\n/fd91ie7Y6woCFY7d5aeno7OnTtjx44dWLZsGbp06eLyurccuvY85IHsk02bNqFFixaIiorCVVdd\nhTfffNPrMj/55BMkJiYiPj4eMTExuOaaa/Doo486Xref999991089thjqFatGqKjo3H69Olcve9w\nVLx4ca/n3bwIZP+6B/9DsX8vR0W5vYaTpKQkPP744zh06BAWLVrkmB7IZ7uvHOLuuWcTEhLwww8/\nYNOmTY7UBB06dHCUT01NxZgxY1C9enVERkbiqquuwj//+U+XO/mcc+a+8sorqFOnDiIjI7F79270\n7NkTf/75J4YNG4ZKlSohKioKTZs29bgA4ryMWbNmOZZx3XXX4ZtvvvF4Hzn57rV3714MHDgQcXFx\nqFChAiZMmAAA+PXXX9G7d2/ExsbiyiuvdMn5m56ejtKlS2Ps2LEe6/7jjz9QrFgxTJkyxXOnhaEn\nnngCERER2Lhxo8v04cOHo2TJkvjuu+8c0wLZV4A5LlJSUhAXF4f4+HgMGTIEJ0+e9CiXlJTkcjzZ\necuZ+84776BFixYoU6YMYmNj0bhxY0ybNi23b/uy1r59e5+pRZz3V3btt2fPnj7Xcc8996BkyZJY\nsWIFAN85r7du3YouXbogLi4O0dHRSEpKwpdffpkP77pomDdvHpKTk1GxYkVERkaiQYMGXu8aVlVM\nnDgRVapUQXR0NJKTk7F7926vvyN37dqFG2+8EaVKlUK1atUwefJkzJs3zyMPeaC/jZjzuvBhz2ui\ny8QPP/yAtm3bomrVqnjkkUcQHR2Nf/3rX+jduzeWLVuGXr16uZQfOXIkKlSogCeeeALp6ekAgKNH\nj+L6669HREQERo8ejXLlymHNmjW4++67kZaW5jFo27PPPotSpUph2LBhKF68OF599VUUL14cNpsN\nJ0+exJNPPomvvvoKCxYsQK1atfDYY4855nX/sX7gwAGsXLkSffv2RUJCAo4cOYKZM2ciKSkJP/74\nIypVquQoq6p49tlnERERgXHjxiE1NRVTpkzBwIEDsWXLlmBv2jw5cuQIGjZsGHD5Dz/8ELVq1cL1\n118f8Dz//ve/sWzZMowcORIxMTGYNm0a+vTpg0OHDqFs2bIAsrZ33759UbduXTz77LOOL32ffvop\nunbtitq1a+PJJ5/E2bNnMW3aNLRt2xbbt29H9erVXZbRr18/1KpVC8899xy2b9+O2bNno2LFinj2\n2WcddXLfv+np6Wjbti1++uknDBs2DNdeey3++usvrFy5Er/99pujnu4CPa6feOIJPPfcc7jnnnvQ\nsmVLnDp1Ct988w22b9+O5OTkgLdlOLn11luxe/dujB49GjVq1MDRo0fxySef4JdffnHsk/nz5yMm\nJgYPPvggSpcujQ0bNmDChAk4ffq0yw/SevXqYd++fS7LnzZtGnr16oWBAwfiwoULeOedd9CvXz98\n+OGHuPnmm13KBnKMFWU5bed2aWlp6NKlC7Zt24b333/fY7sDvnMsBrJPvv32W9x8882oXLkyJk2a\nhIyMDEyaNAnlypVzWeaPP/6IHj16oGnTppg0aRJKliyJffv2ef3hZn/9oYcecvS8Ju8C3b/egnOh\n2r9FQVFqr+Hirrvuwj/+8Q+sW7cOw4YNC/iz3df2dJ/+yiuv4P7770dMTAwee+wxqCoqVqwIADh7\n9izatWuHP/74AyNGjEC1atXw5Zdf4pFHHsHhw4c9BnibO3cuzp8/j3vvvRclS5ZE2bJlcc899yAp\nKQn79+/HqFGjULNmTSxduhQpKSlITU316FSwePFipKWl4b777oOIYMqUKbjttttw4MABR0+/nH73\nuv3221G/fn1MmTIFq1evxuTJk1G2bFnMnDkTycnJmDJlCt5++22MGzcO1113Hdq2bYvo6Gjccsst\nePfddzF16lSXbbZ48WIAwMCBA/O0b4MpNTUVx44dc5kmIihbtiwef/xxfPjhhxg2bBi+++47REdH\nY+3atZgzZw4mT57sSElz7ty5gPdVz5498eWXX2LEiBG45ppr8MEHH2Dw4MEex5yvu4vcj8NPPvkE\nAwYMQMeOHfHPf/4TALB7925s2bKlyA587W+fPvbYYxg+fLjLa2+99RbWrVvnuJAcSPu9//77Pdab\nmZmJIUOGYOnSpVi+fLnL+dp9f27YsAFdu3ZFixYtHBeL5s2bhw4dOuDzzz9HixYtgrU5Cj1v+1NV\nPTqpvfHGG2jYsCF69eqFYsWKYdWqVRg5ciRUFSNGjHCUe/jhh/H888+jV69e6NSpE3bu3InOnTvj\n/PnzLsv7448/0L59e0RERODRRx9FqVKlMHv2bK93/wX626go5zAvtFQ17B8AmgHQbdu2KREZ8+fP\nV5vN5mgXycnJ2rRpU7148aJLuTZt2ujVV1/tMp+I6I033qiZmZkuZYcNG6ZVqlTREydOuEy/4447\nND4+Xs+dO6eqqps2bVIR0caNG2tGRoaj3IABA9Rms2m3bt1c5m/durUmJCS4TKtZs6YOGTLE8f+F\nCxc83uOhQ4c0MjJSn376acc0+7obNGjgsu5p06apzWbTH374wcvWCo233npLRUTnz58fUPlTp06p\niOgtt9wS8DpERCMjI/Xnn392TNu1a5eKiE6fPt0xbeLEiSoieuedd3oso2nTplqpUiU9efKkyzIi\nIiI0JSXFYxnDhw93mf/WW2/V8uXLu0xz378TJkxQm82mK1as8PleDh48qCKiCxYscEwL9Lhu2rSp\n9ujRw+eyCwPnNn3y5EkVEX3xxRf9zmNvk87uu+8+LV26tEubSklJ8WiD7vNmZGRoo0aN9KabbnKZ\nHugxVlTltJ2rZp2Ha9asqSVLltSVK1f6LDtx4kS12Wwu0wLdJz169NDSpUvr4cOHHdP279+vxYsX\nd1nmyy+/rDabTY8fP+6zHvZzb506dfT8+fMBv9fC6ptvvvE4HwWqMO7foqKotNeC5v6d1Ju4uDht\n3ry5qgb+2e5tezqv79ChQ45pDRs21Pbt23uUnTRpksbExOj+/ftdpj/yyCNavHhx/e2331Q16ztI\nXFycHjt2zKWsfZsvWbLEMS0jI0Nbt26tZcqU0bS0NJdllC9fXlNTUx1lV65cqTabTVevXu2YltPv\nXiNGjHBMu3TpklarVk0jIiL0hRdecEw/efKklipVyuX717p169Rms+natWtd3lOTJk28bq9QsLcx\nb4+oqChHue+//15Lliyp99xzj548eVKrVKmi119/vV66dMlRJtB9tXz5co/vWZmZmdquXTu12Wwu\n5/6kpCSv28r9u9WYMWM0Pj4+OBulkAt0nzr74osvtESJEi6/M3Lafl988UXNyMjQ22+/XaOjo/XT\nTz91mW/Tpk1qs9l08+bNjml169bVrl27upQ7d+6c1qpVSzt37uzyntzPO0WFv/1pfzRq1MhR3tvv\nky5dumidOnUc/x85ckSLFy+ut912m0u5J598UkXE5Tw2atQojYiI0J07dzqmnThxQq+44gqPfZKX\n30YUfNu2bVMACqCZ5jEuzLQhRJeBEydOYOPGjejbt6/jiqj90alTJ+zduxf/+9//HOVFBMOHD/e4\n2rhs2TL06NEDly5d8lhGamqqxwjDgwcPdskVZe8t7H6bz/XXX49ff/3V76AIzvkEMzMzcfz4cZQq\nVQpXX32115GNhw4d6rLuxMREqCoOHDjgb1MVmD179uD+++9HmzZtMGjQoIDmOXXqFAAgJiYmR+vq\n2LGjy2AojRo1QpkyZTy2hYjgvvvuc5l2+PBh7Ny5E0OGDEFsbKzLMjp27IiPPvrIYxn33nuvy7TE\nxEQcO3YMaWlpPuu4bNkyNGnSxO+tfe5yclzHxcXhhx9+8OhdXFhFRUWhRIkS2LRpk9dbWO2cc4em\npaXh2LFjaNu2Lc6cOeNx67G/eU+ePIkTJ04gMTHRa3sL9BgranLTzp0dPXrUkUInp7LbJ5mZmVi/\nfj169+7t6IEImNRD7j1G4+LiAAAffPBBtgPipqSksLd1gArj/r2cFcX2Gk5Kly6N06dP5/g7a169\n9957SExMRGxsrMu6kpOTkZGR4ZE+oE+fPh53FK1ZswaVKlVC//79HdPsdymmpaVh8+bNLuX79++P\nMmXKOP53/46am+9ew4YNc/xvs9nQokULqCqGDBnimB4bG4urr77a5bP5pptuwpVXXunoaQ2Yu9p2\n7dqFu+66K/sNWEBEBDNmzMCnn37q8lizZo2jTIMGDfDkk09i1qxZ6Ny5M44fP+5Ih2gX6L766KOP\nULx4cZfvxSKCUaNG5bpdxcXFIS0trUimVfMmkH1qd/jwYfTt2xfNmjXD9OnTHdNz2n4vXLiAPn36\n4KOPPsKaNWuyvftyx44d2Lt3L+644w6X5Z8+fRrJyckeyy/KfO3PTz/9FI0bN3Yp6/wb49SpUzh2\n7BjatWuHAwcOONLNrV+/HpcuXXLpiQ3Aa3rMtWvX4oYbbnBZT1xcHO68806Psnn5bUThjWlDiC4D\n+/btg6ri8ccfd0nNYSciOHr0KK688krHNPeRn//880+cPHkSb775JmbOnOlzGc7cf8DZv4B7m56Z\nmYnU1FTEx8d7fQ+qipdffhkzZszAzz//jEuXLjnWW65cOY/y7uuwL/fEiRNel1+Qjh49im7duiE+\nPh5Lly4N+JYk+w+dnOaQ9fZDOj4+3uu2cM/tdejQIQBA3bp1PcrWq1cP69atw9mzZxEVFeWYbr+V\n1XldgNn2pUuX9lrH/fv3o0+fPtm8E1c5Oa6feuop9O7dG3Xr1kXDhg1x8803Y+DAgY7bSAubEiVK\nYMqUKXjooYdQsWJFtGrVCt27d8egQYNcAhs//vgjHn30UWzcuNFx8QMw2yY1NdXvOj788ENMnjwZ\nO3bscLk9z1sKg5wcY0VFbtu5nYjgzTffxJgxY9C5c2d8/vnnuOqqqwKeP7t9cvToUZw9exZ16tTx\nKOc+7fbbb8ecOXMwfPhwPPzww0hOTsatt96KPn36eLwv988O8q6w7t/LVVFtr+EkLS0NFStWzNV3\n1rzYu3cvvvvuO5QvX97nupx5O8cdOnTI6/6uV68eVNXxXcrOfX/bLzjY93cwvnvFxsYiMjLSI9Ae\nGxvrMpaIiODOO+/EG2+8gXPnziEyMhKLFi1CZGRkjr+X5beWLVtmO7jfuHHj8M477+Drr7/GM888\ng6uvvtrl9UD31S+//IIrr7zSY+By9+XlxMiRI7F06VJ07doVlStXRqdOndCvXz907tw518ss7ALZ\np5cuXUK/fv2QmZmJZcuWuXRoymn7feaZZ5Ceno41a9a4jO3jy969ewHA5wVNm82G1NRUl4tMRZmv\n/RkfH++STuSLL77AE088ga+++gpnzpxxTLf/PomJiXG0RffPuPj4eI94waFDh9C6dWuP9Xr7zMzL\nbyMKbwxeE10G7D2aH3roIZ9fkNxP7s5fiJ2XMXDgQAwePNjrMtyvqtp7Pi9fvhy9e/f2mO7OX0+G\nyZMnY8KECRg2bBiefvpplC1bFjabDQ888IDXHtu5WUdBOHXqFDp37oxTp07h888/d8nVnZ2YmBhU\nrlzZZdCZQORkW7jv99xsr4La9jk5rhMTE7F//36sWLEC69atw+zZszF16lTMnDnT406AwuKBBx5A\nz549sXz5cqxduxYTJkzAs88+i40bN6JJkyZITU1Fu3btEBcXh6effhq1atVCZGQktm3bhocfftil\n3TgPZAKY/Ku9evVCUlISZsyYgSuvvBLFixfH3LlzsWTJEo+6hGt7C5W8tHNn9erVw8cff4z27duj\nY8eO+OKLL1ClSpWA5g3mPomMjMRnn32GjRs3YvXq1fj444/x7rvvIjk5GevWrXMJiLmfQ8i3wrh/\nL0dFub2Gi99//x2pqamoU6dOjj7bfb0XeweHQGRmZqJjx474+9//7nV7uweQvZ3jTp8+7XLhODvZ\n7e9gffcK9LgaNGgQnn/+eSxfvhz9+/fHkiVL0LNnzxzf6RcO9u/f7wg4evu+HOi2VVWvx5e3+QM9\nDsuXL48dO3Zg7dq1WLNmDdasWYN58+Zh8ODBmDdvXkD1KooeeughbN26FevXr/e4aBVI+12+fDmu\nvfZaAECXLl3w8ccfY8qUKUhKSsr2TjH7+ejFF19EkyZNvJbx1TGHvNu/fz9uuukm1KtXDy+99BKq\nVauGEiVKYPXq1Xj55Zf93omdVzn5bUSFD4PXRJeBWrVqATCpN7yNhh2I8uXLIyYmBpcuXcrxMpYs\nWeISvM6N999/Hx06dMCsWbNcpp88edLr1fZwdP78efTo0QP79u3D+vXrc9V7o3v37pg1axa2bt2a\no0Ebc8vew+inn37yeG3Pnj0oV65cUIJVtWvXxvfff5+jeXJ6XMfFxWHw4MEYPHgwzpw5g8TEREyc\nOLHQBq8B01N+7NixGDt2LPbv348mTZrgxRdfxMKFC7Fx40acOHECK1asQJs2bRzz7N+/32M5Bw4c\ncPnxtWzZMkRFRWHt2rUoVizrq8CcOXPy9w1dBoLRzp01b94cK1asQNeuXdGxY0f8+9//xhVXXJHn\nelaoUAFRUVFeU+nYf/i7a9++Pdq3b48XXngBzz77LB577DFs3Lgx158rFNj+zU0gi/s3MGyv4WHh\nwoUQEXTp0iVHn+323nenTp1yScNx8OBBj7K+Aoy1a9dGWloa2rdvn8vamyClt/2we/duAECNGjVy\ntLyC+u5l16BBA1x77bVYvHgxqlSpgl9++cUlNUNhoapISUlBbGwsxo4di8mTJ6NPnz4uv0Fq1qzp\nNaht31f2bV+zZk1s3LgRZ86ccel97W2fxMfH4+eff/aY7t7jHgCKFSuGbt26oVu3bgCAESNG4M03\n38Tjjz/uOPYpyzvvvINXXnnFMVipu0Da74MPPugIXrdq1Qr33XcfunXrhr59++KDDz7wekeh8/IB\n04EoHM+dhdGqVatw4cIFrFq1yuUC7/r1613K2c+b+/btczmHHj9+3OPOzho1agT0+bhp06aAfxtR\n4cOc10SXgfLlyyMpKQkzZ87E4cOHPV7/66+/sl2GzWbDbbfdhvfffx8//PBDjpbx7rvv5qzCXkRE\nRHj8gF+6dCl+//33PC+7IGRmZqJfv3746quv8N577+G6667L1XLGjx+PUqVK4e677/a4FQ4wH77T\npk3La3UdKlWqhKZNm2LBggUut1Z9//33WLdunePLd17ddttt2LlzJ1asWBHwPDk5rp1vkQWAUqVK\noU6dOh6jVRcWZ8+e9ah7QkICYmJiHNOLFSsGVXXpRXDhwgW8/vrrHstLSkpy+T8iIgIigoyMDMe0\ngwcP5mj/FEXBaufu2rdvjyVLlmDv3r3o0qWL3/zxgbLZbEhOTsby5ctd2s++ffvw8ccfu5T1lv6l\nSZMmUNVC24bCSXb7Nzc9Zbl/s8f2Gh42bNjg6AE3YMCAHH22165dG6rqknc2PT0dCxcu9JgvOjra\n6xgR/fr1w5YtW7Bu3TqP11JTUwPqxf3II4/g8OHDLt93L126hFdffRUxMTG48cYbs12Gs4L67uXs\nrrvuwtq1a/Hyyy+jXLly6NKlS9DXkd9efPFFfPXVV5g1axaeeuoptGnTBiNGjHD5Dti1a1e/+6pd\nu3aOchcvXsSMGTMc5TIzM/Hqq696nJNr166NPXv2uKRF2LlzJ7744guXcu7fRQE40teFY9sMte+/\n/5+ShsQAACAASURBVB7Dhw/HoEGDcP/993stE0j7df8d2qFDB7z77rtYs2ZNtnndmzdvjtq1a+OF\nF15Aenq6x+uB/IYmV/ZOMc6/T1JTUzF//nyXcsnJyYiIiPD43fLqq696LLNz587YsmULdu3a5Zh2\n/PhxvP322y7l7PGEQH4bUeHDntdEl4np06cjMTERjRo1wvDhw1GrVi0cOXIEW7Zswe+//45vv/3W\nUdZXL6/nnnsOmzZtwvXXX4/hw4ejfv36OH78OLZt24YNGzYE9AGe2zQC3bt3x6RJkzB06FC0bt0a\n3333HRYvXuy4Ih7u/va3v2HVqlXo2bMn/vrrL5eBcQB4HVDCm1q1auHtt99G//79Ua9ePQwaNAgN\nGzbEhQsX8OWXX2Lp0qUug/MEw/PPP4+uXbuiVatWGDZsGM6cOYPXXnsN8fHxeOKJJ4KyjnHjxuG9\n995D3759MWTIEDRv3hzHjh3DqlWrMHPmTJ+5qQM9ruvXr4+kpCQ0b94cZcuWxddff4333nsPo0eP\nDkr9C4q9/fz3v/9FcnIy+vXrh/r166NYsWJYtmwZjh49ijvuuAMA0Lp1a8THx2PQoEGO97lo0aKA\nAmHdu3fH1KlT0blzZwwYMABHjhzB66+/jquuusrliyG5ClY7BzzPlb1798asWbMwbNgwdO/eHWvX\nrnUZdCY3Jk6ciHXr1qF169YYMWIEMjIyMH36dDRq1Ag7duxwlHvqqafw2WefoVu3bqhRowaOHDmC\nGTNmoHr16l57Ql3Opk+fjpMnTzounK5cuRK//vorAGD06NEB32bP/Rt6bK8FS1Xx0UcfYffu3cjI\nyMCRI0ewYcMGfPLJJ0hISMDKlSsdt/AH+tneqVMnVK9eHUOHDsW4ceNgs9kwb948VKhQwdEu7Zo3\nb4433ngDkydPRp06dVChQgW0b98e48aNw8qVK9G9e3ekpKSgefPmSE9Px65du7Bs2TIcPHjQI2+0\nu3vuuQczZ85ESkoKvvnmG9SsWRNLly7Fli1b8MorryA6OjrH26sgvns5u/POOzF+/HgsX74cI0eO\n9JlyJFScjx93rVu3xrlz5zBhwgQMGTIEXbt2BQDMmzcPTZs2xYgRIxwBzED3VY8ePdC2bVs8/PDD\n+Pnnn1G/fn0sW7bM67gzQ4cOxdSpU9GpUycMGzYMR44cwcyZM9GwYUOXiw933303jh8/jg4dOqBq\n1ao4ePAgXnvtNTRt2hT16tXLj80W1rLbp0OGDIGIoG3bth7n59atWyMhISHX7bdnz56YN28eBg0a\nhJiYGLzxxhsu9bITEcyePRtdu3ZFgwYNMGTIEFSpUgW///47Nm7ciNjYWHbssAT6G79Tp04oXrw4\nunfvjnvvvRenT5/G7NmzUbFiRZcLlhUqVMADDzyAqVOnolevXujSpQt27tyJjz/+GOXLl3f5PTN+\n/HgsWrQIycnJGD16NKKjozF79mzUqFEDJ06ccJTNy28jKgRUNewfAJoB0G3btikRGfPmzVObzaY7\nduxwTPv55581JSVFK1eurCVLltRq1appz549ddmyZY4y8+fPV5vN5rM9/fnnnzpq1CitUaOGlixZ\nUitXrqwdO3bUOXPmOMps2rRJbTabvv/++y7z+lr2xIkT1Waz6bFjxxzTEhISdOjQoY7/z58/r+PG\njdMqVapodHS0tmvXTrdu3art27fXDh06ZLvugwcPqs1m0wULFgSy+YIuKSlJbTabz0dO7du3T++9\n916tVauWRkZGamxsrCYmJurrr7+uFy5ccJSz2Ww6evRoj/ndt6+3feBsw4YNmpiYqNHR0RoXF6e9\ne/fWPXv2uJTxtQz7fj906JDP9auqnjhxQkePHq3VqlXTyMhIrV69ug4dOlSPHz+uqr73YSDH9TPP\nPKOtWrXSsmXLanR0tNavX1+fe+45zcjI8Pp+w5Fz+zl27JiOGjVK69evrzExMRofH6833HCDx3G/\nZcsWbd26tUZHR2vVqlX1kUce0U8++URtNptu3rzZUS4lJUVr1arlMu+8efP06quv1qioKK1fv74u\nWLDAsY+dBXqMFQXBauf+zsMvvvii2mw27dmzp166dEknTpyoERERLmVysk82btyozZs318jISL3q\nqqt07ty5+tBDD2mpUqVcytxyyy1atWpVjYyM1KpVq+rAgQN13759jjK+zr2Xm5o1a/rcv87nOH8K\n4/69HBXl9lrQ7NvI/oiMjNTKlStr586d9bXXXtO0tDSPeXx9tn/wwQcu5b799lu94YYbNDIyUmvW\nrKmvvPKK1+8dR44c0R49emhsbKzabDZt376947X09HR99NFHtW7duhoZGakVKlTQtm3b6ksvveT4\nnmD/DjJ16lSv7/HPP//UYcOGaYUKFTQyMlKbNGmiCxcudCnjbxk2m02feuopl2l5+e6VkpKiZcqU\n8VhPUlKSNm7c2Ot76Natm9psNv3qq6+8vh4q7seP+2Pu3Ll63XXXaY0aNfTUqVMu806bNk1tNpsu\nXbrUMS2QfaVqvpcOHjxY4+LiND4+XlNSUnTnzp1ev4u+/fbbWqdOHY2MjNRmzZrpJ5984vHdatmy\nZdqlSxetVKmS43gdOXKkHjlyJMhbLPxlt08XLFigCQkJfl+3y0v7nTFjhtpsNh0/fryqZn2Xcf6O\nrKq6c+dO7dOnj5YvX14jIyM1ISFB+/fvrxs3bvR4T4F+F7icZBc/cD/vfPjhh9q0aVMtVaqU1qpV\nS1944QVH7MJ5+2Vm/j97dx4W1Xm3D/w+M8MygggCiqgYERL0JURBrRLFJWpajFgUt2oW4iupxhob\nK5pGNHGpGsyPtPqmaYwKXrjExCTFSzTW1jXEGEGJouICLhgQUZAdZjm/P+icOMyAwgBzdO5PLi7h\nOWfOPDNcXyD3PPN99OKyZctEb29v0cnJSRw9erSYnZ0tenh4iHPmzDG6j8zMTHHYsGGiWq0WfXx8\nxA8++EBcv369qFAoxMLCQuk8S/7fiFpeenq6CEAEECxamAsL4mOw2ZIgCMEA0tPT0x+6Wy2RrVi/\nfj3mz5+PK1euoGfPntaeDhERPSYiIyNx/vx5s7096fHH7++Thd9PaikTJkzAuXPncOnSJWtPhYjI\nrPv378PNzQ2rVq3CO++80+i58+fPx8aNG1FeXs7V1TKVkZGBkJAQAAgRRTHDkmux5zXRY+rkyZNw\ncnJq8iYxraGl21gQUctijdqu+n02L1++jNTUVIs2L6OWZUl98vv7ZOH3U56ehN+h+fn52Lt3L155\n5RVrT4WoRT0J9WmrqqurTcYSEhIgCILJfj31fz/evXsXycnJGDp0KINrG8Ge10SPma+++gqHDh3C\n9u3bERMT0+gOym1lzJgx1p6C7FVUVDx0UydPT09ZfD/pycMabRtyrHNfX1+8+uqr8PX1xbVr1/DJ\nJ5/A0dERCxcubLM5PCla6/trSX3y+9t8rFd6VI/z79Br167h+PHj+Oyzz2Bvb4+YmBhrT4moRT3O\n9WnrPv/8cyQmJmLs2LFwcnLCsWPHsHPnTvz617/G4MGDjc4dPHgwhg8fjoCAABQUFGDz5s0oKytD\nXFyclWZPbY1tQ4geM76+vigvL8eECROQkJAAtVpt7SnRI3j//ffx/vvvN3hcEATk5ubCx8enDWdF\nRC1JjnU+c+ZMHDp0CAUFBXBwcEBoaCj+8pe/4LnnnmuzOTwp+P19svD7SbYgKSkJ0dHReOqpp/Dh\nhx8iMjLS2lMiIgIAnD59GosWLcKZM2dQWlqKzp07IyoqCitWrEC7du2Mzl2yZAm+/PJL5OXlQRAE\nhISEYNmyZXxnksy1ZNsQhtdERG3g2rVryMnJafScIUOGwN7evo1mREQtjXX+ZOP398nC7ycRERFR\n62nJ8JptQ4iI2sBTTz2Fp556ytrTIKJWxDp/svH7+2Th95OIiIjo8cDmqkRksePHj1t7CkTUCNYo\nkXyxPonkjTVKJF+sTyLbwPCaiCz2wQcfWHsKRNQI1iiRfLE+ieSNNUokX6xPItvAntdEZLHKykqT\nTRWISD5Yo0TyxfokkjfWKJF8sT6J5Ksle15z5TURWYx/MBDJG2uUSL5Yn0Tyxholki/WJ5FtYHhN\nRERERERERERERLLD8JqIiIiIiIiIiIiIZIfhNRFZbOHChdaeAhE1gjVKJF+sTyJ5Y40SyRfrk8g2\nMLwmIov5+PhYewpE1AjWKJF8sT6J5I01SiRfrE8i2yCIomjtOTyUIAjBANLT09MRHBxs7ekQERER\nERERERERkRkZGRkICQkBgBBRFDMsuRZXXhMRERG1sFOnTmHu3LkIDAyEs7MzevTogSlTpuDy5ctG\n50VHR0OhUJh89OnTx+x1RVHEgwsPjh07hvHjx8PHxwdqtRpdunTBb37zG6SlpZm9fVpaGoYMGQIn\nJyd06dIFb731FioqKlrugRMREREREbUglbUnQERERPSkWbt2LdLS0jBp0iQEBQWhoKAA69evR3Bw\nMH744QejcNrR0RGbNm0yCqU7dOggfS6KIrRaLbRardE5KpUKFy9ehFKpxOzZs+Hl5YXi4mIkJycj\nLCwMqampGDNmjHT+mTNnMGrUKPTp0wcJCQnIy8tDfHw8rly5gr1797byM0JERERERNR0bBtCRBa7\nePEiAgICrD0NImoAa7TtnThxAv3794dK9cs6gStXriAwMBCTJ0/G1q1bAdStvN69ezdKS0vNXken\n06GmpqbR+1KpVLC3t5e+rqqqgq+vL/r164fU1FRpPDw8HD/99BOys7Ph5OQEANi0aRNiYmLw7bff\nYtSoUc1+vNR8rE8ieWONEskX65NIvtg2hIhkJTY21tpTIKJGsEbb3qBBg4yCawDw8/NDYGAgLly4\nYHK+KIooLy83GqsfXOfm5iI3N9fktlqtFrW1tdLXarUanp6eKCkpkcbKyspw8OBBvPzyy1JwDQCv\nvPIKnJycsGvXrqY/SGoRrE8ieWONEskX65PINjC8JiKLbdiwwdpTIKJGsEbl4/bt2/Dw8DAaq6ys\nRPv27eHi4gJ3d3fMnTsXFRUVRoE0ULdy+qWXXjJ73eLiYty5cwfZ2dn485//jKysLKOV1GfPnoVW\nqzWsfpDY2dmhb9++OH36dAs9Qmoq1ieRvLFGieSL9UlkG9jzmogs5uPjY+0pEFEjWKPykJycjFu3\nbmHlypXSmLe3N2JjYxEcHAy9Xo/9+/fj448/RmZmJvbt2weF4pd1BoIgQBAEs9d++eWXcfDgQQCA\nvb093njjDSxZskQ6np+fD0EQ0KVLF5PbdunSBcePH2+ph0lNxPokkjfWKJF8sT6JbAPDayIiIqJW\ndvHiRcydOxfPP/88XnnlFWl81apVRudNnjwZ/v7+WLJkCb766itERkZCr9dDFEVkZGRAr9dDr9cb\nhdoAsGLFCrz11lsoLCzE1q1bUVtbC41GI/XCrqqqAgA4ODiYzM3R0VE6TkREREREJCcMr4mIiIha\niSiKyM/PR3h4OFxdXbFlyxaUlpZCp9OZ/dDr9XjppZcQFxeH1NRUhIaGmlzT09PTJLx+9tlnAdT1\nu54xYwaCg4MRHR0t9bJWq9UAYHbzx+rqauk4ERERERGRnLDnNRFZbO3atdaeAhE1gjVqGVEUpU0R\nq6qqUF5ejvv37+PevXu4c+cOCgoK8PPPP+PGjRu4du0arl69ikuXLuHChQs4efIkXnjhBZSUlGDD\nhg2oqalBXl4e8vPzUVhYiLt376KkpARlZWWorKxEdXU1FAoFXF1djTZcrD+fxtjZ2SEiIgJfffWV\nFFZ36dJFCtLry8/Ph7e3t+VPFDUL65NI3lijRPLF+iSyDVx5TUQWq6ystPYUiKgRrNG6wFev1ze4\n2vlh481RW1uLuXPn4saNG9i0aRN69uxp9jxBEKBQKKBUKqFUKlFdXY3i4mJ07twZzs7OUCgU0jkK\nhQIq1cP/fKusrIQoiigrK4ODgwMCAwOhUqlw6tQpREVFSedpNBqcOXMGU6ZMadZjJMuxPonkjTVK\nJF+sTyLbIDxs9Y4cCIIQDCA9PT0dwcHB1p4OERERWUljYbPhmFarNTnH0De6NRnCZ0PY/Pvf/x5H\njhxBUlISRo0aJR0znGeYp4uLi9FGjLGxsfjwww+xc+dOjB07VhrPzc0FAKMQ/M6dO/D09AQAqFQq\n2Nvbo6SkBEFBQVAqldJtACA8PBw//fQTsrOz4eTkBADYtGkTYmJisH//fowePbpVnx8iIiIiIrIN\nGRkZCAkJAYAQURQzLLkWV14TERFRm2osfH7YSujWDqAfDJfNfTR03BBYG8yfPx8HDx5EREQEdDod\nvv32W6P7mT59Oq5fv45+/fph2rRpCAgIAADs378f+/btQ3h4OMaNGwe9Xi/dJjw8HAqFAllZWdJY\nZGQkunbtiv79+8Pb2xs3b95EYmIi8vPzpX7XBqtWrcLzzz+PsLAwxMTEIC8vDx9++CFefPFFBtdE\nRERERCRLXHlNRERETfZgwNzUdhxtHUA3FjjXH3swgLbEiBEjcPTo0QaP63Q63L9/H/PmzcOJEyfw\n888/Q6fTwc/PDzNmzMCCBQsgCAJqamqk56tPnz5QKBQ4d+6cdJ2NGzfiyy+/xKVLl1BSUgI3NzcM\nHjwYCxcuNLvZY1paGhYtWoSMjAy0b98eU6ZMwV/+8hdpJTYREREREZGlWnLlNcNrIrJYUVERPDw8\nrD0NImpAQzUqimKT22+0VQAtCEKzVkC3ZAAtB3q9HrW1tUYrsOuzt7d/pD7YJE/8HUokb6xRIvli\nfRLJF9uGEJGsvP7660hJSbH2NIhskrkAun7g/Morr+Czzz4z2we6NTUUQD/KSmiFQtGqc3tcKBQK\nODo6QqfTSS8mAHXPrUqleuLCelvE36FE8sYaJZIv1ieRbWB4TUQWe++996w9BaLHmiiKTW690ZQA\netasWSgrK2vW3ARBaHSV88OOUcswPKf05OHvUCJ5Y40SyRfrk8g2MLwmIouxnQ9RnaaGzw8ea019\n+vRpdvjMwJSodfF3KJG8sUaJ5Iv1SWQbGF4TERE9oCmBc/3x1u4D3ZwNCA3jbC1BREREREREjxuG\n10RE9MQxFzA/rP1GWwXQ9QNmhUIBlUrV6ApoBtBERERERERkixheE5HFNm3ahJkzZ1p7GvSEeTBs\nbmo/aGsE0I+6EtoaATRrlEi+WJ9E8sYaJZIv1ieRbWB4TUQWy8jI4B8NZJYois1eAf0oGxFaQhCE\nZveAftw2ImSNEskX65NI3lijRPLF+iSyDUJrr05rCYIgBANIT09PZ0N+IqI2Vj+Abko/6LYMoB+l\n/caDxx63AJqIiIiIiIjocZCRkYGQkBAACBFFMcOSa3HlNRGRDRBFscmtNx481poEQWjyBoQMoImI\niIiIiIiefAyviYgeI81pv2H4vLU1dQX0g+cQEREREREREdXH8JrIhhw5cgQjRowwGRcEAd9//z0G\nDhyIqqoqbN68GSkpKTh79izKy8vh5+eHmJgYxMTEGK10LUYx7uIutNDCDnbohE5oj/Y4duwY1q1b\nh9OnT+POnTtwdXVF3759ERcXh9DQ0Abnd//+ffj7+6OoqAhffvklJkyY0CrPg7U1tfXGg+Ot3eqp\nORsQGsatsREhkVydOnUKiYmJOHz4MK5duwZ3d3cMGjQIK1euhL+/v3RedHQ0kpKSTG4fEBCA8+fP\nS18b2vcYWvEIggCVSoVDhw5h27ZtOH78OPLy8uDl5YWRI0dixYoV8PLyMrqmVqvFqlWrsHXrVty6\ndQtdu3bF66+/jsWLF/NFJCIiIiIikiWG10Q2aP78+ejfv7/RmJ+fHwAgJycH8+bNw6hRo7BgwQK4\nuLjgwIEDmDNnDk6ePInNmzcjH/nIQQ7KUAYAeC/iPbyX8h4u4zLc4IaMSxlQKpWYPXs2vLy8UFxc\njOTkZISFhSE1NRVjxowxO6+4uDhUV1c/FiHogwFzU1dAt3YAXT9gbsoK6Mfhuaemi4iIQEpKirWn\nYVPWrl2LtLQ0TJo0CUFBQSgoKMD69esRHByMH374AX369JHOdXR0xKZNm4x+NnTo0AFAXWit1Wqh\n0WhM7kOj0SA2NhYlJSWYNGkS/P39kZOTg/Xr12Pv3r04c+YMOnXqJJ0/ffp07N69GzNnzkRISAhO\nnDiBuLg43Lx5E5988kkrPhvUGNYnkbyxRonki/VJZBsYXhPZoCFDhjS4qtnLywvnzp1D7969pbFZ\ns2Zh5syZSExMxMtLXkaNb43RbcbNHSd9Xoxi9J7ZG5NmToI3vKXx2bNnw9fXFx999JHZ8DorKwuf\nfPIJli1bhqVLl1r6EB+JIVBuzirotgigm7oBoeGDATTVN3fuXGtPweYsWLAAO3bsgEr1y59akydP\nRmBgINasWYOtW7dK4yqVCtOmTTO5hiiKqKmpaXTj0zVr1iA0NBSOjo7SO2NefPFFDBs2DBs2bMDy\n5csB1K0E/+KLL7Bs2TIsW7YMABATEwN3d3ckJCRg7ty5CAwMbJHHTk3D+iSSN9YokXyxPolsA8Nr\nIhtVXl4OtVpt8lZxd3d3uLu7m5wfGRmJxMREHLtwDAN9B0rj+Tn58PbzNjpXDz3O4Ryc4IQOqFs9\nqFar4enpiZKSErPzmTdvHiZOnIghQ4Y0KRg2vJW+qRsQPvj2+9YiCEKTW29wI0JqDQ2924Faz6BB\ng0zG/Pz8EBgYiAsXLpgcE0URFRUVcHZ2lsY0Go3Rz6nc3FwAQM+ePaUxQyummpoaODo6QhAEDB06\nFB07djS6n2PHjkEQBEyZMsXofqdOnYoPP/wQn3/+OcNrK2F9Eskba5RIvlifRLaB4TWRDYqOjkZZ\nWRmUSiWGDh2K+Ph4hISENHqb/Px8AICLh4vR+OKRi6FQKLAlZ4vRuB56ZJVl4ZnaZ1BUVISkpCRk\nZWXh3XffNbn2rl27cOLECfz000+4evUqAKCiogL37t17aDDdFgF0YyufG9ukkAE0EdV3+/Ztk5C4\nsrIS7du3R2VlJdzc3DBt2jSsWbPG5GdIeHg4FAoFsrKyTK5raC9iZ2eHiooKlJeXw8PDQzpeU1P3\njhm1Wm10u3bt2gEA0tPTW+TxERERERERtSSG10Q2xN7eHlFRUQgPD4eHhwfOnz+PdevWISwsDGlp\naXjuuefM3k6j0eD/ffT/4OXrhacHPG10TBAEQAAg1oUner1e+vjDxD/g9MHT0n3PmDEDr776Kq5d\nuyYFz5WVlfjjH/+Il19+GdXV1fj5558BAHfv3pUCc0s1FkA/SksOIqKWkJycjFu3bmHlypXSmLe3\nN2JjYxEcHAy9Xo/9+/fj448/RmZmJvbt2yeF0lqt1uhnrLkXxwzhdUJCAjQaDaZOnSode+aZZyCK\nIr777jv06NFDGj969CgA4NatW634yImIiIiIiJqH4TWRDRk8eDAGDx4sff3SSy9h4sSJCAoKwjvv\nvIPU1FSzt3vzzTdx6eIlLE9dbhKYJOYm4uC2g2aD5vGx4/Hy9JdRkVeBf/7znygvL8e9e/eklX4A\n8Mknn0Cn0+F///d/Hzr/pgTO9cfYB5ps2TfffIPf/va31p6GzdJqtTh79izefPNNDBgwAMOGDcOl\nS5dQU1ODyMhI1NTUoKamBrW1tRg/fjyqqqqwa9curF69GkOHDpWu8+mnnwIAamtr4ejoaHI/oiji\nyJEjWL58OaZMmYJhw4ZJx8LDw9GjRw/86U9/glqtljZsXLJkCezs7FBVVdX6TwSZxfokkjfWKJF8\nsT6JbAPDayIb16tXL4wfPx5ff/01RFE0CXnj4+Px2Wef4e1VbyPkRfOtRb7b/R3+Z8T/mIx379Md\nvj184VHrgbFjx2Ly5MmIi4vD3/72NyiVSuTn5yMpKQmrV69G9+7doVQqpX7bnp6e8PX1ZQBN1AJ2\n7NjBP+wtpNfrpZC5oY/a2lpUV1ejtrbWaLy4uBhr166FnZ0doqKicODAgUbva8iQIdi1axcyMjKM\nwmsDrVZr9nbZ2dnSC5IbN240Oubg4IDU1FRMnjwZUVFREEURjo6O+OCDD7By5UqjXtvUtlifRPLG\nGiWSL9YnkW1geE1E6N69O2pra002C0tMTMTixYsxZ84c/OGdP+ACTDcZA4DY5FjU1NRAoVBIH4ZW\nHT3deqKzojOUSiWioqLwwQcfoEePHnBwcMD777+P7t27IyIiAtXV1QDqel0DdRtKFhYWwsfHh6E1\nkYU+//xza09BFvR6vUmw/Cjhc01NTYOB8cNUVVXhb3/7G6qrq7Fw4UJ06NDhobexs7ND+/btUVZW\nZva4ubnk5eUhIiICbm5u2Lt3L5ycnEzO6d27N86ePYsLFy6guLgYffr0gaOjI+bPn4/hw4c3+bFR\ny2B9Eskba5RIvlifRLaB4TUR4erVq3B0dDQKrlNSUjBr1ixERUVhw4YNqEIVLuIiRIgmt1e3U0Pd\nTm0ybgc7dEVXKFHXN7qqqgqiKKKsrAwODg64efMmrly5gl69ehndThAEzJ49G4IgoLi4GC4uLibX\nJiLbJIpiowG0IYQ2N67RaNp0rhqNBv/3f/+HwsJC/PGPf4SXl5fJOQqFAg4ODnBwcIC9vT0cHR2h\n0+lQVlaGbt26wdfXF3Z2dlCpVNJH/ZYh9+7dQ0REBLRaLb799lt07ty50Xn17t1b+jw1NRV6vR6j\nR49umQdNRERERETUghheE9mQoqIieHh4GI1lZmZiz549GDt2rDR29OhRTJ06FcOHD0dycjIAQA01\nPOCBO7hjdPv8nLpe1118u0hjJXdK4OrpahRcl5SUYPfu3fDx8ZHmsGrVKhQVFRld79y5c4iLi8Oi\nRYswePBgs6sHiejxJooiNBpNs1ZA19bWWnv6JgRBMAqf7e3tYWdnh+XLl+PatWv461//ipEjR0oh\nteFcg/otO2JjYwEAEydORLdu3aTx3NxcAEDPnj2lscrKSkRGRqKgoAAHDx6Er6/vI8+7qqoKB6gS\n5QAAIABJREFUcXFx8Pb2NtrckYiIiIiISC4YXhPZkClTpkCtViM0NBSdOnVCVlYWNm7cCGdnZ6xe\nvRoAcOPGDUREREChUGDChAnYtWuXdPsqVEEfpIfPsz7S2OKRi6FQKLAlZ4s0tvQ3S9GpWye8+KsX\n4d3JG9evX0diYiLy8/ONrhcaGmoyxw4dOkAURQwYMAARERGt8TQQUQtpKIBuLHw2HBdF03dxWNuD\nwfKDQbO5jwfPeTCINpg/fz6+++47REREwMXFBadOnTI6Pn36dFy/fh39+vXDtGnTEBAQAADYv38/\n9u3bh/DwcPz2t781Wi0eHh4OhUKBrKwsaSw6Ohrp6el47bXXcPHiRWRnZ0vHnJ2dMX78eOnrKVOm\nwNvbG3369EFpaSk2b96M3NxcpKam8oVCIiIiIiKSJYbXRDYkMjIS27ZtQ0JCAkpLS+Hp6YmoqCgs\nXbpUWq2Xm5sr9VmdO3euyTUWLluIns/2hA46AHUrDu8X3Tc6J3xmOE7tPIUNH21ASUkJ3NzcMHjw\nYCxcuNBsYF0fe1wTtazo6Ghs2bLF7DGtVtvk9huGY3q9vo0fycPZ29sbrYBuLHx+8Bx7e/sW/dmT\nmZkJQRCwZ88e7Nmzx+T49OnT4erqinHjxuHgwYPYunUrdDod/Pz8sGbNGixYsABKZd07VwwBtiAI\nJnM8e/YsBEFAUlISkpKSjI716NHDKLweMGAAtmzZgk8//RRqtRphYWHYuXMnnn322RZ73NR0jdUn\nEVkfa5RIvlifRLZBkOPKp/oEQQgGkJ6eno7g4GBrT4fI5pWjHDdwAz/jZ2ihxeEdhzF82nDYwx7d\n0A090AMOcLD2NIlshk6na3QF9N69exEWFmb2HDkG0Ia+zs1ZAa1QKKw9/Ran0+mg1Wqh0+mMxgVB\nkPpg80W/x9eOHTswbdo0a0+DiBrAGiWSL9YnkXxlZGQgJCQEAEJEUcyw5FoMr4mo2bTQ4j7uQwst\nVFDBFa5Sj2siahq9Xt/k9huGj/qhphyoVKomh8+GjycxgG4Jer0eoihCFEUIgiCtzCYiIiIiIpKT\nlgyv2TaEiJpNBRXc4W7taRDJhl6vb3CTwcbC55qaGmi1WmtP34RCoWhy+w3DB4PVlsdQn4iIiIiI\nbA3DayIiogeIomgSNj/KCuja2lrU1tZae/omFApFs1dAq1T8M4GIiIiIiIish/9XSkQWO378OIYM\nGWLtaRAZeTBoftT2G4Zz5UYQhIcG0A2tgLazs2ONEskY65NI3lijRPLF+iSyDQyvichiH3zwAf9o\noFah0Wia3H7DcI4c93RoTvsNQwBtyYZ8rFEi+WJ9Eskba5RIvlifRLaBGzYSkcUqKyvRrl07a0+D\nZEqr1Ta5/YbhHL1eb+3pm7Czs2ty+w3DuCUBtCVYo0TyxfokkjfWKJF8sT6J5IsbNhKRrPAPhief\nTqdrVvuNmpoa6HQ6a0/fhEqlatYKaHt7+8dy0zzWKJF8sT6J5I01SiRfrE8i28DwmojIRuj1+ia3\n3zAE1Vqt1trTN6FUKpscPhs+HscAmoiIiIiIiMjWMLwmInqMiKLY7BXQGo3G2tM3oVAomtx+w/Ch\nVCqtPX0iIiIiIiIiakUMr4lsyJEjRzBixAiTcUEQ8P3332PgwIGoqqrC5s2bkZKSgrNnz6K8vBx+\nfn6IiYlBTEyMyYpVPfRYuHAh1sWvg4C6fr7Hjh3DunXrcPr0ady5cweurq7o27cv4uLiEBoaanT7\n1atXIyUlBVevXkVZWRm6d++OsWPH4t1334WHh0frPRlWJIqiFCg3JXw2fC43giA0ewW0SsVfQ21h\n4cKFiI+Pt/Y0bMqpU6eQmJiIw4cP49q1a3B3d8egQYOwcuVK+Pv7S+dFR0cjKSnJ5PYBAQE4f/68\nybhhrxJD//T//Oc/2LZtG44fP468vDx4eXlh5MiRWLFiBby8vExu+49//AP/+Mc/cOXKFTg5OSE4\nOBhxcXEYPHhwSz58agLWJ5G8sUaJ5Iv1SWQbmBoQ2aD58+ejf//+RmN+fn4AgJycHMybNw+jRo3C\nggUL4OLiggMHDmDOnDk4efIkNm/eDD30uI3buImbuId7qPSpxAEcgCc80R3dcenSJSiVSsyePRte\nXl4oLi5GcnIywsLCkJqaijFjxkj3m56ejn79+mHatGlo3749Lly4gE8//RSpqak4c+YM1Gp1mz43\nTVFbW9vk9huGMTlq7gpoOzs7a0+dHsLHx8faU7A5a9euRVpaGiZNmoSgoCAUFBRg/fr1CA4Oxg8/\n/IA+ffpI5zo6OmLTpk14cBPtDh06SJ+LogitVgutVmt0jkqlwqJFi1BcXIxJkybB398fOTk5WL9+\nPfbu3YszZ86gU6dO0vl/+tOfkJCQgFdeeQVvvvkmSkpK8Mknn2DYsGFIS0sz+b1AbYP1SSRvrFEi\n+WJ9EtkG4cH/CZIrQRCCAaSnp6cjODjY2tMhemwZVl5/+eWXmDBhgtlz7t69i8LCQvTu3dtofObM\nmUhMTMTZy2dxz/ceylHe4P24wx190Rd2+CXUrKqqgq+vL/r164fU1NRG5/nVV19h0qRJ2LFjByZP\nntyER9h0Wq22ye03DB9y/PnZ0CaDjYXPhgDasJKTiCx34sQJ9O/f3+jdBVeuXEFgYCAmT56MrVu3\nAqhbeb17926UlpaavY5hs9SGpKWlISwszKiGjx07hmHDhmHJkiVYvny5dB0XFxeMGzcOO3fulG5/\n7do1+Pr64q233kJCQoLFj5uIiIiIiCgjIwMhISEAECKKYoYl1+LKayIbVV5eDrVabdI32N3dHe7u\n7ibnR0ZGIjExESkXUtDXt680np+TDwDo4ttFGruLu0hHOgZgAJSou75arYanpydKSkoeOrcePXpA\nFMVHOheoC6CbEz7X1NRAr9c/0n20JTs7uya33zAcYwBNJA+DBg0yGfPz80NgYCAuXLhgckwURVRU\nVMDZ2Vkaqx9c5+bmAgB69uwpjYWGhkobqtrb2wMAhg4dio4dOxrdj0ajQVVVldFKbADw9PSEQqFA\nu3btmvMwiYiIiIiIWhXDayIbFB0djbKyMiiVSgwdOhTx8fGGV8QalJ9fF1I7ejgajS8euRgKhQJb\ncrYYjZegBOfLzsO71htFRUVISkpCVlYW3n33XbPXv3PnDioqKnD+/Hm89957UKlUeOqpp3Du3LlG\n22/U1NRAp9NZ8Gy0DpVK1awV0Pb29iZ9xYnoyXH79m0EBgYajVVWVqJ9+/aorKyEm5sbpk2bhrVr\n15r8LAgPD4dCoUBWVpbJdbVaLZRKJZRKJSoqKlBeXm60b4CjoyN+9atfITExEYMGDUJYWBju3buH\nFStWwN3dHbNmzWqdB0xERERERGQBhtdENsTe3h5RUVEIDw+Hh4cHzp8/j3Xr1iEsLAxpaWl47rnn\nzN5Oo9Eg4aMEePl64ekBTxsdEwRBWvUn6kXodDpoNBpotVq8Gvkqzhw6A6BuNfH48eMxdOhQHDhw\nwCh8LiwsxIIFC6Rrurm54fXXX8e1a9dw7dq11nkyHoFSqWxW+Ozg4GCyop3Imi5evIiAgABrT8Pm\nJScn49atW1i5cqU05u3tjdjYWAQHB0Ov12P//v34+OOPkZmZiX379hkF2IIgNPruCkOAnZCQAI1G\ng6lTpxod37ZtGyZPnowZM2ZIY7169cLx48fx1FNPtdwDpSZhfRLJG2uUSL5Yn0S2gT2viWzc1atX\nERQUhGHDhjXYizomJgabNm3C8tTlCHnRdIX2whcWInJ5pMkK6MKrhbDPtkdFQQW+//57eHp6YsqU\nKXBwcDA6T6fT4fLly9BoNLh58yZOnz6NESNGIDQ01OLHp1AomtR+48FzH+xVS/Q4i4iIQEpKirWn\nYdMuXryIQYMG4dlnn8XRo0cbDaH/8pe/IC4uDps2bcL48eOh1+uNPpydnRt8gezHH3/E6NGjERUV\nhe3btxsdKywsxMKFC9GhQwe88MILKCgowJo1a6BWq3H8+HF07NixRR8zPRrWJ5G8sUaJ5Iv1SSRf\nLdnzmuE1EeF3v/sdvv76a1RWVpoEKvHx8Vi0aBHeXvU2Rr0zyuztTx45iRrB/IZiHj97wKnUCTqd\nDitXrkSXLl0QExPT6HyuXr2K+Ph4vPnmm3j22WchCEKzV0Db2dk1el9EtuDGjRvcjd0KdDodtFot\n8vPzMWLECOj1euzduxceHh7SMXP/VlZWIiwsDJGRkXj//fdNruvt7S31t35QdnY2Ro8ejaeeegpH\njhyBk5OTdEyv16Nv374YMWIE/vrXv0rjV65cwf/8z//g7bffxurVq1vniaBGsT6J5I01SiRfrE8i\n+eKGjUTUorp3747a2lqTzcISExOxePFizJkzB3PemYNLuGT29p18OuHmzZvmL/7f18eUSiWCg4OR\nmpqKDh06wNnZucHg+aWXXsLWrVtRWFiI1157zWxIQ0SPjn/UN59Op2s0aG7smCiKKC8vxxtvvIGS\nkhJs3LgR1dXVyMvLa/Q+7e3t0aFDB9y/f9/scXMbzebl5SEiIgJubm7Yu3evUXANAEeOHMG5c+eQ\nkJBgNO7n54fevXvju+++a+IzQy2F9Ukkb6xRIvlifRLZBobXRISrV6/C0dHRKLhOSUnBrFmzEBUV\nhQ0bNqAQhQ3evkOHDhBFEXZ2dlCpVEYfz/d9Hh0dOsLe3h4XLlwAALzwwgtGG4mZYwjTGVwTkaX0\nen2zwmetVgtL3qFWW1uLt99+G3l5efj4448b7SutUCigVCqhUqlQXV2NkpISdO7cGS4uLlAoFEYf\n9d9Rcu/ePURERECj0eDw4cPo3LmzyfVv374NQRDMbnBr2KeAiIiIiIhIbhheE9mQoqIik9A4MzMT\ne/bswdixY6Wxo0ePYurUqRg+fDiSk5MBAJ7whCMcUY1qo9vn5+QDAHr69pTGSu6UwNXNFW5wQxd0\nqRsrKcHu3bvh4+MjzcHQpkStVhtdc/fu3SguLsaAAQNa6JET0eNOr9c3K3zW6XRmVyq3BkEQoFKp\noFQqoVAosHjxYmRlZeGzzz7DCy+8AKVSKQXUSqVSmpurq6vRxoyxsbEA6vo4PtiHOjc3FwDQs+cv\nP28rKysRGRmJgoIC/Otf/0KvXr3Mzu3pp5+GKIrYuXMnxowZI41nZGQgOzsbv//971v0uSAiIiIi\nImoJDK+JbMiUKVOgVqsRGhqKTp06ISsrCxs3boSzs7PU6/TGjRuIiIiAQqHAhAkTsGvXLun2hSiE\nY5Ajej77S3CyeORiVJZV4ou7X0hjS3+zFB7dPDD8V8NxrtM5XL9+HYmJicjPzze63uXLlzFq1ChM\nmTIFAQEBUCgU+PHHH7Ft2zb4+vpi3rx5bfCsED351q5di0WLFll7GlIA3ZwQui0D6AcD5vr/PuyY\nwfz58/Gf//xH+nl66NAho/uZPn06rl+/jn79+mHatGkICAgAAOzfvx/79u1DeHg4xo0bZ/S4w8PD\noVAokJWVJY1FR0cjPT0dr776Ki5evIhLl35p7+Ts7Izx48cDAIKDgzF69GgkJSXh/v37GDNmDH7+\n+Wds2LABTk5OeOutt1rl+aSHk0t9EpF5rFEi+WJ9EtkGhtdENiQyMhLbtm1DQkICSktL4enpiaio\nKCxduhS+vr4A6lb2lZWVAQDmzp1rco1Zy2YZhdeCIEh9rQ3GzByDH3b+gE0fbUJJSQnc3NwwePBg\nLFy4EKGhodJ53bp1Q1RUFA4dOoStW7dCo9GgR48emDdvHv785z/Dzc2tFZ4FIttTWVnZYtcSRbHZ\nK6DNtaxoDYYAuqnhs+HflpCZmQlBELBnzx7s2bPH5Pj06dPh6uqKcePG4eDBg9i6dSt0Oh38/Pyw\nZs0aLFiwAAqFAtXV1VLrEkEQTDbVPXv2LARBwNatW7F161ajYz169JDCa6CuHdS6deuwc+dOfPvt\nt7C3t0dYWBiWL18Of3//Fnnc1HQtWZ9E1PJYo0Tyxfoksg2CJb0c24ogCMEA0tPT0xEcHGzt6RDZ\nND30uIiLyEMe9DBdCamEEr7wRS+Yf+s6EclDc8Jnw79txZIV0PVD3seVKIqoqalpcOW5IAjSfgNE\nRERERERykJGRgZCQEAAIEUUxw5Jr8f90iKhJFFCgD/qgF3ohD3m4h3vQQgsVVOiETvCGN+xg9/AL\nEZHFLFkB3VYvXluyAvpJCaAtIQgCHB0dpU0nDSH2g6vL+TwREREREdGTiuE1ETWLAxzQ67//EVHz\n1Q+cHzV81mq1bRZAKxSKZoXPho0LyXIKhQL29vbWngYREREREVGbYnhNRBYrKiqCh4eHtadBZDWG\nVbFNbb+h0+naZCPCkpISdOzYsdkroBlAE7Ue/g4lkjfWKJF8sT6JbAPDayKy2Ouvv46UlBRrT4PI\nInq9vtkroNsigAbqWkU0J3yeOHGi2U0Dicj6+DuUSN5Yo0Tyxfoksg0Mr4nIYu+99561p0AEoG5z\nu+aEz4bP24KhV3FTwucHz22O999/v4UfBRG1FP4OJZI31iiRfLE+iWwDw2sislhwcLC1p0BPEFEU\nm9V+w/B5W2luD2iVqu1/9bJGieSL9Ukkb6xRIvlifRLZBobXRETUKixZAd1WGxE2N3xWKpUQBKFN\n5khERERERERkqxheExFRg5oTPhv+bcsAujntN7gRIREREREREZG8MbwmIott2rQJM2fOtPY0qAGG\ncLk5IXRbBdAKhaLZK6AZQD8ca5RIvlifRPLGGiWSL9YnkW1geE1EFsvIyOAfDa1Mr9c3ewW0Xq9v\nkzkqFIpmhc8qlYoBdCtjjRLJF+uTSN5Yo0Tyxfoksg1CW62qs4QgCMEA0tPT09mQn8gCR44cwYgR\nI0zGBUHA999/j4EDB6KqqgqbN29GSkoKzp49i/Lycvj5+SEmJgYxMTFGIWMRinAP96CDDiqo4AlP\nuMIVx44dw7p163D69GncuXMHrq6u6Nu3L+Li4hAaGirdvin39SQwBNDNCaHbKoAWBKHJ7Tce/JyI\n6pw6dQqJiYk4fPgwrl27Bnd3dwwaNAgrV66Ev7+/dF50dDSSkpJMbh8QEIDz589LXxs2MjX8LDDU\n6uHDh7Ft2zYcP34ceXl58PLywsiRI7FixQp4eXlJt79+/Tp69uzZ4HxnzZqFf/zjHy3x0ImIiIiI\nyMZlZGQgJCQEAEJEUcyw5FpceU1kg+bPn4/+/fsbjfn5+QEAcnJyMG/ePIwaNQoLFiyAi4sLDhw4\ngDlz5uDkyZPYvHkzbuEWcpCDClQYXeMqrqIDOiD9UjqUSiVmz54NLy8vFBcXIzk5GWFhYUhNTcWY\nMWMe+b7kxhAgNWcFtE6na5M5GkKt5qyAZgBN1DLWrl2LtLQ0TJo0CUFBQSgoKMD69esRHByMH374\nAX369JHOdXR0xKZNm4za9HTo0AFA3c8cjUYDrVZrch8ajQaxsbEoKSnBpEmT4O/vj5ycHKxfvx57\n9+7FmTNn0KlTJwCAp6cnkpOTTa6xb98+bN++HS+++GJLPwVEREREREQW48prIhtiWHn95ZdfYsKE\nCWbPuXv3LgoLC9G7d2+j8ZkzZyIxMREHLh+AxlfT6P0IEBCIQHRFV2msqqoKvr6+6NevH1JTUx/p\nvi5fvgxfX9/mPNRGGQLopobPhn/bSnPDZ6VSCUEQ2myeRGTqxIkT6N+/P1SqX9YJXLlyBYGBgZg8\neTK2bt0KoG7l9e7du1FaWmpyDVEUUVNT0+g7L9LS0hAaGgpHR0fp3SrHjh3DsGHDsGTJEixfvrzR\neY4ePRqnTp3C7du3YW9v35yHSkREREREZIQrr4nIYuXl5VCr1SYrbd3d3eHu7m5yfmRkJBITE/Hd\nhe8w0HegNJ6fkw8A6OLbRRoTISILWXCCE1zhCgBQq9Xw9PRESUnJI9/XhQsXGg2vLVkB3VYv3Fmy\nApoBNNHja9CgQSZjfn5+CAwMxIULF0yOiaKIiooKODs7S2MajcYouM7NzQUAo/YfhlZM1dXVUKvV\nEAQBQ4cORceOHc3ez4MKCgpw6NAhvPbaawyuiYiIiIhIlhheE9mg6OholJWVQalUYujQoYiPjze8\nItag/Py6kNrFw8VofPHIxbh/5z6+qfjGaFwPPbLKshBQG4CioiIkJSUhKysL7777rtF55gLmy5cv\n111Dr8eNGzcaDKbbKoBWKBTNXgH9pPXtpsdTREQEUlJSrD0NAnD79m0EBgYajVVWVqJ9+/aorKyE\nm5sbpk2bhjVr1pj8/AgPD4dCoUBWVpbZa2u1WtjZ2aGiogLl5eXw8PBodC47duyAKIqYPn26ZQ+K\nLML6JJI31iiRfLE+iWwDw2siG2Jvb4+oqCiEh4fDw8MD58+fx7p16xAWFoa0tDQ899xzZm+n0Wjw\n4UcfwsvXC08PeNromCAIaOfSDjpt3UZihg+dTod3JryDzH9nAgDs7OwwdepUREVF4dy5c1IIXT+A\n1mq1WL9+Pbp27YrOnTujsLCwRR67QqFocvhs+JcBND3u5s6da+0pEIDk5GTcunULK1eulMa8vb0R\nGxuL4OBg6PV67N+/Hx9//DEyMzOxb98+6eePKIrSuzH0er3Zn0uG8DohIQEajQZTp05tdD7bt29H\nly5dMHz48JZ7kNRkrE8ieWONEskX65PINrDnNZGNu3r1KoKCgjBs2DCpF3V9MTEx2LRpE5anLkfI\ni6YrtO8W3UV5ebnJeN6FPLTPa4+KvArs3bsX3bp1w4IFC6BWqxucz6pVq5CSkoKPPvoIgwcPNjom\nCEKzVkCrVCoG0ERkVRcvXsSgQYMQGBiIb7/9FjqdDhqNRtqMsba2FlqtFhqNBp988gk+/vhjrF27\nFqNGjYJOpzNqH+Lv799gm48ff/wRo0ePRlRUFLZv397gfC5fvoxnnnkGCxYsQHx8fIs/XiIiIiIi\nsl3seU1ELaZXr14YP348vv76a6OVfQbx8fH47LPP8Paqt80G1wAa7M3crXc39OzWEx1rOuI3v/kN\nZsyYgeXLlyM+Pt5swPzpp5/in//8J/785z9jxowZZoNpIiJrM4TMD4bPD/5b/6OwsBCzZ8+Go6Mj\n3nzzTRw/frzR648ZMwZ///vf8d1332HYsGEmxxvaODY7OxsTJ05EUFAQNm7c2Oh9JCcnQxAE/O53\nv3v0B05ERERERNTGGF4TEbp3747a2lqTzcISExOxePFizJkzB/PemYfzOG/29g4ODtLb2Ot/+Lv7\no7OyM5RKJSZNmoT4+Hj07t0bDg4ORtdITEzE6tWrMWfOHKO31BMRtYYHVz7XD6EbC6LNtTtqTEVF\nBWJjY1FZWYl169ahY8eOD72Nvb09XFxcUFpaanJMqVSavf+8vDxERETAzc0Ne/fuhZOTU6P3sWPH\nDjzzzDPo16/fIz8WIiIiIiKitsbwmohw9epVODo6GgXXKSkpmDVrFqKiorBhwwZUoQoCBIgwDU0y\nD2Yi9LehJuN2sIMPfKBE3Yrp6upqiKKIsrIyo/C6/n0RUcv65ptv8Nvf/tba02hxht75DbXfaCiU\nrq2tbZMNX2tra/Hee+/h559/xpo1a9C9e3cAgEqlgkqlgr29PVQqFezs7GBnZyd9Xltbi/v376N7\n9+7w9fU12gDW3Dtd7t27h4iICGi1Wnz77bfo3Llzo/P64YcfcOXKFb5QKBNPan0SPSlYo0Tyxfok\nsg0Mr4lsSFFRETw8PIzGMjMzsWfPHowdO1YaO3r0KKZOnYrhw4cjOTkZAKCGGp7wRCGMN1DMz8nH\n/o37jcLrkjslcPV0RVd0lYLrkpIS7N69Gz4+PkZzMHdfRNSyduzYIds/7PV6/SO336h/TkPtM9qC\nUqk0CpzrfygUCrzxxhvIzs7G9u3b8etf/1o639CDv6amBhqNxuiFQwCIjY0FAIwdO9Zoj4Dc3FwA\nQM+ePaWxyspKREZGoqCgAP/+97/h6+v70Llv374dgiBg2rRpFj8PZDk51ycRsUaJ5Iz1SWQbuGEj\nkQ154YUXoFarERoaik6dOiErKwsbN26Eg4MD0tLS8Mwzz+DGjRsICgqCVqtFfHw8XFxcpNtXoQr6\nID18nvWRxl596lUoFApsydkijc3rPw+dunXCi796Ed6dvHH9+nUkJiYiPz8fu3btQmRkJAA0el8A\nEBQUhGeffbaVnxUispQois1qv6HRaKweQBvC54ZC6AePPXjOwzaBnT9/Pv72t78hIiICkyZNMjk+\nffp0XL9+Hf369cO0adMQEBAAANi/fz/27duH8PBwfPPNN6itrZVu07t3bygUCmRlZUljU6ZMwd69\ne/Haa69h5MiRRiuznZ2dMX78eKP71ev16Nq1K3x9ffHdd98163kjIiIiIiJqDDdsJKJmiYyMxLZt\n25CQkIDS0lJ4enoiKioKS5culVbr5ebmoqysDAAwd+5ck2ssXLYQvZ7tBQ00AP67WWO9d7GPnTkW\nP+78ERs+2oCSkhK4ublh8ODBWLhwIUJDf1mh/bD7WrZsGcNrojYiiqLRquaHtd94cEyr1Vpt3oIg\nNNh+48GvzZ3TmpvAZmZmQhAE7NmzB3v27DE5Pn36dLi6umLcuHE4ePAgtm7dCp1OBz8/P6xZswYL\nFiyQ+ltrNL/8vK3fNuTs2bMQBAFJSUlISkoyOtajRw+T8PrgwYMoLCxEXFxcCz9iIiIiIiKilseV\n10TUZJWoxA3cwC3ckkJsAHCAA7r/9z8HODRyBSJqLY/agsPc6mhrEQShwZXPjYXSdnZ2rRpAy4Wh\nt3f9VeqCIEjPgble2ERERERERNbAlddEZFXt0A4BCIA//FGKUuiggwoquMAFCjT+VnoieriGgueG\nVj4bVklrtdo22YjQHEMA3dT2G4avqWGGDRtFUYRerwdQ93w/rHUJERERERHR447/t0hEzaaEEm5w\nQ3R0NLZs2fLwGxDZEMNqWXPtNx4WTLd0AP3hhx9iwYIFj3SuuWD5Ye03DP9y9W/rEgSaorYBAAAg\nAElEQVTBJlaa2xr+DiWSN9YokXyxPolsA8NrIrLYmDFjrD0FolZhCKAba7/RUIsOwwpZa1AqlUYB\n9IgRI9CtW7eHbk7IAJqo7fF3KJG8sUaJ5Iv1SWQb2POaiIieaHq9vsntNwxf1+8x3JaUSmWz+kCr\nVCq2kyAiIiIiIiKrYc9rIiKyKaIoNho6N/a1NQNohULxyO036p/DAJqIiIiIiIhsHcNrIiJqE6Io\nPrQFR2MbFVqLIAiPvPFg/XPYn5iIiIiIiIio+RheE5HFjh8/jiFDhlh7GtRGHrbS2dwmhVqtFlqt\ntsU3InxUgiCYDZoba79h+HgSAmjWKJF8sT6J5I01SiRfrE8i28Dwmogs9sEHH/CPhseMTqdrdOVz\nYy06rLlXQkMhc2PtNwxf2zLWKJF8sT6J5I01SiRfrE8i28ANG4nIYpWVlWjXrp21p2FzdDpdg+03\nGmvBodForBpANxQ0P2xzQpVKBUEQrDbvxxlrlEi+WJ9E8sYaJZIv1ieRfHHDRiJqliNHjmDEiBEm\n44Ig4Pvvv8fAgQNRVVWFzZs3IyUlBWfPnkV5eTn8/PwQExODmJgYk03kdNBB2U5Z9y/q2iscO3YM\n69atw+nTp3Hnzh24urqib9++iIuLQ2hoqNHt//Wvf2Hnzp04efIkLly4AB8fH+Tk5LTekyAzer2+\nye03DJ/r9XqrzVupVJoEzA9uQNjY6mgG0G2Pf9S3vVOnTiExMRGHDx/GtWvX4O7ujkGDBmHlypXw\n9/eXzouOjkZSUpLJ7QMCAnD+/HmT8QdfeBIEAf/5z3+wbds2HD9+HHl5efDy8sLIkSOxYsUKeHl5\nNTi/+/fvw9/fH0VFRfjyyy8xYcIECx8xNRfrk0jeWKNE8sX6JLINDK+JbND8+fPRv39/ozE/Pz8A\nQE5ODubNm4dRo0ZhwYIFcHFxwYEDBzBnzhycPHkSmzdvhg46FKAAN3ETJSiRruEBD3RHd2RfyoZS\nqcTs2bPh5eWF4uJiJCcnIywsDKmpqRgzZox0m+3bt2PXrl0IDg5G165d2+YJaGF6vb7RPtCNbU6o\n0+msNm+lUvnQdhsNteCo/yIGERlbu3Yt0tLSMGnSJAQFBaGgoADr169HcHAwfvjhB/Tp00c619HR\nEZs2bTIKpjt06CB9btjstH7bHqVSiUWLFqG4uBiTJk2Cv78/cnJysH79euzduxdnzpxBp06dzM4v\nLi4O1dXVfDGJiIiIiIhkjW1DiGyIYeV1Y6vs7t69i8LCQvTu3dtofObMmUhMTMRPl3/CPd97qEBF\ng/fjClcEIxj2sJfGqqqq4Ovri379+iE1NVUaLygogKenJ5RKJcaNG4esrCyrrLw2hENNbb+h0Wis\nGkArFIpHbr9R/5wnYSNCIrk6ceIE+vfvb9Rv/cqVKwgMDMTkyZOxdetWAHUrr3fv3o3S0lKz19Hp\ndKipqWnwftLS0hAWFmb0roZjx45h2LBhWLJkCZYvX25ym6ysLPTr1w/Lli3D0qVL8cUXX3DlNRER\nERERtRi2DSEii5WXl0OtVpsEmO7u7nB3dzc5PzIyEomJidhzYQ/6+vaVxvNz8vH56s8xf+N8aawE\nJUhHOgZioNRKRK1Ww9PTEyUlJUbXbext7c3R0MrnxtpvGL62FkEQmtx+w3AOA2h6FAsXLkR8fLy1\np2FTBg0aZDLm5+eHwMBAXLhwweSYKIqoqKiAs7OzNFY/uM7NzQUA9OzZUxoLDQ2Vfn7Z29e9YDh0\n6FB07NjR7P0AwLx58zBx4kQMGTLEqv3vqQ7rk0jeWKNE8sX6JLINDK+JbFB0dDTKysqgVCoxdOhQ\nxMfHG14Ra1B+fj4AwNHD0Wh88cjFqCqvMgqvAeA+7iOrLAtda7uiqKgISUlJyMrKwrvvvvvQ+TXU\nguNhmxPWf0t9WxIEwShoftT2G4Z/iVqTj4+PtadA/3X79m0EBgYajVVWVqJ9+/aorKyEm5sbpk2b\nhrVr15q05wkPD4dCoUBWVpbJdbVaLZRKJZRKJSoqKlBeXg4PDw+T87744gucOHECFy9etKn9BeSM\n9Ukkb6xRIvlifRLZBiYmRDbE3t4eUVFRCA8Ph4eHB86fP49169YhLCwMaWlpeO6558zeTqPRIOGj\nBHj5euHpAU8bHRMEAU4dnExuI+pF/G/U/+LUv05J9z1jxgzMmDEDly9fNhtEFxUVoaqqCv/+979b\n/sE/ovohc0MtOMydQyRXf/jDH6w9BQKQnJyMW7duYeXKldKYt7c3YmNjERwcDL1ej/379+Pjjz9G\nZmYm9u3bZxRgC4LQaI9qQ4CdkJAAjUaDqVOnGh2vrq7GwoUL8fbbb6N79+4Mr2WC9Ukkb6xRIvli\nfRLZBobXRDZk8ODBGDx4sPT1Sy+9hIkTJyIoKAjvvPOOUS/qB7355pvIvpiN5anLTVYCJuYmouhO\nEXJzcqHT6aQPAAh7IwzhY8JR9nMZDh48iNu3b+PChQtwdHQ0dzcttmpapVKZtN94lCBapVJx8zIi\nahUXL17E3Llz8fzzz+OVV16RxletWmV03uTJk+Hv748lS5bg66+/xsSJE6Vj58+fb/Q+dDodjhw5\nguXLl2PKlCkYNmyY0fHVq1dDq9XinXfeaYFHRERERERE1PoYXhPZuF69emH8+PH4+uuvIYqiSXgb\nHx+Pzz77DG+vehshL5pvLaLRaFBVVWUy7v2MN7p6doVrpStGjhyJuXPn4sMPP3yk1iFKpbLJK58N\nn9cP2ImIrKmwsBBjx46Fm5sbvvjii4e+SPbHP/4RcXFxOHTokFF4XVpaiqqqKnh4eJjtd5+dnS29\nILlx40ajY9euXcO6devw97//He3atWuZB0ZERERERNTKGF4TEbp3747a2lqTzcISExOxePFizJkz\nB3PemYNLuGT29rdzbsPezd5kXKFQwMHOAc7OzlCpVBg5ciSSkpLQpUsXODk5mYTPbm5uuH37NkaP\nHs0AmqgFXbx4EQEBAdaehk0qLS3Fiy++iNLSUhw/fvyRNql1dHSEu7s7iouLpTGdToc7d+5Aq9Wi\nrKwM3bp1M3oXS15eHiIiIuDm5oa9e/fCycm4ndPSpUvRrVs3DB06FNevXwfwy14Gd+7cwfXr1+Hj\n48N3n1gB65NI3lijRPLF+iSyDQyviQhXr16Fo6OjUXCdkpKCWbNmISoqChs2bMAd3Gnw9l+t+Qqx\nO2KhUCikDcOUSiUEhYAhGAJn1F13586dEEUR3t7eZjcSM7TtYHBN1LJiY2ORkpJi7WnYnJqaGowb\nNw5XrlzBv//9bzzzzDOPdLvy8nIUFRUZ/Zy8d+8etFotgLre1/b29kbHIiIioNFocPjwYXTu3Nnk\nmjdv3sSVK1fQq1cvo3FBEDB79mwIgoDi4mK4uLg056GSBVifRPLGGiWSL9YnkW1geE1kQ+qHIQCQ\nmZmJPXv2YOzYsdLY0aNHMXXqVAwfPhzJyckAAA94QA01qmDcHiQ/Jx+TF0+Gc/tfgu+SOyVw9XSF\nO9yl4LqkpAS7d++Gj4+P2eCaiFrPhg0brD0Fm6PX6zF58mScOHECKSkpGDhwoMk5NTU10Gg0Ri8c\nAsDy5csBAGPGjAEA1NbWori4GHl5eQCAgQMHSi/yVVZWIjIyEgUFBfjXv/5lEk4brFq1CkVFRUZj\n586dQ1xcHBYtWoTBgwebrNamtsH6JJI31iiRfLE+iWwDw2siGzJlyhSo1WqEhoaiU6dOyMrKwsaN\nG+Hs7IzVq1cDAG7cuIGIiAgoFApMmDABu3btkm5fiEI4Bjmi57M9pbHFIxdDoVBgS84WaWzpb5bC\no5sHRvxqBH7q9BOuX7+OxMRE5OfnG10PAM6ePSu9Wn7lyhXcv39f2sDsueeew0svvdRqzweRrfDx\n8bH2FGzO22+/jT179iAiIgJFRUXYtm2b0fHp06ejoKAA/fr1w7Rp06S3vO7fvx/79u1DeHg4xo0b\nB71ej9u3b0MURbzxxhtQKpXIzs6WrhMdHY309HS8+uqryM7OxqVLv7R3cnZ2xvjx4wEAoaGhJnPs\n0KEDRFHEgAEDEBER0RpPAz0C1ieRvLFGieSL9UlkGxhe/3/27j0+qure//9r78lMEkK4JURAMSYk\nCBRRE8sBCohApQXFolyLPYoc4vdQS1HqEftV9FCtIPqlLei3LQrIAbFVq7/wBa1HrVBFQBLxcFUg\n3CHcL7nPbf/+iLPNJJNkcoEM5P3kMY8ye++1Z+2pa5O8Z81niTQjo0aNYsWKFcyfP58LFy7Qvn17\nRo8ezaxZs0hNTQVg3759FBQUAPDQQw9VOUfWU1lB4bVhGFCpROrtk29n4xsbeeV3r3Du3Dnatm1L\n3759efTRR6sEKLm5ucyaNStoW+D5fffdp/BaRC5LX331FYZhsGrVKlatWlVl/8SJE2nTpg133nkn\nH374IcuWLcPn85GWlsacOXOYMWMGpmly4sQJiouLgfL7bVRU8I9uW7duxTAMli1bxrJly4L2JScn\n2+F1dVTjWkREREREIplhWVZT96FWhmFkADk5OTlkZGQ0dXdEmjU/fr7hGw5yED/+KvudOEkllRRS\nQrQWEZFw+Xw+Nm/ejGmaREVF0a5dO9q3bx90TKD+tcPhaKJeioiIiIiIBMvNzSUzMxMg07Ks3Iac\nS6uiiUidmJh0oxuDGMT1XE972rNq7ira057v8T0GMUjBtUiEmTt3blN3Qerh0KFDlJSUUFRURFlZ\nGUlJSZimaS+OGx0dTUxMjILry5zGp0hk0xgViVwanyLNg8qGiEi9uHCR8u2f7OJsMsls6i6JSDUC\nZSfk8lFSUsKBAwfs56mpqcTGxjZhj+Ri0fgUiWwaoyKRS+NTpHlQ2RARERGRCLNt2zZOnToFlC+s\nePPNNzdxj0RERERERMKjsiEiIiIiV6jTp0/bwTVAenp6E/ZGRERERESk6Si8FhEREYkQfr+fPXv2\n2M+vvvpqWrZs2YQ9EhERERERaToKr0WkwSrOEBSRyKMxevkILNII4HQ6SUnRArhXOo1PkcimMSoS\nuTQ+RZoHhdci0mAPPPBAU3dBRGqgMXp5KCsrC1qksUuXLkRFaW3tK53Gp0hk0xgViVwanyLNg8Jr\nEWmwp59+uqm7ICI10Bi9POzduxe/3w9AfHw8V111VRP3SC4FjU+RyKYxKhK5ND5FmgeF1yLSYBkZ\nGU3dBRGpgcZo5Dt79iwnTpywn3ft2hXDMJqwR3KpaHyKRDaNUZHIpfEp0jwovBYRERFpQn6/n927\nd9vPO3bsSHx8fBP2SEREREREJDIovBZpRtauXYtpmlUeDoeDTZs2AVBSUsJLL73EsGHD6NSpE61a\ntSIjI4M//vGP9tfZASwsTnCCXexiG9vYxS7OcAaAjz/+mMmTJ3P99dcTFxdHly5dmDJlCvn5+UH9\nOXDgQMj+BB4PPvjgpXtzREQa0ebNm3nooYfo2bMnLVu2JDk5mXHjxgWF1ACTJk0iKiqKPn36MHjw\nYAYPHkz37t3p0aNH0HGWZeHxeHC73bjdbjweD36/P+z7LcBzzz1H3759SUpKIjY2lq5du/Lwww9r\nsSMREREREYlYWgVIpBmaPn06t9xyS9C2tLQ0APLy8pg2bRpDhw5lxowZtGrVig8++ICpU6eyadMm\nFi9ezCEOkUceJZQA8PdX/86wycPYz37iiWfGYzMoOFvAmDFjSE9PJy8vjwULFrB69Wq2bNlCUlIS\nAO3bt2f58uVV+vfee+/x+uuvM2zYsIv8Tog0D6+++iqTJ09u6m40K3PnzmX9+vWMGTOGXr16kZ+f\nz4IFC8jIyGDjxo12OO3z+XC5XDz66KNYlkWHDh1o164drVu3Br4Lrb1eb5XX8Hg8PProo5w/f77W\n+y1ATk4ON998MxMmTCA+Pp6dO3fy5z//mTVr1rBlyxZiY2MvzZsjQTQ+RSKbxqhI5NL4FGkeFF6L\nNEP9+/fn7rvvDrmvQ4cObNu2je7du9vbpkyZwuTJk1m6dCkTn5iIJ9UT1GZP7h6GTS4Pmgso4N75\n9zK2/1g609k+ZtiwYdx6660sXLiQ2bNnA9CiRQt++tOfVunDkiVLaNWqFXfccUeDr1VEIDc3Vz/Y\nX2IzZsxg5cqVREV996PW2LFj6dmzJ3PmzGHZsmUAFBQU4HA4GDJkCC1btiQzM9OudW1ZFmVlZUHf\neqls7ty59OvXj+joaBwOBxD6fgvw1ltvVWnfp08fxowZw6pVqxg7dmyjXLvUjcanSGTTGBWJXBqf\nIs2DyoaINFOFhYX4fL4q2xMSEoKC64BRo0YB8NnOz4K2H8s7xt0zgoPw7/X/HjvYwVnO2tsGDBhA\nu3bt2LlzZ439ys/P5x//+Af33HMPLpcr7OsRkeq99NJLTd2FZqdPnz5BwTWUf8OlZ8+e9n3w/Pnz\nlJaWAuVBdceOHYMWaXS73UHB9b59+9i3b1/QOfv16wdAWVkZlmUB4d9vAZKTk7Esi3PnztXjKqUx\naHyKRDaNUZHIpfEp0jwovBZphiZNmkSrVq2IiYlh8ODB5OTk1Nrm2LFjALRKbBW0febgmfx66K+r\nHG9hsZ/99vOioiIKCwtJTEys8XVWrlyJZVlMnDgxjCsREbm8HD9+nMTERCzLsutfl5aWMmLECDp3\n7kxCQgIPPfRQyA8Yhw8fXuM3UgKlRWq7354+fZrjx4/zz3/+k2nTphEVFcWgQYMa5wJFREREREQa\nkcqGiDQjLpeL0aNHM3z4cBITE9mxYwcvvPACAwcOZP369dx4440h23k8Hl783Yt0SO1A1+93Ddpn\nGAYWFidPnCQqKsp+OJ1OjjuOU2qWEkMM8+fPx+PxMH78+Br7+Prrr9OxY0cFKSJyxVm+fDlHjhzh\nmWee4ejRoxQWFpKQkMBPf/pTRowYgWmavP/++7z88st89dVXvPfee5jmd/MMDMMImpldmdfrxel0\n1ni/PX78OB07drSfd+7cmZUrV9K1a9cqx4qIiIiIiDQ1hdcizUjfvn3p27ev/fyOO+7gnnvuoVev\nXjz++OOsWbMmZLuf//zn7N61m9lrZgcFKQBL9y3lwP4D7Nq1K2TbPcf3cPx/jjNnzhz69++P2+1m\n3bp1uFwuYmJicLlcREdHEx0dzZEjR8jJyWH69OmNd9EiIhFg165dPPTQQ/zgBz9g/PjxfPHFFwD8\n27/9G2lpaVxzzTVAeV3s9PR0nnjiCd555x3uuece+xw7duywS4OEYlkWa9euZfbs2YwbN45bb721\nyjHt2rXjww8/pLS0lC+//JK//e1vFBQUNPLVioiIiIiINA6F1yLNXJcuXbjrrrt45513sCyryqy+\nefPm8corr/DIs4+QOSwz5DkWTl7IyKdGhtx3+OBhFs5fSKdOnRg9enSVeq0VZWdnA+ULOS5atCgo\n2K78CBV+Bx5Op7PG2Ykizc3IkSPt8SWX3okTJxgxYgRt27blzTffZP/+/XaJj7i4ODp16hR0/MMP\nP8yTTz5p1/8P8Pv9uN1uHA4HUVFRVe5zX3/9tf2B5KJFi0L2xel0MnjwYKC8DMngwYP5wQ9+QFJS\nEsOHD2/My5YwaXyKRDaNUZHIpfEp0jwovBYROnfujNvtpqioiJYtW9rbly5dysyZM5k6dSrTHp/G\nDnaEbP+DCT8Iuf3CiQssf245cXFx/OIXvyA6OrrGfnzxxRd06NCBa6+9FihfrMztdtd5VqBhGDUG\n34HwO9R2p9NZp9cSuRw89NBDTd2FZuvChQsMGzaMCxcu8OmnnxIXFxf0TZW0tLQq32iJiYkhISGB\ns2fPBm0PBN6hPmg8fPgwI0eOpG3btqxevZq4uLiw+te3b186duzIihUrFF43EY1PkcimMSoSuTQ+\nRZoHhdciwt69e4mJiQkKrrOzs5kyZQqjR49m4cKFlFLKLnbhx1+l/Z3334nH48Hr9dqPcyfPseSB\nJThw8Pvf/56EhATKysrsRyCYDti3bx8nT55k5MjQM7jrwrIs+3XqyjTNes/4jorSLVUi0+23397U\nXWiWysrKuPPOO9mzZw8fffQRXbt2JTc3196flJRE27Ztq7QrLCzk1KlTQQsu+nw+/P7y+2/le82Z\nM2cYOXIkXq+Xv//971x11VV16mdpaSnnz5+vUxtpPBqfIpFNY1Qkcml8ijQPSlpEmpHKYQjAV199\nxapVqxgxYoS9bd26dYwfP55BgwaxfPlyAGKIoT3tOc7xoPbH8o4B0DH1uwXASotLeeaOZyg8Vci6\nT9Zx0003hexP4CvwZWVl/OpXv8IwDH75y19y1VVXBQXdoR6BGYiNze/3U1paSmlpaZ3bmqZZ42zv\nmsJvh8NxEa5GRJqK3+9n7NixbNiwgezsbHr37k1+fr79TRLTNLnmmmsoLCwM+uAQYPbs2cB3v5BZ\nloXH42H//v04HA7S09PtY4uLixk1ahT5+fl89NFHpKamhuxPcXExhmEQGxsbtP3tt9/m7NmzfP/7\n32+0axcREREREWksRk0L/0QKwzAygJycnBwyMjKaujsil60hQ4YQGxtLv379SEpKYvv27SxatIjo\n6GjWr1/P9ddfz8GDB+nVqxder5d58+bRqlUru30ppfh7+el8Q2d7233X3YdpmizJW2Jvm/2T2WzI\n3sD9k+9nyKAhQX1o2bIld911V9A2v9/P1VdfTWpqKp999llY1+L3+6sE2oEgvKaH2+2+aMF3Q0RF\nRdVa6qS6cieVSw6ISNObPn06f/jDHxg5ciRjxozB5/Oxd+9e+/6TlZUFwM0338yECRPo1q0bAO+/\n/z7vvfcew4cP591338XtduPxePD5fNx00004HA62b99uv864ceNYvXo1999/P0OGVH+//eqrrxg6\ndCjjxo2jW7dumKbJF198wYoVK7j22mv54osvQs4CFxERERERqavc3FwyMzMBMi3Lyq3t+JoovBZp\nRhYuXMiKFSvYs2cPFy5coH379gwdOpRZs2bZs/XWrl1rL+YVyn889R8MnTUUDx4A7k+5H3eJm9fz\nX7ePmZQyiRMHT4Rsn5ycTF5eXtC2Dz74gB//+McsWLCAqVOnNvQya+Xz+aoNtmsLvwNf248kUVFR\n9Zrx7XK5FHw3E++++y4/+clPmrobzcptt93GunXrqt3v8XgoKChg2rRpbNiwgaNHj+Lz+UhLS+Pe\ne+9lxowZOBwOysrKKCoqAiAjIwPTNNm2bZt9nh49enDo0KGQr1Hxfnv69GmeeOIJ1q1bx6FDh/B4\nPCQnJ3PHHXfw61//mnbt2jXi1UtdaHyKRDaNUZHIpfEpErkUXotIkyqhhEMc4jCHcePmuXHP8fhf\nHieWWDp/+8fJlbnwodfrDRl6l5aW1hp+R+L91uVy1WvGt8vlqrJgnESucePG8Ze//KWpu9FsFRYW\nsnnzZvt5r169wg6Li4uL7dnalWtdm6ZJVFQUDodD4/EypvEpEtk0RkUil8anSORSeC0iEcGPnwIK\n8OLFiZN44jFQgFIdj8dTrxnfbrc7YoPv6havrCn8drlcTd11kUvqyy+/tBdETExMpGfPnmG183g8\ndv39Fi1aYJqm/e0PwzD0zQkREREREYlIjRlea8FGEak3E5PWtG7qblw2nE4nTqezyuJstQks1lZT\nuF3TvovF7XbX6/yGYVRbv7u2cidO55U5o1+uXMePH7eDa8Mw6NKlS1jtLMuirKwMKL93BBZ11eKu\nIiIiIiLSnCi8FhGJcIZh2OU94uPj69TWsqxqw+3ayp14PJ6Lcj2WZVFaWmrPKK0L0zRrXLyyphnf\nlUsuiFxsXq+XvXv32s+Tk5OJjY0Nq23gGxeBD3tERERERESaI/0mLyJyBQsEX/UJv/x+f62zuqvb\nF6jR29j8fj8lJSWUlJTUua3D4ah28crawm/NdpX6OHDggP3thJiYGDp37hxWu8DYA4iOjlY9axER\nERERabYUXotIg02aNIklS5Y0dTekkZmmSUxMDDExMXVu6/f7a6zjXVP47fP5LsLVgM/no7i4mOLi\n4jq3jYqKqvOM70BAHgl1iTVGL72ioiIOHz5sP+/SpUvYH4IEyoU4HA6VymkGND5FIpvGqEjk0vgU\naR4UXotIg91+++1N3QWJMKZpEhsbG3aJhIq8Xm+tM76rK3cSWMyusXm9XrxeL0VFRXVu63Q6qy1l\nUtuM78aacasxeunt2bPHXmi1Xbt2tG/fPqx2gf/WAJULaSY0PkUim8aoSOTS+BRpHozAL1aRzDCM\nDCAnJyeHjIyMpu6OiIhEKK/XW+8Z35H472HFoLsu5U6cTqdKTTShkydPsn37dqC8dM/3v/99WrRo\nUWs7y7IoKirCsiycTme9vvUgIiIiIiLS1HJzc8nMzATItCwrtyHn0sxrERG5YkRFRREVFUVcXFyd\n27rd7hpnfNcUfl8sgT4VFBTUqV1gkc/6zPh2uVwX6WqaB5/Px549e+znnTt3Diu4Bi3SKCIiIiIi\nUpnCaxEREbCD25YtW9apnWVZeDyesELvyuVOAovyNTbLsuodrFdc5LOu4XdUlH6sOHjwoP2+u1wu\nkpOTw2pXcZFGl8ulmfMiIiIiIiIovBZpVtauXcttt91WZbthGHz++ef07t2bkpISFi9eTHZ2Nlu3\nbqWwsJC0tDSysrLIysqqsvicBw/rPl3HwP4DcVK+sNjHH3/MihUr+PTTTzl8+DAdOnRg8ODB/OY3\nv6FDhw5VXt/j8TBv3jz+67/+i/3799O6dWtuueUW/vznP9OpU6eL82aINJLALGeXy0V8fHyd2gZC\n5vrM+PZ4PGG/zp49e0hLSwu7T6WlpZSWltbpWqC81nlNi1fWNOP7Sgi+S0pKOHjwIABff/01mzZt\nIisri/3795OQkECfPn145plnSE9Pt9tMmjSJ1157rcq5unXrxo4dO4K2BUrbBGZnG4ZRp/vtf//3\nf/PGG2+wadMmdu7cybXXXkteXl5jvw1SR59++in9+/dv6m6ISDU0RkUil8anSAdw4lUAACAASURB\nVPNw+f+mKCJ1Nn36dG655ZagbYFgKy8vj2nTpjF06FBmzJhBq1at+OCDD5g6dSqbNm1i8eLF+PBx\nlKMc4hAXuMDTzz/N0/2fph3t6ExnHnvsMc6ePcuYMWNIT08nLy+PBQsWsHr1arZs2UJSUpL9ul6v\nl+HDh7NhwwamTJlCr169OHv2LBs3buT8+fMKr+WKZhgGMTEx9aptHJipW93ilRUfr7zyCr1797af\nBxYEbGx+v5+SkhJKSkrq3NbhcNR7xnflD9WaSsVFGt966y127NjBmDFj6NWrF/n5+SxYsICMjAw2\nbtxIjx497HYxMTEsXLgQy7Ls62ndurW93+/3By3kGOBwOOp0v3399df561//SkZGBldfffVFfjck\nXM8//7x+8RaJYBqjIpFL41OkedCCjSLNSGDm9VtvvcXdd98d8pjTp09z4sQJunfvHrR98uTJLF26\nlC27t3Am9QwlfBdOlRaXEtPiu/Bt/6f7mdR/EtF8V7P1n//8J7feeitPPPEEs2fPtrc///zzzJo1\ni88++yxQzF9EGllxcXFQ3WWfz1enGd8VA3Kfz9eEVxJaVFRUjbO6a9rXWMH36dOn2bp1q/3csiwG\nDBgQNKN8z5499OzZk7Fjx7Js2TKgfOb122+/zZEjR0Iu0uj1emssL7N+/XoGDBgQVGqkuvttfn4+\n7du3x+FwcOedd7J9+3bNvI4AlceniEQWjVGRyKXxKRK5tGCjiDRYYWEhsbGxOByOoO0JCQkkJCRU\nOX7UqFEsXbqU/7fz/3Fz6s329mN5xwDomNrR3nZd/+vIIYfe9Cbq29vMgAEDaNeuHTt37rSPsyyL\nP/zhD9x9991kZmbi8/lwu93ExsY26rWKNHeVf6h3OBy0aNGiXj/se73eGhevrKncid/vb6xLqtIn\nr9dLUVFRnds6nc5qg+2ayp1UDIv9fj+7d++2z3nNNdeELNOSlpZGz549g+6DgffEsizcbndQeB24\nJwbs27cPgJSUFHtbv3797OMCizyGut8CIcs2SdPTL90ikU1jVCRyaXyKNA8Kr0WaoUmTJlFQUIDD\n4WDAgAHMmzev1lnPx46Vh9SxicHB8szBMzFNkyV5S4K2X+ACBzhAF7oAUFRURGFhIYmJifYxO3bs\n4OjRo9xwww1kZWWxbNky3G43N9xwA7///e8ZNGhQI1ytiDSmqKgooqKiiIuLq3PbwMKW4ZY7qRh+\nX6xvink8HjweD4WFhXVuGwi2S0tLKSwsDJoBXlhYGDL0Pn78OD179gTKg2ufz0dxcTGdOnWiuLiY\ntm3bMmHCBObOnVtlVvjw4cMxTZPt27dX6YvP58Pn8+FwOELeb0VERERERC5HCq9FmhGXy8Xo0aMZ\nPnw4iYmJ7NixgxdeeIGBAweyfv16brzxxpDtPB4P8383nw6pHej6/a5B+wzDACP06x3mMKmkYmAw\nf/58PB4P48ePt/cHZir+n//zf0hISGDRokVYlsVvf/tbfvzjH/PFF1/YIY+IXP6cTidOp7NebSsG\n3XUtd3KxuN1uSkpKOHXqlB2ut27dOqh8SEUbNmzgyJEjDB06lNdee4327dtjGAYTJkwgNTUVh8PB\n5s2befnll9m4cSNvvvmmvZhlVFSUvUhjdbxeLw6HI+T9VkRERERE5HKk8FqkGenbty99+/a1n99x\nxx3cc8899OrVi8cff5w1a9aEbPfzn/+cr3d9zew1s6vMBFy6byn/d/r/5fSp05imiWmaOBwOTNOk\nzFHGUesoX3/+NbNnz2bcuHHceuutdtvATMfCwkK++uore3HGwYMHk5aWxvPPP2/XhRWR+nv00UeZ\nN29eU3ejQVwuFy6Xi/j4+Dq1C5TjqK6USU3hdzjBd0FBgR1cB0qNhJKfn88bb7xBly5d6NOnD5Zl\nYVkW48eP5+TJk/Zr3XHHHTgcDrKzs/nzn//MwIED7XP88Y9/xDAMzp07R5s2baq8hs/nY+3atSHv\ntxK5roTxKXIl0xgViVwanyLNg8JrkWauS5cu3HXXXbzzzjtYllVlVt+8efN45ZVXeOTZR8gcFrq0\nSJur2nD+/PmQ+45tOcbMyTNJTU0lKyuLnJwcexbhmTNnAMjMzMTv95Ofn09UVBQtW7akT58+fPbZ\nZ/ZMwppmG4pIza699tqm7kKTMQzDLtlRV36/v8qM74rlTk6fPk1eXh5erxePx0P79u2xLIuysjK8\nXq99ngsXLrBgwQJatGhBVlYWhmHY4XNJSUmVkHzo0KFkZ2fz5ZdfBoXXUB7GV1wEsqKvv/7a/kBy\n0aJFdb5eaRrNeXyKXA40RkUil8anSPOg8FpE6Ny5M263m6KiIlq2bGlvX7p0KTNnzmTq1KlMfXwq\n3/BNyPZDJw+loKCgyvazR8/y+5//nvj4eF544QVcLpcdAgH2DMUWLVpw8ODBoLZOp5NTp06xadMm\noHyBuUDoXfnhcDhwOp3VHiPS3P3iF79o6i5clkzTJCYmJuRsar/fz+bNm+natbyUUqdOney/B/aX\nlZVx8uRJRowYgd/vZ+XKlVx99dV4PB6gvMzHmTNngu6NgSA7Pj4+5H0VqLLQLsDhw4cZOXIkbdu2\nZfXq1fWqSS5NQ+NTJLJpjIpELo1PkeZBqY6IsHfvXmJiYoKC6+zsbKZMmcLo0aNZuHAhJzlZbfvW\nrVsTFxeHz+ezFyC7cPoCL01+Cb/fz5/+9Cc6duyI1+vF6/XaX7FPTU0lKiqKU6dOVTnnqVOngr4W\nH1iMLBB8h8swjBqD70D4Xd12EZFQjhw5QnFxMVD+YVtKSkrQ/kAZpZ/97Gfs37+fjz76iN69e+P3\n+ykqKgIgOjoal8tV5dznz5/n3//930lLS+PGG2+0752BR+U2Z86cYeTIkXg8Hj755BOuuuqqi3TV\nIiIiIiIil5bCa5Fm5NSpUyQmJgZt++qrr1i1ahUjRoywt61bt47x48czaNAgli9fDkAiicQSSwkl\nQe2P5R0DoGNqR3tbaXEps382m4ITBfzzk39y0003BbXx+Xx2CHP77bfzwQcfAHDdddfh9Xr5+uuv\n2bZtG+PGjaNly5b2sT6fzw6+w2VZlt2+rsIJvqsLvxV8i1y5ysrK2L9/v/08JSWlykKUfr+fsWPH\nsmHDBrKzs+nduzeAPbPaNE38fj+FhYVBHxwCPPvsswCMGDGC1q1b29v37dsHBM+8Li4uZtSoUeTn\n5/Pf//3fdOnSpfEuVEREREREpIkZdQ2CmoJhGBlATk5ODhkZGU3dHZHL1pAhQ4iNjaVfv34kJSWx\nfft2Fi1aRHR0NOvXr+f666/n4MGD9OrVC6/Xy7x582jVqpXd/iQnie4VTcoN380wvO+6+/D7/PzX\nof+yt83+yWw2ZG9g4uSJ/GjQj4L60LJlS+666y77+c6dO/mXf/kX4uPj+eUvf4nf72fBggX4/X5y\nc3Pp2LFjUPuKsw8rhuDVPSoecykZhhFW6B2q3EnlRTFFGmrXrl1069atqbtxxdi5cyfHjx8Hyu9p\nmZmZVeryT58+nT/84Q+MHDmSMWPGAN+VEgG4//77OXLkCDfffDMTJkyw//95//33ee+99xg+fDhv\nv/02Pp/PPmf37t0xTZPt27fb28aNG8fq1au57777GDJkSFA/Kt9vt27dSnZ2NgDLly/nxIkTPPLI\nIwDceOON3HHHHY32Hkn4ND5FIpvGqEjk0vgUiVy5ublkZmYCZFqWlduQcym8FmlGFi5cyIoVK9iz\nZw8XLlygffv2DB06lFmzZpGamgrA2rVrGTx4cLXnePCpBxk5a6T9/P6U+zl34hzvFr0btO3kwdBl\nRpKTk8nLywvatmXLFh577DE+//xzTNNkyJAhPP/88406g9CyrFrD7ur2VwyPLgXTNMMqaxJqv4Jv\nCWXkyJF2aCkNc+7cObZs2WI/z8jICPqQL+C2225j3bp11Z7H5/Nx/vx5pk2bxoYNGzh69Cg+n4+0\ntDTuvfdeZsyYgWmalJaW2t846dGjB6Zpsm3bNvs8PXr04NChQyFfo/L99rXXXuOBBx4Ieex9993H\n4sWLa754uSg0PkUim8aoSOTS+BSJXAqvRaTJWFjsZjcHOICP8lD3xMETJF2bBIALF13oQjLJTdnN\nRhWo413XGd8ejwe/339J+2qaZo2LV1Y349vhcCj4voIdPHhQq7E3Asuy2Lx5s12zukOHDmHP9nG7\n3fas67i4uLDHm2VZuN3uaj9EMwwDl8ulUkWXMY1PkcimMSoSuTQ+RSJXY4bXqnktInViYNCVrqSQ\nwlGOcoYzJFybQBRRJJFEBzrg4MoKUQILr1WuaRsOv98fduhdeX99gu+KZQnqqqaFK2ub+V25ZIJE\nFv1Q3ziOHj1qB9cOh8P+xkptAgE0gMvlqtMHRYZhEB0dbd9LAveFQGkihdaXP41PkcimMSoSuTQ+\nRZoHhdciUi9OnCR/+0eqZ5omLpcLl8tV57YVg++Ks7nDmfldn2/V+Hw+fD5fvcLvcIPuUMeIXA7c\nbndQCY6UlJSwx3VZWRmWZdmzpOsjcC8RERERERFpTpQaiIhEqIYE36EC7nDLndQn+K7vopiGYdSp\npnfl7SKXyr59++zSHXFxcXTq1CmsdoEPnQBiYmL0LQUREREREZE6UHgtIg02d+5cHnvssabuhlTg\ncDhwOBxER0fXuW1NC1c2dvBtWVaDgu+6zvgO1PpubsG3xmjDXLhwgWPHjtnP09PTwy79EfgmQ+C/\nUZHKND5FIpvGqEjk0vgUaR70W5SINFhxcXFTd0EaUX2Db8uyagy4a9tXV5Zl4fF47FmtdWGaZrWL\nV9Y2+/tyXNhSY7T+LMti9+7d9vOkpCTatGkTVttAmR8on3UtEorGp0hk0xgViVwanyLNg1Gfr4df\naoZhZAA5OTk5ZGRkNHV3RESkkdUWfNcUftcn+G6IQPAd7qKWFQPyyzH4bu6OHTvG119/DZR/sNO7\nd++wPtixLIuioiIsy8LlctXrWxAiIiIiIiKXo9zcXDIzMwEyLcvKbci5NPNaRESaXMUSIHXl9/tr\nndVd3UKXfr+/Xq/ndrtxu911bls55K7LApeqlXzpeTyeoEUak5OTww6hG2ORRhERERERkeZO4bWI\niFzWTNPENE2cTmed2/r9/jrV9G5o8O3z+fD5fHYd5LqoaeHK2sJvBd/1s3//frssTYsWLbjmmmvC\naldxkcbo6Gi9/yIiIiIiIvWk8FpEGuzUqVMkJiY2dTdE6sw0TVwuV71mxgaC6JoC7+pC8fqU7Kpv\n8G0YBgUFBSQmJtZrxndzVVhYyJEjR+znaWlp9VqksT4fqkjzon9DRSKbxqhI5NL4FGkemu9vpSLS\naB544AGys7Obuhsil1RgYcv6Bt91XdQysL+uwbdlWcyePZvnn3++zv00DKNOi1lW3n45q7hIY2Ji\nIu3atQurXcVFGlXnWsKhf0NFIpvGqEjk0vgUaR4UXos0I2vXruW2226rst0wDD7//HN69+5NSUkJ\nixcvJjs7m61bt1JYWEhaWhpZWVlkZWXZMw/9+DnBCc5wholPT2QnO2lPexJJ5OOPP2bFihV8+umn\nHD58mA4dOjB48GB+85vf0KFDh6DXHjRoEOvWravSpx/96EesWbPm4rwRIk0sEHzXJ9wMJ+SuXOs7\nKyurXv20LMs+X11VrGMeavHKmgLxpl7Y8vjx45w/f96+jrS0tLDaWZZlz7r+n//5H1auXMknn3zC\n/v37SUhIoE+fPjzzzDOkp6fbbSZNmsRrr71W5VzdunVjx44dQeeuWK4m8P5+8sknYd9vAdavX89/\n/Md/8OWXX9KqVSvGjh3Lb3/7W+Li4sJ/g6RRPf30003dBRGpgcaoSOTS+BRpHhReizRD06dP55Zb\nbgnaFghn8vLymDZtGkOHDmXGjBm0atWKDz74gKlTp7Jp0yYWL17MAQ6QRx5llIc0rTNac+DbP3HE\n8chjj1B4tpAxY8aQnp5OXl4eCxYsYPXq1WzZsoWkpCT7dQ3DoHPnzsyZMydoRmmnTp0uwTshcvkJ\nBLx1Cb4zMjKwLKvOdb0rbq8ry7LweDx27ee6ME2zznW9Gyv49nq97N27136enJxMTExMWG3dbre9\nSOPvfvc71q9fz5gxY+jVqxf5+fksWLCAjIwMNm7cSI8ePex2MTExvPrqq0H3wNatWwPfvY+hPkDw\ner08+uijnD9/Pqz77ZYtWxg6dCg9evRg/vz5HD58mHnz5rFnzx5Wr15d5/dKGkdGRkZTd0FEaqAx\nKhK5ND5Fmoc6h9eGYQwAHgUygY7ATyzLyv52XxTwLPBjIBU4D3wIzLQs61iFc7QFFgJ3AH7gbeCX\nlmUVNehqRCQs/fv35+677w65r0OHDmzbto3u3bvb26ZMmcLkyZNZunQp458Yjz+1+oXqiiji3vn3\nMqb/GJJJtrcPGzaMW2+9lYULFzJ79uygNq1bt2bChAkNvCoRqUnFmdB15ff77Zrbgdnc4db6rk/w\n7ff7cbvduN3uOretHHyHG4I7HA5M02T//v3268bExHDttdfWqc9QXi5kxowZrFy5Muj9Hjt2LD17\n9mTOnDksW7bM3h4VFRXyHhiYyV3T4qBz586lX79+REdH26Vaqrvf/vrXv6Zdu3asXbvWnmmdnJxM\nVlYWH374IUOHDg3rWkVERERERC6V+sy8jgO2AIspD50ragHcBPwn8D9AW+APwP8H9K5w3OvAVcAQ\nwAUsBf4E3FuP/ohIPRQWFhIbG1ulLm1CQgIJCQlVjh81ahRLly5lw84N9E79bjgfyyv/XKpjakd7\nW8/+PdnFLuKJpx3ldWIHDBhAu3bt2LlzZ8j++Hw+SktL9dV1kQhkmiamaeJ0OsOehRzg9/vrVNO7\n4vOaQtuaXq++wbfX62X//v12re+0tDT27dsX1szv0tJS4LtFGvv06VPl/GlpafTs2TPkfdCyLIqK\nimjZsqW9ze12B70H+/btAyAlJcXe1q9fP6B8kcjY2FgMwwh5vy0oKODDDz9kxowZQffZf/3Xf+Xh\nhx/mr3/9q8JrERERERGJOHUOry3Leh94H8AwDKPSvgvAsIrbDMN4CNhoGMY1lmUdNgyj+7fHZFqW\n9eW3x/wCWG0Yxq8sy8qv36WISLgmTZpEQUEBDoeDAQMGMG/ePDIzM2tsc+xYeUjdKrFV0PaZg2dS\nWlTKX07+JWi7hcV+9tvhdVFREYWFhSFXg969ezdxcXG43W6uuuoqpkyZwqxZs+o1Q1REqnr11VeZ\nPHlyk7y2aZq4XK56LWxZMfiua63vui5sCXD06FG7zEnLli3xer0cP3681nYOh4PY2FhM08Tv91cJ\nuys+P3bsGN/73vcoLS2173HFxcXEx8dTXFxM27ZtmTBhAnPmzKlSAmX48OGYpsn27dtD9sPr9eJ0\nOkPeb7du3YrX661yr3c6ndx00018+eWXdXqvpPE05fgUkdppjIpELo1PkebhUiRDbQALOPft8z7A\n2UBw/a0Pvz3mXyifpS0iF4HL5WL06NEMHz6cxMREduzYwQsvvMDAgQNZv349N954Y8h2Ho+HF3/3\nIh1SO9D1+12D9hmGgdftxevxls/OdHwXtpzkJCWUEEss8+fPx+PxMH78+KD2aWlpDB48mBtuuIGi\noiLeeustnnnmGXbv3s3KlSsb/00QaYZyc3Mvyx/sGxJ8hwq6awq/T58+TUlJCVB+X7vqqqvCfi2n\n0xnWjO+///3vHDt2jEmTJpGbmwuUB/T33nsvPXr0sBfPffnll9m0aRNvvvkmTqfTnvke4Pf7Q9b2\nDoTXoe63x44dwzAMOnbsWKVdx44d+fTTT8O+Xmlcl+v4FGkuNEZFIpfGp0jzYNRnZpLd2DD8VKh5\nHWJ/NPAZsMOyrH/9dtvjwL9altW90rHHgVmWZf0pxHkygJycnBwV5BdpZHv37qVXr17ceuutrFmz\nJuQxWVlZvPrqq8xeM5vMYVVnaJ86eYozZ84A5aFPIGhxOBx0Le7KwS8O8m//9m/86Ec/4qWXXrJn\nIDqdzqD/DZQwefDBB3nllVf4/PPP6d27d5XXExFpTD6fj02bNlFWVr4I7TXXXEPnzp3DCr/9fj+G\nYeDz+SgqKqp2xveBAwfIysoiNTWVl19+mUpfXguybNkyFi1axNy5cxk2bFiV/Z06dSI2NjZk2y++\n+IIf/vCHjB49mtdff93evnz5cu677z42btxYZcHe++67j1WrVtn3cRERERERkYbIzc0NfOsz07Ks\n3Iac66LNvP528cY3KZ9RPTWcJt8eW62HH36Y1q1bB22bMGGCFnoTaYAuXbpw11138c4772BZVpVA\nZd68ebzyyis88uwjIYNrIKgmq2VZQQu7bf2frTzxiydISUnh3//939m7d2+1fQksKPfDH/6QRYsW\nsXz5cuLj44MC7oqPiuF3qFmIIiLhOHjwoB1cR0dHk5KSgsPhIDo6usZ2fr+foqIiu53T6QwZcOfn\n5/P444/Tpk0b/vSnP9G2bduQwXjAuHHjeOWVV9iwYUPI8LryWgUBX3/9Nffccw+9evVi0aJFQfsC\nYXfgOisqLS2tNgwXERERERGpycqVK6t8c/78+fONdv6LEl5XCK47A4MtyyqssDsfSKp0vIPyxR1r\nLCw5f/58zbwWuQg6d+6M2+2usljY0qVLmTlzJlOnTmXa49PYwY6Q7R0OBy6XC5/Ph9/vt2cenj12\nloW/XEh8fDzPPfdcreGIZVl4PB7i4+MBOH78eFj1ZqE8+K48k7umv1d8ruBbpPkqKSnh4MGD9vMu\nXbpUGw5XFgiCA4tZBj6Aq1iv/8KFC/zsZz+jqKiITz/9lOuvvz7kuQIf/AXC7Hbt2lFWVkZiYiJ+\nv9++vwZqald2+PBhRo4cSdu2bVm9enWVxW87duyIZVn2+gUVHTt2jE6dOoV1zSIiIiIiIhWFmlhc\nYeZ1gzV6eF0huE4FbrMs62ylQz4H2hiGcXOFutdDKJ95vbGx+yMitdu7dy8xMTFBwXV2djZTpkxh\n9OjRLFy4kDLK2MUu/PirtE9ITCAhMcF+7vf5OX/qPC/c9QIOHLzzzjtcc801eL1ePB6PHc4E/u7x\neOyZ2lC+aBpAmzZtwr4Gy7JqrTdbHdM0Q5YxqSnwDjwUfItc3nbv3m1/4NamTRuSkpJqaVEucB8D\niImJCVkGpKysjDvvvJM9e/bw0UcfVRtcA0HBd2FhIadPn6ZDhw5VvnEWypkzZxg5ciRer5e///3v\nIet19+zZk6ioKDZv3szo0aPt7R6Phy1btjBu3LhwLltEREREROSSqnN4bRhGHJBGedgMkGoYxo3A\nGeAo8DZwE3AH4DQMI/Ab1BnLsjyWZe0yDOPvwCLDMP4dcAELgJWWZeU37HJEpCanTp0iMTExaNtX\nX33FqlWrGDFihL1t3bp1jB8/nkGDBrF8+XIAookmiSTyCR6mx/KO8fspv2fOR3Psbe4yN/858j85\nl3+OdZ+s46abbgrZn4KCAqKjo4MWY/N6vbz00ksYhsHEiRPp3r17tYF35a/d11c4C61Vx+FwVFvD\nu7bwu6aatyKNaeTIkWRnh1yeolk7ffp0UL3+9PT0sNpZlmXPunY6nSFnQvv9fsaOHcuGDRvIzs4O\nWb+/rKwMj8cT9MEhwOzZswG4/fbbg7bv27cPgJSUFHtbcXExo0aNIj8/n48++ojU1NSQfW7VqhVD\nhw5l+fLlPPnkk/bM7GXLllFUVMTYsWPDunZpfBqfIpFNY1Qkcml8ijQP9Zl5fQvwD8rrU1vAi99u\nfw34T+DOb7dv+XZ7oJb1bcC6b7f9FFgIfAj4gbeAX9ajLyJSB+PGjSM2NpZ+/fqRlJTE9u3bWbRo\nES1btuS5554Dymu/jhw5EtM0ufvuu/nrX/9qty+lFH8vP51v6Gxvmzl4Jp4yT9DrPP/T5/nmi2+Y\nNHkS27dvZ/v27fa+li1bctdddwHlXyMJfL0kLS2NkpIS/va3v/H555/z4IMPMnDgwLCvzbKsoCA7\nVOBdXfhdsdZsXQXqe4eqI1sbh8MRMuiuKfAO/F2kLh566KGm7kLE8fv97N69235+9dVXVym1UR2P\nx2PX+q/44VtFjzzyCKtWrWLkyJGcOnWKFStWBO2fOHEi+fn53HzzzUyYMIFu3boB8P777/Pee+8x\nfPhwRo0aFfSh2vDhwzFNM+ieOmnSJHJycrj//vvZtWsXu3btsvdVvN8CPPvss/zgBz9g4MCBZGVl\ncfjwYV588UWGDRvGD3/4w7CuXRqfxqdIZNMYFYlcGp8izYMR+KpsJDMMIwPIycnJUc1rkQZYuHAh\nK1asYM+ePVy4cIH27dszdOhQZs2aZc/WW7t2LYMHD672HI899RhDZw3FTXmgcn/K/RimwZK9S+xj\nJqVM4sTBEyHbJycnk5eXB8D+/fuZOXMmX3zxBfn5+ZimSffu3ZkyZQpTpkxprMuuld/vrzKLu7qg\nu/LfKy5WeSkFZnvWp9SJiMCBAwfsmcwul4vevXuHNT4qL9JYXXh92223sW7dupD7oPyDr/PnzzNt\n2jQ2bNjA0aNH8fl8pKWlce+99zJjxgwcDgcej8cuqdSjRw9M02Tbtm32eXr06MGhQ4dCvkbF+23A\n+vXreeyxx8jNzSU+Pp5x48bx29/+NuzgXkREREREpDYVal5nWpaV25BzKbwWkToro4xD3/4p47sZ\nx3HE0ZnOXM3VOHE2YQ8vnUDwHaqMSW3hd1ME34G6uvUJv8NdxE4k0pWWlrJp0yZ7DHbr1o0OHTqE\n1bakpASv14tpmrRo0eKSlP/x+/322gAVBer1OxwOlSESEREREZGI0ZjhtabgiUidRRNNGmmkkkoR\nRfjwEUUULWlZe+MrjGmauFyuamdf1iQQSFVetDKc8Lu+HzxalhU0k7MuDMOoNtiuHH5XDsa1sKVE\nkr1799rBdevWrcMOrivW14+Ojr5kgbFpmkRHR2NZlv0wDEPjSkREREREb7PEBwAAIABJREFUrngK\nr0Wk3kxM4onn3Xff5Sc/+UlTd+eyEwikoqOj69zW5/NVCbjDCb8bGnzXd2HLwAzR+oTfCugaTmP0\nO2fPnuXkyZP28/os0thUJXgMw9AM6yuQxqdIZNMYFYlcGp8izYPCaxFpsJUrV+qHhkvM4XDgcDjq\nFXxXV9oknPC7vvx+f72Db4fDUWtJk+rCbwV95TRGy4VapLFly/C+MVKxxn19xp1IdTQ+RSKbxqhI\n5NL4FGkeVPNaRETCYllWjQF3xX2Vj6lcq/dSqTiDO1QAXlP4reD7ynPo0CH27t0LlC962rt3b5zO\n2uvzh7tIo4iIiIiIiKjmtYiINIHAYo/1KZdgWVbIRSsrlzQJFX43JPj2+Xz1bl/bbO/qwu+mKCch\ntSsrK2P//v3285SUlLCCa8D+xoBpmmG3ERERERERkYbTb9giInLRBRZ7dDqdxMbG1qmt3+8PGXCH\nE34HyjzUR+CcpaWldW4bTuAdKvx2OBz17q/UbO/evfYHGfHx8XTs2DGsdoFvFMClXaRRRERERERE\nFF6LiEiEM00Tl8tVr1INgeC7usC7pvC7IWW1PB6PHXjWRWB2e13DbwXfNTt37hwnTpywn6enp4cV\nQluWZX94oVn1IiIiIiIil55+CxORBps0aRJLlixp6m6IVNHQ4Lu6Gt61zQSvb/BtWVaDgu/qypj8\n+te/Zv78+dWG36Zp1qu/l4PKizR27NiRVq1ahdVWizTKpaB/Q0Uim8aoSOTS+BRpHhRei0iD3X77\n7U3dBZFGZ5om0dHR9QotfT5ftTW8awu/68uyLNxut12fuaJu3bqxZ8+eatuaplmljEnFxS6rm+19\nOQTfx44dsxdbdDgcpKSkhNUu8H4CuFyuiL9OuXzp31CRyKYxKhK5ND5FmgejIV+LvlQMw8gAcnJy\ncsjIyGjq7ohcttauXcttt91WZbthGHz++ef07t2bkpISFi9eTHZ2Nlu3bqWwsJC0tDSysrLIysqq\nEuCUUYYXL1FEEU15yPfxxx+zYsUKPv30Uw4fPkyHDh0YPHgwv/nNb+jQoUO1/Tt//jzp6emcOnWK\nt956i7vvvrtx3wCRy0BtC1jWFIY3BYfDEbKGdzjh98WuH+12u9m4caNd6zo9PZ2rr746rLalpaV4\nPB4MwyAuLq7Ofd28eTNLly7lk08+Yf/+/SQkJNCnTx+eeeYZ0tPT7eMmTZrEa6+9VqV9t27d2LFj\nR9A2y7LsWf2GYWAYBvn5+fzud79j06ZNbN68mcLCQj755BMGDhxY5Zxer5dnn32WZcuWceTIEa6+\n+moeeOABZs6cqbIzIiIiIiLSaHJzc8nMzATItCwrtyHn0sxrkWZo+vTp3HLLLUHb0tLSAMjLy2Pa\ntGkMHTqUGTNm0KpVKz744AOmTp3Kpk2bWLx4MR48HOUohzhEIYX2OdrQhs505rHHHuPs2bOMGTOG\n9PR08vLyWLBgAatXr2bLli0kJSWF7NeTTz5JaWmpFkSTZq2+tZUty7IXF6xr+B0Id+vD5/Ph8/ko\nKyurc9uKAXddwm+HwxHWfWLfvn32tcXFxdVrkcaYmJh63ZPmzp3L+vXrGTNmDL169SI/P58FCxaQ\nkZHBxo0b6dGjh31sTEwMr776alC5mdatW9t/r7hoaUUOh4MdO3Ywb9480tPT6dWrF59//nm1fZo4\ncSJvv/02kydPJjMzkw0bNvDkk09y6NAh/vjHP9b5GkVERERERC42hdcizVD//v2rndXcoUMHtm3b\nRvfu3e1tU6ZMYfLkySxdupTpT0znTOoZSimt0vbct38emP8AD/R/wJ6JDTBs2DBuvfVWFi5cyOzZ\ns6u03b59O3/84x956qmnmDVrViNcpUjzEljssb7BdyDEri78rriv4jGNEXzXR6jAu+K2kpISvvnm\nG0zTxOFw0L17d/x+f1jlPwJBfEMWaZwxYwYrV64Maj927Fh69uzJnDlzWLZsWdC1TJgwIeR5vF5v\nyFIwUP7+3XDDDRw9epSkpCT+9re/VRteb968mTfffJOnnnqKp556CoCsrCwSEhKYP38+Dz30ED17\n9qzXtYqIiIiIiFwsCq9FmqnCwkJiY2OrfFU8ISGBhISEKsePGjWKpUuXsnrnam5OvdnefizvGN9s\n/oZbx95qb0vpn8IXfMG/8C84cQIwYMAA2rVrx86dO0P2Z9q0adxzzz3079+/3ovdiUhon376Kf37\n9692v2EY9qKWsbGxdTp3xVnB1ZU0qS78DiyGWB+B1ygtrfpBmmVZHDx40N7XqlUr+95TMeQPFX47\nHA5M08Q0TWJjY/H5fEH7wtWnT58q29LS0ujZs2fI+6BlWRQVFdGyZUt7m8/nCwqu9+3bBxBUtzsu\nLg6g2oA74J///CeGYTBu3Lig7ePHj+fFF1/kL3/5i8LrJlLb+BSRpqUxKhK5ND5FmgeF1yLN0KRJ\nkygoKMDhcDBgwADmzZsXqEVUrWPHjgEQmxgcbM0cPJPzJ88HhdcAhRRykIN0oQsARUVFFBYWkpiY\nWOXcb775Jhs2bGDXrl3k5eU15NJEJITnn3/+ov1gb5omLpfLDr/rwu/3h1XPO1T4XdOHXOfPn7eD\na9M0ad++vb0vMMu8usUxA/WtQy1+GQi+q6vhXXkxy4rBeGDG9/Hjx6uExMXFxcTHx1NcXEzbtm2Z\nMGECc+bMqRKWDx8+HNM02b59e5V++3y+Gj8MCMwmr/zhRIsWLQDIycmptq1cXBdzfIpIw2mMikQu\njU+R5kHhtUgz4nK5GD16NMOHDycxMZEdO3bwwgsvMHDgQNavX8+NN94Ysp3H42H+7+bTIbUDXb/f\nNWifYRi0uapNyHaHOEQKKZiYzJ8/H4/Hw/jx44OOKS0t5dFHH+WRRx6hc+fOCq9FLoI33nijqbsQ\nkmmaREdHEx0dXfvBlfh8vpD1vEtKSjh37hxt2rTB5/Nx1VVX2X8PHFtd8B0dHY1hGFiWFXImc23B\nd00Mw+Djjz/myJEjTJkyha1btxIVFUVsbCwPPvggvXr1wjAM1q5dy8svv8yXX37J6tWriYqKsoPv\nwCKNNb0n1bn++uuxLIvPPvuM5ORke/u6desAOHLkSJ2vSRpHpI5PESmnMSoSuTQ+RZoHhdcizUjf\nvn3p27ev/fyOO+7gnnvuoVevXjz++OOsWbMmZLuf//znfL3ra2avmV2lXuzSfUs5fOgwmzZuCppx\naNeKLYYDuQeYPXs2d955J927d+fcuXN2YPXcc8/h9Xp5/PHHL+q1izRngdm1VxKHw4HD4agSfO/e\nvZs2bco/UGvRogW33HJLlftWxbA7EIAHHoFQPC4uLuTM7/o6cOAAL774Ij179mTAgAGcPn0aKK+D\nXVHXrl1p0aIFixcv5k9/+hO33347hmHgcDhYs2YNpmlSWlpKTExMldeoaeb18OHDSU5O5le/+hWx\nsbH2go1PPPGEXSNcmsaVOD5FriQaoyKRS+NTpHlQeC3SzHXp0oW77rqLd955B8uyqszqmzdvHq+8\n8gqPPPsImcNClxZxu92UlZXZX0uv6OAXB1n41EI6duzIkCFDePfdd+19p06dYs6cOTzwwAOsWbOG\nmJgYdu3aBcA333zDl19+aYfcFR8ul8ueISkiElBYWBg0gzg9PT3kAo2hFmIsLi7G5/PhcDhq/EWo\npvImlUudBLadPHmSxx9/nPj4eJ5++ula711jxoxhyZIlbNy4kdtvvx3Lsuxz1Vd0dDRr1qxh7Nix\njB49GsuyiImJ4fnnn+eZZ54JqrUtIiIiIiISKRReiwidO3fG7XZXWSxs6dKlzJw5k6lTpzL18al8\nwzch21cXqFw4cYHlzy0nLi6OX/ziF1VmSK5atYq2bdvSuXNndu/eDWCXDdm2bRuWZdGuXbtqg55A\niF0x0K7p4XK5iImJwel0KvgWuQJ9881396j27dvTtm3bsNoFZlYDtZYwCQTfoWY+h3LhwgVuvfVW\n3G43//jHP+jSpUtY4Xfr1q0pKCjANM0qM6pDBfLh6N69O1u3bmXnzp2cPXuWHj16EBMTw/Tp0xk0\naFC9zikiIiIiInIxKbwWEfbu3UtMTExQcJ2dnc2UKVMYPXo0Cxcu5BSnqm3//u/fZ/jDw4O+hn/h\n9AXe+NUb+L1+pj08jVatWlVpd+bMGU6cOMETTzxRZd/rr78OwPz586ssMBYQWFCtoKCgTtdrGEaV\nsDvc8Ls+i9KJNLVHH32UefPmNXU3Lqr8/HwuXLgAlIe7Xbp0CaudZVn2t0ZcLleVRRIboqysjDvv\nvJM9e/bw0UcfVVmosTqFhYWcO3eO6667jvT0dPx+v/3w+XxVZo0HhPuhXPfu3e2/r1mzBr/fzw9/\n+MOw2krjaw7jU+RypjEqErk0PkWaB4XXIs3IqVOnSExMDNr21VdfsWrVKkaMGGFvW7duHePHj2fQ\noEEsX74cgEQSaUELiikOan8s7xht27clLT3N3lZaXMrM22ZSeq6Udf9Yx/e+9z27rEjFh8vl4uTJ\nk0GzDffu3cvKlSv58Y9/zHXXXVevhdxqEwirQpU5qY1hGCFnc4cTflcXOIlcbNdee21Td+GiCtw7\nApKTk8OeGe12u+2SSY354ZTf72fs2LFs2LCB7OxsevfuXeWYsrIyPB5PlZIds2fPBuD2228HysN4\n0zTZt28fACkpKSFfs64zsktKSnjyySfp1KlTlcV05dK50senyOVOY1Qkcml8ijQPhmVZTd2HWhmG\nkQHk5OTkkJGR0dTdEblsDRkyhNjYWPr160dSUhLbt29n0aJFREdHs379eq6//noOHjxIr1698Hq9\nzJs3L2jG9ClO4erlIuWG74KT+667D9M0WZK3xN42+yez2ZC9gYmTJ/KjQT8K6kPLli256667qu3j\n2rVrue2223jrrbcYNWoUZWVlQTW1q3tUPqYhC6tdLKZpVlu/u7YZ3wq+Raq3Z88eDh8+DEBMTAy9\ne/cOK8j1+/0UFRXZ7ZxOZ6P1afr06fzhD39g5MiRjBkzpsr+iRMncuDAAW6++WYmTJhAt27dAHj/\n/fd57733GD58OG+//bZdzgTKZ0ybpsn27duDzjV37lwMw+Cbb77hjTfe4IEHHrAD7v/9v/+3fdy4\ncePo1KkTPXr04MKFCyxevJh9+/axZs0alQ0REREREZFGk5ubS2ZmJkCmZVm5DTmX0hCRZmTUqFGs\nWLGC+fPnc+HCBdq3b8/o0aOZNWsWqampAOzbt88uw/HQQw9VOcf/eup/BYXXhmFApW+q532Vh2EY\nvL74dV5f/HrQvuTk5BrDa/uc3/5vTExM2DMoK/L7/WGH3qWlpUHHNmRRtNr6VFJSQklJSZ3bOhyO\nGhevrOlR3/q4IpeDoqIiO7iG6hdpDKW0tBQoH1+NGVxD+bdaDMNg1apVrFq1qsr+iRMn0qZNG+68\n804+/PBDli1bhs/nIy0tjTlz5jBjxgxM06S0tJTARAPDMEKWBvnNb34TdN9csmSJ/feK4fX3v/99\nlixZwp///GdiY2MZOHAgb7zxBjfccEOjXruIiIiIiEhj0cxrEamzPezhAAfwUHV2czTRpJFGZzo3\nQc8ah8/nq/eM74qzJCNFVFRUtYtX1hR+u1wuBd8S8bZs2cK5c+cASEhICDuI9Xq99gdJLVq0aNRa\n143Jsizcbne195ZAuZNI7b+IiIiIiDQ/mnktIk0qjTRSSOEYxzjNafbs2kNatzSu4iqSSMLk8g48\nHQ4HLVq0oEWLFnVu6/V6Q87mDif89vv9F+FqsBfRDJRHqAun01mvGd8ulyvsxePk4tu1a5ddluJK\ncuLECTu4NgyDtLS0WlqUsyzLnnXtdDojOvgN1Nm3LIv/n707D4+qvvcH/j5nlsxkheyAYcmiiBBL\nQhFUwmLENmJURCBXLo+Uhl6RB5W44NWqRStgqNgL7cUqEKkUa91+oYL1urA1YAoUCgGEbGwhgQRI\nMllmPb8/0nPMMEtmJgmZZN4vHp5kzsz3LAPfCbznM5+vxWJRXicEQYBKpfLrcyfP9NX5SdRXcI4S\n+S/OT6LAwPCaiHyiggo3/PvXL5/9JQoLC3v6lPyCWq2GWq32Kfg2m81eVXy3D8i761M0ZrMZZrMZ\nBoPB67EdVXW7uk+j0TD47mLPPvtsn5ujVqsVpaWlyu3BgwdDr9d7NLb9Io3dsShsdxAEoctbm5B/\n6Ivzk6gv4Rwl8l+cn0SBgeE1EXXa2rVre/oU+gSNRuNzONU+8Pa24ru7mEwmmEwmpYe6p+Q2CK5C\nb3ftThjuOdcX5+jp06dhMpkAAEFBQR6vNi/3w5fH8Y0S6ml9cX4S9SWco0T+i/OTKDAwvCaiTvM0\nNKLuo9VqodVqERYW5tU4uZ+uq8Ur3YXfcgDY1SRJ8jlYF0XR54pvtbrv/kjsa3O0ubkZZ8+eVW4n\nJyd73D5D/nslimKf/jOn3qOvzU+ivoZzlMh/cX4SBQb+r42IKIDJbRN8aZ0gV7B6W/Hd2toKi8XS\nDVfTdk6tra1KP2NviKLoto+3u/CbfYevr9LSUqVVTv/+/RETE+PROLn/OwDodDpWXRMREREREfk5\nhtdEROQTURSh0+mg0+m8Hmuz2dy2MnEXfndn8N3S0oKWlhavx6pUqg5Db1ftTkSxdy9wer3V1tbi\n8uXLALxfpFGuuvb3RRqJiIiIiIioDcNrIuq0lStX4rnnnuvp06BeRBRF6PV6jxfYa89qtbpdvNJd\nxbfNZuuGq2k7p+bmZjQ3N3s9Vq1W+1TxrdVqPQ6++8octdlsdos03nDDDQgJCfForNlshs1mU3qq\nE/mLvjI/ifoqzlEi/8X5SRQYGF4TUaf5EtgR+UqlUiE4OBjBwcFej7VYLD5XfHdX8C23smhqavJ6\nrNzrvKPwu7q6GpcuXbILvntjy4wzZ84oLWG0Wi2GDBni0Ti50l8ex2p38if8GUrk3zhHifwX5ydR\nYBDknpH+TBCENAAHDhw4gLS0tJ4+HSIiCkBms9nrim/5cf74s9aT0NtZu5OeqlpuaWlBcXGx8lze\nfPPNiIuL83isxWKBKIoIDg7ulcE9ERERERFRb3Hw4EGkp6cDQLokSQc7sy9WXhMREXlAo9FAo9Eg\nNDTUq3GSJDkE3960O+kuJpMJJpMJjY2NXo2TF/n0NPxuf1uj0fh8vmVlZUpwHRER4XFwzUUaiYiI\niIiIei+G10QBZOfOnZg8ebLDdkEQsHfvXowdOxYtLS3YsGEDCgsLceTIERgMBiQnJ2PBggVYsGCB\n8nF7K6yoQQ0u4zIssEANNWIRixjE4NtvvsXmzZuxZ88enDt3DvHx8ZgyZQpeffVVxMfHK8f19FhE\nvZncY1mr1SIsLMyrsZIkuQy3O6r4NpvN3XI9kiShtbVVad/hDVEU3S5e6ariu6mpCbW1tcp+UlJS\nPD5XuV2IWq2+ros07t+/HwUFBdixYwcqKysRFRWFcePG4bXXXrM7/3nz5uG9995zGD98+HAcO3ZM\nuS1JEiwWi9K+RhAEqNVqXLx4EW+99RaKi4uxf/9+GAwG7NixAxkZGQ77lCQJb7/9Nt5++22UlpYi\nJCQEaWlp+OUvf4nx48d3w7NARERERETUOQyviQLQk08+iTFjxthtS05OBgCUl5dj8eLFyMzMRF5e\nHsLDw/Hll19i4cKFKC4uxoYNG1Dx718mtFWF1tfWIyI6AudwDnroseS5JTBcMeDhhx9GSkoKysvL\nsWbNGnz++ec4dOgQYmNjPT4WUSCTq5yDgoK8Hmuz2ZSAu6qqCqGhoR5XfMuVyl3NZrN5HXxLkoS6\nujrYbDao1Wr0798f9fX1HrU7EUURkiRBFEWfnsPOWLlyJYqKivDwww8jNTUV1dXVWLNmDdLS0vDd\nd99hxIgRymN1Oh3Wr19v114mIiJCuX6TyQSr1epwDIvFgiNHjiA/Px8pKSlITU3F3r17XZ7T008/\njdWrV2Pu3Ll4/PHHcfXqVaxbtw4TJ05EUVGRw88Fuj5qa2sRHR3d06dBRC5wjhL5L85PosDAntdE\nAUSuvP7oo48wffp0p4+pq6vDxYsXcfPNN9ttnz9/PgoKCvD5qc+BRPsxr2S/glcKX1FuH91zFA/d\n+RCGYZiybffu3Zg4cSJefPFFLFu2zKNjnTp1ComJ1xyMiLyWnZ2NwsJCjx8vL3DoquLbXfjtLGTt\njKamJqW1iSiKiI6O9uhTGaIoIj4+HoIgwGAwwGw2d1jx7azdia+fANm3bx/GjBkDtfqHOoHS0lKM\nHDkSM2fOxKZNmwC0VV5//PHHaGhocNiHXDnubrHQpqYmmM1mxMXF4bPPPsPMmTPx7bffOlReW61W\nhIeH47777sMHH3ygbK+srERiYiKeeOIJrF692qdrpc7xdn4S0fXFOUrkvzg/ifwXe14TUacZDAbo\n9XqHj9FHRUUhKirK4fEPPvggCgoKUHy8GGMTxyrbL5RfwE9+/hO7x468cyS+x/cIRzii0LavCRMm\nIDIyEsePH/f4WMePH2d4TdQFXnnlFa8eL4oi9Ho99Hq918eyWq0dLmDp6r5rQ1qr1QqDwaDcDgsL\n8zhMjoiIgCAIsFgsqK+vB+DbivQajcbl4pXu2p3cdtttDv21k5OTMXLkSLvXQZkkSWhqarLrqW4y\nmeyek4qKCgDAsGE/vDEYEhICADAajW4XBjWbzWhpaVE++SKLiYlRFrKknuHt/CSi64tzlMh/cX4S\nBQaG10QBaN68eWhsbIRKpcKECROQn58vvyPm0oULFwAA4dHhdtuXTlkKURQxLnucw5hKVCrhdVNT\nEwwGg0cf65KPxY+AEXWN6/mpJZVKheDgYJ/CUIvFYhdmHz9+HP3794fFYoFWq8WAAQOcLn55bXCr\n1WqV41+9erVT12M2m33uH94+6Ja/P3PmDJKSknDo0CEEBQWhsbERzc3NCA0NRUtLC/r374/Zs2dj\nxYoVDm8uZmVlQRRFlJSUOD2eu6p3nU6H2267DQUFBRg3bhwyMjJw+fJlvPrqq4iKikJubq5P10id\nx08VEvk3zlEi/8X5SRQYGF4TBRCtVosZM2YgKysL0dHROHbsGFatWoWMjAwUFRXh1ltvdTrObDbj\nN2/9BvGJ8bjxxzfa3ScIAiAANqutrSKyXaFhLWrRjGYEIxirV6+G2WzG7Nmz3Z6j2WzGW2+9hcTE\nRPz4xz/u9DUTUe+hVquhVqsREhKCq1evKq0/gLb/nISHh7scK4fack9tOXS+4YYbOmx30l1MJhNM\nJpPS9mTfvn2ora3Fvffei+LiYgBt1eB33303Bg8eDEmSUFJSgv/93//Fjh07sHbtWmg0Gmg0GqjV\nalgsFoiiiJaWFqdV8e7aiwDA5s2bMXPmTMyZM0fZlpSUhD179mDo0KFdd+FERERERERdhOE1UQAZ\nP348xo8fr9yeNm0aHnroIaSmpuL555/Htm3bnI57/PHHcerEKSzbtszhI/sFFQW4cvkKzpw5A0EQ\nIAgCVCoVRFGEKIo40noEld9VYtmyZcjOzsaIESNw+fJlJaRSqVTKV/lYJ06cwLZt23zuNUtEvZvN\nZsOpU6eU2wMGDHAbXANQQl65whloa6nR0euIJEkuq7k7andiMpk8vqbq6mp88MEHSEpKwrhxP3xS\n5YEHHrB73JgxYxAbG4vCwkL83//9n13v6vXr1wNoC8WdhdcdrWMSGhqKW265BbfffjvuuusuVFdX\nY8WKFbj//vuxZ88eREZGenw9RERERERE1wPDa6IAl5SUhPvvvx+ffvopJEly6NGan5+Pd999F0t+\nvQTp9zhvLfLNpm+Qfn86JEmCJEl21X//OvwvPPOLZ5CUlIQnn3xS6dl6LUEQsHnzZrz77rt44okn\nMHToUJSVlSnh9rVBd/tt1360nojsrV+/HvPnz+/p0/BYVVUVmpqaALRVY7fv8eyOzWZTAmWtVuvR\nG2CCIECr1UKr1SIsLMyr85QXVHTXx9tkMqG6uhovv/wywsLC8NRTT0Gr1bptRZKZmYnCwkL885//\ndFh4EYDdIpCestlsyMzMxOTJk/Hb3/5W2X7XXXfhlltuQX5+PpYvX+71fqnzetv8JAo0nKNE/ovz\nkygwMLwmIiQkJMBkMjksFlZQUIClS5di4cKFWPz8YhzDMafjz5ScwZ2z74TNZoPNZoPVaoXNZsPl\nqst467G3EBYWhjfffNPt4m9bt27FmjVr8NBDDyEnJ0dZYM0TgiA4BNztg21X96nValZ3U0A4ePBg\nr/mHvclksnuTa9iwYdBqtR6PlSQJoih6PKYzBEGATqeDTqdz+ZiGhgZMnDgRFosFe/bswU033QTg\nh6DdVejdr18/mM1mREVFwWKx2P3WaDRen+vOnTtx9OhRrF692m57cnIybr75Zvz973/3ep/UNXrT\n/CQKRJyjRP6L85MoMDC8JiKUlZVBp9PZBdeFhYXIzc3FjBkzsHbtWhhhxAmcgA2OPVWfeucph22N\ndY1YnrUcIkRs374dCQkJsFqtSvgif2+1WvHll19i+fLlyMzMxPPPPw+LxeLV+csf+/dlUTVRFF0G\n264qveWvDL6pt/jd737X06fgsfLycmXhwZCQEAwcONCjcVarVXkNCAoKcvgUSU8wGo247777UFpa\niq+//loJroG21x5XwbfBYMDVq1cxbNgw3HLLLR4fz91rUk1NDQRBcLqoo9ls9vp1l7pOb5qfRIGI\nc5TIf3F+EgUGhtdEAaS2thbR0dF22w4fPoytW7fi3nvvVbbt2rULs2fPxqRJk/D+++8DAIIQhDjE\n4QIu2I2/UN52e0DiAGVba3Mrfpn1S1y5cAW7duzCyJEjXZ7Trl278PTTT2PSpEn461//Co1GA0mS\nlGDbWeB9bfh97VdvtG8z4C05+HZV8a3RaJxWfzP4JnKuoaEB1dXVyu2UlBSPQmi5fQfww6KPPc1m\ns2HmzJnYt28fCgsLMXbsWIfHGI1GmM1muzcOAWDZsmUAgKlTp9rIo5XeAAAgAElEQVRtlyvSXbVR\ncddC6cYbb4QkSfjggw/s9nvw4EF8//33+K//+i/PLoyIiIiIiOg66vn/3RHRdTNr1izo9Xrcfvvt\niI2NRUlJCd555x2EhoYqvU7PnDmD7OxsiKKI6dOn48MPP1TGG2GELdWGG0bdoGxbOmUpRFHExvKN\nyrY3/uMNnPzHScybPw8lJSUoKSlR7gsNDcX999/f4bEAIDU1FaNGjfLqGuW2Jc6CbXeht8VisevV\n7emxTCaTT+F3R1Xd7qrA/aGilKirSZKEkydPKrfj4uLQr18/j8a2f+NKXqyxpy1ZsgRbt25FdnY2\namtrsXnzZrv7H3nkEVRXV2P06NHIycnB8OHDAQBffPEFtm/fjqysLDz44IN2ry9ZWVkQRdHuNRUA\nVq5cCVEU8f3330OSJGzatAm7d+8GALzwwgsAgLS0NNx999147733UF9fj6lTp6Kqqgpr165FSEgI\nnnjiie58OoiIiIiIiHwidLQyvT8QBCENwIEDBw4gLS2tp0+HqNdau3YtNm/ejNLSUjQ0NCAmJgaZ\nmZl46aWXkJiYCKCtL+qUKVNc7mPpy0uR+VImjGircnx02KMQRAEby34Ir3827GeoOVPjdPyQIUNQ\nXl7u0bFefvllvPTSS15fp69sNpvPFd/eBt+d0VHQ7a7fN5G/qqqqUsJrlUqFsWPHehRES5KEpqYm\nSJIErVbrN+H15MmTsWvXLpf3W61W1NfXY/Hixdi3bx+qqqpgtVqRnJyMOXPmIC8vDyqVChaLRQmw\nR4wYAVEUcfToUbt9hYaGOn1TSxAEu3YgRqMRq1atwgcffICKigpotVpkZGRg2bJlSE1N7aIrJyIi\nIiKiQHfw4EGkp6cDQLokSQc7sy+G10TkNRNMOIdzOIuzaEELXsl+Ba8UvoIwhCEBCRiEQVDB9cfX\n+6L2gbe7im9n4fj1eh2WF7b0ZCFLZ1+p98rOzkZhYWFPn4ZLJpMJxcXFStCalJSEhIQEj8a2trbC\nbDZDEASEhIT0yU8myG+sXduXun3P/r543YHC3+cnUaDjHCXyX5yfRP6rK8NrluERkde00CIRiRiG\nYWhBC55b9BwykIFgBPf0qfUYORDWarVej3XXv7ujim9vgu/OLGzZPvjuqOL72q8MvnveokWLevoU\n3KqsrFSC2eDgYAwaNMijcf64SGN3EEURWq1WWRMAaJuTffV6A42/z0+iQMc5SuS/OD+JAgMrr4mI\nejFPQ29nFd/XS/vqUG8rvrmwZd/X2NiIAwcOKLdvvfVW9O/f36Oxzc3NsFqtUKlUCA4O3DfPiIiI\niIiI/Akrr4mICACU8NfbPr+SJHlV6X1t+O0NeWFLX8jBt7uA29V9DL79nyRJOHXqlHI7JibG4+Da\nbDYrizTqdLpuOT8iIiIiIiLqWQyviYgCkNwGxJdFHG02m089vs1ms9cLW8rBty/ht7v+3R0tcsl2\nDNdHTU0NGhoaALS9UZGUlOTROEmSYDS2LRqr1Wr5RgUREREREVEfxfCaiDrts88+wwMPPNDTp0HX\niSiKEEURGo3G67Fy8O1txbfFYvE6+JZDc190tHilu4pvfwy+/XGOWiwWlJWVKbeHDBnicQW1yWSC\nJEkQBMGnPvNE/sQf5ycR/YBzlMh/cX4SBQaG10TUaVu2bOE/GsgjnQ2+ve3xLX/v7foO8ji5utdT\ngiB0quK7u/jjHK2srFQWW9Tr9UhISPBonNVqVSrx+/IijRQ4/HF+EtEPOEeJ/BfnJ1Fg4IKNRETU\n5127WKWz266qv6/Xz0m5lYs3ld7tw+/exGAwYP/+/crtUaNGISoqyqOxXKSRiIiIiIjIv3HBRiIi\nIi/IAa8vLSY6WrzSXfjtTfAtSRLMZrNSjeyN9j3Mva347ongu7S0VPk+Ojra4+C6/SKN3i5SSkRE\nRERERL0Pw2siIiI3fA14JUlyWfEth7DuKr69PZavwbcoim5bmbir/vZlocSLFy/i6tWrANpCd18W\nadRoNL2u2pyIiIiIiIi8x/CaKIDs3LkTkydPdtguCAL27t2LsWPHoqWlBRs2bEBhYSGOHDkCg8GA\n5ORkLFiwAAsWLLALqyRIaEELrLBCDTX00AMAvvnmG2zevBl79uzBuXPnEB8fjylTpuDVV19FfHy8\nw/GLiorw7LPP4p///CfCw8Mxc+ZMvP766wgJCem+J4Oom7Wvhva2SlgOvjtqaeLqPm/YbDalh7S3\n5ODb0/BbEAR8//33sNlsEEURQ4YMgV6v9+hY7Rdp7A1V1/v370dBQQF27NiByspKREVFYdy4cXjt\ntdeQkpKiPG7evHl47733HMYPHz4cx44ds9smSZJSzS8IAgRBQHV1Nd566y0UFxdj//79MBgM2LFj\nBzIyMuzGnj59GsOGDXN5vrm5uXj77bc7c8lERERERERdjuE1UQB68sknMWbMGLttycnJAIDy8nIs\nXrwYmZmZyMvLQ3h4OL788kssXLgQxcXF2LBhA0ww4RzO4SzOogUteHPem1iycQnCEIYEJOC5557D\nlStX8PDDDyMlJQXl5eVYs2YNPv/8cxw6dAixsbHKcQ8dOoTMzEyMGDECq1evxrlz55Cfn4/S0lJ8\n/vnn1/V5IfIX7YNvb9lsNoeq7oULF2L16tUdtjyx2WxeH8tkMnkcftfU1KC2thYAoNPpEBERAYPB\n0GHoLYoizGYzRFFEcHBwr1ikceXKlSgqKsLDDz+M1NRUVFdXY82aNUhLS8N3332HESNGKI/V6XRY\nv369XZuZiIgI5Xt5sdJrK/JFUcSxY8eQn5+PlJQUpKamYu/evU7PJyYmBu+//77D9u3bt+NPf/oT\n7rnnns5eMvlo3rx52LhxY0+fBhG5wDlK5L84P4kCA8NrogB05513Yvr06U7vi4+Px9GjR3HzzTcr\n23JzczF//nwUFBTgiRefwOXEyzDCqNyfNrVtIdVGNOIYjmHe6nmYd+c8pRIbAO655x5MnDgRa9eu\nxbJly5Tt//3f/43IyEjs3LlTqbQeMmQIFixYgK+++gqZmZldeu1EfZ0oihBFERqNRtl23333IS4u\nrsOxcvDtqqrbXfjdUfBtNBpRV1en3I6JifG41YlWq4UoikpY3tHilXJbEWf3Xa/gOy8vD1u2bLF7\nA2LmzJkYOXIkVqxYgU2bNinb1Wo1cnJynO7HYrG4fHPAZrNh1KhRqKqqQmxsLD755BOX4XVwcDD+\n4z/+w2H7xo0bER4ejmnTpnlzedSFpk6d2tOnQERucI4S+S/OT6LAwPCaKEAZDAbo9XqHvrFRUVFO\nF0978MEHUVBQgM+Pf460xDRl+4XyC7jptpvsHpt4ZyIO4ABuw23QoC1AmzBhAiIjI3H8+HHlcY2N\njfjqq6+Ql5dn1yJk7ty5eOqpp/Dhhx8yvCbqAq6C0Ws5C749JYfarkLv48ePIyQkBDabDcHBwYiL\ni1Puc7ewpXxOAJSgWx4n98D2lCAIHi9k6azHtzfGjRvnsC05ORkjR460ex2USZKEpqYmhIaGKtuu\nDa4rKioAwK79h/za6Uvrl+rqanz77bd49NFHfVrMlLqGp/OTiHoG5yiR/+L8JAoMDK+JAtC8efPQ\n2NgIlUqFCRMmID8/H+np6W7HXLhwAQAQHB1st33plKUQRREby+0/rmWAAZWoRAraers2NTXBYDAg\nOjpaecyRI0dgsVgcjq3RaPCjH/0I//znP32+RiK6vtwtbHnp0iVoNBrExMRAEASMGTPG7g0rd6G3\n2WxWKsKDgoIcKsPdBd/XkiTJafsNT8itXHwJv9s/LzU1NRg5cqTdvpubmxEWFobm5mb0798fOTk5\nWLFihcPzmZWVBVEUUVJS4nB+VqvV67YvW7ZsgSRJeOSRR7waR0REREREdL0wvCYKIFqtFjNmzEBW\nVhaio6Nx7NgxrFq1ChkZGSgqKsKtt97qdJzZbMbqt1YjPjEeN/74Rrv7BEEAXHwK/xzOIQlJECFi\n9erVMJvNmD17tnL/hQsXIAgCBgwY4DB2wIAB2LNnj+8XS0R+wWq1oqysTLmdkJDgsBirHPBeW/1r\nNBphMpkgCAJCQkKctvzwpM2Jq0UuvSFJksdtTq4lB99ffPEFzp8/j0WLFqG8vBwqlQphYWF4/PHH\nceutt0IQBHzzzTf4/e9/j0OHDmHbtm12bUfkRRpd8Xaxzj/96U8YMGAAJk2a5PU1ERERERERXQ8M\nr4kCyPjx4zF+/Hjl9rRp0/DQQw8hNTUVzz//PLZt2+Z03OOPP47vT3yPZduWKR/flxVUFODA/x3A\n5brLysf75d9GlRFVUhW+L/oey5Ytw6xZszBx4kRlbEtLCwAgKCjI4Zg6nU65n4g6Z8+ePbjzzjt7\n5Nhnz55Fa2srgLY30AYPHuzROLm/tTzOVWjrruLbHUmS3FZ8uwq95a/eHuvUqVN4/fXXkZqaikmT\nJikLV86ZM8fusbfccgvCwsLw9ttv45133sFPf/pTpVf3119/DVEUYTKZnLb58Kby+tSpUzhw4ADy\n8vJ6xQKYfVlPzk8i6hjnKJH/4vwkCgwMr4kCXFJSEu6//358+umnkCTJIcTIz8/Hu+++iyW/XoL0\ne5y3FvnkN58g939znd63/fB2PPPoM0hKSsJjjz2GQ4cOKR+pv3LlCgDg9OnTGDBggN3H7w0GA3Q6\nnbI427WhORF57o033uiRf9i3tLTg9OnTyu3k5GS7SmJ35H7Wvvbg7ohcDa1Wq52+geaO3H7E0/C7\npqYGeXl5CA8Px/LlyzsMi3NycvCHP/wB+/btw09+8hMlLJervvv16+fbRbfz/vvvQxAEp4s40vXV\nU/OTiDzDOUrkvzg/iQIDw2siQkJCAkwmk8NiYQUFBVi6dCkWLlyIhc8vxEmcdDp+4dsLYbY5fpT+\nctVlvPXYWwgLC8Obb76JoKAgu0XF9Ho9JEnCyZMnkZCQYDe2vLwc/fr1w6FDhwC0BVjOFlDzpPcs\ng28KdB988EGPHLesrEzpSR0REYHY2FiPxrVv66HT6fyuMlgQBGg0Go9C9YaGBuTk5KC1tRV79uxB\nSkqKR6F3v3790NjYCK1Wq/T8lp/LrnhN27JlC2666SaMHj260/uizump+UlEnuEcJfJfnJ9EgYHh\nNRGhrKwMOp3OLrguLCxEbm4uZsyYgbVr16IOdS7Hxw+Kh8Vigc1mU3431DXgd/N+B8kqoeD9Agwc\nOFAJbOQF2BITE6FSqXDixAncddddyv4sFgtOnjyJu+++W9kmtxBoH357ylXY7cmCa/4WmhH5Ijg4\nuOMHdbG6ujqlNQYApKSkeDROkiSl6lqj0fjUEsRfGI1G3HfffSgtLcXXX3+Nm266CUDH1eQGgwFX\nrlxBQkICBg0apGyXX19dPSeevl599913KC0txWuvvebF1VB36Yn5SUSe4xwl8l+cn0SBgeE1UQCp\nra1FdHS03bbDhw9j69atuPfee5Vtu3btwuzZszFp0iS8//77AIAoRCEEIWhCk934C+UXAAADEn9Y\ndLG1uRX5s/PRcLEBu3fsxo9+9COHc5ErCadMmYKvvvoKr7/+OnQ6HSwWC/74xz+itbUV06dPR//+\n/R0qEr3p6wpACc194S707ij8ZvBNgcpms6G0tFS5PWjQILs3x9yR39wC4LSvc29hs9kwc+ZM7Nu3\nD4WFhRg7dqzDY4xGI8xms8Nzs2zZMgDA1KlT7bbLLViGDRvm9JieVmT/6U9/giAIyMnJ8ejxRERE\nREREPYXhNVEAmTVrFvR6PW6//XbExsaipKQE77zzDkJDQ7F8+XIAwJkzZ5CdnQ1RFDF9+nR8+OGH\nyvha1EKbqsWwUT8EJ0unLIUoithYvlHZ9sZ/vIGT/ziJR+Y/gpKSEpSUlCj3hYaG4v7771cWdVyx\nYgXuuOMOZGdnY8GCBTh37hx+85vf4J577sHcuXOdXofNZnO7kJq77+WP3XtKHidXgnpKEASfQm/5\nK1FvdvbsWWXBVY1G4zJsvZbNZlPmWlBQUK9u+bNkyRJs3boV2dnZqK2txebNm+3uf+SRR1BdXY3R\no0cjJycHw4cPBwB88cUX2L59O7KyspCdnW33xltWVhZEUbR7TQWAlStXQhAEnDx5EpIkYdOmTdi9\nezcA4IUXXrB7rM1mw4cffohx48Z5/OdCRERERETUUwRvg5yeIAhCGoADBw4cQFpaWk+fDlGvtXbt\nWmzevBmlpaVoaGhATEwMMjMz8dJLLyExMREAsHPnTkyZMsXlPh57+TFMe2macvvRYY/CcMWAj65+\nZLft0plLTscPGTIE5eXldtuKiorw3HPP4eDBgwgLC8OsWbPw+uuvIyQkpDOX65SzXrOeLLjmS/Dt\nK3khOU9Cb2cV30TXeuaZZ5Cfn39djmU0GvHdd98p1dPDhw9HfHy8R2NbWlpgsVggiiKCg4N79acX\nJk+ejF27drm832q1or6+HosXL8a+fftQVVUFq9WK5ORkzJkzB3l5eRBFEa2trcprz4gRIyCKIo4e\nPWq3r9DQUKfPlSAISu9w2Zdffomf/vSnWLNmDRYuXNgFV0qddT3nJxF5j3OUyH9xfhL5r4MHDyI9\nPR0A0iVJOtiZfbHymiiALFq0CIsWLXL7mIkTJ3bYYqMc5ahEJUwwoaCiAP9vzf9T7tNDjwMVBzAI\ng9zswd7tt9+uVAl2Nzng9aUdgasKb0/Cb2+Cb0mSYDabYTY7LoLZETn4dtfH21UQ3purXMm9wYMH\nX7djlZWVKcF1eHg44uLiPBrXfpHGoKCgXh1cA8C3337b4WMiIiLw3nvvuX2MTqeDyWSC1WrFsWPH\nHO4XRRFms9njN66mTp3qcxsl6h7Xc34Skfc4R4n8F+cnUWBg5TUR+cQKK6pRjcu4DAss0ECDGMQg\nFrEQ0LtDp64mSZLLim+z2ewyAJe/Xi+iKPoUeqtUKgbfBAC4cuUKDh8+rNxOT09HWFhYh+MkSUJz\nczNsNhvUajX0en13nmavJEmSXc9/uTURP21BRERERET+hpXXRNTjVFBh0L9/kXvtq6GDgoK8GisH\n3x21NHF1nzdsNhtMJpNXY2Ry8O1t+M3gu++w2Ww4deqUcnvgwIEeBdeA/SKN3s6RQCEIAjQaTU+f\nBhERERER0XXF8JqIyI+1D769ZbPZOmxr4uo+OUj05lgmk8mn8NuTnt6u7uvtrSX6kvPnz6O5uRkA\noFarMXToUI/GtX/TpLcv0khERERERERdi+E1EXXaiRMnMHz48J4+DbqGKIoQRdGnak05+HZV1e0u\n/PY2+Jb344uOFq/UaDQuq78DSXfPUaPRiMrKSuV2YmKix33lTSYTJEny+e8qUW/Hn6FE/o1zlMh/\ncX4SBYbA+t87EXWLZ599FoWFhT19GtSFOht8+xJ6WywWrxa2BH5Y5M9oNHo1Tu4X7Kqq213Lk97Y\nY7i752h5ebnyBkRoaCgGDBjg0Ti57zvQNxZpJPIFf4YS+TfOUSL/xflJFBgYXhNRp61du7anT4H8\niCiKHlfdXqujxSvd9fv2JviWF7/zZUFMuZWLL+F3TwXf3TlH6+vrUVNTo9xOSUnxKISWJAmtra0A\n4HNrHKK+gD9Difwb5yiR/+L8JAoM/J8iEXXa4MGDe/oUqI+QA15fwm9P2py4CsS9IUkSzGazUjHs\njfY9zN2F3s6+dqYXdHfNUUmS7BZpjIuLQ0REhEdj27eY4SKNFMj4M5TIv3GOEvkvzk+iwMDwmoiI\n+gRfK5slSepwYUt3X709lq/BtyiKHQbdrsLv7loEsaqqCgaDAUDb85+UlOTROEmSlFYvWq2WizQS\nERERERGRUwyviYgooLWvhva2AlhuP+Jr+O0Nm80Gk8nk1RiZ3L/cVR9vV6G3u+DbZDKhoqJCuT1s\n2DCPK+aNRiMkSYIgCD63mCEiIiIiIqK+j+E1EXXaypUr8dxzz/X0aRBdd4IgQKPR+LywpS+hd/t2\nG54qKCjA3LlzvT5HAC77d587dw51dXUQRRFhYWEICQlBU1OTXfjtrPd1+0UadTodF2mkgMefoUT+\njXOUyH9xfhIFBobXRAFk586dmDx5ssN2QRCwd+9ejB07Fi0tLdiwYQMKCwtx5MgRGAwGJCcnY8GC\nBViwYIFShWmFFRdwAXWoQ1lzGf6FfyH2378uVl/EW2+9heLiYuzfvx8GgwE7duxARkaGw7EtFgt+\n/etfY9OmTTh//jwGDRqEn/3sZ1i6dGmPLW5HdD2IoqhURHvLZrN5XfGt1Wp9Cr6d9QVvaWlBeXm5\ncjssLMyu97XM1eKVcrV7a2uryx7fvd3+/ftRUFCAHTt2oLKyElFRURg3bhxee+01pKSkKI+bN28e\n3nvvPYfxw4cPx7Fjx5TbcpW//OcnCAJUKhUuXbrk8estAJjNZuTn5+OPf/wjKisrERERgTFjxuAP\nf/gDBg4c2MXPAnmiubm5p0+BiNzgHCXyX5yfRIGh9//vkIi89uSTT2LMmDF225KTkwEA5eXlWLx4\nMTIzM5GXl4fw8HB8+eWXWLhwIYqLi7FhwwaUoQyVqIQZbdWT0381HVX//hWEINR+X4v8/HykpKQg\nNTUVe/fudXkujzzyCD7++GPMnz8f6enp2LdvH375y1/i7NmzWLduXfc9CUS9mCiKXrXbaD+X2gfe\n7ha5dHafJEmQJAkXLlxQ9hcREYGQkBCnx702+FapVEpYL7cOcUYOt10F2+7u85c3vVauXImioiI8\n/PDDSE1NRXV1NdasWYO0tDR89913GDFihPJYnU6H9evX2z0f8sKXkiTBZDI5bTNjsVjwr3/9y+PX\nW4vFgqysLOzbtw+5ublITU3FlStX8N1336G+vp7hdQ/51a9+1dOnQERucI4S+S/OT6LAwPCaKADd\neeedmD59utP74uPjcfToUdx8883KttzcXMyfPx8FBQV4+MWHISS6/pi/EUZoxmiwv24/RvcbjY8/\n/thlmLJ//3785S9/wcsvv4yXX34ZALBgwQJERUVh9erVWLRoEUaOHNmJKyWia8lV0L70mrZarTh3\n7hzq6+thtVohCAJGjRoFlUrVYfgtSZJSUS3fdqUzC1u272HuS4/vrpKXl4ctW7bYVZHPnDkTI0eO\nxIoVK7Bp0yZlu1qtRk5OjsM+JElCa2ur2+dq9OjROHv2LOLi4vDZZ5+5Da/ffPNN7N69G3//+9+R\nnp7u45URERERERFdPwyviQKUwWCAXq93CGuioqIQFRXl8PgHH3wQBQUF+Mfxf2Bs4lhl+4XytgrM\nAYkDlG26EB1qUINa1Lo9h927d0MQBMyaNctu++zZs/Gb3/wGf/7znxleE/kRSZJw7tw5JfgeNmwY\nBg0a5NHYpqYmmEwm2Gw2qNXqDluetL/t7Tn6GnyLouhxxfe14fi1C1uOGzfOYf/JyckYOXIkjh8/\n7vS8m5qaEBoaqmwzmUx2wbW8QOawYcOUbXLVu7tKdnn///M//4Pp06cjPT0dVqsVJpMJer3ew2eH\niIiIiIjo+mN4TRSA5s2bh8bGRqhUKkyYMAH5+fkdVuHJbQLCo8Ptti+dshQA8F6lY8/WSlS63afR\naAQAh/AkODgYAHDgwAG344nIM7W1tYiOju70fioqKpRQWK/XIyEhwaNxVqtVCa11Op1Xfb4lSfKp\nzYn81Rs2mw0mk8mrMTI5+O4o/L5w4QJuueUWtLS0QK1WQ5IkNDc3IywsDM3Nzejfvz9ycnKwfPly\nhzcXs7KyIIoiSkpKnJ6Ds9YismPHjqGqqgqjRo3CggULsGnTJphMJowaNQq//e1vMWnSJJ+umzqv\nq+YnEXUPzlEi/8X5SRQYGF4TBRCtVosZM2YgKysL0dHROHbsGFatWoWMjAwUFRXh1ltvdTrObDbj\nN2/9BvGJ8bjxxzfa3ScIAq5evOp0XC1qYYTR5fncdNNNkCQJf//73zFkyBBl+65duwAA58+f9/YS\niciJn/3sZygsLOzUPgwGg92cTElJcag2dkV+o6p9z2tPtW8D4i2bzeZxj+9rK77dBcGujmUymdyG\n39u3b8eFCxfw85//HEeOHFGub+7cuUoP7L179+L3v/89iouL8Ze//AUajUZZ3LP9sZw99+4W45QX\n1HzzzTcRFRWFd955B5Ik4fXXX8dPf/pT/OMf/+AnXXpIV8xPIuo+nKNE/ovzkygwMLwmCiDjx4/H\n+PHjldvTpk3DQw89hNTUVDz//PPYtm2b03GPP/44Tp04hWXbljkEJgUVBTi29xgMjQYIguDw+6qx\nLdg2mUwwGo1KCCMIArKysjBkyBA8/fTT0Ov1yoKNL774IjQaDVpaWrrvySAKIK+88kqn9yGHnwAQ\nHR2NyMhIj8aZzWYlCA4KCur0eXhDfr3xNjAHfgi+Pa34bv+9sxC5srISq1atQmpqKrKyspTtjz32\nmN3jMjIyEBcXh7fffhsfffQRfvKTnyj3ya/RJpMJOp3O4Rju2oYYDAbl6+HDh5XFGadMmYLk5GS8\n8cYbdn246frpivlJRN2Hc5TIf3F+EgUGhtdEAS4pKQn3338/Pv30U0iSBEGwX4wxPz8f7777Lpb8\negnS73HeWiTxR4kuqw1bW1sBAI2Njbhy5YrdfYIg4I9//CMWLFiAGTNmQJIk6HQ6LFu2DPn5+QgO\nDkZzc7Nd4N3+KxF5Ji0trVPja2pqUF9fD6Bt3iYlJXk0TpIkpepao9F06YKI3a2zwXf7MLu6uhqz\nZs1C//79sXHjRkRGRjqt+DabzZAkCTk5OfjDH/6Affv22YXX7c/NW3J7pjvuuEMJrgHghhtuwB13\n3IGioiKv90ldo7Pzk4i6F+cokf/i/CQKDAyviQgJCQkwmUwOi4UVFBRg6dKlWLhwIZ54/gmUwHmf\nVXmxMkmSlIpDSZIgSRI0ouvgR5IkJCcn45tvvsHJkydRX1+PG2+8EUFBQVi6dCnGjx+PhoYGp2Pl\nyu72YbazgNvVY4jIMxaLBWVlZcrtIUOGeLzIn7zgoCAI150Lr94AACAASURBVL3quieJoqgsatnQ\n0ICZM2fCYDBgz549uOmmm9yOlYPsyMhIGI1GxMbGKn2/bTYbbDabT28CyIF1XFycw32xsbE4dOiQ\n1/skIiIiIiLqbgyviQhlZWXQ6XR2wXVhYSFyc3MxY8YMrF27FiaYcBzHYYPjx+HVGucvJTroYAtr\ne3xkZCRiY2OV8EUOuuWvo0ePVm7/7W9/g81mw6RJkyAIgtOPwsvhuLser650FHB3FIYTBZLTp08r\nn6zQ6XQeL9LYfvHDoKCggJw7RqMR9913H0pLS/H11193GFwDbX3BW1paUFdXh7i4OISEhHh8PHcV\n2aNGjYJGo3G6lkBVVRViYmI8Pg4REREREdH1ws/dEwWQ2tpah22HDx/G1q1bcc899yjbdu3ahdmz\nZ2PSpEl4//33AQBaaBEHx4q9C+UX8OGKD50e7wbcAPHfLzNy+KtWq6HVahEUFAS9Xo/g4GCEhoYi\nLCwMERERCAoKwhtvvIGBAwciNzcXcXFxiIuLQ0xMjNJnt3///oiIiEBYWBhCQ0MREhICvV6PoKAg\naLVaqNVqqFQql2GZXMVosViUXtwtLS1obm6GwWBAY2Mj6uvrceXKFVy+fBm1tbW4dOkSampqUFNT\ng0uXLqG2thaXL1/GlStXUF9fj8bGRhgMBjQ3N6OlpQVGoxEmk0lpCeCuFy1Rd1u/fr1P45qamnDu\n3DnldnJyssdVv51ZpLEvsNlsmDlzJvbt24ePPvoIY8eOdXiM0WhUelG3t2zZMgDA1KlT7bZXVFSg\noqLC5THd/dmEhoYiKysLRUVFOHnypLL9xIkTKCoqcjgWXT++zk8iuj44R4n8F+cnUWBg5TVRAJk1\naxb0ej1uv/12xMbGoqSkBO+88w5CQ0OxfPlyAMCZM2eQnZ0NURQxffp0fPjhD8G0EUZIqRIGjRqk\nbFs6ZSkMVwyYuXSm3bE+fu1jJAgJOFFyApIkYdOmTdi9ezcA4IUXXrA7p4EDB2LEiBFoaGjAhg0b\nUFFRgW3btikVh4Ig+Nwr99oKb2dV364e46riW158zlueVHbL3zu7TeSrgwcPYv78+V6PKy0tVeZB\nZGQkoqOjPRon93IGrv8ijf5iyZIl2Lp1K7Kzs1FbW4vNmzfb3f/II4+guroao0ePRk5ODoYPHw4A\n+OKLL7B9+3ZkZWXhwQcftFtPICsrC6IooqTEvoXTypUrIYoivv/+e7evt6+//jq+/vprTJ48GU88\n8QRsNhvWrFmD6OhoPP/88931VFAHfJ2fRHR9cI4S+S/OT6LAIPSGakBBENIAHDhw4AAb8hN1wtq1\na7F582aUlpaioaEBMTExyMzMxEsvvYTExEQAwM6dOzFlyhSX+1j68lLc/dLdaEXbQoyPDnsUgihg\nY9lG5TFhCMMEcYLTwFUQBCXUAoBVq1Zh48aNqKyshF6vR0ZGBn71q19h1KhRXXXZPvMl8HYXfHeG\nL+1NGHxTZ1y6dEkJSQVBwI9//GMEBwd3OE6SJDQ1NbX1vNdooNPpuvtU/dLkyZOxa9cul/dbrVbU\n19dj8eLF2LdvH6qqqmC1WpGcnIw5c+YgLy8PKpVK+YQIAIwYMQKiKOLo0aN2+woNDfXo9RYADh06\nhOeeew579+6FKIq466678MYbb3i8CCcREREREVFHDh48iPT0dABIlyTpYGf2xfCaiLxmhhnncR5n\ncRZNaFK2RyACCUjAAAyACr5VSvcVngTcrr52R/Dta49vCkxWqxXFxcVK64/Bgwcrb3B1RG6ZIwgC\nQkJC+OZJF7DZbHbV7DKVSqW0SSIiIiIiIvIXXRles20IEXlNAw2G/vtXK1phgQUaaBCEwGwP4Iwc\n/HobKsnhtSeBt7NtzsiP9Vb79iXeBN5c2LL3O3PmjBJca7VaDBkyxKNx7Rdp1Gq1/HvQRURRhFar\nhUajUd7c4qcqiIiIiIgoEDC8JqJO0SEwWwJ0l/aBlK/Bt7dV3/L37vbn63V42t7k2m3Uc1paWnDm\nzBnlti+LNIqiGJCLNHY3BtZERERERBRoGF4TUadlZ2ejsLCwp08j4LUPjL3labW3q8e4258v1+Fr\nmxMGe855M0fbL9LYr18/xMbGejSufVsLnU7HPwsiD/FnKJF/4xwl8l+cn0SBgeE1EXXaokWLevoU\nqJM6G3x3pse3s/1ZrVafr8ObxSzbP6Yvh62eztG6ujrU1dUpt1NSUjwaJ0mSUnWt0WjYg5nIC/wZ\nSuTfOEeJ/BfnJ1Fg4IKNRETUY3wJvLtrYUtf2pv0peDbZrOhuLgYra2tAIAbbrgBycnJHo01mUxK\neB0SEsLFPomIiIiIiAIYF2wkIqI+QRAEn6t03QXe7QNuZ49xV/HtS9W3r4ta+lPwffbsWSW41mg0\nGDp0qEfjbDabElwHBQUxuCYiIiIiIqIuw/CaiIh6JTn49iX89rXNiase3vJ2b4NvZwtbehOCd5XW\n1lacPn1auZ2UlAS12rN/InCRRiIiIiIiIuouDK+JqNM+++wzPPDAAz19GkQek4Nfb4Pv9tXcvvT6\ndrc/bzkLvl2F31u3bsUDDzxg95j2ysrKlHOIiIhAfHy8R+fQfpHGoKAgv6kiJ+pN+DOUyL9xjhL5\nL85PosDA8JqIOm3Lli38RwMFhM4ubOlN4O3JwpaeBt/vv/8+JkyY4PQ6GhoaUFlZqYTaw4cPR1NT\nk9tQXD6+XHWtVqs9rtQmInv8GUrk3zhHifwX5ydRYGBjSqIAsnPnTiWIav9bpVKhuLgYANDS0oLf\n/e53uOeeezBw4ECEh4cjLS0N69atcwjJJEgwwIA//PkPaEKTsr26uhpLly7FlClTEB4eDlEUsWvX\nLqfnJEkS1q1bh9GjRyMsLAzx8fHIysrC3r17u++JIOoBcvirVquh0WgQFBQEnU6H4OBghIaGIiws\nDBEREejXrx8iIyMRHR2NmJgYxMXFIS4uDrGxsYiOjkZkZCT69++PiIgIhIeHIzQ0FCEhIdDr9QgK\nCoJWq4VarYZKpVKC5rffftvuXOTQ22QyobS0FBaLBSaTCf369QMANDY2or6+HlevXsXly5dRW1uL\nS5cuoaamBjU1Nbh06RIuXryIxsZGNDc3w2g0orGxEQaDAU1NTWhpaYHRaITJZILFYoHVau3yBTb9\n3f79+7Fo0SKMHDkSoaGhGDJkCGbNmoVTp07ZPW7evHlOX5dHjBjhsE/5z81qtSqvx9683k6aNMnp\nsbKysrr+CSCP/fnPf+7pUyAiNzhHifwX5ydRYGCZFFEAevLJJzFmzBi7bcnJyQCA8vJyLF68GJmZ\nmcjLy0N4eDi+/PJLLFy4EMXFxdiwYQOMMOIczuEszqIVrco+QhGKBCSg9PtS5OfnIyUlBampqW6D\n6KeffhqrV6/G3Llz8fjjj+Pq1atYt24dJk6ciKKiIofzJApEna34dlXZfe7cOVitVmi1WqhUKiQm\nJkIUxQ4rvuVAGmhrwSIv9OjJdXjS5sTV4pa9ycqVK1FUVISHH34YqampqK6uxpo1a5CWlobvvvvO\nLpzW6XRYv3693fMdERGhfG+z2exatMhEUcSxY8c8fr0VBAEJCQlYsWKF3bEGDhzYFZdMRERERETU\n5RheEwWgO++8E9OnT3d6X3x8PI4ePYqbb75Z2Zabm4v58+ejoKAAT7z4BOoS62CCyWGsAQYcx3GI\nY0ScrTuLgf0G4uOPP3YZplitVqxbtw4zZ85EQUGBsn3GjBlITEzE5s2bGV4TdZK8sOW1jEYjLl68\nCL1eDwC48cYbERsba/cYVy1MWltblWpqjUbjNCB3FXx7u6hl++twFXh3FIL3RPCdl5eHLVu22LVT\nmTlzJkaOHIkVK1Zg06ZNyna1Wo2cnByn+5Gr4p2x2WwYNWoUqqqqEBsbi08++aTDT61ERES4PBYR\nEREREZG/YXhNFKAMBgP0er1DqBUVFYWoqCiHxz/44IMoKCjAX4//FemJ6cr2C+UXAAADEgco22wh\nNpSiFNGIdnsOZrMZLS0tDoFZTEwMRFFEcHCw19dFRJ4pLy9XguSwsDAMGDDA4TFy8N3+dcJqtcJi\nsUCtVkOv17vsde3ropYdBd++hN/eBt7tv/fVuHHjHLYlJydj5MiROH78uMN9kiShqakJoaGhyrZr\ng+uKigoAwLBhw5RtISEhAOAy4HbGarWitbVVGUtEREREROSvGF4TBaB58+ahsbERKpUKEyZMQH5+\nPtLT092OuXChLaQOibYPO5ZOWYrGukZ80viJ3fZmNOM0Trvdp06nw2233YaCggKMGzcOGRkZuHz5\nMl599VVERUUhNzfXh6sjomvNmzcPGzduVG5fvXoVNTU1yu2UlBSPqpPlqmug40Ua5eDXWdV3R8dw\ntriluxC8/W1n5O3eBt/t27V42t6ko+C7pqYGI0eOtNvW3NyMsLAwNDc3o3///sjJycGKFSscnrus\nrCyIooiSkhKH/bbvg+3OqVOnEBISApPJhLi4OOTm5uKll17igps96Nr5SUT+hXOUyH9xfhIFBv5P\nhSiAaLVazJgxA1lZWYiOjsaxY8ewatUqZGRkoKioCLfeeqvTcWazGavfWo34xHjc+OMb7e4TBAFB\nwUFOx53DOdjgPkzZvHkzZs6ciTlz5ijbkpKSsGfPHgwdOtS7CyQip6ZOnap8L0mS3aKB8fHxCA8P\n92g/ZrNZCUiDgpzP+85q3+bD1+Db16pvd/vz9Trah9l/+ctfcP78ebz44otoamqCKIqIjY1FXl4e\nRo8eDQD429/+ht///vc4fPgwtm/fbheCd9QCpaNwPjk5GVOmTMGoUaPQ1NSEjz76CK+99hpOnTqF\nLVu2eH2N1DXaz08i8j+co0T+i/OTKDAIzj6a628EQUgDcODAgQNIS0vr6dMh6lPKysqQmpqKiRMn\nYtu2bU4fs2DBAqxfvx7Lti1D+j2OFdqXLl5CXV2d0l5ApVJBrVZDpVLh4mcX8fyi5/Hxxx8jI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q1dvOgXag7U46EuwuXboU27Ztc2mXMmfOHIwaNQqrVq3Cli1bXM4xPz/f7RjqQpq++qSPGTMG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\nau1oq+pftmwZCgoKAm5zu90Bq7m7C79dLhci8WeIrlqZBKv4rq2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UFxf7BddxcXH99pxE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6NSiWq1WjFt2rRhn2OxWJQheJG3gYEwXK7Wlv/f7XYrt/WCwcFEUYTb7Ybb7UZra6vu6w8e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Bu117bf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\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Q8UhTsVgs+Hw+otFonrhdVVXF2rVr2bt3b8E+119/PWvWrOHyyy8X4rWmaXmCe2dnJwCN\njY1i2TnnnCMigAwB+73vfS8VFRV555mYmOCZZ57ha1/7Wl5EyBVXXMFXvvIVHnroISleHydOlv4p\nkZyoyD4qkSxeZP+USE4O5ixe67r+JPAkgFJ8rvc1wPd1XX/srW2uAIaAS4CHFEVZA3yQycyTHW9t\n82XgD4qifF3X9cKnQIlEsuBceeWVTExMYDabee9738vNN99s5BMJggSFcA0QGph0OL/no+/J2+76\n91+PyWRia8dW3G438XiciYkJqqqqCoTxI8Fms1FfX4/NZmNsbIxAIABMZj6PjIywatUqNmzYQCwW\nIxgM0t/fnyd09/f309PTQ01NDY2NjUeVyex0Olm+fDnLly9H13Wi0agQs0OhUIEYlkql6OnpEVEY\nPp9PiNkVFRVzen1GRkZ47bXX8oTrM888c8FFXUVRcLlcuFyuopm62Wy2aFaxES0yk0CYyWSIxWLi\nmjKZDFarVbjZp2IIhMWc216vd17ea+8UPvGJTxzvJiwKbDabGIybjqZpxOPxkq7t6QNTM5HL5QiF\nQnmzQAw6Ojqor6/n8ccfF+/Zzs5OHnjgAZ599tm8yJDp57zwwgsxmUzs3r274LjZbBaz2YzJZCIe\njxOLxfIGDHfu3Ekulyu4r1utVk499VR27Ngx6+uTzC+yf0okixvZRyWSxYvsnxLJycG8Zl4ritII\n1AB/NJbpuh5VFOVl4GzgIeAsYNwQrt/iGUAH3g38fj7bJJFIZsZms/Gxj32MCy+8kMrKSvbs2cMt\nt9zCueeey0svvcS73vUuAFRU+ng7zziXzfG7235HTVMNq85YlXdMRVFAgUMc4lT/qSI/ORqNFp3q\nPxdUVSUYDGI2m1m2bBnt7e1Eo1EhEqmqyt69e+np6aG9vZ3169fT2tpKR0cHw8PDmEwmkdEcjUY5\nePAgy5cvp7m5GafTeVRtUxQFv9+P3++npaUFVVUJhUJCzI5GowX7GMLYwYMHRUSJ4cr2+/0l86BH\nRkbYsWPHvDuu5wOr1SqiVqaj63qeQDhVKBwYGGBiYkJsazKZ8Hg8JWNCZhIIYdKpXyqz2OFwzM/F\nSt4xGBE2Xq+X2tragvXpdLqksD09PqgUW7ZsIRwOc/HFF9Pf309/fz8A//Ef/8GGDRvYs2cPW7Zs\nASAYDHLw4EER7+N0OlEUZcYikrlcDpvNxq233ko2m+Wyyy4T6wYGBlAUpagjfenSpbzwwguzugaJ\nRCKRSCQSiUQiOZbMd8HGGiZF6KFpy4feWmdsk1cZSdd1VVGU0JRtJBLJMeLss8/m7LPPFr9/6EMf\n4qMf/Sjr1q3jW9/6Fk888QQAQwyRJSu2u+uLd9G7r5cbn7iRaCTK8PCwEFjueP0OnE4nE0yQdWWx\nWCzkcjnC4XDRGIq5cOjQIdLpNDApvLe1tWEymejt7WXPnj3CqRiLxfjrX/9KbW0tbW1tnHLKKdTW\n1jIyMkJvb68QfVVVpbOzk+7ubmpra2lubp63XGqz2Sxc1TApfo2Ojgoxe3r+rqZpjI6OMjo6CkyK\nwFMjRgxhupjjesOGDXlRAIsVRVFEQcxly5YBk3+DN998k6qqKnK5HKlUCkVRCAQCIr94YmIiz5E9\nG2KxGLFYjIGBgYJ1NputpGvb4/EckyKbkhMLu92e15+noqoqsVispLidy+UYHBzkwQcfpLm5mbPO\nOkvs++KLLzIwMMDnP/95ABEBEo/H84qVAtx99904HA5CoVDRwaFcLsdf//pXbrzxRjZu3Mj73vc+\nsc6I8ykWVeRwOOYU9yORSCQSiUQikUgkx4r5Fq9LoTApah/tNhKJ5BjQ3NzMxRdfzCOPPIKu6yiK\nQpK3hY3f3PwbnrrvKT75b5/k9A+ezpP/50konxRApkY1mM1mBiYG8MV9aJqGx+Mhm82SSCSOqF3J\nZJJDhw6J31taWsT56uvrqa6uZv/+/XR1dYlt+vr6GBoaorW1lcrKSurq6mhsbKS7u5uOjg4hdmua\nJuI8ampqaGlpOWqX+HTsdju1tbXC1RmLxYSQPTY2Ri6Xy9s+m83muTPdbjc2m43BwUFc6zEslgAA\nIABJREFULhdmsxmbzcaGDRvweDzz2tZjRTKZZO/eveI9YbFYaG5uprm5uUBA1jQtTyCc7tw2RL/Z\nkMlk8gYKpqIoihC1p4rbxs9HEzNzvHjhhRf4m7/5m+PdjHcsZrNZzLooRnd3N+973/sIBALceeed\n2O12otEoQ0ND/O53v+OCCy4oGNgrNoBi5M2XisTZv3+/GHy8995789YZM0uMwb+ppFKpo555Ijly\nZP+USBY3so9KJIsX2T8lkpOD+RavB5kUoavJd18vAXZM2WbJ1J0URTED5RQ6tvP4yle+UvBg+IlP\nfELmHEkkC0B9fT2ZTIZ4PJ4njD59/9P87Pqf8aEvfIiN39oIOjz5kye54JsXCFHFZrNhNptRVZXw\neJh0ZNJxrOs6O3fupKOjA13XeeaZZ5iYmChwv5YSCIPBoHDelpWVsWRJ3q0Em81Ge3s79fX17Ny5\nk3A4LNb19/czPj5OQ0MDbreblStX0tTUxKFDhzh48GCe63BwcJDBwUEqKytpaWkp6rScDwwHcmNj\nI5qmEQ6HhZgdDocLXMbDw8N5rnGfz8eZZ55JJpNB07QTzi0cDofZt2+fEO0VRaGxsVE4sqdjMpnE\n+6MYyWSyZNa2EV0zG4zs8mIxLzA5SFPKte12u2eMdThe/PCHP5Rf7I8T0WiUSy65hHg8zgsvvEBr\na6tYd8MNN2C1Wvn2t79NLpcjFouJ/pDJZBgZGaG8vByLJf/rWjH3dG9vLxdddBHl5eX84Q9/KJiJ\nsXTpUnRdLzoTYWBgoGS/kyw8sn9KJIsb2UclksWL7J8SyeLgV7/6Fb/61a/ylkUikXk7vjKXKdgF\nOyuKBlyi6/qjU5b1Azfrun7rW7/7mBSlr9B1/f9TFGU1sBvYMKVg4wXAE0BdsYKNiqKsB1599dVX\nWb9+/RG3VyKRzJ6Pfexj/N//+3+F6DfAAHc/ejc3ffQm3vOP7+Fbv/7W5IY67Ni2g1AkJBx9iqJg\nsViwWq0sO7QMR9pBJBIRMRk9PT3cd999fPWrX2XVqlVFz2+32/NEQUN0MfJfzzjjjBljMnRd59Ch\nQ+zduxeXy4XJZBJifH19PW1tbUIg1zSN/v5+gsFgXuaygd/vZ+XKldTU1BwzYTKbzTI2NibE7OnC\ntdlspr6+XmQ3WywWAoGAiDVY7E7svr4+urq6xPVYrVZaW1uPOlamFIYwWMq1rarqvJxnqsBeTNye\nLkIeKxKJxKLIQz/ZSKfTXHDBBWzfvp0//vGPnHnmmXnrr7zySh544IGCgSpFUcSsl7/85S80NzeT\nTCZJpVKkUilaW1vz7kWhUIjzzz+fcDjMiy++SHNzc0FbotEolZWVfPWrX+UHP/iBWJ7NZgkEAmzc\nuLHArS05Nsj+KZEsbmQflUgWL7J/SiSLl+3btxvF4k/XdX370Rxrzk/RiqK4gRYmHdYATYqivAsI\n6breA9wG/KuiKEGgC/g+0MtbhRh1Xd+nKMpTwL2KonwesAF3AL8qJlxLJJKFZXR0lMrKyrxlr7/+\nOo899hj/7b/9N7Fs33P7+MFlP2Ddeeu47hfXvb2xAqedcRq6phOLxYhEIvQc6CGTyVDlq6LaUU3W\nnEVVVSFeG1EdM5FOp4Vwq+t6XqyGx+NhaGioqEDo8/mwWCwoikJDQwOVlZV0d3czNjYmoil6enoY\nHBxk9erVNDQ0YDKZqKuro7a2lqGhIYLBIOPj46ItkUiEbdu24fF4aG5upq6ubsFdzlarlZqaGmpq\nahgbG+Pll1+mtraWiYkJkskky5Ytyys6mMvlGBoaYmhocgKLw+HIy8su5tQ8HqiqysGDBxkefrv0\ngdvtZs2aNQtaRNFisVBWVlZUHNd1nUQiUdS1HY1GC7LJZ8Jw0E91/U/F5XIVFbe9Xu+CfvGWX+qP\nPZqmcemll7JlyxYeffTRAuEa4JprruEjH/lI3rLh4WE+97nP8alPfYoLL7yQlStX4vV68fv9dHZ2\nYrfb84TrRCLBRz7yEQYHB3n66aeLCtcwOVPj/PPP5xe/+AWbNm0Sg38PPPAA8XicSy+9dB6vXjIX\nZP+USBY3so9KJIsX2T8lkpODOTuvFUV5H/BnCvOpf67r+qff2ua7wOeAMuB54Iu6rgenHKMMuBP4\nMKABvwGu0XW9aBCudF5LJAvHBz7wAZxOJ+eccw5Llixh9+7d3Hvvvdjtdl566SVaW1s5dOgQ69at\nI5vLcuXNV+Ly5X9JaFzXSGN7IwBqTuXK5itBgUf+8gg1uRrsdjsOh4Mbb7yRVCpFMBjk2Wef5R/+\n4R8oLy8nnU7zd3/3dyXbGI/HhSPaZDJRWVk5o3jsdDrx+Xz4/X4CgQBOp5NcLkd3d3eBcF5WVkZ7\ne3uBqDk2Nsabb77JyMhIwfEdDgfNzc0sX758wZ20oVCI7du3C2ew1Wo1Ri+FuB8KhdA0bcbj+Hw+\nIWZXVFSUzMxdSNLpNHv37iUWi4lllZWVrFy58ri0Z7Zks9mSwnYsFjvsaz9bLBZLSde2x+NZ1K+R\npJBrr72W22+/nYsuuoiPf/zjBesvv/zyovt1d3fT2NjILbfcwhe+8IW899eaNWswmUzs3r1bLNu4\ncSN/+MMf+OQnP8n73//+vHujx+Ph4osvFr/v2LGD97znPaxZs4bPfe5z9Pb28p//+Z+cd955ojiv\nRCKRSCQSiUQikRwt8+m8PqrYkGOFFK8lkoXjzjvv5Je//CXBYJBoNEpVVRXnn38+N9xwA01NTQD8\n5S9/4f3vf3/JY/zTd/6Jy294W4j5VOOnMCtmtr6wlWxm0nVtMplobm4uGruhKAqpVKogyiEajRIK\nhejs7BQCjt/vn3VhsfLyclwul3Ala5pGJBIhEolgt9txuVw4nU5cLhetra2sX7++4NiRSIRgMCiK\nJk7FarXS2NhIY2PjghTxGx8f59VXX80Trjds2FCQ+ayqKqFQSIjZpbKaDUwmExUVFULM9vl8Cx6H\nEolE2LdvX15RxRUrVlBXV7eg511oNE0TgyvFxO3ZzDKYLR6Pp6S4vVic9ZK3+du//Vuee+65kutL\nRdV0d3fT1NTEzTffzLXXXpvn/G9ra8NkMrFr1668ZT09PUWP1dDQQEdHR96yl156iW9+85ts374d\nr9fLxo0b+fd///cZY5gkEolEIpFIJBKJZC5I8VoikRw3smR5gzcY4W1H8n3X3cdnbv6M+L2OOtpo\nQ1cnC98lEglyuRzZbJZEIiGKOtbU1OB0Omd0L+/evZvh4WHS6TQWi0VEZ0wVC42s7anYbDZRaHFk\nZCRPRMxmswwMDORFg8Ck87W5uZkVK1bg9/vzBEKTyURfXx+9vb0FTluz2UxDQwNNTU2zFtYPx3Th\n2mKxcMYZZ5QsVjiVdHqyQKYhZh8u9sJms1FZWSnE7Pm6BoPBwUEOHjyYl9e9evVqysvL5/U8i5F0\nOp03GDP1XzweL8g6PlJsNltBvrbxs9vt5pvf/CY333zzvJxLcmzRNI10Oj3je8VqtWK1Wo9hqyTz\nyXXXXSf7p0SyiJF9VCJZvMj+KZEsXo5r5rVEIjm5sWLldE4nSpRDHGKMMZYuX4oLF0tYwnKW4+Kt\nWBHz2+7nSCSCxWIhm82STqfRNI1YLEY6ncZms+F0OgvEl/HxcUZGRlAUBYfDwemnn47X6y1oUyaT\nKXBt67pONptlfHy8wP1qtVpZvnw5FRUV9PX1CXE3l8uxf/9+ent7qa2tLRBxbTYbdrudeDxONBoV\n7m2Xy0UwGKSrq4u6ujqam5uPqmBiOBw+YuEaJotd1tbWUltbC0AsFhNC9tTscINMJkN/f79wl7vd\nbiFkBwKBIxbFNE2jo6ODwcG3yxk4nU7a2trmXSBfrNjtdvFaTkfTNCFqFxO3p/+dZiKTyTA6Osro\n6GjBOpPJxMjICE888URR17YUPRc3JpMJh8OBqqrkcjl0XRfFHC0Wi8j4l5y4LF++/Hg3QSKRzIDs\noxLJ4kX2T4nk5EA6ryUSyTFB13Xi8Tjj4+NCYHM4HJSXlwvntdVqxel0YrPZ0DSNbdu2iSKLy5Yt\nY9WqVbM6VyaTEW5sp9NJIpEoGkliuLZHR0dFrIiBoihUVlZSXV1dNGs4l8sxNjbG6OgouVxOCOyG\nmN3Q0EBbWxu1tbX4fL5Zx4qEw2G2bduWJ1xv2LABv98/q/0Ph1FQ0BCzw+HwjI5ORVEoKysTAmxZ\nWdmsilVmMhn27duXF2FSUVHBqlWrFjwn/J1CMpksKWwb/WI+cDgcJV3bLpdLCqMSiUQikUgkEolE\nIpkTMjZEIpGcsORyOTo6OoT4Vl5eLmJEDFHUYrEwNjbGoUOHUBQFq9XKmWeeOSuHqJFBDJOu18OJ\nxkbW9vDwMK+99hqHDh0imUySSCRIpVJYrVaWLl1aMuJC0zRCoRDDw8N5ec4GXq+XJUuWEAgEZox1\nUBRFOK4Nx63FYuH0008vKCY5n2SzWcbGxoSYbbx2pbBYLAQCASFmF3OYT0xMsHfv3jzH+/Lly6mv\nr5dC6DyRy+VKurYnJiZK5inPFbPZXODUNn73er1yIEIikUgkEolEIpFIJAVI8VoikZzQTExM0Nvb\nSyaTwePx4HQ60XUds9mM2Wwmm83y2muvoes6NptNOJhnQyqVIpvNYjKZjsg1Ojw8zK5du4jH46iq\nSjKZJJlMioxuVVWFSDhVIJzqaC6WMW1EcRQrjmgymbBarYRCIRGh4nK5OOuss6irqzumAmEymRRC\n9ujo6GELDjocDiFkV1ZWigKXhovdbDazatUqAoHAsWi+hMlZDsZsg2Ku7cNloM8Fl8tVclDmZImG\nkUgkEolEIpFIJBJJPlK8lkgki4p9+/axevXqWW+v6zqdnZ3CYez1ekV0hclkoru7m5GRyYKQbreb\ndevW4XK5cDgcM4rRqqoKR/fhCkHOhKqqBIPBPBHWaFtTUxOrVq3CZDKJWIfp/3p6ejh06FDRaAdD\n7J0av5HJZBgaGhKvgaIoLFmyBLvdDkwKhKXcry6X64iucTbo+mTBTUPMDoVCBcUqp24biUTIZrN4\nvV48Hg+BQIC1a9cuaBsls2NqH81kMiVd27FYrOTfeK5YLJa89+x01/Zs4mckkpOBuX6GSiSSY4vs\noxLJ4kX2T4lk8SLFa4lEsqi46KKLePTRR+e0z9jYGKFQCIClS5eSTqdJp9MicsJisWA2m1m9erUo\n0qgoCk6nE4fDUSB8GW5TTdOwWCzz4vqMx+Ps3r2boaGhvOVOp5O1a9dSU1NTct9sNktnZye7du2i\nt7dXRJEYTm6LxSJiN0ZHR4VgqCgKVVVVOByOWbXREAinittTBcJied1HiqqqhEIhIWYbedaapjE2\nNpbn6HU4HFRWVgpXdinXueTYMNs+asTuFMuHj0ajh3XizxZFUXC73SVd28bAjURyMnAkn6ESieTY\nIfuoRLJ4kf1TIlm8SPFaIpEsKg4dOjTnSs/ZbJauri5g0nldU1NDIpHgpZdeIhaLAbBkyRLWrl2L\noihkMpk8Z7LD4cDhcAhxdmqRRrfbPa+uzsHBQXbt2kUymcxbXl1dzSmnnILb7Z5x/3A4TDAYZGBg\nAJgU2lOpFOPj4/T392O327Hb7aiqis/nm9e2ezyekq7t2QrkpUin0/T09PD6668TCoVE5rfX68Xv\n9xcI1TabLU/QlrESx44j6aPFSKfTJYVto9/OBzabraRre777t0RyvJmv/imRSBYG2UclksWL7J8S\nyeJFitcSieQdQX9/vygQ2NjYyNDQEPv37yedTqOqKu3t7VitVmw2Gx6PB1VVSaVSTL1v2e12HA4H\n6XRaZGQvhGtTVVUOHDhAR0dHQZRIS0sLLS0th3U5x2IxgsEgfX19JBIJenp6RG62xWLh7LPP5tRT\nT0VRlKJZxRMTE8RiMebrvm0IhMXEbY/Hc1iBcHR0lAMHDojXI5PJUFZWRi6XY2xsTMTClMLIAa+q\nqiIQCMyqIKdk8aKqKrFYrKS4fbj3w2wxmUx4PJ6S4rZ8H0kkEolEIpFIJBLJ8UWK1xKJ5B1BPB6n\nv78fAJ/Px4EDB4SYu2LFCrxer3BTw2T2s8fjIZPJkEqlhGiqqqpwY3u93gWNpojFYuzcuZPR0dG8\n5W63m7Vr17JkyZLDHmNkZIQnn3xS5FwrikJdXZ1wlNbX19Pc3FzU0a1pWp5AOF0oNNzPR4uiKHi9\n3qLCttfrZXBwkJ6eHrG93W5nzZo1eDwe0U6jgOXIyAjhcHhG0V1RFMrKyoSYPTUTXPLOYHpG/FRx\nu1g+/JHicDhKCttHUsRVIpFIJBKJRCKRSCRzQ4rXEonkHYGu63R3d5PNZunr6xNCrtvt5vTTT88r\nimiI2iaTSURepNNp4vG4EGzNZjN2ux2Xy7Xg7su+vj727NmTl/MMUFNTw9q1a0tGYsRiMbZu3Uo2\nmyWXyxEKhXA6nQVucUVRWLp0KS0tLfj9/lm3K5VKFS0iOTExIVzuR4MhngMiuqWiooLW1lYqKirw\ner243e4CgTCbzTI2NibE7MO1xWKxEAgEhJhtiOKSdya5XC5PzJ7+s9H/jxaz2VwwIDN1UOZIi7xK\nJBKJRCKRSCQSieRtpHgtkUgWjJtuuokbbriBtWvX8sYbbwCTjsmf/vSnPProo+zcuZNYLEZLSwuf\n/NwnufhzF/Pjm3/Ml7/5ZSqpxEHpHOWnn36a733ve+zYsQO73c4HPvABNm3ahKZp7N+/n7KyMux2\nO6eeeiplZWViP03TmJiYIJFICPeuzWbD7/eTyWTIZDJks9k8p65RtNFmsy2Y0zKbzXLgwAE6Ozvz\nXMVms5lVq1bR1NSU16ZYLMYrr7wiit6ZTCZOO+00ysrK6O7upqOjo0AMh8ns75aWFgKBwFG1N5fL\nFXVtz1YgVFW1YDu73V4gVhsDDKUiSSwWC4lEgtHRUSFmH84x7nQ68/KybTbbUb0WJxubN2/mm9/8\n5vFuxhFhFGMt5dou1meOFJfLVSBsG+/buWa0b9u2jfvvv59nn32Wrq4uAoEAZ511FjfddBMrV64s\nuo8Rl7Rv3z5uueUWvvrVrxasn1rc1Ww2MzQ0xG233cbWrVvZtm0bsViMZ599lnPPPbfg+Llcjn/7\nt3/jgQceoK+vj9raWj796U9z/fXXz2txV8ncOJH75zuReDzOD3/4Q7Zu3crWrVsZHx/n/vvv54or\nrijY9qGHHuLWW29l3759mM1m1q5dyTe+8VkuvPB8oAzwlTzP888/zy233MKOHTsYGRmhrKyMU089\nlU2bNnHOOecs3AVK5ozsoxLJ4kX2T4lk8TKf4rW0GEkkEkFfXx+bN28ucLl2dHRw9dVXc/755/O1\nr30Nk8/E4//vcb7yha/wh61/oKq+il3swoSJKqpophnftAe2xx9/nEsuuYQNGzawefNmotEot912\nGy+//DKbN2/GZDKRSCSor6/PE65hUgz1+/24XC4ikYgQrEdGRrBYLCImQFVVEokEuVxOODnNZrNw\nNs+3iG21WjnllFOor69n586dhEIhYFJg2rt3Lz09PbS3t1NZWVlSuK6srASgubmZFStW0NfXRzAY\nzHMmDw8PMzw8THl5OStXrqS6uvqI2muxWCgrKyt4feFtgbBY1vbUgnxTRXq321206KMRGRIOh4u2\nwxAIDWG7qakJRVFIpVLEYjFCoVBerjhMDqD09PSIqBKfzyeE7IqKCim8HYb5jOU41hizMdxuN0uX\nLi1Yn8lkCt63xu8TExNzyohPJBIkEgkGBwcL1lmt1hld29NjbjZv3sxLL73Exz/+cdatW8fg4CB3\n3HEH69ev5+WXX6atra3gHD/60Y/o6ekpOnMhl8sVvZZdu3Zx8803s3LlStatW8df//rXktd3+eWX\n8/DDD3PVVVdx+umns2XLFjZt2kRPTw8/+clPZvsySeaZE7l/vhMZHR3l+9//Pg0NDZx66qk8++yz\nRbe74447uOaaa/jwhy/kyiuvJpUa4f77/x8f+tBn+O1v/5VLLjmHSQG7ESj83D5w4ABms5nPf/7z\n1NTUMD4+zi9+8QvOPfdcnnjiCS644IKFvEzJHJB9VCJZvMj+KZGcHEjntUQiEVx22WWi0N7Y2Jhw\nXo+NjTE8PMyaNWvopZfd7EZH59arbuWZ+5/hvjfvY2nT26KSGTOncipVVIllp5xyCrlcjj179gih\n8Y033mD9+vV8+MMf5rLLLsNsNvPBD34Qr9dbso26rpNMJolEIqIAnCHKGs7IbDZLIpHIc/OaTCac\nTicOh2NBnNi6rtPb28uePXuEQG0QCASIx+N5jsnTTjuNqqqqYodC13UGBgYIBoNEIpGC9T6fj5aW\nFpYuXXpMcqF7enqEKzyVSpHNZvH7/WSzWSFqTxebjxSLxSIGT3K5HJlMBk3TcLlcOByOoiK1yWSi\noqJCiNk+n0/mGkuAt2NuSkWSTO+rR4ohsE8VtQ8ePMh73vMeysvLRSxQMBhk7dq1XHrppTzwwAN5\nxxgeHqa1tZWvf/3rbNq0STivjQK2pYjH4+RyOaqrq3nkkUe49NJL+fOf/1zgvN62bRtnnnkm3/nO\nd/jOd74jll933XXceuutvPbaa6xdu3ZeXg+J5EQmm80yPj7OkiVLePXVVznjjDOKOq9bW1spL/ex\nZctmYPJeMjGRoLb2v/OBD5zKI4/cMGXrlUDzYc+dTCZpamritNNO44knnpi/i5JIJBKJRCI5xkjn\ntUQimXeee+45fvvb37J9+3a+/OUv560LBAIEAgFGGRXCNcA5HzmHZ+5/hp69PXnidW9HLwMM8I9N\n/4gHD+Pj4+zdu5dvfOMbeeLjmjVraGho4IUXXuCyyy6jpqaGdDo9o3itKIooumaIT5qmMT4+TiKR\nwO/3Y7Va8fv95HI5kskk6XQaTdOIx+MkEgkhYs+n8KsoCvX19VRXV7N//366urqASVfoli1b0HWd\n6upqqqqqZhSujWMtW7aMZcuWMTIyQjAYzCsQGY1G2b59Oy6Xi+bmZurr6xfEeayqKm+++Sajo6OY\nzWbcbjc1NTWsXr06L6PbeG1LubbnIhDmcrkCx3Y2m2VkZIRYLEYul8NqteJ0OnE6nbhcLlwuF6lU\nitHRUfbu3YvNZsuLGJlr3IPkncPUCJva2tqC9alUqqRr28h2nw26rhOLxYjFYqIILcCjjz4KTMbr\nGK7txsZGduzYQX9/Pz6fT8TuXH/99axZs4bLL7+cTZs2AZP3j6nCdWdnJwCNjY1imVHYNZ1Oz+gy\nf/7551EUhY0bN+Ytv+yyy/jP//xPfv3rX0vxWiJhcpbFbIovR6NRWlsDGMI1gNfrwuNx4nTm17Ho\n6HgOGKKpaeY4EKfTSVVVVcmZSxKJRCKRSCQnI1K8lkgkaJrG1VdfzWc/+9kZxYuDHBTCNUBoYDIm\nw1eZHxFy/fuvx2Qysb5jPWtZSzqdBigQEbu6urDZbIRCIVRVZcmSJUSjUSoqKmYUllVVJZfL4XK5\n8Hq9omhjOp1mZGQEt9uNx+PBYrHg9XpxuVwkk0lSqZSIx0gmkzgcDpxO57yK2Dabjfb2durr69m2\nbRvBYFCITwMDA/j9/jkJzYYAOz4+TjAYzIszSCQS7Ny5kwMHDtDU1ERDQ8O8FapMpVLs3bs3L77E\nyN6e/nqZTCYRnbBs2bKCY6XT6QLHq/EvHo8fNtbBarVSXl5OeXm5aFssFmNoaCjP9W2I2i6XK0/Y\nrqqqoq6ujurqagKBwIIX85ScOBhFR4sNJhkZ76XEbWPmx2xIp9Ok02lGR0cZGhpi2bJlPP7448Bk\n/xkaGuLnP/85P/7xj9m3bx8w6ahOp9N594sLL7wQk8nE7t27C86h6/qMMyBK3YddLhcAr7766qyv\nRyKRwHnnnc7DDz/FnXc+yoc//G5SqSy33/57otEE1157Sd6273//9ZhMZjo6+guOMzExQSaTYXR0\nlJ///Ofs3r2bb3/728fqMiQSiUQikUgWPVK8lkgk/PjHP+bQoUP86U9/KrnNBBOMMy5+z2Vz/O62\n31HTVEN1Y36Wo6IooMAAA7TSSnV1NWVlZbz44otim1gsxp49e+ju7gbA7/ejKAqqqhKLxfD5Shc5\nMkQYs9kshEojr9mICUgmk/h8PpxOJ2azGY/HUyBiJ5PJPBF7Pt3LNpsNi8VCdXU1g4ODqKoqnJ8v\nvvgi9fX1tLW1zbrwYHl5OWeccQYTExMEg0H6+vqE6JtOp9m7dy/BYJCGhgaamprynNFzJRwOs3//\nfhG7oigKjY2NRYXp2WC324UIPx2jGGcp13YxgdAQHCsrK9E0jUQiIVyvxnGmYzKZcDgcuN1ulixZ\nQm1tLcuXL6euro6ysrJ3vKA9Ojoq8tUls8NsNpfMiAeKZsQbv5fKX9yyZQvhcJiLL75YLNM0jfvu\nu48zzjgDgB07dgCwf/9+XnzxRWw2G3a7HafTKQbCUqlU0bz5meJFWltb0XWdF198kYaGBrH8ueee\nAyZrHkiOD7J/noho3HHHZxgd7efqq3/C1VdPZsZXVfn54x//gzPPbM3bWlEUFEUHRoD8z8JLL72U\np556Cpj87vDP//zP/Ou//uuxuAjJLJF9VCJZvMj+KZGcHEjxWiI5yQmFQnznO9/hhhtuoKKiouR2\no4zm/X7XF++id18vNz5xIz/6zI/47u+/C2/FDN/feT8AKiohQlQr1fzzP/8zP/zhD/nWt77FVVdd\nxcsvv8x//dd/CXHS7/djMpnQNI1IJFJSvM5ms0KgMQRaI2/W4XAwMTFBIpFAVdW8KBGLxYLJZMLt\nduN0OkmlUiSTSXRdF1nOhkBksRzdrTGRSPDKK6+QTqcJBAKUl5fjcDjyYgh6enoYHBxk9erVNDQ0\nzDqj2ev1ctppp7F69WqCwSCHDh0SbstsNkswGKSjo4Ply5fT3NwsXJWzpb+/n87OTiGMW61WWltb\nSwp4R4tRjNPv9xddn0wmS7q2E4kEJpMJj8eTl5Mdj8eFkG0MdBgidyKRYGRkRDgLvlBTAAAgAElE\nQVRXjYGNQCDAsmXLqK6uzism6fP5REzNicynP/1pEWEhmR8MZ3+xAqq5XK7gfbt7925+/etf09zc\nzFlnnSW2ffHFFxkYGODzn/983jGMARWjQO3ExAT33XcfMDmLw+l0Fgx+TUxMAJP52UZxU4P29nbq\n6ur4yle+Qjwep729ne3bt7Np0yasVisTExMF+0iODZ/+9Kf56U9/erybISmCMdtpbGwsr3+YTDE0\nbZyGhkouu+y9fOAD7YTDce655ykuuui7/OlP/8Epp7wd79P51vciGGO6eL1582a+/vWv09PTw89/\n/nMymQzZbHbWg9uShUd+hkokixfZPyWSkwMpXkskJznf/va3CQQCfOlLX5pxO5W3HX2/ufk3PHXf\nU3zy3z7J6Recjss/6Wh2Op1CwJ6+34033sjY2Bi33HILmzdvRlEUNmzYwIUXXshjjz0mhMJwOEwq\nlSKdThe4h3VdF2Kk1WotcEobTkmXy0UkEsmLEjEETkVRMJlMwrFtiNiapomp/VarFZfLdURuXEO4\nTqVSwKSwvn79eqqrqwmFQuzcuVM4g7PZLDt37qSnp4f29vY5CcROp5P29nZWrVpFZ2cnXV1dwimt\naRpdXV10d3ezbNkyWlpaZnSyG/sEg0GGh4fFMpfLRVtbW1GH57HCyLYuJRAa4mAxB6yqqmQyGSFk\nG8umoqoqkUiESCRCR0cHNptNvFe8Xi8WiwWz2ZwnZk//+WgHO44F3/3ud493E04qLBYLFRUVYkBw\neHiYK664giVLlvDcc8/h9XqJRqMMDAzwP/7H/+Cyyy5j5cqVQnyeCUVRCIfDxONxXC4XdrtdvAeN\nwcBMJiPuQVO5++67ufbaa/mXf/kXdF3Hbrdz3XXX8eMf/1jcDyXHni9+8YvytV+kGDUbstksqVQK\nVVXfypcf45prbsFiMXH77VeSyWRIJpOsWfMJ/umffsJ3vvMLfvObTUWOWDibaN26deLnyy+/nPXr\n13PllVfy0EMPLdRlSeaI/AyVSBYvsn9KJCcHi/+JWyKRLBjBYJB7772XH/3oR2LKuOFEzmazdHd3\n4/P5KC8vx/LW7eLp+5/mZ9f/jA994UNs/NZGNFWjob0BXdfJZDLY7PlOIWM/q9XKPffcw/e+9z1+\n//vf4/V6qa2t5dZbb8VkMtHc3IzVahVFiiKRSEHBpEwmg67rKIoyYyyGUbDPcOAa0RRGlIghxiqK\nIoo3ptNpkskkqqqSzWaJRCJYLBYhYs/GeZtMJtm2bVuecL1u3TohvFZUVPDe976X7u5u9u3bJ4Sm\ncDjM888/z4oVK2htbZ2T28put7N69Wqam5vp7u6ms7NTnF/Xdfr6+ujr66O6upqWlpai7vp0Os2+\nffvyhLPKykpWrly5IIUg5wuLxZKXhT0VI9t8qrAdiUQYHBykv7+f0dFREolEQUZwJpMhFAoRCk3m\nuTudTiFku93uovnoLpdLiNnTxe3FUixy/fr1x7sJJy3RaJQPfvCDRKNRXnjhBZYunSxu6/F4+MlP\nJqMGrr/+evFe6ejoACZnWdhsNnw+H7lcTgyuaZqGqqqYzWYxmKeqKg6HQ4jYNput6KDT2rVreeaZ\nZ3jzzTeJRCKsXLkSu93Ov//7v3P22Wcf14Gqk5m3qrBLFhmqqorPiEQiwcDAgPjcHhnp5Pnn93LD\nDf9ILBYT30/cbjvt7XX89a/7Shx15kcvq9XKRRddxObNm4sO4kuOD/IzVCJZvMj+KZGcHEjxWiI5\niTFyk6+++mq+/OUvF6xvamrimmuu4b/+67+opJItj27hR5/9EX/zsb/hC3d+AQCT2YTNZiOTyZDL\n5TCbzZgtk4KnGTPl5AuL8Xic1atXA5MCy7Zt2zjrrLNwu93ApFhoREUEAgEhnmqaJhxQdrv9sGKy\noih4PB6cTifRaJRkMkkulyMUCuFwOPIKJyqKgsPhwG63C/dULpcTU//NZjNOp3PG8yaTSV555RWS\nyaQ4Znt7OzU1NXnbmUwmGhsbWbp0KXv27MnLme3q6qK/v5+2tjbq6urmFFVhtVppaWmhsbGR3t5e\nDh48mFdscWhoiKGhISoqKli5cqUYGIhGo+zbt0+8tgANDQ3U19fP+tyLESNKxu12C7FwKplMhnA4\nTHd3N729vfT29gpBO5VKCcHCyEUfGRkRsTOGmO1wOFAURcSRTC2maWCxWPKE7anittfrnddioZLF\nRzqd5sMf/jDBYJA//vGPtLbm5+D29PQwPj5OW1tb3nJFUbjzzju56667eOmll4Qzc2BggEgkgqZp\nuFwucrkcJpMJu92eV/h0yZIlM/bhqeueeOIJNE3j4osvPuH7vURypGSzWRKJBPF4XPyfTqfz4r6M\nwR1d19m1a3J5Mpkik8lgNpsxmUxvZdQ7KV2HuLD2w3QSiQS6rjMxMSHFa4lEIpFIJBKkeC2RnNSs\nXbuWRx55pGD5t7/9bWKxGLfffjtNTU0AbH9uOz+47AesO28d1/3iurztLRYLqqqK6bThrjCKSeHM\npjOx8nb0RjwezxNrn3zySQYHB7nrrrvEsrKyMpFFPTExIaI0DDex2WyeU5yH2WymvLxcRInkcjkR\nS2K4aQ2R2HB02+128SBrZGzHYjESiYRwak8VllOpVFHhuphoauBwOFi/fj3Lly9n586d4gE5k8nw\n2muvcejQIdrb2w8b91Hseg3xeWBggGAwmFfAMBQK8fLLL+Pz+SgrKyMWiwnRy2w209raOmP2+TsF\nm83GkiVLWLJkiSiUl0qlGB0dFXnBRmZ6MpkUAnUymWRiYoKBgQEsFgter1fEjBRzzBsDJoaTeyqG\nwF7KtS1FixMbTdO49NJL2bJlC48++ihnnnlmwTbXXHMNH/nIR/KWDQ8P87nPfY5PfepTXHjhhaxY\nsUKsO3jwILlcjsbGRrxeL+l0WkSEOBwOMQg1PDxMIpE4bOZ9Mplk06ZNLFu2jMsuu+zoL1oiOQEw\naiNMF6oPh8lkIpvNEo/HqalZiqIo/OlPe/n4x8/GZrPh9/tJJDS2bj3Auee25+3b0TEAuGlqCohl\nIyMjBYWMw+EwDz/8MMuXL5cFyCQSiUQikUjeQtFLWwMWDYqirAdeffXVV+W0EInkGPC3f/u3jI2N\n8cYbbwBw6NAh1q1bRy6X48qbr8Tpy49CGAgO8E+b/olUKoWu6/xL279gNpsJdgRxM+mo/uUvf8lP\nf/pT1qxZg9Pp5I033uCZZ57hM5/5DHfffbc4lq7rdHZ2oqoqNpuNhoYGcrmcEIZdLtcRR1nouk48\nHmdiYkIIthaLBb/fX1IozGazJJPJPGeyyWTC4XAIsWjr1q1zEq6no2kaHR0dHDhwIC+TWVEUGhsb\nWbVq1RHlbxsMDQ0RDAaFgKrrOuFwmFgshs1mo6qqimXLlnHKKafMucDjO5mJiQlGRkYYHR1ldHRU\n/G2MgY2pwraRm24UgPR4PEcduWJERhQTt0tFmJTif/2v/8VVV111VO2RzI1rr72W22+/nYsuuoiP\nf/zjBesvv/zyovt1d3fT2NjILbfcwpe//GWRZa+qKqtXr0ZRFJ5//nmqqqoYHx8nl8txxx13oCgK\nBw4c4A9/+AMf/ehHqa+vx2Kx8L3vfU9EkmzcuJFly5bR1tZGNBrlpz/9KZ2dnTzxxBOcd955C/Za\nSGZG9s+FI5fLifu1IVjPJl/84YcfJpVKEQqF+OUvf8nf//3f09jYiKZp/MM//APpdJq77/4xf/7z\ns7z73Sv56Ef/hkQiwz33PMnQUJg///kHvOc9p4jjrVjxSUwmBx0d3WLZhg0bqKur493vfjdLliyh\nu7ub+++/n4GBAR566KGCgS3J8UP2UYlk8SL7p0SyeNm+fbsRj3e6ruvbj+ZY0nktkUiKMtVZ3NnZ\nKfKQ/+eX/mfBti2nt3D5dy7HZrORTqcnHczYhXANUFVVRSgU4n//7/9NJpOhtbWVu+++m8985jMF\n5/X7/YRCITKZDPF4XAjNxYo0zvWaPB4PDoeDaDRKKpUil8sxNjaG0+nE5/MVHN9qtWK1WoWAbmTO\nJhIJwuEwe/bsIZfLiddr7dq1cxKuYVIMb2lpoba2lt27dzMwMABMiswdHR0iSqS2tvaIrru6uloU\njNy3bx979+4VLrNMJsPY2BgOh4P+/n5WrFhxQhQgPBZ4vV68Xi9NTU1omsb4+Dijo6OMjIwQDofx\n+/0F+6iqSiqVIpVKYTabsdvtmM1m4d438lJnQyaTEcL5dEwmEx6Pp6S4PX2wY/v27fKL/THm9ddf\nR1EUHnvsMR577LGC9aXEa3j7/mu1WtF1PW8Az4g5MplMlJWVMT4+zm233ZY3g+S3v/2t+Pmzn/0s\nLpeLQCDAGWecwc9+9jPuuecenE4n5557Lg8++CDt7e3FGyI5Jsj+OT+oqprnpp6tUG1EQrlcLhE3\ntXHjRg4dOgRM9qOnnnpKbN/e3k5DQwM33PBdzjnncR5//BFuvPFBAE4/vYWf//xrecL15DFsKEr+\nZ+tVV13Fgw8+yG233UY4HKa8vJyzzz6b6667jnPOOedoXw7JPCL7qESyeJH9UyI5OZDOa4lEMmfC\nhOmkk2GG0Xn7HmLGTHm6nOpENW7dLaIUVFVl69atQjBdvny5iCMpRi6Xo7OzE3i7GJ4RsTCXHOjD\nkUqliEajQlA0BMGZzmOIk5FIhNdff51EIgFMikynnXbavGTGDg8Ps2vXrrzMapgsotje3o7H4zmi\n48ZiMfbu3UskEmF4eJhwOCxEz6liWWNjI42NjXMqHHmyYYj+hpg9/W81HavVSiAQwOPxYLfbUVU1\nr5hkNBoV76X5wOFwlBS2XS7XvPYjycKTy+Xo6+sjEokAk/dQo3/qus7g4KDIaV+6dCl2u51QKJQX\nGQQIEVsWZpSc6KiqWuCoNgZ4ZsJkMuWJ1G63uyAKTNM0RkZGGBgYELOuEokEo6OjOBwOysrKsNls\n1NTUUFdXh8mUIZd7k1SqA0VRcTqdmEzG8SqBFW/9L5FIJBKJRHLyMJ/OayleSySSIyZFihAhVFQs\nWKikEotuIRaLkc1mhYu6u7ub7u7JqbJ2u50zzzzzsA7qgYEBYrEYiqJQWVmJy+VaEDFV13VisVhe\n9rPVasXv95c8XzqdZuvWrYTDYfFg29raSk1NDXa7HafTedTuZVVVCQaDBINBIUrB5IN3U1MTq1at\nmpMLfXh4OO9YJpOJ+vp6wuEwhw4dyjsHTOZfL1++nObmZhE5IClNIpEQESMjIyMi6qEUTqeTqqoq\nKisrqaqqwmazkcvl8sTs6T9PjZQ5Gsxmc1629lRx2+v1Suf9IsXIxrfZbLzrXe9CURRMJpMoGtrf\n34+u6yiKwrJly3C5XGSz2aIittvtpqKiQorYkhMCVVVJJpNCpDYc1Yd7hjGE6qlitTFroRiapjE6\nOkp/f7/4bNd1nbGxMTRNE6K1zWajqakprybFZLHfGFZrFK/XAZgAP0yZgSaRSCQSiURyMiHFa4lE\nsqjRNI1IJIKu62SzWfbu3SvWtbW1sWTJksMewxBjADweD9XV1QvqFs3lckQikbyiTS6XC6/XmycS\np9Nptm3bJgos6rpOS0sLFRUVeQKwzWbD6XQeVVY1TBa53L17N0NDQ3nLnU4na9eupaamZsb9dV2n\nq6srr1Cmw+FgzZo1uN2TD9WpVIrOzk66uroKYi1MJhO1tbU0Nzfj9XqP6lpOFnRdJxKJCDE7FAoV\nDA5Mx+/3CzG7oqKiYGBC13USiYQQs6eL27OZGj9bjNkOxcRtOZBxfNB1nZdffhlVVfH7/axdu7Zg\nm1gsJiKHTCYTdXV1Iss/k8kQCoVE/JOBFLEliw0jlmu6o/pwzyuKohR1VM+mNoAhWg8MDOR9B0in\n0ySTybzB84qKiqLxWpFIBFWddF3L+6REIpFIJBKJFK+Pd3MkEsksyGazTExMsHfvXuLxODabjbKy\nMk499dRZ7Z/L5ejt7SWXy2E2m1mxYsUxiTpIJpNEo1HhcjWZTHi9XuFifOWVV4RwDZNifH19Pbqu\niwfdqQ5Zq9WK0+k8atf44OAgu3btKpgWXV1dzSmnnCKE6Klks1n2799POBwWy8rKymhtbS0qqmez\nWbq7u+no6Mh7gDeoqamhpaWF8vLyo7qWkw1VVRkbGxNi9nQX7HTMZjMVFRVCzJ4a61KKTCaTJ2ZP\nFbenFig9WqxW64yu7bkUkZTMnlgsxuuvvw5AXV0dDQ0NRbcLh8OMjIwAk8Vo6+rq8vr6TCJ2IBAo\nWbhWIlkINE0rcFTPRaieKlZPRnXM7f6jaRpjY2P09/fnfebpuo6maaJwNEzelxsaGqisLIz/UFVV\nRPr4fD45e0UikUgkEokEKV4f7+ZIJJJpXHTRRTz66KMFy/v6+njttdeASafwu9/97qIi63QMl2ks\nFhPC29KlS48463muaJpGLBbLKxapKAoHDhzIc7iuWbOG5cuXF7Q9k8mQTCbzXMwWi0WI2Ecqwquq\nyoEDB+jo6CiIEmlpaaGlpUU4dhOJBHv27Mlrb21tLQ0NDYd9wFdVlZ6eHg4ePFg0h7myspKWlhaq\nqqqO6DpOdlKplIgXGR0dPaxr2m63i3iRysrKObv6NE3jQx/6EPfee29R17YxPf5oMXLpS7m2pTB6\n5PT19dHV1QVMDpjNNIA0NjZGKBQCJmeA1NXVFTj5S4nYHo+HiooK+bc6xpT6DH0nYQjVUx3ViURi\nVkK10+kUIrUhWh/NQJkhWg8MDBTcf10uF5lMJu/z2+Px0NTUVHKGQiqVIpFIoCgKZWVlsqbAO5CT\noY9KJCcqsn9KJIuX+RSvpTVAIpEcNV/60pcKlmmaRk9PDyaTCU3TKC8vn7Xols1m0TQNp9MpxJVI\nJHLMxGuTyYTP58PpdBKNRonFYuzcuVM4yJ1OJ21tbQXCNUw+aNvtdux2uxCxs9msyDM2m804nU7s\ndvucH3DNZjNr1qyhvr6enTt3Mjo6Cky+1gcOHKCvr4+1a9diNps5cOBAnnu8paVlVnEtxnlWrFjB\n8uXL6e/vJxgM5olco6OjjI6O4vf7aWlpYenSpfJhfQ44HA7q6uqoq6sDIBqNitd0dHS0INs6nU7T\n19cnol88Hg9VVVVUVVURCAQO6/IzmUxce+211NbWUltbW7A+lUqVdG1PnWVwOKbmxxuRP1Ox2+1F\nXds+n2/ei7G+0zD6n6Ioh43vCQQC5HI5MTDR399PbW1tnthnFJurqKhgbGxM/J2Nv58UsY8txT5D\nT2Q0TSOVShU4qg8Xn2QI1dMd1XOp73C4doVCIfr7+wtEa7/fj9PpZGRkRNyDjfz4pUuXziiWGzUO\nrFarvI+9Q3mn9VGJ5J2E7J8SycmBdF5LJJIFobu7m87OTjH19l3vehcOhwOv1zvjw52macTjcWBS\n7BofHxcxCw0NDQtStHEmstksL774IiMjI+LBu6WlhTVr1uB0Omf1oJrNZkkmk3kOV5PJhMPhmHUm\nZzH6+vry3NW6rovXqra2FqvVis1mY82aNUeVV63rOkNDQwSDQcbHxwvWu91uWlpaqKurk7ERR4mm\naYyPjwtXdjgcntGZaDKZKCsrE67ssrKyef0bqKrKxMRESXF7ekb6kWLE85SKJDna7PgTnW3btpFO\np3G5XJx22mmH3V7XdQYGBsS91O12zzjIlE6nCYVCBYMVHo+HQCBwzO+7khMHQ6ie7qg+nFANFHVU\nz5dQPRWj6GIp0bq6ulrUJzBwOBw0NTUddtBc13Vxn/Z4PLKvSCQSiUQikbyFjA2RSCSLmlQqxdat\nW8XDa3Nzs4gLOVwxIyNuw2Qy4XK5SKfT9PT0AAiR7liRzWbZtm0b0WgUXddJpVLU1dWxbNkyYNK9\n6Pf7Zy2s5XI5kslkXramoig4HI4jyus02njgwAEOHjzI2NiYyMQ2mUw0Njbyvve9b16LsY2NjREM\nBhkeHi5YZzzsNzQ0yMzPeSKTyeTlZRtiZCmsViuBQECI2Qs9WyGRSJQUtotFzhwpTqezpGt7toNI\nJyqZTIZXXnkFgCVLlrBy5cpZ7adpGn19fUKs8/l8VFdXz7hPKRHb6/VSUVEhhbmTHONzcKpIHY/H\nZy1UT3VUL5RQPb29htN6er0In89HbW0tmqbR2dmZN7hcVVXF8uXLZ9U+o74HMO+DhxKJRCKRSCQn\nMlK8lkgki5pdu3aJSAu/389pp51GIpEQIkopJ6Uh7sLkg64hgPb09JBKpYQgeyweDqcK1watra3U\n1tYSiUTEg65ROGouxepUVRUi9tRMbbvdfkRTpJPJJNu3bycYDOY5LcvLy/F6vbS3txctMnU0RCIR\ngsEgAwMDBc5gq9VKY2MjjY2NUuyaZ+LxeF5etjFdvRROp1NEjFRWVh7Tv4cRXVFK3J6N4DUbzGaz\ncGhPF7Y9Hs8JP5AyOjrK/v37gclZH4cToKeiqiq9vb3iflVRUUEgEDjsful0mrGxsYLBEilinzwY\nQvVUR/VshWqHw1HgqD6W/XAm0drr9VJbW4vH46G3t5fBwUGxzmKxsGLFCioqKmZ9LuO7jcViwefz\nzds1SCQSiUQikZzoSPFaIpEsKn73u99xySWXABAKhXjjjTfEug0bNuDxeNB1nYmJCeGq9vl8eWKv\nUaRR0zRR3NAgGo0yNDQEQHV19YI/IJYSrlesWCHamkwm8wQ4Q0CbSzE9Y7p1MpnME4ANEXs2D/vj\n4+Ps27cPVVXRdZ1IJEIqlSrIqq2traWtrW1eXdgwmZF78OBBent7C0QNs9lMQ0MDTU1Ncy4yKDk8\nxnR1Q8weHx8vKSxt2bKFs846C7/fL8Ts8vLyBXc+lkLXdeLxeIG4bfx8uCKWc8Htdpd0bc93f1gI\nOjo6GBgYAGD9+vVz7kvZbJbe3l4R8VJVVUVZWdms9k2lUoRCISliLzBTP0OPF8Uc1dPz94tht9uF\nUG2I1cdrwEjXdcbHx+nv7y+Y+WGI1j6fj2QyWVCQ2Ofz0dTUNOf3dDgcFjU65OfcO5fF0EclEklx\nZP+USBYvUryWSCQLyk033cQNN9zA2rVr84RogJdeeolvfOMb7NixA5/Px6WXXkpvby8PP/wwmqax\nbds28UBYW1ubN8VdVVV+9rOfceedd7J//368Xi8XXXQRmzdvxuv1ijgNt9udJ2wb03o1TcPhcFBf\nX79g157NZnn11VeJRCJi2cqVK2lqairYVtO0gngEu92O3++f08O7pmmk0+mCglZGcchSsSS9vb10\nd3cL4dtqtbJ69WqcTif79++nq6srb3uLxSJE+Pl2r6dSKTo6Ouju7i7IQTaZTNTW1tLS0nLMim6e\njORyOcbGxoSYPbXI5g9/+EO+8Y1v5G1vNpupqKgQYvZicg1mMpmSru2JiYkZc8DngtVqndG1fTT9\nZNu2bdx///08++yzdHV1EQgEOOuss7jppptKRn+oqkp7ezv79u3jlltu4atf/Sqvv/46sVgMq9XK\nGWecURCRMjg4yG233cbWrVvZtm0bsViMZ599lnPPPVdsk06n6e3tRVVVHnzwQR5++GE6Ojpwu92s\nX7+eTZs2cfbZZ5e8llIits/no6Ki4qTPJD9aNm7cyK9//etjdr5iGdWzya83hGoj/sPlci2av/34\n+Dh9fX0ForXH46G2tha/3w/A0NAQPT094rM2lUrx+9//nj179rB161bGx8e5//77ueKKK/KOM9O9\n4LzzzuPpp5+ecTBwNv0UJuuFNDY2ljzOZz/7We6+++6S6yULw7HuoxKJZPbI/imRLF7mU7w+sefS\nSiSSeaevr4/NmzcXFRlfe+01zj//fNra2vj+rd9nX+8+fnLzTzj1/afyZ/6MPqqTzqRxMim4Gk5l\ng3vuuYcvfvGLnHfeedx0000MDw9z11138eqrr/LMM89gs9mw2+0FD4mGUzscDpNKpUilUgvimJyL\ncG20q6ysDJfLRSQSIZvNkk6nGRkZwe12z1r8MplMOJ1OHA6HELFVVSWTyZDJZLBarTidTuEIU1WV\nYDDIyMiIOIbH42HNmjXCcd3e3k59fT07d+4kHA4Dk+Lm7t276enpob29fU5Tow+Hw+Ggra2NlpYW\nurq68jJENU2jp6eHnp4eli5dysqVK4WQIJk/LBYL1dXVIlYilUqJeJEbbrihwM2sqiojIyPifWS3\n26msrBQRI8fTRWiz2fj/2Xvz6DiqO+3/qep9V3dr39WStVkWxnbAA5jYwEAgYAgJhoQTyDDBMwnE\nzhuS8/K+/CDDmjDkDQEyAwyTSULAmMwJJCSESTAmOOzB8iJvslpqba2t1fve1VX1+6Opa5W7Jct2\nS2rZ93MO55iqrqVLdav6Pve5z7e4uDhn3I0gCIhEIjnF7VAodMIolelwHAev1wuv15u1jmEYGI3G\nGcXtEzk0H330Ubz//vu44YYb0NnZifHxcTz11FNYtWoVPvroI7S3t2dt88QTT2B4eJgI1DzPEzFO\nGuCTRDeGYaBUKnHkyBE89thjWLZsGTo7O/HBBx9k7Vej0aCiogLf+ta38POf/xzXXXcd/umf/gmx\nWAzPPPMMPvvZz+L999/HmjVrcn4XrVaLyspKJBIJeL1eck7SNaci9ukxn53uZDKZ5aiei1CtVqtl\nbmqDwVCQf9+5itapVAoul0v2ftfr9TCbzXj88cdRV1eHlStX4i9/+UvO47zwwgtZyz744AP8+7//\nOy699FIoFAkAQwAmAKQAKAAUAagFUIyenp4TtlMgMzMi17HeeOMNbNu2DVdcccUJrwkl/1BhjEIp\nXGj7pFDODqjzmkKhyLjpppvg9XqJi3O68/qqq67C/v378ULPC0gYMkLYn372Jzy5+Unc//r9KGos\ngiAIsCQsuKTkEtRUHnNIcxyHsrIyrFy5En/4wx+IkPbuu+/iuuuuw2OPPYZ//ud/hl6vz1l8LZVK\nYXBwEMDcCo+dLOl0Grt37yZCL5DJl21sbJzT9lLsSTgcPq0oEWlfqVSKFACCBEoAACAASURBVK+U\nUCqVYFkWfX19MgdkaWkpGhsbc7q+RFHE0NAQDh8+nCXq1dTUoL29fV6m/vM8j6GhIfT19WVljgJA\ncXExli1blvcsbsrMhEIh4sr2er0njAQwmUxEzLbb7UsmOzqRSGTFkEj/najg5cmg0WhmFLYNBgM+\n+ugjrFmzRnbdnE4nOjo6sGnTJjz//POy/U1OTqKlpQXf/e53ce+99+JHP/oRvv71r+PgwYPQ6/Ww\n2+05B5yk71RSUoLf/OY32LRpE95+++0sRyfP8zCbzVi/fj1+8pOfgGVZVFdXY2xsDA6HA1u3bsXj\njz8+p+8ej8fh8/myBEOLxQKr1VqQIufZQCqVkuVTx2KxOQ3mSEL19IKKhf43DAQCcLvdWW3aYDCg\nqqpKFo3j9/sxMDAguxZlZWWoqakBz/Pw+/0oLS3F7t278ZnPfCan8zoXt956K1588UX09PwBs/9U\nMCEabQHHKVFUVDRrO52Jv//7v8cnn3yCiYkJGtdDoVAoFAplSUCd1xQKZV7YtWsXXnnlFXR1deFb\n3/qWbF04HMaOHTtw4103EuEaAC695VI8+7+exZsvvIkv3vtFAECyKImPQh9B4AXU1dQByBRxDAQC\n2LRpE3Q6HTiOA8/zuOiii2A0GvGb3/wGW7duzSlcA5nOtV6vJwJxcXFx3vJ6T1e4BjIuSIPBAK1W\ni3A4jFgsRjrFsVjspKJEpOKNGo2GiNgcx8Hv96Ovrw+CIEClUpHCiFVVVbPuq66uDhUVFTh06BCG\nh4fJuuHhYYyPj6O1tRV1dXUzXvtTQaFQoKGhAXV1dXC73XA6nYhEImT91NQUpqamYLVaSRG6fB6f\nko0krDocDnJvSmL29HtfQorocLlcZJaBFDFisVgWpHDqqaDVaqHValFaWpq1jud5ImjniiSZiyNV\nQpplMX0GhATLsjCZTPD7/TJx22azYfny5Th8+HDWNnfffTfa2tpw880349577wWQKYwqDejpdDq4\nXC4AkMUKGAwGADihSMlxHOLxOKqrqwFkHOyjo6OwWq1gWRZ6vX7O312n06GqqgrxeBxer5cMUAWD\nQQSDQVgsFthstiUz4LEUmS5US47quQjVKpUqS6heSmLoyYjW0iDq9DaqVqvR0NBAHNksy+Z8VpyI\nZDKJ1157DRddtBJ1dTwyTusM/f2ZjHqHo+LTJWEYDAcBrD3p4wCZyJG3334bX/va15bU34pCoVAo\nFAolX9BeBYVCAZARMrZs2YLbb78dHR0dWeu7u7uRTqdRu7pWtlypUqKhswGufS6yrLS0FNcbrsfa\n9Wvx/s73AYDkWet0OjIdX4ra0Gg02L9//wmFDovFglgsRoo/zrXo2GzkEq4dDsdJCdfTUSgUM0aJ\nGI1GGI3GkxJp1Wo11Go1hoaG0N/fT8Q1nufR0NAAm80GURRPuE+1Wo2VK1eitrYW3d3dpBglx3Ho\n7u4mUSL5uKbTYVkWNTU1qK6uxsTEBHp7e2XX2u/3429/+xuMRiOamppQVVVVsKLomYRCoSCxHK2t\nrUilUmRAwePxZDlqBUGAz+eDz+dDT08PVCoV7HY7EbMlAbXQkdrnTPd5LBab0bWdawbBTAiCQITc\n43G5XKipqcHvfvc74tweGBjA888/j507d8raciKRAMMwZEDrqquuAsuyOHjwYNZ+OY6bsWAnkBH1\nzz//fLz00ktYtWoV2tvbEQgE8Mwzz8But+P222+f8/eT0Ol0qK6unlHELioqgtVqpSL2acJxXJZQ\nLcUyzYZKpZKJ1EtNqJ5OMBiE2+2WDYICGdG6srISVqtVtjwajaKvr08Wl2S1WlFfX58XV/lrr72G\nYDCIm25amzWQfskld4NlWfT3/3za0jiAwwBWnvSxXnrpJYiiiJtvvvm0zplCoVAoFAplqUJ7ExQK\nBQDw9NNPY2hoCDt37sy53j3mBsMwsFXIp62LgoiJwQmkUxlR1WKxQKvTgmEYJJkk+dyyZcvAMAze\ne+893HrrrVAoFNBoNDh06BC8Xi8YhoHf78/qgE7HYDBAqVQinU4TYeR04HkeXV1dWcL1TMXUTgYp\nszcajSISiUAQBITDYcTjcZjN5jlndguCgL6+PkxMTECj0UClUkGpVKK2thZarZaIGVJm9omEX5vN\nhnXr1mFwcBBHjhwhYnggEMBf//pX1NfXo6WlJe8CB8MwKC8vR3l5OaamptDb24upqSmyPhKJYO/e\nvejp6UFjYyNqa2vz5qynAP/wD/+An//85zOuV6vVqKysRGVlJYCM8CPlZU9NTWU5OjmOw/j4OMbH\nxwFksmOn52UvVYFMr9dDr9ejvLw8a106nc5yak//92zCscSHH36IQCCAa6+9FhMTE5iYmAAA/OAH\nP8CaNWtw5MgRfPjhhwCAo0ePwuVyQalUwmg0yoTsmTjRObz44ovYtGkTvvGNb5BltbW1+O///m/U\n1tbOsuXsSCJ2LBaDz+cjInYgECBObCpiz8z09nmqQrVSqcxyVEs1EJYyM4nWer0eVVVVWb8ZRFHE\n2NgY3G43KezKsizq6upQUlKSt/N68cUXodGocP31F+L4Jplpp7m2mgSQzLViVrZt24aKigqsX7/+\nFM6Ukg9O9A6lUCiLB22fFMrZAe1FUCgU+Hw+fP/738d99903YxG/8XhGpFJp5I6lYDAIS5kF485x\nsCxLMoxf518HAAQQQBGKYLfbsWnTJvzyl79Ea2srrrvuOvT29uK73/0u1Go1OI5DOByeVbxmGAZm\nsxk+nw+pVAqxWOykprpPRxKu/X4/WdbQ0JAX4VpCcpjrdDri3Eyn0/D5fNBqtbBYLLMKtKlUCocP\nH0Y4HCbLSktLyTnG43Ekk0mStx2Px0lkwmz7ZVkWDQ0NJErE7XaTdQMDAxgdHUV7ezuqq6vnJcpD\ncvwGAgE4nU6MjY2RdfF4HAcOHMDRo0fR0NCA+vr6JSuEFhKXX375SX1eEr/q6+shiiICgQARs/1+\nf5ZIGovFMDQ0hKGhIQBAUVEREbOtVusZMRChVCphs9lyPiNFUUQ0Gp3RtZ1MJjE+Po7t27ejsbER\na9ceiw947733MDY2RgRlKYs8Go0ScVur1cLn8+GZZ56BRqOB1+uF3W7POo8T5ZgbjUYsX74cF1xw\nAS655BL09PTgpz/9KW677Tb87ne/Q3t7+2m1eUn8j8Vi8Hq9SCQS5P6hInY2HMchFovhM5/5DJxO\nJ6LRKJmlNBtKpTLLUX0mCNXTCYVCcLvdsvcfkLnHJKf18fdqMplEf3+/bBuDwYDGxsa8FnkOh8P4\n85//hM99bjWsVlPWepfrFzNsKQBwz7AuN729vdi9ezfuuusuGq21iJzsO5RCoSwctH1SKGcHtPdA\noVBwzz33wG63484775zxM6wu4+jlknIHJsuysJRa4B32ZnKolXKRKo44ipBxSD/77LNIJBL43ve+\nh+9+97tgGAY33ngjGhoa8PrrGbFbEIRZ3cMWiwU+nw/AsTzYk4XneezZs4fsBwDq6+vR3Nx80vua\nCwqFAlarlUSJpNNpJBIJJJNJmEwmGAyGrE5pOBzG4cOHZY67uro61NQcK4JpNBqh1+sRj8eJSBSP\nxxGPx6HRaKDT6WYVibRaLVatWkWiRCRnWyqVwt69ezE0NIQVK1bAbDbn+YpkKCoqwpo1axCJROB0\nOuF2u4komkql0NPTg76+PtTV1cHhcORVfDjb+PKXv3zK2zIMA6vVCqvViubmZlLMVYoYOV5cAjKO\nW2lwQqFQwG63EzF7vu6nxUQaqDIajcS9Pp2RkRGsW7cOdrsdTz/9NDQaDUKhECYmJvDb3/4Wl19+\nedZMkukFtafHHCSTyVMSfwVBwGWXXYYNGzbgiSeeIMsuuOACXHrppXjyySfxwAMP5KUYriRiR6NR\n+Hy+nCK2zWY7IwY15ko6nc5yVEtC9Zo1a2Tvo+lIQvV0sfpMfhbOJFpLOeu5RGsA8Hq9GBwcJLOJ\nGIZBRUUFKisr8x5F9fLLLyOZTOHGG9dl3cM8L4BhGLDsTEJzYobluXnhhRfAMAy+8pWvnOLZUvLB\n6bxDKRTK/ELbJ4VydkDFawrlLMfpdOK5557DE088QRy4oigikUiA4zgMDg7CbDajtKIUoijCNybv\nYJstZqTCKdgqbaQA0nQYHOvAmc1mvPrqqxgcHERPTw9qamqwbNkyXHzxxSguLobRaEQsFoPRaJzx\nfKUp9JFIBJFIBOl0+qSEHEm49nq9ZJkUlTHfaDQalJSUIBqNIhwOQxRFhEIhUtBRcs5NTEzA6XQS\n8UqhUKC5uTmn05JlWRgMBuh0OiQSCSQSCQiCgGQyiWQyCbVaDZ1ON2vGZ3FxMT772c+iv78fR48e\nJe5Nn8+HXbt2oaGhAc3NzXnJCc2F0WjEypUr0dLSgv7+fgwODpJzSKfT6Ovrg8vlQnV1NZqampZM\nvvKZilKpRFlZGRE5E4kEcWV7PJ4s5yjP85icnMTk5CSAzKCJ5L4vKSk5o4U4ICPGXXPNNYhEInj3\n3Xdlz5r77rsPKpUK9913H9LpNCKRCGn3iUQCPp8PZrM5q+2dyjV75513cODAATz++ONkGcuyuOCC\nC9DU1ISuri6EQiEolcqcz5pTQRJbZxKxpUzsM03ETqfTiMViRKSORqOy7OWZUCgUOR3VZ4PjNhwO\nw+12k3oMEjqdDpWVlbDZbDmvQzqdxuDgoOydrtFo4HA4YDJlu6LzwbZt22A2m/C5z63OEqml306Z\nwpinNjNsOi+99BJaWlpw7rnnnva+KBQKhUKhUJYqVLymUM5ypFzILVu24Fvf+lbWeofDga1bt+Ib\n//INKJQK9H7Si3VfWkfWp7k0+vf147M3fhZMDqeRHtmdt5KSEthsNiiVSkQiEXR1deH6668HkHHc\nJpPJWadAWywW4hIOBoNzFlpyCdd1dXULIlxLSA5NrVaLUCiERCJBnKwajQY+n49EBQCZjntbW9sJ\nHeYsy0Kv1xMROx6PQxAEpFIppFIpqFQq6HS6GSM4WJYlBRMPHjxIojxEUUR/fz+JEqmqqsrfxTgO\nnU6H5cuXY9myZXC5XHC5XCRrWRAEDA0NYXh4GBUVFWhqaso5WEJZeLRaLWpqasisgFAoRMRsr9eb\nFWWRSCQwMjKCkZERAIDJZCJCtt1uP6MiJZLJJK655ho4nU689dZbWc+a4eFh+P1+nHfeebLlDMPg\nlVdewauvvort27dj9erVpF1Lg1Iny8TEBBiGyfp7SMKxNOvB5/ORwpb5YrqI7fV6SdyR3++XxYks\nRRGb53mZm3quQrU08Hi8o/psEKqnE4lEMDIykiVaa7VaVFVVzShaA5lnjcvlkg2YFRcXo7a2dt6e\nI+Pj49i1axduvvkm6HS6rPVS+5rZ7Z29zUx89NFHcDqdeOihh07lVCkUCoVCoVDOGM6cHiKFQjkl\nOjo68Oqrr2Ytv+eeexCJRPDkk0/C4XCg0dyIVZetws4XduIr934FWkPG+ffW828hEU1g3aZ1su1H\nekZg19thrpFHBHAcRzp3Go0G3/nOd8DzPO666y6o1WqkUilEo1EoFIoZO596vZ58NhgMztq5leB5\nHnv37pUJ17W1tWhtbT3xRZoHpPzcRCJBROze3l5Eo1FoNBqo1WrYbDa0tLScVCecYRhSvFHKBed5\nHhzHgeM4KJVKImLnumY6nQ5r1qzB5OQkDhw4gGg0CiAjOHZ1dZEokdnc8aeLWq1GS0sLGhsbMTQ0\nhL6+PiIGiaKI0dFRjI6OorS0FE1NTXlziZ7JvPvuu7jooosW5FhmsxlmsxmNjY3geR5+v5+I2dOL\no0qEw2GEw2G4XC6wLAur1UrE7KKioiUr5gmCgE2bNuHDDz/Ea6+9liVQA8DWrVvxhS98QbZscnIS\nmzdvxtVXX43LLrsMzc3N5Jq6XC6oVKoZr8ls4m9zczNEUcT27dtl+ZBdXV04evQobr/9drAsC0EQ\n4PF4yCyXfCKJtJFIBD6fD8lkEoIgEBG7qKgIRUVFBSti8zyf5aiWilPOhjS4ON1RPV2oXsj2WShE\nIhG43W4Eg0HZcq1WS5zWMwnAgiDA7XbL6iUolUrU1dXN+/tg27ZtEEURX/rSjVAoygAcm43G8wIE\nQcDAwAR0Oj1aWqqP25oFUAWga87HYhiGTokvAM7GNkqhLBVo+6RQzg6Y6ZmKhQrDMKsA7N69ezdW\nrVq12KdDoZwVbNiwAV6vF/v37yfLfrfnd7jxwhtR01aDKzdfiamRKbzy/16B1qTF9ontsu2vYq/C\n363/O7y38z2y7Ic//CH27duH1atXQ6vV4ve//z127NiBhx9+GHfffTeJ0eB5HgqFAmazeUaRRiog\nBwAVFRWziiw8z2Pfvn3k80BGuG5razula5NvwuEw9u7di1AoRCIDqqur0dbWlpciXJKILWWBAhmR\nS6fTzTolned5OJ1OOJ1OWYE+lmXhcDjQ3Ny8ICITz/Nwu92koNnxWK1WNDU1oaysbMkKnfPNxo0b\n8dprry32aSCVSpF4kampKcRisVk/r1KpZBEjSyky5tvf/jaefPJJbNy4ETfccEPW+ptvvjnndoOD\ng2hoaMAdd9yB2267DaWlpaSQbVtbG1iWxcGDB2XbPProo2AYBj09PXj55Zdx2223oaGhAUBmIFLi\niiuuwI4dO3Ddddfh8ssvx+joKH76058inU7jk08+QVVVFUZHRyGKIhiGQWVl5SkXxZ0L00VsCZZl\nC0LE5nke8XiciNSSo/pEv5slofp4R/VsucuF0j4Xgmg0ipGRkVMSrYFMUd/+/n7Zu8BkMsHhcJzy\n+/Lf/u3fEAgE4Ha78cwzz+D6668nMR1btmyRxY+sXr0a4+PjOHDgAIqK0mCY3WRdMplCPB5HZ+ed\nUCqV6O//uew4Dz30OhimHAcPHsT27dtnbKdARqCvqqqCw+HAe++9B8ricja1UQplqUHbJ4VSuHR1\ndWH16tUAsFoUxbmN3s8AFa8pFEpONmzYAJ/Ph3379pFlPHj81/v/hSf+9xNwdjmhN+lx8Y0X46b/\n7yYUlcinmH9e8XmsX78eb731Fln229/+Fo888gjJVe7s7MRdd91FIkOATH6lNH1YrVbPKErzPA+X\nywVRFKHT6VBdfbzDKYMgCNi7d69MuK6pqUF7e/vJX5R5wOPxoLe3F4IgkKzqyspKIlbp9XqYTKa8\niDgcxyEWi5EoDiAjtEhO7ZmE32g0ioMHD8riTICMS7ujowPl5eWnfW5zQRRFjI2Nwel0ZgkfAIjb\ndz4KdC11YrHYvIqQp0o0GoXH44HH44HX65Xdm7nQ6/UoKSkhgvapxGcsFBs2bMCuXbtmXH98fIfE\n4OAgHA4HvvnNb+LLX/4yWlpaSDxBe3s7WJbFgQMHZNsYjcac7ZdhGNmgVTKZxI9+9CNs374dLpcL\narUaF198MR544AF0dnYCyAjKkqOVZVlUV1fnZRBtJkRRJHEi0wvULqSILQhCTkf1iX4jMwyT01F9\nss+fQm2f+SQajcLtdmfNvtBoNKisrITdbj/hdZucnMTQ0BAZTGUYBlVVVaioqDitgcuGhgYMDQ3l\nXOdyuVBbWwsA6O3tRWtrK+644w488sgjn/4+OQJg4NPvmHm/rlz5LSgUCvT1TRevjWDZi+fUTgHg\nz3/+M6688ko89dRT+OY3v3nK342SH86GNkqhLFVo+6RQChcqXlMolEUjjTS60Y0JTORcz4BBDWrQ\nilawONYRlaZbAxmH1WzF/xKJBPmsVLAqF5OTk0TErKuryxKyBEHAvn37SKE4oHCEa1EUMTg4SHJ/\ngcx1aW1thUKhIA50ICPimEwm6PX6vDiL0+k0yc6VmB43MpOAILnNjp8mX1ZWhuXLly+oK9bj8cDp\ndGJqaiprnV6vR2NjI2pqago2foCSjSAICAaDxJXt9/tljv9cFBUVETF7qWYm5+LgwYMIBAJQKpU4\n99xzwfP8rEKqWq3Oa8bv9JktSqUS1dXV81awVUIUReLEPl7EtlqtKCoqysuglCAIWY7qkxGqp4vV\nOp2ODpSdgGg0itHRUfj9ftnykxGtOY7DwMCAbB86nQ4Oh2NB3ztSsVFRFI/7beIE0I9QKAhBEKBW\nq6HXT8+2tgFYCaBwB9soFAqFQqFQ8g0VrykUyqITQQRDGIIPPvDgoYQSpShFNaqhy1GQSMpeVigU\ncxodj0QiSKVSYBgGZrM5pyiVTCaJW0oSsSRyCdfV1dVob29f9GiJdDqNnp4eWUfcYrGgtbWVCESC\nICASiSAajRJRRa1Ww2w2581tKk2Ln15cjGEYaLXaGUUZnudx9OhR9Pf3Z0WJNDU1oampaUEFRL/f\nD6fTifHx8ax1Go0GDocDdXV18y68UfKPVMhUErPD4fCsn1coFLDb7UTMNpvNs36+UBEEAbt370Yq\nlYLRaMQ555wDINP20um0zHUq1QaYj2ea1+uFz5fJ81Wr1aiurl6Qtp1PEVsSqqc7qmOx2JyEap1O\nJ3NUU6H65IjFYnC73VmitVqtRmVlJYqLi+d0PQOBgKx4LwCUlpYuyuDk9Jlhx9+HPB+D17sfSqUX\nRqMGarUWgAVADYD8FT+lUCgUCoVCWSpQ8ZpCoSwpOI4jAqler59Th1MQBIRCIQiCMGv+9fDwMBKJ\nBFiWRUNDAyk4tn//flnMRaEI17FYDIcPH5a5lysrK1FfX5+zI89xHEKhkMwlrdfrYTab8yakSALP\n8Xmukoid6+8ViUTQ3d2d5Xw2GAzo6OhAaWlpXs5troTDYfT19WFkZCRLmFKpVKirq0NDQwO0Wu2C\nnhclf8TjcVle9vQ2kQutVivLy14qf/toNIp9+/ZBFEVUVFTA4XAs2rlMTEwQsU6r1aKqqmrBBNyZ\nRGyFQgGr1QqLxSI7F0EQkEgkshzVJ3LvS0L18Y7qM8XFv9DEYjGMjo6SgQ+JkxWteZ7HyMiI7D2u\nUqnQ0NCAoqLFEYNjsRgSiQQUCgUsFkvWOikSpby8nA50UCgUCoVCOeuh4jWFQikovve97+Gxxx7L\nuU7KMxVFEWq1+qSyU6e7nDQaTc7pwaFQiHRuS0tLYTKZsoTrqqoqLF++fNGFa6/XS/K+gYyTsLGx\nEWVlZSfcNh6PZ0WJmM1m6HS6vH0vSfw5fgp9Zgq0PmcsgdvtxqFDh2TubSDTee/o6CBZvQtFPB5H\nX18fhoaGsjKFWZZFbW3tgk81LwRma6NLlVAoRPKyfT7fjBnSEiaTSZaXXaji5Pj4OPr6+gAALS0t\nKC4uXrRzkXLmpeJ4BoPhtPOFT+UcwuEwfD4fcd+Kogie56FWq8GyLHFXn0ioBiBzVEsxIIt9L5wJ\n7TMej8PtducUrSsqKlBSUjJnQTcWi6Gvr082yFtUVISGhoZFnUUTDAbB8zx0Ol3Wuy0QCCAWi0Gl\nUslmgVHODM6ENkqhnKnQ9kmhFC75FK/zF5BIoVDOWqRiRrlIpVIQRREMw5x03IVSqYRer0csFkMy\nmYRSqcwSv41GI6ampsDzPPx+PwYGBmTCdWVl5aIL16IoYnh4WFYQSq1Wo62tDSaTaU770Ol00Gg0\nCIfDRKSROssWiyUvHXqWZaHX66HT6YiILQgCUqkUUqkUVCoV9Hq97FhVVVUoLS3F0aNHSQFNICPA\neTweNDc3w+FwLJgLTSoi2dzcDJfLJZtuLggCBgYGMDg4iMrKSjQ1NS3ZaImTZbY2ulQxm82kSKfU\n/iUxO1dBz3A4jHA4jP7+fhJBIYnZRUVFiz64JSEN2LEsO2PB2oWCYRiUl5fD7XYTV/Pk5OScBtzy\niUqlgsFggMfjweTkpEyonv7cOv5veLyjuhCE6lws5fYZj8cxOjoKr9crW65SqYjTeq7XXBRFjI+P\ny2bQSIOOCz2b53gEQSADZLnet9LsgEIuIks5dZZyG6VQznRo+6RQzg6o85pCocwbgiAQx96JijTO\nRjgcBsdxM+ZfT01Nwev1oq+vD4IgkONUVFRgxYoViypKSRnR0zv2JpMJbW1tp9zJ5TgOwWCQdJal\nQmImkymvIrEoikgmk4jH4zJXqzSooFKpZNc2FAqhu7s7y3lnNBqxYsWKRXGQchyHwcFBuFyuLHc4\nkCk22dTUBJvNtuDnRpk/UqkUiRjxeDxZRUaPR6VSkXiR4uLiRXPmi6KIPXv2IB6PQ6vVYtWqVQUh\nqksRDtIzx2azwW635/04oiiSgr3T4z+Od1RLTuvpzyWNRoPS0lKUlJTAaDTOOFuEkh8SiQRxWk/v\nS6hUKuK0PpmBgmQyCZfLRQZvgIzT3+FwLPgMnlwkk0lEo1EwDJM12MXzPBk0t1qtBXG+FAqFQqFQ\nKIsNjQ2hUChLgpMt0jgTJ8q/TiaTeOutt+D1eqHRaGCxWApCuI7H4zh8+DBisRhZVlZWhsbGxtMW\nmUVRJFEikrAjXZt8d5xFUUQqlUI8Hkc6nSbLFQoFcYRL11kURYyMjODQoUOynFog49Jub29flOxh\nSXzr6+sjAyrTsdlsaGpqWnBHKWVhiEQiRMz2er2y4m+5MBgMRMy22+0L5qZMJpPYs2cPeJ6H3W5H\na2vrghx3LnAch5GREfIMKCkpOe3s4ekZ1ZJgfaL4F+BYHj8AMitHEkoVCgVsNlte6wJQjpFIJIjT\n+njRury8HKWlpSftbvf5fBgYGJC9XyoqKhY0Y/1ESIPoarU6a0ZEPB4nhSnLysoK0t1PoVAoFAqF\nstDQ2BAKhVLwpNNpIkKcTM51LqTp81LmczweJ2K4KIro6elBOBwGkBEySktL0dHRsajCtd/vR09P\nD+mMMwwDh8OBioqKvOxfcltrtVqEQiEyUOD3+0mUSL5chwzDQKPRQKPRgOM4xGIxcBwHnucRiUQQ\ni8Wg0+mg1WrBMAxqampQVlaGnp4eDAwMkP243W5MTEygpaVlxgKV84VCoUBdXR1qamowNjYGp9Mp\nc/j5fD58/PHHMJvNaGpqQkVFRcGIJpTTx2g0wmg0or6+HoIgIBgM8Da7lwAAIABJREFUEld2IBDI\ncvZKgurg4CBxWkpittVqnbd7Y7p4O9dIoYVCioEYGRmBIAjweDxQKpVzjjY53lEdi8VkYuVMSPUO\npPiP46OLRFFEKBSCz+cj7x2PxwO/3w+r1UpF7Dwxm2hdVlZ2SqJtOp3G0NCQrPCvWq2Gw+EoqEgn\nURTJvTpbZMj0QRQKhUKhUCgUSv6gzmsKhXLaHDlyROYQnF6kUaVS5c1pG4/HyfR/o9EIlUqFAwcO\nYHR0FMlkEsFgEDabDWvXrp2XKe1zxe12Y2BggHTwVSoVWltbYbFY5u2YqVQKwWCQOEoZhoHBYIDR\naJwX4SadTiMWi8nc1QzDEBFbOmYgEEB3dzcCgYBse7PZjBUrVixqXMfk5CR6e3uzYk6AjPO2sbER\n1dXVZ4QYcXwbpRwjnU7D6/USMTsSicz6eYVCQYo+lpSU5FVkHhgYgNvtBsMw6OjoKCgBTyIWi2F0\ndJTUMqisrMyaWSNFLEx3VM9FqFar1bJ8aoPBMOe4KWmGjt/vlx1LqVQSJ3YhRLDkopDbZzKZxOjo\nKKampmSitVKpRHl5+Sk7jaX8+WQySZbZ7XbU1dUVXNwLx3FkgLyoqCjrnerxeMBxHPR6/WnPRqAU\nJoXcRimUsx3aPimUwoXGhlAolIJi48aNeO2118j/J5NJpFIpIqDmSzAQRRGRSIQItCMjIxgbGyPr\nAKChoQEajQZ1dXULLlTwPA+n0wmPx0OWGY1GtLa2LkhUhiiKiMViCIfD8x4lIiE54afnSTMMQ6b1\nsywLURQxNDSEw4cPZ8U11NTUoL29fVGLXPl8PjidTlmhTwmtVouGhgbU1dXlpSjmYnF8G6XMTDwe\nJxEjU1NTMnEtF1qtVpaXfTptvbu7G6FQCGq1Gueee27BiXgSkUiEPHt5nofVagXHcUSsPlEsC3BM\nqJ5eUDEfbUwSsX0+X1ZWf6GK2IXYPmcTrSWn9ancn4IgYHR0FGNjY2S/0syYxaiLMBdisRgSiQSU\nSmXWgJIgCBgfHweQEbZPJyKNUrgUYhulUCgZaPukUAoXKl5TKJSCYmhoiFR6zleRxpmQpvwfOXIE\nExMTJKqirKwM1dXVJHeysrJyQYuuJRIJHDlyRObaLCkpQVNT04I7d3meRzgclmVtS1ng8yWG8TyP\nRCKBRCJBBAkpbkSn00GhUCCVSuHQoUMYHh6WbSs50xdjwGE6oVAITqeTuEqno1KpUF9fTwZHlhrT\n2yhl7kiRFNPzso+PGDkes9ksy8uea/tPp9PYs2cPUqkUmZlQaKRSKeKo9ng8mJiYQDqdhkKhQFFR\n0YzfVaVSZTmq53vAaimJ2IXUPpPJJMbGxuDxePIqWgOZ92R/f7/sPWkymeBwOAr6uRoMBsHzPHQ6\nXdZAcCKRILN3SktLC3bAiXJ6FFIbpVAocmj7pFAKl3yK1zQEkEKhZPHQQw+BZVl0dnZmrXv//fdx\n0UUXwWAwoLyiHP+w9R8QtAcxhCHEECMuRZZlc3biduzYgUsuuYRkx55//vl44YUX5nxuDMNgaGgI\nY2NjEAQBHMehtLQUnZ2dsum6wWDwFL75qREMBrFv3z7SIWcYBg0NDWhpaVmUyAlJSCouLiaDB8lk\nEh6PB+FwOEuYzdcxDQYDrFYr9Ho9GIaBKIpIJBLw+/0Ih8NgWRYrV67EhRdeKHOvcRyH7u5uvPvu\nu1nxIguJ2WzGqlWrsGHDhqxMbo7j0Nvbi7feegvd3d0kvmapQH/UnxoMw8BisaCxsRFr167F5z73\nOaxduxZNTU0zxgCFQiH09/fjo48+wv/8z//ggw8+QG9vLwKBAERRxCeffII777wTHR0dMBqNqKur\nw4033oju7m7iWD4+R5rnebS3t4NlWTz22GPgOA7pdPqEbfnNN98kz2ubzYYbbrgBg4ODc/ruHMch\nEAjA7Xajt7cXe/fuxd69e9Hb24vR0VFwHEcER57nEQwGSVSUxWJBZWUlli1bhpUrV+Lcc89Fc3Mz\nqqqqYLVaF2SmBcuyKCoqQn19PYqLi8mzOJ1OY3JyEoODgwiFQvPyPDxZCqF9plIpDA4OYv/+/Zic\nnJS5oisrK9HZ2YmqqqpTFmc9Hg8OHDgge09WVVWhpaWl4ITraDSK73//+7jyyitht9thtVqxffv2\nrMF4lmWh1+tRXV2N6upqqNVqsCwLlmVxxRUXA/Cf8Fi7d+/G1VdfjYqKCphMJpxzzjl46qmnTjhI\nRllYCqGNUiiU3ND2SaGcHVB7AIVCkeF2u/Hoo4/mLMK1d+9eXHbZZWhub8aWx7dgcGQQ2x7bhoPO\ng3jg9Qcg8iJMogn1TD0qtBVZrrbXXnsNX/jCF3DBBRfg/vvvB8Mw+PWvf41bbrkFXq8XW7duPeH5\nHT58GOPj41CpVOA4DmazGW1tbaTDaDKZEA6HEY1GwXHcvEc9jI2Nob+/n3T0lUolWlpaYLVa5/W4\nc0GtVqO4uBjRaBSRSASCICAcDiMej8NsNs9LlInUmdfpdEgkEojH4xAEAclkEslkEmq1GiaTCevW\nrcPg4CCOHDlC8mkDgQD++te/or6+Hi0tLYsWJWIwGLBixQo0Nzejv78fg4ODRFTkeR4DAwMYHBxE\nVVUVmpqaCq6wHmX+UCgUKCkpQUlJCdra2pBKpYgr2+PxZA1qCIKAqakpTE1N4ciRI1CpVPjRj36E\nAwcO4Itf/CK+853vYHx8HE899RRef/11PPPMM2hoaMiKJvjxj3+M4eFhMAwDnudlsRwKhQJqtTrr\nefuHP/wB1113HdasWYNHH30UoVAIP/nJT7Bu3Trs2bNHVhdAivyYnlE9Pc9+JiwWC/R6PXieh1ar\nJWJxIRVIZFkWVqsVFosFwWAQfr+fXMOJiQn4fD7YbDaYTKaCcWIvJKlUijitpwumCoUCZWVlKC8v\nPy03McdxGBgYILOigMysLIfDMedinwvN1NQUHnzwQdTV1aGzsxO7du3KOSD/wgsvIBwOI52OQqsN\nQqdL4m9/68GTT76GK65oBfARADOAegCVWcfp6urChRdeiObmZtx9993Q6/V44403sHXrVvT39+Px\nxx9fgG9LoVAoFAqFUvjQ2BAKhSLjpptugtfrJUXM9u/fT9ZdddVV2LN/D57ueRpaQ0b4/NPP/oQn\nNz+Jh/7nISy/eHnGeadQYY1qDUpRKtv3FVdcgUOHDsHlcpFOIM/zaG1thdFoxJ49e2Y9t8OHD2No\naAhAZjq/lCetUChgsVjAsizi8ThGRkYAADabbd4KNwqCgL6+PllOsl6vR1tb27zlS58OPM8jFArJ\nxDWtVguLxTKv7nBRFJFMJhGPx2VT91UqFXQ6HQRBwKFDh+B2u2XbqdVqtLe3o7q6etEFJY7jMDg4\nmFVcTKK8vBxNTU0FMWBBWVwikYgsYiRX9vORI0ewbNkyMluhpKQE4XAYGzZswIYNG3D//fejs7MT\nOp0OoihiZGQEnZ2d2Lp1Kx544AE88sgj2LJli2yfUkTPdNF4+fLlSKfTOHToEGnj+/fvx6pVq/DN\nb34Td999NxGqT5TrDWQG5qbnUxsMBmg0GoiiiLGxMRIXZTAYUFGRPXhZKEjRU5KILaFWq2Gz2WA0\nGgv23PNJKpXC+Pg4JicnZaI1y7JEtD7dwd9gMAiXyyUbCCkpKUFtbW1BF8LlOA5+vx+lpaXYtWsX\n1q9fj2effRa333677HOiKGJy8ijU6m7o9WpoNGp8/es/wS9+8SaGhp5HZeX03x+NAJbJtt+8eTN+\n9atfYXx8XDaTY/369di3b59M8KdQKBQKhUJZatDYEAqFMi/s2rULr7zySk63Tzgcxo4dO3DxVy8m\nwjUAXHrLpVCoFHjn5XeOZR0rGPyh5w84PHxYto9QKASr1SpzLykUChQXF59Q8J0uXAOZbMnzzz8f\nCoWCFHIURRE6nY44dqVp7PkmlUqhu7tbJlzb7Xacc845BSlcA5nrbLVaYbfbyfVPJBKYnJwk124+\nkIo3FhUVwWQykWNzHEfE9OXLl2Pt2rUyF14qlcLevXvx/vvvIxQKzcu5zRWVSoWmpiZceumlWLFi\nRVZBrvHxcbz77rv44IMPZMU6C4lHH310sU/hrMBoNKK+vh6f+cxncPnll+PCCy9ES0sLbDYbEZal\nATcgE08wMDCAqakpVFdXw+l0IhwOk5kSHMfhnnvuQUtLC2688cYZj9vf348jR46Qduz3+3H48GFc\ne+21iEQiGB0dhdPphCiKqK+vx8svv4yRkRH4fL6cwrVUmK68vByNjY3o7OzEqlWr0NraipqaGths\nNhL1wDAMysvLyUyOaDSKycnJvF7XfCI5sevr62G328nfRRJzh4aG5i1eaSYWsn1yHIehoSHs378f\n4+PjRLhmWRYVFRU455xzUFNTc1rCtSAIGBoaQk9PDxGuVSoVli1bhoaGhoIWroHMuZaWlkIURTIz\nKNc5p1IhqNUHwDD8p3UdOLzyyntYv77zOOEa6O9/F/3978mWhcNhMog8nfLy8oL9LXG2Qt+hFErh\nQtsnhXJ2QGNDKBQKgExnc8uWLbj99tvR0dGRtb67uxvpdBpNq5tky5UqJYrKitC3py/z/0olGJbB\n19u+jjXr1+DjnR+Tz65fvx7/+q//ivvuuw+33norGIbBiy++iN27d+PXv/71jOd25MgRmXBdXFyM\nc845BwqFAkaj8dNpu2kkEgnodDpYLBZ4PB7wPI9IJJLXWIdwOIzDhw/LnGS1tbWoqalZEm49jUaD\nkpISRKNRItCEQiHEYjFYLJZ5yx6VnKEajQapVArxeBwcx5HikkqlEueffz7J15UckT6fD7t27UJD\nQwOam5vnPQZmNhQKBerr61FbW4uxsTE4nU6ZsC7FQ1gsFjQ1NRWU+3R68U7KwsCyLGw2G2w2G5qb\nm5FOp8k94vF4ZEXrRFFEMBhEdXU1AoEAPvzwQyiVSgSDQWzbtg1vvPHGrPfSVVddBZZlsWfPHiQS\nCQwMDADIRPH09PTIPqvVauFyuUhchkKhyOmoPpl7l2VZVFZWYmRkBKlUCqFQCEqlct5mvuQD6e8z\nPU5EEAQiYi+kE3sh2ifHcRgfH8fExESW07q0tBQVFRV5eb7GYjH09/fLvpPFYkFDQ8OiRUGdKsfH\n8xxPOu0Cw6TBMAwUCgV+//sPEQhEcfPNG7I+e8kld4NlFejvdwPI3E/r16/Hr3/9a2zevBnf+c53\noNfr8cc//hG//e1v8dhjj83b96KcPPQdSqEULrR9UihnB1S8plAoAICnn34aQ0ND2LlzZ871fWN9\nYBgGtgpb1rqW81pw6L1DpAMHZMRKjuGQQgpqZDqs9913H1wuFx5++GE89NBDADJTzH/zm9/gmmuu\nyXncnp4eWYExu92OlStXkuOoVCpotVqSryw5Br1eL5keni/xemJiAn19faTjr1Ao0NzcXNACTS4Y\nhoHRaIRWq0UoFEIikSAxMTqdDmazeV6dcWq1Gmq1GhzHIR6PI5VKged5xGIx2O12FBcXo6+vD2Nj\nYwAywl5/fz9GR0fR3t6OqqqqeTu3ucCyLKqqqlBZWYnJyUn09vbKpncHg0Hs3r0bBoMBTU1NqKqq\nWnSn4f3337+ox6dkBvbKy8tRXl4OAIjH4/B4PJiamsKLL74In8+HTZs2ESewSqXCgw8+iMsuuwxG\noxG9vb1kO6nNpFIpJJNJ8DwPQRAwPDyMeDwOhmFgMpmwb98+2TmEQiG4XC4AmXa4YsUKaLXavIiz\nUmG/kZERpNNp+Hw+Ujy2kFEoFETEDgQCCAQCWSK23W6HwWCYNxF7PtvniUTr8vLyvIjKoihiYmIC\nIyMjMjd3TU0NSktLC2Yg72SYLl5nn78AQRgG8OmgPQO8+OLb0GhU+OIXL8zaF8MwYBgRwBSAEgDA\n7bffjoMHD+LZZ5/Ff/7nf5J9/fSnP8XmzZvn4ytRThH6DqVQChfaPimUswMqXlMoFPh8Pnz/+9/H\nfffdB5stW5wGgKn4FABApZE7swRegEqjQiqRyri2Pu3fvc6/DgDww48ylAHIiCXNzc244YYbcP31\n14PnefzHf/wHbr75ZuzYsQPnnXeebN89PT3EQQhkhOtzzz03SwjU6XRIp9NIp9OIRqMwm80wmUwI\nBoOIx+NIJpOn5SgWBAEDAwMYHR2VHbOtrS0rQmIpoVQqYbPZkEgkEAqFkE6nyfUyGo3zKtYAGXFO\npVLJjiuJHg0NDSgvL8fRo0dJlm4ikUBXVxeGhoawYsWKRS/2xTAMysrKUFZWBq/XC6fTKYtLiEaj\n2LdvH3p6euBwOFBXV3dahc8oZxY6nQ61tbWIxWJ47rnn0NHRgY0bN0Kv15OYJpfLhR/84AcAjglp\nfr8fXV1d0Gq10Ol0UKlU+NOf/iTbN8Mw+MIXvoBf/epX+MUvfoGvfe1r4DgOP/zhD8msBmn7fKJS\nqYiALQgCPB4PlErlorfVuaBQKGC321FUVJQlYo+NjUGj0RAn9lIgnU4T0Xp6tjfLsigpKUFFRUXe\nnNCpVAoulwvBYJAs0+v1cDgcS/odmSuzXkIUw+D5jNtPoVAgHI7hj3/8BFdffR7MZkPW512uX3z6\nLy8k8ZplWTQ2NuJzn/scNm3aBI1Gg5deegl33nknysvLsXHjxjx/IwqFQqFQKJSlCe1FUygU3HPP\nPbDb7bjzzjtn/IxKlxGtuaS8M8eyLLgkB7VODYbNFjp5HOs033HHHfj444/R1XUsq/+GG27A8uXL\nsXXrVnzwwQdk+dGjR2XCtc1myylcAxmhxmAwIBQKQRAEImBLHelQKISSkpITXIXccByHI0eOyDrl\nVqsVLS0tZ4wQqdVqodFoEIlESNaulEdtsVjmfaq3UqmEyWSCXq8nIrYoitBqtVixYgU8Hg8GBgaI\nADM1NYV33nkHDocDzc3Ni+5qBjIDK3a7HcFgEE6nE2NjYyQzN5FI4NChQ+jt7UVDQ8OSnD5PmR8m\nJyfx+c9/HiaTCQ899BCJRIpGo7jtttuwefNm1NbWygqtCoIAjUaDeDyORCIBpVIJtVoNg8EAk8mE\nkpISGI1GPPvss1Cr1XjuuefwzDPPgGEYXH755bjtttvw7LPPzpsIq9FoUFFRgdHRUYiiiPHxcVRW\nVi4ZEXMmETuZTC4JEXsm0ZphGBIPks/nj9/vx8DAgEzoLS8vR3V1tayA6FIjnU7LnOrHw3EJ8m+l\nUomXXnoHySSHm276LDguDZZloVDk+v5p8q8f/vCHeOqpp9Db20vax5e+9CVccskluOOOO3D11Vcv\n6WtIoVAoFAqFki/oLyIK5SzH6XTiueeew5YtW+B2uzE4OIiBgQEkEglwHIfBwUH4/X5UVFRAFEX4\nxnzyHTDA1MgU7JW5ozNU+FT05jj813/9Fz7/+c/L1iuVSlx55ZX45JNPSOe3t7eXTG0HMsL1qlWr\nZhUpFQoFDAYDOZYkfgIgovbJIjlnpwvX1dXVaG9vP2OEawkpZqCkpIS41DmOw9TUFAKBgEwEmS+k\nDHOr1Qq9Xv/pNOuM4HLOOeegvLycOMEFQYDT6cTbb7+N8fHxeT+3uWKxWLB69Wps2LABdXV1MuGB\n4zgcPXoUO3bswIEDB2SC5HwzNTW1YMeizI1QKIQrrrgCoVAITzzxBOx2O7RaLVQqFX784x+D53nc\neuut0Gq1MJlM5N6PRqPw+/1QKpVgWRZarRZGoxEsyyIajcLj8cDv95OZLaOjo/jrX/+Knp4evPHG\nGwgEAsTxOV/o9XoSjyKKIsbGxnIWhixkJBG7vr4eVquVtGVJxB4aGiKzQk6XfLTPdDoNt9uN/fv3\nY3R0lDyzpWdoZ2cn6urq8iZc8zwPl8uF3t5e8u5Wq9VoaWlBbW3tkhddpe800++OVOrYbwqFQoEX\nX3wbFoseV1yxisT45ObYb4enn34al1xySdbAzsaNGzE6OiobwKcsLvQdSqEULrR9UihnB0v7lyWF\nQjlt3G43RFHEli1biCvU4XDgo48+InEHDz74IC7ouAAKpQK9n/TKtk9zaRz95CgaV2YLIUooYYUV\nAOD1epFOp3OKoBzHQRAECIKA3t5e9Pf3k3VWq/WEwrWEWq0mgnU8HidZ14IgIBwOz/2iIPNDaN++\nfUgkMu4qlmXR0tKC+vr6JZndOVekImtWq5Vc81gsBo/Hg2g0StzE8wnLstDr9bDZbDAYDGBZFhqN\nBg0NDWhvb4fZbCbCSDwex9/+9jd8/PHHeROS8oHBYEBnZycuvfRSNDY2ygY7JNFn586d2Lt3r6xw\n33xx2223zfsxKHMnmUzimmuugdPpxPbt21FZWQkAxM07PDwMv9+P1atXo729HZ2dnfjiF78IhmHw\ny1/+Etdeey0mJydhtVpJAVQgc29Fo1G43W4cOHAA+/btQywWQ0dHBxwOBwRBwDvvvIO1a9eSwb75\nwmg0khkvgiBgdHR01hiGQkWhUKC4uJiI2NLzP5lMYnR0FMPDw6f97Dmd9jldtHa73UinM87e6aJ1\nfX19XovxRiIRHDx4EB6Phyyz2WxYvnw5LBZL3o6zmEj36kwD1amUGqKogVKpxMSED3/5y3586UsX\nQanMvDdnFu+PzQI73h1//LGlvyVl8aHvUAqlcKHtk0I5OzizrIMUCuWk6ejowKuvvpq1/J577kEk\nEsGTTz4Jh8OBCnMFzrvsPOx8YSe+cu9XoDVkROK3nn8LoiBi3aZ1su1HekbQoG+AsibzmCktLUVR\nURFeffVVPPDAA6RDGIlE8Pvf/x5tbW0YHh6WCddFRUVzFq4lpudfi6IIlmVJ4ca5dKpFUcTQ0BCG\nh4fJMo1Gg7a2toKdJj4f6HQ6EiUSjUbJNYzH4zCbzQsSe8EwDHQ6HbRaLZLJJDl2W1sbJiYmMDEx\nQQrXTUxMwOPxoKmpCU1NTQURJQJkIlna29vR1NSEgYEBuFwupFIpACAF9oaHh1FRUYGmpqZ5K273\nL//yL/OyX8rJIwgCNm3ahA8//BCvvfYaWlpaMDg4SGY/AMDWrVtx3XXXkXsFyAhdW7ZswcaNG3HB\nBRegtbWVFD/t7++HUqlETU2N7FjJZJK0FYVCge3bt2N8fBxPPPHEgnzXoqIi8DwPn8+HdDqN0dFR\nVFdXF0z7PBkkEbuoqAh+vx/BYBCiKCKRSGB0dBRarZYMuJ0sp9I+pefe+Pi4TORkGAbFxcWorKzM\nq2ANZO7dsbExEgkDZETaurq6U47mKkQEQSDXNJd4LYoiUikOLFsOnW4CL730Z4gi8OUvbyCfOX6Q\nu79/DIABDsexWWrNzc1488034ff7YbVaybFffvllmEymeZ0dQTk56DuUQilcaPukUM4OmIVw0Z0u\nDMOsArB79+7dWLVq1WKfDoVyVrBhwwZ4vV7s37+fLHtnzzu44sIrUNNWgys3X4mpkSm88v9ewYr1\nK/DgHx+UbX8VexXWrV+Hd3a+Q5Y98sgjuPfee7Fy5UrccsstSKfT+NnPfoaenh78+Mc/RmtrK/ls\nUVERVq9efUrxHDzPIxQKQRRFxONxxGKZokrV1dWzFihLp9Po6emB3+8nyywWC1pbWzPFKM9SOI5D\nKBSSTfvX6/UyB/RCkBEMMi7TdDqNZDIJt9uNYDAIjuOIg81gMKCjowOlpaULdm5zhed5DA0Noa+v\nL2dsSHFxMZqams4oIYgi59vf/jaefPJJbNy4ETfccAPcbjei0SgUCgWqq6tlDiKO44gL8+DBgzj/\n/POxdetWfOUrX0F9fT2SySSi0SguueQSMAyDv/zlL6iqqkI4HMbzzz+P119/HStXroRer8fHH3+M\nnTt3YuPGjfi///f/wmg0oqioCEVFRXkv3Hg8ExMTCIVCADIDOlVVVUs+ViKdTstEbAmtVgu73T5v\nGd+SaD0xMSFzskuidUVFBZmBlE+SyST6+/tls5iMRiMcDse8HG8xSaVS+MlPfoJQKASv14tnn30W\n119/Pc4991wAwDe+8Y1PZ2VxKCnpxd/93TcxMeGHy/ULpNNpMAwDjUY+wFtffytYVov+/kGybNu2\nbfjqV78Kh8OBzZs3Q6fTYdu2bfjoo4/w8MMP4+67717Ir02hUCgUCoWSV7q6urB69WoAWC2KYteJ\nPj8bVLymUCg52bBhA3w+H/bt2ydb/vv3f4//87//D5xdTuhNelx848X42iNfI05sIJNzfbnicqxf\nvx5vvfWWbPvt27fjiSeewNGjR5FMJtHZ2YlbbrkFDQ0N5DOnI1xLpFIpRCIR4vpjWRYmk4nksB5P\nLBbD4cOHZYJiZWUl6uvrl7zIki/i8ThCoRARiVmWhdlshk6nW/AoFUnE5jgOgUAAo6OjSKVS4DiO\nOObKy8vR0dEx78LcqSAIAtxuN5xOZ87YkKKiIixbtgxlZWVndEzN2ciGDRuwa9euGdcfHyOQSqWQ\nTqfxySefYMOGDbjzzjuxefNmlJaWQqVSIRaL4bzzzgMAvPLKKzCbzWhqasKePXvwve99D93d3YjH\n46irq8P111+Pa6+9NuuYWq2WCNlSfnY+kXKvpXgNg8GAioqKM+LenknE1ul0sNlseROxeZ7H5OQk\nxsfHs0Rru92OysrKeRORp6amMDg4KMvRrqioQGVl5Rn5foxEIli+fDlGRkZyrj948CDMZjMYhkE4\nPI62tjW4667r8fDDt4LnebAsC7V6+oA3g4aGr4Nl1ejr65Pt680338QPfvADHDx4EKFQCC0tLbjj\njjtw++23z+M3pFAoFAqFQpl/qHhNoVAWlRBCcMGFCUxAwLGiREooUYlK1KMeesytw97f34/e3mM5\n2lLBu3w4naPRKJLJJAKBAHFDORyOrCnrPp8PPT09so55U1MTysrKTvsczjSk/PBYLEaEGrVaDYvF\nsijudI7jEI/HEY/HMTExgampKYiiSByrCoUCzc3NcDgcBSmyiKKIiYkJOJ1OmeNfwmg0oqmp6Yxw\nqlKyCYfDOHToENLpNEpLS7Fs2bKcn+M4Dj09PeA4DrFYDFVvjyExAAAgAElEQVRVVTAYDNDr9Uin\n04jFYiRGB8jMjGhubpbF+4iiiEgkgkAggEAgMGPBUKVSSYRss9mct+K00oCNVEfAbDafUc9YScQO\nBAKy5TqdDna7/ZQH0Xieh8fjwdjY2IKL1ul0GgMDA/D5jhVq1mg0aGxsPGNjtERRRCAQgCiKMBgM\nOaNXfD4fEokE1Go1iouLASQBuJBMuiCKKSiVyk+zrxlkMq7rAdgW8mtQKBQKhUKhLDpUvKZQKAVB\nEkn44ccLP3sBX/3Hr8IOO5QnEaXvcrlw9OhR8v/5FK6BTCc0FAohFoshGAyCZVmUlJSQbEkgUxht\ncPDYNF61Wo22tjaSPUvJDcdxCAaDRCxjGAZ6vR4mk2lRRNZ0Oo14PI5gMIiRkREirksittFoxIoV\nKz4VGgqTqakpOJ1OWRE0CZ1Oh8bGRtTW1p5SXvDPfvYz/OM//mM+TpOSR6SBCwBobGyccWZIOp3G\nhx9+CJ7nEQ6HsXbtWjAMQ6J7IpEIkskkxsbGSLyPRqNBS0vLjMJmIpFAIBCA3+9HJBLJWYxVOoYk\nZp9uhjLP8xgZGSHPDZvNBrvdfoKtlhbpdBo+nw/BYFC2fDYRO1f7nEm0BkBE6/mcVRIKhdDf3y/L\nXS8uLkZtbW3eBjQKkXQ6TSJuioqKcr7PpEKLRqMRZrMZgDQ4FALD+KDTKaBQqAFYABTezB/KyUPf\noRRK4ULbJ4VSuORTvKY2LgqFcspooEE5yuHqcqEMZSclXA8MDMiEa7PZnFfhGsgIL0ajERqNBizL\nyhxVPM/jyJEjMuHaZDJh5cqVVLieAyqVCna7nXTuRVFENBqFx+OZ0dE5nyiVSphMJlRUVGD58uWo\nqamBUqmEWq2GXq9HKpXChx9+iK6uLuL8LDSKi4uxdu1arFu3DpWVlbJ18XgcBw4cwI4dO3D06FGZ\noDQXurpO67cCZZ6Q8oOVSuWs8RLS7JF4PA69Xg+lUgmGYSAIAhQKBbRaLTQaDaqqqojAnEwmcfjw\nYRLVcTxarRbl5eVoa2vDueeei8bGRtjtdpkwKYoigsEgBgcHsW/fPhw4cAAjIyMzit0nQqFQoLKy\nkhzD5/NlOZWXOkqlEqWlpaivr5cVCY7H4xgZGZG5zyWmt09BEDAxMYHu7m4MDQ3JhGubzYYVK1ag\nsbFx3oRrqZDskSNHyHNGqVSiqakJDofjjBau8f+z9+ZhktX1vf/rnNr36up9m16ne1aWYQC50RkE\nQQM+xIgwEhIV5EL08Yd6CWriFtZkBIkGvAr3GsSIogYT8V58ntwIRsCAMIDpmZ6e3qv3vfb9VJ3f\nH53zpauXWXu6m5nv63nm6Z5Tp6pPnTrfc+q8P+/v+wNif5tMpmWFa03TxCythcWcQqEAqOh6Gapa\nD1QhheszB3kNlUg2LnJ8SiRnB9J5LZFI1pzBwUGOHDki/u/1etm9e/dpi53IZDJMT08Tj8dFU6tg\nMCgaOQJUVlbS0tIioxlOgkKhIBzuBjabDZ/Pt25ChxFvMjg4yOzsLIAoWhQKBdra2jZ8nnk8Hqev\nr4+RkZH/Ekbewmw209DQQFNT04bM9JYcm0KhQEdHB/F4HIfDwY4dO4piPgx0XRfHQTgcpqmpiaam\nJjRNw+Fw4HA40HWdZDKJpmkinsMYjyaTic2bNwuH6PFsVywWE/EiCxu1LsRisRTFi5zIjIBMJlN0\nXFdXV5+xMRS5XI65uTnh5jVwOp2UlpYKZ3yhUBBO68XFqUAgQE1NzWlrAmmQSqXo6+srOpd7vV6a\nm5uXPTbPRKLRKJqmYbfbl93fyWSScDiMoihUVlaKa0g2myWTyWAymU775ySRSCQSiUTydkDGhkgk\nkrctwWCQrq4u8X+Px8OFF1542vOSo9EoY2NjxONxpqamhJCjKApNTU1LnK6SEyebzRKJRIRzTVEU\nXC7XaWkAd7wYglB/f3+RI9wQJ3bs2EEgsLGzSFOpFP39/UUN0wxUVaWuru6MzqA9U0kmk3R2dpLJ\nZPD5fOzYsWPZ9fL5PB0dHYRCIaanp3nXu96Fx+Mhm81is9lwuVzA/LFuOKJNJhPDw8PC2a0oCi0t\nLSd1rBtiXTgcXra5KLzVvLWkpASfz3dcQmcymWRsbAxd11EUZU3E2fXkaCJ2oVBgdnZ2iWhdUlJC\nbW3tmuyXyclJhoeHRUFBURTq6+vPqqaxhUJBzATweDzLfi8JhUKkUiksFgvl5eVieSqVQtM0rFbr\nKcfrSCQSiUQikZwJrKZ4fWbP/ZNIJBuKoaGhJcL16XRcL8Tj8RCPxxkcHATmBQOHw0F7ezt+v/+0\n//2zAaN5VTKZJBaLCTEtlUrh9XrXxSGsqiqVlZWUl5czODjI0NAQhUIBs9mMpmkcOHCAsrIytm/f\nvmGdhQ6Hg+3bt7N582YGBgYYGBgQBYJCocDQ0BBDQ0PU1NTQ2tpaFFUg2bikUinxOR4tqshoyJjJ\nZFBVlbKyMjRNAygqZqiqisPhIJlMks/naWpqYnh4mFAohK7r9Pb20tjYSEVFxQltp9PpxOl0UlNT\nIwpU4XCYSCQihE5D9DOEP5fLhd/vp6SkZEXh1el0UlVVxfj4OLquMz4+Tl1d3Rkr/FksFiorKykp\nKREidiQSob+/n1wuJwoRZrN5TUXrXC7HwMBAUXyLkbF/JhcTlmNh4XWlWUNGgWHx9cIYiyfTk0Ai\nkUgkEolEcnSkeC2RSNaEoaEhDh8+LP7vdrvZvXv3mgiG+Xyevr6+ogZa+Xyec889d8VmZpKTw3Bb\n2+12YrGYENJCoRDJZHLdokRUVaW5uZna2lo6OzuZnZ1FVVVMJhOhUIiXXnqJTZs20dTUtGGjRKxW\nK+3t7bS0tDA0NERfX19Rdu7Y2BhjY2OUl5fT2tq6oZtTSiCRSFAoFFBVdUXX/HwTuDi5XI50Oo3X\n68VisRSJxguxWCxYrVay2SzZbJampibMZrNoAjo4OEg2m6Wuru6kttlqtVJeXk55ebloHmmI1gtd\nw4lEgkQiwejoKDabTcSLLG7o6na7KS8vZ3p6mkKhwNjYGHV1dWtS0FwvzGYzJpOJWCxWNFMlk8lg\nNpuprKxcMxE/HA4XFcMA8ffPRhHW2A8Wi2VZt3k+n18x79qYyXo27jeJRCKRSCSS083GvEOXSCRv\nK6655pqjPj48PLxEuL7wwgvXRLjOZDJ0dHQwNTWFzWbDbDbj8/mora09qYZjkuPDZDLh9/spKysT\nQpSRPR6LxdZt39tsNs4//3x27dqF2WwWQoSu6wSDQV555RWmpqY29LFhNptpbm7m8ssv59xzzxWx\nEQbT09P8x3/8By+++CITExPoun7MMSpZW3RdF5EeFotlxSKakSdfKBTIZrMi9sMQgBeKZgZ2u100\nUc1kMjQ2NlJdXS0eHxsbY3Bw8JSPcWOMNzY2cu6557J9+/Zloz8ymQyTk5McOXKEN954g97eXmZm\nZoRQ6Pf7xfvSNI2xsbEl8ThnAkaE0cGDBxkYGCCfz+P1eiktLWX//v00NDRQV1cnZlOMj4+vmDd+\nquTzedE0eaFg29bWRkNDw1kpwOq6XrQvlmPh57Hw+4txvKqqetZErJxtyGuoRLJxkeNTIjk7kM5r\niURyynzqU59a8bGRkRE6OzvF/9fScR2JROjq6ipylW3ZsgVFUdB1nXA4jNVqPStv1NcKI0okkUgQ\nj8dFMzgjSmS9nO+BQIB3vvOdBINBuru7xTTxVCrFwYMHKS0tpampCY/Hs2HFCFVV2bRpE/X19UxM\nTNDT01M0uyAUCvHqq6/i8XjYt2+fcPlK1h+juRscXbzWNI14PC7WNSI/Fn6OhUKh6BymKAoOh4NE\nIoGmaWSzWerr67FarQSDQQCmpqbQNI3m5uZVOSaMGRcul4u6ujoymQyRSIRQKEQ0GhVCeT6fZ25u\njrm5ORRFwe12C1e2pmlEo1Gy2SxjY2PU1taeEcdroVBgbm6OsbGxopkSgCikfvnLX6a9vZ3Z2VmR\nKx6Px4nH47jdbgKBwKo5sROJxJIeACUlJTQ2Np7RjvdjoWmaOE5X2g/G7AKLxVJ0bMrIkDOfo33P\nlUgk64scnxLJ2YFs2CiRSE4bIyMjHDp0SPzf5XJx4YUXrsl06ImJCfr6+oqm8m7ZsgWfz0d/fz+a\npmGxWCgrK8Pr9W5YgfJMIp/PE41Gi0QTu92Oz+db15v+dDpNZ2cn4+PjWCwWEWtiMpmoqamhpqYG\nh8PxthDSpqenhbN1MUaO7aZNm6TIss4YhTVN06isrKS1tXXJOrquk0wm6ejoYGJiglwux/vf/34c\nDge6rhMKhYCVG8ul02kymYwQlk0mE7Ozs/T394vzotfrpbW19bRG+eTz+aKc7IXFxIXY7XY0TcNk\nMuFwOHC73VRXV79tz826rjM7O7usaO31eqmrq1s2LiaTyTA3N7ekOabb7aa0tPSkC79Grvjo6Kj4\n/I0C2InmoJ+JJJNJ0uk0ZrNZNHRejFH0cblcRb0FjAggu91+VhcAJBKJRCKRSBaymg0bN/6duEQi\nOa10dnZy/fXX09LSgsvlory8nL179/J//s//WbLuI488wrZt27Db7dTV1XHHHXeQTCaXfd3R0dEl\nwnUkEuHyyy/H5XIRCAS47rrrhBNwtSgUCvT29tLb2ytu0B0OB+eddx4lJSWoqorP50NVVXK5HJlM\nZsX3IFldTCYTJSUllJaWCrEsnU4zNTVFPB5ft6gOu93Orl27uPjii7FYLCSTSXK5HJqmMTw8TEdH\nB2NjYyQSiQ0fZ1BeXs4ll1zCO9/5TqqqqooeM1zl//Zv/0ZPT8+KIqLk9PLaa6/xmc98hhtuuIEr\nrriCSy+9lH379tHT01O0nhEVkk6nSafT3HHHHbhcLh566CEURREFiMW51wY2mw2TycTzzz/P5Zdf\njt/vp6mpiVtvvZXnnnsOgGg0SldXV1Fe9WpjMpkIBAI0Nzdz3nnnsXXrVqqrq5c0cE2n0+RyOVF4\n7O3tpaenRzSnfLtgiNYHDx6kv7+/SLj2er1s3bqVLVu2rJhzbrPZqK6uZtOmTUXrxONxgsEgExMT\nJ/x5ZTIZurq6GBkZEedZl8vF9u3bz3rhOpFI8NWvfpU/+qM/orW1Fb/fz/e///0l6+XzeXK5HE88\n8QR79uzB6XRSXl7Oe97zHg4ePAhw1ALnxMQEX/jCF7jsssvwer2oqspvfvObZdfVNI277rqLlpYW\n7HY7LS0t3HfffRv++iORSCQSiURyupCxIRLJWU4wGCQej/Oxj32MmpoakskkTz/9NNdccw2PPfYY\nt9xyCwCf//zneeCBB7j++uv5zGc+w4HOA/z9w3/PC50vcPcv78aChXLK2cQmEqMJcTMH4HQ6mZ6e\n5rrrrmP37t3s37+faDTKN77xDd71rnfxxhtvUFpaesrvJZvN0tXVRTQaFcsCgQBtbW1FzkKv10s4\nHEZRFFKpFGazGbPZvCaOcMm8OFNeXk4ikRD519FoVDR0XK/PoaysjL1799Lf3y+yYC0WC4lEgu7u\nbsrKyqisrMTlcuFwODa0e7mkpIQLL7yQeDxOb28vo6OjQuQ0xklvby8NDQ00NzfLxqVryP79+/nN\nb37D3r17aWtrQ1VV/tf/+l/s2rWLV155hW3btgFvRYak02meffZZZmdnURSFbDYrGqEWCgU0TVt2\nzCiKwo9//GNuvfVWLrvsMu666y4cDgdHjhxB13UsFgu5XI5kMsnhw4dpb28/7ceBoih4PB48Hg/1\n9fWk02nC4TChUEg4jf1+v4gbiUajjI2NUV1dLeJFNup52nDDj46OFs0ugXl3fG1t7YqO3uUwROxM\nJsPs7CyJRAKAWCxGLBbD4/EQCASO6cSenZ0lGAwWFQGqq6vPmFiWU2VmZoZ77rmH+vp6duzYwUsv\nvbTsetlsls9+9rP8/Oc/58/+7M/49Kc/TSKR4MCBA0xNTbF9ewMm0zAwAWQBE+AH6oEKjhw5wgMP\nPMDmzZs555xz+I//+I8Vt+nGG2/k6aef5uMf/zgXXHABL7/8Ml/+8pcZHh7mO9/5zurvBIlEIpFI\nJJINjowNkUgkS9B1nV27dpHJZOjs7GRiYoJNmzZx44038p3Hv8MbvEGYML/41i/4zu3f4fq/vJ6P\n3vtRACLhCNHeKHVzdai6itPp5KKLLmLXrl1omkZnZ6cQ/f7zP/+TXbt28dnPfpYHHnjglLY5Fotx\n+PDhIkeakQe83LTzkZERUqkUuq5TWlqKqqp4vd4NLUieiRg5twvdiQ6HY90/i1QqxaFDhxgfHwfm\nM06NfzU1Nfj9fqxWK06n87RGLqwWP/7xj9m+fTtDQ0NL3HtGdEBzc/OS5o+S1efFF1/E4XCQTqdx\nOp1s27aN4eFhduzYwfXXXy9cn4lEgmAwyBtvvMGf//mfc9ttt/HQQw9x//33c/vtt5PJZETMxnLR\nIcFgkG3btnHLLbdw9913A/NuW+N4TaVSdHd3F2Vvt7W1rdsxoGmaiBeZnZ1ldnZWHKsej0c4tZ1O\npxCyXS7XuseKGKL12NjYklk8xyta/8u//Asf+MAHjrpOOp1mbm5OiNgL/8ZyIramaQSDQWZnZ8Uy\nm81GU1PTCYnoZzq5XI7x8XHcbjf/+Z//yWWXXcb3vvc9PvKRjxSt973vfY+bb755yWOZTIp8vgOT\naQKbbaVCgotEYgu5nAW/38/TTz/N9ddfz/PPP8+ePXuK1nzttde46KKL+OpXv8pXv/pVsfzOO+/k\n7/7u73jzzTfZsWPHqr1/yfFxPGNUIpGsD3J8SiQbFxkbIpFITiuKolBfX084HAbgt7/9Lfl8nmv3\nXcurvEqY+eV7P7wXXdd57gfzU9Aj4Qijo6MMzgxyIHUAu8POhRdeKJx9f/zHf1wkSJ5zzjls3bqV\np5566pS2d2pqio6ODiFcm0wmtm7dyqZNm1YUNhbmVWYyGXRdX9foirMVs9lMIBAgEAgUiWrT09Pr\n+nk4HA52797NxRdfjMvlEg7VRCLB4OAg/f39xGKxY+b4bhR+9rOfsWPHDt7znvfQ1tZWJHQWCgUG\nBwd5/vnnef3114tmLkhWn/POO08c1zabDavVSmtrKzt27ODw4cPAfESBkXn95JNP0tDQwI033lj0\nOsa5Tdd1uru76erqKnr829/+NoVCgXvvvVfMIEilUsKB73A42Lp1K06nE5gX8RbPXFlLzGYzpaWl\ntLS0sHv3bnbv3k0gEMBisRCLxYTInkwmGRsbo7OzkzfffJOBgQFCodC6RCqEQiEOHTpEb29vkXDt\ndrtpb29n69atxyUU/+hHPzrmOna7nZqaGurr64sKDLFYjGAwyOTkpDgPxWIxDh06VCRcl5aWsn37\ndilcL8JisVBSUgJw1ELkI488wvnnn88111wjxuY8B1HV8SIXe3//OP394wuencDlOojff+xZAy+8\n8AKKorBv376i5R/+8IcpFAr8+Mc/Pu73Jlk9jmeMSiSS9UGOT4nk7GDj28UkEsmakEwmSaVSRCIR\nfv7zn/PLX/6SG264AUCIwnOOOcwLThs2p038NIRrgG9//Nuoqsp1Pddht9uFCL444xTmXXSdnZ1M\nTU2dcPZmoVAgGAyKvwvzN/jbtm0TgsxKuN1uTCYT+XyeTCaD3W4nn8+TTCal+3QdsNvt2Gw24vE4\n8XicQqEgmjv6fL6TblJ2qlRUVLB3716Ro65pGpqmkcvliMViVFRUUFlZSS6Xw2w243A4sFqt6+4G\nXYwheFitVtrb22lubmZoaKgok1fXdUZHRxkdHRVNBAOBwHpu9hlJKpUSIqPb7RbHyuTkpHBUGsdZ\nR0cH//qv/8qjjz66JC7DEMsKhQJXXXUVqqrS19cnlv/qV79iy5Yt/N//+3+58847GR0dxe/3c9tt\nt3H//fejKApWq5UtW7bQ09NDLBYjn89z5MgRWlpa1vWzV1WV8vJyXC6XaHiYSCRE1IlBLpdjenqa\n6elpMXumpKTktJ8zjHiQxU5rt9tNbW1tUXH0eDgRQdIQsdPpNLOzs2IbotEokUiEdDpNMpkUx4HZ\nbKahoWFVornORHRdF8fUSuJ1JBLhjTfe4GMf+xj3338/3/nOd4jH4zQ3N3DXXddx7bV/UCReX3bZ\nF1BVlf7+xxe8SgboAs4/6vYYRZrF35eM7zQHDhw4sTcoWRVk0UAi2bjI8SmRnB1I8VoikQBwxx13\n8OijjwLzwsG1117Lww8/DEB7ezu6rvPvL/071+29Tjzn4G/mc61nRmeKBGRFUbBYLEzbp2mnncrK\nSvx+/5IsydnZWTo7O4H5Bo8nIl7ncjmOHDkihHGYz0ptb29fMn1+ORRFwefzMTc3h6ZpqKpKoVAg\nk8lgsVjWTSw9mzHycB0OB5FIhEwmQy6XY2ZmBqfTicfjWZcoEZPJRHt7O3V1dRw6dIjJyUkhLg4P\nDzM3N0d9fT1er5dYLIbJZMLhcGCz2TaciG1gsVhoaWmhsbGR0dFRent7i+IIJicnmZycJBAI0Nra\nSmVl5Tpu7ZlFIpGgUChgMplEoewHP/gBo6Oj3HvvvcC8eJ1MJnnwwQe57LLL2L1795J84oXHlqIo\nKIqCpmni3NXT04PJZOLmm2/m85//PNu3b+enP/0p+/fvR9d1/vZv/xaYF+za2tro7+8nFAqh6zq9\nvb00NjauezM/p9NJVVUV4+Pj2Gw2VFWlsrKSVColZj0YTvJCoUA4HBbXBJfLhd/vp6Sk5JjFzOMl\nHA4zOjq6JLrD5XJRW1uL3+9flb9zPNjtdmpra0mlUszNzREOhxkfHxfFKIfDQUVFBW1tbRs2J3wj\nsLAYstL1paurC13X+Zd/+RdsNhsPPvggXq+Xb37zPj7yka/j8zm5+up3iPXnx+NyrzQFpJd7QGB8\n33rppZdoaGgQy43mjgu/a0kkEolEIpGcLUjxWiKRAPDZz36W6667jrGxMX7yk58IRzLA+eefz/kX\nn89T+5/CX+PnnHefw1DnEN/65LcwWUxkk2/lTFssFr7X/z0sVgtx4oQIUaKUcNttt/G1r32Nv/zL\nv+TjH/84kUiEz3/+8+LGcXGDq6ORSCQ4fPhwUU5ybW0tDQ0NJ9SAyuv1Mjc3B8zniRrxEIlEApPJ\nJPOv1wkjPiCVShGNRoUjPp1O4/F4cDqd6yIKu1wuLrroIiYmJjh48CCpVIp8Pk80GuXw4cNUVFRQ\nU1MjHOTJZBKHw4Hdbt+wIrbJZBLZ8GNjY/T29hbFRszNzfG73/0Or9dLa2sr1dXVssnbKVAoFERj\nQqvVit1up6uri0996lP8wR/8AR/5yEdEZMj3v/99BgYGuOeee3C73UuiaRYeUx0dHZhMJjRNw2Kx\noCiKiN3Zv38/f/EXfwHA+9//fkKhEI888gh/9Vd/JSIkTCYTLS0tBINBpqenARgcHCSbzVJXV7cW\nu2ZF3G435eXlTE9PUygUmJ6epq6ujvLycvL5vIjvCYfDRT0PEokEiUSC0dFRbDabyMn2eDwnfAxv\nJNF6McZsj2g0KoR8RVFwuVzYbDYikUhRLJOkmIWu65WOC6MgEg6HeeWVV9i9ezeQ4g//0MLmzR/n\nb//2p1xxxS7y+Ty5XI5Dh76N07l0phnowNhRt+eqq66ioaGBv/iLv8DhcIiGjV/60pewWCwn9F1J\nIpFIJBKJ5ExBfpOVSCQAtLW10dbWBsCf/umf8r73vY/3v//9/O53vwPgkZ89wq37buUbH/8Guq5j\nMpv44//xx/z++d8zcmQEmBeuGxoasFjfcj6n/8tldPfddzM7O8uDDz7I/v37URSFK6+8kptvvplH\nH30Ut9t9XNs5MzNDd3e3uElXVZXW1taTcghaLBZcLheJRIJYLEYgECCfz1MoFEgkEng8ng0rOp4N\nGO7leDwu3KqRSIRUKoXX6103d3xVVRXl5eV0d3fT399PoVCgUCgwMTHBzMwMDQ0NlJWVAfMC2kIR\ne6MKv4qiUFtbS21tLVNTU/T29hbl5UajUV5//XVcLhfNzc3U19fL4s5JkE6nhVhmsViIRqNcffXV\nlJSU8NOf/lS4p2OxGF//+tf58Ic/TFlZGR6PRxTaYD7KaeG5ablseIfDQTKZ5MMf/rBYZrPZuO66\n6/jVr37Fyy+/zBVXXCFeR1VVmpqaMJvNolHp2NgYmqbR0NCwrudCv99PPp8XM2XGxsaoq6vDZDIJ\nUdrIIQ6FQoTD4aJIj0wmI2YTmEwmfD4ffr8fn8931Jk6kch8HJZRcDBwOp3U1taKrOT1IpfLMTg4\nSCgUEvtCVdWi2JJIJEIkEhEudCliF2OMx+WuJ0ZElFHIN4omPT09FAqz2Gwz7N27lWeeeY2RkREx\nU6iyspLNm1tX+ItHF59tNhvPPvss119/PR/60IfQdR273c7XvvY17r333uP+riSRSCQSiURyJiG/\nwUokkmW59tpr+fM//3N6enrYvHkzFdUVPPCbBxjrGyM0EaJ2cy3+Cj9/WvunmC1mnE4nNTU1WG3F\nN4AK84KHxWLhscce47777qO7u1tk6v7Jn/wJqqrS0tJy1O3RdZ2hoSGGh4fFMpvNxtatW0/pZs7n\n8wk3XTwex+PxEIvF0DSNVCq1atPNJSeHkWPrcDiIRqNkMhmy2ayIEvF6vesiCBtNQevr6+no6GBm\nZgaYFzv6+vqYmJhg8+bNOBwOIaqlUinsdjsOh2PNt/mmm27i8ccfP/aKzOd8V1RUMDc3R29vL5OT\nk+KxRCJBR0cH3d3dNDc3zxerjiOmRzLPQvFa0zSuuuoqotEoL774IlVVVcB8s8ZvfOMb5HI53v3u\ndzM9PU1paakQlGdnZ+nr66O2tla8rlHMW0hNTQ29vb1FkS9GkULXdebm5kTe/0Lq6+uxWq0Eg0Fg\nviGupmk0Nzeva/GltLQUTdOIRqNks1nGxsaora0V22S4jV0uF3V1dWQyGSKRCKFQiGg0KgR+QwSf\nm5tDURTcbrcQwI2c4bUWrU9kfBqEw2HhjjeoqKgQhblJPcEAACAASURBVKVkMsnc3Jxw6hoxKz6f\n76wXsfP5PNlslkwmI2b3RKNR8f0iGAzy6quviiagxnHg9XrFOLRY4ihKlkDAjablmZ6eA+ZnAiQS\nCTKZDNu2bUNVFxd9jl0E2rp1Kx0dHRw+fJhQKMS2bduw2+185jOf4dJLL12t3SA5AU5mjEokkrVB\njk+J5Ozg7P3mKpFIjopxwxuJRABwMZ/NWtNSQ01LDQDBziBz43O844/eQWNT47Kv46RY/C0vL6e8\nvByYF1z+/d//nXe84x1HbZKoaRrd3d1FzkOv18uWLVtO2X3rdDpFE7BIJEJJSQkOh4NUKkU6ncZs\nNsv86w2AxWJZMUrEELfXwxnqdru55JJLGB0dpbOzU0TZJBIJ3nzzTaqqqmhpaUHXdXRdF8eVzWbD\n4XCsmXv5yiuvPOHnBAIBLrroIqLRKH19fYyOjgoBMJPJcPjwYZGN3NTUJHN1jwOjWaOmaXzyk5+k\nt7eXX/3qV7S3twOImR/BYJBYLMZHP/rRoucrisLXv/51HnroIZ5//nna29spFArCib1QkLzgggvo\n7e1ldHSUxsZGsXxiYgJFUSgrKyOTyWA2m5cImZWVlZjNZvr7+4XQrWkara2t6yp6VlRUkM/nSSQS\npNNpJiYmqK6uXnbs22w2UYjJ5/NEIhEh4BoFBF3XicVixGIxhoeH0XVduGwXZtYbxdmSkpLTcp45\nkfGZz+cZGRkpKipZLBYaGxuLRHWn04nT6SSZTDI7O0s6nUbX9TNaxDZiO3K5HNlslmw2u+R3Y/zB\nfHHUKH5omibiQXK5nBCuNU2jtLSUkpISUaScX24DFCYnI1itZhwOK6lUSkSOzceQLHesHH9D6K1b\nt4rfn332WQqFAldcccUJ7hXJanAy11CJRLI2yPEpkZwdnDnfWCUSyUkxPT0txGQDTdN44okncDgc\nbNu2DYAKKrBhI8P8jb2u6/zD5/4Bu8vOJx7+RNHzx/vnnUlbm7fixbvi337ggQeYmJjgW9/61orr\npFIpOjs7i3Ieq6qqVs0FaDRunJmZEU3SnE6nmC4s8683FkaUSCwWI5lMiiZtyWTymBEAp5Pa2loq\nKiro7u5mYGBAiLwTExNMT0+zefNmqquryWQy6LpOOp0uErFPt4B0ww03nPRzvV4v559/Pu3t7fT1\n9TE0NCScvrlcjp6eHvr6+ti0aRMtLS1ytsIK6LpOPB6nUCjwla98hddff51nnnmGiy66SKxjiGo3\n3HADTU1NeL1e/H4/ZWVlTE9P86lPfYobb7yRP/zDP6S5uRmLxUI2m2VwcBCz2UxLSwvZbBar1cq+\nfft46qmn+O53v8s999wjtuHxxx8nEAj8V27v/DnW7XYvEWVLS0sxm83/FZFQIBqN0tXVRVtb27oV\n9BRFoaqqitHRUdLpNIlEgqmpqWM2FDWZTAQCAQKBgPgcjJxso6AUDoeL+iioqkpJSQlNTU3U19ef\n1nPL8Y7PZDJJX19f0fXQ5/PR1NS04mdiiNiJRIK5ubllRexAILChr3FGgWYlYdr4aYyf48U45hfP\nXFAUBZvNhtVqRdM07HY7V111FT/84Q+ZnJzkiiuuQFVVentHeO65g/zBH2zB4/EQj8dxOBxMT8fx\n+5dG+YAJqDnh959Kpfjyl79MTU1NUQyQZO04lWuoRCI5vcjxKZGcHUjxWiI5y7ntttuIRqPs2bOH\n2tpaJiYmePLJJzly5AgPPfSQEKL+x2f+B5PpScrOK0PLaTz/5PP0vNbDHU/cQXldsfj9hcu+gKqq\nHOg/IJY9+eSTPP300+zZswe3283/+3//j3/6p3/illtu4QMf+MCy2zY3N8eRI0eEA0pRFFpaWsT0\n+tXC6/UyOzsrbuiNqeeRSARd12X+9QbDyHR1Op1EIhEhZBhRIifTkG01sFgsbN++XUSJGDMF8vk8\nXV1djIyMsGPHDtxuN6lUikKhQCaTIZPJYLVacTgcGzqCw+l0snPnTtra2ujv7ycYDAoHa6FQYHBw\nkGAwSG1tLa2trXg8nnXe4o1FNpslnU7z8MMP88ILL3D11VczMzPDk08+KdbJZDJcf/31VFZWcv75\n5xMIBNi+fTs+n4+hoSEAtm3bxvve9z5UVUVRFEwmEx/60IdQVZWOjg4h8L3vfe/j8ssv52/+5m+Y\nnp7m3HPP5Z//+Z/57W9/y2OPPYbX6xVi+koRST6fjy1bttDT00MulyOZTHL48GHa29uXxI2sFaqq\nUlNTw8jICNlslmg0Kpq8Hg+KouDxePB4PPj9fgYGBkSTUkVR0HUdi8WC3+/H6XQyMzPD7OysKCT4\n/f41n2Wg6zqTk5PCHQ7z+6G+vv6Ywr2BcV1bScQ2MrHXUsQuFArH5ZRe3Kz0ZFEUBavVisViEY1N\nzWYzTz31FMlkkqmpKQAOHTokigE33ngjVquVL33pSzz33HN89KMf5fbbbyedTvPjH/+IfD7PHXf8\nEYVCAYvFgq7rfOpTT2C32+jv/17R37/33mdQlN9x6NAh0ZT1hRdeAOCLX/yiWG/fvn3U1NSwbds2\notEo//AP/8DAwADPPvvsUWepSSQSiUQikZypKMs1+dloKIqyCzhw4MABdu3atd6bI5GcUfzkJz/h\nu9/9Lh0dHczOzuLxeLjgggu4/fbbufrqq8V6TzzxBN/85jfp7u0GFdouauOGL93Azj07l7zmx5o+\nhkW1MNz3Vj71q6++yuc+9zk6OjpIpVK0t7fzyU9+kltuuWXZ7RoeHhaZqzDfTGnLli14vSs7uU+F\niYkJYrEYAA0NDVitVnK5nFhmt9ulo3QDYkRxRKNR4Z4zmUwiSmQ9t2tkZITOzs6iTFqYd2lv3boV\nRVFIpVKiOAPzArjD4XhbRNXkcjmCwSD9/f0iamEh803LNq97U7uNQiQSoaenh1tvvZXf//73K64X\nCoX4zW9+QzKZxGazcemll2KxWBgaGmL79u3ce++93HbbbcD8sZ7NZtm9ezdms5kjR46QzWbFWEgm\nk9x333380z/9E3Nzc7S3t/OFL3xBuDcNQRreilBajlQqRXd3t/icLRYLbW1t6yqk5XI5RkZGhNu2\nvLwcv99/XM+Nx+OMjIwI0drAarWKHgrRaHRFJ6/T6RRCtsvlOq2FzWw2S39/f9G2Op1OWlpaTukc\nl0gkmJ2dLRq7RmHwVEXsQqGApmnLitGLf64GiqJgsViEML3cT6vVitlsFp9VNpslHo+jKAq7du0S\nxaHFvPzyy9TW1uL1epmZmeH222/n17/+NZqmsX37du6440NcfnktmUyGsbExdF3nQx/6e6xWC319\nCzNYPajqu5Y9VoxGrQYPPvggjz/+OIODgzgcDvbs2cNdd93Fzp1Lv29JJBKJRCKRbFRef/11Lrjg\nAoALdF1//VReS4rXEonkhMiT5yAHGWdcLDv44kF2vHMHACoqm9hEO+2iWeMJvX4+T09PT1G2pMfj\nYcuWLafV7ZZOp0WzppKSEsrKyoB50caYou3xeDa0M/Zsxog1MIQ4mM+s9fl865rpms1mOXLkCIOD\ng0XLzWYz7e3tNDQ0iOagC8ULk8mE0+nEarWuijD24osv8s53vvOUX2c58vk8w8PD9PX1Fe1/g9LS\nUjZv3rwknuhsY2JigsHBQfL5PJs2baK+vr7ocUPgy2QyPP/88xQKBfx+P+95z3tE3AxQlMtssVhI\np9Mi4mJhfu9CERsQQt7i4ymVSonMbLfbveKshWw2S3d3t/iMTSYTmzdvPm0FxeMhk8kwMjIi3md1\ndfVRG/jG43FGR0dFLwcDu91OTU0NgUBAvP9CoUAsFhPxIssVaADh0vb7/Xi93pMSfVcan3NzcwwO\nDhadG6qqqqirq1u12SXxeFw07zRQVVW8p4XvR9f1ZZ3SywnUq8WxBOmFLuoTwWiqaLFYVpwlYszo\ngfnvBePj48KdnclkMJlMbNmyhZKSMJ2dvyCZnC92t7W1UVKysJBSBpwLyO8Pb1dO5zVUIpGcGnJ8\nSiQbFyleSySSdSdJkiGGmGOOO6+5kwefeZAKKqijDhsnJzKn02kOHz5MIpEQyyoqKmhtbV2TGIih\noSFxQ9rY2IiqqqKhl6ZpIh97PSIpJMdHNpstasimKAoul+uootxaEA6H6ejoEA3BDLxeLzt37iQQ\nCJDNZkVDPwNVVXE4HNjt9lMSsa+55hqeeeaZk37+8VAoFBgfH6e3t3eJoxXmIyhaW1tXbLB3ptPf\n38/4+DgWi4Xm5mZRIDMwMtwnJyc5cGA+cqmlpcX4wkc+n0fTNBE3A/MFmmQyiaIoeL3eJYWaxSK2\n4VBdKPYtzOI2m804nc4VPx9N0+jp6REzUowop0AgsEp76cRJJpPC8aooCjU1NUtmySQSCUZGRo5L\ntD7a3zGE7Hg8vuw6qqri9XopKSnB5/Md9wyKxeMzn88TDAaLirhWq5Xm5ubTUizQdZ1IJMLU1JSY\nDWIca4Zj2egDsVr3DcfrlD4d523j/RYKBZxO54oROPF4nGg0SiqVIhaLiUx0Xdex2WyiSebk5CSv\nvPIiFssUJSUZLr54F6pqBvzAJkBGKL3dWYtrqEQiOTnk+JRINi5SvJZIJBsKo8nhqRAOhzly5EiR\n6NjU1ERNzYk3NzpZjJt3mI88MESCQqEg8q/NZrPMv97g6LpOMpkkFottuCiRoaEhDh8+vGS6fH19\nPdu2bRNxNYYb1kBVVex2O3a7/aTEnNUYoyfC5OQkPT09hEKhJY+5XC5aW1upra3d0E3iVhNN0+jq\n6iISieB0Otm8eXORQ7hQKAhH88GDB0WEwcUXX0xDQ8OS10un02iahtVqFaK3y+VadnaKruvk8/mj\nitj5fF4Isna7/aizXPL5PP39/UWfbWNjIxUVFSexZ1aHeDzO+Pj8bCBVVamrq8Nms5FIJBgdHV1S\nNLLZbNTU1FBaWnpS48kokhl50Ysb/hm4XC6RJX208bdwfMbjcfr7+4uaRwYCARobG09qFslCZ/Ti\nnwt/13UdXdfRNI1MJlMUZ2RkRdtstmNe+8xm87Lu6MVO6fUsJmqaJgpsPp9vxfPQ7Owsw8PDzM3N\nic9HVVWqq6vF81wuF2+88Qbd3d3k83laWlq4+OKL1+y9SNaGtb6GSiSS40eOT4lk47Ka4rVs2CiR\nSE6ZU/3CMDY2xsDAgHB0WSwW2tvbjzu/dLXweDzMzMwIsdoQr1VVxeVyEY/H0TSNdDq9riKo5OgY\nbmu73U4sFiOZTJLP5wmFQiSTyXWLElEUhYaGBqqrq+ns7BQxNTCf8T4xMcGWLVtoaGjA6/WKOJFM\nJiPEzVQqhd1ux+FwnJD4s9Zf6isrK6msrGRubo7e3l4mJyfFY4lEgt///vccOXKE5uZmGhoa1jXa\nZS1Ip9OiYGGxWJY4PY1YCFVVhSh8tCaEC6MtVFWlUCisKKAaTelMJlORiG0Il4aYaLfbSafTpNNp\nsf5ymEwmWlpaCAaDTE9PAzA4OEg2m6Wuru4E98zq4Ha7KS8vZ3p6mkKhQH9/P4BwiBucqmhtYLVa\nKS8vp7y8nHw+XxQvsrDolEgkhIBus9lEFMfiprJOp1PMXDBc5DC/rxsaGpa49IElmdLLCdOGKH28\nGEUNw2ltiNiGqA2IZpd2u31Zx/TbYWaSMRZNJtOKx3k6naarq4t4PC7Gq9PppLW1VWRUm0wmMpkM\nk5OTQuzftGnT2rwJyZoihTGJZOMix6dEcnZwZt8tSiSSDU2hUKC3t1e4nWH+C8i2bdtWnMZ7OjGm\nfIfDYdLpNJlMRjgQrVarEHdSqRRms1nmX29wTCYTfr8fp9MpokQymQzT09O43W7cbve6OOitVivn\nnXcemzZtoqOjQzgAc7kcHR0dDA8Ps3PnTiFyOZ1OIWIbDSrT6TQ2mw2Hw7Gh3cuBQICLLrqISCRC\nb28v4+PjQkxLp9N0dnbS09NDU1MTTU1Nb4tGlSfDwkxzh8OxbLwHzDt6Dcetw+FY8YZssXht/H40\njiViL3wsmUwedXyoqkpTUxNms1k4nsfGxtA0jYaGhnUZV36/n3g8Tm9vL/F4HLPZTElJiXAN19TU\nUFZWturiqnGe8fv9YtZHKBQiHA4XZcAbIufk5CQmkwmfz4ff78fn8wk3uxHfks/nsdlsVFVVkc1m\nCQaDSzKmFzqjT3X7V3JKWywWstmsiM4yUFUVt9uN3+9/W4jVi1lYSFqOmZkZ+vr6xGwEk8lEdXU1\n9fX1qKoqos1MJhNjY2NizLrd7mULDRKJRCKRSCSSU0OK1xKJZF3IZDJ0dXUVOePKysrYvHnzuopx\nPp9PTDGPRCJFU+EdDgeapqFpGolEAq/X+7a8cT/bsFqtlJWVkUgkhDgUi8VIpVJ4vd51KZTAvLD7\nrne9i2AwSFdXlxCHwuEwL7zwAo2NjbS3t2O1WnG73TidTlE80XVduGQNEXsju5d9Ph8XXHABiUSC\nvr4+hoeHhdiay+Xo7u6mr6+PTZs20dLScsbNbDCyzI1GnAtZ6JqORqNCWFvYgHExJyNeGywUsY0c\n40KhIPKMDVH0eGaY1NfXY7VaCQaDAExNTaFpGs3NzWt6bjRyr+fm5sT2a5pGPB5n+/btVFRUrMn2\nGLM+XC4XdXV1ZDIZIpEIoVBIFNCMPGljW+PxOMlkElVVMZlMqKoqeissnJ1xophMphUbHC78eTzX\n29LSUtHY0Sh6zM7OEg6HRb732+VaWCgUxLl2sXitaRqDg4PMzMwIB73FYmH79u2UlJSI5y+Moxof\nHxfrVldXy6K2RCKRSCQSyWlg497pSiSStw133nknDzzwwHGvH41G6erqKppe3dDQQH19/enYvBPC\narXicDhIpVJEo1FKS0vFzb0hTESjUQqFAolEYt3cu5ITQ1EU3G43DodDNOAyBCS73X7U3NPTieFg\nNaJERkdHxWODg4OMjY2xbds26urqUFUVp9OJw+EQIrbRvC+TyYhjdznx5ETH6OnC5XJxzjnn0NbW\nRn9/P8FgUAhJ+XyegYEBgsEgtbW1tLa2FuVCv10xzhW6rmOxWJYIwsb7N5lMIoYDOKqDc+E5x/j9\nRJ24iyMiDBFbVVWy2Sz5fF64co9GZWUlZrOZ/v5+dF1nbm4OTdNobW097QWVVCrF6Ogoc3NzYpkR\nyeFyufD5fBQKhdN6js7n80W50stlSudyORHDYcT/GEXQbDbLz372Mz74wQ8KR7au62Lmz+JtV1V1\nxRzphf9fzX2vKAoejwe3200sFmNubk4I8TMzM4RCobeNiL2wr8bCfRSNRunr6xPNUDVNw+v10tTU\nJIRroCg3PpFIEIlERENnGRly5rJRrqESiWQpcnxKJGcHUryWSCSnzIncsE1MTNDX11eU6dne3k4g\nEDhdm3fC+P1+4W6NxWJF2dtGg6Z4PE4ul5P5128zTCaTaJ5miA5GRIzH48Hlcq1LMcJut7Nr1y4R\nJWJMV89ms7z55psMDQ2xc+dOvF4viqLgcDiw2+1kMhlSqZSIgchms0IgXSg6bjRRxW63s23bNlpb\nWwkGg/T394tiVqFQYHh4mOHhYaqqqti8efOa59+vJplMRghmRvzQQhaK17Ozs+L3lfKuYV44M7Ku\njeO1UCig6/oJH7+LRWxVVYU7OBKJ4PF4jtmor7S0FLPZTE9PD4VCQRQo29raTksUTCqVYmxsTOwv\nA4vFQk1NDYFAgImJCdLpNIlEgqmpKSorK0/obyyMVFlOmDZ+LozTOBpG8cnpdJJMJkV8iMlkIhAI\nYLPZ8Pl8WCwW8vm8KE75/X5KS0sJBALrPsNCURS8Xi8ej+dtK2IvjAxRFIVCocDIyAhjY2NiHZPJ\nRFVVlYidWohRJDKZTIyMjIjzlvG+JWcmG+0aKpFI3kKOT4nk7EA5kUYu64WiKLuAAwcOHGDXrl3r\nvTkSieQkMJpoTUxMiGUOh4OtW7duuEYbuq4zMDBAPp/HarXS0NCwZJ1kMilyLr1e74aObJAsj67r\nJBIJYrGYKKaYzWZ8Pp/IOl8PjLHS3d1d5KZVFIWmpiba2tqK3NW6rpPNZotylWH+vRgi9kafHZDP\n5xkaGqKvr49UKrXk8bKyMlpbWykvL1+HrTs15ubmGBgYIJ1OU1paSnt7e5HgvDAX+Ve/+hWZTAan\n08l73/veo0YQpNNpNE3DbDaL11iNGQS6rpPL5cQME5PJhN1uF+7eox1L8Xicnp4eIRDabDba29tX\nLZonnU4Lp/XC768Wi4Xq6mrKy8vF+8/n80XiYiAQoLS0VESkHMspbbyHU8XI3DYKBJFIhEgkgslk\nwmw2Y7PZqK2txWKxEAqFiMfjyzZZNNzPJSUl+P3+dT1HGei6TjQaFW57A0OQ32jRWrquEw6H0XUd\nl8sl+m4YGdYwn1vd2NgoehGUlpYW7WujAbDVauWVV15hamqKbDbLli1bOP/88zf8uVYikUgkEolk\nrXj99de54IILAC7Qdf31U3ktqbZIJJLTTjab5ciRI0QiEbEsEAjQ1ta2IUVfRVHw+Xwi3zOZTC4R\n2B0Oh3CcxePxDXeTLjk2RpSI3W4nGo0KMXB2dhaHw4HX6123KJHW1lZqa2s5dOiQaIin6zr9/f0i\nSqS2tla8D5vNhs1mEyK2EVMQi8UwmUw4HI5jumfXE5PJRFNTEw0NDYyOjtLX11eUhz8zM8PMzAx+\nv5/W1laqqqo27HtZTDqdJpfLiSiLhdu90HUdCoWE0Or3+4+ZnWucbxYKnYbYfCoYYqvP5yMWi4lY\nDEPUtlqtmM3mZfe/2+1my5YtdHd3izibw4cP09bWhsvlOultSqfTwmm9WLSuqqqioqICRVHQNE0c\n/9lsVsSYZLNZRkdHsVqtq3aeNhzry+VIL4zwMPZVKpWir68PRVHETAKv10tzc7Nwp1dVVQnHezgc\nFrND4C2hOBqNEgwGcTqdolHkes0YMa6VXq+3SMTO5/NMT08zNze3oURsTdPE8RMKhRgeHhYFQkVR\nqKmpoba2VhTQjM/YYGEmfCQSEWPbbDZTU1PztjknSSQSiUQikbzd2HiqkUQiOaOIx+McPnxY5EjC\nfJOvTZs2begbPUO8hvmb1MXitSF8Lsy/9ng867GpklPEbDYTCARIp9NEo1EhgGUyGdxu97oJQw6H\ng927dzM1NcXBgweFOzCdTvP666+LKJGFudCGYJbL5UilUiK72GgKZ8SNbNSxp6oq9fX11NXVMTk5\nSW9vL6FQSDweDod57bXXcLvdtLS0iCzwjYrh7s/n89hsthUjQ8xmMzMzM0JYO1pkiMFC8dqIEDne\npo3Hg8Viwel0kslkRCQJzMegZLPZFUVsY0ZNd3c3yWSSXC5HV1cXmzdvxuv1ntA2GE7rqakpEWWS\nz+fRdR2/34/dbmdmZqaoad5ijAgOI0fa6XQeszBwLEH6eFzoC5mcnCxqUqooCvX19VRWVi55DbPZ\nTGlpqXCJx2IxwuEw4XC46DqaTCZFo0qLxSKE7PUouh1LxDbiRNZbxF4YcbKwmG6z2WhtbRXX8IXN\nGhdu78LxNTk5KYo6paWl8vovkUgkEolEchqRsSESyVlOZ2cnf/3Xf82BAweYmJjA6XSybds27rzz\nTt7//vcXrfvII4/wP//n/6S/v5+ysjI+sO8DfOKeTzA8NEz7lnYqqMDFW+66qakpent7KRQK3H77\n7bz55pvLboPFYim6Kd8ojI+Pi+zhpqamZV3imUxGiIpOp3PVpsdL1gdd14nH40VT9y0WCz6f77Rk\n9x4v+Xye3t5eMZ4MVFWlubmZtra2ZQUrQ4g/ePAgmzdvBijKzN7Iwq/BzMwMvb29Rc0MDex2Oy0t\nLTQ0NKyLS/5YZLNZenp6CIfDuN1umpubOXLkCN/73vf49a9/zeDgIIFAgEsuuYSrr75afCZ79uyh\noqJCvE4+n2fnzp10dXWxf/9+Pv3pTwPz5x8jt1fTNOx2uyi0PfHEE9x0001LtklRFMbHx4tefyUW\niu+qqmKz2YRgZ7zWSiK2pmn09PQIB72iKLS0tIj+Brquo2nasrEdRk51KBQqiqNQVVVkLp/Isatp\nGslkElVVRbHK7Xav6JRerXGRzWYZGBgoEkqdTifNzc1FBdGuri62bNlyzNdLJpNCyDauTYsx9pGR\nwbwe5y0j93zx52fseyO7f60ZGxsTorNxHnW5XPzkJz/htdde43e/+x2hUIhvfvObXHvttbjdblFw\nuemmm3jiiSeWvGZ1dTW//OUv2bFjB6qaAaaALKACfmC+EPXCCy/w4IMP8sYbbzA9PY3f7+e8887j\ny1/+Mv/tv/23Ja+by+V44IEH+Md//EcGBwfx+Xzs3r2bxx57jJqamtOzgyQrcrxjVCKRrD1yfEok\nGxcZGyKRSFaNYDBIPB7nYx/7GDU1NSSTSZ5++mmuueYaHnvsMW655RYAPv/5z/PAAw9w/fXXc/Nn\nbubVzld59OFHeaXzFUwWE3/9zF9zhCOUUkqL3kJkMMLo6Kj4O7fccouILjBIJBLcdtttvPe9713z\n9308+Hw+IRBEIpFl3ZA2mw1N08hkMiSTScxm84aMQpEcH0aurMPhIBKJiGZ7MzMzOJ1OPB7Puoik\nRmPTuro6Dh06xOTkJIDIbB0dHWXHjh1UVVUVPc9sNuPxeLj//vv50Y9+RCaTQdd1kskkqVQKm82G\nw+HYkMKvQVlZGWVlZUQiEXp7e4saq6XTaQ4dOkRPTw9NTU00Njaua5FhMYb7HRCNNPfv389vf/tb\nPvjBD7J161amp6f59re/zS9+8Qvuvfde2trailycuq7z0EMPMTw8jKIowoFsxHioqirE1sXOa0VR\nuOeee2hsbCxafrwNMI1CRyKREM5up9MpRGfDzbzYiW1kRldWVhKPxwmFQuTzeSYnJ0XDVOP5CzEi\nMxYLs6qq4vF4VnTuGk7oxe7ohb9nMhkxblRVpba29rTmRodCIQYHB4uysysrK6mvr1/yHj73uc/x\nzDPPHPM1jYaPNTU15HI5IWRHIhHx2RcKBbEcq7z/YQAAIABJREFU5sVZv98v9vtaoKqqcIEbTmzj\nuJ2amiqKE1kLETufzxMMBsVsqkKhgNlspqmpiXg8zn333UdDQwPnnXcev/71r8W+XHwusdvtPPLI\nI8RiMSYmJkRkWEWFE1V9E5gGFpuC3EAj3d3dmEwmPvGJT1BVVUUoFOIHP/gBe/bs4dlnn+XKK68U\nz9A0jauuuoqXX36Z//7f/zvnnHMOoVCIV155hUgkIsXrdeB4x6hEIll75PiUSM4OpPNaIpEsQdd1\ndu3aRSaTobOzk4mJCTZt2sSNN97IVx7/Ckc4AsAvvvULvnP7d/j0//40V940f+OV1/JMTU5RPlaO\nPzMvkPj9ftrb25dM1X7yySf5sz/7M370ox+xb9++tX2Tx0kwGCSbzWI2m2lsbFz2RtvIIjXcies9\nNVqyeqRSKfHZwlsimtPpXNfojYmJCQ4ePLikuWFlZSXbt29fki88NDTEpk2bKBQKpFIp0ul0kXBo\niNhvh8JLPB6nr6+PkZGRJWKtyWSisbGRpqYmHA7HOm3hW0xMTDA8PEw2m6W6uprm5mZefvlldu/e\nTTabpVAoYLPZOHDgAHv27OEd73gHd911F+9+97uB+XPLyMgI55xzDp/+9Ke5++67uf/++7n99tsB\nxGsY7muLxSKcok888QQ333wzr7766il/dzKy1A3Rr1AokMlkSKVSQqA3BOtCoVB0/tN1ndnZ2aKm\neD6fr0hAX0m0NiI0ysrKsNvtS4Rp4+fxnm/D4bBw8JvNZurq6o4ZIXKi5PN5hoeHmZqaEsusVitN\nTU34fL5ln2OMz1P5mwvjRVaKULHZbCJe5ETd66eC4cQ2RGyDtXBiJxIJent7SafTokjncDhobW0V\nMwlCoRAVFRUcOHCACy+8kL/7u7/juuuuo6qqSuyjm266iaeffprR0VEOHz7M7OwsoVCI5mY/55yT\nx+k81nHUCBQ7A1OpFM3NzZx//vk8++yzYvnXvvY1vvKVr/DSSy8ZbiXJOnOqY1QikZw+5PiUSDYu\n0nktkUhOK0Ye52uvvQbAb3/7W/L5PO/d914hXAPs/fBevv3/fZvfP/d7rrzpSrKZLOPj44z3jTPM\nMJe4LqG1spWGhoZlb5KffPJJ3G4311xzzZq9txPF5/MxPT2NpmkkEomifGGDxfnXyWRy2fUkbz+M\n2QLxeFy4TyORCKlUCq/Xu24u36qqKsrLy+nu7qa/v1+IuJOTk0xPT9Pa2kpra6sQa4wv9UbTQIfD\nQTqdJp1OCyEyk8lgtVpxOByrLuitJm63m3PPPZe2tjYGBgYYHBwUglg+n6evr4+BgQHq6upoaWlZ\n17FoNHQzXNcA73jHO4ryqc1mM16vl/r6ekZHR4tmeORyOb74xS/S3t7Ovn37uPvuu4te3xD8BgYG\nyOfztLS0LLsd8Xgcp9O5olipaZqI7lj80/h9YbFjccxTPp8XWdTGdplMJkwmE4qiUFZWhslkIhqN\nAhCLxTCbzZSXl4uxZbVaqaiowGw2Y7FYqKmpoaamZlULKn6/n3w+LzKZx8bGqKurW7WZB4lEgr6+\nPtLptFhWUlJCY2PjUcfUqd50m0wmIUobMytCoRDhcJhkMinWM9znk5OTmEwmUUTw+XyndcwvdGJH\nIhHhxDec2KFQiEAggMfjWTURW9d1xsfHGR4eRtf1omNxYc8Ni8WybITO4rxr49hOpVKMj4+j6zpm\nc476+hms1uJZWf398012m5urFywdBJzAW5+1w+GgvLxcuOSNv/P3f//3fPCDH+SCCy4gn8+TzWY3\nRDHubEYKYxLJxkWOT4nk7ECK1xKJBEDECEQiEX7+85/zy1/+khtuuAF4q3nRnGMOP2+55WzO+SnX\nPQd6SMQTTE5OUigUePijD6OoCnt/v5emkqZl/97MzAz/9m//xg033LChb8o8Ho9opBaJRFYUwkwm\nE06nk0QiQTabJZ1Oy/zrMwTDTe9wOIhGoyImwYgSWS+nvclkYuvWrdTX19PR0cHMzAww73Ls7u4W\nUSLLCTOqquJ0OnE4HMJBa4gk2WxWiK0bKYJjMQ6Hg23bttHa2srg4CADAwPiXFUoFBgaGmJoaIia\nmhpaW1tXdL2eLoy8cV3XsVgsRecDIwfYENRmZ2eJRCLU19cL8VrXdV5++WV++MMf8txzzy0r6hnL\nPvCBD6AoCgcOHEDXdbFc13UuvfRS4vE4VquVvXv38ld/9VfU1tYWCdML3bBHw4jZsFgsRXEYhlBt\nNI80BGibzSZ6AdhsNkKhEBMTE+TzeaLRqBDrS0pKxOtUVlZSVVV12mYBlJaWomka0WiUbDbL2NgY\ntbW1pzSGDaF0dHRUiJyqqtLQ0EB5eflqbfpxoSgKLpcLl8tFXV0dmUxGCMbRaFRsnyHiz83NiQKs\nIYCfrmuyqqoii3uhiJ3L5ZicnBRxIqcqYmcyGfr6+kSxBOaP3fLyckpLS4/rtRef+4yiQGNjI+l0\nGpfLxZVXXsQll/wpZnNx8eOyy76Aqqr09z++6FUHiMX8ZLPzUVRPPPEEhw4d4otf/KJYo7Ozk7Gx\nMXbu3Mmtt97K97//fbLZLDt37uSb3/wml1566QnvD4lEIpFIJJK3O1K8lkgkANxxxx08+uijwPwN\n5rXXXsvDDz8MQHt7O7qu87uXfkfz3mbxnIO/OQjAzMgM4+PjYrmiKJjNZjIlGbJksbJUAHvqqafI\n5/PceOONp/NtnTImkwmPx0M0GuX/Z+/MwyMr63z/OefUvqaSVCWVvZPQG93N0mwNCKIDCGizPcIw\ncMdB8UHncnEQGPTKKN6ro+hcHhh1GJ3rQHtBFrdRB0ZHEZQBEbBZmu5Od2ffk6qk9r3qnPtHfF9S\nnfSeXqDP53n6QVOVOkud91Tq+/7ezy+bzUq362KIJcjFYtH0X78LsVqt1NXVValEstks+XxehttH\nQyXi8XjYsGEDY2NjbNu2TVZ9ZjIZ/vCHP9DY2MiaNWsWDaQURZHBorhuRZhUKpWwWCwyxD6ampS9\nYbPZWL58OV1dXQwNDS2ofB0fH2d8fJxgMEh3dzf19fVHZL9E1TWwx/DaYrFQKBT46U9/yuzsLDfc\ncIPUfpTLZW6//XY+/OEPc9pppzE8PCx/X9d1OdkgPOaKohCPx5mamqJcLjMxMcFll13G+vXrcbvd\n9PT08Nhjj3HllVfy8MMP71fDRoGqqlWajvnh9HyVh8VikS7u+dXaqqrK5yeTSXp6euRj5XKZxsZG\nwuEwjY2NR6TqPxQKUalUyGQy5PN5JicnCYfDB3WNFwoF+vv7ZWNKmPNMd3V1HRMTmHa7nVAoJI85\nkUhIT7a4Pg3DIJVKkUqlGBkZweFwyCDb4/Es+eTcvkJsUYnt8XgO+D2ZmZlhYGCgqlFkKBSSk4x7\n+0yeP4mz++d8Y2Mjf/M3f4PX66VYLPLCC//FT37yLJOTEzz//D9UnSNFUVh8t3Ncc80V/PKXz8lt\n3Hzzzdx9993yGbt27QLgvvvuo66ujn/5l3/BMAz+/u//nksuuYRXXnmFNWvWHMAZMTExMTExMTF5\n52OmKiYmJgDcdtttfPjDH2Z8fJwnn3ySSqUil4afcsopnHzmyfzg3h9Q11THugvWMbxtmG/99bfQ\nrBr5zNtBkdPpZNPAJjSLho5OjBgNNCzY3ve//32CwSB/9md/dsSO8WDx+/2ygiuRSOy1ks7tdssl\n9JlM5og1pDI5cgiVSCqVIpvNygZp2Wz2sC+/3xvNzc2EQiF27tzJwMCADAcnJyf5p3/6Jz73uc/R\n2dm5aBClKAp2u12G2LlcjlKpRLlcJpVKoWmaPO5j9XrWNI3Ozk46OjoYHR2lr6+vyqEciUSIRCIE\nAgG6u7tpaGg4rMeSy+VkgCaCW6BKGaJpGq+++ioPPvggK1as4NJLL5W+8oceeojt27fz+OOPL3jt\nV199lVQqJZ/7ve99D4/HQywWY3R0lFKpRGdnJ52db082tre3097ezj333MM3v/lN2YxXTLJpmrbH\n/2qaJu9rokkkUFV9vRjlcln+XjKZJJ1Oo6oqpVJJhr0OhwNd1zEMo8oTfbgxDIN0Oi3fo76+vgNu\naJhOp5mZmalyr/v9flRVZevWrfv9Og8//DB/9Vd/dUDbPlTEJEM2myWbze7xvRSrNMRKjcOxykQ0\n/tzdxa9pGg6HA6vVus+xWqlUiEajVWNe0zSp5UgkEsCcYmdPbNu2DZib8HrrrbeqjvWKK64gnU5L\nRc9f/MUHOekkF9/85i958MF/44MfPA3DMMhkMjzzzBeora1ddBv33nsrd9zxOUZGRti0aZNc/SDC\ncrH/6XSaN954QzZnfN/73kd3dzdf+9rX+N73vrfXc2Gy9Nx7773cddddR3s3TExMFsEcnyYmxwdm\neG1iYgLA8uXLWb58OQA33HADH/jAB/jgBz/Iyy+/DMA3f/xNbr72Zu7/2P1z/kiLxpWfvpI3n3uT\n/jf6gbkv7fX19Sjq218yKyxcjj4wMMBLL73Erbfe+o5obOhwOHA4HOTzeZLJJHV1dXvcb7FkW1Tm\n7smTbfLORlVV/H4/LpeLRCIhFQxCJXIkm6HNx2q1cuKJJ0qVyOzsLDBXBbx9+3ZGRkZYu3btXquP\nRSVtqVSSzfgqlQrpdJpsNovT6cThcByzIbaqqrS1tdHa2srk5CS9vb1VTtlYLMYrr7yC1+ulq6vr\nkJURe0JUXovgX2xjvjIkGo1y7bXX4na7ue222+S9JZVK8YUvfIHbbruNcDhc9brRaJRQKITT6WRy\nclIGzKIi2jAMKpVKVfAsgugNGzawatUqtm/fTnNz8365nueH7YAM2uaqS5U9NgcEZGidSCSqqloV\nRaGhoUFOkBYKBUZHRwmFQkd08keslhG9CiqVyn5VS+u6zuzsbFUTSk3TZGPJ+VW/+8PewuPDiVhZ\n5PV65XjPZrNVTnNRrS3CX4fDgdPpxOVyLenKIqHemr+aQKwu0DQNu92OxWJZ9L6Tz+dlbwqB8Elr\nmiav392v5d0Rxy22Pf+aLRaLzMzMYBgGxWKR2lo7N9zwHr71rf/k+ee38f73n0g6nWZ2dhar1Sqd\n4rvv7rp13cBc5fT111/Pqaeeyo033siTTz4p9xvgnHPOkcE1QEtLC+eccw4vvvjiAZxVk6Vivjve\nxMTk2MIcnyYmxwdmeG1iYrIoV199NZ/4xCfYtWsXJ5xwAs3hZr7+u68z3jdObDJG8wnN1IRquKH5\nBjpP6qSpqQmXe2HVmpWFQcSjjz6Koij8xV/8xZE4lCXB7/fL5nbpdFou7V8Mi8WCy+WSmpFCoSCr\nLk3eXeyuEtF1XaoIhErkaODz+Tj77LMZHR1l27Ztcqyl02l+//vf09zczOrVq/ca1FmtVqxWq3Q3\nFwoFeXzzQ+xjdQJKURTC4TDhcJhIJEJvb6/0gsNc08DXX3+dHTt20NXVRVtb25I17tN1XVZei7BP\nIAK2bDbLxRdfLIPqQCAgfddf//rXKZVKXHXVVVIXMjo6CszpYKanp/H7/fh8PgqFAqqqomkaVqsV\nn8+HrusyQBMhtqqqqKpKc3MzY2Njh6y0EOdKUZQFgaAIPIVjWVR367qO2+3G5/OhaRrlcploNCor\nr6PRKA0NDUf0fllTU0MqlULXdcrlMrqu73X7uVyOaDRKuVyW58Dj8ex1UnNf/PVf//VB/d5SYrVa\ncblc1NXVUalUZJCdy+UWTF6USiWSyaRs8OpyuZZsVYbNZsPtdpPP52WIDXPBshhPohLbMAxisRix\nWAx42yFfX19f9Rkt3hfRSHR/zsX8SRRd19E0jVQqhaqqGIaB31+H12tQU+NmZmZuVYGY6KtUKkxM\nTOyhiZilajsbN27k3nvvlX8niMC6oWHhirVQKMTrr7++75NosuR88YtfPNq7YGJisgfM8Wlicnxg\nhtcmJiaLksvlAGS1VZAg29lOU1cTTV1zX66Gtg0xOzHLRR+9aI/BdS0Ll84+9thjdHZ2csYZZxzG\nI1haPB4PkUgEXddJJBJ7Da8BWX0333+9VMGYybGFoiiyKZ1wo1cqFWKxmFSJHA33uaIotLa20tDQ\nwI4dOxgcHJSPjY2NMTU1xYoVK+jo6Nhr6GaxWPB6vbhcLhlii+ZluVxOrkw4lq/vYDBIMBgkFovR\n29vL5OSkfCyXy/HWW2+xc+dOqR051OrfQqGwqO9aVH4WCgWuuuoqent7+dKXvkQ4HMZms8mmkiMj\nI8RiMdavX1/1uoqi8O1vf5vvfOc7/OQnP6G1tVVqPESALcJf0XtA/BPBnQiI16xZg9VqPejAVWge\nYO7+KMLoyclJpqam8Pv98nhUVSUYDBIOh2WzR+HELhaLDAwMyOPQNI0TTjhhn/fYpURUfouQNhwO\nL1gxo+s6Y2NjTExMyIDRYrHQ3t4uJx3ejei6TiqVIh6PE4/Hq6qy56NpmvRki8mJQ6VSqcjtzg/Q\n7XY7brebiYkJWTkOc9qu7u7uqsmifD5PNptFVVVqamoWbGP+cUYiEQA6Ojqqxl6xWGRsbEz+PZTL\n5VixYg0Wy8vEYmlCoRpqa2uJRCJSr9TS0rIH73V1KJ3NZqVz3G63s3btWqxWK2NjYwt+U7j7TUxM\nTExMTEyON8zw2sTkOCcSiSz4MlQul9m0aRNOp5PVq1cD4MJFkCAR5r7cGYbBv/7tv+JwO7jk5kuq\nfn+if65541mdZ6FR/QX29ddfZ/v27XzhC184XId0WFBVFZ/PRzweJ5/Pk8/n91m56HK5ZCWfqNY+\nVlULJoeOCEeESqRUKlEoFIhEIrjd7sPS+Gx/sNlsrF27VqpEhEKjXC6zdetWqRLZk6NVoGkaHo9H\nhtjCT5vL5WSI7XQ6j+kQOxAIcPrpp5NOp+nt7WVsbEyGYsVikZ6eHnp7e2lvb6ezs/Ogq5P31KxR\n3A8+8pGP8NJLL/Hoo49itVopFAo4HA4ZmH7qU5/iiiuuqFJyRCIRbrnlFjZu3MjFF19MS0sLVqsV\nXdcZHByUFadCfaEoCqFQSL5PFouF3/3ud7z++ut8/OMfr6pWFZWmohHj/lynNpuNcrlMuVwmnU6T\nTCaZnp5eoAcJhUIynJ//uyLEBujq6mJgYIBMJoPFYpHV8Pu6JpcKu91OOBxmfHwcwzCYnJycW030\nJwd2Lpejv7+/ShPi9Xrp7Ox816+qEYokv99Pe3s72WxWBsrz/dKlUkl65cXnpWjKuKcmx/tC0zTq\n6uqoqampCrGj0Sg7duyQrn6r1UpTUxMtLS0Lrt3543BvzB9ru082ZrNZBgYGUFWVfD6P0+lkaGiY\n73//JwCcf/6JskLc6XQyPh6jpqZa9xOJxAkGW4GA/Fk8HudHP/oRbW1tUuXk8Xi49NJLeeqpp9i5\nc6fUufX09PDiiy/yyU9+cn9Pn4mJiYmJiYnJuwYzvDYxOc65+eabSSaTnHfeeTQ3NzM5Ocmjjz7K\njh07uO++++SX97/5m78hkU/gPXnOjfnso8+y69Vd3L7pdmyO6i+mn3nfZ9BUjb7+vgXbe+SRR1AU\nheuuu+6IHN9S4vf7ZfCXSCT2GWypqorH45H+62w2Kxusmbx7sdls1NfXk81mpY4gnU6Ty+WOmkok\nGo1SX1/Pueeey/DwMNu3b5ehTjKZ5IUXXqC1tZXVq1fvM2hSVRW3243T6SSfz5PL5TAMQ07q2Gy2\nJffhLjUej4eTTz6ZFStW0NfXx/DwsAxdy+UyfX19DAwM0NraSldX1wGPW9Hwcn64BnOVpJ/97Gd5\n+umn2bhxIyMjI7K5Zn19PdFolOuvv56TTz6Zk08+WVYoA1If0tXVxXvf+15UVaVQKGCz2bj11luB\nuSaPhmEQiUS47bbbOPHEE1m/fj2BQIA333yTJ598kubmZm666SYKhYJ8jyqViqyihrebOM4PtXcP\nBRVFwWq1Eo1GSSaTlEoleQ4VRZGV1nsKdxVFkSF2sVikq6uLwcFBEokE5XJZVsKHQqEDOvcHi8vl\norGxkYmJCQzDYGJigpaWFhKJBMPDw3KSQ1EUmpubCYfDSzYZKcbnOwHRvLGpqYlSqSRD5UQiUeWW\nFj+HuYrompoaAoHAATfFhLdDbLfbzbZt24hGozIsLhaLhEKhRbUtoikl7D28/ta3vsXk5KQcY089\n9RTj4+MA3HrrrQwPD3P55Zdz/vnny54GW7Zs4dVXX+X889dy1VXnUqlUmJ2dRdd1brllE3a7nf7+\nh+Q2Lrnk87S0dHHmmecRCoUYGhri4YcfZmJiQvquBX//93/PM888wwUXXMCnPvUpdF3nG9/4BvX1\n9Xz2s5894PNncui8k8aoicnxhjk+TUyOD5T5Xb2PVRRFORX44x//+EdOPfXUo707JibvKp588km+\n+93vsmXLFmZmZvB6vaxfv55bb72Vyy67TD5v06ZNPPDAA+zq3YWhGiw/YznX3X0da89byz0b7+Ge\nn90jn3vjshtxqA4G+gaqtmUYBm1tbYTDYdkI8p3G2NgY2WwWRVFYtmzZflWZimXLMPcl/t1eqWfy\nNpVKhVQqVdVMxm63H3GVyMaNG/nZz34m/3+xWGTbtm2MjIxUPc9qtbJy5Ura29v3O5gTwfXublzh\n0D2SDfgOFqGuEPqK+Qh3dnd3t9Rg7Iv+/n4mJyexWq00NzfT1NQkmwJeeumlvPDCC/K54u8wcb7n\nVy6LfSuXywwPD3PiiSfy6U9/mo997GMoikIymcRut3PJJZegKApPPPEE+XyemZkZnnjiCV577TWi\n0SiFQoGGhgYuvPBCbrvtNurr69F1nUqlgmEYqKqKoijyv4shwmwRZM/MzDA9PS392jAX/NfV1e01\ntN4ThmFQKBTo7+9nZmZGnpPW1lba29sP6LUOhXg8Lpv/RaPRKuWT0+mks7NzySchdx+f70TEvU6E\n1ntq5Gm326Ve5EAa28bjcfr7+ykWi+i6TrFYxGazUVtbK98fh8NBbW2tfH+KxaKsDg8EAnu8tpct\nWyaD693p6+sjEonw6U9/mp07d5JIJDAMg5aWFjZu3Mg99/wtPt9Odux4XWpFPvzhb2C1WujrE+G1\nwoMP/pHHH/8lPT09xONxAoEAGzZs4M477+Tss89esN3XX3+du+66i9///veoqsr73/9+vva1r9HV\n1bVf58tkaXk3jFETk3cr5vg0MTl22bx5s1CxrTcMY/OhvJYZXpuYmBwwKVIMMsgkk1So0Lu5l+5T\nu7FipZlm2mnHydFpVHe4SafTTEzMaVGCweBeHZrzSaVSshJzqXygJu8cisWiVInAXCjn8XjweDxH\nRCWzefPmRT8/Z2dn2bJlC8lksurnNTU1rF27dr+vb3g7fMzlclUBrMViwel0YrPZjnltTrlcZmho\niP7+/qpKZEFDQwPd3d171VkUi0UZwHo8HlpbW6mtraVYLFIsFtE0DafTSblc5tlnnyUWi2Gz2Tjn\nnHP26LOtVCqUSiV0XWdkZEROEoiJEREq+3w+crkckUhENoyFuYmExsZG6uvrqa2tldXOuwflhmFI\nN7ZoxDh/QkJUl0ajUSqVinRUO51OAoEAtbW1BAKBQ9LjCA3KfOdvKBSis7PziE2EDAwMsGPHDsrl\nMhaLhUAgQENDA62trYfl3r2n8flORTjxY7EY8Xi8avJuPpqm4ff7qampwe/3L/r+imtefO7C3D2l\no6ODmpoaYrGYDJQFIsSGOZ+51WqVXuw97e/k5OSfGjH6qyYnZmZm+MUvfiEry30+H/X19Zx00kk0\nNTWhKAqzs5O8+ebPsdmmsVoNTj75ZKxWC6ACIaAD2P97qcmxx7ttjJqYvJswx6eJybGLGV6bmJgc\nE5QoESdOmTJWrAQILHBcv9swDIPBwUHK5TI2m22/KwJ1XSeZTKLrOpqmmf7r4xDDMMhkMqTTaRkI\nisDxYN3KS4Gu6wwNDdHT00O5XK56rKOjgxUrVhyQs1Ys1c9ms1WvJ0JO0UzwWKZSqTA2NkZvb2+V\n51hQW1tLd3c3DQ0NCx4TmolkMkkgEKCjo0N6wiuVCjabDZvNRiwW44UXXiCbzeLz+Tj//PP3qZTR\ndZ2JiQmmpqbQNI14PM7MzIysPvV4PHi9XgzDYGpqimQySaFQkCG2x+OhsbERn89HQ0MDTqeTQqFQ\n1WByPkIfAnPO7ampKdmwUxAIBKivr5eBtZisEMoRq9V6UO/3xMQEAwMDMmCvqamRjunDtWqhUqkw\nOjrK1NQUqVSKXC6Hpmm0t7ezcuXKo+KsfzdQKBRIJBLEYjGSySSLffcQE3qiKtvpdJLNZunt7a0K\nv71eL11dXVX3zHK5vGiIXalU8Pl81NbW7vUeWygUZLV/MBiUkzvDw8Py3ig0PevWrWPZsmUsW7ZM\n/v5bb73Ftm3bKJdLLFvmZ8OG05gLrv2AudLKxMTExMTE5PhkKcPrY1dIaWJicsxjxUqQxSsF362I\nyunZ2VmKxSLZbHa/HJ67+69zudxBuT9N3rmIcMbpdJJMJsnlcpTLZWZnZ3E4HPj9/qNSka+qKsuW\nLSMcDrNt27aqitfBwUHGx8dZvXo1LS0t+xVCCpexzWaTIbbwIafTabLZLE6nUzYVPBbRNI22tjZa\nW1uZmJhg165dVdXps7OzvPzyy/h8Prq7uwmHwzLYnN+s0WKx4HA4pKJD/AzmqqYLhQIwpxPanwkM\nVVUJBoOMjIxgt9tRVRWn0ynd2Ha7nVwuh9vtpquri3w+T19fH8VikXw+Tzqdpq+vj2AwKO9dDQ0N\n1NXVUalUZJBdLBYxDINiscjExATRaBRd12UwLZqTitBaNG6sVCqUy2UymYy8lkUl9+5NIff13osm\nj319fZTLZeLxuPRgi+trKcdLNpulr6+PXC4HzIWkLpeLmpoaLBYLk5OTS+q5Pp6w2+2EQiFCoRCV\nSoVEIiE92WKsGIZBKpUilUoxMjIidVsOh0Ne6y0tLbLaeT4Wi4VgMEggEJAhdrlclh7+crlMfX39\nHj9zxeSOqqqoqsrk5CSjo6Py/gxzqxeBtPVJAAAgAElEQVQaGhpob2+v0geVSiUmJib+NFGn0NS0\nFmhc+pNoYmJiYmJiYnIcY4bXJiYmJgeI3++XX2gTicR+h9Ai+MnlcuTzeSwWywFVtJq8O9A0TTYu\nmx+yFAoFvF4vbrf7qARkDoeDU089lba2NrZs2SJdscVikddff53h4WHWrl2Lz+fb79e0Wq34/X7K\n5bLUWOi6TiaTqQqxj9WKVkVRaGpqoqmpiUgkwq5du2SFJsw1u9y8eTMul4uuri5aW1tleG21WuWx\niYBOhGMwF4CLQLu+vn6/33Or1SqDaqFDEOqQZDKJpmnS0+xyuTj77LPZuXMnU1NTsjHj9PQ08Xic\nxsZG8vk8TqeTUCiEz+fD5XJRLpcZGxtjbGyMfD4vVwqUSiXcbjehUAiv14vdbsdut6NpmnyPd3ef\nG4Yhw20RDCuKsmhTyN3PQV1dHRaLhV27dslJv127dtHV1UWlUkHTtEMOsYUyYnR0VFbtqqpKW1sb\n9fX18hxkMhmmp6cXrbY32X80TaO2tpba2loMwyCdTktPtlidEIlE5LWSSCSw2+2ccMIJOJ1OKpXK\nHivv54fYk5OTFAoFFEWhUCgwNjaG0+mkrq5uwQoHEV6XSiW2bdsmV1sYhkEikZD366amJoCqz/yp\nqSm5r06nk8ZGM7g2MTExMTExMVlqjs1viyYmJu8ovvvd7x7tXTiiWCwWPB4PMOfA3l21sDccDof0\nemYymQXOWZPjB7vdTjAYlAoZwzBIJpNEIhFZkbtUHMgYra+v5/zzz2fVqlVVoeDs7Cy/+93v2Lp1\n66KKib0h9CiBQEBWGM/34mYymarA81gkGAxy9tlnc8455ywIMLPZLFu2bOHXv/41o6OjFItFGV4D\n8h4hQjfhjoa3A/79RbioYS4IbGlpkdsRTSFFtWk+nyeVSrFu3TrWr18vJ0eEWmR4eJiRkRGSySRD\nQ0Ps3LmTvr4+tmzZwtTUlLzXud1uGhoaWLlyJS0tLdhsNgqFgrxeZ2ZmyOfzOBwO3G43LpcLt9tN\nXV2ddAjPd54LtUwulyOZTMrmjzMzMySTSVmtLxzEK1euxGazYbFYqFQq9Pf3UygUZKC9u2d9fykU\nCuzYsYORkREZXLvdbk488URCoRCqqtLU1CQnGcW+LjXH22eoQFEUvF4vra2trF27ltbWVrLZbJX6\nw+fzEQ6HyWQy9Pb28tprr9HT0yMVNosh7jfhcLjK25/L5RgdHWV0dFQGzuI+NDo6Sn9/f5UmyGKx\nUF9fLyfsampq0DStqhnp+Pi4DL8PplGpyTuD43WMmpi8EzDHp4nJ8YFZeW1iYnLIbN68mY997GNH\nezeOKH6/X1amJhIJ6urq9uv3FEXB7XZL/3Umk8Hr9ZpL0Y9ThErE4XCQTCZl6DgzM4PT6Vyy5p4H\nOkZVVaW7u5vm5ma2bt0qm6UZhkF/f79UiTQ3Nx/Qfggvs3BA5/N5DMOQAaTD4cDpdB7TDU1ra2s5\n44wzSCaT9PX1MTY2JsO2fD4v9SJ1dXW0trYuqgxJp9MyeHM4HHIybH8R2xPnyeFwUKlUpFddNFL0\ner0UCgWi0Sj19fWcffbZDA4O0t/fj6ZpshJ7165duN1uua82mw2fz4fdbqe2tpbm5mZZrbqYXkRo\nS2Du2jEMQ1ZWz69yNQxDNp8slUqUy2UZUu/+OvB2hbbVaqW9vV06sCuVCkNDQyxbtgyr1SpD7AOp\nxJ6dnZX9CwThcJjm5uaqlQCaptHU1FSlkdA07YCame6L4/EzdD6VSoXBwUEikQhutxu3242qqvJz\nVaxQAeQkn5hwEWqXmpoauWpF13XZbDMcDgNInQi8HWI7nU7ZYLVSqchx6HQ66ejoYHBwUF5LHo9H\nrmYQn9eZTIZYLEaxWERRFNra2o7oeTM5chzvY9TE5FjGHJ8mJscHZsNGExMTk4NkaGiIYrGIpmks\nW7bsgALoUqlEKpUC5oIn039tAm+HnyKoEa70o6USEUxPT/PWW28taF5YX1/P2rVrDzh8Fei6Tj6f\nJ5fLVVVb2u12nE7nYWvMt5QIV/Lw8DDFYpFyuSwbs9rtdjo7OwmHw1XjfHR0lFdeeYVSqUQwGOS8\n8847oMD+xRdflNXWqqrS3NzM8PAwuVyOWCyGx+NBVVXpjVZVFYfDIR3V2WyWnp4epqenyWazsrGj\noij4/X5sNhsej4fm5mba2tr2+P7quk6xWJRhtqgInx/Yu1wuXC6X9Bbvzu6Btgi1F/v7tFQqMTQ0\nRKlUkh7t5cuX43K5qiqv9xZil8tlhoeHiUaj8mc2m43Ozs69KnEKhQKjo6PyGMPh8EFf9yZvk06n\n6e3tJZ/Py5+J5pyi4l3XdVKplNSL7Kni2mq1UlNTg8vlQtM0Ockg7p2lUonZ2Vl5vUejUbkCymaz\nUVtbS0tLC42NjRiGwTPPPCOv67a2Nnw+H42NjbjdbgB27dpFT08PqVSKQCDAe9/73n02XTUxMTEx\nMTExOV4wGzaamJiYHAP4/X4ikQiVSoVMJnNAQYbVajX91yYLEM3J0uk06XQaXddlc0cRKh4NQqEQ\n559/Pr29vfT29soALxqN8tvf/pbOzk6WL19+wBXTqqricrlwOp0yxNZ1XYahVqsVl8slVTvHIi6X\ni7Vr17J8+XJee+01RkdHAWRgFo1GSSQSeDweOjo68Pl8xONxWWFcV1d3wOfN7XZTLBaxWCzk83mC\nwSBTU1Pouo7L5SKVSuFwOJidnaWxsVFOEogKbIfDQUtLC+l0Wmow7HY7uq7LZrL19fVUKhUGBgak\n53r3e5wIxR0Oh6ycFu+dqKoXrnPRyFN4ssXExHz/tQj+5nuy5wfaVquVZcuWMTQ0JF/39ddfp7W1\nldraWvl6wsEt7qvi/KZSKakcEdTV1dHe3r7PiRK73U44HGZ8fFx6spuamsyJx4NE13XGx8erVi4I\n1/ju3mhVVfH7/fj9ftrb28lmszLIFiugYC6cFtolXdepqamhXC7Le6fVaqWuro5UKkUkEpEOftGM\ntKGhgdraWtm0UehAxCoGRVHkNSr2XzynoaFhv5qumpiYmJiYmJiYHDhmeG1iYmJykHi9XqLRKIZh\nEI/HD7gKz+FwyFAmk8lgsViO2cZ1JkcO4YF1Op0kEgkKhQKlUoloNIrL5cLr9R4VrYamaaxYsYKW\nlha2bt3K1NQUMBfi9Pb2MjY2xpo1aw6qYZkIhRwOB4VCQTqMS6USiUQCi8WCy+U6pid4rFarDHyF\nBkUEswAjIyMMDQ0RCoWIxWLAnEbkYPQTtbW1jI+PA3PKBV3XaWhoYGRkBLfbTSKRIJvNAnPheiAQ\nqFKECKe0w+Ggu7ubSCRCOp3G4/FIDcfQ0BCBQIDa2loymYwMsRsaGmTl6XxEOG2z2fB6vRSLRRKJ\nBKVSSVaiF4tFisUiqVQKi8Uig2yr1Vq1skBRFNnIcfdAWzSN7OvrIx6PS3d3uVyWTQDFORHNK61W\nK4lEgkgkIrehaRrt7e3U19fv93l3uVw0NjYyMTGBYRhMTEzQ0tJieo4PkHw+T19fn1x9BHPntru7\ne78mA0Q1f1NTE6VSSQbZiUSiqupfXLeAvGYTiQSqquJ0OrHb7SSTSakdKRaLcgzNb+Dp9/vlPUp8\nRs/MzEh1jsVioampydR/mZiYmJiYmJgcJszw2sTExOQg0TQNn89HIpEgl8tRLBYPKFwTvuNEIoFh\nGKTTadN/bSKxWCzU1dXJpnaVSkU24/N6vVXu1SOJ2+3mjDPOYHJykrfeeks2Psvlcrzyyis0NDRw\n4oknLhpw7gtFUWT1ebFYJJfLyQrcZDIpnbPzm/8dKxQKBal7CYVCsslcNBolm83KavXR0VEmJiZQ\nVXXRaub9wefzMTw8LJUgxWIRn8+H0+lE13W8Xi9DQ0M0NzczPT2Ny+VC13Xi8TipVEq69xVFIRAI\nsGbNGgC2b9/OzMwMmqbhdDpJp9Nks1nq6upwOp1kMhn6+/v3GmILbDYbgUCATCaDYRioqiqr6udX\nVgtHtwiyxTHtzvxA2+VyccoppzA4OMjU1BSVSoVIJIJhGNTX18sGqJVKhWQyyfj4OLlcDlVV5YRB\nZ2cnHo8HwzAO6FryeDwEg0EikYisvm1paTmmVwccS0QiEQYHB6s0L+FwmNbW1oOavLVarQSDQYLB\nIJVKhVgsxvj4uLxfABSLRSYmJqSaRNM03G43ra2ttLS0AHPV3eJelkgkGB8fR9d1VFWVVf3zg/Xx\n8XHpa6+vrzcVMiYmJiYmJiYmhxGzxM/E5Dhn27ZtXHPNNXR1deF2uwkGg5x//vn8+7//e9XzVFXd\n479QKLTf2/v1r3/N+9//fmpqavD5fJx22mn84Ac/WOrDOmL4/X75v0UzqANBOI1hzsU63/tpYgJz\nzcOCwSAej0c2I0skEszMzMgl6/ti48aNS75fjY2NXHDBBXR3d1eFTlNTUzz33HPs2LGjKqA6EBRF\nwW63U1NTg9/vl8FgpVIhlUoRi8UWeLKPNvl8XqpArFYrPT09fPWrX+Uv//IvueSSS/j4xz/O1772\nNYaHh2UV9PT0NF/+8pfZsGEDjY2NOBwOOjs7+ehHP8rQ0NCi2zEMA7fbLc/t8PAw1113HatXr+a0\n007jwgsv5G//9m956623iEajpFIpNm/eTDQa5ayzzuLCCy/kz/7sz9iwYQNnnXUWK1euxOv1ctVV\nV3Haaadx0kknyUpisRokEokwOztbVdHa39/PwMDAAg/6fIS2Q4TDPp+PUChEIBCQXmJA6kri8bjc\nViaTqWqmuDuqqtLZ2UlLSws2m02G69lslkAggN/vp1gsMjY2JjUhuq7j9/sJBoPkcjmi0ajcXiqV\nkg1T90VNTY0MNMvlMuPj4wd9rcPhGZ/HGuVymV27dtHX1yfPlc1mY9WqVbS3ty/JqiMxudXS0sKa\nNWtYtWqVvH7nf7babDb8fj/xeJw333yT//W//hcf+chHOOuss1ixYgWPPfYYuq5XNWK98847CQQC\n8u+eU045hQ996EPceeedhEKh3SrGF14Lzz//PJdffjltbW04nU7C4TCXXHIJL7744l6PKZFIEAqF\nUFWVH//4x4d8jkwOjuNhjJqYvFMxx6eJyfGBWXltYnKcMzQ0RDqd5q/+6q9oamoim83yox/9iI0b\nN/Kd73yHm266CYBHHnlE/o6BQZIkz7/yPE/84xOcduVp/Cf/SYgQrbRSR92i23rooYe46aabuOii\ni/jKV76Cpmns2LGDkZGRI3KshwO73Y7D4ZCN9urq6g74S7jVapWvkcvl5DJ3ExOBqqqyslY0GysW\ni1Il4vP59nrd3XLLLYdlvzRNY9WqVbS2trJlyxbZBE/XdXbu3ClVIgcywbU7VqsVv99PqVSSKxx0\nXSeTyZDL5aRz+Wgrd3K5HKVSSapCHnjgAV588UWuvPJKTjnlFKanp3nggQf4n//zf3LrrbfS0NCA\n3W6np6cHt9vNVVddRVdXF/F4nP/7f/8vTz31FG+88YZsHicqlUWg5na7sVqtTE1NkUwm+cu//Evq\n6+uZmJjg6aef5h//8R+5/PLLueyyy7BYLExOTnLPPfdI96/4b09PD9/4xje4+OKLgblJifr6evr6\n+hgaGsIwDOnWLhQK1NbWynBbuNk9Hg8NDQ2LKh8cDgeVSkWuHHC73bLKGqjyZItK1v3ViwC0trZi\ns9lk2C+cx6qqEo/HpWJC0zQaGhqwWCyUy2XZbFfTNNl4UiAqtEWlt9VqXaDqqaurkysCisUi4+Pj\nNDc3H9R1eLjG57FCIpGgr6+v6hzX1dWxbNmyJW/KKiaQcrkcExMTlMtlwuEw5XKZSqWC2+3GMAzp\nY4/H4/zrv/4rjY2NdHZ2snnzZrLZrBxnLpdL3m/tdjvf/va3icfjTExMkE6nqamp+dPnfgoYBqaA\nMqAANUAr0MjOnTvRNI1PfvKTNDY2EovFeOSRRzjvvPN4+umnueiiixY9nr/7u78jn88fcytNjjfe\n7WPUxOSdjDk+TUyOD5RjqWppTyiKcirwxz/+8Y+ceuqpR3t3TEze9RiGwamnnkqhUGDbtm1Vj+XJ\ns5nNJEly/03386uHf8X3hr9HXdPbgXU99ZzESVh5O4AdGhpi9erV3Hzzzdx3331H7FiOBMlkUvp/\nQ6FQVTX2/mIYBqlUinK5jKIo+P3+ox7GmRy7zFeJQHW4fTRDjrGxMbZt27ZgBUFjYyNr1qyR/uJD\noVwuk8vlqpruCd3IfCftkaa/v59IJCJXo4yMjLBmzRpsNpsMdXfu3MnatWs5/fTT+W//7b/hdrsX\n3C8cDgfZbJYrr7ySr371q9x+++1VxyrYsWOHbBCnaRonnHAC6XSa8fFxRkZGuPnmm8lkMnzmM5/B\n5/PhcrlYvXq19AQXi0VUVeWzn/0sjz/+OMPDwzQ1NVVtI51Os23bNunoFrhcLmpra6UKReD1ehep\nQkU2tTUMQ074LUalUpFBdrFYXFBZP18vYrfbq671mZkZ+vv7yWazRKNRrFarrFgNBoO0tbVVBdWi\n30ClUpHb2Vv1tKqqWK3WqlBbVVUmJiZk9bnb7SYcDptB45/QdZ3R0VHpZ4e5a7Wjo4NgMHhYtheJ\nRIhGo6TTafm+KopCOBymubkZTdOoVCokEgn6+/uZnZ0ll8sRDofZvn07N954IzfddBNnnHEGmqbR\n3NyMzWbjvvvu4ze/+Q2vvfYaw8PD8h7c0dHOSSepuN17W3nlBNYD1WqRXC5HZ2cnp5xyCk8//fSC\n39q6dSunnHIKX/jCF/j85z/PD37wA6666qqlO2EmJiYmJiYmJoeRzZs3s379eoD1hmFsPpTXMiuv\nTUxMFqAoCq2trbz66qtVPy9S5BVeIUOGUrHECz9+gXXvXVcVXANs6d/CMMNc3nk5GnPVag8++CC6\nrvPFL34RmFt2fjBO3GMRj8dDNBqVX4gPJrwWDtpkMolhGGQyGamJMDHZHdFsLJVKSZ9yPB4nm81W\naTaONM3NzYRCIXbu3MnAwIAMjyYnJ4lEIixfvpzOzs5DCpgtFot0fufzefL5PIZhkMvlyOfz2O12\nnE7nEW1qWSwWZWW02+3G4XCwfv16KpVKVWVpc3Mz7e3tstHfqlWriMfjjI+PVykK0uk0MBeIi2au\nMOfLzmazLF++vOp1hfN5eHiYdDqNrusEg0FisRiZTIbm5ma8Xi/JZJJAICDvvel0mn//93/nnHPO\nWbTRpsfj4YwzzmB8fJwdO3bIytlsNksulyMUCuF2u+VkRSqVIpVK4fV6aWhokJMVmqZht9tl9bbF\nYlm04lZoH1wuF4ZhVAXZognj/GaYNptNBtniuAYHB6XvOhKJcOaZZ1ZV/quqisPhwGq1ygB7/vYV\nRaFSqcjmkOJx4eueP5EgwvRMJkOpVCKVSskK7+OdXC5Hb29vlVbG6/XS1dW1x8mLQ6FSqTAyMsLM\nzIysrIY5Vc2yZcuqJs5Ev4qmpibC4bCcMBLNHcXvimtk/mRKNBplenr6T9evQVNTBIfDxnwTY3//\nBACdnWFxNoBXgDOBtyd2hBIqHo8veky33norV199Neeee+4xpUgyMTExMTExMTnSmOG1iYkJ8HYY\nkUgk+OlPf8p//Md/cN1111U9p5deMsx9EX35qZfJxDNccP0FC17rM+/7DKqqcmr/qXTQAcAzzzzD\nypUreeqpp7jzzjsZGxsjEAjw3//7f+eLX/ziOzqkFVWvsViMQqFAPp8/qC/noolUOp2mVCqRz+eX\npFLV5N2Jqqr4/X5cLheJREKqFoRKxOv1HpUqZKvVyoknnihVIrOzs8BcuLR9+3ZGRkZYu3Yt9fX1\nh7QdMV6cTqdU7hiGIQNtEWIvtZZgMea7kkWgKqp4529f+LpbWlqw2+2Ew2E6OztZsWIFmzdvZmRk\nhMnJSZ544gkURWHlypVs376d2tpagsEgN910E//1X/9FOp3GZrNRKpVQVZVMJiNfe3Z2lt/97ne8\n+uqrXHDBBbS2thKPx3E4HLKBpNVqxel08h//8R8kk0k2btzI7OwsdXV1i96Lm5qaCAaD9Pb2MjIy\nIsPBqakpbDYb7e3tADKoFCG2cFw7nU5sNpsM+HO53D4n50Q1vcPhwDAMWS0uzvX8cDufz0tFRDAY\nZGZmBpvNRl1dHRMTE/h8vgX3ZKEMqVQqVVXY4j0TVfy6rssgWzxvfqCt6zput5tIJEI2m5Xe7WAw\nKCu1j+REyrHA5OQkw8PDsjJfURSam5tpamo6LPekWCzG4OCgXLlkGAY2m422trY93mfEJISqqgQC\nAerq6ujq6gLmVj8IjzogJ1sKhQLve9/7KBQKuFwu3vveM1i79kOk08243W4slrn3+X1/+huov/+h\n+VsEekilTpD36U2bNrF161Y+97nPLdi/H/zgB7z00kv09PTQ39+/dCfLxMTExMTExOQdiBlem5iY\nAHD77bfz7W9/G5j7Mnf11VfzjW98Qz5epsw4by/9ffbRZ7HarZxz9Tm8+G8vcvYVZ8vHFEUBBUYY\nkeH1rl270DSNj370o9x1112sW7eOH//4x3zpS1+iUqnw5S9/+cgc6GFChNcA8Xh80SrG/cFms5n+\na5MDwmq1UldXJ5exCx90Pp+XKpF/+7d/44orrjii++Xz+Tj77LMZHR1l27Ztsmo3nU7z+9//nubm\nZlavXn3IVZiqquJyuapC7PlVsiKEOpzjSPiuATlmDcOQzd0EjzzyCDMzM1xzzTV4PB557G63m4su\nukgGaj6fj0984hOceeaZ6LpONBplZmZGupxFFWapVJIe6K9//ev88Ic/lOfkwgsv5H/8j/+BxWKh\np6dH+rhnZmZwuVxYLBZ++tOfYrfb+cAHPkA+n2dmZmaPAbbVamXVqlU0Nzezbds22aC2WCyya9cu\nAoEA7e3tZDIZGWInk0mSySQ+n09WYovK8Fwut6gjezFElbXNZsPj8Ui9iAitJycnqxQR3d3d0ltc\nKBTYvn07y5cvX3S1z/wQW1R4i4BaNJwU/wS6rssgW4TadXV1RCIRKpUKMzMzVCoV2YxX07Qq3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hGIZs9ClUKyLIFo0/54fY85tPzg+x6+rqKJfLstp8fHyc5ubmBaGyGJ/zERXV85m/LRFqi0Bb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QLxXiFoi8gMEY9SKpVOypioaZqMYWhubiafz+NyuUgmkzgcDinU2u12FhYWWFhYkC70trY2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\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ss60Q5XiUIlQLBTtyVGs0Guh0OhiNRj53Y4k+Ip8epUSGZLNZdpcKBaTCvGu5XM5Ozlwu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\nUIhFIovFguXLl5s3GAewD8AuAHsA9ADQqlmv99x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XYmBggMfYffv24cILL2SncDab5e3m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58pI/fmCenOOV+OtAAobP5xO5rEkQbUoU4l3Vke5rEkRouLrBYIDD4WBux6aKkP2ZkGDv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NKl/4HV6sSuXX/BoEH5AIDp04eisHBRwnMVsmbNV9i06RCeeGIq5syJDhDcdNNQqFQ/T2wJh/NL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JnUsKTYABza5ih8PBHOeU7U3HZWVlye5DW8bBhoYG7Nu3TzbpkpGRga5du7bJuR0Oh9mkmrSI\nbUvidSQSgcfjgcvlYgUagViByWSrCg43rh+PcZ/D4XA4HA6H0364eM3pNLZtK8PSpVuwYcNulJXV\nIy3NiKFDCzBv3v+hR4/Mdp1r2bItuO66Zdi2bTYGD25eOtTU5MO55z6N7dsr8dFHt2LMmL4QBECh\nOHGXgq5btwMrVmzD1q37sHNnNbp0SUVp6fyk++7dW4v77vsQX35ZjEAgjMGDu2Du3PEYObLXYd/H\n5wvi9df/g48//hm//FIBtzuAoqJ0TJs2DNOmDZN9cSsvr0dBwf0J5xAE4J13pmLixKMvwsc5cu66\n6y786U9/QmVlJd577z1WkItYsmQJtm/fjpUrV8oPTBIdvW/pPtTU1uDXX39N+l6BQ7HzthTxodPp\nkJ+fj169emHAgAFwOBz46KOP8MQTT2DevHmw2+1J3dHxovSxWK5NInW8EBK//XgW7JJmX1M0gTRC\nRZprTe2SRoZI865pG7moLRYLy3/VaDQsvgWIfX4kkBuNxg7LdjUYDNBoNAgGg3A6nUhNTeVL7w9D\ncXExpk+fjj/+8Y+45ppr2HYSIsl5vGDBAnz11VdYtGhRwmTDmjVrADRHRwiCgFtvvRW33norrr/+\nesycORORSATz5s1j7m2pu/9YEIlEcOjQIRa7YbVaodFokJqayt6bYiukQjL1PbfbzSIr9Hq9TIRX\nKpWoq6tj2cxqtRqhUAgWiwXp6enw+XzsWdfpdLLxShRFKBQKtqIBgExQptfVajVzalOhSGmONV2D\nSqWC1WplxR71ej0ikQh27drFPkOr1YrCwkI2Lkv7G8UFAUc+3tD4IXWPJ3NdS8e66upq1j7K+KcJ\nSZ1OB7vd3mbXdSQSQXl5OWpqatg2tVqNgoKCNseKiaLIJiEUCgWMRiPLJI8fQ0RRZLFTkUiERZ4A\nQG5ubtLJOOlKBl6okcPhcDgcDue3BRevOZ3GE098hs2bS/GnPw3GwIF5qK524vnn12Pw4Pn49ttZ\n6Ns3p13ni9dHXC4/Ro9+RiZcA8CcORfir38dl+QMJwZvv70V7733PQYP7oLc3JQW9zt4sBFDhz4O\ntVqJ++47HwaDBkuWbMaYMc/iyy/vxtlnF7X6PqWldbj99ndx3nl9cM89o2Gx6PDvf+/Arbe+g61b\ny/D661MSjrnqqtNxwQUDZNvOPLMQVVXVyM7OStifc3zo2bMnevbsCQC4+uqrMXbsWFx00UXYunUr\nmpqaMHv2bMycORM5OW3rU619cReiyYVIiue47777cPrpp2POnDlMlJ42bRrOOuss7Ny5E48//nj7\nL7ADkC4Xlwox0ngQaeE2qdvzWBLvvqYCdfT+yfKuDQZDQt61z+eD0WgE0Cxeq1QqJm5XV1ejV6/m\nSa2OjgyRYrVaUVtby1yRbYkKOBkRRRHV1dW48MILkZKSgjfeeAM+n489qxRPAQAfffQR5s2bh8mT\nJ+Oqq65qNYOYnp2bbroJBw8exIIFC7Bs2TIIgoDTTjsNM2fOxPz584/551JbW4twOAyHwwG1Wg29\nXo/s7GyZKE9OaiC2yqChoYGJiH6/nxUcJKGZsvMdDgdcLhfsdjt73Wq1wmg0yrK0DQYDc0lTpAiJ\nulTUFGiOWBEEAeFwmMWv0HE6nQ5+v5+dC4gJ3hRlQjEbJFwXFxfL4jl69uwJhULBrj0+g3n37t3o\n27fvEbt9aRyj64t3XVNkEu3ncDiYIGwymZCWlobi4mK2f35+PrsXQOvitdvtRklJiSyn32q1onv3\n7gntaA3pGGc2m9lzEf/eoVCITYiIooj6+nrZJEFLK3xo/5au53Cvc05uiouL0bt3785uBofDSQLv\nnxzOyQH/1xmn07jnntF4552uUKmav8RNnHga+vd/BI8//hmWL7/uiM/tdvsxZswz+PnnCqxceTMT\nrgH874vdibsc9G9/uxT/+Mc1UCoVuPjiF7B9e1UL+61BU5Mf27c/hKKi2Je1qVPPRu/eD+Guu97D\nd9/NbvV9srIs+PXXh9CnTzbbduONw3DDDcuxdOlmPPDABSgsTJcdM3hwF1x1VWJG6osvvoi//OUv\n7b1UzjFiwoQJuPnmm7Fnzx688cYbCIVCmDhxIosLOXDgAACg0d2I8kPlyEnLgVrVLFhLv7iTI1Kl\nUkGj0cDutgMA+vXrh5EjR0Kv17Noi/Xr16O0tBSvvPIK+vZt7nPZ2dno06cPNm/efDwuPymHiwyR\nLhOXFvQ6Hq5hafZ1JBJhAqZ02TwJOwqFghVTA5qd14FAAGlpaRAEQSZKNjQ0IBqNYsmSJbjggguY\nsHksxWuz2Yy6ujqIogiHw3HSidfk1pUW0Ev2u9PpxPjx4+F0OrF69WpYLBaZAEiTLRs2bMBtt92G\nMWPGYOHChYcVOKXP7Ny5c3Hvvfdi+/btsFgs6N+/P+6/P7aKhia8jhVVVVUIh8Nwu92w2+0QBIFF\nhhAk4pJrWbr6AADLbxcEgQnHVPSV7qPJZEJRUREqKirYNhJNaWKIYkrov2g0yhzZkUgEfr+fidd+\nv59NJFHb1Go1KyIYDAbZuKhQKGAwGNiKg2g0iuLiYtZ+nU6HXr16saxuEkel5w6Hw5gzZw5WrVp1\nxPc6HA6z+A9qlxRyKFP8EN0fEq5ra2uZOz0lJQUmk0km5iabKBFFEZWVlTh48CDbT6FQoEuXLsjM\nzGzX2BkIBNhYRn9TpM52IHavPB6PbFUR1VsAYjEpXbt2bfUe0fmStY0XauS0xsyZM/Hxxx93djM4\nHE4SeP/kcE4OuHjN6TSGDi1M2FZUlIH+/XOwc2dywbYteDwBnH/+c/jvfw/iww9vxtix/WWvJ8u8\nVihuxvTpI3Huub3xwAP/wp49NSgqysBTT12O88/vJzt+w4ZduPfe97F9exXy8myYMWM0KiudCedc\nt24HHn10NX79tRLhcAS5uSmYMGEw5s+/pNX2Z2VZ23Sdmzbtxamn5jPhGgD0eg3Gjx+Il176CiUl\nNbLX4klLMyEtLVFUuvTSQVi6dDN27qxOEK8BwOsNQq1WQq1u/jJ75ZWTEvbjdB7kJnQ6nThw4AAa\nGxtlYjIQE7jmr5iPx959DD++8CMGFgxkrxmNRvTr1y+29FyQiyCZylikj91uT1gOfujQISZAxRMK\nhZh40Bm0NTLkeBRqjIeEMKmQRgXhKIc2GAyyyBAqYBYOh+H1emXCnMlkYkJTMBhEY2MjBEHAbbfd\nBo1GA6/XC6PRyMRrKqLX0ddjsVjgdDrh8/kQDAbb5cA8USHhOV6Qjt9GQl5rBAIBTJ48GaWlpVi5\nciV69erFXMUKhYLFaWzduhXXX389hgwZgrfeeos57luCngMpVqsVZ511Fvt93bp1yMvLO6YupUAg\ngPr6evacWa1WWK1WmYNZmqNMojAQ64OhUIgVW0xJSYHb7UZTUxP8fr8sPsRutyMvL485s6WZ35Qf\n7fF4EmI0lEollEolVCoVy/SnWAlp7jG1kQrTer1eKJVKllEPAKmpqdBqtYhGo9i9ezcr/qpWq9G7\nd282jtC4Qp8z0By/9PTTTx/VKg9qP00ySiHR2ul0sgkwtVoNo9HIsrkPHTrE2pabmytrb/yEHxCL\nINm7dy9b+QHEVoQUFRUlzZpujWg0ygpfKpVKWK1Wdl/os6R7H++MprgQQRBQUFDQ6mqE1iJDaBKh\npdc5nBdeeKGzm8DhcFqA908O5+SAi9ecE45Dh1zo3799kSGE2+3H2LHP4fvvy/HBBzdj3Lj+CfsI\nQmLECABs3FiCDz/8EbfeOgJmsw7PPbcel1/+CsrL/4bU1Nhy/B9/3I9x455HTo4Vc+eORzgcxdy5\nn8JuN8nOuWNHJS6++EUMGpSPuXPHQ6tVoaSkBps37z2i60pGIBBCamril0SDIfbF9Ycf9rcqXrdE\nVZUTAGC3JwpajzzyCe699wMIAjBkSFfMn/9/GD26L9LS2pZpyelYamtrkZ4un2AIh8NYvnw59Ho9\n+vbtizvuuAOXXnqpbJ+amhpMmzYN1112HS7pfwkKMgvYa6VVpQCAwuzEySXoAbQyt9KzZ0+IoogV\nK1bICsr98MMP2LVrF26++eb2X2QH0FJkiLRIG21P5sQ+Huj1egSDQZblSg5roDkaJBgMwmw2MwGT\nRLJkeddA7HOmvOTCwtjnSe5GEofMZvMxuU6r1QqnMzaWOJ3OhOf0RILuUUtidHtE6XjiBWmKyZgy\nZQq2bduGjz76COedd15Sp+eOHTswceJEdOvWDe+//36rwvXBgwfh9XrRr1+/FvcBgHfffRfbtm3D\nokWL2n0t7aG6upo57ylPOScnR+a6FgQBarWaFV4ksZTyp0VRRGZmJoLBIBNXSXTWaDQwm80wGAxM\nzCZhlkRcAGwyiMRretZJ4FWr1SxPnjKjqXihtJ1Op5OJpwqFghVqJLFVFEWUlpayZ16pVKJ3796y\nzyw+MoREegDo3r37Ed9raZRJsnoBTU1NcDgcbJJLr9fDZrMhGo1CpVKhsrKSjYMZGRns3kjHQim1\ntbUoLy+XTUZmZ2cjPz//iMYScrQDMde3NK6EniFpQVqj0QiFQoFdu3axc+Tl5bUqmtP5WoqCSla0\nl8OR0qVLl8PvxOFwOgXePzmckwMuXnNOKN588xtUVDgwb97/tftYUQSmTFmKqion/vnPabjwwgGH\nP0hCcXE1du58GN26xWIRRo7shVNOmYsVK77DrbeOBAA89NAqqFQKbN58HzIzYyLRxIlD0Lv3Q7Jz\nrVu3E6FQBGvW3Aabzdjua2kLvXplYdOmEng8ARiNWrZ948YSAEBFhaPd5wyFInjmmS9QWJiO009v\nXn6rUAg4//y+uPTSQcjNtaG0tBaLFn2OceOex6pVf0k6ScA59tx0001oamrC8OHDkZubi+rqarz1\n1lvYtWsXFi1aBIPBgEGDBmHQoEGy4yg+pN/p/XDxaRfLCjeOmjUKCoUCpUtKZcfMe2cehAwB2yu2\nQxRFLF++HBs3bgQAFkMwePBgjB49GsuWLYPT6cSYMWNQWVmJF154AUajEXfccccxvBstc7jIEFpG\nLnXnHe/MUxLkKMtVKrJI812lxRpJ1Pb7/Uy4sVqbZxeoMB8QE6XIzSstqpassFlHoNVqmcu2qalJ\nVizyeHEsRel4QTqZSJ3MBQ0Ad955J1avXo3x48ejoaEBb7/9tuz1yZMnw+12Y+zYsXA4HLjzzjtZ\nQUaisLAQZ5zRHOE0depUbNq0SbbqYePGjXj00UcxZswYpKWlYcuWLVi6dCnGjRuH22+/vd3X3B6q\nqqrg8/kQCASQlZUFhUKBjIwMVpyUoEkbn88HpVKJcDiMQCDAsqRDoRBKS0vZBI0gCMjIyEA0GoXb\n7UYgEGAicCgUYhM+er2exUmQ0EyiNsVmSIsyAjG3uEqlYjnbANgz63A4EnKqVSoVK0haXl6Ouro6\nALFno2fPniyDnogXr2msUSgURzXeSO8pXT/R1NTE+jsJv1lZWUzsDgQCaGxsBBAT9DMzYytrSMwH\n5BN7+/btkxVH1Gg0KCwsREpKy/U5WoOKLgKxCBOtVotIJMImHaQudZ1OB4PBAEEQUFJSwsZvm80G\nu93e6vscLgqqNVc2h8PhcDgcDqfz4eI154ShuLga06evwB//2B3XXDP0iM5RU+OCTqdGfn77ncCj\nR/dhwjUADBiQC4tFh9LS2BfSaDSKL74oxmWXncqEawAoLEzHuHH98cknP7NtKSkxIWnlyv/iuuvO\nOib5ibfcMhyrVv2MiRNfxfz5l8Bo1ODFFzfg++/3AwB8vmC7z/mXv7yN4uJqfPrpbTKhKT8/FWvW\nyMWOq6/+A/r2fRj33PM+F687iSuvvBKvvfYaFi9ejPr6epjNZgwZMgQLFizAhRde2OqxgiAAagCD\nAPwIINy8XUDi8/rgGw+y51gQBCxZsoT9TOI1AHz88cdYuHAhVqxYgc8++wwajQbDhw/Ho48+ih49\nenTEZbebZJEhFDEg3S4t1NgZBbv0ej0ThihnNxqNsrxrKhxH4jW5qP1+P2w2GyscR0jFtOzsbJZ1\nTU5TQRBkYndHY7VaWUSEy+XqsPeKF6Vbi/FoL1IBujWB+mjG9J9++gmCIGDVqlVJc44nT56M+vp6\nVFRUAAAefPDBpPtIxetk7crNzYVKpcLChQvhcrlQUFCAxx57DHfdddcxnUhwuVxwuVxwOp0QBAEW\ni4UJzgBYnjvFdlBkiLRAYigUgtfrRXl5Ofu8lUol0tLSYDQaUVNTw8Rter5IuI2/D1T4koRLWlUh\nnWBQKBRMkFapVCzWRKvVwuPxIBQKsfZSvwNikSFVVVWoqmqOOuvevXvCsy59HpVKpWyi7GgidURR\nZKsoqPYAbW9sbJQJzVarFWazmWV+A/IJrpycHFmMC9D8XDU1NWHv3r2yvOnU1FQUFBQcseAbiUSY\nU12tVrMijW63G263m7mgVSoVjEYje5/Kykr2GWi1WuTn57f6PoeLgkq2AofD4XA4HA6Hc2LB/5XG\nOSGoqWnChRc+D5vNgH/+c9oRCQOCALz66tW48873cP75z2LTphno0SOzzcfn59sSttlsBjQ2ev/X\nRhd8vhCKihKXv8dvu+KK0/Daa5tw441vYNaslTj33N647LJTcfnlgztMyB47tj9eeOFKzJq1EkOG\nzIcoAj16ZOCxxy7BjBkfwGSKCVxNTT74fM3WWo1GmdQNvmDBZ/jHP/6D+fP/LyHnOxk2mxHXXXcW\nnnjiM7z11kpMnnzpYY/hdCwTJ07ExIkT231c165d5bnUZwAoAVAL7Fu6T76zCUA3tFkI1Gq1uP/+\n+2WCdmcijQyROielQjVtP1xBr2MNFXWTukQ9Hg/8fj9CoRD0ej2LEyHnKblKVSqVLALE6/Uygcdo\nNOKFF17AzJkzWYEzOn9782nbg8lkQl1dHROp2iJet+aSlv7eXo6HKN1W1q9ff9h9pH2UJlrC4XBS\nl7hSqcT69esTBOnCwsIEx/bxoLq6mhWkpAz2nJychPZTYTwSRCnXPRAIwOVyITMzkwnNRqMRqamp\nUKlUTNSk7OlQKIRIJAKNRsMmeaQFUGmfeMGc8vnVajXbx2QysXGB2kpjBTmkA4EAlEolTCYTXC4X\nW8kCxD63ZC7geDGYIoKo7z7xxBO477772n2vpXErJIJHIhHU1dXB4/Gw8cRut7PoFOo/Xq9X5nq2\n2Zr/DSSN2Thw4AAqKyvZa0qlEl27dkVGRvtjyQgS16l9KSkpCIfD8Hg88Hg8bLLCaDQyxz0QixiR\n5nO3lnMdfy0tRYJIXdnHe3UI57fDkfZRDodz7OH9k8M5OeDiNafTaWry4fzzn0NTkx+bNs1oc8HC\nZPTpk421a2/HOecswujRz+I//5mB3NxEUToZSmXyLy1HsqRcp1Pj669nYP36XVi9+hesXbsd7767\nDeee2xv//vcdHSaQ3HrrSFx33Vn4+ecKaDRKDBqUj3/8YxMEAejVKybc33HHu1i27Bt2zMiRPfHl\nl3fLzrN06WbMmrUSt946An/967g2vz853BsavB1wNZxOwwJgMAAfgEOIxYgoAKQASOvEdnUALWVY\nxwvVJ0rBLhKWaPk8CdChUAhWq5W5K0mY8vv9zHEan3dN57Lb7fB6vSxjmMQuvV5/TMVahUIBi8WC\nxsZG+P1+eDweaDSaVgXq9iIVnVuL8eiMyYiOgj43El3JiUzC54l0baIooqqqCi6XC9FolBVpNJvN\nLOKDPg+K8CChmSZcHA4HW10giiIKCgoQCASYy5pERnJLUxFScs2Sq1mtViMcDjOxmPKxSeSlZ49E\ncHoGpXEeNGkQCoVgt9uZU5giR/buba5jkZOTg+zs7KT3RRoZEl88UBAEVmS3vfc6GAyye6JSqRAM\nBlFTU8MK5CqVSqSmpsJgMDABPxwOIxqNylzZeXl5Ce31+/2oqKhg8StATOQuKio6bOHQw+HxeJhj\n3GQyIRAIsFUaoihCrVYjLS1NJjYHAgHZREF+fj70ev1h36u1SJDDFXLkcIgj6aMcDuf4wPsnh3Ny\nwMVrTqcSCIRw8cUvoqSkBl98cRd69co66nMOGdIV//rXrbjggucxevSz2LjxXqSlJRYfbC8ZGWbo\n9WqUlNQmvLZnT02SI4BzzumFc87phYULL8ff/rYGDzzwL6xfvwujRvU+6vYQer0Gf/hDc8G9det2\nQq/X4KyzYgWg7rtvLP785+YYFptN7rT8+OOfcOONb+DyywfjhRcmteu99+6N3YuJEy8+0uZzTiT0\nALp1diM6lrZGhnRWoUYpUnFOp9MhFArB5XIxAU2v1zOxRhoZQtuk4jW5EwEgMzMTjzzyCAAwEZsE\nIhL5jpS2OKRJuKutrZW1sTVaEqWT/X6y0FlxNu2hoaEBgUAATqeTuZNbKtRIMTaCIMDn86G6uho+\nn4/FdWi1WnTp0gU2mw2NjY3MYQ00u2RJpBVFEQaDgYnLJEqTeE2FGAVBYOJ1MBhkRR6B2LhArmo6\nFv/P3pvHR1Hl6/9PVe9bupPu7EASCGERUAERFxT1DqAiMC4g6NcFHYe54jKOep1xVBT15zYoehV1\nxgEZWRQFBcdl1HEZBUUWF1YJCdm3Tnrft98ffT+Hqk5ng0CinPfrxYuku+rUqeo6Bf2c5zwfgG0T\ni8XY+IlGo2hqamKT2zabrdP4Cql4LY2poLFH47MnSN3bCoUCgUAAra2tbPypVCoYjUaYTCYmFIui\niFAoBLfbzc7PZrPJROB4PI6mpibU1dWxyRFBEFBQUIDCwsKjfj6Gw2F4PB7mrqbzIMhtLRWu4/E4\nDh06xK6j1WpFVlbX8XBdRYJ0VciRwyGOZIxyOJzjAx+fHM6JQf/+FsT5RROPxzF79l/x9deV2Ljx\nvzFhQknXO3WT884bhjVrbsQVV7yEadOexaef3sFiNI4UURRxwQXD8fbb36Gx0cUc4uXlzfjgg92y\nbR0OX7tojpNPHoBEAgiFokfVj87YvPkgNmzYiZtvngyTKXm+w4fnYfjw9JMCX3zxE6688q+YPHkY\nXnvthg7btdu9sNnkEwB1dQ4sX74ZJ588QJYB3tjogssVQGlpTodudg7neNBRZEjqMvK+LNQohRyO\nKpWKOazdbjdzpkqLNVJudTAYhMVigUqlYgJUPB5nedeiKCI7+3CsEYl7QLIAmtvtZkXnpHS30GFX\nK1NEUYRarUY4HEYwGITJZJJlDncmUHN+ftTX1yMSicDn8yEzMxOCICA3N5flDtPnSisA3G43nE4n\ncwGLogi9Xg+bzYb8/HwWc6HT6diETTQaZeOUokakojQA5vCmMU5jSKPRyBzfFJ9D97FUqJYWbRRF\nER6Ph4mhXq+XFTs1m80YPHhwhxMp0uKH5AKna3Ck93k8Hmeuazo/uj6JRIIVTKXCl/QcpGedVDyW\nusUjkQh++ukn2O12JlprNBqUlpb2SnHXRCIBp9OJWCyGaDTKCmYKggC9Xp92shEA6urqmLNOp9O1\nc4p3BD3XO5qUTHXAczgcDofD4XD6J1y85vQZd9yxDps2/YAZM8bAbvdi1apvZO9fddXpPWovVUOZ\nNesU/PWv/w833LAS06c/jw8/vBUazdEtC1206BL86197cOaZT+B3vzsH0Wgczz//GUaPLsR339Ww\n7R566J/44osDuPji0SgqykJTkxvLln2BQYOycPbZpZ0e48cf67Bx4/cAgPLyFrhcATzyyHsAkgL4\n9OljAADV1W2YPftlzJgxBnl5ZuzaVYeXXvoPTjllIB55ZFaX51Jd3YYZM16AKIq49NJT8cYb22Tv\njxkzAKNHFwIA7r77LRw82IILLhiOggILKivtePnl/8DvD2Pp0jmy/e65ZwNWrvwahw49ikGDel44\nk8PpLTpyU6cK1X1dqJGg/FlRFGE0GhEIBODz+VieL+VdA8mieORYpGJnJL54PB7WlslkYrnWVNiO\nnK+iKCIQCMDhcLA4DxKpjyQuqaPYDoVCIcuptVgsR32tOP2PaDSKlpYWOJ1OAElRl3KqacxJ773m\n5mYcOnQIXq+XCaU6nQ4DBw5kue4AmHhNkAhOY5YEXACyTGcStWkfigwhty/lXpPTmgT2aDTKJo8o\njkOhUMDtdgMAWltb2QoCo9GIsrKyTkXo1Dic1IzqI4EEcIoOkjqrTSYTO4Zer5cJ5/F4HE6nk/Up\nPz+fPfMcDgcqKiqYSEwTX0VFRb32XHQ6nfD7/YhGozAYDGxyy2BITvanE68dDgebjFMoFCgpKemW\n6C9dYZNudYm0kGN/X9HA4XA4HA6Hc6LD/7fG6TO+/74WggBs2vQDNm36od37PRWv05lmrrvuTLS1\n+XDXXW9h9uy/YsOGBf+3rdBu33SuG/pCTYwdOwgffHAr7rzzTdx//yYMHJiJxYtnYM+eBuzb18i2\nmznzZFRVtWL58s3MtTx5chkWLbqEOaI7YseOatx//0bZa/T7tdeewcTrjAwtCgrMeP75z9HW5kNB\ngRm3334B/vSnC2EwaDo9BgBUVtrh8SSdngsXrmn3/gMPTGfi9dSpI7Fs2Rd44YXP4XD4YbHoMHly\nGe699yKccspAeDxemExGdi1FkTuYOH2PdKk+IXVjp0aG9HV+MDmvNRoN9Ho9c6OGw2FWjFGtViMU\nCjEns0ajgSAITLCiJf8kEGZnZ8Pj8aClpYVlXFMhNBJ33G43jEZjh4JQdwoddiYmqdVqtLW1IRqN\nwuVywWKxcJfjL5Dm5mZWnFOtVkOn0yE/Px+RSKRdocaWlhbs3LkT0WgUgUCARUWMHDkSXq9XlkEP\nyPPZSXSkf59JyI7FYiz3OR6PIxQKsXuUonLUajW772lSiPajeB56DlBxR3Jmk2rdPSgAACAASURB\nVOgaj8ehVquh1WoxbNiwLuMmpOJxR8UB7XZ72kKP6aBc7lgsxrLv6brabDbmwNbpdFAoFDL3scvl\nYtnSWq0WNpsNsVgM1dXVbIKJJgPKysqOqihjKm63G21tbexz0Gq1MBgMTMQnd7xCoWCfdTAYRHV1\nNWtj0KBBbAKvK2jCAkgvTkuzzXlkCKcrejJGORzO8YWPTw7nxEA4EnfV8UYQhLEAtm/fvh1jx47t\n6+5weoDdbsf69U/j0kut7WInfkn8+tfLsGdPA/bvf6ivu9InPP/887j55pv7uhv9Frvdi/XrW3Hp\npb/n/7k6TsTjceYg1Ov1TCiiwm8KhQI6nQ6JRAI+nw/AYbGnr/pLxd8sFguys7Oxf/9+1NfXw+l0\noqCgAHl5ecjNzYXD4UBdXR2cTidzLQ4YMIC5C8vLy5n7tbS0FBaLBVdddRWef/55OBwOAMmcW5PJ\nxIQujUaDjIyMtIUOe0NobmtrY2J8QUEBc1pyfjls374dNTU1qK6uRnZ2NvLy8jBx4kQmCpMo2dzc\njOrqavj9fibAUvG94cOHo76+HsFgENFoFCaTCRkZGcjOzsauXbtYJrzf70cgEIDH44HNZoPRaITR\naEQwGITf72cCLOVYh8NhFBYWIjs7m0VsUE52LBZDLBZj+fJ6vR5ms5lFfASDQdjtdibuarVaZGVl\n4aSTTupW4cJgMMieOdJiqVJBdcaMGdi4cWNHTbRrz+fzsVUU1Kfs7Gz4/X4Eg0EIgoDMzEyIosjO\nValUstzoSCSC0tJSiKKI8vJytlKDhOXi4uK0cUJHQjQahcfjgcPhYJ9LTk4O9Hq9rH3qp1arhUql\nQjwex/79+9mkXk5ODgoLC7t93EAgwKJJUj8neu5TxMrRuOA5JwY9GaMcDuf4wscnh9N/2bFjB8aN\nGwcA4xKJxI6jaYs7rzmcHhIKRWTxIwcONOG993bh+uvP7MNe9S3Tp/OCjZz+RbrIkHTLyPvKfUd5\n0eQElzooE4kEXC4XwuEwNBoNjEYji1Hw+/0sZoGEJpVKxc6HxEDK7LVYLNDpdLj//vsRDAaZeJab\nm8sycSn/VhpL0ttkZGQw8drlcnHx+hdGIBBAW1ubLDKEsq7JJOHz+VgmNgmSgiBg0KBBUKlUyMjI\nYNnVwWBQNkakr9OklCAI0Gq1skxpjUaDQCDA9iEoGoQcy/Q+TdRQPymGRJqxbTQasW/fPub2NplM\nGD58eLeEa+Cw85quA8WRSFm0aFG323K73ewaAMkCh1arlUWIAIcn7KTXv62tjbmRzWYzPB4Pampq\n2PuCICA/Px+ZmZm9sgqF3OoUf0QTD3l5ee2uXTqXdE1NDTsfg8GAgoKCHh27o/zs1OMdTcFazolD\nd8coh8M5/vDxyeGcGHDxmtOv8flC8HpDnW6Tnd3xcvdjweDBf8a1107E4MHZOHTIjhdf/AJarRJ3\n3TXluPWhv1FUNKivu8DhyOgoMoQEKnq9s0zUIyFVlO6s2KEUcgECh+MFIpEI4vE4K2pGztVQKIRg\nMIhAIICMjAzo9Xrmmm5tbUVrayvi8TiysrKQlZXMnZ8wYQJ27EhOdmu1WiYe6fV65lB0u92w2WzH\nJNJDqVTCZDLB4/HA5/MhEolw0egXRGNjI3Mv6/V6qFQqWaHG5uZmNDU1sdibRCIBm83GHMmiKMJs\nNiMWi7EJFJqgkQrTPp8P4XAYarVaJrDSxI9arWYCNI1zabRIKBSSCdcqlQrhcBjhcFiWo019JKc4\nZUorFAqMGDGi25Mv0vFO4nc6l293VhXG43G0tLTIhH+TyQSj0QhBENgKElEU2fimaxeLxdiqC3JC\nNzc3s7Z1Oh1KS0shCAKLUTkaQqEQE6xDoRDLEc/MzEwr+ksnGwVBgN1uR1tbG4Dks6O4uLhHzyVq\nT/qslyKNb+ERRpzuwFf+cjj9Fz4+OZwTAy5ec/o1Tz31Lzz44D87fF8QgMrK41sYcNq0k7B27TY0\nNrqg0ahw5pmD8eijszBkSPZx6wOHw+mYjgpxSbNfSaRJzb/uiFRRuiNBOrU4W3cQBEEmpuj1ehap\nQEUZ4/E4jEYj1Go1AoEAywUWBAEWi4X1v6WlhfUhO/vwM4mW5APJIo7SY2dkZDBXptfrlb3fm5Db\nE0jm31qt1mNyHM7xp6GhAW63m7l6DQYD9Ho9vF4vKioqEAwGZWNsyJAh8Hg87F61Wq0QRVEmXgOH\nxzJlNFMuMsU9UAY1FXGkY9B4JWFSo9HIHNckmFPmNnB4lUYsFoPf74dSqYTP50NtbS3rT2FhIcxm\nc7evi7ToJD13jkQYjkajaGpqkj3DMjIyZI5yEtgNBgM7bxJxW1paACQjRzwej6wAZl5eHgYOHMgi\nRqj9I4GeIdRPimShVR4dif60vUqlgt/vl13zoqKiHsd6SNtLFae7KuTI4XA4HA6Hw+l/cPGa06+5\n9tozMGnS0E63ycvLOE69SfLKK9cc1+NxOJyeIS2Q1t3IEBK2OnNN95TU7OiOih0CySX9CoUCRqMR\ner2eFTaLRCIwmUzQaDQsboHiAUjky8g4/AwkkUoQBFmxNbfbzX6Wbg8k3aokmPv9/nZ5vL2FTqeD\nWq1GOByGy+XqtUxdTt/icrng8/ngcrmYGzg/Px+NjY2orq5GPB5nsTyZmZlQq9XsXqaCiDk5OQgE\nAu2c1zRmo9EoK8QIgMWFkKM5Go2yXGdpTAetXNDpdEy4lgqWFLEjiiIMBgNCoRACgQACgQBzAJMo\nTAUOewLFU5B4Tc7wnhAMBtHc3Cxzl+fk5DAHtiiKbFJIqVTKhF4S4p1OJxO46fqqVCoMGTIEFouF\nXWOgY7dyZyQSCXbdpHEc5OIWRRGZmZlpz10a4SEIAiorK9nveXl57Z5XXSGdvEwnTtNz/0jOk8Ph\ncDgcDofTNxy/rAUO5wgoLrbh/POHd/pHreZzMH3Nl19+2dddOCHZs2cPZs+ejSFDhsBgMCA7Oxvn\nnnsu3n333Q73icViGDlyJERRxJIlSw6/EQFwCMDXAL4EsBnAHgCeZCTAPffcg/PPP5/FU3zxxRft\n2q6qqmon0Er//Pa3v+3V8+8IEmHIWRmJROD3+xEKhZg45XK54HQ64fF44Pf74Xa74fV64fP5EAgE\nWGFHcnZKIdGDhCKtVgu9Xg+j0QiTyQSz2YzMzExkZmbCbDazpf16vR5arZZFHkhjQKSiHACWQx2J\nRJCRkcFed7vdTITSarVMEAbACthRO1KH6PLly9nP6cQgo9HIYhOkQndvQ30ih+aJyLZt27Bw4UKM\nGjUKRqMRRUVFmDNnDg4cOMC2SSQSWLFiBWbOnIlBgwbBaDRi9OjRePDBB+F0OhEIBFhRw84Kb5eX\nl+PKK6/EwIEDYTAYMGLECCxevJi5jXuDhoYGNq5MJhMEQYDL5UJ1dTUThrVaLUaMGMHed7vdTDjM\nzc1l97DULU2TSgDg9/tZtI4oijIRmMTPSCTC9pEKqLRtLBaDTqdjDm0qxgiA5cMDyeeH3+9Ha2sr\ne1+n0yEjIwORSKTT652K1O1N/UnHK6+8kvZ1t9uNxsZG9nzQ6XTIyclhfVAoFAiHw+yZJ3Vd06Rb\nZWUlHA4HQqEQVCoVE5LHjBnDzpnOm9rsCeFwGE6nE36/n30+9DnT52c2mztsV3rcmpoaNllgMpmQ\nl5fXo74A8knJdJFynbmyOZyO6GiMcjicvoePTw7nxICrfhwO56iprq7u6y6ckFRVVcHr9eK6665D\nQUEB/H4/3nrrLcyYMQMvv/wybrzxxnb7LF26FDU1NfIv7ZUAygHEUjZ2A6gG9lfvx5NPPomhQ4di\nzJgx2LJlS9r+ZGdn47XXXmv3+vvvv4/Vq1dj6tSpR3yuUjpzSMdiMeb+kwpclB8tFXtIKCOBg35O\ndUmn+703IYEMAHOIer1eRKNRqFQqttxeoVAw4ZJiE6QRH06nk7VlNBpZNEA0GsUPP/yAadOmsTzi\nVEhwokKRgUBAFi3QW1Dhxng8DpfLdcwiSvozjz/+ODZv3owrrrgCY8aMQWNjI5577jmMHTsW33zz\nDUaOHAm/34/58+fjjDPOwIIFC5CVlYWvv/4aixcvxqeffor33nsPiUSCCX00ISKltrYWp512GjIz\nM3HLLbcgKysLW7ZswQMPPIAdO3Zgw4YNR30u8XgcTU1NrFCjVqtlLmwaX5mZmRg2bBji8TgcDgcT\njWkCJz8/XyYyUg42OaoBoKmpiYmfFAECgE3+UHSGNNNerVbLxqs0dxoAW8WgUCig1WqhUCigUqnY\nJJdGo4Hf74der2djkETy7sRYSF3hUhE9HTt27MANN9wg27e1tVU2wWM0GmEwGKBUKmWuayreSMVb\niXA4jJ9++gnV1dWwWq1QKpXQarUYMGAAcnNz2/UhXdRSZ1BxWLoHgeTzS6/XIxwOs77rdLpOnyX0\nGTscDnYfqVQqFBUVHdGztrNIEGlUFI8M4fSE1DHK4XD6D3x8cjgnBly85vxsEMUFWLRoOu6/fzoA\nYMWKzZg/fyUOHTqyzOuqqlaUlNyLp566DHfc8ave7u4Jxbx5845Ju8fiM1q0aBMeeuifsNv/gqys\nzotuFRf/CeefPwx///u1vXLs3ubCCy/EhRdeKHtt4cKFGDt2LJYsWdJOvG5ubsbixYtxzz334L77\n7ku+eADAwc6PMz5nPFr/2QrLBRa8tfGtDsVrvV6f9l5Yvnw5MjIyMH369E6P091Ch505H8l1SQXZ\nqF0qeKjVaqFUKpkLkZzQUhH7eEMuWHKUSl3Xer0eQNK5SLEefr+fZeemRoZI867p/D0eD+6++24A\n6V3XhE6nQyAQQDgchsfjgUaj6fVrIhXJyeEuzTg+EfjDH/6ANWvWyETC2bNnY9SoUXjsscewcuVK\nqNVqbN68GRMmTGBC5TXXXINBgwbhkUcewWeffYbJkyez/UlAlLa5cuVKuN1ubNmyBcOHDwcA3Hjj\njYjFYvjHP/4Bl8vVo/zmdNjtdoRCITidTuZYzszMBJAUlouLi5GTkwONRoPGxkYAkLmu8/LyWJ+p\n0GI4HGbxHzQZ5fP5YDKZmHM6Go0yYZqeDZFIBKFQCGq1mgmYBD0zwuEwE6ilqx3UajWb/PJ6vRAE\ngd3/RqMRCoWCCbBUMLIrpM+s1LiSVJ5//nn2czQaRUtLC8v3pjGjVCrZWCGhORKJsJ/pWQEknym7\nd+9GZWWlrCAmrdLpqK9A187rjiJCSFiPx+NMhFYoFJ3eYyQm+/1+WQHJ4uLiIxKXuxKnpS7vvnre\nc36eSMcoh8PpX/DxyeGcGPD/uXH6BS+88BlEcQHOOOOxbu+TFKe63u7993fhwQc3HUXvOL8kBAHd\num9o258bgiBg4MCBTDyQcs8992DEiBG46qqrki/40E64rmioQEVDhew1g9YACyzA/p73p7GxEZ9+\n+ikuvfRS5pIMBoPw+/3wer1wu91wuVxwOBxwOBxwuVzweDzw+Xzw+/0IBoPMKU1CkBSKAKD4DhKk\nKb7DYrGw2A6TyYSMjAxoNBqoVCq2bV8LGSROarVaCILAHIuRSISJdXq9XhahQMIMidHxeBx2ux1A\nUpiRFmvsLO86lYyMDCYIUgRJbyMVs1wu1zE5Rn9m4sSJ7dytpaWlGDVqFPbu3QsgKbxNnDhR5moF\ngEsuuQSJRAL798sHY21tLXbt2iWLuKHPT5p9DiQFY5ooOVoaGhrgcrng9XqZc1mr1cJgMKCsrAxW\nq5WJxcFgEJFIhOVJKxQKWSyEQqFgAjJdH3LoU2423TvSKA5yaVPb0msbi8WgUqnYs4Nc1fQcIce6\nKIoIh8NoaGhg11AQBNhsNlbUlETd1M+kI8h1LYoii+voilAoxGJYgOQzITMzE0qlkrUhncSTbkfn\n3dzcjF27drG4EY1GA6vViuLi4g4LJkqjljrrZyQSSRsRYjab2fFdLhd7Vlkslk7bi0ajiEajqKur\nY68VFhbCaDR2ea066h+QnMRJV6hRGhnC4XA4HA6Hw/n5wJ3XnH7B6tVbUVJixdath1BR0YLBg7O7\n3qmbvPfej3jhhc/xwAOX9FqbHE5/wu/3sxznd955B++//z7mzp0r22br1q1YuXIlNm/efPhLfXt9\nG+ffcz5EUUTF8or2bzYAkBgapc7ojlzTr7zyChKJBGbMmNEjMbQ7hQ5TRRE6JgAm+gKHhRkSVzoT\nOI43JN4A8rxrek+n07HXyVUYDodhMplkgpXH42EObp1OJ4vjIPFaEIQuRSGlUgmDwQCv18uiQ3pD\n5JSi0Wig1WoRDAbh8Xhgs9m4CxLJaIxRo0ax39MVCiX3stVqlb1+44034ssvv0QwGGSf1+TJk/H4\n449j/vz5ePDBB2G1WvHVV1/hxRdfxG233XbUsTCRSASVlZWora1FPB5nudD5+fmw2Wws012pVLJC\nok6nk92zubm5MhGRRGiK/KFICpqgslqtsskOpVKJaDTK4kco75omXwAwxzMJ1hRXkkgkoFKpWHZ9\nPB5HfX09Oz7FjtCzIjMzk4nvJBh35/rE4/F2RRQ7wuv1sgkoAGyyjSa3qA2p65pc3Xq9nn0ebW1t\niEQi8Pl8LJZl4MCBnfahq8iQeDwOn88nO3fK+peOXfq3CEjGnHS1qiISiaC2tpZNWJjN5naTLd1F\nWpQ33XlIs9CPRUFaDofD4XA4HM6xg//vjdPnVFbasXlzBTZsWICbbnoNq1ZtxX33Xdxr7fegttIv\nmmQ+ahQaTd85jmKxOOLxBFSqnhWE4nTOH/7wB7z00ksAkqLvZZddhueee062zS233IK5c+diwoQJ\nqKqqSr6Ypl6eIAgQICCeOOzuI1E4kUgg2JAUUjweDxwOR5d9e/PNN5Gbm4uzzz6b9a+rXGlp5EdP\nSOcelC4jJ9Gqs0zU4420cB7lXXs8HuYsJUc5bUvinDTGAEi6HdPlXVN+NQC2rL8rDAYDKwbodrth\ntVp7XeS3WCzMGerxeI46vuLnzmuvvYa6ujo8/PDD7LXU6AsAePrpp2E2m/GrX8ljlGj8UL6yIAiY\nOnUqFi9ejEcffRQbN25k291777146KGHjqq/4XAY33zzDStqqFKpoNPpcMoppyAzM5M5jkkA9vv9\niEQibFsSVaWQeC0IArRaLStMqlKpWO403YfS+9FoNCIcDrOxLY0Akjq0U6MxyHENJCcOQqEQE8KN\nRiMTavV6PXQ6HXPudtd5LY1y6SyKI5FIoK2tTTa5Z7VaYTQaWZ611LktncRSKpXQ6XRwu92oqKhg\nx3Q6ndDr9bBarcjNzWUTAB0dv6NijST4k9OazsdoNLZrj54X1N+u8uxjsRiam5vh9Xqh0WigVqsx\naNCgTvfpDGkdg3Tnygs1cjgcDofD4fx84VYnTp+zatU3yMzU4+KLR+Pyy8di1apveq3t669fgRde\n+BxAMjNbFBdAoVjQbru//vU/KC39M7TamzFhwv+HbdsOtdtm//5GXH75S7Ba74BOtxCnnfYoNm36\nXrZNNBrDgw9uQlnZfdDpFsJmuwOTJj2JTz7Z2+O2OuKpp/6Fs856AjbbHdDrF2L8+Efw1ls72m0n\nigtw661rsXr1Vowa9SC02oX48MM9AJJfSJ955mOMGvUgdLqFyMu7CwsWrILT6e/y+NddtwIm062o\nrLRj6tSlMBpvhcVyMxYv/qdsu6qqVojiAixZ8hGWLv2EXd+9exsAAC0tHtxww0rk5d0FnW4hTjll\nMVauTJ+lDADPPPMxiov/BL1+ISZP/gt2766Xvf/jj3W4/voVGDLkXuh0C5GffxduuGEl2tp8adtr\nafFg9uyXYTbfBpvtDtx+++sIhSIdHr+y0g5RXIClSz9p997mzQchigvw+uvfdrj/seT3v/89Pv74\nY6xcuRIXXXQRYrGYzCG3fPly7N69G48//rh8xzQTO5UrKrHnpT1wu92y6A7KVo0HkuJPuvgOig3Q\naDTQ6XSor6/H999/j7lz5yIzMxOZmZmwWCzIyMiAyWSCwWCATqdjwgWJSUcqLKRz3UlfEwRBJnB3\nle16PJAWa9RqtQgEAiy/lwRoEq+piCMJb1qtljkm7XY7+zk17xoA7rzzzi4jQwhBEJjwRDnGvQ0V\noAROzOgQKfv27cPChQtx5plnYvbs2fD5fCxOx+PxsFidRYsW4fPPP8ddd93VzkH//vvvy+JhiOLi\nYpx77rn429/+hvXr12P+/Pl45JFHjiofsq2tDT/++CPq6+tZ7rHFYkFpaSmysrJYxAeQFArp83U6\nnewzT3VdA4czr8nF7fP5WEyGyWSC2+1utw8Jw/F4nEUMAWBCvtSFTZNw5AhXKBRQKBSor69n9zhF\nmdAYFAQBBoMBarWaOZej0Sgbax0hLTbZmfs4FouhqakJc+bMYcfPz8+HyWRiUUkAZK7pWCyGYDAo\nE9737dvHzj0SicBgMMBms8FkMsFkMrHzTYfU3S/dhiJCfD4fE4WNRqMsIoRIJBJwOp1ssiAzM7PL\n57jT6URTUxObbCwpKTkqR3Tqsz71HPvTpCXn58eMGTP6ugscDqcD+PjkcE4MuPOa0+esXv0tLr98\nLJRKBebOnYAXX/wC27dXYdy4oqNue8GCc1Ff78LHH+/FqlXz07qwV63aCq83hAULzoEgCHj88Q9x\n2WUvoaLiESgUyS+Hu3fX4+yzn8SAARb88Y/TYDBo8MYb2zBr1jKsX78AM2eeAgB44IFNeOyxD3DT\nTZNw2mnFcLsD2LatCjt2VOOCC0b0qK2OePbZf2PmzJNx9dWnIxyOYu3abzF79st4992FuPDCUbJt\nP/lkH9at246bb54Mm82I4uLkUvObbnoNK1d+jfnzz8Rtt52Pyko7nnvuU3z3XQ2++upudt7pEAQg\nHk9g2rRnccYZg/Hkk5dh3bqv8cADmxCLxbFokTye5e9/34xQKIrf/nYSNBoVsrIMCAYjmDz5Lzh4\nsAW33HIeioutWLduB6677lW4XAHccsv5sjZeffVreL1BLFw4GcFgBEuX/hsXXPA0fvzxfmRnJ0W2\njz7ag8rKVsyffxby8jKwe3c9XnrpP9izpx5bttwjay+RAGbP/itKSqx47LFL8fXXFXj22U/hdAaw\nYsV1ac+7pMSGs84aglWrtuK22y6Qvbdq1TcwmTRdfnbHirKyMpSVlQEArr76akybNg3Tp0/H1q1b\n4Xa78ac//Ql33303CgoKetSudOk9+4OkKEC50iQEpRMq1q1bB0EQcPXVVx/zWIhEItFu6Xu6ZeTS\nyJD+ALmiqTgcic2RSIQJyFqtlmX1hkIhltOtUqkQDoehUCiYC16tVrOCecDhyJDLL7+82+I19YcK\nOHq9XnbM3kIURWRkZMDhcCAUCrGIkl8adF+SmCn9OxaLobGxETNnzoTJZMJTTz2F+vrDk3LSiZx3\n330Xf/nLXzBnzhzMmzePibCdsXbtWtx0000oLy9nLudZs2YhFovhf/7nfzBv3jzZvdIVsVgMVVVV\naG5uRjgcZhERWq0WAwYMQG5uLgCwqAyVSoVYLAafz8fyqNVqdVrXNe1H+P1+Np7JdRwOh1kuPF0X\nigrxeDzQ6/UypzW5uCORCHsOkCubYiqcTicTxUVRRFZWFvR6Pdra2limMxV0lDrhw+Fwp/crTUp1\nli0eDofR3NyMaDSKa665Bmq1Gjk5OWyFCInRarVa5iSPRqPsOWa322WrN4xGIytymUgk2GfSWUSS\n1HVNYn93IkKkeL1e1t+MjIwun68UcULXqLCwUFZwsqd0JU73t0lLzs+PhQsX9nUXOBxOB/DxyeGc\nGPSPb++cE5bt26uwb18jnn8+mc979tmlKCy0YNWqb3pFvD799BKUleXg44/3Yu7cCWm3qalxoLx8\nMTIykl9Ey8pyMGvWMnz44W5cdNFoAMBtt72O4mIrvv32j1Aqk198fve7c3H22U/gf/5nPRMt33tv\nFy6+eDSWLbuqwz51t62OOHBgsSz6Y+HC83DqqQ9jyZKP24nXP/3UhF27HsCwYYeLYn35ZTleeeUr\nrFlzA+bMOY29ft55wzB16rNYt247rrzyNHRGMBjBRReNwtNPz2b9v+SS/8Xjj3+IW289H1lZh4tC\n1dU5cfDgw7LXli79BPv2NWLVqhvYsRYsOBfnnPMU/vzndzB//lkwGA671Q4ebEF5+WLk5SWjBaZO\nPQmnn/4YHn/8Qzz11OUAgJtvnow77pAvpT/99BLMm/cKvvqqHGedVSp7b8gQG9av/x3rv8mkxbJl\nn+POO3+FUaMK0573NddMxIIFq/DTT00oK0uKAtFoDOvW7cDll4+DVts/HF2XXXYZFixYgAMHDuAf\n//gHIpEIZs+ezeJCampqAAAOrwNVTVUosBZApTzcd+kS9dQiiYImKYBEo1Hm/qO8WfqbRJI1a9Zg\n2LBhOPXUU4/5OXcUGUKOQYokIIGsP7jv4vE4E4hICJOK15Q3rVAo4HQ6WT42FaCkgm0Ux0DtpMu7\nPvPMM3tcBI2iE+LxONxud4+Ezu5gNpuZ6O5yuX5W4jW5eFMF6XQCdeoqBcLj8WDevHnweDx4/fXX\nZUU2CVEU8dVXX+HOO+/ElClT8PTTT8viLjpj2bJlGDt2bDuheMaMGXj11Vexc+dOnH/++R3sLcfr\n9aK8vJzdZy6Xi+VJm0wmqNVqWK1WJh7Te1Q41uVyMUEzJycn7fgjsVoQBDgcDiZ+azQatuIAAHtd\nmm+dSCSgVCpZG5RbTbEX0ucATfiEQiE2PmKxGCvmKs3sp7xqivAhOhOvU4XndPh8PtjtdnZvXHTR\nRbJ4HsrLTm2DhOlAIACHwyG7ZgMGDIBSqURtbS0SiQQyMjJYFFFnYjK1oVAoEAgE2kWEGAyGTp+X\n4XCYFZmlYp2dkUgkUFFRwQR4q9Wa9t7vCV2J07xQI+domTJlSl93gcPhdAAfnxzOiQGPDeH0KatW\nfYO8vAxMnlzGXpszZzzWrt3W4Rf+3ubKK8cz4RoAJk0aikQCqKhIFk5yk+wsOQAAIABJREFUOHz4\n9NP9uOKKsXC5Amht9bI/U6aMxIEDzWhoSC6Ltlh02L27HuXlzWmP1ZO2OkIqXDudfjgcfkyaVIod\nO6rbbTt58jCZcA0Ab765HRaLDhdcMEJ2/FNPHQSjUYNPP93fret2882TZb8vXHgeQqEoPv5YHpFy\n+eVjZcI1ALz//i7k5ZllIrlCIeLWW8+D1xvC55//JNv+178+hQnXAHDaacU4/fRivPfeLvaa9LqE\nQhG0tnpx+uklSCTQ7toIQvv+33LLeUgkIGszldmzx0OjUcqibT74YDdaW724+urTO9zveENL4F0u\nF2pqauBwODBy5EiUlJSgpKQE55yTXGXwyNpHMHj+YOytkX9moiBCpVRBpVRBo9ZAq0k6D5UqJRQ2\nuTBAy+tDoRD8fj+8Xi98Ph+++OILlJeXY968ecdlLHcnMqQ/FWoE2keGJBIJJl5T0TppZAhtr9fr\nYbFYmBuVcoeBpOBMDsZgMMjEcaPR2GP3O2X/AkAoFJL1tzdQqVRM6PJ4PF1GMRwvKHbH7/ezbHe7\n3Y7GxkbU1dWhqqoKlZWVOHToEGpra9HQ0ICWlha0tbWxuJ1QKMQyeNMRjUaxYMECVFdXY/Xq1Rg3\nbhxsNhvy8vJQWFiIoqIiDBw4EIcOHcJNN92E8ePHY/Xq1TCZTNDpdB26R6X3dlNTU9prSuMgXaZ2\nKolEAnV1ddi9ezf7/EkEpokii8XCim7GYjEWxwEk71vKvKbt060AIYe6IAhMbCbhVPq+NJJE2n9y\nZFMblHssnTyg60KTQNLM/oyMDGi1WqhUKvh8PnZMqWhNWd0AOi3aSMJzquBNbTocDrS0tLB+ZWVl\nwWazsf6lit/SZ5XX60VbWxtcLhf7bLVaLU466STk5OSwHHlBEJCfny/L+0+HdGWAz+eTRYQYDAaY\nzeZOBd94PA6n08kmCywWS4fbEg0NDWzSQKfToajo6I0Kna2oSa17wOFwOBwOh8P5+cH/F8fpM+Lx\nOF5/fTvOO28YE4oBYMKEEvzlLx/jk0/24b/+a8Qx78fAgXI3ocWSFH4cjqQAWF7egkQCuO++jfjz\nnze2218QgOZmN/LzzXjooRmYNWsZysrux6hRBbjwwpNw9dUTMXp0YY/b6oh33/0BjzzyHr77rhah\n0OEv76LYXoyjmBApBw40w+kMICfnzg6O72n3eiqiKGDwYJvsNXIiV1W1dtmHqqo2DB2a0+71ESPy\nkUgk35dSWtrelVVWlos33zyc9e1w+LBo0bt4/fVtsnMQBMDlCrTbv7Q0p93voii0678Us1mHSy4Z\ng9Wrv8WDDybz1Vat2oqCAgvOO29Yh/sdK1paWto51qLRKFauXAmdToeRI0fitttuw69//WvZNs3N\nzbjppptw/ZXXY1bZLJTklrD3KhoqAACD8wfL9hEFEWKeCFUgKWTodDoYDAYmfMTjcSamxONxrFmz\nBoIgYObMmfB6vZ06tI+W7kSG9LdCjUD7Yo2BQIAJayTGScVracSIzWZjRdsaGxuZaCcVwaQZyD2J\nDJFC/YpEIvB4PLIIg97AbDYzsfBYuLuldBbfIX3taCZbSLilLGbpz/S3IAi47LLLsHPnTmzcuBFT\np05N29bevXtx2WWXobi4GOvWres0O7m2thZ+vx9jxoxhr5WVleGjjz5CeXk5SksPrzxZvXo1RFGU\nbZuOYDCIgwcPygoJ6vV62Gw2FrchiiKMRiNsNhsr7kqirdvtRiKRgMvl6jTrGjhcbC8WizFnNOU+\n03v0PrmmgcORRjT5EwqFWNyHKIpMSJbGfoTDYVm8CAnXBAnt1C+pAK1WqxEIBDos2kjCM7mdpWJp\nPB5HS0sLG8eiKCInJ0d2bCApxKYK5wDQ2tqKuro62WqY3NxcDBo0CAqFAlVVVSwz3GazQa1Ws+vV\n0Zilgq6RSITdXxqNBgaDoVvj3O12s+tqNpu73MflcrFJFVEUMXjw4KOO8ZAW4kx3b0mF7WMdX8Xh\ncDgcDofDOTZw8ZrTZ/z73/vR0ODC2rXfYs0aeaE7QUi6so+HeN1RvjN9OYzHk3/feeevMHXqSWm3\nJSF00qShOHjwYbzzzvf417/24G9/+wpLlnyMl166GvPnn9WjttLxn/8cwMyZL2Dy5DIsWzYP+flm\nqFQK/P3vX7W7hgCg07X/IhePJ5Cba8Lq1TemFWkoQ7on7Nz5Hczm9FEb6frQG07c1CauuOJlfP11\nJe6+ewpOPnkAjEYN4vEEpk59ll333uCaa87Am2/uwNdfV2D06EJs2vQDFi6c3Gvt94Tf/va3cLvd\nOOecc1BYWIjGxkasWrUK+/fvx5IlS6DX63HKKafglFPkUTQUH3LS+JNwyQWXAA2H3zv/nvMhiiIq\nllfI9nn4jYchDBKw+6fdSCQS+Mc//oEvv/wSAHDvvfcCkEcpvP3225gwYQKKi4sByAUGgoRsqYvz\nSATtdJEhqcvIScDoT5mn5GQlkautLTlpk1qsMRaLscKZVNjSYrHA6/XC4/HA7XYjHo+zYmqEVLz+\n/PPPMXfu3B73URAEZGRkoK2tDbFYDF6v94iF8HTo9XoolUpEo1E4nU5YLJYe3wPdie84WlFaFMV2\nInQ6gbo74tjtt9+OTZs2YcaMGbDb7Vi1apXs/auuugperxdTp06F0+nE7bffjvfff1+2zeDBgzFh\nwuEorBtvvBFffvmlbIzddddd+OCDD3D22Wdj4cKFsFqt2LRpEz788EP85je/QV6efFWOFLvdjkOH\nDsnczfn5+Rg4cCD27dsHj8eDeDwOi8XComooR5oEZRI2fT4fy3RPl3UNHBZsQ6EQ299qtaK5uVkm\nSCqVSmg0Gra6hDKsRVFkxU5jsRhUKpUs51qpVLJjBINB9lmZTCbo9XomeEuPT/el9H6UitckMEuh\nyRAAsvtBmm9N7VC+NQC8/fbbmDVrVlrXdSwWw6FDh2C325nALAgChg0bxiZ7fD4fy+lWKBRsMoGu\nWSp0rSmOiO5jo9HY7ck9ihgBkgVYU0X4VMLhMKqqqthERGFhYY+jjNJBz/Z04y+RSPDIEE6vQGOU\nw+H0P/j45HBODLh4zekzXnvtG+TmmvDCC+1jBd56ayc2bPgOL74YkcVBHAlH6+4kh7FKpcD55w/v\ncnuLRY9rrz0D1157Bvz+MCZNehKLFm3C/Pln9bitVNav3wmdTo0PP7yN5WUDwCuvfNXtNoYMycYn\nn+zDmWcOPuJrG48nUFFhZ0L7t99uxaBBZwIAioraO61TKS624scf69u9vndvw/+1kSV7/cCB9jEs\nBw40sWM5nX78+9/7sXjxDNx770Vsm47iW6hNaV/Ly5sRjye67P+0aSchO9uEVau+wYQJJQgEwn0W\nGXLllVfilVdewYsvvojW1laYTCaMGzcOTz75JC6++OJO92XjYjSSAVJ1h1+noowMA3D/ivvZPoIg\nYPny5exnEq8pTuCTTz5Bc3Mz7rvvPhiNRiZcp3NoU+wIcSSCdleRIUD/K9RIIhoAJvpI866zsrJY\nsTe3241wOIxYLAa9Xg+TyQRRFJkrOhgMIhaLsSKa1D61p1QqsWHDhiMSr4Gk6KPX6+Hz+Vhhxd4S\nggRBgMVigd1uRzQahd/vZ1ERdK90JUynTor09Pg0eZBOmO6JKN1dvv/+ewiCgE2bNmHTpk3t3r/q\nqquYyxYA7r///rTbSMVr6cQNMWnSJGzevBmLFi3CsmXL0NraipKSEjz66KO466670vYtGo0yoZRQ\nq9UYPHgwLBYLYrEYmpqa4HIl463MZjNycpL/Dkgzor1eLxKJBJxOpyzruqMM6EgkwvYBkpMa0WgU\noiiySTEgeV/rdDpZ5Afh9Xqh1+sRDoeh1+vZagTqm7SonyiK0Ov1sFqtaG1thSiKUKlU8Hg87J4A\nwFzeJBpT/0kUTT0fcl3TvSMIAvx+vywmxGAwwGq1yj6vNWvWYNasWTJRXKVSybLGqU8ajQZDhgyR\n9aW2tpb9nJubC1EUO4zKoAmFSCTCrpHRaITRaOz2/5disRi7B1QqVZcTWolEApWVlUzcz8zMbHcN\njoSuxGnpBEZ/mbTk/DyhMcrhcPoffHxyOCcG/eNbPOeEIxiMYMOGnZgzZzx+/ev2xdzy881Ys+Zb\nbNz4A664YtxRHYsK/7ndAVm2dXfJzjZh8uQyvPTSf7Bw4Xmy7GUAsNu9sNmS7qG2Np8s31mvV6O0\nNBu1tY4et5UOhUKEIADRaJyJ14cO2fHOO993+3xmzx6HF174HA899E888oj8H/pYLA6vNwSzuevr\n9L//+ymeeWYOAOCmm27C9On/C7VagQsu6FqUv+ii0fjoo714/fVvWdHIWCyO5577FCaTBueeWybb\n/u23v0d9vRMFBck8za1bK/HNN4dwxx3/BeCwez7VYf300x8j3XfxRAJ4/vnPZM7+Z5/9NwQB7Ype\npqJQiLjyyvFYvfpb7NnTgNGjCzss8HismT17NmbPnt3j/YqKiuRZuKMBFAGoASpXVQIRJAXtTAAD\nAeSgRwLhlClTZO2TGE3iAolRJE5KBch0gnZq3IhU0E4XGSIVyCn3tj8VagQOi1xA+2KNVKROo9FA\nEIR2kSHkrlapVAgEAiwqQafTsbYoCgAATCYT3njjjaPqr8FgYCK52+1GVlbWEU8MporSiUQCfr8f\nsVgMlZWVMJvNiEajvSJKdyZI91WMwKefftrlNqljlO5zcg8TJLx/9tlnadsZP3483n333W71y+12\n4+DBg7I856ysLJSUlLBxQ7EXPp8PKpUKOp2OFWqka6lUKtO6rtNlXQOHM8YDgQBzUqcKz7FYjMVg\n0OQNOXilzxGKClGr1YhEIrI2QqEQVCoVwuEwjEYjcnNz2fWkKA6pmOv3+1kRQxJnpdEtoVCoXTFF\netbQPeZ0OlnRSgDIzMyUrY4gXn/9dZnrWqVSoa6uDnV1dbJik0ajEQUFBbLjOhwO5oDW6XQsd1q6\nH10DKvZI7yuVSmi12h4J1zQpQdEr3VktUVdXx/qoUqmQn5/fKxOJ0kzzdO31tzoHnJ8vr7/+el93\ngcPhdAAfnxzOiQEXrzl9wjvvfAePJ4QZM05O+/7EiYORnW3EqlXfHLV4PW7cICQSwC23rMXUqSdB\noRCYYNpdnn9+LiZNehKjRz+E3/zmbAwenI2mJje2bKlAXZ0TO3f+GQAwcuQiTJ5chnHjBiEry4Bv\nv63Cm2/uwK23nt/jttIxffpoLFnyMaZOXYp58yagqcmNF174HEOH5uCHH2q7dS7nnFOG3/52Eh57\n7AN8910NpkwZCZVKgZ9+asKbb+7As8/OwaWXju20DY1GiQ8+2I1rr12OiRMH4733fsT77+/Cvfde\nBKu162XAN900CS+99AWuu+5VbNtWheJiK9at24EtWyqwdOkcNuFAlJZm4+yzn8TvfncOgsEoli79\nBNnZRtx1V7K6tMmkxTnnDMUTT3yIcDiKwkIL/vWvPaisbG0XL0JUVtoxc+YLmDbtJGzZUoHXXvsG\nV199Ossn74xrrjkDzz77KT777Cc88cRlXW7/syADwEn/9ycBpJqvexMSVLoraNPv6QRt2k/qjiW3\nHb1GYlx/EjCkeddarRbBYJAJ2hQXQEK0tFij1F1NcSIUeWAwGHo171oKRSw4nU5EIhEEAgFWGJKQ\nfnad5UqnKyJI4iIJgh25JNOJ0h1FefySkDrEe5t4PI7a2lrU1x9eDaNQKFBUVMRc1URDQ4PMdW2x\nWKDVahEOh6FSqVjBw1gsBqfTyT6HzlzX4XCYTdyQAzoajbJnA0VnUF81Gg2L9KAse7rvFQoFE49p\nkotEWwBM8CbXL4nFoiiy7HUg6fyORCIsjoSQTn6l5l7T7xQ74nA4ZEUus7Oz242ZdPtHo1FUV1fL\nssa1Wi0yMzOh0+naCebSz42iYEjQpYz1UCgEn8/HnqkKhQJqtZpNOvTkuUgFSYHkxFhXE4JUoJKu\nw8CBA1kMz9GSWpRXinQSs79MWnI4HA6Hw+FwjgwuXnP6hNWrv4Ver+4w01oQBFx88WisXr0VDocP\nmZkGCMKRRYBceumpuPXW87B27TasWrUViUSCidcdtZn6+ogR+di27U948MF38eqrX6O11YucHBNO\nPXUQ7r//cDzDbbedj40bv8dHH+1FKBRFUVEWHn10Fu68c0q32nrggemdnsvkycPw979fg8ce+wC/\n//0bKCmx4YknLkVlpb2deN3Z9Vq27CqMH1+El176D+699x0olSKKi6245pqJOOus0rT7SFEqFfjg\ng1uxYMEq3H33WzCZtFi06BLcd588qqKjPmi1Knz++Z245571WLnya7jdQQwblosVK67D//t/E9u1\nce21Z0AQgGee+Team904/fQSPPfclcjNPSzKrVlzI265ZS1eeOFzJBIJTJ16Ej744FYUFNzdrg+i\nKOD113+D++7biD/+cQOUShG33npeOyGaRIhUxo4dhJNOyse+fY2YO7dnEyE/C/pA3+1M0JbGjaQK\n2rTMXqlUskJrJAJpNJp2hRv7CyRqiaIIjUbDYhpS864p/oNiA7RaLRPAPB4PK26nVquhUqlY7rBU\nvCax+0iRuttJCPR6vTAajez6diRKdxedTodwOMwmIMxmc4cCNaf3CAQCKC8vlwm3RqMRpaWl7TKM\nQ6EQ7HY7cxKbzWZkZ2czBy5FXVAEjM/nYxMxHbmugWQRPxKKdTod+1mpVEKn08Hn8zExlv6WZkmT\niC3tL8XcAGDtUfZ9RkaGzGkNJMVcl8vFJgg0Gg3UajWL9yGRl6J8wuGwTLymSBLqn8vlkgmnOTk5\nnQqoVHzV4/GgoaFBNlFns9mQkZGBaDQKjUYjGwNNTU1s26ysLNZn6XWi6wuATYrpdDp2Xj15LlLh\nVuBwYcfOCAaDqK6uZr+Ta7w3oni6igzpj3UOOBwOh8PhcDhHhtAbhdOONYIgjAWwffv27Rg7tnNH\nKKd/YbfbsX7907j0UmuncRicnw/XX78Cb721E2730r7uSp8yduzDsFqN+Oij27vc1m73Yv36Vlx6\n6e9hs9mOQ+9+uaTGBJDzmEQyaeQILfGngmQGg6HfiBiVlZWIRqPQ6XQYMGAAKioqYLfb4fV6YbVa\nodfrMXjwYIRCIWzfvh0NDQ0wGAwYNmwYSkuTE0xVVVXYtWsXPB4PjEYjRowYgZycHBgMBuzcuZMV\nrjv11PbRTMBhUTqdOzr1byIej8Pr9QIAi43oiu7Gd9TU1CAUCkGhUKC4uLhPIj1OJJqamlBdXc0+\nXxKZCwsL0177qqoq7Ny5E9XV1dDpdBgyZAjGjh3LVj6oVCokEgm0tLSgtbUVgUAASqUSubm5KCkp\nSduHcDiMyspKAEkBmQqRAkkx1uv1orW1FdFoFAaDARqNhhVy9Pl88Pv9bEKLJnhUKhXKysrQ2toK\nl8uFcDjMJrJEUYRWq4XZbIZGo2FFBwVBgMfjgUajgU6ng1arRSQSYe8VFxeze72pqQlerxdKpRJF\nRUUAksI+Cdqp2d02m63Le9nn86GhoQEej4e5mtVqNUpKStjYJDGfJq9CoRD27t3LijSWlZXJYlBC\noZDMVa1Wq9kzMJFIsOus0+m6JWAnEgnY7XY2GZCdnd3p8zQej2P//v1soi47OxuZmZnMPd+RE7+7\nRCIRBINBCIIgW3VCfaVJj944FofD4XA4HA6n5+zYsQPjxo0DgHGJRGLH0bTFvxlyOJyjZsWKFX3d\nhePK9u1V+O67Wlx77cSuN+b0KuTQVqvVzCGp0+mYsEUOUKkzUyp0e71e+P1+hEIhRCKRo8pVPlKi\n0Shzg0ujQQCwwosqlQoKhUIWGaLRaGQRIG63mwlUJpOJFXB0u92sGJtWq4Xb7ca8efPQ0tKCxsZG\n1NbWoqqqChUVFaiqqkJdXR0aGxvR0tICh8MBt9vNrlGqm5rEP+orxZVkZGQgMzMT2dnZyMvLw4AB\nA1BUVITBgwejqKgIAwYMQF5eHhOwMjIyoNfrmZNUEASWBRyLxWROYE7vEolEsH//flZAD0jeWyNH\njmSRDulIjQyh6A0ShdVqNZxOJ2KxGLxeL/tcO8u6bmxsZL/TvS2d2KAJKJqYoskppVLJfpZmHlOE\nSDgcZiKwVLwlkTYajbJMbLVazfKYE4kEc/EajUb2mjQ6hIRQaWZ7OBxGIBCAx+Nh/bFYLMjOzu5S\nuHY6nbjqqqvg8/nYc8FqtWLMmDEycTg1TofysIFkXIj0mUcFGelZmJGRgYyMDLZ/ak2C7kDPFeDw\nyojOqKmpYc8ug8GA/Pz8DotIHgmd5VlLs7CPd2TIokWL+MTbL5Drr7++r7vA4XA6gI9PDufEoP+s\noeZwOD9bRo4c2dddOC7s3l2PbduqsGTJxygstGD27PF93aUTGumyfFqGTq5AEr0oaoRcoeQ2loo3\nJPCkFoY8VqTmXYfDYSby0LFJIKbIECApXun1elbcrq2tjWUDa7VaOBwORKNRBINBJv7q9Xq0tLRg\nwoQJsiiRzujMJU1/SKRUKpWwWq29kiVuMplgt9sRj8fhdDqPOu6E0x6Hw4GKigpZLEV2djaKioo6\nFRQ9Hg+cTic8Hg8EQUBGRgays7MRi8Vk2fKRSAROp5M5fnNycmRFDgEwsdftdrN7W6VSQaPRsPFL\nxRlTxWsg6eg1GAxoa2uTRZYoFApWvLSlpQXRaJRFfZAznKI+SNilLHH6nSaTEokE9Ho9VCoVc2Bn\nZWUBaF+0URRFFuFDdFSYUUo8HkdNTQ3sdjsmTpzIzm/IkCHIzs5GJBKRRSBJiy+63W42kaDRaJCd\nnQ2v18tEayAp6ur1euh0urTiLm3TnbFLmdkAWJud0draira2NnaM4uJidkz6TI6GrvKs6Rp8//33\nWLt2LT777DMcOnQIVqsVEydOxMMPP4yhQ4d2+3ivvvoqrr/+emzbtk22AtTtduOCCy7A7t278fbb\nb2PKlCmyyVPOL4cpU6Z0vRGHw+kT+PjkcE4MuHjN4XB6TOp33QkTJvRNR44zb765HYsXv4fhw/Ow\nZs2NUKv5I7SvkGZZS92EiUSCCVLhcJhl2FJ+dGp+tjT7tiNBm8S53hIkSLADks5rh8PB8ltVKhWC\nwSDLwW5sbGRidzweR1NTEwAwoQoAi0qg86JMWkEQmAh+ySWXdFjcMPXv7ohZZrOZiec+n4+5VI8G\ncog6nU4Eg0GEQqF2wifnyIjFYqiurmb3D5C8b0pKSmC1Wrvcv7GxEW63G/F4HCaTCQaDAUajEeFw\nmOWtt7a2svuPxNb8/HxZO5SZTtnJNKFkNpuZ4Gg0GtnKAXI5C4LAnLR0X1NcCWVBE9FoFE6nE2az\nmUXnkJhOz4pgMMiihmg8kmBN2dVqtZplcAeDQfZskUZQ+P1+BAIBmTBrMpm6HA9+vx8HDx6E3++H\nVqvFhRdeCJVKhREjRrAxS+NbmtlMkR+1tYdrXBQWFsLv98PtdrPrqVKpkJmZ2S63XHqNqL2uoMkk\nIHnPdFUANhAIoKamhv1eVFQEtVrNzqc3nNDU/3R51tJ/G5555hls2bIFV1xxBcaMGYPGxkY899xz\nGDt2LL755pseTbynPhc9Hg9+9atfyYRrALjvvvvwxz/+8WhOj9MPmTt3bl93gcPhdAAfnxzOiQFX\nXjgcTo9Yvvw6LF9+XV93o0944IFL8MADl/R1NzhIL16kFmZM/Z1EbSk9FbRT3dldCdrpsqSbm5sR\nDAahUChQW1vLMnTD4TB0Oh28Xi8TsUmIprxbggRAIOnINplM0Gg0LHpAo9HAaDSiqKhIJsD3FiTs\nBQIB+Hw+aLXaXokCIPEaSBa9y8nJOeo2T3R8Ph/Ky8tljv+MjAwMGTKkW5MDiUSiXWQIFWokUTgW\niyEUCsHlcrEJkOzsbJl4GolEEAgEkEgk4Pf7mUis1+uZg1qtVrO8aRqP5JAm8RgAO6YgCOx+p/ek\nwrRSqWSCLvWVnN/xeFwmqpLjmxzbNObcbjcrDqvRaNgkTzAYRH19PRNzpZnSnYnCjY2NqK6uZscX\nBAEWiwUDBgxgYzQUCsmihSiiRxAENDc3s0gUo9GIeDwuK2qp1+uhVqs7/GzpGQd0L76DVllQPzt7\njsRiMVRWVsriTKhA5rGIDOnMdS0IAv7whz/gtNNOkx1z9uzZGDVqFB577DGsXLnyiI7v9XoxZcoU\n/PDDD9iwYYPM9Uf3EYfD4XA4HA6n9+Dr2jgcDofzs4NEZRKJpG47yr4lsaszIYkELhJjyVGq0+mY\nUEWiGB2DMmWdTifsdjvsdjuam5vR1NSEhoYG1NXVobq6GhUVFTh06BBqamrQ0NCA5uZmtLa2wu12\ny+ILSFQkp6kgCCzigM5Jo9HAZrPBZrMhJycHoiiyfN+srCwMGTIEgwYNkhVks1qtLD/7WCxjN5lM\nLPO4u5EkXUEZ5kDS2dgXmeS/FBKJBOrr67Fr1y52jwmCgEGDBmHEiBHddrW3tbXB4/EgEAiwwqc2\nm41FhqhUKibwut1uNmYo65oyo6m4ajweZz+T458EaqPRKLtXafWEVLwm1zYhza6ncUXCcOpKAho3\n1A+aqAKSucwkfNIzQxojIl0xQedK49NgMLAM8I7GWjgcxr59+3Do0CEmymu1WhQUFKCgoIDtR+I+\nkBTE6XWFQoFIJCLLCTeZTGzFiUajgcFgYDFKHa2gkBbo7Oq54Pf72XkbjcYuRdnq6momrJtMJuTl\n5QGQTzYe7bOIJjWA9EI4HUulUuGMM85ot01paSlGjRqFvXv3HtHxfT4fpk6diu+++w7r16/HtGnT\nZO+ny7wWRRG33nor3nnnHYwePRparRajRo3Chx9+2K79zz77DOPHj4dOp8PQoUPx8ssvp23zo48+\nwqRJk5CZmQmTyYThw4fj3nvvPaJz4nA4HA6Hw+nvcPGa06+57roVKCn5U6+2OXnyX3DeeX9hv1dV\ntUIUF2Dlyi29epwTiQMHytO+/uqrWyCKC7BjR/Vx7hHnl0yqUA3gjG/AAAAgAElEQVS0d2JLBYye\n5DHH43FWzI2KvoVCIfj9fjidTrS0tKC+vh51dXVoaGhAU1MTmpqa0NLSArvdjra2NrhcLvj9/rQF\nISlbV6FQsCKLQFK01Wq1sFgsyMvLQ0lJCROmgKRwNHDgQJjNZuY2jcVi0Ol0TESm60CuVKk4+eWX\nXx7Jpe4UikgAwArW9QaUFRyPx3tNFD/RCIVC2Lt3L6qrq5k4q9PpMGrUKBQUFPRoTNTX1zM3PBXm\nJMcr3XeBQAAul4u5g202G7RaLaLRKFtVACTFWK/Xy9zQ0tUEJFyTcEwRI/Q7Ccs+nw+BQACiKLLo\nECreKM2/lt7/UmFYKnD7/X72nl6vZ+/RBBAVFCUBPpFIwG63s3s9Go0y4ZrGerrJMofDgR9//JFd\nRwCw2WwoLCyEXq/H119/zV4PBoNs3Ov1etlEXX19PfvdZDKxyCC9Xi9zuXfmbpbmXXdGNBplbnu1\nWt1lFEpLSws7P5VKhaKiInafSZ/HR4t0giFV0JWulunsWE1NTbDZbD0+ttfrxbRp07B9+3a8+eab\nuPDCC9ttQ/dgKv/5z39w8803Y+7cuXjyyScRCoVw+eWXs2xwANi5cycuvPBCOBwOLF68GDfccAMW\nL16Md955R9bmnj17cMkllyASiWDx4sVYsmQJZs6cic2bN/f4nDjd41j8G8rhcHoHPj45nBMDHhvC\n6dcIAiCKR18I7Fi3eaLzr399iKFDS9O+1wt13GTs3duAN97YhuuvPwuDBmX1buM/M/bs2YNFixZh\n+/btaGxshF6vx8iRI3HXXXdh+vTpafeJxWIYPXo09u3bh6eeegp33HGH5E0AbQAiSE5tmgHoksvc\nn3nmGWzduhXbtm2D1+vFZ599hnPOOadd+5MnT8YXX3zR7vVp06bhvffe643TluXLpgpbSqVSVsyL\nBBp6jUTf1CgP+rs7Tl/K0SYBjtybFE0gLWwozZlWq9Xw+/2sT4MGDYLX62V53OS4JhFP6tA2m83M\n9ehyuZgbksRrIhgMsgJ0JLoJgoAnnngCZ5999lFf+1QoOiQcDrOs46N1VhqNRigUCsRiMbhcLlgs\nll7qbf9g27ZtWLFiRadF5BKJBF599VVs2LABO3fuRFtbG0pKSjBnzhz8/ve/ZzEZJBZLaW1tRWVl\nJRYtWtThmBMEAbW1te0yqVOhmBsSJSkyhFzXCoUCHo+HOZFp5UBBQQHLLafj6XQ6BINBlh+t0WhY\nMVVpzAWdVygUYseh8UXCLkV30Ps+n48VWKRxRPEfFAFEhEIh6HQ6JmRTXIkoirKcfGpbo9HA7/fD\n7/ejsbERoVCIucG1Wi3bN3U1CNBx1nhxcTE7X41Gw8YnCepAspgrCfRAUrQnoVOhUMBiscBkMskm\nBKTH6Owz7WqbRCIBp9PJnmkWi6XTCQ+fz4e6ujr2e3FxMROP0z2PjxTpxGU6cVqa5d3Rc+i1115D\nXV0dHn744R4f+9prr0VDQwPWrVuHiy++uEf779u3D3v37kVxcTGA5L+VJ598MtauXYv//u//BgA8\n8MADUCqV2Lx5M3JzcwEkY06GDx8ua+ujjz5CJBLB+++/j8zMzB71g3NkHKt/QzkcztHDxyeHc2LA\nxWtOv+Zvf7sG8Xii6w17wEcf3d6r7XGAG2/8zXE71p49DXjwwX/ivPOGnfDidVVVFbxeL6677joU\nFBTA7/fjrbfewowZM/Dyyy/jxhtvbLfP0qVLUVNTIxciQgAOAagDEJZsLADIAfbX7ceTTz6JoUOH\nYsyYMf8/e+cdX0WVv/9n5vZ+00iBAAkIgugiuCIqiKCiqKyCgB1FpfxE7C6LDWVFEAVd1wIrq7L2\nAggWrFgREbAgKAaBACkkubklt7f5/XG/n5OZ3JIEAgly3q8XL8jcKWfKmXCf85zng2+/TT9LQRAE\nFBcXY968eQoxhSIE2gK5QEFOaSqaFolEmFuaYgmaZle3FooWkRc1TFXokNojz8+mKf2E3HkdCoXg\n8XjYv41GI4CEaBWLxVBfX8+Kr8kFXI/Hw8RrvV7PMnepeCLFJZA4p9Pp8Nprrx3w+TeH1WqFw+FA\nPB5HQ0MDc04fKIIgsIKQ5OiWO3SPdObPn49169ZlLCLn9/sxadIkDB48GNOmTUNOTg6+/fZbzJ49\nG5988gkTpenZJLF19+7dqKurAwBcfPHFGDx4MDp16gSTyQQgIcBNmTIFpaWlzQrXAFBTU8MiQSjT\nOSsrC9FoFGq1GqIowufzMde1KIrIyclR9DnKjpYkCbW1tazdVGRUpVIluXqpT1B+sM/nY8UVNRoN\nIpEItFoty3um9xkN8IiiyAZwaHCJRGDqo7R/et6or9J5xONxVuzV6/Wivr4eWVlZUKlUMBgMbJaD\nfACL2g6kzhq32Wzo0aMHGzCjdwf1T3J3C4LAssCprTU1NWw/ubm5yM7OVojmlIudqoghIX8fZYpT\namhoYANnVqu1WTF89+7dbL9FRUWK+9nWkSF0nFT1CzJlYQMJAXn69Ok47bTTcPXVV7f6+DU1NdDr\n9SguLm71tmeffTYTrgHg+OOPh9Vqxc6dOwEknstPP/0UY8aMYcI1AJSWluK8887Du+++y5bR74MV\nK1bg2muvbdVMCs6BcSh/h3I4nIOD908O5+iAi9ecDo1KJSLD96sDQq1u4x02we8Pw2hsv2I9oVAE\nWq36sH6Z0ekO3/kmvtgftsN1aM4777ykacvTp0/HgAEDsHDhwiTxuqamBnPmzMHMmTNx7733JhZ6\nAWwCkCrxQQKwHzgpdhIcPzpgP96Ot99+O6N4DSQEmoOt/E1uyVTuaK/Xq3Bk0meiKLKsaBJ9M4nW\nJCI3FaFTCdQthQQaubBCAhTlZQMJcYWcpPRvs9nM+q3b7Wau0KYCtdfrZdtRzi2QEJwkSYJarWZu\nbHJDkzB+KFCr1TCZTPB6vUxoPtiCZVarlblM3W73n0q8vv322/Hqq69mLCKn1Wqxbt06nHLKKSz7\n/Morr0RxcTEeeughfP755xg2bBgT7LxeL3bv3s2czgBw+umno7S0VCHkffPNN/D7/bjiiita1Naq\nqiqF6zo3N5cNCpHjWe66Jkcw9TtyJlOhQXkcRjQaZcJ1U1GTCioKgsCeBfqZxF3q+/Kijnq9Hn6/\nXxGdQ+8GGkwiwToQCMBkMkEURXYMem8AymxoEpXD4TA6deoEg8HARHO5UE/nUVlZib179zKhlbLG\nCwoKFOtTP6F4EPmMCkEQ4PP5WOwKvTsMBgMKCwuTIjmIlkSGZMrEpkx/un+Z3h2SJKG8vFwxQ6Rp\nkdWWOL1bilycbtr+TMI2kPj9d/755yMrKwtvvvlmq/+PJAgClixZgltuuQUjR47E119/zWZKtIRU\ngndWVhacTidrXyAQQM+eybPYmi6bMGECli5dihtuuAEzZ87EiBEjMGbMGFxyySVcyD5EHMrfoRwO\n5+Dg/ZPDOTrgmdecdsPrDeKWW15HScks6PU3Ij//DpxzzuP48ce9bJ2mmdeUT71w4cd4+unP0aPH\n3TCbZ2DkyCdQUZH4AjBnznsoLp4Jo3E6LrroabhcfsVxhw17DMOHL8zYti1bKnDttS+gR4+7YTBM\nR2HhnbjuumWor/cp1ps9ezVEcSp+/bUKl1/+HLKzb8WQIQtS7nPjxt0Qxal46aX1SZ+tWfMLRHEq\nPvjgF7asstKFSZNeREHBndDrb0S/fg/gv//9RrHdF1/8DlGcitdf/x733LMSxcUzYTLNQENDENFo\nDA88sBq9et0Lg2E6cnNvw5AhC/Dpp8oiRdu3V+OSSxYjJ+c2GAzT8de/zsXq1T8p1mnpvtLh84Uw\nZcpLyM29DTbbzZg48fmk+yKKU/Hgg+8mbdu9+yxMmvQigESG9vjx/wEADBu2EKI4FSrVVHz55e8t\nasfRADmf5dmqxMyZM9GnT59G4SqGJOF6Z9VO7KzaqdjOpDPBXmEH9qPFxGIxJoDIIcEpGAzC5/PB\n4/Ggvr4etbW1qKqqwr59+7B7927s3LkT5eXlqKioQHV1Nerq6uB0OllkBhVdA6DImiUHJLk9TSYT\nrFYrsrOzkZeXh8LCQnTp0gXdu3dHSUkJunXrhs6dO6OgoAC5ubms+JXRaGR5twcLidmCILD2mUwm\nFiMiF99IkPf5fNDr9cxhqdVqEQ6H4Xa7EYlEEI1GYTAYFMKfPB+aBCTK7j7UmEwmJhh5PB6F2/xA\n0Gg0ClG+qUB3JHPKKac0W0ROo9HglFNOYfeauPDCCyFJErZv3w4g8ezX19dj06ZN2LMnUVtApVKh\npKQEvXv3TnKgvvzyyxBFsUWDS4FAADU1NfB6vQASGcupIkPcbjcEQYBOp2N52CRKU7xJMBhU5CdT\nn9Xr9YqsZiAR6yHPNTaZTIosd6Ax1gIAE6ZJ8KVrG4lEmMhJ8SHyjGwaHDIYDGygi2JMALDZDySG\nAwnhOCsrC7FYDBqNBmq1msWb0P1omjVuNBrRr18/JjhTf2w6MEZFLOl94XK54PP5IEmSokBlcXGx\nQqAk0TaTcEvIZ62kIh6Pw+l0sgGK5iJ7yJkPJO5r165dFW2TR4YcbN51qloHcjIJ2x6PByNHjoTH\n48GaNWtYIcnW0qdPH6xZswaBQABnn322IiqlOdJd8wN5V+r1enz55Zf45JNPcPXVV2PLli2YMGEC\nzjnnnIN+93I4HA6Hw+F0RLjzmtNuTJnyMpYv/wE33XQm+vQpgMPhwzff/IFff61C//4Jh4ogIKWL\n5KWXvkMkEsOMGcNRX+/D/PkfYty4JRg+vDe++KIMM2eOxI4dtfjXvz7DHXe8heeea5we2hJTyscf\nb8OuXQ5MmnQaCgqs2Lq1EosXf4Vt2yrx7bczk/Y1btwS9OrVCQ8/fHHaLw4nndQdPXrk4fXXN+LK\nK09RfPbGG5uQnW3E2Wf3AQDU1HgwaNA8qFQiZsw4E7m5ZnzwwVZcf/3/4PWGMGPGcMX2c+a8D51O\njTvuOBvhcBRarRr3378a8+atweTJQ/DXv3aHxxPAxo3l2Lx5D0aMSBxn69ZKnH76AnTpYsc//nEu\nTCYd3nhjIy666BksXz4Vf/tbfwBo0b7SIUnA9OmvISvLiAceuBC//16Dp576HHv21GPt2tubvRfy\n+zV06DGYMeNMPPnkWtxzzygce2ziC2ifPs1Pf/8z4/f7WcG0d955Bx988EGSOLVhwwYsW7YM69at\na+xTLiQ5rofPHA5RFLHzeaWADQAoS98GckrH43GUlZXBZDIhHA4jLy8PV1xxBWbMmMEKDR4slI+r\n1+vZPtVqNcxmMxO15Rm6HQV5fADl/5LgEolEYLVaodPpWExCOByGKIowm82QJAmhUAgNDQ3QaDRM\nZDebzcxNSiKSIAjIzs5mQndDQwNycnIO6bkJggCLxQKn04loNAq/38/E5wPFZrOxARCPx4Ps7D93\nTND+/fvRr18/9rM8BoGorq4GAOTk5CASiaCmpgahUAj/+Mc/sGnTJmzZsgWlpaUpnerRaBRvvfUW\nTjvtNHTt2rXZ9lRXV7OBCKPRCJvNxqIsBEGA3+9HLBaD1+tlgyh2ux1arZb1TToPeeSF0WhkgnDT\niBka2IrH40zE9Xg8LB6D+rvX62XFFlUqleLZkOfck2AqF75VKhUCgQDLradzIrRaLSKRCItLkWdi\nUxwJkMiqpsEhug5VVVWKwaKCggIUFxcz4ZJmidD28vMOhULs2tJ1J7c3bZOdna3oV/TeJTe6/Pyb\nIs/+TreO2+1m69jt9owxH16vF5WVlez6du/ePWm/bRkZQvuiQT45NLMFSBbJQ6EQLrzwQuzYsQOf\nfvopevfufVDtGDhwIN555x2MGjUKZ599Nr766qs2eb+So3/HjuQC2GVlqX/5nnnmmTjzzDPx6KOP\n4uGHH8Y999yDtWvXYvjw4SnX53A4HA6HwzlS4eI1p914//1fcMMNp+ORR8ayZXfc0bJtKyvd2LFj\nDszmhGMrGo3j4YfXIBiMYOPGWexLUk1NA15+eQOeeeYKaDQtd1DeeOMw3Hbb2YplgwaV4PLLl+Kb\nb3bgtNOUUzj79++Cl166rtn9jh8/EI899jFcLj/s9sQUp0gkhpUrf8QllwxgkSazZq2EJEn48cd7\n2HqTJw/F5Zc/h9mzV2PKlCHQ6Rq/oIVCUWzefDe02sYu/f77v+D884/HM8+knx5+882vo3v3HHz/\n/T/YsadNOwOnn/4I/v735Uy8bm5fb731Fi655JK0x9Hr1fj001uhUiXuS3FxFv7+9+V4992fccEF\nJzR73YiSklwMGXIMnnxyLc4661gMHdqrxdv+mbn99tuxePFiAAmRYOzYsXjyyScV69x000247LLL\ncPLJJ6O8vDyxMNmcnXAAo3HEQILUKEzXxxGoTQiwLpcL1dXVigKIkiShoKAAU6dORe/eveH3+7Fm\nzRo8/vjjKCsrw+OPP572HDLFd9DfoiiyYmYGgwEqlYq5sKkgIjk0D9bldyigSAByqVI2cTQaZe5T\no9EIrVaL2tpaOBwOaDQalJaWQqvVIhaLIRAIIBqNQqPRsHgOv9/P4hM0Gg0r9GYymeByuRAOh3H7\n7bfjscceO6TnRwMKwWCQFaI8GOc6FeCLRCJwu93Iysr6006JT1VELlXkzaJFi2Cz2TB48GBUVFQw\n0ZXcxemEawBYs2YN6urqDigyxGq1Mtc1ZWw7HA54vV4YDAaIogiLxQK73Z4kYLpcLhZnQgNMAJjj\nmYjH4yy7ngRKeuZpPRqM8Xq97J1gtVphNpvZYI88h5qeFxK9Sez2+/2wWCyscKRcGKVCobS9VqtF\nVlYWvF4vIpEIgsEgRFGE0Whk8T6VlZXw+/1M2NZqtSgtLVU4l2kACkgIrHIx99Zbb8WsWbMQiUSY\ng1wURej1euZYF0UxqXaAPO+a3pXp+og8BiWVkBwIBNgAG7nm0xGJRLB79272c+fOnVMOVh2uyBC5\nSC5/58TjcYwfPx7r16/HqlWrcPLJJx90O4CEaPzqq69i3LhxOPfcc7F27dqk3PbWIooiRowYgZUr\nV6K6upq5w3fs2IE1a9Yo1nU6nUmFGv/yl78onjFO23LnnXdiwYLUMys5HE77wvsnh3N0wMVrTrth\ntxuwYcNuVFW5UVjYugJf48cPZMI1kBCWAeCqq05RfCkbNKgEr732PSoqnOjePbfF+1cKwxF4vSEM\nGlQCSQI2b96jEK8FAZg69YwW7XfChJPw8MNrsGLFD7j22tMAAB9+uBVudwATJpzE1lu+/AdMmHAS\nYrE4HA4vW37OOX3x+usbsXnzHgwe3IMtv+aawQrhGkhc361bK7FjRw169lRmUAKA0+nD2rXbMWfO\naLjdSvvtOef0xQMPvMvuTXP7as4ROXnyECZcAwmBfNaslXj//V9aJV5zUnPrrbdi3LhxqKysxBtv\nvIFYLKb4Avv8889j69atWLFihXLDCJLY9cIuhMIhOF1OltUsn03gr0qIxz6fL2UsyNy5cxU/jxkz\nBnfffTdee+013HjjjTj55JNTCtMtESWbOu/k08hJ5ATARK2OBglD5EqlKIBIJMKECL1ej0AgwKIa\nNBoNbDYbdDodgsEgGhoaUFNTA51Ox+JH4vE4gsEguy7kbJXHquTm5iryfg8VFouF5RV7PJ4kgaU1\nkDO3rq6uzdzcHZF0ReSaitcLFizAF198gXnz5in6t1qtxgcffMCKIspduHJeeeUVaLXajAONhNvt\nRl1dHUKhEARBgNlsZnnXNIhEGe4Uw1FUVJQkUkYiETgcDgCJfqnRaBAIBJKKNEqShEAgwN455GqO\nRCJMxI3FYqyQID3bZrOZvT9oEEjuMKbBLHLmyrPYabCH3iMketfW1rL3h9FoRF5eHurr69HQ0MBy\nqU0mE4xGI/bv34/a2lqYzWbm5M7OzkZJSUnSABoNMAFQtMPv9yM3NxehUIgNPBmNRgiCgKqqKnYu\nBQUFSfts+oy0NDIkVV40ieQajYZl5qdCkiTs3r2bvW/tdjvy8vKS1mvLyJDm9pWuUONtt92G1atX\nY/To0airq8PLL7+s+LylAzlAcrzHRRddhP/85z+47rrrcMEFF+DDDz886Nk+s2fPxkcffYRTTz0V\n06ZNQzQaxVNPPYXjjz8eP/74I1vvwQcfxJdffonzzz8f3bp1w/79+/HMM8+ga9euOP300w+qDZzU\ntGS2CofDaR94/+Rwjg64eM1pNx55ZCyuueYFFBfPxMCBXTFqVD9cffVglJQ0LzIXFysFEZst4TTr\n0iX1cqfTD1mR92ZxOn2YPftdvP76RtTUNGZNCgKShF4AKClp2ZTRE07ogt698/H66xuZeP366xuR\nm2vGmWcmprLW1jbA5QpgyZKvsHjxV0n7EAQo2gQA3bsnH//BB0fjooueQa9e96FfvyKcd95xuPLK\nU3D88Z0BADt21EKSgHvvXYV77lmV5jgeFBbamt1XpimqgoAkwdtk0qGw0Ibyckemy8VpIb169UKv\nXgkX+pVXXolzzz0XF1xwATZs2ACPx4NZs2bhrrvuSnLtpSNVXAHj/2bXi6IIrVabsrihfJkoirj/\n/vvx6quvYvPmzTj33HMP+DybuvjkYrYoimmnjXcEotGownEaj8eZQC3PxNbpdKitrVUUbiMhyePx\nsHgByuQ2mUyQJAl1dXWIRCIQBIEVriHXaCAQwFVXXYWGhgZ2T5r+3VaCNomSHo8HoVAIwWAwKdO4\nNVitVjgcDpZx/GcTrzMVkZOLZW+99RYefPBBXHPNNZg2bRoqKioQiURgNpuRk5OjcJumEq/9fj9W\nrVqFc889t0XxK1VVVUzMtFgsyM3NZf2MCjQGg0EmFNvt9pQFk2praxVCM4nrTV3XVJQSADsGOaVp\nEMPhcMDlcrHnyWKxwGKxIBQKKTKfyT1NedQqlYr1DXl8COVWx2IxJpRTkUeauZCVlQVRFJkoGYlE\nEAqFYLPZsH//flRVVbH2qtVqlJaWJhUspHsiL9ZKOd0+nw9OpxMTJ05k+d5URNLhcDBBX6fTpdyv\nvEgh0LJijU3XkSQJTqeTOc3tdnvG90FVVRV7d+l0urSiQVtGhtDvo1T7orgqIPncfvrpJwiCgNWr\nV2P16tVJ+22NeJ3qmlxzzTWor6/HnXfeifHjx7MB4qbr0rOXap/y5QMGDMCaNWtwxx134L777kNx\ncTHmzJmDbdu24bfffmPr/e1vf0N5eTmef/551NXVITc3F8OGDcPs2bMzDjxwDpybbrqpvZvA4XDS\nwPsnh3N0wMVrTrsxbtxADB16DFas+AEffbQNjz76MebP/xArVkzDyJHHZdxW7uJVLk/9hau19WvG\njVuC9et34a67zsFf/tIFZrMO8biEkSP/hXg8eWcGgzbFXlJD7uv6eh/MZh1Wr/4ZV145iH0ho/1f\neeUgTJw4OOU+TjihS5PjJ4t1Q4Ycgz/++CfeeecnfPTRNjz33DdYuPATLF58JSZNOo0d5447zk57\nvUl0bm5fB0JLiwrFYvHmV+IoGDt2LKZOnYqysjL873//QyQSwfjx41lcyN69iaKoTq8T5fvLUZRT\nBI268RlSqVSKjFf5H3t2Yhp8YWEhiouLW9QeWq++vv6AzylVsS65WC3PfW2LIottjTzvWq/Xw+v1\nJhVYo+J2TqeTiTXZ2dnsfKhYJe2DRArKyCVndXZ2NhPHdDody8wl0ZvcrXJIyCYx+2AEbYPBgEAg\nwCIe6Fk6EEgMb2hogN/vRzgcVrhWj2TkReS+/vrrtEXkPv30U0yePBmjRo3CE088AVEU0alTJyZe\nNyXVfVu+fDkCgUCLxLp4PK4Qr61WKzp16sREQooJ8nq9LK6ic+fOSfvxer1sdgY5isn9TM8a0Jhz\nDTTOoPD7/exzs9mMYDCI/fv3M/eyXq9nMxJisRgrvtiUUCjEBotIKKQ8bSriGI1GEQgEEA6HWV/M\nzs5m6wJgUSnhcBh+vx87duyA1+tVFFotLS2F1WpNeU3lrmuNRsMK1tIsBVEUkZOTwwYAqBAn/btz\n585J50cDBxSz0jQyo+k9lRe2lUPtoHudafDP4/Fg//5E5V5RFFFSUpL2mG0ZGZJpYJLelWq1Ouk9\ns3bt2oM+NgBMnDgREydOTPnZbbfdhttuu439fP/99+P+++9XrJMqAggAdu5Mri0xbNgwbNy4UbHs\n4osvRpcuXRTrDBs2rKXN53A4HA6Hwzni4eI1p13Jz7di6tQzMHXqGair8+LEE/+Jhx56v1nx+lDi\ncvnx2WeJOI277x7Flu/YUZNhq5Zz6aV/xYMPvoe3396MTp0saGgIKiJD8vLMsFh0iMXiGD782IM6\nlt1uxMSJgzFx4mD4/WEMGbIAs2evxqRJp6G0NOFw12hULTpOpn1lQpKAsrIanHFGYz61zxdCdbVH\nERmSlWWEy+VXbBuJxFBV5VYs+5NG3rYplAvtdruxd+9eOJ1O9O3bV7GOIAh46LWHMPf1ufjh3z/g\nhJLGe6FRa2C32ZEKla31wvAff/wBACmnlrcUeV4rxRTInYQUo5Ap87U9IXEOSAh5VHhPkiQmyJCj\ntLa2FkDiXHNzE/2UYjhCoRBzvZNQFgwGFZnCJCaRQGiz2VghNhLI5UIk7T8ejysc9wcqaAuCAKvV\nivr6esRiMfh8voNyA9rtdhax4vF42DU5kmlJETmVSoUNGzbg8ssvx0knnYRly5YxcU6n06WMKEjn\n8Hz55ZdhNptx4YUXNtu2uro6OBwOxONxqNVqWK1WmEwmRKNRljNPRWL1er1CdCXi8Th7jgEo3Pca\njYblFsfjcfa+UqvVLPeaftZoNAiHw9i9e7fCyWs2mxGPx2EwGBAOh1lsDtDoPCdhmkRbEs8FQYBa\nrYZer2cRJCQAazQaWK1WVlCV+ocoitBoNPB4PPB6vawfkVvaarWmdJ5Te6h/iqLIsrNphotWq4XZ\nbFZsX1tby9axWq1JhS0BKPouievpkJ+HXOClASYAbCZHOryuaQ4AACAASURBVOg+EF26dEmbr970\n/XwwyJ3VTcVr+SyhthDJOwKhUEjRt8vKyvD+++/j2muvbcdWcTgcDofD4bQvHS8UlHNUkBBilPEb\nublmFBXZEApF26lVCcjV3dRhvWjRJ20inB57bAGOP74zXnvte7z++kYUFNgwZMgx7PNEwb0BePv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FEUWQxGJtc1idsAWBQIkBBIKWdaLjjq9Xr4/X6Ew2FIksTif/bv38/2SZEltA7lU8sLMjZ1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\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vx0MPPQTDMLBq1Sp8+ctfxjvvvIPJkycP2K8DQQVVe65/MOdLIDiWGMmfoQLBaEeMT4Fg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fbs2bj66quLPjcphmvQoEF49NFHOYKgrq4O5557LhYvXoyLLrpot46PQCAojaIo3aKN7rvvPrz3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jjVtkwm7zpbcDaZTFKczrdNIQ5D/rzrfCK4yNcXeL1ekskksViMQCCA2WzOKYR6IMTbJSMj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\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mZqxfvx7BYBCvvvoqzjnnHDgcDqxZswaPPfYYZs6cCa/Xi9raWn6TJxVRFKHT6ZBIJBAO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Cxue/TR\njjesqYGTJ7s1t9zcXCzLYtasWd3aTjrXeOqeBQUFebQHBgYCsO+MXzIkJyd3uuq9pYsV99BWV72s\nrIyxY8fy4x//mJCQEEJCQrj22ms7LWkiIiIiIiLiSyobIiI99qMf/cjjv7uLyPnLzMzkpZdeAtpe\nzpiWlsaKFSs6He9wOOjfvz9p114Lzc0efYZhYAD3PPccL/30p17bNp56QV9dXR0nTpw469z+8Ic/\nMHjwYBISEs5pvPxDbW0tAPX19R7XbujQoViWxVtvvUV8fHx7+1/+8hcAjh49SkNDwzkdo6WlpcvV\n10VFRUBb6ZCIiAh+97vfYVkWTz/9NElJSezZs4dvfvOb3T436Rk9Q0XsTTEqYl+KT5G+QclrEemx\nO+64w9dTEOk1Fi5cyJ133klZWRkbNmzA7Xa3r84904kTJ9i2bRspKSmEdfBCv5J16wBY9frrfPjh\nh179xQcPYlkWeXl5XZYmASgvL2ffvn1MmzaNbdu2df/E+rji4mIA9u/fT3h4uEffVVddRXZ2Nl98\n8QVxcXEcPXqUdevW4efnR319vce9y8nJ6fSlimcrG3Ly1Er7kydPsn///vb9TJ48mauuuoqlS5eS\nk5Nz3uco50fPUBF7U4yK2JfiU6RvUPJaRHps5syZvp6CSK8xevRoRo8eDcBdd93F1KlTSUlJYffu\n3V5jN27cSGNjY1sZjy4Sl9/91rcoO7XK+quMsyQ7v+rdd98F4MYbbzznbeTcLFy4kOXLl3utuP/w\nww/57LPPLthxTpcmuemmmzwS4EOHDuWmm27ivffeu2DHknOnZ6iIvSlGRexL8SnSNyh5LSIiYmNp\naWncf//9FBUVERsb69HncDgIDw8nOTkZdu3yqnl9Nq3mub/64r333iMqKooRI0Z06xhydpdffjmL\nFy/m888/p7q6msGDBxMTE8Pdd9/N0KFDL9hxTiesv/GNb3j1RUZGetXXFhERERER8TUlr0VERGys\nvr4egJqaGo92l8vFjh07mDNnDgEBARAVBSUlHe4jMjKSiIgIr/aPw8IwHA5uuOEGbrrppk7nsGfP\nHj7//HN+8YtfMG3atB6cTd+1d+9eAK699tpzuoZOp5Oqqiruuecexo4d69HXr1+/Drfp168fhmF0\nus+xY8fi7+/P0aNHvfrKysoYNGjQWeclIiIiIiLydVLyWkR67N133+U73/mOr6chckmrqKjwSh62\ntLSwfv16goKCiIuL8+jLzc3Fsqy2kiEAw4Z5Ja+Ly8sBKKuq4jtnvojP35/AIUMACA4OJjQ0tNO5\nvfHGGxiGwezZs7scJ50LCQkB2kp3nO0aWpbFkiVLCAkJYd68eQQGBrb3lZy6xx2tgPfz6/pr3WWX\nXUZycjJbt27l448/bi9Pc+DAAd577z0eeOCBbp2TXBh6horYm2JUxL4UnyJ9g5LXItJjS5cu1ZcG\nkR6aN28ex48fZ+LEiURHR+NyuXA4HBw8eJBly5YRHBzsMd7hcBAVFUViYmJbQ3AwjBoFn3zSPmZy\nVhamafLNmBiP5PVTubkYgwdTeOQIlmWRk5PDO++8A8Bjjz3mcZzW1lY2bNjAt7/9bZUMOQ+rVq2i\nurq6fbXzli1bOHLkCAAZGRmEhoayYMECGhoaGD9+PM3NzTgcDj744APWrl1LdHS0x/6Sk5MxTZPC\nwkKP9qVLl+Lv709hYWGX9/Tpp5/mf//3f7nlllt46KGHaG1tZcWKFQwcOJBHHnnkYl4K6YSeoSL2\nphgVsS/Fp0jfYJztzfR2YBhGPJCfn59PfHy8r6cjImeoq6vzSqyJSPds2LCBNWvW8OGHH1JVVUVo\naCgJCQlkZGR4lZkoKiri6quvJjMzk6VLl3ru6OOPobgYgBGzZ2MaBh/++78TfHr1rmFgJiV1WF7C\nMAxaWlo82v7yl7+QlJTEihUrmD9//oU74T5ixIgRHD58uMO+kpIShg8fzvr163nhhRc4dOgQpmly\n/fXX8/jjjzNx4kRaWlpoampq3yYuLg7TNPnoo4/a2/r160dQUNA539N9+/axaNEi8vLyME2TW2+9\nlaVLlzJq1KgLdNbSHXqGitibYlTEvhSfIvZVUFBAQkICQIJlWQU92ZeS1yIiIr1NTQ0cOQLl5eB2\nt7X5+0N0dFt5kVMlLOTS0NraSktLi1cSul+/fvj5+XVaA1tERERERMQXLmTyWmVDREREepvw8LbP\nNddAUxMYBgQEgGn6emZyHkzTJCAgAH9/f6CtJrZhGF2+nFFERERERKQ3UPJaRESkt+rXD4KCfD0L\nuUBOJ6uVtBYRERERkb5CS7BEpMcefvhhX09BRLqgGBWxL8WniL0pRkXsS/Ep0jcoeS0iPTZ8+HBf\nT0FEuqAYFbEvxaeIvSlGRexL8SnSN+iFjSIiIiIiIiIiIiJyQVzIFzZq5bWIiIiIiIiIiIiI2I6S\n1yIiIiIiIiIiIiJiO0pei0iPHThwwNdTEJEuKEZF7EvxKWJvilER+1J8ivQNSl6LSI/9n//zf3w9\nBRHpgmJUxL4UnyL2phgVsS/Fp0jfoOS1iPTYypUrfT0FkUue0+kkPT2dUaNGERISwkI8N2EAACAA\nSURBVKBBg0hMTOTNN9/0GGeaZqefKVOmeO60vh6OHWPlr38NjY0AuFwusrKymDx5MmFhYZimyc6d\nOzudV3NzM08//TTXXHMNQUFBDB48mJSUFMrKyi74NehtamtrWbx4MUlJSURERGCaJjk5OR2OXbly\nJXFxcQQGBjJ06FAyMzOpq6vzGGNZFm63G7fbTWtra3t7d+7ppEmTOvy7k5ycfOFOXLpFz1ARe1OM\nitiX4lOkb/Dz9QRE5NI3fPhwX09B5JJXWlrKyZMnmT17NlFRUdTV1bFp0yZSU1NZvXo1c+fOBeDl\nl1/22nbPnj0sX778H8nrzz+Hw4ehqgqA4QDl5TBoEAc/+4zs7GxiY2MZN24ceXl5nc6ppaWF5ORk\n/va3v3Hfffcxbtw4jh07xvvvv09NTQ1RUVEX+jL0KpWVlSxZsoSYmBjGjx/Pjh07Ohy3aNEisrOz\nSU9PZ8GCBTidTlasWIHT6WT79u20trbS3NyM2+322M40Tfz8/Dhw4MA531PDMBg2bBi/+c1vsCyr\nvV330nf0DBWxN8WoiH0pPkX6BiWvRUREbCApKYmkpCSPtgcffJD4+HiWLVvWnrz+wQ9+4LXtW2+9\nhWEYzEhPh//3/6CjVdGtrfD55/yz203V7t0MSEhg06ZNXSY6ly1bxjvvvMOuXbtISEjo2Qn2QVFR\nUbhcLiIjI8nPz+e6667zGuNyuXj++ee55557WLt2bXt7bGwsGRkZvPHGG9x+++0d7r+1tZWmpibG\njh1LZWUll19++VnvKUB4eDgzZ87s2cmJiIiIiIh8DVQ2RERExKZOr5Ktrq7udExTUxObN29m0qRJ\nRFVXeySui8vLKS4v9xgfEhjIgIoKKCnp8tiWZbF8+XK+973vkZCQgNvtpr6+vmcn1Mf4+/sTGRnZ\n5Zi8vDzcbjfTp0/3aJ8xYwaWZfHHP/7Ro72kpISSM+5dUFAQQUFB3Zqb2+2mtra2W9uIiIiIiIh8\n3ZS8FpEee/bZZ309BZFeo66ujqqqKoqLi3n++efZvn07t912W6fjt27dSnV1NbPS0uCzzzz6Jmdl\ncdujj/Lshg3eGx46BC0tne7X6XRSVlbG2LFj+fGPf0xISAghISFce+21nZa/kO5rPFWL/Mzk8+mf\n9+3b59GenJxMSkqK135aW1u9yop0pqioiJCQEEJDQxkyZAi//OUvaeni74JcXHqGitibYlTEvhSf\nIn2DyoaISI+d+VIxETl/mZmZvPTSS0BbTeO0tDRWrFjR6XiHw0H//v1JS0iAL7/06DMMAwOoO5Ug\n9eB2t9fE7khRURHQVjokIiKC3/3ud1iWxdNPP01SUhJ79uzhm9/8ZvdPUDyMGTMGy7LYtWsXiYmJ\n7e2nf0Fw5osxDcPAMIwO93UuCeirrrqKyZMnM3bsWGpra9m4cSNPPfUURUVF5Obmnv+JyHnTM1TE\n3hSjIval+BTpG5S8FpEee/LJJ309BZFeY+HChdx5552UlZWxYcMG3G53++rcM504cYJt27aRkpJC\n2MmTXv0l69Z1fbCamk67Tp7a38mTJ9m/f3/7C/0mT57MVVddxdKlS8nJyTm3k5JOTZgwgW9961s8\n++yzREVFccstt+B0Opk/fz7+/v5epVqcTmen+3K73R4vYezI7373O4+fZ82axbx58/j973/PwoUL\nuf7668//ZOS86BkqYm+KURH7UnyK9A1KXouIiNjI6NGjGT16NAB33XUXU6dOJSUlhd27d3uN3bhx\nI42NjcyaNQuamjrdZ21dXYf1jY+dWnl99OhRPvnkE4++mlOJ7fj4eOrr6z364+Pj+etf/+q1jXTu\ns1MlXb744guv67Zs2TIeeugh7r33XizLws/Pjzlz5vD+++9TUlLCF1984TH+dAmXCyUzM5Pf/e53\n/M///I+S1yIiIiIiYitKXouIiNhYWloa999/P0VFRcTGxnr0ORwOwsPDSU5Ohr/+ta0USAfcbneH\nJSVO10huamqioaHBo2/AgAEAXH755R32OZ1Or3bp3OnV8x1d67CwMNauXcvhw4eprKwkJiaGwYMH\nM3HiRK688kqve3euta3P1bBhwwD48oyyMyIiIiIiIr6m5LWI9FhlZSUDBw709TREeqXTZSNqzijx\n4XK52LFjB3PmzCEgIAAGDOi0hvWx2lqC/Lwf+cZllwEQEBBAYGCgR9/YsWPx8/OjoqLCq6+yspKI\niAivdulc//79gY6v9WlfXXV/6NAhvvjiC+688078zrh3/fr163D7ruphd+X0SvBBgwZ1e1vpOT1D\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ISBcUoyL2pfgUsTfFqIh9KT5F+gbDsixfz+GsDMOIB/Lz8/OJj4/39XRERERERERERERE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\nXv2WBQsWeO2zZ8+e7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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -627,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -648,37 +602,9 @@ "\n", "First we divide the material up into sentences and fetch their texts from the database.\n", "\n", - "For this we use the function `T.words(nodes, fmt='ha')`, an\n", - "[API function](http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html#texts)\n", - "of the etcbc module of LAF-Fabric that provides several representation of the text corresponding to the given nodes.\n", - "The next cell gives an overview of the available formats." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hp = hebrew primary\n", - "ha = hebrew accent\n", - "hv = hebrew vowel\n", - "hc = hebrew cons\n", - "ea = trans accent\n", - "ev = trans vowel\n", - "ec = trans cons\n", - "pf = phono full\n", - "ps = phono simple\n" - ] - } - ], - "source": [ - "for (acronym, (explanation, method)) in T.formats().items(): print('{} = {}'.format(acronym, explanation))" + "For this we use the function `T.text(nodes, fmt='text-orig-full')`, an\n", + "[API function](https://github.com/ETCBC/text-fabric/wiki/Api#text-representation)\n", + "of Text-Fabric." ] }, { @@ -696,26 +622,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "14m 10s Identifying sentences\n", - "14m 10s Found 743 sentences in 296 verses\n", - "14m 10s Grouping sentences by verse\n", - "14m 10s Getting sentence texts\n", - "14m 10s Done: 682 sentences in 253 verses\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "12m 45s Identifying sentences\n", + "12m 45s Found 743 sentences in 296 verses\n", + "12m 45s Grouping sentences by verse\n", + "12m 45s Getting sentence texts\n", + "12m 45s Done: 682 sentences in 253 verses\n", "('2_Kings', 19, 1, 0) וַיְהִ֗י כִּשְׁמֹ֨עַ֙ הַמֶּ֣לֶךְ חִזְקִיָּ֔הוּ \n", "('2_Kings', 19, 1, 1) וַיִּקְרַ֖ע אֶת־בְּגָדָ֑יו \n", "('2_Kings', 19, 1, 2) וַיִּתְכַּ֣ס בַּשָּׂ֔ק \n", @@ -725,26 +645,24 @@ } ], "source": [ - "msg('Identifying sentences')\n", + "info('Identifying sentences')\n", "crossrefs_lcs = set()\n", "all_sentences = set()\n", "for v in all_verse_nodes:\n", - " for w in L.d('word', v):\n", - " all_sentences.add(L.u('sentence', w))\n", - "msg('Found {} sentences in {} verses'.format(len(all_sentences), len(all_verse_nodes)))\n", + " for w in L.d(v, 'word'):\n", + " all_sentences.add(L.u(w, 'sentence')[0])\n", + "info('Found {} sentences in {} verses'.format(len(all_sentences), len(all_verse_nodes)))\n", "\n", - "msg('Grouping sentences by verse')\n", + "info('Grouping sentences by verse')\n", "focus_sentences = collections.defaultdict(set)\n", "for s in all_sentences:\n", - " fw = L.d('word', s)[0]\n", - " bk = T.book_name(L.u('book', fw), lang=LANG)\n", + " fw = L.d(s, 'word')[0]\n", + " (bk, ch, vs) = T.sectionFromNode(fw, lang=LANG)\n", " if bk not in focus_books: continue\n", - " ch = int(F.chapter.v(L.u('chapter', fw)))\n", " if bk in REFBOOKS and ch not in REFCHAPTERS: continue\n", - " vs = int(F.verse.v(L.u('verse', fw)))\n", " focus_sentences[(bk, ch, vs)].add(s)\n", "\n", - "msg('Getting sentence texts')\n", + "info('Getting sentence texts')\n", "chunks = []\n", "chunk_data = []\n", "# in the next line we order chunks such that the reference sentences come first, i.e.\n", @@ -758,9 +676,9 @@ " key=lambda x: (passage_key(x[0]), x[1])\n", "): \n", " for (sn, s) in enumerate(sorted(sents)):\n", - " chunk_data.append(''.join(T.words(L.d('word', s), fmt='ha').replace('\\n', ' ')))\n", + " chunk_data.append(''.join(T.text(L.d(s, 'word'), fmt='text-orig-full')))\n", " chunks.append((bk, ch, vs, sn))\n", - "msg('Done: {} sentences in {} verses'.format(len(chunks), len(focus_sentences)))\n", + "info('Done: {} sentences in {} verses'.format(len(chunks), len(focus_sentences)))\n", "for i in range(5):\n", " print('{} {}'.format(chunks[i], chunk_data[i]))" ] @@ -781,22 +699,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "14m 18s Comparing sentences and filtering the similar ones\n", - "14m 19s Done: 232221 distances\n" + "13m 03s Comparing sentences and filtering the similar ones\n", + "13m 05s Done: 232221 distances\n" ] } ], "source": [ - "msg('Comparing sentences and filtering the similar ones')\n", + "info('Comparing sentences and filtering the similar ones')\n", "chunk_dist = {}\n", "total_chunks = len(chunks)\n", "for i in range(total_chunks):\n", @@ -804,7 +722,7 @@ " for j in range(i + 1, total_chunks):\n", " c_j = chunk_data[j]\n", " chunk_dist[(i, j)] = round(100 * ratio(c_i, c_j))\n", - "msg('Done: {} distances'.format(len(chunk_dist)))" + "info('Done: {} distances'.format(len(chunk_dist)))" ] }, { @@ -821,23 +739,23 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "14m 23s Analyzing similarities\n" + "13m 25s Analyzing similarities\n" ] }, { "data": { - "image/png": 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K2GYoTZs9tvxJg3r+n6gwc3NV3ssgtMnty8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\nApq/73yR9EV2t4sxfrh4DftrCOGEGOPNOXLaW4Xi53o0r5UgfSC8YabcP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ZIkSZIkSZIk\nSZIk9RY2akiSJEmSJEmSJEmSJEmSJEmSJHUTGzUkSZIkSZIkSZIkSZIkSZIkSZK6iY0akiRJkiRJ\nkiRJkiRJkiRJkiRJ3cRGDUmSJEmSJEmSJEmSJEmSJEmSpG5io4YkSZIkSZIkSZIkSZIkSZIkSVI3\nsVFDkiRJkiRJkiRJkiRJkiRJkiSpm9ioIUmSJEmSJEmSJEmSJEmSJEmS1E3+P9y/1AoRm84iAAAA\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -845,7 +763,7 @@ } ], "source": [ - "msg('Analyzing similarities')\n", + "info('Analyzing similarities')\n", "sim_levels = collections.Counter()\n", "for ((i,j), sim) in chunk_dist.items(): sim_levels[sim] += 1\n", "cumsum = 0\n", @@ -881,28 +799,28 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "14m 31s Writing similarities to disk\n", - "14m 32s Done\n" + "13m 52s Writing similarities to disk\n", + "13m 53s Done\n" ] } ], "source": [ - "msg('Writing similarities to disk')\n", + "info('Writing similarities to disk')\n", "field_template = ('{}\\t' * 8) + '{}\\n'\n", "with open(SIMILAR_FILE, 'w') as f:\n", " f.write(field_template.format('book_1', 'chap_1', 'verse_1', 'sen_1', 'book_2', 'chap2', 'verse_2', 'sen_2', 'sim'))\n", " for ((i,j), sim) in sorted(chunk_dist.items()):\n", " f.write(field_template.format(*chunks[i], *chunks[j], sim))\n", - "msg('Done')" + "info('Done')" ] }, { @@ -924,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true @@ -1055,19 +973,17 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def lex_diff(c1, c2):\n", - " b1 = T.book_node(c1[0], lang=LANG)\n", - " b2 = T.book_node(c2[0], lang=LANG)\n", - " v1 = T.node_of(b1, c1[1], c1[2])\n", - " v2 = T.node_of(b2, c2[1], c2[2])\n", - " lex1 = {F.lex_utf8.v(w).rstrip('/[=') for w in L.d('word', v1)}\n", - " lex2 = {F.lex_utf8.v(w).rstrip('/[=') for w in L.d('word', v2)}\n", + " v1 = T.nodeFromSection(c1, lang=LANG)\n", + " v2 = T.nodeFromSection(c2, lang=LANG)\n", + " lex1 = {F.lex_utf8.v(w) for w in L.d(v1, 'word')}\n", + " lex2 = {F.lex_utf8.v(w) for w in L.d(v2, 'word')}\n", " return (lex1-lex2, lex2-lex1)\n", "\n", "compare_lexemes = {}\n", @@ -1095,18 +1011,11 @@ " return (arep, brep)\n", " \n", "def get_vtext(v, hp):\n", - " if hp == 'h':\n", - " return ''.join('{}'.format(T.words(L.d('word', v), fmt='ha')))\n", - " if hp == 'p':\n", - " return ''.join('{}'.format(T.words(L.d('word', v), fmt='pf')))\n", - " return ''\n", + " return ''.join('{}'.format(T.text(L.d(v, 'word'), fmt='text-orig-full' if hp == 'h' else 'text-phono-full')))\n", "\n", "def print_chunk(v1, v2, hp):\n", - " b1 = T.book_node(v1[0], lang=LANG)\n", - " b2 = T.book_node(v2[0], lang=LANG)\n", - "\n", - " vn1 = T.node_of(b1, v1[1], v1[2])\n", - " vn2 = T.node_of(b2, v2[1], v2[2])\n", + " vn1 = T.nodeFromSection(v1, lang=LANG)\n", + " vn2 = T.nodeFromSection(v2, lang=LANG)\n", " text1 = get_vtext(vn1, hp)\n", " text2 = get_vtext(vn2, hp)\n", " (lexdiff1, lexdiff2) = lex_diff(v1, v2)\n", @@ -1180,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1215,29 +1124,23 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17m 22s reading 1QIsaa\n", - "17m 22s 16862 words in 66 chapters in 1290 verses\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + " 6m 31s reading 1QIsaa\n", + " 6m 31s 16862 words in 66 chapters in 1290 verses\n", "חזונ ישׁעיהו בנ אמוצ אשׁר חזה על יהודה וירושׁלמ ביומי עוזיה יותמ אחז יחזקיה מלכי יהודה\n" ] } ], "source": [ - "msg('reading 1QIsaa')\n", + "info('reading 1QIsaa')\n", "qf = open(QISA_FILE)\n", "qisa = collections.defaultdict(lambda: collections.defaultdict(lambda: []))\n", "nwords = 0\n", @@ -1247,7 +1150,7 @@ " (chapter, verse) = passage.split(',')\n", " qisa[int(chapter)][int(verse)].append(Transcription.to_hebrew_x(word))\n", "qf.close()\n", - "msg('{} words in {} chapters in {} verses'.format(nwords, len(qisa), sum(len(qisa[x]) for x in qisa)))\n", + "info('{} words in {} chapters in {} verses'.format(nwords, len(qisa), sum(len(qisa[x]) for x in qisa)))\n", "print(' '.join(qisa[1][1]))" ] }, @@ -1311,12 +1214,12 @@ ")\n", "\n", "def lines_chapter_mt(ch):\n", - " vn = T.node_of(T.book_node('Isaiah', lang=LANG), ch, 1)\n", - " cn = L.u('chapter', vn)\n", + " vn = T.nodeFromSection(('Isaiah', ch, 1), lang=LANG)\n", + " cn = L.u(vn, 'chapter')[0]\n", " lines = []\n", - " for v in L.d('verse', cn):\n", + " for v in L.d(cn, 'verse'):\n", " vl = F.verse.v(v)\n", - " text = T.words(L.d('word', v), fmt='hc').\\\n", + " text = T.text(L.d(v, 'word'), fmt='text-orig-plain').\\\n", " replace('\\u05BE',' ').\\\n", " replace('\\u05C3', '') # maqef and sof pasuq\n", " #lines.append('{} {}'.format(vl, text))\n", @@ -1367,25 +1270,38 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 4m 05s Writing chapter diffs\n", + " 4m 05s Writing whole Isaiah\n", + " 4m 08s Done\n" + ] + } + ], "source": [ + "info('Writing chapter diffs')\n", "for ch in range(37,40):\n", " ht = open('Isaiah-mt-1QIsaa_{}.html'.format(ch), 'w')\n", " ht.write(mt1q_chapter_diff(ch))\n", " ht.close()\n", "\n", "# Now the whole of Isaiah\n", + "info('Writing whole Isaiah')\n", "ht = open('Isaiah-mt-1QIsaa.html', 'w')\n", "ht.write('''{}'''.format(diffhead))\n", "for ch in range (1, 67):\n", " ht.write(mt1q_chapter_diff(ch, head=False))\n", "ht.write('''''')\n", - "ht.close()" + "ht.close()\n", + "info('Done')" ] }, { @@ -1414,7 +1330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii_TF-checkpoint.ipynb b/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii_TF-checkpoint.ipynb index 603a4413..7659522a 100644 --- a/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii_TF-checkpoint.ipynb +++ b/static/docs/tools/parallel/.ipynb_checkpoints/kings_ii_TF-checkpoint.ipynb @@ -4,15 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "\n", + "\n", "# Kings and parallels\n", "\n", "# 0. Introduction\n", @@ -66,17 +59,21 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ - "import sys,os, re, pickle\n", + "import sys, os, re, pickle\n", "import collections, difflib\n", "from Levenshtein import ratio\n", "\n", + "# (sudo -H) pip(3) install python-Levenshtein\n", + "# brew install freetype # on mac os x\n", + "# (sudo -H) pip(3) install matplotlib\n", + "\n", "from IPython.display import HTML, display_pretty, display_html\n", "from difflib import SequenceMatcher\n", "import networkx as nx\n", @@ -100,8 +97,8 @@ "A previous version of this notebook was based on version 4b,\n", "as archived at DANS, downloadable via DOI\n", "[10.17026/dans-z6y-skyh](http://dx.doi.org/10.17026/dans-z6y-skyh).\n", - "It is also possible to get this data through Github:\n", - "[etcbc/laf-fabric-data](https://github.com/ETCBC/laf-fabric-data).\n", + "It is also possible to get this data through Github in TF format:\n", + "[etcbc/text-fabric-data-legacy](https://github.com/ETCBC/text-fabric-data-legacy).\n", "\n", "The transcription of 1QIsaa is in a file produced by the ETCBC. This file is included \n", "[here](https://shebanq.ancient-data.org/shebanq/static/docs/tools/parallel/1QIsaa_an.txt)\n", @@ -110,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "collapsed": true, "scrolled": true @@ -130,14 +127,14 @@ "\n", "We only use a few data features from the ETCBC database. You see them in the code below.\n", "Their documentation can be found through the SHEBANQ help function or via this direct link:\n", - "[Feature-doc](https://shebanq.ancient-data.org/shebanq/static/docs/featuredoc/features/comments/0_overview.html).\n", + "[Feature-doc](https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html).\n", "Here is the direct link to\n", - "[otype](https://shebanq.ancient-data.org/shebanq/static/docs/featuredoc/features/comments/otype.html)." + "[otype](https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/otype)." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -146,15 +143,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "This is Text-Fabric 1.2.7\n", + "This is Text-Fabric 2.2.1\n", "Api reference : https://github.com/ETCBC/text-fabric/wiki/Api\n", "Tutorial : https://github.com/ETCBC/text-fabric/blob/master/docs/tutorial.ipynb\n", "Data sources : https://github.com/ETCBC/text-fabric-data\n", - "Data docs : https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html\n", + "Data docs : https://etcbc.github.io/text-fabric-data\n", "Shebanq docs : https://shebanq.ancient-data.org/text\n", "Slack team : https://shebanq.slack.com/signup\n", "Questions? Ask shebanq@ancient-data.org for an invite to Slack\n", - "107 features found and 0 ignored\n" + "108 features found and 0 ignored\n" ] } ], @@ -165,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -175,10 +172,10 @@ "output_type": "stream", "text": [ " 0.00s loading features ...\n", - " | 0.00s M otext from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", " | 0.19s B lex_utf8 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.13s B language from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " 4.37s All features loaded/computed - for details use loadLog()\n" + " | 0.12s B language from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.00s Feature overview: 102 nodes; 5 edges; 1 configs; 7 computeds\n", + " 4.85s All features loaded/computed - for details use loadLog()\n" ] } ], @@ -209,53 +206,6 @@ "The following alternative languages are available:" ] }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'am': {'language': 'ኣማርኛ', 'languageEnglish': 'amharic'},\n", - " 'ar': {'language': 'العَرَبِية', 'languageEnglish': 'arabic'},\n", - " 'bn': {'language': 'বাংলা', 'languageEnglish': 'bengali'},\n", - " 'da': {'language': 'Dansk', 'languageEnglish': 'danish'},\n", - " 'de': {'language': 'Deutsch', 'languageEnglish': 'german'},\n", - " 'el': {'language': 'Ελληνικά', 'languageEnglish': 'greek'},\n", - " 'en': {'language': 'English', 'languageEnglish': 'english'},\n", - " 'es': {'language': 'Español', 'languageEnglish': 'spanish'},\n", - " 'fa': {'language': 'فارسی', 'languageEnglish': 'farsi'},\n", - " 'fr': {'language': 'Français', 'languageEnglish': 'french'},\n", - " 'he': {'language': 'עברית', 'languageEnglish': 'hebrew'},\n", - " 'hi': {'language': 'हिन्दी', 'languageEnglish': 'hindi'},\n", - " 'id': {'language': 'Bahasa Indonesia', 'languageEnglish': 'indonesian'},\n", - " 'ja': {'language': '日本語', 'languageEnglish': 'japanese'},\n", - " 'ko': {'language': '한국어', 'languageEnglish': 'korean'},\n", - " 'la': {'language': 'Latina', 'languageEnglish': 'latin'},\n", - " 'nl': {'language': 'Nederlands', 'languageEnglish': 'dutch'},\n", - " 'pa': {'language': 'ਪੰਜਾਬੀ', 'languageEnglish': 'punjabi'},\n", - " 'pt': {'language': 'Português', 'languageEnglish': 'portuguese'},\n", - " 'ru': {'language': 'Русский', 'languageEnglish': 'russian'},\n", - " 'sw': {'language': 'Kiswahili', 'languageEnglish': 'swahili'},\n", - " 'syc': {'language': 'ܠܫܢܐ ܣܘܪܝܝܐ', 'languageEnglish': 'syriac'},\n", - " 'tr': {'language': 'Türkçe', 'languageEnglish': 'turkish'},\n", - " 'ur': {'language': 'اُردُو', 'languageEnglish': 'urdu'},\n", - " 'yo': {'language': 'èdè Yorùbá', 'languageEnglish': 'yoruba'},\n", - " 'zh': {'language': '中文', 'languageEnglish': 'chinese'}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "T.languages" - ] - }, { "cell_type": "code", "execution_count": 8, @@ -263,19 +213,7 @@ "collapsed": false, "scrolled": true }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'API' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mCROSSREF_APP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'parallel'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# directory of computed intermediary results of parallel.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mPRECOMP_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{}/{}{}/{}/{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAPI\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'output_dir'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCROSSREF_APP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stored'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;31m# precomputed list of verse chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mCHUNK_GREP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{}/chunks/chunk_{}_{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPRECOMP_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'O'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'verse'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'API' is not defined" - ] - } - ], + "outputs": [], "source": [ "# the language of the book names\n", "LANG = 'en'\n", @@ -284,10 +222,11 @@ "REFBOOKS = {'2_Kings'}\n", "REFCHAPTERS = set(range(19,26))\n", "\n", + "TF_OUTPUT = os.path.expanduser('~/tf/text-fabric-output')\n", "# the results of the parallel notebook.\n", - "CROSSREF_APP = 'parallel'\n", + "CROSSREF_APP = 'parallels'\n", "# directory of computed intermediary results of parallel.\n", - "PRECOMP_DIR = '{}/{}{}/{}/{}'.format(API['output_dir'], source, version, CROSSREF_APP, 'stored')\n", + "PRECOMP_DIR = '{}/{}{}/{}/{}'.format(TF_OUTPUT, source, version, CROSSREF_APP, 'stored')\n", "# precomputed list of verse chunks\n", "CHUNK_GREP = '{}/chunks/chunk_{}_{}'.format(PRECOMP_DIR, 'O', 'verse')\n", "# precomputed matrix of similarities based on verse chunks and the SET method\n", @@ -316,15 +255,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "book_node = dict()\n", - "for b in T.book_nodes:\n", - " book_name = T.book_name(b, lang=LANG)\n", + "for b in F.otype.s('book'):\n", + " book_name = T.bookName(b, lang=LANG)\n", " book_node[book_name] = b\n", " if book_name == '2_Kings': \n", " book_node[book_name+'r'] = b\n", @@ -357,22 +296,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4m 22s 276 external crossrefs saved; 22 internal crossrefs skipped; from total 24832 crossrefs\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "14m 57s 276 external crossrefs saved; 22 internal crossrefs skipped; from total 24792 crossrefs\n", "2_Kingsr\t19\t1\tIsaiah\t37\t1\t100\n", "2_Kingsr\t19\t2\tIsaiah\t37\t2\t100\n", "2_Kingsr\t19\t3\tIsaiah\t37\t3\t100\n", @@ -391,8 +324,9 @@ "with open(MATRIX_GREP, 'rb') as f: grep_dist = pickle.load(f)\n", "\n", "def get_verse_ref(chunk):\n", - " vn = L.u('verse', chunks[chunk][0])\n", - " return (vn, (T.book_name(L.u('book', vn), lang=LANG), int(F.chapter.v(vn)), int(F.verse.v(vn))))\n", + " sec = T.sectionFromNode(chunks[chunk][0], lang=LANG)\n", + " vn = T.nodeFromSection(sec, lang=LANG)\n", + " return (vn, sec)\n", "\n", "all_verse_nodes = set()\n", "n_internal = 0\n", @@ -424,7 +358,7 @@ " crossrefs.add(((bkx, chx, vsx), (bky, chy, vsy), r))\n", " all_verse_nodes |= {v1, v2}\n", "\n", - "msg('{} external crossrefs saved; {} internal crossrefs skipped; from total {} crossrefs'.format(\n", + "info('{} external crossrefs saved; {} internal crossrefs skipped; from total {} crossrefs'.format(\n", " len(crossrefs), n_internal, len(grep_dist),\n", "))\n", "\n", @@ -451,29 +385,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4m 24s Exporting graph info, assembling sets\n", - " 4m 24s 276 edges, 296 verses, 46 chapters, 10 books\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "15m 53s Exporting graph info, assembling sets\n", + "15m 53s 276 edges, 296 verses, 46 chapters, 10 books\n", "1_Kings 2_Chronicles 2_Kings 2_Kingsr Deuteronomy Exodus Ezekiel Haggai Isaiah Jeremiah\n" ] } ], "source": [ - "msg('Exporting graph info, assembling sets')\n", + "info('Exporting graph info, assembling sets')\n", "ncolfile = open(NCOL_FILE, 'w')\n", "for (x, y, r) in sorted(crossrefs, key=lambda z: (\n", " book_node[z[0][0]], z[0][1], z[0][2], \n", @@ -485,7 +413,7 @@ "all_verses = {(x[0][0]+'r', x[0][1], x[0][2]) for x in crossrefs} | {x[1] for x in crossrefs}\n", "all_chapters = {(x[0], x[1]) for x in all_verses}\n", "all_books = {x[0] for x in all_chapters}\n", - "msg('{} edges, {} verses, {} chapters, {} books'.format(\n", + "info('{} edges, {} verses, {} chapters, {} books'.format(\n", " len(crossrefs), len(all_verses), len(all_chapters), len(all_books),\n", "))\n", "print(' '.join(sorted(all_books)))" @@ -516,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "collapsed": false, "scrolled": false @@ -524,9 +452,9 @@ "outputs": [ { "data": { - "image/png": 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X3/TMn8HVq1czatQo6tSpQ926dXniiSdITU0FLrUoeemll6hVqxZ16tThnXfe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9yWgCLQbXFtWK1WysrKAEhJSQkaLgDe4qftnXPXm0ajkRolQgghhBDXgKxgEEJ0Gc8nw+2tNeGp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\naGho4Ec/+hH79+/HbreTk5PDtm3bWLp06XCdtgBqamoCkwcyM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UWxc+dONmzYwJEjR4jL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87s/boSJ7iUWmknNTZCo5N0UmkyGPQlwGM2am0TeUrpnmK1ZcgL4FTXZ2dsrkCf1Kt97t0L8D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Ol0JJNJdu/ezcsvv0xHR8e8iB1ar+ahueGGGxgfH+df/uVfsNvtmM1mNm/ezIMPPkhjYyMn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\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L5cLpdMritVQqUSwWGRoaAlY6ARKJhCyyVVUlkUhI0QWej1AUO+wGg4FAIIDRaKRYLNLe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K/Xi2q1irm5uRWTKFQVST9HtY561JMMRHLQetI0DbOzswAWwh2j0SgrMDo6OviaybLM+Rm5XA6S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CtojlRIbGyp+T/AaxHx0OmM1m1cRgMBiora1l1qxZdHZ20tnZic/no7a2ltbWVvbs2UMsFmNo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DduHCtWrKCnp4d169ZRVFSE3W5PITDMZjMlJSXU1NTQ1dWFx+PB4XBgMpkoKCigtbWVmpoa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577zz2LRpE93d3VxxxRVMmzYNrVbLvHnzuOSSS3jppZf6DGJi0L/44ouZNWsWS5cu7XOs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\nGgcq2r2pcaihNRj+TVi48DgWLbqfRYuO5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RKJ0NPTg8FgoKamhrq6fKqpvO90Ok13dzep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dY7oYoF15WZaZ0OD1evHxxx8DABRFQSKRGDKeMhAIjNp4yk8qRFTqhqDv9/U8BxIhnOMQqZtD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\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6aG9vF2X0Xq+X4uLicVsFRmOVGIhpnTBFJKvVit/vx+VyTZl1orW1lXfeeYfTp0+Lx6xW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\nXZhMJiZPnozD4SCVShEMBo3MiHQ6TSgUMjIR9DV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NZhRFIZlMZvR30DQNi8VivKYQQggh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d3icgIvKvKCsri5UrV1JUVITNZsPX19etPy8vj6EDB9K7e3dX24SkJHwNg3379//vwE8+4WRZ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\n", 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13i8o9ZB1bt0JoJeqni+A9Qal3QHoBpPCQ2B6Pz/qc4UmoDsO2aeGcT5v/eWnXLizf87Z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q1dvOgXag7U46EuwuXboU27Ztc2mXMmfOHIwaNQqrVq3Cli1bXM4xPz/f7RjqQpq++qSPGTMG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\nau1oq+pftmwZCgoKAm5zu90Bq7m7C79dLhci8WeIrlqZBKv4rq2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UFxf7BddxcXH99pxE\nREREREQ0OMTBPgEiGlx79+7Vgifv3zqdDocOHQIAtLW14aWXXsLixYsxZswYxMXFYWbuTGz4wwYc\nUA7g1l/cikM4hLM4CzfcQZ9rz549uOaaaxATE4ORI0di5cqVqKysHKhLpQiiKApKS0u15bFjx4Y8\nwZ7b7dbCWJPJ1C/nN1g84a9er4fBYIDJZILZbIbVaoXNZkNsbCzi4+ORkJCAkSNHIjExEcnJyUhJ\nSUFKSgpGjRqFpKQkjBw5EiNGjEB8fDzi4uKwceNGxMTEwGKxwGw2w2g0Qq/XQ6fTBQ2aPaG3JElw\nuVxwOp1oa2uDw+FAa2srWlpa0NTUhMbGRu1GRG1tLWpqalBTU4Pa2lrU1dXh0qVLaGhoQFNTE1pa\nWtDa2gq73Y62tjY4nU64XC5IkgRZlrvtM97Q0ICioiItuNbr9UMuuD58+DDuueceTJs2DTabDePH\nj8fq1atRUlLi87jbbrst4HvzlClT0NbWhvb2dkiS1OX3rKioCEuXLkVqaipiY2MxY8YMbNmypUc3\nM6j//OIXvxjsUyCiLnCMEkUujk+i6BBZ5WpENGjuv/9+5OXl+azLyMgAAJSXl+Pee+/FwoUL8cBD\nD8AeZ8dHH3yEJ+96EvsP7cekmZNw6ZtfJSjBRExEJnyraP/617/ixhtvRF5eHjZt2oTm5ma88MIL\nuPbaa3HkyBEkJiYO2LXS4Pvqq6/gcDgAdFTOTpgwIaT9FEXRqq5NJpPW2oN8K6U7y8jI6LKlRk+q\nvb3/DHS8cHt7e19HoIrulpYWHD16VAtejUYjZsyYAavVCkVR+mRiy4GwadMmHDhwACtXrkROTg6q\nq6uxZcsW5Obm4tNPP8XUqVO1x5rNZrz66qtwu93a9zk+Pl6rqHe5XACg3YzwVlxcjKuvvhpZWVlY\nv349rFYr/v73v+O+++5DeXk5nn/++YG7aOrSuHHjBvsUiKgLHKNEkYvjkyg6CN1VOUUCQRByARQV\nFRUhNzd3sE+HaFjZu3cvFixYgJ07d2L58uUBH1NfX4+LFy9i8pTJKEYx6tDR6uH5O57Hh9s+xGsl\nryE1PdVnnzSk4XJcri1ffvnlkCQJJ06c0FopfPHFF8jNzcUDDzyAzZs399MVUqRxOp04dOiQFm5m\nZWVhzJgxIe3b3t4Ot9sNURRhtVqHRFg53IU7maX3ulB/BmlubsaxY8d8Kq6nT5/uF8h31eaku9Yn\nA/VaOnjwIPLy8nzC5tLSUkybNg2rVq3C9u3bAXRUXr/77ruoqakJ6fvUOcBet24d/vSnP6G6uhrx\n8fHa+vnz5+Po0aNoaGjow6siIiIiIiL6VnFxMWbNmgUAs1RVLe7NsVh5TUSa1tZWWCwWvz69iYmJ\nSExMRBnKtOAaAObeNBcfbvsQ50+e9wmvq8qrUIUqjEwfiVSkoqGhASdPnsTDDz/sc+ycnBxMmTIF\nb7/9NsPrKFJWVqaFkLGxsUhNTe1mjw6yLMPt7mhL01+TNFL4BEGATqcLu783gJCC7sbGRpw6dQpA\nRw9xURQxbdq0gJXknorvnlR9hxt4e38djjlz5vity8jIwLRp03Dy5Em/bYqiwG63d9lW5+zZswCA\nKVOmaOfT0tICs9nsE1wDwOjRo3HmzJmwzpmIiIiIiGiwMLwmIgAdVX4tLS3Q6XS49tprsXnzZs9d\nMgCAAgXncM5nn0tVlwAAcUm+/WbXX7e+ozdr+RSkIlVr82CxWPye12q14sSJE7h48SJGjRrV15dF\nEaaxsREXL17UljMzM0MKoVVVRXt7O4DInKSResYTtAYLvpuamlBSUgKTyQSTyQSdToe8vDyf1hnd\ntTnxrvL2LAfiWR9u8O3driXUySwDBd81NTWYNm2atqyqKhwOB1JSUuBwODBixAisXLkSTz31FGJi\nYnzOYcmSJRBFEadPn4bRaATQUWH9zjvvYN26dXjwwQdhtVqxe/du7Nq1izcLiYiIiIhoyOD//omi\nnNFoxIoVK7BkyRIkJSXhxIkTeO655zBv3jwcOHAAM2bMAADUohZOOLX9JLeEXS/swuj00TBZTZAl\nGTp9RwAlCAIgAI1oRAtakJKSgoSEBPz73//2ee76+nqcOHECAHDhwgWG18Ocoig+k9KlpqaGPNHe\ncJ6kcSCcOnUK2dnZg30aYWlubsbhw4chSRIAaMF1QkICAGg3PcKt+A4UeofT67ur44XLE3y/++67\nuHDhAh599FE0NTVBEAQkJSXh3nvvxYwZM6CqKj766CO88sorOHbsGN5//32f6/YcR5IkGAwGCIKA\ntWvX4vjx43j55Zfx2muvAei48bN161asW7cu7HOl/jMUxydRNOEYJYpcHJ9E0YE9r4nIT1lZGXJy\ncvCd73wHu3fv7liHMpTg2+DxxXUv4oPXP8CG3Rvw3ovv4T9f/k8IgqBVxep0Ouj1elyBK3CZ4TI8\n+eST2Lx5Mx5++GHccccdaGpqwiOPPIL9+/fD7Xbj448/xty5cwfrkmkAfPXVVygtLQXQEaLNnj1b\nqxLtiqIocDgcUFUVRqOR4XUPLFu2DAUFBYN9GiHzBNeeNjE6nQ6zZs3CiBEjBvW8OldwhxuCB1JS\nUoKlS5ciOzsbu3bt0kL5QI/fsmULNm3ahNdffx2rVq0KeDyLxaId48UXX8Q//vEPrFq1CiaTCTt2\n7EBhYSF27tyJZcuW9dF3hXprqI1PomjDMUoUuTg+iSJXX/a8ZnhNRAHdeuuteO+99+BwOCAIgk94\nvXPzTrzxyBv4yTM/wepHV+PM0TOwjrQGPE56azoSXYlwu93YtGkTdu3aBVmWIQgCFixYgIkTJ+KN\nN97AkSNHkJOTM5CXSAPI5XLh008/1VoyZGZmYuzYsSHt65mkURAExMTEsNd1D5w7d27IzMYeqcF1\nbwUKuKurq7FgwQIoioIPP/wQycnJ2jZJkrSw3POzWnt7OzIzM/GjH/0IL730UsDn8YTXv/3tb7Fl\nyxaUlJTAav32/fm6665DSUkJKisrw+7XTf1jKI1PomjEMUoUuTg+iSIXJ2wkon6XlpYGl8ulTRRm\nhhkAsGfbHry5/k0svWspVj+6GgBwWcZlcDqdkCQJkiT59Iw1Kh2VtQaDAY899hjuuusuVFZWIjEx\nEePGjcP69R39sdvb23H69GmYTCatutZoNGq/GVgObeXl5drrIiYmBmPGjAlpP+9JGs1mM18HPTRU\nfqhvaWnxC65zc3OHfHANfDuxpUdzczNuvPFGtLS0YP/+/cjIyPB5vNPp1MaMJ8C22WwYOXIkGhoa\nun2+//3f/8V1113nE1wDHRVKDz30ECoqKpCent4HV0a9NVTGJ1G04hglilwcn0TRgeE1EQVUVlYG\ns9kMm80GAEhBCl4veB0vrn0R16y4BndtvUt7rDXGCmuMV0CiApIkwSgZkW3JhsvlgtPphMvlQlJS\nEkaOHAmgox1EUVERpk+fDpPJhPb2dm1Svs48IXbnUNsziRtFrubmZlRXV2vL4UzS6Jnsk5M0Dn8t\nLS347LPPfILrmTNnau8Xw4nT6cQNN9yA0tJSfPTRR5g8ebLfY/R6vRZee3pat7a2or6+HsnJyQGP\nq9PptLFVU1MTcPJJz/fX00uciIiIiIgokjEJIIpydXV1SEpK8ll39OhRFBYW4vrrr9fWHdh3ABtv\n2Yic+Tn4xVu/6PKYVWerAADz0+cjyZLkt93tdsPlcmHz5s2or6/HM888g5iYGK16OxCXywWXy4XW\n1la/bTqdzifY9g64PZOX0eBQVRVnzpzRlj2Td4bCu4qffa6Ht9bWVp/gWhRFzJw5E4mJiYN8Zn1P\nURSsWrUKBw8eREFBAWbPnu33GKfTCbfbDZ1O59P7euPGjQCARYsW+Tz+7NmzAIApU6Zo67KysrBn\nzx40NDRoleuKouDPf/4zYmNjMWnSpD6/NiIiIiIior7G8Jooyq1evRoWiwVz587FqFGjcPz4cbz6\n6quw2WxaUHLu3DksW7YMOlGH+cvnY987+3yOcebwGfzn8/+pLa+/bj30oh7nys9p6/Lz8/Huu+9i\n3rx5sNls2LNnD3bu3Ik777wTa9eu1R6nKIoWVHuqtb2/DtSnX5ZltLW1oa2tzW+bIAh+ldreX7Pn\na/+qqqrSbjjodLqQ2xR4V10bjUb+PfXSpk2b8Mgjjwz2aQQUTcE1ADz44IMoLCzEsmXLUFdXh/z8\nfJ/ta9asQXV1NWbOnIlbbrlFC5n37NmDDz74AIsXL/a5sQgAS5YsgSiKWogNAOvXr8ePfvQjzJ49\nG+vWrYPFYsH//b//F0eOHMEzzzzDT6xEkEgen0TEMUoUyTg+iaIDw2uiKHfTTTchPz8fzz//PJqb\nm5GcnIwVK1bg8ccf14LGs2fPoqWlBQDw4j0v+h3j8msv91nWC3qYBTN0+DYcycrKQkNDA55++mm0\ntbVh8uTJePnll3HnnXf67CuKIsxmM8xms9/zqKqqVW13DrW9+8N23sfpdGpBaGd6vd4v0DYYDNqf\n1HNut9snTJswYULIFdROpxOqqmo3H6h3HA7HYJ9CQJ7g2uVyAfg2uO78aZDh5OjRoxAEAYWFhSgs\nLPTbvmbNGiQkJOCGG27ARx99hD/96U+QZRnp6enYsGED7rvvPr99BEHwu8Fz6623Ijk5GRs3bsRz\nzz2H5uZmTJ48GX/4wx98bhjS4IvU8UlEHThGiSIXxydRdBACVTFGGkEQcgEUFRUVITc3d7BPhyjq\nqVBxERdxDudwCZegQoUIEclIxjiMQyIGp2JSluWgVdtutztg1XZXRFEMWrXNauDunTlzBl9//TUA\nwGq1Ii8vL6TvmSzL2g+iZrOZNxGGqdbWVhw+fFi7sSQIAmbOnBm0n3M0U1VVmxDX+33M0wue70VE\nRERERBRJiouLMWvWLACYpapqcW+OxcprIgqbAAEp3/wCABmyT5X1YNHpdLBYLLBYLH7bPFXbgVqR\nuFyugFXbiqJ0OYmkp0I7UMAd7ZMLtrS0aME10DFJY6gBmyfM1Ol0DK6HKbvdzuA6DIIgwGAwwGAw\naOE1e/kTEREREVE0iO50hYj6RCQE193x7n0diCRJfoG2d9V2IG63O+g2zySSnUNtTzuS4Rw8qaqK\nkpISbTk5OVmbMK47brebkzQOcw6HA5999hmD6x4azu8dREREREREnTG8JqJeq6urG/I9aj0fv7da\nrX7bVFUNOoGky+WCoih++3Q1iSSAgG1IPMtDfSK1mpoaNDc3A+hoveKZcK47nSdpHOrfh0gSKWPU\n4XDg0KFDPsH1FVdcweCaolqkjE8iCoxjlChycXwSRQeG10TUa7fffjsKCgoG+zT6jSAIMJlMQSuB\nu5pEUpKkgPt4HhuITqfzm0TS83WkV21LkoSysjJtefz48QEn3wzE5XJxksZ+EgljNFDF9YwZMzBq\n1KhBPS+iwRYJ45OIguMYJYpcHJ9E0YHhNRH12pNPPjnYpzCoPL1oY2Ji/LYpihK0HYknrO3MM2Fh\noNmzPcFusKrtwZ64raKiQmulYjabkZaWFtJ+nu8T0NEuJJID+qFosMeoJ7j29I/3BNcpKSmDel5E\nkWCwxycRdY1jlChycXwSRQeG10TUa7m5uYN9ChFLFEWYzeaA1ceeSSSDVW0HmkTS01rDU73amV6v\n9+mv7R1w9/fkh3a7HV999ZW2HM4kjZ5Qk5M09o/BHKNtbW04fPiwT3Cdk5PD4JroG/w3lCiycYwS\nRS6OT6LowPCaiGiQdDeJpCzLXU4iGahqW5IkSJIEu93ut00UxYCtSDxf97ba2XuSxsTERCQmJoa0\nHydpHL7a2trw2Wefab3fBUHA9OnTMXr06EE+MyIiIiIiIhoKGF4TEUUonU4Hi8UCi8Xit81Tte1d\nqe0dcAeq2lYUBe3t7VoFbGcGgyFoOxK9vut/Li5evIjGxkYAHQFlRkZGSNfoPUmjwWDgJI3DSHt7\ne8DgOjU1dZDPjIiIiIiIiIYKhtdE1Guvv/467rjjjsE+jajSXdW2JEldVm0H4na7g27T6XRBq7YF\nQUBpaan22HHjxgUM3APxnqSRVdf9Z6DHaHt7Ow4dOsTgmigE/DeUKLJxjBJFLo5PougwuDN7EdGg\n27t3L0RR9Put0+lw6NAhAB0f/X/ppZewePFijBkzBnFxccjNzcVLf3gJNUoN9hfvRx3qIMO/2tdb\nUVERli5ditTUVMTGxmLGjBnYsmULFEUZiEuNKnq9HlarFSNGjEBKSgrS0tIwadIkTJ06FdOnT0d2\ndjYmTpyIsWPHIikpCXFxcTCbzUF7VMuyjLa2NjQ1NeHixYv46quvUF5ejpMnT+If//gHKisrUVtb\nC7vdDrPZjMbGRrS1tQWsAPfgJI0Dp7i4eMCey+l0+lRcA8C0adMYXH/j8OHDuOeeezBt2jTYbDaM\nHz8eq1ev9mm7AwC33XZbwPfmqVOnQpKkLscWACxYsCDg/qIo8kZRhBnI8UlE4eMYJYpcHJ9E0YGV\n10QEALj//vuRl5fns87T+qG8vBz33nsvFi5ciIceegjGOCMKPyjEf931X/h/h/4fHnzjQRzGYRhg\nwFiMxXiMhwW+lbfFxcW4+uqrkZWVhfXr18NqteLvf/877rvvPpSXl+P5558fsGuNdp7wKliA5T2J\npHflttPphCRJPo91Op2ora2FqqqQJAkpKSm4ePGiz2N0Ol3ASSQ9Ny10Ol23bUmod1566aUBeR5P\ncO1wOLR106ZNw5gxYwbk+YeCTZs24cCBA1i5ciVycnJQXV2NLVu2IDc3F59++immTp2qPdZsNuP1\n11+HLMuQJAmqqiI+Pl676SMIAvR6PfR6vd/Nn8ceewxr1671WWe32/Gzn/0Mixcv7v8LpZAN1Pgk\nop7hGCWKXByfRNGBaQERAQCuueYaLF++POC20aNH48svv8SUKVNQi1p8js+RuTYTwh0CPtz2IX7w\n2A+Qmp4KN9yoQAWqUIVZmIU4xGnH+MMf/gBBEPDxxx8jPj4eALB27VrMnz8f27ZtY3gdQQwGAwwG\nA2JiYvy2KYriE2gfO3YMZrMZkiTBbDZrf7feZFmGw+HwCTQ9oZsgCFrLkEBtSYJVglPk8QTX3pOF\nXn755Rg7duwgnlXkeeihh7Bjxw6fGzarVq3CtGnT8Nvf/hbbt2/X1uv1etx8881+N408PL3vZVn2\n+/TCd7/7Xb/H5+fnAwDWrFnTV5dDRERERETUrxheE5GmtbUVFovFb9K8xMREJCYmohGNOIIjUNBR\nMTv3prn4cNuHOH/yPFLTv20JUFFegQu4gJvTb9YqsFtaWgKGm6NHj8aZM2f6+cqor4iiqE0iWVdX\nB1EUkZSUBEEQkJeXB4PBELRq27vNgSe4k2UZsixrkzZ2ptfrA1ZtG41GGAyGAblm6p7T6cThw4f9\nguvLLrtsEM8qMs2ZM8dvXUZGBqZNm4aTJ0/6bXO73bDb7bDZbEGPWVZWBlEUkZ2d3WX7nfz8fNhs\nNixbtqxnJ09ERERERDTAGF4TEYCO/qotLS3Q6XS49tprsXnzZsyaNcvnMWUo04JrALhUdQkAEJcU\n5/O49dethyiKmFU+C1MwBQAwf/58vPPOO1i3bh0efPBBWK1W7N69G7t27cLmzZv7+eqor8my7DNJ\n42WXXaZVagebRNITUjscDrhcLkiS5NOiJBBJkiBJkk8o6iGKol+1tnfAzR7aA8PlcuHw4cNobW3V\n1k2dOpXBdZhqamowbdo0n3UOhwMpKSlwOBwYMWIEVq5ciaeeesrvUxFLliyBKIooKSkJ2oKnrq4O\nH374IX7wgx+EPKEqERERERHRYGN4TRTljEYjVqxYgSVLliApKQknTpzAc889h3nz5uHAgQOYMWMG\nAMABB+pQp+0nuSXsemEXRqePxo6nd+DXhb/WtgmCAAjA1/gaWciCDjqsXbsWx48fx8svv4zXXnsN\nQEdV7datW7Fu3bqBvWjqtfPnz6O9vR1Ax2to/Pjx3e6j0+lgNpu1FgeeoBnoaH/gCbE7V227XK6A\nk9MpioL29nbtPDozGAxB25FEW4/tZcuWoaCgoM+P63K58Nlnn/kE11OmTEFaWlqfP9dw9tZbb+HC\nhQt4+umntXWjR4/GAw88gCuuuAKKomDPnj145ZVX8OWXX+L999/3aanjab8jSVLQ1/bbb78NWZbZ\nMiQC9df4JKK+wTFKFLk4Pomig6Cq6mCfQ7cEQcgFUFRUVITc3NzBPh2iYa+srAw5OTn4zne+g927\ndwMAKlGJk/j2I+0vrnsRH7z+ATbs3oDGxkYkZiVqgaTJZILZbIbJZMJ/GP4DaeaOIOvFF1/EP/7x\nD6xatQomkwk7duxAYWEhdu7cyY+xDyFtbW04dOgQPP9+TJkyBSkpKSHvK0kSRFGE1WoNuTpakiS/\nQNuz7Ha7w74GnU4XtGrbYDAMu6rtDz74AIsWLerTYwYLrseNG9enzzPcnTp1CnPmzMH06dOxb98+\n7bXXudUOADz77LN46qmnsG3bNtx8880Bj2exWAK+fufOnYvy8nJ8/fXX7CUfYfpjfBJR3+EYJYpc\nHJ9Ekau4uNjzaf5ZqqoW9+ZYDK+JKKBbb70V7733HhwOBwRBQOk3vwBg5+adeOORN/CTZ36C1Y+u\nxrnKc7h48WLA46TUpCChLQF/+9vf8Le//Q27du1CcnIybDYbbDYbvv/976O0tBSVlZUMVIaIL7/8\nEnV1HVX48fHxmDlzZkj7SZKEtrY2AIDVavXrrd5TiqJ0WbWtKEr3B/EiCIJPb+3OVdt9dd5Dmdvt\nxmeffYaWlhZtXXZ2dkgV+PStixcv4qqrroKiKPjkk08wevRobVt7e7vPa9flcqGiogJ5eXn44Q9/\niP/5n/8JeEyz2ez3Xnr27FlMmjQJ9957L1544YX+uRgiIiIiIqJv9GV4HV2fmyaikKWlpcHlcmkT\nhenQEdjt2bYHb65/E0vvWorVj64GgKCT7QGAoAqQZRl/+9vfkJ2djfPnz+P8+fM+z7Nv3z7k5+cj\nKysLMTExsNlsiI2Nhc1mg8lk6t8LpbDU19drwTUAZGZmhrSfqqra60Sv1/dpACyKIsxmM8xmc8Dt\nbrcbTqdT+9M74JYkKei5djWJZKBQ22QyQa/XD7uq7c7cbjcOHz7M4LqXmpubsXjxYjQ3N2P//v0+\nwXVndrsd1dXVEAQB8fHxqKqqgqqqAV9rgdbl5+dDEATceuutfXoNRERERERE/Y3hNREFVFZWBrPZ\nDJvNBgBIRCIOFhzEi2tfxDUrrsFdW+/SHjtmzBgkJCRogWB7e3vHR94lGeb2jkCxqakpYAWsLMtQ\nVRV1dXUwGAx+2/V6vValbbPZtHDb8zWrtQeOoig+kzSOHTtWe310x+12a3//A31DwmAwBHxtAR3X\n5F2p3blqO9CnkzyTSDocDr9tgiD4BNqdA+6h/nr1BNfNzc3ausmTJzO4DpPT6cQNN9yA0tJSfPTR\nR5g8ebLfY3Q6HRRFwaVLl1BfXw+gYwLHxsZGjBkzJmhwHWj9jh07kJ6ejtmzZ/f9xRAREREREfUj\nhtdEUa6urg5JSUk+644ePYrCwkJcf/312rrP932O397yW+TMz8Ev3vqF7+M/PIq5N87VlqvKqwAL\nkJOWg4kZE9Ha2oqJEyfixIkTWp9ju92ufVTeYrEE7ZksSRIaGxvR2NgYcHvnMNs76PZMBkh94/z5\n81rbD4PBgIkTJ4a0nycgBjqC60gKcEVRhMVigcVi8dumqircbrdfqO35OtAkkqqqdjuJZFdV2/1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6NSiWq1WjFt2rRhn2OxWJQheJG3gYEwXK7Wlv/f7XYrt/WCwcFEUYTb7Ybb7UZra6vu6w8e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Bu117bf\n7++xnQ7Q9UmDjo4ORZsZiUajgdlsVrQjycnJgdfrhdVqla/ZniaR4zgOXq+3x2uxqqoKDMOguLhY\nDq+sVmuPx03ix2gen+dDZ2cnFi5ciM7OTuzevfucW5UVFxfj448/xpw5c3DVVVfhyy+/xLhx42La\nNlrrod7eX9SYTCbs2rULn3/+OT788EN8/PHHeOeddzBv3jxs27aNAuxBQGOUkPhF45OQ0YHCa0II\ngK7+qm63G1qtFpdffjnWrVuH6dOnK55TiUo5uAaA1vqu3rE3rLxB8bwH5z4IjUaDi6ouwhREnxTJ\n6/XC4/Gct7vllZWVclVtQkKC4qO5PeF5Xg5gjUbjqPjFsKKiQg6uga6PKU+bNk31F3CtVgu73S5/\nlLk7n88XMXmk9HW0alY1giBEDQmBrtYO0YLtaJMshkIhHD9+XDExZVJSEoqKikbUTYpHH310qA8h\nbkmTSCYlJUWsEwQBfr8/aq/t3m7MCIIAr9cLr9erekPGYDDIFdt1dXUoLCxES0sLLBYLRFGEz+dD\neno6fD4fkpOTsWTJEqxduzYimL722muh0Whw8uRJ6ss+DNH4HDzBYBA33HADKioq8Nlnn6GwsLBf\n+5s+fTr++c9/4tprr8VVV12FL774Ag6Ho/cNe5GWlgaz2aw6j8TJkydVt5kzZw7mzJmDp59+Gk88\n8QQefvhhfP7555g7d26/j4co0RglJH7R+CRkdKDfcAgZ5QwGAxYvXoxrr70WqampOHbsGJ5++mlc\nccUV2LNnDy644AIAgAcetOLHic5CXAgfPPsBMvIycMVtV4ANsl29X5muSkcwQD3qUYhC6KEeAj7z\nzDPgOA633XbboJ9nW1sbmpqa5McTJ06MKYSWWg4AXR/hHw3BUGVlJSorK+XHdrsd06dPP+dzlx9K\nM5gAACAASURBVNooqFW7cRzXY9V2b72Kw0mtHRobGyPW6XS6iKptg8GAhoYGAJDbLYwdOxbjx48f\ncW1hpk2bNtSHMCxpNBpYrVZYrVY4nc6I9dIkkmrhtvS+0ROWZcGyLN577z00NjZi6dKl2LNnD4Cu\nsXHHHXdg6tSp0Gg0+PLLL/Hyyy/j+++/x8cff6wYjwzDgGEYhEKhUfEeNdLQ+BwcgiCgtLQUX331\nFbZs2YKZM2cOyH7nzJmDt956C0uWLMHVV1+Nzz//vN+f0tFoNJg3bx4++OADNDQ0yD8vKyoq8PHH\nHyue29bWFjFR4wUXXKCYVJoMLBqjhMQvGp+EjA70Gw4ho9yll16KSy+9VH58/fXX49Zbb0VJSQke\neughfPTRRwCAZiirBl/69Us4U34Gaz5ag+PHj2P37t1IS0tDWloa7vvnfbBarThddxpHcRQ5lhwk\nJCQoQpVdu3ZhzZo1WLp0Ka688spBPUdBEBSVS2PGjEFCQkJM2w73SRr7qqqqSlH51d/gujd6vR4p\nKSmqk/CJoqjord093O7LL+mhUAhtbW1yhTXLsvB4PPJHto1GI8aOHQue59HR0aEIuk0m08CcLBlx\njEYjjEZjRJAEdH1iQ60difR/6X3lzJkzePnll1FcXKyomLzrrrsUk0HOnDkTDocDL7/8Mt566y38\n/Oc/l9cdO3YMQNd7nSiKo+LTIYT05t5778XWrVuxaNEiuFwubNq0SbH+9ttvj3lf3dt73HTTTVi/\nfj1WrFiB66+/Hp988km//43w6KOPYtu2bZg1axZ+9atfIRQK4aWXXsLUqVNx8OBB+Xlr1qzBrl27\ncN111yEnJweNjY34y1/+guzsbNUWbYQQQgghwx2F14SQCBMmTMCNN96I999/Xw5CePDy+nfXvYtP\nXvkEy/60DBfOuxDvvvsu3G43fD4fGhoakJCQAJvNBqPRiPqmeqRyqWAYBhaLBYmJiWhvb8c999yD\nwsJC/OlPf4Lf74/aj3Yg1NXVwefzAeiqwO3LJI0sywIYmZM0dldVVaUI+ZOSkjBt2rQhq+RkGAY2\nmw02mw1jx46NWM+yrCLYDg+3w4PpcKIoRky+p9FoYDQa5X10ZzAYovbattlsI/66IOdGq9XK1293\n0ic6amtrcffdd8Nut+OZZ56BxWKB1+sFy7KqAfTSpUuxfv16fPnll4rwuvu+KbwmBDh06BAYhsHW\nrVuxdevWiPV9Ca/VxtR//dd/obW1FatXr0ZpaSnef/991edKn4xQ22f48mnTpuHjjz/G/fffj0ce\neQRZWVlYu3Ytjh07hvLycvl5N954I2pqavD666/LE5XNnj0bjz76aMw35gkhhBBChhMKrwkhqrKy\nssCyrDxRmBZdvY63b9iO1x98HdevvB5LH1qKpsYm7PvnPjgvdHYFgwE/gsEgvF4vLBYL2FMsGjsa\nYbPZkJCQAI7j8Oyzz8JoNGLZsmXYvn07gK4K3O59igciIAwGg6iurpYf5+bmKqoZe8KyLERRHBWT\nNJ46dSoiuJ4+fXpcn7fBYEBqaqpqz3RBEODxeBTBdkdHB2pra+UbEsCP7UR6ur5YlkVLSwtaWloi\n1kkBe/j1Gn4dx3qtDbZXX30VK1asGOrDID9gGEZumeTz+bB7925FL95QKCR/uiAYDMrtRYxGIxIT\nE1VvsoTvmwwvND4Hx+effz4g+1m2bBmWLVumuu7ee+/FvffeKz/+wx/+gD/84Q+K5/A8330zAFDM\nKyGZPXs29u3bp1h28803IzMzU/Gc2bNnx3r4ZADQGCUkftH4JGR0oPCaEKKqsrISJpNJrhp0wok3\ntryB5+58DpctvgwrX1wJAEhJSYEpaMKYMWPg8/nA8zw4joPf70fQF4S/3I8kSxJCoRAaGxvx3nvv\ngWVZ3H777RAEQe7RynEcWltb0draGnEsUkAYLdzuKSCsqqqSf3GMVsGrRjoPYORP0lhdXY0TJ07I\njxMTE+M+uO6NRqORrw+gqx92WVmZPNEdx3Gw2WxISkqSQ26patvr9cb8OqIowu12w+12o66uLmK9\nyWSKuG6lx1ar9bxdVwcOHKB/2MeR3iaRk26qdG9Z4/F40NHRodp/G+i67kfye9VIReOTSILBoKL9\nyMmTJ/HRRx9h+fLlQ3hUhMYoIfGLxichowOF14SMctJHTsMdOnQIW7duxXXXXScvO7DrAJ687UmU\nzC7B6jdXy8t1eh3uf+1++Hw+1NfXo729HWcrziLEh2AL2iAkCDAYDBBFEf/+97/h9/tRWloKQRBQ\nU1MDhmFgNpvlViMWiyWiCjY8IDx79mzEORiNRtVgGwAaGhrkMKegoIAmaeymuroax48flx8nJiZi\nxowZwzq47q6trQ3Hjx9HKBQC0HUzpKioCGPGjFF9Ps/zit7a3XttR6uiUxMIBBAIBNDc3ByxTgrY\no7UkGcjr7qWXXhqwfZH+iWUSOanaWqvVKpY/8cQTAIAFCxYolp86dQoAIkJwMjzQ+BwaXq8XHo+n\nx+c4nc7z2hoqLy8Py5YtQ15eHqqrq/HXv/4VJpMJq1ev7n1jMmhojBISv2h8EjI6jNxEhhASk6VL\nl8JsNmPWrFlIS0vD0aNHsX79ethsNjkoqa2txaJFi6DT6PDTW36KXX/fpdhHZlEm7Jl2ZGZmIicn\nB6/c8QpEUcT/ue//gMllEAwG8Y9//AMNDQ1yRa9WqwXP82BZFhzHIS0tDW63Gy6XCxqNBmazGTab\nLaZe2NJH610ul7xMFEW0traC53kYjUY4nU7odLqICli1kDYUCo2KSRpramoUwXVCQsKIC67r6upQ\nXV0t97/W6/UoKipCUlJS1G20Wi3sdjvsdnvEOqlndrRe2+G9tHsjCALa29vR3t6uut5isUSt2rZY\nLDG/DokvsUwi19DQgIsuughLly5Ffn4+AGD79u3Ytm0bFi5cqLixCADXXnstNBpNRBuCxx57DAzD\n4OjRoxBFERs3bsQXX3wBAPjd7343iGdJSPx7+umn8cc//jHqeoZhcOrUKWRnZ5+3Y7r66qvx9ttv\no6GhAUajEbNmzcLjjz+OCRMmnLdjIIQQQgiJN4zahFbxhmGYaQD279+/H9OmTRvqwyFkRHnxxRex\nadMmVFRUoLOzE06nE/Pnz8cjjzyCvLw8AMDOnTsxd+7cqPv42SM/w9W/vho8z0Or1eK+i++DXtDj\npRdeQn19PVpbW/HnP/8ZHR0dEdsyDAOn04n169crlgeDQfh8PgiCAL1eD71eD4Zh5OW98fl8cl9Y\njUYDh8MRUcUIAGazOaLi1Wq1wmQyyf8fiWpra1FWViY/loLreOnR3F88z6OyshJNTU3yMpvNhqKi\nokH9nnIcF1G1LYXbbrdbvinSX9KNGLVwOyEhQfVaJ/Fhzpw52LVrV9T1PM+jo6MD99xzD7766iuc\nPXsWPM8jLy8Pt912G37zm99EfH8nTZoErVaLyspKxfJobUQYhpE/iUDIaFVdXa3adzrcZZddNmJ+\nLhJCCCGEnE8HDhzA9OnTAWC6KIoH+rMvCq8JIX3Wilacwim44IKIrvcQn9cHr8eLFC4FM5JnIN2a\nDpZlcezYMZw6dQoulwstLS1ywJ2RkYGUlBQEg0GYTCZYLBYEAoFew73ExETY7XaYTCbodDo5pA6v\nfg2FQnC5XPK+EhIS5F7HvUlKSoLNZgPP83C5XHIY2L0lyXAOCLsH1zabDRdffPGI+QU9EAigvLxc\n8XFwp9OJ/Pz8If2eiaIIr9cbtWo7GAwO2Gup9YiXHo/UGzIjWSgUAsdxUPs3m06nk2/uEUIIIYQQ\nQkg8GMjwmtqGEEL6LOWHPz740IpWLF+0HK++9yo89R7oRT14DQ9YAYPBgAsvvBDZ2dk4fPgw2tra\n0NraiubmZtTV1aGtrQ3jxo0D0FUp7XQ64XA4EAqF0NzcrFqpLQV+QFdVYUpKCtLS0jB58mS5FcSh\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Q8UhTsVgs+Hw+otFonrhdVVXF2rVr2bt3b8E+119/PWvWrOHyyy8X4rWmaXmCe2dnJwCN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hGIZs9ClUKyLIFo0/54fY85tPzg+x6+rqKJfLstp8fHyc5ubmBaGyGJ/zERXV85m/LRFqi0Bb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QLxXiFoi8gMEY9SKpVOypioaZqMYWhubiafz+NyuUgmkzgcDinU2u12FhYWWFhYkC70trY2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\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ss60Q5XiUIlQLBTtyVGs0Guh0OhiNRj53Y4k+Ip8epUSGZLNZdpcKBaTCvGu5XM5Ozlwu\nV1LeNRVipWzoXC43ZtQIrRoht6dSqZxx8dpms3GblEgkPLaVl5ejqqoKCoUCZrMZZrMZ4XCYxfpg\nMIjh4WF4PB6kUineBhUdpfFNJpOxQK9QKHgSLhaLwePxwOFwIBgMzvp+IXRfW61WnozL5XLo6OjI\ni5FwOBzs7BfGvFANhmLjvs1mQ01NDf/e3t7O50IikaCiooK373K5Rk1gms1m/pxgMDgjq1uUSmVe\n3jWwX2SOxWJFJ3FlMhnKy8tLErBpApeEXSGFhRqpH/p8Pv5uMpkMdrudY3VIoCbROZvN8mtVKlWe\n67pYJAf1e4VCgXnz5mHFihVoamric02TN8FgEHv27GGXtdlsxrJly/IyuX0+H3p6euB0OtHU1MRO\nb5VKBbfbjUgkAofDgUgkwlnXuVyO+9p4E2siIiIiIiIiIiJTRxSvRUREpo3b7Z7R7UkkEhZ70un0\ntPJAhe7reDw+Zfc13ZQKRbRSEOZgU/bobHIuqlQqFgRoCXfhvs129/V0hWqKeSGhgsQHjUbDYs5s\nin6ZCjPdRz9tSokMKdZ2STwmSLwm1zKAkvKuQ6EQR5FQkcex3hcKhZBOp5FMJqHRaKDVakc5VKcL\nOa+Bke8oFD3r6+s5vsBsNufF3kilUhbgwuEwj2VKpRKZTIYd2YWxKzSG0bHPZDIshLvd7lkbKTKe\n+3p4eBiJRCIv3kOYyaxWq/naQdErxb7jokWL8kTyXbt28XNKpRI2m42fc7lcee+VSCSwWq08rvp8\nvhmZ4KRMcwAck0OTqGNN4pYqYAsjQ4RCbSqVyhtnqqqq+GehqG232yGXy/l6Q8I1tS3h9VEoXgtX\nwxTuDzmzKQqnrKwMixYtQmtrK7vKqR3E43H09fXhk08+wb59+2CxWDhyh1ZuRCIR9PT0cIFT6sdO\np5NFcLrWkFNfjAyZ2xzs11ARkbmM2D9FRA4NRPFaRERk2mzcuHHGt6lUKtmJFYlEpuXwm2n39WTF\nBcrBForE0Wh01oihQqceFWsTijTCfZ+uE366kFBNbsiJhGq5XF6SUF0MiUQCnU7HGeyRSGRWCnSl\ncCD66KeJ0HU91jkT9m3qW8LIkHQ6jVgsxpNQdC5LcV4Hg0EWvWksGet9VCiR8q5LEccnC4nNANhR\nTUilUjQ1NUGn00GpVEKtViOdTnOes1wuRyKRwPDwcN6kAInb6XQamUyGjxM9LpVKYTAYYLFYOBIp\nl8shHo/D7XZjeHgYwWBw1sQjETR2pdNplJWVwWKx8HN79+6F3W7nc+p0OvP2n8YOen+xSazC+BCX\ny4WBgQH+nVZxACPXssJ4EJlMhmuvvZbHGY/HMyOOdnLNAyN9Q7h6aKxr2EQRIhQhA4yODHE4HHxs\nDAYDt/tMJjMqB5tibLLZbF7xTGB/3jX1cxKmhXEgwv2hyRZaLSBELpejuroay5cv56Kmwvd6PB60\ntbWhu7sbarUaNpuNndaJRILzrs1mM0eyuN1u9Pf3IxgMIpFIcH43FcgUmZsc7NdQEZG5jNg/RUQO\nDUTxWkREZNpcf/31B2S7lB+Zy+WmVbxxptzXdLNOxfwmuw8ajWaUEDJbhB5yIwP7i30JhVphcbHp\nOOEnw3hCdTKZLEmoJgFnIqF6LMg5DyAvo/Vg40D10U8DoWA2njhErkkSY4HRedf0ftqeUOAbj1Ao\nxMJuYWG/Qg503jWwP7ObhL3CsUStVqOhoQEqlSpv7KuqquK+kkqlEAqFOFaBijhSpjHlulPfoXgG\nEsIrKipgNBr5OKTTaQSDQTgcDng8nlnjxha6bhOJRJ772u12IxQKoby8HMCI0Frojqb4J2H9gsJr\nCMW1EG1tbXkFEoWOZopsEXLDDTewAzydTsPn8017cpOuObTqJ5lM8j6Mdx2Uy+WjBGwa84WxWYXt\n3+Fw8M/CYzE8PMz9TavVoqysLG8slclkLF7ncjn+LLVanZefXay/Ud47AHZdFz4PjFzfrFYrWlpa\n0NraytE6RCwWQ1dXF9xuNwvvwkx5GluoUGs4HIbf7+civmVlZeOuChE5+DmYr6EiInMdsX+KiBwa\niH9liYiITJuVK1cekO3KZDJ2NyYSiWkJh5+1+5oQCiHk5i2lCGSp7Nq1C6eddhoaGxuh0+lQXl6O\n4447Di+99NKo1+ZyOdx99904/PDDodVqUV1djfVfXY9dH+9COprOE58MBgMLgoFAAA6HA9deey2O\nP/54GI1GSKVS/Otf/5ryfpNAmUwmEY/HPxOheizIvQqMiByzPee3GAeqj34akLhKQupYCMUwQihe\nCyNDJuO6TqVSiMViyGQyE+Zd06qKWCyGffv24Y477sCxxx4LvV6P+fPn4/TTT8fevXvz3nPfffdh\n7dq1qKyshFqtxoIFC7Bx40b09vaO2j45xrPZLNRqNTKZDHbt2oWbbroJy5cvz/scr9eLqqoq7ifx\neByPPvooNm3ahDPPPBOnnnoqTjvtNGzevBn9/f08DlE0Eh13ioegSTsSQmOxGJRKJcrLy2Gz2fLc\n2LFYjN3YoVDoM5+kE7qvjUYji9XAiPvaZrPx2C4sOkjI5XJotdq8cbvwOy1evDjvc4TxIRTJAYy0\nZ6fTmSfsr1y5EjqdjifK4vE4AoHAtMV/mmihdit01I8Vg0LfVyhg+3y+vGuVMEIGGHGU+/1+/szK\nykp+TliokURtup6TwC4sHiwsBimcLCoGRYYUc10LJ72Ez6nVas7GbmxshMFg4ILO9B6n04mHHnoI\nP/7xj3HqqafiqKOOwiuvvIJoNAqv1wtgxCH+m9/8BkcddRTmz58Po9HIqxSWLFkMYOK/Ed544w2s\nX78edXV10Gg0qKqqwrp16/D2229P+F6RT5eD+RoqIjLXEfuniMihQXHbkIiIiMgsgUSXZPL/s/fm\nUXJV9/XvvrfmuarHarXmCSQEAgkQNrOMZfCgJFYQYDABW1k4iUyMh4CD/d76EeNgYyN7EeNAjJ9h\nQWRD5MR+AeS8JMYGGwNCgJDQgFRSq9Vz1zwP9973R/n77XOrq1o9aaLPZy0tSdVVdzj3nnO79tln\nf0tIp9N13VXjgdzXuVwOxWKxYX7m8bDZbBxPoev6pLZBRRBzuRw0TUMul4PD4YDD4Ziy6NrV1YVM\nJoPbbrsNs2bNQi6Xw7Zt27B+/Xo89thj2LRpE7/39ttvx9atW3Hrp2/F52/7PLLHsnhz15tIvJSA\nNWYF3EBxThGOhQ5Y7Bb4fD4uKtbV1YUHH3wQS5YswXnnnYdXXnll3McoimOiIFYPiiug4mDkBj3Z\nuFwuvu6ZTIYFe8mJR3RdN7r2NMEBwPQeUbwmR2epVGKBezziNYne4mqERm5qis0ol8v4xS9+gQMH\nDuCmm27Ceeedh/7+fjz88MNYtWoVXn31VSxfvhwA8Oabb2LhwoX4kz/5E4RCIRw+fBiPPfYYnnvu\nObz99tsIh8O8CkGcOGlpaUEymcQTTzyBt99+G5/85Cdx8cUXm/bzyiuvIBAIIJvNwuVyYdeuXWhr\na8N1112HTCaD4eFh/PKXv8Tvf/97fPvb38bs2bO5bUioJEGW2pVyiMmJXSqVYLfbEQqFuNgeibuV\nSgXJZBKpVApOpxNer9dUQPNkQe5rTdNQKpWwePFidljH43HEYjG0t7fj2LFjMAwDAwMDmDNnzqht\neDwejrugvGMSRm02G5YvX4633noLQDWCpK+vjwVbt9uNQCDAzvx4PG6KMAGqBRxp5Us2m+VnxVTG\nPBJ/ax3jmqbxs7BRm7W0tGB4eJjd4DabrW7hRNF13dzczNc4l8shHo/zz6gtaBKQ2pQQV/WIsTX1\njpHur0aRIWKsTr1JL1VV0dzcjKamJnZdU1+PxWLYunUr2trasHDhQuzatYtd55qmIZlMwm638+TO\nP/7jN9HcbIfNlgKQRiDgBvC/ADwAZv/xz+gVHgcOHIDFYsFf/dVfIRwOIx6P46mnnsIVV1yB559/\nHuvWrat3aSQSiUQikUhmHMrpsKTzeCiKsgrAG2+88YacWZNIZiDlchmxWAyGYcDtdk96Gb5hGEgk\nEpxF28jNdTzIhSlGbUz2ePL5vMnNRu6+6cQwDKxatQrFYpHdgM888wxuvPFG/Me2/8D6OeuBmlon\nuqHzl3XFocC+xo6iq4ju7m4AVaEmGAwiGAxi27Zt2LhxI37961/jiiuuGLXvM02obgQVqSPB4kRk\nGUvMiA5Rp9M5ZlTH4OAgAHCchsPhwNy5cwFUr93u3buhqioSiQT323PPPbdulq7IkSNHkE6n2Qnr\ndDpxwQUX1D2WSCSCrq4uDA0NIRqN4tprr8WCBQv45wcPHsSKFSuwceNGPPnkkw33uXPnTlx44YV4\n4IEH8MUvfrHuSo94PI6hoSHs27cPS5YsQXt7O2bPnj1qP4899hj27duHcrmMaDSKbDaL5cuXIxwO\nY+/evdi3bx82bdqEG2+8ERs3boTD4UBbWxs8Hg9UVYXD4UClUkG5XIbL5cKsWbM4doQcqwC4YJ7d\nboeiKLyConalCwmW5Ag+WYjxDx6PB++88w6Lrn6/H5dccgkOHjzIx7t48eK69wYJ9ORiFp3DAPD2\n22/zdm02Gy699FK+33RdR09PD1/Pzs7OUcIsFT6sVCqwWCwIBALT8lygyBNy7tPkGxXjbESlUmE3\nerlc5sgYunaGYeB3v/sdt+25557LzutDhw4hEokAqIraq1atgmEYOHz4MBKJBK80oDY4evQoyuUy\nnE4nFEXh/YlOeSKfz/P9V+95XigUUKlUjvuczmazPJERCoWQzWaxb98+DA4OIhwOo6urC7fffju+\n8IUv4JxzzoHVakU+n4eu69i6dSveeOMN/O5338fcuR40Nzc12IsdwCoAwYbHIZ7XwoULccEFF+D5\n558/7vslEolEIpFITld27tyJ1atXA8BqwzB2TmVb0jYmkUimzOOPP35Cty8WdMrlcpOO2RCLPomi\ny0Qh15mY/znZ46G4C6AqEmQymWlfYq8oCubMmcPLugFgy5YtWLNmDdbPXg9jyECukDN9RlVUjguI\ndEWw7+f7YC+OxGdomjZqEoGEaor+IAcmOefFuAaK/qDCnGL0BxW+Op2Ea8CczXqm5V+f6D56opho\nZAgw4riszbsmsY6EQ6vV2tB1KpLJZLgwIY1F9cQ+wzDYVasoClatWjXKWbt48WKsWLECe/fuHXOf\n8+bNAwDEYjGTcH3s2DEcOHAAQHU1gK7rOP/88+FyuUzOcHE/TqcTHR0dsFqt8Pv90HUdkUgEiUQC\nixcvRmdnJ4CRsTWXy7FYTRNO9G/6m4Rqr9cLl8vFtQmKxSIymQw7eltaWhAOh+Hz+Ux54xQ9FIvF\nTNnQJxIx+5rc1zTGpFIpFisJ0U0soqoqC740uSJeo2XLlpmKI4rXWlVVtLW18X4HBweh67qpf9LE\nIDnFM5nMtIw1NH4pisJZzkBVLB3rOUYObDpmEo2JRCLBwrXVamWh2TAMU2TIrFmzAIzE8ADmYsEk\njgMj7n6g/uoIWolAUT7jjQypRzKZ5Pbx+/3wer1YsGABVq9ejc7OTpOz3uFwIJ/Pc2ROdQJDh2EM\n48iRY0gkkg32UkIk8v8iEtk15rEA1X7d2tpqel5LTj1n6jNUIpkJyP4pkcwMpHgtkUimzM6dU5pE\nGxder5eFB3K/TgaK5iAxajKQKxjAtORVOxyOac/BzuVyiEajiEQi2LJlC1544QVcc801AKoxCK+9\n9houWnYR7v3uvQhsCMD7SS8Wf2Yxnn3pWd4GCdjX/l/X4qP3fhSlXSUWrHVdRyqVQqlUYuGGvtST\nUE2TA2eiUN0IincBwCLfmcDJ6KMngvFEhgAj4rXo1q/Nu6bt0c/HE8dQKpVYtKUiho0c97SKIp/P\nw+VymTL7RQYGBtDS0jLq9VgshqGhIezYsQO33347FEUZtZJh06ZNvAKNJtFI3Nc0jUXB2v0EAgH4\n/X44HA4EAgH09/fjrbfewmuvvYZvf/vbUBQFK1eu5DxrKswoxiNVKpVR9/xYInY6nUahUGD3cDgc\nRlNTE18XEn6HhoYwMDCATCYz5SKFx0OceCQXOXHw4EF4vV6+ZplMpmGh4NqinaKga7fbORIGqF4H\nUQh3OBw8qUEu69r+SeOkqqoolUrIZrPTIvJbrVbOJrdYLDxOi/dNo8/5fD5eGROPx5HLVSc8+/r6\n+H3t7e38nI7FYtwvRVG7UCjwufj9fu6D9QoBN4oMESeO64nXdI82igwh6HlFxwKAz8vr9WLx4sVc\n4LNcLsNms8HpdCIQCEDTNDgcGorFEi677P/GRRfdg/nzP4O//MstyGZH/26xdu2Xcc0119U9jnQ6\njWg0iv379+Pv//7vsWfPHn5eS04PztRnqEQyE5D9UyKZGcjMa4lEMmV+8IMfnPB9KIoCn8+HRCLB\n7kAxK3Mi23E6nSw2TDb72m63o1Ao8BfaqYqv9XKwnU7npGNJvvSlL+HRRx8FUP0Cv2HDBjz88MMA\nqku5DcPA1n/fCptqw3c2fQd+tx/f/8X3ceMDNyLgDmDd6mrWpqqoUBUVChTowzr0jM6OQ8oHFQUn\nEkXE+I8zRZgeL263m4W9MyX/+mT00elmvO5JcvsDjfOuSZzLZrPcp8aTd51MJjm+gGgkXicSCc6l\nJgdnrXD21FNPoaenB9/4xjdGfb6zs5NFvZaWFnzve9/DVVddZXqPoih8r1EfI3GZREi32z1qP1ar\nFcFgkKM+/vqv/5rPKRQK4Stf+QouuugiFhCLxSJnG1cqFd6npmkYHh4elXtMIjbVBKCVLWImtsPh\ngNvt5v6TzWY5h7lcLiORSCCZTMLlcsHr9Y7KVZ4OarOvFy1ahN7eXhiGgUwmg76+PoTDYRw8eBBA\n1X3daJKDBGxaWVIoFDjCIhwOo7+/HwMDAwCqhXSbmpr4nAKBABf2TKfT+Na3vjVq+36/n9uSJgGo\nnaeCzWaDy+VCPp833dv0LKsHxT75fD4u5BuPx6FpGp8jMJJpDZgLNYbDYe4LmUwGwEh8DEGisc1m\n4/5crw6EYRimQo3UD0Qm6rqm3y8qlQqvnCCXupiPT9fVZrNB0zTMnevHzTdfikWLWmEYwOuvH8bj\nj/9/2LPnKF5++bum46o63nUAWVSzsEfYuHEjfvWrXwGo/m5xxx134Gtf+9qYxy45uZyJz1CJZKYg\n+6dEMjOQ4rVEIjljcDqdcDqdKBQKyGQycDqdk8pMpW2Q+3oy2dckJJDA1uhL/0Sg5ejk4CwUCtA0\njZ1yE+Guu+7C9ddfj97eXjzzzDNcnAsYEQ9iqRhe3fIqLlx6IQDgE5d8AgtuW4BvbP0G1p6/lrNR\n9z5aXfauaRqUPgWOWQ4WbHRdZ4GA3ILvdxRFgdfrRSqV4vzb8Qihkokx3siQeq5RVVVZ5KtUKiyG\nFQoFdliO55pRX6GCh0Bj8TqVSrHT1OVyjYrV2bdvHzZv3oxLL70Ut95666jPb9++HYVCAXv37sVT\nTz01yvVrGAaee+45/reiKDx5RI7nUqmEvXv3jtoPOdJDoRAKhQK++93vYmBgAL29vXjllVeQz+fR\n1NTEbUlO82KxCJfLxaK8oigolUqIxWLweDyjhN3xithWq5Xd4Pl8np3F5MbO5XKw2Wy8SmM6J4fs\ndjuPsR6PB3PmzMHRo0cBVN3Xl112GYLBIBKJBAqFAhKJBEKhUN1tURSVoih8joZhwOVyYdmyZYjF\nYiiXyyiXy9i3bx/OO+88/lxbWxuOHTvGEwK1me6KoiAYDCIajbKjn67jVJ83drudr4umaezaF1cV\niYgrINra2hCNRqFpGiKRCHK5HOz2aqRUMFjNdC6Xy5xBD4CjaWjSExhxgdP2qe84HA6USiUWkOsd\nCz2bbDZb3cgQig4aS7ymSQEAPAFJEUFiRFTt5Ozs2bP/mMldwP/5P3+ObDbLq47Wrl2Ozs4gfvSj\n3+DHP34BmzZ9jD97+PBP/vivXgBLTMfyrW99C1/+8pfR3d2NJ554giNUTsQEjkQikUgkEsmZiBSv\nJRLJGYXP52ORI51O85fliTAd7msSEOhLptVqnRaHMX1hpy/W5DCbqICzdOlSLF26FABwyy234Npr\nr8UnPvEJvPrqqywYLGhfwMI1AHicHnxizSfw9K+fRrlcHiVK2Ww2QB8R/8npSQLB+81hPRYkbmSz\nWXZdjic/WTJ+JhoZAoCjBGrzrsk5SiiKctyJFprcorGmo6OjYd61pmlIp9PcH2w2m0m8HhwcxMc+\n9jGEQiE8++yzpvMxDAOGYeCyyy6Druu4+uqr8ZGPfASrV6+G3W7HZz7zGdO5ASOOVKfTySsggKrb\ndePGjaP2Q+5Uq9WKzs5OXHzxxcjn8xgYGMC6devwF3/xF7Db7bjyyis5T5j2mcvl4HK5eALA6XTC\nMAy+9wOBwKg2OZ6I7XA4uLAjubHL5TKy2SxyudwoN7bb7YbH45kWMc9qtbJTvVQqYeHChejp6WEB\nt6enB+3t7Vygc2BgAIFAYMzxlwoM0phNxYWXLVuGXbuqOcd9fX1ob29He3s7H0dLSwsGBgagaRoG\nBwfR0dFhujdI5CeXM7mCaeJiKtB1pJgXAOzcr+1vYlFhu92OlpYWDA8PY3BwkB3TCxcu5M/19/dz\nf/N6vdwXxKKZXq+X25S2AYAnhS0WS92VRzRB0KgdxMiQsa4Zua6BkcgQWnlgs9l4gkB8H7nPAWDe\nvDDc7mEe/4eGhhCPx3H99Wvw+OO/wSuv7DeJ18IZjHqFJjUA4Oabb8aqVatw++2345lnnml4/BKJ\nRCKRSCQzidN7nbNEIpHUIObIitmZE4XEBvGL+0ShuBAqajad0BJ7yrLNZDJTylfesGEDduzYgffe\ne49zXttD7aPe1xZsQ1krI1fMsQhnt9vhsDtgt9nh9rrhcrk4qzqdTp/wnNrTFRLggDMr//pMYCIF\n10gMs1gsfC+K4jUJY2JRPY/Hc9xVG9lsFpVKBcVikQX0Rq7rGoNZ5gAAIABJREFUdDrNYjflXVNB\nxXg8jnXr1iGVSuEXv/gFAoEAstksZyqnUimk02lkMhmOkpg1axbOPfdcPPvssyww1rYPUL0H6ZzT\n6TRuvfVWpFIpbN++3VR8EBhxX3s8HnR2dsLhcCAYDKK5uRlLlizB9u3bAVRFRYoGIkjIJZGeRMpy\nuYxoNGoSH0XETGyaJKR2SqfTpiKTVKgwHA4jFAqZMr2z2SwGBwcxODjIUSNTgURRisqYO3cu/+zQ\noUOwWCycF0651OPZpugkzmazaG9v56xnANi7d68pgsbr9fI9lc/nTUIp4XQ6+X4tl8solUocLzVV\nqO6A3W7nbYv9hKBjJkHXZrPB5/PxfZHL5UwTyfUKNQIw5ZoHAgF+nURjinQxDIOfMSKUwU4rfiYb\nGUJtCICLiYqrk2hiyzAMk4Oc7smqa9zDbdjW1oqzzz4by5Ytg9frQjDoRTw+OsO7ytiTvDabDevX\nr8fPf/7zk1bMVCKRSCQSieR0R4rXEolkyqxfv/6k7s/tdvOX6MkWbyTXIgB2EU9mG/QFeToKN9ZC\ny+ZJ8JlK0S4S+JLJJDo6OhAOh9ET7Rn1vp5oD5w2J5oDzbBarLCoFqiKkNvpV1hMAaqCWL1CWzMF\nUQTNZrOTLiR6ojnZfXSqjDcyRJx8qpd3TW5VoHp96PXxRIZQvAFFFAEwFSzVNI0jSRKJBLttQ6EQ\nfD4fMpkMhoeH8fGPfxyHDh3Cz372M8ybN48LmpIQR1CeNU0aUdFDnkByOOB0OlkcF8+zVCrhb/7m\nb9DV1YWf/exnOOuss0adD31G13W0tbWhqamJhbtiscgTZHQc5Myl/0ejUY4PSaVSsNlsPDalUil2\nB9dDURQ4HI5xidgUn9TW1ob29naTQ7dUKiEej6O/v5/rH0wGEj5pmwsWLOCxvFgsoru7G62trfza\n0NDQuCan7HY7F9/VNA3ZbBbLli0zbXvfvn2mz7S0tOBzn/scgGqRw3pjvM/nYzGX4qTIoT4VKPaE\ntl0qlZDP503XUSy+K8aVRKNRvjZerxeFQoEzvFOpFIDqtRRzsOn12lgOej7Z7XYujFov71oU1uu5\nrsWJ5LHEa3GSgER0Gr/FVRnRaNS0T5qcaGpqgq47AYyI5xaLikAggAULFiMez6KtrdGqMH+D10fI\n5XKmiBXJqedMe4ZKJDMJ2T8lkpmBFK8lEsmU2bx580ndn+iAJFfyZKAvx1N1X9NxTLf7GhhxmtMX\n8UKhwF9s6zE0NDTqtUqlgieeeAIulwvLly8HANxwww3oHurG/7z5P/y+4eQwfvmHX+JD53/I9PlI\nXwSRvghgA9BRbX9xGb0Y2zDTEIUOEqtOR052H50q440MEUXPeuK1mB1fqVTYOdlIvCZ3cbFYRC6X\ng6IoXICRVlokk0l2SlPeLbk4SWQm1/Vtt92GHTt24Mknn8TFF1/MQjSJ0E6nk7fv9/vh8/ng8Xiw\ne/du7NmzB6tXr+Yig6qqoqenBwcOHODjtdvtMAwDd911F95++2089NBDWLFiRcP2SiaT0DQNNpsN\ns2fPhtvtxpEjRxCJRLB06VIuZEjvoTxnEtUzmQznJCcSCVMBwWKxiGg0OuZ4UCti0/hbT8QGGrux\ndV1HJpPBwMDApN3YovvaYrFg/vz5/LNIJALDMNg1reu6qTDhWFitVl41QxEs4mRCb2+vaZxWVRV/\n+7d/C2DE6Vt7LpR/bbFYYLFYWGAe61kwXii6hZ6HxWLRNBFHEwS0b6Kvr4+fT+3t7TAMA7FYDIcP\nH+b3tLS0mKJeSIx1uVz87BTPQYwMqY2IobY0DIOfPbUCNT2Dx4oMIVc8UJ18pG3Qa1RLwzAM9PSM\nTPCKwn0oFEI+X0Eq5TXtp1Kp4IEHngUAfPSjF5n2G4n0IRIZAjAi5td7XicSCWzbtg1z585l97/k\n1HOmPUMlkpmE7J8SycxAZl5LJJIps27dupO+T7vdDrfbzcW9XC7XhDNAyV2Yz+dRKBTgcDgmnH1N\nok6lUmERZLoRc7CLxeKYOdh33HEHUqkUrrjiCnR2dqK/vx9PP/009u/fj4ceeojdbl/96lfxzM+e\nwYb7N+CuP7sLfrcfjz7/KCpaBd+87Zumba69Zy1UVUXk1xHAMnLejz76KIrFIg4ePAjDMPCTn/wE\nL730EgDg3nvvnfZ2OF0hsSqXy6FUKqFYLNbNaj2VnIo+OlkmExlCnwPALlKKMyDhmmIzSHDN5/Mc\nyUFOb1Gwo+xiq9UKh8NR1wmqqioXmyPnsqZpCAaD+NrXvobt27dj/fr1yOVy+MUvfmH67M0334xk\nMon58+fjhhtuwDnnnAOPx4Ndu3bhJz/5CUKhEL761a+aPrNp0ya8/PLLPGFntVqxZcsWvPjii7j6\n6qsxNDSErVu3mkSvm2++GUBVJDz33HPxZ3/2Z1i1ahXcbjd27NiBp59+Gn6/H7fccgvnU5NoTyJj\nJpOBqqrQNA3lchnpdBrNzc1IpVJwOp3w+/3sBE4kEnC73fD5fA0nHkjEttvt3GdIxKb+Q5nY1M4e\njwcejwelUomzsalIZalUMmVjj6egYW329bx589DV1cXXvqurCwsXLmT3bTweR0tLy7j6ttVqhcfj\n4Tbx+/1obW1lsXLPnj249NJL+Tg//vGPIx6PIxaLcUHMWuHSYrFw/jUAvk65XK5uTvVEoEk4XdfZ\nQU3jWm1kCFAVoUmIttlsOOuss5BMJlGpVHDo0CE4HA7YbDZTZAgV+QXMRU9p4kc8/nqRIaLLnsTp\nyUSG1HNdi0Vd6Rn54IMPIhKJ8DX7zW9+g/7+ftjtdnz961/HwMAALr/8k7jppstw9tmzYRjA88+/\nhv/6rzdx3XUXYv36S0z7Xbv2HqiqHZHIp/m16667DrNnz8aaNWvQ1taGrq4u/OQnP0FfX5/Muz7N\nOJOeoRLJTEP2T4lkZqCcrkucRRRFWQXgjTfeeAOrVq061YcjkUhOE3Rdx/DwMHRdh91uRygUmvAX\neF3XObfT5XLxsuCJQIW+AEy4sOJEKZfLLLqRqC1+UX/mmWfw+OOP45133kE0GoXP58Pq1atx5513\n4mMfMxePOnLkCL7811/G/7z0PyhXyvjgsg/igc88gFWLzePsgtsWQLWqONR1iMVrYCRDtxYSvmYa\nmUwGpVIJiqLA7/efkImMmYDYnyiCoRE9PT3skAaqArbH40EwGESlUkEikeDrQsVZrVYrZs+ePeYx\n5HI5xGIxZLNZLgzb3NyMzs5OkwiuKAqGhoYQiUTQ3d3NIuX555+PtWvX4re//e2Y51kul3H33Xfj\n17/+NY4cOcJ51x/+8Idx7733Ys6cOVw0EqiKXb/73e84fgEArrrqKrzxxhtj7geoiodf/OIX8fLL\nL6O7u5v39YEPfACf+tSnEAgE2Glqt9vR3t6OYDDIDunu7m4kk0kWtH0+H4t/NpsNzc3NyOfzJgEx\nEAiMS0gmEbo2PoTymOvdA7quI5fLIZvNjooPcTgc8Hg87BxvBDnzgeq9dvToUezfv5+P/4orrkAu\nl8PRo0cBVGNj5s2bd9zzqT1Guta7d+/mVT6zZ8/GOeecY2qD3t5ePh4qEFpLOp1mpzk5+UWX/FTQ\nNA3JZBKlUgmqqnI+O1AVnOlavvfeezhy5AgAoK2tDStXrkSpVML+/fuxd+9eKIqCpqYmXH311fw8\nHBgY4Pvr7LPPhtvthmEY6OrqgqZpLFhTUUSv18tjKMVmiQ5tujdq2xpo/BzWNA3Hjh3jiJ+2tjYA\nVUE7FotBURTMmTMHqqpi9uzZ6O/vr9tOe/fuhdvtxj333IPXX38Fvb190DQNCxeGceONV+Kee26A\n1Woe/xcs+AxU1YVDhw7xaz/84Q/x05/+FPv27UMikUAoFMIHPvABfOUrX8EHP/jBCVw5iUQikUgk\nktOPnTt3YvXq1QCw2jCMnVPZlhSvJRLJGY1Y5CoQCExKfM7n88jn86PiMCa6DVpqf6Jdt7V5p5RZ\nOml6ARwAUG+1vwqgE8DZMAnXRF9fHzKZDBRFQVtbG1RVPSltcDqi6zpSqRR0XYfFYoHf75+ymDQT\nKRaLKJVKHB0gOqNr/x4aGuL2JpHW5/PB5XKhXC4jmUzyygqgKkj6fD6Ew2EWoWv/rlQqGB4exvDw\nMEcj+P1+LF68GE1NTaOO9+DBg+jr60NfXx/C4TA6OzuxaNGiaWsPEnYbxRJ1d3djYGCAo1B8Ph86\nOjpGRaMYhsGuXVGIzOVyOHz4MLLZLPr6+pBMJuHxeOD3+xEOhzlaweVyYefOnbBYLJzJ3dTUhFAo\nBKAqKFLMBgmeiqLA6/WO2xk8GREbqGYhZzIZntgjRMd2IzeuKATbbDa89NJLLDAvWLAAS5cuxaFD\nh1gYXbhwIUcFjQdaAVCpVBCPxxGJRFhsX716tclhXS6XcezYMb6n58yZM2oSjK5jqVSCYRhwOBzc\nVybz/KuFJn3ESQiLxYJgMMixHmIbrVy5kkXg119/HV1dXdB1HZ2dnVi9ejVH+Bw8eJCz0lesWAFF\nUZDP57m4I72PokjE+1ecZABGViOJz2py4FOx1HrE43H+faGjowMOhwOGYaC/vx+FQgFOpxMdHR2I\nRqN47733AIDrTpCgv2LFCq6T4XQ6YbPZYBi9yOXeBFD842SCeK8d5yEqkUgkEolE8j5lOsVrmXkt\nkUimzH/8x3+csn2Lwm06nZ5UAavpzL6mTM4TicViMYkxJL5Per+zAFwB4HxU4zibAbQBWArgKgDn\noOF3bnJeim1H0QNnwuTodEKFy4CRCYbThVPZR0VIeKaYnWKxyDnu2WwWmUwGqVQK+Xyes3dzuRzH\nSZTLZS4gJxaSE0VNGhOsVitKpRLnKVOeM+VKU+Y0OVcpV7pSqbDYWCgUWAgTow7E80mlUigUClxk\nrt77pgJFbJBrnOISLBYLx2sUi0VuC7FIZe12SAgVhXByKVssFhaiK5UKxxTRNbNYLDj77LN5osBq\ntSIajSIWi3HkyuDgIMrlMuczU9G5sYo51jtXn8/Hjm+KsiAHfb1xxW63o6mpCR0dHQgGgzwe67qO\ndDqN/v5+DA8P182IpudHuVyGoiimiYejR4+iWCwiHA7za43cuGOdE62QCYVCphzoPXv2oFKpcP+0\n2WwsZmuaVjcTmSZZKQ+e2oRE/6litVp54o3uA8p7B8xFJcXjLRaLSCaTXMSxra2NM9B1XecJDa/X\ny9ui12glkaIoXAhUhCI9xImmRpEhjVa80OQiUBXKaYKVCqgCVaG6Nus6GAzy/gOBAN+T4r6KxSYU\ni2tQqayAxdKJCT1EJWcEp8szVCKRjEb2T4lkZiDFa4lEMmW2bt16SvdPX7RJqJgolH0NwLREfyKQ\n8GUYxqgl7CcCVVXhdrtZBCmVSiY39sQ3CCAMYCWAiwCsArAQwHEM3eIxpNNpkxA0EwVsyokFwOLf\n6cCJ7qPjFaVTqRTHHtQTpSlnGhgRqiwWC6xWKzv6nU7nKNcl/VtVVfj9flitVu6LtQJ3o2KNwEjh\nVTomsXBhvfiLbDaLSqWCfD7PYqvf75+2dhUhB7LT6WTxzWKxsKuZ2o1E90bbAGAaJ8hJTeI4jack\n4JM4qmkavF4vwuGwSWCMxWKIRqO8vXg8jkQiwZEjQHV8ikajpozysRBFbJpc1HUd+Xx+TBGbJpDa\n29vR2tpqcnwXCgXEYjH09/dzPjMwkn1Nx9nZ2ckTFpqmIRKJsBMdqDrVxdzk8Z4PjZUdHR1wuVxw\nOBwoFAo4cOCAqX9SZAZQvb/EiBiC8q/p3OiZI+ZKTwW73Q6fzwdd1znyhCCnNABewUCvU7HFWbNm\nsRBMExw0eSH2D5rgo76lKAo7vQlN00b14VoXva7r/J5GDvt0Os33DE26AtXJX13X+feAeDzOx+Xx\neEyTLs3Nzfx/EtFHJm5VWK2dUNULMKGHqOSM4FT/niuRSBoj+6dEMjOQ4rVEIpkyP/vZz07p/qk4\nFlD9IjqZL++i+7qR8DMW5BgDYBLgTiSKonBON7nkstnsuByO0wkJAeRsJcGKRLSZJmDTUnIAnHd7\nqplsHyVRmgQscjLn8/kJidKappnuAxJ+akVpu90Oh8MBr9cLv98Pv98Pr9cLj8cDt9vNoi3FVhDU\nxtSPKWagNnud4i8aQU5T+jy5Mxu5qSkvv1gssiBJ9//Jwu12w2Kx8LhD4nu9+66e8xoAO89dLhcL\nxjQJUSqVWMinXGIxLgQAEokEBgYGWMjMZrPo7++H2+02uVWTySSSyeS4J9kURYHT6ZywiA1U74Wm\npiaEw2EEAgEWNTVNM7mxC4WCaewGgMWLF/N2KB88HA6zeNrf3z/hcY3Ga5/Ph/b2do756O7uxiOP\nPGJ6b0tLCx/v8PBw3Wea3W43rfSga5rP56dlApViVADwZFSlUjG5wTs6OvjffX19/O958+ahpaWF\nxd3+/n6+5iReUz8DwC5ycVUBQedOedjAaIFadF3Xi/2iFRIATPEqhmGweO10OmGxWHDs2DH+XDgc\nRiKR4M95vV5uZzoGGt8AnPS+Lzl5nOrfcyUSSWNk/5RIZgZSvJZIJO8LxBiNVCo1YWGBlvwDk3df\nW61WFsBPpmBpt9vZiUrLs6fDfTdefD4fizqJRAJWq5W/xFOEwUwTsCkn1TAMZDKZ0+78xyNKp9Np\nFqUzmQxyuRzHedBS+/GK0m63Gx6PBz6fj0Vpiu8QRWkAnN97vIxkmmSy2Wzc31wuF7swSWyrVCos\nho1VAJKc2vQ5ysAF0NBNnUqleIWB0+k8Ya7rsSDHNMVeUB54Pdd/Pec1MCIekuOZzoNyhKldxPeF\nw2HMmTOHt5FOp3Hs2DEeh0ulEnp6eqDrOpqbm1kIzefziEajExqjpiJiWywWFoxbW1tNhQ0LhQKi\n0SiGh4c5eqVcLpsyww3DQCQSgcPhYMGenOSTgXKV6Znldrs5PkQ8ZsqRpjiWeufndrtN8ReEWDhz\nspTLZY7fAar3el9fH/c1j8fDE5eJRIIjQOjY7XY7mpuboaoqCoUCb0+c2CPEFRa0DQC8mgMwr65o\nFBnSyHWdyWT4uEXXNUWaANXrkkwmTUUfKQ4HAJqamqAoCm+HjlEch2SRXolEIpFIJJITgxSvJRLJ\n+wJFUdgdSZm1E4WW/U+H+/pkisfAiPuccmapSN3JEE2poB0w4nwXHa6apk0tk/sMpDb/erxxCVNl\nOkVpUeBsJEq7XC54PB6TU7qeKG2z2TieoZF4LArhxxOByOUPwCRkOZ1OdnNSm5dKJRb4xooMIQGM\nnJhi3nW9z1UqFWQyGXbu2my2UyJeW61W2O12U/s1GsNE57XYHykGhNzVNpsNwWCQ20EsOihGbCxb\ntsyUEZ3L5VjoBariY39/P9LpNJqamjjvWNM0xGIxU5TDeCAR2+v1TljEJmG+ubl5lBubtpFKpRCP\nx1EoFEzn1dPTg2w2i/b2dj7/wcHBSU9SOp1OLFiwgF3GqqpygUDC5XIhGAwCqLqUqdhm7TlR/jW9\nj55hU131QZMhFMNDznnal+i6FqNE2tvb+TlI0S/i/UaTKiR2i2NC7RhBfZkmAuk9IseLDDEMg2Ne\nxFgnABy1ZbVa4XA4TFnXnZ2dpjZvbm427Yuy3+kYZ2KRYolEIpFIJJKThRSvJRLJ+waHw8FiUyaT\nmbDzbDrc12KhsJMdF6GqKjweD+dOF4vFugXKTgSim42EAoohEAWmSWdyn4GIAv50ZNHSPUU5xMVi\nEfl8HrlcbtpEacpUHq8oTc5McuROBdE9OV7XdS0Oh4Pd1uKEAW1vrGKKVACSnPIkaolFYUVIeM3n\n82MWdTzRkCgrCmuNJkzESQHxPqBrqKoqSqUSbDYbx5EAVaFa13X09fWxQEgxJYsXL8ayZct4W6VS\nCQcOHDA5nGOxGAYHB+F2uxEKhXi72WwWsVhsUmP1ZEVsagdyY7e0tPCxUuHD4eFhAOA4KsMwcPDg\nQVitVrS2tnIb1yuoOF78fj8XglRVlbOhRUKhED+T4vF43WuqqqopmqVcLrPYO9k6COLzixzvtG1a\nVULHrmkaBgYG+LOiqA1U7wcaH5xOJ4aHh5HNZlnEpnuP7j+6N8T6EeLqirEiQ+qNGzTpAsCUE04T\nYBQZkslkWFB3u91wuVw8Ce7z+XiCiNpcVVU+B8qNl0gkEolEIpGcGKR4LZFIpsztt99+qg+B8Xq9\n/MV9MsUbRff1ZIrtnUr3Ne2fcrCBEXfoiRbSxazfVCplcqdRm5KLcyYJ2GL+daM8coqZGUuUTiaT\nLEpns1l21ot5xLXtSkKQzWbD5s2bxyVKU27zdIrS46WROFUPUcQjkdJms5n6rlhYjf4mMbLevsVC\njWJkyFh517quc857I5H7ZFAbcUDZ3fXuCVG8I8QCjIqioFAocCQF9V1qm0QiAUVRWPgDgLlz52Ll\nypUmx+y7775ruv8zmQx6e3uhKAqam5t5jCqXy4hGo5NaLTNVEZsE1ebmZnR0dMDn85kK77a0tJhy\nrpPJJFpaWvicGuVRj5d58+bBarXi/vvvh6qqOHTokGl7qqqira2Nj6GR21vMv6ZJBYqRmswEJonG\nFOfhcDh49YzdbkdrayuL6gMDAywO0+SESDKZ5OvkdDphGAb6+vr4M7QPuv9IvK5UKjAMwzT+TCYy\nhDKrLRaLaQUFnY9hGHA4HOjv7+ef1bqum5qaAIz0Gcrfpt8R6N6TvH85nX7PlUgkZmT/lEhmBlK8\nlkgkU2bdunWn+hAY8QsqFZmaCKL7erJRFyRs1BMUTxZ2u53zfSkHezqKeI0Fua91XTdNHMxUBza5\ndx0OB4thlA2bzWbZKZ1KpSYtSotOabfbDa/XC5/Ph0AgAJ/PB6/XC7fbjY9+9KOnTJQeD+J5jic3\nlvo1ZT0D9SNDxLYTncS1kABG2y0Wi8cVr1OpFL//VOVdE263mwtVkiBHwnot9XKvSTwkJ342m4Wi\nKBynIPbdUqnEjl5x++FwGKtXrzZFk+zevZsn1IBqu/b09KBYLCIQCCAYDLJYTJEdkxkbjidij6eI\nrsViQTAYRDAY5BUs1H8o5/vdd99FNptl97VhGCbX8URRFAXLly/H+eefz2PEkSNHTE50yo4Gqvcp\nucJr8Xg8fM/mcjnu45qmTVjAputKE0JAVaSmfHefz4dsNstCNNHR0WEaV8QVEF6vF62trVAUhVeD\nGIbBKy1qxWvxGBrFghwvMiSXy/GY4Pf7TcdGz3ebzYZCoYBMJgOgGtcSCoXYBU/3BZ0PvUbHTysf\nJO9vTqffcyUSiRnZPyWSmYEUryUSyZS56aabTvUhmHC5XCwgp9PpCYshU3Vfi0ufT7RgPBZWqxVe\nr5dzsHO53AnNwaZ9ASPRIYSqqnC5XCxU5fP5kx6rMl3Uc0oXCgXkcrm6onQ+n4eiKOyGpWXsVFiP\noPuGMowpBkcUpckpTaKa6JSmgmG1zkTg9OujtUwkMkTMc7ZardyGFBkCgIWo8eRdG4bBn6NJl+M5\nrwuFAv9xOBxQVfWUitculwtWq5XPg8a8saJDxP5HjlZRxC4Wi1BVle8ri8WCfD6PcrmMXC6HYrE4\nanxsbm7GxRdfzA50wzDw7rvvslhN++3t7UUymWTXsxh1RMUTJ4MoYlPRT3If0wTe8ZzY1J/cbjfa\n29sxf/58/nk6nUZ/fz8Ln5qmIZFITCnT3uv1YtOmTchms9B1HcPDwxgeHjY9OwKBAGc102qMelA+\nNb2P7n0Skccz9ov9ga5LMpnkybVKpcITRbFYzBR1UhsZIkZ2+P1+LnpJ0Saik1mcPKHxEYDpvq6d\nfDpeZAg9h1RVNfXjcrmMcrnMkSHiOXR2diKVSvG2Q6EQu9ip/cTIEOm6nhmc7s9QiWQmI/unRDIz\nkOK1RCJ530FFpsh1RkLWeJmO7Gv60j8ex9+JhHKwScw/kTnYooBXz/VOwhIJ2IVC4bQSsMmtWqlU\nUC6XjytKi05pilSoJ0origK73Q6Xy8UTCVQ4rJ4o7fF44HK54HQ6R4nS71eRRBSxjge5PwGY2oP6\nHImVBIn5jRzUFE9gGIYp75oib+pFgaRSKQDgoo5iwdhTAYnXEy3aKCI68ulPOp2G3W7n+8/pdKKv\nr48noOqJqH6/H2vWrGG3NQAcOHAA0WiUnbdANXJjcHAQiqIgFArB5/Ox2ByPx5FKpSY9TtFkmShi\nkwP5eCI2FQ0EqvfS7Nmz0dTUxPfI4OAggKrTmcaIrq6uKa0mmT9/Pvx+P0cL9fT0cOwJ0drayteu\nVtwWz1vMv85ms6Z4lvGsRKL+QG0BgN3Vmqaxw7tcLiMej/P2xSgYgiKr6DkEVNuUhHhFUUy53LWT\nvlQoks6tkXhdz3VN4zIAjoMhaHwgoZ5+R6DJFFHMro0MIdGd/i9d1xKJRCKRSCQnHileSySS9yVU\ncAwAuwUnArkuxVzLiSA6YE+U+/rdd9/Fxo0bsWjRIng8HrS2tuLKK6/Ef/7nf5re95nPfAZerxeh\nUIhFIovFguXLl5s3GAewD8AuAHsA9ADQqlmv99x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XYmBggMfYffv24cILL2SncDab5e3m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58pI/fmCenOOV+OtAAobP5xO5rEkQbUoU4l3Vke5rEkRouLrBYIDD4WBux6aKkP2ZkGDv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NKl/4HV6sSuXX/BoEH5AIDp04eisHBRwnMVsmbNV9i06RCeeGIq5syJDhDcdNNQqFQ/T2wJh/NL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JnUsKTYABza5ih8PBHOeU7U3HZWVlye5DW8bBhoYG7Nu3TzbpkpGRga5du7bJuR0Oh9mkmrSI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FEUWQxGJtc1idsAWBQIkBBIKWdaLjjq9Xr4/X6Ew2FIksTif/bv38/2SZEltA7lU8sLMjZ1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\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vx0MPPQTDMLBq1Sp8+ctfxjvvvIPJkycP2K8DQQVVe65/MOdLIDiWGMmfoQLBaEeMT4Fg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fbs2bj66quLPjcphmvQoEF49NFHOYKgrq4O5557LhYvXoyLLrpot46PQCAojaIo3aKN7rvvPrz3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jjVtkwm7zpbcDaZTFKczrdNIQ5D/rzrfCK4yNcXeL1ekskksViMQCCA2WzOKYR6IMTbJSMj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\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mZqxfvx7BYBCvvvoqzjnnHDgcDqxZswaPPfYYZs6cCa/Xi9raWn6TJxVRFKHT6ZBIJBAO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Cxue/TR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\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ISBcUoyL2pfgUsTfFqIh9KT5F+gbDsixfz+GsDMOIB/Lz8/OJj4/39XRERERERERERERE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\nXv2WBQsWeO2zZ8+e7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KmCfudBY8yfEWYAAAVvSURBVEwHp+MSETDGBBhj4ovUhbKMMa87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\neW9pIS+KyAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -653,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -674,7 +602,7 @@ "\n", "First we divide the material up into sentences and fetch their texts from the database.\n", "\n", - "For this we use the function `T.words(nodes, fmt='ha')`, an\n", + "For this we use the function `T.words(nodes, fmt='text-orig-full')`, an\n", "[API function](http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html#texts)\n", "of the etcbc module of LAF-Fabric that provides several representation of the text corresponding to the given nodes.\n", "The next cell gives an overview of the available formats." @@ -682,29 +610,33 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "hp = hebrew primary\n", - "ha = hebrew accent\n", - "hv = hebrew vowel\n", - "hc = hebrew cons\n", - "ea = trans accent\n", - "ev = trans vowel\n", - "ec = trans cons\n", - "pf = phono full\n", - "ps = phono simple\n" - ] + "data": { + "text/plain": [ + "{'lex-orig-full',\n", + " 'lex-orig-plain',\n", + " 'lex-trans-full',\n", + " 'lex-trans-plain',\n", + " 'text-orig-full',\n", + " 'text-orig-full-ketiv',\n", + " 'text-orig-plain',\n", + " 'text-trans-full',\n", + " 'text-trans-full-ketiv',\n", + " 'text-trans-plain'}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "for (acronym, (explanation, method)) in T.formats().items(): print('{} = {}'.format(acronym, explanation))" + "T.formats" ] }, { @@ -1440,7 +1372,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/static/docs/tools/parallel/.ipynb_checkpoints/parallels-checkpoint.ipynb b/static/docs/tools/parallel/.ipynb_checkpoints/parallels-checkpoint.ipynb index aea9d997..c39bd63a 100644 --- a/static/docs/tools/parallel/.ipynb_checkpoints/parallels-checkpoint.ipynb +++ b/static/docs/tools/parallel/.ipynb_checkpoints/parallels-checkpoint.ipynb @@ -4,17 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", + "\n", "\n", "# Parallel Passages in the MT\n", "\n", @@ -30,7 +20,14 @@ "Robert Rezetko and Ian Young.\n", " Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach.\n", " *Ancient Near East Monographs, Number9*. SBL Press Atlanta. 2014. \n", - " [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ)\n", + " [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", "\n", "## 0.3 Open Source\n", "This is an IPython notebook. \n", @@ -101,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -140,8 +137,8 @@ " 2\n", " \n", "\n", - " 37
\n", - " 18
\n", + " 39
\n", + " 19
\n", " 3\n", " \n", "\n", @@ -160,23 +157,23 @@ " 9\n", " \n", "\n", - " 154
\n", - " 70
\n", + " 156
\n", + " 71
\n", " 9\n", " \n", "\n", - " 208
\n", - " 94
\n", + " 214
\n", + " 97
\n", " 10\n", " \n", "\n", - " 309
\n", + " 308
\n", " 138
\n", - " 11\n", + " 10\n", " \n", "\n", - " 473
\n", - " 189
\n", + " 469
\n", + " 188
\n", " 14\n", "     \n", "fixed100LCS\n", @@ -201,28 +198,28 @@ " 3\n", " \n", "\n", - " 85
\n", - " 41
\n", + " 83
\n", + " 40
\n", " 3\n", " \n", "\n", - " 122
\n", - " 56
\n", + " 118
\n", + " 54
\n", " 9\n", " \n", "\n", - " 189
\n", - " 88
\n", + " 193
\n", + " 90
\n", " 9\n", " \n", "\n", - " 287
\n", + " 286
\n", " 132
\n", " 9\n", " \n", "\n", - " 535
\n", - " 214
\n", + " 537
\n", + " 215
\n", " 31\n", "       \n", "fixed50SET\n", @@ -232,8 +229,8 @@ " 0\n", " \n", "\n", - " 4
\n", - " 2
\n", + " 6
\n", + " 3
\n", " 2\n", " \n", "\n", @@ -242,44 +239,44 @@ " 2\n", " \n", "\n", - " 57
\n", - " 26
\n", + " 55
\n", + " 25
\n", " 5\n", " \n", "\n", - " 114
\n", - " 52
\n", + " 104
\n", + " 47
\n", " 7\n", " \n", "\n", - " 186
\n", - " 85
\n", + " 197
\n", + " 90
\n", " 8\n", " \n", "\n", - " 271
\n", - " 124
\n", + " 277
\n", + " 127
\n", " 10\n", " \n", "\n", - " 385
\n", - " 176
\n", + " 394
\n", + " 180
\n", " 12\n", " \n", "\n", - " 535
\n", - " 235
\n", + " 543
\n", + " 239
\n", " 15\n", " \n", "\n", - " 748
\n", - " 315
\n", + " 755
\n", + " 322
\n", " 20\n", " \n", "\n", - " 1187
\n", - " 465
\n", - " 47\n", + " 1183
\n", + " 460
\n", + " 48\n", "     \n", "fixed50LCS\n", "\n", @@ -293,64 +290,64 @@ " 2\n", " \n", "\n", - " 53
\n", - " 25
\n", + " 43
\n", + " 20
\n", " 5\n", " \n", "\n", - " 119
\n", - " 53
\n", + " 125
\n", + " 56
\n", " 11\n", " \n", "\n", - " 196
\n", - " 89
\n", + " 204
\n", + " 93
\n", " 12\n", " \n", "\n", - " 301
\n", - " 135
\n", + " 299
\n", + " 134
\n", " 19\n", " \n", "\n", - " 464
\n", - " 205
\n", + " 470
\n", + " 209
\n", " 20\n", " \n", "\n", - " 761
\n", + " 765
\n", " 312
\n", - " 28\n", + " 29\n", " \n", "\n", - " 1888
\n", - " 552
\n", - " 112\n", + " 1867
\n", + " 553
\n", + " 106\n", "       \n", "fixed20SET\n", "\n", - " 28
\n", - " 14
\n", + " 36
\n", + " 18
\n", " 2\n", " \n", "\n", - " 28
\n", - " 14
\n", + " 36
\n", + " 18
\n", " 2\n", " \n", "\n", - " 105
\n", - " 46
\n", - " 8\n", + " 126
\n", + " 58
\n", + " 6\n", " \n", "\n", - " 174
\n", - " 72
\n", + " 199
\n", + " 84
\n", " 12\n", " \n", "\n", - " 326
\n", - " 143
\n", + " 332
\n", + " 146
\n", " 12\n", " \n", "\n", @@ -359,177 +356,177 @@ " 12\n", " \n", "\n", - " 762
\n", - " 331
\n", + " 760
\n", + " 326
\n", " 12\n", " \n", "\n", - " 1058
\n", - " 452
\n", + " 1096
\n", + " 470
\n", " 13\n", " \n", "\n", - " 1830
\n", - " 733
\n", - " 29\n", + " 1837
\n", + " 739
\n", + " 21\n", " \n", "\n", - " 2787
\n", - " 979
\n", - " 154\n", + " 2826
\n", + " 997
\n", + " 175\n", " \n", "\n", - " 4913
\n", - " 1203
\n", - " 1573\n", + " 4933
\n", + " 1212
\n", + " 1638\n", "     \n", "fixed20LCS\n", "\n", - " 6
\n", - " 3
\n", + " 12
\n", + " 6
\n", " 2\n", " \n", "\n", - " 47
\n", - " 22
\n", + " 62
\n", + " 29
\n", " 4\n", " \n", "\n", - " 149
\n", - " 61
\n", - " 11\n", + " 181
\n", + " 76
\n", + " 12\n", " \n", "\n", - " 311
\n", - " 136
\n", + " 339
\n", + " 149
\n", " 12\n", " \n", "\n", - " 682
\n", - " 299
\n", + " 681
\n", + " 300
\n", " 12\n", " \n", "\n", " 1137
\n", " 470
\n", - " 27\n", + " 26\n", " \n", "\n", - " 2217
\n", - " 838
\n", - " 52\n", + " 2224
\n", + " 844
\n", + " 65\n", " \n", "\n", - " 5971
\n", - " 1223
\n", - " 2709\n", + " 5985
\n", + " 1253
\n", + " 2718\n", " \n", "\n", - " 17656
\n", - " 152
\n", - " 17329\n", + " 17654
\n", + " 163
\n", + " 17307\n", "       \n", "fixed10SET\n", "\n", - " 448
\n", - " 209
\n", + " 462
\n", + " 220
\n", " 5\n", " \n", "\n", - " 448
\n", - " 209
\n", + " 462
\n", + " 220
\n", " 5\n", " \n", "\n", - " 482
\n", - " 220
\n", + " 494
\n", + " 231
\n", " 7\n", " \n", "\n", - " 1114
\n", - " 493
\n", - " 11\n", + " 1109
\n", + " 489
\n", + " 20\n", " \n", "\n", - " 1536
\n", - " 628
\n", - " 36\n", + " 1540
\n", + " 631
\n", + " 39\n", " \n", "\n", - " 2754
\n", - " 1094
\n", - " 74\n", + " 2825
\n", + " 1126
\n", + " 75\n", " \n", "\n", - " 4020
\n", - " 1474
\n", - " 163\n", + " 4079
\n", + " 1506
\n", + " 144\n", " \n", "\n", - " 5785
\n", - " 1850
\n", - " 702\n", + " 5792
\n", + " 1855
\n", + " 669\n", " \n", "\n", - " 10211
\n", - " 2210
\n", - " 4141\n", + " 10165
\n", + " 2189
\n", + " 4304\n", " \n", "\n", - " 14100
\n", - " 2018
\n", - " 9047\n", + " 13984
\n", + " 2008
\n", + " 8877\n", " \n", "\n", - " 23054
\n", - " 1455
\n", - " 19638\n", + " 22932
\n", + " 1442
\n", + " 19576\n", "     \n", "fixed10LCS\n", "\n", - " 239
\n", - " 114
\n", + " 277
\n", + " 135
\n", " 5\n", " \n", "\n", - " 379
\n", - " 182
\n", + " 408
\n", + " 199
\n", " 5\n", " \n", "\n", - " 905
\n", - " 399
\n", - " 12\n", + " 937
\n", + " 423
\n", + " 11\n", " \n", "\n", - " 1917
\n", - " 791
\n", - " 71\n", + " 1980
\n", + " 831
\n", + " 73\n", " \n", "\n", - " 3850
\n", - " 1418
\n", - " 137\n", + " 3894
\n", + " 1440
\n", + " 161\n", " \n", "\n", - " 8552
\n", - " 2342
\n", - " 1980\n", + " 8599
\n", + " 2328
\n", + " 2059\n", " \n", "\n", - " 20382
\n", - " 1926
\n", - " 15724\n", + " 20425
\n", + " 1937
\n", + " 15671\n", " \n", "\n", - " 37700
\n", - " 223
\n", - " 37234\n", + " 37696
\n", + " 218
\n", + " 37229\n", " \n", "\n", " 42450
\n", - " 2
\n", - " 42448\n", + " 4
\n", + " 42444\n", "       \n", "objectchapterSET\n", "\n", @@ -583,8 +580,8 @@ " 2\n", " \n", "\n", - " 56
\n", - " 28
\n", + " 58
\n", + " 29
\n", " 2\n", " \n", "\n", @@ -685,8 +682,8 @@ " 154\n", " \n", "\n", - " 2359
\n", - " 961
\n", + " 2361
\n", + " 962
\n", " 156\n", " \n", "\n", @@ -711,7 +708,7 @@ " \n", "\n", " 6711
\n", - " 1850
\n", + " 1851
\n", " 1476\n", "     \n", "objectverseLCS\n", @@ -741,7 +738,7 @@ " 174\n", " \n", "\n", - " 3685
\n", + " 3682
\n", " 1340
\n", " 190\n", " \n", @@ -751,14 +748,14 @@ " 257\n", " \n", "\n", - " 9046
\n", + " 9050
\n", " 1821
\n", - " 4221\n", + " 4225\n", " \n", "\n", - " 18941
\n", + " 18945
\n", " 380
\n", - " 18073\n", + " 18077\n", "       \n", "objecthalf_verseSET\n", "\n", @@ -812,9 +809,9 @@ " 10993\n", " \n", "\n", - " 28990
\n", + " 28988
\n", " 2031
\n", - " 24008\n", + " 24006\n", "     \n", "objecthalf_verseLCS\n", "\n", @@ -843,19 +840,19 @@ " 2364\n", " \n", "\n", - " 19148
\n", + " 19147
\n", " 3084
\n", " 11090\n", " \n", "\n", - " 28472
\n", + " 28473
\n", " 1894
\n", - " 23864\n", + " 23865\n", " \n", "\n", - " 38180
\n", + " 38182
\n", " 665
\n", - " 36649\n", + " 36651\n", " \n", "\n", " 44011
\n", @@ -864,105 +861,105 @@ "       \n", "objectsentenceSET\n", "\n", - " 19028
\n", - " 4325
\n", - " 1056\n", + " 19031
\n", + " 4324
\n", + " 1055\n", " \n", "\n", - " 19036
\n", - " 4329
\n", - " 1056\n", + " 19039
\n", + " 4328
\n", + " 1055\n", " \n", "\n", - " 19208
\n", - " 4404
\n", - " 1056\n", + " 19214
\n", + " 4406
\n", + " 1055\n", " \n", "\n", - " 19771
\n", - " 4606
\n", - " 1056\n", + " 19777
\n", + " 4608
\n", + " 1055\n", " \n", "\n", - " 22063
\n", - " 5066
\n", - " 1056\n", + " 22082
\n", + " 5073
\n", + " 1055\n", " \n", "\n", - " 25724
\n", - " 4993
\n", - " 4853\n", + " 25751
\n", + " 5000
\n", + " 4864\n", " \n", "\n", - " 26880
\n", - " 5222
\n", - " 5232\n", + " 26905
\n", + " 5229
\n", + " 5245\n", " \n", "\n", - " 33378
\n", - " 4111
\n", - " 17433\n", + " 33410
\n", + " 4109
\n", + " 17521\n", " \n", "\n", - " 38807
\n", - " 3753
\n", - " 24074\n", + " 38818
\n", + " 3746
\n", + " 24132\n", " \n", "\n", - " 41835
\n", - " 3505
\n", - " 28077\n", + " 41825
\n", + " 3497
\n", + " 28097\n", " \n", "\n", - " 53117
\n", - " 1174
\n", - " 50174\n", + " 53097
\n", + " 1172
\n", + " 50162\n", "     \n", "objectsentenceLCS\n", "\n", - " 17532
\n", - " 3981
\n", - " 1054\n", + " 17533
\n", + " 3978
\n", + " 1053\n", " \n", - "\n", - " 18079
\n", - " 4215
\n", - " 1054\n", + "\n", + " 18091
\n", + " 4218
\n", + " 1053\n", " \n", "\n", - " 21246
\n", - " 4993
\n", - " 1054\n", + " 21261
\n", + " 4997
\n", + " 1053\n", " \n", "\n", - " 26473
\n", - " 4853
\n", + " 26488
\n", + " 4855
\n", " 7321\n", " \n", "\n", - " 35626
\n", - " 3470
\n", - " 25548\n", + " 35629
\n", + " 3469
\n", + " 25570\n", " \n", "\n", - " 44307
\n", - " 2293
\n", - " 38261\n", + " 44303
\n", + " 2291
\n", + " 38288\n", " \n", "\n", - " 52535
\n", - " 1197
\n", + " 52528
\n", + " 1199
\n", " 49324\n", " \n", "\n", - " 58863
\n", - " 460
\n", - " 57763\n", + " 58855
\n", + " 463
\n", + " 57753\n", " \n", "\n", - " 62379
\n", - " 105
\n", - " 62134\n", + " 62369
\n", + " 112
\n", + " 62109\n", "       \n", "\n" ], @@ -970,7 +967,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1270,18 +1267,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0.00s This is LAF-Fabric 4.5.18\n", - "API reference: http://laf-fabric.readthedocs.org/en/latest/texts/API-reference.html\n", - "Feature doc: https://shebanq.ancient-data.org/static/docs/featuredoc/texts/welcome.html\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import sys, os, re, collections, pickle, math, difflib, glob\n", "\n", @@ -1293,10 +1279,7 @@ "from difflib import SequenceMatcher\n", "from Levenshtein import ratio\n", "\n", - "import laf\n", - "from laf.fabric import LafFabric\n", - "from etcbc.preprocess import prepare\n", - "fabric = LafFabric()" + "from tf.fabric import Fabric" ] }, { @@ -1317,39 +1300,61 @@ }, "outputs": [ { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "This is Text-Fabric 1.2.7\n", + "Api reference : https://github.com/ETCBC/text-fabric/wiki/Api\n", + "Tutorial : https://github.com/ETCBC/text-fabric/blob/master/docs/tutorial.ipynb\n", + "Data sources : https://github.com/ETCBC/text-fabric-data\n", + "Data docs : https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html\n", + "Shebanq docs : https://shebanq.ancient-data.org/text\n", + "Slack team : https://shebanq.slack.com/signup\n", + "Questions? Ask shebanq@ancient-data.org for an invite to Slack\n", + "107 features found and 0 ignored\n" + ] + } + ], + "source": [ + "source = 'etcbc'\n", + "version = '4c'\n", + "ETCBC = 'hebrew/{}{}'.format(source, version)\n", + "TF = Fabric( modules=ETCBC )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", "output_type": "stream", "text": [ - " 0.00s LOADING API: please wait ... \n", - " 0.00s USING main DATA COMPILED AT: 2015-11-02T15-08-56\n", - " 3.23s LOGFILE=/Users/dirk/SURFdrive/laf-fabric-output/etcbc4b/parallel/__log__parallel.txt\n", - " 3.23s INFO: LOADING PREPARED data: please wait ... \n", - " 3.24s prep prep: G.node_sort\n", - " 3.36s prep prep: G.node_sort_inv\n", - " 3.89s prep prep: L.node_up\n", - " 7.26s prep prep: L.node_down\n", - " 13s prep prep: V.verses\n", - " 13s prep prep: V.books_la\n", - " 13s ETCBC reference: http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html\n", - " 15s INFO: LOADED PREPARED data\n", - " 15s INFO: DATA LOADED FROM SOURCE etcbc4b AND ANNOX lexicon FOR TASK parallel AT 2016-03-03T10-49-34\n" + " 0.00s loading features ...\n", + " | 0.05s B otype from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.00s M otext from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.01s B book from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.01s B chapter from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.01s B verse from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.24s B g_word_utf8 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.10s B trailer_utf8 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.16s B lex from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.02s B label from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " | 0.28s B number from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", + " 5.83s All features loaded/computed - for details use loadLog()\n" ] } ], "source": [ - "version = '4b'\n", - "API = fabric.load('etcbc{}'.format(version), '--', 'parallel', {\n", - " \"xmlids\": {\"node\": False, \"edge\": False},\n", - " \"features\": ('''\n", - " otype\n", - " lex g_word_utf8 trailer_utf8\n", - " book chapter verse label number\n", - " ''',\n", - " ''),\n", - " \"prepare\": prepare,\n", - " \"primary\": False,\n", - "}, verbose='NORMAL')\n", - "exec(fabric.localnames.format(var='fabric'))" + "api = TF.load('''\n", + " otype\n", + " lex g_word_utf8 trailer_utf8\n", + " book chapter verse label number\n", + "''')\n", + "api.makeAvailableIn(globals())" ] }, { @@ -1365,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -1419,8 +1424,7 @@ "CLIQUES_PROGRESS = 1 * KILO\n", "\n", "# locations and hyperlinks\n", - "REMOTE_BASE = 'https://surfdrive.surf.nl/files/public.php?service=files&t=dedf27be7e171ab8a8b151f84ded93e8'\n", - "LOCAL_BASE_COMP = my_file('').rstrip('/')\n", + "LOCAL_BASE_COMP = '/Users/dirk/tf/text-fabric-output/{}{}/parallels'.format(source, version)\n", "LOCAL_BASE_OUTP = 'files'\n", "EXPERIMENT_DIR = 'experiments'\n", "EXPERIMENT_FILE = 'experiments'\n", @@ -1447,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -1568,7 +1572,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -1577,7 +1581,7 @@ "def chunking(do_chunk):\n", " global chunks, book_rank\n", " if not do_chunk:\n", - " msg('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", + " info('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", " meta['# CHUNKS'] = len(chunks)\n", " return\n", "\n", @@ -1588,18 +1592,18 @@ "\n", " if os.path.exists(chunk_path):\n", " with open(chunk_path, 'rb') as f: chunks = pickle.load(f)\n", - " msg('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(\n", + " info('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(\n", " CHUNK_LB, CHUNK_DESC,\n", " len(chunks),\n", " ))\n", " else:\n", - " msg('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))\n", + " info('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))\n", " chunks = []\n", " book_rank = {}\n", " for b in F.otype.s('book'):\n", " book_name = F.book.v(b)\n", " book_rank[book_name] = b\n", - " words = L.d('word', b)\n", + " words = L.d(b, otype='word')\n", " nwords = len(words)\n", " if CHUNK_FIXED:\n", " nchunks = nwords // CHUNK_SIZE\n", @@ -1623,13 +1627,13 @@ " cur_chunk += 1\n", " these_chunks[-1].append(w)\n", " else:\n", - " these_chunks = [L.d('word', c) for c in L.d(CHUNK_OBJECT, b)]\n", + " these_chunks = [L.d(c, otype='word') for c in L.d(b, otype=CHUNK_OBJECT)]\n", "\n", " chunks.extend(these_chunks)\n", "\n", " chunkvolume = sum(len(c) for c in these_chunks)\n", " if VERBOSE:\n", - " msg('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(\n", + " info('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(\n", " CHUNK_LB, CHUNK_DESC,\n", " book_name, nwords, len(these_chunks), \n", " min(len(c) for c in these_chunks), \n", @@ -1637,7 +1641,7 @@ " 'OK' if chunkvolume == nwords else 'ERROR',\n", " ))\n", " with open(chunk_path, 'wb') as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL)\n", - " msg('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", + " info('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", " meta['# CHUNKS'] = len(chunks)" ] }, @@ -1663,7 +1667,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -1672,9 +1676,9 @@ "def preparing(do_prepare):\n", " global chunk_data\n", " if not do_prepare:\n", - " msg('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", + " info('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", " return\n", - " msg('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", + " info('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", " chunk_data = []\n", " if SIMILARITY_METHOD == 'SET':\n", " for c in chunks:\n", @@ -1688,7 +1692,7 @@ " clean_words = (w for w in words if w != '')\n", " this_data = ' '.join(clean_words)\n", " chunk_data.append(this_data)\n", - " msg('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))" + " info('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))" ] }, { @@ -1716,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -1729,7 +1733,7 @@ " meta['LOWEST AVAILABLE SIMILARITY'] = cmin\n", " meta['HIGHEST AVAILABLE SIMILARITY'] = cmax\n", " meta['# EQUAL COMPARISONS'] = nequals\n", - " msg('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " cmin, cmax, nequals,\n", " ))\n", @@ -1749,7 +1753,7 @@ " sim_lb = 'K'\n", " \n", " if not do_sim:\n", - " msg('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", " ))\n", @@ -1764,7 +1768,7 @@ "\n", " if os.path.exists(matrix_path):\n", " with open(matrix_path, 'rb') as f: chunk_dist = pickle.load(f)\n", - " msg('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", " ))\n", @@ -1772,7 +1776,7 @@ " similarity_post()\n", " return\n", "\n", - " msg('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " total_distances // sim_unit, sim_lb, total_distances\n", " ))\n", @@ -1798,7 +1802,7 @@ " wt += 1\n", " if wc == SIMILARITY_PROGRESS:\n", " wc = 0\n", - " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " wt // sim_unit, sim_lb, len(chunk_dist),\n", " ))\n", @@ -1819,14 +1823,14 @@ " wt += 1\n", " if wc == SIMILARITY_PROGRESS:\n", " wc = 0\n", - " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " wt // sim_unit, sim_lb, len(chunk_dist),\n", " ))\n", "\n", " with open(matrix_path, 'wb') as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL)\n", " \n", - " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(\n", + " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", " wt // sim_unit, sim_lb, wt, len(chunk_dist),\n", " ))\n", @@ -1867,7 +1871,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false @@ -1877,11 +1881,11 @@ "def key_chunk(i):\n", " c = chunks[i]\n", " w = c[0]\n", - " return (-len(c), L.u('book', w), L.u('chapter', w), L.u('verse', w))\n", + " return (-len(c), L.u(w, otype='book')[0], L.u(w, otype='chapter')[0], L.u(w, otype='verse')[0])\n", "\n", "def meta_clique_pre():\n", " global similars, passages\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " ))\n", " similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD}\n", @@ -1895,7 +1899,7 @@ " meta['# SIMILAR PASSAGES'] = len(passages) \n", "\n", "def meta_clique_pre2():\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(similars), len(passages),\n", "))\n", @@ -1914,12 +1918,12 @@ " totmn += ln * n\n", " totcn += n\n", " if VERBOSE:\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " n, ln,\n", " ))\n", " meta['# CLIQUES of LENGTH {:>4}'.format(ln)] = n\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " totmn, totcn,\n", " ))\n", @@ -1927,14 +1931,14 @@ "def cliqueing(do_clique):\n", " global cliques\n", " if not do_clique:\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(cliques), len(passages), len(similars), \n", " ))\n", " meta_clique_pre2()\n", " meta_clique_post()\n", " return\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, \n", " ))\n", " meta_clique_pre()\n", @@ -1945,14 +1949,14 @@ " )\n", " if os.path.exists(clique_path):\n", " with open(clique_path, 'rb') as f: cliques = pickle.load(f)\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(cliques), len(passages), len(similars), \n", " ))\n", " meta_clique_post()\n", " return\n", "\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(passages), len(similars), \n", " ))\n", @@ -1985,14 +1989,14 @@ " npc += 1\n", " if npc == CLIQUES_PROGRESS:\n", " npc = 0\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(cliques_unsorted), np,\n", " ))\n", " cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted])\n", " with open(clique_path, 'wb') as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL)\n", " meta_clique_post()\n", - " msg('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", + " info('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(cliques), len(passages), len(similars), \n", " ))" @@ -2021,7 +2025,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -2149,7 +2153,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": { "collapsed": false, "scrolled": false @@ -2159,8 +2163,8 @@ "def xterse_chunk(i):\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = L.u('book', fword)\n", - " chapter = L.u('chapter', fword)\n", + " book = L.u(fword, otype='book')[0]\n", + " chapter = L.u(fword, otype='chapter')[0]\n", " return (book, chapter)\n", "\n", "def xterse_clique(ii):\n", @@ -2169,9 +2173,9 @@ "def terse_chunk(i):\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = L.u('book', fword)\n", - " chapter = L.u('chapter', fword)\n", - " verse = L.u('verse', fword)\n", + " book = L.u(fword, otype='book')[0]\n", + " chapter = L.u(fword, otype='chapter')[0]\n", + " verse = L.u(fword, otype='verse')[0]\n", " return (book, chapter, verse)\n", "\n", "def terse_clique(ii):\n", @@ -2182,7 +2186,7 @@ " book = F.book.v(bk)\n", " chapter = F.chapter.v(ch)\n", " verse = F.verse.v(vs)\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', vs))\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(vs, otype='word'))\n", " verse_label = '{} {}:{}'.format(book, chapter, verse)\n", " htext = '{}{}'.format(verse_label, text)\n", " return '{}'.format(htext)\n", @@ -2194,6 +2198,8 @@ " cnd = ''\n", " (cur_b, cur_c) = (None, None)\n", " for (b, c, v) in vlabels:\n", + " c = str(c)\n", + " v = str(v)\n", " sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '\n", " show_b = b+' ' if cur_b != b else ''\n", " show_c = c+':' if cur_b != b or cur_c != c else ''\n", @@ -2253,9 +2259,9 @@ " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = F.book.v(L.u('book', fword))\n", - " chapter = F.chapter.v(L.u('chapter', fword))\n", - " verse = F.verse.v(L.u('verse', fword))\n", + " book = F.book.v(L.u(fword, otype='book')[0])\n", + " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", + " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", " condensed.setdefault(text, []).append((book, chapter, verse))\n", " result = []\n", @@ -2279,9 +2285,9 @@ " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = F.book.v(L.u('book', fword))\n", - " chapter = F.chapter.v(L.u('chapter', fword))\n", - " verse = F.verse.v(L.u('verse', fword))\n", + " book = F.book.v(L.u(fword, otype='book')[0])\n", + " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", + " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", " condensed.setdefault(text, []).append((book, chapter, verse))\n", " result = []\n", @@ -2301,9 +2307,9 @@ " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = F.book.v(L.u('book', fword))\n", - " chapter = F.chapter.v(L.u('chapter', fword))\n", - " verse = F.verse.v(L.u('verse', fword))\n", + " book = F.book.v(L.u(fword, otype='book')[0])\n", + " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", + " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", " verse_labels.append((book, chapter, verse))\n", " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", " return '

{} {}

'.format(\n", @@ -2315,9 +2321,9 @@ " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", " chunk = chunks[i]\n", " fword = chunk[0]\n", - " book = F.book.v(L.u('book', fword))\n", - " chapter = F.chapter.v(L.u('chapter', fword))\n", - " verse = F.verse.v(L.u('verse', fword))\n", + " book = F.book.v(L.u(fword, otype='book')[0])\n", + " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", + " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", " verse_labels.append((book, chapter, verse))\n", " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", " extra = '+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else ''\n", @@ -2327,9 +2333,9 @@ "\n", "def lines_chapter(c):\n", " lines = []\n", - " for v in L.d('verse', c):\n", + " for v in L.d(c, otype='verse'):\n", " vl = F.verse.v(v)\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', v))\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(v, otype='word'))\n", " lines.append('{} {}'.format(vl, text.replace('\\n', ' ')))\n", " return lines\n", "\n", @@ -2358,7 +2364,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -2367,7 +2373,7 @@ "# generate the table of experiments\n", "def gen_html(standalone=False):\n", " global other_exps\n", - " msg('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))\n", + " info('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))\n", " stats = collections.Counter()\n", " pre = '''\n", "\n", @@ -2439,8 +2445,8 @@ " other_exps = experiments\n", "\n", " for stat in sorted(stats):\n", - " msg('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))\n", - " msg(\"EXPERIMENT: Generated html report\")" + " info('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))\n", + " info(\"EXPERIMENT: Generated html report\")" ] }, { @@ -2454,7 +2460,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -2469,7 +2475,7 @@ "\n", "def printing():\n", " global outputs, bin_cliques, base_name\n", - " msg('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " ))\n", " xt_cliques = {xterse_clique(c) for c in cliques} # chapter cliques as tuples of (b, ch) tuples\n", @@ -2486,7 +2492,7 @@ " chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in {'rec', 'nor', 'dub'}\n", " cdoing = 'involving' if chapters_ok else 'skipping'\n", "\n", - " msg('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " ncliques, cdoing, len(bin_cliques),\n", " ))\n", @@ -2530,7 +2536,7 @@ " nnew = 0\n", " if chapters_ok:\n", " chapter_diffs = []\n", - " msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(bin_cliques),\n", " ))\n", @@ -2548,7 +2554,7 @@ " htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2)\n", " with open(hfilepath, 'w') as f: f.write(htext)\n", " if VERBOSE:\n", - " msg('PRINT ({} {} {} M>{} S>{}): written {}'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): written {}'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " hfilename,\n", " ))\n", @@ -2559,7 +2565,7 @@ " '../{}/{}'.format(CHAPTER_DIR, hfilename), \n", " '{} versus {}'.format(lb1, lb2),\n", " ))\n", - " msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " nnew, nexist,\n", " ))\n", @@ -2639,7 +2645,7 @@ " destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = (\n", " len(passages), len(cliques), l_c_l,\n", " )\n", - " msg('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(\n", + " info('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(\n", " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", " len(cliques), len(allgen_htmls), cdoing, len(bin_cliques)\n", " ))" @@ -2656,7 +2662,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -2747,7 +2753,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -2755,13 +2761,13 @@ "source": [ "def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca)\n", "\n", - "def get_verse_o(o, ca=False): return get_verse_w(L.d('word', o)[0], ca=ca)\n", + "def get_verse_o(o, ca=False): return get_verse_w(L.d(o, otype='word')[0], ca=ca)\n", "\n", "def get_verse_w(w, ca=False):\n", - " book = F.book.v(L.u('book', w))\n", - " chapter = F.chapter.v(L.u('chapter', w))\n", - " verse = F.verse.v(L.u('verse', w))\n", - " if ca: ca = F.number.v(L.u('clause_atom', w))\n", + " book = F.book.v(L.u(w, otype='book')[0])\n", + " chapter = F.chapter.v(L.u(w, otype='chapter')[0])\n", + " verse = F.verse.v(L.u(w, otype='verse')[0])\n", + " if ca: ca = F.number.v(L.u(w, otype='clause_atom')[0])\n", " return (book, chapter, verse, ca) if ca else (book, chapter, verse)\n", "\n", "def key_verse(x):\n", @@ -2794,16 +2800,16 @@ "\n", "def get_crossrefs():\n", " global crossrefs\n", - " msg('CROSSREFS: Fetching crossrefs')\n", + " info('CROSSREFS: Fetching crossrefs')\n", " crossrefs_proto = {}\n", " crossrefs = {}\n", " (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX\n", " sim_thr = SHEBANQ_SIMILARITY\n", " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", " if skip: return\n", - " msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", + " info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", - " msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", + " info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", " len(crossrefs_proto),\n", " ))\n", @@ -2818,7 +2824,7 @@ " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", " total = sum(len(x) for x in crossrefs.values())\n", " f.close()\n", - " msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", + " info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", "\n", "def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to):\n", " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", @@ -2827,14 +2833,14 @@ " preparing(do_prep)\n", " similarity(do_sim)\n", "\n", - " msg('CROSSREFS: Fetching crossrefs')\n", + " info('CROSSREFS: Fetching crossrefs')\n", " crossrefs_proto = {}\n", " crossrefs = {}\n", " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", " if skip: return\n", - " msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", + " info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", - " msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", + " info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", " len(crossrefs_proto),\n", " ))\n", @@ -2849,7 +2855,7 @@ " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", " total = sum(len(x) for x in crossrefs.values())\n", " f.close()\n", - " msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", + " info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", "\n", "def compile_refs():\n", " global refs_compiled\n", @@ -2876,7 +2882,7 @@ " link_target = '{} {}:{}'.format(vy[0], vy[1], vy[2])\n", " these_refs.append('[{}]({})'.format(link_text, link_target))\n", " refs_compiled.append((x, ' '.join(these_refs)))\n", - " msg('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))\n", + " info('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))\n", "\n", "def get_chapter_diffs():\n", " global chapter_diffs\n", @@ -2888,7 +2894,7 @@ " chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(\n", " SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,\n", " )))\n", - " msg('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))\n", + " info('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))\n", "\n", " \n", "def get_clique_refs():\n", @@ -2900,7 +2906,7 @@ " clique_refs.append((j, i, '{}/{}/{}/{}/clique_{}_{}.html#c_{}'.format(\n", " SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i,\n", " )))\n", - " msg('CROSSREFS: Added {} clique references'.format(len(clique_refs)))\n", + " info('CROSSREFS: Added {} clique references'.format(len(clique_refs)))\n", "\n", "sfields = '''\n", " version\n", @@ -2992,7 +2998,7 @@ " '[all variants (clique {})](tool:{})'.format(clique, fl),\n", " ))\n", "\n", - " msg('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))\n", + " info('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))\n", "\n", "def crossrefs2shebanq():\n", " expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,)\n", @@ -3020,2136 +3026,3910 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - " 37s EXPERIMENT: Generating html report\n", - " 37s EXPERIMENT: 240 no results available\n", - " 37s EXPERIMENT: Generated html report\n", - " 37s CHUNKING (F 100): Loaded: 4244 chunks\n", - " 37s CHUNKING (F 100): Made 4244 chunks\n", - " 37s PREPARING (F 100 SET)\n", - " 37s PREPARING (F 100 SET): Done 4244 chunks.\n", - " 37s SIMILARITY (F 100 SET M>50): Loaded: 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 37s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates\n", - " 37s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix\n", - " 37s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages\n", - " 37s CLIQUES (F 100 SET M>50 S>100): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 37s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques\n", - " 37s PRINT (F 100 SET M>50 S>100): sorting out cliques\n", - " 37s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 37s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 37s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 37s PREPARING (F 100 SET): Already prepared\n", - " 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 37s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates\n", - " 37s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix\n", - " 37s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages\n", - " 37s CLIQUES (F 100 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 37s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques\n", - " 37s PRINT (F 100 SET M>50 S>95): sorting out cliques\n", - " 37s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 37s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 37s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 37s PREPARING (F 100 SET): Already prepared\n", - " 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 38s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates\n", - " 38s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix\n", - " 38s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages\n", - " 38s CLIQUES (F 100 SET M>50 S>90): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", - " 38s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques\n", - " 38s PRINT (F 100 SET M>50 S>90): sorting out cliques\n", - " 38s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs\n", - " 38s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs\n", - " 38s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 38s PREPARING (F 100 SET): Already prepared\n", - " 38s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 38s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates\n", - " 38s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix\n", - " 38s CLIQUES (F 100 SET M>50 S>85): 19 relevant similarities between 37 passages\n", - " 38s CLIQUES (F 100 SET M>50 S>85): Loaded: 18 cliques out of 37 chunks from 19 comparisons\n", - " 38s CLIQUES (F 100 SET M>50 S>85): 37 members in 18 cliques\n", - " 38s PRINT (F 100 SET M>50 S>85): sorting out cliques\n", - " 38s PRINT (F 100 SET M>50 S>85): formatting 18 cliques skipping 6 binary chapter diffs\n", - " 38s PRINT (F 100 SET M>50 S>85): formatted 18 cliques (1 files) skipping 6 binary chapter diffs\n", - " 38s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 38s PREPARING (F 100 SET): Already prepared\n", - " 38s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 38s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates\n", - " 38s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix\n", - " 38s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages\n", - " 38s CLIQUES (F 100 SET M>50 S>80): Loaded: 30 cliques out of 64 chunks from 35 comparisons\n", - " 38s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques\n", - " 38s PRINT (F 100 SET M>50 S>80): sorting out cliques\n", - " 38s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs\n", - " 39s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs\n", - " 39s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 39s PREPARING (F 100 SET): Already prepared\n", - " 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 39s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates\n", - " 39s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix\n", - " 39s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages\n", - " 39s CLIQUES (F 100 SET M>50 S>75): Loaded: 40 cliques out of 87 chunks from 63 comparisons\n", - " 39s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques\n", - " 39s PRINT (F 100 SET M>50 S>75): sorting out cliques\n", - " 39s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs\n", - " 39s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", - " 39s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 39s PREPARING (F 100 SET): Already prepared\n", - " 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 39s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates\n", - " 39s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix\n", - " 39s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages\n", - " 39s CLIQUES (F 100 SET M>50 S>70): Loaded: 52 cliques out of 113 chunks from 87 comparisons\n", - " 39s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques\n", - " 39s PRINT (F 100 SET M>50 S>70): sorting out cliques\n", - " 39s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs\n", - " 41s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs\n", - " 41s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 41s PREPARING (F 100 SET): Already prepared\n", - " 41s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 41s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 41s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates\n", - " 41s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix\n", - " 41s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 154 passages\n", - " 41s CLIQUES (F 100 SET M>50 S>65): Loaded: 70 cliques out of 154 chunks from 115 comparisons\n", - " 41s CLIQUES (F 100 SET M>50 S>65): 154 members in 70 cliques\n", - " 41s PRINT (F 100 SET M>50 S>65): sorting out cliques\n", - " 41s PRINT (F 100 SET M>50 S>65): formatting 70 cliques skipping 28 binary chapter diffs\n", - " 42s PRINT (F 100 SET M>50 S>65): formatted 70 cliques (2 files) skipping 28 binary chapter diffs\n", - " 42s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 42s PREPARING (F 100 SET): Already prepared\n", - " 42s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 42s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates\n", - " 42s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix\n", - " 42s CLIQUES (F 100 SET M>50 S>60): 148 relevant similarities between 208 passages\n", - " 42s CLIQUES (F 100 SET M>50 S>60): Loaded: 94 cliques out of 208 chunks from 148 comparisons\n", - " 42s CLIQUES (F 100 SET M>50 S>60): 208 members in 94 cliques\n", - " 42s PRINT (F 100 SET M>50 S>60): sorting out cliques\n", - " 42s PRINT (F 100 SET M>50 S>60): formatting 94 cliques skipping 35 binary chapter diffs\n", - " 44s PRINT (F 100 SET M>50 S>60): formatted 94 cliques (2 files) skipping 35 binary chapter diffs\n", - " 44s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 44s PREPARING (F 100 SET): Already prepared\n", - " 44s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 44s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 44s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates\n", - " 44s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix\n", - " 44s CLIQUES (F 100 SET M>50 S>55): 225 relevant similarities between 309 passages\n", - " 44s CLIQUES (F 100 SET M>50 S>55): Loaded: 138 cliques out of 309 chunks from 225 comparisons\n", - " 44s CLIQUES (F 100 SET M>50 S>55): 309 members in 138 cliques\n", - " 44s PRINT (F 100 SET M>50 S>55): sorting out cliques\n", - " 44s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 54 binary chapter diffs\n", - " 47s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 54 binary chapter diffs\n", - " 47s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 47s PREPARING (F 100 SET): Already prepared\n", - " 47s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", - " 47s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 47s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates\n", - " 47s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix\n", - " 47s CLIQUES (F 100 SET M>50 S>50): 359 relevant similarities between 473 passages\n", - " 47s CLIQUES (F 100 SET M>50 S>50): Loaded: 189 cliques out of 473 chunks from 359 comparisons\n", - " 47s CLIQUES (F 100 SET M>50 S>50): 473 members in 189 cliques\n", - " 47s PRINT (F 100 SET M>50 S>50): sorting out cliques\n", - " 47s PRINT (F 100 SET M>50 S>50): formatting 189 cliques skipping 75 binary chapter diffs\n", - " 53s PRINT (F 100 SET M>50 S>50): formatted 189 cliques (4 files) skipping 75 binary chapter diffs\n", - " 53s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 53s PREPARING (F 100 LCS)\n", - " 54s PREPARING (F 100 LCS): Done 4244 chunks.\n", - " 54s SIMILARITY (F 100 LCS M>60): Loaded: 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 54s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 54s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix\n", - " 54s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", - " 54s CLIQUES (F 100 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 54s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques\n", - " 54s PRINT (F 100 LCS M>60 S>100): sorting out cliques\n", - " 54s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 54s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 54s PREPARING (F 100 LCS): Already prepared\n", - " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 54s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 54s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix\n", - " 54s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages\n", - " 54s CLIQUES (F 100 LCS M>60 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 54s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques\n", - " 54s PRINT (F 100 LCS M>60 S>95): sorting out cliques\n", - " 54s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 54s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 54s PREPARING (F 100 LCS): Already prepared\n", - " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 54s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 54s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix\n", - " 54s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages\n", - " 54s CLIQUES (F 100 LCS M>60 S>90): Loaded: 19 cliques out of 39 chunks from 21 comparisons\n", - " 54s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques\n", - " 54s PRINT (F 100 LCS M>60 S>90): sorting out cliques\n", - " 54s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs\n", - " 54s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs\n", - " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 54s PREPARING (F 100 LCS): Already prepared\n", - " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 54s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 54s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix\n", - " 54s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages\n", - " 54s CLIQUES (F 100 LCS M>60 S>85): Loaded: 29 cliques out of 59 chunks from 31 comparisons\n", - " 54s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques\n", - " 54s PRINT (F 100 LCS M>60 S>85): sorting out cliques\n", - " 54s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs\n", - " 55s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs\n", - " 55s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 55s PREPARING (F 100 LCS): Already prepared\n", - " 55s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 55s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 55s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 55s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix\n", - " 55s CLIQUES (F 100 LCS M>60 S>80): 46 relevant similarities between 85 passages\n", - " 55s CLIQUES (F 100 LCS M>60 S>80): Loaded: 41 cliques out of 85 chunks from 46 comparisons\n", - " 55s CLIQUES (F 100 LCS M>60 S>80): 85 members in 41 cliques\n", - " 55s PRINT (F 100 LCS M>60 S>80): sorting out cliques\n", - " 55s PRINT (F 100 LCS M>60 S>80): formatting 41 cliques skipping 16 binary chapter diffs\n", - " 56s PRINT (F 100 LCS M>60 S>80): formatted 41 cliques (1 files) skipping 16 binary chapter diffs\n", - " 56s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 56s PREPARING (F 100 LCS): Already prepared\n", - " 56s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 56s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 56s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 56s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix\n", - " 56s CLIQUES (F 100 LCS M>60 S>75): 77 relevant similarities between 122 passages\n", - " 56s CLIQUES (F 100 LCS M>60 S>75): Loaded: 56 cliques out of 122 chunks from 77 comparisons\n", - " 56s CLIQUES (F 100 LCS M>60 S>75): 122 members in 56 cliques\n", - " 56s PRINT (F 100 LCS M>60 S>75): sorting out cliques\n", - " 56s PRINT (F 100 LCS M>60 S>75): formatting 56 cliques skipping 25 binary chapter diffs\n", - " 57s PRINT (F 100 LCS M>60 S>75): formatted 56 cliques (2 files) skipping 25 binary chapter diffs\n", - " 57s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 57s PREPARING (F 100 LCS): Already prepared\n", - " 57s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 57s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 57s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix\n", - " 57s CLIQUES (F 100 LCS M>60 S>70): 123 relevant similarities between 189 passages\n", - " 57s CLIQUES (F 100 LCS M>60 S>70): Loaded: 88 cliques out of 189 chunks from 123 comparisons\n", - " 57s CLIQUES (F 100 LCS M>60 S>70): 189 members in 88 cliques\n", - " 57s PRINT (F 100 LCS M>60 S>70): sorting out cliques\n", - " 57s PRINT (F 100 LCS M>60 S>70): formatting 88 cliques skipping 38 binary chapter diffs\n", - " 59s PRINT (F 100 LCS M>60 S>70): formatted 88 cliques (2 files) skipping 38 binary chapter diffs\n", - " 59s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 59s PREPARING (F 100 LCS): Already prepared\n", - " 59s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 59s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 59s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 59s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix\n", - " 59s CLIQUES (F 100 LCS M>60 S>65): 182 relevant similarities between 287 passages\n", - " 59s CLIQUES (F 100 LCS M>60 S>65): Loaded: 132 cliques out of 287 chunks from 182 comparisons\n", - " 59s CLIQUES (F 100 LCS M>60 S>65): 287 members in 132 cliques\n", - " 59s PRINT (F 100 LCS M>60 S>65): sorting out cliques\n", - " 59s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs\n", - " 1m 02s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs\n", - " 1m 02s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 1m 02s PREPARING (F 100 LCS): Already prepared\n", - " 1m 02s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", - " 1m 02s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 1m 02s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 1m 02s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix\n", - " 1m 02s CLIQUES (F 100 LCS M>60 S>60): 393 relevant similarities between 535 passages\n", - " 1m 02s CLIQUES (F 100 LCS M>60 S>60): Loaded: 214 cliques out of 535 chunks from 393 comparisons\n", - " 1m 02s CLIQUES (F 100 LCS M>60 S>60): 535 members in 214 cliques\n", - " 1m 02s PRINT (F 100 LCS M>60 S>60): sorting out cliques\n", - " 1m 02s PRINT (F 100 LCS M>60 S>60): formatting 214 cliques skipping 100 binary chapter diffs\n", - " 1m 08s PRINT (F 100 LCS M>60 S>60): formatted 214 cliques (5 files) skipping 100 binary chapter diffs\n", - " 1m 08s CHUNKING (F 50): Loaded: 8509 chunks\n", - " 1m 08s CHUNKING (F 50): Made 8509 chunks\n", - " 1m 08s PREPARING (F 50 SET)\n", - " 1m 09s PREPARING (F 50 SET): Done 8509 chunks.\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>100): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 1m 09s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 09s PREPARING (F 50 SET): Already prepared\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>95): 2 relevant similarities between 4 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>95): 4 members in 2 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>95): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 1m 09s PRINT (F 50 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 09s PREPARING (F 50 SET): Already prepared\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>90): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>90): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 4 binary chapter diffs\n", - " 1m 09s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 4 binary chapter diffs\n", - " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 09s PREPARING (F 50 SET): Already prepared\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>85): 35 relevant similarities between 57 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>85): Loaded: 26 cliques out of 57 chunks from 35 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>85): 57 members in 26 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>85): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>85): formatting 26 cliques skipping 9 binary chapter diffs\n", - " 1m 09s PRINT (F 50 SET M>50 S>85): formatted 26 cliques (1 files) skipping 9 binary chapter diffs\n", - " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 09s PREPARING (F 50 SET): Already prepared\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>80): 69 relevant similarities between 114 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>80): Loaded: 52 cliques out of 114 chunks from 69 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>80): 114 members in 52 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>80): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>80): formatting 52 cliques skipping 19 binary chapter diffs\n", - " 1m 09s PRINT (F 50 SET M>50 S>80): formatted 52 cliques (2 files) skipping 19 binary chapter diffs\n", - " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 09s PREPARING (F 50 SET): Already prepared\n", - " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>75): 115 relevant similarities between 186 passages\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>75): Loaded: 85 cliques out of 186 chunks from 115 comparisons\n", - " 1m 09s CLIQUES (F 50 SET M>50 S>75): 186 members in 85 cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>75): sorting out cliques\n", - " 1m 09s PRINT (F 50 SET M>50 S>75): formatting 85 cliques skipping 31 binary chapter diffs\n", - " 1m 10s PRINT (F 50 SET M>50 S>75): formatted 85 cliques (2 files) skipping 31 binary chapter diffs\n", - " 1m 10s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 10s PREPARING (F 50 SET): Already prepared\n", - " 1m 10s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 10s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 10s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates\n", - " 1m 10s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix\n", - " 1m 10s CLIQUES (F 50 SET M>50 S>70): 171 relevant similarities between 271 passages\n", - " 1m 10s CLIQUES (F 50 SET M>50 S>70): Loaded: 124 cliques out of 271 chunks from 171 comparisons\n", - " 1m 10s CLIQUES (F 50 SET M>50 S>70): 271 members in 124 cliques\n", - " 1m 10s PRINT (F 50 SET M>50 S>70): sorting out cliques\n", - " 1m 10s PRINT (F 50 SET M>50 S>70): formatting 124 cliques skipping 48 binary chapter diffs\n", - " 1m 11s PRINT (F 50 SET M>50 S>70): formatted 124 cliques (3 files) skipping 48 binary chapter diffs\n", - " 1m 11s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 11s PREPARING (F 50 SET): Already prepared\n", - " 1m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 11s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates\n", - " 1m 11s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix\n", - " 1m 11s CLIQUES (F 50 SET M>50 S>65): 248 relevant similarities between 385 passages\n", - " 1m 11s CLIQUES (F 50 SET M>50 S>65): Loaded: 176 cliques out of 385 chunks from 248 comparisons\n", - " 1m 11s CLIQUES (F 50 SET M>50 S>65): 385 members in 176 cliques\n", - " 1m 11s PRINT (F 50 SET M>50 S>65): sorting out cliques\n", - " 1m 11s PRINT (F 50 SET M>50 S>65): formatting 176 cliques skipping 61 binary chapter diffs\n", - " 1m 12s PRINT (F 50 SET M>50 S>65): formatted 176 cliques (4 files) skipping 61 binary chapter diffs\n", - " 1m 12s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 12s PREPARING (F 50 SET): Already prepared\n", - " 1m 12s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 12s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 12s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates\n", - " 1m 12s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix\n", - " 1m 12s CLIQUES (F 50 SET M>50 S>60): 366 relevant similarities between 535 passages\n", - " 1m 12s CLIQUES (F 50 SET M>50 S>60): Loaded: 235 cliques out of 535 chunks from 366 comparisons\n", - " 1m 12s CLIQUES (F 50 SET M>50 S>60): 535 members in 235 cliques\n", - " 1m 12s PRINT (F 50 SET M>50 S>60): sorting out cliques\n", - " 1m 12s PRINT (F 50 SET M>50 S>60): formatting 235 cliques skipping 78 binary chapter diffs\n", - " 1m 13s PRINT (F 50 SET M>50 S>60): formatted 235 cliques (5 files) skipping 78 binary chapter diffs\n", - " 1m 13s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 13s PREPARING (F 50 SET): Already prepared\n", - " 1m 13s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 13s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 13s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates\n", - " 1m 13s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix\n", - " 1m 13s CLIQUES (F 50 SET M>50 S>55): 537 relevant similarities between 748 passages\n", - " 1m 13s CLIQUES (F 50 SET M>50 S>55): Loaded: 315 cliques out of 748 chunks from 537 comparisons\n", - " 1m 13s CLIQUES (F 50 SET M>50 S>55): 748 members in 315 cliques\n", - " 1m 13s PRINT (F 50 SET M>50 S>55): sorting out cliques\n", - " 1m 13s PRINT (F 50 SET M>50 S>55): formatting 315 cliques skipping 101 binary chapter diffs\n", - " 1m 16s PRINT (F 50 SET M>50 S>55): formatted 315 cliques (7 files) skipping 101 binary chapter diffs\n", - " 1m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 16s PREPARING (F 50 SET): Already prepared\n", - " 1m 16s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", - " 1m 16s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 1m 16s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates\n", - " 1m 16s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix\n", - " 1m 16s CLIQUES (F 50 SET M>50 S>50): 923 relevant similarities between 1187 passages\n", - " 1m 16s CLIQUES (F 50 SET M>50 S>50): Loaded: 465 cliques out of 1187 chunks from 923 comparisons\n", - " 1m 16s CLIQUES (F 50 SET M>50 S>50): 1187 members in 465 cliques\n", - " 1m 16s PRINT (F 50 SET M>50 S>50): sorting out cliques\n", - " 1m 16s PRINT (F 50 SET M>50 S>50): formatting 465 cliques skipping 138 binary chapter diffs\n", - " 1m 20s PRINT (F 50 SET M>50 S>50): formatted 465 cliques (10 files) skipping 138 binary chapter diffs\n", - " 1m 20s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 20s PREPARING (F 50 LCS)\n", - " 1m 21s PREPARING (F 50 LCS): Done 8509 chunks.\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>100): sorting out cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 1m 21s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 21s PREPARING (F 50 LCS): Already prepared\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>95): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>95): sorting out cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs\n", - " 1m 21s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs\n", - " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 21s PREPARING (F 50 LCS): Already prepared\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>90): 28 relevant similarities between 53 passages\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>90): Loaded: 25 cliques out of 53 chunks from 28 comparisons\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>90): 53 members in 25 cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>90): sorting out cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>90): formatting 25 cliques skipping 9 binary chapter diffs\n", - " 1m 21s PRINT (F 50 LCS M>60 S>90): formatted 25 cliques (1 files) skipping 9 binary chapter diffs\n", - " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 21s PREPARING (F 50 LCS): Already prepared\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>85): 75 relevant similarities between 119 passages\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>85): Loaded: 53 cliques out of 119 chunks from 75 comparisons\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>85): 119 members in 53 cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>85): sorting out cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>85): formatting 53 cliques skipping 17 binary chapter diffs\n", - " 1m 21s PRINT (F 50 LCS M>60 S>85): formatted 53 cliques (2 files) skipping 17 binary chapter diffs\n", - " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 21s PREPARING (F 50 LCS): Already prepared\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>80): 122 relevant similarities between 196 passages\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>80): Loaded: 89 cliques out of 196 chunks from 122 comparisons\n", - " 1m 21s CLIQUES (F 50 LCS M>60 S>80): 196 members in 89 cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>80): sorting out cliques\n", - " 1m 21s PRINT (F 50 LCS M>60 S>80): formatting 89 cliques skipping 33 binary chapter diffs\n", - " 1m 22s PRINT (F 50 LCS M>60 S>80): formatted 89 cliques (2 files) skipping 33 binary chapter diffs\n", - " 1m 22s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 22s PREPARING (F 50 LCS): Already prepared\n", - " 1m 22s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 22s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 22s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 1m 22s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix\n", - " 1m 22s CLIQUES (F 50 LCS M>60 S>75): 197 relevant similarities between 301 passages\n", - " 1m 22s CLIQUES (F 50 LCS M>60 S>75): Loaded: 135 cliques out of 301 chunks from 197 comparisons\n", - " 1m 22s CLIQUES (F 50 LCS M>60 S>75): 301 members in 135 cliques\n", - " 1m 22s PRINT (F 50 LCS M>60 S>75): sorting out cliques\n", - " 1m 22s PRINT (F 50 LCS M>60 S>75): formatting 135 cliques skipping 50 binary chapter diffs\n", - " 1m 23s PRINT (F 50 LCS M>60 S>75): formatted 135 cliques (3 files) skipping 50 binary chapter diffs\n", - " 1m 23s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 23s PREPARING (F 50 LCS): Already prepared\n", - " 1m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 23s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 1m 23s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix\n", - " 1m 23s CLIQUES (F 50 LCS M>60 S>70): 312 relevant similarities between 464 passages\n", - " 1m 23s CLIQUES (F 50 LCS M>60 S>70): Loaded: 205 cliques out of 464 chunks from 312 comparisons\n", - " 1m 23s CLIQUES (F 50 LCS M>60 S>70): 464 members in 205 cliques\n", - " 1m 23s PRINT (F 50 LCS M>60 S>70): sorting out cliques\n", - " 1m 23s PRINT (F 50 LCS M>60 S>70): formatting 205 cliques skipping 65 binary chapter diffs\n", - " 1m 24s PRINT (F 50 LCS M>60 S>70): formatted 205 cliques (5 files) skipping 65 binary chapter diffs\n", - " 1m 24s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 24s PREPARING (F 50 LCS): Already prepared\n", - " 1m 24s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 24s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 24s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 1m 24s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix\n", - " 1m 24s CLIQUES (F 50 LCS M>60 S>65): 578 relevant similarities between 761 passages\n", - " 1m 24s CLIQUES (F 50 LCS M>60 S>65): Loaded: 312 cliques out of 761 chunks from 578 comparisons\n", - " 1m 24s CLIQUES (F 50 LCS M>60 S>65): 761 members in 312 cliques\n", - " 1m 24s PRINT (F 50 LCS M>60 S>65): sorting out cliques\n", - " 1m 24s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs\n", - " 1m 26s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs\n", - " 1m 26s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 1m 26s PREPARING (F 50 LCS): Already prepared\n", - " 1m 26s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", - " 1m 26s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", - " 1m 26s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 1m 26s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix\n", - " 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1833 relevant similarities between 1888 passages\n", - " 1m 26s CLIQUES (F 50 LCS M>60 S>60): Loaded: 552 cliques out of 1888 chunks from 1833 comparisons\n", - " 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1888 members in 552 cliques\n", - " 1m 26s PRINT (F 50 LCS M>60 S>60): sorting out cliques\n", - " 1m 26s PRINT (F 50 LCS M>60 S>60): formatting 552 cliques skipping 228 binary chapter diffs\n", - " 1m 33s PRINT (F 50 LCS M>60 S>60): formatted 552 cliques (12 files) skipping 228 binary chapter diffs\n", - " 1m 33s CHUNKING (F 20): Loaded: 21311 chunks\n", - " 1m 33s CHUNKING (F 20): Made 21311 chunks\n", - " 1m 33s PREPARING (F 20 SET)\n", - " 1m 34s PREPARING (F 20 SET): Done 21311 chunks.\n", - " 1m 34s SIMILARITY (F 20 SET M>50): Loaded: 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>100): 14 relevant similarities between 28 passages\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>100): Loaded: 14 cliques out of 28 chunks from 14 comparisons\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>100): 28 members in 14 cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>100): sorting out cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>100): formatting 14 cliques skipping 8 binary chapter diffs\n", - " 1m 34s PRINT (F 20 SET M>50 S>100): formatted 14 cliques (1 files) skipping 8 binary chapter diffs\n", - " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 34s PREPARING (F 20 SET): Already prepared\n", - " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>95): 14 relevant similarities between 28 passages\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>95): Loaded: 14 cliques out of 28 chunks from 14 comparisons\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>95): 28 members in 14 cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>95): sorting out cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>95): formatting 14 cliques skipping 8 binary chapter diffs\n", - " 1m 34s PRINT (F 20 SET M>50 S>95): formatted 14 cliques (1 files) skipping 8 binary chapter diffs\n", - " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 34s PREPARING (F 20 SET): Already prepared\n", - " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>90): 63 relevant similarities between 105 passages\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>90): Loaded: 46 cliques out of 105 chunks from 63 comparisons\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>90): 105 members in 46 cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>90): sorting out cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>90): formatting 46 cliques skipping 22 binary chapter diffs\n", - " 1m 34s PRINT (F 20 SET M>50 S>90): formatted 46 cliques (1 files) skipping 22 binary chapter diffs\n", - " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 34s PREPARING (F 20 SET): Already prepared\n", - " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>85): 125 relevant similarities between 174 passages\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>85): Loaded: 72 cliques out of 174 chunks from 125 comparisons\n", - " 1m 34s CLIQUES (F 20 SET M>50 S>85): 174 members in 72 cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>85): sorting out cliques\n", - " 1m 34s PRINT (F 20 SET M>50 S>85): formatting 72 cliques skipping 34 binary chapter diffs\n", - " 1m 35s PRINT (F 20 SET M>50 S>85): formatted 72 cliques (2 files) skipping 34 binary chapter diffs\n", - " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 35s PREPARING (F 20 SET): Already prepared\n", - " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>80): 230 relevant similarities between 326 passages\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>80): Loaded: 143 cliques out of 326 chunks from 230 comparisons\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>80): 326 members in 143 cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>80): sorting out cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>80): formatting 143 cliques skipping 59 binary chapter diffs\n", - " 1m 35s PRINT (F 20 SET M>50 S>80): formatted 143 cliques (3 files) skipping 59 binary chapter diffs\n", - " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 35s PREPARING (F 20 SET): Already prepared\n", - " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>75): 382 relevant similarities between 528 passages\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>75): Loaded: 227 cliques out of 528 chunks from 382 comparisons\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>75): sorting out cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 83 binary chapter diffs\n", - " 1m 35s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 83 binary chapter diffs\n", - " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 35s PREPARING (F 20 SET): Already prepared\n", - " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 762 passages\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>70): Loaded: 331 cliques out of 762 chunks from 546 comparisons\n", - " 1m 35s CLIQUES (F 20 SET M>50 S>70): 762 members in 331 cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>70): sorting out cliques\n", - " 1m 35s PRINT (F 20 SET M>50 S>70): formatting 331 cliques skipping 107 binary chapter diffs\n", - " 1m 36s PRINT (F 20 SET M>50 S>70): formatted 331 cliques (7 files) skipping 107 binary chapter diffs\n", - " 1m 36s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 36s PREPARING (F 20 SET): Already prepared\n", - " 1m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>65): 787 relevant similarities between 1058 passages\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>65): Loaded: 452 cliques out of 1058 chunks from 787 comparisons\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>65): 1058 members in 452 cliques\n", - " 1m 36s PRINT (F 20 SET M>50 S>65): sorting out cliques\n", - " 1m 36s PRINT (F 20 SET M>50 S>65): formatting 452 cliques skipping 136 binary chapter diffs\n", - " 1m 36s PRINT (F 20 SET M>50 S>65): formatted 452 cliques (10 files) skipping 136 binary chapter diffs\n", - " 1m 36s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 36s PREPARING (F 20 SET): Already prepared\n", - " 1m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>60): 1432 relevant similarities between 1830 passages\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>60): Loaded: 733 cliques out of 1830 chunks from 1432 comparisons\n", - " 1m 36s CLIQUES (F 20 SET M>50 S>60): 1830 members in 733 cliques\n", - " 1m 36s PRINT (F 20 SET M>50 S>60): sorting out cliques\n", - " 1m 36s PRINT (F 20 SET M>50 S>60): formatting 733 cliques skipping 211 binary chapter diffs\n", - " 1m 37s PRINT (F 20 SET M>50 S>60): formatted 733 cliques (15 files) skipping 211 binary chapter diffs\n", - " 1m 37s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 37s PREPARING (F 20 SET): Already prepared\n", - " 1m 37s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 37s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 37s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates\n", - " 1m 37s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix\n", - " 1m 37s CLIQUES (F 20 SET M>50 S>55): 2425 relevant similarities between 2787 passages\n", - " 1m 37s CLIQUES (F 20 SET M>50 S>55): Loaded: 979 cliques out of 2787 chunks from 2425 comparisons\n", - " 1m 37s CLIQUES (F 20 SET M>50 S>55): 2787 members in 979 cliques\n", - " 1m 37s PRINT (F 20 SET M>50 S>55): sorting out cliques\n", - " 1m 37s PRINT (F 20 SET M>50 S>55): formatting 979 cliques skipping 285 binary chapter diffs\n", - " 1m 39s PRINT (F 20 SET M>50 S>55): formatted 979 cliques (20 files) skipping 285 binary chapter diffs\n", - " 1m 39s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 39s PREPARING (F 20 SET): Already prepared\n", - " 1m 39s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", - " 1m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", - " 1m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates\n", - " 1m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix\n", - " 1m 39s CLIQUES (F 20 SET M>50 S>50): 5517 relevant similarities between 4913 passages\n", - " 1m 39s CLIQUES (F 20 SET M>50 S>50): Loaded: 1203 cliques out of 4913 chunks from 5517 comparisons\n", - " 1m 39s CLIQUES (F 20 SET M>50 S>50): 4913 members in 1203 cliques\n", - " 1m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques\n", - " 1m 39s PRINT (F 20 SET M>50 S>50): formatting 1203 cliques skipping 391 binary chapter diffs\n", - " 1m 41s PRINT (F 20 SET M>50 S>50): formatted 1203 cliques (25 files) skipping 391 binary chapter diffs\n", - " 1m 41s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 41s PREPARING (F 20 LCS)\n", - " 1m 42s PREPARING (F 20 LCS): Done 21311 chunks.\n", - " 1m 42s SIMILARITY (F 20 LCS M>60): Loaded: 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>100): 3 relevant similarities between 6 passages\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>100): Loaded: 3 cliques out of 6 chunks from 3 comparisons\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>100): 6 members in 3 cliques\n", - " 1m 42s PRINT (F 20 LCS M>60 S>100): sorting out cliques\n", - " 1m 42s PRINT (F 20 LCS M>60 S>100): formatting 3 cliques skipping 3 binary chapter diffs\n", - " 1m 42s PRINT (F 20 LCS M>60 S>100): formatted 3 cliques (1 files) skipping 3 binary chapter diffs\n", - " 1m 42s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 42s PREPARING (F 20 LCS): Already prepared\n", - " 1m 42s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>95): 25 relevant similarities between 47 passages\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>95): Loaded: 22 cliques out of 47 chunks from 25 comparisons\n", - " 1m 42s CLIQUES (F 20 LCS M>60 S>95): 47 members in 22 cliques\n", - " 1m 42s PRINT (F 20 LCS M>60 S>95): sorting out cliques\n", - " 1m 42s PRINT (F 20 LCS M>60 S>95): formatting 22 cliques skipping 12 binary chapter diffs\n", - " 1m 42s PRINT (F 20 LCS M>60 S>95): formatted 22 cliques (1 files) skipping 12 binary chapter diffs\n", - " 1m 42s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 42s PREPARING (F 20 LCS): Already prepared\n", - " 1m 42s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>90): 95 relevant similarities between 149 passages\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded: 61 cliques out of 149 chunks from 95 comparisons\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>90): 149 members in 61 cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>90): formatting 61 cliques skipping 31 binary chapter diffs\n", - " 1m 43s PRINT (F 20 LCS M>60 S>90): formatted 61 cliques (2 files) skipping 31 binary chapter diffs\n", - " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 43s PREPARING (F 20 LCS): Already prepared\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>85): 212 relevant similarities between 311 passages\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>85): Loaded: 136 cliques out of 311 chunks from 212 comparisons\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>85): 311 members in 136 cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>85): sorting out cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>85): formatting 136 cliques skipping 56 binary chapter diffs\n", - " 1m 43s PRINT (F 20 LCS M>60 S>85): formatted 136 cliques (3 files) skipping 56 binary chapter diffs\n", - " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 43s PREPARING (F 20 LCS): Already prepared\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>80): 467 relevant similarities between 682 passages\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>80): Loaded: 299 cliques out of 682 chunks from 467 comparisons\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>80): 682 members in 299 cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>80): sorting out cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>80): formatting 299 cliques skipping 116 binary chapter diffs\n", - " 1m 43s PRINT (F 20 LCS M>60 S>80): formatted 299 cliques (6 files) skipping 116 binary chapter diffs\n", - " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 43s PREPARING (F 20 LCS): Already prepared\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>75): 874 relevant similarities between 1137 passages\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>75): Loaded: 470 cliques out of 1137 chunks from 874 comparisons\n", - " 1m 43s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>75): sorting out cliques\n", - " 1m 43s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs\n", - " 1m 44s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs\n", - " 1m 44s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 44s PREPARING (F 20 LCS): Already prepared\n", - " 1m 44s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 44s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 1m 44s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix\n", - " 1m 44s CLIQUES (F 20 LCS M>60 S>70): 1944 relevant similarities between 2217 passages\n", - " 1m 44s CLIQUES (F 20 LCS M>60 S>70): Loaded: 838 cliques out of 2217 chunks from 1944 comparisons\n", - " 1m 44s CLIQUES (F 20 LCS M>60 S>70): 2217 members in 838 cliques\n", - " 1m 44s PRINT (F 20 LCS M>60 S>70): sorting out cliques\n", - " 1m 44s PRINT (F 20 LCS M>60 S>70): formatting 838 cliques skipping 313 binary chapter diffs\n", - " 1m 46s PRINT (F 20 LCS M>60 S>70): formatted 838 cliques (17 files) skipping 313 binary chapter diffs\n", - " 1m 46s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 46s PREPARING (F 20 LCS): Already prepared\n", - " 1m 46s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 46s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 1m 46s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix\n", - " 1m 46s CLIQUES (F 20 LCS M>60 S>65): 6983 relevant similarities between 5971 passages\n", - " 1m 46s CLIQUES (F 20 LCS M>60 S>65): Loaded: 1223 cliques out of 5971 chunks from 6983 comparisons\n", - " 1m 46s CLIQUES (F 20 LCS M>60 S>65): 5971 members in 1223 cliques\n", - " 1m 46s PRINT (F 20 LCS M>60 S>65): sorting out cliques\n", - " 1m 46s PRINT (F 20 LCS M>60 S>65): formatting 1223 cliques skipping 553 binary chapter diffs\n", - " 1m 49s PRINT (F 20 LCS M>60 S>65): formatted 1223 cliques (25 files) skipping 553 binary chapter diffs\n", - " 1m 49s CHUNKING (F 20): already chunked into 21311 chunks\n", - " 1m 49s PREPARING (F 20 LCS): Already prepared\n", - " 1m 49s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", - " 1m 50s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", - " 1m 50s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 1m 50s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix\n", - " 1m 50s CLIQUES (F 20 LCS M>60 S>60): 122055 relevant similarities between 17656 passages\n", - " 1m 50s CLIQUES (F 20 LCS M>60 S>60): Loaded: 152 cliques out of 17656 chunks from 122055 comparisons\n", - " 1m 50s CLIQUES (F 20 LCS M>60 S>60): 17656 members in 152 cliques\n", - " 1m 50s PRINT (F 20 LCS M>60 S>60): sorting out cliques\n", - " 1m 50s PRINT (F 20 LCS M>60 S>60): formatting 152 cliques skipping 94 binary chapter diffs\n", - " 1m 50s PRINT (F 20 LCS M>60 S>60): formatted 152 cliques (4 files) skipping 94 binary chapter diffs\n", - " 1m 51s CHUNKING (F 10): Loaded: 42639 chunks\n", - " 1m 51s CHUNKING (F 10): Made 42639 chunks\n", - " 1m 51s PREPARING (F 10 SET)\n", - " 1m 52s PREPARING (F 10 SET): Done 42639 chunks.\n", - " 1m 52s SIMILARITY (F 10 SET M>50): Loaded: 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>100): 275 relevant similarities between 448 passages\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>100): Loaded: 209 cliques out of 448 chunks from 275 comparisons\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>100): 448 members in 209 cliques\n", - " 1m 52s PRINT (F 10 SET M>50 S>100): sorting out cliques\n", - " 1m 52s PRINT (F 10 SET M>50 S>100): formatting 209 cliques skipping 80 binary chapter diffs\n", - " 1m 52s PRINT (F 10 SET M>50 S>100): formatted 209 cliques (5 files) skipping 80 binary chapter diffs\n", - " 1m 52s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 52s PREPARING (F 10 SET): Already prepared\n", - " 1m 52s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>95): 275 relevant similarities between 448 passages\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>95): Loaded: 209 cliques out of 448 chunks from 275 comparisons\n", - " 1m 52s CLIQUES (F 10 SET M>50 S>95): 448 members in 209 cliques\n", - " 1m 52s PRINT (F 10 SET M>50 S>95): sorting out cliques\n", - " 1m 52s PRINT (F 10 SET M>50 S>95): formatting 209 cliques skipping 80 binary chapter diffs\n", - " 1m 53s PRINT (F 10 SET M>50 S>95): formatted 209 cliques (5 files) skipping 80 binary chapter diffs\n", - " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 53s PREPARING (F 10 SET): Already prepared\n", - " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>90): 315 relevant similarities between 482 passages\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>90): Loaded: 220 cliques out of 482 chunks from 315 comparisons\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>90): 482 members in 220 cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>90): sorting out cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>90): formatting 220 cliques skipping 85 binary chapter diffs\n", - " 1m 53s PRINT (F 10 SET M>50 S>90): formatted 220 cliques (5 files) skipping 85 binary chapter diffs\n", - " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 53s PREPARING (F 10 SET): Already prepared\n", - " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>85): 745 relevant similarities between 1114 passages\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>85): Loaded: 493 cliques out of 1114 chunks from 745 comparisons\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>85): 1114 members in 493 cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>85): sorting out cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>85): formatting 493 cliques skipping 193 binary chapter diffs\n", - " 1m 53s PRINT (F 10 SET M>50 S>85): formatted 493 cliques (10 files) skipping 193 binary chapter diffs\n", - " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 53s PREPARING (F 10 SET): Already prepared\n", - " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>80): 1149 relevant similarities between 1536 passages\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>80): Loaded: 628 cliques out of 1536 chunks from 1149 comparisons\n", - " 1m 53s CLIQUES (F 10 SET M>50 S>80): 1536 members in 628 cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>80): sorting out cliques\n", - " 1m 53s PRINT (F 10 SET M>50 S>80): formatting 628 cliques skipping 231 binary chapter diffs\n", - " 1m 54s PRINT (F 10 SET M>50 S>80): formatted 628 cliques (13 files) skipping 231 binary chapter diffs\n", - " 1m 54s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 54s PREPARING (F 10 SET): Already prepared\n", - " 1m 54s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 54s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 54s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates\n", - " 1m 54s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix\n", - " 1m 54s CLIQUES (F 10 SET M>50 S>75): 2107 relevant similarities between 2754 passages\n", - " 1m 54s CLIQUES (F 10 SET M>50 S>75): Loaded: 1094 cliques out of 2754 chunks from 2107 comparisons\n", - " 1m 54s CLIQUES (F 10 SET M>50 S>75): 2754 members in 1094 cliques\n", - " 1m 54s PRINT (F 10 SET M>50 S>75): sorting out cliques\n", - " 1m 54s PRINT (F 10 SET M>50 S>75): formatting 1094 cliques skipping 406 binary chapter diffs\n", - " 1m 55s PRINT (F 10 SET M>50 S>75): formatted 1094 cliques (22 files) skipping 406 binary chapter diffs\n", - " 1m 55s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 55s PREPARING (F 10 SET): Already prepared\n", - " 1m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 55s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates\n", - " 1m 55s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix\n", - " 1m 55s CLIQUES (F 10 SET M>50 S>70): 3560 relevant similarities between 4020 passages\n", - " 1m 55s CLIQUES (F 10 SET M>50 S>70): Loaded: 1474 cliques out of 4020 chunks from 3560 comparisons\n", - " 1m 55s CLIQUES (F 10 SET M>50 S>70): 4020 members in 1474 cliques\n", - " 1m 55s PRINT (F 10 SET M>50 S>70): sorting out cliques\n", - " 1m 55s PRINT (F 10 SET M>50 S>70): formatting 1474 cliques skipping 559 binary chapter diffs\n", - " 1m 57s PRINT (F 10 SET M>50 S>70): formatted 1474 cliques (30 files) skipping 559 binary chapter diffs\n", - " 1m 57s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 57s PREPARING (F 10 SET): Already prepared\n", - " 1m 57s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 57s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates\n", - " 1m 57s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix\n", - " 1m 57s CLIQUES (F 10 SET M>50 S>65): 5481 relevant similarities between 5785 passages\n", - " 1m 57s CLIQUES (F 10 SET M>50 S>65): Loaded: 1850 cliques out of 5785 chunks from 5481 comparisons\n", - " 1m 57s CLIQUES (F 10 SET M>50 S>65): 5785 members in 1850 cliques\n", - " 1m 57s PRINT (F 10 SET M>50 S>65): sorting out cliques\n", - " 1m 57s PRINT (F 10 SET M>50 S>65): formatting 1850 cliques skipping 692 binary chapter diffs\n", - " 1m 59s PRINT (F 10 SET M>50 S>65): formatted 1850 cliques (37 files) skipping 692 binary chapter diffs\n", - " 1m 59s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 1m 59s PREPARING (F 10 SET): Already prepared\n", - " 1m 59s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 1m 59s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 1m 59s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates\n", - " 1m 59s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix\n", - " 1m 59s CLIQUES (F 10 SET M>50 S>60): 13263 relevant similarities between 10211 passages\n", - " 1m 59s CLIQUES (F 10 SET M>50 S>60): Loaded: 2210 cliques out of 10211 chunks from 13263 comparisons\n", - " 1m 59s CLIQUES (F 10 SET M>50 S>60): 10211 members in 2210 cliques\n", - " 1m 59s PRINT (F 10 SET M>50 S>60): sorting out cliques\n", - " 1m 59s PRINT (F 10 SET M>50 S>60): formatting 2210 cliques skipping 845 binary chapter diffs\n", - " 2m 01s PRINT (F 10 SET M>50 S>60): formatted 2210 cliques (45 files) skipping 845 binary chapter diffs\n", - " 2m 01s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 01s PREPARING (F 10 SET): Already prepared\n", - " 2m 01s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 2m 01s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 2m 01s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates\n", - " 2m 01s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix\n", - " 2m 01s CLIQUES (F 10 SET M>50 S>55): 25871 relevant similarities between 14100 passages\n", - " 2m 01s CLIQUES (F 10 SET M>50 S>55): Loaded: 2018 cliques out of 14100 chunks from 25871 comparisons\n", - " 2m 01s CLIQUES (F 10 SET M>50 S>55): 14100 members in 2018 cliques\n", - " 2m 01s PRINT (F 10 SET M>50 S>55): sorting out cliques\n", - " 2m 02s PRINT (F 10 SET M>50 S>55): formatting 2018 cliques skipping 783 binary chapter diffs\n", - " 2m 03s PRINT (F 10 SET M>50 S>55): formatted 2018 cliques (41 files) skipping 783 binary chapter diffs\n", - " 2m 03s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 03s PREPARING (F 10 SET): Already prepared\n", - " 2m 03s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", - " 2m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", - " 2m 03s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates\n", - " 2m 03s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix\n", - " 2m 03s CLIQUES (F 10 SET M>50 S>50): 88877 relevant similarities between 23054 passages\n", - " 2m 03s CLIQUES (F 10 SET M>50 S>50): Loaded: 1455 cliques out of 23054 chunks from 88877 comparisons\n", - " 2m 03s CLIQUES (F 10 SET M>50 S>50): 23054 members in 1455 cliques\n", - " 2m 03s PRINT (F 10 SET M>50 S>50): sorting out cliques\n", - " 2m 03s PRINT (F 10 SET M>50 S>50): formatting 1455 cliques skipping 643 binary chapter diffs\n", - " 2m 04s PRINT (F 10 SET M>50 S>50): formatted 1455 cliques (30 files) skipping 643 binary chapter diffs\n", - " 2m 04s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 04s PREPARING (F 10 LCS)\n", - " 2m 05s PREPARING (F 10 LCS): Done 42639 chunks.\n", - " 2m 07s SIMILARITY (F 10 LCS M>60): Loaded: 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 09s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 09s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 2m 09s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix\n", - " 2m 09s CLIQUES (F 10 LCS M>60 S>100): 139 relevant similarities between 239 passages\n", - " 2m 09s CLIQUES (F 10 LCS M>60 S>100): Loaded: 114 cliques out of 239 chunks from 139 comparisons\n", - " 2m 09s CLIQUES (F 10 LCS M>60 S>100): 239 members in 114 cliques\n", - " 2m 09s PRINT (F 10 LCS M>60 S>100): sorting out cliques\n", - " 2m 09s PRINT (F 10 LCS M>60 S>100): formatting 114 cliques skipping 49 binary chapter diffs\n", - " 2m 09s PRINT (F 10 LCS M>60 S>100): formatted 114 cliques (3 files) skipping 49 binary chapter diffs\n", - " 2m 09s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 09s PREPARING (F 10 LCS): Already prepared\n", - " 2m 09s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 11s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 11s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 2m 11s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix\n", - " 2m 12s CLIQUES (F 10 LCS M>60 S>95): 214 relevant similarities between 379 passages\n", - " 2m 12s CLIQUES (F 10 LCS M>60 S>95): Loaded: 182 cliques out of 379 chunks from 214 comparisons\n", - " 2m 12s CLIQUES (F 10 LCS M>60 S>95): 379 members in 182 cliques\n", - " 2m 12s PRINT (F 10 LCS M>60 S>95): sorting out cliques\n", - " 2m 12s PRINT (F 10 LCS M>60 S>95): formatting 182 cliques skipping 78 binary chapter diffs\n", - " 2m 12s PRINT (F 10 LCS M>60 S>95): formatted 182 cliques (4 files) skipping 78 binary chapter diffs\n", - " 2m 12s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 12s PREPARING (F 10 LCS): Already prepared\n", - " 2m 12s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 13s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 13s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 2m 13s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix\n", - " 2m 14s CLIQUES (F 10 LCS M>60 S>90): 609 relevant similarities between 905 passages\n", - " 2m 14s CLIQUES (F 10 LCS M>60 S>90): Loaded: 399 cliques out of 905 chunks from 609 comparisons\n", - " 2m 14s CLIQUES (F 10 LCS M>60 S>90): 905 members in 399 cliques\n", - " 2m 14s PRINT (F 10 LCS M>60 S>90): sorting out cliques\n", - " 2m 14s PRINT (F 10 LCS M>60 S>90): formatting 399 cliques skipping 160 binary chapter diffs\n", - " 2m 15s PRINT (F 10 LCS M>60 S>90): formatted 399 cliques (8 files) skipping 160 binary chapter diffs\n", - " 2m 15s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 15s PREPARING (F 10 LCS): Already prepared\n", - " 2m 15s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 16s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 2m 16s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix\n", - " 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1396 relevant similarities between 1917 passages\n", - " 2m 17s CLIQUES (F 10 LCS M>60 S>85): Loaded: 791 cliques out of 1917 chunks from 1396 comparisons\n", - " 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1917 members in 791 cliques\n", - " 2m 17s PRINT (F 10 LCS M>60 S>85): sorting out cliques\n", - " 2m 17s PRINT (F 10 LCS M>60 S>85): formatting 791 cliques skipping 297 binary chapter diffs\n", - " 2m 17s PRINT (F 10 LCS M>60 S>85): formatted 791 cliques (16 files) skipping 297 binary chapter diffs\n", - " 2m 17s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 17s PREPARING (F 10 LCS): Already prepared\n", - " 2m 17s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 19s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 19s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 2m 19s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix\n", - " 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3308 relevant similarities between 3850 passages\n", - " 2m 19s CLIQUES (F 10 LCS M>60 S>80): Loaded: 1418 cliques out of 3850 chunks from 3308 comparisons\n", - " 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3850 members in 1418 cliques\n", - " 2m 19s PRINT (F 10 LCS M>60 S>80): sorting out cliques\n", - " 2m 19s PRINT (F 10 LCS M>60 S>80): formatting 1418 cliques skipping 549 binary chapter diffs\n", - " 2m 20s PRINT (F 10 LCS M>60 S>80): formatted 1418 cliques (29 files) skipping 549 binary chapter diffs\n", - " 2m 20s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 20s PREPARING (F 10 LCS): Already prepared\n", - " 2m 20s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 21s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 21s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 2m 21s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix\n", - " 2m 23s CLIQUES (F 10 LCS M>60 S>75): 9225 relevant similarities between 8552 passages\n", - " 2m 23s CLIQUES (F 10 LCS M>60 S>75): Loaded: 2342 cliques out of 8552 chunks from 9225 comparisons\n", - " 2m 23s CLIQUES (F 10 LCS M>60 S>75): 8552 members in 2342 cliques\n", - " 2m 23s PRINT (F 10 LCS M>60 S>75): sorting out cliques\n", - " 2m 23s PRINT (F 10 LCS M>60 S>75): formatting 2342 cliques skipping 989 binary chapter diffs\n", - " 2m 25s PRINT (F 10 LCS M>60 S>75): formatted 2342 cliques (47 files) skipping 989 binary chapter diffs\n", - " 2m 25s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 25s PREPARING (F 10 LCS): Already prepared\n", - " 2m 25s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 26s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 26s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 2m 26s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix\n", - " 2m 27s CLIQUES (F 10 LCS M>60 S>70): 38610 relevant similarities between 20382 passages\n", - " 2m 27s CLIQUES (F 10 LCS M>60 S>70): Loaded: 1926 cliques out of 20382 chunks from 38610 comparisons\n", - " 2m 27s CLIQUES (F 10 LCS M>60 S>70): 20382 members in 1926 cliques\n", - " 2m 27s PRINT (F 10 LCS M>60 S>70): sorting out cliques\n", - " 2m 27s PRINT (F 10 LCS M>60 S>70): formatting 1926 cliques skipping 1004 binary chapter diffs\n", - " 2m 28s PRINT (F 10 LCS M>60 S>70): formatted 1926 cliques (39 files) skipping 1004 binary chapter diffs\n", - " 2m 28s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 28s PREPARING (F 10 LCS): Already prepared\n", - " 2m 28s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 29s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 29s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 2m 29s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix\n", - " 2m 31s CLIQUES (F 10 LCS M>60 S>65): 346682 relevant similarities between 37700 passages\n", - " 2m 31s CLIQUES (F 10 LCS M>60 S>65): Loaded: 223 cliques out of 37700 chunks from 346682 comparisons\n", - " 2m 31s CLIQUES (F 10 LCS M>60 S>65): 37700 members in 223 cliques\n", - " 2m 31s PRINT (F 10 LCS M>60 S>65): sorting out cliques\n", - " 2m 31s PRINT (F 10 LCS M>60 S>65): formatting 223 cliques skipping 142 binary chapter diffs\n", - " 2m 31s PRINT (F 10 LCS M>60 S>65): formatted 223 cliques (5 files) skipping 142 binary chapter diffs\n", - " 2m 31s CHUNKING (F 10): already chunked into 42639 chunks\n", - " 2m 31s PREPARING (F 10 LCS): Already prepared\n", - " 2m 31s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", - " 2m 33s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", - " 2m 33s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 2m 33s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix\n", - " 2m 37s CLIQUES (F 10 LCS M>60 S>60): 2918272 relevant similarities between 42450 passages\n", - " 2m 37s CLIQUES (F 10 LCS M>60 S>60): Loaded: 2 cliques out of 42450 chunks from 2918272 comparisons\n", - " 2m 37s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 2 cliques\n", - " 2m 37s PRINT (F 10 LCS M>60 S>60): sorting out cliques\n", - " 2m 37s PRINT (F 10 LCS M>60 S>60): formatting 2 cliques skipping 1 binary chapter diffs\n", - " 2m 37s PRINT (F 10 LCS M>60 S>60): formatted 2 cliques (1 files) skipping 1 binary chapter diffs\n", - " 2m 37s CHUNKING (O chapter): Loaded: 929 chunks\n", - " 2m 37s CHUNKING (O chapter): Made 929 chunks\n", - " 2m 37s PREPARING (O chapter SET)\n", - " 2m 38s PREPARING (O chapter SET): Done 929 chunks.\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Loaded: 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>100): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0\n", - " 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing\n", - " 2m 38s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>95): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>90): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>90): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>85): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>85): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>80): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>80): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>75): Loaded: 7 cliques out of 14 chunks from 7 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>75): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs\n", - " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 38s PREPARING (O chapter SET): Already prepared\n", - " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>70): Loaded: 10 cliques out of 20 chunks from 10 comparisons\n", - " 2m 38s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>70): sorting out cliques\n", - " 2m 38s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs\n", - " 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10\n", - " 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing\n", - " 2m 47s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs\n", - " 2m 47s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 47s PREPARING (O chapter SET): Already prepared\n", - " 2m 47s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 47s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates\n", - " 2m 47s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix\n", - " 2m 47s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages\n", - " 2m 47s CLIQUES (O chapter SET M>30 S>65): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", - " 2m 47s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques\n", - " 2m 47s PRINT (O chapter SET M>30 S>65): sorting out cliques\n", - " 2m 47s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs\n", - " 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12\n", - " 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing\n", - " 2m 57s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs\n", - " 2m 57s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 2m 57s PREPARING (O chapter SET): Already prepared\n", - " 2m 57s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 2m 57s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 2m 57s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates\n", - " 2m 57s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix\n", - " 2m 57s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages\n", - " 2m 57s CLIQUES (O chapter SET M>30 S>60): Loaded: 17 cliques out of 34 chunks from 17 comparisons\n", - " 2m 57s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques\n", - " 2m 57s PRINT (O chapter SET M>30 S>60): sorting out cliques\n", - " 2m 57s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs\n", - " 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17\n", - " 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing\n", - " 3m 13s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs\n", - " 3m 13s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 3m 13s PREPARING (O chapter SET): Already prepared\n", - " 3m 13s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 3m 13s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 3m 13s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates\n", - " 3m 13s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix\n", - " 3m 13s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages\n", - " 3m 13s CLIQUES (O chapter SET M>30 S>55): Loaded: 22 cliques out of 44 chunks from 22 comparisons\n", - " 3m 13s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques\n", - " 3m 13s PRINT (O chapter SET M>30 S>55): sorting out cliques\n", - " 3m 13s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs\n", - " 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22\n", - " 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing\n", - " 3m 33s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", - " 3m 33s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 3m 33s PREPARING (O chapter SET): Already prepared\n", - " 3m 33s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 3m 33s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 3m 33s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates\n", - " 3m 33s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix\n", - " 3m 33s CLIQUES (O chapter SET M>30 S>50): 28 relevant similarities between 56 passages\n", - " 3m 33s CLIQUES (O chapter SET M>30 S>50): Loaded: 28 cliques out of 56 chunks from 28 comparisons\n", - " 3m 33s CLIQUES (O chapter SET M>30 S>50): 56 members in 28 cliques\n", - " 3m 33s PRINT (O chapter SET M>30 S>50): sorting out cliques\n", - " 3m 33s PRINT (O chapter SET M>30 S>50): formatting 28 cliques involving 28 binary chapter diffs\n", - " 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 28\n", - " 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 28 already existing\n", - " 3m 54s PRINT (O chapter SET M>30 S>50): formatted 28 cliques (1 files) involving 28 binary chapter diffs\n", - " 3m 54s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 3m 54s PREPARING (O chapter SET): Already prepared\n", - " 3m 54s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 3m 54s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 3m 54s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates\n", - " 3m 54s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix\n", - " 3m 54s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages\n", - " 3m 54s CLIQUES (O chapter SET M>30 S>45): Loaded: 39 cliques out of 80 chunks from 42 comparisons\n", - " 3m 54s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques\n", - " 3m 54s PRINT (O chapter SET M>30 S>45): sorting out cliques\n", - " 3m 54s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs\n", - " 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37\n", - " 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing\n", - " 4m 23s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs\n", - " 4m 23s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 23s PREPARING (O chapter SET): Already prepared\n", - " 4m 23s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 23s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 23s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates\n", - " 4m 23s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix\n", - " 4m 23s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages\n", - " 4m 23s CLIQUES (O chapter SET M>30 S>40): Loaded: 62 cliques out of 142 chunks from 87 comparisons\n", - " 4m 23s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques\n", - " 4m 23s PRINT (O chapter SET M>30 S>40): sorting out cliques\n", - " 4m 23s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs\n", - " 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51\n", - " 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing\n", - " 5m 10s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs\n", - " 5m 10s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 5m 10s PREPARING (O chapter SET): Already prepared\n", - " 5m 10s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 5m 10s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 5m 10s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates\n", - " 5m 10s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix\n", - " 5m 10s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages\n", - " 5m 10s CLIQUES (O chapter SET M>30 S>35): Loaded: 53 cliques out of 302 chunks from 352 comparisons\n", - " 5m 10s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques\n", - " 5m 10s PRINT (O chapter SET M>30 S>35): sorting out cliques\n", - " 5m 10s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs\n", - " 6m 02s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs\n", - " 6m 02s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 02s PREPARING (O chapter SET): Already prepared\n", - " 6m 02s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 6m 02s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 6m 02s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates\n", - " 6m 02s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix\n", - " 6m 02s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages\n", - " 6m 02s CLIQUES (O chapter SET M>30 S>30): Loaded: 28 cliques out of 571 chunks from 3445 comparisons\n", - " 6m 02s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques\n", - " 6m 02s PRINT (O chapter SET M>30 S>30): sorting out cliques\n", - " 6m 02s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs\n", - " 6m 24s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs\n", - " 6m 24s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 24s PREPARING (O chapter LCS)\n", - " 6m 25s PREPARING (O chapter LCS): Done 929 chunks.\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): Loaded: 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>100): sorting out cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", - " 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0\n", - " 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing\n", - " 6m 25s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", - " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 25s PREPARING (O chapter LCS): Already prepared\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>95): sorting out cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 6m 25s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 25s PREPARING (O chapter LCS): Already prepared\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>90): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>90): sorting out cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 6m 25s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 25s PREPARING (O chapter LCS): Already prepared\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>85): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>85): sorting out cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs\n", - " 6m 25s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs\n", - " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 25s PREPARING (O chapter LCS): Already prepared\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>80): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", - " 6m 25s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>80): sorting out cliques\n", - " 6m 25s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs\n", - " 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9\n", - " 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing\n", - " 6m 31s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs\n", - " 6m 31s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 31s PREPARING (O chapter LCS): Already prepared\n", - " 6m 31s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 31s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 31s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates\n", - " 6m 31s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix\n", - " 6m 31s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages\n", - " 6m 31s CLIQUES (O chapter LCS M>55 S>75): Loaded: 13 cliques out of 26 chunks from 13 comparisons\n", - " 6m 31s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques\n", - " 6m 31s PRINT (O chapter LCS M>55 S>75): sorting out cliques\n", - " 6m 31s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs\n", - " 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13\n", - " 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing\n", - " 6m 40s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs\n", + " 2m 31s EXPERIMENT: Generating html report\n", + " 2m 31s EXPERIMENT: 240 no results available\n", + " 2m 31s EXPERIMENT: Generated html report\n", + " 2m 31s CHUNKING (F 100): Loaded: 4244 chunks\n", + " 2m 31s CHUNKING (F 100): Made 4244 chunks\n", + " 2m 31s PREPARING (F 100 SET)\n", + " 2m 32s PREPARING (F 100 SET): Done 4244 chunks.\n", + " 2m 32s SIMILARITY (F 100 SET M>50): Loaded: 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>100): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>100): sorting out cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 2m 32s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 32s PREPARING (F 100 SET): Already prepared\n", + " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>95): sorting out cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 2m 32s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 32s PREPARING (F 100 SET): Already prepared\n", + " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>90): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>90): sorting out cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs\n", + " 2m 32s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs\n", + " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 32s PREPARING (F 100 SET): Already prepared\n", + " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>85): 20 relevant similarities between 39 passages\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>85): Loaded: 19 cliques out of 39 chunks from 20 comparisons\n", + " 2m 32s CLIQUES (F 100 SET M>50 S>85): 39 members in 19 cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>85): sorting out cliques\n", + " 2m 32s PRINT (F 100 SET M>50 S>85): formatting 19 cliques skipping 7 binary chapter diffs\n", + " 2m 33s PRINT (F 100 SET M>50 S>85): formatted 19 cliques (1 files) skipping 7 binary chapter diffs\n", + " 2m 33s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 33s PREPARING (F 100 SET): Already prepared\n", + " 2m 33s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 33s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 33s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates\n", + " 2m 33s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix\n", + " 2m 33s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages\n", + " 2m 33s CLIQUES (F 100 SET M>50 S>80): Loaded: 30 cliques out of 64 chunks from 35 comparisons\n", + " 2m 33s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques\n", + " 2m 33s PRINT (F 100 SET M>50 S>80): sorting out cliques\n", + " 2m 33s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs\n", + " 2m 34s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs\n", + " 2m 34s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 34s PREPARING (F 100 SET): Already prepared\n", + " 2m 34s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 34s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 34s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates\n", + " 2m 34s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix\n", + " 2m 34s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages\n", + " 2m 34s CLIQUES (F 100 SET M>50 S>75): Loaded: 40 cliques out of 87 chunks from 63 comparisons\n", + " 2m 34s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques\n", + " 2m 34s PRINT (F 100 SET M>50 S>75): sorting out cliques\n", + " 2m 34s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs\n", + " 2m 35s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", + " 2m 35s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 35s PREPARING (F 100 SET): Already prepared\n", + " 2m 35s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 35s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 35s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates\n", + " 2m 35s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix\n", + " 2m 35s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages\n", + " 2m 35s CLIQUES (F 100 SET M>50 S>70): Loaded: 52 cliques out of 113 chunks from 87 comparisons\n", + " 2m 35s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques\n", + " 2m 35s PRINT (F 100 SET M>50 S>70): sorting out cliques\n", + " 2m 35s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs\n", + " 2m 36s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs\n", + " 2m 36s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 36s PREPARING (F 100 SET): Already prepared\n", + " 2m 36s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 36s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 36s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates\n", + " 2m 36s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix\n", + " 2m 36s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 156 passages\n", + " 2m 36s CLIQUES (F 100 SET M>50 S>65): Loaded: 71 cliques out of 156 chunks from 115 comparisons\n", + " 2m 36s CLIQUES (F 100 SET M>50 S>65): 156 members in 71 cliques\n", + " 2m 36s PRINT (F 100 SET M>50 S>65): sorting out cliques\n", + " 2m 36s PRINT (F 100 SET M>50 S>65): formatting 71 cliques skipping 28 binary chapter diffs\n", + " 2m 37s PRINT (F 100 SET M>50 S>65): formatted 71 cliques (2 files) skipping 28 binary chapter diffs\n", + " 2m 37s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 37s PREPARING (F 100 SET): Already prepared\n", + " 2m 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 37s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates\n", + " 2m 37s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix\n", + " 2m 37s CLIQUES (F 100 SET M>50 S>60): 151 relevant similarities between 214 passages\n", + " 2m 37s CLIQUES (F 100 SET M>50 S>60): Loaded: 97 cliques out of 214 chunks from 151 comparisons\n", + " 2m 37s CLIQUES (F 100 SET M>50 S>60): 214 members in 97 cliques\n", + " 2m 37s PRINT (F 100 SET M>50 S>60): sorting out cliques\n", + " 2m 37s PRINT (F 100 SET M>50 S>60): formatting 97 cliques skipping 37 binary chapter diffs\n", + " 2m 39s PRINT (F 100 SET M>50 S>60): formatted 97 cliques (2 files) skipping 37 binary chapter diffs\n", + " 2m 39s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 39s PREPARING (F 100 SET): Already prepared\n", + " 2m 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 39s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates\n", + " 2m 39s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix\n", + " 2m 39s CLIQUES (F 100 SET M>50 S>55): 223 relevant similarities between 308 passages\n", + " 2m 39s CLIQUES (F 100 SET M>50 S>55): Loaded: 138 cliques out of 308 chunks from 223 comparisons\n", + " 2m 39s CLIQUES (F 100 SET M>50 S>55): 308 members in 138 cliques\n", + " 2m 39s PRINT (F 100 SET M>50 S>55): sorting out cliques\n", + " 2m 39s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 56 binary chapter diffs\n", + " 2m 42s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 56 binary chapter diffs\n", + " 2m 42s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 42s PREPARING (F 100 SET): Already prepared\n", + " 2m 42s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", + " 2m 42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 2m 42s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates\n", + " 2m 42s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix\n", + " 2m 42s CLIQUES (F 100 SET M>50 S>50): 354 relevant similarities between 469 passages\n", + " 2m 42s CLIQUES (F 100 SET M>50 S>50): Loaded: 188 cliques out of 469 chunks from 354 comparisons\n", + " 2m 42s CLIQUES (F 100 SET M>50 S>50): 469 members in 188 cliques\n", + " 2m 42s PRINT (F 100 SET M>50 S>50): sorting out cliques\n", + " 2m 43s PRINT (F 100 SET M>50 S>50): formatting 188 cliques skipping 77 binary chapter diffs\n", + " 2m 48s PRINT (F 100 SET M>50 S>50): formatted 188 cliques (4 files) skipping 77 binary chapter diffs\n", + " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 48s PREPARING (F 100 LCS)\n", + " 2m 48s PREPARING (F 100 LCS): Done 4244 chunks.\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): Loaded: 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>100): sorting out cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 2m 48s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 48s PREPARING (F 100 LCS): Already prepared\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>95): sorting out cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 2m 48s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 48s PREPARING (F 100 LCS): Already prepared\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>90): Loaded: 19 cliques out of 39 chunks from 21 comparisons\n", + " 2m 48s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>90): sorting out cliques\n", + " 2m 48s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs\n", + " 2m 49s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs\n", + " 2m 49s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 49s PREPARING (F 100 LCS): Already prepared\n", + " 2m 49s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 49s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 49s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 2m 49s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix\n", + " 2m 49s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages\n", + " 2m 49s CLIQUES (F 100 LCS M>60 S>85): Loaded: 29 cliques out of 59 chunks from 31 comparisons\n", + " 2m 49s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques\n", + " 2m 49s PRINT (F 100 LCS M>60 S>85): sorting out cliques\n", + " 2m 49s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs\n", + " 2m 50s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs\n", + " 2m 50s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 50s PREPARING (F 100 LCS): Already prepared\n", + " 2m 50s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>80): 45 relevant similarities between 83 passages\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>80): Loaded: 40 cliques out of 83 chunks from 45 comparisons\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>80): 83 members in 40 cliques\n", + " 2m 50s PRINT (F 100 LCS M>60 S>80): sorting out cliques\n", + " 2m 50s PRINT (F 100 LCS M>60 S>80): formatting 40 cliques skipping 16 binary chapter diffs\n", + " 2m 50s PRINT (F 100 LCS M>60 S>80): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", + " 2m 50s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 50s PREPARING (F 100 LCS): Already prepared\n", + " 2m 50s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>75): 75 relevant similarities between 118 passages\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>75): Loaded: 54 cliques out of 118 chunks from 75 comparisons\n", + " 2m 50s CLIQUES (F 100 LCS M>60 S>75): 118 members in 54 cliques\n", + " 2m 50s PRINT (F 100 LCS M>60 S>75): sorting out cliques\n", + " 2m 50s PRINT (F 100 LCS M>60 S>75): formatting 54 cliques skipping 23 binary chapter diffs\n", + " 2m 52s PRINT (F 100 LCS M>60 S>75): formatted 54 cliques (2 files) skipping 23 binary chapter diffs\n", + " 2m 52s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 52s PREPARING (F 100 LCS): Already prepared\n", + " 2m 52s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 52s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 52s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 2m 52s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix\n", + " 2m 52s CLIQUES (F 100 LCS M>60 S>70): 125 relevant similarities between 193 passages\n", + " 2m 52s CLIQUES (F 100 LCS M>60 S>70): Loaded: 90 cliques out of 193 chunks from 125 comparisons\n", + " 2m 52s CLIQUES (F 100 LCS M>60 S>70): 193 members in 90 cliques\n", + " 2m 52s PRINT (F 100 LCS M>60 S>70): sorting out cliques\n", + " 2m 52s PRINT (F 100 LCS M>60 S>70): formatting 90 cliques skipping 40 binary chapter diffs\n", + " 2m 54s PRINT (F 100 LCS M>60 S>70): formatted 90 cliques (2 files) skipping 40 binary chapter diffs\n", + " 2m 54s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 54s PREPARING (F 100 LCS): Already prepared\n", + " 2m 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 54s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 2m 54s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix\n", + " 2m 54s CLIQUES (F 100 LCS M>60 S>65): 181 relevant similarities between 286 passages\n", + " 2m 54s CLIQUES (F 100 LCS M>60 S>65): Loaded: 132 cliques out of 286 chunks from 181 comparisons\n", + " 2m 54s CLIQUES (F 100 LCS M>60 S>65): 286 members in 132 cliques\n", + " 2m 54s PRINT (F 100 LCS M>60 S>65): sorting out cliques\n", + " 2m 54s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs\n", + " 2m 57s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs\n", + " 2m 57s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 2m 57s PREPARING (F 100 LCS): Already prepared\n", + " 2m 57s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", + " 2m 57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 2m 57s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 2m 57s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix\n", + " 2m 57s CLIQUES (F 100 LCS M>60 S>60): 394 relevant similarities between 537 passages\n", + " 2m 57s CLIQUES (F 100 LCS M>60 S>60): Loaded: 215 cliques out of 537 chunks from 394 comparisons\n", + " 2m 57s CLIQUES (F 100 LCS M>60 S>60): 537 members in 215 cliques\n", + " 2m 57s PRINT (F 100 LCS M>60 S>60): sorting out cliques\n", + " 2m 57s PRINT (F 100 LCS M>60 S>60): formatting 215 cliques skipping 101 binary chapter diffs\n", + " 3m 03s PRINT (F 100 LCS M>60 S>60): formatted 215 cliques (5 files) skipping 101 binary chapter diffs\n", + " 3m 03s CHUNKING (F 50): Loaded: 8509 chunks\n", + " 3m 03s CHUNKING (F 50): Made 8509 chunks\n", + " 3m 03s PREPARING (F 50 SET)\n", + " 3m 04s PREPARING (F 50 SET): Done 8509 chunks.\n", + " 3m 04s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>100): sorting out cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 3m 04s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 04s PREPARING (F 50 SET): Already prepared\n", + " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>95): 3 relevant similarities between 6 passages\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>95): Loaded: 3 cliques out of 6 chunks from 3 comparisons\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>95): 6 members in 3 cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>95): sorting out cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>95): formatting 3 cliques skipping 3 binary chapter diffs\n", + " 3m 04s PRINT (F 50 SET M>50 S>95): formatted 3 cliques (1 files) skipping 3 binary chapter diffs\n", + " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 04s PREPARING (F 50 SET): Already prepared\n", + " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>90): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>90): sorting out cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 6 binary chapter diffs\n", + " 3m 04s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 6 binary chapter diffs\n", + " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 04s PREPARING (F 50 SET): Already prepared\n", + " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>85): 34 relevant similarities between 55 passages\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>85): Loaded: 25 cliques out of 55 chunks from 34 comparisons\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>85): 55 members in 25 cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>85): sorting out cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>85): formatting 25 cliques skipping 11 binary chapter diffs\n", + " 3m 04s PRINT (F 50 SET M>50 S>85): formatted 25 cliques (1 files) skipping 11 binary chapter diffs\n", + " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 04s PREPARING (F 50 SET): Already prepared\n", + " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>80): 65 relevant similarities between 104 passages\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>80): Loaded: 47 cliques out of 104 chunks from 65 comparisons\n", + " 3m 04s CLIQUES (F 50 SET M>50 S>80): 104 members in 47 cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>80): sorting out cliques\n", + " 3m 04s PRINT (F 50 SET M>50 S>80): formatting 47 cliques skipping 20 binary chapter diffs\n", + " 3m 05s PRINT (F 50 SET M>50 S>80): formatted 47 cliques (1 files) skipping 20 binary chapter diffs\n", + " 3m 05s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 05s PREPARING (F 50 SET): Already prepared\n", + " 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>75): 121 relevant similarities between 197 passages\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>75): Loaded: 90 cliques out of 197 chunks from 121 comparisons\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>75): 197 members in 90 cliques\n", + " 3m 05s PRINT (F 50 SET M>50 S>75): sorting out cliques\n", + " 3m 05s PRINT (F 50 SET M>50 S>75): formatting 90 cliques skipping 35 binary chapter diffs\n", + " 3m 05s PRINT (F 50 SET M>50 S>75): formatted 90 cliques (2 files) skipping 35 binary chapter diffs\n", + " 3m 05s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 05s PREPARING (F 50 SET): Already prepared\n", + " 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>70): 174 relevant similarities between 277 passages\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>70): Loaded: 127 cliques out of 277 chunks from 174 comparisons\n", + " 3m 05s CLIQUES (F 50 SET M>50 S>70): 277 members in 127 cliques\n", + " 3m 05s PRINT (F 50 SET M>50 S>70): sorting out cliques\n", + " 3m 05s PRINT (F 50 SET M>50 S>70): formatting 127 cliques skipping 47 binary chapter diffs\n", + " 3m 06s PRINT (F 50 SET M>50 S>70): formatted 127 cliques (3 files) skipping 47 binary chapter diffs\n", + " 3m 06s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 06s PREPARING (F 50 SET): Already prepared\n", + " 3m 06s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 06s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 06s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates\n", + " 3m 06s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix\n", + " 3m 06s CLIQUES (F 50 SET M>50 S>65): 254 relevant similarities between 394 passages\n", + " 3m 06s CLIQUES (F 50 SET M>50 S>65): Loaded: 180 cliques out of 394 chunks from 254 comparisons\n", + " 3m 06s CLIQUES (F 50 SET M>50 S>65): 394 members in 180 cliques\n", + " 3m 06s PRINT (F 50 SET M>50 S>65): sorting out cliques\n", + " 3m 06s PRINT (F 50 SET M>50 S>65): formatting 180 cliques skipping 61 binary chapter diffs\n", + " 3m 07s PRINT (F 50 SET M>50 S>65): formatted 180 cliques (4 files) skipping 61 binary chapter diffs\n", + " 3m 07s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 07s PREPARING (F 50 SET): Already prepared\n", + " 3m 07s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 07s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 07s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates\n", + " 3m 07s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix\n", + " 3m 08s CLIQUES (F 50 SET M>50 S>60): 365 relevant similarities between 543 passages\n", + " 3m 08s CLIQUES (F 50 SET M>50 S>60): Loaded: 239 cliques out of 543 chunks from 365 comparisons\n", + " 3m 08s CLIQUES (F 50 SET M>50 S>60): 543 members in 239 cliques\n", + " 3m 08s PRINT (F 50 SET M>50 S>60): sorting out cliques\n", + " 3m 08s PRINT (F 50 SET M>50 S>60): formatting 239 cliques skipping 81 binary chapter diffs\n", + " 3m 09s PRINT (F 50 SET M>50 S>60): formatted 239 cliques (5 files) skipping 81 binary chapter diffs\n", + " 3m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 09s PREPARING (F 50 SET): Already prepared\n", + " 3m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 09s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates\n", + " 3m 09s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix\n", + " 3m 09s CLIQUES (F 50 SET M>50 S>55): 535 relevant similarities between 755 passages\n", + " 3m 09s CLIQUES (F 50 SET M>50 S>55): Loaded: 322 cliques out of 755 chunks from 535 comparisons\n", + " 3m 09s CLIQUES (F 50 SET M>50 S>55): 755 members in 322 cliques\n", + " 3m 09s PRINT (F 50 SET M>50 S>55): sorting out cliques\n", + " 3m 09s PRINT (F 50 SET M>50 S>55): formatting 322 cliques skipping 101 binary chapter diffs\n", + " 3m 11s PRINT (F 50 SET M>50 S>55): formatted 322 cliques (7 files) skipping 101 binary chapter diffs\n", + " 3m 11s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 11s PREPARING (F 50 SET): Already prepared\n", + " 3m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", + " 3m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 3m 11s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates\n", + " 3m 11s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix\n", + " 3m 12s CLIQUES (F 50 SET M>50 S>50): 926 relevant similarities between 1183 passages\n", + " 3m 12s CLIQUES (F 50 SET M>50 S>50): Loaded: 460 cliques out of 1183 chunks from 926 comparisons\n", + " 3m 12s CLIQUES (F 50 SET M>50 S>50): 1183 members in 460 cliques\n", + " 3m 12s PRINT (F 50 SET M>50 S>50): sorting out cliques\n", + " 3m 12s PRINT (F 50 SET M>50 S>50): formatting 460 cliques skipping 132 binary chapter diffs\n", + " 3m 16s PRINT (F 50 SET M>50 S>50): formatted 460 cliques (10 files) skipping 132 binary chapter diffs\n", + " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 16s PREPARING (F 50 LCS)\n", + " 3m 16s PREPARING (F 50 LCS): Done 8509 chunks.\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>100): sorting out cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 3m 16s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 16s PREPARING (F 50 LCS): Already prepared\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>95): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>95): sorting out cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs\n", + " 3m 16s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs\n", + " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 16s PREPARING (F 50 LCS): Already prepared\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>90): 23 relevant similarities between 43 passages\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>90): Loaded: 20 cliques out of 43 chunks from 23 comparisons\n", + " 3m 16s CLIQUES (F 50 LCS M>60 S>90): 43 members in 20 cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>90): sorting out cliques\n", + " 3m 16s PRINT (F 50 LCS M>60 S>90): formatting 20 cliques skipping 6 binary chapter diffs\n", + " 3m 16s PRINT (F 50 LCS M>60 S>90): formatted 20 cliques (1 files) skipping 6 binary chapter diffs\n", + " 3m 17s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 17s PREPARING (F 50 LCS): Already prepared\n", + " 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>85): 77 relevant similarities between 125 passages\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>85): Loaded: 56 cliques out of 125 chunks from 77 comparisons\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>85): 125 members in 56 cliques\n", + " 3m 17s PRINT (F 50 LCS M>60 S>85): sorting out cliques\n", + " 3m 17s PRINT (F 50 LCS M>60 S>85): formatting 56 cliques skipping 21 binary chapter diffs\n", + " 3m 17s PRINT (F 50 LCS M>60 S>85): formatted 56 cliques (2 files) skipping 21 binary chapter diffs\n", + " 3m 17s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 17s PREPARING (F 50 LCS): Already prepared\n", + " 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>80): 129 relevant similarities between 204 passages\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>80): Loaded: 93 cliques out of 204 chunks from 129 comparisons\n", + " 3m 17s CLIQUES (F 50 LCS M>60 S>80): 204 members in 93 cliques\n", + " 3m 17s PRINT (F 50 LCS M>60 S>80): sorting out cliques\n", + " 3m 17s PRINT (F 50 LCS M>60 S>80): formatting 93 cliques skipping 35 binary chapter diffs\n", + " 3m 18s PRINT (F 50 LCS M>60 S>80): formatted 93 cliques (2 files) skipping 35 binary chapter diffs\n", + " 3m 18s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 18s PREPARING (F 50 LCS): Already prepared\n", + " 3m 18s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 18s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 18s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 3m 18s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix\n", + " 3m 18s CLIQUES (F 50 LCS M>60 S>75): 198 relevant similarities between 299 passages\n", + " 3m 18s CLIQUES (F 50 LCS M>60 S>75): Loaded: 134 cliques out of 299 chunks from 198 comparisons\n", + " 3m 18s CLIQUES (F 50 LCS M>60 S>75): 299 members in 134 cliques\n", + " 3m 18s PRINT (F 50 LCS M>60 S>75): sorting out cliques\n", + " 3m 18s PRINT (F 50 LCS M>60 S>75): formatting 134 cliques skipping 51 binary chapter diffs\n", + " 3m 19s PRINT (F 50 LCS M>60 S>75): formatted 134 cliques (3 files) skipping 51 binary chapter diffs\n", + " 3m 19s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 19s PREPARING (F 50 LCS): Already prepared\n", + " 3m 19s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 19s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 19s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 3m 19s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix\n", + " 3m 19s CLIQUES (F 50 LCS M>60 S>70): 314 relevant similarities between 470 passages\n", + " 3m 19s CLIQUES (F 50 LCS M>60 S>70): Loaded: 209 cliques out of 470 chunks from 314 comparisons\n", + " 3m 19s CLIQUES (F 50 LCS M>60 S>70): 470 members in 209 cliques\n", + " 3m 19s PRINT (F 50 LCS M>60 S>70): sorting out cliques\n", + " 3m 19s PRINT (F 50 LCS M>60 S>70): formatting 209 cliques skipping 65 binary chapter diffs\n", + " 3m 20s PRINT (F 50 LCS M>60 S>70): formatted 209 cliques (5 files) skipping 65 binary chapter diffs\n", + " 3m 20s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 20s PREPARING (F 50 LCS): Already prepared\n", + " 3m 20s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 20s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 20s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 3m 20s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix\n", + " 3m 20s CLIQUES (F 50 LCS M>60 S>65): 587 relevant similarities between 765 passages\n", + " 3m 20s CLIQUES (F 50 LCS M>60 S>65): Loaded: 312 cliques out of 765 chunks from 587 comparisons\n", + " 3m 20s CLIQUES (F 50 LCS M>60 S>65): 765 members in 312 cliques\n", + " 3m 20s PRINT (F 50 LCS M>60 S>65): sorting out cliques\n", + " 3m 20s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs\n", + " 3m 23s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs\n", + " 3m 23s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 3m 23s PREPARING (F 50 LCS): Already prepared\n", + " 3m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", + " 3m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", + " 3m 23s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 3m 23s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix\n", + " 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1816 relevant similarities between 1867 passages\n", + " 3m 23s CLIQUES (F 50 LCS M>60 S>60): Loaded: 553 cliques out of 1867 chunks from 1816 comparisons\n", + " 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1867 members in 553 cliques\n", + " 3m 23s PRINT (F 50 LCS M>60 S>60): sorting out cliques\n", + " 3m 23s PRINT (F 50 LCS M>60 S>60): formatting 553 cliques skipping 225 binary chapter diffs\n", + " 3m 30s PRINT (F 50 LCS M>60 S>60): formatted 553 cliques (12 files) skipping 225 binary chapter diffs\n", + " 3m 30s CHUNKING (F 20): Loaded: 21312 chunks\n", + " 3m 30s CHUNKING (F 20): Made 21312 chunks\n", + " 3m 30s PREPARING (F 20 SET)\n", + " 3m 31s PREPARING (F 20 SET): Done 21312 chunks.\n", + " 3m 31s SIMILARITY (F 20 SET M>50): Loaded: 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>100): 18 relevant similarities between 36 passages\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>100): Loaded: 18 cliques out of 36 chunks from 18 comparisons\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>100): 36 members in 18 cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>100): sorting out cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>100): formatting 18 cliques skipping 11 binary chapter diffs\n", + " 3m 31s PRINT (F 20 SET M>50 S>100): formatted 18 cliques (1 files) skipping 11 binary chapter diffs\n", + " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 31s PREPARING (F 20 SET): Already prepared\n", + " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>95): 18 relevant similarities between 36 passages\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>95): Loaded: 18 cliques out of 36 chunks from 18 comparisons\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>95): 36 members in 18 cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>95): sorting out cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>95): formatting 18 cliques skipping 11 binary chapter diffs\n", + " 3m 31s PRINT (F 20 SET M>50 S>95): formatted 18 cliques (1 files) skipping 11 binary chapter diffs\n", + " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 31s PREPARING (F 20 SET): Already prepared\n", + " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>90): 72 relevant similarities between 126 passages\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>90): Loaded: 58 cliques out of 126 chunks from 72 comparisons\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>90): 126 members in 58 cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>90): sorting out cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>90): formatting 58 cliques skipping 24 binary chapter diffs\n", + " 3m 31s PRINT (F 20 SET M>50 S>90): formatted 58 cliques (2 files) skipping 24 binary chapter diffs\n", + " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 31s PREPARING (F 20 SET): Already prepared\n", + " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>85): 133 relevant similarities between 199 passages\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>85): Loaded: 84 cliques out of 199 chunks from 133 comparisons\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>85): 199 members in 84 cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>85): sorting out cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>85): formatting 84 cliques skipping 37 binary chapter diffs\n", + " 3m 31s PRINT (F 20 SET M>50 S>85): formatted 84 cliques (2 files) skipping 37 binary chapter diffs\n", + " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 31s PREPARING (F 20 SET): Already prepared\n", + " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>80): 224 relevant similarities between 332 passages\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>80): Loaded: 146 cliques out of 332 chunks from 224 comparisons\n", + " 3m 31s CLIQUES (F 20 SET M>50 S>80): 332 members in 146 cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>80): sorting out cliques\n", + " 3m 31s PRINT (F 20 SET M>50 S>80): formatting 146 cliques skipping 62 binary chapter diffs\n", + " 3m 32s PRINT (F 20 SET M>50 S>80): formatted 146 cliques (3 files) skipping 62 binary chapter diffs\n", + " 3m 32s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 32s PREPARING (F 20 SET): Already prepared\n", + " 3m 32s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>75): 372 relevant similarities between 528 passages\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>75): Loaded: 227 cliques out of 528 chunks from 372 comparisons\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques\n", + " 3m 32s PRINT (F 20 SET M>50 S>75): sorting out cliques\n", + " 3m 32s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 82 binary chapter diffs\n", + " 3m 32s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 82 binary chapter diffs\n", + " 3m 32s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 32s PREPARING (F 20 SET): Already prepared\n", + " 3m 32s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 760 passages\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>70): Loaded: 326 cliques out of 760 chunks from 546 comparisons\n", + " 3m 32s CLIQUES (F 20 SET M>50 S>70): 760 members in 326 cliques\n", + " 3m 32s PRINT (F 20 SET M>50 S>70): sorting out cliques\n", + " 3m 32s PRINT (F 20 SET M>50 S>70): formatting 326 cliques skipping 107 binary chapter diffs\n", + " 3m 33s PRINT (F 20 SET M>50 S>70): formatted 326 cliques (7 files) skipping 107 binary chapter diffs\n", + " 3m 33s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 33s PREPARING (F 20 SET): Already prepared\n", + " 3m 33s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 33s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 33s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates\n", + " 3m 33s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix\n", + " 3m 33s CLIQUES (F 20 SET M>50 S>65): 803 relevant similarities between 1096 passages\n", + " 3m 33s CLIQUES (F 20 SET M>50 S>65): Loaded: 470 cliques out of 1096 chunks from 803 comparisons\n", + " 3m 33s CLIQUES (F 20 SET M>50 S>65): 1096 members in 470 cliques\n", + " 3m 33s PRINT (F 20 SET M>50 S>65): sorting out cliques\n", + " 3m 33s PRINT (F 20 SET M>50 S>65): formatting 470 cliques skipping 144 binary chapter diffs\n", + " 3m 34s PRINT (F 20 SET M>50 S>65): formatted 470 cliques (10 files) skipping 144 binary chapter diffs\n", + " 3m 34s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 34s PREPARING (F 20 SET): Already prepared\n", + " 3m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 34s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates\n", + " 3m 34s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix\n", + " 3m 34s CLIQUES (F 20 SET M>50 S>60): 1414 relevant similarities between 1837 passages\n", + " 3m 34s CLIQUES (F 20 SET M>50 S>60): Loaded: 739 cliques out of 1837 chunks from 1414 comparisons\n", + " 3m 34s CLIQUES (F 20 SET M>50 S>60): 1837 members in 739 cliques\n", + " 3m 34s PRINT (F 20 SET M>50 S>60): sorting out cliques\n", + " 3m 34s PRINT (F 20 SET M>50 S>60): formatting 739 cliques skipping 213 binary chapter diffs\n", + " 3m 36s PRINT (F 20 SET M>50 S>60): formatted 739 cliques (15 files) skipping 213 binary chapter diffs\n", + " 3m 36s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 36s PREPARING (F 20 SET): Already prepared\n", + " 3m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 36s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates\n", + " 3m 36s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix\n", + " 3m 36s CLIQUES (F 20 SET M>50 S>55): 2453 relevant similarities between 2826 passages\n", + " 3m 36s CLIQUES (F 20 SET M>50 S>55): Loaded: 997 cliques out of 2826 chunks from 2453 comparisons\n", + " 3m 36s CLIQUES (F 20 SET M>50 S>55): 2826 members in 997 cliques\n", + " 3m 36s PRINT (F 20 SET M>50 S>55): sorting out cliques\n", + " 3m 36s PRINT (F 20 SET M>50 S>55): formatting 997 cliques skipping 296 binary chapter diffs\n", + " 3m 39s PRINT (F 20 SET M>50 S>55): formatted 997 cliques (20 files) skipping 296 binary chapter diffs\n", + " 3m 39s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 39s PREPARING (F 20 SET): Already prepared\n", + " 3m 39s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", + " 3m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", + " 3m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates\n", + " 3m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix\n", + " 3m 39s CLIQUES (F 20 SET M>50 S>50): 5559 relevant similarities between 4933 passages\n", + " 3m 39s CLIQUES (F 20 SET M>50 S>50): Loaded: 1212 cliques out of 4933 chunks from 5559 comparisons\n", + " 3m 39s CLIQUES (F 20 SET M>50 S>50): 4933 members in 1212 cliques\n", + " 3m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques\n", + " 3m 39s PRINT (F 20 SET M>50 S>50): formatting 1212 cliques skipping 416 binary chapter diffs\n", + " 3m 42s PRINT (F 20 SET M>50 S>50): formatted 1212 cliques (25 files) skipping 416 binary chapter diffs\n", + " 3m 42s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 42s PREPARING (F 20 LCS)\n", + " 3m 43s PREPARING (F 20 LCS): Done 21312 chunks.\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): Loaded: 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>100): 6 relevant similarities between 12 passages\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>100): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>100): 12 members in 6 cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>100): sorting out cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>100): formatting 6 cliques skipping 5 binary chapter diffs\n", + " 3m 43s PRINT (F 20 LCS M>60 S>100): formatted 6 cliques (1 files) skipping 5 binary chapter diffs\n", + " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 43s PREPARING (F 20 LCS): Already prepared\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>95): 33 relevant similarities between 62 passages\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>95): Loaded: 29 cliques out of 62 chunks from 33 comparisons\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>95): 62 members in 29 cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>95): sorting out cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>95): formatting 29 cliques skipping 14 binary chapter diffs\n", + " 3m 43s PRINT (F 20 LCS M>60 S>95): formatted 29 cliques (1 files) skipping 14 binary chapter diffs\n", + " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 43s PREPARING (F 20 LCS): Already prepared\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>90): 115 relevant similarities between 181 passages\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded: 76 cliques out of 181 chunks from 115 comparisons\n", + " 3m 43s CLIQUES (F 20 LCS M>60 S>90): 181 members in 76 cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques\n", + " 3m 43s PRINT (F 20 LCS M>60 S>90): formatting 76 cliques skipping 33 binary chapter diffs\n", + " 3m 43s PRINT (F 20 LCS M>60 S>90): formatted 76 cliques (2 files) skipping 33 binary chapter diffs\n", + " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 43s PREPARING (F 20 LCS): Already prepared\n", + " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>85): 232 relevant similarities between 339 passages\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>85): Loaded: 149 cliques out of 339 chunks from 232 comparisons\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>85): 339 members in 149 cliques\n", + " 3m 44s PRINT (F 20 LCS M>60 S>85): sorting out cliques\n", + " 3m 44s PRINT (F 20 LCS M>60 S>85): formatting 149 cliques skipping 57 binary chapter diffs\n", + " 3m 44s PRINT (F 20 LCS M>60 S>85): formatted 149 cliques (3 files) skipping 57 binary chapter diffs\n", + " 3m 44s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 44s PREPARING (F 20 LCS): Already prepared\n", + " 3m 44s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>80): 470 relevant similarities between 681 passages\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>80): Loaded: 300 cliques out of 681 chunks from 470 comparisons\n", + " 3m 44s CLIQUES (F 20 LCS M>60 S>80): 681 members in 300 cliques\n", + " 3m 44s PRINT (F 20 LCS M>60 S>80): sorting out cliques\n", + " 3m 44s PRINT (F 20 LCS M>60 S>80): formatting 300 cliques skipping 106 binary chapter diffs\n", + " 3m 45s PRINT (F 20 LCS M>60 S>80): formatted 300 cliques (6 files) skipping 106 binary chapter diffs\n", + " 3m 45s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 45s PREPARING (F 20 LCS): Already prepared\n", + " 3m 45s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 45s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 45s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 3m 45s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix\n", + " 3m 45s CLIQUES (F 20 LCS M>60 S>75): 876 relevant similarities between 1137 passages\n", + " 3m 45s CLIQUES (F 20 LCS M>60 S>75): Loaded: 470 cliques out of 1137 chunks from 876 comparisons\n", + " 3m 45s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques\n", + " 3m 45s PRINT (F 20 LCS M>60 S>75): sorting out cliques\n", + " 3m 45s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs\n", + " 3m 46s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs\n", + " 3m 46s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 46s PREPARING (F 20 LCS): Already prepared\n", + " 3m 46s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 46s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 3m 46s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix\n", + " 3m 46s CLIQUES (F 20 LCS M>60 S>70): 1935 relevant similarities between 2224 passages\n", + " 3m 46s CLIQUES (F 20 LCS M>60 S>70): Loaded: 844 cliques out of 2224 chunks from 1935 comparisons\n", + " 3m 46s CLIQUES (F 20 LCS M>60 S>70): 2224 members in 844 cliques\n", + " 3m 46s PRINT (F 20 LCS M>60 S>70): sorting out cliques\n", + " 3m 46s PRINT (F 20 LCS M>60 S>70): formatting 844 cliques skipping 306 binary chapter diffs\n", + " 3m 48s PRINT (F 20 LCS M>60 S>70): formatted 844 cliques (17 files) skipping 306 binary chapter diffs\n", + " 3m 48s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 48s PREPARING (F 20 LCS): Already prepared\n", + " 3m 48s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 48s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 48s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 3m 48s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix\n", + " 3m 48s CLIQUES (F 20 LCS M>60 S>65): 6898 relevant similarities between 5985 passages\n", + " 3m 48s CLIQUES (F 20 LCS M>60 S>65): Loaded: 1253 cliques out of 5985 chunks from 6898 comparisons\n", + " 3m 48s CLIQUES (F 20 LCS M>60 S>65): 5985 members in 1253 cliques\n", + " 3m 48s PRINT (F 20 LCS M>60 S>65): sorting out cliques\n", + " 3m 49s PRINT (F 20 LCS M>60 S>65): formatting 1253 cliques skipping 575 binary chapter diffs\n", + " 3m 52s PRINT (F 20 LCS M>60 S>65): formatted 1253 cliques (26 files) skipping 575 binary chapter diffs\n", + " 3m 52s CHUNKING (F 20): already chunked into 21312 chunks\n", + " 3m 52s PREPARING (F 20 LCS): Already prepared\n", + " 3m 52s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", + " 3m 52s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", + " 3m 52s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 3m 52s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix\n", + " 3m 53s CLIQUES (F 20 LCS M>60 S>60): 121585 relevant similarities between 17654 passages\n", + " 3m 53s CLIQUES (F 20 LCS M>60 S>60): Loaded: 163 cliques out of 17654 chunks from 121585 comparisons\n", + " 3m 53s CLIQUES (F 20 LCS M>60 S>60): 17654 members in 163 cliques\n", + " 3m 53s PRINT (F 20 LCS M>60 S>60): sorting out cliques\n", + " 3m 53s PRINT (F 20 LCS M>60 S>60): formatting 163 cliques skipping 104 binary chapter diffs\n", + " 3m 53s PRINT (F 20 LCS M>60 S>60): formatted 163 cliques (4 files) skipping 104 binary chapter diffs\n", + " 3m 53s CHUNKING (F 10): Loaded: 42640 chunks\n", + " 3m 53s CHUNKING (F 10): Made 42640 chunks\n", + " 3m 53s PREPARING (F 10 SET)\n", + " 3m 54s PREPARING (F 10 SET): Done 42640 chunks.\n", + " 3m 54s SIMILARITY (F 10 SET M>50): Loaded: 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>100): 269 relevant similarities between 462 passages\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>100): Loaded: 220 cliques out of 462 chunks from 269 comparisons\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>100): 462 members in 220 cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>100): sorting out cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>100): formatting 220 cliques skipping 83 binary chapter diffs\n", + " 3m 55s PRINT (F 10 SET M>50 S>100): formatted 220 cliques (5 files) skipping 83 binary chapter diffs\n", + " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 55s PREPARING (F 10 SET): Already prepared\n", + " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>95): 269 relevant similarities between 462 passages\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>95): Loaded: 220 cliques out of 462 chunks from 269 comparisons\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>95): 462 members in 220 cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>95): sorting out cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>95): formatting 220 cliques skipping 83 binary chapter diffs\n", + " 3m 55s PRINT (F 10 SET M>50 S>95): formatted 220 cliques (5 files) skipping 83 binary chapter diffs\n", + " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 55s PREPARING (F 10 SET): Already prepared\n", + " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>90): 307 relevant similarities between 494 passages\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>90): Loaded: 231 cliques out of 494 chunks from 307 comparisons\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>90): 494 members in 231 cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>90): sorting out cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>90): formatting 231 cliques skipping 88 binary chapter diffs\n", + " 3m 55s PRINT (F 10 SET M>50 S>90): formatted 231 cliques (5 files) skipping 88 binary chapter diffs\n", + " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 55s PREPARING (F 10 SET): Already prepared\n", + " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>85): 732 relevant similarities between 1109 passages\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>85): Loaded: 489 cliques out of 1109 chunks from 732 comparisons\n", + " 3m 55s CLIQUES (F 10 SET M>50 S>85): 1109 members in 489 cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>85): sorting out cliques\n", + " 3m 55s PRINT (F 10 SET M>50 S>85): formatting 489 cliques skipping 186 binary chapter diffs\n", + " 3m 56s PRINT (F 10 SET M>50 S>85): formatted 489 cliques (10 files) skipping 186 binary chapter diffs\n", + " 3m 56s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 56s PREPARING (F 10 SET): Already prepared\n", + " 3m 56s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 56s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 56s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates\n", + " 3m 56s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix\n", + " 3m 56s CLIQUES (F 10 SET M>50 S>80): 1140 relevant similarities between 1540 passages\n", + " 3m 56s CLIQUES (F 10 SET M>50 S>80): Loaded: 631 cliques out of 1540 chunks from 1140 comparisons\n", + " 3m 56s CLIQUES (F 10 SET M>50 S>80): 1540 members in 631 cliques\n", + " 3m 56s PRINT (F 10 SET M>50 S>80): sorting out cliques\n", + " 3m 56s PRINT (F 10 SET M>50 S>80): formatting 631 cliques skipping 218 binary chapter diffs\n", + " 3m 57s PRINT (F 10 SET M>50 S>80): formatted 631 cliques (13 files) skipping 218 binary chapter diffs\n", + " 3m 57s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 57s PREPARING (F 10 SET): Already prepared\n", + " 3m 57s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 57s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates\n", + " 3m 57s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix\n", + " 3m 57s CLIQUES (F 10 SET M>50 S>75): 2144 relevant similarities between 2825 passages\n", + " 3m 57s CLIQUES (F 10 SET M>50 S>75): Loaded: 1126 cliques out of 2825 chunks from 2144 comparisons\n", + " 3m 57s CLIQUES (F 10 SET M>50 S>75): 2825 members in 1126 cliques\n", + " 3m 57s PRINT (F 10 SET M>50 S>75): sorting out cliques\n", + " 3m 57s PRINT (F 10 SET M>50 S>75): formatting 1126 cliques skipping 418 binary chapter diffs\n", + " 3m 58s PRINT (F 10 SET M>50 S>75): formatted 1126 cliques (23 files) skipping 418 binary chapter diffs\n", + " 3m 58s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 3m 58s PREPARING (F 10 SET): Already prepared\n", + " 3m 58s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 3m 58s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 3m 58s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates\n", + " 3m 58s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix\n", + " 3m 58s CLIQUES (F 10 SET M>50 S>70): 3512 relevant similarities between 4079 passages\n", + " 3m 58s CLIQUES (F 10 SET M>50 S>70): Loaded: 1506 cliques out of 4079 chunks from 3512 comparisons\n", + " 3m 58s CLIQUES (F 10 SET M>50 S>70): 4079 members in 1506 cliques\n", + " 3m 58s PRINT (F 10 SET M>50 S>70): sorting out cliques\n", + " 3m 59s PRINT (F 10 SET M>50 S>70): formatting 1506 cliques skipping 570 binary chapter diffs\n", + " 4m 00s PRINT (F 10 SET M>50 S>70): formatted 1506 cliques (31 files) skipping 570 binary chapter diffs\n", + " 4m 00s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 00s PREPARING (F 10 SET): Already prepared\n", + " 4m 00s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 4m 00s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 4m 00s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates\n", + " 4m 00s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix\n", + " 4m 01s CLIQUES (F 10 SET M>50 S>65): 5448 relevant similarities between 5792 passages\n", + " 4m 01s CLIQUES (F 10 SET M>50 S>65): Loaded: 1855 cliques out of 5792 chunks from 5448 comparisons\n", + " 4m 01s CLIQUES (F 10 SET M>50 S>65): 5792 members in 1855 cliques\n", + " 4m 01s PRINT (F 10 SET M>50 S>65): sorting out cliques\n", + " 4m 01s PRINT (F 10 SET M>50 S>65): formatting 1855 cliques skipping 679 binary chapter diffs\n", + " 4m 03s PRINT (F 10 SET M>50 S>65): formatted 1855 cliques (38 files) skipping 679 binary chapter diffs\n", + " 4m 03s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 03s PREPARING (F 10 SET): Already prepared\n", + " 4m 03s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 4m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 4m 03s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates\n", + " 4m 03s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix\n", + " 4m 03s CLIQUES (F 10 SET M>50 S>60): 13180 relevant similarities between 10165 passages\n", + " 4m 03s CLIQUES (F 10 SET M>50 S>60): Loaded: 2189 cliques out of 10165 chunks from 13180 comparisons\n", + " 4m 03s CLIQUES (F 10 SET M>50 S>60): 10165 members in 2189 cliques\n", + " 4m 03s PRINT (F 10 SET M>50 S>60): sorting out cliques\n", + " 4m 03s PRINT (F 10 SET M>50 S>60): formatting 2189 cliques skipping 823 binary chapter diffs\n", + " 4m 05s PRINT (F 10 SET M>50 S>60): formatted 2189 cliques (44 files) skipping 823 binary chapter diffs\n", + " 4m 05s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 05s PREPARING (F 10 SET): Already prepared\n", + " 4m 05s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 4m 05s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 4m 05s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates\n", + " 4m 05s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix\n", + " 4m 06s CLIQUES (F 10 SET M>50 S>55): 25713 relevant similarities between 13984 passages\n", + " 4m 06s CLIQUES (F 10 SET M>50 S>55): Loaded: 2008 cliques out of 13984 chunks from 25713 comparisons\n", + " 4m 06s CLIQUES (F 10 SET M>50 S>55): 13984 members in 2008 cliques\n", + " 4m 06s PRINT (F 10 SET M>50 S>55): sorting out cliques\n", + " 4m 06s PRINT (F 10 SET M>50 S>55): formatting 2008 cliques skipping 777 binary chapter diffs\n", + " 4m 08s PRINT (F 10 SET M>50 S>55): formatted 2008 cliques (41 files) skipping 777 binary chapter diffs\n", + " 4m 08s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 08s PREPARING (F 10 SET): Already prepared\n", + " 4m 08s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", + " 4m 08s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", + " 4m 08s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates\n", + " 4m 08s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix\n", + " 4m 09s CLIQUES (F 10 SET M>50 S>50): 89309 relevant similarities between 22932 passages\n", + " 4m 09s CLIQUES (F 10 SET M>50 S>50): Loaded: 1442 cliques out of 22932 chunks from 89309 comparisons\n", + " 4m 09s CLIQUES (F 10 SET M>50 S>50): 22932 members in 1442 cliques\n", + " 4m 09s PRINT (F 10 SET M>50 S>50): sorting out cliques\n", + " 4m 10s PRINT (F 10 SET M>50 S>50): formatting 1442 cliques skipping 627 binary chapter diffs\n", + " 4m 11s PRINT (F 10 SET M>50 S>50): formatted 1442 cliques (29 files) skipping 627 binary chapter diffs\n", + " 4m 11s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 11s PREPARING (F 10 LCS)\n", + " 4m 12s PREPARING (F 10 LCS): Done 42640 chunks.\n", + " 4m 13s SIMILARITY (F 10 LCS M>60): Loaded: 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 14s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 14s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 4m 14s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix\n", + " 4m 15s CLIQUES (F 10 LCS M>60 S>100): 152 relevant similarities between 277 passages\n", + " 4m 15s CLIQUES (F 10 LCS M>60 S>100): Loaded: 135 cliques out of 277 chunks from 152 comparisons\n", + " 4m 15s CLIQUES (F 10 LCS M>60 S>100): 277 members in 135 cliques\n", + " 4m 15s PRINT (F 10 LCS M>60 S>100): sorting out cliques\n", + " 4m 15s PRINT (F 10 LCS M>60 S>100): formatting 135 cliques skipping 52 binary chapter diffs\n", + " 4m 15s PRINT (F 10 LCS M>60 S>100): formatted 135 cliques (3 files) skipping 52 binary chapter diffs\n", + " 4m 15s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 15s PREPARING (F 10 LCS): Already prepared\n", + " 4m 15s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 16s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 4m 16s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix\n", + " 4m 17s CLIQUES (F 10 LCS M>60 S>95): 221 relevant similarities between 408 passages\n", + " 4m 17s CLIQUES (F 10 LCS M>60 S>95): Loaded: 199 cliques out of 408 chunks from 221 comparisons\n", + " 4m 17s CLIQUES (F 10 LCS M>60 S>95): 408 members in 199 cliques\n", + " 4m 17s PRINT (F 10 LCS M>60 S>95): sorting out cliques\n", + " 4m 17s PRINT (F 10 LCS M>60 S>95): formatting 199 cliques skipping 80 binary chapter diffs\n", + " 4m 17s PRINT (F 10 LCS M>60 S>95): formatted 199 cliques (4 files) skipping 80 binary chapter diffs\n", + " 4m 17s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 17s PREPARING (F 10 LCS): Already prepared\n", + " 4m 17s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 18s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 18s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 4m 18s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix\n", + " 4m 19s CLIQUES (F 10 LCS M>60 S>90): 603 relevant similarities between 937 passages\n", + " 4m 19s CLIQUES (F 10 LCS M>60 S>90): Loaded: 423 cliques out of 937 chunks from 603 comparisons\n", + " 4m 19s CLIQUES (F 10 LCS M>60 S>90): 937 members in 423 cliques\n", + " 4m 19s PRINT (F 10 LCS M>60 S>90): sorting out cliques\n", + " 4m 19s PRINT (F 10 LCS M>60 S>90): formatting 423 cliques skipping 163 binary chapter diffs\n", + " 4m 19s PRINT (F 10 LCS M>60 S>90): formatted 423 cliques (9 files) skipping 163 binary chapter diffs\n", + " 4m 19s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 19s PREPARING (F 10 LCS): Already prepared\n", + " 4m 19s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 20s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 20s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 4m 20s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix\n", + " 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1391 relevant similarities between 1980 passages\n", + " 4m 21s CLIQUES (F 10 LCS M>60 S>85): Loaded: 831 cliques out of 1980 chunks from 1391 comparisons\n", + " 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1980 members in 831 cliques\n", + " 4m 21s PRINT (F 10 LCS M>60 S>85): sorting out cliques\n", + " 4m 21s PRINT (F 10 LCS M>60 S>85): formatting 831 cliques skipping 309 binary chapter diffs\n", + " 4m 22s PRINT (F 10 LCS M>60 S>85): formatted 831 cliques (17 files) skipping 309 binary chapter diffs\n", + " 4m 22s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 22s PREPARING (F 10 LCS): Already prepared\n", + " 4m 22s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 23s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 23s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 4m 23s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix\n", + " 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3271 relevant similarities between 3894 passages\n", + " 4m 24s CLIQUES (F 10 LCS M>60 S>80): Loaded: 1440 cliques out of 3894 chunks from 3271 comparisons\n", + " 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3894 members in 1440 cliques\n", + " 4m 24s PRINT (F 10 LCS M>60 S>80): sorting out cliques\n", + " 4m 24s PRINT (F 10 LCS M>60 S>80): formatting 1440 cliques skipping 553 binary chapter diffs\n", + " 4m 26s PRINT (F 10 LCS M>60 S>80): formatted 1440 cliques (29 files) skipping 553 binary chapter diffs\n", + " 4m 26s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 26s PREPARING (F 10 LCS): Already prepared\n", + " 4m 26s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 27s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 27s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 4m 27s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix\n", + " 4m 28s CLIQUES (F 10 LCS M>60 S>75): 9197 relevant similarities between 8599 passages\n", + " 4m 28s CLIQUES (F 10 LCS M>60 S>75): Loaded: 2328 cliques out of 8599 chunks from 9197 comparisons\n", + " 4m 28s CLIQUES (F 10 LCS M>60 S>75): 8599 members in 2328 cliques\n", + " 4m 28s PRINT (F 10 LCS M>60 S>75): sorting out cliques\n", + " 4m 28s PRINT (F 10 LCS M>60 S>75): formatting 2328 cliques skipping 955 binary chapter diffs\n", + " 4m 30s PRINT (F 10 LCS M>60 S>75): formatted 2328 cliques (47 files) skipping 955 binary chapter diffs\n", + " 4m 30s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 30s PREPARING (F 10 LCS): Already prepared\n", + " 4m 30s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 32s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 32s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 4m 32s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix\n", + " 4m 33s CLIQUES (F 10 LCS M>60 S>70): 38515 relevant similarities between 20425 passages\n", + " 4m 33s CLIQUES (F 10 LCS M>60 S>70): Loaded: 1937 cliques out of 20425 chunks from 38515 comparisons\n", + " 4m 33s CLIQUES (F 10 LCS M>60 S>70): 20425 members in 1937 cliques\n", + " 4m 33s PRINT (F 10 LCS M>60 S>70): sorting out cliques\n", + " 4m 34s PRINT (F 10 LCS M>60 S>70): formatting 1937 cliques skipping 993 binary chapter diffs\n", + " 4m 35s PRINT (F 10 LCS M>60 S>70): formatted 1937 cliques (39 files) skipping 993 binary chapter diffs\n", + " 4m 35s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 35s PREPARING (F 10 LCS): Already prepared\n", + " 4m 35s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 36s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 36s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 4m 36s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix\n", + " 4m 39s CLIQUES (F 10 LCS M>60 S>65): 346407 relevant similarities between 37696 passages\n", + " 4m 39s CLIQUES (F 10 LCS M>60 S>65): Loaded: 218 cliques out of 37696 chunks from 346407 comparisons\n", + " 4m 39s CLIQUES (F 10 LCS M>60 S>65): 37696 members in 218 cliques\n", + " 4m 39s PRINT (F 10 LCS M>60 S>65): sorting out cliques\n", + " 4m 40s PRINT (F 10 LCS M>60 S>65): formatting 218 cliques skipping 131 binary chapter diffs\n", + " 4m 40s PRINT (F 10 LCS M>60 S>65): formatted 218 cliques (5 files) skipping 131 binary chapter diffs\n", + " 4m 40s CHUNKING (F 10): already chunked into 42640 chunks\n", + " 4m 40s PREPARING (F 10 LCS): Already prepared\n", + " 4m 40s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", + " 4m 41s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", + " 4m 41s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 4m 41s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix\n", + " 4m 45s CLIQUES (F 10 LCS M>60 S>60): 2916528 relevant similarities between 42450 passages\n", + " 4m 45s CLIQUES (F 10 LCS M>60 S>60): Loaded: 4 cliques out of 42450 chunks from 2916528 comparisons\n", + " 4m 45s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 4 cliques\n", + " 4m 45s PRINT (F 10 LCS M>60 S>60): sorting out cliques\n", + " 4m 46s PRINT (F 10 LCS M>60 S>60): formatting 4 cliques skipping 3 binary chapter diffs\n", + " 4m 46s PRINT (F 10 LCS M>60 S>60): formatted 4 cliques (1 files) skipping 3 binary chapter diffs\n", + " 4m 46s CHUNKING (O chapter): Loaded: 929 chunks\n", + " 4m 46s CHUNKING (O chapter): Made 929 chunks\n", + " 4m 46s PREPARING (O chapter SET)\n", + " 4m 47s PREPARING (O chapter SET): Done 929 chunks.\n", + " 4m 47s SIMILARITY (O chapter SET M>30): Loaded: 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 47s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates\n", + " 4m 47s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>100): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0\n", + " 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing\n", + " 4m 48s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>95): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>90): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>90): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>85): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>85): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>80): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>80): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>75): Loaded: 7 cliques out of 14 chunks from 7 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>75): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs\n", + " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 48s PREPARING (O chapter SET): Already prepared\n", + " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>70): Loaded: 10 cliques out of 20 chunks from 10 comparisons\n", + " 4m 48s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>70): sorting out cliques\n", + " 4m 48s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs\n", + " 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10\n", + " 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing\n", + " 4m 55s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs\n", + " 4m 55s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 55s PREPARING (O chapter SET): Already prepared\n", + " 4m 55s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 55s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): Composing cliques out of 24 chunks from 12 comparisons\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques\n", + " 4m 55s CLIQUES (O chapter SET M>30 S>65): Composed and saved 12 cliques out of 24 chunks from 12 comparisons\n", + " 4m 55s PRINT (O chapter SET M>30 S>65): sorting out cliques\n", + " 4m 55s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs\n", + " 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12\n", + " 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing\n", + " 5m 05s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs\n", + " 5m 05s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 5m 05s PREPARING (O chapter SET): Already prepared\n", + " 5m 05s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 5m 05s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): Composing cliques out of 34 chunks from 17 comparisons\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques\n", + " 5m 05s CLIQUES (O chapter SET M>30 S>60): Composed and saved 17 cliques out of 34 chunks from 17 comparisons\n", + " 5m 05s PRINT (O chapter SET M>30 S>60): sorting out cliques\n", + " 5m 05s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs\n", + " 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17\n", + " 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing\n", + " 5m 20s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs\n", + " 5m 20s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 5m 20s PREPARING (O chapter SET): Already prepared\n", + " 5m 20s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 5m 20s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): Composing cliques out of 44 chunks from 22 comparisons\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques\n", + " 5m 20s CLIQUES (O chapter SET M>30 S>55): Composed and saved 22 cliques out of 44 chunks from 22 comparisons\n", + " 5m 20s PRINT (O chapter SET M>30 S>55): sorting out cliques\n", + " 5m 20s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs\n", + " 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22\n", + " 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing\n", + " 5m 39s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", + " 5m 39s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 5m 39s PREPARING (O chapter SET): Already prepared\n", + " 5m 39s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 5m 39s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): 29 relevant similarities between 58 passages\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): Composing cliques out of 58 chunks from 29 comparisons\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): 58 members in 29 cliques\n", + " 5m 39s CLIQUES (O chapter SET M>30 S>50): Composed and saved 29 cliques out of 58 chunks from 29 comparisons\n", + " 5m 39s PRINT (O chapter SET M>30 S>50): sorting out cliques\n", + " 5m 39s PRINT (O chapter SET M>30 S>50): formatting 29 cliques involving 29 binary chapter diffs\n", + " 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 29\n", + " 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 29 already existing\n", + " 6m 04s PRINT (O chapter SET M>30 S>50): formatted 29 cliques (1 files) involving 29 binary chapter diffs\n", + " 6m 04s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 04s PREPARING (O chapter SET): Already prepared\n", + " 6m 04s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 6m 04s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): Composing cliques out of 80 chunks from 42 comparisons\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques\n", + " 6m 04s CLIQUES (O chapter SET M>30 S>45): Composed and saved 39 cliques out of 80 chunks from 42 comparisons\n", + " 6m 04s PRINT (O chapter SET M>30 S>45): sorting out cliques\n", + " 6m 04s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs\n", + " 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37\n", + " 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing\n", + " 6m 40s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs\n", " 6m 40s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 40s PREPARING (O chapter LCS): Already prepared\n", - " 6m 40s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 40s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 40s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates\n", - " 6m 40s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix\n", - " 6m 40s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages\n", - " 6m 40s CLIQUES (O chapter LCS M>55 S>70): Loaded: 19 cliques out of 38 chunks from 19 comparisons\n", - " 6m 40s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques\n", - " 6m 40s PRINT (O chapter LCS M>55 S>70): sorting out cliques\n", - " 6m 40s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs\n", - " 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19\n", - " 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing\n", - " 6m 55s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs\n", - " 6m 55s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 55s PREPARING (O chapter LCS): Already prepared\n", - " 6m 55s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 6m 55s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 6m 55s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates\n", - " 6m 55s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix\n", - " 6m 55s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages\n", - " 6m 55s CLIQUES (O chapter LCS M>55 S>65): Loaded: 22 cliques out of 44 chunks from 22 comparisons\n", - " 6m 55s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques\n", - " 6m 55s PRINT (O chapter LCS M>55 S>65): sorting out cliques\n", - " 6m 55s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs\n", - " 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22\n", - " 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing\n", - " 7m 12s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", - " 7m 12s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 7m 12s PREPARING (O chapter LCS): Already prepared\n", - " 7m 12s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 7m 12s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 7m 12s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates\n", - " 7m 12s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix\n", - " 7m 12s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages\n", - " 7m 12s CLIQUES (O chapter LCS M>55 S>60): Loaded: 26 cliques out of 52 chunks from 26 comparisons\n", - " 7m 12s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques\n", - " 7m 12s PRINT (O chapter LCS M>55 S>60): sorting out cliques\n", - " 7m 12s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs\n", - " 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26\n", - " 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing\n", - " 7m 32s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs\n", - " 7m 32s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 7m 32s PREPARING (O chapter LCS): Already prepared\n", - " 7m 32s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - " 7m 32s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - " 7m 32s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates\n", - " 7m 32s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix\n", - " 7m 32s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages\n", - " 7m 32s CLIQUES (O chapter LCS M>55 S>55): Loaded: 49 cliques out of 102 chunks from 53 comparisons\n", - " 7m 32s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques\n", - " 7m 32s PRINT (O chapter LCS M>55 S>55): sorting out cliques\n", - " 7m 32s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs\n", - " 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46\n", - " 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing\n", - " 8m 04s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs\n", - " 8m 04s CHUNKING (O verse): Loaded: 23213 chunks\n", - " 8m 04s CHUNKING (O verse): Made 23213 chunks\n", - " 8m 04s PREPARING (O verse SET)\n", - " 8m 05s PREPARING (O verse SET): Done 23213 chunks.\n", - " 8m 05s SIMILARITY (O verse SET M>50): Loaded: 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 05s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 05s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates\n", - " 8m 05s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix\n", - " 8m 05s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages\n", - " 8m 05s CLIQUES (O verse SET M>50 S>100): Loaded: 388 cliques out of 993 chunks from 4506 comparisons\n", - " 8m 05s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques\n", - " 8m 05s PRINT (O verse SET M>50 S>100): sorting out cliques\n", - " 8m 05s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs\n", - " 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100\n", - " 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing\n", - " 8m 06s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs\n", - " 8m 06s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 06s PREPARING (O verse SET): Already prepared\n", - " 8m 06s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 06s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates\n", - " 8m 06s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix\n", - " 8m 06s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages\n", - " 8m 06s CLIQUES (O verse SET M>50 S>95): Loaded: 406 cliques out of 1029 chunks from 4524 comparisons\n", - " 8m 06s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques\n", - " 8m 06s PRINT (O verse SET M>50 S>95): sorting out cliques\n", - " 8m 06s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs\n", - " 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103\n", - " 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing\n", - " 8m 06s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs\n", - " 8m 06s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 06s PREPARING (O verse SET): Already prepared\n", - " 8m 06s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 06s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates\n", - " 8m 06s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix\n", - " 8m 06s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages\n", - " 8m 06s CLIQUES (O verse SET M>50 S>90): Loaded: 526 cliques out of 1286 chunks from 4700 comparisons\n", - " 8m 06s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques\n", - " 8m 06s PRINT (O verse SET M>50 S>90): sorting out cliques\n", - " 8m 06s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs\n", - " 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133\n", - " 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing\n", - " 8m 07s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs\n", - " 8m 07s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 07s PREPARING (O verse SET): Already prepared\n", - " 8m 07s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 07s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 07s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates\n", - " 8m 07s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix\n", - " 8m 07s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages\n", - " 8m 07s CLIQUES (O verse SET M>50 S>85): Loaded: 651 cliques out of 1573 chunks from 4932 comparisons\n", - " 8m 07s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques\n", - " 8m 07s PRINT (O verse SET M>50 S>85): sorting out cliques\n", - " 8m 07s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs\n", - " 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151\n", - " 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing\n", - " 8m 08s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs\n", - " 8m 08s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 08s PREPARING (O verse SET): Already prepared\n", - " 8m 08s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 08s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 08s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates\n", - " 8m 08s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix\n", - " 8m 08s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages\n", - " 8m 08s CLIQUES (O verse SET M>50 S>80): Loaded: 800 cliques out of 1958 chunks from 10653 comparisons\n", - " 8m 08s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques\n", - " 8m 08s PRINT (O verse SET M>50 S>80): sorting out cliques\n", - " 8m 08s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs\n", - " 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174\n", - " 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing\n", - " 8m 09s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs\n", - " 8m 09s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 09s PREPARING (O verse SET): Already prepared\n", - " 8m 09s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 09s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates\n", - " 8m 09s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix\n", - " 8m 09s CLIQUES (O verse SET M>50 S>75): 11181 relevant similarities between 2359 passages\n", - " 8m 09s CLIQUES (O verse SET M>50 S>75): Loaded: 961 cliques out of 2359 chunks from 11181 comparisons\n", - " 8m 09s CLIQUES (O verse SET M>50 S>75): 2359 members in 961 cliques\n", - " 8m 09s PRINT (O verse SET M>50 S>75): sorting out cliques\n", - " 8m 09s PRINT (O verse SET M>50 S>75): formatting 961 cliques involving 210 binary chapter diffs\n", - " 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210\n", - " 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing\n", - " 8m 11s PRINT (O verse SET M>50 S>75): formatted 961 cliques (20 files) involving 210 binary chapter diffs\n", - " 8m 11s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 11s PREPARING (O verse SET): Already prepared\n", - " 8m 11s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 11s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 11s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates\n", - " 8m 11s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix\n", - " 8m 11s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages\n", - " 8m 11s CLIQUES (O verse SET M>50 S>70): Loaded: 1094 cliques out of 2720 chunks from 11704 comparisons\n", - " 8m 11s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques\n", - " 8m 11s PRINT (O verse SET M>50 S>70): sorting out cliques\n", - " 8m 11s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs\n", - " 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237\n", - " 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing\n", - " 8m 12s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs\n", - " 8m 12s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 12s PREPARING (O verse SET): Already prepared\n", - " 8m 12s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 12s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 12s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates\n", - " 8m 12s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix\n", - " 8m 13s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages\n", - " 8m 13s CLIQUES (O verse SET M>50 S>65): Loaded: 1235 cliques out of 3139 chunks from 14353 comparisons\n", - " 8m 13s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques\n", - " 8m 13s PRINT (O verse SET M>50 S>65): sorting out cliques\n", - " 8m 13s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs\n", - " 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284\n", - " 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing\n", - " 8m 15s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs\n", - " 8m 15s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 15s PREPARING (O verse SET): Already prepared\n", - " 8m 15s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 15s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 15s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", - " 8m 15s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", - " 8m 15s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", - " 8m 15s CLIQUES (O verse SET M>50 S>60): Loaded: 1439 cliques out of 3877 chunks from 16055 comparisons\n", - " 8m 15s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", - " 8m 15s PRINT (O verse SET M>50 S>60): sorting out cliques\n", - " 8m 15s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", - " 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", - " 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", - " 8m 17s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n", - " 8m 17s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 17s PREPARING (O verse SET): Already prepared\n", - " 8m 17s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 17s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 17s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates\n", - " 8m 17s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix\n", - " 8m 17s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages\n", - " 8m 17s CLIQUES (O verse SET M>50 S>55): Loaded: 1638 cliques out of 4735 chunks from 18754 comparisons\n", - " 8m 17s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques\n", - " 8m 17s PRINT (O verse SET M>50 S>55): sorting out cliques\n", - " 8m 17s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs\n", - " 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447\n", - " 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing\n", - " 8m 20s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs\n", - " 8m 20s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 20s PREPARING (O verse SET): Already prepared\n", - " 8m 20s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", - " 8m 20s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - " 8m 20s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates\n", - " 8m 20s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix\n", - " 8m 20s CLIQUES (O verse SET M>50 S>50): 24832 relevant similarities between 6711 passages\n", - " 8m 20s CLIQUES (O verse SET M>50 S>50): Loaded: 1850 cliques out of 6711 chunks from 24832 comparisons\n", - " 8m 20s CLIQUES (O verse SET M>50 S>50): 6711 members in 1850 cliques\n", - " 8m 20s PRINT (O verse SET M>50 S>50): sorting out cliques\n", - " 8m 20s PRINT (O verse SET M>50 S>50): formatting 1850 cliques skipping 560 binary chapter diffs\n", - " 8m 24s PRINT (O verse SET M>50 S>50): formatted 1850 cliques (37 files) skipping 560 binary chapter diffs\n", - " 8m 24s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 24s PREPARING (O verse LCS)\n", - " 8m 25s PREPARING (O verse LCS): Done 23213 chunks.\n", - " 8m 25s SIMILARITY (O verse LCS M>60): Loaded: 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 25s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 25s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates\n", - " 8m 25s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix\n", - " 8m 25s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages\n", - " 8m 25s CLIQUES (O verse LCS M>60 S>100): Loaded: 295 cliques out of 793 chunks from 4204 comparisons\n", - " 8m 25s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques\n", - " 8m 25s PRINT (O verse LCS M>60 S>100): sorting out cliques\n", - " 8m 25s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs\n", - " 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80\n", - " 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing\n", - " 8m 26s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs\n", - " 8m 26s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 26s PREPARING (O verse LCS): Already prepared\n", - " 8m 26s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 26s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 26s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates\n", - " 8m 26s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix\n", - " 8m 26s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages\n", - " 8m 26s CLIQUES (O verse LCS M>60 S>95): Loaded: 504 cliques out of 1235 chunks from 4489 comparisons\n", - " 8m 26s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques\n", - " 8m 26s PRINT (O verse LCS M>60 S>95): sorting out cliques\n", - " 8m 26s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs\n", - " 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120\n", - " 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing\n", - " 8m 27s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs\n", - " 8m 27s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 27s PREPARING (O verse LCS): Already prepared\n", - " 8m 27s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 27s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 27s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates\n", - " 8m 27s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix\n", - " 8m 27s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages\n", - " 8m 27s CLIQUES (O verse LCS M>60 S>90): Loaded: 724 cliques out of 1754 chunks from 5538 comparisons\n", - " 8m 27s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques\n", - " 8m 27s PRINT (O verse LCS M>60 S>90): sorting out cliques\n", - " 8m 27s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs\n", - " 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151\n", - " 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing\n", - " 8m 28s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs\n", - " 8m 28s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 28s PREPARING (O verse LCS): Already prepared\n", - " 8m 28s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 28s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 28s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates\n", - " 8m 28s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix\n", - " 8m 28s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages\n", - " 8m 28s CLIQUES (O verse LCS M>60 S>85): Loaded: 938 cliques out of 2296 chunks from 7871 comparisons\n", - " 8m 28s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques\n", - " 8m 28s PRINT (O verse LCS M>60 S>85): sorting out cliques\n", - " 8m 28s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs\n", - " 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179\n", - " 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing\n", - " 8m 29s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs\n", - " 8m 29s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 29s PREPARING (O verse LCS): Already prepared\n", - " 8m 29s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 29s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 29s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates\n", - " 8m 29s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix\n", - " 8m 29s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages\n", - " 8m 29s CLIQUES (O verse LCS M>60 S>80): Loaded: 1141 cliques out of 2925 chunks from 9461 comparisons\n", - " 8m 29s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques\n", - " 8m 29s PRINT (O verse LCS M>60 S>80): sorting out cliques\n", - " 8m 29s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs\n", - " 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251\n", - " 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing\n", - " 8m 31s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs\n", - " 8m 31s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 31s PREPARING (O verse LCS): Already prepared\n", - " 8m 31s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 31s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates\n", - " 8m 31s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix\n", - " 8m 31s CLIQUES (O verse LCS M>60 S>75): 15543 relevant similarities between 3685 passages\n", - " 8m 31s CLIQUES (O verse LCS M>60 S>75): Loaded: 1340 cliques out of 3685 chunks from 15543 comparisons\n", - " 8m 31s CLIQUES (O verse LCS M>60 S>75): 3685 members in 1340 cliques\n", - " 8m 31s PRINT (O verse LCS M>60 S>75): sorting out cliques\n", - " 8m 31s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs\n", - " 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346\n", - " 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing\n", - " 8m 33s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs\n", - " 8m 33s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 33s PREPARING (O verse LCS): Already prepared\n", - " 8m 33s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 33s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 33s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates\n", - " 8m 33s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix\n", - " 8m 33s CLIQUES (O verse LCS M>60 S>70): 19834 relevant similarities between 4958 passages\n", - " 8m 33s CLIQUES (O verse LCS M>60 S>70): Loaded: 1644 cliques out of 4958 chunks from 19834 comparisons\n", - " 8m 33s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques\n", - " 8m 33s PRINT (O verse LCS M>60 S>70): sorting out cliques\n", - " 8m 33s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs\n", - " 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504\n", - " 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing\n", - " 8m 37s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs\n", - " 8m 37s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 37s PREPARING (O verse LCS): Already prepared\n", - " 8m 37s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 37s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 37s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates\n", - " 8m 37s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix\n", - " 8m 37s CLIQUES (O verse LCS M>60 S>65): 31841 relevant similarities between 9046 passages\n", - " 8m 37s CLIQUES (O verse LCS M>60 S>65): Loaded: 1821 cliques out of 9046 chunks from 31841 comparisons\n", - " 8m 37s CLIQUES (O verse LCS M>60 S>65): 9046 members in 1821 cliques\n", - " 8m 37s PRINT (O verse LCS M>60 S>65): sorting out cliques\n", - " 8m 37s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs\n", - " 8m 40s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs\n", - " 8m 40s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 8m 40s PREPARING (O verse LCS): Already prepared\n", - " 8m 40s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", - " 8m 40s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 8m 40s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", - " 8m 40s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", - " 8m 40s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages\n", - " 8m 40s CLIQUES (O verse LCS M>60 S>60): Loaded: 380 cliques out of 18941 chunks from 113614 comparisons\n", - " 8m 40s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques\n", - " 8m 40s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", - " 8m 40s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", - " 8m 41s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n", - " 8m 41s CHUNKING (O half_verse): Loaded: 45180 chunks\n", - " 8m 41s CHUNKING (O half_verse): Made 45180 chunks\n", - " 8m 41s PREPARING (O half_verse SET)\n", - " 8m 42s PREPARING (O half_verse SET): Done 45180 chunks.\n", - " 8m 42s SIMILARITY (O half_verse SET M>50): Loaded: 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 42s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 42s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates\n", - " 8m 42s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix\n", - " 8m 42s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages\n", - " 8m 42s CLIQUES (O half_verse SET M>50 S>100): Loaded: 1725 cliques out of 4327 chunks from 10239 comparisons\n", - " 8m 42s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques\n", - " 8m 42s PRINT (O half_verse SET M>50 S>100): sorting out cliques\n", - " 8m 42s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs\n", - " 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573\n", - " 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing\n", - " 8m 43s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs\n", - " 8m 43s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 43s PREPARING (O half_verse SET): Already prepared\n", - " 8m 43s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 43s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 43s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates\n", - " 8m 43s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix\n", - " 8m 43s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages\n", - " 8m 43s CLIQUES (O half_verse SET M>50 S>95): Loaded: 1728 cliques out of 4333 chunks from 10242 comparisons\n", - " 8m 43s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques\n", - " 8m 43s PRINT (O half_verse SET M>50 S>95): sorting out cliques\n", - " 8m 43s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs\n", - " 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573\n", - " 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing\n", - " 8m 44s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs\n", - " 8m 44s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 44s PREPARING (O half_verse SET): Already prepared\n", - " 8m 44s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 44s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 44s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates\n", - " 8m 44s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix\n", - " 8m 44s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages\n", - " 8m 44s CLIQUES (O half_verse SET M>50 S>90): Loaded: 1863 cliques out of 4618 chunks from 10410 comparisons\n", - " 8m 44s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques\n", - " 8m 44s PRINT (O half_verse SET M>50 S>90): sorting out cliques\n", - " 8m 44s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs\n", - " 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587\n", - " 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing\n", - " 8m 45s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs\n", - " 8m 45s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 45s PREPARING (O half_verse SET): Already prepared\n", - " 8m 45s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 45s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 45s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates\n", - " 8m 45s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix\n", - " 8m 45s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages\n", - " 8m 45s CLIQUES (O half_verse SET M>50 S>85): Loaded: 2072 cliques out of 5145 chunks from 11111 comparisons\n", - " 8m 45s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques\n", - " 8m 45s PRINT (O half_verse SET M>50 S>85): sorting out cliques\n", - " 8m 45s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs\n", - " 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640\n", - " 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing\n", - " 8m 46s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs\n", - " 8m 46s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 46s PREPARING (O half_verse SET): Already prepared\n", - " 8m 46s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 46s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 46s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates\n", - " 8m 46s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix\n", - " 8m 46s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages\n", - " 8m 46s CLIQUES (O half_verse SET M>50 S>80): Loaded: 2474 cliques out of 6422 chunks from 20178 comparisons\n", - " 8m 46s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques\n", - " 8m 46s PRINT (O half_verse SET M>50 S>80): sorting out cliques\n", - " 8m 46s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs\n", - " 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769\n", - " 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing\n", - " 8m 47s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs\n", - " 8m 47s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 47s PREPARING (O half_verse SET): Already prepared\n", - " 8m 47s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 47s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 47s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates\n", - " 8m 47s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix\n", - " 8m 47s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages\n", - " 8m 47s CLIQUES (O half_verse SET M>50 S>75): Loaded: 2888 cliques out of 8265 chunks from 23717 comparisons\n", - " 8m 47s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques\n", - " 8m 47s PRINT (O half_verse SET M>50 S>75): sorting out cliques\n", - " 8m 47s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs\n", - " 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919\n", - " 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing\n", - " 8m 49s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs\n", - " 8m 49s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 49s PREPARING (O half_verse SET): Already prepared\n", - " 8m 49s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 49s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 49s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates\n", - " 8m 49s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix\n", - " 8m 49s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages\n", - " 8m 49s CLIQUES (O half_verse SET M>50 S>70): Loaded: 3193 cliques out of 9388 chunks from 25560 comparisons\n", - " 8m 49s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques\n", - " 8m 49s PRINT (O half_verse SET M>50 S>70): sorting out cliques\n", - " 8m 49s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs\n", - " 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014\n", - " 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing\n", - " 8m 51s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs\n", - " 8m 51s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 51s PREPARING (O half_verse SET): Already prepared\n", - " 8m 51s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 51s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 51s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates\n", - " 8m 51s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix\n", - " 8m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages\n", - " 8m 51s CLIQUES (O half_verse SET M>50 S>65): Loaded: 3342 cliques out of 12162 chunks from 37453 comparisons\n", - " 8m 51s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques\n", - " 8m 51s PRINT (O half_verse SET M>50 S>65): sorting out cliques\n", - " 8m 52s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs\n", - " 8m 53s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs\n", - " 8m 53s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 53s PREPARING (O half_verse SET): Already prepared\n", - " 8m 53s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 54s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 54s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates\n", - " 8m 54s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix\n", - " 8m 54s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages\n", - " 8m 54s CLIQUES (O half_verse SET M>50 S>60): Loaded: 3424 cliques out of 16476 chunks from 55384 comparisons\n", - " 8m 54s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques\n", - " 8m 54s PRINT (O half_verse SET M>50 S>60): sorting out cliques\n", - " 8m 54s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs\n", - " 8m 56s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs\n", - " 8m 56s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 56s PREPARING (O half_verse SET): Already prepared\n", - " 8m 56s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 56s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 56s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates\n", - " 8m 56s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix\n", - " 8m 57s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages\n", - " 8m 57s CLIQUES (O half_verse SET M>50 S>55): Loaded: 3184 cliques out of 19519 chunks from 70089 comparisons\n", - " 8m 57s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques\n", - " 8m 57s PRINT (O half_verse SET M>50 S>55): sorting out cliques\n", - " 8m 57s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs\n", - " 8m 59s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs\n", - " 8m 59s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 8m 59s PREPARING (O half_verse SET): Already prepared\n", - " 8m 59s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", - " 8m 59s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 8m 59s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates\n", - " 8m 59s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix\n", - " 9m 00s CLIQUES (O half_verse SET M>50 S>50): 179842 relevant similarities between 28990 passages\n", - " 9m 00s CLIQUES (O half_verse SET M>50 S>50): Loaded: 2031 cliques out of 28990 chunks from 179842 comparisons\n", - " 9m 00s CLIQUES (O half_verse SET M>50 S>50): 28990 members in 2031 cliques\n", - " 9m 00s PRINT (O half_verse SET M>50 S>50): sorting out cliques\n", - " 9m 00s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs\n", - " 9m 02s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs\n", - " 9m 02s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 02s PREPARING (O half_verse LCS)\n", - " 9m 03s PREPARING (O half_verse LCS): Done 45180 chunks.\n", - " 9m 04s SIMILARITY (O half_verse LCS M>60): Loaded: 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 05s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 05s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates\n", - " 9m 05s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix\n", - " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages\n", - " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): Loaded: 1514 cliques out of 3799 chunks from 9270 comparisons\n", - " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques\n", - " 9m 06s PRINT (O half_verse LCS M>60 S>100): sorting out cliques\n", - " 9m 06s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs\n", - " 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493\n", - " 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing\n", - " 9m 06s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs\n", - " 9m 06s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 06s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 06s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 07s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 07s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates\n", - " 9m 07s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix\n", - " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages\n", - " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): Loaded: 1771 cliques out of 4342 chunks from 9663 comparisons\n", - " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques\n", - " 9m 08s PRINT (O half_verse LCS M>60 S>95): sorting out cliques\n", - " 9m 08s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs\n", - " 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543\n", - " 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing\n", - " 9m 09s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs\n", - " 9m 09s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 09s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 09s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 10s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 10s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates\n", - " 9m 10s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix\n", - " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages\n", - " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): Loaded: 2336 cliques out of 5776 chunks from 12125 comparisons\n", - " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques\n", - " 9m 11s PRINT (O half_verse LCS M>60 S>90): sorting out cliques\n", - " 9m 11s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs\n", - " 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732\n", - " 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing\n", - " 9m 12s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs\n", - " 9m 12s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 12s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 12s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 13s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 13s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates\n", - " 9m 13s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix\n", - " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages\n", - " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): Loaded: 2983 cliques out of 7970 chunks from 17551 comparisons\n", - " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques\n", - " 9m 14s PRINT (O half_verse LCS M>60 S>85): sorting out cliques\n", - " 9m 14s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs\n", - " 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975\n", - " 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing\n", - " 9m 16s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs\n", - " 9m 16s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 16s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 16s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 18s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 18s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates\n", - " 9m 18s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix\n", - " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages\n", - " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): Loaded: 3540 cliques out of 12504 chunks from 27273 comparisons\n", - " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques\n", - " 9m 19s PRINT (O half_verse LCS M>60 S>80): sorting out cliques\n", - " 9m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs\n", - " 9m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs\n", - " 9m 21s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 21s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 21s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 22s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 22s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates\n", - " 9m 22s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix\n", - " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 53981 relevant similarities between 19148 passages\n", - " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): Loaded: 3084 cliques out of 19148 chunks from 53981 comparisons\n", - " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 19148 members in 3084 cliques\n", - " 9m 24s PRINT (O half_verse LCS M>60 S>75): sorting out cliques\n", - " 9m 24s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs\n", - " 9m 26s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs\n", - " 9m 26s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 26s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 26s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 27s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 27s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates\n", - " 9m 27s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix\n", - " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 126162 relevant similarities between 28472 passages\n", - " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): Loaded: 1894 cliques out of 28472 chunks from 126162 comparisons\n", - " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 28472 members in 1894 cliques\n", - " 9m 28s PRINT (O half_verse LCS M>60 S>70): sorting out cliques\n", - " 9m 28s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs\n", - " 9m 30s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs\n", - " 9m 30s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 30s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 30s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 31s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 31s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates\n", - " 9m 31s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix\n", - " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 393325 relevant similarities between 38180 passages\n", - " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): Loaded: 665 cliques out of 38180 chunks from 393325 comparisons\n", - " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 38180 members in 665 cliques\n", - " 9m 32s PRINT (O half_verse LCS M>60 S>65): sorting out cliques\n", - " 9m 32s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs\n", - " 9m 33s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs\n", - " 9m 33s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 9m 33s PREPARING (O half_verse LCS): Already prepared\n", - " 9m 33s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", - " 9m 34s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 9m 34s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates\n", - " 9m 34s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix\n", - " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 2017661 relevant similarities between 44011 passages\n", - " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): Loaded: 89 cliques out of 44011 chunks from 2017661 comparisons\n", - " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques\n", - " 9m 38s PRINT (O half_verse LCS M>60 S>60): sorting out cliques\n", - " 9m 38s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs\n", - " 9m 38s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs\n", - " 9m 38s CHUNKING (O sentence): Loaded: 63586 chunks\n", - " 9m 38s CHUNKING (O sentence): Made 63586 chunks\n", - " 9m 38s PREPARING (O sentence SET)\n", - " 9m 39s PREPARING (O sentence SET): Done 63586 chunks.\n", - " 9m 43s SIMILARITY (O sentence SET M>50): Loaded: 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - " 9m 45s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - " 9m 45s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates\n", - " 9m 45s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix\n", - " 9m 49s CLIQUES (O sentence SET M>50 S>100): 938441 relevant similarities between 19028 passages\n", - " 9m 49s CLIQUES (O sentence SET M>50 S>100): Loaded: 4325 cliques out of 19028 chunks from 938441 comparisons\n", - " 9m 49s CLIQUES (O sentence SET M>50 S>100): 19028 members in 4325 cliques\n", - " 9m 49s PRINT (O sentence SET M>50 S>100): sorting out cliques\n", - " 9m 49s PRINT (O sentence SET M>50 S>100): formatting 4325 cliques involving 1528 binary chapter diffs\n", - " 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528\n", - " 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs: 0 newly created and 1528 already existing\n", - " 9m 52s PRINT (O sentence SET M>50 S>100): formatted 4325 cliques (87 files) involving 1528 binary chapter diffs\n", - " 9m 52s CHUNKING (O sentence): already chunked into 63586 chunks\n", - " 9m 52s PREPARING (O sentence SET): Already prepared\n", - " 9m 52s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - " 9m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - " 9m 55s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates\n", - " 9m 55s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix\n", - " 9m 57s CLIQUES (O sentence SET M>50 S>95): 938445 relevant similarities between 19036 passages\n", - " 9m 57s CLIQUES (O sentence SET M>50 S>95): Loaded: 4329 cliques out of 19036 chunks from 938445 comparisons\n", - " 9m 57s CLIQUES (O sentence SET M>50 S>95): 19036 members in 4329 cliques\n", - " 9m 57s PRINT (O sentence SET M>50 S>95): sorting out cliques\n", - " 9m 57s PRINT (O sentence SET M>50 S>95): formatting 4329 cliques involving 1529 binary chapter diffs\n", - " 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529\n", - " 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing\n", - " 9m 59s PRINT (O sentence SET M>50 S>95): formatted 4329 cliques (87 files) involving 1529 binary chapter diffs\n", - " 9m 59s CHUNKING (O sentence): already chunked into 63586 chunks\n", - " 9m 59s PREPARING (O sentence SET): Already prepared\n", - " 9m 59s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 02s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 02s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates\n", - "10m 02s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix\n", - "10m 04s CLIQUES (O sentence SET M>50 S>90): 938584 relevant similarities between 19208 passages\n", - "10m 04s CLIQUES (O sentence SET M>50 S>90): Loaded: 4404 cliques out of 19208 chunks from 938584 comparisons\n", - "10m 04s CLIQUES (O sentence SET M>50 S>90): 19208 members in 4404 cliques\n", - "10m 04s PRINT (O sentence SET M>50 S>90): sorting out cliques\n", - "10m 04s PRINT (O sentence SET M>50 S>90): formatting 4404 cliques involving 1536 binary chapter diffs\n", - "10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1536\n", - "10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1536 already existing\n", - "10m 06s PRINT (O sentence SET M>50 S>90): formatted 4404 cliques (89 files) involving 1536 binary chapter diffs\n", - "10m 06s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 06s PREPARING (O sentence SET): Already prepared\n", - "10m 06s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 08s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 08s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates\n", - "10m 08s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix\n", - "10m 10s CLIQUES (O sentence SET M>50 S>85): 939433 relevant similarities between 19771 passages\n", - "10m 10s CLIQUES (O sentence SET M>50 S>85): Loaded: 4606 cliques out of 19771 chunks from 939433 comparisons\n", - "10m 10s CLIQUES (O sentence SET M>50 S>85): 19771 members in 4606 cliques\n", - "10m 10s PRINT (O sentence SET M>50 S>85): sorting out cliques\n", - "10m 10s PRINT (O sentence SET M>50 S>85): formatting 4606 cliques involving 1587 binary chapter diffs\n", - "10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1587\n", - "10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1587 already existing\n", - "10m 12s PRINT (O sentence SET M>50 S>85): formatted 4606 cliques (93 files) involving 1587 binary chapter diffs\n", - "10m 12s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 12s PREPARING (O sentence SET): Already prepared\n", - "10m 12s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 15s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 15s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates\n", - "10m 15s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix\n", - "10m 17s CLIQUES (O sentence SET M>50 S>80): 961541 relevant similarities between 22063 passages\n", - "10m 17s CLIQUES (O sentence SET M>50 S>80): Loaded: 5066 cliques out of 22063 chunks from 961541 comparisons\n", - "10m 17s CLIQUES (O sentence SET M>50 S>80): 22063 members in 5066 cliques\n", - "10m 17s PRINT (O sentence SET M>50 S>80): sorting out cliques\n", - "10m 17s PRINT (O sentence SET M>50 S>80): formatting 5066 cliques involving 1745 binary chapter diffs\n", - "10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1745\n", - "10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs: 0 newly created and 1745 already existing\n", - "10m 19s PRINT (O sentence SET M>50 S>80): formatted 5066 cliques (102 files) involving 1745 binary chapter diffs\n", - "10m 19s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 19s PREPARING (O sentence SET): Already prepared\n", - "10m 19s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 21s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 21s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates\n", - "10m 21s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix\n", - "10m 28s CLIQUES (O sentence SET M>50 S>75): 1009869 relevant similarities between 25724 passages\n", - "10m 28s CLIQUES (O sentence SET M>50 S>75): Loaded: 4993 cliques out of 25724 chunks from 1009869 comparisons\n", - "10m 28s CLIQUES (O sentence SET M>50 S>75): 25724 members in 4993 cliques\n", - "10m 28s PRINT (O sentence SET M>50 S>75): sorting out cliques\n", - "10m 29s PRINT (O sentence SET M>50 S>75): formatting 4993 cliques skipping 1743 binary chapter diffs\n", - "10m 37s PRINT (O sentence SET M>50 S>75): formatted 4993 cliques (100 files) skipping 1743 binary chapter diffs\n", - "10m 37s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 37s PREPARING (O sentence SET): Already prepared\n", - "10m 37s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 39s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 39s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates\n", - "10m 39s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix\n", - "10m 42s CLIQUES (O sentence SET M>50 S>70): 1012567 relevant similarities between 26880 passages\n", - "10m 42s CLIQUES (O sentence SET M>50 S>70): Loaded: 5222 cliques out of 26880 chunks from 1012567 comparisons\n", - "10m 42s CLIQUES (O sentence SET M>50 S>70): 26880 members in 5222 cliques\n", - "10m 42s PRINT (O sentence SET M>50 S>70): sorting out cliques\n", - "10m 42s PRINT (O sentence SET M>50 S>70): formatting 5222 cliques skipping 1819 binary chapter diffs\n", - "10m 45s PRINT (O sentence SET M>50 S>70): formatted 5222 cliques (105 files) skipping 1819 binary chapter diffs\n", - "10m 45s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 45s PREPARING (O sentence SET): Already prepared\n", - "10m 45s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 47s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 47s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates\n", - "10m 47s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix\n", - "10m 50s CLIQUES (O sentence SET M>50 S>65): 1332342 relevant similarities between 33378 passages\n", - "10m 50s CLIQUES (O sentence SET M>50 S>65): Loaded: 4111 cliques out of 33378 chunks from 1332342 comparisons\n", - "10m 50s CLIQUES (O sentence SET M>50 S>65): 33378 members in 4111 cliques\n", - "10m 50s PRINT (O sentence SET M>50 S>65): sorting out cliques\n", - "10m 50s PRINT (O sentence SET M>50 S>65): formatting 4111 cliques skipping 1474 binary chapter diffs\n", - "10m 53s PRINT (O sentence SET M>50 S>65): formatted 4111 cliques (83 files) skipping 1474 binary chapter diffs\n", - "10m 53s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "10m 53s PREPARING (O sentence SET): Already prepared\n", - "10m 53s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "10m 56s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "10m 56s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates\n", - "10m 56s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix\n", - "10m 58s CLIQUES (O sentence SET M>50 S>60): 1431575 relevant similarities between 38807 passages\n", - "10m 58s CLIQUES (O sentence SET M>50 S>60): Loaded: 3753 cliques out of 38807 chunks from 1431575 comparisons\n", - "10m 58s CLIQUES (O sentence SET M>50 S>60): 38807 members in 3753 cliques\n", - "10m 58s PRINT (O sentence SET M>50 S>60): sorting out cliques\n", - "10m 58s PRINT (O sentence SET M>50 S>60): formatting 3753 cliques skipping 1386 binary chapter diffs\n", - "11m 01s PRINT (O sentence SET M>50 S>60): formatted 3753 cliques (76 files) skipping 1386 binary chapter diffs\n", - "11m 01s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "11m 01s PREPARING (O sentence SET): Already prepared\n", - "11m 01s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "11m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "11m 03s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates\n", - "11m 03s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix\n", - "11m 06s CLIQUES (O sentence SET M>50 S>55): 1459808 relevant similarities between 41835 passages\n", - "11m 06s CLIQUES (O sentence SET M>50 S>55): Loaded: 3505 cliques out of 41835 chunks from 1459808 comparisons\n", - "11m 06s CLIQUES (O sentence SET M>50 S>55): 41835 members in 3505 cliques\n", - "11m 06s PRINT (O sentence SET M>50 S>55): sorting out cliques\n", - "11m 06s PRINT (O sentence SET M>50 S>55): formatting 3505 cliques skipping 1341 binary chapter diffs\n", - "11m 11s PRINT (O sentence SET M>50 S>55): formatted 3505 cliques (71 files) skipping 1341 binary chapter diffs\n", - "11m 11s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "11m 11s PREPARING (O sentence SET): Already prepared\n", - "11m 11s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", - "11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", - "11m 14s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates\n", - "11m 14s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix\n", - "11m 20s CLIQUES (O sentence SET M>50 S>50): 3959201 relevant similarities between 53117 passages\n", - "11m 20s CLIQUES (O sentence SET M>50 S>50): Loaded: 1174 cliques out of 53117 chunks from 3959201 comparisons\n", - "11m 20s CLIQUES (O sentence SET M>50 S>50): 53117 members in 1174 cliques\n", - "11m 20s PRINT (O sentence SET M>50 S>50): sorting out cliques\n", - "11m 20s PRINT (O sentence SET M>50 S>50): formatting 1174 cliques skipping 468 binary chapter diffs\n", - "11m 22s PRINT (O sentence SET M>50 S>50): formatted 1174 cliques (24 files) skipping 468 binary chapter diffs\n", - "11m 22s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "11m 22s PREPARING (O sentence LCS)\n", - "11m 23s PREPARING (O sentence LCS): Done 63586 chunks.\n", - "11m 29s SIMILARITY (O sentence LCS M>60): Loaded: 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "11m 35s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "11m 35s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates\n", - "11m 35s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix\n", - "11m 40s CLIQUES (O sentence LCS M>60 S>100): 903811 relevant similarities between 17532 passages\n", - "11m 40s CLIQUES (O sentence LCS M>60 S>100): Loaded: 3981 cliques out of 17532 chunks from 903811 comparisons\n", - "11m 40s CLIQUES (O sentence LCS M>60 S>100): 17532 members in 3981 cliques\n", - "11m 40s PRINT (O sentence LCS M>60 S>100): sorting out cliques\n", - "11m 40s PRINT (O sentence LCS M>60 S>100): formatting 3981 cliques skipping 1364 binary chapter diffs\n", - "11m 42s PRINT (O sentence LCS M>60 S>100): formatted 3981 cliques (80 files) skipping 1364 binary chapter diffs\n", - "11m 42s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "11m 42s PREPARING (O sentence LCS): Already prepared\n", - "11m 42s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "11m 47s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "11m 47s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates\n", - "11m 47s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix\n", - "11m 54s CLIQUES (O sentence LCS M>60 S>95): 904511 relevant similarities between 18079 passages\n", - "11m 54s CLIQUES (O sentence LCS M>60 S>95): Loaded: 4215 cliques out of 18079 chunks from 904511 comparisons\n", - "11m 54s CLIQUES (O sentence LCS M>60 S>95): 18079 members in 4215 cliques\n", - "11m 54s PRINT (O sentence LCS M>60 S>95): sorting out cliques\n", - "11m 55s PRINT (O sentence LCS M>60 S>95): formatting 4215 cliques skipping 1418 binary chapter diffs\n", - "11m 57s PRINT (O sentence LCS M>60 S>95): formatted 4215 cliques (85 files) skipping 1418 binary chapter diffs\n", - "11m 57s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "11m 57s PREPARING (O sentence LCS): Already prepared\n", - "11m 57s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "12m 04s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "12m 04s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates\n", - "12m 04s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix\n", - "12m 08s CLIQUES (O sentence LCS M>60 S>90): 915567 relevant similarities between 21246 passages\n", - "12m 08s CLIQUES (O sentence LCS M>60 S>90): Loaded: 4993 cliques out of 21246 chunks from 915567 comparisons\n", - "12m 08s CLIQUES (O sentence LCS M>60 S>90): 21246 members in 4993 cliques\n", - "12m 08s PRINT (O sentence LCS M>60 S>90): sorting out cliques\n", - "12m 08s PRINT (O sentence LCS M>60 S>90): formatting 4993 cliques involving 1704 binary chapter diffs\n", - "12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1704\n", - "12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 0 newly created and 1704 already existing\n", - "12m 11s PRINT (O sentence LCS M>60 S>90): formatted 4993 cliques (100 files) involving 1704 binary chapter diffs\n", - "12m 11s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "12m 11s PREPARING (O sentence LCS): Already prepared\n", - "12m 11s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "12m 16s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates\n", - "12m 16s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix\n", - "12m 22s CLIQUES (O sentence LCS M>60 S>85): 980912 relevant similarities between 26473 passages\n", - "12m 22s CLIQUES (O sentence LCS M>60 S>85): Loaded: 4853 cliques out of 26473 chunks from 980912 comparisons\n", - "12m 22s CLIQUES (O sentence LCS M>60 S>85): 26473 members in 4853 cliques\n", - "12m 22s PRINT (O sentence LCS M>60 S>85): sorting out cliques\n", - "12m 22s PRINT (O sentence LCS M>60 S>85): formatting 4853 cliques skipping 1709 binary chapter diffs\n", - "12m 24s PRINT (O sentence LCS M>60 S>85): formatted 4853 cliques (98 files) skipping 1709 binary chapter diffs\n", - "12m 24s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "12m 24s PREPARING (O sentence LCS): Already prepared\n", - "12m 24s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "12m 30s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "12m 30s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates\n", - "12m 30s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix\n", - "12m 35s CLIQUES (O sentence LCS M>60 S>80): 1301411 relevant similarities between 35626 passages\n", - "12m 35s CLIQUES (O sentence LCS M>60 S>80): Loaded: 3470 cliques out of 35626 chunks from 1301411 comparisons\n", - "12m 35s CLIQUES (O sentence LCS M>60 S>80): 35626 members in 3470 cliques\n", - "12m 35s PRINT (O sentence LCS M>60 S>80): sorting out cliques\n", - "12m 35s PRINT (O sentence LCS M>60 S>80): formatting 3470 cliques skipping 1296 binary chapter diffs\n", - "12m 37s PRINT (O sentence LCS M>60 S>80): formatted 3470 cliques (70 files) skipping 1296 binary chapter diffs\n", - "12m 37s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "12m 37s PREPARING (O sentence LCS): Already prepared\n", - "12m 37s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "12m 43s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "12m 43s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates\n", - "12m 43s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix\n", - "12m 48s CLIQUES (O sentence LCS M>60 S>75): 1620210 relevant similarities between 44307 passages\n", - "12m 48s CLIQUES (O sentence LCS M>60 S>75): Loaded: 2293 cliques out of 44307 chunks from 1620210 comparisons\n", - "12m 48s CLIQUES (O sentence LCS M>60 S>75): 44307 members in 2293 cliques\n", - "12m 48s PRINT (O sentence LCS M>60 S>75): sorting out cliques\n", - "12m 48s PRINT (O sentence LCS M>60 S>75): formatting 2293 cliques skipping 889 binary chapter diffs\n", - "12m 50s PRINT (O sentence LCS M>60 S>75): formatted 2293 cliques (46 files) skipping 889 binary chapter diffs\n", - "12m 50s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "12m 50s PREPARING (O sentence LCS): Already prepared\n", - "12m 50s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "12m 55s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "12m 55s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates\n", - "12m 55s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix\n", - "13m 02s CLIQUES (O sentence LCS M>60 S>70): 2182513 relevant similarities between 52535 passages\n", - "13m 02s CLIQUES (O sentence LCS M>60 S>70): Loaded: 1197 cliques out of 52535 chunks from 2182513 comparisons\n", - "13m 02s CLIQUES (O sentence LCS M>60 S>70): 52535 members in 1197 cliques\n", - "13m 02s PRINT (O sentence LCS M>60 S>70): sorting out cliques\n", - "13m 02s PRINT (O sentence LCS M>60 S>70): formatting 1197 cliques skipping 455 binary chapter diffs\n", - "13m 03s PRINT (O sentence LCS M>60 S>70): formatted 1197 cliques (24 files) skipping 455 binary chapter diffs\n", - "13m 03s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "13m 03s PREPARING (O sentence LCS): Already prepared\n", - "13m 03s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "13m 09s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "13m 09s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates\n", - "13m 09s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix\n", - "13m 19s CLIQUES (O sentence LCS M>60 S>65): 4831555 relevant similarities between 58863 passages\n", - "13m 19s CLIQUES (O sentence LCS M>60 S>65): Loaded: 460 cliques out of 58863 chunks from 4831555 comparisons\n", - "13m 19s CLIQUES (O sentence LCS M>60 S>65): 58863 members in 460 cliques\n", - "13m 19s PRINT (O sentence LCS M>60 S>65): sorting out cliques\n", - "13m 20s PRINT (O sentence LCS M>60 S>65): formatting 460 cliques skipping 207 binary chapter diffs\n", - "13m 21s PRINT (O sentence LCS M>60 S>65): formatted 460 cliques (10 files) skipping 207 binary chapter diffs\n", - "13m 21s CHUNKING (O sentence): already chunked into 63586 chunks\n", - "13m 21s PREPARING (O sentence LCS): Already prepared\n", - "13m 21s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", - "13m 27s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", - "13m 27s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates\n", - "13m 27s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix\n", - "13m 41s CLIQUES (O sentence LCS M>60 S>60): 10271722 relevant similarities between 62379 passages\n", - "13m 41s CLIQUES (O sentence LCS M>60 S>60): Loaded: 105 cliques out of 62379 chunks from 10271722 comparisons\n", - "13m 41s CLIQUES (O sentence LCS M>60 S>60): 62379 members in 105 cliques\n", - "13m 41s PRINT (O sentence LCS M>60 S>60): sorting out cliques\n", - "13m 41s PRINT (O sentence LCS M>60 S>60): formatting 105 cliques skipping 55 binary chapter diffs\n", - "13m 42s PRINT (O sentence LCS M>60 S>60): formatted 105 cliques (3 files) skipping 55 binary chapter diffs\n", - "13m 42s EXPERIMENT: Generating html report\n", - "13m 42s EXPERIMENT: 36 messy results: deprecated\n", - "13m 42s EXPERIMENT: 22 mixed quality: take care\n", - "13m 42s EXPERIMENT: 75 no results available\n", - "13m 42s EXPERIMENT: 9 unassessed quality: inspection needed\n", - "13m 42s EXPERIMENT: 80 method deprecated\n", - "13m 42s EXPERIMENT: 18 promising results: recommended\n", - "13m 42s EXPERIMENT: Generated html report\n", - "13m 42s EXPERIMENT: Generating html report(standalone)\n", - "13m 42s EXPERIMENT: 36 messy results: deprecated\n", - "13m 42s EXPERIMENT: 22 mixed quality: take care\n", - "13m 42s EXPERIMENT: 75 no results available\n", - "13m 42s EXPERIMENT: 9 unassessed quality: inspection needed\n", - "13m 42s EXPERIMENT: 80 method deprecated\n", - "13m 42s EXPERIMENT: 18 promising results: recommended\n", - "13m 42s EXPERIMENT: Generated html report\n" + " 6m 40s PREPARING (O chapter SET): Already prepared\n", + " 6m 40s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 6m 40s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): Composing cliques out of 142 chunks from 87 comparisons\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques\n", + " 6m 40s CLIQUES (O chapter SET M>30 S>40): Composed and saved 62 cliques out of 142 chunks from 87 comparisons\n", + " 6m 40s PRINT (O chapter SET M>30 S>40): sorting out cliques\n", + " 6m 40s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs\n", + " 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51\n", + " 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing\n", + " 7m 37s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs\n", + " 7m 37s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 7m 37s PREPARING (O chapter SET): Already prepared\n", + " 7m 37s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 7m 37s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): Composing cliques out of 302 chunks from 352 comparisons\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques\n", + " 7m 37s CLIQUES (O chapter SET M>30 S>35): Composed and saved 53 cliques out of 302 chunks from 352 comparisons\n", + " 7m 37s PRINT (O chapter SET M>30 S>35): sorting out cliques\n", + " 7m 37s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs\n", + " 8m 36s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs\n", + " 8m 36s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 8m 36s PREPARING (O chapter SET): Already prepared\n", + " 8m 36s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 8m 36s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): Composing cliques out of 571 chunks from 3445 comparisons\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques\n", + " 8m 36s CLIQUES (O chapter SET M>30 S>30): Composed and saved 28 cliques out of 571 chunks from 3445 comparisons\n", + " 8m 36s PRINT (O chapter SET M>30 S>30): sorting out cliques\n", + " 8m 36s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs\n", + " 9m 01s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs\n", + " 9m 01s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 9m 01s PREPARING (O chapter LCS)\n", + " 9m 02s PREPARING (O chapter LCS): Done 929 chunks.\n", + " 9m 02s SIMILARITY (O chapter LCS M>55): Computing 431 K (431056) comparisons and saving entries in matrix\n", + " 9m 16s SIMILARITY (O chapter LCS M>55): Computed 4 K comparisons and saved 1 entries in matrix\n", + " 9m 28s SIMILARITY (O chapter LCS M>55): Computed 8 K comparisons and saved 2 entries in matrix\n", + " 9m 39s SIMILARITY (O chapter LCS M>55): Computed 12 K comparisons and saved 2 entries in matrix\n", + " 9m 52s SIMILARITY (O chapter LCS M>55): Computed 17 K comparisons and saved 2 entries in matrix\n", + "10m 08s SIMILARITY (O chapter LCS M>55): Computed 21 K comparisons and saved 2 entries in matrix\n", + "10m 26s SIMILARITY (O chapter LCS M>55): Computed 25 K comparisons and saved 2 entries in matrix\n", + "10m 45s SIMILARITY (O chapter LCS M>55): Computed 30 K comparisons and saved 2 entries in matrix\n", + "11m 00s SIMILARITY (O chapter LCS M>55): Computed 34 K comparisons and saved 3 entries in matrix\n", + "11m 18s SIMILARITY (O chapter LCS M>55): Computed 38 K comparisons and saved 3 entries in matrix\n", + "11m 33s SIMILARITY (O chapter LCS M>55): Computed 43 K comparisons and saved 3 entries in matrix\n", + "11m 45s SIMILARITY (O chapter LCS M>55): Computed 47 K comparisons and saved 3 entries in matrix\n", + "12m 00s SIMILARITY (O chapter LCS M>55): Computed 51 K comparisons and saved 3 entries in matrix\n", + "12m 18s SIMILARITY (O chapter LCS M>55): Computed 56 K comparisons and saved 3 entries in matrix\n", + "12m 32s SIMILARITY (O chapter LCS M>55): Computed 60 K comparisons and saved 3 entries in matrix\n", + "12m 46s SIMILARITY (O chapter LCS M>55): Computed 64 K comparisons and saved 4 entries in matrix\n", + "13m 01s SIMILARITY (O chapter LCS M>55): Computed 68 K comparisons and saved 8 entries in matrix\n", + "13m 19s SIMILARITY (O chapter LCS M>55): Computed 73 K comparisons and saved 9 entries in matrix\n", + "13m 36s SIMILARITY (O chapter LCS M>55): Computed 77 K comparisons and saved 9 entries in matrix\n", + "13m 49s SIMILARITY (O chapter LCS M>55): Computed 81 K comparisons and saved 10 entries in matrix\n", + "14m 07s SIMILARITY (O chapter LCS M>55): Computed 86 K comparisons and saved 10 entries in matrix\n", + "14m 25s SIMILARITY (O chapter LCS M>55): Computed 90 K comparisons and saved 11 entries in matrix\n", + "14m 43s SIMILARITY (O chapter LCS M>55): Computed 94 K comparisons and saved 11 entries in matrix\n", + "14m 57s SIMILARITY (O chapter LCS M>55): Computed 99 K comparisons and saved 11 entries in matrix\n", + "15m 16s SIMILARITY (O chapter LCS M>55): Computed 103 K comparisons and saved 11 entries in matrix\n", + "15m 40s SIMILARITY (O chapter LCS M>55): Computed 107 K comparisons and saved 11 entries in matrix\n", + "15m 53s SIMILARITY (O chapter LCS M>55): Computed 112 K comparisons and saved 11 entries in matrix\n", + "16m 11s SIMILARITY (O chapter LCS M>55): Computed 116 K comparisons and saved 11 entries in matrix\n", + "16m 27s SIMILARITY (O chapter LCS M>55): Computed 120 K comparisons and saved 11 entries in matrix\n", + "16m 42s SIMILARITY (O chapter LCS M>55): Computed 124 K comparisons and saved 12 entries in matrix\n", + "17m 00s SIMILARITY (O chapter LCS M>55): Computed 129 K comparisons and saved 12 entries in matrix\n", + "17m 20s SIMILARITY (O chapter LCS M>55): Computed 133 K comparisons and saved 12 entries in matrix\n", + "17m 33s SIMILARITY (O chapter LCS M>55): Computed 137 K comparisons and saved 13 entries in matrix\n", + "17m 47s SIMILARITY (O chapter LCS M>55): Computed 142 K comparisons and saved 13 entries in matrix\n", + "17m 59s SIMILARITY (O chapter LCS M>55): Computed 146 K comparisons and saved 13 entries in matrix\n", + "18m 10s SIMILARITY (O chapter LCS M>55): Computed 150 K comparisons and saved 13 entries in matrix\n", + "18m 31s SIMILARITY (O chapter LCS M>55): Computed 155 K comparisons and saved 13 entries in matrix\n", + "18m 42s SIMILARITY (O chapter LCS M>55): Computed 159 K comparisons and saved 13 entries in matrix\n", + "19m 01s SIMILARITY (O chapter LCS M>55): Computed 163 K comparisons and saved 13 entries in matrix\n", + "19m 14s SIMILARITY (O chapter LCS M>55): Computed 168 K comparisons and saved 13 entries in matrix\n", + "19m 30s SIMILARITY (O chapter LCS M>55): Computed 172 K comparisons and saved 14 entries in matrix\n", + "19m 45s SIMILARITY (O chapter LCS M>55): Computed 176 K comparisons and saved 14 entries in matrix\n", + "20m 05s SIMILARITY (O chapter LCS M>55): Computed 181 K comparisons and saved 14 entries in matrix\n", + "20m 17s SIMILARITY (O chapter LCS M>55): Computed 185 K comparisons and saved 14 entries in matrix\n", + "20m 35s SIMILARITY (O chapter LCS M>55): Computed 189 K comparisons and saved 14 entries in matrix\n", + "20m 45s SIMILARITY (O chapter LCS M>55): Computed 193 K comparisons and saved 14 entries in matrix\n", + "21m 01s SIMILARITY (O chapter LCS M>55): Computed 198 K comparisons and saved 14 entries in matrix\n", + "21m 20s SIMILARITY (O chapter LCS M>55): Computed 202 K comparisons and saved 15 entries in matrix\n", + "21m 34s SIMILARITY (O chapter LCS M>55): Computed 206 K comparisons and saved 15 entries in matrix\n", + "21m 47s SIMILARITY (O chapter LCS M>55): Computed 211 K comparisons and saved 16 entries in matrix\n", + "21m 58s SIMILARITY (O chapter LCS M>55): Computed 215 K comparisons and saved 19 entries in matrix\n", + "22m 15s SIMILARITY (O chapter LCS M>55): Computed 219 K comparisons and saved 20 entries in matrix\n", + "22m 32s SIMILARITY (O chapter LCS M>55): Computed 224 K comparisons and saved 21 entries in matrix\n", + "22m 50s SIMILARITY (O chapter LCS M>55): Computed 228 K comparisons and saved 23 entries in matrix\n", + "23m 14s SIMILARITY (O chapter LCS M>55): Computed 232 K comparisons and saved 26 entries in matrix\n", + "23m 31s SIMILARITY (O chapter LCS M>55): Computed 237 K comparisons and saved 28 entries in matrix\n", + "23m 48s SIMILARITY (O chapter LCS M>55): Computed 241 K comparisons and saved 29 entries in matrix\n", + "24m 04s SIMILARITY (O chapter LCS M>55): Computed 245 K comparisons and saved 29 entries in matrix\n", + "24m 19s SIMILARITY (O chapter LCS M>55): Computed 249 K comparisons and saved 35 entries in matrix\n", + "24m 34s SIMILARITY (O chapter LCS M>55): Computed 254 K comparisons and saved 40 entries in matrix\n", + "24m 42s SIMILARITY (O chapter LCS M>55): Computed 258 K comparisons and saved 41 entries in matrix\n", + "24m 50s SIMILARITY (O chapter LCS M>55): Computed 262 K comparisons and saved 41 entries in matrix\n", + "24m 56s SIMILARITY (O chapter LCS M>55): Computed 267 K comparisons and saved 41 entries in matrix\n", + "25m 04s SIMILARITY (O chapter LCS M>55): Computed 271 K comparisons and saved 41 entries in matrix\n", + "25m 12s SIMILARITY (O chapter LCS M>55): Computed 275 K comparisons and saved 41 entries in matrix\n", + "25m 21s SIMILARITY (O chapter LCS M>55): Computed 280 K comparisons and saved 41 entries in matrix\n", + "25m 28s SIMILARITY (O chapter LCS M>55): Computed 284 K comparisons and saved 41 entries in matrix\n", + "25m 34s SIMILARITY (O chapter LCS M>55): Computed 288 K comparisons and saved 41 entries in matrix\n", + "25m 43s SIMILARITY (O chapter LCS M>55): Computed 293 K comparisons and saved 41 entries in matrix\n", + "25m 55s SIMILARITY (O chapter LCS M>55): Computed 297 K comparisons and saved 41 entries in matrix\n", + "26m 04s SIMILARITY (O chapter LCS M>55): Computed 301 K comparisons and saved 41 entries in matrix\n", + "26m 16s SIMILARITY (O chapter LCS M>55): Computed 306 K comparisons and saved 42 entries in matrix\n", + "26m 31s SIMILARITY (O chapter LCS M>55): Computed 310 K comparisons and saved 42 entries in matrix\n", + "26m 42s SIMILARITY (O chapter LCS M>55): Computed 314 K comparisons and saved 42 entries in matrix\n", + "26m 56s SIMILARITY (O chapter LCS M>55): Computed 318 K comparisons and saved 42 entries in matrix\n", + "27m 04s SIMILARITY (O chapter LCS M>55): Computed 323 K comparisons and saved 42 entries in matrix\n", + "27m 16s SIMILARITY (O chapter LCS M>55): Computed 327 K comparisons and saved 42 entries in matrix\n", + "27m 26s SIMILARITY (O chapter LCS M>55): Computed 331 K comparisons and saved 42 entries in matrix\n", + "27m 38s SIMILARITY (O chapter LCS M>55): Computed 336 K comparisons and saved 42 entries in matrix\n", + "27m 48s SIMILARITY (O chapter LCS M>55): Computed 340 K comparisons and saved 42 entries in matrix\n", + "27m 52s SIMILARITY (O chapter LCS M>55): Computed 344 K comparisons and saved 42 entries in matrix\n", + "27m 58s SIMILARITY (O chapter LCS M>55): Computed 349 K comparisons and saved 42 entries in matrix\n", + "28m 03s SIMILARITY (O chapter LCS M>55): Computed 353 K comparisons and saved 42 entries in matrix\n", + "28m 08s SIMILARITY (O chapter LCS M>55): Computed 357 K comparisons and saved 42 entries in matrix\n", + "28m 14s SIMILARITY (O chapter LCS M>55): Computed 362 K comparisons and saved 43 entries in matrix\n", + "28m 20s SIMILARITY (O chapter LCS M>55): Computed 366 K comparisons and saved 43 entries in matrix\n", + "28m 23s SIMILARITY (O chapter LCS M>55): Computed 370 K comparisons and saved 44 entries in matrix\n", + "28m 26s SIMILARITY (O chapter LCS M>55): Computed 374 K comparisons and saved 44 entries in matrix\n", + "28m 31s SIMILARITY (O chapter LCS M>55): Computed 379 K comparisons and saved 44 entries in matrix\n", + "28m 34s SIMILARITY (O chapter LCS M>55): Computed 383 K comparisons and saved 44 entries in matrix\n", + "28m 37s SIMILARITY (O chapter LCS M>55): Computed 387 K comparisons and saved 45 entries in matrix\n", + "28m 43s SIMILARITY (O chapter LCS M>55): Computed 392 K comparisons and saved 46 entries in matrix\n", + "28m 46s SIMILARITY (O chapter LCS M>55): Computed 396 K comparisons and saved 47 entries in matrix\n", + "28m 51s SIMILARITY (O chapter LCS M>55): Computed 400 K comparisons and saved 48 entries in matrix\n", + "28m 55s SIMILARITY (O chapter LCS M>55): Computed 405 K comparisons and saved 49 entries in matrix\n", + "29m 00s SIMILARITY (O chapter LCS M>55): Computed 409 K comparisons and saved 49 entries in matrix\n", + "29m 06s SIMILARITY (O chapter LCS M>55): Computed 413 K comparisons and saved 49 entries in matrix\n", + "29m 14s SIMILARITY (O chapter LCS M>55): Computed 418 K comparisons and saved 49 entries in matrix\n", + "29m 23s SIMILARITY (O chapter LCS M>55): Computed 422 K comparisons and saved 49 entries in matrix\n", + "29m 37s SIMILARITY (O chapter LCS M>55): Computed 426 K comparisons and saved 52 entries in matrix\n", + "29m 55s SIMILARITY (O chapter LCS M>55): Computed 431 K comparisons and saved 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): Computed 431 K (431056) comparisons and saved 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): Composing cliques out of 0 chunks from 0 comparisons\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>100): Composed and saved 0 cliques out of 0 chunks from 0 comparisons\n", + "29m 56s PRINT (O chapter LCS M>55 S>100): sorting out cliques\n", + "29m 56s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", + "29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0\n", + "29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing\n", + "29m 56s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", + "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", + "29m 56s PREPARING (O chapter LCS): Already prepared\n", + "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): Composing cliques out of 2 chunks from 1 comparisons\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>95): Composed and saved 1 cliques out of 2 chunks from 1 comparisons\n", + "29m 56s PRINT (O chapter LCS M>55 S>95): sorting out cliques\n", + "29m 56s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", + "29m 56s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", + "29m 56s PREPARING (O chapter LCS): Already prepared\n", + "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): Composing cliques out of 4 chunks from 2 comparisons\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>90): Composed and saved 2 cliques out of 4 chunks from 2 comparisons\n", + "29m 56s PRINT (O chapter LCS M>55 S>90): sorting out cliques\n", + "29m 56s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs\n", + "29m 56s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", + "29m 56s PREPARING (O chapter LCS): Already prepared\n", + "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): Composing cliques out of 12 chunks from 6 comparisons\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>85): Composed and saved 6 cliques out of 12 chunks from 6 comparisons\n", + "29m 56s PRINT (O chapter LCS M>55 S>85): sorting out cliques\n", + "29m 56s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs\n", + "29m 56s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs\n", + "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", + "29m 56s PREPARING (O chapter LCS): Already prepared\n", + "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): Composing cliques out of 18 chunks from 9 comparisons\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques\n", + "29m 56s CLIQUES (O chapter LCS M>55 S>80): Composed and saved 9 cliques out of 18 chunks from 9 comparisons\n", + "29m 56s PRINT (O chapter LCS M>55 S>80): sorting out cliques\n", + "29m 56s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs\n", + "29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9\n", + "29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing\n", + "30m 02s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs\n", + "30m 02s CHUNKING (O chapter): already chunked into 929 chunks\n", + "30m 02s PREPARING (O chapter LCS): Already prepared\n", + "30m 02s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "30m 02s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): Composing cliques out of 26 chunks from 13 comparisons\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques\n", + "30m 02s CLIQUES (O chapter LCS M>55 S>75): Composed and saved 13 cliques out of 26 chunks from 13 comparisons\n", + "30m 02s PRINT (O chapter LCS M>55 S>75): sorting out cliques\n", + "30m 02s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs\n", + "30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13\n", + "30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing\n", + "30m 11s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs\n", + "30m 11s CHUNKING (O chapter): already chunked into 929 chunks\n", + "30m 11s PREPARING (O chapter LCS): Already prepared\n", + "30m 11s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "30m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): Composing cliques out of 38 chunks from 19 comparisons\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques\n", + "30m 11s CLIQUES (O chapter LCS M>55 S>70): Composed and saved 19 cliques out of 38 chunks from 19 comparisons\n", + "30m 11s PRINT (O chapter LCS M>55 S>70): sorting out cliques\n", + "30m 11s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs\n", + "30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19\n", + "30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing\n", + "30m 27s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs\n", + "30m 27s CHUNKING (O chapter): already chunked into 929 chunks\n", + "30m 27s PREPARING (O chapter LCS): Already prepared\n", + "30m 27s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "30m 27s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): Composing cliques out of 44 chunks from 22 comparisons\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques\n", + "30m 27s CLIQUES (O chapter LCS M>55 S>65): Composed and saved 22 cliques out of 44 chunks from 22 comparisons\n", + "30m 27s PRINT (O chapter LCS M>55 S>65): sorting out cliques\n", + "30m 27s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs\n", + "30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22\n", + "30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing\n", + "30m 47s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", + "30m 47s CHUNKING (O chapter): already chunked into 929 chunks\n", + "30m 47s PREPARING (O chapter LCS): Already prepared\n", + "30m 47s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "30m 47s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "30m 47s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates\n", + "30m 47s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix\n", + "30m 47s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages\n", + "30m 47s CLIQUES (O chapter LCS M>55 S>60): Composing cliques out of 52 chunks from 26 comparisons\n", + "30m 48s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques\n", + "30m 48s CLIQUES (O chapter LCS M>55 S>60): Composed and saved 26 cliques out of 52 chunks from 26 comparisons\n", + "30m 48s PRINT (O chapter LCS M>55 S>60): sorting out cliques\n", + "30m 48s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs\n", + "30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26\n", + "30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing\n", + "31m 11s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs\n", + "31m 11s CHUNKING (O chapter): already chunked into 929 chunks\n", + "31m 11s PREPARING (O chapter LCS): Already prepared\n", + "31m 11s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + "31m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): Composing cliques out of 102 chunks from 53 comparisons\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques\n", + "31m 11s CLIQUES (O chapter LCS M>55 S>55): Composed and saved 49 cliques out of 102 chunks from 53 comparisons\n", + "31m 11s PRINT (O chapter LCS M>55 S>55): sorting out cliques\n", + "31m 11s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs\n", + "31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46\n", + "31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing\n", + "31m 48s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs\n", + "31m 48s CHUNKING (O verse)\n", + "31m 49s CHUNKING (O verse): Made 23213 chunks\n", + "31m 49s PREPARING (O verse SET)\n", + "31m 50s PREPARING (O verse SET): Done 23213 chunks.\n", + "31m 50s SIMILARITY (O verse SET M>50): Computing 269 M (269410078) comparisons and saving entries in matrix\n", + "31m 56s SIMILARITY (O verse SET M>50): Computed 2 M comparisons and saved 78 entries in matrix\n", + "32m 02s SIMILARITY (O verse SET M>50): Computed 5 M comparisons and saved 235 entries in matrix\n", + "32m 08s SIMILARITY (O verse SET M>50): Computed 8 M comparisons and saved 322 entries in matrix\n", + "32m 14s SIMILARITY (O verse SET M>50): Computed 10 M comparisons and saved 335 entries in matrix\n", + "32m 20s SIMILARITY (O verse SET M>50): Computed 13 M comparisons and saved 351 entries in matrix\n", + "32m 25s SIMILARITY (O verse SET M>50): Computed 16 M comparisons and saved 376 entries in matrix\n", + "32m 31s SIMILARITY (O verse SET M>50): Computed 18 M comparisons and saved 389 entries in matrix\n", + "32m 37s SIMILARITY (O verse SET M>50): Computed 21 M comparisons and saved 544 entries in matrix\n", + "32m 43s SIMILARITY (O verse SET M>50): Computed 24 M comparisons and saved 575 entries in matrix\n", + "32m 49s SIMILARITY (O verse SET M>50): Computed 26 M comparisons and saved 613 entries in matrix\n", + "32m 55s SIMILARITY (O verse SET M>50): Computed 29 M comparisons and saved 636 entries in matrix\n", + "33m 01s SIMILARITY (O verse SET M>50): Computed 32 M comparisons and saved 666 entries in matrix\n", + "33m 07s SIMILARITY (O verse SET M>50): Computed 35 M comparisons and saved 684 entries in matrix\n", + "33m 13s SIMILARITY (O verse SET M>50): Computed 37 M comparisons and saved 1101 entries in matrix\n", + "33m 19s SIMILARITY (O verse SET M>50): Computed 40 M comparisons and saved 1318 entries in matrix\n", + "33m 25s SIMILARITY (O verse SET M>50): Computed 43 M comparisons and saved 1848 entries in matrix\n", + "33m 31s SIMILARITY (O verse SET M>50): Computed 45 M comparisons and saved 2090 entries in matrix\n", + "33m 37s SIMILARITY (O verse SET M>50): Computed 48 M comparisons and saved 2125 entries in matrix\n", + "33m 43s SIMILARITY (O verse SET M>50): Computed 51 M comparisons and saved 2521 entries in matrix\n", + "33m 48s SIMILARITY (O verse SET M>50): Computed 53 M comparisons and saved 3522 entries in matrix\n", + "33m 54s SIMILARITY (O verse SET M>50): Computed 56 M comparisons and saved 3597 entries in matrix\n", + "34m 00s SIMILARITY (O verse SET M>50): Computed 59 M comparisons and saved 3847 entries in matrix\n", + "34m 06s SIMILARITY (O verse SET M>50): Computed 61 M comparisons and saved 4268 entries in matrix\n", + "34m 12s SIMILARITY (O verse SET M>50): Computed 64 M comparisons and saved 5626 entries in matrix\n", + "34m 18s SIMILARITY (O verse SET M>50): Computed 67 M comparisons and saved 6320 entries in matrix\n", + "34m 24s SIMILARITY (O verse SET M>50): Computed 70 M comparisons and saved 7034 entries in matrix\n", + "34m 30s SIMILARITY (O verse SET M>50): Computed 72 M comparisons and saved 7890 entries in matrix\n", + "34m 36s SIMILARITY (O verse SET M>50): Computed 75 M comparisons and saved 9304 entries in matrix\n", + "34m 41s SIMILARITY (O verse SET M>50): Computed 78 M comparisons and saved 9839 entries in matrix\n", + "34m 47s SIMILARITY (O verse SET M>50): Computed 80 M comparisons and saved 10997 entries in matrix\n", + "34m 53s SIMILARITY (O verse SET M>50): Computed 83 M comparisons and saved 12113 entries in matrix\n", + "34m 59s SIMILARITY (O verse SET M>50): Computed 86 M comparisons and saved 12712 entries in matrix\n", + "35m 05s SIMILARITY (O verse SET M>50): Computed 88 M comparisons and saved 13345 entries in matrix\n", + "35m 11s SIMILARITY (O verse SET M>50): Computed 91 M comparisons and saved 14140 entries in matrix\n", + "35m 16s SIMILARITY (O verse SET M>50): Computed 94 M comparisons and saved 14375 entries in matrix\n", + "35m 22s SIMILARITY (O verse SET M>50): Computed 96 M comparisons and saved 14945 entries in matrix\n", + "35m 28s SIMILARITY (O verse SET M>50): Computed 99 M comparisons and saved 15321 entries in matrix\n", + "35m 34s SIMILARITY (O verse SET M>50): Computed 102 M comparisons and saved 15714 entries in matrix\n", + "35m 40s SIMILARITY (O verse SET M>50): Computed 105 M comparisons and saved 15978 entries in matrix\n", + "35m 46s SIMILARITY (O verse SET M>50): Computed 107 M comparisons and saved 16103 entries in matrix\n", + "35m 52s SIMILARITY (O verse SET M>50): Computed 110 M comparisons and saved 16239 entries in matrix\n", + "35m 58s SIMILARITY (O verse SET M>50): Computed 113 M comparisons and saved 16359 entries in matrix\n", + "36m 04s SIMILARITY (O verse SET M>50): Computed 115 M comparisons and saved 16433 entries in matrix\n", + "36m 10s SIMILARITY (O verse SET M>50): Computed 118 M comparisons and saved 16469 entries in matrix\n", + "36m 16s SIMILARITY (O verse SET M>50): Computed 121 M comparisons and saved 16583 entries in matrix\n", + "36m 22s SIMILARITY (O verse SET M>50): Computed 123 M comparisons and saved 16619 entries in matrix\n", + "36m 28s SIMILARITY (O verse SET M>50): Computed 126 M comparisons and saved 16669 entries in matrix\n", + "36m 33s SIMILARITY (O verse SET M>50): Computed 129 M comparisons and saved 16834 entries in matrix\n", + "36m 39s SIMILARITY (O verse SET M>50): Computed 132 M comparisons and saved 16878 entries in matrix\n", + "36m 45s SIMILARITY (O verse SET M>50): Computed 134 M comparisons and saved 16899 entries in matrix\n", + "36m 51s SIMILARITY (O verse SET M>50): Computed 137 M comparisons and saved 16916 entries in matrix\n", + "36m 58s SIMILARITY (O verse SET M>50): Computed 140 M comparisons and saved 16926 entries in matrix\n", + "37m 04s SIMILARITY (O verse SET M>50): Computed 142 M comparisons and saved 16953 entries in matrix\n", + "37m 10s SIMILARITY (O verse SET M>50): Computed 145 M comparisons and saved 16980 entries in matrix\n", + "37m 16s SIMILARITY (O verse SET M>50): Computed 148 M comparisons and saved 17084 entries in matrix\n", + "37m 22s SIMILARITY (O verse SET M>50): Computed 150 M comparisons and saved 17098 entries in matrix\n", + "37m 28s SIMILARITY (O verse SET M>50): Computed 153 M comparisons and saved 17119 entries in matrix\n", + "37m 34s SIMILARITY (O verse SET M>50): Computed 156 M comparisons and saved 17305 entries in matrix\n", + "37m 40s SIMILARITY (O verse SET M>50): Computed 158 M comparisons and saved 17341 entries in matrix\n", + "37m 46s SIMILARITY (O verse SET M>50): Computed 161 M comparisons and saved 17365 entries in matrix\n", + "37m 52s SIMILARITY (O verse SET M>50): Computed 164 M comparisons and saved 17543 entries in matrix\n", + "37m 58s SIMILARITY (O verse SET M>50): Computed 167 M comparisons and saved 17680 entries in matrix\n", + "38m 04s SIMILARITY (O verse SET M>50): Computed 169 M comparisons and saved 17948 entries in matrix\n", + "38m 10s SIMILARITY (O verse SET M>50): Computed 172 M comparisons and saved 18586 entries in matrix\n", + "38m 16s SIMILARITY (O verse SET M>50): Computed 175 M comparisons and saved 18899 entries in matrix\n", + "38m 22s SIMILARITY (O verse SET M>50): Computed 177 M comparisons and saved 18991 entries in matrix\n", + "38m 27s SIMILARITY (O verse SET M>50): Computed 180 M comparisons and saved 19389 entries in matrix\n", + "38m 33s SIMILARITY (O verse SET M>50): Computed 183 M comparisons and saved 19718 entries in matrix\n", + "38m 39s SIMILARITY (O verse SET M>50): Computed 185 M comparisons and saved 19823 entries in matrix\n", + "38m 45s SIMILARITY (O verse SET M>50): Computed 188 M comparisons and saved 19961 entries in matrix\n", + "38m 50s SIMILARITY (O verse SET M>50): Computed 191 M comparisons and saved 19967 entries in matrix\n", + "38m 56s SIMILARITY (O verse SET M>50): Computed 193 M comparisons and saved 20103 entries in matrix\n", + "39m 02s SIMILARITY (O verse SET M>50): Computed 196 M comparisons and saved 20158 entries in matrix\n", + "39m 08s SIMILARITY (O verse SET M>50): Computed 199 M comparisons and saved 20162 entries in matrix\n", + "39m 13s SIMILARITY (O verse SET M>50): Computed 202 M comparisons and saved 20312 entries in matrix\n", + "39m 19s SIMILARITY (O verse SET M>50): Computed 204 M comparisons and saved 20523 entries in matrix\n", + "39m 25s SIMILARITY (O verse SET M>50): Computed 207 M comparisons and saved 20771 entries in matrix\n", + "39m 31s SIMILARITY (O verse SET M>50): Computed 210 M comparisons and saved 21114 entries in matrix\n", + "39m 37s SIMILARITY (O verse SET M>50): Computed 212 M comparisons and saved 21360 entries in matrix\n", + "39m 42s SIMILARITY (O verse SET M>50): Computed 215 M comparisons and saved 21383 entries in matrix\n", + "39m 48s SIMILARITY (O verse SET M>50): Computed 218 M comparisons and saved 21935 entries in matrix\n", + "39m 53s SIMILARITY (O verse SET M>50): Computed 220 M comparisons and saved 22457 entries in matrix\n", + "39m 59s SIMILARITY (O verse SET M>50): Computed 223 M comparisons and saved 22720 entries in matrix\n", + "40m 05s SIMILARITY (O verse SET M>50): Computed 226 M comparisons and saved 22863 entries in matrix\n", + "40m 10s SIMILARITY (O verse SET M>50): Computed 228 M comparisons and saved 22938 entries in matrix\n", + "40m 16s SIMILARITY (O verse SET M>50): Computed 231 M comparisons and saved 22961 entries in matrix\n", + "40m 21s SIMILARITY (O verse SET M>50): Computed 234 M comparisons and saved 22979 entries in matrix\n", + "40m 27s SIMILARITY (O verse SET M>50): Computed 237 M comparisons and saved 23043 entries in matrix\n", + "40m 32s SIMILARITY (O verse SET M>50): Computed 239 M comparisons and saved 23373 entries in matrix\n", + "40m 37s SIMILARITY (O verse SET M>50): Computed 242 M comparisons and saved 23590 entries in matrix\n", + "40m 42s SIMILARITY (O verse SET M>50): Computed 245 M comparisons and saved 23770 entries in matrix\n", + "40m 47s SIMILARITY (O verse SET M>50): Computed 247 M comparisons and saved 23807 entries in matrix\n", + "40m 52s SIMILARITY (O verse SET M>50): Computed 250 M comparisons and saved 23901 entries in matrix\n", + "40m 57s SIMILARITY (O verse SET M>50): Computed 253 M comparisons and saved 24012 entries in matrix\n", + "41m 03s SIMILARITY (O verse SET M>50): Computed 255 M comparisons and saved 24091 entries in matrix\n", + "41m 08s SIMILARITY (O verse SET M>50): Computed 258 M comparisons and saved 24138 entries in matrix\n", + "41m 13s SIMILARITY (O verse SET M>50): Computed 261 M comparisons and saved 24182 entries in matrix\n", + "41m 19s SIMILARITY (O verse SET M>50): Computed 264 M comparisons and saved 24220 entries in matrix\n", + "41m 25s SIMILARITY (O verse SET M>50): Computed 266 M comparisons and saved 24413 entries in matrix\n", + "41m 31s SIMILARITY (O verse SET M>50): Computed 269 M comparisons and saved 24792 entries in matrix\n", + "41m 31s SIMILARITY (O verse SET M>50): Computed 269 M (269410078) comparisons and saved 24792 entries in matrix\n", + "41m 31s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): Composing cliques out of 993 chunks from 4506 comparisons\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques\n", + "41m 31s CLIQUES (O verse SET M>50 S>100): Composed and saved 388 cliques out of 993 chunks from 4506 comparisons\n", + "41m 31s PRINT (O verse SET M>50 S>100): sorting out cliques\n", + "41m 31s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs\n", + "41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100\n", + "41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing\n", + "41m 32s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs\n", + "41m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 32s PREPARING (O verse SET): Already prepared\n", + "41m 32s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): Composing cliques out of 1029 chunks from 4524 comparisons\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): Composed 400 cliques out of 1000 chunks\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques\n", + "41m 32s CLIQUES (O verse SET M>50 S>95): Composed and saved 406 cliques out of 1029 chunks from 4524 comparisons\n", + "41m 32s PRINT (O verse SET M>50 S>95): sorting out cliques\n", + "41m 32s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs\n", + "41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103\n", + "41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing\n", + "41m 32s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs\n", + "41m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 32s PREPARING (O verse SET): Already prepared\n", + "41m 32s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 32s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates\n", + "41m 32s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix\n", + "41m 33s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages\n", + "41m 33s CLIQUES (O verse SET M>50 S>90): Composing cliques out of 1286 chunks from 4700 comparisons\n", + "41m 33s CLIQUES (O verse SET M>50 S>90): Composed 467 cliques out of 1000 chunks\n", + "41m 33s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques\n", + "41m 33s CLIQUES (O verse SET M>50 S>90): Composed and saved 526 cliques out of 1286 chunks from 4700 comparisons\n", + "41m 33s PRINT (O verse SET M>50 S>90): sorting out cliques\n", + "41m 33s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs\n", + "41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133\n", + "41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing\n", + "41m 34s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs\n", + "41m 34s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 34s PREPARING (O verse SET): Already prepared\n", + "41m 34s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 34s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 34s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates\n", + "41m 34s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix\n", + "41m 34s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages\n", + "41m 34s CLIQUES (O verse SET M>50 S>85): Composing cliques out of 1573 chunks from 4932 comparisons\n", + "41m 34s CLIQUES (O verse SET M>50 S>85): Composed 473 cliques out of 1000 chunks\n", + "41m 35s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques\n", + "41m 35s CLIQUES (O verse SET M>50 S>85): Composed and saved 651 cliques out of 1573 chunks from 4932 comparisons\n", + "41m 35s PRINT (O verse SET M>50 S>85): sorting out cliques\n", + "41m 35s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs\n", + "41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151\n", + "41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing\n", + "41m 35s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs\n", + "41m 35s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 35s PREPARING (O verse SET): Already prepared\n", + "41m 35s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 35s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 35s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates\n", + "41m 35s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix\n", + "41m 35s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages\n", + "41m 35s CLIQUES (O verse SET M>50 S>80): Composing cliques out of 1958 chunks from 10653 comparisons\n", + "41m 36s CLIQUES (O verse SET M>50 S>80): Composed 487 cliques out of 1000 chunks\n", + "41m 37s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques\n", + "41m 37s CLIQUES (O verse SET M>50 S>80): Composed and saved 800 cliques out of 1958 chunks from 10653 comparisons\n", + "41m 37s PRINT (O verse SET M>50 S>80): sorting out cliques\n", + "41m 37s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs\n", + "41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174\n", + "41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing\n", + "41m 38s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs\n", + "41m 38s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 38s PREPARING (O verse SET): Already prepared\n", + "41m 38s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 38s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 38s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates\n", + "41m 38s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix\n", + "41m 38s CLIQUES (O verse SET M>50 S>75): 11182 relevant similarities between 2361 passages\n", + "41m 38s CLIQUES (O verse SET M>50 S>75): Composing cliques out of 2361 chunks from 11182 comparisons\n", + "41m 38s CLIQUES (O verse SET M>50 S>75): Composed 497 cliques out of 1000 chunks\n", + "41m 39s CLIQUES (O verse SET M>50 S>75): Composed 897 cliques out of 2000 chunks\n", + "41m 40s CLIQUES (O verse SET M>50 S>75): 2361 members in 962 cliques\n", + "41m 40s CLIQUES (O verse SET M>50 S>75): Composed and saved 962 cliques out of 2361 chunks from 11182 comparisons\n", + "41m 40s PRINT (O verse SET M>50 S>75): sorting out cliques\n", + "41m 40s PRINT (O verse SET M>50 S>75): formatting 962 cliques involving 210 binary chapter diffs\n", + "41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210\n", + "41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing\n", + "41m 41s PRINT (O verse SET M>50 S>75): formatted 962 cliques (20 files) involving 210 binary chapter diffs\n", + "41m 41s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 41s PREPARING (O verse SET): Already prepared\n", + "41m 41s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 41s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 41s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates\n", + "41m 41s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix\n", + "41m 41s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages\n", + "41m 41s CLIQUES (O verse SET M>50 S>70): Composing cliques out of 2720 chunks from 11704 comparisons\n", + "41m 42s CLIQUES (O verse SET M>50 S>70): Composed 515 cliques out of 1000 chunks\n", + "41m 42s CLIQUES (O verse SET M>50 S>70): Composed 893 cliques out of 2000 chunks\n", + "41m 44s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques\n", + "41m 44s CLIQUES (O verse SET M>50 S>70): Composed and saved 1094 cliques out of 2720 chunks from 11704 comparisons\n", + "41m 44s PRINT (O verse SET M>50 S>70): sorting out cliques\n", + "41m 44s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs\n", + "41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237\n", + "41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing\n", + "41m 45s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs\n", + "41m 45s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 45s PREPARING (O verse SET): Already prepared\n", + "41m 45s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 45s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 45s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates\n", + "41m 45s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix\n", + "41m 45s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages\n", + "41m 45s CLIQUES (O verse SET M>50 S>65): Composing cliques out of 3139 chunks from 14353 comparisons\n", + "41m 46s CLIQUES (O verse SET M>50 S>65): Composed 524 cliques out of 1000 chunks\n", + "41m 47s CLIQUES (O verse SET M>50 S>65): Composed 901 cliques out of 2000 chunks\n", + "41m 48s CLIQUES (O verse SET M>50 S>65): Composed 1205 cliques out of 3000 chunks\n", + "41m 48s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques\n", + "41m 48s CLIQUES (O verse SET M>50 S>65): Composed and saved 1235 cliques out of 3139 chunks from 14353 comparisons\n", + "41m 48s PRINT (O verse SET M>50 S>65): sorting out cliques\n", + "41m 48s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs\n", + "41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284\n", + "41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing\n", + "41m 50s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs\n", + "41m 50s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 50s PREPARING (O verse SET): Already prepared\n", + "41m 50s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 50s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 50s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", + "41m 50s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", + "41m 51s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", + "41m 51s CLIQUES (O verse SET M>50 S>60): Composing cliques out of 3877 chunks from 16055 comparisons\n", + "41m 51s CLIQUES (O verse SET M>50 S>60): Composed 549 cliques out of 1000 chunks\n", + "41m 52s CLIQUES (O verse SET M>50 S>60): Composed 928 cliques out of 2000 chunks\n", + "41m 53s CLIQUES (O verse SET M>50 S>60): Composed 1239 cliques out of 3000 chunks\n", + "41m 55s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", + "41m 55s CLIQUES (O verse SET M>50 S>60): Composed and saved 1439 cliques out of 3877 chunks from 16055 comparisons\n", + "41m 55s PRINT (O verse SET M>50 S>60): sorting out cliques\n", + "41m 55s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", + "41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", + "41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", + "41m 58s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n", + "41m 58s CHUNKING (O verse): already chunked into 23213 chunks\n", + "41m 58s PREPARING (O verse SET): Already prepared\n", + "41m 58s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "41m 58s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "41m 58s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates\n", + "41m 58s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix\n", + "41m 58s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages\n", + "41m 58s CLIQUES (O verse SET M>50 S>55): Composing cliques out of 4735 chunks from 18754 comparisons\n", + "41m 58s CLIQUES (O verse SET M>50 S>55): Composed 600 cliques out of 1000 chunks\n", + "41m 59s CLIQUES (O verse SET M>50 S>55): Composed 973 cliques out of 2000 chunks\n", + "42m 01s CLIQUES (O verse SET M>50 S>55): Composed 1236 cliques out of 3000 chunks\n", + "42m 03s CLIQUES (O verse SET M>50 S>55): Composed 1508 cliques out of 4000 chunks\n", + "42m 05s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques\n", + "42m 05s CLIQUES (O verse SET M>50 S>55): Composed and saved 1638 cliques out of 4735 chunks from 18754 comparisons\n", + "42m 05s PRINT (O verse SET M>50 S>55): sorting out cliques\n", + "42m 05s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs\n", + "42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447\n", + "42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing\n", + "42m 09s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs\n", + "42m 09s CHUNKING (O verse): already chunked into 23213 chunks\n", + "42m 09s PREPARING (O verse SET): Already prepared\n", + "42m 09s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", + "42m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "42m 09s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates\n", + "42m 09s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix\n", + "42m 09s CLIQUES (O verse SET M>50 S>50): 24792 relevant similarities between 6711 passages\n", + "42m 09s CLIQUES (O verse SET M>50 S>50): Composing cliques out of 6711 chunks from 24792 comparisons\n", + "42m 09s CLIQUES (O verse SET M>50 S>50): Composed 642 cliques out of 1000 chunks\n", + "42m 10s CLIQUES (O verse SET M>50 S>50): Composed 1029 cliques out of 2000 chunks\n", + "42m 12s CLIQUES (O verse SET M>50 S>50): Composed 1309 cliques out of 3000 chunks\n", + "42m 14s CLIQUES (O verse SET M>50 S>50): Composed 1490 cliques out of 4000 chunks\n", + "42m 16s CLIQUES (O verse SET M>50 S>50): Composed 1634 cliques out of 5000 chunks\n", + "42m 19s CLIQUES (O verse SET M>50 S>50): Composed 1803 cliques out of 6000 chunks\n", + "42m 22s CLIQUES (O verse SET M>50 S>50): 6711 members in 1851 cliques\n", + "42m 22s CLIQUES (O verse SET M>50 S>50): Composed and saved 1851 cliques out of 6711 chunks from 24792 comparisons\n", + "42m 22s PRINT (O verse SET M>50 S>50): sorting out cliques\n", + "42m 22s PRINT (O verse SET M>50 S>50): formatting 1851 cliques skipping 560 binary chapter diffs\n", + "42m 26s PRINT (O verse SET M>50 S>50): formatted 1851 cliques (38 files) skipping 560 binary chapter diffs\n", + "42m 26s CHUNKING (O verse): already chunked into 23213 chunks\n", + "42m 26s PREPARING (O verse LCS)\n", + "42m 27s PREPARING (O verse LCS): Done 23213 chunks.\n", + "42m 27s SIMILARITY (O verse LCS M>60): Computing 269 M (269410078) comparisons and saving entries in matrix\n", + "42m 42s SIMILARITY (O verse LCS M>60): Computed 2 M comparisons and saved 1501 entries in matrix\n", + "42m 56s SIMILARITY (O verse LCS M>60): Computed 5 M comparisons and saved 2936 entries in matrix\n", + "43m 10s SIMILARITY (O verse LCS M>60): Computed 8 M comparisons and saved 3564 entries in matrix\n", + "43m 24s SIMILARITY (O verse LCS M>60): Computed 10 M comparisons and saved 4565 entries in matrix\n", + "43m 39s SIMILARITY (O verse LCS M>60): Computed 13 M comparisons and saved 5385 entries in matrix\n", + "43m 54s SIMILARITY (O verse LCS M>60): Computed 16 M comparisons and saved 6042 entries in matrix\n", + "44m 08s SIMILARITY (O verse LCS M>60): Computed 18 M comparisons and saved 6742 entries in matrix\n", + "44m 23s SIMILARITY (O verse LCS M>60): Computed 21 M comparisons and saved 7378 entries in matrix\n", + "44m 37s SIMILARITY (O verse LCS M>60): Computed 24 M comparisons and saved 8027 entries in matrix\n", + "44m 51s SIMILARITY (O verse LCS M>60): Computed 26 M comparisons and saved 8728 entries in matrix\n", + "45m 06s SIMILARITY (O verse LCS M>60): Computed 29 M comparisons and saved 9408 entries in matrix\n", + "45m 20s SIMILARITY (O verse LCS M>60): Computed 32 M comparisons and saved 10133 entries in matrix\n", + "45m 35s SIMILARITY (O verse LCS M>60): Computed 35 M comparisons and saved 10805 entries in matrix\n", + "45m 50s SIMILARITY (O verse LCS M>60): Computed 37 M comparisons and saved 12556 entries in matrix\n", + "46m 07s SIMILARITY (O verse LCS M>60): Computed 40 M comparisons and saved 13740 entries in matrix\n", + "46m 22s SIMILARITY (O verse LCS M>60): Computed 43 M comparisons and saved 15130 entries in matrix\n", + "46m 38s SIMILARITY (O verse LCS M>60): Computed 45 M comparisons and saved 17285 entries in matrix\n", + "46m 52s SIMILARITY (O verse LCS M>60): Computed 48 M comparisons and saved 17993 entries in matrix\n", + "47m 06s SIMILARITY (O verse LCS M>60): Computed 51 M comparisons and saved 18867 entries in matrix\n", + "47m 22s SIMILARITY (O verse LCS M>60): Computed 53 M comparisons and saved 20756 entries in matrix\n", + "47m 37s SIMILARITY (O verse LCS M>60): Computed 56 M comparisons and saved 21911 entries in matrix\n", + "47m 53s SIMILARITY (O verse LCS M>60): Computed 59 M comparisons and saved 22554 entries in matrix\n", + "48m 08s SIMILARITY (O verse LCS M>60): Computed 61 M comparisons and saved 23826 entries in matrix\n", + "48m 24s SIMILARITY (O verse LCS M>60): Computed 64 M comparisons and saved 26427 entries in matrix\n", + "48m 39s SIMILARITY (O verse LCS M>60): Computed 67 M comparisons and saved 28174 entries in matrix\n", + "48m 56s SIMILARITY (O verse LCS M>60): Computed 70 M comparisons and saved 29670 entries in matrix\n", + "49m 10s SIMILARITY (O verse LCS M>60): Computed 72 M comparisons and saved 31882 entries in matrix\n", + "49m 24s SIMILARITY (O verse LCS M>60): Computed 75 M comparisons and saved 34628 entries in matrix\n", + "49m 38s SIMILARITY (O verse LCS M>60): Computed 78 M comparisons and saved 36056 entries in matrix\n", + "49m 52s SIMILARITY (O verse LCS M>60): Computed 80 M comparisons and saved 38367 entries in matrix\n", + "50m 06s SIMILARITY (O verse LCS M>60): Computed 83 M comparisons and saved 40398 entries in matrix\n", + "50m 22s SIMILARITY (O verse LCS M>60): Computed 86 M comparisons and saved 42021 entries in matrix\n", + "50m 36s SIMILARITY (O verse LCS M>60): Computed 88 M comparisons and saved 43894 entries in matrix\n", + "50m 52s SIMILARITY (O verse LCS M>60): Computed 91 M comparisons and saved 45753 entries in matrix\n", + "51m 07s SIMILARITY (O verse LCS M>60): Computed 94 M comparisons and saved 46875 entries in matrix\n", + "51m 21s SIMILARITY (O verse LCS M>60): Computed 96 M comparisons and saved 48256 entries in matrix\n", + "51m 35s SIMILARITY (O verse LCS M>60): Computed 99 M comparisons and saved 49589 entries in matrix\n", + "51m 49s SIMILARITY (O verse LCS M>60): Computed 102 M comparisons and saved 51140 entries in matrix\n", + "52m 06s SIMILARITY (O verse LCS M>60): Computed 105 M comparisons and saved 52455 entries in matrix\n", + "52m 22s SIMILARITY (O verse LCS M>60): Computed 107 M comparisons and saved 53548 entries in matrix\n", + "52m 38s SIMILARITY (O verse LCS M>60): Computed 110 M comparisons and saved 54504 entries in matrix\n", + "52m 53s SIMILARITY (O verse LCS M>60): Computed 113 M comparisons and saved 55274 entries in matrix\n", + "53m 09s SIMILARITY (O verse LCS M>60): Computed 115 M comparisons and saved 56249 entries in matrix\n", + "53m 24s SIMILARITY (O verse LCS M>60): Computed 118 M comparisons and saved 57165 entries in matrix\n", + "53m 41s SIMILARITY (O verse LCS M>60): Computed 121 M comparisons and saved 57967 entries in matrix\n", + "53m 58s SIMILARITY (O verse LCS M>60): Computed 123 M comparisons and saved 58548 entries in matrix\n", + "54m 13s SIMILARITY (O verse LCS M>60): Computed 126 M comparisons and saved 58838 entries in matrix\n", + "54m 28s SIMILARITY (O verse LCS M>60): Computed 129 M comparisons and saved 59705 entries in matrix\n", + "54m 45s SIMILARITY (O verse LCS M>60): Computed 132 M comparisons and saved 60340 entries in matrix\n", + "55m 01s SIMILARITY (O verse LCS M>60): Computed 134 M comparisons and saved 60941 entries in matrix\n", + "55m 17s SIMILARITY (O verse LCS M>60): Computed 137 M comparisons and saved 61487 entries in matrix\n", + "55m 34s SIMILARITY (O verse LCS M>60): Computed 140 M comparisons and saved 62068 entries in matrix\n", + "55m 50s SIMILARITY (O verse LCS M>60): Computed 142 M comparisons and saved 62663 entries in matrix\n", + "56m 06s SIMILARITY (O verse LCS M>60): Computed 145 M comparisons and saved 63515 entries in matrix\n", + "56m 22s SIMILARITY (O verse LCS M>60): Computed 148 M comparisons and saved 64341 entries in matrix\n", + "56m 39s SIMILARITY (O verse LCS M>60): Computed 150 M comparisons and saved 64776 entries in matrix\n", + "56m 55s SIMILARITY (O verse LCS M>60): Computed 153 M comparisons and saved 65315 entries in matrix\n", + "57m 11s SIMILARITY (O verse LCS M>60): Computed 156 M comparisons and saved 66046 entries in matrix\n", + "57m 27s SIMILARITY (O verse LCS M>60): Computed 158 M comparisons and saved 66949 entries in matrix\n", + "57m 44s SIMILARITY (O verse LCS M>60): Computed 161 M comparisons and saved 67488 entries in matrix\n", + "57m 58s SIMILARITY (O verse LCS M>60): Computed 164 M comparisons and saved 68477 entries in matrix\n", + "58m 14s SIMILARITY (O verse LCS M>60): Computed 167 M comparisons and saved 69155 entries in matrix\n", + "58m 32s SIMILARITY (O verse LCS M>60): Computed 169 M comparisons and saved 70076 entries in matrix\n", + "58m 49s SIMILARITY (O verse LCS M>60): Computed 172 M comparisons and saved 71970 entries in matrix\n", + "59m 04s SIMILARITY (O verse LCS M>60): Computed 175 M comparisons and saved 73156 entries in matrix\n", + "59m 19s SIMILARITY (O verse LCS M>60): Computed 177 M comparisons and saved 73905 entries in matrix\n", + "59m 33s SIMILARITY (O verse LCS M>60): Computed 180 M comparisons and saved 75097 entries in matrix\n", + "59m 48s SIMILARITY (O verse LCS M>60): Computed 183 M comparisons and saved 76287 entries in matrix\n", + " 1h 00m 03s SIMILARITY (O verse LCS M>60): Computed 185 M comparisons and saved 76917 entries in matrix\n", + " 1h 00m 16s SIMILARITY (O verse LCS M>60): Computed 188 M comparisons and saved 77780 entries in matrix\n", + " 1h 00m 29s SIMILARITY (O verse LCS M>60): Computed 191 M comparisons and saved 78262 entries in matrix\n", + " 1h 00m 43s SIMILARITY (O verse LCS M>60): Computed 193 M comparisons and saved 78790 entries in matrix\n", + " 1h 00m 55s SIMILARITY (O verse LCS M>60): Computed 196 M comparisons and saved 79379 entries in matrix\n", + " 1h 01m 09s SIMILARITY (O verse LCS M>60): Computed 199 M comparisons and saved 79827 entries in matrix\n", + " 1h 01m 23s SIMILARITY (O verse LCS M>60): Computed 202 M comparisons and saved 80744 entries in matrix\n", + " 1h 01m 37s SIMILARITY (O verse LCS M>60): Computed 204 M comparisons and saved 82026 entries in matrix\n", + " 1h 01m 52s SIMILARITY (O verse LCS M>60): Computed 207 M comparisons and saved 83358 entries in matrix\n", + " 1h 02m 07s SIMILARITY (O verse LCS M>60): Computed 210 M comparisons and saved 85216 entries in matrix\n", + " 1h 02m 24s SIMILARITY (O verse LCS M>60): Computed 212 M comparisons and saved 86251 entries in matrix\n", + " 1h 02m 38s SIMILARITY (O verse LCS M>60): Computed 215 M comparisons and saved 86674 entries in matrix\n", + " 1h 02m 51s SIMILARITY (O verse LCS M>60): Computed 218 M comparisons and saved 88268 entries in matrix\n", + " 1h 03m 04s SIMILARITY (O verse LCS M>60): Computed 220 M comparisons and saved 89741 entries in matrix\n", + " 1h 03m 17s SIMILARITY (O verse LCS M>60): Computed 223 M comparisons and saved 90901 entries in matrix\n", + " 1h 03m 31s SIMILARITY (O verse LCS M>60): Computed 226 M comparisons and saved 91828 entries in matrix\n", + " 1h 03m 44s SIMILARITY (O verse LCS M>60): Computed 228 M comparisons and saved 92351 entries in matrix\n", + " 1h 03m 56s SIMILARITY (O verse LCS M>60): Computed 231 M comparisons and saved 93036 entries in matrix\n", + " 1h 04m 09s SIMILARITY (O verse LCS M>60): Computed 234 M comparisons and saved 93626 entries in matrix\n", + " 1h 04m 22s SIMILARITY (O verse LCS M>60): Computed 237 M comparisons and saved 94732 entries in matrix\n", + " 1h 04m 30s SIMILARITY (O verse LCS M>60): Computed 239 M comparisons and saved 96489 entries in matrix\n", + " 1h 04m 39s SIMILARITY (O verse LCS M>60): Computed 242 M comparisons and saved 98333 entries in matrix\n", + " 1h 04m 48s SIMILARITY (O verse LCS M>60): Computed 245 M comparisons and saved 99952 entries in matrix\n", + " 1h 04m 57s SIMILARITY (O verse LCS M>60): Computed 247 M comparisons and saved 101344 entries in matrix\n", + " 1h 05m 06s SIMILARITY (O verse LCS M>60): Computed 250 M comparisons and saved 102948 entries in matrix\n", + " 1h 05m 15s SIMILARITY (O verse LCS M>60): Computed 253 M comparisons and saved 105178 entries in matrix\n", + " 1h 05m 24s SIMILARITY (O verse LCS M>60): Computed 255 M comparisons and saved 106484 entries in matrix\n", + " 1h 05m 33s SIMILARITY (O verse LCS M>60): Computed 258 M comparisons and saved 107701 entries in matrix\n", + " 1h 05m 43s SIMILARITY (O verse LCS M>60): Computed 261 M comparisons and saved 108625 entries in matrix\n", + " 1h 05m 56s SIMILARITY (O verse LCS M>60): Computed 264 M comparisons and saved 109194 entries in matrix\n", + " 1h 06m 13s SIMILARITY (O verse LCS M>60): Computed 266 M comparisons and saved 110696 entries in matrix\n", + " 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed 269 M comparisons and saved 113632 entries in matrix\n", + " 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed 269 M (269410078) comparisons and saved 113632 entries in matrix\n", + " 1h 06m 30s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates\n", + " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix\n", + " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages\n", + " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): Composing cliques out of 793 chunks from 4204 comparisons\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): Composed and saved 295 cliques out of 793 chunks from 4204 comparisons\n", + " 1h 06m 31s PRINT (O verse LCS M>60 S>100): sorting out cliques\n", + " 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs\n", + " 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80\n", + " 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing\n", + " 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs\n", + " 1h 06m 31s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 31s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 31s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composing cliques out of 1235 chunks from 4489 comparisons\n", + " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composed 457 cliques out of 1000 chunks\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): Composed and saved 504 cliques out of 1235 chunks from 4489 comparisons\n", + " 1h 06m 32s PRINT (O verse LCS M>60 S>95): sorting out cliques\n", + " 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs\n", + " 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120\n", + " 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing\n", + " 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs\n", + " 1h 06m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 32s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 32s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages\n", + " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): Composing cliques out of 1754 chunks from 5538 comparisons\n", + " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed 471 cliques out of 1000 chunks\n", + " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques\n", + " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed and saved 724 cliques out of 1754 chunks from 5538 comparisons\n", + " 1h 06m 33s PRINT (O verse LCS M>60 S>90): sorting out cliques\n", + " 1h 06m 33s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs\n", + " 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151\n", + " 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing\n", + " 1h 06m 34s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs\n", + " 1h 06m 34s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 34s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 34s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 34s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates\n", + " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix\n", + " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages\n", + " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): Composing cliques out of 2296 chunks from 7871 comparisons\n", + " 1h 06m 35s CLIQUES (O verse LCS M>60 S>85): Composed 478 cliques out of 1000 chunks\n", + " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed 874 cliques out of 2000 chunks\n", + " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques\n", + " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed and saved 938 cliques out of 2296 chunks from 7871 comparisons\n", + " 1h 06m 36s PRINT (O verse LCS M>60 S>85): sorting out cliques\n", + " 1h 06m 36s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs\n", + " 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179\n", + " 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing\n", + " 1h 06m 37s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs\n", + " 1h 06m 37s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 37s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 37s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 38s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates\n", + " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix\n", + " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages\n", + " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composing cliques out of 2925 chunks from 9461 comparisons\n", + " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composed 486 cliques out of 1000 chunks\n", + " 1h 06m 39s CLIQUES (O verse LCS M>60 S>80): Composed 871 cliques out of 2000 chunks\n", + " 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques\n", + " 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): Composed and saved 1141 cliques out of 2925 chunks from 9461 comparisons\n", + " 1h 06m 41s PRINT (O verse LCS M>60 S>80): sorting out cliques\n", + " 1h 06m 41s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs\n", + " 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251\n", + " 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing\n", + " 1h 06m 42s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs\n", + " 1h 06m 42s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 42s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 42s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 42s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates\n", + " 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix\n", + " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): 15540 relevant similarities between 3682 passages\n", + " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composing cliques out of 3682 chunks from 15540 comparisons\n", + " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composed 518 cliques out of 1000 chunks\n", + " 1h 06m 44s CLIQUES (O verse LCS M>60 S>75): Composed 886 cliques out of 2000 chunks\n", + " 1h 06m 46s CLIQUES (O verse LCS M>60 S>75): Composed 1220 cliques out of 3000 chunks\n", + " 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): 3682 members in 1340 cliques\n", + " 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): Composed and saved 1340 cliques out of 3682 chunks from 15540 comparisons\n", + " 1h 06m 47s PRINT (O verse LCS M>60 S>75): sorting out cliques\n", + " 1h 06m 47s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs\n", + " 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346\n", + " 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing\n", + " 1h 06m 50s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs\n", + " 1h 06m 50s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 06m 50s PREPARING (O verse LCS): Already prepared\n", + " 1h 06m 50s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 06m 50s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates\n", + " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix\n", + " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): 19833 relevant similarities between 4958 passages\n", + " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composing cliques out of 4958 chunks from 19833 comparisons\n", + " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composed 549 cliques out of 1000 chunks\n", + " 1h 06m 51s CLIQUES (O verse LCS M>60 S>70): Composed 914 cliques out of 2000 chunks\n", + " 1h 06m 53s CLIQUES (O verse LCS M>60 S>70): Composed 1239 cliques out of 3000 chunks\n", + " 1h 06m 55s CLIQUES (O verse LCS M>60 S>70): Composed 1491 cliques out of 4000 chunks\n", + " 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques\n", + " 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): Composed and saved 1644 cliques out of 4958 chunks from 19833 comparisons\n", + " 1h 06m 58s PRINT (O verse LCS M>60 S>70): sorting out cliques\n", + " 1h 06m 58s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs\n", + " 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504\n", + " 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing\n", + " 1h 07m 02s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs\n", + " 1h 07m 02s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 07m 02s PREPARING (O verse LCS): Already prepared\n", + " 1h 07m 02s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 07m 02s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates\n", + " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix\n", + " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): 31844 relevant similarities between 9050 passages\n", + " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): Composing cliques out of 9050 chunks from 31844 comparisons\n", + " 1h 07m 03s CLIQUES (O verse LCS M>60 S>65): Composed 596 cliques out of 1000 chunks\n", + " 1h 07m 04s CLIQUES (O verse LCS M>60 S>65): Composed 975 cliques out of 2000 chunks\n", + " 1h 07m 05s CLIQUES (O verse LCS M>60 S>65): Composed 1258 cliques out of 3000 chunks\n", + " 1h 07m 08s CLIQUES (O verse LCS M>60 S>65): Composed 1468 cliques out of 4000 chunks\n", + " 1h 07m 11s CLIQUES (O verse LCS M>60 S>65): Composed 1570 cliques out of 5000 chunks\n", + " 1h 07m 14s CLIQUES (O verse LCS M>60 S>65): Composed 1698 cliques out of 6000 chunks\n", + " 1h 07m 18s CLIQUES (O verse LCS M>60 S>65): Composed 1902 cliques out of 7000 chunks\n", + " 1h 07m 23s CLIQUES (O verse LCS M>60 S>65): Composed 1932 cliques out of 8000 chunks\n", + " 1h 07m 27s CLIQUES (O verse LCS M>60 S>65): Composed 1823 cliques out of 9000 chunks\n", + " 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): 9050 members in 1821 cliques\n", + " 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): Composed and saved 1821 cliques out of 9050 chunks from 31844 comparisons\n", + " 1h 07m 28s PRINT (O verse LCS M>60 S>65): sorting out cliques\n", + " 1h 07m 28s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs\n", + " 1h 07m 32s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs\n", + " 1h 07m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 1h 07m 32s PREPARING (O verse LCS): Already prepared\n", + " 1h 07m 32s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", + " 1h 07m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", + " 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", + " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): 113632 relevant similarities between 18945 passages\n", + " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composing cliques out of 18945 chunks from 113632 comparisons\n", + " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composed 477 cliques out of 1000 chunks\n", + " 1h 07m 34s CLIQUES (O verse LCS M>60 S>60): Composed 671 cliques out of 2000 chunks\n", + " 1h 07m 36s CLIQUES (O verse LCS M>60 S>60): Composed 756 cliques out of 3000 chunks\n", + " 1h 07m 37s CLIQUES (O verse LCS M>60 S>60): Composed 770 cliques out of 4000 chunks\n", + " 1h 07m 39s CLIQUES (O verse LCS M>60 S>60): Composed 796 cliques out of 5000 chunks\n", + " 1h 07m 41s CLIQUES (O verse LCS M>60 S>60): Composed 817 cliques out of 6000 chunks\n", + " 1h 07m 44s CLIQUES (O verse LCS M>60 S>60): Composed 751 cliques out of 7000 chunks\n", + " 1h 07m 46s CLIQUES (O verse LCS M>60 S>60): Composed 741 cliques out of 8000 chunks\n", + " 1h 07m 49s CLIQUES (O verse LCS M>60 S>60): Composed 729 cliques out of 9000 chunks\n", + " 1h 07m 52s CLIQUES (O verse LCS M>60 S>60): Composed 706 cliques out of 10000 chunks\n", + " 1h 07m 55s CLIQUES (O verse LCS M>60 S>60): Composed 673 cliques out of 11000 chunks\n", + " 1h 07m 58s CLIQUES (O verse LCS M>60 S>60): Composed 646 cliques out of 12000 chunks\n", + " 1h 08m 02s CLIQUES (O verse LCS M>60 S>60): Composed 619 cliques out of 13000 chunks\n", + " 1h 08m 05s CLIQUES (O verse LCS M>60 S>60): Composed 588 cliques out of 14000 chunks\n", + " 1h 08m 09s CLIQUES (O verse LCS M>60 S>60): Composed 557 cliques out of 15000 chunks\n", + " 1h 08m 13s CLIQUES (O verse LCS M>60 S>60): Composed 541 cliques out of 16000 chunks\n", + " 1h 08m 18s CLIQUES (O verse LCS M>60 S>60): Composed 492 cliques out of 17000 chunks\n", + " 1h 08m 22s CLIQUES (O verse LCS M>60 S>60): Composed 431 cliques out of 18000 chunks\n", + " 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): 18945 members in 380 cliques\n", + " 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): Composed and saved 380 cliques out of 18945 chunks from 113632 comparisons\n", + " 1h 08m 28s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", + " 1h 08m 28s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", + " 1h 08m 29s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n", + " 1h 08m 29s CHUNKING (O half_verse)\n", + " 1h 08m 31s CHUNKING (O half_verse): Made 45180 chunks\n", + " 1h 08m 31s PREPARING (O half_verse SET)\n", + " 1h 08m 31s PREPARING (O half_verse SET): Done 45180 chunks.\n", + " 1h 08m 31s SIMILARITY (O half_verse SET M>50): Computing 1020 M (1020593610) comparisons and saving entries in matrix\n", + " 1h 08m 49s SIMILARITY (O half_verse SET M>50): Computed 10 M comparisons and saved 1962 entries in matrix\n", + " 1h 09m 06s SIMILARITY (O half_verse SET M>50): Computed 20 M comparisons and saved 3531 entries in matrix\n", + " 1h 09m 21s SIMILARITY (O half_verse SET M>50): Computed 30 M comparisons and saved 4614 entries in matrix\n", + " 1h 09m 39s SIMILARITY (O half_verse SET M>50): Computed 40 M comparisons and saved 7012 entries in matrix\n", + " 1h 09m 55s SIMILARITY (O half_verse SET M>50): Computed 51 M comparisons and saved 8344 entries in matrix\n", + " 1h 10m 12s SIMILARITY (O half_verse SET M>50): Computed 61 M comparisons and saved 10034 entries in matrix\n", + " 1h 10m 28s SIMILARITY (O half_verse SET M>50): Computed 71 M comparisons and saved 12065 entries in matrix\n", + " 1h 10m 45s SIMILARITY (O half_verse SET M>50): Computed 81 M comparisons and saved 13253 entries in matrix\n", + " 1h 11m 01s SIMILARITY (O half_verse SET M>50): Computed 91 M comparisons and saved 14525 entries in matrix\n", + " 1h 11m 16s SIMILARITY (O half_verse SET M>50): Computed 102 M comparisons and saved 15999 entries in matrix\n", + " 1h 11m 31s SIMILARITY (O half_verse SET M>50): Computed 112 M comparisons and saved 17424 entries in matrix\n", + " 1h 11m 45s SIMILARITY (O half_verse SET M>50): Computed 122 M comparisons and saved 18649 entries in matrix\n", + " 1h 12m 01s SIMILARITY (O half_verse SET M>50): Computed 132 M comparisons and saved 19526 entries in matrix\n", + " 1h 12m 18s SIMILARITY (O half_verse SET M>50): Computed 142 M comparisons and saved 22474 entries in matrix\n", + " 1h 12m 34s SIMILARITY (O half_verse SET M>50): Computed 153 M comparisons and saved 25421 entries in matrix\n", + " 1h 12m 51s SIMILARITY (O half_verse SET M>50): Computed 163 M comparisons and saved 28332 entries in matrix\n", + " 1h 13m 07s SIMILARITY (O half_verse SET M>50): Computed 173 M comparisons and saved 30622 entries in matrix\n", + " 1h 13m 24s SIMILARITY (O half_verse SET M>50): Computed 183 M comparisons and saved 31931 entries in matrix\n", + " 1h 13m 40s SIMILARITY (O half_verse SET M>50): Computed 193 M comparisons and saved 33509 entries in matrix\n", + " 1h 13m 56s SIMILARITY (O half_verse SET M>50): Computed 204 M comparisons and saved 37341 entries in matrix\n", + " 1h 14m 12s SIMILARITY (O half_verse SET M>50): Computed 214 M comparisons and saved 39804 entries in matrix\n", + " 1h 14m 28s SIMILARITY (O half_verse SET M>50): Computed 224 M comparisons and saved 40887 entries in matrix\n", + " 1h 14m 44s SIMILARITY (O half_verse SET M>50): Computed 234 M comparisons and saved 43204 entries in matrix\n", + " 1h 15m 01s SIMILARITY (O half_verse SET M>50): Computed 244 M comparisons and saved 47125 entries in matrix\n", + " 1h 15m 17s SIMILARITY (O half_verse SET M>50): Computed 255 M comparisons and saved 50158 entries in matrix\n", + " 1h 15m 34s SIMILARITY (O half_verse SET M>50): Computed 265 M comparisons and saved 52236 entries in matrix\n", + " 1h 15m 50s SIMILARITY (O half_verse SET M>50): Computed 275 M comparisons and saved 56284 entries in matrix\n", + " 1h 16m 06s SIMILARITY (O half_verse SET M>50): Computed 285 M comparisons and saved 60584 entries in matrix\n", + " 1h 16m 22s SIMILARITY (O half_verse SET M>50): Computed 295 M comparisons and saved 63095 entries in matrix\n", + " 1h 16m 38s SIMILARITY (O half_verse SET M>50): Computed 306 M comparisons and saved 66565 entries in matrix\n", + " 1h 16m 54s SIMILARITY (O half_verse SET M>50): Computed 316 M comparisons and saved 70425 entries in matrix\n", + " 1h 17m 10s SIMILARITY (O half_verse SET M>50): Computed 326 M comparisons and saved 72570 entries in matrix\n", + " 1h 17m 27s SIMILARITY (O half_verse SET M>50): Computed 336 M comparisons and saved 75773 entries in matrix\n", + " 1h 17m 43s SIMILARITY (O half_verse SET M>50): Computed 347 M comparisons and saved 77750 entries in matrix\n", + " 1h 17m 59s SIMILARITY (O half_verse SET M>50): Computed 357 M comparisons and saved 79787 entries in matrix\n", + " 1h 18m 16s SIMILARITY (O half_verse SET M>50): Computed 367 M comparisons and saved 81979 entries in matrix\n", + " 1h 18m 31s SIMILARITY (O half_verse SET M>50): Computed 377 M comparisons and saved 83955 entries in matrix\n", + " 1h 18m 47s SIMILARITY (O half_verse SET M>50): Computed 387 M comparisons and saved 86785 entries in matrix\n", + " 1h 19m 04s SIMILARITY (O half_verse SET M>50): Computed 398 M comparisons and saved 88967 entries in matrix\n", + " 1h 19m 20s SIMILARITY (O half_verse SET M>50): Computed 408 M comparisons and saved 90872 entries in matrix\n", + " 1h 19m 34s SIMILARITY (O half_verse SET M>50): Computed 418 M comparisons and saved 93080 entries in matrix\n", + " 1h 19m 49s SIMILARITY (O half_verse SET M>50): Computed 428 M comparisons and saved 94351 entries in matrix\n", + " 1h 20m 04s SIMILARITY (O half_verse SET M>50): Computed 438 M comparisons and saved 95927 entries in matrix\n", + " 1h 20m 21s SIMILARITY (O half_verse SET M>50): Computed 449 M comparisons and saved 96905 entries in matrix\n", + " 1h 20m 38s SIMILARITY (O half_verse SET M>50): Computed 459 M comparisons and saved 98144 entries in matrix\n", + " 1h 20m 54s SIMILARITY (O half_verse SET M>50): Computed 469 M comparisons and saved 98961 entries in matrix\n", + " 1h 21m 10s SIMILARITY (O half_verse SET M>50): Computed 479 M comparisons and saved 99614 entries in matrix\n", + " 1h 21m 27s SIMILARITY (O half_verse SET M>50): Computed 489 M comparisons and saved 101085 entries in matrix\n", + " 1h 21m 43s SIMILARITY (O half_verse SET M>50): Computed 500 M comparisons and saved 102493 entries in matrix\n", + " 1h 21m 59s SIMILARITY (O half_verse SET M>50): Computed 510 M comparisons and saved 103196 entries in matrix\n", + " 1h 22m 16s SIMILARITY (O half_verse SET M>50): Computed 520 M comparisons and saved 104523 entries in matrix\n", + " 1h 22m 32s SIMILARITY (O half_verse SET M>50): Computed 530 M comparisons and saved 105444 entries in matrix\n", + " 1h 22m 49s SIMILARITY (O half_verse SET M>50): Computed 540 M comparisons and saved 106395 entries in matrix\n", + " 1h 23m 05s SIMILARITY (O half_verse SET M>50): Computed 551 M comparisons and saved 107694 entries in matrix\n", + " 1h 23m 21s SIMILARITY (O half_verse SET M>50): Computed 561 M comparisons and saved 108689 entries in matrix\n", + " 1h 23m 38s SIMILARITY (O half_verse SET M>50): Computed 571 M comparisons and saved 109532 entries in matrix\n", + " 1h 23m 53s SIMILARITY (O half_verse SET M>50): Computed 581 M comparisons and saved 110249 entries in matrix\n", + " 1h 24m 07s SIMILARITY (O half_verse SET M>50): Computed 591 M comparisons and saved 111869 entries in matrix\n", + " 1h 24m 22s SIMILARITY (O half_verse SET M>50): Computed 602 M comparisons and saved 113205 entries in matrix\n", + " 1h 24m 38s SIMILARITY (O half_verse SET M>50): Computed 612 M comparisons and saved 114725 entries in matrix\n", + " 1h 24m 54s SIMILARITY (O half_verse SET M>50): Computed 622 M comparisons and saved 116242 entries in matrix\n", + " 1h 25m 10s SIMILARITY (O half_verse SET M>50): Computed 632 M comparisons and saved 117170 entries in matrix\n", + " 1h 25m 27s SIMILARITY (O half_verse SET M>50): Computed 642 M comparisons and saved 118632 entries in matrix\n", + " 1h 25m 43s SIMILARITY (O half_verse SET M>50): Computed 653 M comparisons and saved 121290 entries in matrix\n", + " 1h 25m 59s SIMILARITY (O half_verse SET M>50): Computed 663 M comparisons and saved 123350 entries in matrix\n", + " 1h 26m 15s SIMILARITY (O half_verse SET M>50): Computed 673 M comparisons and saved 124858 entries in matrix\n", + " 1h 26m 32s SIMILARITY (O half_verse SET M>50): Computed 683 M comparisons and saved 126931 entries in matrix\n", + " 1h 26m 48s SIMILARITY (O half_verse SET M>50): Computed 694 M comparisons and saved 129318 entries in matrix\n", + " 1h 27m 04s SIMILARITY (O half_verse SET M>50): Computed 704 M comparisons and saved 130325 entries in matrix\n", + " 1h 27m 19s SIMILARITY (O half_verse SET M>50): Computed 714 M comparisons and saved 131412 entries in matrix\n", + " 1h 27m 35s SIMILARITY (O half_verse SET M>50): Computed 724 M comparisons and saved 132234 entries in matrix\n", + " 1h 27m 50s SIMILARITY (O half_verse SET M>50): Computed 734 M comparisons and saved 133067 entries in matrix\n", + " 1h 28m 06s SIMILARITY (O half_verse SET M>50): Computed 745 M comparisons and saved 134096 entries in matrix\n", + " 1h 28m 22s SIMILARITY (O half_verse SET M>50): Computed 755 M comparisons and saved 134572 entries in matrix\n", + " 1h 28m 38s SIMILARITY (O half_verse SET M>50): Computed 765 M comparisons and saved 136234 entries in matrix\n", + " 1h 28m 54s SIMILARITY (O half_verse SET M>50): Computed 775 M comparisons and saved 138257 entries in matrix\n", + " 1h 29m 09s SIMILARITY (O half_verse SET M>50): Computed 785 M comparisons and saved 139986 entries in matrix\n", + " 1h 29m 26s SIMILARITY (O half_verse SET M>50): Computed 796 M comparisons and saved 142234 entries in matrix\n", + " 1h 29m 42s SIMILARITY (O half_verse SET M>50): Computed 806 M comparisons and saved 144260 entries in matrix\n", + " 1h 29m 57s SIMILARITY (O half_verse SET M>50): Computed 816 M comparisons and saved 144956 entries in matrix\n", + " 1h 30m 13s SIMILARITY (O half_verse SET M>50): Computed 826 M comparisons and saved 148044 entries in matrix\n", + " 1h 30m 29s SIMILARITY (O half_verse SET M>50): Computed 836 M comparisons and saved 151016 entries in matrix\n", + " 1h 30m 44s SIMILARITY (O half_verse SET M>50): Computed 847 M comparisons and saved 153676 entries in matrix\n", + " 1h 30m 59s SIMILARITY (O half_verse SET M>50): Computed 857 M comparisons and saved 155349 entries in matrix\n", + " 1h 31m 15s SIMILARITY (O half_verse SET M>50): Computed 867 M comparisons and saved 156458 entries in matrix\n", + " 1h 31m 30s SIMILARITY (O half_verse SET M>50): Computed 877 M comparisons and saved 157434 entries in matrix\n", + " 1h 31m 45s SIMILARITY (O half_verse SET M>50): Computed 887 M comparisons and saved 158073 entries in matrix\n", + " 1h 32m 00s SIMILARITY (O half_verse SET M>50): Computed 898 M comparisons and saved 159599 entries in matrix\n", + " 1h 32m 12s SIMILARITY (O half_verse SET M>50): Computed 908 M comparisons and saved 161827 entries in matrix\n", + " 1h 32m 25s SIMILARITY (O half_verse SET M>50): Computed 918 M comparisons and saved 164277 entries in matrix\n", + " 1h 32m 38s SIMILARITY (O half_verse SET M>50): Computed 928 M comparisons and saved 166159 entries in matrix\n", + " 1h 32m 52s SIMILARITY (O half_verse SET M>50): Computed 938 M comparisons and saved 167991 entries in matrix\n", + " 1h 33m 06s SIMILARITY (O half_verse SET M>50): Computed 949 M comparisons and saved 169802 entries in matrix\n", + " 1h 33m 20s SIMILARITY (O half_verse SET M>50): Computed 959 M comparisons and saved 172125 entries in matrix\n", + " 1h 33m 34s SIMILARITY (O half_verse SET M>50): Computed 969 M comparisons and saved 173838 entries in matrix\n", + " 1h 33m 49s SIMILARITY (O half_verse SET M>50): Computed 979 M comparisons and saved 174914 entries in matrix\n", + " 1h 34m 04s SIMILARITY (O half_verse SET M>50): Computed 989 M comparisons and saved 175787 entries in matrix\n", + " 1h 34m 18s SIMILARITY (O half_verse SET M>50): Computed 1000 M comparisons and saved 176399 entries in matrix\n", + " 1h 34m 36s SIMILARITY (O half_verse SET M>50): Computed 1010 M comparisons and saved 177068 entries in matrix\n", + " 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed 1020 M comparisons and saved 179781 entries in matrix\n", + " 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed 1020 M (1020593610) comparisons and saved 179781 entries in matrix\n", + " 1h 34m 55s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates\n", + " 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix\n", + " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages\n", + " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composing cliques out of 4327 chunks from 10239 comparisons\n", + " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composed 455 cliques out of 1000 chunks\n", + " 1h 34m 57s CLIQUES (O half_verse SET M>50 S>100): Composed 829 cliques out of 2000 chunks\n", + " 1h 34m 59s CLIQUES (O half_verse SET M>50 S>100): Composed 1258 cliques out of 3000 chunks\n", + " 1h 35m 02s CLIQUES (O half_verse SET M>50 S>100): Composed 1653 cliques out of 4000 chunks\n", + " 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques\n", + " 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): Composed and saved 1725 cliques out of 4327 chunks from 10239 comparisons\n", + " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): sorting out cliques\n", + " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs\n", + " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573\n", + " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing\n", + " 1h 35m 04s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs\n", + " 1h 35m 04s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 35m 04s PREPARING (O half_verse SET): Already prepared\n", + " 1h 35m 04s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 35m 05s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates\n", + " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix\n", + " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages\n", + " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composing cliques out of 4333 chunks from 10242 comparisons\n", + " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composed 453 cliques out of 1000 chunks\n", + " 1h 35m 06s CLIQUES (O half_verse SET M>50 S>95): Composed 829 cliques out of 2000 chunks\n", + " 1h 35m 08s CLIQUES (O half_verse SET M>50 S>95): Composed 1258 cliques out of 3000 chunks\n", + " 1h 35m 10s CLIQUES (O half_verse SET M>50 S>95): Composed 1653 cliques out of 4000 chunks\n", + " 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques\n", + " 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): Composed and saved 1728 cliques out of 4333 chunks from 10242 comparisons\n", + " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): sorting out cliques\n", + " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs\n", + " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573\n", + " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing\n", + " 1h 35m 12s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs\n", + " 1h 35m 12s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 35m 12s PREPARING (O half_verse SET): Already prepared\n", + " 1h 35m 12s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 35m 13s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates\n", + " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix\n", + " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages\n", + " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composing cliques out of 4618 chunks from 10410 comparisons\n", + " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composed 470 cliques out of 1000 chunks\n", + " 1h 35m 15s CLIQUES (O half_verse SET M>50 S>90): Composed 869 cliques out of 2000 chunks\n", + " 1h 35m 17s CLIQUES (O half_verse SET M>50 S>90): Composed 1279 cliques out of 3000 chunks\n", + " 1h 35m 19s CLIQUES (O half_verse SET M>50 S>90): Composed 1675 cliques out of 4000 chunks\n", + " 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques\n", + " 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): Composed and saved 1863 cliques out of 4618 chunks from 10410 comparisons\n", + " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): sorting out cliques\n", + " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs\n", + " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587\n", + " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing\n", + " 1h 35m 22s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs\n", + " 1h 35m 22s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 35m 22s PREPARING (O half_verse SET): Already prepared\n", + " 1h 35m 22s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 35m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates\n", + " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix\n", + " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages\n", + " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composing cliques out of 5145 chunks from 11111 comparisons\n", + " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composed 490 cliques out of 1000 chunks\n", + " 1h 35m 24s CLIQUES (O half_verse SET M>50 S>85): Composed 940 cliques out of 2000 chunks\n", + " 1h 35m 26s CLIQUES (O half_verse SET M>50 S>85): Composed 1275 cliques out of 3000 chunks\n", + " 1h 35m 28s CLIQUES (O half_verse SET M>50 S>85): Composed 1678 cliques out of 4000 chunks\n", + " 1h 35m 31s CLIQUES (O half_verse SET M>50 S>85): Composed 2041 cliques out of 5000 chunks\n", + " 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques\n", + " 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): Composed and saved 2072 cliques out of 5145 chunks from 11111 comparisons\n", + " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): sorting out cliques\n", + " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs\n", + " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640\n", + " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing\n", + " 1h 35m 33s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs\n", + " 1h 35m 33s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 35m 33s PREPARING (O half_verse SET): Already prepared\n", + " 1h 35m 33s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 35m 34s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates\n", + " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix\n", + " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages\n", + " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composing cliques out of 6422 chunks from 20178 comparisons\n", + " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composed 527 cliques out of 1000 chunks\n", + " 1h 35m 35s CLIQUES (O half_verse SET M>50 S>80): Composed 945 cliques out of 2000 chunks\n", + " 1h 35m 37s CLIQUES (O half_verse SET M>50 S>80): Composed 1384 cliques out of 3000 chunks\n", + " 1h 35m 39s CLIQUES (O half_verse SET M>50 S>80): Composed 1742 cliques out of 4000 chunks\n", + " 1h 35m 43s CLIQUES (O half_verse SET M>50 S>80): Composed 2048 cliques out of 5000 chunks\n", + " 1h 35m 46s CLIQUES (O half_verse SET M>50 S>80): Composed 2372 cliques out of 6000 chunks\n", + " 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques\n", + " 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): Composed and saved 2474 cliques out of 6422 chunks from 20178 comparisons\n", + " 1h 35m 48s PRINT (O half_verse SET M>50 S>80): sorting out cliques\n", + " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs\n", + " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769\n", + " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing\n", + " 1h 35m 50s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs\n", + " 1h 35m 50s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 35m 50s PREPARING (O half_verse SET): Already prepared\n", + " 1h 35m 50s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 35m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates\n", + " 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix\n", + " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages\n", + " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composing cliques out of 8265 chunks from 23717 comparisons\n", + " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composed 536 cliques out of 1000 chunks\n", + " 1h 35m 52s CLIQUES (O half_verse SET M>50 S>75): Composed 988 cliques out of 2000 chunks\n", + " 1h 35m 54s CLIQUES (O half_verse SET M>50 S>75): Composed 1408 cliques out of 3000 chunks\n", + " 1h 35m 56s CLIQUES (O half_verse SET M>50 S>75): Composed 1737 cliques out of 4000 chunks\n", + " 1h 36m 00s CLIQUES (O half_verse SET M>50 S>75): Composed 2148 cliques out of 5000 chunks\n", + " 1h 36m 04s CLIQUES (O half_verse SET M>50 S>75): Composed 2500 cliques out of 6000 chunks\n", + " 1h 36m 08s CLIQUES (O half_verse SET M>50 S>75): Composed 2729 cliques out of 7000 chunks\n", + " 1h 36m 13s CLIQUES (O half_verse SET M>50 S>75): Composed 2854 cliques out of 8000 chunks\n", + " 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques\n", + " 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): Composed and saved 2888 cliques out of 8265 chunks from 23717 comparisons\n", + " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): sorting out cliques\n", + " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs\n", + " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919\n", + " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing\n", + " 1h 36m 18s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs\n", + " 1h 36m 18s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 36m 18s PREPARING (O half_verse SET): Already prepared\n", + " 1h 36m 18s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 36m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates\n", + " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix\n", + " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages\n", + " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): Composing cliques out of 9388 chunks from 25560 comparisons\n", + " 1h 36m 19s CLIQUES (O half_verse SET M>50 S>70): Composed 558 cliques out of 1000 chunks\n", + " 1h 36m 20s CLIQUES (O half_verse SET M>50 S>70): Composed 1029 cliques out of 2000 chunks\n", + " 1h 36m 22s CLIQUES (O half_verse SET M>50 S>70): Composed 1456 cliques out of 3000 chunks\n", + " 1h 36m 24s CLIQUES (O half_verse SET M>50 S>70): Composed 1836 cliques out of 4000 chunks\n", + " 1h 36m 27s CLIQUES (O half_verse SET M>50 S>70): Composed 2118 cliques out of 5000 chunks\n", + " 1h 36m 31s CLIQUES (O half_verse SET M>50 S>70): Composed 2431 cliques out of 6000 chunks\n", + " 1h 36m 35s CLIQUES (O half_verse SET M>50 S>70): Composed 2756 cliques out of 7000 chunks\n", + " 1h 36m 40s CLIQUES (O half_verse SET M>50 S>70): Composed 3017 cliques out of 8000 chunks\n", + " 1h 36m 45s CLIQUES (O half_verse SET M>50 S>70): Composed 3173 cliques out of 9000 chunks\n", + " 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques\n", + " 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): Composed and saved 3193 cliques out of 9388 chunks from 25560 comparisons\n", + " 1h 36m 47s PRINT (O half_verse SET M>50 S>70): sorting out cliques\n", + " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs\n", + " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014\n", + " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing\n", + " 1h 36m 50s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs\n", + " 1h 36m 50s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 36m 50s PREPARING (O half_verse SET): Already prepared\n", + " 1h 36m 50s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 36m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates\n", + " 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix\n", + " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages\n", + " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composing cliques out of 12162 chunks from 37453 comparisons\n", + " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composed 574 cliques out of 1000 chunks\n", + " 1h 36m 52s CLIQUES (O half_verse SET M>50 S>65): Composed 1045 cliques out of 2000 chunks\n", + " 1h 36m 54s CLIQUES (O half_verse SET M>50 S>65): Composed 1468 cliques out of 3000 chunks\n", + " 1h 36m 56s CLIQUES (O half_verse SET M>50 S>65): Composed 1894 cliques out of 4000 chunks\n", + " 1h 36m 58s CLIQUES (O half_verse SET M>50 S>65): Composed 2269 cliques out of 5000 chunks\n", + " 1h 37m 02s CLIQUES (O half_verse SET M>50 S>65): Composed 2552 cliques out of 6000 chunks\n", + " 1h 37m 06s CLIQUES (O half_verse SET M>50 S>65): Composed 2758 cliques out of 7000 chunks\n", + " 1h 37m 10s CLIQUES (O half_verse SET M>50 S>65): Composed 3034 cliques out of 8000 chunks\n", + " 1h 37m 15s CLIQUES (O half_verse SET M>50 S>65): Composed 3276 cliques out of 9000 chunks\n", + " 1h 37m 21s CLIQUES (O half_verse SET M>50 S>65): Composed 3416 cliques out of 10000 chunks\n", + " 1h 37m 28s CLIQUES (O half_verse SET M>50 S>65): Composed 3641 cliques out of 11000 chunks\n", + " 1h 37m 36s CLIQUES (O half_verse SET M>50 S>65): Composed 3425 cliques out of 12000 chunks\n", + " 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques\n", + " 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): Composed and saved 3342 cliques out of 12162 chunks from 37453 comparisons\n", + " 1h 37m 38s PRINT (O half_verse SET M>50 S>65): sorting out cliques\n", + " 1h 37m 38s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs\n", + " 1h 37m 41s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs\n", + " 1h 37m 41s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 37m 41s PREPARING (O half_verse SET): Already prepared\n", + " 1h 37m 41s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 37m 41s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates\n", + " 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix\n", + " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages\n", + " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composing cliques out of 16476 chunks from 55384 comparisons\n", + " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composed 603 cliques out of 1000 chunks\n", + " 1h 37m 44s CLIQUES (O half_verse SET M>50 S>60): Composed 1110 cliques out of 2000 chunks\n", + " 1h 37m 45s CLIQUES (O half_verse SET M>50 S>60): Composed 1550 cliques out of 3000 chunks\n", + " 1h 37m 48s CLIQUES (O half_verse SET M>50 S>60): Composed 1927 cliques out of 4000 chunks\n", + " 1h 37m 51s CLIQUES (O half_verse SET M>50 S>60): Composed 2271 cliques out of 5000 chunks\n", + " 1h 37m 55s CLIQUES (O half_verse SET M>50 S>60): Composed 2547 cliques out of 6000 chunks\n", + " 1h 38m 00s CLIQUES (O half_verse SET M>50 S>60): Composed 2804 cliques out of 7000 chunks\n", + " 1h 38m 05s CLIQUES (O half_verse SET M>50 S>60): Composed 2992 cliques out of 8000 chunks\n", + " 1h 38m 11s CLIQUES (O half_verse SET M>50 S>60): Composed 3182 cliques out of 9000 chunks\n", + " 1h 38m 17s CLIQUES (O half_verse SET M>50 S>60): Composed 3442 cliques out of 10000 chunks\n", + " 1h 38m 24s CLIQUES (O half_verse SET M>50 S>60): Composed 3591 cliques out of 11000 chunks\n", + " 1h 38m 32s CLIQUES (O half_verse SET M>50 S>60): Composed 3732 cliques out of 12000 chunks\n", + " 1h 38m 41s CLIQUES (O half_verse SET M>50 S>60): Composed 3939 cliques out of 13000 chunks\n", + " 1h 38m 50s CLIQUES (O half_verse SET M>50 S>60): Composed 3797 cliques out of 14000 chunks\n", + " 1h 38m 59s CLIQUES (O half_verse SET M>50 S>60): Composed 3797 cliques out of 15000 chunks\n", + " 1h 39m 09s CLIQUES (O half_verse SET M>50 S>60): Composed 3527 cliques out of 16000 chunks\n", + " 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques\n", + " 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): Composed and saved 3424 cliques out of 16476 chunks from 55384 comparisons\n", + " 1h 39m 14s PRINT (O half_verse SET M>50 S>60): sorting out cliques\n", + " 1h 39m 14s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs\n", + " 1h 39m 18s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs\n", + " 1h 39m 18s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 39m 18s PREPARING (O half_verse SET): Already prepared\n", + " 1h 39m 18s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 39m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates\n", + " 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix\n", + " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages\n", + " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composing cliques out of 19519 chunks from 70089 comparisons\n", + " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composed 604 cliques out of 1000 chunks\n", + " 1h 39m 20s CLIQUES (O half_verse SET M>50 S>55): Composed 1098 cliques out of 2000 chunks\n", + " 1h 39m 22s CLIQUES (O half_verse SET M>50 S>55): Composed 1587 cliques out of 3000 chunks\n", + " 1h 39m 25s CLIQUES (O half_verse SET M>50 S>55): Composed 1974 cliques out of 4000 chunks\n", + " 1h 39m 28s CLIQUES (O half_verse SET M>50 S>55): Composed 2356 cliques out of 5000 chunks\n", + " 1h 39m 32s CLIQUES (O half_verse SET M>50 S>55): Composed 2683 cliques out of 6000 chunks\n", + " 1h 39m 37s CLIQUES (O half_verse SET M>50 S>55): Composed 2971 cliques out of 7000 chunks\n", + " 1h 39m 42s CLIQUES (O half_verse SET M>50 S>55): Composed 3126 cliques out of 8000 chunks\n", + " 1h 39m 48s CLIQUES (O half_verse SET M>50 S>55): Composed 3277 cliques out of 9000 chunks\n", + " 1h 39m 54s CLIQUES (O half_verse SET M>50 S>55): Composed 3271 cliques out of 10000 chunks\n", + " 1h 40m 01s CLIQUES (O half_verse SET M>50 S>55): Composed 3316 cliques out of 11000 chunks\n", + " 1h 40m 08s CLIQUES (O half_verse SET M>50 S>55): Composed 3241 cliques out of 12000 chunks\n", + " 1h 40m 16s CLIQUES (O half_verse SET M>50 S>55): Composed 3384 cliques out of 13000 chunks\n", + " 1h 40m 25s CLIQUES (O half_verse SET M>50 S>55): Composed 3387 cliques out of 14000 chunks\n", + " 1h 40m 34s CLIQUES (O half_verse SET M>50 S>55): Composed 3459 cliques out of 15000 chunks\n", + " 1h 40m 43s CLIQUES (O half_verse SET M>50 S>55): Composed 3567 cliques out of 16000 chunks\n", + " 1h 40m 53s CLIQUES (O half_verse SET M>50 S>55): Composed 3471 cliques out of 17000 chunks\n", + " 1h 41m 03s CLIQUES (O half_verse SET M>50 S>55): Composed 3480 cliques out of 18000 chunks\n", + " 1h 41m 13s CLIQUES (O half_verse SET M>50 S>55): Composed 3279 cliques out of 19000 chunks\n", + " 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques\n", + " 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): Composed and saved 3184 cliques out of 19519 chunks from 70089 comparisons\n", + " 1h 41m 19s PRINT (O half_verse SET M>50 S>55): sorting out cliques\n", + " 1h 41m 20s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs\n", + " 1h 41m 23s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs\n", + " 1h 41m 23s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 41m 23s PREPARING (O half_verse SET): Already prepared\n", + " 1h 41m 23s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", + " 1h 41m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates\n", + " 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix\n", + " 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): 179781 relevant similarities between 28988 passages\n", + " 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): Composing cliques out of 28988 chunks from 179781 comparisons\n", + " 1h 41m 25s CLIQUES (O half_verse SET M>50 S>50): Composed 652 cliques out of 1000 chunks\n", + " 1h 41m 26s CLIQUES (O half_verse SET M>50 S>50): Composed 1202 cliques out of 2000 chunks\n", + " 1h 41m 27s CLIQUES (O half_verse SET M>50 S>50): Composed 1587 cliques out of 3000 chunks\n", + " 1h 41m 30s CLIQUES (O half_verse SET M>50 S>50): Composed 1958 cliques out of 4000 chunks\n", + " 1h 41m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 2279 cliques out of 5000 chunks\n", + " 1h 41m 37s CLIQUES (O half_verse SET M>50 S>50): Composed 2478 cliques out of 6000 chunks\n", + " 1h 41m 41s CLIQUES (O half_verse SET M>50 S>50): Composed 2663 cliques out of 7000 chunks\n", + " 1h 41m 46s CLIQUES (O half_verse SET M>50 S>50): Composed 2828 cliques out of 8000 chunks\n", + " 1h 41m 52s CLIQUES (O half_verse SET M>50 S>50): Composed 2961 cliques out of 9000 chunks\n", + " 1h 41m 58s CLIQUES (O half_verse SET M>50 S>50): Composed 3058 cliques out of 10000 chunks\n", + " 1h 42m 04s CLIQUES (O half_verse SET M>50 S>50): Composed 3206 cliques out of 11000 chunks\n", + " 1h 42m 11s CLIQUES (O half_verse SET M>50 S>50): Composed 3278 cliques out of 12000 chunks\n", + " 1h 42m 18s CLIQUES (O half_verse SET M>50 S>50): Composed 3296 cliques out of 13000 chunks\n", + " 1h 42m 26s CLIQUES (O half_verse SET M>50 S>50): Composed 3353 cliques out of 14000 chunks\n", + " 1h 42m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 3280 cliques out of 15000 chunks\n", + " 1h 42m 42s CLIQUES (O half_verse SET M>50 S>50): Composed 3372 cliques out of 16000 chunks\n", + " 1h 42m 50s CLIQUES (O half_verse SET M>50 S>50): Composed 3259 cliques out of 17000 chunks\n", + " 1h 42m 59s CLIQUES (O half_verse SET M>50 S>50): Composed 3240 cliques out of 18000 chunks\n", + " 1h 43m 09s CLIQUES (O half_verse SET M>50 S>50): Composed 3378 cliques out of 19000 chunks\n", + " 1h 43m 18s CLIQUES (O half_verse SET M>50 S>50): Composed 3281 cliques out of 20000 chunks\n", + " 1h 43m 27s CLIQUES (O half_verse SET M>50 S>50): Composed 3127 cliques out of 21000 chunks\n", + " 1h 43m 37s CLIQUES (O half_verse SET M>50 S>50): Composed 3111 cliques out of 22000 chunks\n", + " 1h 43m 48s CLIQUES (O half_verse SET M>50 S>50): Composed 3080 cliques out of 23000 chunks\n", + " 1h 43m 58s CLIQUES (O half_verse SET M>50 S>50): Composed 2926 cliques out of 24000 chunks\n", + " 1h 44m 08s CLIQUES (O half_verse SET M>50 S>50): Composed 2778 cliques out of 25000 chunks\n", + " 1h 44m 20s CLIQUES (O half_verse SET M>50 S>50): Composed 2738 cliques out of 26000 chunks\n", + " 1h 44m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 2711 cliques out of 27000 chunks\n", + " 1h 44m 43s CLIQUES (O half_verse SET M>50 S>50): Composed 2378 cliques out of 28000 chunks\n", + " 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): 28988 members in 2031 cliques\n", + " 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): Composed and saved 2031 cliques out of 28988 chunks from 179781 comparisons\n", + " 1h 44m 55s PRINT (O half_verse SET M>50 S>50): sorting out cliques\n", + " 1h 44m 56s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs\n", + " 1h 44m 58s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs\n", + " 1h 44m 58s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 1h 44m 58s PREPARING (O half_verse LCS)\n", + " 1h 44m 58s PREPARING (O half_verse LCS): Done 45180 chunks.\n", + " 1h 44m 58s SIMILARITY (O half_verse LCS M>60): Computing 1020 M (1020593610) comparisons and saving entries in matrix\n", + " 1h 45m 19s SIMILARITY (O half_verse LCS M>60): Computed 10 M comparisons and saved 23129 entries in matrix\n", + " 1h 45m 40s SIMILARITY (O half_verse LCS M>60): Computed 20 M comparisons and saved 45727 entries in matrix\n", + " 1h 46m 00s SIMILARITY (O half_verse LCS M>60): Computed 30 M comparisons and saved 62396 entries in matrix\n", + " 1h 46m 21s SIMILARITY (O half_verse LCS M>60): Computed 40 M comparisons and saved 86192 entries in matrix\n", + " 1h 46m 43s SIMILARITY (O half_verse LCS M>60): Computed 51 M comparisons and saved 105378 entries in matrix\n", + " 1h 47m 03s SIMILARITY (O half_verse LCS M>60): Computed 61 M comparisons and saved 124304 entries in matrix\n", + " 1h 47m 24s SIMILARITY (O half_verse LCS M>60): Computed 71 M comparisons and saved 143728 entries in matrix\n", + " 1h 47m 45s SIMILARITY (O half_verse LCS M>60): Computed 81 M comparisons and saved 161716 entries in matrix\n", + " 1h 48m 05s SIMILARITY (O half_verse LCS M>60): Computed 91 M comparisons and saved 180158 entries in matrix\n", + " 1h 48m 25s SIMILARITY (O half_verse LCS M>60): Computed 102 M comparisons and saved 198767 entries in matrix\n", + " 1h 48m 46s SIMILARITY (O half_verse LCS M>60): Computed 112 M comparisons and saved 217110 entries in matrix\n", + " 1h 49m 06s SIMILARITY (O half_verse LCS M>60): Computed 122 M comparisons and saved 234406 entries in matrix\n", + " 1h 49m 27s SIMILARITY (O half_verse LCS M>60): Computed 132 M comparisons and saved 251791 entries in matrix\n", + " 1h 49m 49s SIMILARITY (O half_verse LCS M>60): Computed 142 M comparisons and saved 277921 entries in matrix\n", + " 1h 50m 11s SIMILARITY (O half_verse LCS M>60): Computed 153 M comparisons and saved 301012 entries in matrix\n", + " 1h 50m 34s SIMILARITY (O half_verse LCS M>60): Computed 163 M comparisons and saved 322004 entries in matrix\n", + " 1h 50m 56s SIMILARITY (O half_verse LCS M>60): Computed 173 M comparisons and saved 345960 entries in matrix\n", + " 1h 51m 16s SIMILARITY (O half_verse LCS M>60): Computed 183 M comparisons and saved 366300 entries in matrix\n", + " 1h 51m 37s SIMILARITY (O half_verse LCS M>60): Computed 193 M comparisons and saved 381274 entries in matrix\n", + " 1h 51m 59s SIMILARITY (O half_verse LCS M>60): Computed 204 M comparisons and saved 401543 entries in matrix\n", + " 1h 52m 21s SIMILARITY (O half_verse LCS M>60): Computed 214 M comparisons and saved 424607 entries in matrix\n", + " 1h 52m 42s SIMILARITY (O half_verse LCS M>60): Computed 224 M comparisons and saved 434786 entries in matrix\n", + " 1h 53m 04s SIMILARITY (O half_verse LCS M>60): Computed 234 M comparisons and saved 451987 entries in matrix\n", + " 1h 53m 26s SIMILARITY (O half_verse LCS M>60): Computed 244 M comparisons and saved 476196 entries in matrix\n", + " 1h 53m 48s SIMILARITY (O half_verse LCS M>60): Computed 255 M comparisons and saved 495694 entries in matrix\n", + " 1h 54m 11s SIMILARITY (O half_verse LCS M>60): Computed 265 M comparisons and saved 511994 entries in matrix\n", + " 1h 54m 31s SIMILARITY (O half_verse LCS M>60): Computed 275 M comparisons and saved 538859 entries in matrix\n", + " 1h 54m 52s SIMILARITY (O half_verse LCS M>60): Computed 285 M comparisons and saved 566545 entries in matrix\n", + " 1h 55m 12s SIMILARITY (O half_verse LCS M>60): Computed 295 M comparisons and saved 585782 entries in matrix\n", + " 1h 55m 32s SIMILARITY (O half_verse LCS M>60): Computed 306 M comparisons and saved 605411 entries in matrix\n", + " 1h 55m 55s SIMILARITY (O half_verse LCS M>60): Computed 316 M comparisons and saved 623888 entries in matrix\n", + " 1h 56m 16s SIMILARITY (O half_verse LCS M>60): Computed 326 M comparisons and saved 645994 entries in matrix\n", + " 1h 56m 37s SIMILARITY (O half_verse LCS M>60): Computed 336 M comparisons and saved 672491 entries in matrix\n", + " 1h 57m 00s SIMILARITY (O half_verse LCS M>60): Computed 347 M comparisons and saved 690907 entries in matrix\n", + " 1h 57m 20s SIMILARITY (O half_verse LCS M>60): Computed 357 M comparisons and saved 710440 entries in matrix\n", + " 1h 57m 42s SIMILARITY (O half_verse LCS M>60): Computed 367 M comparisons and saved 727551 entries in matrix\n", + " 1h 58m 01s SIMILARITY (O half_verse LCS M>60): Computed 377 M comparisons and saved 747143 entries in matrix\n", + " 1h 58m 23s SIMILARITY (O half_verse LCS M>60): Computed 387 M comparisons and saved 769713 entries in matrix\n", + " 1h 58m 46s SIMILARITY (O half_verse LCS M>60): Computed 398 M comparisons and saved 791579 entries in matrix\n", + " 1h 59m 08s SIMILARITY (O half_verse LCS M>60): Computed 408 M comparisons and saved 813039 entries in matrix\n", + " 1h 59m 31s SIMILARITY (O half_verse LCS M>60): Computed 418 M comparisons and saved 834185 entries in matrix\n", + " 1h 59m 52s SIMILARITY (O half_verse LCS M>60): Computed 428 M comparisons and saved 855462 entries in matrix\n", + " 2h 00m 14s SIMILARITY (O half_verse LCS M>60): Computed 438 M comparisons and saved 879387 entries in matrix\n", + " 2h 00m 35s SIMILARITY (O half_verse LCS M>60): Computed 449 M comparisons and saved 896300 entries in matrix\n", + " 2h 00m 59s SIMILARITY (O half_verse LCS M>60): Computed 459 M comparisons and saved 914630 entries in matrix\n", + " 2h 01m 23s SIMILARITY (O half_verse LCS M>60): Computed 469 M comparisons and saved 928008 entries in matrix\n", + " 2h 01m 44s SIMILARITY (O half_verse LCS M>60): Computed 479 M comparisons and saved 939511 entries in matrix\n", + " 2h 02m 08s SIMILARITY (O half_verse LCS M>60): Computed 489 M comparisons and saved 956514 entries in matrix\n", + " 2h 02m 29s SIMILARITY (O half_verse LCS M>60): Computed 500 M comparisons and saved 973079 entries in matrix\n", + " 2h 02m 52s SIMILARITY (O half_verse LCS M>60): Computed 510 M comparisons and saved 988171 entries in matrix\n", + " 2h 03m 15s SIMILARITY (O half_verse LCS M>60): Computed 520 M comparisons and saved 1007501 entries in matrix\n", + " 2h 03m 37s SIMILARITY (O half_verse LCS M>60): Computed 530 M comparisons and saved 1026297 entries in matrix\n", + " 2h 04m 00s SIMILARITY (O half_verse LCS M>60): Computed 540 M comparisons and saved 1044626 entries in matrix\n", + " 2h 04m 22s SIMILARITY (O half_verse LCS M>60): Computed 551 M comparisons and saved 1065472 entries in matrix\n", + " 2h 04m 44s SIMILARITY (O half_verse LCS M>60): Computed 561 M comparisons and saved 1084725 entries in matrix\n", + " 2h 05m 06s SIMILARITY (O half_verse LCS M>60): Computed 571 M comparisons and saved 1098963 entries in matrix\n", + " 2h 05m 28s SIMILARITY (O half_verse LCS M>60): Computed 581 M comparisons and saved 1113424 entries in matrix\n", + " 2h 05m 50s SIMILARITY (O half_verse LCS M>60): Computed 591 M comparisons and saved 1131189 entries in matrix\n", + " 2h 06m 13s SIMILARITY (O half_verse LCS M>60): Computed 602 M comparisons and saved 1152117 entries in matrix\n", + " 2h 06m 35s SIMILARITY (O half_verse LCS M>60): Computed 612 M comparisons and saved 1169776 entries in matrix\n", + " 2h 06m 56s SIMILARITY (O half_verse LCS M>60): Computed 622 M comparisons and saved 1190128 entries in matrix\n", + " 2h 07m 18s SIMILARITY (O half_verse LCS M>60): Computed 632 M comparisons and saved 1206090 entries in matrix\n", + " 2h 07m 41s SIMILARITY (O half_verse LCS M>60): Computed 642 M comparisons and saved 1223130 entries in matrix\n", + " 2h 08m 03s SIMILARITY (O half_verse LCS M>60): Computed 653 M comparisons and saved 1244760 entries in matrix\n", + " 2h 08m 25s SIMILARITY (O half_verse LCS M>60): Computed 663 M comparisons and saved 1264850 entries in matrix\n", + " 2h 08m 47s SIMILARITY (O half_verse LCS M>60): Computed 673 M comparisons and saved 1283072 entries in matrix\n", + " 2h 09m 09s SIMILARITY (O half_verse LCS M>60): Computed 683 M comparisons and saved 1299660 entries in matrix\n", + " 2h 09m 31s SIMILARITY (O half_verse LCS M>60): Computed 694 M comparisons and saved 1317246 entries in matrix\n", + " 2h 09m 52s SIMILARITY (O half_verse LCS M>60): Computed 704 M comparisons and saved 1333767 entries in matrix\n", + " 2h 10m 11s SIMILARITY (O half_verse LCS M>60): Computed 714 M comparisons and saved 1350133 entries in matrix\n", + " 2h 10m 30s SIMILARITY (O half_verse LCS M>60): Computed 724 M comparisons and saved 1365013 entries in matrix\n", + " 2h 10m 50s SIMILARITY (O half_verse LCS M>60): Computed 734 M comparisons and saved 1379520 entries in matrix\n", + " 2h 11m 08s SIMILARITY (O half_verse LCS M>60): Computed 745 M comparisons and saved 1397432 entries in matrix\n", + " 2h 11m 28s SIMILARITY (O half_verse LCS M>60): Computed 755 M comparisons and saved 1411286 entries in matrix\n", + " 2h 11m 48s SIMILARITY (O half_verse LCS M>60): Computed 765 M comparisons and saved 1430539 entries in matrix\n", + " 2h 12m 08s SIMILARITY (O half_verse LCS M>60): Computed 775 M comparisons and saved 1450873 entries in matrix\n", + " 2h 12m 29s SIMILARITY (O half_verse LCS M>60): Computed 785 M comparisons and saved 1471293 entries in matrix\n", + " 2h 12m 50s SIMILARITY (O half_verse LCS M>60): Computed 796 M comparisons and saved 1493379 entries in matrix\n", + " 2h 13m 13s SIMILARITY (O half_verse LCS M>60): Computed 806 M comparisons and saved 1511949 entries in matrix\n", + " 2h 13m 32s SIMILARITY (O half_verse LCS M>60): Computed 816 M comparisons and saved 1525887 entries in matrix\n", + " 2h 13m 52s SIMILARITY (O half_verse LCS M>60): Computed 826 M comparisons and saved 1544656 entries in matrix\n", + " 2h 14m 12s SIMILARITY (O half_verse LCS M>60): Computed 836 M comparisons and saved 1564089 entries in matrix\n", + " 2h 14m 30s SIMILARITY (O half_verse LCS M>60): Computed 847 M comparisons and saved 1582466 entries in matrix\n", + " 2h 14m 50s SIMILARITY (O half_verse LCS M>60): Computed 857 M comparisons and saved 1600534 entries in matrix\n", + " 2h 15m 10s SIMILARITY (O half_verse LCS M>60): Computed 867 M comparisons and saved 1616006 entries in matrix\n", + " 2h 15m 28s SIMILARITY (O half_verse LCS M>60): Computed 877 M comparisons and saved 1634862 entries in matrix\n", + " 2h 15m 46s SIMILARITY (O half_verse LCS M>60): Computed 887 M comparisons and saved 1650701 entries in matrix\n", + " 2h 16m 05s SIMILARITY (O half_verse LCS M>60): Computed 898 M comparisons and saved 1671631 entries in matrix\n", + " 2h 16m 20s SIMILARITY (O half_verse LCS M>60): Computed 908 M comparisons and saved 1704592 entries in matrix\n", + " 2h 16m 34s SIMILARITY (O half_verse LCS M>60): Computed 918 M comparisons and saved 1741561 entries in matrix\n", + " 2h 16m 49s SIMILARITY (O half_verse LCS M>60): Computed 928 M comparisons and saved 1776301 entries in matrix\n", + " 2h 17m 04s SIMILARITY (O half_verse LCS M>60): Computed 938 M comparisons and saved 1811487 entries in matrix\n", + " 2h 17m 19s SIMILARITY (O half_verse LCS M>60): Computed 949 M comparisons and saved 1846878 entries in matrix\n", + " 2h 17m 33s SIMILARITY (O half_verse LCS M>60): Computed 959 M comparisons and saved 1882694 entries in matrix\n", + " 2h 17m 49s SIMILARITY (O half_verse LCS M>60): Computed 969 M comparisons and saved 1913165 entries in matrix\n", + " 2h 18m 04s SIMILARITY (O half_verse LCS M>60): Computed 979 M comparisons and saved 1941507 entries in matrix\n", + " 2h 18m 20s SIMILARITY (O half_verse LCS M>60): Computed 989 M comparisons and saved 1965409 entries in matrix\n", + " 2h 18m 38s SIMILARITY (O half_verse LCS M>60): Computed 1000 M comparisons and saved 1981708 entries in matrix\n", + " 2h 19m 02s SIMILARITY (O half_verse LCS M>60): Computed 1010 M comparisons and saved 1993912 entries in matrix\n", + " 2h 19m 25s SIMILARITY (O half_verse LCS M>60): Computed 1020 M comparisons and saved 2017735 entries in matrix\n", + " 2h 19m 26s SIMILARITY (O half_verse LCS M>60): Computed 1020 M (1020593610) comparisons and saved 2017735 entries in matrix\n", + " 2h 19m 28s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates\n", + " 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix\n", + " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages\n", + " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composing cliques out of 3799 chunks from 9270 comparisons\n", + " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composed 450 cliques out of 1000 chunks\n", + " 2h 19m 30s CLIQUES (O half_verse LCS M>60 S>100): Composed 823 cliques out of 2000 chunks\n", + " 2h 19m 32s CLIQUES (O half_verse LCS M>60 S>100): Composed 1246 cliques out of 3000 chunks\n", + " 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques\n", + " 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): Composed and saved 1514 cliques out of 3799 chunks from 9270 comparisons\n", + " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): sorting out cliques\n", + " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs\n", + " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493\n", + " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing\n", + " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs\n", + " 2h 19m 34s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 19m 34s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 19m 34s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 19m 36s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates\n", + " 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix\n", + " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages\n", + " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composing cliques out of 4342 chunks from 9663 comparisons\n", + " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composed 469 cliques out of 1000 chunks\n", + " 2h 19m 38s CLIQUES (O half_verse LCS M>60 S>95): Composed 848 cliques out of 2000 chunks\n", + " 2h 19m 40s CLIQUES (O half_verse LCS M>60 S>95): Composed 1272 cliques out of 3000 chunks\n", + " 2h 19m 42s CLIQUES (O half_verse LCS M>60 S>95): Composed 1689 cliques out of 4000 chunks\n", + " 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques\n", + " 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): Composed and saved 1771 cliques out of 4342 chunks from 9663 comparisons\n", + " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): sorting out cliques\n", + " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs\n", + " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543\n", + " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing\n", + " 2h 19m 44s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs\n", + " 2h 19m 44s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 19m 44s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 19m 44s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 19m 46s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates\n", + " 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix\n", + " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages\n", + " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composing cliques out of 5776 chunks from 12125 comparisons\n", + " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composed 482 cliques out of 1000 chunks\n", + " 2h 19m 48s CLIQUES (O half_verse LCS M>60 S>90): Composed 913 cliques out of 2000 chunks\n", + " 2h 19m 50s CLIQUES (O half_verse LCS M>60 S>90): Composed 1282 cliques out of 3000 chunks\n", + " 2h 19m 52s CLIQUES (O half_verse LCS M>60 S>90): Composed 1718 cliques out of 4000 chunks\n", + " 2h 19m 55s CLIQUES (O half_verse LCS M>60 S>90): Composed 2094 cliques out of 5000 chunks\n", + " 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques\n", + " 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): Composed and saved 2336 cliques out of 5776 chunks from 12125 comparisons\n", + " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): sorting out cliques\n", + " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs\n", + " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732\n", + " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing\n", + " 2h 19m 59s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs\n", + " 2h 19m 59s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 19m 59s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 19m 59s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 20m 00s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates\n", + " 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix\n", + " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages\n", + " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composing cliques out of 7970 chunks from 17551 comparisons\n", + " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composed 526 cliques out of 1000 chunks\n", + " 2h 20m 03s CLIQUES (O half_verse LCS M>60 S>85): Composed 959 cliques out of 2000 chunks\n", + " 2h 20m 05s CLIQUES (O half_verse LCS M>60 S>85): Composed 1352 cliques out of 3000 chunks\n", + " 2h 20m 07s CLIQUES (O half_verse LCS M>60 S>85): Composed 1694 cliques out of 4000 chunks\n", + " 2h 20m 10s CLIQUES (O half_verse LCS M>60 S>85): Composed 2079 cliques out of 5000 chunks\n", + " 2h 20m 13s CLIQUES (O half_verse LCS M>60 S>85): Composed 2430 cliques out of 6000 chunks\n", + " 2h 20m 17s CLIQUES (O half_verse LCS M>60 S>85): Composed 2773 cliques out of 7000 chunks\n", + " 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques\n", + " 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): Composed and saved 2983 cliques out of 7970 chunks from 17551 comparisons\n", + " 2h 20m 22s PRINT (O half_verse LCS M>60 S>85): sorting out cliques\n", + " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs\n", + " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975\n", + " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing\n", + " 2h 20m 24s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs\n", + " 2h 20m 24s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 20m 24s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 20m 24s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 20m 26s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates\n", + " 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix\n", + " 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages\n", + " 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): Composing cliques out of 12504 chunks from 27273 comparisons\n", + " 2h 20m 28s CLIQUES (O half_verse LCS M>60 S>80): Composed 538 cliques out of 1000 chunks\n", + " 2h 20m 29s CLIQUES (O half_verse LCS M>60 S>80): Composed 956 cliques out of 2000 chunks\n", + " 2h 20m 30s CLIQUES (O half_verse LCS M>60 S>80): Composed 1379 cliques out of 3000 chunks\n", + " 2h 20m 32s CLIQUES (O half_verse LCS M>60 S>80): Composed 1735 cliques out of 4000 chunks\n", + " 2h 20m 35s CLIQUES (O half_verse LCS M>60 S>80): Composed 2057 cliques out of 5000 chunks\n", + " 2h 20m 39s CLIQUES (O half_verse LCS M>60 S>80): Composed 2332 cliques out of 6000 chunks\n", + " 2h 20m 43s CLIQUES (O half_verse LCS M>60 S>80): Composed 2700 cliques out of 7000 chunks\n", + " 2h 20m 48s CLIQUES (O half_verse LCS M>60 S>80): Composed 2968 cliques out of 8000 chunks\n", + " 2h 20m 54s CLIQUES (O half_verse LCS M>60 S>80): Composed 3278 cliques out of 9000 chunks\n", + " 2h 21m 00s CLIQUES (O half_verse LCS M>60 S>80): Composed 3433 cliques out of 10000 chunks\n", + " 2h 21m 07s CLIQUES (O half_verse LCS M>60 S>80): Composed 3579 cliques out of 11000 chunks\n", + " 2h 21m 14s CLIQUES (O half_verse LCS M>60 S>80): Composed 3589 cliques out of 12000 chunks\n", + " 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques\n", + " 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): Composed and saved 3540 cliques out of 12504 chunks from 27273 comparisons\n", + " 2h 21m 18s PRINT (O half_verse LCS M>60 S>80): sorting out cliques\n", + " 2h 21m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs\n", + " 2h 21m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs\n", + " 2h 21m 21s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 21m 21s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 21m 21s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 21m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates\n", + " 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix\n", + " 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): 53979 relevant similarities between 19147 passages\n", + " 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): Composing cliques out of 19147 chunks from 53979 comparisons\n", + " 2h 21m 25s CLIQUES (O half_verse LCS M>60 S>75): Composed 561 cliques out of 1000 chunks\n", + " 2h 21m 26s CLIQUES (O half_verse LCS M>60 S>75): Composed 1031 cliques out of 2000 chunks\n", + " 2h 21m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed 1399 cliques out of 3000 chunks\n", + " 2h 21m 30s CLIQUES (O half_verse LCS M>60 S>75): Composed 1756 cliques out of 4000 chunks\n", + " 2h 21m 33s CLIQUES (O half_verse LCS M>60 S>75): Composed 2091 cliques out of 5000 chunks\n", + " 2h 21m 36s CLIQUES (O half_verse LCS M>60 S>75): Composed 2372 cliques out of 6000 chunks\n", + " 2h 21m 40s CLIQUES (O half_verse LCS M>60 S>75): Composed 2584 cliques out of 7000 chunks\n", + " 2h 21m 45s CLIQUES (O half_verse LCS M>60 S>75): Composed 2783 cliques out of 8000 chunks\n", + " 2h 21m 51s CLIQUES (O half_verse LCS M>60 S>75): Composed 2943 cliques out of 9000 chunks\n", + " 2h 21m 57s CLIQUES (O half_verse LCS M>60 S>75): Composed 3223 cliques out of 10000 chunks\n", + " 2h 22m 04s CLIQUES (O half_verse LCS M>60 S>75): Composed 3329 cliques out of 11000 chunks\n", + " 2h 22m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed 3391 cliques out of 12000 chunks\n", + " 2h 22m 18s CLIQUES (O half_verse LCS M>60 S>75): Composed 3510 cliques out of 13000 chunks\n", + " 2h 22m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed 3569 cliques out of 14000 chunks\n", + " 2h 22m 35s CLIQUES (O half_verse LCS M>60 S>75): Composed 3562 cliques out of 15000 chunks\n", + " 2h 22m 44s CLIQUES (O half_verse LCS M>60 S>75): Composed 3512 cliques out of 16000 chunks\n", + " 2h 22m 53s CLIQUES (O half_verse LCS M>60 S>75): Composed 3420 cliques out of 17000 chunks\n", + " 2h 23m 02s CLIQUES (O half_verse LCS M>60 S>75): Composed 3340 cliques out of 18000 chunks\n", + " 2h 23m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed 3115 cliques out of 19000 chunks\n", + " 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): 19147 members in 3084 cliques\n", + " 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): Composed and saved 3084 cliques out of 19147 chunks from 53979 comparisons\n", + " 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): sorting out cliques\n", + " 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs\n", + " 2h 23m 16s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs\n", + " 2h 23m 16s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 23m 16s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 23m 16s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 23m 17s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates\n", + " 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix\n", + " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): 126164 relevant similarities between 28473 passages\n", + " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composing cliques out of 28473 chunks from 126164 comparisons\n", + " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composed 580 cliques out of 1000 chunks\n", + " 2h 23m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed 1067 cliques out of 2000 chunks\n", + " 2h 23m 22s CLIQUES (O half_verse LCS M>60 S>70): Composed 1458 cliques out of 3000 chunks\n", + " 2h 23m 24s CLIQUES (O half_verse LCS M>60 S>70): Composed 1714 cliques out of 4000 chunks\n", + " 2h 23m 27s CLIQUES (O half_verse LCS M>60 S>70): Composed 1935 cliques out of 5000 chunks\n", + " 2h 23m 31s CLIQUES (O half_verse LCS M>60 S>70): Composed 2138 cliques out of 6000 chunks\n", + " 2h 23m 35s CLIQUES (O half_verse LCS M>60 S>70): Composed 2387 cliques out of 7000 chunks\n", + " 2h 23m 40s CLIQUES (O half_verse LCS M>60 S>70): Composed 2541 cliques out of 8000 chunks\n", + " 2h 23m 45s CLIQUES (O half_verse LCS M>60 S>70): Composed 2652 cliques out of 9000 chunks\n", + " 2h 23m 50s CLIQUES (O half_verse LCS M>60 S>70): Composed 2723 cliques out of 10000 chunks\n", + " 2h 23m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed 2793 cliques out of 11000 chunks\n", + " 2h 24m 02s CLIQUES (O half_verse LCS M>60 S>70): Composed 2710 cliques out of 12000 chunks\n", + " 2h 24m 09s CLIQUES (O half_verse LCS M>60 S>70): Composed 2725 cliques out of 13000 chunks\n", + " 2h 24m 16s CLIQUES (O half_verse LCS M>60 S>70): Composed 2698 cliques out of 14000 chunks\n", + " 2h 24m 23s CLIQUES (O half_verse LCS M>60 S>70): Composed 2745 cliques out of 15000 chunks\n", + " 2h 24m 30s CLIQUES (O half_verse LCS M>60 S>70): Composed 2779 cliques out of 16000 chunks\n", + " 2h 24m 38s CLIQUES (O half_verse LCS M>60 S>70): Composed 2785 cliques out of 17000 chunks\n", + " 2h 24m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed 2739 cliques out of 18000 chunks\n", + " 2h 24m 54s CLIQUES (O half_verse LCS M>60 S>70): Composed 2659 cliques out of 19000 chunks\n", + " 2h 25m 03s CLIQUES (O half_verse LCS M>60 S>70): Composed 2611 cliques out of 20000 chunks\n", + " 2h 25m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed 2597 cliques out of 21000 chunks\n", + " 2h 25m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed 2491 cliques out of 22000 chunks\n", + " 2h 25m 28s CLIQUES (O half_verse LCS M>60 S>70): Composed 2432 cliques out of 23000 chunks\n", + " 2h 25m 37s CLIQUES (O half_verse LCS M>60 S>70): Composed 2342 cliques out of 24000 chunks\n", + " 2h 25m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed 2231 cliques out of 25000 chunks\n", + " 2h 25m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed 2125 cliques out of 26000 chunks\n", + " 2h 26m 04s CLIQUES (O half_verse LCS M>60 S>70): Composed 2057 cliques out of 27000 chunks\n", + " 2h 26m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed 1923 cliques out of 28000 chunks\n", + " 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): 28473 members in 1894 cliques\n", + " 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): Composed and saved 1894 cliques out of 28473 chunks from 126164 comparisons\n", + " 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): sorting out cliques\n", + " 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs\n", + " 2h 26m 19s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs\n", + " 2h 26m 19s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 26m 19s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 26m 19s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 26m 21s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates\n", + " 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix\n", + " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): 393352 relevant similarities between 38182 passages\n", + " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composing cliques out of 38182 chunks from 393352 comparisons\n", + " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composed 581 cliques out of 1000 chunks\n", + " 2h 26m 24s CLIQUES (O half_verse LCS M>60 S>65): Composed 1010 cliques out of 2000 chunks\n", + " 2h 26m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed 1224 cliques out of 3000 chunks\n", + " 2h 26m 28s CLIQUES (O half_verse LCS M>60 S>65): Composed 1371 cliques out of 4000 chunks\n", + " 2h 26m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed 1516 cliques out of 5000 chunks\n", + " 2h 26m 34s CLIQUES (O half_verse LCS M>60 S>65): Composed 1613 cliques out of 6000 chunks\n", + " 2h 26m 37s CLIQUES (O half_verse LCS M>60 S>65): Composed 1629 cliques out of 7000 chunks\n", + " 2h 26m 41s CLIQUES (O half_verse LCS M>60 S>65): Composed 1628 cliques out of 8000 chunks\n", + " 2h 26m 45s CLIQUES (O half_verse LCS M>60 S>65): Composed 1684 cliques out of 9000 chunks\n", + " 2h 26m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed 1668 cliques out of 10000 chunks\n", + " 2h 26m 53s CLIQUES (O half_verse LCS M>60 S>65): Composed 1624 cliques out of 11000 chunks\n", + " 2h 26m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed 1601 cliques out of 12000 chunks\n", + " 2h 27m 02s CLIQUES (O half_verse LCS M>60 S>65): Composed 1520 cliques out of 13000 chunks\n", + " 2h 27m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed 1498 cliques out of 14000 chunks\n", + " 2h 27m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed 1418 cliques out of 15000 chunks\n", + " 2h 27m 16s CLIQUES (O half_verse LCS M>60 S>65): Composed 1319 cliques out of 16000 chunks\n", + " 2h 27m 22s CLIQUES (O half_verse LCS M>60 S>65): Composed 1332 cliques out of 17000 chunks\n", + " 2h 27m 27s CLIQUES (O half_verse LCS M>60 S>65): Composed 1291 cliques out of 18000 chunks\n", + " 2h 27m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed 1221 cliques out of 19000 chunks\n", + " 2h 27m 36s CLIQUES (O half_verse LCS M>60 S>65): Composed 1167 cliques out of 20000 chunks\n", + " 2h 27m 42s CLIQUES (O half_verse LCS M>60 S>65): Composed 1123 cliques out of 21000 chunks\n", + " 2h 27m 47s CLIQUES (O half_verse LCS M>60 S>65): Composed 1106 cliques out of 22000 chunks\n", + " 2h 27m 52s CLIQUES (O half_verse LCS M>60 S>65): Composed 1121 cliques out of 23000 chunks\n", + " 2h 27m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed 1105 cliques out of 24000 chunks\n", + " 2h 28m 05s CLIQUES (O half_verse LCS M>60 S>65): Composed 1075 cliques out of 25000 chunks\n", + " 2h 28m 09s CLIQUES (O half_verse LCS M>60 S>65): Composed 1026 cliques out of 26000 chunks\n", + " 2h 28m 15s CLIQUES (O half_verse LCS M>60 S>65): Composed 1009 cliques out of 27000 chunks\n", + " 2h 28m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed 974 cliques out of 28000 chunks\n", + " 2h 28m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed 907 cliques out of 29000 chunks\n", + " 2h 28m 32s CLIQUES (O half_verse LCS M>60 S>65): Composed 892 cliques out of 30000 chunks\n", + " 2h 28m 38s CLIQUES (O half_verse LCS M>60 S>65): Composed 865 cliques out of 31000 chunks\n", + " 2h 28m 43s CLIQUES (O half_verse LCS M>60 S>65): Composed 837 cliques out of 32000 chunks\n", + " 2h 28m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed 799 cliques out of 33000 chunks\n", + " 2h 28m 54s CLIQUES (O half_verse LCS M>60 S>65): Composed 757 cliques out of 34000 chunks\n", + " 2h 29m 01s CLIQUES (O half_verse LCS M>60 S>65): Composed 731 cliques out of 35000 chunks\n", + " 2h 29m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed 703 cliques out of 36000 chunks\n", + " 2h 29m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed 687 cliques out of 37000 chunks\n", + " 2h 29m 18s CLIQUES (O half_verse LCS M>60 S>65): Composed 671 cliques out of 38000 chunks\n", + " 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): 38182 members in 665 cliques\n", + " 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed and saved 665 cliques out of 38182 chunks from 393352 comparisons\n", + " 2h 29m 20s PRINT (O half_verse LCS M>60 S>65): sorting out cliques\n", + " 2h 29m 21s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs\n", + " 2h 29m 22s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs\n", + " 2h 29m 22s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 2h 29m 22s PREPARING (O half_verse LCS): Already prepared\n", + " 2h 29m 22s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", + " 2h 29m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates\n", + " 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix\n", + " 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): 2017735 relevant similarities between 44011 passages\n", + " 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): Composing cliques out of 44011 chunks from 2017735 comparisons\n", + " 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 373 cliques out of 1000 chunks\n", + " 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 444 cliques out of 2000 chunks\n", + " 2h 29m 29s CLIQUES (O half_verse LCS M>60 S>60): Composed 473 cliques out of 3000 chunks\n", + " 2h 29m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed 487 cliques out of 4000 chunks\n", + " 2h 29m 32s CLIQUES (O half_verse LCS M>60 S>60): Composed 425 cliques out of 5000 chunks\n", + " 2h 29m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed 392 cliques out of 6000 chunks\n", + " 2h 29m 34s CLIQUES (O half_verse LCS M>60 S>60): Composed 350 cliques out of 7000 chunks\n", + " 2h 29m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed 370 cliques out of 8000 chunks\n", + " 2h 29m 37s CLIQUES (O half_verse LCS M>60 S>60): Composed 323 cliques out of 9000 chunks\n", + " 2h 29m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed 299 cliques out of 10000 chunks\n", + " 2h 29m 40s CLIQUES (O half_verse LCS M>60 S>60): Composed 271 cliques out of 11000 chunks\n", + " 2h 29m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed 265 cliques out of 12000 chunks\n", + " 2h 29m 43s CLIQUES (O half_verse LCS M>60 S>60): Composed 255 cliques out of 13000 chunks\n", + " 2h 29m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed 242 cliques out of 14000 chunks\n", + " 2h 29m 46s CLIQUES (O half_verse LCS M>60 S>60): Composed 224 cliques out of 15000 chunks\n", + " 2h 29m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed 226 cliques out of 16000 chunks\n", + " 2h 29m 50s CLIQUES (O half_verse LCS M>60 S>60): Composed 208 cliques out of 17000 chunks\n", + " 2h 29m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed 190 cliques out of 18000 chunks\n", + " 2h 29m 53s CLIQUES (O half_verse LCS M>60 S>60): Composed 183 cliques out of 19000 chunks\n", + " 2h 29m 55s CLIQUES (O half_verse LCS M>60 S>60): Composed 178 cliques out of 20000 chunks\n", + " 2h 29m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed 177 cliques out of 21000 chunks\n", + " 2h 29m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed 171 cliques out of 22000 chunks\n", + " 2h 30m 01s CLIQUES (O half_verse LCS M>60 S>60): Composed 160 cliques out of 23000 chunks\n", + " 2h 30m 03s CLIQUES (O half_verse LCS M>60 S>60): Composed 147 cliques out of 24000 chunks\n", + " 2h 30m 05s CLIQUES (O half_verse LCS M>60 S>60): Composed 143 cliques out of 25000 chunks\n", + " 2h 30m 08s CLIQUES (O half_verse LCS M>60 S>60): Composed 132 cliques out of 26000 chunks\n", + " 2h 30m 10s CLIQUES (O half_verse LCS M>60 S>60): Composed 129 cliques out of 27000 chunks\n", + " 2h 30m 13s CLIQUES (O half_verse LCS M>60 S>60): Composed 130 cliques out of 28000 chunks\n", + " 2h 30m 15s CLIQUES (O half_verse LCS M>60 S>60): Composed 129 cliques out of 29000 chunks\n", + " 2h 30m 17s CLIQUES (O half_verse LCS M>60 S>60): Composed 126 cliques out of 30000 chunks\n", + " 2h 30m 20s CLIQUES (O half_verse LCS M>60 S>60): Composed 111 cliques out of 31000 chunks\n", + " 2h 30m 22s CLIQUES (O half_verse LCS M>60 S>60): Composed 107 cliques out of 32000 chunks\n", + " 2h 30m 25s CLIQUES (O half_verse LCS M>60 S>60): Composed 106 cliques out of 33000 chunks\n", + " 2h 30m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 101 cliques out of 34000 chunks\n", + " 2h 30m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed 99 cliques out of 35000 chunks\n", + " 2h 30m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed 95 cliques out of 36000 chunks\n", + " 2h 30m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed 98 cliques out of 37000 chunks\n", + " 2h 30m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed 95 cliques out of 38000 chunks\n", + " 2h 30m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed 90 cliques out of 39000 chunks\n", + " 2h 30m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed 90 cliques out of 40000 chunks\n", + " 2h 30m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 41000 chunks\n", + " 2h 30m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 42000 chunks\n", + " 2h 30m 54s CLIQUES (O half_verse LCS M>60 S>60): Composed 88 cliques out of 43000 chunks\n", + " 2h 30m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 44000 chunks\n", + " 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques\n", + " 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed and saved 89 cliques out of 44011 chunks from 2017735 comparisons\n", + " 2h 30m 59s PRINT (O half_verse LCS M>60 S>60): sorting out cliques\n", + " 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs\n", + " 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs\n", + " 2h 31m 00s CHUNKING (O sentence)\n", + " 2h 31m 02s CHUNKING (O sentence): Made 63570 chunks\n", + " 2h 31m 02s PREPARING (O sentence SET)\n", + " 2h 31m 02s PREPARING (O sentence SET): Done 63570 chunks.\n", + " 2h 31m 02s SIMILARITY (O sentence SET M>50): Computing 2020 M (2020540665) comparisons and saving entries in matrix\n", + " 2h 31m 27s SIMILARITY (O sentence SET M>50): Computed 20 M comparisons and saved 45808 entries in matrix\n", + " 2h 31m 51s SIMILARITY (O sentence SET M>50): Computed 40 M comparisons and saved 70941 entries in matrix\n", + " 2h 32m 15s SIMILARITY (O sentence SET M>50): Computed 60 M comparisons and saved 120842 entries in matrix\n", + " 2h 32m 39s SIMILARITY (O sentence SET M>50): Computed 80 M comparisons and saved 204627 entries in matrix\n", + " 2h 33m 03s SIMILARITY (O sentence SET M>50): Computed 101 M comparisons and saved 269586 entries in matrix\n", + " 2h 33m 27s SIMILARITY (O sentence SET M>50): Computed 121 M comparisons and saved 356837 entries in matrix\n", + " 2h 33m 50s SIMILARITY (O sentence SET M>50): Computed 141 M comparisons and saved 443278 entries in matrix\n", + " 2h 34m 13s SIMILARITY (O sentence SET M>50): Computed 161 M comparisons and saved 524421 entries in matrix\n", + " 2h 34m 37s SIMILARITY (O sentence SET M>50): Computed 181 M comparisons and saved 585724 entries in matrix\n", + " 2h 35m 00s SIMILARITY (O sentence SET M>50): Computed 202 M comparisons and saved 624605 entries in matrix\n", + " 2h 35m 24s SIMILARITY (O sentence SET M>50): Computed 222 M comparisons and saved 683267 entries in matrix\n", + " 2h 35m 48s SIMILARITY (O sentence SET M>50): Computed 242 M comparisons and saved 738052 entries in matrix\n", + " 2h 36m 13s SIMILARITY (O sentence SET M>50): Computed 262 M comparisons and saved 806254 entries in matrix\n", + " 2h 36m 37s SIMILARITY (O sentence SET M>50): Computed 282 M comparisons and saved 852940 entries in matrix\n", + " 2h 37m 00s SIMILARITY (O sentence SET M>50): Computed 303 M comparisons and saved 942155 entries in matrix\n", + " 2h 37m 24s SIMILARITY (O sentence SET M>50): Computed 323 M comparisons and saved 1006926 entries in matrix\n", + " 2h 37m 49s SIMILARITY (O sentence SET M>50): Computed 343 M comparisons and saved 1057719 entries in matrix\n", + " 2h 38m 13s SIMILARITY (O sentence SET M>50): Computed 363 M comparisons and saved 1091220 entries in matrix\n", + " 2h 38m 38s SIMILARITY (O sentence SET M>50): Computed 383 M comparisons and saved 1135567 entries in matrix\n", + " 2h 39m 01s SIMILARITY (O sentence SET M>50): Computed 404 M comparisons and saved 1155716 entries in matrix\n", + " 2h 39m 26s SIMILARITY (O sentence SET M>50): Computed 424 M comparisons and saved 1162605 entries in matrix\n", + " 2h 39m 51s SIMILARITY (O sentence SET M>50): Computed 444 M comparisons and saved 1226546 entries in matrix\n", + " 2h 40m 16s SIMILARITY (O sentence SET M>50): Computed 464 M comparisons and saved 1235733 entries in matrix\n", + " 2h 40m 41s SIMILARITY (O sentence SET M>50): Computed 484 M comparisons and saved 1249303 entries in matrix\n", + " 2h 41m 06s SIMILARITY (O sentence SET M>50): Computed 505 M comparisons and saved 1267306 entries in matrix\n", + " 2h 41m 31s SIMILARITY (O sentence SET M>50): Computed 525 M comparisons and saved 1281368 entries in matrix\n", + " 2h 41m 55s SIMILARITY (O sentence SET M>50): Computed 545 M comparisons and saved 1306272 entries in matrix\n", + " 2h 42m 20s SIMILARITY (O sentence SET M>50): Computed 565 M comparisons and saved 1339875 entries in matrix\n", + " 2h 42m 45s SIMILARITY (O sentence SET M>50): Computed 585 M comparisons and saved 1358545 entries in matrix\n", + " 2h 43m 10s SIMILARITY (O sentence SET M>50): Computed 606 M comparisons and saved 1378717 entries in matrix\n", + " 2h 43m 35s SIMILARITY (O sentence SET M>50): Computed 626 M comparisons and saved 1411294 entries in matrix\n", + " 2h 43m 59s SIMILARITY (O sentence SET M>50): Computed 646 M comparisons and saved 1457168 entries in matrix\n", + " 2h 44m 24s SIMILARITY (O sentence SET M>50): Computed 666 M comparisons and saved 1487159 entries in matrix\n", + " 2h 44m 48s SIMILARITY (O sentence SET M>50): Computed 686 M comparisons and saved 1546006 entries in matrix\n", + " 2h 45m 13s SIMILARITY (O sentence SET M>50): Computed 707 M comparisons and saved 1565381 entries in matrix\n", + " 2h 45m 38s SIMILARITY (O sentence SET M>50): Computed 727 M comparisons and saved 1588602 entries in matrix\n", + " 2h 46m 02s SIMILARITY (O sentence SET M>50): Computed 747 M comparisons and saved 1626854 entries in matrix\n", + " 2h 46m 27s SIMILARITY (O sentence SET M>50): Computed 767 M comparisons and saved 1654679 entries in matrix\n", + " 2h 46m 52s SIMILARITY (O sentence SET M>50): Computed 788 M comparisons and saved 1673231 entries in matrix\n", + " 2h 47m 16s SIMILARITY (O sentence SET M>50): Computed 808 M comparisons and saved 1703760 entries in matrix\n", + " 2h 47m 40s SIMILARITY (O sentence SET M>50): Computed 828 M comparisons and saved 1731144 entries in matrix\n", + " 2h 48m 04s SIMILARITY (O sentence SET M>50): Computed 848 M comparisons and saved 1770357 entries in matrix\n", + " 2h 48m 29s SIMILARITY (O sentence SET M>50): Computed 868 M comparisons and saved 1817758 entries in matrix\n", + " 2h 48m 53s SIMILARITY (O sentence SET M>50): Computed 889 M comparisons and saved 1841653 entries in matrix\n", + " 2h 49m 18s SIMILARITY (O sentence SET M>50): Computed 909 M comparisons and saved 1852758 entries in matrix\n", + " 2h 49m 42s SIMILARITY (O sentence SET M>50): Computed 929 M comparisons and saved 1896595 entries in matrix\n", + " 2h 50m 06s SIMILARITY (O sentence SET M>50): Computed 949 M comparisons and saved 1955361 entries in matrix\n", + " 2h 50m 30s SIMILARITY (O sentence SET M>50): Computed 969 M comparisons and saved 1997274 entries in matrix\n", + " 2h 50m 53s SIMILARITY (O sentence SET M>50): Computed 990 M comparisons and saved 2065177 entries in matrix\n", + " 2h 51m 17s SIMILARITY (O sentence SET M>50): Computed 1010 M comparisons and saved 2136092 entries in matrix\n", + " 2h 51m 41s SIMILARITY (O sentence SET M>50): Computed 1030 M comparisons and saved 2184132 entries in matrix\n", + " 2h 52m 04s SIMILARITY (O sentence SET M>50): Computed 1050 M comparisons and saved 2249609 entries in matrix\n", + " 2h 52m 28s SIMILARITY (O sentence SET M>50): Computed 1070 M comparisons and saved 2305726 entries in matrix\n", + " 2h 52m 52s SIMILARITY (O sentence SET M>50): Computed 1091 M comparisons and saved 2363991 entries in matrix\n", + " 2h 53m 15s SIMILARITY (O sentence SET M>50): Computed 1111 M comparisons and saved 2408947 entries in matrix\n", + " 2h 53m 39s SIMILARITY (O sentence SET M>50): Computed 1131 M comparisons and saved 2463678 entries in matrix\n", + " 2h 54m 02s SIMILARITY (O sentence SET M>50): Computed 1151 M comparisons and saved 2514424 entries in matrix\n", + " 2h 54m 26s SIMILARITY (O sentence SET M>50): Computed 1171 M comparisons and saved 2564808 entries in matrix\n", + " 2h 54m 50s SIMILARITY (O sentence SET M>50): Computed 1192 M comparisons and saved 2607470 entries in matrix\n", + " 2h 55m 14s SIMILARITY (O sentence SET M>50): Computed 1212 M comparisons and saved 2670243 entries in matrix\n", + " 2h 55m 37s SIMILARITY (O sentence SET M>50): Computed 1232 M comparisons and saved 2714094 entries in matrix\n", + " 2h 56m 01s SIMILARITY (O sentence SET M>50): Computed 1252 M comparisons and saved 2752488 entries in matrix\n", + " 2h 56m 25s SIMILARITY (O sentence SET M>50): Computed 1272 M comparisons and saved 2814356 entries in matrix\n", + " 2h 56m 51s SIMILARITY (O sentence SET M>50): Computed 1293 M comparisons and saved 2827848 entries in matrix\n", + " 2h 57m 15s SIMILARITY (O sentence SET M>50): Computed 1313 M comparisons and saved 2864217 entries in matrix\n", + " 2h 57m 38s SIMILARITY (O sentence SET M>50): Computed 1333 M comparisons and saved 2935628 entries in matrix\n", + " 2h 58m 01s SIMILARITY (O sentence SET M>50): Computed 1353 M comparisons and saved 3005907 entries in matrix\n", + " 2h 58m 24s SIMILARITY (O sentence SET M>50): Computed 1373 M comparisons and saved 3085992 entries in matrix\n", + " 2h 58m 47s SIMILARITY (O sentence SET M>50): Computed 1394 M comparisons and saved 3153997 entries in matrix\n", + " 2h 59m 11s SIMILARITY (O sentence SET M>50): Computed 1414 M comparisons and saved 3179667 entries in matrix\n", + " 2h 59m 36s SIMILARITY (O sentence SET M>50): Computed 1434 M comparisons and saved 3213666 entries in matrix\n", + " 2h 59m 59s SIMILARITY (O sentence SET M>50): Computed 1454 M comparisons and saved 3241136 entries in matrix\n", + " 3h 00m 23s SIMILARITY (O sentence SET M>50): Computed 1474 M comparisons and saved 3256962 entries in matrix\n", + " 3h 00m 46s SIMILARITY (O sentence SET M>50): Computed 1495 M comparisons and saved 3277580 entries in matrix\n", + " 3h 01m 09s SIMILARITY (O sentence SET M>50): Computed 1515 M comparisons and saved 3306752 entries in matrix\n", + " 3h 01m 31s SIMILARITY (O sentence SET M>50): Computed 1535 M comparisons and saved 3330735 entries in matrix\n", + " 3h 01m 54s SIMILARITY (O sentence SET M>50): Computed 1555 M comparisons and saved 3354290 entries in matrix\n", + " 3h 02m 17s SIMILARITY (O sentence SET M>50): Computed 1576 M comparisons and saved 3386386 entries in matrix\n", + " 3h 02m 40s SIMILARITY (O sentence SET M>50): Computed 1596 M comparisons and saved 3431895 entries in matrix\n", + " 3h 03m 04s SIMILARITY (O sentence SET M>50): Computed 1616 M comparisons and saved 3469842 entries in matrix\n", + " 3h 03m 28s SIMILARITY (O sentence SET M>50): Computed 1636 M comparisons and saved 3513213 entries in matrix\n", + " 3h 03m 52s SIMILARITY (O sentence SET M>50): Computed 1656 M comparisons and saved 3552466 entries in matrix\n", + " 3h 04m 16s SIMILARITY (O sentence SET M>50): Computed 1677 M comparisons and saved 3582525 entries in matrix\n", + " 3h 04m 39s SIMILARITY (O sentence SET M>50): Computed 1697 M comparisons and saved 3603609 entries in matrix\n", + " 3h 05m 03s SIMILARITY (O sentence SET M>50): Computed 1717 M comparisons and saved 3646709 entries in matrix\n", + " 3h 05m 26s SIMILARITY (O sentence SET M>50): Computed 1737 M comparisons and saved 3681527 entries in matrix\n", + " 3h 05m 50s SIMILARITY (O sentence SET M>50): Computed 1757 M comparisons and saved 3707553 entries in matrix\n", + " 3h 06m 14s SIMILARITY (O sentence SET M>50): Computed 1778 M comparisons and saved 3732364 entries in matrix\n", + " 3h 06m 38s SIMILARITY (O sentence SET M>50): Computed 1798 M comparisons and saved 3752245 entries in matrix\n", + " 3h 07m 01s SIMILARITY (O sentence SET M>50): Computed 1818 M comparisons and saved 3772955 entries in matrix\n", + " 3h 07m 24s SIMILARITY (O sentence SET M>50): Computed 1838 M comparisons and saved 3791878 entries in matrix\n", + " 3h 07m 48s SIMILARITY (O sentence SET M>50): Computed 1858 M comparisons and saved 3831199 entries in matrix\n", + " 3h 08m 13s SIMILARITY (O sentence SET M>50): Computed 1879 M comparisons and saved 3848114 entries in matrix\n", + " 3h 08m 38s SIMILARITY (O sentence SET M>50): Computed 1899 M comparisons and saved 3862274 entries in matrix\n", + " 3h 09m 06s SIMILARITY (O sentence SET M>50): Computed 1919 M comparisons and saved 3874162 entries in matrix\n", + " 3h 09m 29s SIMILARITY (O sentence SET M>50): Computed 1939 M comparisons and saved 3888285 entries in matrix\n", + " 3h 09m 53s SIMILARITY (O sentence SET M>50): Computed 1959 M comparisons and saved 3905689 entries in matrix\n", + " 3h 10m 17s SIMILARITY (O sentence SET M>50): Computed 1980 M comparisons and saved 3917473 entries in matrix\n", + " 3h 10m 42s SIMILARITY (O sentence SET M>50): Computed 2000 M comparisons and saved 3936282 entries in matrix\n", + " 3h 11m 08s SIMILARITY (O sentence SET M>50): Computed 2020 M comparisons and saved 3958946 entries in matrix\n", + " 3h 11m 11s SIMILARITY (O sentence SET M>50): Computed 2020 M (2020540665) comparisons and saved 3958946 entries in matrix\n", + " 3h 11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates\n", + " 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix\n", + " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): 937604 relevant similarities between 19031 passages\n", + " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composing cliques out of 19031 chunks from 937604 comparisons\n", + " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composed 511 cliques out of 1000 chunks\n", + " 3h 11m 19s CLIQUES (O sentence SET M>50 S>100): Composed 876 cliques out of 2000 chunks\n", + " 3h 11m 21s CLIQUES (O sentence SET M>50 S>100): Composed 1294 cliques out of 3000 chunks\n", + " 3h 11m 23s CLIQUES (O sentence SET M>50 S>100): Composed 1693 cliques out of 4000 chunks\n", + " 3h 11m 26s CLIQUES (O sentence SET M>50 S>100): Composed 2040 cliques out of 5000 chunks\n", + " 3h 11m 30s CLIQUES (O sentence SET M>50 S>100): Composed 2425 cliques out of 6000 chunks\n", + " 3h 11m 34s CLIQUES (O sentence SET M>50 S>100): Composed 2696 cliques out of 7000 chunks\n", + " 3h 11m 39s CLIQUES (O sentence SET M>50 S>100): Composed 2979 cliques out of 8000 chunks\n", + " 3h 11m 45s CLIQUES (O sentence SET M>50 S>100): Composed 3256 cliques out of 9000 chunks\n", + " 3h 11m 51s CLIQUES (O sentence SET M>50 S>100): Composed 3482 cliques out of 10000 chunks\n", + " 3h 11m 58s CLIQUES (O sentence SET M>50 S>100): Composed 3646 cliques out of 11000 chunks\n", + " 3h 12m 06s CLIQUES (O sentence SET M>50 S>100): Composed 3795 cliques out of 12000 chunks\n", + " 3h 12m 14s CLIQUES (O sentence SET M>50 S>100): Composed 3939 cliques out of 13000 chunks\n", + " 3h 12m 23s CLIQUES (O sentence SET M>50 S>100): Composed 4008 cliques out of 14000 chunks\n", + " 3h 12m 32s CLIQUES (O sentence SET M>50 S>100): Composed 4112 cliques out of 15000 chunks\n", + " 3h 12m 43s CLIQUES (O sentence SET M>50 S>100): Composed 4183 cliques out of 16000 chunks\n", + " 3h 12m 53s CLIQUES (O sentence SET M>50 S>100): Composed 4269 cliques out of 17000 chunks\n", + " 3h 13m 05s CLIQUES (O sentence SET M>50 S>100): Composed 4305 cliques out of 18000 chunks\n", + " 3h 13m 16s CLIQUES (O sentence SET M>50 S>100): Composed 4324 cliques out of 19000 chunks\n", + " 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): 19031 members in 4324 cliques\n", + " 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): Composed and saved 4324 cliques out of 19031 chunks from 937604 comparisons\n", + " 3h 13m 17s PRINT (O sentence SET M>50 S>100): sorting out cliques\n", + " 3h 13m 18s PRINT (O sentence SET M>50 S>100): formatting 4324 cliques involving 1528 binary chapter diffs\n", + " 3h 13m 18s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528\n", + " 3h 13m 19s PRINT (O sentence SET M>50 S>100): Chapter diffs: 7 newly created and 1521 already existing\n", + " 3h 13m 21s PRINT (O sentence SET M>50 S>100): formatted 4324 cliques (87 files) involving 1528 binary chapter diffs\n", + " 3h 13m 21s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 13m 21s PREPARING (O sentence SET): Already prepared\n", + " 3h 13m 21s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 13m 25s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates\n", + " 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix\n", + " 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): 937608 relevant similarities between 19039 passages\n", + " 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): Composing cliques out of 19039 chunks from 937608 comparisons\n", + " 3h 13m 29s CLIQUES (O sentence SET M>50 S>95): Composed 511 cliques out of 1000 chunks\n", + " 3h 13m 30s CLIQUES (O sentence SET M>50 S>95): Composed 876 cliques out of 2000 chunks\n", + " 3h 13m 31s CLIQUES (O sentence SET M>50 S>95): Composed 1297 cliques out of 3000 chunks\n", + " 3h 13m 34s CLIQUES (O sentence SET M>50 S>95): Composed 1691 cliques out of 4000 chunks\n", + " 3h 13m 37s CLIQUES (O sentence SET M>50 S>95): Composed 2042 cliques out of 5000 chunks\n", + " 3h 13m 40s CLIQUES (O sentence SET M>50 S>95): Composed 2425 cliques out of 6000 chunks\n", + " 3h 13m 45s CLIQUES (O sentence SET M>50 S>95): Composed 2699 cliques out of 7000 chunks\n", + " 3h 13m 50s CLIQUES (O sentence SET M>50 S>95): Composed 2979 cliques out of 8000 chunks\n", + " 3h 13m 55s CLIQUES (O sentence SET M>50 S>95): Composed 3259 cliques out of 9000 chunks\n", + " 3h 14m 02s CLIQUES (O sentence SET M>50 S>95): Composed 3485 cliques out of 10000 chunks\n", + " 3h 14m 09s CLIQUES (O sentence SET M>50 S>95): Composed 3649 cliques out of 11000 chunks\n", + " 3h 14m 16s CLIQUES (O sentence SET M>50 S>95): Composed 3799 cliques out of 12000 chunks\n", + " 3h 14m 25s CLIQUES (O sentence SET M>50 S>95): Composed 3942 cliques out of 13000 chunks\n", + " 3h 14m 33s CLIQUES (O sentence SET M>50 S>95): Composed 4012 cliques out of 14000 chunks\n", + " 3h 14m 43s CLIQUES (O sentence SET M>50 S>95): Composed 4115 cliques out of 15000 chunks\n", + " 3h 14m 53s CLIQUES (O sentence SET M>50 S>95): Composed 4186 cliques out of 16000 chunks\n", + " 3h 15m 03s CLIQUES (O sentence SET M>50 S>95): Composed 4272 cliques out of 17000 chunks\n", + " 3h 15m 15s CLIQUES (O sentence SET M>50 S>95): Composed 4309 cliques out of 18000 chunks\n", + " 3h 15m 26s CLIQUES (O sentence SET M>50 S>95): Composed 4328 cliques out of 19000 chunks\n", + " 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): 19039 members in 4328 cliques\n", + " 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): Composed and saved 4328 cliques out of 19039 chunks from 937608 comparisons\n", + " 3h 15m 27s PRINT (O sentence SET M>50 S>95): sorting out cliques\n", + " 3h 15m 28s PRINT (O sentence SET M>50 S>95): formatting 4328 cliques involving 1529 binary chapter diffs\n", + " 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529\n", + " 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing\n", + " 3h 15m 31s PRINT (O sentence SET M>50 S>95): formatted 4328 cliques (87 files) involving 1529 binary chapter diffs\n", + " 3h 15m 31s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 15m 31s PREPARING (O sentence SET): Already prepared\n", + " 3h 15m 31s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 15m 34s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates\n", + " 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix\n", + " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): 937734 relevant similarities between 19214 passages\n", + " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composing cliques out of 19214 chunks from 937734 comparisons\n", + " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composed 484 cliques out of 1000 chunks\n", + " 3h 15m 39s CLIQUES (O sentence SET M>50 S>90): Composed 880 cliques out of 2000 chunks\n", + " 3h 15m 40s CLIQUES (O sentence SET M>50 S>90): Composed 1288 cliques out of 3000 chunks\n", + " 3h 15m 43s CLIQUES (O sentence SET M>50 S>90): Composed 1677 cliques out of 4000 chunks\n", + " 3h 15m 46s CLIQUES (O sentence SET M>50 S>90): Composed 2031 cliques out of 5000 chunks\n", + " 3h 15m 49s CLIQUES (O sentence SET M>50 S>90): Composed 2429 cliques out of 6000 chunks\n", + " 3h 15m 54s CLIQUES (O sentence SET M>50 S>90): Composed 2718 cliques out of 7000 chunks\n", + " 3h 15m 58s CLIQUES (O sentence SET M>50 S>90): Composed 3019 cliques out of 8000 chunks\n", + " 3h 16m 04s CLIQUES (O sentence SET M>50 S>90): Composed 3282 cliques out of 9000 chunks\n", + " 3h 16m 10s CLIQUES (O sentence SET M>50 S>90): Composed 3532 cliques out of 10000 chunks\n", + " 3h 16m 17s CLIQUES (O sentence SET M>50 S>90): Composed 3699 cliques out of 11000 chunks\n", + " 3h 16m 25s CLIQUES (O sentence SET M>50 S>90): Composed 3845 cliques out of 12000 chunks\n", + " 3h 16m 33s CLIQUES (O sentence SET M>50 S>90): Composed 4002 cliques out of 13000 chunks\n", + " 3h 16m 41s CLIQUES (O sentence SET M>50 S>90): Composed 4078 cliques out of 14000 chunks\n", + " 3h 16m 51s CLIQUES (O sentence SET M>50 S>90): Composed 4179 cliques out of 15000 chunks\n", + " 3h 17m 01s CLIQUES (O sentence SET M>50 S>90): Composed 4256 cliques out of 16000 chunks\n", + " 3h 17m 11s CLIQUES (O sentence SET M>50 S>90): Composed 4340 cliques out of 17000 chunks\n", + " 3h 17m 23s CLIQUES (O sentence SET M>50 S>90): Composed 4381 cliques out of 18000 chunks\n", + " 3h 17m 34s CLIQUES (O sentence SET M>50 S>90): Composed 4404 cliques out of 19000 chunks\n", + " 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): 19214 members in 4406 cliques\n", + " 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): Composed and saved 4406 cliques out of 19214 chunks from 937734 comparisons\n", + " 3h 17m 38s PRINT (O sentence SET M>50 S>90): sorting out cliques\n", + " 3h 17m 38s PRINT (O sentence SET M>50 S>90): formatting 4406 cliques involving 1537 binary chapter diffs\n", + " 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1537\n", + " 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1537 already existing\n", + " 3h 17m 41s PRINT (O sentence SET M>50 S>90): formatted 4406 cliques (89 files) involving 1537 binary chapter diffs\n", + " 3h 17m 41s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 17m 41s PREPARING (O sentence SET): Already prepared\n", + " 3h 17m 41s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 17m 44s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates\n", + " 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix\n", + " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): 938584 relevant similarities between 19777 passages\n", + " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composing cliques out of 19777 chunks from 938584 comparisons\n", + " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composed 493 cliques out of 1000 chunks\n", + " 3h 17m 49s CLIQUES (O sentence SET M>50 S>85): Composed 910 cliques out of 2000 chunks\n", + " 3h 17m 51s CLIQUES (O sentence SET M>50 S>85): Composed 1283 cliques out of 3000 chunks\n", + " 3h 17m 53s CLIQUES (O sentence SET M>50 S>85): Composed 1662 cliques out of 4000 chunks\n", + " 3h 17m 56s CLIQUES (O sentence SET M>50 S>85): Composed 2063 cliques out of 5000 chunks\n", + " 3h 17m 59s CLIQUES (O sentence SET M>50 S>85): Composed 2427 cliques out of 6000 chunks\n", + " 3h 18m 04s CLIQUES (O sentence SET M>50 S>85): Composed 2795 cliques out of 7000 chunks\n", + " 3h 18m 09s CLIQUES (O sentence SET M>50 S>85): Composed 3047 cliques out of 8000 chunks\n", + " 3h 18m 14s CLIQUES (O sentence SET M>50 S>85): Composed 3345 cliques out of 9000 chunks\n", + " 3h 18m 20s CLIQUES (O sentence SET M>50 S>85): Composed 3583 cliques out of 10000 chunks\n", + " 3h 18m 27s CLIQUES (O sentence SET M>50 S>85): Composed 3799 cliques out of 11000 chunks\n", + " 3h 18m 35s CLIQUES (O sentence SET M>50 S>85): Composed 3972 cliques out of 12000 chunks\n", + " 3h 18m 43s CLIQUES (O sentence SET M>50 S>85): Composed 4129 cliques out of 13000 chunks\n", + " 3h 18m 52s CLIQUES (O sentence SET M>50 S>85): Composed 4244 cliques out of 14000 chunks\n", + " 3h 19m 01s CLIQUES (O sentence SET M>50 S>85): Composed 4306 cliques out of 15000 chunks\n", + " 3h 19m 11s CLIQUES (O sentence SET M>50 S>85): Composed 4411 cliques out of 16000 chunks\n", + " 3h 19m 22s CLIQUES (O sentence SET M>50 S>85): Composed 4500 cliques out of 17000 chunks\n", + " 3h 19m 33s CLIQUES (O sentence SET M>50 S>85): Composed 4571 cliques out of 18000 chunks\n", + " 3h 19m 46s CLIQUES (O sentence SET M>50 S>85): Composed 4596 cliques out of 19000 chunks\n", + " 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): 19777 members in 4608 cliques\n", + " 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): Composed and saved 4608 cliques out of 19777 chunks from 938584 comparisons\n", + " 3h 19m 56s PRINT (O sentence SET M>50 S>85): sorting out cliques\n", + " 3h 19m 56s PRINT (O sentence SET M>50 S>85): formatting 4608 cliques involving 1589 binary chapter diffs\n", + " 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1589\n", + " 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1589 already existing\n", + " 3h 19m 59s PRINT (O sentence SET M>50 S>85): formatted 4608 cliques (93 files) involving 1589 binary chapter diffs\n", + " 3h 19m 59s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 19m 59s PREPARING (O sentence SET): Already prepared\n", + " 3h 19m 59s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 20m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates\n", + " 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix\n", + " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): 960796 relevant similarities between 22082 passages\n", + " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composing cliques out of 22082 chunks from 960796 comparisons\n", + " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composed 492 cliques out of 1000 chunks\n", + " 3h 20m 07s CLIQUES (O sentence SET M>50 S>80): Composed 1040 cliques out of 2000 chunks\n", + " 3h 20m 09s CLIQUES (O sentence SET M>50 S>80): Composed 1463 cliques out of 3000 chunks\n", + " 3h 20m 11s CLIQUES (O sentence SET M>50 S>80): Composed 1841 cliques out of 4000 chunks\n", + " 3h 20m 14s CLIQUES (O sentence SET M>50 S>80): Composed 2270 cliques out of 5000 chunks\n", + " 3h 20m 18s CLIQUES (O sentence SET M>50 S>80): Composed 2408 cliques out of 6000 chunks\n", + " 3h 20m 22s CLIQUES (O sentence SET M>50 S>80): Composed 2656 cliques out of 7000 chunks\n", + " 3h 20m 27s CLIQUES (O sentence SET M>50 S>80): Composed 2940 cliques out of 8000 chunks\n", + " 3h 20m 33s CLIQUES (O sentence SET M>50 S>80): Composed 3239 cliques out of 9000 chunks\n", + " 3h 20m 39s CLIQUES (O sentence SET M>50 S>80): Composed 3458 cliques out of 10000 chunks\n", + " 3h 20m 46s CLIQUES (O sentence SET M>50 S>80): Composed 3713 cliques out of 11000 chunks\n", + " 3h 20m 54s CLIQUES (O sentence SET M>50 S>80): Composed 3992 cliques out of 12000 chunks\n", + " 3h 21m 02s CLIQUES (O sentence SET M>50 S>80): Composed 4223 cliques out of 13000 chunks\n", + " 3h 21m 11s CLIQUES (O sentence SET M>50 S>80): Composed 4386 cliques out of 14000 chunks\n", + " 3h 21m 21s CLIQUES (O sentence SET M>50 S>80): Composed 4536 cliques out of 15000 chunks\n", + " 3h 21m 31s CLIQUES (O sentence SET M>50 S>80): Composed 4686 cliques out of 16000 chunks\n", + " 3h 21m 41s CLIQUES (O sentence SET M>50 S>80): Composed 4756 cliques out of 17000 chunks\n", + " 3h 21m 53s CLIQUES (O sentence SET M>50 S>80): Composed 4852 cliques out of 18000 chunks\n", + " 3h 22m 05s CLIQUES (O sentence SET M>50 S>80): Composed 4931 cliques out of 19000 chunks\n", + " 3h 22m 18s CLIQUES (O sentence SET M>50 S>80): Composed 5015 cliques out of 20000 chunks\n", + " 3h 22m 31s CLIQUES (O sentence SET M>50 S>80): Composed 5050 cliques out of 21000 chunks\n", + " 3h 22m 45s CLIQUES (O sentence SET M>50 S>80): Composed 5072 cliques out of 22000 chunks\n", + " 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): 22082 members in 5073 cliques\n", + " 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): Composed and saved 5073 cliques out of 22082 chunks from 960796 comparisons\n", + " 3h 22m 47s PRINT (O sentence SET M>50 S>80): sorting out cliques\n", + " 3h 22m 47s PRINT (O sentence SET M>50 S>80): formatting 5073 cliques involving 1748 binary chapter diffs\n", + " 3h 22m 47s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1748\n", + " 3h 22m 48s PRINT (O sentence SET M>50 S>80): Chapter diffs: 2 newly created and 1746 already existing\n", + " 3h 22m 51s PRINT (O sentence SET M>50 S>80): formatted 5073 cliques (102 files) involving 1748 binary chapter diffs\n", + " 3h 22m 51s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 22m 51s PREPARING (O sentence SET): Already prepared\n", + " 3h 22m 51s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 22m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates\n", + " 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix\n", + " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): 1009309 relevant similarities between 25751 passages\n", + " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composing cliques out of 25751 chunks from 1009309 comparisons\n", + " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composed 517 cliques out of 1000 chunks\n", + " 3h 23m 00s CLIQUES (O sentence SET M>50 S>75): Composed 1017 cliques out of 2000 chunks\n", + " 3h 23m 02s CLIQUES (O sentence SET M>50 S>75): Composed 1456 cliques out of 3000 chunks\n", + " 3h 23m 04s CLIQUES (O sentence SET M>50 S>75): Composed 1823 cliques out of 4000 chunks\n", + " 3h 23m 07s CLIQUES (O sentence SET M>50 S>75): Composed 2257 cliques out of 5000 chunks\n", + " 3h 23m 11s CLIQUES (O sentence SET M>50 S>75): Composed 2696 cliques out of 6000 chunks\n", + " 3h 23m 15s CLIQUES (O sentence SET M>50 S>75): Composed 2955 cliques out of 7000 chunks\n", + " 3h 23m 20s CLIQUES (O sentence SET M>50 S>75): Composed 3272 cliques out of 8000 chunks\n", + " 3h 23m 26s CLIQUES (O sentence SET M>50 S>75): Composed 3671 cliques out of 9000 chunks\n", + " 3h 23m 33s CLIQUES (O sentence SET M>50 S>75): Composed 3821 cliques out of 10000 chunks\n", + " 3h 23m 39s CLIQUES (O sentence SET M>50 S>75): Composed 3773 cliques out of 11000 chunks\n", + " 3h 23m 47s CLIQUES (O sentence SET M>50 S>75): Composed 3823 cliques out of 12000 chunks\n", + " 3h 23m 55s CLIQUES (O sentence SET M>50 S>75): Composed 3891 cliques out of 13000 chunks\n", + " 3h 24m 04s CLIQUES (O sentence SET M>50 S>75): Composed 4004 cliques out of 14000 chunks\n", + " 3h 24m 13s CLIQUES (O sentence SET M>50 S>75): Composed 4138 cliques out of 15000 chunks\n", + " 3h 24m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4263 cliques out of 16000 chunks\n", + " 3h 24m 34s CLIQUES (O sentence SET M>50 S>75): Composed 4341 cliques out of 17000 chunks\n", + " 3h 24m 45s CLIQUES (O sentence SET M>50 S>75): Composed 4387 cliques out of 18000 chunks\n", + " 3h 24m 57s CLIQUES (O sentence SET M>50 S>75): Composed 4531 cliques out of 19000 chunks\n", + " 3h 25m 10s CLIQUES (O sentence SET M>50 S>75): Composed 4638 cliques out of 20000 chunks\n", + " 3h 25m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4699 cliques out of 21000 chunks\n", + " 3h 25m 37s CLIQUES (O sentence SET M>50 S>75): Composed 4803 cliques out of 22000 chunks\n", + " 3h 25m 52s CLIQUES (O sentence SET M>50 S>75): Composed 4893 cliques out of 23000 chunks\n", + " 3h 26m 07s CLIQUES (O sentence SET M>50 S>75): Composed 4963 cliques out of 24000 chunks\n", + " 3h 26m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4988 cliques out of 25000 chunks\n", + " 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): 25751 members in 5000 cliques\n", + " 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): Composed and saved 5000 cliques out of 25751 chunks from 1009309 comparisons\n", + " 3h 26m 36s PRINT (O sentence SET M>50 S>75): sorting out cliques\n", + " 3h 26m 36s PRINT (O sentence SET M>50 S>75): formatting 5000 cliques skipping 1744 binary chapter diffs\n", + " 3h 26m 40s PRINT (O sentence SET M>50 S>75): formatted 5000 cliques (100 files) skipping 1744 binary chapter diffs\n", + " 3h 26m 40s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 26m 40s PREPARING (O sentence SET): Already prepared\n", + " 3h 26m 40s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 26m 43s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates\n", + " 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix\n", + " 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): 1012009 relevant similarities between 26905 passages\n", + " 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): Composing cliques out of 26905 chunks from 1012009 comparisons\n", + " 3h 26m 48s CLIQUES (O sentence SET M>50 S>70): Composed 537 cliques out of 1000 chunks\n", + " 3h 26m 49s CLIQUES (O sentence SET M>50 S>70): Composed 985 cliques out of 2000 chunks\n", + " 3h 26m 50s CLIQUES (O sentence SET M>50 S>70): Composed 1462 cliques out of 3000 chunks\n", + " 3h 26m 53s CLIQUES (O sentence SET M>50 S>70): Composed 1835 cliques out of 4000 chunks\n", + " 3h 26m 55s CLIQUES (O sentence SET M>50 S>70): Composed 2167 cliques out of 5000 chunks\n", + " 3h 26m 59s CLIQUES (O sentence SET M>50 S>70): Composed 2504 cliques out of 6000 chunks\n", + " 3h 27m 03s CLIQUES (O sentence SET M>50 S>70): Composed 2905 cliques out of 7000 chunks\n", + " 3h 27m 08s CLIQUES (O sentence SET M>50 S>70): Composed 3149 cliques out of 8000 chunks\n", + " 3h 27m 14s CLIQUES (O sentence SET M>50 S>70): Composed 3459 cliques out of 9000 chunks\n", + " 3h 27m 20s CLIQUES (O sentence SET M>50 S>70): Composed 3832 cliques out of 10000 chunks\n", + " 3h 27m 27s CLIQUES (O sentence SET M>50 S>70): Composed 4078 cliques out of 11000 chunks\n", + " 3h 27m 35s CLIQUES (O sentence SET M>50 S>70): Composed 4002 cliques out of 12000 chunks\n", + " 3h 27m 43s CLIQUES (O sentence SET M>50 S>70): Composed 4017 cliques out of 13000 chunks\n", + " 3h 27m 52s CLIQUES (O sentence SET M>50 S>70): Composed 4080 cliques out of 14000 chunks\n", + " 3h 28m 01s CLIQUES (O sentence SET M>50 S>70): Composed 4191 cliques out of 15000 chunks\n", + " 3h 28m 11s CLIQUES (O sentence SET M>50 S>70): Composed 4347 cliques out of 16000 chunks\n", + " 3h 28m 21s CLIQUES (O sentence SET M>50 S>70): Composed 4483 cliques out of 17000 chunks\n", + " 3h 28m 32s CLIQUES (O sentence SET M>50 S>70): Composed 4559 cliques out of 18000 chunks\n", + " 3h 28m 44s CLIQUES (O sentence SET M>50 S>70): Composed 4607 cliques out of 19000 chunks\n", + " 3h 28m 57s CLIQUES (O sentence SET M>50 S>70): Composed 4721 cliques out of 20000 chunks\n", + " 3h 29m 09s CLIQUES (O sentence SET M>50 S>70): Composed 4855 cliques out of 21000 chunks\n", + " 3h 29m 23s CLIQUES (O sentence SET M>50 S>70): Composed 4924 cliques out of 22000 chunks\n", + " 3h 29m 38s CLIQUES (O sentence SET M>50 S>70): Composed 5029 cliques out of 23000 chunks\n", + " 3h 29m 53s CLIQUES (O sentence SET M>50 S>70): Composed 5101 cliques out of 24000 chunks\n", + " 3h 30m 09s CLIQUES (O sentence SET M>50 S>70): Composed 5183 cliques out of 25000 chunks\n", + " 3h 30m 25s CLIQUES (O sentence SET M>50 S>70): Composed 5214 cliques out of 26000 chunks\n", + " 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): 26905 members in 5229 cliques\n", + " 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): Composed and saved 5229 cliques out of 26905 chunks from 1012009 comparisons\n", + " 3h 30m 41s PRINT (O sentence SET M>50 S>70): sorting out cliques\n", + " 3h 30m 42s PRINT (O sentence SET M>50 S>70): formatting 5229 cliques skipping 1820 binary chapter diffs\n", + " 3h 30m 46s PRINT (O sentence SET M>50 S>70): formatted 5229 cliques (105 files) skipping 1820 binary chapter diffs\n", + " 3h 30m 46s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 30m 46s PREPARING (O sentence SET): Already prepared\n", + " 3h 30m 46s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 30m 49s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates\n", + " 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix\n", + " 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): 1332004 relevant similarities between 33410 passages\n", + " 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): Composing cliques out of 33410 chunks from 1332004 comparisons\n", + " 3h 30m 54s CLIQUES (O sentence SET M>50 S>65): Composed 524 cliques out of 1000 chunks\n", + " 3h 30m 55s CLIQUES (O sentence SET M>50 S>65): Composed 1016 cliques out of 2000 chunks\n", + " 3h 30m 56s CLIQUES (O sentence SET M>50 S>65): Composed 1499 cliques out of 3000 chunks\n", + " 3h 30m 59s CLIQUES (O sentence SET M>50 S>65): Composed 1916 cliques out of 4000 chunks\n", + " 3h 31m 02s CLIQUES (O sentence SET M>50 S>65): Composed 2262 cliques out of 5000 chunks\n", + " 3h 31m 05s CLIQUES (O sentence SET M>50 S>65): Composed 2620 cliques out of 6000 chunks\n", + " 3h 31m 10s CLIQUES (O sentence SET M>50 S>65): Composed 2904 cliques out of 7000 chunks\n", + " 3h 31m 15s CLIQUES (O sentence SET M>50 S>65): Composed 3114 cliques out of 8000 chunks\n", + " 3h 31m 21s CLIQUES (O sentence SET M>50 S>65): Composed 3365 cliques out of 9000 chunks\n", + " 3h 31m 27s CLIQUES (O sentence SET M>50 S>65): Composed 3548 cliques out of 10000 chunks\n", + " 3h 31m 33s CLIQUES (O sentence SET M>50 S>65): Composed 3707 cliques out of 11000 chunks\n", + " 3h 31m 41s CLIQUES (O sentence SET M>50 S>65): Composed 3974 cliques out of 12000 chunks\n", + " 3h 31m 49s CLIQUES (O sentence SET M>50 S>65): Composed 4225 cliques out of 13000 chunks\n", + " 3h 31m 58s CLIQUES (O sentence SET M>50 S>65): Composed 4391 cliques out of 14000 chunks\n", + " 3h 32m 07s CLIQUES (O sentence SET M>50 S>65): Composed 4501 cliques out of 15000 chunks\n", + " 3h 32m 17s CLIQUES (O sentence SET M>50 S>65): Composed 4570 cliques out of 16000 chunks\n", + " 3h 32m 27s CLIQUES (O sentence SET M>50 S>65): Composed 4832 cliques out of 17000 chunks\n", + " 3h 32m 38s CLIQUES (O sentence SET M>50 S>65): Composed 5000 cliques out of 18000 chunks\n", + " 3h 32m 50s CLIQUES (O sentence SET M>50 S>65): Composed 5240 cliques out of 19000 chunks\n", + " 3h 33m 01s CLIQUES (O sentence SET M>50 S>65): Composed 5073 cliques out of 20000 chunks\n", + " 3h 33m 12s CLIQUES (O sentence SET M>50 S>65): Composed 4793 cliques out of 21000 chunks\n", + " 3h 33m 23s CLIQUES (O sentence SET M>50 S>65): Composed 4698 cliques out of 22000 chunks\n", + " 3h 33m 35s CLIQUES (O sentence SET M>50 S>65): Composed 4651 cliques out of 23000 chunks\n", + " 3h 33m 47s CLIQUES (O sentence SET M>50 S>65): Composed 4583 cliques out of 24000 chunks\n", + " 3h 34m 00s CLIQUES (O sentence SET M>50 S>65): Composed 4556 cliques out of 25000 chunks\n", + " 3h 34m 14s CLIQUES (O sentence SET M>50 S>65): Composed 4493 cliques out of 26000 chunks\n", + " 3h 34m 29s CLIQUES (O sentence SET M>50 S>65): Composed 4477 cliques out of 27000 chunks\n", + " 3h 34m 46s CLIQUES (O sentence SET M>50 S>65): Composed 4454 cliques out of 28000 chunks\n", + " 3h 35m 02s CLIQUES (O sentence SET M>50 S>65): Composed 4335 cliques out of 29000 chunks\n", + " 3h 35m 18s CLIQUES (O sentence SET M>50 S>65): Composed 4262 cliques out of 30000 chunks\n", + " 3h 35m 35s CLIQUES (O sentence SET M>50 S>65): Composed 4127 cliques out of 31000 chunks\n", + " 3h 35m 54s CLIQUES (O sentence SET M>50 S>65): Composed 4080 cliques out of 32000 chunks\n", + " 3h 36m 14s CLIQUES (O sentence SET M>50 S>65): Composed 4104 cliques out of 33000 chunks\n", + " 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): 33410 members in 4109 cliques\n", + " 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): Composed and saved 4109 cliques out of 33410 chunks from 1332004 comparisons\n", + " 3h 36m 24s PRINT (O sentence SET M>50 S>65): sorting out cliques\n", + " 3h 36m 25s PRINT (O sentence SET M>50 S>65): formatting 4109 cliques skipping 1470 binary chapter diffs\n", + " 3h 36m 28s PRINT (O sentence SET M>50 S>65): formatted 4109 cliques (83 files) skipping 1470 binary chapter diffs\n", + " 3h 36m 28s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 36m 28s PREPARING (O sentence SET): Already prepared\n", + " 3h 36m 28s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 36m 31s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates\n", + " 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix\n", + " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): 1431430 relevant similarities between 38818 passages\n", + " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composing cliques out of 38818 chunks from 1431430 comparisons\n", + " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composed 541 cliques out of 1000 chunks\n", + " 3h 36m 37s CLIQUES (O sentence SET M>50 S>60): Composed 1043 cliques out of 2000 chunks\n", + " 3h 36m 39s CLIQUES (O sentence SET M>50 S>60): Composed 1463 cliques out of 3000 chunks\n", + " 3h 36m 41s CLIQUES (O sentence SET M>50 S>60): Composed 1851 cliques out of 4000 chunks\n", + " 3h 36m 44s CLIQUES (O sentence SET M>50 S>60): Composed 2147 cliques out of 5000 chunks\n", + " 3h 36m 48s CLIQUES (O sentence SET M>50 S>60): Composed 2487 cliques out of 6000 chunks\n", + " 3h 36m 52s CLIQUES (O sentence SET M>50 S>60): Composed 2732 cliques out of 7000 chunks\n", + " 3h 36m 57s CLIQUES (O sentence SET M>50 S>60): Composed 3043 cliques out of 8000 chunks\n", + " 3h 37m 03s CLIQUES (O sentence SET M>50 S>60): Composed 3330 cliques out of 9000 chunks\n", + " 3h 37m 09s CLIQUES (O sentence SET M>50 S>60): Composed 3583 cliques out of 10000 chunks\n", + " 3h 37m 16s CLIQUES (O sentence SET M>50 S>60): Composed 3885 cliques out of 11000 chunks\n", + " 3h 37m 23s CLIQUES (O sentence SET M>50 S>60): Composed 4252 cliques out of 12000 chunks\n", + " 3h 37m 31s CLIQUES (O sentence SET M>50 S>60): Composed 4277 cliques out of 13000 chunks\n", + " 3h 37m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4252 cliques out of 14000 chunks\n", + " 3h 37m 48s CLIQUES (O sentence SET M>50 S>60): Composed 4222 cliques out of 15000 chunks\n", + " 3h 37m 57s CLIQUES (O sentence SET M>50 S>60): Composed 4393 cliques out of 16000 chunks\n", + " 3h 38m 07s CLIQUES (O sentence SET M>50 S>60): Composed 4560 cliques out of 17000 chunks\n", + " 3h 38m 18s CLIQUES (O sentence SET M>50 S>60): Composed 4650 cliques out of 18000 chunks\n", + " 3h 38m 29s CLIQUES (O sentence SET M>50 S>60): Composed 4600 cliques out of 19000 chunks\n", + " 3h 38m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4518 cliques out of 20000 chunks\n", + " 3h 38m 50s CLIQUES (O sentence SET M>50 S>60): Composed 4516 cliques out of 21000 chunks\n", + " 3h 39m 02s CLIQUES (O sentence SET M>50 S>60): Composed 4545 cliques out of 22000 chunks\n", + " 3h 39m 14s CLIQUES (O sentence SET M>50 S>60): Composed 4565 cliques out of 23000 chunks\n", + " 3h 39m 27s CLIQUES (O sentence SET M>50 S>60): Composed 4671 cliques out of 24000 chunks\n", + " 3h 39m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4520 cliques out of 25000 chunks\n", + " 3h 39m 51s CLIQUES (O sentence SET M>50 S>60): Composed 4267 cliques out of 26000 chunks\n", + " 3h 40m 03s CLIQUES (O sentence SET M>50 S>60): Composed 4194 cliques out of 27000 chunks\n", + " 3h 40m 15s CLIQUES (O sentence SET M>50 S>60): Composed 4163 cliques out of 28000 chunks\n", + " 3h 40m 28s CLIQUES (O sentence SET M>50 S>60): Composed 4115 cliques out of 29000 chunks\n", + " 3h 40m 41s CLIQUES (O sentence SET M>50 S>60): Composed 4079 cliques out of 30000 chunks\n", + " 3h 40m 56s CLIQUES (O sentence SET M>50 S>60): Composed 4032 cliques out of 31000 chunks\n", + " 3h 41m 13s CLIQUES (O sentence SET M>50 S>60): Composed 4015 cliques out of 32000 chunks\n", + " 3h 41m 31s CLIQUES (O sentence SET M>50 S>60): Composed 4029 cliques out of 33000 chunks\n", + " 3h 41m 51s CLIQUES (O sentence SET M>50 S>60): Composed 3972 cliques out of 34000 chunks\n", + " 3h 42m 08s CLIQUES (O sentence SET M>50 S>60): Composed 3878 cliques out of 35000 chunks\n", + " 3h 42m 26s CLIQUES (O sentence SET M>50 S>60): Composed 3790 cliques out of 36000 chunks\n", + " 3h 42m 47s CLIQUES (O sentence SET M>50 S>60): Composed 3724 cliques out of 37000 chunks\n", + " 3h 43m 09s CLIQUES (O sentence SET M>50 S>60): Composed 3734 cliques out of 38000 chunks\n", + " 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): 38818 members in 3746 cliques\n", + " 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): Composed and saved 3746 cliques out of 38818 chunks from 1431430 comparisons\n", + " 3h 43m 31s PRINT (O sentence SET M>50 S>60): sorting out cliques\n", + " 3h 43m 32s PRINT (O sentence SET M>50 S>60): formatting 3746 cliques skipping 1382 binary chapter diffs\n", + " 3h 43m 35s PRINT (O sentence SET M>50 S>60): formatted 3746 cliques (75 files) skipping 1382 binary chapter diffs\n", + " 3h 43m 35s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 43m 35s PREPARING (O sentence SET): Already prepared\n", + " 3h 43m 35s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 43m 38s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates\n", + " 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix\n", + " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): 1459649 relevant similarities between 41825 passages\n", + " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composing cliques out of 41825 chunks from 1459649 comparisons\n", + " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composed 547 cliques out of 1000 chunks\n", + " 3h 43m 44s CLIQUES (O sentence SET M>50 S>55): Composed 1057 cliques out of 2000 chunks\n", + " 3h 43m 46s CLIQUES (O sentence SET M>50 S>55): Composed 1503 cliques out of 3000 chunks\n", + " 3h 43m 48s CLIQUES (O sentence SET M>50 S>55): Composed 1917 cliques out of 4000 chunks\n", + " 3h 43m 52s CLIQUES (O sentence SET M>50 S>55): Composed 2303 cliques out of 5000 chunks\n", + " 3h 43m 55s CLIQUES (O sentence SET M>50 S>55): Composed 2658 cliques out of 6000 chunks\n", + " 3h 44m 00s CLIQUES (O sentence SET M>50 S>55): Composed 2824 cliques out of 7000 chunks\n", + " 3h 44m 04s CLIQUES (O sentence SET M>50 S>55): Composed 3086 cliques out of 8000 chunks\n", + " 3h 44m 10s CLIQUES (O sentence SET M>50 S>55): Composed 3169 cliques out of 9000 chunks\n", + " 3h 44m 16s CLIQUES (O sentence SET M>50 S>55): Composed 3323 cliques out of 10000 chunks\n", + " 3h 44m 22s CLIQUES (O sentence SET M>50 S>55): Composed 3473 cliques out of 11000 chunks\n", + " 3h 44m 29s CLIQUES (O sentence SET M>50 S>55): Composed 3459 cliques out of 12000 chunks\n", + " 3h 44m 36s CLIQUES (O sentence SET M>50 S>55): Composed 3499 cliques out of 13000 chunks\n", + " 3h 44m 44s CLIQUES (O sentence SET M>50 S>55): Composed 3650 cliques out of 14000 chunks\n", + " 3h 44m 52s CLIQUES (O sentence SET M>50 S>55): Composed 3810 cliques out of 15000 chunks\n", + " 3h 45m 01s CLIQUES (O sentence SET M>50 S>55): Composed 3790 cliques out of 16000 chunks\n", + " 3h 45m 09s CLIQUES (O sentence SET M>50 S>55): Composed 3780 cliques out of 17000 chunks\n", + " 3h 45m 18s CLIQUES (O sentence SET M>50 S>55): Composed 3760 cliques out of 18000 chunks\n", + " 3h 45m 28s CLIQUES (O sentence SET M>50 S>55): Composed 3878 cliques out of 19000 chunks\n", + " 3h 45m 40s CLIQUES (O sentence SET M>50 S>55): Composed 3996 cliques out of 20000 chunks\n", + " 3h 45m 51s CLIQUES (O sentence SET M>50 S>55): Composed 4035 cliques out of 21000 chunks\n", + " 3h 46m 03s CLIQUES (O sentence SET M>50 S>55): Composed 4055 cliques out of 22000 chunks\n", + " 3h 46m 14s CLIQUES (O sentence SET M>50 S>55): Composed 4031 cliques out of 23000 chunks\n", + " 3h 46m 25s CLIQUES (O sentence SET M>50 S>55): Composed 4074 cliques out of 24000 chunks\n", + " 3h 46m 38s CLIQUES (O sentence SET M>50 S>55): Composed 4143 cliques out of 25000 chunks\n", + " 3h 46m 51s CLIQUES (O sentence SET M>50 S>55): Composed 4176 cliques out of 26000 chunks\n", + " 3h 47m 05s CLIQUES (O sentence SET M>50 S>55): Composed 4303 cliques out of 27000 chunks\n", + " 3h 47m 19s CLIQUES (O sentence SET M>50 S>55): Composed 4180 cliques out of 28000 chunks\n", + " 3h 47m 31s CLIQUES (O sentence SET M>50 S>55): Composed 3952 cliques out of 29000 chunks\n", + " 3h 47m 43s CLIQUES (O sentence SET M>50 S>55): Composed 3894 cliques out of 30000 chunks\n", + " 3h 47m 57s CLIQUES (O sentence SET M>50 S>55): Composed 3885 cliques out of 31000 chunks\n", + " 3h 48m 10s CLIQUES (O sentence SET M>50 S>55): Composed 3846 cliques out of 32000 chunks\n", + " 3h 48m 26s CLIQUES (O sentence SET M>50 S>55): Composed 3817 cliques out of 33000 chunks\n", + " 3h 48m 41s CLIQUES (O sentence SET M>50 S>55): Composed 3776 cliques out of 34000 chunks\n", + " 3h 48m 59s CLIQUES (O sentence SET M>50 S>55): Composed 3761 cliques out of 35000 chunks\n", + " 3h 49m 19s CLIQUES (O sentence SET M>50 S>55): Composed 3775 cliques out of 36000 chunks\n", + " 3h 49m 40s CLIQUES (O sentence SET M>50 S>55): Composed 3718 cliques out of 37000 chunks\n", + " 3h 49m 58s CLIQUES (O sentence SET M>50 S>55): Composed 3624 cliques out of 38000 chunks\n", + " 3h 50m 18s CLIQUES (O sentence SET M>50 S>55): Composed 3539 cliques out of 39000 chunks\n", + " 3h 50m 39s CLIQUES (O sentence SET M>50 S>55): Composed 3474 cliques out of 40000 chunks\n", + " 3h 51m 04s CLIQUES (O sentence SET M>50 S>55): Composed 3485 cliques out of 41000 chunks\n", + " 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): 41825 members in 3497 cliques\n", + " 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): Composed and saved 3497 cliques out of 41825 chunks from 1459649 comparisons\n", + " 3h 51m 26s PRINT (O sentence SET M>50 S>55): sorting out cliques\n", + " 3h 51m 27s PRINT (O sentence SET M>50 S>55): formatting 3497 cliques skipping 1340 binary chapter diffs\n", + " 3h 51m 30s PRINT (O sentence SET M>50 S>55): formatted 3497 cliques (70 files) skipping 1340 binary chapter diffs\n", + " 3h 51m 30s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 51m 30s PREPARING (O sentence SET): Already prepared\n", + " 3h 51m 30s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", + " 3h 51m 33s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", + " 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates\n", + " 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix\n", + " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): 3958946 relevant similarities between 53097 passages\n", + " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composing cliques out of 53097 chunks from 3958946 comparisons\n", + " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composed 571 cliques out of 1000 chunks\n", + " 3h 51m 42s CLIQUES (O sentence SET M>50 S>50): Composed 1008 cliques out of 2000 chunks\n", + " 3h 51m 44s CLIQUES (O sentence SET M>50 S>50): Composed 1426 cliques out of 3000 chunks\n", + " 3h 51m 46s CLIQUES (O sentence SET M>50 S>50): Composed 1736 cliques out of 4000 chunks\n", + " 3h 51m 49s CLIQUES (O sentence SET M>50 S>50): Composed 2052 cliques out of 5000 chunks\n", + " 3h 51m 52s CLIQUES (O sentence SET M>50 S>50): Composed 2192 cliques out of 6000 chunks\n", + " 3h 51m 56s CLIQUES (O sentence SET M>50 S>50): Composed 2423 cliques out of 7000 chunks\n", + " 3h 52m 01s CLIQUES (O sentence SET M>50 S>50): Composed 2614 cliques out of 8000 chunks\n", + " 3h 52m 06s CLIQUES (O sentence SET M>50 S>50): Composed 2756 cliques out of 9000 chunks\n", + " 3h 52m 11s CLIQUES (O sentence SET M>50 S>50): Composed 2808 cliques out of 10000 chunks\n", + " 3h 52m 17s CLIQUES (O sentence SET M>50 S>50): Composed 3000 cliques out of 11000 chunks\n", + " 3h 52m 22s CLIQUES (O sentence SET M>50 S>50): Composed 3004 cliques out of 12000 chunks\n", + " 3h 52m 29s CLIQUES (O sentence SET M>50 S>50): Composed 2995 cliques out of 13000 chunks\n", + " 3h 52m 35s CLIQUES (O sentence SET M>50 S>50): Composed 3110 cliques out of 14000 chunks\n", + " 3h 52m 42s CLIQUES (O sentence SET M>50 S>50): Composed 3191 cliques out of 15000 chunks\n", + " 3h 52m 50s CLIQUES (O sentence SET M>50 S>50): Composed 3179 cliques out of 16000 chunks\n", + " 3h 52m 57s CLIQUES (O sentence SET M>50 S>50): Composed 3148 cliques out of 17000 chunks\n", + " 3h 53m 06s CLIQUES (O sentence SET M>50 S>50): Composed 3260 cliques out of 18000 chunks\n", + " 3h 53m 14s CLIQUES (O sentence SET M>50 S>50): Composed 3363 cliques out of 19000 chunks\n", + " 3h 53m 24s CLIQUES (O sentence SET M>50 S>50): Composed 3416 cliques out of 20000 chunks\n", + " 3h 53m 31s CLIQUES (O sentence SET M>50 S>50): Composed 3274 cliques out of 21000 chunks\n", + " 3h 53m 40s CLIQUES (O sentence SET M>50 S>50): Composed 3166 cliques out of 22000 chunks\n", + " 3h 53m 48s CLIQUES (O sentence SET M>50 S>50): Composed 3062 cliques out of 23000 chunks\n", + " 3h 53m 58s CLIQUES (O sentence SET M>50 S>50): Composed 3140 cliques out of 24000 chunks\n", + " 3h 54m 07s CLIQUES (O sentence SET M>50 S>50): Composed 3112 cliques out of 25000 chunks\n", + " 3h 54m 17s CLIQUES (O sentence SET M>50 S>50): Composed 3145 cliques out of 26000 chunks\n", + " 3h 54m 29s CLIQUES (O sentence SET M>50 S>50): Composed 3255 cliques out of 27000 chunks\n", + " 3h 54m 40s CLIQUES (O sentence SET M>50 S>50): Composed 3246 cliques out of 28000 chunks\n", + " 3h 54m 50s CLIQUES (O sentence SET M>50 S>50): Composed 3125 cliques out of 29000 chunks\n", + " 3h 54m 59s CLIQUES (O sentence SET M>50 S>50): Composed 3010 cliques out of 30000 chunks\n", + " 3h 55m 09s CLIQUES (O sentence SET M>50 S>50): Composed 2938 cliques out of 31000 chunks\n", + " 3h 55m 21s CLIQUES (O sentence SET M>50 S>50): Composed 2956 cliques out of 32000 chunks\n", + " 3h 55m 31s CLIQUES (O sentence SET M>50 S>50): Composed 2928 cliques out of 33000 chunks\n", + " 3h 55m 44s CLIQUES (O sentence SET M>50 S>50): Composed 2962 cliques out of 34000 chunks\n", + " 3h 55m 59s CLIQUES (O sentence SET M>50 S>50): Composed 3076 cliques out of 35000 chunks\n", + " 3h 56m 13s CLIQUES (O sentence SET M>50 S>50): Composed 3081 cliques out of 36000 chunks\n", + " 3h 56m 23s CLIQUES (O sentence SET M>50 S>50): Composed 2934 cliques out of 37000 chunks\n", + " 3h 56m 34s CLIQUES (O sentence SET M>50 S>50): Composed 2737 cliques out of 38000 chunks\n", + " 3h 56m 45s CLIQUES (O sentence SET M>50 S>50): Composed 2647 cliques out of 39000 chunks\n", + " 3h 56m 57s CLIQUES (O sentence SET M>50 S>50): Composed 2631 cliques out of 40000 chunks\n", + " 3h 57m 09s CLIQUES (O sentence SET M>50 S>50): Composed 2531 cliques out of 41000 chunks\n", + " 3h 57m 21s CLIQUES (O sentence SET M>50 S>50): Composed 2481 cliques out of 42000 chunks\n", + " 3h 57m 34s CLIQUES (O sentence SET M>50 S>50): Composed 2438 cliques out of 43000 chunks\n", + " 3h 57m 47s CLIQUES (O sentence SET M>50 S>50): Composed 2401 cliques out of 44000 chunks\n", + " 3h 57m 55s CLIQUES (O sentence SET M>50 S>50): Composed 1968 cliques out of 45000 chunks\n", + " 3h 58m 03s CLIQUES (O sentence SET M>50 S>50): Composed 1819 cliques out of 46000 chunks\n", + " 3h 58m 09s CLIQUES (O sentence SET M>50 S>50): Composed 1762 cliques out of 47000 chunks\n", + " 3h 58m 19s CLIQUES (O sentence SET M>50 S>50): Composed 1653 cliques out of 48000 chunks\n", + " 3h 58m 27s CLIQUES (O sentence SET M>50 S>50): Composed 1539 cliques out of 49000 chunks\n", + " 3h 58m 37s CLIQUES (O sentence SET M>50 S>50): Composed 1450 cliques out of 50000 chunks\n", + " 3h 58m 47s CLIQUES (O sentence SET M>50 S>50): Composed 1331 cliques out of 51000 chunks\n", + " 3h 58m 54s CLIQUES (O sentence SET M>50 S>50): Composed 1237 cliques out of 52000 chunks\n", + " 3h 59m 05s CLIQUES (O sentence SET M>50 S>50): Composed 1176 cliques out of 53000 chunks\n", + " 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): 53097 members in 1172 cliques\n", + " 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): Composed and saved 1172 cliques out of 53097 chunks from 3958946 comparisons\n", + " 3h 59m 07s PRINT (O sentence SET M>50 S>50): sorting out cliques\n", + " 3h 59m 08s PRINT (O sentence SET M>50 S>50): formatting 1172 cliques skipping 470 binary chapter diffs\n", + " 3h 59m 10s PRINT (O sentence SET M>50 S>50): formatted 1172 cliques (24 files) skipping 470 binary chapter diffs\n", + " 3h 59m 10s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 3h 59m 10s PREPARING (O sentence LCS)\n", + " 3h 59m 10s PREPARING (O sentence LCS): Done 63570 chunks.\n", + " 3h 59m 10s SIMILARITY (O sentence LCS M>60): Computing 2020 M (2020540665) comparisons and saving entries in matrix\n", + " 3h 59m 35s SIMILARITY (O sentence LCS M>60): Computed 20 M comparisons and saved 125670 entries in matrix\n", + " 4h 00m 03s SIMILARITY (O sentence LCS M>60): Computed 40 M comparisons and saved 204624 entries in matrix\n", + " 4h 00m 28s SIMILARITY (O sentence LCS M>60): Computed 60 M comparisons and saved 308337 entries in matrix\n", + " 4h 00m 51s SIMILARITY (O sentence LCS M>60): Computed 80 M comparisons and saved 449060 entries in matrix\n", + " 4h 01m 15s SIMILARITY (O sentence LCS M>60): Computed 101 M comparisons and saved 569966 entries in matrix\n", + " 4h 01m 38s SIMILARITY (O sentence LCS M>60): Computed 121 M comparisons and saved 713424 entries in matrix\n", + " 4h 02m 00s SIMILARITY (O sentence LCS M>60): Computed 141 M comparisons and saved 854622 entries in matrix\n", + " 4h 02m 23s SIMILARITY (O sentence LCS M>60): Computed 161 M comparisons and saved 1003547 entries in matrix\n", + " 4h 02m 47s SIMILARITY (O sentence LCS M>60): Computed 181 M comparisons and saved 1128089 entries in matrix\n", + " 4h 03m 11s SIMILARITY (O sentence LCS M>60): Computed 202 M comparisons and saved 1229587 entries in matrix\n", + " 4h 03m 34s SIMILARITY (O sentence LCS M>60): Computed 222 M comparisons and saved 1346415 entries in matrix\n", + " 4h 03m 58s SIMILARITY (O sentence LCS M>60): Computed 242 M comparisons and saved 1461744 entries in matrix\n", + " 4h 04m 21s SIMILARITY (O sentence LCS M>60): Computed 262 M comparisons and saved 1586425 entries in matrix\n", + " 4h 04m 45s SIMILARITY (O sentence LCS M>60): Computed 282 M comparisons and saved 1687522 entries in matrix\n", + " 4h 05m 07s SIMILARITY (O sentence LCS M>60): Computed 303 M comparisons and saved 1866431 entries in matrix\n", + " 4h 05m 32s SIMILARITY (O sentence LCS M>60): Computed 323 M comparisons and saved 2001292 entries in matrix\n", + " 4h 05m 58s SIMILARITY (O sentence LCS M>60): Computed 343 M comparisons and saved 2113727 entries in matrix\n", + " 4h 06m 23s SIMILARITY (O sentence LCS M>60): Computed 363 M comparisons and saved 2217744 entries in matrix\n", + " 4h 06m 48s SIMILARITY (O sentence LCS M>60): Computed 383 M comparisons and saved 2338942 entries in matrix\n", + " 4h 07m 12s SIMILARITY (O sentence LCS M>60): Computed 404 M comparisons and saved 2429384 entries in matrix\n", + " 4h 07m 41s SIMILARITY (O sentence LCS M>60): Computed 424 M comparisons and saved 2474476 entries in matrix\n", + " 4h 08m 06s SIMILARITY (O sentence LCS M>60): Computed 444 M comparisons and saved 2597349 entries in matrix\n", + " 4h 08m 37s SIMILARITY (O sentence LCS M>60): Computed 464 M comparisons and saved 2640723 entries in matrix\n", + " 4h 09m 05s SIMILARITY (O sentence LCS M>60): Computed 484 M comparisons and saved 2700790 entries in matrix\n", + " 4h 09m 33s SIMILARITY (O sentence LCS M>60): Computed 505 M comparisons and saved 2776579 entries in matrix\n", + " 4h 09m 59s SIMILARITY (O sentence LCS M>60): Computed 525 M comparisons and saved 2855300 entries in matrix\n", + " 4h 10m 25s SIMILARITY (O sentence LCS M>60): Computed 545 M comparisons and saved 2950778 entries in matrix\n", + " 4h 10m 52s SIMILARITY (O sentence LCS M>60): Computed 565 M comparisons and saved 3049115 entries in matrix\n", + " 4h 11m 18s SIMILARITY (O sentence LCS M>60): Computed 585 M comparisons and saved 3127277 entries in matrix\n", + " 4h 11m 47s SIMILARITY (O sentence LCS M>60): Computed 606 M comparisons and saved 3195359 entries in matrix\n", + " 4h 12m 16s SIMILARITY (O sentence LCS M>60): Computed 626 M comparisons and saved 3280692 entries in matrix\n", + " 4h 12m 42s SIMILARITY (O sentence LCS M>60): Computed 646 M comparisons and saved 3395665 entries in matrix\n", + " 4h 13m 08s SIMILARITY (O sentence LCS M>60): Computed 666 M comparisons and saved 3483599 entries in matrix\n", + " 4h 13m 31s SIMILARITY (O sentence LCS M>60): Computed 686 M comparisons and saved 3606181 entries in matrix\n", + " 4h 14m 01s SIMILARITY (O sentence LCS M>60): Computed 707 M comparisons and saved 3668148 entries in matrix\n", + " 4h 14m 29s SIMILARITY (O sentence LCS M>60): Computed 727 M comparisons and saved 3732591 entries in matrix\n", + " 4h 14m 57s SIMILARITY (O sentence LCS M>60): Computed 747 M comparisons and saved 3835399 entries in matrix\n", + " 4h 15m 25s SIMILARITY (O sentence LCS M>60): Computed 767 M comparisons and saved 3922964 entries in matrix\n", + " 4h 15m 52s SIMILARITY (O sentence LCS M>60): Computed 788 M comparisons and saved 3999296 entries in matrix\n", + " 4h 16m 16s SIMILARITY (O sentence LCS M>60): Computed 808 M comparisons and saved 4096313 entries in matrix\n", + " 4h 16m 43s SIMILARITY (O sentence LCS M>60): Computed 828 M comparisons and saved 4197206 entries in matrix\n", + " 4h 17m 08s SIMILARITY (O sentence LCS M>60): Computed 848 M comparisons and saved 4290970 entries in matrix\n", + " 4h 17m 33s SIMILARITY (O sentence LCS M>60): Computed 868 M comparisons and saved 4403470 entries in matrix\n", + " 4h 18m 00s SIMILARITY (O sentence LCS M>60): Computed 889 M comparisons and saved 4489840 entries in matrix\n", + " 4h 18m 31s SIMILARITY (O sentence LCS M>60): Computed 909 M comparisons and saved 4540049 entries in matrix\n", + " 4h 18m 57s SIMILARITY (O sentence LCS M>60): Computed 929 M comparisons and saved 4641605 entries in matrix\n", + " 4h 19m 21s SIMILARITY (O sentence LCS M>60): Computed 949 M comparisons and saved 4763900 entries in matrix\n", + " 4h 19m 44s SIMILARITY (O sentence LCS M>60): Computed 969 M comparisons and saved 4867130 entries in matrix\n", + " 4h 20m 07s SIMILARITY (O sentence LCS M>60): Computed 990 M comparisons and saved 5006563 entries in matrix\n", + " 4h 20m 30s SIMILARITY (O sentence LCS M>60): Computed 1010 M comparisons and saved 5154413 entries in matrix\n", + " 4h 20m 54s SIMILARITY (O sentence LCS M>60): Computed 1030 M comparisons and saved 5266887 entries in matrix\n", + " 4h 21m 18s SIMILARITY (O sentence LCS M>60): Computed 1050 M comparisons and saved 5389996 entries in matrix\n", + " 4h 21m 41s SIMILARITY (O sentence LCS M>60): Computed 1070 M comparisons and saved 5529990 entries in matrix\n", + " 4h 22m 04s SIMILARITY (O sentence LCS M>60): Computed 1091 M comparisons and saved 5664009 entries in matrix\n", + " 4h 22m 27s SIMILARITY (O sentence LCS M>60): Computed 1111 M comparisons and saved 5779132 entries in matrix\n", + " 4h 22m 50s SIMILARITY (O sentence LCS M>60): Computed 1131 M comparisons and saved 5897038 entries in matrix\n", + " 4h 23m 13s SIMILARITY (O sentence LCS M>60): Computed 1151 M comparisons and saved 6019779 entries in matrix\n", + " 4h 23m 37s SIMILARITY (O sentence LCS M>60): Computed 1171 M comparisons and saved 6123416 entries in matrix\n", + " 4h 24m 01s SIMILARITY (O sentence LCS M>60): Computed 1192 M comparisons and saved 6226770 entries in matrix\n", + " 4h 24m 24s SIMILARITY (O sentence LCS M>60): Computed 1212 M comparisons and saved 6372406 entries in matrix\n", + " 4h 24m 47s SIMILARITY (O sentence LCS M>60): Computed 1232 M comparisons and saved 6489159 entries in matrix\n", + " 4h 25m 11s SIMILARITY (O sentence LCS M>60): Computed 1252 M comparisons and saved 6591841 entries in matrix\n", + " 4h 25m 35s SIMILARITY (O sentence LCS M>60): Computed 1272 M comparisons and saved 6724627 entries in matrix\n", + " 4h 26m 05s SIMILARITY (O sentence LCS M>60): Computed 1293 M comparisons and saved 6776806 entries in matrix\n", + " 4h 26m 31s SIMILARITY (O sentence LCS M>60): Computed 1313 M comparisons and saved 6870691 entries in matrix\n", + " 4h 26m 53s SIMILARITY (O sentence LCS M>60): Computed 1333 M comparisons and saved 7016662 entries in matrix\n", + " 4h 27m 16s SIMILARITY (O sentence LCS M>60): Computed 1353 M comparisons and saved 7160786 entries in matrix\n", + " 4h 27m 37s SIMILARITY (O sentence LCS M>60): Computed 1373 M comparisons and saved 7304076 entries in matrix\n", + " 4h 27m 58s SIMILARITY (O sentence LCS M>60): Computed 1394 M comparisons and saved 7442832 entries in matrix\n", + " 4h 28m 25s SIMILARITY (O sentence LCS M>60): Computed 1414 M comparisons and saved 7518860 entries in matrix\n", + " 4h 28m 51s SIMILARITY (O sentence LCS M>60): Computed 1434 M comparisons and saved 7608922 entries in matrix\n", + " 4h 29m 15s SIMILARITY (O sentence LCS M>60): Computed 1454 M comparisons and saved 7700130 entries in matrix\n", + " 4h 29m 39s SIMILARITY (O sentence LCS M>60): Computed 1474 M comparisons and saved 7774516 entries in matrix\n", + " 4h 30m 02s SIMILARITY (O sentence LCS M>60): Computed 1495 M comparisons and saved 7856910 entries in matrix\n", + " 4h 30m 25s SIMILARITY (O sentence LCS M>60): Computed 1515 M comparisons and saved 7944698 entries in matrix\n", + " 4h 30m 46s SIMILARITY (O sentence LCS M>60): Computed 1535 M comparisons and saved 8045412 entries in matrix\n", + " 4h 31m 08s SIMILARITY (O sentence LCS M>60): Computed 1555 M comparisons and saved 8141211 entries in matrix\n", + " 4h 31m 31s SIMILARITY (O sentence LCS M>60): Computed 1576 M comparisons and saved 8245481 entries in matrix\n", + " 4h 31m 54s SIMILARITY (O sentence LCS M>60): Computed 1596 M comparisons and saved 8373793 entries in matrix\n", + " 4h 32m 17s SIMILARITY (O sentence LCS M>60): Computed 1616 M comparisons and saved 8494098 entries in matrix\n", + " 4h 32m 42s SIMILARITY (O sentence LCS M>60): Computed 1636 M comparisons and saved 8618198 entries in matrix\n", + " 4h 33m 08s SIMILARITY (O sentence LCS M>60): Computed 1656 M comparisons and saved 8731397 entries in matrix\n", + " 4h 33m 36s SIMILARITY (O sentence LCS M>60): Computed 1677 M comparisons and saved 8822721 entries in matrix\n", + " 4h 34m 01s SIMILARITY (O sentence LCS M>60): Computed 1697 M comparisons and saved 8909346 entries in matrix\n", + " 4h 34m 24s SIMILARITY (O sentence LCS M>60): Computed 1717 M comparisons and saved 9026706 entries in matrix\n", + " 4h 34m 48s SIMILARITY (O sentence LCS M>60): Computed 1737 M comparisons and saved 9129077 entries in matrix\n", + " 4h 35m 11s SIMILARITY (O sentence LCS M>60): Computed 1757 M comparisons and saved 9217047 entries in matrix\n", + " 4h 35m 35s SIMILARITY (O sentence LCS M>60): Computed 1778 M comparisons and saved 9310495 entries in matrix\n", + " 4h 36m 00s SIMILARITY (O sentence LCS M>60): Computed 1798 M comparisons and saved 9389003 entries in matrix\n", + " 4h 36m 23s SIMILARITY (O sentence LCS M>60): Computed 1818 M comparisons and saved 9478471 entries in matrix\n", + " 4h 36m 46s SIMILARITY (O sentence LCS M>60): Computed 1838 M comparisons and saved 9562926 entries in matrix\n", + " 4h 37m 09s SIMILARITY (O sentence LCS M>60): Computed 1858 M comparisons and saved 9671444 entries in matrix\n", + " 4h 37m 32s SIMILARITY (O sentence LCS M>60): Computed 1879 M comparisons and saved 9752741 entries in matrix\n", + " 4h 37m 54s SIMILARITY (O sentence LCS M>60): Computed 1899 M comparisons and saved 9833579 entries in matrix\n", + " 4h 38m 18s SIMILARITY (O sentence LCS M>60): Computed 1919 M comparisons and saved 9906769 entries in matrix\n", + " 4h 38m 41s SIMILARITY (O sentence LCS M>60): Computed 1939 M comparisons and saved 9986245 entries in matrix\n", + " 4h 39m 06s SIMILARITY (O sentence LCS M>60): Computed 1959 M comparisons and saved 10067146 entries in matrix\n", + " 4h 39m 31s SIMILARITY (O sentence LCS M>60): Computed 1980 M comparisons and saved 10128099 entries in matrix\n", + " 4h 39m 59s SIMILARITY (O sentence LCS M>60): Computed 2000 M comparisons and saved 10200826 entries in matrix\n", + " 4h 40m 31s SIMILARITY (O sentence LCS M>60): Computed 2020 M comparisons and saved 10279985 entries in matrix\n", + " 4h 40m 40s SIMILARITY (O sentence LCS M>60): Computed 2020 M (2020540665) comparisons and saved 10279985 entries in matrix\n", + " 4h 40m 51s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates\n", + " 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix\n", + " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): 903431 relevant similarities between 17533 passages\n", + " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composing cliques out of 17533 chunks from 903431 comparisons\n", + " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composed 469 cliques out of 1000 chunks\n", + " 4h 41m 01s CLIQUES (O sentence LCS M>60 S>100): Composed 877 cliques out of 2000 chunks\n", + " 4h 41m 03s CLIQUES (O sentence LCS M>60 S>100): Composed 1228 cliques out of 3000 chunks\n", + " 4h 41m 05s CLIQUES (O sentence LCS M>60 S>100): Composed 1612 cliques out of 4000 chunks\n", + " 4h 41m 08s CLIQUES (O sentence LCS M>60 S>100): Composed 1997 cliques out of 5000 chunks\n", + " 4h 41m 12s CLIQUES (O sentence LCS M>60 S>100): Composed 2303 cliques out of 6000 chunks\n", + " 4h 41m 17s CLIQUES (O sentence LCS M>60 S>100): Composed 2599 cliques out of 7000 chunks\n", + " 4h 41m 22s CLIQUES (O sentence LCS M>60 S>100): Composed 2880 cliques out of 8000 chunks\n", + " 4h 41m 28s CLIQUES (O sentence LCS M>60 S>100): Composed 3109 cliques out of 9000 chunks\n", + " 4h 41m 34s CLIQUES (O sentence LCS M>60 S>100): Composed 3290 cliques out of 10000 chunks\n", + " 4h 41m 41s CLIQUES (O sentence LCS M>60 S>100): Composed 3478 cliques out of 11000 chunks\n", + " 4h 41m 49s CLIQUES (O sentence LCS M>60 S>100): Composed 3590 cliques out of 12000 chunks\n", + " 4h 41m 58s CLIQUES (O sentence LCS M>60 S>100): Composed 3665 cliques out of 13000 chunks\n", + " 4h 42m 07s CLIQUES (O sentence LCS M>60 S>100): Composed 3777 cliques out of 14000 chunks\n", + " 4h 42m 17s CLIQUES (O sentence LCS M>60 S>100): Composed 3878 cliques out of 15000 chunks\n", + " 4h 42m 27s CLIQUES (O sentence LCS M>60 S>100): Composed 3942 cliques out of 16000 chunks\n", + " 4h 42m 38s CLIQUES (O sentence LCS M>60 S>100): Composed 3970 cliques out of 17000 chunks\n", + " 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): 17533 members in 3978 cliques\n", + " 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): Composed and saved 3978 cliques out of 17533 chunks from 903431 comparisons\n", + " 4h 42m 45s PRINT (O sentence LCS M>60 S>100): sorting out cliques\n", + " 4h 42m 45s PRINT (O sentence LCS M>60 S>100): formatting 3978 cliques skipping 1364 binary chapter diffs\n", + " 4h 42m 48s PRINT (O sentence LCS M>60 S>100): formatted 3978 cliques (80 files) skipping 1364 binary chapter diffs\n", + " 4h 42m 48s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 4h 42m 48s PREPARING (O sentence LCS): Already prepared\n", + " 4h 42m 48s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 4h 42m 57s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates\n", + " 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix\n", + " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): 904132 relevant similarities between 18091 passages\n", + " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composing cliques out of 18091 chunks from 904132 comparisons\n", + " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composed 478 cliques out of 1000 chunks\n", + " 4h 43m 06s CLIQUES (O sentence LCS M>60 S>95): Composed 855 cliques out of 2000 chunks\n", + " 4h 43m 08s CLIQUES (O sentence LCS M>60 S>95): Composed 1280 cliques out of 3000 chunks\n", + " 4h 43m 10s CLIQUES (O sentence LCS M>60 S>95): Composed 1680 cliques out of 4000 chunks\n", + " 4h 43m 13s CLIQUES (O sentence LCS M>60 S>95): Composed 2032 cliques out of 5000 chunks\n", + " 4h 43m 17s CLIQUES (O sentence LCS M>60 S>95): Composed 2411 cliques out of 6000 chunks\n", + " 4h 43m 22s CLIQUES (O sentence LCS M>60 S>95): Composed 2654 cliques out of 7000 chunks\n", + " 4h 43m 27s CLIQUES (O sentence LCS M>60 S>95): Composed 2966 cliques out of 8000 chunks\n", + " 4h 43m 33s CLIQUES (O sentence LCS M>60 S>95): Composed 3253 cliques out of 9000 chunks\n", + " 4h 43m 39s CLIQUES (O sentence LCS M>60 S>95): Composed 3431 cliques out of 10000 chunks\n", + " 4h 43m 47s CLIQUES (O sentence LCS M>60 S>95): Composed 3606 cliques out of 11000 chunks\n", + " 4h 43m 55s CLIQUES (O sentence LCS M>60 S>95): Composed 3776 cliques out of 12000 chunks\n", + " 4h 44m 03s CLIQUES (O sentence LCS M>60 S>95): Composed 3861 cliques out of 13000 chunks\n", + " 4h 44m 13s CLIQUES (O sentence LCS M>60 S>95): Composed 3972 cliques out of 14000 chunks\n", + " 4h 44m 23s CLIQUES (O sentence LCS M>60 S>95): Composed 4063 cliques out of 15000 chunks\n", + " 4h 44m 33s CLIQUES (O sentence LCS M>60 S>95): Composed 4152 cliques out of 16000 chunks\n", + " 4h 44m 44s CLIQUES (O sentence LCS M>60 S>95): Composed 4193 cliques out of 17000 chunks\n", + " 4h 44m 56s CLIQUES (O sentence LCS M>60 S>95): Composed 4217 cliques out of 18000 chunks\n", + " 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): 18091 members in 4218 cliques\n", + " 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): Composed and saved 4218 cliques out of 18091 chunks from 904132 comparisons\n", + " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): sorting out cliques\n", + " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): formatting 4218 cliques involving 1419 binary chapter diffs\n", + " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): Chapter diffs needed: 1419\n", + " 4h 45m 08s PRINT (O sentence LCS M>60 S>95): Chapter diffs: 67 newly created and 1352 already existing\n", + " 4h 45m 11s PRINT (O sentence LCS M>60 S>95): formatted 4218 cliques (85 files) involving 1419 binary chapter diffs\n", + " 4h 45m 11s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 4h 45m 11s PREPARING (O sentence LCS): Already prepared\n", + " 4h 45m 11s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 4h 45m 21s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates\n", + " 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix\n", + " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): 915208 relevant similarities between 21261 passages\n", + " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composing cliques out of 21261 chunks from 915208 comparisons\n", + " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composed 483 cliques out of 1000 chunks\n", + " 4h 45m 29s CLIQUES (O sentence LCS M>60 S>90): Composed 936 cliques out of 2000 chunks\n", + " 4h 45m 31s CLIQUES (O sentence LCS M>60 S>90): Composed 1287 cliques out of 3000 chunks\n", + " 4h 45m 33s CLIQUES (O sentence LCS M>60 S>90): Composed 1691 cliques out of 4000 chunks\n", + " 4h 45m 36s CLIQUES (O sentence LCS M>60 S>90): Composed 2062 cliques out of 5000 chunks\n", + " 4h 45m 40s CLIQUES (O sentence LCS M>60 S>90): Composed 2407 cliques out of 6000 chunks\n", + " 4h 45m 45s CLIQUES (O sentence LCS M>60 S>90): Composed 2762 cliques out of 7000 chunks\n", + " 4h 45m 50s CLIQUES (O sentence LCS M>60 S>90): Composed 3103 cliques out of 8000 chunks\n", + " 4h 45m 56s CLIQUES (O sentence LCS M>60 S>90): Composed 3332 cliques out of 9000 chunks\n", + " 4h 46m 03s CLIQUES (O sentence LCS M>60 S>90): Composed 3634 cliques out of 10000 chunks\n", + " 4h 46m 10s CLIQUES (O sentence LCS M>60 S>90): Composed 3904 cliques out of 11000 chunks\n", + " 4h 46m 18s CLIQUES (O sentence LCS M>60 S>90): Composed 4127 cliques out of 12000 chunks\n", + " 4h 46m 27s CLIQUES (O sentence LCS M>60 S>90): Composed 4303 cliques out of 13000 chunks\n", + " 4h 46m 37s CLIQUES (O sentence LCS M>60 S>90): Composed 4451 cliques out of 14000 chunks\n", + " 4h 46m 46s CLIQUES (O sentence LCS M>60 S>90): Composed 4601 cliques out of 15000 chunks\n", + " 4h 46m 57s CLIQUES (O sentence LCS M>60 S>90): Composed 4684 cliques out of 16000 chunks\n", + " 4h 47m 08s CLIQUES (O sentence LCS M>60 S>90): Composed 4786 cliques out of 17000 chunks\n", + " 4h 47m 21s CLIQUES (O sentence LCS M>60 S>90): Composed 4866 cliques out of 18000 chunks\n", + " 4h 47m 33s CLIQUES (O sentence LCS M>60 S>90): Composed 4929 cliques out of 19000 chunks\n", + " 4h 47m 47s CLIQUES (O sentence LCS M>60 S>90): Composed 4970 cliques out of 20000 chunks\n", + " 4h 48m 01s CLIQUES (O sentence LCS M>60 S>90): Composed 4995 cliques out of 21000 chunks\n", + " 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): 21261 members in 4997 cliques\n", + " 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): Composed and saved 4997 cliques out of 21261 chunks from 915208 comparisons\n", + " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): sorting out cliques\n", + " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): formatting 4997 cliques involving 1703 binary chapter diffs\n", + " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1703\n", + " 4h 48m 06s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 2 newly created and 1701 already existing\n", + " 4h 48m 09s PRINT (O sentence LCS M>60 S>90): formatted 4997 cliques (100 files) involving 1703 binary chapter diffs\n", + " 4h 48m 09s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 4h 48m 09s PREPARING (O sentence LCS): Already prepared\n", + " 4h 48m 09s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 4h 48m 19s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates\n", + " 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix\n", + " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): 980755 relevant similarities between 26488 passages\n", + " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composing cliques out of 26488 chunks from 980755 comparisons\n", + " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composed 488 cliques out of 1000 chunks\n", + " 4h 48m 28s CLIQUES (O sentence LCS M>60 S>85): Composed 940 cliques out of 2000 chunks\n", + " 4h 48m 30s CLIQUES (O sentence LCS M>60 S>85): Composed 1315 cliques out of 3000 chunks\n", + " 4h 48m 32s CLIQUES (O sentence LCS M>60 S>85): Composed 1672 cliques out of 4000 chunks\n", + " 4h 48m 35s CLIQUES (O sentence LCS M>60 S>85): Composed 2063 cliques out of 5000 chunks\n", + " 4h 48m 39s CLIQUES (O sentence LCS M>60 S>85): Composed 2408 cliques out of 6000 chunks\n", + " 4h 48m 44s CLIQUES (O sentence LCS M>60 S>85): Composed 2693 cliques out of 7000 chunks\n", + " 4h 48m 49s CLIQUES (O sentence LCS M>60 S>85): Composed 2956 cliques out of 8000 chunks\n", + " 4h 48m 55s CLIQUES (O sentence LCS M>60 S>85): Composed 3253 cliques out of 9000 chunks\n", + " 4h 49m 02s CLIQUES (O sentence LCS M>60 S>85): Composed 3542 cliques out of 10000 chunks\n", + " 4h 49m 09s CLIQUES (O sentence LCS M>60 S>85): Composed 3728 cliques out of 11000 chunks\n", + " 4h 49m 17s CLIQUES (O sentence LCS M>60 S>85): Composed 3912 cliques out of 12000 chunks\n", + " 4h 49m 26s CLIQUES (O sentence LCS M>60 S>85): Composed 4083 cliques out of 13000 chunks\n", + " 4h 49m 36s CLIQUES (O sentence LCS M>60 S>85): Composed 4328 cliques out of 14000 chunks\n", + " 4h 49m 46s CLIQUES (O sentence LCS M>60 S>85): Composed 4464 cliques out of 15000 chunks\n", + " 4h 49m 56s CLIQUES (O sentence LCS M>60 S>85): Composed 4558 cliques out of 16000 chunks\n", + " 4h 50m 08s CLIQUES (O sentence LCS M>60 S>85): Composed 4608 cliques out of 17000 chunks\n", + " 4h 50m 20s CLIQUES (O sentence LCS M>60 S>85): Composed 4635 cliques out of 18000 chunks\n", + " 4h 50m 32s CLIQUES (O sentence LCS M>60 S>85): Composed 4710 cliques out of 19000 chunks\n", + " 4h 50m 45s CLIQUES (O sentence LCS M>60 S>85): Composed 4787 cliques out of 20000 chunks\n", + " 4h 50m 59s CLIQUES (O sentence LCS M>60 S>85): Composed 4826 cliques out of 21000 chunks\n", + " 4h 51m 14s CLIQUES (O sentence LCS M>60 S>85): Composed 4853 cliques out of 22000 chunks\n", + " 4h 51m 28s CLIQUES (O sentence LCS M>60 S>85): Composed 4877 cliques out of 23000 chunks\n", + " 4h 51m 44s CLIQUES (O sentence LCS M>60 S>85): Composed 4859 cliques out of 24000 chunks\n", + " 4h 52m 00s CLIQUES (O sentence LCS M>60 S>85): Composed 4827 cliques out of 25000 chunks\n", + " 4h 52m 17s CLIQUES (O sentence LCS M>60 S>85): Composed 4851 cliques out of 26000 chunks\n", + " 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): 26488 members in 4855 cliques\n", + " 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): Composed and saved 4855 cliques out of 26488 chunks from 980755 comparisons\n", + " 4h 52m 27s PRINT (O sentence LCS M>60 S>85): sorting out cliques\n", + " 4h 52m 28s PRINT (O sentence LCS M>60 S>85): formatting 4855 cliques skipping 1711 binary chapter diffs\n", + " 4h 52m 31s PRINT (O sentence LCS M>60 S>85): formatted 4855 cliques (98 files) skipping 1711 binary chapter diffs\n", + " 4h 52m 31s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 4h 52m 31s PREPARING (O sentence LCS): Already prepared\n", + " 4h 52m 31s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 4h 52m 41s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates\n", + " 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix\n", + " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): 1301831 relevant similarities between 35629 passages\n", + " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composing cliques out of 35629 chunks from 1301831 comparisons\n", + " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composed 505 cliques out of 1000 chunks\n", + " 4h 52m 50s CLIQUES (O sentence LCS M>60 S>80): Composed 932 cliques out of 2000 chunks\n", + " 4h 52m 52s CLIQUES (O sentence LCS M>60 S>80): Composed 1346 cliques out of 3000 chunks\n", + " 4h 52m 54s CLIQUES (O sentence LCS M>60 S>80): Composed 1725 cliques out of 4000 chunks\n", + " 4h 52m 58s CLIQUES (O sentence LCS M>60 S>80): Composed 2000 cliques out of 5000 chunks\n", + " 4h 53m 01s CLIQUES (O sentence LCS M>60 S>80): Composed 2295 cliques out of 6000 chunks\n", + " 4h 53m 06s CLIQUES (O sentence LCS M>60 S>80): Composed 2537 cliques out of 7000 chunks\n", + " 4h 53m 11s CLIQUES (O sentence LCS M>60 S>80): Composed 2867 cliques out of 8000 chunks\n", + " 4h 53m 17s CLIQUES (O sentence LCS M>60 S>80): Composed 3061 cliques out of 9000 chunks\n", + " 4h 53m 23s CLIQUES (O sentence LCS M>60 S>80): Composed 3188 cliques out of 10000 chunks\n", + " 4h 53m 31s CLIQUES (O sentence LCS M>60 S>80): Composed 3259 cliques out of 11000 chunks\n", + " 4h 53m 38s CLIQUES (O sentence LCS M>60 S>80): Composed 3454 cliques out of 12000 chunks\n", + " 4h 53m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3687 cliques out of 13000 chunks\n", + " 4h 53m 56s CLIQUES (O sentence LCS M>60 S>80): Composed 3826 cliques out of 14000 chunks\n", + " 4h 54m 05s CLIQUES (O sentence LCS M>60 S>80): Composed 3891 cliques out of 15000 chunks\n", + " 4h 54m 15s CLIQUES (O sentence LCS M>60 S>80): Composed 3887 cliques out of 16000 chunks\n", + " 4h 54m 25s CLIQUES (O sentence LCS M>60 S>80): Composed 3942 cliques out of 17000 chunks\n", + " 4h 54m 35s CLIQUES (O sentence LCS M>60 S>80): Composed 3938 cliques out of 18000 chunks\n", + " 4h 54m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3994 cliques out of 19000 chunks\n", + " 4h 54m 58s CLIQUES (O sentence LCS M>60 S>80): Composed 4005 cliques out of 20000 chunks\n", + " 4h 55m 10s CLIQUES (O sentence LCS M>60 S>80): Composed 4071 cliques out of 21000 chunks\n", + " 4h 55m 21s CLIQUES (O sentence LCS M>60 S>80): Composed 4023 cliques out of 22000 chunks\n", + " 4h 55m 33s CLIQUES (O sentence LCS M>60 S>80): Composed 3965 cliques out of 23000 chunks\n", + " 4h 55m 44s CLIQUES (O sentence LCS M>60 S>80): Composed 3889 cliques out of 24000 chunks\n", + " 4h 55m 56s CLIQUES (O sentence LCS M>60 S>80): Composed 3810 cliques out of 25000 chunks\n", + " 4h 56m 09s CLIQUES (O sentence LCS M>60 S>80): Composed 3734 cliques out of 26000 chunks\n", + " 4h 56m 22s CLIQUES (O sentence LCS M>60 S>80): Composed 3702 cliques out of 27000 chunks\n", + " 4h 56m 37s CLIQUES (O sentence LCS M>60 S>80): Composed 3705 cliques out of 28000 chunks\n", + " 4h 56m 52s CLIQUES (O sentence LCS M>60 S>80): Composed 3704 cliques out of 29000 chunks\n", + " 4h 57m 08s CLIQUES (O sentence LCS M>60 S>80): Composed 3682 cliques out of 30000 chunks\n", + " 4h 57m 21s CLIQUES (O sentence LCS M>60 S>80): Composed 3637 cliques out of 31000 chunks\n", + " 4h 57m 33s CLIQUES (O sentence LCS M>60 S>80): Composed 3604 cliques out of 32000 chunks\n", + " 4h 57m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3537 cliques out of 33000 chunks\n", + " 4h 58m 01s CLIQUES (O sentence LCS M>60 S>80): Composed 3492 cliques out of 34000 chunks\n", + " 4h 58m 15s CLIQUES (O sentence LCS M>60 S>80): Composed 3476 cliques out of 35000 chunks\n", + " 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): 35629 members in 3469 cliques\n", + " 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): Composed and saved 3469 cliques out of 35629 chunks from 1301831 comparisons\n", + " 4h 58m 26s PRINT (O sentence LCS M>60 S>80): sorting out cliques\n", + " 4h 58m 27s PRINT (O sentence LCS M>60 S>80): formatting 3469 cliques skipping 1291 binary chapter diffs\n", + " 4h 58m 29s PRINT (O sentence LCS M>60 S>80): formatted 3469 cliques (70 files) skipping 1291 binary chapter diffs\n", + " 4h 58m 29s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 4h 58m 29s PREPARING (O sentence LCS): Already prepared\n", + " 4h 58m 29s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 4h 58m 39s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates\n", + " 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix\n", + " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): 1620905 relevant similarities between 44303 passages\n", + " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composing cliques out of 44303 chunks from 1620905 comparisons\n", + " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composed 511 cliques out of 1000 chunks\n", + " 4h 58m 49s CLIQUES (O sentence LCS M>60 S>75): Composed 937 cliques out of 2000 chunks\n", + " 4h 58m 51s CLIQUES (O sentence LCS M>60 S>75): Composed 1325 cliques out of 3000 chunks\n", + " 4h 58m 53s CLIQUES (O sentence LCS M>60 S>75): Composed 1670 cliques out of 4000 chunks\n", + " 4h 58m 56s CLIQUES (O sentence LCS M>60 S>75): Composed 1940 cliques out of 5000 chunks\n", + " 4h 59m 00s CLIQUES (O sentence LCS M>60 S>75): Composed 2172 cliques out of 6000 chunks\n", + " 4h 59m 05s CLIQUES (O sentence LCS M>60 S>75): Composed 2355 cliques out of 7000 chunks\n", + " 4h 59m 09s CLIQUES (O sentence LCS M>60 S>75): Composed 2554 cliques out of 8000 chunks\n", + " 4h 59m 15s CLIQUES (O sentence LCS M>60 S>75): Composed 2741 cliques out of 9000 chunks\n", + " 4h 59m 21s CLIQUES (O sentence LCS M>60 S>75): Composed 2821 cliques out of 10000 chunks\n", + " 4h 59m 28s CLIQUES (O sentence LCS M>60 S>75): Composed 2925 cliques out of 11000 chunks\n", + " 4h 59m 35s CLIQUES (O sentence LCS M>60 S>75): Composed 3093 cliques out of 12000 chunks\n", + " 4h 59m 42s CLIQUES (O sentence LCS M>60 S>75): Composed 3200 cliques out of 13000 chunks\n", + " 4h 59m 50s CLIQUES (O sentence LCS M>60 S>75): Composed 3227 cliques out of 14000 chunks\n", + " 4h 59m 58s CLIQUES (O sentence LCS M>60 S>75): Composed 3153 cliques out of 15000 chunks\n", + " 5h 00m 06s CLIQUES (O sentence LCS M>60 S>75): Composed 3205 cliques out of 16000 chunks\n", + " 5h 00m 15s CLIQUES (O sentence LCS M>60 S>75): Composed 3181 cliques out of 17000 chunks\n", + " 5h 00m 25s CLIQUES (O sentence LCS M>60 S>75): Composed 3207 cliques out of 18000 chunks\n", + " 5h 00m 36s CLIQUES (O sentence LCS M>60 S>75): Composed 3213 cliques out of 19000 chunks\n", + " 5h 00m 45s CLIQUES (O sentence LCS M>60 S>75): Composed 3221 cliques out of 20000 chunks\n", + " 5h 00m 55s CLIQUES (O sentence LCS M>60 S>75): Composed 3184 cliques out of 21000 chunks\n", + " 5h 01m 04s CLIQUES (O sentence LCS M>60 S>75): Composed 3109 cliques out of 22000 chunks\n", + " 5h 01m 14s CLIQUES (O sentence LCS M>60 S>75): Composed 3080 cliques out of 23000 chunks\n", + " 5h 01m 24s CLIQUES (O sentence LCS M>60 S>75): Composed 3047 cliques out of 24000 chunks\n", + " 5h 01m 36s CLIQUES (O sentence LCS M>60 S>75): Composed 2977 cliques out of 25000 chunks\n", + " 5h 01m 52s CLIQUES (O sentence LCS M>60 S>75): Composed 2947 cliques out of 26000 chunks\n", + " 5h 02m 06s CLIQUES (O sentence LCS M>60 S>75): Composed 2871 cliques out of 27000 chunks\n", + " 5h 02m 18s CLIQUES (O sentence LCS M>60 S>75): Composed 2848 cliques out of 28000 chunks\n", + " 5h 02m 31s CLIQUES (O sentence LCS M>60 S>75): Composed 2825 cliques out of 29000 chunks\n", + " 5h 02m 41s CLIQUES (O sentence LCS M>60 S>75): Composed 2783 cliques out of 30000 chunks\n", + " 5h 02m 54s CLIQUES (O sentence LCS M>60 S>75): Composed 2759 cliques out of 31000 chunks\n", + " 5h 03m 05s CLIQUES (O sentence LCS M>60 S>75): Composed 2696 cliques out of 32000 chunks\n", + " 5h 03m 17s CLIQUES (O sentence LCS M>60 S>75): Composed 2609 cliques out of 33000 chunks\n", + " 5h 03m 29s CLIQUES (O sentence LCS M>60 S>75): Composed 2544 cliques out of 34000 chunks\n", + " 5h 03m 40s CLIQUES (O sentence LCS M>60 S>75): Composed 2469 cliques out of 35000 chunks\n", + " 5h 03m 53s CLIQUES (O sentence LCS M>60 S>75): Composed 2463 cliques out of 36000 chunks\n", + " 5h 04m 03s CLIQUES (O sentence LCS M>60 S>75): Composed 2453 cliques out of 37000 chunks\n", + " 5h 04m 13s CLIQUES (O sentence LCS M>60 S>75): Composed 2444 cliques out of 38000 chunks\n", + " 5h 04m 24s CLIQUES (O sentence LCS M>60 S>75): Composed 2407 cliques out of 39000 chunks\n", + " 5h 04m 34s CLIQUES (O sentence LCS M>60 S>75): Composed 2376 cliques out of 40000 chunks\n", + " 5h 04m 45s CLIQUES (O sentence LCS M>60 S>75): Composed 2341 cliques out of 41000 chunks\n", + " 5h 04m 56s CLIQUES (O sentence LCS M>60 S>75): Composed 2304 cliques out of 42000 chunks\n", + " 5h 05m 07s CLIQUES (O sentence LCS M>60 S>75): Composed 2296 cliques out of 43000 chunks\n", + " 5h 05m 19s CLIQUES (O sentence LCS M>60 S>75): Composed 2292 cliques out of 44000 chunks\n", + " 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): 44303 members in 2291 cliques\n", + " 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): Composed and saved 2291 cliques out of 44303 chunks from 1620905 comparisons\n", + " 5h 05m 24s PRINT (O sentence LCS M>60 S>75): sorting out cliques\n", + " 5h 05m 25s PRINT (O sentence LCS M>60 S>75): formatting 2291 cliques skipping 888 binary chapter diffs\n", + " 5h 05m 27s PRINT (O sentence LCS M>60 S>75): formatted 2291 cliques (46 files) skipping 888 binary chapter diffs\n", + " 5h 05m 27s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 5h 05m 27s PREPARING (O sentence LCS): Already prepared\n", + " 5h 05m 27s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 5h 05m 37s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates\n", + " 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix\n", + " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): 2184827 relevant similarities between 52528 passages\n", + " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composing cliques out of 52528 chunks from 2184827 comparisons\n", + " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composed 501 cliques out of 1000 chunks\n", + " 5h 05m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 931 cliques out of 2000 chunks\n", + " 5h 05m 50s CLIQUES (O sentence LCS M>60 S>70): Composed 1217 cliques out of 3000 chunks\n", + " 5h 05m 52s CLIQUES (O sentence LCS M>60 S>70): Composed 1494 cliques out of 4000 chunks\n", + " 5h 05m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 1737 cliques out of 5000 chunks\n", + " 5h 05m 59s CLIQUES (O sentence LCS M>60 S>70): Composed 1924 cliques out of 6000 chunks\n", + " 5h 06m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 2023 cliques out of 7000 chunks\n", + " 5h 06m 07s CLIQUES (O sentence LCS M>60 S>70): Composed 2080 cliques out of 8000 chunks\n", + " 5h 06m 12s CLIQUES (O sentence LCS M>60 S>70): Composed 2197 cliques out of 9000 chunks\n", + " 5h 06m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 10000 chunks\n", + " 5h 06m 23s CLIQUES (O sentence LCS M>60 S>70): Composed 2133 cliques out of 11000 chunks\n", + " 5h 06m 29s CLIQUES (O sentence LCS M>60 S>70): Composed 2203 cliques out of 12000 chunks\n", + " 5h 06m 35s CLIQUES (O sentence LCS M>60 S>70): Composed 2190 cliques out of 13000 chunks\n", + " 5h 06m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 2189 cliques out of 14000 chunks\n", + " 5h 06m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 2105 cliques out of 15000 chunks\n", + " 5h 06m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 16000 chunks\n", + " 5h 07m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 17000 chunks\n", + " 5h 07m 10s CLIQUES (O sentence LCS M>60 S>70): Composed 2128 cliques out of 18000 chunks\n", + " 5h 07m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 2099 cliques out of 19000 chunks\n", + " 5h 07m 24s CLIQUES (O sentence LCS M>60 S>70): Composed 2060 cliques out of 20000 chunks\n", + " 5h 07m 30s CLIQUES (O sentence LCS M>60 S>70): Composed 1970 cliques out of 21000 chunks\n", + " 5h 07m 39s CLIQUES (O sentence LCS M>60 S>70): Composed 1984 cliques out of 22000 chunks\n", + " 5h 07m 46s CLIQUES (O sentence LCS M>60 S>70): Composed 1936 cliques out of 23000 chunks\n", + " 5h 07m 54s CLIQUES (O sentence LCS M>60 S>70): Composed 1911 cliques out of 24000 chunks\n", + " 5h 08m 04s CLIQUES (O sentence LCS M>60 S>70): Composed 1914 cliques out of 25000 chunks\n", + " 5h 08m 12s CLIQUES (O sentence LCS M>60 S>70): Composed 1859 cliques out of 26000 chunks\n", + " 5h 08m 20s CLIQUES (O sentence LCS M>60 S>70): Composed 1789 cliques out of 27000 chunks\n", + " 5h 08m 27s CLIQUES (O sentence LCS M>60 S>70): Composed 1745 cliques out of 28000 chunks\n", + " 5h 08m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1687 cliques out of 29000 chunks\n", + " 5h 08m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 1660 cliques out of 30000 chunks\n", + " 5h 08m 50s CLIQUES (O sentence LCS M>60 S>70): Composed 1631 cliques out of 31000 chunks\n", + " 5h 08m 58s CLIQUES (O sentence LCS M>60 S>70): Composed 1600 cliques out of 32000 chunks\n", + " 5h 09m 07s CLIQUES (O sentence LCS M>60 S>70): Composed 1552 cliques out of 33000 chunks\n", + " 5h 09m 18s CLIQUES (O sentence LCS M>60 S>70): Composed 1506 cliques out of 34000 chunks\n", + " 5h 09m 27s CLIQUES (O sentence LCS M>60 S>70): Composed 1426 cliques out of 35000 chunks\n", + " 5h 09m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1413 cliques out of 36000 chunks\n", + " 5h 09m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 1399 cliques out of 37000 chunks\n", + " 5h 09m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 1383 cliques out of 38000 chunks\n", + " 5h 09m 56s CLIQUES (O sentence LCS M>60 S>70): Composed 1374 cliques out of 39000 chunks\n", + " 5h 10m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 1349 cliques out of 40000 chunks\n", + " 5h 10m 11s CLIQUES (O sentence LCS M>60 S>70): Composed 1306 cliques out of 41000 chunks\n", + " 5h 10m 20s CLIQUES (O sentence LCS M>60 S>70): Composed 1259 cliques out of 42000 chunks\n", + " 5h 10m 28s CLIQUES (O sentence LCS M>60 S>70): Composed 1231 cliques out of 43000 chunks\n", + " 5h 10m 40s CLIQUES (O sentence LCS M>60 S>70): Composed 1243 cliques out of 44000 chunks\n", + " 5h 10m 49s CLIQUES (O sentence LCS M>60 S>70): Composed 1250 cliques out of 45000 chunks\n", + " 5h 10m 58s CLIQUES (O sentence LCS M>60 S>70): Composed 1249 cliques out of 46000 chunks\n", + " 5h 11m 08s CLIQUES (O sentence LCS M>60 S>70): Composed 1234 cliques out of 47000 chunks\n", + " 5h 11m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 1227 cliques out of 48000 chunks\n", + " 5h 11m 25s CLIQUES (O sentence LCS M>60 S>70): Composed 1219 cliques out of 49000 chunks\n", + " 5h 11m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1205 cliques out of 50000 chunks\n", + " 5h 11m 44s CLIQUES (O sentence LCS M>60 S>70): Composed 1205 cliques out of 51000 chunks\n", + " 5h 11m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 1201 cliques out of 52000 chunks\n", + " 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): 52528 members in 1199 cliques\n", + " 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): Composed and saved 1199 cliques out of 52528 chunks from 2184827 comparisons\n", + " 5h 12m 03s PRINT (O sentence LCS M>60 S>70): sorting out cliques\n", + " 5h 12m 05s PRINT (O sentence LCS M>60 S>70): formatting 1199 cliques skipping 455 binary chapter diffs\n", + " 5h 12m 06s PRINT (O sentence LCS M>60 S>70): formatted 1199 cliques (24 files) skipping 455 binary chapter diffs\n", + " 5h 12m 06s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 5h 12m 06s PREPARING (O sentence LCS): Already prepared\n", + " 5h 12m 06s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 5h 12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates\n", + " 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix\n", + " 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): 4834493 relevant similarities between 58855 passages\n", + " 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): Composing cliques out of 58855 chunks from 4834493 comparisons\n", + " 5h 12m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 479 cliques out of 1000 chunks\n", + " 5h 12m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 743 cliques out of 2000 chunks\n", + " 5h 12m 31s CLIQUES (O sentence LCS M>60 S>65): Composed 968 cliques out of 3000 chunks\n", + " 5h 12m 33s CLIQUES (O sentence LCS M>60 S>65): Composed 1082 cliques out of 4000 chunks\n", + " 5h 12m 36s CLIQUES (O sentence LCS M>60 S>65): Composed 1164 cliques out of 5000 chunks\n", + " 5h 12m 38s CLIQUES (O sentence LCS M>60 S>65): Composed 1217 cliques out of 6000 chunks\n", + " 5h 12m 41s CLIQUES (O sentence LCS M>60 S>65): Composed 1183 cliques out of 7000 chunks\n", + " 5h 12m 45s CLIQUES (O sentence LCS M>60 S>65): Composed 1240 cliques out of 8000 chunks\n", + " 5h 12m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 1156 cliques out of 9000 chunks\n", + " 5h 12m 51s CLIQUES (O sentence LCS M>60 S>65): Composed 1157 cliques out of 10000 chunks\n", + " 5h 12m 54s CLIQUES (O sentence LCS M>60 S>65): Composed 1075 cliques out of 11000 chunks\n", + " 5h 12m 58s CLIQUES (O sentence LCS M>60 S>65): Composed 1014 cliques out of 12000 chunks\n", + " 5h 13m 02s CLIQUES (O sentence LCS M>60 S>65): Composed 998 cliques out of 13000 chunks\n", + " 5h 13m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 976 cliques out of 14000 chunks\n", + " 5h 13m 09s CLIQUES (O sentence LCS M>60 S>65): Composed 930 cliques out of 15000 chunks\n", + " 5h 13m 13s CLIQUES (O sentence LCS M>60 S>65): Composed 891 cliques out of 16000 chunks\n", + " 5h 13m 18s CLIQUES (O sentence LCS M>60 S>65): Composed 886 cliques out of 17000 chunks\n", + " 5h 13m 22s CLIQUES (O sentence LCS M>60 S>65): Composed 836 cliques out of 18000 chunks\n", + " 5h 13m 26s CLIQUES (O sentence LCS M>60 S>65): Composed 818 cliques out of 19000 chunks\n", + " 5h 13m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 798 cliques out of 20000 chunks\n", + " 5h 13m 34s CLIQUES (O sentence LCS M>60 S>65): Composed 799 cliques out of 21000 chunks\n", + " 5h 13m 39s CLIQUES (O sentence LCS M>60 S>65): Composed 778 cliques out of 22000 chunks\n", + " 5h 13m 45s CLIQUES (O sentence LCS M>60 S>65): Composed 764 cliques out of 23000 chunks\n", + " 5h 13m 49s CLIQUES (O sentence LCS M>60 S>65): Composed 743 cliques out of 24000 chunks\n", + " 5h 13m 52s CLIQUES (O sentence LCS M>60 S>65): Composed 717 cliques out of 25000 chunks\n", + " 5h 13m 56s CLIQUES (O sentence LCS M>60 S>65): Composed 707 cliques out of 26000 chunks\n", + " 5h 14m 00s CLIQUES (O sentence LCS M>60 S>65): Composed 692 cliques out of 27000 chunks\n", + " 5h 14m 06s CLIQUES (O sentence LCS M>60 S>65): Composed 684 cliques out of 28000 chunks\n", + " 5h 14m 10s CLIQUES (O sentence LCS M>60 S>65): Composed 650 cliques out of 29000 chunks\n", + " 5h 14m 15s CLIQUES (O sentence LCS M>60 S>65): Composed 639 cliques out of 30000 chunks\n", + " 5h 14m 21s CLIQUES (O sentence LCS M>60 S>65): Composed 623 cliques out of 31000 chunks\n", + " 5h 14m 26s CLIQUES (O sentence LCS M>60 S>65): Composed 598 cliques out of 32000 chunks\n", + " 5h 14m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 586 cliques out of 33000 chunks\n", + " 5h 14m 34s CLIQUES (O sentence LCS M>60 S>65): Composed 580 cliques out of 34000 chunks\n", + " 5h 14m 39s CLIQUES (O sentence LCS M>60 S>65): Composed 570 cliques out of 35000 chunks\n", + " 5h 14m 43s CLIQUES (O sentence LCS M>60 S>65): Composed 564 cliques out of 36000 chunks\n", + " 5h 14m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 554 cliques out of 37000 chunks\n", + " 5h 14m 53s CLIQUES (O sentence LCS M>60 S>65): Composed 540 cliques out of 38000 chunks\n", + " 5h 14m 59s CLIQUES (O sentence LCS M>60 S>65): Composed 530 cliques out of 39000 chunks\n", + " 5h 15m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 514 cliques out of 40000 chunks\n", + " 5h 15m 10s CLIQUES (O sentence LCS M>60 S>65): Composed 499 cliques out of 41000 chunks\n", + " 5h 15m 15s CLIQUES (O sentence LCS M>60 S>65): Composed 500 cliques out of 42000 chunks\n", + " 5h 15m 19s CLIQUES (O sentence LCS M>60 S>65): Composed 499 cliques out of 43000 chunks\n", + " 5h 15m 24s CLIQUES (O sentence LCS M>60 S>65): Composed 495 cliques out of 44000 chunks\n", + " 5h 15m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 491 cliques out of 45000 chunks\n", + " 5h 15m 35s CLIQUES (O sentence LCS M>60 S>65): Composed 484 cliques out of 46000 chunks\n", + " 5h 15m 41s CLIQUES (O sentence LCS M>60 S>65): Composed 479 cliques out of 47000 chunks\n", + " 5h 15m 47s CLIQUES (O sentence LCS M>60 S>65): Composed 472 cliques out of 48000 chunks\n", + " 5h 15m 54s CLIQUES (O sentence LCS M>60 S>65): Composed 467 cliques out of 49000 chunks\n", + " 5h 16m 00s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 50000 chunks\n", + " 5h 16m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 51000 chunks\n", + " 5h 16m 11s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 52000 chunks\n", + " 5h 16m 17s CLIQUES (O sentence LCS M>60 S>65): Composed 467 cliques out of 53000 chunks\n", + " 5h 16m 23s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 54000 chunks\n", + " 5h 16m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 55000 chunks\n", + " 5h 16m 35s CLIQUES (O sentence LCS M>60 S>65): Composed 465 cliques out of 56000 chunks\n", + " 5h 16m 42s CLIQUES (O sentence LCS M>60 S>65): Composed 464 cliques out of 57000 chunks\n", + " 5h 16m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 463 cliques out of 58000 chunks\n", + " 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): 58855 members in 463 cliques\n", + " 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): Composed and saved 463 cliques out of 58855 chunks from 4834493 comparisons\n", + " 5h 16m 56s PRINT (O sentence LCS M>60 S>65): sorting out cliques\n", + " 5h 16m 57s PRINT (O sentence LCS M>60 S>65): formatting 463 cliques skipping 209 binary chapter diffs\n", + " 5h 16m 58s PRINT (O sentence LCS M>60 S>65): formatted 463 cliques (10 files) skipping 209 binary chapter diffs\n", + " 5h 16m 58s CHUNKING (O sentence): already chunked into 63570 chunks\n", + " 5h 16m 58s PREPARING (O sentence LCS): Already prepared\n", + " 5h 16m 58s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", + " 5h 17m 08s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", + " 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates\n", + " 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix\n", + " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): 10279985 relevant similarities between 62369 passages\n", + " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composing cliques out of 62369 chunks from 10279985 comparisons\n", + " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composed 317 cliques out of 1000 chunks\n", + " 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 374 cliques out of 2000 chunks\n", + " 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 400 cliques out of 3000 chunks\n", + " 5h 17m 30s CLIQUES (O sentence LCS M>60 S>60): Composed 381 cliques out of 4000 chunks\n", + " 5h 17m 31s CLIQUES (O sentence LCS M>60 S>60): Composed 375 cliques out of 5000 chunks\n", + " 5h 17m 32s CLIQUES (O sentence LCS M>60 S>60): Composed 348 cliques out of 6000 chunks\n", + " 5h 17m 33s CLIQUES (O sentence LCS M>60 S>60): Composed 309 cliques out of 7000 chunks\n", + " 5h 17m 34s CLIQUES (O sentence LCS M>60 S>60): Composed 298 cliques out of 8000 chunks\n", + " 5h 17m 36s CLIQUES (O sentence LCS M>60 S>60): Composed 272 cliques out of 9000 chunks\n", + " 5h 17m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 246 cliques out of 10000 chunks\n", + " 5h 17m 39s CLIQUES (O sentence LCS M>60 S>60): Composed 243 cliques out of 11000 chunks\n", + " 5h 17m 40s CLIQUES (O sentence LCS M>60 S>60): Composed 221 cliques out of 12000 chunks\n", + " 5h 17m 42s CLIQUES (O sentence LCS M>60 S>60): Composed 214 cliques out of 13000 chunks\n", + " 5h 17m 43s CLIQUES (O sentence LCS M>60 S>60): Composed 209 cliques out of 14000 chunks\n", + " 5h 17m 44s CLIQUES (O sentence LCS M>60 S>60): Composed 190 cliques out of 15000 chunks\n", + " 5h 17m 46s CLIQUES (O sentence LCS M>60 S>60): Composed 175 cliques out of 16000 chunks\n", + " 5h 17m 48s CLIQUES (O sentence LCS M>60 S>60): Composed 169 cliques out of 17000 chunks\n", + " 5h 17m 49s CLIQUES (O sentence LCS M>60 S>60): Composed 162 cliques out of 18000 chunks\n", + " 5h 17m 51s CLIQUES (O sentence LCS M>60 S>60): Composed 160 cliques out of 19000 chunks\n", + " 5h 17m 52s CLIQUES (O sentence LCS M>60 S>60): Composed 151 cliques out of 20000 chunks\n", + " 5h 17m 54s CLIQUES (O sentence LCS M>60 S>60): Composed 141 cliques out of 21000 chunks\n", + " 5h 17m 55s CLIQUES (O sentence LCS M>60 S>60): Composed 133 cliques out of 22000 chunks\n", + " 5h 17m 57s CLIQUES (O sentence LCS M>60 S>60): Composed 134 cliques out of 23000 chunks\n", + " 5h 17m 59s CLIQUES (O sentence LCS M>60 S>60): Composed 132 cliques out of 24000 chunks\n", + " 5h 18m 02s CLIQUES (O sentence LCS M>60 S>60): Composed 126 cliques out of 25000 chunks\n", + " 5h 18m 04s CLIQUES (O sentence LCS M>60 S>60): Composed 124 cliques out of 26000 chunks\n", + " 5h 18m 07s CLIQUES (O sentence LCS M>60 S>60): Composed 120 cliques out of 27000 chunks\n", + " 5h 18m 09s CLIQUES (O sentence LCS M>60 S>60): Composed 119 cliques out of 28000 chunks\n", + " 5h 18m 11s CLIQUES (O sentence LCS M>60 S>60): Composed 119 cliques out of 29000 chunks\n", + " 5h 18m 14s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 30000 chunks\n", + " 5h 18m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 31000 chunks\n", + " 5h 18m 19s CLIQUES (O sentence LCS M>60 S>60): Composed 116 cliques out of 32000 chunks\n", + " 5h 18m 22s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 33000 chunks\n", + " 5h 18m 25s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 34000 chunks\n", + " 5h 18m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 35000 chunks\n", + " 5h 18m 31s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 36000 chunks\n", + " 5h 18m 34s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 37000 chunks\n", + " 5h 18m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 38000 chunks\n", + " 5h 18m 40s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 39000 chunks\n", + " 5h 18m 43s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 40000 chunks\n", + " 5h 18m 47s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 41000 chunks\n", + " 5h 18m 50s CLIQUES (O sentence LCS M>60 S>60): Composed 115 cliques out of 42000 chunks\n", + " 5h 18m 54s CLIQUES (O sentence LCS M>60 S>60): Composed 114 cliques out of 43000 chunks\n", + " 5h 18m 57s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 44000 chunks\n", + " 5h 19m 01s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 45000 chunks\n", + " 5h 19m 05s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 46000 chunks\n", + " 5h 19m 08s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 47000 chunks\n", + " 5h 19m 12s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 48000 chunks\n", + " 5h 19m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 49000 chunks\n", + " 5h 19m 20s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 50000 chunks\n", + " 5h 19m 24s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 51000 chunks\n", + " 5h 19m 29s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 52000 chunks\n", + " 5h 19m 33s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 53000 chunks\n", + " 5h 19m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 54000 chunks\n", + " 5h 19m 42s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 55000 chunks\n", + " 5h 19m 46s CLIQUES (O sentence LCS M>60 S>60): Composed 114 cliques out of 56000 chunks\n", + " 5h 19m 51s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 57000 chunks\n", + " 5h 19m 56s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 58000 chunks\n", + " 5h 20m 00s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 59000 chunks\n", + " 5h 20m 05s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 60000 chunks\n", + " 5h 20m 11s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 61000 chunks\n", + " 5h 20m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 62000 chunks\n", + " 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): 62369 members in 112 cliques\n", + " 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): Composed and saved 112 cliques out of 62369 chunks from 10279985 comparisons\n", + " 5h 20m 20s PRINT (O sentence LCS M>60 S>60): sorting out cliques\n", + " 5h 20m 21s PRINT (O sentence LCS M>60 S>60): formatting 112 cliques skipping 61 binary chapter diffs\n", + " 5h 20m 22s PRINT (O sentence LCS M>60 S>60): formatted 112 cliques (3 files) skipping 61 binary chapter diffs\n", + " 5h 20m 22s EXPERIMENT: Generating html report\n", + " 5h 20m 22s EXPERIMENT: 35 messy results: deprecated\n", + " 5h 20m 22s EXPERIMENT: 23 mixed quality: take care\n", + " 5h 20m 22s EXPERIMENT: 75 no results available\n", + " 5h 20m 22s EXPERIMENT: 9 unassessed quality: inspection needed\n", + " 5h 20m 22s EXPERIMENT: 80 method deprecated\n", + " 5h 20m 22s EXPERIMENT: 18 promising results: recommended\n", + " 5h 20m 22s EXPERIMENT: Generated html report\n", + " 5h 20m 22s EXPERIMENT: Generating html report(standalone)\n", + " 5h 20m 22s EXPERIMENT: 35 messy results: deprecated\n", + " 5h 20m 22s EXPERIMENT: 23 mixed quality: take care\n", + " 5h 20m 22s EXPERIMENT: 75 no results available\n", + " 5h 20m 22s EXPERIMENT: 9 unassessed quality: inspection needed\n", + " 5h 20m 22s EXPERIMENT: 80 method deprecated\n", + " 5h 20m 22s EXPERIMENT: 18 promising results: recommended\n", + " 5h 20m 22s EXPERIMENT: Generated html report\n" ] } ], @@ -5166,7 +6946,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -5195,7 +6975,7 @@ "" ] }, - "execution_count": 16, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -5356,7 +7136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/static/docs/tools/parallel/Isaiah-mt-1QIsaa.html b/static/docs/tools/parallel/Isaiah-mt-1QIsaa.html index fc92a76b..dfa380a0 100644 --- a/static/docs/tools/parallel/Isaiah-mt-1QIsaa.html +++ b/static/docs/tools/parallel/Isaiah-mt-1QIsaa.html @@ -30,57 +30,58 @@
Isaiah 1 MT
Isaiah 1 1QIsaa - n1חזונ ישעיהו בנ אמוצ אשר חזה על יהודה וירושלמ בימי n1חזונ ישעיהו בנ אמוצ אשר חזה על יהודה וירושלמ ביומי - >עזיהו יותמ אחז יחזקיהו מלכי יהודה> עוזיה יותמ אחז יחזקיה מלכי יהודה - 2שמעו שמימ והאזיני ארצ כי יהוה דבר בנימ גדלתי ורוממ2שמעו שמימ והאזיני הארצ כיא יהוה דבר בנימ גדלתי ורו - >תי והמ פשעו בי>ממתי והמה פשעו בי - 3ידע שור קנהו וחמור אבוס בעליו ישראל לא ידע עמי לא 3ידע שור קוניהו וחמור אבוס בעליו ישראל לוא ידע ועמי - >התבוננ> לוא התבוננ - 4הוי גוי חטא עמ כבד עונ זרע מרעימ בנימ משחיתימ עזבו4הוי גוי חוטה עמ כבד עוונ זרע מרעימ בנימ משחיתימ עז - > את יהוה נאצו את קדוש ישראל נזרו אחור>בו את יהוה נאצו את קדוש ישראל נזרו אחור - 5על מה תכו עוד תוסיפו סרה כל ראש לחלי וכל לבב דוי5על מה תכו עוד תוסיפו סרה כול ראוש לחולי וכול לבב ד + t1חזון ישׁעיהו בן אמוץ אשׁר חזה על יהודה וירושׁלם ביt1חזונ ישעיהו בנ אמוצ אשר חזה על יהודה וירושלמ ביומי + >מי עזיהו יותם אחז יחזקיהו מלכי יהודה > עוזיה יותמ אחז יחזקיה מלכי יהודה + 2שׁמעו שׁמים והאזיני ארץ כי יהוה דבר בנים גדלתי ורו2שמעו שמימ והאזיני הארצ כיא יהוה דבר בנימ גדלתי ורו + >ממתי והם פשׁעו בי >ממתי והמה פשעו בי + 3ידע שׁור קנהו וחמור אבוס בעליו ישׂראל לא ידע עמי ל3ידע שור קוניהו וחמור אבוס בעליו ישראל לוא ידע ועמי + >א התבונן > לוא התבוננ + 4הוי׀ גוי חטא עם כבד עון זרע מרעים בנים משׁחיתים עז4הוי גוי חוטה עמ כבד עוונ זרע מרעימ בנימ משחיתימ עז + >בו את יהוה נאצו את קדושׁ ישׂראל נזרו אחור >בו את יהוה נאצו את קדוש ישראל נזרו אחור + 5על מה תכו עוד תוסיפו סרה כל ראשׁ לחלי וכל לבב דוי 5על מה תכו עוד תוסיפו סרה כול ראוש לחולי וכול לבב ד  >וה - 6מכפ רגל ועד ראש אינ בו מתמ פצע וחבורה ומכה טריה לא6מכפ רגל ועד רואש אינ בו מתמ פצע וחבורה ומכה טריה ל - > זרו ולא חבשו ולא רככה בשמנ>וא זרו ולוא חובשו ולוא רככה בשמנ - 7ארצכמ שממה עריכמ שרפות אש אדמתכמ לנגדכמ זרימ אכלימ7ארצכמ שממה עריכמ שרופות אש אדמתכמ לנגדכמ זרימ אוכל - > אתה ושממה כמהפכת זרימ>ימ אותה ושממו עליה כמאפכת זרימ - 8ונותרה בת ציונ כסכה בכרמ כמלונה במקשה כעיר נצורה8ונתרת בת ציונ כסוכה בכרמ וכמלונה במקשה כעיר נצורה - 9לולי יהוה צבאות הותיר לנו שריד כמעט כסדמ היינו לעמ9לולי יהוה צבאות הותיר לנו שריד כמעט כסודמ היינו לע - >רה דמינו>ומרה דמינו - 10שמעו דבר יהוה קציני סדמ האזינו תורת אלהינו עמ עמרה10שמעו דבר יהוה קציני סודמ ואזינו תורת אלוהינו עמ עו -  >מרה - 11למה לי רב זבחיכמ יאמר יהוה שבעתי עלות אילימ וחלב מ11למה לי רוב זבחיכמ יואמר יהוה שבעתי עולות אילימ וחל - >ריאימ ודמ פרימ וכבשימ ועתודימ לא חפצתי>ב מריאימ ודמ פרימ וכבשימ ועתודימ לוא חפצתי - 12כי תבאו לראות פני מי בקש זאת מידכמ רמס חצרי12כיא תבאו לראות פני מי בקש זואת מידכמ לרמוס חצרי - 13לא תוסיפו הביא מנחת שוא קטרת תועבה היא לי חדש ושבת13לוא תוסיפו להביא מנחת שוא קטרת תועבה היא לי חודש ו - > קרא מקרא לא אוכל אונ ועצרה>שבת קרא מקרא לוא אוכל אונ ועצרתה - 14חדשיכמ ומועדיכמ שנאה נפשי היו עלי לטרח נלאיתי נשא14חודשיכמ ומועדיכמ שנאה נפשי היו עלי לטרח נלאיתי נשו -  >א - 15ובפרשכמ כפיכמ אעלימ עיני מכמ גמ כי תרבו תפלה אינני15ובפרשכמ כפיכמ אעלימ עיני מכמ גמ כי הרבו תפלה אינני - > שמע ידיכמ דמימ מלאו> שומע ידיכמה דמימ מלאו אצבעותיכמ בעאונ - 16רחצו הזכו הסירו רע מעלליכמ מנגד עיני חדלו הרע16רחצו והזכו והסירו רוע מעלליכמ מנגד עיני חדלו הרע - 17למדו היטב דרשו משפט אשרו חמוצ שפטו יתומ ריבו אלמנה17למדו היטיב דרושו משפט אשרו חמוצ שפטו יאתומ ריבו אל -  >מנה - 18לכו נא ונוכחה יאמר יהוה אמ יהיו חטאיכמ כשנימ כשלג 18לכו נא ונוכחה יואמר יהוה אמ יהיו חטאיכמ כשני כשלג  - >ילבינו אמ יאדימו כתולע כצמר יהיו>ילבינו אמ ידומו כתולע כצמר יהיו - 19אמ תאבו ושמעתמ טוב הארצ תאכלו19אמ תאבו ושמעתמ טוב הארצ תאכלו - n20ואמ תמאנו ומריתמ חרב תאכלו כי פי יהוה דברn20ואמ תמאנו ומריתמ בחרב תאכלו כיא פי יהוה דבר - 21איכה היתה לזונה קריה נאמנה מלאתי משפט צדק ילינ בה 21היכה הייתה לזונה קריה נאמנה מלאתי משפט צדק ילינ בה - >ועתה מרצחימ> ועתה מרצחימ - 22כספכ היה לסיגימ סבאכ מהול במימ22כספכ היו לסוגימ סבאכ מהול במימ - 23שריכ סוררימ וחברי גנבימ כלו אהב שחד ורדפ שלמנימ ית23שריכי סוררימ וחברי גנבימ כולמ אוהבי שוחד רודפי שלמ - >ומ לא ישפטו וריב אלמנה לא יבוא אליהמ>ונימ יאתומ לוא ישפטו וריב אלמנה לוא יבוא אליהמ - 24לכנ נאמ האדונ יהוה צבאות אביר ישראל הוי אנחמ מצרי 24לכנ נאומ האדונ יהוה צבאות אביר ישראל הוה אנחמ מצרי - >ואנקמה מאויבי>ו ואנקמ מהאיבו - 25ואשיבה ידי עליכ ואצרפ כבר סיגיכ ואסירה כל בדיליכ25והשיב ידי עליכ ואצרפ כבר סוגיכ ואסיר כול בדיליכ - 26ואשיבה שפטיכ כבראשנה ויעציכ כבתחלה אחרי כנ יקרא לכ26ואשיבה שופטיכ כבראישונה ויעציכ כבתחלה אחרי כנ יקרא - > עיר הצדק קריה נאמנה>ו לכ עיר הצדק קריה נאמנה - 27ציונ במשפט תפדה ושביה בצדקה27ציונ במשפט תפדה ושביה בצדקה - t28ושבר פשעימ וחטאימ יחדו ועזבי יהוה יכלוt28ושבר פושעימ וחטאימ יחדו ועוזבי יהוה יכלו - 29כי יבשו מאילימ אשר חמדתמ ותחפרו מהגנות אשר בחרתמ29כיא יבושו מאלימ אשר חמדתמ ותחפורו מהגנות אשר בחרתמ - 30כי תהיו כאלה נבלת עלה וכגנה אשר מימ אינ לה30כי תהיו כאלה נובלת עלה וכגנה אשר אינ מימ לה - 31והיה החסנ לנערת ופעלו לניצוצ ובערו שניהמ יחדו ואינ31והיה החסנכמ לנעורת ופעלכמ לניצוצ ובערו שניהמ יחדו  - > מכבה>ואינ מכבה + 6מכף רגל ועד ראשׁ אין בו מתם פצע וחבורה ומכה טריה ל6מכפ רגל ועד רואש אינ בו מתמ פצע וחבורה ומכה טריה ל + >א זרו ולא חבשׁו ולא רככה בשׁמן >וא זרו ולוא חובשו ולוא רככה בשמנ + 7ארצכם שׁממה עריכם שׂרפות אשׁ אדמתכם לנגדכם זרים אכ7ארצכמ שממה עריכמ שרופות אש אדמתכמ לנגדכמ זרימ אוכל + >לים אתה ושׁממה כמהפכת זרים >ימ אותה ושממו עליה כמאפכת זרימ + 8ונותרה בת ציון כסכה בכרם כמלונה במקשׁה כעיר נצורה 8ונתרת בת ציונ כסוכה בכרמ וכמלונה במקשה כעיר נצורה + 9לולי יהוה צבאות הותיר לנו שׂריד כמעט כסדם היינו לע9לולי יהוה צבאות הותיר לנו שריד כמעט כסודמ היינו לע + >מרה דמינו ס >ומרה דמינו + 10שׁמעו דבר יהוה קציני סדם האזינו תורת אלהינו עם עמר10שמעו דבר יהוה קציני סודמ ואזינו תורת אלוהינו עמ עו + >ה >מרה + 11למה לי רב זבחיכם יאמר יהוה שׂבעתי עלות אילים וחלב 11למה לי רוב זבחיכמ יואמר יהוה שבעתי עולות אילימ וחל + >מריאים ודם פרים וכבשׂים ועתודים לא חפצתי >ב מריאימ ודמ פרימ וכבשימ ועתודימ לוא חפצתי + 12כי תבאו לראות פני מי בקשׁ זאת מידכם רמס חצרי 12כיא תבאו לראות פני מי בקש זואת מידכמ לרמוס חצרי + 13לא תוסיפו הביא מנחת שׁוא קטרת תועבה היא לי חדשׁ וש13לוא תוסיפו להביא מנחת שוא קטרת תועבה היא לי חודש ו + >ׁבת קרא מקרא לא אוכל און ועצרה >שבת קרא מקרא לוא אוכל אונ ועצרתה + 14חדשׁיכם ומועדיכם שׂנאה נפשׁי היו עלי לטרח נלאיתי נ14חודשיכמ ומועדיכמ שנאה נפשי היו עלי לטרח נלאיתי נשו + >שׂא >א + 15ובפרשׂכם כפיכם אעלים עיני מכם גם כי תרבו תפלה איננ15ובפרשכמ כפיכמ אעלימ עיני מכמ גמ כי הרבו תפלה אינני + >י שׁמע ידיכם דמים מלאו > שומע ידיכמה דמימ מלאו אצבעותיכמ בעאונ + 16רחצו הזכו הסירו רע מעלליכם מנגד עיני חדלו הרע 16רחצו והזכו והסירו רוע מעלליכמ מנגד עיני חדלו הרע + 17למדו היטב דרשׁו משׁפט אשׁרו חמוץ שׁפטו יתום ריבו א17למדו היטיב דרושו משפט אשרו חמוצ שפטו יאתומ ריבו אל + >למנה ס >מנה + 18לכו נא ונוכחה יאמר יהוה אם יהיו חטאיכם כשׁנים כשׁל18לכו נא ונוכחה יואמר יהוה אמ יהיו חטאיכמ כשני כשלג  + >ג ילבינו אם יאדימו כתולע כצמר יהיו >ילבינו אמ ידומו כתולע כצמר יהיו + 19אם תאבו ושׁמעתם טוב הארץ תאכלו 19אמ תאבו ושמעתמ טוב הארצ תאכלו + 20ואם תמאנו ומריתם חרב תאכלו כי פי יהוה דבר ס 20ואמ תמאנו ומריתמ בחרב תאכלו כיא פי יהוה דבר + 21איכה היתה לזונה קריה נאמנה מלאתי משׁפט צדק ילין בה21היכה הייתה לזונה קריה נאמנה מלאתי משפט צדק ילינ בה + > ועתה מרצחים > ועתה מרצחימ + 22כספך היה לסיגים סבאך מהול במים 22כספכ היו לסוגימ סבאכ מהול במימ + 23שׂריך סוררים וחברי גנבים כלו אהב שׁחד ורדף שׁלמנים23שריכי סוררימ וחברי גנבימ כולמ אוהבי שוחד רודפי שלמ + > יתום לא ישׁפטו וריב אלמנה לא יבוא אליהם פ >ונימ יאתומ לוא ישפטו וריב אלמנה לוא יבוא אליהמ + 24לכן נאם האדון יהוה צבאות אביר ישׂראל הוי אנחם מצרי24לכנ נאומ האדונ יהוה צבאות אביר ישראל הוה אנחמ מצרי + > ואנקמה מאויבי >ו ואנקמ מהאיבו + 25ואשׁיבה ידי עליך ואצרף כבר סיגיך ואסירה כל בדיליך 25והשיב ידי עליכ ואצרפ כבר סוגיכ ואסיר כול בדיליכ + 26ואשׁיבה שׁפטיך כבראשׁנה ויעציך כבתחלה אחרי כן יקרא26ואשיבה שופטיכ כבראישונה ויעציכ כבתחלה אחרי כנ יקרא + > לך עיר הצדק קריה נאמנה >ו לכ עיר הצדק קריה נאמנה + 27ציון במשׁפט תפדה ושׁביה בצדקה 27ציונ במשפט תפדה ושביה בצדקה + 28ושׁבר פשׁעים וחטאים יחדו ועזבי יהוה יכלו 28ושבר פושעימ וחטאימ יחדו ועוזבי יהוה יכלו + 29כי יבשׁו מאילים אשׁר חמדתם ותחפרו מהגנות אשׁר בחרת29כיא יבושו מאלימ אשר חמדתמ ותחפורו מהגנות אשר בחרתמ + >ם   + 30כי תהיו כאלה נבלת עלה וכגנה אשׁר מים אין לה 30כי תהיו כאלה נובלת עלה וכגנה אשר אינ מימ לה + 31והיה החסן לנערת ופעלו לניצוץ ובערו שׁניהם יחדו ואי31והיה החסנכמ לנעורת ופעלכמ לניצוצ ובערו שניהמ יחדו  + >ן מכבה ס >ואינ מכבה - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 2 MT
Isaiah 2 1QIsaa
n1הדבר אשר חזה ישעיהו בנ אמוצ על יהודה וירושלמn1הדבר אשר חזה ישעיה בנ אמוצ על יהודה וירושלימ
2והיה באחרית הימימ נכונ יהיה הר בית יהוה בראש ההרימ2והיה באחרית הימימ נכונ יהיה הר בית יהוה בראש הרימ 
> ונשא מגבעות ונהרו אליו כל הגוימ>ונשא מגבעות ונהרו עלוהי כול הגואימ
3והלכו עמימ רבימ ואמרו לכו ונעלה אל הר יהוה אל בית 3והלכו עמימ רבימ ואמרו לכו ונעלה אל בית אלוהי יעקוב
>אלהי יעקב וירנו מדרכיו ונלכה בארחתיו כי מציונ תצא > וירונו מדרכיו ונאלכה באורחתיו כיא מציונ תצא תורה 
>תורה ודבר יהוה מירושלמ>ודבר יהוה מירושלימ
4ושפט בינ הגוימ והוכיח לעמימ רבימ וכתתו חרבותמ לאתי4ושפט בינ הגואימ וה והוכיח לעמימ רבימ וכתתו את חרבו
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5בית יעקב לכו ונלכה באור יהוה5בית יעקוב לכו ונלכה באור יהוה
6כי נטשתה עמכ בית יעקב כי מלאו מקדמ ועננימ כפלשתימ 6כיא נטשתה עמכ בית יעקוב כיא מלאו מקדמ ועוננימ כפלש
>ובילדי נכרימ ישפיקו>תימ ובילדי נכריאימ ישפיקו
7ותמלא ארצו כספ וזהב ואינ קצה לאצרתיו ותמלא ארצו סו7ותמלא ארצו כספ וזהב ואינ קצ לאוצרותיו ותמלא ארצו ס
>סימ ואינ קצה למרכבתיו>וסימ ואינ קצ למרכבותיו
8ותמלא ארצו אלילימ למעשה ידיו ישתחוו לאשר עשו אצבעת8ותמלא ארצו אלילימ למעשה ידיו ישתחוו לאשר עשו אצבעו
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9וישח אדמ וישפל איש ואל תשא להמ9וישח אדמ וישפל איש
10בוא בצור והטמנ בעפר מפני פחד יהוה ומהדר גאנו
11עיני גבהות אדמ שפל ושח רומ אנשימ ונשגב יהוה לבדו ב10ועיני גבהות אדמ תשפלנה וישח רומ אנשימ ונשגב יהוה ל
>יומ ההוא>בדו ביומ ההוא
12כי יומ ליהוה צבאות על כל גאה ורמ ועל כל נשא ושפל11כיא יומ ליהוה צבאות על כל גאה ורמ ונשא ושפל
13ועל כל ארזי הלבנונ הרמימ והנשאימ ועל כל אלוני הבשנ12ועל כל ארזי הלבנונ הרמימ והנשאימ ועל כל אלוני הבשנ
n14ועל כל ההרימ הרמימ ועל כל הגבעות הנשאותn13ועל כל ההרימ הרומימ ועל כול הגבעות הנשאות
15ועל כל מגדל גבה ועל כל חומה בצורה14ועל כול מגדל גבה ועל כול חומה בצורה
16ועל כל אניות תרשיש ועל כל שכיות החמדה15ועל כול אניות תרשיש ועל כול שכיות החמדה
17ושח גבהות האדמ ושפל רומ אנשימ ונשגב יהוה לבדו ביומ16ושח גבהות האדמ ושפל רומ אנשימ ונשגב יהוה לבדו ביומ
> ההוא> ההוא
t18והאלילימ כליל יחלפt17והאלילימ כליל יחלופו
19ובאו במערות צרימ ובמחלות עפר מפני פחד יהוה ומהדר ג18ובאו במערות צורימ ובמחלות עפר מפני פחד יהוה ומהדר 
>אונו בקומו לערצ הארצ>גאונו בקומו לערוצ הארצ
20ביומ ההוא ישליכ האדמ את אלילי כספו ואת אלילי זהבו 19ביומ ההוא ישליכ האדמ את אלילי כספו ואת אלילי זהבו 
>אשר עשו לו להשתחות לחפר פרות ולעטלפימ>אשר עשו אצבעותיו להשתחות לחפרפרימ ולעטלפימ
21לבוא בנקרות הצרימ ובסעפי הסלעימ מפני פחד יהוה ומהד20לבוא בנקרות הצורימ ובסעפי הסלעימ מפני פחד יהוה ומה
>ר גאונו בקומו לערצ הארצ>דר גאונו בקומו לערוצ הארצ
22חדלו לכמ מנ האדמ אשר נשמה באפו כי במה נחשב הוא21חדלו לכמה מנ האדמ אשר נשמה באפו כיא במה נחשב הוא
t1הדבר אשׁר חזה ישׁעיהו בן אמוץ על יהודה וירושׁלם t1הדבר אשר חזה ישעיה בנ אמוצ על יהודה וירושלימ
2והיה׀ באחרית הימים נכון יהיה הר בית יהוה בראשׁ ההר2והיה באחרית הימימ נכונ יהיה הר בית יהוה בראש הרימ 
>ים ונשׂא מגבעות ונהרו אליו כל הגוים >ונשא מגבעות ונהרו עלוהי כול הגואימ
3והלכו עמים רבים ואמרו לכו׀ ונעלה אל הר יהוה אל בית3והלכו עמימ רבימ ואמרו לכו ונעלה אל בית אלוהי יעקוב
> אלהי יעקב וירנו מדרכיו ונלכה בארחתיו כי מציון תצא> וירונו מדרכיו ונאלכה באורחתיו כיא מציונ תצא תורה 
> תורה ודבר יהוה מירושׁלם >ודבר יהוה מירושלימ
4ושׁפט בין הגוים והוכיח לעמים רבים וכתתו חרבותם לאת4ושפט בינ הגואימ וה והוכיח לעמימ רבימ וכתתו את חרבו
>ים וחניתותיהם למזמרות לא ישׂא גוי אל גוי חרב ולא י>תמ לאתימ וחניתותיהמ למזמרות ולוא ישא גוי אל גוי חר
>למדו עוד מלחמה פ >ב ולוא ילמדו עוד מלחמה
5בית יעקב לכו ונלכה באור יהוה 5בית יעקוב לכו ונלכה באור יהוה
6כי נטשׁתה עמך בית יעקב כי מלאו מקדם ועננים כפלשׁתי6כיא נטשתה עמכ בית יעקוב כיא מלאו מקדמ ועוננימ כפלש
>ם ובילדי נכרים ישׂפיקו >תימ ובילדי נכריאימ ישפיקו
7ותמלא ארצו כסף וזהב ואין קצה לאצרתיו ותמלא ארצו סו7ותמלא ארצו כספ וזהב ואינ קצ לאוצרותיו ותמלא ארצו ס
>סים ואין קצה למרכבתיו >וסימ ואינ קצ למרכבותיו
8ותמלא ארצו אלילים למעשׂה ידיו ישׁתחוו לאשׁר עשׂו א8ותמלא ארצו אלילימ למעשה ידיו ישתחוו לאשר עשו אצבעו
>צבעתיו >תיו
9וישׁח אדם וישׁפל אישׁ ואל תשׂא להם 9וישח אדמ וישפל איש
10בוא בצור והטמן בעפר מפני פחד יהוה ומהדר גאנו 
11עיני גבהות אדם שׁפל ושׁח רום אנשׁים ונשׂגב יהוה לב10ועיני גבהות אדמ תשפלנה וישח רומ אנשימ ונשגב יהוה ל
>דו ביום ההוא ס >בדו ביומ ההוא
12כי יום ליהוה צבאות על כל גאה ורם ועל כל נשׂא ושׁפל11כיא יומ ליהוה צבאות על כל גאה ורמ ונשא ושפל
>  
13ועל כל ארזי הלבנון הרמים והנשׂאים ועל כל אלוני הבש12ועל כל ארזי הלבנונ הרמימ והנשאימ ועל כל אלוני הבשנ
>ׁן  
14ועל כל ההרים הרמים ועל כל הגבעות הנשׂאות 13ועל כל ההרימ הרומימ ועל כול הגבעות הנשאות
15ועל כל מגדל גבה ועל כל חומה בצורה 14ועל כול מגדל גבה ועל כול חומה בצורה
16ועל כל אניות תרשׁישׁ ועל כל שׂכיות החמדה 15ועל כול אניות תרשיש ועל כול שכיות החמדה
17ושׁח גבהות האדם ושׁפל רום אנשׁים ונשׂגב יהוה לבדו 16ושח גבהות האדמ ושפל רומ אנשימ ונשגב יהוה לבדו ביומ
>ביום ההוא > ההוא
18והאלילים כליל יחלף 17והאלילימ כליל יחלופו
19ובאו במערות צרים ובמחלות עפר מפני פחד יהוה ומהדר ג18ובאו במערות צורימ ובמחלות עפר מפני פחד יהוה ומהדר 
>אונו בקומו לערץ הארץ >גאונו בקומו לערוצ הארצ
20ביום ההוא ישׁליך האדם את אלילי כספו ואת אלילי זהבו19ביומ ההוא ישליכ האדמ את אלילי כספו ואת אלילי זהבו 
> אשׁר עשׂו לו להשׁתחות לחפר פרות ולעטלפים >אשר עשו אצבעותיו להשתחות לחפרפרימ ולעטלפימ
21לבוא בנקרות הצרים ובסעפי הסלעים מפני פחד יהוה ומהד20לבוא בנקרות הצורימ ובסעפי הסלעימ מפני פחד יהוה ומה
>ר גאונו בקומו לערץ הארץ >דר גאונו בקומו לערוצ הארצ
22חדלו לכם מן האדם אשׁר נשׁמה באפו כי במה נחשׁב הוא 21חדלו לכמה מנ האדמ אשר נשמה באפו כיא במה נחשב הוא
>פ  
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Isaiah 3 MT
Isaiah 3 1QIsaa
n1כי הנה האדונ יהוה צבאות מסיר מירושלמ ומיהודה משענ n1כיא הנה האדונ יהוה צבאות מהסיר מירושלמ ומיהודה משע
>ומשענה כל משענ לחמ וכל משענ מימ>נ ומשענה כל משענ לחמ וכול משענ מימ
2גבור ואיש מלחמה שופט ונביא וקסמ וזקנ2גבור ואיש מלחמה שופט ונביא וקוסמ וזקנ
3שר חמשימ ונשוא פנימ ויועצ וחכמ חרשימ ונבונ לחש3שר חמשימ ונשא פנימ ויעצ וחכמ חרשימ ונבונ לחש
4ונתתי נערימ שריהמ ותעלולימ ימשלו במ4ונתתי נערימ שריהמ ותעלולימ ימשולו במ
5ונגש העמ איש באיש ואיש ברעהו ירהבו הנער בזקנ והנקל5ונגש העמ איש באיש ואיש ברעהו ירהבו הנער בזקנ והנקל
>ה בנכבד>ה בנכבד
t6כי יתפש איש באחיו בית אביו שמלה לכה קצינ תהיה לנו t6כיא יתפוש איש באחיהו בית אביו שמלה לכה קצינ תהיה ל
>והמכשלה הזאת תחת ידכ>נו והמכשלה הזאות תחת ידיכ
7ישא ביומ ההוא לאמר לא אהיה חבש ובביתי אינ לחמ ואינ7וישא ביומ ההוא לאמור לוא אהיה חובש ובביתי אינ לחמ 
> שמלה לא תשימני קצינ עמ>ואינ שלמה לוא תשימוני קצינ עמ
8כי כשלה ירושלמ ויהודה נפל כי לשונמ ומעלליהמ אל יהו8כי כשלה ירושלימ ויהודה נפלה כי לשונמ ומעלליהמ על י
>ה למרות עני כבודו>הוה למרות עיני כבודו
9הכרת פניהמ ענתה במ וחטאתמ כסדמ הגידו לא כחדו אוי ל9הכרות פניהמ ענתה במ וחטאתמ כסודמ הגידו ולוא כחדו א
>נפשמ כי גמלו להמ רעה>וי לנפשמ כיא גמלו להמ רעה
10אמרו צדיק כי טוב כי פרי מעלליהמ יאכלו10אמורו לצדיק כיא טוב כיא פרי מעלליהמה יאכלו
11אוי לרשע רע כי גמול ידיו יעשה לו11אוי לרשע רע כיא גמול ידו ישוב לוא
12עמי נגשיו מעולל ונשימ משלו בו עמי מאשריכ מתעימ ודר12עמי נגשו מעולל ונשימ משלו בו עמי משריכ מתעימ ודרכי
>כ ארחתיכ בלעו> אורחותיכ בלעו
13נצב לריב יהוה ועמד לדינ עמימ13נצב לריב יהוה עומד לדינ עמימ
14יהוה במשפט יבוא עמ זקני עמו ושריו ואתמ בערתמ הכרמ 14יהוה במשפט יבוא עמ זקני עמו ושריו ואתמה בערתמ הכרמ
>גזלת העני בבתיכמ> גזלת העני בבתיכמ
15מה לכמ תדכאו עמי ופני עניימ תטחנו נאמ אדני יהוה צב15מלכמה תדכאו עמי ופני עניימ תטחנו נואמ אדוני יהוה צ
>אות>באות
16ויאמר יהוה יענ כי גבהו בנות ציונ ותלכנה נטויות גרו16ויואמר יהוה יענ כיא גבהו בנות ציונ ותלכנה נטיות גר
>נ ומשקרות עינימ הלוכ וטפפ תלכנה וברגליהמ תעכסנה>ונ ומשקרות עינימ הלוכ וטופפ תלכנה וברגליהנה תעכסנה
17ושפח אדני קדקד בנות ציונ ויהוה פתהנ יערה17ושפח יהוה קדקד בנות ציונ ואדוני פתהנ יערה
18ביומ ההוא יסיר אדני את תפארת העכסימ והשביסימ והשהר18ביומ ההוא יסיר אדוני את תפארת העכיסימ והשבישימ והש
>נימ>הרנימ
19הנטיפות והשירות והרעלות19והנטפות והשירות והרעלות
20הפארימ והצעדות והקשרימ ובתי הנפש והלחשימ20והפארימ והצעדות וקשרימ ובתי הנפש והלחשימ
21הטבעות ונזמי האפ21והטבעות ונזמי האפ
22המחלצות והמעטפות והמטפחות והחריטימ22והמחלצות והמעטפות והחריטימ
23והגלינימ והסדינימ והצניפות והרדידימ23והגליונימ והסדינימ והצניפות והרדידימ
24והיה תחת בשמ מק יהיה ותחת חגורה נקפה ותחת מעשה מקש24ויהיו תחת הבשמ מק ותחות הגורה נקפה ותחות מעשה מקשה
>ה קרחה ותחת פתיגיל מחגרת שק כי תחת יפי> קרחה ותחת פתיגיל מחגרת שק כי תחת יפי בשת
25מתיכ בחרב יפלו וגבורתכ במלחמה25מתיכ בחרב יפולו וגבורותיכ במלחמה
26ואנו ואבלו פתחיה ונקתה לארצ תשב26ואנו ואבלו פתחיה ונקתה לארצ תשב
t1כי הנה האדון יהוה צבאות מסיר מירושׁלם ומיהודה משׁעt1כיא הנה האדונ יהוה צבאות מהסיר מירושלמ ומיהודה משע
>ן ומשׁענה כל משׁען לחם וכל משׁען מים >נ ומשענה כל משענ לחמ וכול משענ מימ
2גבור ואישׁ מלחמה שׁופט ונביא וקסם וזקן 2גבור ואיש מלחמה שופט ונביא וקוסמ וזקנ
3שׂר חמשׁים ונשׂוא פנים ויועץ וחכם חרשׁים ונבון לחש3שר חמשימ ונשא פנימ ויעצ וחכמ חרשימ ונבונ לחש
>ׁ  
4ונתתי נערים שׂריהם ותעלולים ימשׁלו בם 4ונתתי נערימ שריהמ ותעלולימ ימשולו במ
5ונגשׂ העם אישׁ באישׁ ואישׁ ברעהו ירהבו הנער בזקן ו5ונגש העמ איש באיש ואיש ברעהו ירהבו הנער בזקנ והנקל
>הנקלה בנכבד >ה בנכבד
6כי יתפשׂ אישׁ באחיו בית אביו שׂמלה לכה קצין תהיה ל6כיא יתפוש איש באחיהו בית אביו שמלה לכה קצינ תהיה ל
>נו והמכשׁלה הזאת תחת ידך >נו והמכשלה הזאות תחת ידיכ
7ישׂא ביום ההוא׀ לאמר לא אהיה חבשׁ ובביתי אין לחם ו7וישא ביומ ההוא לאמור לוא אהיה חובש ובביתי אינ לחמ 
>אין שׂמלה לא תשׂימני קצין עם >ואינ שלמה לוא תשימוני קצינ עמ
8כי כשׁלה ירושׁלם ויהודה נפל כי לשׁונם ומעלליהם אל 8כי כשלה ירושלימ ויהודה נפלה כי לשונמ ומעלליהמ על י
>יהוה למרות עני כבודו >הוה למרות עיני כבודו
9הכרת פניהם ענתה בם וחטאתם כסדם הגידו לא כחדו אוי ל9הכרות פניהמ ענתה במ וחטאתמ כסודמ הגידו ולוא כחדו א
>נפשׁם כי גמלו להם רעה >וי לנפשמ כיא גמלו להמ רעה
10אמרו צדיק כי טוב כי פרי מעלליהם יאכלו 10אמורו לצדיק כיא טוב כיא פרי מעלליהמה יאכלו
11אוי לרשׁע רע כי גמול ידיו יעשׂה לו 11אוי לרשע רע כיא גמול ידו ישוב לוא
12עמי נגשׂיו מעולל ונשׁים משׁלו בו עמי מאשׁריך מתעים12עמי נגשו מעולל ונשימ משלו בו עמי משריכ מתעימ ודרכי
> ודרך ארחתיך בלעו ס > אורחותיכ בלעו
13נצב לריב יהוה ועמד לדין עמים 13נצב לריב יהוה עומד לדינ עמימ
14יהוה במשׁפט יבוא עם זקני עמו ושׂריו ואתם בערתם הכר14יהוה במשפט יבוא עמ זקני עמו ושריו ואתמה בערתמ הכרמ
>ם גזלת העני בבתיכם > גזלת העני בבתיכמ
15מלכם תדכאו עמי ופני עניים תטחנו נאם אדני יהוה צבאו15מלכמה תדכאו עמי ופני עניימ תטחנו נואמ אדוני יהוה צ
>ת ס >באות
16ויאמר יהוה יען כי גבהו בנות ציון ותלכנה נטוות גרון16ויואמר יהוה יענ כיא גבהו בנות ציונ ותלכנה נטיות גר
> ומשׂקרות עינים הלוך וטפף תלכנה וברגליהם תעכסנה >ונ ומשקרות עינימ הלוכ וטופפ תלכנה וברגליהנה תעכסנה
17ושׂפח אדני קדקד בנות ציון ויהוה פתהן יערה ס 17ושפח יהוה קדקד בנות ציונ ואדוני פתהנ יערה
18ביום ההוא יסיר אדני את תפארת העכסים והשׁביסים והשׂ18ביומ ההוא יסיר אדוני את תפארת העכיסימ והשבישימ והש
>הרנים >הרנימ
19הנטיפות והשׁירות והרעלות 19והנטפות והשירות והרעלות
20הפארים והצעדות והקשׁרים ובתי הנפשׁ והלחשׁים 20והפארימ והצעדות וקשרימ ובתי הנפש והלחשימ
21הטבעות ונזמי האף 21והטבעות ונזמי האפ
22המחלצות והמעטפות והמטפחות והחריטים 22והמחלצות והמעטפות והחריטימ
23והגלינים והסדינים והצניפות והרדידים 23והגליונימ והסדינימ והצניפות והרדידימ
24והיה תחת בשׂם מק יהיה ותחת חגורה נקפה ותחת מעשׂה מ24ויהיו תחת הבשמ מק ותחות הגורה נקפה ותחות מעשה מקשה
>קשׁה קרחה ותחת פתיגיל מחגרת שׂק כי תחת יפי > קרחה ותחת פתיגיל מחגרת שק כי תחת יפי בשת
25מתיך בחרב יפלו וגבורתך במלחמה 25מתיכ בחרב יפולו וגבורותיכ במלחמה
26ואנו ואבלו פתחיה ונקתה לארץ תשׁב 26ואנו ואבלו פתחיה ונקתה לארצ תשב
- - - - - - - - - - - + + + + + + + + + + + + +

Isaiah 4 MT
Isaiah 4 1QIsaa
t1והחזיקו שבע נשימ באיש אחד ביומ ההוא לאמר לחמנו נאכt1והחזיקה שבע נשימ באיש אחד ביומ ההוא לאמור לחמנו נא
>ל ושמלתנו נלבש רק יקרא שמכ עלינו אספ חרפתנו>כל ושלמתנו נלבש רק יקרא שמכ עלינו אספ חרפתנו
2ביומ ההוא יהיה צמח יהוה לצבי ולכבוד ופרי הארצ לגאו2ביומ ההוא יהיה צמח יהוה לצבי ולכבוד ופרי הארצ לגאו
>נ ולתפארת לפליטת ישראל>נ ולתפארת לפליטת ישראל ויהודה
3והיה הנשאר בציונ והנותר בירושלמ קדוש יאמר לו כל הכ3ויהיה הנשאר בציונ והנותר בירושלמ קדוש יאמר לו כול 
>תוב לחיימ בירושלמ>הכתוב לחיימ בירושלמ
4אמ רחצ אדני את צאת בנות ציונ ואת דמי ירושלמ ידיח מ4אמ רחצ אדוני את צאת בנות ציונ ואת דמי ירושלמ ידיח 
>קרבה ברוח משפט וברוח בער>מקרבה ברוח משפט וברוח סער
5וברא יהוה על כל מכונ הר ציונ ועל מקראה עננ יוממ וע5ויברא יהוה על כול מכונ הר ציונ ועל מקראה עננ יוממ
>שנ ונגה אש להבה לילה כי על כל כבוד חפה 
6וסכה תהיה לצל יוממ מחרב ולמחסה ולמסתור מזרמ וממטר6מחרב ולמחסה ולמסתור מזרמ וממטר
t1והחזיקו שׁבע נשׁים באישׁ אחד ביום ההוא לאמר לחמנו t1והחזיקה שבע נשימ באיש אחד ביומ ההוא לאמור לחמנו נא
>נאכל ושׂמלתנו נלבשׁ רק יקרא שׁמך עלינו אסף חרפתנו >כל ושלמתנו נלבש רק יקרא שמכ עלינו אספ חרפתנו
>ס  
2ביום ההוא יהיה צמח יהוה לצבי ולכבוד ופרי הארץ לגאו2ביומ ההוא יהיה צמח יהוה לצבי ולכבוד ופרי הארצ לגאו
>ן ולתפארת לפליטת ישׂראל >נ ולתפארת לפליטת ישראל ויהודה
3והיה׀ הנשׁאר בציון והנותר בירושׁלם קדושׁ יאמר לו כ3ויהיה הנשאר בציונ והנותר בירושלמ קדוש יאמר לו כול 
>ל הכתוב לחיים בירושׁלם >הכתוב לחיימ בירושלמ
4אם׀ רחץ אדני את צאת בנות ציון ואת דמי ירושׁלם ידיח4אמ רחצ אדוני את צאת בנות ציונ ואת דמי ירושלמ ידיח 
> מקרבה ברוח משׁפט וברוח בער >מקרבה ברוח משפט וברוח סער
5וברא יהוה על כל מכון הר ציון ועל מקראה ענן׀ יומם ו5ויברא יהוה על כול מכונ הר ציונ ועל מקראה עננ יוממ
>עשׁן ונגה אשׁ להבה לילה כי על כל כבוד חפה  
6וסכה תהיה לצל יומם מחרב ולמחסה ולמסתור מזרם וממטר 6מחרב ולמחסה ולמסתור מזרמ וממטר
>פ  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 5 MT
Isaiah 5 1QIsaa
n1אשירה נא לידידי שירת דודי לכרמו כרמ היה לידידי בקרn1אשירה לידידי שירת דודי לכרמו כרמ היהא לידידי בקרנ 
>נ בנ שמנ>בנ שמנ
2ויעזקהו ויסקלהו ויטעהו שרק ויבנ מגדל בתוכו וגמ יקב2ויעזקהו ויסקולהו ויטעהו שורק ויבנא מגדל בתוכו וגמ 
> חצב בו ויקו לעשות ענבימ ויעש באשימ>יקב חצב בו ויקו לעשות ענבימ ויעשה באושימ
3ועתה יושב ירושלמ ואיש יהודה שפטו נא ביני ובינ כרמי3ועתה יושבי ירושלמ ואיש יהודה שפוטונה ביני ובינ כרמ
 >י
4מה לעשות עוד לכרמי ולא עשיתי בו מדוע קויתי לעשות ע4מה לעשות עוד בכרמי ולוא עשיתי בו מדוע קויתי לעשות 
>נבימ ויעש באשימ>ענבימ וישה באושימ
5ועתה אודיעה נא אתכמ את אשר אני עשה לכרמי הסר משוכת5ואתה אודיע נא אתכמה את אשר אני עושא לכרמי אסיר מסו
>ו והיה לבער פרצ גדרו והיה למרמס>כתו ויהיה בער פרצ גדרו ויהיה למרמס
6ואשיתהו בתה לא יזמר ולא יעדר ועלה שמיר ושית ועל הע6ואשיתהו בתה ולוא יזמר ולוא יעדר ועלה שמיר ושית ועל
>בימ אצוה מהמטיר עליו מטר> העבימ אצוה מהמטיר עליו מטר
7כי כרמ יהוה צבאות בית ישראל ואיש יהודה נטע שעשועיו7כי כרמ יהוה צבאות בית ישראל ואיש יהודה נטע שעשועו 
> ויקו למשפט והנה משפח לצדקה והנה צעקה>ויקו למשפט והנה למשפח לצדקה והנה צעקה
8הוי מגיעי בית בבית שדה בשדה יקריבו עד אפס מקומ והו8הוי מגיעי בית בית שדה בשדה יקריבו עד אפס מקומ וישת
>שבתמ לבדכמ בקרב הארצ>מ לבדכמ בקרב הארצ
9באזני יהוה צבאות אמ לא בתימ רבימ לשמה יהיו גדלימ ו9באוזני יהוה צבאות אמ לוא בתימ רבימ לשמה יהיו גדולי
>טובימ מאינ יושב>מ וטובימ מאינ יושב
10כי עשרת צמדי כרמ יעשו בת אחת וזרע חמר יעשה איפה10כי עשרת צמדי כרמ יעשו בת אחד וזרע חמר יעשה איפה
11הוי משכימי בבקר שכר ירדפו מאחרי בנשפ יינ ידליקמ11הוי משכימי בבקר שכר ירדופו מאחזי בנשפ יינ ידליקמ
12והיה כנור ונבל תפ וחליל ויינ משתיהמ ואת פעל יהוה ל12והיה כנור ונבל וחליל ויינ משתיהמ ואת פעלת יהוה לוא
>א יביטו ומעשה ידיו לא ראו> הביטו ומעשה ידיו לוא ראו
13לכנ גלה עמי מבלי דעת וכבודו מתי רעב והמונו צחה צמא13לכנ גלה עמי מבלי דעת וכבודי מתי רעב והמונו צחה צמא
14לכנ הרחיבה שאול נפשה ופערה פיה לבלי חק וירד הדרה ו14לכנ הרחיבה שאול נפשה ופערה פיה לבלי חוק וירד הדרה 
>המונה ושאונה ועלז בה>והמונה ושאונה ועלז בה
15וישח אדמ וישפל איש ועיני גבהימ תשפלנה15ישח אדמ וישפל איש ועיני גבהימ תשפלנה
16ויגבה יהוה צבאות במשפט והאל הקדוש נקדש בצדקה16ויגבה יהוה צבאות במשפט והאל הקדוש נקדש בצדקה
n17ורעו כבשימ כדברמ וחרבות מחימ גרימ יאכלוn17ורעו כבושימ כדברמ וחרבות מיחימ גרימ יאכלו
18הוי משכי העונ בחבלי השוא וכעבות העגלה חטאה18הוי משכי העוונ בחבלי השי וכעבות העגלה חטאה
19האמרימ ימהר יחישה מעשהו למענ נראה ותקרב ותבואה עצת19האומרימ ימהר יחיש מעשיהו למענ נראה ותקרבה ותבואה ע
> קדוש ישראל ונדעה>צת קדוש ישראל ונדע
20הוי האמרימ לרע טוב ולטוב רע שמימ חשכ לאור ואור לחש20הוי האומרימ לרע טוב ולטוב רע שמימ חושכ לאור ואור ל
>כ שמימ מר למתוק ומתוק למר>חושכ שמימ מר למתוק ומתוק למר
21הוי חכמימ בעיניהמ ונגד פניהמ נבנימ21הוי חכמימ בעיניהמ ונגד פניהמ נבונימ
22הוי גבורימ לשתות יינ ואנשי חיל למסכ שכר22הוי גבורימ לשתות יינ ואנשי חיל למסכ שכר
t23מצדיקי רשע עקב שחד וצדקת צדיקימ יסירו ממנוt23מצדיקי רשע עקב שחוד וצדקת צדיקימ יסירו ממנו
24לכנ כאכל קש לשונ אש וחשש להבה ירפה שרשמ כמק יהיה ו24לכנ כאכל קש לשונ אש ואש לוהבת ירפה שרשמ כמק יהיה ו
>פרחמ כאבק יעלה כי מאסו את תורת יהוה צבאות ואת אמרת>פרחמ כאבק יעלה כיא מאסו את תורת יהוה צבאות ואת אמר
> קדוש ישראל נאצו>ת קדוש ישראל נאצו
25על כנ חרה אפ יהוה בעמו ויט ידו עליו ויכהו וירגזו ה25על כנ חרה אפ יהוה בעמו ויט ידיו עליו ויכהו וירגזו 
>הרימ ותהי נבלתמ כסוחה בקרב חוצות בכל זאת לא שב אפו>ההרימ ותהיה נבלתמ כסוחה בקרב חוצות בכול זאות לוא ש
> ועוד ידו נטויה>ב אפו ועוד ידיו נטויה
26ונשא נס לגוימ מרחוק ושרק לו מקצה הארצ והנה מהרה קל26ונשא נס לגואימ מרחוק ושרק לוא מקצה הארצ והנה מהרה 
> יבוא>קל יבוא
27אינ עיפ ואינ כושל בו לא ינומ ולא יישנ ולא נפתח אזו27אינ יעפ ואינ כושל ולוא ינומ ולוא יישנ ולוא נפתחה א
>ר חלציו ולא נתק שרוכ נעליו>זור חלציו ולוא נתק שרוכ נעליו
28אשר חציו שנונימ וכל קשתתיו דרכות פרסות סוסיו כצר נ28אשר חציו שנונימ וכול קשתותיו דרוכות פרסות סוסיו כצ
>חשבו וגלגליו כסופה>ור נחשבו וגלגליו כסופה
29שאגה לו כלביא ישאג ככפירימ וינהמ ויאחז טרפ ויפליט 29שאגה לו כלביא ישאג וככפירימ ינהמ ויאחז טרפ ויפליט 
>ואינ מציל>ואינ מציל
30וינהמ עליו ביומ ההוא כנהמת ימ ונבט לארצ והנה חשכ צ30ינהמ עליו ביומ ההוא כנהמת ימ ונבט לארצ והנה חושכ צ
>ר ואור חשכ בעריפיה>ר ואור חשכ בעריפיה
t1אשׁירה נא לידידי שׁירת דודי לכרמו כרם היה לידידי בt1אשירה לידידי שירת דודי לכרמו כרמ היהא לידידי בקרנ 
>קרן בן שׁמן >בנ שמנ
2ויעזקהו ויסקלהו ויטעהו שׂרק ויבן מגדל בתוכו וגם יק2ויעזקהו ויסקולהו ויטעהו שורק ויבנא מגדל בתוכו וגמ 
>ב חצב בו ויקו לעשׂות ענבים ויעשׂ באשׁים >יקב חצב בו ויקו לעשות ענבימ ויעשה באושימ
3ועתה יושׁב ירושׁלם ואישׁ יהודה שׁפטו נא ביני ובין 3ועתה יושבי ירושלמ ואיש יהודה שפוטונה ביני ובינ כרמ
>כרמי >י
4מה לעשׂות עוד לכרמי ולא עשׂיתי בו מדוע קויתי לעשׂו4מה לעשות עוד בכרמי ולוא עשיתי בו מדוע קויתי לעשות 
>ת ענבים ויעשׂ באשׁים >ענבימ וישה באושימ
5ועתה אודיעה נא אתכם את אשׁר אני עשׂה לכרמי הסר משׂ5ואתה אודיע נא אתכמה את אשר אני עושא לכרמי אסיר מסו
>וכתו והיה לבער פרץ גדרו והיה למרמס >כתו ויהיה בער פרצ גדרו ויהיה למרמס
6ואשׁיתהו בתה לא יזמר ולא יעדר ועלה שׁמיר ושׁית ועל6ואשיתהו בתה ולוא יזמר ולוא יעדר ועלה שמיר ושית ועל
> העבים אצוה מהמטיר עליו מטר > העבימ אצוה מהמטיר עליו מטר
7כי כרם יהוה צבאות בית ישׂראל ואישׁ יהודה נטע שׁעשׁ7כי כרמ יהוה צבאות בית ישראל ואיש יהודה נטע שעשועו 
>ועיו ויקו למשׁפט והנה משׂפח לצדקה והנה צעקה ס >ויקו למשפט והנה למשפח לצדקה והנה צעקה
8הוי מגיעי בית בבית שׂדה בשׂדה יקריבו עד אפס מקום ו8הוי מגיעי בית בית שדה בשדה יקריבו עד אפס מקומ וישת
>הושׁבתם לבדכם בקרב הארץ >מ לבדכמ בקרב הארצ
9באזני יהוה צבאות אם לא בתים רבים לשׁמה יהיו גדלים 9באוזני יהוה צבאות אמ לוא בתימ רבימ לשמה יהיו גדולי
>וטובים מאין יושׁב >מ וטובימ מאינ יושב
10כי עשׂרת צמדי כרם יעשׂו בת אחת וזרע חמר יעשׂה איפה10כי עשרת צמדי כרמ יעשו בת אחד וזרע חמר יעשה איפה
> פ  
11הוי משׁכימי בבקר שׁכר ירדפו מאחרי בנשׁף יין ידליקם11הוי משכימי בבקר שכר ירדופו מאחזי בנשפ יינ ידליקמ
>  
12והיה כנור ונבל תף וחליל ויין משׁתיהם ואת פעל יהוה 12והיה כנור ונבל וחליל ויינ משתיהמ ואת פעלת יהוה לוא
>לא יביטו ומעשׂה ידיו לא ראו > הביטו ומעשה ידיו לוא ראו
13לכן גלה עמי מבלי דעת וכבודו מתי רעב והמונו צחה צמא13לכנ גלה עמי מבלי דעת וכבודי מתי רעב והמונו צחה צמא
>  
14לכן הרחיבה שׁאול נפשׁה ופערה פיה לבלי חק וירד הדרה14לכנ הרחיבה שאול נפשה ופערה פיה לבלי חוק וירד הדרה 
> והמונה ושׁאונה ועלז בה >והמונה ושאונה ועלז בה
15וישׁח אדם וישׁפל אישׁ ועיני גבהים תשׁפלנה 15ישח אדמ וישפל איש ועיני גבהימ תשפלנה
16ויגבה יהוה צבאות במשׁפט והאל הקדושׁ נקדשׁ בצדקה 16ויגבה יהוה צבאות במשפט והאל הקדוש נקדש בצדקה
17ורעו כבשׂים כדברם וחרבות מחים גרים יאכלו 17ורעו כבושימ כדברמ וחרבות מיחימ גרימ יאכלו
18הוי משׁכי העון בחבלי השׁוא וכעבות העגלה חטאה 18הוי משכי העוונ בחבלי השי וכעבות העגלה חטאה
19האמרים ימהר׀ יחישׁה מעשׂהו למען נראה ותקרב ותבואה 19האומרימ ימהר יחיש מעשיהו למענ נראה ותקרבה ותבואה ע
>עצת קדושׁ ישׂראל ונדעה ס >צת קדוש ישראל ונדע
20הוי האמרים לרע טוב ולטוב רע שׂמים חשׁך לאור ואור ל20הוי האומרימ לרע טוב ולטוב רע שמימ חושכ לאור ואור ל
>חשׁך שׂמים מר למתוק ומתוק למר ס >חושכ שמימ מר למתוק ומתוק למר
21הוי חכמים בעיניהם ונגד פניהם נבנים 21הוי חכמימ בעיניהמ ונגד פניהמ נבונימ
22הוי גבורים לשׁתות יין ואנשׁי חיל למסך שׁכר 22הוי גבורימ לשתות יינ ואנשי חיל למסכ שכר
23מצדיקי רשׁע עקב שׁחד וצדקת צדיקים יסירו ממנו ס 23מצדיקי רשע עקב שחוד וצדקת צדיקימ יסירו ממנו
24לכן כאכל קשׁ לשׁון אשׁ וחשׁשׁ להבה ירפה שׁרשׁם כמק24לכנ כאכל קש לשונ אש ואש לוהבת ירפה שרשמ כמק יהיה ו
> יהיה ופרחם כאבק יעלה כי מאסו את תורת יהוה צבאות ו>פרחמ כאבק יעלה כיא מאסו את תורת יהוה צבאות ואת אמר
>את אמרת קדושׁ ישׂראל נאצו >ת קדוש ישראל נאצו
25על כן חרה אף יהוה בעמו ויט ידו עליו ויכהו וירגזו ה25על כנ חרה אפ יהוה בעמו ויט ידיו עליו ויכהו וירגזו 
>הרים ותהי נבלתם כסוחה בקרב חוצות בכל זאת לא שׁב אפ>ההרימ ותהיה נבלתמ כסוחה בקרב חוצות בכול זאות לוא ש
>ו ועוד ידו נטויה >ב אפו ועוד ידיו נטויה
26ונשׂא נס לגוים מרחוק ושׁרק לו מקצה הארץ והנה מהרה 26ונשא נס לגואימ מרחוק ושרק לוא מקצה הארצ והנה מהרה 
>קל יבוא >קל יבוא
27אין עיף ואין כושׁל בו לא ינום ולא יישׁן ולא נפתח א27אינ יעפ ואינ כושל ולוא ינומ ולוא יישנ ולוא נפתחה א
>זור חלציו ולא נתק שׂרוך נעליו >זור חלציו ולוא נתק שרוכ נעליו
28אשׁר חציו שׁנונים וכל קשׁתתיו דרכות פרסות סוסיו כצ28אשר חציו שנונימ וכול קשתותיו דרוכות פרסות סוסיו כצ
>ר נחשׁבו וגלגליו כסופה >ור נחשבו וגלגליו כסופה
29שׁאגה לו כלביא ושׁאג ככפירים וינהם ויאחז טרף ויפלי29שאגה לו כלביא ישאג וככפירימ ינהמ ויאחז טרפ ויפליט 
>ט ואין מציל >ואינ מציל
30וינהם עליו ביום ההוא כנהמת ים ונבט לארץ והנה חשׁך 30ינהמ עליו ביומ ההוא כנהמת ימ ונבט לארצ והנה חושכ צ
>צר ואור חשׁך בעריפיה פ >ר ואור חשכ בעריפיה
- - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 6 MT
Isaiah 6 1QIsaa
t1בשנת מות המלכ עזיהו ואראה את אדני ישב על כסא רמ ונt1בשנת מות המלכ עוזיה אראה את אדוני יושב על כסאו רמ 
>שא ושוליו מלאימ את ההיכל>ונשא ושוליו מלאימ את ההיכל
2שרפימ עמדימ ממעל לו שש כנפימ שש כנפימ לאחד בשתימ י2שרפימ עומדימ ממעלה לו שש כנפימ לאחד בשתימ יכסה פני
>כסה פניו ובשתימ יכסה רגליו ובשתימ יעופפ>ו ובשתימ יכסה רגליו ובשתימ יעופפ
3וקרא זה אל זה ואמר קדוש קדוש קדוש יהוה צבאות מלא כ3וקראימ זה אל זה קדוש קדוש יהוה צבאות מלא כול הארצ 
>ל הארצ כבודו>כבודו
4וינעו אמות הספימ מקול הקורא והבית ימלא עשנ4וינועו אמות הספימ מקול הקורה והבית נמלא עשנ
5ואמר אוי לי כי נדמיתי כי איש טמא שפתימ אנכי ובתוכ 5ואמר אי לי כי נדמיתי כיא איש טמה שפתימ אנוכי ובתוכ
>עמ טמא שפתימ אנכי יושב כי את המלכ יהוה צבאות ראו ע> עמ טמא שפתימ אנוכי יושב כיא את המלכ יהוה צבאות רא
>יני>ו עיני
6ויעפ אלי אחד מנ השרפימ ובידו רצפה במלקחימ לקח מעל 6ויעופ אלי אחד מנ השרפימ ובידו רצפה במלקחימ לקח מעל
>המזבח> המזבח
7ויגע על פי ויאמר הנה נגע זה על שפתיכ וסר עונכ וחטא7ויגע על פי ויואמר הנה נגע זה על שפתיכ וסר עוונכ וח
>תכ תכפר>טאותיכ תכפר
8ואשמע את קול אדני אמר את מי אשלח ומי ילכ לנו ואמר 8ואשמע את קול אדוני אמר את מי אשלח ומי ילכ לנו ואמר
>הנני שלחני>ה הנני שלחני
9ויאמר לכ ואמרת לעמ הזה שמעו שמוע ואל תבינו וראו רא9ויואמר לכ ואמרתה לעמ הזה שמעו שמוע ועל תבינו ראו ר
>ו ואל תדעו>או ועל תדעו
10השמנ לב העמ הזה ואזניו הכבד ועיניו השע פנ יראה בעי10השמ לב העמ הזה ואוזניו הכבד ועיניו השע פנ יראה בעי
>ניו ובאזניו ישמע ולבבו יבינ ושב ורפא לו>ניו ובאוזניו ישמעו בלבבו יבינ ושב ורפא לו
11ואמר עד מתי אדני ויאמר עד אשר אמ שאו ערימ מאינ יוש11ואמרה עד מתי יהוה ויואמר עד אשר אמ שאו ערימ מאינ י
>ב ובתימ מאינ אדמ והאדמה תשאה שממה>ושב ובתימ מאינ אדמ והאדמה תשאה שממה
12ורחק יהוה את האדמ ורבה העזובה בקרב הארצ12ורחק יהוה את האדמ ורבה עזובה בקרב הארצ
13ועוד בה עשריה ושבה והיתה לבער כאלה וכאלונ אשר בשלכ13ועוד בה עשיריה ושבה והייתה לבער כאלה וכאלונ אשר מש
>ת מצבת במ זרע קדש מצבתה>לכת מצבת במה זרע הקודש מצבתה
t1בשׁנת מות המלך עזיהו ואראה את אדני ישׁב על כסא רם t1בשנת מות המלכ עוזיה אראה את אדוני יושב על כסאו רמ 
>ונשׂא ושׁוליו מלאים את ההיכל >ונשא ושוליו מלאימ את ההיכל
2שׂרפים עמדים׀ ממעל לו שׁשׁ כנפים שׁשׁ כנפים לאחד ב2שרפימ עומדימ ממעלה לו שש כנפימ לאחד בשתימ יכסה פני
>שׁתים׀ יכסה פניו ובשׁתים יכסה רגליו ובשׁתים יעופף >ו ובשתימ יכסה רגליו ובשתימ יעופפ
3וקרא זה אל זה ואמר קדושׁ׀ קדושׁ קדושׁ יהוה צבאות מ3וקראימ זה אל זה קדוש קדוש יהוה צבאות מלא כול הארצ 
>לא כל הארץ כבודו >כבודו
4וינעו אמות הספים מקול הקורא והבית ימלא עשׁן 4וינועו אמות הספימ מקול הקורה והבית נמלא עשנ
5ואמר אוי לי כי נדמיתי כי אישׁ טמא שׂפתים אנכי ובתו5ואמר אי לי כי נדמיתי כיא איש טמה שפתימ אנוכי ובתוכ
>ך עם טמא שׂפתים אנכי יושׁב כי את המלך יהוה צבאות ר> עמ טמא שפתימ אנוכי יושב כיא את המלכ יהוה צבאות רא
>או עיני >ו עיני
6ויעף אלי אחד מן השׂרפים ובידו רצפה במלקחים לקח מעל6ויעופ אלי אחד מנ השרפימ ובידו רצפה במלקחימ לקח מעל
> המזבח > המזבח
7ויגע על פי ויאמר הנה נגע זה על שׂפתיך וסר עונך וחט7ויגע על פי ויואמר הנה נגע זה על שפתיכ וסר עוונכ וח
>אתך תכפר >טאותיכ תכפר
8ואשׁמע את קול אדני אמר את מי אשׁלח ומי ילך לנו ואמ8ואשמע את קול אדוני אמר את מי אשלח ומי ילכ לנו ואמר
>ר הנני שׁלחני >ה הנני שלחני
9ויאמר לך ואמרת לעם הזה שׁמעו שׁמוע ואל תבינו וראו 9ויואמר לכ ואמרתה לעמ הזה שמעו שמוע ועל תבינו ראו ר
>ראו ואל תדעו >או ועל תדעו
10השׁמן לב העם הזה ואזניו הכבד ועיניו השׁע פן יראה ב10השמ לב העמ הזה ואוזניו הכבד ועיניו השע פנ יראה בעי
>עיניו ובאזניו ישׁמע ולבבו יבין ושׁב ורפא לו >ניו ובאוזניו ישמעו בלבבו יבינ ושב ורפא לו
11ואמר עד מתי אדני ויאמר עד אשׁר אם שׁאו ערים מאין י11ואמרה עד מתי יהוה ויואמר עד אשר אמ שאו ערימ מאינ י
>ושׁב ובתים מאין אדם והאדמה תשׁאה שׁממה >ושב ובתימ מאינ אדמ והאדמה תשאה שממה
12ורחק יהוה את האדם ורבה העזובה בקרב הארץ 12ורחק יהוה את האדמ ורבה עזובה בקרב הארצ
13ועוד בה עשׂריה ושׁבה והיתה לבער כאלה וכאלון אשׁר ב13ועוד בה עשיריה ושבה והייתה לבער כאלה וכאלונ אשר מש
>שׁלכת מצבת בם זרע קדשׁ מצבתה פ >לכת מצבת במה זרע הקודש מצבתה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 7 MT
Isaiah 7 1QIsaa
n1ויהי בימי אחז בנ יותמ בנ עזיהו מלכ יהודה עלה רצינ n1ויהי בימי אחז בנ יותמ בנ עוזיה מלכ יהודה עלה רצינ 
>מלכ ארמ ופקח בנ רמליהו מלכ ישראל ירושלמ למלחמה עלי>מלכ ארמ ופקח בנ רומליה מלכ ישראל ירושלמ למלחמה עלי
>ה ולא יכל להלחמ עליה>ה ולוא יכלו להלחמ עליה
2ויגד לבית דוד לאמר נחה ארמ על אפרימ וינע לבבו ולבב2ויגד לבית דויד לאמור נחה ארמ על אפרימ וינע לבב עמו
> עמו כנוע עצי יער מפני רוח> כנע עצי היער מפני הרוח
3ויאמר יהוה אל ישעיהו צא נא לקראת אחז אתה ושאר ישוב3ויואמר יהוה אל ישעיה צא נא לקראת אחז אתה ושאר ישוב
> בנכ אל קצה תעלת הברכה העליונה אל מסלת שדה כובס> בנכ אל קצה תעלת הברכה העליונה אל מסלת שדה כובס
4ואמרת אליו השמר והשקט אל תירא ולבבכ אל ירכ משני זנ4ואמרת אליו השמר והשקט ואל תירא ולבבכ אל ירכ משני ז
>בות האודימ העשנימ האלה בחרי אפ רצינ וארמ ובנ רמליה>נבות האודימ העושנימ האלה כי בחורי אפ רצינ וארמ ובנ
>ו> רמליה
5יענ כי יעצ עליכ ארמ רעה אפרימ ובנ רמליהו לאמר5יענ כי יעצ עליכ ארמ רעה אפרימ ובנ רומליה לאמור
6נעלה ביהודה ונקיצנה ונבקענה אלינו ונמליכ מלכ בתוכה6נעלה ביהודה ונקיצנה ונבקענה אלינו ונמליכ מלכ בתוכה
> את בנ טבאל> את בנ טבאל
n7כה אמר אדני יהוה לא תקומ ולא תהיהn7כה אמר אדוני יהוה לוא תקומ ולוא תהיה
8כי ראש ארמ דמשק וראש דמשק רצינ ובעוד ששימ וחמש שנה8כיא ראוש ארמ דרמשק וראוש דרמשק רצינ ובעוד ששימ וחמ
> יחת אפרימ מעמ>ש שנה יחת אפרימ מעמ
9וראש אפרימ שמרונ וראש שמרונ בנ רמליהו אמ לא תאמינו9וראוש אפרימ שומרונ וראוש שומרונ בנ רומליה אמ לוא ת
> כי לא תאמנו>אמינו כיא לוא תאמינו
10ויוספ יהוה דבר אל אחז לאמר10ויוספ יהוה דבר אל אחז לאמור
11שאל לכ אות מעמ יהוה אלהיכ העמק שאלה או הגבה למעלה11שאל לכ אות מעמ יהוה אלוהיכ העמק שאלה או הגבה למעלה
12ויאמר אחז לא אשאל ולא אנסה את יהוה12ויואמר אחז לוא אשאל ולוא אנסה את יהוה
13ויאמר שמעו נא בית דוד המעט מכמ הלאות אנשימ כי תלאו13ויואמר שמעו נה בית דויד המעט מכמה הלאות אנשימ כי ת
> גמ את אלהי>לאו גמ את אלוהי
14לכנ יתנ אדני הוא לכמ אות הנה העלמה הרה וילדת בנ וק14לכנ יתנ יהוה הוה לכמה אות הנה העלמה הרה וילדת בנ ו
>ראת שמו עמנו אל>קרא שמו עמנואל
15חמאה ודבש יאכל לדעתו מאוס ברע ובחור בטוב15חמאה ודבש יאכל לדעתו מאוס ברע ובחר בטוב
16כי בטרמ ידע הנער מאס ברע ובחר בטוב תעזב האדמה אשר 16כי בטרמ ידע הנער מאס ברע ובחור בטוב תעזב האדמה אשר
>אתה קצ מפני שני מלכיה> אתה קצ מפני שני מלכיה
17יביא יהוה עליכ ועל עמכ ועל בית אביכ ימימ אשר לא בא17ויביא יהוה עליכ ועל עמכ ועל בית אביכ ימימ אשר לוא 
>ו למיומ סור אפרימ מעל יהודה את מלכ אשור>באו למיומ סור אפרימ מעל יהודה את מלכ אשור
18והיה ביומ ההוא ישרק יהוה לזבוב אשר בקצה יארי מצרימ18והיה ביומ ההוא ישרוק יהוה לזבוב אשר בקצה יארי מצרי
> ולדבורה אשר בארצ אשור>מ ולדבורא אשר בארצ אשור
19ובאו ונחו כלמ בנחלי הבתות ובנקיקי הסלעימ ובכל הנעצ19ובאו ונחו כולמ בנחלי הבתות ובנקיקי הסלעימ ובכול הנ
>וצימ ובכל הנהללימ>עצוצימ ובכול הנהלילימ
20ביומ ההוא יגלח אדני בתער השכירה בעברי נהר במלכ אשו20ביומ ההוא יגלח אדוני בתער השכירה בעברי נהר במלכ אש
>ר את הראש ושער הרגלימ וגמ את הזקנ תספה>ור את הראוש ושער הרגלימ וגמ אתה הזקנ תספה
21והיה ביומ ההוא יחיה איש עגלת בקר ושתי צאנ21והיה ביומ ההוא יחיה איש עגלת בקר ושתי צאנ
t22והיה מרב עשות חלב יאכל חמאה כי חמאה ודבש יאכל כל הt22והיה מרוב עשות חלב יאכל חמאה כיא חמאה ודבש יאכל הנ
>נותר בקרב הארצ>ותר בקרב הארצ
23והיה ביומ ההוא יהיה כל מקומ אשר יהיה שמ אלפ גפנ בא23והיה ביומ ההוא כול המקומ אשר יהיה שמ אלפ גפנ באלפ 
>לפ כספ לשמיר ולשית יהיה>כספ לשמיר ולשית יהיה
24בחצימ ובקשת יבוא שמה כי שמיר ושית תהיה כל הארצ24בחצימ ובקשתות יבוא שמה כיא שמיר ושית תהיה כול הארצ
25וכל ההרימ אשר במעדר יעדרונ לא תבוא שמה יראת שמיר ו25וכול ההרימ אשר במעדר יעדרונ לוא תבוא שמה יראת ברזל
>שית והיה למשלח שור ולמרמס שה> שמיר ושית יהיה למשלח שור ולמרמס שה
t1ויהי בימי אחז בן יותם בן עזיהו מלך יהודה עלה רצין t1ויהי בימי אחז בנ יותמ בנ עוזיה מלכ יהודה עלה רצינ 
>מלך ארם ופקח בן רמליהו מלך ישׂראל ירושׁלם למלחמה ע>מלכ ארמ ופקח בנ רומליה מלכ ישראל ירושלמ למלחמה עלי
>ליה ולא יכל להלחם עליה >ה ולוא יכלו להלחמ עליה
2ויגד לבית דוד לאמר נחה ארם על אפרים וינע לבבו ולבב2ויגד לבית דויד לאמור נחה ארמ על אפרימ וינע לבב עמו
> עמו כנוע עצי יער מפני רוח > כנע עצי היער מפני הרוח
3ויאמר יהוה אל ישׁעיהו צא נא לקראת אחז אתה ושׁאר יש3ויואמר יהוה אל ישעיה צא נא לקראת אחז אתה ושאר ישוב
>ׁוב בנך אל קצה תעלת הברכה העליונה אל מסלת שׂדה כוב> בנכ אל קצה תעלת הברכה העליונה אל מסלת שדה כובס
>ס  
4ואמרת אליו השׁמר והשׁקט אל תירא ולבבך אל ירך משׁני4ואמרת אליו השמר והשקט ואל תירא ולבבכ אל ירכ משני ז
> זנבות האודים העשׁנים האלה בחרי אף רצין וארם ובן ר>נבות האודימ העושנימ האלה כי בחורי אפ רצינ וארמ ובנ
>מליהו > רמליה
5יען כי יעץ עליך ארם רעה אפרים ובן רמליהו לאמר 5יענ כי יעצ עליכ ארמ רעה אפרימ ובנ רומליה לאמור
6נעלה ביהודה ונקיצנה ונבקענה אלינו ונמליך מלך בתוכה6נעלה ביהודה ונקיצנה ונבקענה אלינו ונמליכ מלכ בתוכה
> את בן טבאל ס > את בנ טבאל
7כה אמר אדני יהוה לא תקום ולא תהיה 7כה אמר אדוני יהוה לוא תקומ ולוא תהיה
8כי ראשׁ ארם דמשׂק וראשׁ דמשׂק רצין ובעוד שׁשׁים וח8כיא ראוש ארמ דרמשק וראוש דרמשק רצינ ובעוד ששימ וחמ
>משׁ שׁנה יחת אפרים מעם >ש שנה יחת אפרימ מעמ
9וראשׁ אפרים שׁמרון וראשׁ שׁמרון בן רמליהו אם לא תא9וראוש אפרימ שומרונ וראוש שומרונ בנ רומליה אמ לוא ת
>מינו כי לא תאמנו ס >אמינו כיא לוא תאמינו
10ויוסף יהוה דבר אל אחז לאמר 10ויוספ יהוה דבר אל אחז לאמור
11שׁאל לך אות מעם יהוה אלהיך העמק שׁאלה או הגבה למעל11שאל לכ אות מעמ יהוה אלוהיכ העמק שאלה או הגבה למעלה
>ה  
12ויאמר אחז לא אשׁאל ולא אנסה את יהוה 12ויואמר אחז לוא אשאל ולוא אנסה את יהוה
13ויאמר שׁמעו נא בית דוד המעט מכם הלאות אנשׁים כי תל13ויואמר שמעו נה בית דויד המעט מכמה הלאות אנשימ כי ת
>או גם את אלהי >לאו גמ את אלוהי
14לכן יתן אדני הוא לכם אות הנה העלמה הרה וילדת בן וק14לכנ יתנ יהוה הוה לכמה אות הנה העלמה הרה וילדת בנ ו
>ראת שׁמו עמנו אל >קרא שמו עמנואל
15חמאה ודבשׁ יאכל לדעתו מאוס ברע ובחור בטוב 15חמאה ודבש יאכל לדעתו מאוס ברע ובחר בטוב
16כי בטרם ידע הנער מאס ברע ובחר בטוב תעזב האדמה אשׁר16כי בטרמ ידע הנער מאס ברע ובחור בטוב תעזב האדמה אשר
> אתה קץ מפני שׁני מלכיה > אתה קצ מפני שני מלכיה
17יביא יהוה עליך ועל עמך ועל בית אביך ימים אשׁר לא ב17ויביא יהוה עליכ ועל עמכ ועל בית אביכ ימימ אשר לוא 
>או למיום סור אפרים מעל יהודה את מלך אשׁור פ >באו למיומ סור אפרימ מעל יהודה את מלכ אשור
18והיה׀ ביום ההוא ישׁרק יהוה לזבוב אשׁר בקצה יארי מצ18והיה ביומ ההוא ישרוק יהוה לזבוב אשר בקצה יארי מצרי
>רים ולדבורה אשׁר בארץ אשׁור >מ ולדבורא אשר בארצ אשור
19ובאו ונחו כלם בנחלי הבתות ובנקיקי הסלעים ובכל הנעצ19ובאו ונחו כולמ בנחלי הבתות ובנקיקי הסלעימ ובכול הנ
>וצים ובכל הנהללים >עצוצימ ובכול הנהלילימ
20ביום ההוא יגלח אדני בתער השׂכירה בעברי נהר במלך אש20ביומ ההוא יגלח אדוני בתער השכירה בעברי נהר במלכ אש
>ׁור את הראשׁ ושׂער הרגלים וגם את הזקן תספה ס >ור את הראוש ושער הרגלימ וגמ אתה הזקנ תספה
21והיה ביום ההוא יחיה אישׁ עגלת בקר ושׁתי צאן 21והיה ביומ ההוא יחיה איש עגלת בקר ושתי צאנ
22והיה מרב עשׂות חלב יאכל חמאה כי חמאה ודבשׁ יאכל כל22והיה מרוב עשות חלב יאכל חמאה כיא חמאה ודבש יאכל הנ
> הנותר בקרב הארץ >ותר בקרב הארצ
23והיה ביום ההוא יהיה כל מקום אשׁר יהיה שׁם אלף גפן 23והיה ביומ ההוא כול המקומ אשר יהיה שמ אלפ גפנ באלפ 
>באלף כסף לשׁמיר ולשׁית יהיה >כספ לשמיר ולשית יהיה
24בחצים ובקשׁת יבוא שׁמה כי שׁמיר ושׁית תהיה כל הארץ24בחצימ ובקשתות יבוא שמה כיא שמיר ושית תהיה כול הארצ
>  
25וכל ההרים אשׁר במעדר יעדרון לא תבוא שׁמה יראת שׁמי25וכול ההרימ אשר במעדר יעדרונ לוא תבוא שמה יראת ברזל
>ר ושׁית והיה למשׁלח שׁור ולמרמס שׂה פ > שמיר ושית יהיה למשלח שור ולמרמס שה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 8 MT
Isaiah 8 1QIsaa
n1ויאמר יהוה אלי קח לכ גליונ גדול וכתב עליו בחרט אנוn1ויאומר יהוה אלי קח לכ גליונ גדול וכתוב עליו בחרט א
>ש למהר שלל חש בז>נוש למהר שלל חש בז
2ואעידה לי עדימ נאמנימ את אוריה הכהנ ואת זכריהו בנ 2והעד לי עדימ נאמנימ את אוריה הכוהנ ואת זכריה בנ יב
>יברכיהו>רכיה
3ואקרב אל הנביאה ותהר ותלד בנ ויאמר יהוה אלי קרא שמ3ואקרב אל הנביא ותהר ותלד בנ ויאומר יהוה אלי קרא שמ
>ו מהר שלל חש בז>ו מהר שלל חש בז
4כי בטרמ ידע הנער קרא אבי ואמי ישא את חיל דמשק ואת 4כיא בטרמ ידע הנער לקראו אביו ואמו ישא את חיל דרמשק
>שלל שמרונ לפני מלכ אשור> ואת שלל שומרונ לפני מלכ אשור
5ויספ יהוה דבר אלי עוד לאמר5ויוספ יהוה דבר אלי עוד לאמור
6יענ כי מאס העמ הזה את מי השלח ההלכימ לאט ומשוש את 6יענ כיא מאס העמ הזה את מי השולח ההולכימ לאוט ומשוש
>רצינ ובנ רמליהו> את רצינ ואת בנ רומליה
7ולכנ הנה אדני מעלה עליהמ את מי הנהר העצומימ והרבימ7ולכנ הנה אדוני מעלה עליהמ את מי הנהר ה העצומימ והר
> את מלכ אשור ואת כל כבודו ועלה על כל אפיקיו והלכ ע>בימ את מלכ אשור ואת כול כבודו ועלה על כול אפיקיו ו
>ל כל גדותיו>הלכ על כול גדוותיו
8וחלפ ביהודה שטפ ועבר עד צואר יגיע והיה מטות כנפיו 8וחלפ ביהודה שטפ ועבר עד צואר יגיע והיה מטות כנפיו 
>מלא רחב ארצכ עמנו אל>מלוא רחב ארצכ עמנואל
9רעו עמימ וחתו והאזינו כל מרחקי ארצ התאזרו וחתו התא9רעו עמימ וחתו והאזינו כול מרחקי הארצ התאזרו וחותו
>זרו וחתו 
10עצו עצה ותפר דברו דבר ולא יקומ כי עמנו אל10עצו עצה ותפר דברו דבר ולוא יקומ כיא עמנואל
11כי כה אמר יהוה אלי כחזקת היד ויסרני מלכת בדרכ העמ 11כי כה אמר יהוה אלי כחזקת יד יסירנו מלכת בדרכ העמ ה
>הזה לאמר>זה לאמור
12לא תאמרונ קשר לכל אשר יאמר העמ הזה קשר ואת מוראו ל12לוא תאמרו קשר לכול אשר יואמר העמ הזה קשר ואת מוראו
>א תיראו ולא תעריצו> לוא תיראו ולוא תעריצו
13את יהוה צבאות אתו תקדישו והוא מוראכמ והוא מערצכמ13את יהוה צבאות אותו תקדישו והוא מוראכמ והוא מערצכמ
14והיה למקדש ולאבנ נגפ ולצור מכשול לשני בתי ישראל לפ14ויהיא למקדש ולאבנ נגפ ולצר מכשול לשני בתי ישראל לפ
>ח ולמוקש ליושב ירושלמ>ח ולמוקש ליושב ירושלימ
15וכשלו במ רבימ ונפלו ונשברו ונוקשו ונלכדו15וכשלו במ רבימ ונפלו ונשברו ונוקשו ונלכדו
t16צור תעודה חתומ תורה בלמדיt16צור תעודה וחתומ תורה בלמדי
17וחכיתי ליהוה המסתיר פניו מבית יעקב וקויתי לו17וחכיתי ליהוה המסתיר את פניו מבית יעקוב וקויתי לו
18הנה אנכי והילדימ אשר נתנ לי יהוה לאתות ולמופתימ בי18אנה אנוכי והילדימ אשר נתנ לי יהוה לאות ולמופת בישר
>שראל מעמ יהוה צבאות השכנ בהר ציונ>אל מעמ יהוה צבאות השוכנ בהר ציונ
19וכי יאמרו אליכמ דרשו אל האבות ואל הידענימ המצפצפימ19וכי יואמרו אליכמה דרשו אל האובות ואל הידעונימ המצפ
> והמהגימ הלוא עמ אל אלהיו ידרש בעד החיימ אל המתימ>צפימ והמהגימ הלוא עמ אל אלוהו ידרוש בעד חיימ אל המ
 >יתימ
20לתורה ולתעודה אמ לא יאמרו כדבר הזה אשר אינ לו שחר20לתורה ולתעודה אמ לוא יואמרו כדבר הזה אשר אינ לו שח
 >ר
21ועבר בה נקשה ורעב והיה כי ירעב והתקצפ וקלל במלכו ו21ועבר בה ונקשה ורעב והיה כיא ירעב יתקצפ וקלל במלכו 
>באלהיו ופנה למעלה>ובאלוהו ופנה למעלה
22ואל ארצ יביט והנה צרה וחשכה מעופ צוקה ואפלה מנדח22ואל הארצ יביט והנה צרה וחשוכה מעיפ צוקה ואפלה מנדח
23כי לא מועפ לאשר מוצק לה כעת הראשונ הקל ארצה זבלונ 23כילו מעופפ לאשר מוצק לה כעת הרישונ הקל ארצ זבולונ 
>וארצה נפתלי והאחרונ הכביד דרכ הימ עבר הירדנ גליל ה>והארצ נפתלי והאחרונ הכביד דרכ הימ עבר הירדנ גליל ה
>גוימ>גואימ
t1ויאמר יהוה אלי קח לך גליון גדול וכתב עליו בחרט אנוt1ויאומר יהוה אלי קח לכ גליונ גדול וכתוב עליו בחרט א
>שׁ למהר שׁלל חשׁ בז >נוש למהר שלל חש בז
2ואעידה לי עדים נאמנים את אוריה הכהן ואת זכריהו בן 2והעד לי עדימ נאמנימ את אוריה הכוהנ ואת זכריה בנ יב
>יברכיהו >רכיה
3ואקרב אל הנביאה ותהר ותלד בן ויאמר יהוה אלי קרא שׁ3ואקרב אל הנביא ותהר ותלד בנ ויאומר יהוה אלי קרא שמ
>מו מהר שׁלל חשׁ בז >ו מהר שלל חש בז
4כי בטרם ידע הנער קרא אבי ואמי ישׂא׀ את חיל דמשׂק ו4כיא בטרמ ידע הנער לקראו אביו ואמו ישא את חיל דרמשק
>את שׁלל שׁמרון לפני מלך אשׁור ס > ואת שלל שומרונ לפני מלכ אשור
5ויסף יהוה דבר אלי עוד לאמר 5ויוספ יהוה דבר אלי עוד לאמור
6יען כי מאס העם הזה את מי השׁלח ההלכים לאט ומשׂושׂ 6יענ כיא מאס העמ הזה את מי השולח ההולכימ לאוט ומשוש
>את רצין ובן רמליהו > את רצינ ואת בנ רומליה
7ולכן הנה אדני מעלה עליהם את מי הנהר העצומים והרבים7ולכנ הנה אדוני מעלה עליהמ את מי הנהר ה העצומימ והר
> את מלך אשׁור ואת כל כבודו ועלה על כל אפיקיו והלך >בימ את מלכ אשור ואת כול כבודו ועלה על כול אפיקיו ו
>על כל גדותיו >הלכ על כול גדוותיו
8וחלף ביהודה שׁטף ועבר עד צואר יגיע והיה מטות כנפיו8וחלפ ביהודה שטפ ועבר עד צואר יגיע והיה מטות כנפיו 
> מלא רחב ארצך עמנו אל ס >מלוא רחב ארצכ עמנואל
9רעו עמים וחתו והאזינו כל מרחקי ארץ התאזרו וחתו התא9רעו עמימ וחתו והאזינו כול מרחקי הארצ התאזרו וחותו
>זרו וחתו  
10עצו עצה ותפר דברו דבר ולא יקום כי עמנו אל ס 10עצו עצה ותפר דברו דבר ולוא יקומ כיא עמנואל
11כי כה אמר יהוה אלי כחזקת היד ויסרני מלכת בדרך העם 11כי כה אמר יהוה אלי כחזקת יד יסירנו מלכת בדרכ העמ ה
>הזה לאמר >זה לאמור
12לא תאמרון קשׁר לכל אשׁר יאמר העם הזה קשׁר ואת מורא12לוא תאמרו קשר לכול אשר יואמר העמ הזה קשר ואת מוראו
>ו לא תיראו ולא תעריצו > לוא תיראו ולוא תעריצו
13את יהוה צבאות אתו תקדישׁו והוא מוראכם והוא מערצכם 13את יהוה צבאות אותו תקדישו והוא מוראכמ והוא מערצכמ
14והיה למקדשׁ ולאבן נגף ולצור מכשׁול לשׁני בתי ישׂרא14ויהיא למקדש ולאבנ נגפ ולצר מכשול לשני בתי ישראל לפ
>ל לפח ולמוקשׁ ליושׁב ירושׁלם >ח ולמוקש ליושב ירושלימ
15וכשׁלו בם רבים ונפלו ונשׁברו ונוקשׁו ונלכדו ס 15וכשלו במ רבימ ונפלו ונשברו ונוקשו ונלכדו
16צור תעודה חתום תורה בלמדי 16צור תעודה וחתומ תורה בלמדי
17וחכיתי ליהוה המסתיר פניו מבית יעקב וקויתי לו 17וחכיתי ליהוה המסתיר את פניו מבית יעקוב וקויתי לו
18הנה אנכי והילדים אשׁר נתן לי יהוה לאתות ולמופתים ב18אנה אנוכי והילדימ אשר נתנ לי יהוה לאות ולמופת בישר
>ישׂראל מעם יהוה צבאות השׁכן בהר ציון ס >אל מעמ יהוה צבאות השוכנ בהר ציונ
19וכי יאמרו אליכם דרשׁו אל האבות ואל הידענים המצפצפי19וכי יואמרו אליכמה דרשו אל האובות ואל הידעונימ המצפ
>ם והמהגים הלוא עם אל אלהיו ידרשׁ בעד החיים אל המתי>צפימ והמהגימ הלוא עמ אל אלוהו ידרוש בעד חיימ אל המ
>ם >יתימ
20לתורה ולתעודה אם לא יאמרו כדבר הזה אשׁר אין לו שׁח20לתורה ולתעודה אמ לוא יואמרו כדבר הזה אשר אינ לו שח
>ר >ר
21ועבר בה נקשׁה ורעב והיה כי ירעב והתקצף וקלל במלכו 21ועבר בה ונקשה ורעב והיה כיא ירעב יתקצפ וקלל במלכו 
>ובאלהיו ופנה למעלה >ובאלוהו ופנה למעלה
22ואל ארץ יביט והנה צרה וחשׁכה מעוף צוקה ואפלה מנדח 22ואל הארצ יביט והנה צרה וחשוכה מעיפ צוקה ואפלה מנדח
23כי לא מועף לאשׁר מוצק לה כעת הראשׁון הקל ארצה זבלו23כילו מעופפ לאשר מוצק לה כעת הרישונ הקל ארצ זבולונ 
>ן וארצה נפתלי והאחרון הכביד דרך הים עבר הירדן גליל>והארצ נפתלי והאחרונ הכביד דרכ הימ עבר הירדנ גליל ה
> הגוים >גואימ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 9 MT
Isaiah 9 1QIsaa
n1העמ ההלכימ בחשכ ראו אור גדול ישבי בארצ צלמות אור נn1העמ ההולכימ בחושכ ראו אור גדול יושבי בארצ צלמות או
>גה עליהמ>ר נגה עליהמ
2הרבית הגוי לו הגדלת השמחה שמחו לפניכ כשמחת בקציר כ2הרביתה הגוי לוא הגדלתה השמחה שמחו לפניכ כשמחת בקצי
>אשר יגילו בחלקמ שלל>ר כאשר יגילו בחלקמ שלל
3כי את על סבלו ואת מטה שכמו שבט הנגש בו החתת כיומ מ3כי את עולסבלו ואת מטה שכמו שבט הנוגש בו והחתת כיומ
>דינ> מדימ
4כי כל סאונ סאנ ברעש ושמלה מגוללה בדמימ והיתה לשרפה4כי כול סאונ סאנ ברעש ושמלה מגוללה בדמימ והיתה לשרפ
> מאכלת אש>ה מאכלת אש
5כי ילד ילד לנו בנ נתנ לנו ותהי המשרה על שכמו ויקרא5כי ילד יולד לנו בנ נתנ לנו ותהייהמשורה על שכמו וקר
> שמו פלא יועצ אל גבור אביעד שר שלומ>א שמו פלא יועצ אל גבור אבי עד שר השלומ
6למרבה המשרה ולשלומ אינ קצ על כסא דוד ועל ממלכתו לה6למרבה המשורה ולשלומ אינ קצ על כסה דויד ועל ממלכתו 
>כינ אתה ולסעדה במשפט ובצדקה מעתה ועד עולמ קנאת יהו>להכינ אותו ולסעדו במשפט ובצדקה מעתה ועד עולמ קנאת 
>ה צבאות תעשה זאת>יהוה צבאות תעשה זאות
7דבר שלח אדני ביעקב ונפל בישראל7דבר שלח יהוה ביעקוב ונפל בישראל
8וידעו העמ כלו אפרימ ויושב שמרונ בגאוה ובגדל לבב לא8וירעו העמ כלו אפרימ ויושבי שמרונ בגאוה ובגדל לבב ל
>מר>אמור
9לבנימ נפלו וגזית נבנה שקמימ גדעו וארזימ נחליפ9לבנימ נפלו וגזית נבנה שקמימ גדעו וארזימ נחליפ
n10וישגב יהוה את צרי רצינ עליו ואת איביו יסכסכn10וישגב יהוה את צרי רציאנ עליו ואת אויביו יסכסכ
11ארמ מקדמ ופלשתימ מאחור ויאכלו את ישראל בכל פה בכל 11ארמ מקדמ ופלשתיימ מאחור ויאכלו את ישראל בכול פה וב
>זאת לא שב אפו ועוד ידו נטויה>כול זוות לוא שב אפו ועוד ידיו נטויה
12והעמ לא שב עד המכהו ואת יהוה צבאות לא דרשו12והעמ לוא שב על המכהו ואת יהוה צבאות לוא דרשו
13ויכרת יהוה מישראל ראש וזנב כפה ואגמונ יומ אחד13ויכרת יהוה מישראל ראש וזנב כפה ואגמנ ביומ אחד
14זקנ ונשוא פנימ הוא הראש ונביא מורה שקר הוא הזנב14זקנ ונשא פנימ הוא הרואש ונביא מורה שקר הוא הזנב
15ויהיו מאשרי העמ הזה מתעימ ומאשריו מבלעימ15ויהיו מאשרי העמ הזה מתעימ ומאשריו מבלעימ
t16על כנ על בחוריו לא ישמח אדני ואת יתמיו ואת אלמנתיוt16על כנ על בחוריו לוא יחמול אדוני ואת יתומיו ואת אלמ
> לא ירחמ כי כלו חנפ ומרע וכל פה דבר נבלה בכל זאת ל>נותיו לוא ירחמ כי כולו חנפ ומרע וכול פה דבר נבלה ב
>א שב אפו ועוד ידו נטויה>כול זואת לוא שב אפו ועוד ידיו נטויה
17כי בערה כאש רשעה שמיר ושית תאכל ותצת בסבכי היער וי17כי בערה כאש רשעה שמיר ושית תואכל ותצת בסבכי היער ו
>תאבכו גאות עשנ>יתאבכו גיאות עשנ
18בעברת יהוה צבאות נעתמ ארצ ויהי העמ כמאכלת אש איש א18מעברת יהוה צבאות נתעמ הארצ ויהיו העמ כמאכלת אש איש
>ל אחיו לא יחמלו> אל אחיו לוא יחמולו
19ויגזר על ימינ ורעב ויאכל על שמאול ולא שבעו איש בשר19ויגזר על ימינ ורעב ויאכל ועל שמאול ולוא שבעו איש ב
> זרעו יאכלו>שר זרועו
20מנשה את אפרימ ואפרימ את מנשה יחדו המה על יהודה בכל20ויאכל מנשה את אפרימ ואפרימ את מנשה יחדו המה על יהו
> זאת לא שב אפו ועוד ידו נטויה>דה ובכול זואת לוא שב אפו ועוד ידיו נטויה
t1העם ההלכים בחשׁך ראו אור גדול ישׁבי בארץ צלמות אורt1העמ ההולכימ בחושכ ראו אור גדול יושבי בארצ צלמות או
> נגה עליהם >ר נגה עליהמ
2הרבית הגוי לא הגדלת השׂמחה שׂמחו לפניך כשׂמחת בקצי2הרביתה הגוי לוא הגדלתה השמחה שמחו לפניכ כשמחת בקצי
>ר כאשׁר יגילו בחלקם שׁלל >ר כאשר יגילו בחלקמ שלל
3כי׀ את על סבלו ואת מטה שׁכמו שׁבט הנגשׂ בו החתת כי3כי את עולסבלו ואת מטה שכמו שבט הנוגש בו והחתת כיומ
>ום מדין > מדימ
4כי כל סאון סאן ברעשׁ ושׂמלה מגוללה בדמים והיתה לשׂ4כי כול סאונ סאנ ברעש ושמלה מגוללה בדמימ והיתה לשרפ
>רפה מאכלת אשׁ >ה מאכלת אש
5כי ילד ילד לנו בן נתן לנו ותהי המשׂרה על שׁכמו ויק5כי ילד יולד לנו בנ נתנ לנו ותהייהמשורה על שכמו וקר
>רא שׁמו פלא יועץ אל גבור אביעד שׂר שׁלום >א שמו פלא יועצ אל גבור אבי עד שר השלומ
6למרבה המשׂרה ולשׁלום אין קץ על כסא דוד ועל ממלכתו 6למרבה המשורה ולשלומ אינ קצ על כסה דויד ועל ממלכתו 
>להכין אתה ולסעדה במשׁפט ובצדקה מעתה ועד עולם קנאת >להכינ אותו ולסעדו במשפט ובצדקה מעתה ועד עולמ קנאת 
>יהוה צבאות תעשׂה זאת ס >יהוה צבאות תעשה זאות
7דבר שׁלח אדני ביעקב ונפל בישׂראל 7דבר שלח יהוה ביעקוב ונפל בישראל
8וידעו העם כלו אפרים ויושׁב שׁמרון בגאוה ובגדל לבב 8וירעו העמ כלו אפרימ ויושבי שמרונ בגאוה ובגדל לבב ל
>לאמר >אמור
9לבנים נפלו וגזית נבנה שׁקמים גדעו וארזים נחליף 9לבנימ נפלו וגזית נבנה שקמימ גדעו וארזימ נחליפ
10וישׂגב יהוה את צרי רצין עליו ואת איביו יסכסך 10וישגב יהוה את צרי רציאנ עליו ואת אויביו יסכסכ
11ארם מקדם ופלשׁתים מאחור ויאכלו את ישׂראל בכל פה בכ11ארמ מקדמ ופלשתיימ מאחור ויאכלו את ישראל בכול פה וב
>ל זאת לא שׁב אפו ועוד ידו נטויה >כול זוות לוא שב אפו ועוד ידיו נטויה
12והעם לא שׁב עד המכהו ואת יהוה צבאות לא דרשׁו ס 12והעמ לוא שב על המכהו ואת יהוה צבאות לוא דרשו
13ויכרת יהוה מישׂראל ראשׁ וזנב כפה ואגמון יום אחד 13ויכרת יהוה מישראל ראש וזנב כפה ואגמנ ביומ אחד
14זקן ונשׂוא פנים הוא הראשׁ ונביא מורה שׁקר הוא הזנב14זקנ ונשא פנימ הוא הרואש ונביא מורה שקר הוא הזנב
>  
15ויהיו מאשׁרי העם הזה מתעים ומאשׁריו מבלעים 15ויהיו מאשרי העמ הזה מתעימ ומאשריו מבלעימ
16על כן על בחוריו לא ישׂמח׀ אדני ואת יתמיו ואת אלמנת16על כנ על בחוריו לוא יחמול אדוני ואת יתומיו ואת אלמ
>יו לא ירחם כי כלו חנף ומרע וכל פה דבר נבלה בכל זאת>נותיו לוא ירחמ כי כולו חנפ ומרע וכול פה דבר נבלה ב
> לא שׁב אפו ועוד ידו נטויה >כול זואת לוא שב אפו ועוד ידיו נטויה
17כי בערה כאשׁ רשׁעה שׁמיר ושׁית תאכל ותצת בסבכי היע17כי בערה כאש רשעה שמיר ושית תואכל ותצת בסבכי היער ו
>ר ויתאבכו גאות עשׁן >יתאבכו גיאות עשנ
18בעברת יהוה צבאות נעתם ארץ ויהי העם כמאכלת אשׁ אישׁ18מעברת יהוה צבאות נתעמ הארצ ויהיו העמ כמאכלת אש איש
> אל אחיו לא יחמלו > אל אחיו לוא יחמולו
19ויגזר על ימין ורעב ויאכל על שׂמאול ולא שׂבעו אישׁ 19ויגזר על ימינ ורעב ויאכל ועל שמאול ולוא שבעו איש ב
>בשׂר זרעו יאכלו >שר זרועו
20מנשׁה את אפרים ואפרים את מנשׁה יחדו המה על יהודה ב20ויאכל מנשה את אפרימ ואפרימ את מנשה יחדו המה על יהו
>כל זאת לא שׁב אפו ועוד ידו נטויה ס >דה ובכול זואת לוא שב אפו ועוד ידיו נטויה
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Isaiah 10 MT
Isaiah 10 1QIsaa
n1הוי החקקימ חקקי אונ ומכתבימ עמל כתבוn1הוי חוקקימ חוקקי אונ ומכתבימ עמל כתבו
2להטות מדינ דלימ ולגזל משפט עניי עמי להיות אלמנות ש2להטות מדינ דלימ ולגזול משפט עניי עמי להיות אלמנות 
>ללמ ואת יתומימ יבזו>שללמ ואת יתומימ יבוזו
3ומה תעשו ליומ פקדה ולשואה ממרחק תבוא על מי תנוסו ל3ומה תעשו ליומ פקודה ולשאה ממרחק תבוא על מי תנוסו ל
>עזרה ואנה תעזבו כבודכמ>עזרה ואנה תעזובו כבודכמ
4בלתי כרע תחת אסיר ותחת הרוגימ יפלו בכל זאת לא שב א4בלתי כרע תחת אסור ותחת הרוגימ יפלו ובכול זואת לוא 
>פו ועוד ידו נטויה>שב אפו ועוד ידיו נטויה
5הוי אשור שבט אפי ומטה הוא בידמ זעמי5הוי אשור שבט אפי ומטה הוא בידמ זעמי
n6בגוי חנפ אשלחנו ועל עמ עברתי אצונו לשלל שלל ולבז בn6בגוי חנפ אשלחנו ועל עמ עברתי אצונו לשלול שלל ולבז 
>ז ולשומו מרמס כחמר חוצות>בז ולשומ מרמס כחמר חוצות
7והוא לא כנ ידמה ולבבו לא כנ יחשב כי להשמיד בלבבו ו7והוא לוא כנ ידמה ולבבו לוא כנ יחשוב כיא להשמיד בלב
>להכרית גוימ לא מעט>בו ולהכרית גואימ לוא מעט
8כי יאמר הלא שרי יחדו מלכימ8כיא יואמר הלוא שרי יחדו מלכימ
9הלא ככרכמיש כלנו אמ לא כארפד חמת אמ לא כדמשק שמרונ9הלוא ככרכמיש כלנו אמ לוא כארפד חמת אמ לוא כדרמשק ש
 >ומרונ
10כאשר מצאה ידי לממלכת האליל ופסיליהמ מירושלמ ומשמרו10כאשר מצאה ידי לממלכות האלילימ ופסיליהמ מירושלימ ומ
>נ>שומרונ
11הלא כאשר עשיתי לשמרונ ולאליליה כנ אעשה לירושלמ ולע11הלוא כאשר עשיתי לשומרונ ולאליליה כנ אעשה לירושלימ 
>צביה>ולעצביה
12והיה כי יבצע אדני את כל מעשהו בהר ציונ ובירושלמ אפ12כי יבצע אדוני את כול מעשוהי בהר ציונ ובירושלימ אפק
>קד על פרי גדל לבב מלכ אשור ועל תפארת רומ עיניו>וד על פרי גודל לבב מלכ אשור ועל תפארת רומ עיניו
13כי אמר בכח ידי עשיתי ובחכמתי כי נבנותי ואסיר גבולת13כי יואמר בכוח ידי עשיתי ובחכמתי כי נבונותי ואסיר ג
> עמימ ועתודותיהמ שושתי ואוריד כאביר יושבימ>בלות עמימ ועתידותיהמה שושיתי ואוריד יושבימ
14ותמצא כקנ ידי לחיל העמימ וכאספ ביצימ עזבות כל הארצ14ותמצא כקנ ידי לחיל העמימ וכאסופ בצימ עזבות כול האר
> אני אספתי ולא היה נדד כנפ ופצה פה ומצפצפ>צ אני אספתי ולוא היה נודד כנפ ופוצה פה ומצפצפ
15היתפאר הגרזנ על החצב בו אמ יתגדל המשור על מניפו כה15היתפאר הגרזנ על החוצב בו אמ יתגדל המשור על מניפיו 
>ניפ שבט ואת מרימיו כהרימ מטה לא עצ>כהניפ שבט את מרימיו כהרימ מטה לוא עצ
16לכנ ישלח האדונ יהוה צבאות במשמניו רזונ ותחת כבדו י16לכנ ישלח האדונ יהוה צבאות במשמניו רזונ ותחת כבודו 
>קד יקד כיקוד אש>יקד יקוד כיקד אש
17והיה אור ישראל לאש וקדושו ללהבה ובערה ואכלה שיתו ו17והיה אור ישראל לאש וקדושו ללהבה ובערה ואכלה שיתו ו
>שמירו ביומ אחד>שמירו ביומ אחד
18וכבוד יערו וכרמלו מנפש ועד בשר יכלה והיה כמסס נסס18וכבוד יערו וכרמלו מנפש ועד בשר יכלה והיה כמסס נסס
19ושאר עצ יערו מספר יהיו ונער יכתבמ19ושאר עצ יערו מספר יהיו ונער יכתבמ
t20והיה ביומ ההוא לא יוסיפ עוד שאר ישראל ופליטת בית יt20והיה ביומ ההוא לוא יוסיפ עוד שאר ישראל ופליטת בית 
>עקב להשענ על מכהו ונשענ על יהוה קדוש ישראל באמת>יעקוב להשענ על מכהו ונשענ אל יהוה קדוש ישראל באמת
21שאר ישוב שאר יעקב אל אל גבור21שאר ישוב שאר יעקוב אל אל גבור
22כי אמ יהיה עמכ ישראל כחול הימ שאר ישוב בו כליונ חר22כיא אמ יהיה עמכ ישראל כחול הימ שאר ישוב בו כליונ ח
>וצ שוטפ צדקה>רוצ שוטפ צדקה
23כי כלה ונחרצה אדני יהוה צבאות עשה בקרב כל הארצ23כי כלה ונחרצה אדוני יהוה צבאות עושה בקרב כול הארצ
24לכנ כה אמר אדני יהוה צבאות אל תירא עמי ישב ציונ מא24לכנ כוה אמר אדוני יהוה צבאות אל תירא עמי יושב ציונ
>שור בשבט יככה ומטהו ישא עליכ בדרכ מצרימ> מאשור משבט יככה ומטו ישא עליכ בדרכ מצרימ
25כי עוד מעט מזער וכלה זעמ ואפי על תבליתמ25כי עוד מעט מזער וכלה זעמ ואפי על תבלותמ
26ועורר עליו יהוה צבאות שוט כמכת מדינ בצור עורב ומטה26ויעיר עליו יהוה צבאות שוט כמכת מדינ בצור עורב ומטה
>ו על הימ ונשאו בדרכ מצרימ>ו על הימ ונשאו בדרכ מצרימ
27והיה ביומ ההוא יסור סבלו מעל שכמכ ועלו מעל צוארכ ו27והיה ביומ ההוא יסור סבלו מעל שכמכ ועולו מעל צוארכ 
>חבל על מפני שמנ>וחבל עול מפני שמנ
28בא על עית עבר במגרונ למכמש יפקיד כליו28בא על עיתה עבר במגרונ למכמש יפקוד כליו
29עברו מעברה גבע מלונ לנו חרדה הרמה גבעת שאול נסה29עבר במעברה גבע מלונ לנו חרדה הרמה גבעת שאול נסה
30צהלי קולכ בת גלימ הקשיבי לישה עניה ענתות30צהלי קולכ בת גלימ הקשיבי ליש עניה ענתות
31נדדה מדמנה ישבי הגבימ העיזו31נדדה מרמנה יושבי הגבימ העיזו
32עוד היומ בנב לעמד ינפפ ידו הר בת ציונ גבעת ירושלמ32עוד היומ בנב לעמוד ינופ ידיו הר בת ציונ גבעת ירושל
 >ימ
33הנה האדונ יהוה צבאות מסעפ פארה במערצה ורמי הקומה ג33הנה האדונ יהוה צבאות מסעפ פארה במערצה ורמי הקומה ג
>דועימ והגבהימ ישפלו>דועימ והגבהימ ישפלו
34ונקפ סבכי היער בברזל והלבנונ באדיר יפול34ונקפ סבכי היער בברזל והלבנונ באדיר יפול
t1הוי החקקים חקקי און ומכתבים עמל כתבו t1הוי חוקקימ חוקקי אונ ומכתבימ עמל כתבו
2להטות מדין דלים ולגזל משׁפט עניי עמי להיות אלמנות 2להטות מדינ דלימ ולגזול משפט עניי עמי להיות אלמנות 
>שׁללם ואת יתומים יבזו >שללמ ואת יתומימ יבוזו
3ומה תעשׂו ליום פקדה ולשׁואה ממרחק תבוא על מי תנוסו3ומה תעשו ליומ פקודה ולשאה ממרחק תבוא על מי תנוסו ל
> לעזרה ואנה תעזבו כבודכם >עזרה ואנה תעזובו כבודכמ
4בלתי כרע תחת אסיר ותחת הרוגים יפלו בכל זאת לא שׁב 4בלתי כרע תחת אסור ותחת הרוגימ יפלו ובכול זואת לוא 
>אפו ועוד ידו נטויה ס >שב אפו ועוד ידיו נטויה
5הוי אשׁור שׁבט אפי ומטה הוא בידם זעמי 5הוי אשור שבט אפי ומטה הוא בידמ זעמי
6בגוי חנף אשׁלחנו ועל עם עברתי אצונו לשׁלל שׁלל ולב6בגוי חנפ אשלחנו ועל עמ עברתי אצונו לשלול שלל ולבז 
>ז בז ולשׂימו מרמס כחמר חוצות >בז ולשומ מרמס כחמר חוצות
7והוא לא כן ידמה ולבבו לא כן יחשׁב כי להשׁמיד בלבבו7והוא לוא כנ ידמה ולבבו לוא כנ יחשוב כיא להשמיד בלב
> ולהכרית גוים לא מעט >בו ולהכרית גואימ לוא מעט
8כי יאמר הלא שׂרי יחדו מלכים 8כיא יואמר הלוא שרי יחדו מלכימ
9הלא ככרכמישׁ כלנו אם לא כארפד חמת אם לא כדמשׂק שׁמ9הלוא ככרכמיש כלנו אמ לוא כארפד חמת אמ לוא כדרמשק ש
>רון >ומרונ
10כאשׁר מצאה ידי לממלכת האליל ופסיליהם מירושׁלם ומשׁ10כאשר מצאה ידי לממלכות האלילימ ופסיליהמ מירושלימ ומ
>מרון >שומרונ
11הלא כאשׁר עשׂיתי לשׁמרון ולאליליה כן אעשׂה לירושׁל11הלוא כאשר עשיתי לשומרונ ולאליליה כנ אעשה לירושלימ 
>ם ולעצביה ס >ולעצביה
12והיה כי יבצע אדני את כל מעשׂהו בהר ציון ובירושׁלם 12כי יבצע אדוני את כול מעשוהי בהר ציונ ובירושלימ אפק
>אפקד על פרי גדל לבב מלך אשׁור ועל תפארת רום עיניו >וד על פרי גודל לבב מלכ אשור ועל תפארת רומ עיניו
13כי אמר בכח ידי עשׂיתי ובחכמתי כי נבנותי ואסיר׀ גבו13כי יואמר בכוח ידי עשיתי ובחכמתי כי נבונותי ואסיר ג
>לת עמים ועתידתיהם שׁושׂתי ואוריד כאביר יושׁבים >בלות עמימ ועתידותיהמה שושיתי ואוריד יושבימ
14ותמצא כקן׀ ידי לחיל העמים וכאסף ביצים עזבות כל האר14ותמצא כקנ ידי לחיל העמימ וכאסופ בצימ עזבות כול האר
>ץ אני אספתי ולא היה נדד כנף ופצה פה ומצפצף >צ אני אספתי ולוא היה נודד כנפ ופוצה פה ומצפצפ
15היתפאר הגרזן על החצב בו אם יתגדל המשׂור על מניפו כ15היתפאר הגרזנ על החוצב בו אמ יתגדל המשור על מניפיו 
>הניף שׁבט ואת מרימיו כהרים מטה לא עץ >כהניפ שבט את מרימיו כהרימ מטה לוא עצ
16לכן ישׁלח האדון יהוה צבאות במשׁמניו רזון ותחת כבדו16לכנ ישלח האדונ יהוה צבאות במשמניו רזונ ותחת כבודו 
> יקד יקד כיקוד אשׁ >יקד יקוד כיקד אש
17והיה אור ישׂראל לאשׁ וקדושׁו ללהבה ובערה ואכלה שׁי17והיה אור ישראל לאש וקדושו ללהבה ובערה ואכלה שיתו ו
>תו ושׁמירו ביום אחד >שמירו ביומ אחד
18וכבוד יערו וכרמלו מנפשׁ ועד בשׂר יכלה והיה כמסס נס18וכבוד יערו וכרמלו מנפש ועד בשר יכלה והיה כמסס נסס
>ס  
19ושׁאר עץ יערו מספר יהיו ונער יכתבם פ 19ושאר עצ יערו מספר יהיו ונער יכתבמ
20והיה׀ ביום ההוא לא יוסיף עוד שׁאר ישׂראל ופליטת בי20והיה ביומ ההוא לוא יוסיפ עוד שאר ישראל ופליטת בית 
>ת יעקב להשׁען על מכהו ונשׁען על יהוה קדושׁ ישׂראל >יעקוב להשענ על מכהו ונשענ אל יהוה קדוש ישראל באמת
>באמת  
21שׁאר ישׁוב שׁאר יעקב אל אל גבור 21שאר ישוב שאר יעקוב אל אל גבור
22כי אם יהיה עמך ישׂראל כחול הים שׁאר ישׁוב בו כליון22כיא אמ יהיה עמכ ישראל כחול הימ שאר ישוב בו כליונ ח
> חרוץ שׁוטף צדקה >רוצ שוטפ צדקה
23כי כלה ונחרצה אדני יהוה צבאות עשׂה בקרב כל הארץ ס 23כי כלה ונחרצה אדוני יהוה צבאות עושה בקרב כול הארצ
24לכן כה אמר אדני יהוה צבאות אל תירא עמי ישׁב ציון מ24לכנ כוה אמר אדוני יהוה צבאות אל תירא עמי יושב ציונ
>אשׁור בשׁבט יככה ומטהו ישׂא עליך בדרך מצרים > מאשור משבט יככה ומטו ישא עליכ בדרכ מצרימ
25כי עוד מעט מזער וכלה זעם ואפי על תבליתם 25כי עוד מעט מזער וכלה זעמ ואפי על תבלותמ
26ועורר עליו יהוה צבאות שׁוט כמכת מדין בצור עורב ומט26ויעיר עליו יהוה צבאות שוט כמכת מדינ בצור עורב ומטה
>הו על הים ונשׂאו בדרך מצרים >ו על הימ ונשאו בדרכ מצרימ
27והיה׀ ביום ההוא יסור סבלו מעל שׁכמך ועלו מעל צוארך27והיה ביומ ההוא יסור סבלו מעל שכמכ ועולו מעל צוארכ 
> וחבל על מפני שׁמן >וחבל עול מפני שמנ
28בא על עית עבר במגרון למכמשׂ יפקיד כליו 28בא על עיתה עבר במגרונ למכמש יפקוד כליו
29עברו מעברה גבע מלון לנו חרדה הרמה גבעת שׁאול נסה 29עבר במעברה גבע מלונ לנו חרדה הרמה גבעת שאול נסה
30צהלי קולך בת גלים הקשׁיבי לישׁה עניה ענתות 30צהלי קולכ בת גלימ הקשיבי ליש עניה ענתות
31נדדה מדמנה ישׁבי הגבים העיזו 31נדדה מרמנה יושבי הגבימ העיזו
32עוד היום בנב לעמד ינפף ידו הר בית ציון גבעת ירושׁל32עוד היומ בנב לעמוד ינופ ידיו הר בת ציונ גבעת ירושל
>ם ס >ימ
33הנה האדון יהוה צבאות מסעף פארה במערצה ורמי הקומה ג33הנה האדונ יהוה צבאות מסעפ פארה במערצה ורמי הקומה ג
>דועים והגבהים ישׁפלו >דועימ והגבהימ ישפלו
34ונקף סבכי היער בברזל והלבנון באדיר יפול ס 34ונקפ סבכי היער בברזל והלבנונ באדיר יפול
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Isaiah 11 MT
Isaiah 11 1QIsaa
n1ויצא חטר מגזע ישי ונצר משרשיו יפרהn1ויצא חטר מגזע ישי ונצר משורשיו יפרה
t1ויצא חטר מגזע ישׁי ונצר משׁרשׁיו יפרה t1ויצא חטר מגזע ישי ונצר משורשיו יפרה
2ונחה עליו רוח יהוה רוח חכמה ובינה רוח עצה וגבורה ר2ונחה עליו רוח יהוה רוח חכמה ובינה רוח עצה וגבורה ר
>וח דעת ויראת יהוה>וח דעת ויראת יהוה
t3והריחו ביראת יהוה ולא למראה עיניו ישפוט ולא למשמע t3והריחו ביראת יהוה ולוא למראה עניו ישפוט ולוא למשמע
>אזניו יוכיח> אוזנו יוכיח
4ושפט בצדק דלימ והוכיח במישור לענוי ארצ והכה ארצ בש4ושפט בצדק דלימ והוכיח במישור לעניי הארצ והכה הארצ 
>בט פיו וברוח שפתיו ימית רשע>בשבט פיו וברוח שפתיו יומת רשע
5והיה צדק אזור מתניו והאמונה אזור חלציו5והיה צדק אזור מתניו ואמונה אזור חלציו
6וגר זאב עמ כבש ונמר עמ גדי ירבצ ועגל וכפיר ומריא י6וגר זאב עמ כבש ונמר עמ גדי ירבצ ועגל וכפיר ימרו יח
>חדו ונער קטנ נהג במ>דו ונער קטנ נהג במה
7ופרה ודב תרעינה יחדו ירבצו ילדיהנ ואריה כבקר יאכל 7ופרה ודב תרעינה יחדו ורבצו ילדיהנ ואריה כבקר יאכל 
>תבנ>תבנ
8ושעשע יונק על חר פתנ ועל מאורת צפעוני גמול ידו הדה8וישעשע יונק על חור פתנ ועל מאורות צפעונימ גמול ידו
 > הדה
9לא ירעו ולא ישחיתו בכל הר קדשי כי מלאה הארצ דעה את9לוא ירעו ולוא ישחיתו בהר קדשי כי תמלאה הארצ דעה את
> יהוה כמימ לימ מכסימ> יהוה כמימ לימ מכסימ
10והיה ביומ ההוא שרש ישי אשר עמד לנס עמימ אליו גוימ 10והיה ביומ ההוא שרש ישי אשר עמד לנס עמימ אליו גואימ
>ידרשו והיתה מנחתו כבוד> ידרושו והיא מנוחתו כבוד
11והיה ביומ ההוא יוסיפ אדני שנית ידו לקנות את שאר עמ11והיה ביומ ההוא יוסיפ אדוני שנית ידו לקנות את שאר ע
>ו אשר ישאר מאשור וממצרימ ומפתרוס ומכוש ומעילמ ומשנ>מו אשר ישאר מאשור וממצרימ ומפתרוס ומכוש ומעילמ ומש
>ער ומחמת ומאיי הימ>נער ומחמת ומאיי הימ
12ונשא נס לגוימ ואספ נדחי ישראל ונפצות יהודה יקבצ מא12ונשה נס לגואימ ואספ נדחי ישראל ונפוצות יהודה יקבצ 
>רבע כנפות הארצ>מכנפות הארצ
13וסרה קנאת אפרימ וצררי יהודה יכרתו אפרימ לא יקנא את13וסרה קנאת אפרימ וצוררי יהודה יכרתו אפרימ לוא יקנא 
> יהודה ויהודה לא יצר את אפרימ>את יהודה ויהודה לוא יצר את אפרימ
14ועפו בכתפ פלשתימ ימה יחדו יבזו את בני קדמ אדומ ומו14ועפו בכתפ פלשתיימ ימה יחדו ובזזו את בני קדמ אדומ ו
>אב משלוח ידמ ובני עמונ משמעתמ>מואב משלוח ידמ ובני עמונ משמעתמ
15והחרימ יהוה את לשונ ימ מצרימ והניפ ידו על הנהר בעי15והחרימ יהוה את לשונ ימ מצרימ והניפ ידיו על הנהר בע
>מ רוחו והכהו לשבעה נחלימ והדריכ בנעלימ>יימ רוח והכהו לשבעת נחלימ והדריכי בנעלימ
16והיתה מסלה לשאר עמו אשר ישאר מאשור כאשר היתה לישרא16והייתה מסלה לשאר עמו אשר ישאר מאשור כאשר הייתה ליש
>ל ביומ עלתו מארצ מצרימ>ראל ביומ עלות מארצ מצרימ
>וח דעת ויראת יהוה >וח דעת ויראת יהוה
3והריחו ביראת יהוה ולא למראה עיניו ישׁפוט ולא למשׁמ3והריחו ביראת יהוה ולוא למראה עניו ישפוט ולוא למשמע
>ע אזניו יוכיח > אוזנו יוכיח
4ושׁפט בצדק דלים והוכיח במישׁור לענוי ארץ והכה ארץ 4ושפט בצדק דלימ והוכיח במישור לעניי הארצ והכה הארצ 
>בשׁבט פיו וברוח שׂפתיו ימית רשׁע >בשבט פיו וברוח שפתיו יומת רשע
5והיה צדק אזור מתניו והאמונה אזור חלציו 5והיה צדק אזור מתניו ואמונה אזור חלציו
6וגר זאב עם כבשׂ ונמר עם גדי ירבץ ועגל וכפיר ומריא 6וגר זאב עמ כבש ונמר עמ גדי ירבצ ועגל וכפיר ימרו יח
>יחדו ונער קטן נהג בם >דו ונער קטנ נהג במה
7ופרה ודב תרעינה יחדו ירבצו ילדיהן ואריה כבקר יאכל 7ופרה ודב תרעינה יחדו ורבצו ילדיהנ ואריה כבקר יאכל 
>תבן >תבנ
8ושׁעשׁע יונק על חר פתן ועל מאורת צפעוני גמול ידו ה8וישעשע יונק על חור פתנ ועל מאורות צפעונימ גמול ידו
>דה > הדה
9לא ירעו ולא ישׁחיתו בכל הר קדשׁי כי מלאה הארץ דעה 9לוא ירעו ולוא ישחיתו בהר קדשי כי תמלאה הארצ דעה את
>את יהוה כמים לים מכסים פ > יהוה כמימ לימ מכסימ
10והיה ביום ההוא שׁרשׁ ישׁי אשׁר עמד לנס עמים אליו ג10והיה ביומ ההוא שרש ישי אשר עמד לנס עמימ אליו גואימ
>וים ידרשׁו והיתה מנחתו כבוד פ > ידרושו והיא מנוחתו כבוד
11והיה׀ ביום ההוא יוסיף אדני׀ שׁנית ידו לקנות את שׁא11והיה ביומ ההוא יוסיפ אדוני שנית ידו לקנות את שאר ע
>ר עמו אשׁר ישׁאר מאשׁור וממצרים ומפתרוס ומכושׁ ומע>מו אשר ישאר מאשור וממצרימ ומפתרוס ומכוש ומעילמ ומש
>ילם ומשׁנער ומחמת ומאיי הים >נער ומחמת ומאיי הימ
12ונשׂא נס לגוים ואסף נדחי ישׂראל ונפצות יהודה יקבץ 12ונשה נס לגואימ ואספ נדחי ישראל ונפוצות יהודה יקבצ 
>מארבע כנפות הארץ >מכנפות הארצ
13וסרה קנאת אפרים וצררי יהודה יכרתו אפרים לא יקנא את13וסרה קנאת אפרימ וצוררי יהודה יכרתו אפרימ לוא יקנא 
> יהודה ויהודה לא יצר את אפרים >את יהודה ויהודה לוא יצר את אפרימ
14ועפו בכתף פלשׁתים ימה יחדו יבזו את בני קדם אדום ומ14ועפו בכתפ פלשתיימ ימה יחדו ובזזו את בני קדמ אדומ ו
>ואב משׁלוח ידם ובני עמון משׁמעתם >מואב משלוח ידמ ובני עמונ משמעתמ
15והחרים יהוה את לשׁון ים מצרים והניף ידו על הנהר בע15והחרימ יהוה את לשונ ימ מצרימ והניפ ידיו על הנהר בע
>ים רוחו והכהו לשׁבעה נחלים והדריך בנעלים >יימ רוח והכהו לשבעת נחלימ והדריכי בנעלימ
16והיתה מסלה לשׁאר עמו אשׁר ישׁאר מאשׁור כאשׁר היתה 16והייתה מסלה לשאר עמו אשר ישאר מאשור כאשר הייתה ליש
>לישׂראל ביום עלתו מארץ מצרים >ראל ביומ עלות מארצ מצרימ
- - - - - - - - - + + + + + + + + + +

Isaiah 12 MT
Isaiah 12 1QIsaa
t1ואמרת ביומ ההוא אודכ יהוה כי אנפת בי ישב אפכ ותנחמt1ואמרתה ביומ ההוא אודכה יהוה כי אנפתה בי ושב אפכה ו
>ני>תנחמני
2הנה אל ישועתי אבטח ולא אפחד כי עזי וזמרת יה יהוה ו2הנה אל אל ישועתי אבטח ולוא אפחד כיא עוזי וזמרתיה י
>יהי לי לישועה>הוה היהא לי לישועה
3ושאבתמ מימ בששונ ממעיני הישועה3ושאבתמה מימ בששונ ממעיני הישועה
4ואמרתמ ביומ ההוא הודו ליהוה קראו בשמו הודיעו בעמימ4ואמרתה ביומ ההוא אודו ליהוה קראו בשמו הודיעו בעמימ
> עלילתיו הזכירו כי נשגב שמו> עלילותיו הזכירו כי נשגב שמו
5זמרו יהוה כי גאות עשה מודעת זאת בכל הארצ5זמרו ליהוה כי גאות עשה מודעות זואת בכול הארצ
6צהלי ורני יושבת ציונ כי גדול בקרבכ קדוש ישראל6צהלי ורני יושבת ציונ כיא גדול בקרבכ קדוש ישראל
t1ואמרת ביום ההוא אודך יהוה כי אנפת בי ישׁב אפך ותנחt1ואמרתה ביומ ההוא אודכה יהוה כי אנפתה בי ושב אפכה ו
>מני >תנחמני
2הנה אל ישׁועתי אבטח ולא אפחד כי עזי וזמרת יה יהוה 2הנה אל אל ישועתי אבטח ולוא אפחד כיא עוזי וזמרתיה י
>ויהי לי לישׁועה >הוה היהא לי לישועה
3ושׁאבתם מים בשׂשׂון ממעיני הישׁועה 3ושאבתמה מימ בששונ ממעיני הישועה
4ואמרתם ביום ההוא הודו ליהוה קראו בשׁמו הודיעו בעמי4ואמרתה ביומ ההוא אודו ליהוה קראו בשמו הודיעו בעמימ
>ם עלילתיו הזכירו כי נשׂגב שׁמו > עלילותיו הזכירו כי נשגב שמו
5זמרו יהוה כי גאות עשׂה מידעת זאת בכל הארץ 5זמרו ליהוה כי גאות עשה מודעות זואת בכול הארצ
6צהלי ורני יושׁבת ציון כי גדול בקרבך קדושׁ ישׂראל פ6צהלי ורני יושבת ציונ כיא גדול בקרבכ קדוש ישראל
>  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 13 MT
Isaiah 13 1QIsaa
n1משא בבל אשר חזה ישעיהו בנ אמוצn1משא בבל אשר חוזה ישעיה בנ אמוצ
2על הר נשפה שאו נס הרימו קול להמ הניפו יד ויבאו פתח2על הר נשפה שאו נס הרימו קול להמ הניפו יד יבוא פתחי
>י נדיבימ> נדיבימ
3אני צויתי למקדשי גמ קראתי גבורי לאפי עליזי גאותי3אני צויתי למקדשי גמ קראתי גבורי לאפי עליזי גאותי
n4קול המונ בהרימ דמות עמ רב קול שאונ ממלכות גוימ נאסn4קול המונ בהרימ דמות עמ רב קול שאונ ממלכות גואימ נס
>פימ יהוה צבאות מפקד צבא מלחמה>פימ יהוה צבאות מפקד צבא מלחמה
5באימ מארצ מרחק מקצה השמימ יהוה וכלי זעמו לחבל כל ה5באימ מארצ מרחק מקצה השמימ יהוה וכלי זעמו לחבל כל ה
>ארצ>ארצ
6הילילו כי קרוב יומ יהוה כשד משדי יבוא6הילילו כי קרוב יומ יהוה כשד משדי יבוא
n7על כנ כל ידימ תרפינה וכל לבב אנוש ימסn7על כנ כול ידינ תרפינה וכל לבב אנוש ימס
8ונבהלו צירימ וחבלימ יאחזונ כיולדה יחילונ איש אל רע8ונבהלו צירימ וחבלימ יאחזונ כיולדה יחילונ איש אל רע
>הו יתמהו פני להבימ פניהמ>הו יתמהו ופני להבימ פניהמ
9הנה יומ יהוה בא אכזרי ועברה וחרונ אפ לשומ הארצ לשמ9הנה יומ יהוה בא אגזרי ועברה וחרונ אפ לשומ ארצ לשמה
>ה וחטאיה ישמיד ממנה> וחטאימ ישמיד ממנה
10כי כוכבי השמימ וכסיליהמ לא יהלו אורמ חשכ השמש בצאת10כי כוכבי השמימ וכסליהמ לוא יאירו אורמ חשכ השמש בצא
>ו וירח לא יגיה אורו>תו וירח לוא יגיה אורו
11ופקדתי על תבל רעה ועל רשעימ עונמ והשבתי גאונ זדימ 11ופקדתי על תבל רעה ועל רשעימ עוונמ והשבתי גאונ זדימ
>וגאות עריצימ אשפיל> וגאות עריצימ אשפיל
12אוקיר אנוש מפז ואדמ מכתמ אופיר12אוקר אנוש מפז ואדמ מכתמ אופיר
13על כנ שמימ ארגיז ותרעש הארצ ממקומה בעברת יהוה צבאו13על כנ שמימ ארגיז ותרעש הארצ ממקומה בעברת יהוה צבאו
>ת וביומ חרונ אפו>ת וביומ חרונ אפו
t14והיה כצבי מדח וכצאנ ואינ מקבצ איש אל עמו יפנו ואישt14והיו כצבי מדח וכצאונ ואינ מקבצ איש אל עמו יפנו ואי
> אל ארצו ינוסו>ש אל ארצו ינוסו
15כל הנמצא ידקר וכל הנספה יפול בחרב15כול הנמצא ידקר וכול הנספה יפול בחרב
16ועלליהמ ירטשו לעיניהמ ישסו בתיהמ ונשיהמ תשכבנה16ועילוליהמה ירוטשו לעיניהמ וישסו בתיהמ ונשיהמה תשכב
 >נה
17הנני מעיר עליהמ את מדי אשר כספ לא יחשבו וזהב לא יח17הנני מעיר עליהמ את מדי אשר כספ לוא יחשוב וזהב לוא 
>פצו בו>יחפצו בו
18וקשתות נערימ תרטשנה ופרי בטנ לא ירחמו על בנימ לא ת18וקשתות נערימ תרטשנה ועל פרי בטנ לוא ירחמו ועל בנימ
>חוס עינמ> לוא תחוס עינמ
19והיתה בבל צבי ממלכות תפארת גאונ כשדימ כמהפכת אלהימ19והיתה בבל צבי ממלכת תפראת גאונ כשדיימ כמאפכת אלוהי
> את סדמ ואת עמרה>מ את סודמ ואת עומרה
20לא תשב לנצח ולא תשכנ עד דור ודור ולא יהל שמ ערבי ו20לוא תשב לנצח ולוא תשכונ עד דור ודור ולוא יהל שמה ע
>רעימ לא ירבצו שמ>רבי ורועימ לוא ירביצו שמ
21ורבצו שמ ציימ ומלאו בתיהמ אחימ ושכנו שמ בנות יענה 21ורבצו שמ ציימ ומלאו בתיהמ אחימ ושכנו שמה בנות יענה
>ושעירימ ירקדו שמ> ושעירימ ירקדו שמ
22וענה איימ באלמנותיו ותנימ בהיכלי ענג וקרוב לבוא עת22וענה אימ באלמנותו ותנימ בהיכלו ענוגו קרוב לבוא עתה
>ה וימיה לא ימשכו> וימיה לוא ימשכו עוד
t1משׂא בבל אשׁר חזה ישׁעיהו בן אמוץ t1משא בבל אשר חוזה ישעיה בנ אמוצ
2על הר נשׁפה שׂאו נס הרימו קול להם הניפו יד ויבאו פ2על הר נשפה שאו נס הרימו קול להמ הניפו יד יבוא פתחי
>תחי נדיבים > נדיבימ
3אני צויתי למקדשׁי גם קראתי גבורי לאפי עליזי גאותי 3אני צויתי למקדשי גמ קראתי גבורי לאפי עליזי גאותי
4קול המון בהרים דמות עם רב קול שׁאון ממלכות גוים נא4קול המונ בהרימ דמות עמ רב קול שאונ ממלכות גואימ נס
>ספים יהוה צבאות מפקד צבא מלחמה >פימ יהוה צבאות מפקד צבא מלחמה
5באים מארץ מרחק מקצה השׁמים יהוה וכלי זעמו לחבל כל 5באימ מארצ מרחק מקצה השמימ יהוה וכלי זעמו לחבל כל ה
>הארץ >ארצ
6הילילו כי קרוב יום יהוה כשׁד משׁדי יבוא 6הילילו כי קרוב יומ יהוה כשד משדי יבוא
7על כן כל ידים תרפינה וכל לבב אנושׁ ימס 7על כנ כול ידינ תרפינה וכל לבב אנוש ימס
8ונבהלו׀ צירים וחבלים יאחזון כיולדה יחילון אישׁ אל 8ונבהלו צירימ וחבלימ יאחזונ כיולדה יחילונ איש אל רע
>רעהו יתמהו פני להבים פניהם >הו יתמהו ופני להבימ פניהמ
9הנה יום יהוה בא אכזרי ועברה וחרון אף לשׂום הארץ לש9הנה יומ יהוה בא אגזרי ועברה וחרונ אפ לשומ ארצ לשמה
>ׁמה וחטאיה ישׁמיד ממנה > וחטאימ ישמיד ממנה
10כי כוכבי השׁמים וכסיליהם לא יהלו אורם חשׁך השׁמשׁ 10כי כוכבי השמימ וכסליהמ לוא יאירו אורמ חשכ השמש בצא
>בצאתו וירח לא יגיה אורו >תו וירח לוא יגיה אורו
11ופקדתי על תבל רעה ועל רשׁעים עונם והשׁבתי גאון זדי11ופקדתי על תבל רעה ועל רשעימ עוונמ והשבתי גאונ זדימ
>ם וגאות עריצים אשׁפיל > וגאות עריצימ אשפיל
12אוקיר אנושׁ מפז ואדם מכתם אופיר 12אוקר אנוש מפז ואדמ מכתמ אופיר
13על כן שׁמים ארגיז ותרעשׁ הארץ ממקומה בעברת יהוה צב13על כנ שמימ ארגיז ותרעש הארצ ממקומה בעברת יהוה צבאו
>אות וביום חרון אפו >ת וביומ חרונ אפו
14והיה כצבי מדח וכצאן ואין מקבץ אישׁ אל עמו יפנו ואי14והיו כצבי מדח וכצאונ ואינ מקבצ איש אל עמו יפנו ואי
>שׁ אל ארצו ינוסו >ש אל ארצו ינוסו
15כל הנמצא ידקר וכל הנספה יפול בחרב 15כול הנמצא ידקר וכול הנספה יפול בחרב
16ועלליהם ירטשׁו לעיניהם ישׁסו בתיהם ונשׁיהם תשׁגלנה16ועילוליהמה ירוטשו לעיניהמ וישסו בתיהמ ונשיהמה תשכב
> >נה
17הנני מעיר עליהם את מדי אשׁר כסף לא יחשׁבו וזהב לא 17הנני מעיר עליהמ את מדי אשר כספ לוא יחשוב וזהב לוא 
>יחפצו בו >יחפצו בו
18וקשׁתות נערים תרטשׁנה ופרי בטן לא ירחמו על בנים לא18וקשתות נערימ תרטשנה ועל פרי בטנ לוא ירחמו ועל בנימ
> תחוס עינם > לוא תחוס עינמ
19והיתה בבל צבי ממלכות תפארת גאון כשׂדים כמהפכת אלהי19והיתה בבל צבי ממלכת תפראת גאונ כשדיימ כמאפכת אלוהי
>ם את סדם ואת עמרה >מ את סודמ ואת עומרה
20לא תשׁב לנצח ולא תשׁכן עד דור ודור ולא יהל שׁם ערב20לוא תשב לנצח ולוא תשכונ עד דור ודור ולוא יהל שמה ע
>י ורעים לא ירבצו שׁם >רבי ורועימ לוא ירביצו שמ
21ורבצו שׁם ציים ומלאו בתיהם אחים ושׁכנו שׁם בנות יע21ורבצו שמ ציימ ומלאו בתיהמ אחימ ושכנו שמה בנות יענה
>נה ושׂעירים ירקדו שׁם > ושעירימ ירקדו שמ
22וענה איים באלמנותיו ותנים בהיכלי ענג וקרוב לבוא עת22וענה אימ באלמנותו ותנימ בהיכלו ענוגו קרוב לבוא עתה
>ה וימיה לא ימשׁכו > וימיה לוא ימשכו עוד
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Isaiah 14 MT
Isaiah 14 1QIsaa
n1כי ירחמ יהוה את יעקב ובחר עוד בישראל והניחמ על אדמn1כי ירחמ יהוה את יעקוב ובחר עוד בישראל והניחמ על אד
>תמ ונלוה הגר עליהמ ונספחו על בית יעקב>מתמ ונלוא הגר עליהמ ונספחו על בית יעקוב
2ולקחומ עמימ והביאומ אל מקוממ והתנחלומ בית ישראל על2ולקחומ עמימ רבימ והביאומ אל אדמתמ ואל מקוממ והתנחל
> אדמת יהוה לעבדימ ולשפחות והיו שבימ לשביהמ ורדו בנ>ומ בית ישראל אל אדמת יהוה לעבדימ ולשפחות והיו שובי
>גשיהמ>מ לשוביהמ ורדימ בנוגשיהמ
3והיה ביומ הניח יהוה לכ מעצבכ ומרגזכ ומנ העבדה הקשה3והיה ביומ הניח יהוה לכ מעצבכ ומרוגזכ ומנ העבודה הק
> אשר עבד בכ>שה אשר עבדו בכה
4ונשאת המשל הזה על מלכ בבל ואמרת איכ שבת נגש שבתה מ4ונשאתה את המשל הזה על מלכ בבל ואמרתה איכה שבת נוגש
>דהבה> שבתה מרהבה
5שבר יהוה מטה רשעימ שבט משלימ5שבר יהוה מטה רשעימ שבט מושלימ
6מכה עמימ בעברה מכת בלתי סרה רדה באפ גוימ מרדפ בלי 6מכה עמימ בעברה מכת בלתי סרה רודה באפ גואימ מרדפ בל
>חשכ>י חשכ
7נחה שקטה כל הארצ פצחו רנה7נחה שקטה כול הארצ פצחו רונה
8גמ ברושימ שמחו לכ ארזי לבנונ מאז שכבת לא יעלה הכרת8גמ ברושימ שמחו לכ ארזי הלבנונ מאז שכבתה ולוא יעלה 
> עלינו>הכורת עלינו
9שאול מתחת רגזה לכ לקראת בואכ עורר לכ רפאימ כל עתוד9שאול מתחת רגזה לכה לקרת בואכ עוררה לכה רפאימ כול ע
>י ארצ הקימ מכסאותמ כל מלכי גוימ>תודי ארצ הקימה מכסאותמ כול מלכי גואימ
10כלמ יענו ויאמרו אליכ גמ אתה חלית כמונו אלינו נמשלת10כולמ יענו ויאמרו אליכ גמ אתה חליתה כמונו אלינו נמש
 >לתה
11הורד שאול גאונכ המית נבליכ תחתיכ יצע רמה ומכסיכ תו11הורד שאול גאונכ המות נבלתכ תחתיכ יצע רמה ומכסכ תול
>לעה>עה
12איכ נפלת משמימ הילל בנ שחר נגדעת לארצ חולש על גוימ12היכה נפלתה מהשמימ היליל בנ שחר נגדעתה לארצ חולש על
 > גוי
13ואתה אמרת בלבבכ השמימ אעלה ממעל לכוכבי אל ארימ כסא13ואתה אמרתה בלבבכה השמימ אעלה ממעלה לכוכבי אל ארימ 
>י ואשב בהר מועד בירכתי צפונ>כסאי אשב בהר מועד בירכתי צפונ
14אעלה על במתי עב אדמה לעליונ14אעלה על בומתי עב אדמה לעליונ
15אכ אל שאול תורד אל ירכתי בור15אכ אל שאול תורד אל ירכתי בור
n16ראיכ אליכ ישגיחו אליכ יתבוננו הזה האיש מרגיז הארצ n16רואיכ אליכ ישגיחו אליכה יתבוננו הזה האיש המרגיז הא
>מרעיש ממלכות>רצ המרעיש ממלכות
17שמ תבל כמדבר ועריו הרס אסיריו לא פתח ביתה17שמ תבל כמדבר עריו הרס אסיריו לוא פתח ביתה
18כל מלכי גוימ כלמ שכבו בכבוד איש בביתו18כול מלכי גואימ שכבו בכבוד איש בביתו
19ואתה השלכת מקברכ כנצר נתעב לבוש הרגימ מטעני חרב יו19ואתה הושלכתה מקוברכ כנצר נתעב לבש הרוגימ מטעני חרב
>רדי אל אבני בור כפגר מובס> יורדו אל אבני בור כפגר מובס
20לא תחד אתמ בקבורה כי ארצכ שחת עמכ הרגת לא יקרא לעו20לוא תחת אותמ בקבורה כי ארצכ שחתה עמכ הרגתה לוא יקר
>למ זרע מרעימ>או לעולמ זרע מרעימ
21הכינו לבניו מטבח בעונ אבותמ בל יקמו וירשו ארצ ומלא21הכינו לבניו מטבח בעוונ אבותמ בל יקומו וירשו ארצ ומ
>ו פני תבל ערימ>לו פני תבל ערימ
22וקמתי עליהמ נאמ יהוה צבאות והכרתי לבבל שמ ושאר וני22וקמתי עליהמה נואמ יהוה צבאות והכרתי לבבל שמ ושארית
>נ ונכד נאמ יהוה> נינ ונכד נואמ יהוה
23ושמתיה למורש קפד ואגמי מימ וטאטאתיה במטאטא השמד נא23ושמתי למירש קפז אגמי מימ וטאטאתי במטאטא השמד נואמ 
>מ יהוה צבאות>יהוה צבאות
24נשבע יהוה צבאות לאמר אמ לא כאשר דמיתי כנ היתה וכאש24נשבע יהוה צבאות לאמור אמ לוא כאשר דמיתי כנ תהיה וכ
>ר יעצתי היא תקומ>אשר יעצתי היא תקומ
25לשבר אשור בארצי ועל הרי אבוסנו וסר מעליהמ עלו וסבל25לשבור אשור בארצי ועל הרי אבוסנו וסר מעליכמה עלו וס
>ו מעל שכמו יסור>בלו מעל שכמכה יסור
26זאת העצה היעוצה על כל הארצ וזאת היד הנטויה על כל ה26זואת העצה היעוצה על כול הארצ וזות היד הנטויה על כו
>גוימ>ל הגואימ
27כי יהוה צבאות יעצ ומי יפר וידו הנטויה ומי ישיבנה27כיא יהוה צבאות יעצ ומי יפר וידיו הנטויה ומי ישיבנה
28בשנת מות המלכ אחז היה המשא הזה28בשנת מות המלכ אחז היה המשא הזה
t29אל תשמחי פלשת כלכ כי נשבר שבט מככ כי משרש נחש יצא t29אל תשמחי פלשת כולכ כי נשבר שבט מככה כי משורש נחש י
>צפע ופריו שרפ מעופפ>צא צפע ופריו שרפ מעופפ
30ורעו בכורי דלימ ואביונימ לבטח ירבצו והמתי ברעב שרש30ורעו בכורי דלימ ואביונימ לבטח ירבצו והמתי ברעב שור
>כ ושאריתכ יהרג>שכ ושאריתכ אהרוג
31הילילי שער זעקי עיר נמוג פלשת כלכ כי מצפונ עשנ בא 31הילילי שער זעקי עיר נמוג פלשת כולכ כי מצפונ עשנ בא
>ואינ בודד במועדיו> ואינ מודד במודעיו
32ומה יענה מלאכי גוי כי יהוה יסד ציונ ובה יחסו עניי 32ומה יענו מלכי גוי כי יהוה יסד ציונ ובו יחסו עניי ע
>עמו>מו
t1כי ירחם יהוה את יעקב ובחר עוד בישׂראל והניחם על אדt1כי ירחמ יהוה את יעקוב ובחר עוד בישראל והניחמ על אד
>מתם ונלוה הגר עליהם ונספחו על בית יעקב >מתמ ונלוא הגר עליהמ ונספחו על בית יעקוב
2ולקחום עמים והביאום אל מקומם והתנחלום בית ישׂראל ע2ולקחומ עמימ רבימ והביאומ אל אדמתמ ואל מקוממ והתנחל
>ל אדמת יהוה לעבדים ולשׁפחות והיו שׁבים לשׁביהם ורד>ומ בית ישראל אל אדמת יהוה לעבדימ ולשפחות והיו שובי
>ו בנגשׂיהם ס >מ לשוביהמ ורדימ בנוגשיהמ
3והיה ביום הניח יהוה לך מעצבך ומרגזך ומן העבדה הקשׁ3והיה ביומ הניח יהוה לכ מעצבכ ומרוגזכ ומנ העבודה הק
>ה אשׁר עבד בך >שה אשר עבדו בכה
4ונשׂאת המשׁל הזה על מלך בבל ואמרת איך שׁבת נגשׂ שׁ4ונשאתה את המשל הזה על מלכ בבל ואמרתה איכה שבת נוגש
>בתה מדהבה > שבתה מרהבה
5שׁבר יהוה מטה רשׁעים שׁבט משׁלים 5שבר יהוה מטה רשעימ שבט מושלימ
6מכה עמים בעברה מכת בלתי סרה רדה באף גוים מרדף בלי 6מכה עמימ בעברה מכת בלתי סרה רודה באפ גואימ מרדפ בל
>חשׂך >י חשכ
7נחה שׁקטה כל הארץ פצחו רנה 7נחה שקטה כול הארצ פצחו רונה
8גם ברושׁים שׂמחו לך ארזי לבנון מאז שׁכבת לא יעלה ה8גמ ברושימ שמחו לכ ארזי הלבנונ מאז שכבתה ולוא יעלה 
>כרת עלינו >הכורת עלינו
9שׁאול מתחת רגזה לך לקראת בואך עורר לך רפאים כל עתו9שאול מתחת רגזה לכה לקרת בואכ עוררה לכה רפאימ כול ע
>די ארץ הקים מכסאותם כל מלכי גוים >תודי ארצ הקימה מכסאותמ כול מלכי גואימ
10כלם יענו ויאמרו אליך גם אתה חלית כמונו אלינו נמשׁל10כולמ יענו ויאמרו אליכ גמ אתה חליתה כמונו אלינו נמש
>ת >לתה
11הורד שׁאול גאונך המית נבליך תחתיך יצע רמה ומכסיך ת11הורד שאול גאונכ המות נבלתכ תחתיכ יצע רמה ומכסכ תול
>ולעה >עה
12איך נפלת משׁמים הילל בן שׁחר נגדעת לארץ חולשׁ על ג12היכה נפלתה מהשמימ היליל בנ שחר נגדעתה לארצ חולש על
>וים > גוי
13ואתה אמרת בלבבך השׁמים אעלה ממעל לכוכבי אל ארים כס13ואתה אמרתה בלבבכה השמימ אעלה ממעלה לכוכבי אל ארימ 
>אי ואשׁב בהר מועד בירכתי צפון >כסאי אשב בהר מועד בירכתי צפונ
14אעלה על במתי עב אדמה לעליון 14אעלה על בומתי עב אדמה לעליונ
15אך אל שׁאול תורד אל ירכתי בור 15אכ אל שאול תורד אל ירכתי בור
16ראיך אליך ישׁגיחו אליך יתבוננו הזה האישׁ מרגיז האר16רואיכ אליכ ישגיחו אליכה יתבוננו הזה האיש המרגיז הא
>ץ מרעישׁ ממלכות >רצ המרעיש ממלכות
17שׂם תבל כמדבר ועריו הרס אסיריו לא פתח ביתה 17שמ תבל כמדבר עריו הרס אסיריו לוא פתח ביתה
18כל מלכי גוים כלם שׁכבו בכבוד אישׁ בביתו 18כול מלכי גואימ שכבו בכבוד איש בביתו
19ואתה השׁלכת מקברך כנצר נתעב לבושׁ הרגים מטעני חרב 19ואתה הושלכתה מקוברכ כנצר נתעב לבש הרוגימ מטעני חרב
>יורדי אל אבני בור כפגר מובס > יורדו אל אבני בור כפגר מובס
20לא תחד אתם בקבורה כי ארצך שׁחת עמך הרגת לא יקרא לע20לוא תחת אותמ בקבורה כי ארצכ שחתה עמכ הרגתה לוא יקר
>ולם זרע מרעים >או לעולמ זרע מרעימ
21הכינו לבניו מטבח בעון אבותם בל יקמו וירשׁו ארץ ומל21הכינו לבניו מטבח בעוונ אבותמ בל יקומו וירשו ארצ ומ
>או פני תבל ערים >לו פני תבל ערימ
22וקמתי עליהם נאם יהוה צבאות והכרתי לבבל שׁם ושׁאר ו22וקמתי עליהמה נואמ יהוה צבאות והכרתי לבבל שמ ושארית
>נין ונכד נאם יהוה > נינ ונכד נואמ יהוה
23ושׂמתיה למורשׁ קפד ואגמי מים וטאטאתיה במטאטא השׁמד23ושמתי למירש קפז אגמי מימ וטאטאתי במטאטא השמד נואמ 
> נאם יהוה צבאות פ >יהוה צבאות
24נשׁבע יהוה צבאות לאמר אם לא כאשׁר דמיתי כן היתה וכ24נשבע יהוה צבאות לאמור אמ לוא כאשר דמיתי כנ תהיה וכ
>אשׁר יעצתי היא תקום >אשר יעצתי היא תקומ
25לשׁבר אשׁור בארצי ועל הרי אבוסנו וסר מעליהם עלו וס25לשבור אשור בארצי ועל הרי אבוסנו וסר מעליכמה עלו וס
>בלו מעל שׁכמו יסור >בלו מעל שכמכה יסור
26זאת העצה היעוצה על כל הארץ וזאת היד הנטויה על כל ה26זואת העצה היעוצה על כול הארצ וזות היד הנטויה על כו
>גוים >ל הגואימ
27כי יהוה צבאות יעץ ומי יפר וידו הנטויה ומי ישׁיבנה 27כיא יהוה צבאות יעצ ומי יפר וידיו הנטויה ומי ישיבנה
>פ  
28בשׁנת מות המלך אחז היה המשׂא הזה 28בשנת מות המלכ אחז היה המשא הזה
29אל תשׂמחי פלשׁת כלך כי נשׁבר שׁבט מכך כי משׁרשׁ נח29אל תשמחי פלשת כולכ כי נשבר שבט מככה כי משורש נחש י
>שׁ יצא צפע ופריו שׂרף מעופף >צא צפע ופריו שרפ מעופפ
30ורעו בכורי דלים ואביונים לבטח ירבצו והמתי ברעב שׁר30ורעו בכורי דלימ ואביונימ לבטח ירבצו והמתי ברעב שור
>שׁך ושׁאריתך יהרג >שכ ושאריתכ אהרוג
31הילילי שׁער זעקי עיר נמוג פלשׁת כלך כי מצפון עשׁן 31הילילי שער זעקי עיר נמוג פלשת כולכ כי מצפונ עשנ בא
>בא ואין בודד במועדיו > ואינ מודד במודעיו
32ומה יענה מלאכי גוי כי יהוה יסד ציון ובה יחסו עניי 32ומה יענו מלכי גוי כי יהוה יסד ציונ ובו יחסו עניי ע
>עמו ס >מו
- - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + +

Isaiah 15 MT
Isaiah 15 1QIsaa
n1משא מואב כי בליל שדד ער מואב נדמה כי בליל שדד קיר n1משא מואב כי בלילה שודד עיר מואב ונדמה כי בלילה שוד
>מואב נדמה>ד עיר מואב נדמה
2עלה הבית ודיבנ הבמות לבכי על נבו ועל מידבא מואב יי2עלה הבית ודיבונ הבאמות לבכי על נבו ועל מידבה מואב 
>ליל בכל ראשיו קרחה כל זקנ גרועה>יליל בכול ראושו קרחה וכל זקנ גרועה
3בחוצתיו חגרו שק על גגותיה וברחבתיה כלה ייליל ירד ב3בחוצותיה חגורו שק על גגותיה וברחובתיה כלה יהיליל ו
>בכי>ירד בבכי
4ותזעק חשבונ ואלעלה עד יהצ נשמע קולמ על כנ חלצי מוא4ותזעק חשבונ ואלעלה עד יהצ נשמע קולמ על כנ חלצי מוא
>ב יריעו נפשו ירעה לו>ב יריעו נפשו ירע לו
5לבי למואב יזעק בריחה עד צער עגלת שלשיה כי מעלה הלו5לבי למואב יזעק ברחוה עד צעור עגלת שלישיה כי מעלה ה
>חית בבכי יעלה בו כי דרכ חורנימ זעקת שבר יעערו>לוחות בבכי יעלה בו כי דרכ חורונימ זעקת שבר ערו
6כי מי נמרימ משמות יהיו כי יבש חציר כלה דשא ירק לא 6כי מי נמרימ משמות יהיו כי יבש חציר כלה דשא ירוק לו
>היה>א אהיא
7על כנ יתרה עשה ופקדתמ על נחל הערבימ ישאומ7על כנ יתרה עשה ופקודתמ על נחל ערבי תישאומ
8כי הקיפה הזעקה את גבול מואב עד אגלימ יללתה ובאר אי8כי הקיפה הזעקה את גבול מואב עד אגלימ יללתה ובאר אי
>לימ יללתה>לימ יללתה
t9כי מי דימונ מלאו דמ כי אשית על דימונ נוספות לפליטתt9כי מי דיבונ מלאו דמ כי אשית על דיבונ נוספת לפליטת 
> מואב אריה ולשארית אדמה>מואב ארוה לשארית אדמה
t1משׂא מואב כי בליל שׁדד ער מואב נדמה כי בליל שׁדד קt1משא מואב כי בלילה שודד עיר מואב ונדמה כי בלילה שוד
>יר מואב נדמה >ד עיר מואב נדמה
2עלה הבית ודיבן הבמות לבכי על נבו ועל מידבא מואב יי2עלה הבית ודיבונ הבאמות לבכי על נבו ועל מידבה מואב 
>ליל בכל ראשׁיו קרחה כל זקן גרועה >יליל בכול ראושו קרחה וכל זקנ גרועה
3בחוצתיו חגרו שׂק על גגותיה וברחבתיה כלה ייליל ירד 3בחוצותיה חגורו שק על גגותיה וברחובתיה כלה יהיליל ו
>בבכי >ירד בבכי
4ותזעק חשׁבון ואלעלה עד יהץ נשׁמע קולם על כן חלצי מ4ותזעק חשבונ ואלעלה עד יהצ נשמע קולמ על כנ חלצי מוא
>ואב יריעו נפשׁו ירעה לו >ב יריעו נפשו ירע לו
5לבי למואב יזעק בריחה עד צער עגלת שׁלשׁיה כי׀ מעלה 5לבי למואב יזעק ברחוה עד צעור עגלת שלישיה כי מעלה ה
>הלוחית בבכי יעלה בו כי דרך חורנים זעקת שׁבר יעערו >לוחות בבכי יעלה בו כי דרכ חורונימ זעקת שבר ערו
6כי מי נמרים משׁמות יהיו כי יבשׁ חציר כלה דשׁא ירק 6כי מי נמרימ משמות יהיו כי יבש חציר כלה דשא ירוק לו
>לא היה >א אהיא
7על כן יתרה עשׂה ופקדתם על נחל הערבים ישׂאום 7על כנ יתרה עשה ופקודתמ על נחל ערבי תישאומ
8כי הקיפה הזעקה את גבול מואב עד אגלים יללתה ובאר אי8כי הקיפה הזעקה את גבול מואב עד אגלימ יללתה ובאר אי
>לים יללתה >לימ יללתה
9כי מי דימון מלאו דם כי אשׁית על דימון נוספות לפליט9כי מי דיבונ מלאו דמ כי אשית על דיבונ נוספת לפליטת 
>ת מואב אריה ולשׁארית אדמה >מואב ארוה לשארית אדמה
- - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 16 MT
Isaiah 16 1QIsaa
n1שלחו כר משל ארצ מסלע מדברה אל הר בת ציונn1שלחו כרמשל ארצ מסלה מדברה אל הר בת ציונ
2והיה כעופ נודד קנ משלח תהיינה בנות מואב מעברת לארנ2והיא כעופ נודד קנ משלח תהינה בנות מואב מעברת לארנו
>ונ>נ
3הביאי עצה עשו פלילה שיתי כליל צלכ בתוכ צהרימ סתרי 3הביו עצה עשו פלילה שיתי כליל צלכ בתוכ צהרימ סתרי נ
>נדחימ נדד אל תגלי>דחימ נודד אל תגלי
4יגורו בכ נדחי מואב הוי סתר למו מפני שודד כי אפס המ4יגורו בכ נדחי מואב הוי סתר למו מפני שודד כי אפס המ
>צ כלה שד תמו רמס מנ הארצ>וצ כלה שד תמ רומס מנ הארצ
5והוכנ בחסד כסא וישב עליו באמת באהל דוד שפט ודרש מש5והוכנ בחסד כסא וישב עליו באמת באוהלו דויד שופט ודו
>פט ומהר צדק>רש משפט ומהר צדק
6שמענו גאונ מואב גא מאד גאותו וגאונו ועברתו לא כנ ב6שמענו גאונ מואב גאה מואד גאתו וגאונו ועברתו לכנ בד
>דיו>יו
7לכנ ייליל מואב למואב כלה ייליל לאשישי קיר חרשת תהג7ולכנ לוא ייליל מואב למואב כלה ייליל לאשישי קיר חרש
>ו אכ נכאימ>ת תהגו אכ נכאימ
8כי שדמות חשבונ אמלל גפנ שבמה בעלי גוימ הלמו שרוקיה8כי שדמות חשבונ אמללה גפנ שבמה
> עד יעזר נגעו תעו מדבר שלחותיה נטשו עברו ימ 
9על כנ אבכה בבכי יעזר גפנ שבמה אריוכ דמעתי חשבונ וא9ארזיכ דמעתי חשבונ ואלעלה כיא על קיצכ ועל קצירכ היד
>לעלה כי על קיצכ ועל קצירכ הידד נפל>ד נפל
10ונאספ שמחה וגיל מנ הכרמל ובכרמימ לא ירננ לא ירעע י10ונאספ שמחה וגיל מנ הכרמל ובכרמימ לוא ירננו ולוא יר
>ינ ביקבימ לא ידרכ הדרכ הידד השבתי>ועע יינ ביקבימ לוא ידרוכ הדורכ הידד השבתי
11על כנ מעי למואב ככנור יהמו וקרבי לקיר חרש11על כנ מעי למואב ככנור יהמו וקרבי לקיר חרש
n12והיה כי נראה כי נלאה מואב על הבמה ובא אל מקדשו להתn12יהיה כי נראה כי בא מואב על הבמה ובא אל מקדשיו להתפ
>פלל ולא יוכל>לל ולוא יוכל
13זה הדבר אשר דבר יהוה אל מואב מאז13זה הדבר אשר דבר יהוה אל מואב מאז
t14ועתה דבר יהוה לאמר בשלש שנימ כשני שכיר ונקלה כבוד t14ועתה דבר יהוה לאמור בשלוש שנימ כשני שכיר ונקלה כבו
>מואב בכל ההמונ הרב ושאר מעט מזער לוא כביר>ד מואב בכול המונ הרב ושאר מעט מזער ולוא כבוד
t1שׁלחו כר משׁל ארץ מסלע מדברה אל הר בת ציון t1שלחו כרמשל ארצ מסלה מדברה אל הר בת ציונ
2והיה כעוף נודד קן משׁלח תהיינה בנות מואב מעברת לאר2והיא כעופ נודד קנ משלח תהינה בנות מואב מעברת לארנו
>נון >נ
3הביאו עצה עשׂו פלילה שׁיתי כליל צלך בתוך צהרים סתר3הביו עצה עשו פלילה שיתי כליל צלכ בתוכ צהרימ סתרי נ
>י נדחים נדד אל תגלי >דחימ נודד אל תגלי
4יגורו בך נדחי מואב הוי סתר למו מפני שׁודד כי אפס ה4יגורו בכ נדחי מואב הוי סתר למו מפני שודד כי אפס המ
>מץ כלה שׁד תמו רמס מן הארץ >וצ כלה שד תמ רומס מנ הארצ
5והוכן בחסד כסא וישׁב עליו באמת באהל דוד שׁפט ודרשׁ5והוכנ בחסד כסא וישב עליו באמת באוהלו דויד שופט ודו
> משׁפט ומהר צדק >רש משפט ומהר צדק
6שׁמענו גאון מואב גא מאד גאותו וגאונו ועברתו לא כן 6שמענו גאונ מואב גאה מואד גאתו וגאונו ועברתו לכנ בד
>בדיו ס >יו
7לכן ייליל מואב למואב כלה ייליל לאשׁישׁי קיר חרשׂת 7ולכנ לוא ייליל מואב למואב כלה ייליל לאשישי קיר חרש
>תהגו אך נכאים >ת תהגו אכ נכאימ
8כי שׁדמות חשׁבון אמלל גפן שׂבמה בעלי גוים הלמו שׂר8כי שדמות חשבונ אמללה גפנ שבמה
>וקיה עד יעזר נגעו תעו מדבר שׁלחותיה נטשׁו עברו ים  
9על כן אבכה בבכי יעזר גפן שׂבמה אריוך דמעתי חשׁבון 9ארזיכ דמעתי חשבונ ואלעלה כיא על קיצכ ועל קצירכ היד
>ואלעלה כי על קיצך ועל קצירך הידד נפל >ד נפל
10ונאסף שׂמחה וגיל מן הכרמל ובכרמים לא ירנן לא ירעע 10ונאספ שמחה וגיל מנ הכרמל ובכרמימ לוא ירננו ולוא יר
>יין ביקבים לא ידרך הדרך הידד השׁבתי >ועע יינ ביקבימ לוא ידרוכ הדורכ הידד השבתי
11על כן מעי למואב ככנור יהמו וקרבי לקיר חרשׂ 11על כנ מעי למואב ככנור יהמו וקרבי לקיר חרש
12והיה כי נראה כי נלאה מואב על הבמה ובא אל מקדשׁו לה12יהיה כי נראה כי בא מואב על הבמה ובא אל מקדשיו להתפ
>תפלל ולא יוכל >לל ולוא יוכל
13זה הדבר אשׁר דבר יהוה אל מואב מאז 13זה הדבר אשר דבר יהוה אל מואב מאז
14ועתה דבר יהוה לאמר בשׁלשׁ שׁנים כשׁני שׂכיר ונקלה 14ועתה דבר יהוה לאמור בשלוש שנימ כשני שכיר ונקלה כבו
>כבוד מואב בכל ההמון הרב ושׁאר מעט מזער לוא כביר ס >ד מואב בכול המונ הרב ושאר מעט מזער ולוא כבוד
- - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 17 MT
Isaiah 17 1QIsaa
t1משא דמשק הנה דמשק מוסר מעיר והיתה מעי מפלהt1משא דרמשק הנה דרמשק מוסר מעיר והיית מעי מפלה
2עזבות ערי ערער לעדרימ תהיינה ורבצו ואינ מחריד2עזובות ערי עורערו לעדרימ תהינה ורבצו ואינ מחריד
3ונשבת מבצר מאפרימ וממלכה מדמשק ושאר ארמ ככבוד בני 3ונשבת מבצר מאפרימ וממלכה מדרמשק ושאר ארמ ככבוד בני
>ישראל יהיו נאמ יהוה צבאות> ישראל יהיה נואמ יהוה צבאות
4והיה ביומ ההוא ידל כבוד יעקב ומשמנ בשרו ירזה4והיה ביומ ההוא ידל כבוד יעקוב ומשמנ בשרו ירזה
5והיה כאספ קציר קמה וזרעו שבלימ יקצור והיה כמלקט שב5והיה כאספ קציר קמה וזרעו שבלימ וקציר והיה כמלקט שב
>לימ בעמק רפאימ>לימ בעמק רפאימ
6ונשאר בו עוללת כנקפ זית שנימ שלשה גרגרימ בראש אמיר6ונשאר בו עוללות כנקפ זית שנימ שלושה גדגרימ ברואש א
> ארבעה חמשה בסעפיה פריה נאמ יהוה אלהי ישראל>מיר ארבעה חמשה בסעפי פריה נואמ יהוה אלוהי ישראל
7ביומ ההוא ישעה האדמ על עשהו ועיניו אל קדוש ישראל ת7ביומ ההוא ישעה האדמ על עושוהי ועיניו אל קדוש ישראל
>ראינה> תראינה
8ולא ישעה אל המזבחות מעשה ידיו ואשר עשו אצבעתיו לא 8ולוא ישעה על המזבחות מעשיו אשר עשו אצבעותיו ולוא י
>יראה והאשרימ והחמנימ>ראה האשרימ והחמנימ
9ביומ ההוא יהיו ערי מעזו כעזובת החרש והאמיר אשר עזב9ביומ ההוא יהיו ערי מעוזו כעזובות החרש והאמיר אשר ע
>ו מפני בני ישראל והיתה שממה>זבו מפני בני ישראל והייתה שממה
10כי שכחת אלהי ישעכ וצור מעזכ לא זכרת על כנ תטעי נטע10כי שכחתי אלוהי ישעכ וצור מעוזכ לוא זכרת עלכנ תטעי 
>י נעמנימ וזמרת זר תזרענו>נטעי נעמונימ וזמורת זר תזרוענו
11ביומ נטעכ תשגשגי ובבקר זרעכ תפריחי נד קציר ביומ נח11ביומ נטעכ תשגשגשי ובבקר זרעכ תפריחי נד קציר ביומ נ
>לה וכאב אנוש>חלה וכאוב אנוש
12הוי המונ עמימ רבימ כהמות ימימ יהמיונ ושאונ לאמימ כ12הוי המונ עמימ רבימ כהמות ימימ יהמיונ ושאונ לאומימ 
>שאונ מימ כבירימ ישאונ>כשאונ מימ כבדימ ושאונ
13לאמימ כשאונ מימ רבימ ישאונ וגער בו ונס ממרחק ורדפ 13לאומימ כשאונ מימ רבימ ישאונ ויגער בו ונס ממרחק ורד
>כמצ הרימ לפני רוח וכגלגל לפני סופה>פ כמצ הרימ לפני רוח וכגלגל לפני סופה
14לעת ערב והנה בלהה בטרמ בקר איננו זה חלק שוסינו וגו14לעת ערב והנה בלהה בטרמ בקר ואיננו זה חלק שוסינו וג
>רל לבזזינו>ורל לבוזזינו
t1משׂא דמשׂק הנה דמשׂק מוסר מעיר והיתה מעי מפלה t1משא דרמשק הנה דרמשק מוסר מעיר והיית מעי מפלה
2עזבות ערי ערער לעדרים תהיינה ורבצו ואין מחריד 2עזובות ערי עורערו לעדרימ תהינה ורבצו ואינ מחריד
3ונשׁבת מבצר מאפרים וממלכה מדמשׂק ושׁאר ארם ככבוד ב3ונשבת מבצר מאפרימ וממלכה מדרמשק ושאר ארמ ככבוד בני
>ני ישׂראל יהיו נאם יהוה צבאות ס > ישראל יהיה נואמ יהוה צבאות
4והיה ביום ההוא ידל כבוד יעקב ומשׁמן בשׂרו ירזה 4והיה ביומ ההוא ידל כבוד יעקוב ומשמנ בשרו ירזה
5והיה כאסף קציר קמה וזרעו שׁבלים יקצור והיה כמלקט ש5והיה כאספ קציר קמה וזרעו שבלימ וקציר והיה כמלקט שב
>ׁבלים בעמק רפאים >לימ בעמק רפאימ
6ונשׁאר בו עוללת כנקף זית שׁנים שׁלשׁה גרגרים בראשׁ6ונשאר בו עוללות כנקפ זית שנימ שלושה גדגרימ ברואש א
> אמיר ארבעה חמשׁה בסעפיה פריה נאם יהוה אלהי ישׂראל>מיר ארבעה חמשה בסעפי פריה נואמ יהוה אלוהי ישראל
> ס  
7ביום ההוא ישׁעה האדם על עשׂהו ועיניו אל קדושׁ ישׂר7ביומ ההוא ישעה האדמ על עושוהי ועיניו אל קדוש ישראל
>אל תראינה > תראינה
8ולא ישׁעה אל המזבחות מעשׂה ידיו ואשׁר עשׂו אצבעתיו8ולוא ישעה על המזבחות מעשיו אשר עשו אצבעותיו ולוא י
> לא יראה והאשׁרים והחמנים >ראה האשרימ והחמנימ
9ביום ההוא יהיו׀ ערי מעזו כעזובת החרשׁ והאמיר אשׁר 9ביומ ההוא יהיו ערי מעוזו כעזובות החרש והאמיר אשר ע
>עזבו מפני בני ישׂראל והיתה שׁממה >זבו מפני בני ישראל והייתה שממה
10כי שׁכחת אלהי ישׁעך וצור מעזך לא זכרת על כן תטעי נ10כי שכחתי אלוהי ישעכ וצור מעוזכ לוא זכרת עלכנ תטעי 
>טעי נעמנים וזמרת זר תזרענו >נטעי נעמונימ וזמורת זר תזרוענו
11ביום נטעך תשׂגשׂגי ובבקר זרעך תפריחי נד קציר ביום 11ביומ נטעכ תשגשגשי ובבקר זרעכ תפריחי נד קציר ביומ נ
>נחלה וכאב אנושׁ ס >חלה וכאוב אנוש
12הוי המון עמים רבים כהמות ימים יהמיון ושׁאון לאמים 12הוי המונ עמימ רבימ כהמות ימימ יהמיונ ושאונ לאומימ 
>כשׁאון מים כבירים ישׁאון >כשאונ מימ כבדימ ושאונ
13לאמים כשׁאון מים רבים ישׁאון וגער בו ונס ממרחק ורד13לאומימ כשאונ מימ רבימ ישאונ ויגער בו ונס ממרחק ורד
>ף כמץ הרים לפני רוח וכגלגל לפני סופה >פ כמצ הרימ לפני רוח וכגלגל לפני סופה
14לעת ערב והנה בלהה בטרם בקר איננו זה חלק שׁוסינו וג14לעת ערב והנה בלהה בטרמ בקר ואיננו זה חלק שוסינו וג
>ורל לבזזינו ס >ורל לבוזזינו
- - - - - - - - - - - - - - - + + + + + + + + + + + + + + +

Isaiah 18 MT
Isaiah 18 1QIsaa
t1הוי ארצ צלצל כנפימ אשר מעבר לנהרי כושt1הוי ארצ צל צל כנפימ אשר מעבר לנהרי כוש
2השלח בימ צירימ ובכלי גמא על פני מימ לכו מלאכימ קלי2השולח בימ צירימ ובכלי גמא עלפני מימ לכו מלאכימ קלי
>מ אל גוי ממשכ ומורט אל עמ נורא מנ הוא והלאה גוי קו>מ לגוי ממשכ וממורט אל עמ נורא מנ הוא והלאה גוי קוק
> קו ומבוסה אשר בזאו נהרימ ארצו>ו ומבוסה אשר בזאי נהרימ ארצו
3כל ישבי תבל ושכני ארצ כנשא נס הרימ תראו וכתקע שופר3כול יושבי תבל ושוכני ארצ כנשא נס הרימ תראו וכתקוע 
> תשמעו>שופר תשמעו
4כי כה אמר יהוה אלי אשקטה ואביטה במכוני כחמ צח עלי 4כיא כה אמר יהוה אלי אשקוטה ואביטה במכוני כחמ צח על
>אור כעב טל בחמ קציר>י אור כעב טל בחמ קציר
5כי לפני קציר כתמ פרח ובסר גמל יהיה נצה וכרת הזלזלי5כי לפני קציר כתמ פרח ובסור גמול יהיה נצה וכרת הזלז
>מ במזמרות ואת הנטישות הסיר התז>לימ במזמרות ואת הנטישות הסיר התז
6יעזבו יחדו לעיט הרימ ולבהמת הארצ וקצ עליו העיט וכל6ועזבו יחדו לעיט הרימ ולבהמות ארצ וקצ עליו העיט וכו
> בהמת הארצ עליו תחרפ>ל בהמות הארצ עליו תחרפ
7בעת ההיא יובל שי ליהוה צבאות עמ ממשכ ומורט ומעמ נו7בעתה ההיא יובל שי ליהוה צבאות מעמ ממשכ וממרט ומעמ 
>רא מנ הוא והלאה גוי קו קו ומבוסה אשר בזאו נהרימ אר>נורא מהוא והלאה גוי קוקו ומבוסא אשר בזאי נהרימ ארצ
>צו אל מקומ שמ יהוה צבאות הר ציונ>ו אל מקומ שמ יהוה הר ציונ
t1הוי ארץ צלצל כנפים אשׁר מעבר לנהרי כושׁ t1הוי ארצ צל צל כנפימ אשר מעבר לנהרי כוש
2השׁלח בים צירים ובכלי גמא על פני מים לכו׀ מלאכים ק2השולח בימ צירימ ובכלי גמא עלפני מימ לכו מלאכימ קלי
>לים אל גוי ממשׁך ומורט אל עם נורא מן הוא והלאה גוי>מ לגוי ממשכ וממורט אל עמ נורא מנ הוא והלאה גוי קוק
> קו קו ומבוסה אשׁר בזאו נהרים ארצו >ו ומבוסה אשר בזאי נהרימ ארצו
3כל ישׁבי תבל ושׁכני ארץ כנשׂא נס הרים תראו וכתקע ש3כול יושבי תבל ושוכני ארצ כנשא נס הרימ תראו וכתקוע 
>ׁופר תשׁמעו ס >שופר תשמעו
4כי כה אמר יהוה אלי אשׁקוטה ואביטה במכוני כחם צח על4כיא כה אמר יהוה אלי אשקוטה ואביטה במכוני כחמ צח על
>י אור כעב טל בחם קציר >י אור כעב טל בחמ קציר
5כי לפני קציר כתם פרח ובסר גמל יהיה נצה וכרת הזלזלי5כי לפני קציר כתמ פרח ובסור גמול יהיה נצה וכרת הזלז
>ם במזמרות ואת הנטישׁות הסיר התז >לימ במזמרות ואת הנטישות הסיר התז
6יעזבו יחדו לעיט הרים ולבהמת הארץ וקץ עליו העיט וכל6ועזבו יחדו לעיט הרימ ולבהמות ארצ וקצ עליו העיט וכו
> בהמת הארץ עליו תחרף >ל בהמות הארצ עליו תחרפ
7בעת ההיא יובל שׁי ליהוה צבאות עם ממשׁך ומורט ומעם 7בעתה ההיא יובל שי ליהוה צבאות מעמ ממשכ וממרט ומעמ 
>נורא מן הוא והלאה גוי׀ קו קו ומבוסה אשׁר בזאו נהרי>נורא מהוא והלאה גוי קוקו ומבוסא אשר בזאי נהרימ ארצ
>ם ארצו אל מקום שׁם יהוה צבאות הר ציון ס >ו אל מקומ שמ יהוה הר ציונ
- - - - - - - - - - + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 19 MT
Isaiah 19 1QIsaa
n1משא מצרימ הנה יהוה רכב על עב קל ובא מצרימ ונעו אליn1משא מצרימ הנה יהוה רוכב על עב קל ובא מצרימ ונעו אל
>לי מצרימ מפניו ולבב מצרימ ימס בקרבו>ילי מצרימ מפניו ולבב מצרימ ימס בקרבו
2וסכסכתי מצרימ במצרימ ונלחמו איש באחיו ואיש ברעהו ע2וסכסכתי מצרימ במצרימ ונלחמו איש באחיו ואיש ברעהו ו
>יר בעיר ממלכה בממלכה>עיר בעיר ממלכה בממלכה
3ונבקה רוח מצרימ בקרבו ועצתו אבלע ודרשו אל האלילימ 3ונבקה רוח מצרימ בקרבו ועצתו אבלע ודרשו אל אלילימ ו
>ואל האטימ ואל האבות ואל הידענימ>אל האטימ ואל האובות ואל הידעונימ
4וסכרתי את מצרימ ביד אדנימ קשה ומלכ עז ימשל במ נאמ 4וסכרתי את מצרימ ביד אדונימ קשה ומלכ עז ימשל במ נוא
>האדונ יהוה צבאות>מ האדונ יהוה צבאות
5ונשתו מימ מהימ ונהר יחרב ויבש5ונשתו מימ מהימ ונהר יחרב ויבש
n6והאזניחו נהרות דללו וחרבו יארי מצור קנה וסופ קמלוn6והזניחו הנהרות ודללו וחרבו יאורי מצור קנה וסופ וקמ
t1משׂא מצרים הנה יהוה רכב על עב קל ובא מצרים ונעו אלt1משא מצרימ הנה יהוה רוכב על עב קל ובא מצרימ ונעו אל
>ילי מצרים מפניו ולבב מצרים ימס בקרבו >ילי מצרימ מפניו ולבב מצרימ ימס בקרבו
2וסכסכתי מצרים במצרים ונלחמו אישׁ באחיו ואישׁ ברעהו2וסכסכתי מצרימ במצרימ ונלחמו איש באחיו ואיש ברעהו ו
> עיר בעיר ממלכה בממלכה >עיר בעיר ממלכה בממלכה
3ונבקה רוח מצרים בקרבו ועצתו אבלע ודרשׁו אל האלילים3ונבקה רוח מצרימ בקרבו ועצתו אבלע ודרשו אל אלילימ ו
> ואל האטים ואל האבות ואל הידענים >אל האטימ ואל האובות ואל הידעונימ
4וסכרתי את מצרים ביד אדנים קשׁה ומלך עז ימשׁל בם נא4וסכרתי את מצרימ ביד אדונימ קשה ומלכ עז ימשל במ נוא
>ם האדון יהוה צבאות >מ האדונ יהוה צבאות
5ונשׁתו מים מהים ונהר יחרב ויבשׁ 5ונשתו מימ מהימ ונהר יחרב ויבש
6והאזניחו נהרות דללו וחרבו יארי מצור קנה וסוף קמלו 6והזניחו הנהרות ודללו וחרבו יאורי מצור קנה וסופ וקמ
 >לו
7ערות על יאור על פי יאור וכל מזרע יאור ייבש נדפ ואי7ערות על יאור על פי יאור וכול מזרע יאור יבש ונדפ וא
>ננו>ינ בו
8ואנו הדיגימ ואבלו כל משליכי ביאור חכה ופרשי מכמרת 8ואנו הדגימ ואבלו כול משליכי ביאור חכה ופרשי מכמרת 
>על פני מימ אמללו>על פני מימ אמללו
9ובשו עבדי פשתימ שריקות וארגימ חורי9יבושו עובדי פשתימ שריקות ואורגימ חורו
10והיו שתתיה מדכאימ כל עשי שכר אגמי נפש10והיו שותתיה מדכאימ כל עושי שכר אגמי נפש
11אכ אולימ שרי צענ חכמי יעצי פרעה עצה נבערה איכ תאמר11אכ אולימ שרי צענ חכמיה יועצי פרעוה עצה נבערה איכ ת
>ו אל פרעה בנ חכמימ אני בנ מלכי קדמ>אמרו אל פרעה בנ חמימ אני בנ מלכי קדמ
12אימ אפוא חכמיכ ויגידו נא לכ וידעו מה יעצ יהוה צבאו12אימ אפוא חכמיכ ויגידונא לכ וידעו מה יעצ יהוה צבאות
>ת על מצרימ> על מצרימ
13נואלו שרי צענ נשאו שרי נפ התעו את מצרימ פנת שבטיה13נאולו שרי צענ נשיאי שרי נפ התעו את מצרימ פנת שבטיה
14יהוה מסכ בקרבה רוח עועימ והתעו את מצרימ בכל מעשהו 14יהוה מסכ בקרבה רוח עועיימ והתעו את מצרימ בכול מעשה
>כהתעות שכור בקיאו>ו כהתעות שכור בקיאו
15ולא יהיה למצרימ מעשה אשר יעשה ראש וזנב כפה ואגמונ15ולוא יהיה למצרימ מעשה אשר יעשה ראוש וזנב כפה ואגמנ
16ביומ ההוא יהיה מצרימ כנשימ וחרד ופחד מפני תנופת יד16ביומ הוא יהיה מצרימ כנשימ וחרדו ופחדו מפני תנופת י
> יהוה צבאות אשר הוא מניפ עליו>ד יהוה צבאות אשר הוא מהניפ ידו עליה
17והיתה אדמת יהודה למצרימ לחגא כל אשר יזכיר אתה אליו17והיית אדמת יהודה למצרימ לחוגה כול אשר יזכיר אותה א
> יפחד מפני עצת יהוה צבאות אשר הוא יועצ עליו>ליו יפחד מפני עצת יהוה צבאות אשר הוא יועצ עליו
18ביומ ההוא יהיו חמש ערימ בארצ מצרימ מדברות שפת כנענ18ביומ ההוא יהיו חמש ערימ בארצ מצרימ מדברות שפת כנענ
> ונשבעות ליהוה צבאות עיר ההרס יאמר לאחת> ונשבעות ליהוה צבאות עיר החרס יאמר לאחת
19ביומ ההוא יהיה מזבח ליהוה בתוכ ארצ מצרימ ומצבה אצל19ביומ ההוא יהיה מזבח ליהוה בתוכ ארצ מצרימ ומצבה אצל
> גבולה ליהוה> גבולה ליהוה
n20והיה לאות ולעד ליהוה צבאות בארצ מצרימ כי יצעקו אל n20והייה לאות ולעד ליהוה צבאות בארצ מצרימ כי יצעקו אל
>יהוה מפני לחצימ וישלח להמ מושיע ורב והצילמ> יהוה מפני לוחצימ ושלח להמ מושיע וירד והצילמ
21ונודע יהוה למצרימ וידעו מצרימ את יהוה ביומ ההוא וע21ונודע יהוה למצרימ וידעו מצרימ את יהוה ביומ ההוא יע
>בדו זבח ומנחה ונדרו נדר ליהוה ושלמו>בדו זבח ומנחה ונדרו נדר ליהוה ושלמו
22ונגפ יהוה את מצרימ נגפ ורפוא ושבו עד יהוה ונעתר לה22ונגפ יהוה את מצרימ נגפ ונרפו ושבו עד יהוה ונעתר לה
>מ ורפאמ>מ ורפאמ
23ביומ ההוא תהיה מסלה ממצרימ אשורה ובא אשור במצרימ ו23ביומ ההוא תהיה מסלה ממצרימ אשורה ובא אשור במצרימ ו
>מצרימ באשור ועבדו מצרימ את אשור>מצרימ באשור ועבדו את אשור
24ביומ ההוא יהיה ישראל שלישיה למצרימ ולאשור ברכה בקר24ביומ ההוא יהיה ישראל שלישיה למצרימ ולאשור ברכה בקר
>ב הארצ>ב הארצ
t25אשר ברכו יהוה צבאות לאמר ברוכ עמי מצרימ ומעשה ידי t25אשר ברכו יהוה צבאות לאמור ברוכ עמי מצרימ ומעשה ידי
>אשור ונחלתי ישראל> אשור ונחלתי ישראל
7ערות על יאור על פי יאור וכל מזרע יאור ייבשׁ נדף וא7ערות על יאור על פי יאור וכול מזרע יאור יבש ונדפ וא
>יננו >ינ בו
8ואנו הדיגים ואבלו כל משׁליכי ביאור חכה ופרשׂי מכמר8ואנו הדגימ ואבלו כול משליכי ביאור חכה ופרשי מכמרת 
>ת על פני מים אמללו >על פני מימ אמללו
9ובשׁו עבדי פשׁתים שׂריקות וארגים חורי 9יבושו עובדי פשתימ שריקות ואורגימ חורו
10והיו שׁתתיה מדכאים כל עשׂי שׂכר אגמי נפשׁ 10והיו שותתיה מדכאימ כל עושי שכר אגמי נפש
11אך אולים שׂרי צען חכמי יעצי פרעה עצה נבערה איך תאמ11אכ אולימ שרי צענ חכמיה יועצי פרעוה עצה נבערה איכ ת
>רו אל פרעה בן חכמים אני בן מלכי קדם >אמרו אל פרעה בנ חמימ אני בנ מלכי קדמ
12אים אפוא חכמיך ויגידו נא לך וידעו מה יעץ יהוה צבאו12אימ אפוא חכמיכ ויגידונא לכ וידעו מה יעצ יהוה צבאות
>ת על מצרים > על מצרימ
13נואלו שׂרי צען נשׁאו שׂרי נף התעו את מצרים פנת שׁב13נאולו שרי צענ נשיאי שרי נפ התעו את מצרימ פנת שבטיה
>טיה  
14יהוה מסך בקרבה רוח עועים והתעו את מצרים בכל מעשׂהו14יהוה מסכ בקרבה רוח עועיימ והתעו את מצרימ בכול מעשה
> כהתעות שׁכור בקיאו >ו כהתעות שכור בקיאו
15ולא יהיה למצרים מעשׂה אשׁר יעשׂה ראשׁ וזנב כפה ואג15ולוא יהיה למצרימ מעשה אשר יעשה ראוש וזנב כפה ואגמנ
>מון ס  
16ביום ההוא יהיה מצרים כנשׁים וחרד׀ ופחד מפני תנופת 16ביומ הוא יהיה מצרימ כנשימ וחרדו ופחדו מפני תנופת י
>יד יהוה צבאות אשׁר הוא מניף עליו >ד יהוה צבאות אשר הוא מהניפ ידו עליה
17והיתה אדמת יהודה למצרים לחגא כל אשׁר יזכיר אתה אלי17והיית אדמת יהודה למצרימ לחוגה כול אשר יזכיר אותה א
>ו יפחד מפני עצת יהוה צבאות אשׁר הוא יועץ עליו ס >ליו יפחד מפני עצת יהוה צבאות אשר הוא יועצ עליו
18ביום ההוא יהיו חמשׁ ערים בארץ מצרים מדברות שׂפת כנ18ביומ ההוא יהיו חמש ערימ בארצ מצרימ מדברות שפת כנענ
>ען ונשׁבעות ליהוה צבאות עיר ההרס יאמר לאחת ס > ונשבעות ליהוה צבאות עיר החרס יאמר לאחת
19ביום ההוא יהיה מזבח ליהוה בתוך ארץ מצרים ומצבה אצל19ביומ ההוא יהיה מזבח ליהוה בתוכ ארצ מצרימ ומצבה אצל
> גבולה ליהוה > גבולה ליהוה
20והיה לאות ולעד ליהוה צבאות בארץ מצרים כי יצעקו אל 20והייה לאות ולעד ליהוה צבאות בארצ מצרימ כי יצעקו אל
>יהוה מפני לחצים וישׁלח להם מושׁיע ורב והצילם > יהוה מפני לוחצימ ושלח להמ מושיע וירד והצילמ
21ונודע יהוה למצרים וידעו מצרים את יהוה ביום ההוא וע21ונודע יהוה למצרימ וידעו מצרימ את יהוה ביומ ההוא יע
>בדו זבח ומנחה ונדרו נדר ליהוה ושׁלמו >בדו זבח ומנחה ונדרו נדר ליהוה ושלמו
22ונגף יהוה את מצרים נגף ורפוא ושׁבו עד יהוה ונעתר ל22ונגפ יהוה את מצרימ נגפ ונרפו ושבו עד יהוה ונעתר לה
>הם ורפאם >מ ורפאמ
23ביום ההוא תהיה מסלה ממצרים אשׁורה ובא אשׁור במצרים23ביומ ההוא תהיה מסלה ממצרימ אשורה ובא אשור במצרימ ו
> ומצרים באשׁור ועבדו מצרים את אשׁור ס >מצרימ באשור ועבדו את אשור
24ביום ההוא יהיה ישׂראל שׁלישׁיה למצרים ולאשׁור ברכה24ביומ ההוא יהיה ישראל שלישיה למצרימ ולאשור ברכה בקר
> בקרב הארץ >ב הארצ
25אשׁר ברכו יהוה צבאות לאמר ברוך עמי מצרים ומעשׂה יד25אשר ברכו יהוה צבאות לאמור ברוכ עמי מצרימ ומעשה ידי
>י אשׁור ונחלתי ישׂראל ס > אשור ונחלתי ישראל
- - - - - - - - - - - - + + + + + + + + + + + +

Isaiah 20 MT
Isaiah 20 1QIsaa
t1בשנת בא תרתנ אשדודה בשלח אתו סרגונ מלכ אשור וילחמ t1בשנת בא תורתנ אשדודה בשלח אותו סרגונ מלכ אשור וילח
>באשדוד וילכדה>מ באשדוד וילכודה
2בעת ההיא דבר יהוה ביד ישעיהו בנ אמוצ לאמר לכ ופתחת2בעת ההיא דבר יהוה ביד ישעיה בנ אמוצ לאמור לכ ופתחת
> השק מעל מתניכ ונעלכ תחלצ מעל רגליכ ויעש כנ הלכ ער> השק מעל מתניכ ונעליכ תחליצ מעל רגליכ ויעש כנ הלוכ
>ומ ויחפ> ערומ ויחפ
3ויאמר יהוה כאשר הלכ עבדי ישעיהו ערומ ויחפ שלש שנימ3ויואמר יהוה כאשר הלכ עבדי ישעיה ערומ ויחפ שלוש שני
> אות ומופת על מצרימ ועל כוש>מ אות ומפת על מצרימ ועל כוש
4כנ ינהג מלכ אשור את שבי מצרימ ואת גלות כוש נערימ ו4כנ ינהג מלכ אשור את שבי מצרימ ואת גולת כוש נערימ ו
>זקנימ ערומ ויחפ וחשופי שת ערות מצרימ>זקנימ ערומ ויחפ וחשופי שת ערות מצרימ
5וחתו ובשו מכוש מבטמ ומנ מצרימ תפארתמ5וחתו ויבושו מכוש מבטחמ וממצרימ תפארתמ
6ואמר ישב האי הזה ביומ ההוא הנה כה מבטנו אשר נסנו ש6ואמר יושב האיי הזה ביומ ההוא הנה כה מבטנו אשר נסמכ
>מ לעזרה להנצל מפני מלכ אשור ואיכ נמלט אנחנו> שמ לעזרה להנצל מפני מלכ אשור ואיכ נמלט אנחנו
t1בשׁנת בא תרתן אשׁדודה בשׁלח אתו סרגון מלך אשׁור ויt1בשנת בא תורתנ אשדודה בשלח אותו סרגונ מלכ אשור וילח
>לחם באשׁדוד וילכדה >מ באשדוד וילכודה
2בעת ההיא דבר יהוה ביד ישׁעיהו בן אמוץ לאמר לך ופתח2בעת ההיא דבר יהוה ביד ישעיה בנ אמוצ לאמור לכ ופתחת
>ת השׂק מעל מתניך ונעלך תחלץ מעל רגליך ויעשׂ כן הלך> השק מעל מתניכ ונעליכ תחליצ מעל רגליכ ויעש כנ הלוכ
> ערום ויחף ס > ערומ ויחפ
3ויאמר יהוה כאשׁר הלך עבדי ישׁעיהו ערום ויחף שׁלשׁ 3ויואמר יהוה כאשר הלכ עבדי ישעיה ערומ ויחפ שלוש שני
>שׁנים אות ומופת על מצרים ועל כושׁ >מ אות ומפת על מצרימ ועל כוש
4כן ינהג מלך אשׁור את שׁבי מצרים ואת גלות כושׁ נערי4כנ ינהג מלכ אשור את שבי מצרימ ואת גולת כוש נערימ ו
>ם וזקנים ערום ויחף וחשׂופי שׁת ערות מצרים >זקנימ ערומ ויחפ וחשופי שת ערות מצרימ
5וחתו ובשׁו מכושׁ מבטם ומן מצרים תפארתם 5וחתו ויבושו מכוש מבטחמ וממצרימ תפארתמ
6ואמר ישׁב האי הזה ביום ההוא הנה כה מבטנו אשׁר נסנו6ואמר יושב האיי הזה ביומ ההוא הנה כה מבטנו אשר נסמכ
> שׁם לעזרה להנצל מפני מלך אשׁור ואיך נמלט אנחנו ס > שמ לעזרה להנצל מפני מלכ אשור ואיכ נמלט אנחנו
- - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 21 MT
Isaiah 21 1QIsaa
t1משא מדבר ימ כסופות בנגב לחלפ ממדבר בא מארצ נוראהt1משא דבר ימ כספות בנגב לחלפ ממדבר בא מארצ נוראה
2חזות קשה הגד לי הבוגד בוגד והשודד שודד עלי עילמ צו2חזות קשה היגד לי הבוגד בוגד והשודד שודד עלי עילמ צ
>רי מדי כל אנחתה השבתי>ירי מדי כול אנחתה השבתי
3על כנ מלאו מתני חלחלה צירימ אחזוני כצירי יולדה נעו3על כנ מלאו מתני חלחלה צירימ אחזוני כצירי יולדה נעו
>יתי משמע נבהלתי מראות>יתי משמוע נבהלתי מראות
4תעה לבבי פלצות בעתתני את נשפ חשקי שמ לי לחרדה4תועה ולבבי פלצות בעתתני את נשפ השקי שמ לי לחרדה
5ערכ השלחנ צפה הצפית אכול שתה קומו השרימ משחו מגנ5ערוכ השלחנ צופה הצפית אכול שתה קומו השרימ משחו מגנ
6כי כה אמר אלי אדני לכ העמד המצפה אשר יראה יגיד6כי כה אמר אלי אדוני לכ העמד המצפה אשר יראה ויגיד
7וראה רכב צמד פרשימ רכב חמור רכב גמל והקשיב קשב רב 7וראה רכב צמד איש פרשימ רוכב חמור רוכב גמל והקשב קש
>קשב>ב רב קשב
8ויקרא אריה על מצפה אדני אנכי עמד תמיד יוממ ועל משמ8ויקרא הראה על מצפה אדוני אנוכי עומד תמיד יוממ ועל 
>רתי אנכי נצב כל הלילות>משמרתי אנוכי נצב כול הלילות
9והנה זה בא רכב איש צמד פרשימ ויענ ויאמר נפלה נפלה 9והנה זה בא רוכב איש צמד פרשימ ויעני ויואמר נפלה נפ
>בבל וכל פסילי אלהיה שבר לארצ>לה בבל וכול פסילי אלוהיה שברו לארצ
10מדשתי ובנ גרני אשר שמעתי מאת יהוה צבאות אלהי ישראל10מדשתי ובנ גדרי אשר שמעתי מאת יהוה צבאות אלוהי ישרא
> הגדתי לכמ>ל הגדתי לכמ
11משא דומה אלי קרא משעיר שמר מה מלילה שמר מה מליל11משא דומה אלי קרא משעיר שומר מה מליל שומר מה מליל
12אמר שמר אתה בקר וגמ לילה אמ תבעיונ בעיו שבו אתיו12אמר שומר אתה בוקר וגמ לילה אמ תבעונ בעו שובו אתיו
13משא בערב ביער בערב תלינו ארחות דדנימ13משא בערב ביער בערב תלינו אורחות דודנימ
14לקראת צמא התיו מימ ישבי ארצ תימא בלחמו קדמו נדד14לקראת צמא האתיו מימ יושבי ארצ תימא בלחמ קדמו נודד
15כי מפני חרבות נדדו מפני חרב נטושה ומפני קשת דרוכה 15כי מפני הרבות נדד מפני חרב נטושה ומפני קשת דרוכה ו
>ומפני כבד מלחמה>מפני כבוד מלחמה
16כי כה אמר אדני אלי בעוד שנה כשני שכיר וכלה כל כבוד16כי כה אמר יהוה אלי בעוד שלוש שנימ כשני שכיר יכלה כ
> קדר>בוד קדר
17ושאר מספר קשת גבורי בני קדר ימעטו כי יהוה אלהי ישר17ושאר מספר קשת גבורי בני קדר ימעטו כי יהוה אלוהי יש
>אל דבר>ראל דבר
t1משׂא מדבר ים כסופות בנגב לחלף ממדבר בא מארץ נוראה t1משא דבר ימ כספות בנגב לחלפ ממדבר בא מארצ נוראה
2חזות קשׁה הגד לי הבוגד׀ בוגד והשׁודד׀ שׁודד עלי עי2חזות קשה היגד לי הבוגד בוגד והשודד שודד עלי עילמ צ
>לם צורי מדי כל אנחתה השׁבתי >ירי מדי כול אנחתה השבתי
3על כן מלאו מתני חלחלה צירים אחזוני כצירי יולדה נעו3על כנ מלאו מתני חלחלה צירימ אחזוני כצירי יולדה נעו
>יתי משׁמע נבהלתי מראות >יתי משמוע נבהלתי מראות
4תעה לבבי פלצות בעתתני את נשׁף חשׁקי שׂם לי לחרדה 4תועה ולבבי פלצות בעתתני את נשפ השקי שמ לי לחרדה
5ערך השׁלחן צפה הצפית אכול שׁתה קומו השׂרים משׁחו מ5ערוכ השלחנ צופה הצפית אכול שתה קומו השרימ משחו מגנ
>גן פ  
6כי כה אמר אלי אדני לך העמד המצפה אשׁר יראה יגיד 6כי כה אמר אלי אדוני לכ העמד המצפה אשר יראה ויגיד
7וראה רכב צמד פרשׁים רכב חמור רכב גמל והקשׁיב קשׁב 7וראה רכב צמד איש פרשימ רוכב חמור רוכב גמל והקשב קש
>רב קשׁב >ב רב קשב
8ויקרא אריה על מצפה׀ אדני אנכי עמד תמיד יומם ועל מש8ויקרא הראה על מצפה אדוני אנוכי עומד תמיד יוממ ועל 
>ׁמרתי אנכי נצב כל הלילות >משמרתי אנוכי נצב כול הלילות
9והנה זה בא רכב אישׁ צמד פרשׁים ויען ויאמר נפלה נפל9והנה זה בא רוכב איש צמד פרשימ ויעני ויואמר נפלה נפ
>ה בבל וכל פסילי אלהיה שׁבר לארץ >לה בבל וכול פסילי אלוהיה שברו לארצ
10מדשׁתי ובן גרני אשׁר שׁמעתי מאת יהוה צבאות אלהי יש10מדשתי ובנ גדרי אשר שמעתי מאת יהוה צבאות אלוהי ישרא
>ׂראל הגדתי לכם ס >ל הגדתי לכמ
11משׂא דומה אלי קרא משׂעיר שׁמר מה מלילה שׁמר מה מלי11משא דומה אלי קרא משעיר שומר מה מליל שומר מה מליל
>ל  
12אמר שׁמר אתה בקר וגם לילה אם תבעיון בעיו שׁבו אתיו12אמר שומר אתה בוקר וגמ לילה אמ תבעונ בעו שובו אתיו
> ס  
13משׂא בערב ביער בערב תלינו ארחות דדנים 13משא בערב ביער בערב תלינו אורחות דודנימ
14לקראת צמא התיו מים ישׁבי ארץ תימא בלחמו קדמו נדד 14לקראת צמא האתיו מימ יושבי ארצ תימא בלחמ קדמו נודד
15כי מפני חרבות נדדו מפני׀ חרב נטושׁה ומפני קשׁת דרו15כי מפני הרבות נדד מפני חרב נטושה ומפני קשת דרוכה ו
>כה ומפני כבד מלחמה ס >מפני כבוד מלחמה
16כי כה אמר אדני אלי בעוד שׁנה כשׁני שׂכיר וכלה כל כ16כי כה אמר יהוה אלי בעוד שלוש שנימ כשני שכיר יכלה כ
>בוד קדר >בוד קדר
17ושׁאר מספר קשׁת גבורי בני קדר ימעטו כי יהוה אלהי י17ושאר מספר קשת גבורי בני קדר ימעטו כי יהוה אלוהי יש
>שׂראל דבר ס >ראל דבר
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 22 MT
Isaiah 22 1QIsaa
n1משא גיא חזיונ מה לכ אפוא כי עלית כלכ לגגותn1משא גי חזוונ מלכי אפוא כי עליתי כולכ לגגות
2תשאות מלאה עיר הומיה קריה עליזה חלליכ לא חללי חרב 2תשאות מלאה עיר הומיה קריה עליזה חלליכ לוא חללי חרב
>ולא מתי מלחמה> ולוא מיתי מלחמה
3כל קציניכ נדדו יחד מקשת אסרו כל נמצאיכ אסרו יחדו מ3כול קציניכ נדדו יחד מקשת אסורה כול נמצאיכ אסרו יחד
>רחוק ברחו>ו מרחוק ברחו
4על כנ אמרתי שעו מני אמרר בבכי אל תאיצו לנחמני על ש4על כנ אמרתי שועו ממני ואמרר בבכי אל תוצוו לנחמני ע
>ד בת עמי>ל שד בת עמי
5כי יומ מהומה ומבוסה ומבוכה לאדני יהוה צבאות בגיא ח5כי יומ מהומה ומבוסה ומבוכה לאדוני יהוה צבאות בגי ח
>זיונ מקרקר קר ושוע אל ההר>זיונ מקרקר קדשו על ההר
6ועילמ נשא אשפה ברכב אדמ פרשימ וקיר ערה מגנ6ועילמ נשא אשפא ברכב אדמ פרשימ וקור ערה מגנ
7ויהי מבחר עמקיכ מלאו רכב והפרשימ שת שתו השערה7והיה מבחר עמקיכ מלאו רכב והפרשימ שת שתו השערה
8ויגל את מסכ יהודה ותבט ביומ ההוא אל נשק בית היער8ויגל את מסכ יהודה ותבט ביומ ההוא אל נשק בית היער
n9ואת בקיעי עיר דוד ראיתמ כי רבו ותקבצו את מי הברכה n9ואת בקיעי עיר דויד ראיתמה כי רבו ותקבצו את מי הברכ
>התחתונה>ה התחתונה
10ואת בתי ירושלמ ספרתמ ותתצו הבתימ לבצר החומה10ואת בתי ירושלמ ספרתמ ותתוצו הבתימ לבצור החומה
11ומקוה עשיתמ בינ החמתימ למי הברכה הישנה ולא הבטתמ א11ומקוה עשיתמ בינ החומות למי הברכה הישנה ולוא הבטתמה
>ל עשיה ויצרה מרחוק לא ראיתמ> על עושיה ויצרה מרחוק לוא ראיתמ
12ויקרא אדני יהוה צבאות ביומ ההוא לבכי ולמספד ולקרחה12ויקרא אדוני יהוה צבאות ביומ ההוא לבכי ולמספד ולקרח
> ולחגר שק>ה ולחגור שק
13והנה ששונ ושמחה הרג בקר ושחט צאנ אכל בשר ושתות יינ13והנה ששונ ושמחה הרג בקר ושחט צואנ אכול בשר ושתות י
> אכול ושתו כי מחר נמות>ינ אכול ושתו כי מחר נמות
14ונגלה באזני יהוה צבאות אמ יכפר העונ הזה לכמ עד תמת14ונגלה באוזני יהוה צבאות אמ יכפר לכמ ה עוונ הזה לכמ
>ונ אמר אדני יהוה צבאות>ה עד תמותונ אמר אדוני יהוה צבאות
15כה אמר אדני יהוה צבאות לכ בא אל הסכנ הזה על שבנא א15כה אמר אדוני יהוה צבאות לכ בוא אל הסוכנ הזה אל שבנ
>שר על הבית>א אשר על הבית
16מה לכ פה ומי לכ פה כי חצבת לכ פה קבר חצבי מרומ קבר16מהלכ פה ומי לכ פה כי חצבתה לכה פה קבר חצבי מרומ קב
>ו חקקי בסלע משכנ לו>רו חוקקי בסלע משכנ לו
17הנה יהוה מטלטלכ טלטלה גבר ועטכ עטה17הנה יהוה מטלטלכ טלטלה גבר יעוטכ עטה
18צנופ יצנפכ צנפה כדור אל ארצ רחבת ידימ שמה תמות ושמ18צניפ וצנפכה צנפה כדור אל ארצ רחבת ידימ שמה תמות וש
>ה מרכבות כבודכ קלונ בית אדניכ>מה מרכבות כבודכ קלונ בית אדוניכ
19והדפתיכ ממצבכ וממעמדכ יהרסכ19והדפתיכ ממצבכ וממעמדכ הרסכ
20והיה ביומ ההוא וקראתי לעבדי לאליקימ בנ חלקיהו20והיה ביומ ההוא וקרתי לעבדי לאליקימ בנ חלקיה
21והלבשתיו כתנתכ ואבנטכ אחזקנו וממשלתכ אתנ בידו והיה21והלבשתיו כתנותכ ואבניטכ אחזקנו וממשלתכ אתנ בידו וה
> לאב ליושב ירושלמ ולבית יהודה>יה לאב ליושב ירושלמ ולבית יהודה
22ונתתי מפתח בית דוד על שכמו ופתח ואינ סגר וסגר ואינ22ונתתי מפתח בית דויד על שכמו ופתח ואינ סוגר וסגר וא
> פתח>ינ פותח
23ותקעתיו יתד במקומ נאמנ והיה לכסא כבוד לבית אביו23ותקעתיו יתד במקומ נאמנ והיה לכסא כבוד לבית אביו
t24ותלו עליו כל כבוד בית אביו הצאצאימ והצפעות כל כלי t24ותלו עליו כול כביד בית אביו הצאצאימ והצפעות כול כל
>הקטנ מכלי האגנות ועד כל כלי הנבלימ>י קטנ מכלי האגנות ועד כול כלי הנבלימ
25ביומ ההוא נאמ יהוה צבאות תמוש היתד התקועה במקומ נא25ביומ ההוא נואמ יהוה צבאות תמוש היתד התקועה במקומ נ
>מנ ונגדעה ונפלה ונכרת המשא אשר עליה כי יהוה דבר>אמנ ונגדעה ונפלה ונכרת המשא אשר עליה כי יהוה דבר
t1משׂא גיא חזיון מה לך אפוא כי עלית כלך לגגות t1משא גי חזוונ מלכי אפוא כי עליתי כולכ לגגות
2תשׁאות׀ מלאה עיר הומיה קריה עליזה חלליך לא חללי חר2תשאות מלאה עיר הומיה קריה עליזה חלליכ לוא חללי חרב
>ב ולא מתי מלחמה > ולוא מיתי מלחמה
3כל קציניך נדדו יחד מקשׁת אסרו כל נמצאיך אסרו יחדו 3כול קציניכ נדדו יחד מקשת אסורה כול נמצאיכ אסרו יחד
>מרחוק ברחו >ו מרחוק ברחו
4על כן אמרתי שׁעו מני אמרר בבכי אל תאיצו לנחמני על 4על כנ אמרתי שועו ממני ואמרר בבכי אל תוצוו לנחמני ע
>שׁד בת עמי >ל שד בת עמי
5כי יום מהומה ומבוסה ומבוכה לאדני יהוה צבאות בגיא ח5כי יומ מהומה ומבוסה ומבוכה לאדוני יהוה צבאות בגי ח
>זיון מקרקר קר ושׁוע אל ההר >זיונ מקרקר קדשו על ההר
6ועילם נשׂא אשׁפה ברכב אדם פרשׁים וקיר ערה מגן 6ועילמ נשא אשפא ברכב אדמ פרשימ וקור ערה מגנ
7ויהי מבחר עמקיך מלאו רכב והפרשׁים שׁת שׁתו השׁערה 7והיה מבחר עמקיכ מלאו רכב והפרשימ שת שתו השערה
8ויגל את מסך יהודה ותבט ביום ההוא אל נשׁק בית היער 8ויגל את מסכ יהודה ותבט ביומ ההוא אל נשק בית היער
9ואת בקיעי עיר דוד ראיתם כי רבו ותקבצו את מי הברכה 9ואת בקיעי עיר דויד ראיתמה כי רבו ותקבצו את מי הברכ
>התחתונה >ה התחתונה
10ואת בתי ירושׁלם ספרתם ותתצו הבתים לבצר החומה 10ואת בתי ירושלמ ספרתמ ותתוצו הבתימ לבצור החומה
11ומקוה׀ עשׂיתם בין החמתים למי הברכה הישׁנה ולא הבטת11ומקוה עשיתמ בינ החומות למי הברכה הישנה ולוא הבטתמה
>ם אל עשׂיה ויצרה מרחוק לא ראיתם > על עושיה ויצרה מרחוק לוא ראיתמ
12ויקרא אדני יהוה צבאות ביום ההוא לבכי ולמספד ולקרחה12ויקרא אדוני יהוה צבאות ביומ ההוא לבכי ולמספד ולקרח
> ולחגר שׂק >ה ולחגור שק
13והנה׀ שׂשׂון ושׂמחה הרג׀ בקר ושׁחט צאן אכל בשׂר וש13והנה ששונ ושמחה הרג בקר ושחט צואנ אכול בשר ושתות י
>ׁתות יין אכול ושׁתו כי מחר נמות >ינ אכול ושתו כי מחר נמות
14ונגלה באזני יהוה צבאות אם יכפר העון הזה לכם עד תמת14ונגלה באוזני יהוה צבאות אמ יכפר לכמ ה עוונ הזה לכמ
>ון אמר אדני יהוה צבאות פ >ה עד תמותונ אמר אדוני יהוה צבאות
15כה אמר אדני יהוה צבאות לך בא אל הסכן הזה על שׁבנא 15כה אמר אדוני יהוה צבאות לכ בוא אל הסוכנ הזה אל שבנ
>אשׁר על הבית >א אשר על הבית
16מה לך פה ומי לך פה כי חצבת לך פה קבר חצבי מרום קבר16מהלכ פה ומי לכ פה כי חצבתה לכה פה קבר חצבי מרומ קב
>ו חקקי בסלע משׁכן לו >רו חוקקי בסלע משכנ לו
17הנה יהוה מטלטלך טלטלה גבר ועטך עטה 17הנה יהוה מטלטלכ טלטלה גבר יעוטכ עטה
18צנוף יצנפך צנפה כדור אל ארץ רחבת ידים שׁמה תמות וש18צניפ וצנפכה צנפה כדור אל ארצ רחבת ידימ שמה תמות וש
>ׁמה מרכבות כבודך קלון בית אדניך >מה מרכבות כבודכ קלונ בית אדוניכ
19והדפתיך ממצבך וממעמדך יהרסך 19והדפתיכ ממצבכ וממעמדכ הרסכ
20והיה ביום ההוא וקראתי לעבדי לאליקים בן חלקיהו 20והיה ביומ ההוא וקרתי לעבדי לאליקימ בנ חלקיה
21והלבשׁתיו כתנתך ואבנטך אחזקנו וממשׁלתך אתן בידו וה21והלבשתיו כתנותכ ואבניטכ אחזקנו וממשלתכ אתנ בידו וה
>יה לאב ליושׁב ירושׁלם ולבית יהודה >יה לאב ליושב ירושלמ ולבית יהודה
22ונתתי מפתח בית דוד על שׁכמו ופתח ואין סגר וסגר ואי22ונתתי מפתח בית דויד על שכמו ופתח ואינ סוגר וסגר וא
>ן פתח >ינ פותח
23ותקעתיו יתד במקום נאמן והיה לכסא כבוד לבית אביו 23ותקעתיו יתד במקומ נאמנ והיה לכסא כבוד לבית אביו
24ותלו עליו כל׀ כבוד בית אביו הצאצאים והצפעות כל כלי24ותלו עליו כול כביד בית אביו הצאצאימ והצפעות כול כל
> הקטן מכלי האגנות ועד כל כלי הנבלים >י קטנ מכלי האגנות ועד כול כלי הנבלימ
25ביום ההוא נאם יהוה צבאות תמושׁ היתד התקועה במקום נ25ביומ ההוא נואמ יהוה צבאות תמוש היתד התקועה במקומ נ
>אמן ונגדעה ונפלה ונכרת המשׂא אשׁר עליה כי יהוה דבר>אמנ ונגדעה ונפלה ונכרת המשא אשר עליה כי יהוה דבר
> ס  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 23 MT
Isaiah 23 1QIsaa
n1משא צר הילילו אניות תרשיש כי שדד מבית מבוא מארצ כתn1משא צר אילילו אניות תרשיש כי שודד מבית מבוא מארצ כ
>ימ נגלה למו>תיימ נגלה למו
2דמו ישבי אי סחר צידונ עבר ימ מלאוכ2דמו יושבי אי סחר צידונ עברו ימ מלאכיכ
3ובמימ רבימ זרע שחר קציר יאור תבואתה ותהי סחר גוימ3ובמימ רבימ זרע שחר קציר יאור תבואתה ותהי סחר גואימ
4בושי צידונ כי אמר ימ מעוז הימ לאמר לא חלתי ולא ילד4בושי צידנ כי אמרה ימ מעוז הימ לאמור לוא חלתי ולוא 
>תי ולא גדלתי בחורימ רוממתי בתולות>ילדתי ולוא גדלתי בחורימ רוממתי בתולות
5כאשר שמע למצרימ יחילו כשמע צר5כאשר שמע למצרימ יחילו כשמע צר
n6עברו תרשישה הילילו ישבי איn6עוברי תרשישה הילילו יושבי אי
7הזאת לכמ עליזה מימי קדמ קדמתה יבלוה רגליה מרחוק לג7הזואת לכמה העלוזה מימי קדמ קדמותה ובליה רגליה מרחק
>ור> לגור
8מי יעצ זאת על צר המעטירה אשר סחריה שרימ כנעניה נכב8מי יעצ זואת על צר המעטרה אשר סוחריה שרימ כנעניה נכ
>די ארצ>בדי ארצ
9יהוה צבאות יעצה לחלל גאונ כל צבי להקל כל נכבדי ארצ9יהוה צבאות יעצה לחלל כול גאונ צבי להקל כול נכבדי א
 >רצ
10עברי ארצכ כיאר בת תרשיש אינ מזח עוד10עבדי ארצכ כיאור בת תרשיש אינ מזח עוד
11ידו נטה על הימ הרגיז ממלכות יהוה צוה אל כנענ לשמד 11ידו נטה על הימ הרגיז ממלכות יהוה צוה אל כנענ להשמי
>מעזניה>ד מעוזיה
12ויאמר לא תוסיפי עוד לעלוז המעשקה בתולת בת צידונ כת12ויואמר לוא תוסיפי עוד לעלוז מעשקה בתולת בת צידונ כ
>ימ קומי עברי גמ שמ לא ינוח לכ>תיימ קומי עברי גמ שמ לוא ינוח לכ
13הנ ארצ כשדימ זה העמ לא היה אשור יסדה לציימ הקימו ב13הנה ארצ כשדיימ זה העמ לוא היה אשור יסדה לציינ הקימ
>חוניו עררו ארמנותיה שמה למפלה>וה בחיניה עוררו ארמנותיה שמה למפלה
14הילילו אניות תרשיש כי שדד מעזכנ14הילילו אניות תרשיש כי שודד מעוזכ
15והיה ביומ ההוא ונשכחת צר שבעימ שנה כימי מלכ אחד מק15והיה ביומ הוא לצר כשירת הזונה
>צ שבעימ שנה יהיה לצר כשירת הזונה 
16קחי כנור סבי עיר זונה נשכחה היטיבי נגנ הרבי שיר למ16קחי כנור סבי עיר זונה נשכחה היטיבי נגנ הרבי שיר למ
>ענ תזכרי>ענ תזכרי
t17והיה מקצ שבעימ שנה יפקד יהוה את צר ושבה לאתננה וזנt17והיה מקצ שבעינ שנה יפקוד יהוה את צר ושבה לאתננה וז
>תה את כל ממלכות הארצ על פני האדמה>נתה את ממלכות הארצ על פני האדמה
18והיה סחרה ואתננה קדש ליהוה לא יאצר ולא יחסנ כי ליש18והיה סחרה ואתננה קודש ליהוה לוא יאצר ולוא יחסנ כי 
>בימ לפני יהוה יהיה סחרה לאכל לשבעה ולמכסה עתיק>ליושבימ לפני יהוה יהיה סחרה לאכול לשבעה ולמכסה עתי
 >ק
t1משׂא צר הילילו׀ אניות תרשׁישׁ כי שׁדד מבית מבוא מאt1משא צר אילילו אניות תרשיש כי שודד מבית מבוא מארצ כ
>רץ כתים נגלה למו >תיימ נגלה למו
2דמו ישׁבי אי סחר צידון עבר ים מלאוך 2דמו יושבי אי סחר צידונ עברו ימ מלאכיכ
3ובמים רבים זרע שׁחר קציר יאור תבואתה ותהי סחר גוים3ובמימ רבימ זרע שחר קציר יאור תבואתה ותהי סחר גואימ
>  
4בושׁי צידון כי אמר ים מעוז הים לאמר לא חלתי ולא יל4בושי צידנ כי אמרה ימ מעוז הימ לאמור לוא חלתי ולוא 
>דתי ולא גדלתי בחורים רוממתי בתולות >ילדתי ולוא גדלתי בחורימ רוממתי בתולות
5כאשׁר שׁמע למצרים יחילו כשׁמע צר 5כאשר שמע למצרימ יחילו כשמע צר
6עברו תרשׁישׁה הילילו ישׁבי אי 6עוברי תרשישה הילילו יושבי אי
7הזאת לכם עליזה מימי קדם קדמתה יבלוה רגליה מרחוק לג7הזואת לכמה העלוזה מימי קדמ קדמותה ובליה רגליה מרחק
>ור > לגור
8מי יעץ זאת על צר המעטירה אשׁר סחריה שׂרים כנעניה נ8מי יעצ זואת על צר המעטרה אשר סוחריה שרימ כנעניה נכ
>כבדי ארץ >בדי ארצ
9יהוה צבאות יעצה לחלל גאון כל צבי להקל כל נכבדי ארץ9יהוה צבאות יעצה לחלל כול גאונ צבי להקל כול נכבדי א
> >רצ
10עברי ארצך כיאר בת תרשׁישׁ אין מזח עוד 10עבדי ארצכ כיאור בת תרשיש אינ מזח עוד
11ידו נטה על הים הרגיז ממלכות יהוה צוה אל כנען לשׁמד11ידו נטה על הימ הרגיז ממלכות יהוה צוה אל כנענ להשמי
> מעזניה >ד מעוזיה
12ויאמר לא תוסיפי עוד לעלוז המעשׁקה בתולת בת צידון כ12ויואמר לוא תוסיפי עוד לעלוז מעשקה בתולת בת צידונ כ
>תיים קומי עברי גם שׁם לא ינוח לך >תיימ קומי עברי גמ שמ לוא ינוח לכ
13הן׀ ארץ כשׂדים זה העם לא היה אשׁור יסדה לציים הקימ13הנה ארצ כשדיימ זה העמ לוא היה אשור יסדה לציינ הקימ
>ו בחיניו עררו ארמנותיה שׂמה למפלה >וה בחיניה עוררו ארמנותיה שמה למפלה
14הילילו אניות תרשׁישׁ כי שׁדד מעזכן ס 14הילילו אניות תרשיש כי שודד מעוזכ
15והיה ביום ההוא ונשׁכחת צר שׁבעים שׁנה כימי מלך אחד15והיה ביומ הוא לצר כשירת הזונה
> מקץ שׁבעים שׁנה יהיה לצר כשׁירת הזונה  
16קחי כנור סבי עיר זונה נשׁכחה היטיבי נגן הרבי שׁיר 16קחי כנור סבי עיר זונה נשכחה היטיבי נגנ הרבי שיר למ
>למען תזכרי >ענ תזכרי
17והיה מקץ׀ שׁבעים שׁנה יפקד יהוה את צר ושׁבה לאתננה17והיה מקצ שבעינ שנה יפקוד יהוה את צר ושבה לאתננה וז
> וזנתה את כל ממלכות הארץ על פני האדמה >נתה את ממלכות הארצ על פני האדמה
18והיה סחרה ואתננה קדשׁ ליהוה לא יאצר ולא יחסן כי לי18והיה סחרה ואתננה קודש ליהוה לוא יאצר ולוא יחסנ כי 
>שׁבים לפני יהוה יהיה סחרה לאכל לשׂבעה ולמכסה עתיק >ליושבימ לפני יהוה יהיה סחרה לאכול לשבעה ולמכסה עתי
>פ >ק
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 24 MT
Isaiah 24 1QIsaa
n1הנה יהוה בוקק הארצ ובולקה ועוה פניה והפיצ ישביהn1הנה יהוה בוקק האדמה ובולקה ועוה פניה והפיצ יושביה
2והיה כעמ ככהנ כעבד כאדניו כשפחה כגברתה כקונה כמוכר2והיה כעמ ככוהנ כעבד כאדוניו כשפחה כגברתה כקונה כמו
> כמלוה כלוה כנשה כאשר נשא בו>כר כמלוה כלוה כנושה כאשר נשא בו
3הבוק תבוק הארצ והבוז תבוז כי יהוה דבר את הדבר הזה3הבוק תבוק הארצ והבוז תבוז כי יהוה דבר את הדבר הזה
n4אבלה נבלה הארצ אמללה נבלה תבל אמללו מרומ עמ הארצn4אבלה נבלה הארצ אמללה נבלה תבל אמלל מרומ עמ הארצ
5והארצ חנפה תחת ישביה כי עברו תורת חלפו חק הפרו ברי5והארצ חנפה תחת יושביה כי עברו תורות חלפו חוק הפירו
>ת עולמ> ברית עולמ
6על כנ אלה אכלה ארצ ויאשמו ישבי בה על כנ חרו ישבי א6על כנ אלה אכלה וישמו יושבי בה עלכנ חורו יושבי ארצ 
>רצ ונשאר אנוש מזער>ונשאר אנוש מזער
7אבל תירוש אמללה גפנ נאנחו כל שמחי לב7אבל תירוש אמללה גפנ נאנחו כול שמחי לב
8שבת משוש תפימ חדל שאונ עליזימ שבת משוש כנור8שבת משוש תפימ חדל שאונ עליזימ שבת משוש כנור
n9בשיר לא ישתו יינ ימר שכר לשתיוn9בשיר לוא ישתו יינ וימר שכר לשותיו
10נשברה קרית תהו סגר כל בית מבוא10נשברה קרית תהו סגר כול בית מבוא
11צוחה על היינ בחוצות ערבה כל שמחה גלה משוש הארצ11צוחה על היינ בחוצות ערבה כול שמחה גלה משוש הארצ
12נשאר בעיר שמה ושאיה יכת שער12נשאר בעיר שמה ושאיה יוכת שער
13כי כה יהיה בקרב הארצ בתוכ העמימ כנקפ זית כעוללת אמ13כי כה יהיה בקרב הארצ בתוכ העמימ כנקפ זית כעוללת אמ
> כלה בציר> כלה בציר
n14המה ישאו קולמ ירנו בגאונ יהוה צהלו מימn14המה ישאו קולמ ירונו בגאונ יהוה צהלו מימ
15על כנ בארימ כבדו יהוה באיי הימ שמ יהוה אלהי ישראל15על כנ בארימ כבדו יהוה באיי הימ שמ יהוה אלוהי ישראל
16מכנפ הארצ זמרת שמענו צבי לצדיק ואמר רזי לי רזי לי 16מכנפ הארצ זמרת שמענו צבי לצדיק ואמר רזי לי רזי לי 
>אוי לי בגדימ בגדו ובגד בוגדימ בגדו>אוי לי בוגדימ בגדו ובגד בוגדימ בגדו
17פחד ופחת ופח עליכ יושב הארצ17פחד ופחת ופח עליכ יושב הארצ
t18והיה הנס מקול הפחד יפל אל הפחת והעולה מתוכ הפחת ילt18והיה הנס מקול הפחד יפול אל הפחת והעולה מתוכ הפחת י
>כד בפח כי ארבות ממרומ נפתחו וירעשו מוסדי ארצ>לכד בפח כי ארבות ממרומ נפתחו וירעשו מוסדי ארצ
19רעה התרעעה הארצ פור התפוררה ארצ מוט התמוטטה ארצ19רעה התרועעה הארצ פור התפוררה ארצ מוט התמוטטה ארצ
20נוע תנוע ארצ כשכור והתנודדה כמלונה וכבד עליה פשעה 20נוע תנוע הארצ כשכור והתנודדא וכמלונה וכבד עליה פשע
>ונפלה ולא תסיפ קומ>ה ונפל ולוא תוסיפ קומ
21והיה ביומ ההוא יפקד יהוה על צבא המרומ במרומ ועל מל21והיה ביומ ההוא יפקוד יהוה על צבא המרומ במרומ ועל מ
>כי האדמה על האדמה>לכי האדמה על האדמה
22ואספו אספה אסיר על בור וסגרו על מסגר ומרב ימימ יפק22אספו אספה על בור וסגרו על מסגר ומרוב ימימ יפקדו
>דו 
23וחפרה הלבנה ובושה החמה כי מלכ יהוה צבאות בהר ציונ 23וחפרה הלבנה ובושה החמה כי מלכ יהוה צבאות בהר ציונ 
>ובירושלמ ונגד זקניו כבוד>ובירושלמ ונגד זקניו כבוד
t1הנה יהוה בוקק הארץ ובולקה ועוה פניה והפיץ ישׁביה t1הנה יהוה בוקק האדמה ובולקה ועוה פניה והפיצ יושביה
2והיה כעם ככהן כעבד כאדניו כשׁפחה כגברתה כקונה כמוכ2והיה כעמ ככוהנ כעבד כאדוניו כשפחה כגברתה כקונה כמו
>ר כמלוה כלוה כנשׁה כאשׁר נשׁא בו >כר כמלוה כלוה כנושה כאשר נשא בו
3הבוק׀ תבוק הארץ והבוז׀ תבוז כי יהוה דבר את הדבר הז3הבוק תבוק הארצ והבוז תבוז כי יהוה דבר את הדבר הזה
>ה  
4אבלה נבלה הארץ אמללה נבלה תבל אמללו מרום עם הארץ 4אבלה נבלה הארצ אמללה נבלה תבל אמלל מרומ עמ הארצ
5והארץ חנפה תחת ישׁביה כי עברו תורת חלפו חק הפרו בר5והארצ חנפה תחת יושביה כי עברו תורות חלפו חוק הפירו
>ית עולם > ברית עולמ
6על כן אלה אכלה ארץ ויאשׁמו ישׁבי בה על כן חרו ישׁב6על כנ אלה אכלה וישמו יושבי בה עלכנ חורו יושבי ארצ 
>י ארץ ונשׁאר אנושׁ מזער >ונשאר אנוש מזער
7אבל תירושׁ אמללה גפן נאנחו כל שׂמחי לב 7אבל תירוש אמללה גפנ נאנחו כול שמחי לב
8שׁבת משׂושׂ תפים חדל שׁאון עליזים שׁבת משׂושׂ כנור8שבת משוש תפימ חדל שאונ עליזימ שבת משוש כנור
>  
9בשׁיר לא ישׁתו יין ימר שׁכר לשׁתיו 9בשיר לוא ישתו יינ וימר שכר לשותיו
10נשׁברה קרית תהו סגר כל בית מבוא 10נשברה קרית תהו סגר כול בית מבוא
11צוחה על היין בחוצות ערבה כל שׂמחה גלה משׂושׂ הארץ 11צוחה על היינ בחוצות ערבה כול שמחה גלה משוש הארצ
12נשׁאר בעיר שׁמה ושׁאיה יכת שׁער 12נשאר בעיר שמה ושאיה יוכת שער
13כי כה יהיה בקרב הארץ בתוך העמים כנקף זית כעוללת אם13כי כה יהיה בקרב הארצ בתוכ העמימ כנקפ זית כעוללת אמ
> כלה בציר > כלה בציר
14המה ישׂאו קולם ירנו בגאון יהוה צהלו מים 14המה ישאו קולמ ירונו בגאונ יהוה צהלו מימ
15על כן בארים כבדו יהוה באיי הים שׁם יהוה אלהי ישׂרא15על כנ בארימ כבדו יהוה באיי הימ שמ יהוה אלוהי ישראל
>ל ס  
16מכנף הארץ זמרת שׁמענו צבי לצדיק ואמר רזי לי רזי לי16מכנפ הארצ זמרת שמענו צבי לצדיק ואמר רזי לי רזי לי 
> אוי לי בגדים בגדו ובגד בוגדים בגדו >אוי לי בוגדימ בגדו ובגד בוגדימ בגדו
17פחד ופחת ופח עליך יושׁב הארץ 17פחד ופחת ופח עליכ יושב הארצ
18והיה הנס מקול הפחד יפל אל הפחת והעולה מתוך הפחת יל18והיה הנס מקול הפחד יפול אל הפחת והעולה מתוכ הפחת י
>כד בפח כי ארבות ממרום נפתחו וירעשׁו מוסדי ארץ >לכד בפח כי ארבות ממרומ נפתחו וירעשו מוסדי ארצ
19רעה התרעעה הארץ פור התפוררה ארץ מוט התמוטטה ארץ 19רעה התרועעה הארצ פור התפוררה ארצ מוט התמוטטה ארצ
20נוע תנוע ארץ כשׁכור והתנודדה כמלונה וכבד עליה פשׁע20נוע תנוע הארצ כשכור והתנודדא וכמלונה וכבד עליה פשע
>ה ונפלה ולא תסיף קום ס >ה ונפל ולוא תוסיפ קומ
21והיה ביום ההוא יפקד יהוה על צבא המרום במרום ועל מל21והיה ביומ ההוא יפקוד יהוה על צבא המרומ במרומ ועל מ
>כי האדמה על האדמה >לכי האדמה על האדמה
22ואספו אספה אסיר על בור וסגרו על מסגר ומרב ימים יפק22אספו אספה על בור וסגרו על מסגר ומרוב ימימ יפקדו
>דו  
23וחפרה הלבנה ובושׁה החמה כי מלך יהוה צבאות בהר ציון23וחפרה הלבנה ובושה החמה כי מלכ יהוה צבאות בהר ציונ 
> ובירושׁלם ונגד זקניו כבוד פ >ובירושלמ ונגד זקניו כבוד
- - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + +

Isaiah 25 MT
Isaiah 25 1QIsaa
t1יהוה אלהי אתה ארוממכ אודה שמכ כי עשית פלא עצות מרחt1יהוה אלוהי אתה ארוממכ אודה שמכ כי עשיתה פלא אצית מ
>וק אמונה אמנ>רחוק אמונה אמנ
2כי שמת מעיר לגל קריה בצורה למפלה ארמונ זרימ מעיר ל2כי שמתה מעיר לגל קריה בצורה למפלה ארמונ זרימ מעיר 
>עולמ לא יבנה>לעולמ לוא יבנה
3על כנ יכבדוכ עמ עז קרית גוימ עריצימ ייראוכ3עלכנ יכבדוכ עמ עז קרית גוימ עריצימ ייראוכ
4כי היית מעוז לדל מעוז לאביונ בצר לו מחסה מזרמ צל מ4כי הייתה מעוז לדל מעוז לאביונ בצר לו מחסה מזרמ צל 
>חרב כי רוח עריצימ כזרמ קיר>מחורב כי רוח עריצימ כזרמ קיר
5כחרב בציונ שאונ זרימ תכניע חרב בצל עב זמיר עריצימ 5כחורב בציונ שאונ זרימ תכניע חורב בצל עב זמיר עריצי
>יענה>מ יענה
6ועשה יהוה צבאות לכל העמימ בהר הזה משתה שמנימ משתה 6ועשה יהוה צבאות לכול העמימ בהר הזה משתה שמנימ משתה
>שמרימ שמנימ ממחימ שמרימ מזקקימ> שמרימ שמנימ ממחימ שמרימ מזוקקימ
7ובלע בהר הזה פני הלוט הלוט על כל העמימ והמסכה הנסו7ובלע בהר הזה פנו הלוט הלוט על כול העמימ והמסכה הנס
>כה על כל הגוימ>וכה על כול הגואימ
8בלע המות לנצח ומחה אדני יהוה דמעה מעל כל פנימ וחרפ8בלע המות לנצח ומחה אדוני יהוה דמעה מעל כול פנימ וח
>ת עמו יסיר מעל כל הארצ כי יהוה דבר>רפת עמו יסיר מעל כול הארצ כי יהוה דבר
9ואמר ביומ ההוא הנה אלהינו זה קוינו לו ויושיענו זה 9ואמרת ביומ ההוא הנה יהוה אלוהינו זה קוינו לו ויושי
>יהוה קוינו לו נגילה ונשמחה בישועתו>ענו זה יהוה קוינו לו נגילה ונשמח בישועתו
10כי תנוח יד יהוה בהר הזה ונדוש מואב תחתיו כהדוש מתב10כי תנוח יד יהוה בהר הזה ונדש מואב תחתיו כחדוש מתבנ
>נ במו מדמנה> במי מדמנה
11ופרש ידיו בקרבו כאשר יפרש השחה לשחות והשפיל גאותו 11ופרש ידיו בקרבו כאשר יפרש השוחה לשחות והשפיל גאותו
>עמ ארבות ידיו> עמ ארבות ידיו
12ומבצר משגב חומתיכ השח השפיל הגיע לארצ עד עפר12ומבצר משגב חומותיכ השחה השפיל יגיע לארצ עד עפר
t1יהוה אלהי אתה ארוממך אודה שׁמך כי עשׂית פלא עצות מt1יהוה אלוהי אתה ארוממכ אודה שמכ כי עשיתה פלא אצית מ
>רחוק אמונה אמן >רחוק אמונה אמנ
2כי שׂמת מעיר לגל קריה בצורה למפלה ארמון זרים מעיר 2כי שמתה מעיר לגל קריה בצורה למפלה ארמונ זרימ מעיר 
>לעולם לא יבנה >לעולמ לוא יבנה
3על כן יכבדוך עם עז קרית גוים עריצים ייראוך 3עלכנ יכבדוכ עמ עז קרית גוימ עריצימ ייראוכ
4כי היית מעוז לדל מעוז לאביון בצר לו מחסה מזרם צל מ4כי הייתה מעוז לדל מעוז לאביונ בצר לו מחסה מזרמ צל 
>חרב כי רוח עריצים כזרם קיר >מחורב כי רוח עריצימ כזרמ קיר
5כחרב בציון שׁאון זרים תכניע חרב בצל עב זמיר עריצים5כחורב בציונ שאונ זרימ תכניע חורב בצל עב זמיר עריצי
> יענה פ >מ יענה
6ועשׂה יהוה צבאות לכל העמים בהר הזה משׁתה שׁמנים מש6ועשה יהוה צבאות לכול העמימ בהר הזה משתה שמנימ משתה
>ׁתה שׁמרים שׁמנים ממחים שׁמרים מזקקים > שמרימ שמנימ ממחימ שמרימ מזוקקימ
7ובלע בהר הזה פני הלוט׀ הלוט על כל העמים והמסכה הנס7ובלע בהר הזה פנו הלוט הלוט על כול העמימ והמסכה הנס
>וכה על כל הגוים >וכה על כול הגואימ
8בלע המות לנצח ומחה אדני יהוה דמעה מעל כל פנים וחרפ8בלע המות לנצח ומחה אדוני יהוה דמעה מעל כול פנימ וח
>ת עמו יסיר מעל כל הארץ כי יהוה דבר פ >רפת עמו יסיר מעל כול הארצ כי יהוה דבר
9ואמר ביום ההוא הנה אלהינו זה קוינו לו ויושׁיענו זה9ואמרת ביומ ההוא הנה יהוה אלוהינו זה קוינו לו ויושי
> יהוה קוינו לו נגילה ונשׂמחה בישׁועתו >ענו זה יהוה קוינו לו נגילה ונשמח בישועתו
10כי תנוח יד יהוה בהר הזה ונדושׁ מואב תחתיו כהדושׁ מ10כי תנוח יד יהוה בהר הזה ונדש מואב תחתיו כחדוש מתבנ
>תבן במי מדמנה > במי מדמנה
11ופרשׂ ידיו בקרבו כאשׁר יפרשׂ השׂחה לשׂחות והשׁפיל 11ופרש ידיו בקרבו כאשר יפרש השוחה לשחות והשפיל גאותו
>גאותו עם ארבות ידיו > עמ ארבות ידיו
12ומבצר משׂגב חומתיך השׁח השׁפיל הגיע לארץ עד עפר ס 12ומבצר משגב חומותיכ השחה השפיל יגיע לארצ עד עפר
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 26 MT
Isaiah 26 1QIsaa
n1ביומ ההוא יושר השיר הזה בארצ יהודה עיר עז לנו ישועn1ביומ ההוא ישיר השיר הזואת בארצ יהודה עיר עוז לנו י
>ה ישית חומות וחל>שועה ישית חומות וחל
2פתחו שערימ ויבא גוי צדיק שמר אמנימ2פתחו שעריכ ויבוא גוי צדיק שומר אמונימ
3יצר סמוכ תצר שלומ שלומ כי בכ בטוח3יצר סמוכ תצר שלומ שלומ כי בכה
4בטחו ביהוה עדי עד כי ביה יהוה צור עולמימ4בטחו ביהוה עדי עד כי ביה יהוה צור עולמימ
n5כי השח ישבי מרומ קריה נשגבה ישפילנה ישפילה עד ארצ n5כי השת יושבי מרומ קריה נשגבה ישפילנה עדי ארצ יגיענ
>יגיענה עד עפר>ה עדי עפר
6תרמסנה רגל רגלי עני פעמי דלימ6תרמסנה רגלי עניימ פעמי דלימ
7ארח לצדיק מישרימ ישר מעגל צדיק תפלס7אורח לצדיק מישרימ ישר מעגל צדק תפלט
8אפ ארח משפטיכ יהוה קוינוכ לשמכ ולזכרכ תאות נפש8אפ אורח משפטיכ יהוה קוינו לשמכ ולתורתכ תאית נפש
9נפשי אויתיכ בלילה אפ רוחי בקרבי אשחרכ כי כאשר משפט9נפשי אויתיכ בלילה אפ רוחי בקרבי אשחרכה כי כאשר משפ
>יכ לארצ צדק למדו ישבי תבל>טיכ לארצ צדק למדו יושבי תבל
10יחנ רשע בל למד צדק בארצ נכחות יעול ובל יראה גאות י10יחונ רשע בל למד צדק בארצ נכוחות יעיל ובל יראה גאות
>הוה> יהוה
11יהוה רמה ידכ בל יחזיונ יחזו ויבשו קנאת עמ אפ אש צר11יהוה רמה ידכה בל יחזיונ ויחזו ויבושו קנאת העמ אפ א
>יכ תאכלמ>ש צריכ תאכלמ
12יהוה תשפת שלומ לנו כי גמ כל מעשינו פעלת לנו12יהוה תשפוט שלומ לנו כי גמ כול מעשינו פעלתה לנו
13יהוה אלהינו בעלונו אדנימ זולתכ לבד בכ נזכיר שמכ13יהוה אלוהינו בעלונו אדונימ זולתכ לבד בכ נזכור שמכ
14מתימ בל יחיו רפאימ בל יקמו לכנ פקדת ותשמידמ ותאבד 14מיתימ בל יחיו רפאימ בל יקומו לכנ פקדת ותשמידמ ותאס
>כל זכר למו>ר כול זכר למו
15יספת לגוי יהוה יספת לגוי נכבדת רחקת כל קצוי ארצ15יספת לגוי יהוה יספתה לגוי נכבדת רחקת כול קצוי ארצ
16יהוה בצר פקדוכ צקונ לחש מוסרכ למו16יהוה בצר פקדוכ צקונ לחשו מוסריכ למו
17כמו הרה תקריב ללדת תחיל תזעק בחבליה כנ היינו מפניכ17כמו הרה תקריב ללדת תחיל תזעק בחבליה כנ היינו מפניכ
> יהוה> יהוה
t18הרינו חלנו כמו ילדנו רוח ישועת בל נעשה ארצ ובל יפלt18הרינו חלנו כמו ילדנו רוח ישועתכ בל נעשה ארצ ובל יפ
>ו ישבי תבל>ולו יושבי תבל
19יחיו מתיכ נבלתי יקומונ הקיצו ורננו שכני עפר כי טל 19יחיו מיתיכ נבלתי יקומונ יקיצו וירננו שוכני עפר כי 
>אורת טלכ וארצ רפאימ תפיל>טל אורות טלכ וארצ רפאימ תפיל
20לכ עמי בא בחדריכ וסגר דלתכ בעדכ חבי כמעט רגע עד יע20לכ עמי בא בחדריכ וסגר דלתיכ בעדכ חבו כמעט רגע עד י
>בר זעמ>עבור זעמ
21כי הנה יהוה יצא ממקומו לפקד עונ ישב הארצ עליו וגלת21כי יהוה יצא ממקומו לפקוד עוונ יושב הארצ עליו וגלתה
>ה הארצ את דמיה ולא תכסה עוד על הרוגיה> הארצ את דמיה ולוא תכסה עוד על הרוגיה
t1ביום ההוא יושׁר השׁיר הזה בארץ יהודה עיר עז לנו ישt1ביומ ההוא ישיר השיר הזואת בארצ יהודה עיר עוז לנו י
>ׁועה ישׁית חומות וחל >שועה ישית חומות וחל
2פתחו שׁערים ויבא גוי צדיק שׁמר אמנים 2פתחו שעריכ ויבוא גוי צדיק שומר אמונימ
3יצר סמוך תצר שׁלום׀ שׁלום כי בך בטוח 3יצר סמוכ תצר שלומ שלומ כי בכה
4בטחו ביהוה עדי עד כי ביה יהוה צור עולמים 4בטחו ביהוה עדי עד כי ביה יהוה צור עולמימ
5כי השׁח ישׁבי מרום קריה נשׂגבה ישׁפילנה ישׁפילה עד5כי השת יושבי מרומ קריה נשגבה ישפילנה עדי ארצ יגיענ
> ארץ יגיענה עד עפר >ה עדי עפר
6תרמסנה רגל רגלי עני פעמי דלים 6תרמסנה רגלי עניימ פעמי דלימ
7ארח לצדיק מישׁרים ישׁר מעגל צדיק תפלס 7אורח לצדיק מישרימ ישר מעגל צדק תפלט
8אף ארח משׁפטיך יהוה קוינוך לשׁמך ולזכרך תאות נפשׁ 8אפ אורח משפטיכ יהוה קוינו לשמכ ולתורתכ תאית נפש
9נפשׁי אויתיך בלילה אף רוחי בקרבי אשׁחרך כי כאשׁר מ9נפשי אויתיכ בלילה אפ רוחי בקרבי אשחרכה כי כאשר משפ
>שׁפטיך לארץ צדק למדו ישׁבי תבל >טיכ לארצ צדק למדו יושבי תבל
10יחן רשׁע בל למד צדק בארץ נכחות יעול ובל יראה גאות 10יחונ רשע בל למד צדק בארצ נכוחות יעיל ובל יראה גאות
>יהוה ס > יהוה
11יהוה רמה ידך בל יחזיון יחזו ויבשׁו קנאת עם אף אשׁ 11יהוה רמה ידכה בל יחזיונ ויחזו ויבושו קנאת העמ אפ א
>צריך תאכלם ס >ש צריכ תאכלמ
12יהוה תשׁפת שׁלום לנו כי גם כל מעשׂינו פעלת לנו 12יהוה תשפוט שלומ לנו כי גמ כול מעשינו פעלתה לנו
13יהוה אלהינו בעלונו אדנים זולתך לבד בך נזכיר שׁמך 13יהוה אלוהינו בעלונו אדונימ זולתכ לבד בכ נזכור שמכ
14מתים בל יחיו רפאים בל יקמו לכן פקדת ותשׁמידם ותאבד14מיתימ בל יחיו רפאימ בל יקומו לכנ פקדת ותשמידמ ותאס
> כל זכר למו >ר כול זכר למו
15יספת לגוי יהוה יספת לגוי נכבדת רחקת כל קצוי ארץ 15יספת לגוי יהוה יספתה לגוי נכבדת רחקת כול קצוי ארצ
16יהוה בצר פקדוך צקון לחשׁ מוסרך למו 16יהוה בצר פקדוכ צקונ לחשו מוסריכ למו
17כמו הרה תקריב ללדת תחיל תזעק בחבליה כן היינו מפניך17כמו הרה תקריב ללדת תחיל תזעק בחבליה כנ היינו מפניכ
> יהוה > יהוה
18הרינו חלנו כמו ילדנו רוח ישׁועת בל נעשׂה ארץ ובל י18הרינו חלנו כמו ילדנו רוח ישועתכ בל נעשה ארצ ובל יפ
>פלו ישׁבי תבל >ולו יושבי תבל
19יחיו מתיך נבלתי יקומון הקיצו ורננו שׁכני עפר כי טל19יחיו מיתיכ נבלתי יקומונ יקיצו וירננו שוכני עפר כי 
> אורת טלך וארץ רפאים תפיל ס >טל אורות טלכ וארצ רפאימ תפיל
20לך עמי בא בחדריך וסגר דלתיך בעדך חבי כמעט רגע עד י20לכ עמי בא בחדריכ וסגר דלתיכ בעדכ חבו כמעט רגע עד י
>עבור זעם >עבור זעמ
21כי הנה יהוה יצא ממקומו לפקד עון ישׁב הארץ עליו וגל21כי יהוה יצא ממקומו לפקוד עוונ יושב הארצ עליו וגלתה
>תה הארץ את דמיה ולא תכסה עוד על הרוגיה ס > הארצ את דמיה ולוא תכסה עוד על הרוגיה
- - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 27 MT
Isaiah 27 1QIsaa
n1ביומ ההוא יפקד יהוה בחרבו הקשה והגדולה והחזקה על לn1ביומ ההוא יפקוד יהוה בחרבו הקשה והגדולה והחזקה על 
>ויתנ נחש ברח ועל לויתנ נחש עקלתונ והרג את התנינ אש>לויתנ נחש בורח ועל לויתנ נחש עקלתונ והרג את התנינ 
>ר בימ>אשר בימ
2ביומ ההוא כרמ חמד ענו לה2ביומ ההוא כרמ חומר ענו לה
3אני יהוה נצרה לרגעימ אשקנה פנ יפקד עליה לילה ויומ 3אני יהוה נצרה לרגעימ אשקנה פנ יפקוד עליה לילה ויומ
>אצרנה> אצורנה
4חמה אינ לי מי יתנני שמיר שית במלחמה אפשעה בה אציתנ4חמה אינ לי מי יתנני שמיר ושית במלחמה אפשעה בה ואצי
>ה יחד>תנה יחדו
5או יחזק במעוזי יעשה שלומ לי שלומ יעשה לי5או יחזק במעוזי יעשה שלומ לי שלומ יעשה לי
n6הבאימ ישרש יעקב יציצ ופרח ישראל ומלאו פני תבל תנובn6הבאימ ישריש יעקוב ויציצ ויפרח ישראל ומלאו פני תבל 
>ה>תנובה
7הכמכת מכהו הכהו אמ כהרג הרגיו הרג7הכמכת מכהו הכהו אמ כהרג הורגיו הרג
8בסאסאה בשלחה תריבנה הגה ברוחו הקשה ביומ קדימ8בסאסאה בשלחה תריבנה הגה ברוחו הקשה ביומ קדימ
n9לכנ בזאת יכפר עונ יעקב וזה כל פרי הסר חטאתו בשומו n9לכנ בזואת יכפר עוונ יעקוב וזה כול פרי הסיר חטאוו ב
>כל אבני מזבח כאבני גר מנפצות לא יקמו אשרימ וחמנימ>שומו כול אבני מזבח כאבני גיר מנפצות לוא יקומו אשרי
 >מ וחמנימ
10כי עיר בצורה בדד נוה משלח ונעזב כמדבר שמ ירעה עגל 10כי עיר בצורה בדד נוה משלח ונעזב כמדבר שמ ירעה עגל 
>ושמ ירבצ וכלה סעפיה>ושמ ירבצ וכלה סעפיה
t11ביבש קצירה תשברנה נשימ באות מאירות אותה כי לא עמ בt11ביבש קצירה תשברנה נשימ באות מאירות אותה כי לוא עמ 
>ינות הוא על כנ לא ירחמנו עשהו ויצרו לא יחננו>בינות הוא על כנ לוא ירחמנו עושהו ויוצרו לוא יחוננו
12והיה ביומ ההוא יחבט יהוה משבלת הנהר עד נחל מצרימ ו12והיה ביומ ההוא יחבוט יהוה משבל הנהר עד נחל מצרימ ו
>אתמ תלקטו לאחד אחד בני ישראל>אתמה תלקטו לאחד אחד בני ישראל
13והיה ביומ ההוא יתקע בשופר גדול ובאו האבדימ בארצ אש13והיה ביומ ההוא יתקע בשופר גדול ובאו האובדימ בארצ א
>ור והנדחימ בארצ מצרימ והשתחוו ליהוה בהר הקדש בירוש>שור והנדחימ בארצ מצרימ והשתחו ליהוה בהר הקדש בירוש
>למ>למ
t1ביום ההוא יפקד יהוה בחרבו הקשׁה והגדולה והחזקה על t1ביומ ההוא יפקוד יהוה בחרבו הקשה והגדולה והחזקה על 
>לויתן נחשׁ ברח ועל לויתן נחשׁ עקלתון והרג את התנין>לויתנ נחש בורח ועל לויתנ נחש עקלתונ והרג את התנינ 
> אשׁר בים ס >אשר בימ
2ביום ההוא כרם חמד ענו לה 2ביומ ההוא כרמ חומר ענו לה
3אני יהוה נצרה לרגעים אשׁקנה פן יפקד עליה לילה ויום3אני יהוה נצרה לרגעימ אשקנה פנ יפקוד עליה לילה ויומ
> אצרנה > אצורנה
4חמה אין לי מי יתנני שׁמיר שׁית במלחמה אפשׂעה בה אצ4חמה אינ לי מי יתנני שמיר ושית במלחמה אפשעה בה ואצי
>יתנה יחד >תנה יחדו
5או יחזק במעוזי יעשׂה שׁלום לי שׁלום יעשׂה לי 5או יחזק במעוזי יעשה שלומ לי שלומ יעשה לי
6הבאים ישׁרשׁ יעקב יציץ ופרח ישׂראל ומלאו פני תבל ת6הבאימ ישריש יעקוב ויציצ ויפרח ישראל ומלאו פני תבל 
>נובה ס >תנובה
7הכמכת מכהו הכהו אם כהרג הרגיו הרג 7הכמכת מכהו הכהו אמ כהרג הורגיו הרג
8בסאסאה בשׁלחה תריבנה הגה ברוחו הקשׁה ביום קדים 8בסאסאה בשלחה תריבנה הגה ברוחו הקשה ביומ קדימ
9לכן בזאת יכפר עון יעקב וזה כל פרי הסר חטאתו בשׂומו9לכנ בזואת יכפר עוונ יעקוב וזה כול פרי הסיר חטאוו ב
>׀ כל אבני מזבח כאבני גר מנפצות לא יקמו אשׁרים וחמנ>שומו כול אבני מזבח כאבני גיר מנפצות לוא יקומו אשרי
>ים >מ וחמנימ
10כי עיר בצורה בדד נוה משׁלח ונעזב כמדבר שׁם ירעה עג10כי עיר בצורה בדד נוה משלח ונעזב כמדבר שמ ירעה עגל 
>ל ושׁם ירבץ וכלה סעפיה >ושמ ירבצ וכלה סעפיה
11ביבשׁ קצירה תשׁברנה נשׁים באות מאירות אותה כי לא ע11ביבש קצירה תשברנה נשימ באות מאירות אותה כי לוא עמ 
>ם בינות הוא על כן לא ירחמנו עשׂהו ויצרו לא יחננו ס>בינות הוא על כנ לוא ירחמנו עושהו ויוצרו לוא יחוננו
>  
12והיה ביום ההוא יחבט יהוה משׁבלת הנהר עד נחל מצרים 12והיה ביומ ההוא יחבוט יהוה משבל הנהר עד נחל מצרימ ו
>ואתם תלקטו לאחד אחד בני ישׂראל ס >אתמה תלקטו לאחד אחד בני ישראל
13והיה׀ ביום ההוא יתקע בשׁופר גדול ובאו האבדים בארץ 13והיה ביומ ההוא יתקע בשופר גדול ובאו האובדימ בארצ א
>אשׁור והנדחים בארץ מצרים והשׁתחוו ליהוה בהר הקדשׁ >שור והנדחימ בארצ מצרימ והשתחו ליהוה בהר הקדש בירוש
>בירושׁלם >למ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 28 MT
Isaiah 28 1QIsaa
n1הוי עטרת גאות שכרי אפרימ וציצ נבל צבי תפארתו אשר עn1הוי עטרת גאונ שכורי אפרימ וציצ נבל צבי תפארתו אשר 
>ל ראש גיא שמנימ הלומי יינ>על ראש גאי שמנימ הלומי יינ
2הנה חזק ואמצ לאדני כזרמ ברד שער קטב כזרמ מימ כבירי2הנה בחזק ואמצ ליהוה כזרמ ברד שער קטב כזרמ מימ כברי
>מ שטפימ הניח לארצ ביד>מ שוטפימ והניח לארצ ביד
3ברגלימ תרמסנה עטרת גאות שכורי אפרימ3ברגלימ תרמסנה עטרת גאות שכורי אפרימ
n4והיתה ציצת נבל צבי תפארתו אשר על ראש גיא שמנימ כבכn4והייתה ציצת נבל צבי תפארתו אשר על ראש גאי שמנימ כב
>ורה בטרמ קיצ אשר יראה הראה אותה בעודה בכפו יבלענה>כרה בטרמ קיצ אשר יראה הרואה אותה בעודנה בכפו יבלענ
 >ה
5ביומ ההוא יהיה יהוה צבאות לעטרת צבי ולצפירת תפארה 5ביומ ההוא יהיה יהוה צבאות לעטרת צבי ולצפירת תפארה 
>לשאר עמו>לשאר עמו
n6ולרוח משפט ליושב על המשפט ולגבורה משיבי מלחמה שערהn6ולרוח משפט ליושב על המשפט ולגבורה משיבי מלחמה שער
7וגמ אלה ביינ שגו ובשכר תעו כהנ ונביא שגו בשכר נבלע7גמ אלה ביינ שגו ובשכר תעו כוהנ ונבי שגו בשכר נבלעו
>ו מנ היינ תעו מנ השכר שגו בראה פקו פליליה> מנ היינ תעו מנ השכר שגו בראה פקו פליליה
8כי כל שלחנות מלאו קיא צאה בלי מקומ8כי כול שלחנות מלאו קיה צאה בלי מקומ
9את מי יורה דעה ואת מי יבינ שמועה גמולי מחלב עתיקי 9את מי יורה דעה ואת מי יבינ שמועה גמולי מחלב עתיקי 
>משדימ>משדימ
n10כי צו לצו צו לצו קו לקו קו לקו זעיר שמ זעיר שמn10כי צי לצי צי לצי קו לקו קו לקו זעיר שמ זעיר שמ
11כי בלעגי שפה ובלשונ אחרת ידבר אל העמ הזה11כי בלעגי שפה ובלשונ אחרת ידבר אל העמ הזה
t12אשר אמר אליהמ זאת המנוחה הניחו לעיפ וזאת המרגעה ולt12אשר אמר אליהמה זואת המנוחה הניחו ליעופ וזואת המרגע
>א אבוא שמוע>ה ולוא אבו לשמוע
13והיה להמ דבר יהוה צו לצו צו לצו קו לקו קו לקו זעיר13והיה להמ דבר יהוה צי לצי צי לצי קו לקו קו לקו זעיר
> שמ זעיר שמ למענ ילכו וכשלו אחור ונשברו ונוקשו ונל> שמ זעיר שמ למענ ילכו וכשלו אחור ונשברו ונוקשו ונל
>כדו>כדו
14לכנ שמעו דבר יהוה אנשי לצונ משלי העמ הזה אשר בירוש14לכנ שמע דבר יהוה אנשי לצונ משלי העמ הזה אשר בירושל
>למ>ימ
15כי אמרתמ כרתנו ברית את מות ועמ שאול עשינו חזה שוט 15כי אמרתמ כרתנו ברית את מות ועמ שאול עשינו חזה שוט 
>שוטפ כי יעבר לא יבואנו כי שמנו כזב מחסנו ובשקר נסת>שוטפ כי יבור לוא יבואנו כי שמנו כזב מחסני ובשקר נס
>רנו>תרנו
16לכנ כה אמר אדני יהוה הנני יסד בציונ אבנ אבנ בחנ פנ16לכנ כה אמר אדוני יהוה הנני מיסד בציונ אבנ אבנ בחנ 
>ת יקרת מוסד מוסד המאמינ לא יחיש>פנת יקרת מוסד מוסד המאמינ לוא יחיש
17ושמתי משפט לקו וצדקה למשקלת ויעה ברד מחסה כזב וסתר17ושמתי משפט לקו וצדקה למשקלת ויעה ברד ממחסה כזב וסת
> מימ ישטפו>ר מימ ושטפו
18וכפר בריתכמ את מות וחזותכמ את שאול לא תקומ שוט שוט18וכפר את בריתכמה את מות וחזותכמ את שאול לוא תקומ שו
>פ כי יעבר והייתמ לו למרמס>ט שוטפ כי יעבור והייתמה לו למרמס
19מדי עברו יקח אתכמ כי בבקר בבקר יעבר ביומ ובלילה וה19מדי עברו יקח אתכמה כי בבקר בבקר יעבור ביומ ובלילה 
>יה רק זועה הבינ שמועה>רק זועה הבינ שמועה
20כי קצר המצע מהשתרע והמסכה צרה כהתכנס20כי קצר המצע משתריימ והמסכסכה בהתכנס
21כי כהר פרצימ יקומ יהוה כעמק בגבעונ ירגז לעשות מעשה21כי בהר פרצימ יקומ יהוה בעמק בגבעונ ירגז לעשות מעשה
>ו זר מעשהו ולעבד עבדתו נכריה עבדתו>ו זר מעשהו ולעבד עבדתו נכריה עבדתו
22ועתה אל תתלוצצו פנ יחזקו מוסריכמ כי כלה ונחרצה שמע22ואתה אל תתלוצצו פנ יחזקו מוסרותיכמ כי כלה ונחרצה ש
>תי מאת אדני יהוה צבאות על כל הארצ>מעתי מאת יהוה צבאות על כל הארצ
23האזינו ושמעו קולי הקשיבו ושמעו אמרתי23אזינו ושמעו קולי הקשיבו ושמעו אמרתי
24הכל היומ יחרש החרש לזרע יפתח וישדד אדמתו24הכול היומ יחרוש החורש לזרוע ופתח וישדד אדמתו
25הלוא אמ שוה פניה והפיצ קצח וכמנ יזרק ושמ חטה שורה 25הלוא אמ שוה פניה והפיצ קצח וכימנ וזרק ושמ חטה שורה
>ושערה נסמנ וכסמת גבלתו> ושעורה נסמנ וכסמת גבולותו
26ויסרו למשפט אלהיו יורנו26ויסרהו למשפט אלוהו יורנו
27כי לא בחרוצ יודש קצח ואופנ עגלה על כמנ יוסב כי במט27כי לוא בחרוצ ידש קצח ואפנ עגלה על כמנ יסוב כי במטה
>ה יחבט קצח וכמנ בשבט> יחבט קצח וכמנ בשבט
28לחמ יודק כי לא לנצח אדוש ידושנו והממ גלגל עגלתו ופ28יודק כי לוא לנצח הדש ידושנו והממ גלגל עגלתו ופרשיו
>רשיו לא ידקנו> לוא ידיקנו
29גמ זאת מעמ יהוה צבאות יצאה הפליא עצה הגדיל תושיה29גמ זואת מעמ יהוה צבאות יצאה הפלה עצה והגדיל תושיה
t1הוי עטרת גאות שׁכרי אפרים וציץ נבל צבי תפארתו אשׁרt1הוי עטרת גאונ שכורי אפרימ וציצ נבל צבי תפארתו אשר 
> על ראשׁ גיא שׁמנים הלומי יין >על ראש גאי שמנימ הלומי יינ
2הנה חזק ואמץ לאדני כזרם ברד שׂער קטב כזרם מים כביר2הנה בחזק ואמצ ליהוה כזרמ ברד שער קטב כזרמ מימ כברי
>ים שׁטפים הניח לארץ ביד >מ שוטפימ והניח לארצ ביד
3ברגלים תרמסנה עטרת גאות שׁכורי אפרים 3ברגלימ תרמסנה עטרת גאות שכורי אפרימ
4והיתה ציצת נבל צבי תפארתו אשׁר על ראשׁ גיא שׁמנים 4והייתה ציצת נבל צבי תפארתו אשר על ראש גאי שמנימ כב
>כבכורה בטרם קיץ אשׁר יראה הראה אותה בעודה בכפו יבל>כרה בטרמ קיצ אשר יראה הרואה אותה בעודנה בכפו יבלענ
>ענה ס >ה
5ביום ההוא יהיה יהוה צבאות לעטרת צבי ולצפירת תפארה 5ביומ ההוא יהיה יהוה צבאות לעטרת צבי ולצפירת תפארה 
>לשׁאר עמו >לשאר עמו
6ולרוח משׁפט ליושׁב על המשׁפט ולגבורה משׁיבי מלחמה 6ולרוח משפט ליושב על המשפט ולגבורה משיבי מלחמה שער
>שׁערה ס  
7וגם אלה ביין שׁגו ובשׁכר תעו כהן ונביא שׁגו בשׁכר 7גמ אלה ביינ שגו ובשכר תעו כוהנ ונבי שגו בשכר נבלעו
>נבלעו מן היין תעו מן השׁכר שׁגו בראה פקו פליליה > מנ היינ תעו מנ השכר שגו בראה פקו פליליה
8כי כל שׁלחנות מלאו קיא צאה בלי מקום ס 8כי כול שלחנות מלאו קיה צאה בלי מקומ
9את מי יורה דעה ואת מי יבין שׁמועה גמולי מחלב עתיקי9את מי יורה דעה ואת מי יבינ שמועה גמולי מחלב עתיקי 
> משׁדים >משדימ
10כי צו לצו צו לצו קו לקו קו לקו זעיר שׁם זעיר שׁם 10כי צי לצי צי לצי קו לקו קו לקו זעיר שמ זעיר שמ
11כי בלעגי שׂפה ובלשׁון אחרת ידבר אל העם הזה 11כי בלעגי שפה ובלשונ אחרת ידבר אל העמ הזה
12אשׁר׀ אמר אליהם זאת המנוחה הניחו לעיף וזאת המרגעה 12אשר אמר אליהמה זואת המנוחה הניחו ליעופ וזואת המרגע
>ולא אבוא שׁמוע >ה ולוא אבו לשמוע
13והיה להם דבר יהוה צו לצו צו לצו קו לקו קו לקו זעיר13והיה להמ דבר יהוה צי לצי צי לצי קו לקו קו לקו זעיר
> שׁם זעיר שׁם למען ילכו וכשׁלו אחור ונשׁברו ונוקשׁ> שמ זעיר שמ למענ ילכו וכשלו אחור ונשברו ונוקשו ונל
>ו ונלכדו פ >כדו
14לכן שׁמעו דבר יהוה אנשׁי לצון משׁלי העם הזה אשׁר ב14לכנ שמע דבר יהוה אנשי לצונ משלי העמ הזה אשר בירושל
>ירושׁלם >ימ
15כי אמרתם כרתנו ברית את מות ועם שׁאול עשׂינו חזה שׁ15כי אמרתמ כרתנו ברית את מות ועמ שאול עשינו חזה שוט 
>יט שׁוטף כי עבר לא יבואנו כי שׂמנו כזב מחסנו ובשׁק>שוטפ כי יבור לוא יבואנו כי שמנו כזב מחסני ובשקר נס
>ר נסתרנו ס >תרנו
16לכן כה אמר אדני יהוה הנני יסד בציון אבן אבן בחן פנ16לכנ כה אמר אדוני יהוה הנני מיסד בציונ אבנ אבנ בחנ 
>ת יקרת מוסד מוסד המאמין לא יחישׁ >פנת יקרת מוסד מוסד המאמינ לוא יחיש
17ושׂמתי משׁפט לקו וצדקה למשׁקלת ויעה ברד מחסה כזב ו17ושמתי משפט לקו וצדקה למשקלת ויעה ברד ממחסה כזב וסת
>סתר מים ישׁטפו >ר מימ ושטפו
18וכפר בריתכם את מות וחזותכם את שׁאול לא תקום שׁוט ש18וכפר את בריתכמה את מות וחזותכמ את שאול לוא תקומ שו
>ׁוטף כי יעבר והייתם לו למרמס >ט שוטפ כי יעבור והייתמה לו למרמס
19מדי עברו יקח אתכם כי בבקר בבקר יעבר ביום ובלילה וה19מדי עברו יקח אתכמה כי בבקר בבקר יעבור ביומ ובלילה 
>יה רק זועה הבין שׁמועה >רק זועה הבינ שמועה
20כי קצר המצע מהשׂתרע והמסכה צרה כהתכנס 20כי קצר המצע משתריימ והמסכסכה בהתכנס
21כי כהר פרצים יקום יהוה כעמק בגבעון ירגז לעשׂות מעש21כי בהר פרצימ יקומ יהוה בעמק בגבעונ ירגז לעשות מעשה
>ׂהו זר מעשׂהו ולעבד עבדתו נכריה עבדתו >ו זר מעשהו ולעבד עבדתו נכריה עבדתו
22ועתה אל תתלוצצו פן יחזקו מוסריכם כי כלה ונחרצה שׁמ22ואתה אל תתלוצצו פנ יחזקו מוסרותיכמ כי כלה ונחרצה ש
>עתי מאת אדני יהוה צבאות על כל הארץ >מעתי מאת יהוה צבאות על כל הארצ
23האזינו ושׁמעו קולי הקשׁיבו ושׁמעו אמרתי 23אזינו ושמעו קולי הקשיבו ושמעו אמרתי
24הכל היום יחרשׁ החרשׁ לזרע יפתח וישׂדד אדמתו 24הכול היומ יחרוש החורש לזרוע ופתח וישדד אדמתו
25הלוא אם שׁוה פניה והפיץ קצח וכמן יזרק ושׂם חטה שׂו25הלוא אמ שוה פניה והפיצ קצח וכימנ וזרק ושמ חטה שורה
>רה ושׂערה נסמן וכסמת גבלתו > ושעורה נסמנ וכסמת גבולותו
26ויסרו למשׁפט אלהיו יורנו 26ויסרהו למשפט אלוהו יורנו
27כי לא בחרוץ יודשׁ קצח ואופן עגלה על כמן יוסב כי במ27כי לוא בחרוצ ידש קצח ואפנ עגלה על כמנ יסוב כי במטה
>טה יחבט קצח וכמן בשׁבט > יחבט קצח וכמנ בשבט
28לחם יודק כי לא לנצח אדושׁ ידושׁנו והמם גלגל עגלתו 28יודק כי לוא לנצח הדש ידושנו והממ גלגל עגלתו ופרשיו
>ופרשׁיו לא ידקנו > לוא ידיקנו
29גם זאת מעם יהוה צבאות יצאה הפליא עצה הגדיל תושׁיה 29גמ זואת מעמ יהוה צבאות יצאה הפלה עצה והגדיל תושיה
>ס  
- - - - - + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 29 MT
Isaiah 29 1QIsaa
n1הוי אריאל אריאל קרית חנה דוד ספו שנה על שנה חגימ יn1הוי ארואל ארואל קרית חנה דויד ספי שנה על שנה חגימ 
>נקפו>ינקפו
2והציקותי לאריאל והיתה תאניה ואניה והיתה לי כאריאל2והציקותי לארואל והייתה תאניה ואניה והייתה לי כארוא
 >ל
3וחניתי כדור עליכ וצרתי עליכ מצב והקימתי עליכ מצרת3וחניתי כדור עליכ וצרתי עליכ מצב והקימותי עליכ מצוד
t1הוי אריאל אריאל קרית חנה דוד ספו שׁנה על שׁנה חגיםt1הוי ארואל ארואל קרית חנה דויד ספי שנה על שנה חגימ 
> ינקפו >ינקפו
2והציקותי לאריאל והיתה תאניה ואניה והיתה לי כאריאל 2והציקותי לארואל והייתה תאניה ואניה והייתה לי כארוא
 >ל
3וחניתי כדור עליך וצרתי עליך מצב והקימתי עליך מצרת 3וחניתי כדור עליכ וצרתי עליכ מצב והקימותי עליכ מצוד
 >ות
4ושפלת מארצ תדברי ומעפר תשח אמרתכ והיה כאוב מארצ קו4ושפלת מארצ תדברי ומעפר תשח אמרתכ והיה כאוב מארצ קו
>לכ ומעפר אמרתכ תצפצפ>לכ ומעפר אמרתכ תצפצפ
n5והיה כאבק דק המונ זריכ וכמצ עבר המונ עריצימ והיה לn5והיה כאבק דק המונ זדיכ וכמוצ עובר המונ עריצימ והיה
>פתע פתאמ> לפתע פתאמ
6מעמ יהוה צבאות תפקד ברעמ וברעש וקול גדול סופה וסער6מעמ יהוה צבאות תפקד ברעמ וברעש וקול גדול סופה וסער
>ה ולהב אש אוכלה>ה ולהב אש אוכלה
n7והיה כחלומ חזונ לילה המונ כל הגוימ הצבאימ על אריאלn7והיה כחלומ חזונ לילה המונ כול הגואימ הצובאימ על אר
> וכל צביה ומצדתה והמציקימ לה>ואל וכול צביה ומצרתה והמצוקימ לה
8והיה כאשר יחלמ הרעב והנה אוכל והקיצ וריקה נפשו וכא8והיה כאשר יחלומ הרעב והנה אוכל והקיצ וריקה נפשיו ו
>שר יחלמ הצמא והנה שתה והקיצ והנה עיפ ונפשו שוקקה כ>כאשר יחלומ הצמא והנה שותה והקיצ והנה עיפ ונפשו שקי
>נ יהיה המונ כל הגוימ הצבאימ על הר ציונ>קה כנ יהיה המונ כול הגואימ הצבאימ על הר ציונ
9התמהמהו ותמהו השתעשעו ושעו שכרו ולא יינ נעו ולא שכ9התמהמהו ותמהו התשתעשעו ושועו שכרונ ולוא מיינ נעווו
>ר>לשכר
10כי נסכ עליכמ יהוה רוח תרדמה ויעצמ את עיניכמ את הנב10כי נסכ עליכמה יהוה רוח תרדמה ויעצמ את עיניכמ את הנ
>יאימ ואת ראשיכמ החזימ כסה>ביאימ ואת ראשיכמ החוזימ כסה
11ותהי לכמ חזות הכל כדברי הספר החתומ אשר יתנו אתו אל11ותהיה לכמ חזות הכול כדברי הספר החתומ אשר יתנו אותו
> יודע ספר לאמר קרא נא זה ואמר לא אוכל כי חתומ הוא> אל יודע הספר לאמור קרא נא זה ויואמר לוא אוכל כי ח
 >תומ הוא
12ונתנ הספר על אשר לא ידע ספר לאמר קרא נא זה ואמר לא12ונתנו הספר אל אשר לוא יודע ספר לאמור קרא נא זה ויו
> ידעתי ספר>אמר לוא ידעתי ספר
13ויאמר אדני יענ כי נגש העמ הזה בפיו ובשפתיו כבדוני 13ויואמר אדוני יענ כי נגש העמ הזה בפיו ובשפתיו כבדונ
>ולבו רחק ממני ותהי יראתמ אתי מצות אנשימ מלמדה>י ולבו רחוק ממני ותהיה יראת אותי כמצות אנשימ מלמדה
14לכנ הנני יוספ להפליא את העמ הזה הפלא ופלא ואבדה חכ14לכנ הנה אנוכי יוספ להפלה את העמ הזה הפלה ופלה ואבד
>מת חכמיו ובינת נבניו תסתתר>ה חכמת חכמיו ובינות נבוניו תסתתר
15הוי המעמיקימ מיהוה לסתר עצה והיה במחשכ מעשיהמ ויאמ15הוי המעמיקימ מיהוה לסתר עצה ויהי במחשכ מעשיהמ ויוא
>רו מי ראנו ומי יודענו>מרו מי ראינו ומי ידענו
16הפככמ אמ כחמר היצר יחשב כי יאמר מעשה לעשהו לא עשני16הפכ מכמ אמ כחמ היוצר יחשב כי יואמר מעשה לעושהו לוא
> ויצר אמר ליוצרו לא הבינ> עשני ויצר חמר ליוצריו לוא הבינ
17הלוא עוד מעט מזער ושב לבנונ לכרמל והכרמל ליער יחשב17הלוא עוד מעט מזער ושב לבנונ לכרמל והכרמל ליער יחשב
n18ושמעו ביומ ההוא החרשימ דברי ספר ומאפל ומחשכ עיני עn18ושמעו ביומ ההוא החרשימ דברי ספר ומאפלה ומחושכ עיני
>ורימ תראינה> עורימ תראינה
19ויספו ענוימ ביהוה שמחה ואביוני אדמ בקדוש ישראל יגי19ויספו עניימ ביהוה שמחה ואביוני אדמ בקדוש ישראל יגי
>לו>לו
20כי אפס עריצ וכלה לצ ונכרתו כל שקדי אונ20כי אפס עריצ וכלה ליצ ונכרתו כול שוקדי אונ
21מחטיאי אדמ בדבר ולמוכיח בשער יקשונ ויטו בתהו צדיק21מחטיאי אדמ בדבר ולמוכיח בשער יקשונ ויטו בתהו צדיק
t22לכנ כה אמר יהוה אל בית יעקב אשר פדה את אברהמ לא עתt22לכנ כה אמר יהוה אל בית יעקוב אשר פדה את אברהמ לוא 
>ה יבוש יעקב ולא עתה פניו יחורו>עתה יבוש יעקוב ולוא עתה פניו יחורו
23כי בראתו ילדיו מעשה ידי בקרבו יקדישו שמי והקדישו א23כי בראותו ילדיו מעשה ידי בקרבו יקדשו שמי והקדישו א
>ת קדוש יעקב ואת אלהי ישראל יעריצו>ת קדוש יעקוב ואת אלוהי ישראל יעריצו
24וידעו תעי רוח בינה ורוגנימ ילמדו לקח24וידעו תועי רוח בינה ורוגנימ ילמדו לקח
4ושׁפלת מארץ תדברי ומעפר תשׁח אמרתך והיה כאוב מארץ 4ושפלת מארצ תדברי ומעפר תשח אמרתכ והיה כאוב מארצ קו
>קולך ומעפר אמרתך תצפצף >לכ ומעפר אמרתכ תצפצפ
5והיה כאבק דק המון זריך וכמץ עבר המון עריצים והיה ל5והיה כאבק דק המונ זדיכ וכמוצ עובר המונ עריצימ והיה
>פתע פתאם > לפתע פתאמ
6מעם יהוה צבאות תפקד ברעם וברעשׁ וקול גדול סופה וסע6מעמ יהוה צבאות תפקד ברעמ וברעש וקול גדול סופה וסער
>רה ולהב אשׁ אוכלה >ה ולהב אש אוכלה
7והיה כחלום חזון לילה המון כל הגוים הצבאים על אריאל7והיה כחלומ חזונ לילה המונ כול הגואימ הצובאימ על אר
> וכל צביה ומצדתה והמציקים לה >ואל וכול צביה ומצרתה והמצוקימ לה
8והיה כאשׁר יחלם הרעב והנה אוכל והקיץ וריקה נפשׁו ו8והיה כאשר יחלומ הרעב והנה אוכל והקיצ וריקה נפשיו ו
>כאשׁר יחלם הצמא והנה שׁתה והקיץ והנה עיף ונפשׁו שׁ>כאשר יחלומ הצמא והנה שותה והקיצ והנה עיפ ונפשו שקי
>וקקה כן יהיה המון כל הגוים הצבאים על הר ציון ס >קה כנ יהיה המונ כול הגואימ הצבאימ על הר ציונ
9התמהמהו ותמהו השׁתעשׁעו ושׁעו שׁכרו ולא יין נעו ול9התמהמהו ותמהו התשתעשעו ושועו שכרונ ולוא מיינ נעווו
>א שׁכר >לשכר
10כי נסך עליכם יהוה רוח תרדמה ויעצם את עיניכם את הנב10כי נסכ עליכמה יהוה רוח תרדמה ויעצמ את עיניכמ את הנ
>יאים ואת ראשׁיכם החזים כסה >ביאימ ואת ראשיכמ החוזימ כסה
11ותהי לכם חזות הכל כדברי הספר החתום אשׁר יתנו אתו א11ותהיה לכמ חזות הכול כדברי הספר החתומ אשר יתנו אותו
>ל יודע הספר לאמר קרא נא זה ואמר לא אוכל כי חתום הו> אל יודע הספר לאמור קרא נא זה ויואמר לוא אוכל כי ח
>א >תומ הוא
12ונתן הספר על אשׁר לא ידע ספר לאמר קרא נא זה ואמר ל12ונתנו הספר אל אשר לוא יודע ספר לאמור קרא נא זה ויו
>א ידעתי ספר ס >אמר לוא ידעתי ספר
13ויאמר אדני יען כי נגשׁ העם הזה בפיו ובשׂפתיו כבדונ13ויואמר אדוני יענ כי נגש העמ הזה בפיו ובשפתיו כבדונ
>י ולבו רחק ממני ותהי יראתם אתי מצות אנשׁים מלמדה >י ולבו רחוק ממני ותהיה יראת אותי כמצות אנשימ מלמדה
14לכן הנני יוסף להפליא את העם הזה הפלא ופלא ואבדה חכ14לכנ הנה אנוכי יוספ להפלה את העמ הזה הפלה ופלה ואבד
>מת חכמיו ובינת נבניו תסתתר ס >ה חכמת חכמיו ובינות נבוניו תסתתר
15הוי המעמיקים מיהוה לסתר עצה והיה במחשׁך מעשׂיהם וי15הוי המעמיקימ מיהוה לסתר עצה ויהי במחשכ מעשיהמ ויוא
>אמרו מי ראנו ומי יודענו >מרו מי ראינו ומי ידענו
16הפככם אם כחמר היצר יחשׁב כי יאמר מעשׂה לעשׂהו לא ע16הפכ מכמ אמ כחמ היוצר יחשב כי יואמר מעשה לעושהו לוא
>שׂני ויצר אמר ליוצרו לא הבין > עשני ויצר חמר ליוצריו לוא הבינ
17הלוא עוד מעט מזער ושׁב לבנון לכרמל והכרמל ליער יחש17הלוא עוד מעט מזער ושב לבנונ לכרמל והכרמל ליער יחשב
>ׁב  
18ושׁמעו ביום ההוא החרשׁים דברי ספר ומאפל ומחשׁך עינ18ושמעו ביומ ההוא החרשימ דברי ספר ומאפלה ומחושכ עיני
>י עורים תראינה > עורימ תראינה
19ויספו ענוים ביהוה שׂמחה ואביוני אדם בקדושׁ ישׂראל 19ויספו עניימ ביהוה שמחה ואביוני אדמ בקדוש ישראל יגי
>יגילו >לו
20כי אפס עריץ וכלה לץ ונכרתו כל שׁקדי און 20כי אפס עריצ וכלה ליצ ונכרתו כול שוקדי אונ
21מחטיאי אדם בדבר ולמוכיח בשׁער יקשׁון ויטו בתהו צדי21מחטיאי אדמ בדבר ולמוכיח בשער יקשונ ויטו בתהו צדיק
>ק ס  
22לכן כה אמר יהוה אל בית יעקב אשׁר פדה את אברהם לא ע22לכנ כה אמר יהוה אל בית יעקוב אשר פדה את אברהמ לוא 
>תה יבושׁ יעקב ולא עתה פניו יחורו >עתה יבוש יעקוב ולוא עתה פניו יחורו
23כי בראתו ילדיו מעשׂה ידי בקרבו יקדישׁו שׁמי והקדיש23כי בראותו ילדיו מעשה ידי בקרבו יקדשו שמי והקדישו א
>ׁו את קדושׁ יעקב ואת אלהי ישׂראל יעריצו >ת קדוש יעקוב ואת אלוהי ישראל יעריצו
24וידעו תעי רוח בינה ורוגנים ילמדו לקח 24וידעו תועי רוח בינה ורוגנימ ילמדו לקח
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 30 MT
Isaiah 30 1QIsaa
t1הוי בנימ סוררימ נאמ יהוה לעשות עצה ולא מני ולנסכ מt1הוי בנימ סוררימ נואמ יהוה לעשות עצה ולוא ממני ולנס
>סכה ולא רוחי למענ ספות חטאת על חטאת>כ מסכה ולוא רוחי למענ ספות חטאת על חטאת
2ההלכימ לרדת מצרימ ופי לא שאלו לעוז במעוז פרעה ולחס2ההולכימ לרדת מצרימ ופי לוא שאלו לעוז במעוז פרעוה ו
>ות בצל מצרימ>לחסות בצל מצרימ
3והיה לכמ מעוז פרעה לבשת והחסות בצל מצרימ לכלמה3והיה לכמ מעוז פרעה לבשת והחסות בצל מצרימ לכמה
4כי היו בצענ שריו ומלאכיו חנס יגיעו4כי היה בצענ שריו ומלאכיו חנס יגיעו
5כל הבאיש על עמ לא יועילו למו לא לעזר ולא להועיל כי5כלה באש על עמ לוא יועילו למו לוא לעזרה ולוא תועיל 
> לבשת וגמ לחרפה>כי לבשת וגמ לחרפה
6משא בהמות נגב בארצ צרה וצוקה לביא וליש מהמ אפעה וש6משא בהמות נגב בארצ צרה וציה וצוקה לביא וליש ואינ מ
>רפ מעופפ ישאו על כתפ עירימ חילהמ ועל דבשת גמלימ או>ימ אפעה ושרפ מעופפ ישא על כתפ עירימ חילמ ועל דבשת 
>צרתמ על עמ לא יועילו>גמלימ אוצרותמ על עמ לוא יועילו
7ומצרימ הבל וריק יעזרו לכנ קראתי לזאת רהב המ שבת7ומצרימ הבל וריק יעזרו לכנ קראתי לזואת רהבהמ שבת
8עתה בוא כתבה על לוח אתמ ועל ספר חקה ותהי ליומ אחרו8עתה בוא כותבהא על לוח אותמ ועל ספר חקה ותהי ליומ א
>נ לעד עד עולמ>חרונ לעד עד עולמ
9כי עמ מרי הוא בנימ כחשימ בנימ לא אבו שמוע תורת יהו9כי עמ מרי הוא בנימ כחשימ בנימ לוא אבו לשמוע תורת י
>ה>הוה
10אשר אמרו לראימ לא תראו ולחזימ לא תחזו לנו נכחות דב10אשר אמרו לראימ לוא תראו ולחוזימ לוא תחזו לנו נכחות
>רו לנו חלקות חזו מהתלות> דברו לנו חלקות חזו מתלות
11סורו מני דרכ הטו מני ארח השביתו מפנינו את קדוש ישר11תסירו מנו דרכ הטו מני ארח השביתו מפנינו את קדוש יש
>אל>ראל
12לכנ כה אמר קדוש ישראל יענ מאסכמ בדבר הזה ותבטחו בע12לכנ כה אמר קדוש ישראל יענ מאסכמ בדבר הזה ותבטחו בע
>שק ונלוז ותשענו עליו>ושק ותעלוז ותשענו עליו
13לכנ יהיה לכמ העונ הזה כפרצ נפל נבעה בחומה נשגבה אש13לכנ יהיה לכמ העוונ הזה כפרצ נופל נבעה בחומה נשגבה 
>ר פתאמ לפתע יבוא שברה>אשר פתאמ לפתע יבוא שברה
14ושברה כשבר נבל יוצרימ כתות לא יחמל ולא ימצא במכתתו14ושברה כשבר נבל יוצרימ כתות לוא יחמולו ולוא ימצא במ
> חרש לחתות אש מיקוד ולחשפ מימ מגבא>כתתו חרש לחתות אש מיקוד ולחסופ מימ מגבה
15כי כה אמר אדני יהוה קדוש ישראל בשובה ונחת תושעונ ב15כי כה אמר אדוני יהוה קדוש ישראל בשיבה ונחת תושעונ 
>השקט ובבטחה תהיה גבורתכמ ולא אביתמ>בהשקט ובבטחה תהיה גבורתכמ ולוא אביתמ
16ותאמרו לא כי על סוס ננוס על כנ תנוסונ ועל קל נרכב 16ותאמרו לוא כי אל סוס ננוס עלכנ תנוסונ ואל קל נרכב 
>על כנ יקלו רדפיכמ>עלכנ יקלו רודפיכמ
17אלפ אחד מפני גערת אחד מפני גערת חמשה תנסו עד אמ נו17אלפ אחד מפני גערת אחד ומפני חמשה תנוסו עד אמ נותרת
>תרתמ כתרנ על ראש ההר וכנס על הגבעה>מ כתרנ על ראוש הר וכנס על הגבעה
18ולכנ יחכה יהוה לחננכמ ולכנ ירומ לרחמכמ כי אלהי משפ18ולכנ יחכה יהוה לחונכמ ולכנ ירימ לרחמכמ כיא אלוהי מ
>ט יהוה אשרי כל חוכי לו>שפט יהוה אשרי כול חוכי לו
19כי עמ בציונ ישב בירושלמ בכו לא תבכה חנונ יחנכ לקול19כי עמ בציונ ישב ובירושלמ בכו לוא תבכו חנונ יחונכ י
> זעקכ כשמעתו ענכ>הוה לקול זעקכ כשמועתו ענכ
20ונתנ לכמ אדני לחמ צר ומימ לחצ ולא יכנפ עוד מוריכ ו20ונתנ לכמ אדוני לחמ צר ומי לחצ ולוא יכנפו עוד מוראי
>היו עיניכ ראות את מוריכ>כ והיו עיניכ ראות את מוריכ
21ואזניכ תשמענה דבר מאחריכ לאמר זה הדרכ לכו בו כי תא21ואוזניכ תשמענה דבר מאחריכ לאמור זה הדרכ לכו בו כי 
>מינו וכי תשמאילו>תיאמינו וכי תשמאילו
22וטמאתמ את צפוי פסילי כספכ ואת אפדת מסכת זהבכ תזרמ 22וטמיתמ את צפוי פסילי כספכ ואת אפודות מסכות זהבכ תז
>כמו דוה צא תאמר לו>רמ כמו דוה צא תאמר לו
23ונתנ מטר זרעכ אשר תזרע את האדמה ולחמ תבואת האדמה ו23ונתנ מטר זרעכ אשר תזרע את האדמה ולחמ תבואת האדמה י
>היה דשנ ושמנ ירעה מקניכ ביומ ההוא כר נרחב>היה דשנ ושמנ זרעה מקניכ ביומ ההוא כר נרהב
24והאלפימ והעירימ עבדי האדמה בליל חמיצ יאכלו אשר זרה24והאלפימ והעירימ עובדי האדמה בליל חמצ יאכלו אשר יזר
> ברחת ובמזרה>ה ברחת ובמזרה
25והיה על כל הר גבה ועל כל גבעה נשאה פלגימ יבלי מימ 25והיה על כול הר גבה ועל כול גבעה נשאה פלגימ יובלי מ
>ביומ הרג רב בנפל מגדלימ>ימ ביומ הרג רב בנפל מגדלימ
26והיה אור הלבנה כאור החמה ואור החמה יהיה שבעתימ כאו26והיה אור הלבנה כאור החמה ואור החמה יהיה שבעתימ כאו
>ר שבעת הימימ ביומ חבש יהוה את שבר עמו ומחצ מכתו יר>ר שבעת הימימ ביומ חבוש יהוה את שבר עמו ומחצ מכתו י
>פא>רפא
27הנה שמ יהוה בא ממרחק בער אפו וכבד משאה שפתיו מלאו 27הנה שמ יהוה בא ממרחק בוער אפו וכבד משאה שפתיו מלאו
>זעמ ולשונו כאש אכלת> זעמ ולשונו כאש אוכלת
28ורוחו כנחל שוטפ עד צואר יחצה להנפה גוימ בנפת שוא ו28ורוחו כנחל שוטפ עד צואר וחצה לנפה גואימ בנפת שוא ו
>רסנ מתעה על לחיי עמימ>רסנ מתעה על לוחיי עמימ
29השיר יהיה לכמ כליל התקדש חג ושמחת לבב כהולכ בחליל 29השיר והיה לכמה כליל התקדישו חג ושמחת לבב כהולכ בחל
>לבוא בהר יהוה אל צור ישראל>יל לבוא בהר יהוה אל צור ישראל
30והשמיע יהוה את הוד קולו ונחת זרועו יראה בזעפ אפ ול30השמיע השמיע יהוה את הוד קולו ונחת זרועו יראה בזעפ 
>הב אש אוכלה נפצ וזרמ ואבנ ברד>אפ ולהב אש אוכלה נפצ וזרמ ואבנ ברד
31כי מקול יהוה יחת אשור בשבט יכה31כי מקול יהוה יחת אשור בשבט יאכה
32והיה כל מעבר מטה מוסדה אשר יניח יהוה עליו בתפימ וב32והיה כול מעבר מטה מוסדו אשר יניח יהוה עליו בתפימ ו
>כנרות ובמלחמות תנופה נלחמ במ>בכנרות ובמלחמות תנופה נלחמ בה
33כי ערוכ מאתמול תפתה גמ היא למלכ הוכנ העמיק הרחב מד33כי ערוכ מאתמול תפתח גמ היה למלכ יוכנ הכיני והעמיקי
>רתה אש ועצימ הרבה נשמת יהוה כנחל גפרית בערה בה> הרחיבי מדורתה אש ועצימ הרבה נשמת יהוה כנחל גפרית 
 >בערה בה
t1הוי בנים סוררים נאם יהוה לעשׂות עצה ולא מני ולנסך t1הוי בנימ סוררימ נואמ יהוה לעשות עצה ולוא ממני ולנס
>מסכה ולא רוחי למען ספות חטאת על חטאת >כ מסכה ולוא רוחי למענ ספות חטאת על חטאת
2ההלכים לרדת מצרים ופי לא שׁאלו לעוז במעוז פרעה ולח2ההולכימ לרדת מצרימ ופי לוא שאלו לעוז במעוז פרעוה ו
>סות בצל מצרים >לחסות בצל מצרימ
3והיה לכם מעוז פרעה לבשׁת והחסות בצל מצרים לכלמה 3והיה לכמ מעוז פרעה לבשת והחסות בצל מצרימ לכמה
4כי היו בצען שׂריו ומלאכיו חנס יגיעו 4כי היה בצענ שריו ומלאכיו חנס יגיעו
5כל הבאישׁ על עם לא יועילו למו לא לעזר ולא להועיל כ5כלה באש על עמ לוא יועילו למו לוא לעזרה ולוא תועיל 
>י לבשׁת וגם לחרפה ס >כי לבשת וגמ לחרפה
6משׂא בהמות נגב בארץ צרה וצוקה לביא ולישׁ מהם אפעה 6משא בהמות נגב בארצ צרה וציה וצוקה לביא וליש ואינ מ
>ושׂרף מעופף ישׂאו על כתף עירים חילהם ועל דבשׁת גמל>ימ אפעה ושרפ מעופפ ישא על כתפ עירימ חילמ ועל דבשת 
>ים אוצרתם על עם לא יועילו >גמלימ אוצרותמ על עמ לוא יועילו
7ומצרים הבל וריק יעזרו לכן קראתי לזאת רהב הם שׁבת 7ומצרימ הבל וריק יעזרו לכנ קראתי לזואת רהבהמ שבת
8עתה בוא כתבה על לוח אתם ועל ספר חקה ותהי ליום אחרו8עתה בוא כותבהא על לוח אותמ ועל ספר חקה ותהי ליומ א
>ן לעד עד עולם >חרונ לעד עד עולמ
9כי עם מרי הוא בנים כחשׁים בנים לא אבו שׁמוע תורת י9כי עמ מרי הוא בנימ כחשימ בנימ לוא אבו לשמוע תורת י
>הוה >הוה
10אשׁר אמרו לראים לא תראו ולחזים לא תחזו לנו נכחות ד10אשר אמרו לראימ לוא תראו ולחוזימ לוא תחזו לנו נכחות
>ברו לנו חלקות חזו מהתלות > דברו לנו חלקות חזו מתלות
11סורו מני דרך הטו מני ארח השׁביתו מפנינו את קדושׁ י11תסירו מנו דרכ הטו מני ארח השביתו מפנינו את קדוש יש
>שׂראל ס >ראל
12לכן כה אמר קדושׁ ישׂראל יען מאסכם בדבר הזה ותבטחו 12לכנ כה אמר קדוש ישראל יענ מאסכמ בדבר הזה ותבטחו בע
>בעשׁק ונלוז ותשׁענו עליו >ושק ותעלוז ותשענו עליו
13לכן יהיה לכם העון הזה כפרץ נפל נבעה בחומה נשׂגבה א13לכנ יהיה לכמ העוונ הזה כפרצ נופל נבעה בחומה נשגבה 
>שׁר פתאם לפתע יבוא שׁברה >אשר פתאמ לפתע יבוא שברה
14ושׁברה כשׁבר נבל יוצרים כתות לא יחמל ולא ימצא במכת14ושברה כשבר נבל יוצרימ כתות לוא יחמולו ולוא ימצא במ
>תו חרשׂ לחתות אשׁ מיקוד ולחשׂף מים מגבא פ >כתתו חרש לחתות אש מיקוד ולחסופ מימ מגבה
15כי כה אמר אדני יהוה קדושׁ ישׂראל בשׁובה ונחת תושׁע15כי כה אמר אדוני יהוה קדוש ישראל בשיבה ונחת תושעונ 
>ון בהשׁקט ובבטחה תהיה גבורתכם ולא אביתם >בהשקט ובבטחה תהיה גבורתכמ ולוא אביתמ
16ותאמרו לא כי על סוס ננוס על כן תנוסון ועל קל נרכב 16ותאמרו לוא כי אל סוס ננוס עלכנ תנוסונ ואל קל נרכב 
>על כן יקלו רדפיכם >עלכנ יקלו רודפיכמ
17אלף אחד מפני גערת אחד מפני גערת חמשׁה תנסו עד אם נ17אלפ אחד מפני גערת אחד ומפני חמשה תנוסו עד אמ נותרת
>ותרתם כתרן על ראשׁ ההר וכנס על הגבעה >מ כתרנ על ראוש הר וכנס על הגבעה
18ולכן יחכה יהוה לחננכם ולכן ירום לרחמכם כי אלהי משׁ18ולכנ יחכה יהוה לחונכמ ולכנ ירימ לרחמכמ כיא אלוהי מ
>פט יהוה אשׁרי כל חוכי לו ס >שפט יהוה אשרי כול חוכי לו
19כי עם בציון ישׁב בירושׁלם בכו לא תבכה חנון יחנך לק19כי עמ בציונ ישב ובירושלמ בכו לוא תבכו חנונ יחונכ י
>ול זעקך כשׁמעתו ענך >הוה לקול זעקכ כשמועתו ענכ
20ונתן לכם אדני לחם צר ומים לחץ ולא יכנף עוד מוריך ו20ונתנ לכמ אדוני לחמ צר ומי לחצ ולוא יכנפו עוד מוראי
>היו עיניך ראות את מוריך >כ והיו עיניכ ראות את מוריכ
21ואזניך תשׁמענה דבר מאחריך לאמר זה הדרך לכו בו כי ת21ואוזניכ תשמענה דבר מאחריכ לאמור זה הדרכ לכו בו כי 
>אמינו וכי תשׂמאילו >תיאמינו וכי תשמאילו
22וטמאתם את צפוי פסילי כספך ואת אפדת מסכת זהבך תזרם 22וטמיתמ את צפוי פסילי כספכ ואת אפודות מסכות זהבכ תז
>כמו דוה צא תאמר לו >רמ כמו דוה צא תאמר לו
23ונתן מטר זרעך אשׁר תזרע את האדמה ולחם תבואת האדמה 23ונתנ מטר זרעכ אשר תזרע את האדמה ולחמ תבואת האדמה י
>והיה דשׁן ושׁמן ירעה מקניך ביום ההוא כר נרחב >היה דשנ ושמנ זרעה מקניכ ביומ ההוא כר נרהב
24והאלפים והעירים עבדי האדמה בליל חמיץ יאכלו אשׁר זר24והאלפימ והעירימ עובדי האדמה בליל חמצ יאכלו אשר יזר
>ה ברחת ובמזרה >ה ברחת ובמזרה
25והיה׀ על כל הר גבה ועל כל גבעה נשׂאה פלגים יבלי מי25והיה על כול הר גבה ועל כול גבעה נשאה פלגימ יובלי מ
>ם ביום הרג רב בנפל מגדלים >ימ ביומ הרג רב בנפל מגדלימ
26והיה אור הלבנה כאור החמה ואור החמה יהיה שׁבעתים כא26והיה אור הלבנה כאור החמה ואור החמה יהיה שבעתימ כאו
>ור שׁבעת הימים ביום חבשׁ יהוה את שׁבר עמו ומחץ מכת>ר שבעת הימימ ביומ חבוש יהוה את שבר עמו ומחצ מכתו י
>ו ירפא ס >רפא
27הנה שׁם יהוה בא ממרחק בער אפו וכבד משׂאה שׂפתיו מל27הנה שמ יהוה בא ממרחק בוער אפו וכבד משאה שפתיו מלאו
>או זעם ולשׁונו כאשׁ אכלת > זעמ ולשונו כאש אוכלת
28ורוחו כנחל שׁוטף עד צואר יחצה להנפה גוים בנפת שׁוא28ורוחו כנחל שוטפ עד צואר וחצה לנפה גואימ בנפת שוא ו
> ורסן מתעה על לחיי עמים >רסנ מתעה על לוחיי עמימ
29השׁיר יהיה לכם כליל התקדשׁ חג ושׂמחת לבב כהולך בחל29השיר והיה לכמה כליל התקדישו חג ושמחת לבב כהולכ בחל
>יל לבוא בהר יהוה אל צור ישׂראל >יל לבוא בהר יהוה אל צור ישראל
30והשׁמיע יהוה את הוד קולו ונחת זרועו יראה בזעף אף ו30השמיע השמיע יהוה את הוד קולו ונחת זרועו יראה בזעפ 
>להב אשׁ אוכלה נפץ וזרם ואבן ברד >אפ ולהב אש אוכלה נפצ וזרמ ואבנ ברד
31כי מקול יהוה יחת אשׁור בשׁבט יכה 31כי מקול יהוה יחת אשור בשבט יאכה
32והיה כל מעבר מטה מוסדה אשׁר יניח יהוה עליו בתפים ו32והיה כול מעבר מטה מוסדו אשר יניח יהוה עליו בתפימ ו
>בכנרות ובמלחמות תנופה נלחם בה >בכנרות ובמלחמות תנופה נלחמ בה
33כי ערוך מאתמול תפתה גם הוא למלך הוכן העמיק הרחב מד33כי ערוכ מאתמול תפתח גמ היה למלכ יוכנ הכיני והעמיקי
>רתה אשׁ ועצים הרבה נשׁמת יהוה כנחל גפרית בערה בה ס> הרחיבי מדורתה אש ועצימ הרבה נשמת יהוה כנחל גפרית 
> >בערה בה
- - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + +

Isaiah 31 MT
Isaiah 31 1QIsaa
n1הוי הירדימ מצרימ לעזרה על סוסימ ישענו ויבטחו על רכn1הוי היורדימ למצרימ לעזרה על סוסימ ישענו ויבטחו על 
>ב כי רב ועל פרשימ כי עצמו מאד ולא שעו על קדוש ישרא>הרכב כי רב ועל פרשימ כי עצמו מאדה ולוא שעו אל קדוש
>ל ואת יהוה לא דרשו> ישראל ואת יהוה לוא דרשו
2וגמ הוא חכמ ויבא רע ואת דבריו לא הסיר וקמ על בית מ2וגמ הוא חכמ ויביא רע ואת דבריו לוא הסיר וקמ על בית
>רעימ ועל עזרת פעלי אונ> מרעימ ועל עזרת פועלי אונ
3ומצרימ אדמ ולא אל וסוסיהמ בשר ולא רוח ויהוה יטה יד3ומצרימ אדמ ולוא אל וסוסיהמה בשר ולוא ריה ויהוה יטה
>ו וכשל עוזר ונפל עזר ויחדו כלמ יכליונ> ידו וכשל עוזר ונפל עזר יחדו כולמ יכליונ
4כי כה אמר יהוה אלי כאשר יהגה האריה והכפיר על טרפו 4כי כה אמר יהוה אלי כאשר יהגה האריה והכפיר על טרפיו
>אשר יקרא עליו מלא רעימ מקולמ לא יחת ומהמונמ לא יענ> אשר יקרא אליו מלאו רועימ מקולמ לוא יחת ומהמונמ לו
>ה כנ ירד יהוה צבאות לצבא על הר ציונ ועל גבעתה>א יענה כנ ירד יהוה צבאות לצבא על הר ציונ ועל גבעתה
5כצפרימ עפות כנ יגנ יהוה צבאות על ירושלמ גנונ והציל5כצפורימ עפות כנ יגנ יהוה צבאות על ירושלמ גנונ והצי
> פסח והמליט>ל ופסח והפליט
6שובו לאשר העמיקו סרה בני ישראל6שוביו לאשר לאשר העמיקו סרה בני ישראל
7כי ביומ ההוא ימאסונ איש אלילי כספו ואלילי זהבו אשר7כי ביומ ההוא ימאסונ איש אלילי כספו ואלילי זהבו אשר
> עשו לכמ ידיכמ חטא> עשו לכמ ידיכמ חטא
t8ונפל אשור בחרב לא איש וחרב לא אדמ תאכלנו ונס לו מפt8ונפל אשור בחרב לוא איש וחרב לואאדמ תאכולנו ונס ולו
>ני חרב ובחוריו למס יהיו>א מפני חרב ובחוריו למס יהיו
9וסלעו ממגור יעבור וחתו מנס שריו נאמ יהוה אשר אור ל9וסלעו ממגור יעבור וחתו מנוס שריו נואמ יהוה אשר אור
>ו בציונ ותנור לו בירושלמ> לו בציונ ותנור לו בירושלמ
t1הוי הירדים מצרים לעזרה על סוסים ישׁענו ויבטחו על רt1הוי היורדימ למצרימ לעזרה על סוסימ ישענו ויבטחו על 
>כב כי רב ועל פרשׁים כי עצמו מאד ולא שׁעו על קדושׁ >הרכב כי רב ועל פרשימ כי עצמו מאדה ולוא שעו אל קדוש
>ישׂראל ואת יהוה לא דרשׁו > ישראל ואת יהוה לוא דרשו
2וגם הוא חכם ויבא רע ואת דבריו לא הסיר וקם על בית מ2וגמ הוא חכמ ויביא רע ואת דבריו לוא הסיר וקמ על בית
>רעים ועל עזרת פעלי און > מרעימ ועל עזרת פועלי אונ
3ומצרים אדם ולא אל וסוסיהם בשׂר ולא רוח ויהוה יטה י3ומצרימ אדמ ולוא אל וסוסיהמה בשר ולוא ריה ויהוה יטה
>דו וכשׁל עוזר ונפל עזר ויחדו כלם יכליון ס > ידו וכשל עוזר ונפל עזר יחדו כולמ יכליונ
4כי כה אמר יהוה׀ אלי כאשׁר יהגה האריה והכפיר על טרפ4כי כה אמר יהוה אלי כאשר יהגה האריה והכפיר על טרפיו
>ו אשׁר יקרא עליו מלא רעים מקולם לא יחת ומהמונם לא > אשר יקרא אליו מלאו רועימ מקולמ לוא יחת ומהמונמ לו
>יענה כן ירד יהוה צבאות לצבא על הר ציון ועל גבעתה >א יענה כנ ירד יהוה צבאות לצבא על הר ציונ ועל גבעתה
5כצפרים עפות כן יגן יהוה צבאות על ירושׁלם גנון והצי5כצפורימ עפות כנ יגנ יהוה צבאות על ירושלמ גנונ והצי
>ל פסח והמליט >ל ופסח והפליט
6שׁובו לאשׁר העמיקו סרה בני ישׂראל 6שוביו לאשר לאשר העמיקו סרה בני ישראל
7כי ביום ההוא ימאסון אישׁ אלילי כספו ואלילי זהבו אש7כי ביומ ההוא ימאסונ איש אלילי כספו ואלילי זהבו אשר
>ׁר עשׂו לכם ידיכם חטא > עשו לכמ ידיכמ חטא
8ונפל אשׁור בחרב לא אישׁ וחרב לא אדם תאכלנו ונס לו 8ונפל אשור בחרב לוא איש וחרב לואאדמ תאכולנו ונס ולו
>מפני חרב ובחוריו למס יהיו >א מפני חרב ובחוריו למס יהיו
9וסלעו ממגור יעבור וחתו מנס שׂריו נאם יהוה אשׁר אור9וסלעו ממגור יעבור וחתו מנוס שריו נואמ יהוה אשר אור
> לו בציון ותנור לו בירושׁלם ס > לו בציונ ותנור לו בירושלמ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 32 MT
Isaiah 32 1QIsaa
n1הנ לצדק ימלכ מלכ ולשרימ למשפט ישרוn1הנה לצדק ימלוכ מלכ ולשרימ למשפט ישרו
2והיה איש כמחבא רוח וסתר זרמ כפלגי מימ בציונ כצל סל2והיה איש כמחבה רוח וסתר זרמ כפלגי מימ בציינ בצל סל
>ע כבד בארצ עיפה>ע כבד בארצ עיפה
3ולא תשעינה עיני ראימ ואזני שמעימ תקשבנה3ולוא תשענה עיני ראימ ואזני שומעימ תקשבנה
4ולבב נמהרימ יבינ לדעת ולשונ עלגימ תמהר לדבר צחות4ולבב נמהרימ יבינ לדעת ולשונ עלגימ תמהר לדבר צוחות
5לא יקרא עוד לנבל נדיב ולכילי לא יאמר שוע5לוא יקראו עוד לנבל נדיב ולכילי לוא יואמר שוע
6כי נבל נבלה ידבר ולבו יעשה אונ לעשות חנפ ולדבר אל 6כי נבל נבלה ידבר ולבו חושב אונ לעשות חנפ ולדבר אל 
>יהוה תועה להריק נפש רעב ומשקה צמא יחסיר>יהוה תועה להריק נפש רעב ומשקה צמא יחסיר
7וכלי כליו רעימ הוא זמות יעצ לחבל עניימ באמרי שקר ו7וכילי כליו רעימ והוא זמות יעצ לחבל עניימ באמרי שקר
>בדבר אביונ משפט> ובדבר אביונימ משפט
8ונדיב נדיבות יעצ והוא על נדיבות יקומ8ונדיב נדיבות יעצ והוא על נדיבות יקומ
n9נשימ שאננות קמנה שמענה קולי בנות בטחות האזנה אמרתיn9נשימ שאננות קומנה שמענה קולי בנות בוטחות האזינה אמ
 >רתי
10ימימ על שנה תרגזנה בטחות כי כלה בציר אספ בלי יבוא10ימימ על שנה תרגזנה הבוטחות כי כלה בציר אספ בל יבוא
11חרדו שאננות רגזה בטחות פשטה וערה וחגורה על חלצימ11חרדו שאננות רגזה בוטחות פשטה יערו חגרנה וספדנה על 
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12על שדימ ספדימ על שדי חמד על גפנ פריה12על שדימ סופדימ על שדי חמדה על גפנ פריה
13על אדמת עמי קוצ שמיר תעלה כי על כל בתי משוש קריה ע13על אדמת עמי קוצ ושמיר תעלה כי על כול בתי משוש קריה
>ליזה> עליזה
14כי ארמונ נטש המונ עיר עזב עפל ובחנ היה בעד מערות ע14כי ארמונ נטש המונ עיר עזב עופל ובחנ היה בעד מערות 
>ד עולמ משוש פראימ מרעה עדרימ>עד עולמ משוש פראימ מרעה לעדרימ
15עד יערה עלינו רוח ממרומ והיה מדבר לכרמל והכרמל ליע15עד יערה עלינו רוח ממרומ והיה מדבר לכרמל וכרמל ליער
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16ושכנ במדבר משפט וצדקה בכרמל תשב16ושכנ במדבר משפט וצדקה בכרמל תשב
t17והיה מעשה הצדקה שלומ ועבדת הצדקה השקט ובטח עד עולמt17והיה מעשה הצדקה לשלומ ועבודת הצדקה השקט ובטח עד עו
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18וישב עמי בנוה שלומ ובמשכנות מבטחימ ובמנוחת שאננות18וישב עמי בנוה שלומ ובמשכנות מבטחימ ובמנוחות שאננות
19וברד ברדת היער ובשפלה תשפל העיר19וברד ברדת היער ובשפלה תשפל היער
20אשריכמ זרעי על כל מימ משלחי רגל השור והחמור20אשריכמה זורעי על כול מימ ומשלחי רגל השור והחמור
t1הן לצדק ימלך מלך ולשׂרים למשׁפט ישׂרו t1הנה לצדק ימלוכ מלכ ולשרימ למשפט ישרו
2והיה אישׁ כמחבא רוח וסתר זרם כפלגי מים בציון כצל ס2והיה איש כמחבה רוח וסתר זרמ כפלגי מימ בציינ בצל סל
>לע כבד בארץ עיפה >ע כבד בארצ עיפה
3ולא תשׁעינה עיני ראים ואזני שׁמעים תקשׁבנה 3ולוא תשענה עיני ראימ ואזני שומעימ תקשבנה
4ולבב נמהרים יבין לדעת ולשׁון עלגים תמהר לדבר צחות 4ולבב נמהרימ יבינ לדעת ולשונ עלגימ תמהר לדבר צוחות
5לא יקרא עוד לנבל נדיב ולכילי לא יאמר שׁוע 5לוא יקראו עוד לנבל נדיב ולכילי לוא יואמר שוע
6כי נבל נבלה ידבר ולבו יעשׂה און לעשׂות חנף ולדבר א6כי נבל נבלה ידבר ולבו חושב אונ לעשות חנפ ולדבר אל 
>ל יהוה תועה להריק נפשׁ רעב ומשׁקה צמא יחסיר >יהוה תועה להריק נפש רעב ומשקה צמא יחסיר
7וכלי כליו רעים הוא זמות יעץ לחבל ענוים באמרי שׁקר 7וכילי כליו רעימ והוא זמות יעצ לחבל עניימ באמרי שקר
>ובדבר אביון משׁפט > ובדבר אביונימ משפט
8ונדיב נדיבות יעץ והוא על נדיבות יקום פ 8ונדיב נדיבות יעצ והוא על נדיבות יקומ
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10ימים על שׁנה תרגזנה בטחות כי כלה בציר אסף בלי יבוא10ימימ על שנה תרגזנה הבוטחות כי כלה בציר אספ בל יבוא
>  
11חרדו שׁאננות רגזה בטחות פשׁטה וערה וחגורה על חלצים11חרדו שאננות רגזה בוטחות פשטה יערו חגרנה וספדנה על 
> >החלצימ
12על שׁדים ספדים על שׂדי חמד על גפן פריה 12על שדימ סופדימ על שדי חמדה על גפנ פריה
13על אדמת עמי קוץ שׁמיר תעלה כי על כל בתי משׂושׂ קרי13על אדמת עמי קוצ ושמיר תעלה כי על כול בתי משוש קריה
>ה עליזה > עליזה
14כי ארמון נטשׁ המון עיר עזב עפל ובחן היה בעד מערות 14כי ארמונ נטש המונ עיר עזב עופל ובחנ היה בעד מערות 
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15עד יערה עלינו רוח ממרום והיה מדבר לכרמל וכרמל ליער15עד יערה עלינו רוח ממרומ והיה מדבר לכרמל וכרמל ליער
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16ושׁכן במדבר משׁפט וצדקה בכרמל תשׁב 16ושכנ במדבר משפט וצדקה בכרמל תשב
17והיה מעשׂה הצדקה שׁלום ועבדת הצדקה השׁקט ובטח עד ע17והיה מעשה הצדקה לשלומ ועבודת הצדקה השקט ובטח עד עו
>ולם >למ
18וישׁב עמי בנוה שׁלום ובמשׁכנות מבטחים ובמנוחת שׁאנ18וישב עמי בנוה שלומ ובמשכנות מבטחימ ובמנוחות שאננות
>נות  
19וברד ברדת היער ובשׁפלה תשׁפל העיר 19וברד ברדת היער ובשפלה תשפל היער
20אשׁריכם זרעי על כל מים משׁלחי רגל השׁור והחמור ס 20אשריכמה זורעי על כול מימ ומשלחי רגל השור והחמור
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Isaiah 33 1QIsaa
n1הוי שודד ואתה לא שדוד ובוגד ולא בגדו בו כהתמכ שודדn1הוי שודד ואתה לוא שדוד ובוגד ולוא בגדו בו כהתמככ ש
> תושד כנלתכ לבגד יבגדו בכ>ודד תושד ככלותכ לבגוד יבגודו בכ
2יהוה חננו לכ קוינו היה זרעמ לבקרימ אפ ישועתנו בעת 2יהוה חוננו לכה קוינו והיה זרעמ לבקרימ אפ הושעתנו ב
>צרה>עת צרה
3מקול המונ נדדו עמימ מרוממתכ נפצו גוימ3מקול המונ נדדו עמימ מדממתכ נפצו גוימ
4ואספ שללכמ אספ החסיל כמשק גבימ שוקק בו4ואספ שללכמ אספ החסיל משק גבימ שקק בו
5נשגב יהוה כי שכנ מרומ מלא ציונ משפט וצדקה5נשגב יהוה כי שכנ מרומ מלא ציונ משפט וצדקה
n6והיה אמונת עתיכ חסנ ישועת חכמת ודעת יראת יהוה היא n6יהיה אמונת עתיכ חסנ וישועות חכמת ודעת יראת יהוה הי
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7הנ אראלמ צעקו חצה מלאכי שלומ מר יבכיונ7הנ אראלמ זעקו חצה מלאכי שלומ מר יבכוונ
8נשמו מסלות שבת עבר ארח הפר ברית מאס ערימ לא חשב אנ8נשמו מסלות שבת עובר ארח הפר ברית מאס עדימ לוא חשב 
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9אבל אמללה ארצ החפיר לבנונ קמל היה השרונ כערבה ונער9אבל אמללה הארצ חפור לבנונ קמל היה השרונ כערבה נוער
> בשנ וכרמל> בשנ וכרמל
10עתה אקומ יאמר יהוה עתה ארוממ עתה אנשא10עתה אקומ אמר יהוה עתה אתרוממ עתה הנשא
11תהרו חשש תלדו קש רוחכמ אש תאכלכמ11תהרו חששה תלדו קש רוחכמ אש תאכלכמ
12והיו עמימ משרפות שיד קוצימ כסוחימ באש יצתו12ויהיו עמימ משרפות שיד קוצימ כסוחימ באש יצתו
13שמעו רחוקימ אשר עשיתי ודעו קרובימ גברתי13שמעו רחוקימ אשר עשיתי ידעו קרובימ גבורתי
14פחדו בציונ חטאימ אחזה רעדה חנפימ מי יגור לנו אש או14פחדו בציונ חטאימ אחזה רעדה חנפימ מי יגור לנו אש או
>כלה מי יגור לנו מוקדי עולמ>כלה מי יגור לנו מוקדי עולמ
t15הלכ צדקות ודבר מישרימ מאס בבצע מעשקות נער כפיו מתמt15הולכ צדקות וידבר מישרימ מאס בבצע מעשקות נער כפו מת
>כ בשחד אטמ אזנו משמע דמימ ועצמ עיניו מראות ברע>מכ בשחוד אוטמ אוזניו משמוע דמימ יעצמ עיניו מראות ב
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16הוא מרומימ ישכנ מצדות סלעימ משגבו לחמו נתנ מימיו נ16הוא מרומימ ישכונ מצדות סלעימ משגבו לחמו נתנ מימיו 
>אמנימ>נאמנימ
17מלכ ביפיו תחזינה עיניכ תראינה ארצ מרחקימ17מלכ ביופיו תחזיונ עיניכ תראינה ארצ מרחקימ
18לבכ יהגה אימה איה ספר איה שקל איה ספר את המגדלימ18לבכה יהגה אימה איה ספר איה שקל איה ספר את המגדלימ
19את עמ נועז לא תראה עמ עמקי שפה משמוע נלעג לשונ אינ19את עמ נועז לוא תיראו עמ עמקי שפה משמיע נלעג לשונ א
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20חזה ציונ קרית מועדנו עיניכ תראינה ירושלמ נוה שאננ 20חזה ציונ קרית מועדינו עיניכ תראינה ירושלמ נוה שאננ
>אהל בל יצענ בל יסע יתדתיו לנצח וכל חבליו בל ינתקו> אהל בל יצענ בל יסע יתדותו לנצח וכול חבליו בל ינתק
 >ו
21כי אמ שמ אדיר יהוה לנו מקומ נהרימ יארימ רחבי ידימ 21כי אמ שמ אדיר יהוה לנו מקומ נהרות יארימ רחבי ידימ 
>בל תלכ בו אני שיט וצי אדיר לא יעברנו>בל תלב בו אני שט וצי אדיר לוא יעברנו
22כי יהוה שפטנו יהוה מחקקנו יהוה מלכנו הוא יושיענו22כי יהוה שופטנו ויהוה מחוקקנו ויהוה מלכנו והוא יושי
 >ענו
23נטשו חבליכ בל יחזקו כנ תרנמ בל פרשו נס אז חלק עד ש23נטשו חבליכ בל יחזקו כי תרנמ בל פרש נס אז חלק עד של
>לל מרבה פסחימ בזזו בז>ל מרובה פסחימ בזזו בז
24ובל יאמר שכנ חליתי העמ הישב בה נשא עונ24ובל יואמר שוכנ חליתי העמ היושב בה נשא עוונ
t1הוי שׁודד ואתה לא שׁדוד ובוגד ולא בגדו בו כהתמך שׁt1הוי שודד ואתה לוא שדוד ובוגד ולוא בגדו בו כהתמככ ש
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2יהוה חננו לך קוינו היה זרעם לבקרים אף ישׁועתנו בעת2יהוה חוננו לכה קוינו והיה זרעמ לבקרימ אפ הושעתנו ב
> צרה >עת צרה
3מקול המון נדדו עמים מרוממתך נפצו גוים 3מקול המונ נדדו עמימ מדממתכ נפצו גוימ
4ואסף שׁללכם אסף החסיל כמשׁק גבים שׁוקק בו 4ואספ שללכמ אספ החסיל משק גבימ שקק בו
5נשׂגב יהוה כי שׁכן מרום מלא ציון משׁפט וצדקה 5נשגב יהוה כי שכנ מרומ מלא ציונ משפט וצדקה
6והיה אמונת עתיך חסן ישׁועת חכמת ודעת יראת יהוה היא6יהיה אמונת עתיכ חסנ וישועות חכמת ודעת יראת יהוה הי
> אוצרו ס >א אוצרו
7הן אראלם צעקו חצה מלאכי שׁלום מר יבכיון 7הנ אראלמ זעקו חצה מלאכי שלומ מר יבכוונ
8נשׁמו מסלות שׁבת עבר ארח הפר ברית מאס ערים לא חשׁב8נשמו מסלות שבת עובר ארח הפר ברית מאס עדימ לוא חשב 
> אנושׁ >אנוש
9אבל אמללה ארץ החפיר לבנון קמל היה השׁרון כערבה ונע9אבל אמללה הארצ חפור לבנונ קמל היה השרונ כערבה נוער
>ר בשׁן וכרמל > בשנ וכרמל
10עתה אקום יאמר יהוה עתה ארומם עתה אנשׂא 10עתה אקומ אמר יהוה עתה אתרוממ עתה הנשא
11תהרו חשׁשׁ תלדו קשׁ רוחכם אשׁ תאכלכם 11תהרו חששה תלדו קש רוחכמ אש תאכלכמ
12והיו עמים משׂרפות שׂיד קוצים כסוחים באשׁ יצתו ס 12ויהיו עמימ משרפות שיד קוצימ כסוחימ באש יצתו
13שׁמעו רחוקים אשׁר עשׂיתי ודעו קרובים גברתי 13שמעו רחוקימ אשר עשיתי ידעו קרובימ גבורתי
14פחדו בציון חטאים אחזה רעדה חנפים מי׀ יגור לנו אשׁ 14פחדו בציונ חטאימ אחזה רעדה חנפימ מי יגור לנו אש או
>אוכלה מי יגור לנו מוקדי עולם >כלה מי יגור לנו מוקדי עולמ
15הלך צדקות ודבר מישׁרים מאס בבצע מעשׁקות נער כפיו מ15הולכ צדקות וידבר מישרימ מאס בבצע מעשקות נער כפו מת
>תמך בשׁחד אטם אזנו משׁמע דמים ועצם עיניו מראות ברע>מכ בשחוד אוטמ אוזניו משמוע דמימ יעצמ עיניו מראות ב
> >רע
16הוא מרומים ישׁכן מצדות סלעים משׂגבו לחמו נתן מימיו16הוא מרומימ ישכונ מצדות סלעימ משגבו לחמו נתנ מימיו 
> נאמנים >נאמנימ
17מלך ביפיו תחזינה עיניך תראינה ארץ מרחקים 17מלכ ביופיו תחזיונ עיניכ תראינה ארצ מרחקימ
18לבך יהגה אימה איה ספר איה שׁקל איה ספר את המגדלים 18לבכה יהגה אימה איה ספר איה שקל איה ספר את המגדלימ
19את עם נועז לא תראה עם עמקי שׂפה משׁמוע נלעג לשׁון 19את עמ נועז לוא תיראו עמ עמקי שפה משמיע נלעג לשונ א
>אין בינה >ינ בינה
20חזה ציון קרית מועדנו עיניך תראינה ירושׁלם נוה שׁאנ20חזה ציונ קרית מועדינו עיניכ תראינה ירושלמ נוה שאננ
>ן אהל בל יצען בל יסע יתדתיו לנצח וכל חבליו בל ינתק> אהל בל יצענ בל יסע יתדותו לנצח וכול חבליו בל ינתק
>ו >ו
21כי אם שׁם אדיר יהוה לנו מקום נהרים יארים רחבי ידים21כי אמ שמ אדיר יהוה לנו מקומ נהרות יארימ רחבי ידימ 
> בל תלך בו אני שׁיט וצי אדיר לא יעברנו >בל תלב בו אני שט וצי אדיר לוא יעברנו
22כי יהוה שׁפטנו יהוה מחקקנו יהוה מלכנו הוא יושׁיענו22כי יהוה שופטנו ויהוה מחוקקנו ויהוה מלכנו והוא יושי
> >ענו
23נטשׁו חבליך בל יחזקו כן תרנם בל פרשׂו נס אז חלק עד23נטשו חבליכ בל יחזקו כי תרנמ בל פרש נס אז חלק עד של
> שׁלל מרבה פסחים בזזו בז >ל מרובה פסחימ בזזו בז
24ובל יאמר שׁכן חליתי העם הישׁב בה נשׂא עון 24ובל יואמר שוכנ חליתי העמ היושב בה נשא עוונ
- - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + +

Isaiah 34 MT
Isaiah 34 1QIsaa
t1קרבו גוימ לשמע ולאמימ הקשיבו תשמע הארצ ומלאה תבל וt1קרובו גואימ לשמוע ולאומימ הקשיבו תשמע הארצ ומלואה 
>כל צאצאיה>תבל וכול צאצאיה
2כי קצפ ליהוה על כל הגוימ וחמה על כל צבאמ החריממ נת2כיא קצפ ליהוה על כול הגואימ וחמה על כול צבאמ החרימ
>נמ לטבח>מ ונתנמ לטבוח
3וחלליהמ ישלכו ופגריהמ יעלה באשמ ונמסו הרימ מדממ3וחלליהמ יושלכו ופגריהמה יעלה באושמה ונמסו ההרימ מד
 >ממ
4ונמקו כל צבא השמימ ונגלו כספר השמימ וכל צבאמ יבול 4והעמקימ יתבקעו וכול צבא השמימ יפולו ונגלו כספר השמ
>כנבל עלה מגפנ וכנבלת מתאנה>ימ וכול צבאמ יבול כנובל עלה מגופנ וכנובלת מנ תאנה
5כי רותה בשמימ חרבי הנה על אדומ תרד ועל עמ חרמי למש5כיא תראה בשמימ חרביא הנה על אדומ תרד ועל עמ חרמי ל
>פט>משפט
6חרב ליהוה מלאה דמ הדשנה מחלב מדמ כרימ ועתודימ מחלב6חרב ליהוה מלאה דמ הדשנה מחלב מדמ כרימ ועתודימ מחלב
> כליות אילימ כי זבח ליהוה בבצרה וטבח גדול בארצ אדו> כלאיות אילימ כיא זבח ליהוה בבוצרה וטבח גדול בארצ 
>מ>אדומ
7וירדו ראמימ עממ ופרימ עמ אבירימ ורותה ארצמ מדמ ועפ7וירדו ראמימ עממ ופרימ עמ אבירימ ורותה ארצמה מדמ וע
>רמ מחלב ידשנ>פרמ מחלב ידשנ
8כי יומ נקמ ליהוה שנת שלומימ לריב ציונ8כיא יומ נקמ ליהוה שנת שלומימ לריב ציונ
t1קרבו גוים לשׁמע ולאמים הקשׁיבו תשׁמע הארץ ומלאה תבt1קרובו גואימ לשמוע ולאומימ הקשיבו תשמע הארצ ומלואה 
>ל וכל צאצאיה >תבל וכול צאצאיה
2כי קצף ליהוה על כל הגוים וחמה על כל צבאם החרימם נת2כיא קצפ ליהוה על כול הגואימ וחמה על כול צבאמ החרימ
>נם לטבח >מ ונתנמ לטבוח
3וחלליהם ישׁלכו ופגריהם יעלה באשׁם ונמסו הרים מדמם 3וחלליהמ יושלכו ופגריהמה יעלה באושמה ונמסו ההרימ מד
 >ממ
4ונמקו כל צבא השׁמים ונגלו כספר השׁמים וכל צבאם יבו4והעמקימ יתבקעו וכול צבא השמימ יפולו ונגלו כספר השמ
>ל כנבל עלה מגפן וכנבלת מתאנה >ימ וכול צבאמ יבול כנובל עלה מגופנ וכנובלת מנ תאנה
5כי רותה בשׁמים חרבי הנה על אדום תרד ועל עם חרמי למ5כיא תראה בשמימ חרביא הנה על אדומ תרד ועל עמ חרמי ל
>שׁפט >משפט
6חרב ליהוה מלאה דם הדשׁנה מחלב מדם כרים ועתודים מחל6חרב ליהוה מלאה דמ הדשנה מחלב מדמ כרימ ועתודימ מחלב
>ב כליות אילים כי זבח ליהוה בבצרה וטבח גדול בארץ אד> כלאיות אילימ כיא זבח ליהוה בבוצרה וטבח גדול בארצ 
>ום >אדומ
7וירדו ראמים עמם ופרים עם אבירים ורותה ארצם מדם ועפ7וירדו ראמימ עממ ופרימ עמ אבירימ ורותה ארצמה מדמ וע
>רם מחלב ידשׁן >פרמ מחלב ידשנ
8כי יום נקם ליהוה שׁנת שׁלומים לריב ציון 8כיא יומ נקמ ליהוה שנת שלומימ לריב ציונ
9ונהפכו נחליה לזפת ועפרה לגפרית והיתה ארצה לזפת בער9ונהפכו נחליה לזפת ועפרה לגפרית והייתה ארצה לזפת וב
>ה>ערה
10לילה ויוממ לא תכבה לעולמ יעלה עשנה מדור לדור תחרב 10לילה ויוממ ולוא תכובה לעולמ ועלה עשנה מדור לדור ות
>לנצח נצחימ אינ עבר בה>חרב לנצח נצחימ ואינ עובר בהא
11וירשוה קאת וקפוד וינשופ וערב ישכנו בה ונטה עליה קו11וירשוהה קאת וקפוד וינשופ ועורב ישכונו בהא ונטא עלי
> תהו ואבני בהו>הא קו ותהו ואבני בהו
12חריה ואינ שמ מלוכה יקראו וכל שריה יהיו אפס12וחריה ואינ שמה מלוכה יקראו וכול שריה יהיו אפס
13ועלתה ארמנתיה סירימ קמוש וחוח במבצריה והיתה נוה תנ13ועלתה ארמונותיה סירימ קמוש וחוח במבצריהא והייתה נו
>ימ חציר לבנות יענה>ה תנימ חצר לבנות יענה
14ופגשו ציימ את איימ ושעיר על רעהו יקרא אכ שמ הרגיעה14ופגשו ציימ את אייאמימ ושעיר על רעהו יקרא אכ שמה יר
> לילית ומצאה לה מנוח>גיעו ליליות ומצאו להמה מנוח
15שמה קננה קפוז ותמלט ובקעה ודגרה בצלה אכ שמ נקבצו ד15שמה קננה קופד ותמלט ובקעה ודגרה בצלה אכ אכ שמה נקב
>יות אשה רעותה>צו דוות אשה רעותה
16דרשו מעל ספר יהוה וקראו אחת מהנה לא נעדרה אשה רעות16דרושו מעל ספר יהוה וקראו ואחת לוא נעדרה אשה רעותה 
>ה לא פקדו כי פי הוא צוה ורוחו הוא קבצנ>כיא פיהו הוא צוה ורוחהו היאה קבצמ
17והוא הפיל להנ גורל וידו חלקתה להמ בקו עד עולמ יירש17והואה הפיל להנה גורל וידיו חלקת להנה בקו עד עולמ י
>וה לדור ודור ישכנו בה>רשוה א לדור ודור ישכנו בה
>ה >ערה
10לילה ויומם לא תכבה לעולם יעלה עשׁנה מדור לדור תחרב10לילה ויוממ ולוא תכובה לעולמ ועלה עשנה מדור לדור ות
> לנצח נצחים אין עבר בה >חרב לנצח נצחימ ואינ עובר בהא
11וירשׁוה קאת וקפוד וינשׁוף וערב ישׁכנו בה ונטה עליה11וירשוהה קאת וקפוד וינשופ ועורב ישכונו בהא ונטא עלי
> קו תהו ואבני בהו >הא קו ותהו ואבני בהו
12חריה ואין שׁם מלוכה יקראו וכל שׂריה יהיו אפס 12וחריה ואינ שמה מלוכה יקראו וכול שריה יהיו אפס
13ועלתה ארמנתיה סירים קמושׂ וחוח במבצריה והיתה נוה ת13ועלתה ארמונותיה סירימ קמוש וחוח במבצריהא והייתה נו
>נים חציר לבנות יענה >ה תנימ חצר לבנות יענה
14ופגשׁו ציים את איים ושׂעיר על רעהו יקרא אך שׁם הרג14ופגשו ציימ את אייאמימ ושעיר על רעהו יקרא אכ שמה יר
>יעה לילית ומצאה לה מנוח >גיעו ליליות ומצאו להמה מנוח
15שׁמה קננה קפוז ותמלט ובקעה ודגרה בצלה אך שׁם נקבצו15שמה קננה קופד ותמלט ובקעה ודגרה בצלה אכ אכ שמה נקב
> דיות אשׁה רעותה >צו דוות אשה רעותה
16דרשׁו מעל ספר יהוה וקראו אחת מהנה לא נעדרה אשׁה רע16דרושו מעל ספר יהוה וקראו ואחת לוא נעדרה אשה רעותה 
>ותה לא פקדו כי פי הוא צוה ורוחו הוא קבצן >כיא פיהו הוא צוה ורוחהו היאה קבצמ
17והוא הפיל להן גורל וידו חלקתה להם בקו עד עולם יירש17והואה הפיל להנה גורל וידיו חלקת להנה בקו עד עולמ י
>ׁוה לדור ודור ישׁכנו בה ס >רשוה א לדור ודור ישכנו בה
- - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + +

Isaiah 35 MT
Isaiah 35 1QIsaa
f1יששומ מדבר וציה ותגל ערבה ותפרח כחבצלתf1יששומ מדבר וציה ותגל ערבה ותפרח כחבצלת
t2פרח תפרח ותגל אפ גילת ורננ כבוד הלבנונ נתנ לה הדר t2פרח תפרח ותגל אפ גילת ורננ כבוד לבנונ ב נתנ לה הדר
>הכרמל והשרונ המה יראו כבוד יהוה הדר אלהינו> הכרמל והשרונ המה יראו כבוד יהוה הדר אלהינו
3חזקו ידימ רפות וברכימ כשלות אמצו3חזקו ידימ רפות וברכימ כושלות אמצו
4אמרו לנמהרי לב חזקו אל תיראו הנה אלהיכמ נקמ יבוא ג4אמרו לנמהרי לב חזקו אל תיראו הנה אלוהכמה נקמ יביא 
>מול אלהימ הוא יבוא וישעכמ>גמול אלוהימ הואה יביא ויושעכמה
5אז תפקחנה עיני עורימ ואזני חרשימ תפתחנה5אז תפקחנה עיני עורימ ואוזני חרשימ תפתחנה
6אז ידלג כאיל פסח ותרנ לשונ אלמ כי נבקעו במדבר מימ 6אז ידלג כאיאל פסח ותרונ לשונ אלמ כיא נבקעו במדבר מ
>ונחלימ בערבה>ימ ונחלימ בערבה ילכו
7והיה השרב לאגמ וצמאונ למבועי מימ בנוה תנימ רבצה חצ7והיה השרב לאגמ וצמאונ למבועי מימ בנוה תנימ רבצ חצי
>יר לקנה וגמא>ר לקנה וגומא
8והיה שמ מסלול ודרכ ודרכ הקדש יקרא לה לא יעברנו טמא8יהיה שמה שמה מסולל ודרכ הקודש יקראו לה לוא יעובורנ
> והוא למו הלכ דרכ ואוילימ לא יתעו>ה הואה ולמי הולכ דרכ ואוילימ לוא יתעו
9לא יהיה שמ אריה ופריצ חיות בל יעלנה לא תמצא שמ והל9לוא יהיה שמה אריה ופריצ חיות בל לוא יעלנה ולוא ימצ
>כו גאולימ>א שמ והלכו גאולימ
10ופדויי יהוה ישבונ ובאו ציונ ברנה ושמחת עולמ על ראש10ופדויי יהוה ישובו ובאו ציונ ברונה ושמחת עולמ על רא
>מ ששונ ושמחה ישיגו ונסו יגונ ואנחה>ושמ ששונ ושמחה ישיגו ונס יגונ ואנחה
t1ישׂשׂום מדבר וציה ותגל ערבה ותפרח כחבצלת t1יששומ מדבר וציה ותגל ערבה ותפרח כחבצלת
2פרח תפרח ותגל אף גילת ורנן כבוד הלבנון נתן לה הדר 2פרח תפרח ותגל אפ גילת ורננ כבוד לבנונ ב נתנ לה הדר
>הכרמל והשׁרון המה יראו כבוד יהוה הדר אלהינו ס > הכרמל והשרונ המה יראו כבוד יהוה הדר אלהינו
3חזקו ידים רפות וברכים כשׁלות אמצו 3חזקו ידימ רפות וברכימ כושלות אמצו
4אמרו לנמהרי לב חזקו אל תיראו הנה אלהיכם נקם יבוא ג4אמרו לנמהרי לב חזקו אל תיראו הנה אלוהכמה נקמ יביא 
>מול אלהים הוא יבוא וישׁעכם >גמול אלוהימ הואה יביא ויושעכמה
5אז תפקחנה עיני עורים ואזני חרשׁים תפתחנה 5אז תפקחנה עיני עורימ ואוזני חרשימ תפתחנה
6אז ידלג כאיל פסח ותרן לשׁון אלם כי נבקעו במדבר מים6אז ידלג כאיאל פסח ותרונ לשונ אלמ כיא נבקעו במדבר מ
> ונחלים בערבה >ימ ונחלימ בערבה ילכו
7והיה השׁרב לאגם וצמאון למבועי מים בנוה תנים רבצה ח7והיה השרב לאגמ וצמאונ למבועי מימ בנוה תנימ רבצ חצי
>ציר לקנה וגמא >ר לקנה וגומא
8והיה שׁם מסלול ודרך ודרך הקדשׁ יקרא לה לא יעברנו ט8יהיה שמה שמה מסולל ודרכ הקודש יקראו לה לוא יעובורנ
>מא והוא למו הלך דרך ואוילים לא יתעו >ה הואה ולמי הולכ דרכ ואוילימ לוא יתעו
9לא יהיה שׁם אריה ופריץ חיות בל יעלנה לא תמצא שׁם ו9לוא יהיה שמה אריה ופריצ חיות בל לוא יעלנה ולוא ימצ
>הלכו גאולים >א שמ והלכו גאולימ
10ופדויי יהוה ישׁבון ובאו ציון ברנה ושׂמחת עולם על ר10ופדויי יהוה ישובו ובאו ציונ ברונה ושמחת עולמ על רא
>אשׁם שׂשׂון ושׂמחה ישׂיגו ונסו יגון ואנחה פ >ושמ ששונ ושמחה ישיגו ונס יגונ ואנחה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 36 MT
Isaiah 36 1QIsaa
t1ויהי בארבע עשרה שנה למלכ חזקיהו עלה סנחריב מלכ אשוt1ויהי בארבע עשרה שנה למלכ חזקיה עלה סנחריב מלכ אשור
>ר על כל ערי יהודה הבצרות ויתפשמ> על כול ערי יהודה הבצורות ויתפושמ
2וישלח מלכ אשור את רב שקה מלכיש ירושלמה אל המלכ חזק2וישלח מלכ אשו את רב שקה מלכיש ירושלימ אל המלכ חזקי
>יהו בחיל כבד ויעמד בתעלת הברכה העליונה במסלת שדה כ>ה בחיל כבד מאודה ויעמוד בתעלת הברכה העליונה במסלת 
>ובס>שדי כובס
3ויצא אליו אליקימ בנ חלקיהו אשר על הבית ושבנא הספר 3ויצא אליו אליקימ בנ חלקיה אשר על הבית ושובנא הסופר
>ויואח בנ אספ המזכיר> ויואח בנ אספ המזכיר
4ויאמר אליהמ רב שקה אמרו נא אל חזקיהו כה אמר המלכ ה4ויואמר אליהמה רב שקה אמורו נא אל חזקיה כוה אמר המל
>גדול מלכ אשור מה הבטחונ הזה אשר בטחת>כ הגדול מלכ אשור מה הבטחונ הזה אשר אתה בטחתה בו
5אמרתי אכ דבר שפתימ עצה וגבורה למלחמה עתה על מי בטח5אמרתה אכ דבר שפתימ עצה וגבורא למלחמה עתה על מיא בט
>ת כי מרדת בי>חתה כיא מרדתה ביא
6הנה בטחת על משענת הקנה הרצוצ הזה על מצרימ אשר יסמכ6הנה בטחתה על משענת הקנה הרצוצ הזה על מצרימ אשר יסמ
> איש עליו ובא בכפו ונקבה כנ פרעה מלכ מצרימ לכל הבט>כ איש עליו ובא בכפו ונקבה כנ פרעוה מלכ מצרימ לכול 
>חימ עליו>הבוטחימ עליו
7וכי תאמר אלי אל יהוה אלהינו בטחנו הלוא הוא אשר הסי7וכיא תואמרו אלי על יהוה אלוהינו בטחנו הלוא הואה אש
>ר חזקיהו את במתיו ואת מזבחתיו ויאמר ליהודה ולירושל>ר הסיר חזקיה את במותיו ואת מזבחותיו ויואמר ליהודה 
>מ לפני המזבח הזה תשתחוו>ולירושלימ לפני המזבח הזה תשתחוו
8ועתה התערב נא את אדני המלכ אשור ואתנה לכ אלפימ סוס8ועתה התערבונא את אדוני המלכ אשור ואתנה לכה אלפימ ס
>ימ אמ תוכל לתת לכ רכבימ עליהמ>וסימ אמ תוכל לתת לכה רוכבימ עליהמה
9ואיכ תשיב את פני פחת אחד עבדי אדני הקטנימ ותבטח לכ9ואיכה תשיב את פני פחת אחד מעבדי אדוני הקטנימ ותבטח
> על מצרימ לרכב ולפרשימ> לכמ על מצרימ לרכב ולפרשימ
10ועתה המבלעדי יהוה עליתי על הארצ הזאת להשחיתה יהוה 10ועתה המבלעדי יהוה עליתי על הארצ הזואת להשחיתה יהוה
>אמר אלי עלה אל הארצ הזאת והשחיתה> אמר אלי עלה אל הארצ הזות להשחיתה
11ויאמר אליקימ ושבנא ויואח אל רב שקה דבר נא אל עבדיכ11ויואמרו אליו אליקימ ושובנא ויואח דברנא עמ עבדיכ אר
> ארמית כי שמעימ אנחנו ואל תדבר אלינו יהודית באזני >מית כיא שומעימ אנחנו ואל תדבר את הדברימ האלה באוזנ
>העמ אשר על החומה>י האנשימ היושבימ על החומה
12ויאמר רב שקה האל אדניכ ואליכ שלחני אדני לדבר את הד12ויואמר רב שקה האליכמה ועל אדוניכמה שלחני אדוני לדב
>ברימ האלה הלא על האנשימ הישבימ על החומה לאכל את צו>ר את הדברימ האלה הלוא על האנשימ היושבימ על החומה ל
>אתמ ולשתות את מימי רגליהמ עמכמ>אכול את חריהמה ולשתות את שיניהמה עמכמה
13ויעמד רב שקה ויקרא בקול גדול יהודית ויאמר שמעו את 13ויעמוד רב השקה ויקרא בקול גדול יהודית ויואמר שמעו 
>דברי המלכ הגדול מלכ אשור>את דברי המלכ הגדול מלכ אשור
14כה אמר המלכ אל ישא לכמ חזקיהו כי לא יוכל להציל אתכ14כוה אמר מלכ אשור אל ישא לכמה יחזקיה כיא לוא יוכל ל
>מ>הציל אתכמה
15ואל יבטח אתכמ חזקיהו אל יהוה לאמר הצל יצילנו יהוה 15ואל יבטח אתכמה חוזקיה אל יהוה לאמור הצל יצילנו יהו
>לא תנתנ העיר הזאת ביד מלכ אשור>ה ולוא תנתנ העיר הזואת ביד מלכ אשור
16אל תשמעו אל חזקיהו ס כי כה אמר המלכ אשור עשו אתי ב16אל תשמעו אל חוזקיה כיא כוה אמר מלכ אשור עשו אתי בר
>רכה וצאו אלי ואכלו איש גפנו ואיש תאנתו ושתו איש מי>כה וצאו אלי ואכולו איש את גפנו ואיש את תנתו ושתו א
> בורו>יש מי בורו
17עד באי ולקחתי אתכמ אל ארצ כארצכמ ארצ דגנ ותירוש אר17עד בואי ולקחתי אתכמה אל ארצ כארצכמה אל ארצ דגנ ותי
>צ לחמ וכרמימ>רוש ארצ לחמ וכרמימ
18פנ יסית אתכמ חזקיהו לאמר יהוה יצילנו ההצילו אלהי ה18פנ יסית אתכמה חוזקיה לאמור יהוה יצילנו ההצילו אלוה
>גוימ איש את ארצו מיד מלכ אשור>י הגואימ איש ארצו מיד מלכ אשור
19איה אלהי חמת וארפד איה אלהי ספרוימ וכי הצילו את שמ19איה אלוהי חמת וארפד איה אלוהי ספריימ וכיא ההצילו א
>רונ מידי>ת שומרונ מידי
20מי בכל אלהי הארצות האלה אשר הצילו את ארצמ מידי כי 20מיא בכול אלוהי הארצות האלה אשר הצילו את ארצמ מידי 
>יציל יהוה את ירושלמ מידי>כיא יציל יהוה את ירושלימ מידי
21ויחרישו ולא ענו אתו דבר כי מצות המלכ היא לאמר לא ת21והחרישו ולוא ענו אותוה דבר כיא מצות המלכ היה לאמור
>ענהו> לוא תענוהו
22ויבא אליקימ בנ חלקיהו אשר על הבית ושבנא הסופר ויוא22ויבוא אליקימ בנ חלקיה אשר על הבית ושובנא הסופר ויו
>ח בנ אספ המזכיר אל חזקיהו קרועי בגדימ ויגידו לו את>אח בנ אספ המזכיר אל חוזקיה קרועי בגדימ ויגידו לוא 
> דברי רב שקה>את דברי רב שקה
t1ויהי בארבע עשׂרה שׁנה למלך חזקיהו עלה סנחריב מלך אt1ויהי בארבע עשרה שנה למלכ חזקיה עלה סנחריב מלכ אשור
>שׁור על כל ערי יהודה הבצרות ויתפשׂם > על כול ערי יהודה הבצורות ויתפושמ
2וישׁלח מלך אשׁור׀ את רב שׁקה מלכישׁ ירושׁלמה אל המ2וישלח מלכ אשו את רב שקה מלכיש ירושלימ אל המלכ חזקי
>לך חזקיהו בחיל כבד ויעמד בתעלת הברכה העליונה במסלת>ה בחיל כבד מאודה ויעמוד בתעלת הברכה העליונה במסלת 
> שׂדה כובס >שדי כובס
3ויצא אליו אליקים בן חלקיהו אשׁר על הבית ושׁבנא הספ3ויצא אליו אליקימ בנ חלקיה אשר על הבית ושובנא הסופר
>ר ויואח בן אסף המזכיר > ויואח בנ אספ המזכיר
4ויאמר אליהם רב שׁקה אמרו נא אל חזקיהו כה אמר המלך 4ויואמר אליהמה רב שקה אמורו נא אל חזקיה כוה אמר המל
>הגדול מלך אשׁור מה הבטחון הזה אשׁר בטחת >כ הגדול מלכ אשור מה הבטחונ הזה אשר אתה בטחתה בו
5אמרתי אך דבר שׂפתים עצה וגבורה למלחמה עתה על מי בט5אמרתה אכ דבר שפתימ עצה וגבורא למלחמה עתה על מיא בט
>חת כי מרדת בי >חתה כיא מרדתה ביא
6הנה בטחת על משׁענת הקנה הרצוץ הזה על מצרים אשׁר יס6הנה בטחתה על משענת הקנה הרצוצ הזה על מצרימ אשר יסמ
>מך אישׁ עליו ובא בכפו ונקבה כן פרעה מלך מצרים לכל >כ איש עליו ובא בכפו ונקבה כנ פרעוה מלכ מצרימ לכול 
>הבטחים עליו >הבוטחימ עליו
7וכי תאמר אלי אל יהוה אלהינו בטחנו הלוא הוא אשׁר הס7וכיא תואמרו אלי על יהוה אלוהינו בטחנו הלוא הואה אש
>יר חזקיהו את במתיו ואת מזבחתיו ויאמר ליהודה ולירוש>ר הסיר חזקיה את במותיו ואת מזבחותיו ויואמר ליהודה 
>ׁלם לפני המזבח הזה תשׁתחוו >ולירושלימ לפני המזבח הזה תשתחוו
8ועתה התערב נא את אדני המלך אשׁור ואתנה לך אלפים סו8ועתה התערבונא את אדוני המלכ אשור ואתנה לכה אלפימ ס
>סים אם תוכל לתת לך רכבים עליהם >וסימ אמ תוכל לתת לכה רוכבימ עליהמה
9ואיך תשׁיב את פני פחת אחד עבדי אדני הקטנים ותבטח ל9ואיכה תשיב את פני פחת אחד מעבדי אדוני הקטנימ ותבטח
>ך על מצרים לרכב ולפרשׁים > לכמ על מצרימ לרכב ולפרשימ
10ועתה המבלעדי יהוה עליתי על הארץ הזאת להשׁחיתה יהוה10ועתה המבלעדי יהוה עליתי על הארצ הזואת להשחיתה יהוה
> אמר אלי עלה אל הארץ הזאת והשׁחיתה > אמר אלי עלה אל הארצ הזות להשחיתה
11ויאמר אליקים ושׁבנא ויואח אל רב שׁקה דבר נא אל עבד11ויואמרו אליו אליקימ ושובנא ויואח דברנא עמ עבדיכ אר
>יך ארמית כי שׁמעים אנחנו ואל תדבר אלינו יהודית באז>מית כיא שומעימ אנחנו ואל תדבר את הדברימ האלה באוזנ
>ני העם אשׁר על החומה >י האנשימ היושבימ על החומה
12ויאמר רב שׁקה האל אדניך ואליך שׁלחני אדני לדבר את 12ויואמר רב שקה האליכמה ועל אדוניכמה שלחני אדוני לדב
>הדברים האלה הלא על האנשׁים הישׁבים על החומה לאכל א>ר את הדברימ האלה הלוא על האנשימ היושבימ על החומה ל
>ת חראיהם ולשׁתות את שׁיניהם עמכם >אכול את חריהמה ולשתות את שיניהמה עמכמה
13ויעמד רב שׁקה ויקרא בקול גדול יהודית ויאמר שׁמעו א13ויעמוד רב השקה ויקרא בקול גדול יהודית ויואמר שמעו 
>ת דברי המלך הגדול מלך אשׁור >את דברי המלכ הגדול מלכ אשור
14כה אמר המלך אל ישׁא לכם חזקיהו כי לא יוכל להציל את14כוה אמר מלכ אשור אל ישא לכמה יחזקיה כיא לוא יוכל ל
>כם >הציל אתכמה
15ואל יבטח אתכם חזקיהו אל יהוה לאמר הצל יצילנו יהוה 15ואל יבטח אתכמה חוזקיה אל יהוה לאמור הצל יצילנו יהו
>לא תנתן העיר הזאת ביד מלך אשׁור >ה ולוא תנתנ העיר הזואת ביד מלכ אשור
16אל תשׁמעו אל חזקיהו ס כי כה אמר המלך אשׁור עשׂו את16אל תשמעו אל חוזקיה כיא כוה אמר מלכ אשור עשו אתי בר
>י ברכה וצאו אלי ואכלו אישׁ גפנו ואישׁ תאנתו ושׁתו >כה וצאו אלי ואכולו איש את גפנו ואיש את תנתו ושתו א
>אישׁ מי בורו >יש מי בורו
17עד באי ולקחתי אתכם אל ארץ כארצכם ארץ דגן ותירושׁ א17עד בואי ולקחתי אתכמה אל ארצ כארצכמה אל ארצ דגנ ותי
>רץ לחם וכרמים >רוש ארצ לחמ וכרמימ
18פן יסית אתכם חזקיהו לאמר יהוה יצילנו ההצילו אלהי ה18פנ יסית אתכמה חוזקיה לאמור יהוה יצילנו ההצילו אלוה
>גוים אישׁ את ארצו מיד מלך אשׁור >י הגואימ איש ארצו מיד מלכ אשור
19איה אלהי חמת וארפד איה אלהי ספרוים וכי הצילו את שׁ19איה אלוהי חמת וארפד איה אלוהי ספריימ וכיא ההצילו א
>מרון מידי >ת שומרונ מידי
20מי בכל אלהי הארצות האלה אשׁר הצילו את ארצם מידי כי20מיא בכול אלוהי הארצות האלה אשר הצילו את ארצמ מידי 
> יציל יהוה את ירושׁלם מידי >כיא יציל יהוה את ירושלימ מידי
21ויחרישׁו ולא ענו אתו דבר כי מצות המלך היא לאמר לא 21והחרישו ולוא ענו אותוה דבר כיא מצות המלכ היה לאמור
>תענהו > לוא תענוהו
22ויבא אליקים בן חלקיהו אשׁר על הבית ושׁבנא הסופר וי22ויבוא אליקימ בנ חלקיה אשר על הבית ושובנא הסופר ויו
>ואח בן אסף המזכיר אל חזקיהו קרועי בגדים ויגידו לו >אח בנ אספ המזכיר אל חוזקיה קרועי בגדימ ויגידו לוא 
>את דברי רב שׁקה ס >את דברי רב שקה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 37 MT
Isaiah 37 1QIsaa
t1ויהי כשמע המלכ חזקיהו ויקרע את בגדיו ויתכס בשק ויבt1ויהי כשמוע חוזקיה המלכ ויקרע את בגדיו ויתכס בשק וי
>א בית יהוה>בוא בית יהוה
2וישלח את אליקימ אשר על הבית ואת שבנא הסופר ואת זקנ2וישלח את אליקימ אשר על הבית ואת שובנא הסופר ואת זק
>י הכהנימ מתכסימ בשקימ אל ישעיהו בנ אמוצ הנביא>ני הכוהנימ מתכסימ בשקימ אל ישעיה בנ אמוצ הנביא
3ויאמרו אליו כה אמר חזקיהו יומ צרה ותוכחה ונאצה היו3ויואמרו אליו כוה אמר יחזקיה יומ צרה ותוכחה ונאצה ה
>מ הזה כי באו בנימ עד משבר וכח אינ ללדה>יומ הזה כיא באו בנימ עד משבר וכוח אינ ללדה
4אולי ישמע יהוה אלהיכ את דברי רב שקה אשר שלחו מלכ א4אולי ישמע יהוה אלוהיכה את דברי רב שקה אשר שלחו מלכ
>שור אדניו לחרפ אלהימ חי והוכיח בדברימ אשר שמע יהוה> אשור אדוניו לחרפ אלוהימ חי והוכיח בדברימ אשר שמע 
> אלהיכ ונשאת תפלה בעד השארית הנמצאה>יהוה אלוהיכה ונשאתה תפלה בעד השארית הנמצאימ בעיר ה
 >זואת
5ויבאו עבדי המלכ חזקיהו אל ישעיהו5ויבואו עבדי המלכ יחוזקיה אל ישעיה
6ויאמר אליהמ ישעיהו כה תאמרונ אל אדניכמ כה אמר יהוה6א ויואמר להמה ישעיה כוה תואמרו אל אדוניכמה כוה אמר
> אל תירא מפני הדברימ אשר שמעת אשר גדפו נערי מלכ אש> יהוה אל תירא מפני הדברימ ב אשר שמעתה אשר גדפו נער
>ור אותי>י מלכ אשור אותי
7הנני נותנ בו רוח ושמע שמועה ושב אל ארצו והפלתיו בח7הנני נותנ רוח בוא ושמע שמועה ושב לארצו והפלתיו בחר
>רב בארצו>ב בארצו
8וישב רב שקה וימצא את מלכ אשור נלחמ על לבנה כי שמע 8וישוב רב שקה וימצא את מלכ אשור נלחמ על לבנה כיא שמ
>כי נסע מלכיש>ע כיא נסע מלכיש
9וישמע על תרהקה מלכ כוש לאמר יצא להלחמ אתכ וישמע וי9וישמע אל תרהקה מלכ כוש לאמור יצא להלחמ אתכה וישמע 
>שלח מלאכימ אל חזקיהו לאמר>וישוב וישלח מלאכימ אל יחוזקיה לאמור
10כה תאמרונ אל חזקיהו מלכ יהודה לאמר אל ישאכ אלהיכ א10כוה תומרו אל חוזקיה מלכ יהודה לאמור אל ישייכה אלוה
>שר אתה בוטח בו לאמר לא תנתנ ירושלמ ביד מלכ אשור>יכה אשר אתה בוטח בוא לאמור לוא תנתנ ירושלימ ביד מל
 >כ אשור
11הנה אתה שמעת אשר עשו מלכי אשור לכל הארצות להחריממ 11הנה אתה שמעתה את אשר עשו מלכי אשור לכול הארצות להח
>ואתה תנצל>ריממ ואתה תנצל
12ההצילו אותמ אלהי הגוימ אשר השחיתו אבותי את גוזנ וא12ההצילו אותמ אלוהי הגואימ אשר השחיתו אבותי את גוזנ 
>ת חרנ ורצפ ובני עדנ אשר בתלשר>ואת חרנ ורצפ ובני עדנ אשר בתלשר
13איה מלכ חמת ומלכ ארפד ומלכ לעיר ספרוימ הנע ועוה13איה מלכ חמת ומלכ ארפד ומלכ לעיר וספריימ ונע ועוה ו
t1ויהי כשׁמע המלך חזקיהו ויקרע את בגדיו ויתכס בשׂק וt1ויהי כשמוע חוזקיה המלכ ויקרע את בגדיו ויתכס בשק וי
>יבא בית יהוה >בוא בית יהוה
2וישׁלח את אליקים אשׁר על הבית ואת׀ שׁבנא הסופר ואת2וישלח את אליקימ אשר על הבית ואת שובנא הסופר ואת זק
> זקני הכהנים מתכסים בשׂקים אל ישׁעיהו בן אמוץ הנבי>ני הכוהנימ מתכסימ בשקימ אל ישעיה בנ אמוצ הנביא
>א  
3ויאמרו אליו כה אמר חזקיהו יום צרה ותוכחה ונאצה היו3ויואמרו אליו כוה אמר יחזקיה יומ צרה ותוכחה ונאצה ה
>ם הזה כי באו בנים עד משׁבר וכח אין ללדה >יומ הזה כיא באו בנימ עד משבר וכוח אינ ללדה
4אולי ישׁמע יהוה אלהיך את׀ דברי רב שׁקה אשׁר שׁלחו 4אולי ישמע יהוה אלוהיכה את דברי רב שקה אשר שלחו מלכ
>מלך אשׁור׀ אדניו לחרף אלהים חי והוכיח בדברים אשׁר > אשור אדוניו לחרפ אלוהימ חי והוכיח בדברימ אשר שמע 
>שׁמע יהוה אלהיך ונשׂאת תפלה בעד השׁארית הנמצאה >יהוה אלוהיכה ונשאתה תפלה בעד השארית הנמצאימ בעיר ה
 >זואת
5ויבאו עבדי המלך חזקיהו אל ישׁעיהו 5ויבואו עבדי המלכ יחוזקיה אל ישעיה
6ויאמר אליהם ישׁעיהו כה תאמרון אל אדניכם כה׀ אמר יה6א ויואמר להמה ישעיה כוה תואמרו אל אדוניכמה כוה אמר
>וה אל תירא מפני הדברים אשׁר שׁמעת אשׁר גדפו נערי מ> יהוה אל תירא מפני הדברימ ב אשר שמעתה אשר גדפו נער
>לך אשׁור אותי >י מלכ אשור אותי
7הנני נותן בו רוח ושׁמע שׁמועה ושׁב אל ארצו והפלתיו7הנני נותנ רוח בוא ושמע שמועה ושב לארצו והפלתיו בחר
> בחרב בארצו >ב בארצו
8וישׁב רב שׁקה וימצא את מלך אשׁור נלחם על לבנה כי ש8וישוב רב שקה וימצא את מלכ אשור נלחמ על לבנה כיא שמ
>ׁמע כי נסע מלכישׁ >ע כיא נסע מלכיש
9וישׁמע על תרהקה מלך כושׁ לאמר יצא להלחם אתך וישׁמע9וישמע אל תרהקה מלכ כוש לאמור יצא להלחמ אתכה וישמע 
> וישׁלח מלאכים אל חזקיהו לאמר >וישוב וישלח מלאכימ אל יחוזקיה לאמור
10כה תאמרון אל חזקיהו מלך יהודה לאמר אל ישׁאך אלהיך 10כוה תומרו אל חוזקיה מלכ יהודה לאמור אל ישייכה אלוה
>אשׁר אתה בוטח בו לאמר לא תנתן ירושׁלם ביד מלך אשׁו>יכה אשר אתה בוטח בוא לאמור לוא תנתנ ירושלימ ביד מל
>ר >כ אשור
11הנה׀ אתה שׁמעת אשׁר עשׂו מלכי אשׁור לכל הארצות להח11הנה אתה שמעתה את אשר עשו מלכי אשור לכול הארצות להח
>רימם ואתה תנצל >ריממ ואתה תנצל
12ההצילו אותם אלהי הגוים אשׁר השׁחיתו אבותי את גוזן 12ההצילו אותמ אלוהי הגואימ אשר השחיתו אבותי את גוזנ 
>ואת חרן ורצף ובני עדן אשׁר בתלשׂר >ואת חרנ ורצפ ובני עדנ אשר בתלשר
13איה מלך חמת ומלך ארפד ומלך לעיר ספרוים הנע ועוה 13איה מלכ חמת ומלכ ארפד ומלכ לעיר וספריימ ונע ועוה ו
 >שומרונ
14ויקח חזקיהו את הספרימ מיד המלאכימ ויקראהו ויעל בית14ויקח חוזקיה את הספרימ מיד המלאכימ ויקראמ ויעלה בית
> יהוה ויפרשהו חזקיהו לפני יהוה> יהוה ויפרושה חוזקיה לפני יהוה
15ויתפלל חזקיהו אל יהוה לאמר15ויתפלל חוזקיה אל יהוה לאמור
16יהוה צבאות אלהי ישראל ישב הכרבימ אתה הוא האלהימ לב16יהוה צבאות אלוהי ישראל יושב הכרובימ אתה הואה האלוה
>דכ לכל ממלכות הארצ אתה עשית את השמימ ואת הארצ>ימ לבדכה לכול ממלכות הארצ אתה עשיתה את השמימ ואת ה
 >ארצ
17הטה יהוה אזנכ ושמע פקח יהוה עינכ וראה ושמע את כל ד17הטא יהוה אוזנכה ושמעה פקח יהוה עיניכה וראה ושמע את
>ברי סנחריב אשר שלח לחרפ אלהימ חי> כול דברי סנחריב אשר שלח לחרפ אלוהימ חי
18אמנמ יהוה החריבו מלכי אשור את כל הארצות ואת ארצמ18אמנמ יהוה החריבו מלכי אשור את כול הארצות
19ונתנ את אלהיהמ באש כי לא אלהימ המה כי אמ מעשה ידי 19ויתנו את אלוהיהמה באש כיא לוא אלוהימ המה כיא אמ מע
>אדמ עצ ואבנ ויאבדומ>שי ידי אדמ עצ ואבנ ויאבדומ
20ועתה יהוה אלהינו הושיענו מידו וידעו כל ממלכות הארצ20ועתה יהוה אלוהינו אושיענו מידו וידעו כול ממלכות הא
> כי אתה יהוה לבדכ>רצ כיא אתה יהוה אלוהימ לבדכה
21וישלח ישעיהו בנ אמוצ אל חזקיהו לאמר כה אמר יהוה אל21וישלח ישעיה בנ אמוצ על יחוזקיה לאמור כוה אמר יהוה 
>הי ישראל אשר התפללת אלי אל סנחריב מלכ אשור>אלוהי ישראל אשר התפללתה אליו אל סרחריב מלכ אשור
22זה הדבר אשר דבר יהוה עליו בזה לכ לעגה לכ בתולת בת 22זה הדבר אשר דבר יהוה עליו בזה לכה לעגה לכה בתולת ב
>ציונ אחריכ ראש הניעה בת ירושלמ>ת ציונ אחריכה ראושה הניעה בת ירושלימ
23את מי חרפת וגדפת ועל מי הרימותה קול ותשא מרומ עיני23את מיא חרפתה וגדפתה ועל מיא הרימותה קול ותשא מרומ 
>כ אל קדוש ישראל>עיניכה אל קדוש ישראל
24ביד עבדיכ חרפת אדני ותאמר ברב רכבי אני עליתי מרומ 24ביד עבדיכה חרפתה אדוני ותומר ברוב רכבי אני עליתי מ
>הרימ ירכתי לבנונ ואכרת קומת ארזיו מבחר ברשיו ואבוא>רומ הרימ ירכתי לבנונ ואכרותה קומת ארזיו מבחר ברושי
> מרומ קצו יער כרמלו>ו ואבוא מרומ קצו יער כרמליו
25אני קרתי ושתיתי מימ ואחרב בכפ פעמי כל יארי מצור25אני קראתי ושתיתי מימ זרימ ואחריבה בכפ פעמי כול יאר
14ויקח חזקיהו את הספרים מיד המלאכים ויקראהו ויעל בית14ויקח חוזקיה את הספרימ מיד המלאכימ ויקראמ ויעלה בית
> יהוה ויפרשׂהו חזקיהו לפני יהוה > יהוה ויפרושה חוזקיה לפני יהוה
15ויתפלל חזקיהו אל יהוה לאמר 15ויתפלל חוזקיה אל יהוה לאמור
16יהוה צבאות אלהי ישׂראל ישׁב הכרבים אתה הוא האלהים 16יהוה צבאות אלוהי ישראל יושב הכרובימ אתה הואה האלוה
>לבדך לכל ממלכות הארץ אתה עשׂית את השׁמים ואת הארץ >ימ לבדכה לכול ממלכות הארצ אתה עשיתה את השמימ ואת ה
 >ארצ
17הטה יהוה׀ אזנך ושׁמע פקח יהוה עינך וראה ושׁמע את כ17הטא יהוה אוזנכה ושמעה פקח יהוה עיניכה וראה ושמע את
>ל דברי סנחריב אשׁר שׁלח לחרף אלהים חי > כול דברי סנחריב אשר שלח לחרפ אלוהימ חי
18אמנם יהוה החריבו מלכי אשׁור את כל הארצות ואת ארצם 18אמנמ יהוה החריבו מלכי אשור את כול הארצות
19ונתן את אלהיהם באשׁ כי לא אלהים המה כי אם מעשׂה יד19ויתנו את אלוהיהמה באש כיא לוא אלוהימ המה כיא אמ מע
>י אדם עץ ואבן ויאבדום >שי ידי אדמ עצ ואבנ ויאבדומ
20ועתה יהוה אלהינו הושׁיענו מידו וידעו כל ממלכות האר20ועתה יהוה אלוהינו אושיענו מידו וידעו כול ממלכות הא
>ץ כי אתה יהוה לבדך >רצ כיא אתה יהוה אלוהימ לבדכה
21וישׁלח ישׁעיהו בן אמוץ אל חזקיהו לאמר כה אמר יהוה 21וישלח ישעיה בנ אמוצ על יחוזקיה לאמור כוה אמר יהוה 
>אלהי ישׂראל אשׁר התפללת אלי אל סנחריב מלך אשׁור >אלוהי ישראל אשר התפללתה אליו אל סרחריב מלכ אשור
22זה הדבר אשׁר דבר יהוה עליו בזה לך לעגה לך בתולת בת22זה הדבר אשר דבר יהוה עליו בזה לכה לעגה לכה בתולת ב
> ציון אחריך ראשׁ הניעה בת ירושׁלם >ת ציונ אחריכה ראושה הניעה בת ירושלימ
23את מי חרפת וגדפת ועל מי הרימותה קול ותשׂא מרום עינ23את מיא חרפתה וגדפתה ועל מיא הרימותה קול ותשא מרומ 
>יך אל קדושׁ ישׂראל >עיניכה אל קדוש ישראל
24ביד עבדיך חרפת׀ אדני ותאמר ברב רכבי אני עליתי מרום24ביד עבדיכה חרפתה אדוני ותומר ברוב רכבי אני עליתי מ
> הרים ירכתי לבנון ואכרת קומת ארזיו מבחר ברשׁיו ואב>רומ הרימ ירכתי לבנונ ואכרותה קומת ארזיו מבחר ברושי
>וא מרום קצו יער כרמלו >ו ואבוא מרומ קצו יער כרמליו
25אני קרתי ושׁתיתי מים ואחרב בכף פעמי כל יארי מצור 25אני קראתי ושתיתי מימ זרימ ואחריבה בכפ פעמי כול יאר
 >י מצור
26הלוא שמעת למרחוק אותה עשיתי מימי קדמ ויצרתיה עתה ה26הלוא שמעתה למרחוק אותה עשיתי מימי קדמ יצרתיה עתה ה
>באתיה ותהי להשאות גלימ נצימ ערימ בצרות>ביאותיה ותהי לשאוות גלימ נצורימ ערימ בצורות
27וישביהנ קצרי יד חתו ובשו היו עשב שדה וירק דשא חציר27ויושביהנה קצרי יד חתו ויבשו היו עשב שדה ירק דשה חצ
> גגות ושדמה לפני קמה>יר גגות הנשדפ לפני קדימ
28ושבתכ וצאתכ ובואכ ידעתי ואת התרגזכ אלי28קומכה ושבתכה וצאתכה ובואכה ידעתיא ואת הרגזכה אלי
29יענ התרגזכ אלי ושאננכ עלה באזני ושמתי חחי באפכ ומת29ושאננכה עלה באוזני ושמתי חחי באפכה ומתגי בשפאותיכה
>גי בשפתיכ והשיבתיכ בדרכ אשר באת בה> והשיבותיכה בדרכ אשר בתה בה
30וזה לכ האות אכול השנה ספיח ובשנה השנית שחיס ובשנה 30וזה לכה האות אכולו השנה ספיח ובשנה השנית שעיס ובשנ
>השלישית זרעו וקצרו ונטעו כרמימ ואכלו פרימ>ה השלישית זרעו וקצורו ונטוע כרמימ ואכולו פרימ
31ויספה פליטת בית יהודה הנשארה שרש למטה ועשה פרי למע31ואספה פליטת בית יהודה והנמצא שורש למטה ועשה פרי מע
>לה>לה
32כי מירושלמ תצא שארית ופליטה מהר ציונ קנאת יהוה צבא32כיא מציונ תצא שארית ופליטא מירושלימ קנאת יהוה צבאו
>ות תעשה זאת>ת תעשה זואת
33לכנ כה אמר יהוה אל מלכ אשור לא יבוא אל העיר הזאת ו33לכנ כוה אמר יהוה אל מלכ אשור לוא יבוא אל העיר הזאו
>לא יורה שמ חצ ולא יקדמנה מגנ ולא ישפכ עליה סללה>ת ולוא ישפוכ עליהא סוללה ולוא ירא שמ חצ ולוא יקדמנ
 >ה מגנ
34בדרכ אשר בא בה ישוב ואל העיר הזאת לא יבוא נאמ יהוה34בדרכ אשר בא באה ישוב ואל העיר הזואת לוא יבוא נואומ
 > יהוה
35וגנותי על העיר הזאת להושיעה למעני ולמענ דוד עבדי35וגנותי על העיר הזואת להושיעה למעני ולמענ דויד עבדי
36ויצא מלאכ יהוה ויכה במחנה אשור מאה ושמנימ וחמשה אל36ויצא מלאכ יהוה ויכ במחנה אשור מאה ושמונימ וחמשא אל
>פ וישכימו בבקר והנה כלמ פגרימ מתימ>פ וישכימו בבוקר והנה כולמ פגרימ מיתימ
37ויסע וילכ וישב סנחריב מלכ אשור וישב בנינוה37ויסע וילכ וישווב סנחריב מלכ אשור וישב בנינוה
38ויהי הוא משתחוה בית נסרכ אלהיו ואדרמלכ ושראצר בניו38ויהי הואה משתחוה בבית נסרכ אלוהיו ואדרמלכ ושראוצר 
> הכהו בחרב והמה נמלטו ארצ אררט וימלכ אסר חדנ בנו ת>בניו הכהו בחרב והמה נמלטו ארצ הוררט וימלוכ אסרחודנ
>חתיו> בניו תחתיו
26הלוא שׁמעת למרחוק אותה עשׂיתי מימי קדם ויצרתיה עתה26הלוא שמעתה למרחוק אותה עשיתי מימי קדמ יצרתיה עתה ה
> הבאתיה ותהי להשׁאות גלים נצים ערים בצרות >ביאותיה ותהי לשאוות גלימ נצורימ ערימ בצורות
27וישׁביהן קצרי יד חתו ובשׁו היו עשׂב שׂדה וירק דשׁא27ויושביהנה קצרי יד חתו ויבשו היו עשב שדה ירק דשה חצ
> חציר גגות ושׁדמה לפני קמה >יר גגות הנשדפ לפני קדימ
28ושׁבתך וצאתך ובואך ידעתי ואת התרגזך אלי 28קומכה ושבתכה וצאתכה ובואכה ידעתיא ואת הרגזכה אלי
29יען התרגזך אלי ושׁאננך עלה באזני ושׂמתי חחי באפך ו29ושאננכה עלה באוזני ושמתי חחי באפכה ומתגי בשפאותיכה
>מתגי בשׂפתיך והשׁיבתיך בדרך אשׁר באת בה > והשיבותיכה בדרכ אשר בתה בה
30וזה לך האות אכול השׁנה ספיח ובשׁנה השׁנית שׁחיס וב30וזה לכה האות אכולו השנה ספיח ובשנה השנית שעיס ובשנ
>שׁנה השׁלישׁית זרעו וקצרו ונטעו כרמים ואכול פרים >ה השלישית זרעו וקצורו ונטוע כרמימ ואכולו פרימ
31ויספה פליטת בית יהודה הנשׁארה שׁרשׁ למטה ועשׂה פרי31ואספה פליטת בית יהודה והנמצא שורש למטה ועשה פרי מע
> למעלה >לה
32כי מירושׁלם תצא שׁארית ופליטה מהר ציון קנאת יהוה צ32כיא מציונ תצא שארית ופליטא מירושלימ קנאת יהוה צבאו
>באות תעשׂה זאת ס >ת תעשה זואת
33לכן כה אמר יהוה אל מלך אשׁור לא יבוא אל העיר הזאת 33לכנ כוה אמר יהוה אל מלכ אשור לוא יבוא אל העיר הזאו
>ולא יורה שׁם חץ ולא יקדמנה מגן ולא ישׁפך עליה סללה>ת ולוא ישפוכ עליהא סוללה ולוא ירא שמ חצ ולוא יקדמנ
> >ה מגנ
34בדרך אשׁר בא בה ישׁוב ואל העיר הזאת לא יבוא נאם יה34בדרכ אשר בא באה ישוב ואל העיר הזואת לוא יבוא נואומ
>וה > יהוה
35וגנותי על העיר הזאת להושׁיעה למעני ולמען דוד עבדי 35וגנותי על העיר הזואת להושיעה למעני ולמענ דויד עבדי
>ס  
36ויצא׀ מלאך יהוה ויכה במחנה אשׁור מאה ושׁמנים וחמשׁ36ויצא מלאכ יהוה ויכ במחנה אשור מאה ושמונימ וחמשא אל
>ה אלף וישׁכימו בבקר והנה כלם פגרים מתים >פ וישכימו בבוקר והנה כולמ פגרימ מיתימ
37ויסע וילך וישׁב סנחריב מלך אשׁור וישׁב בנינוה 37ויסע וילכ וישווב סנחריב מלכ אשור וישב בנינוה
38ויהי הוא משׁתחוה בית׀ נסרך אלהיו ואדרמלך ושׂראצר ב38ויהי הואה משתחוה בבית נסרכ אלוהיו ואדרמלכ ושראוצר 
>ניו הכהו בחרב והמה נמלטו ארץ אררט וימלך אסר חדן בנ>בניו הכהו בחרב והמה נמלטו ארצ הוררט וימלוכ אסרחודנ
>ו תחתיו ס > בניו תחתיו
- - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - + + + + + + + + - - - - - - - + + + + + + + - - + + +

Isaiah 38 MT
Isaiah 38 1QIsaa
t1בימימ ההמ חלה חזקיהו למות ויבוא אליו ישעיהו בנ אמוt1בימימ ההמה חלה יחוזקיה למות ויבוא אליו ישעיה בנ אמ
>צ הנביא ויאמר אליו כה אמר יהוה צו לביתכ כי מת אתה >וצ הנביא ויואמר אליו כוה אמר יהוה צוי לביתכה כיא מ
>ולא תחיה>ית אתה ולוא תחיה
2ויסב חזקיהו פניו אל הקיר ויתפלל אל יהוה2ויסוב יחוזקיה פניו אל הקיר ויתפלל אל יהוה
3ויאמר אנה יהוה זכר נא את אשר התהלכתי לפניכ באמת וב3ויואמר אנה יהוה זכורנא את אשר התהלכתי לפניכה באמת 
>לב שלמ והטוב בעיניכ עשיתי ויבכ חזקיהו בכי גדול>ובלבב שלמ והטוב בעיניכה עשיתי ויבכא יחוזקיה בכי גד
 >ול
4ויהי דבר יהוה אל ישעיהו לאמר4ויהי דבר יהוה אל ישעיה לאמור
5הלוכ ואמרת אל חזקיהו כה אמר יהוה אלהי דוד אביכ שמע5הלוכ ואמרתה אל יחוזקיה כוה אמר יהוה אלוהי דויד אבי
>תי את תפלתכ ראיתי את דמעתכ הנני יוספ על ימיכ חמש ע>כה שמעתי את תפלתכה וראיתי את דמעתכה הנני יוספ על י
>שרה שנה>מיכה חמש עשרה שנה
6ומכפ מלכ אשור אצילכ ואת העיר הזאת וגנותי על העיר ה6ומכפ מלכ אשור אצילכה ואת העיר הזואת וגנותי על העיר
>זאת> הזואת למעני ולמענ דויד עבדי
7וזה לכ האות מאת יהוה אשר יעשה יהוה את הדבר הזה אשר7וזה לכה האות מאת יהוה אשר יעשה יהוה את הדבר הזה אש
> דבר>ר דבר
8הנני משיב את צל המעלות אשר ירדה במעלות אחז בשמש אח8הנני משיב את צל המעלות אשר ירדה במעלות עלית אחז את
>רנית עשר מעלות ותשב השמש עשר מעלות במעלות אשר ירדה> השמש אחורנית עשר מעלות ותשוב השמש עשר מעלות במעלו
 >ת אשר ירדה
9מכתב לחזקיהו מלכ יהודה בחלתו ויחי מחליו9מכתב ליחוזקיה מלכ יהודה בחוליותיו ויחי מחוליו
10אני אמרתי בדמי ימי אלכה בשערי שאול פקדתי יתר שנותי10אני אמרתי בדמי וימי אלכה בשערי שאול פקודתי ומר שנו
 >תי
11אמרתי לא אראה יה יה בארצ החיימ לא אביט אדמ עוד עמ 11אמרתי לוא אראה יה בארצ חיימ ולוא אביט אדמ עוד עמ י
>יושבי חדל>ושבי חדל
t1בימים ההם חלה חזקיהו למות ויבוא אליו ישׁעיהו בן אמt1בימימ ההמה חלה יחוזקיה למות ויבוא אליו ישעיה בנ אמ
>וץ הנביא ויאמר אליו כה אמר יהוה צו לביתך כי מת אתה>וצ הנביא ויואמר אליו כוה אמר יהוה צוי לביתכה כיא מ
> ולא תחיה >ית אתה ולוא תחיה
2ויסב חזקיהו פניו אל הקיר ויתפלל אל יהוה 2ויסוב יחוזקיה פניו אל הקיר ויתפלל אל יהוה
3ויאמר אנה יהוה זכר נא את אשׁר התהלכתי לפניך באמת ו3ויואמר אנה יהוה זכורנא את אשר התהלכתי לפניכה באמת 
>בלב שׁלם והטוב בעיניך עשׂיתי ויבך חזקיהו בכי גדול >ובלבב שלמ והטוב בעיניכה עשיתי ויבכא יחוזקיה בכי גד
>ס >ול
4ויהי דבר יהוה אל ישׁעיהו לאמר 4ויהי דבר יהוה אל ישעיה לאמור
5הלוך ואמרת אל חזקיהו כה אמר יהוה אלהי דוד אביך שׁמ5הלוכ ואמרתה אל יחוזקיה כוה אמר יהוה אלוהי דויד אבי
>עתי את תפלתך ראיתי את דמעתך הנני יוסף על ימיך חמשׁ>כה שמעתי את תפלתכה וראיתי את דמעתכה הנני יוספ על י
> עשׂרה שׁנה >מיכה חמש עשרה שנה
6ומכף מלך אשׁור אצילך ואת העיר הזאת וגנותי על העיר 6ומכפ מלכ אשור אצילכה ואת העיר הזואת וגנותי על העיר
>הזאת > הזואת למעני ולמענ דויד עבדי
7וזה לך האות מאת יהוה אשׁר יעשׂה יהוה את הדבר הזה א7וזה לכה האות מאת יהוה אשר יעשה יהוה את הדבר הזה אש
>שׁר דבר >ר דבר
8הנני משׁיב את צל המעלות אשׁר ירדה במעלות אחז בשׁמש8הנני משיב את צל המעלות אשר ירדה במעלות עלית אחז את
>ׁ אחרנית עשׂר מעלות ותשׁב השׁמשׁ עשׂר מעלות במעלות> השמש אחורנית עשר מעלות ותשוב השמש עשר מעלות במעלו
> אשׁר ירדה ס >ת אשר ירדה
9מכתב לחזקיהו מלך יהודה בחלתו ויחי מחליו 9מכתב ליחוזקיה מלכ יהודה בחוליותיו ויחי מחוליו
10אני אמרתי בדמי ימי אלכה בשׁערי שׁאול פקדתי יתר שׁנ10אני אמרתי בדמי וימי אלכה בשערי שאול פקודתי ומר שנו
>ותי >תי
11אמרתי לא אראה יה יה בארץ החיים לא אביט אדם עוד עם 11אמרתי לוא אראה יה בארצ חיימ ולוא אביט אדמ עוד עמ י
>יושׁבי חדל >ושבי חדל
12דורי נסע ונגלה מני כאהל רעי קפדתי כארג חיי מדלה יב12דורי נסע יכלה מני כאוהל רעי ספרתי כאורג חיי מדלה י
>צעני מיומ עד לילה תשלימני>בצעני מיומ עד לילה תשלימני
13שויתי עד בקר כארי כנ ישבר כל עצמותי מיומ עד לילה ת13שפותי עד בוקר כארי כנ ישבור כול עצמותי מיומ עד ליל
>שלימני>ה תשלימני
14כסוס עגור כנ אצפצפ אהגה כיונה דלו עיני למרומ אדני 14כסוס עוגר כנ אצפצפ אהגה כיונא דלו עיני למרומ אדוני
>עשקה לי ערבני> עושקה לי וערבני
15מה אדבר ואמר לי והוא עשה אדדה כל שנותי על מר נפשי15מה אדבר ואומר לוא והיאה עשה ליא אדודה כול שנותי על
 > מור נפשיא
16אדני עליהמ יחיו ולכל בהנ חיי רוחי ותחלימני והחיני16אדוני עליהמה וחיו ולכול בהמה חיו רוחו ותחלימני והח
>צעני מיום עד לילה תשׁלימני >בצעני מיומ עד לילה תשלימני
13שׁויתי עד בקר כארי כן ישׁבר כל עצמותי מיום עד לילה13שפותי עד בוקר כארי כנ ישבור כול עצמותי מיומ עד ליל
> תשׁלימני >ה תשלימני
14כסוס עגור כן אצפצף אהגה כיונה דלו עיני למרום אדני 14כסוס עוגר כנ אצפצפ אהגה כיונא דלו עיני למרומ אדוני
>עשׁקה לי ערבני > עושקה לי וערבני
15מה אדבר ואמר לי והוא עשׂה אדדה כל שׁנותי על מר נפש15מה אדבר ואומר לוא והיאה עשה ליא אדודה כול שנותי על
>ׁי > מור נפשיא
16אדני עליהם יחיו ולכל בהן חיי רוחי ותחלימני והחיני 16אדוני עליהמה וחיו ולכול בהמה חיו רוחו ותחלימני והח
 >יני
17הנה לשלומ מר לי מר ואתה חשקת נפשי משחת בלי כי השלכ17הנ לשלומ מר ליא מאודה ואתה חשקתה נפשי משחת כלי כיא
>ת אחרי גוכ כל חטאי> השלכתה אחרי גוכה כול חטאי
18כי לא שאול תודכ מות יהללכ לא ישברו יורדי בור אל אמ18כיא לוא שאול תודכה ולוא מות יהללכה ולוא ישברו יורד
>תכ>י בור אל אמתכה
19חי חי הוא יודכ כמוני היומ אב לבנימ יודיע אל אמתכ19חי חי הוא יודכה כמוני היומ אב לבנימ יודיע אל אמתכה
20יהוה להושיעני ונגנותי ננגנ כל ימי חיינו על בית יהו20יהוה להושיעני חי חי יודכ כמוני היומ אב לבנימ יהודי
>ה>ע אלוה אמתכ יהוה להושיעני ונגנותי ננגנ כול ימי חיי
17הנה לשׁלום מר לי מר ואתה חשׁקת נפשׁי משׁחת בלי כי 17הנ לשלומ מר ליא מאודה ואתה חשקתה נפשי משחת כלי כיא
>השׁלכת אחרי גוך כל חטאי > השלכתה אחרי גוכה כול חטאי
18כי לא שׁאול תודך מות יהללך לא ישׂברו יורדי בור אל 18כיא לוא שאול תודכה ולוא מות יהללכה ולוא ישברו יורד
>אמתך >י בור אל אמתכה
19חי חי הוא יודך כמוני היום אב לבנים יודיע אל אמתך 19חי חי הוא יודכה כמוני היומ אב לבנימ יודיע אל אמתכה
20יהוה להושׁיעני ונגנותי ננגן כל ימי חיינו על בית יה20יהוה להושיעני חי חי יודכ כמוני היומ אב לבנימ יהודי
>וה >ע אלוה אמתכ יהוה להושיעני ונגנותי ננגנ כול ימי חיי
 >נו על בית יהוה
21ויאמר ישעיהו ישאו דבלת תאנימ וימרחו על השחינ ויחי21ויאומר ישעיהו דבלת תאנימ וימרחו על השחינ ויחי
22ויאמר חזקיהו מה אות כי אעלה בית יהוה22ויאמר חזקיה מה אות כי אעלה בית יהוה
21ויאמר ישׁעיהו ישׂאו דבלת תאנים וימרחו על השׁחין וי21ויאומר ישעיהו דבלת תאנימ וימרחו על השחינ ויחי
>חי  
22ויאמר חזקיהו מה אות כי אעלה בית יהוה ס 22ויאמר חזקיה מה אות כי אעלה בית יהוה
- - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + +

Isaiah 39 MT
Isaiah 39 1QIsaa
t1בעת ההוא שלח מרדכ בלאדנ בנ בלאדנ מלכ בבל ספרימ ומנt1בעת ההיא שלח מרודכ בלאדונ בנ בלאדונ מלכ בבל ספרימ 
>חה אל חזקיהו וישמע כי חלה ויחזק>ומנחה אל יחוזקיה וישמע כיא חלה ויחיה
2וישמח עליהמ חזקיהו ויראמ את בית נכתו את הכספ ואת ה2וישמח עליהמה יחוזקיה ויראמ את כול בית נכתיו את הכס
>זהב ואת הבשמימ ואת השמנ הטוב ואת כל בית כליו ואת כ>פ ואת הזהב ואת הבשמימ ואת השמנ הטוב ואת כול בית כל
>ל אשר נמצא באצרתיו לא היה דבר אשר לא הראמ חזקיהו ב>יו ואת כול אשר נמצא באוצרותיו לוא היה דבר אשר לוא 
>ביתו ובכל ממשלתו>הראמ יחוזקיה בביתו ובכול ממלכתו
3ויבא ישעיהו הנביא אל המלכ חזקיהו ויאמר אליו מה אמר3ויבוא ישעיה הנביא אל המלכ יחוזקיה ויואמר אליו מה א
>ו האנשימ האלה ומאינ יבאו אליכ ויאמר חזקיהו מארצ רח>מרו האנשימ האלה ומאינ יבואו אליכה ויואמר יחוזקיה מ
>וקה באו אלי מבבל>ארצ רחוקה באו אלי מבבל
4ויאמר מה ראו בביתכ ויאמר חזקיהו את כל אשר בביתי רא4ויואמר מה ראו בביתכה ויואמר יחוזקיה את כול אשר בבי
>ו לא היה דבר אשר לא הראיתימ באוצרתי>תי ראו לוא היה דבר אשר לוא הראיתימ באוצרותי
5ויאמר ישעיהו אל חזקיהו שמע דבר יהוה צבאות5ויואמר ישעיה אל יחוזקיה שמע דבר יהוה צבאות
6הנה ימימ באימ ונשא כל אשר בביתכ ואשר אצרו אבתיכ עד6הנה ימימ באימ ונשאו כול אשר בביתכה ואשר אצרו אבותי
> היומ הזה בבל לא יותר דבר אמר יהוה>כה עד היומ הזה בבל יבואו ולוא יותר דבר אמר יהוה
7ומבניכ אשר יצאו ממכ אשר תוליד יקחו והיו סריסימ בהי7ומבניכה אשר יצאו ממעיכה אשר תוליד יקחו ויהיו סריסי
>כל מלכ בבל>מ בהיכל מלכ בבל
8ויאמר חזקיהו אל ישעיהו טוב דבר יהוה אשר דברת ויאמר8ויואמר יחוזקיה אל ישעיה טוב דבר יהוה אשר דברתה ויו
> כי יהיה שלומ ואמת בימי>אמר כיא יהיה שלומ ואמת בימי
t1בעת ההוא שׁלח מרדך בלאדן בן בלאדן מלך בבל ספרים ומt1בעת ההיא שלח מרודכ בלאדונ בנ בלאדונ מלכ בבל ספרימ 
>נחה אל חזקיהו וישׁמע כי חלה ויחזק >ומנחה אל יחוזקיה וישמע כיא חלה ויחיה
2וישׂמח עליהם חזקיהו ויראם את בית נכתה את הכסף ואת 2וישמח עליהמה יחוזקיה ויראמ את כול בית נכתיו את הכס
>הזהב ואת הבשׂמים ואת׀ השׁמן הטוב ואת כל בית כליו ו>פ ואת הזהב ואת הבשמימ ואת השמנ הטוב ואת כול בית כל
>את כל אשׁר נמצא באצרתיו לא היה דבר אשׁר לא הראם חז>יו ואת כול אשר נמצא באוצרותיו לוא היה דבר אשר לוא 
>קיהו בביתו ובכל ממשׁלתו >הראמ יחוזקיה בביתו ובכול ממלכתו
3ויבא ישׁעיהו הנביא אל המלך חזקיהו ויאמר אליו מה אמ3ויבוא ישעיה הנביא אל המלכ יחוזקיה ויואמר אליו מה א
>רו׀ האנשׁים האלה ומאין יבאו אליך ויאמר חזקיהו מארץ>מרו האנשימ האלה ומאינ יבואו אליכה ויואמר יחוזקיה מ
> רחוקה באו אלי מבבל >ארצ רחוקה באו אלי מבבל
4ויאמר מה ראו בביתך ויאמר חזקיהו את כל אשׁר בביתי ר4ויואמר מה ראו בביתכה ויואמר יחוזקיה את כול אשר בבי
>או לא היה דבר אשׁר לא הראיתים באוצרתי >תי ראו לוא היה דבר אשר לוא הראיתימ באוצרותי
5ויאמר ישׁעיהו אל חזקיהו שׁמע דבר יהוה צבאות 5ויואמר ישעיה אל יחוזקיה שמע דבר יהוה צבאות
6הנה ימים באים ונשׂא׀ כל אשׁר בביתך ואשׁר אצרו אבתי6הנה ימימ באימ ונשאו כול אשר בביתכה ואשר אצרו אבותי
>ך עד היום הזה בבל לא יותר דבר אמר יהוה >כה עד היומ הזה בבל יבואו ולוא יותר דבר אמר יהוה
7ומבניך אשׁר יצאו ממך אשׁר תוליד יקחו והיו סריסים ב7ומבניכה אשר יצאו ממעיכה אשר תוליד יקחו ויהיו סריסי
>היכל מלך בבל >מ בהיכל מלכ בבל
8ויאמר חזקיהו אל ישׁעיהו טוב דבר יהוה אשׁר דברת ויא8ויואמר יחוזקיה אל ישעיה טוב דבר יהוה אשר דברתה ויו
>מר כי יהיה שׁלום ואמת בימי פ >אמר כיא יהיה שלומ ואמת בימי
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 40 MT
Isaiah 40 1QIsaa
n1נחמו נחמו עמי יאמר אלהיכמn1נחמו נחמו עמי יואמר אלוהיכמה
2דברו על לב ירושלמ וקראו אליה כי מלאה צבאה כי נרצה 2דברו על לב ירושלימ וקראו אליהא כיא מלא צבאה כיא נר
>עונה כי לקחה מיד יהוה כפלימ בכל חטאתיה>צא עוונה כיא לקחה מיד יהוה כפלימ בכול חטאותיה
3קול קורא במדבר פנו דרכ יהוה ישרו בערבה מסלה לאלהינ3קול קורא במדבר פנו דרכ יהוה וישרו בערבה מסלה לאלוה
>ו>ינו
4כל גיא ינשא וכל הר וגבעה ישפלו והיה העקב למישור וה4כול גי ינשא וכול הר וגבעה ישפלו והיה העקב למישור ו
>רכסימ לבקעה>הרוכסימ לבקעה
5ונגלה כבוד יהוה וראו כל בשר יחדו כי פי יהוה דבר5ונגלה כבוד יהוה וראו כול בשר יחדיו כיא פיא יהוה דב
 >ר
6קול אמר קרא ואמר מה אקרא כל הבשר חציר וכל חסדו כצי6קול אומר קרא ואומרה מה אקרא כול הבשר חציר וכול חסד
>צ השדה>יו כציצ השדה
7יבש חציר נבל ציצ כי רוח יהוה נשבה בו אכנ חציר העמ7יבש חציר נבל ציצ כי רוח יייי נשבה בוא הכנ חציר העמ
8יבש חציר נבל ציצ ודבר אלהינו יקומ לעולמ8יבש חציל נבל ציצ ודבר אלוהינו אלוהינו יקומ לעולמ
9על הר גבה עלי לכ מבשרת ציונ הרימי בכח קולכ מבשרת י9על הר גבה עלי לכי מבשרת ציונ הרימי בכוח קולכ מבשרת
>רושלמ הרימי אל תיראי אמרי לערי יהודה הנה אלהיכמ> ירושלימ הרימי אל תיראי אמורי לערי יהודה הנה אלוהה
 >כמה
10הנה אדני יהוה בחזק יבוא וזרעו משלה לו הנה שכרו אתו10הנה אדוני יהוה בחוזק יבוא וזרועו משלה לוא הנה שכרו
> ופעלתו לפניו> אתו ופעלתיו לפניו
11כרעה עדרו ירעה בזרעו יקבצ טלאימ ובחיקו ישא עלות ינ11כרועה עדרו ירעה בזרועו יקבצ טלימ ובחיקוה ישא עולות
>הל> ינהל
12מי מדד בשעלו מימ ושמימ בזרת תכנ וכל בשלש עפר הארצ 12מיא מדד בשועלו מי ימ ושמימ בזרתו תכנ וכל בשליש עפר
>ושקל בפלס הרימ וגבעות במאזנימ> הארצ ושקל בפלס הרימ וגבעות במוזנימ
13מי תכנ את רוח יהוה ואיש עצתו יודיענו13מיא תכנ את רוח יהוה איש עצתו יודיענה
14את מי נועצ ויבינהו וילמדהו בארח משפט וילמדהו דעת ו14את מי נועצ ויבינהו וילמדהו באורח משפט וילמדהו דעת 
>דרכ תבונות יודיענו>ודרכ תבונות יודיענו
15הנ גוימ כמר מדלי וכשחק מאזנימ נחשבו הנ איימ כדק יט15הנ גואימ כמר מדלי וכשחק מזנימ נחשבו הנ איימ כדק וי
>ול>טול
16ולבנונ אינ די בער וחיתו אינ די עולה16ולבנונ אינ די בער וחיתו אינ די עולה
t17כל הגוימ כאינ נגדו מאפס ותהו נחשבו לוt17כול הגואימ כאינ נגדו וכאפס ותהוו נחשבו לו
18ואל מי תדמיונ אל ומה דמות תערכו לו18ואל מיא תדמיוני אל ומה דמות תערוכו לי
19הפסל נסכ חרש וצרפ בזהב ירקענו ורתקות כספ צורפ19הפסל ויעשה מסכ חרש וצורפ בזהב וירקענו ורתקות כספ צ
 >ורפ
20המסכנ תרומה עצ לא ירקב יבחר חרש חכמ יבקש לו להכינ 20עצ לוא ירבק ובחר חרש חכמ ובשקלו להוכינ פסל לוא ימו
>פסל לא ימוט>ט
21הלוא תדעו הלוא תשמעו הלוא הגד מראש לכמ הלוא הבינתמ21הלוא תדעו הלוא תשמעו הלוא הוגד מרוש לכמה הלוא הבינ
> מוסדות הארצ>ותמה מוסדות ארצ
22הישב על חוג הארצ וישביה כחגבימ הנוטה כדק שמימ וימת22היושב על חוג הארצ ויושביהא כחגבימ הנוטה כדוק שמימ 
>חמ כאהל לשבת>וימתחמ כאוהל לשבת
23הנותנ רוזנימ לאינ שפטי ארצ כתהו עשה23הנותנ רוזנימ לאינ שופטי ארצ כתהו עשה
24אפ בל נטעו אפ בל זרעו אפ בל שרש בארצ גזעמ וגמ נשפ 24אפ בל נטעו אפ בל זרעו אפ בל שרשו בארצ גזעמ גמ נשפ 
>בהמ ויבשו וסערה כקש תשאמ>בהמה וייבשו וסערה כקש תשאמ
25ואל מי תדמיוני ואשוה יאמר קדוש25אל מיא תדמיוני ואשוא יואמר קדוש
26שאו מרומ עיניכמ וראו מי ברא אלה המוציא במספר צבאמ 26שאו מרומ עיניכמה וראו מי ברא אלה המוציא במספר צבאמ
>לכלמ בשמ יקרא מרב אונימ ואמיצ כח איש לא נעדר> לכולמ בשמ יקרא מרוב אונימ ואמצ כוחו ואיש לוא נעדר
27למה תאמר יעקב ותדבר ישראל נסתרה דרכי מיהוה ומאלהי 27למה תאומר יעקוב ותדבר ישראל נסתרה דרכי מיהוה ומאלו
>משפטי יעבור>הי משפטי יעבור
28הלוא ידעת אמ לא שמעת אלהי עולמ יהוה בורא קצות הארצ28הלוא ידעתה אמ לוא שמעתה אלוהי עולמ יהוה בורא קצוות
> לא ייעפ ולא ייגע אינ חקר לתבונתו> הארצ לוא יעפ ולוא יגע ואינ חקר לתבונתיו
29נתנ ליעפ כח ולאינ אונימ עצמה ירבה29הנותנ ליעפ כוח ולאינ אונימ עוצמה ירבה
30ויעפו נערימ ויגעו ובחורימ כשול יכשלו30ויעפו נערימ ויגעו ובחורימ כשול יכשולו
31וקוי יהוה יחליפו כח יעלו אבר כנשרימ ירוצו ולא ייגע31וקוי יהוה יחליפו כוח ויעלו אבר כנשרימ ירוצו ולוא י
>ו ילכו ולא ייעפו>יגעו ילכו ולוא יעופו
t1נחמו נחמו עמי יאמר אלהיכם t1נחמו נחמו עמי יואמר אלוהיכמה
2דברו על לב ירושׁלם וקראו אליה כי מלאה צבאה כי נרצה2דברו על לב ירושלימ וקראו אליהא כיא מלא צבאה כיא נר
> עונה כי לקחה מיד יהוה כפלים בכל חטאתיה ס >צא עוונה כיא לקחה מיד יהוה כפלימ בכול חטאותיה
3קול קורא במדבר פנו דרך יהוה ישׁרו בערבה מסלה לאלהי3קול קורא במדבר פנו דרכ יהוה וישרו בערבה מסלה לאלוה
>נו >ינו
4כל גיא ינשׂא וכל הר וגבעה ישׁפלו והיה העקב למישׁור4כול גי ינשא וכול הר וגבעה ישפלו והיה העקב למישור ו
> והרכסים לבקעה >הרוכסימ לבקעה
5ונגלה כבוד יהוה וראו כל בשׂר יחדו כי פי יהוה דבר ס5ונגלה כבוד יהוה וראו כול בשר יחדיו כיא פיא יהוה דב
> >ר
6קול אמר קרא ואמר מה אקרא כל הבשׂר חציר וכל חסדו כצ6קול אומר קרא ואומרה מה אקרא כול הבשר חציר וכול חסד
>יץ השׂדה >יו כציצ השדה
7יבשׁ חציר נבל ציץ כי רוח יהוה נשׁבה בו אכן חציר הע7יבש חציר נבל ציצ כי רוח יייי נשבה בוא הכנ חציר העמ
>ם  
8יבשׁ חציר נבל ציץ ודבר אלהינו יקום לעולם ס 8יבש חציל נבל ציצ ודבר אלוהינו אלוהינו יקומ לעולמ
9על הר גבה עלי לך מבשׂרת ציון הרימי בכח קולך מבשׂרת9על הר גבה עלי לכי מבשרת ציונ הרימי בכוח קולכ מבשרת
> ירושׁלם הרימי אל תיראי אמרי לערי יהודה הנה אלהיכם> ירושלימ הרימי אל תיראי אמורי לערי יהודה הנה אלוהה
> >כמה
10הנה אדני יהוה בחזק יבוא וזרעו משׁלה לו הנה שׂכרו א10הנה אדוני יהוה בחוזק יבוא וזרועו משלה לוא הנה שכרו
>תו ופעלתו לפניו > אתו ופעלתיו לפניו
11כרעה עדרו ירעה בזרעו יקבץ טלאים ובחיקו ישׂא עלות י11כרועה עדרו ירעה בזרועו יקבצ טלימ ובחיקוה ישא עולות
>נהל ס > ינהל
12מי מדד בשׁעלו מים ושׁמים בזרת תכן וכל בשׁלשׁ עפר ה12מיא מדד בשועלו מי ימ ושמימ בזרתו תכנ וכל בשליש עפר
>ארץ ושׁקל בפלס הרים וגבעות במאזנים > הארצ ושקל בפלס הרימ וגבעות במוזנימ
13מי תכן את רוח יהוה ואישׁ עצתו יודיענו 13מיא תכנ את רוח יהוה איש עצתו יודיענה
14את מי נועץ ויבינהו וילמדהו בארח משׁפט וילמדהו דעת 14את מי נועצ ויבינהו וילמדהו באורח משפט וילמדהו דעת 
>ודרך תבונות יודיענו >ודרכ תבונות יודיענו
15הן גוים כמר מדלי וכשׁחק מאזנים נחשׁבו הן איים כדק 15הנ גואימ כמר מדלי וכשחק מזנימ נחשבו הנ איימ כדק וי
>יטול >טול
16ולבנון אין די בער וחיתו אין די עולה ס 16ולבנונ אינ די בער וחיתו אינ די עולה
17כל הגוים כאין נגדו מאפס ותהו נחשׁבו לו 17כול הגואימ כאינ נגדו וכאפס ותהוו נחשבו לו
18ואל מי תדמיון אל ומה דמות תערכו לו 18ואל מיא תדמיוני אל ומה דמות תערוכו לי
19הפסל נסך חרשׁ וצרף בזהב ירקענו ורתקות כסף צורף 19הפסל ויעשה מסכ חרש וצורפ בזהב וירקענו ורתקות כספ צ
 >ורפ
20המסכן תרומה עץ לא ירקב יבחר חרשׁ חכם יבקשׁ לו להכי20עצ לוא ירבק ובחר חרש חכמ ובשקלו להוכינ פסל לוא ימו
>ן פסל לא ימוט >ט
21הלוא תדעו הלוא תשׁמעו הלוא הגד מראשׁ לכם הלוא הבינ21הלוא תדעו הלוא תשמעו הלוא הוגד מרוש לכמה הלוא הבינ
>תם מוסדות הארץ >ותמה מוסדות ארצ
22הישׁב על חוג הארץ וישׁביה כחגבים הנוטה כדק שׁמים ו22היושב על חוג הארצ ויושביהא כחגבימ הנוטה כדוק שמימ 
>ימתחם כאהל לשׁבת >וימתחמ כאוהל לשבת
23הנותן רוזנים לאין שׁפטי ארץ כתהו עשׂה 23הנותנ רוזנימ לאינ שופטי ארצ כתהו עשה
24אף בל נטעו אף בל זרעו אף בל שׁרשׁ בארץ גזעם וגם נש24אפ בל נטעו אפ בל זרעו אפ בל שרשו בארצ גזעמ גמ נשפ 
>ׁף בהם ויבשׁו וסערה כקשׁ תשׂאם ס >בהמה וייבשו וסערה כקש תשאמ
25ואל מי תדמיוני ואשׁוה יאמר קדושׁ 25אל מיא תדמיוני ואשוא יואמר קדוש
26שׂאו מרום עיניכם וראו מי ברא אלה המוציא במספר צבאם26שאו מרומ עיניכמה וראו מי ברא אלה המוציא במספר צבאמ
> לכלם בשׁם יקרא מרב אונים ואמיץ כח אישׁ לא נעדר ס > לכולמ בשמ יקרא מרוב אונימ ואמצ כוחו ואיש לוא נעדר
27למה תאמר יעקב ותדבר ישׂראל נסתרה דרכי מיהוה ומאלהי27למה תאומר יעקוב ותדבר ישראל נסתרה דרכי מיהוה ומאלו
> משׁפטי יעבור >הי משפטי יעבור
28הלוא ידעת אם לא שׁמעת אלהי עולם׀ יהוה בורא קצות הא28הלוא ידעתה אמ לוא שמעתה אלוהי עולמ יהוה בורא קצוות
>רץ לא ייעף ולא ייגע אין חקר לתבונתו > הארצ לוא יעפ ולוא יגע ואינ חקר לתבונתיו
29נתן ליעף כח ולאין אונים עצמה ירבה 29הנותנ ליעפ כוח ולאינ אונימ עוצמה ירבה
30ויעפו נערים ויגעו ובחורים כשׁול יכשׁלו 30ויעפו נערימ ויגעו ובחורימ כשול יכשולו
31וקוי יהוה יחליפו כח יעלו אבר כנשׁרים ירוצו ולא ייג31וקוי יהוה יחליפו כוח ויעלו אבר כנשרימ ירוצו ולוא י
>עו ילכו ולא ייעפו פ >יגעו ילכו ולוא יעופו
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 41 MT
Isaiah 41 1QIsaa
t1החרישו אלי איימ ולאמימ יחליפו כח יגשו אז ידברו יחדt1החרישו אלי איימ ולאומימ יחליפו כוח יגושו אז ידברו 
>ו למשפט נקרבה>יחדיו למשפט נקרבה
2מי העיר ממזרח צדק יקראהו לרגלו יתנ לפניו גוימ ומלכ2מי העיר ממזרח צדק ויקראהו לרגליו ויתנ לפניו גואימ 
>ימ ירד יתנ כעפר חרבו כקש נדפ קשתו>ומלכימ יוריד ויתנ כעפר חרבו כקש נודפ קשתו
3ירדפמ יעבור שלומ ארח ברגליו לא יבוא3וירדפמ ויעבור שלומ אורח ברגליו לוא יבינו
4מי פעל ועשה קרא הדרות מראש אני יהוה ראשונ ואת אחרנ4מיא פעל ועשה קורה הדורות מרואש אני יהוה רישונ ואת 
>ימ אני הוא>אחרונימ אני הואה
5ראו איימ וייראו קצות הארצ יחרדו קרבו ויאתיונ5ראו איימ ויראו קצאוות הארצ יחדו קרבו ואתיונ
6איש את רעהו יעזרו ולאחיו יאמר חזק6איש את רעיהו יעזורו לאחיהו יואמר חזק
7ויחזק חרש את צרפ מחליק פטיש את הולמ פעמ אמר לדבק ט7ויחזק חרש את צורפ מחליק פלטיש את אולמ פעמ יואמר לד
>וב הוא ויחזקהו במסמרימ לא ימוט>בק טוב הואה ויחזקהו במסמרימ לוא ימוט
8ואתה ישראל עבדי יעקב אשר בחרתיכ זרע אברהמ אהבי8ואתה ישראל עבדי יעקוב אשר בחרתיכה זרע אברהמ אוהבי
9אשר החזקתיכ מקצות הארצ ומאציליה קראתיכ ואמר לכ עבד9אשר החזקתיכה מקצוות הארצ ומאציליהא קראתיכה ואומרה 
>י אתה בחרתיכ ולא מאסתיכ>לכה עבדי אתה בחרתיכה ולוא מאסתיכה
10אל תירא כי עמכ אני אל תשתע כי אני אלהיכ אמצתיכ אפ 10אל תירא כיא עמכה אני אל תשתע כיא אני אלוהיכה אמצתי
>עזרתיכ אפ תמכתיכ בימינ צדקי>כה אפ עזרתיכה אפ תמכתיכה בימינ צדקי
11הנ יבשו ויכלמו כל הנחרימ בכ יהיו כאינ ויאבדו אנשי 11הנ יבושו ויכלמו כול הנחרימ בכה יובדו כול אנשי ריבכ
>ריבכ>ה
12תבקשמ ולא תמצאמ אנשי מצתכ יהיו כאינ וכאפס אנשי מלח12ואנשי מצתכה יהיו כאינ וכאפס אנשי מלחמתכה
>מתכ 
13כי אני יהוה אלהיכ מחזיק ימינכ האמר לכ אל תירא אני 13כיא אני יהוה אלוהיכה מחזיק ימינכה האומר לכה אל תיר
>עזרתיכ>א אני עזרתיכה
14אל תיראי תולעת יעקב מתי ישראל אני עזרתיכ נאמ יהוה 14אל תיראי תולעת יעקוב ומיתי ישראל אני עזרתיכה נאומ 
>וגאלכ קדוש ישראל>יהוה וגואלכה קדוש ישראל
15הנה שמתיכ למורג חרוצ חדש בעל פיפיות תדוש הרימ ותדק15הנה שמתיכה למורג חרוצ חדש בעל פיפיות תדוש הרימ ותד
> וגבעות כמצ תשימ>ק וגבעות כמוצ תשימ
16תזרמ ורוח תשאמ וסערה תפיצ אותמ ואתה תגיל ביהוה בקד16תזרמ ורוח תשאמ וסערה תפיצ אותמה ואתה תגיל ביהוה וב
>וש ישראל תתהלל>קדוש ישראל תתהלל
17העניימ והאביונימ מבקשימ מימ ואינ לשונמ בצמא נשתה א17העניימ האביונימ המבקשימ מימ ואינ לשונמה בצמה נשתה 
>ני יהוה אענמ אלהי ישראל לא אעזבמ>אני יהוה אענמ אלוהי ישראל לוא אעזובמ
18אפתח על שפיימ נהרות ובתוכ בקעות מעינות אשימ מדבר ל18אפתחה על שפאימ נהרות ובתוכ בקעות מעינימ אשימה המדב
>אגמ מימ וארצ ציה למוצאי מימ>ר לאגמ מימ וארצ ציאה למוצאי מימ
19אתנ במדבר ארז שטה והדס ועצ שמנ אשימ בערבה ברוש תדה19אתנה במדבר ארז שטה והדס ועצ שמנ אשימה בערבה בראוש 
>ר ותאשור יחדו>תרהר ותאשור יחדו
20למענ יראו וידעו וישימו וישכילו יחדו כי יד יהוה עשת20למענ יראו וידעו ויבינו וישכילו יחדיו כיא יד יהוה ע
>ה זאת וקדוש ישראל בראה>שתה זואת וקדוש ישראל בראה
21קרבו ריבכמ יאמר יהוה הגישו עצמותיכמ יאמר מלכ יעקב21קרבו ריבכמה יואמר יהוה הגישו עצמותיכמה יואמר מלכ י
 >עקוב
22יגישו ויגידו לנו את אשר תקרינה הראשנות מה הנה הגיד22יגישו ויגידו לנו את אשר תקראונ הראישונות מה הנה הג
>ו ונשימה לבנו ונדעה אחריתנ או הבאות השמיענו>ידו ונשימה לבנו ונדעה או אחרונות או הבאות השמיעונו
23הגידו האתיות לאחור ונדעה כי אלהימ אתמ אפ תיטיבו ות23הגידו האותיותלאחור ונדעה כיא אלוהימ אתמה אפ תיטיבו
>רעו ונשתעה ונראה יחדו> ותרעו ונשמעה ונראה יחדיו
24הנ אתמ מאינ ופעלכמ מאפע תועבה יבחר בכמ24הנה אתמה מאינ ופועלכמה תועבה יבחר בכמה
25העירותי מצפונ ויאת ממזרח שמש יקרא בשמי ויבא סגנימ 25העירות מצפונ ויאתיו ממזרח שמש ויקרא בשמו ויבואו סג
>כמו חמר וכמו יוצר ירמס טיט>נימ כמו חמר וכמו יוצר וירמוס טיט
26מי הגיד מראש ונדעה ומלפנימ ונאמר צדיק אפ אינ מגיד 26מיא הגיד מרוש ונדעה מלפנימ ונאומרה צדק אפ אינ מגיד
>אפ אינ משמיע אפ אינ שמע אמריכמ> אפ אינ משמיע אפ אינ שומע אמריכמה
27ראשונ לציונ הנה הנמ ולירושלמ מבשר אתנ27רישונ לציונ הנה הנומה ולירושלימ מבשר אתנ
28וארא ואינ איש ומאלה ואינ יועצ ואשאלמ וישיבו דבר28ואראה ואינ איש ומאלה ואינ יועצ אשאלמ וישיבו דבר
29הנ כלמ אונ אפס מעשיהמ רוח ותהו נסכיהמ29הנה כולמ אינ ואפס מעשיהמה רוח ותוהו נסיכיהמה
t1החרישׁו אלי איים ולאמים יחליפו כח יגשׁו אז ידברו יt1החרישו אלי איימ ולאומימ יחליפו כוח יגושו אז ידברו 
>חדו למשׁפט נקרבה >יחדיו למשפט נקרבה
2מי העיר ממזרח צדק יקראהו לרגלו יתן לפניו גוים ומלכ2מי העיר ממזרח צדק ויקראהו לרגליו ויתנ לפניו גואימ 
>ים ירד יתן כעפר חרבו כקשׁ נדף קשׁתו >ומלכימ יוריד ויתנ כעפר חרבו כקש נודפ קשתו
3ירדפם יעבור שׁלום ארח ברגליו לא יבוא 3וירדפמ ויעבור שלומ אורח ברגליו לוא יבינו
4מי פעל ועשׂה קרא הדרות מראשׁ אני יהוה ראשׁון ואת א4מיא פעל ועשה קורה הדורות מרואש אני יהוה רישונ ואת 
>חרנים אני הוא >אחרונימ אני הואה
5ראו איים וייראו קצות הארץ יחרדו קרבו ויאתיון 5ראו איימ ויראו קצאוות הארצ יחדו קרבו ואתיונ
6אישׁ את רעהו יעזרו ולאחיו יאמר חזק 6איש את רעיהו יעזורו לאחיהו יואמר חזק
7ויחזק חרשׁ את צרף מחליק פטישׁ את הולם פעם אמר לדבק7ויחזק חרש את צורפ מחליק פלטיש את אולמ פעמ יואמר לד
> טוב הוא ויחזקהו במסמרים לא ימוט ס >בק טוב הואה ויחזקהו במסמרימ לוא ימוט
8ואתה ישׂראל עבדי יעקב אשׁר בחרתיך זרע אברהם אהבי 8ואתה ישראל עבדי יעקוב אשר בחרתיכה זרע אברהמ אוהבי
9אשׁר החזקתיך מקצות הארץ ומאציליה קראתיך ואמר לך עב9אשר החזקתיכה מקצוות הארצ ומאציליהא קראתיכה ואומרה 
>די אתה בחרתיך ולא מאסתיך >לכה עבדי אתה בחרתיכה ולוא מאסתיכה
10אל תירא כי עמך אני אל תשׁתע כי אני אלהיך אמצתיך אף10אל תירא כיא עמכה אני אל תשתע כיא אני אלוהיכה אמצתי
> עזרתיך אף תמכתיך בימין צדקי >כה אפ עזרתיכה אפ תמכתיכה בימינ צדקי
11הן יבשׁו ויכלמו כל הנחרים בך יהיו כאין ויאבדו אנשׁ11הנ יבושו ויכלמו כול הנחרימ בכה יובדו כול אנשי ריבכ
>י ריבך >ה
12תבקשׁם ולא תמצאם אנשׁי מצתך יהיו כאין וכאפס אנשׁי 12ואנשי מצתכה יהיו כאינ וכאפס אנשי מלחמתכה
>מלחמתך  
13כי אני יהוה אלהיך מחזיק ימינך האמר לך אל תירא אני 13כיא אני יהוה אלוהיכה מחזיק ימינכה האומר לכה אל תיר
>עזרתיך ס >א אני עזרתיכה
14אל תיראי תולעת יעקב מתי ישׂראל אני עזרתיך נאם יהוה14אל תיראי תולעת יעקוב ומיתי ישראל אני עזרתיכה נאומ 
> וגאלך קדושׁ ישׂראל >יהוה וגואלכה קדוש ישראל
15הנה שׂמתיך למורג חרוץ חדשׁ בעל פיפיות תדושׁ הרים ו15הנה שמתיכה למורג חרוצ חדש בעל פיפיות תדוש הרימ ותד
>תדק וגבעות כמץ תשׂים >ק וגבעות כמוצ תשימ
16תזרם ורוח תשׂאם וסערה תפיץ אותם ואתה תגיל ביהוה בק16תזרמ ורוח תשאמ וסערה תפיצ אותמה ואתה תגיל ביהוה וב
>דושׁ ישׂראל תתהלל פ >קדוש ישראל תתהלל
17העניים והאביונים מבקשׁים מים ואין לשׁונם בצמא נשׁת17העניימ האביונימ המבקשימ מימ ואינ לשונמה בצמה נשתה 
>ה אני יהוה אענם אלהי ישׂראל לא אעזבם >אני יהוה אענמ אלוהי ישראל לוא אעזובמ
18אפתח על שׁפיים נהרות ובתוך בקעות מעינות אשׂים מדבר18אפתחה על שפאימ נהרות ובתוכ בקעות מעינימ אשימה המדב
> לאגם מים וארץ ציה למוצאי מים >ר לאגמ מימ וארצ ציאה למוצאי מימ
19אתן במדבר ארז שׁטה והדס ועץ שׁמן אשׂים בערבה ברושׁ19אתנה במדבר ארז שטה והדס ועצ שמנ אשימה בערבה בראוש 
> תדהר ותאשׁור יחדו >תרהר ותאשור יחדו
20למען יראו וידעו וישׂימו וישׂכילו יחדו כי יד יהוה ע20למענ יראו וידעו ויבינו וישכילו יחדיו כיא יד יהוה ע
>שׂתה זאת וקדושׁ ישׂראל בראה פ >שתה זואת וקדוש ישראל בראה
21קרבו ריבכם יאמר יהוה הגישׁו עצמותיכם יאמר מלך יעקב21קרבו ריבכמה יואמר יהוה הגישו עצמותיכמה יואמר מלכ י
> >עקוב
22יגישׁו ויגידו לנו את אשׁר תקרינה הראשׁנות׀ מה הנה 22יגישו ויגידו לנו את אשר תקראונ הראישונות מה הנה הג
>הגידו ונשׂימה לבנו ונדעה אחריתן או הבאות השׁמיענו >ידו ונשימה לבנו ונדעה או אחרונות או הבאות השמיעונו
23הגידו האתיות לאחור ונדעה כי אלהים אתם אף תיטיבו ות23הגידו האותיותלאחור ונדעה כיא אלוהימ אתמה אפ תיטיבו
>רעו ונשׁתעה ונרא יחדו > ותרעו ונשמעה ונראה יחדיו
24הן אתם מאין ופעלכם מאפע תועבה יבחר בכם 24הנה אתמה מאינ ופועלכמה תועבה יבחר בכמה
25העירותי מצפון ויאת ממזרח שׁמשׁ יקרא בשׁמי ויבא סגנ25העירות מצפונ ויאתיו ממזרח שמש ויקרא בשמו ויבואו סג
>ים כמו חמר וכמו יוצר ירמס טיט >נימ כמו חמר וכמו יוצר וירמוס טיט
26מי הגיד מראשׁ ונדעה ומלפנים ונאמר צדיק אף אין מגיד26מיא הגיד מרוש ונדעה מלפנימ ונאומרה צדק אפ אינ מגיד
> אף אין משׁמיע אף אין שׁמע אמריכם > אפ אינ משמיע אפ אינ שומע אמריכמה
27ראשׁון לציון הנה הנם ולירושׁלם מבשׂר אתן 27רישונ לציונ הנה הנומה ולירושלימ מבשר אתנ
28וארא ואין אישׁ ומאלה ואין יועץ ואשׁאלם וישׁיבו דבר28ואראה ואינ איש ומאלה ואינ יועצ אשאלמ וישיבו דבר
>  
29הן כלם און אפס מעשׂיהם רוח ותהו נסכיהם פ 29הנה כולמ אינ ואפס מעשיהמה רוח ותוהו נסיכיהמה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 42 MT
Isaiah 42 1QIsaa
n1הנ עבדי אתמכ בו בחירי רצתה נפשי נתתי רוחי עליו משפn1הנה עבדי אתמוכה בו בחירי רצתה נפשי נתתי רוחי עליו 
>ט לגוימ יוציא>ומשפטו לגואימ יוציא
2לא יצעק ולא ישא ולא ישמיע בחוצ קולו2לוא יזעק ולוא ישא ולוא ישמיע בחוצ קולו
3קנה רצוצ לא ישבור ופשתה כהה לא יכבנה לאמת יוציא מש3קנה רצוצ לוא ישבור ופשתה כהה לוא יכבה לאמת יוציא מ
>פט>שפט
4לא יכהה ולא ירוצ עד ישימ בארצ משפט ולתורתו איימ יי4ולוא יכהה ולוא ירוצ עד ישימ בארצ משפט ולתורתיו איי
>חילו>מ ינחילו
5כה אמר האל יהוה בורא השמימ ונוטיהמ רקע הארצ וצאצאי5כוה אמר האל האלוהימ בורה השמימ ונוטיהמה רוקע הארצ 
>ה נתנ נשמה לעמ עליה ורוח להלכימ בה>וצאצאיה נותנ נשמה לעמ עליהא ורוח להולכימ בה
6אני יהוה קראתיכ בצדק ואחזק בידכ ואצרכ ואתנכ לברית 6אני ייייי קרתיכה בצדק ואחזיקה בידכה ואצורכה ואתנכה
>עמ לאור גוימ> לברית עמ לאור גואימ
7לפקח עינימ עורות להוציא ממסגר אסיר מבית כלא ישבי ח7לפקוח עינימ עורות להוציא ממסגר אסור ומבית כלא יושב
>שכ>י חושכ
8אני יהוה הוא שמי וכבודי לאחר לא אתנ ותהלתי לפסילימ8אני יהוה הואה ושמי וכבודי לאחר לוא אתנ ותהלתי לפסי
 >לימ
9הראשנות הנה באו וחדשות אני מגיד בטרמ תצמחנה אשמיע 9הרישונות הנה באו והחדשות אני מגיד בטרמ תצמחנה אשמי
>אתכמ>ע אתכמה
10שירו ליהוה שיר חדש תהלתו מקצה הארצ יורדי הימ ומלאו10שירו ליהוה שיר חדש ותהלתו מקצה הארצ יורדי הימ ומלו
> איימ וישביהמ>או איימ ויושביהמ
11ישאו מדבר ועריו חצרימ תשב קדר ירנו ישבי סלע מראש ה11ישא מדבר עריו וחצרימ תשב קדר וירונו יושבי סלע מראו
>רימ יצוחו>ש הררימ יצריחו
12ישימו ליהוה כבוד ותהלתו באיימ יגידו12ישימו ליהוה כבוד ותהלתו באיימ יגידו
n13יהוה כגבור יצא כאיש מלחמות יעיר קנאה יריע אפ יצריחn13יהוה כגבור יצא כאיש מלחמות יעיר קנאה יודיע אפ יצרי
> על איביו יתגבר>ח על אויביו יתגבר
14החשיתי מעולמ אחריש אתאפק כיולדה אפעה אשמ ואשאפ יחד14אחשיתי אכ מעולמ אחריש אתאפקה כיולדה אפעה אשמה ואשו
 >פה יחדיו
15אחריב הרימ וגבעות וכל עשבמ אוביש ושמתי נהרות לאיימ15אחריבה הרימ וגבעות וכול עשבמ אוביש ושמתי נהרות לאי
> ואגמימ אוביש>ימ ואגמימ אוביש
16והולכתי עורימ בדרכ לא ידעו בנתיבות לא ידעו אדריכמ 16והוליכתי עורימ בדרכ ולוא ידעו בנתיבות לוא ידעו אדר
>אשימ מחשכ לפניהמ לאור ומעקשימ למישור אלה הדברימ עש>יכמ אשימה מהשוכימ לפניהמה לאור ומעקשימ למישור אלה 
>יתמ ולא עזבתימ>הדברימ עשיתימ ולוא עזבתימ
17נסגו אחור יבשו בשת הבטחימ בפסל האמרימ למסכה אתמ אל17נסגו אחור ובושו בושת הבוטחימ בפסל האמרימ למסכה אתמ
>הינו>ה אלוהינו
18החרשימ שמעו והעורימ הביטו לראות18החרשימ שמעו והעורימ הביטו לראות
t19מי עור כי אמ עבדי וחרש כמלאכי אשלח מי עור כמשלמ ועt19מי עור כיא אמ עבדי וחרש כמלאכי אשלח מי עואר כמשלמ 
>ור כעבד יהוה>ועואר כעבד יהוה
20ראות רבות ולא תשמר פקוח אזנימ ולא ישמע20ראיתה רבות ולוא תשמור פתחו אוזנימ ולוא ישמע
21יהוה חפצ למענ צדקו יגדיל תורה ויאדיר21יהוה חפצ למענ צדקו ויגדל תורה ויאדרהה
22והוא עמ בזוז ושסוי הפח בחורימ כלמ ובבתי כלאימ החבא22והואה עמ בזוז ושסוי הפח בחורימ כולמ ובבתי כלאימ הו
>ו היו לבז ואינ מציל משסה ואינ אמר השב>חבאו היו לבז ואינ מציל למשוסה ואינ אומר השב
23מי בכמ יאזינ זאת יקשב וישמע לאחור23מיא בכמה ויאזינ זואת ויקשב וישמע לאחור
24מי נתנ למשיסה יעקב וישראל לבזזימ הלוא יהוה זו חטאנ24מיא נתנ למשוסה יעקוב וישראל לבוזזימ הלוא יהוה זה ח
>ו לו ולא אבו בדרכיו הלוכ ולא שמעו בתורתו>טאנו לו ולוא אבו בדרכיו להלוכ ולוא שמעו בתורתיו
25וישפכ עליו חמה אפו ועזוז מלחמה ותלהטהו מסביב ולא י25וישפוכ עליו חמת אפוא ועוזז מלחמה ותלהטהו מסביב ולו
>דע ותבער בו ולא ישימ על לב>א ידע ותבער בו ולוא ישימ על לב
t1הן עבדי אתמך בו בחירי רצתה נפשׁי נתתי רוחי עליו משt1הנה עבדי אתמוכה בו בחירי רצתה נפשי נתתי רוחי עליו 
>ׁפט לגוים יוציא >ומשפטו לגואימ יוציא
2לא יצעק ולא ישׂא ולא ישׁמיע בחוץ קולו 2לוא יזעק ולוא ישא ולוא ישמיע בחוצ קולו
3קנה רצוץ לא ישׁבור ופשׁתה כהה לא יכבנה לאמת יוציא 3קנה רצוצ לוא ישבור ופשתה כהה לוא יכבה לאמת יוציא מ
>משׁפט >שפט
4לא יכהה ולא ירוץ עד ישׂים בארץ משׁפט ולתורתו איים 4ולוא יכהה ולוא ירוצ עד ישימ בארצ משפט ולתורתיו איי
>ייחילו פ >מ ינחילו
5כה אמר האל׀ יהוה בורא השׁמים ונוטיהם רקע הארץ וצאצ5כוה אמר האל האלוהימ בורה השמימ ונוטיהמה רוקע הארצ 
>איה נתן נשׁמה לעם עליה ורוח להלכים בה >וצאצאיה נותנ נשמה לעמ עליהא ורוח להולכימ בה
6אני יהוה קראתיך בצדק ואחזק בידך ואצרך ואתנך לברית 6אני ייייי קרתיכה בצדק ואחזיקה בידכה ואצורכה ואתנכה
>עם לאור גוים > לברית עמ לאור גואימ
7לפקח עינים עורות להוציא ממסגר אסיר מבית כלא ישׁבי 7לפקוח עינימ עורות להוציא ממסגר אסור ומבית כלא יושב
>חשׁך >י חושכ
8אני יהוה הוא שׁמי וכבודי לאחר לא אתן ותהלתי לפסילי8אני יהוה הואה ושמי וכבודי לאחר לוא אתנ ותהלתי לפסי
>ם >לימ
9הראשׁנות הנה באו וחדשׁות אני מגיד בטרם תצמחנה אשׁמ9הרישונות הנה באו והחדשות אני מגיד בטרמ תצמחנה אשמי
>יע אתכם פ >ע אתכמה
10שׁירו ליהוה שׁיר חדשׁ תהלתו מקצה הארץ יורדי הים ומ10שירו ליהוה שיר חדש ותהלתו מקצה הארצ יורדי הימ ומלו
>לאו איים וישׁביהם >או איימ ויושביהמ
11ישׂאו מדבר ועריו חצרים תשׁב קדר ירנו ישׁבי סלע מרא11ישא מדבר עריו וחצרימ תשב קדר וירונו יושבי סלע מראו
>שׁ הרים יצוחו >ש הררימ יצריחו
12ישׂימו ליהוה כבוד ותהלתו באיים יגידו 12ישימו ליהוה כבוד ותהלתו באיימ יגידו
13יהוה כגבור יצא כאישׁ מלחמות יעיר קנאה יריע אף יצרי13יהוה כגבור יצא כאיש מלחמות יעיר קנאה יודיע אפ יצרי
>ח על איביו יתגבר ס >ח על אויביו יתגבר
14החשׁיתי מעולם אחרישׁ אתאפק כיולדה אפעה אשׁם ואשׁאף14אחשיתי אכ מעולמ אחריש אתאפקה כיולדה אפעה אשמה ואשו
> יחד >פה יחדיו
15אחריב הרים וגבעות וכל עשׂבם אובישׁ ושׂמתי נהרות לא15אחריבה הרימ וגבעות וכול עשבמ אוביש ושמתי נהרות לאי
>יים ואגמים אובישׁ >ימ ואגמימ אוביש
16והולכתי עורים בדרך לא ידעו בנתיבות לא ידעו אדריכם 16והוליכתי עורימ בדרכ ולוא ידעו בנתיבות לוא ידעו אדר
>אשׂים מחשׁך לפניהם לאור ומעקשׁים למישׁור אלה הדברי>יכמ אשימה מהשוכימ לפניהמה לאור ומעקשימ למישור אלה 
>ם עשׂיתם ולא עזבתים >הדברימ עשיתימ ולוא עזבתימ
17נסגו אחור יבשׁו בשׁת הבטחים בפסל האמרים למסכה אתם 17נסגו אחור ובושו בושת הבוטחימ בפסל האמרימ למסכה אתמ
>אלהינו ס >ה אלוהינו
18החרשׁים שׁמעו והעורים הביטו לראות 18החרשימ שמעו והעורימ הביטו לראות
19מי עור כי אם עבדי וחרשׁ כמלאכי אשׁלח מי עור כמשׁלם19מי עור כיא אמ עבדי וחרש כמלאכי אשלח מי עואר כמשלמ 
> ועור כעבד יהוה >ועואר כעבד יהוה
20ראית רבות ולא תשׁמר פקוח אזנים ולא ישׁמע 20ראיתה רבות ולוא תשמור פתחו אוזנימ ולוא ישמע
21יהוה חפץ למען צדקו יגדיל תורה ויאדיר 21יהוה חפצ למענ צדקו ויגדל תורה ויאדרהה
22והוא עם בזוז ושׁסוי הפח בחורים כלם ובבתי כלאים החב22והואה עמ בזוז ושסוי הפח בחורימ כולמ ובבתי כלאימ הו
>או היו לבז ואין מציל משׁסה ואין אמר השׁב >חבאו היו לבז ואינ מציל למשוסה ואינ אומר השב
23מי בכם יאזין זאת יקשׁב וישׁמע לאחור 23מיא בכמה ויאזינ זואת ויקשב וישמע לאחור
24מי נתן למשׁוסה יעקב וישׂראל לבזזים הלוא יהוה זו חט24מיא נתנ למשוסה יעקוב וישראל לבוזזימ הלוא יהוה זה ח
>אנו לו ולא אבו בדרכיו הלוך ולא שׁמעו בתורתו >טאנו לו ולוא אבו בדרכיו להלוכ ולוא שמעו בתורתיו
25וישׁפך עליו חמה אפו ועזוז מלחמה ותלהטהו מסביב ולא 25וישפוכ עליו חמת אפוא ועוזז מלחמה ותלהטהו מסביב ולו
>ידע ותבער בו ולא ישׂים על לב פ >א ידע ותבער בו ולוא ישימ על לב
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 43 MT
Isaiah 43 1QIsaa
t1ועתה כה אמר יהוה בראכ יעקב ויצרכ ישראל אל תירא כי t1ועתה כוה אמר יהוה בוראיכה יעקוב ויוצריכה ישראל אל 
>גאלתיכ קראתי בשמכ לי אתה>תירא כיא גאלתיכה קראתי בשמכה ליא אתה
2כי תעבר במימ אתכ אני ובנהרות לא ישטפוכ כי תלכ במו 2כיא תעבור במימ אתכה אני ובנהרות לוא ישטפוכ כיא תלכ
>אש לא תכוה ולהבה לא תבער בכ> במו אש לוא תכוה ולהבה לוא תבער בכה
3כי אני יהוה אלהיכ קדוש ישראל מושיעכ נתתי כפרכ מצרי3אני יהוה אלוהיכה קדוש ישראל גואלכ ונתתי מצרימ כופר
>מ כוש וסבא תחתיכ>כ כוש וסבאימ תחתיכה
4מאשר יקרת בעיני נכבדת ואני אהבתיכ ואתנ אדמ תחתיכ ו4מאשר יקרתה בעיני נכבדתה ואני אהבתיכה אתנ האדמ תחתי
>לאמימ תחת נפשכ>כה ולאומימ תחת נפשכה
5אל תירא כי אתכ אני ממזרח אביא זרעכ וממערב אקבצכ5אל תירא כיא אתכה אני ממזרח אביא זרעכה וממערב אקבצכ
 >ה
6אמר לצפונ תני ולתימנ אל תכלאי הביאי בני מרחוק ובנו6אומר לצפונ תני ולתימנ אל תכלאי הביאו בני מרחוק ובנ
>תי מקצה הארצ>ותי מקצוי הארצ
7כל הנקרא בשמי ולכבודי בראתיו יצרתיו אפ עשיתיו7כול הנקרא בשמי ולכבודי בראתיהו יצרתיהו אפ עשיתיהו
8הוציא עמ עור ועינימ יש וחרשימ ואזנימ למו8הוציאו עמ עואר עינימ יש וחרשימ ואוזנימ למו
9כל הגוימ נקבצו יחדו ויאספו לאמימ מי בהמ יגיד זאת ו9כול הגואימ נקבצו יחדיו ויאספו לאומימ מי בהמה ויגיד
>ראשנות ישמיענו יתנו עדיהמ ויצדקו וישמעו ויאמרו אמת>ו זואת ורישונות ישמיעו יתנו עדיהמה ויצדקו וישמיעו 
 >ויואמרו אמת
10אתמ עדי נאמ יהוה ועבדי אשר בחרתי למענ תדעו ותאמינו10אתמה עדי נואמ יהוה עבדי אשר בחרתי למענ תדעו ותאמינ
> לי ותבינו כי אני הוא לפני לא נוצר אל ואחרי לא יהי>ו ליא ותבינו כיא אני הואה לפני לוא נוצר אל ואחרי ל
>ה>וא היה
11אנכי אנכי יהוה ואינ מבלעדי מושיע11אנוכי אנוכי יהוה ואינ מבלעדי מושיע
12אנכי הגדתי והושעתי והשמעתי ואינ בכמ זר ואתמ עדי נא12אנוכי הגדתי והושעתי והשמעתי ואינ בכמה זר ואתמה עדי
>מ יהוה ואני אל> נואמ יהוה אני אל
13גמ מיומ אני הוא ואינ מידי מציל אפעל ומי ישיבנה13גמ מיומ אני הואה ואינ מידי מציל אפעולה ומי ישיבנה
14כה אמר יהוה גאלכמ קדוש ישראל למענכמ שלחתי בבלה והו14כוה אמר יהוה גואלכמה קדוש ישראל למענכמה שלחתי בבבל
>רדתי בריחימ כלמ וכשדימ באניות רנתמ> והורדתי בריחימ כולמ וכשדיימ באוניות רנתמה
15אני יהוה קדושכמ בורא ישראל מלככמ15אני יהוה קדושכמה בורא ישראל מלככמה
16כה אמר יהוה הנותנ בימ דרכ ובמימ עזימ נתיבה16כוה אמר יהוה הנותנ בימ דרכ ובמימ עזימ נתיבה
17המוציא רכב וסוס חיל ועזוז יחדו ישכבו בל יקומו דעכו17המוציא רכב וסוס וחיל ועוזוז יחדיו ישכובו בל יקומו 
> כפשתה כבו>דעכו כפשתה כבו
18אל תזכרו ראשנות וקדמניות אל תתבננו18אל תזכור רישונות וקדמוניות אל תתבוננו
19הנני עשה חדשה עתה תצמח הלוא תדעוה אפ אשימ במדבר דר19הנני עושה חדשה ועתה תצמח הלוא תדעו אפ אשימ במדבר ד
>כ בישמונ נהרות>רכ בישומונ נתיבות
20תכבדני חית השדה תנימ ובנות יענה כי נתתי במדבר מימ 20תכבדני חית השדה תנימ ובנות יענה כיא אתנ במדבר מימ 
>נהרות בישימנ להשקות עמי בחירי>נהרות בישומונ להשקות עמי ובחירי
21עמ זו יצרתי לי תהלתי יספרו21עמ זה יצרתי לי ותהלתי יואמרו
22ולא אתי קראת יעקב כי יגעת בי ישראל22ולוא אותי קראתה יעקוב כיא יגעתה ביא ישראל
23לא הביאת לי שה עלתיכ וזבחיכ לא כבדתני לא העבדתיכ ב23לוא הביאותה לי שה לעולה ובזבחיכה לוא כבדתני ולוא ע
>מנחה ולא הוגעתיכ בלבונה>שיתה ליא מנחה ולוא הוגעתיכה בלבונה
24לא קנית לי בכספ קנה וחלב זבחיכ לא הרויתני אכ העבדת24לוא קניתה ליא בכספ קנה וחלב זבחיכה לוא הרויתני אכ 
>ני בחטאותיכ הוגעתני בעונתיכ>העבדתני בחטאותיכה הוגעתני בעונכה
25אנכי אנכי הוא מחה פשעיכ למעני וחטאתיכ לא אזכר25אנוכי אנוכי הואה מוחה פשעכה למעני וחטאתיכה לוא אזכ
 >ור עוד
26הזכירני נשפטה יחד ספר אתה למענ תצדק26הזכירוני נשפטה יחדיו ספר אתה למענ תצדק
27אביכ הראשונ חטא ומליציכ פשעו בי27אביכה הרישונ חטא ומליציכה פשעו ביא
28ואחלל שרי קדש ואתנה לחרמ יעקב וישראל לגדופימ28ואחללה שרי קודש ואתנ לחרמ יעקוב וישראל לגודפימ
t1ועתה כה אמר יהוה בראך יעקב ויצרך ישׂראל אל תירא כיt1ועתה כוה אמר יהוה בוראיכה יעקוב ויוצריכה ישראל אל 
> גאלתיך קראתי בשׁמך לי אתה >תירא כיא גאלתיכה קראתי בשמכה ליא אתה
2כי תעבר במים אתך אני ובנהרות לא ישׁטפוך כי תלך במו2כיא תעבור במימ אתכה אני ובנהרות לוא ישטפוכ כיא תלכ
> אשׁ לא תכוה ולהבה לא תבער בך > במו אש לוא תכוה ולהבה לוא תבער בכה
3כי אני יהוה אלהיך קדושׁ ישׂראל מושׁיעך נתתי כפרך מ3אני יהוה אלוהיכה קדוש ישראל גואלכ ונתתי מצרימ כופר
>צרים כושׁ וסבא תחתיך >כ כוש וסבאימ תחתיכה
4מאשׁר יקרת בעיני נכבדת ואני אהבתיך ואתן אדם תחתיך 4מאשר יקרתה בעיני נכבדתה ואני אהבתיכה אתנ האדמ תחתי
>ולאמים תחת נפשׁך >כה ולאומימ תחת נפשכה
5אל תירא כי אתך אני ממזרח אביא זרעך וממערב אקבצך 5אל תירא כיא אתכה אני ממזרח אביא זרעכה וממערב אקבצכ
 >ה
6אמר לצפון תני ולתימן אל תכלאי הביאי בני מרחוק ובנו6אומר לצפונ תני ולתימנ אל תכלאי הביאו בני מרחוק ובנ
>תי מקצה הארץ >ותי מקצוי הארצ
7כל הנקרא בשׁמי ולכבודי בראתיו יצרתיו אף עשׂיתיו 7כול הנקרא בשמי ולכבודי בראתיהו יצרתיהו אפ עשיתיהו
8הוציא עם עור ועינים ישׁ וחרשׁים ואזנים למו 8הוציאו עמ עואר עינימ יש וחרשימ ואוזנימ למו
9כל הגוים נקבצו יחדו ויאספו לאמים מי בהם יגיד זאת ו9כול הגואימ נקבצו יחדיו ויאספו לאומימ מי בהמה ויגיד
>ראשׁנות ישׁמיענו יתנו עדיהם ויצדקו וישׁמעו ויאמרו >ו זואת ורישונות ישמיעו יתנו עדיהמה ויצדקו וישמיעו 
>אמת >ויואמרו אמת
10אתם עדי נאם יהוה ועבדי אשׁר בחרתי למען תדעו ותאמינ10אתמה עדי נואמ יהוה עבדי אשר בחרתי למענ תדעו ותאמינ
>ו לי ותבינו כי אני הוא לפני לא נוצר אל ואחרי לא יה>ו ליא ותבינו כיא אני הואה לפני לוא נוצר אל ואחרי ל
>יה ס >וא היה
11אנכי אנכי יהוה ואין מבלעדי מושׁיע 11אנוכי אנוכי יהוה ואינ מבלעדי מושיע
12אנכי הגדתי והושׁעתי והשׁמעתי ואין בכם זר ואתם עדי 12אנוכי הגדתי והושעתי והשמעתי ואינ בכמה זר ואתמה עדי
>נאם יהוה ואני אל > נואמ יהוה אני אל
13גם מיום אני הוא ואין מידי מציל אפעל ומי ישׁיבנה ס 13גמ מיומ אני הואה ואינ מידי מציל אפעולה ומי ישיבנה
14כה אמר יהוה גאלכם קדושׁ ישׂראל למענכם שׁלחתי בבלה 14כוה אמר יהוה גואלכמה קדוש ישראל למענכמה שלחתי בבבל
>והורדתי בריחים כלם וכשׂדים באניות רנתם > והורדתי בריחימ כולמ וכשדיימ באוניות רנתמה
15אני יהוה קדושׁכם בורא ישׂראל מלככם ס 15אני יהוה קדושכמה בורא ישראל מלככמה
16כה אמר יהוה הנותן בים דרך ובמים עזים נתיבה 16כוה אמר יהוה הנותנ בימ דרכ ובמימ עזימ נתיבה
17המוציא רכב וסוס חיל ועזוז יחדו ישׁכבו בל יקומו דעכ17המוציא רכב וסוס וחיל ועוזוז יחדיו ישכובו בל יקומו 
>ו כפשׁתה כבו >דעכו כפשתה כבו
18אל תזכרו ראשׁנות וקדמניות אל תתבננו 18אל תזכור רישונות וקדמוניות אל תתבוננו
19הנני עשׂה חדשׁה עתה תצמח הלוא תדעוה אף אשׂים במדבר19הנני עושה חדשה ועתה תצמח הלוא תדעו אפ אשימ במדבר ד
> דרך בישׁמון נהרות >רכ בישומונ נתיבות
20תכבדני חית השׂדה תנים ובנות יענה כי נתתי במדבר מים20תכבדני חית השדה תנימ ובנות יענה כיא אתנ במדבר מימ 
> נהרות בישׁימן להשׁקות עמי בחירי >נהרות בישומונ להשקות עמי ובחירי
21עם זו יצרתי לי תהלתי יספרו ס 21עמ זה יצרתי לי ותהלתי יואמרו
22ולא אתי קראת יעקב כי יגעת בי ישׂראל 22ולוא אותי קראתה יעקוב כיא יגעתה ביא ישראל
23לא הביאת לי שׂה עלתיך וזבחיך לא כבדתני לא העבדתיך 23לוא הביאותה לי שה לעולה ובזבחיכה לוא כבדתני ולוא ע
>במנחה ולא הוגעתיך בלבונה >שיתה ליא מנחה ולוא הוגעתיכה בלבונה
24לא קנית לי בכסף קנה וחלב זבחיך לא הרויתני אך העבדת24לוא קניתה ליא בכספ קנה וחלב זבחיכה לוא הרויתני אכ 
>ני בחטאותיך הוגעתני בעונתיך ס >העבדתני בחטאותיכה הוגעתני בעונכה
25אנכי אנכי הוא מחה פשׁעיך למעני וחטאתיך לא אזכר 25אנוכי אנוכי הואה מוחה פשעכה למעני וחטאתיכה לוא אזכ
 >ור עוד
26הזכירני נשׁפטה יחד ספר אתה למען תצדק 26הזכירוני נשפטה יחדיו ספר אתה למענ תצדק
27אביך הראשׁון חטא ומליציך פשׁעו בי 27אביכה הרישונ חטא ומליציכה פשעו ביא
28ואחלל שׂרי קדשׁ ואתנה לחרם יעקב וישׂראל לגדופים ס 28ואחללה שרי קודש ואתנ לחרמ יעקוב וישראל לגודפימ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 44 MT
Isaiah 44 1QIsaa
n1ועתה שמע יעקב עבדי וישראל בחרתי בוn1ועתה שמע יעקוב עבדי וישראל בחרתי בוא
2כה אמר יהוה עשכ ויצרכ מבטנ יעזרכ אל תירא עבדי יעקב2כוה אמר יהוה עושכה ויוצרכה מבטנ ועוזרכה אל תירא עב
> וישרונ בחרתי בו>די יעקוב וישורונ בחרתי בוא
3כי אצק מימ על צמא ונזלימ על יבשה אצק רוחי על זרעכ 3כיא אצק מימ על צמא ונוזלימ על יבשה כנ אצק רוחי על 
>וברכתי על צאצאיכ>זרעכה וברכתי על צאצאיכה
4וצמחו בבינ חציר כערבימ על יבלי מימ4יצמחו כבינ חציר כערבימ על יובלי מימ
5זה יאמר ליהוה אני וזה יקרא בשמ יעקב וזה יכתב ידו ל5זה יואמר ליהוה אני וזה יקרא בשמ יעקוב וזה יכתוב יד
>יהוה ובשמ ישראל יכנה>והי ליהוה ובשמ ישראל יכנה
6כה אמר יהוה מלכ ישראל וגאלו יהוה צבאות אני ראשונ ו6כוה אמר יהוה מלכ ישראל וגואליו יהוה צבאות שמו אני 
>אני אחרונ ומבלעדי אינ אלהימ>רישונ ואני אחרונ ומבלעדי אינ אלוהימ
7ומי כמוני יקרא ויגידה ויערכה לי משומי עמ עולמ ואתי7ומיא כמוני יקרא ויגידה ויערוכהה לוא משימו עמ עולמ 
>ות ואשר תבאנה יגידו למו>ואותיות יואמר אשר תבואינה יגידו למו
8אל תפחדו ואל תרהו הלא מאז השמעתיכ והגדתי ואתמ עדי 8אל תפחדו ואל תיראו הלוא מאז השמעתיכה והגדתי ואתמה 
>היש אלוה מבלעדי ואינ צור בל ידעתי>עדי היש אלוה מבלעדי ואינ צור בל ידעתי
9יצרי פסל כלמ תהו וחמודיהמ בל יועילו ועדיהמ המה בל 9ויצר פסל כולמה תהו וחמודיהמה בל יועילו ועדיהמה המה
>יראו ובל ידעו למענ יבשו> בל יראו בל ידעו למענ יבושו
10מי יצר אל ופסל נסכ לבלתי הועיל10מי יצר אל ופסל נסכ לבלתי הועיל
t11הנ כל חבריו יבשו וחרשימ המה מאדמ יתקבצו כלמ יעמדו t11הנה כול חובריו יבושו וחרשימ המה מאדמ יתקבצו כולמ ו
>יפחדו יבשו יחד>עמודו ופחדו יבושו יחדיו
12חרש ברזל מעצד ופעל בפחמ ובמקבות יצרהו ויפעלהו בזרו12חרש ברזל מעצד יפעל בפחמ ובמקבות ויצורהו ויפעלהו בז
>ע כחו גמ רעב ואינ כח לא שתה מימ וייעפ>רוע כוחוה גמ רעב ואינ כוח לוא שותה מימ ויועפ
13חרש עצימ נטה קו יתארהו בשרד יעשהו במקצעות ובמחוגה 13חרש עצימ נטהו קו יתארהו בשרד ועשהו במקצעות ובמחגה 
>יתארהו ויעשהו כתבנית איש כתפארת אדמ לשבת בית>יתארהו ויעשהו כתבנית איש כתפארת אדמ לשבת בית
14לכרת לו ארזימ ויקח תרזה ואלונ ויאמצ לו בעצי יער נט14לכרות לוא ארזימ ויקח תרזה אלונ ויאמצ לוא בעצי יער 
>ע ארנ וגשמ יגדל>נטע אורנ וגשמ יגדל
15והיה לאדמ לבער ויקח מהמ ויחמ אפ ישיק ואפה לחמ אפ י15והגה לאדמ לבער ויקח מהמה ויחומ אפ ישיק ואפה לחמ או
>פעל אל וישתחו עשהו פסל ויסגד למו> יפעל אל וישתחו עשהו פסל ויסגוד למו
16חציו שרפ במו אש על חציו בשר יאכל יצלה צלי וישבע אפ16חציו שרפ במו אש ועל וחציו בשר ויאכל ועל גחליו ישב 
> יחמ ויאמר האח חמותי ראיתי אור>ויחמ ויואמר האח חמותי נגד אור
17ושאריתו לאל עשה לפסלו יסגד לו וישתחו ויתפלל אליו ו17ושריתו לאל עשה לבליו עצ יסגוד לו וישתחוה ויתפלל אל
>יאמר הצילני כי אלי אתה>יו ויואמר הצילני כיא אלי אתה
18לא ידעו ולא יבינו כי טח מראות עיניהמ מהשכיל לבתמ18לוא ידעו ולוא יבינו כיא טח מראות עיניהמה מהשכל לבו
 >תמה
19ולא ישיב אל לבו ולא דעת ולא תבונה לאמר חציו שרפתי 19ולוא ישיב אל לבו ולוא דעת ולוא תבונה לאמור לאמור ח
>במו אש ואפ אפיתי על גחליו לחמ אצלה בשר ואכל ויתרו >ציו שרפתי במו אש ואפ אפיתי על גחליו לחמ ואצלה בשר 
>לתועבה אעשה לבול עצ אסגוד>ואוכלה ויתרו לתועבות אעשה לבלוי עצ אסגוד
20רעה אפר לב הותל הטהו ולא יציל את נפשו ולא יאמר הלו20רועה אפר לב הותל הטהו ולוא יוכיל נפשו ולוא יואמר ש
>א שקר בימיני>קר בימיני
21זכר אלה יעקב וישראל כי עבדי אתה יצרתיכ עבד לי אתה 21זכור אלה יעקוב ישראל כיא עבדי אתה יצרתיכה עבד לי א
>ישראל לא תנשני>תה ישראל לוא תשאני
22מחיתי כעב פשעיכ וכעננ חטאותיכ שובה אלי כי גאלתיכ22מחיתי כעב פשעכה וכעננ חטאותיכה שובה אלי כיא גאלתיכ
 >ה
23רנו שמימ כי עשה יהוה הריעו תחתיות ארצ פצחו הרימ רנ23רונו שמימ כיא עשה יהוה הריעו תחתיות הארצ פצחו הרימ
>ה יער וכל עצ בו כי גאל יהוה יעקב ובישראל יתפאר> רונה יער כול עצ בו כיא גאל יהוה יעקוב ובישראל יתפ
 >אר
24כה אמר יהוה גאלכ ויצרכ מבטנ אנכי יהוה עשה כל נטה ש24כוה אמר יהוה גואלכה ויוצרכה מבטנ אנוכי יהוה עושה כ
>מימ לבדי רקע הארצ מאתי>ול נוטה שמימ לבדי רוקע הארצ מיא אתי
25מפר אתות בדימ וקסמימ יהולל משיב חכמימ אחור ודעתמ י25מפר אותות בדימ וקסמימ יהולל משיב חכמימ אחור ודעתמ 
>שכל>יסכל
26מקימ דבר עבדו ועצת מלאכיו ישלימ האמר לירושלמ תושב 26מקימ דבר עבדו ועצת מלאכיו ישלימ האומר לירושלימ תשב
>ולערי יהודה תבנינה וחרבותיה אקוממ> ולערי יהודה תבנינה וחרבותיה אקוממ
27האמר לצולה חרבי ונהרתיכ אוביש27האומר לצולה חרבי ונהרותיכ אוביש
28האמר לכורש רעי וכל חפצי ישלמ ולאמר לירושלמ תבנה וה28האומר לכורש רעי וכול חפצי ישלימ ולאמור לירושלימ תב
>יכל תוסד>נה והיכל יתיסד
t1ועתה שׁמע יעקב עבדי וישׂראל בחרתי בו t1ועתה שמע יעקוב עבדי וישראל בחרתי בוא
2כה אמר יהוה עשׂך ויצרך מבטן יעזרך אל תירא עבדי יעק2כוה אמר יהוה עושכה ויוצרכה מבטנ ועוזרכה אל תירא עב
>ב וישׁרון בחרתי בו >די יעקוב וישורונ בחרתי בוא
3כי אצק מים על צמא ונזלים על יבשׁה אצק רוחי על זרעך3כיא אצק מימ על צמא ונוזלימ על יבשה כנ אצק רוחי על 
> וברכתי על צאצאיך >זרעכה וברכתי על צאצאיכה
4וצמחו בבין חציר כערבים על יבלי מים 4יצמחו כבינ חציר כערבימ על יובלי מימ
5זה יאמר ליהוה אני וזה יקרא בשׁם יעקב וזה יכתב ידו 5זה יואמר ליהוה אני וזה יקרא בשמ יעקוב וזה יכתוב יד
>ליהוה ובשׁם ישׂראל יכנה פ >והי ליהוה ובשמ ישראל יכנה
6כה אמר יהוה מלך ישׂראל וגאלו יהוה צבאות אני ראשׁון6כוה אמר יהוה מלכ ישראל וגואליו יהוה צבאות שמו אני 
> ואני אחרון ומבלעדי אין אלהים >רישונ ואני אחרונ ומבלעדי אינ אלוהימ
7ומי כמוני יקרא ויגידה ויערכה לי משׂומי עם עולם ואת7ומיא כמוני יקרא ויגידה ויערוכהה לוא משימו עמ עולמ 
>יות ואשׁר תבאנה יגידו למו >ואותיות יואמר אשר תבואינה יגידו למו
8אל תפחדו ואל תרהו הלא מאז השׁמעתיך והגדתי ואתם עדי8אל תפחדו ואל תיראו הלוא מאז השמעתיכה והגדתי ואתמה 
> הישׁ אלוה מבלעדי ואין צור בל ידעתי >עדי היש אלוה מבלעדי ואינ צור בל ידעתי
9יצרי פסל כלם תהו וחמודיהם בל יועילו ועדיהם המה בל 9ויצר פסל כולמה תהו וחמודיהמה בל יועילו ועדיהמה המה
>יראו ובל ידעו למען יבשׁו > בל יראו בל ידעו למענ יבושו
10מי יצר אל ופסל נסך לבלתי הועיל 10מי יצר אל ופסל נסכ לבלתי הועיל
11הן כל חבריו יבשׁו וחרשׁים המה מאדם יתקבצו כלם יעמד11הנה כול חובריו יבושו וחרשימ המה מאדמ יתקבצו כולמ ו
>ו יפחדו יבשׁו יחד >עמודו ופחדו יבושו יחדיו
12חרשׁ ברזל מעצד ופעל בפחם ובמקבות יצרהו ויפעלהו בזר12חרש ברזל מעצד יפעל בפחמ ובמקבות ויצורהו ויפעלהו בז
>וע כחו גם רעב ואין כח לא שׁתה מים וייעף >רוע כוחוה גמ רעב ואינ כוח לוא שותה מימ ויועפ
13חרשׁ עצים נטה קו יתארהו בשׂרד יעשׂהו במקצעות ובמחו13חרש עצימ נטהו קו יתארהו בשרד ועשהו במקצעות ובמחגה 
>גה יתארהו ויעשׂהו כתבנית אישׁ כתפארת אדם לשׁבת בית>יתארהו ויעשהו כתבנית איש כתפארת אדמ לשבת בית
>  
14לכרת לו ארזים ויקח תרזה ואלון ויאמץ לו בעצי יער נט14לכרות לוא ארזימ ויקח תרזה אלונ ויאמצ לוא בעצי יער 
>ע ארן וגשׁם יגדל >נטע אורנ וגשמ יגדל
15והיה לאדם לבער ויקח מהם ויחם אף ישׂיק ואפה לחם אף 15והגה לאדמ לבער ויקח מהמה ויחומ אפ ישיק ואפה לחמ או
>יפעל אל וישׁתחו עשׂהו פסל ויסגד למו > יפעל אל וישתחו עשהו פסל ויסגוד למו
16חציו שׂרף במו אשׁ על חציו בשׂר יאכל יצלה צלי וישׂב16חציו שרפ במו אש ועל וחציו בשר ויאכל ועל גחליו ישב 
>ע אף יחם ויאמר האח חמותי ראיתי אור >ויחמ ויואמר האח חמותי נגד אור
17ושׁאריתו לאל עשׂה לפסלו יסגוד לו וישׁתחו ויתפלל אל17ושריתו לאל עשה לבליו עצ יסגוד לו וישתחוה ויתפלל אל
>יו ויאמר הצילני כי אלי אתה >יו ויואמר הצילני כיא אלי אתה
18לא ידעו ולא יבינו כי טח מראות עיניהם מהשׂכיל לבתם 18לוא ידעו ולוא יבינו כיא טח מראות עיניהמה מהשכל לבו
 >תמה
19ולא ישׁיב אל לבו ולא דעת ולא תבונה לאמר חציו שׂרפת19ולוא ישיב אל לבו ולוא דעת ולוא תבונה לאמור לאמור ח
>י במו אשׁ ואף אפיתי על גחליו לחם אצלה בשׂר ואכל וי>ציו שרפתי במו אש ואפ אפיתי על גחליו לחמ ואצלה בשר 
>תרו לתועבה אעשׂה לבול עץ אסגוד >ואוכלה ויתרו לתועבות אעשה לבלוי עצ אסגוד
20רעה אפר לב הותל הטהו ולא יציל את נפשׁו ולא יאמר הל20רועה אפר לב הותל הטהו ולוא יוכיל נפשו ולוא יואמר ש
>וא שׁקר בימיני ס >קר בימיני
21זכר אלה יעקב וישׂראל כי עבדי אתה יצרתיך עבד לי אתה21זכור אלה יעקוב ישראל כיא עבדי אתה יצרתיכה עבד לי א
> ישׂראל לא תנשׁני >תה ישראל לוא תשאני
22מחיתי כעב פשׁעיך וכענן חטאותיך שׁובה אלי כי גאלתיך22מחיתי כעב פשעכה וכעננ חטאותיכה שובה אלי כיא גאלתיכ
> >ה
23רנו שׁמים כי עשׂה יהוה הריעו תחתיות ארץ פצחו הרים 23רונו שמימ כיא עשה יהוה הריעו תחתיות הארצ פצחו הרימ
>רנה יער וכל עץ בו כי גאל יהוה יעקב ובישׂראל יתפאר > רונה יער כול עצ בו כיא גאל יהוה יעקוב ובישראל יתפ
>פ >אר
24כה אמר יהוה גאלך ויצרך מבטן אנכי יהוה עשׂה כל נטה 24כוה אמר יהוה גואלכה ויוצרכה מבטנ אנוכי יהוה עושה כ
>שׁמים לבדי רקע הארץ מי אתי >ול נוטה שמימ לבדי רוקע הארצ מיא אתי
25מפר אתות בדים וקסמים יהולל משׁיב חכמים אחור ודעתם 25מפר אותות בדימ וקסמימ יהולל משיב חכמימ אחור ודעתמ 
>ישׂכל >יסכל
26מקים דבר עבדו ועצת מלאכיו ישׁלים האמר לירושׁלם תוש26מקימ דבר עבדו ועצת מלאכיו ישלימ האומר לירושלימ תשב
>ׁב ולערי יהודה תבנינה וחרבותיה אקומם > ולערי יהודה תבנינה וחרבותיה אקוממ
27האמר לצולה חרבי ונהרתיך אובישׁ 27האומר לצולה חרבי ונהרותיכ אוביש
28האמר לכורשׁ רעי וכל חפצי ישׁלם ולאמר לירושׁלם תבנה28האומר לכורש רעי וכול חפצי ישלימ ולאמור לירושלימ תב
> והיכל תוסד ס >נה והיכל יתיסד
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Isaiah 45 MT
Isaiah 45 1QIsaa
t1כה אמר יהוה למשיחו לכורש אשר החזקתי בימינו לרד לפנt1כוה אמר יהוה למשיחו לכורש אשר החזקתי בימינו לרד לפ
>יו גוימ ומתני מלכימ אפתח לפתח לפניו דלתימ ושערימ ל>ניו גואימ ומתני מלכימ אפתח לפתוח לפניו דלתות ושערי
>א יסגרו>מ לוא יסגרו
2אני לפניכ אלכ והדורימ אישר דלתות נחושה אשבר ובריחי2אני לפניכה אלכ והררימ יאושר דלתות נחושה אשבור וברי
> ברזל אגדע>חי ברזל אגדע
3ונתתי לכ אוצרות חשכ ומטמני מסתרימ למענ תדע כי אני 3ונתתי לכה אוצרות חושכ ומטמוני מסתרימ למענ תדע כיא 
>יהוה הקורא בשמכ אלהי ישראל>אני יהוה הקורה בשמכה אלוהי ישראל
4למענ עבדי יעקב וישראל בחירי ואקרא לכ בשמכ אכנכ ולא4למענ עבדי יעקוב ישראל בחירי ואקרא לכה ובשמ הכינכה 
> ידעתני>ולוא ידעתני
5אני יהוה ואינ עוד זולתי אינ אלהימ אאזרכ ולא ידעתני5אני יהוה ואינ עוד זולתי ואינ אלוהימ אאזרכה ולוא יד
 >עתני
6למענ ידעו ממזרח שמש וממערבה כי אפס בלעדי אני יהוה 6למענ ידעו ממזרח שמש וממערב כיא אפס בלעדי אני יהוה 
>ואינ עוד>ואינ עוד
7יוצר אור ובורא חשכ עשה שלומ ובורא רע אני יהוה עשה 7יוצר אור ובורה חושכ עושה טוב ובורה רע אני יהוה עוש
>כל אלה>ה כול אלה
8הרעיפו שמימ ממעל ושחקימ יזלו צדק תפתח ארצ ויפרו יש8הריעו שמימ ממעלה ושחקימ ייזל צדק האמר לארצ ויפרח י
>ע וצדקה תצמיח יחד אני יהוה בראתיו>שע וצדקה תצמיח
9הוי רב את יצרו חרש את חרשי אדמה היאמר חמר ליצרו מה9הוי רב את יוצריו חרש את חורשי האדמה הוי האומר וצרו
> תעשה ופעלכ אינ ידימ לו> מה תעשה ופועלכה אינ אדמ ידימ לו
10הוי אמר לאב מה תוליד ולאשה מה תחילינ10הוי האומר לאב מה תוליד ולאשה מה תחולינ
11כה אמר יהוה קדוש ישראל ויצרו האתיות שאלוני על בני 11כוה אמר יהוה קדוש ישראל יוצר האותות שאלוני על בני 
>ועל פעל ידי תצוני>ועל פועל ידי תצווני
12אנכי עשיתי ארצ ואדמ עליה בראתי אני ידי נטו שמימ וכ12אנוכי עשיתי ארצ ואדמ עליהא בראתי אני ידי נטו שמימ 
>ל צבאמ צויתי>וכול צבאמ צויתי
13אנכי העירתהו בצדק וכל דרכיו אישר הוא יבנה עירי וגל13אנוכי העירותיהו בצדק וכול דרכיו אישר הואה יבנה עיר
>ותי ישלח לא במחיר ולא בשחד אמר יהוה צבאות>י וגלתיא ישלח לוא במחיר ולוא בשוחוד אמר יהוה צבאות
14כה אמר יהוה יגיע מצרימ וסחר כוש וסבאימ אנשי מדה על14כוה אמר יהוה יגיע מצרימ וסחר כוש סבאימ אנשי מדות ע
>יכ יעברו ולכ יהיו אחריכ ילכו בזקימ יעברו ואליכ ישת>ליכ יעבורו ולכ יהיו אחריכ ילכו בזקימ יעבורו ואליכי
>חוו אליכ יתפללו אכ בכ אל ואינ עוד אפס אלהימ> ישתחווה ואליכי יתפללו אכ בכי אל ואינ עוד אפס אלוה
 >ימ
15אכנ אתה אל מסתתר אלהי ישראל מושיע15אכנ אתה אל מסתתר אלוהי ישראל מושיע
16בושו וגמ נכלמו כלמ יחדו הלכו בכלמה חרשי צירימ16בושו וגמ נכלמו כולמה יחדיו וילכו בכלמה חורשי צורימ
17ישראל נושע ביהוה תשועת עולמימ לא תבשו ולא תכלמו עד17ישראל נושע ביהוה תשועת עולמימ לוא תבושו ולוא תכלמו
> עולמי עד> עד עולמי עד
18כי כה אמר יהוה בורא השמימ הוא האלהימ יצר הארצ ועשה18כיא כוה אמר יהוה בורה השמימ הואה האלוהימ ויוצר האר
> הוא כוננה לא תהו בראה לשבת יצרה אני יהוה ואינ עוד>צ ועשיה והואה כוננה לוא לתהו בראה לשבת יצרה אני יה
 >וה ואינ עוד
19לא בסתר דברתי במקומ ארצ חשכ לא אמרתי לזרע יעקב תהו19לוא בסתר דברתי במקומ ארצ חושכ לוא אמרתי לזרע יעקוב
> בקשוני אני יהוה דבר צדק מגיד מישרימ> תהו בקשוני אני יהוה דובר צדק מגיד מישרימ
20הקבצו ובאו התנגשו יחדו פליטי הגוימ לא ידעו הנשאימ 20הקבצו ובואו התנגשו ואתיו פליטי הגואימ לוא ידעו הנו
>את עצ פסלמ ומתפללימ אל אל לא יושיע>שאימ את עצ פסלמה ומתפללימ אל אל לוא יושיע
21הגידו והגישו אפ יועצו יחדו מי השמיע זאת מקדמ מאז ה21הגידו והגישו אפ יועצו יחדיו מיא השמיע זואת מקדמ מא
>גידה הלוא אני יהוה ואינ עוד אלהימ מבלעדי אל צדיק ו>ז הגידה הלוא אני יהוה ואינ עוד אלוהימ מבלעדי אל צד
>מושיע אינ זולתי>יק ומושיע ואינ זולתי
22פנו אלי והושעו כל אפסי ארצ כי אני אל ואינ עוד22פנו אלי והושיעו כול אפסי ארצ כיא אני אל ואינ עוד
23בי נשבעתי יצא מפי צדקה דבר ולא ישוב כי לי תכרע כל 23ביא נשבעתי יצא מפיא צדקה דבר ולוא ישוב כיא ליא תכר
>ברכ תשבע כל לשונ>ע כול בירכ ותשבע כול לשונ
24אכ ביהוה לי אמר צדקות ועז עדיו יבוא ויבשו כל הנחרי24אכ ביהוה ליא יאמר צדקות ועוז עדיו יבואו יבושו כול 
>מ בו>הנחרימ בו
25ביהוה יצדקו ויתהללו כל זרע ישראל25ביהוה יצדקו ויתהללו כול זרע ישראל
t1כה אמר יהוה למשׁיחו לכורשׁ אשׁר החזקתי בימינו לרד t1כוה אמר יהוה למשיחו לכורש אשר החזקתי בימינו לרד לפ
>לפניו גוים ומתני מלכים אפתח לפתח לפניו דלתים ושׁער>ניו גואימ ומתני מלכימ אפתח לפתוח לפניו דלתות ושערי
>ים לא יסגרו >מ לוא יסגרו
2אני לפניך אלך והדורים אושׁר דלתות נחושׁה אשׁבר ובר2אני לפניכה אלכ והררימ יאושר דלתות נחושה אשבור וברי
>יחי ברזל אגדע >חי ברזל אגדע
3ונתתי לך אוצרות חשׁך ומטמני מסתרים למען תדע כי אני3ונתתי לכה אוצרות חושכ ומטמוני מסתרימ למענ תדע כיא 
> יהוה הקורא בשׁמך אלהי ישׂראל >אני יהוה הקורה בשמכה אלוהי ישראל
4למען עבדי יעקב וישׂראל בחירי ואקרא לך בשׁמך אכנך ו4למענ עבדי יעקוב ישראל בחירי ואקרא לכה ובשמ הכינכה 
>לא ידעתני >ולוא ידעתני
5אני יהוה ואין עוד זולתי אין אלהים אאזרך ולא ידעתני5אני יהוה ואינ עוד זולתי ואינ אלוהימ אאזרכה ולוא יד
> >עתני
6למען ידעו ממזרח שׁמשׁ וממערבה כי אפס בלעדי אני יהו6למענ ידעו ממזרח שמש וממערב כיא אפס בלעדי אני יהוה 
>ה ואין עוד >ואינ עוד
7יוצר אור ובורא חשׁך עשׂה שׁלום ובורא רע אני יהוה ע7יוצר אור ובורה חושכ עושה טוב ובורה רע אני יהוה עוש
>שׂה כל אלה ס >ה כול אלה
8הרעיפו שׁמים ממעל ושׁחקים יזלו צדק תפתח ארץ ויפרו 8הריעו שמימ ממעלה ושחקימ ייזל צדק האמר לארצ ויפרח י
>ישׁע וצדקה תצמיח יחד אני יהוה בראתיו ס >שע וצדקה תצמיח
9הוי רב את יצרו חרשׂ את חרשׂי אדמה היאמר חמר ליצרו 9הוי רב את יוצריו חרש את חורשי האדמה הוי האומר וצרו
>מה תעשׂה ופעלך אין ידים לו ס > מה תעשה ופועלכה אינ אדמ ידימ לו
10הוי אמר לאב מה תוליד ולאשׁה מה תחילין ס 10הוי האומר לאב מה תוליד ולאשה מה תחולינ
11כה אמר יהוה קדושׁ ישׂראל ויצרו האתיות שׁאלוני על ב11כוה אמר יהוה קדוש ישראל יוצר האותות שאלוני על בני 
>ני ועל פעל ידי תצוני >ועל פועל ידי תצווני
12אנכי עשׂיתי ארץ ואדם עליה בראתי אני ידי נטו שׁמים 12אנוכי עשיתי ארצ ואדמ עליהא בראתי אני ידי נטו שמימ 
>וכל צבאם צויתי >וכול צבאמ צויתי
13אנכי העירתהו בצדק וכל דרכיו אישׁר הוא יבנה עירי וג13אנוכי העירותיהו בצדק וכול דרכיו אישר הואה יבנה עיר
>לותי ישׁלח לא במחיר ולא בשׁחד אמר יהוה צבאות פ >י וגלתיא ישלח לוא במחיר ולוא בשוחוד אמר יהוה צבאות
14כה׀ אמר יהוה יגיע מצרים וסחר כושׁ וסבאים אנשׁי מדה14כוה אמר יהוה יגיע מצרימ וסחר כוש סבאימ אנשי מדות ע
> עליך יעברו ולך יהיו אחריך ילכו בזקים יעברו ואליך >ליכ יעבורו ולכ יהיו אחריכ ילכו בזקימ יעבורו ואליכי
>ישׁתחוו אליך יתפללו אך בך אל ואין עוד אפס אלהים > ישתחווה ואליכי יתפללו אכ בכי אל ואינ עוד אפס אלוה
 >ימ
15אכן אתה אל מסתתר אלהי ישׂראל מושׁיע 15אכנ אתה אל מסתתר אלוהי ישראל מושיע
16בושׁו וגם נכלמו כלם יחדו הלכו בכלמה חרשׁי צירים 16בושו וגמ נכלמו כולמה יחדיו וילכו בכלמה חורשי צורימ
17ישׂראל נושׁע ביהוה תשׁועת עולמים לא תבשׁו ולא תכלמ17ישראל נושע ביהוה תשועת עולמימ לוא תבושו ולוא תכלמו
>ו עד עולמי עד פ > עד עולמי עד
18כי כה אמר יהוה בורא השׁמים הוא האלהים יצר הארץ ועש18כיא כוה אמר יהוה בורה השמימ הואה האלוהימ ויוצר האר
>ׂה הוא כוננה לא תהו בראה לשׁבת יצרה אני יהוה ואין >צ ועשיה והואה כוננה לוא לתהו בראה לשבת יצרה אני יה
>עוד >וה ואינ עוד
19לא בסתר דברתי במקום ארץ חשׁך לא אמרתי לזרע יעקב תה19לוא בסתר דברתי במקומ ארצ חושכ לוא אמרתי לזרע יעקוב
>ו בקשׁוני אני יהוה דבר צדק מגיד מישׁרים > תהו בקשוני אני יהוה דובר צדק מגיד מישרימ
20הקבצו ובאו התנגשׁו יחדו פליטי הגוים לא ידעו הנשׂאי20הקבצו ובואו התנגשו ואתיו פליטי הגואימ לוא ידעו הנו
>ם את עץ פסלם ומתפללים אל אל לא יושׁיע >שאימ את עצ פסלמה ומתפללימ אל אל לוא יושיע
21הגידו והגישׁו אף יועצו יחדו מי השׁמיע זאת מקדם מאז21הגידו והגישו אפ יועצו יחדיו מיא השמיע זואת מקדמ מא
> הגידה הלוא אני יהוה ואין עוד אלהים מבלעדי אל צדיק>ז הגידה הלוא אני יהוה ואינ עוד אלוהימ מבלעדי אל צד
> ומושׁיע אין זולתי >יק ומושיע ואינ זולתי
22פנו אלי והושׁעו כל אפסי ארץ כי אני אל ואין עוד 22פנו אלי והושיעו כול אפסי ארצ כיא אני אל ואינ עוד
23בי נשׁבעתי יצא מפי צדקה דבר ולא ישׁוב כי לי תכרע כ23ביא נשבעתי יצא מפיא צדקה דבר ולוא ישוב כיא ליא תכר
>ל ברך תשׁבע כל לשׁון >ע כול בירכ ותשבע כול לשונ
24אך ביהוה לי אמר צדקות ועז עדיו יבוא ויבשׁו כל הנחר24אכ ביהוה ליא יאמר צדקות ועוז עדיו יבואו יבושו כול 
>ים בו >הנחרימ בו
25ביהוה יצדקו ויתהללו כל זרע ישׂראל 25ביהוה יצדקו ויתהללו כול זרע ישראל
- - - - - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - - - + + + + + + + + + +

Isaiah 46 MT
Isaiah 46 1QIsaa
n1כרע בל קרס נבו היו עצביהמ לחיה ולבהמה נשאתיכמ עמוסn1כרע בל קרס נבו היו עצביהמה לחיה לבהמה נשאותיכמה עמ
>ות משא לעיפה>וסות משמועיהמה
2קרסו כרעו יחדו לא יכלו מלט משא ונפשמ בשבי הלכה2קרסו כרעו יחדיו ולוא יוכלו מלט משא ונפשמה בשבי הלכ
 >ו
3שמעו אלי בית יעקב וכל שארית בית ישראל העמסימ מני ב3שמע אלי בית יעקוב וכול שארית בית ישראל עומסימ ממני
>טנ הנשאימ מני רחמ> בטנ ונושאימ מני רחמ
4ועד זקנה אני הוא ועד שיבה אני אסבל אני עשיתי ואני 4עד זקנה אני הואה ועד שיבה אני אסבול אני עשיתי ואני
>אשא ואני אסבל ואמלט> אשא ואנוכי אסבול ואמלטה
5למי תדמיוני ותשוו ותמשלוני ונדמה5למי תדמיוני ותשוי ותמשלוני ואדמה
6הזלימ זהב מכיס וכספ בקנה ישקלו ישכרו צורפ ויעשהו א6הזלימ זהב בכיס וכספ בקנה ישקולו ישכורו צורפ ויעשה 
>ל יסגדו אפ ישתחוו>אל ויסגודו אפ ישתחו
7ישאהו על כתפ יסבלהו ויניחהו תחתיו ויעמד ממקומו לא 7וישאוהי על כתפ יסבלוהי ויניחוהי תחתיו ויעמוד ממקומ
>ימיש אפ יצעק אליו ולא יענה מצרתו לא יושיענו>ו לוא ימוש אפ יזעק עליו ולוא יענה מצרתו לוא יושיענ
t1כרע בל קרס נבו היו עצביהם לחיה ולבהמה נשׂאתיכם עמוt1כרע בל קרס נבו היו עצביהמה לחיה לבהמה נשאותיכמה עמ
>סות משׂא לעיפה >וסות משמועיהמה
2קרסו כרעו יחדו לא יכלו מלט משׂא ונפשׁם בשׁבי הלכה 2קרסו כרעו יחדיו ולוא יוכלו מלט משא ונפשמה בשבי הלכ
>ס >ו
3שׁמעו אלי בית יעקב וכל שׁארית בית ישׂראל העמסים מנ3שמע אלי בית יעקוב וכול שארית בית ישראל עומסימ ממני
>י בטן הנשׂאים מני רחם > בטנ ונושאימ מני רחמ
4ועד זקנה אני הוא ועד שׂיבה אני אסבל אני עשׂיתי ואנ4עד זקנה אני הואה ועד שיבה אני אסבול אני עשיתי ואני
>י אשׂא ואני אסבל ואמלט ס > אשא ואנוכי אסבול ואמלטה
5למי תדמיוני ותשׁוו ותמשׁלוני ונדמה 5למי תדמיוני ותשוי ותמשלוני ואדמה
6הזלים זהב מכיס וכסף בקנה ישׁקלו ישׂכרו צורף ויעשׂה6הזלימ זהב בכיס וכספ בקנה ישקולו ישכורו צורפ ויעשה 
>ו אל יסגדו אף ישׁתחוו >אל ויסגודו אפ ישתחו
7ישׂאהו על כתף יסבלהו ויניחהו תחתיו ויעמד ממקומו לא7וישאוהי על כתפ יסבלוהי ויניחוהי תחתיו ויעמוד ממקומ
> ימישׁ אף יצעק אליו ולא יענה מצרתו לא יושׁיענו ס >ו לוא ימוש אפ יזעק עליו ולוא יענה מצרתו לוא יושיענ
 >ו
8זכרו זאת והתאששו השיבו פושעימ על לב8זכורו זואת והתאוששו השיבו פושעימ על לב
9זכרו ראשנות מעולמ כי אנכי אל ואינ עוד אלהימ ואפס כ9זכורו רישונות מעולמ כיא אני אל ואינ עוד אלוהימ ואפ
>מוני>ס כמוני
10מגיד מראשית אחרית ומקדמ אשר לא נעשו אמר עצתי תקומ 10מגיד מראישית אחרות ומקדמ אשר לוא נעשו אמר עצתי תקו
>וכל חפצי אעשה>מ וכול חפצי יעשה
11קרא ממזרח עיט מארצ מרחק איש עצתי אפ דברתי אפ אביאנ11קורה ממזרח עיט מארצ מרחק איש עצתו אפ דברתי אפ אביא
>ה יצרתי אפ אעשנה>נה יצרתיה אפ אעשנה
12שמעו אלי אבירי לב הרחוקימ מצדקה12שמעו אלי אבירי לב הרחוקימ מצדקה
t13קרבתי צדקתי לא תרחק ותשועתי לא תאחר ונתתי בציונ תשt13קרובה צדקתי ולוא תרחק ותשועתי ולוא תאחר נתתי בציונ
>ועה לישראל תפארתי> תשועה ולישראל תפארתי
8זכרו זאת והתאשׁשׁו השׁיבו פושׁעים על לב 8זכורו זואת והתאוששו השיבו פושעימ על לב
9זכרו ראשׁנות מעולם כי אנכי אל ואין עוד אלהים ואפס 9זכורו רישונות מעולמ כיא אני אל ואינ עוד אלוהימ ואפ
>כמוני >ס כמוני
10מגיד מראשׁית אחרית ומקדם אשׁר לא נעשׂו אמר עצתי תק10מגיד מראישית אחרות ומקדמ אשר לוא נעשו אמר עצתי תקו
>ום וכל חפצי אעשׂה >מ וכול חפצי יעשה
11קרא ממזרח עיט מארץ מרחק אישׁ עצתו אף דברתי אף אביא11קורה ממזרח עיט מארצ מרחק איש עצתו אפ דברתי אפ אביא
>נה יצרתי אף אעשׂנה ס >נה יצרתיה אפ אעשנה
12שׁמעו אלי אבירי לב הרחוקים מצדקה 12שמעו אלי אבירי לב הרחוקימ מצדקה
13קרבתי צדקתי לא תרחק ותשׁועתי לא תאחר ונתתי בציון ת13קרובה צדקתי ולוא תרחק ותשועתי ולוא תאחר נתתי בציונ
>שׁועה לישׂראל תפארתי ס > תשועה ולישראל תפארתי
- - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 47 MT
Isaiah 47 1QIsaa
n1רדי ושבי על עפר בתולת בת בבל שבי לארצ אינ כסא בת כn1רדי ושבי על עפר בתולת בת בבל שבי על הארצ אינ כסא ב
>שדימ כי לא תוסיפי יקראו לכ רכה וענגה>ת כשדיימ כיא לוא תוסיפי וקראו לכ רכה וענוגה
2קחי רחימ וטחני קמח גלי צמתכ חשפי שבל גלי שוק עברי 2קחי רחימ וטחני קמח גלי צמתכ חשופי שוליכ גלי שוק עב
>נהרות>ורי נהרות
3תגל ערותכ גמ תראה חרפתכ נקמ אקח ולא אפגע אדמ3תגלה ערותכ גמ תראה חרפתכ נקמ אקח ולוא אפגע אדמ
4גאלנו יהוה צבאות שמו קדוש ישראל4גאלנו יהוה צבאות שמו קדוש ישראל
t5שבי דוממ ובאי בחשכ בת כשדימ כי לא תוסיפי יקראו לכ t5שבי דממה ובואי בחושכ בת כשדיימ כיא לוא תוסיפי וקרא
>גברת ממלכות>ו לכ גבורת ממלכות
6קצפתי על עמי חללתי נחלתי ואתנמ בידכ לא שמת להמ רחמ6קצפתי על עמי וחללתי נחלתי ואתנמ בידכ לוא שמתי להמה
>ימ על זקנ הכבדת עלכ מאד> רחמימ על זקנ הכבדתי עולכ מואדה
7ותאמרי לעולמ אהיה גברת עד לא שמת אלה על לבכ לא זכר7ותואמרי לעולמ אהיה גבורת עוד לוא שמתי אלה על לבכי 
>ת אחריתה>לוא זכרתי אחרונה
8ועתה שמעי זאת עדינה היושבת לבטח האמרה בלבבה אני וא8ועתה שמעי זואת עודנה היושבת לבטח האומרה בלבבה אני 
>פסי עוד לא אשב אלמנה ולא אדע שכול>ואפסי עוד לוא אשב עלמנה ולוא אראה שכול
9ותבאנה לכ שתי אלה רגע ביומ אחד שכול ואלמנ כתממ באו9ותבואינה לכ שתי אלה רגע ביומ אחד שכול ואלמנה כתוממ
> עליכ ברב כשפיכ בעצמת חבריכ מאד> באו עליכ ברוב כשפיכ בעצמת חובריכ מואדה
10ותבטחי ברעתכ אמרת אינ ראני חכמתכ ודעתכ היא שובבתכ 10ותבטחי בדעתכ אמרתי אינ רואני חכמתכ ודעתכ היאה שובב
>ותאמרי בלבכ אני ואפסי עוד>תכ ותואמרי בלבבכ אני ואפסי עוד
11ובא עליכ רעה לא תדעי שחרה ותפל עליכ הוה לא תוכלי כ11ובאה עליכ רעה ולוא תדעי שחרה ותפול עליכ הויה לוא ת
>פרה ותבא עליכ פתאמ שואה לא תדעי>וכלי לכפרה ותבוא עליכ פתאומ שאה ולוא תדעי
12עמדי נא בחבריכ וברב כשפיכ באשר יגעת מנעוריכ אולי ת12ועמודינא בחובריכ וברוב כשפיכ באשר יגעתי מנעוריכ וע
>וכלי הועיל אולי תערוצי>ד היומ
13נלאית ברב עצתיכ יעמדו נא ויושיעכ הברי שמימ החזימ ב13כרוב עצתכ יעמודו נא ויושיעוכ חוברי שמימ והחוזימ בכ
>כוכבימ מודיעמ לחדשימ מאשר יבאו עליכ>וכבימ מודעימ לחרשימ מאשר יבוא עליהמה
14הנה היו כקש אש שרפתמ לא יצילו את נפשמ מיד להבה אינ14הנה היו כקש אש שרפתמ לוא הצילו את נפשמ מיד להבה אי
> גחלת לחממ אור לשבת נגדו>נ גחלת לחוממ אור לשבת נגדו
15כנ היו לכ אשר יגעת סחריכ מנעוריכ איש לעברו תעו אינ15כנ היו לכ אשר יגעתי סוחריכ מנעוריכ איש לעברו תעו א
> מושיעכ>ינ מושיעכ
t1רדי׀ ושׁבי על עפר בתולת בת בבל שׁבי לארץ אין כסא בt1רדי ושבי על עפר בתולת בת בבל שבי על הארצ אינ כסא ב
>ת כשׂדים כי לא תוסיפי יקראו לך רכה וענגה >ת כשדיימ כיא לוא תוסיפי וקראו לכ רכה וענוגה
2קחי רחים וטחני קמח גלי צמתך חשׂפי שׁבל גלי שׁוק עב2קחי רחימ וטחני קמח גלי צמתכ חשופי שוליכ גלי שוק עב
>רי נהרות >ורי נהרות
3תגל ערותך גם תראה חרפתך נקם אקח ולא אפגע אדם ס 3תגלה ערותכ גמ תראה חרפתכ נקמ אקח ולוא אפגע אדמ
4גאלנו יהוה צבאות שׁמו קדושׁ ישׂראל 4גאלנו יהוה צבאות שמו קדוש ישראל
5שׁבי דומם ובאי בחשׁך בת כשׂדים כי לא תוסיפי יקראו 5שבי דממה ובואי בחושכ בת כשדיימ כיא לוא תוסיפי וקרא
>לך גברת ממלכות >ו לכ גבורת ממלכות
6קצפתי על עמי חללתי נחלתי ואתנם בידך לא שׂמת להם רח6קצפתי על עמי וחללתי נחלתי ואתנמ בידכ לוא שמתי להמה
>מים על זקן הכבדת עלך מאד > רחמימ על זקנ הכבדתי עולכ מואדה
7ותאמרי לעולם אהיה גברת עד לא שׂמת אלה על לבך לא זכ7ותואמרי לעולמ אהיה גבורת עוד לוא שמתי אלה על לבכי 
>רת אחריתה ס >לוא זכרתי אחרונה
8ועתה שׁמעי זאת עדינה היושׁבת לבטח האמרה בלבבה אני 8ועתה שמעי זואת עודנה היושבת לבטח האומרה בלבבה אני 
>ואפסי עוד לא אשׁב אלמנה ולא אדע שׁכול >ואפסי עוד לוא אשב עלמנה ולוא אראה שכול
9ותבאנה לך שׁתי אלה רגע ביום אחד שׁכול ואלמן כתמם ב9ותבואינה לכ שתי אלה רגע ביומ אחד שכול ואלמנה כתוממ
>או עליך ברב כשׁפיך בעצמת חבריך מאד > באו עליכ ברוב כשפיכ בעצמת חובריכ מואדה
10ותבטחי ברעתך אמרת אין ראני חכמתך ודעתך היא שׁובבתך10ותבטחי בדעתכ אמרתי אינ רואני חכמתכ ודעתכ היאה שובב
> ותאמרי בלבך אני ואפסי עוד >תכ ותואמרי בלבבכ אני ואפסי עוד
11ובא עליך רעה לא תדעי שׁחרה ותפל עליך הוה לא תוכלי 11ובאה עליכ רעה ולוא תדעי שחרה ותפול עליכ הויה לוא ת
>כפרה ותבא עליך פתאם שׁואה לא תדעי >וכלי לכפרה ותבוא עליכ פתאומ שאה ולוא תדעי
12עמדי נא בחבריך וברב כשׁפיך באשׁר יגעת מנעוריך אולי12ועמודינא בחובריכ וברוב כשפיכ באשר יגעתי מנעוריכ וע
> תוכלי הועיל אולי תערוצי >ד היומ
13נלאית ברב עצתיך יעמדו נא ויושׁיעך הברו שׁמים החזים13כרוב עצתכ יעמודו נא ויושיעוכ חוברי שמימ והחוזימ בכ
> בכוכבים מודיעם לחדשׁים מאשׁר יבאו עליך >וכבימ מודעימ לחרשימ מאשר יבוא עליהמה
14הנה היו כקשׁ אשׁ שׂרפתם לא יצילו את נפשׁם מיד להבה14הנה היו כקש אש שרפתמ לוא הצילו את נפשמ מיד להבה אי
> אין גחלת לחמם אור לשׁבת נגדו >נ גחלת לחוממ אור לשבת נגדו
15כן היו לך אשׁר יגעת סחריך מנעוריך אישׁ לעברו תעו א15כנ היו לכ אשר יגעתי סוחריכ מנעוריכ איש לעברו תעו א
>ין מושׁיעך ס >ינ מושיעכ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 48 MT
Isaiah 48 1QIsaa
t1שמעו זאת בית יעקב הנקראימ בשמ ישראל וממי יהודה יצאt1שמעו זואת בית יעקוב הנקראימ בשמ ישראל וממי יהודה י
>ו הנשבעימ בשמ יהוה ובאלהי ישראל יזכירו לא באמת ולא>צאו הנשבעימ בשמ יהוה ובאלוהי ישראל יזכירו לוא באמת
> בצדקה> ולוא בצדקה
2כי מעיר הקדש נקראו ועל אלהי ישראל נסמכו יהוה צבאות2כיא מעיר הקודש נקראו ועל אלוהי ישראל נסמכו יהוה צב
> שמו>אות שמו
3הראשנות מאז הגדתי ומפי יצאו ואשמיעמ פתאמ עשיתי ותב3הרישונות מאז הגדתי ומפי יצאה ואשמיעמ פתאומ עשיתי ו
>אנה>תבואינה
4מדעתי כי קשה אתה וגיד ברזל ערפכ ומצחכ נחושה4מאדltֵתי כיא קשה אתה וגיד ברזל עורפכה ומצחכה נחושה
5ואגיד לכ מאז בטרמ תבוא השמעתיכ פנ תאמר עצבי עשמ ופ5ואגידה לכה מאז בטרמ תבוא השמעתיכה פנ תואמר עצבי עש
>סלי ונסכי צומ>מ פסלי ונסכי צומ
6שמעת חזה כלה ואתמ הלוא תגידו השמעתיכ חדשות מעתה ונ6שמעתה חזה כולה ואתמה הלוא תגידו השמעתיכה חדשות מעת
>צרות ולא ידעתמ>ה ונצורות לוא ידעתנ
7עתה נבראו ולא מאז ולפני יומ ולא שמעתמ פנ תאמר הנה 7עתה נבראו ולוא מאז ולפני יומ לוא שמעתימ פנ תואמר ה
>ידעתינ>נה ידעתימ
8גמ לא שמעת גמ לא ידעת גמ מאז לא פתחה אזנכ כי ידעתי8וגמ לוא שמעתי גמ לוא ידעתה גמ מאז לוא פתחת אוזנכה 
> בגוד תבגוד ופשע מבטנ קרא לכ>כיא ידעתי כיא בגוד תבגוד ופושע מבטנ יקראו לכה
9למענ שמי אאריכ אפי ותהלתי אחטמ לכ לבלתי הכריתכ9למענ שמי אאריכ אפי ותהלתי אחטומ לכה לבלתי הכריתכה
10הנה צרפתיכ ולא בכספ בחרתיכ בכור עני10הנה צרפתיכה ולוא בכספ בחנתיכה בכור עני
11למעני למעני אעשה כי איכ יחל וכבודי לאחר לא אתנ11למעני למעני אעשה כיא איכה איחל וכבודי לאחר לוא אתנ
12שמע אלי יעקב וישראל מקראי אני הוא אני ראשונ אפ אני12שמע אלה יעקוב וישראל מקראי אני הואה אני רישונ אפ א
> אחרונ>ני אחרונ
13אפ ידי יסדה ארצ וימיני טפחה שמימ קרא אני אליהמ יעמ13אפ ידי יסדו ארצ וימיני טפחה שמימ קורה אני אליהמה ו
>דו יחדו>יעמודו יחדיו
14הקבצו כלכמ ושמעו מי בהמ הגיד את אלה יהוה אהבו יעשה14יקבצו כולמ וישמעו מי בהמ ויגיד את אלה יהוה אוהבי ו
> חפצו בבבל וזרעו כשדימ>ישה חפצי בבבל זרועו כשדיימ
15אני אני דברתי אפ קראתיו הביאתיו והצליח דרכו15אני אני דברתי אפ קראתי והביאותיהו והצליחה דרכוהי
16קרבו אלי שמעו זאת לא מראש בסתר דברתי מעת היותה שמ 16קרובו אלי ושמעו זואת לוא מרוש בסתר דברתי בעת היותה
>אני ועתה אדני יהוה שלחני ורוחו> שמה אני ועתה אדוני יהוה שלחני ורוחו
17כה אמר יהוה גאלכ קדוש ישראל אני יהוה אלהיכ מלמדכ ל17כוה אמר יהוה גואלכה קדוש ישראל אני יהוה אלוהיכה מל
>הועיל מדריככ בדרכ תלכ>מדכה להועיל הדריכה בדרכ אשר תלכ בה
18לוא הקשבת למצותי ויהי כנהר שלומכ וצדקתכ כגלי הימ18ולוא הקשבתה אל מצוותי והיה כנהר שלומכה וצדקתכ כגלי
 > הימ
19ויהי כחול זרעכ וצאצאי מעיכ כמעתיו לא יכרת ולא ישמד19ויהי כחול זרעכה וצאצאיכה כמעותיו לוא יכרת ולוא ישמ
> שמו מלפני>ד שמו מלפני
20צאו מבבל ברחו מכשדימ בקול רנה הגידו השמיעו זאת הוצ20צאו מבבל ברחו מכשדיימ בקול רונה הגידו והשמיעו זואת
>יאוה עד קצה הארצ אמרו גאל יהוה עבדו יעקב> עד קצוי הארצ אמרו גאל יהוה את עבדו יעקוב
21ולא צמאו בחרבות הוליכמ מימ מצור הזיל למו ויבקע צור21ולוא צמאו בחרבות הוליכו מימ מצור הזיב למו ויבקע צו
> ויזבו מימ>ר ויזובו מימ
22אינ שלומ אמר יהוה לרשעימ22ואינ שלומ אמר יהוה לרשעימ
t1שׁמעו זאת בית יעקב הנקראים בשׁם ישׂראל וממי יהודה t1שמעו זואת בית יעקוב הנקראימ בשמ ישראל וממי יהודה י
>יצאו הנשׁבעים׀ בשׁם יהוה ובאלהי ישׂראל יזכירו לא ב>צאו הנשבעימ בשמ יהוה ובאלוהי ישראל יזכירו לוא באמת
>אמת ולא בצדקה > ולוא בצדקה
2כי מעיר הקדשׁ נקראו ועל אלהי ישׂראל נסמכו יהוה צבא2כיא מעיר הקודש נקראו ועל אלוהי ישראל נסמכו יהוה צב
>ות שׁמו ס >אות שמו
3הראשׁנות מאז הגדתי ומפי יצאו ואשׁמיעם פתאם עשׂיתי 3הרישונות מאז הגדתי ומפי יצאה ואשמיעמ פתאומ עשיתי ו
>ותבאנה >תבואינה
4מדעתי כי קשׁה אתה וגיד ברזל ערפך ומצחך נחושׁה 4מאדltֵתי כיא קשה אתה וגיד ברזל עורפכה ומצחכה נחושה
5ואגיד לך מאז בטרם תבוא השׁמעתיך פן תאמר עצבי עשׂם 5ואגידה לכה מאז בטרמ תבוא השמעתיכה פנ תואמר עצבי עש
>ופסלי ונסכי צום >מ פסלי ונסכי צומ
6שׁמעת חזה כלה ואתם הלוא תגידו השׁמעתיך חדשׁות מעתה6שמעתה חזה כולה ואתמה הלוא תגידו השמעתיכה חדשות מעת
> ונצרות ולא ידעתם >ה ונצורות לוא ידעתנ
7עתה נבראו ולא מאז ולפני יום ולא שׁמעתם פן תאמר הנה7עתה נבראו ולוא מאז ולפני יומ לוא שמעתימ פנ תואמר ה
> ידעתין >נה ידעתימ
8גם לא שׁמעת גם לא ידעת גם מאז לא פתחה אזנך כי ידעת8וגמ לוא שמעתי גמ לוא ידעתה גמ מאז לוא פתחת אוזנכה 
>י בגוד תבגוד ופשׁע מבטן קרא לך >כיא ידעתי כיא בגוד תבגוד ופושע מבטנ יקראו לכה
9למען שׁמי אאריך אפי ותהלתי אחטם לך לבלתי הכריתך 9למענ שמי אאריכ אפי ותהלתי אחטומ לכה לבלתי הכריתכה
10הנה צרפתיך ולא בכסף בחרתיך בכור עני 10הנה צרפתיכה ולוא בכספ בחנתיכה בכור עני
11למעני למעני אעשׂה כי איך יחל וכבודי לאחר לא אתן ס 11למעני למעני אעשה כיא איכה איחל וכבודי לאחר לוא אתנ
12שׁמע אלי יעקב וישׂראל מקראי אני הוא אני ראשׁון אף 12שמע אלה יעקוב וישראל מקראי אני הואה אני רישונ אפ א
>אני אחרון >ני אחרונ
13אף ידי יסדה ארץ וימיני טפחה שׁמים קרא אני אליהם יע13אפ ידי יסדו ארצ וימיני טפחה שמימ קורה אני אליהמה ו
>מדו יחדו >יעמודו יחדיו
14הקבצו כלכם ושׁמעו מי בהם הגיד את אלה יהוה אהבו יעש14יקבצו כולמ וישמעו מי בהמ ויגיד את אלה יהוה אוהבי ו
>ׂה חפצו בבבל וזרעו כשׂדים >ישה חפצי בבבל זרועו כשדיימ
15אני אני דברתי אף קראתיו הביאתיו והצליח דרכו 15אני אני דברתי אפ קראתי והביאותיהו והצליחה דרכוהי
16קרבו אלי שׁמעו זאת לא מראשׁ בסתר דברתי מעת היותה ש16קרובו אלי ושמעו זואת לוא מרוש בסתר דברתי בעת היותה
>ׁם אני ועתה אדני יהוה שׁלחני ורוחו פ > שמה אני ועתה אדוני יהוה שלחני ורוחו
17כה אמר יהוה גאלך קדושׁ ישׂראל אני יהוה אלהיך מלמדך17כוה אמר יהוה גואלכה קדוש ישראל אני יהוה אלוהיכה מל
> להועיל מדריכך בדרך תלך >מדכה להועיל הדריכה בדרכ אשר תלכ בה
18לוא הקשׁבת למצותי ויהי כנהר שׁלומך וצדקתך כגלי הים18ולוא הקשבתה אל מצוותי והיה כנהר שלומכה וצדקתכ כגלי
> > הימ
19ויהי כחול זרעך וצאצאי מעיך כמעתיו לא יכרת ולא ישׁמ19ויהי כחול זרעכה וצאצאיכה כמעותיו לוא יכרת ולוא ישמ
>ד שׁמו מלפני >ד שמו מלפני
20צאו מבבל ברחו מכשׂדים בקול רנה הגידו השׁמיעו זאת ה20צאו מבבל ברחו מכשדיימ בקול רונה הגידו והשמיעו זואת
>וציאוה עד קצה הארץ אמרו גאל יהוה עבדו יעקב > עד קצוי הארצ אמרו גאל יהוה את עבדו יעקוב
21ולא צמאו בחרבות הוליכם מים מצור הזיל למו ויבקע צור21ולוא צמאו בחרבות הוליכו מימ מצור הזיב למו ויבקע צו
> ויזבו מים >ר ויזובו מימ
22אין שׁלום אמר יהוה לרשׁעים ס 22ואינ שלומ אמר יהוה לרשעימ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 49 MT
Isaiah 49 1QIsaa
t1שמעו איימ אלי והקשיבו לאמימ מרחוק יהוה מבטנ קראני t1שמעו איימ אלי הקשיבו לאומימ מרחוק יהוה מבטנ קראני 
>ממעי אמי הזכיר שמי>ממעי אמי הזכיר שמי
2וישמ פי כחרב חדה בצל ידו החביאני וישימני לחצ ברור 2וישמ פי כחרב חדה בצל ידיו החביאני וישימני לכחצ ברו
>באשפתו הסתירני>ר באשפתיו הסתירני
3ויאמר לי עבדי אתה ישראל אשר בכ אתפאר3ויואמר לי עבדי אתה ישראל אשר בכה אתפאר
4ואני אמרתי לריק יגעתי לתהו והבל כחי כליתי אכנ משפט4אני אמרתי לריק יגעתי לתוה ולהבל כוחי כליתי אכנ משפ
>י את יהוה ופעלתי את אלהי>טי את יהוה ופועלתי את אלוהי
5ועתה אמר יהוה יצרי מבטנ לעבד לו לשובב יעקב אליו וי5ועתה אמר יהוה יוצרכ מבטנ לעבד לו לשובב יעקוב אליו 
>שראל לו יאספ ואכבד בעיני יהוה ואלהי היה עזי>וישראל לו יאספ ואכבדה בעיני יהוה ואלוהי היה עזרי
6ויאמר נקל מהיותכ לי עבד להקימ את שבטי יעקב ונצורי 6ויואמר נקל מהיותכה לי עבד להקימ את שבטי ישראל ונצי
>ישראל להשיב ונתתיכ לאור גוימ להיות ישועתי עד קצה ה>רי יעקוב להשיב ונתתיכ לאור גואימ להיות ישועתי עד ק
>ארצ>צוי הארצ
7כה אמר יהוה גאל ישראל קדושו לבזה נפש למתעב גוי לעב7כוה אמר אדוני יהוה גואלכה ישראל קדושו לבזוי נפש למ
>ד משלימ מלכימ יראו וקמו שרימ וישתחוו למענ יהוה אשר>תעבי גוי לעבד מושלימ מלכימ ראו וקמו ושרימ יהשתחוו 
> נאמנ קדש ישראל ויבחרכ>למענ יהוה אשר נאמנ קדוש ישראל יבחרכה
8כה אמר יהוה בעת רצונ עניתיכ וביומ ישועה עזרתיכ ואצ8כוה אמר יהוה בעת רצונ אענכה וביומ ישועה אעזרכה ואצ
>רכ ואתנכ לברית עמ להקימ ארצ להנחיל נחלות שממות>ורכה ואתנכה לברית עמ להקימ ארצ להנחיל נחלות שוממות
9לאמר לאסורימ צאו לאשר בחשכ הגלו על דרכימ ירעו ובכל9לאמור לאסורימ צאו ולאשר בחושכ הגלו על כול הרימ ירע
> שפיימ מרעיתמ>ו ובכול שפאימ מרעיתמ
10לא ירעבו ולא יצמאו ולא יכמ שרב ושמש כי מרחממ ינהגמ10לוא ירעבו ולוא יצמאו ולוא יכמ שוב ושמש כיא מרחממ י
> ועל מבועי מימ ינהלמ>נהגמ ועל מבועי מימ ינהלמ
11ושמתי כל הרי לדרכ ומסלתי ירמונ11ושמתי כול הרי לדרכ ומס ומסלתי ירומונ
12הנה אלה מרחוק יבאו והנה אלה מצפונ ומימ ואלה מארצ ס12הנה אלה מרחוק יבואו והנה אלה מצפונ ומימ ואלה מארצ 
>ינימ>סוניימ
13רנו שמימ וגילי ארצ ופצחו הרימ רנה כי נחמ יהוה עמו 13רונו שמימ וגילי ארצ פצחו הרימ רונה כיא מנחמ יהוה ע
>ועניו ירחמ>מו ועניו ירחמ
14ותאמר ציונ עזבני יהוה ואדני שכחני14ותואמר ציונ עזבני יהוה ואדוני שכחני
15התשכח אשה עולה מרחמ בנ בטנה גמ אלה תשכחנה ואנכי לא15התשכח אשה עולה מרחמ בנ בטנה גמ אלה תשכחנה ואנוכי ל
> אשכחכ>וא אשכחכי
16הנ על כפימ חקתיכ חומתיכ נגדי תמיד16הנה על כפימ חוקותיכ וחומותיכ נגדי תמיד
17מהרו בניכ מהרסיכ ומחרביכ ממכ יצאו17מהרו בוניכ מהורסיכ ומחריביכ ממכ יצאו
18שאי סביב עיניכ וראי כלמ נקבצו באו לכ חי אני נאמ יה18סאי סביב עיניכ וראי כולמ נקבצו באו לכי חי אני נואמ
>וה כי כלמ כעדי תלבשי ותקשרימ ככלה> יהוה כיא כולמ כעדי תלבשי ותקשרימ ככלה
19כי חרבתיכ ושממתיכ וארצ הרסתיכ כי עתה תצרי מיושב ור19כיא חרבותיכ ושוממותיכ וארצ הרוסתכ כיא עתה תצרי מיו
>חקו מבלעיכ>שב ורחקו מבלעיכ
20עוד יאמרו באזניכ בני שכליכ צר לי המקומ גשה לי ואשב20עוד יואמרו באוזניכ בני שכוליכ צר לי המקומ גשה לי ו
>ה>אשבה
21ואמרת בלבבכ מי ילד לי את אלה ואני שכולה וגלמודה גל21ואמרת בלבבכ מיא ילד לי את אלה ואני שכולה וגלמודה ו
>ה וסורה ואלה מי גדל הנ אני נשארתי לבדי אלה איפה המ>גולה וסרה אלה מיא גדל הנה אני נשארתי לבדי אלה איפו
 > המה
22כה אמר אדני יהוה הנה אשא אל גוימ ידי ואל עמימ ארימ22כיא כוה אמר יהוה הנה אשא אל גואימ ידי ואל העמימ אר
> נסי והביאו בניכ בחצנ ובנתיכ על כתפ תנשאנה>ימ נסי והביאו בניכ בחוצנ ובנותיכ על כתפ תנשנה
23והיו מלכימ אמניכ ושרותיהמ מיניקתיכ אפימ ארצ ישתחוו23והוי מלכימ אמניכ ושרותיהמה מינקותיכ אפימ ארצ ישתחו
> לכ ועפר רגליכ ילחכו וידעת כי אני יהוה אשר לא יבשו>ו לכ ועפר רגליכ ילחכו וידעתי כיא אני יהוה אשר לוא 
> קוי>יבושו קוי
24היקח מגבור מלקוח ואמ שבי צדיק ימלט24היקחו מגבור מלקוח אמ שבי עריצ ימלט
25כי כה אמר יהוה גמ שבי גבור יקח ומלקוח עריצ ימלט וא25כיא כוה אמר יהוה גמ מלקוח גבור ילקח ושובי עריצ ימל
>ת יריבכ אנכי אריב ואת בניכ אנכי אושיע>ט ואת רוביכ אנוכי אריב ואת בניכ אנוכי אושיע
26והאכלתי את מוניכ את בשרמ וכעסיס דממ ישכרונ וידעו כ26ואוכלתי את מוניכ את בשרמ וכעסיס דממ ישכרו וידעו כו
>ל בשר כי אני יהוה מושיעכ וגאלכ אביר יעקב>ל בשר כיא אני יהוה מושיעכ וגואלכי אביר יעקוב
t1שׁמעו איים אלי והקשׁיבו לאמים מרחוק יהוה מבטן קראנt1שמעו איימ אלי הקשיבו לאומימ מרחוק יהוה מבטנ קראני 
>י ממעי אמי הזכיר שׁמי >ממעי אמי הזכיר שמי
2וישׂם פי כחרב חדה בצל ידו החביאני וישׂימני לחץ ברו2וישמ פי כחרב חדה בצל ידיו החביאני וישימני לכחצ ברו
>ר באשׁפתו הסתירני >ר באשפתיו הסתירני
3ויאמר לי עבדי אתה ישׂראל אשׁר בך אתפאר 3ויואמר לי עבדי אתה ישראל אשר בכה אתפאר
4ואני אמרתי לריק יגעתי לתהו והבל כחי כליתי אכן משׁפ4אני אמרתי לריק יגעתי לתוה ולהבל כוחי כליתי אכנ משפ
>טי את יהוה ופעלתי את אלהי >טי את יהוה ופועלתי את אלוהי
5ועתה׀ אמר יהוה יצרי מבטן לעבד לו לשׁובב יעקב אליו 5ועתה אמר יהוה יוצרכ מבטנ לעבד לו לשובב יעקוב אליו 
>וישׂראל לא יאסף ואכבד בעיני יהוה ואלהי היה עזי >וישראל לו יאספ ואכבדה בעיני יהוה ואלוהי היה עזרי
6ויאמר נקל מהיותך לי עבד להקים את שׁבטי יעקב ונצירי6ויואמר נקל מהיותכה לי עבד להקימ את שבטי ישראל ונצי
> ישׂראל להשׁיב ונתתיך לאור גוים להיות ישׁועתי עד ק>רי יעקוב להשיב ונתתיכ לאור גואימ להיות ישועתי עד ק
>צה הארץ ס >צוי הארצ
7כה אמר יהוה גאל ישׂראל קדושׁו לבזה נפשׁ למתעב גוי 7כוה אמר אדוני יהוה גואלכה ישראל קדושו לבזוי נפש למ
>לעבד משׁלים מלכים יראו וקמו שׂרים וישׁתחוו למען יה>תעבי גוי לעבד מושלימ מלכימ ראו וקמו ושרימ יהשתחוו 
>וה אשׁר נאמן קדשׁ ישׂראל ויבחרך >למענ יהוה אשר נאמנ קדוש ישראל יבחרכה
8כה׀ אמר יהוה בעת רצון עניתיך וביום ישׁועה עזרתיך ו8כוה אמר יהוה בעת רצונ אענכה וביומ ישועה אעזרכה ואצ
>אצרך ואתנך לברית עם להקים ארץ להנחיל נחלות שׁממות >ורכה ואתנכה לברית עמ להקימ ארצ להנחיל נחלות שוממות
9לאמר לאסורים צאו לאשׁר בחשׁך הגלו על דרכים ירעו וב9לאמור לאסורימ צאו ולאשר בחושכ הגלו על כול הרימ ירע
>כל שׁפיים מרעיתם >ו ובכול שפאימ מרעיתמ
10לא ירעבו ולא יצמאו ולא יכם שׁרב ושׁמשׁ כי מרחמם ינ10לוא ירעבו ולוא יצמאו ולוא יכמ שוב ושמש כיא מרחממ י
>הגם ועל מבועי מים ינהלם >נהגמ ועל מבועי מימ ינהלמ
11ושׂמתי כל הרי לדרך ומסלתי ירמון 11ושמתי כול הרי לדרכ ומס ומסלתי ירומונ
12הנה אלה מרחוק יבאו והנה אלה מצפון ומים ואלה מארץ ס12הנה אלה מרחוק יבואו והנה אלה מצפונ ומימ ואלה מארצ 
>ינים >סוניימ
13רנו שׁמים וגילי ארץ יפצחו הרים רנה כי נחם יהוה עמו13רונו שמימ וגילי ארצ פצחו הרימ רונה כיא מנחמ יהוה ע
> ועניו ירחם ס >מו ועניו ירחמ
14ותאמר ציון עזבני יהוה ואדני שׁכחני 14ותואמר ציונ עזבני יהוה ואדוני שכחני
15התשׁכח אשׁה עולה מרחם בן בטנה גם אלה תשׁכחנה ואנכי15התשכח אשה עולה מרחמ בנ בטנה גמ אלה תשכחנה ואנוכי ל
> לא אשׁכחך >וא אשכחכי
16הן על כפים חקתיך חומתיך נגדי תמיד 16הנה על כפימ חוקותיכ וחומותיכ נגדי תמיד
17מהרו בניך מהרסיך ומחרביך ממך יצאו 17מהרו בוניכ מהורסיכ ומחריביכ ממכ יצאו
18שׂאי סביב עיניך וראי כלם נקבצו באו לך חי אני נאם י18סאי סביב עיניכ וראי כולמ נקבצו באו לכי חי אני נואמ
>הוה כי כלם כעדי תלבשׁי ותקשׁרים ככלה > יהוה כיא כולמ כעדי תלבשי ותקשרימ ככלה
19כי חרבתיך ושׁממתיך וארץ הרסתיך כי עתה תצרי מיושׁב 19כיא חרבותיכ ושוממותיכ וארצ הרוסתכ כיא עתה תצרי מיו
>ורחקו מבלעיך >שב ורחקו מבלעיכ
20עוד יאמרו באזניך בני שׁכליך צר לי המקום גשׁה לי וא20עוד יואמרו באוזניכ בני שכוליכ צר לי המקומ גשה לי ו
>שׁבה >אשבה
21ואמרת בלבבך מי ילד לי את אלה ואני שׁכולה וגלמודה ג21ואמרת בלבבכ מיא ילד לי את אלה ואני שכולה וגלמודה ו
>לה׀ וסורה ואלה מי גדל הן אני נשׁארתי לבדי אלה איפה>גולה וסרה אלה מיא גדל הנה אני נשארתי לבדי אלה איפו
> הם פ > המה
22כה אמר אדני יהוה הנה אשׂא אל גוים ידי ואל עמים ארי22כיא כוה אמר יהוה הנה אשא אל גואימ ידי ואל העמימ אר
>ם נסי והביאו בניך בחצן ובנתיך על כתף תנשׂאנה >ימ נסי והביאו בניכ בחוצנ ובנותיכ על כתפ תנשנה
23והיו מלכים אמניך ושׂרותיהם מיניקתיך אפים ארץ ישׁתח23והוי מלכימ אמניכ ושרותיהמה מינקותיכ אפימ ארצ ישתחו
>וו לך ועפר רגליך ילחכו וידעת כי אני יהוה אשׁר לא י>ו לכ ועפר רגליכ ילחכו וידעתי כיא אני יהוה אשר לוא 
>בשׁו קוי ס >יבושו קוי
24היקח מגבור מלקוח ואם שׁבי צדיק ימלט 24היקחו מגבור מלקוח אמ שבי עריצ ימלט
25כי כה׀ אמר יהוה גם שׁבי גבור יקח ומלקוח עריץ ימלט 25כיא כוה אמר יהוה גמ מלקוח גבור ילקח ושובי עריצ ימל
>ואת יריבך אנכי אריב ואת בניך אנכי אושׁיע >ט ואת רוביכ אנוכי אריב ואת בניכ אנוכי אושיע
26והאכלתי את מוניך את בשׂרם וכעסיס דמם ישׁכרון וידעו26ואוכלתי את מוניכ את בשרמ וכעסיס דממ ישכרו וידעו כו
> כל בשׂר כי אני יהוה מושׁיעך וגאלך אביר יעקב ס >ל בשר כיא אני יהוה מושיעכ וגואלכי אביר יעקוב
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Isaiah 50 1QIsaa
t1כה אמר יהוה אי זה ספר כריתות אמכמ אשר שלחתיה או מיt1כוה אמר יהוה אי זה ספר כריתות אמכמה אשר שלחתיה או 
> מנושי אשר מכרתי אתכמ לו הנ בעונתיכמ נמכרתמ ובפשעי>מי מנושי אשר מכרתי אתכמה לו הנה בעוונותיכמה נמכרתמ
>כמ שלחה אמכמ>ה ובפשעיכמה שולחה אמכמה
2מדוע באתי ואינ איש קראתי ואינ עונה הקצור קצרה ידי 2מדוע באתי ואינ איש קראתי ואינ עונה הקצור קצרה ידי 
>מפדות ואמ אינ בי כח להציל הנ בגערתי אחריב ימ אשימ >מפדות אמ אינ בי כוח להציל הנה בגערתי אחריב ימ אשימ
>נהרות מדבר תבאש דגתמ מאינ מימ ותמת בצמא> נהרות מדבר תיבש דגתמ מאינ מימ ותמות בצמא
3אלביש שמימ קדרות ושק אשימ כסותמ3אלבישה שמימ קדרות ושק אשימ כסותמ
4אדני יהוה נתנ לי לשונ למודימ לדעת לעות את יעפ דבר 4אדוני יהוה נתנ לי לשונ למודימ לדעת לעות את יעפ דב 
>יעיר בבקר בבקר יעיר לי אזנ לשמע כלמודימ>ויעיר בבוקר בבוקר ויעיר לי אוזנ לשמוע כלמודימ
5אדני יהוה פתח לי אזנ ואנכי לא מריתי אחור לא נסוגתי5אדוני אלוהימ פתח לי אוזנ ואנוכי לוא מריתי אחור לוא
 > נסגותי
6גוי נתתי למכימ ולחיי למרטימ פני לא הסתרתי מכלמות ו6גוי נתתי למכימ ולחיי למטלימ פני לוא הסירותי מכלמות
>רק> ורוק
7ואדני יהוה יעזר לי על כנ לא נכלמתי על כנ שמתי פני 7ואדוני יהוה יעזור לי על כנ לוא נכלמתי על כנ שמתי פ
>כחלמיש ואדע כי לא אבוש>ני כחלמיש ואדltֵה כיא לוא אבוש
8קרוב מצדיקי מי יריב אתי נעמדה יחד מי בעל משפטי יגש8קרוב מצדיקי מי יריב אתי נעמודה יחדיו מי בעל משפטי 
> אלי>יגש אלי
9הנ אדני יהוה יעזר לי מי הוא ירשיעני הנ כלמ כבגד יב9הנה אדוני יהוה יעזור ליא מי הואה ירשיעני הנה כולמ 
>לו עש יאכלמ>כבגד יבלו עש יאכולמ
10מי בכמ ירא יהוה שמע בקול עבדו אשר הלכ חשכימ ואינ נ10מי בכמה יראי יהוה שומע בקול עבדו אשר הלכו חשוכימ ו
>גה לו יבטח בשמ יהוה וישענ באלהיו>אינ נוגה לו יבטח בשמ יהוה וישענ באלוהיו
11הנ כלכמ קדחי אש מאזרי זיקות לכו באור אשכמ ובזיקות 11הנה כולמ קודחי אש מאזרי זיקות לכו באור אשכמה ובזיק
>בערתמ מידי היתה זאת לכמ למעצבה תשכבונ>ות בערתמה מידי הייתה זואת לכמה למעצבה תשכבו
t1כה׀ אמר יהוה אי זה ספר כריתות אמכם אשׁר שׁלחתיה אוt1כוה אמר יהוה אי זה ספר כריתות אמכמה אשר שלחתיה או 
> מי מנושׁי אשׁר מכרתי אתכם לו הן בעונתיכם נמכרתם ו>מי מנושי אשר מכרתי אתכמה לו הנה בעוונותיכמה נמכרתמ
>בפשׁעיכם שׁלחה אמכם >ה ובפשעיכמה שולחה אמכמה
2מדוע באתי ואין אישׁ קראתי ואין עונה הקצור קצרה ידי2מדוע באתי ואינ איש קראתי ואינ עונה הקצור קצרה ידי 
> מפדות ואם אין בי כח להציל הן בגערתי אחריב ים אשׂי>מפדות אמ אינ בי כוח להציל הנה בגערתי אחריב ימ אשימ
>ם נהרות מדבר תבאשׁ דגתם מאין מים ותמת בצמא > נהרות מדבר תיבש דגתמ מאינ מימ ותמות בצמא
3אלבישׁ שׁמים קדרות ושׂק אשׂים כסותם ס 3אלבישה שמימ קדרות ושק אשימ כסותמ
4אדני יהוה נתן לי לשׁון למודים לדעת לעות את יעף דבר4אדוני יהוה נתנ לי לשונ למודימ לדעת לעות את יעפ דב 
> יעיר׀ בבקר בבקר יעיר לי אזן לשׁמע כלמודים >ויעיר בבוקר בבוקר ויעיר לי אוזנ לשמוע כלמודימ
5אדני יהוה פתח לי אזן ואנכי לא מריתי אחור לא נסוגתי5אדוני אלוהימ פתח לי אוזנ ואנוכי לוא מריתי אחור לוא
> > נסגותי
6גוי נתתי למכים ולחיי למרטים פני לא הסתרתי מכלמות ו6גוי נתתי למכימ ולחיי למטלימ פני לוא הסירותי מכלמות
>רק > ורוק
7ואדני יהוה יעזר לי על כן לא נכלמתי על כן שׂמתי פני7ואדוני יהוה יעזור לי על כנ לוא נכלמתי על כנ שמתי פ
> כחלמישׁ ואדע כי לא אבושׁ >ני כחלמיש ואדltֵה כיא לוא אבוש
8קרוב מצדיקי מי יריב אתי נעמדה יחד מי בעל משׁפטי יג8קרוב מצדיקי מי יריב אתי נעמודה יחדיו מי בעל משפטי 
>שׁ אלי >יגש אלי
9הן אדני יהוה יעזר לי מי הוא ירשׁיעני הן כלם כבגד י9הנה אדוני יהוה יעזור ליא מי הואה ירשיעני הנה כולמ 
>בלו עשׁ יאכלם >כבגד יבלו עש יאכולמ
10מי בכם ירא יהוה שׁמע בקול עבדו אשׁר׀ הלך חשׁכים וא10מי בכמה יראי יהוה שומע בקול עבדו אשר הלכו חשוכימ ו
>ין נגה לו יבטח בשׁם יהוה וישׁען באלהיו >אינ נוגה לו יבטח בשמ יהוה וישענ באלוהיו
11הן כלכם קדחי אשׁ מאזרי זיקות לכו׀ באור אשׁכם ובזיק11הנה כולמ קודחי אש מאזרי זיקות לכו באור אשכמה ובזיק
>ות בערתם מידי היתה זאת לכם למעצבה תשׁכבון פ >ות בערתמה מידי הייתה זואת לכמה למעצבה תשכבו
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Isaiah 51 MT
Isaiah 51 1QIsaa
t1שמעו אלי רדפי צדק מבקשי יהוה הביטו אל צור חצבתמ ואt1שמעו אלי רודפי צדק מבקשי יהוה הביטו אל צור חצבתמה 
>ל מקבת בור נקרתמ>ואל מקבת בור נקרתמה
2הביטו אל אברהמ אביכמ ואל שרה תחוללכמ כי אחד קראתיו2הביטו אל אברהמ אביכמה ואל שרה תחוללכמה כיא אחד קרת
> ואברכהו וארבהו>יהו ואפרהו וארבהו
3כי נחמ יהוה ציונ נחמ כל חרבתיה וישמ מדברה כעדנ וער3כיא נחמ יהוה ציונ נחמ כול חרבותיה וישמ מדברה כעדנ 
>בתה כגנ יהוה ששונ ושמחה ימצא בה תודה וקול זמרה>וערבתה כגנ יהוה ששונ ושמחה ימצאו בה תודה וקול זמרה
 > נס יגונ ואנחה
4הקשיבו אלי עמי ולאומי אלי האזינו כי תורה מאתי תצא 4אקשיבו אלי עמי ולאומי אלי האזינו כיא תורה מאתי תצא
>ומשפטי לאור עמימ ארגיע> ומשפטי לאור עמימ ארגיע
5קרוב צדקי יצא ישעי וזרעי עמימ ישפטו אלי איימ יקוו 5קרוב צדקי יצא ישעי וזרועו עמימ ישפוטו אליו איימ יק
>ואל זרעי ייחלונ>וו ואל זרועו יוחילונ
6שאו לשמימ עיניכמ והביטו אל הארצ מתחת כי שמימ כעשנ 6שאו שמימ עיניכמה והביטו אל הארצ מתחתה וראו מי ברא 
>נמלחו והארצ כבגד תבלה וישביה כמו כנ ימותונ וישועתי>את אלה ויושביה כמו כנ ימותונ וישועתי לעולמ תהיה וצ
> לעולמ תהיה וצדקתי לא תחת>דקתי לוא תחת
7שמעו אלי ידעי צדק עמ תורתי בלבמ אל תיראו חרפת אנוש7שמעו אלי יודעי צדק עמ תורתי בלבמ אל תיראו חרפת אנו
> ומגדפתמ אל תחתו>ש וממגדפותמ אל תחתו
8כי כבגד יאכלמ עש וכצמר יאכלמ סס וצדקתי לעולמ תהיה 8כיא כבגד יואכולמ עש וכצמר יאכלמ סס וצדקתי לעולמ תה
>וישועתי לדור דורימ>יה וישועתי לדור דורימ
9עורי עורי לבשי עז זרוע יהוה עורי כימי קדמ דרות עול9עורי עורי לבשי עוז זרוע יהוה עורי כימי קדמ דורות ע
>מימ הלוא את היא המחצבת רהב מחוללת תנינ>ולמימ הלוא אתי היאה המוחצת רחוב מחללת תנימ
10הלוא את היא המחרבת ימ מי תהומ רבה השמה מעמקי ימ דר10הלוא אתי היאה המחרבת ימ מי תהומ רבא השמה במעמקי ימ
>כ לעבר גאולימ> דרכ לעבור גאולימ
11ופדויי יהוה ישובונ ובאו ציונ ברנה ושמחת עולמ על רא11ופזורי יהוה ישובו ובאו ציונ ברונה ושמחת עולמ על רו
>שמ ששונ ושמחה ישיגונ נסו יגונ ואנחה>אשיהמה ששונ ושמחה ישיגו ונס יגונ ואנחה
12אנכי אנכי הוא מנחמכמ מי את ותיראי מאנוש ימות ומבנ 12אנוכי אנוכי הואה מנחמכמה מי אתי ותיראי מאנוש ימות 
>אדמ חציר ינתנ>ומבנ אדמ חציר נתנ
13ותשכח יהוה עשכ נוטה שמימ ויסד ארצ ותפחד תמיד כל הי13ותשכחי את יהוה עשכה נוטה שמימ ויסד ארצ ותפחד תמיד 
>ומ מפני חמת המציק כאשר כוננ להשחית ואיה חמת המציק>כול היומ מפני חמת המציק כאשר כוננ להשחית ואיה חמת 
 >המציק
14מהר צעה להפתח ולא ימות לשחת ולא יחסר לחמו14מהר צרה להפתח ולוא ימות לשחת ולוא יחסר לחמו
15ואנכי יהוה אלהיכ רגע הימ ויהמו גליו יהוה צבאות שמו15אנוכי יהוה אלוהיכה רוגע הימ ויהמו גליו יהוה צבאות 
 >שמו
16ואשימ דברי בפיכ ובצל ידי כסיתיכ לנטע שמימ וליסד אר16אשימ דברי בפיכה ובצל ידי כסיתיכה לנטוע שמימ וליסד 
>צ ולאמר לציונ עמי אתה>ארצ ולאמור לציונ עמיא אתה
17התעוררי התעוררי קומי ירושלמ אשר שתית מיד יהוה את כ17התעוררי התעוררי קומי ירושלימ אשר שתיתי מיד יהוה את
>וס חמתו את קבעת כוס התרעלה שתית מצית> כוס חמתו את קובעת כוס התרעלה שתיתי מצית
18אינ מנהל לה מכל בנימ ילדה ואינ מחזיק בידה מכל בנימ18אינ מנחל לכ מכול בנימ ילדה ואינ מחזיק בידה מכול בנ
> גדלה>ימ גדלה
19שתימ הנה קראתיכ מי ינוד לכ השד והשבר והרעב והחרב מ19שתימ המה קראתכי מי ינוד לכי השד והשבר והרעב והחרב 
>י אנחמכ>מי ינחמכ
20בניכ עלפו שכבו בראש כל חוצות כתוא מכמר המלאימ חמת 20בניכ עולפו שוכבו בראוש כול חוצות כתו מוכמר המלאימ 
>יהוה גערת אלהיכ>חמת יהוה גערת אלוהיכ
21לכנ שמעי נא זאת עניה ושכרת ולא מיינ21לכנ שמעי נא זואת עניה שכורת ולוא מיינ
22כה אמר אדניכ יהוה ואלהיכ יריב עמו הנה לקחתי מידכ א22כוה אמר אדוניכ יהוה אלוהיכ יריב עמוא הנה לקחתי מיד
>ת כוס התרעלה את קבעת כוס חמתי לא תוסיפי לשתותה עוד>כ את כוס התרעלה את קובעת כוס חמתי לוא תוסיפי לשתות
 >ו עוד
23ושמתיה ביד מוגיכ אשר אמרו לנפשכ שחי ונעברה ותשימי 23ושמתיהו ביד מוגיכ ומעניכ אשר אמרו לנפשכי שוחי ונעב
>כארצ גוכ וכחוצ לעברימ>ורה ותשימי כארצ גוכ וכחוצ לעוברימ
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>ואל מקבת בור נקרתם >ואל מקבת בור נקרתמה
2הביטו אל אברהם אביכם ואל שׂרה תחוללכם כי אחד קראתי2הביטו אל אברהמ אביכמה ואל שרה תחוללכמה כיא אחד קרת
>ו ואברכהו וארבהו ס >יהו ואפרהו וארבהו
3כי נחם יהוה ציון נחם כל חרבתיה וישׂם מדברה כעדן וע3כיא נחמ יהוה ציונ נחמ כול חרבותיה וישמ מדברה כעדנ 
>רבתה כגן יהוה שׂשׂון ושׂמחה ימצא בה תודה וקול זמרה>וערבתה כגנ יהוה ששונ ושמחה ימצאו בה תודה וקול זמרה
> ס > נס יגונ ואנחה
4הקשׁיבו אלי עמי ולאומי אלי האזינו כי תורה מאתי תצא4אקשיבו אלי עמי ולאומי אלי האזינו כיא תורה מאתי תצא
> ומשׁפטי לאור עמים ארגיע > ומשפטי לאור עמימ ארגיע
5קרוב צדקי יצא ישׁעי וזרעי עמים ישׁפטו אלי איים יקו5קרוב צדקי יצא ישעי וזרועו עמימ ישפוטו אליו איימ יק
>ו ואל זרעי ייחלון >וו ואל זרועו יוחילונ
6שׂאו לשׁמים עיניכם והביטו אל הארץ מתחת כי שׁמים כע6שאו שמימ עיניכמה והביטו אל הארצ מתחתה וראו מי ברא 
>שׁן נמלחו והארץ כבגד תבלה וישׁביה כמו כן ימותון וי>את אלה ויושביה כמו כנ ימותונ וישועתי לעולמ תהיה וצ
>שׁועתי לעולם תהיה וצדקתי לא תחת ס >דקתי לוא תחת
7שׁמעו אלי ידעי צדק עם תורתי בלבם אל תיראו חרפת אנו7שמעו אלי יודעי צדק עמ תורתי בלבמ אל תיראו חרפת אנו
>שׁ ומגדפתם אל תחתו >ש וממגדפותמ אל תחתו
8כי כבגד יאכלם עשׁ וכצמר יאכלם סס וצדקתי לעולם תהיה8כיא כבגד יואכולמ עש וכצמר יאכלמ סס וצדקתי לעולמ תה
> וישׁועתי לדור דורים ס >יה וישועתי לדור דורימ
9עורי עורי לבשׁי עז זרוע יהוה עורי כימי קדם דרות עו9עורי עורי לבשי עוז זרוע יהוה עורי כימי קדמ דורות ע
>למים הלוא את היא המחצבת רהב מחוללת תנין >ולמימ הלוא אתי היאה המוחצת רחוב מחללת תנימ
10הלוא את היא המחרבת ים מי תהום רבה השׂמה מעמקי ים ד10הלוא אתי היאה המחרבת ימ מי תהומ רבא השמה במעמקי ימ
>רך לעבר גאולים > דרכ לעבור גאולימ
11ופדויי יהוה ישׁובון ובאו ציון ברנה ושׂמחת עולם על 11ופזורי יהוה ישובו ובאו ציונ ברונה ושמחת עולמ על רו
>ראשׁם שׂשׂון ושׂמחה ישׂיגון נסו יגון ואנחה ס >אשיהמה ששונ ושמחה ישיגו ונס יגונ ואנחה
12אנכי אנכי הוא מנחמכם מי את ותיראי מאנושׁ ימות ומבן12אנוכי אנוכי הואה מנחמכמה מי אתי ותיראי מאנוש ימות 
> אדם חציר ינתן >ומבנ אדמ חציר נתנ
13ותשׁכח יהוה עשׂך נוטה שׁמים ויסד ארץ ותפחד תמיד כל13ותשכחי את יהוה עשכה נוטה שמימ ויסד ארצ ותפחד תמיד 
> היום מפני חמת המציק כאשׁר כונן להשׁחית ואיה חמת ה>כול היומ מפני חמת המציק כאשר כוננ להשחית ואיה חמת 
>מציק >המציק
14מהר צעה להפתח ולא ימות לשׁחת ולא יחסר לחמו 14מהר צרה להפתח ולוא ימות לשחת ולוא יחסר לחמו
15ואנכי יהוה אלהיך רגע הים ויהמו גליו יהוה צבאות שׁמ15אנוכי יהוה אלוהיכה רוגע הימ ויהמו גליו יהוה צבאות 
>ו >שמו
16ואשׂים דברי בפיך ובצל ידי כסיתיך לנטע שׁמים וליסד 16אשימ דברי בפיכה ובצל ידי כסיתיכה לנטוע שמימ וליסד 
>ארץ ולאמר לציון עמי אתה ס >ארצ ולאמור לציונ עמיא אתה
17התעוררי התעוררי קומי ירושׁלם אשׁר שׁתית מיד יהוה א17התעוררי התעוררי קומי ירושלימ אשר שתיתי מיד יהוה את
>ת כוס חמתו את קבעת כוס התרעלה שׁתית מצית > כוס חמתו את קובעת כוס התרעלה שתיתי מצית
18אין מנהל לה מכל בנים ילדה ואין מחזיק בידה מכל בנים18אינ מנחל לכ מכול בנימ ילדה ואינ מחזיק בידה מכול בנ
> גדלה >ימ גדלה
19שׁתים הנה קראתיך מי ינוד לך השׁד והשׁבר והרעב והחר19שתימ המה קראתכי מי ינוד לכי השד והשבר והרעב והחרב 
>ב מי אנחמך >מי ינחמכ
20בניך עלפו שׁכבו בראשׁ כל חוצות כתוא מכמר המלאים חמ20בניכ עולפו שוכבו בראוש כול חוצות כתו מוכמר המלאימ 
>ת יהוה גערת אלהיך >חמת יהוה גערת אלוהיכ
21לכן שׁמעי נא זאת עניה ושׁכרת ולא מיין ס 21לכנ שמעי נא זואת עניה שכורת ולוא מיינ
22כה אמר אדניך יהוה ואלהיך יריב עמו הנה לקחתי מידך א22כוה אמר אדוניכ יהוה אלוהיכ יריב עמוא הנה לקחתי מיד
>ת כוס התרעלה את קבעת כוס חמתי לא תוסיפי לשׁתותה עו>כ את כוס התרעלה את קובעת כוס חמתי לוא תוסיפי לשתות
>ד >ו עוד
23ושׂמתיה ביד מוגיך אשׁר אמרו לנפשׁך שׁחי ונעברה ותש23ושמתיהו ביד מוגיכ ומעניכ אשר אמרו לנפשכי שוחי ונעב
>ׂימי כארץ גוך וכחוץ לעברים ס >ורה ותשימי כארצ גוכ וכחוצ לעוברימ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 52 MT
Isaiah 52 1QIsaa
t1עורי עורי לבשי עזכ ציונ לבשי בגדי תפארתכ ירושלמ עיt1עורי עורי לבשי עוז ציונ לבשי בגדי תפארתכ ירושלמ עי
>ר הקדש כי לא יוסיפ יבא בכ עוד ערל וטמא>ר הקודש כיא לוא יוסיפ ויבוא בכ ערל וטמא
2התנערי מעפר קומי שבי ירושלמ התפתחי מוסרי צוארכ שבי2התנערי מעפר וקומי ושבי ירושלימ התפתחו מוסרי צורכ ש
>ה בת ציונ>ביה בת ציונ
3כי כה אמר יהוה חנמ נמכרתמ ולא בכספ תגאלו3כיא כוה אמר יהוה חנמ נמכרתמה ולוא בכספ תגאלו
4כי כה אמר אדני יהוה מצרימ ירד עמי בראשנה לגור שמ ו4כיא כוה אמר יהוה מצרימ ירד עמי ברישונה לגור שמה וא
>אשור באפס עשקו>שור באפס עשקו
5ועתה מה לי פה נאמ יהוה כי לקח עמי חנמ משליו יהיליל5ועתה מה לי פה נואמ יהוה כי לוקח עמיא חנמ משלו והול
>ו נאמ יהוה ותמיד כל היומ שמי מנאצ>לו נואמ ותמיד כול היומ שמי מנואצ
6לכנ ידע עמי שמי לכנ ביומ ההוא כי אני הוא המדבר הננ6לכנ ידע עמיא שמי ביומ ההואה כי אני הואה המדבר הנני
>י 
7מה נאוו על ההרימ רגלי מבשר משמיע שלומ מבשר טוב משמ7מה נאוו על ההרימ רגלי מבשר מבשר שלומ משמיע טוב משמ
>יע ישועה אמר לציונ מלכ אלהיכ>יע ישועה אומר לציונ מלכ אלוהיכ
8קול צפיכ נשאו קול יחדו ירננו כי עינ בעינ יראו בשוב8קול צופיכ נשאו קולמ יחדיו ירננו כיא עינ בעינ יראו 
> יהוה ציונ>בשוב יהוה ציונ ברחמימ
9פצחו רננו יחדו חרבות ירושלמ כי נחמ יהוה עמו גאל יר9פצחו רונה יחדיו חרבות ירושלימ כיא נחמ יהוה עמו וגא
>ושלמ>ל את ירושלימ
10חשפ יהוה את זרוע קדשו לעיני כל הגוימ וראו כל אפסי 10חשפ יהוה את זרוע קודשו לעיני כול הגואימ וראו כול א
>ארצ את ישועת אלהינו>פסי הארצ את ישועת אלוהינו
11סורו סורו צאו משמ טמא אל תגעו צאו מתוכה הברו נשאי 11סורו סורו צאו משמה בטמה אל תגעו צאו מתוכה הברו נוש
>כלי יהוה>אי כלי יהוה
12כי לא בחפזונ תצאו ובמנוסה לא תלכונ כי הלכ לפניכמ י12כיא לוא בחפזונ תצאו ובמנוסא לוא תלכונ כיא הולכ לפנ
>הוה ומאספכמ אלהי ישראל>יכמה יהוה ומאספכמה אלוהי ישראל אלוהי כול הארצ יקרא
13הנה ישכיל עבדי ירומ ונשא וגבה מאד13הנה ישכיל עבדי וירומ ונשא וגבה מואדה
14כאשר שממו עליכ רבימ כנ משחת מאיש מראהו ותארו מבני 14כאשר שממו עליכה רבימ כנ משחתי מאיש מראהו ותוארו מב
>אדמ>ני האדמ
15כנ יזה גוימ רבימ עליו יקפצו מלכימ פיהמ כי אשר לא ס15כנ יזה גואימ רבימ עליו וקפצו מלכימ פיהמה כיא את אש
>פר להמ ראו ואשר לא שמעו התבוננו>ר לוא סופר להמה ראו ואת אשר לוא שמעו התבוננו
t1עורי עורי לבשׁי עזך ציון לבשׁי׀ בגדי תפארתך ירושׁלt1עורי עורי לבשי עוז ציונ לבשי בגדי תפארתכ ירושלמ עי
>ם עיר הקדשׁ כי לא יוסיף יבא בך עוד ערל וטמא >ר הקודש כיא לוא יוסיפ ויבוא בכ ערל וטמא
2התנערי מעפר קומי שׁבי ירושׁלם התפתחו מוסרי צוארך ש2התנערי מעפר וקומי ושבי ירושלימ התפתחו מוסרי צורכ ש
>ׁביה בת ציון ס >ביה בת ציונ
3כי כה אמר יהוה חנם נמכרתם ולא בכסף תגאלו 3כיא כוה אמר יהוה חנמ נמכרתמה ולוא בכספ תגאלו
4כי כה אמר אדני יהוה מצרים ירד עמי בראשׁנה לגור שׁם4כיא כוה אמר יהוה מצרימ ירד עמי ברישונה לגור שמה וא
> ואשׁור באפס עשׁקו >שור באפס עשקו
5ועתה מי לי פה נאם יהוה כי לקח עמי חנם משׁלו יהיליל5ועתה מה לי פה נואמ יהוה כי לוקח עמיא חנמ משלו והול
>ו נאם יהוה ותמיד כל היום שׁמי מנאץ >לו נואמ ותמיד כול היומ שמי מנואצ
6לכן ידע עמי שׁמי לכן ביום ההוא כי אני הוא המדבר הנ6לכנ ידע עמיא שמי ביומ ההואה כי אני הואה המדבר הנני
>ני  
7מה נאוו על ההרים רגלי מבשׂר משׁמיע שׁלום מבשׂר טוב7מה נאוו על ההרימ רגלי מבשר מבשר שלומ משמיע טוב משמ
> משׁמיע ישׁועה אמר לציון מלך אלהיך >יע ישועה אומר לציונ מלכ אלוהיכ
8קול צפיך נשׂאו קול יחדו ירננו כי עין בעין יראו בשׁ8קול צופיכ נשאו קולמ יחדיו ירננו כיא עינ בעינ יראו 
>וב יהוה ציון >בשוב יהוה ציונ ברחמימ
9פצחו רננו יחדו חרבות ירושׁלם כי נחם יהוה עמו גאל י9פצחו רונה יחדיו חרבות ירושלימ כיא נחמ יהוה עמו וגא
>רושׁלם >ל את ירושלימ
10חשׂף יהוה את זרוע קדשׁו לעיני כל הגוים וראו כל אפס10חשפ יהוה את זרוע קודשו לעיני כול הגואימ וראו כול א
>י ארץ את ישׁועת אלהינו ס >פסי הארצ את ישועת אלוהינו
11סורו סורו צאו משׁם טמא אל תגעו צאו מתוכה הברו נשׂא11סורו סורו צאו משמה בטמה אל תגעו צאו מתוכה הברו נוש
>י כלי יהוה >אי כלי יהוה
12כי לא בחפזון תצאו ובמנוסה לא תלכון כי הלך לפניכם י12כיא לוא בחפזונ תצאו ובמנוסא לוא תלכונ כיא הולכ לפנ
>הוה ומאספכם אלהי ישׂראל ס >יכמה יהוה ומאספכמה אלוהי ישראל אלוהי כול הארצ יקרא
13הנה ישׂכיל עבדי ירום ונשׂא וגבה מאד 13הנה ישכיל עבדי וירומ ונשא וגבה מואדה
14כאשׁר שׁממו עליך רבים כן משׁחת מאישׁ מראהו ותארו מ14כאשר שממו עליכה רבימ כנ משחתי מאיש מראהו ותוארו מב
>בני אדם >ני האדמ
15כן יזה גוים רבים עליו יקפצו מלכים פיהם כי אשׁר לא 15כנ יזה גואימ רבימ עליו וקפצו מלכימ פיהמה כיא את אש
>ספר להם ראו ואשׁר לא שׁמעו התבוננו >ר לוא סופר להמה ראו ואת אשר לוא שמעו התבוננו
- - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 53 MT
Isaiah 53 1QIsaa
t1מי האמינ לשמעתנו וזרוע יהוה על מי נגלתהt1מי האמינ לשמועתנו וזרוע יהוה אל מי נגלתה
2ויעל כיונק לפניו וכשרש מארצ ציה לא תאר לו ולא הדר 2ויעל כיונק לפניו וכשורש מארצ ציאה לוא תאור לו ולוא
>ונראהו ולא מראה ונחמדהו> הדר לו ונראנו ולוא מראה ונחמדנו
3נבזה וחדל אישימ איש מכאבות וידוע חלי וכמסתר פנימ מ3נבזה וחדל אישימ ואיש מכאובות ויודע חולי וכמסתיר פנ
>מנו נבזה ולא חשבנהו>ימ ממנו ונבוזהו ולוא חשבנוהו
4אכנ חלינו הוא נשא ומכאבינו סבלמ ואנחנו חשבנהו נגוע4אכנ חוליינו הואה נשא ומכאובינו סבלמ ואנחנו חשבנוהי
> מכה אלהימ ומענה> נגוע ומוכה אלוהימ ומעונה
5והוא מחלל מפשענו מדכא מעונתינו מוסר שלומנו עליו וב5והואה מחולל מפשעינו ומדוכא מעוונותינו ומוסר שלומנו
>חברתו נרפא לנו> עליו ובחבורתיו נרפא לנו
6כלנו כצאנ תעינו איש לדרכו פנינו ויהוה הפגיע בו את 6כולנו כצואנ תעינו איש לדרכו פנינו ויהוה הפגיע בו א
>עונ כלנו>ת עוונ כולנו
7נגש והוא נענה ולא יפתח פיו כשה לטבח יובל וכרחל לפנ7נגש והואה נענה ולוא יפתח פיהו כשה לטבוח יובל כרחל 
>י גזזיה נאלמה ולא יפתח פיו>לפני גוזזיה נאלמה ולוא פתח פיהו
8מעצר וממשפט לקח ואת דורו מי ישוחח כי נגזר מארצ חיי8מעוצר וממשפט לוקח ואת דורו מיא ישוחח כיא נגזר מארצ
>מ מפשע עמי נגע למו> חיימ מפשע עמו נוגע למו
9ויתנ את רשעימ קברו ואת עשיר במתיו על לא חמס עשה ול9ויתנו את רשעימ קברו ועמת עשיר בומתו על לוא חמס עשה
>א מרמה בפיו> ולוא מרמה בפיהו
10ויהוה חפצ דכאו החלי אמ תשימ אשמ נפשו יראה זרע יארי10ויהוה חפצ דכאו ויחללהו אמ תשימ אשמ נפשו יראה זרע ו
>כ ימימ וחפצ יהוה בידו יצלח>יארכ ימימ וחפצ יהוה בידו יצלח
11מעמל נפשו יראה ישבע בדעתו יצדיק צדיק עבדי לרבימ וע11מעמל נפשוה יראה אור וישבע ובדעתו יצדיק צדיק עבדו ל
>ונתמ הוא יסבל>רבימ ועוונותמ הואה יסבול
12לכנ אחלק לו ברבימ ואת עצומימ יחלק שלל תחת אשר הערה12לכנ אחלק לו ברבימ ואת עצומימ יחלק שלל תחת אשר הערה
> למות נפשו ואת פשעימ נמנה והוא חטא רבימ נשא ולפשעי> למות נפשו ואת פושעימ נמנה והואה חטאי רבימ נשא ולפ
>מ יפגיע>שעיהמה יפגע
t1מי האמין לשׁמעתנו וזרוע יהוה על מי נגלתה t1מי האמינ לשמועתנו וזרוע יהוה אל מי נגלתה
2ויעל כיונק לפניו וכשׁרשׁ מארץ ציה לא תאר לו ולא הד2ויעל כיונק לפניו וכשורש מארצ ציאה לוא תאור לו ולוא
>ר ונראהו ולא מראה ונחמדהו > הדר לו ונראנו ולוא מראה ונחמדנו
3נבזה וחדל אישׁים אישׁ מכאבות וידוע חלי וכמסתר פנים3נבזה וחדל אישימ ואיש מכאובות ויודע חולי וכמסתיר פנ
> ממנו נבזה ולא חשׁבנהו >ימ ממנו ונבוזהו ולוא חשבנוהו
4אכן חלינו הוא נשׂא ומכאבינו סבלם ואנחנו חשׁבנהו נג4אכנ חוליינו הואה נשא ומכאובינו סבלמ ואנחנו חשבנוהי
>וע מכה אלהים ומענה > נגוע ומוכה אלוהימ ומעונה
5והוא מחלל מפשׁענו מדכא מעונתינו מוסר שׁלומנו עליו 5והואה מחולל מפשעינו ומדוכא מעוונותינו ומוסר שלומנו
>ובחברתו נרפא לנו > עליו ובחבורתיו נרפא לנו
6כלנו כצאן תעינו אישׁ לדרכו פנינו ויהוה הפגיע בו את6כולנו כצואנ תעינו איש לדרכו פנינו ויהוה הפגיע בו א
> עון כלנו >ת עוונ כולנו
7נגשׂ והוא נענה ולא יפתח פיו כשׂה לטבח יובל וכרחל ל7נגש והואה נענה ולוא יפתח פיהו כשה לטבוח יובל כרחל 
>פני גזזיה נאלמה ולא יפתח פיו >לפני גוזזיה נאלמה ולוא פתח פיהו
8מעצר וממשׁפט לקח ואת דורו מי ישׂוחח כי נגזר מארץ ח8מעוצר וממשפט לוקח ואת דורו מיא ישוחח כיא נגזר מארצ
>יים מפשׁע עמי נגע למו > חיימ מפשע עמו נוגע למו
9ויתן את רשׁעים קברו ואת עשׁיר במתיו על לא חמס עשׂה9ויתנו את רשעימ קברו ועמת עשיר בומתו על לוא חמס עשה
> ולא מרמה בפיו > ולוא מרמה בפיהו
10ויהוה חפץ דכאו החלי אם תשׂים אשׁם נפשׁו יראה זרע י10ויהוה חפצ דכאו ויחללהו אמ תשימ אשמ נפשו יראה זרע ו
>אריך ימים וחפץ יהוה בידו יצלח >יארכ ימימ וחפצ יהוה בידו יצלח
11מעמל נפשׁו יראה ישׂבע בדעתו יצדיק צדיק עבדי לרבים 11מעמל נפשוה יראה אור וישבע ובדעתו יצדיק צדיק עבדו ל
>ועונתם הוא יסבל >רבימ ועוונותמ הואה יסבול
12לכן אחלק לו ברבים ואת עצומים יחלק שׁלל תחת אשׁר הע12לכנ אחלק לו ברבימ ואת עצומימ יחלק שלל תחת אשר הערה
>רה למות נפשׁו ואת פשׁעים נמנה והוא חטא רבים נשׂא ו> למות נפשו ואת פושעימ נמנה והואה חטאי רבימ נשא ולפ
>לפשׁעים יפגיע ס >שעיהמה יפגע
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Isaiah 54 MT
Isaiah 54 1QIsaa
t1רני עקרה לא ילדה פצחי רנה וצהלי לא חלה כי רבימ בניt1רוני עקרה ולוא ילדה פצחי רונה וצהלי ולוא חלה כיא ר
> שוממה מבני בעולה אמר יהוה>בימ בני שוממה מבני בעולה אמר יהוה
2הרחיבי מקומ אהלכ ויריעות משכנותיכ יטו אל תחשכי האר2ארחיבי מקומ אהלכי ויריעות משכנותיכ יטי ואל תחשוכי 
>יכי מיתריכ ויתדתיכ חזקי>האריכי מיתריכ ויתדותיכ חזקי
3כי ימינ ושמאול תפרצי וזרעכ גוימ יירש וערימ נשמות י3כיא ימינ ושמואל תפרוצי וזרעכ גואימ יירשו וערימ נשמ
>ושיבו>ות יושיבו
4אל תיראי כי לא תבושי ואל תכלמי כי לא תחפירי כי בשת4אל תיראי כיא לוא תבושי ואל תכלמי כיא לוא תחפורי כי
> עלומיכ תשכחי וחרפת אלמנותיכ לא תזכרי עוד>א בושת עלומיכ תשכחי וחרפת אלמנותיכ לוא תזכורי עוד
5כי בעליכ עשיכ יהוה צבאות שמו וגאלכ קדוש ישראל אלהי5כיא בעלכי עושיכ יהוה צבאות שמו וגואלכי קדוש ישראל 
> כל הארצ יקרא>אלוהי כול הארצ יקרה
6כי כאשה עזובה ועצובת רוח קראכ יהוה ואשת נעורימ כי 6כיא כאשה עזובה ועצובת רוח קראכ יהוה ואשת נעורימ כי
>תמאס אמר אלהיכ>א תמאס אמר יהוה אלוהיכ
7ברגע קטנ עזבתיכ וברחמימ גדלימ אקבצכ7ברוגע קטנ עזבתיכ וברחמימ גדולימ אקבצכ
8בשצפ קצפ הסתרתי פני רגע ממכ ובחסד עולמ רחמתיכ אמר 8בשצפ קצפ הסתרתי פני רוגע ממכ ובחסדי עולמ רחמתיכ אמ
>גאלכ יהוה>ר גואלכי יהוה
9כי מי נח זאת לי אשר נשבעתי מעבר מי נח עוד על הארצ 9כימ נוח זואת לי אשר נשבעתי מעבור מי נוח עוד על האר
>כנ נשבעתי מקצפ עליכ ומגער בכ>צ כנ נשבעתי מקצופ עליכ עוד ומגעור בכ
10כי ההרימ ימושו והגבעות תמוטנה וחסדי מאתכ לא ימוש ו10כיא ההרימ ימושו והגבעות תתמוטינה וחסדי מאתכ לוא ימ
>ברית שלומי לא תמוט אמר מרחמכ יהוה>וש וברית שלומי לוא תמוט אמר מרחמכי יהוה
11עניה סערה לא נחמה הנה אנכי מרביצ בפוכ אבניכ ויסדתי11ענייה סחורה לוא נחמה הנה אנוכי מרביצ בפוכ אבניכ וי
>כ בספירימ>סודותיכ בספירימ
12ושמתי כדכד שמשתיכ ושעריכ לאבני אקדח וכל גבולכ לאבנ12ושמתי כדכוד שמשותיכ ושעריכ לאבני אוקדח וכול גבוליכ
>י חפצ> לאבני חפצ
13וכל בניכ למודי יהוה ורב שלומ בניכ13וכול בניכ למודי יהוה ורב שלומ בוניכי
14בצדקה תכונני רחקי מעשק כי לא תיראי וממחתה כי לא תק14בצדקה תתכונני רחקי מעושק כיא לוא תיראי וממחתה כיא 
>רב אליכ>לוא תקרב אליכ
15הנ גור יגור אפס מאותי מי גר אתכ עליכ יפול15הנה גור יגור אכס מאתי מי יגר אתכ עליכ יפולו
16הנה אנכי בראתי חרש נפח באש פחמ ומוציא כלי למעשהו ו16הנה אנוכי בראתי חרש נופח באש פחמ ומוציא כלי למעשוה
>אנכי בראתי משחית לחבל>י אנוכי בראתי משחית לחבל
17כל כלי יוצר עליכ לא יצלח וכל לשונ תקומ אתכ למשפט ת17כול כלי יוצר עליכ לוא יצלח זואת נחלת עבדי יהוה וצד
>רשיעי זאת נחלת עבדי יהוה וצדקתמ מאתי נאמ יהוה>קתמ מאתי נואמ יהוה
t1רני עקרה לא ילדה פצחי רנה וצהלי לא חלה כי רבים בניt1רוני עקרה ולוא ילדה פצחי רונה וצהלי ולוא חלה כיא ר
> שׁוממה מבני בעולה אמר יהוה >בימ בני שוממה מבני בעולה אמר יהוה
2הרחיבי׀ מקום אהלך ויריעות משׁכנותיך יטו אל תחשׂכי 2ארחיבי מקומ אהלכי ויריעות משכנותיכ יטי ואל תחשוכי 
>האריכי מיתריך ויתדתיך חזקי >האריכי מיתריכ ויתדותיכ חזקי
3כי ימין ושׂמאול תפרצי וזרעך גוים יירשׁ וערים נשׁמו3כיא ימינ ושמואל תפרוצי וזרעכ גואימ יירשו וערימ נשמ
>ת יושׁיבו >ות יושיבו
4אל תיראי כי לא תבושׁי ואל תכלמי כי לא תחפירי כי בש4אל תיראי כיא לוא תבושי ואל תכלמי כיא לוא תחפורי כי
>ׁת עלומיך תשׁכחי וחרפת אלמנותיך לא תזכרי עוד >א בושת עלומיכ תשכחי וחרפת אלמנותיכ לוא תזכורי עוד
5כי בעליך עשׂיך יהוה צבאות שׁמו וגאלך קדושׁ ישׂראל 5כיא בעלכי עושיכ יהוה צבאות שמו וגואלכי קדוש ישראל 
>אלהי כל הארץ יקרא >אלוהי כול הארצ יקרה
6כי כאשׁה עזובה ועצובת רוח קראך יהוה ואשׁת נעורים כ6כיא כאשה עזובה ועצובת רוח קראכ יהוה ואשת נעורימ כי
>י תמאס אמר אלהיך >א תמאס אמר יהוה אלוהיכ
7ברגע קטן עזבתיך וברחמים גדלים אקבצך 7ברוגע קטנ עזבתיכ וברחמימ גדולימ אקבצכ
8בשׁצף קצף הסתרתי פני רגע ממך ובחסד עולם רחמתיך אמר8בשצפ קצפ הסתרתי פני רוגע ממכ ובחסדי עולמ רחמתיכ אמ
> גאלך יהוה ס >ר גואלכי יהוה
9כי מי נח זאת לי אשׁר נשׁבעתי מעבר מי נח עוד על האר9כימ נוח זואת לי אשר נשבעתי מעבור מי נוח עוד על האר
>ץ כן נשׁבעתי מקצף עליך ומגער בך >צ כנ נשבעתי מקצופ עליכ עוד ומגעור בכ
10כי ההרים ימושׁו והגבעות תמוטנה וחסדי מאתך לא ימושׁ10כיא ההרימ ימושו והגבעות תתמוטינה וחסדי מאתכ לוא ימ
> וברית שׁלומי לא תמוט אמר מרחמך יהוה ס >וש וברית שלומי לוא תמוט אמר מרחמכי יהוה
11עניה סערה לא נחמה הנה אנכי מרביץ בפוך אבניך ויסדתי11ענייה סחורה לוא נחמה הנה אנוכי מרביצ בפוכ אבניכ וי
>ך בספירים >סודותיכ בספירימ
12ושׂמתי כדכד שׁמשׁתיך ושׁעריך לאבני אקדח וכל גבולך 12ושמתי כדכוד שמשותיכ ושעריכ לאבני אוקדח וכול גבוליכ
>לאבני חפץ > לאבני חפצ
13וכל בניך למודי יהוה ורב שׁלום בניך 13וכול בניכ למודי יהוה ורב שלומ בוניכי
14בצדקה תכונני רחקי מעשׁק כי לא תיראי וממחתה כי לא ת14בצדקה תתכונני רחקי מעושק כיא לוא תיראי וממחתה כיא 
>קרב אליך >לוא תקרב אליכ
15הן גור יגור אפס מאותי מי גר אתך עליך יפול 15הנה גור יגור אכס מאתי מי יגר אתכ עליכ יפולו
16הן אנכי בראתי חרשׁ נפח באשׁ פחם ומוציא כלי למעשׂהו16הנה אנוכי בראתי חרש נופח באש פחמ ומוציא כלי למעשוה
> ואנכי בראתי משׁחית לחבל >י אנוכי בראתי משחית לחבל
17כל כלי יוצר עליך לא יצלח וכל לשׁון תקום אתך למשׁפט17כול כלי יוצר עליכ לוא יצלח זואת נחלת עבדי יהוה וצד
> תרשׁיעי זאת נחלת עבדי יהוה וצדקתם מאתי נאם יהוה ס>קתמ מאתי נואמ יהוה
>  
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Isaiah 55 MT
Isaiah 55 1QIsaa
t1הוי כל צמא לכו למימ ואשר אינ לו כספ לכו שברו ואכלוt1הוי כול צמא לכו למימ ואשר אינ לו כספ לכו שבורו בלו
> ולכו שברו בלוא כספ ובלוא מחיר יינ וחלב>א כספ ובלוא מחיר יינ וחלב
2למה תשקלו כספ בלוא לחמ ויגיעכמ בלוא לשבעה שמעו שמו2למה תשקולו כספ בלוא לחמ ויגיעכמ בלוא שבעה שמעו שמע
>ע אלי ואכלו טוב ותתענג בדשנ נפשכמ>וא אלי ואכולו טוב ותתענג בדשנ נפשכמ
3הטו אזנכמ ולכו אלי שמעו ותחי נפשכמ ואכרתה לכמ ברית3הטו אוזנכמה ולכו אלי ושמעו ותחי נפשכמה ואכרות לכמה
> עולמ חסדי דוד הנאמנימ> ברית עולמ חסדי דויד הנאמנימ
4הנ עד לאומימ נתתיו נגיד ומצוה לאמימ4הנה עד לאומימ נתתיהו נגיד ומצוה לאומימ
5הנ גוי לא תדע תקרא וגוי לא ידעוכ אליכ ירוצו למענ י5הנה גוי לוא תדע תקרא וגוי לוא ידעכה אליכה ירוצ למע
>הוה אלהיכ ולקדוש ישראל כי פארכ>נ יהוה אלוהיכ ולקדוש ישראל כיא פארכ
6דרשו יהוה בהמצאו קראהו בהיותו קרוב6דרושו יהוה בהמצאו קראוהי בהיותו קרוב
7יעזב רשע דרכו ואיש אונ מחשבתיו וישב אל יהוה וירחמה7יעזב רשע דרכו ואיש אונ מחשבותיו וישוב אל יהוה וירח
>ו ואל אלהינו כי ירבה לסלוח>מהו ואל אלוהינו כיא ירבה לסלוח
8כי לא מחשבותי מחשבותיכמ ולא דרכיכמ דרכי נאמ יהוה8כיא לוא מחשבותי מחשבותיכמ ולוא דרכיכמה דרכי נואמ י
 >הוה
9כי גבהו שמימ מארצ כנ גבהו דרכי מדרכיכמ ומחשבתי ממח9כיא כגובה שמימ מארצ כנ גבהו דרכי מדרכיכמה ומחשבותי
>שבתיכמ> ממחשבותיכמה
10כי כאשר ירד הגשמ והשלג מנ השמימ ושמה לא ישוב כי אמ10כיא כאשר ירד הגשמ והשלג מנ השמימ ושמה לוא ישוב כיא
> הרוה את הארצ והולידה והצמיחה ונתנ זרע לזרע ולחמ ל> אמ הרוה את הארצ והולידה והצמיחה ונתנ זרע לזורע ול
>אכל>חמ לאכול
11כנ יהיה דברי אשר יצא מפי לא ישוב אלי ריקמ כי אמ עש11כנ יהיה דברי אשר יצא מפי לוא ישוב אלי ריקמ כיא אמ 
>ה את אשר חפצתי והצליח אשר שלחתיו>עשה את אשר חפצתי והצליח את אשר שלחתיו
12כי בשמחה תצאו ובשלומ תובלונ ההרימ והגבעות יפצחו לפ12כיא בשמחה תצאו ובשלומ תלכו ההרימ והגבעות יפצחו לפנ
>ניכמ רנה וכל עצי השדה ימחאו כפ>יכמה רונה וכול עצי השדה ימחוא כפ
13תחת הנעצוצ יעלה ברוש ותחת הסרפד יעלה הדס והיה ליהו13תחת הנעצוצ יעלה בראוש ותחת הסרפוד יעלה אדס והיו לי
>ה לשמ לאות עולמ לא יכרת>הוה לאות ולשמ עולמ לוא יכרת
t1הוי כל צמא לכו למים ואשׁר אין לו כסף לכו שׁברו ואכt1הוי כול צמא לכו למימ ואשר אינ לו כספ לכו שבורו בלו
>לו ולכו שׁברו בלוא כסף ובלוא מחיר יין וחלב >א כספ ובלוא מחיר יינ וחלב
2למה תשׁקלו כסף בלוא לחם ויגיעכם בלוא לשׂבעה שׁמעו 2למה תשקולו כספ בלוא לחמ ויגיעכמ בלוא שבעה שמעו שמע
>שׁמוע אלי ואכלו טוב ותתענג בדשׁן נפשׁכם >וא אלי ואכולו טוב ותתענג בדשנ נפשכמ
3הטו אזנכם ולכו אלי שׁמעו ותחי נפשׁכם ואכרתה לכם בר3הטו אוזנכמה ולכו אלי ושמעו ותחי נפשכמה ואכרות לכמה
>ית עולם חסדי דוד הנאמנים > ברית עולמ חסדי דויד הנאמנימ
4הן עד לאומים נתתיו נגיד ומצוה לאמים 4הנה עד לאומימ נתתיהו נגיד ומצוה לאומימ
5הן גוי לא תדע תקרא וגוי לא ידעוך אליך ירוצו למען י5הנה גוי לוא תדע תקרא וגוי לוא ידעכה אליכה ירוצ למע
>הוה אלהיך ולקדושׁ ישׂראל כי פארך ס >נ יהוה אלוהיכ ולקדוש ישראל כיא פארכ
6דרשׁו יהוה בהמצאו קראהו בהיותו קרוב 6דרושו יהוה בהמצאו קראוהי בהיותו קרוב
7יעזב רשׁע דרכו ואישׁ און מחשׁבתיו וישׁב אל יהוה וי7יעזב רשע דרכו ואיש אונ מחשבותיו וישוב אל יהוה וירח
>רחמהו ואל אלהינו כי ירבה לסלוח >מהו ואל אלוהינו כיא ירבה לסלוח
8כי לא מחשׁבותי מחשׁבותיכם ולא דרכיכם דרכי נאם יהוה8כיא לוא מחשבותי מחשבותיכמ ולוא דרכיכמה דרכי נואמ י
> >הוה
9כי גבהו שׁמים מארץ כן גבהו דרכי מדרכיכם ומחשׁבתי מ9כיא כגובה שמימ מארצ כנ גבהו דרכי מדרכיכמה ומחשבותי
>מחשׁבתיכם > ממחשבותיכמה
10כי כאשׁר ירד הגשׁם והשׁלג מן השׁמים ושׁמה לא ישׁוב10כיא כאשר ירד הגשמ והשלג מנ השמימ ושמה לוא ישוב כיא
> כי אם הרוה את הארץ והולידה והצמיחה ונתן זרע לזרע > אמ הרוה את הארצ והולידה והצמיחה ונתנ זרע לזורע ול
>ולחם לאכל >חמ לאכול
11כן יהיה דברי אשׁר יצא מפי לא ישׁוב אלי ריקם כי אם 11כנ יהיה דברי אשר יצא מפי לוא ישוב אלי ריקמ כיא אמ 
>עשׂה את אשׁר חפצתי והצליח אשׁר שׁלחתיו >עשה את אשר חפצתי והצליח את אשר שלחתיו
12כי בשׂמחה תצאו ובשׁלום תובלון ההרים והגבעות יפצחו 12כיא בשמחה תצאו ובשלומ תלכו ההרימ והגבעות יפצחו לפנ
>לפניכם רנה וכל עצי השׂדה ימחאו כף >יכמה רונה וכול עצי השדה ימחוא כפ
13תחת הנעצוץ יעלה ברושׁ תחת הסרפד יעלה הדס והיה ליהו13תחת הנעצוצ יעלה בראוש ותחת הסרפוד יעלה אדס והיו לי
>ה לשׁם לאות עולם לא יכרת ס >הוה לאות ולשמ עולמ לוא יכרת
- - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 56 MT
Isaiah 56 1QIsaa
t1כה אמר יהוה שמרו משפט ועשו צדקה כי קרובה ישועתי לבt1כיא כוה אמר יהוה שמורו משפט ועשו צדקה כיא קרובה יש
>וא וצדקתי להגלות>ועתי לבוא וצדקתי להגלות
2אשרי אנוש יעשה זאת ובנ אדמ יחזיק בה שמר שבת מחללו 2אשרי אנוש יעשה זואת ובנ אדמ יחזיק בה שומר שבת מחלל
>ושמר ידו מעשות כל רע>ה ושומר ידיו מעשות כול רע
3ואל יאמר בנ הנכר הנלוה אל יהוה לאמר הבדל יבדילני י3אל יואמר בנ הנכר הנלוא אל יהוה לאמור הבדל יבדילני 
>הוה מעל עמו ואל יאמר הסריס הנ אני עצ יבש>יהוה מעל עמו ואל יואמר הסריס הנה אנוכי עצ יבש
4כי כה אמר יהוה לסריסימ אשר ישמרו את שבתותי ובחרו ב4כיא כוה אמר יהוה לסריסימ אשר ישמורו את שבתותי ויבח
>אשר חפצתי ומחזיקימ בבריתי>ורו באשר חפצתי ומחזיקימ בבריתי
5ונתתי להמ בביתי ובחומתי יד ושמ טוב מבנימ ומבנות שמ5ונתתי להמה בביתי ובחומותי יד ושמ טוב מבנימ ומנ בנו
> עולמ אתנ לו אשר לא יכרת>ת שמ עולמ אתנ להמה אשר לוא יכרת
6ובני הנכר הנלוימ על יהוה לשרתו ולאהבה את שמ יהוה ל6ובני הנכר הנלויימ אל יהוה להיות לו לעבדימ ולברכ את
>היות לו לעבדימ כל שמר שבת מחללו ומחזיקימ בבריתי> שמ יהוה ושומרימ את השבת מחללה ומחזיקימ בבריתי
7והביאותימ אל הר קדשי ושמחתימ בבית תפלתי עולתיהמ וז7והביאותימ אל הר קודשי ושמחתימ בבית תפלתי עולותיהמה
>בחיהמ לרצונ על מזבחי כי ביתי בית תפלה יקרא לכל העמ> וזבחיהמה יעלו לרצונ על מזבחי כיא ביתי בית תפלה יק
>ימ>רה לכול העמימ
8נאמ אדני יהוה מקבצ נדחי ישראל עוד אקבצ עליו לנקבצי8נואמ אדוני יהוה מקבצ נדחי ישראל עוד אקבצ עליו לנקב
>ו>ציו
9כל חיתו שדי אתיו לאכל כל חיתו ביער9כול חיות שדה אתיו לאכול וכול חיות ביער
10צפיו עורימ כלמ לא ידעו כלמ כלבימ אלמימ לא יוכלו לנ10צופיו עורימ כולמ לוא ידעו כולמ כלבימ אלמימ לוא יוכ
>בח הזימ שכבימ אהבי לנומ>לו לנבוח המה חוזימ שוכבימ אוהבימ לנואמ
11והכלבימ עזי נפש לא ידעו שבעה והמה רעימ לא ידעו הבי11והכלבימ עזי נפש לוא ידעו שבעה והמה הרועימ לוא ידעו
>נ כלמ לדרכמ פנו איש לבצעו מקצהו> הבינ כולמ לדרכמ פנו איש לבצעו מקצהו
12אתיו אקחה יינ ונסבאה שכר והיה כזה יומ מחר גדול יתר12אתיו ונקח יינ ונסבה שכר ויהי כזה היומ ומחר גדול ית
> מאד>ר מואד
t1כה אמר יהוה שׁמרו משׁפט ועשׂו צדקה כי קרובה ישׁועתt1כיא כוה אמר יהוה שמורו משפט ועשו צדקה כיא קרובה יש
>י לבוא וצדקתי להגלות >ועתי לבוא וצדקתי להגלות
2אשׁרי אנושׁ יעשׂה זאת ובן אדם יחזיק בה שׁמר שׁבת מ2אשרי אנוש יעשה זואת ובנ אדמ יחזיק בה שומר שבת מחלל
>חללו ושׁמר ידו מעשׂות כל רע ס >ה ושומר ידיו מעשות כול רע
3ואל יאמר בן הנכר הנלוה אל יהוה לאמר הבדל יבדילני י3אל יואמר בנ הנכר הנלוא אל יהוה לאמור הבדל יבדילני 
>הוה מעל עמו ואל יאמר הסריס הן אני עץ יבשׁ ס >יהוה מעל עמו ואל יואמר הסריס הנה אנוכי עצ יבש
4כי כה׀ אמר יהוה לסריסים אשׁר ישׁמרו את שׁבתותי ובח4כיא כוה אמר יהוה לסריסימ אשר ישמורו את שבתותי ויבח
>רו באשׁר חפצתי ומחזיקים בבריתי >ורו באשר חפצתי ומחזיקימ בבריתי
5ונתתי להם בביתי ובחומתי יד ושׁם טוב מבנים ומבנות ש5ונתתי להמה בביתי ובחומותי יד ושמ טוב מבנימ ומנ בנו
>ׁם עולם אתן לו אשׁר לא יכרת ס >ת שמ עולמ אתנ להמה אשר לוא יכרת
6ובני הנכר הנלוים על יהוה לשׁרתו ולאהבה את שׁם יהוה6ובני הנכר הנלויימ אל יהוה להיות לו לעבדימ ולברכ את
> להיות לו לעבדים כל שׁמר שׁבת מחללו ומחזיקים בברית> שמ יהוה ושומרימ את השבת מחללה ומחזיקימ בבריתי
>י  
7והביאותים אל הר קדשׁי ושׂמחתים בבית תפלתי עולתיהם 7והביאותימ אל הר קודשי ושמחתימ בבית תפלתי עולותיהמה
>וזבחיהם לרצון על מזבחי כי ביתי בית תפלה יקרא לכל ה> וזבחיהמה יעלו לרצונ על מזבחי כיא ביתי בית תפלה יק
>עמים >רה לכול העמימ
8נאם אדני יהוה מקבץ נדחי ישׂראל עוד אקבץ עליו לנקבצ8נואמ אדוני יהוה מקבצ נדחי ישראל עוד אקבצ עליו לנקב
>יו >ציו
9כל חיתו שׂדי אתיו לאכל כל חיתו ביער ס 9כול חיות שדה אתיו לאכול וכול חיות ביער
10צפו עורים כלם לא ידעו כלם כלבים אלמים לא יוכלו לנב10צופיו עורימ כולמ לוא ידעו כולמ כלבימ אלמימ לוא יוכ
>ח הזים שׁכבים אהבי לנום >לו לנבוח המה חוזימ שוכבימ אוהבימ לנואמ
11והכלבים עזי נפשׁ לא ידעו שׂבעה והמה רעים לא ידעו ה11והכלבימ עזי נפש לוא ידעו שבעה והמה הרועימ לוא ידעו
>בין כלם לדרכם פנו אישׁ לבצעו מקצהו > הבינ כולמ לדרכמ פנו איש לבצעו מקצהו
12אתיו אקחה יין ונסבאה שׁכר והיה כזה יום מחר גדול ית12אתיו ונקח יינ ונסבה שכר ויהי כזה היומ ומחר גדול ית
>ר מאד >ר מואד
- - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + +

Isaiah 57 MT
Isaiah 57 1QIsaa
t1הצדיק אבד ואינ איש שמ על לב ואנשי חסד נאספימ באינ t1והצדיק אובד ואינ איש שמ על לב ואנשי החסד נאספימ בא
>מבינ כי מפני הרעה נאספ הצדיק>ינ מבינ כיא מפני הרעה נאספ הצדיק
2יבוא שלומ ינוחו על משכבותמ הלכ נכחו2ויבוא שלומ וינוחו על משכבותיו הלוכ נוכחה
3ואתמ קרבו הנה בני עננה זרע מנאפ ותזנה3ואתמה קרובו הנה בני עננה זרע מנאפ ותזנו
4על מי תתענגו על מי תרחיבו פה תאריכו לשונ הלוא אתמ 4על מיא תתענגו ועל מיא תרחיבו פה תאריכו לשונ הלוא א
>ילדי פשע זרע שקר>תמה ילודי פשע זרע שקר
5הנחמימ באלימ תחת כל עצ רעננ שחטי הילדימ בנחלימ תחת5הנחמימ באלימ תחת כול עצ רעננ שוחטי הילדימ בנחלימ ת
> סעפי הסלעימ>חת שעפי הסלעימ
6בחלקי נחל חלקכ המ המ גורלכ גמ להמ שפכת נסכ העלית מ6בחלקי נחל חלקכה שמה המה גורלכה גמ להמה שפכתה נסכ ה
>נחה העל אלה אנחמ>עליתה מנחה העל אלה אנחמ
7על הר גבה ונשא שמת משכבכ גמ שמ עלית לזבח זבח7על הר גבה ונשא שמת משכבכה גמ שמ עלית לזבוח זבח
8ואחר הדלת והמזוזה שמת זכרונכ כי מאתי גלית ותעלי הר8ואחר הדלת והמזוזה שמתה זכרונכה כיא מאתי גליתה ותעל
>חבת משכבכ ותכרת לכ מהמ אהבת משכבמ יד חזית>ו הרחבת משכבכ ותכרותו לכה מהמה אהבת משכבמה יד חזית
9ותשרי למלכ בשמנ ותרבי רקחיכ ותשלחי צריכ עד מרחק ות9ותשרי למלכ בשמנ ותרבי רוקחיכ ותשלחי ציריכ עד מרחק 
>שפילי עד שאול>ותשפולי עד שאול
10ברב דרככ יגעת לא אמרת נואש חית ידכ מצאת על כנ לא ח10ברוב דרכיכ יגעת לוא אמרת נואש חית ידכ מצת על כנ לו
>לית>א חלית
11ואת מי דאגת ותיראי כי תכזבי ואותי לא זכרת לא שמת ע11ואת מי דאגת ותיראיני כיא תכזבי ואותי לוא זכרתי ולו
>ל לבכ הלא אני מחשה ומעלמ ואותי לא תיראי>א שמתי אלה על לבכה הלוא אני מחשה ומעולמ ואותי לוא 
t1הצדיק אבד ואין אישׁ שׂם על לב ואנשׁי חסד נאספים באt1והצדיק אובד ואינ איש שמ על לב ואנשי החסד נאספימ בא
>ין מבין כי מפני הרעה נאסף הצדיק >ינ מבינ כיא מפני הרעה נאספ הצדיק
2יבוא שׁלום ינוחו על משׁכבותם הלך נכחו 2ויבוא שלומ וינוחו על משכבותיו הלוכ נוכחה
3ואתם קרבו הנה בני עננה זרע מנאף ותזנה 3ואתמה קרובו הנה בני עננה זרע מנאפ ותזנו
4על מי תתענגו על מי תרחיבו פה תאריכו לשׁון הלוא אתם4על מיא תתענגו ועל מיא תרחיבו פה תאריכו לשונ הלוא א
> ילדי פשׁע זרע שׁקר >תמה ילודי פשע זרע שקר
5הנחמים באלים תחת כל עץ רענן שׁחטי הילדים בנחלים תח5הנחמימ באלימ תחת כול עצ רעננ שוחטי הילדימ בנחלימ ת
>ת סעפי הסלעים >חת שעפי הסלעימ
6בחלקי נחל חלקך הם הם גורלך גם להם שׁפכת נסך העלית 6בחלקי נחל חלקכה שמה המה גורלכה גמ להמה שפכתה נסכ ה
>מנחה העל אלה אנחם >עליתה מנחה העל אלה אנחמ
7על הר גבה ונשׂא שׂמת משׁכבך גם שׁם עלית לזבח זבח 7על הר גבה ונשא שמת משכבכה גמ שמ עלית לזבוח זבח
8ואחר הדלת והמזוזה שׂמת זכרונך כי מאתי גלית ותעלי ה8ואחר הדלת והמזוזה שמתה זכרונכה כיא מאתי גליתה ותעל
>רחבת משׁכבך ותכרת לך מהם אהבת משׁכבם יד חזית >ו הרחבת משכבכ ותכרותו לכה מהמה אהבת משכבמה יד חזית
9ותשׁרי למלך בשׁמן ותרבי רקחיך ותשׁלחי צריך עד מרחק9ותשרי למלכ בשמנ ותרבי רוקחיכ ותשלחי ציריכ עד מרחק 
> ותשׁפילי עד שׁאול >ותשפולי עד שאול
10ברב דרכך יגעת לא אמרת נואשׁ חית ידך מצאת על כן לא 10ברוב דרכיכ יגעת לוא אמרת נואש חית ידכ מצת על כנ לו
>חלית >א חלית
11ואת מי דאגת ותיראי כי תכזבי ואותי לא זכרת לא שׂמת 11ואת מי דאגת ותיראיני כיא תכזבי ואותי לוא זכרתי ולו
>על לבך הלא אני מחשׁה ומעלם ואותי לא תיראי >א שמתי אלה על לבכה הלוא אני מחשה ומעולמ ואותי לוא 
 >תיראי
12אני אגיד צדקתכ ואת מעשיכ ולא יועילוכ12אני אגיד צדקתכ ואת מעשיכ ולוא יועילוכ קובציכ
13בזעקכ יצילכ קבוציכ ואת כלמ ישא רוח יקח הבל והחוסה 13בזעקכ יצילוכ קובציכ ואת כולמ ישא רוח ויקח הבל וחוס
>בי ינחל ארצ ויירש הר קדשי>ה ביא ינחל ארצ וירש הר קודשי
14ואמר סלו סלו פנו דרכ הרימו מכשול מדרכ עמי14ויואמר סולו סולו המסלה פנו דרכ הרימו מכשול מדרכ עמ
 >יא
15כי כה אמר רמ ונשא שכנ עד וקדוש שמו מרומ וקדוש אשכו15כיא כוה אמר רמ ונשא שוכנ עד וקדוש שמו במרומ ובקודש
>נ ואת דכא ושפל רוח להחיות רוח שפלימ ולהחיות לב נדכ> ישכונ ואת דכא ושפל רוח לחיות רוח שפלימ ולחיות לב 
>אימ>נדכאימ
16כי לא לעולמ אריב ולא לנצח אקצופ כי רוח מלפני יעטופ16כיא לוא לעולמ אריב ולוא לנצח אקצופ כיא רוח מלפני י
> ונשמות אני עשיתי>עטופ ונשמות אני עשיתי
17בעונ בצעו קצפתי ואכהו הסתר ואקצפ וילכ שובב בדרכ לב17בעוונ בצעו קצפתי ואכהו ואהסתר ואקצופה וילכ שובב בד
>ו>רכ לבי
18דרכיו ראיתי וארפאהו ואנחהו ואשלמ נחמימ לו ולאבליו18דרכיו ראיתי וארפאהו ואשלמ לוא תנחומימ לוא ולאבליו
19בורא ניב שפתימ שלומ שלומ לרחוק ולקרוב אמר יהוה ורפ19בבורה ניב שפתימ שלומ לרחוק ולקרוב אמר יהוה ורפתיהו
>אתיו 
20והרשעימ כימ נגרש כי השקט לא יוכל ויגרשו מימיו רפש 20והרשעימ כימ נגרשו כיא לאשקוט לוא יוכלויתגרשו מימיו
>וטיט> רפש וטיט
21אינ שלומ אמר אלהי לרשעימ21ואינ שלומ אמר אלוהי לרשעימ
12אני אגיד צדקתך ואת מעשׂיך ולא יועילוך 12אני אגיד צדקתכ ואת מעשיכ ולוא יועילוכ קובציכ
13בזעקך יצילך קבוציך ואת כלם ישׂא רוח יקח הבל והחוסה13בזעקכ יצילוכ קובציכ ואת כולמ ישא רוח ויקח הבל וחוס
> בי ינחל ארץ ויירשׁ הר קדשׁי >ה ביא ינחל ארצ וירש הר קודשי
14ואמר סלו סלו פנו דרך הרימו מכשׁול מדרך עמי ס 14ויואמר סולו סולו המסלה פנו דרכ הרימו מכשול מדרכ עמ
 >יא
15כי כה אמר רם ונשׂא שׁכן עד וקדושׁ שׁמו מרום וקדושׁ15כיא כוה אמר רמ ונשא שוכנ עד וקדוש שמו במרומ ובקודש
> אשׁכון ואת דכא ושׁפל רוח להחיות רוח שׁפלים ולהחיו> ישכונ ואת דכא ושפל רוח לחיות רוח שפלימ ולחיות לב 
>ת לב נדכאים >נדכאימ
16כי לא לעולם אריב ולא לנצח אקצוף כי רוח מלפני יעטוף16כיא לוא לעולמ אריב ולוא לנצח אקצופ כיא רוח מלפני י
> ונשׁמות אני עשׂיתי >עטופ ונשמות אני עשיתי
17בעון בצעו קצפתי ואכהו הסתר ואקצף וילך שׁובב בדרך ל17בעוונ בצעו קצפתי ואכהו ואהסתר ואקצופה וילכ שובב בד
>בו >רכ לבי
18דרכיו ראיתי וארפאהו ואנחהו ואשׁלם נחמים לו ולאבליו18דרכיו ראיתי וארפאהו ואשלמ לוא תנחומימ לוא ולאבליו
>  
19בורא נוב שׂפתים שׁלום׀ שׁלום לרחוק ולקרוב אמר יהוה19בבורה ניב שפתימ שלומ לרחוק ולקרוב אמר יהוה ורפתיהו
> ורפאתיו  
20והרשׁעים כים נגרשׁ כי השׁקט לא יוכל ויגרשׁו מימיו 20והרשעימ כימ נגרשו כיא לאשקוט לוא יוכלויתגרשו מימיו
>רפשׁ וטיט > רפש וטיט
21אין שׁלום אמר אלהי לרשׁעים ס 21ואינ שלומ אמר אלוהי לרשעימ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 58 MT
Isaiah 58 1QIsaa
t1קרא בגרונ אל תחשכ כשופר הרמ קולכ והגד לעמי פשעמ ולt1קרא בגרונ אל תחשוכ כשופר הרמ קולכה והגד לעמיא פשעי
>בית יעקב חטאתמ>המה ולבית יעקוב חטאותמה
2ואותי יומ יומ ידרשונ ודעת דרכי יחפצונ כגוי אשר צדק2אותי יומ ויומ ידרושו ודעת דרכי יחפצונ כגוי אשר צדק
>ה עשה ומשפט אלהיו לא עזב ישאלוני משפטי צדק קרבת אל>ה עשה ומשפט אלוהו לוא עזב ישאלוני משפטי צדק קרבת א
>הימ יחפצונ>לוהימ יחפצו
3למה צמנו ולא ראית ענינו נפשנו ולא תדע הנ ביומ צמכמ3למה צמנו ולוא ראיתה ענינו נפשותינו ולוא תדע הנ ביו
> תמצאו חפצ וכל עצביכמ תנגשו>מ צומכמה תמצאו חפצ וכולעצביכמ תנגשו
4הנ לריב ומצה תצומו ולהכות באגרפ רשע לא תצומו כיומ 4הנה לריב ולמצא תצומו ולהכות בגורפ רשע לוא תצומו כי
>להשמיע במרומ קולכמ>ומ לשמיע במרומ קולכמה
5הכזה יהיה צומ אבחרהו יומ ענות אדמ נפשו הלכפ כאגמנ 5הכזה יהיה צומ אבחרהו יומ ענות אדמ נפשו הלכופ כאוגמ
>ראשו ושק ואפר יציע הלזה תקרא צומ ויומ רצונ ליהוה>נ רואשו שק ואפר יציע הלזה תקראו צומ יומ רצונ ליהוה
6הלוא זה צומ אבחרהו פתח חרצבות רשע התר אגדות מוטה ו6הלוא זה הצומ אשר אבחרהו פתח חרצבות רשע והתר אגודות
>שלח רצוצימ חפשימ וכל מוטה תנתקו> מטה ושלח רצוצימ חופשיימ וכול מוטה תנתקו
7הלוא פרס לרעב לחמכ ועניימ מרודימ תביא בית כי תראה 7הלוא פרוס לרעב לחמכה וענויימ מרודימ תביא בית כיא ת
>ערמ וכסיתו ומבשרכ לא תתעלמ>ראה ערומ וכסיתו בגד ומבשרכה לוא תתעל
8אז יבקע כשחר אורכ וארכתכ מהרה תצמח והלכ לפניכ צדקכ8אז יבקע כשחר אורכה וארוכתכה מהרה תצמח והלכ לפניכה 
> כבוד יהוה יאספכ>צדקכה וכבוד יהוה יאספכה
9אז תקרא ויהוה יענה תשוע ויאמר הנני אמ תסיר מתוככ מ9אז תקרא ויהוה יענה תשוע ויואמר הנני אמ תסיר מתוככה
>וטה שלח אצבע ודבר אונ> מוטה ושלוח אצבע ודבר אונ
10ותפק לרעב נפשכ ונפש נענה תשביע וזרח בחשכ אורכ ואפל10ותפק לרעב נפשכה ונפש נענה תשביע וזרח בחושכ אורכה ו
>תכ כצהרימ>אפלתכה כצהורימ
11ונחכ יהוה תמיד והשביע בצחצחות נפשכ ועצמתיכ יחליצ ו11ונחכה יהוה תמיד והשביע בצצחות נפשכה ועצמותיכה יחלי
>היית כגנ רוה וכמוצא מימ אשר לא יכזבו מימיו>צו והייתה כגנ רוה וכמוצא מימ אשר לוא יכזבו מימיו
12ובנו ממכ חרבות עולמ מוסדי דור ודור תקוממ וקרא לכ ג12ובנו ממכה חרבות עולמ מוסדי דור ודור תקוממ וקראו לכ
>דר פרצ משבב נתיבות לשבת> גודר פרצ משובב נתיבות לשבת
13אמ תשיב משבת רגלכ עשות חפציכ ביומ קדשי וקראת לשבת 13אמ תשיב משבת רגלכה מעשות חפציכה ביומ קודשי וקראתה 
>ענג לקדוש יהוה מכבד וכבדתו מעשות דרכיכ ממצוא חפצכ >לשבת עונג ולקדוש יהוה מכבד וכבדתו מעשות דרכיכה וממ
>ודבר דבר>צוא חפצכה ודבר דבר
14אז תתענג על יהוה והרכבתיכ על במתי ארצ והאכלתיכ נחל14אז תתענג על יהוה והרכיבכה על בומתי ארצ והאכילכה נח
>ת יעקב אביכ כי פי יהוה דבר>לת יעקוב אביכה כיא פי יהוה דבר
t1קרא בגרון אל תחשׂך כשׁופר הרם קולך והגד לעמי פשׁעםt1קרא בגרונ אל תחשוכ כשופר הרמ קולכה והגד לעמיא פשעי
> ולבית יעקב חטאתם >המה ולבית יעקוב חטאותמה
2ואותי יום יום ידרשׁון ודעת דרכי יחפצון כגוי אשׁר צ2אותי יומ ויומ ידרושו ודעת דרכי יחפצונ כגוי אשר צדק
>דקה עשׂה ומשׁפט אלהיו לא עזב ישׁאלוני משׁפטי צדק ק>ה עשה ומשפט אלוהו לוא עזב ישאלוני משפטי צדק קרבת א
>רבת אלהים יחפצון >לוהימ יחפצו
3למה צמנו ולא ראית ענינו נפשׁנו ולא תדע הן ביום צמכ3למה צמנו ולוא ראיתה ענינו נפשותינו ולוא תדע הנ ביו
>ם תמצאו חפץ וכל עצביכם תנגשׂו >מ צומכמה תמצאו חפצ וכולעצביכמ תנגשו
4הן לריב ומצה תצומו ולהכות באגרף רשׁע לא תצומו כיום4הנה לריב ולמצא תצומו ולהכות בגורפ רשע לוא תצומו כי
> להשׁמיע במרום קולכם >ומ לשמיע במרומ קולכמה
5הכזה יהיה צום אבחרהו יום ענות אדם נפשׁו הלכף כאגמן5הכזה יהיה צומ אבחרהו יומ ענות אדמ נפשו הלכופ כאוגמ
> ראשׁו ושׂק ואפר יציע הלזה תקרא צום ויום רצון ליהו>נ רואשו שק ואפר יציע הלזה תקראו צומ יומ רצונ ליהוה
>ה  
6הלוא זה צום אבחרהו פתח חרצבות רשׁע התר אגדות מוטה 6הלוא זה הצומ אשר אבחרהו פתח חרצבות רשע והתר אגודות
>ושׁלח רצוצים חפשׁים וכל מוטה תנתקו > מטה ושלח רצוצימ חופשיימ וכול מוטה תנתקו
7הלוא פרס לרעב לחמך ועניים מרודים תביא בית כי תראה 7הלוא פרוס לרעב לחמכה וענויימ מרודימ תביא בית כיא ת
>ערם וכסיתו ומבשׂרך לא תתעלם >ראה ערומ וכסיתו בגד ומבשרכה לוא תתעל
8אז יבקע כשׁחר אורך וארכתך מהרה תצמח והלך לפניך צדק8אז יבקע כשחר אורכה וארוכתכה מהרה תצמח והלכ לפניכה 
>ך כבוד יהוה יאספך >צדקכה וכבוד יהוה יאספכה
9אז תקרא ויהוה יענה תשׁוע ויאמר הנני אם תסיר מתוכך 9אז תקרא ויהוה יענה תשוע ויואמר הנני אמ תסיר מתוככה
>מוטה שׁלח אצבע ודבר און > מוטה ושלוח אצבע ודבר אונ
10ותפק לרעב נפשׁך ונפשׁ נענה תשׂביע וזרח בחשׁך אורך 10ותפק לרעב נפשכה ונפש נענה תשביע וזרח בחושכ אורכה ו
>ואפלתך כצהרים >אפלתכה כצהורימ
11ונחך יהוה תמיד והשׂביע בצחצחות נפשׁך ועצמתיך יחליץ11ונחכה יהוה תמיד והשביע בצצחות נפשכה ועצמותיכה יחלי
> והיית כגן רוה וכמוצא מים אשׁר לא יכזבו מימיו >צו והייתה כגנ רוה וכמוצא מימ אשר לוא יכזבו מימיו
12ובנו ממך חרבות עולם מוסדי דור ודור תקומם וקרא לך ג12ובנו ממכה חרבות עולמ מוסדי דור ודור תקוממ וקראו לכ
>דר פרץ משׁבב נתיבות לשׁבת > גודר פרצ משובב נתיבות לשבת
13אם תשׁיב משׁבת רגלך עשׂות חפציך ביום קדשׁי וקראת ל13אמ תשיב משבת רגלכה מעשות חפציכה ביומ קודשי וקראתה 
>שׁבת ענג לקדושׁ יהוה מכבד וכבדתו מעשׂות דרכיך ממצו>לשבת עונג ולקדוש יהוה מכבד וכבדתו מעשות דרכיכה וממ
>א חפצך ודבר דבר >צוא חפצכה ודבר דבר
14אז תתענג על יהוה והרכבתיך על במותי ארץ והאכלתיך נח14אז תתענג על יהוה והרכיבכה על בומתי ארצ והאכילכה נח
>לת יעקב אביך כי פי יהוה דבר ס >לת יעקוב אביכה כיא פי יהוה דבר
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 59 MT
Isaiah 59 1QIsaa
t1הנ לא קצרה יד יהוה מהושיע ולא כבדה אזנו משמועt1הנה לוא קצרא יד יהוה מהושיע ולוא כבדו אוזניו משמוע
2כי אמ עונתיכמ היו מבדלימ בינכמ לבינ אלהיכמ וחטאותי2כיא אמ עוונותיכמה היו מבדילימ בינכמה לבינ אלוהיכמה
>כמ הסתירו פנימ מכמ משמוע> וחטאותיכמה הסתירו פנימ מכמה משמוע
3כי כפיכמ נגאלו בדמ ואצבעותיכמ בעונ שפתותיכמ דברו ש3כיא כפיכמה נגאלו בדמ ואצבעותיכמה בעוונ לשונכמה עול
>קר לשונכמ עולה תהגה>ה תהגה
4אינ קרא בצדק ואינ נשפט באמונה בטוח על תהו ודבר שוא4אינ קורה בצדק ואינ נשפט באמונה בטחו על תהו ודבר שו
> הרו עמל והוליד אונ> הרוה עמל והולידו אונ
5ביצי צפעוני בקעו וקורי עכביש יארגו האכל מביציהמ ימ5בצי צפעונימ יבקעו וקורי עכביש יירגו האוכל מבציהמה 
>ות והזורה תבקע אפעה>ימות והאזורה תבקע אפע
6קוריהמ לא יהיו לבגד ולא יתכסו במעשיהמ מעשיהמ מעשי 6קוריהמ לוא יהיו לבגד ולוא יכסו במעשיהמה מעשיהמה מע
>אונ ופעל חמס בכפיהמ>שי אונ ופועול חמס בכפיהמ
7רגליהמ לרע ירצו וימהרו לשפכ דמ נקי מחשבותיהמ מחשבו7רגליהמה לרע ירוצו וימהרו לשפוכ דמ נקיא מחשבותיהמה 
>ת אונ שד ושבר במסלותמ>מחשבות אונ שד ושבר וחמס במסלותיהמה
8דרכ שלומ לא ידעו ואינ משפט במעגלותמ נתיבותיהמ עקשו8דרכ שלומ לוא ידעו ואינ משפט במעגלותיהמה נתיבותי המ
> להמ כל דרכ בה לא ידע שלומ>ה עקשו להמה כול הדורכ בה לוא ידע שלומ
9על כנ רחק משפט ממנו ולא תשיגנו צדקה נקוה לאור והנה9על כנ רחק משפט ממנו ולוא תשיגנו צדקה נקוה לאור והנ
> חשכ לנגהות באפלות נהלכ>ה חושכ לנגהות באפלה נהלכ
10נגששה כעורימ קיר וכאינ עינימ נגששה כשלנו בצהרימ כנ10נגשש כעורימ קיר וכאינ עינימ נגששה כשלנו בצהורימ כנ
>שפ באשמנימ כמתימ>שפ באשמונימ כמיתימ
11נהמה כדבימ כלנו וכיונימ הגה נהגה נקוה למשפט ואינ ל11נהמה כדבימ כולנו כיונימ הגוא נהגה נקוה למשפט ואינ 
>ישועה רחקה ממנו>ולישועה רחקה ממנו
12כי רבו פשעינו נגדכ וחטאותינו ענתה בנו כי פשעינו את12כיא רבו פשעינו נגדכה וחטאותינו ענוא בנו כיא פשעינו
>נו ועונתינו ידענומ> אתנו ועוונותינו ידענומ
13פשע וכחש ביהוה ונסוג מאחר אלהינו דבר עשק וסרה הרו 13פשועו וכחש ביהוה ונסוג מאחר אלוהינו ודברו עושק וסר
>והגו מלב דברי שקר>ה והגוא מלב דברי שקר
14והסג אחור משפט וצדקה מרחוק תעמד כי כשלה ברחוב אמת 14ואסיג אחור משפט וצדקה מרחוק תעמוד כיא כשלה ברחוב א
>ונכחה לא תוכל לבוא>מת ונכוחה לוא תוכל לבוא
15ותהי האמת נעדרת וסר מרע משתולל וירא יהוה וירע בעינ15ותהי האמת נעדרת וסר מרע משתולל וירא יהוה וירע בעינ
>יו כי אינ משפט>יו כיא אינ משפט
16וירא כי אינ איש וישתוממ כי אינ מפגיע ותושע לו זרעו16וירא כיא אינ איש וישתוממ כיא אינ מפגיע ותושע לוא ז
> וצדקתו היא סמכתהו>רועו וצדקתיו היא סמכתו
17וילבש צדקה כשרינ וכובע ישועה בראשו וילבש בגדי נקמ 17וילבש צדקה כשרינ וכובע ישועה ברואשיו וילבש בגדי נק
>תלבשת ויעט כמעיל קנאה>מ תלבושת ויעט כמעיל קנאא
18כעל גמלות כעל ישלמ חמה לצריו גמול לאיביו לאיימ גמו18כעל גמולות כעל ישלמ חמה לצריו גמול לאויביו לאיימ ג
>ל ישלמ>מול ישלמ
19וייראו ממערב את שמ יהוה וממזרח שמש את כבודו כי יבו19וייראו ממערב את שמ יהוה וממזרח שמש את כבודיו כיא י
>א כנהר צר רוח יהוה נססה בו>בוא כנהר צור רוח יהוה נוססה בוה
20ובא לציונ גואל ולשבי פשע ביעקב נאמ יהוה20ובא אל ציונ גואל ולשבי פשע ביעקוב נואמ יהוה
21ואני זאת בריתי אותמ אמר יהוה רוחי אשר עליכ ודברי א21ואני זואת בריתי אתמ אמר יהוה ורוחי אשר עליכה ודברי
>שר שמתי בפיכ לא ימושו מפיכ ומפי זרעכ ומפי זרע זרעכ> אשר שמתי בפיכה לוא ימושו מפיכה ומפי זרעכה ומפי זר
> אמר יהוה מעתה ועד עולמ>ע זרעכה מעתה ועד עולמ
t1הן לא קצרה יד יהוה מהושׁיע ולא כבדה אזנו משׁמוע t1הנה לוא קצרא יד יהוה מהושיע ולוא כבדו אוזניו משמוע
2כי אם עונתיכם היו מבדלים בינכם לבין אלהיכם וחטאותי2כיא אמ עוונותיכמה היו מבדילימ בינכמה לבינ אלוהיכמה
>כם הסתירו פנים מכם משׁמוע > וחטאותיכמה הסתירו פנימ מכמה משמוע
3כי כפיכם נגאלו בדם ואצבעותיכם בעון שׂפתותיכם דברו 3כיא כפיכמה נגאלו בדמ ואצבעותיכמה בעוונ לשונכמה עול
>שׁקר לשׁונכם עולה תהגה >ה תהגה
4אין קרא בצדק ואין נשׁפט באמונה בטוח על תהו ודבר שׁ4אינ קורה בצדק ואינ נשפט באמונה בטחו על תהו ודבר שו
>וא הרו עמל והוליד און > הרוה עמל והולידו אונ
5ביצי צפעוני בקעו וקורי עכבישׁ יארגו האכל מביציהם י5בצי צפעונימ יבקעו וקורי עכביש יירגו האוכל מבציהמה 
>מות והזורה תבקע אפעה >ימות והאזורה תבקע אפע
6קוריהם לא יהיו לבגד ולא יתכסו במעשׂיהם מעשׂיהם מעש6קוריהמ לוא יהיו לבגד ולוא יכסו במעשיהמה מעשיהמה מע
>ׂי און ופעל חמס בכפיהם >שי אונ ופועול חמס בכפיהמ
7רגליהם לרע ירצו וימהרו לשׁפך דם נקי מחשׁבותיהם מחש7רגליהמה לרע ירוצו וימהרו לשפוכ דמ נקיא מחשבותיהמה 
>ׁבות און שׁד ושׁבר במסלותם >מחשבות אונ שד ושבר וחמס במסלותיהמה
8דרך שׁלום לא ידעו ואין משׁפט במעגלותם נתיבותיהם עק8דרכ שלומ לוא ידעו ואינ משפט במעגלותיהמה נתיבותי המ
>שׁו להם כל דרך בה לא ידע שׁלום >ה עקשו להמה כול הדורכ בה לוא ידע שלומ
9על כן רחק משׁפט ממנו ולא תשׂיגנו צדקה נקוה לאור וה9על כנ רחק משפט ממנו ולוא תשיגנו צדקה נקוה לאור והנ
>נה חשׁך לנגהות באפלות נהלך >ה חושכ לנגהות באפלה נהלכ
10נגשׁשׁה כעורים קיר וכאין עינים נגשׁשׁה כשׁלנו בצהר10נגשש כעורימ קיר וכאינ עינימ נגששה כשלנו בצהורימ כנ
>ים כנשׁף באשׁמנים כמתים >שפ באשמונימ כמיתימ
11נהמה כדבים כלנו וכיונים הגה נהגה נקוה למשׁפט ואין 11נהמה כדבימ כולנו כיונימ הגוא נהגה נקוה למשפט ואינ 
>לישׁועה רחקה ממנו >ולישועה רחקה ממנו
12כי רבו פשׁעינו נגדך וחטאותינו ענתה בנו כי פשׁעינו 12כיא רבו פשעינו נגדכה וחטאותינו ענוא בנו כיא פשעינו
>אתנו ועונתינו ידענום > אתנו ועוונותינו ידענומ
13פשׁע וכחשׁ ביהוה ונסוג מאחר אלהינו דבר עשׁק וסרה ה13פשועו וכחש ביהוה ונסוג מאחר אלוהינו ודברו עושק וסר
>רו והגו מלב דברי שׁקר >ה והגוא מלב דברי שקר
14והסג אחור משׁפט וצדקה מרחוק תעמד כי כשׁלה ברחוב אמ14ואסיג אחור משפט וצדקה מרחוק תעמוד כיא כשלה ברחוב א
>ת ונכחה לא תוכל לבוא >מת ונכוחה לוא תוכל לבוא
15ותהי האמת נעדרת וסר מרע משׁתולל וירא יהוה וירע בעי15ותהי האמת נעדרת וסר מרע משתולל וירא יהוה וירע בעינ
>ניו כי אין משׁפט >יו כיא אינ משפט
16וירא כי אין אישׁ וישׁתומם כי אין מפגיע ותושׁע לו ז16וירא כיא אינ איש וישתוממ כיא אינ מפגיע ותושע לוא ז
>רעו וצדקתו היא סמכתהו >רועו וצדקתיו היא סמכתו
17וילבשׁ צדקה כשׁרין וכובע ישׁועה בראשׁו וילבשׁ בגדי17וילבש צדקה כשרינ וכובע ישועה ברואשיו וילבש בגדי נק
> נקם תלבשׁת ויעט כמעיל קנאה >מ תלבושת ויעט כמעיל קנאא
18כעל גמלות כעל ישׁלם חמה לצריו גמול לאיביו לאיים גמ18כעל גמולות כעל ישלמ חמה לצריו גמול לאויביו לאיימ ג
>ול ישׁלם >מול ישלמ
19וייראו ממערב את שׁם יהוה וממזרח שׁמשׁ את כבודו כי 19וייראו ממערב את שמ יהוה וממזרח שמש את כבודיו כיא י
>יבוא כנהר צר רוח יהוה נססה בו >בוא כנהר צור רוח יהוה נוססה בוה
20ובא לציון גואל ולשׁבי פשׁע ביעקב נאם יהוה 20ובא אל ציונ גואל ולשבי פשע ביעקוב נואמ יהוה
21ואני זאת בריתי אותם אמר יהוה רוחי אשׁר עליך ודברי 21ואני זואת בריתי אתמ אמר יהוה ורוחי אשר עליכה ודברי
>אשׁר שׂמתי בפיך לא ימושׁו מפיך ומפי זרעך ומפי זרע > אשר שמתי בפיכה לוא ימושו מפיכה ומפי זרעכה ומפי זר
>זרעך אמר יהוה מעתה ועד עולם ס >ע זרעכה מעתה ועד עולמ
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 60 MT
Isaiah 60 1QIsaa
t1קומי אורי כי בא אורכ וכבוד יהוה עליכ זרחt1קומי אורי כיא בא אורכ כבוד יהוה עליכ זרח
2כי הנה החשכ יכסה ארצ וערפל לאמימ ועליכ יזרח יהוה ו2כיא הנה החושכ יכסה ארצ וערפל לאומימ ועליכ יזרח יהו
>כבודו עליכ יראה>ה וכבודו עליכ יראה
3והלכו גוימ לאורכ ומלכימ לנגה זרחכ3והלכו גואימ לאורכ ומלכימ לנגד זרחכ
4שאי סביב עיניכ וראי כלמ נקבצו באו לכ בניכ מרחוק יב4שאי סביב עיניכ וראי כולמה נקבצו באו לכ בניכ מרחוק 
>או ובנתיכ על צד תאמנה>יבואו ובנותיכ על צד תאמנה
5אז תראי ונהרת ופחד ורחב לבבכ כי יהפכ עליכ המונ ימ 5אז תראי ונהר ורחב לבבכ כיא יהפכ אליכ המונ ימ חיל ג
>חיל גוימ יבאו לכ>ואימ יבואו לכ
6שפעת גמלימ תכסכ בכרי מדינ ועיפה כלמ משבא יבאו זהב 6שפעת גמלימ תכסכ בכרי מדימ ועיפו כולמ משבאו יבואו ז
>ולבונה ישאו ותהלת יהוה יבשרו>הב ולבונה ישאו ותהלת יהוה יבשרו
7כל צאנ קדר יקבצו לכ אילי נביות ישרתונכ יעלו על רצו7כול צואנ קדר יקבצו לכ אילי נבאות ישרתונכ ויעלו לרצ
>נ מזבחי ובית תפארתי אפאר>ונ על מזבחי ובית תפארתי אפאר
8מי אלה כעב תעופינה וכיונימ אל ארבתיהמ8מיא אלה כעב תעופפנה וכיונימ אל ארבותיהמה
9כי לי איימ יקוו ואניות תרשיש בראשנה להביא בניכ מרח9כיא ליא איימ יקוו ואניות תרשיש ברישונה להביא בני מ
>וק כספמ וזהבמ אתמ לשמ יהוה אלהיכ ולקדוש ישראל כי פ>רחוק כספמ וזהבמ אתמ לשמ יהוה אלוהיכ ולקדוש ישראל כ
>ארכ>יא פארכ
10ובנו בני נכר חמתיכ ומלכיהמ ישרתונכ כי בקצפי הכיתיכ10ובנו בני נכר חומותיכ ומלכיהמה ישרתונכ כיא בקצפי הכ
> וברצוני רחמתיכ>יתיכ וברצוני רחמתיכ
11ופתחו שעריכ תמיד יוממ ולילה לא יסגרו להביא אליכ חי11ופתחו שעריכ תמיד יוממ ולילה ולוא יסגרו להביא אליכ 
>ל גוימ ומלכיהמ נהוגימ>חיל גואימ ומלכיהמה נהוגימ
12כי הגוי והממלכה אשר לא יעבדוכ יאבדו והגוימ חרב יחר12כיא הגוי והממלכה אשר לוא יעבודוכי יאבדו והגואימ חר
>בו>וב יחרבו
13כבוד הלבנונ אליכ יבוא ברוש תדהר ותאשור יחדו לפאר מ13כבוד הלבנונ נתנ לכ ואליכ יבוא ברוש ותהרהר ותאשור י
>קומ מקדשי ומקומ רגלי אכבד>חדיו לפאר מקומ מקדשי ומקומ רגלי אכבד
14והלכו אליכ שחוח בני מעניכ והשתחוו על כפות רגליכ כל14ואהלכו אליכ שחוח כול בני מעניכ והשתחוו על כפות רגל
> מנאציכ וקראו לכ עיר יהוה ציונ קדוש ישראל>יכ כול מנאציכ וקראו לכ עיר יהוה ציונ קדוש ישראל
15תחת היותכ עזובה ושנואה ואינ עובר ושמתיכ לגאונ עולמ15תחת הייותכ עזובה ושנואה ואינ עובר ושמתיכ לגאונ עול
> משוש דור ודור>מ משוש דור ודור
16וינקת חלב גוימ ושד מלכימ תינקי וידעת כי אני יהוה מ16וינקתי חלב גואימ ושד מלכימ תינקי וידעתי כיא אני יה
>ושיעכ וגאלכ אביר יעקב>וה מושיעכ וגואלכ אביר יעקוב
17תחת הנחשת אביא זהב ותחת הברזל אביא כספ ותחת העצימ 17תחת הנחושת אביא זהב ותחת הברזל אביא כספ ותחת העצימ
>נחשת ותחת האבנימ ברזל ושמתי פקדתכ שלומ ונגשיכ צדקה> נחושת ותחת האבנימ ברזל ושמתי פקודתכ שלומ ונוגשיכ 
 >צדקה
18לא ישמע עוד חמס בארצכ שד ושבר בגבוליכ וקראת ישועה 18ולוא ישמע עוד חמס בארצכ שד ושבר בגבוליכ וקראתה היש
>חומתיכ ושעריכ תהלה>ועה חומותיכ ושעריכ תהלה
19לא יהיה לכ עוד השמש לאור יוממ ולנגה הירח לא יאיר ל19לוא יהיה לכ עוד השמש לאור יוממ ולנוגה הירח בלילה ל
>כ והיה לכ יהוה לאור עולמ ואלהיכ לתפארתכ>וא יאיר לכ והיה לכ יהוה לאור עולמ ואלוהיכ לתפארתכ
20לא יבוא עוד שמשכ וירחכ לא יאספ כי יהוה יהיה לכ לאו20לוא יבוא שמשכ וירחכ לוא יאספ כיא יהוה יהיה לכ לאור
>ר עולמ ושלמו ימי אבלכ> עולמ ושלמו ימי אבלכ
21ועמכ כלמ צדיקימ לעולמ יירשו ארצ נצר מטעי מעשה ידי 21ועמכ כולמ צדיקימ לעולמ ירשו ארצ נצר מטעי יהוה מעשי
>להתפאר> ידיו להתפאר
22הקטנ יהיה לאלפ והצעיר לגוי עצומ אני יהוה בעתה אחיש22הקטנ יהיה לאלפ והצעיר לגוי עצומ אני יהוה בעתה אחיש
>נה>נה
t1קומי אורי כי בא אורך וכבוד יהוה עליך זרח t1קומי אורי כיא בא אורכ כבוד יהוה עליכ זרח
2כי הנה החשׁך יכסה ארץ וערפל לאמים ועליך יזרח יהוה 2כיא הנה החושכ יכסה ארצ וערפל לאומימ ועליכ יזרח יהו
>וכבודו עליך יראה >ה וכבודו עליכ יראה
3והלכו גוים לאורך ומלכים לנגה זרחך 3והלכו גואימ לאורכ ומלכימ לנגד זרחכ
4שׂאי סביב עיניך וראי כלם נקבצו באו לך בניך מרחוק י4שאי סביב עיניכ וראי כולמה נקבצו באו לכ בניכ מרחוק 
>באו ובנתיך על צד תאמנה >יבואו ובנותיכ על צד תאמנה
5אז תראי ונהרת ופחד ורחב לבבך כי יהפך עליך המון ים 5אז תראי ונהר ורחב לבבכ כיא יהפכ אליכ המונ ימ חיל ג
>חיל גוים יבאו לך >ואימ יבואו לכ
6שׁפעת גמלים תכסך בכרי מדין ועיפה כלם משׁבא יבאו זה6שפעת גמלימ תכסכ בכרי מדימ ועיפו כולמ משבאו יבואו ז
>ב ולבונה ישׂאו ותהלת יהוה יבשׂרו >הב ולבונה ישאו ותהלת יהוה יבשרו
7כל צאן קדר יקבצו לך אילי נביות ישׁרתונך יעלו על רצ7כול צואנ קדר יקבצו לכ אילי נבאות ישרתונכ ויעלו לרצ
>ון מזבחי ובית תפארתי אפאר >ונ על מזבחי ובית תפארתי אפאר
8מי אלה כעב תעופינה וכיונים אל ארבתיהם 8מיא אלה כעב תעופפנה וכיונימ אל ארבותיהמה
9כי לי׀ איים יקוו ואניות תרשׁישׁ בראשׁנה להביא בניך9כיא ליא איימ יקוו ואניות תרשיש ברישונה להביא בני מ
> מרחוק כספם וזהבם אתם לשׁם יהוה אלהיך ולקדושׁ ישׂר>רחוק כספמ וזהבמ אתמ לשמ יהוה אלוהיכ ולקדוש ישראל כ
>אל כי פארך >יא פארכ
10ובנו בני נכר חמתיך ומלכיהם ישׁרתונך כי בקצפי הכיתי10ובנו בני נכר חומותיכ ומלכיהמה ישרתונכ כיא בקצפי הכ
>ך וברצוני רחמתיך >יתיכ וברצוני רחמתיכ
11ופתחו שׁעריך תמיד יומם ולילה לא יסגרו להביא אליך ח11ופתחו שעריכ תמיד יוממ ולילה ולוא יסגרו להביא אליכ 
>יל גוים ומלכיהם נהוגים >חיל גואימ ומלכיהמה נהוגימ
12כי הגוי והממלכה אשׁר לא יעבדוך יאבדו והגוים חרב יח12כיא הגוי והממלכה אשר לוא יעבודוכי יאבדו והגואימ חר
>רבו >וב יחרבו
13כבוד הלבנון אליך יבוא ברושׁ תדהר ותאשׁור יחדו לפאר13כבוד הלבנונ נתנ לכ ואליכ יבוא ברוש ותהרהר ותאשור י
> מקום מקדשׁי ומקום רגלי אכבד >חדיו לפאר מקומ מקדשי ומקומ רגלי אכבד
14והלכו אליך שׁחוח בני מעניך והשׁתחוו על כפות רגליך 14ואהלכו אליכ שחוח כול בני מעניכ והשתחוו על כפות רגל
>כל מנאציך וקראו לך עיר יהוה ציון קדושׁ ישׂראל >יכ כול מנאציכ וקראו לכ עיר יהוה ציונ קדוש ישראל
15תחת היותך עזובה ושׂנואה ואין עובר ושׂמתיך לגאון עו15תחת הייותכ עזובה ושנואה ואינ עובר ושמתיכ לגאונ עול
>לם משׂושׂ דור ודור >מ משוש דור ודור
16וינקת חלב גוים ושׁד מלכים תינקי וידעת כי אני יהוה 16וינקתי חלב גואימ ושד מלכימ תינקי וידעתי כיא אני יה
>מושׁיעך וגאלך אביר יעקב >וה מושיעכ וגואלכ אביר יעקוב
17תחת הנחשׁת אביא זהב ותחת הברזל אביא כסף ותחת העצים17תחת הנחושת אביא זהב ותחת הברזל אביא כספ ותחת העצימ
> נחשׁת ותחת האבנים ברזל ושׂמתי פקדתך שׁלום ונגשׂיך> נחושת ותחת האבנימ ברזל ושמתי פקודתכ שלומ ונוגשיכ 
> צדקה >צדקה
18לא ישׁמע עוד חמס בארצך שׁד ושׁבר בגבוליך וקראת ישׁ18ולוא ישמע עוד חמס בארצכ שד ושבר בגבוליכ וקראתה היש
>ועה חומתיך ושׁעריך תהלה >ועה חומותיכ ושעריכ תהלה
19לא יהיה לך עוד השׁמשׁ לאור יומם ולנגה הירח לא יאיר19לוא יהיה לכ עוד השמש לאור יוממ ולנוגה הירח בלילה ל
> לך והיה לך יהוה לאור עולם ואלהיך לתפארתך >וא יאיר לכ והיה לכ יהוה לאור עולמ ואלוהיכ לתפארתכ
20לא יבוא עוד שׁמשׁך וירחך לא יאסף כי יהוה יהיה לך ל20לוא יבוא שמשכ וירחכ לוא יאספ כיא יהוה יהיה לכ לאור
>אור עולם ושׁלמו ימי אבלך > עולמ ושלמו ימי אבלכ
21ועמך כלם צדיקים לעולם יירשׁו ארץ נצר מטעו מעשׂה יד21ועמכ כולמ צדיקימ לעולמ ירשו ארצ נצר מטעי יהוה מעשי
>י להתפאר > ידיו להתפאר
22הקטן יהיה לאלף והצעיר לגוי עצום אני יהוה בעתה אחיש22הקטנ יהיה לאלפ והצעיר לגוי עצומ אני יהוה בעתה אחיש
>ׁנה ס >נה
- - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 61 MT
Isaiah 61 1QIsaa
t1רוח אדני יהוה עלי יענ משח יהוה אתי לבשר ענוימ שלחנt1רוח יהוה עלי יענ משח יהוה אותי לבשר ענוימ שלחני ול
>י לחבש לנשברי לב לקרא לשבוימ דרור ולאסורימ פקח קוח>חבוש לנשברי לב לקרוא לשבויימ דרור ולאסורימ פקחקוח
2לקרא שנת רצונ ליהוה ויומ נקמ לאלהינו לנחמ כל אבלימ2לקרוא שנת רצונ ליהוה יומ נקמ לאלוהינו לנחמ כול אבי
 >לימ
3לשומ לאבלי ציונ לתת להמ פאר תחת אפר שמנ ששונ תחת א3לשומ לאבילי ציונ לתת להמה פאר תחת אפר שמנ ששונ תחת
>בל מעטה תהלה תחת רוח כהה וקרא להמ אילי הצדק מטע יה> אבל מעטה תהלה תחת רוח כהה וקראו להמה אילי הצדק מט
>וה להתפאר>ע יהוה להתפאר
4ובנו חרבות עולמ שממות ראשנימ יקוממו וחדשו ערי חרב 4ובנו חרבות עולמ שוממות ריאשונימ יקוממו וחדשו ערי ח
>שממות דור ודור>ורב שוממות דור ודור יקוממו
5ועמדו זרימ ורעו צאנכמ ובני נכר אכריכמ וכרמיכמ5ועמדו זרימ ורעו צואנכמה ובני נכר אכריכמה וכורמיכמה
6ואתמ כהני יהוה תקראו משרתי אלהינו יאמר לכמ חיל גוי6ואתמה כוהני יהוה תקרוא ומשרתי אלוהינו יאמר לכמה חי
>מ תאכלו ובכבודמ תתימרו>ל גואימ תואכלו ובכבודמ תתיאמרו
7תחת בשתכמ משנה וכלמה ירנו חלקמ לכנ בארצמ משנה יירש7תחת בושתכמה משנה וכלמה ירונו חלקכמה לכנ משנה בארצמ
>ו שמחת עולמ תהיה להמ> תירשו שמחת עולמ תהיה לכמה
8כי אני יהוה אהב משפט שנא גזל בעולה ונתתי פעלתמ באמ8כיא אני יהוה אוהב משפט ושונה גזול בעולה ונתתי פעול
>ת וברית עולמ אכרות להמ>תכמ באמת וברית עולמ אכרות לכמה
9ונודע בגוימ זרעמ וצאצאיהמ בתוכ העמימ כל ראיהמ יכיר9ונודע בגואימ זרעכמה וצאצאיכמה בתוכ העמימ כול רואיה
>ומ כי המ זרע ברכ יהוה>מה יכירומ כיא המה זרע ברכ יהוה
10שוש אשיש ביהוה תגל נפשי באלהי כי הלבישני בגדי ישע 10שיש אשיש ביהוה תגל נפשי באלוהי כיא הלבישני בגדי יש
>מעיל צדקה יעטני כחתנ יכהנ פאר וככלה תעדה כליה>ע מעיל צדקה יעטני כחתנ ככוהנ פאר וככלה תעדה כליהא
11כי כארצ תוציא צמחה וכגנה זרועיה תצמיח כנ אדני יהוה11כיא כארצ תוציא צמחה וכגנה זרועיה תצמיח כנ יהוה אלו
> יצמיח צדקה ותהלה נגד כל הגוימ>הימ יצמיח צדקה ותהלה נגד כול הגואימ
t1רוח אדני יהוה עלי יען משׁח יהוה אתי לבשׂר ענוים שׁt1רוח יהוה עלי יענ משח יהוה אותי לבשר ענוימ שלחני ול
>לחני לחבשׁ לנשׁברי לב לקרא לשׁבוים דרור ולאסורים פ>חבוש לנשברי לב לקרוא לשבויימ דרור ולאסורימ פקחקוח
>קח קוח  
2לקרא שׁנת רצון ליהוה ויום נקם לאלהינו לנחם כל אבלי2לקרוא שנת רצונ ליהוה יומ נקמ לאלוהינו לנחמ כול אבי
>ם >לימ
3לשׂום׀ לאבלי ציון לתת להם פאר תחת אפר שׁמן שׂשׂון 3לשומ לאבילי ציונ לתת להמה פאר תחת אפר שמנ ששונ תחת
>תחת אבל מעטה תהלה תחת רוח כהה וקרא להם אילי הצדק מ> אבל מעטה תהלה תחת רוח כהה וקראו להמה אילי הצדק מט
>טע יהוה להתפאר >ע יהוה להתפאר
4ובנו חרבות עולם שׁממות ראשׁנים יקוממו וחדשׁו ערי ח4ובנו חרבות עולמ שוממות ריאשונימ יקוממו וחדשו ערי ח
>רב שׁממות דור ודור >ורב שוממות דור ודור יקוממו
5ועמדו זרים ורעו צאנכם ובני נכר אכריכם וכרמיכם 5ועמדו זרימ ורעו צואנכמה ובני נכר אכריכמה וכורמיכמה
6ואתם כהני יהוה תקראו משׁרתי אלהינו יאמר לכם חיל גו6ואתמה כוהני יהוה תקרוא ומשרתי אלוהינו יאמר לכמה חי
>ים תאכלו ובכבודם תתימרו >ל גואימ תואכלו ובכבודמ תתיאמרו
7תחת בשׁתכם משׁנה וכלמה ירנו חלקם לכן בארצם משׁנה י7תחת בושתכמה משנה וכלמה ירונו חלקכמה לכנ משנה בארצמ
>ירשׁו שׂמחת עולם תהיה להם > תירשו שמחת עולמ תהיה לכמה
8כי אני יהוה אהב משׁפט שׂנא גזל בעולה ונתתי פעלתם ב8כיא אני יהוה אוהב משפט ושונה גזול בעולה ונתתי פעול
>אמת וברית עולם אכרות להם >תכמ באמת וברית עולמ אכרות לכמה
9ונודע בגוים זרעם וצאצאיהם בתוך העמים כל ראיהם יכיר9ונודע בגואימ זרעכמה וצאצאיכמה בתוכ העמימ כול רואיה
>ום כי הם זרע ברך יהוה ס >מה יכירומ כיא המה זרע ברכ יהוה
10שׂושׂ אשׂישׂ ביהוה תגל נפשׁי באלהי כי הלבישׁני בגד10שיש אשיש ביהוה תגל נפשי באלוהי כיא הלבישני בגדי יש
>י ישׁע מעיל צדקה יעטני כחתן יכהן פאר וככלה תעדה כל>ע מעיל צדקה יעטני כחתנ ככוהנ פאר וככלה תעדה כליהא
>יה  
11כי כארץ תוציא צמחה וכגנה זרועיה תצמיח כן׀ אדני יהו11כיא כארצ תוציא צמחה וכגנה זרועיה תצמיח כנ יהוה אלו
>ה יצמיח צדקה ותהלה נגד כל הגוים >הימ יצמיח צדקה ותהלה נגד כול הגואימ
- - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 62 MT
Isaiah 62 1QIsaa
t1למענ ציונ לא אחשה ולמענ ירושלמ לא אשקוט עד יצא כנגt1למענ ציונ ולוא אחריש ולמענ ירושלימ לוא אשקוט עד יצ
>ה צדקה וישועתה כלפיד יבער>א כנוגה צדקה וישועתה כלפיד תבער
2וראו גוימ צדקכ וכל מלכימ כבודכ וקרא לכ שמ חדש אשר 2וראו גואימ צדקכי וכול מלכימ כבודכ וקראו לכ שמ חדש 
>פי יהוה יקבנו>אשר פי יהוה יקובנו
3והיית עטרת תפארת ביד יהוה וצניפ מלוכה בכפ אלהיכ3והיית עטרת תפארת ביד יהוה וצנופ מלוכה בכפ אלוהיכי
4לא יאמר לכ עוד עזובה ולארצכ לא יאמר עוד שממה כי לכ4ולוא יאמר לכי עוד עזובה ולארצכ לוא יאמר עוד שוממה 
> יקרא חפצי בה ולארצכ בעולה כי חפצ יהוה בכ וארצכ תב>כיא לכי יקראו חפצי בהא ולארצכ בעולה כיא חפצ יהוה ב
>על>כי וארצכ תבעל
5כי יבעל בחור בתולה יבעלוכ בניכ ומשוש חתנ על כלה יש5כיא כבעול בחור בתולה יבעלוכי בניכ ומשוש חתנ על כלה
>יש עליכ אלהיכ> ישיש עליכ אלוהיכ
6על חומתיכ ירושלמ הפקדתי שמרימ כל היומ וכל הלילה תמ6על חומותיכ ירושלימ הפקדתי שומרימ כול היומ וכול הלי
>יד לא יחשו המזכרימ את יהוה אל דמי לכמ>לה לוא יחשו המזכירימ את יהוה אל דמי לכמה
7ואל תתנו דמי לו עד יכוננ ועד ישימ את ירושלמ תהלה ב7ואל תתנו דמי לו עד יכינ ועד יכוננ ועד ישימ את ירוש
>ארצ>לימ תהלה בארצ
8נשבע יהוה בימינו ובזרוע עזו אמ אתנ את דגנכ עוד מאכ8נשבע יהוה בימינו ובזרוע עוזו אמ אתנ עוד דגנכ מאכל 
>ל לאיביכ ואמ ישתו בני נכר תירושכ אשר יגעת בו>לאוביכ אמ ישתו בני נכר תירושכ אשר יגעתי בוה
9כי מאספיו יאכלהו והללו את יהוה ומקבציו ישתהו בחצרו9כיא אמ מאספוהי יאכולוהי ויהללו את שמ יהוה ומקבצו י
>ת קדשי>שתוהי בחצרות קודשי אמר אלוהיכ
10עברו עברו בשערימ פנו דרכ העמ סלו סלו המסלה סקלו מא10עבורו בשערימ פנו דרכ העמ סולו סולו המסלה סקולו מאב
>בנ הרימו נס על העמימ>נ הנגפ אמורו בעמימ
11הנה יהוה השמיע אל קצה הארצ אמרו לבת ציונ הנה ישעכ 11הנה יהוה השמיעו אל קצוי הארצ אמורו לבת ציונ הנה יש
>בא הנה שכרו אתו ופעלתו לפניו>עכ בא הנה שכרו אתו ופעלתיו לפניו
12וקראו להמ עמ הקדש גאולי יהוה ולכ יקרא דרושה עיר לא12וקראו להמה עמ הקודש גאולי יהוה ולכי יקראו דרושה עי
> נעזבה>ר לוא נעזבה
t1למען ציון לא אחשׁה ולמען ירושׁלם לא אשׁקוט עד יצא t1למענ ציונ ולוא אחריש ולמענ ירושלימ לוא אשקוט עד יצ
>כנגה צדקה וישׁועתה כלפיד יבער >א כנוגה צדקה וישועתה כלפיד תבער
2וראו גוים צדקך וכל מלכים כבודך וקרא לך שׁם חדשׁ אש2וראו גואימ צדקכי וכול מלכימ כבודכ וקראו לכ שמ חדש 
>ׁר פי יהוה יקבנו >אשר פי יהוה יקובנו
3והיית עטרת תפארת ביד יהוה וצנוף מלוכה בכף אלהיך 3והיית עטרת תפארת ביד יהוה וצנופ מלוכה בכפ אלוהיכי
4לא יאמר לך עוד עזובה ולארצך לא יאמר עוד שׁממה כי ל4ולוא יאמר לכי עוד עזובה ולארצכ לוא יאמר עוד שוממה 
>ך יקרא חפצי בה ולארצך בעולה כי חפץ יהוה בך וארצך ת>כיא לכי יקראו חפצי בהא ולארצכ בעולה כיא חפצ יהוה ב
>בעל >כי וארצכ תבעל
5כי יבעל בחור בתולה יבעלוך בניך ומשׂושׂ חתן על כלה 5כיא כבעול בחור בתולה יבעלוכי בניכ ומשוש חתנ על כלה
>ישׂישׂ עליך אלהיך > ישיש עליכ אלוהיכ
6על חומתיך ירושׁלם הפקדתי שׁמרים כל היום וכל הלילה 6על חומותיכ ירושלימ הפקדתי שומרימ כול היומ וכול הלי
>תמיד לא יחשׁו המזכרים את יהוה אל דמי לכם >לה לוא יחשו המזכירימ את יהוה אל דמי לכמה
7ואל תתנו דמי לו עד יכונן ועד ישׂים את ירושׁלם תהלה7ואל תתנו דמי לו עד יכינ ועד יכוננ ועד ישימ את ירוש
> בארץ >לימ תהלה בארצ
8נשׁבע יהוה בימינו ובזרוע עזו אם אתן את דגנך עוד מא8נשבע יהוה בימינו ובזרוע עוזו אמ אתנ עוד דגנכ מאכל 
>כל לאיביך ואם ישׁתו בני נכר תירושׁך אשׁר יגעת בו >לאוביכ אמ ישתו בני נכר תירושכ אשר יגעתי בוה
9כי מאספיו יאכלהו והללו את יהוה ומקבציו ישׁתהו בחצר9כיא אמ מאספוהי יאכולוהי ויהללו את שמ יהוה ומקבצו י
>ות קדשׁי ס >שתוהי בחצרות קודשי אמר אלוהיכ
10עברו עברו בשׁערים פנו דרך העם סלו סלו המסלה סקלו מ10עבורו בשערימ פנו דרכ העמ סולו סולו המסלה סקולו מאב
>אבן הרימו נס על העמים >נ הנגפ אמורו בעמימ
11הנה יהוה השׁמיע אל קצה הארץ אמרו לבת ציון הנה ישׁע11הנה יהוה השמיעו אל קצוי הארצ אמורו לבת ציונ הנה יש
>ך בא הנה שׂכרו אתו ופעלתו לפניו >עכ בא הנה שכרו אתו ופעלתיו לפניו
12וקראו להם עם הקדשׁ גאולי יהוה ולך יקרא דרושׁה עיר 12וקראו להמה עמ הקודש גאולי יהוה ולכי יקראו דרושה עי
>לא נעזבה ס >ר לוא נעזבה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Isaiah 63 MT
Isaiah 63 1QIsaa
t1מי זה בא מאדומ חמוצ בגדימ מבצרה זה הדור בלבושו צעהt1מיא זה בא מאדומ חמוצ בגדימ מבוצרה זה הדר בלבושו צו
> ברב כחו אני מדבר בצדקה רב להושיע>עה ברוב כוחוה אני מדבר בעדקה רב להושיע
2מדוע אדמ ללבושכ ובגדיכ כדרכ בגת2מדוע אדומ ללבושכה ובגדיכ כדורכ בגד
3פורה דרכתי לבדי ומעמימ אינ איש אתי ואדרכמ באפי ואר3פורה דרכתי לבדי ומעמי אינ איש אתי וכול מלבושי גאלת
>מסמ בחמתי ויז נצחמ על בגדי וכל מלבושי אגאלתי>י
4כי יומ נקמ בלבי ושנת גאולי באה4כיא יומ נקמ בלבי ושנת גאולי באה
5ואביט ואינ עזר ואשתוממ ואינ סומכ ותושע לי זרעי וחמ5ואביט ואינ עוזר ואשתוממ ואינ תומכ ותושע ליא זרועי 
>תי היא סמכתני>וחמתיא היא סמכתני
6ואבוס עמימ באפי ואשכרמ בחמתי ואוריד לארצ נצחמ6ואבוסה עמימ באפיא ואשכירמה בחמתי ואורידה לארצ נצחמ
7חסדי יהוה אזכיר תהלת יהוה כעל כל אשר גמלנו יהוה ור7חסדי יהוה אזכיר תהלת יהוה כעל כול אשר גמלנו יהוה ו
>ב טוב לבית ישראל אשר גמלמ כרחמיו וכרב חסדיו>רב טוב לבית ישראל אשר גמאלמ כרחמיו וכרוב חסדיו
8ויאמר אכ עמי המה בנימ לא ישקרו ויהי להמ למושיע8ויואמר אכ עמי המה בנימ לוא ישקרו ויהי להמה למושיע
9בכל צרתמ לו צר ומלאכ פניו הושיעמ באהבתו ובחמלתו הו9בכול צרתמה לוא צר ומלאכ פניו הושיעמה באהבתיו ובחומ
>א גאלמ וינטלמ וינשאמ כל ימי עולמ>לתיו הואה גאלמה וינשאמ וינטלמ כול ימי עולמ
10והמה מרו ועצבו את רוח קדשו ויהפכ להמ לאויב הוא נלח10והמה מרו ועצבו את רוח קודשיו ויהפכ להמה לאויב והוא
>מ במ>ה נלחמ במ
11ויזכר ימי עולמ משה עמו איה המעלמ מימ את רעי צאנו א11ויזכור ימי עולמ מושה עמוא איה המעלה מימ את רועי צו
>יה השמ בקרבו את רוח קדשו>אנו איה השמ בקרבו את רוח קודשו
12מוליכ לימינ משה זרוע תפארתו בוקע מימ מפניהמ לעשות 12ומוליכ לימינ מושה זרוע תפארתיו בוקע מימ מפניהמה לע
>לו שמ עולמ>שות שמ עולמ
13מוליכמ בתהמות כסוס במדבר לא יכשלו13מוליכמ בתומות כסוס במדבר לוא יכשלו
14כבהמה בבקעה תרד רוח יהוה תניחנו כנ נהגת עמכ לעשות 14כבהמה בבקעה תרד רוח יהוה תניחנו כיא נהגתה עמכה לעש
>לכ שמ תפארת>ות לכה שמ תפארת
15הבט משמימ וראה מזבל קדשכ ותפארתכ איה קנאתכ וגבורתכ15הבט מנ השמימ וראה מזבול קודשכה ותפארתכה איה קנאתכה
> המונ מעיכ ורחמיכ אלי התאפקו> וגבורתכה המונ מעיכ ורחמיכ אלי התאפקו
16כי אתה אבינו כי אברהמ לא ידענו וישראל לא יכירנו את16כיא אתה אבינו ואברהמ לוא ידענו וישראל לוא הכירנו א
>ה יהוה אבינו גאלנו מעולמ שמכ>תה הואה יהוה אבינו גואלנו מעולמ שמכה
17למה תתענו יהוה מדרכיכ תקשיח לבנו מיראתכ שוב למענ ע17למה יהוה תתענו מדרכיכה תקשיח לבנו מיראתכ שוב למענ 
>בדיכ שבטי נחלתכ>עבדיכ שבט נחלתכ
18למצער ירשו עמ קדשכ צרינו בוססו מקדשכ18למצער ירש עמ קודשכ צרינו בססו מקדשכה
19היינו מעולמ לא משלת במ לא נקרא שמכ עליהמ לוא קרעת 19הוינו מעולמ לוא משלתה במ לוא נקרא שמכה עליהמה לוא 
>שמימ ירדת מפניכ הרימ נזלו>קרעתה שמימ וירדתה מפניכה הרימ נזלו
t1מי זה׀ בא מאדום חמוץ בגדים מבצרה זה הדור בלבושׁו צt1מיא זה בא מאדומ חמוצ בגדימ מבוצרה זה הדר בלבושו צו
>עה ברב כחו אני מדבר בצדקה רב להושׁיע >עה ברוב כוחוה אני מדבר בעדקה רב להושיע
2מדוע אדם ללבושׁך ובגדיך כדרך בגת 2מדוע אדומ ללבושכה ובגדיכ כדורכ בגד
3פורה׀ דרכתי לבדי ומעמים אין אישׁ אתי ואדרכם באפי ו3פורה דרכתי לבדי ומעמי אינ איש אתי וכול מלבושי גאלת
>ארמסם בחמתי ויז נצחם על בגדי וכל מלבושׁי אגאלתי >י
4כי יום נקם בלבי ושׁנת גאולי באה 4כיא יומ נקמ בלבי ושנת גאולי באה
5ואביט ואין עזר ואשׁתומם ואין סומך ותושׁע לי זרעי ו5ואביט ואינ עוזר ואשתוממ ואינ תומכ ותושע ליא זרועי 
>חמתי היא סמכתני >וחמתיא היא סמכתני
6ואבוס עמים באפי ואשׁכרם בחמתי ואוריד לארץ נצחם ס 6ואבוסה עמימ באפיא ואשכירמה בחמתי ואורידה לארצ נצחמ
7חסדי יהוה׀ אזכיר תהלת יהוה כעל כל אשׁר גמלנו יהוה 7חסדי יהוה אזכיר תהלת יהוה כעל כול אשר גמלנו יהוה ו
>ורב טוב לבית ישׂראל אשׁר גמלם כרחמיו וכרב חסדיו >רב טוב לבית ישראל אשר גמאלמ כרחמיו וכרוב חסדיו
8ויאמר אך עמי המה בנים לא ישׁקרו ויהי להם למושׁיע 8ויואמר אכ עמי המה בנימ לוא ישקרו ויהי להמה למושיע
9בכל צרתם׀ לא צר ומלאך פניו הושׁיעם באהבתו ובחמלתו 9בכול צרתמה לוא צר ומלאכ פניו הושיעמה באהבתיו ובחומ
>הוא גאלם וינטלם וינשׂאם כל ימי עולם >לתיו הואה גאלמה וינשאמ וינטלמ כול ימי עולמ
10והמה מרו ועצבו את רוח קדשׁו ויהפך להם לאויב הוא נל10והמה מרו ועצבו את רוח קודשיו ויהפכ להמה לאויב והוא
>חם בם >ה נלחמ במ
11ויזכר ימי עולם משׁה עמו איה׀ המעלם מים את רעי צאנו11ויזכור ימי עולמ מושה עמוא איה המעלה מימ את רועי צו
> איה השׂם בקרבו את רוח קדשׁו >אנו איה השמ בקרבו את רוח קודשו
12מוליך לימין משׁה זרוע תפארתו בוקע מים מפניהם לעשׂו12ומוליכ לימינ מושה זרוע תפארתיו בוקע מימ מפניהמה לע
>ת לו שׁם עולם >שות שמ עולמ
13מוליכם בתהמות כסוס במדבר לא יכשׁלו 13מוליכמ בתומות כסוס במדבר לוא יכשלו
14כבהמה בבקעה תרד רוח יהוה תניחנו כן נהגת עמך לעשׂות14כבהמה בבקעה תרד רוח יהוה תניחנו כיא נהגתה עמכה לעש
> לך שׁם תפארת >ות לכה שמ תפארת
15הבט משׁמים וראה מזבל קדשׁך ותפארתך איה קנאתך וגבור15הבט מנ השמימ וראה מזבול קודשכה ותפארתכה איה קנאתכה
>תך המון מעיך ורחמיך אלי התאפקו > וגבורתכה המונ מעיכ ורחמיכ אלי התאפקו
16כי אתה אבינו כי אברהם לא ידענו וישׂראל לא יכירנו א16כיא אתה אבינו ואברהמ לוא ידענו וישראל לוא הכירנו א
>תה יהוה אבינו גאלנו מעולם שׁמך >תה הואה יהוה אבינו גואלנו מעולמ שמכה
17למה תתענו יהוה מדרכיך תקשׁיח לבנו מיראתך שׁוב למען17למה יהוה תתענו מדרכיכה תקשיח לבנו מיראתכ שוב למענ 
> עבדיך שׁבטי נחלתך >עבדיכ שבט נחלתכ
18למצער ירשׁו עם קדשׁך צרינו בוססו מקדשׁך 18למצער ירש עמ קודשכ צרינו בססו מקדשכה
19היינו מעולם לא משׁלת בם לא נקרא שׁמך עליהם לוא קרע19הוינו מעולמ לוא משלתה במ לוא נקרא שמכה עליהמה לוא 
>ת שׁמים ירדת מפניך הרים נזלו >קרעתה שמימ וירדתה מפניכה הרימ נזלו
- - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + +

Isaiah 64 MT
Isaiah 64 1QIsaa
t1כקדח אש המסימ מימ תבעה אש להודיע שמכ לצריכ מפניכ גt1כקדוח אש עמוסימ מימ תבעה אש לצריכה להודיע שמכה לצר
>וימ ירגזו>יכהמפניכה גואימ ירגזו
2בעשותכ נוראות לא נקוה ירדת מפניכ הרימ נזלו2בעשותכה נוראות נקוה ירדתה מפניכה הרימ נזלו
3ומעולמ לא שמעו לא האזינו עינ לא ראתה אלהימ זולתכ י3מעולמ לוא שמעו ולוא האזינו ועינ לוא ראתה אלוהימ זו
>עשה למחכה לו>לתכ יעשה למחכה לו
4פגעת את שש ועשה צדק בדרכיכ יזכרוכ הנ אתה קצפת ונחט4פגעתה את שש ועושה צדק בדרכיכה יזכורוכה הנה אתה קצפ
>א בהמ עולמ ונושע>תה ונחטא בהמה עולמ ונושע
5ונהי כטמא כלנו וכבגד עדימ כל צדקתינו ונבל כעלה כלנ5ונהיה כטמא כולנו כבגד עדימ כול צדקותינו ונבולה כעל
>ו ועוננו כרוח ישאנו>ה כולנו ועוונותינו כרוח ישאונו
6ואינ קורא בשמכ מתעורר להחזיק בכ כי הסתרת פניכ ממנו6ואינ קורה בשמכה מתעורר לאחזיק בכה כיא הסתרתה פניכה
> ותמוגנו ביד עוננו> ממנו ותמגדנו ביד עווננו
7ועתה יהוה אבינו אתה אנחנו החמר ואתה יצרנו ומעשה יד7ואתה יהוה אבינו אתה ואנחנו חמר ואתה יוצרנו ומעשה י
>כ כלנו>דיכה כולנו
8אל תקצפ יהוה עד מאד ואל לעד תזכר עונ הנ הבט נא עמכ8אל תקצופ יהוה עד מואדה ואל לעת תזכור עוונ הנה הבטנ
> כלנו>ה עמכה כולנו
9ערי קדשכ היו מדבר ציונ מדבר היתה ירושלמ שממה9ערי קודשכה היו מדבר ציונ כמדבר הייתה ירושלימ שוממה
10בית קדשנו ותפארתנו אשר הללוכ אבתינו היה לשרפת אש ו10בית קודשנו ותפארתנו אשר הללוכה אבותינו היו לשרפת א
>כל מחמדינו היה לחרבה>ש וכול מחמודינו היו לחורבה
11העל אלה תתאפק יהוה תחשה ותעננו עד מאד11העל אלה תתאפק יהוה תחשה ותעננו עד מואדה
t1כקדח אשׁ המסים מים תבעה אשׁ להודיע שׁמך לצריך מפניt1כקדוח אש עמוסימ מימ תבעה אש לצריכה להודיע שמכה לצר
>ך גוים ירגזו >יכהמפניכה גואימ ירגזו
2בעשׂותך נוראות לא נקוה ירדת מפניך הרים נזלו 2בעשותכה נוראות נקוה ירדתה מפניכה הרימ נזלו
3ומעולם לא שׁמעו לא האזינו עין לא ראתה אלהים זולתך 3מעולמ לוא שמעו ולוא האזינו ועינ לוא ראתה אלוהימ זו
>יעשׂה למחכה לו >לתכ יעשה למחכה לו
4פגעת את שׂשׂ ועשׂה צדק בדרכיך יזכרוך הן אתה קצפת ו4פגעתה את שש ועושה צדק בדרכיכה יזכורוכה הנה אתה קצפ
>נחטא בהם עולם ונושׁע >תה ונחטא בהמה עולמ ונושע
5ונהי כטמא כלנו וכבגד עדים כל צדקתינו ונבל כעלה כלנ5ונהיה כטמא כולנו כבגד עדימ כול צדקותינו ונבולה כעל
>ו ועוננו כרוח ישׂאנו >ה כולנו ועוונותינו כרוח ישאונו
6ואין קורא בשׁמך מתעורר להחזיק בך כי הסתרת פניך ממנ6ואינ קורה בשמכה מתעורר לאחזיק בכה כיא הסתרתה פניכה
>ו ותמוגנו ביד עוננו > ממנו ותמגדנו ביד עווננו
7ועתה יהוה אבינו אתה אנחנו החמר ואתה יצרנו ומעשׂה י7ואתה יהוה אבינו אתה ואנחנו חמר ואתה יוצרנו ומעשה י
>דך כלנו >דיכה כולנו
8אל תקצף יהוה עד מאד ואל לעד תזכר עון הן הבט נא עמך8אל תקצופ יהוה עד מואדה ואל לעת תזכור עוונ הנה הבטנ
> כלנו >ה עמכה כולנו
9ערי קדשׁך היו מדבר ציון מדבר היתה ירושׁלם שׁממה 9ערי קודשכה היו מדבר ציונ כמדבר הייתה ירושלימ שוממה
10בית קדשׁנו ותפארתנו אשׁר הללוך אבתינו היה לשׂרפת א10בית קודשנו ותפארתנו אשר הללוכה אבותינו היו לשרפת א
>שׁ וכל מחמדינו היה לחרבה >ש וכול מחמודינו היו לחורבה
11העל אלה תתאפק יהוה תחשׁה ותעננו עד מאד ס 11העל אלה תתאפק יהוה תחשה ותעננו עד מואדה
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + +

Isaiah 65 MT
Isaiah 65 1QIsaa
t1נדרשתי ללוא שאלו נמצאתי ללא בקשני אמרתי הנני הנני t1נדרשתי ללוא שאלוני נמציתי ללוא בקשוני אמרתי הנני ה
>אל גוי לא קרא בשמי>נני אל גוי לוא קרא בשמיא
2פרשתי ידי כל היומ אל עמ סורר ההלכימ הדרכ לא טוב אח2פרשתי ידי כול היומ אל עמ סורה ההולכימ הדרכ לואטוב 
>ר מחשבתיהמ>אחר מחשבותיהמה
3העמ המכעיסימ אותי על פני תמיד זבחימ בגנות ומקטרימ 3העמ המכעיסימ אותי על פני תמיד המה זובחימ בגנות וינ
>על הלבנימ>קו ידימ על האבנימ
4הישבימ בקברימ ובנצורימ ילינו האכלימ בשר החזיר ומרק4היושבימ בקברימ ובנצירימ ילינו האוכלימ בשר החוזיר ו
> פגלימ כליהמ>מרק פגולימ בכליהמה
5האמרימ קרב אליכ אל תגש בי כי קדשתיכ אלה עשנ באפי א5האומרימ קרב אליכה אל תגע ביא קדשתיכה אלה עשנ באפי 
>ש יקדת כל היומ>אש יוקדת כול היומ
6הנה כתובה לפני לא אחשה כי אמ שלמתי ושלמתי על חיקמ6הנה כתובה לפני לוא אחשה כיא אמ שלמתי ושלמתי אל חיק
 >מ
7עונתיכמ ועונת אבותיכמ יחדו אמר יהוה אשר קטרו על הה7עוונותיכמה ועונות אבותיכמה יחדו אמר יהוה אשר קטורו
>רימ ועל הגבעות חרפוני ומדתי פעלתמ ראשנה אל חיקמ> על הרימ ועל הגבעות חרפוני ומדותי פועלתמה רישונה א
 >ל חיקמה
8כה אמר יהוה כאשר ימצא התירוש באשכול ואמר אל תשחיתה8כוה אמר יהוה כאשר ימצא התירוש באשכול ויואמר אל תשח
>ו כי ברכה בו כנ אעשה למענ עבדי לבלתי השחית הכל>יתוהי כיא ברכה בוא כנ אעשה למענ עבדי לבלתי השחית ה
 >כול
9והוצאתי מיעקב זרע ומיהודה יורש הרי וירשוה בחירי וע9והוציתי מיעקוב זרע ומיהודה ירש הרי וירשוהי בחירי ו
>בדי ישכנו שמה>עבדי ישכונו שמה
10והיה השרונ לנוה צאנ ועמק עכור לרבצ בקר לעמי אשר דר10והיאה השרונ לנוי צואנ ועמק עכור למרבצ בקר לעמי אשר
>שוני> דרשוני
11ואתמ עזבי יהוה השכחימ את הר קדשי הערכימ לגד שלחנ ו11ואתמה עוזבי יהוה השכחימ את הר קודשי העורכימ לגד שו
>הממלאימ למני ממסכ>לחנ וממלאימ למני מסכה
12ומניתי אתכמ לחרב וכלכמ לטבח תכרעו יענ קראתי ולא ענ12ומניתי אתכמה לחרב וכולכמה לטבחה תכרעו יענ קראתי ול
>יתמ דברתי ולא שמעתמ ותעשו הרע בעיני ובאשר לא חפצתי>וא עניתמה דברתי ולוא שמעתמה ותעשו הרע בעיני ובאשר 
> בחרתמ>לוא חפצתי בחרתמה
13לכנ כה אמר אדני יהוה הנה עבדי יאכלו ואתמ תרעבו הנה13לכנ כוה אמר אדוני יהוה הנה עבדי יואכלו ואתמה תרעבו
> עבדי ישתו ואתמ תצמאו הנה עבדי ישמחו ואתמ תבשו> הנה עבדי ישתו ואתמה תצמאו הנה עבדי ישמחו ואתמה תב
 >ושו
14הנה עבדי ירנו מטוב לב ואתמ תצעקו מכאב לב ומשבר רוח14הנה עבדי ירננו בטוב לב ואתמה תזעקו מכאוב לב ומשברו
> תילילו>נ רוח תילילו
15והנחתמ שמכמ לשבועה לבחירי והמיתכ אדני יהוה ולעבדיו15והנחתמה שמכמה לשבועה לבחירי והמיתכה אדוני יהוה תמי
> יקרא שמ אחר>ד
16אשר המתברכ בארצ יתברכ באלהי אמנ והנשבע בארצ ישבע ב16והיה הנשבע באלוהי אמנ והנשבע בארצ ישבע באלוהי אמנ 
>אלהי אמנ כי נשכחו הצרות הראשנות וכי נסתרו מעיני>כיא נשכחו הצרות הרישונות וכיא נסתרו מעיני
17כי הנני בורא שמימ חדשימ וארצ חדשה ולא תזכרנה הראשנ17כיא הנני בורא שמימ חדשימ וארצ חדשה ולוא תזכרנה הרי
>ות ולא תעלינה על לב>שונות ולוא תעלינא על לב
18כי אמ שישו וגילו עדי עד אשר אני בורא כי הנני בורא 18כיא אמ שיש וגיל עדי עד אשר אני בורא כיא הנני בורא 
>את ירושלמ גילה ועמה משוש>את ירושלימ גילה ועמה משוש
19וגלתי בירושלמ וששתי בעמי ולא ישמע בה עוד קול בכי ו19וגלתי בירושלימ וששתי בעמיא ולוא ישמע בה עוד קול בכ
>קול זעקה>י וקול זעקה
20לא יהיה משמ עוד עול ימימ וזקנ אשר לא ימלא את ימיו 20ולוא יהיה משמה עוד עויל ימימ וזקנ אשר לוא ימלה את 
>כי הנער בנ מאה שנה ימות והחוטא בנ מאה שנה יקלל>ימיו כיא הנער בנ מאה שנה ימות והחוטא בנ מאה שנה יק
 >ולל
21ובנו בתימ וישבו ונטעו כרמימ ואכלו פרימ21ובנו בתימ וישבו ונטעו כרמימ ואכלו את פריאמ
22לא יבנו ואחר ישב לא יטעו ואחר יאכל כי כימי העצ ימי22לוא יבנו ואחר ישב לוא יטעו ואחר יואכל כיא כימי עצ 
> עמי ומעשה ידיהמ יבלו בחירי>ימי עמיא ומעשה ידיהמה יבלו בחירי
t1נדרשׁתי ללוא שׁאלו נמצאתי ללא בקשׁני אמרתי הנני הנt1נדרשתי ללוא שאלוני נמציתי ללוא בקשוני אמרתי הנני ה
>ני אל גוי לא קרא בשׁמי >נני אל גוי לוא קרא בשמיא
2פרשׂתי ידי כל היום אל עם סורר ההלכים הדרך לא טוב א2פרשתי ידי כול היומ אל עמ סורה ההולכימ הדרכ לואטוב 
>חר מחשׁבתיהם >אחר מחשבותיהמה
3העם המכעיסים אותי על פני תמיד זבחים בגנות ומקטרים 3העמ המכעיסימ אותי על פני תמיד המה זובחימ בגנות וינ
>על הלבנים >קו ידימ על האבנימ
4הישׁבים בקברים ובנצורים ילינו האכלים בשׂר החזיר ופ4היושבימ בקברימ ובנצירימ ילינו האוכלימ בשר החוזיר ו
>רק פגלים כליהם >מרק פגולימ בכליהמה
5האמרים קרב אליך אל תגשׁ בי כי קדשׁתיך אלה עשׁן באפ5האומרימ קרב אליכה אל תגע ביא קדשתיכה אלה עשנ באפי 
>י אשׁ יקדת כל היום >אש יוקדת כול היומ
6הנה כתובה לפני לא אחשׂה כי אם שׁלמתי ושׁלמתי על חי6הנה כתובה לפני לוא אחשה כיא אמ שלמתי ושלמתי אל חיק
>קם >מ
7עונתיכם ועונת אבותיכם יחדו אמר יהוה אשׁר קטרו על ה7עוונותיכמה ועונות אבותיכמה יחדו אמר יהוה אשר קטורו
>הרים ועל הגבעות חרפוני ומדתי פעלתם ראשׁנה על חיקם > על הרימ ועל הגבעות חרפוני ומדותי פועלתמה רישונה א
>ס >ל חיקמה
8כה׀ אמר יהוה כאשׁר ימצא התירושׁ באשׁכול ואמר אל תש8כוה אמר יהוה כאשר ימצא התירוש באשכול ויואמר אל תשח
>ׁחיתהו כי ברכה בו כן אעשׂה למען עבדי לבלתי השׁחית >יתוהי כיא ברכה בוא כנ אעשה למענ עבדי לבלתי השחית ה
>הכל >כול
9והוצאתי מיעקב זרע ומיהודה יורשׁ הרי וירשׁוה בחירי 9והוציתי מיעקוב זרע ומיהודה ירש הרי וירשוהי בחירי ו
>ועבדי ישׁכנו שׁמה >עבדי ישכונו שמה
10והיה השׁרון לנוה צאן ועמק עכור לרבץ בקר לעמי אשׁר 10והיאה השרונ לנוי צואנ ועמק עכור למרבצ בקר לעמי אשר
>דרשׁוני > דרשוני
11ואתם עזבי יהוה השׁכחים את הר קדשׁי הערכים לגד שׁלח11ואתמה עוזבי יהוה השכחימ את הר קודשי העורכימ לגד שו
>ן והממלאים למני ממסך >לחנ וממלאימ למני מסכה
12ומניתי אתכם לחרב וכלכם לטבח תכרעו יען קראתי ולא ענ12ומניתי אתכמה לחרב וכולכמה לטבחה תכרעו יענ קראתי ול
>יתם דברתי ולא שׁמעתם ותעשׂו הרע בעיני ובאשׁר לא חפ>וא עניתמה דברתי ולוא שמעתמה ותעשו הרע בעיני ובאשר 
>צתי בחרתם פ >לוא חפצתי בחרתמה
13לכן כה אמר׀ אדני יהוה הנה עבדי׀ יאכלו ואתם תרעבו ה13לכנ כוה אמר אדוני יהוה הנה עבדי יואכלו ואתמה תרעבו
>נה עבדי ישׁתו ואתם תצמאו הנה עבדי ישׂמחו ואתם תבשׁ> הנה עבדי ישתו ואתמה תצמאו הנה עבדי ישמחו ואתמה תב
>ו >ושו
14הנה עבדי ירנו מטוב לב ואתם תצעקו מכאב לב ומשׁבר רו14הנה עבדי ירננו בטוב לב ואתמה תזעקו מכאוב לב ומשברו
>ח תילילו >נ רוח תילילו
15והנחתם שׁמכם לשׁבועה לבחירי והמיתך אדני יהוה ולעבד15והנחתמה שמכמה לשבועה לבחירי והמיתכה אדוני יהוה תמי
>יו יקרא שׁם אחר >ד
16אשׁר המתברך בארץ יתברך באלהי אמן והנשׁבע בארץ ישׁב16והיה הנשבע באלוהי אמנ והנשבע בארצ ישבע באלוהי אמנ 
>ע באלהי אמן כי נשׁכחו הצרות הראשׁנות וכי נסתרו מעי>כיא נשכחו הצרות הרישונות וכיא נסתרו מעיני
>ני  
17כי הנני בורא שׁמים חדשׁים וארץ חדשׁה ולא תזכרנה הר17כיא הנני בורא שמימ חדשימ וארצ חדשה ולוא תזכרנה הרי
>אשׁנות ולא תעלינה על לב >שונות ולוא תעלינא על לב
18כי אם שׂישׂו וגילו עדי עד אשׁר אני בורא כי הנני בו18כיא אמ שיש וגיל עדי עד אשר אני בורא כיא הנני בורא 
>רא את ירושׁלם גילה ועמה משׂושׂ >את ירושלימ גילה ועמה משוש
19וגלתי בירושׁלם ושׂשׂתי בעמי ולא ישׁמע בה עוד קול ב19וגלתי בירושלימ וששתי בעמיא ולוא ישמע בה עוד קול בכ
>כי וקול זעקה >י וקול זעקה
20לא יהיה משׁם עוד עול ימים וזקן אשׁר לא ימלא את ימי20ולוא יהיה משמה עוד עויל ימימ וזקנ אשר לוא ימלה את 
>ו כי הנער בן מאה שׁנה ימות והחוטא בן מאה שׁנה יקלל>ימיו כיא הנער בנ מאה שנה ימות והחוטא בנ מאה שנה יק
> >ולל
21ובנו בתים וישׁבו ונטעו כרמים ואכלו פרים 21ובנו בתימ וישבו ונטעו כרמימ ואכלו את פריאמ
22לא יבנו ואחר ישׁב לא יטעו ואחר יאכל כי כימי העץ ימ22לוא יבנו ואחר ישב לוא יטעו ואחר יואכל כיא כימי עצ 
>י עמי ומעשׂה ידיהם יבלו בחירי >ימי עמיא ומעשה ידיהמה יבלו בחירי
23לא ייגעו לריק ולא ילדו לבהלה כי זרע ברוכי יהוה המה23לוא יגעו לריק ולוא ילדו לבהלה כיא זרע ברכ יהוה המה
> וצאצאיהמ אתמ> וצאצאיהמה אתמ
24והיה טרמ יקראו ואני אענה עוד המ מדברימ ואני אשמע24והיה טרמ יקראו ואני אענה עוד המה מדברימ ואני אשמע
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>מו לא ירעו ולא ישחיתו בכל הר קדשי אמר יהוה>ו לוא ירעו ולוא ישחיתו בכול הר קודשי אמר יהוה
> וצאצאיהם אתם > וצאצאיהמה אתמ
24והיה טרם יקראו ואני אענה עוד הם מדברים ואני אשׁמע 24והיה טרמ יקראו ואני אענה עוד המה מדברימ ואני אשמע
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>חמו לא ירעו ולא ישׁחיתו בכל הר קדשׁי אמר יהוה ס >ו לוא ירעו ולוא ישחיתו בכול הר קודשי אמר יהוה
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Isaiah 66 MT
Isaiah 66 1QIsaa
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>ר תבנו לי ואי זה מקומ מנוחתי>שר תבנו ליא ואיזה מקומ מנוחתי
2ואת כל אלה ידי עשתה ויהיו כל אלה נאמ יהוה ואל זה א2ואת כול אלה ידי עשתה והיו כול אלה נואמ יהוה ואל זה
>ביט אל עני ונכה רוח וחרד על דברי> אביט אל עניא ונכאי רוח והחורד לדברי
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>יר מזכיר לבנה מברכ אונ גמ המה בחרו בדרכיהמ ובשקוצי>חוזיר מזכיר לבונה מברכ אונ גמ המה בחרו בדרכיהמה וב
>המ נפשמ חפצה>שקוציהמה נפשמה חפצה
4גמ אני אבחר בתעלליהמ ומגורתמ אביא להמ יענ קראתי וא4גמ אני אבחר בתעלוליהמה ובמגורותיהמה אביא להמה יענ 
>ינ עונה דברתי ולא שמעו ויעשו הרע בעיני ובאשר לא חפ>קראתי ואינ עונה דברתי ולוא שמעו ויעשו את הרע בעיני
>צתי בחרו> ובאשר לוא חפצתי בחרו
5שמעו דבר יהוה החרדימ אל דברו אמרו אחיכמ שנאיכמ מנד5שמעו דבר יהוה החרדימ אל דבריו אמרו אחיכמה שונאיכמה
>יכמ למענ שמי יכבד יהוה ונראה בשמחתכמ והמ יבשו> מנדיכמ למענ שמי יכבד יהוה יראה בשמחתכמה והמה יבוש
 >ו
6קול שאונ מעיר קול מהיכל קול יהוה משלמ גמול לאיביו6קול שאונ בעיר קול מהיכל קול יהוה משלמ גמול לאויביו
7בטרמ תחיל ילדה בטרמ יבוא חבל לה והמליטה זכר7בטרמ תחיל ילדה בטרמ יבוא חבל לה המליטה זכר
8מי שמע כזאת מי ראה כאלה היוחל ארצ ביומ אחד אמ יולד8מיא שמע כזואת ומיא יראה כאלה התחיל ארצ ביומ אחד אמ
> גוי פעמ אחת כי חלה גמ ילדה ציונ את בניה> יולד גוי פעמ אחת כיא חלה גמ ילדה ציונ את בניהא
9האני אשביר ולא אוליד יאמר יהוה אמ אני המוליד ועצרת9האני אשביר ולוא אוליד יואמר יהוה אמ אניא המוליד וא
>י אמר אלהיכ>עצורה אמר אלוהיכ
10שמחו את ירושלמ וגילו בה כל אהביה שישו אתה משוש כל 10שמחו את ירושלימ וגילו בהא כול אוהביהא שישו אתה משו
>המתאבלימ עליה>ש כול המתאבלימ עליהא
11למענ תינקו ושבעתמ משד תנחמיה למענ תמצו והתענגתמ מז11למענ תינקו ושבעתמה משד תנחומיהא למענ תמוצו והתענגת
>יז כבודה>מה ממזוז כבודה
12כי כה אמר יהוה הנני נטה אליה כנהר שלומ וכנחל שוטפ 12כוה אמר יהוה הנני נוטה אליהא כנהר שלומ וכנחל שוטפ 
>כבוד גוימ וינקתמ על צד תנשאו ועל ברכימ תשעשעו>כבוד גואימ ויונקותיהמה על צד תנשינה ועל בורכימ תשת
 >עשעו
13כאיש אשר אמו תנחמנו כנ אנכי אנחמכמ ובירושלמ תנחמו13כאיש אשר אמו תנחמנו כנ אנוכי אנחמכמה ובירושלימ תתנ
 >חמו
14וראיתמ ושש לבכמ ועצמותיכמ כדשא תפרחנה ונודעה יד יה14וראיתמה ושש לבכמה ועצמותיכמה כדשא תפרחנה ונודע יד 
>וה את עבדיו וזעמ את איביו>יהוה את עבדיו וזעמ את איביו
15כי הנה יהוה באש יבוא וכסופה מרכבתיו להשיב בחמה אפו15כיא הנה יהוה באש יבוא ובסופה מרכבותיו להשיב בחמה א
> וגערתו בלהבי אש>פו אפו וגערתיו בלהבי אש
16כי באש יהוה נשפט ובחרבו את כל בשר ורבו חללי יהוה16כיא באש יהוה יבוא לשפוט ובחרבו את כול הבשר ורבו חל
 >ליו
17המתקדשימ והמטהרימ אל הגנות אחר אחת בתוכ אכלי בשר ה17המתקדשימ והמטהרימ אל הגנות אחר אחת בתוכ אוכלי בשר 
>חזיר והשקצ והעכבר יחדו יספו נאמ יהוה>החוזיר והשקוצ והעכבר יחדיו אמר יהוה
18ואנכי מעשיהמ ומחשבתיהמ באה לקבצ את כל הגוימ והלשנו18ואנוכי מעשיהמה ומחשבותיהמה באו לקבצ את כול הגואימ 
>ת ובאו וראו את כבודי>והלשונות ובאו וראו את כבודי
19ושמתי בהמ אות ושלחתי מהמ פליטימ אל הגוימ תרשיש פול19ושמתי בהמה אותות ושלחתי מהמה פליטימ אל הגואימ תרשי
> ולוד משכי קשת תבל ויונ האיימ הרחקימ אשר לא שמעו א>ש פול ולוד משוכ קשת תובל ויואנ האימ הרחוקימ אשר לו
>ת שמעי ולא ראו את כבודי והגידו את כבודי בגוימ>א שמעו את שמעי ולוא ראוו את כבודי והגידו את כבודי 
 >בגואימ
20והביאו את כל אחיכמ מכל הגוימ מנחה ליהוה בסוסימ ובר20והביאו את כול כול אחיכמה מכל הגואימ מנחה ליהוה בסו
>כב ובצבימ ובפרדימ ובכרכרות על הר קדשי ירושלמ אמר י>סימ וברכבמ ובצובימ וב ובפרדימ ובכורכובות אל הר קדש
>הוה כאשר יביאו בני ישראל את המנחה בכלי טהור בית יה>י ירושלימ אמר יהוה כאשר יביאו בני ישראל את המנחה ב
>וה>כלי טהור בית יהוה
21וגמ מהמ אקח לכהנימ ללוימ אמר יהוה21וגמ מהמה אקח ליא לכוהנימ ללויימ אמר יהוה
22כי כאשר השמימ החדשימ והארצ החדשה אשר אני עשה עמדימ22כיא כאשר השמימ חדשימ והארצ החדשה אשר אני עושה עומד
> לפני נאמ יהוה כנ יעמד זרעכמ ושמכמ>ימ לפני נואמ יהוה כנ יעמוד זרעכמה ושמכמה
23והיה מדי חדש בחדשו ומדי שבת בשבתו יבוא כל בשר להשת23והיה מדי חודש בחודשו ומדי שבת בשבתה יבוא כול הבשר 
>חות לפני אמר יהוה>להשתחות לפני אמר יהוה
24ויצאו וראו בפגרי האנשימ הפשעימ בי כי תולעתמ לא תמו24ויצאו וראו בפגרי האנשימ הפושעימ ביא כיא תולעתמ לוא
>ת ואשמ לא תכבה והיו דראונ לכל בשר> תמות ואשהמה לוא תכבה והיו דראונ לכול הבשר
t1כה אמר יהוה השׁמים כסאי והארץ הדם רגלי אי זה בית אt1כוה אמר יהוה השמימ כסאי והארצ הדומ רגלי איזה בית א
>שׁר תבנו לי ואי זה מקום מנוחתי >שר תבנו ליא ואיזה מקומ מנוחתי
2ואת כל אלה ידי עשׂתה ויהיו כל אלה נאם יהוה ואל זה 2ואת כול אלה ידי עשתה והיו כול אלה נואמ יהוה ואל זה
>אביט אל עני ונכה רוח וחרד על דברי > אביט אל עניא ונכאי רוח והחורד לדברי
3שׁוחט השׁור מכה אישׁ זובח השׂה ערף כלב מעלה מנחה ד3שוחט השור כמכה איש זובח השא עורפ כלב מעלה מנחה דמ 
>ם חזיר מזכיר לבנה מברך און גם המה בחרו בדרכיהם ובש>חוזיר מזכיר לבונה מברכ אונ גמ המה בחרו בדרכיהמה וב
>ׁקוציהם נפשׁם חפצה >שקוציהמה נפשמה חפצה
4גם אני אבחר בתעלליהם ומגורתם אביא להם יען קראתי וא4גמ אני אבחר בתעלוליהמה ובמגורותיהמה אביא להמה יענ 
>ין עונה דברתי ולא שׁמעו ויעשׂו הרע בעיני ובאשׁר לא>קראתי ואינ עונה דברתי ולוא שמעו ויעשו את הרע בעיני
> חפצתי בחרו ס > ובאשר לוא חפצתי בחרו
5שׁמעו דבר יהוה החרדים אל דברו אמרו אחיכם שׂנאיכם מ5שמעו דבר יהוה החרדימ אל דבריו אמרו אחיכמה שונאיכמה
>נדיכם למען שׁמי יכבד יהוה ונראה בשׂמחתכם והם יבשׁו> מנדיכמ למענ שמי יכבד יהוה יראה בשמחתכמה והמה יבוש
> >ו
6קול שׁאון מעיר קול מהיכל קול יהוה משׁלם גמול לאיבי6קול שאונ בעיר קול מהיכל קול יהוה משלמ גמול לאויביו
>ו  
7בטרם תחיל ילדה בטרם יבוא חבל לה והמליטה זכר 7בטרמ תחיל ילדה בטרמ יבוא חבל לה המליטה זכר
8מי שׁמע כזאת מי ראה כאלה היוחל ארץ ביום אחד אם יול8מיא שמע כזואת ומיא יראה כאלה התחיל ארצ ביומ אחד אמ
>ד גוי פעם אחת כי חלה גם ילדה ציון את בניה > יולד גוי פעמ אחת כיא חלה גמ ילדה ציונ את בניהא
9האני אשׁביר ולא אוליד יאמר יהוה אם אני המוליד ועצר9האני אשביר ולוא אוליד יואמר יהוה אמ אניא המוליד וא
>תי אמר אלהיך ס >עצורה אמר אלוהיכ
10שׂמחו את ירושׁלם וגילו בה כל אהביה שׂישׂו אתה משׂו10שמחו את ירושלימ וגילו בהא כול אוהביהא שישו אתה משו
>שׂ כל המתאבלים עליה >ש כול המתאבלימ עליהא
11למען תינקו ושׂבעתם משׁד תנחמיה למען תמצו והתענגתם 11למענ תינקו ושבעתמה משד תנחומיהא למענ תמוצו והתענגת
>מזיז כבודה ס >מה ממזוז כבודה
12כי כה׀ אמר יהוה הנני נטה אליה כנהר שׁלום וכנחל שׁו12כוה אמר יהוה הנני נוטה אליהא כנהר שלומ וכנחל שוטפ 
>טף כבוד גוים וינקתם על צד תנשׂאו ועל ברכים תשׁעשׁע>כבוד גואימ ויונקותיהמה על צד תנשינה ועל בורכימ תשת
>ו >עשעו
13כאישׁ אשׁר אמו תנחמנו כן אנכי אנחמכם ובירושׁלם תנח13כאיש אשר אמו תנחמנו כנ אנוכי אנחמכמה ובירושלימ תתנ
>מו >חמו
14וראיתם ושׂשׂ לבכם ועצמותיכם כדשׁא תפרחנה ונודעה יד14וראיתמה ושש לבכמה ועצמותיכמה כדשא תפרחנה ונודע יד 
> יהוה את עבדיו וזעם את איביו >יהוה את עבדיו וזעמ את איביו
15כי הנה יהוה באשׁ יבוא וכסופה מרכבתיו להשׁיב בחמה א15כיא הנה יהוה באש יבוא ובסופה מרכבותיו להשיב בחמה א
>פו וגערתו בלהבי אשׁ >פו אפו וגערתיו בלהבי אש
16כי באשׁ יהוה נשׁפט ובחרבו את כל בשׂר ורבו חללי יהו16כיא באש יהוה יבוא לשפוט ובחרבו את כול הבשר ורבו חל
>ה >ליו
17המתקדשׁים והמטהרים אל הגנות אחר אחד בתוך אכלי בשׂר17המתקדשימ והמטהרימ אל הגנות אחר אחת בתוכ אוכלי בשר 
> החזיר והשׁקץ והעכבר יחדו יספו נאם יהוה >החוזיר והשקוצ והעכבר יחדיו אמר יהוה
18ואנכי מעשׂיהם ומחשׁבתיהם באה לקבץ את כל הגוים והלש18ואנוכי מעשיהמה ומחשבותיהמה באו לקבצ את כול הגואימ 
>ׁנות ובאו וראו את כבודי >והלשונות ובאו וראו את כבודי
19ושׂמתי בהם אות ושׁלחתי מהם׀ פליטים אל הגוים תרשׁיש19ושמתי בהמה אותות ושלחתי מהמה פליטימ אל הגואימ תרשי
>ׁ פול ולוד משׁכי קשׁת תבל ויון האיים הרחקים אשׁר ל>ש פול ולוד משוכ קשת תובל ויואנ האימ הרחוקימ אשר לו
>א שׁמעו את שׁמעי ולא ראו את כבודי והגידו את כבודי >א שמעו את שמעי ולוא ראוו את כבודי והגידו את כבודי 
>בגוים >בגואימ
20והביאו את כל אחיכם מכל הגוים׀ מנחה׀ ליהוה בסוסים ו20והביאו את כול כול אחיכמה מכל הגואימ מנחה ליהוה בסו
>ברכב ובצבים ובפרדים ובכרכרות על הר קדשׁי ירושׁלם א>סימ וברכבמ ובצובימ וב ובפרדימ ובכורכובות אל הר קדש
>מר יהוה כאשׁר יביאו בני ישׂראל את המנחה בכלי טהור >י ירושלימ אמר יהוה כאשר יביאו בני ישראל את המנחה ב
>בית יהוה >כלי טהור בית יהוה
21וגם מהם אקח לכהנים ללוים אמר יהוה 21וגמ מהמה אקח ליא לכוהנימ ללויימ אמר יהוה
22כי כאשׁר השׁמים החדשׁים והארץ החדשׁה אשׁר אני עשׂה22כיא כאשר השמימ חדשימ והארצ החדשה אשר אני עושה עומד
> עמדים לפני נאם יהוה כן יעמד זרעכם ושׁמכם >ימ לפני נואמ יהוה כנ יעמוד זרעכמה ושמכמה
23והיה מדי חדשׁ בחדשׁו ומדי שׁבת בשׁבתו יבוא כל בשׂר23והיה מדי חודש בחודשו ומדי שבת בשבתה יבוא כול הבשר 
> להשׁתחות לפני אמר יהוה >להשתחות לפני אמר יהוה
24ויצאו וראו בפגרי האנשׁים הפשׁעים בי כי תולעתם לא ת24ויצאו וראו בפגרי האנשימ הפושעימ ביא כיא תולעתמ לוא
>מות ואשׁם לא תכבה והיו דראון לכל בשׂר > תמות ואשהמה לוא תכבה והיו דראונ לכול הבשר
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Isaiah 37 MT
Isaiah 37 1QIsaa - t1ויהי כשמע המלכ חזקיהו ויקרע את בגדיו ויתכס בשק ויבt1ויהי כשמוע חוזקיה המלכ ויקרע את בגדיו ויתכס בשק וי - >א בית יהוה>בוא בית יהוה - 2וישלח את אליקימ אשר על הבית ואת שבנא הסופר ואת זקנ2וישלח את אליקימ אשר על הבית ואת שובנא הסופר ואת זק - >י הכהנימ מתכסימ בשקימ אל ישעיהו בנ אמוצ הנביא>ני הכוהנימ מתכסימ בשקימ אל ישעיה בנ אמוצ הנביא - 3ויאמרו אליו כה אמר חזקיהו יומ צרה ותוכחה ונאצה היו3ויואמרו אליו כוה אמר יחזקיה יומ צרה ותוכחה ונאצה ה - >מ הזה כי באו בנימ עד משבר וכח אינ ללדה>יומ הזה כיא באו בנימ עד משבר וכוח אינ ללדה - 4אולי ישמע יהוה אלהיכ את דברי רב שקה אשר שלחו מלכ א4אולי ישמע יהוה אלוהיכה את דברי רב שקה אשר שלחו מלכ - >שור אדניו לחרפ אלהימ חי והוכיח בדברימ אשר שמע יהוה> אשור אדוניו לחרפ אלוהימ חי והוכיח בדברימ אשר שמע  - > אלהיכ ונשאת תפלה בעד השארית הנמצאה>יהוה אלוהיכה ונשאתה תפלה בעד השארית הנמצאימ בעיר ה -  >זואת - 5ויבאו עבדי המלכ חזקיהו אל ישעיהו5ויבואו עבדי המלכ יחוזקיה אל ישעיה - 6ויאמר אליהמ ישעיהו כה תאמרונ אל אדניכמ כה אמר יהוה6א ויואמר להמה ישעיה כוה תואמרו אל אדוניכמה כוה אמר - > אל תירא מפני הדברימ אשר שמעת אשר גדפו נערי מלכ אש> יהוה אל תירא מפני הדברימ ב אשר שמעתה אשר גדפו נער - >ור אותי>י מלכ אשור אותי - 7הנני נותנ בו רוח ושמע שמועה ושב אל ארצו והפלתיו בח7הנני נותנ רוח בוא ושמע שמועה ושב לארצו והפלתיו בחר - >רב בארצו>ב בארצו - 8וישב רב שקה וימצא את מלכ אשור נלחמ על לבנה כי שמע 8וישוב רב שקה וימצא את מלכ אשור נלחמ על לבנה כיא שמ - >כי נסע מלכיש>ע כיא נסע מלכיש - 9וישמע על תרהקה מלכ כוש לאמר יצא להלחמ אתכ וישמע וי9וישמע אל תרהקה מלכ כוש לאמור יצא להלחמ אתכה וישמע  - >שלח מלאכימ אל חזקיהו לאמר>וישוב וישלח מלאכימ אל יחוזקיה לאמור - 10כה תאמרונ אל חזקיהו מלכ יהודה לאמר אל ישאכ אלהיכ א10כוה תומרו אל חוזקיה מלכ יהודה לאמור אל ישייכה אלוה - >שר אתה בוטח בו לאמר לא תנתנ ירושלמ ביד מלכ אשור>יכה אשר אתה בוטח בוא לאמור לוא תנתנ ירושלימ ביד מל -  >כ אשור - 11הנה אתה שמעת אשר עשו מלכי אשור לכל הארצות להחריממ 11הנה אתה שמעתה את אשר עשו מלכי אשור לכול הארצות להח - >ואתה תנצל>ריממ ואתה תנצל - 12ההצילו אותמ אלהי הגוימ אשר השחיתו אבותי את גוזנ וא12ההצילו אותמ אלוהי הגואימ אשר השחיתו אבותי את גוזנ  - >ת חרנ ורצפ ובני עדנ אשר בתלשר>ואת חרנ ורצפ ובני עדנ אשר בתלשר - 13איה מלכ חמת ומלכ ארפד ומלכ לעיר ספרוימ הנע ועוה13איה מלכ חמת ומלכ ארפד ומלכ לעיר וספריימ ונע ועוה ו + t1ויהי כשׁמע המלך חזקיהו ויקרע את בגדיו ויתכס בשׂק וt1ויהי כשמוע חוזקיה המלכ ויקרע את בגדיו ויתכס בשק וי + >יבא בית יהוה >בוא בית יהוה + 2וישׁלח את אליקים אשׁר על הבית ואת׀ שׁבנא הסופר ואת2וישלח את אליקימ אשר על הבית ואת שובנא הסופר ואת זק + > זקני הכהנים מתכסים בשׂקים אל ישׁעיהו בן אמוץ הנבי>ני הכוהנימ מתכסימ בשקימ אל ישעיה בנ אמוצ הנביא + >א   + 3ויאמרו אליו כה אמר חזקיהו יום צרה ותוכחה ונאצה היו3ויואמרו אליו כוה אמר יחזקיה יומ צרה ותוכחה ונאצה ה + >ם הזה כי באו בנים עד משׁבר וכח אין ללדה >יומ הזה כיא באו בנימ עד משבר וכוח אינ ללדה + 4אולי ישׁמע יהוה אלהיך את׀ דברי רב שׁקה אשׁר שׁלחו 4אולי ישמע יהוה אלוהיכה את דברי רב שקה אשר שלחו מלכ + >מלך אשׁור׀ אדניו לחרף אלהים חי והוכיח בדברים אשׁר > אשור אדוניו לחרפ אלוהימ חי והוכיח בדברימ אשר שמע  + >שׁמע יהוה אלהיך ונשׂאת תפלה בעד השׁארית הנמצאה >יהוה אלוהיכה ונשאתה תפלה בעד השארית הנמצאימ בעיר ה +  >זואת + 5ויבאו עבדי המלך חזקיהו אל ישׁעיהו 5ויבואו עבדי המלכ יחוזקיה אל ישעיה + 6ויאמר אליהם ישׁעיהו כה תאמרון אל אדניכם כה׀ אמר יה6א ויואמר להמה ישעיה כוה תואמרו אל אדוניכמה כוה אמר + >וה אל תירא מפני הדברים אשׁר שׁמעת אשׁר גדפו נערי מ> יהוה אל תירא מפני הדברימ ב אשר שמעתה אשר גדפו נער + >לך אשׁור אותי >י מלכ אשור אותי + 7הנני נותן בו רוח ושׁמע שׁמועה ושׁב אל ארצו והפלתיו7הנני נותנ רוח בוא ושמע שמועה ושב לארצו והפלתיו בחר + > בחרב בארצו >ב בארצו + 8וישׁב רב שׁקה וימצא את מלך אשׁור נלחם על לבנה כי ש8וישוב רב שקה וימצא את מלכ אשור נלחמ על לבנה כיא שמ + >ׁמע כי נסע מלכישׁ >ע כיא נסע מלכיש + 9וישׁמע על תרהקה מלך כושׁ לאמר יצא להלחם אתך וישׁמע9וישמע אל תרהקה מלכ כוש לאמור יצא להלחמ אתכה וישמע  + > וישׁלח מלאכים אל חזקיהו לאמר >וישוב וישלח מלאכימ אל יחוזקיה לאמור + 10כה תאמרון אל חזקיהו מלך יהודה לאמר אל ישׁאך אלהיך 10כוה תומרו אל חוזקיה מלכ יהודה לאמור אל ישייכה אלוה + >אשׁר אתה בוטח בו לאמר לא תנתן ירושׁלם ביד מלך אשׁו>יכה אשר אתה בוטח בוא לאמור לוא תנתנ ירושלימ ביד מל + >ר >כ אשור + 11הנה׀ אתה שׁמעת אשׁר עשׂו מלכי אשׁור לכל הארצות להח11הנה אתה שמעתה את אשר עשו מלכי אשור לכול הארצות להח + >רימם ואתה תנצל >ריממ ואתה תנצל + 12ההצילו אותם אלהי הגוים אשׁר השׁחיתו אבותי את גוזן 12ההצילו אותמ אלוהי הגואימ אשר השחיתו אבותי את גוזנ  + >ואת חרן ורצף ובני עדן אשׁר בתלשׂר >ואת חרנ ורצפ ובני עדנ אשר בתלשר + 13איה מלך חמת ומלך ארפד ומלך לעיר ספרוים הנע ועוה 13איה מלכ חמת ומלכ ארפד ומלכ לעיר וספריימ ונע ועוה ו  >שומרונ - 14ויקח חזקיהו את הספרימ מיד המלאכימ ויקראהו ויעל בית14ויקח חוזקיה את הספרימ מיד המלאכימ ויקראמ ויעלה בית - > יהוה ויפרשהו חזקיהו לפני יהוה> יהוה ויפרושה חוזקיה לפני יהוה - 15ויתפלל חזקיהו אל יהוה לאמר15ויתפלל חוזקיה אל יהוה לאמור - 16יהוה צבאות אלהי ישראל ישב הכרבימ אתה הוא האלהימ לב16יהוה צבאות אלוהי ישראל יושב הכרובימ אתה הואה האלוה - >דכ לכל ממלכות הארצ אתה עשית את השמימ ואת הארצ>ימ לבדכה לכול ממלכות הארצ אתה עשיתה את השמימ ואת ה -  >ארצ - 17הטה יהוה אזנכ ושמע פקח יהוה עינכ וראה ושמע את כל ד17הטא יהוה אוזנכה ושמעה פקח יהוה עיניכה וראה ושמע את - >ברי סנחריב אשר שלח לחרפ אלהימ חי> כול דברי סנחריב אשר שלח לחרפ אלוהימ חי - 18אמנמ יהוה החריבו מלכי אשור את כל הארצות ואת ארצמ18אמנמ יהוה החריבו מלכי אשור את כול הארצות - 19ונתנ את אלהיהמ באש כי לא אלהימ המה כי אמ מעשה ידי 19ויתנו את אלוהיהמה באש כיא לוא אלוהימ המה כיא אמ מע - >אדמ עצ ואבנ ויאבדומ>שי ידי אדמ עצ ואבנ ויאבדומ - 20ועתה יהוה אלהינו הושיענו מידו וידעו כל ממלכות הארצ20ועתה יהוה אלוהינו אושיענו מידו וידעו כול ממלכות הא - > כי אתה יהוה לבדכ>רצ כיא אתה יהוה אלוהימ לבדכה - 21וישלח ישעיהו בנ אמוצ אל חזקיהו לאמר כה אמר יהוה אל21וישלח ישעיה בנ אמוצ על יחוזקיה לאמור כוה אמר יהוה  - >הי ישראל אשר התפללת אלי אל סנחריב מלכ אשור>אלוהי ישראל אשר התפללתה אליו אל סרחריב מלכ אשור - 22זה הדבר אשר דבר יהוה עליו בזה לכ לעגה לכ בתולת בת 22זה הדבר אשר דבר יהוה עליו בזה לכה לעגה לכה בתולת ב - >ציונ אחריכ ראש הניעה בת ירושלמ>ת ציונ אחריכה ראושה הניעה בת ירושלימ - 23את מי חרפת וגדפת ועל מי הרימותה קול ותשא מרומ עיני23את מיא חרפתה וגדפתה ועל מיא הרימותה קול ותשא מרומ  - >כ אל קדוש ישראל>עיניכה אל קדוש ישראל - 24ביד עבדיכ חרפת אדני ותאמר ברב רכבי אני עליתי מרומ 24ביד עבדיכה חרפתה אדוני ותומר ברוב רכבי אני עליתי מ - >הרימ ירכתי לבנונ ואכרת קומת ארזיו מבחר ברשיו ואבוא>רומ הרימ ירכתי לבנונ ואכרותה קומת ארזיו מבחר ברושי - > מרומ קצו יער כרמלו>ו ואבוא מרומ קצו יער כרמליו - 25אני קרתי ושתיתי מימ ואחרב בכפ פעמי כל יארי מצור25אני קראתי ושתיתי מימ זרימ ואחריבה בכפ פעמי כול יאר + 14ויקח חזקיהו את הספרים מיד המלאכים ויקראהו ויעל בית14ויקח חוזקיה את הספרימ מיד המלאכימ ויקראמ ויעלה בית + > יהוה ויפרשׂהו חזקיהו לפני יהוה > יהוה ויפרושה חוזקיה לפני יהוה + 15ויתפלל חזקיהו אל יהוה לאמר 15ויתפלל חוזקיה אל יהוה לאמור + 16יהוה צבאות אלהי ישׂראל ישׁב הכרבים אתה הוא האלהים 16יהוה צבאות אלוהי ישראל יושב הכרובימ אתה הואה האלוה + >לבדך לכל ממלכות הארץ אתה עשׂית את השׁמים ואת הארץ >ימ לבדכה לכול ממלכות הארצ אתה עשיתה את השמימ ואת ה +  >ארצ + 17הטה יהוה׀ אזנך ושׁמע פקח יהוה עינך וראה ושׁמע את כ17הטא יהוה אוזנכה ושמעה פקח יהוה עיניכה וראה ושמע את + >ל דברי סנחריב אשׁר שׁלח לחרף אלהים חי > כול דברי סנחריב אשר שלח לחרפ אלוהימ חי + 18אמנם יהוה החריבו מלכי אשׁור את כל הארצות ואת ארצם 18אמנמ יהוה החריבו מלכי אשור את כול הארצות + 19ונתן את אלהיהם באשׁ כי לא אלהים המה כי אם מעשׂה יד19ויתנו את אלוהיהמה באש כיא לוא אלוהימ המה כיא אמ מע + >י אדם עץ ואבן ויאבדום >שי ידי אדמ עצ ואבנ ויאבדומ + 20ועתה יהוה אלהינו הושׁיענו מידו וידעו כל ממלכות האר20ועתה יהוה אלוהינו אושיענו מידו וידעו כול ממלכות הא + >ץ כי אתה יהוה לבדך >רצ כיא אתה יהוה אלוהימ לבדכה + 21וישׁלח ישׁעיהו בן אמוץ אל חזקיהו לאמר כה אמר יהוה 21וישלח ישעיה בנ אמוצ על יחוזקיה לאמור כוה אמר יהוה  + >אלהי ישׂראל אשׁר התפללת אלי אל סנחריב מלך אשׁור >אלוהי ישראל אשר התפללתה אליו אל סרחריב מלכ אשור + 22זה הדבר אשׁר דבר יהוה עליו בזה לך לעגה לך בתולת בת22זה הדבר אשר דבר יהוה עליו בזה לכה לעגה לכה בתולת ב + > ציון אחריך ראשׁ הניעה בת ירושׁלם >ת ציונ אחריכה ראושה הניעה בת ירושלימ + 23את מי חרפת וגדפת ועל מי הרימותה קול ותשׂא מרום עינ23את מיא חרפתה וגדפתה ועל מיא הרימותה קול ותשא מרומ  + >יך אל קדושׁ ישׂראל >עיניכה אל קדוש ישראל + 24ביד עבדיך חרפת׀ אדני ותאמר ברב רכבי אני עליתי מרום24ביד עבדיכה חרפתה אדוני ותומר ברוב רכבי אני עליתי מ + > הרים ירכתי לבנון ואכרת קומת ארזיו מבחר ברשׁיו ואב>רומ הרימ ירכתי לבנונ ואכרותה קומת ארזיו מבחר ברושי + >וא מרום קצו יער כרמלו >ו ואבוא מרומ קצו יער כרמליו + 25אני קרתי ושׁתיתי מים ואחרב בכף פעמי כל יארי מצור 25אני קראתי ושתיתי מימ זרימ ואחריבה בכפ פעמי כול יאר  >י מצור - 26הלוא שמעת למרחוק אותה עשיתי מימי קדמ ויצרתיה עתה ה26הלוא שמעתה למרחוק אותה עשיתי מימי קדמ יצרתיה עתה ה - >באתיה ותהי להשאות גלימ נצימ ערימ בצרות>ביאותיה ותהי לשאוות גלימ נצורימ ערימ בצורות - 27וישביהנ קצרי יד חתו ובשו היו עשב שדה וירק דשא חציר27ויושביהנה קצרי יד חתו ויבשו היו עשב שדה ירק דשה חצ - > גגות ושדמה לפני קמה>יר גגות הנשדפ לפני קדימ - 28ושבתכ וצאתכ ובואכ ידעתי ואת התרגזכ אלי28קומכה ושבתכה וצאתכה ובואכה ידעתיא ואת הרגזכה אלי - 29יענ התרגזכ אלי ושאננכ עלה באזני ושמתי חחי באפכ ומת29ושאננכה עלה באוזני ושמתי חחי באפכה ומתגי בשפאותיכה - >גי בשפתיכ והשיבתיכ בדרכ אשר באת בה> והשיבותיכה בדרכ אשר בתה בה - 30וזה לכ האות אכול השנה ספיח ובשנה השנית שחיס ובשנה 30וזה לכה האות אכולו השנה ספיח ובשנה השנית שעיס ובשנ - >השלישית זרעו וקצרו ונטעו כרמימ ואכלו פרימ>ה השלישית זרעו וקצורו ונטוע כרמימ ואכולו פרימ - 31ויספה פליטת בית יהודה הנשארה שרש למטה ועשה פרי למע31ואספה פליטת בית יהודה והנמצא שורש למטה ועשה פרי מע - >לה>לה - 32כי מירושלמ תצא שארית ופליטה מהר ציונ קנאת יהוה צבא32כיא מציונ תצא שארית ופליטא מירושלימ קנאת יהוה צבאו - >ות תעשה זאת>ת תעשה זואת - 33לכנ כה אמר יהוה אל מלכ אשור לא יבוא אל העיר הזאת ו33לכנ כוה אמר יהוה אל מלכ אשור לוא יבוא אל העיר הזאו - >לא יורה שמ חצ ולא יקדמנה מגנ ולא ישפכ עליה סללה>ת ולוא ישפוכ עליהא סוללה ולוא ירא שמ חצ ולוא יקדמנ -  >ה מגנ - 34בדרכ אשר בא בה ישוב ואל העיר הזאת לא יבוא נאמ יהוה34בדרכ אשר בא באה ישוב ואל העיר הזואת לוא יבוא נואומ -  > יהוה - 35וגנותי על העיר הזאת להושיעה למעני ולמענ דוד עבדי35וגנותי על העיר הזואת להושיעה למעני ולמענ דויד עבדי - 36ויצא מלאכ יהוה ויכה במחנה אשור מאה ושמנימ וחמשה אל36ויצא מלאכ יהוה ויכ במחנה אשור מאה ושמונימ וחמשא אל - >פ וישכימו בבקר והנה כלמ פגרימ מתימ>פ וישכימו בבוקר והנה כולמ פגרימ מיתימ - 37ויסע וילכ וישב סנחריב מלכ אשור וישב בנינוה37ויסע וילכ וישווב סנחריב מלכ אשור וישב בנינוה - 38ויהי הוא משתחוה בית נסרכ אלהיו ואדרמלכ ושראצר בניו38ויהי הואה משתחוה בבית נסרכ אלוהיו ואדרמלכ ושראוצר  - > הכהו בחרב והמה נמלטו ארצ אררט וימלכ אסר חדנ בנו ת>בניו הכהו בחרב והמה נמלטו ארצ הוררט וימלוכ אסרחודנ - >חתיו> בניו תחתיו + 26הלוא שׁמעת למרחוק אותה עשׂיתי מימי קדם ויצרתיה עתה26הלוא שמעתה למרחוק אותה עשיתי מימי קדמ יצרתיה עתה ה + > הבאתיה ותהי להשׁאות גלים נצים ערים בצרות >ביאותיה ותהי לשאוות גלימ נצורימ ערימ בצורות + 27וישׁביהן קצרי יד חתו ובשׁו היו עשׂב שׂדה וירק דשׁא27ויושביהנה קצרי יד חתו ויבשו היו עשב שדה ירק דשה חצ + > חציר גגות ושׁדמה לפני קמה >יר גגות הנשדפ לפני קדימ + 28ושׁבתך וצאתך ובואך ידעתי ואת התרגזך אלי 28קומכה ושבתכה וצאתכה ובואכה ידעתיא ואת הרגזכה אלי + 29יען התרגזך אלי ושׁאננך עלה באזני ושׂמתי חחי באפך ו29ושאננכה עלה באוזני ושמתי חחי באפכה ומתגי בשפאותיכה + >מתגי בשׂפתיך והשׁיבתיך בדרך אשׁר באת בה > והשיבותיכה בדרכ אשר בתה בה + 30וזה לך האות אכול השׁנה ספיח ובשׁנה השׁנית שׁחיס וב30וזה לכה האות אכולו השנה ספיח ובשנה השנית שעיס ובשנ + >שׁנה השׁלישׁית זרעו וקצרו ונטעו כרמים ואכול פרים >ה השלישית זרעו וקצורו ונטוע כרמימ ואכולו פרימ + 31ויספה פליטת בית יהודה הנשׁארה שׁרשׁ למטה ועשׂה פרי31ואספה פליטת בית יהודה והנמצא שורש למטה ועשה פרי מע + > למעלה >לה + 32כי מירושׁלם תצא שׁארית ופליטה מהר ציון קנאת יהוה צ32כיא מציונ תצא שארית ופליטא מירושלימ קנאת יהוה צבאו + >באות תעשׂה זאת ס >ת תעשה זואת + 33לכן כה אמר יהוה אל מלך אשׁור לא יבוא אל העיר הזאת 33לכנ כוה אמר יהוה אל מלכ אשור לוא יבוא אל העיר הזאו + >ולא יורה שׁם חץ ולא יקדמנה מגן ולא ישׁפך עליה סללה>ת ולוא ישפוכ עליהא סוללה ולוא ירא שמ חצ ולוא יקדמנ + > >ה מגנ + 34בדרך אשׁר בא בה ישׁוב ואל העיר הזאת לא יבוא נאם יה34בדרכ אשר בא באה ישוב ואל העיר הזואת לוא יבוא נואומ + >וה > יהוה + 35וגנותי על העיר הזאת להושׁיעה למעני ולמען דוד עבדי 35וגנותי על העיר הזואת להושיעה למעני ולמענ דויד עבדי + >ס   + 36ויצא׀ מלאך יהוה ויכה במחנה אשׁור מאה ושׁמנים וחמשׁ36ויצא מלאכ יהוה ויכ במחנה אשור מאה ושמונימ וחמשא אל + >ה אלף וישׁכימו בבקר והנה כלם פגרים מתים >פ וישכימו בבוקר והנה כולמ פגרימ מיתימ + 37ויסע וילך וישׁב סנחריב מלך אשׁור וישׁב בנינוה 37ויסע וילכ וישווב סנחריב מלכ אשור וישב בנינוה + 38ויהי הוא משׁתחוה בית׀ נסרך אלהיו ואדרמלך ושׂראצר ב38ויהי הואה משתחוה בבית נסרכ אלוהיו ואדרמלכ ושראוצר  + >ניו הכהו בחרב והמה נמלטו ארץ אררט וימלך אסר חדן בנ>בניו הכהו בחרב והמה נמלטו ארצ הוררט וימלוכ אסרחודנ + >ו תחתיו ס > בניו תחתיו \ No newline at end of file diff --git a/static/docs/tools/parallel/Isaiah-mt-1QIsaa_38.html b/static/docs/tools/parallel/Isaiah-mt-1QIsaa_38.html index 993913df..3a71b19d 100644 --- a/static/docs/tools/parallel/Isaiah-mt-1QIsaa_38.html +++ b/static/docs/tools/parallel/Isaiah-mt-1QIsaa_38.html @@ -30,48 +30,49 @@
Isaiah 38 MT
Isaiah 38 1QIsaa - t1בימימ ההמ חלה חזקיהו למות ויבוא אליו ישעיהו בנ אמוt1בימימ ההמה חלה יחוזקיה למות ויבוא אליו ישעיה בנ אמ - >צ הנביא ויאמר אליו כה אמר יהוה צו לביתכ כי מת אתה >וצ הנביא ויואמר אליו כוה אמר יהוה צוי לביתכה כיא מ - >ולא תחיה>ית אתה ולוא תחיה - 2ויסב חזקיהו פניו אל הקיר ויתפלל אל יהוה2ויסוב יחוזקיה פניו אל הקיר ויתפלל אל יהוה - 3ויאמר אנה יהוה זכר נא את אשר התהלכתי לפניכ באמת וב3ויואמר אנה יהוה זכורנא את אשר התהלכתי לפניכה באמת  - >לב שלמ והטוב בעיניכ עשיתי ויבכ חזקיהו בכי גדול>ובלבב שלמ והטוב בעיניכה עשיתי ויבכא יחוזקיה בכי גד -  >ול - 4ויהי דבר יהוה אל ישעיהו לאמר4ויהי דבר יהוה אל ישעיה לאמור - 5הלוכ ואמרת אל חזקיהו כה אמר יהוה אלהי דוד אביכ שמע5הלוכ ואמרתה אל יחוזקיה כוה אמר יהוה אלוהי דויד אבי - >תי את תפלתכ ראיתי את דמעתכ הנני יוספ על ימיכ חמש ע>כה שמעתי את תפלתכה וראיתי את דמעתכה הנני יוספ על י - >שרה שנה>מיכה חמש עשרה שנה - 6ומכפ מלכ אשור אצילכ ואת העיר הזאת וגנותי על העיר ה6ומכפ מלכ אשור אצילכה ואת העיר הזואת וגנותי על העיר - >זאת> הזואת למעני ולמענ דויד עבדי - 7וזה לכ האות מאת יהוה אשר יעשה יהוה את הדבר הזה אשר7וזה לכה האות מאת יהוה אשר יעשה יהוה את הדבר הזה אש - > דבר>ר דבר - 8הנני משיב את צל המעלות אשר ירדה במעלות אחז בשמש אח8הנני משיב את צל המעלות אשר ירדה במעלות עלית אחז את - >רנית עשר מעלות ותשב השמש עשר מעלות במעלות אשר ירדה> השמש אחורנית עשר מעלות ותשוב השמש עשר מעלות במעלו -  >ת אשר ירדה - 9מכתב לחזקיהו מלכ יהודה בחלתו ויחי מחליו9מכתב ליחוזקיה מלכ יהודה בחוליותיו ויחי מחוליו - 10אני אמרתי בדמי ימי אלכה בשערי שאול פקדתי יתר שנותי10אני אמרתי בדמי וימי אלכה בשערי שאול פקודתי ומר שנו -  >תי - 11אמרתי לא אראה יה יה בארצ החיימ לא אביט אדמ עוד עמ 11אמרתי לוא אראה יה בארצ חיימ ולוא אביט אדמ עוד עמ י - >יושבי חדל>ושבי חדל + t1בימים ההם חלה חזקיהו למות ויבוא אליו ישׁעיהו בן אמt1בימימ ההמה חלה יחוזקיה למות ויבוא אליו ישעיה בנ אמ + >וץ הנביא ויאמר אליו כה אמר יהוה צו לביתך כי מת אתה>וצ הנביא ויואמר אליו כוה אמר יהוה צוי לביתכה כיא מ + > ולא תחיה >ית אתה ולוא תחיה + 2ויסב חזקיהו פניו אל הקיר ויתפלל אל יהוה 2ויסוב יחוזקיה פניו אל הקיר ויתפלל אל יהוה + 3ויאמר אנה יהוה זכר נא את אשׁר התהלכתי לפניך באמת ו3ויואמר אנה יהוה זכורנא את אשר התהלכתי לפניכה באמת  + >בלב שׁלם והטוב בעיניך עשׂיתי ויבך חזקיהו בכי גדול >ובלבב שלמ והטוב בעיניכה עשיתי ויבכא יחוזקיה בכי גד + >ס >ול + 4ויהי דבר יהוה אל ישׁעיהו לאמר 4ויהי דבר יהוה אל ישעיה לאמור + 5הלוך ואמרת אל חזקיהו כה אמר יהוה אלהי דוד אביך שׁמ5הלוכ ואמרתה אל יחוזקיה כוה אמר יהוה אלוהי דויד אבי + >עתי את תפלתך ראיתי את דמעתך הנני יוסף על ימיך חמשׁ>כה שמעתי את תפלתכה וראיתי את דמעתכה הנני יוספ על י + > עשׂרה שׁנה >מיכה חמש עשרה שנה + 6ומכף מלך אשׁור אצילך ואת העיר הזאת וגנותי על העיר 6ומכפ מלכ אשור אצילכה ואת העיר הזואת וגנותי על העיר + >הזאת > הזואת למעני ולמענ דויד עבדי + 7וזה לך האות מאת יהוה אשׁר יעשׂה יהוה את הדבר הזה א7וזה לכה האות מאת יהוה אשר יעשה יהוה את הדבר הזה אש + >שׁר דבר >ר דבר + 8הנני משׁיב את צל המעלות אשׁר ירדה במעלות אחז בשׁמש8הנני משיב את צל המעלות אשר ירדה במעלות עלית אחז את + >ׁ אחרנית עשׂר מעלות ותשׁב השׁמשׁ עשׂר מעלות במעלות> השמש אחורנית עשר מעלות ותשוב השמש עשר מעלות במעלו + > אשׁר ירדה ס >ת אשר ירדה + 9מכתב לחזקיהו מלך יהודה בחלתו ויחי מחליו 9מכתב ליחוזקיה מלכ יהודה בחוליותיו ויחי מחוליו + 10אני אמרתי בדמי ימי אלכה בשׁערי שׁאול פקדתי יתר שׁנ10אני אמרתי בדמי וימי אלכה בשערי שאול פקודתי ומר שנו + >ותי >תי + 11אמרתי לא אראה יה יה בארץ החיים לא אביט אדם עוד עם 11אמרתי לוא אראה יה בארצ חיימ ולוא אביט אדמ עוד עמ י + >יושׁבי חדל >ושבי חדל 12דורי נסע ונגלה מני כאהל רעי קפדתי כארג חיי מדלה יב12דורי נסע יכלה מני כאוהל רעי ספרתי כאורג חיי מדלה י - >צעני מיומ עד לילה תשלימני>בצעני מיומ עד לילה תשלימני - 13שויתי עד בקר כארי כנ ישבר כל עצמותי מיומ עד לילה ת13שפותי עד בוקר כארי כנ ישבור כול עצמותי מיומ עד ליל - >שלימני>ה תשלימני - 14כסוס עגור כנ אצפצפ אהגה כיונה דלו עיני למרומ אדני 14כסוס עוגר כנ אצפצפ אהגה כיונא דלו עיני למרומ אדוני - >עשקה לי ערבני> עושקה לי וערבני - 15מה אדבר ואמר לי והוא עשה אדדה כל שנותי על מר נפשי15מה אדבר ואומר לוא והיאה עשה ליא אדודה כול שנותי על -  > מור נפשיא - 16אדני עליהמ יחיו ולכל בהנ חיי רוחי ותחלימני והחיני16אדוני עליהמה וחיו ולכול בהמה חיו רוחו ותחלימני והח + >צעני מיום עד לילה תשׁלימני >בצעני מיומ עד לילה תשלימני + 13שׁויתי עד בקר כארי כן ישׁבר כל עצמותי מיום עד לילה13שפותי עד בוקר כארי כנ ישבור כול עצמותי מיומ עד ליל + > תשׁלימני >ה תשלימני + 14כסוס עגור כן אצפצף אהגה כיונה דלו עיני למרום אדני 14כסוס עוגר כנ אצפצפ אהגה כיונא דלו עיני למרומ אדוני + >עשׁקה לי ערבני > עושקה לי וערבני + 15מה אדבר ואמר לי והוא עשׂה אדדה כל שׁנותי על מר נפש15מה אדבר ואומר לוא והיאה עשה ליא אדודה כול שנותי על + >ׁי > מור נפשיא + 16אדני עליהם יחיו ולכל בהן חיי רוחי ותחלימני והחיני 16אדוני עליהמה וחיו ולכול בהמה חיו רוחו ותחלימני והח  >יני - 17הנה לשלומ מר לי מר ואתה חשקת נפשי משחת בלי כי השלכ17הנ לשלומ מר ליא מאודה ואתה חשקתה נפשי משחת כלי כיא - >ת אחרי גוכ כל חטאי> השלכתה אחרי גוכה כול חטאי - 18כי לא שאול תודכ מות יהללכ לא ישברו יורדי בור אל אמ18כיא לוא שאול תודכה ולוא מות יהללכה ולוא ישברו יורד - >תכ>י בור אל אמתכה - 19חי חי הוא יודכ כמוני היומ אב לבנימ יודיע אל אמתכ19חי חי הוא יודכה כמוני היומ אב לבנימ יודיע אל אמתכה - 20יהוה להושיעני ונגנותי ננגנ כל ימי חיינו על בית יהו20יהוה להושיעני חי חי יודכ כמוני היומ אב לבנימ יהודי - >ה>ע אלוה אמתכ יהוה להושיעני ונגנותי ננגנ כול ימי חיי + 17הנה לשׁלום מר לי מר ואתה חשׁקת נפשׁי משׁחת בלי כי 17הנ לשלומ מר ליא מאודה ואתה חשקתה נפשי משחת כלי כיא + >השׁלכת אחרי גוך כל חטאי > השלכתה אחרי גוכה כול חטאי + 18כי לא שׁאול תודך מות יהללך לא ישׂברו יורדי בור אל 18כיא לוא שאול תודכה ולוא מות יהללכה ולוא ישברו יורד + >אמתך >י בור אל אמתכה + 19חי חי הוא יודך כמוני היום אב לבנים יודיע אל אמתך 19חי חי הוא יודכה כמוני היומ אב לבנימ יודיע אל אמתכה + 20יהוה להושׁיעני ונגנותי ננגן כל ימי חיינו על בית יה20יהוה להושיעני חי חי יודכ כמוני היומ אב לבנימ יהודי + >וה >ע אלוה אמתכ יהוה להושיעני ונגנותי ננגנ כול ימי חיי  >נו על בית יהוה - 21ויאמר ישעיהו ישאו דבלת תאנימ וימרחו על השחינ ויחי21ויאומר ישעיהו דבלת תאנימ וימרחו על השחינ ויחי - 22ויאמר חזקיהו מה אות כי אעלה בית יהוה22ויאמר חזקיה מה אות כי אעלה בית יהוה + 21ויאמר ישׁעיהו ישׂאו דבלת תאנים וימרחו על השׁחין וי21ויאומר ישעיהו דבלת תאנימ וימרחו על השחינ ויחי + >חי   + 22ויאמר חזקיהו מה אות כי אעלה בית יהוה ס 22ויאמר חזקיה מה אות כי אעלה בית יהוה \ No newline at end of file diff --git a/static/docs/tools/parallel/Isaiah-mt-1QIsaa_39.html b/static/docs/tools/parallel/Isaiah-mt-1QIsaa_39.html index e7a30fd0..f2869de6 100644 --- a/static/docs/tools/parallel/Isaiah-mt-1QIsaa_39.html +++ b/static/docs/tools/parallel/Isaiah-mt-1QIsaa_39.html @@ -30,23 +30,23 @@
Isaiah 39 MT
Isaiah 39 1QIsaa - t1בעת ההוא שלח מרדכ בלאדנ בנ בלאדנ מלכ בבל ספרימ ומנt1בעת ההיא שלח מרודכ בלאדונ בנ בלאדונ מלכ בבל ספרימ  - >חה אל חזקיהו וישמע כי חלה ויחזק>ומנחה אל יחוזקיה וישמע כיא חלה ויחיה - 2וישמח עליהמ חזקיהו ויראמ את בית נכתו את הכספ ואת ה2וישמח עליהמה יחוזקיה ויראמ את כול בית נכתיו את הכס - >זהב ואת הבשמימ ואת השמנ הטוב ואת כל בית כליו ואת כ>פ ואת הזהב ואת הבשמימ ואת השמנ הטוב ואת כול בית כל - >ל אשר נמצא באצרתיו לא היה דבר אשר לא הראמ חזקיהו ב>יו ואת כול אשר נמצא באוצרותיו לוא היה דבר אשר לוא  - >ביתו ובכל ממשלתו>הראמ יחוזקיה בביתו ובכול ממלכתו - 3ויבא ישעיהו הנביא אל המלכ חזקיהו ויאמר אליו מה אמר3ויבוא ישעיה הנביא אל המלכ יחוזקיה ויואמר אליו מה א - >ו האנשימ האלה ומאינ יבאו אליכ ויאמר חזקיהו מארצ רח>מרו האנשימ האלה ומאינ יבואו אליכה ויואמר יחוזקיה מ - >וקה באו אלי מבבל>ארצ רחוקה באו אלי מבבל - 4ויאמר מה ראו בביתכ ויאמר חזקיהו את כל אשר בביתי רא4ויואמר מה ראו בביתכה ויואמר יחוזקיה את כול אשר בבי - >ו לא היה דבר אשר לא הראיתימ באוצרתי>תי ראו לוא היה דבר אשר לוא הראיתימ באוצרותי - 5ויאמר ישעיהו אל חזקיהו שמע דבר יהוה צבאות5ויואמר ישעיה אל יחוזקיה שמע דבר יהוה צבאות - 6הנה ימימ באימ ונשא כל אשר בביתכ ואשר אצרו אבתיכ עד6הנה ימימ באימ ונשאו כול אשר בביתכה ואשר אצרו אבותי - > היומ הזה בבל לא יותר דבר אמר יהוה>כה עד היומ הזה בבל יבואו ולוא יותר דבר אמר יהוה - 7ומבניכ אשר יצאו ממכ אשר תוליד יקחו והיו סריסימ בהי7ומבניכה אשר יצאו ממעיכה אשר תוליד יקחו ויהיו סריסי - >כל מלכ בבל>מ בהיכל מלכ בבל - 8ויאמר חזקיהו אל ישעיהו טוב דבר יהוה אשר דברת ויאמר8ויואמר יחוזקיה אל ישעיה טוב דבר יהוה אשר דברתה ויו - > כי יהיה שלומ ואמת בימי>אמר כיא יהיה שלומ ואמת בימי + t1בעת ההוא שׁלח מרדך בלאדן בן בלאדן מלך בבל ספרים ומt1בעת ההיא שלח מרודכ בלאדונ בנ בלאדונ מלכ בבל ספרימ  + >נחה אל חזקיהו וישׁמע כי חלה ויחזק >ומנחה אל יחוזקיה וישמע כיא חלה ויחיה + 2וישׂמח עליהם חזקיהו ויראם את בית נכתה את הכסף ואת 2וישמח עליהמה יחוזקיה ויראמ את כול בית נכתיו את הכס + >הזהב ואת הבשׂמים ואת׀ השׁמן הטוב ואת כל בית כליו ו>פ ואת הזהב ואת הבשמימ ואת השמנ הטוב ואת כול בית כל + >את כל אשׁר נמצא באצרתיו לא היה דבר אשׁר לא הראם חז>יו ואת כול אשר נמצא באוצרותיו לוא היה דבר אשר לוא  + >קיהו בביתו ובכל ממשׁלתו >הראמ יחוזקיה בביתו ובכול ממלכתו + 3ויבא ישׁעיהו הנביא אל המלך חזקיהו ויאמר אליו מה אמ3ויבוא ישעיה הנביא אל המלכ יחוזקיה ויואמר אליו מה א + >רו׀ האנשׁים האלה ומאין יבאו אליך ויאמר חזקיהו מארץ>מרו האנשימ האלה ומאינ יבואו אליכה ויואמר יחוזקיה מ + > רחוקה באו אלי מבבל >ארצ רחוקה באו אלי מבבל + 4ויאמר מה ראו בביתך ויאמר חזקיהו את כל אשׁר בביתי ר4ויואמר מה ראו בביתכה ויואמר יחוזקיה את כול אשר בבי + >או לא היה דבר אשׁר לא הראיתים באוצרתי >תי ראו לוא היה דבר אשר לוא הראיתימ באוצרותי + 5ויאמר ישׁעיהו אל חזקיהו שׁמע דבר יהוה צבאות 5ויואמר ישעיה אל יחוזקיה שמע דבר יהוה צבאות + 6הנה ימים באים ונשׂא׀ כל אשׁר בביתך ואשׁר אצרו אבתי6הנה ימימ באימ ונשאו כול אשר בביתכה ואשר אצרו אבותי + >ך עד היום הזה בבל לא יותר דבר אמר יהוה >כה עד היומ הזה בבל יבואו ולוא יותר דבר אמר יהוה + 7ומבניך אשׁר יצאו ממך אשׁר תוליד יקחו והיו סריסים ב7ומבניכה אשר יצאו ממעיכה אשר תוליד יקחו ויהיו סריסי + >היכל מלך בבל >מ בהיכל מלכ בבל + 8ויאמר חזקיהו אל ישׁעיהו טוב דבר יהוה אשׁר דברת ויא8ויואמר יחוזקיה אל ישעיה טוב דבר יהוה אשר דברתה ויו + >מר כי יהיה שׁלום ואמת בימי פ >אמר כיא יהיה שלומ ואמת בימי \ No newline at end of file diff --git a/static/docs/tools/parallel/images/tf-small.png b/static/docs/tools/parallel/images/tf-small.png new file mode 100644 index 0000000000000000000000000000000000000000..43687b51ad7a75a23521e75c20f027a39255ec41 GIT binary patch literal 79182 zcmY(p1ymeC(459tGcSYtEZyWRpl^HNl;;6U@#Qqr8WOOAO4ke0MftvHM}U zX=u0w#NL9}?uRMS=xcRAkEMKSVjHQc=y6)fvMG7xRM=#`vsC**@U@FNraswI@8d?*T;cyuZy%1rm{A1_ll9KLr*h zI~Vw0&1>s<=&2|RSvY@YGqZFyw_<~QcKL@50|SN#{i}Yq@-U->e0Fql7lMdT{})5( zU;RH|c52H1qIfunQ0u9vQ%X6zSyA$`@vw1Fi=t9eQi9zqt%WqDW&gMNzc&$TTMrKx 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Parallel Passages in the MT

0. Introduction

0.1 Motivation

We want to make a list of all parallel passages in the Masoretic Text (MT) of the Hebrew Bible.

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Here is a quote that triggered Dirk to write this notebook:

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Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible.

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Robert Rezetko and Ian Young. - Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach. - Ancient Near East Monographs, Number9. SBL Press Atlanta. 2014. - PDF Open access available

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0.3 Open Source

This is an IPython notebook. -It contains a working program to carry out the computations needed to obtain the results reported here.

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You can download this notebook and run it on your computer, provided you have -LAF-Fabric installed. -An easy way to do that is describe here.

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It is a pity that we cannot compare our results with the Accordance resource mentioned above, since that resource has not been published in an accessible manner. We also do not have the information how this resource has been constructed on the basis of the raw data. In contrast with that, we present our results in a completely reproducible manner. This notebook itself can serve as the method of replication, provided you have obtained the necessary resources. See SHEBANQ sources, which are all Open Access.

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0.4 What are parallel passages?

The notion of parallel passage is not a simple, straightforward one. -There are parallels on the basis of lexical content in the passages on the one hand, -but on the other hand there are also correspondences in certain syntactical structures, -or even in similarities in text structure.

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In this notebook we do select a straightforward notion of parallel, based on lexical content only. -We investigate two measures of similarity, one that ignores word order completely, and one that takes word order into account.

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Two kinds of short-comings of this approach must be mentioned:

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  1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels)
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  2. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history.
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For a more full treatment of parallel passages, see

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Wido Th. van Peursen and Eep Talstra. - Computer-Assisted Analysis of Parallel Texts in the Bible - - The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles. - Vetus Testamentum</i> 57, pp. 45-72. - 2007, Brill, Leiden.

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Note that our method fails to identify any parallels with Chronica_II 32. Van Peursen and Talstra state about this chapter and 2 Kings 18:

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These chapters differ so much, that it is sometimes impossible to establish which verses should be considered parallel.

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In this notebook we produce a set of cliques, a clique being a set of passages that are quite similar, based on lexical information.

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0.5 Authors

This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer.

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Dirk Roorda while discussing ideas with -Martijn Naaijer.

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0.6 Status

Last modified: 2016-03-03 Added experiments based on chapter chunks and lower similarities.

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165 experiments have been carried out, of which 18 with promising results. -All results can be easily inspected, just by clicking in your browser. -One of the experiments has been chosen as the basis for -crossref -annotations in SHEBANQ.

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1. Results

Click in a green cell to see interesting results. The numbers in the cell indicate

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  • the number of passages that have a variant elsewhere
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  • the number of cliques they form (cliques are sets of similar passages)
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  • the number of passages in the biggest clique
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Below the results is an account of the method that we used, followed by the actual code to produce these results.

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In [24]:
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# run this cell after all other cells
-HTML(other_exps)
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Out[24]:
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no results available
promising results: recommended
messy results: deprecated
mixed quality: take care
method deprecated
unassessed quality: inspection needed
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- 1055 - 		- 25751
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- 4864 - 		- 26905
- 5229
- 5245 - 		- 33410
- 4109
- 17521 - 		- 38818
- 3746
- 24132 - 		- 41825
- 3497
- 28097 - 		- 53097
- 1172
- 50162 - 		    
objectsentenceLCS - 17533
- 3978
- 1053 - 		- 18091
- 4218
- 1053 - 		- 21261
- 4997
- 1053 - 		- 26488
- 4855
- 7321 - 		- 35629
- 3469
- 25570 - 		- 44303
- 2291
- 38288 - 		- 52528
- 1199
- 49324 - 		- 58855
- 463
- 57753 - 		- 62369
- 112
- 62109 - 		      
- -
- -
- -
-
- -
-
-
-
-
-
-

2. Experiments

We have conducted 165 experiments, all corresponding to a specific choice of parameters. -Every experiment is an attempt to identify variants and collect them in cliques.

-

The table gives an overview of the experiments conducted.

-

Every row corresponds to a particular way of chunking and a method of measuring the similarity.

-

There are columns for each similarity threshold that we have tried. -The idea is that chunks are similar if their similarity is above the threshold.

-

The outcomes of one experiment have been added to SHEBANQ as the note set -crossref. -The experiment chosen for this is currently

-
    -
  • chunking: object verse
    • -
  • similarity method: SET
    • -
  • similarity threshold: 65
    • -
-

2.1 Assessing the outcomes

Not all experiments lead to useful results. -We have indicated the value of a result by a color coding, based on objective characteristics, -such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking. -These numbers are shown in the cells.

-

2.1.1 Assessment criteria

If the method is based on fixed chunks, we deprecated the method and the results. -Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case.

-

Otherwise, we consider the ll, the length of the longest clique, and nc, the number of cliques. -We set three quality parameters:

-
    -
  • REC_CLIQUE_RATIO = 5 : recommended clique ratio
    • -
  • DUB_CLIQUE_RATIO = 15 : dubious clique ratio
    • -
  • DEP_CLIQUE_RATIO = 25 : deprecated clique ratio
    • -
-

where the clique ratio is $100 (ll/nc)$, -i.e. the length of the longest clique divided by the number of cliques as percentage.

-

An experiment is recommended if its clique ratio is between the recommended and dubious clique ratios.

-

It is dubious if its clique ratio is between the dubious and deprecated clique ratios.

-

It is deprecated if its clique ratio is above the deprecated clique ratio.

-

2.2 Inspecting results

If you click on the hyperlink in the cell, you are taken to a page that gives you -all the details of the results:

-
    -
  1. A link to a file with all cliques (which are the sets of similar passages)
    2. -
  2. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for -experiments with outcomes that are labeled as promising or unassessed quality or mixed results.
    3. -
-

To get into the variants quickly, inspect the list (2) and click through -to see the actual variant material in chapter context.

-

Not all variants occur here, so continue with (1) to see the remaining cliques.

-

Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters. -In that case you have to consult the big list (1)

-

All these results can be downloaded from the -SHEBANQ github repo -After downloading the whole directory, open experiments.html in your browser.

- -
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3. Method

Here we discuss the method we used to arrive at a list of parallel passages -in the Masoretic Text (MT) of the Hebrew Bible.

-

3.1 Similarity

We have to find passages in the MT that are similar. -Therefore we chunk the text in some way, and then compute the similarities between pairs of chunks.

-

There are many ways to define and compute similarity between texts. -Here, we have tried two methods SET and LCS. -Both methods define similarity as the fraction of common material with respect to the total material.

-

3.1.1 SET

The SET method reduces textual chunks to sets of lexemes. -This method abstracts from the order and number of occurrences of words in chunks.

-

We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets):

-$$ s_{\rm set}(C_1, C_2) = {\vert C_1 \cap C_2\vert \over \vert C_1 \cup C_2 \vert} $$

where $\vert X \vert$ is the number of elements in set $X$.

-

3.1.2 LCS

The LCS method is less reductive: chunks are strings of lexemes, -so the order and number of occurrences of words is retained.

-

We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings):

-$$ s_{\rm lcs}(C_1, C_2) = {\vert {\rm LCS}(C_1,C_2)\vert \over \vert C_1\vert + \vert C_2 \vert - -\vert {\rm LCS}(C_1,C_2)\vert} $$

where ${\rm LCS}(C_1, C_2)$ is the -longest common subsequence -of $C_1$ and $C_2$ and -$\vert X\vert$ is the length of sequence $X$.

-

It remains to be seen whether we need the extra sophistication of LCS. -The risk is that LCS could fail to spot related passages when there is a large amount of transposition going on. -The results should have the last word.

-

We need to compute the LCS efficiently, and for this we used the python Levenshtein module:

-

pip install python-Levenshtein

-

whose documentation is -here.

-

3.2 Performance

Similarity computation is the part where the heavy lifting occurs. -It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000), -you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), -you need to do ~ 2,000,000,000 ones! -The computation of a single similarity should be really fast.

-

Besides that, we use two ways to economize:

-
    -
  • after having computed a matrix for a specific set of parameter values, we save the matrix to disk; -new runs can load the matrix from disk in a matter of seconds;
    • -
  • we do not store low similarity values in the matrix, low being < MATRIX_THRESHOLD.
    • -
-

The LCS method is more complicated. -We have tried the ratio method from the difflib package that is present in the standard python distribution. -This is unbearably slow for our purposes. -The ratio method in the Levenshtein package is much quicker.

-

See the table for an indication of the amount of work to create the similarity matrix -and the performance per similarity method.

-

The matrix threshold is the lower bound of similarities that are stored in the matrix. -If a pair of chunks has a lower similarity, no entry will be made in the matrix.

-

The computing has been done on a Macbook Air (11", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM).

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
chunk typechunk sizesimilarity methodmatrix threshold# of comparisonssize of matrix (KB)computing time (min)
fixed100LCS609,003,6467?
fixed100SET509,003,6467?
fixed50LCS6036,197,28637?
fixed50SET5036,197,28618?
fixed20LCS60227,068,7052,400?
fixed20SET50227,068,705113?
fixed10LCS60909,020,84159,000?
fixed10SET50909,020,8411,800?
objectverseLCS60269,410,0782,30031
objectverseSET50269,410,07850914
objecthalf_verseLCS601,016,396,24140,00050
objecthalf_verseSET501,016,396,2413,60041
objectsentenceLCS602,055,975,750212,00068
objectsentenceSET502,055,975,75082,00063
- -
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4. Workflow

4.1 Chunking

There are several ways to chunk the text:

-
    -
  • fixed chunks of approximately CHUNK_SIZE words
    • -
  • by object, such as verse, sentence and even chapter
    • -
-

After chunking, we prepare the chunks for similarity measuring.

-

4.1.1 Fixed chunking

Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results. -The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries. -It is to be expected that fixed chunks in variant passages will be mutually out of phase, -meaning that the chunks involved in these passages are not aligned with each other. -So they will have a lower similarity than they could have if they were aligned. -This is a source of artificial noise in the outcome and/or missed cases.

-

If the chunking respects "natural" boundaries in the text, there is far less misalignment.

-

4.1.2 Object chunking

We can also chunk by object, such as verse, half_verse or sentence.

-

Chunking by verse is very much like chunking in fixed chunks of size 20, performance-wise.

-

Chunking by half_verse is comparable to fixed chunks of size 10.

-

Chunking by sentence will generate an enormous amount of -false positives, because there are very many very short sentences (down to 1-word) in the text. -Besides that, the performance overhead is huge.

-

The half_verses seem to be a very interesting candidate. -They are smaller than verses, but there are less degenerate cases compared to with sentences. -From the table above it can be read that half verses require only half as many similarity computations as sentences.

-

4.2 Preparing

We prepare the chunks for the application of the chosen method of similarity computation (SET or LCS).

-

In both cases we reduce the text to a sequence of transliterated consonantal lexemes without disambiguation. -In fact, we go one step further: we remove the consonants (alef, wav, yod) that are often silent.

-

For SET, we represent each chunk as the set of its reduced lexemes.

-

For LCS, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces.

-

4.3 Cliques

After having computed a sufficient part of the similarity matrix, we set a value for SIMILARITY_THRESHOLD. -All pairs of chunks having at least that similarity are deemed interesting.

-

We organize the members of such pairs in cliques, groups of chunks of which each member is -similar (similarity > SIMILARITY_THRESHOLD) to at least one other member.

-

We start with no cliques and walk through the pairs whose similarity is above SIMILARITY_THRESHOLD, -and try to put each member into a clique.

-

If there is not yet a clique, we make the member in question into a new singleton clique.

-

If there are cliques, we find the cliques that have a member similar to the member in question. -If we find several, we merge them all into one clique.

-

If there is no such clique, we put the member in a new singleton clique.

-

NB: Cliques may drift, meaning that they contain members that are completely different from each other. -They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other.

-

4.3.1 Organizing the cliques

In order to accomodate cases where there are many corresponding verses in corresponding chapters, we produce -chapter-by-chapter diffs in the following way.

-

We make a list of all chapters that are involved in cliques. -This yields a list of chapter cliques. -For all binary chapters cliques, we generate a colorful diff rendering (as html) for the complete two chapters.

-

We only do this for promising experiments.

-

4.3.2 Evaluating clique sets

Not all clique sets are equally worth while. -For example, if we set the SIMILARITY_THRESHOLD too low, we might get one gigantic clique, especially -in combination with a fine-grained chunking. In other words: we suffer from clique drifting.

-

We detect clique drifting by looking at the size of the largest clique. -If that is large compared to the total number of chunks, we deem the results unsatisfactory.

-

On the other hand, when the SIMILARITY_THRESHOLD is too high, you might miss a lot of correspondences, -especially when chunks are large, or when we have fixed-size chunks that are out of phase.

-

We deem the results of experiments based on a partioning into fixed length chunks as unsatisfactory, although it -might be interesting to inspect what exactly the damage is.

-

At the moment, we have not yet analysed the relative merits of the similarity methods SET and LCS.

- -
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-
-
-
-
-
-

5. Implementation

The rest is code. From here we fire up the engines and start computing.

- -
-
-
-
-
-
In [1]:
-
-
-
import sys, os, re, collections, pickle, math, difflib, glob
-
-from IPython.display import HTML, display
-import matplotlib.pyplot as plt
-%matplotlib inline
-PICKLE_PROTOCOL = 3
-
-from difflib import SequenceMatcher
-from Levenshtein import ratio
-
-from tf.fabric import Fabric
-
- -
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- -
-
-
-
-
-
-

5.1 Loading the feature data

We load the features we need from the ETCBC database.

- -
-
-
-
-
-
In [2]:
-
-
-
source = 'etcbc'
-version = '4c'
-ETCBC = 'hebrew/{}{}'.format(source, version)
-TF = Fabric( modules=ETCBC )
-
- -
-
-
- -
-
- - -
-
-
This is Text-Fabric 1.2.7
-Api reference : https://github.com/ETCBC/text-fabric/wiki/Api
-Tutorial      : https://github.com/ETCBC/text-fabric/blob/master/docs/tutorial.html
-Data sources  : https://github.com/ETCBC/text-fabric-data
-Data docs     : https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html
-Shebanq docs  : https://shebanq.ancient-data.org/text
-Slack team    : https://shebanq.slack.com/signup
-Questions? Ask shebanq@ancient-data.org for an invite to Slack
-107 features found and 0 ignored
-
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- -
-
- -
-
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-
In [3]:
-
-
-
api = TF.load('''
-    otype
-    lex g_word_utf8 trailer_utf8
-    book chapter verse label number
-''')
-api.makeAvailableIn(globals())
-
- -
-
-
- -
-
- - -
-
-
  0.00s loading features ...
-   |     0.05s B otype                from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.00s M otext                from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.01s B book                 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.01s B chapter              from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.01s B verse                from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.24s B g_word_utf8          from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.10s B trailer_utf8         from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.16s B lex                  from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.02s B label                from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-   |     0.28s B number               from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c
-  5.83s All features loaded/computed - for details use loadLog()
-
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- -
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- -
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-
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-

5.2 Configuration

Here are the parameters on which the results crucially depend.

-

There are also parameters that control the reporting of the results, such as file locations.

- -
-
-
-
-
-
In [4]:
-
-
-
# chunking
-CHUNK_LABELS = {True: 'fixed', False: 'object'}
-CHUNK_LBS = {True: 'F', False: 'O'}
-CHUNK_SIZES = (100, 50, 20, 10)
-CHUNK_OBJECTS = ('chapter', 'verse','half_verse','sentence')
-
-# preparing
-EXCLUDED_CONS = '[>WJ=/\[]'             # weed out weak consonants
-EXCLUDED_PAT = re.compile(EXCLUDED_CONS)
-
-# similarity
-MATRIX_THRESHOLD = 50
-SIM_METHODS = ('SET', 'LCS')
-SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30)
-
-# printing
-DEP_CLIQUE_RATIO = 25
-DUB_CLIQUE_RATIO = 15
-REC_CLIQUE_RATIO =  5
-LARGE_CLIQUE_SIZE = 50
-CLIQUES_PER_FILE = 50
-
-# assessing results
-VALUE_LABELS = dict(
-    mis='no results available',
-    rec='promising results: recommended',
-    dep='messy results: deprecated',
-    dub='mixed quality: take care',
-    out='method deprecated',
-    nor='unassessed quality: inspection needed',
-    lr='this experiment is the last one run',
-)
-
-# crossrefs for SHEBANQ
-SHEBANQ_MATRIX = (False, 'verse', 'SET')
-SHEBANQ_SIMILARITY = 65
-SHEBANQ_TOOL = 'parallel'
-CROSSREF_STATUS = '!'
-CROSSREF_KEYWORD = 'crossref'
-
-# progress indication
-VERBOSE = False
-MEGA = 1000000
-KILO = 1000
-SIMILARITY_PROGRESS = 5 * MEGA
-CLIQUES_PROGRESS = 1 * KILO
-
-# locations and hyperlinks
-LOCAL_BASE_COMP = '/Users/dirk/tf/text-fabric-output/{}{}/parallels'.format(source, version)
-LOCAL_BASE_OUTP = 'files'
-EXPERIMENT_DIR = 'experiments'
-EXPERIMENT_FILE = 'experiments'
-EXPERIMENT_PATH = '{}/{}.txt'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)
-EXPERIMENT_HTML = '{}/{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)
-NOTES_FILE = 'crossref'
-NOTES_PATH = '{}/{}.csv'.format(LOCAL_BASE_OUTP, NOTES_FILE)
-STORED_CLIQUE_DIR = 'stored/cliques'
-STORED_MATRIX_DIR = 'stored/matrices'
-STORED_CHUNK_DIR = 'stored/chunks'
-CHAPTER_DIR = 'chapters'
-CROSSREF_DB_FILE = 'crossrefdb.csv'
-CROSSREF_DB_PATH = '{}/{}'.format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE)
-
- -
-
-
- -
-
-
-
-
-
-

5.3 Experiment settings

For each experiment we have to adapt the configuration settings to the parameters that define the experiment.

- -
-
-
-
-
-
In [5]:
-
-
-
def reset_params():
-    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
-    global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD
-    global meta
-    meta = collections.OrderedDict()
-    
-    # chunking
-    CHUNK_FIXED = None                      # kind of chunking: fixed size or by object
-    CHUNK_SIZE = None                       # only relevant for CHUNK_FIXED = True
-    CHUNK_OBJECT = None                     # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell
-    CHUNK_LB = None                         # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ
-    CHUNK_DESC = None                       # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ
-    # similarity
-    MATRIX_THRESHOLD = None                 # minimal similarity used to fill the matrix of similarities
-    SIMILARITY_METHOD = None                # see SIM_METHODS in next cell
-    SIMILARITY_THRESHOLD = None             # minimal similarity used to put elements together in cliques
-    meta = collections.OrderedDict()
-
-def set_matrix_threshold(sim_m=None, chunk_o=None):
-    global MATRIX_THRESHOLD
-    the_sim_m = SIMILARITY_METHOD if sim_m == None else sim_m
-    the_chunk_o = CHUNK_OBJECT if chunk_o == None else chunk_o
-    MATRIX_THRESHOLD = 50 if the_sim_m == 'SET' else 60
-    if the_sim_m == 'SET':
-        if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 30
-        else: MATRIX_THRESHOLD = 50
-    else:
-        if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 55
-        else: MATRIX_THRESHOLD = 60
-
-def do_params_chunk(chunk_f, chunk_i):
-    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
-    do_chunk = False
-    if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):
-        do_chunk = True
-        CHUNK_FIXED = chunk_f
-        if chunk_f: CHUNK_SIZE = chunk_i
-        else: CHUNK_OBJECT = chunk_i
-
-    CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]
-    CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT
-
-    for p in (
-        '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),
-        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),
-    ):
-        if not os.path.exists(p): os.makedirs(p)
-
-    return do_chunk
-
-def do_params(chunk_f, chunk_i, sim_m, sim_thr):
-    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
-    global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD
-    global meta
-    do_chunk = False
-    do_prep = False
-    do_sim = False
-    do_clique = False
-    
-    meta = collections.OrderedDict()
-    if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):
-        do_chunk = True
-        do_prep = True
-        do_sim = True
-        do_clique = True
-        CHUNK_FIXED = chunk_f
-        if chunk_f: CHUNK_SIZE = chunk_i
-        else: CHUNK_OBJECT = chunk_i
-    if sim_m != SIMILARITY_METHOD:
-        do_prep = True
-        do_sim = True
-        do_clique = True
-        SIMILARITY_METHOD = sim_m
-    if sim_thr != SIMILARITY_THRESHOLD:
-        do_clique = True
-        SIMILARITY_THRESHOLD = sim_thr
-    set_matrix_threshold()
-    if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD : return (False, False, False, False, True)
-
-    CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]
-    CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT
-
-    meta['CHUNK TYPE'] = 'FIXED {}'.format(CHUNK_SIZE) if CHUNK_FIXED else 'OBJECT {}'.format(CHUNK_OBJECT)
-    meta['MATRIX THRESHOLD'] = MATRIX_THRESHOLD
-    meta['SIMILARITY METHOD'] = SIMILARITY_METHOD
-    meta['SIMILARITY THRESHOLD'] = SIMILARITY_THRESHOLD
-    
-    
-    for p in (
-        '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),
-        '{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR),
-        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR),
-        '{}/{}'.format(LOCAL_BASE_COMP, STORED_MATRIX_DIR),
-        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),
-    ):
-        if not os.path.exists(p): os.makedirs(p)
-
-    return (do_chunk, do_prep, do_sim, do_clique, False)
-
-reset_params()
-
- -
-
-
- -
-
-
-
-
-
-

5.4 Chunking

We divide the text into chunks to be compared. The result is chunks, -which is a list of lists. -Every chunk is a list of word nodes.

- -
-
-
-
-
-
In [6]:
-
-
-
def chunking(do_chunk):
-    global chunks, book_rank
-    if not do_chunk:
-        info('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))
-        meta['# CHUNKS'] = len(chunks)
-        return
-
-    chunk_path = '{}/{}/chunk_{}_{}'.format(
-        LOCAL_BASE_COMP, STORED_CHUNK_DIR,
-        CHUNK_LB, CHUNK_DESC,
-    )
-
-    if os.path.exists(chunk_path):
-        with open(chunk_path, 'rb') as f: chunks = pickle.load(f)
-        info('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(
-            CHUNK_LB, CHUNK_DESC,
-            len(chunks),
-        ))
-    else:
-        info('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))
-        chunks = []
-        book_rank = {}
-        for b in F.otype.s('book'):
-            book_name = F.book.v(b)
-            book_rank[book_name] = b
-            words = L.d(b, otype='word')
-            nwords = len(words)
-            if CHUNK_FIXED:
-                nchunks = nwords // CHUNK_SIZE
-                if nchunks == 0: 
-                    nchunks = 1
-                    common_incr = nwords
-                    special_incr = 0
-                else:            
-                    rem = nwords % CHUNK_SIZE
-                    common_incr = rem // nchunks
-                    special_incr = rem % nchunks
-                word_in_chunk = -1
-                cur_chunk = -1
-                these_chunks = []
-
-                for w in words:
-                    word_in_chunk += 1
-                    if word_in_chunk == 0 or (word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0)):
-                        word_in_chunk = 0
-                        these_chunks.append([])
-                        cur_chunk += 1
-                    these_chunks[-1].append(w)
-            else:
-                these_chunks = [L.d(c, otype='word') for c in L.d(b, otype=CHUNK_OBJECT)]
-
-            chunks.extend(these_chunks)
-
-            chunkvolume = sum(len(c) for c in these_chunks)
-            if VERBOSE:
-                info('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(
-                    CHUNK_LB, CHUNK_DESC,
-                    book_name, nwords, len(these_chunks), 
-                    min(len(c) for c in these_chunks), 
-                    max(len(c) for c in these_chunks),
-                    'OK' if chunkvolume == nwords else 'ERROR',
-                ))
-        with  open(chunk_path, 'wb') as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL)
-    info('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))
-    meta['# CHUNKS'] = len(chunks)
-
- -
-
-
- -
-
-
-
-
-
-

5.5 Preparing

In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing.

-

5.5.1 Preparing for SET comparison

We reduce words to their lexemes (dictionary entries) and from them we also remove the alef, waw, and yods. -The lexeme feature also contains characters (/ [ =) to disambiguate homonyms. We also remove these. -If we end up with something empty, we skip it. -Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content.

-

5.5.2 Preparing for LCS comparison

Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity.

- -
-
-
-
-
-
In [7]:
-
-
-
def preparing(do_prepare):
-    global chunk_data
-    if not do_prepare:
-        info('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))
-        return
-    info('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))
-    chunk_data = []
-    if SIMILARITY_METHOD == 'SET':
-        for c in chunks:
-            words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)
-            clean_words = (w for w in words if w != '')
-            this_data = frozenset(clean_words)
-            chunk_data.append(this_data)
-    else:
-        for c in chunks:
-            words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)
-            clean_words = (w for w in words if w != '')
-            this_data = ' '.join(clean_words)
-            chunk_data.append(this_data)
-    info('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))
-
- -
-
-
- -
-
-
-
-
-
-

5.6 Similarity computation

Here we implement our two ways of similarity computation. -Both need a massive amount of work, especially for experiments with many small chunks. -The similarities are stored in a matrix, a data structure that stores a similarity number for each pair of chunk indices. -Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold. -We store matrices for re-use.

-

5.6.1 SET similarity

The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union. -Intuitively, we compute the proportion of what two chunks have in common against their total material.

-

In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0.

-

5.6.2 LCS similarity

The core is the method ratio(), taken from the Levenshtein module. -Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance.

- -
-
-
-
-
-
In [8]:
-
-
-
def similarity_post():
-    nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100})
-    cmin = min(chunk_dist.values()) if len(chunk_dist) else '!empty set!'
-    cmax = max(chunk_dist.values()) if len(chunk_dist) else '!empty set!'
-    meta['LOWEST  AVAILABLE SIMILARITY'] = cmin
-    meta['HIGHEST AVAILABLE SIMILARITY'] = cmax
-    meta['# EQUAL COMPARISONS'] = nequals
-    info('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-        cmin, cmax, nequals,
-    ))
-    
-def similarity(do_sim):
-    global chunk_dist
-    total_chunks = len(chunks) 
-    total_distances = total_chunks * (total_chunks - 1) // 2
-    meta['# SIMILARITY COMPARISONS'] = total_distances
-    
-    SIMILARITY_PROGRESS = total_distances // 100
-    if SIMILARITY_PROGRESS >= MEGA:
-        sim_unit = MEGA
-        sim_lb = 'M'
-    else:
-        sim_unit = KILO
-        sim_lb = 'K'
-    
-    if not do_sim:
-        info('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-            total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),
-        ))
-        meta['# STORED SIMILARITIES'] = len(chunk_dist)
-        similarity_post()
-        return
-
-    matrix_path = '{}/{}/matrix_{}_{}_{}_{}'.format(
-        LOCAL_BASE_COMP, STORED_MATRIX_DIR,
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-    )
-
-    if os.path.exists(matrix_path):
-        with open(matrix_path, 'rb') as f: chunk_dist = pickle.load(f)
-        info('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-            total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),
-        ))
-        meta['# STORED SIMILARITIES'] = len(chunk_dist)
-        similarity_post()
-        return
-
-    info('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-        total_distances // sim_unit, sim_lb, total_distances
-    ))
-
-    chunk_dist = {}
-    wc = 0
-    wt = 0
-    if SIMILARITY_METHOD == 'SET':
-        # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union
-        for i in range(total_chunks):
-            c_i = chunk_data[i]
-            for j in range(i + 1, total_chunks):
-                c_j = chunk_data[j]
-                u = len(c_i | c_j)
-                
-                # HERE COMES THE SIMILARITY COMPUTATION
-                d = 100 * len(c_i & c_j) / u if u != 0 else 0
-                
-                # HERE WE STORE THE OUTCOME
-                if d >= MATRIX_THRESHOLD:
-                    chunk_dist[(i,j)] = d
-                wc += 1
-                wt += 1
-                if wc == SIMILARITY_PROGRESS:
-                    wc = 0
-                    info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(
-                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-                        wt // sim_unit, sim_lb, len(chunk_dist),
-                    ))
-    elif SIMILARITY_METHOD == 'LCS':
-        # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length
-        for i in range(total_chunks):
-            c_i = chunk_data[i]
-            for j in range(i + 1, total_chunks):
-                c_j = chunk_data[j]
-
-                # HERE COMES THE SIMILARITY COMPUTATION
-                d = 100 * ratio(c_i, c_j)
-
-                # HERE WE STORE THE OUTCOME
-                if d >= MATRIX_THRESHOLD:
-                    chunk_dist[(i,j)] = d
-                wc += 1
-                wt += 1
-                if wc == SIMILARITY_PROGRESS:
-                    wc = 0
-                    info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(
-                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-                        wt // sim_unit, sim_lb, len(chunk_dist),
-                    ))
-
-    with  open(matrix_path, 'wb') as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL)
-        
-    info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
-        wt // sim_unit, sim_lb, wt, len(chunk_dist),
-    ))
-    
-    meta['# STORED SIMILARITIES'] = len(chunk_dist)
-    similarity_post()
-
- -
-
-
- -
-
-
-
-
-
-

5.7 Cliques

Based on the value for the SIMILARITY_THRESHOLD we use the similarity matrix to pick the interesting -similar pairs out of it. From these pairs we lump together our cliques.

-

Our list of experiments will select various values for SIMILARITY_THRESHOLD, which will result -in various types of cliqueing behaviour.

-

We store computed cliques for re-use.

-

5.7.1 Selecting passages

We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages.

-

5.7.2 Growing cliques

We inspect all passages in our set, and try to add them to the cliques we are growing. -We start with an empty set of cliques. -Each passage is added to a clique with which it has enough familiarity, otherwise it is added to a new clique. -Enough familiarity means: the passage is similar to at least one member of the clique, and the similarity is at least SIMILARITY_THRESHOLD. -It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged. -This may lead to growing very large cliques if SIMILARITY_THRESHOLD is too low.

- -
-
-
-
-
-
In [9]:
-
-
-
def key_chunk(i):
-    c = chunks[i]
-    w = c[0]
-    return  (-len(c), L.u(w, otype='book')[0], L.u(w, otype='chapter')[0], L.u(w, otype='verse')[0])
-
-def meta_clique_pre():
-    global similars, passages
-    info('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-    ))
-    similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD}
-    passage_set = set()
-    for (i,j) in similars:
-        passage_set.add(i)
-        passage_set.add(j)
-    passages = sorted(passage_set, key=key_chunk)
-
-    meta['# SIMILAR COMPARISONS'] = len(similars)
-    meta['# SIMILAR PASSAGES'] = len(passages)    
-
-def meta_clique_pre2():
-    info('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(
-    CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-    len(similars), len(passages),
-))
-
-
-def meta_clique_post():
-    global l_c_l
-    meta['# CLIQUES'] = len(cliques)
-    scliques = collections.Counter()
-    for c in cliques:
-        scliques[len(c)] += 1
-    l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0
-    totmn = 0
-    totcn = 0
-    for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]):
-        totmn += ln * n
-        totcn += n
-        if VERBOSE:
-            info('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(
-                CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-                n, ln,
-            ))
-        meta['# CLIQUES of LENGTH {:>4}'.format(ln)] = n
-    info('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-        totmn, totcn,
-    ))
-    
-def cliqueing(do_clique):
-    global cliques
-    if not do_clique:
-        info('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-            len(cliques), len(passages), len(similars),            
-        ))
-        meta_clique_pre2()
-        meta_clique_post()
-        return
-    info('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,        
-    ))
-    meta_clique_pre()
-    meta_clique_pre2()
-    clique_path = '{}/{}/clique_{}_{}_{}_{}_{}'.format(
-        LOCAL_BASE_COMP, STORED_CLIQUE_DIR,
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-    )
-    if os.path.exists(clique_path):
-        with open(clique_path, 'rb') as f: cliques = pickle.load(f)
-        info('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-            len(cliques), len(passages), len(similars),            
-        ))
-        meta_clique_post()
-        return
-
-    info('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-        len(passages), len(similars),            
-    ))
-    cliques_unsorted = []
-    np = 0
-    npc = 0
-    for i in passages:
-        added = None
-        removable = set()
-        for (k, c) in enumerate(cliques_unsorted):
-            origc = tuple(c)
-            for j in origc:            
-                d = chunk_dist.get((i,j), 0) if i < j else chunk_dist.get((j,i), 0) if j < i else 0
-                if d >= SIMILARITY_THRESHOLD:
-                    if added == None:    # the passage has not been added to any clique yet
-                        c.add(i)
-                        added = k        # remember that we added the passage to this clique
-                    else:                # the passage has alreay been added to another clique:
-                                         # we merge this clique with that one
-                        cliques_unsorted[added] |= c
-                        removable.add(k) # we remember that we have merged this clicque into another one,
-                                         # so we can throw away this clicque later 
-                    break
-        if added == None:
-            cliques_unsorted.append({i})
-        else:
-            if len(removable):
-                cliques_unsorted = [c for (k,c) in enumerate(cliques_unsorted) if k not in removable]
-        np += 1
-        npc += 1
-        if npc == CLIQUES_PROGRESS:
-            npc = 0
-            info('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(
-                CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-                len(cliques_unsorted), np,
-            ))
-    cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted])
-    with  open(clique_path, 'wb') as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL)
-    meta_clique_post()
-    info('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-        len(cliques), len(passages), len(similars),            
-    ))
-
- -
-
-
- -
-
-
-
-
-
-

5.8 Output

We deliver the output of our experiments in various ways, all in HTML.

-

We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it.

-

For every (good) experiment, we produce a big list of its cliques, and for -every such clique, we produce a diff-view of its members.

-

Big cliques will be split into several files.

-

Clique listings will also contain metadata: the value of the experiment parameters.

-

5.8.1 Format definitions

Here are the definitions for formatting the (HTML) output.

- -
-
-
-
-
-
In [10]:
-
-
-
# clique lists
-css = '''
-td.vl {
-    font-family: Verdana, Arial, sans-serif;
-    font-size: small;
-    text-align: right;
-    color: #aaaaaa;
-    width: 10%;
-    direction: ltr;
-    border-left: 2px solid #aaaaaa;
-    border-right: 2px solid #aaaaaa;
-}
-td.ht {
-    font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif;
-    font-size: x-large;
-    line-height: 1.7;
-    text-align: right;
-    direction: rtl;
-}
-table.ht {
-    width: 100%;
-    direction: rtl;
-    border-collapse: collapse;
-}
-td.ht {
-    border-left: 2px solid #aaaaaa;
-    border-right: 2px solid #aaaaaa;
-}
-tr.ht.tb {
-    border-top: 2px solid #aaaaaa;
-    border-left: 2px solid #aaaaaa;
-    border-right: 2px solid #aaaaaa;
-}
-tr.ht.bb {
-    border-bottom: 2px solid #aaaaaa;
-    border-left: 2px solid #aaaaaa;
-    border-right: 2px solid #aaaaaa;
-}
-span.m {
-    background-color: #aaaaff;
-}
-span.f {
-    background-color: #ffaaaa;
-}
-span.x {
-    background-color: #ffffaa;
-    color: #bb0000;
-}
-span.delete {
-    background-color: #ffaaaa;
-}
-span.insert {
-    background-color: #aaffaa;
-}
-span.replace {
-    background-color: #ffff00;
-}
-
-'''
-
-# chapter diffs
-diffhead = '''
-<head>
-    <meta http-equiv="Content-Type"
-          content="text/html; charset=UTF-8" />
-    <title></title>
-    <style type="text/css">
-        table.diff {
-            font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; 
-            font-size: x-large;
-            text-align: right;
-        }
-        .diff_header {background-color:#e0e0e0}
-        td.diff_header {text-align:right}
-        .diff_next {background-color:#c0c0c0}
-        .diff_add {background-color:#aaffaa}
-        .diff_chg {background-color:#ffff77}
-        .diff_sub {background-color:#ffaaaa}
-    </style>
-</head>
-'''
-
-# table of experiments
-ecss = '''
-<style type="text/css">
-.mis {background-color: #cccccc;}
-.rec {background-color: #aaffaa;}
-.dep {background-color: #ffaaaa;}
-.dub {background-color: #ffddaa;}
-.out {background-color: #ffddff;}
-.nor {background-color: #fcfcff;}
-.ps  {font-weight: normal;}
-.mx  {font-style: italic;}
-.cl  {font-weight: bold;}
-.lr  {font-weight: bold; background-color: #ffffaa;}
-p,td {font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: small;}
-td   {border: 1pt solid #000000; padding: 4pt;}
-table {border: 1pt solid #000000; border-collapse: collapse;}
-</style>
-'''
-
-legend = '''
-<table>
-<tr><td class="mis">{mis}</td></tr>
-<tr><td class="rec">{rec}</td></tr>
-<tr><td class="dep">{dep}</td></tr>
-<tr><td class="dub">{dub}</td></tr>
-<tr><td class="out">{out}</td></tr>
-<tr><td class="nor">{nor}</td></tr>
-</table>
-'''.format(**VALUE_LABELS)
-
- -
-
-
- -
-
-
-
-
-
-

5.8.2 Formatting clique lists

-
-
-
-
-
-
In [17]:
-
-
-
def xterse_chunk(i):
-    chunk = chunks[i]
-    fword = chunk[0]
-    book = L.u(fword, otype='book')[0]
-    chapter = L.u(fword, otype='chapter')[0]
-    return (book, chapter)
-
-def xterse_clique(ii):
-    return tuple(sorted({xterse_chunk(i) for i in ii}))
-
-def terse_chunk(i):
-    chunk = chunks[i]
-    fword = chunk[0]
-    book = L.u(fword, otype='book')[0]
-    chapter = L.u(fword, otype='chapter')[0]
-    verse = L.u(fword, otype='verse')[0]
-    return (book, chapter, verse)
-
-def terse_clique(ii):
-    return tuple(sorted({terse_chunk(i) for i in ii}))
-
-def verse_chunk(i):
-    (bk, ch, vs) = i
-    book = F.book.v(bk)
-    chapter = F.chapter.v(ch)
-    verse = F.verse.v(vs)
-    text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(vs, otype='word'))
-    verse_label = '<td class="vl">{} {}:{}</td>'.format(book, chapter, verse)
-    htext = '{}<td class="ht">{}</td>'.format(verse_label, text)
-    return '<tr class="ht">{}</tr>'.format(htext)
-
-def verse_clique(ii):
-    return '<table class="ht">{}</table>\n'.format(''.join(verse_chunk(i) for i in sorted(ii)))
-
-def condense(vlabels):
-    cnd = ''
-    (cur_b, cur_c) = (None, None)
-    for (b, c, v) in vlabels:
-        c = str(c)
-        v = str(v)
-        sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '
-        show_b = b+' ' if cur_b != b else ''
-        show_c = c+':' if cur_b != b or cur_c != c else ''
-        (cur_b, cur_c) = (b, c)
-        cnd += '{}{}{}{}'.format(sep, show_b, show_c, v)
-    return cnd
-
-def print_diff(a, b):
-    arep = ''
-    brep = ''
-    for (lb, ai, aj, bi, bj) in SequenceMatcher(isjunk=None, a=a, b=b, autojunk=False).get_opcodes():
-        if lb == 'equal':
-            arep += a[ai:aj]
-            brep += b[bi:bj]
-        elif lb == 'delete':
-            arep += '<span class="{}">{}</span>'.format(lb, a[ai:aj])
-        elif lb == 'insert':
-            brep += '<span class="{}">{}</span>'.format(lb, b[bi:bj])
-        else:
-            arep += '<span class="{}">{}</span>'.format(lb, a[ai:aj])
-            brep += '<span class="{}">{}</span>'.format(lb, b[bi:bj])
-    return (arep, brep)
-    
-def print_chunk_fine(prev, text, verse_labels, prevlabels):
-    if prev == None:
-        return '''
-<tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
-'''.format(
-            condense(verse_labels), 
-            text,
-        )
-    else:
-        (prevline, textline) = print_diff(prev, text)
-        return '''
-<tr class="ht tb"><td class="vl">{}</td><td class="ht">{}</td></tr>
-<tr class="ht bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
-'''.format(
-    condense(prevlabels) if prevlabels != None else 'previous',
-    prevline,
-    condense(verse_labels), 
-    textline,
-)
-
-def print_chunk_coarse(text, verse_labels):
-    return '''
-<tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
-'''.format(
-            condense(verse_labels), 
-            text,
-        )
-
-def print_clique(ii, ncliques):
-    return print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii)
-    
-def print_clique_fine(ii):
-    condensed = collections.OrderedDict()
-    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):
-        chunk = chunks[i]
-        fword = chunk[0]
-        book = F.book.v(L.u(fword, otype='book')[0])
-        chapter = F.chapter.v(L.u(fword, otype='chapter')[0])
-        verse = F.verse.v(L.u(fword, otype='verse')[0])
-        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)
-        condensed.setdefault(text, []).append((book, chapter, verse))
-    result = []
-    nv = len(condensed.items())
-    prev = None
-    for (text, verse_labels) in condensed.items():
-        if prev == None:
-            if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None))
-            else:
-                prev = text
-                prevlabels = verse_labels
-                continue
-        else:
-            result.append(print_chunk_fine(prev, text, verse_labels, prevlabels))
-            prev = text
-            prevlabels = None
-    return '<table class="ht">{}</table>\n'.format(''.join(result))
-
-def print_clique_coarse(ii):
-    condensed = collections.OrderedDict()
-    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:
-        chunk = chunks[i]
-        fword = chunk[0]
-        book = F.book.v(L.u(fword, otype='book')[0])
-        chapter = F.chapter.v(L.u(fword, otype='chapter')[0])
-        verse = F.verse.v(L.u(fword, otype='verse')[0])
-        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)
-        condensed.setdefault(text, []).append((book, chapter, verse))
-    result = []
-    nv = len(condensed.items())
-    prev = None
-    for (text, verse_labels) in condensed.items():
-        result.append(print_chunk_coarse(text, verse_labels))
-    if len(ii) > LARGE_CLIQUE_SIZE:
-        result.append(print_chunk_coarse('+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE),[]))
-    return '<table class="ht">{}</table>\n'.format(''.join(result))
-
-def index_clique(bnm, n, ii, ncliques):
-    return index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii)
-    
-def index_clique_fine(bnm, n, ii):
-    verse_labels = []
-    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):
-        chunk = chunks[i]
-        fword = chunk[0]
-        book = F.book.v(L.u(fword, otype='book')[0])
-        chapter = F.chapter.v(L.u(fword, otype='chapter')[0])
-        verse = F.verse.v(L.u(fword, otype='verse')[0])
-        verse_labels.append((book, chapter, verse))
-        reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)
-    return '<p><b>{}</b> <a href="{}.html#c_{}">{}</a></p>'.format(
-        n, reffl, n, condense(verse_labels),
-    )
-
-def index_clique_coarse(bnm, n, ii):
-    verse_labels = []
-    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:
-        chunk = chunks[i]
-        fword = chunk[0]
-        book = F.book.v(L.u(fword, otype='book')[0])
-        chapter = F.chapter.v(L.u(fword, otype='chapter')[0])
-        verse = F.verse.v(L.u(fword, otype='verse')[0])
-        verse_labels.append((book, chapter, verse))
-        reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)
-    extra = '+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else ''
-    return '<p><b>{}</b> <a href="{}.html#c_{}">{}{}</a></p>'.format(
-        n, reffl, n, condense(verse_labels), extra,
-    )
-
-def lines_chapter(c):
-    lines = []
-    for v in L.d(c, otype='verse'):
-        vl = F.verse.v(v)
-        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(v, otype='word'))
-        lines.append('{} {}'.format(vl, text.replace('\n', ' ')))
-    return lines
-
-def compare_chapters(c1, c2, lb1, lb2):
-    dh = difflib.HtmlDiff(wrapcolumn=80)
-    table_html = dh.make_table(
-        lines_chapter(c1), 
-        lines_chapter(c2), 
-        fromdesc=lb1, 
-        todesc=lb2, 
-        context=False, 
-        numlines=5,
-    )
-    htext = '''<html>{}<body>{}</body></html>'''.format(diffhead, table_html)
-    return htext
-
- -
-
-
- -
-
-
-
-
-
-

5.8.3 Compiling the table of experiments

Here we generate the table of experiments, complete with the coloring according to their assessments.

- -
-
-
-
-
-
In [18]:
-
-
-
# generate the table of experiments
-def gen_html(standalone=False):
-    global other_exps
-    info('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))
-    stats = collections.Counter()
-    pre = '''
-<html>
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
-{}
-</head>
-<body>
-'''.format(ecss) if standalone else ''
-    
-    post = '''
-</body></html>
-''' if standalone else ''
-
-    experiments = '''
-{}
-{}
-<table>
-<tr><th>chunk type</th><th>chunk size</th><th>similarity method</th>{}</tr>
-'''.format(pre, legend, ''.join('<th>{}</th>'.format(sim_thr) for sim_thr in SIMILARITIES))
-    
-    for chunk_f in (True, False):
-        if chunk_f:
-            chunk_items = CHUNK_SIZES
-        else:
-            chunk_items = CHUNK_OBJECTS
-        chunk_lb = CHUNK_LBS[chunk_f]
-        for chunk_i in chunk_items:
-            for sim_m in SIM_METHODS:
-                set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i)
-                these_outputs = outputs.get(MATRIX_THRESHOLD, {})
-                experiments += '<tr><td>{}</td><td>{}</td><td>{}</td>'.format(
-                    CHUNK_LABELS[chunk_f], chunk_i, sim_m,
-                )
-                for sim_thr in SIMILARITIES:
-                    okey = (chunk_lb, chunk_i, sim_m, sim_thr)
-                    values = these_outputs.get(okey)
-                    if values == None:
-                        result = '<td class="mis">&nbsp;</td>'
-                        stats['mis'] += 1
-                    else:
-                        (npassages, ncliques, longest_clique_len) = values
-                        cls = assess_exp(chunk_f, npassages, ncliques, longest_clique_len)
-                        stats[cls] += 1
-                        (lr_el, lr_lb) = ('', '')
-                        if (CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD) == (
-                            chunk_lb, chunk_i, sim_m, sim_thr,
-                        ):
-                            lr_el = '<span class="lr">*</span>'
-                            lr_lb = VALUE_LABELS['lr']
-                        result = '''
-<td class="{}" title="{}">{}
-    <span class="ps">{}</span><br/>
-    <a target="_blank" href="{}{}/{}_{}_{}_M{}_S{}.html"><span class="cl">{}</span></a><br/>
-    <span class="mx">{}</span>
-    </td>'''.format(
-        cls, lr_lb, lr_el, npassages,
-        '' if standalone else LOCAL_BASE_OUTP+'/', 
-        EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr,
-        ncliques, longest_clique_len,
-    )
-                    experiments += result
-                experiments += '</tr>\n'
-    experiments += '</table>\n{}'.format(post)
-    if standalone:
-        with open(EXPERIMENT_HTML, 'w') as f:
-            f.write(experiments)
-    else:
-        other_exps = experiments
-
-    for stat in sorted(stats):
-        info('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))
-    info("EXPERIMENT: Generated html report")
-
- -
-
-
- -
-
-
-
-
-
-

5.8.4 High level formatting functions

Here everything concerning output is brought together.

- -
-
-
-
-
-
In [19]:
-
-
-
def assess_exp(cf, np, nc, ll):
-    return 'out' if cf else \
-    'rec' if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else \
-    'dep' if ll > nc * DEP_CLIQUE_RATIO / 100 else \
-    'dub' if ll > nc * DUB_CLIQUE_RATIO / 100 else \
-    'nor'
-
-def printing():
-    global outputs, bin_cliques, base_name
-    info('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-    ))
-    xt_cliques = {xterse_clique(c) for c in cliques}     # chapter cliques as tuples of (b, ch) tuples
-    bin_cliques = {c for c in xt_cliques if len(c) == 2} # chapter cliques with exactly two chapters
-    # all chapters that occur in binary chapter cliques
-    bin_chapters = {c[0] for c in bin_cliques} | {c[1] for c in bin_cliques}
-    meta['# BINARY CHAPTER DIFFS'] = len(bin_cliques)
-
-    # We generate one kind of info for binary chapter cliques (the majority of cases).
-    # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they
-    # have member chunks in the same chapter, or in multiple (more than two) chapters.
-    
-    ncliques = len(cliques)
-    chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in {'rec', 'nor', 'dub'}
-    cdoing = 'involving' if chapters_ok else 'skipping'
-
-    info('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-        ncliques, cdoing, len(bin_cliques),
-    ))
-    meta_html = '\n'.join('{:<40} : {:>10}'.format(k, str(meta[k])) for k in meta)
-
-    base_name = '{}_{}_{}_M{}_S{}'.format(
-        CHUNK_LB,
-        CHUNK_DESC,
-        SIMILARITY_METHOD,
-        MATRIX_THRESHOLD,
-        SIMILARITY_THRESHOLD, 
-    )
-    param_spec = '''
-<table>
-<tr><th>chunking method</th><td>{}</td></tr>
-<tr><th>chunking description</th><td>{}</td></tr>
-<tr><th>similarity method</th><td>{}</td></tr>
-<tr><th>similarity threshold</th><td>{}</td></tr>
-</table>
-    '''.format(
-        CHUNK_LABELS[CHUNK_FIXED],
-        CHUNK_DESC,
-        SIMILARITY_METHOD, 
-        SIMILARITY_THRESHOLD, 
-    )
-    param_lab = 'chunk-{}-{}-sim-{}-m{}-s{}'.format(
-        CHUNK_LB,
-        CHUNK_DESC,
-        SIMILARITY_METHOD,
-        MATRIX_THRESHOLD,
-        SIMILARITY_THRESHOLD, 
-    )
-    index_name = base_name
-    all_name = '{}_{}'.format('all', base_name)
-    cliques_name = '{}_{}'.format('clique', base_name)
-
-    clique_links = []
-    clique_links.append(('{}/{}.html'.format(base_name, all_name), 'Big list of all cliques'))
-
-    nexist = 0
-    nnew = 0
-    if chapters_ok:
-        chapter_diffs = []
-        info('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-            len(bin_cliques),
-        ))
-
-        bcc_text = '<p>These results look good, so a binary chapter comparison has been generated</p>'
-        for cl in sorted(bin_cliques):
-            lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))
-            lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))
-            hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')
-            hfilepath = '{}/{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename)
-            chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(
-                SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,
-            )))
-            if not os.path.exists(hfilepath):
-                htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2)
-                with open(hfilepath, 'w') as f: f.write(htext)
-                if VERBOSE:
-                    info('PRINT ({} {} {} M>{} S>{}): written {}'.format(
-                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-                        hfilename,
-                    ))
-                nnew += 1
-            else:
-                nexist += 1
-            clique_links.append((
-                '../{}/{}'.format(CHAPTER_DIR, hfilename), 
-                '{} versus {}'.format(lb1, lb2),
-            ))
-        info('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(
-            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-            nnew, nexist,
-        ))
-    else:
-        bcc_text = '<p>These results look dubious at best, so no binary chapter comparison has been generated</p>'
-
-
-    allgeni_html = (index_clique(cliques_name, i, c, ncliques) for (i,c) in enumerate(cliques))
-    
-    allgen_htmls = []
-    allgen_html = ''
-    
-    for (i, c) in enumerate(cliques):
-        if i % CLIQUES_PER_FILE == 0:
-            if i > 0:
-                allgen_htmls.append(allgen_html)
-            allgen_html = ''
-        allgen_html += '<h3><a name="c_{}">Clique {}</a></h3>\n{}'.format(i, i, print_clique(c, ncliques))
-    allgen_htmls.append(allgen_html)
-
-    index_html_tpl = '''
-{}
-<h1>Binary chapter comparisons</h1>
-{}
-{}
-    '''
-
-    content_file_tpl = '''<html>
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
-<title>{}</title>
-<style type="text/css">
-{}
-</style>
-</head>
-<body>
-<h1>{}</h1>
-{}
-<p><a href="#meta">more parameters and stats</a></p>
-{}
-<h1><a name="meta">Parameters and stats</a></h1>
-<pre>{}</pre>
-</body>
-</html>'''
-    
-    a_tpl_file = '<p><a target="_blank" href="{}">{}</a></p>'
-
-    index_html_file = index_html_tpl.format(
-        a_tpl_file.format(*clique_links[0]),
-        bcc_text,
-        '\n'.join(a_tpl_file.format(*c) for c in clique_links[1:]),
-    )
-
-    listing_html = '{}\n'.format(
-        '\n'.join(allgeni_html),
-    )
-
-    for (subdir, fname, content_html, tit) in (
-        (None, index_name, index_html_file, 'Index '+param_lab),
-        (base_name, all_name, listing_html, 'Listing '+param_lab),
-        (base_name, cliques_name, allgen_htmls, 'Cliques '+param_lab),
-    ): 
-        subdir = '' if subdir == None else (subdir + '/')
-        subdirabs = '{}/{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir)
-        if not os.path.exists(subdirabs): os.makedirs(subdirabs)
-
-        if type(content_html) is list:
-            for (i, c_h) in enumerate(content_html):
-                fn = '{}_{}'.format(fname, i)
-                t = '{}_{}'.format(tit, i)
-                with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn), 'w') as f: 
-                    f.write(content_file_tpl.format(t, css, t, param_spec, c_h, meta_html))
-        else:
-            with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname), 'w') as f: 
-                f.write(content_file_tpl.format(tit, css, tit, param_spec, content_html, meta_html))
-    destination = outputs.setdefault(MATRIX_THRESHOLD, {})
-    destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = (
-        len(passages), len(cliques), l_c_l,
-    )
-    info('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(
-        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
-        len(cliques), len(allgen_htmls), cdoing, len(bin_cliques)
-    ))
-
- -
-
-
- -
-
-
-
-
-
-

5.9 Running experiments

The workflows of doing a single experiment, and then all experiments, are defined.

- -
-
-
-
-
-
In [20]:
-
-
-
outputs = {}
-
-def writeoutputs():
-    global outputs
-    with open(EXPERIMENT_PATH, 'wb') as f:
-        pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL)
-
-def readoutputs():
-    global outputs
-    if not os.path.exists(EXPERIMENT_PATH):
-        outputs = {}
-    else:
-        with open(EXPERIMENT_PATH, 'rb') as f:
-            outputs = pickle.load(f)
-
-def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index):
-    if do_index:
-        readoutputs()
-    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
-    if skip: return
-    chunking(do_chunk)
-    preparing(do_prep)
-    similarity(do_sim)
-    cliqueing(do_clique)
-    printing()
-    if do_index:
-        writeoutputs()
-        gen_html()
-
-def do_only_chunk(chunk_f, chunk_i):
-    do_chunk = do_params_chunk(chunk_f, chunk_i)
-    chunking(do_chunk)
-
-def reset_experiments():
-    global outputs
-    readoutputs()
-    outputs = {}
-    reset_params()
-    writeoutputs()
-    gen_html()
-
-def do_all_experiments(no_fixed=False, only_object=None):
-    global outputs
-    reset_experiments()
-    for chunk_f in (False,) if no_fixed else (True, False):
-        if chunk_f:
-            chunk_items = CHUNK_SIZES
-        else:
-            chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)
-        for chunk_i in chunk_items:
-            for sim_m in SIM_METHODS:
-                for sim_thr in SIMILARITIES:
-                    do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False)
-    writeoutputs()
-    gen_html()
-    gen_html(standalone=True)
-
-def do_all_chunks(no_fixed=False, only_object=None):
-    global outputs
-    reset_experiments()
-    for chunk_f in (False,) if no_fixed else (True, False):
-        if chunk_f:
-            chunk_items = CHUNK_SIZES
-        else:
-            chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)
-        for chunk_i in chunk_items:
-            do_only_chunk(chunk_f, chunk_i)
-    
-def show_all_experiments():
-    readoutputs()
-    gen_html()
-    gen_html(standalone=True)
-
- -
-
-
- -
-
-
-
-
-
-

6. SHEBANQ annotations

Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages.

- -
-
-
-
-
-
In [21]:
-
-
-
def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca)
-
-def get_verse_o(o, ca=False): return get_verse_w(L.d(o, otype='word')[0], ca=ca)
-
-def get_verse_w(w, ca=False):
-    book = F.book.v(L.u(w, otype='book')[0])
-    chapter = F.chapter.v(L.u(w, otype='chapter')[0])
-    verse = F.verse.v(L.u(w, otype='verse')[0])
-    if ca: ca = F.number.v(L.u(w, otype='clause_atom')[0])
-    return (book, chapter, verse, ca) if ca else (book, chapter, verse)
-
-def key_verse(x):
-    return  (book_rank[x[0]], int(x[1]), int(x[2]))
-
-MAX_REFS = 10
-
-def condensex(vlabels):
-    cnd = []
-    (cur_b, cur_c) = (None, None)
-    for (b, c, v, d) in vlabels:
-        sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '
-        show_b = b+' ' if cur_b != b else ''
-        show_c = c+':' if cur_b != b or cur_c != c else ''
-        (cur_b, cur_c) = (b, c)
-        cnd.append('{}{}{}{}{}'.format(sep, show_b, show_c, v, d))
-    return cnd
-
-dfields = '''
-    book1
-    chapter1
-    verse1
-    book2
-    chapter2
-    verse2
-    similarity
-'''.strip().split()
-
-dfields_fmt = ('{}\t' * (len(dfields) - 1)) + '{}\n' 
-
-def get_crossrefs():
-    global crossrefs
-    info('CROSSREFS: Fetching crossrefs')
-    crossrefs_proto = {}
-    crossrefs = {}
-    (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX
-    sim_thr = SHEBANQ_SIMILARITY
-    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
-    if skip: return
-    info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))
-    crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}
-    info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(
-        CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,
-        len(crossrefs_proto),
-    ))
-    f = open(CROSSREF_DB_PATH, 'w')
-    f.write('{}\n'.format('\t'.join(dfields)))        
-    for ((x,y), d) in crossrefs_proto:
-        vx = get_verse(x)
-        vy = get_verse(y)
-        rd = int(round(d))
-        crossrefs.setdefault(x, {})[vy] = rd
-        crossrefs.setdefault(y, {})[vx] = rd
-        f.write(dfields_fmt.format(*(vx+vy+(rd,))))
-    total = sum(len(x) for x in crossrefs.values())
-    f.close()
-    info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))
-
-def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to):
-    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
-    if skip: return
-    chunking(do_chunk)
-    preparing(do_prep)
-    similarity(do_sim)
-
-    info('CROSSREFS: Fetching crossrefs')
-    crossrefs_proto = {}
-    crossrefs = {}
-    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
-    if skip: return
-    info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))
-    crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}
-    info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(
-        CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,
-        len(crossrefs_proto),
-    ))
-    f = open('files/{}'.format(write_to), 'w')
-    f.write('{}\n'.format('\t'.join(dfields)))        
-    for ((x,y), d) in crossrefs_proto:
-        vx = get_verse(x)
-        vy = get_verse(y)
-        rd = int(round(d))
-        crossrefs.setdefault(x, {})[vy] = rd
-        crossrefs.setdefault(y, {})[vx] = rd
-        f.write(dfields_fmt.format(*(vx+vy+(rd,))))
-    total = sum(len(x) for x in crossrefs.values())
-    f.close()
-    info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))
-
-def compile_refs():
-    global refs_compiled
-    refs_grouped = []
-    for x in sorted(crossrefs):
-        refs = crossrefs[x]
-        vys = sorted(refs.keys(), key=key_verse)
-        currefs = []
-        for vy in vys:
-            nr = len(currefs)
-            if nr == MAX_REFS:
-                refs_grouped.append((x, tuple(currefs)))
-                currefs = []            
-            currefs.append(vy)
-        if len(currefs):
-            refs_grouped.append((x, tuple(currefs)))
-    refs_compiled = []
-    for (x, vys) in refs_grouped:
-        vysd = [(vy[0], vy[1], vy[2], ' ~{}%'.format(crossrefs[x][vy])) for vy in vys]
-        vysl = condensex(vysd)
-        these_refs = []
-        for (i, vy) in enumerate(vysd):
-            link_text = vysl[i]
-            link_target = '{} {}:{}'.format(vy[0], vy[1], vy[2])
-            these_refs.append('[{}]({})'.format(link_text, link_target))
-        refs_compiled.append((x, ' '.join(these_refs)))
-    info('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))
-
-def get_chapter_diffs():
-    global chapter_diffs
-    chapter_diffs = []
-    for cl in sorted(bin_cliques):
-        lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))
-        lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))
-        hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')
-        chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(
-            SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,
-        )))
-    info('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))
-
-        
-def get_clique_refs():
-    global clique_refs
-    clique_refs = []
-    for (i, c) in enumerate(cliques):
-        for j in c:
-            seq = i // CLIQUES_PER_FILE
-            clique_refs.append((j, i, '{}/{}/{}/{}/clique_{}_{}.html#c_{}'.format(
-                SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i,
-            )))
-    info('CROSSREFS: Added {} clique references'.format(len(clique_refs)))
-
-sfields = '''
-    version
-    book
-    chapter
-    verse
-    clause_atom
-    is_shared
-    is_published
-    status
-    keywords
-    ntext
-'''.strip().split()
-
-sfields_fmt = ('{}\t' * (len(sfields) - 1)) + '{}\n' 
-
-def generate_notes():
-    with open(NOTES_PATH, 'w') as f:
-        f.write('{}\n'.format('\t'.join(sfields)))        
-        x = next(F.otype.s('word'))
-        (bk, ch, vs, ca) = get_verse(x, ca=True)
-        f.write(sfields_fmt.format(
-            version,
-            bk,
-            ch,
-            vs,
-            ca,
-            'T',
-            '',
-            CROSSREF_STATUS,
-            CROSSREF_KEYWORD,
-            '''The crossref notes are the result of a computation without manual tweaks.
-Parameters: chunk by verse, similarity method SET with threshold 65.
-[Here](tool=parallel) is an account of the generation method.'''.replace('\n', ' ')
-        ))
-        for (lb1, ch1, lb2, ch2, fl) in chapter_diffs:
-            (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True)
-            (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True)
-            f.write(sfields_fmt.format(
-                version,
-                bk1,
-                ch1,
-                vs1,
-                ca1,
-                'T',
-                '',
-                CROSSREF_STATUS,
-                CROSSREF_KEYWORD,
-                '[chapter diff with {}](tool:{})'.format(lb2, fl),
-            ))
-            f.write(sfields_fmt.format(
-                version,
-                bk2,
-                ch2,
-                vs2,
-                ca2,
-                'T',
-                '',
-                CROSSREF_STATUS,
-                CROSSREF_KEYWORD,
-                '[chapter diff with {}](tool:{})'.format(lb1, fl),
-            ))
-        for (x, refs) in refs_compiled:
-            (bk, ch, vs, ca) = get_verse(x, ca=True)
-            f.write(sfields_fmt.format(
-                version,
-                bk,
-                ch,
-                vs,
-                ca,
-                'T',
-                '',
-                CROSSREF_STATUS,
-                CROSSREF_KEYWORD,
-                refs,
-            ))
-        for (chunk, clique, fl) in clique_refs:
-            (bk, ch, vs, ca) = get_verse(chunk, ca=True)
-            f.write(sfields_fmt.format(
-                version,
-                bk,
-                ch,
-                vs,
-                ca,
-                'T',
-                '',
-                CROSSREF_STATUS,
-                CROSSREF_KEYWORD,
-                '[all variants (clique {})](tool:{})'.format(clique, fl),
-            ))
-
-    info('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))
-
-def crossrefs2shebanq():
-    expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,)
-    do_experiment(*(expr+(True,)))
-    get_crossrefs()
-    compile_refs()
-    get_chapter_diffs()
-    get_clique_refs()
-    generate_notes()
-
- -
-
-
- -
-
-
-
-
-
-

7. Main

In the cell below you can select the experiments you want to carry out.

-

The previous cells contain just definitions and parameters. -The next cell will do work.

-

If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8).

- -
-
-
-
-
-
In [22]:
-
-
-
reset_params()
-#do_experiment(False, 'sentence', 'LCS', 60, False)
-do_all_experiments()
-#do_all_experiments(no_fixed=True, only_object='chapter')
-#crossrefs2shebanq()
-#show_all_experiments()
-#get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt')
-#do_all_chunks()
-
- -
-
-
- -
-
- - -
-
-
 2m 31s EXPERIMENT: Generating html report
- 2m 31s EXPERIMENT: 240 no results available
- 2m 31s EXPERIMENT: Generated html report
- 2m 31s CHUNKING (F 100): Loaded:  4244 chunks
- 2m 31s CHUNKING (F 100): Made 4244 chunks
- 2m 31s PREPARING (F 100 SET)
- 2m 32s PREPARING (F 100 SET): Done 4244 chunks.
- 2m 32s SIMILARITY (F 100 SET M>50): Loaded:  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 32s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates
- 2m 32s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix
- 2m 32s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages
- 2m 32s CLIQUES (F 100 SET M>50 S>100): Loaded:     1 cliques out of      2 chunks from 1 comparisons
- 2m 32s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques
- 2m 32s PRINT (F 100 SET M>50 S>100): sorting out cliques
- 2m 32s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs
- 2m 32s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
- 2m 32s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 32s PREPARING (F 100 SET): Already prepared
- 2m 32s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 32s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates
- 2m 32s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix
- 2m 32s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages
- 2m 32s CLIQUES (F 100 SET M>50 S>95): Loaded:     2 cliques out of      4 chunks from 2 comparisons
- 2m 32s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques
- 2m 32s PRINT (F 100 SET M>50 S>95): sorting out cliques
- 2m 32s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs
- 2m 32s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
- 2m 32s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 32s PREPARING (F 100 SET): Already prepared
- 2m 32s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 32s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates
- 2m 32s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix
- 2m 32s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages
- 2m 32s CLIQUES (F 100 SET M>50 S>90): Loaded:     9 cliques out of     18 chunks from 9 comparisons
- 2m 32s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques
- 2m 32s PRINT (F 100 SET M>50 S>90): sorting out cliques
- 2m 32s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs
- 2m 32s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs
- 2m 32s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 32s PREPARING (F 100 SET): Already prepared
- 2m 32s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 32s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates
- 2m 32s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix
- 2m 32s CLIQUES (F 100 SET M>50 S>85): 20 relevant similarities between 39 passages
- 2m 32s CLIQUES (F 100 SET M>50 S>85): Loaded:    19 cliques out of     39 chunks from 20 comparisons
- 2m 32s CLIQUES (F 100 SET M>50 S>85): 39 members in 19 cliques
- 2m 32s PRINT (F 100 SET M>50 S>85): sorting out cliques
- 2m 32s PRINT (F 100 SET M>50 S>85): formatting 19 cliques skipping 7 binary chapter diffs
- 2m 33s PRINT (F 100 SET M>50 S>85): formatted 19 cliques (1 files) skipping 7 binary chapter diffs
- 2m 33s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 33s PREPARING (F 100 SET): Already prepared
- 2m 33s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 33s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 33s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates
- 2m 33s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix
- 2m 33s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages
- 2m 33s CLIQUES (F 100 SET M>50 S>80): Loaded:    30 cliques out of     64 chunks from 35 comparisons
- 2m 33s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques
- 2m 33s PRINT (F 100 SET M>50 S>80): sorting out cliques
- 2m 33s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs
- 2m 34s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs
- 2m 34s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 34s PREPARING (F 100 SET): Already prepared
- 2m 34s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 34s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 34s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates
- 2m 34s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix
- 2m 34s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages
- 2m 34s CLIQUES (F 100 SET M>50 S>75): Loaded:    40 cliques out of     87 chunks from 63 comparisons
- 2m 34s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques
- 2m 34s PRINT (F 100 SET M>50 S>75): sorting out cliques
- 2m 34s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs
- 2m 35s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs
- 2m 35s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 35s PREPARING (F 100 SET): Already prepared
- 2m 35s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 35s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 35s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates
- 2m 35s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix
- 2m 35s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages
- 2m 35s CLIQUES (F 100 SET M>50 S>70): Loaded:    52 cliques out of    113 chunks from 87 comparisons
- 2m 35s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques
- 2m 35s PRINT (F 100 SET M>50 S>70): sorting out cliques
- 2m 35s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs
- 2m 36s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs
- 2m 36s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 36s PREPARING (F 100 SET): Already prepared
- 2m 36s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 36s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 36s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates
- 2m 36s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix
- 2m 36s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 156 passages
- 2m 36s CLIQUES (F 100 SET M>50 S>65): Loaded:    71 cliques out of    156 chunks from 115 comparisons
- 2m 36s CLIQUES (F 100 SET M>50 S>65): 156 members in 71 cliques
- 2m 36s PRINT (F 100 SET M>50 S>65): sorting out cliques
- 2m 36s PRINT (F 100 SET M>50 S>65): formatting 71 cliques skipping 28 binary chapter diffs
- 2m 37s PRINT (F 100 SET M>50 S>65): formatted 71 cliques (2 files) skipping 28 binary chapter diffs
- 2m 37s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 37s PREPARING (F 100 SET): Already prepared
- 2m 37s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 37s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates
- 2m 37s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix
- 2m 37s CLIQUES (F 100 SET M>50 S>60): 151 relevant similarities between 214 passages
- 2m 37s CLIQUES (F 100 SET M>50 S>60): Loaded:    97 cliques out of    214 chunks from 151 comparisons
- 2m 37s CLIQUES (F 100 SET M>50 S>60): 214 members in 97 cliques
- 2m 37s PRINT (F 100 SET M>50 S>60): sorting out cliques
- 2m 37s PRINT (F 100 SET M>50 S>60): formatting 97 cliques skipping 37 binary chapter diffs
- 2m 39s PRINT (F 100 SET M>50 S>60): formatted 97 cliques (2 files) skipping 37 binary chapter diffs
- 2m 39s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 39s PREPARING (F 100 SET): Already prepared
- 2m 39s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 39s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates
- 2m 39s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix
- 2m 39s CLIQUES (F 100 SET M>50 S>55): 223 relevant similarities between 308 passages
- 2m 39s CLIQUES (F 100 SET M>50 S>55): Loaded:   138 cliques out of    308 chunks from 223 comparisons
- 2m 39s CLIQUES (F 100 SET M>50 S>55): 308 members in 138 cliques
- 2m 39s PRINT (F 100 SET M>50 S>55): sorting out cliques
- 2m 39s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 56 binary chapter diffs
- 2m 42s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 56 binary chapter diffs
- 2m 42s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 42s PREPARING (F 100 SET): Already prepared
- 2m 42s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 354 entries in matrix
- 2m 42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
- 2m 42s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates
- 2m 42s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix
- 2m 42s CLIQUES (F 100 SET M>50 S>50): 354 relevant similarities between 469 passages
- 2m 42s CLIQUES (F 100 SET M>50 S>50): Loaded:   188 cliques out of    469 chunks from 354 comparisons
- 2m 42s CLIQUES (F 100 SET M>50 S>50): 469 members in 188 cliques
- 2m 42s PRINT (F 100 SET M>50 S>50): sorting out cliques
- 2m 43s PRINT (F 100 SET M>50 S>50): formatting 188 cliques skipping 77 binary chapter diffs
- 2m 48s PRINT (F 100 SET M>50 S>50): formatted 188 cliques (4 files) skipping 77 binary chapter diffs
- 2m 48s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 48s PREPARING (F 100 LCS)
- 2m 48s PREPARING (F 100 LCS): Done 4244 chunks.
- 2m 48s SIMILARITY (F 100 LCS M>60): Loaded:  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 48s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates
- 2m 48s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix
- 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages
- 2m 48s CLIQUES (F 100 LCS M>60 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
- 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques
- 2m 48s PRINT (F 100 LCS M>60 S>100): sorting out cliques
- 2m 48s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs
- 2m 48s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
- 2m 48s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 48s PREPARING (F 100 LCS): Already prepared
- 2m 48s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 48s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates
- 2m 48s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix
- 2m 48s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages
- 2m 48s CLIQUES (F 100 LCS M>60 S>95): Loaded:     2 cliques out of      4 chunks from 2 comparisons
- 2m 48s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques
- 2m 48s PRINT (F 100 LCS M>60 S>95): sorting out cliques
- 2m 48s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs
- 2m 48s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
- 2m 48s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 48s PREPARING (F 100 LCS): Already prepared
- 2m 48s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 48s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates
- 2m 48s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix
- 2m 48s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages
- 2m 48s CLIQUES (F 100 LCS M>60 S>90): Loaded:    19 cliques out of     39 chunks from 21 comparisons
- 2m 48s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques
- 2m 48s PRINT (F 100 LCS M>60 S>90): sorting out cliques
- 2m 48s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs
- 2m 49s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs
- 2m 49s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 49s PREPARING (F 100 LCS): Already prepared
- 2m 49s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 49s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 49s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates
- 2m 49s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix
- 2m 49s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages
- 2m 49s CLIQUES (F 100 LCS M>60 S>85): Loaded:    29 cliques out of     59 chunks from 31 comparisons
- 2m 49s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques
- 2m 49s PRINT (F 100 LCS M>60 S>85): sorting out cliques
- 2m 49s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs
- 2m 50s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs
- 2m 50s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 50s PREPARING (F 100 LCS): Already prepared
- 2m 50s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 50s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates
- 2m 50s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix
- 2m 50s CLIQUES (F 100 LCS M>60 S>80): 45 relevant similarities between 83 passages
- 2m 50s CLIQUES (F 100 LCS M>60 S>80): Loaded:    40 cliques out of     83 chunks from 45 comparisons
- 2m 50s CLIQUES (F 100 LCS M>60 S>80): 83 members in 40 cliques
- 2m 50s PRINT (F 100 LCS M>60 S>80): sorting out cliques
- 2m 50s PRINT (F 100 LCS M>60 S>80): formatting 40 cliques skipping 16 binary chapter diffs
- 2m 50s PRINT (F 100 LCS M>60 S>80): formatted 40 cliques (1 files) skipping 16 binary chapter diffs
- 2m 50s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 50s PREPARING (F 100 LCS): Already prepared
- 2m 50s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 50s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates
- 2m 50s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix
- 2m 50s CLIQUES (F 100 LCS M>60 S>75): 75 relevant similarities between 118 passages
- 2m 50s CLIQUES (F 100 LCS M>60 S>75): Loaded:    54 cliques out of    118 chunks from 75 comparisons
- 2m 50s CLIQUES (F 100 LCS M>60 S>75): 118 members in 54 cliques
- 2m 50s PRINT (F 100 LCS M>60 S>75): sorting out cliques
- 2m 50s PRINT (F 100 LCS M>60 S>75): formatting 54 cliques skipping 23 binary chapter diffs
- 2m 52s PRINT (F 100 LCS M>60 S>75): formatted 54 cliques (2 files) skipping 23 binary chapter diffs
- 2m 52s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 52s PREPARING (F 100 LCS): Already prepared
- 2m 52s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 52s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 52s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates
- 2m 52s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix
- 2m 52s CLIQUES (F 100 LCS M>60 S>70): 125 relevant similarities between 193 passages
- 2m 52s CLIQUES (F 100 LCS M>60 S>70): Loaded:    90 cliques out of    193 chunks from 125 comparisons
- 2m 52s CLIQUES (F 100 LCS M>60 S>70): 193 members in 90 cliques
- 2m 52s PRINT (F 100 LCS M>60 S>70): sorting out cliques
- 2m 52s PRINT (F 100 LCS M>60 S>70): formatting 90 cliques skipping 40 binary chapter diffs
- 2m 54s PRINT (F 100 LCS M>60 S>70): formatted 90 cliques (2 files) skipping 40 binary chapter diffs
- 2m 54s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 54s PREPARING (F 100 LCS): Already prepared
- 2m 54s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 54s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates
- 2m 54s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix
- 2m 54s CLIQUES (F 100 LCS M>60 S>65): 181 relevant similarities between 286 passages
- 2m 54s CLIQUES (F 100 LCS M>60 S>65): Loaded:   132 cliques out of    286 chunks from 181 comparisons
- 2m 54s CLIQUES (F 100 LCS M>60 S>65): 286 members in 132 cliques
- 2m 54s PRINT (F 100 LCS M>60 S>65): sorting out cliques
- 2m 54s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs
- 2m 57s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs
- 2m 57s CHUNKING (F 100): already chunked into 4244 chunks
- 2m 57s PREPARING (F 100 LCS): Already prepared
- 2m 57s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 394 entries in matrix
- 2m 57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
- 2m 57s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates
- 2m 57s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix
- 2m 57s CLIQUES (F 100 LCS M>60 S>60): 394 relevant similarities between 537 passages
- 2m 57s CLIQUES (F 100 LCS M>60 S>60): Loaded:   215 cliques out of    537 chunks from 394 comparisons
- 2m 57s CLIQUES (F 100 LCS M>60 S>60): 537 members in 215 cliques
- 2m 57s PRINT (F 100 LCS M>60 S>60): sorting out cliques
- 2m 57s PRINT (F 100 LCS M>60 S>60): formatting 215 cliques skipping 101 binary chapter diffs
- 3m 03s PRINT (F 100 LCS M>60 S>60): formatted 215 cliques (5 files) skipping 101 binary chapter diffs
- 3m 03s CHUNKING (F 50): Loaded:  8509 chunks
- 3m 03s CHUNKING (F 50): Made 8509 chunks
- 3m 03s PREPARING (F 50 SET)
- 3m 04s PREPARING (F 50 SET): Done 8509 chunks.
- 3m 04s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 04s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates
- 3m 04s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix
- 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages
- 3m 04s CLIQUES (F 50 SET M>50 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
- 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques
- 3m 04s PRINT (F 50 SET M>50 S>100): sorting out cliques
- 3m 04s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs
- 3m 04s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
- 3m 04s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 04s PREPARING (F 50 SET): Already prepared
- 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 04s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates
- 3m 04s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix
- 3m 04s CLIQUES (F 50 SET M>50 S>95): 3 relevant similarities between 6 passages
- 3m 04s CLIQUES (F 50 SET M>50 S>95): Loaded:     3 cliques out of      6 chunks from 3 comparisons
- 3m 04s CLIQUES (F 50 SET M>50 S>95): 6 members in 3 cliques
- 3m 04s PRINT (F 50 SET M>50 S>95): sorting out cliques
- 3m 04s PRINT (F 50 SET M>50 S>95): formatting 3 cliques skipping 3 binary chapter diffs
- 3m 04s PRINT (F 50 SET M>50 S>95): formatted 3 cliques (1 files) skipping 3 binary chapter diffs
- 3m 04s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 04s PREPARING (F 50 SET): Already prepared
- 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 04s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates
- 3m 04s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix
- 3m 04s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages
- 3m 04s CLIQUES (F 50 SET M>50 S>90): Loaded:    12 cliques out of     24 chunks from 12 comparisons
- 3m 04s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques
- 3m 04s PRINT (F 50 SET M>50 S>90): sorting out cliques
- 3m 04s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 6 binary chapter diffs
- 3m 04s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 6 binary chapter diffs
- 3m 04s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 04s PREPARING (F 50 SET): Already prepared
- 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 04s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates
- 3m 04s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix
- 3m 04s CLIQUES (F 50 SET M>50 S>85): 34 relevant similarities between 55 passages
- 3m 04s CLIQUES (F 50 SET M>50 S>85): Loaded:    25 cliques out of     55 chunks from 34 comparisons
- 3m 04s CLIQUES (F 50 SET M>50 S>85): 55 members in 25 cliques
- 3m 04s PRINT (F 50 SET M>50 S>85): sorting out cliques
- 3m 04s PRINT (F 50 SET M>50 S>85): formatting 25 cliques skipping 11 binary chapter diffs
- 3m 04s PRINT (F 50 SET M>50 S>85): formatted 25 cliques (1 files) skipping 11 binary chapter diffs
- 3m 04s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 04s PREPARING (F 50 SET): Already prepared
- 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 04s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates
- 3m 04s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix
- 3m 04s CLIQUES (F 50 SET M>50 S>80): 65 relevant similarities between 104 passages
- 3m 04s CLIQUES (F 50 SET M>50 S>80): Loaded:    47 cliques out of    104 chunks from 65 comparisons
- 3m 04s CLIQUES (F 50 SET M>50 S>80): 104 members in 47 cliques
- 3m 04s PRINT (F 50 SET M>50 S>80): sorting out cliques
- 3m 04s PRINT (F 50 SET M>50 S>80): formatting 47 cliques skipping 20 binary chapter diffs
- 3m 05s PRINT (F 50 SET M>50 S>80): formatted 47 cliques (1 files) skipping 20 binary chapter diffs
- 3m 05s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 05s PREPARING (F 50 SET): Already prepared
- 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 05s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates
- 3m 05s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix
- 3m 05s CLIQUES (F 50 SET M>50 S>75): 121 relevant similarities between 197 passages
- 3m 05s CLIQUES (F 50 SET M>50 S>75): Loaded:    90 cliques out of    197 chunks from 121 comparisons
- 3m 05s CLIQUES (F 50 SET M>50 S>75): 197 members in 90 cliques
- 3m 05s PRINT (F 50 SET M>50 S>75): sorting out cliques
- 3m 05s PRINT (F 50 SET M>50 S>75): formatting 90 cliques skipping 35 binary chapter diffs
- 3m 05s PRINT (F 50 SET M>50 S>75): formatted 90 cliques (2 files) skipping 35 binary chapter diffs
- 3m 05s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 05s PREPARING (F 50 SET): Already prepared
- 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 05s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates
- 3m 05s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix
- 3m 05s CLIQUES (F 50 SET M>50 S>70): 174 relevant similarities between 277 passages
- 3m 05s CLIQUES (F 50 SET M>50 S>70): Loaded:   127 cliques out of    277 chunks from 174 comparisons
- 3m 05s CLIQUES (F 50 SET M>50 S>70): 277 members in 127 cliques
- 3m 05s PRINT (F 50 SET M>50 S>70): sorting out cliques
- 3m 05s PRINT (F 50 SET M>50 S>70): formatting 127 cliques skipping 47 binary chapter diffs
- 3m 06s PRINT (F 50 SET M>50 S>70): formatted 127 cliques (3 files) skipping 47 binary chapter diffs
- 3m 06s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 06s PREPARING (F 50 SET): Already prepared
- 3m 06s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 06s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 06s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates
- 3m 06s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix
- 3m 06s CLIQUES (F 50 SET M>50 S>65): 254 relevant similarities between 394 passages
- 3m 06s CLIQUES (F 50 SET M>50 S>65): Loaded:   180 cliques out of    394 chunks from 254 comparisons
- 3m 06s CLIQUES (F 50 SET M>50 S>65): 394 members in 180 cliques
- 3m 06s PRINT (F 50 SET M>50 S>65): sorting out cliques
- 3m 06s PRINT (F 50 SET M>50 S>65): formatting 180 cliques skipping 61 binary chapter diffs
- 3m 07s PRINT (F 50 SET M>50 S>65): formatted 180 cliques (4 files) skipping 61 binary chapter diffs
- 3m 07s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 07s PREPARING (F 50 SET): Already prepared
- 3m 07s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 07s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 07s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates
- 3m 07s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix
- 3m 08s CLIQUES (F 50 SET M>50 S>60): 365 relevant similarities between 543 passages
- 3m 08s CLIQUES (F 50 SET M>50 S>60): Loaded:   239 cliques out of    543 chunks from 365 comparisons
- 3m 08s CLIQUES (F 50 SET M>50 S>60): 543 members in 239 cliques
- 3m 08s PRINT (F 50 SET M>50 S>60): sorting out cliques
- 3m 08s PRINT (F 50 SET M>50 S>60): formatting 239 cliques skipping 81 binary chapter diffs
- 3m 09s PRINT (F 50 SET M>50 S>60): formatted 239 cliques (5 files) skipping 81 binary chapter diffs
- 3m 09s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 09s PREPARING (F 50 SET): Already prepared
- 3m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 09s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates
- 3m 09s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix
- 3m 09s CLIQUES (F 50 SET M>50 S>55): 535 relevant similarities between 755 passages
- 3m 09s CLIQUES (F 50 SET M>50 S>55): Loaded:   322 cliques out of    755 chunks from 535 comparisons
- 3m 09s CLIQUES (F 50 SET M>50 S>55): 755 members in 322 cliques
- 3m 09s PRINT (F 50 SET M>50 S>55): sorting out cliques
- 3m 09s PRINT (F 50 SET M>50 S>55): formatting 322 cliques skipping 101 binary chapter diffs
- 3m 11s PRINT (F 50 SET M>50 S>55): formatted 322 cliques (7 files) skipping 101 binary chapter diffs
- 3m 11s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 11s PREPARING (F 50 SET): Already prepared
- 3m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix
- 3m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
- 3m 11s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates
- 3m 11s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix
- 3m 12s CLIQUES (F 50 SET M>50 S>50): 926 relevant similarities between 1183 passages
- 3m 12s CLIQUES (F 50 SET M>50 S>50): Loaded:   460 cliques out of   1183 chunks from 926 comparisons
- 3m 12s CLIQUES (F 50 SET M>50 S>50): 1183 members in 460 cliques
- 3m 12s PRINT (F 50 SET M>50 S>50): sorting out cliques
- 3m 12s PRINT (F 50 SET M>50 S>50): formatting 460 cliques skipping 132 binary chapter diffs
- 3m 16s PRINT (F 50 SET M>50 S>50): formatted 460 cliques (10 files) skipping 132 binary chapter diffs
- 3m 16s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 16s PREPARING (F 50 LCS)
- 3m 16s PREPARING (F 50 LCS): Done 8509 chunks.
- 3m 16s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 16s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates
- 3m 16s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix
- 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages
- 3m 16s CLIQUES (F 50 LCS M>60 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
- 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques
- 3m 16s PRINT (F 50 LCS M>60 S>100): sorting out cliques
- 3m 16s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs
- 3m 16s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
- 3m 16s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 16s PREPARING (F 50 LCS): Already prepared
- 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 16s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates
- 3m 16s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix
- 3m 16s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages
- 3m 16s CLIQUES (F 50 LCS M>60 S>95): Loaded:     6 cliques out of     12 chunks from 6 comparisons
- 3m 16s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques
- 3m 16s PRINT (F 50 LCS M>60 S>95): sorting out cliques
- 3m 16s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs
- 3m 16s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs
- 3m 16s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 16s PREPARING (F 50 LCS): Already prepared
- 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 16s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates
- 3m 16s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix
- 3m 16s CLIQUES (F 50 LCS M>60 S>90): 23 relevant similarities between 43 passages
- 3m 16s CLIQUES (F 50 LCS M>60 S>90): Loaded:    20 cliques out of     43 chunks from 23 comparisons
- 3m 16s CLIQUES (F 50 LCS M>60 S>90): 43 members in 20 cliques
- 3m 16s PRINT (F 50 LCS M>60 S>90): sorting out cliques
- 3m 16s PRINT (F 50 LCS M>60 S>90): formatting 20 cliques skipping 6 binary chapter diffs
- 3m 16s PRINT (F 50 LCS M>60 S>90): formatted 20 cliques (1 files) skipping 6 binary chapter diffs
- 3m 17s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 17s PREPARING (F 50 LCS): Already prepared
- 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 17s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates
- 3m 17s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix
- 3m 17s CLIQUES (F 50 LCS M>60 S>85): 77 relevant similarities between 125 passages
- 3m 17s CLIQUES (F 50 LCS M>60 S>85): Loaded:    56 cliques out of    125 chunks from 77 comparisons
- 3m 17s CLIQUES (F 50 LCS M>60 S>85): 125 members in 56 cliques
- 3m 17s PRINT (F 50 LCS M>60 S>85): sorting out cliques
- 3m 17s PRINT (F 50 LCS M>60 S>85): formatting 56 cliques skipping 21 binary chapter diffs
- 3m 17s PRINT (F 50 LCS M>60 S>85): formatted 56 cliques (2 files) skipping 21 binary chapter diffs
- 3m 17s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 17s PREPARING (F 50 LCS): Already prepared
- 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 17s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates
- 3m 17s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix
- 3m 17s CLIQUES (F 50 LCS M>60 S>80): 129 relevant similarities between 204 passages
- 3m 17s CLIQUES (F 50 LCS M>60 S>80): Loaded:    93 cliques out of    204 chunks from 129 comparisons
- 3m 17s CLIQUES (F 50 LCS M>60 S>80): 204 members in 93 cliques
- 3m 17s PRINT (F 50 LCS M>60 S>80): sorting out cliques
- 3m 17s PRINT (F 50 LCS M>60 S>80): formatting 93 cliques skipping 35 binary chapter diffs
- 3m 18s PRINT (F 50 LCS M>60 S>80): formatted 93 cliques (2 files) skipping 35 binary chapter diffs
- 3m 18s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 18s PREPARING (F 50 LCS): Already prepared
- 3m 18s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 18s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 18s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates
- 3m 18s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix
- 3m 18s CLIQUES (F 50 LCS M>60 S>75): 198 relevant similarities between 299 passages
- 3m 18s CLIQUES (F 50 LCS M>60 S>75): Loaded:   134 cliques out of    299 chunks from 198 comparisons
- 3m 18s CLIQUES (F 50 LCS M>60 S>75): 299 members in 134 cliques
- 3m 18s PRINT (F 50 LCS M>60 S>75): sorting out cliques
- 3m 18s PRINT (F 50 LCS M>60 S>75): formatting 134 cliques skipping 51 binary chapter diffs
- 3m 19s PRINT (F 50 LCS M>60 S>75): formatted 134 cliques (3 files) skipping 51 binary chapter diffs
- 3m 19s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 19s PREPARING (F 50 LCS): Already prepared
- 3m 19s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 19s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 19s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates
- 3m 19s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix
- 3m 19s CLIQUES (F 50 LCS M>60 S>70): 314 relevant similarities between 470 passages
- 3m 19s CLIQUES (F 50 LCS M>60 S>70): Loaded:   209 cliques out of    470 chunks from 314 comparisons
- 3m 19s CLIQUES (F 50 LCS M>60 S>70): 470 members in 209 cliques
- 3m 19s PRINT (F 50 LCS M>60 S>70): sorting out cliques
- 3m 19s PRINT (F 50 LCS M>60 S>70): formatting 209 cliques skipping 65 binary chapter diffs
- 3m 20s PRINT (F 50 LCS M>60 S>70): formatted 209 cliques (5 files) skipping 65 binary chapter diffs
- 3m 20s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 20s PREPARING (F 50 LCS): Already prepared
- 3m 20s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 20s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 20s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates
- 3m 20s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix
- 3m 20s CLIQUES (F 50 LCS M>60 S>65): 587 relevant similarities between 765 passages
- 3m 20s CLIQUES (F 50 LCS M>60 S>65): Loaded:   312 cliques out of    765 chunks from 587 comparisons
- 3m 20s CLIQUES (F 50 LCS M>60 S>65): 765 members in 312 cliques
- 3m 20s PRINT (F 50 LCS M>60 S>65): sorting out cliques
- 3m 20s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs
- 3m 23s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs
- 3m 23s CHUNKING (F 50): already chunked into 8509 chunks
- 3m 23s PREPARING (F 50 LCS): Already prepared
- 3m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix
- 3m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%
- 3m 23s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates
- 3m 23s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix
- 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1816 relevant similarities between 1867 passages
- 3m 23s CLIQUES (F 50 LCS M>60 S>60): Loaded:   553 cliques out of   1867 chunks from 1816 comparisons
- 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1867 members in 553 cliques
- 3m 23s PRINT (F 50 LCS M>60 S>60): sorting out cliques
- 3m 23s PRINT (F 50 LCS M>60 S>60): formatting 553 cliques skipping 225 binary chapter diffs
- 3m 30s PRINT (F 50 LCS M>60 S>60): formatted 553 cliques (12 files) skipping 225 binary chapter diffs
- 3m 30s CHUNKING (F 20): Loaded: 21312 chunks
- 3m 30s CHUNKING (F 20): Made 21312 chunks
- 3m 30s PREPARING (F 20 SET)
- 3m 31s PREPARING (F 20 SET): Done 21312 chunks.
- 3m 31s SIMILARITY (F 20 SET M>50): Loaded:   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 31s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates
- 3m 31s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix
- 3m 31s CLIQUES (F 20 SET M>50 S>100): 18 relevant similarities between 36 passages
- 3m 31s CLIQUES (F 20 SET M>50 S>100): Loaded:    18 cliques out of     36 chunks from 18 comparisons
- 3m 31s CLIQUES (F 20 SET M>50 S>100): 36 members in 18 cliques
- 3m 31s PRINT (F 20 SET M>50 S>100): sorting out cliques
- 3m 31s PRINT (F 20 SET M>50 S>100): formatting 18 cliques skipping 11 binary chapter diffs
- 3m 31s PRINT (F 20 SET M>50 S>100): formatted 18 cliques (1 files) skipping 11 binary chapter diffs
- 3m 31s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 31s PREPARING (F 20 SET): Already prepared
- 3m 31s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 31s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates
- 3m 31s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix
- 3m 31s CLIQUES (F 20 SET M>50 S>95): 18 relevant similarities between 36 passages
- 3m 31s CLIQUES (F 20 SET M>50 S>95): Loaded:    18 cliques out of     36 chunks from 18 comparisons
- 3m 31s CLIQUES (F 20 SET M>50 S>95): 36 members in 18 cliques
- 3m 31s PRINT (F 20 SET M>50 S>95): sorting out cliques
- 3m 31s PRINT (F 20 SET M>50 S>95): formatting 18 cliques skipping 11 binary chapter diffs
- 3m 31s PRINT (F 20 SET M>50 S>95): formatted 18 cliques (1 files) skipping 11 binary chapter diffs
- 3m 31s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 31s PREPARING (F 20 SET): Already prepared
- 3m 31s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 31s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates
- 3m 31s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix
- 3m 31s CLIQUES (F 20 SET M>50 S>90): 72 relevant similarities between 126 passages
- 3m 31s CLIQUES (F 20 SET M>50 S>90): Loaded:    58 cliques out of    126 chunks from 72 comparisons
- 3m 31s CLIQUES (F 20 SET M>50 S>90): 126 members in 58 cliques
- 3m 31s PRINT (F 20 SET M>50 S>90): sorting out cliques
- 3m 31s PRINT (F 20 SET M>50 S>90): formatting 58 cliques skipping 24 binary chapter diffs
- 3m 31s PRINT (F 20 SET M>50 S>90): formatted 58 cliques (2 files) skipping 24 binary chapter diffs
- 3m 31s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 31s PREPARING (F 20 SET): Already prepared
- 3m 31s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 31s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates
- 3m 31s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix
- 3m 31s CLIQUES (F 20 SET M>50 S>85): 133 relevant similarities between 199 passages
- 3m 31s CLIQUES (F 20 SET M>50 S>85): Loaded:    84 cliques out of    199 chunks from 133 comparisons
- 3m 31s CLIQUES (F 20 SET M>50 S>85): 199 members in 84 cliques
- 3m 31s PRINT (F 20 SET M>50 S>85): sorting out cliques
- 3m 31s PRINT (F 20 SET M>50 S>85): formatting 84 cliques skipping 37 binary chapter diffs
- 3m 31s PRINT (F 20 SET M>50 S>85): formatted 84 cliques (2 files) skipping 37 binary chapter diffs
- 3m 31s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 31s PREPARING (F 20 SET): Already prepared
- 3m 31s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 31s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates
- 3m 31s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix
- 3m 31s CLIQUES (F 20 SET M>50 S>80): 224 relevant similarities between 332 passages
- 3m 31s CLIQUES (F 20 SET M>50 S>80): Loaded:   146 cliques out of    332 chunks from 224 comparisons
- 3m 31s CLIQUES (F 20 SET M>50 S>80): 332 members in 146 cliques
- 3m 31s PRINT (F 20 SET M>50 S>80): sorting out cliques
- 3m 31s PRINT (F 20 SET M>50 S>80): formatting 146 cliques skipping 62 binary chapter diffs
- 3m 32s PRINT (F 20 SET M>50 S>80): formatted 146 cliques (3 files) skipping 62 binary chapter diffs
- 3m 32s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 32s PREPARING (F 20 SET): Already prepared
- 3m 32s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 32s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates
- 3m 32s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix
- 3m 32s CLIQUES (F 20 SET M>50 S>75): 372 relevant similarities between 528 passages
- 3m 32s CLIQUES (F 20 SET M>50 S>75): Loaded:   227 cliques out of    528 chunks from 372 comparisons
- 3m 32s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques
- 3m 32s PRINT (F 20 SET M>50 S>75): sorting out cliques
- 3m 32s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 82 binary chapter diffs
- 3m 32s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 82 binary chapter diffs
- 3m 32s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 32s PREPARING (F 20 SET): Already prepared
- 3m 32s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 32s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates
- 3m 32s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix
- 3m 32s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 760 passages
- 3m 32s CLIQUES (F 20 SET M>50 S>70): Loaded:   326 cliques out of    760 chunks from 546 comparisons
- 3m 32s CLIQUES (F 20 SET M>50 S>70): 760 members in 326 cliques
- 3m 32s PRINT (F 20 SET M>50 S>70): sorting out cliques
- 3m 32s PRINT (F 20 SET M>50 S>70): formatting 326 cliques skipping 107 binary chapter diffs
- 3m 33s PRINT (F 20 SET M>50 S>70): formatted 326 cliques (7 files) skipping 107 binary chapter diffs
- 3m 33s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 33s PREPARING (F 20 SET): Already prepared
- 3m 33s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 33s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 33s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates
- 3m 33s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix
- 3m 33s CLIQUES (F 20 SET M>50 S>65): 803 relevant similarities between 1096 passages
- 3m 33s CLIQUES (F 20 SET M>50 S>65): Loaded:   470 cliques out of   1096 chunks from 803 comparisons
- 3m 33s CLIQUES (F 20 SET M>50 S>65): 1096 members in 470 cliques
- 3m 33s PRINT (F 20 SET M>50 S>65): sorting out cliques
- 3m 33s PRINT (F 20 SET M>50 S>65): formatting 470 cliques skipping 144 binary chapter diffs
- 3m 34s PRINT (F 20 SET M>50 S>65): formatted 470 cliques (10 files) skipping 144 binary chapter diffs
- 3m 34s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 34s PREPARING (F 20 SET): Already prepared
- 3m 34s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 34s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates
- 3m 34s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix
- 3m 34s CLIQUES (F 20 SET M>50 S>60): 1414 relevant similarities between 1837 passages
- 3m 34s CLIQUES (F 20 SET M>50 S>60): Loaded:   739 cliques out of   1837 chunks from 1414 comparisons
- 3m 34s CLIQUES (F 20 SET M>50 S>60): 1837 members in 739 cliques
- 3m 34s PRINT (F 20 SET M>50 S>60): sorting out cliques
- 3m 34s PRINT (F 20 SET M>50 S>60): formatting 739 cliques skipping 213 binary chapter diffs
- 3m 36s PRINT (F 20 SET M>50 S>60): formatted 739 cliques (15 files) skipping 213 binary chapter diffs
- 3m 36s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 36s PREPARING (F 20 SET): Already prepared
- 3m 36s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 36s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates
- 3m 36s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix
- 3m 36s CLIQUES (F 20 SET M>50 S>55): 2453 relevant similarities between 2826 passages
- 3m 36s CLIQUES (F 20 SET M>50 S>55): Loaded:   997 cliques out of   2826 chunks from 2453 comparisons
- 3m 36s CLIQUES (F 20 SET M>50 S>55): 2826 members in 997 cliques
- 3m 36s PRINT (F 20 SET M>50 S>55): sorting out cliques
- 3m 36s PRINT (F 20 SET M>50 S>55): formatting 997 cliques skipping 296 binary chapter diffs
- 3m 39s PRINT (F 20 SET M>50 S>55): formatted 997 cliques (20 files) skipping 296 binary chapter diffs
- 3m 39s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 39s PREPARING (F 20 SET): Already prepared
- 3m 39s SIMILARITY (F 20 SET M>50): Using   227 M (227090016) comparisons with 5559 entries in matrix
- 3m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%
- 3m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates
- 3m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix
- 3m 39s CLIQUES (F 20 SET M>50 S>50): 5559 relevant similarities between 4933 passages
- 3m 39s CLIQUES (F 20 SET M>50 S>50): Loaded:  1212 cliques out of   4933 chunks from 5559 comparisons
- 3m 39s CLIQUES (F 20 SET M>50 S>50): 4933 members in 1212 cliques
- 3m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques
- 3m 39s PRINT (F 20 SET M>50 S>50): formatting 1212 cliques skipping 416 binary chapter diffs
- 3m 42s PRINT (F 20 SET M>50 S>50): formatted 1212 cliques (25 files) skipping 416 binary chapter diffs
- 3m 42s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 42s PREPARING (F 20 LCS)
- 3m 43s PREPARING (F 20 LCS): Done 21312 chunks.
- 3m 43s SIMILARITY (F 20 LCS M>60): Loaded:   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 43s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates
- 3m 43s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix
- 3m 43s CLIQUES (F 20 LCS M>60 S>100): 6 relevant similarities between 12 passages
- 3m 43s CLIQUES (F 20 LCS M>60 S>100): Loaded:     6 cliques out of     12 chunks from 6 comparisons
- 3m 43s CLIQUES (F 20 LCS M>60 S>100): 12 members in 6 cliques
- 3m 43s PRINT (F 20 LCS M>60 S>100): sorting out cliques
- 3m 43s PRINT (F 20 LCS M>60 S>100): formatting 6 cliques skipping 5 binary chapter diffs
- 3m 43s PRINT (F 20 LCS M>60 S>100): formatted 6 cliques (1 files) skipping 5 binary chapter diffs
- 3m 43s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 43s PREPARING (F 20 LCS): Already prepared
- 3m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 43s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates
- 3m 43s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix
- 3m 43s CLIQUES (F 20 LCS M>60 S>95): 33 relevant similarities between 62 passages
- 3m 43s CLIQUES (F 20 LCS M>60 S>95): Loaded:    29 cliques out of     62 chunks from 33 comparisons
- 3m 43s CLIQUES (F 20 LCS M>60 S>95): 62 members in 29 cliques
- 3m 43s PRINT (F 20 LCS M>60 S>95): sorting out cliques
- 3m 43s PRINT (F 20 LCS M>60 S>95): formatting 29 cliques skipping 14 binary chapter diffs
- 3m 43s PRINT (F 20 LCS M>60 S>95): formatted 29 cliques (1 files) skipping 14 binary chapter diffs
- 3m 43s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 43s PREPARING (F 20 LCS): Already prepared
- 3m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates
- 3m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix
- 3m 43s CLIQUES (F 20 LCS M>60 S>90): 115 relevant similarities between 181 passages
- 3m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded:    76 cliques out of    181 chunks from 115 comparisons
- 3m 43s CLIQUES (F 20 LCS M>60 S>90): 181 members in 76 cliques
- 3m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques
- 3m 43s PRINT (F 20 LCS M>60 S>90): formatting 76 cliques skipping 33 binary chapter diffs
- 3m 43s PRINT (F 20 LCS M>60 S>90): formatted 76 cliques (2 files) skipping 33 binary chapter diffs
- 3m 43s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 43s PREPARING (F 20 LCS): Already prepared
- 3m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 44s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates
- 3m 44s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix
- 3m 44s CLIQUES (F 20 LCS M>60 S>85): 232 relevant similarities between 339 passages
- 3m 44s CLIQUES (F 20 LCS M>60 S>85): Loaded:   149 cliques out of    339 chunks from 232 comparisons
- 3m 44s CLIQUES (F 20 LCS M>60 S>85): 339 members in 149 cliques
- 3m 44s PRINT (F 20 LCS M>60 S>85): sorting out cliques
- 3m 44s PRINT (F 20 LCS M>60 S>85): formatting 149 cliques skipping 57 binary chapter diffs
- 3m 44s PRINT (F 20 LCS M>60 S>85): formatted 149 cliques (3 files) skipping 57 binary chapter diffs
- 3m 44s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 44s PREPARING (F 20 LCS): Already prepared
- 3m 44s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 44s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates
- 3m 44s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix
- 3m 44s CLIQUES (F 20 LCS M>60 S>80): 470 relevant similarities between 681 passages
- 3m 44s CLIQUES (F 20 LCS M>60 S>80): Loaded:   300 cliques out of    681 chunks from 470 comparisons
- 3m 44s CLIQUES (F 20 LCS M>60 S>80): 681 members in 300 cliques
- 3m 44s PRINT (F 20 LCS M>60 S>80): sorting out cliques
- 3m 44s PRINT (F 20 LCS M>60 S>80): formatting 300 cliques skipping 106 binary chapter diffs
- 3m 45s PRINT (F 20 LCS M>60 S>80): formatted 300 cliques (6 files) skipping 106 binary chapter diffs
- 3m 45s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 45s PREPARING (F 20 LCS): Already prepared
- 3m 45s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 45s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 45s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates
- 3m 45s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix
- 3m 45s CLIQUES (F 20 LCS M>60 S>75): 876 relevant similarities between 1137 passages
- 3m 45s CLIQUES (F 20 LCS M>60 S>75): Loaded:   470 cliques out of   1137 chunks from 876 comparisons
- 3m 45s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques
- 3m 45s PRINT (F 20 LCS M>60 S>75): sorting out cliques
- 3m 45s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs
- 3m 46s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs
- 3m 46s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 46s PREPARING (F 20 LCS): Already prepared
- 3m 46s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 46s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates
- 3m 46s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix
- 3m 46s CLIQUES (F 20 LCS M>60 S>70): 1935 relevant similarities between 2224 passages
- 3m 46s CLIQUES (F 20 LCS M>60 S>70): Loaded:   844 cliques out of   2224 chunks from 1935 comparisons
- 3m 46s CLIQUES (F 20 LCS M>60 S>70): 2224 members in 844 cliques
- 3m 46s PRINT (F 20 LCS M>60 S>70): sorting out cliques
- 3m 46s PRINT (F 20 LCS M>60 S>70): formatting 844 cliques skipping 306 binary chapter diffs
- 3m 48s PRINT (F 20 LCS M>60 S>70): formatted 844 cliques (17 files) skipping 306 binary chapter diffs
- 3m 48s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 48s PREPARING (F 20 LCS): Already prepared
- 3m 48s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 48s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 48s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates
- 3m 48s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix
- 3m 48s CLIQUES (F 20 LCS M>60 S>65): 6898 relevant similarities between 5985 passages
- 3m 48s CLIQUES (F 20 LCS M>60 S>65): Loaded:  1253 cliques out of   5985 chunks from 6898 comparisons
- 3m 48s CLIQUES (F 20 LCS M>60 S>65): 5985 members in 1253 cliques
- 3m 48s PRINT (F 20 LCS M>60 S>65): sorting out cliques
- 3m 49s PRINT (F 20 LCS M>60 S>65): formatting 1253 cliques skipping 575 binary chapter diffs
- 3m 52s PRINT (F 20 LCS M>60 S>65): formatted 1253 cliques (26 files) skipping 575 binary chapter diffs
- 3m 52s CHUNKING (F 20): already chunked into 21312 chunks
- 3m 52s PREPARING (F 20 LCS): Already prepared
- 3m 52s SIMILARITY (F 20 LCS M>60): Using   227 M (227090016) comparisons with 121585 entries in matrix
- 3m 52s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%
- 3m 52s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates
- 3m 52s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix
- 3m 53s CLIQUES (F 20 LCS M>60 S>60): 121585 relevant similarities between 17654 passages
- 3m 53s CLIQUES (F 20 LCS M>60 S>60): Loaded:   163 cliques out of  17654 chunks from 121585 comparisons
- 3m 53s CLIQUES (F 20 LCS M>60 S>60): 17654 members in 163 cliques
- 3m 53s PRINT (F 20 LCS M>60 S>60): sorting out cliques
- 3m 53s PRINT (F 20 LCS M>60 S>60): formatting 163 cliques skipping 104 binary chapter diffs
- 3m 53s PRINT (F 20 LCS M>60 S>60): formatted 163 cliques (4 files) skipping 104 binary chapter diffs
- 3m 53s CHUNKING (F 10): Loaded: 42640 chunks
- 3m 53s CHUNKING (F 10): Made 42640 chunks
- 3m 53s PREPARING (F 10 SET)
- 3m 54s PREPARING (F 10 SET): Done 42640 chunks.
- 3m 54s SIMILARITY (F 10 SET M>50): Loaded:   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 55s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates
- 3m 55s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix
- 3m 55s CLIQUES (F 10 SET M>50 S>100): 269 relevant similarities between 462 passages
- 3m 55s CLIQUES (F 10 SET M>50 S>100): Loaded:   220 cliques out of    462 chunks from 269 comparisons
- 3m 55s CLIQUES (F 10 SET M>50 S>100): 462 members in 220 cliques
- 3m 55s PRINT (F 10 SET M>50 S>100): sorting out cliques
- 3m 55s PRINT (F 10 SET M>50 S>100): formatting 220 cliques skipping 83 binary chapter diffs
- 3m 55s PRINT (F 10 SET M>50 S>100): formatted 220 cliques (5 files) skipping 83 binary chapter diffs
- 3m 55s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 55s PREPARING (F 10 SET): Already prepared
- 3m 55s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 55s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates
- 3m 55s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix
- 3m 55s CLIQUES (F 10 SET M>50 S>95): 269 relevant similarities between 462 passages
- 3m 55s CLIQUES (F 10 SET M>50 S>95): Loaded:   220 cliques out of    462 chunks from 269 comparisons
- 3m 55s CLIQUES (F 10 SET M>50 S>95): 462 members in 220 cliques
- 3m 55s PRINT (F 10 SET M>50 S>95): sorting out cliques
- 3m 55s PRINT (F 10 SET M>50 S>95): formatting 220 cliques skipping 83 binary chapter diffs
- 3m 55s PRINT (F 10 SET M>50 S>95): formatted 220 cliques (5 files) skipping 83 binary chapter diffs
- 3m 55s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 55s PREPARING (F 10 SET): Already prepared
- 3m 55s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 55s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates
- 3m 55s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix
- 3m 55s CLIQUES (F 10 SET M>50 S>90): 307 relevant similarities between 494 passages
- 3m 55s CLIQUES (F 10 SET M>50 S>90): Loaded:   231 cliques out of    494 chunks from 307 comparisons
- 3m 55s CLIQUES (F 10 SET M>50 S>90): 494 members in 231 cliques
- 3m 55s PRINT (F 10 SET M>50 S>90): sorting out cliques
- 3m 55s PRINT (F 10 SET M>50 S>90): formatting 231 cliques skipping 88 binary chapter diffs
- 3m 55s PRINT (F 10 SET M>50 S>90): formatted 231 cliques (5 files) skipping 88 binary chapter diffs
- 3m 55s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 55s PREPARING (F 10 SET): Already prepared
- 3m 55s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 55s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates
- 3m 55s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix
- 3m 55s CLIQUES (F 10 SET M>50 S>85): 732 relevant similarities between 1109 passages
- 3m 55s CLIQUES (F 10 SET M>50 S>85): Loaded:   489 cliques out of   1109 chunks from 732 comparisons
- 3m 55s CLIQUES (F 10 SET M>50 S>85): 1109 members in 489 cliques
- 3m 55s PRINT (F 10 SET M>50 S>85): sorting out cliques
- 3m 55s PRINT (F 10 SET M>50 S>85): formatting 489 cliques skipping 186 binary chapter diffs
- 3m 56s PRINT (F 10 SET M>50 S>85): formatted 489 cliques (10 files) skipping 186 binary chapter diffs
- 3m 56s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 56s PREPARING (F 10 SET): Already prepared
- 3m 56s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 56s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 56s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates
- 3m 56s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix
- 3m 56s CLIQUES (F 10 SET M>50 S>80): 1140 relevant similarities between 1540 passages
- 3m 56s CLIQUES (F 10 SET M>50 S>80): Loaded:   631 cliques out of   1540 chunks from 1140 comparisons
- 3m 56s CLIQUES (F 10 SET M>50 S>80): 1540 members in 631 cliques
- 3m 56s PRINT (F 10 SET M>50 S>80): sorting out cliques
- 3m 56s PRINT (F 10 SET M>50 S>80): formatting 631 cliques skipping 218 binary chapter diffs
- 3m 57s PRINT (F 10 SET M>50 S>80): formatted 631 cliques (13 files) skipping 218 binary chapter diffs
- 3m 57s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 57s PREPARING (F 10 SET): Already prepared
- 3m 57s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 57s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates
- 3m 57s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix
- 3m 57s CLIQUES (F 10 SET M>50 S>75): 2144 relevant similarities between 2825 passages
- 3m 57s CLIQUES (F 10 SET M>50 S>75): Loaded:  1126 cliques out of   2825 chunks from 2144 comparisons
- 3m 57s CLIQUES (F 10 SET M>50 S>75): 2825 members in 1126 cliques
- 3m 57s PRINT (F 10 SET M>50 S>75): sorting out cliques
- 3m 57s PRINT (F 10 SET M>50 S>75): formatting 1126 cliques skipping 418 binary chapter diffs
- 3m 58s PRINT (F 10 SET M>50 S>75): formatted 1126 cliques (23 files) skipping 418 binary chapter diffs
- 3m 58s CHUNKING (F 10): already chunked into 42640 chunks
- 3m 58s PREPARING (F 10 SET): Already prepared
- 3m 58s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 3m 58s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 3m 58s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates
- 3m 58s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix
- 3m 58s CLIQUES (F 10 SET M>50 S>70): 3512 relevant similarities between 4079 passages
- 3m 58s CLIQUES (F 10 SET M>50 S>70): Loaded:  1506 cliques out of   4079 chunks from 3512 comparisons
- 3m 58s CLIQUES (F 10 SET M>50 S>70): 4079 members in 1506 cliques
- 3m 58s PRINT (F 10 SET M>50 S>70): sorting out cliques
- 3m 59s PRINT (F 10 SET M>50 S>70): formatting 1506 cliques skipping 570 binary chapter diffs
- 4m 00s PRINT (F 10 SET M>50 S>70): formatted 1506 cliques (31 files) skipping 570 binary chapter diffs
- 4m 00s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 00s PREPARING (F 10 SET): Already prepared
- 4m 00s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 4m 00s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 4m 00s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates
- 4m 00s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix
- 4m 01s CLIQUES (F 10 SET M>50 S>65): 5448 relevant similarities between 5792 passages
- 4m 01s CLIQUES (F 10 SET M>50 S>65): Loaded:  1855 cliques out of   5792 chunks from 5448 comparisons
- 4m 01s CLIQUES (F 10 SET M>50 S>65): 5792 members in 1855 cliques
- 4m 01s PRINT (F 10 SET M>50 S>65): sorting out cliques
- 4m 01s PRINT (F 10 SET M>50 S>65): formatting 1855 cliques skipping 679 binary chapter diffs
- 4m 03s PRINT (F 10 SET M>50 S>65): formatted 1855 cliques (38 files) skipping 679 binary chapter diffs
- 4m 03s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 03s PREPARING (F 10 SET): Already prepared
- 4m 03s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 4m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 4m 03s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates
- 4m 03s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix
- 4m 03s CLIQUES (F 10 SET M>50 S>60): 13180 relevant similarities between 10165 passages
- 4m 03s CLIQUES (F 10 SET M>50 S>60): Loaded:  2189 cliques out of  10165 chunks from 13180 comparisons
- 4m 03s CLIQUES (F 10 SET M>50 S>60): 10165 members in 2189 cliques
- 4m 03s PRINT (F 10 SET M>50 S>60): sorting out cliques
- 4m 03s PRINT (F 10 SET M>50 S>60): formatting 2189 cliques skipping 823 binary chapter diffs
- 4m 05s PRINT (F 10 SET M>50 S>60): formatted 2189 cliques (44 files) skipping 823 binary chapter diffs
- 4m 05s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 05s PREPARING (F 10 SET): Already prepared
- 4m 05s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 4m 05s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 4m 05s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates
- 4m 05s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix
- 4m 06s CLIQUES (F 10 SET M>50 S>55): 25713 relevant similarities between 13984 passages
- 4m 06s CLIQUES (F 10 SET M>50 S>55): Loaded:  2008 cliques out of  13984 chunks from 25713 comparisons
- 4m 06s CLIQUES (F 10 SET M>50 S>55): 13984 members in 2008 cliques
- 4m 06s PRINT (F 10 SET M>50 S>55): sorting out cliques
- 4m 06s PRINT (F 10 SET M>50 S>55): formatting 2008 cliques skipping 777 binary chapter diffs
- 4m 08s PRINT (F 10 SET M>50 S>55): formatted 2008 cliques (41 files) skipping 777 binary chapter diffs
- 4m 08s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 08s PREPARING (F 10 SET): Already prepared
- 4m 08s SIMILARITY (F 10 SET M>50): Using   909 M (909063480) comparisons with 89309 entries in matrix
- 4m 08s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%
- 4m 08s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates
- 4m 08s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix
- 4m 09s CLIQUES (F 10 SET M>50 S>50): 89309 relevant similarities between 22932 passages
- 4m 09s CLIQUES (F 10 SET M>50 S>50): Loaded:  1442 cliques out of  22932 chunks from 89309 comparisons
- 4m 09s CLIQUES (F 10 SET M>50 S>50): 22932 members in 1442 cliques
- 4m 09s PRINT (F 10 SET M>50 S>50): sorting out cliques
- 4m 10s PRINT (F 10 SET M>50 S>50): formatting 1442 cliques skipping 627 binary chapter diffs
- 4m 11s PRINT (F 10 SET M>50 S>50): formatted 1442 cliques (29 files) skipping 627 binary chapter diffs
- 4m 11s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 11s PREPARING (F 10 LCS)
- 4m 12s PREPARING (F 10 LCS): Done 42640 chunks.
- 4m 13s SIMILARITY (F 10 LCS M>60): Loaded:   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 14s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 14s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates
- 4m 14s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix
- 4m 15s CLIQUES (F 10 LCS M>60 S>100): 152 relevant similarities between 277 passages
- 4m 15s CLIQUES (F 10 LCS M>60 S>100): Loaded:   135 cliques out of    277 chunks from 152 comparisons
- 4m 15s CLIQUES (F 10 LCS M>60 S>100): 277 members in 135 cliques
- 4m 15s PRINT (F 10 LCS M>60 S>100): sorting out cliques
- 4m 15s PRINT (F 10 LCS M>60 S>100): formatting 135 cliques skipping 52 binary chapter diffs
- 4m 15s PRINT (F 10 LCS M>60 S>100): formatted 135 cliques (3 files) skipping 52 binary chapter diffs
- 4m 15s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 15s PREPARING (F 10 LCS): Already prepared
- 4m 15s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 16s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates
- 4m 16s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix
- 4m 17s CLIQUES (F 10 LCS M>60 S>95): 221 relevant similarities between 408 passages
- 4m 17s CLIQUES (F 10 LCS M>60 S>95): Loaded:   199 cliques out of    408 chunks from 221 comparisons
- 4m 17s CLIQUES (F 10 LCS M>60 S>95): 408 members in 199 cliques
- 4m 17s PRINT (F 10 LCS M>60 S>95): sorting out cliques
- 4m 17s PRINT (F 10 LCS M>60 S>95): formatting 199 cliques skipping 80 binary chapter diffs
- 4m 17s PRINT (F 10 LCS M>60 S>95): formatted 199 cliques (4 files) skipping 80 binary chapter diffs
- 4m 17s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 17s PREPARING (F 10 LCS): Already prepared
- 4m 17s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 18s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 18s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates
- 4m 18s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix
- 4m 19s CLIQUES (F 10 LCS M>60 S>90): 603 relevant similarities between 937 passages
- 4m 19s CLIQUES (F 10 LCS M>60 S>90): Loaded:   423 cliques out of    937 chunks from 603 comparisons
- 4m 19s CLIQUES (F 10 LCS M>60 S>90): 937 members in 423 cliques
- 4m 19s PRINT (F 10 LCS M>60 S>90): sorting out cliques
- 4m 19s PRINT (F 10 LCS M>60 S>90): formatting 423 cliques skipping 163 binary chapter diffs
- 4m 19s PRINT (F 10 LCS M>60 S>90): formatted 423 cliques (9 files) skipping 163 binary chapter diffs
- 4m 19s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 19s PREPARING (F 10 LCS): Already prepared
- 4m 19s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 20s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 20s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates
- 4m 20s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix
- 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1391 relevant similarities between 1980 passages
- 4m 21s CLIQUES (F 10 LCS M>60 S>85): Loaded:   831 cliques out of   1980 chunks from 1391 comparisons
- 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1980 members in 831 cliques
- 4m 21s PRINT (F 10 LCS M>60 S>85): sorting out cliques
- 4m 21s PRINT (F 10 LCS M>60 S>85): formatting 831 cliques skipping 309 binary chapter diffs
- 4m 22s PRINT (F 10 LCS M>60 S>85): formatted 831 cliques (17 files) skipping 309 binary chapter diffs
- 4m 22s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 22s PREPARING (F 10 LCS): Already prepared
- 4m 22s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 23s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 23s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates
- 4m 23s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix
- 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3271 relevant similarities between 3894 passages
- 4m 24s CLIQUES (F 10 LCS M>60 S>80): Loaded:  1440 cliques out of   3894 chunks from 3271 comparisons
- 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3894 members in 1440 cliques
- 4m 24s PRINT (F 10 LCS M>60 S>80): sorting out cliques
- 4m 24s PRINT (F 10 LCS M>60 S>80): formatting 1440 cliques skipping 553 binary chapter diffs
- 4m 26s PRINT (F 10 LCS M>60 S>80): formatted 1440 cliques (29 files) skipping 553 binary chapter diffs
- 4m 26s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 26s PREPARING (F 10 LCS): Already prepared
- 4m 26s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 27s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 27s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates
- 4m 27s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix
- 4m 28s CLIQUES (F 10 LCS M>60 S>75): 9197 relevant similarities between 8599 passages
- 4m 28s CLIQUES (F 10 LCS M>60 S>75): Loaded:  2328 cliques out of   8599 chunks from 9197 comparisons
- 4m 28s CLIQUES (F 10 LCS M>60 S>75): 8599 members in 2328 cliques
- 4m 28s PRINT (F 10 LCS M>60 S>75): sorting out cliques
- 4m 28s PRINT (F 10 LCS M>60 S>75): formatting 2328 cliques skipping 955 binary chapter diffs
- 4m 30s PRINT (F 10 LCS M>60 S>75): formatted 2328 cliques (47 files) skipping 955 binary chapter diffs
- 4m 30s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 30s PREPARING (F 10 LCS): Already prepared
- 4m 30s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 32s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 32s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates
- 4m 32s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix
- 4m 33s CLIQUES (F 10 LCS M>60 S>70): 38515 relevant similarities between 20425 passages
- 4m 33s CLIQUES (F 10 LCS M>60 S>70): Loaded:  1937 cliques out of  20425 chunks from 38515 comparisons
- 4m 33s CLIQUES (F 10 LCS M>60 S>70): 20425 members in 1937 cliques
- 4m 33s PRINT (F 10 LCS M>60 S>70): sorting out cliques
- 4m 34s PRINT (F 10 LCS M>60 S>70): formatting 1937 cliques skipping 993 binary chapter diffs
- 4m 35s PRINT (F 10 LCS M>60 S>70): formatted 1937 cliques (39 files) skipping 993 binary chapter diffs
- 4m 35s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 35s PREPARING (F 10 LCS): Already prepared
- 4m 35s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 36s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 36s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates
- 4m 36s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix
- 4m 39s CLIQUES (F 10 LCS M>60 S>65): 346407 relevant similarities between 37696 passages
- 4m 39s CLIQUES (F 10 LCS M>60 S>65): Loaded:   218 cliques out of  37696 chunks from 346407 comparisons
- 4m 39s CLIQUES (F 10 LCS M>60 S>65): 37696 members in 218 cliques
- 4m 39s PRINT (F 10 LCS M>60 S>65): sorting out cliques
- 4m 40s PRINT (F 10 LCS M>60 S>65): formatting 218 cliques skipping 131 binary chapter diffs
- 4m 40s PRINT (F 10 LCS M>60 S>65): formatted 218 cliques (5 files) skipping 131 binary chapter diffs
- 4m 40s CHUNKING (F 10): already chunked into 42640 chunks
- 4m 40s PREPARING (F 10 LCS): Already prepared
- 4m 40s SIMILARITY (F 10 LCS M>60): Using   909 M (909063480) comparisons with 2916528 entries in matrix
- 4m 41s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%
- 4m 41s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates
- 4m 41s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix
- 4m 45s CLIQUES (F 10 LCS M>60 S>60): 2916528 relevant similarities between 42450 passages
- 4m 45s CLIQUES (F 10 LCS M>60 S>60): Loaded:     4 cliques out of  42450 chunks from 2916528 comparisons
- 4m 45s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 4 cliques
- 4m 45s PRINT (F 10 LCS M>60 S>60): sorting out cliques
- 4m 46s PRINT (F 10 LCS M>60 S>60): formatting 4 cliques skipping 3 binary chapter diffs
- 4m 46s PRINT (F 10 LCS M>60 S>60): formatted 4 cliques (1 files) skipping 3 binary chapter diffs
- 4m 46s CHUNKING (O chapter): Loaded:   929 chunks
- 4m 46s CHUNKING (O chapter): Made 929 chunks
- 4m 46s PREPARING (O chapter SET)
- 4m 47s PREPARING (O chapter SET): Done 929 chunks.
- 4m 47s SIMILARITY (O chapter SET M>30): Loaded:   431 K (431056) comparisons with 3445 entries in matrix
- 4m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 47s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates
- 4m 47s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques
- 4m 48s PRINT (O chapter SET M>30 S>100): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0
- 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing
- 4m 48s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>95): Loaded:     1 cliques out of      2 chunks from 1 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques
- 4m 48s PRINT (O chapter SET M>30 S>95): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>90): Loaded:     1 cliques out of      2 chunks from 1 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques
- 4m 48s PRINT (O chapter SET M>30 S>90): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>85): Loaded:     1 cliques out of      2 chunks from 1 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques
- 4m 48s PRINT (O chapter SET M>30 S>85): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>80): Loaded:     2 cliques out of      4 chunks from 2 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques
- 4m 48s PRINT (O chapter SET M>30 S>80): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>75): Loaded:     7 cliques out of     14 chunks from 7 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques
- 4m 48s PRINT (O chapter SET M>30 S>75): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs
- 4m 48s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 48s PREPARING (O chapter SET): Already prepared
- 4m 48s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 48s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates
- 4m 48s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix
- 4m 48s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages
- 4m 48s CLIQUES (O chapter SET M>30 S>70): Loaded:    10 cliques out of     20 chunks from 10 comparisons
- 4m 48s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques
- 4m 48s PRINT (O chapter SET M>30 S>70): sorting out cliques
- 4m 48s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs
- 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10
- 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing
- 4m 55s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs
- 4m 55s CHUNKING (O chapter): already chunked into 929 chunks
- 4m 55s PREPARING (O chapter SET): Already prepared
- 4m 55s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 4m 55s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 4m 55s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates
- 4m 55s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix
- 4m 55s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages
- 4m 55s CLIQUES (O chapter SET M>30 S>65): Composing cliques out of     24 chunks from 12 comparisons
- 4m 55s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques
- 4m 55s CLIQUES (O chapter SET M>30 S>65): Composed and saved    12 cliques out of     24 chunks from 12 comparisons
- 4m 55s PRINT (O chapter SET M>30 S>65): sorting out cliques
- 4m 55s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs
- 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12
- 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing
- 5m 05s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs
- 5m 05s CHUNKING (O chapter): already chunked into 929 chunks
- 5m 05s PREPARING (O chapter SET): Already prepared
- 5m 05s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 5m 05s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 5m 05s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates
- 5m 05s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix
- 5m 05s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages
- 5m 05s CLIQUES (O chapter SET M>30 S>60): Composing cliques out of     34 chunks from 17 comparisons
- 5m 05s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques
- 5m 05s CLIQUES (O chapter SET M>30 S>60): Composed and saved    17 cliques out of     34 chunks from 17 comparisons
- 5m 05s PRINT (O chapter SET M>30 S>60): sorting out cliques
- 5m 05s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs
- 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17
- 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing
- 5m 20s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs
- 5m 20s CHUNKING (O chapter): already chunked into 929 chunks
- 5m 20s PREPARING (O chapter SET): Already prepared
- 5m 20s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 5m 20s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 5m 20s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates
- 5m 20s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix
- 5m 20s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages
- 5m 20s CLIQUES (O chapter SET M>30 S>55): Composing cliques out of     44 chunks from 22 comparisons
- 5m 20s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques
- 5m 20s CLIQUES (O chapter SET M>30 S>55): Composed and saved    22 cliques out of     44 chunks from 22 comparisons
- 5m 20s PRINT (O chapter SET M>30 S>55): sorting out cliques
- 5m 20s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs
- 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22
- 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing
- 5m 39s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs
- 5m 39s CHUNKING (O chapter): already chunked into 929 chunks
- 5m 39s PREPARING (O chapter SET): Already prepared
- 5m 39s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 5m 39s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 5m 39s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates
- 5m 39s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix
- 5m 39s CLIQUES (O chapter SET M>30 S>50): 29 relevant similarities between 58 passages
- 5m 39s CLIQUES (O chapter SET M>30 S>50): Composing cliques out of     58 chunks from 29 comparisons
- 5m 39s CLIQUES (O chapter SET M>30 S>50): 58 members in 29 cliques
- 5m 39s CLIQUES (O chapter SET M>30 S>50): Composed and saved    29 cliques out of     58 chunks from 29 comparisons
- 5m 39s PRINT (O chapter SET M>30 S>50): sorting out cliques
- 5m 39s PRINT (O chapter SET M>30 S>50): formatting 29 cliques involving 29 binary chapter diffs
- 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 29
- 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 29 already existing
- 6m 04s PRINT (O chapter SET M>30 S>50): formatted 29 cliques (1 files) involving 29 binary chapter diffs
- 6m 04s CHUNKING (O chapter): already chunked into 929 chunks
- 6m 04s PREPARING (O chapter SET): Already prepared
- 6m 04s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 6m 04s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 6m 04s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates
- 6m 04s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix
- 6m 04s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages
- 6m 04s CLIQUES (O chapter SET M>30 S>45): Composing cliques out of     80 chunks from 42 comparisons
- 6m 04s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques
- 6m 04s CLIQUES (O chapter SET M>30 S>45): Composed and saved    39 cliques out of     80 chunks from 42 comparisons
- 6m 04s PRINT (O chapter SET M>30 S>45): sorting out cliques
- 6m 04s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs
- 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37
- 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing
- 6m 40s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs
- 6m 40s CHUNKING (O chapter): already chunked into 929 chunks
- 6m 40s PREPARING (O chapter SET): Already prepared
- 6m 40s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 6m 40s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 6m 40s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates
- 6m 40s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix
- 6m 40s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages
- 6m 40s CLIQUES (O chapter SET M>30 S>40): Composing cliques out of    142 chunks from 87 comparisons
- 6m 40s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques
- 6m 40s CLIQUES (O chapter SET M>30 S>40): Composed and saved    62 cliques out of    142 chunks from 87 comparisons
- 6m 40s PRINT (O chapter SET M>30 S>40): sorting out cliques
- 6m 40s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs
- 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51
- 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing
- 7m 37s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs
- 7m 37s CHUNKING (O chapter): already chunked into 929 chunks
- 7m 37s PREPARING (O chapter SET): Already prepared
- 7m 37s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 7m 37s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 7m 37s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates
- 7m 37s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix
- 7m 37s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages
- 7m 37s CLIQUES (O chapter SET M>30 S>35): Composing cliques out of    302 chunks from 352 comparisons
- 7m 37s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques
- 7m 37s CLIQUES (O chapter SET M>30 S>35): Composed and saved    53 cliques out of    302 chunks from 352 comparisons
- 7m 37s PRINT (O chapter SET M>30 S>35): sorting out cliques
- 7m 37s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs
- 8m 36s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs
- 8m 36s CHUNKING (O chapter): already chunked into 929 chunks
- 8m 36s PREPARING (O chapter SET): Already prepared
- 8m 36s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
- 8m 36s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
- 8m 36s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates
- 8m 36s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix
- 8m 36s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages
- 8m 36s CLIQUES (O chapter SET M>30 S>30): Composing cliques out of    571 chunks from 3445 comparisons
- 8m 36s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques
- 8m 36s CLIQUES (O chapter SET M>30 S>30): Composed and saved    28 cliques out of    571 chunks from 3445 comparisons
- 8m 36s PRINT (O chapter SET M>30 S>30): sorting out cliques
- 8m 36s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs
- 9m 01s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs
- 9m 01s CHUNKING (O chapter): already chunked into 929 chunks
- 9m 01s PREPARING (O chapter LCS)
- 9m 02s PREPARING (O chapter LCS): Done 929 chunks.
- 9m 02s SIMILARITY (O chapter LCS M>55): Computing   431 K (431056) comparisons and saving entries in matrix
- 9m 16s SIMILARITY (O chapter LCS M>55): Computed     4 K comparisons and saved 1 entries in matrix
- 9m 28s SIMILARITY (O chapter LCS M>55): Computed     8 K comparisons and saved 2 entries in matrix
- 9m 39s SIMILARITY (O chapter LCS M>55): Computed    12 K comparisons and saved 2 entries in matrix
- 9m 52s SIMILARITY (O chapter LCS M>55): Computed    17 K comparisons and saved 2 entries in matrix
-10m 08s SIMILARITY (O chapter LCS M>55): Computed    21 K comparisons and saved 2 entries in matrix
-10m 26s SIMILARITY (O chapter LCS M>55): Computed    25 K comparisons and saved 2 entries in matrix
-10m 45s SIMILARITY (O chapter LCS M>55): Computed    30 K comparisons and saved 2 entries in matrix
-11m 00s SIMILARITY (O chapter LCS M>55): Computed    34 K comparisons and saved 3 entries in matrix
-11m 18s SIMILARITY (O chapter LCS M>55): Computed    38 K comparisons and saved 3 entries in matrix
-11m 33s SIMILARITY (O chapter LCS M>55): Computed    43 K comparisons and saved 3 entries in matrix
-11m 45s SIMILARITY (O chapter LCS M>55): Computed    47 K comparisons and saved 3 entries in matrix
-12m 00s SIMILARITY (O chapter LCS M>55): Computed    51 K comparisons and saved 3 entries in matrix
-12m 18s SIMILARITY (O chapter LCS M>55): Computed    56 K comparisons and saved 3 entries in matrix
-12m 32s SIMILARITY (O chapter LCS M>55): Computed    60 K comparisons and saved 3 entries in matrix
-12m 46s SIMILARITY (O chapter LCS M>55): Computed    64 K comparisons and saved 4 entries in matrix
-13m 01s SIMILARITY (O chapter LCS M>55): Computed    68 K comparisons and saved 8 entries in matrix
-13m 19s SIMILARITY (O chapter LCS M>55): Computed    73 K comparisons and saved 9 entries in matrix
-13m 36s SIMILARITY (O chapter LCS M>55): Computed    77 K comparisons and saved 9 entries in matrix
-13m 49s SIMILARITY (O chapter LCS M>55): Computed    81 K comparisons and saved 10 entries in matrix
-14m 07s SIMILARITY (O chapter LCS M>55): Computed    86 K comparisons and saved 10 entries in matrix
-14m 25s SIMILARITY (O chapter LCS M>55): Computed    90 K comparisons and saved 11 entries in matrix
-14m 43s SIMILARITY (O chapter LCS M>55): Computed    94 K comparisons and saved 11 entries in matrix
-14m 57s SIMILARITY (O chapter LCS M>55): Computed    99 K comparisons and saved 11 entries in matrix
-15m 16s SIMILARITY (O chapter LCS M>55): Computed   103 K comparisons and saved 11 entries in matrix
-15m 40s SIMILARITY (O chapter LCS M>55): Computed   107 K comparisons and saved 11 entries in matrix
-15m 53s SIMILARITY (O chapter LCS M>55): Computed   112 K comparisons and saved 11 entries in matrix
-16m 11s SIMILARITY (O chapter LCS M>55): Computed   116 K comparisons and saved 11 entries in matrix
-16m 27s SIMILARITY (O chapter LCS M>55): Computed   120 K comparisons and saved 11 entries in matrix
-16m 42s SIMILARITY (O chapter LCS M>55): Computed   124 K comparisons and saved 12 entries in matrix
-17m 00s SIMILARITY (O chapter LCS M>55): Computed   129 K comparisons and saved 12 entries in matrix
-17m 20s SIMILARITY (O chapter LCS M>55): Computed   133 K comparisons and saved 12 entries in matrix
-17m 33s SIMILARITY (O chapter LCS M>55): Computed   137 K comparisons and saved 13 entries in matrix
-17m 47s SIMILARITY (O chapter LCS M>55): Computed   142 K comparisons and saved 13 entries in matrix
-17m 59s SIMILARITY (O chapter LCS M>55): Computed   146 K comparisons and saved 13 entries in matrix
-18m 10s SIMILARITY (O chapter LCS M>55): Computed   150 K comparisons and saved 13 entries in matrix
-18m 31s SIMILARITY (O chapter LCS M>55): Computed   155 K comparisons and saved 13 entries in matrix
-18m 42s SIMILARITY (O chapter LCS M>55): Computed   159 K comparisons and saved 13 entries in matrix
-19m 01s SIMILARITY (O chapter LCS M>55): Computed   163 K comparisons and saved 13 entries in matrix
-19m 14s SIMILARITY (O chapter LCS M>55): Computed   168 K comparisons and saved 13 entries in matrix
-19m 30s SIMILARITY (O chapter LCS M>55): Computed   172 K comparisons and saved 14 entries in matrix
-19m 45s SIMILARITY (O chapter LCS M>55): Computed   176 K comparisons and saved 14 entries in matrix
-20m 05s SIMILARITY (O chapter LCS M>55): Computed   181 K comparisons and saved 14 entries in matrix
-20m 17s SIMILARITY (O chapter LCS M>55): Computed   185 K comparisons and saved 14 entries in matrix
-20m 35s SIMILARITY (O chapter LCS M>55): Computed   189 K comparisons and saved 14 entries in matrix
-20m 45s SIMILARITY (O chapter LCS M>55): Computed   193 K comparisons and saved 14 entries in matrix
-21m 01s SIMILARITY (O chapter LCS M>55): Computed   198 K comparisons and saved 14 entries in matrix
-21m 20s SIMILARITY (O chapter LCS M>55): Computed   202 K comparisons and saved 15 entries in matrix
-21m 34s SIMILARITY (O chapter LCS M>55): Computed   206 K comparisons and saved 15 entries in matrix
-21m 47s SIMILARITY (O chapter LCS M>55): Computed   211 K comparisons and saved 16 entries in matrix
-21m 58s SIMILARITY (O chapter LCS M>55): Computed   215 K comparisons and saved 19 entries in matrix
-22m 15s SIMILARITY (O chapter LCS M>55): Computed   219 K comparisons and saved 20 entries in matrix
-22m 32s SIMILARITY (O chapter LCS M>55): Computed   224 K comparisons and saved 21 entries in matrix
-22m 50s SIMILARITY (O chapter LCS M>55): Computed   228 K comparisons and saved 23 entries in matrix
-23m 14s SIMILARITY (O chapter LCS M>55): Computed   232 K comparisons and saved 26 entries in matrix
-23m 31s SIMILARITY (O chapter LCS M>55): Computed   237 K comparisons and saved 28 entries in matrix
-23m 48s SIMILARITY (O chapter LCS M>55): Computed   241 K comparisons and saved 29 entries in matrix
-24m 04s SIMILARITY (O chapter LCS M>55): Computed   245 K comparisons and saved 29 entries in matrix
-24m 19s SIMILARITY (O chapter LCS M>55): Computed   249 K comparisons and saved 35 entries in matrix
-24m 34s SIMILARITY (O chapter LCS M>55): Computed   254 K comparisons and saved 40 entries in matrix
-24m 42s SIMILARITY (O chapter LCS M>55): Computed   258 K comparisons and saved 41 entries in matrix
-24m 50s SIMILARITY (O chapter LCS M>55): Computed   262 K comparisons and saved 41 entries in matrix
-24m 56s SIMILARITY (O chapter LCS M>55): Computed   267 K comparisons and saved 41 entries in matrix
-25m 04s SIMILARITY (O chapter LCS M>55): Computed   271 K comparisons and saved 41 entries in matrix
-25m 12s SIMILARITY (O chapter LCS M>55): Computed   275 K comparisons and saved 41 entries in matrix
-25m 21s SIMILARITY (O chapter LCS M>55): Computed   280 K comparisons and saved 41 entries in matrix
-25m 28s SIMILARITY (O chapter LCS M>55): Computed   284 K comparisons and saved 41 entries in matrix
-25m 34s SIMILARITY (O chapter LCS M>55): Computed   288 K comparisons and saved 41 entries in matrix
-25m 43s SIMILARITY (O chapter LCS M>55): Computed   293 K comparisons and saved 41 entries in matrix
-25m 55s SIMILARITY (O chapter LCS M>55): Computed   297 K comparisons and saved 41 entries in matrix
-26m 04s SIMILARITY (O chapter LCS M>55): Computed   301 K comparisons and saved 41 entries in matrix
-26m 16s SIMILARITY (O chapter LCS M>55): Computed   306 K comparisons and saved 42 entries in matrix
-26m 31s SIMILARITY (O chapter LCS M>55): Computed   310 K comparisons and saved 42 entries in matrix
-26m 42s SIMILARITY (O chapter LCS M>55): Computed   314 K comparisons and saved 42 entries in matrix
-26m 56s SIMILARITY (O chapter LCS M>55): Computed   318 K comparisons and saved 42 entries in matrix
-27m 04s SIMILARITY (O chapter LCS M>55): Computed   323 K comparisons and saved 42 entries in matrix
-27m 16s SIMILARITY (O chapter LCS M>55): Computed   327 K comparisons and saved 42 entries in matrix
-27m 26s SIMILARITY (O chapter LCS M>55): Computed   331 K comparisons and saved 42 entries in matrix
-27m 38s SIMILARITY (O chapter LCS M>55): Computed   336 K comparisons and saved 42 entries in matrix
-27m 48s SIMILARITY (O chapter LCS M>55): Computed   340 K comparisons and saved 42 entries in matrix
-27m 52s SIMILARITY (O chapter LCS M>55): Computed   344 K comparisons and saved 42 entries in matrix
-27m 58s SIMILARITY (O chapter LCS M>55): Computed   349 K comparisons and saved 42 entries in matrix
-28m 03s SIMILARITY (O chapter LCS M>55): Computed   353 K comparisons and saved 42 entries in matrix
-28m 08s SIMILARITY (O chapter LCS M>55): Computed   357 K comparisons and saved 42 entries in matrix
-28m 14s SIMILARITY (O chapter LCS M>55): Computed   362 K comparisons and saved 43 entries in matrix
-28m 20s SIMILARITY (O chapter LCS M>55): Computed   366 K comparisons and saved 43 entries in matrix
-28m 23s SIMILARITY (O chapter LCS M>55): Computed   370 K comparisons and saved 44 entries in matrix
-28m 26s SIMILARITY (O chapter LCS M>55): Computed   374 K comparisons and saved 44 entries in matrix
-28m 31s SIMILARITY (O chapter LCS M>55): Computed   379 K comparisons and saved 44 entries in matrix
-28m 34s SIMILARITY (O chapter LCS M>55): Computed   383 K comparisons and saved 44 entries in matrix
-28m 37s SIMILARITY (O chapter LCS M>55): Computed   387 K comparisons and saved 45 entries in matrix
-28m 43s SIMILARITY (O chapter LCS M>55): Computed   392 K comparisons and saved 46 entries in matrix
-28m 46s SIMILARITY (O chapter LCS M>55): Computed   396 K comparisons and saved 47 entries in matrix
-28m 51s SIMILARITY (O chapter LCS M>55): Computed   400 K comparisons and saved 48 entries in matrix
-28m 55s SIMILARITY (O chapter LCS M>55): Computed   405 K comparisons and saved 49 entries in matrix
-29m 00s SIMILARITY (O chapter LCS M>55): Computed   409 K comparisons and saved 49 entries in matrix
-29m 06s SIMILARITY (O chapter LCS M>55): Computed   413 K comparisons and saved 49 entries in matrix
-29m 14s SIMILARITY (O chapter LCS M>55): Computed   418 K comparisons and saved 49 entries in matrix
-29m 23s SIMILARITY (O chapter LCS M>55): Computed   422 K comparisons and saved 49 entries in matrix
-29m 37s SIMILARITY (O chapter LCS M>55): Computed   426 K comparisons and saved 52 entries in matrix
-29m 55s SIMILARITY (O chapter LCS M>55): Computed   431 K comparisons and saved 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): Computed   431 K (431056) comparisons and saved 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-29m 56s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates
-29m 56s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix
-29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages
-29m 56s CLIQUES (O chapter LCS M>55 S>100): Composing cliques out of      0 chunks from 0 comparisons
-29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques
-29m 56s CLIQUES (O chapter LCS M>55 S>100): Composed and saved     0 cliques out of      0 chunks from 0 comparisons
-29m 56s PRINT (O chapter LCS M>55 S>100): sorting out cliques
-29m 56s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs
-29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0
-29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing
-29m 56s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs
-29m 56s CHUNKING (O chapter): already chunked into 929 chunks
-29m 56s PREPARING (O chapter LCS): Already prepared
-29m 56s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-29m 56s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates
-29m 56s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix
-29m 56s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages
-29m 56s CLIQUES (O chapter LCS M>55 S>95): Composing cliques out of      2 chunks from 1 comparisons
-29m 56s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques
-29m 56s CLIQUES (O chapter LCS M>55 S>95): Composed and saved     1 cliques out of      2 chunks from 1 comparisons
-29m 56s PRINT (O chapter LCS M>55 S>95): sorting out cliques
-29m 56s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs
-29m 56s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
-29m 56s CHUNKING (O chapter): already chunked into 929 chunks
-29m 56s PREPARING (O chapter LCS): Already prepared
-29m 56s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-29m 56s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates
-29m 56s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix
-29m 56s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages
-29m 56s CLIQUES (O chapter LCS M>55 S>90): Composing cliques out of      4 chunks from 2 comparisons
-29m 56s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques
-29m 56s CLIQUES (O chapter LCS M>55 S>90): Composed and saved     2 cliques out of      4 chunks from 2 comparisons
-29m 56s PRINT (O chapter LCS M>55 S>90): sorting out cliques
-29m 56s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs
-29m 56s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
-29m 56s CHUNKING (O chapter): already chunked into 929 chunks
-29m 56s PREPARING (O chapter LCS): Already prepared
-29m 56s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-29m 56s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates
-29m 56s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix
-29m 56s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages
-29m 56s CLIQUES (O chapter LCS M>55 S>85): Composing cliques out of     12 chunks from 6 comparisons
-29m 56s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques
-29m 56s CLIQUES (O chapter LCS M>55 S>85): Composed and saved     6 cliques out of     12 chunks from 6 comparisons
-29m 56s PRINT (O chapter LCS M>55 S>85): sorting out cliques
-29m 56s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs
-29m 56s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs
-29m 56s CHUNKING (O chapter): already chunked into 929 chunks
-29m 56s PREPARING (O chapter LCS): Already prepared
-29m 56s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-29m 56s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates
-29m 56s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix
-29m 56s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages
-29m 56s CLIQUES (O chapter LCS M>55 S>80): Composing cliques out of     18 chunks from 9 comparisons
-29m 56s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques
-29m 56s CLIQUES (O chapter LCS M>55 S>80): Composed and saved     9 cliques out of     18 chunks from 9 comparisons
-29m 56s PRINT (O chapter LCS M>55 S>80): sorting out cliques
-29m 56s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs
-29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9
-29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing
-30m 02s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs
-30m 02s CHUNKING (O chapter): already chunked into 929 chunks
-30m 02s PREPARING (O chapter LCS): Already prepared
-30m 02s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-30m 02s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-30m 02s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates
-30m 02s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix
-30m 02s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages
-30m 02s CLIQUES (O chapter LCS M>55 S>75): Composing cliques out of     26 chunks from 13 comparisons
-30m 02s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques
-30m 02s CLIQUES (O chapter LCS M>55 S>75): Composed and saved    13 cliques out of     26 chunks from 13 comparisons
-30m 02s PRINT (O chapter LCS M>55 S>75): sorting out cliques
-30m 02s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs
-30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13
-30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing
-30m 11s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs
-30m 11s CHUNKING (O chapter): already chunked into 929 chunks
-30m 11s PREPARING (O chapter LCS): Already prepared
-30m 11s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-30m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-30m 11s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates
-30m 11s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix
-30m 11s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages
-30m 11s CLIQUES (O chapter LCS M>55 S>70): Composing cliques out of     38 chunks from 19 comparisons
-30m 11s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques
-30m 11s CLIQUES (O chapter LCS M>55 S>70): Composed and saved    19 cliques out of     38 chunks from 19 comparisons
-30m 11s PRINT (O chapter LCS M>55 S>70): sorting out cliques
-30m 11s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs
-30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19
-30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing
-30m 27s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs
-30m 27s CHUNKING (O chapter): already chunked into 929 chunks
-30m 27s PREPARING (O chapter LCS): Already prepared
-30m 27s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-30m 27s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-30m 27s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates
-30m 27s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix
-30m 27s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages
-30m 27s CLIQUES (O chapter LCS M>55 S>65): Composing cliques out of     44 chunks from 22 comparisons
-30m 27s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques
-30m 27s CLIQUES (O chapter LCS M>55 S>65): Composed and saved    22 cliques out of     44 chunks from 22 comparisons
-30m 27s PRINT (O chapter LCS M>55 S>65): sorting out cliques
-30m 27s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs
-30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22
-30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing
-30m 47s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs
-30m 47s CHUNKING (O chapter): already chunked into 929 chunks
-30m 47s PREPARING (O chapter LCS): Already prepared
-30m 47s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-30m 47s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-30m 47s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates
-30m 47s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix
-30m 47s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages
-30m 47s CLIQUES (O chapter LCS M>55 S>60): Composing cliques out of     52 chunks from 26 comparisons
-30m 48s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques
-30m 48s CLIQUES (O chapter LCS M>55 S>60): Composed and saved    26 cliques out of     52 chunks from 26 comparisons
-30m 48s PRINT (O chapter LCS M>55 S>60): sorting out cliques
-30m 48s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs
-30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26
-30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing
-31m 11s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs
-31m 11s CHUNKING (O chapter): already chunked into 929 chunks
-31m 11s PREPARING (O chapter LCS): Already prepared
-31m 11s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
-31m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
-31m 11s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates
-31m 11s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix
-31m 11s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages
-31m 11s CLIQUES (O chapter LCS M>55 S>55): Composing cliques out of    102 chunks from 53 comparisons
-31m 11s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques
-31m 11s CLIQUES (O chapter LCS M>55 S>55): Composed and saved    49 cliques out of    102 chunks from 53 comparisons
-31m 11s PRINT (O chapter LCS M>55 S>55): sorting out cliques
-31m 11s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs
-31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46
-31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing
-31m 48s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs
-31m 48s CHUNKING (O verse)
-31m 49s CHUNKING (O verse): Made 23213 chunks
-31m 49s PREPARING (O verse SET)
-31m 50s PREPARING (O verse SET): Done 23213 chunks.
-31m 50s SIMILARITY (O verse SET M>50): Computing   269 M (269410078) comparisons and saving entries in matrix
-31m 56s SIMILARITY (O verse SET M>50): Computed     2 M comparisons and saved 78 entries in matrix
-32m 02s SIMILARITY (O verse SET M>50): Computed     5 M comparisons and saved 235 entries in matrix
-32m 08s SIMILARITY (O verse SET M>50): Computed     8 M comparisons and saved 322 entries in matrix
-32m 14s SIMILARITY (O verse SET M>50): Computed    10 M comparisons and saved 335 entries in matrix
-32m 20s SIMILARITY (O verse SET M>50): Computed    13 M comparisons and saved 351 entries in matrix
-32m 25s SIMILARITY (O verse SET M>50): Computed    16 M comparisons and saved 376 entries in matrix
-32m 31s SIMILARITY (O verse SET M>50): Computed    18 M comparisons and saved 389 entries in matrix
-32m 37s SIMILARITY (O verse SET M>50): Computed    21 M comparisons and saved 544 entries in matrix
-32m 43s SIMILARITY (O verse SET M>50): Computed    24 M comparisons and saved 575 entries in matrix
-32m 49s SIMILARITY (O verse SET M>50): Computed    26 M comparisons and saved 613 entries in matrix
-32m 55s SIMILARITY (O verse SET M>50): Computed    29 M comparisons and saved 636 entries in matrix
-33m 01s SIMILARITY (O verse SET M>50): Computed    32 M comparisons and saved 666 entries in matrix
-33m 07s SIMILARITY (O verse SET M>50): Computed    35 M comparisons and saved 684 entries in matrix
-33m 13s SIMILARITY (O verse SET M>50): Computed    37 M comparisons and saved 1101 entries in matrix
-33m 19s SIMILARITY (O verse SET M>50): Computed    40 M comparisons and saved 1318 entries in matrix
-33m 25s SIMILARITY (O verse SET M>50): Computed    43 M comparisons and saved 1848 entries in matrix
-33m 31s SIMILARITY (O verse SET M>50): Computed    45 M comparisons and saved 2090 entries in matrix
-33m 37s SIMILARITY (O verse SET M>50): Computed    48 M comparisons and saved 2125 entries in matrix
-33m 43s SIMILARITY (O verse SET M>50): Computed    51 M comparisons and saved 2521 entries in matrix
-33m 48s SIMILARITY (O verse SET M>50): Computed    53 M comparisons and saved 3522 entries in matrix
-33m 54s SIMILARITY (O verse SET M>50): Computed    56 M comparisons and saved 3597 entries in matrix
-34m 00s SIMILARITY (O verse SET M>50): Computed    59 M comparisons and saved 3847 entries in matrix
-34m 06s SIMILARITY (O verse SET M>50): Computed    61 M comparisons and saved 4268 entries in matrix
-34m 12s SIMILARITY (O verse SET M>50): Computed    64 M comparisons and saved 5626 entries in matrix
-34m 18s SIMILARITY (O verse SET M>50): Computed    67 M comparisons and saved 6320 entries in matrix
-34m 24s SIMILARITY (O verse SET M>50): Computed    70 M comparisons and saved 7034 entries in matrix
-34m 30s SIMILARITY (O verse SET M>50): Computed    72 M comparisons and saved 7890 entries in matrix
-34m 36s SIMILARITY (O verse SET M>50): Computed    75 M comparisons and saved 9304 entries in matrix
-34m 41s SIMILARITY (O verse SET M>50): Computed    78 M comparisons and saved 9839 entries in matrix
-34m 47s SIMILARITY (O verse SET M>50): Computed    80 M comparisons and saved 10997 entries in matrix
-34m 53s SIMILARITY (O verse SET M>50): Computed    83 M comparisons and saved 12113 entries in matrix
-34m 59s SIMILARITY (O verse SET M>50): Computed    86 M comparisons and saved 12712 entries in matrix
-35m 05s SIMILARITY (O verse SET M>50): Computed    88 M comparisons and saved 13345 entries in matrix
-35m 11s SIMILARITY (O verse SET M>50): Computed    91 M comparisons and saved 14140 entries in matrix
-35m 16s SIMILARITY (O verse SET M>50): Computed    94 M comparisons and saved 14375 entries in matrix
-35m 22s SIMILARITY (O verse SET M>50): Computed    96 M comparisons and saved 14945 entries in matrix
-35m 28s SIMILARITY (O verse SET M>50): Computed    99 M comparisons and saved 15321 entries in matrix
-35m 34s SIMILARITY (O verse SET M>50): Computed   102 M comparisons and saved 15714 entries in matrix
-35m 40s SIMILARITY (O verse SET M>50): Computed   105 M comparisons and saved 15978 entries in matrix
-35m 46s SIMILARITY (O verse SET M>50): Computed   107 M comparisons and saved 16103 entries in matrix
-35m 52s SIMILARITY (O verse SET M>50): Computed   110 M comparisons and saved 16239 entries in matrix
-35m 58s SIMILARITY (O verse SET M>50): Computed   113 M comparisons and saved 16359 entries in matrix
-36m 04s SIMILARITY (O verse SET M>50): Computed   115 M comparisons and saved 16433 entries in matrix
-36m 10s SIMILARITY (O verse SET M>50): Computed   118 M comparisons and saved 16469 entries in matrix
-36m 16s SIMILARITY (O verse SET M>50): Computed   121 M comparisons and saved 16583 entries in matrix
-36m 22s SIMILARITY (O verse SET M>50): Computed   123 M comparisons and saved 16619 entries in matrix
-36m 28s SIMILARITY (O verse SET M>50): Computed   126 M comparisons and saved 16669 entries in matrix
-36m 33s SIMILARITY (O verse SET M>50): Computed   129 M comparisons and saved 16834 entries in matrix
-36m 39s SIMILARITY (O verse SET M>50): Computed   132 M comparisons and saved 16878 entries in matrix
-36m 45s SIMILARITY (O verse SET M>50): Computed   134 M comparisons and saved 16899 entries in matrix
-36m 51s SIMILARITY (O verse SET M>50): Computed   137 M comparisons and saved 16916 entries in matrix
-36m 58s SIMILARITY (O verse SET M>50): Computed   140 M comparisons and saved 16926 entries in matrix
-37m 04s SIMILARITY (O verse SET M>50): Computed   142 M comparisons and saved 16953 entries in matrix
-37m 10s SIMILARITY (O verse SET M>50): Computed   145 M comparisons and saved 16980 entries in matrix
-37m 16s SIMILARITY (O verse SET M>50): Computed   148 M comparisons and saved 17084 entries in matrix
-37m 22s SIMILARITY (O verse SET M>50): Computed   150 M comparisons and saved 17098 entries in matrix
-37m 28s SIMILARITY (O verse SET M>50): Computed   153 M comparisons and saved 17119 entries in matrix
-37m 34s SIMILARITY (O verse SET M>50): Computed   156 M comparisons and saved 17305 entries in matrix
-37m 40s SIMILARITY (O verse SET M>50): Computed   158 M comparisons and saved 17341 entries in matrix
-37m 46s SIMILARITY (O verse SET M>50): Computed   161 M comparisons and saved 17365 entries in matrix
-37m 52s SIMILARITY (O verse SET M>50): Computed   164 M comparisons and saved 17543 entries in matrix
-37m 58s SIMILARITY (O verse SET M>50): Computed   167 M comparisons and saved 17680 entries in matrix
-38m 04s SIMILARITY (O verse SET M>50): Computed   169 M comparisons and saved 17948 entries in matrix
-38m 10s SIMILARITY (O verse SET M>50): Computed   172 M comparisons and saved 18586 entries in matrix
-38m 16s SIMILARITY (O verse SET M>50): Computed   175 M comparisons and saved 18899 entries in matrix
-38m 22s SIMILARITY (O verse SET M>50): Computed   177 M comparisons and saved 18991 entries in matrix
-38m 27s SIMILARITY (O verse SET M>50): Computed   180 M comparisons and saved 19389 entries in matrix
-38m 33s SIMILARITY (O verse SET M>50): Computed   183 M comparisons and saved 19718 entries in matrix
-38m 39s SIMILARITY (O verse SET M>50): Computed   185 M comparisons and saved 19823 entries in matrix
-38m 45s SIMILARITY (O verse SET M>50): Computed   188 M comparisons and saved 19961 entries in matrix
-38m 50s SIMILARITY (O verse SET M>50): Computed   191 M comparisons and saved 19967 entries in matrix
-38m 56s SIMILARITY (O verse SET M>50): Computed   193 M comparisons and saved 20103 entries in matrix
-39m 02s SIMILARITY (O verse SET M>50): Computed   196 M comparisons and saved 20158 entries in matrix
-39m 08s SIMILARITY (O verse SET M>50): Computed   199 M comparisons and saved 20162 entries in matrix
-39m 13s SIMILARITY (O verse SET M>50): Computed   202 M comparisons and saved 20312 entries in matrix
-39m 19s SIMILARITY (O verse SET M>50): Computed   204 M comparisons and saved 20523 entries in matrix
-39m 25s SIMILARITY (O verse SET M>50): Computed   207 M comparisons and saved 20771 entries in matrix
-39m 31s SIMILARITY (O verse SET M>50): Computed   210 M comparisons and saved 21114 entries in matrix
-39m 37s SIMILARITY (O verse SET M>50): Computed   212 M comparisons and saved 21360 entries in matrix
-39m 42s SIMILARITY (O verse SET M>50): Computed   215 M comparisons and saved 21383 entries in matrix
-39m 48s SIMILARITY (O verse SET M>50): Computed   218 M comparisons and saved 21935 entries in matrix
-39m 53s SIMILARITY (O verse SET M>50): Computed   220 M comparisons and saved 22457 entries in matrix
-39m 59s SIMILARITY (O verse SET M>50): Computed   223 M comparisons and saved 22720 entries in matrix
-40m 05s SIMILARITY (O verse SET M>50): Computed   226 M comparisons and saved 22863 entries in matrix
-40m 10s SIMILARITY (O verse SET M>50): Computed   228 M comparisons and saved 22938 entries in matrix
-40m 16s SIMILARITY (O verse SET M>50): Computed   231 M comparisons and saved 22961 entries in matrix
-40m 21s SIMILARITY (O verse SET M>50): Computed   234 M comparisons and saved 22979 entries in matrix
-40m 27s SIMILARITY (O verse SET M>50): Computed   237 M comparisons and saved 23043 entries in matrix
-40m 32s SIMILARITY (O verse SET M>50): Computed   239 M comparisons and saved 23373 entries in matrix
-40m 37s SIMILARITY (O verse SET M>50): Computed   242 M comparisons and saved 23590 entries in matrix
-40m 42s SIMILARITY (O verse SET M>50): Computed   245 M comparisons and saved 23770 entries in matrix
-40m 47s SIMILARITY (O verse SET M>50): Computed   247 M comparisons and saved 23807 entries in matrix
-40m 52s SIMILARITY (O verse SET M>50): Computed   250 M comparisons and saved 23901 entries in matrix
-40m 57s SIMILARITY (O verse SET M>50): Computed   253 M comparisons and saved 24012 entries in matrix
-41m 03s SIMILARITY (O verse SET M>50): Computed   255 M comparisons and saved 24091 entries in matrix
-41m 08s SIMILARITY (O verse SET M>50): Computed   258 M comparisons and saved 24138 entries in matrix
-41m 13s SIMILARITY (O verse SET M>50): Computed   261 M comparisons and saved 24182 entries in matrix
-41m 19s SIMILARITY (O verse SET M>50): Computed   264 M comparisons and saved 24220 entries in matrix
-41m 25s SIMILARITY (O verse SET M>50): Computed   266 M comparisons and saved 24413 entries in matrix
-41m 31s SIMILARITY (O verse SET M>50): Computed   269 M comparisons and saved 24792 entries in matrix
-41m 31s SIMILARITY (O verse SET M>50): Computed   269 M (269410078) comparisons and saved 24792 entries in matrix
-41m 31s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 31s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates
-41m 31s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix
-41m 31s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages
-41m 31s CLIQUES (O verse SET M>50 S>100): Composing cliques out of    993 chunks from 4506 comparisons
-41m 31s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques
-41m 31s CLIQUES (O verse SET M>50 S>100): Composed and saved   388 cliques out of    993 chunks from 4506 comparisons
-41m 31s PRINT (O verse SET M>50 S>100): sorting out cliques
-41m 31s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs
-41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100
-41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing
-41m 32s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs
-41m 32s CHUNKING (O verse): already chunked into 23213 chunks
-41m 32s PREPARING (O verse SET): Already prepared
-41m 32s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 32s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates
-41m 32s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix
-41m 32s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages
-41m 32s CLIQUES (O verse SET M>50 S>95): Composing cliques out of   1029 chunks from 4524 comparisons
-41m 32s CLIQUES (O verse SET M>50 S>95): Composed   400 cliques out of   1000 chunks
-41m 32s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques
-41m 32s CLIQUES (O verse SET M>50 S>95): Composed and saved   406 cliques out of   1029 chunks from 4524 comparisons
-41m 32s PRINT (O verse SET M>50 S>95): sorting out cliques
-41m 32s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs
-41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103
-41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing
-41m 32s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs
-41m 32s CHUNKING (O verse): already chunked into 23213 chunks
-41m 32s PREPARING (O verse SET): Already prepared
-41m 32s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 32s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates
-41m 32s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix
-41m 33s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages
-41m 33s CLIQUES (O verse SET M>50 S>90): Composing cliques out of   1286 chunks from 4700 comparisons
-41m 33s CLIQUES (O verse SET M>50 S>90): Composed   467 cliques out of   1000 chunks
-41m 33s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques
-41m 33s CLIQUES (O verse SET M>50 S>90): Composed and saved   526 cliques out of   1286 chunks from 4700 comparisons
-41m 33s PRINT (O verse SET M>50 S>90): sorting out cliques
-41m 33s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs
-41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133
-41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing
-41m 34s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs
-41m 34s CHUNKING (O verse): already chunked into 23213 chunks
-41m 34s PREPARING (O verse SET): Already prepared
-41m 34s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 34s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 34s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates
-41m 34s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix
-41m 34s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages
-41m 34s CLIQUES (O verse SET M>50 S>85): Composing cliques out of   1573 chunks from 4932 comparisons
-41m 34s CLIQUES (O verse SET M>50 S>85): Composed   473 cliques out of   1000 chunks
-41m 35s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques
-41m 35s CLIQUES (O verse SET M>50 S>85): Composed and saved   651 cliques out of   1573 chunks from 4932 comparisons
-41m 35s PRINT (O verse SET M>50 S>85): sorting out cliques
-41m 35s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs
-41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151
-41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing
-41m 35s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs
-41m 35s CHUNKING (O verse): already chunked into 23213 chunks
-41m 35s PREPARING (O verse SET): Already prepared
-41m 35s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 35s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 35s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates
-41m 35s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix
-41m 35s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages
-41m 35s CLIQUES (O verse SET M>50 S>80): Composing cliques out of   1958 chunks from 10653 comparisons
-41m 36s CLIQUES (O verse SET M>50 S>80): Composed   487 cliques out of   1000 chunks
-41m 37s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques
-41m 37s CLIQUES (O verse SET M>50 S>80): Composed and saved   800 cliques out of   1958 chunks from 10653 comparisons
-41m 37s PRINT (O verse SET M>50 S>80): sorting out cliques
-41m 37s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs
-41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174
-41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing
-41m 38s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs
-41m 38s CHUNKING (O verse): already chunked into 23213 chunks
-41m 38s PREPARING (O verse SET): Already prepared
-41m 38s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 38s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 38s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates
-41m 38s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix
-41m 38s CLIQUES (O verse SET M>50 S>75): 11182 relevant similarities between 2361 passages
-41m 38s CLIQUES (O verse SET M>50 S>75): Composing cliques out of   2361 chunks from 11182 comparisons
-41m 38s CLIQUES (O verse SET M>50 S>75): Composed   497 cliques out of   1000 chunks
-41m 39s CLIQUES (O verse SET M>50 S>75): Composed   897 cliques out of   2000 chunks
-41m 40s CLIQUES (O verse SET M>50 S>75): 2361 members in 962 cliques
-41m 40s CLIQUES (O verse SET M>50 S>75): Composed and saved   962 cliques out of   2361 chunks from 11182 comparisons
-41m 40s PRINT (O verse SET M>50 S>75): sorting out cliques
-41m 40s PRINT (O verse SET M>50 S>75): formatting 962 cliques involving 210 binary chapter diffs
-41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210
-41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing
-41m 41s PRINT (O verse SET M>50 S>75): formatted 962 cliques (20 files) involving 210 binary chapter diffs
-41m 41s CHUNKING (O verse): already chunked into 23213 chunks
-41m 41s PREPARING (O verse SET): Already prepared
-41m 41s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 41s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 41s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates
-41m 41s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix
-41m 41s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages
-41m 41s CLIQUES (O verse SET M>50 S>70): Composing cliques out of   2720 chunks from 11704 comparisons
-41m 42s CLIQUES (O verse SET M>50 S>70): Composed   515 cliques out of   1000 chunks
-41m 42s CLIQUES (O verse SET M>50 S>70): Composed   893 cliques out of   2000 chunks
-41m 44s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques
-41m 44s CLIQUES (O verse SET M>50 S>70): Composed and saved  1094 cliques out of   2720 chunks from 11704 comparisons
-41m 44s PRINT (O verse SET M>50 S>70): sorting out cliques
-41m 44s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs
-41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237
-41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing
-41m 45s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs
-41m 45s CHUNKING (O verse): already chunked into 23213 chunks
-41m 45s PREPARING (O verse SET): Already prepared
-41m 45s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 45s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 45s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates
-41m 45s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix
-41m 45s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages
-41m 45s CLIQUES (O verse SET M>50 S>65): Composing cliques out of   3139 chunks from 14353 comparisons
-41m 46s CLIQUES (O verse SET M>50 S>65): Composed   524 cliques out of   1000 chunks
-41m 47s CLIQUES (O verse SET M>50 S>65): Composed   901 cliques out of   2000 chunks
-41m 48s CLIQUES (O verse SET M>50 S>65): Composed  1205 cliques out of   3000 chunks
-41m 48s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques
-41m 48s CLIQUES (O verse SET M>50 S>65): Composed and saved  1235 cliques out of   3139 chunks from 14353 comparisons
-41m 48s PRINT (O verse SET M>50 S>65): sorting out cliques
-41m 48s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs
-41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284
-41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing
-41m 50s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs
-41m 50s CHUNKING (O verse): already chunked into 23213 chunks
-41m 50s PREPARING (O verse SET): Already prepared
-41m 50s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 50s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 50s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates
-41m 50s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix
-41m 51s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages
-41m 51s CLIQUES (O verse SET M>50 S>60): Composing cliques out of   3877 chunks from 16055 comparisons
-41m 51s CLIQUES (O verse SET M>50 S>60): Composed   549 cliques out of   1000 chunks
-41m 52s CLIQUES (O verse SET M>50 S>60): Composed   928 cliques out of   2000 chunks
-41m 53s CLIQUES (O verse SET M>50 S>60): Composed  1239 cliques out of   3000 chunks
-41m 55s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques
-41m 55s CLIQUES (O verse SET M>50 S>60): Composed and saved  1439 cliques out of   3877 chunks from 16055 comparisons
-41m 55s PRINT (O verse SET M>50 S>60): sorting out cliques
-41m 55s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs
-41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358
-41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing
-41m 58s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs
-41m 58s CHUNKING (O verse): already chunked into 23213 chunks
-41m 58s PREPARING (O verse SET): Already prepared
-41m 58s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-41m 58s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-41m 58s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates
-41m 58s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix
-41m 58s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages
-41m 58s CLIQUES (O verse SET M>50 S>55): Composing cliques out of   4735 chunks from 18754 comparisons
-41m 58s CLIQUES (O verse SET M>50 S>55): Composed   600 cliques out of   1000 chunks
-41m 59s CLIQUES (O verse SET M>50 S>55): Composed   973 cliques out of   2000 chunks
-42m 01s CLIQUES (O verse SET M>50 S>55): Composed  1236 cliques out of   3000 chunks
-42m 03s CLIQUES (O verse SET M>50 S>55): Composed  1508 cliques out of   4000 chunks
-42m 05s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques
-42m 05s CLIQUES (O verse SET M>50 S>55): Composed and saved  1638 cliques out of   4735 chunks from 18754 comparisons
-42m 05s PRINT (O verse SET M>50 S>55): sorting out cliques
-42m 05s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs
-42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447
-42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing
-42m 09s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs
-42m 09s CHUNKING (O verse): already chunked into 23213 chunks
-42m 09s PREPARING (O verse SET): Already prepared
-42m 09s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24792 entries in matrix
-42m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-42m 09s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates
-42m 09s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix
-42m 09s CLIQUES (O verse SET M>50 S>50): 24792 relevant similarities between 6711 passages
-42m 09s CLIQUES (O verse SET M>50 S>50): Composing cliques out of   6711 chunks from 24792 comparisons
-42m 09s CLIQUES (O verse SET M>50 S>50): Composed   642 cliques out of   1000 chunks
-42m 10s CLIQUES (O verse SET M>50 S>50): Composed  1029 cliques out of   2000 chunks
-42m 12s CLIQUES (O verse SET M>50 S>50): Composed  1309 cliques out of   3000 chunks
-42m 14s CLIQUES (O verse SET M>50 S>50): Composed  1490 cliques out of   4000 chunks
-42m 16s CLIQUES (O verse SET M>50 S>50): Composed  1634 cliques out of   5000 chunks
-42m 19s CLIQUES (O verse SET M>50 S>50): Composed  1803 cliques out of   6000 chunks
-42m 22s CLIQUES (O verse SET M>50 S>50): 6711 members in 1851 cliques
-42m 22s CLIQUES (O verse SET M>50 S>50): Composed and saved  1851 cliques out of   6711 chunks from 24792 comparisons
-42m 22s PRINT (O verse SET M>50 S>50): sorting out cliques
-42m 22s PRINT (O verse SET M>50 S>50): formatting 1851 cliques skipping 560 binary chapter diffs
-42m 26s PRINT (O verse SET M>50 S>50): formatted 1851 cliques (38 files) skipping 560 binary chapter diffs
-42m 26s CHUNKING (O verse): already chunked into 23213 chunks
-42m 26s PREPARING (O verse LCS)
-42m 27s PREPARING (O verse LCS): Done 23213 chunks.
-42m 27s SIMILARITY (O verse LCS M>60): Computing   269 M (269410078) comparisons and saving entries in matrix
-42m 42s SIMILARITY (O verse LCS M>60): Computed     2 M comparisons and saved 1501 entries in matrix
-42m 56s SIMILARITY (O verse LCS M>60): Computed     5 M comparisons and saved 2936 entries in matrix
-43m 10s SIMILARITY (O verse LCS M>60): Computed     8 M comparisons and saved 3564 entries in matrix
-43m 24s SIMILARITY (O verse LCS M>60): Computed    10 M comparisons and saved 4565 entries in matrix
-43m 39s SIMILARITY (O verse LCS M>60): Computed    13 M comparisons and saved 5385 entries in matrix
-43m 54s SIMILARITY (O verse LCS M>60): Computed    16 M comparisons and saved 6042 entries in matrix
-44m 08s SIMILARITY (O verse LCS M>60): Computed    18 M comparisons and saved 6742 entries in matrix
-44m 23s SIMILARITY (O verse LCS M>60): Computed    21 M comparisons and saved 7378 entries in matrix
-44m 37s SIMILARITY (O verse LCS M>60): Computed    24 M comparisons and saved 8027 entries in matrix
-44m 51s SIMILARITY (O verse LCS M>60): Computed    26 M comparisons and saved 8728 entries in matrix
-45m 06s SIMILARITY (O verse LCS M>60): Computed    29 M comparisons and saved 9408 entries in matrix
-45m 20s SIMILARITY (O verse LCS M>60): Computed    32 M comparisons and saved 10133 entries in matrix
-45m 35s SIMILARITY (O verse LCS M>60): Computed    35 M comparisons and saved 10805 entries in matrix
-45m 50s SIMILARITY (O verse LCS M>60): Computed    37 M comparisons and saved 12556 entries in matrix
-46m 07s SIMILARITY (O verse LCS M>60): Computed    40 M comparisons and saved 13740 entries in matrix
-46m 22s SIMILARITY (O verse LCS M>60): Computed    43 M comparisons and saved 15130 entries in matrix
-46m 38s SIMILARITY (O verse LCS M>60): Computed    45 M comparisons and saved 17285 entries in matrix
-46m 52s SIMILARITY (O verse LCS M>60): Computed    48 M comparisons and saved 17993 entries in matrix
-47m 06s SIMILARITY (O verse LCS M>60): Computed    51 M comparisons and saved 18867 entries in matrix
-47m 22s SIMILARITY (O verse LCS M>60): Computed    53 M comparisons and saved 20756 entries in matrix
-47m 37s SIMILARITY (O verse LCS M>60): Computed    56 M comparisons and saved 21911 entries in matrix
-47m 53s SIMILARITY (O verse LCS M>60): Computed    59 M comparisons and saved 22554 entries in matrix
-48m 08s SIMILARITY (O verse LCS M>60): Computed    61 M comparisons and saved 23826 entries in matrix
-48m 24s SIMILARITY (O verse LCS M>60): Computed    64 M comparisons and saved 26427 entries in matrix
-48m 39s SIMILARITY (O verse LCS M>60): Computed    67 M comparisons and saved 28174 entries in matrix
-48m 56s SIMILARITY (O verse LCS M>60): Computed    70 M comparisons and saved 29670 entries in matrix
-49m 10s SIMILARITY (O verse LCS M>60): Computed    72 M comparisons and saved 31882 entries in matrix
-49m 24s SIMILARITY (O verse LCS M>60): Computed    75 M comparisons and saved 34628 entries in matrix
-49m 38s SIMILARITY (O verse LCS M>60): Computed    78 M comparisons and saved 36056 entries in matrix
-49m 52s SIMILARITY (O verse LCS M>60): Computed    80 M comparisons and saved 38367 entries in matrix
-50m 06s SIMILARITY (O verse LCS M>60): Computed    83 M comparisons and saved 40398 entries in matrix
-50m 22s SIMILARITY (O verse LCS M>60): Computed    86 M comparisons and saved 42021 entries in matrix
-50m 36s SIMILARITY (O verse LCS M>60): Computed    88 M comparisons and saved 43894 entries in matrix
-50m 52s SIMILARITY (O verse LCS M>60): Computed    91 M comparisons and saved 45753 entries in matrix
-51m 07s SIMILARITY (O verse LCS M>60): Computed    94 M comparisons and saved 46875 entries in matrix
-51m 21s SIMILARITY (O verse LCS M>60): Computed    96 M comparisons and saved 48256 entries in matrix
-51m 35s SIMILARITY (O verse LCS M>60): Computed    99 M comparisons and saved 49589 entries in matrix
-51m 49s SIMILARITY (O verse LCS M>60): Computed   102 M comparisons and saved 51140 entries in matrix
-52m 06s SIMILARITY (O verse LCS M>60): Computed   105 M comparisons and saved 52455 entries in matrix
-52m 22s SIMILARITY (O verse LCS M>60): Computed   107 M comparisons and saved 53548 entries in matrix
-52m 38s SIMILARITY (O verse LCS M>60): Computed   110 M comparisons and saved 54504 entries in matrix
-52m 53s SIMILARITY (O verse LCS M>60): Computed   113 M comparisons and saved 55274 entries in matrix
-53m 09s SIMILARITY (O verse LCS M>60): Computed   115 M comparisons and saved 56249 entries in matrix
-53m 24s SIMILARITY (O verse LCS M>60): Computed   118 M comparisons and saved 57165 entries in matrix
-53m 41s SIMILARITY (O verse LCS M>60): Computed   121 M comparisons and saved 57967 entries in matrix
-53m 58s SIMILARITY (O verse LCS M>60): Computed   123 M comparisons and saved 58548 entries in matrix
-54m 13s SIMILARITY (O verse LCS M>60): Computed   126 M comparisons and saved 58838 entries in matrix
-54m 28s SIMILARITY (O verse LCS M>60): Computed   129 M comparisons and saved 59705 entries in matrix
-54m 45s SIMILARITY (O verse LCS M>60): Computed   132 M comparisons and saved 60340 entries in matrix
-55m 01s SIMILARITY (O verse LCS M>60): Computed   134 M comparisons and saved 60941 entries in matrix
-55m 17s SIMILARITY (O verse LCS M>60): Computed   137 M comparisons and saved 61487 entries in matrix
-55m 34s SIMILARITY (O verse LCS M>60): Computed   140 M comparisons and saved 62068 entries in matrix
-55m 50s SIMILARITY (O verse LCS M>60): Computed   142 M comparisons and saved 62663 entries in matrix
-56m 06s SIMILARITY (O verse LCS M>60): Computed   145 M comparisons and saved 63515 entries in matrix
-56m 22s SIMILARITY (O verse LCS M>60): Computed   148 M comparisons and saved 64341 entries in matrix
-56m 39s SIMILARITY (O verse LCS M>60): Computed   150 M comparisons and saved 64776 entries in matrix
-56m 55s SIMILARITY (O verse LCS M>60): Computed   153 M comparisons and saved 65315 entries in matrix
-57m 11s SIMILARITY (O verse LCS M>60): Computed   156 M comparisons and saved 66046 entries in matrix
-57m 27s SIMILARITY (O verse LCS M>60): Computed   158 M comparisons and saved 66949 entries in matrix
-57m 44s SIMILARITY (O verse LCS M>60): Computed   161 M comparisons and saved 67488 entries in matrix
-57m 58s SIMILARITY (O verse LCS M>60): Computed   164 M comparisons and saved 68477 entries in matrix
-58m 14s SIMILARITY (O verse LCS M>60): Computed   167 M comparisons and saved 69155 entries in matrix
-58m 32s SIMILARITY (O verse LCS M>60): Computed   169 M comparisons and saved 70076 entries in matrix
-58m 49s SIMILARITY (O verse LCS M>60): Computed   172 M comparisons and saved 71970 entries in matrix
-59m 04s SIMILARITY (O verse LCS M>60): Computed   175 M comparisons and saved 73156 entries in matrix
-59m 19s SIMILARITY (O verse LCS M>60): Computed   177 M comparisons and saved 73905 entries in matrix
-59m 33s SIMILARITY (O verse LCS M>60): Computed   180 M comparisons and saved 75097 entries in matrix
-59m 48s SIMILARITY (O verse LCS M>60): Computed   183 M comparisons and saved 76287 entries in matrix
- 1h 00m 03s SIMILARITY (O verse LCS M>60): Computed   185 M comparisons and saved 76917 entries in matrix
- 1h 00m 16s SIMILARITY (O verse LCS M>60): Computed   188 M comparisons and saved 77780 entries in matrix
- 1h 00m 29s SIMILARITY (O verse LCS M>60): Computed   191 M comparisons and saved 78262 entries in matrix
- 1h 00m 43s SIMILARITY (O verse LCS M>60): Computed   193 M comparisons and saved 78790 entries in matrix
- 1h 00m 55s SIMILARITY (O verse LCS M>60): Computed   196 M comparisons and saved 79379 entries in matrix
- 1h 01m 09s SIMILARITY (O verse LCS M>60): Computed   199 M comparisons and saved 79827 entries in matrix
- 1h 01m 23s SIMILARITY (O verse LCS M>60): Computed   202 M comparisons and saved 80744 entries in matrix
- 1h 01m 37s SIMILARITY (O verse LCS M>60): Computed   204 M comparisons and saved 82026 entries in matrix
- 1h 01m 52s SIMILARITY (O verse LCS M>60): Computed   207 M comparisons and saved 83358 entries in matrix
- 1h 02m 07s SIMILARITY (O verse LCS M>60): Computed   210 M comparisons and saved 85216 entries in matrix
- 1h 02m 24s SIMILARITY (O verse LCS M>60): Computed   212 M comparisons and saved 86251 entries in matrix
- 1h 02m 38s SIMILARITY (O verse LCS M>60): Computed   215 M comparisons and saved 86674 entries in matrix
- 1h 02m 51s SIMILARITY (O verse LCS M>60): Computed   218 M comparisons and saved 88268 entries in matrix
- 1h 03m 04s SIMILARITY (O verse LCS M>60): Computed   220 M comparisons and saved 89741 entries in matrix
- 1h 03m 17s SIMILARITY (O verse LCS M>60): Computed   223 M comparisons and saved 90901 entries in matrix
- 1h 03m 31s SIMILARITY (O verse LCS M>60): Computed   226 M comparisons and saved 91828 entries in matrix
- 1h 03m 44s SIMILARITY (O verse LCS M>60): Computed   228 M comparisons and saved 92351 entries in matrix
- 1h 03m 56s SIMILARITY (O verse LCS M>60): Computed   231 M comparisons and saved 93036 entries in matrix
- 1h 04m 09s SIMILARITY (O verse LCS M>60): Computed   234 M comparisons and saved 93626 entries in matrix
- 1h 04m 22s SIMILARITY (O verse LCS M>60): Computed   237 M comparisons and saved 94732 entries in matrix
- 1h 04m 30s SIMILARITY (O verse LCS M>60): Computed   239 M comparisons and saved 96489 entries in matrix
- 1h 04m 39s SIMILARITY (O verse LCS M>60): Computed   242 M comparisons and saved 98333 entries in matrix
- 1h 04m 48s SIMILARITY (O verse LCS M>60): Computed   245 M comparisons and saved 99952 entries in matrix
- 1h 04m 57s SIMILARITY (O verse LCS M>60): Computed   247 M comparisons and saved 101344 entries in matrix
- 1h 05m 06s SIMILARITY (O verse LCS M>60): Computed   250 M comparisons and saved 102948 entries in matrix
- 1h 05m 15s SIMILARITY (O verse LCS M>60): Computed   253 M comparisons and saved 105178 entries in matrix
- 1h 05m 24s SIMILARITY (O verse LCS M>60): Computed   255 M comparisons and saved 106484 entries in matrix
- 1h 05m 33s SIMILARITY (O verse LCS M>60): Computed   258 M comparisons and saved 107701 entries in matrix
- 1h 05m 43s SIMILARITY (O verse LCS M>60): Computed   261 M comparisons and saved 108625 entries in matrix
- 1h 05m 56s SIMILARITY (O verse LCS M>60): Computed   264 M comparisons and saved 109194 entries in matrix
- 1h 06m 13s SIMILARITY (O verse LCS M>60): Computed   266 M comparisons and saved 110696 entries in matrix
- 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed   269 M comparisons and saved 113632 entries in matrix
- 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed   269 M (269410078) comparisons and saved 113632 entries in matrix
- 1h 06m 30s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates
- 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix
- 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages
- 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): Composing cliques out of    793 chunks from 4204 comparisons
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): Composed and saved   295 cliques out of    793 chunks from 4204 comparisons
- 1h 06m 31s PRINT (O verse LCS M>60 S>100): sorting out cliques
- 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs
- 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80
- 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing
- 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs
- 1h 06m 31s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 31s PREPARING (O verse LCS): Already prepared
- 1h 06m 31s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composing cliques out of   1235 chunks from 4489 comparisons
- 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composed   457 cliques out of   1000 chunks
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): Composed and saved   504 cliques out of   1235 chunks from 4489 comparisons
- 1h 06m 32s PRINT (O verse LCS M>60 S>95): sorting out cliques
- 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs
- 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120
- 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing
- 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs
- 1h 06m 32s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 32s PREPARING (O verse LCS): Already prepared
- 1h 06m 32s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages
- 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): Composing cliques out of   1754 chunks from 5538 comparisons
- 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed   471 cliques out of   1000 chunks
- 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques
- 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed and saved   724 cliques out of   1754 chunks from 5538 comparisons
- 1h 06m 33s PRINT (O verse LCS M>60 S>90): sorting out cliques
- 1h 06m 33s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs
- 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151
- 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing
- 1h 06m 34s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs
- 1h 06m 34s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 34s PREPARING (O verse LCS): Already prepared
- 1h 06m 34s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 34s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates
- 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix
- 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages
- 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): Composing cliques out of   2296 chunks from 7871 comparisons
- 1h 06m 35s CLIQUES (O verse LCS M>60 S>85): Composed   478 cliques out of   1000 chunks
- 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed   874 cliques out of   2000 chunks
- 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques
- 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed and saved   938 cliques out of   2296 chunks from 7871 comparisons
- 1h 06m 36s PRINT (O verse LCS M>60 S>85): sorting out cliques
- 1h 06m 36s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs
- 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179
- 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing
- 1h 06m 37s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs
- 1h 06m 37s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 37s PREPARING (O verse LCS): Already prepared
- 1h 06m 37s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 38s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates
- 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix
- 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages
- 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composing cliques out of   2925 chunks from 9461 comparisons
- 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composed   486 cliques out of   1000 chunks
- 1h 06m 39s CLIQUES (O verse LCS M>60 S>80): Composed   871 cliques out of   2000 chunks
- 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques
- 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): Composed and saved  1141 cliques out of   2925 chunks from 9461 comparisons
- 1h 06m 41s PRINT (O verse LCS M>60 S>80): sorting out cliques
- 1h 06m 41s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs
- 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251
- 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing
- 1h 06m 42s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs
- 1h 06m 42s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 42s PREPARING (O verse LCS): Already prepared
- 1h 06m 42s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 42s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates
- 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix
- 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): 15540 relevant similarities between 3682 passages
- 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composing cliques out of   3682 chunks from 15540 comparisons
- 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composed   518 cliques out of   1000 chunks
- 1h 06m 44s CLIQUES (O verse LCS M>60 S>75): Composed   886 cliques out of   2000 chunks
- 1h 06m 46s CLIQUES (O verse LCS M>60 S>75): Composed  1220 cliques out of   3000 chunks
- 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): 3682 members in 1340 cliques
- 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): Composed and saved  1340 cliques out of   3682 chunks from 15540 comparisons
- 1h 06m 47s PRINT (O verse LCS M>60 S>75): sorting out cliques
- 1h 06m 47s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs
- 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346
- 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing
- 1h 06m 50s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs
- 1h 06m 50s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 06m 50s PREPARING (O verse LCS): Already prepared
- 1h 06m 50s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 06m 50s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates
- 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix
- 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): 19833 relevant similarities between 4958 passages
- 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composing cliques out of   4958 chunks from 19833 comparisons
- 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composed   549 cliques out of   1000 chunks
- 1h 06m 51s CLIQUES (O verse LCS M>60 S>70): Composed   914 cliques out of   2000 chunks
- 1h 06m 53s CLIQUES (O verse LCS M>60 S>70): Composed  1239 cliques out of   3000 chunks
- 1h 06m 55s CLIQUES (O verse LCS M>60 S>70): Composed  1491 cliques out of   4000 chunks
- 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques
- 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): Composed and saved  1644 cliques out of   4958 chunks from 19833 comparisons
- 1h 06m 58s PRINT (O verse LCS M>60 S>70): sorting out cliques
- 1h 06m 58s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs
- 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504
- 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing
- 1h 07m 02s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs
- 1h 07m 02s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 07m 02s PREPARING (O verse LCS): Already prepared
- 1h 07m 02s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 07m 02s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates
- 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix
- 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): 31844 relevant similarities between 9050 passages
- 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): Composing cliques out of   9050 chunks from 31844 comparisons
- 1h 07m 03s CLIQUES (O verse LCS M>60 S>65): Composed   596 cliques out of   1000 chunks
- 1h 07m 04s CLIQUES (O verse LCS M>60 S>65): Composed   975 cliques out of   2000 chunks
- 1h 07m 05s CLIQUES (O verse LCS M>60 S>65): Composed  1258 cliques out of   3000 chunks
- 1h 07m 08s CLIQUES (O verse LCS M>60 S>65): Composed  1468 cliques out of   4000 chunks
- 1h 07m 11s CLIQUES (O verse LCS M>60 S>65): Composed  1570 cliques out of   5000 chunks
- 1h 07m 14s CLIQUES (O verse LCS M>60 S>65): Composed  1698 cliques out of   6000 chunks
- 1h 07m 18s CLIQUES (O verse LCS M>60 S>65): Composed  1902 cliques out of   7000 chunks
- 1h 07m 23s CLIQUES (O verse LCS M>60 S>65): Composed  1932 cliques out of   8000 chunks
- 1h 07m 27s CLIQUES (O verse LCS M>60 S>65): Composed  1823 cliques out of   9000 chunks
- 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): 9050 members in 1821 cliques
- 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): Composed and saved  1821 cliques out of   9050 chunks from 31844 comparisons
- 1h 07m 28s PRINT (O verse LCS M>60 S>65): sorting out cliques
- 1h 07m 28s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs
- 1h 07m 32s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs
- 1h 07m 32s CHUNKING (O verse): already chunked into 23213 chunks
- 1h 07m 32s PREPARING (O verse LCS): Already prepared
- 1h 07m 32s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113632 entries in matrix
- 1h 07m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
- 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates
- 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix
- 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): 113632 relevant similarities between 18945 passages
- 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composing cliques out of  18945 chunks from 113632 comparisons
- 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composed   477 cliques out of   1000 chunks
- 1h 07m 34s CLIQUES (O verse LCS M>60 S>60): Composed   671 cliques out of   2000 chunks
- 1h 07m 36s CLIQUES (O verse LCS M>60 S>60): Composed   756 cliques out of   3000 chunks
- 1h 07m 37s CLIQUES (O verse LCS M>60 S>60): Composed   770 cliques out of   4000 chunks
- 1h 07m 39s CLIQUES (O verse LCS M>60 S>60): Composed   796 cliques out of   5000 chunks
- 1h 07m 41s CLIQUES (O verse LCS M>60 S>60): Composed   817 cliques out of   6000 chunks
- 1h 07m 44s CLIQUES (O verse LCS M>60 S>60): Composed   751 cliques out of   7000 chunks
- 1h 07m 46s CLIQUES (O verse LCS M>60 S>60): Composed   741 cliques out of   8000 chunks
- 1h 07m 49s CLIQUES (O verse LCS M>60 S>60): Composed   729 cliques out of   9000 chunks
- 1h 07m 52s CLIQUES (O verse LCS M>60 S>60): Composed   706 cliques out of  10000 chunks
- 1h 07m 55s CLIQUES (O verse LCS M>60 S>60): Composed   673 cliques out of  11000 chunks
- 1h 07m 58s CLIQUES (O verse LCS M>60 S>60): Composed   646 cliques out of  12000 chunks
- 1h 08m 02s CLIQUES (O verse LCS M>60 S>60): Composed   619 cliques out of  13000 chunks
- 1h 08m 05s CLIQUES (O verse LCS M>60 S>60): Composed   588 cliques out of  14000 chunks
- 1h 08m 09s CLIQUES (O verse LCS M>60 S>60): Composed   557 cliques out of  15000 chunks
- 1h 08m 13s CLIQUES (O verse LCS M>60 S>60): Composed   541 cliques out of  16000 chunks
- 1h 08m 18s CLIQUES (O verse LCS M>60 S>60): Composed   492 cliques out of  17000 chunks
- 1h 08m 22s CLIQUES (O verse LCS M>60 S>60): Composed   431 cliques out of  18000 chunks
- 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): 18945 members in 380 cliques
- 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): Composed and saved   380 cliques out of  18945 chunks from 113632 comparisons
- 1h 08m 28s PRINT (O verse LCS M>60 S>60): sorting out cliques
- 1h 08m 28s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs
- 1h 08m 29s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs
- 1h 08m 29s CHUNKING (O half_verse)
- 1h 08m 31s CHUNKING (O half_verse): Made 45180 chunks
- 1h 08m 31s PREPARING (O half_verse SET)
- 1h 08m 31s PREPARING (O half_verse SET): Done 45180 chunks.
- 1h 08m 31s SIMILARITY (O half_verse SET M>50): Computing  1020 M (1020593610) comparisons and saving entries in matrix
- 1h 08m 49s SIMILARITY (O half_verse SET M>50): Computed    10 M comparisons and saved 1962 entries in matrix
- 1h 09m 06s SIMILARITY (O half_verse SET M>50): Computed    20 M comparisons and saved 3531 entries in matrix
- 1h 09m 21s SIMILARITY (O half_verse SET M>50): Computed    30 M comparisons and saved 4614 entries in matrix
- 1h 09m 39s SIMILARITY (O half_verse SET M>50): Computed    40 M comparisons and saved 7012 entries in matrix
- 1h 09m 55s SIMILARITY (O half_verse SET M>50): Computed    51 M comparisons and saved 8344 entries in matrix
- 1h 10m 12s SIMILARITY (O half_verse SET M>50): Computed    61 M comparisons and saved 10034 entries in matrix
- 1h 10m 28s SIMILARITY (O half_verse SET M>50): Computed    71 M comparisons and saved 12065 entries in matrix
- 1h 10m 45s SIMILARITY (O half_verse SET M>50): Computed    81 M comparisons and saved 13253 entries in matrix
- 1h 11m 01s SIMILARITY (O half_verse SET M>50): Computed    91 M comparisons and saved 14525 entries in matrix
- 1h 11m 16s SIMILARITY (O half_verse SET M>50): Computed   102 M comparisons and saved 15999 entries in matrix
- 1h 11m 31s SIMILARITY (O half_verse SET M>50): Computed   112 M comparisons and saved 17424 entries in matrix
- 1h 11m 45s SIMILARITY (O half_verse SET M>50): Computed   122 M comparisons and saved 18649 entries in matrix
- 1h 12m 01s SIMILARITY (O half_verse SET M>50): Computed   132 M comparisons and saved 19526 entries in matrix
- 1h 12m 18s SIMILARITY (O half_verse SET M>50): Computed   142 M comparisons and saved 22474 entries in matrix
- 1h 12m 34s SIMILARITY (O half_verse SET M>50): Computed   153 M comparisons and saved 25421 entries in matrix
- 1h 12m 51s SIMILARITY (O half_verse SET M>50): Computed   163 M comparisons and saved 28332 entries in matrix
- 1h 13m 07s SIMILARITY (O half_verse SET M>50): Computed   173 M comparisons and saved 30622 entries in matrix
- 1h 13m 24s SIMILARITY (O half_verse SET M>50): Computed   183 M comparisons and saved 31931 entries in matrix
- 1h 13m 40s SIMILARITY (O half_verse SET M>50): Computed   193 M comparisons and saved 33509 entries in matrix
- 1h 13m 56s SIMILARITY (O half_verse SET M>50): Computed   204 M comparisons and saved 37341 entries in matrix
- 1h 14m 12s SIMILARITY (O half_verse SET M>50): Computed   214 M comparisons and saved 39804 entries in matrix
- 1h 14m 28s SIMILARITY (O half_verse SET M>50): Computed   224 M comparisons and saved 40887 entries in matrix
- 1h 14m 44s SIMILARITY (O half_verse SET M>50): Computed   234 M comparisons and saved 43204 entries in matrix
- 1h 15m 01s SIMILARITY (O half_verse SET M>50): Computed   244 M comparisons and saved 47125 entries in matrix
- 1h 15m 17s SIMILARITY (O half_verse SET M>50): Computed   255 M comparisons and saved 50158 entries in matrix
- 1h 15m 34s SIMILARITY (O half_verse SET M>50): Computed   265 M comparisons and saved 52236 entries in matrix
- 1h 15m 50s SIMILARITY (O half_verse SET M>50): Computed   275 M comparisons and saved 56284 entries in matrix
- 1h 16m 06s SIMILARITY (O half_verse SET M>50): Computed   285 M comparisons and saved 60584 entries in matrix
- 1h 16m 22s SIMILARITY (O half_verse SET M>50): Computed   295 M comparisons and saved 63095 entries in matrix
- 1h 16m 38s SIMILARITY (O half_verse SET M>50): Computed   306 M comparisons and saved 66565 entries in matrix
- 1h 16m 54s SIMILARITY (O half_verse SET M>50): Computed   316 M comparisons and saved 70425 entries in matrix
- 1h 17m 10s SIMILARITY (O half_verse SET M>50): Computed   326 M comparisons and saved 72570 entries in matrix
- 1h 17m 27s SIMILARITY (O half_verse SET M>50): Computed   336 M comparisons and saved 75773 entries in matrix
- 1h 17m 43s SIMILARITY (O half_verse SET M>50): Computed   347 M comparisons and saved 77750 entries in matrix
- 1h 17m 59s SIMILARITY (O half_verse SET M>50): Computed   357 M comparisons and saved 79787 entries in matrix
- 1h 18m 16s SIMILARITY (O half_verse SET M>50): Computed   367 M comparisons and saved 81979 entries in matrix
- 1h 18m 31s SIMILARITY (O half_verse SET M>50): Computed   377 M comparisons and saved 83955 entries in matrix
- 1h 18m 47s SIMILARITY (O half_verse SET M>50): Computed   387 M comparisons and saved 86785 entries in matrix
- 1h 19m 04s SIMILARITY (O half_verse SET M>50): Computed   398 M comparisons and saved 88967 entries in matrix
- 1h 19m 20s SIMILARITY (O half_verse SET M>50): Computed   408 M comparisons and saved 90872 entries in matrix
- 1h 19m 34s SIMILARITY (O half_verse SET M>50): Computed   418 M comparisons and saved 93080 entries in matrix
- 1h 19m 49s SIMILARITY (O half_verse SET M>50): Computed   428 M comparisons and saved 94351 entries in matrix
- 1h 20m 04s SIMILARITY (O half_verse SET M>50): Computed   438 M comparisons and saved 95927 entries in matrix
- 1h 20m 21s SIMILARITY (O half_verse SET M>50): Computed   449 M comparisons and saved 96905 entries in matrix
- 1h 20m 38s SIMILARITY (O half_verse SET M>50): Computed   459 M comparisons and saved 98144 entries in matrix
- 1h 20m 54s SIMILARITY (O half_verse SET M>50): Computed   469 M comparisons and saved 98961 entries in matrix
- 1h 21m 10s SIMILARITY (O half_verse SET M>50): Computed   479 M comparisons and saved 99614 entries in matrix
- 1h 21m 27s SIMILARITY (O half_verse SET M>50): Computed   489 M comparisons and saved 101085 entries in matrix
- 1h 21m 43s SIMILARITY (O half_verse SET M>50): Computed   500 M comparisons and saved 102493 entries in matrix
- 1h 21m 59s SIMILARITY (O half_verse SET M>50): Computed   510 M comparisons and saved 103196 entries in matrix
- 1h 22m 16s SIMILARITY (O half_verse SET M>50): Computed   520 M comparisons and saved 104523 entries in matrix
- 1h 22m 32s SIMILARITY (O half_verse SET M>50): Computed   530 M comparisons and saved 105444 entries in matrix
- 1h 22m 49s SIMILARITY (O half_verse SET M>50): Computed   540 M comparisons and saved 106395 entries in matrix
- 1h 23m 05s SIMILARITY (O half_verse SET M>50): Computed   551 M comparisons and saved 107694 entries in matrix
- 1h 23m 21s SIMILARITY (O half_verse SET M>50): Computed   561 M comparisons and saved 108689 entries in matrix
- 1h 23m 38s SIMILARITY (O half_verse SET M>50): Computed   571 M comparisons and saved 109532 entries in matrix
- 1h 23m 53s SIMILARITY (O half_verse SET M>50): Computed   581 M comparisons and saved 110249 entries in matrix
- 1h 24m 07s SIMILARITY (O half_verse SET M>50): Computed   591 M comparisons and saved 111869 entries in matrix
- 1h 24m 22s SIMILARITY (O half_verse SET M>50): Computed   602 M comparisons and saved 113205 entries in matrix
- 1h 24m 38s SIMILARITY (O half_verse SET M>50): Computed   612 M comparisons and saved 114725 entries in matrix
- 1h 24m 54s SIMILARITY (O half_verse SET M>50): Computed   622 M comparisons and saved 116242 entries in matrix
- 1h 25m 10s SIMILARITY (O half_verse SET M>50): Computed   632 M comparisons and saved 117170 entries in matrix
- 1h 25m 27s SIMILARITY (O half_verse SET M>50): Computed   642 M comparisons and saved 118632 entries in matrix
- 1h 25m 43s SIMILARITY (O half_verse SET M>50): Computed   653 M comparisons and saved 121290 entries in matrix
- 1h 25m 59s SIMILARITY (O half_verse SET M>50): Computed   663 M comparisons and saved 123350 entries in matrix
- 1h 26m 15s SIMILARITY (O half_verse SET M>50): Computed   673 M comparisons and saved 124858 entries in matrix
- 1h 26m 32s SIMILARITY (O half_verse SET M>50): Computed   683 M comparisons and saved 126931 entries in matrix
- 1h 26m 48s SIMILARITY (O half_verse SET M>50): Computed   694 M comparisons and saved 129318 entries in matrix
- 1h 27m 04s SIMILARITY (O half_verse SET M>50): Computed   704 M comparisons and saved 130325 entries in matrix
- 1h 27m 19s SIMILARITY (O half_verse SET M>50): Computed   714 M comparisons and saved 131412 entries in matrix
- 1h 27m 35s SIMILARITY (O half_verse SET M>50): Computed   724 M comparisons and saved 132234 entries in matrix
- 1h 27m 50s SIMILARITY (O half_verse SET M>50): Computed   734 M comparisons and saved 133067 entries in matrix
- 1h 28m 06s SIMILARITY (O half_verse SET M>50): Computed   745 M comparisons and saved 134096 entries in matrix
- 1h 28m 22s SIMILARITY (O half_verse SET M>50): Computed   755 M comparisons and saved 134572 entries in matrix
- 1h 28m 38s SIMILARITY (O half_verse SET M>50): Computed   765 M comparisons and saved 136234 entries in matrix
- 1h 28m 54s SIMILARITY (O half_verse SET M>50): Computed   775 M comparisons and saved 138257 entries in matrix
- 1h 29m 09s SIMILARITY (O half_verse SET M>50): Computed   785 M comparisons and saved 139986 entries in matrix
- 1h 29m 26s SIMILARITY (O half_verse SET M>50): Computed   796 M comparisons and saved 142234 entries in matrix
- 1h 29m 42s SIMILARITY (O half_verse SET M>50): Computed   806 M comparisons and saved 144260 entries in matrix
- 1h 29m 57s SIMILARITY (O half_verse SET M>50): Computed   816 M comparisons and saved 144956 entries in matrix
- 1h 30m 13s SIMILARITY (O half_verse SET M>50): Computed   826 M comparisons and saved 148044 entries in matrix
- 1h 30m 29s SIMILARITY (O half_verse SET M>50): Computed   836 M comparisons and saved 151016 entries in matrix
- 1h 30m 44s SIMILARITY (O half_verse SET M>50): Computed   847 M comparisons and saved 153676 entries in matrix
- 1h 30m 59s SIMILARITY (O half_verse SET M>50): Computed   857 M comparisons and saved 155349 entries in matrix
- 1h 31m 15s SIMILARITY (O half_verse SET M>50): Computed   867 M comparisons and saved 156458 entries in matrix
- 1h 31m 30s SIMILARITY (O half_verse SET M>50): Computed   877 M comparisons and saved 157434 entries in matrix
- 1h 31m 45s SIMILARITY (O half_verse SET M>50): Computed   887 M comparisons and saved 158073 entries in matrix
- 1h 32m 00s SIMILARITY (O half_verse SET M>50): Computed   898 M comparisons and saved 159599 entries in matrix
- 1h 32m 12s SIMILARITY (O half_verse SET M>50): Computed   908 M comparisons and saved 161827 entries in matrix
- 1h 32m 25s SIMILARITY (O half_verse SET M>50): Computed   918 M comparisons and saved 164277 entries in matrix
- 1h 32m 38s SIMILARITY (O half_verse SET M>50): Computed   928 M comparisons and saved 166159 entries in matrix
- 1h 32m 52s SIMILARITY (O half_verse SET M>50): Computed   938 M comparisons and saved 167991 entries in matrix
- 1h 33m 06s SIMILARITY (O half_verse SET M>50): Computed   949 M comparisons and saved 169802 entries in matrix
- 1h 33m 20s SIMILARITY (O half_verse SET M>50): Computed   959 M comparisons and saved 172125 entries in matrix
- 1h 33m 34s SIMILARITY (O half_verse SET M>50): Computed   969 M comparisons and saved 173838 entries in matrix
- 1h 33m 49s SIMILARITY (O half_verse SET M>50): Computed   979 M comparisons and saved 174914 entries in matrix
- 1h 34m 04s SIMILARITY (O half_verse SET M>50): Computed   989 M comparisons and saved 175787 entries in matrix
- 1h 34m 18s SIMILARITY (O half_verse SET M>50): Computed  1000 M comparisons and saved 176399 entries in matrix
- 1h 34m 36s SIMILARITY (O half_verse SET M>50): Computed  1010 M comparisons and saved 177068 entries in matrix
- 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed  1020 M comparisons and saved 179781 entries in matrix
- 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed  1020 M (1020593610) comparisons and saved 179781 entries in matrix
- 1h 34m 55s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates
- 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix
- 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages
- 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composing cliques out of   4327 chunks from 10239 comparisons
- 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composed   455 cliques out of   1000 chunks
- 1h 34m 57s CLIQUES (O half_verse SET M>50 S>100): Composed   829 cliques out of   2000 chunks
- 1h 34m 59s CLIQUES (O half_verse SET M>50 S>100): Composed  1258 cliques out of   3000 chunks
- 1h 35m 02s CLIQUES (O half_verse SET M>50 S>100): Composed  1653 cliques out of   4000 chunks
- 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques
- 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): Composed and saved  1725 cliques out of   4327 chunks from 10239 comparisons
- 1h 35m 03s PRINT (O half_verse SET M>50 S>100): sorting out cliques
- 1h 35m 03s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs
- 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573
- 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing
- 1h 35m 04s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs
- 1h 35m 04s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 35m 04s PREPARING (O half_verse SET): Already prepared
- 1h 35m 04s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 35m 05s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates
- 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix
- 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages
- 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composing cliques out of   4333 chunks from 10242 comparisons
- 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composed   453 cliques out of   1000 chunks
- 1h 35m 06s CLIQUES (O half_verse SET M>50 S>95): Composed   829 cliques out of   2000 chunks
- 1h 35m 08s CLIQUES (O half_verse SET M>50 S>95): Composed  1258 cliques out of   3000 chunks
- 1h 35m 10s CLIQUES (O half_verse SET M>50 S>95): Composed  1653 cliques out of   4000 chunks
- 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques
- 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): Composed and saved  1728 cliques out of   4333 chunks from 10242 comparisons
- 1h 35m 11s PRINT (O half_verse SET M>50 S>95): sorting out cliques
- 1h 35m 11s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs
- 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573
- 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing
- 1h 35m 12s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs
- 1h 35m 12s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 35m 12s PREPARING (O half_verse SET): Already prepared
- 1h 35m 12s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 35m 13s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates
- 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix
- 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages
- 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composing cliques out of   4618 chunks from 10410 comparisons
- 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composed   470 cliques out of   1000 chunks
- 1h 35m 15s CLIQUES (O half_verse SET M>50 S>90): Composed   869 cliques out of   2000 chunks
- 1h 35m 17s CLIQUES (O half_verse SET M>50 S>90): Composed  1279 cliques out of   3000 chunks
- 1h 35m 19s CLIQUES (O half_verse SET M>50 S>90): Composed  1675 cliques out of   4000 chunks
- 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques
- 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): Composed and saved  1863 cliques out of   4618 chunks from 10410 comparisons
- 1h 35m 21s PRINT (O half_verse SET M>50 S>90): sorting out cliques
- 1h 35m 21s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs
- 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587
- 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing
- 1h 35m 22s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs
- 1h 35m 22s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 35m 22s PREPARING (O half_verse SET): Already prepared
- 1h 35m 22s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 35m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates
- 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix
- 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages
- 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composing cliques out of   5145 chunks from 11111 comparisons
- 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composed   490 cliques out of   1000 chunks
- 1h 35m 24s CLIQUES (O half_verse SET M>50 S>85): Composed   940 cliques out of   2000 chunks
- 1h 35m 26s CLIQUES (O half_verse SET M>50 S>85): Composed  1275 cliques out of   3000 chunks
- 1h 35m 28s CLIQUES (O half_verse SET M>50 S>85): Composed  1678 cliques out of   4000 chunks
- 1h 35m 31s CLIQUES (O half_verse SET M>50 S>85): Composed  2041 cliques out of   5000 chunks
- 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques
- 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): Composed and saved  2072 cliques out of   5145 chunks from 11111 comparisons
- 1h 35m 32s PRINT (O half_verse SET M>50 S>85): sorting out cliques
- 1h 35m 32s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs
- 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640
- 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing
- 1h 35m 33s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs
- 1h 35m 33s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 35m 33s PREPARING (O half_verse SET): Already prepared
- 1h 35m 33s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 35m 34s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates
- 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix
- 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages
- 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composing cliques out of   6422 chunks from 20178 comparisons
- 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composed   527 cliques out of   1000 chunks
- 1h 35m 35s CLIQUES (O half_verse SET M>50 S>80): Composed   945 cliques out of   2000 chunks
- 1h 35m 37s CLIQUES (O half_verse SET M>50 S>80): Composed  1384 cliques out of   3000 chunks
- 1h 35m 39s CLIQUES (O half_verse SET M>50 S>80): Composed  1742 cliques out of   4000 chunks
- 1h 35m 43s CLIQUES (O half_verse SET M>50 S>80): Composed  2048 cliques out of   5000 chunks
- 1h 35m 46s CLIQUES (O half_verse SET M>50 S>80): Composed  2372 cliques out of   6000 chunks
- 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques
- 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): Composed and saved  2474 cliques out of   6422 chunks from 20178 comparisons
- 1h 35m 48s PRINT (O half_verse SET M>50 S>80): sorting out cliques
- 1h 35m 49s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs
- 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769
- 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing
- 1h 35m 50s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs
- 1h 35m 50s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 35m 50s PREPARING (O half_verse SET): Already prepared
- 1h 35m 50s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 35m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates
- 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix
- 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages
- 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composing cliques out of   8265 chunks from 23717 comparisons
- 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composed   536 cliques out of   1000 chunks
- 1h 35m 52s CLIQUES (O half_verse SET M>50 S>75): Composed   988 cliques out of   2000 chunks
- 1h 35m 54s CLIQUES (O half_verse SET M>50 S>75): Composed  1408 cliques out of   3000 chunks
- 1h 35m 56s CLIQUES (O half_verse SET M>50 S>75): Composed  1737 cliques out of   4000 chunks
- 1h 36m 00s CLIQUES (O half_verse SET M>50 S>75): Composed  2148 cliques out of   5000 chunks
- 1h 36m 04s CLIQUES (O half_verse SET M>50 S>75): Composed  2500 cliques out of   6000 chunks
- 1h 36m 08s CLIQUES (O half_verse SET M>50 S>75): Composed  2729 cliques out of   7000 chunks
- 1h 36m 13s CLIQUES (O half_verse SET M>50 S>75): Composed  2854 cliques out of   8000 chunks
- 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques
- 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): Composed and saved  2888 cliques out of   8265 chunks from 23717 comparisons
- 1h 36m 15s PRINT (O half_verse SET M>50 S>75): sorting out cliques
- 1h 36m 15s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs
- 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919
- 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing
- 1h 36m 18s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs
- 1h 36m 18s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 36m 18s PREPARING (O half_verse SET): Already prepared
- 1h 36m 18s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 36m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates
- 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix
- 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages
- 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): Composing cliques out of   9388 chunks from 25560 comparisons
- 1h 36m 19s CLIQUES (O half_verse SET M>50 S>70): Composed   558 cliques out of   1000 chunks
- 1h 36m 20s CLIQUES (O half_verse SET M>50 S>70): Composed  1029 cliques out of   2000 chunks
- 1h 36m 22s CLIQUES (O half_verse SET M>50 S>70): Composed  1456 cliques out of   3000 chunks
- 1h 36m 24s CLIQUES (O half_verse SET M>50 S>70): Composed  1836 cliques out of   4000 chunks
- 1h 36m 27s CLIQUES (O half_verse SET M>50 S>70): Composed  2118 cliques out of   5000 chunks
- 1h 36m 31s CLIQUES (O half_verse SET M>50 S>70): Composed  2431 cliques out of   6000 chunks
- 1h 36m 35s CLIQUES (O half_verse SET M>50 S>70): Composed  2756 cliques out of   7000 chunks
- 1h 36m 40s CLIQUES (O half_verse SET M>50 S>70): Composed  3017 cliques out of   8000 chunks
- 1h 36m 45s CLIQUES (O half_verse SET M>50 S>70): Composed  3173 cliques out of   9000 chunks
- 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques
- 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): Composed and saved  3193 cliques out of   9388 chunks from 25560 comparisons
- 1h 36m 47s PRINT (O half_verse SET M>50 S>70): sorting out cliques
- 1h 36m 48s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs
- 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014
- 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing
- 1h 36m 50s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs
- 1h 36m 50s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 36m 50s PREPARING (O half_verse SET): Already prepared
- 1h 36m 50s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 36m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates
- 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix
- 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages
- 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composing cliques out of  12162 chunks from 37453 comparisons
- 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composed   574 cliques out of   1000 chunks
- 1h 36m 52s CLIQUES (O half_verse SET M>50 S>65): Composed  1045 cliques out of   2000 chunks
- 1h 36m 54s CLIQUES (O half_verse SET M>50 S>65): Composed  1468 cliques out of   3000 chunks
- 1h 36m 56s CLIQUES (O half_verse SET M>50 S>65): Composed  1894 cliques out of   4000 chunks
- 1h 36m 58s CLIQUES (O half_verse SET M>50 S>65): Composed  2269 cliques out of   5000 chunks
- 1h 37m 02s CLIQUES (O half_verse SET M>50 S>65): Composed  2552 cliques out of   6000 chunks
- 1h 37m 06s CLIQUES (O half_verse SET M>50 S>65): Composed  2758 cliques out of   7000 chunks
- 1h 37m 10s CLIQUES (O half_verse SET M>50 S>65): Composed  3034 cliques out of   8000 chunks
- 1h 37m 15s CLIQUES (O half_verse SET M>50 S>65): Composed  3276 cliques out of   9000 chunks
- 1h 37m 21s CLIQUES (O half_verse SET M>50 S>65): Composed  3416 cliques out of  10000 chunks
- 1h 37m 28s CLIQUES (O half_verse SET M>50 S>65): Composed  3641 cliques out of  11000 chunks
- 1h 37m 36s CLIQUES (O half_verse SET M>50 S>65): Composed  3425 cliques out of  12000 chunks
- 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques
- 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): Composed and saved  3342 cliques out of  12162 chunks from 37453 comparisons
- 1h 37m 38s PRINT (O half_verse SET M>50 S>65): sorting out cliques
- 1h 37m 38s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs
- 1h 37m 41s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs
- 1h 37m 41s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 37m 41s PREPARING (O half_verse SET): Already prepared
- 1h 37m 41s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 37m 41s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates
- 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix
- 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages
- 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composing cliques out of  16476 chunks from 55384 comparisons
- 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composed   603 cliques out of   1000 chunks
- 1h 37m 44s CLIQUES (O half_verse SET M>50 S>60): Composed  1110 cliques out of   2000 chunks
- 1h 37m 45s CLIQUES (O half_verse SET M>50 S>60): Composed  1550 cliques out of   3000 chunks
- 1h 37m 48s CLIQUES (O half_verse SET M>50 S>60): Composed  1927 cliques out of   4000 chunks
- 1h 37m 51s CLIQUES (O half_verse SET M>50 S>60): Composed  2271 cliques out of   5000 chunks
- 1h 37m 55s CLIQUES (O half_verse SET M>50 S>60): Composed  2547 cliques out of   6000 chunks
- 1h 38m 00s CLIQUES (O half_verse SET M>50 S>60): Composed  2804 cliques out of   7000 chunks
- 1h 38m 05s CLIQUES (O half_verse SET M>50 S>60): Composed  2992 cliques out of   8000 chunks
- 1h 38m 11s CLIQUES (O half_verse SET M>50 S>60): Composed  3182 cliques out of   9000 chunks
- 1h 38m 17s CLIQUES (O half_verse SET M>50 S>60): Composed  3442 cliques out of  10000 chunks
- 1h 38m 24s CLIQUES (O half_verse SET M>50 S>60): Composed  3591 cliques out of  11000 chunks
- 1h 38m 32s CLIQUES (O half_verse SET M>50 S>60): Composed  3732 cliques out of  12000 chunks
- 1h 38m 41s CLIQUES (O half_verse SET M>50 S>60): Composed  3939 cliques out of  13000 chunks
- 1h 38m 50s CLIQUES (O half_verse SET M>50 S>60): Composed  3797 cliques out of  14000 chunks
- 1h 38m 59s CLIQUES (O half_verse SET M>50 S>60): Composed  3797 cliques out of  15000 chunks
- 1h 39m 09s CLIQUES (O half_verse SET M>50 S>60): Composed  3527 cliques out of  16000 chunks
- 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques
- 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): Composed and saved  3424 cliques out of  16476 chunks from 55384 comparisons
- 1h 39m 14s PRINT (O half_verse SET M>50 S>60): sorting out cliques
- 1h 39m 14s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs
- 1h 39m 18s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs
- 1h 39m 18s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 39m 18s PREPARING (O half_verse SET): Already prepared
- 1h 39m 18s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 39m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates
- 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix
- 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages
- 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composing cliques out of  19519 chunks from 70089 comparisons
- 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composed   604 cliques out of   1000 chunks
- 1h 39m 20s CLIQUES (O half_verse SET M>50 S>55): Composed  1098 cliques out of   2000 chunks
- 1h 39m 22s CLIQUES (O half_verse SET M>50 S>55): Composed  1587 cliques out of   3000 chunks
- 1h 39m 25s CLIQUES (O half_verse SET M>50 S>55): Composed  1974 cliques out of   4000 chunks
- 1h 39m 28s CLIQUES (O half_verse SET M>50 S>55): Composed  2356 cliques out of   5000 chunks
- 1h 39m 32s CLIQUES (O half_verse SET M>50 S>55): Composed  2683 cliques out of   6000 chunks
- 1h 39m 37s CLIQUES (O half_verse SET M>50 S>55): Composed  2971 cliques out of   7000 chunks
- 1h 39m 42s CLIQUES (O half_verse SET M>50 S>55): Composed  3126 cliques out of   8000 chunks
- 1h 39m 48s CLIQUES (O half_verse SET M>50 S>55): Composed  3277 cliques out of   9000 chunks
- 1h 39m 54s CLIQUES (O half_verse SET M>50 S>55): Composed  3271 cliques out of  10000 chunks
- 1h 40m 01s CLIQUES (O half_verse SET M>50 S>55): Composed  3316 cliques out of  11000 chunks
- 1h 40m 08s CLIQUES (O half_verse SET M>50 S>55): Composed  3241 cliques out of  12000 chunks
- 1h 40m 16s CLIQUES (O half_verse SET M>50 S>55): Composed  3384 cliques out of  13000 chunks
- 1h 40m 25s CLIQUES (O half_verse SET M>50 S>55): Composed  3387 cliques out of  14000 chunks
- 1h 40m 34s CLIQUES (O half_verse SET M>50 S>55): Composed  3459 cliques out of  15000 chunks
- 1h 40m 43s CLIQUES (O half_verse SET M>50 S>55): Composed  3567 cliques out of  16000 chunks
- 1h 40m 53s CLIQUES (O half_verse SET M>50 S>55): Composed  3471 cliques out of  17000 chunks
- 1h 41m 03s CLIQUES (O half_verse SET M>50 S>55): Composed  3480 cliques out of  18000 chunks
- 1h 41m 13s CLIQUES (O half_verse SET M>50 S>55): Composed  3279 cliques out of  19000 chunks
- 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques
- 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): Composed and saved  3184 cliques out of  19519 chunks from 70089 comparisons
- 1h 41m 19s PRINT (O half_verse SET M>50 S>55): sorting out cliques
- 1h 41m 20s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs
- 1h 41m 23s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs
- 1h 41m 23s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 41m 23s PREPARING (O half_verse SET): Already prepared
- 1h 41m 23s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179781 entries in matrix
- 1h 41m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
- 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates
- 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix
- 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): 179781 relevant similarities between 28988 passages
- 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): Composing cliques out of  28988 chunks from 179781 comparisons
- 1h 41m 25s CLIQUES (O half_verse SET M>50 S>50): Composed   652 cliques out of   1000 chunks
- 1h 41m 26s CLIQUES (O half_verse SET M>50 S>50): Composed  1202 cliques out of   2000 chunks
- 1h 41m 27s CLIQUES (O half_verse SET M>50 S>50): Composed  1587 cliques out of   3000 chunks
- 1h 41m 30s CLIQUES (O half_verse SET M>50 S>50): Composed  1958 cliques out of   4000 chunks
- 1h 41m 33s CLIQUES (O half_verse SET M>50 S>50): Composed  2279 cliques out of   5000 chunks
- 1h 41m 37s CLIQUES (O half_verse SET M>50 S>50): Composed  2478 cliques out of   6000 chunks
- 1h 41m 41s CLIQUES (O half_verse SET M>50 S>50): Composed  2663 cliques out of   7000 chunks
- 1h 41m 46s CLIQUES (O half_verse SET M>50 S>50): Composed  2828 cliques out of   8000 chunks
- 1h 41m 52s CLIQUES (O half_verse SET M>50 S>50): Composed  2961 cliques out of   9000 chunks
- 1h 41m 58s CLIQUES (O half_verse SET M>50 S>50): Composed  3058 cliques out of  10000 chunks
- 1h 42m 04s CLIQUES (O half_verse SET M>50 S>50): Composed  3206 cliques out of  11000 chunks
- 1h 42m 11s CLIQUES (O half_verse SET M>50 S>50): Composed  3278 cliques out of  12000 chunks
- 1h 42m 18s CLIQUES (O half_verse SET M>50 S>50): Composed  3296 cliques out of  13000 chunks
- 1h 42m 26s CLIQUES (O half_verse SET M>50 S>50): Composed  3353 cliques out of  14000 chunks
- 1h 42m 33s CLIQUES (O half_verse SET M>50 S>50): Composed  3280 cliques out of  15000 chunks
- 1h 42m 42s CLIQUES (O half_verse SET M>50 S>50): Composed  3372 cliques out of  16000 chunks
- 1h 42m 50s CLIQUES (O half_verse SET M>50 S>50): Composed  3259 cliques out of  17000 chunks
- 1h 42m 59s CLIQUES (O half_verse SET M>50 S>50): Composed  3240 cliques out of  18000 chunks
- 1h 43m 09s CLIQUES (O half_verse SET M>50 S>50): Composed  3378 cliques out of  19000 chunks
- 1h 43m 18s CLIQUES (O half_verse SET M>50 S>50): Composed  3281 cliques out of  20000 chunks
- 1h 43m 27s CLIQUES (O half_verse SET M>50 S>50): Composed  3127 cliques out of  21000 chunks
- 1h 43m 37s CLIQUES (O half_verse SET M>50 S>50): Composed  3111 cliques out of  22000 chunks
- 1h 43m 48s CLIQUES (O half_verse SET M>50 S>50): Composed  3080 cliques out of  23000 chunks
- 1h 43m 58s CLIQUES (O half_verse SET M>50 S>50): Composed  2926 cliques out of  24000 chunks
- 1h 44m 08s CLIQUES (O half_verse SET M>50 S>50): Composed  2778 cliques out of  25000 chunks
- 1h 44m 20s CLIQUES (O half_verse SET M>50 S>50): Composed  2738 cliques out of  26000 chunks
- 1h 44m 33s CLIQUES (O half_verse SET M>50 S>50): Composed  2711 cliques out of  27000 chunks
- 1h 44m 43s CLIQUES (O half_verse SET M>50 S>50): Composed  2378 cliques out of  28000 chunks
- 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): 28988 members in 2031 cliques
- 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): Composed and saved  2031 cliques out of  28988 chunks from 179781 comparisons
- 1h 44m 55s PRINT (O half_verse SET M>50 S>50): sorting out cliques
- 1h 44m 56s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs
- 1h 44m 58s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs
- 1h 44m 58s CHUNKING (O half_verse): already chunked into 45180 chunks
- 1h 44m 58s PREPARING (O half_verse LCS)
- 1h 44m 58s PREPARING (O half_verse LCS): Done 45180 chunks.
- 1h 44m 58s SIMILARITY (O half_verse LCS M>60): Computing  1020 M (1020593610) comparisons and saving entries in matrix
- 1h 45m 19s SIMILARITY (O half_verse LCS M>60): Computed    10 M comparisons and saved 23129 entries in matrix
- 1h 45m 40s SIMILARITY (O half_verse LCS M>60): Computed    20 M comparisons and saved 45727 entries in matrix
- 1h 46m 00s SIMILARITY (O half_verse LCS M>60): Computed    30 M comparisons and saved 62396 entries in matrix
- 1h 46m 21s SIMILARITY (O half_verse LCS M>60): Computed    40 M comparisons and saved 86192 entries in matrix
- 1h 46m 43s SIMILARITY (O half_verse LCS M>60): Computed    51 M comparisons and saved 105378 entries in matrix
- 1h 47m 03s SIMILARITY (O half_verse LCS M>60): Computed    61 M comparisons and saved 124304 entries in matrix
- 1h 47m 24s SIMILARITY (O half_verse LCS M>60): Computed    71 M comparisons and saved 143728 entries in matrix
- 1h 47m 45s SIMILARITY (O half_verse LCS M>60): Computed    81 M comparisons and saved 161716 entries in matrix
- 1h 48m 05s SIMILARITY (O half_verse LCS M>60): Computed    91 M comparisons and saved 180158 entries in matrix
- 1h 48m 25s SIMILARITY (O half_verse LCS M>60): Computed   102 M comparisons and saved 198767 entries in matrix
- 1h 48m 46s SIMILARITY (O half_verse LCS M>60): Computed   112 M comparisons and saved 217110 entries in matrix
- 1h 49m 06s SIMILARITY (O half_verse LCS M>60): Computed   122 M comparisons and saved 234406 entries in matrix
- 1h 49m 27s SIMILARITY (O half_verse LCS M>60): Computed   132 M comparisons and saved 251791 entries in matrix
- 1h 49m 49s SIMILARITY (O half_verse LCS M>60): Computed   142 M comparisons and saved 277921 entries in matrix
- 1h 50m 11s SIMILARITY (O half_verse LCS M>60): Computed   153 M comparisons and saved 301012 entries in matrix
- 1h 50m 34s SIMILARITY (O half_verse LCS M>60): Computed   163 M comparisons and saved 322004 entries in matrix
- 1h 50m 56s SIMILARITY (O half_verse LCS M>60): Computed   173 M comparisons and saved 345960 entries in matrix
- 1h 51m 16s SIMILARITY (O half_verse LCS M>60): Computed   183 M comparisons and saved 366300 entries in matrix
- 1h 51m 37s SIMILARITY (O half_verse LCS M>60): Computed   193 M comparisons and saved 381274 entries in matrix
- 1h 51m 59s SIMILARITY (O half_verse LCS M>60): Computed   204 M comparisons and saved 401543 entries in matrix
- 1h 52m 21s SIMILARITY (O half_verse LCS M>60): Computed   214 M comparisons and saved 424607 entries in matrix
- 1h 52m 42s SIMILARITY (O half_verse LCS M>60): Computed   224 M comparisons and saved 434786 entries in matrix
- 1h 53m 04s SIMILARITY (O half_verse LCS M>60): Computed   234 M comparisons and saved 451987 entries in matrix
- 1h 53m 26s SIMILARITY (O half_verse LCS M>60): Computed   244 M comparisons and saved 476196 entries in matrix
- 1h 53m 48s SIMILARITY (O half_verse LCS M>60): Computed   255 M comparisons and saved 495694 entries in matrix
- 1h 54m 11s SIMILARITY (O half_verse LCS M>60): Computed   265 M comparisons and saved 511994 entries in matrix
- 1h 54m 31s SIMILARITY (O half_verse LCS M>60): Computed   275 M comparisons and saved 538859 entries in matrix
- 1h 54m 52s SIMILARITY (O half_verse LCS M>60): Computed   285 M comparisons and saved 566545 entries in matrix
- 1h 55m 12s SIMILARITY (O half_verse LCS M>60): Computed   295 M comparisons and saved 585782 entries in matrix
- 1h 55m 32s SIMILARITY (O half_verse LCS M>60): Computed   306 M comparisons and saved 605411 entries in matrix
- 1h 55m 55s SIMILARITY (O half_verse LCS M>60): Computed   316 M comparisons and saved 623888 entries in matrix
- 1h 56m 16s SIMILARITY (O half_verse LCS M>60): Computed   326 M comparisons and saved 645994 entries in matrix
- 1h 56m 37s SIMILARITY (O half_verse LCS M>60): Computed   336 M comparisons and saved 672491 entries in matrix
- 1h 57m 00s SIMILARITY (O half_verse LCS M>60): Computed   347 M comparisons and saved 690907 entries in matrix
- 1h 57m 20s SIMILARITY (O half_verse LCS M>60): Computed   357 M comparisons and saved 710440 entries in matrix
- 1h 57m 42s SIMILARITY (O half_verse LCS M>60): Computed   367 M comparisons and saved 727551 entries in matrix
- 1h 58m 01s SIMILARITY (O half_verse LCS M>60): Computed   377 M comparisons and saved 747143 entries in matrix
- 1h 58m 23s SIMILARITY (O half_verse LCS M>60): Computed   387 M comparisons and saved 769713 entries in matrix
- 1h 58m 46s SIMILARITY (O half_verse LCS M>60): Computed   398 M comparisons and saved 791579 entries in matrix
- 1h 59m 08s SIMILARITY (O half_verse LCS M>60): Computed   408 M comparisons and saved 813039 entries in matrix
- 1h 59m 31s SIMILARITY (O half_verse LCS M>60): Computed   418 M comparisons and saved 834185 entries in matrix
- 1h 59m 52s SIMILARITY (O half_verse LCS M>60): Computed   428 M comparisons and saved 855462 entries in matrix
- 2h 00m 14s SIMILARITY (O half_verse LCS M>60): Computed   438 M comparisons and saved 879387 entries in matrix
- 2h 00m 35s SIMILARITY (O half_verse LCS M>60): Computed   449 M comparisons and saved 896300 entries in matrix
- 2h 00m 59s SIMILARITY (O half_verse LCS M>60): Computed   459 M comparisons and saved 914630 entries in matrix
- 2h 01m 23s SIMILARITY (O half_verse LCS M>60): Computed   469 M comparisons and saved 928008 entries in matrix
- 2h 01m 44s SIMILARITY (O half_verse LCS M>60): Computed   479 M comparisons and saved 939511 entries in matrix
- 2h 02m 08s SIMILARITY (O half_verse LCS M>60): Computed   489 M comparisons and saved 956514 entries in matrix
- 2h 02m 29s SIMILARITY (O half_verse LCS M>60): Computed   500 M comparisons and saved 973079 entries in matrix
- 2h 02m 52s SIMILARITY (O half_verse LCS M>60): Computed   510 M comparisons and saved 988171 entries in matrix
- 2h 03m 15s SIMILARITY (O half_verse LCS M>60): Computed   520 M comparisons and saved 1007501 entries in matrix
- 2h 03m 37s SIMILARITY (O half_verse LCS M>60): Computed   530 M comparisons and saved 1026297 entries in matrix
- 2h 04m 00s SIMILARITY (O half_verse LCS M>60): Computed   540 M comparisons and saved 1044626 entries in matrix
- 2h 04m 22s SIMILARITY (O half_verse LCS M>60): Computed   551 M comparisons and saved 1065472 entries in matrix
- 2h 04m 44s SIMILARITY (O half_verse LCS M>60): Computed   561 M comparisons and saved 1084725 entries in matrix
- 2h 05m 06s SIMILARITY (O half_verse LCS M>60): Computed   571 M comparisons and saved 1098963 entries in matrix
- 2h 05m 28s SIMILARITY (O half_verse LCS M>60): Computed   581 M comparisons and saved 1113424 entries in matrix
- 2h 05m 50s SIMILARITY (O half_verse LCS M>60): Computed   591 M comparisons and saved 1131189 entries in matrix
- 2h 06m 13s SIMILARITY (O half_verse LCS M>60): Computed   602 M comparisons and saved 1152117 entries in matrix
- 2h 06m 35s SIMILARITY (O half_verse LCS M>60): Computed   612 M comparisons and saved 1169776 entries in matrix
- 2h 06m 56s SIMILARITY (O half_verse LCS M>60): Computed   622 M comparisons and saved 1190128 entries in matrix
- 2h 07m 18s SIMILARITY (O half_verse LCS M>60): Computed   632 M comparisons and saved 1206090 entries in matrix
- 2h 07m 41s SIMILARITY (O half_verse LCS M>60): Computed   642 M comparisons and saved 1223130 entries in matrix
- 2h 08m 03s SIMILARITY (O half_verse LCS M>60): Computed   653 M comparisons and saved 1244760 entries in matrix
- 2h 08m 25s SIMILARITY (O half_verse LCS M>60): Computed   663 M comparisons and saved 1264850 entries in matrix
- 2h 08m 47s SIMILARITY (O half_verse LCS M>60): Computed   673 M comparisons and saved 1283072 entries in matrix
- 2h 09m 09s SIMILARITY (O half_verse LCS M>60): Computed   683 M comparisons and saved 1299660 entries in matrix
- 2h 09m 31s SIMILARITY (O half_verse LCS M>60): Computed   694 M comparisons and saved 1317246 entries in matrix
- 2h 09m 52s SIMILARITY (O half_verse LCS M>60): Computed   704 M comparisons and saved 1333767 entries in matrix
- 2h 10m 11s SIMILARITY (O half_verse LCS M>60): Computed   714 M comparisons and saved 1350133 entries in matrix
- 2h 10m 30s SIMILARITY (O half_verse LCS M>60): Computed   724 M comparisons and saved 1365013 entries in matrix
- 2h 10m 50s SIMILARITY (O half_verse LCS M>60): Computed   734 M comparisons and saved 1379520 entries in matrix
- 2h 11m 08s SIMILARITY (O half_verse LCS M>60): Computed   745 M comparisons and saved 1397432 entries in matrix
- 2h 11m 28s SIMILARITY (O half_verse LCS M>60): Computed   755 M comparisons and saved 1411286 entries in matrix
- 2h 11m 48s SIMILARITY (O half_verse LCS M>60): Computed   765 M comparisons and saved 1430539 entries in matrix
- 2h 12m 08s SIMILARITY (O half_verse LCS M>60): Computed   775 M comparisons and saved 1450873 entries in matrix
- 2h 12m 29s SIMILARITY (O half_verse LCS M>60): Computed   785 M comparisons and saved 1471293 entries in matrix
- 2h 12m 50s SIMILARITY (O half_verse LCS M>60): Computed   796 M comparisons and saved 1493379 entries in matrix
- 2h 13m 13s SIMILARITY (O half_verse LCS M>60): Computed   806 M comparisons and saved 1511949 entries in matrix
- 2h 13m 32s SIMILARITY (O half_verse LCS M>60): Computed   816 M comparisons and saved 1525887 entries in matrix
- 2h 13m 52s SIMILARITY (O half_verse LCS M>60): Computed   826 M comparisons and saved 1544656 entries in matrix
- 2h 14m 12s SIMILARITY (O half_verse LCS M>60): Computed   836 M comparisons and saved 1564089 entries in matrix
- 2h 14m 30s SIMILARITY (O half_verse LCS M>60): Computed   847 M comparisons and saved 1582466 entries in matrix
- 2h 14m 50s SIMILARITY (O half_verse LCS M>60): Computed   857 M comparisons and saved 1600534 entries in matrix
- 2h 15m 10s SIMILARITY (O half_verse LCS M>60): Computed   867 M comparisons and saved 1616006 entries in matrix
- 2h 15m 28s SIMILARITY (O half_verse LCS M>60): Computed   877 M comparisons and saved 1634862 entries in matrix
- 2h 15m 46s SIMILARITY (O half_verse LCS M>60): Computed   887 M comparisons and saved 1650701 entries in matrix
- 2h 16m 05s SIMILARITY (O half_verse LCS M>60): Computed   898 M comparisons and saved 1671631 entries in matrix
- 2h 16m 20s SIMILARITY (O half_verse LCS M>60): Computed   908 M comparisons and saved 1704592 entries in matrix
- 2h 16m 34s SIMILARITY (O half_verse LCS M>60): Computed   918 M comparisons and saved 1741561 entries in matrix
- 2h 16m 49s SIMILARITY (O half_verse LCS M>60): Computed   928 M comparisons and saved 1776301 entries in matrix
- 2h 17m 04s SIMILARITY (O half_verse LCS M>60): Computed   938 M comparisons and saved 1811487 entries in matrix
- 2h 17m 19s SIMILARITY (O half_verse LCS M>60): Computed   949 M comparisons and saved 1846878 entries in matrix
- 2h 17m 33s SIMILARITY (O half_verse LCS M>60): Computed   959 M comparisons and saved 1882694 entries in matrix
- 2h 17m 49s SIMILARITY (O half_verse LCS M>60): Computed   969 M comparisons and saved 1913165 entries in matrix
- 2h 18m 04s SIMILARITY (O half_verse LCS M>60): Computed   979 M comparisons and saved 1941507 entries in matrix
- 2h 18m 20s SIMILARITY (O half_verse LCS M>60): Computed   989 M comparisons and saved 1965409 entries in matrix
- 2h 18m 38s SIMILARITY (O half_verse LCS M>60): Computed  1000 M comparisons and saved 1981708 entries in matrix
- 2h 19m 02s SIMILARITY (O half_verse LCS M>60): Computed  1010 M comparisons and saved 1993912 entries in matrix
- 2h 19m 25s SIMILARITY (O half_verse LCS M>60): Computed  1020 M comparisons and saved 2017735 entries in matrix
- 2h 19m 26s SIMILARITY (O half_verse LCS M>60): Computed  1020 M (1020593610) comparisons and saved 2017735 entries in matrix
- 2h 19m 28s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates
- 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix
- 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages
- 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composing cliques out of   3799 chunks from 9270 comparisons
- 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composed   450 cliques out of   1000 chunks
- 2h 19m 30s CLIQUES (O half_verse LCS M>60 S>100): Composed   823 cliques out of   2000 chunks
- 2h 19m 32s CLIQUES (O half_verse LCS M>60 S>100): Composed  1246 cliques out of   3000 chunks
- 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques
- 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): Composed and saved  1514 cliques out of   3799 chunks from 9270 comparisons
- 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): sorting out cliques
- 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs
- 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493
- 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing
- 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs
- 2h 19m 34s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 19m 34s PREPARING (O half_verse LCS): Already prepared
- 2h 19m 34s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 19m 36s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates
- 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix
- 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages
- 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composing cliques out of   4342 chunks from 9663 comparisons
- 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composed   469 cliques out of   1000 chunks
- 2h 19m 38s CLIQUES (O half_verse LCS M>60 S>95): Composed   848 cliques out of   2000 chunks
- 2h 19m 40s CLIQUES (O half_verse LCS M>60 S>95): Composed  1272 cliques out of   3000 chunks
- 2h 19m 42s CLIQUES (O half_verse LCS M>60 S>95): Composed  1689 cliques out of   4000 chunks
- 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques
- 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): Composed and saved  1771 cliques out of   4342 chunks from 9663 comparisons
- 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): sorting out cliques
- 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs
- 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543
- 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing
- 2h 19m 44s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs
- 2h 19m 44s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 19m 44s PREPARING (O half_verse LCS): Already prepared
- 2h 19m 44s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 19m 46s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates
- 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix
- 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages
- 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composing cliques out of   5776 chunks from 12125 comparisons
- 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composed   482 cliques out of   1000 chunks
- 2h 19m 48s CLIQUES (O half_verse LCS M>60 S>90): Composed   913 cliques out of   2000 chunks
- 2h 19m 50s CLIQUES (O half_verse LCS M>60 S>90): Composed  1282 cliques out of   3000 chunks
- 2h 19m 52s CLIQUES (O half_verse LCS M>60 S>90): Composed  1718 cliques out of   4000 chunks
- 2h 19m 55s CLIQUES (O half_verse LCS M>60 S>90): Composed  2094 cliques out of   5000 chunks
- 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques
- 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): Composed and saved  2336 cliques out of   5776 chunks from 12125 comparisons
- 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): sorting out cliques
- 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs
- 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732
- 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing
- 2h 19m 59s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs
- 2h 19m 59s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 19m 59s PREPARING (O half_verse LCS): Already prepared
- 2h 19m 59s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 20m 00s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates
- 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix
- 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages
- 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composing cliques out of   7970 chunks from 17551 comparisons
- 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composed   526 cliques out of   1000 chunks
- 2h 20m 03s CLIQUES (O half_verse LCS M>60 S>85): Composed   959 cliques out of   2000 chunks
- 2h 20m 05s CLIQUES (O half_verse LCS M>60 S>85): Composed  1352 cliques out of   3000 chunks
- 2h 20m 07s CLIQUES (O half_verse LCS M>60 S>85): Composed  1694 cliques out of   4000 chunks
- 2h 20m 10s CLIQUES (O half_verse LCS M>60 S>85): Composed  2079 cliques out of   5000 chunks
- 2h 20m 13s CLIQUES (O half_verse LCS M>60 S>85): Composed  2430 cliques out of   6000 chunks
- 2h 20m 17s CLIQUES (O half_verse LCS M>60 S>85): Composed  2773 cliques out of   7000 chunks
- 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques
- 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): Composed and saved  2983 cliques out of   7970 chunks from 17551 comparisons
- 2h 20m 22s PRINT (O half_verse LCS M>60 S>85): sorting out cliques
- 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs
- 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975
- 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing
- 2h 20m 24s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs
- 2h 20m 24s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 20m 24s PREPARING (O half_verse LCS): Already prepared
- 2h 20m 24s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 20m 26s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates
- 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix
- 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages
- 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): Composing cliques out of  12504 chunks from 27273 comparisons
- 2h 20m 28s CLIQUES (O half_verse LCS M>60 S>80): Composed   538 cliques out of   1000 chunks
- 2h 20m 29s CLIQUES (O half_verse LCS M>60 S>80): Composed   956 cliques out of   2000 chunks
- 2h 20m 30s CLIQUES (O half_verse LCS M>60 S>80): Composed  1379 cliques out of   3000 chunks
- 2h 20m 32s CLIQUES (O half_verse LCS M>60 S>80): Composed  1735 cliques out of   4000 chunks
- 2h 20m 35s CLIQUES (O half_verse LCS M>60 S>80): Composed  2057 cliques out of   5000 chunks
- 2h 20m 39s CLIQUES (O half_verse LCS M>60 S>80): Composed  2332 cliques out of   6000 chunks
- 2h 20m 43s CLIQUES (O half_verse LCS M>60 S>80): Composed  2700 cliques out of   7000 chunks
- 2h 20m 48s CLIQUES (O half_verse LCS M>60 S>80): Composed  2968 cliques out of   8000 chunks
- 2h 20m 54s CLIQUES (O half_verse LCS M>60 S>80): Composed  3278 cliques out of   9000 chunks
- 2h 21m 00s CLIQUES (O half_verse LCS M>60 S>80): Composed  3433 cliques out of  10000 chunks
- 2h 21m 07s CLIQUES (O half_verse LCS M>60 S>80): Composed  3579 cliques out of  11000 chunks
- 2h 21m 14s CLIQUES (O half_verse LCS M>60 S>80): Composed  3589 cliques out of  12000 chunks
- 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques
- 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): Composed and saved  3540 cliques out of  12504 chunks from 27273 comparisons
- 2h 21m 18s PRINT (O half_verse LCS M>60 S>80): sorting out cliques
- 2h 21m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs
- 2h 21m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs
- 2h 21m 21s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 21m 21s PREPARING (O half_verse LCS): Already prepared
- 2h 21m 21s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 21m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates
- 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix
- 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): 53979 relevant similarities between 19147 passages
- 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): Composing cliques out of  19147 chunks from 53979 comparisons
- 2h 21m 25s CLIQUES (O half_verse LCS M>60 S>75): Composed   561 cliques out of   1000 chunks
- 2h 21m 26s CLIQUES (O half_verse LCS M>60 S>75): Composed  1031 cliques out of   2000 chunks
- 2h 21m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed  1399 cliques out of   3000 chunks
- 2h 21m 30s CLIQUES (O half_verse LCS M>60 S>75): Composed  1756 cliques out of   4000 chunks
- 2h 21m 33s CLIQUES (O half_verse LCS M>60 S>75): Composed  2091 cliques out of   5000 chunks
- 2h 21m 36s CLIQUES (O half_verse LCS M>60 S>75): Composed  2372 cliques out of   6000 chunks
- 2h 21m 40s CLIQUES (O half_verse LCS M>60 S>75): Composed  2584 cliques out of   7000 chunks
- 2h 21m 45s CLIQUES (O half_verse LCS M>60 S>75): Composed  2783 cliques out of   8000 chunks
- 2h 21m 51s CLIQUES (O half_verse LCS M>60 S>75): Composed  2943 cliques out of   9000 chunks
- 2h 21m 57s CLIQUES (O half_verse LCS M>60 S>75): Composed  3223 cliques out of  10000 chunks
- 2h 22m 04s CLIQUES (O half_verse LCS M>60 S>75): Composed  3329 cliques out of  11000 chunks
- 2h 22m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed  3391 cliques out of  12000 chunks
- 2h 22m 18s CLIQUES (O half_verse LCS M>60 S>75): Composed  3510 cliques out of  13000 chunks
- 2h 22m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed  3569 cliques out of  14000 chunks
- 2h 22m 35s CLIQUES (O half_verse LCS M>60 S>75): Composed  3562 cliques out of  15000 chunks
- 2h 22m 44s CLIQUES (O half_verse LCS M>60 S>75): Composed  3512 cliques out of  16000 chunks
- 2h 22m 53s CLIQUES (O half_verse LCS M>60 S>75): Composed  3420 cliques out of  17000 chunks
- 2h 23m 02s CLIQUES (O half_verse LCS M>60 S>75): Composed  3340 cliques out of  18000 chunks
- 2h 23m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed  3115 cliques out of  19000 chunks
- 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): 19147 members in 3084 cliques
- 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): Composed and saved  3084 cliques out of  19147 chunks from 53979 comparisons
- 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): sorting out cliques
- 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs
- 2h 23m 16s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs
- 2h 23m 16s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 23m 16s PREPARING (O half_verse LCS): Already prepared
- 2h 23m 16s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 23m 17s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates
- 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix
- 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): 126164 relevant similarities between 28473 passages
- 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composing cliques out of  28473 chunks from 126164 comparisons
- 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composed   580 cliques out of   1000 chunks
- 2h 23m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed  1067 cliques out of   2000 chunks
- 2h 23m 22s CLIQUES (O half_verse LCS M>60 S>70): Composed  1458 cliques out of   3000 chunks
- 2h 23m 24s CLIQUES (O half_verse LCS M>60 S>70): Composed  1714 cliques out of   4000 chunks
- 2h 23m 27s CLIQUES (O half_verse LCS M>60 S>70): Composed  1935 cliques out of   5000 chunks
- 2h 23m 31s CLIQUES (O half_verse LCS M>60 S>70): Composed  2138 cliques out of   6000 chunks
- 2h 23m 35s CLIQUES (O half_verse LCS M>60 S>70): Composed  2387 cliques out of   7000 chunks
- 2h 23m 40s CLIQUES (O half_verse LCS M>60 S>70): Composed  2541 cliques out of   8000 chunks
- 2h 23m 45s CLIQUES (O half_verse LCS M>60 S>70): Composed  2652 cliques out of   9000 chunks
- 2h 23m 50s CLIQUES (O half_verse LCS M>60 S>70): Composed  2723 cliques out of  10000 chunks
- 2h 23m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed  2793 cliques out of  11000 chunks
- 2h 24m 02s CLIQUES (O half_verse LCS M>60 S>70): Composed  2710 cliques out of  12000 chunks
- 2h 24m 09s CLIQUES (O half_verse LCS M>60 S>70): Composed  2725 cliques out of  13000 chunks
- 2h 24m 16s CLIQUES (O half_verse LCS M>60 S>70): Composed  2698 cliques out of  14000 chunks
- 2h 24m 23s CLIQUES (O half_verse LCS M>60 S>70): Composed  2745 cliques out of  15000 chunks
- 2h 24m 30s CLIQUES (O half_verse LCS M>60 S>70): Composed  2779 cliques out of  16000 chunks
- 2h 24m 38s CLIQUES (O half_verse LCS M>60 S>70): Composed  2785 cliques out of  17000 chunks
- 2h 24m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed  2739 cliques out of  18000 chunks
- 2h 24m 54s CLIQUES (O half_verse LCS M>60 S>70): Composed  2659 cliques out of  19000 chunks
- 2h 25m 03s CLIQUES (O half_verse LCS M>60 S>70): Composed  2611 cliques out of  20000 chunks
- 2h 25m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed  2597 cliques out of  21000 chunks
- 2h 25m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed  2491 cliques out of  22000 chunks
- 2h 25m 28s CLIQUES (O half_verse LCS M>60 S>70): Composed  2432 cliques out of  23000 chunks
- 2h 25m 37s CLIQUES (O half_verse LCS M>60 S>70): Composed  2342 cliques out of  24000 chunks
- 2h 25m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed  2231 cliques out of  25000 chunks
- 2h 25m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed  2125 cliques out of  26000 chunks
- 2h 26m 04s CLIQUES (O half_verse LCS M>60 S>70): Composed  2057 cliques out of  27000 chunks
- 2h 26m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed  1923 cliques out of  28000 chunks
- 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): 28473 members in 1894 cliques
- 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): Composed and saved  1894 cliques out of  28473 chunks from 126164 comparisons
- 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): sorting out cliques
- 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs
- 2h 26m 19s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs
- 2h 26m 19s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 26m 19s PREPARING (O half_verse LCS): Already prepared
- 2h 26m 19s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 26m 21s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates
- 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix
- 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): 393352 relevant similarities between 38182 passages
- 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composing cliques out of  38182 chunks from 393352 comparisons
- 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composed   581 cliques out of   1000 chunks
- 2h 26m 24s CLIQUES (O half_verse LCS M>60 S>65): Composed  1010 cliques out of   2000 chunks
- 2h 26m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed  1224 cliques out of   3000 chunks
- 2h 26m 28s CLIQUES (O half_verse LCS M>60 S>65): Composed  1371 cliques out of   4000 chunks
- 2h 26m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed  1516 cliques out of   5000 chunks
- 2h 26m 34s CLIQUES (O half_verse LCS M>60 S>65): Composed  1613 cliques out of   6000 chunks
- 2h 26m 37s CLIQUES (O half_verse LCS M>60 S>65): Composed  1629 cliques out of   7000 chunks
- 2h 26m 41s CLIQUES (O half_verse LCS M>60 S>65): Composed  1628 cliques out of   8000 chunks
- 2h 26m 45s CLIQUES (O half_verse LCS M>60 S>65): Composed  1684 cliques out of   9000 chunks
- 2h 26m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed  1668 cliques out of  10000 chunks
- 2h 26m 53s CLIQUES (O half_verse LCS M>60 S>65): Composed  1624 cliques out of  11000 chunks
- 2h 26m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed  1601 cliques out of  12000 chunks
- 2h 27m 02s CLIQUES (O half_verse LCS M>60 S>65): Composed  1520 cliques out of  13000 chunks
- 2h 27m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed  1498 cliques out of  14000 chunks
- 2h 27m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed  1418 cliques out of  15000 chunks
- 2h 27m 16s CLIQUES (O half_verse LCS M>60 S>65): Composed  1319 cliques out of  16000 chunks
- 2h 27m 22s CLIQUES (O half_verse LCS M>60 S>65): Composed  1332 cliques out of  17000 chunks
- 2h 27m 27s CLIQUES (O half_verse LCS M>60 S>65): Composed  1291 cliques out of  18000 chunks
- 2h 27m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed  1221 cliques out of  19000 chunks
- 2h 27m 36s CLIQUES (O half_verse LCS M>60 S>65): Composed  1167 cliques out of  20000 chunks
- 2h 27m 42s CLIQUES (O half_verse LCS M>60 S>65): Composed  1123 cliques out of  21000 chunks
- 2h 27m 47s CLIQUES (O half_verse LCS M>60 S>65): Composed  1106 cliques out of  22000 chunks
- 2h 27m 52s CLIQUES (O half_verse LCS M>60 S>65): Composed  1121 cliques out of  23000 chunks
- 2h 27m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed  1105 cliques out of  24000 chunks
- 2h 28m 05s CLIQUES (O half_verse LCS M>60 S>65): Composed  1075 cliques out of  25000 chunks
- 2h 28m 09s CLIQUES (O half_verse LCS M>60 S>65): Composed  1026 cliques out of  26000 chunks
- 2h 28m 15s CLIQUES (O half_verse LCS M>60 S>65): Composed  1009 cliques out of  27000 chunks
- 2h 28m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed   974 cliques out of  28000 chunks
- 2h 28m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed   907 cliques out of  29000 chunks
- 2h 28m 32s CLIQUES (O half_verse LCS M>60 S>65): Composed   892 cliques out of  30000 chunks
- 2h 28m 38s CLIQUES (O half_verse LCS M>60 S>65): Composed   865 cliques out of  31000 chunks
- 2h 28m 43s CLIQUES (O half_verse LCS M>60 S>65): Composed   837 cliques out of  32000 chunks
- 2h 28m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed   799 cliques out of  33000 chunks
- 2h 28m 54s CLIQUES (O half_verse LCS M>60 S>65): Composed   757 cliques out of  34000 chunks
- 2h 29m 01s CLIQUES (O half_verse LCS M>60 S>65): Composed   731 cliques out of  35000 chunks
- 2h 29m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed   703 cliques out of  36000 chunks
- 2h 29m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed   687 cliques out of  37000 chunks
- 2h 29m 18s CLIQUES (O half_verse LCS M>60 S>65): Composed   671 cliques out of  38000 chunks
- 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): 38182 members in 665 cliques
- 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed and saved   665 cliques out of  38182 chunks from 393352 comparisons
- 2h 29m 20s PRINT (O half_verse LCS M>60 S>65): sorting out cliques
- 2h 29m 21s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs
- 2h 29m 22s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs
- 2h 29m 22s CHUNKING (O half_verse): already chunked into 45180 chunks
- 2h 29m 22s PREPARING (O half_verse LCS): Already prepared
- 2h 29m 22s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017735 entries in matrix
- 2h 29m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
- 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates
- 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix
- 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): 2017735 relevant similarities between 44011 passages
- 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): Composing cliques out of  44011 chunks from 2017735 comparisons
- 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed   373 cliques out of   1000 chunks
- 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed   444 cliques out of   2000 chunks
- 2h 29m 29s CLIQUES (O half_verse LCS M>60 S>60): Composed   473 cliques out of   3000 chunks
- 2h 29m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed   487 cliques out of   4000 chunks
- 2h 29m 32s CLIQUES (O half_verse LCS M>60 S>60): Composed   425 cliques out of   5000 chunks
- 2h 29m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed   392 cliques out of   6000 chunks
- 2h 29m 34s CLIQUES (O half_verse LCS M>60 S>60): Composed   350 cliques out of   7000 chunks
- 2h 29m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed   370 cliques out of   8000 chunks
- 2h 29m 37s CLIQUES (O half_verse LCS M>60 S>60): Composed   323 cliques out of   9000 chunks
- 2h 29m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed   299 cliques out of  10000 chunks
- 2h 29m 40s CLIQUES (O half_verse LCS M>60 S>60): Composed   271 cliques out of  11000 chunks
- 2h 29m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed   265 cliques out of  12000 chunks
- 2h 29m 43s CLIQUES (O half_verse LCS M>60 S>60): Composed   255 cliques out of  13000 chunks
- 2h 29m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed   242 cliques out of  14000 chunks
- 2h 29m 46s CLIQUES (O half_verse LCS M>60 S>60): Composed   224 cliques out of  15000 chunks
- 2h 29m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed   226 cliques out of  16000 chunks
- 2h 29m 50s CLIQUES (O half_verse LCS M>60 S>60): Composed   208 cliques out of  17000 chunks
- 2h 29m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed   190 cliques out of  18000 chunks
- 2h 29m 53s CLIQUES (O half_verse LCS M>60 S>60): Composed   183 cliques out of  19000 chunks
- 2h 29m 55s CLIQUES (O half_verse LCS M>60 S>60): Composed   178 cliques out of  20000 chunks
- 2h 29m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed   177 cliques out of  21000 chunks
- 2h 29m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed   171 cliques out of  22000 chunks
- 2h 30m 01s CLIQUES (O half_verse LCS M>60 S>60): Composed   160 cliques out of  23000 chunks
- 2h 30m 03s CLIQUES (O half_verse LCS M>60 S>60): Composed   147 cliques out of  24000 chunks
- 2h 30m 05s CLIQUES (O half_verse LCS M>60 S>60): Composed   143 cliques out of  25000 chunks
- 2h 30m 08s CLIQUES (O half_verse LCS M>60 S>60): Composed   132 cliques out of  26000 chunks
- 2h 30m 10s CLIQUES (O half_verse LCS M>60 S>60): Composed   129 cliques out of  27000 chunks
- 2h 30m 13s CLIQUES (O half_verse LCS M>60 S>60): Composed   130 cliques out of  28000 chunks
- 2h 30m 15s CLIQUES (O half_verse LCS M>60 S>60): Composed   129 cliques out of  29000 chunks
- 2h 30m 17s CLIQUES (O half_verse LCS M>60 S>60): Composed   126 cliques out of  30000 chunks
- 2h 30m 20s CLIQUES (O half_verse LCS M>60 S>60): Composed   111 cliques out of  31000 chunks
- 2h 30m 22s CLIQUES (O half_verse LCS M>60 S>60): Composed   107 cliques out of  32000 chunks
- 2h 30m 25s CLIQUES (O half_verse LCS M>60 S>60): Composed   106 cliques out of  33000 chunks
- 2h 30m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed   101 cliques out of  34000 chunks
- 2h 30m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed    99 cliques out of  35000 chunks
- 2h 30m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed    95 cliques out of  36000 chunks
- 2h 30m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed    98 cliques out of  37000 chunks
- 2h 30m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed    95 cliques out of  38000 chunks
- 2h 30m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed    90 cliques out of  39000 chunks
- 2h 30m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed    90 cliques out of  40000 chunks
- 2h 30m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed    89 cliques out of  41000 chunks
- 2h 30m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed    89 cliques out of  42000 chunks
- 2h 30m 54s CLIQUES (O half_verse LCS M>60 S>60): Composed    88 cliques out of  43000 chunks
- 2h 30m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed    89 cliques out of  44000 chunks
- 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques
- 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed and saved    89 cliques out of  44011 chunks from 2017735 comparisons
- 2h 30m 59s PRINT (O half_verse LCS M>60 S>60): sorting out cliques
- 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs
- 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs
- 2h 31m 00s CHUNKING (O sentence)
- 2h 31m 02s CHUNKING (O sentence): Made 63570 chunks
- 2h 31m 02s PREPARING (O sentence SET)
- 2h 31m 02s PREPARING (O sentence SET): Done 63570 chunks.
- 2h 31m 02s SIMILARITY (O sentence SET M>50): Computing  2020 M (2020540665) comparisons and saving entries in matrix
- 2h 31m 27s SIMILARITY (O sentence SET M>50): Computed    20 M comparisons and saved 45808 entries in matrix
- 2h 31m 51s SIMILARITY (O sentence SET M>50): Computed    40 M comparisons and saved 70941 entries in matrix
- 2h 32m 15s SIMILARITY (O sentence SET M>50): Computed    60 M comparisons and saved 120842 entries in matrix
- 2h 32m 39s SIMILARITY (O sentence SET M>50): Computed    80 M comparisons and saved 204627 entries in matrix
- 2h 33m 03s SIMILARITY (O sentence SET M>50): Computed   101 M comparisons and saved 269586 entries in matrix
- 2h 33m 27s SIMILARITY (O sentence SET M>50): Computed   121 M comparisons and saved 356837 entries in matrix
- 2h 33m 50s SIMILARITY (O sentence SET M>50): Computed   141 M comparisons and saved 443278 entries in matrix
- 2h 34m 13s SIMILARITY (O sentence SET M>50): Computed   161 M comparisons and saved 524421 entries in matrix
- 2h 34m 37s SIMILARITY (O sentence SET M>50): Computed   181 M comparisons and saved 585724 entries in matrix
- 2h 35m 00s SIMILARITY (O sentence SET M>50): Computed   202 M comparisons and saved 624605 entries in matrix
- 2h 35m 24s SIMILARITY (O sentence SET M>50): Computed   222 M comparisons and saved 683267 entries in matrix
- 2h 35m 48s SIMILARITY (O sentence SET M>50): Computed   242 M comparisons and saved 738052 entries in matrix
- 2h 36m 13s SIMILARITY (O sentence SET M>50): Computed   262 M comparisons and saved 806254 entries in matrix
- 2h 36m 37s SIMILARITY (O sentence SET M>50): Computed   282 M comparisons and saved 852940 entries in matrix
- 2h 37m 00s SIMILARITY (O sentence SET M>50): Computed   303 M comparisons and saved 942155 entries in matrix
- 2h 37m 24s SIMILARITY (O sentence SET M>50): Computed   323 M comparisons and saved 1006926 entries in matrix
- 2h 37m 49s SIMILARITY (O sentence SET M>50): Computed   343 M comparisons and saved 1057719 entries in matrix
- 2h 38m 13s SIMILARITY (O sentence SET M>50): Computed   363 M comparisons and saved 1091220 entries in matrix
- 2h 38m 38s SIMILARITY (O sentence SET M>50): Computed   383 M comparisons and saved 1135567 entries in matrix
- 2h 39m 01s SIMILARITY (O sentence SET M>50): Computed   404 M comparisons and saved 1155716 entries in matrix
- 2h 39m 26s SIMILARITY (O sentence SET M>50): Computed   424 M comparisons and saved 1162605 entries in matrix
- 2h 39m 51s SIMILARITY (O sentence SET M>50): Computed   444 M comparisons and saved 1226546 entries in matrix
- 2h 40m 16s SIMILARITY (O sentence SET M>50): Computed   464 M comparisons and saved 1235733 entries in matrix
- 2h 40m 41s SIMILARITY (O sentence SET M>50): Computed   484 M comparisons and saved 1249303 entries in matrix
- 2h 41m 06s SIMILARITY (O sentence SET M>50): Computed   505 M comparisons and saved 1267306 entries in matrix
- 2h 41m 31s SIMILARITY (O sentence SET M>50): Computed   525 M comparisons and saved 1281368 entries in matrix
- 2h 41m 55s SIMILARITY (O sentence SET M>50): Computed   545 M comparisons and saved 1306272 entries in matrix
- 2h 42m 20s SIMILARITY (O sentence SET M>50): Computed   565 M comparisons and saved 1339875 entries in matrix
- 2h 42m 45s SIMILARITY (O sentence SET M>50): Computed   585 M comparisons and saved 1358545 entries in matrix
- 2h 43m 10s SIMILARITY (O sentence SET M>50): Computed   606 M comparisons and saved 1378717 entries in matrix
- 2h 43m 35s SIMILARITY (O sentence SET M>50): Computed   626 M comparisons and saved 1411294 entries in matrix
- 2h 43m 59s SIMILARITY (O sentence SET M>50): Computed   646 M comparisons and saved 1457168 entries in matrix
- 2h 44m 24s SIMILARITY (O sentence SET M>50): Computed   666 M comparisons and saved 1487159 entries in matrix
- 2h 44m 48s SIMILARITY (O sentence SET M>50): Computed   686 M comparisons and saved 1546006 entries in matrix
- 2h 45m 13s SIMILARITY (O sentence SET M>50): Computed   707 M comparisons and saved 1565381 entries in matrix
- 2h 45m 38s SIMILARITY (O sentence SET M>50): Computed   727 M comparisons and saved 1588602 entries in matrix
- 2h 46m 02s SIMILARITY (O sentence SET M>50): Computed   747 M comparisons and saved 1626854 entries in matrix
- 2h 46m 27s SIMILARITY (O sentence SET M>50): Computed   767 M comparisons and saved 1654679 entries in matrix
- 2h 46m 52s SIMILARITY (O sentence SET M>50): Computed   788 M comparisons and saved 1673231 entries in matrix
- 2h 47m 16s SIMILARITY (O sentence SET M>50): Computed   808 M comparisons and saved 1703760 entries in matrix
- 2h 47m 40s SIMILARITY (O sentence SET M>50): Computed   828 M comparisons and saved 1731144 entries in matrix
- 2h 48m 04s SIMILARITY (O sentence SET M>50): Computed   848 M comparisons and saved 1770357 entries in matrix
- 2h 48m 29s SIMILARITY (O sentence SET M>50): Computed   868 M comparisons and saved 1817758 entries in matrix
- 2h 48m 53s SIMILARITY (O sentence SET M>50): Computed   889 M comparisons and saved 1841653 entries in matrix
- 2h 49m 18s SIMILARITY (O sentence SET M>50): Computed   909 M comparisons and saved 1852758 entries in matrix
- 2h 49m 42s SIMILARITY (O sentence SET M>50): Computed   929 M comparisons and saved 1896595 entries in matrix
- 2h 50m 06s SIMILARITY (O sentence SET M>50): Computed   949 M comparisons and saved 1955361 entries in matrix
- 2h 50m 30s SIMILARITY (O sentence SET M>50): Computed   969 M comparisons and saved 1997274 entries in matrix
- 2h 50m 53s SIMILARITY (O sentence SET M>50): Computed   990 M comparisons and saved 2065177 entries in matrix
- 2h 51m 17s SIMILARITY (O sentence SET M>50): Computed  1010 M comparisons and saved 2136092 entries in matrix
- 2h 51m 41s SIMILARITY (O sentence SET M>50): Computed  1030 M comparisons and saved 2184132 entries in matrix
- 2h 52m 04s SIMILARITY (O sentence SET M>50): Computed  1050 M comparisons and saved 2249609 entries in matrix
- 2h 52m 28s SIMILARITY (O sentence SET M>50): Computed  1070 M comparisons and saved 2305726 entries in matrix
- 2h 52m 52s SIMILARITY (O sentence SET M>50): Computed  1091 M comparisons and saved 2363991 entries in matrix
- 2h 53m 15s SIMILARITY (O sentence SET M>50): Computed  1111 M comparisons and saved 2408947 entries in matrix
- 2h 53m 39s SIMILARITY (O sentence SET M>50): Computed  1131 M comparisons and saved 2463678 entries in matrix
- 2h 54m 02s SIMILARITY (O sentence SET M>50): Computed  1151 M comparisons and saved 2514424 entries in matrix
- 2h 54m 26s SIMILARITY (O sentence SET M>50): Computed  1171 M comparisons and saved 2564808 entries in matrix
- 2h 54m 50s SIMILARITY (O sentence SET M>50): Computed  1192 M comparisons and saved 2607470 entries in matrix
- 2h 55m 14s SIMILARITY (O sentence SET M>50): Computed  1212 M comparisons and saved 2670243 entries in matrix
- 2h 55m 37s SIMILARITY (O sentence SET M>50): Computed  1232 M comparisons and saved 2714094 entries in matrix
- 2h 56m 01s SIMILARITY (O sentence SET M>50): Computed  1252 M comparisons and saved 2752488 entries in matrix
- 2h 56m 25s SIMILARITY (O sentence SET M>50): Computed  1272 M comparisons and saved 2814356 entries in matrix
- 2h 56m 51s SIMILARITY (O sentence SET M>50): Computed  1293 M comparisons and saved 2827848 entries in matrix
- 2h 57m 15s SIMILARITY (O sentence SET M>50): Computed  1313 M comparisons and saved 2864217 entries in matrix
- 2h 57m 38s SIMILARITY (O sentence SET M>50): Computed  1333 M comparisons and saved 2935628 entries in matrix
- 2h 58m 01s SIMILARITY (O sentence SET M>50): Computed  1353 M comparisons and saved 3005907 entries in matrix
- 2h 58m 24s SIMILARITY (O sentence SET M>50): Computed  1373 M comparisons and saved 3085992 entries in matrix
- 2h 58m 47s SIMILARITY (O sentence SET M>50): Computed  1394 M comparisons and saved 3153997 entries in matrix
- 2h 59m 11s SIMILARITY (O sentence SET M>50): Computed  1414 M comparisons and saved 3179667 entries in matrix
- 2h 59m 36s SIMILARITY (O sentence SET M>50): Computed  1434 M comparisons and saved 3213666 entries in matrix
- 2h 59m 59s SIMILARITY (O sentence SET M>50): Computed  1454 M comparisons and saved 3241136 entries in matrix
- 3h 00m 23s SIMILARITY (O sentence SET M>50): Computed  1474 M comparisons and saved 3256962 entries in matrix
- 3h 00m 46s SIMILARITY (O sentence SET M>50): Computed  1495 M comparisons and saved 3277580 entries in matrix
- 3h 01m 09s SIMILARITY (O sentence SET M>50): Computed  1515 M comparisons and saved 3306752 entries in matrix
- 3h 01m 31s SIMILARITY (O sentence SET M>50): Computed  1535 M comparisons and saved 3330735 entries in matrix
- 3h 01m 54s SIMILARITY (O sentence SET M>50): Computed  1555 M comparisons and saved 3354290 entries in matrix
- 3h 02m 17s SIMILARITY (O sentence SET M>50): Computed  1576 M comparisons and saved 3386386 entries in matrix
- 3h 02m 40s SIMILARITY (O sentence SET M>50): Computed  1596 M comparisons and saved 3431895 entries in matrix
- 3h 03m 04s SIMILARITY (O sentence SET M>50): Computed  1616 M comparisons and saved 3469842 entries in matrix
- 3h 03m 28s SIMILARITY (O sentence SET M>50): Computed  1636 M comparisons and saved 3513213 entries in matrix
- 3h 03m 52s SIMILARITY (O sentence SET M>50): Computed  1656 M comparisons and saved 3552466 entries in matrix
- 3h 04m 16s SIMILARITY (O sentence SET M>50): Computed  1677 M comparisons and saved 3582525 entries in matrix
- 3h 04m 39s SIMILARITY (O sentence SET M>50): Computed  1697 M comparisons and saved 3603609 entries in matrix
- 3h 05m 03s SIMILARITY (O sentence SET M>50): Computed  1717 M comparisons and saved 3646709 entries in matrix
- 3h 05m 26s SIMILARITY (O sentence SET M>50): Computed  1737 M comparisons and saved 3681527 entries in matrix
- 3h 05m 50s SIMILARITY (O sentence SET M>50): Computed  1757 M comparisons and saved 3707553 entries in matrix
- 3h 06m 14s SIMILARITY (O sentence SET M>50): Computed  1778 M comparisons and saved 3732364 entries in matrix
- 3h 06m 38s SIMILARITY (O sentence SET M>50): Computed  1798 M comparisons and saved 3752245 entries in matrix
- 3h 07m 01s SIMILARITY (O sentence SET M>50): Computed  1818 M comparisons and saved 3772955 entries in matrix
- 3h 07m 24s SIMILARITY (O sentence SET M>50): Computed  1838 M comparisons and saved 3791878 entries in matrix
- 3h 07m 48s SIMILARITY (O sentence SET M>50): Computed  1858 M comparisons and saved 3831199 entries in matrix
- 3h 08m 13s SIMILARITY (O sentence SET M>50): Computed  1879 M comparisons and saved 3848114 entries in matrix
- 3h 08m 38s SIMILARITY (O sentence SET M>50): Computed  1899 M comparisons and saved 3862274 entries in matrix
- 3h 09m 06s SIMILARITY (O sentence SET M>50): Computed  1919 M comparisons and saved 3874162 entries in matrix
- 3h 09m 29s SIMILARITY (O sentence SET M>50): Computed  1939 M comparisons and saved 3888285 entries in matrix
- 3h 09m 53s SIMILARITY (O sentence SET M>50): Computed  1959 M comparisons and saved 3905689 entries in matrix
- 3h 10m 17s SIMILARITY (O sentence SET M>50): Computed  1980 M comparisons and saved 3917473 entries in matrix
- 3h 10m 42s SIMILARITY (O sentence SET M>50): Computed  2000 M comparisons and saved 3936282 entries in matrix
- 3h 11m 08s SIMILARITY (O sentence SET M>50): Computed  2020 M comparisons and saved 3958946 entries in matrix
- 3h 11m 11s SIMILARITY (O sentence SET M>50): Computed  2020 M (2020540665) comparisons and saved 3958946 entries in matrix
- 3h 11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates
- 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix
- 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): 937604 relevant similarities between 19031 passages
- 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composing cliques out of  19031 chunks from 937604 comparisons
- 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composed   511 cliques out of   1000 chunks
- 3h 11m 19s CLIQUES (O sentence SET M>50 S>100): Composed   876 cliques out of   2000 chunks
- 3h 11m 21s CLIQUES (O sentence SET M>50 S>100): Composed  1294 cliques out of   3000 chunks
- 3h 11m 23s CLIQUES (O sentence SET M>50 S>100): Composed  1693 cliques out of   4000 chunks
- 3h 11m 26s CLIQUES (O sentence SET M>50 S>100): Composed  2040 cliques out of   5000 chunks
- 3h 11m 30s CLIQUES (O sentence SET M>50 S>100): Composed  2425 cliques out of   6000 chunks
- 3h 11m 34s CLIQUES (O sentence SET M>50 S>100): Composed  2696 cliques out of   7000 chunks
- 3h 11m 39s CLIQUES (O sentence SET M>50 S>100): Composed  2979 cliques out of   8000 chunks
- 3h 11m 45s CLIQUES (O sentence SET M>50 S>100): Composed  3256 cliques out of   9000 chunks
- 3h 11m 51s CLIQUES (O sentence SET M>50 S>100): Composed  3482 cliques out of  10000 chunks
- 3h 11m 58s CLIQUES (O sentence SET M>50 S>100): Composed  3646 cliques out of  11000 chunks
- 3h 12m 06s CLIQUES (O sentence SET M>50 S>100): Composed  3795 cliques out of  12000 chunks
- 3h 12m 14s CLIQUES (O sentence SET M>50 S>100): Composed  3939 cliques out of  13000 chunks
- 3h 12m 23s CLIQUES (O sentence SET M>50 S>100): Composed  4008 cliques out of  14000 chunks
- 3h 12m 32s CLIQUES (O sentence SET M>50 S>100): Composed  4112 cliques out of  15000 chunks
- 3h 12m 43s CLIQUES (O sentence SET M>50 S>100): Composed  4183 cliques out of  16000 chunks
- 3h 12m 53s CLIQUES (O sentence SET M>50 S>100): Composed  4269 cliques out of  17000 chunks
- 3h 13m 05s CLIQUES (O sentence SET M>50 S>100): Composed  4305 cliques out of  18000 chunks
- 3h 13m 16s CLIQUES (O sentence SET M>50 S>100): Composed  4324 cliques out of  19000 chunks
- 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): 19031 members in 4324 cliques
- 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): Composed and saved  4324 cliques out of  19031 chunks from 937604 comparisons
- 3h 13m 17s PRINT (O sentence SET M>50 S>100): sorting out cliques
- 3h 13m 18s PRINT (O sentence SET M>50 S>100): formatting 4324 cliques involving 1528 binary chapter diffs
- 3h 13m 18s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528
- 3h 13m 19s PRINT (O sentence SET M>50 S>100): Chapter diffs: 7 newly created and 1521 already existing
- 3h 13m 21s PRINT (O sentence SET M>50 S>100): formatted 4324 cliques (87 files) involving 1528 binary chapter diffs
- 3h 13m 21s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 13m 21s PREPARING (O sentence SET): Already prepared
- 3h 13m 21s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 13m 25s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates
- 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix
- 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): 937608 relevant similarities between 19039 passages
- 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): Composing cliques out of  19039 chunks from 937608 comparisons
- 3h 13m 29s CLIQUES (O sentence SET M>50 S>95): Composed   511 cliques out of   1000 chunks
- 3h 13m 30s CLIQUES (O sentence SET M>50 S>95): Composed   876 cliques out of   2000 chunks
- 3h 13m 31s CLIQUES (O sentence SET M>50 S>95): Composed  1297 cliques out of   3000 chunks
- 3h 13m 34s CLIQUES (O sentence SET M>50 S>95): Composed  1691 cliques out of   4000 chunks
- 3h 13m 37s CLIQUES (O sentence SET M>50 S>95): Composed  2042 cliques out of   5000 chunks
- 3h 13m 40s CLIQUES (O sentence SET M>50 S>95): Composed  2425 cliques out of   6000 chunks
- 3h 13m 45s CLIQUES (O sentence SET M>50 S>95): Composed  2699 cliques out of   7000 chunks
- 3h 13m 50s CLIQUES (O sentence SET M>50 S>95): Composed  2979 cliques out of   8000 chunks
- 3h 13m 55s CLIQUES (O sentence SET M>50 S>95): Composed  3259 cliques out of   9000 chunks
- 3h 14m 02s CLIQUES (O sentence SET M>50 S>95): Composed  3485 cliques out of  10000 chunks
- 3h 14m 09s CLIQUES (O sentence SET M>50 S>95): Composed  3649 cliques out of  11000 chunks
- 3h 14m 16s CLIQUES (O sentence SET M>50 S>95): Composed  3799 cliques out of  12000 chunks
- 3h 14m 25s CLIQUES (O sentence SET M>50 S>95): Composed  3942 cliques out of  13000 chunks
- 3h 14m 33s CLIQUES (O sentence SET M>50 S>95): Composed  4012 cliques out of  14000 chunks
- 3h 14m 43s CLIQUES (O sentence SET M>50 S>95): Composed  4115 cliques out of  15000 chunks
- 3h 14m 53s CLIQUES (O sentence SET M>50 S>95): Composed  4186 cliques out of  16000 chunks
- 3h 15m 03s CLIQUES (O sentence SET M>50 S>95): Composed  4272 cliques out of  17000 chunks
- 3h 15m 15s CLIQUES (O sentence SET M>50 S>95): Composed  4309 cliques out of  18000 chunks
- 3h 15m 26s CLIQUES (O sentence SET M>50 S>95): Composed  4328 cliques out of  19000 chunks
- 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): 19039 members in 4328 cliques
- 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): Composed and saved  4328 cliques out of  19039 chunks from 937608 comparisons
- 3h 15m 27s PRINT (O sentence SET M>50 S>95): sorting out cliques
- 3h 15m 28s PRINT (O sentence SET M>50 S>95): formatting 4328 cliques involving 1529 binary chapter diffs
- 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529
- 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing
- 3h 15m 31s PRINT (O sentence SET M>50 S>95): formatted 4328 cliques (87 files) involving 1529 binary chapter diffs
- 3h 15m 31s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 15m 31s PREPARING (O sentence SET): Already prepared
- 3h 15m 31s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 15m 34s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates
- 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix
- 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): 937734 relevant similarities between 19214 passages
- 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composing cliques out of  19214 chunks from 937734 comparisons
- 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composed   484 cliques out of   1000 chunks
- 3h 15m 39s CLIQUES (O sentence SET M>50 S>90): Composed   880 cliques out of   2000 chunks
- 3h 15m 40s CLIQUES (O sentence SET M>50 S>90): Composed  1288 cliques out of   3000 chunks
- 3h 15m 43s CLIQUES (O sentence SET M>50 S>90): Composed  1677 cliques out of   4000 chunks
- 3h 15m 46s CLIQUES (O sentence SET M>50 S>90): Composed  2031 cliques out of   5000 chunks
- 3h 15m 49s CLIQUES (O sentence SET M>50 S>90): Composed  2429 cliques out of   6000 chunks
- 3h 15m 54s CLIQUES (O sentence SET M>50 S>90): Composed  2718 cliques out of   7000 chunks
- 3h 15m 58s CLIQUES (O sentence SET M>50 S>90): Composed  3019 cliques out of   8000 chunks
- 3h 16m 04s CLIQUES (O sentence SET M>50 S>90): Composed  3282 cliques out of   9000 chunks
- 3h 16m 10s CLIQUES (O sentence SET M>50 S>90): Composed  3532 cliques out of  10000 chunks
- 3h 16m 17s CLIQUES (O sentence SET M>50 S>90): Composed  3699 cliques out of  11000 chunks
- 3h 16m 25s CLIQUES (O sentence SET M>50 S>90): Composed  3845 cliques out of  12000 chunks
- 3h 16m 33s CLIQUES (O sentence SET M>50 S>90): Composed  4002 cliques out of  13000 chunks
- 3h 16m 41s CLIQUES (O sentence SET M>50 S>90): Composed  4078 cliques out of  14000 chunks
- 3h 16m 51s CLIQUES (O sentence SET M>50 S>90): Composed  4179 cliques out of  15000 chunks
- 3h 17m 01s CLIQUES (O sentence SET M>50 S>90): Composed  4256 cliques out of  16000 chunks
- 3h 17m 11s CLIQUES (O sentence SET M>50 S>90): Composed  4340 cliques out of  17000 chunks
- 3h 17m 23s CLIQUES (O sentence SET M>50 S>90): Composed  4381 cliques out of  18000 chunks
- 3h 17m 34s CLIQUES (O sentence SET M>50 S>90): Composed  4404 cliques out of  19000 chunks
- 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): 19214 members in 4406 cliques
- 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): Composed and saved  4406 cliques out of  19214 chunks from 937734 comparisons
- 3h 17m 38s PRINT (O sentence SET M>50 S>90): sorting out cliques
- 3h 17m 38s PRINT (O sentence SET M>50 S>90): formatting 4406 cliques involving 1537 binary chapter diffs
- 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1537
- 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1537 already existing
- 3h 17m 41s PRINT (O sentence SET M>50 S>90): formatted 4406 cliques (89 files) involving 1537 binary chapter diffs
- 3h 17m 41s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 17m 41s PREPARING (O sentence SET): Already prepared
- 3h 17m 41s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 17m 44s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates
- 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix
- 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): 938584 relevant similarities between 19777 passages
- 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composing cliques out of  19777 chunks from 938584 comparisons
- 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composed   493 cliques out of   1000 chunks
- 3h 17m 49s CLIQUES (O sentence SET M>50 S>85): Composed   910 cliques out of   2000 chunks
- 3h 17m 51s CLIQUES (O sentence SET M>50 S>85): Composed  1283 cliques out of   3000 chunks
- 3h 17m 53s CLIQUES (O sentence SET M>50 S>85): Composed  1662 cliques out of   4000 chunks
- 3h 17m 56s CLIQUES (O sentence SET M>50 S>85): Composed  2063 cliques out of   5000 chunks
- 3h 17m 59s CLIQUES (O sentence SET M>50 S>85): Composed  2427 cliques out of   6000 chunks
- 3h 18m 04s CLIQUES (O sentence SET M>50 S>85): Composed  2795 cliques out of   7000 chunks
- 3h 18m 09s CLIQUES (O sentence SET M>50 S>85): Composed  3047 cliques out of   8000 chunks
- 3h 18m 14s CLIQUES (O sentence SET M>50 S>85): Composed  3345 cliques out of   9000 chunks
- 3h 18m 20s CLIQUES (O sentence SET M>50 S>85): Composed  3583 cliques out of  10000 chunks
- 3h 18m 27s CLIQUES (O sentence SET M>50 S>85): Composed  3799 cliques out of  11000 chunks
- 3h 18m 35s CLIQUES (O sentence SET M>50 S>85): Composed  3972 cliques out of  12000 chunks
- 3h 18m 43s CLIQUES (O sentence SET M>50 S>85): Composed  4129 cliques out of  13000 chunks
- 3h 18m 52s CLIQUES (O sentence SET M>50 S>85): Composed  4244 cliques out of  14000 chunks
- 3h 19m 01s CLIQUES (O sentence SET M>50 S>85): Composed  4306 cliques out of  15000 chunks
- 3h 19m 11s CLIQUES (O sentence SET M>50 S>85): Composed  4411 cliques out of  16000 chunks
- 3h 19m 22s CLIQUES (O sentence SET M>50 S>85): Composed  4500 cliques out of  17000 chunks
- 3h 19m 33s CLIQUES (O sentence SET M>50 S>85): Composed  4571 cliques out of  18000 chunks
- 3h 19m 46s CLIQUES (O sentence SET M>50 S>85): Composed  4596 cliques out of  19000 chunks
- 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): 19777 members in 4608 cliques
- 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): Composed and saved  4608 cliques out of  19777 chunks from 938584 comparisons
- 3h 19m 56s PRINT (O sentence SET M>50 S>85): sorting out cliques
- 3h 19m 56s PRINT (O sentence SET M>50 S>85): formatting 4608 cliques involving 1589 binary chapter diffs
- 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1589
- 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1589 already existing
- 3h 19m 59s PRINT (O sentence SET M>50 S>85): formatted 4608 cliques (93 files) involving 1589 binary chapter diffs
- 3h 19m 59s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 19m 59s PREPARING (O sentence SET): Already prepared
- 3h 19m 59s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 20m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates
- 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix
- 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): 960796 relevant similarities between 22082 passages
- 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composing cliques out of  22082 chunks from 960796 comparisons
- 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composed   492 cliques out of   1000 chunks
- 3h 20m 07s CLIQUES (O sentence SET M>50 S>80): Composed  1040 cliques out of   2000 chunks
- 3h 20m 09s CLIQUES (O sentence SET M>50 S>80): Composed  1463 cliques out of   3000 chunks
- 3h 20m 11s CLIQUES (O sentence SET M>50 S>80): Composed  1841 cliques out of   4000 chunks
- 3h 20m 14s CLIQUES (O sentence SET M>50 S>80): Composed  2270 cliques out of   5000 chunks
- 3h 20m 18s CLIQUES (O sentence SET M>50 S>80): Composed  2408 cliques out of   6000 chunks
- 3h 20m 22s CLIQUES (O sentence SET M>50 S>80): Composed  2656 cliques out of   7000 chunks
- 3h 20m 27s CLIQUES (O sentence SET M>50 S>80): Composed  2940 cliques out of   8000 chunks
- 3h 20m 33s CLIQUES (O sentence SET M>50 S>80): Composed  3239 cliques out of   9000 chunks
- 3h 20m 39s CLIQUES (O sentence SET M>50 S>80): Composed  3458 cliques out of  10000 chunks
- 3h 20m 46s CLIQUES (O sentence SET M>50 S>80): Composed  3713 cliques out of  11000 chunks
- 3h 20m 54s CLIQUES (O sentence SET M>50 S>80): Composed  3992 cliques out of  12000 chunks
- 3h 21m 02s CLIQUES (O sentence SET M>50 S>80): Composed  4223 cliques out of  13000 chunks
- 3h 21m 11s CLIQUES (O sentence SET M>50 S>80): Composed  4386 cliques out of  14000 chunks
- 3h 21m 21s CLIQUES (O sentence SET M>50 S>80): Composed  4536 cliques out of  15000 chunks
- 3h 21m 31s CLIQUES (O sentence SET M>50 S>80): Composed  4686 cliques out of  16000 chunks
- 3h 21m 41s CLIQUES (O sentence SET M>50 S>80): Composed  4756 cliques out of  17000 chunks
- 3h 21m 53s CLIQUES (O sentence SET M>50 S>80): Composed  4852 cliques out of  18000 chunks
- 3h 22m 05s CLIQUES (O sentence SET M>50 S>80): Composed  4931 cliques out of  19000 chunks
- 3h 22m 18s CLIQUES (O sentence SET M>50 S>80): Composed  5015 cliques out of  20000 chunks
- 3h 22m 31s CLIQUES (O sentence SET M>50 S>80): Composed  5050 cliques out of  21000 chunks
- 3h 22m 45s CLIQUES (O sentence SET M>50 S>80): Composed  5072 cliques out of  22000 chunks
- 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): 22082 members in 5073 cliques
- 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): Composed and saved  5073 cliques out of  22082 chunks from 960796 comparisons
- 3h 22m 47s PRINT (O sentence SET M>50 S>80): sorting out cliques
- 3h 22m 47s PRINT (O sentence SET M>50 S>80): formatting 5073 cliques involving 1748 binary chapter diffs
- 3h 22m 47s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1748
- 3h 22m 48s PRINT (O sentence SET M>50 S>80): Chapter diffs: 2 newly created and 1746 already existing
- 3h 22m 51s PRINT (O sentence SET M>50 S>80): formatted 5073 cliques (102 files) involving 1748 binary chapter diffs
- 3h 22m 51s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 22m 51s PREPARING (O sentence SET): Already prepared
- 3h 22m 51s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 22m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates
- 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix
- 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): 1009309 relevant similarities between 25751 passages
- 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composing cliques out of  25751 chunks from 1009309 comparisons
- 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composed   517 cliques out of   1000 chunks
- 3h 23m 00s CLIQUES (O sentence SET M>50 S>75): Composed  1017 cliques out of   2000 chunks
- 3h 23m 02s CLIQUES (O sentence SET M>50 S>75): Composed  1456 cliques out of   3000 chunks
- 3h 23m 04s CLIQUES (O sentence SET M>50 S>75): Composed  1823 cliques out of   4000 chunks
- 3h 23m 07s CLIQUES (O sentence SET M>50 S>75): Composed  2257 cliques out of   5000 chunks
- 3h 23m 11s CLIQUES (O sentence SET M>50 S>75): Composed  2696 cliques out of   6000 chunks
- 3h 23m 15s CLIQUES (O sentence SET M>50 S>75): Composed  2955 cliques out of   7000 chunks
- 3h 23m 20s CLIQUES (O sentence SET M>50 S>75): Composed  3272 cliques out of   8000 chunks
- 3h 23m 26s CLIQUES (O sentence SET M>50 S>75): Composed  3671 cliques out of   9000 chunks
- 3h 23m 33s CLIQUES (O sentence SET M>50 S>75): Composed  3821 cliques out of  10000 chunks
- 3h 23m 39s CLIQUES (O sentence SET M>50 S>75): Composed  3773 cliques out of  11000 chunks
- 3h 23m 47s CLIQUES (O sentence SET M>50 S>75): Composed  3823 cliques out of  12000 chunks
- 3h 23m 55s CLIQUES (O sentence SET M>50 S>75): Composed  3891 cliques out of  13000 chunks
- 3h 24m 04s CLIQUES (O sentence SET M>50 S>75): Composed  4004 cliques out of  14000 chunks
- 3h 24m 13s CLIQUES (O sentence SET M>50 S>75): Composed  4138 cliques out of  15000 chunks
- 3h 24m 23s CLIQUES (O sentence SET M>50 S>75): Composed  4263 cliques out of  16000 chunks
- 3h 24m 34s CLIQUES (O sentence SET M>50 S>75): Composed  4341 cliques out of  17000 chunks
- 3h 24m 45s CLIQUES (O sentence SET M>50 S>75): Composed  4387 cliques out of  18000 chunks
- 3h 24m 57s CLIQUES (O sentence SET M>50 S>75): Composed  4531 cliques out of  19000 chunks
- 3h 25m 10s CLIQUES (O sentence SET M>50 S>75): Composed  4638 cliques out of  20000 chunks
- 3h 25m 23s CLIQUES (O sentence SET M>50 S>75): Composed  4699 cliques out of  21000 chunks
- 3h 25m 37s CLIQUES (O sentence SET M>50 S>75): Composed  4803 cliques out of  22000 chunks
- 3h 25m 52s CLIQUES (O sentence SET M>50 S>75): Composed  4893 cliques out of  23000 chunks
- 3h 26m 07s CLIQUES (O sentence SET M>50 S>75): Composed  4963 cliques out of  24000 chunks
- 3h 26m 23s CLIQUES (O sentence SET M>50 S>75): Composed  4988 cliques out of  25000 chunks
- 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): 25751 members in 5000 cliques
- 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): Composed and saved  5000 cliques out of  25751 chunks from 1009309 comparisons
- 3h 26m 36s PRINT (O sentence SET M>50 S>75): sorting out cliques
- 3h 26m 36s PRINT (O sentence SET M>50 S>75): formatting 5000 cliques skipping 1744 binary chapter diffs
- 3h 26m 40s PRINT (O sentence SET M>50 S>75): formatted 5000 cliques (100 files) skipping 1744 binary chapter diffs
- 3h 26m 40s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 26m 40s PREPARING (O sentence SET): Already prepared
- 3h 26m 40s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 26m 43s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates
- 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix
- 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): 1012009 relevant similarities between 26905 passages
- 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): Composing cliques out of  26905 chunks from 1012009 comparisons
- 3h 26m 48s CLIQUES (O sentence SET M>50 S>70): Composed   537 cliques out of   1000 chunks
- 3h 26m 49s CLIQUES (O sentence SET M>50 S>70): Composed   985 cliques out of   2000 chunks
- 3h 26m 50s CLIQUES (O sentence SET M>50 S>70): Composed  1462 cliques out of   3000 chunks
- 3h 26m 53s CLIQUES (O sentence SET M>50 S>70): Composed  1835 cliques out of   4000 chunks
- 3h 26m 55s CLIQUES (O sentence SET M>50 S>70): Composed  2167 cliques out of   5000 chunks
- 3h 26m 59s CLIQUES (O sentence SET M>50 S>70): Composed  2504 cliques out of   6000 chunks
- 3h 27m 03s CLIQUES (O sentence SET M>50 S>70): Composed  2905 cliques out of   7000 chunks
- 3h 27m 08s CLIQUES (O sentence SET M>50 S>70): Composed  3149 cliques out of   8000 chunks
- 3h 27m 14s CLIQUES (O sentence SET M>50 S>70): Composed  3459 cliques out of   9000 chunks
- 3h 27m 20s CLIQUES (O sentence SET M>50 S>70): Composed  3832 cliques out of  10000 chunks
- 3h 27m 27s CLIQUES (O sentence SET M>50 S>70): Composed  4078 cliques out of  11000 chunks
- 3h 27m 35s CLIQUES (O sentence SET M>50 S>70): Composed  4002 cliques out of  12000 chunks
- 3h 27m 43s CLIQUES (O sentence SET M>50 S>70): Composed  4017 cliques out of  13000 chunks
- 3h 27m 52s CLIQUES (O sentence SET M>50 S>70): Composed  4080 cliques out of  14000 chunks
- 3h 28m 01s CLIQUES (O sentence SET M>50 S>70): Composed  4191 cliques out of  15000 chunks
- 3h 28m 11s CLIQUES (O sentence SET M>50 S>70): Composed  4347 cliques out of  16000 chunks
- 3h 28m 21s CLIQUES (O sentence SET M>50 S>70): Composed  4483 cliques out of  17000 chunks
- 3h 28m 32s CLIQUES (O sentence SET M>50 S>70): Composed  4559 cliques out of  18000 chunks
- 3h 28m 44s CLIQUES (O sentence SET M>50 S>70): Composed  4607 cliques out of  19000 chunks
- 3h 28m 57s CLIQUES (O sentence SET M>50 S>70): Composed  4721 cliques out of  20000 chunks
- 3h 29m 09s CLIQUES (O sentence SET M>50 S>70): Composed  4855 cliques out of  21000 chunks
- 3h 29m 23s CLIQUES (O sentence SET M>50 S>70): Composed  4924 cliques out of  22000 chunks
- 3h 29m 38s CLIQUES (O sentence SET M>50 S>70): Composed  5029 cliques out of  23000 chunks
- 3h 29m 53s CLIQUES (O sentence SET M>50 S>70): Composed  5101 cliques out of  24000 chunks
- 3h 30m 09s CLIQUES (O sentence SET M>50 S>70): Composed  5183 cliques out of  25000 chunks
- 3h 30m 25s CLIQUES (O sentence SET M>50 S>70): Composed  5214 cliques out of  26000 chunks
- 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): 26905 members in 5229 cliques
- 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): Composed and saved  5229 cliques out of  26905 chunks from 1012009 comparisons
- 3h 30m 41s PRINT (O sentence SET M>50 S>70): sorting out cliques
- 3h 30m 42s PRINT (O sentence SET M>50 S>70): formatting 5229 cliques skipping 1820 binary chapter diffs
- 3h 30m 46s PRINT (O sentence SET M>50 S>70): formatted 5229 cliques (105 files) skipping 1820 binary chapter diffs
- 3h 30m 46s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 30m 46s PREPARING (O sentence SET): Already prepared
- 3h 30m 46s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 30m 49s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates
- 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix
- 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): 1332004 relevant similarities between 33410 passages
- 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): Composing cliques out of  33410 chunks from 1332004 comparisons
- 3h 30m 54s CLIQUES (O sentence SET M>50 S>65): Composed   524 cliques out of   1000 chunks
- 3h 30m 55s CLIQUES (O sentence SET M>50 S>65): Composed  1016 cliques out of   2000 chunks
- 3h 30m 56s CLIQUES (O sentence SET M>50 S>65): Composed  1499 cliques out of   3000 chunks
- 3h 30m 59s CLIQUES (O sentence SET M>50 S>65): Composed  1916 cliques out of   4000 chunks
- 3h 31m 02s CLIQUES (O sentence SET M>50 S>65): Composed  2262 cliques out of   5000 chunks
- 3h 31m 05s CLIQUES (O sentence SET M>50 S>65): Composed  2620 cliques out of   6000 chunks
- 3h 31m 10s CLIQUES (O sentence SET M>50 S>65): Composed  2904 cliques out of   7000 chunks
- 3h 31m 15s CLIQUES (O sentence SET M>50 S>65): Composed  3114 cliques out of   8000 chunks
- 3h 31m 21s CLIQUES (O sentence SET M>50 S>65): Composed  3365 cliques out of   9000 chunks
- 3h 31m 27s CLIQUES (O sentence SET M>50 S>65): Composed  3548 cliques out of  10000 chunks
- 3h 31m 33s CLIQUES (O sentence SET M>50 S>65): Composed  3707 cliques out of  11000 chunks
- 3h 31m 41s CLIQUES (O sentence SET M>50 S>65): Composed  3974 cliques out of  12000 chunks
- 3h 31m 49s CLIQUES (O sentence SET M>50 S>65): Composed  4225 cliques out of  13000 chunks
- 3h 31m 58s CLIQUES (O sentence SET M>50 S>65): Composed  4391 cliques out of  14000 chunks
- 3h 32m 07s CLIQUES (O sentence SET M>50 S>65): Composed  4501 cliques out of  15000 chunks
- 3h 32m 17s CLIQUES (O sentence SET M>50 S>65): Composed  4570 cliques out of  16000 chunks
- 3h 32m 27s CLIQUES (O sentence SET M>50 S>65): Composed  4832 cliques out of  17000 chunks
- 3h 32m 38s CLIQUES (O sentence SET M>50 S>65): Composed  5000 cliques out of  18000 chunks
- 3h 32m 50s CLIQUES (O sentence SET M>50 S>65): Composed  5240 cliques out of  19000 chunks
- 3h 33m 01s CLIQUES (O sentence SET M>50 S>65): Composed  5073 cliques out of  20000 chunks
- 3h 33m 12s CLIQUES (O sentence SET M>50 S>65): Composed  4793 cliques out of  21000 chunks
- 3h 33m 23s CLIQUES (O sentence SET M>50 S>65): Composed  4698 cliques out of  22000 chunks
- 3h 33m 35s CLIQUES (O sentence SET M>50 S>65): Composed  4651 cliques out of  23000 chunks
- 3h 33m 47s CLIQUES (O sentence SET M>50 S>65): Composed  4583 cliques out of  24000 chunks
- 3h 34m 00s CLIQUES (O sentence SET M>50 S>65): Composed  4556 cliques out of  25000 chunks
- 3h 34m 14s CLIQUES (O sentence SET M>50 S>65): Composed  4493 cliques out of  26000 chunks
- 3h 34m 29s CLIQUES (O sentence SET M>50 S>65): Composed  4477 cliques out of  27000 chunks
- 3h 34m 46s CLIQUES (O sentence SET M>50 S>65): Composed  4454 cliques out of  28000 chunks
- 3h 35m 02s CLIQUES (O sentence SET M>50 S>65): Composed  4335 cliques out of  29000 chunks
- 3h 35m 18s CLIQUES (O sentence SET M>50 S>65): Composed  4262 cliques out of  30000 chunks
- 3h 35m 35s CLIQUES (O sentence SET M>50 S>65): Composed  4127 cliques out of  31000 chunks
- 3h 35m 54s CLIQUES (O sentence SET M>50 S>65): Composed  4080 cliques out of  32000 chunks
- 3h 36m 14s CLIQUES (O sentence SET M>50 S>65): Composed  4104 cliques out of  33000 chunks
- 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): 33410 members in 4109 cliques
- 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): Composed and saved  4109 cliques out of  33410 chunks from 1332004 comparisons
- 3h 36m 24s PRINT (O sentence SET M>50 S>65): sorting out cliques
- 3h 36m 25s PRINT (O sentence SET M>50 S>65): formatting 4109 cliques skipping 1470 binary chapter diffs
- 3h 36m 28s PRINT (O sentence SET M>50 S>65): formatted 4109 cliques (83 files) skipping 1470 binary chapter diffs
- 3h 36m 28s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 36m 28s PREPARING (O sentence SET): Already prepared
- 3h 36m 28s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 36m 31s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates
- 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix
- 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): 1431430 relevant similarities between 38818 passages
- 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composing cliques out of  38818 chunks from 1431430 comparisons
- 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composed   541 cliques out of   1000 chunks
- 3h 36m 37s CLIQUES (O sentence SET M>50 S>60): Composed  1043 cliques out of   2000 chunks
- 3h 36m 39s CLIQUES (O sentence SET M>50 S>60): Composed  1463 cliques out of   3000 chunks
- 3h 36m 41s CLIQUES (O sentence SET M>50 S>60): Composed  1851 cliques out of   4000 chunks
- 3h 36m 44s CLIQUES (O sentence SET M>50 S>60): Composed  2147 cliques out of   5000 chunks
- 3h 36m 48s CLIQUES (O sentence SET M>50 S>60): Composed  2487 cliques out of   6000 chunks
- 3h 36m 52s CLIQUES (O sentence SET M>50 S>60): Composed  2732 cliques out of   7000 chunks
- 3h 36m 57s CLIQUES (O sentence SET M>50 S>60): Composed  3043 cliques out of   8000 chunks
- 3h 37m 03s CLIQUES (O sentence SET M>50 S>60): Composed  3330 cliques out of   9000 chunks
- 3h 37m 09s CLIQUES (O sentence SET M>50 S>60): Composed  3583 cliques out of  10000 chunks
- 3h 37m 16s CLIQUES (O sentence SET M>50 S>60): Composed  3885 cliques out of  11000 chunks
- 3h 37m 23s CLIQUES (O sentence SET M>50 S>60): Composed  4252 cliques out of  12000 chunks
- 3h 37m 31s CLIQUES (O sentence SET M>50 S>60): Composed  4277 cliques out of  13000 chunks
- 3h 37m 39s CLIQUES (O sentence SET M>50 S>60): Composed  4252 cliques out of  14000 chunks
- 3h 37m 48s CLIQUES (O sentence SET M>50 S>60): Composed  4222 cliques out of  15000 chunks
- 3h 37m 57s CLIQUES (O sentence SET M>50 S>60): Composed  4393 cliques out of  16000 chunks
- 3h 38m 07s CLIQUES (O sentence SET M>50 S>60): Composed  4560 cliques out of  17000 chunks
- 3h 38m 18s CLIQUES (O sentence SET M>50 S>60): Composed  4650 cliques out of  18000 chunks
- 3h 38m 29s CLIQUES (O sentence SET M>50 S>60): Composed  4600 cliques out of  19000 chunks
- 3h 38m 39s CLIQUES (O sentence SET M>50 S>60): Composed  4518 cliques out of  20000 chunks
- 3h 38m 50s CLIQUES (O sentence SET M>50 S>60): Composed  4516 cliques out of  21000 chunks
- 3h 39m 02s CLIQUES (O sentence SET M>50 S>60): Composed  4545 cliques out of  22000 chunks
- 3h 39m 14s CLIQUES (O sentence SET M>50 S>60): Composed  4565 cliques out of  23000 chunks
- 3h 39m 27s CLIQUES (O sentence SET M>50 S>60): Composed  4671 cliques out of  24000 chunks
- 3h 39m 39s CLIQUES (O sentence SET M>50 S>60): Composed  4520 cliques out of  25000 chunks
- 3h 39m 51s CLIQUES (O sentence SET M>50 S>60): Composed  4267 cliques out of  26000 chunks
- 3h 40m 03s CLIQUES (O sentence SET M>50 S>60): Composed  4194 cliques out of  27000 chunks
- 3h 40m 15s CLIQUES (O sentence SET M>50 S>60): Composed  4163 cliques out of  28000 chunks
- 3h 40m 28s CLIQUES (O sentence SET M>50 S>60): Composed  4115 cliques out of  29000 chunks
- 3h 40m 41s CLIQUES (O sentence SET M>50 S>60): Composed  4079 cliques out of  30000 chunks
- 3h 40m 56s CLIQUES (O sentence SET M>50 S>60): Composed  4032 cliques out of  31000 chunks
- 3h 41m 13s CLIQUES (O sentence SET M>50 S>60): Composed  4015 cliques out of  32000 chunks
- 3h 41m 31s CLIQUES (O sentence SET M>50 S>60): Composed  4029 cliques out of  33000 chunks
- 3h 41m 51s CLIQUES (O sentence SET M>50 S>60): Composed  3972 cliques out of  34000 chunks
- 3h 42m 08s CLIQUES (O sentence SET M>50 S>60): Composed  3878 cliques out of  35000 chunks
- 3h 42m 26s CLIQUES (O sentence SET M>50 S>60): Composed  3790 cliques out of  36000 chunks
- 3h 42m 47s CLIQUES (O sentence SET M>50 S>60): Composed  3724 cliques out of  37000 chunks
- 3h 43m 09s CLIQUES (O sentence SET M>50 S>60): Composed  3734 cliques out of  38000 chunks
- 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): 38818 members in 3746 cliques
- 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): Composed and saved  3746 cliques out of  38818 chunks from 1431430 comparisons
- 3h 43m 31s PRINT (O sentence SET M>50 S>60): sorting out cliques
- 3h 43m 32s PRINT (O sentence SET M>50 S>60): formatting 3746 cliques skipping 1382 binary chapter diffs
- 3h 43m 35s PRINT (O sentence SET M>50 S>60): formatted 3746 cliques (75 files) skipping 1382 binary chapter diffs
- 3h 43m 35s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 43m 35s PREPARING (O sentence SET): Already prepared
- 3h 43m 35s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 43m 38s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates
- 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix
- 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): 1459649 relevant similarities between 41825 passages
- 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composing cliques out of  41825 chunks from 1459649 comparisons
- 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composed   547 cliques out of   1000 chunks
- 3h 43m 44s CLIQUES (O sentence SET M>50 S>55): Composed  1057 cliques out of   2000 chunks
- 3h 43m 46s CLIQUES (O sentence SET M>50 S>55): Composed  1503 cliques out of   3000 chunks
- 3h 43m 48s CLIQUES (O sentence SET M>50 S>55): Composed  1917 cliques out of   4000 chunks
- 3h 43m 52s CLIQUES (O sentence SET M>50 S>55): Composed  2303 cliques out of   5000 chunks
- 3h 43m 55s CLIQUES (O sentence SET M>50 S>55): Composed  2658 cliques out of   6000 chunks
- 3h 44m 00s CLIQUES (O sentence SET M>50 S>55): Composed  2824 cliques out of   7000 chunks
- 3h 44m 04s CLIQUES (O sentence SET M>50 S>55): Composed  3086 cliques out of   8000 chunks
- 3h 44m 10s CLIQUES (O sentence SET M>50 S>55): Composed  3169 cliques out of   9000 chunks
- 3h 44m 16s CLIQUES (O sentence SET M>50 S>55): Composed  3323 cliques out of  10000 chunks
- 3h 44m 22s CLIQUES (O sentence SET M>50 S>55): Composed  3473 cliques out of  11000 chunks
- 3h 44m 29s CLIQUES (O sentence SET M>50 S>55): Composed  3459 cliques out of  12000 chunks
- 3h 44m 36s CLIQUES (O sentence SET M>50 S>55): Composed  3499 cliques out of  13000 chunks
- 3h 44m 44s CLIQUES (O sentence SET M>50 S>55): Composed  3650 cliques out of  14000 chunks
- 3h 44m 52s CLIQUES (O sentence SET M>50 S>55): Composed  3810 cliques out of  15000 chunks
- 3h 45m 01s CLIQUES (O sentence SET M>50 S>55): Composed  3790 cliques out of  16000 chunks
- 3h 45m 09s CLIQUES (O sentence SET M>50 S>55): Composed  3780 cliques out of  17000 chunks
- 3h 45m 18s CLIQUES (O sentence SET M>50 S>55): Composed  3760 cliques out of  18000 chunks
- 3h 45m 28s CLIQUES (O sentence SET M>50 S>55): Composed  3878 cliques out of  19000 chunks
- 3h 45m 40s CLIQUES (O sentence SET M>50 S>55): Composed  3996 cliques out of  20000 chunks
- 3h 45m 51s CLIQUES (O sentence SET M>50 S>55): Composed  4035 cliques out of  21000 chunks
- 3h 46m 03s CLIQUES (O sentence SET M>50 S>55): Composed  4055 cliques out of  22000 chunks
- 3h 46m 14s CLIQUES (O sentence SET M>50 S>55): Composed  4031 cliques out of  23000 chunks
- 3h 46m 25s CLIQUES (O sentence SET M>50 S>55): Composed  4074 cliques out of  24000 chunks
- 3h 46m 38s CLIQUES (O sentence SET M>50 S>55): Composed  4143 cliques out of  25000 chunks
- 3h 46m 51s CLIQUES (O sentence SET M>50 S>55): Composed  4176 cliques out of  26000 chunks
- 3h 47m 05s CLIQUES (O sentence SET M>50 S>55): Composed  4303 cliques out of  27000 chunks
- 3h 47m 19s CLIQUES (O sentence SET M>50 S>55): Composed  4180 cliques out of  28000 chunks
- 3h 47m 31s CLIQUES (O sentence SET M>50 S>55): Composed  3952 cliques out of  29000 chunks
- 3h 47m 43s CLIQUES (O sentence SET M>50 S>55): Composed  3894 cliques out of  30000 chunks
- 3h 47m 57s CLIQUES (O sentence SET M>50 S>55): Composed  3885 cliques out of  31000 chunks
- 3h 48m 10s CLIQUES (O sentence SET M>50 S>55): Composed  3846 cliques out of  32000 chunks
- 3h 48m 26s CLIQUES (O sentence SET M>50 S>55): Composed  3817 cliques out of  33000 chunks
- 3h 48m 41s CLIQUES (O sentence SET M>50 S>55): Composed  3776 cliques out of  34000 chunks
- 3h 48m 59s CLIQUES (O sentence SET M>50 S>55): Composed  3761 cliques out of  35000 chunks
- 3h 49m 19s CLIQUES (O sentence SET M>50 S>55): Composed  3775 cliques out of  36000 chunks
- 3h 49m 40s CLIQUES (O sentence SET M>50 S>55): Composed  3718 cliques out of  37000 chunks
- 3h 49m 58s CLIQUES (O sentence SET M>50 S>55): Composed  3624 cliques out of  38000 chunks
- 3h 50m 18s CLIQUES (O sentence SET M>50 S>55): Composed  3539 cliques out of  39000 chunks
- 3h 50m 39s CLIQUES (O sentence SET M>50 S>55): Composed  3474 cliques out of  40000 chunks
- 3h 51m 04s CLIQUES (O sentence SET M>50 S>55): Composed  3485 cliques out of  41000 chunks
- 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): 41825 members in 3497 cliques
- 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): Composed and saved  3497 cliques out of  41825 chunks from 1459649 comparisons
- 3h 51m 26s PRINT (O sentence SET M>50 S>55): sorting out cliques
- 3h 51m 27s PRINT (O sentence SET M>50 S>55): formatting 3497 cliques skipping 1340 binary chapter diffs
- 3h 51m 30s PRINT (O sentence SET M>50 S>55): formatted 3497 cliques (70 files) skipping 1340 binary chapter diffs
- 3h 51m 30s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 51m 30s PREPARING (O sentence SET): Already prepared
- 3h 51m 30s SIMILARITY (O sentence SET M>50): Using  2020 M (2020540665) comparisons with 3958946 entries in matrix
- 3h 51m 33s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%
- 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates
- 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix
- 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): 3958946 relevant similarities between 53097 passages
- 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composing cliques out of  53097 chunks from 3958946 comparisons
- 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composed   571 cliques out of   1000 chunks
- 3h 51m 42s CLIQUES (O sentence SET M>50 S>50): Composed  1008 cliques out of   2000 chunks
- 3h 51m 44s CLIQUES (O sentence SET M>50 S>50): Composed  1426 cliques out of   3000 chunks
- 3h 51m 46s CLIQUES (O sentence SET M>50 S>50): Composed  1736 cliques out of   4000 chunks
- 3h 51m 49s CLIQUES (O sentence SET M>50 S>50): Composed  2052 cliques out of   5000 chunks
- 3h 51m 52s CLIQUES (O sentence SET M>50 S>50): Composed  2192 cliques out of   6000 chunks
- 3h 51m 56s CLIQUES (O sentence SET M>50 S>50): Composed  2423 cliques out of   7000 chunks
- 3h 52m 01s CLIQUES (O sentence SET M>50 S>50): Composed  2614 cliques out of   8000 chunks
- 3h 52m 06s CLIQUES (O sentence SET M>50 S>50): Composed  2756 cliques out of   9000 chunks
- 3h 52m 11s CLIQUES (O sentence SET M>50 S>50): Composed  2808 cliques out of  10000 chunks
- 3h 52m 17s CLIQUES (O sentence SET M>50 S>50): Composed  3000 cliques out of  11000 chunks
- 3h 52m 22s CLIQUES (O sentence SET M>50 S>50): Composed  3004 cliques out of  12000 chunks
- 3h 52m 29s CLIQUES (O sentence SET M>50 S>50): Composed  2995 cliques out of  13000 chunks
- 3h 52m 35s CLIQUES (O sentence SET M>50 S>50): Composed  3110 cliques out of  14000 chunks
- 3h 52m 42s CLIQUES (O sentence SET M>50 S>50): Composed  3191 cliques out of  15000 chunks
- 3h 52m 50s CLIQUES (O sentence SET M>50 S>50): Composed  3179 cliques out of  16000 chunks
- 3h 52m 57s CLIQUES (O sentence SET M>50 S>50): Composed  3148 cliques out of  17000 chunks
- 3h 53m 06s CLIQUES (O sentence SET M>50 S>50): Composed  3260 cliques out of  18000 chunks
- 3h 53m 14s CLIQUES (O sentence SET M>50 S>50): Composed  3363 cliques out of  19000 chunks
- 3h 53m 24s CLIQUES (O sentence SET M>50 S>50): Composed  3416 cliques out of  20000 chunks
- 3h 53m 31s CLIQUES (O sentence SET M>50 S>50): Composed  3274 cliques out of  21000 chunks
- 3h 53m 40s CLIQUES (O sentence SET M>50 S>50): Composed  3166 cliques out of  22000 chunks
- 3h 53m 48s CLIQUES (O sentence SET M>50 S>50): Composed  3062 cliques out of  23000 chunks
- 3h 53m 58s CLIQUES (O sentence SET M>50 S>50): Composed  3140 cliques out of  24000 chunks
- 3h 54m 07s CLIQUES (O sentence SET M>50 S>50): Composed  3112 cliques out of  25000 chunks
- 3h 54m 17s CLIQUES (O sentence SET M>50 S>50): Composed  3145 cliques out of  26000 chunks
- 3h 54m 29s CLIQUES (O sentence SET M>50 S>50): Composed  3255 cliques out of  27000 chunks
- 3h 54m 40s CLIQUES (O sentence SET M>50 S>50): Composed  3246 cliques out of  28000 chunks
- 3h 54m 50s CLIQUES (O sentence SET M>50 S>50): Composed  3125 cliques out of  29000 chunks
- 3h 54m 59s CLIQUES (O sentence SET M>50 S>50): Composed  3010 cliques out of  30000 chunks
- 3h 55m 09s CLIQUES (O sentence SET M>50 S>50): Composed  2938 cliques out of  31000 chunks
- 3h 55m 21s CLIQUES (O sentence SET M>50 S>50): Composed  2956 cliques out of  32000 chunks
- 3h 55m 31s CLIQUES (O sentence SET M>50 S>50): Composed  2928 cliques out of  33000 chunks
- 3h 55m 44s CLIQUES (O sentence SET M>50 S>50): Composed  2962 cliques out of  34000 chunks
- 3h 55m 59s CLIQUES (O sentence SET M>50 S>50): Composed  3076 cliques out of  35000 chunks
- 3h 56m 13s CLIQUES (O sentence SET M>50 S>50): Composed  3081 cliques out of  36000 chunks
- 3h 56m 23s CLIQUES (O sentence SET M>50 S>50): Composed  2934 cliques out of  37000 chunks
- 3h 56m 34s CLIQUES (O sentence SET M>50 S>50): Composed  2737 cliques out of  38000 chunks
- 3h 56m 45s CLIQUES (O sentence SET M>50 S>50): Composed  2647 cliques out of  39000 chunks
- 3h 56m 57s CLIQUES (O sentence SET M>50 S>50): Composed  2631 cliques out of  40000 chunks
- 3h 57m 09s CLIQUES (O sentence SET M>50 S>50): Composed  2531 cliques out of  41000 chunks
- 3h 57m 21s CLIQUES (O sentence SET M>50 S>50): Composed  2481 cliques out of  42000 chunks
- 3h 57m 34s CLIQUES (O sentence SET M>50 S>50): Composed  2438 cliques out of  43000 chunks
- 3h 57m 47s CLIQUES (O sentence SET M>50 S>50): Composed  2401 cliques out of  44000 chunks
- 3h 57m 55s CLIQUES (O sentence SET M>50 S>50): Composed  1968 cliques out of  45000 chunks
- 3h 58m 03s CLIQUES (O sentence SET M>50 S>50): Composed  1819 cliques out of  46000 chunks
- 3h 58m 09s CLIQUES (O sentence SET M>50 S>50): Composed  1762 cliques out of  47000 chunks
- 3h 58m 19s CLIQUES (O sentence SET M>50 S>50): Composed  1653 cliques out of  48000 chunks
- 3h 58m 27s CLIQUES (O sentence SET M>50 S>50): Composed  1539 cliques out of  49000 chunks
- 3h 58m 37s CLIQUES (O sentence SET M>50 S>50): Composed  1450 cliques out of  50000 chunks
- 3h 58m 47s CLIQUES (O sentence SET M>50 S>50): Composed  1331 cliques out of  51000 chunks
- 3h 58m 54s CLIQUES (O sentence SET M>50 S>50): Composed  1237 cliques out of  52000 chunks
- 3h 59m 05s CLIQUES (O sentence SET M>50 S>50): Composed  1176 cliques out of  53000 chunks
- 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): 53097 members in 1172 cliques
- 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): Composed and saved  1172 cliques out of  53097 chunks from 3958946 comparisons
- 3h 59m 07s PRINT (O sentence SET M>50 S>50): sorting out cliques
- 3h 59m 08s PRINT (O sentence SET M>50 S>50): formatting 1172 cliques skipping 470 binary chapter diffs
- 3h 59m 10s PRINT (O sentence SET M>50 S>50): formatted 1172 cliques (24 files) skipping 470 binary chapter diffs
- 3h 59m 10s CHUNKING (O sentence): already chunked into 63570 chunks
- 3h 59m 10s PREPARING (O sentence LCS)
- 3h 59m 10s PREPARING (O sentence LCS): Done 63570 chunks.
- 3h 59m 10s SIMILARITY (O sentence LCS M>60): Computing  2020 M (2020540665) comparisons and saving entries in matrix
- 3h 59m 35s SIMILARITY (O sentence LCS M>60): Computed    20 M comparisons and saved 125670 entries in matrix
- 4h 00m 03s SIMILARITY (O sentence LCS M>60): Computed    40 M comparisons and saved 204624 entries in matrix
- 4h 00m 28s SIMILARITY (O sentence LCS M>60): Computed    60 M comparisons and saved 308337 entries in matrix
- 4h 00m 51s SIMILARITY (O sentence LCS M>60): Computed    80 M comparisons and saved 449060 entries in matrix
- 4h 01m 15s SIMILARITY (O sentence LCS M>60): Computed   101 M comparisons and saved 569966 entries in matrix
- 4h 01m 38s SIMILARITY (O sentence LCS M>60): Computed   121 M comparisons and saved 713424 entries in matrix
- 4h 02m 00s SIMILARITY (O sentence LCS M>60): Computed   141 M comparisons and saved 854622 entries in matrix
- 4h 02m 23s SIMILARITY (O sentence LCS M>60): Computed   161 M comparisons and saved 1003547 entries in matrix
- 4h 02m 47s SIMILARITY (O sentence LCS M>60): Computed   181 M comparisons and saved 1128089 entries in matrix
- 4h 03m 11s SIMILARITY (O sentence LCS M>60): Computed   202 M comparisons and saved 1229587 entries in matrix
- 4h 03m 34s SIMILARITY (O sentence LCS M>60): Computed   222 M comparisons and saved 1346415 entries in matrix
- 4h 03m 58s SIMILARITY (O sentence LCS M>60): Computed   242 M comparisons and saved 1461744 entries in matrix
- 4h 04m 21s SIMILARITY (O sentence LCS M>60): Computed   262 M comparisons and saved 1586425 entries in matrix
- 4h 04m 45s SIMILARITY (O sentence LCS M>60): Computed   282 M comparisons and saved 1687522 entries in matrix
- 4h 05m 07s SIMILARITY (O sentence LCS M>60): Computed   303 M comparisons and saved 1866431 entries in matrix
- 4h 05m 32s SIMILARITY (O sentence LCS M>60): Computed   323 M comparisons and saved 2001292 entries in matrix
- 4h 05m 58s SIMILARITY (O sentence LCS M>60): Computed   343 M comparisons and saved 2113727 entries in matrix
- 4h 06m 23s SIMILARITY (O sentence LCS M>60): Computed   363 M comparisons and saved 2217744 entries in matrix
- 4h 06m 48s SIMILARITY (O sentence LCS M>60): Computed   383 M comparisons and saved 2338942 entries in matrix
- 4h 07m 12s SIMILARITY (O sentence LCS M>60): Computed   404 M comparisons and saved 2429384 entries in matrix
- 4h 07m 41s SIMILARITY (O sentence LCS M>60): Computed   424 M comparisons and saved 2474476 entries in matrix
- 4h 08m 06s SIMILARITY (O sentence LCS M>60): Computed   444 M comparisons and saved 2597349 entries in matrix
- 4h 08m 37s SIMILARITY (O sentence LCS M>60): Computed   464 M comparisons and saved 2640723 entries in matrix
- 4h 09m 05s SIMILARITY (O sentence LCS M>60): Computed   484 M comparisons and saved 2700790 entries in matrix
- 4h 09m 33s SIMILARITY (O sentence LCS M>60): Computed   505 M comparisons and saved 2776579 entries in matrix
- 4h 09m 59s SIMILARITY (O sentence LCS M>60): Computed   525 M comparisons and saved 2855300 entries in matrix
- 4h 10m 25s SIMILARITY (O sentence LCS M>60): Computed   545 M comparisons and saved 2950778 entries in matrix
- 4h 10m 52s SIMILARITY (O sentence LCS M>60): Computed   565 M comparisons and saved 3049115 entries in matrix
- 4h 11m 18s SIMILARITY (O sentence LCS M>60): Computed   585 M comparisons and saved 3127277 entries in matrix
- 4h 11m 47s SIMILARITY (O sentence LCS M>60): Computed   606 M comparisons and saved 3195359 entries in matrix
- 4h 12m 16s SIMILARITY (O sentence LCS M>60): Computed   626 M comparisons and saved 3280692 entries in matrix
- 4h 12m 42s SIMILARITY (O sentence LCS M>60): Computed   646 M comparisons and saved 3395665 entries in matrix
- 4h 13m 08s SIMILARITY (O sentence LCS M>60): Computed   666 M comparisons and saved 3483599 entries in matrix
- 4h 13m 31s SIMILARITY (O sentence LCS M>60): Computed   686 M comparisons and saved 3606181 entries in matrix
- 4h 14m 01s SIMILARITY (O sentence LCS M>60): Computed   707 M comparisons and saved 3668148 entries in matrix
- 4h 14m 29s SIMILARITY (O sentence LCS M>60): Computed   727 M comparisons and saved 3732591 entries in matrix
- 4h 14m 57s SIMILARITY (O sentence LCS M>60): Computed   747 M comparisons and saved 3835399 entries in matrix
- 4h 15m 25s SIMILARITY (O sentence LCS M>60): Computed   767 M comparisons and saved 3922964 entries in matrix
- 4h 15m 52s SIMILARITY (O sentence LCS M>60): Computed   788 M comparisons and saved 3999296 entries in matrix
- 4h 16m 16s SIMILARITY (O sentence LCS M>60): Computed   808 M comparisons and saved 4096313 entries in matrix
- 4h 16m 43s SIMILARITY (O sentence LCS M>60): Computed   828 M comparisons and saved 4197206 entries in matrix
- 4h 17m 08s SIMILARITY (O sentence LCS M>60): Computed   848 M comparisons and saved 4290970 entries in matrix
- 4h 17m 33s SIMILARITY (O sentence LCS M>60): Computed   868 M comparisons and saved 4403470 entries in matrix
- 4h 18m 00s SIMILARITY (O sentence LCS M>60): Computed   889 M comparisons and saved 4489840 entries in matrix
- 4h 18m 31s SIMILARITY (O sentence LCS M>60): Computed   909 M comparisons and saved 4540049 entries in matrix
- 4h 18m 57s SIMILARITY (O sentence LCS M>60): Computed   929 M comparisons and saved 4641605 entries in matrix
- 4h 19m 21s SIMILARITY (O sentence LCS M>60): Computed   949 M comparisons and saved 4763900 entries in matrix
- 4h 19m 44s SIMILARITY (O sentence LCS M>60): Computed   969 M comparisons and saved 4867130 entries in matrix
- 4h 20m 07s SIMILARITY (O sentence LCS M>60): Computed   990 M comparisons and saved 5006563 entries in matrix
- 4h 20m 30s SIMILARITY (O sentence LCS M>60): Computed  1010 M comparisons and saved 5154413 entries in matrix
- 4h 20m 54s SIMILARITY (O sentence LCS M>60): Computed  1030 M comparisons and saved 5266887 entries in matrix
- 4h 21m 18s SIMILARITY (O sentence LCS M>60): Computed  1050 M comparisons and saved 5389996 entries in matrix
- 4h 21m 41s SIMILARITY (O sentence LCS M>60): Computed  1070 M comparisons and saved 5529990 entries in matrix
- 4h 22m 04s SIMILARITY (O sentence LCS M>60): Computed  1091 M comparisons and saved 5664009 entries in matrix
- 4h 22m 27s SIMILARITY (O sentence LCS M>60): Computed  1111 M comparisons and saved 5779132 entries in matrix
- 4h 22m 50s SIMILARITY (O sentence LCS M>60): Computed  1131 M comparisons and saved 5897038 entries in matrix
- 4h 23m 13s SIMILARITY (O sentence LCS M>60): Computed  1151 M comparisons and saved 6019779 entries in matrix
- 4h 23m 37s SIMILARITY (O sentence LCS M>60): Computed  1171 M comparisons and saved 6123416 entries in matrix
- 4h 24m 01s SIMILARITY (O sentence LCS M>60): Computed  1192 M comparisons and saved 6226770 entries in matrix
- 4h 24m 24s SIMILARITY (O sentence LCS M>60): Computed  1212 M comparisons and saved 6372406 entries in matrix
- 4h 24m 47s SIMILARITY (O sentence LCS M>60): Computed  1232 M comparisons and saved 6489159 entries in matrix
- 4h 25m 11s SIMILARITY (O sentence LCS M>60): Computed  1252 M comparisons and saved 6591841 entries in matrix
- 4h 25m 35s SIMILARITY (O sentence LCS M>60): Computed  1272 M comparisons and saved 6724627 entries in matrix
- 4h 26m 05s SIMILARITY (O sentence LCS M>60): Computed  1293 M comparisons and saved 6776806 entries in matrix
- 4h 26m 31s SIMILARITY (O sentence LCS M>60): Computed  1313 M comparisons and saved 6870691 entries in matrix
- 4h 26m 53s SIMILARITY (O sentence LCS M>60): Computed  1333 M comparisons and saved 7016662 entries in matrix
- 4h 27m 16s SIMILARITY (O sentence LCS M>60): Computed  1353 M comparisons and saved 7160786 entries in matrix
- 4h 27m 37s SIMILARITY (O sentence LCS M>60): Computed  1373 M comparisons and saved 7304076 entries in matrix
- 4h 27m 58s SIMILARITY (O sentence LCS M>60): Computed  1394 M comparisons and saved 7442832 entries in matrix
- 4h 28m 25s SIMILARITY (O sentence LCS M>60): Computed  1414 M comparisons and saved 7518860 entries in matrix
- 4h 28m 51s SIMILARITY (O sentence LCS M>60): Computed  1434 M comparisons and saved 7608922 entries in matrix
- 4h 29m 15s SIMILARITY (O sentence LCS M>60): Computed  1454 M comparisons and saved 7700130 entries in matrix
- 4h 29m 39s SIMILARITY (O sentence LCS M>60): Computed  1474 M comparisons and saved 7774516 entries in matrix
- 4h 30m 02s SIMILARITY (O sentence LCS M>60): Computed  1495 M comparisons and saved 7856910 entries in matrix
- 4h 30m 25s SIMILARITY (O sentence LCS M>60): Computed  1515 M comparisons and saved 7944698 entries in matrix
- 4h 30m 46s SIMILARITY (O sentence LCS M>60): Computed  1535 M comparisons and saved 8045412 entries in matrix
- 4h 31m 08s SIMILARITY (O sentence LCS M>60): Computed  1555 M comparisons and saved 8141211 entries in matrix
- 4h 31m 31s SIMILARITY (O sentence LCS M>60): Computed  1576 M comparisons and saved 8245481 entries in matrix
- 4h 31m 54s SIMILARITY (O sentence LCS M>60): Computed  1596 M comparisons and saved 8373793 entries in matrix
- 4h 32m 17s SIMILARITY (O sentence LCS M>60): Computed  1616 M comparisons and saved 8494098 entries in matrix
- 4h 32m 42s SIMILARITY (O sentence LCS M>60): Computed  1636 M comparisons and saved 8618198 entries in matrix
- 4h 33m 08s SIMILARITY (O sentence LCS M>60): Computed  1656 M comparisons and saved 8731397 entries in matrix
- 4h 33m 36s SIMILARITY (O sentence LCS M>60): Computed  1677 M comparisons and saved 8822721 entries in matrix
- 4h 34m 01s SIMILARITY (O sentence LCS M>60): Computed  1697 M comparisons and saved 8909346 entries in matrix
- 4h 34m 24s SIMILARITY (O sentence LCS M>60): Computed  1717 M comparisons and saved 9026706 entries in matrix
- 4h 34m 48s SIMILARITY (O sentence LCS M>60): Computed  1737 M comparisons and saved 9129077 entries in matrix
- 4h 35m 11s SIMILARITY (O sentence LCS M>60): Computed  1757 M comparisons and saved 9217047 entries in matrix
- 4h 35m 35s SIMILARITY (O sentence LCS M>60): Computed  1778 M comparisons and saved 9310495 entries in matrix
- 4h 36m 00s SIMILARITY (O sentence LCS M>60): Computed  1798 M comparisons and saved 9389003 entries in matrix
- 4h 36m 23s SIMILARITY (O sentence LCS M>60): Computed  1818 M comparisons and saved 9478471 entries in matrix
- 4h 36m 46s SIMILARITY (O sentence LCS M>60): Computed  1838 M comparisons and saved 9562926 entries in matrix
- 4h 37m 09s SIMILARITY (O sentence LCS M>60): Computed  1858 M comparisons and saved 9671444 entries in matrix
- 4h 37m 32s SIMILARITY (O sentence LCS M>60): Computed  1879 M comparisons and saved 9752741 entries in matrix
- 4h 37m 54s SIMILARITY (O sentence LCS M>60): Computed  1899 M comparisons and saved 9833579 entries in matrix
- 4h 38m 18s SIMILARITY (O sentence LCS M>60): Computed  1919 M comparisons and saved 9906769 entries in matrix
- 4h 38m 41s SIMILARITY (O sentence LCS M>60): Computed  1939 M comparisons and saved 9986245 entries in matrix
- 4h 39m 06s SIMILARITY (O sentence LCS M>60): Computed  1959 M comparisons and saved 10067146 entries in matrix
- 4h 39m 31s SIMILARITY (O sentence LCS M>60): Computed  1980 M comparisons and saved 10128099 entries in matrix
- 4h 39m 59s SIMILARITY (O sentence LCS M>60): Computed  2000 M comparisons and saved 10200826 entries in matrix
- 4h 40m 31s SIMILARITY (O sentence LCS M>60): Computed  2020 M comparisons and saved 10279985 entries in matrix
- 4h 40m 40s SIMILARITY (O sentence LCS M>60): Computed  2020 M (2020540665) comparisons and saved 10279985 entries in matrix
- 4h 40m 51s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates
- 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix
- 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): 903431 relevant similarities between 17533 passages
- 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composing cliques out of  17533 chunks from 903431 comparisons
- 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composed   469 cliques out of   1000 chunks
- 4h 41m 01s CLIQUES (O sentence LCS M>60 S>100): Composed   877 cliques out of   2000 chunks
- 4h 41m 03s CLIQUES (O sentence LCS M>60 S>100): Composed  1228 cliques out of   3000 chunks
- 4h 41m 05s CLIQUES (O sentence LCS M>60 S>100): Composed  1612 cliques out of   4000 chunks
- 4h 41m 08s CLIQUES (O sentence LCS M>60 S>100): Composed  1997 cliques out of   5000 chunks
- 4h 41m 12s CLIQUES (O sentence LCS M>60 S>100): Composed  2303 cliques out of   6000 chunks
- 4h 41m 17s CLIQUES (O sentence LCS M>60 S>100): Composed  2599 cliques out of   7000 chunks
- 4h 41m 22s CLIQUES (O sentence LCS M>60 S>100): Composed  2880 cliques out of   8000 chunks
- 4h 41m 28s CLIQUES (O sentence LCS M>60 S>100): Composed  3109 cliques out of   9000 chunks
- 4h 41m 34s CLIQUES (O sentence LCS M>60 S>100): Composed  3290 cliques out of  10000 chunks
- 4h 41m 41s CLIQUES (O sentence LCS M>60 S>100): Composed  3478 cliques out of  11000 chunks
- 4h 41m 49s CLIQUES (O sentence LCS M>60 S>100): Composed  3590 cliques out of  12000 chunks
- 4h 41m 58s CLIQUES (O sentence LCS M>60 S>100): Composed  3665 cliques out of  13000 chunks
- 4h 42m 07s CLIQUES (O sentence LCS M>60 S>100): Composed  3777 cliques out of  14000 chunks
- 4h 42m 17s CLIQUES (O sentence LCS M>60 S>100): Composed  3878 cliques out of  15000 chunks
- 4h 42m 27s CLIQUES (O sentence LCS M>60 S>100): Composed  3942 cliques out of  16000 chunks
- 4h 42m 38s CLIQUES (O sentence LCS M>60 S>100): Composed  3970 cliques out of  17000 chunks
- 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): 17533 members in 3978 cliques
- 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): Composed and saved  3978 cliques out of  17533 chunks from 903431 comparisons
- 4h 42m 45s PRINT (O sentence LCS M>60 S>100): sorting out cliques
- 4h 42m 45s PRINT (O sentence LCS M>60 S>100): formatting 3978 cliques skipping 1364 binary chapter diffs
- 4h 42m 48s PRINT (O sentence LCS M>60 S>100): formatted 3978 cliques (80 files) skipping 1364 binary chapter diffs
- 4h 42m 48s CHUNKING (O sentence): already chunked into 63570 chunks
- 4h 42m 48s PREPARING (O sentence LCS): Already prepared
- 4h 42m 48s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 4h 42m 57s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates
- 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix
- 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): 904132 relevant similarities between 18091 passages
- 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composing cliques out of  18091 chunks from 904132 comparisons
- 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composed   478 cliques out of   1000 chunks
- 4h 43m 06s CLIQUES (O sentence LCS M>60 S>95): Composed   855 cliques out of   2000 chunks
- 4h 43m 08s CLIQUES (O sentence LCS M>60 S>95): Composed  1280 cliques out of   3000 chunks
- 4h 43m 10s CLIQUES (O sentence LCS M>60 S>95): Composed  1680 cliques out of   4000 chunks
- 4h 43m 13s CLIQUES (O sentence LCS M>60 S>95): Composed  2032 cliques out of   5000 chunks
- 4h 43m 17s CLIQUES (O sentence LCS M>60 S>95): Composed  2411 cliques out of   6000 chunks
- 4h 43m 22s CLIQUES (O sentence LCS M>60 S>95): Composed  2654 cliques out of   7000 chunks
- 4h 43m 27s CLIQUES (O sentence LCS M>60 S>95): Composed  2966 cliques out of   8000 chunks
- 4h 43m 33s CLIQUES (O sentence LCS M>60 S>95): Composed  3253 cliques out of   9000 chunks
- 4h 43m 39s CLIQUES (O sentence LCS M>60 S>95): Composed  3431 cliques out of  10000 chunks
- 4h 43m 47s CLIQUES (O sentence LCS M>60 S>95): Composed  3606 cliques out of  11000 chunks
- 4h 43m 55s CLIQUES (O sentence LCS M>60 S>95): Composed  3776 cliques out of  12000 chunks
- 4h 44m 03s CLIQUES (O sentence LCS M>60 S>95): Composed  3861 cliques out of  13000 chunks
- 4h 44m 13s CLIQUES (O sentence LCS M>60 S>95): Composed  3972 cliques out of  14000 chunks
- 4h 44m 23s CLIQUES (O sentence LCS M>60 S>95): Composed  4063 cliques out of  15000 chunks
- 4h 44m 33s CLIQUES (O sentence LCS M>60 S>95): Composed  4152 cliques out of  16000 chunks
- 4h 44m 44s CLIQUES (O sentence LCS M>60 S>95): Composed  4193 cliques out of  17000 chunks
- 4h 44m 56s CLIQUES (O sentence LCS M>60 S>95): Composed  4217 cliques out of  18000 chunks
- 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): 18091 members in 4218 cliques
- 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): Composed and saved  4218 cliques out of  18091 chunks from 904132 comparisons
- 4h 44m 58s PRINT (O sentence LCS M>60 S>95): sorting out cliques
- 4h 44m 58s PRINT (O sentence LCS M>60 S>95): formatting 4218 cliques involving 1419 binary chapter diffs
- 4h 44m 58s PRINT (O sentence LCS M>60 S>95): Chapter diffs needed: 1419
- 4h 45m 08s PRINT (O sentence LCS M>60 S>95): Chapter diffs: 67 newly created and 1352 already existing
- 4h 45m 11s PRINT (O sentence LCS M>60 S>95): formatted 4218 cliques (85 files) involving 1419 binary chapter diffs
- 4h 45m 11s CHUNKING (O sentence): already chunked into 63570 chunks
- 4h 45m 11s PREPARING (O sentence LCS): Already prepared
- 4h 45m 11s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 4h 45m 21s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates
- 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix
- 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): 915208 relevant similarities between 21261 passages
- 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composing cliques out of  21261 chunks from 915208 comparisons
- 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composed   483 cliques out of   1000 chunks
- 4h 45m 29s CLIQUES (O sentence LCS M>60 S>90): Composed   936 cliques out of   2000 chunks
- 4h 45m 31s CLIQUES (O sentence LCS M>60 S>90): Composed  1287 cliques out of   3000 chunks
- 4h 45m 33s CLIQUES (O sentence LCS M>60 S>90): Composed  1691 cliques out of   4000 chunks
- 4h 45m 36s CLIQUES (O sentence LCS M>60 S>90): Composed  2062 cliques out of   5000 chunks
- 4h 45m 40s CLIQUES (O sentence LCS M>60 S>90): Composed  2407 cliques out of   6000 chunks
- 4h 45m 45s CLIQUES (O sentence LCS M>60 S>90): Composed  2762 cliques out of   7000 chunks
- 4h 45m 50s CLIQUES (O sentence LCS M>60 S>90): Composed  3103 cliques out of   8000 chunks
- 4h 45m 56s CLIQUES (O sentence LCS M>60 S>90): Composed  3332 cliques out of   9000 chunks
- 4h 46m 03s CLIQUES (O sentence LCS M>60 S>90): Composed  3634 cliques out of  10000 chunks
- 4h 46m 10s CLIQUES (O sentence LCS M>60 S>90): Composed  3904 cliques out of  11000 chunks
- 4h 46m 18s CLIQUES (O sentence LCS M>60 S>90): Composed  4127 cliques out of  12000 chunks
- 4h 46m 27s CLIQUES (O sentence LCS M>60 S>90): Composed  4303 cliques out of  13000 chunks
- 4h 46m 37s CLIQUES (O sentence LCS M>60 S>90): Composed  4451 cliques out of  14000 chunks
- 4h 46m 46s CLIQUES (O sentence LCS M>60 S>90): Composed  4601 cliques out of  15000 chunks
- 4h 46m 57s CLIQUES (O sentence LCS M>60 S>90): Composed  4684 cliques out of  16000 chunks
- 4h 47m 08s CLIQUES (O sentence LCS M>60 S>90): Composed  4786 cliques out of  17000 chunks
- 4h 47m 21s CLIQUES (O sentence LCS M>60 S>90): Composed  4866 cliques out of  18000 chunks
- 4h 47m 33s CLIQUES (O sentence LCS M>60 S>90): Composed  4929 cliques out of  19000 chunks
- 4h 47m 47s CLIQUES (O sentence LCS M>60 S>90): Composed  4970 cliques out of  20000 chunks
- 4h 48m 01s CLIQUES (O sentence LCS M>60 S>90): Composed  4995 cliques out of  21000 chunks
- 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): 21261 members in 4997 cliques
- 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): Composed and saved  4997 cliques out of  21261 chunks from 915208 comparisons
- 4h 48m 05s PRINT (O sentence LCS M>60 S>90): sorting out cliques
- 4h 48m 05s PRINT (O sentence LCS M>60 S>90): formatting 4997 cliques involving 1703 binary chapter diffs
- 4h 48m 05s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1703
- 4h 48m 06s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 2 newly created and 1701 already existing
- 4h 48m 09s PRINT (O sentence LCS M>60 S>90): formatted 4997 cliques (100 files) involving 1703 binary chapter diffs
- 4h 48m 09s CHUNKING (O sentence): already chunked into 63570 chunks
- 4h 48m 09s PREPARING (O sentence LCS): Already prepared
- 4h 48m 09s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 4h 48m 19s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates
- 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix
- 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): 980755 relevant similarities between 26488 passages
- 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composing cliques out of  26488 chunks from 980755 comparisons
- 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composed   488 cliques out of   1000 chunks
- 4h 48m 28s CLIQUES (O sentence LCS M>60 S>85): Composed   940 cliques out of   2000 chunks
- 4h 48m 30s CLIQUES (O sentence LCS M>60 S>85): Composed  1315 cliques out of   3000 chunks
- 4h 48m 32s CLIQUES (O sentence LCS M>60 S>85): Composed  1672 cliques out of   4000 chunks
- 4h 48m 35s CLIQUES (O sentence LCS M>60 S>85): Composed  2063 cliques out of   5000 chunks
- 4h 48m 39s CLIQUES (O sentence LCS M>60 S>85): Composed  2408 cliques out of   6000 chunks
- 4h 48m 44s CLIQUES (O sentence LCS M>60 S>85): Composed  2693 cliques out of   7000 chunks
- 4h 48m 49s CLIQUES (O sentence LCS M>60 S>85): Composed  2956 cliques out of   8000 chunks
- 4h 48m 55s CLIQUES (O sentence LCS M>60 S>85): Composed  3253 cliques out of   9000 chunks
- 4h 49m 02s CLIQUES (O sentence LCS M>60 S>85): Composed  3542 cliques out of  10000 chunks
- 4h 49m 09s CLIQUES (O sentence LCS M>60 S>85): Composed  3728 cliques out of  11000 chunks
- 4h 49m 17s CLIQUES (O sentence LCS M>60 S>85): Composed  3912 cliques out of  12000 chunks
- 4h 49m 26s CLIQUES (O sentence LCS M>60 S>85): Composed  4083 cliques out of  13000 chunks
- 4h 49m 36s CLIQUES (O sentence LCS M>60 S>85): Composed  4328 cliques out of  14000 chunks
- 4h 49m 46s CLIQUES (O sentence LCS M>60 S>85): Composed  4464 cliques out of  15000 chunks
- 4h 49m 56s CLIQUES (O sentence LCS M>60 S>85): Composed  4558 cliques out of  16000 chunks
- 4h 50m 08s CLIQUES (O sentence LCS M>60 S>85): Composed  4608 cliques out of  17000 chunks
- 4h 50m 20s CLIQUES (O sentence LCS M>60 S>85): Composed  4635 cliques out of  18000 chunks
- 4h 50m 32s CLIQUES (O sentence LCS M>60 S>85): Composed  4710 cliques out of  19000 chunks
- 4h 50m 45s CLIQUES (O sentence LCS M>60 S>85): Composed  4787 cliques out of  20000 chunks
- 4h 50m 59s CLIQUES (O sentence LCS M>60 S>85): Composed  4826 cliques out of  21000 chunks
- 4h 51m 14s CLIQUES (O sentence LCS M>60 S>85): Composed  4853 cliques out of  22000 chunks
- 4h 51m 28s CLIQUES (O sentence LCS M>60 S>85): Composed  4877 cliques out of  23000 chunks
- 4h 51m 44s CLIQUES (O sentence LCS M>60 S>85): Composed  4859 cliques out of  24000 chunks
- 4h 52m 00s CLIQUES (O sentence LCS M>60 S>85): Composed  4827 cliques out of  25000 chunks
- 4h 52m 17s CLIQUES (O sentence LCS M>60 S>85): Composed  4851 cliques out of  26000 chunks
- 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): 26488 members in 4855 cliques
- 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): Composed and saved  4855 cliques out of  26488 chunks from 980755 comparisons
- 4h 52m 27s PRINT (O sentence LCS M>60 S>85): sorting out cliques
- 4h 52m 28s PRINT (O sentence LCS M>60 S>85): formatting 4855 cliques skipping 1711 binary chapter diffs
- 4h 52m 31s PRINT (O sentence LCS M>60 S>85): formatted 4855 cliques (98 files) skipping 1711 binary chapter diffs
- 4h 52m 31s CHUNKING (O sentence): already chunked into 63570 chunks
- 4h 52m 31s PREPARING (O sentence LCS): Already prepared
- 4h 52m 31s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 4h 52m 41s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates
- 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix
- 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): 1301831 relevant similarities between 35629 passages
- 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composing cliques out of  35629 chunks from 1301831 comparisons
- 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composed   505 cliques out of   1000 chunks
- 4h 52m 50s CLIQUES (O sentence LCS M>60 S>80): Composed   932 cliques out of   2000 chunks
- 4h 52m 52s CLIQUES (O sentence LCS M>60 S>80): Composed  1346 cliques out of   3000 chunks
- 4h 52m 54s CLIQUES (O sentence LCS M>60 S>80): Composed  1725 cliques out of   4000 chunks
- 4h 52m 58s CLIQUES (O sentence LCS M>60 S>80): Composed  2000 cliques out of   5000 chunks
- 4h 53m 01s CLIQUES (O sentence LCS M>60 S>80): Composed  2295 cliques out of   6000 chunks
- 4h 53m 06s CLIQUES (O sentence LCS M>60 S>80): Composed  2537 cliques out of   7000 chunks
- 4h 53m 11s CLIQUES (O sentence LCS M>60 S>80): Composed  2867 cliques out of   8000 chunks
- 4h 53m 17s CLIQUES (O sentence LCS M>60 S>80): Composed  3061 cliques out of   9000 chunks
- 4h 53m 23s CLIQUES (O sentence LCS M>60 S>80): Composed  3188 cliques out of  10000 chunks
- 4h 53m 31s CLIQUES (O sentence LCS M>60 S>80): Composed  3259 cliques out of  11000 chunks
- 4h 53m 38s CLIQUES (O sentence LCS M>60 S>80): Composed  3454 cliques out of  12000 chunks
- 4h 53m 47s CLIQUES (O sentence LCS M>60 S>80): Composed  3687 cliques out of  13000 chunks
- 4h 53m 56s CLIQUES (O sentence LCS M>60 S>80): Composed  3826 cliques out of  14000 chunks
- 4h 54m 05s CLIQUES (O sentence LCS M>60 S>80): Composed  3891 cliques out of  15000 chunks
- 4h 54m 15s CLIQUES (O sentence LCS M>60 S>80): Composed  3887 cliques out of  16000 chunks
- 4h 54m 25s CLIQUES (O sentence LCS M>60 S>80): Composed  3942 cliques out of  17000 chunks
- 4h 54m 35s CLIQUES (O sentence LCS M>60 S>80): Composed  3938 cliques out of  18000 chunks
- 4h 54m 47s CLIQUES (O sentence LCS M>60 S>80): Composed  3994 cliques out of  19000 chunks
- 4h 54m 58s CLIQUES (O sentence LCS M>60 S>80): Composed  4005 cliques out of  20000 chunks
- 4h 55m 10s CLIQUES (O sentence LCS M>60 S>80): Composed  4071 cliques out of  21000 chunks
- 4h 55m 21s CLIQUES (O sentence LCS M>60 S>80): Composed  4023 cliques out of  22000 chunks
- 4h 55m 33s CLIQUES (O sentence LCS M>60 S>80): Composed  3965 cliques out of  23000 chunks
- 4h 55m 44s CLIQUES (O sentence LCS M>60 S>80): Composed  3889 cliques out of  24000 chunks
- 4h 55m 56s CLIQUES (O sentence LCS M>60 S>80): Composed  3810 cliques out of  25000 chunks
- 4h 56m 09s CLIQUES (O sentence LCS M>60 S>80): Composed  3734 cliques out of  26000 chunks
- 4h 56m 22s CLIQUES (O sentence LCS M>60 S>80): Composed  3702 cliques out of  27000 chunks
- 4h 56m 37s CLIQUES (O sentence LCS M>60 S>80): Composed  3705 cliques out of  28000 chunks
- 4h 56m 52s CLIQUES (O sentence LCS M>60 S>80): Composed  3704 cliques out of  29000 chunks
- 4h 57m 08s CLIQUES (O sentence LCS M>60 S>80): Composed  3682 cliques out of  30000 chunks
- 4h 57m 21s CLIQUES (O sentence LCS M>60 S>80): Composed  3637 cliques out of  31000 chunks
- 4h 57m 33s CLIQUES (O sentence LCS M>60 S>80): Composed  3604 cliques out of  32000 chunks
- 4h 57m 47s CLIQUES (O sentence LCS M>60 S>80): Composed  3537 cliques out of  33000 chunks
- 4h 58m 01s CLIQUES (O sentence LCS M>60 S>80): Composed  3492 cliques out of  34000 chunks
- 4h 58m 15s CLIQUES (O sentence LCS M>60 S>80): Composed  3476 cliques out of  35000 chunks
- 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): 35629 members in 3469 cliques
- 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): Composed and saved  3469 cliques out of  35629 chunks from 1301831 comparisons
- 4h 58m 26s PRINT (O sentence LCS M>60 S>80): sorting out cliques
- 4h 58m 27s PRINT (O sentence LCS M>60 S>80): formatting 3469 cliques skipping 1291 binary chapter diffs
- 4h 58m 29s PRINT (O sentence LCS M>60 S>80): formatted 3469 cliques (70 files) skipping 1291 binary chapter diffs
- 4h 58m 29s CHUNKING (O sentence): already chunked into 63570 chunks
- 4h 58m 29s PREPARING (O sentence LCS): Already prepared
- 4h 58m 29s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 4h 58m 39s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates
- 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix
- 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): 1620905 relevant similarities between 44303 passages
- 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composing cliques out of  44303 chunks from 1620905 comparisons
- 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composed   511 cliques out of   1000 chunks
- 4h 58m 49s CLIQUES (O sentence LCS M>60 S>75): Composed   937 cliques out of   2000 chunks
- 4h 58m 51s CLIQUES (O sentence LCS M>60 S>75): Composed  1325 cliques out of   3000 chunks
- 4h 58m 53s CLIQUES (O sentence LCS M>60 S>75): Composed  1670 cliques out of   4000 chunks
- 4h 58m 56s CLIQUES (O sentence LCS M>60 S>75): Composed  1940 cliques out of   5000 chunks
- 4h 59m 00s CLIQUES (O sentence LCS M>60 S>75): Composed  2172 cliques out of   6000 chunks
- 4h 59m 05s CLIQUES (O sentence LCS M>60 S>75): Composed  2355 cliques out of   7000 chunks
- 4h 59m 09s CLIQUES (O sentence LCS M>60 S>75): Composed  2554 cliques out of   8000 chunks
- 4h 59m 15s CLIQUES (O sentence LCS M>60 S>75): Composed  2741 cliques out of   9000 chunks
- 4h 59m 21s CLIQUES (O sentence LCS M>60 S>75): Composed  2821 cliques out of  10000 chunks
- 4h 59m 28s CLIQUES (O sentence LCS M>60 S>75): Composed  2925 cliques out of  11000 chunks
- 4h 59m 35s CLIQUES (O sentence LCS M>60 S>75): Composed  3093 cliques out of  12000 chunks
- 4h 59m 42s CLIQUES (O sentence LCS M>60 S>75): Composed  3200 cliques out of  13000 chunks
- 4h 59m 50s CLIQUES (O sentence LCS M>60 S>75): Composed  3227 cliques out of  14000 chunks
- 4h 59m 58s CLIQUES (O sentence LCS M>60 S>75): Composed  3153 cliques out of  15000 chunks
- 5h 00m 06s CLIQUES (O sentence LCS M>60 S>75): Composed  3205 cliques out of  16000 chunks
- 5h 00m 15s CLIQUES (O sentence LCS M>60 S>75): Composed  3181 cliques out of  17000 chunks
- 5h 00m 25s CLIQUES (O sentence LCS M>60 S>75): Composed  3207 cliques out of  18000 chunks
- 5h 00m 36s CLIQUES (O sentence LCS M>60 S>75): Composed  3213 cliques out of  19000 chunks
- 5h 00m 45s CLIQUES (O sentence LCS M>60 S>75): Composed  3221 cliques out of  20000 chunks
- 5h 00m 55s CLIQUES (O sentence LCS M>60 S>75): Composed  3184 cliques out of  21000 chunks
- 5h 01m 04s CLIQUES (O sentence LCS M>60 S>75): Composed  3109 cliques out of  22000 chunks
- 5h 01m 14s CLIQUES (O sentence LCS M>60 S>75): Composed  3080 cliques out of  23000 chunks
- 5h 01m 24s CLIQUES (O sentence LCS M>60 S>75): Composed  3047 cliques out of  24000 chunks
- 5h 01m 36s CLIQUES (O sentence LCS M>60 S>75): Composed  2977 cliques out of  25000 chunks
- 5h 01m 52s CLIQUES (O sentence LCS M>60 S>75): Composed  2947 cliques out of  26000 chunks
- 5h 02m 06s CLIQUES (O sentence LCS M>60 S>75): Composed  2871 cliques out of  27000 chunks
- 5h 02m 18s CLIQUES (O sentence LCS M>60 S>75): Composed  2848 cliques out of  28000 chunks
- 5h 02m 31s CLIQUES (O sentence LCS M>60 S>75): Composed  2825 cliques out of  29000 chunks
- 5h 02m 41s CLIQUES (O sentence LCS M>60 S>75): Composed  2783 cliques out of  30000 chunks
- 5h 02m 54s CLIQUES (O sentence LCS M>60 S>75): Composed  2759 cliques out of  31000 chunks
- 5h 03m 05s CLIQUES (O sentence LCS M>60 S>75): Composed  2696 cliques out of  32000 chunks
- 5h 03m 17s CLIQUES (O sentence LCS M>60 S>75): Composed  2609 cliques out of  33000 chunks
- 5h 03m 29s CLIQUES (O sentence LCS M>60 S>75): Composed  2544 cliques out of  34000 chunks
- 5h 03m 40s CLIQUES (O sentence LCS M>60 S>75): Composed  2469 cliques out of  35000 chunks
- 5h 03m 53s CLIQUES (O sentence LCS M>60 S>75): Composed  2463 cliques out of  36000 chunks
- 5h 04m 03s CLIQUES (O sentence LCS M>60 S>75): Composed  2453 cliques out of  37000 chunks
- 5h 04m 13s CLIQUES (O sentence LCS M>60 S>75): Composed  2444 cliques out of  38000 chunks
- 5h 04m 24s CLIQUES (O sentence LCS M>60 S>75): Composed  2407 cliques out of  39000 chunks
- 5h 04m 34s CLIQUES (O sentence LCS M>60 S>75): Composed  2376 cliques out of  40000 chunks
- 5h 04m 45s CLIQUES (O sentence LCS M>60 S>75): Composed  2341 cliques out of  41000 chunks
- 5h 04m 56s CLIQUES (O sentence LCS M>60 S>75): Composed  2304 cliques out of  42000 chunks
- 5h 05m 07s CLIQUES (O sentence LCS M>60 S>75): Composed  2296 cliques out of  43000 chunks
- 5h 05m 19s CLIQUES (O sentence LCS M>60 S>75): Composed  2292 cliques out of  44000 chunks
- 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): 44303 members in 2291 cliques
- 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): Composed and saved  2291 cliques out of  44303 chunks from 1620905 comparisons
- 5h 05m 24s PRINT (O sentence LCS M>60 S>75): sorting out cliques
- 5h 05m 25s PRINT (O sentence LCS M>60 S>75): formatting 2291 cliques skipping 888 binary chapter diffs
- 5h 05m 27s PRINT (O sentence LCS M>60 S>75): formatted 2291 cliques (46 files) skipping 888 binary chapter diffs
- 5h 05m 27s CHUNKING (O sentence): already chunked into 63570 chunks
- 5h 05m 27s PREPARING (O sentence LCS): Already prepared
- 5h 05m 27s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 5h 05m 37s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates
- 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix
- 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): 2184827 relevant similarities between 52528 passages
- 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composing cliques out of  52528 chunks from 2184827 comparisons
- 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composed   501 cliques out of   1000 chunks
- 5h 05m 48s CLIQUES (O sentence LCS M>60 S>70): Composed   931 cliques out of   2000 chunks
- 5h 05m 50s CLIQUES (O sentence LCS M>60 S>70): Composed  1217 cliques out of   3000 chunks
- 5h 05m 52s CLIQUES (O sentence LCS M>60 S>70): Composed  1494 cliques out of   4000 chunks
- 5h 05m 55s CLIQUES (O sentence LCS M>60 S>70): Composed  1737 cliques out of   5000 chunks
- 5h 05m 59s CLIQUES (O sentence LCS M>60 S>70): Composed  1924 cliques out of   6000 chunks
- 5h 06m 03s CLIQUES (O sentence LCS M>60 S>70): Composed  2023 cliques out of   7000 chunks
- 5h 06m 07s CLIQUES (O sentence LCS M>60 S>70): Composed  2080 cliques out of   8000 chunks
- 5h 06m 12s CLIQUES (O sentence LCS M>60 S>70): Composed  2197 cliques out of   9000 chunks
- 5h 06m 17s CLIQUES (O sentence LCS M>60 S>70): Composed  2153 cliques out of  10000 chunks
- 5h 06m 23s CLIQUES (O sentence LCS M>60 S>70): Composed  2133 cliques out of  11000 chunks
- 5h 06m 29s CLIQUES (O sentence LCS M>60 S>70): Composed  2203 cliques out of  12000 chunks
- 5h 06m 35s CLIQUES (O sentence LCS M>60 S>70): Composed  2190 cliques out of  13000 chunks
- 5h 06m 41s CLIQUES (O sentence LCS M>60 S>70): Composed  2189 cliques out of  14000 chunks
- 5h 06m 48s CLIQUES (O sentence LCS M>60 S>70): Composed  2105 cliques out of  15000 chunks
- 5h 06m 55s CLIQUES (O sentence LCS M>60 S>70): Composed  2153 cliques out of  16000 chunks
- 5h 07m 03s CLIQUES (O sentence LCS M>60 S>70): Composed  2153 cliques out of  17000 chunks
- 5h 07m 10s CLIQUES (O sentence LCS M>60 S>70): Composed  2128 cliques out of  18000 chunks
- 5h 07m 17s CLIQUES (O sentence LCS M>60 S>70): Composed  2099 cliques out of  19000 chunks
- 5h 07m 24s CLIQUES (O sentence LCS M>60 S>70): Composed  2060 cliques out of  20000 chunks
- 5h 07m 30s CLIQUES (O sentence LCS M>60 S>70): Composed  1970 cliques out of  21000 chunks
- 5h 07m 39s CLIQUES (O sentence LCS M>60 S>70): Composed  1984 cliques out of  22000 chunks
- 5h 07m 46s CLIQUES (O sentence LCS M>60 S>70): Composed  1936 cliques out of  23000 chunks
- 5h 07m 54s CLIQUES (O sentence LCS M>60 S>70): Composed  1911 cliques out of  24000 chunks
- 5h 08m 04s CLIQUES (O sentence LCS M>60 S>70): Composed  1914 cliques out of  25000 chunks
- 5h 08m 12s CLIQUES (O sentence LCS M>60 S>70): Composed  1859 cliques out of  26000 chunks
- 5h 08m 20s CLIQUES (O sentence LCS M>60 S>70): Composed  1789 cliques out of  27000 chunks
- 5h 08m 27s CLIQUES (O sentence LCS M>60 S>70): Composed  1745 cliques out of  28000 chunks
- 5h 08m 34s CLIQUES (O sentence LCS M>60 S>70): Composed  1687 cliques out of  29000 chunks
- 5h 08m 41s CLIQUES (O sentence LCS M>60 S>70): Composed  1660 cliques out of  30000 chunks
- 5h 08m 50s CLIQUES (O sentence LCS M>60 S>70): Composed  1631 cliques out of  31000 chunks
- 5h 08m 58s CLIQUES (O sentence LCS M>60 S>70): Composed  1600 cliques out of  32000 chunks
- 5h 09m 07s CLIQUES (O sentence LCS M>60 S>70): Composed  1552 cliques out of  33000 chunks
- 5h 09m 18s CLIQUES (O sentence LCS M>60 S>70): Composed  1506 cliques out of  34000 chunks
- 5h 09m 27s CLIQUES (O sentence LCS M>60 S>70): Composed  1426 cliques out of  35000 chunks
- 5h 09m 34s CLIQUES (O sentence LCS M>60 S>70): Composed  1413 cliques out of  36000 chunks
- 5h 09m 41s CLIQUES (O sentence LCS M>60 S>70): Composed  1399 cliques out of  37000 chunks
- 5h 09m 48s CLIQUES (O sentence LCS M>60 S>70): Composed  1383 cliques out of  38000 chunks
- 5h 09m 56s CLIQUES (O sentence LCS M>60 S>70): Composed  1374 cliques out of  39000 chunks
- 5h 10m 03s CLIQUES (O sentence LCS M>60 S>70): Composed  1349 cliques out of  40000 chunks
- 5h 10m 11s CLIQUES (O sentence LCS M>60 S>70): Composed  1306 cliques out of  41000 chunks
- 5h 10m 20s CLIQUES (O sentence LCS M>60 S>70): Composed  1259 cliques out of  42000 chunks
- 5h 10m 28s CLIQUES (O sentence LCS M>60 S>70): Composed  1231 cliques out of  43000 chunks
- 5h 10m 40s CLIQUES (O sentence LCS M>60 S>70): Composed  1243 cliques out of  44000 chunks
- 5h 10m 49s CLIQUES (O sentence LCS M>60 S>70): Composed  1250 cliques out of  45000 chunks
- 5h 10m 58s CLIQUES (O sentence LCS M>60 S>70): Composed  1249 cliques out of  46000 chunks
- 5h 11m 08s CLIQUES (O sentence LCS M>60 S>70): Composed  1234 cliques out of  47000 chunks
- 5h 11m 17s CLIQUES (O sentence LCS M>60 S>70): Composed  1227 cliques out of  48000 chunks
- 5h 11m 25s CLIQUES (O sentence LCS M>60 S>70): Composed  1219 cliques out of  49000 chunks
- 5h 11m 34s CLIQUES (O sentence LCS M>60 S>70): Composed  1205 cliques out of  50000 chunks
- 5h 11m 44s CLIQUES (O sentence LCS M>60 S>70): Composed  1205 cliques out of  51000 chunks
- 5h 11m 55s CLIQUES (O sentence LCS M>60 S>70): Composed  1201 cliques out of  52000 chunks
- 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): 52528 members in 1199 cliques
- 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): Composed and saved  1199 cliques out of  52528 chunks from 2184827 comparisons
- 5h 12m 03s PRINT (O sentence LCS M>60 S>70): sorting out cliques
- 5h 12m 05s PRINT (O sentence LCS M>60 S>70): formatting 1199 cliques skipping 455 binary chapter diffs
- 5h 12m 06s PRINT (O sentence LCS M>60 S>70): formatted 1199 cliques (24 files) skipping 455 binary chapter diffs
- 5h 12m 06s CHUNKING (O sentence): already chunked into 63570 chunks
- 5h 12m 06s PREPARING (O sentence LCS): Already prepared
- 5h 12m 06s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 5h 12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates
- 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix
- 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): 4834493 relevant similarities between 58855 passages
- 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): Composing cliques out of  58855 chunks from 4834493 comparisons
- 5h 12m 29s CLIQUES (O sentence LCS M>60 S>65): Composed   479 cliques out of   1000 chunks
- 5h 12m 30s CLIQUES (O sentence LCS M>60 S>65): Composed   743 cliques out of   2000 chunks
- 5h 12m 31s CLIQUES (O sentence LCS M>60 S>65): Composed   968 cliques out of   3000 chunks
- 5h 12m 33s CLIQUES (O sentence LCS M>60 S>65): Composed  1082 cliques out of   4000 chunks
- 5h 12m 36s CLIQUES (O sentence LCS M>60 S>65): Composed  1164 cliques out of   5000 chunks
- 5h 12m 38s CLIQUES (O sentence LCS M>60 S>65): Composed  1217 cliques out of   6000 chunks
- 5h 12m 41s CLIQUES (O sentence LCS M>60 S>65): Composed  1183 cliques out of   7000 chunks
- 5h 12m 45s CLIQUES (O sentence LCS M>60 S>65): Composed  1240 cliques out of   8000 chunks
- 5h 12m 48s CLIQUES (O sentence LCS M>60 S>65): Composed  1156 cliques out of   9000 chunks
- 5h 12m 51s CLIQUES (O sentence LCS M>60 S>65): Composed  1157 cliques out of  10000 chunks
- 5h 12m 54s CLIQUES (O sentence LCS M>60 S>65): Composed  1075 cliques out of  11000 chunks
- 5h 12m 58s CLIQUES (O sentence LCS M>60 S>65): Composed  1014 cliques out of  12000 chunks
- 5h 13m 02s CLIQUES (O sentence LCS M>60 S>65): Composed   998 cliques out of  13000 chunks
- 5h 13m 05s CLIQUES (O sentence LCS M>60 S>65): Composed   976 cliques out of  14000 chunks
- 5h 13m 09s CLIQUES (O sentence LCS M>60 S>65): Composed   930 cliques out of  15000 chunks
- 5h 13m 13s CLIQUES (O sentence LCS M>60 S>65): Composed   891 cliques out of  16000 chunks
- 5h 13m 18s CLIQUES (O sentence LCS M>60 S>65): Composed   886 cliques out of  17000 chunks
- 5h 13m 22s CLIQUES (O sentence LCS M>60 S>65): Composed   836 cliques out of  18000 chunks
- 5h 13m 26s CLIQUES (O sentence LCS M>60 S>65): Composed   818 cliques out of  19000 chunks
- 5h 13m 30s CLIQUES (O sentence LCS M>60 S>65): Composed   798 cliques out of  20000 chunks
- 5h 13m 34s CLIQUES (O sentence LCS M>60 S>65): Composed   799 cliques out of  21000 chunks
- 5h 13m 39s CLIQUES (O sentence LCS M>60 S>65): Composed   778 cliques out of  22000 chunks
- 5h 13m 45s CLIQUES (O sentence LCS M>60 S>65): Composed   764 cliques out of  23000 chunks
- 5h 13m 49s CLIQUES (O sentence LCS M>60 S>65): Composed   743 cliques out of  24000 chunks
- 5h 13m 52s CLIQUES (O sentence LCS M>60 S>65): Composed   717 cliques out of  25000 chunks
- 5h 13m 56s CLIQUES (O sentence LCS M>60 S>65): Composed   707 cliques out of  26000 chunks
- 5h 14m 00s CLIQUES (O sentence LCS M>60 S>65): Composed   692 cliques out of  27000 chunks
- 5h 14m 06s CLIQUES (O sentence LCS M>60 S>65): Composed   684 cliques out of  28000 chunks
- 5h 14m 10s CLIQUES (O sentence LCS M>60 S>65): Composed   650 cliques out of  29000 chunks
- 5h 14m 15s CLIQUES (O sentence LCS M>60 S>65): Composed   639 cliques out of  30000 chunks
- 5h 14m 21s CLIQUES (O sentence LCS M>60 S>65): Composed   623 cliques out of  31000 chunks
- 5h 14m 26s CLIQUES (O sentence LCS M>60 S>65): Composed   598 cliques out of  32000 chunks
- 5h 14m 30s CLIQUES (O sentence LCS M>60 S>65): Composed   586 cliques out of  33000 chunks
- 5h 14m 34s CLIQUES (O sentence LCS M>60 S>65): Composed   580 cliques out of  34000 chunks
- 5h 14m 39s CLIQUES (O sentence LCS M>60 S>65): Composed   570 cliques out of  35000 chunks
- 5h 14m 43s CLIQUES (O sentence LCS M>60 S>65): Composed   564 cliques out of  36000 chunks
- 5h 14m 48s CLIQUES (O sentence LCS M>60 S>65): Composed   554 cliques out of  37000 chunks
- 5h 14m 53s CLIQUES (O sentence LCS M>60 S>65): Composed   540 cliques out of  38000 chunks
- 5h 14m 59s CLIQUES (O sentence LCS M>60 S>65): Composed   530 cliques out of  39000 chunks
- 5h 15m 05s CLIQUES (O sentence LCS M>60 S>65): Composed   514 cliques out of  40000 chunks
- 5h 15m 10s CLIQUES (O sentence LCS M>60 S>65): Composed   499 cliques out of  41000 chunks
- 5h 15m 15s CLIQUES (O sentence LCS M>60 S>65): Composed   500 cliques out of  42000 chunks
- 5h 15m 19s CLIQUES (O sentence LCS M>60 S>65): Composed   499 cliques out of  43000 chunks
- 5h 15m 24s CLIQUES (O sentence LCS M>60 S>65): Composed   495 cliques out of  44000 chunks
- 5h 15m 29s CLIQUES (O sentence LCS M>60 S>65): Composed   491 cliques out of  45000 chunks
- 5h 15m 35s CLIQUES (O sentence LCS M>60 S>65): Composed   484 cliques out of  46000 chunks
- 5h 15m 41s CLIQUES (O sentence LCS M>60 S>65): Composed   479 cliques out of  47000 chunks
- 5h 15m 47s CLIQUES (O sentence LCS M>60 S>65): Composed   472 cliques out of  48000 chunks
- 5h 15m 54s CLIQUES (O sentence LCS M>60 S>65): Composed   467 cliques out of  49000 chunks
- 5h 16m 00s CLIQUES (O sentence LCS M>60 S>65): Composed   466 cliques out of  50000 chunks
- 5h 16m 05s CLIQUES (O sentence LCS M>60 S>65): Composed   466 cliques out of  51000 chunks
- 5h 16m 11s CLIQUES (O sentence LCS M>60 S>65): Composed   466 cliques out of  52000 chunks
- 5h 16m 17s CLIQUES (O sentence LCS M>60 S>65): Composed   467 cliques out of  53000 chunks
- 5h 16m 23s CLIQUES (O sentence LCS M>60 S>65): Composed   466 cliques out of  54000 chunks
- 5h 16m 29s CLIQUES (O sentence LCS M>60 S>65): Composed   466 cliques out of  55000 chunks
- 5h 16m 35s CLIQUES (O sentence LCS M>60 S>65): Composed   465 cliques out of  56000 chunks
- 5h 16m 42s CLIQUES (O sentence LCS M>60 S>65): Composed   464 cliques out of  57000 chunks
- 5h 16m 48s CLIQUES (O sentence LCS M>60 S>65): Composed   463 cliques out of  58000 chunks
- 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): 58855 members in 463 cliques
- 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): Composed and saved   463 cliques out of  58855 chunks from 4834493 comparisons
- 5h 16m 56s PRINT (O sentence LCS M>60 S>65): sorting out cliques
- 5h 16m 57s PRINT (O sentence LCS M>60 S>65): formatting 463 cliques skipping 209 binary chapter diffs
- 5h 16m 58s PRINT (O sentence LCS M>60 S>65): formatted 463 cliques (10 files) skipping 209 binary chapter diffs
- 5h 16m 58s CHUNKING (O sentence): already chunked into 63570 chunks
- 5h 16m 58s PREPARING (O sentence LCS): Already prepared
- 5h 16m 58s SIMILARITY (O sentence LCS M>60): Using  2020 M (2020540665) comparisons with 10279985 entries in matrix
- 5h 17m 08s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%
- 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates
- 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix
- 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): 10279985 relevant similarities between 62369 passages
- 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composing cliques out of  62369 chunks from 10279985 comparisons
- 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composed   317 cliques out of   1000 chunks
- 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed   374 cliques out of   2000 chunks
- 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed   400 cliques out of   3000 chunks
- 5h 17m 30s CLIQUES (O sentence LCS M>60 S>60): Composed   381 cliques out of   4000 chunks
- 5h 17m 31s CLIQUES (O sentence LCS M>60 S>60): Composed   375 cliques out of   5000 chunks
- 5h 17m 32s CLIQUES (O sentence LCS M>60 S>60): Composed   348 cliques out of   6000 chunks
- 5h 17m 33s CLIQUES (O sentence LCS M>60 S>60): Composed   309 cliques out of   7000 chunks
- 5h 17m 34s CLIQUES (O sentence LCS M>60 S>60): Composed   298 cliques out of   8000 chunks
- 5h 17m 36s CLIQUES (O sentence LCS M>60 S>60): Composed   272 cliques out of   9000 chunks
- 5h 17m 37s CLIQUES (O sentence LCS M>60 S>60): Composed   246 cliques out of  10000 chunks
- 5h 17m 39s CLIQUES (O sentence LCS M>60 S>60): Composed   243 cliques out of  11000 chunks
- 5h 17m 40s CLIQUES (O sentence LCS M>60 S>60): Composed   221 cliques out of  12000 chunks
- 5h 17m 42s CLIQUES (O sentence LCS M>60 S>60): Composed   214 cliques out of  13000 chunks
- 5h 17m 43s CLIQUES (O sentence LCS M>60 S>60): Composed   209 cliques out of  14000 chunks
- 5h 17m 44s CLIQUES (O sentence LCS M>60 S>60): Composed   190 cliques out of  15000 chunks
- 5h 17m 46s CLIQUES (O sentence LCS M>60 S>60): Composed   175 cliques out of  16000 chunks
- 5h 17m 48s CLIQUES (O sentence LCS M>60 S>60): Composed   169 cliques out of  17000 chunks
- 5h 17m 49s CLIQUES (O sentence LCS M>60 S>60): Composed   162 cliques out of  18000 chunks
- 5h 17m 51s CLIQUES (O sentence LCS M>60 S>60): Composed   160 cliques out of  19000 chunks
- 5h 17m 52s CLIQUES (O sentence LCS M>60 S>60): Composed   151 cliques out of  20000 chunks
- 5h 17m 54s CLIQUES (O sentence LCS M>60 S>60): Composed   141 cliques out of  21000 chunks
- 5h 17m 55s CLIQUES (O sentence LCS M>60 S>60): Composed   133 cliques out of  22000 chunks
- 5h 17m 57s CLIQUES (O sentence LCS M>60 S>60): Composed   134 cliques out of  23000 chunks
- 5h 17m 59s CLIQUES (O sentence LCS M>60 S>60): Composed   132 cliques out of  24000 chunks
- 5h 18m 02s CLIQUES (O sentence LCS M>60 S>60): Composed   126 cliques out of  25000 chunks
- 5h 18m 04s CLIQUES (O sentence LCS M>60 S>60): Composed   124 cliques out of  26000 chunks
- 5h 18m 07s CLIQUES (O sentence LCS M>60 S>60): Composed   120 cliques out of  27000 chunks
- 5h 18m 09s CLIQUES (O sentence LCS M>60 S>60): Composed   119 cliques out of  28000 chunks
- 5h 18m 11s CLIQUES (O sentence LCS M>60 S>60): Composed   119 cliques out of  29000 chunks
- 5h 18m 14s CLIQUES (O sentence LCS M>60 S>60): Composed   118 cliques out of  30000 chunks
- 5h 18m 16s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  31000 chunks
- 5h 18m 19s CLIQUES (O sentence LCS M>60 S>60): Composed   116 cliques out of  32000 chunks
- 5h 18m 22s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  33000 chunks
- 5h 18m 25s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  34000 chunks
- 5h 18m 28s CLIQUES (O sentence LCS M>60 S>60): Composed   118 cliques out of  35000 chunks
- 5h 18m 31s CLIQUES (O sentence LCS M>60 S>60): Composed   118 cliques out of  36000 chunks
- 5h 18m 34s CLIQUES (O sentence LCS M>60 S>60): Composed   118 cliques out of  37000 chunks
- 5h 18m 37s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  38000 chunks
- 5h 18m 40s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  39000 chunks
- 5h 18m 43s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  40000 chunks
- 5h 18m 47s CLIQUES (O sentence LCS M>60 S>60): Composed   117 cliques out of  41000 chunks
- 5h 18m 50s CLIQUES (O sentence LCS M>60 S>60): Composed   115 cliques out of  42000 chunks
- 5h 18m 54s CLIQUES (O sentence LCS M>60 S>60): Composed   114 cliques out of  43000 chunks
- 5h 18m 57s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  44000 chunks
- 5h 19m 01s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  45000 chunks
- 5h 19m 05s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  46000 chunks
- 5h 19m 08s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  47000 chunks
- 5h 19m 12s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  48000 chunks
- 5h 19m 16s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  49000 chunks
- 5h 19m 20s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  50000 chunks
- 5h 19m 24s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  51000 chunks
- 5h 19m 29s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  52000 chunks
- 5h 19m 33s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  53000 chunks
- 5h 19m 37s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  54000 chunks
- 5h 19m 42s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  55000 chunks
- 5h 19m 46s CLIQUES (O sentence LCS M>60 S>60): Composed   114 cliques out of  56000 chunks
- 5h 19m 51s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  57000 chunks
- 5h 19m 56s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  58000 chunks
- 5h 20m 00s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  59000 chunks
- 5h 20m 05s CLIQUES (O sentence LCS M>60 S>60): Composed   113 cliques out of  60000 chunks
- 5h 20m 11s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  61000 chunks
- 5h 20m 16s CLIQUES (O sentence LCS M>60 S>60): Composed   112 cliques out of  62000 chunks
- 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): 62369 members in 112 cliques
- 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): Composed and saved   112 cliques out of  62369 chunks from 10279985 comparisons
- 5h 20m 20s PRINT (O sentence LCS M>60 S>60): sorting out cliques
- 5h 20m 21s PRINT (O sentence LCS M>60 S>60): formatting 112 cliques skipping 61 binary chapter diffs
- 5h 20m 22s PRINT (O sentence LCS M>60 S>60): formatted 112 cliques (3 files) skipping 61 binary chapter diffs
- 5h 20m 22s EXPERIMENT: Generating html report
- 5h 20m 22s EXPERIMENT:  35 messy results: deprecated
- 5h 20m 22s EXPERIMENT:  23 mixed quality: take care
- 5h 20m 22s EXPERIMENT:  75 no results available
- 5h 20m 22s EXPERIMENT:   9 unassessed quality: inspection needed
- 5h 20m 22s EXPERIMENT:  80 method deprecated
- 5h 20m 22s EXPERIMENT:  18 promising results: recommended
- 5h 20m 22s EXPERIMENT: Generated html report
- 5h 20m 22s EXPERIMENT: Generating html report(standalone)
- 5h 20m 22s EXPERIMENT:  35 messy results: deprecated
- 5h 20m 22s EXPERIMENT:  23 mixed quality: take care
- 5h 20m 22s EXPERIMENT:  75 no results available
- 5h 20m 22s EXPERIMENT:   9 unassessed quality: inspection needed
- 5h 20m 22s EXPERIMENT:  80 method deprecated
- 5h 20m 22s EXPERIMENT:  18 promising results: recommended
- 5h 20m 22s EXPERIMENT: Generated html report
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8. Overview of the similarities

Here are the plots of two similarity matrices

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  • with verses as chunks and SET as similarity method
    • -
  • with verses as chunks and LCS as similarity method
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Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic.

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In [28]:
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do_experiment(False, 'verse', 'SET', 60, False)
-distances = collections.Counter()
-for (x, d) in chunk_dist.items():
-    distances[int(round(d))] += 1
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-x = range(MATRIX_THRESHOLD, 101)
-fig = plt.figure(figsize=[15, 4])
-plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')
-plt.axis([MATRIX_THRESHOLD, 101, 0, 15])
-plt.xlabel('similarity as %')
-plt.ylabel('log # similarities')
-plt.xticks(x, x, rotation='vertical')
-plt.margins(0.2)
-plt.subplots_adjust(bottom=0.15);
-plt.title('distances');
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31m 00s CHUNKING (O verse): Loaded: 23213 chunks
-31m 00s CHUNKING (O verse): Made 23213 chunks
-31m 00s PREPARING (O verse SET)
-31m 01s PREPARING (O verse SET): Done 23213 chunks.
-31m 02s SIMILARITY (O verse SET M>50): Loaded:   269 M (269410078) comparisons with 24832 entries in matrix
-31m 02s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
-31m 02s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates
-31m 02s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix
-31m 04s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages
-31m 04s CLIQUES (O verse SET M>50 S>60): Loaded:  1439 cliques out of   3877 chunks from 16055 comparisons
-31m 04s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques
-31m 04s PRINT (O verse SET M>50 S>60): sorting out cliques
-31m 04s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs
-31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358
-31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing
-31m 06s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs
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In [29]:
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do_experiment(False, 'verse', 'LCS', 60, False)
-distances = collections.Counter()
-for (x, d) in chunk_dist.items():
-    distances[int(round(d))] += 1
-
-x = range(MATRIX_THRESHOLD, 101)
-fig = plt.figure(figsize=[15, 4])
-plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')
-plt.axis([MATRIX_THRESHOLD, 101, 0, 15])
-plt.xlabel('similarity as %')
-plt.ylabel('log # similarities')
-plt.xticks(x, x, rotation='vertical')
-plt.margins(0.2)
-plt.subplots_adjust(bottom=0.15);
-plt.title('distances');
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33m 46s CHUNKING (O verse): already chunked into 23213 chunks
-33m 46s PREPARING (O verse LCS)
-33m 47s PREPARING (O verse LCS): Done 23213 chunks.
-33m 47s SIMILARITY (O verse LCS M>60): Loaded:   269 M (269410078) comparisons with 113614 entries in matrix
-33m 47s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
-33m 47s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates
-33m 47s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix
-33m 47s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages
-33m 47s CLIQUES (O verse LCS M>60 S>60): Loaded:   380 cliques out of  18941 chunks from 113614 comparisons
-33m 47s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques
-33m 47s PRINT (O verse LCS M>60 S>60): sorting out cliques
-33m 47s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs
-33m 48s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs
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- - - - - - diff --git a/static/docs/tools/parallel/parallels_TF.ipynb b/static/docs/tools/parallel/parallels_TF.ipynb deleted file mode 100644 index b3c7ce59..00000000 --- a/static/docs/tools/parallel/parallels_TF.ipynb +++ /dev/null @@ -1,7147 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Parallel Passages in the MT\n", - "\n", - "# 0. Introduction\n", - "\n", - "## 0.1 Motivation\n", - "We want to make a list of **all** parallel passages in the Masoretic Text (MT) of the Hebrew Bible.\n", - "\n", - "Here is a quote that triggered Dirk to write this notebook:\n", - "\n", - "> Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible.\n", - "\n", - "Robert Rezetko and Ian Young.\n", - " Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach.\n", - " *Ancient Near East Monographs, Number9*. SBL Press Atlanta. 2014. \n", - " [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ)\n", - "\n", - "## 0.3 Open Source\n", - "This is an IPython notebook. \n", - "It contains a working program to carry out the computations needed to obtain the results reported here.\n", - "\n", - "You can download this notebook and run it on your computer, provided you have\n", - "[LAF-Fabric](http://laf-fabric.readthedocs.org/en/latest/texts/welcome.html) installed.\n", - "An easy way to do that is describe [here](laf-fabric.readthedocs.org/texts/getting-started.html).\n", - "\n", - "It is a pity that we cannot compare our results with the Accordance resource mentioned above, since that resource has not been published in an accessible manner. We also do not have the information how this resource has been constructed on the basis of the raw data. In contrast with that, we present our results in a completely reproducible manner. This notebook itself can serve as the method of replication, provided you have obtained the necessary resources. See [SHEBANQ sources](https://shebanq.ancient-data.org/sources), which are all Open Access.\n", - "\n", - "## 0.4 What are parallel passages?\n", - "The notion of *parallel passage* is not a simple, straightforward one.\n", - "There are parallels on the basis of lexical content in the passages on the one hand, \n", - "but on the other hand there are also correspondences in certain syntactical structures, \n", - "or even in similarities in text structure.\n", - "\n", - "In this notebook we do select a straightforward notion of parallel, based on lexical content only.\n", - "We investigate two measures of similarity, one that ignores word order completely, and one that takes word order into account.\n", - "\n", - "Two kinds of short-comings of this approach must be mentioned:\n", - "\n", - "1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels)\n", - "1. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history.\n", - "\n", - "For a more full treatment of parallel passages, see\n", - "\n", - "Wido Th. van Peursen and Eep Talstra.\n", - " Computer-Assisted Analysis of Parallel Texts in the Bible - \n", - " The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles.\n", - " Vetus Testamentum 57, pp. 45-72.\n", - " 2007, Brill, Leiden.\n", - " \n", - "Note that our method fails to identify any parallels with Chronica_II 32. Van Peursen and Talstra state about this chapter and 2 Kings 18: \n", - "\n", - "> These chapters differ so much, that it is sometimes impossible to establish which verses should be considered parallel.\n", - "\n", - "In this notebook we produce a set of *cliques*, a clique being a set of passages that are *quite* similar, based on lexical information.\n", - "\n", - "\n", - "## 0.5 Authors\n", - "This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer.\n", - "\n", - "[Dirk Roorda](mailto:dirk.roorda@dans.knaw.nl) while discussing ideas with \n", - "[Martijn Naaijer](mailto:m.naaijer@vu.nl). \n", - "\n", - "\n", - "## 0.6 Status\n", - "\n", - "**Last modified: 2016-03-03** Added experiments based on chapter chunks and lower similarities.\n", - "\n", - "165 experiments have been carried out, of which 18 with promising results.\n", - "All results can be easily inspected, just by clicking in your browser.\n", - "One of the experiments has been chosen as the basis for\n", - "[crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v)\n", - "annotations in SHEBANQ.\n", - "\n", - "# 1. Results\n", - "\n", - "Click in a green cell to see interesting results. The numbers in the cell indicate\n", - "\n", - "* the number of passages that have a variant elsewhere\n", - "* the number of *cliques* they form (cliques are sets of similar passages)\n", - "* the number of passages in the biggest clique\n", - "\n", - "Below the results is an account of the method that we used, followed by the actual code to produce these results." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
no results available
promising results: recommended
messy results: deprecated
mixed quality: take care
method deprecated
unassessed quality: inspection needed
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chunk typechunk sizesimilarity method1009590858075706560555045403530
fixed100SET\n", - " 2
\n", - " 1
\n", - " 2\n", - " 		\n", - " 4
\n", - " 2
\n", - " 2\n", - " 		\n", - " 18
\n", - " 9
\n", - " 2\n", - " 		\n", - " 39
\n", - " 19
\n", - " 3\n", - " 		\n", - " 64
\n", - " 30
\n", - " 6\n", - " 		\n", - " 87
\n", - " 40
\n", - " 9\n", - " 		\n", - " 113
\n", - " 52
\n", - " 9\n", - " 		\n", - " 156
\n", - " 71
\n", - " 9\n", - " 		\n", - " 214
\n", - " 97
\n", - " 10\n", - " 		\n", - " 308
\n", - " 138
\n", - " 10\n", - " 		\n", - " 469
\n", - " 188
\n", - " 14\n", - " 		    
fixed100LCS\n", - " 0
\n", - " 0
\n", - " 0\n", - " 		\n", - " 4
\n", - " 2
\n", - " 2\n", - " 		\n", - " 39
\n", - " 19
\n", - " 3\n", - " 		\n", - " 59
\n", - " 29
\n", - " 3\n", - " 		\n", - " 83
\n", - " 40
\n", - " 3\n", - " 		\n", - " 118
\n", - " 54
\n", - " 9\n", - " 		\n", - " 193
\n", - " 90
\n", - " 9\n", - " 		\n", - " 286
\n", - " 132
\n", - " 9\n", - " 		\n", - " 537
\n", - " 215
\n", - " 31\n", - " 		      
fixed50SET\n", - " 0
\n", - " 0
\n", - " 0\n", - " 		\n", - " 6
\n", - " 3
\n", - " 2\n", - " 		\n", - " 24
\n", - " 12
\n", - " 2\n", - " 		\n", - " 55
\n", - " 25
\n", - " 5\n", - " 		\n", - " 104
\n", - " 47
\n", - " 7\n", - " 		\n", - " 197
\n", - " 90
\n", - " 8\n", - " 		\n", - " 277
\n", - " 127
\n", - " 10\n", - " 		\n", - " 394
\n", - " 180
\n", - " 12\n", - " 		\n", - " 543
\n", - " 239
\n", - " 15\n", - " 		\n", - " 755
\n", - " 322
\n", - " 20\n", - " 		\n", - " 1183
\n", - " 460
\n", - " 48\n", - " 		    
fixed50LCS\n", - " 0
\n", - " 0
\n", - " 0\n", - " 		\n", - " 12
\n", - " 6
\n", - " 2\n", - " 		\n", - " 43
\n", - " 20
\n", - " 5\n", - " 		\n", - " 125
\n", - " 56
\n", - " 11\n", - " 		\n", - " 204
\n", - " 93
\n", - " 12\n", - " 		\n", - " 299
\n", - " 134
\n", - " 19\n", - " 		\n", - " 470
\n", - " 209
\n", - " 20\n", - " 		\n", - " 765
\n", - " 312
\n", - " 29\n", - " 		\n", - " 1867
\n", - " 553
\n", - " 106\n", - " 		      
fixed20SET\n", - " 36
\n", - " 18
\n", - " 2\n", - " 		\n", - " 36
\n", - " 18
\n", - " 2\n", - " 		\n", - " 126
\n", - " 58
\n", - " 6\n", - " 		\n", - " 199
\n", - " 84
\n", - " 12\n", - " 		\n", - " 332
\n", - " 146
\n", - " 12\n", - " 		\n", - " 528
\n", - " 227
\n", - " 12\n", - " 		\n", - " 760
\n", - " 326
\n", - " 12\n", - " 		\n", - " 1096
\n", - " 470
\n", - " 13\n", - " 		\n", - " 1837
\n", - " 739
\n", - " 21\n", - " 		\n", - " 2826
\n", - " 997
\n", - " 175\n", - " 		\n", - " 4933
\n", - " 1212
\n", - " 1638\n", - " 		    
fixed20LCS\n", - " 12
\n", - " 6
\n", - " 2\n", - " 		\n", - " 62
\n", - " 29
\n", - " 4\n", - " 		\n", - " 181
\n", - " 76
\n", - " 12\n", - " 		\n", - " 339
\n", - " 149
\n", - " 12\n", - " 		\n", - " 681
\n", - " 300
\n", - " 12\n", - " 		\n", - " 1137
\n", - " 470
\n", - " 26\n", - " 		\n", - " 2224
\n", - " 844
\n", - " 65\n", - " 		\n", - " 5985
\n", - " 1253
\n", - " 2718\n", - " 		\n", - " 17654
\n", - " 163
\n", - " 17307\n", - " 		      
fixed10SET\n", - " 462
\n", - " 220
\n", - " 5\n", - " 		\n", - " 462
\n", - " 220
\n", - " 5\n", - " 		\n", - " 494
\n", - " 231
\n", - " 7\n", - " 		\n", - " 1109
\n", - " 489
\n", - " 20\n", - " 		\n", - " 1540
\n", - " 631
\n", - " 39\n", - " 		\n", - " 2825
\n", - " 1126
\n", - " 75\n", - " 		\n", - " 4079
\n", - " 1506
\n", - " 144\n", - " 		\n", - " 5792
\n", - " 1855
\n", - " 669\n", - " 		\n", - " 10165
\n", - " 2189
\n", - " 4304\n", - " 		\n", - " 13984
\n", - " 2008
\n", - " 8877\n", - " 		\n", - " 22932
\n", - " 1442
\n", - " 19576\n", - " 		    
fixed10LCS\n", - " 277
\n", - " 135
\n", - " 5\n", - " 		\n", - " 408
\n", - " 199
\n", - " 5\n", - " 		\n", - " 937
\n", - " 423
\n", - " 11\n", - " 		\n", - " 1980
\n", - " 831
\n", - " 73\n", - " 		\n", - " 3894
\n", - " 1440
\n", - " 161\n", - " 		\n", - " 8599
\n", - " 2328
\n", - " 2059\n", - " 		\n", - " 20425
\n", - " 1937
\n", - " 15671\n", - " 		\n", - " 37696
\n", - " 218
\n", - " 37229\n", - " 		\n", - " 42450
\n", - " 4
\n", - " 42444\n", - " 		      
objectchapterSET\n", - " 0
\n", - " 0
\n", - " 0\n", - " 		\n", - " 2
\n", - " 1
\n", - " 2\n", - " 		\n", - " 2
\n", - " 1
\n", - " 2\n", - " 		\n", - " 2
\n", - " 1
\n", - " 2\n", - " 		\n", - " 4
\n", - " 2
\n", - " 2\n", - " 		\n", - " 14
\n", - " 7
\n", - " 2\n", - " 		\n", - " 20
\n", - " 10
\n", - " 2\n", - " 		\n", - " 24
\n", - " 12
\n", - " 2\n", - " 		\n", - " 34
\n", - " 17
\n", - " 2\n", - " 		\n", - " 44
\n", - " 22
\n", - " 2\n", - " 		\n", - " 58
\n", - " 29
\n", - " 2\n", - " 		\n", - " 80
\n", - " 39
\n", - " 3\n", - " 		\n", - " 142
\n", - " 62
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\n", - " 61\n", - " 		\n", - " 571
\n", - " 28
\n", - " 496\n", - "
objectchapterLCS\n", - " 0
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\n", - " 26
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\n", - " 49
\n", - " 4\n", - " 		     
objectverseSET\n", - " 993
\n", - " 388
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\n", - " 526
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\n", - " 651
\n", - " 70\n", - " 		\n", - " 1958
\n", - " 800
\n", - " 154\n", - " 		\n", - " 2361
\n", - " 962
\n", - " 156\n", - " 		\n", - " 2720
\n", - " 1094
\n", - " 166\n", - " 		\n", - " 3139
\n", - " 1235
\n", - " 172\n", - " 		\n", - " 3877
\n", - " 1439
\n", - " 202\n", - " 		\n", - " 4735
\n", - " 1638
\n", - " 388\n", - " 		\n", - " 6711
\n", - " 1851
\n", - " 1476\n", - " 		    
objectverseLCS\n", - " 793
\n", - " 295
\n", - " 69\n", - " 		\n", - " 1235
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\n", - " 724
\n", - " 74\n", - " 		\n", - " 2296
\n", - " 938
\n", - " 160\n", - " 		\n", - " 2925
\n", - " 1141
\n", - " 174\n", - " 		\n", - " 3682
\n", - " 1340
\n", - " 190\n", - " 		\n", - " 4958
\n", - " 1644
\n", - " 257\n", - " 		\n", - " 9050
\n", - " 1821
\n", - " 4225\n", - " 		\n", - " 18945
\n", - " 380
\n", - " 18077\n", - " 		      
objecthalf_verseSET\n", - " 4327
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\n", - " 1863
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\n", - " 2072
\n", - " 70\n", - " 		\n", - " 6422
\n", - " 2474
\n", - " 195\n", - " 		\n", - " 8265
\n", - " 2888
\n", - " 536\n", - " 		\n", - " 9388
\n", - " 3193
\n", - " 681\n", - " 		\n", - " 12162
\n", - " 3342
\n", - " 2842\n", - " 		\n", - " 16476
\n", - " 3424
\n", - " 6915\n", - " 		\n", - " 19519
\n", - " 3184
\n", - " 10993\n", - " 		\n", - " 28988
\n", - " 2031
\n", - " 24006\n", - " 		    
objecthalf_verseLCS\n", - " 3799
\n", - " 1514
\n", - " 69\n", - " 		\n", - " 4342
\n", - " 1771
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\n", - " 2336
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\n", - " 665
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\n", - " 89
\n", - " 43822\n", - " 		      
objectsentenceSET\n", - " 19031
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\n", - " 1055\n", - " 		\n", - " 19039
\n", - " 4328
\n", - " 1055\n", - " 		\n", - " 19214
\n", - " 4406
\n", - " 1055\n", - " 		\n", - " 19777
\n", - " 4608
\n", - " 1055\n", - " 		\n", - " 22082
\n", - " 5073
\n", - " 1055\n", - " 		\n", - " 25751
\n", - " 5000
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\n", - " 3746
\n", - " 24132\n", - " 		\n", - " 41825
\n", - " 3497
\n", - " 28097\n", - " 		\n", - " 53097
\n", - " 1172
\n", - " 50162\n", - " 		    
objectsentenceLCS\n", - " 17533
\n", - " 3978
\n", - " 1053\n", - " 		\n", - " 18091
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\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# run this cell after all other cells\n", - "HTML(other_exps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Experiments\n", - "\n", - "We have conducted 165 experiments, all corresponding to a specific choice of parameters.\n", - "Every experiment is an attempt to identify variants and collect them in *cliques*.\n", - "\n", - "The table gives an overview of the experiments conducted.\n", - "\n", - "Every *row* corresponds to a particular way of chunking and a method of measuring the similarity.\n", - "\n", - "There are *columns* for each similarity *threshold* that we have tried.\n", - "The idea is that chunks are similar if their similarity is above the threshold.\n", - "\n", - "The outcomes of one experiment have been added to SHEBANQ as the note set\n", - "[crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v).\n", - "The experiment chosen for this is currently\n", - "\n", - "* *chunking*: **object verse**\n", - "* *similarity method*: **SET**\n", - "* *similarity threshold*: **65**\n", - "\n", - "\n", - "## 2.1 Assessing the outcomes\n", - "\n", - "Not all experiments lead to useful results.\n", - "We have indicated the value of a result by a color coding, based on objective characteristics,\n", - "such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking.\n", - "These numbers are shown in the cells.\n", - "\n", - "### 2.1.1 Assessment criteria\n", - "\n", - "If the method is based on *fixed* chunks, we deprecated the method and the results.\n", - "Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case.\n", - "\n", - "Otherwise, we consider the *ll*, the length of the longest clique, and *nc*, the number of cliques.\n", - "We set three quality parameters:\n", - "* `REC_CLIQUE_RATIO` = 5 : recommended clique ratio\n", - "* `DUB_CLIQUE_RATIO` = 15 : dubious clique ratio\n", - "* `DEP_CLIQUE_RATIO` = 25 : deprecated clique ratio\n", - "\n", - "where the *clique ratio* is $100 (ll/nc)$, \n", - "i.e. the length of the longest clique divided by the number of cliques as percentage.\n", - "\n", - "An experiment is *recommended* if its clique ratio is between the recommended and dubious clique ratios.\n", - "\n", - "It is *dubious* if its clique ratio is between the dubious and deprecated clique ratios.\n", - "\n", - "It is *deprecated* if its clique ratio is above the deprecated clique ratio.\n", - "\n", - "# 2.2 Inspecting results\n", - "If you click on the hyperlink in the cell, you are taken to a page that gives you\n", - "all the details of the results:\n", - "\n", - "1. A link to a file with all *cliques* (which are the sets of similar passages)\n", - "1. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for\n", - " experiments with outcomes that are labeled as *promising* or *unassessed quality* or *mixed results*.\n", - "\n", - "To get into the variants quickly, inspect the list (2) and click through \n", - "to see the actual variant material in chapter context.\n", - "\n", - "Not all variants occur here, so continue with (1) to see the remaining cliques.\n", - "\n", - "Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters.\n", - "In that case you have to consult the big list (1)\n", - "\n", - "All these results can be downloaded from the\n", - "[SHEBANQ github repo](https://github.com/ETCBC/shebanq/tree/master/static/docs/tools/parallel/files)\n", - "After downloading the whole directory, open ``experiments.html`` in your browser." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Method\n", - "\n", - "Here we discuss the method we used to arrive at a list of parallel passages \n", - "in the Masoretic Text (MT) of the Hebrew Bible.\n", - "\n", - "## 3.1 Similarity\n", - "\n", - "We have to find passages in the MT that are *similar*.\n", - "Therefore we *chunk* the text in some way, and then compute the similarities between pairs of chunks.\n", - "\n", - "There are many ways to define and compute similarity between texts.\n", - "Here, we have tried two methods ``SET`` and ``LCS``.\n", - "Both methods define similarity as the fraction of common material with respect to the total material.\n", - "\n", - "### 3.1.1 SET\n", - "\n", - "The ``SET`` method reduces textual chunks to *sets* of *lexemes*.\n", - "This method abstracts from the order and number of occurrences of words in chunks.\n", - "\n", - "We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets):\n", - "\n", - "$$ s_{\\rm set}(C_1, C_2) = {\\vert C_1 \\cap C_2\\vert \\over \\vert C_1 \\cup C_2 \\vert} $$\n", - "\n", - "where $\\vert X \\vert$ is the number of elements in set $X$.\n", - "\n", - "### 3.1.2 LCS\n", - "\n", - "The ``LCS`` method is less reductive: chunks are *strings* of *lexemes*, \n", - "so the order and number of occurrences of words is retained.\n", - "\n", - "We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings):\n", - "\n", - "$$ s_{\\rm lcs}(C_1, C_2) = {\\vert {\\rm LCS}(C_1,C_2)\\vert \\over \\vert C_1\\vert + \\vert C_2 \\vert - \n", - "\\vert {\\rm LCS}(C_1,C_2)\\vert} $$\n", - "\n", - "where ${\\rm LCS}(C_1, C_2)$ is the\n", - "[longest common subsequence](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem)\n", - "of $C_1$ and $C_2$ and\n", - "$\\vert X\\vert$ is the length of sequence $X$.\n", - "\n", - "It remains to be seen whether we need the extra sophistication of ``LCS``.\n", - "The risk is that ``LCS`` could fail to spot related passages when there is a large amount of transposition going on.\n", - "The results should have the last word. \n", - "\n", - "We need to compute the LCS efficiently, and for this we used the python ``Levenshtein`` module:\n", - "\n", - "``pip install python-Levenshtein``\n", - "\n", - "whose documentation is\n", - "[here](http://www.coli.uni-saarland.de/courses/LT1/2011/slides/Python-Levenshtein.html).\n", - "\n", - "## 3.2 Performance\n", - "\n", - "Similarity computation is the part where the heavy lifting occurs.\n", - "It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000),\n", - "you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), \n", - "you need to do ~ 2,000,000,000 ones!\n", - "The computation of a single similarity should be *really* fast.\n", - "\n", - "Besides that, we use two ways to economize:\n", - "\n", - "* after having computed a matrix for a specific set of parameter values, we save the matrix to disk;\n", - " new runs can load the matrix from disk in a matter of seconds;\n", - "* we do not store low similarity values in the matrix, low being < ``MATRIX_THRESHOLD``.\n", - "\n", - "The ``LCS`` method is more complicated.\n", - "We have tried the ``ratio`` method from the ``difflib`` package that is present in the standard python distribution.\n", - "This is unbearably slow for our purposes.\n", - "The ``ratio`` method in the ``Levenshtein`` package is much quicker.\n", - "\n", - "See the table for an indication of the amount of work to create the similarity matrix\n", - "and the performance per similarity method.\n", - "\n", - "The *matrix threshold* is the lower bound of similarities that are stored in the matrix.\n", - "If a pair of chunks has a lower similarity, no entry will be made in the matrix.\n", - "\n", - "The computing has been done on a Macbook Air (11\", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM).\n", - "\n", - "|chunk type |chunk size|similarity method|matrix threshold|# of comparisons|size of matrix (KB)|computing time (min)|\n", - "|:----------|---------:|----------------:|---------------:|---------------:|------------------:|-------------------:|\n", - "|fixed |100 |LCS |60 | 9,003,646| 7| ? |\n", - "|fixed |100 |SET |50 | 9,003,646| 7| ? |\n", - "|fixed |50 |LCS |60 | 36,197,286| 37| ? |\n", - "|fixed |50 |SET |50 | 36,197,286| 18| ? |\n", - "|fixed |20 |LCS |60 | 227,068,705| 2,400| ? |\n", - "|fixed |20 |SET |50 | 227,068,705| 113| ? |\n", - "|fixed |10 |LCS |60 | 909,020,841| 59,000| ? |\n", - "|fixed |10 |SET |50 | 909,020,841| 1,800| ? |\n", - "|object |verse |LCS |60 | 269,410,078| 2,300| 31|\n", - "|object |verse |SET |50 | 269,410,078| 509| 14|\n", - "|object |half_verse|LCS |60 | 1,016,396,241| 40,000| 50|\n", - "|object |half_verse|SET |50 | 1,016,396,241| 3,600| 41|\n", - "|object |sentence |LCS |60 | 2,055,975,750| 212,000| 68|\n", - "|object |sentence |SET |50 | 2,055,975,750| 82,000| 63|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Workflow\n", - "\n", - "## 4.1 Chunking\n", - "\n", - "There are several ways to chunk the text:\n", - "\n", - "* fixed chunks of approximately ``CHUNK_SIZE`` words\n", - "* by object, such as verse, sentence and even chapter\n", - "\n", - "After chunking, we prepare the chunks for similarity measuring.\n", - "\n", - "### 4.1.1 Fixed chunking\n", - "Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results.\n", - "The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries.\n", - "It is to be expected that fixed chunks in variant passages will be mutually *out of phase*, \n", - "meaning that the chunks involved in these passages are not aligned with each other.\n", - "So they will have a lower similarity than they could have if they were aligned.\n", - "This is a source of artificial noise in the outcome and/or missed cases.\n", - "\n", - "If the chunking respects \"natural\" boundaries in the text, there is far less misalignment.\n", - "\n", - "### 4.1.2 Object chunking\n", - "We can also chunk by object, such as verse, half_verse or sentence.\n", - "\n", - "Chunking by *verse* is very much like chunking in fixed chunks of size 20, performance-wise.\n", - "\n", - "Chunking by *half_verse* is comparable to fixed chunks of size 10.\n", - "\n", - "Chunking by *sentence* will generate an enormous amount of\n", - "false positives, because there are very many very short sentences (down to 1-word) in the text.\n", - "Besides that, the performance overhead is huge.\n", - "\n", - "The *half_verses* seem to be a very interesting candidate. \n", - "They are smaller than verses, but there are less *degenerate cases* compared to with sentences. \n", - "From the table above it can be read that half verses require only half as many similarity computations as sentences.\n", - "\n", - "\n", - "## 4.2 Preparing\n", - "\n", - "We prepare the chunks for the application of the chosen method of similarity computation (``SET`` or ``LCS``).\n", - "\n", - "In both cases we reduce the text to a sequence of transliterated consonantal *lexemes* without disambiguation.\n", - "In fact, we go one step further: we remove the consonants (alef, wav, yod) that are often silent.\n", - "\n", - "For ``SET``, we represent each chunk as the set of its reduced lexemes.\n", - "\n", - "For ``LCS``, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces.\n", - "\n", - "## 4.3 Cliques\n", - "\n", - "After having computed a sufficient part of the similarity matrix, we set a value for ``SIMILARITY_THRESHOLD``.\n", - "All pairs of chunks having at least that similarity are deemed *interesting*.\n", - "\n", - "We organize the members of such pairs in *cliques*, groups of chunks of which each member is \n", - "similar (*similarity* > ``SIMILARITY_THRESHOLD``) to at least one other member.\n", - "\n", - "We start with no cliques and walk through the pairs whose similarity is above ``SIMILARITY_THRESHOLD``, \n", - "and try to put each member into a clique.\n", - "\n", - "If there is not yet a clique, we make the member in question into a new singleton clique.\n", - "\n", - "If there are cliques, we find the cliques that have a member similar to the member in question.\n", - "If we find several, we merge them all into one clique.\n", - "\n", - "If there is no such clique, we put the member in a new singleton clique.\n", - "\n", - "NB: Cliques may *drift*, meaning that they contain members that are completely different from each other.\n", - "They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other.\n", - "\n", - "### 4.3.1 Organizing the cliques\n", - "In order to accomodate cases where there are many corresponding verses in corresponding chapters, we produce\n", - "chapter-by-chapter diffs in the following way.\n", - "\n", - "We make a list of all chapters that are involved in cliques.\n", - "This yields a list of chapter cliques.\n", - "For all *binary* chapters cliques, we generate a colorful diff rendering (as html) for the complete two chapters.\n", - "\n", - "We only do this for *promising* experiments.\n", - "\n", - "### 4.3.2 Evaluating clique sets\n", - "\n", - "Not all clique sets are equally worth while.\n", - "For example, if we set the ``SIMILARITY_THRESHOLD`` too low, we might get one gigantic clique, especially\n", - "in combination with a fine-grained chunking. In other words: we suffer from *clique drifting*.\n", - "\n", - "We detect clique drifting by looking at the size of the largest clique.\n", - "If that is large compared to the total number of chunks, we deem the results unsatisfactory.\n", - "\n", - "On the other hand, when the ``SIMILARITY_THRESHOLD`` is too high, you might miss a lot of correspondences,\n", - "especially when chunks are large, or when we have fixed-size chunks that are out of phase.\n", - "\n", - "We deem the results of experiments based on a partioning into fixed length chunks as unsatisfactory, although it\n", - "might be interesting to inspect what exactly the damage is.\n", - "\n", - "At the moment, we have not yet analysed the relative merits of the similarity methods ``SET`` and ``LCS``." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Implementation\n", - "\n", - "\n", - "The rest is code. From here we fire up the engines and start computing." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import sys, os, re, collections, pickle, math, difflib, glob\n", - "\n", - "from IPython.display import HTML, display\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "PICKLE_PROTOCOL = 3\n", - "\n", - "from difflib import SequenceMatcher\n", - "from Levenshtein import ratio\n", - "\n", - "from tf.fabric import Fabric" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.1 Loading the feature data\n", - "\n", - "We load the features we need from the ETCBC database." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is Text-Fabric 1.2.7\n", - "Api reference : https://github.com/ETCBC/text-fabric/wiki/Api\n", - "Tutorial : https://github.com/ETCBC/text-fabric/blob/master/docs/tutorial.ipynb\n", - "Data sources : https://github.com/ETCBC/text-fabric-data\n", - "Data docs : https://etcbc.github.io/text-fabric-data/features/hebrew/etcbc4c/0_overview.html\n", - "Shebanq docs : https://shebanq.ancient-data.org/text\n", - "Slack team : https://shebanq.slack.com/signup\n", - "Questions? Ask shebanq@ancient-data.org for an invite to Slack\n", - "107 features found and 0 ignored\n" - ] - } - ], - "source": [ - "source = 'etcbc'\n", - "version = '4c'\n", - "ETCBC = 'hebrew/{}{}'.format(source, version)\n", - "TF = Fabric( modules=ETCBC )" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.00s loading features ...\n", - " | 0.05s B otype from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.00s M otext from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.01s B book from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.01s B chapter from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.01s B verse from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.24s B g_word_utf8 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.10s B trailer_utf8 from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.16s B lex from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.02s B label from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " | 0.28s B number from /Users/dirk/github/text-fabric-data/hebrew/etcbc4c\n", - " 5.83s All features loaded/computed - for details use loadLog()\n" - ] - } - ], - "source": [ - "api = TF.load('''\n", - " otype\n", - " lex g_word_utf8 trailer_utf8\n", - " book chapter verse label number\n", - "''')\n", - "api.makeAvailableIn(globals())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.2 Configuration\n", - "\n", - "Here are the parameters on which the results crucially depend.\n", - "\n", - "There are also parameters that control the reporting of the results, such as file locations." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# chunking\n", - "CHUNK_LABELS = {True: 'fixed', False: 'object'}\n", - "CHUNK_LBS = {True: 'F', False: 'O'}\n", - "CHUNK_SIZES = (100, 50, 20, 10)\n", - "CHUNK_OBJECTS = ('chapter', 'verse','half_verse','sentence')\n", - "\n", - "# preparing\n", - "EXCLUDED_CONS = '[>WJ=/\\[]' # weed out weak consonants\n", - "EXCLUDED_PAT = re.compile(EXCLUDED_CONS)\n", - "\n", - "# similarity\n", - "MATRIX_THRESHOLD = 50\n", - "SIM_METHODS = ('SET', 'LCS')\n", - "SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30)\n", - "\n", - "# printing\n", - "DEP_CLIQUE_RATIO = 25\n", - "DUB_CLIQUE_RATIO = 15\n", - "REC_CLIQUE_RATIO = 5\n", - "LARGE_CLIQUE_SIZE = 50\n", - "CLIQUES_PER_FILE = 50\n", - "\n", - "# assessing results\n", - "VALUE_LABELS = dict(\n", - " mis='no results available',\n", - " rec='promising results: recommended',\n", - " dep='messy results: deprecated',\n", - " dub='mixed quality: take care',\n", - " out='method deprecated',\n", - " nor='unassessed quality: inspection needed',\n", - " lr='this experiment is the last one run',\n", - ")\n", - "\n", - "# crossrefs for SHEBANQ\n", - "SHEBANQ_MATRIX = (False, 'verse', 'SET')\n", - "SHEBANQ_SIMILARITY = 65\n", - "SHEBANQ_TOOL = 'parallel'\n", - "CROSSREF_STATUS = '!'\n", - "CROSSREF_KEYWORD = 'crossref'\n", - "\n", - "# progress indication\n", - "VERBOSE = False\n", - "MEGA = 1000000\n", - "KILO = 1000\n", - "SIMILARITY_PROGRESS = 5 * MEGA\n", - "CLIQUES_PROGRESS = 1 * KILO\n", - "\n", - "# locations and hyperlinks\n", - "LOCAL_BASE_COMP = '/Users/dirk/tf/text-fabric-output/{}{}/parallels'.format(source, version)\n", - "LOCAL_BASE_OUTP = 'files'\n", - "EXPERIMENT_DIR = 'experiments'\n", - "EXPERIMENT_FILE = 'experiments'\n", - "EXPERIMENT_PATH = '{}/{}.txt'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)\n", - "EXPERIMENT_HTML = '{}/{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)\n", - "NOTES_FILE = 'crossref'\n", - "NOTES_PATH = '{}/{}.csv'.format(LOCAL_BASE_OUTP, NOTES_FILE)\n", - "STORED_CLIQUE_DIR = 'stored/cliques'\n", - "STORED_MATRIX_DIR = 'stored/matrices'\n", - "STORED_CHUNK_DIR = 'stored/chunks'\n", - "CHAPTER_DIR = 'chapters'\n", - "CROSSREF_DB_FILE = 'crossrefdb.csv'\n", - "CROSSREF_DB_PATH = '{}/{}'.format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.3 Experiment settings\n", - "\n", - "For each experiment we have to adapt the configuration settings to the parameters that define the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def reset_params():\n", - " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", - " global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD\n", - " global meta\n", - " meta = collections.OrderedDict()\n", - " \n", - " # chunking\n", - " CHUNK_FIXED = None # kind of chunking: fixed size or by object\n", - " CHUNK_SIZE = None # only relevant for CHUNK_FIXED = True\n", - " CHUNK_OBJECT = None # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell\n", - " CHUNK_LB = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ\n", - " CHUNK_DESC = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ\n", - " # similarity\n", - " MATRIX_THRESHOLD = None # minimal similarity used to fill the matrix of similarities\n", - " SIMILARITY_METHOD = None # see SIM_METHODS in next cell\n", - " SIMILARITY_THRESHOLD = None # minimal similarity used to put elements together in cliques\n", - " meta = collections.OrderedDict()\n", - "\n", - "def set_matrix_threshold(sim_m=None, chunk_o=None):\n", - " global MATRIX_THRESHOLD\n", - " the_sim_m = SIMILARITY_METHOD if sim_m == None else sim_m\n", - " the_chunk_o = CHUNK_OBJECT if chunk_o == None else chunk_o\n", - " MATRIX_THRESHOLD = 50 if the_sim_m == 'SET' else 60\n", - " if the_sim_m == 'SET':\n", - " if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 30\n", - " else: MATRIX_THRESHOLD = 50\n", - " else:\n", - " if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 55\n", - " else: MATRIX_THRESHOLD = 60\n", - "\n", - "def do_params_chunk(chunk_f, chunk_i):\n", - " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", - " do_chunk = False\n", - " if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):\n", - " do_chunk = True\n", - " CHUNK_FIXED = chunk_f\n", - " if chunk_f: CHUNK_SIZE = chunk_i\n", - " else: CHUNK_OBJECT = chunk_i\n", - "\n", - " CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]\n", - " CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT\n", - "\n", - " for p in (\n", - " '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),\n", - " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),\n", - " ):\n", - " if not os.path.exists(p): os.makedirs(p)\n", - "\n", - " return do_chunk\n", - "\n", - "def do_params(chunk_f, chunk_i, sim_m, sim_thr):\n", - " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", - " global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD\n", - " global meta\n", - " do_chunk = False\n", - " do_prep = False\n", - " do_sim = False\n", - " do_clique = False\n", - " \n", - " meta = collections.OrderedDict()\n", - " if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):\n", - " do_chunk = True\n", - " do_prep = True\n", - " do_sim = True\n", - " do_clique = True\n", - " CHUNK_FIXED = chunk_f\n", - " if chunk_f: CHUNK_SIZE = chunk_i\n", - " else: CHUNK_OBJECT = chunk_i\n", - " if sim_m != SIMILARITY_METHOD:\n", - " do_prep = True\n", - " do_sim = True\n", - " do_clique = True\n", - " SIMILARITY_METHOD = sim_m\n", - " if sim_thr != SIMILARITY_THRESHOLD:\n", - " do_clique = True\n", - " SIMILARITY_THRESHOLD = sim_thr\n", - " set_matrix_threshold()\n", - " if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD : return (False, False, False, False, True)\n", - "\n", - " CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]\n", - " CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT\n", - "\n", - " meta['CHUNK TYPE'] = 'FIXED {}'.format(CHUNK_SIZE) if CHUNK_FIXED else 'OBJECT {}'.format(CHUNK_OBJECT)\n", - " meta['MATRIX THRESHOLD'] = MATRIX_THRESHOLD\n", - " meta['SIMILARITY METHOD'] = SIMILARITY_METHOD\n", - " meta['SIMILARITY THRESHOLD'] = SIMILARITY_THRESHOLD\n", - " \n", - " \n", - " for p in (\n", - " '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),\n", - " '{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR),\n", - " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR),\n", - " '{}/{}'.format(LOCAL_BASE_COMP, STORED_MATRIX_DIR),\n", - " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),\n", - " ):\n", - " if not os.path.exists(p): os.makedirs(p)\n", - "\n", - " return (do_chunk, do_prep, do_sim, do_clique, False)\n", - "\n", - "reset_params()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.4 Chunking\n", - "\n", - "We divide the text into chunks to be compared. The result is ``chunks``,\n", - "which is a list of lists.\n", - "Every chunk is a list of word nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def chunking(do_chunk):\n", - " global chunks, book_rank\n", - " if not do_chunk:\n", - " info('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", - " meta['# CHUNKS'] = len(chunks)\n", - " return\n", - "\n", - " chunk_path = '{}/{}/chunk_{}_{}'.format(\n", - " LOCAL_BASE_COMP, STORED_CHUNK_DIR,\n", - " CHUNK_LB, CHUNK_DESC,\n", - " )\n", - "\n", - " if os.path.exists(chunk_path):\n", - " with open(chunk_path, 'rb') as f: chunks = pickle.load(f)\n", - " info('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(\n", - " CHUNK_LB, CHUNK_DESC,\n", - " len(chunks),\n", - " ))\n", - " else:\n", - " info('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))\n", - " chunks = []\n", - " book_rank = {}\n", - " for b in F.otype.s('book'):\n", - " book_name = F.book.v(b)\n", - " book_rank[book_name] = b\n", - " words = L.d(b, otype='word')\n", - " nwords = len(words)\n", - " if CHUNK_FIXED:\n", - " nchunks = nwords // CHUNK_SIZE\n", - " if nchunks == 0: \n", - " nchunks = 1\n", - " common_incr = nwords\n", - " special_incr = 0\n", - " else: \n", - " rem = nwords % CHUNK_SIZE\n", - " common_incr = rem // nchunks\n", - " special_incr = rem % nchunks\n", - " word_in_chunk = -1\n", - " cur_chunk = -1\n", - " these_chunks = []\n", - "\n", - " for w in words:\n", - " word_in_chunk += 1\n", - " if word_in_chunk == 0 or (word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0)):\n", - " word_in_chunk = 0\n", - " these_chunks.append([])\n", - " cur_chunk += 1\n", - " these_chunks[-1].append(w)\n", - " else:\n", - " these_chunks = [L.d(c, otype='word') for c in L.d(b, otype=CHUNK_OBJECT)]\n", - "\n", - " chunks.extend(these_chunks)\n", - "\n", - " chunkvolume = sum(len(c) for c in these_chunks)\n", - " if VERBOSE:\n", - " info('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(\n", - " CHUNK_LB, CHUNK_DESC,\n", - " book_name, nwords, len(these_chunks), \n", - " min(len(c) for c in these_chunks), \n", - " max(len(c) for c in these_chunks),\n", - " 'OK' if chunkvolume == nwords else 'ERROR',\n", - " ))\n", - " with open(chunk_path, 'wb') as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL)\n", - " info('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", - " meta['# CHUNKS'] = len(chunks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.5 Preparing\n", - "\n", - "In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing.\n", - "\n", - "### 5.5.1 Preparing for SET comparison\n", - "\n", - "We reduce words to their lexemes (dictionary entries) and from them we also remove the alef, waw, and yods.\n", - "The lexeme feature also contains characters (`/ [ =`) to disambiguate homonyms. We also remove these.\n", - "If we end up with something empty, we skip it.\n", - "Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content.\n", - "\n", - "### 5.5.2 Preparing for LCS comparison\n", - "\n", - "Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def preparing(do_prepare):\n", - " global chunk_data\n", - " if not do_prepare:\n", - " info('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", - " return\n", - " info('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", - " chunk_data = []\n", - " if SIMILARITY_METHOD == 'SET':\n", - " for c in chunks:\n", - " words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)\n", - " clean_words = (w for w in words if w != '')\n", - " this_data = frozenset(clean_words)\n", - " chunk_data.append(this_data)\n", - " else:\n", - " for c in chunks:\n", - " words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)\n", - " clean_words = (w for w in words if w != '')\n", - " this_data = ' '.join(clean_words)\n", - " chunk_data.append(this_data)\n", - " info('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.6 Similarity computation\n", - "\n", - "Here we implement our two ways of similarity computation.\n", - "Both need a massive amount of work, especially for experiments with many small chunks.\n", - "The similarities are stored in a ``matrix``, a data structure that stores a similarity number for each pair of chunk indices.\n", - "Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold.\n", - "We store matrices for re-use.\n", - "\n", - "### 5.6.1 SET similarity\n", - "The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union.\n", - "Intuitively, we compute the proportion of what two chunks have in common against their total material.\n", - "\n", - "In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0.\n", - "\n", - "### 5.6.2 LCS similarity\n", - "The core is the method `ratio()`, taken from the Levenshtein module. \n", - "Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def similarity_post():\n", - " nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100})\n", - " cmin = min(chunk_dist.values()) if len(chunk_dist) else '!empty set!'\n", - " cmax = max(chunk_dist.values()) if len(chunk_dist) else '!empty set!'\n", - " meta['LOWEST AVAILABLE SIMILARITY'] = cmin\n", - " meta['HIGHEST AVAILABLE SIMILARITY'] = cmax\n", - " meta['# EQUAL COMPARISONS'] = nequals\n", - " info('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " cmin, cmax, nequals,\n", - " ))\n", - " \n", - "def similarity(do_sim):\n", - " global chunk_dist\n", - " total_chunks = len(chunks) \n", - " total_distances = total_chunks * (total_chunks - 1) // 2\n", - " meta['# SIMILARITY COMPARISONS'] = total_distances\n", - " \n", - " SIMILARITY_PROGRESS = total_distances // 100\n", - " if SIMILARITY_PROGRESS >= MEGA:\n", - " sim_unit = MEGA\n", - " sim_lb = 'M'\n", - " else:\n", - " sim_unit = KILO\n", - " sim_lb = 'K'\n", - " \n", - " if not do_sim:\n", - " info('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", - " ))\n", - " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", - " similarity_post()\n", - " return\n", - "\n", - " matrix_path = '{}/{}/matrix_{}_{}_{}_{}'.format(\n", - " LOCAL_BASE_COMP, STORED_MATRIX_DIR,\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " )\n", - "\n", - " if os.path.exists(matrix_path):\n", - " with open(matrix_path, 'rb') as f: chunk_dist = pickle.load(f)\n", - " info('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", - " ))\n", - " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", - " similarity_post()\n", - " return\n", - "\n", - " info('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " total_distances // sim_unit, sim_lb, total_distances\n", - " ))\n", - "\n", - " chunk_dist = {}\n", - " wc = 0\n", - " wt = 0\n", - " if SIMILARITY_METHOD == 'SET':\n", - " # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union\n", - " for i in range(total_chunks):\n", - " c_i = chunk_data[i]\n", - " for j in range(i + 1, total_chunks):\n", - " c_j = chunk_data[j]\n", - " u = len(c_i | c_j)\n", - " \n", - " # HERE COMES THE SIMILARITY COMPUTATION\n", - " d = 100 * len(c_i & c_j) / u if u != 0 else 0\n", - " \n", - " # HERE WE STORE THE OUTCOME\n", - " if d >= MATRIX_THRESHOLD:\n", - " chunk_dist[(i,j)] = d\n", - " wc += 1\n", - " wt += 1\n", - " if wc == SIMILARITY_PROGRESS:\n", - " wc = 0\n", - " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " wt // sim_unit, sim_lb, len(chunk_dist),\n", - " ))\n", - " elif SIMILARITY_METHOD == 'LCS':\n", - " # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length\n", - " for i in range(total_chunks):\n", - " c_i = chunk_data[i]\n", - " for j in range(i + 1, total_chunks):\n", - " c_j = chunk_data[j]\n", - "\n", - " # HERE COMES THE SIMILARITY COMPUTATION\n", - " d = 100 * ratio(c_i, c_j)\n", - "\n", - " # HERE WE STORE THE OUTCOME\n", - " if d >= MATRIX_THRESHOLD:\n", - " chunk_dist[(i,j)] = d\n", - " wc += 1\n", - " wt += 1\n", - " if wc == SIMILARITY_PROGRESS:\n", - " wc = 0\n", - " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " wt // sim_unit, sim_lb, len(chunk_dist),\n", - " ))\n", - "\n", - " with open(matrix_path, 'wb') as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL)\n", - " \n", - " info('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", - " wt // sim_unit, sim_lb, wt, len(chunk_dist),\n", - " ))\n", - " \n", - " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", - " similarity_post()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "scrolled": true - }, - "source": [ - "## 5.7 Cliques\n", - "\n", - "Based on the value for the ``SIMILARITY_THRESHOLD`` we use the similarity matrix to pick the *interesting*\n", - "similar pairs out of it. From these pairs we lump together our cliques.\n", - "\n", - "Our list of experiments will select various values for ``SIMILARITY_THRESHOLD``, which will result\n", - "in various types of cliqueing behaviour.\n", - "\n", - "We store computed cliques for re-use.\n", - "\n", - "## 5.7.1 Selecting passages\n", - "\n", - "We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages.\n", - "\n", - "## 5.7.2 Growing cliques\n", - "We inspect all passages in our set, and try to add them to the cliques we are growing.\n", - "We start with an empty set of cliques.\n", - "Each passage is added to a clique with which it has *enough familiarity*, otherwise it is added to a new clique.\n", - "*Enough familiarity means*: the passage is similar to at least one member of the clique, and the similarity is at least ``SIMILARITY_THRESHOLD``. \n", - "It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged.\n", - "This may lead to growing very large cliques if ``SIMILARITY_THRESHOLD`` is too low." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "def key_chunk(i):\n", - " c = chunks[i]\n", - " w = c[0]\n", - " return (-len(c), L.u(w, otype='book')[0], L.u(w, otype='chapter')[0], L.u(w, otype='verse')[0])\n", - "\n", - "def meta_clique_pre():\n", - " global similars, passages\n", - " info('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " ))\n", - " similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD}\n", - " passage_set = set()\n", - " for (i,j) in similars:\n", - " passage_set.add(i)\n", - " passage_set.add(j)\n", - " passages = sorted(passage_set, key=key_chunk)\n", - "\n", - " meta['# SIMILAR COMPARISONS'] = len(similars)\n", - " meta['# SIMILAR PASSAGES'] = len(passages) \n", - "\n", - "def meta_clique_pre2():\n", - " info('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(similars), len(passages),\n", - "))\n", - "\n", - "\n", - "def meta_clique_post():\n", - " global l_c_l\n", - " meta['# CLIQUES'] = len(cliques)\n", - " scliques = collections.Counter()\n", - " for c in cliques:\n", - " scliques[len(c)] += 1\n", - " l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0\n", - " totmn = 0\n", - " totcn = 0\n", - " for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]):\n", - " totmn += ln * n\n", - " totcn += n\n", - " if VERBOSE:\n", - " info('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " n, ln,\n", - " ))\n", - " meta['# CLIQUES of LENGTH {:>4}'.format(ln)] = n\n", - " info('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " totmn, totcn,\n", - " ))\n", - " \n", - "def cliqueing(do_clique):\n", - " global cliques\n", - " if not do_clique:\n", - " info('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(cliques), len(passages), len(similars), \n", - " ))\n", - " meta_clique_pre2()\n", - " meta_clique_post()\n", - " return\n", - " info('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, \n", - " ))\n", - " meta_clique_pre()\n", - " meta_clique_pre2()\n", - " clique_path = '{}/{}/clique_{}_{}_{}_{}_{}'.format(\n", - " LOCAL_BASE_COMP, STORED_CLIQUE_DIR,\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " )\n", - " if os.path.exists(clique_path):\n", - " with open(clique_path, 'rb') as f: cliques = pickle.load(f)\n", - " info('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(cliques), len(passages), len(similars), \n", - " ))\n", - " meta_clique_post()\n", - " return\n", - "\n", - " info('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(passages), len(similars), \n", - " ))\n", - " cliques_unsorted = []\n", - " np = 0\n", - " npc = 0\n", - " for i in passages:\n", - " added = None\n", - " removable = set()\n", - " for (k, c) in enumerate(cliques_unsorted):\n", - " origc = tuple(c)\n", - " for j in origc: \n", - " d = chunk_dist.get((i,j), 0) if i < j else chunk_dist.get((j,i), 0) if j < i else 0\n", - " if d >= SIMILARITY_THRESHOLD:\n", - " if added == None: # the passage has not been added to any clique yet\n", - " c.add(i)\n", - " added = k # remember that we added the passage to this clique\n", - " else: # the passage has alreay been added to another clique:\n", - " # we merge this clique with that one\n", - " cliques_unsorted[added] |= c\n", - " removable.add(k) # we remember that we have merged this clicque into another one,\n", - " # so we can throw away this clicque later \n", - " break\n", - " if added == None:\n", - " cliques_unsorted.append({i})\n", - " else:\n", - " if len(removable):\n", - " cliques_unsorted = [c for (k,c) in enumerate(cliques_unsorted) if k not in removable]\n", - " np += 1\n", - " npc += 1\n", - " if npc == CLIQUES_PROGRESS:\n", - " npc = 0\n", - " info('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(cliques_unsorted), np,\n", - " ))\n", - " cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted])\n", - " with open(clique_path, 'wb') as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL)\n", - " meta_clique_post()\n", - " info('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(cliques), len(passages), len(similars), \n", - " ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.8 Output\n", - "\n", - "We deliver the output of our experiments in various ways, all in HTML.\n", - "\n", - "We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it.\n", - "\n", - "For every (*good*) experiment, we produce a big list of its cliques, and for \n", - "every such clique, we produce a diff-view of its members.\n", - "\n", - "Big cliques will be split into several files.\n", - "\n", - "Clique listings will also contain metadata: the value of the experiment parameters.\n", - "\n", - "### 5.8.1 Format definitions\n", - "Here are the definitions for formatting the (HTML) output." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# clique lists\n", - "css = '''\n", - "td.vl {\n", - " font-family: Verdana, Arial, sans-serif;\n", - " font-size: small;\n", - " text-align: right;\n", - " color: #aaaaaa;\n", - " width: 10%;\n", - " direction: ltr;\n", - " border-left: 2px solid #aaaaaa;\n", - " border-right: 2px solid #aaaaaa;\n", - "}\n", - "td.ht {\n", - " font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif;\n", - " font-size: x-large;\n", - " line-height: 1.7;\n", - " text-align: right;\n", - " direction: rtl;\n", - "}\n", - "table.ht {\n", - " width: 100%;\n", - " direction: rtl;\n", - " border-collapse: collapse;\n", - "}\n", - "td.ht {\n", - " border-left: 2px solid #aaaaaa;\n", - " border-right: 2px solid #aaaaaa;\n", - "}\n", - "tr.ht.tb {\n", - " border-top: 2px solid #aaaaaa;\n", - " border-left: 2px solid #aaaaaa;\n", - " border-right: 2px solid #aaaaaa;\n", - "}\n", - "tr.ht.bb {\n", - " border-bottom: 2px solid #aaaaaa;\n", - " border-left: 2px solid #aaaaaa;\n", - " border-right: 2px solid #aaaaaa;\n", - "}\n", - "span.m {\n", - " background-color: #aaaaff;\n", - "}\n", - "span.f {\n", - " background-color: #ffaaaa;\n", - "}\n", - "span.x {\n", - " background-color: #ffffaa;\n", - " color: #bb0000;\n", - "}\n", - "span.delete {\n", - " background-color: #ffaaaa;\n", - "}\n", - "span.insert {\n", - " background-color: #aaffaa;\n", - "}\n", - "span.replace {\n", - " background-color: #ffff00;\n", - "}\n", - "\n", - "'''\n", - "\n", - "# chapter diffs\n", - "diffhead = '''\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "'''\n", - "\n", - "# table of experiments\n", - "ecss = '''\n", - "\n", - "'''\n", - "\n", - "legend = '''\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
{mis}
{rec}
{dep}
{dub}
{out}
{nor}
\n", - "'''.format(**VALUE_LABELS)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.8.2 Formatting clique lists" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "def xterse_chunk(i):\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = L.u(fword, otype='book')[0]\n", - " chapter = L.u(fword, otype='chapter')[0]\n", - " return (book, chapter)\n", - "\n", - "def xterse_clique(ii):\n", - " return tuple(sorted({xterse_chunk(i) for i in ii}))\n", - "\n", - "def terse_chunk(i):\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = L.u(fword, otype='book')[0]\n", - " chapter = L.u(fword, otype='chapter')[0]\n", - " verse = L.u(fword, otype='verse')[0]\n", - " return (book, chapter, verse)\n", - "\n", - "def terse_clique(ii):\n", - " return tuple(sorted({terse_chunk(i) for i in ii}))\n", - "\n", - "def verse_chunk(i):\n", - " (bk, ch, vs) = i\n", - " book = F.book.v(bk)\n", - " chapter = F.chapter.v(ch)\n", - " verse = F.verse.v(vs)\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(vs, otype='word'))\n", - " verse_label = '{} {}:{}'.format(book, chapter, verse)\n", - " htext = '{}{}'.format(verse_label, text)\n", - " return '{}'.format(htext)\n", - "\n", - "def verse_clique(ii):\n", - " return '{}
\\n'.format(''.join(verse_chunk(i) for i in sorted(ii)))\n", - "\n", - "def condense(vlabels):\n", - " cnd = ''\n", - " (cur_b, cur_c) = (None, None)\n", - " for (b, c, v) in vlabels:\n", - " c = str(c)\n", - " v = str(v)\n", - " sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '\n", - " show_b = b+' ' if cur_b != b else ''\n", - " show_c = c+':' if cur_b != b or cur_c != c else ''\n", - " (cur_b, cur_c) = (b, c)\n", - " cnd += '{}{}{}{}'.format(sep, show_b, show_c, v)\n", - " return cnd\n", - "\n", - "def print_diff(a, b):\n", - " arep = ''\n", - " brep = ''\n", - " for (lb, ai, aj, bi, bj) in SequenceMatcher(isjunk=None, a=a, b=b, autojunk=False).get_opcodes():\n", - " if lb == 'equal':\n", - " arep += a[ai:aj]\n", - " brep += b[bi:bj]\n", - " elif lb == 'delete':\n", - " arep += '{}'.format(lb, a[ai:aj])\n", - " elif lb == 'insert':\n", - " brep += '{}'.format(lb, b[bi:bj])\n", - " else:\n", - " arep += '{}'.format(lb, a[ai:aj])\n", - " brep += '{}'.format(lb, b[bi:bj])\n", - " return (arep, brep)\n", - " \n", - "def print_chunk_fine(prev, text, verse_labels, prevlabels):\n", - " if prev == None:\n", - " return '''\n", - "{}{}\n", - "'''.format(\n", - " condense(verse_labels), \n", - " text,\n", - " )\n", - " else:\n", - " (prevline, textline) = print_diff(prev, text)\n", - " return '''\n", - "{}{}\n", - "{}{}\n", - "'''.format(\n", - " condense(prevlabels) if prevlabels != None else 'previous',\n", - " prevline,\n", - " condense(verse_labels), \n", - " textline,\n", - ")\n", - "\n", - "def print_chunk_coarse(text, verse_labels):\n", - " return '''\n", - "{}{}\n", - "'''.format(\n", - " condense(verse_labels), \n", - " text,\n", - " )\n", - "\n", - "def print_clique(ii, ncliques):\n", - " return print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii)\n", - " \n", - "def print_clique_fine(ii):\n", - " condensed = collections.OrderedDict()\n", - " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = F.book.v(L.u(fword, otype='book')[0])\n", - " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", - " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", - " condensed.setdefault(text, []).append((book, chapter, verse))\n", - " result = []\n", - " nv = len(condensed.items())\n", - " prev = None\n", - " for (text, verse_labels) in condensed.items():\n", - " if prev == None:\n", - " if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None))\n", - " else:\n", - " prev = text\n", - " prevlabels = verse_labels\n", - " continue\n", - " else:\n", - " result.append(print_chunk_fine(prev, text, verse_labels, prevlabels))\n", - " prev = text\n", - " prevlabels = None\n", - " return '{}
\\n'.format(''.join(result))\n", - "\n", - "def print_clique_coarse(ii):\n", - " condensed = collections.OrderedDict()\n", - " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = F.book.v(L.u(fword, otype='book')[0])\n", - " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", - " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", - " condensed.setdefault(text, []).append((book, chapter, verse))\n", - " result = []\n", - " nv = len(condensed.items())\n", - " prev = None\n", - " for (text, verse_labels) in condensed.items():\n", - " result.append(print_chunk_coarse(text, verse_labels))\n", - " if len(ii) > LARGE_CLIQUE_SIZE:\n", - " result.append(print_chunk_coarse('+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE),[]))\n", - " return '{}
\\n'.format(''.join(result))\n", - "\n", - "def index_clique(bnm, n, ii, ncliques):\n", - " return index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii)\n", - " \n", - "def index_clique_fine(bnm, n, ii):\n", - " verse_labels = []\n", - " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = F.book.v(L.u(fword, otype='book')[0])\n", - " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", - " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", - " verse_labels.append((book, chapter, verse))\n", - " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", - " return '

{} {}

'.format(\n", - " n, reffl, n, condense(verse_labels),\n", - " )\n", - "\n", - "def index_clique_coarse(bnm, n, ii):\n", - " verse_labels = []\n", - " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", - " chunk = chunks[i]\n", - " fword = chunk[0]\n", - " book = F.book.v(L.u(fword, otype='book')[0])\n", - " chapter = F.chapter.v(L.u(fword, otype='chapter')[0])\n", - " verse = F.verse.v(L.u(fword, otype='verse')[0])\n", - " verse_labels.append((book, chapter, verse))\n", - " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", - " extra = '+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else ''\n", - " return '

{} {}{}

'.format(\n", - " n, reffl, n, condense(verse_labels), extra,\n", - " )\n", - "\n", - "def lines_chapter(c):\n", - " lines = []\n", - " for v in L.d(c, otype='verse'):\n", - " vl = F.verse.v(v)\n", - " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d(v, otype='word'))\n", - " lines.append('{} {}'.format(vl, text.replace('\\n', ' ')))\n", - " return lines\n", - "\n", - "def compare_chapters(c1, c2, lb1, lb2):\n", - " dh = difflib.HtmlDiff(wrapcolumn=80)\n", - " table_html = dh.make_table(\n", - " lines_chapter(c1), \n", - " lines_chapter(c2), \n", - " fromdesc=lb1, \n", - " todesc=lb2, \n", - " context=False, \n", - " numlines=5,\n", - " )\n", - " htext = '''{}{}'''.format(diffhead, table_html)\n", - " return htext" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.8.3 Compiling the table of experiments\n", - "\n", - "Here we generate the table of experiments, complete with the coloring according to their assessments." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# generate the table of experiments\n", - "def gen_html(standalone=False):\n", - " global other_exps\n", - " info('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))\n", - " stats = collections.Counter()\n", - " pre = '''\n", - "\n", - "\n", - "\n", - "{}\n", - "\n", - "\n", - "'''.format(ecss) if standalone else ''\n", - " \n", - " post = '''\n", - "\n", - "''' if standalone else ''\n", - "\n", - " experiments = '''\n", - "{}\n", - "{}\n", - "\n", - "{}\n", - "'''.format(pre, legend, ''.join(''.format(sim_thr) for sim_thr in SIMILARITIES))\n", - " \n", - " for chunk_f in (True, False):\n", - " if chunk_f:\n", - " chunk_items = CHUNK_SIZES\n", - " else:\n", - " chunk_items = CHUNK_OBJECTS\n", - " chunk_lb = CHUNK_LBS[chunk_f]\n", - " for chunk_i in chunk_items:\n", - " for sim_m in SIM_METHODS:\n", - " set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i)\n", - " these_outputs = outputs.get(MATRIX_THRESHOLD, {})\n", - " experiments += ''.format(\n", - " CHUNK_LABELS[chunk_f], chunk_i, sim_m,\n", - " )\n", - " for sim_thr in SIMILARITIES:\n", - " okey = (chunk_lb, chunk_i, sim_m, sim_thr)\n", - " values = these_outputs.get(okey)\n", - " if values == None:\n", - " result = ''\n", - " stats['mis'] += 1\n", - " else:\n", - " (npassages, ncliques, longest_clique_len) = values\n", - " cls = assess_exp(chunk_f, npassages, ncliques, longest_clique_len)\n", - " stats[cls] += 1\n", - " (lr_el, lr_lb) = ('', '')\n", - " if (CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD) == (\n", - " chunk_lb, chunk_i, sim_m, sim_thr,\n", - " ):\n", - " lr_el = '*'\n", - " lr_lb = VALUE_LABELS['lr']\n", - " result = '''\n", - "'''.format(\n", - " cls, lr_lb, lr_el, npassages,\n", - " '' if standalone else LOCAL_BASE_OUTP+'/', \n", - " EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr,\n", - " ncliques, longest_clique_len,\n", - " )\n", - " experiments += result\n", - " experiments += '\\n'\n", - " experiments += '
chunk typechunk sizesimilarity method
{}
{}{}{} {}\n", - " {}
\n", - " {}
\n", - " {}\n", - "
\\n{}'.format(post)\n", - " if standalone:\n", - " with open(EXPERIMENT_HTML, 'w') as f:\n", - " f.write(experiments)\n", - " else:\n", - " other_exps = experiments\n", - "\n", - " for stat in sorted(stats):\n", - " info('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))\n", - " info(\"EXPERIMENT: Generated html report\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.8.4 High level formatting functions\n", - "\n", - "Here everything concerning output is brought together." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def assess_exp(cf, np, nc, ll):\n", - " return 'out' if cf else \\\n", - " 'rec' if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else \\\n", - " 'dep' if ll > nc * DEP_CLIQUE_RATIO / 100 else \\\n", - " 'dub' if ll > nc * DUB_CLIQUE_RATIO / 100 else \\\n", - " 'nor'\n", - "\n", - "def printing():\n", - " global outputs, bin_cliques, base_name\n", - " info('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " ))\n", - " xt_cliques = {xterse_clique(c) for c in cliques} # chapter cliques as tuples of (b, ch) tuples\n", - " bin_cliques = {c for c in xt_cliques if len(c) == 2} # chapter cliques with exactly two chapters\n", - " # all chapters that occur in binary chapter cliques\n", - " bin_chapters = {c[0] for c in bin_cliques} | {c[1] for c in bin_cliques}\n", - " meta['# BINARY CHAPTER DIFFS'] = len(bin_cliques)\n", - "\n", - " # We generate one kind of info for binary chapter cliques (the majority of cases).\n", - " # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they\n", - " # have member chunks in the same chapter, or in multiple (more than two) chapters.\n", - " \n", - " ncliques = len(cliques)\n", - " chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in {'rec', 'nor', 'dub'}\n", - " cdoing = 'involving' if chapters_ok else 'skipping'\n", - "\n", - " info('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " ncliques, cdoing, len(bin_cliques),\n", - " ))\n", - " meta_html = '\\n'.join('{:<40} : {:>10}'.format(k, str(meta[k])) for k in meta)\n", - "\n", - " base_name = '{}_{}_{}_M{}_S{}'.format(\n", - " CHUNK_LB,\n", - " CHUNK_DESC,\n", - " SIMILARITY_METHOD,\n", - " MATRIX_THRESHOLD,\n", - " SIMILARITY_THRESHOLD, \n", - " )\n", - " param_spec = '''\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
chunking method{}
chunking description{}
similarity method{}
similarity threshold{}
\n", - " '''.format(\n", - " CHUNK_LABELS[CHUNK_FIXED],\n", - " CHUNK_DESC,\n", - " SIMILARITY_METHOD, \n", - " SIMILARITY_THRESHOLD, \n", - " )\n", - " param_lab = 'chunk-{}-{}-sim-{}-m{}-s{}'.format(\n", - " CHUNK_LB,\n", - " CHUNK_DESC,\n", - " SIMILARITY_METHOD,\n", - " MATRIX_THRESHOLD,\n", - " SIMILARITY_THRESHOLD, \n", - " )\n", - " index_name = base_name\n", - " all_name = '{}_{}'.format('all', base_name)\n", - " cliques_name = '{}_{}'.format('clique', base_name)\n", - "\n", - " clique_links = []\n", - " clique_links.append(('{}/{}.html'.format(base_name, all_name), 'Big list of all cliques'))\n", - "\n", - " nexist = 0\n", - " nnew = 0\n", - " if chapters_ok:\n", - " chapter_diffs = []\n", - " info('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(bin_cliques),\n", - " ))\n", - "\n", - " bcc_text = '

These results look good, so a binary chapter comparison has been generated

'\n", - " for cl in sorted(bin_cliques):\n", - " lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))\n", - " lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))\n", - " hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')\n", - " hfilepath = '{}/{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename)\n", - " chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(\n", - " SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,\n", - " )))\n", - " if not os.path.exists(hfilepath):\n", - " htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2)\n", - " with open(hfilepath, 'w') as f: f.write(htext)\n", - " if VERBOSE:\n", - " info('PRINT ({} {} {} M>{} S>{}): written {}'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " hfilename,\n", - " ))\n", - " nnew += 1\n", - " else:\n", - " nexist += 1\n", - " clique_links.append((\n", - " '../{}/{}'.format(CHAPTER_DIR, hfilename), \n", - " '{} versus {}'.format(lb1, lb2),\n", - " ))\n", - " info('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " nnew, nexist,\n", - " ))\n", - " else:\n", - " bcc_text = '

These results look dubious at best, so no binary chapter comparison has been generated

'\n", - "\n", - "\n", - " allgeni_html = (index_clique(cliques_name, i, c, ncliques) for (i,c) in enumerate(cliques))\n", - " \n", - " allgen_htmls = []\n", - " allgen_html = ''\n", - " \n", - " for (i, c) in enumerate(cliques):\n", - " if i % CLIQUES_PER_FILE == 0:\n", - " if i > 0:\n", - " allgen_htmls.append(allgen_html)\n", - " allgen_html = ''\n", - " allgen_html += '

Clique {}

\\n{}'.format(i, i, print_clique(c, ncliques))\n", - " allgen_htmls.append(allgen_html)\n", - "\n", - " index_html_tpl = '''\n", - "{}\n", - "

Binary chapter comparisons

\n", - "{}\n", - "{}\n", - " '''\n", - "\n", - " content_file_tpl = '''\n", - "\n", - "\n", - "{}\n", - "\n", - "\n", - "\n", - "

{}

\n", - "{}\n", - "

more parameters and stats

\n", - "{}\n", - "

Parameters and stats

\n", - "
{}
\n", - "\n", - "'''\n", - " \n", - " a_tpl_file = '

{}

'\n", - "\n", - " index_html_file = index_html_tpl.format(\n", - " a_tpl_file.format(*clique_links[0]),\n", - " bcc_text,\n", - " '\\n'.join(a_tpl_file.format(*c) for c in clique_links[1:]),\n", - " )\n", - "\n", - " listing_html = '{}\\n'.format(\n", - " '\\n'.join(allgeni_html),\n", - " )\n", - "\n", - " for (subdir, fname, content_html, tit) in (\n", - " (None, index_name, index_html_file, 'Index '+param_lab),\n", - " (base_name, all_name, listing_html, 'Listing '+param_lab),\n", - " (base_name, cliques_name, allgen_htmls, 'Cliques '+param_lab),\n", - " ): \n", - " subdir = '' if subdir == None else (subdir + '/')\n", - " subdirabs = '{}/{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir)\n", - " if not os.path.exists(subdirabs): os.makedirs(subdirabs)\n", - "\n", - " if type(content_html) is list:\n", - " for (i, c_h) in enumerate(content_html):\n", - " fn = '{}_{}'.format(fname, i)\n", - " t = '{}_{}'.format(tit, i)\n", - " with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn), 'w') as f: \n", - " f.write(content_file_tpl.format(t, css, t, param_spec, c_h, meta_html))\n", - " else:\n", - " with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname), 'w') as f: \n", - " f.write(content_file_tpl.format(tit, css, tit, param_spec, content_html, meta_html))\n", - " destination = outputs.setdefault(MATRIX_THRESHOLD, {})\n", - " destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = (\n", - " len(passages), len(cliques), l_c_l,\n", - " )\n", - " info('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(\n", - " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", - " len(cliques), len(allgen_htmls), cdoing, len(bin_cliques)\n", - " ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.9 Running experiments\n", - "\n", - "The workflows of doing a single experiment, and then all experiments, are defined." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "outputs = {}\n", - "\n", - "def writeoutputs():\n", - " global outputs\n", - " with open(EXPERIMENT_PATH, 'wb') as f:\n", - " pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL)\n", - "\n", - "def readoutputs():\n", - " global outputs\n", - " if not os.path.exists(EXPERIMENT_PATH):\n", - " outputs = {}\n", - " else:\n", - " with open(EXPERIMENT_PATH, 'rb') as f:\n", - " outputs = pickle.load(f)\n", - "\n", - "def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index):\n", - " if do_index:\n", - " readoutputs()\n", - " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", - " if skip: return\n", - " chunking(do_chunk)\n", - " preparing(do_prep)\n", - " similarity(do_sim)\n", - " cliqueing(do_clique)\n", - " printing()\n", - " if do_index:\n", - " writeoutputs()\n", - " gen_html()\n", - "\n", - "def do_only_chunk(chunk_f, chunk_i):\n", - " do_chunk = do_params_chunk(chunk_f, chunk_i)\n", - " chunking(do_chunk)\n", - "\n", - "def reset_experiments():\n", - " global outputs\n", - " readoutputs()\n", - " outputs = {}\n", - " reset_params()\n", - " writeoutputs()\n", - " gen_html()\n", - "\n", - "def do_all_experiments(no_fixed=False, only_object=None):\n", - " global outputs\n", - " reset_experiments()\n", - " for chunk_f in (False,) if no_fixed else (True, False):\n", - " if chunk_f:\n", - " chunk_items = CHUNK_SIZES\n", - " else:\n", - " chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)\n", - " for chunk_i in chunk_items:\n", - " for sim_m in SIM_METHODS:\n", - " for sim_thr in SIMILARITIES:\n", - " do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False)\n", - " writeoutputs()\n", - " gen_html()\n", - " gen_html(standalone=True)\n", - "\n", - "def do_all_chunks(no_fixed=False, only_object=None):\n", - " global outputs\n", - " reset_experiments()\n", - " for chunk_f in (False,) if no_fixed else (True, False):\n", - " if chunk_f:\n", - " chunk_items = CHUNK_SIZES\n", - " else:\n", - " chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)\n", - " for chunk_i in chunk_items:\n", - " do_only_chunk(chunk_f, chunk_i)\n", - " \n", - "def show_all_experiments():\n", - " readoutputs()\n", - " gen_html()\n", - " gen_html(standalone=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. SHEBANQ annotations\n", - "\n", - "Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca)\n", - "\n", - "def get_verse_o(o, ca=False): return get_verse_w(L.d(o, otype='word')[0], ca=ca)\n", - "\n", - "def get_verse_w(w, ca=False):\n", - " book = F.book.v(L.u(w, otype='book')[0])\n", - " chapter = F.chapter.v(L.u(w, otype='chapter')[0])\n", - " verse = F.verse.v(L.u(w, otype='verse')[0])\n", - " if ca: ca = F.number.v(L.u(w, otype='clause_atom')[0])\n", - " return (book, chapter, verse, ca) if ca else (book, chapter, verse)\n", - "\n", - "def key_verse(x):\n", - " return (book_rank[x[0]], int(x[1]), int(x[2]))\n", - "\n", - "MAX_REFS = 10\n", - "\n", - "def condensex(vlabels):\n", - " cnd = []\n", - " (cur_b, cur_c) = (None, None)\n", - " for (b, c, v, d) in vlabels:\n", - " sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '\n", - " show_b = b+' ' if cur_b != b else ''\n", - " show_c = c+':' if cur_b != b or cur_c != c else ''\n", - " (cur_b, cur_c) = (b, c)\n", - " cnd.append('{}{}{}{}{}'.format(sep, show_b, show_c, v, d))\n", - " return cnd\n", - "\n", - "dfields = '''\n", - " book1\n", - " chapter1\n", - " verse1\n", - " book2\n", - " chapter2\n", - " verse2\n", - " similarity\n", - "'''.strip().split()\n", - "\n", - "dfields_fmt = ('{}\\t' * (len(dfields) - 1)) + '{}\\n' \n", - "\n", - "def get_crossrefs():\n", - " global crossrefs\n", - " info('CROSSREFS: Fetching crossrefs')\n", - " crossrefs_proto = {}\n", - " crossrefs = {}\n", - " (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX\n", - " sim_thr = SHEBANQ_SIMILARITY\n", - " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", - " if skip: return\n", - " info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", - " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", - " info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", - " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", - " len(crossrefs_proto),\n", - " ))\n", - " f = open(CROSSREF_DB_PATH, 'w')\n", - " f.write('{}\\n'.format('\\t'.join(dfields))) \n", - " for ((x,y), d) in crossrefs_proto:\n", - " vx = get_verse(x)\n", - " vy = get_verse(y)\n", - " rd = int(round(d))\n", - " crossrefs.setdefault(x, {})[vy] = rd\n", - " crossrefs.setdefault(y, {})[vx] = rd\n", - " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", - " total = sum(len(x) for x in crossrefs.values())\n", - " f.close()\n", - " info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", - "\n", - "def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to):\n", - " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", - " if skip: return\n", - " chunking(do_chunk)\n", - " preparing(do_prep)\n", - " similarity(do_sim)\n", - "\n", - " info('CROSSREFS: Fetching crossrefs')\n", - " crossrefs_proto = {}\n", - " crossrefs = {}\n", - " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", - " if skip: return\n", - " info('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", - " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", - " info('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", - " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", - " len(crossrefs_proto),\n", - " ))\n", - " f = open('files/{}'.format(write_to), 'w')\n", - " f.write('{}\\n'.format('\\t'.join(dfields))) \n", - " for ((x,y), d) in crossrefs_proto:\n", - " vx = get_verse(x)\n", - " vy = get_verse(y)\n", - " rd = int(round(d))\n", - " crossrefs.setdefault(x, {})[vy] = rd\n", - " crossrefs.setdefault(y, {})[vx] = rd\n", - " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", - " total = sum(len(x) for x in crossrefs.values())\n", - " f.close()\n", - " info('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", - "\n", - "def compile_refs():\n", - " global refs_compiled\n", - " refs_grouped = []\n", - " for x in sorted(crossrefs):\n", - " refs = crossrefs[x]\n", - " vys = sorted(refs.keys(), key=key_verse)\n", - " currefs = []\n", - " for vy in vys:\n", - " nr = len(currefs)\n", - " if nr == MAX_REFS:\n", - " refs_grouped.append((x, tuple(currefs)))\n", - " currefs = [] \n", - " currefs.append(vy)\n", - " if len(currefs):\n", - " refs_grouped.append((x, tuple(currefs)))\n", - " refs_compiled = []\n", - " for (x, vys) in refs_grouped:\n", - " vysd = [(vy[0], vy[1], vy[2], ' ~{}%'.format(crossrefs[x][vy])) for vy in vys]\n", - " vysl = condensex(vysd)\n", - " these_refs = []\n", - " for (i, vy) in enumerate(vysd):\n", - " link_text = vysl[i]\n", - " link_target = '{} {}:{}'.format(vy[0], vy[1], vy[2])\n", - " these_refs.append('[{}]({})'.format(link_text, link_target))\n", - " refs_compiled.append((x, ' '.join(these_refs)))\n", - " info('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))\n", - "\n", - "def get_chapter_diffs():\n", - " global chapter_diffs\n", - " chapter_diffs = []\n", - " for cl in sorted(bin_cliques):\n", - " lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))\n", - " lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))\n", - " hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')\n", - " chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(\n", - " SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,\n", - " )))\n", - " info('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))\n", - "\n", - " \n", - "def get_clique_refs():\n", - " global clique_refs\n", - " clique_refs = []\n", - " for (i, c) in enumerate(cliques):\n", - " for j in c:\n", - " seq = i // CLIQUES_PER_FILE\n", - " clique_refs.append((j, i, '{}/{}/{}/{}/clique_{}_{}.html#c_{}'.format(\n", - " SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i,\n", - " )))\n", - " info('CROSSREFS: Added {} clique references'.format(len(clique_refs)))\n", - "\n", - "sfields = '''\n", - " version\n", - " book\n", - " chapter\n", - " verse\n", - " clause_atom\n", - " is_shared\n", - " is_published\n", - " status\n", - " keywords\n", - " ntext\n", - "'''.strip().split()\n", - "\n", - "sfields_fmt = ('{}\\t' * (len(sfields) - 1)) + '{}\\n' \n", - "\n", - "def generate_notes():\n", - " with open(NOTES_PATH, 'w') as f:\n", - " f.write('{}\\n'.format('\\t'.join(sfields))) \n", - " x = next(F.otype.s('word'))\n", - " (bk, ch, vs, ca) = get_verse(x, ca=True)\n", - " f.write(sfields_fmt.format(\n", - " version,\n", - " bk,\n", - " ch,\n", - " vs,\n", - " ca,\n", - " 'T',\n", - " '',\n", - " CROSSREF_STATUS,\n", - " CROSSREF_KEYWORD,\n", - " '''The crossref notes are the result of a computation without manual tweaks.\n", - "Parameters: chunk by verse, similarity method SET with threshold 65.\n", - "[Here](tool=parallel) is an account of the generation method.'''.replace('\\n', ' ')\n", - " ))\n", - " for (lb1, ch1, lb2, ch2, fl) in chapter_diffs:\n", - " (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True)\n", - " (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True)\n", - " f.write(sfields_fmt.format(\n", - " version,\n", - " bk1,\n", - " ch1,\n", - " vs1,\n", - " ca1,\n", - " 'T',\n", - " '',\n", - " CROSSREF_STATUS,\n", - " CROSSREF_KEYWORD,\n", - " '[chapter diff with {}](tool:{})'.format(lb2, fl),\n", - " ))\n", - " f.write(sfields_fmt.format(\n", - " version,\n", - " bk2,\n", - " ch2,\n", - " vs2,\n", - " ca2,\n", - " 'T',\n", - " '',\n", - " CROSSREF_STATUS,\n", - " CROSSREF_KEYWORD,\n", - " '[chapter diff with {}](tool:{})'.format(lb1, fl),\n", - " ))\n", - " for (x, refs) in refs_compiled:\n", - " (bk, ch, vs, ca) = get_verse(x, ca=True)\n", - " f.write(sfields_fmt.format(\n", - " version,\n", - " bk,\n", - " ch,\n", - " vs,\n", - " ca,\n", - " 'T',\n", - " '',\n", - " CROSSREF_STATUS,\n", - " CROSSREF_KEYWORD,\n", - " refs,\n", - " ))\n", - " for (chunk, clique, fl) in clique_refs:\n", - " (bk, ch, vs, ca) = get_verse(chunk, ca=True)\n", - " f.write(sfields_fmt.format(\n", - " version,\n", - " bk,\n", - " ch,\n", - " vs,\n", - " ca,\n", - " 'T',\n", - " '',\n", - " CROSSREF_STATUS,\n", - " CROSSREF_KEYWORD,\n", - " '[all variants (clique {})](tool:{})'.format(clique, fl),\n", - " ))\n", - "\n", - " info('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))\n", - "\n", - "def crossrefs2shebanq():\n", - " expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,)\n", - " do_experiment(*(expr+(True,)))\n", - " get_crossrefs()\n", - " compile_refs()\n", - " get_chapter_diffs()\n", - " get_clique_refs()\n", - " generate_notes()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7. Main\n", - "\n", - "In the cell below you can select the experiments you want to carry out.\n", - "\n", - "The previous cells contain just definitions and parameters.\n", - "The next cell will do work.\n", - "\n", - "If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2m 31s EXPERIMENT: Generating html report\n", - " 2m 31s EXPERIMENT: 240 no results available\n", - " 2m 31s EXPERIMENT: Generated html report\n", - " 2m 31s CHUNKING (F 100): Loaded: 4244 chunks\n", - " 2m 31s CHUNKING (F 100): Made 4244 chunks\n", - " 2m 31s PREPARING (F 100 SET)\n", - " 2m 32s PREPARING (F 100 SET): Done 4244 chunks.\n", - " 2m 32s SIMILARITY (F 100 SET M>50): Loaded: 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>100): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>100): sorting out cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 2m 32s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 32s PREPARING (F 100 SET): Already prepared\n", - " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>95): sorting out cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 2m 32s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 32s PREPARING (F 100 SET): Already prepared\n", - " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>90): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>90): sorting out cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs\n", - " 2m 32s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs\n", - " 2m 32s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 32s PREPARING (F 100 SET): Already prepared\n", - " 2m 32s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 32s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>85): 20 relevant similarities between 39 passages\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>85): Loaded: 19 cliques out of 39 chunks from 20 comparisons\n", - " 2m 32s CLIQUES (F 100 SET M>50 S>85): 39 members in 19 cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>85): sorting out cliques\n", - " 2m 32s PRINT (F 100 SET M>50 S>85): formatting 19 cliques skipping 7 binary chapter diffs\n", - " 2m 33s PRINT (F 100 SET M>50 S>85): formatted 19 cliques (1 files) skipping 7 binary chapter diffs\n", - " 2m 33s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 33s PREPARING (F 100 SET): Already prepared\n", - " 2m 33s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 33s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 33s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates\n", - " 2m 33s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix\n", - " 2m 33s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages\n", - " 2m 33s CLIQUES (F 100 SET M>50 S>80): Loaded: 30 cliques out of 64 chunks from 35 comparisons\n", - " 2m 33s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques\n", - " 2m 33s PRINT (F 100 SET M>50 S>80): sorting out cliques\n", - " 2m 33s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs\n", - " 2m 34s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs\n", - " 2m 34s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 34s PREPARING (F 100 SET): Already prepared\n", - " 2m 34s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 34s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 34s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates\n", - " 2m 34s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix\n", - " 2m 34s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages\n", - " 2m 34s CLIQUES (F 100 SET M>50 S>75): Loaded: 40 cliques out of 87 chunks from 63 comparisons\n", - " 2m 34s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques\n", - " 2m 34s PRINT (F 100 SET M>50 S>75): sorting out cliques\n", - " 2m 34s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs\n", - " 2m 35s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", - " 2m 35s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 35s PREPARING (F 100 SET): Already prepared\n", - " 2m 35s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 35s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 35s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates\n", - " 2m 35s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix\n", - " 2m 35s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages\n", - " 2m 35s CLIQUES (F 100 SET M>50 S>70): Loaded: 52 cliques out of 113 chunks from 87 comparisons\n", - " 2m 35s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques\n", - " 2m 35s PRINT (F 100 SET M>50 S>70): sorting out cliques\n", - " 2m 35s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs\n", - " 2m 36s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs\n", - " 2m 36s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 36s PREPARING (F 100 SET): Already prepared\n", - " 2m 36s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 36s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 36s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates\n", - " 2m 36s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix\n", - " 2m 36s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 156 passages\n", - " 2m 36s CLIQUES (F 100 SET M>50 S>65): Loaded: 71 cliques out of 156 chunks from 115 comparisons\n", - " 2m 36s CLIQUES (F 100 SET M>50 S>65): 156 members in 71 cliques\n", - " 2m 36s PRINT (F 100 SET M>50 S>65): sorting out cliques\n", - " 2m 36s PRINT (F 100 SET M>50 S>65): formatting 71 cliques skipping 28 binary chapter diffs\n", - " 2m 37s PRINT (F 100 SET M>50 S>65): formatted 71 cliques (2 files) skipping 28 binary chapter diffs\n", - " 2m 37s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 37s PREPARING (F 100 SET): Already prepared\n", - " 2m 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 37s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates\n", - " 2m 37s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix\n", - " 2m 37s CLIQUES (F 100 SET M>50 S>60): 151 relevant similarities between 214 passages\n", - " 2m 37s CLIQUES (F 100 SET M>50 S>60): Loaded: 97 cliques out of 214 chunks from 151 comparisons\n", - " 2m 37s CLIQUES (F 100 SET M>50 S>60): 214 members in 97 cliques\n", - " 2m 37s PRINT (F 100 SET M>50 S>60): sorting out cliques\n", - " 2m 37s PRINT (F 100 SET M>50 S>60): formatting 97 cliques skipping 37 binary chapter diffs\n", - " 2m 39s PRINT (F 100 SET M>50 S>60): formatted 97 cliques (2 files) skipping 37 binary chapter diffs\n", - " 2m 39s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 39s PREPARING (F 100 SET): Already prepared\n", - " 2m 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 39s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates\n", - " 2m 39s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix\n", - " 2m 39s CLIQUES (F 100 SET M>50 S>55): 223 relevant similarities between 308 passages\n", - " 2m 39s CLIQUES (F 100 SET M>50 S>55): Loaded: 138 cliques out of 308 chunks from 223 comparisons\n", - " 2m 39s CLIQUES (F 100 SET M>50 S>55): 308 members in 138 cliques\n", - " 2m 39s PRINT (F 100 SET M>50 S>55): sorting out cliques\n", - " 2m 39s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 56 binary chapter diffs\n", - " 2m 42s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 56 binary chapter diffs\n", - " 2m 42s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 42s PREPARING (F 100 SET): Already prepared\n", - " 2m 42s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 354 entries in matrix\n", - " 2m 42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", - " 2m 42s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates\n", - " 2m 42s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix\n", - " 2m 42s CLIQUES (F 100 SET M>50 S>50): 354 relevant similarities between 469 passages\n", - " 2m 42s CLIQUES (F 100 SET M>50 S>50): Loaded: 188 cliques out of 469 chunks from 354 comparisons\n", - " 2m 42s CLIQUES (F 100 SET M>50 S>50): 469 members in 188 cliques\n", - " 2m 42s PRINT (F 100 SET M>50 S>50): sorting out cliques\n", - " 2m 43s PRINT (F 100 SET M>50 S>50): formatting 188 cliques skipping 77 binary chapter diffs\n", - " 2m 48s PRINT (F 100 SET M>50 S>50): formatted 188 cliques (4 files) skipping 77 binary chapter diffs\n", - " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 48s PREPARING (F 100 LCS)\n", - " 2m 48s PREPARING (F 100 LCS): Done 4244 chunks.\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): Loaded: 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>100): sorting out cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 2m 48s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 48s PREPARING (F 100 LCS): Already prepared\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>95): sorting out cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 2m 48s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 2m 48s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 48s PREPARING (F 100 LCS): Already prepared\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 48s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>90): Loaded: 19 cliques out of 39 chunks from 21 comparisons\n", - " 2m 48s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>90): sorting out cliques\n", - " 2m 48s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs\n", - " 2m 49s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs\n", - " 2m 49s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 49s PREPARING (F 100 LCS): Already prepared\n", - " 2m 49s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 49s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 49s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 2m 49s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix\n", - " 2m 49s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages\n", - " 2m 49s CLIQUES (F 100 LCS M>60 S>85): Loaded: 29 cliques out of 59 chunks from 31 comparisons\n", - " 2m 49s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques\n", - " 2m 49s PRINT (F 100 LCS M>60 S>85): sorting out cliques\n", - " 2m 49s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs\n", - " 2m 50s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs\n", - " 2m 50s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 50s PREPARING (F 100 LCS): Already prepared\n", - " 2m 50s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>80): 45 relevant similarities between 83 passages\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>80): Loaded: 40 cliques out of 83 chunks from 45 comparisons\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>80): 83 members in 40 cliques\n", - " 2m 50s PRINT (F 100 LCS M>60 S>80): sorting out cliques\n", - " 2m 50s PRINT (F 100 LCS M>60 S>80): formatting 40 cliques skipping 16 binary chapter diffs\n", - " 2m 50s PRINT (F 100 LCS M>60 S>80): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", - " 2m 50s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 50s PREPARING (F 100 LCS): Already prepared\n", - " 2m 50s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 50s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>75): 75 relevant similarities between 118 passages\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>75): Loaded: 54 cliques out of 118 chunks from 75 comparisons\n", - " 2m 50s CLIQUES (F 100 LCS M>60 S>75): 118 members in 54 cliques\n", - " 2m 50s PRINT (F 100 LCS M>60 S>75): sorting out cliques\n", - " 2m 50s PRINT (F 100 LCS M>60 S>75): formatting 54 cliques skipping 23 binary chapter diffs\n", - " 2m 52s PRINT (F 100 LCS M>60 S>75): formatted 54 cliques (2 files) skipping 23 binary chapter diffs\n", - " 2m 52s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 52s PREPARING (F 100 LCS): Already prepared\n", - " 2m 52s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 52s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 52s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 2m 52s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix\n", - " 2m 52s CLIQUES (F 100 LCS M>60 S>70): 125 relevant similarities between 193 passages\n", - " 2m 52s CLIQUES (F 100 LCS M>60 S>70): Loaded: 90 cliques out of 193 chunks from 125 comparisons\n", - " 2m 52s CLIQUES (F 100 LCS M>60 S>70): 193 members in 90 cliques\n", - " 2m 52s PRINT (F 100 LCS M>60 S>70): sorting out cliques\n", - " 2m 52s PRINT (F 100 LCS M>60 S>70): formatting 90 cliques skipping 40 binary chapter diffs\n", - " 2m 54s PRINT (F 100 LCS M>60 S>70): formatted 90 cliques (2 files) skipping 40 binary chapter diffs\n", - " 2m 54s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 54s PREPARING (F 100 LCS): Already prepared\n", - " 2m 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 54s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 2m 54s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix\n", - " 2m 54s CLIQUES (F 100 LCS M>60 S>65): 181 relevant similarities between 286 passages\n", - " 2m 54s CLIQUES (F 100 LCS M>60 S>65): Loaded: 132 cliques out of 286 chunks from 181 comparisons\n", - " 2m 54s CLIQUES (F 100 LCS M>60 S>65): 286 members in 132 cliques\n", - " 2m 54s PRINT (F 100 LCS M>60 S>65): sorting out cliques\n", - " 2m 54s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs\n", - " 2m 57s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs\n", - " 2m 57s CHUNKING (F 100): already chunked into 4244 chunks\n", - " 2m 57s PREPARING (F 100 LCS): Already prepared\n", - " 2m 57s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 394 entries in matrix\n", - " 2m 57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", - " 2m 57s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 2m 57s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix\n", - " 2m 57s CLIQUES (F 100 LCS M>60 S>60): 394 relevant similarities between 537 passages\n", - " 2m 57s CLIQUES (F 100 LCS M>60 S>60): Loaded: 215 cliques out of 537 chunks from 394 comparisons\n", - " 2m 57s CLIQUES (F 100 LCS M>60 S>60): 537 members in 215 cliques\n", - " 2m 57s PRINT (F 100 LCS M>60 S>60): sorting out cliques\n", - " 2m 57s PRINT (F 100 LCS M>60 S>60): formatting 215 cliques skipping 101 binary chapter diffs\n", - " 3m 03s PRINT (F 100 LCS M>60 S>60): formatted 215 cliques (5 files) skipping 101 binary chapter diffs\n", - " 3m 03s CHUNKING (F 50): Loaded: 8509 chunks\n", - " 3m 03s CHUNKING (F 50): Made 8509 chunks\n", - " 3m 03s PREPARING (F 50 SET)\n", - " 3m 04s PREPARING (F 50 SET): Done 8509 chunks.\n", - " 3m 04s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>100): sorting out cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 3m 04s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 04s PREPARING (F 50 SET): Already prepared\n", - " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>95): 3 relevant similarities between 6 passages\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>95): Loaded: 3 cliques out of 6 chunks from 3 comparisons\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>95): 6 members in 3 cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>95): sorting out cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>95): formatting 3 cliques skipping 3 binary chapter diffs\n", - " 3m 04s PRINT (F 50 SET M>50 S>95): formatted 3 cliques (1 files) skipping 3 binary chapter diffs\n", - " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 04s PREPARING (F 50 SET): Already prepared\n", - " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>90): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>90): sorting out cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 6 binary chapter diffs\n", - " 3m 04s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 6 binary chapter diffs\n", - " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 04s PREPARING (F 50 SET): Already prepared\n", - " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>85): 34 relevant similarities between 55 passages\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>85): Loaded: 25 cliques out of 55 chunks from 34 comparisons\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>85): 55 members in 25 cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>85): sorting out cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>85): formatting 25 cliques skipping 11 binary chapter diffs\n", - " 3m 04s PRINT (F 50 SET M>50 S>85): formatted 25 cliques (1 files) skipping 11 binary chapter diffs\n", - " 3m 04s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 04s PREPARING (F 50 SET): Already prepared\n", - " 3m 04s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 04s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>80): 65 relevant similarities between 104 passages\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>80): Loaded: 47 cliques out of 104 chunks from 65 comparisons\n", - " 3m 04s CLIQUES (F 50 SET M>50 S>80): 104 members in 47 cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>80): sorting out cliques\n", - " 3m 04s PRINT (F 50 SET M>50 S>80): formatting 47 cliques skipping 20 binary chapter diffs\n", - " 3m 05s PRINT (F 50 SET M>50 S>80): formatted 47 cliques (1 files) skipping 20 binary chapter diffs\n", - " 3m 05s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 05s PREPARING (F 50 SET): Already prepared\n", - " 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>75): 121 relevant similarities between 197 passages\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>75): Loaded: 90 cliques out of 197 chunks from 121 comparisons\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>75): 197 members in 90 cliques\n", - " 3m 05s PRINT (F 50 SET M>50 S>75): sorting out cliques\n", - " 3m 05s PRINT (F 50 SET M>50 S>75): formatting 90 cliques skipping 35 binary chapter diffs\n", - " 3m 05s PRINT (F 50 SET M>50 S>75): formatted 90 cliques (2 files) skipping 35 binary chapter diffs\n", - " 3m 05s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 05s PREPARING (F 50 SET): Already prepared\n", - " 3m 05s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 05s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>70): 174 relevant similarities between 277 passages\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>70): Loaded: 127 cliques out of 277 chunks from 174 comparisons\n", - " 3m 05s CLIQUES (F 50 SET M>50 S>70): 277 members in 127 cliques\n", - " 3m 05s PRINT (F 50 SET M>50 S>70): sorting out cliques\n", - " 3m 05s PRINT (F 50 SET M>50 S>70): formatting 127 cliques skipping 47 binary chapter diffs\n", - " 3m 06s PRINT (F 50 SET M>50 S>70): formatted 127 cliques (3 files) skipping 47 binary chapter diffs\n", - " 3m 06s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 06s PREPARING (F 50 SET): Already prepared\n", - " 3m 06s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 06s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 06s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates\n", - " 3m 06s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix\n", - " 3m 06s CLIQUES (F 50 SET M>50 S>65): 254 relevant similarities between 394 passages\n", - " 3m 06s CLIQUES (F 50 SET M>50 S>65): Loaded: 180 cliques out of 394 chunks from 254 comparisons\n", - " 3m 06s CLIQUES (F 50 SET M>50 S>65): 394 members in 180 cliques\n", - " 3m 06s PRINT (F 50 SET M>50 S>65): sorting out cliques\n", - " 3m 06s PRINT (F 50 SET M>50 S>65): formatting 180 cliques skipping 61 binary chapter diffs\n", - " 3m 07s PRINT (F 50 SET M>50 S>65): formatted 180 cliques (4 files) skipping 61 binary chapter diffs\n", - " 3m 07s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 07s PREPARING (F 50 SET): Already prepared\n", - " 3m 07s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 07s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 07s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates\n", - " 3m 07s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix\n", - " 3m 08s CLIQUES (F 50 SET M>50 S>60): 365 relevant similarities between 543 passages\n", - " 3m 08s CLIQUES (F 50 SET M>50 S>60): Loaded: 239 cliques out of 543 chunks from 365 comparisons\n", - " 3m 08s CLIQUES (F 50 SET M>50 S>60): 543 members in 239 cliques\n", - " 3m 08s PRINT (F 50 SET M>50 S>60): sorting out cliques\n", - " 3m 08s PRINT (F 50 SET M>50 S>60): formatting 239 cliques skipping 81 binary chapter diffs\n", - " 3m 09s PRINT (F 50 SET M>50 S>60): formatted 239 cliques (5 files) skipping 81 binary chapter diffs\n", - " 3m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 09s PREPARING (F 50 SET): Already prepared\n", - " 3m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 09s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates\n", - " 3m 09s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix\n", - " 3m 09s CLIQUES (F 50 SET M>50 S>55): 535 relevant similarities between 755 passages\n", - " 3m 09s CLIQUES (F 50 SET M>50 S>55): Loaded: 322 cliques out of 755 chunks from 535 comparisons\n", - " 3m 09s CLIQUES (F 50 SET M>50 S>55): 755 members in 322 cliques\n", - " 3m 09s PRINT (F 50 SET M>50 S>55): sorting out cliques\n", - " 3m 09s PRINT (F 50 SET M>50 S>55): formatting 322 cliques skipping 101 binary chapter diffs\n", - " 3m 11s PRINT (F 50 SET M>50 S>55): formatted 322 cliques (7 files) skipping 101 binary chapter diffs\n", - " 3m 11s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 11s PREPARING (F 50 SET): Already prepared\n", - " 3m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 926 entries in matrix\n", - " 3m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", - " 3m 11s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates\n", - " 3m 11s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix\n", - " 3m 12s CLIQUES (F 50 SET M>50 S>50): 926 relevant similarities between 1183 passages\n", - " 3m 12s CLIQUES (F 50 SET M>50 S>50): Loaded: 460 cliques out of 1183 chunks from 926 comparisons\n", - " 3m 12s CLIQUES (F 50 SET M>50 S>50): 1183 members in 460 cliques\n", - " 3m 12s PRINT (F 50 SET M>50 S>50): sorting out cliques\n", - " 3m 12s PRINT (F 50 SET M>50 S>50): formatting 460 cliques skipping 132 binary chapter diffs\n", - " 3m 16s PRINT (F 50 SET M>50 S>50): formatted 460 cliques (10 files) skipping 132 binary chapter diffs\n", - " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 16s PREPARING (F 50 LCS)\n", - " 3m 16s PREPARING (F 50 LCS): Done 8509 chunks.\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>100): sorting out cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", - " 3m 16s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", - " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 16s PREPARING (F 50 LCS): Already prepared\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>95): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>95): sorting out cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs\n", - " 3m 16s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs\n", - " 3m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 16s PREPARING (F 50 LCS): Already prepared\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 16s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>90): 23 relevant similarities between 43 passages\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>90): Loaded: 20 cliques out of 43 chunks from 23 comparisons\n", - " 3m 16s CLIQUES (F 50 LCS M>60 S>90): 43 members in 20 cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>90): sorting out cliques\n", - " 3m 16s PRINT (F 50 LCS M>60 S>90): formatting 20 cliques skipping 6 binary chapter diffs\n", - " 3m 16s PRINT (F 50 LCS M>60 S>90): formatted 20 cliques (1 files) skipping 6 binary chapter diffs\n", - " 3m 17s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 17s PREPARING (F 50 LCS): Already prepared\n", - " 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>85): 77 relevant similarities between 125 passages\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>85): Loaded: 56 cliques out of 125 chunks from 77 comparisons\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>85): 125 members in 56 cliques\n", - " 3m 17s PRINT (F 50 LCS M>60 S>85): sorting out cliques\n", - " 3m 17s PRINT (F 50 LCS M>60 S>85): formatting 56 cliques skipping 21 binary chapter diffs\n", - " 3m 17s PRINT (F 50 LCS M>60 S>85): formatted 56 cliques (2 files) skipping 21 binary chapter diffs\n", - " 3m 17s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 17s PREPARING (F 50 LCS): Already prepared\n", - " 3m 17s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 17s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>80): 129 relevant similarities between 204 passages\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>80): Loaded: 93 cliques out of 204 chunks from 129 comparisons\n", - " 3m 17s CLIQUES (F 50 LCS M>60 S>80): 204 members in 93 cliques\n", - " 3m 17s PRINT (F 50 LCS M>60 S>80): sorting out cliques\n", - " 3m 17s PRINT (F 50 LCS M>60 S>80): formatting 93 cliques skipping 35 binary chapter diffs\n", - " 3m 18s PRINT (F 50 LCS M>60 S>80): formatted 93 cliques (2 files) skipping 35 binary chapter diffs\n", - " 3m 18s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 18s PREPARING (F 50 LCS): Already prepared\n", - " 3m 18s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 18s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 18s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 3m 18s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix\n", - " 3m 18s CLIQUES (F 50 LCS M>60 S>75): 198 relevant similarities between 299 passages\n", - " 3m 18s CLIQUES (F 50 LCS M>60 S>75): Loaded: 134 cliques out of 299 chunks from 198 comparisons\n", - " 3m 18s CLIQUES (F 50 LCS M>60 S>75): 299 members in 134 cliques\n", - " 3m 18s PRINT (F 50 LCS M>60 S>75): sorting out cliques\n", - " 3m 18s PRINT (F 50 LCS M>60 S>75): formatting 134 cliques skipping 51 binary chapter diffs\n", - " 3m 19s PRINT (F 50 LCS M>60 S>75): formatted 134 cliques (3 files) skipping 51 binary chapter diffs\n", - " 3m 19s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 19s PREPARING (F 50 LCS): Already prepared\n", - " 3m 19s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 19s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 19s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 3m 19s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix\n", - " 3m 19s CLIQUES (F 50 LCS M>60 S>70): 314 relevant similarities between 470 passages\n", - " 3m 19s CLIQUES (F 50 LCS M>60 S>70): Loaded: 209 cliques out of 470 chunks from 314 comparisons\n", - " 3m 19s CLIQUES (F 50 LCS M>60 S>70): 470 members in 209 cliques\n", - " 3m 19s PRINT (F 50 LCS M>60 S>70): sorting out cliques\n", - " 3m 19s PRINT (F 50 LCS M>60 S>70): formatting 209 cliques skipping 65 binary chapter diffs\n", - " 3m 20s PRINT (F 50 LCS M>60 S>70): formatted 209 cliques (5 files) skipping 65 binary chapter diffs\n", - " 3m 20s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 20s PREPARING (F 50 LCS): Already prepared\n", - " 3m 20s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 20s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 20s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 3m 20s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix\n", - " 3m 20s CLIQUES (F 50 LCS M>60 S>65): 587 relevant similarities between 765 passages\n", - " 3m 20s CLIQUES (F 50 LCS M>60 S>65): Loaded: 312 cliques out of 765 chunks from 587 comparisons\n", - " 3m 20s CLIQUES (F 50 LCS M>60 S>65): 765 members in 312 cliques\n", - " 3m 20s PRINT (F 50 LCS M>60 S>65): sorting out cliques\n", - " 3m 20s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs\n", - " 3m 23s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs\n", - " 3m 23s CHUNKING (F 50): already chunked into 8509 chunks\n", - " 3m 23s PREPARING (F 50 LCS): Already prepared\n", - " 3m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1816 entries in matrix\n", - " 3m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 98.58156028368793. 0 are 100%\n", - " 3m 23s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 3m 23s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix\n", - " 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1816 relevant similarities between 1867 passages\n", - " 3m 23s CLIQUES (F 50 LCS M>60 S>60): Loaded: 553 cliques out of 1867 chunks from 1816 comparisons\n", - " 3m 23s CLIQUES (F 50 LCS M>60 S>60): 1867 members in 553 cliques\n", - " 3m 23s PRINT (F 50 LCS M>60 S>60): sorting out cliques\n", - " 3m 23s PRINT (F 50 LCS M>60 S>60): formatting 553 cliques skipping 225 binary chapter diffs\n", - " 3m 30s PRINT (F 50 LCS M>60 S>60): formatted 553 cliques (12 files) skipping 225 binary chapter diffs\n", - " 3m 30s CHUNKING (F 20): Loaded: 21312 chunks\n", - " 3m 30s CHUNKING (F 20): Made 21312 chunks\n", - " 3m 30s PREPARING (F 20 SET)\n", - " 3m 31s PREPARING (F 20 SET): Done 21312 chunks.\n", - " 3m 31s SIMILARITY (F 20 SET M>50): Loaded: 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>100): 18 relevant similarities between 36 passages\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>100): Loaded: 18 cliques out of 36 chunks from 18 comparisons\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>100): 36 members in 18 cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>100): sorting out cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>100): formatting 18 cliques skipping 11 binary chapter diffs\n", - " 3m 31s PRINT (F 20 SET M>50 S>100): formatted 18 cliques (1 files) skipping 11 binary chapter diffs\n", - " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 31s PREPARING (F 20 SET): Already prepared\n", - " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>95): 18 relevant similarities between 36 passages\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>95): Loaded: 18 cliques out of 36 chunks from 18 comparisons\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>95): 36 members in 18 cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>95): sorting out cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>95): formatting 18 cliques skipping 11 binary chapter diffs\n", - " 3m 31s PRINT (F 20 SET M>50 S>95): formatted 18 cliques (1 files) skipping 11 binary chapter diffs\n", - " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 31s PREPARING (F 20 SET): Already prepared\n", - " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>90): 72 relevant similarities between 126 passages\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>90): Loaded: 58 cliques out of 126 chunks from 72 comparisons\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>90): 126 members in 58 cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>90): sorting out cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>90): formatting 58 cliques skipping 24 binary chapter diffs\n", - " 3m 31s PRINT (F 20 SET M>50 S>90): formatted 58 cliques (2 files) skipping 24 binary chapter diffs\n", - " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 31s PREPARING (F 20 SET): Already prepared\n", - " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>85): 133 relevant similarities between 199 passages\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>85): Loaded: 84 cliques out of 199 chunks from 133 comparisons\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>85): 199 members in 84 cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>85): sorting out cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>85): formatting 84 cliques skipping 37 binary chapter diffs\n", - " 3m 31s PRINT (F 20 SET M>50 S>85): formatted 84 cliques (2 files) skipping 37 binary chapter diffs\n", - " 3m 31s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 31s PREPARING (F 20 SET): Already prepared\n", - " 3m 31s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 31s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>80): 224 relevant similarities between 332 passages\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>80): Loaded: 146 cliques out of 332 chunks from 224 comparisons\n", - " 3m 31s CLIQUES (F 20 SET M>50 S>80): 332 members in 146 cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>80): sorting out cliques\n", - " 3m 31s PRINT (F 20 SET M>50 S>80): formatting 146 cliques skipping 62 binary chapter diffs\n", - " 3m 32s PRINT (F 20 SET M>50 S>80): formatted 146 cliques (3 files) skipping 62 binary chapter diffs\n", - " 3m 32s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 32s PREPARING (F 20 SET): Already prepared\n", - " 3m 32s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>75): 372 relevant similarities between 528 passages\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>75): Loaded: 227 cliques out of 528 chunks from 372 comparisons\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques\n", - " 3m 32s PRINT (F 20 SET M>50 S>75): sorting out cliques\n", - " 3m 32s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 82 binary chapter diffs\n", - " 3m 32s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 82 binary chapter diffs\n", - " 3m 32s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 32s PREPARING (F 20 SET): Already prepared\n", - " 3m 32s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 32s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 760 passages\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>70): Loaded: 326 cliques out of 760 chunks from 546 comparisons\n", - " 3m 32s CLIQUES (F 20 SET M>50 S>70): 760 members in 326 cliques\n", - " 3m 32s PRINT (F 20 SET M>50 S>70): sorting out cliques\n", - " 3m 32s PRINT (F 20 SET M>50 S>70): formatting 326 cliques skipping 107 binary chapter diffs\n", - " 3m 33s PRINT (F 20 SET M>50 S>70): formatted 326 cliques (7 files) skipping 107 binary chapter diffs\n", - " 3m 33s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 33s PREPARING (F 20 SET): Already prepared\n", - " 3m 33s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 33s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 33s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates\n", - " 3m 33s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix\n", - " 3m 33s CLIQUES (F 20 SET M>50 S>65): 803 relevant similarities between 1096 passages\n", - " 3m 33s CLIQUES (F 20 SET M>50 S>65): Loaded: 470 cliques out of 1096 chunks from 803 comparisons\n", - " 3m 33s CLIQUES (F 20 SET M>50 S>65): 1096 members in 470 cliques\n", - " 3m 33s PRINT (F 20 SET M>50 S>65): sorting out cliques\n", - " 3m 33s PRINT (F 20 SET M>50 S>65): formatting 470 cliques skipping 144 binary chapter diffs\n", - " 3m 34s PRINT (F 20 SET M>50 S>65): formatted 470 cliques (10 files) skipping 144 binary chapter diffs\n", - " 3m 34s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 34s PREPARING (F 20 SET): Already prepared\n", - " 3m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 34s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates\n", - " 3m 34s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix\n", - " 3m 34s CLIQUES (F 20 SET M>50 S>60): 1414 relevant similarities between 1837 passages\n", - " 3m 34s CLIQUES (F 20 SET M>50 S>60): Loaded: 739 cliques out of 1837 chunks from 1414 comparisons\n", - " 3m 34s CLIQUES (F 20 SET M>50 S>60): 1837 members in 739 cliques\n", - " 3m 34s PRINT (F 20 SET M>50 S>60): sorting out cliques\n", - " 3m 34s PRINT (F 20 SET M>50 S>60): formatting 739 cliques skipping 213 binary chapter diffs\n", - " 3m 36s PRINT (F 20 SET M>50 S>60): formatted 739 cliques (15 files) skipping 213 binary chapter diffs\n", - " 3m 36s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 36s PREPARING (F 20 SET): Already prepared\n", - " 3m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 36s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates\n", - " 3m 36s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix\n", - " 3m 36s CLIQUES (F 20 SET M>50 S>55): 2453 relevant similarities between 2826 passages\n", - " 3m 36s CLIQUES (F 20 SET M>50 S>55): Loaded: 997 cliques out of 2826 chunks from 2453 comparisons\n", - " 3m 36s CLIQUES (F 20 SET M>50 S>55): 2826 members in 997 cliques\n", - " 3m 36s PRINT (F 20 SET M>50 S>55): sorting out cliques\n", - " 3m 36s PRINT (F 20 SET M>50 S>55): formatting 997 cliques skipping 296 binary chapter diffs\n", - " 3m 39s PRINT (F 20 SET M>50 S>55): formatted 997 cliques (20 files) skipping 296 binary chapter diffs\n", - " 3m 39s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 39s PREPARING (F 20 SET): Already prepared\n", - " 3m 39s SIMILARITY (F 20 SET M>50): Using 227 M (227090016) comparisons with 5559 entries in matrix\n", - " 3m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 18 are 100%\n", - " 3m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates\n", - " 3m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix\n", - " 3m 39s CLIQUES (F 20 SET M>50 S>50): 5559 relevant similarities between 4933 passages\n", - " 3m 39s CLIQUES (F 20 SET M>50 S>50): Loaded: 1212 cliques out of 4933 chunks from 5559 comparisons\n", - " 3m 39s CLIQUES (F 20 SET M>50 S>50): 4933 members in 1212 cliques\n", - " 3m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques\n", - " 3m 39s PRINT (F 20 SET M>50 S>50): formatting 1212 cliques skipping 416 binary chapter diffs\n", - " 3m 42s PRINT (F 20 SET M>50 S>50): formatted 1212 cliques (25 files) skipping 416 binary chapter diffs\n", - " 3m 42s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 42s PREPARING (F 20 LCS)\n", - " 3m 43s PREPARING (F 20 LCS): Done 21312 chunks.\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): Loaded: 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>100): 6 relevant similarities between 12 passages\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>100): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>100): 12 members in 6 cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>100): sorting out cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>100): formatting 6 cliques skipping 5 binary chapter diffs\n", - " 3m 43s PRINT (F 20 LCS M>60 S>100): formatted 6 cliques (1 files) skipping 5 binary chapter diffs\n", - " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 43s PREPARING (F 20 LCS): Already prepared\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>95): 33 relevant similarities between 62 passages\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>95): Loaded: 29 cliques out of 62 chunks from 33 comparisons\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>95): 62 members in 29 cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>95): sorting out cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>95): formatting 29 cliques skipping 14 binary chapter diffs\n", - " 3m 43s PRINT (F 20 LCS M>60 S>95): formatted 29 cliques (1 files) skipping 14 binary chapter diffs\n", - " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 43s PREPARING (F 20 LCS): Already prepared\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>90): 115 relevant similarities between 181 passages\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded: 76 cliques out of 181 chunks from 115 comparisons\n", - " 3m 43s CLIQUES (F 20 LCS M>60 S>90): 181 members in 76 cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques\n", - " 3m 43s PRINT (F 20 LCS M>60 S>90): formatting 76 cliques skipping 33 binary chapter diffs\n", - " 3m 43s PRINT (F 20 LCS M>60 S>90): formatted 76 cliques (2 files) skipping 33 binary chapter diffs\n", - " 3m 43s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 43s PREPARING (F 20 LCS): Already prepared\n", - " 3m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>85): 232 relevant similarities between 339 passages\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>85): Loaded: 149 cliques out of 339 chunks from 232 comparisons\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>85): 339 members in 149 cliques\n", - " 3m 44s PRINT (F 20 LCS M>60 S>85): sorting out cliques\n", - " 3m 44s PRINT (F 20 LCS M>60 S>85): formatting 149 cliques skipping 57 binary chapter diffs\n", - " 3m 44s PRINT (F 20 LCS M>60 S>85): formatted 149 cliques (3 files) skipping 57 binary chapter diffs\n", - " 3m 44s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 44s PREPARING (F 20 LCS): Already prepared\n", - " 3m 44s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>80): 470 relevant similarities between 681 passages\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>80): Loaded: 300 cliques out of 681 chunks from 470 comparisons\n", - " 3m 44s CLIQUES (F 20 LCS M>60 S>80): 681 members in 300 cliques\n", - " 3m 44s PRINT (F 20 LCS M>60 S>80): sorting out cliques\n", - " 3m 44s PRINT (F 20 LCS M>60 S>80): formatting 300 cliques skipping 106 binary chapter diffs\n", - " 3m 45s PRINT (F 20 LCS M>60 S>80): formatted 300 cliques (6 files) skipping 106 binary chapter diffs\n", - " 3m 45s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 45s PREPARING (F 20 LCS): Already prepared\n", - " 3m 45s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 45s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 45s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 3m 45s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix\n", - " 3m 45s CLIQUES (F 20 LCS M>60 S>75): 876 relevant similarities between 1137 passages\n", - " 3m 45s CLIQUES (F 20 LCS M>60 S>75): Loaded: 470 cliques out of 1137 chunks from 876 comparisons\n", - " 3m 45s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques\n", - " 3m 45s PRINT (F 20 LCS M>60 S>75): sorting out cliques\n", - " 3m 45s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs\n", - " 3m 46s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs\n", - " 3m 46s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 46s PREPARING (F 20 LCS): Already prepared\n", - " 3m 46s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 46s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 3m 46s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix\n", - " 3m 46s CLIQUES (F 20 LCS M>60 S>70): 1935 relevant similarities between 2224 passages\n", - " 3m 46s CLIQUES (F 20 LCS M>60 S>70): Loaded: 844 cliques out of 2224 chunks from 1935 comparisons\n", - " 3m 46s CLIQUES (F 20 LCS M>60 S>70): 2224 members in 844 cliques\n", - " 3m 46s PRINT (F 20 LCS M>60 S>70): sorting out cliques\n", - " 3m 46s PRINT (F 20 LCS M>60 S>70): formatting 844 cliques skipping 306 binary chapter diffs\n", - " 3m 48s PRINT (F 20 LCS M>60 S>70): formatted 844 cliques (17 files) skipping 306 binary chapter diffs\n", - " 3m 48s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 48s PREPARING (F 20 LCS): Already prepared\n", - " 3m 48s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 48s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 48s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 3m 48s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix\n", - " 3m 48s CLIQUES (F 20 LCS M>60 S>65): 6898 relevant similarities between 5985 passages\n", - " 3m 48s CLIQUES (F 20 LCS M>60 S>65): Loaded: 1253 cliques out of 5985 chunks from 6898 comparisons\n", - " 3m 48s CLIQUES (F 20 LCS M>60 S>65): 5985 members in 1253 cliques\n", - " 3m 48s PRINT (F 20 LCS M>60 S>65): sorting out cliques\n", - " 3m 49s PRINT (F 20 LCS M>60 S>65): formatting 1253 cliques skipping 575 binary chapter diffs\n", - " 3m 52s PRINT (F 20 LCS M>60 S>65): formatted 1253 cliques (26 files) skipping 575 binary chapter diffs\n", - " 3m 52s CHUNKING (F 20): already chunked into 21312 chunks\n", - " 3m 52s PREPARING (F 20 LCS): Already prepared\n", - " 3m 52s SIMILARITY (F 20 LCS M>60): Using 227 M (227090016) comparisons with 121585 entries in matrix\n", - " 3m 52s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 6 are 100%\n", - " 3m 52s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 3m 52s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix\n", - " 3m 53s CLIQUES (F 20 LCS M>60 S>60): 121585 relevant similarities between 17654 passages\n", - " 3m 53s CLIQUES (F 20 LCS M>60 S>60): Loaded: 163 cliques out of 17654 chunks from 121585 comparisons\n", - " 3m 53s CLIQUES (F 20 LCS M>60 S>60): 17654 members in 163 cliques\n", - " 3m 53s PRINT (F 20 LCS M>60 S>60): sorting out cliques\n", - " 3m 53s PRINT (F 20 LCS M>60 S>60): formatting 163 cliques skipping 104 binary chapter diffs\n", - " 3m 53s PRINT (F 20 LCS M>60 S>60): formatted 163 cliques (4 files) skipping 104 binary chapter diffs\n", - " 3m 53s CHUNKING (F 10): Loaded: 42640 chunks\n", - " 3m 53s CHUNKING (F 10): Made 42640 chunks\n", - " 3m 53s PREPARING (F 10 SET)\n", - " 3m 54s PREPARING (F 10 SET): Done 42640 chunks.\n", - " 3m 54s SIMILARITY (F 10 SET M>50): Loaded: 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>100): 269 relevant similarities between 462 passages\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>100): Loaded: 220 cliques out of 462 chunks from 269 comparisons\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>100): 462 members in 220 cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>100): sorting out cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>100): formatting 220 cliques skipping 83 binary chapter diffs\n", - " 3m 55s PRINT (F 10 SET M>50 S>100): formatted 220 cliques (5 files) skipping 83 binary chapter diffs\n", - " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 55s PREPARING (F 10 SET): Already prepared\n", - " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>95): 269 relevant similarities between 462 passages\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>95): Loaded: 220 cliques out of 462 chunks from 269 comparisons\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>95): 462 members in 220 cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>95): sorting out cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>95): formatting 220 cliques skipping 83 binary chapter diffs\n", - " 3m 55s PRINT (F 10 SET M>50 S>95): formatted 220 cliques (5 files) skipping 83 binary chapter diffs\n", - " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 55s PREPARING (F 10 SET): Already prepared\n", - " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>90): 307 relevant similarities between 494 passages\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>90): Loaded: 231 cliques out of 494 chunks from 307 comparisons\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>90): 494 members in 231 cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>90): sorting out cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>90): formatting 231 cliques skipping 88 binary chapter diffs\n", - " 3m 55s PRINT (F 10 SET M>50 S>90): formatted 231 cliques (5 files) skipping 88 binary chapter diffs\n", - " 3m 55s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 55s PREPARING (F 10 SET): Already prepared\n", - " 3m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>85): 732 relevant similarities between 1109 passages\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>85): Loaded: 489 cliques out of 1109 chunks from 732 comparisons\n", - " 3m 55s CLIQUES (F 10 SET M>50 S>85): 1109 members in 489 cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>85): sorting out cliques\n", - " 3m 55s PRINT (F 10 SET M>50 S>85): formatting 489 cliques skipping 186 binary chapter diffs\n", - " 3m 56s PRINT (F 10 SET M>50 S>85): formatted 489 cliques (10 files) skipping 186 binary chapter diffs\n", - " 3m 56s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 56s PREPARING (F 10 SET): Already prepared\n", - " 3m 56s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 56s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 56s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates\n", - " 3m 56s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix\n", - " 3m 56s CLIQUES (F 10 SET M>50 S>80): 1140 relevant similarities between 1540 passages\n", - " 3m 56s CLIQUES (F 10 SET M>50 S>80): Loaded: 631 cliques out of 1540 chunks from 1140 comparisons\n", - " 3m 56s CLIQUES (F 10 SET M>50 S>80): 1540 members in 631 cliques\n", - " 3m 56s PRINT (F 10 SET M>50 S>80): sorting out cliques\n", - " 3m 56s PRINT (F 10 SET M>50 S>80): formatting 631 cliques skipping 218 binary chapter diffs\n", - " 3m 57s PRINT (F 10 SET M>50 S>80): formatted 631 cliques (13 files) skipping 218 binary chapter diffs\n", - " 3m 57s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 57s PREPARING (F 10 SET): Already prepared\n", - " 3m 57s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 57s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates\n", - " 3m 57s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix\n", - " 3m 57s CLIQUES (F 10 SET M>50 S>75): 2144 relevant similarities between 2825 passages\n", - " 3m 57s CLIQUES (F 10 SET M>50 S>75): Loaded: 1126 cliques out of 2825 chunks from 2144 comparisons\n", - " 3m 57s CLIQUES (F 10 SET M>50 S>75): 2825 members in 1126 cliques\n", - " 3m 57s PRINT (F 10 SET M>50 S>75): sorting out cliques\n", - " 3m 57s PRINT (F 10 SET M>50 S>75): formatting 1126 cliques skipping 418 binary chapter diffs\n", - " 3m 58s PRINT (F 10 SET M>50 S>75): formatted 1126 cliques (23 files) skipping 418 binary chapter diffs\n", - " 3m 58s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 3m 58s PREPARING (F 10 SET): Already prepared\n", - " 3m 58s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 3m 58s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 3m 58s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates\n", - " 3m 58s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix\n", - " 3m 58s CLIQUES (F 10 SET M>50 S>70): 3512 relevant similarities between 4079 passages\n", - " 3m 58s CLIQUES (F 10 SET M>50 S>70): Loaded: 1506 cliques out of 4079 chunks from 3512 comparisons\n", - " 3m 58s CLIQUES (F 10 SET M>50 S>70): 4079 members in 1506 cliques\n", - " 3m 58s PRINT (F 10 SET M>50 S>70): sorting out cliques\n", - " 3m 59s PRINT (F 10 SET M>50 S>70): formatting 1506 cliques skipping 570 binary chapter diffs\n", - " 4m 00s PRINT (F 10 SET M>50 S>70): formatted 1506 cliques (31 files) skipping 570 binary chapter diffs\n", - " 4m 00s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 00s PREPARING (F 10 SET): Already prepared\n", - " 4m 00s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 4m 00s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 4m 00s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates\n", - " 4m 00s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix\n", - " 4m 01s CLIQUES (F 10 SET M>50 S>65): 5448 relevant similarities between 5792 passages\n", - " 4m 01s CLIQUES (F 10 SET M>50 S>65): Loaded: 1855 cliques out of 5792 chunks from 5448 comparisons\n", - " 4m 01s CLIQUES (F 10 SET M>50 S>65): 5792 members in 1855 cliques\n", - " 4m 01s PRINT (F 10 SET M>50 S>65): sorting out cliques\n", - " 4m 01s PRINT (F 10 SET M>50 S>65): formatting 1855 cliques skipping 679 binary chapter diffs\n", - " 4m 03s PRINT (F 10 SET M>50 S>65): formatted 1855 cliques (38 files) skipping 679 binary chapter diffs\n", - " 4m 03s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 03s PREPARING (F 10 SET): Already prepared\n", - " 4m 03s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 4m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 4m 03s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates\n", - " 4m 03s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix\n", - " 4m 03s CLIQUES (F 10 SET M>50 S>60): 13180 relevant similarities between 10165 passages\n", - " 4m 03s CLIQUES (F 10 SET M>50 S>60): Loaded: 2189 cliques out of 10165 chunks from 13180 comparisons\n", - " 4m 03s CLIQUES (F 10 SET M>50 S>60): 10165 members in 2189 cliques\n", - " 4m 03s PRINT (F 10 SET M>50 S>60): sorting out cliques\n", - " 4m 03s PRINT (F 10 SET M>50 S>60): formatting 2189 cliques skipping 823 binary chapter diffs\n", - " 4m 05s PRINT (F 10 SET M>50 S>60): formatted 2189 cliques (44 files) skipping 823 binary chapter diffs\n", - " 4m 05s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 05s PREPARING (F 10 SET): Already prepared\n", - " 4m 05s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 4m 05s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 4m 05s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates\n", - " 4m 05s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix\n", - " 4m 06s CLIQUES (F 10 SET M>50 S>55): 25713 relevant similarities between 13984 passages\n", - " 4m 06s CLIQUES (F 10 SET M>50 S>55): Loaded: 2008 cliques out of 13984 chunks from 25713 comparisons\n", - " 4m 06s CLIQUES (F 10 SET M>50 S>55): 13984 members in 2008 cliques\n", - " 4m 06s PRINT (F 10 SET M>50 S>55): sorting out cliques\n", - " 4m 06s PRINT (F 10 SET M>50 S>55): formatting 2008 cliques skipping 777 binary chapter diffs\n", - " 4m 08s PRINT (F 10 SET M>50 S>55): formatted 2008 cliques (41 files) skipping 777 binary chapter diffs\n", - " 4m 08s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 08s PREPARING (F 10 SET): Already prepared\n", - " 4m 08s SIMILARITY (F 10 SET M>50): Using 909 M (909063480) comparisons with 89309 entries in matrix\n", - " 4m 08s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 269 are 100%\n", - " 4m 08s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates\n", - " 4m 08s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix\n", - " 4m 09s CLIQUES (F 10 SET M>50 S>50): 89309 relevant similarities between 22932 passages\n", - " 4m 09s CLIQUES (F 10 SET M>50 S>50): Loaded: 1442 cliques out of 22932 chunks from 89309 comparisons\n", - " 4m 09s CLIQUES (F 10 SET M>50 S>50): 22932 members in 1442 cliques\n", - " 4m 09s PRINT (F 10 SET M>50 S>50): sorting out cliques\n", - " 4m 10s PRINT (F 10 SET M>50 S>50): formatting 1442 cliques skipping 627 binary chapter diffs\n", - " 4m 11s PRINT (F 10 SET M>50 S>50): formatted 1442 cliques (29 files) skipping 627 binary chapter diffs\n", - " 4m 11s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 11s PREPARING (F 10 LCS)\n", - " 4m 12s PREPARING (F 10 LCS): Done 42640 chunks.\n", - " 4m 13s SIMILARITY (F 10 LCS M>60): Loaded: 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 14s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 14s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates\n", - " 4m 14s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix\n", - " 4m 15s CLIQUES (F 10 LCS M>60 S>100): 152 relevant similarities between 277 passages\n", - " 4m 15s CLIQUES (F 10 LCS M>60 S>100): Loaded: 135 cliques out of 277 chunks from 152 comparisons\n", - " 4m 15s CLIQUES (F 10 LCS M>60 S>100): 277 members in 135 cliques\n", - " 4m 15s PRINT (F 10 LCS M>60 S>100): sorting out cliques\n", - " 4m 15s PRINT (F 10 LCS M>60 S>100): formatting 135 cliques skipping 52 binary chapter diffs\n", - " 4m 15s PRINT (F 10 LCS M>60 S>100): formatted 135 cliques (3 files) skipping 52 binary chapter diffs\n", - " 4m 15s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 15s PREPARING (F 10 LCS): Already prepared\n", - " 4m 15s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 16s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates\n", - " 4m 16s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix\n", - " 4m 17s CLIQUES (F 10 LCS M>60 S>95): 221 relevant similarities between 408 passages\n", - " 4m 17s CLIQUES (F 10 LCS M>60 S>95): Loaded: 199 cliques out of 408 chunks from 221 comparisons\n", - " 4m 17s CLIQUES (F 10 LCS M>60 S>95): 408 members in 199 cliques\n", - " 4m 17s PRINT (F 10 LCS M>60 S>95): sorting out cliques\n", - " 4m 17s PRINT (F 10 LCS M>60 S>95): formatting 199 cliques skipping 80 binary chapter diffs\n", - " 4m 17s PRINT (F 10 LCS M>60 S>95): formatted 199 cliques (4 files) skipping 80 binary chapter diffs\n", - " 4m 17s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 17s PREPARING (F 10 LCS): Already prepared\n", - " 4m 17s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 18s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 18s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates\n", - " 4m 18s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix\n", - " 4m 19s CLIQUES (F 10 LCS M>60 S>90): 603 relevant similarities between 937 passages\n", - " 4m 19s CLIQUES (F 10 LCS M>60 S>90): Loaded: 423 cliques out of 937 chunks from 603 comparisons\n", - " 4m 19s CLIQUES (F 10 LCS M>60 S>90): 937 members in 423 cliques\n", - " 4m 19s PRINT (F 10 LCS M>60 S>90): sorting out cliques\n", - " 4m 19s PRINT (F 10 LCS M>60 S>90): formatting 423 cliques skipping 163 binary chapter diffs\n", - " 4m 19s PRINT (F 10 LCS M>60 S>90): formatted 423 cliques (9 files) skipping 163 binary chapter diffs\n", - " 4m 19s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 19s PREPARING (F 10 LCS): Already prepared\n", - " 4m 19s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 20s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 20s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates\n", - " 4m 20s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix\n", - " 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1391 relevant similarities between 1980 passages\n", - " 4m 21s CLIQUES (F 10 LCS M>60 S>85): Loaded: 831 cliques out of 1980 chunks from 1391 comparisons\n", - " 4m 21s CLIQUES (F 10 LCS M>60 S>85): 1980 members in 831 cliques\n", - " 4m 21s PRINT (F 10 LCS M>60 S>85): sorting out cliques\n", - " 4m 21s PRINT (F 10 LCS M>60 S>85): formatting 831 cliques skipping 309 binary chapter diffs\n", - " 4m 22s PRINT (F 10 LCS M>60 S>85): formatted 831 cliques (17 files) skipping 309 binary chapter diffs\n", - " 4m 22s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 22s PREPARING (F 10 LCS): Already prepared\n", - " 4m 22s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 23s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 23s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates\n", - " 4m 23s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix\n", - " 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3271 relevant similarities between 3894 passages\n", - " 4m 24s CLIQUES (F 10 LCS M>60 S>80): Loaded: 1440 cliques out of 3894 chunks from 3271 comparisons\n", - " 4m 24s CLIQUES (F 10 LCS M>60 S>80): 3894 members in 1440 cliques\n", - " 4m 24s PRINT (F 10 LCS M>60 S>80): sorting out cliques\n", - " 4m 24s PRINT (F 10 LCS M>60 S>80): formatting 1440 cliques skipping 553 binary chapter diffs\n", - " 4m 26s PRINT (F 10 LCS M>60 S>80): formatted 1440 cliques (29 files) skipping 553 binary chapter diffs\n", - " 4m 26s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 26s PREPARING (F 10 LCS): Already prepared\n", - " 4m 26s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 27s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 27s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates\n", - " 4m 27s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix\n", - " 4m 28s CLIQUES (F 10 LCS M>60 S>75): 9197 relevant similarities between 8599 passages\n", - " 4m 28s CLIQUES (F 10 LCS M>60 S>75): Loaded: 2328 cliques out of 8599 chunks from 9197 comparisons\n", - " 4m 28s CLIQUES (F 10 LCS M>60 S>75): 8599 members in 2328 cliques\n", - " 4m 28s PRINT (F 10 LCS M>60 S>75): sorting out cliques\n", - " 4m 28s PRINT (F 10 LCS M>60 S>75): formatting 2328 cliques skipping 955 binary chapter diffs\n", - " 4m 30s PRINT (F 10 LCS M>60 S>75): formatted 2328 cliques (47 files) skipping 955 binary chapter diffs\n", - " 4m 30s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 30s PREPARING (F 10 LCS): Already prepared\n", - " 4m 30s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 32s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 32s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates\n", - " 4m 32s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix\n", - " 4m 33s CLIQUES (F 10 LCS M>60 S>70): 38515 relevant similarities between 20425 passages\n", - " 4m 33s CLIQUES (F 10 LCS M>60 S>70): Loaded: 1937 cliques out of 20425 chunks from 38515 comparisons\n", - " 4m 33s CLIQUES (F 10 LCS M>60 S>70): 20425 members in 1937 cliques\n", - " 4m 33s PRINT (F 10 LCS M>60 S>70): sorting out cliques\n", - " 4m 34s PRINT (F 10 LCS M>60 S>70): formatting 1937 cliques skipping 993 binary chapter diffs\n", - " 4m 35s PRINT (F 10 LCS M>60 S>70): formatted 1937 cliques (39 files) skipping 993 binary chapter diffs\n", - " 4m 35s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 35s PREPARING (F 10 LCS): Already prepared\n", - " 4m 35s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 36s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 36s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates\n", - " 4m 36s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix\n", - " 4m 39s CLIQUES (F 10 LCS M>60 S>65): 346407 relevant similarities between 37696 passages\n", - " 4m 39s CLIQUES (F 10 LCS M>60 S>65): Loaded: 218 cliques out of 37696 chunks from 346407 comparisons\n", - " 4m 39s CLIQUES (F 10 LCS M>60 S>65): 37696 members in 218 cliques\n", - " 4m 39s PRINT (F 10 LCS M>60 S>65): sorting out cliques\n", - " 4m 40s PRINT (F 10 LCS M>60 S>65): formatting 218 cliques skipping 131 binary chapter diffs\n", - " 4m 40s PRINT (F 10 LCS M>60 S>65): formatted 218 cliques (5 files) skipping 131 binary chapter diffs\n", - " 4m 40s CHUNKING (F 10): already chunked into 42640 chunks\n", - " 4m 40s PREPARING (F 10 LCS): Already prepared\n", - " 4m 40s SIMILARITY (F 10 LCS M>60): Using 909 M (909063480) comparisons with 2916528 entries in matrix\n", - " 4m 41s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 152 are 100%\n", - " 4m 41s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates\n", - " 4m 41s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix\n", - " 4m 45s CLIQUES (F 10 LCS M>60 S>60): 2916528 relevant similarities between 42450 passages\n", - " 4m 45s CLIQUES (F 10 LCS M>60 S>60): Loaded: 4 cliques out of 42450 chunks from 2916528 comparisons\n", - " 4m 45s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 4 cliques\n", - " 4m 45s PRINT (F 10 LCS M>60 S>60): sorting out cliques\n", - " 4m 46s PRINT (F 10 LCS M>60 S>60): formatting 4 cliques skipping 3 binary chapter diffs\n", - " 4m 46s PRINT (F 10 LCS M>60 S>60): formatted 4 cliques (1 files) skipping 3 binary chapter diffs\n", - " 4m 46s CHUNKING (O chapter): Loaded: 929 chunks\n", - " 4m 46s CHUNKING (O chapter): Made 929 chunks\n", - " 4m 46s PREPARING (O chapter SET)\n", - " 4m 47s PREPARING (O chapter SET): Done 929 chunks.\n", - " 4m 47s SIMILARITY (O chapter SET M>30): Loaded: 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 47s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates\n", - " 4m 47s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>100): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0\n", - " 4m 48s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing\n", - " 4m 48s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>95): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>90): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>90): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>85): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>85): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>80): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>80): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>75): Loaded: 7 cliques out of 14 chunks from 7 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>75): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs\n", - " 4m 48s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 48s PREPARING (O chapter SET): Already prepared\n", - " 4m 48s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 48s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>70): Loaded: 10 cliques out of 20 chunks from 10 comparisons\n", - " 4m 48s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>70): sorting out cliques\n", - " 4m 48s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs\n", - " 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10\n", - " 4m 48s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing\n", - " 4m 55s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs\n", - " 4m 55s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 4m 55s PREPARING (O chapter SET): Already prepared\n", - " 4m 55s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 4m 55s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): Composing cliques out of 24 chunks from 12 comparisons\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques\n", - " 4m 55s CLIQUES (O chapter SET M>30 S>65): Composed and saved 12 cliques out of 24 chunks from 12 comparisons\n", - " 4m 55s PRINT (O chapter SET M>30 S>65): sorting out cliques\n", - " 4m 55s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs\n", - " 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12\n", - " 4m 55s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing\n", - " 5m 05s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs\n", - " 5m 05s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 5m 05s PREPARING (O chapter SET): Already prepared\n", - " 5m 05s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 5m 05s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): Composing cliques out of 34 chunks from 17 comparisons\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques\n", - " 5m 05s CLIQUES (O chapter SET M>30 S>60): Composed and saved 17 cliques out of 34 chunks from 17 comparisons\n", - " 5m 05s PRINT (O chapter SET M>30 S>60): sorting out cliques\n", - " 5m 05s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs\n", - " 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17\n", - " 5m 05s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing\n", - " 5m 20s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs\n", - " 5m 20s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 5m 20s PREPARING (O chapter SET): Already prepared\n", - " 5m 20s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 5m 20s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): Composing cliques out of 44 chunks from 22 comparisons\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques\n", - " 5m 20s CLIQUES (O chapter SET M>30 S>55): Composed and saved 22 cliques out of 44 chunks from 22 comparisons\n", - " 5m 20s PRINT (O chapter SET M>30 S>55): sorting out cliques\n", - " 5m 20s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs\n", - " 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22\n", - " 5m 20s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing\n", - " 5m 39s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", - " 5m 39s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 5m 39s PREPARING (O chapter SET): Already prepared\n", - " 5m 39s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 5m 39s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): 29 relevant similarities between 58 passages\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): Composing cliques out of 58 chunks from 29 comparisons\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): 58 members in 29 cliques\n", - " 5m 39s CLIQUES (O chapter SET M>30 S>50): Composed and saved 29 cliques out of 58 chunks from 29 comparisons\n", - " 5m 39s PRINT (O chapter SET M>30 S>50): sorting out cliques\n", - " 5m 39s PRINT (O chapter SET M>30 S>50): formatting 29 cliques involving 29 binary chapter diffs\n", - " 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 29\n", - " 5m 39s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 29 already existing\n", - " 6m 04s PRINT (O chapter SET M>30 S>50): formatted 29 cliques (1 files) involving 29 binary chapter diffs\n", - " 6m 04s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 04s PREPARING (O chapter SET): Already prepared\n", - " 6m 04s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 6m 04s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): Composing cliques out of 80 chunks from 42 comparisons\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques\n", - " 6m 04s CLIQUES (O chapter SET M>30 S>45): Composed and saved 39 cliques out of 80 chunks from 42 comparisons\n", - " 6m 04s PRINT (O chapter SET M>30 S>45): sorting out cliques\n", - " 6m 04s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs\n", - " 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37\n", - " 6m 04s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing\n", - " 6m 40s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs\n", - " 6m 40s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 6m 40s PREPARING (O chapter SET): Already prepared\n", - " 6m 40s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 6m 40s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): Composing cliques out of 142 chunks from 87 comparisons\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques\n", - " 6m 40s CLIQUES (O chapter SET M>30 S>40): Composed and saved 62 cliques out of 142 chunks from 87 comparisons\n", - " 6m 40s PRINT (O chapter SET M>30 S>40): sorting out cliques\n", - " 6m 40s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs\n", - " 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51\n", - " 6m 40s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing\n", - " 7m 37s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs\n", - " 7m 37s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 7m 37s PREPARING (O chapter SET): Already prepared\n", - " 7m 37s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 7m 37s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): Composing cliques out of 302 chunks from 352 comparisons\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques\n", - " 7m 37s CLIQUES (O chapter SET M>30 S>35): Composed and saved 53 cliques out of 302 chunks from 352 comparisons\n", - " 7m 37s PRINT (O chapter SET M>30 S>35): sorting out cliques\n", - " 7m 37s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs\n", - " 8m 36s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs\n", - " 8m 36s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 8m 36s PREPARING (O chapter SET): Already prepared\n", - " 8m 36s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", - " 8m 36s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): Composing cliques out of 571 chunks from 3445 comparisons\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques\n", - " 8m 36s CLIQUES (O chapter SET M>30 S>30): Composed and saved 28 cliques out of 571 chunks from 3445 comparisons\n", - " 8m 36s PRINT (O chapter SET M>30 S>30): sorting out cliques\n", - " 8m 36s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs\n", - " 9m 01s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs\n", - " 9m 01s CHUNKING (O chapter): already chunked into 929 chunks\n", - " 9m 01s PREPARING (O chapter LCS)\n", - " 9m 02s PREPARING (O chapter LCS): Done 929 chunks.\n", - " 9m 02s SIMILARITY (O chapter LCS M>55): Computing 431 K (431056) comparisons and saving entries in matrix\n", - " 9m 16s SIMILARITY (O chapter LCS M>55): Computed 4 K comparisons and saved 1 entries in matrix\n", - " 9m 28s SIMILARITY (O chapter LCS M>55): Computed 8 K comparisons and saved 2 entries in matrix\n", - " 9m 39s SIMILARITY (O chapter LCS M>55): Computed 12 K comparisons and saved 2 entries in matrix\n", - " 9m 52s SIMILARITY (O chapter LCS M>55): Computed 17 K comparisons and saved 2 entries in matrix\n", - "10m 08s SIMILARITY (O chapter LCS M>55): Computed 21 K comparisons and saved 2 entries in matrix\n", - "10m 26s SIMILARITY (O chapter LCS M>55): Computed 25 K comparisons and saved 2 entries in matrix\n", - "10m 45s SIMILARITY (O chapter LCS M>55): Computed 30 K comparisons and saved 2 entries in matrix\n", - "11m 00s SIMILARITY (O chapter LCS M>55): Computed 34 K comparisons and saved 3 entries in matrix\n", - "11m 18s SIMILARITY (O chapter LCS M>55): Computed 38 K comparisons and saved 3 entries in matrix\n", - "11m 33s SIMILARITY (O chapter LCS M>55): Computed 43 K comparisons and saved 3 entries in matrix\n", - "11m 45s SIMILARITY (O chapter LCS M>55): Computed 47 K comparisons and saved 3 entries in matrix\n", - "12m 00s SIMILARITY (O chapter LCS M>55): Computed 51 K comparisons and saved 3 entries in matrix\n", - "12m 18s SIMILARITY (O chapter LCS M>55): Computed 56 K comparisons and saved 3 entries in matrix\n", - "12m 32s SIMILARITY (O chapter LCS M>55): Computed 60 K comparisons and saved 3 entries in matrix\n", - "12m 46s SIMILARITY (O chapter LCS M>55): Computed 64 K comparisons and saved 4 entries in matrix\n", - "13m 01s SIMILARITY (O chapter LCS M>55): Computed 68 K comparisons and saved 8 entries in matrix\n", - "13m 19s SIMILARITY (O chapter LCS M>55): Computed 73 K comparisons and saved 9 entries in matrix\n", - "13m 36s SIMILARITY (O chapter LCS M>55): Computed 77 K comparisons and saved 9 entries in matrix\n", - "13m 49s SIMILARITY (O chapter LCS M>55): Computed 81 K comparisons and saved 10 entries in matrix\n", - "14m 07s SIMILARITY (O chapter LCS M>55): Computed 86 K comparisons and saved 10 entries in matrix\n", - "14m 25s SIMILARITY (O chapter LCS M>55): Computed 90 K comparisons and saved 11 entries in matrix\n", - "14m 43s SIMILARITY (O chapter LCS M>55): Computed 94 K comparisons and saved 11 entries in matrix\n", - "14m 57s SIMILARITY (O chapter LCS M>55): Computed 99 K comparisons and saved 11 entries in matrix\n", - "15m 16s SIMILARITY (O chapter LCS M>55): Computed 103 K comparisons and saved 11 entries in matrix\n", - "15m 40s SIMILARITY (O chapter LCS M>55): Computed 107 K comparisons and saved 11 entries in matrix\n", - "15m 53s SIMILARITY (O chapter LCS M>55): Computed 112 K comparisons and saved 11 entries in matrix\n", - "16m 11s SIMILARITY (O chapter LCS M>55): Computed 116 K comparisons and saved 11 entries in matrix\n", - "16m 27s SIMILARITY (O chapter LCS M>55): Computed 120 K comparisons and saved 11 entries in matrix\n", - "16m 42s SIMILARITY (O chapter LCS M>55): Computed 124 K comparisons and saved 12 entries in matrix\n", - "17m 00s SIMILARITY (O chapter LCS M>55): Computed 129 K comparisons and saved 12 entries in matrix\n", - "17m 20s SIMILARITY (O chapter LCS M>55): Computed 133 K comparisons and saved 12 entries in matrix\n", - "17m 33s SIMILARITY (O chapter LCS M>55): Computed 137 K comparisons and saved 13 entries in matrix\n", - "17m 47s SIMILARITY (O chapter LCS M>55): Computed 142 K comparisons and saved 13 entries in matrix\n", - "17m 59s SIMILARITY (O chapter LCS M>55): Computed 146 K comparisons and saved 13 entries in matrix\n", - "18m 10s SIMILARITY (O chapter LCS M>55): Computed 150 K comparisons and saved 13 entries in matrix\n", - "18m 31s SIMILARITY (O chapter LCS M>55): Computed 155 K comparisons and saved 13 entries in matrix\n", - "18m 42s SIMILARITY (O chapter LCS M>55): Computed 159 K comparisons and saved 13 entries in matrix\n", - "19m 01s SIMILARITY (O chapter LCS M>55): Computed 163 K comparisons and saved 13 entries in matrix\n", - "19m 14s SIMILARITY (O chapter LCS M>55): Computed 168 K comparisons and saved 13 entries in matrix\n", - "19m 30s SIMILARITY (O chapter LCS M>55): Computed 172 K comparisons and saved 14 entries in matrix\n", - "19m 45s SIMILARITY (O chapter LCS M>55): Computed 176 K comparisons and saved 14 entries in matrix\n", - "20m 05s SIMILARITY (O chapter LCS M>55): Computed 181 K comparisons and saved 14 entries in matrix\n", - "20m 17s SIMILARITY (O chapter LCS M>55): Computed 185 K comparisons and saved 14 entries in matrix\n", - "20m 35s SIMILARITY (O chapter LCS M>55): Computed 189 K comparisons and saved 14 entries in matrix\n", - "20m 45s SIMILARITY (O chapter LCS M>55): Computed 193 K comparisons and saved 14 entries in matrix\n", - "21m 01s SIMILARITY (O chapter LCS M>55): Computed 198 K comparisons and saved 14 entries in matrix\n", - "21m 20s SIMILARITY (O chapter LCS M>55): Computed 202 K comparisons and saved 15 entries in matrix\n", - "21m 34s SIMILARITY (O chapter LCS M>55): Computed 206 K comparisons and saved 15 entries in matrix\n", - "21m 47s SIMILARITY (O chapter LCS M>55): Computed 211 K comparisons and saved 16 entries in matrix\n", - "21m 58s SIMILARITY (O chapter LCS M>55): Computed 215 K comparisons and saved 19 entries in matrix\n", - "22m 15s SIMILARITY (O chapter LCS M>55): Computed 219 K comparisons and saved 20 entries in matrix\n", - "22m 32s SIMILARITY (O chapter LCS M>55): Computed 224 K comparisons and saved 21 entries in matrix\n", - "22m 50s SIMILARITY (O chapter LCS M>55): Computed 228 K comparisons and saved 23 entries in matrix\n", - "23m 14s SIMILARITY (O chapter LCS M>55): Computed 232 K comparisons and saved 26 entries in matrix\n", - "23m 31s SIMILARITY (O chapter LCS M>55): Computed 237 K comparisons and saved 28 entries in matrix\n", - "23m 48s SIMILARITY (O chapter LCS M>55): Computed 241 K comparisons and saved 29 entries in matrix\n", - "24m 04s SIMILARITY (O chapter LCS M>55): Computed 245 K comparisons and saved 29 entries in matrix\n", - "24m 19s SIMILARITY (O chapter LCS M>55): Computed 249 K comparisons and saved 35 entries in matrix\n", - "24m 34s SIMILARITY (O chapter LCS M>55): Computed 254 K comparisons and saved 40 entries in matrix\n", - "24m 42s SIMILARITY (O chapter LCS M>55): Computed 258 K comparisons and saved 41 entries in matrix\n", - "24m 50s SIMILARITY (O chapter LCS M>55): Computed 262 K comparisons and saved 41 entries in matrix\n", - "24m 56s SIMILARITY (O chapter LCS M>55): Computed 267 K comparisons and saved 41 entries in matrix\n", - "25m 04s SIMILARITY (O chapter LCS M>55): Computed 271 K comparisons and saved 41 entries in matrix\n", - "25m 12s SIMILARITY (O chapter LCS M>55): Computed 275 K comparisons and saved 41 entries in matrix\n", - "25m 21s SIMILARITY (O chapter LCS M>55): Computed 280 K comparisons and saved 41 entries in matrix\n", - "25m 28s SIMILARITY (O chapter LCS M>55): Computed 284 K comparisons and saved 41 entries in matrix\n", - "25m 34s SIMILARITY (O chapter LCS M>55): Computed 288 K comparisons and saved 41 entries in matrix\n", - "25m 43s SIMILARITY (O chapter LCS M>55): Computed 293 K comparisons and saved 41 entries in matrix\n", - "25m 55s SIMILARITY (O chapter LCS M>55): Computed 297 K comparisons and saved 41 entries in matrix\n", - "26m 04s SIMILARITY (O chapter LCS M>55): Computed 301 K comparisons and saved 41 entries in matrix\n", - "26m 16s SIMILARITY (O chapter LCS M>55): Computed 306 K comparisons and saved 42 entries in matrix\n", - "26m 31s SIMILARITY (O chapter LCS M>55): Computed 310 K comparisons and saved 42 entries in matrix\n", - "26m 42s SIMILARITY (O chapter LCS M>55): Computed 314 K comparisons and saved 42 entries in matrix\n", - "26m 56s SIMILARITY (O chapter LCS M>55): Computed 318 K comparisons and saved 42 entries in matrix\n", - "27m 04s SIMILARITY (O chapter LCS M>55): Computed 323 K comparisons and saved 42 entries in matrix\n", - "27m 16s SIMILARITY (O chapter LCS M>55): Computed 327 K comparisons and saved 42 entries in matrix\n", - "27m 26s SIMILARITY (O chapter LCS M>55): Computed 331 K comparisons and saved 42 entries in matrix\n", - "27m 38s SIMILARITY (O chapter LCS M>55): Computed 336 K comparisons and saved 42 entries in matrix\n", - "27m 48s SIMILARITY (O chapter LCS M>55): Computed 340 K comparisons and saved 42 entries in matrix\n", - "27m 52s SIMILARITY (O chapter LCS M>55): Computed 344 K comparisons and saved 42 entries in matrix\n", - "27m 58s SIMILARITY (O chapter LCS M>55): Computed 349 K comparisons and saved 42 entries in matrix\n", - "28m 03s SIMILARITY (O chapter LCS M>55): Computed 353 K comparisons and saved 42 entries in matrix\n", - "28m 08s SIMILARITY (O chapter LCS M>55): Computed 357 K comparisons and saved 42 entries in matrix\n", - "28m 14s SIMILARITY (O chapter LCS M>55): Computed 362 K comparisons and saved 43 entries in matrix\n", - "28m 20s SIMILARITY (O chapter LCS M>55): Computed 366 K comparisons and saved 43 entries in matrix\n", - "28m 23s SIMILARITY (O chapter LCS M>55): Computed 370 K comparisons and saved 44 entries in matrix\n", - "28m 26s SIMILARITY (O chapter LCS M>55): Computed 374 K comparisons and saved 44 entries in matrix\n", - "28m 31s SIMILARITY (O chapter LCS M>55): Computed 379 K comparisons and saved 44 entries in matrix\n", - "28m 34s SIMILARITY (O chapter LCS M>55): Computed 383 K comparisons and saved 44 entries in matrix\n", - "28m 37s SIMILARITY (O chapter LCS M>55): Computed 387 K comparisons and saved 45 entries in matrix\n", - "28m 43s SIMILARITY (O chapter LCS M>55): Computed 392 K comparisons and saved 46 entries in matrix\n", - "28m 46s SIMILARITY (O chapter LCS M>55): Computed 396 K comparisons and saved 47 entries in matrix\n", - "28m 51s SIMILARITY (O chapter LCS M>55): Computed 400 K comparisons and saved 48 entries in matrix\n", - "28m 55s SIMILARITY (O chapter LCS M>55): Computed 405 K comparisons and saved 49 entries in matrix\n", - "29m 00s SIMILARITY (O chapter LCS M>55): Computed 409 K comparisons and saved 49 entries in matrix\n", - "29m 06s SIMILARITY (O chapter LCS M>55): Computed 413 K comparisons and saved 49 entries in matrix\n", - "29m 14s SIMILARITY (O chapter LCS M>55): Computed 418 K comparisons and saved 49 entries in matrix\n", - "29m 23s SIMILARITY (O chapter LCS M>55): Computed 422 K comparisons and saved 49 entries in matrix\n", - "29m 37s SIMILARITY (O chapter LCS M>55): Computed 426 K comparisons and saved 52 entries in matrix\n", - "29m 55s SIMILARITY (O chapter LCS M>55): Computed 431 K comparisons and saved 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): Computed 431 K (431056) comparisons and saved 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): Composing cliques out of 0 chunks from 0 comparisons\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>100): Composed and saved 0 cliques out of 0 chunks from 0 comparisons\n", - "29m 56s PRINT (O chapter LCS M>55 S>100): sorting out cliques\n", - "29m 56s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", - "29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0\n", - "29m 56s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing\n", - "29m 56s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", - "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", - "29m 56s PREPARING (O chapter LCS): Already prepared\n", - "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): Composing cliques out of 2 chunks from 1 comparisons\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>95): Composed and saved 1 cliques out of 2 chunks from 1 comparisons\n", - "29m 56s PRINT (O chapter LCS M>55 S>95): sorting out cliques\n", - "29m 56s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", - "29m 56s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", - "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", - "29m 56s PREPARING (O chapter LCS): Already prepared\n", - "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): Composing cliques out of 4 chunks from 2 comparisons\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>90): Composed and saved 2 cliques out of 4 chunks from 2 comparisons\n", - "29m 56s PRINT (O chapter LCS M>55 S>90): sorting out cliques\n", - "29m 56s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs\n", - "29m 56s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", - "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", - "29m 56s PREPARING (O chapter LCS): Already prepared\n", - "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): Composing cliques out of 12 chunks from 6 comparisons\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>85): Composed and saved 6 cliques out of 12 chunks from 6 comparisons\n", - "29m 56s PRINT (O chapter LCS M>55 S>85): sorting out cliques\n", - "29m 56s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs\n", - "29m 56s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs\n", - "29m 56s CHUNKING (O chapter): already chunked into 929 chunks\n", - "29m 56s PREPARING (O chapter LCS): Already prepared\n", - "29m 56s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "29m 56s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): Composing cliques out of 18 chunks from 9 comparisons\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques\n", - "29m 56s CLIQUES (O chapter LCS M>55 S>80): Composed and saved 9 cliques out of 18 chunks from 9 comparisons\n", - "29m 56s PRINT (O chapter LCS M>55 S>80): sorting out cliques\n", - "29m 56s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs\n", - "29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9\n", - "29m 56s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing\n", - "30m 02s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs\n", - "30m 02s CHUNKING (O chapter): already chunked into 929 chunks\n", - "30m 02s PREPARING (O chapter LCS): Already prepared\n", - "30m 02s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "30m 02s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): Composing cliques out of 26 chunks from 13 comparisons\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques\n", - "30m 02s CLIQUES (O chapter LCS M>55 S>75): Composed and saved 13 cliques out of 26 chunks from 13 comparisons\n", - "30m 02s PRINT (O chapter LCS M>55 S>75): sorting out cliques\n", - "30m 02s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs\n", - "30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13\n", - "30m 02s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing\n", - "30m 11s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs\n", - "30m 11s CHUNKING (O chapter): already chunked into 929 chunks\n", - "30m 11s PREPARING (O chapter LCS): Already prepared\n", - "30m 11s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "30m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): Composing cliques out of 38 chunks from 19 comparisons\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques\n", - "30m 11s CLIQUES (O chapter LCS M>55 S>70): Composed and saved 19 cliques out of 38 chunks from 19 comparisons\n", - "30m 11s PRINT (O chapter LCS M>55 S>70): sorting out cliques\n", - "30m 11s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs\n", - "30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19\n", - "30m 11s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing\n", - "30m 27s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs\n", - "30m 27s CHUNKING (O chapter): already chunked into 929 chunks\n", - "30m 27s PREPARING (O chapter LCS): Already prepared\n", - "30m 27s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "30m 27s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): Composing cliques out of 44 chunks from 22 comparisons\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques\n", - "30m 27s CLIQUES (O chapter LCS M>55 S>65): Composed and saved 22 cliques out of 44 chunks from 22 comparisons\n", - "30m 27s PRINT (O chapter LCS M>55 S>65): sorting out cliques\n", - "30m 27s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs\n", - "30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22\n", - "30m 27s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing\n", - "30m 47s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", - "30m 47s CHUNKING (O chapter): already chunked into 929 chunks\n", - "30m 47s PREPARING (O chapter LCS): Already prepared\n", - "30m 47s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "30m 47s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "30m 47s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates\n", - "30m 47s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix\n", - "30m 47s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages\n", - "30m 47s CLIQUES (O chapter LCS M>55 S>60): Composing cliques out of 52 chunks from 26 comparisons\n", - "30m 48s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques\n", - "30m 48s CLIQUES (O chapter LCS M>55 S>60): Composed and saved 26 cliques out of 52 chunks from 26 comparisons\n", - "30m 48s PRINT (O chapter LCS M>55 S>60): sorting out cliques\n", - "30m 48s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs\n", - "30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26\n", - "30m 48s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing\n", - "31m 11s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs\n", - "31m 11s CHUNKING (O chapter): already chunked into 929 chunks\n", - "31m 11s PREPARING (O chapter LCS): Already prepared\n", - "31m 11s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", - "31m 11s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): Composing cliques out of 102 chunks from 53 comparisons\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques\n", - "31m 11s CLIQUES (O chapter LCS M>55 S>55): Composed and saved 49 cliques out of 102 chunks from 53 comparisons\n", - "31m 11s PRINT (O chapter LCS M>55 S>55): sorting out cliques\n", - "31m 11s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs\n", - "31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46\n", - "31m 11s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing\n", - "31m 48s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs\n", - "31m 48s CHUNKING (O verse)\n", - "31m 49s CHUNKING (O verse): Made 23213 chunks\n", - "31m 49s PREPARING (O verse SET)\n", - "31m 50s PREPARING (O verse SET): Done 23213 chunks.\n", - "31m 50s SIMILARITY (O verse SET M>50): Computing 269 M (269410078) comparisons and saving entries in matrix\n", - "31m 56s SIMILARITY (O verse SET M>50): Computed 2 M comparisons and saved 78 entries in matrix\n", - "32m 02s SIMILARITY (O verse SET M>50): Computed 5 M comparisons and saved 235 entries in matrix\n", - "32m 08s SIMILARITY (O verse SET M>50): Computed 8 M comparisons and saved 322 entries in matrix\n", - "32m 14s SIMILARITY (O verse SET M>50): Computed 10 M comparisons and saved 335 entries in matrix\n", - "32m 20s SIMILARITY (O verse SET M>50): Computed 13 M comparisons and saved 351 entries in matrix\n", - "32m 25s SIMILARITY (O verse SET M>50): Computed 16 M comparisons and saved 376 entries in matrix\n", - "32m 31s SIMILARITY (O verse SET M>50): Computed 18 M comparisons and saved 389 entries in matrix\n", - "32m 37s SIMILARITY (O verse SET M>50): Computed 21 M comparisons and saved 544 entries in matrix\n", - "32m 43s SIMILARITY (O verse SET M>50): Computed 24 M comparisons and saved 575 entries in matrix\n", - "32m 49s SIMILARITY (O verse SET M>50): Computed 26 M comparisons and saved 613 entries in matrix\n", - "32m 55s SIMILARITY (O verse SET M>50): Computed 29 M comparisons and saved 636 entries in matrix\n", - "33m 01s SIMILARITY (O verse SET M>50): Computed 32 M comparisons and saved 666 entries in matrix\n", - "33m 07s SIMILARITY (O verse SET M>50): Computed 35 M comparisons and saved 684 entries in matrix\n", - "33m 13s SIMILARITY (O verse SET M>50): Computed 37 M comparisons and saved 1101 entries in matrix\n", - "33m 19s SIMILARITY (O verse SET M>50): Computed 40 M comparisons and saved 1318 entries in matrix\n", - "33m 25s SIMILARITY (O verse SET M>50): Computed 43 M comparisons and saved 1848 entries in matrix\n", - "33m 31s SIMILARITY (O verse SET M>50): Computed 45 M comparisons and saved 2090 entries in matrix\n", - "33m 37s SIMILARITY (O verse SET M>50): Computed 48 M comparisons and saved 2125 entries in matrix\n", - "33m 43s SIMILARITY (O verse SET M>50): Computed 51 M comparisons and saved 2521 entries in matrix\n", - "33m 48s SIMILARITY (O verse SET M>50): Computed 53 M comparisons and saved 3522 entries in matrix\n", - "33m 54s SIMILARITY (O verse SET M>50): Computed 56 M comparisons and saved 3597 entries in matrix\n", - "34m 00s SIMILARITY (O verse SET M>50): Computed 59 M comparisons and saved 3847 entries in matrix\n", - "34m 06s SIMILARITY (O verse SET M>50): Computed 61 M comparisons and saved 4268 entries in matrix\n", - "34m 12s SIMILARITY (O verse SET M>50): Computed 64 M comparisons and saved 5626 entries in matrix\n", - "34m 18s SIMILARITY (O verse SET M>50): Computed 67 M comparisons and saved 6320 entries in matrix\n", - "34m 24s SIMILARITY (O verse SET M>50): Computed 70 M comparisons and saved 7034 entries in matrix\n", - "34m 30s SIMILARITY (O verse SET M>50): Computed 72 M comparisons and saved 7890 entries in matrix\n", - "34m 36s SIMILARITY (O verse SET M>50): Computed 75 M comparisons and saved 9304 entries in matrix\n", - "34m 41s SIMILARITY (O verse SET M>50): Computed 78 M comparisons and saved 9839 entries in matrix\n", - "34m 47s SIMILARITY (O verse SET M>50): Computed 80 M comparisons and saved 10997 entries in matrix\n", - "34m 53s SIMILARITY (O verse SET M>50): Computed 83 M comparisons and saved 12113 entries in matrix\n", - "34m 59s SIMILARITY (O verse SET M>50): Computed 86 M comparisons and saved 12712 entries in matrix\n", - "35m 05s SIMILARITY (O verse SET M>50): Computed 88 M comparisons and saved 13345 entries in matrix\n", - "35m 11s SIMILARITY (O verse SET M>50): Computed 91 M comparisons and saved 14140 entries in matrix\n", - "35m 16s SIMILARITY (O verse SET M>50): Computed 94 M comparisons and saved 14375 entries in matrix\n", - "35m 22s SIMILARITY (O verse SET M>50): Computed 96 M comparisons and saved 14945 entries in matrix\n", - "35m 28s SIMILARITY (O verse SET M>50): Computed 99 M comparisons and saved 15321 entries in matrix\n", - "35m 34s SIMILARITY (O verse SET M>50): Computed 102 M comparisons and saved 15714 entries in matrix\n", - "35m 40s SIMILARITY (O verse SET M>50): Computed 105 M comparisons and saved 15978 entries in matrix\n", - "35m 46s SIMILARITY (O verse SET M>50): Computed 107 M comparisons and saved 16103 entries in matrix\n", - "35m 52s SIMILARITY (O verse SET M>50): Computed 110 M comparisons and saved 16239 entries in matrix\n", - "35m 58s SIMILARITY (O verse SET M>50): Computed 113 M comparisons and saved 16359 entries in matrix\n", - "36m 04s SIMILARITY (O verse SET M>50): Computed 115 M comparisons and saved 16433 entries in matrix\n", - "36m 10s SIMILARITY (O verse SET M>50): Computed 118 M comparisons and saved 16469 entries in matrix\n", - "36m 16s SIMILARITY (O verse SET M>50): Computed 121 M comparisons and saved 16583 entries in matrix\n", - "36m 22s SIMILARITY (O verse SET M>50): Computed 123 M comparisons and saved 16619 entries in matrix\n", - "36m 28s SIMILARITY (O verse SET M>50): Computed 126 M comparisons and saved 16669 entries in matrix\n", - "36m 33s SIMILARITY (O verse SET M>50): Computed 129 M comparisons and saved 16834 entries in matrix\n", - "36m 39s SIMILARITY (O verse SET M>50): Computed 132 M comparisons and saved 16878 entries in matrix\n", - "36m 45s SIMILARITY (O verse SET M>50): Computed 134 M comparisons and saved 16899 entries in matrix\n", - "36m 51s SIMILARITY (O verse SET M>50): Computed 137 M comparisons and saved 16916 entries in matrix\n", - "36m 58s SIMILARITY (O verse SET M>50): Computed 140 M comparisons and saved 16926 entries in matrix\n", - "37m 04s SIMILARITY (O verse SET M>50): Computed 142 M comparisons and saved 16953 entries in matrix\n", - "37m 10s SIMILARITY (O verse SET M>50): Computed 145 M comparisons and saved 16980 entries in matrix\n", - "37m 16s SIMILARITY (O verse SET M>50): Computed 148 M comparisons and saved 17084 entries in matrix\n", - "37m 22s SIMILARITY (O verse SET M>50): Computed 150 M comparisons and saved 17098 entries in matrix\n", - "37m 28s SIMILARITY (O verse SET M>50): Computed 153 M comparisons and saved 17119 entries in matrix\n", - "37m 34s SIMILARITY (O verse SET M>50): Computed 156 M comparisons and saved 17305 entries in matrix\n", - "37m 40s SIMILARITY (O verse SET M>50): Computed 158 M comparisons and saved 17341 entries in matrix\n", - "37m 46s SIMILARITY (O verse SET M>50): Computed 161 M comparisons and saved 17365 entries in matrix\n", - "37m 52s SIMILARITY (O verse SET M>50): Computed 164 M comparisons and saved 17543 entries in matrix\n", - "37m 58s SIMILARITY (O verse SET M>50): Computed 167 M comparisons and saved 17680 entries in matrix\n", - "38m 04s SIMILARITY (O verse SET M>50): Computed 169 M comparisons and saved 17948 entries in matrix\n", - "38m 10s SIMILARITY (O verse SET M>50): Computed 172 M comparisons and saved 18586 entries in matrix\n", - "38m 16s SIMILARITY (O verse SET M>50): Computed 175 M comparisons and saved 18899 entries in matrix\n", - "38m 22s SIMILARITY (O verse SET M>50): Computed 177 M comparisons and saved 18991 entries in matrix\n", - "38m 27s SIMILARITY (O verse SET M>50): Computed 180 M comparisons and saved 19389 entries in matrix\n", - "38m 33s SIMILARITY (O verse SET M>50): Computed 183 M comparisons and saved 19718 entries in matrix\n", - "38m 39s SIMILARITY (O verse SET M>50): Computed 185 M comparisons and saved 19823 entries in matrix\n", - "38m 45s SIMILARITY (O verse SET M>50): Computed 188 M comparisons and saved 19961 entries in matrix\n", - "38m 50s SIMILARITY (O verse SET M>50): Computed 191 M comparisons and saved 19967 entries in matrix\n", - "38m 56s SIMILARITY (O verse SET M>50): Computed 193 M comparisons and saved 20103 entries in matrix\n", - "39m 02s SIMILARITY (O verse SET M>50): Computed 196 M comparisons and saved 20158 entries in matrix\n", - "39m 08s SIMILARITY (O verse SET M>50): Computed 199 M comparisons and saved 20162 entries in matrix\n", - "39m 13s SIMILARITY (O verse SET M>50): Computed 202 M comparisons and saved 20312 entries in matrix\n", - "39m 19s SIMILARITY (O verse SET M>50): Computed 204 M comparisons and saved 20523 entries in matrix\n", - "39m 25s SIMILARITY (O verse SET M>50): Computed 207 M comparisons and saved 20771 entries in matrix\n", - "39m 31s SIMILARITY (O verse SET M>50): Computed 210 M comparisons and saved 21114 entries in matrix\n", - "39m 37s SIMILARITY (O verse SET M>50): Computed 212 M comparisons and saved 21360 entries in matrix\n", - "39m 42s SIMILARITY (O verse SET M>50): Computed 215 M comparisons and saved 21383 entries in matrix\n", - "39m 48s SIMILARITY (O verse SET M>50): Computed 218 M comparisons and saved 21935 entries in matrix\n", - "39m 53s SIMILARITY (O verse SET M>50): Computed 220 M comparisons and saved 22457 entries in matrix\n", - "39m 59s SIMILARITY (O verse SET M>50): Computed 223 M comparisons and saved 22720 entries in matrix\n", - "40m 05s SIMILARITY (O verse SET M>50): Computed 226 M comparisons and saved 22863 entries in matrix\n", - "40m 10s SIMILARITY (O verse SET M>50): Computed 228 M comparisons and saved 22938 entries in matrix\n", - "40m 16s SIMILARITY (O verse SET M>50): Computed 231 M comparisons and saved 22961 entries in matrix\n", - "40m 21s SIMILARITY (O verse SET M>50): Computed 234 M comparisons and saved 22979 entries in matrix\n", - "40m 27s SIMILARITY (O verse SET M>50): Computed 237 M comparisons and saved 23043 entries in matrix\n", - "40m 32s SIMILARITY (O verse SET M>50): Computed 239 M comparisons and saved 23373 entries in matrix\n", - "40m 37s SIMILARITY (O verse SET M>50): Computed 242 M comparisons and saved 23590 entries in matrix\n", - "40m 42s SIMILARITY (O verse SET M>50): Computed 245 M comparisons and saved 23770 entries in matrix\n", - "40m 47s SIMILARITY (O verse SET M>50): Computed 247 M comparisons and saved 23807 entries in matrix\n", - "40m 52s SIMILARITY (O verse SET M>50): Computed 250 M comparisons and saved 23901 entries in matrix\n", - "40m 57s SIMILARITY (O verse SET M>50): Computed 253 M comparisons and saved 24012 entries in matrix\n", - "41m 03s SIMILARITY (O verse SET M>50): Computed 255 M comparisons and saved 24091 entries in matrix\n", - "41m 08s SIMILARITY (O verse SET M>50): Computed 258 M comparisons and saved 24138 entries in matrix\n", - "41m 13s SIMILARITY (O verse SET M>50): Computed 261 M comparisons and saved 24182 entries in matrix\n", - "41m 19s SIMILARITY (O verse SET M>50): Computed 264 M comparisons and saved 24220 entries in matrix\n", - "41m 25s SIMILARITY (O verse SET M>50): Computed 266 M comparisons and saved 24413 entries in matrix\n", - "41m 31s SIMILARITY (O verse SET M>50): Computed 269 M comparisons and saved 24792 entries in matrix\n", - "41m 31s SIMILARITY (O verse SET M>50): Computed 269 M (269410078) comparisons and saved 24792 entries in matrix\n", - "41m 31s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): Composing cliques out of 993 chunks from 4506 comparisons\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques\n", - "41m 31s CLIQUES (O verse SET M>50 S>100): Composed and saved 388 cliques out of 993 chunks from 4506 comparisons\n", - "41m 31s PRINT (O verse SET M>50 S>100): sorting out cliques\n", - "41m 31s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs\n", - "41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100\n", - "41m 31s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing\n", - "41m 32s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs\n", - "41m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 32s PREPARING (O verse SET): Already prepared\n", - "41m 32s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): Composing cliques out of 1029 chunks from 4524 comparisons\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): Composed 400 cliques out of 1000 chunks\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques\n", - "41m 32s CLIQUES (O verse SET M>50 S>95): Composed and saved 406 cliques out of 1029 chunks from 4524 comparisons\n", - "41m 32s PRINT (O verse SET M>50 S>95): sorting out cliques\n", - "41m 32s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs\n", - "41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103\n", - "41m 32s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing\n", - "41m 32s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs\n", - "41m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 32s PREPARING (O verse SET): Already prepared\n", - "41m 32s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 32s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 32s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates\n", - "41m 32s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix\n", - "41m 33s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages\n", - "41m 33s CLIQUES (O verse SET M>50 S>90): Composing cliques out of 1286 chunks from 4700 comparisons\n", - "41m 33s CLIQUES (O verse SET M>50 S>90): Composed 467 cliques out of 1000 chunks\n", - "41m 33s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques\n", - "41m 33s CLIQUES (O verse SET M>50 S>90): Composed and saved 526 cliques out of 1286 chunks from 4700 comparisons\n", - "41m 33s PRINT (O verse SET M>50 S>90): sorting out cliques\n", - "41m 33s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs\n", - "41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133\n", - "41m 33s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing\n", - "41m 34s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs\n", - "41m 34s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 34s PREPARING (O verse SET): Already prepared\n", - "41m 34s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 34s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 34s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates\n", - "41m 34s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix\n", - "41m 34s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages\n", - "41m 34s CLIQUES (O verse SET M>50 S>85): Composing cliques out of 1573 chunks from 4932 comparisons\n", - "41m 34s CLIQUES (O verse SET M>50 S>85): Composed 473 cliques out of 1000 chunks\n", - "41m 35s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques\n", - "41m 35s CLIQUES (O verse SET M>50 S>85): Composed and saved 651 cliques out of 1573 chunks from 4932 comparisons\n", - "41m 35s PRINT (O verse SET M>50 S>85): sorting out cliques\n", - "41m 35s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs\n", - "41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151\n", - "41m 35s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing\n", - "41m 35s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs\n", - "41m 35s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 35s PREPARING (O verse SET): Already prepared\n", - "41m 35s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 35s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 35s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates\n", - "41m 35s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix\n", - "41m 35s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages\n", - "41m 35s CLIQUES (O verse SET M>50 S>80): Composing cliques out of 1958 chunks from 10653 comparisons\n", - "41m 36s CLIQUES (O verse SET M>50 S>80): Composed 487 cliques out of 1000 chunks\n", - "41m 37s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques\n", - "41m 37s CLIQUES (O verse SET M>50 S>80): Composed and saved 800 cliques out of 1958 chunks from 10653 comparisons\n", - "41m 37s PRINT (O verse SET M>50 S>80): sorting out cliques\n", - "41m 37s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs\n", - "41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174\n", - "41m 37s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing\n", - "41m 38s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs\n", - "41m 38s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 38s PREPARING (O verse SET): Already prepared\n", - "41m 38s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 38s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 38s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates\n", - "41m 38s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix\n", - "41m 38s CLIQUES (O verse SET M>50 S>75): 11182 relevant similarities between 2361 passages\n", - "41m 38s CLIQUES (O verse SET M>50 S>75): Composing cliques out of 2361 chunks from 11182 comparisons\n", - "41m 38s CLIQUES (O verse SET M>50 S>75): Composed 497 cliques out of 1000 chunks\n", - "41m 39s CLIQUES (O verse SET M>50 S>75): Composed 897 cliques out of 2000 chunks\n", - "41m 40s CLIQUES (O verse SET M>50 S>75): 2361 members in 962 cliques\n", - "41m 40s CLIQUES (O verse SET M>50 S>75): Composed and saved 962 cliques out of 2361 chunks from 11182 comparisons\n", - "41m 40s PRINT (O verse SET M>50 S>75): sorting out cliques\n", - "41m 40s PRINT (O verse SET M>50 S>75): formatting 962 cliques involving 210 binary chapter diffs\n", - "41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210\n", - "41m 40s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing\n", - "41m 41s PRINT (O verse SET M>50 S>75): formatted 962 cliques (20 files) involving 210 binary chapter diffs\n", - "41m 41s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 41s PREPARING (O verse SET): Already prepared\n", - "41m 41s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 41s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 41s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates\n", - "41m 41s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix\n", - "41m 41s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages\n", - "41m 41s CLIQUES (O verse SET M>50 S>70): Composing cliques out of 2720 chunks from 11704 comparisons\n", - "41m 42s CLIQUES (O verse SET M>50 S>70): Composed 515 cliques out of 1000 chunks\n", - "41m 42s CLIQUES (O verse SET M>50 S>70): Composed 893 cliques out of 2000 chunks\n", - "41m 44s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques\n", - "41m 44s CLIQUES (O verse SET M>50 S>70): Composed and saved 1094 cliques out of 2720 chunks from 11704 comparisons\n", - "41m 44s PRINT (O verse SET M>50 S>70): sorting out cliques\n", - "41m 44s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs\n", - "41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237\n", - "41m 44s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing\n", - "41m 45s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs\n", - "41m 45s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 45s PREPARING (O verse SET): Already prepared\n", - "41m 45s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 45s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 45s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates\n", - "41m 45s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix\n", - "41m 45s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages\n", - "41m 45s CLIQUES (O verse SET M>50 S>65): Composing cliques out of 3139 chunks from 14353 comparisons\n", - "41m 46s CLIQUES (O verse SET M>50 S>65): Composed 524 cliques out of 1000 chunks\n", - "41m 47s CLIQUES (O verse SET M>50 S>65): Composed 901 cliques out of 2000 chunks\n", - "41m 48s CLIQUES (O verse SET M>50 S>65): Composed 1205 cliques out of 3000 chunks\n", - "41m 48s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques\n", - "41m 48s CLIQUES (O verse SET M>50 S>65): Composed and saved 1235 cliques out of 3139 chunks from 14353 comparisons\n", - "41m 48s PRINT (O verse SET M>50 S>65): sorting out cliques\n", - "41m 48s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs\n", - "41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284\n", - "41m 48s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing\n", - "41m 50s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs\n", - "41m 50s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 50s PREPARING (O verse SET): Already prepared\n", - "41m 50s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 50s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 50s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", - "41m 50s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", - "41m 51s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", - "41m 51s CLIQUES (O verse SET M>50 S>60): Composing cliques out of 3877 chunks from 16055 comparisons\n", - "41m 51s CLIQUES (O verse SET M>50 S>60): Composed 549 cliques out of 1000 chunks\n", - "41m 52s CLIQUES (O verse SET M>50 S>60): Composed 928 cliques out of 2000 chunks\n", - "41m 53s CLIQUES (O verse SET M>50 S>60): Composed 1239 cliques out of 3000 chunks\n", - "41m 55s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", - "41m 55s CLIQUES (O verse SET M>50 S>60): Composed and saved 1439 cliques out of 3877 chunks from 16055 comparisons\n", - "41m 55s PRINT (O verse SET M>50 S>60): sorting out cliques\n", - "41m 55s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", - "41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", - "41m 55s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", - "41m 58s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n", - "41m 58s CHUNKING (O verse): already chunked into 23213 chunks\n", - "41m 58s PREPARING (O verse SET): Already prepared\n", - "41m 58s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "41m 58s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "41m 58s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates\n", - "41m 58s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix\n", - "41m 58s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages\n", - "41m 58s CLIQUES (O verse SET M>50 S>55): Composing cliques out of 4735 chunks from 18754 comparisons\n", - "41m 58s CLIQUES (O verse SET M>50 S>55): Composed 600 cliques out of 1000 chunks\n", - "41m 59s CLIQUES (O verse SET M>50 S>55): Composed 973 cliques out of 2000 chunks\n", - "42m 01s CLIQUES (O verse SET M>50 S>55): Composed 1236 cliques out of 3000 chunks\n", - "42m 03s CLIQUES (O verse SET M>50 S>55): Composed 1508 cliques out of 4000 chunks\n", - "42m 05s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques\n", - "42m 05s CLIQUES (O verse SET M>50 S>55): Composed and saved 1638 cliques out of 4735 chunks from 18754 comparisons\n", - "42m 05s PRINT (O verse SET M>50 S>55): sorting out cliques\n", - "42m 05s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs\n", - "42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447\n", - "42m 05s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing\n", - "42m 09s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs\n", - "42m 09s CHUNKING (O verse): already chunked into 23213 chunks\n", - "42m 09s PREPARING (O verse SET): Already prepared\n", - "42m 09s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24792 entries in matrix\n", - "42m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "42m 09s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates\n", - "42m 09s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix\n", - "42m 09s CLIQUES (O verse SET M>50 S>50): 24792 relevant similarities between 6711 passages\n", - "42m 09s CLIQUES (O verse SET M>50 S>50): Composing cliques out of 6711 chunks from 24792 comparisons\n", - "42m 09s CLIQUES (O verse SET M>50 S>50): Composed 642 cliques out of 1000 chunks\n", - "42m 10s CLIQUES (O verse SET M>50 S>50): Composed 1029 cliques out of 2000 chunks\n", - "42m 12s CLIQUES (O verse SET M>50 S>50): Composed 1309 cliques out of 3000 chunks\n", - "42m 14s CLIQUES (O verse SET M>50 S>50): Composed 1490 cliques out of 4000 chunks\n", - "42m 16s CLIQUES (O verse SET M>50 S>50): Composed 1634 cliques out of 5000 chunks\n", - "42m 19s CLIQUES (O verse SET M>50 S>50): Composed 1803 cliques out of 6000 chunks\n", - "42m 22s CLIQUES (O verse SET M>50 S>50): 6711 members in 1851 cliques\n", - "42m 22s CLIQUES (O verse SET M>50 S>50): Composed and saved 1851 cliques out of 6711 chunks from 24792 comparisons\n", - "42m 22s PRINT (O verse SET M>50 S>50): sorting out cliques\n", - "42m 22s PRINT (O verse SET M>50 S>50): formatting 1851 cliques skipping 560 binary chapter diffs\n", - "42m 26s PRINT (O verse SET M>50 S>50): formatted 1851 cliques (38 files) skipping 560 binary chapter diffs\n", - "42m 26s CHUNKING (O verse): already chunked into 23213 chunks\n", - "42m 26s PREPARING (O verse LCS)\n", - "42m 27s PREPARING (O verse LCS): Done 23213 chunks.\n", - "42m 27s SIMILARITY (O verse LCS M>60): Computing 269 M (269410078) comparisons and saving entries in matrix\n", - "42m 42s SIMILARITY (O verse LCS M>60): Computed 2 M comparisons and saved 1501 entries in matrix\n", - "42m 56s SIMILARITY (O verse LCS M>60): Computed 5 M comparisons and saved 2936 entries in matrix\n", - "43m 10s SIMILARITY (O verse LCS M>60): Computed 8 M comparisons and saved 3564 entries in matrix\n", - "43m 24s SIMILARITY (O verse LCS M>60): Computed 10 M comparisons and saved 4565 entries in matrix\n", - "43m 39s SIMILARITY (O verse LCS M>60): Computed 13 M comparisons and saved 5385 entries in matrix\n", - "43m 54s SIMILARITY (O verse LCS M>60): Computed 16 M comparisons and saved 6042 entries in matrix\n", - "44m 08s SIMILARITY (O verse LCS M>60): Computed 18 M comparisons and saved 6742 entries in matrix\n", - "44m 23s SIMILARITY (O verse LCS M>60): Computed 21 M comparisons and saved 7378 entries in matrix\n", - "44m 37s SIMILARITY (O verse LCS M>60): Computed 24 M comparisons and saved 8027 entries in matrix\n", - "44m 51s SIMILARITY (O verse LCS M>60): Computed 26 M comparisons and saved 8728 entries in matrix\n", - "45m 06s SIMILARITY (O verse LCS M>60): Computed 29 M comparisons and saved 9408 entries in matrix\n", - "45m 20s SIMILARITY (O verse LCS M>60): Computed 32 M comparisons and saved 10133 entries in matrix\n", - "45m 35s SIMILARITY (O verse LCS M>60): Computed 35 M comparisons and saved 10805 entries in matrix\n", - "45m 50s SIMILARITY (O verse LCS M>60): Computed 37 M comparisons and saved 12556 entries in matrix\n", - "46m 07s SIMILARITY (O verse LCS M>60): Computed 40 M comparisons and saved 13740 entries in matrix\n", - "46m 22s SIMILARITY (O verse LCS M>60): Computed 43 M comparisons and saved 15130 entries in matrix\n", - "46m 38s SIMILARITY (O verse LCS M>60): Computed 45 M comparisons and saved 17285 entries in matrix\n", - "46m 52s SIMILARITY (O verse LCS M>60): Computed 48 M comparisons and saved 17993 entries in matrix\n", - "47m 06s SIMILARITY (O verse LCS M>60): Computed 51 M comparisons and saved 18867 entries in matrix\n", - "47m 22s SIMILARITY (O verse LCS M>60): Computed 53 M comparisons and saved 20756 entries in matrix\n", - "47m 37s SIMILARITY (O verse LCS M>60): Computed 56 M comparisons and saved 21911 entries in matrix\n", - "47m 53s SIMILARITY (O verse LCS M>60): Computed 59 M comparisons and saved 22554 entries in matrix\n", - "48m 08s SIMILARITY (O verse LCS M>60): Computed 61 M comparisons and saved 23826 entries in matrix\n", - "48m 24s SIMILARITY (O verse LCS M>60): Computed 64 M comparisons and saved 26427 entries in matrix\n", - "48m 39s SIMILARITY (O verse LCS M>60): Computed 67 M comparisons and saved 28174 entries in matrix\n", - "48m 56s SIMILARITY (O verse LCS M>60): Computed 70 M comparisons and saved 29670 entries in matrix\n", - "49m 10s SIMILARITY (O verse LCS M>60): Computed 72 M comparisons and saved 31882 entries in matrix\n", - "49m 24s SIMILARITY (O verse LCS M>60): Computed 75 M comparisons and saved 34628 entries in matrix\n", - "49m 38s SIMILARITY (O verse LCS M>60): Computed 78 M comparisons and saved 36056 entries in matrix\n", - "49m 52s SIMILARITY (O verse LCS M>60): Computed 80 M comparisons and saved 38367 entries in matrix\n", - "50m 06s SIMILARITY (O verse LCS M>60): Computed 83 M comparisons and saved 40398 entries in matrix\n", - "50m 22s SIMILARITY (O verse LCS M>60): Computed 86 M comparisons and saved 42021 entries in matrix\n", - "50m 36s SIMILARITY (O verse LCS M>60): Computed 88 M comparisons and saved 43894 entries in matrix\n", - "50m 52s SIMILARITY (O verse LCS M>60): Computed 91 M comparisons and saved 45753 entries in matrix\n", - "51m 07s SIMILARITY (O verse LCS M>60): Computed 94 M comparisons and saved 46875 entries in matrix\n", - "51m 21s SIMILARITY (O verse LCS M>60): Computed 96 M comparisons and saved 48256 entries in matrix\n", - "51m 35s SIMILARITY (O verse LCS M>60): Computed 99 M comparisons and saved 49589 entries in matrix\n", - "51m 49s SIMILARITY (O verse LCS M>60): Computed 102 M comparisons and saved 51140 entries in matrix\n", - "52m 06s SIMILARITY (O verse LCS M>60): Computed 105 M comparisons and saved 52455 entries in matrix\n", - "52m 22s SIMILARITY (O verse LCS M>60): Computed 107 M comparisons and saved 53548 entries in matrix\n", - "52m 38s SIMILARITY (O verse LCS M>60): Computed 110 M comparisons and saved 54504 entries in matrix\n", - "52m 53s SIMILARITY (O verse LCS M>60): Computed 113 M comparisons and saved 55274 entries in matrix\n", - "53m 09s SIMILARITY (O verse LCS M>60): Computed 115 M comparisons and saved 56249 entries in matrix\n", - "53m 24s SIMILARITY (O verse LCS M>60): Computed 118 M comparisons and saved 57165 entries in matrix\n", - "53m 41s SIMILARITY (O verse LCS M>60): Computed 121 M comparisons and saved 57967 entries in matrix\n", - "53m 58s SIMILARITY (O verse LCS M>60): Computed 123 M comparisons and saved 58548 entries in matrix\n", - "54m 13s SIMILARITY (O verse LCS M>60): Computed 126 M comparisons and saved 58838 entries in matrix\n", - "54m 28s SIMILARITY (O verse LCS M>60): Computed 129 M comparisons and saved 59705 entries in matrix\n", - "54m 45s SIMILARITY (O verse LCS M>60): Computed 132 M comparisons and saved 60340 entries in matrix\n", - "55m 01s SIMILARITY (O verse LCS M>60): Computed 134 M comparisons and saved 60941 entries in matrix\n", - "55m 17s SIMILARITY (O verse LCS M>60): Computed 137 M comparisons and saved 61487 entries in matrix\n", - "55m 34s SIMILARITY (O verse LCS M>60): Computed 140 M comparisons and saved 62068 entries in matrix\n", - "55m 50s SIMILARITY (O verse LCS M>60): Computed 142 M comparisons and saved 62663 entries in matrix\n", - "56m 06s SIMILARITY (O verse LCS M>60): Computed 145 M comparisons and saved 63515 entries in matrix\n", - "56m 22s SIMILARITY (O verse LCS M>60): Computed 148 M comparisons and saved 64341 entries in matrix\n", - "56m 39s SIMILARITY (O verse LCS M>60): Computed 150 M comparisons and saved 64776 entries in matrix\n", - "56m 55s SIMILARITY (O verse LCS M>60): Computed 153 M comparisons and saved 65315 entries in matrix\n", - "57m 11s SIMILARITY (O verse LCS M>60): Computed 156 M comparisons and saved 66046 entries in matrix\n", - "57m 27s SIMILARITY (O verse LCS M>60): Computed 158 M comparisons and saved 66949 entries in matrix\n", - "57m 44s SIMILARITY (O verse LCS M>60): Computed 161 M comparisons and saved 67488 entries in matrix\n", - "57m 58s SIMILARITY (O verse LCS M>60): Computed 164 M comparisons and saved 68477 entries in matrix\n", - "58m 14s SIMILARITY (O verse LCS M>60): Computed 167 M comparisons and saved 69155 entries in matrix\n", - "58m 32s SIMILARITY (O verse LCS M>60): Computed 169 M comparisons and saved 70076 entries in matrix\n", - "58m 49s SIMILARITY (O verse LCS M>60): Computed 172 M comparisons and saved 71970 entries in matrix\n", - "59m 04s SIMILARITY (O verse LCS M>60): Computed 175 M comparisons and saved 73156 entries in matrix\n", - "59m 19s SIMILARITY (O verse LCS M>60): Computed 177 M comparisons and saved 73905 entries in matrix\n", - "59m 33s SIMILARITY (O verse LCS M>60): Computed 180 M comparisons and saved 75097 entries in matrix\n", - "59m 48s SIMILARITY (O verse LCS M>60): Computed 183 M comparisons and saved 76287 entries in matrix\n", - " 1h 00m 03s SIMILARITY (O verse LCS M>60): Computed 185 M comparisons and saved 76917 entries in matrix\n", - " 1h 00m 16s SIMILARITY (O verse LCS M>60): Computed 188 M comparisons and saved 77780 entries in matrix\n", - " 1h 00m 29s SIMILARITY (O verse LCS M>60): Computed 191 M comparisons and saved 78262 entries in matrix\n", - " 1h 00m 43s SIMILARITY (O verse LCS M>60): Computed 193 M comparisons and saved 78790 entries in matrix\n", - " 1h 00m 55s SIMILARITY (O verse LCS M>60): Computed 196 M comparisons and saved 79379 entries in matrix\n", - " 1h 01m 09s SIMILARITY (O verse LCS M>60): Computed 199 M comparisons and saved 79827 entries in matrix\n", - " 1h 01m 23s SIMILARITY (O verse LCS M>60): Computed 202 M comparisons and saved 80744 entries in matrix\n", - " 1h 01m 37s SIMILARITY (O verse LCS M>60): Computed 204 M comparisons and saved 82026 entries in matrix\n", - " 1h 01m 52s SIMILARITY (O verse LCS M>60): Computed 207 M comparisons and saved 83358 entries in matrix\n", - " 1h 02m 07s SIMILARITY (O verse LCS M>60): Computed 210 M comparisons and saved 85216 entries in matrix\n", - " 1h 02m 24s SIMILARITY (O verse LCS M>60): Computed 212 M comparisons and saved 86251 entries in matrix\n", - " 1h 02m 38s SIMILARITY (O verse LCS M>60): Computed 215 M comparisons and saved 86674 entries in matrix\n", - " 1h 02m 51s SIMILARITY (O verse LCS M>60): Computed 218 M comparisons and saved 88268 entries in matrix\n", - " 1h 03m 04s SIMILARITY (O verse LCS M>60): Computed 220 M comparisons and saved 89741 entries in matrix\n", - " 1h 03m 17s SIMILARITY (O verse LCS M>60): Computed 223 M comparisons and saved 90901 entries in matrix\n", - " 1h 03m 31s SIMILARITY (O verse LCS M>60): Computed 226 M comparisons and saved 91828 entries in matrix\n", - " 1h 03m 44s SIMILARITY (O verse LCS M>60): Computed 228 M comparisons and saved 92351 entries in matrix\n", - " 1h 03m 56s SIMILARITY (O verse LCS M>60): Computed 231 M comparisons and saved 93036 entries in matrix\n", - " 1h 04m 09s SIMILARITY (O verse LCS M>60): Computed 234 M comparisons and saved 93626 entries in matrix\n", - " 1h 04m 22s SIMILARITY (O verse LCS M>60): Computed 237 M comparisons and saved 94732 entries in matrix\n", - " 1h 04m 30s SIMILARITY (O verse LCS M>60): Computed 239 M comparisons and saved 96489 entries in matrix\n", - " 1h 04m 39s SIMILARITY (O verse LCS M>60): Computed 242 M comparisons and saved 98333 entries in matrix\n", - " 1h 04m 48s SIMILARITY (O verse LCS M>60): Computed 245 M comparisons and saved 99952 entries in matrix\n", - " 1h 04m 57s SIMILARITY (O verse LCS M>60): Computed 247 M comparisons and saved 101344 entries in matrix\n", - " 1h 05m 06s SIMILARITY (O verse LCS M>60): Computed 250 M comparisons and saved 102948 entries in matrix\n", - " 1h 05m 15s SIMILARITY (O verse LCS M>60): Computed 253 M comparisons and saved 105178 entries in matrix\n", - " 1h 05m 24s SIMILARITY (O verse LCS M>60): Computed 255 M comparisons and saved 106484 entries in matrix\n", - " 1h 05m 33s SIMILARITY (O verse LCS M>60): Computed 258 M comparisons and saved 107701 entries in matrix\n", - " 1h 05m 43s SIMILARITY (O verse LCS M>60): Computed 261 M comparisons and saved 108625 entries in matrix\n", - " 1h 05m 56s SIMILARITY (O verse LCS M>60): Computed 264 M comparisons and saved 109194 entries in matrix\n", - " 1h 06m 13s SIMILARITY (O verse LCS M>60): Computed 266 M comparisons and saved 110696 entries in matrix\n", - " 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed 269 M comparisons and saved 113632 entries in matrix\n", - " 1h 06m 30s SIMILARITY (O verse LCS M>60): Computed 269 M (269410078) comparisons and saved 113632 entries in matrix\n", - " 1h 06m 30s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates\n", - " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix\n", - " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages\n", - " 1h 06m 30s CLIQUES (O verse LCS M>60 S>100): Composing cliques out of 793 chunks from 4204 comparisons\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>100): Composed and saved 295 cliques out of 793 chunks from 4204 comparisons\n", - " 1h 06m 31s PRINT (O verse LCS M>60 S>100): sorting out cliques\n", - " 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs\n", - " 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80\n", - " 1h 06m 31s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing\n", - " 1h 06m 31s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs\n", - " 1h 06m 31s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 31s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 31s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composing cliques out of 1235 chunks from 4489 comparisons\n", - " 1h 06m 31s CLIQUES (O verse LCS M>60 S>95): Composed 457 cliques out of 1000 chunks\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>95): Composed and saved 504 cliques out of 1235 chunks from 4489 comparisons\n", - " 1h 06m 32s PRINT (O verse LCS M>60 S>95): sorting out cliques\n", - " 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs\n", - " 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120\n", - " 1h 06m 32s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing\n", - " 1h 06m 32s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs\n", - " 1h 06m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 32s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 32s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages\n", - " 1h 06m 32s CLIQUES (O verse LCS M>60 S>90): Composing cliques out of 1754 chunks from 5538 comparisons\n", - " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed 471 cliques out of 1000 chunks\n", - " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques\n", - " 1h 06m 33s CLIQUES (O verse LCS M>60 S>90): Composed and saved 724 cliques out of 1754 chunks from 5538 comparisons\n", - " 1h 06m 33s PRINT (O verse LCS M>60 S>90): sorting out cliques\n", - " 1h 06m 33s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs\n", - " 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151\n", - " 1h 06m 33s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing\n", - " 1h 06m 34s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs\n", - " 1h 06m 34s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 34s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 34s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 34s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates\n", - " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix\n", - " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages\n", - " 1h 06m 34s CLIQUES (O verse LCS M>60 S>85): Composing cliques out of 2296 chunks from 7871 comparisons\n", - " 1h 06m 35s CLIQUES (O verse LCS M>60 S>85): Composed 478 cliques out of 1000 chunks\n", - " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed 874 cliques out of 2000 chunks\n", - " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques\n", - " 1h 06m 36s CLIQUES (O verse LCS M>60 S>85): Composed and saved 938 cliques out of 2296 chunks from 7871 comparisons\n", - " 1h 06m 36s PRINT (O verse LCS M>60 S>85): sorting out cliques\n", - " 1h 06m 36s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs\n", - " 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179\n", - " 1h 06m 36s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing\n", - " 1h 06m 37s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs\n", - " 1h 06m 37s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 37s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 37s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 38s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates\n", - " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix\n", - " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages\n", - " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composing cliques out of 2925 chunks from 9461 comparisons\n", - " 1h 06m 38s CLIQUES (O verse LCS M>60 S>80): Composed 486 cliques out of 1000 chunks\n", - " 1h 06m 39s CLIQUES (O verse LCS M>60 S>80): Composed 871 cliques out of 2000 chunks\n", - " 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques\n", - " 1h 06m 41s CLIQUES (O verse LCS M>60 S>80): Composed and saved 1141 cliques out of 2925 chunks from 9461 comparisons\n", - " 1h 06m 41s PRINT (O verse LCS M>60 S>80): sorting out cliques\n", - " 1h 06m 41s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs\n", - " 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251\n", - " 1h 06m 41s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing\n", - " 1h 06m 42s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs\n", - " 1h 06m 42s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 42s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 42s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 42s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates\n", - " 1h 06m 42s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix\n", - " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): 15540 relevant similarities between 3682 passages\n", - " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composing cliques out of 3682 chunks from 15540 comparisons\n", - " 1h 06m 43s CLIQUES (O verse LCS M>60 S>75): Composed 518 cliques out of 1000 chunks\n", - " 1h 06m 44s CLIQUES (O verse LCS M>60 S>75): Composed 886 cliques out of 2000 chunks\n", - " 1h 06m 46s CLIQUES (O verse LCS M>60 S>75): Composed 1220 cliques out of 3000 chunks\n", - " 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): 3682 members in 1340 cliques\n", - " 1h 06m 47s CLIQUES (O verse LCS M>60 S>75): Composed and saved 1340 cliques out of 3682 chunks from 15540 comparisons\n", - " 1h 06m 47s PRINT (O verse LCS M>60 S>75): sorting out cliques\n", - " 1h 06m 47s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs\n", - " 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346\n", - " 1h 06m 47s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing\n", - " 1h 06m 50s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs\n", - " 1h 06m 50s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 06m 50s PREPARING (O verse LCS): Already prepared\n", - " 1h 06m 50s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 06m 50s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates\n", - " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix\n", - " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): 19833 relevant similarities between 4958 passages\n", - " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composing cliques out of 4958 chunks from 19833 comparisons\n", - " 1h 06m 50s CLIQUES (O verse LCS M>60 S>70): Composed 549 cliques out of 1000 chunks\n", - " 1h 06m 51s CLIQUES (O verse LCS M>60 S>70): Composed 914 cliques out of 2000 chunks\n", - " 1h 06m 53s CLIQUES (O verse LCS M>60 S>70): Composed 1239 cliques out of 3000 chunks\n", - " 1h 06m 55s CLIQUES (O verse LCS M>60 S>70): Composed 1491 cliques out of 4000 chunks\n", - " 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques\n", - " 1h 06m 58s CLIQUES (O verse LCS M>60 S>70): Composed and saved 1644 cliques out of 4958 chunks from 19833 comparisons\n", - " 1h 06m 58s PRINT (O verse LCS M>60 S>70): sorting out cliques\n", - " 1h 06m 58s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs\n", - " 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504\n", - " 1h 06m 58s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing\n", - " 1h 07m 02s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs\n", - " 1h 07m 02s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 07m 02s PREPARING (O verse LCS): Already prepared\n", - " 1h 07m 02s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 07m 02s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates\n", - " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix\n", - " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): 31844 relevant similarities between 9050 passages\n", - " 1h 07m 02s CLIQUES (O verse LCS M>60 S>65): Composing cliques out of 9050 chunks from 31844 comparisons\n", - " 1h 07m 03s CLIQUES (O verse LCS M>60 S>65): Composed 596 cliques out of 1000 chunks\n", - " 1h 07m 04s CLIQUES (O verse LCS M>60 S>65): Composed 975 cliques out of 2000 chunks\n", - " 1h 07m 05s CLIQUES (O verse LCS M>60 S>65): Composed 1258 cliques out of 3000 chunks\n", - " 1h 07m 08s CLIQUES (O verse LCS M>60 S>65): Composed 1468 cliques out of 4000 chunks\n", - " 1h 07m 11s CLIQUES (O verse LCS M>60 S>65): Composed 1570 cliques out of 5000 chunks\n", - " 1h 07m 14s CLIQUES (O verse LCS M>60 S>65): Composed 1698 cliques out of 6000 chunks\n", - " 1h 07m 18s CLIQUES (O verse LCS M>60 S>65): Composed 1902 cliques out of 7000 chunks\n", - " 1h 07m 23s CLIQUES (O verse LCS M>60 S>65): Composed 1932 cliques out of 8000 chunks\n", - " 1h 07m 27s CLIQUES (O verse LCS M>60 S>65): Composed 1823 cliques out of 9000 chunks\n", - " 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): 9050 members in 1821 cliques\n", - " 1h 07m 28s CLIQUES (O verse LCS M>60 S>65): Composed and saved 1821 cliques out of 9050 chunks from 31844 comparisons\n", - " 1h 07m 28s PRINT (O verse LCS M>60 S>65): sorting out cliques\n", - " 1h 07m 28s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs\n", - " 1h 07m 32s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs\n", - " 1h 07m 32s CHUNKING (O verse): already chunked into 23213 chunks\n", - " 1h 07m 32s PREPARING (O verse LCS): Already prepared\n", - " 1h 07m 32s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113632 entries in matrix\n", - " 1h 07m 32s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - " 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", - " 1h 07m 32s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", - " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): 113632 relevant similarities between 18945 passages\n", - " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composing cliques out of 18945 chunks from 113632 comparisons\n", - " 1h 07m 33s CLIQUES (O verse LCS M>60 S>60): Composed 477 cliques out of 1000 chunks\n", - " 1h 07m 34s CLIQUES (O verse LCS M>60 S>60): Composed 671 cliques out of 2000 chunks\n", - " 1h 07m 36s CLIQUES (O verse LCS M>60 S>60): Composed 756 cliques out of 3000 chunks\n", - " 1h 07m 37s CLIQUES (O verse LCS M>60 S>60): Composed 770 cliques out of 4000 chunks\n", - " 1h 07m 39s CLIQUES (O verse LCS M>60 S>60): Composed 796 cliques out of 5000 chunks\n", - " 1h 07m 41s CLIQUES (O verse LCS M>60 S>60): Composed 817 cliques out of 6000 chunks\n", - " 1h 07m 44s CLIQUES (O verse LCS M>60 S>60): Composed 751 cliques out of 7000 chunks\n", - " 1h 07m 46s CLIQUES (O verse LCS M>60 S>60): Composed 741 cliques out of 8000 chunks\n", - " 1h 07m 49s CLIQUES (O verse LCS M>60 S>60): Composed 729 cliques out of 9000 chunks\n", - " 1h 07m 52s CLIQUES (O verse LCS M>60 S>60): Composed 706 cliques out of 10000 chunks\n", - " 1h 07m 55s CLIQUES (O verse LCS M>60 S>60): Composed 673 cliques out of 11000 chunks\n", - " 1h 07m 58s CLIQUES (O verse LCS M>60 S>60): Composed 646 cliques out of 12000 chunks\n", - " 1h 08m 02s CLIQUES (O verse LCS M>60 S>60): Composed 619 cliques out of 13000 chunks\n", - " 1h 08m 05s CLIQUES (O verse LCS M>60 S>60): Composed 588 cliques out of 14000 chunks\n", - " 1h 08m 09s CLIQUES (O verse LCS M>60 S>60): Composed 557 cliques out of 15000 chunks\n", - " 1h 08m 13s CLIQUES (O verse LCS M>60 S>60): Composed 541 cliques out of 16000 chunks\n", - " 1h 08m 18s CLIQUES (O verse LCS M>60 S>60): Composed 492 cliques out of 17000 chunks\n", - " 1h 08m 22s CLIQUES (O verse LCS M>60 S>60): Composed 431 cliques out of 18000 chunks\n", - " 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): 18945 members in 380 cliques\n", - " 1h 08m 28s CLIQUES (O verse LCS M>60 S>60): Composed and saved 380 cliques out of 18945 chunks from 113632 comparisons\n", - " 1h 08m 28s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", - " 1h 08m 28s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", - " 1h 08m 29s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n", - " 1h 08m 29s CHUNKING (O half_verse)\n", - " 1h 08m 31s CHUNKING (O half_verse): Made 45180 chunks\n", - " 1h 08m 31s PREPARING (O half_verse SET)\n", - " 1h 08m 31s PREPARING (O half_verse SET): Done 45180 chunks.\n", - " 1h 08m 31s SIMILARITY (O half_verse SET M>50): Computing 1020 M (1020593610) comparisons and saving entries in matrix\n", - " 1h 08m 49s SIMILARITY (O half_verse SET M>50): Computed 10 M comparisons and saved 1962 entries in matrix\n", - " 1h 09m 06s SIMILARITY (O half_verse SET M>50): Computed 20 M comparisons and saved 3531 entries in matrix\n", - " 1h 09m 21s SIMILARITY (O half_verse SET M>50): Computed 30 M comparisons and saved 4614 entries in matrix\n", - " 1h 09m 39s SIMILARITY (O half_verse SET M>50): Computed 40 M comparisons and saved 7012 entries in matrix\n", - " 1h 09m 55s SIMILARITY (O half_verse SET M>50): Computed 51 M comparisons and saved 8344 entries in matrix\n", - " 1h 10m 12s SIMILARITY (O half_verse SET M>50): Computed 61 M comparisons and saved 10034 entries in matrix\n", - " 1h 10m 28s SIMILARITY (O half_verse SET M>50): Computed 71 M comparisons and saved 12065 entries in matrix\n", - " 1h 10m 45s SIMILARITY (O half_verse SET M>50): Computed 81 M comparisons and saved 13253 entries in matrix\n", - " 1h 11m 01s SIMILARITY (O half_verse SET M>50): Computed 91 M comparisons and saved 14525 entries in matrix\n", - " 1h 11m 16s SIMILARITY (O half_verse SET M>50): Computed 102 M comparisons and saved 15999 entries in matrix\n", - " 1h 11m 31s SIMILARITY (O half_verse SET M>50): Computed 112 M comparisons and saved 17424 entries in matrix\n", - " 1h 11m 45s SIMILARITY (O half_verse SET M>50): Computed 122 M comparisons and saved 18649 entries in matrix\n", - " 1h 12m 01s SIMILARITY (O half_verse SET M>50): Computed 132 M comparisons and saved 19526 entries in matrix\n", - " 1h 12m 18s SIMILARITY (O half_verse SET M>50): Computed 142 M comparisons and saved 22474 entries in matrix\n", - " 1h 12m 34s SIMILARITY (O half_verse SET M>50): Computed 153 M comparisons and saved 25421 entries in matrix\n", - " 1h 12m 51s SIMILARITY (O half_verse SET M>50): Computed 163 M comparisons and saved 28332 entries in matrix\n", - " 1h 13m 07s SIMILARITY (O half_verse SET M>50): Computed 173 M comparisons and saved 30622 entries in matrix\n", - " 1h 13m 24s SIMILARITY (O half_verse SET M>50): Computed 183 M comparisons and saved 31931 entries in matrix\n", - " 1h 13m 40s SIMILARITY (O half_verse SET M>50): Computed 193 M comparisons and saved 33509 entries in matrix\n", - " 1h 13m 56s SIMILARITY (O half_verse SET M>50): Computed 204 M comparisons and saved 37341 entries in matrix\n", - " 1h 14m 12s SIMILARITY (O half_verse SET M>50): Computed 214 M comparisons and saved 39804 entries in matrix\n", - " 1h 14m 28s SIMILARITY (O half_verse SET M>50): Computed 224 M comparisons and saved 40887 entries in matrix\n", - " 1h 14m 44s SIMILARITY (O half_verse SET M>50): Computed 234 M comparisons and saved 43204 entries in matrix\n", - " 1h 15m 01s SIMILARITY (O half_verse SET M>50): Computed 244 M comparisons and saved 47125 entries in matrix\n", - " 1h 15m 17s SIMILARITY (O half_verse SET M>50): Computed 255 M comparisons and saved 50158 entries in matrix\n", - " 1h 15m 34s SIMILARITY (O half_verse SET M>50): Computed 265 M comparisons and saved 52236 entries in matrix\n", - " 1h 15m 50s SIMILARITY (O half_verse SET M>50): Computed 275 M comparisons and saved 56284 entries in matrix\n", - " 1h 16m 06s SIMILARITY (O half_verse SET M>50): Computed 285 M comparisons and saved 60584 entries in matrix\n", - " 1h 16m 22s SIMILARITY (O half_verse SET M>50): Computed 295 M comparisons and saved 63095 entries in matrix\n", - " 1h 16m 38s SIMILARITY (O half_verse SET M>50): Computed 306 M comparisons and saved 66565 entries in matrix\n", - " 1h 16m 54s SIMILARITY (O half_verse SET M>50): Computed 316 M comparisons and saved 70425 entries in matrix\n", - " 1h 17m 10s SIMILARITY (O half_verse SET M>50): Computed 326 M comparisons and saved 72570 entries in matrix\n", - " 1h 17m 27s SIMILARITY (O half_verse SET M>50): Computed 336 M comparisons and saved 75773 entries in matrix\n", - " 1h 17m 43s SIMILARITY (O half_verse SET M>50): Computed 347 M comparisons and saved 77750 entries in matrix\n", - " 1h 17m 59s SIMILARITY (O half_verse SET M>50): Computed 357 M comparisons and saved 79787 entries in matrix\n", - " 1h 18m 16s SIMILARITY (O half_verse SET M>50): Computed 367 M comparisons and saved 81979 entries in matrix\n", - " 1h 18m 31s SIMILARITY (O half_verse SET M>50): Computed 377 M comparisons and saved 83955 entries in matrix\n", - " 1h 18m 47s SIMILARITY (O half_verse SET M>50): Computed 387 M comparisons and saved 86785 entries in matrix\n", - " 1h 19m 04s SIMILARITY (O half_verse SET M>50): Computed 398 M comparisons and saved 88967 entries in matrix\n", - " 1h 19m 20s SIMILARITY (O half_verse SET M>50): Computed 408 M comparisons and saved 90872 entries in matrix\n", - " 1h 19m 34s SIMILARITY (O half_verse SET M>50): Computed 418 M comparisons and saved 93080 entries in matrix\n", - " 1h 19m 49s SIMILARITY (O half_verse SET M>50): Computed 428 M comparisons and saved 94351 entries in matrix\n", - " 1h 20m 04s SIMILARITY (O half_verse SET M>50): Computed 438 M comparisons and saved 95927 entries in matrix\n", - " 1h 20m 21s SIMILARITY (O half_verse SET M>50): Computed 449 M comparisons and saved 96905 entries in matrix\n", - " 1h 20m 38s SIMILARITY (O half_verse SET M>50): Computed 459 M comparisons and saved 98144 entries in matrix\n", - " 1h 20m 54s SIMILARITY (O half_verse SET M>50): Computed 469 M comparisons and saved 98961 entries in matrix\n", - " 1h 21m 10s SIMILARITY (O half_verse SET M>50): Computed 479 M comparisons and saved 99614 entries in matrix\n", - " 1h 21m 27s SIMILARITY (O half_verse SET M>50): Computed 489 M comparisons and saved 101085 entries in matrix\n", - " 1h 21m 43s SIMILARITY (O half_verse SET M>50): Computed 500 M comparisons and saved 102493 entries in matrix\n", - " 1h 21m 59s SIMILARITY (O half_verse SET M>50): Computed 510 M comparisons and saved 103196 entries in matrix\n", - " 1h 22m 16s SIMILARITY (O half_verse SET M>50): Computed 520 M comparisons and saved 104523 entries in matrix\n", - " 1h 22m 32s SIMILARITY (O half_verse SET M>50): Computed 530 M comparisons and saved 105444 entries in matrix\n", - " 1h 22m 49s SIMILARITY (O half_verse SET M>50): Computed 540 M comparisons and saved 106395 entries in matrix\n", - " 1h 23m 05s SIMILARITY (O half_verse SET M>50): Computed 551 M comparisons and saved 107694 entries in matrix\n", - " 1h 23m 21s SIMILARITY (O half_verse SET M>50): Computed 561 M comparisons and saved 108689 entries in matrix\n", - " 1h 23m 38s SIMILARITY (O half_verse SET M>50): Computed 571 M comparisons and saved 109532 entries in matrix\n", - " 1h 23m 53s SIMILARITY (O half_verse SET M>50): Computed 581 M comparisons and saved 110249 entries in matrix\n", - " 1h 24m 07s SIMILARITY (O half_verse SET M>50): Computed 591 M comparisons and saved 111869 entries in matrix\n", - " 1h 24m 22s SIMILARITY (O half_verse SET M>50): Computed 602 M comparisons and saved 113205 entries in matrix\n", - " 1h 24m 38s SIMILARITY (O half_verse SET M>50): Computed 612 M comparisons and saved 114725 entries in matrix\n", - " 1h 24m 54s SIMILARITY (O half_verse SET M>50): Computed 622 M comparisons and saved 116242 entries in matrix\n", - " 1h 25m 10s SIMILARITY (O half_verse SET M>50): Computed 632 M comparisons and saved 117170 entries in matrix\n", - " 1h 25m 27s SIMILARITY (O half_verse SET M>50): Computed 642 M comparisons and saved 118632 entries in matrix\n", - " 1h 25m 43s SIMILARITY (O half_verse SET M>50): Computed 653 M comparisons and saved 121290 entries in matrix\n", - " 1h 25m 59s SIMILARITY (O half_verse SET M>50): Computed 663 M comparisons and saved 123350 entries in matrix\n", - " 1h 26m 15s SIMILARITY (O half_verse SET M>50): Computed 673 M comparisons and saved 124858 entries in matrix\n", - " 1h 26m 32s SIMILARITY (O half_verse SET M>50): Computed 683 M comparisons and saved 126931 entries in matrix\n", - " 1h 26m 48s SIMILARITY (O half_verse SET M>50): Computed 694 M comparisons and saved 129318 entries in matrix\n", - " 1h 27m 04s SIMILARITY (O half_verse SET M>50): Computed 704 M comparisons and saved 130325 entries in matrix\n", - " 1h 27m 19s SIMILARITY (O half_verse SET M>50): Computed 714 M comparisons and saved 131412 entries in matrix\n", - " 1h 27m 35s SIMILARITY (O half_verse SET M>50): Computed 724 M comparisons and saved 132234 entries in matrix\n", - " 1h 27m 50s SIMILARITY (O half_verse SET M>50): Computed 734 M comparisons and saved 133067 entries in matrix\n", - " 1h 28m 06s SIMILARITY (O half_verse SET M>50): Computed 745 M comparisons and saved 134096 entries in matrix\n", - " 1h 28m 22s SIMILARITY (O half_verse SET M>50): Computed 755 M comparisons and saved 134572 entries in matrix\n", - " 1h 28m 38s SIMILARITY (O half_verse SET M>50): Computed 765 M comparisons and saved 136234 entries in matrix\n", - " 1h 28m 54s SIMILARITY (O half_verse SET M>50): Computed 775 M comparisons and saved 138257 entries in matrix\n", - " 1h 29m 09s SIMILARITY (O half_verse SET M>50): Computed 785 M comparisons and saved 139986 entries in matrix\n", - " 1h 29m 26s SIMILARITY (O half_verse SET M>50): Computed 796 M comparisons and saved 142234 entries in matrix\n", - " 1h 29m 42s SIMILARITY (O half_verse SET M>50): Computed 806 M comparisons and saved 144260 entries in matrix\n", - " 1h 29m 57s SIMILARITY (O half_verse SET M>50): Computed 816 M comparisons and saved 144956 entries in matrix\n", - " 1h 30m 13s SIMILARITY (O half_verse SET M>50): Computed 826 M comparisons and saved 148044 entries in matrix\n", - " 1h 30m 29s SIMILARITY (O half_verse SET M>50): Computed 836 M comparisons and saved 151016 entries in matrix\n", - " 1h 30m 44s SIMILARITY (O half_verse SET M>50): Computed 847 M comparisons and saved 153676 entries in matrix\n", - " 1h 30m 59s SIMILARITY (O half_verse SET M>50): Computed 857 M comparisons and saved 155349 entries in matrix\n", - " 1h 31m 15s SIMILARITY (O half_verse SET M>50): Computed 867 M comparisons and saved 156458 entries in matrix\n", - " 1h 31m 30s SIMILARITY (O half_verse SET M>50): Computed 877 M comparisons and saved 157434 entries in matrix\n", - " 1h 31m 45s SIMILARITY (O half_verse SET M>50): Computed 887 M comparisons and saved 158073 entries in matrix\n", - " 1h 32m 00s SIMILARITY (O half_verse SET M>50): Computed 898 M comparisons and saved 159599 entries in matrix\n", - " 1h 32m 12s SIMILARITY (O half_verse SET M>50): Computed 908 M comparisons and saved 161827 entries in matrix\n", - " 1h 32m 25s SIMILARITY (O half_verse SET M>50): Computed 918 M comparisons and saved 164277 entries in matrix\n", - " 1h 32m 38s SIMILARITY (O half_verse SET M>50): Computed 928 M comparisons and saved 166159 entries in matrix\n", - " 1h 32m 52s SIMILARITY (O half_verse SET M>50): Computed 938 M comparisons and saved 167991 entries in matrix\n", - " 1h 33m 06s SIMILARITY (O half_verse SET M>50): Computed 949 M comparisons and saved 169802 entries in matrix\n", - " 1h 33m 20s SIMILARITY (O half_verse SET M>50): Computed 959 M comparisons and saved 172125 entries in matrix\n", - " 1h 33m 34s SIMILARITY (O half_verse SET M>50): Computed 969 M comparisons and saved 173838 entries in matrix\n", - " 1h 33m 49s SIMILARITY (O half_verse SET M>50): Computed 979 M comparisons and saved 174914 entries in matrix\n", - " 1h 34m 04s SIMILARITY (O half_verse SET M>50): Computed 989 M comparisons and saved 175787 entries in matrix\n", - " 1h 34m 18s SIMILARITY (O half_verse SET M>50): Computed 1000 M comparisons and saved 176399 entries in matrix\n", - " 1h 34m 36s SIMILARITY (O half_verse SET M>50): Computed 1010 M comparisons and saved 177068 entries in matrix\n", - " 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed 1020 M comparisons and saved 179781 entries in matrix\n", - " 1h 34m 55s SIMILARITY (O half_verse SET M>50): Computed 1020 M (1020593610) comparisons and saved 179781 entries in matrix\n", - " 1h 34m 55s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates\n", - " 1h 34m 55s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix\n", - " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages\n", - " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composing cliques out of 4327 chunks from 10239 comparisons\n", - " 1h 34m 56s CLIQUES (O half_verse SET M>50 S>100): Composed 455 cliques out of 1000 chunks\n", - " 1h 34m 57s CLIQUES (O half_verse SET M>50 S>100): Composed 829 cliques out of 2000 chunks\n", - " 1h 34m 59s CLIQUES (O half_verse SET M>50 S>100): Composed 1258 cliques out of 3000 chunks\n", - " 1h 35m 02s CLIQUES (O half_verse SET M>50 S>100): Composed 1653 cliques out of 4000 chunks\n", - " 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques\n", - " 1h 35m 03s CLIQUES (O half_verse SET M>50 S>100): Composed and saved 1725 cliques out of 4327 chunks from 10239 comparisons\n", - " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): sorting out cliques\n", - " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs\n", - " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573\n", - " 1h 35m 03s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing\n", - " 1h 35m 04s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs\n", - " 1h 35m 04s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 35m 04s PREPARING (O half_verse SET): Already prepared\n", - " 1h 35m 04s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 35m 05s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates\n", - " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix\n", - " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages\n", - " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composing cliques out of 4333 chunks from 10242 comparisons\n", - " 1h 35m 05s CLIQUES (O half_verse SET M>50 S>95): Composed 453 cliques out of 1000 chunks\n", - " 1h 35m 06s CLIQUES (O half_verse SET M>50 S>95): Composed 829 cliques out of 2000 chunks\n", - " 1h 35m 08s CLIQUES (O half_verse SET M>50 S>95): Composed 1258 cliques out of 3000 chunks\n", - " 1h 35m 10s CLIQUES (O half_verse SET M>50 S>95): Composed 1653 cliques out of 4000 chunks\n", - " 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques\n", - " 1h 35m 11s CLIQUES (O half_verse SET M>50 S>95): Composed and saved 1728 cliques out of 4333 chunks from 10242 comparisons\n", - " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): sorting out cliques\n", - " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs\n", - " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573\n", - " 1h 35m 11s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing\n", - " 1h 35m 12s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs\n", - " 1h 35m 12s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 35m 12s PREPARING (O half_verse SET): Already prepared\n", - " 1h 35m 12s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 35m 13s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates\n", - " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix\n", - " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages\n", - " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composing cliques out of 4618 chunks from 10410 comparisons\n", - " 1h 35m 13s CLIQUES (O half_verse SET M>50 S>90): Composed 470 cliques out of 1000 chunks\n", - " 1h 35m 15s CLIQUES (O half_verse SET M>50 S>90): Composed 869 cliques out of 2000 chunks\n", - " 1h 35m 17s CLIQUES (O half_verse SET M>50 S>90): Composed 1279 cliques out of 3000 chunks\n", - " 1h 35m 19s CLIQUES (O half_verse SET M>50 S>90): Composed 1675 cliques out of 4000 chunks\n", - " 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques\n", - " 1h 35m 21s CLIQUES (O half_verse SET M>50 S>90): Composed and saved 1863 cliques out of 4618 chunks from 10410 comparisons\n", - " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): sorting out cliques\n", - " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs\n", - " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587\n", - " 1h 35m 21s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing\n", - " 1h 35m 22s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs\n", - " 1h 35m 22s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 35m 22s PREPARING (O half_verse SET): Already prepared\n", - " 1h 35m 22s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 35m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates\n", - " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix\n", - " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages\n", - " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composing cliques out of 5145 chunks from 11111 comparisons\n", - " 1h 35m 23s CLIQUES (O half_verse SET M>50 S>85): Composed 490 cliques out of 1000 chunks\n", - " 1h 35m 24s CLIQUES (O half_verse SET M>50 S>85): Composed 940 cliques out of 2000 chunks\n", - " 1h 35m 26s CLIQUES (O half_verse SET M>50 S>85): Composed 1275 cliques out of 3000 chunks\n", - " 1h 35m 28s CLIQUES (O half_verse SET M>50 S>85): Composed 1678 cliques out of 4000 chunks\n", - " 1h 35m 31s CLIQUES (O half_verse SET M>50 S>85): Composed 2041 cliques out of 5000 chunks\n", - " 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques\n", - " 1h 35m 32s CLIQUES (O half_verse SET M>50 S>85): Composed and saved 2072 cliques out of 5145 chunks from 11111 comparisons\n", - " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): sorting out cliques\n", - " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs\n", - " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640\n", - " 1h 35m 32s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing\n", - " 1h 35m 33s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs\n", - " 1h 35m 33s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 35m 33s PREPARING (O half_verse SET): Already prepared\n", - " 1h 35m 33s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 35m 34s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates\n", - " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix\n", - " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages\n", - " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composing cliques out of 6422 chunks from 20178 comparisons\n", - " 1h 35m 34s CLIQUES (O half_verse SET M>50 S>80): Composed 527 cliques out of 1000 chunks\n", - " 1h 35m 35s CLIQUES (O half_verse SET M>50 S>80): Composed 945 cliques out of 2000 chunks\n", - " 1h 35m 37s CLIQUES (O half_verse SET M>50 S>80): Composed 1384 cliques out of 3000 chunks\n", - " 1h 35m 39s CLIQUES (O half_verse SET M>50 S>80): Composed 1742 cliques out of 4000 chunks\n", - " 1h 35m 43s CLIQUES (O half_verse SET M>50 S>80): Composed 2048 cliques out of 5000 chunks\n", - " 1h 35m 46s CLIQUES (O half_verse SET M>50 S>80): Composed 2372 cliques out of 6000 chunks\n", - " 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques\n", - " 1h 35m 48s CLIQUES (O half_verse SET M>50 S>80): Composed and saved 2474 cliques out of 6422 chunks from 20178 comparisons\n", - " 1h 35m 48s PRINT (O half_verse SET M>50 S>80): sorting out cliques\n", - " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs\n", - " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769\n", - " 1h 35m 49s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing\n", - " 1h 35m 50s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs\n", - " 1h 35m 50s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 35m 50s PREPARING (O half_verse SET): Already prepared\n", - " 1h 35m 50s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 35m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates\n", - " 1h 35m 50s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix\n", - " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages\n", - " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composing cliques out of 8265 chunks from 23717 comparisons\n", - " 1h 35m 51s CLIQUES (O half_verse SET M>50 S>75): Composed 536 cliques out of 1000 chunks\n", - " 1h 35m 52s CLIQUES (O half_verse SET M>50 S>75): Composed 988 cliques out of 2000 chunks\n", - " 1h 35m 54s CLIQUES (O half_verse SET M>50 S>75): Composed 1408 cliques out of 3000 chunks\n", - " 1h 35m 56s CLIQUES (O half_verse SET M>50 S>75): Composed 1737 cliques out of 4000 chunks\n", - " 1h 36m 00s CLIQUES (O half_verse SET M>50 S>75): Composed 2148 cliques out of 5000 chunks\n", - " 1h 36m 04s CLIQUES (O half_verse SET M>50 S>75): Composed 2500 cliques out of 6000 chunks\n", - " 1h 36m 08s CLIQUES (O half_verse SET M>50 S>75): Composed 2729 cliques out of 7000 chunks\n", - " 1h 36m 13s CLIQUES (O half_verse SET M>50 S>75): Composed 2854 cliques out of 8000 chunks\n", - " 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques\n", - " 1h 36m 15s CLIQUES (O half_verse SET M>50 S>75): Composed and saved 2888 cliques out of 8265 chunks from 23717 comparisons\n", - " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): sorting out cliques\n", - " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs\n", - " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919\n", - " 1h 36m 15s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing\n", - " 1h 36m 18s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs\n", - " 1h 36m 18s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 36m 18s PREPARING (O half_verse SET): Already prepared\n", - " 1h 36m 18s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 36m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates\n", - " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix\n", - " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages\n", - " 1h 36m 18s CLIQUES (O half_verse SET M>50 S>70): Composing cliques out of 9388 chunks from 25560 comparisons\n", - " 1h 36m 19s CLIQUES (O half_verse SET M>50 S>70): Composed 558 cliques out of 1000 chunks\n", - " 1h 36m 20s CLIQUES (O half_verse SET M>50 S>70): Composed 1029 cliques out of 2000 chunks\n", - " 1h 36m 22s CLIQUES (O half_verse SET M>50 S>70): Composed 1456 cliques out of 3000 chunks\n", - " 1h 36m 24s CLIQUES (O half_verse SET M>50 S>70): Composed 1836 cliques out of 4000 chunks\n", - " 1h 36m 27s CLIQUES (O half_verse SET M>50 S>70): Composed 2118 cliques out of 5000 chunks\n", - " 1h 36m 31s CLIQUES (O half_verse SET M>50 S>70): Composed 2431 cliques out of 6000 chunks\n", - " 1h 36m 35s CLIQUES (O half_verse SET M>50 S>70): Composed 2756 cliques out of 7000 chunks\n", - " 1h 36m 40s CLIQUES (O half_verse SET M>50 S>70): Composed 3017 cliques out of 8000 chunks\n", - " 1h 36m 45s CLIQUES (O half_verse SET M>50 S>70): Composed 3173 cliques out of 9000 chunks\n", - " 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques\n", - " 1h 36m 47s CLIQUES (O half_verse SET M>50 S>70): Composed and saved 3193 cliques out of 9388 chunks from 25560 comparisons\n", - " 1h 36m 47s PRINT (O half_verse SET M>50 S>70): sorting out cliques\n", - " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs\n", - " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014\n", - " 1h 36m 48s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing\n", - " 1h 36m 50s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs\n", - " 1h 36m 50s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 36m 50s PREPARING (O half_verse SET): Already prepared\n", - " 1h 36m 50s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 36m 50s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates\n", - " 1h 36m 50s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix\n", - " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages\n", - " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composing cliques out of 12162 chunks from 37453 comparisons\n", - " 1h 36m 51s CLIQUES (O half_verse SET M>50 S>65): Composed 574 cliques out of 1000 chunks\n", - " 1h 36m 52s CLIQUES (O half_verse SET M>50 S>65): Composed 1045 cliques out of 2000 chunks\n", - " 1h 36m 54s CLIQUES (O half_verse SET M>50 S>65): Composed 1468 cliques out of 3000 chunks\n", - " 1h 36m 56s CLIQUES (O half_verse SET M>50 S>65): Composed 1894 cliques out of 4000 chunks\n", - " 1h 36m 58s CLIQUES (O half_verse SET M>50 S>65): Composed 2269 cliques out of 5000 chunks\n", - " 1h 37m 02s CLIQUES (O half_verse SET M>50 S>65): Composed 2552 cliques out of 6000 chunks\n", - " 1h 37m 06s CLIQUES (O half_verse SET M>50 S>65): Composed 2758 cliques out of 7000 chunks\n", - " 1h 37m 10s CLIQUES (O half_verse SET M>50 S>65): Composed 3034 cliques out of 8000 chunks\n", - " 1h 37m 15s CLIQUES (O half_verse SET M>50 S>65): Composed 3276 cliques out of 9000 chunks\n", - " 1h 37m 21s CLIQUES (O half_verse SET M>50 S>65): Composed 3416 cliques out of 10000 chunks\n", - " 1h 37m 28s CLIQUES (O half_verse SET M>50 S>65): Composed 3641 cliques out of 11000 chunks\n", - " 1h 37m 36s CLIQUES (O half_verse SET M>50 S>65): Composed 3425 cliques out of 12000 chunks\n", - " 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques\n", - " 1h 37m 38s CLIQUES (O half_verse SET M>50 S>65): Composed and saved 3342 cliques out of 12162 chunks from 37453 comparisons\n", - " 1h 37m 38s PRINT (O half_verse SET M>50 S>65): sorting out cliques\n", - " 1h 37m 38s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs\n", - " 1h 37m 41s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs\n", - " 1h 37m 41s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 37m 41s PREPARING (O half_verse SET): Already prepared\n", - " 1h 37m 41s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 37m 41s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates\n", - " 1h 37m 41s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix\n", - " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages\n", - " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composing cliques out of 16476 chunks from 55384 comparisons\n", - " 1h 37m 42s CLIQUES (O half_verse SET M>50 S>60): Composed 603 cliques out of 1000 chunks\n", - " 1h 37m 44s CLIQUES (O half_verse SET M>50 S>60): Composed 1110 cliques out of 2000 chunks\n", - " 1h 37m 45s CLIQUES (O half_verse SET M>50 S>60): Composed 1550 cliques out of 3000 chunks\n", - " 1h 37m 48s CLIQUES (O half_verse SET M>50 S>60): Composed 1927 cliques out of 4000 chunks\n", - " 1h 37m 51s CLIQUES (O half_verse SET M>50 S>60): Composed 2271 cliques out of 5000 chunks\n", - " 1h 37m 55s CLIQUES (O half_verse SET M>50 S>60): Composed 2547 cliques out of 6000 chunks\n", - " 1h 38m 00s CLIQUES (O half_verse SET M>50 S>60): Composed 2804 cliques out of 7000 chunks\n", - " 1h 38m 05s CLIQUES (O half_verse SET M>50 S>60): Composed 2992 cliques out of 8000 chunks\n", - " 1h 38m 11s CLIQUES (O half_verse SET M>50 S>60): Composed 3182 cliques out of 9000 chunks\n", - " 1h 38m 17s CLIQUES (O half_verse SET M>50 S>60): Composed 3442 cliques out of 10000 chunks\n", - " 1h 38m 24s CLIQUES (O half_verse SET M>50 S>60): Composed 3591 cliques out of 11000 chunks\n", - " 1h 38m 32s CLIQUES (O half_verse SET M>50 S>60): Composed 3732 cliques out of 12000 chunks\n", - " 1h 38m 41s CLIQUES (O half_verse SET M>50 S>60): Composed 3939 cliques out of 13000 chunks\n", - " 1h 38m 50s CLIQUES (O half_verse SET M>50 S>60): Composed 3797 cliques out of 14000 chunks\n", - " 1h 38m 59s CLIQUES (O half_verse SET M>50 S>60): Composed 3797 cliques out of 15000 chunks\n", - " 1h 39m 09s CLIQUES (O half_verse SET M>50 S>60): Composed 3527 cliques out of 16000 chunks\n", - " 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques\n", - " 1h 39m 14s CLIQUES (O half_verse SET M>50 S>60): Composed and saved 3424 cliques out of 16476 chunks from 55384 comparisons\n", - " 1h 39m 14s PRINT (O half_verse SET M>50 S>60): sorting out cliques\n", - " 1h 39m 14s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs\n", - " 1h 39m 18s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs\n", - " 1h 39m 18s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 39m 18s PREPARING (O half_verse SET): Already prepared\n", - " 1h 39m 18s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 39m 18s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates\n", - " 1h 39m 18s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix\n", - " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages\n", - " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composing cliques out of 19519 chunks from 70089 comparisons\n", - " 1h 39m 19s CLIQUES (O half_verse SET M>50 S>55): Composed 604 cliques out of 1000 chunks\n", - " 1h 39m 20s CLIQUES (O half_verse SET M>50 S>55): Composed 1098 cliques out of 2000 chunks\n", - " 1h 39m 22s CLIQUES (O half_verse SET M>50 S>55): Composed 1587 cliques out of 3000 chunks\n", - " 1h 39m 25s CLIQUES (O half_verse SET M>50 S>55): Composed 1974 cliques out of 4000 chunks\n", - " 1h 39m 28s CLIQUES (O half_verse SET M>50 S>55): Composed 2356 cliques out of 5000 chunks\n", - " 1h 39m 32s CLIQUES (O half_verse SET M>50 S>55): Composed 2683 cliques out of 6000 chunks\n", - " 1h 39m 37s CLIQUES (O half_verse SET M>50 S>55): Composed 2971 cliques out of 7000 chunks\n", - " 1h 39m 42s CLIQUES (O half_verse SET M>50 S>55): Composed 3126 cliques out of 8000 chunks\n", - " 1h 39m 48s CLIQUES (O half_verse SET M>50 S>55): Composed 3277 cliques out of 9000 chunks\n", - " 1h 39m 54s CLIQUES (O half_verse SET M>50 S>55): Composed 3271 cliques out of 10000 chunks\n", - " 1h 40m 01s CLIQUES (O half_verse SET M>50 S>55): Composed 3316 cliques out of 11000 chunks\n", - " 1h 40m 08s CLIQUES (O half_verse SET M>50 S>55): Composed 3241 cliques out of 12000 chunks\n", - " 1h 40m 16s CLIQUES (O half_verse SET M>50 S>55): Composed 3384 cliques out of 13000 chunks\n", - " 1h 40m 25s CLIQUES (O half_verse SET M>50 S>55): Composed 3387 cliques out of 14000 chunks\n", - " 1h 40m 34s CLIQUES (O half_verse SET M>50 S>55): Composed 3459 cliques out of 15000 chunks\n", - " 1h 40m 43s CLIQUES (O half_verse SET M>50 S>55): Composed 3567 cliques out of 16000 chunks\n", - " 1h 40m 53s CLIQUES (O half_verse SET M>50 S>55): Composed 3471 cliques out of 17000 chunks\n", - " 1h 41m 03s CLIQUES (O half_verse SET M>50 S>55): Composed 3480 cliques out of 18000 chunks\n", - " 1h 41m 13s CLIQUES (O half_verse SET M>50 S>55): Composed 3279 cliques out of 19000 chunks\n", - " 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques\n", - " 1h 41m 19s CLIQUES (O half_verse SET M>50 S>55): Composed and saved 3184 cliques out of 19519 chunks from 70089 comparisons\n", - " 1h 41m 19s PRINT (O half_verse SET M>50 S>55): sorting out cliques\n", - " 1h 41m 20s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs\n", - " 1h 41m 23s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs\n", - " 1h 41m 23s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 41m 23s PREPARING (O half_verse SET): Already prepared\n", - " 1h 41m 23s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179781 entries in matrix\n", - " 1h 41m 23s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", - " 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates\n", - " 1h 41m 23s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix\n", - " 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): 179781 relevant similarities between 28988 passages\n", - " 1h 41m 24s CLIQUES (O half_verse SET M>50 S>50): Composing cliques out of 28988 chunks from 179781 comparisons\n", - " 1h 41m 25s CLIQUES (O half_verse SET M>50 S>50): Composed 652 cliques out of 1000 chunks\n", - " 1h 41m 26s CLIQUES (O half_verse SET M>50 S>50): Composed 1202 cliques out of 2000 chunks\n", - " 1h 41m 27s CLIQUES (O half_verse SET M>50 S>50): Composed 1587 cliques out of 3000 chunks\n", - " 1h 41m 30s CLIQUES (O half_verse SET M>50 S>50): Composed 1958 cliques out of 4000 chunks\n", - " 1h 41m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 2279 cliques out of 5000 chunks\n", - " 1h 41m 37s CLIQUES (O half_verse SET M>50 S>50): Composed 2478 cliques out of 6000 chunks\n", - " 1h 41m 41s CLIQUES (O half_verse SET M>50 S>50): Composed 2663 cliques out of 7000 chunks\n", - " 1h 41m 46s CLIQUES (O half_verse SET M>50 S>50): Composed 2828 cliques out of 8000 chunks\n", - " 1h 41m 52s CLIQUES (O half_verse SET M>50 S>50): Composed 2961 cliques out of 9000 chunks\n", - " 1h 41m 58s CLIQUES (O half_verse SET M>50 S>50): Composed 3058 cliques out of 10000 chunks\n", - " 1h 42m 04s CLIQUES (O half_verse SET M>50 S>50): Composed 3206 cliques out of 11000 chunks\n", - " 1h 42m 11s CLIQUES (O half_verse SET M>50 S>50): Composed 3278 cliques out of 12000 chunks\n", - " 1h 42m 18s CLIQUES (O half_verse SET M>50 S>50): Composed 3296 cliques out of 13000 chunks\n", - " 1h 42m 26s CLIQUES (O half_verse SET M>50 S>50): Composed 3353 cliques out of 14000 chunks\n", - " 1h 42m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 3280 cliques out of 15000 chunks\n", - " 1h 42m 42s CLIQUES (O half_verse SET M>50 S>50): Composed 3372 cliques out of 16000 chunks\n", - " 1h 42m 50s CLIQUES (O half_verse SET M>50 S>50): Composed 3259 cliques out of 17000 chunks\n", - " 1h 42m 59s CLIQUES (O half_verse SET M>50 S>50): Composed 3240 cliques out of 18000 chunks\n", - " 1h 43m 09s CLIQUES (O half_verse SET M>50 S>50): Composed 3378 cliques out of 19000 chunks\n", - " 1h 43m 18s CLIQUES (O half_verse SET M>50 S>50): Composed 3281 cliques out of 20000 chunks\n", - " 1h 43m 27s CLIQUES (O half_verse SET M>50 S>50): Composed 3127 cliques out of 21000 chunks\n", - " 1h 43m 37s CLIQUES (O half_verse SET M>50 S>50): Composed 3111 cliques out of 22000 chunks\n", - " 1h 43m 48s CLIQUES (O half_verse SET M>50 S>50): Composed 3080 cliques out of 23000 chunks\n", - " 1h 43m 58s CLIQUES (O half_verse SET M>50 S>50): Composed 2926 cliques out of 24000 chunks\n", - " 1h 44m 08s CLIQUES (O half_verse SET M>50 S>50): Composed 2778 cliques out of 25000 chunks\n", - " 1h 44m 20s CLIQUES (O half_verse SET M>50 S>50): Composed 2738 cliques out of 26000 chunks\n", - " 1h 44m 33s CLIQUES (O half_verse SET M>50 S>50): Composed 2711 cliques out of 27000 chunks\n", - " 1h 44m 43s CLIQUES (O half_verse SET M>50 S>50): Composed 2378 cliques out of 28000 chunks\n", - " 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): 28988 members in 2031 cliques\n", - " 1h 44m 55s CLIQUES (O half_verse SET M>50 S>50): Composed and saved 2031 cliques out of 28988 chunks from 179781 comparisons\n", - " 1h 44m 55s PRINT (O half_verse SET M>50 S>50): sorting out cliques\n", - " 1h 44m 56s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs\n", - " 1h 44m 58s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs\n", - " 1h 44m 58s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 1h 44m 58s PREPARING (O half_verse LCS)\n", - " 1h 44m 58s PREPARING (O half_verse LCS): Done 45180 chunks.\n", - " 1h 44m 58s SIMILARITY (O half_verse LCS M>60): Computing 1020 M (1020593610) comparisons and saving entries in matrix\n", - " 1h 45m 19s SIMILARITY (O half_verse LCS M>60): Computed 10 M comparisons and saved 23129 entries in matrix\n", - " 1h 45m 40s SIMILARITY (O half_verse LCS M>60): Computed 20 M comparisons and saved 45727 entries in matrix\n", - " 1h 46m 00s SIMILARITY (O half_verse LCS M>60): Computed 30 M comparisons and saved 62396 entries in matrix\n", - " 1h 46m 21s SIMILARITY (O half_verse LCS M>60): Computed 40 M comparisons and saved 86192 entries in matrix\n", - " 1h 46m 43s SIMILARITY (O half_verse LCS M>60): Computed 51 M comparisons and saved 105378 entries in matrix\n", - " 1h 47m 03s SIMILARITY (O half_verse LCS M>60): Computed 61 M comparisons and saved 124304 entries in matrix\n", - " 1h 47m 24s SIMILARITY (O half_verse LCS M>60): Computed 71 M comparisons and saved 143728 entries in matrix\n", - " 1h 47m 45s SIMILARITY (O half_verse LCS M>60): Computed 81 M comparisons and saved 161716 entries in matrix\n", - " 1h 48m 05s SIMILARITY (O half_verse LCS M>60): Computed 91 M comparisons and saved 180158 entries in matrix\n", - " 1h 48m 25s SIMILARITY (O half_verse LCS M>60): Computed 102 M comparisons and saved 198767 entries in matrix\n", - " 1h 48m 46s SIMILARITY (O half_verse LCS M>60): Computed 112 M comparisons and saved 217110 entries in matrix\n", - " 1h 49m 06s SIMILARITY (O half_verse LCS M>60): Computed 122 M comparisons and saved 234406 entries in matrix\n", - " 1h 49m 27s SIMILARITY (O half_verse LCS M>60): Computed 132 M comparisons and saved 251791 entries in matrix\n", - " 1h 49m 49s SIMILARITY (O half_verse LCS M>60): Computed 142 M comparisons and saved 277921 entries in matrix\n", - " 1h 50m 11s SIMILARITY (O half_verse LCS M>60): Computed 153 M comparisons and saved 301012 entries in matrix\n", - " 1h 50m 34s SIMILARITY (O half_verse LCS M>60): Computed 163 M comparisons and saved 322004 entries in matrix\n", - " 1h 50m 56s SIMILARITY (O half_verse LCS M>60): Computed 173 M comparisons and saved 345960 entries in matrix\n", - " 1h 51m 16s SIMILARITY (O half_verse LCS M>60): Computed 183 M comparisons and saved 366300 entries in matrix\n", - " 1h 51m 37s SIMILARITY (O half_verse LCS M>60): Computed 193 M comparisons and saved 381274 entries in matrix\n", - " 1h 51m 59s SIMILARITY (O half_verse LCS M>60): Computed 204 M comparisons and saved 401543 entries in matrix\n", - " 1h 52m 21s SIMILARITY (O half_verse LCS M>60): Computed 214 M comparisons and saved 424607 entries in matrix\n", - " 1h 52m 42s SIMILARITY (O half_verse LCS M>60): Computed 224 M comparisons and saved 434786 entries in matrix\n", - " 1h 53m 04s SIMILARITY (O half_verse LCS M>60): Computed 234 M comparisons and saved 451987 entries in matrix\n", - " 1h 53m 26s SIMILARITY (O half_verse LCS M>60): Computed 244 M comparisons and saved 476196 entries in matrix\n", - " 1h 53m 48s SIMILARITY (O half_verse LCS M>60): Computed 255 M comparisons and saved 495694 entries in matrix\n", - " 1h 54m 11s SIMILARITY (O half_verse LCS M>60): Computed 265 M comparisons and saved 511994 entries in matrix\n", - " 1h 54m 31s SIMILARITY (O half_verse LCS M>60): Computed 275 M comparisons and saved 538859 entries in matrix\n", - " 1h 54m 52s SIMILARITY (O half_verse LCS M>60): Computed 285 M comparisons and saved 566545 entries in matrix\n", - " 1h 55m 12s SIMILARITY (O half_verse LCS M>60): Computed 295 M comparisons and saved 585782 entries in matrix\n", - " 1h 55m 32s SIMILARITY (O half_verse LCS M>60): Computed 306 M comparisons and saved 605411 entries in matrix\n", - " 1h 55m 55s SIMILARITY (O half_verse LCS M>60): Computed 316 M comparisons and saved 623888 entries in matrix\n", - " 1h 56m 16s SIMILARITY (O half_verse LCS M>60): Computed 326 M comparisons and saved 645994 entries in matrix\n", - " 1h 56m 37s SIMILARITY (O half_verse LCS M>60): Computed 336 M comparisons and saved 672491 entries in matrix\n", - " 1h 57m 00s SIMILARITY (O half_verse LCS M>60): Computed 347 M comparisons and saved 690907 entries in matrix\n", - " 1h 57m 20s SIMILARITY (O half_verse LCS M>60): Computed 357 M comparisons and saved 710440 entries in matrix\n", - " 1h 57m 42s SIMILARITY (O half_verse LCS M>60): Computed 367 M comparisons and saved 727551 entries in matrix\n", - " 1h 58m 01s SIMILARITY (O half_verse LCS M>60): Computed 377 M comparisons and saved 747143 entries in matrix\n", - " 1h 58m 23s SIMILARITY (O half_verse LCS M>60): Computed 387 M comparisons and saved 769713 entries in matrix\n", - " 1h 58m 46s SIMILARITY (O half_verse LCS M>60): Computed 398 M comparisons and saved 791579 entries in matrix\n", - " 1h 59m 08s SIMILARITY (O half_verse LCS M>60): Computed 408 M comparisons and saved 813039 entries in matrix\n", - " 1h 59m 31s SIMILARITY (O half_verse LCS M>60): Computed 418 M comparisons and saved 834185 entries in matrix\n", - " 1h 59m 52s SIMILARITY (O half_verse LCS M>60): Computed 428 M comparisons and saved 855462 entries in matrix\n", - " 2h 00m 14s SIMILARITY (O half_verse LCS M>60): Computed 438 M comparisons and saved 879387 entries in matrix\n", - " 2h 00m 35s SIMILARITY (O half_verse LCS M>60): Computed 449 M comparisons and saved 896300 entries in matrix\n", - " 2h 00m 59s SIMILARITY (O half_verse LCS M>60): Computed 459 M comparisons and saved 914630 entries in matrix\n", - " 2h 01m 23s SIMILARITY (O half_verse LCS M>60): Computed 469 M comparisons and saved 928008 entries in matrix\n", - " 2h 01m 44s SIMILARITY (O half_verse LCS M>60): Computed 479 M comparisons and saved 939511 entries in matrix\n", - " 2h 02m 08s SIMILARITY (O half_verse LCS M>60): Computed 489 M comparisons and saved 956514 entries in matrix\n", - " 2h 02m 29s SIMILARITY (O half_verse LCS M>60): Computed 500 M comparisons and saved 973079 entries in matrix\n", - " 2h 02m 52s SIMILARITY (O half_verse LCS M>60): Computed 510 M comparisons and saved 988171 entries in matrix\n", - " 2h 03m 15s SIMILARITY (O half_verse LCS M>60): Computed 520 M comparisons and saved 1007501 entries in matrix\n", - " 2h 03m 37s SIMILARITY (O half_verse LCS M>60): Computed 530 M comparisons and saved 1026297 entries in matrix\n", - " 2h 04m 00s SIMILARITY (O half_verse LCS M>60): Computed 540 M comparisons and saved 1044626 entries in matrix\n", - " 2h 04m 22s SIMILARITY (O half_verse LCS M>60): Computed 551 M comparisons and saved 1065472 entries in matrix\n", - " 2h 04m 44s SIMILARITY (O half_verse LCS M>60): Computed 561 M comparisons and saved 1084725 entries in matrix\n", - " 2h 05m 06s SIMILARITY (O half_verse LCS M>60): Computed 571 M comparisons and saved 1098963 entries in matrix\n", - " 2h 05m 28s SIMILARITY (O half_verse LCS M>60): Computed 581 M comparisons and saved 1113424 entries in matrix\n", - " 2h 05m 50s SIMILARITY (O half_verse LCS M>60): Computed 591 M comparisons and saved 1131189 entries in matrix\n", - " 2h 06m 13s SIMILARITY (O half_verse LCS M>60): Computed 602 M comparisons and saved 1152117 entries in matrix\n", - " 2h 06m 35s SIMILARITY (O half_verse LCS M>60): Computed 612 M comparisons and saved 1169776 entries in matrix\n", - " 2h 06m 56s SIMILARITY (O half_verse LCS M>60): Computed 622 M comparisons and saved 1190128 entries in matrix\n", - " 2h 07m 18s SIMILARITY (O half_verse LCS M>60): Computed 632 M comparisons and saved 1206090 entries in matrix\n", - " 2h 07m 41s SIMILARITY (O half_verse LCS M>60): Computed 642 M comparisons and saved 1223130 entries in matrix\n", - " 2h 08m 03s SIMILARITY (O half_verse LCS M>60): Computed 653 M comparisons and saved 1244760 entries in matrix\n", - " 2h 08m 25s SIMILARITY (O half_verse LCS M>60): Computed 663 M comparisons and saved 1264850 entries in matrix\n", - " 2h 08m 47s SIMILARITY (O half_verse LCS M>60): Computed 673 M comparisons and saved 1283072 entries in matrix\n", - " 2h 09m 09s SIMILARITY (O half_verse LCS M>60): Computed 683 M comparisons and saved 1299660 entries in matrix\n", - " 2h 09m 31s SIMILARITY (O half_verse LCS M>60): Computed 694 M comparisons and saved 1317246 entries in matrix\n", - " 2h 09m 52s SIMILARITY (O half_verse LCS M>60): Computed 704 M comparisons and saved 1333767 entries in matrix\n", - " 2h 10m 11s SIMILARITY (O half_verse LCS M>60): Computed 714 M comparisons and saved 1350133 entries in matrix\n", - " 2h 10m 30s SIMILARITY (O half_verse LCS M>60): Computed 724 M comparisons and saved 1365013 entries in matrix\n", - " 2h 10m 50s SIMILARITY (O half_verse LCS M>60): Computed 734 M comparisons and saved 1379520 entries in matrix\n", - " 2h 11m 08s SIMILARITY (O half_verse LCS M>60): Computed 745 M comparisons and saved 1397432 entries in matrix\n", - " 2h 11m 28s SIMILARITY (O half_verse LCS M>60): Computed 755 M comparisons and saved 1411286 entries in matrix\n", - " 2h 11m 48s SIMILARITY (O half_verse LCS M>60): Computed 765 M comparisons and saved 1430539 entries in matrix\n", - " 2h 12m 08s SIMILARITY (O half_verse LCS M>60): Computed 775 M comparisons and saved 1450873 entries in matrix\n", - " 2h 12m 29s SIMILARITY (O half_verse LCS M>60): Computed 785 M comparisons and saved 1471293 entries in matrix\n", - " 2h 12m 50s SIMILARITY (O half_verse LCS M>60): Computed 796 M comparisons and saved 1493379 entries in matrix\n", - " 2h 13m 13s SIMILARITY (O half_verse LCS M>60): Computed 806 M comparisons and saved 1511949 entries in matrix\n", - " 2h 13m 32s SIMILARITY (O half_verse LCS M>60): Computed 816 M comparisons and saved 1525887 entries in matrix\n", - " 2h 13m 52s SIMILARITY (O half_verse LCS M>60): Computed 826 M comparisons and saved 1544656 entries in matrix\n", - " 2h 14m 12s SIMILARITY (O half_verse LCS M>60): Computed 836 M comparisons and saved 1564089 entries in matrix\n", - " 2h 14m 30s SIMILARITY (O half_verse LCS M>60): Computed 847 M comparisons and saved 1582466 entries in matrix\n", - " 2h 14m 50s SIMILARITY (O half_verse LCS M>60): Computed 857 M comparisons and saved 1600534 entries in matrix\n", - " 2h 15m 10s SIMILARITY (O half_verse LCS M>60): Computed 867 M comparisons and saved 1616006 entries in matrix\n", - " 2h 15m 28s SIMILARITY (O half_verse LCS M>60): Computed 877 M comparisons and saved 1634862 entries in matrix\n", - " 2h 15m 46s SIMILARITY (O half_verse LCS M>60): Computed 887 M comparisons and saved 1650701 entries in matrix\n", - " 2h 16m 05s SIMILARITY (O half_verse LCS M>60): Computed 898 M comparisons and saved 1671631 entries in matrix\n", - " 2h 16m 20s SIMILARITY (O half_verse LCS M>60): Computed 908 M comparisons and saved 1704592 entries in matrix\n", - " 2h 16m 34s SIMILARITY (O half_verse LCS M>60): Computed 918 M comparisons and saved 1741561 entries in matrix\n", - " 2h 16m 49s SIMILARITY (O half_verse LCS M>60): Computed 928 M comparisons and saved 1776301 entries in matrix\n", - " 2h 17m 04s SIMILARITY (O half_verse LCS M>60): Computed 938 M comparisons and saved 1811487 entries in matrix\n", - " 2h 17m 19s SIMILARITY (O half_verse LCS M>60): Computed 949 M comparisons and saved 1846878 entries in matrix\n", - " 2h 17m 33s SIMILARITY (O half_verse LCS M>60): Computed 959 M comparisons and saved 1882694 entries in matrix\n", - " 2h 17m 49s SIMILARITY (O half_verse LCS M>60): Computed 969 M comparisons and saved 1913165 entries in matrix\n", - " 2h 18m 04s SIMILARITY (O half_verse LCS M>60): Computed 979 M comparisons and saved 1941507 entries in matrix\n", - " 2h 18m 20s SIMILARITY (O half_verse LCS M>60): Computed 989 M comparisons and saved 1965409 entries in matrix\n", - " 2h 18m 38s SIMILARITY (O half_verse LCS M>60): Computed 1000 M comparisons and saved 1981708 entries in matrix\n", - " 2h 19m 02s SIMILARITY (O half_verse LCS M>60): Computed 1010 M comparisons and saved 1993912 entries in matrix\n", - " 2h 19m 25s SIMILARITY (O half_verse LCS M>60): Computed 1020 M comparisons and saved 2017735 entries in matrix\n", - " 2h 19m 26s SIMILARITY (O half_verse LCS M>60): Computed 1020 M (1020593610) comparisons and saved 2017735 entries in matrix\n", - " 2h 19m 28s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates\n", - " 2h 19m 28s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix\n", - " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages\n", - " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composing cliques out of 3799 chunks from 9270 comparisons\n", - " 2h 19m 29s CLIQUES (O half_verse LCS M>60 S>100): Composed 450 cliques out of 1000 chunks\n", - " 2h 19m 30s CLIQUES (O half_verse LCS M>60 S>100): Composed 823 cliques out of 2000 chunks\n", - " 2h 19m 32s CLIQUES (O half_verse LCS M>60 S>100): Composed 1246 cliques out of 3000 chunks\n", - " 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques\n", - " 2h 19m 34s CLIQUES (O half_verse LCS M>60 S>100): Composed and saved 1514 cliques out of 3799 chunks from 9270 comparisons\n", - " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): sorting out cliques\n", - " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs\n", - " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493\n", - " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing\n", - " 2h 19m 34s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs\n", - " 2h 19m 34s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 19m 34s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 19m 34s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 19m 36s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates\n", - " 2h 19m 36s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix\n", - " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages\n", - " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composing cliques out of 4342 chunks from 9663 comparisons\n", - " 2h 19m 37s CLIQUES (O half_verse LCS M>60 S>95): Composed 469 cliques out of 1000 chunks\n", - " 2h 19m 38s CLIQUES (O half_verse LCS M>60 S>95): Composed 848 cliques out of 2000 chunks\n", - " 2h 19m 40s CLIQUES (O half_verse LCS M>60 S>95): Composed 1272 cliques out of 3000 chunks\n", - " 2h 19m 42s CLIQUES (O half_verse LCS M>60 S>95): Composed 1689 cliques out of 4000 chunks\n", - " 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques\n", - " 2h 19m 43s CLIQUES (O half_verse LCS M>60 S>95): Composed and saved 1771 cliques out of 4342 chunks from 9663 comparisons\n", - " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): sorting out cliques\n", - " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs\n", - " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543\n", - " 2h 19m 43s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing\n", - " 2h 19m 44s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs\n", - " 2h 19m 44s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 19m 44s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 19m 44s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 19m 46s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates\n", - " 2h 19m 46s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix\n", - " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages\n", - " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composing cliques out of 5776 chunks from 12125 comparisons\n", - " 2h 19m 47s CLIQUES (O half_verse LCS M>60 S>90): Composed 482 cliques out of 1000 chunks\n", - " 2h 19m 48s CLIQUES (O half_verse LCS M>60 S>90): Composed 913 cliques out of 2000 chunks\n", - " 2h 19m 50s CLIQUES (O half_verse LCS M>60 S>90): Composed 1282 cliques out of 3000 chunks\n", - " 2h 19m 52s CLIQUES (O half_verse LCS M>60 S>90): Composed 1718 cliques out of 4000 chunks\n", - " 2h 19m 55s CLIQUES (O half_verse LCS M>60 S>90): Composed 2094 cliques out of 5000 chunks\n", - " 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques\n", - " 2h 19m 58s CLIQUES (O half_verse LCS M>60 S>90): Composed and saved 2336 cliques out of 5776 chunks from 12125 comparisons\n", - " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): sorting out cliques\n", - " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs\n", - " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732\n", - " 2h 19m 58s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing\n", - " 2h 19m 59s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs\n", - " 2h 19m 59s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 19m 59s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 19m 59s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 20m 00s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates\n", - " 2h 20m 00s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix\n", - " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages\n", - " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composing cliques out of 7970 chunks from 17551 comparisons\n", - " 2h 20m 02s CLIQUES (O half_verse LCS M>60 S>85): Composed 526 cliques out of 1000 chunks\n", - " 2h 20m 03s CLIQUES (O half_verse LCS M>60 S>85): Composed 959 cliques out of 2000 chunks\n", - " 2h 20m 05s CLIQUES (O half_verse LCS M>60 S>85): Composed 1352 cliques out of 3000 chunks\n", - " 2h 20m 07s CLIQUES (O half_verse LCS M>60 S>85): Composed 1694 cliques out of 4000 chunks\n", - " 2h 20m 10s CLIQUES (O half_verse LCS M>60 S>85): Composed 2079 cliques out of 5000 chunks\n", - " 2h 20m 13s CLIQUES (O half_verse LCS M>60 S>85): Composed 2430 cliques out of 6000 chunks\n", - " 2h 20m 17s CLIQUES (O half_verse LCS M>60 S>85): Composed 2773 cliques out of 7000 chunks\n", - " 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques\n", - " 2h 20m 22s CLIQUES (O half_verse LCS M>60 S>85): Composed and saved 2983 cliques out of 7970 chunks from 17551 comparisons\n", - " 2h 20m 22s PRINT (O half_verse LCS M>60 S>85): sorting out cliques\n", - " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs\n", - " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975\n", - " 2h 20m 23s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing\n", - " 2h 20m 24s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs\n", - " 2h 20m 24s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 20m 24s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 20m 24s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 20m 26s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates\n", - " 2h 20m 26s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix\n", - " 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages\n", - " 2h 20m 27s CLIQUES (O half_verse LCS M>60 S>80): Composing cliques out of 12504 chunks from 27273 comparisons\n", - " 2h 20m 28s CLIQUES (O half_verse LCS M>60 S>80): Composed 538 cliques out of 1000 chunks\n", - " 2h 20m 29s CLIQUES (O half_verse LCS M>60 S>80): Composed 956 cliques out of 2000 chunks\n", - " 2h 20m 30s CLIQUES (O half_verse LCS M>60 S>80): Composed 1379 cliques out of 3000 chunks\n", - " 2h 20m 32s CLIQUES (O half_verse LCS M>60 S>80): Composed 1735 cliques out of 4000 chunks\n", - " 2h 20m 35s CLIQUES (O half_verse LCS M>60 S>80): Composed 2057 cliques out of 5000 chunks\n", - " 2h 20m 39s CLIQUES (O half_verse LCS M>60 S>80): Composed 2332 cliques out of 6000 chunks\n", - " 2h 20m 43s CLIQUES (O half_verse LCS M>60 S>80): Composed 2700 cliques out of 7000 chunks\n", - " 2h 20m 48s CLIQUES (O half_verse LCS M>60 S>80): Composed 2968 cliques out of 8000 chunks\n", - " 2h 20m 54s CLIQUES (O half_verse LCS M>60 S>80): Composed 3278 cliques out of 9000 chunks\n", - " 2h 21m 00s CLIQUES (O half_verse LCS M>60 S>80): Composed 3433 cliques out of 10000 chunks\n", - " 2h 21m 07s CLIQUES (O half_verse LCS M>60 S>80): Composed 3579 cliques out of 11000 chunks\n", - " 2h 21m 14s CLIQUES (O half_verse LCS M>60 S>80): Composed 3589 cliques out of 12000 chunks\n", - " 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques\n", - " 2h 21m 18s CLIQUES (O half_verse LCS M>60 S>80): Composed and saved 3540 cliques out of 12504 chunks from 27273 comparisons\n", - " 2h 21m 18s PRINT (O half_verse LCS M>60 S>80): sorting out cliques\n", - " 2h 21m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs\n", - " 2h 21m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs\n", - " 2h 21m 21s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 21m 21s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 21m 21s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 21m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates\n", - " 2h 21m 23s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix\n", - " 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): 53979 relevant similarities between 19147 passages\n", - " 2h 21m 24s CLIQUES (O half_verse LCS M>60 S>75): Composing cliques out of 19147 chunks from 53979 comparisons\n", - " 2h 21m 25s CLIQUES (O half_verse LCS M>60 S>75): Composed 561 cliques out of 1000 chunks\n", - " 2h 21m 26s CLIQUES (O half_verse LCS M>60 S>75): Composed 1031 cliques out of 2000 chunks\n", - " 2h 21m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed 1399 cliques out of 3000 chunks\n", - " 2h 21m 30s CLIQUES (O half_verse LCS M>60 S>75): Composed 1756 cliques out of 4000 chunks\n", - " 2h 21m 33s CLIQUES (O half_verse LCS M>60 S>75): Composed 2091 cliques out of 5000 chunks\n", - " 2h 21m 36s CLIQUES (O half_verse LCS M>60 S>75): Composed 2372 cliques out of 6000 chunks\n", - " 2h 21m 40s CLIQUES (O half_verse LCS M>60 S>75): Composed 2584 cliques out of 7000 chunks\n", - " 2h 21m 45s CLIQUES (O half_verse LCS M>60 S>75): Composed 2783 cliques out of 8000 chunks\n", - " 2h 21m 51s CLIQUES (O half_verse LCS M>60 S>75): Composed 2943 cliques out of 9000 chunks\n", - " 2h 21m 57s CLIQUES (O half_verse LCS M>60 S>75): Composed 3223 cliques out of 10000 chunks\n", - " 2h 22m 04s CLIQUES (O half_verse LCS M>60 S>75): Composed 3329 cliques out of 11000 chunks\n", - " 2h 22m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed 3391 cliques out of 12000 chunks\n", - " 2h 22m 18s CLIQUES (O half_verse LCS M>60 S>75): Composed 3510 cliques out of 13000 chunks\n", - " 2h 22m 27s CLIQUES (O half_verse LCS M>60 S>75): Composed 3569 cliques out of 14000 chunks\n", - " 2h 22m 35s CLIQUES (O half_verse LCS M>60 S>75): Composed 3562 cliques out of 15000 chunks\n", - " 2h 22m 44s CLIQUES (O half_verse LCS M>60 S>75): Composed 3512 cliques out of 16000 chunks\n", - " 2h 22m 53s CLIQUES (O half_verse LCS M>60 S>75): Composed 3420 cliques out of 17000 chunks\n", - " 2h 23m 02s CLIQUES (O half_verse LCS M>60 S>75): Composed 3340 cliques out of 18000 chunks\n", - " 2h 23m 11s CLIQUES (O half_verse LCS M>60 S>75): Composed 3115 cliques out of 19000 chunks\n", - " 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): 19147 members in 3084 cliques\n", - " 2h 23m 13s CLIQUES (O half_verse LCS M>60 S>75): Composed and saved 3084 cliques out of 19147 chunks from 53979 comparisons\n", - " 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): sorting out cliques\n", - " 2h 23m 13s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs\n", - " 2h 23m 16s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs\n", - " 2h 23m 16s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 23m 16s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 23m 16s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 23m 17s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates\n", - " 2h 23m 17s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix\n", - " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): 126164 relevant similarities between 28473 passages\n", - " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composing cliques out of 28473 chunks from 126164 comparisons\n", - " 2h 23m 19s CLIQUES (O half_verse LCS M>60 S>70): Composed 580 cliques out of 1000 chunks\n", - " 2h 23m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed 1067 cliques out of 2000 chunks\n", - " 2h 23m 22s CLIQUES (O half_verse LCS M>60 S>70): Composed 1458 cliques out of 3000 chunks\n", - " 2h 23m 24s CLIQUES (O half_verse LCS M>60 S>70): Composed 1714 cliques out of 4000 chunks\n", - " 2h 23m 27s CLIQUES (O half_verse LCS M>60 S>70): Composed 1935 cliques out of 5000 chunks\n", - " 2h 23m 31s CLIQUES (O half_verse LCS M>60 S>70): Composed 2138 cliques out of 6000 chunks\n", - " 2h 23m 35s CLIQUES (O half_verse LCS M>60 S>70): Composed 2387 cliques out of 7000 chunks\n", - " 2h 23m 40s CLIQUES (O half_verse LCS M>60 S>70): Composed 2541 cliques out of 8000 chunks\n", - " 2h 23m 45s CLIQUES (O half_verse LCS M>60 S>70): Composed 2652 cliques out of 9000 chunks\n", - " 2h 23m 50s CLIQUES (O half_verse LCS M>60 S>70): Composed 2723 cliques out of 10000 chunks\n", - " 2h 23m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed 2793 cliques out of 11000 chunks\n", - " 2h 24m 02s CLIQUES (O half_verse LCS M>60 S>70): Composed 2710 cliques out of 12000 chunks\n", - " 2h 24m 09s CLIQUES (O half_verse LCS M>60 S>70): Composed 2725 cliques out of 13000 chunks\n", - " 2h 24m 16s CLIQUES (O half_verse LCS M>60 S>70): Composed 2698 cliques out of 14000 chunks\n", - " 2h 24m 23s CLIQUES (O half_verse LCS M>60 S>70): Composed 2745 cliques out of 15000 chunks\n", - " 2h 24m 30s CLIQUES (O half_verse LCS M>60 S>70): Composed 2779 cliques out of 16000 chunks\n", - " 2h 24m 38s CLIQUES (O half_verse LCS M>60 S>70): Composed 2785 cliques out of 17000 chunks\n", - " 2h 24m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed 2739 cliques out of 18000 chunks\n", - " 2h 24m 54s CLIQUES (O half_verse LCS M>60 S>70): Composed 2659 cliques out of 19000 chunks\n", - " 2h 25m 03s CLIQUES (O half_verse LCS M>60 S>70): Composed 2611 cliques out of 20000 chunks\n", - " 2h 25m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed 2597 cliques out of 21000 chunks\n", - " 2h 25m 20s CLIQUES (O half_verse LCS M>60 S>70): Composed 2491 cliques out of 22000 chunks\n", - " 2h 25m 28s CLIQUES (O half_verse LCS M>60 S>70): Composed 2432 cliques out of 23000 chunks\n", - " 2h 25m 37s CLIQUES (O half_verse LCS M>60 S>70): Composed 2342 cliques out of 24000 chunks\n", - " 2h 25m 46s CLIQUES (O half_verse LCS M>60 S>70): Composed 2231 cliques out of 25000 chunks\n", - " 2h 25m 56s CLIQUES (O half_verse LCS M>60 S>70): Composed 2125 cliques out of 26000 chunks\n", - " 2h 26m 04s CLIQUES (O half_verse LCS M>60 S>70): Composed 2057 cliques out of 27000 chunks\n", - " 2h 26m 12s CLIQUES (O half_verse LCS M>60 S>70): Composed 1923 cliques out of 28000 chunks\n", - " 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): 28473 members in 1894 cliques\n", - " 2h 26m 17s CLIQUES (O half_verse LCS M>60 S>70): Composed and saved 1894 cliques out of 28473 chunks from 126164 comparisons\n", - " 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): sorting out cliques\n", - " 2h 26m 17s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs\n", - " 2h 26m 19s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs\n", - " 2h 26m 19s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 26m 19s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 26m 19s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 26m 21s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates\n", - " 2h 26m 21s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix\n", - " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): 393352 relevant similarities between 38182 passages\n", - " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composing cliques out of 38182 chunks from 393352 comparisons\n", - " 2h 26m 23s CLIQUES (O half_verse LCS M>60 S>65): Composed 581 cliques out of 1000 chunks\n", - " 2h 26m 24s CLIQUES (O half_verse LCS M>60 S>65): Composed 1010 cliques out of 2000 chunks\n", - " 2h 26m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed 1224 cliques out of 3000 chunks\n", - " 2h 26m 28s CLIQUES (O half_verse LCS M>60 S>65): Composed 1371 cliques out of 4000 chunks\n", - " 2h 26m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed 1516 cliques out of 5000 chunks\n", - " 2h 26m 34s CLIQUES (O half_verse LCS M>60 S>65): Composed 1613 cliques out of 6000 chunks\n", - " 2h 26m 37s CLIQUES (O half_verse LCS M>60 S>65): Composed 1629 cliques out of 7000 chunks\n", - " 2h 26m 41s CLIQUES (O half_verse LCS M>60 S>65): Composed 1628 cliques out of 8000 chunks\n", - " 2h 26m 45s CLIQUES (O half_verse LCS M>60 S>65): Composed 1684 cliques out of 9000 chunks\n", - " 2h 26m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed 1668 cliques out of 10000 chunks\n", - " 2h 26m 53s CLIQUES (O half_verse LCS M>60 S>65): Composed 1624 cliques out of 11000 chunks\n", - " 2h 26m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed 1601 cliques out of 12000 chunks\n", - " 2h 27m 02s CLIQUES (O half_verse LCS M>60 S>65): Composed 1520 cliques out of 13000 chunks\n", - " 2h 27m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed 1498 cliques out of 14000 chunks\n", - " 2h 27m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed 1418 cliques out of 15000 chunks\n", - " 2h 27m 16s CLIQUES (O half_verse LCS M>60 S>65): Composed 1319 cliques out of 16000 chunks\n", - " 2h 27m 22s CLIQUES (O half_verse LCS M>60 S>65): Composed 1332 cliques out of 17000 chunks\n", - " 2h 27m 27s CLIQUES (O half_verse LCS M>60 S>65): Composed 1291 cliques out of 18000 chunks\n", - " 2h 27m 31s CLIQUES (O half_verse LCS M>60 S>65): Composed 1221 cliques out of 19000 chunks\n", - " 2h 27m 36s CLIQUES (O half_verse LCS M>60 S>65): Composed 1167 cliques out of 20000 chunks\n", - " 2h 27m 42s CLIQUES (O half_verse LCS M>60 S>65): Composed 1123 cliques out of 21000 chunks\n", - " 2h 27m 47s CLIQUES (O half_verse LCS M>60 S>65): Composed 1106 cliques out of 22000 chunks\n", - " 2h 27m 52s CLIQUES (O half_verse LCS M>60 S>65): Composed 1121 cliques out of 23000 chunks\n", - " 2h 27m 58s CLIQUES (O half_verse LCS M>60 S>65): Composed 1105 cliques out of 24000 chunks\n", - " 2h 28m 05s CLIQUES (O half_verse LCS M>60 S>65): Composed 1075 cliques out of 25000 chunks\n", - " 2h 28m 09s CLIQUES (O half_verse LCS M>60 S>65): Composed 1026 cliques out of 26000 chunks\n", - " 2h 28m 15s CLIQUES (O half_verse LCS M>60 S>65): Composed 1009 cliques out of 27000 chunks\n", - " 2h 28m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed 974 cliques out of 28000 chunks\n", - " 2h 28m 26s CLIQUES (O half_verse LCS M>60 S>65): Composed 907 cliques out of 29000 chunks\n", - " 2h 28m 32s CLIQUES (O half_verse LCS M>60 S>65): Composed 892 cliques out of 30000 chunks\n", - " 2h 28m 38s CLIQUES (O half_verse LCS M>60 S>65): Composed 865 cliques out of 31000 chunks\n", - " 2h 28m 43s CLIQUES (O half_verse LCS M>60 S>65): Composed 837 cliques out of 32000 chunks\n", - " 2h 28m 49s CLIQUES (O half_verse LCS M>60 S>65): Composed 799 cliques out of 33000 chunks\n", - " 2h 28m 54s CLIQUES (O half_verse LCS M>60 S>65): Composed 757 cliques out of 34000 chunks\n", - " 2h 29m 01s CLIQUES (O half_verse LCS M>60 S>65): Composed 731 cliques out of 35000 chunks\n", - " 2h 29m 07s CLIQUES (O half_verse LCS M>60 S>65): Composed 703 cliques out of 36000 chunks\n", - " 2h 29m 12s CLIQUES (O half_verse LCS M>60 S>65): Composed 687 cliques out of 37000 chunks\n", - " 2h 29m 18s CLIQUES (O half_verse LCS M>60 S>65): Composed 671 cliques out of 38000 chunks\n", - " 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): 38182 members in 665 cliques\n", - " 2h 29m 20s CLIQUES (O half_verse LCS M>60 S>65): Composed and saved 665 cliques out of 38182 chunks from 393352 comparisons\n", - " 2h 29m 20s PRINT (O half_verse LCS M>60 S>65): sorting out cliques\n", - " 2h 29m 21s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs\n", - " 2h 29m 22s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs\n", - " 2h 29m 22s CHUNKING (O half_verse): already chunked into 45180 chunks\n", - " 2h 29m 22s PREPARING (O half_verse LCS): Already prepared\n", - " 2h 29m 22s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017735 entries in matrix\n", - " 2h 29m 23s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", - " 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates\n", - " 2h 29m 23s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix\n", - " 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): 2017735 relevant similarities between 44011 passages\n", - " 2h 29m 27s CLIQUES (O half_verse LCS M>60 S>60): Composing cliques out of 44011 chunks from 2017735 comparisons\n", - " 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 373 cliques out of 1000 chunks\n", - " 2h 29m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 444 cliques out of 2000 chunks\n", - " 2h 29m 29s CLIQUES (O half_verse LCS M>60 S>60): Composed 473 cliques out of 3000 chunks\n", - " 2h 29m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed 487 cliques out of 4000 chunks\n", - " 2h 29m 32s CLIQUES (O half_verse LCS M>60 S>60): Composed 425 cliques out of 5000 chunks\n", - " 2h 29m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed 392 cliques out of 6000 chunks\n", - " 2h 29m 34s CLIQUES (O half_verse LCS M>60 S>60): Composed 350 cliques out of 7000 chunks\n", - " 2h 29m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed 370 cliques out of 8000 chunks\n", - " 2h 29m 37s CLIQUES (O half_verse LCS M>60 S>60): Composed 323 cliques out of 9000 chunks\n", - " 2h 29m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed 299 cliques out of 10000 chunks\n", - " 2h 29m 40s CLIQUES (O half_verse LCS M>60 S>60): Composed 271 cliques out of 11000 chunks\n", - " 2h 29m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed 265 cliques out of 12000 chunks\n", - " 2h 29m 43s CLIQUES (O half_verse LCS M>60 S>60): Composed 255 cliques out of 13000 chunks\n", - " 2h 29m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed 242 cliques out of 14000 chunks\n", - " 2h 29m 46s CLIQUES (O half_verse LCS M>60 S>60): Composed 224 cliques out of 15000 chunks\n", - " 2h 29m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed 226 cliques out of 16000 chunks\n", - " 2h 29m 50s CLIQUES (O half_verse LCS M>60 S>60): Composed 208 cliques out of 17000 chunks\n", - " 2h 29m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed 190 cliques out of 18000 chunks\n", - " 2h 29m 53s CLIQUES (O half_verse LCS M>60 S>60): Composed 183 cliques out of 19000 chunks\n", - " 2h 29m 55s CLIQUES (O half_verse LCS M>60 S>60): Composed 178 cliques out of 20000 chunks\n", - " 2h 29m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed 177 cliques out of 21000 chunks\n", - " 2h 29m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed 171 cliques out of 22000 chunks\n", - " 2h 30m 01s CLIQUES (O half_verse LCS M>60 S>60): Composed 160 cliques out of 23000 chunks\n", - " 2h 30m 03s CLIQUES (O half_verse LCS M>60 S>60): Composed 147 cliques out of 24000 chunks\n", - " 2h 30m 05s CLIQUES (O half_verse LCS M>60 S>60): Composed 143 cliques out of 25000 chunks\n", - " 2h 30m 08s CLIQUES (O half_verse LCS M>60 S>60): Composed 132 cliques out of 26000 chunks\n", - " 2h 30m 10s CLIQUES (O half_verse LCS M>60 S>60): Composed 129 cliques out of 27000 chunks\n", - " 2h 30m 13s CLIQUES (O half_verse LCS M>60 S>60): Composed 130 cliques out of 28000 chunks\n", - " 2h 30m 15s CLIQUES (O half_verse LCS M>60 S>60): Composed 129 cliques out of 29000 chunks\n", - " 2h 30m 17s CLIQUES (O half_verse LCS M>60 S>60): Composed 126 cliques out of 30000 chunks\n", - " 2h 30m 20s CLIQUES (O half_verse LCS M>60 S>60): Composed 111 cliques out of 31000 chunks\n", - " 2h 30m 22s CLIQUES (O half_verse LCS M>60 S>60): Composed 107 cliques out of 32000 chunks\n", - " 2h 30m 25s CLIQUES (O half_verse LCS M>60 S>60): Composed 106 cliques out of 33000 chunks\n", - " 2h 30m 28s CLIQUES (O half_verse LCS M>60 S>60): Composed 101 cliques out of 34000 chunks\n", - " 2h 30m 30s CLIQUES (O half_verse LCS M>60 S>60): Composed 99 cliques out of 35000 chunks\n", - " 2h 30m 33s CLIQUES (O half_verse LCS M>60 S>60): Composed 95 cliques out of 36000 chunks\n", - " 2h 30m 36s CLIQUES (O half_verse LCS M>60 S>60): Composed 98 cliques out of 37000 chunks\n", - " 2h 30m 39s CLIQUES (O half_verse LCS M>60 S>60): Composed 95 cliques out of 38000 chunks\n", - " 2h 30m 42s CLIQUES (O half_verse LCS M>60 S>60): Composed 90 cliques out of 39000 chunks\n", - " 2h 30m 45s CLIQUES (O half_verse LCS M>60 S>60): Composed 90 cliques out of 40000 chunks\n", - " 2h 30m 48s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 41000 chunks\n", - " 2h 30m 51s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 42000 chunks\n", - " 2h 30m 54s CLIQUES (O half_verse LCS M>60 S>60): Composed 88 cliques out of 43000 chunks\n", - " 2h 30m 57s CLIQUES (O half_verse LCS M>60 S>60): Composed 89 cliques out of 44000 chunks\n", - " 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques\n", - " 2h 30m 59s CLIQUES (O half_verse LCS M>60 S>60): Composed and saved 89 cliques out of 44011 chunks from 2017735 comparisons\n", - " 2h 30m 59s PRINT (O half_verse LCS M>60 S>60): sorting out cliques\n", - " 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs\n", - " 2h 31m 00s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs\n", - " 2h 31m 00s CHUNKING (O sentence)\n", - " 2h 31m 02s CHUNKING (O sentence): Made 63570 chunks\n", - " 2h 31m 02s PREPARING (O sentence SET)\n", - " 2h 31m 02s PREPARING (O sentence SET): Done 63570 chunks.\n", - " 2h 31m 02s SIMILARITY (O sentence SET M>50): Computing 2020 M (2020540665) comparisons and saving entries in matrix\n", - " 2h 31m 27s SIMILARITY (O sentence SET M>50): Computed 20 M comparisons and saved 45808 entries in matrix\n", - " 2h 31m 51s SIMILARITY (O sentence SET M>50): Computed 40 M comparisons and saved 70941 entries in matrix\n", - " 2h 32m 15s SIMILARITY (O sentence SET M>50): Computed 60 M comparisons and saved 120842 entries in matrix\n", - " 2h 32m 39s SIMILARITY (O sentence SET M>50): Computed 80 M comparisons and saved 204627 entries in matrix\n", - " 2h 33m 03s SIMILARITY (O sentence SET M>50): Computed 101 M comparisons and saved 269586 entries in matrix\n", - " 2h 33m 27s SIMILARITY (O sentence SET M>50): Computed 121 M comparisons and saved 356837 entries in matrix\n", - " 2h 33m 50s SIMILARITY (O sentence SET M>50): Computed 141 M comparisons and saved 443278 entries in matrix\n", - " 2h 34m 13s SIMILARITY (O sentence SET M>50): Computed 161 M comparisons and saved 524421 entries in matrix\n", - " 2h 34m 37s SIMILARITY (O sentence SET M>50): Computed 181 M comparisons and saved 585724 entries in matrix\n", - " 2h 35m 00s SIMILARITY (O sentence SET M>50): Computed 202 M comparisons and saved 624605 entries in matrix\n", - " 2h 35m 24s SIMILARITY (O sentence SET M>50): Computed 222 M comparisons and saved 683267 entries in matrix\n", - " 2h 35m 48s SIMILARITY (O sentence SET M>50): Computed 242 M comparisons and saved 738052 entries in matrix\n", - " 2h 36m 13s SIMILARITY (O sentence SET M>50): Computed 262 M comparisons and saved 806254 entries in matrix\n", - " 2h 36m 37s SIMILARITY (O sentence SET M>50): Computed 282 M comparisons and saved 852940 entries in matrix\n", - " 2h 37m 00s SIMILARITY (O sentence SET M>50): Computed 303 M comparisons and saved 942155 entries in matrix\n", - " 2h 37m 24s SIMILARITY (O sentence SET M>50): Computed 323 M comparisons and saved 1006926 entries in matrix\n", - " 2h 37m 49s SIMILARITY (O sentence SET M>50): Computed 343 M comparisons and saved 1057719 entries in matrix\n", - " 2h 38m 13s SIMILARITY (O sentence SET M>50): Computed 363 M comparisons and saved 1091220 entries in matrix\n", - " 2h 38m 38s SIMILARITY (O sentence SET M>50): Computed 383 M comparisons and saved 1135567 entries in matrix\n", - " 2h 39m 01s SIMILARITY (O sentence SET M>50): Computed 404 M comparisons and saved 1155716 entries in matrix\n", - " 2h 39m 26s SIMILARITY (O sentence SET M>50): Computed 424 M comparisons and saved 1162605 entries in matrix\n", - " 2h 39m 51s SIMILARITY (O sentence SET M>50): Computed 444 M comparisons and saved 1226546 entries in matrix\n", - " 2h 40m 16s SIMILARITY (O sentence SET M>50): Computed 464 M comparisons and saved 1235733 entries in matrix\n", - " 2h 40m 41s SIMILARITY (O sentence SET M>50): Computed 484 M comparisons and saved 1249303 entries in matrix\n", - " 2h 41m 06s SIMILARITY (O sentence SET M>50): Computed 505 M comparisons and saved 1267306 entries in matrix\n", - " 2h 41m 31s SIMILARITY (O sentence SET M>50): Computed 525 M comparisons and saved 1281368 entries in matrix\n", - " 2h 41m 55s SIMILARITY (O sentence SET M>50): Computed 545 M comparisons and saved 1306272 entries in matrix\n", - " 2h 42m 20s SIMILARITY (O sentence SET M>50): Computed 565 M comparisons and saved 1339875 entries in matrix\n", - " 2h 42m 45s SIMILARITY (O sentence SET M>50): Computed 585 M comparisons and saved 1358545 entries in matrix\n", - " 2h 43m 10s SIMILARITY (O sentence SET M>50): Computed 606 M comparisons and saved 1378717 entries in matrix\n", - " 2h 43m 35s SIMILARITY (O sentence SET M>50): Computed 626 M comparisons and saved 1411294 entries in matrix\n", - " 2h 43m 59s SIMILARITY (O sentence SET M>50): Computed 646 M comparisons and saved 1457168 entries in matrix\n", - " 2h 44m 24s SIMILARITY (O sentence SET M>50): Computed 666 M comparisons and saved 1487159 entries in matrix\n", - " 2h 44m 48s SIMILARITY (O sentence SET M>50): Computed 686 M comparisons and saved 1546006 entries in matrix\n", - " 2h 45m 13s SIMILARITY (O sentence SET M>50): Computed 707 M comparisons and saved 1565381 entries in matrix\n", - " 2h 45m 38s SIMILARITY (O sentence SET M>50): Computed 727 M comparisons and saved 1588602 entries in matrix\n", - " 2h 46m 02s SIMILARITY (O sentence SET M>50): Computed 747 M comparisons and saved 1626854 entries in matrix\n", - " 2h 46m 27s SIMILARITY (O sentence SET M>50): Computed 767 M comparisons and saved 1654679 entries in matrix\n", - " 2h 46m 52s SIMILARITY (O sentence SET M>50): Computed 788 M comparisons and saved 1673231 entries in matrix\n", - " 2h 47m 16s SIMILARITY (O sentence SET M>50): Computed 808 M comparisons and saved 1703760 entries in matrix\n", - " 2h 47m 40s SIMILARITY (O sentence SET M>50): Computed 828 M comparisons and saved 1731144 entries in matrix\n", - " 2h 48m 04s SIMILARITY (O sentence SET M>50): Computed 848 M comparisons and saved 1770357 entries in matrix\n", - " 2h 48m 29s SIMILARITY (O sentence SET M>50): Computed 868 M comparisons and saved 1817758 entries in matrix\n", - " 2h 48m 53s SIMILARITY (O sentence SET M>50): Computed 889 M comparisons and saved 1841653 entries in matrix\n", - " 2h 49m 18s SIMILARITY (O sentence SET M>50): Computed 909 M comparisons and saved 1852758 entries in matrix\n", - " 2h 49m 42s SIMILARITY (O sentence SET M>50): Computed 929 M comparisons and saved 1896595 entries in matrix\n", - " 2h 50m 06s SIMILARITY (O sentence SET M>50): Computed 949 M comparisons and saved 1955361 entries in matrix\n", - " 2h 50m 30s SIMILARITY (O sentence SET M>50): Computed 969 M comparisons and saved 1997274 entries in matrix\n", - " 2h 50m 53s SIMILARITY (O sentence SET M>50): Computed 990 M comparisons and saved 2065177 entries in matrix\n", - " 2h 51m 17s SIMILARITY (O sentence SET M>50): Computed 1010 M comparisons and saved 2136092 entries in matrix\n", - " 2h 51m 41s SIMILARITY (O sentence SET M>50): Computed 1030 M comparisons and saved 2184132 entries in matrix\n", - " 2h 52m 04s SIMILARITY (O sentence SET M>50): Computed 1050 M comparisons and saved 2249609 entries in matrix\n", - " 2h 52m 28s SIMILARITY (O sentence SET M>50): Computed 1070 M comparisons and saved 2305726 entries in matrix\n", - " 2h 52m 52s SIMILARITY (O sentence SET M>50): Computed 1091 M comparisons and saved 2363991 entries in matrix\n", - " 2h 53m 15s SIMILARITY (O sentence SET M>50): Computed 1111 M comparisons and saved 2408947 entries in matrix\n", - " 2h 53m 39s SIMILARITY (O sentence SET M>50): Computed 1131 M comparisons and saved 2463678 entries in matrix\n", - " 2h 54m 02s SIMILARITY (O sentence SET M>50): Computed 1151 M comparisons and saved 2514424 entries in matrix\n", - " 2h 54m 26s SIMILARITY (O sentence SET M>50): Computed 1171 M comparisons and saved 2564808 entries in matrix\n", - " 2h 54m 50s SIMILARITY (O sentence SET M>50): Computed 1192 M comparisons and saved 2607470 entries in matrix\n", - " 2h 55m 14s SIMILARITY (O sentence SET M>50): Computed 1212 M comparisons and saved 2670243 entries in matrix\n", - " 2h 55m 37s SIMILARITY (O sentence SET M>50): Computed 1232 M comparisons and saved 2714094 entries in matrix\n", - " 2h 56m 01s SIMILARITY (O sentence SET M>50): Computed 1252 M comparisons and saved 2752488 entries in matrix\n", - " 2h 56m 25s SIMILARITY (O sentence SET M>50): Computed 1272 M comparisons and saved 2814356 entries in matrix\n", - " 2h 56m 51s SIMILARITY (O sentence SET M>50): Computed 1293 M comparisons and saved 2827848 entries in matrix\n", - " 2h 57m 15s SIMILARITY (O sentence SET M>50): Computed 1313 M comparisons and saved 2864217 entries in matrix\n", - " 2h 57m 38s SIMILARITY (O sentence SET M>50): Computed 1333 M comparisons and saved 2935628 entries in matrix\n", - " 2h 58m 01s SIMILARITY (O sentence SET M>50): Computed 1353 M comparisons and saved 3005907 entries in matrix\n", - " 2h 58m 24s SIMILARITY (O sentence SET M>50): Computed 1373 M comparisons and saved 3085992 entries in matrix\n", - " 2h 58m 47s SIMILARITY (O sentence SET M>50): Computed 1394 M comparisons and saved 3153997 entries in matrix\n", - " 2h 59m 11s SIMILARITY (O sentence SET M>50): Computed 1414 M comparisons and saved 3179667 entries in matrix\n", - " 2h 59m 36s SIMILARITY (O sentence SET M>50): Computed 1434 M comparisons and saved 3213666 entries in matrix\n", - " 2h 59m 59s SIMILARITY (O sentence SET M>50): Computed 1454 M comparisons and saved 3241136 entries in matrix\n", - " 3h 00m 23s SIMILARITY (O sentence SET M>50): Computed 1474 M comparisons and saved 3256962 entries in matrix\n", - " 3h 00m 46s SIMILARITY (O sentence SET M>50): Computed 1495 M comparisons and saved 3277580 entries in matrix\n", - " 3h 01m 09s SIMILARITY (O sentence SET M>50): Computed 1515 M comparisons and saved 3306752 entries in matrix\n", - " 3h 01m 31s SIMILARITY (O sentence SET M>50): Computed 1535 M comparisons and saved 3330735 entries in matrix\n", - " 3h 01m 54s SIMILARITY (O sentence SET M>50): Computed 1555 M comparisons and saved 3354290 entries in matrix\n", - " 3h 02m 17s SIMILARITY (O sentence SET M>50): Computed 1576 M comparisons and saved 3386386 entries in matrix\n", - " 3h 02m 40s SIMILARITY (O sentence SET M>50): Computed 1596 M comparisons and saved 3431895 entries in matrix\n", - " 3h 03m 04s SIMILARITY (O sentence SET M>50): Computed 1616 M comparisons and saved 3469842 entries in matrix\n", - " 3h 03m 28s SIMILARITY (O sentence SET M>50): Computed 1636 M comparisons and saved 3513213 entries in matrix\n", - " 3h 03m 52s SIMILARITY (O sentence SET M>50): Computed 1656 M comparisons and saved 3552466 entries in matrix\n", - " 3h 04m 16s SIMILARITY (O sentence SET M>50): Computed 1677 M comparisons and saved 3582525 entries in matrix\n", - " 3h 04m 39s SIMILARITY (O sentence SET M>50): Computed 1697 M comparisons and saved 3603609 entries in matrix\n", - " 3h 05m 03s SIMILARITY (O sentence SET M>50): Computed 1717 M comparisons and saved 3646709 entries in matrix\n", - " 3h 05m 26s SIMILARITY (O sentence SET M>50): Computed 1737 M comparisons and saved 3681527 entries in matrix\n", - " 3h 05m 50s SIMILARITY (O sentence SET M>50): Computed 1757 M comparisons and saved 3707553 entries in matrix\n", - " 3h 06m 14s SIMILARITY (O sentence SET M>50): Computed 1778 M comparisons and saved 3732364 entries in matrix\n", - " 3h 06m 38s SIMILARITY (O sentence SET M>50): Computed 1798 M comparisons and saved 3752245 entries in matrix\n", - " 3h 07m 01s SIMILARITY (O sentence SET M>50): Computed 1818 M comparisons and saved 3772955 entries in matrix\n", - " 3h 07m 24s SIMILARITY (O sentence SET M>50): Computed 1838 M comparisons and saved 3791878 entries in matrix\n", - " 3h 07m 48s SIMILARITY (O sentence SET M>50): Computed 1858 M comparisons and saved 3831199 entries in matrix\n", - " 3h 08m 13s SIMILARITY (O sentence SET M>50): Computed 1879 M comparisons and saved 3848114 entries in matrix\n", - " 3h 08m 38s SIMILARITY (O sentence SET M>50): Computed 1899 M comparisons and saved 3862274 entries in matrix\n", - " 3h 09m 06s SIMILARITY (O sentence SET M>50): Computed 1919 M comparisons and saved 3874162 entries in matrix\n", - " 3h 09m 29s SIMILARITY (O sentence SET M>50): Computed 1939 M comparisons and saved 3888285 entries in matrix\n", - " 3h 09m 53s SIMILARITY (O sentence SET M>50): Computed 1959 M comparisons and saved 3905689 entries in matrix\n", - " 3h 10m 17s SIMILARITY (O sentence SET M>50): Computed 1980 M comparisons and saved 3917473 entries in matrix\n", - " 3h 10m 42s SIMILARITY (O sentence SET M>50): Computed 2000 M comparisons and saved 3936282 entries in matrix\n", - " 3h 11m 08s SIMILARITY (O sentence SET M>50): Computed 2020 M comparisons and saved 3958946 entries in matrix\n", - " 3h 11m 11s SIMILARITY (O sentence SET M>50): Computed 2020 M (2020540665) comparisons and saved 3958946 entries in matrix\n", - " 3h 11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates\n", - " 3h 11m 14s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix\n", - " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): 937604 relevant similarities between 19031 passages\n", - " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composing cliques out of 19031 chunks from 937604 comparisons\n", - " 3h 11m 18s CLIQUES (O sentence SET M>50 S>100): Composed 511 cliques out of 1000 chunks\n", - " 3h 11m 19s CLIQUES (O sentence SET M>50 S>100): Composed 876 cliques out of 2000 chunks\n", - " 3h 11m 21s CLIQUES (O sentence SET M>50 S>100): Composed 1294 cliques out of 3000 chunks\n", - " 3h 11m 23s CLIQUES (O sentence SET M>50 S>100): Composed 1693 cliques out of 4000 chunks\n", - " 3h 11m 26s CLIQUES (O sentence SET M>50 S>100): Composed 2040 cliques out of 5000 chunks\n", - " 3h 11m 30s CLIQUES (O sentence SET M>50 S>100): Composed 2425 cliques out of 6000 chunks\n", - " 3h 11m 34s CLIQUES (O sentence SET M>50 S>100): Composed 2696 cliques out of 7000 chunks\n", - " 3h 11m 39s CLIQUES (O sentence SET M>50 S>100): Composed 2979 cliques out of 8000 chunks\n", - " 3h 11m 45s CLIQUES (O sentence SET M>50 S>100): Composed 3256 cliques out of 9000 chunks\n", - " 3h 11m 51s CLIQUES (O sentence SET M>50 S>100): Composed 3482 cliques out of 10000 chunks\n", - " 3h 11m 58s CLIQUES (O sentence SET M>50 S>100): Composed 3646 cliques out of 11000 chunks\n", - " 3h 12m 06s CLIQUES (O sentence SET M>50 S>100): Composed 3795 cliques out of 12000 chunks\n", - " 3h 12m 14s CLIQUES (O sentence SET M>50 S>100): Composed 3939 cliques out of 13000 chunks\n", - " 3h 12m 23s CLIQUES (O sentence SET M>50 S>100): Composed 4008 cliques out of 14000 chunks\n", - " 3h 12m 32s CLIQUES (O sentence SET M>50 S>100): Composed 4112 cliques out of 15000 chunks\n", - " 3h 12m 43s CLIQUES (O sentence SET M>50 S>100): Composed 4183 cliques out of 16000 chunks\n", - " 3h 12m 53s CLIQUES (O sentence SET M>50 S>100): Composed 4269 cliques out of 17000 chunks\n", - " 3h 13m 05s CLIQUES (O sentence SET M>50 S>100): Composed 4305 cliques out of 18000 chunks\n", - " 3h 13m 16s CLIQUES (O sentence SET M>50 S>100): Composed 4324 cliques out of 19000 chunks\n", - " 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): 19031 members in 4324 cliques\n", - " 3h 13m 17s CLIQUES (O sentence SET M>50 S>100): Composed and saved 4324 cliques out of 19031 chunks from 937604 comparisons\n", - " 3h 13m 17s PRINT (O sentence SET M>50 S>100): sorting out cliques\n", - " 3h 13m 18s PRINT (O sentence SET M>50 S>100): formatting 4324 cliques involving 1528 binary chapter diffs\n", - " 3h 13m 18s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528\n", - " 3h 13m 19s PRINT (O sentence SET M>50 S>100): Chapter diffs: 7 newly created and 1521 already existing\n", - " 3h 13m 21s PRINT (O sentence SET M>50 S>100): formatted 4324 cliques (87 files) involving 1528 binary chapter diffs\n", - " 3h 13m 21s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 13m 21s PREPARING (O sentence SET): Already prepared\n", - " 3h 13m 21s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 13m 25s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates\n", - " 3h 13m 25s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix\n", - " 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): 937608 relevant similarities between 19039 passages\n", - " 3h 13m 28s CLIQUES (O sentence SET M>50 S>95): Composing cliques out of 19039 chunks from 937608 comparisons\n", - " 3h 13m 29s CLIQUES (O sentence SET M>50 S>95): Composed 511 cliques out of 1000 chunks\n", - " 3h 13m 30s CLIQUES (O sentence SET M>50 S>95): Composed 876 cliques out of 2000 chunks\n", - " 3h 13m 31s CLIQUES (O sentence SET M>50 S>95): Composed 1297 cliques out of 3000 chunks\n", - " 3h 13m 34s CLIQUES (O sentence SET M>50 S>95): Composed 1691 cliques out of 4000 chunks\n", - " 3h 13m 37s CLIQUES (O sentence SET M>50 S>95): Composed 2042 cliques out of 5000 chunks\n", - " 3h 13m 40s CLIQUES (O sentence SET M>50 S>95): Composed 2425 cliques out of 6000 chunks\n", - " 3h 13m 45s CLIQUES (O sentence SET M>50 S>95): Composed 2699 cliques out of 7000 chunks\n", - " 3h 13m 50s CLIQUES (O sentence SET M>50 S>95): Composed 2979 cliques out of 8000 chunks\n", - " 3h 13m 55s CLIQUES (O sentence SET M>50 S>95): Composed 3259 cliques out of 9000 chunks\n", - " 3h 14m 02s CLIQUES (O sentence SET M>50 S>95): Composed 3485 cliques out of 10000 chunks\n", - " 3h 14m 09s CLIQUES (O sentence SET M>50 S>95): Composed 3649 cliques out of 11000 chunks\n", - " 3h 14m 16s CLIQUES (O sentence SET M>50 S>95): Composed 3799 cliques out of 12000 chunks\n", - " 3h 14m 25s CLIQUES (O sentence SET M>50 S>95): Composed 3942 cliques out of 13000 chunks\n", - " 3h 14m 33s CLIQUES (O sentence SET M>50 S>95): Composed 4012 cliques out of 14000 chunks\n", - " 3h 14m 43s CLIQUES (O sentence SET M>50 S>95): Composed 4115 cliques out of 15000 chunks\n", - " 3h 14m 53s CLIQUES (O sentence SET M>50 S>95): Composed 4186 cliques out of 16000 chunks\n", - " 3h 15m 03s CLIQUES (O sentence SET M>50 S>95): Composed 4272 cliques out of 17000 chunks\n", - " 3h 15m 15s CLIQUES (O sentence SET M>50 S>95): Composed 4309 cliques out of 18000 chunks\n", - " 3h 15m 26s CLIQUES (O sentence SET M>50 S>95): Composed 4328 cliques out of 19000 chunks\n", - " 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): 19039 members in 4328 cliques\n", - " 3h 15m 27s CLIQUES (O sentence SET M>50 S>95): Composed and saved 4328 cliques out of 19039 chunks from 937608 comparisons\n", - " 3h 15m 27s PRINT (O sentence SET M>50 S>95): sorting out cliques\n", - " 3h 15m 28s PRINT (O sentence SET M>50 S>95): formatting 4328 cliques involving 1529 binary chapter diffs\n", - " 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529\n", - " 3h 15m 28s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing\n", - " 3h 15m 31s PRINT (O sentence SET M>50 S>95): formatted 4328 cliques (87 files) involving 1529 binary chapter diffs\n", - " 3h 15m 31s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 15m 31s PREPARING (O sentence SET): Already prepared\n", - " 3h 15m 31s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 15m 34s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates\n", - " 3h 15m 34s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix\n", - " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): 937734 relevant similarities between 19214 passages\n", - " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composing cliques out of 19214 chunks from 937734 comparisons\n", - " 3h 15m 38s CLIQUES (O sentence SET M>50 S>90): Composed 484 cliques out of 1000 chunks\n", - " 3h 15m 39s CLIQUES (O sentence SET M>50 S>90): Composed 880 cliques out of 2000 chunks\n", - " 3h 15m 40s CLIQUES (O sentence SET M>50 S>90): Composed 1288 cliques out of 3000 chunks\n", - " 3h 15m 43s CLIQUES (O sentence SET M>50 S>90): Composed 1677 cliques out of 4000 chunks\n", - " 3h 15m 46s CLIQUES (O sentence SET M>50 S>90): Composed 2031 cliques out of 5000 chunks\n", - " 3h 15m 49s CLIQUES (O sentence SET M>50 S>90): Composed 2429 cliques out of 6000 chunks\n", - " 3h 15m 54s CLIQUES (O sentence SET M>50 S>90): Composed 2718 cliques out of 7000 chunks\n", - " 3h 15m 58s CLIQUES (O sentence SET M>50 S>90): Composed 3019 cliques out of 8000 chunks\n", - " 3h 16m 04s CLIQUES (O sentence SET M>50 S>90): Composed 3282 cliques out of 9000 chunks\n", - " 3h 16m 10s CLIQUES (O sentence SET M>50 S>90): Composed 3532 cliques out of 10000 chunks\n", - " 3h 16m 17s CLIQUES (O sentence SET M>50 S>90): Composed 3699 cliques out of 11000 chunks\n", - " 3h 16m 25s CLIQUES (O sentence SET M>50 S>90): Composed 3845 cliques out of 12000 chunks\n", - " 3h 16m 33s CLIQUES (O sentence SET M>50 S>90): Composed 4002 cliques out of 13000 chunks\n", - " 3h 16m 41s CLIQUES (O sentence SET M>50 S>90): Composed 4078 cliques out of 14000 chunks\n", - " 3h 16m 51s CLIQUES (O sentence SET M>50 S>90): Composed 4179 cliques out of 15000 chunks\n", - " 3h 17m 01s CLIQUES (O sentence SET M>50 S>90): Composed 4256 cliques out of 16000 chunks\n", - " 3h 17m 11s CLIQUES (O sentence SET M>50 S>90): Composed 4340 cliques out of 17000 chunks\n", - " 3h 17m 23s CLIQUES (O sentence SET M>50 S>90): Composed 4381 cliques out of 18000 chunks\n", - " 3h 17m 34s CLIQUES (O sentence SET M>50 S>90): Composed 4404 cliques out of 19000 chunks\n", - " 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): 19214 members in 4406 cliques\n", - " 3h 17m 38s CLIQUES (O sentence SET M>50 S>90): Composed and saved 4406 cliques out of 19214 chunks from 937734 comparisons\n", - " 3h 17m 38s PRINT (O sentence SET M>50 S>90): sorting out cliques\n", - " 3h 17m 38s PRINT (O sentence SET M>50 S>90): formatting 4406 cliques involving 1537 binary chapter diffs\n", - " 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1537\n", - " 3h 17m 38s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1537 already existing\n", - " 3h 17m 41s PRINT (O sentence SET M>50 S>90): formatted 4406 cliques (89 files) involving 1537 binary chapter diffs\n", - " 3h 17m 41s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 17m 41s PREPARING (O sentence SET): Already prepared\n", - " 3h 17m 41s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 17m 44s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates\n", - " 3h 17m 44s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix\n", - " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): 938584 relevant similarities between 19777 passages\n", - " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composing cliques out of 19777 chunks from 938584 comparisons\n", - " 3h 17m 48s CLIQUES (O sentence SET M>50 S>85): Composed 493 cliques out of 1000 chunks\n", - " 3h 17m 49s CLIQUES (O sentence SET M>50 S>85): Composed 910 cliques out of 2000 chunks\n", - " 3h 17m 51s CLIQUES (O sentence SET M>50 S>85): Composed 1283 cliques out of 3000 chunks\n", - " 3h 17m 53s CLIQUES (O sentence SET M>50 S>85): Composed 1662 cliques out of 4000 chunks\n", - " 3h 17m 56s CLIQUES (O sentence SET M>50 S>85): Composed 2063 cliques out of 5000 chunks\n", - " 3h 17m 59s CLIQUES (O sentence SET M>50 S>85): Composed 2427 cliques out of 6000 chunks\n", - " 3h 18m 04s CLIQUES (O sentence SET M>50 S>85): Composed 2795 cliques out of 7000 chunks\n", - " 3h 18m 09s CLIQUES (O sentence SET M>50 S>85): Composed 3047 cliques out of 8000 chunks\n", - " 3h 18m 14s CLIQUES (O sentence SET M>50 S>85): Composed 3345 cliques out of 9000 chunks\n", - " 3h 18m 20s CLIQUES (O sentence SET M>50 S>85): Composed 3583 cliques out of 10000 chunks\n", - " 3h 18m 27s CLIQUES (O sentence SET M>50 S>85): Composed 3799 cliques out of 11000 chunks\n", - " 3h 18m 35s CLIQUES (O sentence SET M>50 S>85): Composed 3972 cliques out of 12000 chunks\n", - " 3h 18m 43s CLIQUES (O sentence SET M>50 S>85): Composed 4129 cliques out of 13000 chunks\n", - " 3h 18m 52s CLIQUES (O sentence SET M>50 S>85): Composed 4244 cliques out of 14000 chunks\n", - " 3h 19m 01s CLIQUES (O sentence SET M>50 S>85): Composed 4306 cliques out of 15000 chunks\n", - " 3h 19m 11s CLIQUES (O sentence SET M>50 S>85): Composed 4411 cliques out of 16000 chunks\n", - " 3h 19m 22s CLIQUES (O sentence SET M>50 S>85): Composed 4500 cliques out of 17000 chunks\n", - " 3h 19m 33s CLIQUES (O sentence SET M>50 S>85): Composed 4571 cliques out of 18000 chunks\n", - " 3h 19m 46s CLIQUES (O sentence SET M>50 S>85): Composed 4596 cliques out of 19000 chunks\n", - " 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): 19777 members in 4608 cliques\n", - " 3h 19m 56s CLIQUES (O sentence SET M>50 S>85): Composed and saved 4608 cliques out of 19777 chunks from 938584 comparisons\n", - " 3h 19m 56s PRINT (O sentence SET M>50 S>85): sorting out cliques\n", - " 3h 19m 56s PRINT (O sentence SET M>50 S>85): formatting 4608 cliques involving 1589 binary chapter diffs\n", - " 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1589\n", - " 3h 19m 56s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1589 already existing\n", - " 3h 19m 59s PRINT (O sentence SET M>50 S>85): formatted 4608 cliques (93 files) involving 1589 binary chapter diffs\n", - " 3h 19m 59s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 19m 59s PREPARING (O sentence SET): Already prepared\n", - " 3h 19m 59s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 20m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates\n", - " 3h 20m 03s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix\n", - " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): 960796 relevant similarities between 22082 passages\n", - " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composing cliques out of 22082 chunks from 960796 comparisons\n", - " 3h 20m 06s CLIQUES (O sentence SET M>50 S>80): Composed 492 cliques out of 1000 chunks\n", - " 3h 20m 07s CLIQUES (O sentence SET M>50 S>80): Composed 1040 cliques out of 2000 chunks\n", - " 3h 20m 09s CLIQUES (O sentence SET M>50 S>80): Composed 1463 cliques out of 3000 chunks\n", - " 3h 20m 11s CLIQUES (O sentence SET M>50 S>80): Composed 1841 cliques out of 4000 chunks\n", - " 3h 20m 14s CLIQUES (O sentence SET M>50 S>80): Composed 2270 cliques out of 5000 chunks\n", - " 3h 20m 18s CLIQUES (O sentence SET M>50 S>80): Composed 2408 cliques out of 6000 chunks\n", - " 3h 20m 22s CLIQUES (O sentence SET M>50 S>80): Composed 2656 cliques out of 7000 chunks\n", - " 3h 20m 27s CLIQUES (O sentence SET M>50 S>80): Composed 2940 cliques out of 8000 chunks\n", - " 3h 20m 33s CLIQUES (O sentence SET M>50 S>80): Composed 3239 cliques out of 9000 chunks\n", - " 3h 20m 39s CLIQUES (O sentence SET M>50 S>80): Composed 3458 cliques out of 10000 chunks\n", - " 3h 20m 46s CLIQUES (O sentence SET M>50 S>80): Composed 3713 cliques out of 11000 chunks\n", - " 3h 20m 54s CLIQUES (O sentence SET M>50 S>80): Composed 3992 cliques out of 12000 chunks\n", - " 3h 21m 02s CLIQUES (O sentence SET M>50 S>80): Composed 4223 cliques out of 13000 chunks\n", - " 3h 21m 11s CLIQUES (O sentence SET M>50 S>80): Composed 4386 cliques out of 14000 chunks\n", - " 3h 21m 21s CLIQUES (O sentence SET M>50 S>80): Composed 4536 cliques out of 15000 chunks\n", - " 3h 21m 31s CLIQUES (O sentence SET M>50 S>80): Composed 4686 cliques out of 16000 chunks\n", - " 3h 21m 41s CLIQUES (O sentence SET M>50 S>80): Composed 4756 cliques out of 17000 chunks\n", - " 3h 21m 53s CLIQUES (O sentence SET M>50 S>80): Composed 4852 cliques out of 18000 chunks\n", - " 3h 22m 05s CLIQUES (O sentence SET M>50 S>80): Composed 4931 cliques out of 19000 chunks\n", - " 3h 22m 18s CLIQUES (O sentence SET M>50 S>80): Composed 5015 cliques out of 20000 chunks\n", - " 3h 22m 31s CLIQUES (O sentence SET M>50 S>80): Composed 5050 cliques out of 21000 chunks\n", - " 3h 22m 45s CLIQUES (O sentence SET M>50 S>80): Composed 5072 cliques out of 22000 chunks\n", - " 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): 22082 members in 5073 cliques\n", - " 3h 22m 47s CLIQUES (O sentence SET M>50 S>80): Composed and saved 5073 cliques out of 22082 chunks from 960796 comparisons\n", - " 3h 22m 47s PRINT (O sentence SET M>50 S>80): sorting out cliques\n", - " 3h 22m 47s PRINT (O sentence SET M>50 S>80): formatting 5073 cliques involving 1748 binary chapter diffs\n", - " 3h 22m 47s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1748\n", - " 3h 22m 48s PRINT (O sentence SET M>50 S>80): Chapter diffs: 2 newly created and 1746 already existing\n", - " 3h 22m 51s PRINT (O sentence SET M>50 S>80): formatted 5073 cliques (102 files) involving 1748 binary chapter diffs\n", - " 3h 22m 51s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 22m 51s PREPARING (O sentence SET): Already prepared\n", - " 3h 22m 51s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 22m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates\n", - " 3h 22m 55s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix\n", - " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): 1009309 relevant similarities between 25751 passages\n", - " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composing cliques out of 25751 chunks from 1009309 comparisons\n", - " 3h 22m 59s CLIQUES (O sentence SET M>50 S>75): Composed 517 cliques out of 1000 chunks\n", - " 3h 23m 00s CLIQUES (O sentence SET M>50 S>75): Composed 1017 cliques out of 2000 chunks\n", - " 3h 23m 02s CLIQUES (O sentence SET M>50 S>75): Composed 1456 cliques out of 3000 chunks\n", - " 3h 23m 04s CLIQUES (O sentence SET M>50 S>75): Composed 1823 cliques out of 4000 chunks\n", - " 3h 23m 07s CLIQUES (O sentence SET M>50 S>75): Composed 2257 cliques out of 5000 chunks\n", - " 3h 23m 11s CLIQUES (O sentence SET M>50 S>75): Composed 2696 cliques out of 6000 chunks\n", - " 3h 23m 15s CLIQUES (O sentence SET M>50 S>75): Composed 2955 cliques out of 7000 chunks\n", - " 3h 23m 20s CLIQUES (O sentence SET M>50 S>75): Composed 3272 cliques out of 8000 chunks\n", - " 3h 23m 26s CLIQUES (O sentence SET M>50 S>75): Composed 3671 cliques out of 9000 chunks\n", - " 3h 23m 33s CLIQUES (O sentence SET M>50 S>75): Composed 3821 cliques out of 10000 chunks\n", - " 3h 23m 39s CLIQUES (O sentence SET M>50 S>75): Composed 3773 cliques out of 11000 chunks\n", - " 3h 23m 47s CLIQUES (O sentence SET M>50 S>75): Composed 3823 cliques out of 12000 chunks\n", - " 3h 23m 55s CLIQUES (O sentence SET M>50 S>75): Composed 3891 cliques out of 13000 chunks\n", - " 3h 24m 04s CLIQUES (O sentence SET M>50 S>75): Composed 4004 cliques out of 14000 chunks\n", - " 3h 24m 13s CLIQUES (O sentence SET M>50 S>75): Composed 4138 cliques out of 15000 chunks\n", - " 3h 24m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4263 cliques out of 16000 chunks\n", - " 3h 24m 34s CLIQUES (O sentence SET M>50 S>75): Composed 4341 cliques out of 17000 chunks\n", - " 3h 24m 45s CLIQUES (O sentence SET M>50 S>75): Composed 4387 cliques out of 18000 chunks\n", - " 3h 24m 57s CLIQUES (O sentence SET M>50 S>75): Composed 4531 cliques out of 19000 chunks\n", - " 3h 25m 10s CLIQUES (O sentence SET M>50 S>75): Composed 4638 cliques out of 20000 chunks\n", - " 3h 25m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4699 cliques out of 21000 chunks\n", - " 3h 25m 37s CLIQUES (O sentence SET M>50 S>75): Composed 4803 cliques out of 22000 chunks\n", - " 3h 25m 52s CLIQUES (O sentence SET M>50 S>75): Composed 4893 cliques out of 23000 chunks\n", - " 3h 26m 07s CLIQUES (O sentence SET M>50 S>75): Composed 4963 cliques out of 24000 chunks\n", - " 3h 26m 23s CLIQUES (O sentence SET M>50 S>75): Composed 4988 cliques out of 25000 chunks\n", - " 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): 25751 members in 5000 cliques\n", - " 3h 26m 36s CLIQUES (O sentence SET M>50 S>75): Composed and saved 5000 cliques out of 25751 chunks from 1009309 comparisons\n", - " 3h 26m 36s PRINT (O sentence SET M>50 S>75): sorting out cliques\n", - " 3h 26m 36s PRINT (O sentence SET M>50 S>75): formatting 5000 cliques skipping 1744 binary chapter diffs\n", - " 3h 26m 40s PRINT (O sentence SET M>50 S>75): formatted 5000 cliques (100 files) skipping 1744 binary chapter diffs\n", - " 3h 26m 40s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 26m 40s PREPARING (O sentence SET): Already prepared\n", - " 3h 26m 40s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 26m 43s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates\n", - " 3h 26m 43s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix\n", - " 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): 1012009 relevant similarities between 26905 passages\n", - " 3h 26m 47s CLIQUES (O sentence SET M>50 S>70): Composing cliques out of 26905 chunks from 1012009 comparisons\n", - " 3h 26m 48s CLIQUES (O sentence SET M>50 S>70): Composed 537 cliques out of 1000 chunks\n", - " 3h 26m 49s CLIQUES (O sentence SET M>50 S>70): Composed 985 cliques out of 2000 chunks\n", - " 3h 26m 50s CLIQUES (O sentence SET M>50 S>70): Composed 1462 cliques out of 3000 chunks\n", - " 3h 26m 53s CLIQUES (O sentence SET M>50 S>70): Composed 1835 cliques out of 4000 chunks\n", - " 3h 26m 55s CLIQUES (O sentence SET M>50 S>70): Composed 2167 cliques out of 5000 chunks\n", - " 3h 26m 59s CLIQUES (O sentence SET M>50 S>70): Composed 2504 cliques out of 6000 chunks\n", - " 3h 27m 03s CLIQUES (O sentence SET M>50 S>70): Composed 2905 cliques out of 7000 chunks\n", - " 3h 27m 08s CLIQUES (O sentence SET M>50 S>70): Composed 3149 cliques out of 8000 chunks\n", - " 3h 27m 14s CLIQUES (O sentence SET M>50 S>70): Composed 3459 cliques out of 9000 chunks\n", - " 3h 27m 20s CLIQUES (O sentence SET M>50 S>70): Composed 3832 cliques out of 10000 chunks\n", - " 3h 27m 27s CLIQUES (O sentence SET M>50 S>70): Composed 4078 cliques out of 11000 chunks\n", - " 3h 27m 35s CLIQUES (O sentence SET M>50 S>70): Composed 4002 cliques out of 12000 chunks\n", - " 3h 27m 43s CLIQUES (O sentence SET M>50 S>70): Composed 4017 cliques out of 13000 chunks\n", - " 3h 27m 52s CLIQUES (O sentence SET M>50 S>70): Composed 4080 cliques out of 14000 chunks\n", - " 3h 28m 01s CLIQUES (O sentence SET M>50 S>70): Composed 4191 cliques out of 15000 chunks\n", - " 3h 28m 11s CLIQUES (O sentence SET M>50 S>70): Composed 4347 cliques out of 16000 chunks\n", - " 3h 28m 21s CLIQUES (O sentence SET M>50 S>70): Composed 4483 cliques out of 17000 chunks\n", - " 3h 28m 32s CLIQUES (O sentence SET M>50 S>70): Composed 4559 cliques out of 18000 chunks\n", - " 3h 28m 44s CLIQUES (O sentence SET M>50 S>70): Composed 4607 cliques out of 19000 chunks\n", - " 3h 28m 57s CLIQUES (O sentence SET M>50 S>70): Composed 4721 cliques out of 20000 chunks\n", - " 3h 29m 09s CLIQUES (O sentence SET M>50 S>70): Composed 4855 cliques out of 21000 chunks\n", - " 3h 29m 23s CLIQUES (O sentence SET M>50 S>70): Composed 4924 cliques out of 22000 chunks\n", - " 3h 29m 38s CLIQUES (O sentence SET M>50 S>70): Composed 5029 cliques out of 23000 chunks\n", - " 3h 29m 53s CLIQUES (O sentence SET M>50 S>70): Composed 5101 cliques out of 24000 chunks\n", - " 3h 30m 09s CLIQUES (O sentence SET M>50 S>70): Composed 5183 cliques out of 25000 chunks\n", - " 3h 30m 25s CLIQUES (O sentence SET M>50 S>70): Composed 5214 cliques out of 26000 chunks\n", - " 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): 26905 members in 5229 cliques\n", - " 3h 30m 41s CLIQUES (O sentence SET M>50 S>70): Composed and saved 5229 cliques out of 26905 chunks from 1012009 comparisons\n", - " 3h 30m 41s PRINT (O sentence SET M>50 S>70): sorting out cliques\n", - " 3h 30m 42s PRINT (O sentence SET M>50 S>70): formatting 5229 cliques skipping 1820 binary chapter diffs\n", - " 3h 30m 46s PRINT (O sentence SET M>50 S>70): formatted 5229 cliques (105 files) skipping 1820 binary chapter diffs\n", - " 3h 30m 46s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 30m 46s PREPARING (O sentence SET): Already prepared\n", - " 3h 30m 46s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 30m 49s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates\n", - " 3h 30m 49s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix\n", - " 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): 1332004 relevant similarities between 33410 passages\n", - " 3h 30m 53s CLIQUES (O sentence SET M>50 S>65): Composing cliques out of 33410 chunks from 1332004 comparisons\n", - " 3h 30m 54s CLIQUES (O sentence SET M>50 S>65): Composed 524 cliques out of 1000 chunks\n", - " 3h 30m 55s CLIQUES (O sentence SET M>50 S>65): Composed 1016 cliques out of 2000 chunks\n", - " 3h 30m 56s CLIQUES (O sentence SET M>50 S>65): Composed 1499 cliques out of 3000 chunks\n", - " 3h 30m 59s CLIQUES (O sentence SET M>50 S>65): Composed 1916 cliques out of 4000 chunks\n", - " 3h 31m 02s CLIQUES (O sentence SET M>50 S>65): Composed 2262 cliques out of 5000 chunks\n", - " 3h 31m 05s CLIQUES (O sentence SET M>50 S>65): Composed 2620 cliques out of 6000 chunks\n", - " 3h 31m 10s CLIQUES (O sentence SET M>50 S>65): Composed 2904 cliques out of 7000 chunks\n", - " 3h 31m 15s CLIQUES (O sentence SET M>50 S>65): Composed 3114 cliques out of 8000 chunks\n", - " 3h 31m 21s CLIQUES (O sentence SET M>50 S>65): Composed 3365 cliques out of 9000 chunks\n", - " 3h 31m 27s CLIQUES (O sentence SET M>50 S>65): Composed 3548 cliques out of 10000 chunks\n", - " 3h 31m 33s CLIQUES (O sentence SET M>50 S>65): Composed 3707 cliques out of 11000 chunks\n", - " 3h 31m 41s CLIQUES (O sentence SET M>50 S>65): Composed 3974 cliques out of 12000 chunks\n", - " 3h 31m 49s CLIQUES (O sentence SET M>50 S>65): Composed 4225 cliques out of 13000 chunks\n", - " 3h 31m 58s CLIQUES (O sentence SET M>50 S>65): Composed 4391 cliques out of 14000 chunks\n", - " 3h 32m 07s CLIQUES (O sentence SET M>50 S>65): Composed 4501 cliques out of 15000 chunks\n", - " 3h 32m 17s CLIQUES (O sentence SET M>50 S>65): Composed 4570 cliques out of 16000 chunks\n", - " 3h 32m 27s CLIQUES (O sentence SET M>50 S>65): Composed 4832 cliques out of 17000 chunks\n", - " 3h 32m 38s CLIQUES (O sentence SET M>50 S>65): Composed 5000 cliques out of 18000 chunks\n", - " 3h 32m 50s CLIQUES (O sentence SET M>50 S>65): Composed 5240 cliques out of 19000 chunks\n", - " 3h 33m 01s CLIQUES (O sentence SET M>50 S>65): Composed 5073 cliques out of 20000 chunks\n", - " 3h 33m 12s CLIQUES (O sentence SET M>50 S>65): Composed 4793 cliques out of 21000 chunks\n", - " 3h 33m 23s CLIQUES (O sentence SET M>50 S>65): Composed 4698 cliques out of 22000 chunks\n", - " 3h 33m 35s CLIQUES (O sentence SET M>50 S>65): Composed 4651 cliques out of 23000 chunks\n", - " 3h 33m 47s CLIQUES (O sentence SET M>50 S>65): Composed 4583 cliques out of 24000 chunks\n", - " 3h 34m 00s CLIQUES (O sentence SET M>50 S>65): Composed 4556 cliques out of 25000 chunks\n", - " 3h 34m 14s CLIQUES (O sentence SET M>50 S>65): Composed 4493 cliques out of 26000 chunks\n", - " 3h 34m 29s CLIQUES (O sentence SET M>50 S>65): Composed 4477 cliques out of 27000 chunks\n", - " 3h 34m 46s CLIQUES (O sentence SET M>50 S>65): Composed 4454 cliques out of 28000 chunks\n", - " 3h 35m 02s CLIQUES (O sentence SET M>50 S>65): Composed 4335 cliques out of 29000 chunks\n", - " 3h 35m 18s CLIQUES (O sentence SET M>50 S>65): Composed 4262 cliques out of 30000 chunks\n", - " 3h 35m 35s CLIQUES (O sentence SET M>50 S>65): Composed 4127 cliques out of 31000 chunks\n", - " 3h 35m 54s CLIQUES (O sentence SET M>50 S>65): Composed 4080 cliques out of 32000 chunks\n", - " 3h 36m 14s CLIQUES (O sentence SET M>50 S>65): Composed 4104 cliques out of 33000 chunks\n", - " 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): 33410 members in 4109 cliques\n", - " 3h 36m 24s CLIQUES (O sentence SET M>50 S>65): Composed and saved 4109 cliques out of 33410 chunks from 1332004 comparisons\n", - " 3h 36m 24s PRINT (O sentence SET M>50 S>65): sorting out cliques\n", - " 3h 36m 25s PRINT (O sentence SET M>50 S>65): formatting 4109 cliques skipping 1470 binary chapter diffs\n", - " 3h 36m 28s PRINT (O sentence SET M>50 S>65): formatted 4109 cliques (83 files) skipping 1470 binary chapter diffs\n", - " 3h 36m 28s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 36m 28s PREPARING (O sentence SET): Already prepared\n", - " 3h 36m 28s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 36m 31s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates\n", - " 3h 36m 31s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix\n", - " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): 1431430 relevant similarities between 38818 passages\n", - " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composing cliques out of 38818 chunks from 1431430 comparisons\n", - " 3h 36m 36s CLIQUES (O sentence SET M>50 S>60): Composed 541 cliques out of 1000 chunks\n", - " 3h 36m 37s CLIQUES (O sentence SET M>50 S>60): Composed 1043 cliques out of 2000 chunks\n", - " 3h 36m 39s CLIQUES (O sentence SET M>50 S>60): Composed 1463 cliques out of 3000 chunks\n", - " 3h 36m 41s CLIQUES (O sentence SET M>50 S>60): Composed 1851 cliques out of 4000 chunks\n", - " 3h 36m 44s CLIQUES (O sentence SET M>50 S>60): Composed 2147 cliques out of 5000 chunks\n", - " 3h 36m 48s CLIQUES (O sentence SET M>50 S>60): Composed 2487 cliques out of 6000 chunks\n", - " 3h 36m 52s CLIQUES (O sentence SET M>50 S>60): Composed 2732 cliques out of 7000 chunks\n", - " 3h 36m 57s CLIQUES (O sentence SET M>50 S>60): Composed 3043 cliques out of 8000 chunks\n", - " 3h 37m 03s CLIQUES (O sentence SET M>50 S>60): Composed 3330 cliques out of 9000 chunks\n", - " 3h 37m 09s CLIQUES (O sentence SET M>50 S>60): Composed 3583 cliques out of 10000 chunks\n", - " 3h 37m 16s CLIQUES (O sentence SET M>50 S>60): Composed 3885 cliques out of 11000 chunks\n", - " 3h 37m 23s CLIQUES (O sentence SET M>50 S>60): Composed 4252 cliques out of 12000 chunks\n", - " 3h 37m 31s CLIQUES (O sentence SET M>50 S>60): Composed 4277 cliques out of 13000 chunks\n", - " 3h 37m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4252 cliques out of 14000 chunks\n", - " 3h 37m 48s CLIQUES (O sentence SET M>50 S>60): Composed 4222 cliques out of 15000 chunks\n", - " 3h 37m 57s CLIQUES (O sentence SET M>50 S>60): Composed 4393 cliques out of 16000 chunks\n", - " 3h 38m 07s CLIQUES (O sentence SET M>50 S>60): Composed 4560 cliques out of 17000 chunks\n", - " 3h 38m 18s CLIQUES (O sentence SET M>50 S>60): Composed 4650 cliques out of 18000 chunks\n", - " 3h 38m 29s CLIQUES (O sentence SET M>50 S>60): Composed 4600 cliques out of 19000 chunks\n", - " 3h 38m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4518 cliques out of 20000 chunks\n", - " 3h 38m 50s CLIQUES (O sentence SET M>50 S>60): Composed 4516 cliques out of 21000 chunks\n", - " 3h 39m 02s CLIQUES (O sentence SET M>50 S>60): Composed 4545 cliques out of 22000 chunks\n", - " 3h 39m 14s CLIQUES (O sentence SET M>50 S>60): Composed 4565 cliques out of 23000 chunks\n", - " 3h 39m 27s CLIQUES (O sentence SET M>50 S>60): Composed 4671 cliques out of 24000 chunks\n", - " 3h 39m 39s CLIQUES (O sentence SET M>50 S>60): Composed 4520 cliques out of 25000 chunks\n", - " 3h 39m 51s CLIQUES (O sentence SET M>50 S>60): Composed 4267 cliques out of 26000 chunks\n", - " 3h 40m 03s CLIQUES (O sentence SET M>50 S>60): Composed 4194 cliques out of 27000 chunks\n", - " 3h 40m 15s CLIQUES (O sentence SET M>50 S>60): Composed 4163 cliques out of 28000 chunks\n", - " 3h 40m 28s CLIQUES (O sentence SET M>50 S>60): Composed 4115 cliques out of 29000 chunks\n", - " 3h 40m 41s CLIQUES (O sentence SET M>50 S>60): Composed 4079 cliques out of 30000 chunks\n", - " 3h 40m 56s CLIQUES (O sentence SET M>50 S>60): Composed 4032 cliques out of 31000 chunks\n", - " 3h 41m 13s CLIQUES (O sentence SET M>50 S>60): Composed 4015 cliques out of 32000 chunks\n", - " 3h 41m 31s CLIQUES (O sentence SET M>50 S>60): Composed 4029 cliques out of 33000 chunks\n", - " 3h 41m 51s CLIQUES (O sentence SET M>50 S>60): Composed 3972 cliques out of 34000 chunks\n", - " 3h 42m 08s CLIQUES (O sentence SET M>50 S>60): Composed 3878 cliques out of 35000 chunks\n", - " 3h 42m 26s CLIQUES (O sentence SET M>50 S>60): Composed 3790 cliques out of 36000 chunks\n", - " 3h 42m 47s CLIQUES (O sentence SET M>50 S>60): Composed 3724 cliques out of 37000 chunks\n", - " 3h 43m 09s CLIQUES (O sentence SET M>50 S>60): Composed 3734 cliques out of 38000 chunks\n", - " 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): 38818 members in 3746 cliques\n", - " 3h 43m 31s CLIQUES (O sentence SET M>50 S>60): Composed and saved 3746 cliques out of 38818 chunks from 1431430 comparisons\n", - " 3h 43m 31s PRINT (O sentence SET M>50 S>60): sorting out cliques\n", - " 3h 43m 32s PRINT (O sentence SET M>50 S>60): formatting 3746 cliques skipping 1382 binary chapter diffs\n", - " 3h 43m 35s PRINT (O sentence SET M>50 S>60): formatted 3746 cliques (75 files) skipping 1382 binary chapter diffs\n", - " 3h 43m 35s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 43m 35s PREPARING (O sentence SET): Already prepared\n", - " 3h 43m 35s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 43m 38s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates\n", - " 3h 43m 38s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix\n", - " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): 1459649 relevant similarities between 41825 passages\n", - " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composing cliques out of 41825 chunks from 1459649 comparisons\n", - " 3h 43m 43s CLIQUES (O sentence SET M>50 S>55): Composed 547 cliques out of 1000 chunks\n", - " 3h 43m 44s CLIQUES (O sentence SET M>50 S>55): Composed 1057 cliques out of 2000 chunks\n", - " 3h 43m 46s CLIQUES (O sentence SET M>50 S>55): Composed 1503 cliques out of 3000 chunks\n", - " 3h 43m 48s CLIQUES (O sentence SET M>50 S>55): Composed 1917 cliques out of 4000 chunks\n", - " 3h 43m 52s CLIQUES (O sentence SET M>50 S>55): Composed 2303 cliques out of 5000 chunks\n", - " 3h 43m 55s CLIQUES (O sentence SET M>50 S>55): Composed 2658 cliques out of 6000 chunks\n", - " 3h 44m 00s CLIQUES (O sentence SET M>50 S>55): Composed 2824 cliques out of 7000 chunks\n", - " 3h 44m 04s CLIQUES (O sentence SET M>50 S>55): Composed 3086 cliques out of 8000 chunks\n", - " 3h 44m 10s CLIQUES (O sentence SET M>50 S>55): Composed 3169 cliques out of 9000 chunks\n", - " 3h 44m 16s CLIQUES (O sentence SET M>50 S>55): Composed 3323 cliques out of 10000 chunks\n", - " 3h 44m 22s CLIQUES (O sentence SET M>50 S>55): Composed 3473 cliques out of 11000 chunks\n", - " 3h 44m 29s CLIQUES (O sentence SET M>50 S>55): Composed 3459 cliques out of 12000 chunks\n", - " 3h 44m 36s CLIQUES (O sentence SET M>50 S>55): Composed 3499 cliques out of 13000 chunks\n", - " 3h 44m 44s CLIQUES (O sentence SET M>50 S>55): Composed 3650 cliques out of 14000 chunks\n", - " 3h 44m 52s CLIQUES (O sentence SET M>50 S>55): Composed 3810 cliques out of 15000 chunks\n", - " 3h 45m 01s CLIQUES (O sentence SET M>50 S>55): Composed 3790 cliques out of 16000 chunks\n", - " 3h 45m 09s CLIQUES (O sentence SET M>50 S>55): Composed 3780 cliques out of 17000 chunks\n", - " 3h 45m 18s CLIQUES (O sentence SET M>50 S>55): Composed 3760 cliques out of 18000 chunks\n", - " 3h 45m 28s CLIQUES (O sentence SET M>50 S>55): Composed 3878 cliques out of 19000 chunks\n", - " 3h 45m 40s CLIQUES (O sentence SET M>50 S>55): Composed 3996 cliques out of 20000 chunks\n", - " 3h 45m 51s CLIQUES (O sentence SET M>50 S>55): Composed 4035 cliques out of 21000 chunks\n", - " 3h 46m 03s CLIQUES (O sentence SET M>50 S>55): Composed 4055 cliques out of 22000 chunks\n", - " 3h 46m 14s CLIQUES (O sentence SET M>50 S>55): Composed 4031 cliques out of 23000 chunks\n", - " 3h 46m 25s CLIQUES (O sentence SET M>50 S>55): Composed 4074 cliques out of 24000 chunks\n", - " 3h 46m 38s CLIQUES (O sentence SET M>50 S>55): Composed 4143 cliques out of 25000 chunks\n", - " 3h 46m 51s CLIQUES (O sentence SET M>50 S>55): Composed 4176 cliques out of 26000 chunks\n", - " 3h 47m 05s CLIQUES (O sentence SET M>50 S>55): Composed 4303 cliques out of 27000 chunks\n", - " 3h 47m 19s CLIQUES (O sentence SET M>50 S>55): Composed 4180 cliques out of 28000 chunks\n", - " 3h 47m 31s CLIQUES (O sentence SET M>50 S>55): Composed 3952 cliques out of 29000 chunks\n", - " 3h 47m 43s CLIQUES (O sentence SET M>50 S>55): Composed 3894 cliques out of 30000 chunks\n", - " 3h 47m 57s CLIQUES (O sentence SET M>50 S>55): Composed 3885 cliques out of 31000 chunks\n", - " 3h 48m 10s CLIQUES (O sentence SET M>50 S>55): Composed 3846 cliques out of 32000 chunks\n", - " 3h 48m 26s CLIQUES (O sentence SET M>50 S>55): Composed 3817 cliques out of 33000 chunks\n", - " 3h 48m 41s CLIQUES (O sentence SET M>50 S>55): Composed 3776 cliques out of 34000 chunks\n", - " 3h 48m 59s CLIQUES (O sentence SET M>50 S>55): Composed 3761 cliques out of 35000 chunks\n", - " 3h 49m 19s CLIQUES (O sentence SET M>50 S>55): Composed 3775 cliques out of 36000 chunks\n", - " 3h 49m 40s CLIQUES (O sentence SET M>50 S>55): Composed 3718 cliques out of 37000 chunks\n", - " 3h 49m 58s CLIQUES (O sentence SET M>50 S>55): Composed 3624 cliques out of 38000 chunks\n", - " 3h 50m 18s CLIQUES (O sentence SET M>50 S>55): Composed 3539 cliques out of 39000 chunks\n", - " 3h 50m 39s CLIQUES (O sentence SET M>50 S>55): Composed 3474 cliques out of 40000 chunks\n", - " 3h 51m 04s CLIQUES (O sentence SET M>50 S>55): Composed 3485 cliques out of 41000 chunks\n", - " 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): 41825 members in 3497 cliques\n", - " 3h 51m 26s CLIQUES (O sentence SET M>50 S>55): Composed and saved 3497 cliques out of 41825 chunks from 1459649 comparisons\n", - " 3h 51m 26s PRINT (O sentence SET M>50 S>55): sorting out cliques\n", - " 3h 51m 27s PRINT (O sentence SET M>50 S>55): formatting 3497 cliques skipping 1340 binary chapter diffs\n", - " 3h 51m 30s PRINT (O sentence SET M>50 S>55): formatted 3497 cliques (70 files) skipping 1340 binary chapter diffs\n", - " 3h 51m 30s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 51m 30s PREPARING (O sentence SET): Already prepared\n", - " 3h 51m 30s SIMILARITY (O sentence SET M>50): Using 2020 M (2020540665) comparisons with 3958946 entries in matrix\n", - " 3h 51m 33s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 937604 are 100%\n", - " 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates\n", - " 3h 51m 33s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix\n", - " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): 3958946 relevant similarities between 53097 passages\n", - " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composing cliques out of 53097 chunks from 3958946 comparisons\n", - " 3h 51m 41s CLIQUES (O sentence SET M>50 S>50): Composed 571 cliques out of 1000 chunks\n", - " 3h 51m 42s CLIQUES (O sentence SET M>50 S>50): Composed 1008 cliques out of 2000 chunks\n", - " 3h 51m 44s CLIQUES (O sentence SET M>50 S>50): Composed 1426 cliques out of 3000 chunks\n", - " 3h 51m 46s CLIQUES (O sentence SET M>50 S>50): Composed 1736 cliques out of 4000 chunks\n", - " 3h 51m 49s CLIQUES (O sentence SET M>50 S>50): Composed 2052 cliques out of 5000 chunks\n", - " 3h 51m 52s CLIQUES (O sentence SET M>50 S>50): Composed 2192 cliques out of 6000 chunks\n", - " 3h 51m 56s CLIQUES (O sentence SET M>50 S>50): Composed 2423 cliques out of 7000 chunks\n", - " 3h 52m 01s CLIQUES (O sentence SET M>50 S>50): Composed 2614 cliques out of 8000 chunks\n", - " 3h 52m 06s CLIQUES (O sentence SET M>50 S>50): Composed 2756 cliques out of 9000 chunks\n", - " 3h 52m 11s CLIQUES (O sentence SET M>50 S>50): Composed 2808 cliques out of 10000 chunks\n", - " 3h 52m 17s CLIQUES (O sentence SET M>50 S>50): Composed 3000 cliques out of 11000 chunks\n", - " 3h 52m 22s CLIQUES (O sentence SET M>50 S>50): Composed 3004 cliques out of 12000 chunks\n", - " 3h 52m 29s CLIQUES (O sentence SET M>50 S>50): Composed 2995 cliques out of 13000 chunks\n", - " 3h 52m 35s CLIQUES (O sentence SET M>50 S>50): Composed 3110 cliques out of 14000 chunks\n", - " 3h 52m 42s CLIQUES (O sentence SET M>50 S>50): Composed 3191 cliques out of 15000 chunks\n", - " 3h 52m 50s CLIQUES (O sentence SET M>50 S>50): Composed 3179 cliques out of 16000 chunks\n", - " 3h 52m 57s CLIQUES (O sentence SET M>50 S>50): Composed 3148 cliques out of 17000 chunks\n", - " 3h 53m 06s CLIQUES (O sentence SET M>50 S>50): Composed 3260 cliques out of 18000 chunks\n", - " 3h 53m 14s CLIQUES (O sentence SET M>50 S>50): Composed 3363 cliques out of 19000 chunks\n", - " 3h 53m 24s CLIQUES (O sentence SET M>50 S>50): Composed 3416 cliques out of 20000 chunks\n", - " 3h 53m 31s CLIQUES (O sentence SET M>50 S>50): Composed 3274 cliques out of 21000 chunks\n", - " 3h 53m 40s CLIQUES (O sentence SET M>50 S>50): Composed 3166 cliques out of 22000 chunks\n", - " 3h 53m 48s CLIQUES (O sentence SET M>50 S>50): Composed 3062 cliques out of 23000 chunks\n", - " 3h 53m 58s CLIQUES (O sentence SET M>50 S>50): Composed 3140 cliques out of 24000 chunks\n", - " 3h 54m 07s CLIQUES (O sentence SET M>50 S>50): Composed 3112 cliques out of 25000 chunks\n", - " 3h 54m 17s CLIQUES (O sentence SET M>50 S>50): Composed 3145 cliques out of 26000 chunks\n", - " 3h 54m 29s CLIQUES (O sentence SET M>50 S>50): Composed 3255 cliques out of 27000 chunks\n", - " 3h 54m 40s CLIQUES (O sentence SET M>50 S>50): Composed 3246 cliques out of 28000 chunks\n", - " 3h 54m 50s CLIQUES (O sentence SET M>50 S>50): Composed 3125 cliques out of 29000 chunks\n", - " 3h 54m 59s CLIQUES (O sentence SET M>50 S>50): Composed 3010 cliques out of 30000 chunks\n", - " 3h 55m 09s CLIQUES (O sentence SET M>50 S>50): Composed 2938 cliques out of 31000 chunks\n", - " 3h 55m 21s CLIQUES (O sentence SET M>50 S>50): Composed 2956 cliques out of 32000 chunks\n", - " 3h 55m 31s CLIQUES (O sentence SET M>50 S>50): Composed 2928 cliques out of 33000 chunks\n", - " 3h 55m 44s CLIQUES (O sentence SET M>50 S>50): Composed 2962 cliques out of 34000 chunks\n", - " 3h 55m 59s CLIQUES (O sentence SET M>50 S>50): Composed 3076 cliques out of 35000 chunks\n", - " 3h 56m 13s CLIQUES (O sentence SET M>50 S>50): Composed 3081 cliques out of 36000 chunks\n", - " 3h 56m 23s CLIQUES (O sentence SET M>50 S>50): Composed 2934 cliques out of 37000 chunks\n", - " 3h 56m 34s CLIQUES (O sentence SET M>50 S>50): Composed 2737 cliques out of 38000 chunks\n", - " 3h 56m 45s CLIQUES (O sentence SET M>50 S>50): Composed 2647 cliques out of 39000 chunks\n", - " 3h 56m 57s CLIQUES (O sentence SET M>50 S>50): Composed 2631 cliques out of 40000 chunks\n", - " 3h 57m 09s CLIQUES (O sentence SET M>50 S>50): Composed 2531 cliques out of 41000 chunks\n", - " 3h 57m 21s CLIQUES (O sentence SET M>50 S>50): Composed 2481 cliques out of 42000 chunks\n", - " 3h 57m 34s CLIQUES (O sentence SET M>50 S>50): Composed 2438 cliques out of 43000 chunks\n", - " 3h 57m 47s CLIQUES (O sentence SET M>50 S>50): Composed 2401 cliques out of 44000 chunks\n", - " 3h 57m 55s CLIQUES (O sentence SET M>50 S>50): Composed 1968 cliques out of 45000 chunks\n", - " 3h 58m 03s CLIQUES (O sentence SET M>50 S>50): Composed 1819 cliques out of 46000 chunks\n", - " 3h 58m 09s CLIQUES (O sentence SET M>50 S>50): Composed 1762 cliques out of 47000 chunks\n", - " 3h 58m 19s CLIQUES (O sentence SET M>50 S>50): Composed 1653 cliques out of 48000 chunks\n", - " 3h 58m 27s CLIQUES (O sentence SET M>50 S>50): Composed 1539 cliques out of 49000 chunks\n", - " 3h 58m 37s CLIQUES (O sentence SET M>50 S>50): Composed 1450 cliques out of 50000 chunks\n", - " 3h 58m 47s CLIQUES (O sentence SET M>50 S>50): Composed 1331 cliques out of 51000 chunks\n", - " 3h 58m 54s CLIQUES (O sentence SET M>50 S>50): Composed 1237 cliques out of 52000 chunks\n", - " 3h 59m 05s CLIQUES (O sentence SET M>50 S>50): Composed 1176 cliques out of 53000 chunks\n", - " 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): 53097 members in 1172 cliques\n", - " 3h 59m 07s CLIQUES (O sentence SET M>50 S>50): Composed and saved 1172 cliques out of 53097 chunks from 3958946 comparisons\n", - " 3h 59m 07s PRINT (O sentence SET M>50 S>50): sorting out cliques\n", - " 3h 59m 08s PRINT (O sentence SET M>50 S>50): formatting 1172 cliques skipping 470 binary chapter diffs\n", - " 3h 59m 10s PRINT (O sentence SET M>50 S>50): formatted 1172 cliques (24 files) skipping 470 binary chapter diffs\n", - " 3h 59m 10s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 3h 59m 10s PREPARING (O sentence LCS)\n", - " 3h 59m 10s PREPARING (O sentence LCS): Done 63570 chunks.\n", - " 3h 59m 10s SIMILARITY (O sentence LCS M>60): Computing 2020 M (2020540665) comparisons and saving entries in matrix\n", - " 3h 59m 35s SIMILARITY (O sentence LCS M>60): Computed 20 M comparisons and saved 125670 entries in matrix\n", - " 4h 00m 03s SIMILARITY (O sentence LCS M>60): Computed 40 M comparisons and saved 204624 entries in matrix\n", - " 4h 00m 28s SIMILARITY (O sentence LCS M>60): Computed 60 M comparisons and saved 308337 entries in matrix\n", - " 4h 00m 51s SIMILARITY (O sentence LCS M>60): Computed 80 M comparisons and saved 449060 entries in matrix\n", - " 4h 01m 15s SIMILARITY (O sentence LCS M>60): Computed 101 M comparisons and saved 569966 entries in matrix\n", - " 4h 01m 38s SIMILARITY (O sentence LCS M>60): Computed 121 M comparisons and saved 713424 entries in matrix\n", - " 4h 02m 00s SIMILARITY (O sentence LCS M>60): Computed 141 M comparisons and saved 854622 entries in matrix\n", - " 4h 02m 23s SIMILARITY (O sentence LCS M>60): Computed 161 M comparisons and saved 1003547 entries in matrix\n", - " 4h 02m 47s SIMILARITY (O sentence LCS M>60): Computed 181 M comparisons and saved 1128089 entries in matrix\n", - " 4h 03m 11s SIMILARITY (O sentence LCS M>60): Computed 202 M comparisons and saved 1229587 entries in matrix\n", - " 4h 03m 34s SIMILARITY (O sentence LCS M>60): Computed 222 M comparisons and saved 1346415 entries in matrix\n", - " 4h 03m 58s SIMILARITY (O sentence LCS M>60): Computed 242 M comparisons and saved 1461744 entries in matrix\n", - " 4h 04m 21s SIMILARITY (O sentence LCS M>60): Computed 262 M comparisons and saved 1586425 entries in matrix\n", - " 4h 04m 45s SIMILARITY (O sentence LCS M>60): Computed 282 M comparisons and saved 1687522 entries in matrix\n", - " 4h 05m 07s SIMILARITY (O sentence LCS M>60): Computed 303 M comparisons and saved 1866431 entries in matrix\n", - " 4h 05m 32s SIMILARITY (O sentence LCS M>60): Computed 323 M comparisons and saved 2001292 entries in matrix\n", - " 4h 05m 58s SIMILARITY (O sentence LCS M>60): Computed 343 M comparisons and saved 2113727 entries in matrix\n", - " 4h 06m 23s SIMILARITY (O sentence LCS M>60): Computed 363 M comparisons and saved 2217744 entries in matrix\n", - " 4h 06m 48s SIMILARITY (O sentence LCS M>60): Computed 383 M comparisons and saved 2338942 entries in matrix\n", - " 4h 07m 12s SIMILARITY (O sentence LCS M>60): Computed 404 M comparisons and saved 2429384 entries in matrix\n", - " 4h 07m 41s SIMILARITY (O sentence LCS M>60): Computed 424 M comparisons and saved 2474476 entries in matrix\n", - " 4h 08m 06s SIMILARITY (O sentence LCS M>60): Computed 444 M comparisons and saved 2597349 entries in matrix\n", - " 4h 08m 37s SIMILARITY (O sentence LCS M>60): Computed 464 M comparisons and saved 2640723 entries in matrix\n", - " 4h 09m 05s SIMILARITY (O sentence LCS M>60): Computed 484 M comparisons and saved 2700790 entries in matrix\n", - " 4h 09m 33s SIMILARITY (O sentence LCS M>60): Computed 505 M comparisons and saved 2776579 entries in matrix\n", - " 4h 09m 59s SIMILARITY (O sentence LCS M>60): Computed 525 M comparisons and saved 2855300 entries in matrix\n", - " 4h 10m 25s SIMILARITY (O sentence LCS M>60): Computed 545 M comparisons and saved 2950778 entries in matrix\n", - " 4h 10m 52s SIMILARITY (O sentence LCS M>60): Computed 565 M comparisons and saved 3049115 entries in matrix\n", - " 4h 11m 18s SIMILARITY (O sentence LCS M>60): Computed 585 M comparisons and saved 3127277 entries in matrix\n", - " 4h 11m 47s SIMILARITY (O sentence LCS M>60): Computed 606 M comparisons and saved 3195359 entries in matrix\n", - " 4h 12m 16s SIMILARITY (O sentence LCS M>60): Computed 626 M comparisons and saved 3280692 entries in matrix\n", - " 4h 12m 42s SIMILARITY (O sentence LCS M>60): Computed 646 M comparisons and saved 3395665 entries in matrix\n", - " 4h 13m 08s SIMILARITY (O sentence LCS M>60): Computed 666 M comparisons and saved 3483599 entries in matrix\n", - " 4h 13m 31s SIMILARITY (O sentence LCS M>60): Computed 686 M comparisons and saved 3606181 entries in matrix\n", - " 4h 14m 01s SIMILARITY (O sentence LCS M>60): Computed 707 M comparisons and saved 3668148 entries in matrix\n", - " 4h 14m 29s SIMILARITY (O sentence LCS M>60): Computed 727 M comparisons and saved 3732591 entries in matrix\n", - " 4h 14m 57s SIMILARITY (O sentence LCS M>60): Computed 747 M comparisons and saved 3835399 entries in matrix\n", - " 4h 15m 25s SIMILARITY (O sentence LCS M>60): Computed 767 M comparisons and saved 3922964 entries in matrix\n", - " 4h 15m 52s SIMILARITY (O sentence LCS M>60): Computed 788 M comparisons and saved 3999296 entries in matrix\n", - " 4h 16m 16s SIMILARITY (O sentence LCS M>60): Computed 808 M comparisons and saved 4096313 entries in matrix\n", - " 4h 16m 43s SIMILARITY (O sentence LCS M>60): Computed 828 M comparisons and saved 4197206 entries in matrix\n", - " 4h 17m 08s SIMILARITY (O sentence LCS M>60): Computed 848 M comparisons and saved 4290970 entries in matrix\n", - " 4h 17m 33s SIMILARITY (O sentence LCS M>60): Computed 868 M comparisons and saved 4403470 entries in matrix\n", - " 4h 18m 00s SIMILARITY (O sentence LCS M>60): Computed 889 M comparisons and saved 4489840 entries in matrix\n", - " 4h 18m 31s SIMILARITY (O sentence LCS M>60): Computed 909 M comparisons and saved 4540049 entries in matrix\n", - " 4h 18m 57s SIMILARITY (O sentence LCS M>60): Computed 929 M comparisons and saved 4641605 entries in matrix\n", - " 4h 19m 21s SIMILARITY (O sentence LCS M>60): Computed 949 M comparisons and saved 4763900 entries in matrix\n", - " 4h 19m 44s SIMILARITY (O sentence LCS M>60): Computed 969 M comparisons and saved 4867130 entries in matrix\n", - " 4h 20m 07s SIMILARITY (O sentence LCS M>60): Computed 990 M comparisons and saved 5006563 entries in matrix\n", - " 4h 20m 30s SIMILARITY (O sentence LCS M>60): Computed 1010 M comparisons and saved 5154413 entries in matrix\n", - " 4h 20m 54s SIMILARITY (O sentence LCS M>60): Computed 1030 M comparisons and saved 5266887 entries in matrix\n", - " 4h 21m 18s SIMILARITY (O sentence LCS M>60): Computed 1050 M comparisons and saved 5389996 entries in matrix\n", - " 4h 21m 41s SIMILARITY (O sentence LCS M>60): Computed 1070 M comparisons and saved 5529990 entries in matrix\n", - " 4h 22m 04s SIMILARITY (O sentence LCS M>60): Computed 1091 M comparisons and saved 5664009 entries in matrix\n", - " 4h 22m 27s SIMILARITY (O sentence LCS M>60): Computed 1111 M comparisons and saved 5779132 entries in matrix\n", - " 4h 22m 50s SIMILARITY (O sentence LCS M>60): Computed 1131 M comparisons and saved 5897038 entries in matrix\n", - " 4h 23m 13s SIMILARITY (O sentence LCS M>60): Computed 1151 M comparisons and saved 6019779 entries in matrix\n", - " 4h 23m 37s SIMILARITY (O sentence LCS M>60): Computed 1171 M comparisons and saved 6123416 entries in matrix\n", - " 4h 24m 01s SIMILARITY (O sentence LCS M>60): Computed 1192 M comparisons and saved 6226770 entries in matrix\n", - " 4h 24m 24s SIMILARITY (O sentence LCS M>60): Computed 1212 M comparisons and saved 6372406 entries in matrix\n", - " 4h 24m 47s SIMILARITY (O sentence LCS M>60): Computed 1232 M comparisons and saved 6489159 entries in matrix\n", - " 4h 25m 11s SIMILARITY (O sentence LCS M>60): Computed 1252 M comparisons and saved 6591841 entries in matrix\n", - " 4h 25m 35s SIMILARITY (O sentence LCS M>60): Computed 1272 M comparisons and saved 6724627 entries in matrix\n", - " 4h 26m 05s SIMILARITY (O sentence LCS M>60): Computed 1293 M comparisons and saved 6776806 entries in matrix\n", - " 4h 26m 31s SIMILARITY (O sentence LCS M>60): Computed 1313 M comparisons and saved 6870691 entries in matrix\n", - " 4h 26m 53s SIMILARITY (O sentence LCS M>60): Computed 1333 M comparisons and saved 7016662 entries in matrix\n", - " 4h 27m 16s SIMILARITY (O sentence LCS M>60): Computed 1353 M comparisons and saved 7160786 entries in matrix\n", - " 4h 27m 37s SIMILARITY (O sentence LCS M>60): Computed 1373 M comparisons and saved 7304076 entries in matrix\n", - " 4h 27m 58s SIMILARITY (O sentence LCS M>60): Computed 1394 M comparisons and saved 7442832 entries in matrix\n", - " 4h 28m 25s SIMILARITY (O sentence LCS M>60): Computed 1414 M comparisons and saved 7518860 entries in matrix\n", - " 4h 28m 51s SIMILARITY (O sentence LCS M>60): Computed 1434 M comparisons and saved 7608922 entries in matrix\n", - " 4h 29m 15s SIMILARITY (O sentence LCS M>60): Computed 1454 M comparisons and saved 7700130 entries in matrix\n", - " 4h 29m 39s SIMILARITY (O sentence LCS M>60): Computed 1474 M comparisons and saved 7774516 entries in matrix\n", - " 4h 30m 02s SIMILARITY (O sentence LCS M>60): Computed 1495 M comparisons and saved 7856910 entries in matrix\n", - " 4h 30m 25s SIMILARITY (O sentence LCS M>60): Computed 1515 M comparisons and saved 7944698 entries in matrix\n", - " 4h 30m 46s SIMILARITY (O sentence LCS M>60): Computed 1535 M comparisons and saved 8045412 entries in matrix\n", - " 4h 31m 08s SIMILARITY (O sentence LCS M>60): Computed 1555 M comparisons and saved 8141211 entries in matrix\n", - " 4h 31m 31s SIMILARITY (O sentence LCS M>60): Computed 1576 M comparisons and saved 8245481 entries in matrix\n", - " 4h 31m 54s SIMILARITY (O sentence LCS M>60): Computed 1596 M comparisons and saved 8373793 entries in matrix\n", - " 4h 32m 17s SIMILARITY (O sentence LCS M>60): Computed 1616 M comparisons and saved 8494098 entries in matrix\n", - " 4h 32m 42s SIMILARITY (O sentence LCS M>60): Computed 1636 M comparisons and saved 8618198 entries in matrix\n", - " 4h 33m 08s SIMILARITY (O sentence LCS M>60): Computed 1656 M comparisons and saved 8731397 entries in matrix\n", - " 4h 33m 36s SIMILARITY (O sentence LCS M>60): Computed 1677 M comparisons and saved 8822721 entries in matrix\n", - " 4h 34m 01s SIMILARITY (O sentence LCS M>60): Computed 1697 M comparisons and saved 8909346 entries in matrix\n", - " 4h 34m 24s SIMILARITY (O sentence LCS M>60): Computed 1717 M comparisons and saved 9026706 entries in matrix\n", - " 4h 34m 48s SIMILARITY (O sentence LCS M>60): Computed 1737 M comparisons and saved 9129077 entries in matrix\n", - " 4h 35m 11s SIMILARITY (O sentence LCS M>60): Computed 1757 M comparisons and saved 9217047 entries in matrix\n", - " 4h 35m 35s SIMILARITY (O sentence LCS M>60): Computed 1778 M comparisons and saved 9310495 entries in matrix\n", - " 4h 36m 00s SIMILARITY (O sentence LCS M>60): Computed 1798 M comparisons and saved 9389003 entries in matrix\n", - " 4h 36m 23s SIMILARITY (O sentence LCS M>60): Computed 1818 M comparisons and saved 9478471 entries in matrix\n", - " 4h 36m 46s SIMILARITY (O sentence LCS M>60): Computed 1838 M comparisons and saved 9562926 entries in matrix\n", - " 4h 37m 09s SIMILARITY (O sentence LCS M>60): Computed 1858 M comparisons and saved 9671444 entries in matrix\n", - " 4h 37m 32s SIMILARITY (O sentence LCS M>60): Computed 1879 M comparisons and saved 9752741 entries in matrix\n", - " 4h 37m 54s SIMILARITY (O sentence LCS M>60): Computed 1899 M comparisons and saved 9833579 entries in matrix\n", - " 4h 38m 18s SIMILARITY (O sentence LCS M>60): Computed 1919 M comparisons and saved 9906769 entries in matrix\n", - " 4h 38m 41s SIMILARITY (O sentence LCS M>60): Computed 1939 M comparisons and saved 9986245 entries in matrix\n", - " 4h 39m 06s SIMILARITY (O sentence LCS M>60): Computed 1959 M comparisons and saved 10067146 entries in matrix\n", - " 4h 39m 31s SIMILARITY (O sentence LCS M>60): Computed 1980 M comparisons and saved 10128099 entries in matrix\n", - " 4h 39m 59s SIMILARITY (O sentence LCS M>60): Computed 2000 M comparisons and saved 10200826 entries in matrix\n", - " 4h 40m 31s SIMILARITY (O sentence LCS M>60): Computed 2020 M comparisons and saved 10279985 entries in matrix\n", - " 4h 40m 40s SIMILARITY (O sentence LCS M>60): Computed 2020 M (2020540665) comparisons and saved 10279985 entries in matrix\n", - " 4h 40m 51s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates\n", - " 4h 40m 51s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix\n", - " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): 903431 relevant similarities between 17533 passages\n", - " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composing cliques out of 17533 chunks from 903431 comparisons\n", - " 4h 41m 00s CLIQUES (O sentence LCS M>60 S>100): Composed 469 cliques out of 1000 chunks\n", - " 4h 41m 01s CLIQUES (O sentence LCS M>60 S>100): Composed 877 cliques out of 2000 chunks\n", - " 4h 41m 03s CLIQUES (O sentence LCS M>60 S>100): Composed 1228 cliques out of 3000 chunks\n", - " 4h 41m 05s CLIQUES (O sentence LCS M>60 S>100): Composed 1612 cliques out of 4000 chunks\n", - " 4h 41m 08s CLIQUES (O sentence LCS M>60 S>100): Composed 1997 cliques out of 5000 chunks\n", - " 4h 41m 12s CLIQUES (O sentence LCS M>60 S>100): Composed 2303 cliques out of 6000 chunks\n", - " 4h 41m 17s CLIQUES (O sentence LCS M>60 S>100): Composed 2599 cliques out of 7000 chunks\n", - " 4h 41m 22s CLIQUES (O sentence LCS M>60 S>100): Composed 2880 cliques out of 8000 chunks\n", - " 4h 41m 28s CLIQUES (O sentence LCS M>60 S>100): Composed 3109 cliques out of 9000 chunks\n", - " 4h 41m 34s CLIQUES (O sentence LCS M>60 S>100): Composed 3290 cliques out of 10000 chunks\n", - " 4h 41m 41s CLIQUES (O sentence LCS M>60 S>100): Composed 3478 cliques out of 11000 chunks\n", - " 4h 41m 49s CLIQUES (O sentence LCS M>60 S>100): Composed 3590 cliques out of 12000 chunks\n", - " 4h 41m 58s CLIQUES (O sentence LCS M>60 S>100): Composed 3665 cliques out of 13000 chunks\n", - " 4h 42m 07s CLIQUES (O sentence LCS M>60 S>100): Composed 3777 cliques out of 14000 chunks\n", - " 4h 42m 17s CLIQUES (O sentence LCS M>60 S>100): Composed 3878 cliques out of 15000 chunks\n", - " 4h 42m 27s CLIQUES (O sentence LCS M>60 S>100): Composed 3942 cliques out of 16000 chunks\n", - " 4h 42m 38s CLIQUES (O sentence LCS M>60 S>100): Composed 3970 cliques out of 17000 chunks\n", - " 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): 17533 members in 3978 cliques\n", - " 4h 42m 45s CLIQUES (O sentence LCS M>60 S>100): Composed and saved 3978 cliques out of 17533 chunks from 903431 comparisons\n", - " 4h 42m 45s PRINT (O sentence LCS M>60 S>100): sorting out cliques\n", - " 4h 42m 45s PRINT (O sentence LCS M>60 S>100): formatting 3978 cliques skipping 1364 binary chapter diffs\n", - " 4h 42m 48s PRINT (O sentence LCS M>60 S>100): formatted 3978 cliques (80 files) skipping 1364 binary chapter diffs\n", - " 4h 42m 48s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 4h 42m 48s PREPARING (O sentence LCS): Already prepared\n", - " 4h 42m 48s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 4h 42m 57s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates\n", - " 4h 42m 57s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix\n", - " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): 904132 relevant similarities between 18091 passages\n", - " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composing cliques out of 18091 chunks from 904132 comparisons\n", - " 4h 43m 05s CLIQUES (O sentence LCS M>60 S>95): Composed 478 cliques out of 1000 chunks\n", - " 4h 43m 06s CLIQUES (O sentence LCS M>60 S>95): Composed 855 cliques out of 2000 chunks\n", - " 4h 43m 08s CLIQUES (O sentence LCS M>60 S>95): Composed 1280 cliques out of 3000 chunks\n", - " 4h 43m 10s CLIQUES (O sentence LCS M>60 S>95): Composed 1680 cliques out of 4000 chunks\n", - " 4h 43m 13s CLIQUES (O sentence LCS M>60 S>95): Composed 2032 cliques out of 5000 chunks\n", - " 4h 43m 17s CLIQUES (O sentence LCS M>60 S>95): Composed 2411 cliques out of 6000 chunks\n", - " 4h 43m 22s CLIQUES (O sentence LCS M>60 S>95): Composed 2654 cliques out of 7000 chunks\n", - " 4h 43m 27s CLIQUES (O sentence LCS M>60 S>95): Composed 2966 cliques out of 8000 chunks\n", - " 4h 43m 33s CLIQUES (O sentence LCS M>60 S>95): Composed 3253 cliques out of 9000 chunks\n", - " 4h 43m 39s CLIQUES (O sentence LCS M>60 S>95): Composed 3431 cliques out of 10000 chunks\n", - " 4h 43m 47s CLIQUES (O sentence LCS M>60 S>95): Composed 3606 cliques out of 11000 chunks\n", - " 4h 43m 55s CLIQUES (O sentence LCS M>60 S>95): Composed 3776 cliques out of 12000 chunks\n", - " 4h 44m 03s CLIQUES (O sentence LCS M>60 S>95): Composed 3861 cliques out of 13000 chunks\n", - " 4h 44m 13s CLIQUES (O sentence LCS M>60 S>95): Composed 3972 cliques out of 14000 chunks\n", - " 4h 44m 23s CLIQUES (O sentence LCS M>60 S>95): Composed 4063 cliques out of 15000 chunks\n", - " 4h 44m 33s CLIQUES (O sentence LCS M>60 S>95): Composed 4152 cliques out of 16000 chunks\n", - " 4h 44m 44s CLIQUES (O sentence LCS M>60 S>95): Composed 4193 cliques out of 17000 chunks\n", - " 4h 44m 56s CLIQUES (O sentence LCS M>60 S>95): Composed 4217 cliques out of 18000 chunks\n", - " 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): 18091 members in 4218 cliques\n", - " 4h 44m 58s CLIQUES (O sentence LCS M>60 S>95): Composed and saved 4218 cliques out of 18091 chunks from 904132 comparisons\n", - " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): sorting out cliques\n", - " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): formatting 4218 cliques involving 1419 binary chapter diffs\n", - " 4h 44m 58s PRINT (O sentence LCS M>60 S>95): Chapter diffs needed: 1419\n", - " 4h 45m 08s PRINT (O sentence LCS M>60 S>95): Chapter diffs: 67 newly created and 1352 already existing\n", - " 4h 45m 11s PRINT (O sentence LCS M>60 S>95): formatted 4218 cliques (85 files) involving 1419 binary chapter diffs\n", - " 4h 45m 11s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 4h 45m 11s PREPARING (O sentence LCS): Already prepared\n", - " 4h 45m 11s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 4h 45m 21s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates\n", - " 4h 45m 21s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix\n", - " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): 915208 relevant similarities between 21261 passages\n", - " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composing cliques out of 21261 chunks from 915208 comparisons\n", - " 4h 45m 28s CLIQUES (O sentence LCS M>60 S>90): Composed 483 cliques out of 1000 chunks\n", - " 4h 45m 29s CLIQUES (O sentence LCS M>60 S>90): Composed 936 cliques out of 2000 chunks\n", - " 4h 45m 31s CLIQUES (O sentence LCS M>60 S>90): Composed 1287 cliques out of 3000 chunks\n", - " 4h 45m 33s CLIQUES (O sentence LCS M>60 S>90): Composed 1691 cliques out of 4000 chunks\n", - " 4h 45m 36s CLIQUES (O sentence LCS M>60 S>90): Composed 2062 cliques out of 5000 chunks\n", - " 4h 45m 40s CLIQUES (O sentence LCS M>60 S>90): Composed 2407 cliques out of 6000 chunks\n", - " 4h 45m 45s CLIQUES (O sentence LCS M>60 S>90): Composed 2762 cliques out of 7000 chunks\n", - " 4h 45m 50s CLIQUES (O sentence LCS M>60 S>90): Composed 3103 cliques out of 8000 chunks\n", - " 4h 45m 56s CLIQUES (O sentence LCS M>60 S>90): Composed 3332 cliques out of 9000 chunks\n", - " 4h 46m 03s CLIQUES (O sentence LCS M>60 S>90): Composed 3634 cliques out of 10000 chunks\n", - " 4h 46m 10s CLIQUES (O sentence LCS M>60 S>90): Composed 3904 cliques out of 11000 chunks\n", - " 4h 46m 18s CLIQUES (O sentence LCS M>60 S>90): Composed 4127 cliques out of 12000 chunks\n", - " 4h 46m 27s CLIQUES (O sentence LCS M>60 S>90): Composed 4303 cliques out of 13000 chunks\n", - " 4h 46m 37s CLIQUES (O sentence LCS M>60 S>90): Composed 4451 cliques out of 14000 chunks\n", - " 4h 46m 46s CLIQUES (O sentence LCS M>60 S>90): Composed 4601 cliques out of 15000 chunks\n", - " 4h 46m 57s CLIQUES (O sentence LCS M>60 S>90): Composed 4684 cliques out of 16000 chunks\n", - " 4h 47m 08s CLIQUES (O sentence LCS M>60 S>90): Composed 4786 cliques out of 17000 chunks\n", - " 4h 47m 21s CLIQUES (O sentence LCS M>60 S>90): Composed 4866 cliques out of 18000 chunks\n", - " 4h 47m 33s CLIQUES (O sentence LCS M>60 S>90): Composed 4929 cliques out of 19000 chunks\n", - " 4h 47m 47s CLIQUES (O sentence LCS M>60 S>90): Composed 4970 cliques out of 20000 chunks\n", - " 4h 48m 01s CLIQUES (O sentence LCS M>60 S>90): Composed 4995 cliques out of 21000 chunks\n", - " 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): 21261 members in 4997 cliques\n", - " 4h 48m 05s CLIQUES (O sentence LCS M>60 S>90): Composed and saved 4997 cliques out of 21261 chunks from 915208 comparisons\n", - " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): sorting out cliques\n", - " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): formatting 4997 cliques involving 1703 binary chapter diffs\n", - " 4h 48m 05s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1703\n", - " 4h 48m 06s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 2 newly created and 1701 already existing\n", - " 4h 48m 09s PRINT (O sentence LCS M>60 S>90): formatted 4997 cliques (100 files) involving 1703 binary chapter diffs\n", - " 4h 48m 09s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 4h 48m 09s PREPARING (O sentence LCS): Already prepared\n", - " 4h 48m 09s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 4h 48m 19s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates\n", - " 4h 48m 19s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix\n", - " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): 980755 relevant similarities between 26488 passages\n", - " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composing cliques out of 26488 chunks from 980755 comparisons\n", - " 4h 48m 27s CLIQUES (O sentence LCS M>60 S>85): Composed 488 cliques out of 1000 chunks\n", - " 4h 48m 28s CLIQUES (O sentence LCS M>60 S>85): Composed 940 cliques out of 2000 chunks\n", - " 4h 48m 30s CLIQUES (O sentence LCS M>60 S>85): Composed 1315 cliques out of 3000 chunks\n", - " 4h 48m 32s CLIQUES (O sentence LCS M>60 S>85): Composed 1672 cliques out of 4000 chunks\n", - " 4h 48m 35s CLIQUES (O sentence LCS M>60 S>85): Composed 2063 cliques out of 5000 chunks\n", - " 4h 48m 39s CLIQUES (O sentence LCS M>60 S>85): Composed 2408 cliques out of 6000 chunks\n", - " 4h 48m 44s CLIQUES (O sentence LCS M>60 S>85): Composed 2693 cliques out of 7000 chunks\n", - " 4h 48m 49s CLIQUES (O sentence LCS M>60 S>85): Composed 2956 cliques out of 8000 chunks\n", - " 4h 48m 55s CLIQUES (O sentence LCS M>60 S>85): Composed 3253 cliques out of 9000 chunks\n", - " 4h 49m 02s CLIQUES (O sentence LCS M>60 S>85): Composed 3542 cliques out of 10000 chunks\n", - " 4h 49m 09s CLIQUES (O sentence LCS M>60 S>85): Composed 3728 cliques out of 11000 chunks\n", - " 4h 49m 17s CLIQUES (O sentence LCS M>60 S>85): Composed 3912 cliques out of 12000 chunks\n", - " 4h 49m 26s CLIQUES (O sentence LCS M>60 S>85): Composed 4083 cliques out of 13000 chunks\n", - " 4h 49m 36s CLIQUES (O sentence LCS M>60 S>85): Composed 4328 cliques out of 14000 chunks\n", - " 4h 49m 46s CLIQUES (O sentence LCS M>60 S>85): Composed 4464 cliques out of 15000 chunks\n", - " 4h 49m 56s CLIQUES (O sentence LCS M>60 S>85): Composed 4558 cliques out of 16000 chunks\n", - " 4h 50m 08s CLIQUES (O sentence LCS M>60 S>85): Composed 4608 cliques out of 17000 chunks\n", - " 4h 50m 20s CLIQUES (O sentence LCS M>60 S>85): Composed 4635 cliques out of 18000 chunks\n", - " 4h 50m 32s CLIQUES (O sentence LCS M>60 S>85): Composed 4710 cliques out of 19000 chunks\n", - " 4h 50m 45s CLIQUES (O sentence LCS M>60 S>85): Composed 4787 cliques out of 20000 chunks\n", - " 4h 50m 59s CLIQUES (O sentence LCS M>60 S>85): Composed 4826 cliques out of 21000 chunks\n", - " 4h 51m 14s CLIQUES (O sentence LCS M>60 S>85): Composed 4853 cliques out of 22000 chunks\n", - " 4h 51m 28s CLIQUES (O sentence LCS M>60 S>85): Composed 4877 cliques out of 23000 chunks\n", - " 4h 51m 44s CLIQUES (O sentence LCS M>60 S>85): Composed 4859 cliques out of 24000 chunks\n", - " 4h 52m 00s CLIQUES (O sentence LCS M>60 S>85): Composed 4827 cliques out of 25000 chunks\n", - " 4h 52m 17s CLIQUES (O sentence LCS M>60 S>85): Composed 4851 cliques out of 26000 chunks\n", - " 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): 26488 members in 4855 cliques\n", - " 4h 52m 27s CLIQUES (O sentence LCS M>60 S>85): Composed and saved 4855 cliques out of 26488 chunks from 980755 comparisons\n", - " 4h 52m 27s PRINT (O sentence LCS M>60 S>85): sorting out cliques\n", - " 4h 52m 28s PRINT (O sentence LCS M>60 S>85): formatting 4855 cliques skipping 1711 binary chapter diffs\n", - " 4h 52m 31s PRINT (O sentence LCS M>60 S>85): formatted 4855 cliques (98 files) skipping 1711 binary chapter diffs\n", - " 4h 52m 31s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 4h 52m 31s PREPARING (O sentence LCS): Already prepared\n", - " 4h 52m 31s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 4h 52m 41s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates\n", - " 4h 52m 41s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix\n", - " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): 1301831 relevant similarities between 35629 passages\n", - " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composing cliques out of 35629 chunks from 1301831 comparisons\n", - " 4h 52m 49s CLIQUES (O sentence LCS M>60 S>80): Composed 505 cliques out of 1000 chunks\n", - " 4h 52m 50s CLIQUES (O sentence LCS M>60 S>80): Composed 932 cliques out of 2000 chunks\n", - " 4h 52m 52s CLIQUES (O sentence LCS M>60 S>80): Composed 1346 cliques out of 3000 chunks\n", - " 4h 52m 54s CLIQUES (O sentence LCS M>60 S>80): Composed 1725 cliques out of 4000 chunks\n", - " 4h 52m 58s CLIQUES (O sentence LCS M>60 S>80): Composed 2000 cliques out of 5000 chunks\n", - " 4h 53m 01s CLIQUES (O sentence LCS M>60 S>80): Composed 2295 cliques out of 6000 chunks\n", - " 4h 53m 06s CLIQUES (O sentence LCS M>60 S>80): Composed 2537 cliques out of 7000 chunks\n", - " 4h 53m 11s CLIQUES (O sentence LCS M>60 S>80): Composed 2867 cliques out of 8000 chunks\n", - " 4h 53m 17s CLIQUES (O sentence LCS M>60 S>80): Composed 3061 cliques out of 9000 chunks\n", - " 4h 53m 23s CLIQUES (O sentence LCS M>60 S>80): Composed 3188 cliques out of 10000 chunks\n", - " 4h 53m 31s CLIQUES (O sentence LCS M>60 S>80): Composed 3259 cliques out of 11000 chunks\n", - " 4h 53m 38s CLIQUES (O sentence LCS M>60 S>80): Composed 3454 cliques out of 12000 chunks\n", - " 4h 53m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3687 cliques out of 13000 chunks\n", - " 4h 53m 56s CLIQUES (O sentence LCS M>60 S>80): Composed 3826 cliques out of 14000 chunks\n", - " 4h 54m 05s CLIQUES (O sentence LCS M>60 S>80): Composed 3891 cliques out of 15000 chunks\n", - " 4h 54m 15s CLIQUES (O sentence LCS M>60 S>80): Composed 3887 cliques out of 16000 chunks\n", - " 4h 54m 25s CLIQUES (O sentence LCS M>60 S>80): Composed 3942 cliques out of 17000 chunks\n", - " 4h 54m 35s CLIQUES (O sentence LCS M>60 S>80): Composed 3938 cliques out of 18000 chunks\n", - " 4h 54m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3994 cliques out of 19000 chunks\n", - " 4h 54m 58s CLIQUES (O sentence LCS M>60 S>80): Composed 4005 cliques out of 20000 chunks\n", - " 4h 55m 10s CLIQUES (O sentence LCS M>60 S>80): Composed 4071 cliques out of 21000 chunks\n", - " 4h 55m 21s CLIQUES (O sentence LCS M>60 S>80): Composed 4023 cliques out of 22000 chunks\n", - " 4h 55m 33s CLIQUES (O sentence LCS M>60 S>80): Composed 3965 cliques out of 23000 chunks\n", - " 4h 55m 44s CLIQUES (O sentence LCS M>60 S>80): Composed 3889 cliques out of 24000 chunks\n", - " 4h 55m 56s CLIQUES (O sentence LCS M>60 S>80): Composed 3810 cliques out of 25000 chunks\n", - " 4h 56m 09s CLIQUES (O sentence LCS M>60 S>80): Composed 3734 cliques out of 26000 chunks\n", - " 4h 56m 22s CLIQUES (O sentence LCS M>60 S>80): Composed 3702 cliques out of 27000 chunks\n", - " 4h 56m 37s CLIQUES (O sentence LCS M>60 S>80): Composed 3705 cliques out of 28000 chunks\n", - " 4h 56m 52s CLIQUES (O sentence LCS M>60 S>80): Composed 3704 cliques out of 29000 chunks\n", - " 4h 57m 08s CLIQUES (O sentence LCS M>60 S>80): Composed 3682 cliques out of 30000 chunks\n", - " 4h 57m 21s CLIQUES (O sentence LCS M>60 S>80): Composed 3637 cliques out of 31000 chunks\n", - " 4h 57m 33s CLIQUES (O sentence LCS M>60 S>80): Composed 3604 cliques out of 32000 chunks\n", - " 4h 57m 47s CLIQUES (O sentence LCS M>60 S>80): Composed 3537 cliques out of 33000 chunks\n", - " 4h 58m 01s CLIQUES (O sentence LCS M>60 S>80): Composed 3492 cliques out of 34000 chunks\n", - " 4h 58m 15s CLIQUES (O sentence LCS M>60 S>80): Composed 3476 cliques out of 35000 chunks\n", - " 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): 35629 members in 3469 cliques\n", - " 4h 58m 26s CLIQUES (O sentence LCS M>60 S>80): Composed and saved 3469 cliques out of 35629 chunks from 1301831 comparisons\n", - " 4h 58m 26s PRINT (O sentence LCS M>60 S>80): sorting out cliques\n", - " 4h 58m 27s PRINT (O sentence LCS M>60 S>80): formatting 3469 cliques skipping 1291 binary chapter diffs\n", - " 4h 58m 29s PRINT (O sentence LCS M>60 S>80): formatted 3469 cliques (70 files) skipping 1291 binary chapter diffs\n", - " 4h 58m 29s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 4h 58m 29s PREPARING (O sentence LCS): Already prepared\n", - " 4h 58m 29s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 4h 58m 39s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates\n", - " 4h 58m 39s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix\n", - " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): 1620905 relevant similarities between 44303 passages\n", - " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composing cliques out of 44303 chunks from 1620905 comparisons\n", - " 4h 58m 48s CLIQUES (O sentence LCS M>60 S>75): Composed 511 cliques out of 1000 chunks\n", - " 4h 58m 49s CLIQUES (O sentence LCS M>60 S>75): Composed 937 cliques out of 2000 chunks\n", - " 4h 58m 51s CLIQUES (O sentence LCS M>60 S>75): Composed 1325 cliques out of 3000 chunks\n", - " 4h 58m 53s CLIQUES (O sentence LCS M>60 S>75): Composed 1670 cliques out of 4000 chunks\n", - " 4h 58m 56s CLIQUES (O sentence LCS M>60 S>75): Composed 1940 cliques out of 5000 chunks\n", - " 4h 59m 00s CLIQUES (O sentence LCS M>60 S>75): Composed 2172 cliques out of 6000 chunks\n", - " 4h 59m 05s CLIQUES (O sentence LCS M>60 S>75): Composed 2355 cliques out of 7000 chunks\n", - " 4h 59m 09s CLIQUES (O sentence LCS M>60 S>75): Composed 2554 cliques out of 8000 chunks\n", - " 4h 59m 15s CLIQUES (O sentence LCS M>60 S>75): Composed 2741 cliques out of 9000 chunks\n", - " 4h 59m 21s CLIQUES (O sentence LCS M>60 S>75): Composed 2821 cliques out of 10000 chunks\n", - " 4h 59m 28s CLIQUES (O sentence LCS M>60 S>75): Composed 2925 cliques out of 11000 chunks\n", - " 4h 59m 35s CLIQUES (O sentence LCS M>60 S>75): Composed 3093 cliques out of 12000 chunks\n", - " 4h 59m 42s CLIQUES (O sentence LCS M>60 S>75): Composed 3200 cliques out of 13000 chunks\n", - " 4h 59m 50s CLIQUES (O sentence LCS M>60 S>75): Composed 3227 cliques out of 14000 chunks\n", - " 4h 59m 58s CLIQUES (O sentence LCS M>60 S>75): Composed 3153 cliques out of 15000 chunks\n", - " 5h 00m 06s CLIQUES (O sentence LCS M>60 S>75): Composed 3205 cliques out of 16000 chunks\n", - " 5h 00m 15s CLIQUES (O sentence LCS M>60 S>75): Composed 3181 cliques out of 17000 chunks\n", - " 5h 00m 25s CLIQUES (O sentence LCS M>60 S>75): Composed 3207 cliques out of 18000 chunks\n", - " 5h 00m 36s CLIQUES (O sentence LCS M>60 S>75): Composed 3213 cliques out of 19000 chunks\n", - " 5h 00m 45s CLIQUES (O sentence LCS M>60 S>75): Composed 3221 cliques out of 20000 chunks\n", - " 5h 00m 55s CLIQUES (O sentence LCS M>60 S>75): Composed 3184 cliques out of 21000 chunks\n", - " 5h 01m 04s CLIQUES (O sentence LCS M>60 S>75): Composed 3109 cliques out of 22000 chunks\n", - " 5h 01m 14s CLIQUES (O sentence LCS M>60 S>75): Composed 3080 cliques out of 23000 chunks\n", - " 5h 01m 24s CLIQUES (O sentence LCS M>60 S>75): Composed 3047 cliques out of 24000 chunks\n", - " 5h 01m 36s CLIQUES (O sentence LCS M>60 S>75): Composed 2977 cliques out of 25000 chunks\n", - " 5h 01m 52s CLIQUES (O sentence LCS M>60 S>75): Composed 2947 cliques out of 26000 chunks\n", - " 5h 02m 06s CLIQUES (O sentence LCS M>60 S>75): Composed 2871 cliques out of 27000 chunks\n", - " 5h 02m 18s CLIQUES (O sentence LCS M>60 S>75): Composed 2848 cliques out of 28000 chunks\n", - " 5h 02m 31s CLIQUES (O sentence LCS M>60 S>75): Composed 2825 cliques out of 29000 chunks\n", - " 5h 02m 41s CLIQUES (O sentence LCS M>60 S>75): Composed 2783 cliques out of 30000 chunks\n", - " 5h 02m 54s CLIQUES (O sentence LCS M>60 S>75): Composed 2759 cliques out of 31000 chunks\n", - " 5h 03m 05s CLIQUES (O sentence LCS M>60 S>75): Composed 2696 cliques out of 32000 chunks\n", - " 5h 03m 17s CLIQUES (O sentence LCS M>60 S>75): Composed 2609 cliques out of 33000 chunks\n", - " 5h 03m 29s CLIQUES (O sentence LCS M>60 S>75): Composed 2544 cliques out of 34000 chunks\n", - " 5h 03m 40s CLIQUES (O sentence LCS M>60 S>75): Composed 2469 cliques out of 35000 chunks\n", - " 5h 03m 53s CLIQUES (O sentence LCS M>60 S>75): Composed 2463 cliques out of 36000 chunks\n", - " 5h 04m 03s CLIQUES (O sentence LCS M>60 S>75): Composed 2453 cliques out of 37000 chunks\n", - " 5h 04m 13s CLIQUES (O sentence LCS M>60 S>75): Composed 2444 cliques out of 38000 chunks\n", - " 5h 04m 24s CLIQUES (O sentence LCS M>60 S>75): Composed 2407 cliques out of 39000 chunks\n", - " 5h 04m 34s CLIQUES (O sentence LCS M>60 S>75): Composed 2376 cliques out of 40000 chunks\n", - " 5h 04m 45s CLIQUES (O sentence LCS M>60 S>75): Composed 2341 cliques out of 41000 chunks\n", - " 5h 04m 56s CLIQUES (O sentence LCS M>60 S>75): Composed 2304 cliques out of 42000 chunks\n", - " 5h 05m 07s CLIQUES (O sentence LCS M>60 S>75): Composed 2296 cliques out of 43000 chunks\n", - " 5h 05m 19s CLIQUES (O sentence LCS M>60 S>75): Composed 2292 cliques out of 44000 chunks\n", - " 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): 44303 members in 2291 cliques\n", - " 5h 05m 24s CLIQUES (O sentence LCS M>60 S>75): Composed and saved 2291 cliques out of 44303 chunks from 1620905 comparisons\n", - " 5h 05m 24s PRINT (O sentence LCS M>60 S>75): sorting out cliques\n", - " 5h 05m 25s PRINT (O sentence LCS M>60 S>75): formatting 2291 cliques skipping 888 binary chapter diffs\n", - " 5h 05m 27s PRINT (O sentence LCS M>60 S>75): formatted 2291 cliques (46 files) skipping 888 binary chapter diffs\n", - " 5h 05m 27s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 5h 05m 27s PREPARING (O sentence LCS): Already prepared\n", - " 5h 05m 27s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 5h 05m 37s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates\n", - " 5h 05m 37s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix\n", - " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): 2184827 relevant similarities between 52528 passages\n", - " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composing cliques out of 52528 chunks from 2184827 comparisons\n", - " 5h 05m 47s CLIQUES (O sentence LCS M>60 S>70): Composed 501 cliques out of 1000 chunks\n", - " 5h 05m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 931 cliques out of 2000 chunks\n", - " 5h 05m 50s CLIQUES (O sentence LCS M>60 S>70): Composed 1217 cliques out of 3000 chunks\n", - " 5h 05m 52s CLIQUES (O sentence LCS M>60 S>70): Composed 1494 cliques out of 4000 chunks\n", - " 5h 05m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 1737 cliques out of 5000 chunks\n", - " 5h 05m 59s CLIQUES (O sentence LCS M>60 S>70): Composed 1924 cliques out of 6000 chunks\n", - " 5h 06m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 2023 cliques out of 7000 chunks\n", - " 5h 06m 07s CLIQUES (O sentence LCS M>60 S>70): Composed 2080 cliques out of 8000 chunks\n", - " 5h 06m 12s CLIQUES (O sentence LCS M>60 S>70): Composed 2197 cliques out of 9000 chunks\n", - " 5h 06m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 10000 chunks\n", - " 5h 06m 23s CLIQUES (O sentence LCS M>60 S>70): Composed 2133 cliques out of 11000 chunks\n", - " 5h 06m 29s CLIQUES (O sentence LCS M>60 S>70): Composed 2203 cliques out of 12000 chunks\n", - " 5h 06m 35s CLIQUES (O sentence LCS M>60 S>70): Composed 2190 cliques out of 13000 chunks\n", - " 5h 06m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 2189 cliques out of 14000 chunks\n", - " 5h 06m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 2105 cliques out of 15000 chunks\n", - " 5h 06m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 16000 chunks\n", - " 5h 07m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 2153 cliques out of 17000 chunks\n", - " 5h 07m 10s CLIQUES (O sentence LCS M>60 S>70): Composed 2128 cliques out of 18000 chunks\n", - " 5h 07m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 2099 cliques out of 19000 chunks\n", - " 5h 07m 24s CLIQUES (O sentence LCS M>60 S>70): Composed 2060 cliques out of 20000 chunks\n", - " 5h 07m 30s CLIQUES (O sentence LCS M>60 S>70): Composed 1970 cliques out of 21000 chunks\n", - " 5h 07m 39s CLIQUES (O sentence LCS M>60 S>70): Composed 1984 cliques out of 22000 chunks\n", - " 5h 07m 46s CLIQUES (O sentence LCS M>60 S>70): Composed 1936 cliques out of 23000 chunks\n", - " 5h 07m 54s CLIQUES (O sentence LCS M>60 S>70): Composed 1911 cliques out of 24000 chunks\n", - " 5h 08m 04s CLIQUES (O sentence LCS M>60 S>70): Composed 1914 cliques out of 25000 chunks\n", - " 5h 08m 12s CLIQUES (O sentence LCS M>60 S>70): Composed 1859 cliques out of 26000 chunks\n", - " 5h 08m 20s CLIQUES (O sentence LCS M>60 S>70): Composed 1789 cliques out of 27000 chunks\n", - " 5h 08m 27s CLIQUES (O sentence LCS M>60 S>70): Composed 1745 cliques out of 28000 chunks\n", - " 5h 08m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1687 cliques out of 29000 chunks\n", - " 5h 08m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 1660 cliques out of 30000 chunks\n", - " 5h 08m 50s CLIQUES (O sentence LCS M>60 S>70): Composed 1631 cliques out of 31000 chunks\n", - " 5h 08m 58s CLIQUES (O sentence LCS M>60 S>70): Composed 1600 cliques out of 32000 chunks\n", - " 5h 09m 07s CLIQUES (O sentence LCS M>60 S>70): Composed 1552 cliques out of 33000 chunks\n", - " 5h 09m 18s CLIQUES (O sentence LCS M>60 S>70): Composed 1506 cliques out of 34000 chunks\n", - " 5h 09m 27s CLIQUES (O sentence LCS M>60 S>70): Composed 1426 cliques out of 35000 chunks\n", - " 5h 09m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1413 cliques out of 36000 chunks\n", - " 5h 09m 41s CLIQUES (O sentence LCS M>60 S>70): Composed 1399 cliques out of 37000 chunks\n", - " 5h 09m 48s CLIQUES (O sentence LCS M>60 S>70): Composed 1383 cliques out of 38000 chunks\n", - " 5h 09m 56s CLIQUES (O sentence LCS M>60 S>70): Composed 1374 cliques out of 39000 chunks\n", - " 5h 10m 03s CLIQUES (O sentence LCS M>60 S>70): Composed 1349 cliques out of 40000 chunks\n", - " 5h 10m 11s CLIQUES (O sentence LCS M>60 S>70): Composed 1306 cliques out of 41000 chunks\n", - " 5h 10m 20s CLIQUES (O sentence LCS M>60 S>70): Composed 1259 cliques out of 42000 chunks\n", - " 5h 10m 28s CLIQUES (O sentence LCS M>60 S>70): Composed 1231 cliques out of 43000 chunks\n", - " 5h 10m 40s CLIQUES (O sentence LCS M>60 S>70): Composed 1243 cliques out of 44000 chunks\n", - " 5h 10m 49s CLIQUES (O sentence LCS M>60 S>70): Composed 1250 cliques out of 45000 chunks\n", - " 5h 10m 58s CLIQUES (O sentence LCS M>60 S>70): Composed 1249 cliques out of 46000 chunks\n", - " 5h 11m 08s CLIQUES (O sentence LCS M>60 S>70): Composed 1234 cliques out of 47000 chunks\n", - " 5h 11m 17s CLIQUES (O sentence LCS M>60 S>70): Composed 1227 cliques out of 48000 chunks\n", - " 5h 11m 25s CLIQUES (O sentence LCS M>60 S>70): Composed 1219 cliques out of 49000 chunks\n", - " 5h 11m 34s CLIQUES (O sentence LCS M>60 S>70): Composed 1205 cliques out of 50000 chunks\n", - " 5h 11m 44s CLIQUES (O sentence LCS M>60 S>70): Composed 1205 cliques out of 51000 chunks\n", - " 5h 11m 55s CLIQUES (O sentence LCS M>60 S>70): Composed 1201 cliques out of 52000 chunks\n", - " 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): 52528 members in 1199 cliques\n", - " 5h 12m 03s CLIQUES (O sentence LCS M>60 S>70): Composed and saved 1199 cliques out of 52528 chunks from 2184827 comparisons\n", - " 5h 12m 03s PRINT (O sentence LCS M>60 S>70): sorting out cliques\n", - " 5h 12m 05s PRINT (O sentence LCS M>60 S>70): formatting 1199 cliques skipping 455 binary chapter diffs\n", - " 5h 12m 06s PRINT (O sentence LCS M>60 S>70): formatted 1199 cliques (24 files) skipping 455 binary chapter diffs\n", - " 5h 12m 06s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 5h 12m 06s PREPARING (O sentence LCS): Already prepared\n", - " 5h 12m 06s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 5h 12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates\n", - " 5h 12m 16s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix\n", - " 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): 4834493 relevant similarities between 58855 passages\n", - " 5h 12m 28s CLIQUES (O sentence LCS M>60 S>65): Composing cliques out of 58855 chunks from 4834493 comparisons\n", - " 5h 12m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 479 cliques out of 1000 chunks\n", - " 5h 12m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 743 cliques out of 2000 chunks\n", - " 5h 12m 31s CLIQUES (O sentence LCS M>60 S>65): Composed 968 cliques out of 3000 chunks\n", - " 5h 12m 33s CLIQUES (O sentence LCS M>60 S>65): Composed 1082 cliques out of 4000 chunks\n", - " 5h 12m 36s CLIQUES (O sentence LCS M>60 S>65): Composed 1164 cliques out of 5000 chunks\n", - " 5h 12m 38s CLIQUES (O sentence LCS M>60 S>65): Composed 1217 cliques out of 6000 chunks\n", - " 5h 12m 41s CLIQUES (O sentence LCS M>60 S>65): Composed 1183 cliques out of 7000 chunks\n", - " 5h 12m 45s CLIQUES (O sentence LCS M>60 S>65): Composed 1240 cliques out of 8000 chunks\n", - " 5h 12m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 1156 cliques out of 9000 chunks\n", - " 5h 12m 51s CLIQUES (O sentence LCS M>60 S>65): Composed 1157 cliques out of 10000 chunks\n", - " 5h 12m 54s CLIQUES (O sentence LCS M>60 S>65): Composed 1075 cliques out of 11000 chunks\n", - " 5h 12m 58s CLIQUES (O sentence LCS M>60 S>65): Composed 1014 cliques out of 12000 chunks\n", - " 5h 13m 02s CLIQUES (O sentence LCS M>60 S>65): Composed 998 cliques out of 13000 chunks\n", - " 5h 13m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 976 cliques out of 14000 chunks\n", - " 5h 13m 09s CLIQUES (O sentence LCS M>60 S>65): Composed 930 cliques out of 15000 chunks\n", - " 5h 13m 13s CLIQUES (O sentence LCS M>60 S>65): Composed 891 cliques out of 16000 chunks\n", - " 5h 13m 18s CLIQUES (O sentence LCS M>60 S>65): Composed 886 cliques out of 17000 chunks\n", - " 5h 13m 22s CLIQUES (O sentence LCS M>60 S>65): Composed 836 cliques out of 18000 chunks\n", - " 5h 13m 26s CLIQUES (O sentence LCS M>60 S>65): Composed 818 cliques out of 19000 chunks\n", - " 5h 13m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 798 cliques out of 20000 chunks\n", - " 5h 13m 34s CLIQUES (O sentence LCS M>60 S>65): Composed 799 cliques out of 21000 chunks\n", - " 5h 13m 39s CLIQUES (O sentence LCS M>60 S>65): Composed 778 cliques out of 22000 chunks\n", - " 5h 13m 45s CLIQUES (O sentence LCS M>60 S>65): Composed 764 cliques out of 23000 chunks\n", - " 5h 13m 49s CLIQUES (O sentence LCS M>60 S>65): Composed 743 cliques out of 24000 chunks\n", - " 5h 13m 52s CLIQUES (O sentence LCS M>60 S>65): Composed 717 cliques out of 25000 chunks\n", - " 5h 13m 56s CLIQUES (O sentence LCS M>60 S>65): Composed 707 cliques out of 26000 chunks\n", - " 5h 14m 00s CLIQUES (O sentence LCS M>60 S>65): Composed 692 cliques out of 27000 chunks\n", - " 5h 14m 06s CLIQUES (O sentence LCS M>60 S>65): Composed 684 cliques out of 28000 chunks\n", - " 5h 14m 10s CLIQUES (O sentence LCS M>60 S>65): Composed 650 cliques out of 29000 chunks\n", - " 5h 14m 15s CLIQUES (O sentence LCS M>60 S>65): Composed 639 cliques out of 30000 chunks\n", - " 5h 14m 21s CLIQUES (O sentence LCS M>60 S>65): Composed 623 cliques out of 31000 chunks\n", - " 5h 14m 26s CLIQUES (O sentence LCS M>60 S>65): Composed 598 cliques out of 32000 chunks\n", - " 5h 14m 30s CLIQUES (O sentence LCS M>60 S>65): Composed 586 cliques out of 33000 chunks\n", - " 5h 14m 34s CLIQUES (O sentence LCS M>60 S>65): Composed 580 cliques out of 34000 chunks\n", - " 5h 14m 39s CLIQUES (O sentence LCS M>60 S>65): Composed 570 cliques out of 35000 chunks\n", - " 5h 14m 43s CLIQUES (O sentence LCS M>60 S>65): Composed 564 cliques out of 36000 chunks\n", - " 5h 14m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 554 cliques out of 37000 chunks\n", - " 5h 14m 53s CLIQUES (O sentence LCS M>60 S>65): Composed 540 cliques out of 38000 chunks\n", - " 5h 14m 59s CLIQUES (O sentence LCS M>60 S>65): Composed 530 cliques out of 39000 chunks\n", - " 5h 15m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 514 cliques out of 40000 chunks\n", - " 5h 15m 10s CLIQUES (O sentence LCS M>60 S>65): Composed 499 cliques out of 41000 chunks\n", - " 5h 15m 15s CLIQUES (O sentence LCS M>60 S>65): Composed 500 cliques out of 42000 chunks\n", - " 5h 15m 19s CLIQUES (O sentence LCS M>60 S>65): Composed 499 cliques out of 43000 chunks\n", - " 5h 15m 24s CLIQUES (O sentence LCS M>60 S>65): Composed 495 cliques out of 44000 chunks\n", - " 5h 15m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 491 cliques out of 45000 chunks\n", - " 5h 15m 35s CLIQUES (O sentence LCS M>60 S>65): Composed 484 cliques out of 46000 chunks\n", - " 5h 15m 41s CLIQUES (O sentence LCS M>60 S>65): Composed 479 cliques out of 47000 chunks\n", - " 5h 15m 47s CLIQUES (O sentence LCS M>60 S>65): Composed 472 cliques out of 48000 chunks\n", - " 5h 15m 54s CLIQUES (O sentence LCS M>60 S>65): Composed 467 cliques out of 49000 chunks\n", - " 5h 16m 00s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 50000 chunks\n", - " 5h 16m 05s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 51000 chunks\n", - " 5h 16m 11s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 52000 chunks\n", - " 5h 16m 17s CLIQUES (O sentence LCS M>60 S>65): Composed 467 cliques out of 53000 chunks\n", - " 5h 16m 23s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 54000 chunks\n", - " 5h 16m 29s CLIQUES (O sentence LCS M>60 S>65): Composed 466 cliques out of 55000 chunks\n", - " 5h 16m 35s CLIQUES (O sentence LCS M>60 S>65): Composed 465 cliques out of 56000 chunks\n", - " 5h 16m 42s CLIQUES (O sentence LCS M>60 S>65): Composed 464 cliques out of 57000 chunks\n", - " 5h 16m 48s CLIQUES (O sentence LCS M>60 S>65): Composed 463 cliques out of 58000 chunks\n", - " 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): 58855 members in 463 cliques\n", - " 5h 16m 56s CLIQUES (O sentence LCS M>60 S>65): Composed and saved 463 cliques out of 58855 chunks from 4834493 comparisons\n", - " 5h 16m 56s PRINT (O sentence LCS M>60 S>65): sorting out cliques\n", - " 5h 16m 57s PRINT (O sentence LCS M>60 S>65): formatting 463 cliques skipping 209 binary chapter diffs\n", - " 5h 16m 58s PRINT (O sentence LCS M>60 S>65): formatted 463 cliques (10 files) skipping 209 binary chapter diffs\n", - " 5h 16m 58s CHUNKING (O sentence): already chunked into 63570 chunks\n", - " 5h 16m 58s PREPARING (O sentence LCS): Already prepared\n", - " 5h 16m 58s SIMILARITY (O sentence LCS M>60): Using 2020 M (2020540665) comparisons with 10279985 entries in matrix\n", - " 5h 17m 08s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903431 are 100%\n", - " 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates\n", - " 5h 17m 08s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix\n", - " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): 10279985 relevant similarities between 62369 passages\n", - " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composing cliques out of 62369 chunks from 10279985 comparisons\n", - " 5h 17m 27s CLIQUES (O sentence LCS M>60 S>60): Composed 317 cliques out of 1000 chunks\n", - " 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 374 cliques out of 2000 chunks\n", - " 5h 17m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 400 cliques out of 3000 chunks\n", - " 5h 17m 30s CLIQUES (O sentence LCS M>60 S>60): Composed 381 cliques out of 4000 chunks\n", - " 5h 17m 31s CLIQUES (O sentence LCS M>60 S>60): Composed 375 cliques out of 5000 chunks\n", - " 5h 17m 32s CLIQUES (O sentence LCS M>60 S>60): Composed 348 cliques out of 6000 chunks\n", - " 5h 17m 33s CLIQUES (O sentence LCS M>60 S>60): Composed 309 cliques out of 7000 chunks\n", - " 5h 17m 34s CLIQUES (O sentence LCS M>60 S>60): Composed 298 cliques out of 8000 chunks\n", - " 5h 17m 36s CLIQUES (O sentence LCS M>60 S>60): Composed 272 cliques out of 9000 chunks\n", - " 5h 17m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 246 cliques out of 10000 chunks\n", - " 5h 17m 39s CLIQUES (O sentence LCS M>60 S>60): Composed 243 cliques out of 11000 chunks\n", - " 5h 17m 40s CLIQUES (O sentence LCS M>60 S>60): Composed 221 cliques out of 12000 chunks\n", - " 5h 17m 42s CLIQUES (O sentence LCS M>60 S>60): Composed 214 cliques out of 13000 chunks\n", - " 5h 17m 43s CLIQUES (O sentence LCS M>60 S>60): Composed 209 cliques out of 14000 chunks\n", - " 5h 17m 44s CLIQUES (O sentence LCS M>60 S>60): Composed 190 cliques out of 15000 chunks\n", - " 5h 17m 46s CLIQUES (O sentence LCS M>60 S>60): Composed 175 cliques out of 16000 chunks\n", - " 5h 17m 48s CLIQUES (O sentence LCS M>60 S>60): Composed 169 cliques out of 17000 chunks\n", - " 5h 17m 49s CLIQUES (O sentence LCS M>60 S>60): Composed 162 cliques out of 18000 chunks\n", - " 5h 17m 51s CLIQUES (O sentence LCS M>60 S>60): Composed 160 cliques out of 19000 chunks\n", - " 5h 17m 52s CLIQUES (O sentence LCS M>60 S>60): Composed 151 cliques out of 20000 chunks\n", - " 5h 17m 54s CLIQUES (O sentence LCS M>60 S>60): Composed 141 cliques out of 21000 chunks\n", - " 5h 17m 55s CLIQUES (O sentence LCS M>60 S>60): Composed 133 cliques out of 22000 chunks\n", - " 5h 17m 57s CLIQUES (O sentence LCS M>60 S>60): Composed 134 cliques out of 23000 chunks\n", - " 5h 17m 59s CLIQUES (O sentence LCS M>60 S>60): Composed 132 cliques out of 24000 chunks\n", - " 5h 18m 02s CLIQUES (O sentence LCS M>60 S>60): Composed 126 cliques out of 25000 chunks\n", - " 5h 18m 04s CLIQUES (O sentence LCS M>60 S>60): Composed 124 cliques out of 26000 chunks\n", - " 5h 18m 07s CLIQUES (O sentence LCS M>60 S>60): Composed 120 cliques out of 27000 chunks\n", - " 5h 18m 09s CLIQUES (O sentence LCS M>60 S>60): Composed 119 cliques out of 28000 chunks\n", - " 5h 18m 11s CLIQUES (O sentence LCS M>60 S>60): Composed 119 cliques out of 29000 chunks\n", - " 5h 18m 14s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 30000 chunks\n", - " 5h 18m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 31000 chunks\n", - " 5h 18m 19s CLIQUES (O sentence LCS M>60 S>60): Composed 116 cliques out of 32000 chunks\n", - " 5h 18m 22s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 33000 chunks\n", - " 5h 18m 25s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 34000 chunks\n", - " 5h 18m 28s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 35000 chunks\n", - " 5h 18m 31s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 36000 chunks\n", - " 5h 18m 34s CLIQUES (O sentence LCS M>60 S>60): Composed 118 cliques out of 37000 chunks\n", - " 5h 18m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 38000 chunks\n", - " 5h 18m 40s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 39000 chunks\n", - " 5h 18m 43s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 40000 chunks\n", - " 5h 18m 47s CLIQUES (O sentence LCS M>60 S>60): Composed 117 cliques out of 41000 chunks\n", - " 5h 18m 50s CLIQUES (O sentence LCS M>60 S>60): Composed 115 cliques out of 42000 chunks\n", - " 5h 18m 54s CLIQUES (O sentence LCS M>60 S>60): Composed 114 cliques out of 43000 chunks\n", - " 5h 18m 57s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 44000 chunks\n", - " 5h 19m 01s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 45000 chunks\n", - " 5h 19m 05s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 46000 chunks\n", - " 5h 19m 08s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 47000 chunks\n", - " 5h 19m 12s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 48000 chunks\n", - " 5h 19m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 49000 chunks\n", - " 5h 19m 20s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 50000 chunks\n", - " 5h 19m 24s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 51000 chunks\n", - " 5h 19m 29s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 52000 chunks\n", - " 5h 19m 33s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 53000 chunks\n", - " 5h 19m 37s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 54000 chunks\n", - " 5h 19m 42s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 55000 chunks\n", - " 5h 19m 46s CLIQUES (O sentence LCS M>60 S>60): Composed 114 cliques out of 56000 chunks\n", - " 5h 19m 51s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 57000 chunks\n", - " 5h 19m 56s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 58000 chunks\n", - " 5h 20m 00s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 59000 chunks\n", - " 5h 20m 05s CLIQUES (O sentence LCS M>60 S>60): Composed 113 cliques out of 60000 chunks\n", - " 5h 20m 11s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 61000 chunks\n", - " 5h 20m 16s CLIQUES (O sentence LCS M>60 S>60): Composed 112 cliques out of 62000 chunks\n", - " 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): 62369 members in 112 cliques\n", - " 5h 20m 20s CLIQUES (O sentence LCS M>60 S>60): Composed and saved 112 cliques out of 62369 chunks from 10279985 comparisons\n", - " 5h 20m 20s PRINT (O sentence LCS M>60 S>60): sorting out cliques\n", - " 5h 20m 21s PRINT (O sentence LCS M>60 S>60): formatting 112 cliques skipping 61 binary chapter diffs\n", - " 5h 20m 22s PRINT (O sentence LCS M>60 S>60): formatted 112 cliques (3 files) skipping 61 binary chapter diffs\n", - " 5h 20m 22s EXPERIMENT: Generating html report\n", - " 5h 20m 22s EXPERIMENT: 35 messy results: deprecated\n", - " 5h 20m 22s EXPERIMENT: 23 mixed quality: take care\n", - " 5h 20m 22s EXPERIMENT: 75 no results available\n", - " 5h 20m 22s EXPERIMENT: 9 unassessed quality: inspection needed\n", - " 5h 20m 22s EXPERIMENT: 80 method deprecated\n", - " 5h 20m 22s EXPERIMENT: 18 promising results: recommended\n", - " 5h 20m 22s EXPERIMENT: Generated html report\n", - " 5h 20m 22s EXPERIMENT: Generating html report(standalone)\n", - " 5h 20m 22s EXPERIMENT: 35 messy results: deprecated\n", - " 5h 20m 22s EXPERIMENT: 23 mixed quality: take care\n", - " 5h 20m 22s EXPERIMENT: 75 no results available\n", - " 5h 20m 22s EXPERIMENT: 9 unassessed quality: inspection needed\n", - " 5h 20m 22s EXPERIMENT: 80 method deprecated\n", - " 5h 20m 22s EXPERIMENT: 18 promising results: recommended\n", - " 5h 20m 22s EXPERIMENT: Generated html report\n" - ] - } - ], - "source": [ - "reset_params()\n", - "#do_experiment(False, 'sentence', 'LCS', 60, False)\n", - "do_all_experiments()\n", - "#do_all_experiments(no_fixed=True, only_object='chapter')\n", - "#crossrefs2shebanq()\n", - "#show_all_experiments()\n", - "#get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt')\n", - "#do_all_chunks()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(ecss)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 8. Overview of the similarities\n", - "\n", - "Here are the plots of two similarity matrices\n", - "* with verses as chunks and SET as similarity method\n", - "* with verses as chunks and LCS as similarity method\n", - "\n", - "Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "31m 00s CHUNKING (O verse): Loaded: 23213 chunks\n", - "31m 00s CHUNKING (O verse): Made 23213 chunks\n", - "31m 00s PREPARING (O verse SET)\n", - "31m 01s PREPARING (O verse SET): Done 23213 chunks.\n", - "31m 02s SIMILARITY (O verse SET M>50): Loaded: 269 M (269410078) comparisons with 24832 entries in matrix\n", - "31m 02s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", - "31m 02s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", - "31m 02s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", - "31m 04s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", - "31m 04s CLIQUES (O verse SET M>50 S>60): Loaded: 1439 cliques out of 3877 chunks from 16055 comparisons\n", - "31m 04s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", - "31m 04s PRINT (O verse SET M>50 S>60): sorting out cliques\n", - "31m 04s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", - "31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", - "31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", - "31m 06s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n" - ] - }, - { - "data": { - "image/png": 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3AWiuqvfkeJ4OHqx45JGkh0hERER5WLcOOOssYNMmS+aGDrXPR4zwOzIiomCoXNk6/Z5w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\nqlTw31iJiIgofiVKAE8+Cdx+u1XkJGLTJuDjj+P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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "do_experiment(False, 'verse', 'SET', 60, False)\n", - "distances = collections.Counter()\n", - "for (x, d) in chunk_dist.items():\n", - " distances[int(round(d))] += 1\n", - "\n", - "x = range(MATRIX_THRESHOLD, 101)\n", - "fig = plt.figure(figsize=[15, 4])\n", - "plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')\n", - "plt.axis([MATRIX_THRESHOLD, 101, 0, 15])\n", - "plt.xlabel('similarity as %')\n", - "plt.ylabel('log # similarities')\n", - "plt.xticks(x, x, rotation='vertical')\n", - "plt.margins(0.2)\n", - "plt.subplots_adjust(bottom=0.15);\n", - "plt.title('distances');" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "33m 46s CHUNKING (O verse): already chunked into 23213 chunks\n", - "33m 46s PREPARING (O verse LCS)\n", - "33m 47s PREPARING (O verse LCS): Done 23213 chunks.\n", - "33m 47s SIMILARITY (O verse LCS M>60): Loaded: 269 M (269410078) comparisons with 113614 entries in matrix\n", - "33m 47s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", - "33m 47s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", - "33m 47s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", - "33m 47s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages\n", - "33m 47s CLIQUES (O verse LCS M>60 S>60): Loaded: 380 cliques out of 18941 chunks from 113614 comparisons\n", - "33m 47s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques\n", - "33m 47s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", - "33m 47s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", - "33m 48s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n" - ] - }, - { - "data": { - "image/png": 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84Afw2c82Oprh7cILywf5730PDjmktPIOlRdeKAnd5z8PxxwzdPWqS3s7fO5z\ncMUV5Z6cPh122qmMbT3ppEZHJ/XPz34GEyeWngVDqdoJUG7PzM0rjz+XmRfUKKgzgesz84yIGAEs\nlJmv9TjHZE5S05s+vczc2Nlyd+ON5YPq1luXxObaa8sYvY99rCuBW3NNW4qGytVXw9e+VlpUh2p5\nBHV580349rfLh/lzzy1jpRph8mRoaystg0cd1ZgYhqtx48rYyPPOK/8OdnrllTIe+JBD4IADGhef\n1F9tbXDYYfCpTw1tvYNK5iLiH8D9wPbADsBjlBktq15fLiIWA8ZlZp9DX03mJLWizNJ98aabShfG\nbbeFDTd0+vRG2mGH8p/vQQc1OpLh5e67Ya+9YKutyjfZjRxDCWU5k7Y2+PKXyweyuUlHR0maxo4t\n3cB32qnRERX//Cd89KNw8smle1pPjz4KH/lImSFwm22GPj6pv157rXQNnzSpTJQ2lAabzAWwITAG\nuBp4X+X5qZQWtbFVBLQxcBowHtgYuBM4ODPf7nGeyZwkqWr331/WvHvkEVhiierL6+goLX3t7WW7\n7TZYZZXSyrD55mVba63h2/ra0QG//CUcf3xZ62+vvRodUZeJE0tC97WvlbXtWtnrr5eW5zFj4G9/\ng8UWK19cXHwxfOtbcOihjf0bfO650kPhsMPgq1+d/XlXX11mHb3lljIxk9SMLrkEfvvbMiZ8qA02\nmTsDuAE4JDM3qOy7FzgI+Ehm/riKgDYDbgW2ysw7I+Ik4NXMPLbHeXnssV272traaGtrG2y1kqRh\n7MtfhmWXLWMeBqqjAx54oCRu119ftiWXLC0JbW2w5ZYlSbj99rLddlvpXtiZ2HVuyy5b61fVfCZN\ngi99qYwtPeec5hk/2d3TT5f37eCDW6+19rHHSvJ2+eXl72yrrUor3E47dc2uN3Ei7LxzmT79N79p\nTPfiV14p98fnPgdHHz3n83/1Kzj11LIsy2KL1T8+aaC++lVYd134znfqX1d7ezvt7e3/fn7ccccN\nKplbG/gIcALwMDAVWB/4GnBTZr4w2AAjYnnglsxcs/J8a+DwzNy5x3m2zEmSamLixLIo9d13lwlq\n+tLRUVrzOlvebrihrNPX1lY+oG6zTWmJ68tzz5V1CzuTuzvuKLOedk/uRo0a/PpqzWjMGPjKV8qH\nnqOPbu5ZCp98sryfhxwC3/hGo6OZvXffLX9/Y8aU7c03S5fhnXYqrc2LLNL7dW+8Udbxe/11uOii\noV0C4O0r2sKuAAAbtUlEQVS3yxi5TTYpLbP9aR3MLK2lEyfCX/4ytBPkSHOSWf7fuPLKMmnPUKt2\nApRxmblpRCwEjAN+T2mZ27XKoK4HDsjMRyPiWMoEKIf3OMdkTpJUMz/4QVmS4eyzZ97f0VEmselM\n3m68sayD1dbWlcCttFJ1dXd0lFaV7q13Dz4Ia6/dldxtsUX5oNBqH2Tfead0pbv0UvjjH0vXulYw\nYUJ5f488su9ugEPtuedKt8kxY8oMuOutB5/+dEngNtmk/10nZ8wo70tnS95QrIs1fXqZOXbhhcvf\nwkDGCk+bBtttV+6DwbSgS/Uyfnz5EmXChMZ0Xa42mds6M2+qPP5rtUlct3I3pixNMB/wL2C/zHy1\nxzkmc5Kkmnn99ZI8XXZZ+ZB5/fVdydvyy3d1m9xmG1hxxfrHM3VqWbeoM7m7/fbyQX7UqDJpzuqr\nd22rrVZibLZxeA8+WMbErbdeWdOvFmMSh9Ljj5dJio49tnTFbYSODrjzzpJwjRkD//pXSWp22gl2\n3LH67rm/+115fRdcUCYbqZfM8jt89tmS2A+me+dLL5UvNkaPLuPopGZw4ollsp5TT21M/U25zlx/\nmMxJkmrtd78r3bnWXXfmbpPNstbflCmlS+b48WV9wief7NreeANWXbUkdp0JXvdkb9VVh258VGb5\nYHPMMXDCCbDvvs2XaPbXY4+VhO4nPynj/YbKuHFlrNjll5eW4M6xbx/6EMw3X23ruvrq0u3yF7+o\nX5J0+OHlC5Jrrqlutr8HHyzvx6WXlvGoUqN98pOlO/ZuuzWmfpM5SZIqMkvCtNRSjY5k4N56qyvB\n657odT5+9tmSFHRP8Dp/rrRS6Xb39ttle+edmX8OdN9rr5VxhOeeW1o7W90jj5Q10I4/vqxFVy+Z\nZeHsn/+81Pmtb5XFzIdiFsfx40t3zX32geOOq+1yKT//Ofzf/5VW7qWXrr68MWPgwAPh1lvLlxRS\no7z5Zvmy79lnG7e8ismcJEnDwPTppZtmb8nes8+W1p6RI2HBBcs2u8d9Hev+eM01m3uSk4EaP75M\nKnLiibDnnrUte+pU+NOfStIz77xl4pU99hj6mSaff760Lqy2GpxxRnkfq/WHP5QW2ptuqm3i9fOf\nly8Lbrxx6Nf1kjpdfnlp0b7uusbFYDInSZLUDw88ULpUnXxymVa/WlOmlK69p5wC739/SeI+8YnG\ndkl95x3Yf/8ymcNf/lLGYg7WZZfBAQeUsafrrluzEIHSirnffqV78fnn17YlUeqvb36zfPlx2GGN\ni6HaCVA+08vuV4H7M/P5GsTXV90mc5IkaUjde2+ZWv83v4HP9PYpqB+eeAJOOgnOOqt0bfze92Dj\njWsaZlUyS1fLP/yhtDxssMHAy7jppvL7ufzyMmlJPUydWsbPbbddmRRFGkqZZRbYSy4pS9s0Sl/J\nXH86R3wZ2ArobFxsA+4C3hMRP8zMs2d3oSRJUqvZeOOyNMCOO5ZupLvs0v9r77yzdMm66qoys+N9\n9815TcJGiCjJ0fveV5Kls88uCWx/3XdfWYLgnHPql8gBLLBA+SC9+eaw/vplfKE0VP75z9KSveGG\njY5k9vrTYD0CWC8zP5uZn6UsHJ7AFsDhfV4pSZLUgkaN6loEfcyYvs/t6CjnbLttaan64AdLF8YT\nTmjORK67ffaBiy8us5H+9rf9u2bChLLm1sknly6p9bb88vDXv5bZBO+6q/71SZ2uuAJ22KG5Z+rt\nTzK3amZO7vb8+cq+l4Fp9QlLkiSpsT7wgTImbL/94MorZz0+dSqcfnoZC3f00WXs2OOPw3e/C4st\nNvTxDtbWW5cukyefDN/5Tpn1dHYmTy5dHo88skzgMlQ22aSMPdxttzLJjzQUxo4tyVwz68+Yud8A\nqwEXVHbtDjwNHApcnpnb1i04x8xJkqQG+8c/ShJx7rll8pKXXy5r7J1ySkkyDj20tMo187f3/TFl\nCuy+Oyy0UJl5c5FFZj7+2mtlbcZddmnc+LUf/7gk2O3ttZmJU5qdt98urcJPPQVLLNHYWKqdACWA\nzwBbV3bdDFw0FFmWyZwkSWoGnZN9fPrTZQbIXXctk5q8//2Njqy2pk2Dr3+9jP277LKubqLvvFO6\nVq63XlnovFGJaybsvXdZ3uHss1s/gVbzuvJK+NGPyr3faFUvTRARywObU8bK3V7vWSy71WsyJ0mS\nmsLNN8M115RxdCut1Oho6iezTOJy0kllrNomm5SJR0aMKK2T887b2Pjefhs++tEyAcv3v9/YWDT3\n+s53YJll4KijGh1J9S1znwf+G2gHAvgIcGhmXljjOHur22ROkiSpAS65BA48sIwdnD69LEGwwAKN\njqqYOBG22AJ+/evSSirV2rrrltlaN9us0ZFUn8zdC3yyszUuIpYF/p6ZdV8txWROkiSpce66q4wN\nPOUUWHTRRkczs9tvh512Kq2ljVwDTHOfCRNgyy3LZDvNsFh9tevMzdOjW+VL9G8WTEmSJLWwzTaD\nM89sdBS923zzMgPnrruWxG7ZZcv+adPgrbdKd8zOn7N7PLvjU6eW8lZbrWyrrlp+Lrus4/SGgyuv\nLOsuNkMiNyf9SeauiIgrgT9Vnu8B/K1+IUmSJElzttdeMH48vOc9Jcl6++2yf6GFyrbggmXr7XHP\nfUsuWcZCLrQQzD8/PP98WTT6uuvKjIZPPw1vvFEmheme4HX+7HzccxZQtZ6xY4d26Y1q9HcClM8C\nH648vTEzL6lrVF312s1SkiRJfXrxxZKALbggzDdf/ep5662S1D39dEnwOpO87j9Hjuw92VtrrTIb\naCutQTgcvftuaYF9/PEyAUozqHo2y0YxmZMkSVKryISXXpo1wXvqqdLK9/DDpQVw/fVn3ZZcstHR\nC+Daa+GII+C22xodSZdBJXMR8TplKYJZDgGZmXX/XsFkTpIkSXOLjo6S2I0fP+u28MJdid0GG3Q9\nbpbWoeHisMNKV9vRoxsdSRdb5iRJkqQmlQnPPDNrgvfgg6X7aG8tecsv72Qs9bDRRnDaaWU2y2Zh\nMidJkiS1mEyYNGnm5K7z54wZsPLKsMIKs24rrtj1eKmlhnZWxnfegVdfLduMGbD66qWlqxU88wxs\nvHGZ/GbeeRsdTReTOUmSJGkukQkvv1zWQZs0qWzdH3ffXn8dlltu9sle9w26ErHO7bXXZt3X13GA\nxRcvW0QZN7jEErDmmr1vK67YPEsAnH46/P3v8Kc/zfncodS0yVxEzAPcCTyTmbv0ctxkTpIkSRqk\nqVNh8uTeE72eiWBEVyK2+OJl5s3uz3tuvR0fOXLm+js6Svn/+tes24QJMGVKab3rLdF7z3uGdrH6\n3XeHnXeGL31p6Orsj2ZO5r4DbAYsZjInSZIkDS9vvQVPPDH7ZG/hhUtSt+aaZXmHffeF97639nFM\nm1ZaMB96qKuVsln0lcz1Z9HwuoiIVYBPAT8BvtuoOCRJkiQ1xkILdU3q0lNmaVWcMKEkd/feWyYm\n2X9/OPro2q7Zd+utJWlstkRuThrZQ/WXwKH0vvyBJEmSpGEsoiRXW20F++wDJ5wA999f1vJbZx34\n/e/LJCu1cMUVsMMOtSlrKDW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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "do_experiment(False, 'verse', 'LCS', 60, False)\n", - "distances = collections.Counter()\n", - "for (x, d) in chunk_dist.items():\n", - " distances[int(round(d))] += 1\n", - "\n", - "x = range(MATRIX_THRESHOLD, 101)\n", - "fig = plt.figure(figsize=[15, 4])\n", - "plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')\n", - "plt.axis([MATRIX_THRESHOLD, 101, 0, 15])\n", - "plt.xlabel('similarity as %')\n", - "plt.ylabel('log # similarities')\n", - "plt.xticks(x, x, rotation='vertical')\n", - "plt.margins(0.2)\n", - "plt.subplots_adjust(bottom=0.15);\n", - "plt.title('distances');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/static/docs/tools/parallel/parallels_legacy.html b/static/docs/tools/parallel/parallels_legacy.html new file mode 100644 index 00000000..f5bb92d2 --- /dev/null +++ b/static/docs/tools/parallel/parallels_legacy.html @@ -0,0 +1,6244 @@ + + + +Notebook + + + + + + + + + + + + + + + + + + + + +
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Parallel Passages in the MT

0. Introduction

0.1 Motivation

We want to make a list of all parallel passages in the Masoretic Text (MT) of the Hebrew Bible.

+

Here is a quote that triggered Dirk to write this notebook:

+

Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible.

+
+

Robert Rezetko and Ian Young. + Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach. + Ancient Near East Monographs, Number9. SBL Press Atlanta. 2014. + PDF Open access available

+

0.3 Open Source

This is an IPython notebook. +It contains a working program to carry out the computations needed to obtain the results reported here.

+

You can download this notebook and run it on your computer, provided you have +LAF-Fabric installed. +An easy way to do that is describe here.

+

It is a pity that we cannot compare our results with the Accordance resource mentioned above, since that resource has not been published in an accessible manner. We also do not have the information how this resource has been constructed on the basis of the raw data. In contrast with that, we present our results in a completely reproducible manner. This notebook itself can serve as the method of replication, provided you have obtained the necessary resources. See SHEBANQ sources, which are all Open Access.

+

0.4 What are parallel passages?

The notion of parallel passage is not a simple, straightforward one. +There are parallels on the basis of lexical content in the passages on the one hand, +but on the other hand there are also correspondences in certain syntactical structures, +or even in similarities in text structure.

+

In this notebook we do select a straightforward notion of parallel, based on lexical content only. +We investigate two measures of similarity, one that ignores word order completely, and one that takes word order into account.

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Two kinds of short-comings of this approach must be mentioned:

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  1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels)
    2. +
  2. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history.
    3. +
+

For a more full treatment of parallel passages, see

+

Wido Th. van Peursen and Eep Talstra. + Computer-Assisted Analysis of Parallel Texts in the Bible - + The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles. + Vetus Testamentum</i> 57, pp. 45-72. + 2007, Brill, Leiden.

+

Note that our method fails to identify any parallels with Chronica_II 32. Van Peursen and Talstra state about this chapter and 2 Kings 18:

+

These chapters differ so much, that it is sometimes impossible to establish which verses should be considered parallel.

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In this notebook we produce a set of cliques, a clique being a set of passages that are quite similar, based on lexical information.

+

0.5 Authors

This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer.

+

Dirk Roorda while discussing ideas with +Martijn Naaijer.

+

0.6 Status

Last modified: 2016-03-03 Added experiments based on chapter chunks and lower similarities.

+

165 experiments have been carried out, of which 18 with promising results. +All results can be easily inspected, just by clicking in your browser. +One of the experiments has been chosen as the basis for +crossref +annotations in SHEBANQ.

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1. Results

Click in a green cell to see interesting results. The numbers in the cell indicate

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  • the number of passages that have a variant elsewhere
    • +
  • the number of cliques they form (cliques are sets of similar passages)
    • +
  • the number of passages in the biggest clique
    • +
+

Below the results is an account of the method that we used, followed by the actual code to produce these results.

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In [17]:
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# run this cell after all other cells
+HTML(other_exps)
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Out[17]:
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no results available
promising results: recommended
messy results: deprecated
mixed quality: take care
method deprecated
unassessed quality: inspection needed
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+ 154 + 		+ 2359
+ 961
+ 156 + 		+ 2720
+ 1094
+ 166 + 		+ 3139
+ 1235
+ 172 + 		+ 3877
+ 1439
+ 202 + 		+ 4735
+ 1638
+ 388 + 		+ 6711
+ 1850
+ 1476 + 		    
objectverseLCS + 793
+ 295
+ 69 + 		+ 1235
+ 504
+ 69 + 		+ 1754
+ 724
+ 74 + 		+ 2296
+ 938
+ 160 + 		+ 2925
+ 1141
+ 174 + 		+ 3685
+ 1340
+ 190 + 		+ 4958
+ 1644
+ 257 + 		+ 9046
+ 1821
+ 4221 + 		+ 18941
+ 380
+ 18073 + 		      
objecthalf_verseSET + 4327
+ 1725
+ 70 + 		+ 4333
+ 1728
+ 70 + 		+ 4618
+ 1863
+ 70 + 		+ 5145
+ 2072
+ 70 + 		+ 6422
+ 2474
+ 195 + 		+ 8265
+ 2888
+ 536 + 		+ 9388
+ 3193
+ 681 + 		+ 12162
+ 3342
+ 2842 + 		+ 16476
+ 3424
+ 6915 + 		+ 19519
+ 3184
+ 10993 + 		+ 28990
+ 2031
+ 24008 + 		    
objecthalf_verseLCS + 3799
+ 1514
+ 69 + 		+ 4342
+ 1771
+ 69 + 		+ 5776
+ 2336
+ 74 + 		+ 7970
+ 2983
+ 189 + 		+ 12504
+ 3540
+ 2364 + 		+ 19148
+ 3084
+ 11090 + 		+ 28472
+ 1894
+ 23864 + 		+ 38180
+ 665
+ 36649 + 		+ 44011
+ 89
+ 43822 + 		      
objectsentenceSET + 19028
+ 4325
+ 1056 + 		+ 19036
+ 4329
+ 1056 + 		+ 19208
+ 4404
+ 1056 + 		+ 19771
+ 4606
+ 1056 + 		+ 22063
+ 5066
+ 1056 + 		+ 25724
+ 4993
+ 4853 + 		+ 26880
+ 5222
+ 5232 + 		+ 33378
+ 4111
+ 17433 + 		+ 38807
+ 3753
+ 24074 + 		+ 41835
+ 3505
+ 28077 + 		+ 53117
+ 1174
+ 50174 + 		    
objectsentenceLCS + 17532
+ 3981
+ 1054 + 		+ 18079
+ 4215
+ 1054 + 		+ 21246
+ 4993
+ 1054 + 		+ 26473
+ 4853
+ 7321 + 		+ 35626
+ 3470
+ 25548 + 		+ 44307
+ 2293
+ 38261 + 		+ 52535
+ 1197
+ 49324 + 		+ 58863
+ 460
+ 57763 + 		+ 62379
+ 105
+ 62134 + 		      
+ +
+ +
+ +
+
+ +
+
+
+
+
+
+

2. Experiments

We have conducted 165 experiments, all corresponding to a specific choice of parameters. +Every experiment is an attempt to identify variants and collect them in cliques.

+

The table gives an overview of the experiments conducted.

+

Every row corresponds to a particular way of chunking and a method of measuring the similarity.

+

There are columns for each similarity threshold that we have tried. +The idea is that chunks are similar if their similarity is above the threshold.

+

The outcomes of one experiment have been added to SHEBANQ as the note set +crossref. +The experiment chosen for this is currently

+
    +
  • chunking: object verse
    • +
  • similarity method: SET
    • +
  • similarity threshold: 65
    • +
+

2.1 Assessing the outcomes

Not all experiments lead to useful results. +We have indicated the value of a result by a color coding, based on objective characteristics, +such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking. +These numbers are shown in the cells.

+

2.1.1 Assessment criteria

If the method is based on fixed chunks, we deprecated the method and the results. +Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case.

+

Otherwise, we consider the ll, the length of the longest clique, and nc, the number of cliques. +We set three quality parameters:

+
    +
  • REC_CLIQUE_RATIO = 5 : recommended clique ratio
    • +
  • DUB_CLIQUE_RATIO = 15 : dubious clique ratio
    • +
  • DEP_CLIQUE_RATIO = 25 : deprecated clique ratio
    • +
+

where the clique ratio is $100 (ll/nc)$, +i.e. the length of the longest clique divided by the number of cliques as percentage.

+

An experiment is recommended if its clique ratio is between the recommended and dubious clique ratios.

+

It is dubious if its clique ratio is between the dubious and deprecated clique ratios.

+

It is deprecated if its clique ratio is above the deprecated clique ratio.

+

2.2 Inspecting results

If you click on the hyperlink in the cell, you are taken to a page that gives you +all the details of the results:

+
    +
  1. A link to a file with all cliques (which are the sets of similar passages)
    2. +
  2. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for +experiments with outcomes that are labeled as promising or unassessed quality or mixed results.
    3. +
+

To get into the variants quickly, inspect the list (2) and click through +to see the actual variant material in chapter context.

+

Not all variants occur here, so continue with (1) to see the remaining cliques.

+

Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters. +In that case you have to consult the big list (1)

+

All these results can be downloaded from the +SHEBANQ github repo +After downloading the whole directory, open experiments.html in your browser.

+ +
+
+
+
+
+
+
+
+

3. Method

Here we discuss the method we used to arrive at a list of parallel passages +in the Masoretic Text (MT) of the Hebrew Bible.

+

3.1 Similarity

We have to find passages in the MT that are similar. +Therefore we chunk the text in some way, and then compute the similarities between pairs of chunks.

+

There are many ways to define and compute similarity between texts. +Here, we have tried two methods SET and LCS. +Both methods define similarity as the fraction of common material with respect to the total material.

+

3.1.1 SET

The SET method reduces textual chunks to sets of lexemes. +This method abstracts from the order and number of occurrences of words in chunks.

+

We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets):

+$$ s_{\rm set}(C_1, C_2) = {\vert C_1 \cap C_2\vert \over \vert C_1 \cup C_2 \vert} $$

where $\vert X \vert$ is the number of elements in set $X$.

+

3.1.2 LCS

The LCS method is less reductive: chunks are strings of lexemes, +so the order and number of occurrences of words is retained.

+

We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings):

+$$ s_{\rm lcs}(C_1, C_2) = {\vert {\rm LCS}(C_1,C_2)\vert \over \vert C_1\vert + \vert C_2 \vert - +\vert {\rm LCS}(C_1,C_2)\vert} $$

where ${\rm LCS}(C_1, C_2)$ is the +longest common subsequence +of $C_1$ and $C_2$ and +$\vert X\vert$ is the length of sequence $X$.

+

It remains to be seen whether we need the extra sophistication of LCS. +The risk is that LCS could fail to spot related passages when there is a large amount of transposition going on. +The results should have the last word.

+

We need to compute the LCS efficiently, and for this we used the python Levenshtein module:

+

pip install python-Levenshtein

+

whose documentation is +here.

+

3.2 Performance

Similarity computation is the part where the heavy lifting occurs. +It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000), +you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), +you need to do ~ 2,000,000,000 ones! +The computation of a single similarity should be really fast.

+

Besides that, we use two ways to economize:

+
    +
  • after having computed a matrix for a specific set of parameter values, we save the matrix to disk; +new runs can load the matrix from disk in a matter of seconds;
    • +
  • we do not store low similarity values in the matrix, low being < MATRIX_THRESHOLD.
    • +
+

The LCS method is more complicated. +We have tried the ratio method from the difflib package that is present in the standard python distribution. +This is unbearably slow for our purposes. +The ratio method in the Levenshtein package is much quicker.

+

See the table for an indication of the amount of work to create the similarity matrix +and the performance per similarity method.

+

The matrix threshold is the lower bound of similarities that are stored in the matrix. +If a pair of chunks has a lower similarity, no entry will be made in the matrix.

+

The computing has been done on a Macbook Air (11", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM).

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
chunk typechunk sizesimilarity methodmatrix threshold# of comparisonssize of matrix (KB)computing time (min)
fixed100LCS609,003,6467?
fixed100SET509,003,6467?
fixed50LCS6036,197,28637?
fixed50SET5036,197,28618?
fixed20LCS60227,068,7052,400?
fixed20SET50227,068,705113?
fixed10LCS60909,020,84159,000?
fixed10SET50909,020,8411,800?
objectverseLCS60269,410,0782,30031
objectverseSET50269,410,07850914
objecthalf_verseLCS601,016,396,24140,00050
objecthalf_verseSET501,016,396,2413,60041
objectsentenceLCS602,055,975,750212,00068
objectsentenceSET502,055,975,75082,00063
+ +
+
+
+
+
+
+
+
+

4. Workflow

4.1 Chunking

There are several ways to chunk the text:

+
    +
  • fixed chunks of approximately CHUNK_SIZE words
    • +
  • by object, such as verse, sentence and even chapter
    • +
+

After chunking, we prepare the chunks for similarity measuring.

+

4.1.1 Fixed chunking

Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results. +The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries. +It is to be expected that fixed chunks in variant passages will be mutually out of phase, +meaning that the chunks involved in these passages are not aligned with each other. +So they will have a lower similarity than they could have if they were aligned. +This is a source of artificial noise in the outcome and/or missed cases.

+

If the chunking respects "natural" boundaries in the text, there is far less misalignment.

+

4.1.2 Object chunking

We can also chunk by object, such as verse, half_verse or sentence.

+

Chunking by verse is very much like chunking in fixed chunks of size 20, performance-wise.

+

Chunking by half_verse is comparable to fixed chunks of size 10.

+

Chunking by sentence will generate an enormous amount of +false positives, because there are very many very short sentences (down to 1-word) in the text. +Besides that, the performance overhead is huge.

+

The half_verses seem to be a very interesting candidate. +They are smaller than verses, but there are less degenerate cases compared to with sentences. +From the table above it can be read that half verses require only half as many similarity computations as sentences.

+

4.2 Preparing

We prepare the chunks for the application of the chosen method of similarity computation (SET or LCS).

+

In both cases we reduce the text to a sequence of transliterated consonantal lexemes without disambiguation. +In fact, we go one step further: we remove the consonants (alef, wav, yod) that are often silent.

+

For SET, we represent each chunk as the set of its reduced lexemes.

+

For LCS, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces.

+

4.3 Cliques

After having computed a sufficient part of the similarity matrix, we set a value for SIMILARITY_THRESHOLD. +All pairs of chunks having at least that similarity are deemed interesting.

+

We organize the members of such pairs in cliques, groups of chunks of which each member is +similar (similarity > SIMILARITY_THRESHOLD) to at least one other member.

+

We start with no cliques and walk through the pairs whose similarity is above SIMILARITY_THRESHOLD, +and try to put each member into a clique.

+

If there is not yet a clique, we make the member in question into a new singleton clique.

+

If there are cliques, we find the cliques that have a member similar to the member in question. +If we find several, we merge them all into one clique.

+

If there is no such clique, we put the member in a new singleton clique.

+

NB: Cliques may drift, meaning that they contain members that are completely different from each other. +They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other.

+

4.3.1 Organizing the cliques

In order to accomodate cases where there are many corresponding verses in corresponding chapters, we produce +chapter-by-chapter diffs in the following way.

+

We make a list of all chapters that are involved in cliques. +This yields a list of chapter cliques. +For all binary chapters cliques, we generate a colorful diff rendering (as html) for the complete two chapters.

+

We only do this for promising experiments.

+

4.3.2 Evaluating clique sets

Not all clique sets are equally worth while. +For example, if we set the SIMILARITY_THRESHOLD too low, we might get one gigantic clique, especially +in combination with a fine-grained chunking. In other words: we suffer from clique drifting.

+

We detect clique drifting by looking at the size of the largest clique. +If that is large compared to the total number of chunks, we deem the results unsatisfactory.

+

On the other hand, when the SIMILARITY_THRESHOLD is too high, you might miss a lot of correspondences, +especially when chunks are large, or when we have fixed-size chunks that are out of phase.

+

We deem the results of experiments based on a partioning into fixed length chunks as unsatisfactory, although it +might be interesting to inspect what exactly the damage is.

+

At the moment, we have not yet analysed the relative merits of the similarity methods SET and LCS.

+ +
+
+
+
+
+
+
+
+

5. Implementation

The rest is code. From here we fire up the engines and start computing.

+ +
+
+
+
+
+
In [1]:
+
+
+
import sys, os, re, collections, pickle, math, difflib, glob
+
+from IPython.display import HTML, display
+import matplotlib.pyplot as plt
+%matplotlib inline
+PICKLE_PROTOCOL = 3
+
+from difflib import SequenceMatcher
+from Levenshtein import ratio
+
+import laf
+from laf.fabric import LafFabric
+from etcbc.preprocess import prepare
+fabric = LafFabric()
+
+ +
+
+
+ +
+
+ + +
+
+
  0.00s This is LAF-Fabric 4.5.18
+API reference: http://laf-fabric.readthedocs.org/en/latest/texts/API-reference.html
+Feature doc: https://shebanq.ancient-data.org/static/docs/featuredoc/texts/welcome.html
+
+
+
+
+ +
+
+ +
+
+
+
+
+
+

5.1 Loading the feature data

We load the features we need from the ETCBC database.

+ +
+
+
+
+
+
In [2]:
+
+
+
version = '4b'
+API = fabric.load('etcbc{}'.format(version), '--', 'parallel', {
+    "xmlids": {"node": False, "edge": False},
+    "features": ('''
+        otype
+        lex g_word_utf8 trailer_utf8
+        book chapter verse label number
+    ''',
+    ''),
+    "prepare": prepare,
+    "primary": False,
+}, verbose='NORMAL')
+exec(fabric.localnames.format(var='fabric'))
+
+ +
+
+
+ +
+
+ + +
+
+
  0.00s LOADING API: please wait ... 
+  0.00s USING main  DATA COMPILED AT: 2015-11-02T15-08-56
+  3.23s LOGFILE=/Users/dirk/SURFdrive/laf-fabric-output/etcbc4b/parallel/__log__parallel.txt
+  3.23s INFO: LOADING PREPARED data: please wait ... 
+  3.24s prep prep: G.node_sort
+  3.36s prep prep: G.node_sort_inv
+  3.89s prep prep: L.node_up
+  7.26s prep prep: L.node_down
+    13s prep prep: V.verses
+    13s prep prep: V.books_la
+    13s ETCBC reference: http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html
+    15s INFO: LOADED PREPARED data
+    15s INFO: DATA LOADED FROM SOURCE etcbc4b AND ANNOX lexicon FOR TASK parallel AT 2016-03-03T10-49-34
+
+
+
+ +
+
+ +
+
+
+
+
+
+

5.2 Configuration

Here are the parameters on which the results crucially depend.

+

There are also parameters that control the reporting of the results, such as file locations.

+ +
+
+
+
+
+
In [3]:
+
+
+
# chunking
+CHUNK_LABELS = {True: 'fixed', False: 'object'}
+CHUNK_LBS = {True: 'F', False: 'O'}
+CHUNK_SIZES = (100, 50, 20, 10)
+CHUNK_OBJECTS = ('chapter', 'verse','half_verse','sentence')
+
+# preparing
+EXCLUDED_CONS = '[>WJ=/\[]'             # weed out weak consonants
+EXCLUDED_PAT = re.compile(EXCLUDED_CONS)
+
+# similarity
+MATRIX_THRESHOLD = 50
+SIM_METHODS = ('SET', 'LCS')
+SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30)
+
+# printing
+DEP_CLIQUE_RATIO = 25
+DUB_CLIQUE_RATIO = 15
+REC_CLIQUE_RATIO =  5
+LARGE_CLIQUE_SIZE = 50
+CLIQUES_PER_FILE = 50
+
+# assessing results
+VALUE_LABELS = dict(
+    mis='no results available',
+    rec='promising results: recommended',
+    dep='messy results: deprecated',
+    dub='mixed quality: take care',
+    out='method deprecated',
+    nor='unassessed quality: inspection needed',
+    lr='this experiment is the last one run',
+)
+
+# crossrefs for SHEBANQ
+SHEBANQ_MATRIX = (False, 'verse', 'SET')
+SHEBANQ_SIMILARITY = 65
+SHEBANQ_TOOL = 'parallel'
+CROSSREF_STATUS = '!'
+CROSSREF_KEYWORD = 'crossref'
+
+# progress indication
+VERBOSE = False
+MEGA = 1000000
+KILO = 1000
+SIMILARITY_PROGRESS = 5 * MEGA
+CLIQUES_PROGRESS = 1 * KILO
+
+# locations and hyperlinks
+REMOTE_BASE = 'https://surfdrive.surf.nl/files/public.php?service=files&t=dedf27be7e171ab8a8b151f84ded93e8'
+LOCAL_BASE_COMP = my_file('').rstrip('/')
+LOCAL_BASE_OUTP = 'files'
+EXPERIMENT_DIR = 'experiments'
+EXPERIMENT_FILE = 'experiments'
+EXPERIMENT_PATH = '{}/{}.txt'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)
+EXPERIMENT_HTML = '{}/{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)
+NOTES_FILE = 'crossref'
+NOTES_PATH = '{}/{}.csv'.format(LOCAL_BASE_OUTP, NOTES_FILE)
+STORED_CLIQUE_DIR = 'stored/cliques'
+STORED_MATRIX_DIR = 'stored/matrices'
+STORED_CHUNK_DIR = 'stored/chunks'
+CHAPTER_DIR = 'chapters'
+CROSSREF_DB_FILE = 'crossrefdb.csv'
+CROSSREF_DB_PATH = '{}/{}'.format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE)
+
+ +
+
+
+ +
+
+
+
+
+
+

5.3 Experiment settings

For each experiment we have to adapt the configuration settings to the parameters that define the experiment.

+ +
+
+
+
+
+
In [4]:
+
+
+
def reset_params():
+    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
+    global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD
+    global meta
+    meta = collections.OrderedDict()
+    
+    # chunking
+    CHUNK_FIXED = None                      # kind of chunking: fixed size or by object
+    CHUNK_SIZE = None                       # only relevant for CHUNK_FIXED = True
+    CHUNK_OBJECT = None                     # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell
+    CHUNK_LB = None                         # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ
+    CHUNK_DESC = None                       # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ
+    # similarity
+    MATRIX_THRESHOLD = None                 # minimal similarity used to fill the matrix of similarities
+    SIMILARITY_METHOD = None                # see SIM_METHODS in next cell
+    SIMILARITY_THRESHOLD = None             # minimal similarity used to put elements together in cliques
+    meta = collections.OrderedDict()
+
+def set_matrix_threshold(sim_m=None, chunk_o=None):
+    global MATRIX_THRESHOLD
+    the_sim_m = SIMILARITY_METHOD if sim_m == None else sim_m
+    the_chunk_o = CHUNK_OBJECT if chunk_o == None else chunk_o
+    MATRIX_THRESHOLD = 50 if the_sim_m == 'SET' else 60
+    if the_sim_m == 'SET':
+        if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 30
+        else: MATRIX_THRESHOLD = 50
+    else:
+        if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 55
+        else: MATRIX_THRESHOLD = 60
+
+def do_params_chunk(chunk_f, chunk_i):
+    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
+    do_chunk = False
+    if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):
+        do_chunk = True
+        CHUNK_FIXED = chunk_f
+        if chunk_f: CHUNK_SIZE = chunk_i
+        else: CHUNK_OBJECT = chunk_i
+
+    CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]
+    CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT
+
+    for p in (
+        '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),
+        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),
+    ):
+        if not os.path.exists(p): os.makedirs(p)
+
+    return do_chunk
+
+def do_params(chunk_f, chunk_i, sim_m, sim_thr):
+    global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC
+    global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD
+    global meta
+    do_chunk = False
+    do_prep = False
+    do_sim = False
+    do_clique = False
+    
+    meta = collections.OrderedDict()
+    if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):
+        do_chunk = True
+        do_prep = True
+        do_sim = True
+        do_clique = True
+        CHUNK_FIXED = chunk_f
+        if chunk_f: CHUNK_SIZE = chunk_i
+        else: CHUNK_OBJECT = chunk_i
+    if sim_m != SIMILARITY_METHOD:
+        do_prep = True
+        do_sim = True
+        do_clique = True
+        SIMILARITY_METHOD = sim_m
+    if sim_thr != SIMILARITY_THRESHOLD:
+        do_clique = True
+        SIMILARITY_THRESHOLD = sim_thr
+    set_matrix_threshold()
+    if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD : return (False, False, False, False, True)
+
+    CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]
+    CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT
+
+    meta['CHUNK TYPE'] = 'FIXED {}'.format(CHUNK_SIZE) if CHUNK_FIXED else 'OBJECT {}'.format(CHUNK_OBJECT)
+    meta['MATRIX THRESHOLD'] = MATRIX_THRESHOLD
+    meta['SIMILARITY METHOD'] = SIMILARITY_METHOD
+    meta['SIMILARITY THRESHOLD'] = SIMILARITY_THRESHOLD
+    
+    
+    for p in (
+        '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),
+        '{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR),
+        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR),
+        '{}/{}'.format(LOCAL_BASE_COMP, STORED_MATRIX_DIR),
+        '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),
+    ):
+        if not os.path.exists(p): os.makedirs(p)
+
+    return (do_chunk, do_prep, do_sim, do_clique, False)
+
+reset_params()
+
+ +
+
+
+ +
+
+
+
+
+
+

5.4 Chunking

We divide the text into chunks to be compared. The result is chunks, +which is a list of lists. +Every chunk is a list of word nodes.

+ +
+
+
+
+
+
In [5]:
+
+
+
def chunking(do_chunk):
+    global chunks, book_rank
+    if not do_chunk:
+        msg('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))
+        meta['# CHUNKS'] = len(chunks)
+        return
+
+    chunk_path = '{}/{}/chunk_{}_{}'.format(
+        LOCAL_BASE_COMP, STORED_CHUNK_DIR,
+        CHUNK_LB, CHUNK_DESC,
+    )
+
+    if os.path.exists(chunk_path):
+        with open(chunk_path, 'rb') as f: chunks = pickle.load(f)
+        msg('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(
+            CHUNK_LB, CHUNK_DESC,
+            len(chunks),
+        ))
+    else:
+        msg('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))
+        chunks = []
+        book_rank = {}
+        for b in F.otype.s('book'):
+            book_name = F.book.v(b)
+            book_rank[book_name] = b
+            words = L.d('word', b)
+            nwords = len(words)
+            if CHUNK_FIXED:
+                nchunks = nwords // CHUNK_SIZE
+                if nchunks == 0: 
+                    nchunks = 1
+                    common_incr = nwords
+                    special_incr = 0
+                else:            
+                    rem = nwords % CHUNK_SIZE
+                    common_incr = rem // nchunks
+                    special_incr = rem % nchunks
+                word_in_chunk = -1
+                cur_chunk = -1
+                these_chunks = []
+
+                for w in words:
+                    word_in_chunk += 1
+                    if word_in_chunk == 0 or (word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0)):
+                        word_in_chunk = 0
+                        these_chunks.append([])
+                        cur_chunk += 1
+                    these_chunks[-1].append(w)
+            else:
+                these_chunks = [L.d('word', c) for c in L.d(CHUNK_OBJECT, b)]
+
+            chunks.extend(these_chunks)
+
+            chunkvolume = sum(len(c) for c in these_chunks)
+            if VERBOSE:
+                msg('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(
+                    CHUNK_LB, CHUNK_DESC,
+                    book_name, nwords, len(these_chunks), 
+                    min(len(c) for c in these_chunks), 
+                    max(len(c) for c in these_chunks),
+                    'OK' if chunkvolume == nwords else 'ERROR',
+                ))
+        with  open(chunk_path, 'wb') as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL)
+    msg('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))
+    meta['# CHUNKS'] = len(chunks)
+
+ +
+
+
+ +
+
+
+
+
+
+

5.5 Preparing

In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing.

+

5.5.1 Preparing for SET comparison

We reduce words to their lexemes (dictionary entries) and from them we also remove the alef, waw, and yods. +The lexeme feature also contains characters (/ [ =) to disambiguate homonyms. We also remove these. +If we end up with something empty, we skip it. +Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content.

+

5.5.2 Preparing for LCS comparison

Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity.

+ +
+
+
+
+
+
In [6]:
+
+
+
def preparing(do_prepare):
+    global chunk_data
+    if not do_prepare:
+        msg('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))
+        return
+    msg('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))
+    chunk_data = []
+    if SIMILARITY_METHOD == 'SET':
+        for c in chunks:
+            words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)
+            clean_words = (w for w in words if w != '')
+            this_data = frozenset(clean_words)
+            chunk_data.append(this_data)
+    else:
+        for c in chunks:
+            words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)
+            clean_words = (w for w in words if w != '')
+            this_data = ' '.join(clean_words)
+            chunk_data.append(this_data)
+    msg('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))
+
+ +
+
+
+ +
+
+
+
+
+
+

5.6 Similarity computation

Here we implement our two ways of similarity computation. +Both need a massive amount of work, especially for experiments with many small chunks. +The similarities are stored in a matrix, a data structure that stores a similarity number for each pair of chunk indices. +Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold. +We store matrices for re-use.

+

5.6.1 SET similarity

The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union. +Intuitively, we compute the proportion of what two chunks have in common against their total material.

+

In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0.

+

5.6.2 LCS similarity

The core is the method ratio(), taken from the Levenshtein module. +Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance.

+ +
+
+
+
+
+
In [7]:
+
+
+
def similarity_post():
+    nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100})
+    cmin = min(chunk_dist.values()) if len(chunk_dist) else '!empty set!'
+    cmax = max(chunk_dist.values()) if len(chunk_dist) else '!empty set!'
+    meta['LOWEST  AVAILABLE SIMILARITY'] = cmin
+    meta['HIGHEST AVAILABLE SIMILARITY'] = cmax
+    meta['# EQUAL COMPARISONS'] = nequals
+    msg('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+        cmin, cmax, nequals,
+    ))
+    
+def similarity(do_sim):
+    global chunk_dist
+    total_chunks = len(chunks) 
+    total_distances = total_chunks * (total_chunks - 1) // 2
+    meta['# SIMILARITY COMPARISONS'] = total_distances
+    
+    SIMILARITY_PROGRESS = total_distances // 100
+    if SIMILARITY_PROGRESS >= MEGA:
+        sim_unit = MEGA
+        sim_lb = 'M'
+    else:
+        sim_unit = KILO
+        sim_lb = 'K'
+    
+    if not do_sim:
+        msg('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+            total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),
+        ))
+        meta['# STORED SIMILARITIES'] = len(chunk_dist)
+        similarity_post()
+        return
+
+    matrix_path = '{}/{}/matrix_{}_{}_{}_{}'.format(
+        LOCAL_BASE_COMP, STORED_MATRIX_DIR,
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+    )
+
+    if os.path.exists(matrix_path):
+        with open(matrix_path, 'rb') as f: chunk_dist = pickle.load(f)
+        msg('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+            total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),
+        ))
+        meta['# STORED SIMILARITIES'] = len(chunk_dist)
+        similarity_post()
+        return
+
+    msg('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+        total_distances // sim_unit, sim_lb, total_distances
+    ))
+
+    chunk_dist = {}
+    wc = 0
+    wt = 0
+    if SIMILARITY_METHOD == 'SET':
+        # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union
+        for i in range(total_chunks):
+            c_i = chunk_data[i]
+            for j in range(i + 1, total_chunks):
+                c_j = chunk_data[j]
+                u = len(c_i | c_j)
+                
+                # HERE COMES THE SIMILARITY COMPUTATION
+                d = 100 * len(c_i & c_j) / u if u != 0 else 0
+                
+                # HERE WE STORE THE OUTCOME
+                if d >= MATRIX_THRESHOLD:
+                    chunk_dist[(i,j)] = d
+                wc += 1
+                wt += 1
+                if wc == SIMILARITY_PROGRESS:
+                    wc = 0
+                    msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(
+                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+                        wt // sim_unit, sim_lb, len(chunk_dist),
+                    ))
+    elif SIMILARITY_METHOD == 'LCS':
+        # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length
+        for i in range(total_chunks):
+            c_i = chunk_data[i]
+            for j in range(i + 1, total_chunks):
+                c_j = chunk_data[j]
+
+                # HERE COMES THE SIMILARITY COMPUTATION
+                d = 100 * ratio(c_i, c_j)
+
+                # HERE WE STORE THE OUTCOME
+                if d >= MATRIX_THRESHOLD:
+                    chunk_dist[(i,j)] = d
+                wc += 1
+                wt += 1
+                if wc == SIMILARITY_PROGRESS:
+                    wc = 0
+                    msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(
+                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+                        wt // sim_unit, sim_lb, len(chunk_dist),
+                    ))
+
+    with  open(matrix_path, 'wb') as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL)
+        
+    msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,
+        wt // sim_unit, sim_lb, wt, len(chunk_dist),
+    ))
+    
+    meta['# STORED SIMILARITIES'] = len(chunk_dist)
+    similarity_post()
+
+ +
+
+
+ +
+
+
+
+
+
+

5.7 Cliques

Based on the value for the SIMILARITY_THRESHOLD we use the similarity matrix to pick the interesting +similar pairs out of it. From these pairs we lump together our cliques.

+

Our list of experiments will select various values for SIMILARITY_THRESHOLD, which will result +in various types of cliqueing behaviour.

+

We store computed cliques for re-use.

+

5.7.1 Selecting passages

We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages.

+

5.7.2 Growing cliques

We inspect all passages in our set, and try to add them to the cliques we are growing. +We start with an empty set of cliques. +Each passage is added to a clique with which it has enough familiarity, otherwise it is added to a new clique. +Enough familiarity means: the passage is similar to at least one member of the clique, and the similarity is at least SIMILARITY_THRESHOLD. +It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged. +This may lead to growing very large cliques if SIMILARITY_THRESHOLD is too low.

+ +
+
+
+
+
+
In [8]:
+
+
+
def key_chunk(i):
+    c = chunks[i]
+    w = c[0]
+    return  (-len(c), L.u('book', w), L.u('chapter', w), L.u('verse', w))
+
+def meta_clique_pre():
+    global similars, passages
+    msg('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+    ))
+    similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD}
+    passage_set = set()
+    for (i,j) in similars:
+        passage_set.add(i)
+        passage_set.add(j)
+    passages = sorted(passage_set, key=key_chunk)
+
+    meta['# SIMILAR COMPARISONS'] = len(similars)
+    meta['# SIMILAR PASSAGES'] = len(passages)    
+
+def meta_clique_pre2():
+    msg('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(
+    CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+    len(similars), len(passages),
+))
+
+
+def meta_clique_post():
+    global l_c_l
+    meta['# CLIQUES'] = len(cliques)
+    scliques = collections.Counter()
+    for c in cliques:
+        scliques[len(c)] += 1
+    l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0
+    totmn = 0
+    totcn = 0
+    for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]):
+        totmn += ln * n
+        totcn += n
+        if VERBOSE:
+            msg('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(
+                CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+                n, ln,
+            ))
+        meta['# CLIQUES of LENGTH {:>4}'.format(ln)] = n
+    msg('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+        totmn, totcn,
+    ))
+    
+def cliqueing(do_clique):
+    global cliques
+    if not do_clique:
+        msg('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+            len(cliques), len(passages), len(similars),            
+        ))
+        meta_clique_pre2()
+        meta_clique_post()
+        return
+    msg('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,        
+    ))
+    meta_clique_pre()
+    meta_clique_pre2()
+    clique_path = '{}/{}/clique_{}_{}_{}_{}_{}'.format(
+        LOCAL_BASE_COMP, STORED_CLIQUE_DIR,
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+    )
+    if os.path.exists(clique_path):
+        with open(clique_path, 'rb') as f: cliques = pickle.load(f)
+        msg('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+            len(cliques), len(passages), len(similars),            
+        ))
+        meta_clique_post()
+        return
+
+    msg('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+        len(passages), len(similars),            
+    ))
+    cliques_unsorted = []
+    np = 0
+    npc = 0
+    for i in passages:
+        added = None
+        removable = set()
+        for (k, c) in enumerate(cliques_unsorted):
+            origc = tuple(c)
+            for j in origc:            
+                d = chunk_dist.get((i,j), 0) if i < j else chunk_dist.get((j,i), 0) if j < i else 0
+                if d >= SIMILARITY_THRESHOLD:
+                    if added == None:    # the passage has not been added to any clique yet
+                        c.add(i)
+                        added = k        # remember that we added the passage to this clique
+                    else:                # the passage has alreay been added to another clique:
+                                         # we merge this clique with that one
+                        cliques_unsorted[added] |= c
+                        removable.add(k) # we remember that we have merged this clicque into another one,
+                                         # so we can throw away this clicque later 
+                    break
+        if added == None:
+            cliques_unsorted.append({i})
+        else:
+            if len(removable):
+                cliques_unsorted = [c for (k,c) in enumerate(cliques_unsorted) if k not in removable]
+        np += 1
+        npc += 1
+        if npc == CLIQUES_PROGRESS:
+            npc = 0
+            msg('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(
+                CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+                len(cliques_unsorted), np,
+            ))
+    cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted])
+    with  open(clique_path, 'wb') as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL)
+    meta_clique_post()
+    msg('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+        len(cliques), len(passages), len(similars),            
+    ))
+
+ +
+
+
+ +
+
+
+
+
+
+

5.8 Output

We deliver the output of our experiments in various ways, all in HTML.

+

We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it.

+

For every (good) experiment, we produce a big list of its cliques, and for +every such clique, we produce a diff-view of its members.

+

Big cliques will be split into several files.

+

Clique listings will also contain metadata: the value of the experiment parameters.

+

5.8.1 Format definitions

Here are the definitions for formatting the (HTML) output.

+ +
+
+
+
+
+
In [9]:
+
+
+
# clique lists
+css = '''
+td.vl {
+    font-family: Verdana, Arial, sans-serif;
+    font-size: small;
+    text-align: right;
+    color: #aaaaaa;
+    width: 10%;
+    direction: ltr;
+    border-left: 2px solid #aaaaaa;
+    border-right: 2px solid #aaaaaa;
+}
+td.ht {
+    font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif;
+    font-size: x-large;
+    line-height: 1.7;
+    text-align: right;
+    direction: rtl;
+}
+table.ht {
+    width: 100%;
+    direction: rtl;
+    border-collapse: collapse;
+}
+td.ht {
+    border-left: 2px solid #aaaaaa;
+    border-right: 2px solid #aaaaaa;
+}
+tr.ht.tb {
+    border-top: 2px solid #aaaaaa;
+    border-left: 2px solid #aaaaaa;
+    border-right: 2px solid #aaaaaa;
+}
+tr.ht.bb {
+    border-bottom: 2px solid #aaaaaa;
+    border-left: 2px solid #aaaaaa;
+    border-right: 2px solid #aaaaaa;
+}
+span.m {
+    background-color: #aaaaff;
+}
+span.f {
+    background-color: #ffaaaa;
+}
+span.x {
+    background-color: #ffffaa;
+    color: #bb0000;
+}
+span.delete {
+    background-color: #ffaaaa;
+}
+span.insert {
+    background-color: #aaffaa;
+}
+span.replace {
+    background-color: #ffff00;
+}
+
+'''
+
+# chapter diffs
+diffhead = '''
+<head>
+    <meta http-equiv="Content-Type"
+          content="text/html; charset=UTF-8" />
+    <title></title>
+    <style type="text/css">
+        table.diff {
+            font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; 
+            font-size: x-large;
+            text-align: right;
+        }
+        .diff_header {background-color:#e0e0e0}
+        td.diff_header {text-align:right}
+        .diff_next {background-color:#c0c0c0}
+        .diff_add {background-color:#aaffaa}
+        .diff_chg {background-color:#ffff77}
+        .diff_sub {background-color:#ffaaaa}
+    </style>
+</head>
+'''
+
+# table of experiments
+ecss = '''
+<style type="text/css">
+.mis {background-color: #cccccc;}
+.rec {background-color: #aaffaa;}
+.dep {background-color: #ffaaaa;}
+.dub {background-color: #ffddaa;}
+.out {background-color: #ffddff;}
+.nor {background-color: #fcfcff;}
+.ps  {font-weight: normal;}
+.mx  {font-style: italic;}
+.cl  {font-weight: bold;}
+.lr  {font-weight: bold; background-color: #ffffaa;}
+p,td {font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: small;}
+td   {border: 1pt solid #000000; padding: 4pt;}
+table {border: 1pt solid #000000; border-collapse: collapse;}
+</style>
+'''
+
+legend = '''
+<table>
+<tr><td class="mis">{mis}</td></tr>
+<tr><td class="rec">{rec}</td></tr>
+<tr><td class="dep">{dep}</td></tr>
+<tr><td class="dub">{dub}</td></tr>
+<tr><td class="out">{out}</td></tr>
+<tr><td class="nor">{nor}</td></tr>
+</table>
+'''.format(**VALUE_LABELS)
+
+ +
+
+
+ +
+
+
+
+
+
+

5.8.2 Formatting clique lists

+
+
+
+
+
+
In [10]:
+
+
+
def xterse_chunk(i):
+    chunk = chunks[i]
+    fword = chunk[0]
+    book = L.u('book', fword)
+    chapter = L.u('chapter', fword)
+    return (book, chapter)
+
+def xterse_clique(ii):
+    return tuple(sorted({xterse_chunk(i) for i in ii}))
+
+def terse_chunk(i):
+    chunk = chunks[i]
+    fword = chunk[0]
+    book = L.u('book', fword)
+    chapter = L.u('chapter', fword)
+    verse = L.u('verse', fword)
+    return (book, chapter, verse)
+
+def terse_clique(ii):
+    return tuple(sorted({terse_chunk(i) for i in ii}))
+
+def verse_chunk(i):
+    (bk, ch, vs) = i
+    book = F.book.v(bk)
+    chapter = F.chapter.v(ch)
+    verse = F.verse.v(vs)
+    text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', vs))
+    verse_label = '<td class="vl">{} {}:{}</td>'.format(book, chapter, verse)
+    htext = '{}<td class="ht">{}</td>'.format(verse_label, text)
+    return '<tr class="ht">{}</tr>'.format(htext)
+
+def verse_clique(ii):
+    return '<table class="ht">{}</table>\n'.format(''.join(verse_chunk(i) for i in sorted(ii)))
+
+def condense(vlabels):
+    cnd = ''
+    (cur_b, cur_c) = (None, None)
+    for (b, c, v) in vlabels:
+        sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '
+        show_b = b+' ' if cur_b != b else ''
+        show_c = c+':' if cur_b != b or cur_c != c else ''
+        (cur_b, cur_c) = (b, c)
+        cnd += '{}{}{}{}'.format(sep, show_b, show_c, v)
+    return cnd
+
+def print_diff(a, b):
+    arep = ''
+    brep = ''
+    for (lb, ai, aj, bi, bj) in SequenceMatcher(isjunk=None, a=a, b=b, autojunk=False).get_opcodes():
+        if lb == 'equal':
+            arep += a[ai:aj]
+            brep += b[bi:bj]
+        elif lb == 'delete':
+            arep += '<span class="{}">{}</span>'.format(lb, a[ai:aj])
+        elif lb == 'insert':
+            brep += '<span class="{}">{}</span>'.format(lb, b[bi:bj])
+        else:
+            arep += '<span class="{}">{}</span>'.format(lb, a[ai:aj])
+            brep += '<span class="{}">{}</span>'.format(lb, b[bi:bj])
+    return (arep, brep)
+    
+def print_chunk_fine(prev, text, verse_labels, prevlabels):
+    if prev == None:
+        return '''
+<tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
+'''.format(
+            condense(verse_labels), 
+            text,
+        )
+    else:
+        (prevline, textline) = print_diff(prev, text)
+        return '''
+<tr class="ht tb"><td class="vl">{}</td><td class="ht">{}</td></tr>
+<tr class="ht bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
+'''.format(
+    condense(prevlabels) if prevlabels != None else 'previous',
+    prevline,
+    condense(verse_labels), 
+    textline,
+)
+
+def print_chunk_coarse(text, verse_labels):
+    return '''
+<tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr>
+'''.format(
+            condense(verse_labels), 
+            text,
+        )
+
+def print_clique(ii, ncliques):
+    return print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii)
+    
+def print_clique_fine(ii):
+    condensed = collections.OrderedDict()
+    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):
+        chunk = chunks[i]
+        fword = chunk[0]
+        book = F.book.v(L.u('book', fword))
+        chapter = F.chapter.v(L.u('chapter', fword))
+        verse = F.verse.v(L.u('verse', fword))
+        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)
+        condensed.setdefault(text, []).append((book, chapter, verse))
+    result = []
+    nv = len(condensed.items())
+    prev = None
+    for (text, verse_labels) in condensed.items():
+        if prev == None:
+            if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None))
+            else:
+                prev = text
+                prevlabels = verse_labels
+                continue
+        else:
+            result.append(print_chunk_fine(prev, text, verse_labels, prevlabels))
+            prev = text
+            prevlabels = None
+    return '<table class="ht">{}</table>\n'.format(''.join(result))
+
+def print_clique_coarse(ii):
+    condensed = collections.OrderedDict()
+    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:
+        chunk = chunks[i]
+        fword = chunk[0]
+        book = F.book.v(L.u('book', fword))
+        chapter = F.chapter.v(L.u('chapter', fword))
+        verse = F.verse.v(L.u('verse', fword))
+        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)
+        condensed.setdefault(text, []).append((book, chapter, verse))
+    result = []
+    nv = len(condensed.items())
+    prev = None
+    for (text, verse_labels) in condensed.items():
+        result.append(print_chunk_coarse(text, verse_labels))
+    if len(ii) > LARGE_CLIQUE_SIZE:
+        result.append(print_chunk_coarse('+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE),[]))
+    return '<table class="ht">{}</table>\n'.format(''.join(result))
+
+def index_clique(bnm, n, ii, ncliques):
+    return index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii)
+    
+def index_clique_fine(bnm, n, ii):
+    verse_labels = []
+    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):
+        chunk = chunks[i]
+        fword = chunk[0]
+        book = F.book.v(L.u('book', fword))
+        chapter = F.chapter.v(L.u('chapter', fword))
+        verse = F.verse.v(L.u('verse', fword))
+        verse_labels.append((book, chapter, verse))
+        reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)
+    return '<p><b>{}</b> <a href="{}.html#c_{}">{}</a></p>'.format(
+        n, reffl, n, condense(verse_labels),
+    )
+
+def index_clique_coarse(bnm, n, ii):
+    verse_labels = []
+    for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:
+        chunk = chunks[i]
+        fword = chunk[0]
+        book = F.book.v(L.u('book', fword))
+        chapter = F.chapter.v(L.u('chapter', fword))
+        verse = F.verse.v(L.u('verse', fword))
+        verse_labels.append((book, chapter, verse))
+        reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)
+    extra = '+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else ''
+    return '<p><b>{}</b> <a href="{}.html#c_{}">{}{}</a></p>'.format(
+        n, reffl, n, condense(verse_labels), extra,
+    )
+
+def lines_chapter(c):
+    lines = []
+    for v in L.d('verse', c):
+        vl = F.verse.v(v)
+        text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', v))
+        lines.append('{} {}'.format(vl, text.replace('\n', ' ')))
+    return lines
+
+def compare_chapters(c1, c2, lb1, lb2):
+    dh = difflib.HtmlDiff(wrapcolumn=80)
+    table_html = dh.make_table(
+        lines_chapter(c1), 
+        lines_chapter(c2), 
+        fromdesc=lb1, 
+        todesc=lb2, 
+        context=False, 
+        numlines=5,
+    )
+    htext = '''<html>{}<body>{}</body></html>'''.format(diffhead, table_html)
+    return htext
+
+ +
+
+
+ +
+
+
+
+
+
+

5.8.3 Compiling the table of experiments

Here we generate the table of experiments, complete with the coloring according to their assessments.

+ +
+
+
+
+
+
In [11]:
+
+
+
# generate the table of experiments
+def gen_html(standalone=False):
+    global other_exps
+    msg('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))
+    stats = collections.Counter()
+    pre = '''
+<html>
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
+{}
+</head>
+<body>
+'''.format(ecss) if standalone else ''
+    
+    post = '''
+</body></html>
+''' if standalone else ''
+
+    experiments = '''
+{}
+{}
+<table>
+<tr><th>chunk type</th><th>chunk size</th><th>similarity method</th>{}</tr>
+'''.format(pre, legend, ''.join('<th>{}</th>'.format(sim_thr) for sim_thr in SIMILARITIES))
+    
+    for chunk_f in (True, False):
+        if chunk_f:
+            chunk_items = CHUNK_SIZES
+        else:
+            chunk_items = CHUNK_OBJECTS
+        chunk_lb = CHUNK_LBS[chunk_f]
+        for chunk_i in chunk_items:
+            for sim_m in SIM_METHODS:
+                set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i)
+                these_outputs = outputs.get(MATRIX_THRESHOLD, {})
+                experiments += '<tr><td>{}</td><td>{}</td><td>{}</td>'.format(
+                    CHUNK_LABELS[chunk_f], chunk_i, sim_m,
+                )
+                for sim_thr in SIMILARITIES:
+                    okey = (chunk_lb, chunk_i, sim_m, sim_thr)
+                    values = these_outputs.get(okey)
+                    if values == None:
+                        result = '<td class="mis">&nbsp;</td>'
+                        stats['mis'] += 1
+                    else:
+                        (npassages, ncliques, longest_clique_len) = values
+                        cls = assess_exp(chunk_f, npassages, ncliques, longest_clique_len)
+                        stats[cls] += 1
+                        (lr_el, lr_lb) = ('', '')
+                        if (CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD) == (
+                            chunk_lb, chunk_i, sim_m, sim_thr,
+                        ):
+                            lr_el = '<span class="lr">*</span>'
+                            lr_lb = VALUE_LABELS['lr']
+                        result = '''
+<td class="{}" title="{}">{}
+    <span class="ps">{}</span><br/>
+    <a target="_blank" href="{}{}/{}_{}_{}_M{}_S{}.html"><span class="cl">{}</span></a><br/>
+    <span class="mx">{}</span>
+    </td>'''.format(
+        cls, lr_lb, lr_el, npassages,
+        '' if standalone else LOCAL_BASE_OUTP+'/', 
+        EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr,
+        ncliques, longest_clique_len,
+    )
+                    experiments += result
+                experiments += '</tr>\n'
+    experiments += '</table>\n{}'.format(post)
+    if standalone:
+        with open(EXPERIMENT_HTML, 'w') as f:
+            f.write(experiments)
+    else:
+        other_exps = experiments
+
+    for stat in sorted(stats):
+        msg('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))
+    msg("EXPERIMENT: Generated html report")
+
+ +
+
+
+ +
+
+
+
+
+
+

5.8.4 High level formatting functions

Here everything concerning output is brought together.

+ +
+
+
+
+
+
In [12]:
+
+
+
def assess_exp(cf, np, nc, ll):
+    return 'out' if cf else \
+    'rec' if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else \
+    'dep' if ll > nc * DEP_CLIQUE_RATIO / 100 else \
+    'dub' if ll > nc * DUB_CLIQUE_RATIO / 100 else \
+    'nor'
+
+def printing():
+    global outputs, bin_cliques, base_name
+    msg('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+    ))
+    xt_cliques = {xterse_clique(c) for c in cliques}     # chapter cliques as tuples of (b, ch) tuples
+    bin_cliques = {c for c in xt_cliques if len(c) == 2} # chapter cliques with exactly two chapters
+    # all chapters that occur in binary chapter cliques
+    bin_chapters = {c[0] for c in bin_cliques} | {c[1] for c in bin_cliques}
+    meta['# BINARY CHAPTER DIFFS'] = len(bin_cliques)
+
+    # We generate one kind of info for binary chapter cliques (the majority of cases).
+    # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they
+    # have member chunks in the same chapter, or in multiple (more than two) chapters.
+    
+    ncliques = len(cliques)
+    chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in {'rec', 'nor', 'dub'}
+    cdoing = 'involving' if chapters_ok else 'skipping'
+
+    msg('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+        ncliques, cdoing, len(bin_cliques),
+    ))
+    meta_html = '\n'.join('{:<40} : {:>10}'.format(k, str(meta[k])) for k in meta)
+
+    base_name = '{}_{}_{}_M{}_S{}'.format(
+        CHUNK_LB,
+        CHUNK_DESC,
+        SIMILARITY_METHOD,
+        MATRIX_THRESHOLD,
+        SIMILARITY_THRESHOLD, 
+    )
+    param_spec = '''
+<table>
+<tr><th>chunking method</th><td>{}</td></tr>
+<tr><th>chunking description</th><td>{}</td></tr>
+<tr><th>similarity method</th><td>{}</td></tr>
+<tr><th>similarity threshold</th><td>{}</td></tr>
+</table>
+    '''.format(
+        CHUNK_LABELS[CHUNK_FIXED],
+        CHUNK_DESC,
+        SIMILARITY_METHOD, 
+        SIMILARITY_THRESHOLD, 
+    )
+    param_lab = 'chunk-{}-{}-sim-{}-m{}-s{}'.format(
+        CHUNK_LB,
+        CHUNK_DESC,
+        SIMILARITY_METHOD,
+        MATRIX_THRESHOLD,
+        SIMILARITY_THRESHOLD, 
+    )
+    index_name = base_name
+    all_name = '{}_{}'.format('all', base_name)
+    cliques_name = '{}_{}'.format('clique', base_name)
+
+    clique_links = []
+    clique_links.append(('{}/{}.html'.format(base_name, all_name), 'Big list of all cliques'))
+
+    nexist = 0
+    nnew = 0
+    if chapters_ok:
+        chapter_diffs = []
+        msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+            len(bin_cliques),
+        ))
+
+        bcc_text = '<p>These results look good, so a binary chapter comparison has been generated</p>'
+        for cl in sorted(bin_cliques):
+            lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))
+            lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))
+            hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')
+            hfilepath = '{}/{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename)
+            chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(
+                SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,
+            )))
+            if not os.path.exists(hfilepath):
+                htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2)
+                with open(hfilepath, 'w') as f: f.write(htext)
+                if VERBOSE:
+                    msg('PRINT ({} {} {} M>{} S>{}): written {}'.format(
+                        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+                        hfilename,
+                    ))
+                nnew += 1
+            else:
+                nexist += 1
+            clique_links.append((
+                '../{}/{}'.format(CHAPTER_DIR, hfilename), 
+                '{} versus {}'.format(lb1, lb2),
+            ))
+        msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(
+            CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+            nnew, nexist,
+        ))
+    else:
+        bcc_text = '<p>These results look dubious at best, so no binary chapter comparison has been generated</p>'
+
+
+    allgeni_html = (index_clique(cliques_name, i, c, ncliques) for (i,c) in enumerate(cliques))
+    
+    allgen_htmls = []
+    allgen_html = ''
+    
+    for (i, c) in enumerate(cliques):
+        if i % CLIQUES_PER_FILE == 0:
+            if i > 0:
+                allgen_htmls.append(allgen_html)
+            allgen_html = ''
+        allgen_html += '<h3><a name="c_{}">Clique {}</a></h3>\n{}'.format(i, i, print_clique(c, ncliques))
+    allgen_htmls.append(allgen_html)
+
+    index_html_tpl = '''
+{}
+<h1>Binary chapter comparisons</h1>
+{}
+{}
+    '''
+
+    content_file_tpl = '''<html>
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
+<title>{}</title>
+<style type="text/css">
+{}
+</style>
+</head>
+<body>
+<h1>{}</h1>
+{}
+<p><a href="#meta">more parameters and stats</a></p>
+{}
+<h1><a name="meta">Parameters and stats</a></h1>
+<pre>{}</pre>
+</body>
+</html>'''
+    
+    a_tpl_file = '<p><a target="_blank" href="{}">{}</a></p>'
+
+    index_html_file = index_html_tpl.format(
+        a_tpl_file.format(*clique_links[0]),
+        bcc_text,
+        '\n'.join(a_tpl_file.format(*c) for c in clique_links[1:]),
+    )
+
+    listing_html = '{}\n'.format(
+        '\n'.join(allgeni_html),
+    )
+
+    for (subdir, fname, content_html, tit) in (
+        (None, index_name, index_html_file, 'Index '+param_lab),
+        (base_name, all_name, listing_html, 'Listing '+param_lab),
+        (base_name, cliques_name, allgen_htmls, 'Cliques '+param_lab),
+    ): 
+        subdir = '' if subdir == None else (subdir + '/')
+        subdirabs = '{}/{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir)
+        if not os.path.exists(subdirabs): os.makedirs(subdirabs)
+
+        if type(content_html) is list:
+            for (i, c_h) in enumerate(content_html):
+                fn = '{}_{}'.format(fname, i)
+                t = '{}_{}'.format(tit, i)
+                with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn), 'w') as f: 
+                    f.write(content_file_tpl.format(t, css, t, param_spec, c_h, meta_html))
+        else:
+            with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname), 'w') as f: 
+                f.write(content_file_tpl.format(tit, css, tit, param_spec, content_html, meta_html))
+    destination = outputs.setdefault(MATRIX_THRESHOLD, {})
+    destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = (
+        len(passages), len(cliques), l_c_l,
+    )
+    msg('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(
+        CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,
+        len(cliques), len(allgen_htmls), cdoing, len(bin_cliques)
+    ))
+
+ +
+
+
+ +
+
+
+
+
+
+

5.9 Running experiments

The workflows of doing a single experiment, and then all experiments, are defined.

+ +
+
+
+
+
+
In [13]:
+
+
+
outputs = {}
+
+def writeoutputs():
+    global outputs
+    with open(EXPERIMENT_PATH, 'wb') as f:
+        pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL)
+
+def readoutputs():
+    global outputs
+    if not os.path.exists(EXPERIMENT_PATH):
+        outputs = {}
+    else:
+        with open(EXPERIMENT_PATH, 'rb') as f:
+            outputs = pickle.load(f)
+
+def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index):
+    if do_index:
+        readoutputs()
+    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
+    if skip: return
+    chunking(do_chunk)
+    preparing(do_prep)
+    similarity(do_sim)
+    cliqueing(do_clique)
+    printing()
+    if do_index:
+        writeoutputs()
+        gen_html()
+
+def do_only_chunk(chunk_f, chunk_i):
+    do_chunk = do_params_chunk(chunk_f, chunk_i)
+    chunking(do_chunk)
+
+def reset_experiments():
+    global outputs
+    readoutputs()
+    outputs = {}
+    reset_params()
+    writeoutputs()
+    gen_html()
+
+def do_all_experiments(no_fixed=False, only_object=None):
+    global outputs
+    reset_experiments()
+    for chunk_f in (False,) if no_fixed else (True, False):
+        if chunk_f:
+            chunk_items = CHUNK_SIZES
+        else:
+            chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)
+        for chunk_i in chunk_items:
+            for sim_m in SIM_METHODS:
+                for sim_thr in SIMILARITIES:
+                    do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False)
+    writeoutputs()
+    gen_html()
+    gen_html(standalone=True)
+
+def do_all_chunks(no_fixed=False, only_object=None):
+    global outputs
+    reset_experiments()
+    for chunk_f in (False,) if no_fixed else (True, False):
+        if chunk_f:
+            chunk_items = CHUNK_SIZES
+        else:
+            chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)
+        for chunk_i in chunk_items:
+            do_only_chunk(chunk_f, chunk_i)
+    
+def show_all_experiments():
+    readoutputs()
+    gen_html()
+    gen_html(standalone=True)
+
+ +
+
+
+ +
+
+
+
+
+
+

6. SHEBANQ annotations

Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages.

+ +
+
+
+
+
+
In [14]:
+
+
+
def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca)
+
+def get_verse_o(o, ca=False): return get_verse_w(L.d('word', o)[0], ca=ca)
+
+def get_verse_w(w, ca=False):
+    book = F.book.v(L.u('book', w))
+    chapter = F.chapter.v(L.u('chapter', w))
+    verse = F.verse.v(L.u('verse', w))
+    if ca: ca = F.number.v(L.u('clause_atom', w))
+    return (book, chapter, verse, ca) if ca else (book, chapter, verse)
+
+def key_verse(x):
+    return  (book_rank[x[0]], int(x[1]), int(x[2]))
+
+MAX_REFS = 10
+
+def condensex(vlabels):
+    cnd = []
+    (cur_b, cur_c) = (None, None)
+    for (b, c, v, d) in vlabels:
+        sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '
+        show_b = b+' ' if cur_b != b else ''
+        show_c = c+':' if cur_b != b or cur_c != c else ''
+        (cur_b, cur_c) = (b, c)
+        cnd.append('{}{}{}{}{}'.format(sep, show_b, show_c, v, d))
+    return cnd
+
+dfields = '''
+    book1
+    chapter1
+    verse1
+    book2
+    chapter2
+    verse2
+    similarity
+'''.strip().split()
+
+dfields_fmt = ('{}\t' * (len(dfields) - 1)) + '{}\n' 
+
+def get_crossrefs():
+    global crossrefs
+    msg('CROSSREFS: Fetching crossrefs')
+    crossrefs_proto = {}
+    crossrefs = {}
+    (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX
+    sim_thr = SHEBANQ_SIMILARITY
+    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
+    if skip: return
+    msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))
+    crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}
+    msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(
+        CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,
+        len(crossrefs_proto),
+    ))
+    f = open(CROSSREF_DB_PATH, 'w')
+    f.write('{}\n'.format('\t'.join(dfields)))        
+    for ((x,y), d) in crossrefs_proto:
+        vx = get_verse(x)
+        vy = get_verse(y)
+        rd = int(round(d))
+        crossrefs.setdefault(x, {})[vy] = rd
+        crossrefs.setdefault(y, {})[vx] = rd
+        f.write(dfields_fmt.format(*(vx+vy+(rd,))))
+    total = sum(len(x) for x in crossrefs.values())
+    f.close()
+    msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))
+
+def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to):
+    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
+    if skip: return
+    chunking(do_chunk)
+    preparing(do_prep)
+    similarity(do_sim)
+
+    msg('CROSSREFS: Fetching crossrefs')
+    crossrefs_proto = {}
+    crossrefs = {}
+    (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)
+    if skip: return
+    msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))
+    crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}
+    msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(
+        CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,
+        len(crossrefs_proto),
+    ))
+    f = open('files/{}'.format(write_to), 'w')
+    f.write('{}\n'.format('\t'.join(dfields)))        
+    for ((x,y), d) in crossrefs_proto:
+        vx = get_verse(x)
+        vy = get_verse(y)
+        rd = int(round(d))
+        crossrefs.setdefault(x, {})[vy] = rd
+        crossrefs.setdefault(y, {})[vx] = rd
+        f.write(dfields_fmt.format(*(vx+vy+(rd,))))
+    total = sum(len(x) for x in crossrefs.values())
+    f.close()
+    msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))
+
+def compile_refs():
+    global refs_compiled
+    refs_grouped = []
+    for x in sorted(crossrefs):
+        refs = crossrefs[x]
+        vys = sorted(refs.keys(), key=key_verse)
+        currefs = []
+        for vy in vys:
+            nr = len(currefs)
+            if nr == MAX_REFS:
+                refs_grouped.append((x, tuple(currefs)))
+                currefs = []            
+            currefs.append(vy)
+        if len(currefs):
+            refs_grouped.append((x, tuple(currefs)))
+    refs_compiled = []
+    for (x, vys) in refs_grouped:
+        vysd = [(vy[0], vy[1], vy[2], ' ~{}%'.format(crossrefs[x][vy])) for vy in vys]
+        vysl = condensex(vysd)
+        these_refs = []
+        for (i, vy) in enumerate(vysd):
+            link_text = vysl[i]
+            link_target = '{} {}:{}'.format(vy[0], vy[1], vy[2])
+            these_refs.append('[{}]({})'.format(link_text, link_target))
+        refs_compiled.append((x, ' '.join(these_refs)))
+    msg('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))
+
+def get_chapter_diffs():
+    global chapter_diffs
+    chapter_diffs = []
+    for cl in sorted(bin_cliques):
+        lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))
+        lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))
+        hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')
+        chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(
+            SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,
+        )))
+    msg('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))
+
+        
+def get_clique_refs():
+    global clique_refs
+    clique_refs = []
+    for (i, c) in enumerate(cliques):
+        for j in c:
+            seq = i // CLIQUES_PER_FILE
+            clique_refs.append((j, i, '{}/{}/{}/{}/clique_{}_{}.html#c_{}'.format(
+                SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i,
+            )))
+    msg('CROSSREFS: Added {} clique references'.format(len(clique_refs)))
+
+sfields = '''
+    version
+    book
+    chapter
+    verse
+    clause_atom
+    is_shared
+    is_published
+    status
+    keywords
+    ntext
+'''.strip().split()
+
+sfields_fmt = ('{}\t' * (len(sfields) - 1)) + '{}\n' 
+
+def generate_notes():
+    with open(NOTES_PATH, 'w') as f:
+        f.write('{}\n'.format('\t'.join(sfields)))        
+        x = next(F.otype.s('word'))
+        (bk, ch, vs, ca) = get_verse(x, ca=True)
+        f.write(sfields_fmt.format(
+            version,
+            bk,
+            ch,
+            vs,
+            ca,
+            'T',
+            '',
+            CROSSREF_STATUS,
+            CROSSREF_KEYWORD,
+            '''The crossref notes are the result of a computation without manual tweaks.
+Parameters: chunk by verse, similarity method SET with threshold 65.
+[Here](tool=parallel) is an account of the generation method.'''.replace('\n', ' ')
+        ))
+        for (lb1, ch1, lb2, ch2, fl) in chapter_diffs:
+            (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True)
+            (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True)
+            f.write(sfields_fmt.format(
+                version,
+                bk1,
+                ch1,
+                vs1,
+                ca1,
+                'T',
+                '',
+                CROSSREF_STATUS,
+                CROSSREF_KEYWORD,
+                '[chapter diff with {}](tool:{})'.format(lb2, fl),
+            ))
+            f.write(sfields_fmt.format(
+                version,
+                bk2,
+                ch2,
+                vs2,
+                ca2,
+                'T',
+                '',
+                CROSSREF_STATUS,
+                CROSSREF_KEYWORD,
+                '[chapter diff with {}](tool:{})'.format(lb1, fl),
+            ))
+        for (x, refs) in refs_compiled:
+            (bk, ch, vs, ca) = get_verse(x, ca=True)
+            f.write(sfields_fmt.format(
+                version,
+                bk,
+                ch,
+                vs,
+                ca,
+                'T',
+                '',
+                CROSSREF_STATUS,
+                CROSSREF_KEYWORD,
+                refs,
+            ))
+        for (chunk, clique, fl) in clique_refs:
+            (bk, ch, vs, ca) = get_verse(chunk, ca=True)
+            f.write(sfields_fmt.format(
+                version,
+                bk,
+                ch,
+                vs,
+                ca,
+                'T',
+                '',
+                CROSSREF_STATUS,
+                CROSSREF_KEYWORD,
+                '[all variants (clique {})](tool:{})'.format(clique, fl),
+            ))
+
+    msg('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))
+
+def crossrefs2shebanq():
+    expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,)
+    do_experiment(*(expr+(True,)))
+    get_crossrefs()
+    compile_refs()
+    get_chapter_diffs()
+    get_clique_refs()
+    generate_notes()
+
+ +
+
+
+ +
+
+
+
+
+
+

7. Main

In the cell below you can select the experiments you want to carry out.

+

The previous cells contain just definitions and parameters. +The next cell will do work.

+

If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8).

+ +
+
+
+
+
+
In [15]:
+
+
+
reset_params()
+#do_experiment(False, 'sentence', 'LCS', 60, False)
+do_all_experiments()
+#do_all_experiments(no_fixed=True, only_object='chapter')
+#crossrefs2shebanq()
+#show_all_experiments()
+#get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt')
+#do_all_chunks()
+
+ +
+
+
+ +
+
+ + +
+
+
    37s EXPERIMENT: Generating html report
+    37s EXPERIMENT: 240 no results available
+    37s EXPERIMENT: Generated html report
+    37s CHUNKING (F 100): Loaded:  4244 chunks
+    37s CHUNKING (F 100): Made 4244 chunks
+    37s PREPARING (F 100 SET)
+    37s PREPARING (F 100 SET): Done 4244 chunks.
+    37s SIMILARITY (F 100 SET M>50): Loaded:  9003 K (9003646) comparisons with 359 entries in matrix
+    37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    37s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates
+    37s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix
+    37s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages
+    37s CLIQUES (F 100 SET M>50 S>100): Loaded:     1 cliques out of      2 chunks from 1 comparisons
+    37s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques
+    37s PRINT (F 100 SET M>50 S>100): sorting out cliques
+    37s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs
+    37s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
+    37s CHUNKING (F 100): already chunked into 4244 chunks
+    37s PREPARING (F 100 SET): Already prepared
+    37s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    37s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates
+    37s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix
+    37s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages
+    37s CLIQUES (F 100 SET M>50 S>95): Loaded:     2 cliques out of      4 chunks from 2 comparisons
+    37s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques
+    37s PRINT (F 100 SET M>50 S>95): sorting out cliques
+    37s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs
+    37s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
+    37s CHUNKING (F 100): already chunked into 4244 chunks
+    37s PREPARING (F 100 SET): Already prepared
+    37s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    38s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates
+    38s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix
+    38s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages
+    38s CLIQUES (F 100 SET M>50 S>90): Loaded:     9 cliques out of     18 chunks from 9 comparisons
+    38s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques
+    38s PRINT (F 100 SET M>50 S>90): sorting out cliques
+    38s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs
+    38s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs
+    38s CHUNKING (F 100): already chunked into 4244 chunks
+    38s PREPARING (F 100 SET): Already prepared
+    38s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    38s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates
+    38s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix
+    38s CLIQUES (F 100 SET M>50 S>85): 19 relevant similarities between 37 passages
+    38s CLIQUES (F 100 SET M>50 S>85): Loaded:    18 cliques out of     37 chunks from 19 comparisons
+    38s CLIQUES (F 100 SET M>50 S>85): 37 members in 18 cliques
+    38s PRINT (F 100 SET M>50 S>85): sorting out cliques
+    38s PRINT (F 100 SET M>50 S>85): formatting 18 cliques skipping 6 binary chapter diffs
+    38s PRINT (F 100 SET M>50 S>85): formatted 18 cliques (1 files) skipping 6 binary chapter diffs
+    38s CHUNKING (F 100): already chunked into 4244 chunks
+    38s PREPARING (F 100 SET): Already prepared
+    38s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    38s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates
+    38s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix
+    38s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages
+    38s CLIQUES (F 100 SET M>50 S>80): Loaded:    30 cliques out of     64 chunks from 35 comparisons
+    38s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques
+    38s PRINT (F 100 SET M>50 S>80): sorting out cliques
+    38s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs
+    39s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs
+    39s CHUNKING (F 100): already chunked into 4244 chunks
+    39s PREPARING (F 100 SET): Already prepared
+    39s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    39s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates
+    39s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix
+    39s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages
+    39s CLIQUES (F 100 SET M>50 S>75): Loaded:    40 cliques out of     87 chunks from 63 comparisons
+    39s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques
+    39s PRINT (F 100 SET M>50 S>75): sorting out cliques
+    39s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs
+    39s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs
+    39s CHUNKING (F 100): already chunked into 4244 chunks
+    39s PREPARING (F 100 SET): Already prepared
+    39s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    39s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates
+    39s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix
+    39s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages
+    39s CLIQUES (F 100 SET M>50 S>70): Loaded:    52 cliques out of    113 chunks from 87 comparisons
+    39s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques
+    39s PRINT (F 100 SET M>50 S>70): sorting out cliques
+    39s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs
+    41s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs
+    41s CHUNKING (F 100): already chunked into 4244 chunks
+    41s PREPARING (F 100 SET): Already prepared
+    41s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    41s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    41s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates
+    41s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix
+    41s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 154 passages
+    41s CLIQUES (F 100 SET M>50 S>65): Loaded:    70 cliques out of    154 chunks from 115 comparisons
+    41s CLIQUES (F 100 SET M>50 S>65): 154 members in 70 cliques
+    41s PRINT (F 100 SET M>50 S>65): sorting out cliques
+    41s PRINT (F 100 SET M>50 S>65): formatting 70 cliques skipping 28 binary chapter diffs
+    42s PRINT (F 100 SET M>50 S>65): formatted 70 cliques (2 files) skipping 28 binary chapter diffs
+    42s CHUNKING (F 100): already chunked into 4244 chunks
+    42s PREPARING (F 100 SET): Already prepared
+    42s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    42s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates
+    42s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix
+    42s CLIQUES (F 100 SET M>50 S>60): 148 relevant similarities between 208 passages
+    42s CLIQUES (F 100 SET M>50 S>60): Loaded:    94 cliques out of    208 chunks from 148 comparisons
+    42s CLIQUES (F 100 SET M>50 S>60): 208 members in 94 cliques
+    42s PRINT (F 100 SET M>50 S>60): sorting out cliques
+    42s PRINT (F 100 SET M>50 S>60): formatting 94 cliques skipping 35 binary chapter diffs
+    44s PRINT (F 100 SET M>50 S>60): formatted 94 cliques (2 files) skipping 35 binary chapter diffs
+    44s CHUNKING (F 100): already chunked into 4244 chunks
+    44s PREPARING (F 100 SET): Already prepared
+    44s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    44s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    44s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates
+    44s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix
+    44s CLIQUES (F 100 SET M>50 S>55): 225 relevant similarities between 309 passages
+    44s CLIQUES (F 100 SET M>50 S>55): Loaded:   138 cliques out of    309 chunks from 225 comparisons
+    44s CLIQUES (F 100 SET M>50 S>55): 309 members in 138 cliques
+    44s PRINT (F 100 SET M>50 S>55): sorting out cliques
+    44s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 54 binary chapter diffs
+    47s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 54 binary chapter diffs
+    47s CHUNKING (F 100): already chunked into 4244 chunks
+    47s PREPARING (F 100 SET): Already prepared
+    47s SIMILARITY (F 100 SET M>50): Using  9003 K (9003646) comparisons with 359 entries in matrix
+    47s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%
+    47s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates
+    47s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix
+    47s CLIQUES (F 100 SET M>50 S>50): 359 relevant similarities between 473 passages
+    47s CLIQUES (F 100 SET M>50 S>50): Loaded:   189 cliques out of    473 chunks from 359 comparisons
+    47s CLIQUES (F 100 SET M>50 S>50): 473 members in 189 cliques
+    47s PRINT (F 100 SET M>50 S>50): sorting out cliques
+    47s PRINT (F 100 SET M>50 S>50): formatting 189 cliques skipping 75 binary chapter diffs
+    53s PRINT (F 100 SET M>50 S>50): formatted 189 cliques (4 files) skipping 75 binary chapter diffs
+    53s CHUNKING (F 100): already chunked into 4244 chunks
+    53s PREPARING (F 100 LCS)
+    54s PREPARING (F 100 LCS): Done 4244 chunks.
+    54s SIMILARITY (F 100 LCS M>60): Loaded:  9003 K (9003646) comparisons with 393 entries in matrix
+    54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    54s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates
+    54s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix
+    54s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages
+    54s CLIQUES (F 100 LCS M>60 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
+    54s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques
+    54s PRINT (F 100 LCS M>60 S>100): sorting out cliques
+    54s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs
+    54s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
+    54s CHUNKING (F 100): already chunked into 4244 chunks
+    54s PREPARING (F 100 LCS): Already prepared
+    54s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    54s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates
+    54s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix
+    54s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages
+    54s CLIQUES (F 100 LCS M>60 S>95): Loaded:     2 cliques out of      4 chunks from 2 comparisons
+    54s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques
+    54s PRINT (F 100 LCS M>60 S>95): sorting out cliques
+    54s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs
+    54s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
+    54s CHUNKING (F 100): already chunked into 4244 chunks
+    54s PREPARING (F 100 LCS): Already prepared
+    54s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    54s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates
+    54s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix
+    54s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages
+    54s CLIQUES (F 100 LCS M>60 S>90): Loaded:    19 cliques out of     39 chunks from 21 comparisons
+    54s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques
+    54s PRINT (F 100 LCS M>60 S>90): sorting out cliques
+    54s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs
+    54s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs
+    54s CHUNKING (F 100): already chunked into 4244 chunks
+    54s PREPARING (F 100 LCS): Already prepared
+    54s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    54s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates
+    54s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix
+    54s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages
+    54s CLIQUES (F 100 LCS M>60 S>85): Loaded:    29 cliques out of     59 chunks from 31 comparisons
+    54s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques
+    54s PRINT (F 100 LCS M>60 S>85): sorting out cliques
+    54s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs
+    55s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs
+    55s CHUNKING (F 100): already chunked into 4244 chunks
+    55s PREPARING (F 100 LCS): Already prepared
+    55s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    55s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    55s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates
+    55s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix
+    55s CLIQUES (F 100 LCS M>60 S>80): 46 relevant similarities between 85 passages
+    55s CLIQUES (F 100 LCS M>60 S>80): Loaded:    41 cliques out of     85 chunks from 46 comparisons
+    55s CLIQUES (F 100 LCS M>60 S>80): 85 members in 41 cliques
+    55s PRINT (F 100 LCS M>60 S>80): sorting out cliques
+    55s PRINT (F 100 LCS M>60 S>80): formatting 41 cliques skipping 16 binary chapter diffs
+    56s PRINT (F 100 LCS M>60 S>80): formatted 41 cliques (1 files) skipping 16 binary chapter diffs
+    56s CHUNKING (F 100): already chunked into 4244 chunks
+    56s PREPARING (F 100 LCS): Already prepared
+    56s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    56s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    56s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates
+    56s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix
+    56s CLIQUES (F 100 LCS M>60 S>75): 77 relevant similarities between 122 passages
+    56s CLIQUES (F 100 LCS M>60 S>75): Loaded:    56 cliques out of    122 chunks from 77 comparisons
+    56s CLIQUES (F 100 LCS M>60 S>75): 122 members in 56 cliques
+    56s PRINT (F 100 LCS M>60 S>75): sorting out cliques
+    56s PRINT (F 100 LCS M>60 S>75): formatting 56 cliques skipping 25 binary chapter diffs
+    57s PRINT (F 100 LCS M>60 S>75): formatted 56 cliques (2 files) skipping 25 binary chapter diffs
+    57s CHUNKING (F 100): already chunked into 4244 chunks
+    57s PREPARING (F 100 LCS): Already prepared
+    57s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    57s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates
+    57s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix
+    57s CLIQUES (F 100 LCS M>60 S>70): 123 relevant similarities between 189 passages
+    57s CLIQUES (F 100 LCS M>60 S>70): Loaded:    88 cliques out of    189 chunks from 123 comparisons
+    57s CLIQUES (F 100 LCS M>60 S>70): 189 members in 88 cliques
+    57s PRINT (F 100 LCS M>60 S>70): sorting out cliques
+    57s PRINT (F 100 LCS M>60 S>70): formatting 88 cliques skipping 38 binary chapter diffs
+    59s PRINT (F 100 LCS M>60 S>70): formatted 88 cliques (2 files) skipping 38 binary chapter diffs
+    59s CHUNKING (F 100): already chunked into 4244 chunks
+    59s PREPARING (F 100 LCS): Already prepared
+    59s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+    59s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+    59s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates
+    59s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix
+    59s CLIQUES (F 100 LCS M>60 S>65): 182 relevant similarities between 287 passages
+    59s CLIQUES (F 100 LCS M>60 S>65): Loaded:   132 cliques out of    287 chunks from 182 comparisons
+    59s CLIQUES (F 100 LCS M>60 S>65): 287 members in 132 cliques
+    59s PRINT (F 100 LCS M>60 S>65): sorting out cliques
+    59s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs
+ 1m 02s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs
+ 1m 02s CHUNKING (F 100): already chunked into 4244 chunks
+ 1m 02s PREPARING (F 100 LCS): Already prepared
+ 1m 02s SIMILARITY (F 100 LCS M>60): Using  9003 K (9003646) comparisons with 393 entries in matrix
+ 1m 02s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%
+ 1m 02s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates
+ 1m 02s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix
+ 1m 02s CLIQUES (F 100 LCS M>60 S>60): 393 relevant similarities between 535 passages
+ 1m 02s CLIQUES (F 100 LCS M>60 S>60): Loaded:   214 cliques out of    535 chunks from 393 comparisons
+ 1m 02s CLIQUES (F 100 LCS M>60 S>60): 535 members in 214 cliques
+ 1m 02s PRINT (F 100 LCS M>60 S>60): sorting out cliques
+ 1m 02s PRINT (F 100 LCS M>60 S>60): formatting 214 cliques skipping 100 binary chapter diffs
+ 1m 08s PRINT (F 100 LCS M>60 S>60): formatted 214 cliques (5 files) skipping 100 binary chapter diffs
+ 1m 08s CHUNKING (F 50): Loaded:  8509 chunks
+ 1m 08s CHUNKING (F 50): Made 8509 chunks
+ 1m 08s PREPARING (F 50 SET)
+ 1m 09s PREPARING (F 50 SET): Done 8509 chunks.
+ 1m 09s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>100): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs
+ 1m 09s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
+ 1m 09s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 09s PREPARING (F 50 SET): Already prepared
+ 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>95): 2 relevant similarities between 4 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>95): Loaded:     2 cliques out of      4 chunks from 2 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>95): 4 members in 2 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>95): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs
+ 1m 09s PRINT (F 50 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
+ 1m 09s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 09s PREPARING (F 50 SET): Already prepared
+ 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>90): Loaded:    12 cliques out of     24 chunks from 12 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>90): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 4 binary chapter diffs
+ 1m 09s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 4 binary chapter diffs
+ 1m 09s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 09s PREPARING (F 50 SET): Already prepared
+ 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>85): 35 relevant similarities between 57 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>85): Loaded:    26 cliques out of     57 chunks from 35 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>85): 57 members in 26 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>85): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>85): formatting 26 cliques skipping 9 binary chapter diffs
+ 1m 09s PRINT (F 50 SET M>50 S>85): formatted 26 cliques (1 files) skipping 9 binary chapter diffs
+ 1m 09s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 09s PREPARING (F 50 SET): Already prepared
+ 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>80): 69 relevant similarities between 114 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>80): Loaded:    52 cliques out of    114 chunks from 69 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>80): 114 members in 52 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>80): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>80): formatting 52 cliques skipping 19 binary chapter diffs
+ 1m 09s PRINT (F 50 SET M>50 S>80): formatted 52 cliques (2 files) skipping 19 binary chapter diffs
+ 1m 09s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 09s PREPARING (F 50 SET): Already prepared
+ 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 09s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates
+ 1m 09s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix
+ 1m 09s CLIQUES (F 50 SET M>50 S>75): 115 relevant similarities between 186 passages
+ 1m 09s CLIQUES (F 50 SET M>50 S>75): Loaded:    85 cliques out of    186 chunks from 115 comparisons
+ 1m 09s CLIQUES (F 50 SET M>50 S>75): 186 members in 85 cliques
+ 1m 09s PRINT (F 50 SET M>50 S>75): sorting out cliques
+ 1m 09s PRINT (F 50 SET M>50 S>75): formatting 85 cliques skipping 31 binary chapter diffs
+ 1m 10s PRINT (F 50 SET M>50 S>75): formatted 85 cliques (2 files) skipping 31 binary chapter diffs
+ 1m 10s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 10s PREPARING (F 50 SET): Already prepared
+ 1m 10s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 10s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 10s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates
+ 1m 10s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix
+ 1m 10s CLIQUES (F 50 SET M>50 S>70): 171 relevant similarities between 271 passages
+ 1m 10s CLIQUES (F 50 SET M>50 S>70): Loaded:   124 cliques out of    271 chunks from 171 comparisons
+ 1m 10s CLIQUES (F 50 SET M>50 S>70): 271 members in 124 cliques
+ 1m 10s PRINT (F 50 SET M>50 S>70): sorting out cliques
+ 1m 10s PRINT (F 50 SET M>50 S>70): formatting 124 cliques skipping 48 binary chapter diffs
+ 1m 11s PRINT (F 50 SET M>50 S>70): formatted 124 cliques (3 files) skipping 48 binary chapter diffs
+ 1m 11s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 11s PREPARING (F 50 SET): Already prepared
+ 1m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 11s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates
+ 1m 11s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix
+ 1m 11s CLIQUES (F 50 SET M>50 S>65): 248 relevant similarities between 385 passages
+ 1m 11s CLIQUES (F 50 SET M>50 S>65): Loaded:   176 cliques out of    385 chunks from 248 comparisons
+ 1m 11s CLIQUES (F 50 SET M>50 S>65): 385 members in 176 cliques
+ 1m 11s PRINT (F 50 SET M>50 S>65): sorting out cliques
+ 1m 11s PRINT (F 50 SET M>50 S>65): formatting 176 cliques skipping 61 binary chapter diffs
+ 1m 12s PRINT (F 50 SET M>50 S>65): formatted 176 cliques (4 files) skipping 61 binary chapter diffs
+ 1m 12s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 12s PREPARING (F 50 SET): Already prepared
+ 1m 12s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 12s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 12s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates
+ 1m 12s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix
+ 1m 12s CLIQUES (F 50 SET M>50 S>60): 366 relevant similarities between 535 passages
+ 1m 12s CLIQUES (F 50 SET M>50 S>60): Loaded:   235 cliques out of    535 chunks from 366 comparisons
+ 1m 12s CLIQUES (F 50 SET M>50 S>60): 535 members in 235 cliques
+ 1m 12s PRINT (F 50 SET M>50 S>60): sorting out cliques
+ 1m 12s PRINT (F 50 SET M>50 S>60): formatting 235 cliques skipping 78 binary chapter diffs
+ 1m 13s PRINT (F 50 SET M>50 S>60): formatted 235 cliques (5 files) skipping 78 binary chapter diffs
+ 1m 13s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 13s PREPARING (F 50 SET): Already prepared
+ 1m 13s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 13s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 13s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates
+ 1m 13s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix
+ 1m 13s CLIQUES (F 50 SET M>50 S>55): 537 relevant similarities between 748 passages
+ 1m 13s CLIQUES (F 50 SET M>50 S>55): Loaded:   315 cliques out of    748 chunks from 537 comparisons
+ 1m 13s CLIQUES (F 50 SET M>50 S>55): 748 members in 315 cliques
+ 1m 13s PRINT (F 50 SET M>50 S>55): sorting out cliques
+ 1m 13s PRINT (F 50 SET M>50 S>55): formatting 315 cliques skipping 101 binary chapter diffs
+ 1m 16s PRINT (F 50 SET M>50 S>55): formatted 315 cliques (7 files) skipping 101 binary chapter diffs
+ 1m 16s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 16s PREPARING (F 50 SET): Already prepared
+ 1m 16s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix
+ 1m 16s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%
+ 1m 16s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates
+ 1m 16s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix
+ 1m 16s CLIQUES (F 50 SET M>50 S>50): 923 relevant similarities between 1187 passages
+ 1m 16s CLIQUES (F 50 SET M>50 S>50): Loaded:   465 cliques out of   1187 chunks from 923 comparisons
+ 1m 16s CLIQUES (F 50 SET M>50 S>50): 1187 members in 465 cliques
+ 1m 16s PRINT (F 50 SET M>50 S>50): sorting out cliques
+ 1m 16s PRINT (F 50 SET M>50 S>50): formatting 465 cliques skipping 138 binary chapter diffs
+ 1m 20s PRINT (F 50 SET M>50 S>50): formatted 465 cliques (10 files) skipping 138 binary chapter diffs
+ 1m 20s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 20s PREPARING (F 50 LCS)
+ 1m 21s PREPARING (F 50 LCS): Done 8509 chunks.
+ 1m 21s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 21s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates
+ 1m 21s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix
+ 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages
+ 1m 21s CLIQUES (F 50 LCS M>60 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
+ 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>100): sorting out cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs
+ 1m 21s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs
+ 1m 21s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 21s PREPARING (F 50 LCS): Already prepared
+ 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 21s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates
+ 1m 21s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix
+ 1m 21s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages
+ 1m 21s CLIQUES (F 50 LCS M>60 S>95): Loaded:     6 cliques out of     12 chunks from 6 comparisons
+ 1m 21s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>95): sorting out cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs
+ 1m 21s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs
+ 1m 21s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 21s PREPARING (F 50 LCS): Already prepared
+ 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 21s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates
+ 1m 21s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix
+ 1m 21s CLIQUES (F 50 LCS M>60 S>90): 28 relevant similarities between 53 passages
+ 1m 21s CLIQUES (F 50 LCS M>60 S>90): Loaded:    25 cliques out of     53 chunks from 28 comparisons
+ 1m 21s CLIQUES (F 50 LCS M>60 S>90): 53 members in 25 cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>90): sorting out cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>90): formatting 25 cliques skipping 9 binary chapter diffs
+ 1m 21s PRINT (F 50 LCS M>60 S>90): formatted 25 cliques (1 files) skipping 9 binary chapter diffs
+ 1m 21s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 21s PREPARING (F 50 LCS): Already prepared
+ 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 21s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates
+ 1m 21s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix
+ 1m 21s CLIQUES (F 50 LCS M>60 S>85): 75 relevant similarities between 119 passages
+ 1m 21s CLIQUES (F 50 LCS M>60 S>85): Loaded:    53 cliques out of    119 chunks from 75 comparisons
+ 1m 21s CLIQUES (F 50 LCS M>60 S>85): 119 members in 53 cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>85): sorting out cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>85): formatting 53 cliques skipping 17 binary chapter diffs
+ 1m 21s PRINT (F 50 LCS M>60 S>85): formatted 53 cliques (2 files) skipping 17 binary chapter diffs
+ 1m 21s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 21s PREPARING (F 50 LCS): Already prepared
+ 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 21s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates
+ 1m 21s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix
+ 1m 21s CLIQUES (F 50 LCS M>60 S>80): 122 relevant similarities between 196 passages
+ 1m 21s CLIQUES (F 50 LCS M>60 S>80): Loaded:    89 cliques out of    196 chunks from 122 comparisons
+ 1m 21s CLIQUES (F 50 LCS M>60 S>80): 196 members in 89 cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>80): sorting out cliques
+ 1m 21s PRINT (F 50 LCS M>60 S>80): formatting 89 cliques skipping 33 binary chapter diffs
+ 1m 22s PRINT (F 50 LCS M>60 S>80): formatted 89 cliques (2 files) skipping 33 binary chapter diffs
+ 1m 22s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 22s PREPARING (F 50 LCS): Already prepared
+ 1m 22s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 22s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 22s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates
+ 1m 22s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix
+ 1m 22s CLIQUES (F 50 LCS M>60 S>75): 197 relevant similarities between 301 passages
+ 1m 22s CLIQUES (F 50 LCS M>60 S>75): Loaded:   135 cliques out of    301 chunks from 197 comparisons
+ 1m 22s CLIQUES (F 50 LCS M>60 S>75): 301 members in 135 cliques
+ 1m 22s PRINT (F 50 LCS M>60 S>75): sorting out cliques
+ 1m 22s PRINT (F 50 LCS M>60 S>75): formatting 135 cliques skipping 50 binary chapter diffs
+ 1m 23s PRINT (F 50 LCS M>60 S>75): formatted 135 cliques (3 files) skipping 50 binary chapter diffs
+ 1m 23s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 23s PREPARING (F 50 LCS): Already prepared
+ 1m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 23s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates
+ 1m 23s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix
+ 1m 23s CLIQUES (F 50 LCS M>60 S>70): 312 relevant similarities between 464 passages
+ 1m 23s CLIQUES (F 50 LCS M>60 S>70): Loaded:   205 cliques out of    464 chunks from 312 comparisons
+ 1m 23s CLIQUES (F 50 LCS M>60 S>70): 464 members in 205 cliques
+ 1m 23s PRINT (F 50 LCS M>60 S>70): sorting out cliques
+ 1m 23s PRINT (F 50 LCS M>60 S>70): formatting 205 cliques skipping 65 binary chapter diffs
+ 1m 24s PRINT (F 50 LCS M>60 S>70): formatted 205 cliques (5 files) skipping 65 binary chapter diffs
+ 1m 24s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 24s PREPARING (F 50 LCS): Already prepared
+ 1m 24s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 24s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 24s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates
+ 1m 24s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix
+ 1m 24s CLIQUES (F 50 LCS M>60 S>65): 578 relevant similarities between 761 passages
+ 1m 24s CLIQUES (F 50 LCS M>60 S>65): Loaded:   312 cliques out of    761 chunks from 578 comparisons
+ 1m 24s CLIQUES (F 50 LCS M>60 S>65): 761 members in 312 cliques
+ 1m 24s PRINT (F 50 LCS M>60 S>65): sorting out cliques
+ 1m 24s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs
+ 1m 26s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs
+ 1m 26s CHUNKING (F 50): already chunked into 8509 chunks
+ 1m 26s PREPARING (F 50 LCS): Already prepared
+ 1m 26s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix
+ 1m 26s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%
+ 1m 26s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates
+ 1m 26s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix
+ 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1833 relevant similarities between 1888 passages
+ 1m 26s CLIQUES (F 50 LCS M>60 S>60): Loaded:   552 cliques out of   1888 chunks from 1833 comparisons
+ 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1888 members in 552 cliques
+ 1m 26s PRINT (F 50 LCS M>60 S>60): sorting out cliques
+ 1m 26s PRINT (F 50 LCS M>60 S>60): formatting 552 cliques skipping 228 binary chapter diffs
+ 1m 33s PRINT (F 50 LCS M>60 S>60): formatted 552 cliques (12 files) skipping 228 binary chapter diffs
+ 1m 33s CHUNKING (F 20): Loaded: 21311 chunks
+ 1m 33s CHUNKING (F 20): Made 21311 chunks
+ 1m 33s PREPARING (F 20 SET)
+ 1m 34s PREPARING (F 20 SET): Done 21311 chunks.
+ 1m 34s SIMILARITY (F 20 SET M>50): Loaded:   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 34s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates
+ 1m 34s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix
+ 1m 34s CLIQUES (F 20 SET M>50 S>100): 14 relevant similarities between 28 passages
+ 1m 34s CLIQUES (F 20 SET M>50 S>100): Loaded:    14 cliques out of     28 chunks from 14 comparisons
+ 1m 34s CLIQUES (F 20 SET M>50 S>100): 28 members in 14 cliques
+ 1m 34s PRINT (F 20 SET M>50 S>100): sorting out cliques
+ 1m 34s PRINT (F 20 SET M>50 S>100): formatting 14 cliques skipping 8 binary chapter diffs
+ 1m 34s PRINT (F 20 SET M>50 S>100): formatted 14 cliques (1 files) skipping 8 binary chapter diffs
+ 1m 34s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 34s PREPARING (F 20 SET): Already prepared
+ 1m 34s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 34s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates
+ 1m 34s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix
+ 1m 34s CLIQUES (F 20 SET M>50 S>95): 14 relevant similarities between 28 passages
+ 1m 34s CLIQUES (F 20 SET M>50 S>95): Loaded:    14 cliques out of     28 chunks from 14 comparisons
+ 1m 34s CLIQUES (F 20 SET M>50 S>95): 28 members in 14 cliques
+ 1m 34s PRINT (F 20 SET M>50 S>95): sorting out cliques
+ 1m 34s PRINT (F 20 SET M>50 S>95): formatting 14 cliques skipping 8 binary chapter diffs
+ 1m 34s PRINT (F 20 SET M>50 S>95): formatted 14 cliques (1 files) skipping 8 binary chapter diffs
+ 1m 34s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 34s PREPARING (F 20 SET): Already prepared
+ 1m 34s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 34s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates
+ 1m 34s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix
+ 1m 34s CLIQUES (F 20 SET M>50 S>90): 63 relevant similarities between 105 passages
+ 1m 34s CLIQUES (F 20 SET M>50 S>90): Loaded:    46 cliques out of    105 chunks from 63 comparisons
+ 1m 34s CLIQUES (F 20 SET M>50 S>90): 105 members in 46 cliques
+ 1m 34s PRINT (F 20 SET M>50 S>90): sorting out cliques
+ 1m 34s PRINT (F 20 SET M>50 S>90): formatting 46 cliques skipping 22 binary chapter diffs
+ 1m 34s PRINT (F 20 SET M>50 S>90): formatted 46 cliques (1 files) skipping 22 binary chapter diffs
+ 1m 34s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 34s PREPARING (F 20 SET): Already prepared
+ 1m 34s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 34s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates
+ 1m 34s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix
+ 1m 34s CLIQUES (F 20 SET M>50 S>85): 125 relevant similarities between 174 passages
+ 1m 34s CLIQUES (F 20 SET M>50 S>85): Loaded:    72 cliques out of    174 chunks from 125 comparisons
+ 1m 34s CLIQUES (F 20 SET M>50 S>85): 174 members in 72 cliques
+ 1m 34s PRINT (F 20 SET M>50 S>85): sorting out cliques
+ 1m 34s PRINT (F 20 SET M>50 S>85): formatting 72 cliques skipping 34 binary chapter diffs
+ 1m 35s PRINT (F 20 SET M>50 S>85): formatted 72 cliques (2 files) skipping 34 binary chapter diffs
+ 1m 35s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 35s PREPARING (F 20 SET): Already prepared
+ 1m 35s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 35s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates
+ 1m 35s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix
+ 1m 35s CLIQUES (F 20 SET M>50 S>80): 230 relevant similarities between 326 passages
+ 1m 35s CLIQUES (F 20 SET M>50 S>80): Loaded:   143 cliques out of    326 chunks from 230 comparisons
+ 1m 35s CLIQUES (F 20 SET M>50 S>80): 326 members in 143 cliques
+ 1m 35s PRINT (F 20 SET M>50 S>80): sorting out cliques
+ 1m 35s PRINT (F 20 SET M>50 S>80): formatting 143 cliques skipping 59 binary chapter diffs
+ 1m 35s PRINT (F 20 SET M>50 S>80): formatted 143 cliques (3 files) skipping 59 binary chapter diffs
+ 1m 35s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 35s PREPARING (F 20 SET): Already prepared
+ 1m 35s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 35s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates
+ 1m 35s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix
+ 1m 35s CLIQUES (F 20 SET M>50 S>75): 382 relevant similarities between 528 passages
+ 1m 35s CLIQUES (F 20 SET M>50 S>75): Loaded:   227 cliques out of    528 chunks from 382 comparisons
+ 1m 35s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques
+ 1m 35s PRINT (F 20 SET M>50 S>75): sorting out cliques
+ 1m 35s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 83 binary chapter diffs
+ 1m 35s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 83 binary chapter diffs
+ 1m 35s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 35s PREPARING (F 20 SET): Already prepared
+ 1m 35s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 35s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates
+ 1m 35s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix
+ 1m 35s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 762 passages
+ 1m 35s CLIQUES (F 20 SET M>50 S>70): Loaded:   331 cliques out of    762 chunks from 546 comparisons
+ 1m 35s CLIQUES (F 20 SET M>50 S>70): 762 members in 331 cliques
+ 1m 35s PRINT (F 20 SET M>50 S>70): sorting out cliques
+ 1m 35s PRINT (F 20 SET M>50 S>70): formatting 331 cliques skipping 107 binary chapter diffs
+ 1m 36s PRINT (F 20 SET M>50 S>70): formatted 331 cliques (7 files) skipping 107 binary chapter diffs
+ 1m 36s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 36s PREPARING (F 20 SET): Already prepared
+ 1m 36s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 36s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates
+ 1m 36s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix
+ 1m 36s CLIQUES (F 20 SET M>50 S>65): 787 relevant similarities between 1058 passages
+ 1m 36s CLIQUES (F 20 SET M>50 S>65): Loaded:   452 cliques out of   1058 chunks from 787 comparisons
+ 1m 36s CLIQUES (F 20 SET M>50 S>65): 1058 members in 452 cliques
+ 1m 36s PRINT (F 20 SET M>50 S>65): sorting out cliques
+ 1m 36s PRINT (F 20 SET M>50 S>65): formatting 452 cliques skipping 136 binary chapter diffs
+ 1m 36s PRINT (F 20 SET M>50 S>65): formatted 452 cliques (10 files) skipping 136 binary chapter diffs
+ 1m 36s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 36s PREPARING (F 20 SET): Already prepared
+ 1m 36s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 36s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates
+ 1m 36s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix
+ 1m 36s CLIQUES (F 20 SET M>50 S>60): 1432 relevant similarities between 1830 passages
+ 1m 36s CLIQUES (F 20 SET M>50 S>60): Loaded:   733 cliques out of   1830 chunks from 1432 comparisons
+ 1m 36s CLIQUES (F 20 SET M>50 S>60): 1830 members in 733 cliques
+ 1m 36s PRINT (F 20 SET M>50 S>60): sorting out cliques
+ 1m 36s PRINT (F 20 SET M>50 S>60): formatting 733 cliques skipping 211 binary chapter diffs
+ 1m 37s PRINT (F 20 SET M>50 S>60): formatted 733 cliques (15 files) skipping 211 binary chapter diffs
+ 1m 37s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 37s PREPARING (F 20 SET): Already prepared
+ 1m 37s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 37s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 37s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates
+ 1m 37s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix
+ 1m 37s CLIQUES (F 20 SET M>50 S>55): 2425 relevant similarities between 2787 passages
+ 1m 37s CLIQUES (F 20 SET M>50 S>55): Loaded:   979 cliques out of   2787 chunks from 2425 comparisons
+ 1m 37s CLIQUES (F 20 SET M>50 S>55): 2787 members in 979 cliques
+ 1m 37s PRINT (F 20 SET M>50 S>55): sorting out cliques
+ 1m 37s PRINT (F 20 SET M>50 S>55): formatting 979 cliques skipping 285 binary chapter diffs
+ 1m 39s PRINT (F 20 SET M>50 S>55): formatted 979 cliques (20 files) skipping 285 binary chapter diffs
+ 1m 39s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 39s PREPARING (F 20 SET): Already prepared
+ 1m 39s SIMILARITY (F 20 SET M>50): Using   227 M (227068705) comparisons with 5517 entries in matrix
+ 1m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%
+ 1m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates
+ 1m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix
+ 1m 39s CLIQUES (F 20 SET M>50 S>50): 5517 relevant similarities between 4913 passages
+ 1m 39s CLIQUES (F 20 SET M>50 S>50): Loaded:  1203 cliques out of   4913 chunks from 5517 comparisons
+ 1m 39s CLIQUES (F 20 SET M>50 S>50): 4913 members in 1203 cliques
+ 1m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques
+ 1m 39s PRINT (F 20 SET M>50 S>50): formatting 1203 cliques skipping 391 binary chapter diffs
+ 1m 41s PRINT (F 20 SET M>50 S>50): formatted 1203 cliques (25 files) skipping 391 binary chapter diffs
+ 1m 41s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 41s PREPARING (F 20 LCS)
+ 1m 42s PREPARING (F 20 LCS): Done 21311 chunks.
+ 1m 42s SIMILARITY (F 20 LCS M>60): Loaded:   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 42s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates
+ 1m 42s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix
+ 1m 42s CLIQUES (F 20 LCS M>60 S>100): 3 relevant similarities between 6 passages
+ 1m 42s CLIQUES (F 20 LCS M>60 S>100): Loaded:     3 cliques out of      6 chunks from 3 comparisons
+ 1m 42s CLIQUES (F 20 LCS M>60 S>100): 6 members in 3 cliques
+ 1m 42s PRINT (F 20 LCS M>60 S>100): sorting out cliques
+ 1m 42s PRINT (F 20 LCS M>60 S>100): formatting 3 cliques skipping 3 binary chapter diffs
+ 1m 42s PRINT (F 20 LCS M>60 S>100): formatted 3 cliques (1 files) skipping 3 binary chapter diffs
+ 1m 42s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 42s PREPARING (F 20 LCS): Already prepared
+ 1m 42s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 42s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates
+ 1m 42s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix
+ 1m 42s CLIQUES (F 20 LCS M>60 S>95): 25 relevant similarities between 47 passages
+ 1m 42s CLIQUES (F 20 LCS M>60 S>95): Loaded:    22 cliques out of     47 chunks from 25 comparisons
+ 1m 42s CLIQUES (F 20 LCS M>60 S>95): 47 members in 22 cliques
+ 1m 42s PRINT (F 20 LCS M>60 S>95): sorting out cliques
+ 1m 42s PRINT (F 20 LCS M>60 S>95): formatting 22 cliques skipping 12 binary chapter diffs
+ 1m 42s PRINT (F 20 LCS M>60 S>95): formatted 22 cliques (1 files) skipping 12 binary chapter diffs
+ 1m 42s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 42s PREPARING (F 20 LCS): Already prepared
+ 1m 42s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates
+ 1m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix
+ 1m 43s CLIQUES (F 20 LCS M>60 S>90): 95 relevant similarities between 149 passages
+ 1m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded:    61 cliques out of    149 chunks from 95 comparisons
+ 1m 43s CLIQUES (F 20 LCS M>60 S>90): 149 members in 61 cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>90): formatting 61 cliques skipping 31 binary chapter diffs
+ 1m 43s PRINT (F 20 LCS M>60 S>90): formatted 61 cliques (2 files) skipping 31 binary chapter diffs
+ 1m 43s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 43s PREPARING (F 20 LCS): Already prepared
+ 1m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 43s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates
+ 1m 43s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix
+ 1m 43s CLIQUES (F 20 LCS M>60 S>85): 212 relevant similarities between 311 passages
+ 1m 43s CLIQUES (F 20 LCS M>60 S>85): Loaded:   136 cliques out of    311 chunks from 212 comparisons
+ 1m 43s CLIQUES (F 20 LCS M>60 S>85): 311 members in 136 cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>85): sorting out cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>85): formatting 136 cliques skipping 56 binary chapter diffs
+ 1m 43s PRINT (F 20 LCS M>60 S>85): formatted 136 cliques (3 files) skipping 56 binary chapter diffs
+ 1m 43s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 43s PREPARING (F 20 LCS): Already prepared
+ 1m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 43s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates
+ 1m 43s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix
+ 1m 43s CLIQUES (F 20 LCS M>60 S>80): 467 relevant similarities between 682 passages
+ 1m 43s CLIQUES (F 20 LCS M>60 S>80): Loaded:   299 cliques out of    682 chunks from 467 comparisons
+ 1m 43s CLIQUES (F 20 LCS M>60 S>80): 682 members in 299 cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>80): sorting out cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>80): formatting 299 cliques skipping 116 binary chapter diffs
+ 1m 43s PRINT (F 20 LCS M>60 S>80): formatted 299 cliques (6 files) skipping 116 binary chapter diffs
+ 1m 43s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 43s PREPARING (F 20 LCS): Already prepared
+ 1m 43s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 43s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates
+ 1m 43s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix
+ 1m 43s CLIQUES (F 20 LCS M>60 S>75): 874 relevant similarities between 1137 passages
+ 1m 43s CLIQUES (F 20 LCS M>60 S>75): Loaded:   470 cliques out of   1137 chunks from 874 comparisons
+ 1m 43s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>75): sorting out cliques
+ 1m 43s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs
+ 1m 44s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs
+ 1m 44s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 44s PREPARING (F 20 LCS): Already prepared
+ 1m 44s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 44s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates
+ 1m 44s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix
+ 1m 44s CLIQUES (F 20 LCS M>60 S>70): 1944 relevant similarities between 2217 passages
+ 1m 44s CLIQUES (F 20 LCS M>60 S>70): Loaded:   838 cliques out of   2217 chunks from 1944 comparisons
+ 1m 44s CLIQUES (F 20 LCS M>60 S>70): 2217 members in 838 cliques
+ 1m 44s PRINT (F 20 LCS M>60 S>70): sorting out cliques
+ 1m 44s PRINT (F 20 LCS M>60 S>70): formatting 838 cliques skipping 313 binary chapter diffs
+ 1m 46s PRINT (F 20 LCS M>60 S>70): formatted 838 cliques (17 files) skipping 313 binary chapter diffs
+ 1m 46s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 46s PREPARING (F 20 LCS): Already prepared
+ 1m 46s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 46s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates
+ 1m 46s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix
+ 1m 46s CLIQUES (F 20 LCS M>60 S>65): 6983 relevant similarities between 5971 passages
+ 1m 46s CLIQUES (F 20 LCS M>60 S>65): Loaded:  1223 cliques out of   5971 chunks from 6983 comparisons
+ 1m 46s CLIQUES (F 20 LCS M>60 S>65): 5971 members in 1223 cliques
+ 1m 46s PRINT (F 20 LCS M>60 S>65): sorting out cliques
+ 1m 46s PRINT (F 20 LCS M>60 S>65): formatting 1223 cliques skipping 553 binary chapter diffs
+ 1m 49s PRINT (F 20 LCS M>60 S>65): formatted 1223 cliques (25 files) skipping 553 binary chapter diffs
+ 1m 49s CHUNKING (F 20): already chunked into 21311 chunks
+ 1m 49s PREPARING (F 20 LCS): Already prepared
+ 1m 49s SIMILARITY (F 20 LCS M>60): Using   227 M (227068705) comparisons with 122055 entries in matrix
+ 1m 50s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%
+ 1m 50s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates
+ 1m 50s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix
+ 1m 50s CLIQUES (F 20 LCS M>60 S>60): 122055 relevant similarities between 17656 passages
+ 1m 50s CLIQUES (F 20 LCS M>60 S>60): Loaded:   152 cliques out of  17656 chunks from 122055 comparisons
+ 1m 50s CLIQUES (F 20 LCS M>60 S>60): 17656 members in 152 cliques
+ 1m 50s PRINT (F 20 LCS M>60 S>60): sorting out cliques
+ 1m 50s PRINT (F 20 LCS M>60 S>60): formatting 152 cliques skipping 94 binary chapter diffs
+ 1m 50s PRINT (F 20 LCS M>60 S>60): formatted 152 cliques (4 files) skipping 94 binary chapter diffs
+ 1m 51s CHUNKING (F 10): Loaded: 42639 chunks
+ 1m 51s CHUNKING (F 10): Made 42639 chunks
+ 1m 51s PREPARING (F 10 SET)
+ 1m 52s PREPARING (F 10 SET): Done 42639 chunks.
+ 1m 52s SIMILARITY (F 10 SET M>50): Loaded:   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 52s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates
+ 1m 52s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix
+ 1m 52s CLIQUES (F 10 SET M>50 S>100): 275 relevant similarities between 448 passages
+ 1m 52s CLIQUES (F 10 SET M>50 S>100): Loaded:   209 cliques out of    448 chunks from 275 comparisons
+ 1m 52s CLIQUES (F 10 SET M>50 S>100): 448 members in 209 cliques
+ 1m 52s PRINT (F 10 SET M>50 S>100): sorting out cliques
+ 1m 52s PRINT (F 10 SET M>50 S>100): formatting 209 cliques skipping 80 binary chapter diffs
+ 1m 52s PRINT (F 10 SET M>50 S>100): formatted 209 cliques (5 files) skipping 80 binary chapter diffs
+ 1m 52s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 52s PREPARING (F 10 SET): Already prepared
+ 1m 52s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 52s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates
+ 1m 52s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix
+ 1m 52s CLIQUES (F 10 SET M>50 S>95): 275 relevant similarities between 448 passages
+ 1m 52s CLIQUES (F 10 SET M>50 S>95): Loaded:   209 cliques out of    448 chunks from 275 comparisons
+ 1m 52s CLIQUES (F 10 SET M>50 S>95): 448 members in 209 cliques
+ 1m 52s PRINT (F 10 SET M>50 S>95): sorting out cliques
+ 1m 52s PRINT (F 10 SET M>50 S>95): formatting 209 cliques skipping 80 binary chapter diffs
+ 1m 53s PRINT (F 10 SET M>50 S>95): formatted 209 cliques (5 files) skipping 80 binary chapter diffs
+ 1m 53s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 53s PREPARING (F 10 SET): Already prepared
+ 1m 53s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 53s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates
+ 1m 53s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix
+ 1m 53s CLIQUES (F 10 SET M>50 S>90): 315 relevant similarities between 482 passages
+ 1m 53s CLIQUES (F 10 SET M>50 S>90): Loaded:   220 cliques out of    482 chunks from 315 comparisons
+ 1m 53s CLIQUES (F 10 SET M>50 S>90): 482 members in 220 cliques
+ 1m 53s PRINT (F 10 SET M>50 S>90): sorting out cliques
+ 1m 53s PRINT (F 10 SET M>50 S>90): formatting 220 cliques skipping 85 binary chapter diffs
+ 1m 53s PRINT (F 10 SET M>50 S>90): formatted 220 cliques (5 files) skipping 85 binary chapter diffs
+ 1m 53s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 53s PREPARING (F 10 SET): Already prepared
+ 1m 53s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 53s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates
+ 1m 53s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix
+ 1m 53s CLIQUES (F 10 SET M>50 S>85): 745 relevant similarities between 1114 passages
+ 1m 53s CLIQUES (F 10 SET M>50 S>85): Loaded:   493 cliques out of   1114 chunks from 745 comparisons
+ 1m 53s CLIQUES (F 10 SET M>50 S>85): 1114 members in 493 cliques
+ 1m 53s PRINT (F 10 SET M>50 S>85): sorting out cliques
+ 1m 53s PRINT (F 10 SET M>50 S>85): formatting 493 cliques skipping 193 binary chapter diffs
+ 1m 53s PRINT (F 10 SET M>50 S>85): formatted 493 cliques (10 files) skipping 193 binary chapter diffs
+ 1m 53s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 53s PREPARING (F 10 SET): Already prepared
+ 1m 53s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 53s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates
+ 1m 53s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix
+ 1m 53s CLIQUES (F 10 SET M>50 S>80): 1149 relevant similarities between 1536 passages
+ 1m 53s CLIQUES (F 10 SET M>50 S>80): Loaded:   628 cliques out of   1536 chunks from 1149 comparisons
+ 1m 53s CLIQUES (F 10 SET M>50 S>80): 1536 members in 628 cliques
+ 1m 53s PRINT (F 10 SET M>50 S>80): sorting out cliques
+ 1m 53s PRINT (F 10 SET M>50 S>80): formatting 628 cliques skipping 231 binary chapter diffs
+ 1m 54s PRINT (F 10 SET M>50 S>80): formatted 628 cliques (13 files) skipping 231 binary chapter diffs
+ 1m 54s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 54s PREPARING (F 10 SET): Already prepared
+ 1m 54s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 54s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 54s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates
+ 1m 54s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix
+ 1m 54s CLIQUES (F 10 SET M>50 S>75): 2107 relevant similarities between 2754 passages
+ 1m 54s CLIQUES (F 10 SET M>50 S>75): Loaded:  1094 cliques out of   2754 chunks from 2107 comparisons
+ 1m 54s CLIQUES (F 10 SET M>50 S>75): 2754 members in 1094 cliques
+ 1m 54s PRINT (F 10 SET M>50 S>75): sorting out cliques
+ 1m 54s PRINT (F 10 SET M>50 S>75): formatting 1094 cliques skipping 406 binary chapter diffs
+ 1m 55s PRINT (F 10 SET M>50 S>75): formatted 1094 cliques (22 files) skipping 406 binary chapter diffs
+ 1m 55s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 55s PREPARING (F 10 SET): Already prepared
+ 1m 55s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 55s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates
+ 1m 55s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix
+ 1m 55s CLIQUES (F 10 SET M>50 S>70): 3560 relevant similarities between 4020 passages
+ 1m 55s CLIQUES (F 10 SET M>50 S>70): Loaded:  1474 cliques out of   4020 chunks from 3560 comparisons
+ 1m 55s CLIQUES (F 10 SET M>50 S>70): 4020 members in 1474 cliques
+ 1m 55s PRINT (F 10 SET M>50 S>70): sorting out cliques
+ 1m 55s PRINT (F 10 SET M>50 S>70): formatting 1474 cliques skipping 559 binary chapter diffs
+ 1m 57s PRINT (F 10 SET M>50 S>70): formatted 1474 cliques (30 files) skipping 559 binary chapter diffs
+ 1m 57s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 57s PREPARING (F 10 SET): Already prepared
+ 1m 57s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 57s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates
+ 1m 57s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix
+ 1m 57s CLIQUES (F 10 SET M>50 S>65): 5481 relevant similarities between 5785 passages
+ 1m 57s CLIQUES (F 10 SET M>50 S>65): Loaded:  1850 cliques out of   5785 chunks from 5481 comparisons
+ 1m 57s CLIQUES (F 10 SET M>50 S>65): 5785 members in 1850 cliques
+ 1m 57s PRINT (F 10 SET M>50 S>65): sorting out cliques
+ 1m 57s PRINT (F 10 SET M>50 S>65): formatting 1850 cliques skipping 692 binary chapter diffs
+ 1m 59s PRINT (F 10 SET M>50 S>65): formatted 1850 cliques (37 files) skipping 692 binary chapter diffs
+ 1m 59s CHUNKING (F 10): already chunked into 42639 chunks
+ 1m 59s PREPARING (F 10 SET): Already prepared
+ 1m 59s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 1m 59s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 1m 59s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates
+ 1m 59s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix
+ 1m 59s CLIQUES (F 10 SET M>50 S>60): 13263 relevant similarities between 10211 passages
+ 1m 59s CLIQUES (F 10 SET M>50 S>60): Loaded:  2210 cliques out of  10211 chunks from 13263 comparisons
+ 1m 59s CLIQUES (F 10 SET M>50 S>60): 10211 members in 2210 cliques
+ 1m 59s PRINT (F 10 SET M>50 S>60): sorting out cliques
+ 1m 59s PRINT (F 10 SET M>50 S>60): formatting 2210 cliques skipping 845 binary chapter diffs
+ 2m 01s PRINT (F 10 SET M>50 S>60): formatted 2210 cliques (45 files) skipping 845 binary chapter diffs
+ 2m 01s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 01s PREPARING (F 10 SET): Already prepared
+ 2m 01s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 2m 01s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 2m 01s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates
+ 2m 01s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix
+ 2m 01s CLIQUES (F 10 SET M>50 S>55): 25871 relevant similarities between 14100 passages
+ 2m 01s CLIQUES (F 10 SET M>50 S>55): Loaded:  2018 cliques out of  14100 chunks from 25871 comparisons
+ 2m 01s CLIQUES (F 10 SET M>50 S>55): 14100 members in 2018 cliques
+ 2m 01s PRINT (F 10 SET M>50 S>55): sorting out cliques
+ 2m 02s PRINT (F 10 SET M>50 S>55): formatting 2018 cliques skipping 783 binary chapter diffs
+ 2m 03s PRINT (F 10 SET M>50 S>55): formatted 2018 cliques (41 files) skipping 783 binary chapter diffs
+ 2m 03s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 03s PREPARING (F 10 SET): Already prepared
+ 2m 03s SIMILARITY (F 10 SET M>50): Using   909 M (909020841) comparisons with 88877 entries in matrix
+ 2m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%
+ 2m 03s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates
+ 2m 03s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix
+ 2m 03s CLIQUES (F 10 SET M>50 S>50): 88877 relevant similarities between 23054 passages
+ 2m 03s CLIQUES (F 10 SET M>50 S>50): Loaded:  1455 cliques out of  23054 chunks from 88877 comparisons
+ 2m 03s CLIQUES (F 10 SET M>50 S>50): 23054 members in 1455 cliques
+ 2m 03s PRINT (F 10 SET M>50 S>50): sorting out cliques
+ 2m 03s PRINT (F 10 SET M>50 S>50): formatting 1455 cliques skipping 643 binary chapter diffs
+ 2m 04s PRINT (F 10 SET M>50 S>50): formatted 1455 cliques (30 files) skipping 643 binary chapter diffs
+ 2m 04s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 04s PREPARING (F 10 LCS)
+ 2m 05s PREPARING (F 10 LCS): Done 42639 chunks.
+ 2m 07s SIMILARITY (F 10 LCS M>60): Loaded:   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 09s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 09s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates
+ 2m 09s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix
+ 2m 09s CLIQUES (F 10 LCS M>60 S>100): 139 relevant similarities between 239 passages
+ 2m 09s CLIQUES (F 10 LCS M>60 S>100): Loaded:   114 cliques out of    239 chunks from 139 comparisons
+ 2m 09s CLIQUES (F 10 LCS M>60 S>100): 239 members in 114 cliques
+ 2m 09s PRINT (F 10 LCS M>60 S>100): sorting out cliques
+ 2m 09s PRINT (F 10 LCS M>60 S>100): formatting 114 cliques skipping 49 binary chapter diffs
+ 2m 09s PRINT (F 10 LCS M>60 S>100): formatted 114 cliques (3 files) skipping 49 binary chapter diffs
+ 2m 09s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 09s PREPARING (F 10 LCS): Already prepared
+ 2m 09s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 11s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 11s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates
+ 2m 11s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix
+ 2m 12s CLIQUES (F 10 LCS M>60 S>95): 214 relevant similarities between 379 passages
+ 2m 12s CLIQUES (F 10 LCS M>60 S>95): Loaded:   182 cliques out of    379 chunks from 214 comparisons
+ 2m 12s CLIQUES (F 10 LCS M>60 S>95): 379 members in 182 cliques
+ 2m 12s PRINT (F 10 LCS M>60 S>95): sorting out cliques
+ 2m 12s PRINT (F 10 LCS M>60 S>95): formatting 182 cliques skipping 78 binary chapter diffs
+ 2m 12s PRINT (F 10 LCS M>60 S>95): formatted 182 cliques (4 files) skipping 78 binary chapter diffs
+ 2m 12s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 12s PREPARING (F 10 LCS): Already prepared
+ 2m 12s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 13s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 13s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates
+ 2m 13s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix
+ 2m 14s CLIQUES (F 10 LCS M>60 S>90): 609 relevant similarities between 905 passages
+ 2m 14s CLIQUES (F 10 LCS M>60 S>90): Loaded:   399 cliques out of    905 chunks from 609 comparisons
+ 2m 14s CLIQUES (F 10 LCS M>60 S>90): 905 members in 399 cliques
+ 2m 14s PRINT (F 10 LCS M>60 S>90): sorting out cliques
+ 2m 14s PRINT (F 10 LCS M>60 S>90): formatting 399 cliques skipping 160 binary chapter diffs
+ 2m 15s PRINT (F 10 LCS M>60 S>90): formatted 399 cliques (8 files) skipping 160 binary chapter diffs
+ 2m 15s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 15s PREPARING (F 10 LCS): Already prepared
+ 2m 15s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 16s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates
+ 2m 16s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix
+ 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1396 relevant similarities between 1917 passages
+ 2m 17s CLIQUES (F 10 LCS M>60 S>85): Loaded:   791 cliques out of   1917 chunks from 1396 comparisons
+ 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1917 members in 791 cliques
+ 2m 17s PRINT (F 10 LCS M>60 S>85): sorting out cliques
+ 2m 17s PRINT (F 10 LCS M>60 S>85): formatting 791 cliques skipping 297 binary chapter diffs
+ 2m 17s PRINT (F 10 LCS M>60 S>85): formatted 791 cliques (16 files) skipping 297 binary chapter diffs
+ 2m 17s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 17s PREPARING (F 10 LCS): Already prepared
+ 2m 17s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 19s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 19s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates
+ 2m 19s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix
+ 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3308 relevant similarities between 3850 passages
+ 2m 19s CLIQUES (F 10 LCS M>60 S>80): Loaded:  1418 cliques out of   3850 chunks from 3308 comparisons
+ 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3850 members in 1418 cliques
+ 2m 19s PRINT (F 10 LCS M>60 S>80): sorting out cliques
+ 2m 19s PRINT (F 10 LCS M>60 S>80): formatting 1418 cliques skipping 549 binary chapter diffs
+ 2m 20s PRINT (F 10 LCS M>60 S>80): formatted 1418 cliques (29 files) skipping 549 binary chapter diffs
+ 2m 20s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 20s PREPARING (F 10 LCS): Already prepared
+ 2m 20s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 21s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 21s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates
+ 2m 21s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix
+ 2m 23s CLIQUES (F 10 LCS M>60 S>75): 9225 relevant similarities between 8552 passages
+ 2m 23s CLIQUES (F 10 LCS M>60 S>75): Loaded:  2342 cliques out of   8552 chunks from 9225 comparisons
+ 2m 23s CLIQUES (F 10 LCS M>60 S>75): 8552 members in 2342 cliques
+ 2m 23s PRINT (F 10 LCS M>60 S>75): sorting out cliques
+ 2m 23s PRINT (F 10 LCS M>60 S>75): formatting 2342 cliques skipping 989 binary chapter diffs
+ 2m 25s PRINT (F 10 LCS M>60 S>75): formatted 2342 cliques (47 files) skipping 989 binary chapter diffs
+ 2m 25s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 25s PREPARING (F 10 LCS): Already prepared
+ 2m 25s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 26s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 26s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates
+ 2m 26s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix
+ 2m 27s CLIQUES (F 10 LCS M>60 S>70): 38610 relevant similarities between 20382 passages
+ 2m 27s CLIQUES (F 10 LCS M>60 S>70): Loaded:  1926 cliques out of  20382 chunks from 38610 comparisons
+ 2m 27s CLIQUES (F 10 LCS M>60 S>70): 20382 members in 1926 cliques
+ 2m 27s PRINT (F 10 LCS M>60 S>70): sorting out cliques
+ 2m 27s PRINT (F 10 LCS M>60 S>70): formatting 1926 cliques skipping 1004 binary chapter diffs
+ 2m 28s PRINT (F 10 LCS M>60 S>70): formatted 1926 cliques (39 files) skipping 1004 binary chapter diffs
+ 2m 28s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 28s PREPARING (F 10 LCS): Already prepared
+ 2m 28s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 29s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 29s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates
+ 2m 29s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix
+ 2m 31s CLIQUES (F 10 LCS M>60 S>65): 346682 relevant similarities between 37700 passages
+ 2m 31s CLIQUES (F 10 LCS M>60 S>65): Loaded:   223 cliques out of  37700 chunks from 346682 comparisons
+ 2m 31s CLIQUES (F 10 LCS M>60 S>65): 37700 members in 223 cliques
+ 2m 31s PRINT (F 10 LCS M>60 S>65): sorting out cliques
+ 2m 31s PRINT (F 10 LCS M>60 S>65): formatting 223 cliques skipping 142 binary chapter diffs
+ 2m 31s PRINT (F 10 LCS M>60 S>65): formatted 223 cliques (5 files) skipping 142 binary chapter diffs
+ 2m 31s CHUNKING (F 10): already chunked into 42639 chunks
+ 2m 31s PREPARING (F 10 LCS): Already prepared
+ 2m 31s SIMILARITY (F 10 LCS M>60): Using   909 M (909020841) comparisons with 2918272 entries in matrix
+ 2m 33s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%
+ 2m 33s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates
+ 2m 33s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix
+ 2m 37s CLIQUES (F 10 LCS M>60 S>60): 2918272 relevant similarities between 42450 passages
+ 2m 37s CLIQUES (F 10 LCS M>60 S>60): Loaded:     2 cliques out of  42450 chunks from 2918272 comparisons
+ 2m 37s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 2 cliques
+ 2m 37s PRINT (F 10 LCS M>60 S>60): sorting out cliques
+ 2m 37s PRINT (F 10 LCS M>60 S>60): formatting 2 cliques skipping 1 binary chapter diffs
+ 2m 37s PRINT (F 10 LCS M>60 S>60): formatted 2 cliques (1 files) skipping 1 binary chapter diffs
+ 2m 37s CHUNKING (O chapter): Loaded:   929 chunks
+ 2m 37s CHUNKING (O chapter): Made 929 chunks
+ 2m 37s PREPARING (O chapter SET)
+ 2m 38s PREPARING (O chapter SET): Done 929 chunks.
+ 2m 38s SIMILARITY (O chapter SET M>30): Loaded:   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>100): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0
+ 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing
+ 2m 38s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>95): Loaded:     1 cliques out of      2 chunks from 1 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>95): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>90): Loaded:     1 cliques out of      2 chunks from 1 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>90): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>85): Loaded:     1 cliques out of      2 chunks from 1 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>85): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>80): Loaded:     2 cliques out of      4 chunks from 2 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>80): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>75): Loaded:     7 cliques out of     14 chunks from 7 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>75): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs
+ 2m 38s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 38s PREPARING (O chapter SET): Already prepared
+ 2m 38s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 38s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates
+ 2m 38s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix
+ 2m 38s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages
+ 2m 38s CLIQUES (O chapter SET M>30 S>70): Loaded:    10 cliques out of     20 chunks from 10 comparisons
+ 2m 38s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques
+ 2m 38s PRINT (O chapter SET M>30 S>70): sorting out cliques
+ 2m 38s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs
+ 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10
+ 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing
+ 2m 47s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs
+ 2m 47s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 47s PREPARING (O chapter SET): Already prepared
+ 2m 47s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 47s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates
+ 2m 47s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix
+ 2m 47s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages
+ 2m 47s CLIQUES (O chapter SET M>30 S>65): Loaded:    12 cliques out of     24 chunks from 12 comparisons
+ 2m 47s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques
+ 2m 47s PRINT (O chapter SET M>30 S>65): sorting out cliques
+ 2m 47s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs
+ 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12
+ 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing
+ 2m 57s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs
+ 2m 57s CHUNKING (O chapter): already chunked into 929 chunks
+ 2m 57s PREPARING (O chapter SET): Already prepared
+ 2m 57s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 2m 57s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 2m 57s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates
+ 2m 57s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix
+ 2m 57s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages
+ 2m 57s CLIQUES (O chapter SET M>30 S>60): Loaded:    17 cliques out of     34 chunks from 17 comparisons
+ 2m 57s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques
+ 2m 57s PRINT (O chapter SET M>30 S>60): sorting out cliques
+ 2m 57s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs
+ 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17
+ 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing
+ 3m 13s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs
+ 3m 13s CHUNKING (O chapter): already chunked into 929 chunks
+ 3m 13s PREPARING (O chapter SET): Already prepared
+ 3m 13s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 3m 13s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 3m 13s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates
+ 3m 13s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix
+ 3m 13s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages
+ 3m 13s CLIQUES (O chapter SET M>30 S>55): Loaded:    22 cliques out of     44 chunks from 22 comparisons
+ 3m 13s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques
+ 3m 13s PRINT (O chapter SET M>30 S>55): sorting out cliques
+ 3m 13s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs
+ 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22
+ 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing
+ 3m 33s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs
+ 3m 33s CHUNKING (O chapter): already chunked into 929 chunks
+ 3m 33s PREPARING (O chapter SET): Already prepared
+ 3m 33s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 3m 33s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 3m 33s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates
+ 3m 33s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix
+ 3m 33s CLIQUES (O chapter SET M>30 S>50): 28 relevant similarities between 56 passages
+ 3m 33s CLIQUES (O chapter SET M>30 S>50): Loaded:    28 cliques out of     56 chunks from 28 comparisons
+ 3m 33s CLIQUES (O chapter SET M>30 S>50): 56 members in 28 cliques
+ 3m 33s PRINT (O chapter SET M>30 S>50): sorting out cliques
+ 3m 33s PRINT (O chapter SET M>30 S>50): formatting 28 cliques involving 28 binary chapter diffs
+ 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 28
+ 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 28 already existing
+ 3m 54s PRINT (O chapter SET M>30 S>50): formatted 28 cliques (1 files) involving 28 binary chapter diffs
+ 3m 54s CHUNKING (O chapter): already chunked into 929 chunks
+ 3m 54s PREPARING (O chapter SET): Already prepared
+ 3m 54s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 3m 54s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 3m 54s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates
+ 3m 54s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix
+ 3m 54s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages
+ 3m 54s CLIQUES (O chapter SET M>30 S>45): Loaded:    39 cliques out of     80 chunks from 42 comparisons
+ 3m 54s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques
+ 3m 54s PRINT (O chapter SET M>30 S>45): sorting out cliques
+ 3m 54s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs
+ 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37
+ 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing
+ 4m 23s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs
+ 4m 23s CHUNKING (O chapter): already chunked into 929 chunks
+ 4m 23s PREPARING (O chapter SET): Already prepared
+ 4m 23s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 4m 23s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 4m 23s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates
+ 4m 23s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix
+ 4m 23s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages
+ 4m 23s CLIQUES (O chapter SET M>30 S>40): Loaded:    62 cliques out of    142 chunks from 87 comparisons
+ 4m 23s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques
+ 4m 23s PRINT (O chapter SET M>30 S>40): sorting out cliques
+ 4m 23s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs
+ 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51
+ 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing
+ 5m 10s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs
+ 5m 10s CHUNKING (O chapter): already chunked into 929 chunks
+ 5m 10s PREPARING (O chapter SET): Already prepared
+ 5m 10s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 5m 10s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 5m 10s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates
+ 5m 10s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix
+ 5m 10s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages
+ 5m 10s CLIQUES (O chapter SET M>30 S>35): Loaded:    53 cliques out of    302 chunks from 352 comparisons
+ 5m 10s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques
+ 5m 10s PRINT (O chapter SET M>30 S>35): sorting out cliques
+ 5m 10s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs
+ 6m 02s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs
+ 6m 02s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 02s PREPARING (O chapter SET): Already prepared
+ 6m 02s SIMILARITY (O chapter SET M>30): Using   431 K (431056) comparisons with 3445 entries in matrix
+ 6m 02s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%
+ 6m 02s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates
+ 6m 02s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix
+ 6m 02s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages
+ 6m 02s CLIQUES (O chapter SET M>30 S>30): Loaded:    28 cliques out of    571 chunks from 3445 comparisons
+ 6m 02s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques
+ 6m 02s PRINT (O chapter SET M>30 S>30): sorting out cliques
+ 6m 02s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs
+ 6m 24s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs
+ 6m 24s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 24s PREPARING (O chapter LCS)
+ 6m 25s PREPARING (O chapter LCS): Done 929 chunks.
+ 6m 25s SIMILARITY (O chapter LCS M>55): Loaded:   431 K (431056) comparisons with 53 entries in matrix
+ 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 25s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates
+ 6m 25s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix
+ 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages
+ 6m 25s CLIQUES (O chapter LCS M>55 S>100): Loaded:     0 cliques out of      0 chunks from 0 comparisons
+ 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>100): sorting out cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs
+ 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0
+ 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing
+ 6m 25s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs
+ 6m 25s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 25s PREPARING (O chapter LCS): Already prepared
+ 6m 25s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 25s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates
+ 6m 25s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix
+ 6m 25s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages
+ 6m 25s CLIQUES (O chapter LCS M>55 S>95): Loaded:     1 cliques out of      2 chunks from 1 comparisons
+ 6m 25s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>95): sorting out cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs
+ 6m 25s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs
+ 6m 25s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 25s PREPARING (O chapter LCS): Already prepared
+ 6m 25s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 25s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates
+ 6m 25s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix
+ 6m 25s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages
+ 6m 25s CLIQUES (O chapter LCS M>55 S>90): Loaded:     2 cliques out of      4 chunks from 2 comparisons
+ 6m 25s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>90): sorting out cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs
+ 6m 25s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs
+ 6m 25s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 25s PREPARING (O chapter LCS): Already prepared
+ 6m 25s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 25s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates
+ 6m 25s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix
+ 6m 25s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages
+ 6m 25s CLIQUES (O chapter LCS M>55 S>85): Loaded:     6 cliques out of     12 chunks from 6 comparisons
+ 6m 25s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>85): sorting out cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs
+ 6m 25s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs
+ 6m 25s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 25s PREPARING (O chapter LCS): Already prepared
+ 6m 25s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 25s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates
+ 6m 25s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix
+ 6m 25s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages
+ 6m 25s CLIQUES (O chapter LCS M>55 S>80): Loaded:     9 cliques out of     18 chunks from 9 comparisons
+ 6m 25s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>80): sorting out cliques
+ 6m 25s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs
+ 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9
+ 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing
+ 6m 31s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs
+ 6m 31s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 31s PREPARING (O chapter LCS): Already prepared
+ 6m 31s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 31s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 31s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates
+ 6m 31s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix
+ 6m 31s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages
+ 6m 31s CLIQUES (O chapter LCS M>55 S>75): Loaded:    13 cliques out of     26 chunks from 13 comparisons
+ 6m 31s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques
+ 6m 31s PRINT (O chapter LCS M>55 S>75): sorting out cliques
+ 6m 31s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs
+ 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13
+ 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing
+ 6m 40s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs
+ 6m 40s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 40s PREPARING (O chapter LCS): Already prepared
+ 6m 40s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 40s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 40s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates
+ 6m 40s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix
+ 6m 40s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages
+ 6m 40s CLIQUES (O chapter LCS M>55 S>70): Loaded:    19 cliques out of     38 chunks from 19 comparisons
+ 6m 40s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques
+ 6m 40s PRINT (O chapter LCS M>55 S>70): sorting out cliques
+ 6m 40s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs
+ 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19
+ 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing
+ 6m 55s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs
+ 6m 55s CHUNKING (O chapter): already chunked into 929 chunks
+ 6m 55s PREPARING (O chapter LCS): Already prepared
+ 6m 55s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 6m 55s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 6m 55s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates
+ 6m 55s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix
+ 6m 55s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages
+ 6m 55s CLIQUES (O chapter LCS M>55 S>65): Loaded:    22 cliques out of     44 chunks from 22 comparisons
+ 6m 55s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques
+ 6m 55s PRINT (O chapter LCS M>55 S>65): sorting out cliques
+ 6m 55s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs
+ 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22
+ 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing
+ 7m 12s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs
+ 7m 12s CHUNKING (O chapter): already chunked into 929 chunks
+ 7m 12s PREPARING (O chapter LCS): Already prepared
+ 7m 12s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 7m 12s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 7m 12s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates
+ 7m 12s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix
+ 7m 12s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages
+ 7m 12s CLIQUES (O chapter LCS M>55 S>60): Loaded:    26 cliques out of     52 chunks from 26 comparisons
+ 7m 12s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques
+ 7m 12s PRINT (O chapter LCS M>55 S>60): sorting out cliques
+ 7m 12s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs
+ 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26
+ 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing
+ 7m 32s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs
+ 7m 32s CHUNKING (O chapter): already chunked into 929 chunks
+ 7m 32s PREPARING (O chapter LCS): Already prepared
+ 7m 32s SIMILARITY (O chapter LCS M>55): Using   431 K (431056) comparisons with 53 entries in matrix
+ 7m 32s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%
+ 7m 32s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates
+ 7m 32s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix
+ 7m 32s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages
+ 7m 32s CLIQUES (O chapter LCS M>55 S>55): Loaded:    49 cliques out of    102 chunks from 53 comparisons
+ 7m 32s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques
+ 7m 32s PRINT (O chapter LCS M>55 S>55): sorting out cliques
+ 7m 32s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs
+ 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46
+ 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing
+ 8m 04s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs
+ 8m 04s CHUNKING (O verse): Loaded: 23213 chunks
+ 8m 04s CHUNKING (O verse): Made 23213 chunks
+ 8m 04s PREPARING (O verse SET)
+ 8m 05s PREPARING (O verse SET): Done 23213 chunks.
+ 8m 05s SIMILARITY (O verse SET M>50): Loaded:   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 05s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 05s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates
+ 8m 05s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix
+ 8m 05s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages
+ 8m 05s CLIQUES (O verse SET M>50 S>100): Loaded:   388 cliques out of    993 chunks from 4506 comparisons
+ 8m 05s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques
+ 8m 05s PRINT (O verse SET M>50 S>100): sorting out cliques
+ 8m 05s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs
+ 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100
+ 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing
+ 8m 06s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs
+ 8m 06s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 06s PREPARING (O verse SET): Already prepared
+ 8m 06s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 06s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates
+ 8m 06s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix
+ 8m 06s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages
+ 8m 06s CLIQUES (O verse SET M>50 S>95): Loaded:   406 cliques out of   1029 chunks from 4524 comparisons
+ 8m 06s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques
+ 8m 06s PRINT (O verse SET M>50 S>95): sorting out cliques
+ 8m 06s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs
+ 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103
+ 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing
+ 8m 06s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs
+ 8m 06s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 06s PREPARING (O verse SET): Already prepared
+ 8m 06s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 06s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates
+ 8m 06s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix
+ 8m 06s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages
+ 8m 06s CLIQUES (O verse SET M>50 S>90): Loaded:   526 cliques out of   1286 chunks from 4700 comparisons
+ 8m 06s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques
+ 8m 06s PRINT (O verse SET M>50 S>90): sorting out cliques
+ 8m 06s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs
+ 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133
+ 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing
+ 8m 07s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs
+ 8m 07s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 07s PREPARING (O verse SET): Already prepared
+ 8m 07s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 07s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 07s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates
+ 8m 07s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix
+ 8m 07s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages
+ 8m 07s CLIQUES (O verse SET M>50 S>85): Loaded:   651 cliques out of   1573 chunks from 4932 comparisons
+ 8m 07s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques
+ 8m 07s PRINT (O verse SET M>50 S>85): sorting out cliques
+ 8m 07s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs
+ 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151
+ 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing
+ 8m 08s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs
+ 8m 08s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 08s PREPARING (O verse SET): Already prepared
+ 8m 08s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 08s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 08s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates
+ 8m 08s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix
+ 8m 08s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages
+ 8m 08s CLIQUES (O verse SET M>50 S>80): Loaded:   800 cliques out of   1958 chunks from 10653 comparisons
+ 8m 08s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques
+ 8m 08s PRINT (O verse SET M>50 S>80): sorting out cliques
+ 8m 08s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs
+ 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174
+ 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing
+ 8m 09s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs
+ 8m 09s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 09s PREPARING (O verse SET): Already prepared
+ 8m 09s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 09s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates
+ 8m 09s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix
+ 8m 09s CLIQUES (O verse SET M>50 S>75): 11181 relevant similarities between 2359 passages
+ 8m 09s CLIQUES (O verse SET M>50 S>75): Loaded:   961 cliques out of   2359 chunks from 11181 comparisons
+ 8m 09s CLIQUES (O verse SET M>50 S>75): 2359 members in 961 cliques
+ 8m 09s PRINT (O verse SET M>50 S>75): sorting out cliques
+ 8m 09s PRINT (O verse SET M>50 S>75): formatting 961 cliques involving 210 binary chapter diffs
+ 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210
+ 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing
+ 8m 11s PRINT (O verse SET M>50 S>75): formatted 961 cliques (20 files) involving 210 binary chapter diffs
+ 8m 11s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 11s PREPARING (O verse SET): Already prepared
+ 8m 11s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 11s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 11s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates
+ 8m 11s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix
+ 8m 11s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages
+ 8m 11s CLIQUES (O verse SET M>50 S>70): Loaded:  1094 cliques out of   2720 chunks from 11704 comparisons
+ 8m 11s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques
+ 8m 11s PRINT (O verse SET M>50 S>70): sorting out cliques
+ 8m 11s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs
+ 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237
+ 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing
+ 8m 12s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs
+ 8m 12s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 12s PREPARING (O verse SET): Already prepared
+ 8m 12s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 12s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 12s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates
+ 8m 12s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix
+ 8m 13s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages
+ 8m 13s CLIQUES (O verse SET M>50 S>65): Loaded:  1235 cliques out of   3139 chunks from 14353 comparisons
+ 8m 13s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques
+ 8m 13s PRINT (O verse SET M>50 S>65): sorting out cliques
+ 8m 13s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs
+ 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284
+ 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing
+ 8m 15s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs
+ 8m 15s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 15s PREPARING (O verse SET): Already prepared
+ 8m 15s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 15s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 15s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates
+ 8m 15s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix
+ 8m 15s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages
+ 8m 15s CLIQUES (O verse SET M>50 S>60): Loaded:  1439 cliques out of   3877 chunks from 16055 comparisons
+ 8m 15s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques
+ 8m 15s PRINT (O verse SET M>50 S>60): sorting out cliques
+ 8m 15s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs
+ 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358
+ 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing
+ 8m 17s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs
+ 8m 17s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 17s PREPARING (O verse SET): Already prepared
+ 8m 17s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 17s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 17s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates
+ 8m 17s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix
+ 8m 17s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages
+ 8m 17s CLIQUES (O verse SET M>50 S>55): Loaded:  1638 cliques out of   4735 chunks from 18754 comparisons
+ 8m 17s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques
+ 8m 17s PRINT (O verse SET M>50 S>55): sorting out cliques
+ 8m 17s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs
+ 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447
+ 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing
+ 8m 20s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs
+ 8m 20s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 20s PREPARING (O verse SET): Already prepared
+ 8m 20s SIMILARITY (O verse SET M>50): Using   269 M (269410078) comparisons with 24832 entries in matrix
+ 8m 20s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+ 8m 20s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates
+ 8m 20s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix
+ 8m 20s CLIQUES (O verse SET M>50 S>50): 24832 relevant similarities between 6711 passages
+ 8m 20s CLIQUES (O verse SET M>50 S>50): Loaded:  1850 cliques out of   6711 chunks from 24832 comparisons
+ 8m 20s CLIQUES (O verse SET M>50 S>50): 6711 members in 1850 cliques
+ 8m 20s PRINT (O verse SET M>50 S>50): sorting out cliques
+ 8m 20s PRINT (O verse SET M>50 S>50): formatting 1850 cliques skipping 560 binary chapter diffs
+ 8m 24s PRINT (O verse SET M>50 S>50): formatted 1850 cliques (37 files) skipping 560 binary chapter diffs
+ 8m 24s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 24s PREPARING (O verse LCS)
+ 8m 25s PREPARING (O verse LCS): Done 23213 chunks.
+ 8m 25s SIMILARITY (O verse LCS M>60): Loaded:   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 25s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 25s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates
+ 8m 25s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix
+ 8m 25s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages
+ 8m 25s CLIQUES (O verse LCS M>60 S>100): Loaded:   295 cliques out of    793 chunks from 4204 comparisons
+ 8m 25s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques
+ 8m 25s PRINT (O verse LCS M>60 S>100): sorting out cliques
+ 8m 25s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs
+ 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80
+ 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing
+ 8m 26s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs
+ 8m 26s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 26s PREPARING (O verse LCS): Already prepared
+ 8m 26s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 26s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 26s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates
+ 8m 26s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix
+ 8m 26s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages
+ 8m 26s CLIQUES (O verse LCS M>60 S>95): Loaded:   504 cliques out of   1235 chunks from 4489 comparisons
+ 8m 26s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques
+ 8m 26s PRINT (O verse LCS M>60 S>95): sorting out cliques
+ 8m 26s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs
+ 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120
+ 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing
+ 8m 27s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs
+ 8m 27s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 27s PREPARING (O verse LCS): Already prepared
+ 8m 27s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 27s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 27s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates
+ 8m 27s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix
+ 8m 27s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages
+ 8m 27s CLIQUES (O verse LCS M>60 S>90): Loaded:   724 cliques out of   1754 chunks from 5538 comparisons
+ 8m 27s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques
+ 8m 27s PRINT (O verse LCS M>60 S>90): sorting out cliques
+ 8m 27s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs
+ 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151
+ 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing
+ 8m 28s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs
+ 8m 28s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 28s PREPARING (O verse LCS): Already prepared
+ 8m 28s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 28s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 28s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates
+ 8m 28s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix
+ 8m 28s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages
+ 8m 28s CLIQUES (O verse LCS M>60 S>85): Loaded:   938 cliques out of   2296 chunks from 7871 comparisons
+ 8m 28s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques
+ 8m 28s PRINT (O verse LCS M>60 S>85): sorting out cliques
+ 8m 28s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs
+ 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179
+ 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing
+ 8m 29s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs
+ 8m 29s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 29s PREPARING (O verse LCS): Already prepared
+ 8m 29s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 29s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 29s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates
+ 8m 29s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix
+ 8m 29s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages
+ 8m 29s CLIQUES (O verse LCS M>60 S>80): Loaded:  1141 cliques out of   2925 chunks from 9461 comparisons
+ 8m 29s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques
+ 8m 29s PRINT (O verse LCS M>60 S>80): sorting out cliques
+ 8m 29s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs
+ 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251
+ 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing
+ 8m 31s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs
+ 8m 31s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 31s PREPARING (O verse LCS): Already prepared
+ 8m 31s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 31s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates
+ 8m 31s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix
+ 8m 31s CLIQUES (O verse LCS M>60 S>75): 15543 relevant similarities between 3685 passages
+ 8m 31s CLIQUES (O verse LCS M>60 S>75): Loaded:  1340 cliques out of   3685 chunks from 15543 comparisons
+ 8m 31s CLIQUES (O verse LCS M>60 S>75): 3685 members in 1340 cliques
+ 8m 31s PRINT (O verse LCS M>60 S>75): sorting out cliques
+ 8m 31s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs
+ 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346
+ 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing
+ 8m 33s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs
+ 8m 33s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 33s PREPARING (O verse LCS): Already prepared
+ 8m 33s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 33s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 33s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates
+ 8m 33s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix
+ 8m 33s CLIQUES (O verse LCS M>60 S>70): 19834 relevant similarities between 4958 passages
+ 8m 33s CLIQUES (O verse LCS M>60 S>70): Loaded:  1644 cliques out of   4958 chunks from 19834 comparisons
+ 8m 33s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques
+ 8m 33s PRINT (O verse LCS M>60 S>70): sorting out cliques
+ 8m 33s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs
+ 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504
+ 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing
+ 8m 37s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs
+ 8m 37s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 37s PREPARING (O verse LCS): Already prepared
+ 8m 37s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 37s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 37s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates
+ 8m 37s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix
+ 8m 37s CLIQUES (O verse LCS M>60 S>65): 31841 relevant similarities between 9046 passages
+ 8m 37s CLIQUES (O verse LCS M>60 S>65): Loaded:  1821 cliques out of   9046 chunks from 31841 comparisons
+ 8m 37s CLIQUES (O verse LCS M>60 S>65): 9046 members in 1821 cliques
+ 8m 37s PRINT (O verse LCS M>60 S>65): sorting out cliques
+ 8m 37s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs
+ 8m 40s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs
+ 8m 40s CHUNKING (O verse): already chunked into 23213 chunks
+ 8m 40s PREPARING (O verse LCS): Already prepared
+ 8m 40s SIMILARITY (O verse LCS M>60): Using   269 M (269410078) comparisons with 113614 entries in matrix
+ 8m 40s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+ 8m 40s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates
+ 8m 40s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix
+ 8m 40s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages
+ 8m 40s CLIQUES (O verse LCS M>60 S>60): Loaded:   380 cliques out of  18941 chunks from 113614 comparisons
+ 8m 40s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques
+ 8m 40s PRINT (O verse LCS M>60 S>60): sorting out cliques
+ 8m 40s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs
+ 8m 41s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs
+ 8m 41s CHUNKING (O half_verse): Loaded: 45180 chunks
+ 8m 41s CHUNKING (O half_verse): Made 45180 chunks
+ 8m 41s PREPARING (O half_verse SET)
+ 8m 42s PREPARING (O half_verse SET): Done 45180 chunks.
+ 8m 42s SIMILARITY (O half_verse SET M>50): Loaded:  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 42s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 42s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates
+ 8m 42s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix
+ 8m 42s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages
+ 8m 42s CLIQUES (O half_verse SET M>50 S>100): Loaded:  1725 cliques out of   4327 chunks from 10239 comparisons
+ 8m 42s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques
+ 8m 42s PRINT (O half_verse SET M>50 S>100): sorting out cliques
+ 8m 42s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs
+ 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573
+ 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing
+ 8m 43s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs
+ 8m 43s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 43s PREPARING (O half_verse SET): Already prepared
+ 8m 43s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 43s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 43s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates
+ 8m 43s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix
+ 8m 43s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages
+ 8m 43s CLIQUES (O half_verse SET M>50 S>95): Loaded:  1728 cliques out of   4333 chunks from 10242 comparisons
+ 8m 43s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques
+ 8m 43s PRINT (O half_verse SET M>50 S>95): sorting out cliques
+ 8m 43s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs
+ 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573
+ 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing
+ 8m 44s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs
+ 8m 44s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 44s PREPARING (O half_verse SET): Already prepared
+ 8m 44s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 44s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 44s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates
+ 8m 44s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix
+ 8m 44s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages
+ 8m 44s CLIQUES (O half_verse SET M>50 S>90): Loaded:  1863 cliques out of   4618 chunks from 10410 comparisons
+ 8m 44s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques
+ 8m 44s PRINT (O half_verse SET M>50 S>90): sorting out cliques
+ 8m 44s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs
+ 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587
+ 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing
+ 8m 45s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs
+ 8m 45s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 45s PREPARING (O half_verse SET): Already prepared
+ 8m 45s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 45s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 45s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates
+ 8m 45s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix
+ 8m 45s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages
+ 8m 45s CLIQUES (O half_verse SET M>50 S>85): Loaded:  2072 cliques out of   5145 chunks from 11111 comparisons
+ 8m 45s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques
+ 8m 45s PRINT (O half_verse SET M>50 S>85): sorting out cliques
+ 8m 45s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs
+ 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640
+ 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing
+ 8m 46s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs
+ 8m 46s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 46s PREPARING (O half_verse SET): Already prepared
+ 8m 46s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 46s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 46s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates
+ 8m 46s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix
+ 8m 46s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages
+ 8m 46s CLIQUES (O half_verse SET M>50 S>80): Loaded:  2474 cliques out of   6422 chunks from 20178 comparisons
+ 8m 46s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques
+ 8m 46s PRINT (O half_verse SET M>50 S>80): sorting out cliques
+ 8m 46s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs
+ 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769
+ 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing
+ 8m 47s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs
+ 8m 47s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 47s PREPARING (O half_verse SET): Already prepared
+ 8m 47s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 47s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 47s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates
+ 8m 47s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix
+ 8m 47s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages
+ 8m 47s CLIQUES (O half_verse SET M>50 S>75): Loaded:  2888 cliques out of   8265 chunks from 23717 comparisons
+ 8m 47s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques
+ 8m 47s PRINT (O half_verse SET M>50 S>75): sorting out cliques
+ 8m 47s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs
+ 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919
+ 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing
+ 8m 49s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs
+ 8m 49s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 49s PREPARING (O half_verse SET): Already prepared
+ 8m 49s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 49s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 49s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates
+ 8m 49s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix
+ 8m 49s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages
+ 8m 49s CLIQUES (O half_verse SET M>50 S>70): Loaded:  3193 cliques out of   9388 chunks from 25560 comparisons
+ 8m 49s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques
+ 8m 49s PRINT (O half_verse SET M>50 S>70): sorting out cliques
+ 8m 49s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs
+ 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014
+ 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing
+ 8m 51s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs
+ 8m 51s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 51s PREPARING (O half_verse SET): Already prepared
+ 8m 51s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 51s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 51s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates
+ 8m 51s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix
+ 8m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages
+ 8m 51s CLIQUES (O half_verse SET M>50 S>65): Loaded:  3342 cliques out of  12162 chunks from 37453 comparisons
+ 8m 51s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques
+ 8m 51s PRINT (O half_verse SET M>50 S>65): sorting out cliques
+ 8m 52s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs
+ 8m 53s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs
+ 8m 53s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 53s PREPARING (O half_verse SET): Already prepared
+ 8m 53s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 54s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 54s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates
+ 8m 54s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix
+ 8m 54s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages
+ 8m 54s CLIQUES (O half_verse SET M>50 S>60): Loaded:  3424 cliques out of  16476 chunks from 55384 comparisons
+ 8m 54s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques
+ 8m 54s PRINT (O half_verse SET M>50 S>60): sorting out cliques
+ 8m 54s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs
+ 8m 56s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs
+ 8m 56s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 56s PREPARING (O half_verse SET): Already prepared
+ 8m 56s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 56s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 56s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates
+ 8m 56s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix
+ 8m 57s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages
+ 8m 57s CLIQUES (O half_verse SET M>50 S>55): Loaded:  3184 cliques out of  19519 chunks from 70089 comparisons
+ 8m 57s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques
+ 8m 57s PRINT (O half_verse SET M>50 S>55): sorting out cliques
+ 8m 57s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs
+ 8m 59s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs
+ 8m 59s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 8m 59s PREPARING (O half_verse SET): Already prepared
+ 8m 59s SIMILARITY (O half_verse SET M>50): Using  1020 M (1020593610) comparisons with 179842 entries in matrix
+ 8m 59s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%
+ 8m 59s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates
+ 8m 59s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix
+ 9m 00s CLIQUES (O half_verse SET M>50 S>50): 179842 relevant similarities between 28990 passages
+ 9m 00s CLIQUES (O half_verse SET M>50 S>50): Loaded:  2031 cliques out of  28990 chunks from 179842 comparisons
+ 9m 00s CLIQUES (O half_verse SET M>50 S>50): 28990 members in 2031 cliques
+ 9m 00s PRINT (O half_verse SET M>50 S>50): sorting out cliques
+ 9m 00s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs
+ 9m 02s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs
+ 9m 02s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 02s PREPARING (O half_verse LCS)
+ 9m 03s PREPARING (O half_verse LCS): Done 45180 chunks.
+ 9m 04s SIMILARITY (O half_verse LCS M>60): Loaded:  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 05s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 05s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates
+ 9m 05s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix
+ 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages
+ 9m 06s CLIQUES (O half_verse LCS M>60 S>100): Loaded:  1514 cliques out of   3799 chunks from 9270 comparisons
+ 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques
+ 9m 06s PRINT (O half_verse LCS M>60 S>100): sorting out cliques
+ 9m 06s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs
+ 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493
+ 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing
+ 9m 06s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs
+ 9m 06s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 06s PREPARING (O half_verse LCS): Already prepared
+ 9m 06s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 07s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 07s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates
+ 9m 07s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix
+ 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages
+ 9m 08s CLIQUES (O half_verse LCS M>60 S>95): Loaded:  1771 cliques out of   4342 chunks from 9663 comparisons
+ 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques
+ 9m 08s PRINT (O half_verse LCS M>60 S>95): sorting out cliques
+ 9m 08s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs
+ 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543
+ 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing
+ 9m 09s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs
+ 9m 09s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 09s PREPARING (O half_verse LCS): Already prepared
+ 9m 09s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 10s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 10s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates
+ 9m 10s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix
+ 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages
+ 9m 11s CLIQUES (O half_verse LCS M>60 S>90): Loaded:  2336 cliques out of   5776 chunks from 12125 comparisons
+ 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques
+ 9m 11s PRINT (O half_verse LCS M>60 S>90): sorting out cliques
+ 9m 11s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs
+ 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732
+ 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing
+ 9m 12s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs
+ 9m 12s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 12s PREPARING (O half_verse LCS): Already prepared
+ 9m 12s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 13s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 13s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates
+ 9m 13s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix
+ 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages
+ 9m 14s CLIQUES (O half_verse LCS M>60 S>85): Loaded:  2983 cliques out of   7970 chunks from 17551 comparisons
+ 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques
+ 9m 14s PRINT (O half_verse LCS M>60 S>85): sorting out cliques
+ 9m 14s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs
+ 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975
+ 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing
+ 9m 16s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs
+ 9m 16s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 16s PREPARING (O half_verse LCS): Already prepared
+ 9m 16s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 18s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 18s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates
+ 9m 18s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix
+ 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages
+ 9m 19s CLIQUES (O half_verse LCS M>60 S>80): Loaded:  3540 cliques out of  12504 chunks from 27273 comparisons
+ 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques
+ 9m 19s PRINT (O half_verse LCS M>60 S>80): sorting out cliques
+ 9m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs
+ 9m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs
+ 9m 21s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 21s PREPARING (O half_verse LCS): Already prepared
+ 9m 21s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 22s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 22s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates
+ 9m 22s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix
+ 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 53981 relevant similarities between 19148 passages
+ 9m 24s CLIQUES (O half_verse LCS M>60 S>75): Loaded:  3084 cliques out of  19148 chunks from 53981 comparisons
+ 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 19148 members in 3084 cliques
+ 9m 24s PRINT (O half_verse LCS M>60 S>75): sorting out cliques
+ 9m 24s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs
+ 9m 26s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs
+ 9m 26s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 26s PREPARING (O half_verse LCS): Already prepared
+ 9m 26s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 27s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 27s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates
+ 9m 27s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix
+ 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 126162 relevant similarities between 28472 passages
+ 9m 28s CLIQUES (O half_verse LCS M>60 S>70): Loaded:  1894 cliques out of  28472 chunks from 126162 comparisons
+ 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 28472 members in 1894 cliques
+ 9m 28s PRINT (O half_verse LCS M>60 S>70): sorting out cliques
+ 9m 28s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs
+ 9m 30s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs
+ 9m 30s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 30s PREPARING (O half_verse LCS): Already prepared
+ 9m 30s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 31s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 31s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates
+ 9m 31s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix
+ 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 393325 relevant similarities between 38180 passages
+ 9m 32s CLIQUES (O half_verse LCS M>60 S>65): Loaded:   665 cliques out of  38180 chunks from 393325 comparisons
+ 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 38180 members in 665 cliques
+ 9m 32s PRINT (O half_verse LCS M>60 S>65): sorting out cliques
+ 9m 32s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs
+ 9m 33s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs
+ 9m 33s CHUNKING (O half_verse): already chunked into 45180 chunks
+ 9m 33s PREPARING (O half_verse LCS): Already prepared
+ 9m 33s SIMILARITY (O half_verse LCS M>60): Using  1020 M (1020593610) comparisons with 2017661 entries in matrix
+ 9m 34s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%
+ 9m 34s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates
+ 9m 34s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix
+ 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 2017661 relevant similarities between 44011 passages
+ 9m 38s CLIQUES (O half_verse LCS M>60 S>60): Loaded:    89 cliques out of  44011 chunks from 2017661 comparisons
+ 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques
+ 9m 38s PRINT (O half_verse LCS M>60 S>60): sorting out cliques
+ 9m 38s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs
+ 9m 38s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs
+ 9m 38s CHUNKING (O sentence): Loaded: 63586 chunks
+ 9m 38s CHUNKING (O sentence): Made 63586 chunks
+ 9m 38s PREPARING (O sentence SET)
+ 9m 39s PREPARING (O sentence SET): Done 63586 chunks.
+ 9m 43s SIMILARITY (O sentence SET M>50): Loaded:  2021 M (2021557905) comparisons with 3959201 entries in matrix
+ 9m 45s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+ 9m 45s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates
+ 9m 45s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix
+ 9m 49s CLIQUES (O sentence SET M>50 S>100): 938441 relevant similarities between 19028 passages
+ 9m 49s CLIQUES (O sentence SET M>50 S>100): Loaded:  4325 cliques out of  19028 chunks from 938441 comparisons
+ 9m 49s CLIQUES (O sentence SET M>50 S>100): 19028 members in 4325 cliques
+ 9m 49s PRINT (O sentence SET M>50 S>100): sorting out cliques
+ 9m 49s PRINT (O sentence SET M>50 S>100): formatting 4325 cliques involving 1528 binary chapter diffs
+ 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528
+ 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs: 0 newly created and 1528 already existing
+ 9m 52s PRINT (O sentence SET M>50 S>100): formatted 4325 cliques (87 files) involving 1528 binary chapter diffs
+ 9m 52s CHUNKING (O sentence): already chunked into 63586 chunks
+ 9m 52s PREPARING (O sentence SET): Already prepared
+ 9m 52s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+ 9m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+ 9m 55s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates
+ 9m 55s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix
+ 9m 57s CLIQUES (O sentence SET M>50 S>95): 938445 relevant similarities between 19036 passages
+ 9m 57s CLIQUES (O sentence SET M>50 S>95): Loaded:  4329 cliques out of  19036 chunks from 938445 comparisons
+ 9m 57s CLIQUES (O sentence SET M>50 S>95): 19036 members in 4329 cliques
+ 9m 57s PRINT (O sentence SET M>50 S>95): sorting out cliques
+ 9m 57s PRINT (O sentence SET M>50 S>95): formatting 4329 cliques involving 1529 binary chapter diffs
+ 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529
+ 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing
+ 9m 59s PRINT (O sentence SET M>50 S>95): formatted 4329 cliques (87 files) involving 1529 binary chapter diffs
+ 9m 59s CHUNKING (O sentence): already chunked into 63586 chunks
+ 9m 59s PREPARING (O sentence SET): Already prepared
+ 9m 59s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 02s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 02s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates
+10m 02s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix
+10m 04s CLIQUES (O sentence SET M>50 S>90): 938584 relevant similarities between 19208 passages
+10m 04s CLIQUES (O sentence SET M>50 S>90): Loaded:  4404 cliques out of  19208 chunks from 938584 comparisons
+10m 04s CLIQUES (O sentence SET M>50 S>90): 19208 members in 4404 cliques
+10m 04s PRINT (O sentence SET M>50 S>90): sorting out cliques
+10m 04s PRINT (O sentence SET M>50 S>90): formatting 4404 cliques involving 1536 binary chapter diffs
+10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1536
+10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1536 already existing
+10m 06s PRINT (O sentence SET M>50 S>90): formatted 4404 cliques (89 files) involving 1536 binary chapter diffs
+10m 06s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 06s PREPARING (O sentence SET): Already prepared
+10m 06s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 08s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 08s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates
+10m 08s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix
+10m 10s CLIQUES (O sentence SET M>50 S>85): 939433 relevant similarities between 19771 passages
+10m 10s CLIQUES (O sentence SET M>50 S>85): Loaded:  4606 cliques out of  19771 chunks from 939433 comparisons
+10m 10s CLIQUES (O sentence SET M>50 S>85): 19771 members in 4606 cliques
+10m 10s PRINT (O sentence SET M>50 S>85): sorting out cliques
+10m 10s PRINT (O sentence SET M>50 S>85): formatting 4606 cliques involving 1587 binary chapter diffs
+10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1587
+10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1587 already existing
+10m 12s PRINT (O sentence SET M>50 S>85): formatted 4606 cliques (93 files) involving 1587 binary chapter diffs
+10m 12s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 12s PREPARING (O sentence SET): Already prepared
+10m 12s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 15s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 15s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates
+10m 15s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix
+10m 17s CLIQUES (O sentence SET M>50 S>80): 961541 relevant similarities between 22063 passages
+10m 17s CLIQUES (O sentence SET M>50 S>80): Loaded:  5066 cliques out of  22063 chunks from 961541 comparisons
+10m 17s CLIQUES (O sentence SET M>50 S>80): 22063 members in 5066 cliques
+10m 17s PRINT (O sentence SET M>50 S>80): sorting out cliques
+10m 17s PRINT (O sentence SET M>50 S>80): formatting 5066 cliques involving 1745 binary chapter diffs
+10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1745
+10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs: 0 newly created and 1745 already existing
+10m 19s PRINT (O sentence SET M>50 S>80): formatted 5066 cliques (102 files) involving 1745 binary chapter diffs
+10m 19s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 19s PREPARING (O sentence SET): Already prepared
+10m 19s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 21s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 21s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates
+10m 21s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix
+10m 28s CLIQUES (O sentence SET M>50 S>75): 1009869 relevant similarities between 25724 passages
+10m 28s CLIQUES (O sentence SET M>50 S>75): Loaded:  4993 cliques out of  25724 chunks from 1009869 comparisons
+10m 28s CLIQUES (O sentence SET M>50 S>75): 25724 members in 4993 cliques
+10m 28s PRINT (O sentence SET M>50 S>75): sorting out cliques
+10m 29s PRINT (O sentence SET M>50 S>75): formatting 4993 cliques skipping 1743 binary chapter diffs
+10m 37s PRINT (O sentence SET M>50 S>75): formatted 4993 cliques (100 files) skipping 1743 binary chapter diffs
+10m 37s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 37s PREPARING (O sentence SET): Already prepared
+10m 37s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 39s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 39s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates
+10m 39s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix
+10m 42s CLIQUES (O sentence SET M>50 S>70): 1012567 relevant similarities between 26880 passages
+10m 42s CLIQUES (O sentence SET M>50 S>70): Loaded:  5222 cliques out of  26880 chunks from 1012567 comparisons
+10m 42s CLIQUES (O sentence SET M>50 S>70): 26880 members in 5222 cliques
+10m 42s PRINT (O sentence SET M>50 S>70): sorting out cliques
+10m 42s PRINT (O sentence SET M>50 S>70): formatting 5222 cliques skipping 1819 binary chapter diffs
+10m 45s PRINT (O sentence SET M>50 S>70): formatted 5222 cliques (105 files) skipping 1819 binary chapter diffs
+10m 45s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 45s PREPARING (O sentence SET): Already prepared
+10m 45s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 47s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 47s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates
+10m 47s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix
+10m 50s CLIQUES (O sentence SET M>50 S>65): 1332342 relevant similarities between 33378 passages
+10m 50s CLIQUES (O sentence SET M>50 S>65): Loaded:  4111 cliques out of  33378 chunks from 1332342 comparisons
+10m 50s CLIQUES (O sentence SET M>50 S>65): 33378 members in 4111 cliques
+10m 50s PRINT (O sentence SET M>50 S>65): sorting out cliques
+10m 50s PRINT (O sentence SET M>50 S>65): formatting 4111 cliques skipping 1474 binary chapter diffs
+10m 53s PRINT (O sentence SET M>50 S>65): formatted 4111 cliques (83 files) skipping 1474 binary chapter diffs
+10m 53s CHUNKING (O sentence): already chunked into 63586 chunks
+10m 53s PREPARING (O sentence SET): Already prepared
+10m 53s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+10m 56s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+10m 56s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates
+10m 56s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix
+10m 58s CLIQUES (O sentence SET M>50 S>60): 1431575 relevant similarities between 38807 passages
+10m 58s CLIQUES (O sentence SET M>50 S>60): Loaded:  3753 cliques out of  38807 chunks from 1431575 comparisons
+10m 58s CLIQUES (O sentence SET M>50 S>60): 38807 members in 3753 cliques
+10m 58s PRINT (O sentence SET M>50 S>60): sorting out cliques
+10m 58s PRINT (O sentence SET M>50 S>60): formatting 3753 cliques skipping 1386 binary chapter diffs
+11m 01s PRINT (O sentence SET M>50 S>60): formatted 3753 cliques (76 files) skipping 1386 binary chapter diffs
+11m 01s CHUNKING (O sentence): already chunked into 63586 chunks
+11m 01s PREPARING (O sentence SET): Already prepared
+11m 01s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+11m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+11m 03s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates
+11m 03s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix
+11m 06s CLIQUES (O sentence SET M>50 S>55): 1459808 relevant similarities between 41835 passages
+11m 06s CLIQUES (O sentence SET M>50 S>55): Loaded:  3505 cliques out of  41835 chunks from 1459808 comparisons
+11m 06s CLIQUES (O sentence SET M>50 S>55): 41835 members in 3505 cliques
+11m 06s PRINT (O sentence SET M>50 S>55): sorting out cliques
+11m 06s PRINT (O sentence SET M>50 S>55): formatting 3505 cliques skipping 1341 binary chapter diffs
+11m 11s PRINT (O sentence SET M>50 S>55): formatted 3505 cliques (71 files) skipping 1341 binary chapter diffs
+11m 11s CHUNKING (O sentence): already chunked into 63586 chunks
+11m 11s PREPARING (O sentence SET): Already prepared
+11m 11s SIMILARITY (O sentence SET M>50): Using  2021 M (2021557905) comparisons with 3959201 entries in matrix
+11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%
+11m 14s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates
+11m 14s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix
+11m 20s CLIQUES (O sentence SET M>50 S>50): 3959201 relevant similarities between 53117 passages
+11m 20s CLIQUES (O sentence SET M>50 S>50): Loaded:  1174 cliques out of  53117 chunks from 3959201 comparisons
+11m 20s CLIQUES (O sentence SET M>50 S>50): 53117 members in 1174 cliques
+11m 20s PRINT (O sentence SET M>50 S>50): sorting out cliques
+11m 20s PRINT (O sentence SET M>50 S>50): formatting 1174 cliques skipping 468 binary chapter diffs
+11m 22s PRINT (O sentence SET M>50 S>50): formatted 1174 cliques (24 files) skipping 468 binary chapter diffs
+11m 22s CHUNKING (O sentence): already chunked into 63586 chunks
+11m 22s PREPARING (O sentence LCS)
+11m 23s PREPARING (O sentence LCS): Done 63586 chunks.
+11m 29s SIMILARITY (O sentence LCS M>60): Loaded:  2021 M (2021557905) comparisons with 10271722 entries in matrix
+11m 35s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+11m 35s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates
+11m 35s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix
+11m 40s CLIQUES (O sentence LCS M>60 S>100): 903811 relevant similarities between 17532 passages
+11m 40s CLIQUES (O sentence LCS M>60 S>100): Loaded:  3981 cliques out of  17532 chunks from 903811 comparisons
+11m 40s CLIQUES (O sentence LCS M>60 S>100): 17532 members in 3981 cliques
+11m 40s PRINT (O sentence LCS M>60 S>100): sorting out cliques
+11m 40s PRINT (O sentence LCS M>60 S>100): formatting 3981 cliques skipping 1364 binary chapter diffs
+11m 42s PRINT (O sentence LCS M>60 S>100): formatted 3981 cliques (80 files) skipping 1364 binary chapter diffs
+11m 42s CHUNKING (O sentence): already chunked into 63586 chunks
+11m 42s PREPARING (O sentence LCS): Already prepared
+11m 42s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+11m 47s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+11m 47s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates
+11m 47s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix
+11m 54s CLIQUES (O sentence LCS M>60 S>95): 904511 relevant similarities between 18079 passages
+11m 54s CLIQUES (O sentence LCS M>60 S>95): Loaded:  4215 cliques out of  18079 chunks from 904511 comparisons
+11m 54s CLIQUES (O sentence LCS M>60 S>95): 18079 members in 4215 cliques
+11m 54s PRINT (O sentence LCS M>60 S>95): sorting out cliques
+11m 55s PRINT (O sentence LCS M>60 S>95): formatting 4215 cliques skipping 1418 binary chapter diffs
+11m 57s PRINT (O sentence LCS M>60 S>95): formatted 4215 cliques (85 files) skipping 1418 binary chapter diffs
+11m 57s CHUNKING (O sentence): already chunked into 63586 chunks
+11m 57s PREPARING (O sentence LCS): Already prepared
+11m 57s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+12m 04s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+12m 04s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates
+12m 04s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix
+12m 08s CLIQUES (O sentence LCS M>60 S>90): 915567 relevant similarities between 21246 passages
+12m 08s CLIQUES (O sentence LCS M>60 S>90): Loaded:  4993 cliques out of  21246 chunks from 915567 comparisons
+12m 08s CLIQUES (O sentence LCS M>60 S>90): 21246 members in 4993 cliques
+12m 08s PRINT (O sentence LCS M>60 S>90): sorting out cliques
+12m 08s PRINT (O sentence LCS M>60 S>90): formatting 4993 cliques involving 1704 binary chapter diffs
+12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1704
+12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 0 newly created and 1704 already existing
+12m 11s PRINT (O sentence LCS M>60 S>90): formatted 4993 cliques (100 files) involving 1704 binary chapter diffs
+12m 11s CHUNKING (O sentence): already chunked into 63586 chunks
+12m 11s PREPARING (O sentence LCS): Already prepared
+12m 11s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+12m 16s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates
+12m 16s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix
+12m 22s CLIQUES (O sentence LCS M>60 S>85): 980912 relevant similarities between 26473 passages
+12m 22s CLIQUES (O sentence LCS M>60 S>85): Loaded:  4853 cliques out of  26473 chunks from 980912 comparisons
+12m 22s CLIQUES (O sentence LCS M>60 S>85): 26473 members in 4853 cliques
+12m 22s PRINT (O sentence LCS M>60 S>85): sorting out cliques
+12m 22s PRINT (O sentence LCS M>60 S>85): formatting 4853 cliques skipping 1709 binary chapter diffs
+12m 24s PRINT (O sentence LCS M>60 S>85): formatted 4853 cliques (98 files) skipping 1709 binary chapter diffs
+12m 24s CHUNKING (O sentence): already chunked into 63586 chunks
+12m 24s PREPARING (O sentence LCS): Already prepared
+12m 24s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+12m 30s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+12m 30s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates
+12m 30s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix
+12m 35s CLIQUES (O sentence LCS M>60 S>80): 1301411 relevant similarities between 35626 passages
+12m 35s CLIQUES (O sentence LCS M>60 S>80): Loaded:  3470 cliques out of  35626 chunks from 1301411 comparisons
+12m 35s CLIQUES (O sentence LCS M>60 S>80): 35626 members in 3470 cliques
+12m 35s PRINT (O sentence LCS M>60 S>80): sorting out cliques
+12m 35s PRINT (O sentence LCS M>60 S>80): formatting 3470 cliques skipping 1296 binary chapter diffs
+12m 37s PRINT (O sentence LCS M>60 S>80): formatted 3470 cliques (70 files) skipping 1296 binary chapter diffs
+12m 37s CHUNKING (O sentence): already chunked into 63586 chunks
+12m 37s PREPARING (O sentence LCS): Already prepared
+12m 37s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+12m 43s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+12m 43s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates
+12m 43s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix
+12m 48s CLIQUES (O sentence LCS M>60 S>75): 1620210 relevant similarities between 44307 passages
+12m 48s CLIQUES (O sentence LCS M>60 S>75): Loaded:  2293 cliques out of  44307 chunks from 1620210 comparisons
+12m 48s CLIQUES (O sentence LCS M>60 S>75): 44307 members in 2293 cliques
+12m 48s PRINT (O sentence LCS M>60 S>75): sorting out cliques
+12m 48s PRINT (O sentence LCS M>60 S>75): formatting 2293 cliques skipping 889 binary chapter diffs
+12m 50s PRINT (O sentence LCS M>60 S>75): formatted 2293 cliques (46 files) skipping 889 binary chapter diffs
+12m 50s CHUNKING (O sentence): already chunked into 63586 chunks
+12m 50s PREPARING (O sentence LCS): Already prepared
+12m 50s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+12m 55s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+12m 55s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates
+12m 55s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix
+13m 02s CLIQUES (O sentence LCS M>60 S>70): 2182513 relevant similarities between 52535 passages
+13m 02s CLIQUES (O sentence LCS M>60 S>70): Loaded:  1197 cliques out of  52535 chunks from 2182513 comparisons
+13m 02s CLIQUES (O sentence LCS M>60 S>70): 52535 members in 1197 cliques
+13m 02s PRINT (O sentence LCS M>60 S>70): sorting out cliques
+13m 02s PRINT (O sentence LCS M>60 S>70): formatting 1197 cliques skipping 455 binary chapter diffs
+13m 03s PRINT (O sentence LCS M>60 S>70): formatted 1197 cliques (24 files) skipping 455 binary chapter diffs
+13m 03s CHUNKING (O sentence): already chunked into 63586 chunks
+13m 03s PREPARING (O sentence LCS): Already prepared
+13m 03s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+13m 09s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+13m 09s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates
+13m 09s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix
+13m 19s CLIQUES (O sentence LCS M>60 S>65): 4831555 relevant similarities between 58863 passages
+13m 19s CLIQUES (O sentence LCS M>60 S>65): Loaded:   460 cliques out of  58863 chunks from 4831555 comparisons
+13m 19s CLIQUES (O sentence LCS M>60 S>65): 58863 members in 460 cliques
+13m 19s PRINT (O sentence LCS M>60 S>65): sorting out cliques
+13m 20s PRINT (O sentence LCS M>60 S>65): formatting 460 cliques skipping 207 binary chapter diffs
+13m 21s PRINT (O sentence LCS M>60 S>65): formatted 460 cliques (10 files) skipping 207 binary chapter diffs
+13m 21s CHUNKING (O sentence): already chunked into 63586 chunks
+13m 21s PREPARING (O sentence LCS): Already prepared
+13m 21s SIMILARITY (O sentence LCS M>60): Using  2021 M (2021557905) comparisons with 10271722 entries in matrix
+13m 27s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%
+13m 27s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates
+13m 27s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix
+13m 41s CLIQUES (O sentence LCS M>60 S>60): 10271722 relevant similarities between 62379 passages
+13m 41s CLIQUES (O sentence LCS M>60 S>60): Loaded:   105 cliques out of  62379 chunks from 10271722 comparisons
+13m 41s CLIQUES (O sentence LCS M>60 S>60): 62379 members in 105 cliques
+13m 41s PRINT (O sentence LCS M>60 S>60): sorting out cliques
+13m 41s PRINT (O sentence LCS M>60 S>60): formatting 105 cliques skipping 55 binary chapter diffs
+13m 42s PRINT (O sentence LCS M>60 S>60): formatted 105 cliques (3 files) skipping 55 binary chapter diffs
+13m 42s EXPERIMENT: Generating html report
+13m 42s EXPERIMENT:  36 messy results: deprecated
+13m 42s EXPERIMENT:  22 mixed quality: take care
+13m 42s EXPERIMENT:  75 no results available
+13m 42s EXPERIMENT:   9 unassessed quality: inspection needed
+13m 42s EXPERIMENT:  80 method deprecated
+13m 42s EXPERIMENT:  18 promising results: recommended
+13m 42s EXPERIMENT: Generated html report
+13m 42s EXPERIMENT: Generating html report(standalone)
+13m 42s EXPERIMENT:  36 messy results: deprecated
+13m 42s EXPERIMENT:  22 mixed quality: take care
+13m 42s EXPERIMENT:  75 no results available
+13m 42s EXPERIMENT:   9 unassessed quality: inspection needed
+13m 42s EXPERIMENT:  80 method deprecated
+13m 42s EXPERIMENT:  18 promising results: recommended
+13m 42s EXPERIMENT: Generated html report
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In [16]:
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HTML(ecss)
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+

8. Overview of the similarities

Here are the plots of two similarity matrices

+
    +
  • with verses as chunks and SET as similarity method
    • +
  • with verses as chunks and LCS as similarity method
    • +
+

Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic.

+ +
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+
In [28]:
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+
do_experiment(False, 'verse', 'SET', 60, False)
+distances = collections.Counter()
+for (x, d) in chunk_dist.items():
+    distances[int(round(d))] += 1
+
+x = range(MATRIX_THRESHOLD, 101)
+fig = plt.figure(figsize=[15, 4])
+plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')
+plt.axis([MATRIX_THRESHOLD, 101, 0, 15])
+plt.xlabel('similarity as %')
+plt.ylabel('log # similarities')
+plt.xticks(x, x, rotation='vertical')
+plt.margins(0.2)
+plt.subplots_adjust(bottom=0.15);
+plt.title('distances');
+
+ +
+
+
+ +
+
+ + +
+
+
31m 00s CHUNKING (O verse): Loaded: 23213 chunks
+31m 00s CHUNKING (O verse): Made 23213 chunks
+31m 00s PREPARING (O verse SET)
+31m 01s PREPARING (O verse SET): Done 23213 chunks.
+31m 02s SIMILARITY (O verse SET M>50): Loaded:   269 M (269410078) comparisons with 24832 entries in matrix
+31m 02s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%
+31m 02s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates
+31m 02s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix
+31m 04s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages
+31m 04s CLIQUES (O verse SET M>50 S>60): Loaded:  1439 cliques out of   3877 chunks from 16055 comparisons
+31m 04s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques
+31m 04s PRINT (O verse SET M>50 S>60): sorting out cliques
+31m 04s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs
+31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358
+31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing
+31m 06s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs
+
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In [29]:
+
+
+
do_experiment(False, 'verse', 'LCS', 60, False)
+distances = collections.Counter()
+for (x, d) in chunk_dist.items():
+    distances[int(round(d))] += 1
+
+x = range(MATRIX_THRESHOLD, 101)
+fig = plt.figure(figsize=[15, 4])
+plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')
+plt.axis([MATRIX_THRESHOLD, 101, 0, 15])
+plt.xlabel('similarity as %')
+plt.ylabel('log # similarities')
+plt.xticks(x, x, rotation='vertical')
+plt.margins(0.2)
+plt.subplots_adjust(bottom=0.15);
+plt.title('distances');
+
+ +
+
+
+ +
+
+ + +
+
+
33m 46s CHUNKING (O verse): already chunked into 23213 chunks
+33m 46s PREPARING (O verse LCS)
+33m 47s PREPARING (O verse LCS): Done 23213 chunks.
+33m 47s SIMILARITY (O verse LCS M>60): Loaded:   269 M (269410078) comparisons with 113614 entries in matrix
+33m 47s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%
+33m 47s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates
+33m 47s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix
+33m 47s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages
+33m 47s CLIQUES (O verse LCS M>60 S>60): Loaded:   380 cliques out of  18941 chunks from 113614 comparisons
+33m 47s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques
+33m 47s PRINT (O verse LCS M>60 S>60): sorting out cliques
+33m 47s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs
+33m 48s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs
+
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+ +
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In [ ]:
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+
 
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+ + diff --git a/static/docs/tools/parallel/parallels_legacy.ipynb b/static/docs/tools/parallel/parallels_legacy.ipynb new file mode 100644 index 00000000..aea9d997 --- /dev/null +++ b/static/docs/tools/parallel/parallels_legacy.ipynb @@ -0,0 +1,5364 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Parallel Passages in the MT\n", + "\n", + "# 0. Introduction\n", + "\n", + "## 0.1 Motivation\n", + "We want to make a list of **all** parallel passages in the Masoretic Text (MT) of the Hebrew Bible.\n", + "\n", + "Here is a quote that triggered Dirk to write this notebook:\n", + "\n", + "> Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible.\n", + "\n", + "Robert Rezetko and Ian Young.\n", + " Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach.\n", + " *Ancient Near East Monographs, Number9*. SBL Press Atlanta. 2014. \n", + " [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ)\n", + "\n", + "## 0.3 Open Source\n", + "This is an IPython notebook. \n", + "It contains a working program to carry out the computations needed to obtain the results reported here.\n", + "\n", + "You can download this notebook and run it on your computer, provided you have\n", + "[LAF-Fabric](http://laf-fabric.readthedocs.org/en/latest/texts/welcome.html) installed.\n", + "An easy way to do that is describe [here](laf-fabric.readthedocs.org/texts/getting-started.html).\n", + "\n", + "It is a pity that we cannot compare our results with the Accordance resource mentioned above, since that resource has not been published in an accessible manner. We also do not have the information how this resource has been constructed on the basis of the raw data. In contrast with that, we present our results in a completely reproducible manner. This notebook itself can serve as the method of replication, provided you have obtained the necessary resources. See [SHEBANQ sources](https://shebanq.ancient-data.org/sources), which are all Open Access.\n", + "\n", + "## 0.4 What are parallel passages?\n", + "The notion of *parallel passage* is not a simple, straightforward one.\n", + "There are parallels on the basis of lexical content in the passages on the one hand, \n", + "but on the other hand there are also correspondences in certain syntactical structures, \n", + "or even in similarities in text structure.\n", + "\n", + "In this notebook we do select a straightforward notion of parallel, based on lexical content only.\n", + "We investigate two measures of similarity, one that ignores word order completely, and one that takes word order into account.\n", + "\n", + "Two kinds of short-comings of this approach must be mentioned:\n", + "\n", + "1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels)\n", + "1. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history.\n", + "\n", + "For a more full treatment of parallel passages, see\n", + "\n", + "Wido Th. van Peursen and Eep Talstra.\n", + " Computer-Assisted Analysis of Parallel Texts in the Bible - \n", + " The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles.\n", + " Vetus Testamentum 57, pp. 45-72.\n", + " 2007, Brill, Leiden.\n", + " \n", + "Note that our method fails to identify any parallels with Chronica_II 32. Van Peursen and Talstra state about this chapter and 2 Kings 18: \n", + "\n", + "> These chapters differ so much, that it is sometimes impossible to establish which verses should be considered parallel.\n", + "\n", + "In this notebook we produce a set of *cliques*, a clique being a set of passages that are *quite* similar, based on lexical information.\n", + "\n", + "\n", + "## 0.5 Authors\n", + "This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer.\n", + "\n", + "[Dirk Roorda](mailto:dirk.roorda@dans.knaw.nl) while discussing ideas with \n", + "[Martijn Naaijer](mailto:m.naaijer@vu.nl). \n", + "\n", + "\n", + "## 0.6 Status\n", + "\n", + "**Last modified: 2016-03-03** Added experiments based on chapter chunks and lower similarities.\n", + "\n", + "165 experiments have been carried out, of which 18 with promising results.\n", + "All results can be easily inspected, just by clicking in your browser.\n", + "One of the experiments has been chosen as the basis for\n", + "[crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v)\n", + "annotations in SHEBANQ.\n", + "\n", + "# 1. Results\n", + "\n", + "Click in a green cell to see interesting results. The numbers in the cell indicate\n", + "\n", + "* the number of passages that have a variant elsewhere\n", + "* the number of *cliques* they form (cliques are sets of similar passages)\n", + "* the number of passages in the biggest clique\n", + "\n", + "Below the results is an account of the method that we used, followed by the actual code to produce these results." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
no results available
promising results: recommended
messy results: deprecated
mixed quality: take care
method deprecated
unassessed quality: inspection needed
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
chunk typechunk sizesimilarity method1009590858075706560555045403530
fixed100SET\n", + " 2
\n", + " 1
\n", + " 2\n", + " 		\n", + " 4
\n", + " 2
\n", + " 2\n", + " 		\n", + " 18
\n", + " 9
\n", + " 2\n", + " 		\n", + " 37
\n", + " 18
\n", + " 3\n", + " 		\n", + " 64
\n", + " 30
\n", + " 6\n", + " 		\n", + " 87
\n", + " 40
\n", + " 9\n", + " 		\n", + " 113
\n", + " 52
\n", + " 9\n", + " 		\n", + " 154
\n", + " 70
\n", + " 9\n", + " 		\n", + " 208
\n", + " 94
\n", + " 10\n", + " 		\n", + " 309
\n", + " 138
\n", + " 11\n", + " 		\n", + " 473
\n", + " 189
\n", + " 14\n", + " 		    
fixed100LCS\n", + " 0
\n", + " 0
\n", + " 0\n", + " 		\n", + " 4
\n", + " 2
\n", + " 2\n", + " 		\n", + " 39
\n", + " 19
\n", + " 3\n", + " 		\n", + " 59
\n", + " 29
\n", + " 3\n", + " 		\n", + " 85
\n", + " 41
\n", + " 3\n", + " 		\n", + " 122
\n", + " 56
\n", + " 9\n", + " 		\n", + " 189
\n", + " 88
\n", + " 9\n", + " 		\n", + " 287
\n", + " 132
\n", + " 9\n", + " 		\n", + " 535
\n", + " 214
\n", + " 31\n", + " 		      
fixed50SET\n", + " 0
\n", + " 0
\n", + " 0\n", + " 		\n", + " 4
\n", + " 2
\n", + " 2\n", + " 		\n", + " 24
\n", + " 12
\n", + " 2\n", + " 		\n", + " 57
\n", + " 26
\n", + " 5\n", + " 		\n", + " 114
\n", + " 52
\n", + " 7\n", + " 		\n", + " 186
\n", + " 85
\n", + " 8\n", + " 		\n", + " 271
\n", + " 124
\n", + " 10\n", + " 		\n", + " 385
\n", + " 176
\n", + " 12\n", + " 		\n", + " 535
\n", + " 235
\n", + " 15\n", + " 		\n", + " 748
\n", + " 315
\n", + " 20\n", + " 		\n", + " 1187
\n", + " 465
\n", + " 47\n", + " 		    
fixed50LCS\n", + " 0
\n", + " 0
\n", + " 0\n", + " 		\n", + " 12
\n", + " 6
\n", + " 2\n", + " 		\n", + " 53
\n", + " 25
\n", + " 5\n", + " 		\n", + " 119
\n", + " 53
\n", + " 11\n", + " 		\n", + " 196
\n", + " 89
\n", + " 12\n", + " 		\n", + " 301
\n", + " 135
\n", + " 19\n", + " 		\n", + " 464
\n", + " 205
\n", + " 20\n", + " 		\n", + " 761
\n", + " 312
\n", + " 28\n", + " 		\n", + " 1888
\n", + " 552
\n", + " 112\n", + " 		      
fixed20SET\n", + " 28
\n", + " 14
\n", + " 2\n", + " 		\n", + " 28
\n", + " 14
\n", + " 2\n", + " 		\n", + " 105
\n", + " 46
\n", + " 8\n", + " 		\n", + " 174
\n", + " 72
\n", + " 12\n", + " 		\n", + " 326
\n", + " 143
\n", + " 12\n", + " 		\n", + " 528
\n", + " 227
\n", + " 12\n", + " 		\n", + " 762
\n", + " 331
\n", + " 12\n", + " 		\n", + " 1058
\n", + " 452
\n", + " 13\n", + " 		\n", + " 1830
\n", + " 733
\n", + " 29\n", + " 		\n", + " 2787
\n", + " 979
\n", + " 154\n", + " 		\n", + " 4913
\n", + " 1203
\n", + " 1573\n", + " 		    
fixed20LCS\n", + " 6
\n", + " 3
\n", + " 2\n", + " 		\n", + " 47
\n", + " 22
\n", + " 4\n", + " 		\n", + " 149
\n", + " 61
\n", + " 11\n", + " 		\n", + " 311
\n", + " 136
\n", + " 12\n", + " 		\n", + " 682
\n", + " 299
\n", + " 12\n", + " 		\n", + " 1137
\n", + " 470
\n", + " 27\n", + " 		\n", + " 2217
\n", + " 838
\n", + " 52\n", + " 		\n", + " 5971
\n", + " 1223
\n", + " 2709\n", + " 		\n", + " 17656
\n", + " 152
\n", + " 17329\n", + " 		      
fixed10SET\n", + " 448
\n", + " 209
\n", + " 5\n", + " 		\n", + " 448
\n", + " 209
\n", + " 5\n", + " 		\n", + " 482
\n", + " 220
\n", + " 7\n", + " 		\n", + " 1114
\n", + " 493
\n", + " 11\n", + " 		\n", + " 1536
\n", + " 628
\n", + " 36\n", + " 		\n", + " 2754
\n", + " 1094
\n", + " 74\n", + " 		\n", + " 4020
\n", + " 1474
\n", + " 163\n", + " 		\n", + " 5785
\n", + " 1850
\n", + " 702\n", + " 		\n", + " 10211
\n", + " 2210
\n", + " 4141\n", + " 		\n", + " 14100
\n", + " 2018
\n", + " 9047\n", + " 		\n", + " 23054
\n", + " 1455
\n", + " 19638\n", + " 		    
fixed10LCS\n", + " 239
\n", + " 114
\n", + " 5\n", + " 		\n", + " 379
\n", + " 182
\n", + " 5\n", + " 		\n", + " 905
\n", + " 399
\n", + " 12\n", + " 		\n", + " 1917
\n", + " 791
\n", + " 71\n", + " 		\n", + " 3850
\n", + " 1418
\n", + " 137\n", + " 		\n", + " 8552
\n", + " 2342
\n", + " 1980\n", + " 		\n", + " 20382
\n", + " 1926
\n", + " 15724\n", + " 		\n", + " 37700
\n", + " 223
\n", + " 37234\n", + " 		\n", + " 42450
\n", + " 2
\n", + " 42448\n", + " 		      
objectchapterSET\n", + " 0
\n", + " 0
\n", + " 0\n", + " 		\n", + " 2
\n", + " 1
\n", + " 2\n", + " 		\n", + " 2
\n", + " 1
\n", + " 2\n", + " 		\n", + " 2
\n", + " 1
\n", + " 2\n", + " 		\n", + " 4
\n", + " 2
\n", + " 2\n", + " 		\n", + " 14
\n", + " 7
\n", + " 2\n", + " 		\n", + " 20
\n", + " 10
\n", + " 2\n", + " 		\n", + " 24
\n", + " 12
\n", + " 2\n", + " 		\n", + " 34
\n", + " 17
\n", + " 2\n", + " 		\n", + " 44
\n", + " 22
\n", + " 2\n", + " 		\n", + " 56
\n", + " 28
\n", + " 2\n", + " 		\n", + " 80
\n", + " 39
\n", + " 3\n", + " 		\n", + " 142
\n", + " 62
\n", + " 7\n", + " 		\n", + " 302
\n", + " 53
\n", + " 61\n", + " 		\n", + " 571
\n", + " 28
\n", + " 496\n", + "
objectchapterLCS\n", + " 0
\n", + " 0
\n", + " 0\n", + " 		\n", + " 2
\n", + " 1
\n", + " 2\n", + " 		\n", + " 4
\n", + " 2
\n", + " 2\n", + " 		\n", + " 12
\n", + " 6
\n", + " 2\n", + " 		\n", + " 18
\n", + " 9
\n", + " 2\n", + " 		\n", + " 26
\n", + " 13
\n", + " 2\n", + " 		\n", + " 38
\n", + " 19
\n", + " 2\n", + " 		\n", + " 44
\n", + " 22
\n", + " 2\n", + " 		\n", + " 52
\n", + " 26
\n", + " 2\n", + " 		\n", + " 102
\n", + " 49
\n", + " 4\n", + " 		     
objectverseSET\n", + " 993
\n", + " 388
\n", + " 70\n", + " 		\n", + " 1029
\n", + " 406
\n", + " 70\n", + " 		\n", + " 1286
\n", + " 526
\n", + " 70\n", + " 		\n", + " 1573
\n", + " 651
\n", + " 70\n", + " 		\n", + " 1958
\n", + " 800
\n", + " 154\n", + " 		\n", + " 2359
\n", + " 961
\n", + " 156\n", + " 		\n", + " 2720
\n", + " 1094
\n", + " 166\n", + " 		\n", + " 3139
\n", + " 1235
\n", + " 172\n", + " 		\n", + " 3877
\n", + " 1439
\n", + " 202\n", + " 		\n", + " 4735
\n", + " 1638
\n", + " 388\n", + " 		\n", + " 6711
\n", + " 1850
\n", + " 1476\n", + " 		    
objectverseLCS\n", + " 793
\n", + " 295
\n", + " 69\n", + " 		\n", + " 1235
\n", + " 504
\n", + " 69\n", + " 		\n", + " 1754
\n", + " 724
\n", + " 74\n", + " 		\n", + " 2296
\n", + " 938
\n", + " 160\n", + " 		\n", + " 2925
\n", + " 1141
\n", + " 174\n", + " 		\n", + " 3685
\n", + " 1340
\n", + " 190\n", + " 		\n", + " 4958
\n", + " 1644
\n", + " 257\n", + " 		\n", + " 9046
\n", + " 1821
\n", + " 4221\n", + " 		\n", + " 18941
\n", + " 380
\n", + " 18073\n", + " 		      
objecthalf_verseSET\n", + " 4327
\n", + " 1725
\n", + " 70\n", + " 		\n", + " 4333
\n", + " 1728
\n", + " 70\n", + " 		\n", + " 4618
\n", + " 1863
\n", + " 70\n", + " 		\n", + " 5145
\n", + " 2072
\n", + " 70\n", + " 		\n", + " 6422
\n", + " 2474
\n", + " 195\n", + " 		\n", + " 8265
\n", + " 2888
\n", + " 536\n", + " 		\n", + " 9388
\n", + " 3193
\n", + " 681\n", + " 		\n", + " 12162
\n", + " 3342
\n", + " 2842\n", + " 		\n", + " 16476
\n", + " 3424
\n", + " 6915\n", + " 		\n", + " 19519
\n", + " 3184
\n", + " 10993\n", + " 		\n", + " 28990
\n", + " 2031
\n", + " 24008\n", + " 		    
objecthalf_verseLCS\n", + " 3799
\n", + " 1514
\n", + " 69\n", + " 		\n", + " 4342
\n", + " 1771
\n", + " 69\n", + " 		\n", + " 5776
\n", + " 2336
\n", + " 74\n", + " 		\n", + " 7970
\n", + " 2983
\n", + " 189\n", + " 		\n", + " 12504
\n", + " 3540
\n", + " 2364\n", + " 		\n", + " 19148
\n", + " 3084
\n", + " 11090\n", + " 		\n", + " 28472
\n", + " 1894
\n", + " 23864\n", + " 		\n", + " 38180
\n", + " 665
\n", + " 36649\n", + " 		\n", + " 44011
\n", + " 89
\n", + " 43822\n", + " 		      
objectsentenceSET\n", + " 19028
\n", + " 4325
\n", + " 1056\n", + " 		\n", + " 19036
\n", + " 4329
\n", + " 1056\n", + " 		\n", + " 19208
\n", + " 4404
\n", + " 1056\n", + " 		\n", + " 19771
\n", + " 4606
\n", + " 1056\n", + " 		\n", + " 22063
\n", + " 5066
\n", + " 1056\n", + " 		\n", + " 25724
\n", + " 4993
\n", + " 4853\n", + " 		\n", + " 26880
\n", + " 5222
\n", + " 5232\n", + " 		\n", + " 33378
\n", + " 4111
\n", + " 17433\n", + " 		\n", + " 38807
\n", + " 3753
\n", + " 24074\n", + " 		\n", + " 41835
\n", + " 3505
\n", + " 28077\n", + " 		\n", + " 53117
\n", + " 1174
\n", + " 50174\n", + " 		    
objectsentenceLCS\n", + " 17532
\n", + " 3981
\n", + " 1054\n", + " 		\n", + " 18079
\n", + " 4215
\n", + " 1054\n", + " 		\n", + " 21246
\n", + " 4993
\n", + " 1054\n", + " 		\n", + " 26473
\n", + " 4853
\n", + " 7321\n", + " 		\n", + " 35626
\n", + " 3470
\n", + " 25548\n", + " 		\n", + " 44307
\n", + " 2293
\n", + " 38261\n", + " 		\n", + " 52535
\n", + " 1197
\n", + " 49324\n", + " 		\n", + " 58863
\n", + " 460
\n", + " 57763\n", + " 		\n", + " 62379
\n", + " 105
\n", + " 62134\n", + " 		      
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# run this cell after all other cells\n", + "HTML(other_exps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Experiments\n", + "\n", + "We have conducted 165 experiments, all corresponding to a specific choice of parameters.\n", + "Every experiment is an attempt to identify variants and collect them in *cliques*.\n", + "\n", + "The table gives an overview of the experiments conducted.\n", + "\n", + "Every *row* corresponds to a particular way of chunking and a method of measuring the similarity.\n", + "\n", + "There are *columns* for each similarity *threshold* that we have tried.\n", + "The idea is that chunks are similar if their similarity is above the threshold.\n", + "\n", + "The outcomes of one experiment have been added to SHEBANQ as the note set\n", + "[crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v).\n", + "The experiment chosen for this is currently\n", + "\n", + "* *chunking*: **object verse**\n", + "* *similarity method*: **SET**\n", + "* *similarity threshold*: **65**\n", + "\n", + "\n", + "## 2.1 Assessing the outcomes\n", + "\n", + "Not all experiments lead to useful results.\n", + "We have indicated the value of a result by a color coding, based on objective characteristics,\n", + "such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking.\n", + "These numbers are shown in the cells.\n", + "\n", + "### 2.1.1 Assessment criteria\n", + "\n", + "If the method is based on *fixed* chunks, we deprecated the method and the results.\n", + "Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case.\n", + "\n", + "Otherwise, we consider the *ll*, the length of the longest clique, and *nc*, the number of cliques.\n", + "We set three quality parameters:\n", + "* `REC_CLIQUE_RATIO` = 5 : recommended clique ratio\n", + "* `DUB_CLIQUE_RATIO` = 15 : dubious clique ratio\n", + "* `DEP_CLIQUE_RATIO` = 25 : deprecated clique ratio\n", + "\n", + "where the *clique ratio* is $100 (ll/nc)$, \n", + "i.e. the length of the longest clique divided by the number of cliques as percentage.\n", + "\n", + "An experiment is *recommended* if its clique ratio is between the recommended and dubious clique ratios.\n", + "\n", + "It is *dubious* if its clique ratio is between the dubious and deprecated clique ratios.\n", + "\n", + "It is *deprecated* if its clique ratio is above the deprecated clique ratio.\n", + "\n", + "# 2.2 Inspecting results\n", + "If you click on the hyperlink in the cell, you are taken to a page that gives you\n", + "all the details of the results:\n", + "\n", + "1. A link to a file with all *cliques* (which are the sets of similar passages)\n", + "1. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for\n", + " experiments with outcomes that are labeled as *promising* or *unassessed quality* or *mixed results*.\n", + "\n", + "To get into the variants quickly, inspect the list (2) and click through \n", + "to see the actual variant material in chapter context.\n", + "\n", + "Not all variants occur here, so continue with (1) to see the remaining cliques.\n", + "\n", + "Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters.\n", + "In that case you have to consult the big list (1)\n", + "\n", + "All these results can be downloaded from the\n", + "[SHEBANQ github repo](https://github.com/ETCBC/shebanq/tree/master/static/docs/tools/parallel/files)\n", + "After downloading the whole directory, open ``experiments.html`` in your browser." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Method\n", + "\n", + "Here we discuss the method we used to arrive at a list of parallel passages \n", + "in the Masoretic Text (MT) of the Hebrew Bible.\n", + "\n", + "## 3.1 Similarity\n", + "\n", + "We have to find passages in the MT that are *similar*.\n", + "Therefore we *chunk* the text in some way, and then compute the similarities between pairs of chunks.\n", + "\n", + "There are many ways to define and compute similarity between texts.\n", + "Here, we have tried two methods ``SET`` and ``LCS``.\n", + "Both methods define similarity as the fraction of common material with respect to the total material.\n", + "\n", + "### 3.1.1 SET\n", + "\n", + "The ``SET`` method reduces textual chunks to *sets* of *lexemes*.\n", + "This method abstracts from the order and number of occurrences of words in chunks.\n", + "\n", + "We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets):\n", + "\n", + "$$ s_{\\rm set}(C_1, C_2) = {\\vert C_1 \\cap C_2\\vert \\over \\vert C_1 \\cup C_2 \\vert} $$\n", + "\n", + "where $\\vert X \\vert$ is the number of elements in set $X$.\n", + "\n", + "### 3.1.2 LCS\n", + "\n", + "The ``LCS`` method is less reductive: chunks are *strings* of *lexemes*, \n", + "so the order and number of occurrences of words is retained.\n", + "\n", + "We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings):\n", + "\n", + "$$ s_{\\rm lcs}(C_1, C_2) = {\\vert {\\rm LCS}(C_1,C_2)\\vert \\over \\vert C_1\\vert + \\vert C_2 \\vert - \n", + "\\vert {\\rm LCS}(C_1,C_2)\\vert} $$\n", + "\n", + "where ${\\rm LCS}(C_1, C_2)$ is the\n", + "[longest common subsequence](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem)\n", + "of $C_1$ and $C_2$ and\n", + "$\\vert X\\vert$ is the length of sequence $X$.\n", + "\n", + "It remains to be seen whether we need the extra sophistication of ``LCS``.\n", + "The risk is that ``LCS`` could fail to spot related passages when there is a large amount of transposition going on.\n", + "The results should have the last word. \n", + "\n", + "We need to compute the LCS efficiently, and for this we used the python ``Levenshtein`` module:\n", + "\n", + "``pip install python-Levenshtein``\n", + "\n", + "whose documentation is\n", + "[here](http://www.coli.uni-saarland.de/courses/LT1/2011/slides/Python-Levenshtein.html).\n", + "\n", + "## 3.2 Performance\n", + "\n", + "Similarity computation is the part where the heavy lifting occurs.\n", + "It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000),\n", + "you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), \n", + "you need to do ~ 2,000,000,000 ones!\n", + "The computation of a single similarity should be *really* fast.\n", + "\n", + "Besides that, we use two ways to economize:\n", + "\n", + "* after having computed a matrix for a specific set of parameter values, we save the matrix to disk;\n", + " new runs can load the matrix from disk in a matter of seconds;\n", + "* we do not store low similarity values in the matrix, low being < ``MATRIX_THRESHOLD``.\n", + "\n", + "The ``LCS`` method is more complicated.\n", + "We have tried the ``ratio`` method from the ``difflib`` package that is present in the standard python distribution.\n", + "This is unbearably slow for our purposes.\n", + "The ``ratio`` method in the ``Levenshtein`` package is much quicker.\n", + "\n", + "See the table for an indication of the amount of work to create the similarity matrix\n", + "and the performance per similarity method.\n", + "\n", + "The *matrix threshold* is the lower bound of similarities that are stored in the matrix.\n", + "If a pair of chunks has a lower similarity, no entry will be made in the matrix.\n", + "\n", + "The computing has been done on a Macbook Air (11\", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM).\n", + "\n", + "|chunk type |chunk size|similarity method|matrix threshold|# of comparisons|size of matrix (KB)|computing time (min)|\n", + "|:----------|---------:|----------------:|---------------:|---------------:|------------------:|-------------------:|\n", + "|fixed |100 |LCS |60 | 9,003,646| 7| ? |\n", + "|fixed |100 |SET |50 | 9,003,646| 7| ? |\n", + "|fixed |50 |LCS |60 | 36,197,286| 37| ? |\n", + "|fixed |50 |SET |50 | 36,197,286| 18| ? |\n", + "|fixed |20 |LCS |60 | 227,068,705| 2,400| ? |\n", + "|fixed |20 |SET |50 | 227,068,705| 113| ? |\n", + "|fixed |10 |LCS |60 | 909,020,841| 59,000| ? |\n", + "|fixed |10 |SET |50 | 909,020,841| 1,800| ? |\n", + "|object |verse |LCS |60 | 269,410,078| 2,300| 31|\n", + "|object |verse |SET |50 | 269,410,078| 509| 14|\n", + "|object |half_verse|LCS |60 | 1,016,396,241| 40,000| 50|\n", + "|object |half_verse|SET |50 | 1,016,396,241| 3,600| 41|\n", + "|object |sentence |LCS |60 | 2,055,975,750| 212,000| 68|\n", + "|object |sentence |SET |50 | 2,055,975,750| 82,000| 63|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Workflow\n", + "\n", + "## 4.1 Chunking\n", + "\n", + "There are several ways to chunk the text:\n", + "\n", + "* fixed chunks of approximately ``CHUNK_SIZE`` words\n", + "* by object, such as verse, sentence and even chapter\n", + "\n", + "After chunking, we prepare the chunks for similarity measuring.\n", + "\n", + "### 4.1.1 Fixed chunking\n", + "Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results.\n", + "The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries.\n", + "It is to be expected that fixed chunks in variant passages will be mutually *out of phase*, \n", + "meaning that the chunks involved in these passages are not aligned with each other.\n", + "So they will have a lower similarity than they could have if they were aligned.\n", + "This is a source of artificial noise in the outcome and/or missed cases.\n", + "\n", + "If the chunking respects \"natural\" boundaries in the text, there is far less misalignment.\n", + "\n", + "### 4.1.2 Object chunking\n", + "We can also chunk by object, such as verse, half_verse or sentence.\n", + "\n", + "Chunking by *verse* is very much like chunking in fixed chunks of size 20, performance-wise.\n", + "\n", + "Chunking by *half_verse* is comparable to fixed chunks of size 10.\n", + "\n", + "Chunking by *sentence* will generate an enormous amount of\n", + "false positives, because there are very many very short sentences (down to 1-word) in the text.\n", + "Besides that, the performance overhead is huge.\n", + "\n", + "The *half_verses* seem to be a very interesting candidate. \n", + "They are smaller than verses, but there are less *degenerate cases* compared to with sentences. \n", + "From the table above it can be read that half verses require only half as many similarity computations as sentences.\n", + "\n", + "\n", + "## 4.2 Preparing\n", + "\n", + "We prepare the chunks for the application of the chosen method of similarity computation (``SET`` or ``LCS``).\n", + "\n", + "In both cases we reduce the text to a sequence of transliterated consonantal *lexemes* without disambiguation.\n", + "In fact, we go one step further: we remove the consonants (alef, wav, yod) that are often silent.\n", + "\n", + "For ``SET``, we represent each chunk as the set of its reduced lexemes.\n", + "\n", + "For ``LCS``, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces.\n", + "\n", + "## 4.3 Cliques\n", + "\n", + "After having computed a sufficient part of the similarity matrix, we set a value for ``SIMILARITY_THRESHOLD``.\n", + "All pairs of chunks having at least that similarity are deemed *interesting*.\n", + "\n", + "We organize the members of such pairs in *cliques*, groups of chunks of which each member is \n", + "similar (*similarity* > ``SIMILARITY_THRESHOLD``) to at least one other member.\n", + "\n", + "We start with no cliques and walk through the pairs whose similarity is above ``SIMILARITY_THRESHOLD``, \n", + "and try to put each member into a clique.\n", + "\n", + "If there is not yet a clique, we make the member in question into a new singleton clique.\n", + "\n", + "If there are cliques, we find the cliques that have a member similar to the member in question.\n", + "If we find several, we merge them all into one clique.\n", + "\n", + "If there is no such clique, we put the member in a new singleton clique.\n", + "\n", + "NB: Cliques may *drift*, meaning that they contain members that are completely different from each other.\n", + "They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other.\n", + "\n", + "### 4.3.1 Organizing the cliques\n", + "In order to accomodate cases where there are many corresponding verses in corresponding chapters, we produce\n", + "chapter-by-chapter diffs in the following way.\n", + "\n", + "We make a list of all chapters that are involved in cliques.\n", + "This yields a list of chapter cliques.\n", + "For all *binary* chapters cliques, we generate a colorful diff rendering (as html) for the complete two chapters.\n", + "\n", + "We only do this for *promising* experiments.\n", + "\n", + "### 4.3.2 Evaluating clique sets\n", + "\n", + "Not all clique sets are equally worth while.\n", + "For example, if we set the ``SIMILARITY_THRESHOLD`` too low, we might get one gigantic clique, especially\n", + "in combination with a fine-grained chunking. In other words: we suffer from *clique drifting*.\n", + "\n", + "We detect clique drifting by looking at the size of the largest clique.\n", + "If that is large compared to the total number of chunks, we deem the results unsatisfactory.\n", + "\n", + "On the other hand, when the ``SIMILARITY_THRESHOLD`` is too high, you might miss a lot of correspondences,\n", + "especially when chunks are large, or when we have fixed-size chunks that are out of phase.\n", + "\n", + "We deem the results of experiments based on a partioning into fixed length chunks as unsatisfactory, although it\n", + "might be interesting to inspect what exactly the damage is.\n", + "\n", + "At the moment, we have not yet analysed the relative merits of the similarity methods ``SET`` and ``LCS``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Implementation\n", + "\n", + "\n", + "The rest is code. From here we fire up the engines and start computing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0.00s This is LAF-Fabric 4.5.18\n", + "API reference: http://laf-fabric.readthedocs.org/en/latest/texts/API-reference.html\n", + "Feature doc: https://shebanq.ancient-data.org/static/docs/featuredoc/texts/welcome.html\n", + "\n" + ] + } + ], + "source": [ + "import sys, os, re, collections, pickle, math, difflib, glob\n", + "\n", + "from IPython.display import HTML, display\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "PICKLE_PROTOCOL = 3\n", + "\n", + "from difflib import SequenceMatcher\n", + "from Levenshtein import ratio\n", + "\n", + "import laf\n", + "from laf.fabric import LafFabric\n", + "from etcbc.preprocess import prepare\n", + "fabric = LafFabric()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.1 Loading the feature data\n", + "\n", + "We load the features we need from the ETCBC database." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0.00s LOADING API: please wait ... \n", + " 0.00s USING main DATA COMPILED AT: 2015-11-02T15-08-56\n", + " 3.23s LOGFILE=/Users/dirk/SURFdrive/laf-fabric-output/etcbc4b/parallel/__log__parallel.txt\n", + " 3.23s INFO: LOADING PREPARED data: please wait ... \n", + " 3.24s prep prep: G.node_sort\n", + " 3.36s prep prep: G.node_sort_inv\n", + " 3.89s prep prep: L.node_up\n", + " 7.26s prep prep: L.node_down\n", + " 13s prep prep: V.verses\n", + " 13s prep prep: V.books_la\n", + " 13s ETCBC reference: http://laf-fabric.readthedocs.org/en/latest/texts/ETCBC-reference.html\n", + " 15s INFO: LOADED PREPARED data\n", + " 15s INFO: DATA LOADED FROM SOURCE etcbc4b AND ANNOX lexicon FOR TASK parallel AT 2016-03-03T10-49-34\n" + ] + } + ], + "source": [ + "version = '4b'\n", + "API = fabric.load('etcbc{}'.format(version), '--', 'parallel', {\n", + " \"xmlids\": {\"node\": False, \"edge\": False},\n", + " \"features\": ('''\n", + " otype\n", + " lex g_word_utf8 trailer_utf8\n", + " book chapter verse label number\n", + " ''',\n", + " ''),\n", + " \"prepare\": prepare,\n", + " \"primary\": False,\n", + "}, verbose='NORMAL')\n", + "exec(fabric.localnames.format(var='fabric'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.2 Configuration\n", + "\n", + "Here are the parameters on which the results crucially depend.\n", + "\n", + "There are also parameters that control the reporting of the results, such as file locations." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# chunking\n", + "CHUNK_LABELS = {True: 'fixed', False: 'object'}\n", + "CHUNK_LBS = {True: 'F', False: 'O'}\n", + "CHUNK_SIZES = (100, 50, 20, 10)\n", + "CHUNK_OBJECTS = ('chapter', 'verse','half_verse','sentence')\n", + "\n", + "# preparing\n", + "EXCLUDED_CONS = '[>WJ=/\\[]' # weed out weak consonants\n", + "EXCLUDED_PAT = re.compile(EXCLUDED_CONS)\n", + "\n", + "# similarity\n", + "MATRIX_THRESHOLD = 50\n", + "SIM_METHODS = ('SET', 'LCS')\n", + "SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30)\n", + "\n", + "# printing\n", + "DEP_CLIQUE_RATIO = 25\n", + "DUB_CLIQUE_RATIO = 15\n", + "REC_CLIQUE_RATIO = 5\n", + "LARGE_CLIQUE_SIZE = 50\n", + "CLIQUES_PER_FILE = 50\n", + "\n", + "# assessing results\n", + "VALUE_LABELS = dict(\n", + " mis='no results available',\n", + " rec='promising results: recommended',\n", + " dep='messy results: deprecated',\n", + " dub='mixed quality: take care',\n", + " out='method deprecated',\n", + " nor='unassessed quality: inspection needed',\n", + " lr='this experiment is the last one run',\n", + ")\n", + "\n", + "# crossrefs for SHEBANQ\n", + "SHEBANQ_MATRIX = (False, 'verse', 'SET')\n", + "SHEBANQ_SIMILARITY = 65\n", + "SHEBANQ_TOOL = 'parallel'\n", + "CROSSREF_STATUS = '!'\n", + "CROSSREF_KEYWORD = 'crossref'\n", + "\n", + "# progress indication\n", + "VERBOSE = False\n", + "MEGA = 1000000\n", + "KILO = 1000\n", + "SIMILARITY_PROGRESS = 5 * MEGA\n", + "CLIQUES_PROGRESS = 1 * KILO\n", + "\n", + "# locations and hyperlinks\n", + "REMOTE_BASE = 'https://surfdrive.surf.nl/files/public.php?service=files&t=dedf27be7e171ab8a8b151f84ded93e8'\n", + "LOCAL_BASE_COMP = my_file('').rstrip('/')\n", + "LOCAL_BASE_OUTP = 'files'\n", + "EXPERIMENT_DIR = 'experiments'\n", + "EXPERIMENT_FILE = 'experiments'\n", + "EXPERIMENT_PATH = '{}/{}.txt'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)\n", + "EXPERIMENT_HTML = '{}/{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_FILE)\n", + "NOTES_FILE = 'crossref'\n", + "NOTES_PATH = '{}/{}.csv'.format(LOCAL_BASE_OUTP, NOTES_FILE)\n", + "STORED_CLIQUE_DIR = 'stored/cliques'\n", + "STORED_MATRIX_DIR = 'stored/matrices'\n", + "STORED_CHUNK_DIR = 'stored/chunks'\n", + "CHAPTER_DIR = 'chapters'\n", + "CROSSREF_DB_FILE = 'crossrefdb.csv'\n", + "CROSSREF_DB_PATH = '{}/{}'.format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.3 Experiment settings\n", + "\n", + "For each experiment we have to adapt the configuration settings to the parameters that define the experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def reset_params():\n", + " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", + " global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD\n", + " global meta\n", + " meta = collections.OrderedDict()\n", + " \n", + " # chunking\n", + " CHUNK_FIXED = None # kind of chunking: fixed size or by object\n", + " CHUNK_SIZE = None # only relevant for CHUNK_FIXED = True\n", + " CHUNK_OBJECT = None # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell\n", + " CHUNK_LB = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ\n", + " CHUNK_DESC = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ\n", + " # similarity\n", + " MATRIX_THRESHOLD = None # minimal similarity used to fill the matrix of similarities\n", + " SIMILARITY_METHOD = None # see SIM_METHODS in next cell\n", + " SIMILARITY_THRESHOLD = None # minimal similarity used to put elements together in cliques\n", + " meta = collections.OrderedDict()\n", + "\n", + "def set_matrix_threshold(sim_m=None, chunk_o=None):\n", + " global MATRIX_THRESHOLD\n", + " the_sim_m = SIMILARITY_METHOD if sim_m == None else sim_m\n", + " the_chunk_o = CHUNK_OBJECT if chunk_o == None else chunk_o\n", + " MATRIX_THRESHOLD = 50 if the_sim_m == 'SET' else 60\n", + " if the_sim_m == 'SET':\n", + " if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 30\n", + " else: MATRIX_THRESHOLD = 50\n", + " else:\n", + " if the_chunk_o == 'chapter': MATRIX_THRESHOLD = 55\n", + " else: MATRIX_THRESHOLD = 60\n", + "\n", + "def do_params_chunk(chunk_f, chunk_i):\n", + " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", + " do_chunk = False\n", + " if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):\n", + " do_chunk = True\n", + " CHUNK_FIXED = chunk_f\n", + " if chunk_f: CHUNK_SIZE = chunk_i\n", + " else: CHUNK_OBJECT = chunk_i\n", + "\n", + " CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]\n", + " CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT\n", + "\n", + " for p in (\n", + " '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),\n", + " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),\n", + " ):\n", + " if not os.path.exists(p): os.makedirs(p)\n", + "\n", + " return do_chunk\n", + "\n", + "def do_params(chunk_f, chunk_i, sim_m, sim_thr):\n", + " global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC\n", + " global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD\n", + " global meta\n", + " do_chunk = False\n", + " do_prep = False\n", + " do_sim = False\n", + " do_clique = False\n", + " \n", + " meta = collections.OrderedDict()\n", + " if chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT):\n", + " do_chunk = True\n", + " do_prep = True\n", + " do_sim = True\n", + " do_clique = True\n", + " CHUNK_FIXED = chunk_f\n", + " if chunk_f: CHUNK_SIZE = chunk_i\n", + " else: CHUNK_OBJECT = chunk_i\n", + " if sim_m != SIMILARITY_METHOD:\n", + " do_prep = True\n", + " do_sim = True\n", + " do_clique = True\n", + " SIMILARITY_METHOD = sim_m\n", + " if sim_thr != SIMILARITY_THRESHOLD:\n", + " do_clique = True\n", + " SIMILARITY_THRESHOLD = sim_thr\n", + " set_matrix_threshold()\n", + " if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD : return (False, False, False, False, True)\n", + "\n", + " CHUNK_LB = CHUNK_LBS[CHUNK_FIXED]\n", + " CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT\n", + "\n", + " meta['CHUNK TYPE'] = 'FIXED {}'.format(CHUNK_SIZE) if CHUNK_FIXED else 'OBJECT {}'.format(CHUNK_OBJECT)\n", + " meta['MATRIX THRESHOLD'] = MATRIX_THRESHOLD\n", + " meta['SIMILARITY METHOD'] = SIMILARITY_METHOD\n", + " meta['SIMILARITY THRESHOLD'] = SIMILARITY_THRESHOLD\n", + " \n", + " \n", + " for p in (\n", + " '{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR),\n", + " '{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR),\n", + " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR),\n", + " '{}/{}'.format(LOCAL_BASE_COMP, STORED_MATRIX_DIR),\n", + " '{}/{}'.format(LOCAL_BASE_COMP, STORED_CHUNK_DIR),\n", + " ):\n", + " if not os.path.exists(p): os.makedirs(p)\n", + "\n", + " return (do_chunk, do_prep, do_sim, do_clique, False)\n", + "\n", + "reset_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.4 Chunking\n", + "\n", + "We divide the text into chunks to be compared. The result is ``chunks``,\n", + "which is a list of lists.\n", + "Every chunk is a list of word nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def chunking(do_chunk):\n", + " global chunks, book_rank\n", + " if not do_chunk:\n", + " msg('CHUNKING ({} {}): already chunked into {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", + " meta['# CHUNKS'] = len(chunks)\n", + " return\n", + "\n", + " chunk_path = '{}/{}/chunk_{}_{}'.format(\n", + " LOCAL_BASE_COMP, STORED_CHUNK_DIR,\n", + " CHUNK_LB, CHUNK_DESC,\n", + " )\n", + "\n", + " if os.path.exists(chunk_path):\n", + " with open(chunk_path, 'rb') as f: chunks = pickle.load(f)\n", + " msg('CHUNKING ({} {}): Loaded: {:>5} chunks'.format(\n", + " CHUNK_LB, CHUNK_DESC,\n", + " len(chunks),\n", + " ))\n", + " else:\n", + " msg('CHUNKING ({} {})'.format(CHUNK_LB, CHUNK_DESC))\n", + " chunks = []\n", + " book_rank = {}\n", + " for b in F.otype.s('book'):\n", + " book_name = F.book.v(b)\n", + " book_rank[book_name] = b\n", + " words = L.d('word', b)\n", + " nwords = len(words)\n", + " if CHUNK_FIXED:\n", + " nchunks = nwords // CHUNK_SIZE\n", + " if nchunks == 0: \n", + " nchunks = 1\n", + " common_incr = nwords\n", + " special_incr = 0\n", + " else: \n", + " rem = nwords % CHUNK_SIZE\n", + " common_incr = rem // nchunks\n", + " special_incr = rem % nchunks\n", + " word_in_chunk = -1\n", + " cur_chunk = -1\n", + " these_chunks = []\n", + "\n", + " for w in words:\n", + " word_in_chunk += 1\n", + " if word_in_chunk == 0 or (word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0)):\n", + " word_in_chunk = 0\n", + " these_chunks.append([])\n", + " cur_chunk += 1\n", + " these_chunks[-1].append(w)\n", + " else:\n", + " these_chunks = [L.d('word', c) for c in L.d(CHUNK_OBJECT, b)]\n", + "\n", + " chunks.extend(these_chunks)\n", + "\n", + " chunkvolume = sum(len(c) for c in these_chunks)\n", + " if VERBOSE:\n", + " msg('CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}'.format(\n", + " CHUNK_LB, CHUNK_DESC,\n", + " book_name, nwords, len(these_chunks), \n", + " min(len(c) for c in these_chunks), \n", + " max(len(c) for c in these_chunks),\n", + " 'OK' if chunkvolume == nwords else 'ERROR',\n", + " ))\n", + " with open(chunk_path, 'wb') as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL)\n", + " msg('CHUNKING ({} {}): Made {} chunks'.format(CHUNK_LB, CHUNK_DESC, len(chunks)))\n", + " meta['# CHUNKS'] = len(chunks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Preparing\n", + "\n", + "In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing.\n", + "\n", + "### 5.5.1 Preparing for SET comparison\n", + "\n", + "We reduce words to their lexemes (dictionary entries) and from them we also remove the alef, waw, and yods.\n", + "The lexeme feature also contains characters (`/ [ =`) to disambiguate homonyms. We also remove these.\n", + "If we end up with something empty, we skip it.\n", + "Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content.\n", + "\n", + "### 5.5.2 Preparing for LCS comparison\n", + "\n", + "Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def preparing(do_prepare):\n", + " global chunk_data\n", + " if not do_prepare:\n", + " msg('PREPARING ({} {} {}): Already prepared'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", + " return\n", + " msg('PREPARING ({} {} {})'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD))\n", + " chunk_data = []\n", + " if SIMILARITY_METHOD == 'SET':\n", + " for c in chunks:\n", + " words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)\n", + " clean_words = (w for w in words if w != '')\n", + " this_data = frozenset(clean_words)\n", + " chunk_data.append(this_data)\n", + " else:\n", + " for c in chunks:\n", + " words = (EXCLUDED_PAT.sub('', F.lex.v(w).replace('<', 'O')) for w in c)\n", + " clean_words = (w for w in words if w != '')\n", + " this_data = ' '.join(clean_words)\n", + " chunk_data.append(this_data)\n", + " msg('PREPARING ({} {} {}): Done {} chunks.'.format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Similarity computation\n", + "\n", + "Here we implement our two ways of similarity computation.\n", + "Both need a massive amount of work, especially for experiments with many small chunks.\n", + "The similarities are stored in a ``matrix``, a data structure that stores a similarity number for each pair of chunk indices.\n", + "Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold.\n", + "We store matrices for re-use.\n", + "\n", + "### 5.6.1 SET similarity\n", + "The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union.\n", + "Intuitively, we compute the proportion of what two chunks have in common against their total material.\n", + "\n", + "In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0.\n", + "\n", + "### 5.6.2 LCS similarity\n", + "The core is the method `ratio()`, taken from the Levenshtein module. \n", + "Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def similarity_post():\n", + " nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100})\n", + " cmin = min(chunk_dist.values()) if len(chunk_dist) else '!empty set!'\n", + " cmax = max(chunk_dist.values()) if len(chunk_dist) else '!empty set!'\n", + " meta['LOWEST AVAILABLE SIMILARITY'] = cmin\n", + " meta['HIGHEST AVAILABLE SIMILARITY'] = cmax\n", + " meta['# EQUAL COMPARISONS'] = nequals\n", + " msg('SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " cmin, cmax, nequals,\n", + " ))\n", + " \n", + "def similarity(do_sim):\n", + " global chunk_dist\n", + " total_chunks = len(chunks) \n", + " total_distances = total_chunks * (total_chunks - 1) // 2\n", + " meta['# SIMILARITY COMPARISONS'] = total_distances\n", + " \n", + " SIMILARITY_PROGRESS = total_distances // 100\n", + " if SIMILARITY_PROGRESS >= MEGA:\n", + " sim_unit = MEGA\n", + " sim_lb = 'M'\n", + " else:\n", + " sim_unit = KILO\n", + " sim_lb = 'K'\n", + " \n", + " if not do_sim:\n", + " msg('SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", + " ))\n", + " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", + " similarity_post()\n", + " return\n", + "\n", + " matrix_path = '{}/{}/matrix_{}_{}_{}_{}'.format(\n", + " LOCAL_BASE_COMP, STORED_MATRIX_DIR,\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " )\n", + "\n", + " if os.path.exists(matrix_path):\n", + " with open(matrix_path, 'rb') as f: chunk_dist = pickle.load(f)\n", + " msg('SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist),\n", + " ))\n", + " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", + " similarity_post()\n", + " return\n", + "\n", + " msg('SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " total_distances // sim_unit, sim_lb, total_distances\n", + " ))\n", + "\n", + " chunk_dist = {}\n", + " wc = 0\n", + " wt = 0\n", + " if SIMILARITY_METHOD == 'SET':\n", + " # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union\n", + " for i in range(total_chunks):\n", + " c_i = chunk_data[i]\n", + " for j in range(i + 1, total_chunks):\n", + " c_j = chunk_data[j]\n", + " u = len(c_i | c_j)\n", + " \n", + " # HERE COMES THE SIMILARITY COMPUTATION\n", + " d = 100 * len(c_i & c_j) / u if u != 0 else 0\n", + " \n", + " # HERE WE STORE THE OUTCOME\n", + " if d >= MATRIX_THRESHOLD:\n", + " chunk_dist[(i,j)] = d\n", + " wc += 1\n", + " wt += 1\n", + " if wc == SIMILARITY_PROGRESS:\n", + " wc = 0\n", + " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " wt // sim_unit, sim_lb, len(chunk_dist),\n", + " ))\n", + " elif SIMILARITY_METHOD == 'LCS':\n", + " # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length\n", + " for i in range(total_chunks):\n", + " c_i = chunk_data[i]\n", + " for j in range(i + 1, total_chunks):\n", + " c_j = chunk_data[j]\n", + "\n", + " # HERE COMES THE SIMILARITY COMPUTATION\n", + " d = 100 * ratio(c_i, c_j)\n", + "\n", + " # HERE WE STORE THE OUTCOME\n", + " if d >= MATRIX_THRESHOLD:\n", + " chunk_dist[(i,j)] = d\n", + " wc += 1\n", + " wt += 1\n", + " if wc == SIMILARITY_PROGRESS:\n", + " wc = 0\n", + " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " wt // sim_unit, sim_lb, len(chunk_dist),\n", + " ))\n", + "\n", + " with open(matrix_path, 'wb') as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL)\n", + " \n", + " msg('SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD,\n", + " wt // sim_unit, sim_lb, wt, len(chunk_dist),\n", + " ))\n", + " \n", + " meta['# STORED SIMILARITIES'] = len(chunk_dist)\n", + " similarity_post()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "scrolled": true + }, + "source": [ + "## 5.7 Cliques\n", + "\n", + "Based on the value for the ``SIMILARITY_THRESHOLD`` we use the similarity matrix to pick the *interesting*\n", + "similar pairs out of it. From these pairs we lump together our cliques.\n", + "\n", + "Our list of experiments will select various values for ``SIMILARITY_THRESHOLD``, which will result\n", + "in various types of cliqueing behaviour.\n", + "\n", + "We store computed cliques for re-use.\n", + "\n", + "## 5.7.1 Selecting passages\n", + "\n", + "We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages.\n", + "\n", + "## 5.7.2 Growing cliques\n", + "We inspect all passages in our set, and try to add them to the cliques we are growing.\n", + "We start with an empty set of cliques.\n", + "Each passage is added to a clique with which it has *enough familiarity*, otherwise it is added to a new clique.\n", + "*Enough familiarity means*: the passage is similar to at least one member of the clique, and the similarity is at least ``SIMILARITY_THRESHOLD``. \n", + "It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged.\n", + "This may lead to growing very large cliques if ``SIMILARITY_THRESHOLD`` is too low." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "def key_chunk(i):\n", + " c = chunks[i]\n", + " w = c[0]\n", + " return (-len(c), L.u('book', w), L.u('chapter', w), L.u('verse', w))\n", + "\n", + "def meta_clique_pre():\n", + " global similars, passages\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " ))\n", + " similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD}\n", + " passage_set = set()\n", + " for (i,j) in similars:\n", + " passage_set.add(i)\n", + " passage_set.add(j)\n", + " passages = sorted(passage_set, key=key_chunk)\n", + "\n", + " meta['# SIMILAR COMPARISONS'] = len(similars)\n", + " meta['# SIMILAR PASSAGES'] = len(passages) \n", + "\n", + "def meta_clique_pre2():\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(similars), len(passages),\n", + "))\n", + "\n", + "\n", + "def meta_clique_post():\n", + " global l_c_l\n", + " meta['# CLIQUES'] = len(cliques)\n", + " scliques = collections.Counter()\n", + " for c in cliques:\n", + " scliques[len(c)] += 1\n", + " l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0\n", + " totmn = 0\n", + " totcn = 0\n", + " for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]):\n", + " totmn += ln * n\n", + " totcn += n\n", + " if VERBOSE:\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " n, ln,\n", + " ))\n", + " meta['# CLIQUES of LENGTH {:>4}'.format(ln)] = n\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " totmn, totcn,\n", + " ))\n", + " \n", + "def cliqueing(do_clique):\n", + " global cliques\n", + " if not do_clique:\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(cliques), len(passages), len(similars), \n", + " ))\n", + " meta_clique_pre2()\n", + " meta_clique_post()\n", + " return\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, \n", + " ))\n", + " meta_clique_pre()\n", + " meta_clique_pre2()\n", + " clique_path = '{}/{}/clique_{}_{}_{}_{}_{}'.format(\n", + " LOCAL_BASE_COMP, STORED_CLIQUE_DIR,\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " )\n", + " if os.path.exists(clique_path):\n", + " with open(clique_path, 'rb') as f: cliques = pickle.load(f)\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(cliques), len(passages), len(similars), \n", + " ))\n", + " meta_clique_post()\n", + " return\n", + "\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(passages), len(similars), \n", + " ))\n", + " cliques_unsorted = []\n", + " np = 0\n", + " npc = 0\n", + " for i in passages:\n", + " added = None\n", + " removable = set()\n", + " for (k, c) in enumerate(cliques_unsorted):\n", + " origc = tuple(c)\n", + " for j in origc: \n", + " d = chunk_dist.get((i,j), 0) if i < j else chunk_dist.get((j,i), 0) if j < i else 0\n", + " if d >= SIMILARITY_THRESHOLD:\n", + " if added == None: # the passage has not been added to any clique yet\n", + " c.add(i)\n", + " added = k # remember that we added the passage to this clique\n", + " else: # the passage has alreay been added to another clique:\n", + " # we merge this clique with that one\n", + " cliques_unsorted[added] |= c\n", + " removable.add(k) # we remember that we have merged this clicque into another one,\n", + " # so we can throw away this clicque later \n", + " break\n", + " if added == None:\n", + " cliques_unsorted.append({i})\n", + " else:\n", + " if len(removable):\n", + " cliques_unsorted = [c for (k,c) in enumerate(cliques_unsorted) if k not in removable]\n", + " np += 1\n", + " npc += 1\n", + " if npc == CLIQUES_PROGRESS:\n", + " npc = 0\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(cliques_unsorted), np,\n", + " ))\n", + " cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted])\n", + " with open(clique_path, 'wb') as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL)\n", + " meta_clique_post()\n", + " msg('CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(cliques), len(passages), len(similars), \n", + " ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.8 Output\n", + "\n", + "We deliver the output of our experiments in various ways, all in HTML.\n", + "\n", + "We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it.\n", + "\n", + "For every (*good*) experiment, we produce a big list of its cliques, and for \n", + "every such clique, we produce a diff-view of its members.\n", + "\n", + "Big cliques will be split into several files.\n", + "\n", + "Clique listings will also contain metadata: the value of the experiment parameters.\n", + "\n", + "### 5.8.1 Format definitions\n", + "Here are the definitions for formatting the (HTML) output." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# clique lists\n", + "css = '''\n", + "td.vl {\n", + " font-family: Verdana, Arial, sans-serif;\n", + " font-size: small;\n", + " text-align: right;\n", + " color: #aaaaaa;\n", + " width: 10%;\n", + " direction: ltr;\n", + " border-left: 2px solid #aaaaaa;\n", + " border-right: 2px solid #aaaaaa;\n", + "}\n", + "td.ht {\n", + " font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif;\n", + " font-size: x-large;\n", + " line-height: 1.7;\n", + " text-align: right;\n", + " direction: rtl;\n", + "}\n", + "table.ht {\n", + " width: 100%;\n", + " direction: rtl;\n", + " border-collapse: collapse;\n", + "}\n", + "td.ht {\n", + " border-left: 2px solid #aaaaaa;\n", + " border-right: 2px solid #aaaaaa;\n", + "}\n", + "tr.ht.tb {\n", + " border-top: 2px solid #aaaaaa;\n", + " border-left: 2px solid #aaaaaa;\n", + " border-right: 2px solid #aaaaaa;\n", + "}\n", + "tr.ht.bb {\n", + " border-bottom: 2px solid #aaaaaa;\n", + " border-left: 2px solid #aaaaaa;\n", + " border-right: 2px solid #aaaaaa;\n", + "}\n", + "span.m {\n", + " background-color: #aaaaff;\n", + "}\n", + "span.f {\n", + " background-color: #ffaaaa;\n", + "}\n", + "span.x {\n", + " background-color: #ffffaa;\n", + " color: #bb0000;\n", + "}\n", + "span.delete {\n", + " background-color: #ffaaaa;\n", + "}\n", + "span.insert {\n", + " background-color: #aaffaa;\n", + "}\n", + "span.replace {\n", + " background-color: #ffff00;\n", + "}\n", + "\n", + "'''\n", + "\n", + "# chapter diffs\n", + "diffhead = '''\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "'''\n", + "\n", + "# table of experiments\n", + "ecss = '''\n", + "\n", + "'''\n", + "\n", + "legend = '''\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
{mis}
{rec}
{dep}
{dub}
{out}
{nor}
\n", + "'''.format(**VALUE_LABELS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.8.2 Formatting clique lists" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "def xterse_chunk(i):\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = L.u('book', fword)\n", + " chapter = L.u('chapter', fword)\n", + " return (book, chapter)\n", + "\n", + "def xterse_clique(ii):\n", + " return tuple(sorted({xterse_chunk(i) for i in ii}))\n", + "\n", + "def terse_chunk(i):\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = L.u('book', fword)\n", + " chapter = L.u('chapter', fword)\n", + " verse = L.u('verse', fword)\n", + " return (book, chapter, verse)\n", + "\n", + "def terse_clique(ii):\n", + " return tuple(sorted({terse_chunk(i) for i in ii}))\n", + "\n", + "def verse_chunk(i):\n", + " (bk, ch, vs) = i\n", + " book = F.book.v(bk)\n", + " chapter = F.chapter.v(ch)\n", + " verse = F.verse.v(vs)\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', vs))\n", + " verse_label = '{} {}:{}'.format(book, chapter, verse)\n", + " htext = '{}{}'.format(verse_label, text)\n", + " return '{}'.format(htext)\n", + "\n", + "def verse_clique(ii):\n", + " return '{}
\\n'.format(''.join(verse_chunk(i) for i in sorted(ii)))\n", + "\n", + "def condense(vlabels):\n", + " cnd = ''\n", + " (cur_b, cur_c) = (None, None)\n", + " for (b, c, v) in vlabels:\n", + " sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '\n", + " show_b = b+' ' if cur_b != b else ''\n", + " show_c = c+':' if cur_b != b or cur_c != c else ''\n", + " (cur_b, cur_c) = (b, c)\n", + " cnd += '{}{}{}{}'.format(sep, show_b, show_c, v)\n", + " return cnd\n", + "\n", + "def print_diff(a, b):\n", + " arep = ''\n", + " brep = ''\n", + " for (lb, ai, aj, bi, bj) in SequenceMatcher(isjunk=None, a=a, b=b, autojunk=False).get_opcodes():\n", + " if lb == 'equal':\n", + " arep += a[ai:aj]\n", + " brep += b[bi:bj]\n", + " elif lb == 'delete':\n", + " arep += '{}'.format(lb, a[ai:aj])\n", + " elif lb == 'insert':\n", + " brep += '{}'.format(lb, b[bi:bj])\n", + " else:\n", + " arep += '{}'.format(lb, a[ai:aj])\n", + " brep += '{}'.format(lb, b[bi:bj])\n", + " return (arep, brep)\n", + " \n", + "def print_chunk_fine(prev, text, verse_labels, prevlabels):\n", + " if prev == None:\n", + " return '''\n", + "{}{}\n", + "'''.format(\n", + " condense(verse_labels), \n", + " text,\n", + " )\n", + " else:\n", + " (prevline, textline) = print_diff(prev, text)\n", + " return '''\n", + "{}{}\n", + "{}{}\n", + "'''.format(\n", + " condense(prevlabels) if prevlabels != None else 'previous',\n", + " prevline,\n", + " condense(verse_labels), \n", + " textline,\n", + ")\n", + "\n", + "def print_chunk_coarse(text, verse_labels):\n", + " return '''\n", + "{}{}\n", + "'''.format(\n", + " condense(verse_labels), \n", + " text,\n", + " )\n", + "\n", + "def print_clique(ii, ncliques):\n", + " return print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii)\n", + " \n", + "def print_clique_fine(ii):\n", + " condensed = collections.OrderedDict()\n", + " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = F.book.v(L.u('book', fword))\n", + " chapter = F.chapter.v(L.u('chapter', fword))\n", + " verse = F.verse.v(L.u('verse', fword))\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", + " condensed.setdefault(text, []).append((book, chapter, verse))\n", + " result = []\n", + " nv = len(condensed.items())\n", + " prev = None\n", + " for (text, verse_labels) in condensed.items():\n", + " if prev == None:\n", + " if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None))\n", + " else:\n", + " prev = text\n", + " prevlabels = verse_labels\n", + " continue\n", + " else:\n", + " result.append(print_chunk_fine(prev, text, verse_labels, prevlabels))\n", + " prev = text\n", + " prevlabels = None\n", + " return '{}
\\n'.format(''.join(result))\n", + "\n", + "def print_clique_coarse(ii):\n", + " condensed = collections.OrderedDict()\n", + " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = F.book.v(L.u('book', fword))\n", + " chapter = F.chapter.v(L.u('chapter', fword))\n", + " verse = F.verse.v(L.u('verse', fword))\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in chunk)\n", + " condensed.setdefault(text, []).append((book, chapter, verse))\n", + " result = []\n", + " nv = len(condensed.items())\n", + " prev = None\n", + " for (text, verse_labels) in condensed.items():\n", + " result.append(print_chunk_coarse(text, verse_labels))\n", + " if len(ii) > LARGE_CLIQUE_SIZE:\n", + " result.append(print_chunk_coarse('+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE),[]))\n", + " return '{}
\\n'.format(''.join(result))\n", + "\n", + "def index_clique(bnm, n, ii, ncliques):\n", + " return index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii)\n", + " \n", + "def index_clique_fine(bnm, n, ii):\n", + " verse_labels = []\n", + " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c)):\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = F.book.v(L.u('book', fword))\n", + " chapter = F.chapter.v(L.u('chapter', fword))\n", + " verse = F.verse.v(L.u('verse', fword))\n", + " verse_labels.append((book, chapter, verse))\n", + " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", + " return '

{} {}

'.format(\n", + " n, reffl, n, condense(verse_labels),\n", + " )\n", + "\n", + "def index_clique_coarse(bnm, n, ii):\n", + " verse_labels = []\n", + " for i in sorted(ii, key = lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]:\n", + " chunk = chunks[i]\n", + " fword = chunk[0]\n", + " book = F.book.v(L.u('book', fword))\n", + " chapter = F.chapter.v(L.u('chapter', fword))\n", + " verse = F.verse.v(L.u('verse', fword))\n", + " verse_labels.append((book, chapter, verse))\n", + " reffl = '{}_{}'.format(bnm, n // CLIQUES_PER_FILE)\n", + " extra = '+ {} ...'.format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else ''\n", + " return '

{} {}{}

'.format(\n", + " n, reffl, n, condense(verse_labels), extra,\n", + " )\n", + "\n", + "def lines_chapter(c):\n", + " lines = []\n", + " for v in L.d('verse', c):\n", + " vl = F.verse.v(v)\n", + " text = ''.join('{}{}'.format(F.g_word_utf8.v(w), F.trailer_utf8.v(w)) for w in L.d('word', v))\n", + " lines.append('{} {}'.format(vl, text.replace('\\n', ' ')))\n", + " return lines\n", + "\n", + "def compare_chapters(c1, c2, lb1, lb2):\n", + " dh = difflib.HtmlDiff(wrapcolumn=80)\n", + " table_html = dh.make_table(\n", + " lines_chapter(c1), \n", + " lines_chapter(c2), \n", + " fromdesc=lb1, \n", + " todesc=lb2, \n", + " context=False, \n", + " numlines=5,\n", + " )\n", + " htext = '''{}{}'''.format(diffhead, table_html)\n", + " return htext" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.8.3 Compiling the table of experiments\n", + "\n", + "Here we generate the table of experiments, complete with the coloring according to their assessments." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# generate the table of experiments\n", + "def gen_html(standalone=False):\n", + " global other_exps\n", + " msg('EXPERIMENT: Generating html report{}'.format('(standalone)' if standalone else ''))\n", + " stats = collections.Counter()\n", + " pre = '''\n", + "\n", + "\n", + "\n", + "{}\n", + "\n", + "\n", + "'''.format(ecss) if standalone else ''\n", + " \n", + " post = '''\n", + "\n", + "''' if standalone else ''\n", + "\n", + " experiments = '''\n", + "{}\n", + "{}\n", + "\n", + "{}\n", + "'''.format(pre, legend, ''.join(''.format(sim_thr) for sim_thr in SIMILARITIES))\n", + " \n", + " for chunk_f in (True, False):\n", + " if chunk_f:\n", + " chunk_items = CHUNK_SIZES\n", + " else:\n", + " chunk_items = CHUNK_OBJECTS\n", + " chunk_lb = CHUNK_LBS[chunk_f]\n", + " for chunk_i in chunk_items:\n", + " for sim_m in SIM_METHODS:\n", + " set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i)\n", + " these_outputs = outputs.get(MATRIX_THRESHOLD, {})\n", + " experiments += ''.format(\n", + " CHUNK_LABELS[chunk_f], chunk_i, sim_m,\n", + " )\n", + " for sim_thr in SIMILARITIES:\n", + " okey = (chunk_lb, chunk_i, sim_m, sim_thr)\n", + " values = these_outputs.get(okey)\n", + " if values == None:\n", + " result = ''\n", + " stats['mis'] += 1\n", + " else:\n", + " (npassages, ncliques, longest_clique_len) = values\n", + " cls = assess_exp(chunk_f, npassages, ncliques, longest_clique_len)\n", + " stats[cls] += 1\n", + " (lr_el, lr_lb) = ('', '')\n", + " if (CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD) == (\n", + " chunk_lb, chunk_i, sim_m, sim_thr,\n", + " ):\n", + " lr_el = '*'\n", + " lr_lb = VALUE_LABELS['lr']\n", + " result = '''\n", + "'''.format(\n", + " cls, lr_lb, lr_el, npassages,\n", + " '' if standalone else LOCAL_BASE_OUTP+'/', \n", + " EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr,\n", + " ncliques, longest_clique_len,\n", + " )\n", + " experiments += result\n", + " experiments += '\\n'\n", + " experiments += '
chunk typechunk sizesimilarity method
{}
{}{}{} {}\n", + " {}
\n", + " {}
\n", + " {}\n", + "
\\n{}'.format(post)\n", + " if standalone:\n", + " with open(EXPERIMENT_HTML, 'w') as f:\n", + " f.write(experiments)\n", + " else:\n", + " other_exps = experiments\n", + "\n", + " for stat in sorted(stats):\n", + " msg('EXPERIMENT: {:>3} {}'.format(stats[stat], VALUE_LABELS[stat]))\n", + " msg(\"EXPERIMENT: Generated html report\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.8.4 High level formatting functions\n", + "\n", + "Here everything concerning output is brought together." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def assess_exp(cf, np, nc, ll):\n", + " return 'out' if cf else \\\n", + " 'rec' if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else \\\n", + " 'dep' if ll > nc * DEP_CLIQUE_RATIO / 100 else \\\n", + " 'dub' if ll > nc * DUB_CLIQUE_RATIO / 100 else \\\n", + " 'nor'\n", + "\n", + "def printing():\n", + " global outputs, bin_cliques, base_name\n", + " msg('PRINT ({} {} {} M>{} S>{}): sorting out cliques'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " ))\n", + " xt_cliques = {xterse_clique(c) for c in cliques} # chapter cliques as tuples of (b, ch) tuples\n", + " bin_cliques = {c for c in xt_cliques if len(c) == 2} # chapter cliques with exactly two chapters\n", + " # all chapters that occur in binary chapter cliques\n", + " bin_chapters = {c[0] for c in bin_cliques} | {c[1] for c in bin_cliques}\n", + " meta['# BINARY CHAPTER DIFFS'] = len(bin_cliques)\n", + "\n", + " # We generate one kind of info for binary chapter cliques (the majority of cases).\n", + " # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they\n", + " # have member chunks in the same chapter, or in multiple (more than two) chapters.\n", + " \n", + " ncliques = len(cliques)\n", + " chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in {'rec', 'nor', 'dub'}\n", + " cdoing = 'involving' if chapters_ok else 'skipping'\n", + "\n", + " msg('PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " ncliques, cdoing, len(bin_cliques),\n", + " ))\n", + " meta_html = '\\n'.join('{:<40} : {:>10}'.format(k, str(meta[k])) for k in meta)\n", + "\n", + " base_name = '{}_{}_{}_M{}_S{}'.format(\n", + " CHUNK_LB,\n", + " CHUNK_DESC,\n", + " SIMILARITY_METHOD,\n", + " MATRIX_THRESHOLD,\n", + " SIMILARITY_THRESHOLD, \n", + " )\n", + " param_spec = '''\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
chunking method{}
chunking description{}
similarity method{}
similarity threshold{}
\n", + " '''.format(\n", + " CHUNK_LABELS[CHUNK_FIXED],\n", + " CHUNK_DESC,\n", + " SIMILARITY_METHOD, \n", + " SIMILARITY_THRESHOLD, \n", + " )\n", + " param_lab = 'chunk-{}-{}-sim-{}-m{}-s{}'.format(\n", + " CHUNK_LB,\n", + " CHUNK_DESC,\n", + " SIMILARITY_METHOD,\n", + " MATRIX_THRESHOLD,\n", + " SIMILARITY_THRESHOLD, \n", + " )\n", + " index_name = base_name\n", + " all_name = '{}_{}'.format('all', base_name)\n", + " cliques_name = '{}_{}'.format('clique', base_name)\n", + "\n", + " clique_links = []\n", + " clique_links.append(('{}/{}.html'.format(base_name, all_name), 'Big list of all cliques'))\n", + "\n", + " nexist = 0\n", + " nnew = 0\n", + " if chapters_ok:\n", + " chapter_diffs = []\n", + " msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(bin_cliques),\n", + " ))\n", + "\n", + " bcc_text = '

These results look good, so a binary chapter comparison has been generated

'\n", + " for cl in sorted(bin_cliques):\n", + " lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))\n", + " lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))\n", + " hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')\n", + " hfilepath = '{}/{}/{}'.format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename)\n", + " chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(\n", + " SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,\n", + " )))\n", + " if not os.path.exists(hfilepath):\n", + " htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2)\n", + " with open(hfilepath, 'w') as f: f.write(htext)\n", + " if VERBOSE:\n", + " msg('PRINT ({} {} {} M>{} S>{}): written {}'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " hfilename,\n", + " ))\n", + " nnew += 1\n", + " else:\n", + " nexist += 1\n", + " clique_links.append((\n", + " '../{}/{}'.format(CHAPTER_DIR, hfilename), \n", + " '{} versus {}'.format(lb1, lb2),\n", + " ))\n", + " msg('PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " nnew, nexist,\n", + " ))\n", + " else:\n", + " bcc_text = '

These results look dubious at best, so no binary chapter comparison has been generated

'\n", + "\n", + "\n", + " allgeni_html = (index_clique(cliques_name, i, c, ncliques) for (i,c) in enumerate(cliques))\n", + " \n", + " allgen_htmls = []\n", + " allgen_html = ''\n", + " \n", + " for (i, c) in enumerate(cliques):\n", + " if i % CLIQUES_PER_FILE == 0:\n", + " if i > 0:\n", + " allgen_htmls.append(allgen_html)\n", + " allgen_html = ''\n", + " allgen_html += '

Clique {}

\\n{}'.format(i, i, print_clique(c, ncliques))\n", + " allgen_htmls.append(allgen_html)\n", + "\n", + " index_html_tpl = '''\n", + "{}\n", + "

Binary chapter comparisons

\n", + "{}\n", + "{}\n", + " '''\n", + "\n", + " content_file_tpl = '''\n", + "\n", + "\n", + "{}\n", + "\n", + "\n", + "\n", + "

{}

\n", + "{}\n", + "

more parameters and stats

\n", + "{}\n", + "

Parameters and stats

\n", + "
{}
\n", + "\n", + "'''\n", + " \n", + " a_tpl_file = '

{}

'\n", + "\n", + " index_html_file = index_html_tpl.format(\n", + " a_tpl_file.format(*clique_links[0]),\n", + " bcc_text,\n", + " '\\n'.join(a_tpl_file.format(*c) for c in clique_links[1:]),\n", + " )\n", + "\n", + " listing_html = '{}\\n'.format(\n", + " '\\n'.join(allgeni_html),\n", + " )\n", + "\n", + " for (subdir, fname, content_html, tit) in (\n", + " (None, index_name, index_html_file, 'Index '+param_lab),\n", + " (base_name, all_name, listing_html, 'Listing '+param_lab),\n", + " (base_name, cliques_name, allgen_htmls, 'Cliques '+param_lab),\n", + " ): \n", + " subdir = '' if subdir == None else (subdir + '/')\n", + " subdirabs = '{}/{}/{}'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir)\n", + " if not os.path.exists(subdirabs): os.makedirs(subdirabs)\n", + "\n", + " if type(content_html) is list:\n", + " for (i, c_h) in enumerate(content_html):\n", + " fn = '{}_{}'.format(fname, i)\n", + " t = '{}_{}'.format(tit, i)\n", + " with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn), 'w') as f: \n", + " f.write(content_file_tpl.format(t, css, t, param_spec, c_h, meta_html))\n", + " else:\n", + " with open('{}/{}/{}{}.html'.format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname), 'w') as f: \n", + " f.write(content_file_tpl.format(tit, css, tit, param_spec, content_html, meta_html))\n", + " destination = outputs.setdefault(MATRIX_THRESHOLD, {})\n", + " destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = (\n", + " len(passages), len(cliques), l_c_l,\n", + " )\n", + " msg('PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs'.format(\n", + " CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD,\n", + " len(cliques), len(allgen_htmls), cdoing, len(bin_cliques)\n", + " ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.9 Running experiments\n", + "\n", + "The workflows of doing a single experiment, and then all experiments, are defined." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "outputs = {}\n", + "\n", + "def writeoutputs():\n", + " global outputs\n", + " with open(EXPERIMENT_PATH, 'wb') as f:\n", + " pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL)\n", + "\n", + "def readoutputs():\n", + " global outputs\n", + " if not os.path.exists(EXPERIMENT_PATH):\n", + " outputs = {}\n", + " else:\n", + " with open(EXPERIMENT_PATH, 'rb') as f:\n", + " outputs = pickle.load(f)\n", + "\n", + "def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index):\n", + " if do_index:\n", + " readoutputs()\n", + " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", + " if skip: return\n", + " chunking(do_chunk)\n", + " preparing(do_prep)\n", + " similarity(do_sim)\n", + " cliqueing(do_clique)\n", + " printing()\n", + " if do_index:\n", + " writeoutputs()\n", + " gen_html()\n", + "\n", + "def do_only_chunk(chunk_f, chunk_i):\n", + " do_chunk = do_params_chunk(chunk_f, chunk_i)\n", + " chunking(do_chunk)\n", + "\n", + "def reset_experiments():\n", + " global outputs\n", + " readoutputs()\n", + " outputs = {}\n", + " reset_params()\n", + " writeoutputs()\n", + " gen_html()\n", + "\n", + "def do_all_experiments(no_fixed=False, only_object=None):\n", + " global outputs\n", + " reset_experiments()\n", + " for chunk_f in (False,) if no_fixed else (True, False):\n", + " if chunk_f:\n", + " chunk_items = CHUNK_SIZES\n", + " else:\n", + " chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)\n", + " for chunk_i in chunk_items:\n", + " for sim_m in SIM_METHODS:\n", + " for sim_thr in SIMILARITIES:\n", + " do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False)\n", + " writeoutputs()\n", + " gen_html()\n", + " gen_html(standalone=True)\n", + "\n", + "def do_all_chunks(no_fixed=False, only_object=None):\n", + " global outputs\n", + " reset_experiments()\n", + " for chunk_f in (False,) if no_fixed else (True, False):\n", + " if chunk_f:\n", + " chunk_items = CHUNK_SIZES\n", + " else:\n", + " chunk_items = CHUNK_OBJECTS if only_object==None else (only_object,)\n", + " for chunk_i in chunk_items:\n", + " do_only_chunk(chunk_f, chunk_i)\n", + " \n", + "def show_all_experiments():\n", + " readoutputs()\n", + " gen_html()\n", + " gen_html(standalone=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. SHEBANQ annotations\n", + "\n", + "Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca)\n", + "\n", + "def get_verse_o(o, ca=False): return get_verse_w(L.d('word', o)[0], ca=ca)\n", + "\n", + "def get_verse_w(w, ca=False):\n", + " book = F.book.v(L.u('book', w))\n", + " chapter = F.chapter.v(L.u('chapter', w))\n", + " verse = F.verse.v(L.u('verse', w))\n", + " if ca: ca = F.number.v(L.u('clause_atom', w))\n", + " return (book, chapter, verse, ca) if ca else (book, chapter, verse)\n", + "\n", + "def key_verse(x):\n", + " return (book_rank[x[0]], int(x[1]), int(x[2]))\n", + "\n", + "MAX_REFS = 10\n", + "\n", + "def condensex(vlabels):\n", + " cnd = []\n", + " (cur_b, cur_c) = (None, None)\n", + " for (b, c, v, d) in vlabels:\n", + " sep = '' if cur_b == None else '. ' if cur_b != b else '; ' if cur_c != c else ', '\n", + " show_b = b+' ' if cur_b != b else ''\n", + " show_c = c+':' if cur_b != b or cur_c != c else ''\n", + " (cur_b, cur_c) = (b, c)\n", + " cnd.append('{}{}{}{}{}'.format(sep, show_b, show_c, v, d))\n", + " return cnd\n", + "\n", + "dfields = '''\n", + " book1\n", + " chapter1\n", + " verse1\n", + " book2\n", + " chapter2\n", + " verse2\n", + " similarity\n", + "'''.strip().split()\n", + "\n", + "dfields_fmt = ('{}\\t' * (len(dfields) - 1)) + '{}\\n' \n", + "\n", + "def get_crossrefs():\n", + " global crossrefs\n", + " msg('CROSSREFS: Fetching crossrefs')\n", + " crossrefs_proto = {}\n", + " crossrefs = {}\n", + " (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX\n", + " sim_thr = SHEBANQ_SIMILARITY\n", + " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", + " if skip: return\n", + " msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", + " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", + " msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", + " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", + " len(crossrefs_proto),\n", + " ))\n", + " f = open(CROSSREF_DB_PATH, 'w')\n", + " f.write('{}\\n'.format('\\t'.join(dfields))) \n", + " for ((x,y), d) in crossrefs_proto:\n", + " vx = get_verse(x)\n", + " vy = get_verse(y)\n", + " rd = int(round(d))\n", + " crossrefs.setdefault(x, {})[vy] = rd\n", + " crossrefs.setdefault(y, {})[vx] = rd\n", + " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", + " total = sum(len(x) for x in crossrefs.values())\n", + " f.close()\n", + " msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", + "\n", + "def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to):\n", + " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", + " if skip: return\n", + " chunking(do_chunk)\n", + " preparing(do_prep)\n", + " similarity(do_sim)\n", + "\n", + " msg('CROSSREFS: Fetching crossrefs')\n", + " crossrefs_proto = {}\n", + " crossrefs = {}\n", + " (do_chunk, do_prep, do_sim, do_clique, skip) = do_params(chunk_f, chunk_i, sim_m, sim_thr)\n", + " if skip: return\n", + " msg('CROSSREFS ({} {} {} S>{})'.format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr))\n", + " crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr}\n", + " msg('CROSSREFS ({} {} {} S>{}): found {} pairs'.format(\n", + " CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr,\n", + " len(crossrefs_proto),\n", + " ))\n", + " f = open('files/{}'.format(write_to), 'w')\n", + " f.write('{}\\n'.format('\\t'.join(dfields))) \n", + " for ((x,y), d) in crossrefs_proto:\n", + " vx = get_verse(x)\n", + " vy = get_verse(y)\n", + " rd = int(round(d))\n", + " crossrefs.setdefault(x, {})[vy] = rd\n", + " crossrefs.setdefault(y, {})[vx] = rd\n", + " f.write(dfields_fmt.format(*(vx+vy+(rd,))))\n", + " total = sum(len(x) for x in crossrefs.values())\n", + " f.close()\n", + " msg('CROSSREFS: Found {} crossreferences and wrote {} pairs'.format(total, len(crossrefs_proto)))\n", + "\n", + "def compile_refs():\n", + " global refs_compiled\n", + " refs_grouped = []\n", + " for x in sorted(crossrefs):\n", + " refs = crossrefs[x]\n", + " vys = sorted(refs.keys(), key=key_verse)\n", + " currefs = []\n", + " for vy in vys:\n", + " nr = len(currefs)\n", + " if nr == MAX_REFS:\n", + " refs_grouped.append((x, tuple(currefs)))\n", + " currefs = [] \n", + " currefs.append(vy)\n", + " if len(currefs):\n", + " refs_grouped.append((x, tuple(currefs)))\n", + " refs_compiled = []\n", + " for (x, vys) in refs_grouped:\n", + " vysd = [(vy[0], vy[1], vy[2], ' ~{}%'.format(crossrefs[x][vy])) for vy in vys]\n", + " vysl = condensex(vysd)\n", + " these_refs = []\n", + " for (i, vy) in enumerate(vysd):\n", + " link_text = vysl[i]\n", + " link_target = '{} {}:{}'.format(vy[0], vy[1], vy[2])\n", + " these_refs.append('[{}]({})'.format(link_text, link_target))\n", + " refs_compiled.append((x, ' '.join(these_refs)))\n", + " msg('CROSSREFS: Compiled cross references into {} notes'.format(len(refs_compiled)))\n", + "\n", + "def get_chapter_diffs():\n", + " global chapter_diffs\n", + " chapter_diffs = []\n", + " for cl in sorted(bin_cliques):\n", + " lb1 = '{} {}'.format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1]))\n", + " lb2 = '{} {}'.format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1]))\n", + " hfilename = '{}_vs_{}.html'.format(lb1, lb2).replace(' ','_')\n", + " chapter_diffs.append((lb1, cl[0][1], lb2, cl[1][1], '{}/{}/{}/{}'.format(\n", + " SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename,\n", + " )))\n", + " msg('CROSSREFS: Added {} chapter diffs'.format(2*len(chapter_diffs)))\n", + "\n", + " \n", + "def get_clique_refs():\n", + " global clique_refs\n", + " clique_refs = []\n", + " for (i, c) in enumerate(cliques):\n", + " for j in c:\n", + " seq = i // CLIQUES_PER_FILE\n", + " clique_refs.append((j, i, '{}/{}/{}/{}/clique_{}_{}.html#c_{}'.format(\n", + " SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i,\n", + " )))\n", + " msg('CROSSREFS: Added {} clique references'.format(len(clique_refs)))\n", + "\n", + "sfields = '''\n", + " version\n", + " book\n", + " chapter\n", + " verse\n", + " clause_atom\n", + " is_shared\n", + " is_published\n", + " status\n", + " keywords\n", + " ntext\n", + "'''.strip().split()\n", + "\n", + "sfields_fmt = ('{}\\t' * (len(sfields) - 1)) + '{}\\n' \n", + "\n", + "def generate_notes():\n", + " with open(NOTES_PATH, 'w') as f:\n", + " f.write('{}\\n'.format('\\t'.join(sfields))) \n", + " x = next(F.otype.s('word'))\n", + " (bk, ch, vs, ca) = get_verse(x, ca=True)\n", + " f.write(sfields_fmt.format(\n", + " version,\n", + " bk,\n", + " ch,\n", + " vs,\n", + " ca,\n", + " 'T',\n", + " '',\n", + " CROSSREF_STATUS,\n", + " CROSSREF_KEYWORD,\n", + " '''The crossref notes are the result of a computation without manual tweaks.\n", + "Parameters: chunk by verse, similarity method SET with threshold 65.\n", + "[Here](tool=parallel) is an account of the generation method.'''.replace('\\n', ' ')\n", + " ))\n", + " for (lb1, ch1, lb2, ch2, fl) in chapter_diffs:\n", + " (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True)\n", + " (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True)\n", + " f.write(sfields_fmt.format(\n", + " version,\n", + " bk1,\n", + " ch1,\n", + " vs1,\n", + " ca1,\n", + " 'T',\n", + " '',\n", + " CROSSREF_STATUS,\n", + " CROSSREF_KEYWORD,\n", + " '[chapter diff with {}](tool:{})'.format(lb2, fl),\n", + " ))\n", + " f.write(sfields_fmt.format(\n", + " version,\n", + " bk2,\n", + " ch2,\n", + " vs2,\n", + " ca2,\n", + " 'T',\n", + " '',\n", + " CROSSREF_STATUS,\n", + " CROSSREF_KEYWORD,\n", + " '[chapter diff with {}](tool:{})'.format(lb1, fl),\n", + " ))\n", + " for (x, refs) in refs_compiled:\n", + " (bk, ch, vs, ca) = get_verse(x, ca=True)\n", + " f.write(sfields_fmt.format(\n", + " version,\n", + " bk,\n", + " ch,\n", + " vs,\n", + " ca,\n", + " 'T',\n", + " '',\n", + " CROSSREF_STATUS,\n", + " CROSSREF_KEYWORD,\n", + " refs,\n", + " ))\n", + " for (chunk, clique, fl) in clique_refs:\n", + " (bk, ch, vs, ca) = get_verse(chunk, ca=True)\n", + " f.write(sfields_fmt.format(\n", + " version,\n", + " bk,\n", + " ch,\n", + " vs,\n", + " ca,\n", + " 'T',\n", + " '',\n", + " CROSSREF_STATUS,\n", + " CROSSREF_KEYWORD,\n", + " '[all variants (clique {})](tool:{})'.format(clique, fl),\n", + " ))\n", + "\n", + " msg('CROSSREFS: Generated {} notes'.format(1+len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs)))\n", + "\n", + "def crossrefs2shebanq():\n", + " expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,)\n", + " do_experiment(*(expr+(True,)))\n", + " get_crossrefs()\n", + " compile_refs()\n", + " get_chapter_diffs()\n", + " get_clique_refs()\n", + " generate_notes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Main\n", + "\n", + "In the cell below you can select the experiments you want to carry out.\n", + "\n", + "The previous cells contain just definitions and parameters.\n", + "The next cell will do work.\n", + "\n", + "If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 37s EXPERIMENT: Generating html report\n", + " 37s EXPERIMENT: 240 no results available\n", + " 37s EXPERIMENT: Generated html report\n", + " 37s CHUNKING (F 100): Loaded: 4244 chunks\n", + " 37s CHUNKING (F 100): Made 4244 chunks\n", + " 37s PREPARING (F 100 SET)\n", + " 37s PREPARING (F 100 SET): Done 4244 chunks.\n", + " 37s SIMILARITY (F 100 SET M>50): Loaded: 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 37s CLIQUES (F 100 SET M>50 S>100): fetching similars and chunk candidates\n", + " 37s CLIQUES (F 100 SET M>50 S>100): inspecting the similarity matrix\n", + " 37s CLIQUES (F 100 SET M>50 S>100): 1 relevant similarities between 2 passages\n", + " 37s CLIQUES (F 100 SET M>50 S>100): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 37s CLIQUES (F 100 SET M>50 S>100): 2 members in 1 cliques\n", + " 37s PRINT (F 100 SET M>50 S>100): sorting out cliques\n", + " 37s PRINT (F 100 SET M>50 S>100): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 37s PRINT (F 100 SET M>50 S>100): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 37s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 37s PREPARING (F 100 SET): Already prepared\n", + " 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 37s CLIQUES (F 100 SET M>50 S>95): fetching similars and chunk candidates\n", + " 37s CLIQUES (F 100 SET M>50 S>95): inspecting the similarity matrix\n", + " 37s CLIQUES (F 100 SET M>50 S>95): 2 relevant similarities between 4 passages\n", + " 37s CLIQUES (F 100 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 37s CLIQUES (F 100 SET M>50 S>95): 4 members in 2 cliques\n", + " 37s PRINT (F 100 SET M>50 S>95): sorting out cliques\n", + " 37s PRINT (F 100 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 37s PRINT (F 100 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 37s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 37s PREPARING (F 100 SET): Already prepared\n", + " 37s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 37s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 38s CLIQUES (F 100 SET M>50 S>90): fetching similars and chunk candidates\n", + " 38s CLIQUES (F 100 SET M>50 S>90): inspecting the similarity matrix\n", + " 38s CLIQUES (F 100 SET M>50 S>90): 9 relevant similarities between 18 passages\n", + " 38s CLIQUES (F 100 SET M>50 S>90): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", + " 38s CLIQUES (F 100 SET M>50 S>90): 18 members in 9 cliques\n", + " 38s PRINT (F 100 SET M>50 S>90): sorting out cliques\n", + " 38s PRINT (F 100 SET M>50 S>90): formatting 9 cliques skipping 3 binary chapter diffs\n", + " 38s PRINT (F 100 SET M>50 S>90): formatted 9 cliques (1 files) skipping 3 binary chapter diffs\n", + " 38s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 38s PREPARING (F 100 SET): Already prepared\n", + " 38s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 38s CLIQUES (F 100 SET M>50 S>85): fetching similars and chunk candidates\n", + " 38s CLIQUES (F 100 SET M>50 S>85): inspecting the similarity matrix\n", + " 38s CLIQUES (F 100 SET M>50 S>85): 19 relevant similarities between 37 passages\n", + " 38s CLIQUES (F 100 SET M>50 S>85): Loaded: 18 cliques out of 37 chunks from 19 comparisons\n", + " 38s CLIQUES (F 100 SET M>50 S>85): 37 members in 18 cliques\n", + " 38s PRINT (F 100 SET M>50 S>85): sorting out cliques\n", + " 38s PRINT (F 100 SET M>50 S>85): formatting 18 cliques skipping 6 binary chapter diffs\n", + " 38s PRINT (F 100 SET M>50 S>85): formatted 18 cliques (1 files) skipping 6 binary chapter diffs\n", + " 38s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 38s PREPARING (F 100 SET): Already prepared\n", + " 38s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 38s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 38s CLIQUES (F 100 SET M>50 S>80): fetching similars and chunk candidates\n", + " 38s CLIQUES (F 100 SET M>50 S>80): inspecting the similarity matrix\n", + " 38s CLIQUES (F 100 SET M>50 S>80): 35 relevant similarities between 64 passages\n", + " 38s CLIQUES (F 100 SET M>50 S>80): Loaded: 30 cliques out of 64 chunks from 35 comparisons\n", + " 38s CLIQUES (F 100 SET M>50 S>80): 64 members in 30 cliques\n", + " 38s PRINT (F 100 SET M>50 S>80): sorting out cliques\n", + " 38s PRINT (F 100 SET M>50 S>80): formatting 30 cliques skipping 10 binary chapter diffs\n", + " 39s PRINT (F 100 SET M>50 S>80): formatted 30 cliques (1 files) skipping 10 binary chapter diffs\n", + " 39s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 39s PREPARING (F 100 SET): Already prepared\n", + " 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 39s CLIQUES (F 100 SET M>50 S>75): fetching similars and chunk candidates\n", + " 39s CLIQUES (F 100 SET M>50 S>75): inspecting the similarity matrix\n", + " 39s CLIQUES (F 100 SET M>50 S>75): 63 relevant similarities between 87 passages\n", + " 39s CLIQUES (F 100 SET M>50 S>75): Loaded: 40 cliques out of 87 chunks from 63 comparisons\n", + " 39s CLIQUES (F 100 SET M>50 S>75): 87 members in 40 cliques\n", + " 39s PRINT (F 100 SET M>50 S>75): sorting out cliques\n", + " 39s PRINT (F 100 SET M>50 S>75): formatting 40 cliques skipping 16 binary chapter diffs\n", + " 39s PRINT (F 100 SET M>50 S>75): formatted 40 cliques (1 files) skipping 16 binary chapter diffs\n", + " 39s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 39s PREPARING (F 100 SET): Already prepared\n", + " 39s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 39s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 39s CLIQUES (F 100 SET M>50 S>70): fetching similars and chunk candidates\n", + " 39s CLIQUES (F 100 SET M>50 S>70): inspecting the similarity matrix\n", + " 39s CLIQUES (F 100 SET M>50 S>70): 87 relevant similarities between 113 passages\n", + " 39s CLIQUES (F 100 SET M>50 S>70): Loaded: 52 cliques out of 113 chunks from 87 comparisons\n", + " 39s CLIQUES (F 100 SET M>50 S>70): 113 members in 52 cliques\n", + " 39s PRINT (F 100 SET M>50 S>70): sorting out cliques\n", + " 39s PRINT (F 100 SET M>50 S>70): formatting 52 cliques skipping 21 binary chapter diffs\n", + " 41s PRINT (F 100 SET M>50 S>70): formatted 52 cliques (2 files) skipping 21 binary chapter diffs\n", + " 41s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 41s PREPARING (F 100 SET): Already prepared\n", + " 41s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 41s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 41s CLIQUES (F 100 SET M>50 S>65): fetching similars and chunk candidates\n", + " 41s CLIQUES (F 100 SET M>50 S>65): inspecting the similarity matrix\n", + " 41s CLIQUES (F 100 SET M>50 S>65): 115 relevant similarities between 154 passages\n", + " 41s CLIQUES (F 100 SET M>50 S>65): Loaded: 70 cliques out of 154 chunks from 115 comparisons\n", + " 41s CLIQUES (F 100 SET M>50 S>65): 154 members in 70 cliques\n", + " 41s PRINT (F 100 SET M>50 S>65): sorting out cliques\n", + " 41s PRINT (F 100 SET M>50 S>65): formatting 70 cliques skipping 28 binary chapter diffs\n", + " 42s PRINT (F 100 SET M>50 S>65): formatted 70 cliques (2 files) skipping 28 binary chapter diffs\n", + " 42s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 42s PREPARING (F 100 SET): Already prepared\n", + " 42s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 42s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 42s CLIQUES (F 100 SET M>50 S>60): fetching similars and chunk candidates\n", + " 42s CLIQUES (F 100 SET M>50 S>60): inspecting the similarity matrix\n", + " 42s CLIQUES (F 100 SET M>50 S>60): 148 relevant similarities between 208 passages\n", + " 42s CLIQUES (F 100 SET M>50 S>60): Loaded: 94 cliques out of 208 chunks from 148 comparisons\n", + " 42s CLIQUES (F 100 SET M>50 S>60): 208 members in 94 cliques\n", + " 42s PRINT (F 100 SET M>50 S>60): sorting out cliques\n", + " 42s PRINT (F 100 SET M>50 S>60): formatting 94 cliques skipping 35 binary chapter diffs\n", + " 44s PRINT (F 100 SET M>50 S>60): formatted 94 cliques (2 files) skipping 35 binary chapter diffs\n", + " 44s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 44s PREPARING (F 100 SET): Already prepared\n", + " 44s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 44s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 44s CLIQUES (F 100 SET M>50 S>55): fetching similars and chunk candidates\n", + " 44s CLIQUES (F 100 SET M>50 S>55): inspecting the similarity matrix\n", + " 44s CLIQUES (F 100 SET M>50 S>55): 225 relevant similarities between 309 passages\n", + " 44s CLIQUES (F 100 SET M>50 S>55): Loaded: 138 cliques out of 309 chunks from 225 comparisons\n", + " 44s CLIQUES (F 100 SET M>50 S>55): 309 members in 138 cliques\n", + " 44s PRINT (F 100 SET M>50 S>55): sorting out cliques\n", + " 44s PRINT (F 100 SET M>50 S>55): formatting 138 cliques skipping 54 binary chapter diffs\n", + " 47s PRINT (F 100 SET M>50 S>55): formatted 138 cliques (3 files) skipping 54 binary chapter diffs\n", + " 47s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 47s PREPARING (F 100 SET): Already prepared\n", + " 47s SIMILARITY (F 100 SET M>50): Using 9003 K (9003646) comparisons with 359 entries in matrix\n", + " 47s SIMILARITY (F 100 SET M>50): similarities between 50.0 and 100.0. 1 are 100%\n", + " 47s CLIQUES (F 100 SET M>50 S>50): fetching similars and chunk candidates\n", + " 47s CLIQUES (F 100 SET M>50 S>50): inspecting the similarity matrix\n", + " 47s CLIQUES (F 100 SET M>50 S>50): 359 relevant similarities between 473 passages\n", + " 47s CLIQUES (F 100 SET M>50 S>50): Loaded: 189 cliques out of 473 chunks from 359 comparisons\n", + " 47s CLIQUES (F 100 SET M>50 S>50): 473 members in 189 cliques\n", + " 47s PRINT (F 100 SET M>50 S>50): sorting out cliques\n", + " 47s PRINT (F 100 SET M>50 S>50): formatting 189 cliques skipping 75 binary chapter diffs\n", + " 53s PRINT (F 100 SET M>50 S>50): formatted 189 cliques (4 files) skipping 75 binary chapter diffs\n", + " 53s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 53s PREPARING (F 100 LCS)\n", + " 54s PREPARING (F 100 LCS): Done 4244 chunks.\n", + " 54s SIMILARITY (F 100 LCS M>60): Loaded: 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 54s CLIQUES (F 100 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 54s CLIQUES (F 100 LCS M>60 S>100): inspecting the similarity matrix\n", + " 54s CLIQUES (F 100 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", + " 54s CLIQUES (F 100 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 54s CLIQUES (F 100 LCS M>60 S>100): 0 members in 0 cliques\n", + " 54s PRINT (F 100 LCS M>60 S>100): sorting out cliques\n", + " 54s PRINT (F 100 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 54s PRINT (F 100 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 54s PREPARING (F 100 LCS): Already prepared\n", + " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 54s CLIQUES (F 100 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 54s CLIQUES (F 100 LCS M>60 S>95): inspecting the similarity matrix\n", + " 54s CLIQUES (F 100 LCS M>60 S>95): 2 relevant similarities between 4 passages\n", + " 54s CLIQUES (F 100 LCS M>60 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 54s CLIQUES (F 100 LCS M>60 S>95): 4 members in 2 cliques\n", + " 54s PRINT (F 100 LCS M>60 S>95): sorting out cliques\n", + " 54s PRINT (F 100 LCS M>60 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 54s PRINT (F 100 LCS M>60 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 54s PREPARING (F 100 LCS): Already prepared\n", + " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 54s CLIQUES (F 100 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 54s CLIQUES (F 100 LCS M>60 S>90): inspecting the similarity matrix\n", + " 54s CLIQUES (F 100 LCS M>60 S>90): 21 relevant similarities between 39 passages\n", + " 54s CLIQUES (F 100 LCS M>60 S>90): Loaded: 19 cliques out of 39 chunks from 21 comparisons\n", + " 54s CLIQUES (F 100 LCS M>60 S>90): 39 members in 19 cliques\n", + " 54s PRINT (F 100 LCS M>60 S>90): sorting out cliques\n", + " 54s PRINT (F 100 LCS M>60 S>90): formatting 19 cliques skipping 4 binary chapter diffs\n", + " 54s PRINT (F 100 LCS M>60 S>90): formatted 19 cliques (1 files) skipping 4 binary chapter diffs\n", + " 54s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 54s PREPARING (F 100 LCS): Already prepared\n", + " 54s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 54s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 54s CLIQUES (F 100 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 54s CLIQUES (F 100 LCS M>60 S>85): inspecting the similarity matrix\n", + " 54s CLIQUES (F 100 LCS M>60 S>85): 31 relevant similarities between 59 passages\n", + " 54s CLIQUES (F 100 LCS M>60 S>85): Loaded: 29 cliques out of 59 chunks from 31 comparisons\n", + " 54s CLIQUES (F 100 LCS M>60 S>85): 59 members in 29 cliques\n", + " 54s PRINT (F 100 LCS M>60 S>85): sorting out cliques\n", + " 54s PRINT (F 100 LCS M>60 S>85): formatting 29 cliques skipping 10 binary chapter diffs\n", + " 55s PRINT (F 100 LCS M>60 S>85): formatted 29 cliques (1 files) skipping 10 binary chapter diffs\n", + " 55s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 55s PREPARING (F 100 LCS): Already prepared\n", + " 55s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 55s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 55s CLIQUES (F 100 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 55s CLIQUES (F 100 LCS M>60 S>80): inspecting the similarity matrix\n", + " 55s CLIQUES (F 100 LCS M>60 S>80): 46 relevant similarities between 85 passages\n", + " 55s CLIQUES (F 100 LCS M>60 S>80): Loaded: 41 cliques out of 85 chunks from 46 comparisons\n", + " 55s CLIQUES (F 100 LCS M>60 S>80): 85 members in 41 cliques\n", + " 55s PRINT (F 100 LCS M>60 S>80): sorting out cliques\n", + " 55s PRINT (F 100 LCS M>60 S>80): formatting 41 cliques skipping 16 binary chapter diffs\n", + " 56s PRINT (F 100 LCS M>60 S>80): formatted 41 cliques (1 files) skipping 16 binary chapter diffs\n", + " 56s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 56s PREPARING (F 100 LCS): Already prepared\n", + " 56s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 56s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 56s CLIQUES (F 100 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 56s CLIQUES (F 100 LCS M>60 S>75): inspecting the similarity matrix\n", + " 56s CLIQUES (F 100 LCS M>60 S>75): 77 relevant similarities between 122 passages\n", + " 56s CLIQUES (F 100 LCS M>60 S>75): Loaded: 56 cliques out of 122 chunks from 77 comparisons\n", + " 56s CLIQUES (F 100 LCS M>60 S>75): 122 members in 56 cliques\n", + " 56s PRINT (F 100 LCS M>60 S>75): sorting out cliques\n", + " 56s PRINT (F 100 LCS M>60 S>75): formatting 56 cliques skipping 25 binary chapter diffs\n", + " 57s PRINT (F 100 LCS M>60 S>75): formatted 56 cliques (2 files) skipping 25 binary chapter diffs\n", + " 57s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 57s PREPARING (F 100 LCS): Already prepared\n", + " 57s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 57s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 57s CLIQUES (F 100 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 57s CLIQUES (F 100 LCS M>60 S>70): inspecting the similarity matrix\n", + " 57s CLIQUES (F 100 LCS M>60 S>70): 123 relevant similarities between 189 passages\n", + " 57s CLIQUES (F 100 LCS M>60 S>70): Loaded: 88 cliques out of 189 chunks from 123 comparisons\n", + " 57s CLIQUES (F 100 LCS M>60 S>70): 189 members in 88 cliques\n", + " 57s PRINT (F 100 LCS M>60 S>70): sorting out cliques\n", + " 57s PRINT (F 100 LCS M>60 S>70): formatting 88 cliques skipping 38 binary chapter diffs\n", + " 59s PRINT (F 100 LCS M>60 S>70): formatted 88 cliques (2 files) skipping 38 binary chapter diffs\n", + " 59s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 59s PREPARING (F 100 LCS): Already prepared\n", + " 59s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 59s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 59s CLIQUES (F 100 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 59s CLIQUES (F 100 LCS M>60 S>65): inspecting the similarity matrix\n", + " 59s CLIQUES (F 100 LCS M>60 S>65): 182 relevant similarities between 287 passages\n", + " 59s CLIQUES (F 100 LCS M>60 S>65): Loaded: 132 cliques out of 287 chunks from 182 comparisons\n", + " 59s CLIQUES (F 100 LCS M>60 S>65): 287 members in 132 cliques\n", + " 59s PRINT (F 100 LCS M>60 S>65): sorting out cliques\n", + " 59s PRINT (F 100 LCS M>60 S>65): formatting 132 cliques skipping 55 binary chapter diffs\n", + " 1m 02s PRINT (F 100 LCS M>60 S>65): formatted 132 cliques (3 files) skipping 55 binary chapter diffs\n", + " 1m 02s CHUNKING (F 100): already chunked into 4244 chunks\n", + " 1m 02s PREPARING (F 100 LCS): Already prepared\n", + " 1m 02s SIMILARITY (F 100 LCS M>60): Using 9003 K (9003646) comparisons with 393 entries in matrix\n", + " 1m 02s SIMILARITY (F 100 LCS M>60): similarities between 60.0 and 97.6923076923077. 0 are 100%\n", + " 1m 02s CLIQUES (F 100 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 1m 02s CLIQUES (F 100 LCS M>60 S>60): inspecting the similarity matrix\n", + " 1m 02s CLIQUES (F 100 LCS M>60 S>60): 393 relevant similarities between 535 passages\n", + " 1m 02s CLIQUES (F 100 LCS M>60 S>60): Loaded: 214 cliques out of 535 chunks from 393 comparisons\n", + " 1m 02s CLIQUES (F 100 LCS M>60 S>60): 535 members in 214 cliques\n", + " 1m 02s PRINT (F 100 LCS M>60 S>60): sorting out cliques\n", + " 1m 02s PRINT (F 100 LCS M>60 S>60): formatting 214 cliques skipping 100 binary chapter diffs\n", + " 1m 08s PRINT (F 100 LCS M>60 S>60): formatted 214 cliques (5 files) skipping 100 binary chapter diffs\n", + " 1m 08s CHUNKING (F 50): Loaded: 8509 chunks\n", + " 1m 08s CHUNKING (F 50): Made 8509 chunks\n", + " 1m 08s PREPARING (F 50 SET)\n", + " 1m 09s PREPARING (F 50 SET): Done 8509 chunks.\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Loaded: 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>100): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>100): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 relevant similarities between 0 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>100): 0 members in 0 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>100): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 1m 09s PRINT (F 50 SET M>50 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 09s PREPARING (F 50 SET): Already prepared\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>95): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>95): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>95): 2 relevant similarities between 4 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>95): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>95): 4 members in 2 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>95): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>95): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 1m 09s PRINT (F 50 SET M>50 S>95): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 09s PREPARING (F 50 SET): Already prepared\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>90): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>90): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>90): 12 relevant similarities between 24 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>90): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>90): 24 members in 12 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>90): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>90): formatting 12 cliques skipping 4 binary chapter diffs\n", + " 1m 09s PRINT (F 50 SET M>50 S>90): formatted 12 cliques (1 files) skipping 4 binary chapter diffs\n", + " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 09s PREPARING (F 50 SET): Already prepared\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>85): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>85): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>85): 35 relevant similarities between 57 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>85): Loaded: 26 cliques out of 57 chunks from 35 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>85): 57 members in 26 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>85): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>85): formatting 26 cliques skipping 9 binary chapter diffs\n", + " 1m 09s PRINT (F 50 SET M>50 S>85): formatted 26 cliques (1 files) skipping 9 binary chapter diffs\n", + " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 09s PREPARING (F 50 SET): Already prepared\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>80): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>80): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>80): 69 relevant similarities between 114 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>80): Loaded: 52 cliques out of 114 chunks from 69 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>80): 114 members in 52 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>80): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>80): formatting 52 cliques skipping 19 binary chapter diffs\n", + " 1m 09s PRINT (F 50 SET M>50 S>80): formatted 52 cliques (2 files) skipping 19 binary chapter diffs\n", + " 1m 09s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 09s PREPARING (F 50 SET): Already prepared\n", + " 1m 09s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 09s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>75): fetching similars and chunk candidates\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>75): inspecting the similarity matrix\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>75): 115 relevant similarities between 186 passages\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>75): Loaded: 85 cliques out of 186 chunks from 115 comparisons\n", + " 1m 09s CLIQUES (F 50 SET M>50 S>75): 186 members in 85 cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>75): sorting out cliques\n", + " 1m 09s PRINT (F 50 SET M>50 S>75): formatting 85 cliques skipping 31 binary chapter diffs\n", + " 1m 10s PRINT (F 50 SET M>50 S>75): formatted 85 cliques (2 files) skipping 31 binary chapter diffs\n", + " 1m 10s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 10s PREPARING (F 50 SET): Already prepared\n", + " 1m 10s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 10s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 10s CLIQUES (F 50 SET M>50 S>70): fetching similars and chunk candidates\n", + " 1m 10s CLIQUES (F 50 SET M>50 S>70): inspecting the similarity matrix\n", + " 1m 10s CLIQUES (F 50 SET M>50 S>70): 171 relevant similarities between 271 passages\n", + " 1m 10s CLIQUES (F 50 SET M>50 S>70): Loaded: 124 cliques out of 271 chunks from 171 comparisons\n", + " 1m 10s CLIQUES (F 50 SET M>50 S>70): 271 members in 124 cliques\n", + " 1m 10s PRINT (F 50 SET M>50 S>70): sorting out cliques\n", + " 1m 10s PRINT (F 50 SET M>50 S>70): formatting 124 cliques skipping 48 binary chapter diffs\n", + " 1m 11s PRINT (F 50 SET M>50 S>70): formatted 124 cliques (3 files) skipping 48 binary chapter diffs\n", + " 1m 11s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 11s PREPARING (F 50 SET): Already prepared\n", + " 1m 11s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 11s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 11s CLIQUES (F 50 SET M>50 S>65): fetching similars and chunk candidates\n", + " 1m 11s CLIQUES (F 50 SET M>50 S>65): inspecting the similarity matrix\n", + " 1m 11s CLIQUES (F 50 SET M>50 S>65): 248 relevant similarities between 385 passages\n", + " 1m 11s CLIQUES (F 50 SET M>50 S>65): Loaded: 176 cliques out of 385 chunks from 248 comparisons\n", + " 1m 11s CLIQUES (F 50 SET M>50 S>65): 385 members in 176 cliques\n", + " 1m 11s PRINT (F 50 SET M>50 S>65): sorting out cliques\n", + " 1m 11s PRINT (F 50 SET M>50 S>65): formatting 176 cliques skipping 61 binary chapter diffs\n", + " 1m 12s PRINT (F 50 SET M>50 S>65): formatted 176 cliques (4 files) skipping 61 binary chapter diffs\n", + " 1m 12s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 12s PREPARING (F 50 SET): Already prepared\n", + " 1m 12s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 12s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 12s CLIQUES (F 50 SET M>50 S>60): fetching similars and chunk candidates\n", + " 1m 12s CLIQUES (F 50 SET M>50 S>60): inspecting the similarity matrix\n", + " 1m 12s CLIQUES (F 50 SET M>50 S>60): 366 relevant similarities between 535 passages\n", + " 1m 12s CLIQUES (F 50 SET M>50 S>60): Loaded: 235 cliques out of 535 chunks from 366 comparisons\n", + " 1m 12s CLIQUES (F 50 SET M>50 S>60): 535 members in 235 cliques\n", + " 1m 12s PRINT (F 50 SET M>50 S>60): sorting out cliques\n", + " 1m 12s PRINT (F 50 SET M>50 S>60): formatting 235 cliques skipping 78 binary chapter diffs\n", + " 1m 13s PRINT (F 50 SET M>50 S>60): formatted 235 cliques (5 files) skipping 78 binary chapter diffs\n", + " 1m 13s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 13s PREPARING (F 50 SET): Already prepared\n", + " 1m 13s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 13s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 13s CLIQUES (F 50 SET M>50 S>55): fetching similars and chunk candidates\n", + " 1m 13s CLIQUES (F 50 SET M>50 S>55): inspecting the similarity matrix\n", + " 1m 13s CLIQUES (F 50 SET M>50 S>55): 537 relevant similarities between 748 passages\n", + " 1m 13s CLIQUES (F 50 SET M>50 S>55): Loaded: 315 cliques out of 748 chunks from 537 comparisons\n", + " 1m 13s CLIQUES (F 50 SET M>50 S>55): 748 members in 315 cliques\n", + " 1m 13s PRINT (F 50 SET M>50 S>55): sorting out cliques\n", + " 1m 13s PRINT (F 50 SET M>50 S>55): formatting 315 cliques skipping 101 binary chapter diffs\n", + " 1m 16s PRINT (F 50 SET M>50 S>55): formatted 315 cliques (7 files) skipping 101 binary chapter diffs\n", + " 1m 16s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 16s PREPARING (F 50 SET): Already prepared\n", + " 1m 16s SIMILARITY (F 50 SET M>50): Using 36197 K (36197286) comparisons with 923 entries in matrix\n", + " 1m 16s SIMILARITY (F 50 SET M>50): similarities between 50.0 and 96.96969696969697. 0 are 100%\n", + " 1m 16s CLIQUES (F 50 SET M>50 S>50): fetching similars and chunk candidates\n", + " 1m 16s CLIQUES (F 50 SET M>50 S>50): inspecting the similarity matrix\n", + " 1m 16s CLIQUES (F 50 SET M>50 S>50): 923 relevant similarities between 1187 passages\n", + " 1m 16s CLIQUES (F 50 SET M>50 S>50): Loaded: 465 cliques out of 1187 chunks from 923 comparisons\n", + " 1m 16s CLIQUES (F 50 SET M>50 S>50): 1187 members in 465 cliques\n", + " 1m 16s PRINT (F 50 SET M>50 S>50): sorting out cliques\n", + " 1m 16s PRINT (F 50 SET M>50 S>50): formatting 465 cliques skipping 138 binary chapter diffs\n", + " 1m 20s PRINT (F 50 SET M>50 S>50): formatted 465 cliques (10 files) skipping 138 binary chapter diffs\n", + " 1m 20s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 20s PREPARING (F 50 LCS)\n", + " 1m 21s PREPARING (F 50 LCS): Done 8509 chunks.\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): Loaded: 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>100): inspecting the similarity matrix\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 relevant similarities between 0 passages\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>100): 0 members in 0 cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>100): sorting out cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>100): formatting 0 cliques skipping 0 binary chapter diffs\n", + " 1m 21s PRINT (F 50 LCS M>60 S>100): formatted 0 cliques (1 files) skipping 0 binary chapter diffs\n", + " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 21s PREPARING (F 50 LCS): Already prepared\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>95): inspecting the similarity matrix\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>95): 6 relevant similarities between 12 passages\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>95): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>95): 12 members in 6 cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>95): sorting out cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>95): formatting 6 cliques skipping 3 binary chapter diffs\n", + " 1m 21s PRINT (F 50 LCS M>60 S>95): formatted 6 cliques (1 files) skipping 3 binary chapter diffs\n", + " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 21s PREPARING (F 50 LCS): Already prepared\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>90): inspecting the similarity matrix\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>90): 28 relevant similarities between 53 passages\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>90): Loaded: 25 cliques out of 53 chunks from 28 comparisons\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>90): 53 members in 25 cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>90): sorting out cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>90): formatting 25 cliques skipping 9 binary chapter diffs\n", + " 1m 21s PRINT (F 50 LCS M>60 S>90): formatted 25 cliques (1 files) skipping 9 binary chapter diffs\n", + " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 21s PREPARING (F 50 LCS): Already prepared\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>85): inspecting the similarity matrix\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>85): 75 relevant similarities between 119 passages\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>85): Loaded: 53 cliques out of 119 chunks from 75 comparisons\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>85): 119 members in 53 cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>85): sorting out cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>85): formatting 53 cliques skipping 17 binary chapter diffs\n", + " 1m 21s PRINT (F 50 LCS M>60 S>85): formatted 53 cliques (2 files) skipping 17 binary chapter diffs\n", + " 1m 21s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 21s PREPARING (F 50 LCS): Already prepared\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 21s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>80): inspecting the similarity matrix\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>80): 122 relevant similarities between 196 passages\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>80): Loaded: 89 cliques out of 196 chunks from 122 comparisons\n", + " 1m 21s CLIQUES (F 50 LCS M>60 S>80): 196 members in 89 cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>80): sorting out cliques\n", + " 1m 21s PRINT (F 50 LCS M>60 S>80): formatting 89 cliques skipping 33 binary chapter diffs\n", + " 1m 22s PRINT (F 50 LCS M>60 S>80): formatted 89 cliques (2 files) skipping 33 binary chapter diffs\n", + " 1m 22s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 22s PREPARING (F 50 LCS): Already prepared\n", + " 1m 22s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 22s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 22s CLIQUES (F 50 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 1m 22s CLIQUES (F 50 LCS M>60 S>75): inspecting the similarity matrix\n", + " 1m 22s CLIQUES (F 50 LCS M>60 S>75): 197 relevant similarities between 301 passages\n", + " 1m 22s CLIQUES (F 50 LCS M>60 S>75): Loaded: 135 cliques out of 301 chunks from 197 comparisons\n", + " 1m 22s CLIQUES (F 50 LCS M>60 S>75): 301 members in 135 cliques\n", + " 1m 22s PRINT (F 50 LCS M>60 S>75): sorting out cliques\n", + " 1m 22s PRINT (F 50 LCS M>60 S>75): formatting 135 cliques skipping 50 binary chapter diffs\n", + " 1m 23s PRINT (F 50 LCS M>60 S>75): formatted 135 cliques (3 files) skipping 50 binary chapter diffs\n", + " 1m 23s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 23s PREPARING (F 50 LCS): Already prepared\n", + " 1m 23s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 23s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 23s CLIQUES (F 50 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 1m 23s CLIQUES (F 50 LCS M>60 S>70): inspecting the similarity matrix\n", + " 1m 23s CLIQUES (F 50 LCS M>60 S>70): 312 relevant similarities between 464 passages\n", + " 1m 23s CLIQUES (F 50 LCS M>60 S>70): Loaded: 205 cliques out of 464 chunks from 312 comparisons\n", + " 1m 23s CLIQUES (F 50 LCS M>60 S>70): 464 members in 205 cliques\n", + " 1m 23s PRINT (F 50 LCS M>60 S>70): sorting out cliques\n", + " 1m 23s PRINT (F 50 LCS M>60 S>70): formatting 205 cliques skipping 65 binary chapter diffs\n", + " 1m 24s PRINT (F 50 LCS M>60 S>70): formatted 205 cliques (5 files) skipping 65 binary chapter diffs\n", + " 1m 24s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 24s PREPARING (F 50 LCS): Already prepared\n", + " 1m 24s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 24s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 24s CLIQUES (F 50 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 1m 24s CLIQUES (F 50 LCS M>60 S>65): inspecting the similarity matrix\n", + " 1m 24s CLIQUES (F 50 LCS M>60 S>65): 578 relevant similarities between 761 passages\n", + " 1m 24s CLIQUES (F 50 LCS M>60 S>65): Loaded: 312 cliques out of 761 chunks from 578 comparisons\n", + " 1m 24s CLIQUES (F 50 LCS M>60 S>65): 761 members in 312 cliques\n", + " 1m 24s PRINT (F 50 LCS M>60 S>65): sorting out cliques\n", + " 1m 24s PRINT (F 50 LCS M>60 S>65): formatting 312 cliques skipping 107 binary chapter diffs\n", + " 1m 26s PRINT (F 50 LCS M>60 S>65): formatted 312 cliques (7 files) skipping 107 binary chapter diffs\n", + " 1m 26s CHUNKING (F 50): already chunked into 8509 chunks\n", + " 1m 26s PREPARING (F 50 LCS): Already prepared\n", + " 1m 26s SIMILARITY (F 50 LCS M>60): Using 36197 K (36197286) comparisons with 1833 entries in matrix\n", + " 1m 26s SIMILARITY (F 50 LCS M>60): similarities between 60.0 and 97.52650176678446. 0 are 100%\n", + " 1m 26s CLIQUES (F 50 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 1m 26s CLIQUES (F 50 LCS M>60 S>60): inspecting the similarity matrix\n", + " 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1833 relevant similarities between 1888 passages\n", + " 1m 26s CLIQUES (F 50 LCS M>60 S>60): Loaded: 552 cliques out of 1888 chunks from 1833 comparisons\n", + " 1m 26s CLIQUES (F 50 LCS M>60 S>60): 1888 members in 552 cliques\n", + " 1m 26s PRINT (F 50 LCS M>60 S>60): sorting out cliques\n", + " 1m 26s PRINT (F 50 LCS M>60 S>60): formatting 552 cliques skipping 228 binary chapter diffs\n", + " 1m 33s PRINT (F 50 LCS M>60 S>60): formatted 552 cliques (12 files) skipping 228 binary chapter diffs\n", + " 1m 33s CHUNKING (F 20): Loaded: 21311 chunks\n", + " 1m 33s CHUNKING (F 20): Made 21311 chunks\n", + " 1m 33s PREPARING (F 20 SET)\n", + " 1m 34s PREPARING (F 20 SET): Done 21311 chunks.\n", + " 1m 34s SIMILARITY (F 20 SET M>50): Loaded: 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>100): fetching similars and chunk candidates\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>100): inspecting the similarity matrix\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>100): 14 relevant similarities between 28 passages\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>100): Loaded: 14 cliques out of 28 chunks from 14 comparisons\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>100): 28 members in 14 cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>100): sorting out cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>100): formatting 14 cliques skipping 8 binary chapter diffs\n", + " 1m 34s PRINT (F 20 SET M>50 S>100): formatted 14 cliques (1 files) skipping 8 binary chapter diffs\n", + " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 34s PREPARING (F 20 SET): Already prepared\n", + " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>95): fetching similars and chunk candidates\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>95): inspecting the similarity matrix\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>95): 14 relevant similarities between 28 passages\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>95): Loaded: 14 cliques out of 28 chunks from 14 comparisons\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>95): 28 members in 14 cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>95): sorting out cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>95): formatting 14 cliques skipping 8 binary chapter diffs\n", + " 1m 34s PRINT (F 20 SET M>50 S>95): formatted 14 cliques (1 files) skipping 8 binary chapter diffs\n", + " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 34s PREPARING (F 20 SET): Already prepared\n", + " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>90): fetching similars and chunk candidates\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>90): inspecting the similarity matrix\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>90): 63 relevant similarities between 105 passages\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>90): Loaded: 46 cliques out of 105 chunks from 63 comparisons\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>90): 105 members in 46 cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>90): sorting out cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>90): formatting 46 cliques skipping 22 binary chapter diffs\n", + " 1m 34s PRINT (F 20 SET M>50 S>90): formatted 46 cliques (1 files) skipping 22 binary chapter diffs\n", + " 1m 34s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 34s PREPARING (F 20 SET): Already prepared\n", + " 1m 34s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 34s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>85): fetching similars and chunk candidates\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>85): inspecting the similarity matrix\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>85): 125 relevant similarities between 174 passages\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>85): Loaded: 72 cliques out of 174 chunks from 125 comparisons\n", + " 1m 34s CLIQUES (F 20 SET M>50 S>85): 174 members in 72 cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>85): sorting out cliques\n", + " 1m 34s PRINT (F 20 SET M>50 S>85): formatting 72 cliques skipping 34 binary chapter diffs\n", + " 1m 35s PRINT (F 20 SET M>50 S>85): formatted 72 cliques (2 files) skipping 34 binary chapter diffs\n", + " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 35s PREPARING (F 20 SET): Already prepared\n", + " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>80): fetching similars and chunk candidates\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>80): inspecting the similarity matrix\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>80): 230 relevant similarities between 326 passages\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>80): Loaded: 143 cliques out of 326 chunks from 230 comparisons\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>80): 326 members in 143 cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>80): sorting out cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>80): formatting 143 cliques skipping 59 binary chapter diffs\n", + " 1m 35s PRINT (F 20 SET M>50 S>80): formatted 143 cliques (3 files) skipping 59 binary chapter diffs\n", + " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 35s PREPARING (F 20 SET): Already prepared\n", + " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>75): fetching similars and chunk candidates\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>75): inspecting the similarity matrix\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>75): 382 relevant similarities between 528 passages\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>75): Loaded: 227 cliques out of 528 chunks from 382 comparisons\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>75): 528 members in 227 cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>75): sorting out cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>75): formatting 227 cliques skipping 83 binary chapter diffs\n", + " 1m 35s PRINT (F 20 SET M>50 S>75): formatted 227 cliques (5 files) skipping 83 binary chapter diffs\n", + " 1m 35s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 35s PREPARING (F 20 SET): Already prepared\n", + " 1m 35s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 35s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>70): fetching similars and chunk candidates\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>70): inspecting the similarity matrix\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>70): 546 relevant similarities between 762 passages\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>70): Loaded: 331 cliques out of 762 chunks from 546 comparisons\n", + " 1m 35s CLIQUES (F 20 SET M>50 S>70): 762 members in 331 cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>70): sorting out cliques\n", + " 1m 35s PRINT (F 20 SET M>50 S>70): formatting 331 cliques skipping 107 binary chapter diffs\n", + " 1m 36s PRINT (F 20 SET M>50 S>70): formatted 331 cliques (7 files) skipping 107 binary chapter diffs\n", + " 1m 36s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 36s PREPARING (F 20 SET): Already prepared\n", + " 1m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>65): fetching similars and chunk candidates\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>65): inspecting the similarity matrix\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>65): 787 relevant similarities between 1058 passages\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>65): Loaded: 452 cliques out of 1058 chunks from 787 comparisons\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>65): 1058 members in 452 cliques\n", + " 1m 36s PRINT (F 20 SET M>50 S>65): sorting out cliques\n", + " 1m 36s PRINT (F 20 SET M>50 S>65): formatting 452 cliques skipping 136 binary chapter diffs\n", + " 1m 36s PRINT (F 20 SET M>50 S>65): formatted 452 cliques (10 files) skipping 136 binary chapter diffs\n", + " 1m 36s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 36s PREPARING (F 20 SET): Already prepared\n", + " 1m 36s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 36s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>60): fetching similars and chunk candidates\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>60): inspecting the similarity matrix\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>60): 1432 relevant similarities between 1830 passages\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>60): Loaded: 733 cliques out of 1830 chunks from 1432 comparisons\n", + " 1m 36s CLIQUES (F 20 SET M>50 S>60): 1830 members in 733 cliques\n", + " 1m 36s PRINT (F 20 SET M>50 S>60): sorting out cliques\n", + " 1m 36s PRINT (F 20 SET M>50 S>60): formatting 733 cliques skipping 211 binary chapter diffs\n", + " 1m 37s PRINT (F 20 SET M>50 S>60): formatted 733 cliques (15 files) skipping 211 binary chapter diffs\n", + " 1m 37s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 37s PREPARING (F 20 SET): Already prepared\n", + " 1m 37s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 37s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 37s CLIQUES (F 20 SET M>50 S>55): fetching similars and chunk candidates\n", + " 1m 37s CLIQUES (F 20 SET M>50 S>55): inspecting the similarity matrix\n", + " 1m 37s CLIQUES (F 20 SET M>50 S>55): 2425 relevant similarities between 2787 passages\n", + " 1m 37s CLIQUES (F 20 SET M>50 S>55): Loaded: 979 cliques out of 2787 chunks from 2425 comparisons\n", + " 1m 37s CLIQUES (F 20 SET M>50 S>55): 2787 members in 979 cliques\n", + " 1m 37s PRINT (F 20 SET M>50 S>55): sorting out cliques\n", + " 1m 37s PRINT (F 20 SET M>50 S>55): formatting 979 cliques skipping 285 binary chapter diffs\n", + " 1m 39s PRINT (F 20 SET M>50 S>55): formatted 979 cliques (20 files) skipping 285 binary chapter diffs\n", + " 1m 39s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 39s PREPARING (F 20 SET): Already prepared\n", + " 1m 39s SIMILARITY (F 20 SET M>50): Using 227 M (227068705) comparisons with 5517 entries in matrix\n", + " 1m 39s SIMILARITY (F 20 SET M>50): similarities between 50.0 and 100.0. 14 are 100%\n", + " 1m 39s CLIQUES (F 20 SET M>50 S>50): fetching similars and chunk candidates\n", + " 1m 39s CLIQUES (F 20 SET M>50 S>50): inspecting the similarity matrix\n", + " 1m 39s CLIQUES (F 20 SET M>50 S>50): 5517 relevant similarities between 4913 passages\n", + " 1m 39s CLIQUES (F 20 SET M>50 S>50): Loaded: 1203 cliques out of 4913 chunks from 5517 comparisons\n", + " 1m 39s CLIQUES (F 20 SET M>50 S>50): 4913 members in 1203 cliques\n", + " 1m 39s PRINT (F 20 SET M>50 S>50): sorting out cliques\n", + " 1m 39s PRINT (F 20 SET M>50 S>50): formatting 1203 cliques skipping 391 binary chapter diffs\n", + " 1m 41s PRINT (F 20 SET M>50 S>50): formatted 1203 cliques (25 files) skipping 391 binary chapter diffs\n", + " 1m 41s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 41s PREPARING (F 20 LCS)\n", + " 1m 42s PREPARING (F 20 LCS): Done 21311 chunks.\n", + " 1m 42s SIMILARITY (F 20 LCS M>60): Loaded: 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>100): inspecting the similarity matrix\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>100): 3 relevant similarities between 6 passages\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>100): Loaded: 3 cliques out of 6 chunks from 3 comparisons\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>100): 6 members in 3 cliques\n", + " 1m 42s PRINT (F 20 LCS M>60 S>100): sorting out cliques\n", + " 1m 42s PRINT (F 20 LCS M>60 S>100): formatting 3 cliques skipping 3 binary chapter diffs\n", + " 1m 42s PRINT (F 20 LCS M>60 S>100): formatted 3 cliques (1 files) skipping 3 binary chapter diffs\n", + " 1m 42s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 42s PREPARING (F 20 LCS): Already prepared\n", + " 1m 42s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 42s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>95): inspecting the similarity matrix\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>95): 25 relevant similarities between 47 passages\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>95): Loaded: 22 cliques out of 47 chunks from 25 comparisons\n", + " 1m 42s CLIQUES (F 20 LCS M>60 S>95): 47 members in 22 cliques\n", + " 1m 42s PRINT (F 20 LCS M>60 S>95): sorting out cliques\n", + " 1m 42s PRINT (F 20 LCS M>60 S>95): formatting 22 cliques skipping 12 binary chapter diffs\n", + " 1m 42s PRINT (F 20 LCS M>60 S>95): formatted 22 cliques (1 files) skipping 12 binary chapter diffs\n", + " 1m 42s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 42s PREPARING (F 20 LCS): Already prepared\n", + " 1m 42s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>90): inspecting the similarity matrix\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>90): 95 relevant similarities between 149 passages\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>90): Loaded: 61 cliques out of 149 chunks from 95 comparisons\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>90): 149 members in 61 cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>90): sorting out cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>90): formatting 61 cliques skipping 31 binary chapter diffs\n", + " 1m 43s PRINT (F 20 LCS M>60 S>90): formatted 61 cliques (2 files) skipping 31 binary chapter diffs\n", + " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 43s PREPARING (F 20 LCS): Already prepared\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>85): inspecting the similarity matrix\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>85): 212 relevant similarities between 311 passages\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>85): Loaded: 136 cliques out of 311 chunks from 212 comparisons\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>85): 311 members in 136 cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>85): sorting out cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>85): formatting 136 cliques skipping 56 binary chapter diffs\n", + " 1m 43s PRINT (F 20 LCS M>60 S>85): formatted 136 cliques (3 files) skipping 56 binary chapter diffs\n", + " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 43s PREPARING (F 20 LCS): Already prepared\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>80): inspecting the similarity matrix\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>80): 467 relevant similarities between 682 passages\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>80): Loaded: 299 cliques out of 682 chunks from 467 comparisons\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>80): 682 members in 299 cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>80): sorting out cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>80): formatting 299 cliques skipping 116 binary chapter diffs\n", + " 1m 43s PRINT (F 20 LCS M>60 S>80): formatted 299 cliques (6 files) skipping 116 binary chapter diffs\n", + " 1m 43s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 43s PREPARING (F 20 LCS): Already prepared\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 43s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>75): inspecting the similarity matrix\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>75): 874 relevant similarities between 1137 passages\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>75): Loaded: 470 cliques out of 1137 chunks from 874 comparisons\n", + " 1m 43s CLIQUES (F 20 LCS M>60 S>75): 1137 members in 470 cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>75): sorting out cliques\n", + " 1m 43s PRINT (F 20 LCS M>60 S>75): formatting 470 cliques skipping 162 binary chapter diffs\n", + " 1m 44s PRINT (F 20 LCS M>60 S>75): formatted 470 cliques (10 files) skipping 162 binary chapter diffs\n", + " 1m 44s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 44s PREPARING (F 20 LCS): Already prepared\n", + " 1m 44s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 44s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 44s CLIQUES (F 20 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 1m 44s CLIQUES (F 20 LCS M>60 S>70): inspecting the similarity matrix\n", + " 1m 44s CLIQUES (F 20 LCS M>60 S>70): 1944 relevant similarities between 2217 passages\n", + " 1m 44s CLIQUES (F 20 LCS M>60 S>70): Loaded: 838 cliques out of 2217 chunks from 1944 comparisons\n", + " 1m 44s CLIQUES (F 20 LCS M>60 S>70): 2217 members in 838 cliques\n", + " 1m 44s PRINT (F 20 LCS M>60 S>70): sorting out cliques\n", + " 1m 44s PRINT (F 20 LCS M>60 S>70): formatting 838 cliques skipping 313 binary chapter diffs\n", + " 1m 46s PRINT (F 20 LCS M>60 S>70): formatted 838 cliques (17 files) skipping 313 binary chapter diffs\n", + " 1m 46s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 46s PREPARING (F 20 LCS): Already prepared\n", + " 1m 46s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 46s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 46s CLIQUES (F 20 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 1m 46s CLIQUES (F 20 LCS M>60 S>65): inspecting the similarity matrix\n", + " 1m 46s CLIQUES (F 20 LCS M>60 S>65): 6983 relevant similarities between 5971 passages\n", + " 1m 46s CLIQUES (F 20 LCS M>60 S>65): Loaded: 1223 cliques out of 5971 chunks from 6983 comparisons\n", + " 1m 46s CLIQUES (F 20 LCS M>60 S>65): 5971 members in 1223 cliques\n", + " 1m 46s PRINT (F 20 LCS M>60 S>65): sorting out cliques\n", + " 1m 46s PRINT (F 20 LCS M>60 S>65): formatting 1223 cliques skipping 553 binary chapter diffs\n", + " 1m 49s PRINT (F 20 LCS M>60 S>65): formatted 1223 cliques (25 files) skipping 553 binary chapter diffs\n", + " 1m 49s CHUNKING (F 20): already chunked into 21311 chunks\n", + " 1m 49s PREPARING (F 20 LCS): Already prepared\n", + " 1m 49s SIMILARITY (F 20 LCS M>60): Using 227 M (227068705) comparisons with 122055 entries in matrix\n", + " 1m 50s SIMILARITY (F 20 LCS M>60): similarities between 60.0 and 100.0. 3 are 100%\n", + " 1m 50s CLIQUES (F 20 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 1m 50s CLIQUES (F 20 LCS M>60 S>60): inspecting the similarity matrix\n", + " 1m 50s CLIQUES (F 20 LCS M>60 S>60): 122055 relevant similarities between 17656 passages\n", + " 1m 50s CLIQUES (F 20 LCS M>60 S>60): Loaded: 152 cliques out of 17656 chunks from 122055 comparisons\n", + " 1m 50s CLIQUES (F 20 LCS M>60 S>60): 17656 members in 152 cliques\n", + " 1m 50s PRINT (F 20 LCS M>60 S>60): sorting out cliques\n", + " 1m 50s PRINT (F 20 LCS M>60 S>60): formatting 152 cliques skipping 94 binary chapter diffs\n", + " 1m 50s PRINT (F 20 LCS M>60 S>60): formatted 152 cliques (4 files) skipping 94 binary chapter diffs\n", + " 1m 51s CHUNKING (F 10): Loaded: 42639 chunks\n", + " 1m 51s CHUNKING (F 10): Made 42639 chunks\n", + " 1m 51s PREPARING (F 10 SET)\n", + " 1m 52s PREPARING (F 10 SET): Done 42639 chunks.\n", + " 1m 52s SIMILARITY (F 10 SET M>50): Loaded: 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>100): fetching similars and chunk candidates\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>100): inspecting the similarity matrix\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>100): 275 relevant similarities between 448 passages\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>100): Loaded: 209 cliques out of 448 chunks from 275 comparisons\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>100): 448 members in 209 cliques\n", + " 1m 52s PRINT (F 10 SET M>50 S>100): sorting out cliques\n", + " 1m 52s PRINT (F 10 SET M>50 S>100): formatting 209 cliques skipping 80 binary chapter diffs\n", + " 1m 52s PRINT (F 10 SET M>50 S>100): formatted 209 cliques (5 files) skipping 80 binary chapter diffs\n", + " 1m 52s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 52s PREPARING (F 10 SET): Already prepared\n", + " 1m 52s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 52s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>95): fetching similars and chunk candidates\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>95): inspecting the similarity matrix\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>95): 275 relevant similarities between 448 passages\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>95): Loaded: 209 cliques out of 448 chunks from 275 comparisons\n", + " 1m 52s CLIQUES (F 10 SET M>50 S>95): 448 members in 209 cliques\n", + " 1m 52s PRINT (F 10 SET M>50 S>95): sorting out cliques\n", + " 1m 52s PRINT (F 10 SET M>50 S>95): formatting 209 cliques skipping 80 binary chapter diffs\n", + " 1m 53s PRINT (F 10 SET M>50 S>95): formatted 209 cliques (5 files) skipping 80 binary chapter diffs\n", + " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 53s PREPARING (F 10 SET): Already prepared\n", + " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>90): fetching similars and chunk candidates\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>90): inspecting the similarity matrix\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>90): 315 relevant similarities between 482 passages\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>90): Loaded: 220 cliques out of 482 chunks from 315 comparisons\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>90): 482 members in 220 cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>90): sorting out cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>90): formatting 220 cliques skipping 85 binary chapter diffs\n", + " 1m 53s PRINT (F 10 SET M>50 S>90): formatted 220 cliques (5 files) skipping 85 binary chapter diffs\n", + " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 53s PREPARING (F 10 SET): Already prepared\n", + " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>85): fetching similars and chunk candidates\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>85): inspecting the similarity matrix\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>85): 745 relevant similarities between 1114 passages\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>85): Loaded: 493 cliques out of 1114 chunks from 745 comparisons\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>85): 1114 members in 493 cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>85): sorting out cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>85): formatting 493 cliques skipping 193 binary chapter diffs\n", + " 1m 53s PRINT (F 10 SET M>50 S>85): formatted 493 cliques (10 files) skipping 193 binary chapter diffs\n", + " 1m 53s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 53s PREPARING (F 10 SET): Already prepared\n", + " 1m 53s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 53s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>80): fetching similars and chunk candidates\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>80): inspecting the similarity matrix\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>80): 1149 relevant similarities between 1536 passages\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>80): Loaded: 628 cliques out of 1536 chunks from 1149 comparisons\n", + " 1m 53s CLIQUES (F 10 SET M>50 S>80): 1536 members in 628 cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>80): sorting out cliques\n", + " 1m 53s PRINT (F 10 SET M>50 S>80): formatting 628 cliques skipping 231 binary chapter diffs\n", + " 1m 54s PRINT (F 10 SET M>50 S>80): formatted 628 cliques (13 files) skipping 231 binary chapter diffs\n", + " 1m 54s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 54s PREPARING (F 10 SET): Already prepared\n", + " 1m 54s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 54s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 54s CLIQUES (F 10 SET M>50 S>75): fetching similars and chunk candidates\n", + " 1m 54s CLIQUES (F 10 SET M>50 S>75): inspecting the similarity matrix\n", + " 1m 54s CLIQUES (F 10 SET M>50 S>75): 2107 relevant similarities between 2754 passages\n", + " 1m 54s CLIQUES (F 10 SET M>50 S>75): Loaded: 1094 cliques out of 2754 chunks from 2107 comparisons\n", + " 1m 54s CLIQUES (F 10 SET M>50 S>75): 2754 members in 1094 cliques\n", + " 1m 54s PRINT (F 10 SET M>50 S>75): sorting out cliques\n", + " 1m 54s PRINT (F 10 SET M>50 S>75): formatting 1094 cliques skipping 406 binary chapter diffs\n", + " 1m 55s PRINT (F 10 SET M>50 S>75): formatted 1094 cliques (22 files) skipping 406 binary chapter diffs\n", + " 1m 55s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 55s PREPARING (F 10 SET): Already prepared\n", + " 1m 55s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 55s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 55s CLIQUES (F 10 SET M>50 S>70): fetching similars and chunk candidates\n", + " 1m 55s CLIQUES (F 10 SET M>50 S>70): inspecting the similarity matrix\n", + " 1m 55s CLIQUES (F 10 SET M>50 S>70): 3560 relevant similarities between 4020 passages\n", + " 1m 55s CLIQUES (F 10 SET M>50 S>70): Loaded: 1474 cliques out of 4020 chunks from 3560 comparisons\n", + " 1m 55s CLIQUES (F 10 SET M>50 S>70): 4020 members in 1474 cliques\n", + " 1m 55s PRINT (F 10 SET M>50 S>70): sorting out cliques\n", + " 1m 55s PRINT (F 10 SET M>50 S>70): formatting 1474 cliques skipping 559 binary chapter diffs\n", + " 1m 57s PRINT (F 10 SET M>50 S>70): formatted 1474 cliques (30 files) skipping 559 binary chapter diffs\n", + " 1m 57s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 57s PREPARING (F 10 SET): Already prepared\n", + " 1m 57s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 57s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 57s CLIQUES (F 10 SET M>50 S>65): fetching similars and chunk candidates\n", + " 1m 57s CLIQUES (F 10 SET M>50 S>65): inspecting the similarity matrix\n", + " 1m 57s CLIQUES (F 10 SET M>50 S>65): 5481 relevant similarities between 5785 passages\n", + " 1m 57s CLIQUES (F 10 SET M>50 S>65): Loaded: 1850 cliques out of 5785 chunks from 5481 comparisons\n", + " 1m 57s CLIQUES (F 10 SET M>50 S>65): 5785 members in 1850 cliques\n", + " 1m 57s PRINT (F 10 SET M>50 S>65): sorting out cliques\n", + " 1m 57s PRINT (F 10 SET M>50 S>65): formatting 1850 cliques skipping 692 binary chapter diffs\n", + " 1m 59s PRINT (F 10 SET M>50 S>65): formatted 1850 cliques (37 files) skipping 692 binary chapter diffs\n", + " 1m 59s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 1m 59s PREPARING (F 10 SET): Already prepared\n", + " 1m 59s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 1m 59s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 1m 59s CLIQUES (F 10 SET M>50 S>60): fetching similars and chunk candidates\n", + " 1m 59s CLIQUES (F 10 SET M>50 S>60): inspecting the similarity matrix\n", + " 1m 59s CLIQUES (F 10 SET M>50 S>60): 13263 relevant similarities between 10211 passages\n", + " 1m 59s CLIQUES (F 10 SET M>50 S>60): Loaded: 2210 cliques out of 10211 chunks from 13263 comparisons\n", + " 1m 59s CLIQUES (F 10 SET M>50 S>60): 10211 members in 2210 cliques\n", + " 1m 59s PRINT (F 10 SET M>50 S>60): sorting out cliques\n", + " 1m 59s PRINT (F 10 SET M>50 S>60): formatting 2210 cliques skipping 845 binary chapter diffs\n", + " 2m 01s PRINT (F 10 SET M>50 S>60): formatted 2210 cliques (45 files) skipping 845 binary chapter diffs\n", + " 2m 01s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 01s PREPARING (F 10 SET): Already prepared\n", + " 2m 01s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 2m 01s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 2m 01s CLIQUES (F 10 SET M>50 S>55): fetching similars and chunk candidates\n", + " 2m 01s CLIQUES (F 10 SET M>50 S>55): inspecting the similarity matrix\n", + " 2m 01s CLIQUES (F 10 SET M>50 S>55): 25871 relevant similarities between 14100 passages\n", + " 2m 01s CLIQUES (F 10 SET M>50 S>55): Loaded: 2018 cliques out of 14100 chunks from 25871 comparisons\n", + " 2m 01s CLIQUES (F 10 SET M>50 S>55): 14100 members in 2018 cliques\n", + " 2m 01s PRINT (F 10 SET M>50 S>55): sorting out cliques\n", + " 2m 02s PRINT (F 10 SET M>50 S>55): formatting 2018 cliques skipping 783 binary chapter diffs\n", + " 2m 03s PRINT (F 10 SET M>50 S>55): formatted 2018 cliques (41 files) skipping 783 binary chapter diffs\n", + " 2m 03s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 03s PREPARING (F 10 SET): Already prepared\n", + " 2m 03s SIMILARITY (F 10 SET M>50): Using 909 M (909020841) comparisons with 88877 entries in matrix\n", + " 2m 03s SIMILARITY (F 10 SET M>50): similarities between 50.0 and 100.0. 275 are 100%\n", + " 2m 03s CLIQUES (F 10 SET M>50 S>50): fetching similars and chunk candidates\n", + " 2m 03s CLIQUES (F 10 SET M>50 S>50): inspecting the similarity matrix\n", + " 2m 03s CLIQUES (F 10 SET M>50 S>50): 88877 relevant similarities between 23054 passages\n", + " 2m 03s CLIQUES (F 10 SET M>50 S>50): Loaded: 1455 cliques out of 23054 chunks from 88877 comparisons\n", + " 2m 03s CLIQUES (F 10 SET M>50 S>50): 23054 members in 1455 cliques\n", + " 2m 03s PRINT (F 10 SET M>50 S>50): sorting out cliques\n", + " 2m 03s PRINT (F 10 SET M>50 S>50): formatting 1455 cliques skipping 643 binary chapter diffs\n", + " 2m 04s PRINT (F 10 SET M>50 S>50): formatted 1455 cliques (30 files) skipping 643 binary chapter diffs\n", + " 2m 04s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 04s PREPARING (F 10 LCS)\n", + " 2m 05s PREPARING (F 10 LCS): Done 42639 chunks.\n", + " 2m 07s SIMILARITY (F 10 LCS M>60): Loaded: 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 09s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 09s CLIQUES (F 10 LCS M>60 S>100): fetching similars and chunk candidates\n", + " 2m 09s CLIQUES (F 10 LCS M>60 S>100): inspecting the similarity matrix\n", + " 2m 09s CLIQUES (F 10 LCS M>60 S>100): 139 relevant similarities between 239 passages\n", + " 2m 09s CLIQUES (F 10 LCS M>60 S>100): Loaded: 114 cliques out of 239 chunks from 139 comparisons\n", + " 2m 09s CLIQUES (F 10 LCS M>60 S>100): 239 members in 114 cliques\n", + " 2m 09s PRINT (F 10 LCS M>60 S>100): sorting out cliques\n", + " 2m 09s PRINT (F 10 LCS M>60 S>100): formatting 114 cliques skipping 49 binary chapter diffs\n", + " 2m 09s PRINT (F 10 LCS M>60 S>100): formatted 114 cliques (3 files) skipping 49 binary chapter diffs\n", + " 2m 09s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 09s PREPARING (F 10 LCS): Already prepared\n", + " 2m 09s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 11s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 11s CLIQUES (F 10 LCS M>60 S>95): fetching similars and chunk candidates\n", + " 2m 11s CLIQUES (F 10 LCS M>60 S>95): inspecting the similarity matrix\n", + " 2m 12s CLIQUES (F 10 LCS M>60 S>95): 214 relevant similarities between 379 passages\n", + " 2m 12s CLIQUES (F 10 LCS M>60 S>95): Loaded: 182 cliques out of 379 chunks from 214 comparisons\n", + " 2m 12s CLIQUES (F 10 LCS M>60 S>95): 379 members in 182 cliques\n", + " 2m 12s PRINT (F 10 LCS M>60 S>95): sorting out cliques\n", + " 2m 12s PRINT (F 10 LCS M>60 S>95): formatting 182 cliques skipping 78 binary chapter diffs\n", + " 2m 12s PRINT (F 10 LCS M>60 S>95): formatted 182 cliques (4 files) skipping 78 binary chapter diffs\n", + " 2m 12s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 12s PREPARING (F 10 LCS): Already prepared\n", + " 2m 12s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 13s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 13s CLIQUES (F 10 LCS M>60 S>90): fetching similars and chunk candidates\n", + " 2m 13s CLIQUES (F 10 LCS M>60 S>90): inspecting the similarity matrix\n", + " 2m 14s CLIQUES (F 10 LCS M>60 S>90): 609 relevant similarities between 905 passages\n", + " 2m 14s CLIQUES (F 10 LCS M>60 S>90): Loaded: 399 cliques out of 905 chunks from 609 comparisons\n", + " 2m 14s CLIQUES (F 10 LCS M>60 S>90): 905 members in 399 cliques\n", + " 2m 14s PRINT (F 10 LCS M>60 S>90): sorting out cliques\n", + " 2m 14s PRINT (F 10 LCS M>60 S>90): formatting 399 cliques skipping 160 binary chapter diffs\n", + " 2m 15s PRINT (F 10 LCS M>60 S>90): formatted 399 cliques (8 files) skipping 160 binary chapter diffs\n", + " 2m 15s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 15s PREPARING (F 10 LCS): Already prepared\n", + " 2m 15s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 16s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 16s CLIQUES (F 10 LCS M>60 S>85): fetching similars and chunk candidates\n", + " 2m 16s CLIQUES (F 10 LCS M>60 S>85): inspecting the similarity matrix\n", + " 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1396 relevant similarities between 1917 passages\n", + " 2m 17s CLIQUES (F 10 LCS M>60 S>85): Loaded: 791 cliques out of 1917 chunks from 1396 comparisons\n", + " 2m 17s CLIQUES (F 10 LCS M>60 S>85): 1917 members in 791 cliques\n", + " 2m 17s PRINT (F 10 LCS M>60 S>85): sorting out cliques\n", + " 2m 17s PRINT (F 10 LCS M>60 S>85): formatting 791 cliques skipping 297 binary chapter diffs\n", + " 2m 17s PRINT (F 10 LCS M>60 S>85): formatted 791 cliques (16 files) skipping 297 binary chapter diffs\n", + " 2m 17s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 17s PREPARING (F 10 LCS): Already prepared\n", + " 2m 17s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 19s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 19s CLIQUES (F 10 LCS M>60 S>80): fetching similars and chunk candidates\n", + " 2m 19s CLIQUES (F 10 LCS M>60 S>80): inspecting the similarity matrix\n", + " 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3308 relevant similarities between 3850 passages\n", + " 2m 19s CLIQUES (F 10 LCS M>60 S>80): Loaded: 1418 cliques out of 3850 chunks from 3308 comparisons\n", + " 2m 19s CLIQUES (F 10 LCS M>60 S>80): 3850 members in 1418 cliques\n", + " 2m 19s PRINT (F 10 LCS M>60 S>80): sorting out cliques\n", + " 2m 19s PRINT (F 10 LCS M>60 S>80): formatting 1418 cliques skipping 549 binary chapter diffs\n", + " 2m 20s PRINT (F 10 LCS M>60 S>80): formatted 1418 cliques (29 files) skipping 549 binary chapter diffs\n", + " 2m 20s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 20s PREPARING (F 10 LCS): Already prepared\n", + " 2m 20s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 21s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 21s CLIQUES (F 10 LCS M>60 S>75): fetching similars and chunk candidates\n", + " 2m 21s CLIQUES (F 10 LCS M>60 S>75): inspecting the similarity matrix\n", + " 2m 23s CLIQUES (F 10 LCS M>60 S>75): 9225 relevant similarities between 8552 passages\n", + " 2m 23s CLIQUES (F 10 LCS M>60 S>75): Loaded: 2342 cliques out of 8552 chunks from 9225 comparisons\n", + " 2m 23s CLIQUES (F 10 LCS M>60 S>75): 8552 members in 2342 cliques\n", + " 2m 23s PRINT (F 10 LCS M>60 S>75): sorting out cliques\n", + " 2m 23s PRINT (F 10 LCS M>60 S>75): formatting 2342 cliques skipping 989 binary chapter diffs\n", + " 2m 25s PRINT (F 10 LCS M>60 S>75): formatted 2342 cliques (47 files) skipping 989 binary chapter diffs\n", + " 2m 25s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 25s PREPARING (F 10 LCS): Already prepared\n", + " 2m 25s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 26s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 26s CLIQUES (F 10 LCS M>60 S>70): fetching similars and chunk candidates\n", + " 2m 26s CLIQUES (F 10 LCS M>60 S>70): inspecting the similarity matrix\n", + " 2m 27s CLIQUES (F 10 LCS M>60 S>70): 38610 relevant similarities between 20382 passages\n", + " 2m 27s CLIQUES (F 10 LCS M>60 S>70): Loaded: 1926 cliques out of 20382 chunks from 38610 comparisons\n", + " 2m 27s CLIQUES (F 10 LCS M>60 S>70): 20382 members in 1926 cliques\n", + " 2m 27s PRINT (F 10 LCS M>60 S>70): sorting out cliques\n", + " 2m 27s PRINT (F 10 LCS M>60 S>70): formatting 1926 cliques skipping 1004 binary chapter diffs\n", + " 2m 28s PRINT (F 10 LCS M>60 S>70): formatted 1926 cliques (39 files) skipping 1004 binary chapter diffs\n", + " 2m 28s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 28s PREPARING (F 10 LCS): Already prepared\n", + " 2m 28s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 29s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 29s CLIQUES (F 10 LCS M>60 S>65): fetching similars and chunk candidates\n", + " 2m 29s CLIQUES (F 10 LCS M>60 S>65): inspecting the similarity matrix\n", + " 2m 31s CLIQUES (F 10 LCS M>60 S>65): 346682 relevant similarities between 37700 passages\n", + " 2m 31s CLIQUES (F 10 LCS M>60 S>65): Loaded: 223 cliques out of 37700 chunks from 346682 comparisons\n", + " 2m 31s CLIQUES (F 10 LCS M>60 S>65): 37700 members in 223 cliques\n", + " 2m 31s PRINT (F 10 LCS M>60 S>65): sorting out cliques\n", + " 2m 31s PRINT (F 10 LCS M>60 S>65): formatting 223 cliques skipping 142 binary chapter diffs\n", + " 2m 31s PRINT (F 10 LCS M>60 S>65): formatted 223 cliques (5 files) skipping 142 binary chapter diffs\n", + " 2m 31s CHUNKING (F 10): already chunked into 42639 chunks\n", + " 2m 31s PREPARING (F 10 LCS): Already prepared\n", + " 2m 31s SIMILARITY (F 10 LCS M>60): Using 909 M (909020841) comparisons with 2918272 entries in matrix\n", + " 2m 33s SIMILARITY (F 10 LCS M>60): similarities between 60.0 and 100.0. 139 are 100%\n", + " 2m 33s CLIQUES (F 10 LCS M>60 S>60): fetching similars and chunk candidates\n", + " 2m 33s CLIQUES (F 10 LCS M>60 S>60): inspecting the similarity matrix\n", + " 2m 37s CLIQUES (F 10 LCS M>60 S>60): 2918272 relevant similarities between 42450 passages\n", + " 2m 37s CLIQUES (F 10 LCS M>60 S>60): Loaded: 2 cliques out of 42450 chunks from 2918272 comparisons\n", + " 2m 37s CLIQUES (F 10 LCS M>60 S>60): 42450 members in 2 cliques\n", + " 2m 37s PRINT (F 10 LCS M>60 S>60): sorting out cliques\n", + " 2m 37s PRINT (F 10 LCS M>60 S>60): formatting 2 cliques skipping 1 binary chapter diffs\n", + " 2m 37s PRINT (F 10 LCS M>60 S>60): formatted 2 cliques (1 files) skipping 1 binary chapter diffs\n", + " 2m 37s CHUNKING (O chapter): Loaded: 929 chunks\n", + " 2m 37s CHUNKING (O chapter): Made 929 chunks\n", + " 2m 37s PREPARING (O chapter SET)\n", + " 2m 38s PREPARING (O chapter SET): Done 929 chunks.\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Loaded: 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>100): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>100): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 relevant similarities between 0 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>100): 0 members in 0 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>100): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs needed: 0\n", + " 2m 38s PRINT (O chapter SET M>30 S>100): Chapter diffs: 0 newly created and 0 already existing\n", + " 2m 38s PRINT (O chapter SET M>30 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>95): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>95): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>95): 1 relevant similarities between 2 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>95): 2 members in 1 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>95): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>90): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>90): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>90): 1 relevant similarities between 2 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>90): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>90): 2 members in 1 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>90): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>90): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>90): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>85): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>85): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>85): 1 relevant similarities between 2 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>85): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>85): 2 members in 1 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>85): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>85): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>85): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>80): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>80): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>80): 2 relevant similarities between 4 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>80): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>80): 4 members in 2 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>80): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>80): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>80): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>75): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>75): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>75): 7 relevant similarities between 14 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>75): Loaded: 7 cliques out of 14 chunks from 7 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>75): 14 members in 7 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>75): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>75): formatting 7 cliques skipping 7 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>75): formatted 7 cliques (1 files) skipping 7 binary chapter diffs\n", + " 2m 38s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 38s PREPARING (O chapter SET): Already prepared\n", + " 2m 38s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 38s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>70): fetching similars and chunk candidates\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>70): inspecting the similarity matrix\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>70): 10 relevant similarities between 20 passages\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>70): Loaded: 10 cliques out of 20 chunks from 10 comparisons\n", + " 2m 38s CLIQUES (O chapter SET M>30 S>70): 20 members in 10 cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>70): sorting out cliques\n", + " 2m 38s PRINT (O chapter SET M>30 S>70): formatting 10 cliques involving 10 binary chapter diffs\n", + " 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs needed: 10\n", + " 2m 38s PRINT (O chapter SET M>30 S>70): Chapter diffs: 0 newly created and 10 already existing\n", + " 2m 47s PRINT (O chapter SET M>30 S>70): formatted 10 cliques (1 files) involving 10 binary chapter diffs\n", + " 2m 47s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 47s PREPARING (O chapter SET): Already prepared\n", + " 2m 47s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 47s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 47s CLIQUES (O chapter SET M>30 S>65): fetching similars and chunk candidates\n", + " 2m 47s CLIQUES (O chapter SET M>30 S>65): inspecting the similarity matrix\n", + " 2m 47s CLIQUES (O chapter SET M>30 S>65): 12 relevant similarities between 24 passages\n", + " 2m 47s CLIQUES (O chapter SET M>30 S>65): Loaded: 12 cliques out of 24 chunks from 12 comparisons\n", + " 2m 47s CLIQUES (O chapter SET M>30 S>65): 24 members in 12 cliques\n", + " 2m 47s PRINT (O chapter SET M>30 S>65): sorting out cliques\n", + " 2m 47s PRINT (O chapter SET M>30 S>65): formatting 12 cliques involving 12 binary chapter diffs\n", + " 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs needed: 12\n", + " 2m 47s PRINT (O chapter SET M>30 S>65): Chapter diffs: 0 newly created and 12 already existing\n", + " 2m 57s PRINT (O chapter SET M>30 S>65): formatted 12 cliques (1 files) involving 12 binary chapter diffs\n", + " 2m 57s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 2m 57s PREPARING (O chapter SET): Already prepared\n", + " 2m 57s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 2m 57s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 2m 57s CLIQUES (O chapter SET M>30 S>60): fetching similars and chunk candidates\n", + " 2m 57s CLIQUES (O chapter SET M>30 S>60): inspecting the similarity matrix\n", + " 2m 57s CLIQUES (O chapter SET M>30 S>60): 17 relevant similarities between 34 passages\n", + " 2m 57s CLIQUES (O chapter SET M>30 S>60): Loaded: 17 cliques out of 34 chunks from 17 comparisons\n", + " 2m 57s CLIQUES (O chapter SET M>30 S>60): 34 members in 17 cliques\n", + " 2m 57s PRINT (O chapter SET M>30 S>60): sorting out cliques\n", + " 2m 57s PRINT (O chapter SET M>30 S>60): formatting 17 cliques involving 17 binary chapter diffs\n", + " 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs needed: 17\n", + " 2m 57s PRINT (O chapter SET M>30 S>60): Chapter diffs: 0 newly created and 17 already existing\n", + " 3m 13s PRINT (O chapter SET M>30 S>60): formatted 17 cliques (1 files) involving 17 binary chapter diffs\n", + " 3m 13s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 3m 13s PREPARING (O chapter SET): Already prepared\n", + " 3m 13s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 3m 13s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 3m 13s CLIQUES (O chapter SET M>30 S>55): fetching similars and chunk candidates\n", + " 3m 13s CLIQUES (O chapter SET M>30 S>55): inspecting the similarity matrix\n", + " 3m 13s CLIQUES (O chapter SET M>30 S>55): 22 relevant similarities between 44 passages\n", + " 3m 13s CLIQUES (O chapter SET M>30 S>55): Loaded: 22 cliques out of 44 chunks from 22 comparisons\n", + " 3m 13s CLIQUES (O chapter SET M>30 S>55): 44 members in 22 cliques\n", + " 3m 13s PRINT (O chapter SET M>30 S>55): sorting out cliques\n", + " 3m 13s PRINT (O chapter SET M>30 S>55): formatting 22 cliques involving 22 binary chapter diffs\n", + " 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs needed: 22\n", + " 3m 13s PRINT (O chapter SET M>30 S>55): Chapter diffs: 0 newly created and 22 already existing\n", + " 3m 33s PRINT (O chapter SET M>30 S>55): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", + " 3m 33s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 3m 33s PREPARING (O chapter SET): Already prepared\n", + " 3m 33s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 3m 33s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 3m 33s CLIQUES (O chapter SET M>30 S>50): fetching similars and chunk candidates\n", + " 3m 33s CLIQUES (O chapter SET M>30 S>50): inspecting the similarity matrix\n", + " 3m 33s CLIQUES (O chapter SET M>30 S>50): 28 relevant similarities between 56 passages\n", + " 3m 33s CLIQUES (O chapter SET M>30 S>50): Loaded: 28 cliques out of 56 chunks from 28 comparisons\n", + " 3m 33s CLIQUES (O chapter SET M>30 S>50): 56 members in 28 cliques\n", + " 3m 33s PRINT (O chapter SET M>30 S>50): sorting out cliques\n", + " 3m 33s PRINT (O chapter SET M>30 S>50): formatting 28 cliques involving 28 binary chapter diffs\n", + " 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs needed: 28\n", + " 3m 33s PRINT (O chapter SET M>30 S>50): Chapter diffs: 0 newly created and 28 already existing\n", + " 3m 54s PRINT (O chapter SET M>30 S>50): formatted 28 cliques (1 files) involving 28 binary chapter diffs\n", + " 3m 54s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 3m 54s PREPARING (O chapter SET): Already prepared\n", + " 3m 54s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 3m 54s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 3m 54s CLIQUES (O chapter SET M>30 S>45): fetching similars and chunk candidates\n", + " 3m 54s CLIQUES (O chapter SET M>30 S>45): inspecting the similarity matrix\n", + " 3m 54s CLIQUES (O chapter SET M>30 S>45): 42 relevant similarities between 80 passages\n", + " 3m 54s CLIQUES (O chapter SET M>30 S>45): Loaded: 39 cliques out of 80 chunks from 42 comparisons\n", + " 3m 54s CLIQUES (O chapter SET M>30 S>45): 80 members in 39 cliques\n", + " 3m 54s PRINT (O chapter SET M>30 S>45): sorting out cliques\n", + " 3m 54s PRINT (O chapter SET M>30 S>45): formatting 39 cliques involving 37 binary chapter diffs\n", + " 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs needed: 37\n", + " 3m 54s PRINT (O chapter SET M>30 S>45): Chapter diffs: 0 newly created and 37 already existing\n", + " 4m 23s PRINT (O chapter SET M>30 S>45): formatted 39 cliques (1 files) involving 37 binary chapter diffs\n", + " 4m 23s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 4m 23s PREPARING (O chapter SET): Already prepared\n", + " 4m 23s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 4m 23s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 4m 23s CLIQUES (O chapter SET M>30 S>40): fetching similars and chunk candidates\n", + " 4m 23s CLIQUES (O chapter SET M>30 S>40): inspecting the similarity matrix\n", + " 4m 23s CLIQUES (O chapter SET M>30 S>40): 87 relevant similarities between 142 passages\n", + " 4m 23s CLIQUES (O chapter SET M>30 S>40): Loaded: 62 cliques out of 142 chunks from 87 comparisons\n", + " 4m 23s CLIQUES (O chapter SET M>30 S>40): 142 members in 62 cliques\n", + " 4m 23s PRINT (O chapter SET M>30 S>40): sorting out cliques\n", + " 4m 23s PRINT (O chapter SET M>30 S>40): formatting 62 cliques involving 51 binary chapter diffs\n", + " 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs needed: 51\n", + " 4m 23s PRINT (O chapter SET M>30 S>40): Chapter diffs: 0 newly created and 51 already existing\n", + " 5m 10s PRINT (O chapter SET M>30 S>40): formatted 62 cliques (2 files) involving 51 binary chapter diffs\n", + " 5m 10s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 5m 10s PREPARING (O chapter SET): Already prepared\n", + " 5m 10s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 5m 10s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 5m 10s CLIQUES (O chapter SET M>30 S>35): fetching similars and chunk candidates\n", + " 5m 10s CLIQUES (O chapter SET M>30 S>35): inspecting the similarity matrix\n", + " 5m 10s CLIQUES (O chapter SET M>30 S>35): 352 relevant similarities between 302 passages\n", + " 5m 10s CLIQUES (O chapter SET M>30 S>35): Loaded: 53 cliques out of 302 chunks from 352 comparisons\n", + " 5m 10s CLIQUES (O chapter SET M>30 S>35): 302 members in 53 cliques\n", + " 5m 10s PRINT (O chapter SET M>30 S>35): sorting out cliques\n", + " 5m 10s PRINT (O chapter SET M>30 S>35): formatting 53 cliques skipping 27 binary chapter diffs\n", + " 6m 02s PRINT (O chapter SET M>30 S>35): formatted 53 cliques (2 files) skipping 27 binary chapter diffs\n", + " 6m 02s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 02s PREPARING (O chapter SET): Already prepared\n", + " 6m 02s SIMILARITY (O chapter SET M>30): Using 431 K (431056) comparisons with 3445 entries in matrix\n", + " 6m 02s SIMILARITY (O chapter SET M>30): similarities between 30.0 and 96.83257918552036. 0 are 100%\n", + " 6m 02s CLIQUES (O chapter SET M>30 S>30): fetching similars and chunk candidates\n", + " 6m 02s CLIQUES (O chapter SET M>30 S>30): inspecting the similarity matrix\n", + " 6m 02s CLIQUES (O chapter SET M>30 S>30): 3445 relevant similarities between 571 passages\n", + " 6m 02s CLIQUES (O chapter SET M>30 S>30): Loaded: 28 cliques out of 571 chunks from 3445 comparisons\n", + " 6m 02s CLIQUES (O chapter SET M>30 S>30): 571 members in 28 cliques\n", + " 6m 02s PRINT (O chapter SET M>30 S>30): sorting out cliques\n", + " 6m 02s PRINT (O chapter SET M>30 S>30): formatting 28 cliques skipping 18 binary chapter diffs\n", + " 6m 24s PRINT (O chapter SET M>30 S>30): formatted 28 cliques (1 files) skipping 18 binary chapter diffs\n", + " 6m 24s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 24s PREPARING (O chapter LCS)\n", + " 6m 25s PREPARING (O chapter LCS): Done 929 chunks.\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): Loaded: 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>100): fetching similars and chunk candidates\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>100): inspecting the similarity matrix\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 relevant similarities between 0 passages\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>100): Loaded: 0 cliques out of 0 chunks from 0 comparisons\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>100): 0 members in 0 cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>100): sorting out cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>100): formatting 0 cliques involving 0 binary chapter diffs\n", + " 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs needed: 0\n", + " 6m 25s PRINT (O chapter LCS M>55 S>100): Chapter diffs: 0 newly created and 0 already existing\n", + " 6m 25s PRINT (O chapter LCS M>55 S>100): formatted 0 cliques (1 files) involving 0 binary chapter diffs\n", + " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 25s PREPARING (O chapter LCS): Already prepared\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>95): fetching similars and chunk candidates\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>95): inspecting the similarity matrix\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>95): 1 relevant similarities between 2 passages\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>95): Loaded: 1 cliques out of 2 chunks from 1 comparisons\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>95): 2 members in 1 cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>95): sorting out cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>95): formatting 1 cliques skipping 1 binary chapter diffs\n", + " 6m 25s PRINT (O chapter LCS M>55 S>95): formatted 1 cliques (1 files) skipping 1 binary chapter diffs\n", + " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 25s PREPARING (O chapter LCS): Already prepared\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>90): fetching similars and chunk candidates\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>90): inspecting the similarity matrix\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>90): 2 relevant similarities between 4 passages\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>90): Loaded: 2 cliques out of 4 chunks from 2 comparisons\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>90): 4 members in 2 cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>90): sorting out cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>90): formatting 2 cliques skipping 2 binary chapter diffs\n", + " 6m 25s PRINT (O chapter LCS M>55 S>90): formatted 2 cliques (1 files) skipping 2 binary chapter diffs\n", + " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 25s PREPARING (O chapter LCS): Already prepared\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>85): fetching similars and chunk candidates\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>85): inspecting the similarity matrix\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>85): 6 relevant similarities between 12 passages\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>85): Loaded: 6 cliques out of 12 chunks from 6 comparisons\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>85): 12 members in 6 cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>85): sorting out cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>85): formatting 6 cliques skipping 6 binary chapter diffs\n", + " 6m 25s PRINT (O chapter LCS M>55 S>85): formatted 6 cliques (1 files) skipping 6 binary chapter diffs\n", + " 6m 25s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 25s PREPARING (O chapter LCS): Already prepared\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 25s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>80): fetching similars and chunk candidates\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>80): inspecting the similarity matrix\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>80): 9 relevant similarities between 18 passages\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>80): Loaded: 9 cliques out of 18 chunks from 9 comparisons\n", + " 6m 25s CLIQUES (O chapter LCS M>55 S>80): 18 members in 9 cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>80): sorting out cliques\n", + " 6m 25s PRINT (O chapter LCS M>55 S>80): formatting 9 cliques involving 9 binary chapter diffs\n", + " 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs needed: 9\n", + " 6m 25s PRINT (O chapter LCS M>55 S>80): Chapter diffs: 0 newly created and 9 already existing\n", + " 6m 31s PRINT (O chapter LCS M>55 S>80): formatted 9 cliques (1 files) involving 9 binary chapter diffs\n", + " 6m 31s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 31s PREPARING (O chapter LCS): Already prepared\n", + " 6m 31s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 31s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 31s CLIQUES (O chapter LCS M>55 S>75): fetching similars and chunk candidates\n", + " 6m 31s CLIQUES (O chapter LCS M>55 S>75): inspecting the similarity matrix\n", + " 6m 31s CLIQUES (O chapter LCS M>55 S>75): 13 relevant similarities between 26 passages\n", + " 6m 31s CLIQUES (O chapter LCS M>55 S>75): Loaded: 13 cliques out of 26 chunks from 13 comparisons\n", + " 6m 31s CLIQUES (O chapter LCS M>55 S>75): 26 members in 13 cliques\n", + " 6m 31s PRINT (O chapter LCS M>55 S>75): sorting out cliques\n", + " 6m 31s PRINT (O chapter LCS M>55 S>75): formatting 13 cliques involving 13 binary chapter diffs\n", + " 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs needed: 13\n", + " 6m 31s PRINT (O chapter LCS M>55 S>75): Chapter diffs: 0 newly created and 13 already existing\n", + " 6m 40s PRINT (O chapter LCS M>55 S>75): formatted 13 cliques (1 files) involving 13 binary chapter diffs\n", + " 6m 40s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 40s PREPARING (O chapter LCS): Already prepared\n", + " 6m 40s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 40s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 40s CLIQUES (O chapter LCS M>55 S>70): fetching similars and chunk candidates\n", + " 6m 40s CLIQUES (O chapter LCS M>55 S>70): inspecting the similarity matrix\n", + " 6m 40s CLIQUES (O chapter LCS M>55 S>70): 19 relevant similarities between 38 passages\n", + " 6m 40s CLIQUES (O chapter LCS M>55 S>70): Loaded: 19 cliques out of 38 chunks from 19 comparisons\n", + " 6m 40s CLIQUES (O chapter LCS M>55 S>70): 38 members in 19 cliques\n", + " 6m 40s PRINT (O chapter LCS M>55 S>70): sorting out cliques\n", + " 6m 40s PRINT (O chapter LCS M>55 S>70): formatting 19 cliques involving 19 binary chapter diffs\n", + " 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs needed: 19\n", + " 6m 40s PRINT (O chapter LCS M>55 S>70): Chapter diffs: 0 newly created and 19 already existing\n", + " 6m 55s PRINT (O chapter LCS M>55 S>70): formatted 19 cliques (1 files) involving 19 binary chapter diffs\n", + " 6m 55s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 6m 55s PREPARING (O chapter LCS): Already prepared\n", + " 6m 55s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 6m 55s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 6m 55s CLIQUES (O chapter LCS M>55 S>65): fetching similars and chunk candidates\n", + " 6m 55s CLIQUES (O chapter LCS M>55 S>65): inspecting the similarity matrix\n", + " 6m 55s CLIQUES (O chapter LCS M>55 S>65): 22 relevant similarities between 44 passages\n", + " 6m 55s CLIQUES (O chapter LCS M>55 S>65): Loaded: 22 cliques out of 44 chunks from 22 comparisons\n", + " 6m 55s CLIQUES (O chapter LCS M>55 S>65): 44 members in 22 cliques\n", + " 6m 55s PRINT (O chapter LCS M>55 S>65): sorting out cliques\n", + " 6m 55s PRINT (O chapter LCS M>55 S>65): formatting 22 cliques involving 22 binary chapter diffs\n", + " 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs needed: 22\n", + " 6m 55s PRINT (O chapter LCS M>55 S>65): Chapter diffs: 0 newly created and 22 already existing\n", + " 7m 12s PRINT (O chapter LCS M>55 S>65): formatted 22 cliques (1 files) involving 22 binary chapter diffs\n", + " 7m 12s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 7m 12s PREPARING (O chapter LCS): Already prepared\n", + " 7m 12s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 7m 12s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 7m 12s CLIQUES (O chapter LCS M>55 S>60): fetching similars and chunk candidates\n", + " 7m 12s CLIQUES (O chapter LCS M>55 S>60): inspecting the similarity matrix\n", + " 7m 12s CLIQUES (O chapter LCS M>55 S>60): 26 relevant similarities between 52 passages\n", + " 7m 12s CLIQUES (O chapter LCS M>55 S>60): Loaded: 26 cliques out of 52 chunks from 26 comparisons\n", + " 7m 12s CLIQUES (O chapter LCS M>55 S>60): 52 members in 26 cliques\n", + " 7m 12s PRINT (O chapter LCS M>55 S>60): sorting out cliques\n", + " 7m 12s PRINT (O chapter LCS M>55 S>60): formatting 26 cliques involving 26 binary chapter diffs\n", + " 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs needed: 26\n", + " 7m 12s PRINT (O chapter LCS M>55 S>60): Chapter diffs: 0 newly created and 26 already existing\n", + " 7m 32s PRINT (O chapter LCS M>55 S>60): formatted 26 cliques (1 files) involving 26 binary chapter diffs\n", + " 7m 32s CHUNKING (O chapter): already chunked into 929 chunks\n", + " 7m 32s PREPARING (O chapter LCS): Already prepared\n", + " 7m 32s SIMILARITY (O chapter LCS M>55): Using 431 K (431056) comparisons with 53 entries in matrix\n", + " 7m 32s SIMILARITY (O chapter LCS M>55): similarities between 55.02239283429302 and 97.7977740942458. 0 are 100%\n", + " 7m 32s CLIQUES (O chapter LCS M>55 S>55): fetching similars and chunk candidates\n", + " 7m 32s CLIQUES (O chapter LCS M>55 S>55): inspecting the similarity matrix\n", + " 7m 32s CLIQUES (O chapter LCS M>55 S>55): 53 relevant similarities between 102 passages\n", + " 7m 32s CLIQUES (O chapter LCS M>55 S>55): Loaded: 49 cliques out of 102 chunks from 53 comparisons\n", + " 7m 32s CLIQUES (O chapter LCS M>55 S>55): 102 members in 49 cliques\n", + " 7m 32s PRINT (O chapter LCS M>55 S>55): sorting out cliques\n", + " 7m 32s PRINT (O chapter LCS M>55 S>55): formatting 49 cliques involving 46 binary chapter diffs\n", + " 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs needed: 46\n", + " 7m 32s PRINT (O chapter LCS M>55 S>55): Chapter diffs: 0 newly created and 46 already existing\n", + " 8m 04s PRINT (O chapter LCS M>55 S>55): formatted 49 cliques (1 files) involving 46 binary chapter diffs\n", + " 8m 04s CHUNKING (O verse): Loaded: 23213 chunks\n", + " 8m 04s CHUNKING (O verse): Made 23213 chunks\n", + " 8m 04s PREPARING (O verse SET)\n", + " 8m 05s PREPARING (O verse SET): Done 23213 chunks.\n", + " 8m 05s SIMILARITY (O verse SET M>50): Loaded: 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 05s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 05s CLIQUES (O verse SET M>50 S>100): fetching similars and chunk candidates\n", + " 8m 05s CLIQUES (O verse SET M>50 S>100): inspecting the similarity matrix\n", + " 8m 05s CLIQUES (O verse SET M>50 S>100): 4506 relevant similarities between 993 passages\n", + " 8m 05s CLIQUES (O verse SET M>50 S>100): Loaded: 388 cliques out of 993 chunks from 4506 comparisons\n", + " 8m 05s CLIQUES (O verse SET M>50 S>100): 993 members in 388 cliques\n", + " 8m 05s PRINT (O verse SET M>50 S>100): sorting out cliques\n", + " 8m 05s PRINT (O verse SET M>50 S>100): formatting 388 cliques involving 100 binary chapter diffs\n", + " 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs needed: 100\n", + " 8m 05s PRINT (O verse SET M>50 S>100): Chapter diffs: 0 newly created and 100 already existing\n", + " 8m 06s PRINT (O verse SET M>50 S>100): formatted 388 cliques (8 files) involving 100 binary chapter diffs\n", + " 8m 06s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 06s PREPARING (O verse SET): Already prepared\n", + " 8m 06s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 06s CLIQUES (O verse SET M>50 S>95): fetching similars and chunk candidates\n", + " 8m 06s CLIQUES (O verse SET M>50 S>95): inspecting the similarity matrix\n", + " 8m 06s CLIQUES (O verse SET M>50 S>95): 4524 relevant similarities between 1029 passages\n", + " 8m 06s CLIQUES (O verse SET M>50 S>95): Loaded: 406 cliques out of 1029 chunks from 4524 comparisons\n", + " 8m 06s CLIQUES (O verse SET M>50 S>95): 1029 members in 406 cliques\n", + " 8m 06s PRINT (O verse SET M>50 S>95): sorting out cliques\n", + " 8m 06s PRINT (O verse SET M>50 S>95): formatting 406 cliques involving 103 binary chapter diffs\n", + " 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs needed: 103\n", + " 8m 06s PRINT (O verse SET M>50 S>95): Chapter diffs: 0 newly created and 103 already existing\n", + " 8m 06s PRINT (O verse SET M>50 S>95): formatted 406 cliques (9 files) involving 103 binary chapter diffs\n", + " 8m 06s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 06s PREPARING (O verse SET): Already prepared\n", + " 8m 06s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 06s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 06s CLIQUES (O verse SET M>50 S>90): fetching similars and chunk candidates\n", + " 8m 06s CLIQUES (O verse SET M>50 S>90): inspecting the similarity matrix\n", + " 8m 06s CLIQUES (O verse SET M>50 S>90): 4700 relevant similarities between 1286 passages\n", + " 8m 06s CLIQUES (O verse SET M>50 S>90): Loaded: 526 cliques out of 1286 chunks from 4700 comparisons\n", + " 8m 06s CLIQUES (O verse SET M>50 S>90): 1286 members in 526 cliques\n", + " 8m 06s PRINT (O verse SET M>50 S>90): sorting out cliques\n", + " 8m 06s PRINT (O verse SET M>50 S>90): formatting 526 cliques involving 133 binary chapter diffs\n", + " 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs needed: 133\n", + " 8m 06s PRINT (O verse SET M>50 S>90): Chapter diffs: 0 newly created and 133 already existing\n", + " 8m 07s PRINT (O verse SET M>50 S>90): formatted 526 cliques (11 files) involving 133 binary chapter diffs\n", + " 8m 07s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 07s PREPARING (O verse SET): Already prepared\n", + " 8m 07s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 07s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 07s CLIQUES (O verse SET M>50 S>85): fetching similars and chunk candidates\n", + " 8m 07s CLIQUES (O verse SET M>50 S>85): inspecting the similarity matrix\n", + " 8m 07s CLIQUES (O verse SET M>50 S>85): 4932 relevant similarities between 1573 passages\n", + " 8m 07s CLIQUES (O verse SET M>50 S>85): Loaded: 651 cliques out of 1573 chunks from 4932 comparisons\n", + " 8m 07s CLIQUES (O verse SET M>50 S>85): 1573 members in 651 cliques\n", + " 8m 07s PRINT (O verse SET M>50 S>85): sorting out cliques\n", + " 8m 07s PRINT (O verse SET M>50 S>85): formatting 651 cliques involving 151 binary chapter diffs\n", + " 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs needed: 151\n", + " 8m 07s PRINT (O verse SET M>50 S>85): Chapter diffs: 0 newly created and 151 already existing\n", + " 8m 08s PRINT (O verse SET M>50 S>85): formatted 651 cliques (14 files) involving 151 binary chapter diffs\n", + " 8m 08s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 08s PREPARING (O verse SET): Already prepared\n", + " 8m 08s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 08s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 08s CLIQUES (O verse SET M>50 S>80): fetching similars and chunk candidates\n", + " 8m 08s CLIQUES (O verse SET M>50 S>80): inspecting the similarity matrix\n", + " 8m 08s CLIQUES (O verse SET M>50 S>80): 10653 relevant similarities between 1958 passages\n", + " 8m 08s CLIQUES (O verse SET M>50 S>80): Loaded: 800 cliques out of 1958 chunks from 10653 comparisons\n", + " 8m 08s CLIQUES (O verse SET M>50 S>80): 1958 members in 800 cliques\n", + " 8m 08s PRINT (O verse SET M>50 S>80): sorting out cliques\n", + " 8m 08s PRINT (O verse SET M>50 S>80): formatting 800 cliques involving 174 binary chapter diffs\n", + " 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs needed: 174\n", + " 8m 08s PRINT (O verse SET M>50 S>80): Chapter diffs: 0 newly created and 174 already existing\n", + " 8m 09s PRINT (O verse SET M>50 S>80): formatted 800 cliques (16 files) involving 174 binary chapter diffs\n", + " 8m 09s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 09s PREPARING (O verse SET): Already prepared\n", + " 8m 09s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 09s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 09s CLIQUES (O verse SET M>50 S>75): fetching similars and chunk candidates\n", + " 8m 09s CLIQUES (O verse SET M>50 S>75): inspecting the similarity matrix\n", + " 8m 09s CLIQUES (O verse SET M>50 S>75): 11181 relevant similarities between 2359 passages\n", + " 8m 09s CLIQUES (O verse SET M>50 S>75): Loaded: 961 cliques out of 2359 chunks from 11181 comparisons\n", + " 8m 09s CLIQUES (O verse SET M>50 S>75): 2359 members in 961 cliques\n", + " 8m 09s PRINT (O verse SET M>50 S>75): sorting out cliques\n", + " 8m 09s PRINT (O verse SET M>50 S>75): formatting 961 cliques involving 210 binary chapter diffs\n", + " 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs needed: 210\n", + " 8m 09s PRINT (O verse SET M>50 S>75): Chapter diffs: 0 newly created and 210 already existing\n", + " 8m 11s PRINT (O verse SET M>50 S>75): formatted 961 cliques (20 files) involving 210 binary chapter diffs\n", + " 8m 11s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 11s PREPARING (O verse SET): Already prepared\n", + " 8m 11s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 11s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 11s CLIQUES (O verse SET M>50 S>70): fetching similars and chunk candidates\n", + " 8m 11s CLIQUES (O verse SET M>50 S>70): inspecting the similarity matrix\n", + " 8m 11s CLIQUES (O verse SET M>50 S>70): 11704 relevant similarities between 2720 passages\n", + " 8m 11s CLIQUES (O verse SET M>50 S>70): Loaded: 1094 cliques out of 2720 chunks from 11704 comparisons\n", + " 8m 11s CLIQUES (O verse SET M>50 S>70): 2720 members in 1094 cliques\n", + " 8m 11s PRINT (O verse SET M>50 S>70): sorting out cliques\n", + " 8m 11s PRINT (O verse SET M>50 S>70): formatting 1094 cliques involving 237 binary chapter diffs\n", + " 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs needed: 237\n", + " 8m 11s PRINT (O verse SET M>50 S>70): Chapter diffs: 0 newly created and 237 already existing\n", + " 8m 12s PRINT (O verse SET M>50 S>70): formatted 1094 cliques (22 files) involving 237 binary chapter diffs\n", + " 8m 12s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 12s PREPARING (O verse SET): Already prepared\n", + " 8m 12s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 12s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 12s CLIQUES (O verse SET M>50 S>65): fetching similars and chunk candidates\n", + " 8m 12s CLIQUES (O verse SET M>50 S>65): inspecting the similarity matrix\n", + " 8m 13s CLIQUES (O verse SET M>50 S>65): 14353 relevant similarities between 3139 passages\n", + " 8m 13s CLIQUES (O verse SET M>50 S>65): Loaded: 1235 cliques out of 3139 chunks from 14353 comparisons\n", + " 8m 13s CLIQUES (O verse SET M>50 S>65): 3139 members in 1235 cliques\n", + " 8m 13s PRINT (O verse SET M>50 S>65): sorting out cliques\n", + " 8m 13s PRINT (O verse SET M>50 S>65): formatting 1235 cliques involving 284 binary chapter diffs\n", + " 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs needed: 284\n", + " 8m 13s PRINT (O verse SET M>50 S>65): Chapter diffs: 0 newly created and 284 already existing\n", + " 8m 15s PRINT (O verse SET M>50 S>65): formatted 1235 cliques (25 files) involving 284 binary chapter diffs\n", + " 8m 15s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 15s PREPARING (O verse SET): Already prepared\n", + " 8m 15s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 15s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 15s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", + " 8m 15s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", + " 8m 15s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", + " 8m 15s CLIQUES (O verse SET M>50 S>60): Loaded: 1439 cliques out of 3877 chunks from 16055 comparisons\n", + " 8m 15s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", + " 8m 15s PRINT (O verse SET M>50 S>60): sorting out cliques\n", + " 8m 15s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", + " 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", + " 8m 15s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", + " 8m 17s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n", + " 8m 17s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 17s PREPARING (O verse SET): Already prepared\n", + " 8m 17s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 17s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 17s CLIQUES (O verse SET M>50 S>55): fetching similars and chunk candidates\n", + " 8m 17s CLIQUES (O verse SET M>50 S>55): inspecting the similarity matrix\n", + " 8m 17s CLIQUES (O verse SET M>50 S>55): 18754 relevant similarities between 4735 passages\n", + " 8m 17s CLIQUES (O verse SET M>50 S>55): Loaded: 1638 cliques out of 4735 chunks from 18754 comparisons\n", + " 8m 17s CLIQUES (O verse SET M>50 S>55): 4735 members in 1638 cliques\n", + " 8m 17s PRINT (O verse SET M>50 S>55): sorting out cliques\n", + " 8m 17s PRINT (O verse SET M>50 S>55): formatting 1638 cliques involving 447 binary chapter diffs\n", + " 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs needed: 447\n", + " 8m 17s PRINT (O verse SET M>50 S>55): Chapter diffs: 0 newly created and 447 already existing\n", + " 8m 20s PRINT (O verse SET M>50 S>55): formatted 1638 cliques (33 files) involving 447 binary chapter diffs\n", + " 8m 20s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 20s PREPARING (O verse SET): Already prepared\n", + " 8m 20s SIMILARITY (O verse SET M>50): Using 269 M (269410078) comparisons with 24832 entries in matrix\n", + " 8m 20s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + " 8m 20s CLIQUES (O verse SET M>50 S>50): fetching similars and chunk candidates\n", + " 8m 20s CLIQUES (O verse SET M>50 S>50): inspecting the similarity matrix\n", + " 8m 20s CLIQUES (O verse SET M>50 S>50): 24832 relevant similarities between 6711 passages\n", + " 8m 20s CLIQUES (O verse SET M>50 S>50): Loaded: 1850 cliques out of 6711 chunks from 24832 comparisons\n", + " 8m 20s CLIQUES (O verse SET M>50 S>50): 6711 members in 1850 cliques\n", + " 8m 20s PRINT (O verse SET M>50 S>50): sorting out cliques\n", + " 8m 20s PRINT (O verse SET M>50 S>50): formatting 1850 cliques skipping 560 binary chapter diffs\n", + " 8m 24s PRINT (O verse SET M>50 S>50): formatted 1850 cliques (37 files) skipping 560 binary chapter diffs\n", + " 8m 24s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 24s PREPARING (O verse LCS)\n", + " 8m 25s PREPARING (O verse LCS): Done 23213 chunks.\n", + " 8m 25s SIMILARITY (O verse LCS M>60): Loaded: 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 25s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 25s CLIQUES (O verse LCS M>60 S>100): fetching similars and chunk candidates\n", + " 8m 25s CLIQUES (O verse LCS M>60 S>100): inspecting the similarity matrix\n", + " 8m 25s CLIQUES (O verse LCS M>60 S>100): 4204 relevant similarities between 793 passages\n", + " 8m 25s CLIQUES (O verse LCS M>60 S>100): Loaded: 295 cliques out of 793 chunks from 4204 comparisons\n", + " 8m 25s CLIQUES (O verse LCS M>60 S>100): 793 members in 295 cliques\n", + " 8m 25s PRINT (O verse LCS M>60 S>100): sorting out cliques\n", + " 8m 25s PRINT (O verse LCS M>60 S>100): formatting 295 cliques involving 80 binary chapter diffs\n", + " 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs needed: 80\n", + " 8m 25s PRINT (O verse LCS M>60 S>100): Chapter diffs: 0 newly created and 80 already existing\n", + " 8m 26s PRINT (O verse LCS M>60 S>100): formatted 295 cliques (6 files) involving 80 binary chapter diffs\n", + " 8m 26s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 26s PREPARING (O verse LCS): Already prepared\n", + " 8m 26s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 26s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 26s CLIQUES (O verse LCS M>60 S>95): fetching similars and chunk candidates\n", + " 8m 26s CLIQUES (O verse LCS M>60 S>95): inspecting the similarity matrix\n", + " 8m 26s CLIQUES (O verse LCS M>60 S>95): 4489 relevant similarities between 1235 passages\n", + " 8m 26s CLIQUES (O verse LCS M>60 S>95): Loaded: 504 cliques out of 1235 chunks from 4489 comparisons\n", + " 8m 26s CLIQUES (O verse LCS M>60 S>95): 1235 members in 504 cliques\n", + " 8m 26s PRINT (O verse LCS M>60 S>95): sorting out cliques\n", + " 8m 26s PRINT (O verse LCS M>60 S>95): formatting 504 cliques involving 120 binary chapter diffs\n", + " 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs needed: 120\n", + " 8m 26s PRINT (O verse LCS M>60 S>95): Chapter diffs: 0 newly created and 120 already existing\n", + " 8m 27s PRINT (O verse LCS M>60 S>95): formatted 504 cliques (11 files) involving 120 binary chapter diffs\n", + " 8m 27s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 27s PREPARING (O verse LCS): Already prepared\n", + " 8m 27s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 27s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 27s CLIQUES (O verse LCS M>60 S>90): fetching similars and chunk candidates\n", + " 8m 27s CLIQUES (O verse LCS M>60 S>90): inspecting the similarity matrix\n", + " 8m 27s CLIQUES (O verse LCS M>60 S>90): 5538 relevant similarities between 1754 passages\n", + " 8m 27s CLIQUES (O verse LCS M>60 S>90): Loaded: 724 cliques out of 1754 chunks from 5538 comparisons\n", + " 8m 27s CLIQUES (O verse LCS M>60 S>90): 1754 members in 724 cliques\n", + " 8m 27s PRINT (O verse LCS M>60 S>90): sorting out cliques\n", + " 8m 27s PRINT (O verse LCS M>60 S>90): formatting 724 cliques involving 151 binary chapter diffs\n", + " 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs needed: 151\n", + " 8m 27s PRINT (O verse LCS M>60 S>90): Chapter diffs: 0 newly created and 151 already existing\n", + " 8m 28s PRINT (O verse LCS M>60 S>90): formatted 724 cliques (15 files) involving 151 binary chapter diffs\n", + " 8m 28s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 28s PREPARING (O verse LCS): Already prepared\n", + " 8m 28s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 28s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 28s CLIQUES (O verse LCS M>60 S>85): fetching similars and chunk candidates\n", + " 8m 28s CLIQUES (O verse LCS M>60 S>85): inspecting the similarity matrix\n", + " 8m 28s CLIQUES (O verse LCS M>60 S>85): 7871 relevant similarities between 2296 passages\n", + " 8m 28s CLIQUES (O verse LCS M>60 S>85): Loaded: 938 cliques out of 2296 chunks from 7871 comparisons\n", + " 8m 28s CLIQUES (O verse LCS M>60 S>85): 2296 members in 938 cliques\n", + " 8m 28s PRINT (O verse LCS M>60 S>85): sorting out cliques\n", + " 8m 28s PRINT (O verse LCS M>60 S>85): formatting 938 cliques involving 179 binary chapter diffs\n", + " 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs needed: 179\n", + " 8m 28s PRINT (O verse LCS M>60 S>85): Chapter diffs: 0 newly created and 179 already existing\n", + " 8m 29s PRINT (O verse LCS M>60 S>85): formatted 938 cliques (19 files) involving 179 binary chapter diffs\n", + " 8m 29s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 29s PREPARING (O verse LCS): Already prepared\n", + " 8m 29s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 29s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 29s CLIQUES (O verse LCS M>60 S>80): fetching similars and chunk candidates\n", + " 8m 29s CLIQUES (O verse LCS M>60 S>80): inspecting the similarity matrix\n", + " 8m 29s CLIQUES (O verse LCS M>60 S>80): 9461 relevant similarities between 2925 passages\n", + " 8m 29s CLIQUES (O verse LCS M>60 S>80): Loaded: 1141 cliques out of 2925 chunks from 9461 comparisons\n", + " 8m 29s CLIQUES (O verse LCS M>60 S>80): 2925 members in 1141 cliques\n", + " 8m 29s PRINT (O verse LCS M>60 S>80): sorting out cliques\n", + " 8m 29s PRINT (O verse LCS M>60 S>80): formatting 1141 cliques involving 251 binary chapter diffs\n", + " 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs needed: 251\n", + " 8m 29s PRINT (O verse LCS M>60 S>80): Chapter diffs: 0 newly created and 251 already existing\n", + " 8m 31s PRINT (O verse LCS M>60 S>80): formatted 1141 cliques (23 files) involving 251 binary chapter diffs\n", + " 8m 31s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 31s PREPARING (O verse LCS): Already prepared\n", + " 8m 31s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 31s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 31s CLIQUES (O verse LCS M>60 S>75): fetching similars and chunk candidates\n", + " 8m 31s CLIQUES (O verse LCS M>60 S>75): inspecting the similarity matrix\n", + " 8m 31s CLIQUES (O verse LCS M>60 S>75): 15543 relevant similarities between 3685 passages\n", + " 8m 31s CLIQUES (O verse LCS M>60 S>75): Loaded: 1340 cliques out of 3685 chunks from 15543 comparisons\n", + " 8m 31s CLIQUES (O verse LCS M>60 S>75): 3685 members in 1340 cliques\n", + " 8m 31s PRINT (O verse LCS M>60 S>75): sorting out cliques\n", + " 8m 31s PRINT (O verse LCS M>60 S>75): formatting 1340 cliques involving 346 binary chapter diffs\n", + " 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs needed: 346\n", + " 8m 31s PRINT (O verse LCS M>60 S>75): Chapter diffs: 0 newly created and 346 already existing\n", + " 8m 33s PRINT (O verse LCS M>60 S>75): formatted 1340 cliques (27 files) involving 346 binary chapter diffs\n", + " 8m 33s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 33s PREPARING (O verse LCS): Already prepared\n", + " 8m 33s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 33s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 33s CLIQUES (O verse LCS M>60 S>70): fetching similars and chunk candidates\n", + " 8m 33s CLIQUES (O verse LCS M>60 S>70): inspecting the similarity matrix\n", + " 8m 33s CLIQUES (O verse LCS M>60 S>70): 19834 relevant similarities between 4958 passages\n", + " 8m 33s CLIQUES (O verse LCS M>60 S>70): Loaded: 1644 cliques out of 4958 chunks from 19834 comparisons\n", + " 8m 33s CLIQUES (O verse LCS M>60 S>70): 4958 members in 1644 cliques\n", + " 8m 33s PRINT (O verse LCS M>60 S>70): sorting out cliques\n", + " 8m 33s PRINT (O verse LCS M>60 S>70): formatting 1644 cliques involving 504 binary chapter diffs\n", + " 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs needed: 504\n", + " 8m 33s PRINT (O verse LCS M>60 S>70): Chapter diffs: 0 newly created and 504 already existing\n", + " 8m 37s PRINT (O verse LCS M>60 S>70): formatted 1644 cliques (33 files) involving 504 binary chapter diffs\n", + " 8m 37s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 37s PREPARING (O verse LCS): Already prepared\n", + " 8m 37s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 37s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 37s CLIQUES (O verse LCS M>60 S>65): fetching similars and chunk candidates\n", + " 8m 37s CLIQUES (O verse LCS M>60 S>65): inspecting the similarity matrix\n", + " 8m 37s CLIQUES (O verse LCS M>60 S>65): 31841 relevant similarities between 9046 passages\n", + " 8m 37s CLIQUES (O verse LCS M>60 S>65): Loaded: 1821 cliques out of 9046 chunks from 31841 comparisons\n", + " 8m 37s CLIQUES (O verse LCS M>60 S>65): 9046 members in 1821 cliques\n", + " 8m 37s PRINT (O verse LCS M>60 S>65): sorting out cliques\n", + " 8m 37s PRINT (O verse LCS M>60 S>65): formatting 1821 cliques skipping 699 binary chapter diffs\n", + " 8m 40s PRINT (O verse LCS M>60 S>65): formatted 1821 cliques (37 files) skipping 699 binary chapter diffs\n", + " 8m 40s CHUNKING (O verse): already chunked into 23213 chunks\n", + " 8m 40s PREPARING (O verse LCS): Already prepared\n", + " 8m 40s SIMILARITY (O verse LCS M>60): Using 269 M (269410078) comparisons with 113614 entries in matrix\n", + " 8m 40s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + " 8m 40s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", + " 8m 40s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", + " 8m 40s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages\n", + " 8m 40s CLIQUES (O verse LCS M>60 S>60): Loaded: 380 cliques out of 18941 chunks from 113614 comparisons\n", + " 8m 40s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques\n", + " 8m 40s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", + " 8m 40s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", + " 8m 41s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n", + " 8m 41s CHUNKING (O half_verse): Loaded: 45180 chunks\n", + " 8m 41s CHUNKING (O half_verse): Made 45180 chunks\n", + " 8m 41s PREPARING (O half_verse SET)\n", + " 8m 42s PREPARING (O half_verse SET): Done 45180 chunks.\n", + " 8m 42s SIMILARITY (O half_verse SET M>50): Loaded: 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 42s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 42s CLIQUES (O half_verse SET M>50 S>100): fetching similars and chunk candidates\n", + " 8m 42s CLIQUES (O half_verse SET M>50 S>100): inspecting the similarity matrix\n", + " 8m 42s CLIQUES (O half_verse SET M>50 S>100): 10239 relevant similarities between 4327 passages\n", + " 8m 42s CLIQUES (O half_verse SET M>50 S>100): Loaded: 1725 cliques out of 4327 chunks from 10239 comparisons\n", + " 8m 42s CLIQUES (O half_verse SET M>50 S>100): 4327 members in 1725 cliques\n", + " 8m 42s PRINT (O half_verse SET M>50 S>100): sorting out cliques\n", + " 8m 42s PRINT (O half_verse SET M>50 S>100): formatting 1725 cliques involving 573 binary chapter diffs\n", + " 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs needed: 573\n", + " 8m 42s PRINT (O half_verse SET M>50 S>100): Chapter diffs: 0 newly created and 573 already existing\n", + " 8m 43s PRINT (O half_verse SET M>50 S>100): formatted 1725 cliques (35 files) involving 573 binary chapter diffs\n", + " 8m 43s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 43s PREPARING (O half_verse SET): Already prepared\n", + " 8m 43s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 43s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 43s CLIQUES (O half_verse SET M>50 S>95): fetching similars and chunk candidates\n", + " 8m 43s CLIQUES (O half_verse SET M>50 S>95): inspecting the similarity matrix\n", + " 8m 43s CLIQUES (O half_verse SET M>50 S>95): 10242 relevant similarities between 4333 passages\n", + " 8m 43s CLIQUES (O half_verse SET M>50 S>95): Loaded: 1728 cliques out of 4333 chunks from 10242 comparisons\n", + " 8m 43s CLIQUES (O half_verse SET M>50 S>95): 4333 members in 1728 cliques\n", + " 8m 43s PRINT (O half_verse SET M>50 S>95): sorting out cliques\n", + " 8m 43s PRINT (O half_verse SET M>50 S>95): formatting 1728 cliques involving 573 binary chapter diffs\n", + " 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs needed: 573\n", + " 8m 43s PRINT (O half_verse SET M>50 S>95): Chapter diffs: 0 newly created and 573 already existing\n", + " 8m 44s PRINT (O half_verse SET M>50 S>95): formatted 1728 cliques (35 files) involving 573 binary chapter diffs\n", + " 8m 44s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 44s PREPARING (O half_verse SET): Already prepared\n", + " 8m 44s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 44s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 44s CLIQUES (O half_verse SET M>50 S>90): fetching similars and chunk candidates\n", + " 8m 44s CLIQUES (O half_verse SET M>50 S>90): inspecting the similarity matrix\n", + " 8m 44s CLIQUES (O half_verse SET M>50 S>90): 10410 relevant similarities between 4618 passages\n", + " 8m 44s CLIQUES (O half_verse SET M>50 S>90): Loaded: 1863 cliques out of 4618 chunks from 10410 comparisons\n", + " 8m 44s CLIQUES (O half_verse SET M>50 S>90): 4618 members in 1863 cliques\n", + " 8m 44s PRINT (O half_verse SET M>50 S>90): sorting out cliques\n", + " 8m 44s PRINT (O half_verse SET M>50 S>90): formatting 1863 cliques involving 587 binary chapter diffs\n", + " 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs needed: 587\n", + " 8m 44s PRINT (O half_verse SET M>50 S>90): Chapter diffs: 0 newly created and 587 already existing\n", + " 8m 45s PRINT (O half_verse SET M>50 S>90): formatted 1863 cliques (38 files) involving 587 binary chapter diffs\n", + " 8m 45s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 45s PREPARING (O half_verse SET): Already prepared\n", + " 8m 45s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 45s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 45s CLIQUES (O half_verse SET M>50 S>85): fetching similars and chunk candidates\n", + " 8m 45s CLIQUES (O half_verse SET M>50 S>85): inspecting the similarity matrix\n", + " 8m 45s CLIQUES (O half_verse SET M>50 S>85): 11111 relevant similarities between 5145 passages\n", + " 8m 45s CLIQUES (O half_verse SET M>50 S>85): Loaded: 2072 cliques out of 5145 chunks from 11111 comparisons\n", + " 8m 45s CLIQUES (O half_verse SET M>50 S>85): 5145 members in 2072 cliques\n", + " 8m 45s PRINT (O half_verse SET M>50 S>85): sorting out cliques\n", + " 8m 45s PRINT (O half_verse SET M>50 S>85): formatting 2072 cliques involving 640 binary chapter diffs\n", + " 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs needed: 640\n", + " 8m 45s PRINT (O half_verse SET M>50 S>85): Chapter diffs: 0 newly created and 640 already existing\n", + " 8m 46s PRINT (O half_verse SET M>50 S>85): formatted 2072 cliques (42 files) involving 640 binary chapter diffs\n", + " 8m 46s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 46s PREPARING (O half_verse SET): Already prepared\n", + " 8m 46s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 46s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 46s CLIQUES (O half_verse SET M>50 S>80): fetching similars and chunk candidates\n", + " 8m 46s CLIQUES (O half_verse SET M>50 S>80): inspecting the similarity matrix\n", + " 8m 46s CLIQUES (O half_verse SET M>50 S>80): 20178 relevant similarities between 6422 passages\n", + " 8m 46s CLIQUES (O half_verse SET M>50 S>80): Loaded: 2474 cliques out of 6422 chunks from 20178 comparisons\n", + " 8m 46s CLIQUES (O half_verse SET M>50 S>80): 6422 members in 2474 cliques\n", + " 8m 46s PRINT (O half_verse SET M>50 S>80): sorting out cliques\n", + " 8m 46s PRINT (O half_verse SET M>50 S>80): formatting 2474 cliques involving 769 binary chapter diffs\n", + " 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs needed: 769\n", + " 8m 46s PRINT (O half_verse SET M>50 S>80): Chapter diffs: 0 newly created and 769 already existing\n", + " 8m 47s PRINT (O half_verse SET M>50 S>80): formatted 2474 cliques (50 files) involving 769 binary chapter diffs\n", + " 8m 47s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 47s PREPARING (O half_verse SET): Already prepared\n", + " 8m 47s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 47s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 47s CLIQUES (O half_verse SET M>50 S>75): fetching similars and chunk candidates\n", + " 8m 47s CLIQUES (O half_verse SET M>50 S>75): inspecting the similarity matrix\n", + " 8m 47s CLIQUES (O half_verse SET M>50 S>75): 23717 relevant similarities between 8265 passages\n", + " 8m 47s CLIQUES (O half_verse SET M>50 S>75): Loaded: 2888 cliques out of 8265 chunks from 23717 comparisons\n", + " 8m 47s CLIQUES (O half_verse SET M>50 S>75): 8265 members in 2888 cliques\n", + " 8m 47s PRINT (O half_verse SET M>50 S>75): sorting out cliques\n", + " 8m 47s PRINT (O half_verse SET M>50 S>75): formatting 2888 cliques involving 919 binary chapter diffs\n", + " 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs needed: 919\n", + " 8m 47s PRINT (O half_verse SET M>50 S>75): Chapter diffs: 0 newly created and 919 already existing\n", + " 8m 49s PRINT (O half_verse SET M>50 S>75): formatted 2888 cliques (58 files) involving 919 binary chapter diffs\n", + " 8m 49s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 49s PREPARING (O half_verse SET): Already prepared\n", + " 8m 49s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 49s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 49s CLIQUES (O half_verse SET M>50 S>70): fetching similars and chunk candidates\n", + " 8m 49s CLIQUES (O half_verse SET M>50 S>70): inspecting the similarity matrix\n", + " 8m 49s CLIQUES (O half_verse SET M>50 S>70): 25560 relevant similarities between 9388 passages\n", + " 8m 49s CLIQUES (O half_verse SET M>50 S>70): Loaded: 3193 cliques out of 9388 chunks from 25560 comparisons\n", + " 8m 49s CLIQUES (O half_verse SET M>50 S>70): 9388 members in 3193 cliques\n", + " 8m 49s PRINT (O half_verse SET M>50 S>70): sorting out cliques\n", + " 8m 49s PRINT (O half_verse SET M>50 S>70): formatting 3193 cliques involving 1014 binary chapter diffs\n", + " 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs needed: 1014\n", + " 8m 49s PRINT (O half_verse SET M>50 S>70): Chapter diffs: 0 newly created and 1014 already existing\n", + " 8m 51s PRINT (O half_verse SET M>50 S>70): formatted 3193 cliques (64 files) involving 1014 binary chapter diffs\n", + " 8m 51s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 51s PREPARING (O half_verse SET): Already prepared\n", + " 8m 51s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 51s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 51s CLIQUES (O half_verse SET M>50 S>65): fetching similars and chunk candidates\n", + " 8m 51s CLIQUES (O half_verse SET M>50 S>65): inspecting the similarity matrix\n", + " 8m 51s CLIQUES (O half_verse SET M>50 S>65): 37453 relevant similarities between 12162 passages\n", + " 8m 51s CLIQUES (O half_verse SET M>50 S>65): Loaded: 3342 cliques out of 12162 chunks from 37453 comparisons\n", + " 8m 51s CLIQUES (O half_verse SET M>50 S>65): 12162 members in 3342 cliques\n", + " 8m 51s PRINT (O half_verse SET M>50 S>65): sorting out cliques\n", + " 8m 52s PRINT (O half_verse SET M>50 S>65): formatting 3342 cliques skipping 1072 binary chapter diffs\n", + " 8m 53s PRINT (O half_verse SET M>50 S>65): formatted 3342 cliques (67 files) skipping 1072 binary chapter diffs\n", + " 8m 53s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 53s PREPARING (O half_verse SET): Already prepared\n", + " 8m 53s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 54s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 54s CLIQUES (O half_verse SET M>50 S>60): fetching similars and chunk candidates\n", + " 8m 54s CLIQUES (O half_verse SET M>50 S>60): inspecting the similarity matrix\n", + " 8m 54s CLIQUES (O half_verse SET M>50 S>60): 55384 relevant similarities between 16476 passages\n", + " 8m 54s CLIQUES (O half_verse SET M>50 S>60): Loaded: 3424 cliques out of 16476 chunks from 55384 comparisons\n", + " 8m 54s CLIQUES (O half_verse SET M>50 S>60): 16476 members in 3424 cliques\n", + " 8m 54s PRINT (O half_verse SET M>50 S>60): sorting out cliques\n", + " 8m 54s PRINT (O half_verse SET M>50 S>60): formatting 3424 cliques skipping 1185 binary chapter diffs\n", + " 8m 56s PRINT (O half_verse SET M>50 S>60): formatted 3424 cliques (69 files) skipping 1185 binary chapter diffs\n", + " 8m 56s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 56s PREPARING (O half_verse SET): Already prepared\n", + " 8m 56s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 56s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 56s CLIQUES (O half_verse SET M>50 S>55): fetching similars and chunk candidates\n", + " 8m 56s CLIQUES (O half_verse SET M>50 S>55): inspecting the similarity matrix\n", + " 8m 57s CLIQUES (O half_verse SET M>50 S>55): 70089 relevant similarities between 19519 passages\n", + " 8m 57s CLIQUES (O half_verse SET M>50 S>55): Loaded: 3184 cliques out of 19519 chunks from 70089 comparisons\n", + " 8m 57s CLIQUES (O half_verse SET M>50 S>55): 19519 members in 3184 cliques\n", + " 8m 57s PRINT (O half_verse SET M>50 S>55): sorting out cliques\n", + " 8m 57s PRINT (O half_verse SET M>50 S>55): formatting 3184 cliques skipping 1149 binary chapter diffs\n", + " 8m 59s PRINT (O half_verse SET M>50 S>55): formatted 3184 cliques (64 files) skipping 1149 binary chapter diffs\n", + " 8m 59s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 8m 59s PREPARING (O half_verse SET): Already prepared\n", + " 8m 59s SIMILARITY (O half_verse SET M>50): Using 1020 M (1020593610) comparisons with 179842 entries in matrix\n", + " 8m 59s SIMILARITY (O half_verse SET M>50): similarities between 50.0 and 100.0. 10239 are 100%\n", + " 8m 59s CLIQUES (O half_verse SET M>50 S>50): fetching similars and chunk candidates\n", + " 8m 59s CLIQUES (O half_verse SET M>50 S>50): inspecting the similarity matrix\n", + " 9m 00s CLIQUES (O half_verse SET M>50 S>50): 179842 relevant similarities between 28990 passages\n", + " 9m 00s CLIQUES (O half_verse SET M>50 S>50): Loaded: 2031 cliques out of 28990 chunks from 179842 comparisons\n", + " 9m 00s CLIQUES (O half_verse SET M>50 S>50): 28990 members in 2031 cliques\n", + " 9m 00s PRINT (O half_verse SET M>50 S>50): sorting out cliques\n", + " 9m 00s PRINT (O half_verse SET M>50 S>50): formatting 2031 cliques skipping 802 binary chapter diffs\n", + " 9m 02s PRINT (O half_verse SET M>50 S>50): formatted 2031 cliques (41 files) skipping 802 binary chapter diffs\n", + " 9m 02s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 02s PREPARING (O half_verse LCS)\n", + " 9m 03s PREPARING (O half_verse LCS): Done 45180 chunks.\n", + " 9m 04s SIMILARITY (O half_verse LCS M>60): Loaded: 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 05s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 05s CLIQUES (O half_verse LCS M>60 S>100): fetching similars and chunk candidates\n", + " 9m 05s CLIQUES (O half_verse LCS M>60 S>100): inspecting the similarity matrix\n", + " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 9270 relevant similarities between 3799 passages\n", + " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): Loaded: 1514 cliques out of 3799 chunks from 9270 comparisons\n", + " 9m 06s CLIQUES (O half_verse LCS M>60 S>100): 3799 members in 1514 cliques\n", + " 9m 06s PRINT (O half_verse LCS M>60 S>100): sorting out cliques\n", + " 9m 06s PRINT (O half_verse LCS M>60 S>100): formatting 1514 cliques involving 493 binary chapter diffs\n", + " 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs needed: 493\n", + " 9m 06s PRINT (O half_verse LCS M>60 S>100): Chapter diffs: 0 newly created and 493 already existing\n", + " 9m 06s PRINT (O half_verse LCS M>60 S>100): formatted 1514 cliques (31 files) involving 493 binary chapter diffs\n", + " 9m 06s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 06s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 06s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 07s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 07s CLIQUES (O half_verse LCS M>60 S>95): fetching similars and chunk candidates\n", + " 9m 07s CLIQUES (O half_verse LCS M>60 S>95): inspecting the similarity matrix\n", + " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 9663 relevant similarities between 4342 passages\n", + " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): Loaded: 1771 cliques out of 4342 chunks from 9663 comparisons\n", + " 9m 08s CLIQUES (O half_verse LCS M>60 S>95): 4342 members in 1771 cliques\n", + " 9m 08s PRINT (O half_verse LCS M>60 S>95): sorting out cliques\n", + " 9m 08s PRINT (O half_verse LCS M>60 S>95): formatting 1771 cliques involving 543 binary chapter diffs\n", + " 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs needed: 543\n", + " 9m 08s PRINT (O half_verse LCS M>60 S>95): Chapter diffs: 0 newly created and 543 already existing\n", + " 9m 09s PRINT (O half_verse LCS M>60 S>95): formatted 1771 cliques (36 files) involving 543 binary chapter diffs\n", + " 9m 09s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 09s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 09s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 10s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 10s CLIQUES (O half_verse LCS M>60 S>90): fetching similars and chunk candidates\n", + " 9m 10s CLIQUES (O half_verse LCS M>60 S>90): inspecting the similarity matrix\n", + " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 12125 relevant similarities between 5776 passages\n", + " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): Loaded: 2336 cliques out of 5776 chunks from 12125 comparisons\n", + " 9m 11s CLIQUES (O half_verse LCS M>60 S>90): 5776 members in 2336 cliques\n", + " 9m 11s PRINT (O half_verse LCS M>60 S>90): sorting out cliques\n", + " 9m 11s PRINT (O half_verse LCS M>60 S>90): formatting 2336 cliques involving 732 binary chapter diffs\n", + " 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs needed: 732\n", + " 9m 11s PRINT (O half_verse LCS M>60 S>90): Chapter diffs: 0 newly created and 732 already existing\n", + " 9m 12s PRINT (O half_verse LCS M>60 S>90): formatted 2336 cliques (47 files) involving 732 binary chapter diffs\n", + " 9m 12s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 12s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 12s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 13s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 13s CLIQUES (O half_verse LCS M>60 S>85): fetching similars and chunk candidates\n", + " 9m 13s CLIQUES (O half_verse LCS M>60 S>85): inspecting the similarity matrix\n", + " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 17551 relevant similarities between 7970 passages\n", + " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): Loaded: 2983 cliques out of 7970 chunks from 17551 comparisons\n", + " 9m 14s CLIQUES (O half_verse LCS M>60 S>85): 7970 members in 2983 cliques\n", + " 9m 14s PRINT (O half_verse LCS M>60 S>85): sorting out cliques\n", + " 9m 14s PRINT (O half_verse LCS M>60 S>85): formatting 2983 cliques involving 975 binary chapter diffs\n", + " 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs needed: 975\n", + " 9m 14s PRINT (O half_verse LCS M>60 S>85): Chapter diffs: 0 newly created and 975 already existing\n", + " 9m 16s PRINT (O half_verse LCS M>60 S>85): formatted 2983 cliques (60 files) involving 975 binary chapter diffs\n", + " 9m 16s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 16s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 16s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 18s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 18s CLIQUES (O half_verse LCS M>60 S>80): fetching similars and chunk candidates\n", + " 9m 18s CLIQUES (O half_verse LCS M>60 S>80): inspecting the similarity matrix\n", + " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 27273 relevant similarities between 12504 passages\n", + " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): Loaded: 3540 cliques out of 12504 chunks from 27273 comparisons\n", + " 9m 19s CLIQUES (O half_verse LCS M>60 S>80): 12504 members in 3540 cliques\n", + " 9m 19s PRINT (O half_verse LCS M>60 S>80): sorting out cliques\n", + " 9m 19s PRINT (O half_verse LCS M>60 S>80): formatting 3540 cliques skipping 1230 binary chapter diffs\n", + " 9m 21s PRINT (O half_verse LCS M>60 S>80): formatted 3540 cliques (71 files) skipping 1230 binary chapter diffs\n", + " 9m 21s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 21s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 21s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 22s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 22s CLIQUES (O half_verse LCS M>60 S>75): fetching similars and chunk candidates\n", + " 9m 22s CLIQUES (O half_verse LCS M>60 S>75): inspecting the similarity matrix\n", + " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 53981 relevant similarities between 19148 passages\n", + " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): Loaded: 3084 cliques out of 19148 chunks from 53981 comparisons\n", + " 9m 24s CLIQUES (O half_verse LCS M>60 S>75): 19148 members in 3084 cliques\n", + " 9m 24s PRINT (O half_verse LCS M>60 S>75): sorting out cliques\n", + " 9m 24s PRINT (O half_verse LCS M>60 S>75): formatting 3084 cliques skipping 1134 binary chapter diffs\n", + " 9m 26s PRINT (O half_verse LCS M>60 S>75): formatted 3084 cliques (62 files) skipping 1134 binary chapter diffs\n", + " 9m 26s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 26s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 26s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 27s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 27s CLIQUES (O half_verse LCS M>60 S>70): fetching similars and chunk candidates\n", + " 9m 27s CLIQUES (O half_verse LCS M>60 S>70): inspecting the similarity matrix\n", + " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 126162 relevant similarities between 28472 passages\n", + " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): Loaded: 1894 cliques out of 28472 chunks from 126162 comparisons\n", + " 9m 28s CLIQUES (O half_verse LCS M>60 S>70): 28472 members in 1894 cliques\n", + " 9m 28s PRINT (O half_verse LCS M>60 S>70): sorting out cliques\n", + " 9m 28s PRINT (O half_verse LCS M>60 S>70): formatting 1894 cliques skipping 747 binary chapter diffs\n", + " 9m 30s PRINT (O half_verse LCS M>60 S>70): formatted 1894 cliques (38 files) skipping 747 binary chapter diffs\n", + " 9m 30s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 30s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 30s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 31s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 31s CLIQUES (O half_verse LCS M>60 S>65): fetching similars and chunk candidates\n", + " 9m 31s CLIQUES (O half_verse LCS M>60 S>65): inspecting the similarity matrix\n", + " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 393325 relevant similarities between 38180 passages\n", + " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): Loaded: 665 cliques out of 38180 chunks from 393325 comparisons\n", + " 9m 32s CLIQUES (O half_verse LCS M>60 S>65): 38180 members in 665 cliques\n", + " 9m 32s PRINT (O half_verse LCS M>60 S>65): sorting out cliques\n", + " 9m 32s PRINT (O half_verse LCS M>60 S>65): formatting 665 cliques skipping 287 binary chapter diffs\n", + " 9m 33s PRINT (O half_verse LCS M>60 S>65): formatted 665 cliques (14 files) skipping 287 binary chapter diffs\n", + " 9m 33s CHUNKING (O half_verse): already chunked into 45180 chunks\n", + " 9m 33s PREPARING (O half_verse LCS): Already prepared\n", + " 9m 33s SIMILARITY (O half_verse LCS M>60): Using 1020 M (1020593610) comparisons with 2017661 entries in matrix\n", + " 9m 34s SIMILARITY (O half_verse LCS M>60): similarities between 60.0 and 100.0. 9270 are 100%\n", + " 9m 34s CLIQUES (O half_verse LCS M>60 S>60): fetching similars and chunk candidates\n", + " 9m 34s CLIQUES (O half_verse LCS M>60 S>60): inspecting the similarity matrix\n", + " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 2017661 relevant similarities between 44011 passages\n", + " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): Loaded: 89 cliques out of 44011 chunks from 2017661 comparisons\n", + " 9m 38s CLIQUES (O half_verse LCS M>60 S>60): 44011 members in 89 cliques\n", + " 9m 38s PRINT (O half_verse LCS M>60 S>60): sorting out cliques\n", + " 9m 38s PRINT (O half_verse LCS M>60 S>60): formatting 89 cliques skipping 57 binary chapter diffs\n", + " 9m 38s PRINT (O half_verse LCS M>60 S>60): formatted 89 cliques (2 files) skipping 57 binary chapter diffs\n", + " 9m 38s CHUNKING (O sentence): Loaded: 63586 chunks\n", + " 9m 38s CHUNKING (O sentence): Made 63586 chunks\n", + " 9m 38s PREPARING (O sentence SET)\n", + " 9m 39s PREPARING (O sentence SET): Done 63586 chunks.\n", + " 9m 43s SIMILARITY (O sentence SET M>50): Loaded: 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + " 9m 45s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + " 9m 45s CLIQUES (O sentence SET M>50 S>100): fetching similars and chunk candidates\n", + " 9m 45s CLIQUES (O sentence SET M>50 S>100): inspecting the similarity matrix\n", + " 9m 49s CLIQUES (O sentence SET M>50 S>100): 938441 relevant similarities between 19028 passages\n", + " 9m 49s CLIQUES (O sentence SET M>50 S>100): Loaded: 4325 cliques out of 19028 chunks from 938441 comparisons\n", + " 9m 49s CLIQUES (O sentence SET M>50 S>100): 19028 members in 4325 cliques\n", + " 9m 49s PRINT (O sentence SET M>50 S>100): sorting out cliques\n", + " 9m 49s PRINT (O sentence SET M>50 S>100): formatting 4325 cliques involving 1528 binary chapter diffs\n", + " 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs needed: 1528\n", + " 9m 49s PRINT (O sentence SET M>50 S>100): Chapter diffs: 0 newly created and 1528 already existing\n", + " 9m 52s PRINT (O sentence SET M>50 S>100): formatted 4325 cliques (87 files) involving 1528 binary chapter diffs\n", + " 9m 52s CHUNKING (O sentence): already chunked into 63586 chunks\n", + " 9m 52s PREPARING (O sentence SET): Already prepared\n", + " 9m 52s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + " 9m 55s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + " 9m 55s CLIQUES (O sentence SET M>50 S>95): fetching similars and chunk candidates\n", + " 9m 55s CLIQUES (O sentence SET M>50 S>95): inspecting the similarity matrix\n", + " 9m 57s CLIQUES (O sentence SET M>50 S>95): 938445 relevant similarities between 19036 passages\n", + " 9m 57s CLIQUES (O sentence SET M>50 S>95): Loaded: 4329 cliques out of 19036 chunks from 938445 comparisons\n", + " 9m 57s CLIQUES (O sentence SET M>50 S>95): 19036 members in 4329 cliques\n", + " 9m 57s PRINT (O sentence SET M>50 S>95): sorting out cliques\n", + " 9m 57s PRINT (O sentence SET M>50 S>95): formatting 4329 cliques involving 1529 binary chapter diffs\n", + " 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs needed: 1529\n", + " 9m 57s PRINT (O sentence SET M>50 S>95): Chapter diffs: 0 newly created and 1529 already existing\n", + " 9m 59s PRINT (O sentence SET M>50 S>95): formatted 4329 cliques (87 files) involving 1529 binary chapter diffs\n", + " 9m 59s CHUNKING (O sentence): already chunked into 63586 chunks\n", + " 9m 59s PREPARING (O sentence SET): Already prepared\n", + " 9m 59s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 02s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 02s CLIQUES (O sentence SET M>50 S>90): fetching similars and chunk candidates\n", + "10m 02s CLIQUES (O sentence SET M>50 S>90): inspecting the similarity matrix\n", + "10m 04s CLIQUES (O sentence SET M>50 S>90): 938584 relevant similarities between 19208 passages\n", + "10m 04s CLIQUES (O sentence SET M>50 S>90): Loaded: 4404 cliques out of 19208 chunks from 938584 comparisons\n", + "10m 04s CLIQUES (O sentence SET M>50 S>90): 19208 members in 4404 cliques\n", + "10m 04s PRINT (O sentence SET M>50 S>90): sorting out cliques\n", + "10m 04s PRINT (O sentence SET M>50 S>90): formatting 4404 cliques involving 1536 binary chapter diffs\n", + "10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs needed: 1536\n", + "10m 04s PRINT (O sentence SET M>50 S>90): Chapter diffs: 0 newly created and 1536 already existing\n", + "10m 06s PRINT (O sentence SET M>50 S>90): formatted 4404 cliques (89 files) involving 1536 binary chapter diffs\n", + "10m 06s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 06s PREPARING (O sentence SET): Already prepared\n", + "10m 06s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 08s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 08s CLIQUES (O sentence SET M>50 S>85): fetching similars and chunk candidates\n", + "10m 08s CLIQUES (O sentence SET M>50 S>85): inspecting the similarity matrix\n", + "10m 10s CLIQUES (O sentence SET M>50 S>85): 939433 relevant similarities between 19771 passages\n", + "10m 10s CLIQUES (O sentence SET M>50 S>85): Loaded: 4606 cliques out of 19771 chunks from 939433 comparisons\n", + "10m 10s CLIQUES (O sentence SET M>50 S>85): 19771 members in 4606 cliques\n", + "10m 10s PRINT (O sentence SET M>50 S>85): sorting out cliques\n", + "10m 10s PRINT (O sentence SET M>50 S>85): formatting 4606 cliques involving 1587 binary chapter diffs\n", + "10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs needed: 1587\n", + "10m 10s PRINT (O sentence SET M>50 S>85): Chapter diffs: 0 newly created and 1587 already existing\n", + "10m 12s PRINT (O sentence SET M>50 S>85): formatted 4606 cliques (93 files) involving 1587 binary chapter diffs\n", + "10m 12s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 12s PREPARING (O sentence SET): Already prepared\n", + "10m 12s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 15s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 15s CLIQUES (O sentence SET M>50 S>80): fetching similars and chunk candidates\n", + "10m 15s CLIQUES (O sentence SET M>50 S>80): inspecting the similarity matrix\n", + "10m 17s CLIQUES (O sentence SET M>50 S>80): 961541 relevant similarities between 22063 passages\n", + "10m 17s CLIQUES (O sentence SET M>50 S>80): Loaded: 5066 cliques out of 22063 chunks from 961541 comparisons\n", + "10m 17s CLIQUES (O sentence SET M>50 S>80): 22063 members in 5066 cliques\n", + "10m 17s PRINT (O sentence SET M>50 S>80): sorting out cliques\n", + "10m 17s PRINT (O sentence SET M>50 S>80): formatting 5066 cliques involving 1745 binary chapter diffs\n", + "10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs needed: 1745\n", + "10m 17s PRINT (O sentence SET M>50 S>80): Chapter diffs: 0 newly created and 1745 already existing\n", + "10m 19s PRINT (O sentence SET M>50 S>80): formatted 5066 cliques (102 files) involving 1745 binary chapter diffs\n", + "10m 19s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 19s PREPARING (O sentence SET): Already prepared\n", + "10m 19s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 21s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 21s CLIQUES (O sentence SET M>50 S>75): fetching similars and chunk candidates\n", + "10m 21s CLIQUES (O sentence SET M>50 S>75): inspecting the similarity matrix\n", + "10m 28s CLIQUES (O sentence SET M>50 S>75): 1009869 relevant similarities between 25724 passages\n", + "10m 28s CLIQUES (O sentence SET M>50 S>75): Loaded: 4993 cliques out of 25724 chunks from 1009869 comparisons\n", + "10m 28s CLIQUES (O sentence SET M>50 S>75): 25724 members in 4993 cliques\n", + "10m 28s PRINT (O sentence SET M>50 S>75): sorting out cliques\n", + "10m 29s PRINT (O sentence SET M>50 S>75): formatting 4993 cliques skipping 1743 binary chapter diffs\n", + "10m 37s PRINT (O sentence SET M>50 S>75): formatted 4993 cliques (100 files) skipping 1743 binary chapter diffs\n", + "10m 37s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 37s PREPARING (O sentence SET): Already prepared\n", + "10m 37s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 39s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 39s CLIQUES (O sentence SET M>50 S>70): fetching similars and chunk candidates\n", + "10m 39s CLIQUES (O sentence SET M>50 S>70): inspecting the similarity matrix\n", + "10m 42s CLIQUES (O sentence SET M>50 S>70): 1012567 relevant similarities between 26880 passages\n", + "10m 42s CLIQUES (O sentence SET M>50 S>70): Loaded: 5222 cliques out of 26880 chunks from 1012567 comparisons\n", + "10m 42s CLIQUES (O sentence SET M>50 S>70): 26880 members in 5222 cliques\n", + "10m 42s PRINT (O sentence SET M>50 S>70): sorting out cliques\n", + "10m 42s PRINT (O sentence SET M>50 S>70): formatting 5222 cliques skipping 1819 binary chapter diffs\n", + "10m 45s PRINT (O sentence SET M>50 S>70): formatted 5222 cliques (105 files) skipping 1819 binary chapter diffs\n", + "10m 45s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 45s PREPARING (O sentence SET): Already prepared\n", + "10m 45s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 47s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 47s CLIQUES (O sentence SET M>50 S>65): fetching similars and chunk candidates\n", + "10m 47s CLIQUES (O sentence SET M>50 S>65): inspecting the similarity matrix\n", + "10m 50s CLIQUES (O sentence SET M>50 S>65): 1332342 relevant similarities between 33378 passages\n", + "10m 50s CLIQUES (O sentence SET M>50 S>65): Loaded: 4111 cliques out of 33378 chunks from 1332342 comparisons\n", + "10m 50s CLIQUES (O sentence SET M>50 S>65): 33378 members in 4111 cliques\n", + "10m 50s PRINT (O sentence SET M>50 S>65): sorting out cliques\n", + "10m 50s PRINT (O sentence SET M>50 S>65): formatting 4111 cliques skipping 1474 binary chapter diffs\n", + "10m 53s PRINT (O sentence SET M>50 S>65): formatted 4111 cliques (83 files) skipping 1474 binary chapter diffs\n", + "10m 53s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "10m 53s PREPARING (O sentence SET): Already prepared\n", + "10m 53s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "10m 56s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "10m 56s CLIQUES (O sentence SET M>50 S>60): fetching similars and chunk candidates\n", + "10m 56s CLIQUES (O sentence SET M>50 S>60): inspecting the similarity matrix\n", + "10m 58s CLIQUES (O sentence SET M>50 S>60): 1431575 relevant similarities between 38807 passages\n", + "10m 58s CLIQUES (O sentence SET M>50 S>60): Loaded: 3753 cliques out of 38807 chunks from 1431575 comparisons\n", + "10m 58s CLIQUES (O sentence SET M>50 S>60): 38807 members in 3753 cliques\n", + "10m 58s PRINT (O sentence SET M>50 S>60): sorting out cliques\n", + "10m 58s PRINT (O sentence SET M>50 S>60): formatting 3753 cliques skipping 1386 binary chapter diffs\n", + "11m 01s PRINT (O sentence SET M>50 S>60): formatted 3753 cliques (76 files) skipping 1386 binary chapter diffs\n", + "11m 01s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "11m 01s PREPARING (O sentence SET): Already prepared\n", + "11m 01s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "11m 03s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "11m 03s CLIQUES (O sentence SET M>50 S>55): fetching similars and chunk candidates\n", + "11m 03s CLIQUES (O sentence SET M>50 S>55): inspecting the similarity matrix\n", + "11m 06s CLIQUES (O sentence SET M>50 S>55): 1459808 relevant similarities between 41835 passages\n", + "11m 06s CLIQUES (O sentence SET M>50 S>55): Loaded: 3505 cliques out of 41835 chunks from 1459808 comparisons\n", + "11m 06s CLIQUES (O sentence SET M>50 S>55): 41835 members in 3505 cliques\n", + "11m 06s PRINT (O sentence SET M>50 S>55): sorting out cliques\n", + "11m 06s PRINT (O sentence SET M>50 S>55): formatting 3505 cliques skipping 1341 binary chapter diffs\n", + "11m 11s PRINT (O sentence SET M>50 S>55): formatted 3505 cliques (71 files) skipping 1341 binary chapter diffs\n", + "11m 11s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "11m 11s PREPARING (O sentence SET): Already prepared\n", + "11m 11s SIMILARITY (O sentence SET M>50): Using 2021 M (2021557905) comparisons with 3959201 entries in matrix\n", + "11m 14s SIMILARITY (O sentence SET M>50): similarities between 50.0 and 100.0. 938441 are 100%\n", + "11m 14s CLIQUES (O sentence SET M>50 S>50): fetching similars and chunk candidates\n", + "11m 14s CLIQUES (O sentence SET M>50 S>50): inspecting the similarity matrix\n", + "11m 20s CLIQUES (O sentence SET M>50 S>50): 3959201 relevant similarities between 53117 passages\n", + "11m 20s CLIQUES (O sentence SET M>50 S>50): Loaded: 1174 cliques out of 53117 chunks from 3959201 comparisons\n", + "11m 20s CLIQUES (O sentence SET M>50 S>50): 53117 members in 1174 cliques\n", + "11m 20s PRINT (O sentence SET M>50 S>50): sorting out cliques\n", + "11m 20s PRINT (O sentence SET M>50 S>50): formatting 1174 cliques skipping 468 binary chapter diffs\n", + "11m 22s PRINT (O sentence SET M>50 S>50): formatted 1174 cliques (24 files) skipping 468 binary chapter diffs\n", + "11m 22s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "11m 22s PREPARING (O sentence LCS)\n", + "11m 23s PREPARING (O sentence LCS): Done 63586 chunks.\n", + "11m 29s SIMILARITY (O sentence LCS M>60): Loaded: 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "11m 35s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "11m 35s CLIQUES (O sentence LCS M>60 S>100): fetching similars and chunk candidates\n", + "11m 35s CLIQUES (O sentence LCS M>60 S>100): inspecting the similarity matrix\n", + "11m 40s CLIQUES (O sentence LCS M>60 S>100): 903811 relevant similarities between 17532 passages\n", + "11m 40s CLIQUES (O sentence LCS M>60 S>100): Loaded: 3981 cliques out of 17532 chunks from 903811 comparisons\n", + "11m 40s CLIQUES (O sentence LCS M>60 S>100): 17532 members in 3981 cliques\n", + "11m 40s PRINT (O sentence LCS M>60 S>100): sorting out cliques\n", + "11m 40s PRINT (O sentence LCS M>60 S>100): formatting 3981 cliques skipping 1364 binary chapter diffs\n", + "11m 42s PRINT (O sentence LCS M>60 S>100): formatted 3981 cliques (80 files) skipping 1364 binary chapter diffs\n", + "11m 42s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "11m 42s PREPARING (O sentence LCS): Already prepared\n", + "11m 42s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "11m 47s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "11m 47s CLIQUES (O sentence LCS M>60 S>95): fetching similars and chunk candidates\n", + "11m 47s CLIQUES (O sentence LCS M>60 S>95): inspecting the similarity matrix\n", + "11m 54s CLIQUES (O sentence LCS M>60 S>95): 904511 relevant similarities between 18079 passages\n", + "11m 54s CLIQUES (O sentence LCS M>60 S>95): Loaded: 4215 cliques out of 18079 chunks from 904511 comparisons\n", + "11m 54s CLIQUES (O sentence LCS M>60 S>95): 18079 members in 4215 cliques\n", + "11m 54s PRINT (O sentence LCS M>60 S>95): sorting out cliques\n", + "11m 55s PRINT (O sentence LCS M>60 S>95): formatting 4215 cliques skipping 1418 binary chapter diffs\n", + "11m 57s PRINT (O sentence LCS M>60 S>95): formatted 4215 cliques (85 files) skipping 1418 binary chapter diffs\n", + "11m 57s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "11m 57s PREPARING (O sentence LCS): Already prepared\n", + "11m 57s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "12m 04s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "12m 04s CLIQUES (O sentence LCS M>60 S>90): fetching similars and chunk candidates\n", + "12m 04s CLIQUES (O sentence LCS M>60 S>90): inspecting the similarity matrix\n", + "12m 08s CLIQUES (O sentence LCS M>60 S>90): 915567 relevant similarities between 21246 passages\n", + "12m 08s CLIQUES (O sentence LCS M>60 S>90): Loaded: 4993 cliques out of 21246 chunks from 915567 comparisons\n", + "12m 08s CLIQUES (O sentence LCS M>60 S>90): 21246 members in 4993 cliques\n", + "12m 08s PRINT (O sentence LCS M>60 S>90): sorting out cliques\n", + "12m 08s PRINT (O sentence LCS M>60 S>90): formatting 4993 cliques involving 1704 binary chapter diffs\n", + "12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs needed: 1704\n", + "12m 08s PRINT (O sentence LCS M>60 S>90): Chapter diffs: 0 newly created and 1704 already existing\n", + "12m 11s PRINT (O sentence LCS M>60 S>90): formatted 4993 cliques (100 files) involving 1704 binary chapter diffs\n", + "12m 11s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "12m 11s PREPARING (O sentence LCS): Already prepared\n", + "12m 11s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "12m 16s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "12m 16s CLIQUES (O sentence LCS M>60 S>85): fetching similars and chunk candidates\n", + "12m 16s CLIQUES (O sentence LCS M>60 S>85): inspecting the similarity matrix\n", + "12m 22s CLIQUES (O sentence LCS M>60 S>85): 980912 relevant similarities between 26473 passages\n", + "12m 22s CLIQUES (O sentence LCS M>60 S>85): Loaded: 4853 cliques out of 26473 chunks from 980912 comparisons\n", + "12m 22s CLIQUES (O sentence LCS M>60 S>85): 26473 members in 4853 cliques\n", + "12m 22s PRINT (O sentence LCS M>60 S>85): sorting out cliques\n", + "12m 22s PRINT (O sentence LCS M>60 S>85): formatting 4853 cliques skipping 1709 binary chapter diffs\n", + "12m 24s PRINT (O sentence LCS M>60 S>85): formatted 4853 cliques (98 files) skipping 1709 binary chapter diffs\n", + "12m 24s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "12m 24s PREPARING (O sentence LCS): Already prepared\n", + "12m 24s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "12m 30s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "12m 30s CLIQUES (O sentence LCS M>60 S>80): fetching similars and chunk candidates\n", + "12m 30s CLIQUES (O sentence LCS M>60 S>80): inspecting the similarity matrix\n", + "12m 35s CLIQUES (O sentence LCS M>60 S>80): 1301411 relevant similarities between 35626 passages\n", + "12m 35s CLIQUES (O sentence LCS M>60 S>80): Loaded: 3470 cliques out of 35626 chunks from 1301411 comparisons\n", + "12m 35s CLIQUES (O sentence LCS M>60 S>80): 35626 members in 3470 cliques\n", + "12m 35s PRINT (O sentence LCS M>60 S>80): sorting out cliques\n", + "12m 35s PRINT (O sentence LCS M>60 S>80): formatting 3470 cliques skipping 1296 binary chapter diffs\n", + "12m 37s PRINT (O sentence LCS M>60 S>80): formatted 3470 cliques (70 files) skipping 1296 binary chapter diffs\n", + "12m 37s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "12m 37s PREPARING (O sentence LCS): Already prepared\n", + "12m 37s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "12m 43s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "12m 43s CLIQUES (O sentence LCS M>60 S>75): fetching similars and chunk candidates\n", + "12m 43s CLIQUES (O sentence LCS M>60 S>75): inspecting the similarity matrix\n", + "12m 48s CLIQUES (O sentence LCS M>60 S>75): 1620210 relevant similarities between 44307 passages\n", + "12m 48s CLIQUES (O sentence LCS M>60 S>75): Loaded: 2293 cliques out of 44307 chunks from 1620210 comparisons\n", + "12m 48s CLIQUES (O sentence LCS M>60 S>75): 44307 members in 2293 cliques\n", + "12m 48s PRINT (O sentence LCS M>60 S>75): sorting out cliques\n", + "12m 48s PRINT (O sentence LCS M>60 S>75): formatting 2293 cliques skipping 889 binary chapter diffs\n", + "12m 50s PRINT (O sentence LCS M>60 S>75): formatted 2293 cliques (46 files) skipping 889 binary chapter diffs\n", + "12m 50s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "12m 50s PREPARING (O sentence LCS): Already prepared\n", + "12m 50s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "12m 55s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "12m 55s CLIQUES (O sentence LCS M>60 S>70): fetching similars and chunk candidates\n", + "12m 55s CLIQUES (O sentence LCS M>60 S>70): inspecting the similarity matrix\n", + "13m 02s CLIQUES (O sentence LCS M>60 S>70): 2182513 relevant similarities between 52535 passages\n", + "13m 02s CLIQUES (O sentence LCS M>60 S>70): Loaded: 1197 cliques out of 52535 chunks from 2182513 comparisons\n", + "13m 02s CLIQUES (O sentence LCS M>60 S>70): 52535 members in 1197 cliques\n", + "13m 02s PRINT (O sentence LCS M>60 S>70): sorting out cliques\n", + "13m 02s PRINT (O sentence LCS M>60 S>70): formatting 1197 cliques skipping 455 binary chapter diffs\n", + "13m 03s PRINT (O sentence LCS M>60 S>70): formatted 1197 cliques (24 files) skipping 455 binary chapter diffs\n", + "13m 03s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "13m 03s PREPARING (O sentence LCS): Already prepared\n", + "13m 03s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "13m 09s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "13m 09s CLIQUES (O sentence LCS M>60 S>65): fetching similars and chunk candidates\n", + "13m 09s CLIQUES (O sentence LCS M>60 S>65): inspecting the similarity matrix\n", + "13m 19s CLIQUES (O sentence LCS M>60 S>65): 4831555 relevant similarities between 58863 passages\n", + "13m 19s CLIQUES (O sentence LCS M>60 S>65): Loaded: 460 cliques out of 58863 chunks from 4831555 comparisons\n", + "13m 19s CLIQUES (O sentence LCS M>60 S>65): 58863 members in 460 cliques\n", + "13m 19s PRINT (O sentence LCS M>60 S>65): sorting out cliques\n", + "13m 20s PRINT (O sentence LCS M>60 S>65): formatting 460 cliques skipping 207 binary chapter diffs\n", + "13m 21s PRINT (O sentence LCS M>60 S>65): formatted 460 cliques (10 files) skipping 207 binary chapter diffs\n", + "13m 21s CHUNKING (O sentence): already chunked into 63586 chunks\n", + "13m 21s PREPARING (O sentence LCS): Already prepared\n", + "13m 21s SIMILARITY (O sentence LCS M>60): Using 2021 M (2021557905) comparisons with 10271722 entries in matrix\n", + "13m 27s SIMILARITY (O sentence LCS M>60): similarities between 60.0 and 100.0. 903811 are 100%\n", + "13m 27s CLIQUES (O sentence LCS M>60 S>60): fetching similars and chunk candidates\n", + "13m 27s CLIQUES (O sentence LCS M>60 S>60): inspecting the similarity matrix\n", + "13m 41s CLIQUES (O sentence LCS M>60 S>60): 10271722 relevant similarities between 62379 passages\n", + "13m 41s CLIQUES (O sentence LCS M>60 S>60): Loaded: 105 cliques out of 62379 chunks from 10271722 comparisons\n", + "13m 41s CLIQUES (O sentence LCS M>60 S>60): 62379 members in 105 cliques\n", + "13m 41s PRINT (O sentence LCS M>60 S>60): sorting out cliques\n", + "13m 41s PRINT (O sentence LCS M>60 S>60): formatting 105 cliques skipping 55 binary chapter diffs\n", + "13m 42s PRINT (O sentence LCS M>60 S>60): formatted 105 cliques (3 files) skipping 55 binary chapter diffs\n", + "13m 42s EXPERIMENT: Generating html report\n", + "13m 42s EXPERIMENT: 36 messy results: deprecated\n", + "13m 42s EXPERIMENT: 22 mixed quality: take care\n", + "13m 42s EXPERIMENT: 75 no results available\n", + "13m 42s EXPERIMENT: 9 unassessed quality: inspection needed\n", + "13m 42s EXPERIMENT: 80 method deprecated\n", + "13m 42s EXPERIMENT: 18 promising results: recommended\n", + "13m 42s EXPERIMENT: Generated html report\n", + "13m 42s EXPERIMENT: Generating html report(standalone)\n", + "13m 42s EXPERIMENT: 36 messy results: deprecated\n", + "13m 42s EXPERIMENT: 22 mixed quality: take care\n", + "13m 42s EXPERIMENT: 75 no results available\n", + "13m 42s EXPERIMENT: 9 unassessed quality: inspection needed\n", + "13m 42s EXPERIMENT: 80 method deprecated\n", + "13m 42s EXPERIMENT: 18 promising results: recommended\n", + "13m 42s EXPERIMENT: Generated html report\n" + ] + } + ], + "source": [ + "reset_params()\n", + "#do_experiment(False, 'sentence', 'LCS', 60, False)\n", + "do_all_experiments()\n", + "#do_all_experiments(no_fixed=True, only_object='chapter')\n", + "#crossrefs2shebanq()\n", + "#show_all_experiments()\n", + "#get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt')\n", + "#do_all_chunks()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HTML(ecss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8. Overview of the similarities\n", + "\n", + "Here are the plots of two similarity matrices\n", + "* with verses as chunks and SET as similarity method\n", + "* with verses as chunks and LCS as similarity method\n", + "\n", + "Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "31m 00s CHUNKING (O verse): Loaded: 23213 chunks\n", + "31m 00s CHUNKING (O verse): Made 23213 chunks\n", + "31m 00s PREPARING (O verse SET)\n", + "31m 01s PREPARING (O verse SET): Done 23213 chunks.\n", + "31m 02s SIMILARITY (O verse SET M>50): Loaded: 269 M (269410078) comparisons with 24832 entries in matrix\n", + "31m 02s SIMILARITY (O verse SET M>50): similarities between 50.0 and 100.0. 4506 are 100%\n", + "31m 02s CLIQUES (O verse SET M>50 S>60): fetching similars and chunk candidates\n", + "31m 02s CLIQUES (O verse SET M>50 S>60): inspecting the similarity matrix\n", + "31m 04s CLIQUES (O verse SET M>50 S>60): 16055 relevant similarities between 3877 passages\n", + "31m 04s CLIQUES (O verse SET M>50 S>60): Loaded: 1439 cliques out of 3877 chunks from 16055 comparisons\n", + "31m 04s CLIQUES (O verse SET M>50 S>60): 3877 members in 1439 cliques\n", + "31m 04s PRINT (O verse SET M>50 S>60): sorting out cliques\n", + "31m 04s PRINT (O verse SET M>50 S>60): formatting 1439 cliques involving 358 binary chapter diffs\n", + "31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs needed: 358\n", + "31m 04s PRINT (O verse SET M>50 S>60): Chapter diffs: 0 newly created and 358 already existing\n", + "31m 06s PRINT (O verse SET M>50 S>60): formatted 1439 cliques (29 files) involving 358 binary chapter diffs\n" + ] + }, + { + "data": { + "image/png": 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3AWiuqvfkeJ4OHqx45JGkh0hERER5WLcOOOssYNMmS+aGDrXPR4zwOzIiomCoXNk6/Z5w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\nyJM5MzMzMzOzLvRfN8FuOJskk0wAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "do_experiment(False, 'verse', 'SET', 60, False)\n", + "distances = collections.Counter()\n", + "for (x, d) in chunk_dist.items():\n", + " distances[int(round(d))] += 1\n", + "\n", + "x = range(MATRIX_THRESHOLD, 101)\n", + "fig = plt.figure(figsize=[15, 4])\n", + "plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')\n", + "plt.axis([MATRIX_THRESHOLD, 101, 0, 15])\n", + "plt.xlabel('similarity as %')\n", + "plt.ylabel('log # similarities')\n", + "plt.xticks(x, x, rotation='vertical')\n", + "plt.margins(0.2)\n", + "plt.subplots_adjust(bottom=0.15);\n", + "plt.title('distances');" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "33m 46s CHUNKING (O verse): already chunked into 23213 chunks\n", + "33m 46s PREPARING (O verse LCS)\n", + "33m 47s PREPARING (O verse LCS): Done 23213 chunks.\n", + "33m 47s SIMILARITY (O verse LCS M>60): Loaded: 269 M (269410078) comparisons with 113614 entries in matrix\n", + "33m 47s SIMILARITY (O verse LCS M>60): similarities between 60.0 and 100.0. 4204 are 100%\n", + "33m 47s CLIQUES (O verse LCS M>60 S>60): fetching similars and chunk candidates\n", + "33m 47s CLIQUES (O verse LCS M>60 S>60): inspecting the similarity matrix\n", + "33m 47s CLIQUES (O verse LCS M>60 S>60): 113614 relevant similarities between 18941 passages\n", + "33m 47s CLIQUES (O verse LCS M>60 S>60): Loaded: 380 cliques out of 18941 chunks from 113614 comparisons\n", + "33m 47s CLIQUES (O verse LCS M>60 S>60): 18941 members in 380 cliques\n", + "33m 47s PRINT (O verse LCS M>60 S>60): sorting out cliques\n", + "33m 47s PRINT (O verse LCS M>60 S>60): formatting 380 cliques skipping 190 binary chapter diffs\n", + "33m 48s PRINT (O verse LCS M>60 S>60): formatted 380 cliques (8 files) skipping 190 binary chapter diffs\n" + ] + }, + { + "data": { + "image/png": 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84Afw2c82Oprh7cILywf5730PDjmktPIOlRdeKAnd5z8PxxwzdPWqS3s7fO5z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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "do_experiment(False, 'verse', 'LCS', 60, False)\n", + "distances = collections.Counter()\n", + "for (x, d) in chunk_dist.items():\n", + " distances[int(round(d))] += 1\n", + "\n", + "x = range(MATRIX_THRESHOLD, 101)\n", + "fig = plt.figure(figsize=[15, 4])\n", + "plt.plot(x, [math.log(max((1, distances[y]))) for y in x], 'b-')\n", + "plt.axis([MATRIX_THRESHOLD, 101, 0, 15])\n", + "plt.xlabel('similarity as %')\n", + "plt.ylabel('log # similarities')\n", + "plt.xticks(x, x, rotation='vertical')\n", + "plt.margins(0.2)\n", + "plt.subplots_adjust(bottom=0.15);\n", + "plt.title('distances');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}