ClusterStats.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "f9c391d9",
   "metadata": {},
   "source": [
    "---\n",
    "title: Book Clustering Statistics\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16363b59",
   "metadata": {},
   "source": [
    "This notebook provides statistics on the results of our book clustering.\n",
    "\n",
    "\n",
    "## Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5aee56f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.4     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.5\n",
      "\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.4.4     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse, warn.conflicts=FALSE)\n",
    "library(arrow, warn.conflicts=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cddfa8c",
   "metadata": {},
   "source": [
    "I want to use `theme_minimal()` by default:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "102edec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(theme_minimal())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3246ada4",
   "metadata": {},
   "source": [
    "And default image sizes aren't great:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ef52c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width  = 7,\n",
    "        repr.plot.height = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479c5e31",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "Let's start by getting our clusters and their statistics:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43619a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table\n",
      "41,149,070 rows x 8 columns\n",
      "$ cluster        \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 423896385, 454491654, 424930878, 449145631, 440372971, …\n",
      "$ n_nodes       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…\n",
      "$ n_isbns       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ n_loc_recs    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ n_ol_editions \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ n_ol_works    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ n_gr_books    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…\n",
      "$ n_gr_works    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…\n"
     ]
    }
   ],
   "source": [
    "clusters = read_parquet(\"book-links/cluster-stats.parquet\", as_data_frame=FALSE)\n",
    "glimpse(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f9f949",
   "metadata": {},
   "source": [
    "Describe the count columns for basic descriptive stats:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b04dba65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    n_nodes             n_isbns           n_loc_recs       n_ol_editions     \n",
       " Min.   :     1.00   Min.   :    0.00   Min.   :   0.000   Min.   :    0.00  \n",
       " 1st Qu.:     2.00   1st Qu.:    0.00   1st Qu.:   0.000   1st Qu.:    1.00  \n",
       " Median :     3.00   Median :    1.00   Median :   0.000   Median :    1.00  \n",
       " Mean   :     3.38   Mean   :    1.08   Mean   :   0.235   Mean   :    1.14  \n",
       " 3rd Qu.:     4.00   3rd Qu.:    2.00   3rd Qu.:   0.000   3rd Qu.:    1.00  \n",
       " Max.   :106334.00   Max.   :51481.00   Max.   :1439.000   Max.   :44567.00  \n",
       "   n_ol_works          n_gr_books         n_gr_works       \n",
       " Min.   :   0.0000   Min.   :   0.000   Min.   :  0.00000  \n",
       " 1st Qu.:   1.0000   1st Qu.:   0.000   1st Qu.:  0.00000  \n",
       " Median :   1.0000   Median :   0.000   Median :  0.00000  \n",
       " Mean   :   0.8319   Mean   :   0.057   Mean   :  0.03699  \n",
       " 3rd Qu.:   1.0000   3rd Qu.:   0.000   3rd Qu.:  0.00000  \n",
       " Max.   :2329.0000   Max.   :7378.000   Max.   :295.00000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters %>%\n",
    "  select(-cluster) %>%\n",
    "  collect() %>%\n",
    "  summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e2d679",
   "metadata": {},
   "source": [
    "75% of clusters only contain 2 ISBNs (probably -10 and -13) and one book. OpenLibrary also contributes to the largest number of clusters.\n",
    "\n",
    "## Clusters per Source\n",
    "\n",
    "How many clusters are connected to each source?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3adaa1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>source</th><th scope=col>count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>n_nodes      </td><td>41149070</td></tr>\n",
       "\t<tr><td>n_isbns      </td><td>24073256</td></tr>\n",
       "\t<tr><td>n_loc_recs   </td><td> 9277944</td></tr>\n",
       "\t<tr><td>n_ol_editions</td><td>34655323</td></tr>\n",
       "\t<tr><td>n_ol_works   </td><td>32823913</td></tr>\n",
       "\t<tr><td>n_gr_books   </td><td> 1505224</td></tr>\n",
       "\t<tr><td>n_gr_works   </td><td> 1504700</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{ll}\n",
       " source & count\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t n\\_nodes       & 41149070\\\\\n",
       "\t n\\_isbns       & 24073256\\\\\n",
       "\t n\\_loc\\_recs    &  9277944\\\\\n",
       "\t n\\_ol\\_editions & 34655323\\\\\n",
       "\t n\\_ol\\_works    & 32823913\\\\\n",
       "\t n\\_gr\\_books    &  1505224\\\\\n",
       "\t n\\_gr\\_works    &  1504700\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| source &lt;chr&gt; | count &lt;int&gt; |\n",
       "|---|---|\n",
       "| n_nodes       | 41149070 |\n",
       "| n_isbns       | 24073256 |\n",
       "| n_loc_recs    |  9277944 |\n",
       "| n_ol_editions | 34655323 |\n",
       "| n_ol_works    | 32823913 |\n",
       "| n_gr_books    |  1505224 |\n",
       "| n_gr_works    |  1504700 |\n",
       "\n"
      ],
      "text/plain": [
       "  source        count   \n",
       "1 n_nodes       41149070\n",
       "2 n_isbns       24073256\n",
       "3 n_loc_recs     9277944\n",
       "4 n_ol_editions 34655323\n",
       "5 n_ol_works    32823913\n",
       "6 n_gr_books     1505224\n",
       "7 n_gr_works     1504700"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src_counts = clusters %>%\n",
    "  summarize(across(-cluster, ~ sum(.x > 0))) %>%\n",
    "  collect() %>%\n",
    "  pivot_longer(everything(), names_to=\"source\", values_to=\"count\")\n",
    "src_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88365d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EgSEBIXQrIDIUlASE5BSOIQkgSEJAAhSUBIXAjJDoQkASE5BSGJQ0gSEJIAhCQB\nIXEhJDsQkgSE5BSEJA4hSUBIAhCSBITENQIhnTwlsykBhCQOIUkYZSGVlctsSgAhiUNIEhCS\nAIQkASFxCYV08lTnkZqrMVe0HNp/s2EH29D2u22DoSvLyjt/t/caW+s5vvt4b+xKIKQT+3z+\n2pr9pyx/CxCSOIQkIWkhlS1YsG1LxrHI5WuTnqnIK3XTDc/M2bo1ElL+rFUvuGoIuZUz+9W8\naY0xKzQk/9Ypl8mqqWsrFu+wercISRxCkpC8kDLbCNmRF7mcXzxE7s9mIaV/HbOXfomQdzI8\npCDfR3yLlpPoSll5f3neV6RTZz/WBiO38HkTaokTUuJbxdHTIXUzcx2eHoVHa1F4rF6P0sfZ\nrfBgrR6FB+tpV3iwTuHT2ScZUjFdnHWFC7ivn6HL3Sykgti9FrNK0o979RN05QOXL7JCylYs\nfbaD/jGSvfryQMwtOjyJxQmJcyuAkdYmGRJ7iVOre0MXG/V6ujzGQlodu9fLbDljd6N+kX6t\n05siK6QsJ9AUubEya+Kq5sgt8BMpAD+RxI3Rn0jGkGJ+IsW+G5foJ1L54fSTgZ2Gri5eavVu\n8RpJHF4jSUjeayRjSDGvkQwhhV8jFeb305dGK0h0he53PP0Yud9Dd/vtXKt3i5DEISQJjoV0\nLZO9a5c+PKT83NUlrn1x3rUjpyYcrs9Ysb50wlGrd4uQxCEkCUn8PRJdNFVH3ydoObj/Zs30\nYR/9OXmq4/DewG+beo7tCv0eKbQS2K+2uuXGe3uONFm+W4QkDiFJcOyTDf1+Qny/eNXOIaxA\nSOIQkoRkh9RVHXThyqyKdblzmoddKz2WeBCSOIQkYZR91k45hCQOIUlASAIQkgSExIWQ7EBI\nEhCSUxCSOIQkASEJQEgSEBIXQrIDIUlASE5BSOIQkgSEJAAhSUBIXAjJDoQkASE5BSGJQ0gS\nEJIAhCQBIXEhJDsQkgSE5BSEJA4hSUBIAhCSBITEhZDsQEgSEJJTEJI4hCQBIQlASBIQEhdC\nsgMhSUBITkFI4hCShHEfksoZhpAkICQuhGQHQpKAkJyCkMQhJAkISQBCkoCQuBCSHQhJAkJy\nCkISh5AkICQBCEkCQuJCSHYgJAkIySl9ffx9LBtSOfUHvEMKj6b0YXpVPs5+pQ9zXJ7OsRAS\nwKiHkAAUQEgACiAkAAUQEoACCAlAAYQEoMBoC6nrSqL/7TyyNfFuI6Lr0tUEW33XvmS/g2iq\nC+hI1qACeq83JPrVTPPldrocCo7sWrIGFeC9ecWTYHPoLNYHhzaYpEGFtdQ1x93mb7jqpV+6\ngiP7ysLRRllIVe5s19Lhv6xubxy+1XS3EdUyU9fzHri2rz60cuqJSRNy6YW3sqgMXf1/SB1f\nz1JXmmvSvuFX3w19TuL+C3q263U/8bKRZbmWJXFkZJfblaHn3xt27QOns5CNLC1T5acnLLg/\nx7Vz+HU37ge/3pqTlpV5jJBzbGST9Af2MzG6QvrQdYo0565iq51Xmv2ha/cXDNsas1uy9J5s\n3BwKyVd/ayB0bb0e/Pp1+pYhX8nM0FRYMyeZI+v49Y1B33b9bGAcVyI/C0uqg1/X59wmn6Uf\nCl74yvXPyRzal41+0pBXzFYTnU7Gn/taMkdGrd6SGQjE31B/P3xdZl3gy9DcJb3+na6bwSvf\ncyf6qRo2ukIqLKSLQ+4O4luXNjNzwZ3gteHvfGRrZCWpQiHVZE6dknMmeFU4pOpM+kSgQT8d\nuNAx4UByB8ZkbafP4fInPJm2KjQrQiH5MtjXsmeCV/46Z8D85iNoA/1jJfHpZM7oN5I7rIOL\nBwIh1c7MmjFhR+jKUEhf6F/QmnI2B69c+JKV442ukCZV0cV1vZZsXHiP9Fc+E/xDLPydj2yN\nrCRVMKSTT1wk5Ehm8PlKOKTS/MD4tgUu7MnoSe7AqLv0T/eh+eu8pH3BpuA1oZCu65/RZY0r\n8JE03+Sq5A7Lf/H825MOE87pZFbkJ3dkV3O+JiykrzPf9ZP6rH8JXhsKqUZnf96ULglcuBz4\nDnKNqpAG9Rq6vKef8KVvp6/xjutNpOXixYubFtBFQ3RrZCW5owuG9Hwpe/k5+Sjx0kG9r9PF\nJUKWlLJNszaypT+3MrnjoryLCv2kVj9NR/brmYTcpqPKr6QLD73yOt1+XGfvN5D3XfFfXo+I\n/uxMvdJHEp9OtuNd1wdJHVhX7ikSCOmtJ9npLD3pvwIAAATSSURBVFxLg6GjyjhIF71k2yS2\nT+XcwK5rnvInPFTIqAqJuHbRxR399G19YQHzJXk3Ozt7YhpdrIxuja4kVTCknLzAyI6TL+mg\nsnS6mEqfpRSxTVMDzwXO6PWJjjISevKX9BJy2BUYGX3C9CodVVoGXdSQCzrtnBzWA3+pYtGL\nyR4ZPU0LigjndFJvZKv8FDffqwUsm9cuktXZgZFtISSXjkrPoourZEca26c88Hy4c0KNpSOO\nrpBmsVec5/QbzfqpmGvDzwXCW6MrSRUMKXdTzFXhp3aVs+nC6w68NipenNxhEdI2fwV7+/u4\n3hlzZeip3T39I7qsymLrV4PvSCTZP+utnNNJn3Nmb03uoIqzWTYTs4fWzYu9OvTU7rjO3vJ8\nLvDaaG+GtXeHR1dIG3Poi/aKGX4yu4j9PA39ZiH8nY9sjawkVTCk9TmBb2xwaOGQTrKfQkdd\n7NV0k+tocodF7uauDLyF0OzeGR1Z5F27p0sIGZj9ClutmJXk71jg7n6T1ss5nexbl/RfC5Lg\nU7sPXYFfVYSGFgqp1b2fncgjdNU/q8LawUZXSJ6cZw9Wuuiz5rqJzx88smV28Nrwdz6yNbKS\nRIcPLJ1y4EAracvL3XO0+ungT8NwSP7np+97a2LgJdIbk5P8+5COnJyaAwcO0Nfse11rjryz\npjx4dTikT93lhwqz2K9uujL2Jndk5Nlf7tpZom/jnU76nLM4ySMLYCENvZi59ei+ondC1wRD\nIlVpb9bkLWB/PH2iJ/o1fIzRFRK5t3nFmsDbOE1bi0s3h96WO7F5+NbISvKUBJ5LNxDSu++l\n4l9+GKylMTQnSN+eF156j/3Z6l+5P7njIl8HBlbAfiN7Yf3ysu23gldXHQttv1yxfGPgV/Nn\nCpP8+wLy8caS0l9dYWuc01mQ5JMZVMze4PB/UL58zTvtoWtCrxb8H64sru5lazvWWDzYKAsJ\nYGxCSAAKICQABRASgAIICUABhASgAEICUAAhASiAkFLS3/+F0yMYbxBSSkJIqiGk8ePKr17e\nHPhLaP6P1qx+m/3jHaRsO1s2FbO/uLbhdfLp2tWBz2EXP/qvi4uLbzo10PEIIY0XvVM07fsP\na68R0vzftW89ov2ANfXIz9imzzX26e+/+usZj/6/v9QW0dXH/+Rbjz/++OeOjnecQUjjxRRt\nXjvx//5jQn78jVcH/Af+5N92DQtJy/QSf8432d8bwFM71RDSOHFV+1+htVot8Bc8X9I2Dgvp\nX7H/4etjbT1BSOohpHFis7YltLZJ28q+XNSmDgspEE+jxv5ND4SkGkIaJ1Zph0NrZdrv2Jd2\n7efDQvoRW2/SFhCEpB5CGice+Il0if1E+u5P2OoxhDTiENI4cVX7cWitVktnX1ay10h/9+ds\ntfCBkP7rnzkyyHEMIY0X2dovOgj55Ax7127tgP/gnz7aScgSbZ1/YNsjD4SU+dAdZ0c77iCk\n8aJnkvaNR7/Nfgw1/zft249oPzhHr2z5C+2P/vB7mx8I6b0/ePiHj51xdLzjDEIaP77Y8HLw\nHxjxf/jqqprAJxtI71svb22LfLKB6i4OvBVx9bUSfLJBJYQEoABCAlAAIQEogJAAFEBIAAog\nJAAFEBKAAggJQAGEBKAAQgJQACEBKICQABT4/6BSxtm2nHA0AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(src_counts, aes(y=source, x=count)) +\n",
    "  geom_bar(stat='identity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ebda89",
   "metadata": {},
   "source": [
    "## Distributions\n",
    "\n",
    "Let's look at the distributions of cluster sizes.  Let's first compute histograms\n",
    "of the number of records per cluster for each cluster type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27b2901e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>rec_type</th><th scope=col>nrecs</th><th scope=col>count</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>n_nodes</td><td>2</td><td>10611148</td></tr>\n",
       "\t<tr><td>n_nodes</td><td>1</td><td> 6960647</td></tr>\n",
       "\t<tr><td>n_nodes</td><td>3</td><td> 9248888</td></tr>\n",
       "\t<tr><td>n_nodes</td><td>4</td><td> 8154636</td></tr>\n",
       "\t<tr><td>n_nodes</td><td>5</td><td> 2148226</td></tr>\n",
       "\t<tr><td>n_nodes</td><td>6</td><td> 1107105</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " rec\\_type & nrecs & count\\\\\n",
       " <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t n\\_nodes & 2 & 10611148\\\\\n",
       "\t n\\_nodes & 1 &  6960647\\\\\n",
       "\t n\\_nodes & 3 &  9248888\\\\\n",
       "\t n\\_nodes & 4 &  8154636\\\\\n",
       "\t n\\_nodes & 5 &  2148226\\\\\n",
       "\t n\\_nodes & 6 &  1107105\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| rec_type &lt;fct&gt; | nrecs &lt;int&gt; | count &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| n_nodes | 2 | 10611148 |\n",
       "| n_nodes | 1 |  6960647 |\n",
       "| n_nodes | 3 |  9248888 |\n",
       "| n_nodes | 4 |  8154636 |\n",
       "| n_nodes | 5 |  2148226 |\n",
       "| n_nodes | 6 |  1107105 |\n",
       "\n"
      ],
      "text/plain": [
       "  rec_type nrecs count   \n",
       "1 n_nodes  2     10611148\n",
       "2 n_nodes  1      6960647\n",
       "3 n_nodes  3      9248888\n",
       "4 n_nodes  4      8154636\n",
       "5 n_nodes  5      2148226\n",
       "6 n_nodes  6      1107105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_dists = collect(clusters) %>%\n",
    "  gather(rec_type, nrecs, -cluster, factor_key=TRUE) %>%\n",
    "  summarize(count=n(), .by=c(\"rec_type\", \"nrecs\"))\n",
    "head(size_dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ac748b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mTransformation introduced infinite values in continuous x-axis”\n"
     ]
    },
    {
     "data": {
      "image/png": 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HoUCoVCoRCJRFwut+raYESpVOr1ehcXF0cH0sBoNBqpVCoQCFxdXR0dCzgzPIq1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F6XMa55TWCQm5SHZogWNIL7qRjhQOwOH0\nyPmDT2/Jqa0ysfL69IuHCu4G2bnOC+sbLHC1xngAJBIJi8Vqhee+m4hcLpfL5TweD1aLtRSl\nUkkQBIfT1GBza0Oj0dTW1rLZbBsbo6U0XyMkquo/bix5UPys6WKwS69ZPTbbsa38HNNx5MgR\nFEWjo6OPHz8ulUp79+7dtWtX05eIRKJTp04VFxd7enrGxsaa+YWekZFx9OjRr7/+GkVRAMAP\nP/wAAJg/fz4AgCCIVatWjR8/vl27dib060zt3bv3qVOnCgsLDU95Hjp0qKCgYMGCBSwWKyEh\n4cGDBwRB+Pr6xsTEtLST8HJy7FxdXblc7vXr1ylHeTyez38IBAIWi1X31tn52YnLdu3apaY+\nj6OkpqayWCx/f4qsMl2R9GbcbH3twOgsW6eAl+LVAQDatnVqaI1SHvizBquoE2RXXN97s5Ej\nrgidzwtbx233lbEJhEYkujmdUAutMRFF2GPCjA8jmMSP8+gDVNHgRgiVrOZfU1mhbBp978DZ\njixTn+BitTKtuvREzt2hZ35IKcs1MRMCgUBeKX67/kmdVwcAyCy/tvvafIIqJdp8jhw5snr1\n6kGDBhUUFBQWFkZHR9dt31GSnp7epk2b1atXp6enr1ixom3btllZZuVes9nsuLi4e/fuAQDE\nYvGiRYsWLVokFosBAHfv3o2Li9NFkUzoP3LkyMqVK6Ojo3NyctRqdf2gKUmSn3322YIFC6Ki\noths9oQJE6ZOnXrv3r20tLT169frnMgWpWUjdmq1uqioSPdCKpXm5uYiCOLn54dh2JQpU3bv\n3m1raxsVFUWj0YqKipKTkz/+2KzDkgCAMWPGfP755zt37hw8eHBubu6JEydiY2OZTCYAYNeu\nXW3atHFxcSEIIjs7+9ChQ5GRkeak7kFagr4xAY/SywsLniXPaezSCKZ+ht+9ovPD237GRhv5\nN8LYniZGCVWlqjye7TXBCiNZA9owIj3Vt4uMTUAIBrNwqKLN7/WFovgftTUlru/8irKpfyN6\n8+zjug5fcJniCK0ealz7ydU/r41bgpjoXAGBQCCvBiW12WkliXrCJ5W3nlbdDXCiKHFlPk+e\nPMnOztaVSBMIBKtWrZo5c6axyfPmzQsKCkpISGCxWAqFok+fPgsWLLhw4UKjq/j6+vr7+yck\nJHTq1CkxMTEgIIAkyUuXLo0YMSI+Pj4gIMDX17dR/ZmZmZmZmX5+DfagVSrVjBkzbt++nZyc\nHBQUVFVV9eeff964caMu9PgCwkwt69gVFRUtWrRI97q4uDglJQVF0ZMnTwIAhg8fzufzT548\nefLkSQzDXF1du3fvbr7mtm3bfvXVV/v27Tt//jyfzx89evSUKVN0QywW6+jRoxUVFSiKOjs7\njx8/fsSIEaa1QVoOGg398KOeyVfzc59UkySpcmOnUlQFBhJVFZvdiGPHcOxNt+ukEaUam4BL\nrT1niiF2P4yWrPhXcS7TWBU6mjgQ1fAIeoOUQemdkxVMruu7v1NfA8DogIhrpTmHHlPn8NWn\nQCIskoq8uFZWXYZAIJAXhlBWTCmvkhU10bHr1atXXeHbzp07L1++XKvV0mgUvopMJrt06dLB\ngwdZLBYAgM1mf/TRRzNnzlQqlTqJafr37x8fH79kyZKEhIT+/fuTJBkfHz9ixAjdW3P09+jR\nQ8+rq6mpGThwoFqtTklJcXJyAgDweDx7e/tNmzYtXLgwMjKSwWBQ3kvz0rIL+Pv7nz592tho\nVFRUVFSUaQ2jR48ePXo05VCXLl26dOliKJ8+fbqJoxKQFw+GoVF9/aL6+gEA7hYJU6/oT0AR\nzJ7t3rgilC7ofqjmxtuamluU45L0OHXVFX7kr5g52vR027L4G0cwovxrl/5NOQEh6Ownk2XB\nPwOkQeKzOOWA0+SNGM+J8ioAwOoeseY4dsBUm1kIBAJ5hbDjUOfSCYzILdBcb3uNyWQSBKFU\nKilT5HX7gZ6ez3dyvLy8CIIoKSmhzMvSY8CAAbNmzVKr1QkJCStXriRJMi4uTq1WX716de7c\nuebod3Fx0dN59+7dysrKgwcP6rw63S3Ex8evWrVqyJAhWq126NChGzZs0IUDWw7YKxbyQglz\n7+dp105P2DtgEodh1l45xg1wjEl2HHCL5TmWcoKqPL4mZTxl6TtzYI9qz+wXYHR1iT9N4qsv\nJYnqE8tItdzYVUyMRl3TpGFokE1jaJuWngKBQCAvBg+74CDnbnpCb0FoYNPCdRahc7l07peO\nwsJCFEXd3c36YR8TE6NQKI4fP56VldWvX7+YmJjMzMzjx48rlcp+/fqZo9/wqF9MTMyOHTtm\nzJhx5MiROmFERMSxY8eEQuHFixfz8/NnzJhh5Q2bDXTsIC8UGsqIsltBkwb9J0DYtV1rJbXL\n/u0Tl9BzY8L4xxU3GlGBoHS7ToKu+3ntV6J0iuQ2jfB6+WkH2WML6l3Xh796CKt/UANRPQ+M\nJuxgeIno4k+5n/jVnNtkzL2jbg7b8DNBoVVPvfCrtLFusxAIBPLSQQAyp8eW+ruuPvZhc3tt\nR5EXV2rDxsYmOjp627ZtupKKCoVi69atAwYMMGcfFgDg6OjYsWPHb775JiIiQiAQCASC8PDw\nb775Jjw83NHR0Wr977///u7du6dNm6Y79iGRSHT1WTEM69at28CBAysqKkxc3iy8nHInkFZL\nrUgZf7LaRrOQYAgJhghR2yna/Hi//CYAAJDgSeWtLUnTFscc8nekqIPTAJTObfclza5DzbVR\nhoOERiK+vxhBGZyADyy1ELVj222LVV3Pr5n930+ueh4Yo7I7JvVSu13WODyo7/HhkqrKw5+J\nEnd6f5OCcR30dH7UMeZqac7NcuqKKnXkiaunXtizuseo9vYWbyVDIBDIi0TAcVsy4M8C4cMK\nSZ4j18vXviOCvOhQ0Q8//NCvX7+wsLAuXbrcuHFDJpPVdY03h/79+2/atOmLL56VZRgwYMC3\n33776aefNlH/9OnTORzO1KlTFQpFly5doqKi+vTp4+3tXVFRcf78+R07dlh6m5aCGRZfgTQF\nhUJh2PEWUseDeyUPH5QBgCA4B1Xba5xTNA7/JcwhAABAkHhJbXbvgEnmaCNxqTx3t7FRjfAG\nt81iYNVnDc3TTn29AC+lOOiBamzpNWGoSqAV6PcWI2Q1uKiU21k/KxRFEF9bhyOPKZrP6lEi\nqz385I4rxzbUwVzfTq1W02g0DMPMnA/RodFoNBoNk8mEj85SdDWkYOlESyEIQqVS0en0N6Z0\nIgIQO7aLh11bAcetuQpWt2/fPiQkpO6tQCCIjo429p/U2dl57ty5Tk5OLBZr5MiR27dvr78P\nGxwcHBZmopQVcHd3d3Z2njJlii4lzt3d3cnJacqUKXWF1Uzr1zO1viQkJKRHjx4ZGRl9+/ad\nPHmyQCBgMpkdO3bctGlTTEyMxQ/FQhBYdb15ecUKFL9yXL389PSJ9Lq38oBfNQ76BwtoKP2H\niY/NK/xBVidFq6uuGht2HHCLbtdY8M8IeIm4avgvpNLo0XRFwBGNg/4RXQSjuS86YxOmXyqZ\nIMnJ53++VppjztJsGv3ymE/dbPjmTIYFiq1DV6DY1tb2jfmWfWHAAsXW8YYVKIa8ssCtWMgL\nxc29QVYcgjMN5zBoHLPLuSF2XfeJbkxWV6dQDlcn9rHtsMGKDVkAAOZuy+jspbpmdP+UnTMB\n0XDUrg3cShLXFm8a4vb+fl73BkFHFEF+6Dv5q+sn/85La3RphVaz5va5zVHj6SgMJkEgkFaN\nUCjcunWroTwmJqZPnz4td+3rCzw8AXmhBAQ6hIY9Pw9Pr+loOCfC8y3zFWIcL4d+VwQ9/kRQ\nCh+RxBW1d+cbc/sahT2e4qhEfVgFwxkVevUXSQBAxd4FpFa/z7cTm7ur39S0Kd+M9GtELQDg\nRO69ASe/r1EZPWwLgUAgEIghcCu2mXlNt2JxraokI0FWU8ix83APjqExWnCTRa3G4y9kp94q\nkkjUDo6ccv5eqf3zvjSYwm1B78Pt2vhYqlZRcLA29T1SKzMcYnnECnocs85ayeoE2X6jJZEB\nACSqlnRaCVCNntx72Q2WL/XJfxWufTdxX0JhZqOrD/YO+bl/I0UZ4VasdcCtWKuBW7HWAbdi\nIS8GuBULAbXljy//Pltana97y7Fzj5q+297DVM5pU2AwsKHD2w0d3o4gyMtJuX+fGWPDC9Xa\npZGYEpP50qu6/SsqaLfYYseO7T2Z4dSn4lwbQCj1hpTFJ4VXhwu6HUCoyqOYhvdVf8Chy3Yb\nLcKCEAxM6YhzSvXkJVvHeX7yF8OjveElTIxGM+9Ix/mCR8mlT3q6BVpkMwQCgUBaLXArtrVD\nEnjygXl1Xh0AQC4qubb/Q1zb4gXVUBQpLRYDAGiSNqzCMey8KYzKngiJlZaI1Vp958wcMLYH\n3Va/+rEOVdm52nsfWWcn7+M+9r9OpAc6GptAohTPSiMsLFw/SCvSd/h0iNVm3SAJwILLh5W4\nfjgQAoFAIBBKoGPX2hGWpInK9PcEpdX5lbmNFQpuDhjMhocDUK3S45wo/PMFR9suORH5d/o2\nLWGZT8Nt97WxIUX+fo34oZV2dvN2ODGD3pGyBAnJyhtHF4XoSREA8Nqy3E98y3fP0gqL9Ebb\nCvR70RijXC65VZZnqcEQCAQCaZ1Ax661o5YJKeUqObW8eQnr4Fb/rcL7mMrjLIFJAABiZeWp\nBxv/vLvKIoUsj1h+p58QjLKUIFGd0F1ReNhKWzHUYe9k9sgQgwO7CE3sz86eznk8A5AG/6Fw\nbe21P/KWhigyL9UXzwuLtqPsM0bFpnvxVtoMgUAgkFYGdOxaOzxH6mbJPCejLVObkTbBTn3/\n681KsCrUzpf1JiRm/1YpzTe4zhQc/7n8iO2UQySurL09R+OfWXoAACAASURBVCvJssJUAACg\nodyPooCR40a0mnaMcqrWYQAQKlnprulkvR1VNxv+4cHvdHf1N6esy+2K/PmXDknM272FQCAQ\nSGsGOnatHa6Dj1/ncXpCz/ZvtdzhCT2GjWz34Uc9e0Z5+YQpKCc8qbxFKTcBy3MsjRtEOUTi\niqqLPZQlZyzVqYOopjh1WwejOtzYkFZYpMprcLq2vb37n0PevTf5f362+i3IDDmZey/6xGYR\nrH4CgUAgEJNAxw4CImNXBvWYjqA0AACCYv5dJnYbv+lFGuDrZx8zwK9LZ+qTsL9d/+TjY2Fn\nHn5nfr4dQuMKeh6n8UMpR0lNrejGFK1YvyGYOWAefIAajbKhMg9M4WpslNBQeK4OLJspbbqZ\ns3S5XDzk9A8aAjdnMgQCgUBaJ9CxgwAawyYydtWElZnDPk0cvyKj27gNDLbFZUGajo8g3I5N\nfaRArhafTfv+xL115muj2YY49ksxkmwHSFwuy7GmEzNqz+FMoCiq/B+ITdo8RG1HMUBjML2o\nLyxXUHSkpaRQKvwy5YSZkyEQCATSCoGOHeQZKI1h6xSA0VkvywA6yprV/Xs2nWdsQkL2HpGi\nzHyFCI3DCzV69gKXW5a6Vwfvs36sgW2MDpN0btp8TKoffeSE9DfWJNvdvJ6wOg5m354Z/3ut\nmnrbGgKBQCCtHOjYQV4h+ESo85NV7IIxNHGw4ShJEqW1jy1SaBO00DZ8K0ApujJgHC/rjERY\nNLsto+x/mYDwqJ1gRMu1efQBvbpBfE724FzOfOfi70dqKnL05o/2j3BgWVCJPr4w47Nrxy01\nGwKBQCCtAejYQV4VcJw8sPeuqJzGKOvPKO9LOYfDsCC4BQAAALEJnMcP32IgRnF5gTz/d1yq\n72aZCaOHj+3SfiYmsJ5OQDQNdrRJApfd+yt/WVdNdYNgoRObuytmqidXYP7qf+U9LJHVWmQw\nBAKBQFoD2LJly162DW8UCoWCzabO64KYQKVSFeTXXk9+VsgXVdupna4DrEFHBy7THkNpaq3C\nxTYAMagmZwK6oDPAFZqa24CsO3lA4tLHqpJT8txdJKFiOpty0YyqbeusuviEqKI+J4uQKL02\nWO1yXU9OapS4qITXpcFJZE+uYHpw9xjP4ApF7VNxtTmrXyzKGuXXkU1jqNVqGo2GYVjj10Dq\nodFoNBoNk8mEj85StFotSZKwPbGlEAShUqnodDpsTwxpUWDEDmIWJKGVVudr1aaKfTQRhfz5\noVeEYHKezES0DUr4SlXCCxm7dlx59/uLb1vYkQLhha1j+79rOEASKmnG6uqkvri8wGKLEeBw\naBomMFpnGFU4sfJHGMplDy+QGv2idAyM1tnZ+4+Bszf1HsuhNf6VmVNbOfbcTi0JD8lCIBAI\n5DnQsYM0AkngafHfHf2m/Zn1UUe/Cbny+zvyWur+p01EYN8gZY0macN7EMfOmxTsGKM3M7P8\n2t/pWy3Vr6nWD57Voa66Wp3Uh1Bb3myDgdpuGm5inF7eA1Hrbx8Titri70cCkqC8ZGJQl7U9\nR5uz+GNRxZfJp8y0FAKBQCB6xMXFrVix4lVWaAXQsYM0QnriDw///Q7X1WAjyaJHF678/g7R\nAm3pnZw5oWENisAhWm5ntwkcNtNw8u38sxYvYDLIh8sLxQ+WWKwTAGZ3HxMFUBCAYDIPQ7k8\nPUGcctDYVR42FAVTKDmYfetGpZXHeyEQCKSVk56e/uiRNTVNX5hCK4COHcQUuEb5KFG/PZew\n+GFR+vmWWG7cxA6du3jqqoIgCBIW7u7iyssvoihxotBILFVOd+xheoIi7w/ZY4sDgQAA2/8N\ntP3fAECj/t/EeTzNJu1jWm3b+kISAMntY8YUdnb28bN1NGdpEpCzLu8//OSORQZDIBBI0yFx\npbLoT2nWekXhEVLbgok6EIugvWwDIK808toS3CAbDAAgrrTyMKlp2Bz6xCnhsWNDa4SK8nLJ\nkYP3H94rUXjbA4NuDny2c0ltlju/LZUaanghy1Ulp3FFifEphPj+x3T7CIZDlGV2YwhncgQ9\nzE04aR9JGLaSRTC5CydrltrpttLvz2ciAGSpp3IWuAiGfMLvPROzda5/AR3FNvYaN/afn4z1\npa0PSZJLb5zy4tv3dTdeXQ8CgUCaFa04XXhtFC57qnuLsT0FPY/RBZFN0RkXF4dhWO/evX/+\n+WepVNqnT59FixbRaKYcldOnT//xxx9KpXLIkCFt27Zdu3ZtQkJCnarIyMi9e/cWFhZevHjR\nxJmVS5cu/fTTTyKRKCYmZuHChXUzr1y5smfPnuLiYk9Pz7lz5/bo8Tw6YGKoDpVKtXDhQhzH\nt27dKhaLt23b9uDBA4IgfH19Z8+e3alTJysfU2PAiB3EFAy2HaAqq8u0sW+5RZlMGp/POnH0\noUaNAwCYpQNRjX7V4sKa9OV/D/o+8W2xsspMtSjT0bH/TY7/uxjbA0Hq/vL1XSfh1ZGaGou7\n0wIA6KGutivfMjGBURnJbHiWApdUVR1Z+vTzYHl6vN5kPpNljlengyTJjy4f1hLUSXsQCATS\nzJB4zfUpdV4dAABXFNVcn0ziTaqdnp6evnPnzu+++2727Nlz5szZunXrF198YWL+gQMHYmNj\nXVxcxo4dm5iYOG/evEuXLtWp2rFjx6ZNm6ZNm/bll1+aOPx++/btDz/8MCoqql+/fuvWrZsz\nZ45Ofvr06ejoaBzHx48fr1Qqo6Kizp8/3+hQHUKhcODAgffv31+7di2bzY6JiUlOTo6NjR03\nbhyHw7l79671j6kxYMQOYgqmjb1HcP/ijAZuB4Nt69nelAfTdHJyquX/HZJFNTxO5iKl93Hc\nNpNEiPquWEbZ1d+uL/4o+g8z1aIsN36nHQAAUiupvNgLF6cDg7IppEZcc32S08AHCM2CosHP\n9DtxTU9glvfC1AJ54D6APHfCCEVt6c5pvmsfYTbPS9k5sBpRpUe1QpYlKmtv727RVRAIBGIF\nmpo7WnGanhCX5aqrrjJdBjZFM4Igf/75J5PJBACUlJTExcVt3LjR2OSvvvpq/vz5W7duBQDM\nmjUrJiYmJ+f5bpJGozl9+rSNTSOf5MXFxbm5uW5ubgCAiIiIQYMGffLJJ+Hh4YsXL546derv\nv/8OAJg7dy6O44sWLcrIyAAAmBjSkZeXN2TIkODg4AMHDrDZ7LKyskePHu3fvz88PLwpD8dM\nYMQO0ghdx60XuLevc6cYbH73iVvYPGfTVzURtUpb/y2mcLXJ+pCT9aFhgC299JKl7SgAAAiN\n5zwwFWNSt6bFZXnih59ZqhMAADSNx8xoNSHM0j76K4or5I8u1pc4sbmDvdtbtHh2TblF8yEQ\nCMQ6jNUQIFTmbqEYo3PnzjqvDgDg7+9fWVmpUqkoZ1ZUVOTl5cXGxtZJRo0aVX9CZGRko14d\nAKB79+46rw4AMHDgQC6Xe/369aqqqpycnPHjx9dNmzRpUmZmZm1trYkh3dusrKzu3bsPGDDg\n2LFjurq2zs7OYWFhEydOXLNmzbVr17TaBl9wzQ507CCNwOI6vrXgbN9Zv4cPXdp9wnfDl1zy\naNe/pRd196DoMEEyqHstCOUm0uaMg9BsO+mfC6lDnvOTNHOdpSrpoa7GjlDUh1kykFarnw9X\nsXdB7eU99WugrO81pouzfs9ZE3x69XhScbb58yEQCMQ6aNwgajmvqZm+9Sv86/ZPNRrqggY6\nR4rHe56oU/81AMDOzqzyAra2DVoE8Xg8kUgkEon0hvh8PgDA9JDurUajUSgUNjY2KPrs6wBF\n0eTk5Pnz5ycnJw8bNszLy+vChQvm2GYd0LGDNA6CYu7BMe36fuDXeWyLZtfV4erG69rd+9mb\n/4J0iIq66da13CO5VdbkK7A8Rtsadhv7D0na17Ks9RYpRJ1sePN7NT6PwDhZs+lVDTJncXF5\n+Z651SeW10nsWTbHh71/bMh7fdypPkMNMvBUhGbqhT03y/MsshkCgUAsBeMGsH1n6AlZHrF0\nQecXZoOPjw+GYfU3QOu/Np/61UmEQmF5eXlAQICPjw+NRktLe77d/ODBAwaD4enpaWJI9zY0\nNDQ+Pn7Xrl0LFiwgyWef1Fwud8GCBWfPnq2oqIiOjv7444+tMNVMoGMHaSoqWbVa0fx9S2PH\nhg4a0pZvx6rLgqNJAzApRQTrTsHZb/+NvfR4rxWr2ATO5/jNNjJIih8uVZVb9rvKZm53u/XD\n6WFugE0HmKn/X+ynoxGNfiKd8K912urnPTAQgHRz9Tvw1pzLYz+1ZTQo4Gysp9qXKScsMhgC\ngUCsgB++zSZwHkDpAACAYBy/OfzIX16kAQwGY9KkSWvXri0tLQUAPHjwYM+ePVboycnJ+f77\n7wEAOI5/8sknzs7OQ4cOpdPp06dP//bbb3VJe1lZWRs2bHjnnXcwDDMxVKezS5cuSUlJR44c\neeeddwiCKCkpOX/+PEEQAAAMw2g0mjl7xFYDHTuI9ZRmJZ7d0Pf4iohjy8L+2TKkMu92Myqn\n0dABg4K+ihswdETwMxGJcXLm0CT+lPOP3l1l3Z6sbfgWhn03Y6PSjJWWqUMAa3g7h8NTXe8s\n4n3U29RMkq5X3A4AQOJaZR5FUTp/W8eOjp7mrJ9ZUx51bMPp3PtaAnYbg0AgLQVCs7EN3+oa\nK3Z6K8M1VszvvAulm1tZvbnYuHEjh8Px9vb28fEZNmzYzJkzrWhhPHz48NOnT7u4uNjb2+uK\np+i8rg0bNrRp06ZNmzYeHh7t2rXr2LHjmjVrdJeYGKqjQ4cOly5dOn/+/NSpU6urqydPnszl\ncoOCgpycnJKTk3/88cem374xkLo4IaRZEAqF9vYvYrPypVNddD9hxzhc+zytlc7kvrXwb56D\nrxXaxGKxWq12cHBADKqr4Djx49bkwoJn6QsAITHPh0K3nYZKZnbf1MNvnBWra6WPq/6NMHZQ\n37bDBps2i61QW/vlOcVJ/YNjDdblZ8nb/qa3q8oJG+yx6CSC6X88nc17+H7ifvNXD7JzPvjW\nO64c28antkrkcrlcLre1tYUd2S1FqVQSBMHhGO2SDKFEo9HU1tay2ewWjda81qSnpyMIEhIS\nontbU1Nz//79qKgoE8VKSJJMS0tTqVQdOnRYu3btvn37Hj9+bKiq0RV1px86dOhQP8kPAJCX\nl1dcXOzl5eXt7a13LeWQ3rqlpaVZWVmhoaEODg45OTmVlZUODg6BgYF16XctAXTsmpnW49hd\n+nVmSeZFPaF/l4ndxm2wQpsJxw4AoNHgVy8/fZxVpdHivn72fuGSLVfH608iQRffUZM6L+My\nrXn+irzfRXfeoerfSiIo02nIY4xtVsCsPpL1ibLfGoliqt0uq9wTSKzBsS+6o593XArGc9Kb\n/L8bp399lGy+AV1cfE8Mfd/8+a0K6NhZDXTsrAM6ds1OZWWlVCr18/MDAKSlpfXv33/mzJnf\nfvvty7brJQPr2EGshLL5hLjiSUusRadj/foH9usfCAAoK5WkXH+MkWwcaRhgQ8Ct/FPppUkf\nRu0Ocja6tWoMtu8MgNJFN6cZjCAkoVZXJbO9JliqkzUspBHHjgSM0j70sl4a12SV1zkSPHMr\nNVVPy35+x2X2Thq/Qc+Nld1G9nEP+jDpoEKrNseAW+V5t8rzurj4Wmo5BAKBvCIUFxe//fbb\nhvI5c+aEhYV169bN1dUVRdGCgoKpU6fGxcVZpGHaNMPP/NceGLFrZlpPxO78thHCovt6Qo92\n/fvM/NUKbaYjdnXcTS0+cuA+jhNqp2SFH/W+pB3bdcXwRCbNmnCCJO0ryionvNAV3OCvrFAo\nP3BXsiGJVDVetUjllqjy0q9dTnPwdp68iRs5pr7wWmnOxH92m2lAV2ff48Ng0I4CGLGzGhix\nsw4YsYO8GODhCYiV+HUaYyj07WRNipuZyKTq40ce4jgBAGBU9uRkf4DJKbZHRYqyJ5XW9AQD\nAHCDv0Bo+u3LAACStG/KTzmK7y8mNWKLFHKmRDienmW7bBBrVCPVhpnlUYDQz6vTVheU/DBe\nnpFYX9jLLYDPMPc79WZF3vhzO4ulosanQiAQCOT1Bzp2ECtp03Omb0PfLrjPu94dhrXcirk5\n1ap6oS+6KJRVNIJypsJC96sOhMbjG6laTGhqZI+3VCV0tdS3w7zsOBM6Mrt6NTKPoKFq6jNl\n1ce+0ZOMD7Kge3RK2dM+xzY8FTe1IjwEAoFAXn1gjh3EWhCkx8Tvg3pMr3x6E0VpLoE97dwa\nOX/URLS4/skGVEHdGtXDrp1KK8NQBg21+Og72/tt2ZNtGiF1zE8rzRalvsvxm0vnd0SZjuar\nxTwaLwSA1QYTzOr6bWQBAACQ6tJMvZmfd3ortaIgtbIAmIeKwCf+s/vK2CVMDP6Xh0AgkDcZ\n+CkPaRKO3p0cvSmiR3JRMYLS2LbUzVitw9NTv88YqrJnlvdVuVyqLwxx67Pr6gcltdkYSgt2\n6TWhU5yrbYBFC2EsVw0gjZQARpSFR5WFRxGMzW33P27w52bqZHT2ZHTyVKcWmZjDzh+GqjkG\nmXYIxtP3INk0+slhH5zNe7jp7r85tZXmGFAiq5136eDPMW9gpjAEAoFA6oCHJ4BEImnGjrw4\njpuouNMaKH+cmPHvt0pxKQDAxt435K2vHHy6NnoVQRAkSTb66BIuPL15vWEVYlSrcrugckki\naTIE5zghXSrQBn6eLdN5Ya/DNgzqdmSU4CV7NRlm9Xuhh+7GXEabqZaslGvXXiZumPLtAAAK\nvz81jncA8vw/JmfQJzZDllJOfiKumnzpdyVu7h9wtGvglIDOPZx8zZz/BqP7k0NR1PR5HYgh\nJEnqHt3LNuQ1gyRJgiAQBGn2R0ej0fTapEJaM9CxA837BGpqagQCC3yIN4yq/DuJuyfXr1pM\nY3AGzT9j6xxo+kKJRKJWq+3t7U1/yxIEeSMl/3pyQVmpBDQMqhH0WrXrZZVLAkD1O0YPbb9g\nZNgnltwHKUoZqyw5XV9CGcCjCyIdYq5bohloHpUJpx4EKlNtIUi6VBFwSGv7rHYMO6A70yec\n22MKO7Cn4eSE3LS4u//kS6rNt+GDsD5fdh5ikdlvHnK5XKFQ8Hg8eCrWUuCpWOvQaDRisZjN\nZrfEo4O/TyB1QMeumWk95U4oSdozozQrUU/o13lc9wmbTV9oZrmTOh7eL937W/3WW6Ss7Q9a\nvn4umo6OnoM+jDK3PshzhU9+lKbHEZoaU7MQusuwPJTlamqOAUSVrHLwz6TcZC06UisN3U7Y\nlNaXOU3aKBisH0qUSCQsFutcUca8pIMkMPe/87Eh73Vz9bPE6jcNWO7EaqBjZx2w3AnkxQBj\n6ZDmRFKVSyGspBA2kbCObvMX9fbzf+ZDa+zvGfPqAAAcuhVttRCbwHkuI8s5/u+ZmkVqRLfn\nWKoadbRhxTQSwgQIjftoIatgWP0aKJWHP6s88DGppmh91sstwFyvjgQAgIQio48LAoFAIK8v\n0LGDNCdMG4poJaWw6Xj72A0b2U73GrcxdT60iw91VZTGQTCmc3/TU1Rl/+CKEtNzDOFMiWh8\nEgkYZVGs/FH1JETNha2F6weSBkl1dky2LYMNzAEBAADz0/IgEAgE8hoBHTtIc+IfSdF3i1LY\nLHh62bm68QAAgDB6vntI+/nt3aKtXoLpMhBje5ieQ6gqLFVLD3dH7c3ayWJURmLKBqdilU9S\nahN3AgJXlz/WVOUBkgQAYAj6YVgf8w3Iq4Vl7SAQCOQNBDp2kOYksOuUwG5T6kvax8z3DB1c\n+fRmesLWtPjvy3Ms6GHfKCiKvD29k70Dhy6i6OvAwJ1jeNuHBi9uyhII3ZbfZY9JI+g0G4uT\n1UgNTgjlZk5GlU56EknK/pxFXnmfBz/9NKByZaQiMwkA8GFY9LwO0ah5SYoXi7NulD21xGQI\nBAKBvAbAwxPNTCs/PKGjpiS9Mu8WitKc/bvbOgXcOPZZ7q3DdaM+HUf2mLwVQRr8qLD08ER9\ntFoi81HFgetrq3kn64SohmeT8QmqdPLytvvwo54YZv1vGBJXlp3gAiMZbAjGxWx8GE59eO3+\nh7LMrttHgvIuWxo5P/EfspCfcG6eiQkoi+u97CbDtS0A4MT/2Xvv+Laq+///fe7QHrYk773j\nOM50dkgCTggQRoCyAx/2aKGsjm/hx2ihjA/0Q6FQCqW0oSWMAikrCYGE7EH2cGIn8d6WrD3v\nOr8/FBxZlmVJsZ00Oc8/8tA99yxdx/JL7/MeDfse2PBhLNMuzh//1vk3Dt3vLIUETyQMCZ5I\nDBI8QRgdiMWOMPwkZ1aUzrq1eMZSXWpxw66PG3b20xnN+784tnXZMC7HMBTNUMKRheojD8l7\n5rLWyYrWyzQHnwwaulpb7Fs3N5/K/IhWyExzBruLRbfgrPHWv2lZN13irDFPCsrLYirUgeVW\nSd0avY/kd9tW/SH4elXzoRi3sLr5kNnv7vImWH6NQCAQCGcgRNgRRpbm/V8MzADXvO/z4V2l\nqcEKAIyrRNF0ner4HfLOi5Bw0pzQ2BBHjreI6Ce/iZgh8n+K3lbnvgdin1P7y3l02tA5RRGn\nZ7tmASAEFO0qZHsn0e68gY+U6z6R9M4eiBAzG3nDWJr84bNVHz03/oNn3j28JfadEwgEAuGM\nhZQUI4wsnC+CQYjzOYZ3FYoOFzqYDgQyV/PGXZi17wJD55pcZ8Ds9FvktKoic/6VE36VpIwj\n+RyjK09ZdMhT9zJn2czb9w7WzdfyIRa5pGnvIXroAFWkkiW9fmXvNe8N0Q/TitbFSNAwjjLa\ne2LPoqbFW/w+lp18jIz2hB9ekd60tbN+yNVPzI0BAKx+z5M7vmQo+pYxM2IcSCAQCISBPPXU\nUzRNP/nkk6dxD8RiRxhZktJLBzbq08uGd5WS0v7VVBH2Fr0byFgjyawYSQFkaejdY3G3coLP\nFejd3vjpH9Ze5+c9cS1BK7N1E/+YNPXv0bv52z9zHYy1gCxbkaZ7vBrJhq5BJ++c26fqAIB2\n56rqrwu12+nmnkin99PK+VqZIsYNhPK/e9aIWEpgIIFAIBCC1NTUHD58+PTugQg7wshSccHP\nWYUmtIVmlZUL4yrwNTSFRcaZs/P7LgX9ESEpmqtZj6vpu7p3EliI0ZXTyuzofbyN73ib3nUf\n/p2v+T0suKJ3Vt002fT5bbrHq9nxGVE7hpskaVdhUOohitEtfEBdeWGwPUeT/P6Ft483DpGi\nZSD2gNfic8c7ikAgnLO4rS37Vz2/5f2f7lv5nNMc60EBYaQhR7GEkUVjzDv/zuV7vvxtb8te\nDNiQVTn5sqf0aSXDvtCVPxlXVGw8uL/T6fB3KnuGNMe12GINMugHYvRT37VuvDBKFywGHLvu\nCr6mDz2ePGsFm1wVpT+dl6zKS6YzdbafrRikJu0ge+G1AJ1YElzr3mJVScYrnw62T07JXXn5\nAw7O5xUCC/7zqiM2rzsaUYmZ+ggEwjlIZ936Tf+8W+T9wcu6Le/Ouv61nMpLTmXO4DnmnDlz\n3nnnHbfbPXfu3IceeohhogmVL7744r333vP7/RdffHFZWdnzzz+/du3avqmqqqr++c9/tra2\nrlu3bmD8+/bt2x977LGVK1cqFAoAuO666xBCH374IQD4/f5LLrnk+eefnz59OgBs2rTp3Xff\nbW9vz87Ovuuuu2bOnBm64dBVQucPBAIPPvigKIqvvfaa0+n805/+dODAAUmS8vPzb7/99smT\nJ5/Ks4oCEXaEEceYM3HhT1eIvA9jiZGNYJz/+IkZ4ydmAMB3dYf/vWeIzvIY3OAiD0ytZpOn\n8radsXQWfR22HTemXHgQUfIhpj2/WHHJGP/KOCp99eVfwSLX+/kz3sNrM+5bzhhygo16mVIv\nU2666tHLv36zyTl0+Ei2Rq9iSOIPAoEwNCLv3/7xI32qDgAkntvxyS/TimbJVEkJT1tTU7Nt\n27adO3c++OCDHo/ngQce6Orqevnllwfrv3z58qVLl953331VVVVff/31a6+9Vl9f3zfVli1b\nxo4d++ijjwIATUfweCktLd2wYcOWLVuqq6vb2to+/fRTAGhvb8/Kytq8efOGDRtKS0sB4Isv\nvrjyyitvuumma6655vvvvz/vvPO+/vrrRYsWRV/FarUuWbKE5/kvv/xSqVRWVVWlpaUtXbqU\noqjDhw/v3buXCDvCfz00m6CQSoBs+UyEWYz4KH0mZi9KeP6kqe9Y1s7EYkwZhkV3Pd+7TZYy\nf+hp//dSd4nJ/doWiCW7JAb10Vu9Je8JSbUAgAF8x7a2v3pl7hNbEHNSRBoUmj/Nvf6yr94Y\ncr5ml+3W75Y9PLF6gmmIs2YCgXCO09u6z+/uX70GAe939TT9kD022oHGkCCEPvnkE7lcDgAd\nHR1PPfVUFGH3+OOP33///a+99hoA3HbbbRdccEGfsAMAnue/+OKLKFkDDQbDpEmT1q5dW11d\nvXbt2qqqKozx2rVrb7nllrVr106ePDk5ORkAHnnkkaVLly5btgwA7rrrLlEUH3rooSNHjkRZ\npamp6eKLLx4zZszy5cuVSmVXV9fhw4fff//9iRMnnsrDiRHiY0c4CzE3qxQtS6L3+WTv8zWd\n6xObn9GNMy3YJU+/EGJLpxxrfjsKae6Zmfz6lTGdxiIATCkbrkWiHH48vw007+3+252S3wUA\nkt8temwAMCklZ0nh+FjW/671yOIvX//HkW0x7ZZAIJyriEIgcnuIDS8xpkyZElR1AFBYWGg2\nmwOByGv19PQ0NTUtWXLyo/6KK64I7VBVVTVkLuigpAOA7777rrq6OuwSACwWS319/TXXXNM3\n5Prrr6+trXU4HIOtUldXN2PGjAULFnz66adKpRIAUlNTKysrr7vuuueee27Lli2CMLKluomw\nI5yFcJwg656vPfQbeeeFtLsQCRrACCRZqP+a1dv65qZ72uxHEluC0ZYZ5qzST3oT0UPn32e0\n5bHPLD+/SHn9pBg7I0GlaLgWSSePUJ3bljf+orj5XzFXygAAIABJREFUqarj9yXV/8zU9Fil\n5+Cal2Zfc//48xkUchgxuE3wqR1fPrdrdYOTFJMlEAiRSc6soOgIJ37G7AmnOHNQCQUJnmzy\nfOSzl6C00mpPZgMNfQ0ASUlDHwovWLBg9+7ddrt93bp1CxYsWLBgwdq1a2022549exYsWAAA\ndrsdAHQ6Xd8QvV7f1x5xFZ7nfT6fWq2mqBMSi6KorVu33n///Vu3bl28eHFOTs6aNWuG3FvC\nEGFHOA2IvL/z6IaGXR9bmnfHdOwYJ5mZOgCgvNmK1is0hx/V7XlRv/uPrKM8TM7won9Vzeun\nspCq8C7dpD9F76PMW8ro4hB2AKB7ZC6Vqhm6HwAAsLYKzf5HKX9qX4votgSa9wYfLNdxuOO1\nK1HL3gcnXMBQIb/vgxsFRSz9+eD6Bf/543u12+PaNoFAOEdQaEzjFoSX4S6fd6/GmDdqe8jL\ny6Npuu9IFABCX8fInDlzGIb585//bLPZZs2aNXv2bKvV+uabb7IsO3v27OAqDMMcOnQy2O7A\ngQMymSw7e1CXlXHjxn333Xdvv/32Aw880Fe1VaPRPPDAA1999VVPT8/8+fMffvjheLcaO8TH\njjDaWFr2bF1+v8fWGhQXKQVTs8qr2+s28wFPWsGUsfPuU2jDa97Hy5ixaaVlKUfrzCeDTCVG\nUvQM7NntajjFtXDUY1ZGU6af9Od450RqGVOeyvUMlXzkx3eHeL3q+PXucX+KaIjDvN/y2ZPc\n3f/yi9GcDsPgROGJHV8U6EznZRbHsXUCgXBuUHH+z5S6tKNb/+62NKkNOSUzbi6adsNobkAm\nk11//fXPP//8woULMzIyDhw48O6778Y7iVKpnDVr1ksvvTRnzpzgEfDs2bNfeuml2bNnB22H\nLMvecsstL7744qJFi4qKiurq6l566aU777wzYjRGH1OnTl2/fv3ChQu9Xu9f//rXrq6ugwcP\nLly4kKIomqYZhhnResHEYkcYVXi/a8v7P/XY2vpMRubGnftWvmCu32xv21u36Z2V/7fAY2s/\nxVUQgqW3TpkztwBRIYYpPoINTCs3DWyMCyrqDIK7TgqYE5k3IA7dJ+TNUd7MYG3ciHDth5Pl\nKhR7JhUAABAl6ZZv/37MHkEQEwiEcx2ECquuuejnK3/yu8MXP/RN8YyliBo61/rw8vLLL6tU\nqtzc3Ly8vMWLF996660sy8Y7SXV1td1uDx68AsCCBQvsdnvQwS7ISy+9VFpaWlpampWVVV5e\nPmHChOeee27IacePH79hw4Zvvvlm6dKlvb29N9xwg0ajKSkpSUlJ2bp165//HPcX/tghFjvC\nqNJRu85r74jSIeC17f78ibm3xv3FKwyFgpk5O2/zxsa+Frllpld3LLybdZbHzak1iaf5kGcs\npmSGKOERrponBHc9Flxs8mTNmMcZbYRSHANhx6Zx25vj2gniNBDJKgkAtNqgYeUamcLFxVpJ\nNggviT9b/0GB3lRn6zIqNJcWVN5cNqPfkS6BQCAME7/97W9RSETalClTvv/++1CvuzDS09N3\n7dp16NChQCAwfvz4559/vu+ENGyqKNxzzz2zZ8+urKwMXt55553Tp08fN25cXweDwfDtt982\nNTW1t7fn5OTk5uYOtuGwljFjxuzcubOuri4jI6O3t7e+vt5sNhuNxuLiYmokP0URHgEPp3MZ\nq9VqMBhO9y7OXOo2vbPnq99F70OzymufrTv1tew233O/Wxva4sv9lEs/mUBS3rlQ0bpElt6U\nXe7RqPRzx12Um56fwEKBnnW2LZdjcWjNhGiVqXoboxs3ZE/J4e9d8g+xe4jCFaFgOuCpeE1S\nREhZZ7r2hR0VF9+17l+xzzYYVxRMeGP+qB64jBBer9fr9ep0uoFpSwnR8fv9kiSpVEOHDRFC\n4Xne4XAolcoRPYY7pzCbzW63u6CgAAAOHTpUXV196623vvjii6d7X6cZYrEjjCrqH9PnRmV4\nvmwkJSuzsvTt7Y6+FmXL1Yre2Zz6qKg9hlmvpOx2Vf5OUnabPQAe2Lz2D3NSH1m68D4AkLCw\nuf6j2u4tgsQXGiedX3qrnBn0z5g89YKUSxqce3/ub/sk+uax6HXsecA4//shN0/pFYb3b3S9\nspHb0iQ5/SAN/UyQKFfV3u0e/zJQ/XzpNFVXGS56pLFm85AzxMLnjfuvLZkyLysmuyOBQCCc\nIu3t7TfddNPA9jvuuKOysnL69Onp6ekURbW0tCxduvSpp56Ka4abb755+Hd8uiEWu2GGWOyi\nIwncmjeusHXUROmTUTZ//u3vDcty3V2uN1/f5vVwfS2sXLIVvySqWyL2R5i9d+qH44snvfr9\n0trurX3tqdqCxxZ9qWS1EUf1Eej6xrbt6iFMd4jOuMoHKA5nFPOiv4qt9lh7U6Ik6+XStvNp\n2xXFM7Qzb5BnlClKz/uwYf9vNn2c7bfZWZWdPSVby88nXPCryaeUg/RMgFjsEoZY7BKDWOwI\nowOx2BFGFYqRzbn5rR3//mVPwzYAQIhSaFJ8ru6+DqxCM+WKIc5qYyctXfurx87fsa25u8sl\nlzF797Q7jF8MpuoAACN+zYHlNlwTquoAoMfVuGL/izdWPRt9OXn6oqSqd2w7Inw1DFlDFNzH\nGW1ZHG+DiifoQaIpf6qi6XIqkOyDr3zHtwIAIGpa7vgv2w7LRQ4ADuiz/6/0whaV8cctRS1Q\nO+Aug4iPHYFAIJyhEGFHGG00htzqez7y2Np9zi5dShEjU9Vt+VtrzXecz5lWMKXiggdUSVnD\nuJxKxZ5fXQwAH3+w3+8XeP3h6P2buTVMa4Sw3FjLVMRginMd/E3yrM9img0AAOQz87zNttj7\nAwAgkHWdx6fuEBUWAAAsic37+gqNjXe0PX/ws+fOf/iIzythaYhg2QF3z8sqiW8zBAKBQBgt\niLAjnB7UyVnq5BMCrnzefVmTbuI4zmg0hkYYeR2dTXv/47V3aI15+VOulquST2XF9jYHAACK\nmkYEg4BcTdYDA++IUkxFYGjN0KInYP7eue9BJmmiMvdGRMmH7K95eG5gS1O/01gKgzS0GY/y\nZp4QdgPI8Nsf4e134Lg/AW4rnzXBmPXB0Z2HrZ3JctWivLEVhsx4JyEQCATCCEGEHeHMwm1t\nadj5kcfWLmGho+ZbgfcBAAY4tO61+be/Z8xJvIIyy1IAQLsKoxzFBq1TAcE78E6RqSqmVZIm\nssmTedueKH0w7/Qcfx0AXAd+qZ/yliLr6uhzUlq56bP/8Szbxe9tB5aWz8xzb9kgbRw60R3t\nLOQNEURqEK7nGCTHVxKDoagur3PSh793/Jg25f/2ffd41SX3Vc6Nax4CgUAgjBBE2BHOINqP\nrN26/KcDy0gjAM5r37r8/sW/WB+xQGEsjB2X3tJsV3RcIhj2S7IhTjYRojCW+i5VMv3Vkx6L\ncSF12S/t22NKCCJxNtu2a9UlP9dNeGWI/ahlmp/OCr72r6nDW2OKeZL1zECC2le8PGKsbr0Y\nd+CUIEmrmg+FNf5+18rZmUXjjcN5gE4gEAiExCBO0IQzBYHz/PDJLwaquj7c1hZb+8GE5593\nflFhkREJak3Nr2jPEFlXMJaSlOmZ+rJUbf7Mgp/8fxetNKhiPXBUZFwavRxFGJ5jrwW6Yy0I\nLdl9jie+wYI0dFcAAGCtlTJzBFsjlimXq4ftCHVVU7jaIxAIBMJpgVjsCGcKjvb9AU9v9PhM\nPuBJeH6aRvf8bMbe3e2NDdatIhqqDivYfV1PXvKNWpYU70KIVinzbvIcfTX2If6Oz+VpMSUQ\n4Xa2Ylcgrv3Imy8XlWZR03Ryh6zqu5m3d8Cg+dzjxc3HtyUCgUAgjBDEYkc4UxB5f1RVBwjR\nHmuL29qa8BIIoclV2VdfO14RoWxsBOq6t0o4ppiJMOTpl8bVH0fy6ovcMxD3fpDEqg/fq2g5\nuSXMe+ds+0eRZ9iKwFYYSfwEgUAgnBEQYUc4U9ClD+HIj7H0w2f/7+uX5+/9+venuFZu8tBF\nvQDgrc33PbPqki5nfbzzy1PPV2RE0XYDndsQlk4avbDoF9zHsMQN6Abs2LR4NxNE1jWHtZ18\n1wrO/fau96b1NkYZEiOFOtPFuRWnPg+BQCAQTh0i7AhnCgpdxtj594U1MvLw7PaSyNdufKv+\nh+WnstZVE/+fgu2f/F2K7JbQ4ah7e8tPBYmPeHdwUNL0f6lLH6HkJgCE2DALYbhh0te8rOsz\ntW379ZK3zbH7nq7/6Myrx3T9R+fYez8W+h0aM4VG1U2T49zMCVjLpP6bwM8e/kzHD13iNjoN\nTkvFB7+97Ms3nNyg/pEEAoFAGB3op59++nTv4azC5/MplcPmunTuEAgERFHMH1et0Bg9tlYh\n4NGlFFZe+Oicm/7isjY7umrD+nsdncUzlia8nFqWNCFrgc3b4QpYaaxElnGq+tuQJMcKK6bD\n1YnLbylLm2HS5Ma1BKLk8rQLNWW/1Iz5tffYH7E4tOgRnIe99W/wtl0nTHpY4m27RG+rIuvK\n0G7yWflIpxCPmrE3PrmJeL2sdxJjq0SiUtK0A2AKYxUjqzEVT08vmJFeWGPtiGvCULq8zmW1\n2/J1ptKkBG2KowzP8zzPy+Vymo6jvBsBAARBwBizLHu6N/JfhiRJgUCAZVlSxY4wopBascMM\nqRWbGE6nc2CC4iDfv3Nj17HwAvYKjWnJ4z901G1w9zYpdemZY+YzsgTLL2IMe3e3b9rQ0Nnh\n5LSHPGV/Hthn6bQXVKzO7utK1RZUZMylUBxRR1j0d604pdKQ2vIn5VlXsEn9c/gJUu8N7/M1\nXYnNKRgOeYv/BQBsenHBC3UAIGJped3OV/ev7fI6T2W3D02s/sWkhacyw+hAasUmDKkVmxik\nVixhdCDCbpghwi4xogi7Hz79df0PH4Q1GrIqRSHg6K4LHmsq9elzbnrTlDflVPbw/DPrer1N\nrvG/HXhLIze4A9bg66ykMT+b+65RHUfatu6vsiV/56nsDQBUBXfop7wVeowr2X2uF773f3k4\n1t/i/lVffaXL+KQjjCG38P9Oetpt6jh+wzfvnOJWN179i0JdHAlfTgtE2CUMEXaJQYQdYXQg\nR7HDDDmKTYzgUaxKpRoo7DSG3Ibdn+D+Fb1kSp2j51ifThEC7q5jm4qmXU8zCf6R5jhx0/oG\n3quQVO2Ssjv0FkPL/PxJRzeX39JsPTC78NrYJ0eICXR/k9jG+uDteymZUWaYdnJaBatYUIKv\nHgMNdqnVEcM++l1hmUvQH8MBt+uHf2ORU+RXIYrKUidt7DjW6Y1htsExKbVWv+ehTR8/u/Pr\nT47v8fDcRFM2TZ1ZHr3kKDZhyFFsYpCjWMLoQITdMEOEXWJEEXYKjSkpY6y58Qc+4AYAVqHN\nn3x119GNYd14v8uQValPS6Q+Pcaw7G87OzqcAMA4x0jyXkl5wsBmUOR7+N4wW5fV2zEt/wqN\nPNbatTLjNBD9XO/WBPYWiuTrVBXdE9bIYUF9+TjN5eO8nx2EmBMXAwCWuUV1K6a9osvsPfiN\nWN+Dm2nu0K5qveTtruMCbiuriZp/ZlB2djd93ri/2+vkJNEa8G7uPN7itl2SF1Mk8qhBhF3C\nEGGXGETYEUYHkqCY8F9AVnl1RulmZ88xV29zzdrXGgaczAbxe3oTm/9wTVddrTn4Gglq1fE7\nJNlPRFWLsnxTr78GIEJ+PZe/N01bGPMKSFv5vCz1AtvWq7AYa8q6gUicebBbrlc2ga9/LAWO\nnhYQGGuFxlrBpe7w56xUNl6Nfyj0wCEAYOS99xatEzUth/TZz5Yv7pHrYtpcyHK8FF7HdkX9\n3pvLpk9Ly49pKgKBQCAkypl1OEIgDAZFs7rU0pq1r9k6agbrQzPymnWv7/nytw27PpaECEng\nBqNtwDkmxem51I29YuS1EKJStQWxzx9EnrYw9ZIGWpUf78CTu1LleY790bnvIc+xV6WAJfRW\nhCiK2Ixtsp7p6tq7WOv4k6sEjKq6OxSNV045mvfUgbVUpDqzERhquf2WtpjmIRAIBMIpMLIW\nO7PZvH379oaGhp6enilTplx11VXDOLnFYlmxYkV7e7tOp6uurp4wYcIoLEo4jZibfoii6rSm\ngp2f/UYST1itDn//RvW9/1ZqU2OZmWHCv+GIcrOQdHiw/vOKl+oUiQQHUPKUlEWHPMf/FOj4\nkrPuANzPsoVoVXR7nuA46OzdFnztPvJM8qz/yExzgpfYnXhRL9qTHdaCRLnMPB0AprV7L0nu\n/Ko47rpqA1HQ5OSOQCAQRpyRtdjV1NSsX7+eYZi2trb29vYEZti2bdt77703sN1isTz88MN1\ndXVVVVUMwzz55JObNm0arkUJZyZe+6CBpRmlc7329j5VBwAuS+POT38d48xjxv6o/5DEmbb7\n8pcH8j4bpC+V5Fx0Yfl9K2v+9PdtD6/Y/2KH42jo7V5P2+cHXv77toe/OPiHXk8EGxWilZqy\nXxnP32Scu4bRlgUbaWWmbtyzUVQdpUinZMmhyYolzmb/YWkwPR6/vkFyjEhyYCSoHv8y76eb\nlOW9/pubt/2m9uu7GjbmeyxDj+yPjGLmZSXi/kggEAiEuBhZi938+fPnz58PAEePHh14t62t\nbeXKlR0dHUlJSdOnT585c+bAPl1dXbW14clpAeCTTz4BgGeffVahUACAJEnvvvvunDlzEELR\nFyX896JOjlyQdNEDX3fWfd85IJyio/Z7gfPEkt8uK0t/4cVl36w+5Cl/VdREq7KlOnp3dnb+\ns6sXebkTyd6+q33npqnPzSq8BgAOda7/y6Z7+B9zEa858va9c/4yLvP8iFPJUuanLDoseltB\n4mh1gevQY4MtSslTk6r+bt18cVi76G3lrdtBMSXwzQj+V2cF5vbtzG0/sL5is5BUBwA/adv1\nStmFq9PiCIZ4fOrFuVqSBohAIBBGnNPmY3fw4MFHHnmE47g5c+ZkZma+8cYbH3wQ2SM+Irt3\n754xY0ZQ1QHA3Llze3t7GxuHofAl4YwlJX+qMXdSWGNWebUhuzIYMBsGxpIQiDVSYcGFJZVL\nDkVXdZTfJGmaj6pf6FN1ACBI3Ie7n7R5OwKC9x/bH+FDKkzwov8f2x/1854oc9KqHFpTBIiS\n+L45w33apECP59grEYcHR0mexM9hY/Wgk1hlw7VIlAMAi8WfH/suNRCSxzjqJONNmdeVVCW+\nQwKBQCDEzGmLin3zzTeXLFly4403Bi8LCwt///vfL1myJMZcIT09PSkpKX2XqampANDd3V1Y\nGHug4gk8Ho8gCEP3iw1JkhyOU8oBdm4iiiIAOJ1D1DwYf9mL+7/4jbV1d/AyrWRe+aKnHA6H\nTBeh3pdCk+IXmUDMP47jjvVR7iJRLiks/sxVA0VMQPDubvw2SZHu8oeH5boCvQeaN5SlzB5y\ndUFW3LfUwLuB7jURR/mpPInnpTx9hHsyGjgRADASERJAkkdeOJYYCwyAAAlq2p0n6I8CgELk\np9iaV6VXxjLJAUvH7E/+982Z11QkZ8Sw2CgR/C/n9Xp9vlOtlnuuIUkSxpjn4y2gfK4TTCQe\nCASG8S9OEIZhSNJjQh+nR9hZLJa2trbt27cfOnQo2CIIgiiKbW1tJSUlW7ZsWb16dbDdbDY7\nHI4nnngieFlUVHTrrbeKoogxDk0FJJfLg5MksBlBEIb3E4p83iXMkI+OUZmmXPe2x9rkc7Sr\nDfnKpOzgKFPJAn3GckfnodDOJfMfEgQRIDz1xqCrRyrnmq2rmJt399bGfze5w8uaheJwOuSU\nJuItH+eJ6b9E6nWo6TXsb41tswAAVNYdApMFkgTXlKNVx7A5xDQoo6lnprk/ugc8gijzaA7f\nnVBCuh8JDsZA+VJA2wAUD4CmWptOCruh6PV7HtnxnxXn38ZSZ1bSuGH/E3vuIElxJE0k9CFJ\nEnl0hBHl9Ai74Ffk6urqMANbZmYmAGRkZMyYMSPYsn///ubm5r5Lk8kEADRNK5XKUOtO8LVO\nF1vCrf4kNmowbDZbcnKseWsJfbhcLo7jDAbDwATFAzGaTADhR3vn37HswOoXWg+u4jmP1lgw\nbsGDeROXxLWHgpQJ1pbwcIcxGTPnjF38cc0vo4/d8jWlV4oQyV5ckTvTqDZGHy56Gmzbr41N\n1SEATCnSVIU/U5f9AlEyt9stz9FRy653vbSe29qMBZEdk6p5dJ7Pv0bC7XLz5SrbOMCnpOv6\nVla0XKZovVTQ1/oKPsnzxpc1sM1rbxLdM1LitqmPEEFbnVarJdli4yUQCEiSRDKxxwvP806n\nU6lUkmpshBHl9Ai7lJQUmqYDgUBlZYRv/IWFhX2Cj+M4t9u9ePHisD7FxcV1dXV9l7W1tQih\n/Pz8BDYTi5I4vROeOyCEEn56Sm3K9Gv+MP2aP4i8j2YT+ZNz5YRf13SuD3WJS1KmX1zxM17k\nBClaVjzWMo3x5Hs8oFAs8mf2Kx22aOx9Jk14MpEwsOCxblwoeptj2ybWjH1SO/ap0CaEEJNv\nSH7jKhAxFiUkowHA85VdcXwp48qLbdqYwYixl6uO30xP/iTeoQ7ef+b8dgR3cir/5c5xyHOL\nl74nRh4dYUQ5PcJOoVAsXLjws88+KysrC+afc7vdW7ZsWbRoUYwzLFy48JVXXtmyZcvs2bN7\nenpWrFgxa9YsvT6SpxHhHGMwVWdrP9Tbtp9hlamF01VJWQM7pGjyfr1wxWf7Xqi37KIQU5Q8\nc6z8trVfd3Jsl5zWBcQI/n9Iohn7BHnbiS8e8vZL9ap0KndLj6tZQ2eMT7kyR1/6/dF/pOuK\nx6TNQihyrJKvZXnMqg4AwHP0D5qyXyNawdt287ZdgsBI6RcAWwAAQCP0Y4EsujuHccU+a3zQ\nrnyVpfxX23t8WK9l6xlVfb065avMCY6okrrH6+JEQUYzVr9nc2e91e8Zk5wmYnzU3i2nGIQQ\nJwmVxqzJKRE8JgkEAoEQCyjozjlCNDc3//rXvwYAn89H07RMJqNp+v333wcAjuPeeeedb7/9\nVqVSMQzjcDgqKyufeeaZsBlWrFixc+fO5557buDky5YtW7FihVqtdrvdY8eOfeyxx7RabfRF\nRwGr1WowkLQOceN0OjmOMxqNI/FdFkvi9n8/2rTnRGo6mlVMvOSx0lm3Rhmyfl39mpW1goj9\nOV9y6d9iFM1RD0lyRdM1MstMAExRlMGospg9gvaYt/jvmD0RupFnGH//vHd1ipTwwVgyfztR\ncA6aeDkihnnrfI3v+FqWn9gArdSN/19V0U9D+3je2e76v01xTRsfCPed8Ar6Om/JvwRaerb8\n0o0ppVEG5WmNt4+d9X97v3Nwg4YsXJg79i/zb5TRI/u10+v1er1enU5HjmLjxe/3S5JEzhPj\nhed5h8OhVCpJoANhRBlZYef3+48dO9ZvPYTGjTuZ/srr9ba1tdE0nZqaGpRlYVgsFqfTOVis\nq9Pp7Ojo0Ov1GRkno+2GXHREIcIuMUZU2B1e/+f9q14Ia1xw32cp+ZFzcBw7avnrm9sBgDfs\n8Rb/LZYlEGbVNb+gvdk0jUQRY8bjqnwWs/2MfBUZ834+PzzbtufYq879j8T6Tn6EVuUNNPIZ\nz98sM55MBun79KDjidURBg9VQzYxuLRtvrzPBYq+Zdqd3VHLy1IISUN97Nxdcd6T08IdMIYX\nIuwShgi7xCDCjjA6jOx3YoVCEdGLrg+VSlVaGu37vclkCgZMRESn0w0MfRhyUcK5RsMPHw5s\nbNz18WDCbueOE0EMfMq2GJfAiOdNO+iWbFHEAMAn1YSpOgCo6dxg9bT7bBqH3W80qdLStQDg\nbXw3xiVCiXh06z3+mhSwUKwWSwJggZ1VgmgKiwPi70bGvUdmrhL0tSzFX9RxfFnBZABI93nn\nNXnlvGxTjqox+eRHjYTxkOLyn3U7riiaUGnIoog3EoFAIMTDactjRyCMGv5IJbD87sh1sVyu\nwNFac/C1OECcRQGzrowMXWenEwBgEO+2d/6+zlJ/Imi6tCzlhqWTpEBP7EtEx9f6sa/145PX\nFEstPh++uGC45h8CiVUdvQ0A7m7guhdyU1w1V2wvp7gcAHgASdvGdD+0WCb1ibSh1JpP4BZ/\n8fp4Y9br828o1CVSlpdAIBDOTU5b5QkCYdTQGPMHNmpNBRE7f/ivvV7viRhY2p8asU9EKH8q\nz59wxUOBgb50AJg2N5/MEny0zvzR8n2Mpji8JwBiIrglxI3ES1VrYOqOBIcn6qMh52RPr1Yv\n2TKR4k5Y0xGmZh3J+MWmqBnjIi13oLf97nX/4kSSao5AIBBihQg7wtnPuAUPhbXIlLrS2bcP\n7Nnd5Tp29KQlT955IcJsLEtQvJbtOc/yY4pg1l5Bu/vnGUEg756HhH5uSbVHesSMR8OmQoxW\nN/GPsSwaC3hmtLzK0TiFI1BGRMHiY6FcsS8pgeVqbV2bOo4nvhUCgUA4x6Cffvrp072Hswqf\nz0fydiZAIBAQRVGlUiUQPOF1dNZt/lvj7n/bOw+rk3Nlyn5ul5zP0XVsk8j5Al4rlgQA0KeV\nzrrh9aSMMQOnam9z7t3d3ndJ8XrKmy1q6jHjBwAkKoGKZD3CNIgyUX8EMz7al4UwA0CxjrGi\noltSmIMdCugr+CMXAQ7/KuWHnOJxU8C1A4teAGC0ZcnT/6XIWkIhNmD+fqi3PvSzQioJtiyA\ngZ52pwamRKA4hIO+HDGFYzAiozStpJHUqoovumh6WsEEU/aB3vZ3j2x978i25cd2buw43uTs\nLU1OVdAxye6I8DzP87xcLqfpM6sYxpmPIAgYY5ZN/OGfm0iSFAgEWJYl8TqEEWVko2LPQUhU\nbGIkHBXbfXzLxmV3CJw3eEmzyjlL38wcc8KxzNF9bO1b1wY8J2okUIxs3IKHK87/2aCzdbn+\n8OKGAc34qlsyM7P0te27Pq39dfT9UHyS+vAvqMAJRzrMeCSZnQqkaJQajztylmOGoW64qbK8\nyIcoGa3ODzZKnLX7y3TAsdZDGwxGO8Y4cYdqmmieAAAgAElEQVTl2vek0IJjpwPMuF2TnwWA\ndaljfj/msthrYby38LYGh/npH74Kazco1J9cfHdpUlpi+yFRsQlDomITg0TFEkYHchRL+C9G\n5P3bPnywT9UBgMj7tn/8CO93By+3f/RQn6oDAEngata+6uptGmzCtHRt6Zhw97ic3ORpEybn\nphbPq7w8VRvZM+/kEqzdl/+vvkskqGlvllal8bgGrV0hCNK/P6rxo7w+VQcAlMygyrs5+lqx\noC55yPG7NadB1VGBsAYu40RSvQt6ahf1HBowIDLlyRlpKu3zuyPkbbH6PQ9s+OhU9kggEAhn\nH0TYEf6L6W3d63OFR5UGPFZz0w8A4LV3WNsPhrnli7y/szbaEef1N04sLTup7fILDEv/ZzJF\nIQCQM6p75/wlO6k8+q4EXR0OkTXFpUa/T4h+Vun3C8ePhkfp6ia+qsi+JvpaUUCUTFP+uDLn\n9sDGhjiG4QEvEsKfuwrL7D/OJAXStgTSN/bdnWM+GsskE1Ny3r7gpg3txwKDxE/UWDtaXNZT\n2iiBQCCcXZB0J4T/YoRBqhcE2wU+eDdcUgW8Vs7nkCkjF6DTaOV33ju9p9ttsXgMBlVaujb0\ncDgraczjF33dZj/y1uZ7Le7WyNtC+Jqby1SUiaKR0ahKSlY+8fjnmA0ApgDTmPYjXodwuF+X\nw2/x83pB4miKVbJaAECMJnnGh6Lnud51s8V4sqIw6kLtpD/SiixakY69ThAjaDSsDCBf//iG\noKdcxIwkA53ohnKrk+QOz9i/UN4MSVSBpkGS2wAAAQWcFrMuhcQP+S4uLai8rrjKLwjdXkeU\nbh4+silUkESzz52m0pFMeAQC4ZyC+NgNM8THLjES87HzOjq/eGGWJIlhYy791UatMV8S+RXP\nTOJ8kXPRJWWUT7nid6kF0+Pdqijidd8e+6r+ab9hy2B9EDDzS26uLvz5p59vOsi9IWiPAgr5\nRcOUrLdK0XI1EjQAwCcf8Od8diLMAgAA8o0Tb5jy23zjxOClbetV/o7P+68QTVjRqjzR1wo4\nGDCBUEcpfLIYLP1Tt9AAp+q/FysYsGA4gBleZpkIEgMUf6Co6aeXGvzD8b2SAnRd2dTnZlzB\nUie0sj3gfWbnyk/r9wqSqGZld1ec9+CECxgqXEkTH7uEIT52iUF87AijA4mKHWZIVGxiJBYV\nyyq0ksiZG384cY0BEJTNuSN/4hIAQBStUBvbD38bcazfbWk98HXOuIvk6viE+Der6tZ+e4x2\nZXMp24EKsTz101pSk3Xf7gMHW5hPRFVbuAZDWFS1i5oWmWW6oK33lr2OGW/ofbuva0/ryql5\nV6hkOgBgkyb6mpeBFGqaGvwpIQbztn4nqdpeGHME7Z0CQkgY42Bf6Eag4BgCRPvSaU/miaBg\nTKdZjQYPtbF4aLvdkGCAQ73tVr9nQU45AGDAd67719dNB4NVy3hJ3N7VyEnieZklYQNJVGzC\nkKjYxCBRsYTRgQi7YYYIu8RION1JauEMucbo7m3m/S6tMbei+oFx1T9H6ITzaHLWOF1qkcvS\nxPlsFCMLpjvpQxJ5zmfPqbwk9uV8Xn7Zu7swBiQpGft4SWmWZNYT1rgBGw/IWjE9aKl7Sd7L\negvE3DUCG6EGBi8GBImrzLwAACi5SZ5+kehplAJmRDGIkgMWEK0EHFEYRcpsovBTqiSoyxn6\nHY7WueWYHmZVRcCpGJ4Tg4PWjlvGzFAxsu1djS/tWRN2d4+59X/GzFAy/f6aEmGXMETYJQYR\ndoTRgfjYEf67QRRdOuvW0lm3DtYhb8LleRMuB4CVryx0dNX1u4fB2RNf8luLxSNJJ7QI7UtX\n194fSF/rz/0s3m0Hufg6wzeHLX5/5LudjmN9r9mkSYbz+kWGip6GnlXhVqgo0JU+4fOhu40m\nBRamNWnQYOG4wBg3OCwmheaYPYIzooilBqfFoCDnXwQC4eyHRMUSzhUiREsgkKmilkMYgEoV\n/lU7rJhEXGhkySpZ5BgOANDIk6OMRWxSXOY1ijXGsbNR8bx1KIYzbXKSXNX372B3CQQC4ayH\nWOwIZy1YEmydR/wusz6tVJ2cXTD5qpPeeD9SMPnquOY0mlR5+cnNTba+Frm9TO5UUuBzq4Dr\n//uEBDVmBk0gp2C1habJE7MWrnbWR+wwIWvhka7NDC3LTa6QM/2sTRJnE2z7WH0F7+iXEA5R\nSgwSSOE55ABAMeY8Ickr2aPWXVX4IKMDAKAjCwKKaD1PGacqcHPHd4/WoSO69L+PLXQz8jHd\nNADUpokqMTC/2a0UxDajxysXJcASRR9Xp9pk/cSZ1o/G9DAYoDZNQFqFR/BjwFNScnUyhZPr\nZwUtN2QU68PTE7p4/x5Li9qnnpSWp5UpOm1dtYfWeTxWTf7EyvxJRmLeIxAI/52QqNhhhkTF\nJkbClScGw9ZRs+3DBx3dJ/Kl5U+6ctpVL+z+4qn6nR/29SmZcXPVlb+Pd2Zrr/evf9nRa/EA\ngI46lMe8z4ADACQEbanQmI4G1FRAg1nAKIrBkhDxXknKtCbrAV70A4BGbrih6pmq3EuDtzzH\nX3cd/E2wBFlc0HQB/scV+Fh68JLJS8YMLdb/6OE3fRtesBrkAQAAvwK+WYx2T408UWIBFhQH\nUn97548PBlO8iChGpAGAp0UGiyjYk+L92Wu49E0AwCP6g9zp/8ifjRBgDNfsVdy/UaXmEAC4\n5fiP87z/meBPUWmcgUBAjOB6eH522R/Pu7ZPrr1Xu/33O1d6BA4AtKz86oBt8Y5lRs4NACKi\nPsmZpr3ytw9NWhD/mzwnIFGxiUGiYgmjAxF2wwwRdokxvMKO97tW/XGRx9YW2hiUcb0te3sa\ndwBAWtEsQ/b4xOYXBKnmYFdn0xHHrp9KQmh4BD6WJWtPCbeKUb5Uxl0CIEgyp6A/EmVmvSLl\nwvJ7BMyv2PdiaDtLy3+18LPc5HGBrlXWzZcmtm0AoOWZav/7uIdhSoyKC0oAIf/3x/n97Zxr\nPTfumbDO6B93Q0NhwmsNAAfSN8u7Zw8smBsdb/E/BUNN8PXLZRetTK+c0ci+/okurNt91zl3\n5kYLs63OGbNswa0AsKH92E1r/tbXnuWzvb37PaXYz9vvtZIF8697/ifFk+Pa6jkCEXaJQYQd\nYXQgPnaEs5DWQ6vDVB0A1O/8gPe7jbmTyufdWz7v3oRVHQAwDDVhUmYau7W/qgMAlG2OoC0k\nZY+se66y8RYkDnG+6fCbi1Kq9rSsCmvnxcD3R5cBgOfoKwlvGwDEQAeaXKP52SzFhWXAUEAj\nxYIS7aPzpbLwSFIAgJmbYpo01u+GiLWXx6vqAEDedV7f65+07gKAm3ZHCDy/YfcQz3Zta229\nwwwA7xzeHNp+aef+MFUHANe07nq7Jra3TyAQCGcSRNgRzkK89vaBjZIo+Fzdw7mKo2Ngo2IQ\nm5EktwKA9GOVrShYPR02b4SZrZ52ABB94YI1XkRvS4Tt4a6BjVgfreTDSWK2sVK8JtauoaO4\nkwEu6QEHAKS5InxwpTuGzlrS7rEDQIen308hNeAa2DM14GxzD/3DIhAIhDMNIuwIZyGqpMyB\njYhilNq0YVxFqc8Y2OgfJEEVFTAAAMUNGgPbh0GdmayKMLNBnQUAtDIrrk0OhFblRtgeivBk\nkGPo3caFxA4aShJtlPykwOqW6wCgRxshnLZLP3QljUx1EgBkqPq9L7NMO7Bnj1ybpY4vYppA\nIBDOBIiwI5yFZFdcpEoKF0BFU69lFYlYjAajePpNNBt+JtihNQ3sSTtLaG8WAMi650efM88w\nPt8wobrsjrB2lpbPL7kZAFQlDya8YQCgFBmK7GsGtmsm/XJgI942Z+gZ4/HR9ed+BVTUsNxI\ncOknT04/zZ4CAMunREj999HkQfIB/sj8rNJgbOydFf3e19eZ4/10eK7dT7Or7qyYHe9WCQQC\n4bRDKk8MM6TyRGIkXHkiIjQrTy2YZm7cEfCeyEuSO/7SqVc9Rw34+30qyNUGfVppT/02gfMC\nAEUzPs3iNvtPBE0jDjlypd0Fqvo7kKigKHTe9CnTJo07Zt4hSBwAUIjGIcooU1/6s3l/U8l0\n2UnlCFENlt0SFgFALUu6adrzFRnzAIDRllKsnjNvBDxQIQ0RrcpoipOmL2e0ZQNvsaZy3AG8\neztQIgBAQI5WXY5qKod+CqELRlsf+wr/LRgOSnIb4yxCmO3XGQkiBRRGACDQEoUEwDQAAMUH\nstdwKTsBQKDoD3Knf5QzjQJoSRadSpjUxrASAgCPDP/fBd5vx3BGhUaSRDFSQNjczJJX512n\nYmQAkK8zJitUO7oaeUkEAEmVJKWX5nXXKkUeAEREfZoz1XT543eNO2/gPAQglScShVSeIIwO\nJCp2mCFRsYkx7OlOAl4bRTFOc73P1aNPK9Ua88M68H43AMYYUzQt8n5WrqUYmSTynM9B0TKK\nonjOy7AKjHFoZmO3O6BQsAxz0tQtcF5r2wGB8+rTx4pUstsVsFo9vKLNIux3On1KKa8guUpE\nXomjU9PV6SmpTmcA0576nv0KJZ2uKeu2tzXb9rr9jjHZVRUZc/1egaaRTM4AQKu1rsNaD5RY\napzFyEEp0zKUDGPJ6beoeQdv3SRybr+7l5LrZbo8WvIghQkk7O7ayXD1QLGI0cgMU9nkSZK3\nQ3AfpzX5rGkuDpiBVlGyZMzZKEWa5O8GwIjVI1oJAKK7279/nVBvlQ7pQfQDk4qtPXRRNlNq\nBKEHQCXwHVIHJexo5R0W0HcgnQYc6UKPjBIQ0vpFbSslIMpjojQagWV9wFEWD/DIl+RvrzT7\nsQsrxR5QSQEqr0MWAEHPdxsc2i6t9kBZMZNpSPVrWatnl7ZH4JxTG12CQolNsgMpLo3Ajk/J\no0SDQ6VKAUnncu5Q6Nf52tNtgQmyHBvnbtDYtRlJ4/MnXZJf0dPWc5Cz1LktGQG2kw60+u1V\nptyZbKY6TV9sSGMlqefoUUNpqa3T2kX56p09jFw2Nb1AzrBWp/nIvtUOjzWlaPqk4qlamcIn\n8MlyVU+PJVmtYdUKAGhz2ViaSZOpJYePMp67gY0kKjYxSFQsYXQgwm6YIcIuMYZR2LXVfLP3\n62fdvc0IUSkF06queEaf3s9GZW7aufvzJ20dNaGNiKLkamPA04ulk/5bQaOS1lQw5fKn2+zF\n36ysdToDFIXGlKdefmWFwXjiD5vHw331+eF9ezpEUVKq2PMvKJ42J/WTPS9sa/pIAj40jx3j\nT5c3X8U4KjDrDOR/GkjaC0gEiQZKAsA0nyxvvUzWO11ZttOs+1SAH536MQKEETAmTWayr/EK\n1KNGIgBgjJrtkxWsJ11TN+BMFAFgQBSANPhpKXWisCxCrGFG0pS3Gd1Y3tzY858rkPowkuHw\nDHweDVq3AGrG44u+hvF7gZKCe0M1lbDyMnBH8FQDWsRzv4dZm0AeAIGBvVPQtxeBf/hM2kji\nTLsDOasw48WW2XTHIo1fJgGIDLACCBTu0EvpLkomID+D3Vqr0a5DmA3+YHkafz0u8Opcr1sJ\noR+DCJBWpnDz/hkN7KPrVHlWGiN8NIf6/fnOHrXwyDr1BUdZRkJUslJz70zV0imjVl33zIEI\nu8Qgwo4wOhBhN8wQYZcYwyXsuuu3rnv7+tAWhTbl4oe+UWhOuL45zfXfvLY4eHgaO4iS1QYe\n9OKCvpaUVM2Dj54nk9EY47f/vL3+eG9of83s5e38lshTYVZd+4A/9zNB3RSxg6xnNpcaeWwK\n4u9jukfIMZZWZhjO29L5zgwmN0K51ZNYUsBkDm9sy0Xv3ANSSFwqBkCAF62E2RtDO6Lacvjg\nFhiQxPlUEPXH+OSDiqarEhi7tYB78Go3RuEfg5UdzFsf6WUhx91OJe7USmU9/WJvdY9Vq5ae\nc7nuiLBLDCLsCKMDCZ4gnFXsX/1iWIvfZa7b9E7f5aHvXhUCcddswBKXSX8R2mLuce/c0QoA\ndbXmMFUnahoHU3UAgBHvy/toMFUHAFzq1sFuLaLtI/cbK/o6bRvvpTOiqjqACKoOALJb8Nh+\nFlBAAGo3zApPBYfHHIHcplPYZgRoR4mi7ZLExs5qlE1pjVBZ8b7NKll/J0adD4WpOgBwvbYJ\n80NH4xIIBMKoQYQd4azC2XNsYKMjpDFih1hQUJ1hLd1dLgDo6XaHtYuKCDnhQpHkvVHvD2pE\nT4GR1RCi5whK2CE+dUCOQJMZBljCAABShtKOCSAoAOIL0e2jsDdCAryCSI0DwW5O6oqQBo9A\nIBBOF0TYEc4qQgMd+pCrkvp1SOgYUMThB08qFQsASmW4FELiEEdUSJQnsgMA3wj/wiL6FBLX\neQe868F86XzDf4SHTzgLJjLWqYiQFc+liE0kIkC6ISpeEAgEwmhChB3hrCJ/0pWhl8E/znkT\nTzbmTVqSmGHHKk0PvQxWFQOA8rGpYdqOdZYxUrTctmzv9CjajgoYB7u1Wzp1SRTtvWvKHxCG\ndOGTIpmyOBmqHRve2JMGXQMyRXs0UF8c/9ai9ceMV0iKVoE3CjYV3loQIbXe6vLAwEaODt+i\nfG4hpSfCjkAgnEEQYUc4qxi34OGM0nl9lwigcuHD6SUnE9IWVV1XNPWGeKfNKF8kz/pJ3yXD\nUJctqcjI1AGARiu/7saJCsVJPy1jsvG26a8q6PAq9UFY20RF22XKxqVIiqDtaHeBqvbBYDbj\ncDDskjTHcL9RIo7gHxaVQY1a2opn1BV3asp+L0Yq2HVicFcGrLgGuP4751n4z0/Anjxgwwh9\nfD04QjSuXwmfXA9+ZWQNF6+9DQEAYMbvK/zIX/ippIpQhy10HYzCLXNuufTEJa6IFrv3pvvW\nl/QrIPveVP/vLnb7+543BqbYpH/mojg3TSAQCCMLiYodZkhUbGIMbx67rqMbe1v30awio3Re\nWK6TIJbm3T2NO9zWFoSRKHE0zepSS5Mzyx3dR3tb9gm8HxAGSQJE6VKK0opnpxXNwhgfrunu\naHMqVWz52DSjqZ/xzOUK1Bzscjr8qWmaceMzGIbyBGxbj391uGmfx29XquQ6vTxVm4ccRWJ3\ngdvDJSUp1QZvg2tTh6WN98lZhtKbUGHqBIVrHE0zRaVJTe7N++q39bo6aUmtULKmJENeVm5x\nyrRWW43QszbLfUCD5HZhfLt0q17ZVWT4Adu3C/4OzmuVRAkzBk3qBFZbQCvSeOdh3rxBEty0\nKgfJUoHvBUrOqnKQMgvEgOhrx4KL0U9U5l7H6CqC74XvqnPueUn01CMfi3w6yYWx3yXZJdSe\nAc1jgREw44DSOshpAglDUyGqLZe8clBYRXUPeNIZVxrCLGAEIEkyJ5aLVFEjGHuxSy01puOA\nGgQNJVBIoiiJBUxhAIQkkRIlGYckhDBGWMIIY6AcKqHWhFr0zPxGbPAwjAiAMUIgUqJLJVq0\n3qMZ7p705rxAk4eRb00plzsqS3tldrkgUJTJg7oVwq5cfnwnm+9mnSksTNBctKuDbfcGeHWb\nydBtZBsnqRVpek4SWIrmJNHsc/GSOM6YPT0t77jD7Ob8pkPO1HqfSqEoWDBhpaJzY8dxkxOu\n7TRNAANTmqKoLgHmXPxuTKJiE4NExRJGByLshhki7BJj2BMUnzu4XC6FQkHKAMSL1+v1er06\nnY6UAYgXIuwSgwg7wuhwLn7dJBAIBAKBQDgrIcKOQCAQCAQC4SyBCDsCgUAgEAiEswQi7AgE\nAoFAIBDOEuLNlUAgnHMEPL0yVTJCFABwPgcjUyOEOL9TrkrmfE5GpqToIQIXJJH3edwCVgLj\nZSVBqTEJPO519NLygEKSy5QGhqG5gCDSLrUsyeXiaRq8ooXzUzqtXKs0dFu6VXKdVqv0B3ib\nr50OMEqFyo0dyTJGocoBied9nqONPenJMobNFDFFsf4kjYkX/U6vWcuyDCUDxLodNSqZycsr\nzL5GHS1Hkk2tyfVyVqUqm5KUlEwvSQh8RxmWYbRjPO5G7LXQIMnUqYHWvYwph5JpgVYJllZE\nl4g6JBN9tDwNeF6EXl+rl1Z0QTdNazU+r1uVXyaKXTRkivWNotXGT6yUtza5PWZMYbqX4xVy\n7OvgaUGvlWGn0udEvMeF85WdTS1yRZLWZLT57ILLlaZX414KK1SsgvJ3NQbS092Y81iwwdMj\nsOnKdLW6tEA63FBnYHS8T5DZjA6jvHisZX9zsqudzcqFJNza1mDXmUzZuaqDOzvlol5TIjve\n7NXzjqLibI3GbT5q8CS1BhRJOhXOM5mQ3IlsaUjb2dHg02m8pmTlrtasglybjsG2HrmDUmZo\nPYoUb2OdUi/TGdPBoHM5XCnpOV57V6u5tkRnbKGUtt0NyWkpyCSlZU5wB7g0g1Htl+w+i9zu\n5Nq7VMmUJzff3W43gdaqcOlSMj1NrbnZKe0UkjfYkVY0GTJadUoZzeoZWUtPgy45SydTyGhG\nLdD2gPdwZ7PRLyhyszLdrCrP6BT8SkbGUrStt6XBac5IKUIUcB6HJeBNT0r3iUKuxtDmsRkV\najUjswd8GDBL0RpWwVAUADg4nyCJKr9bqU8FRDk4n9znVGiSRL8NUSpKqQMAQZLcvD9J3i9C\nIiAKnCiwANaAV6lSem1ukDNqlbLX7zEq1AAg+RyIVSAmwRTcQ2L1e5LkKmr4QqywhwOaQiHp\nigBj0W2htSnDtUSERQFb/d7gEyMQhh0SFTvMkKjYxDgDo2IlUTi8/o26TX/lfE5GpjLlVTl6\njvscHUGFh7GEEI2xiCgmo3Tu5Muf1hrzB07i7m3e8vHjvU1bEIgCjRDGtAQShcw6CoFocgIl\nAUezncqS5rxmifUiSUYFDKKiO6QYFwLACDOI02crOy+i7BmIQwAYAAFIgJxuWX2LzuWVAYBG\nxatzfGuVKgnTl9K9BVRgBJ8mjpB5zu6UH2vVe7wsAtBpuJI8h0bJA+CQnv2HYQBAgDD0mhAl\n4WRrv+kEFhjhxyEYdWVgazKMqQNKBADwaGDHDBi/H0xmkCjUWAgrLwNz2snhSi9cuApP2AuM\nABIFFAYM0JGFVl8Kzfnh7wbhnVMObThvp1vjpQW6orZo0bdzNGonvuQLyGsCSgJzGlp9CRwL\nz54jqbpRIClCxmnUL41eTfnxb6u32pKdlEQVNeRcvHqu0RZe50Ni/B1FG5XJG1lJcLDKTcpF\nEw9VlXfRqP+DxgCbi/nayu+u6tzCSgIAiBTlZJRJvAdjVKfLeL3o/CO6AamhAWhETTDltDq7\nLzr6/TXtuzS8n6PZFm16ttRtKOOoVEAIJC/w3dkfZ9/8Jod5SUxVaR+aUH3LmBm1tq4ntn/x\nQ3eTiCUKUecdZx5cr8q10gKC3Xn8y9WefLrpoeYtWlsrULSqbG7q0ldlWRUD95AYIpbeOrTp\nzYMbbAGvgmavLp70WNXFetkgpU1ig9vW7Hzxe+GoGWgkm5ilfayaKdJYVjzt+P4tye+mlLqk\nBfcbL38cscOWgJrn+Y5e81+Ob/+kca9P4HUy5V0Vc+4fP5+lYqpfRyDECBF2wwwRdolxBgq7\nfSufO7LhLzF21hhyLnpwNavQhjbyfvfKVxZ57a2xzHA8C7VFtRGYEH830yPrn9vX62d216SK\n0smHRlO4vKKXkYtJKEJBhRHF5ZXtPWyS8MnNMIw0taJHLhvZErd9IJcO/vwgeNQAAAjDLX/D\nRccj9ONl6M37wZIa2vZD1cGvL94Q2lLSabzJWA/qkFrAAoOW3TlQFA7J0ZKm96//KrQl2a67\n96/XK/wRMq348j/jU3+g/SbVoZ8jadBULIKmyTv2rYj1Ory07J4pt7QrB6SMBgCAuxo23tC6\no+8SMaA6D6j+xiPXHvq+9BvqtBnBy99ULfrroS0W/4lHUdXC/uWjfvm37WqeLnsBM56+Fkaf\nnvfMXlrX7yEnzB/3rX1577ehLfOySv658PaETXf8oS7rzR/gwMnfEUqvkK454Dzwbmi3pAvu\nTb3ljcSWiLAoz9+19p/fdR4Nbbxn3HlPTF08XEsQCEB87AiEiPjdltqNb8fe321tPbb9n2GN\nx3f8K0ZVBwAFnUBFqIBwkvmUUzbgr3hThy5U1QGAKKKedvWPqm5Uv7Y1tulCVR0ACALV0qEd\nrP+wg7VOmLn5xEXR8ciqDgBYDi74LrRBpKS1F2wP65Wf3dBP1QEAI+AF38S/Lfi2emtYmy3J\nuXPKwYjdFW0XAaZk7dVRVB0AMO582p0X8ZZK5G5u3hbxVhLvvbbth9AWNidc1QGAeox4R+Om\nvss/7F3bp+oA4IEN4RnskjysvGt2aIvg6LR980qU/ceOiw+8un9dWOOG9mObOgb5+caA+7XN\noaoOACSHn/uyN6ybfd1feHNDwquEcaC3PUzVAcBfazZ3e53DtQSBAMTHDgA8Ho8gDJttQ5Ik\nh8MxXLOdO4iiCABO55nyAWdtPYBxVJ01AEvrobAfvaWtJvbhtITlPPIN7puUEskC5/EN+BVG\n4D3ZOKrmT68/womSxz+6HzJpXSdepHRH6YVTekIfjUvr8cvDi8OmIH7gOEiNNm3ktShsMdoH\ntvekhsuIIEhQIV5L+4e2dVG+NFHTFPFWvjfy5DleK93/lIaKJLwpFRT6Ts7Aif3+7xX0RviZ\nUr70/g3I0/z/t3fvUU1c+QPAvzOTN0mAACE8AgEUEAQEkRVQsatbH221bmu1umut1Vp3i93V\n9Rzd1a2Pal2rZ1dPdW13a/d4LK7d7motW2q1CopVWxGpggqIQgKE8AoB8s7M74/5bRpDeIhY\nJf1+/mHmzp17b8Jk8s2dO3fKuENxMqzQa220h07f77S1Y8SD7BG0Vup6JpImD6Xpqy7zeL0+\nvvm+XNd5+JlHM8zVhjvjg1QPUjKHw8FJj5ETBnYgEAiG8Hq0wWDAD9ggdHd3s3PZPyaXYu1+\nAxw6/b8RYwwIxTK3f71QfH8X5R19jrQxM2TPOI1LeYg+Kc79haQuPI2bGzCK8vA58pj48DBG\nIfsCGHOfA7As9wycEph5BEMwxD1N9c8ds6sAABmMSURBVPSGE+Cx2D7fNoIheFauWeAeOAp7\ni+IJBigzTZn7v55CmXrb0sXx3NvX2eO2Bsbjr1oHGCjXt+ieAYNdfFpkc28d06MxPEnAkJwM\nFYznz1GQ2HfQ5Vt9hQ5dt3sq5f4/AgChf7BgiE7pMh/PvdfBUv8HfKMek9MmekxgYAcUNcQD\nVzkcfFfvG3ti4nA4j8kZShaW6KuI69De6i8j4fyrSn3W7V+vSp1d9fWHA6yxTQrWPg+ca4wo\nEty/eOQBJn2n+1d1sKzX7/v+DPrNZwCIYJmpxuh+g7AiwDjYMgeDuJb6/wuVcYxZCALPbwVR\nNsZ1VWDhj6yOrBx51zXxpk2SzO/R+O9SPBXXT6uSr8d+M/aaW7ak8liPme1+NxjKYg+4yjHE\n9FUoZbP7VvW28St5gsf0WlHQbR95TPf3/VX2BuBFu2ezNcBp+SjnakpgWFmLxhm/fjHKuuhb\n91sKbAGlbinSzIVDcjKM8gtKC4q40lznmujLE06JGDXo8kVPjer8yzm3REeke48aVx7jMzKT\nGKJTenbIiAC+T6vlnoAy1k+eHKQcwvt8EcIxdgh5QBBk9ovvCn0Vrim9ZSYpbsqMdUGqdLf0\nwIi0MTN/D4TzW+H7Pg+aIFyHxhl55K3wfs7sJbRPGSNyKyc0sDv43shJEWCsDKBOOnx/2Lui\nCABQKjoD/c2uqWHB3UEDjzKZ/r/bCPre/4Jb/1nhFKge+f/L3WL4z1yweuq4KkuFyz9xS5uV\n/0Rgy/d3G3BsnPjPZhLncu7JVDWSKJzSbyN7+tmprAjN98cS5SB/9lWWUh3SMyct1Jmi/gMA\n1qDLHYrveivQQTBN8Z/07CRjfa5IKlAked6TILYmPNXM/77ryKYnLBUALp28Dj2c10YfVmaw\nqyP95B9OeelXSTnOwHT/hO7LEfdcp/4gs/ui6p4U2TPrfJKn99b++7Vn0jyl+Pv/jpjL//PE\nuUFC8aAL9Hklg//ECNcU0S/HyjdtoiSBzhRKGhyyIo/ope9zECRc/ra0mVKX22wVIunenBcx\nqkNDC++KHWJ4V+zgPIZ3xQKA3WqsK/uss+WOyD9MmTit+e7l9oZygqAAaJq20zYrweXzhb6h\n8T/1Dfbc+wIAHU1VFRfz62rqmwgTwdUFCZggv5QbXVyt7Zxfd5sv4yf1eYKQTtTzL9MiHWHx\ntdTHtHCKurjXHZRJQil9eAEmAymAQJV/Rr3jBMd8IZzp5BLA5Vr4AnGUYjxjkpRXfmXt0jMM\nRfJC6jjxRh9JtEpOMeWBhpJgsPjzpA5zE2HvAJroZggG7CLCzgGGAcJBUBYQmIlok+0nUvKq\nhKykOJSV5+8wtfJpAwFAsEEkQxAEBQCMg6DNEsYaQHD5vO5woMxWpobgGwDsJBCMjQs00W4h\nDUY+0e7r36aQ+tgYAAaY7thqSqqnCIZj5ZEEAM8EJIBBStxIZCx84NiJuggw+MLEIiZcA8Ju\nwsFhtAriWioj6SD82xiSJjr8mDoVUauCnK9AdZexCOByGlQmQGwFhDeAhU9UjQRt2P/edQYI\nBwDBSAww6gZIDARDgoMEIKFOSdfLCLsQaCHBABAMQ9IMYWFImibgRlytTt4u6vQZVRXtp5cw\nhJUIaYaoGoZrBU0kUx1LU7SNZ3AI27p5YAeuiKvm+FQQhENwZzZlCSSAtvro1IGd1/zC7YRg\nhME4sskmMolImkNzu2/E3W4ItAjM/Kg6uczA4ToomuoGh4hjFQOQRnFzcYIhb4w4UX9XZunU\niAKvKlOmdCjiaxi/Wy0R7Taug6EJbqefDx0bWPZsMFdKhn73ueX6l3aH7XLAiEZxUFSHhgb4\nzi+yNihGRHF5FMeHyw0W+XZYTd02S5BAMjF0xIzI0Tf12oKqb8Mrz8baukdGpTVGpLVe+1Jh\nrIyQtgm5BEmE+8S+3KpK/7KuosXUFeeveFo1mkNSAHCttf5cQ7XBYjbazBwgI8qNIXfMDj5V\nlyRqjxBFSmQ/69KSdaUEV+gz+mf8yNSh/TCaHbb8O9eqO5oVIunMyNFy0RDclGO9UGu9Uk9w\nSV5mJDcpBAAc3e1d335ia77DlcdIMp4nhe5T0jwIm83W0dFhJpkzuhpNd3uUNPAZVbIPd8gC\nR4RYGNgNMQzsBufxDOyGhc7OToFAwOX2M0kycmM0Go1Go1Qq5fHwm/X+mM1mdkTso27IMMMG\ndkKhEMdho4cKL8UihBBCCHkJDOwQQgghhLwEBnYIIYQQQl4CAzuEEEIIIS+BgR1CCCGEkJfA\nwA4hhBBCyEtgYIcQQggh5CUwsEMIIYQQ8hIY2CGEEEIIeQkM7BBCCCGEvAQGdgghhBBCXgID\nO4QQQgghL8F51A3wNvh058ERCAQ8Ho8giEfdkOFHIBBQFPWoWzH88Hg8kiQ5HDwH3jcul8sw\nzKNuxfBDUZRYLMZDDj1sBH4+EUIIIYS8A16KRQghhBDyEhjYIYQQQgh5CQzsEEIIIYS8BAZ2\nCCGEEEJeAgM7hBBCCCEvgYEdQgghhJCXwAl10JDp7OzUaDQURcXGxg5tyVartba2lsPhREZG\nkuQ9v0YeXqXo8WexWNRqtclkio+P53K5Q1gywzB1dXUWiyUiIkIgELhu0ul07e3tAoEgJCSE\nx+MNYaXo8UfTtEaj6ejoUCqVfn5+Q1u4Tqdra2tTKBSuJdfV1XV0dLhmGz16NE75ifqAgR0a\nAq2trWvWrGlpaQEAhULx/vvv328J7Df0iBEjem46f/78u+++S9O03W739/dfu3Ytm+3BK0XD\n2rp16yoqKtiZOA8cOBAYGHi/JdTU1ISEhAiFQrf0urq6t99+W6vV8vl8mqaXL18+ZcoUADCb\nzUuXLjUYDDwez2azCYXC+fPnP/vss0PyctDj7+DBg8ePH7darQCwatWqyZMn328JjY2NfD5f\nJpO5pZtMph07dpSUlEgkkq6urhkzZixfvpyN3vLy8i5dusTn852Z8/LyMLBDfcDADg0BoVC4\nZMmSqKiozz///Ntvv+2ZwWq11tXV8Xi80NBQjxOvq9XqVatWHT9+3C29qalp165dM2fOXLJk\nid1u3759+7Zt2/bv38/j8fqtFHm3p59+eunSpY2NjTt27Oi5le1ys9lsYWFhPUM31tq1azds\n2JCUlOSaSNP09u3bpVLprl27hELhxx9/vGfPnpiYGJVKxeFw1qxZM3LkSJFIZLVa//3vfx84\ncCAhIQF7i38kUlNTx44dK5fLX3nlFY8Zmpqa9Hq9QqHw9fX1mOFvf/vbyJEjX3zxRbf0Dz74\noLq6eu/evUqlsrS0dMuWLUql8qmnnmK3ZmVlrVmzZghfCPJuGNihISASiSZMmNDb1mPHjuXl\n5QkEApqmCYLIzc3NyMgYYMmnT5/mcDi/+MUvSJLk8XgvvfRSbm5uSUlJZmZm35Uir5ednQ0A\nzc3NPTeVlZXt3r3baDQKhUKDwTB37tz58+cPsNiKigqNRrNt2zaRSAQAc+fOzc/PP3ny5LJl\nyzgcTkpKCpuNx+NNnz798OHDWq0WA7sfCfY3QHd3d89NOp1u586dt2/f9vPza2trGz9+/MqV\nK3v7ReHGarUWFhY+99xzSqUSAFJTUzMyMk6cOOEM7Ox2e01NDZ/PVygU+PxA1C8M7NDDVVxc\nfOTIkY0bNyYkJADAiRMndu7cuXfv3qCgoIHsfvv2bZVK5RzkFBkZKRQKb9++nZmZ+RAbjYaz\npqamrVu3Ll68eMaMGQRBVFdXr1+/PiIiIisrayC7V1dXA0B8fDy7SpJkXFwcm8jS6XQtLS0G\ng6GgoCAsLCw9Pf1hvAo0jNA0vWXLlhEjRmzevFkgEOj1+o0bNx48eHD58uUD2V2tVlutVuch\nBwDx8fEXLlyw2WzsyNELFy6UlpaazWaxWLxw4UJnwIeQRxjYoYcrPz8/MTHR4XBcu3YNANhL\nsWVlZVOnTjWbzTU1NWy2+vp6AKioqGBXCYIYNWoUAHR2dkqlUtcCpVJpZ2fnD/oa0LBy6tQp\niUSiVCqvX7/OpsTExFy6dIkN7G7evEnTNJtO03Rtba2zC0SlUolEoq6uLqFQ6DpgQCqVNjQ0\nOFfPnDlz/Pjxrq4uf3//1atXsx176Mfs2rVrtbW1CxYsqKqqYlOSkpLOnz/PBnZqtdp5yurq\n6mpubnae6ORyeWBgYFdXFwC4nuikUinDMN3d3X5+fnPmzPnVr34llUqNRuORI0fee+89iUQy\nadKkH/QVomEFAzv0cNXX1wuFwsOHDztTVCoVe2drY2Pj1q1b2USHwwEAzlWKog4ePAgAHA7H\nYrG4FmixWDyO0kOIVV9fbzabXQ85AJBIJOzCzp07TSYTu2y1Wg8dOuQM7N58883Y2FgOh8OO\njndyO+TmzZs3b948m81WUFDwxz/+cfPmzW6j9NCPTUNDA0EQn332mWtiSEgIu/Dxxx9fuXKF\nXTYajTU1NZcuXWJX586d++yzz7JHl+tRx5702PS4uDg2USQSvfzyy2VlZYWFhRjYoT7gFyR6\nuPh8/tixYz1ekoiKivroo4/Y5erq6lWrVjlXnYKDg52/bgHAbDYbDIbg4OCH12A03PH5fH9/\n/23btnnc+ve//925/MILL/zhD39wC8vkcrnD4Whra3PeuqjT6Xoeclwud9asWQUFBWfPnsXA\n7keOz+czDLN27Vq3ywus1atXO5c3b97c8+YJuVwOADqdjr1MAQDNzc0ikUgsFvcsLSAggO3h\nQ6g3OEExerjGjBlTXFzsdvGU7Z8biLS0tIaGBucIp+LiYoZh0tLShriVyIukpKSo1ery8nLX\nxIEfcmPGjCFJsqioiF3VarWVlZXsQDp2ahWn9vb21tZWHx+foWg1GsZGjx5NkmRBQYFr4sAP\nuaCgIKVS6Tzk7Hb7119/7TzkXI+65ubm8vLyqKioIWo48k7YY4eGRkFBgc1mq6mpMRqN7Kwl\nEyZMkMlkCxcu/O677377299Onz7dz8+vubm5uLh49erV0dHRAyk2Ozs7ISHhrbfemjVrlslk\nOnbs2PTp08PDw/uu9OG9TPT4+Oabb7Ra7d27dwHg5MmTPj4+8fHxsbGxkyZNOnfu3KZNm2bO\nnBkeHm4wGEpLSzMyMp555pmBFCuTyX7+858fPHjQYDD4+vp+/vnnUVFR7Dx2Z86c+eqrr2Jj\nYyUSSVtb27lz54RCIY5k//Goqam5fv06e830ypUrBoMhKCgoMzNTLpf/8pe/PHjwYH19fWJi\notVqraqqcjgcA5+jZMmSJVu2bHnnnXcSEhK+/vprvV7P9uo1NzevX79+3LhxMpmsra2tsLBQ\nJBI9//zzD/FFouGP2rhx46NuA/IGhw8fvnv3LkEQvr6+Go1Go9EkJSX5+fkJhcIpU6YQBHHz\n5k32O/iJJ55ITEx0u2nfZDLV1tayX5+uCIKYOHEiAFy/ft1gMDzzzDPz5s1zTs7ZW6U/xAtG\nj9qZM2dKS0u7urrkcrlOp9NoNEFBQVFRUQRBTJo0KSgoqLq6uqqqymw2JycnT5w40e0BEgBQ\nXl4+btw4f39/t/Tk5GSFQnHjxo36+vr09PRf//rX7PSwUqnUbDZrNBq1Ws0embm5uXi8/XhU\nVFScOXOmsbFRLpcbjUaNRkPTdGpqKgAkJCQkJyer1eqbN2/q9fqwsLCZM2f2nM2utrY2JCSk\n58/a0NDQMWPG3Llzp7q6Oiws7I033ggLCwMADodDEIRWq62rq2MYZuLEiW+88YbHC74IORFu\nFxcQQgghhNAwhWPsEEIIIYS8BAZ2CCGEEEJeAgM7hBBCCCEvgYEdQgghhJCXwMAOIYQQQshL\n4Dx2CCEP7HZ7fn5+ZWWl0WhcsGBBbGzs4Mo5cOCAwWD4zW9+M7TNQwgh5BFOd4LQsPfWW29N\nnjx5woQJALBz5860tLSf/vSnD1Kg3W7PzMwsKSmJjo4WCAT79u3r49mUDQ0NRUVF7JxeERER\nOTk5oaGhzq0TJkzQaDTsFIYIIYQeNuyxQ2h4u3Xr1oYNG/773/8CQFNT05o1a/75z38+YJn5\n+fmXL19+7733Xn311T6ytba25ubmHjlyhKZpgiAoirLb7SRJzpo1a9++fc6HoA+t9PR0vV7v\nfMocQgghVzjGDqHh7fz58yRJZmdnA8DZs2cBgO26exBVVVUAkJWV1Uee1tbW8ePHHz58+KWX\nXiotLbXZbFarta6u7i9/+cvly5dLSkoesA0IIYQGAXvsEBreiouLk5KS2IcXnT17VqVSsQ8j\n6hvDMGfPni0pKbHb7XFxcdOmTWOfuGU0Gnfs2FFYWAgA+/fvDwwMlMlkK1eu7FnCypUrq6ur\nt2zZsn79emeiUqnMzc1dtGiRXq/3WO/27dsjIiIWLFjgTNFqtfv373/yySdd48iysrJvvvmm\npaXF398/NjY2KyuLbd7GjRsbGhrMZrPzWYiLFy9WqVTsckVFRVFRUXt7e2ho6IwZM4KDg50F\n7tu3jyTJ11577eLFixcvXmxoaNi6dSuXy+2jLoQQGpYYhNBwYzQaf/0/wcHBsbGx7HJYWFhk\nZCS7fOrUqd52b2pqYqMogUDARoQRERFXrlxhGEav1+fk5ERGRgLAuHHjcnJyFixY0LMErVZL\nUVRERITdbu+7qdnZ2ZGRkc5VX1/fp556yjVDaWkpALzzzjvsKk3TixcvBgAfH5+oqCiJRAIA\nS5cuZbfm5OSIxWKBQJDzP6WlpewbsnDhQgDgcrkhISEEQQgEgg8//NBZS2Ji4ujRo+fNm+fn\n5zdp0qSEhASTydR3XQghNBxhYIfQ8NPZ2ZmdnZ2dnZ2cnAwAycnJ2dnZaWlpAJCYmMhuOnz4\ncG+7T506lSCInTt32mw2mqY//fRTsVgcGhpqMBjYDG+++SYA3Lhxo7cS/vWvfwHAa6+91m9T\n7zewO3nyJACsW7fOarWyKbdv3y4sLHTmHzt2bExMjFstL7/8MkmSe/bsYffSarVPPvkkRVEl\nJSVshsTERACYNm2a8zUOpC6EEBp2cIwdQsOPWCwuLi4uLi5+/fXXSZI8c+ZMcXHxunXrAOCL\nL75gN82fP9/jvmVlZadOnZo9e/bq1as5HA5BELNmzVq7dm1DQ8OhQ4cG2ID6+noAYDv2hlZt\nbS0ATJs2jb1OCgDR0dE5OTl97FJTU/OPf/xjxYoVubm57F7BwcF5eXkA8P777zuzEQTx17/+\nle2WG3RdCCH0mMMxdggNY1988UVGRoZMJgOAL7/8MiEhITw8vO9dLl68CACzZ892TZwzZ876\n9esvXLiwYsWKgddOEMT9N7kfkydPFggEL7zwwtKlS6dMmZKRkSEWi/vehe1jU6vVa9eudU0X\niUQVFRXOVYVCERUV9YB1IYTQYw4DO4SGmc7Ozg0bNrDLJ0+ejI6OZqf//fTTT2UyGbv8/PPP\n93ZvbHt7OwAoFArXRHbmOXbTQLD3ZzyM2eliYmLOnTu3devW3bt3b9u2jcvlzpw5c9euXTEx\nMb3t0tzcDACNjY0Wi8U1PSsry3VeZblc/uB1IYTQYw4DO4SGGZvNdvXqVQBobW3t7Ozk8/lX\nr17V6/U6nU6pVLKb+rie6O/vDwBardY1sbGx0blpICZOnEiSZEFBgcPhoChq4I3n8XhWq9U1\npbW11S1Penr60aNH7Xb71atXP/vssz/96U83b968ceNGbx2EbLOXLVu2bNmygbdkcHUhhNBj\nDsfYITTMyGSywsLCwsLCRYsWCYXCc+fOFRYWrlixgqKo06dPs5vmzJnT2+7jx48HgOPHj7sm\nHjt2DAAyMzMH2Ibg4OD58+fX1tbu2LGj51aDwaBWqz3uqFQq3eYWPn36tMecHA4nPT1906ZN\nixYtunXrFjuqDwC4XK5baMhGsXl5ecxgn6PTW10IITTsYGCH0HBVWFiYmZnJ4/EAoKioKDU1\nVSqV9rtXSkrK1KlTjx079uc//5mdrCQ/P//tt98OCQlhZwwZoD179sTExPz+979fvnx5eXk5\nm9jY2Lh///6kpKSysjKPe02dOvXOnTu7d+9mGMZutx86dGjv3r2uGY4ePfrRRx91dXWxq2q1\nuri4WCKRBAQEsClKpVKr1Wo0GucucXFxixYtKiwsfPXVV9nLsgDQ1NT07rvvsg/k6E2/dSGE\n0PDzaG/KRQgNjt1ul0qlmzZtYldDQ0N/97vfDXDfpqYmtnNOKBQ657FzzgzCDGC6E1Zzc/O8\nefNIkgQADofDzutLkuTs2bMbGhrYPG7TnbS0tLAj2EQiEY/Hk8vl7I2rzulOtm7dyp6a5HK5\nUqkkSdLf3/+TTz5xlnDixAmKovh8/ogRI+Li4i5dusQwjMlkeuWVV9iWyOVy9tZXLpfrnPMl\nMTExJSXFrf391oUQQsMOwQz24gVC6BFqa2vbs2fPiy++GBcXxz4u4rnnnktKShrg7gzDFBUV\nOZ88MX36dNfHLbDXc19//fXAwMB+i6qvry8qKtJoNDRNK5XKnJwc1ztzDxw4YDAY2Fs6WEaj\n8ejRo7W1taGhobNnz7ZYLG5PnqisrLx06RI7B7JKpZo+fbpIJHKtsbKy8uTJk62trewMw84n\nT9y5c+f06dNNTU1SqTQiImLy5MnOLkznkyfcGt9vXQghNLxgYIcQQggh5CVwjB1CCCGEkJfA\nwA4hhBBCyEtgYIcQQggh5CUwsEMIIYQQ8hIY2CGEEEIIeQkM7BBCCCGEvAQGdgghhBBCXgID\nO4QQQgghL4GBHUIIIYSQl8DADiGEEELIS2BghxBCCCHkJf4Px2CEklRBB0QAAAAASUVORK5C\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(size_dists) +\n",
    "  aes(x=nrecs, y=count, color=rec_type) +\n",
    "  geom_point() +\n",
    "  scale_x_log10() +\n",
    "  scale_y_log10() +\n",
    "  scale_color_brewer(type=\"qual\", palette=\"Dark2\") +\n",
    "  xlab(\"# of Records\") +\n",
    "  xlab(\"# of Clusters\") +\n",
    "  ggtitle(\"Distribution of cluster counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4f746b",
   "metadata": {},
   "source": [
    "Looks mostly fine - we expect a lot of power laws - but the number of clusters with merged GoodReads works is concerning.\n",
    "\n",
    "## GoodReads Work Merging\n",
    "\n",
    "What's going on with these clusters? Let's take a peek at them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de011ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table (query)\n",
      "10,080 rows x 8 columns\n",
      "$ cluster        \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 100003920, 100127260, 100150667, 100150538, 100260134, …\n",
      "$ n_nodes       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 106334, 9586, 520, 1602, 315, 513, 304, 337, 685, 610, …\n",
      "$ n_isbns       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 51481, 4624, 194, 780, 141, 225, 120, 91, 245, 299, 248…\n",
      "$ n_loc_recs    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 1439, 281, 6, 55, 1, 6, 3, 38, 2, 0, 0, 1, 105, 111, 0,…\n",
      "$ n_ol_editions \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 44567, 3721, 113, 462, 51, 113, 75, 64, 185, 170, 153, …\n",
      "$ n_ol_works    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 1174, 342, 77, 78, 18, 38, 21, 58, 75, 45, 47, 25, 185,…\n",
      "$ n_gr_books    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 7378, 513, 69, 172, 53, 91, 45, 46, 140, 60, 51, 46, 49…\n",
      "$ n_gr_works    \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 295, 105, 61, 55, 51, 40, 40, 40, 38, 36, 34, 31, 30, 3…\n",
      "Call `print()` for query details\n"
     ]
    }
   ],
   "source": [
    "gr_big = clusters %>%\n",
    "  filter(n_gr_works > 1) %>%\n",
    "  arrange(desc(n_gr_works))\n",
    "gr_big %>% glimpse()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7cb9cd",
   "metadata": {},
   "source": [
    "We have a lot of these clusters. What fraction of the GoodReads-affected clusters is this?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88bfe3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Scalar\n",
       "0.0002449630088845264"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(gr_big) / sum(!is.na(clusters$n_gr_books))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78184884",
   "metadata": {},
   "source": [
    "Less than 1%. Not bad, but let's look at these largest clusters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c7a4b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cluster</th><th scope=col>n_nodes</th><th scope=col>n_isbns</th><th scope=col>n_loc_recs</th><th scope=col>n_ol_editions</th><th scope=col>n_ol_works</th><th scope=col>n_gr_books</th><th scope=col>n_gr_works</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>100003920</td><td>106334</td><td>51481</td><td>1439</td><td>44567</td><td>1174</td><td>7378</td><td>295</td></tr>\n",
       "\t<tr><td>100127260</td><td>  9586</td><td> 4624</td><td> 281</td><td> 3721</td><td> 342</td><td> 513</td><td>105</td></tr>\n",
       "\t<tr><td>100150667</td><td>   520</td><td>  194</td><td>   6</td><td>  113</td><td>  77</td><td>  69</td><td> 61</td></tr>\n",
       "\t<tr><td>100150538</td><td>  1602</td><td>  780</td><td>  55</td><td>  462</td><td>  78</td><td> 172</td><td> 55</td></tr>\n",
       "\t<tr><td>100260134</td><td>   315</td><td>  141</td><td>   1</td><td>   51</td><td>  18</td><td>  53</td><td> 51</td></tr>\n",
       "\t<tr><td>101889755</td><td>   337</td><td>   91</td><td>  38</td><td>   64</td><td>  58</td><td>  46</td><td> 40</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " cluster & n\\_nodes & n\\_isbns & n\\_loc\\_recs & n\\_ol\\_editions & n\\_ol\\_works & n\\_gr\\_books & n\\_gr\\_works\\\\\n",
       " <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 100003920 & 106334 & 51481 & 1439 & 44567 & 1174 & 7378 & 295\\\\\n",
       "\t 100127260 &   9586 &  4624 &  281 &  3721 &  342 &  513 & 105\\\\\n",
       "\t 100150667 &    520 &   194 &    6 &   113 &   77 &   69 &  61\\\\\n",
       "\t 100150538 &   1602 &   780 &   55 &   462 &   78 &  172 &  55\\\\\n",
       "\t 100260134 &    315 &   141 &    1 &    51 &   18 &   53 &  51\\\\\n",
       "\t 101889755 &    337 &    91 &   38 &    64 &   58 &   46 &  40\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 8\n",
       "\n",
       "| cluster &lt;int&gt; | n_nodes &lt;int&gt; | n_isbns &lt;int&gt; | n_loc_recs &lt;int&gt; | n_ol_editions &lt;int&gt; | n_ol_works &lt;int&gt; | n_gr_books &lt;int&gt; | n_gr_works &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 100003920 | 106334 | 51481 | 1439 | 44567 | 1174 | 7378 | 295 |\n",
       "| 100127260 |   9586 |  4624 |  281 |  3721 |  342 |  513 | 105 |\n",
       "| 100150667 |    520 |   194 |    6 |   113 |   77 |   69 |  61 |\n",
       "| 100150538 |   1602 |   780 |   55 |   462 |   78 |  172 |  55 |\n",
       "| 100260134 |    315 |   141 |    1 |    51 |   18 |   53 |  51 |\n",
       "| 101889755 |    337 |    91 |   38 |    64 |   58 |   46 |  40 |\n",
       "\n"
      ],
      "text/plain": [
       "  cluster   n_nodes n_isbns n_loc_recs n_ol_editions n_ol_works n_gr_books\n",
       "1 100003920 106334  51481   1439       44567         1174       7378      \n",
       "2 100127260   9586   4624    281        3721          342        513      \n",
       "3 100150667    520    194      6         113           77         69      \n",
       "4 100150538   1602    780     55         462           78        172      \n",
       "5 100260134    315    141      1          51           18         53      \n",
       "6 101889755    337     91     38          64           58         46      \n",
       "  n_gr_works\n",
       "1 295       \n",
       "2 105       \n",
       "3  61       \n",
       "4  55       \n",
       "5  51       \n",
       "6  40       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gr_big %>% head() %>% collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b52648",
   "metadata": {},
   "source": [
    "## Large Cluster Debugging\n",
    "\n",
    "We have some pretty big clusters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0266a846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cluster</th><th scope=col>n_nodes</th><th scope=col>n_isbns</th><th scope=col>n_loc_recs</th><th scope=col>n_ol_editions</th><th scope=col>n_ol_works</th><th scope=col>n_gr_books</th><th scope=col>n_gr_works</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>100003920</td><td>106334</td><td>51481</td><td>1439</td><td>44567</td><td>1174</td><td>7378</td><td>295</td></tr>\n",
       "\t<tr><td>101110712</td><td> 24435</td><td>12152</td><td> 190</td><td>10644</td><td>  69</td><td>1352</td><td> 28</td></tr>\n",
       "\t<tr><td>111780550</td><td> 11281</td><td> 7520</td><td>   0</td><td> 3760</td><td>   1</td><td>   0</td><td>  0</td></tr>\n",
       "\t<tr><td>107938279</td><td> 10678</td><td> 7118</td><td>   0</td><td> 3559</td><td>   1</td><td>   0</td><td>  0</td></tr>\n",
       "\t<tr><td>100685594</td><td> 10118</td><td> 6518</td><td>   7</td><td> 3558</td><td>  35</td><td>   0</td><td>  0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 5 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " cluster & n\\_nodes & n\\_isbns & n\\_loc\\_recs & n\\_ol\\_editions & n\\_ol\\_works & n\\_gr\\_books & n\\_gr\\_works\\\\\n",
       " <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 100003920 & 106334 & 51481 & 1439 & 44567 & 1174 & 7378 & 295\\\\\n",
       "\t 101110712 &  24435 & 12152 &  190 & 10644 &   69 & 1352 &  28\\\\\n",
       "\t 111780550 &  11281 &  7520 &    0 &  3760 &    1 &    0 &   0\\\\\n",
       "\t 107938279 &  10678 &  7118 &    0 &  3559 &    1 &    0 &   0\\\\\n",
       "\t 100685594 &  10118 &  6518 &    7 &  3558 &   35 &    0 &   0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 8\n",
       "\n",
       "| cluster &lt;int&gt; | n_nodes &lt;int&gt; | n_isbns &lt;int&gt; | n_loc_recs &lt;int&gt; | n_ol_editions &lt;int&gt; | n_ol_works &lt;int&gt; | n_gr_books &lt;int&gt; | n_gr_works &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 100003920 | 106334 | 51481 | 1439 | 44567 | 1174 | 7378 | 295 |\n",
       "| 101110712 |  24435 | 12152 |  190 | 10644 |   69 | 1352 |  28 |\n",
       "| 111780550 |  11281 |  7520 |    0 |  3760 |    1 |    0 |   0 |\n",
       "| 107938279 |  10678 |  7118 |    0 |  3559 |    1 |    0 |   0 |\n",
       "| 100685594 |  10118 |  6518 |    7 |  3558 |   35 |    0 |   0 |\n",
       "\n"
      ],
      "text/plain": [
       "  cluster   n_nodes n_isbns n_loc_recs n_ol_editions n_ol_works n_gr_books\n",
       "1 100003920 106334  51481   1439       44567         1174       7378      \n",
       "2 101110712  24435  12152    190       10644           69       1352      \n",
       "3 111780550  11281   7520      0        3760            1          0      \n",
       "4 107938279  10678   7118      0        3559            1          0      \n",
       "5 100685594  10118   6518      7        3558           35          0      \n",
       "  n_gr_works\n",
       "1 295       \n",
       "2  28       \n",
       "3   0       \n",
       "4   0       \n",
       "5   0       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "big = clusters %>% slice_max(n_nodes, n=5, with_ties=FALSE) %>%\n",
    "  collect()\n",
    "big"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c2e106",
   "metadata": {},
   "source": [
    "What is up with this? We should figure out what went wrong, if we can.  What are its ISBNs?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e792ec84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table\n",
      "44,471,261 rows x 7 columns\n",
      "$ isbn_id     \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…\n",
      "$ isbn \u001b[3m\u001b[90m<large_string>\u001b[39m\u001b[23m \"9781593601607\", \"9781483140049\", \"9781872357089\", \"978178…\n",
      "$ LOC        \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1…\n",
      "$ OL         \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…\n",
      "$ GR          \u001b[3m\u001b[90m<int64>\u001b[39m\u001b[23m 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ AZ14       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…\n",
      "$ AZ18       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…\n"
     ]
    }
   ],
   "source": [
    "isbns = read_parquet('book-links/all-isbns.parquet', as_data_frame=FALSE)\n",
    "glimpse(isbns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe17c110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table (query)\n",
      "44,471,261 rows x 2 columns\n",
      "$ isbn_id \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 44471261, 44471260, 44471259, 44471258, 44471257, 44471256, 44…\n",
      "$ cluster \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 944471261, 944471260, 944471259, 944471258, 944471257, 9444712…\n",
      "Call `print()` for query details\n"
     ]
    }
   ],
   "source": [
    "links = read_parquet(\"book-links/isbn-clusters.parquet\", as_data_frame=FALSE) %>%\n",
    "  select(isbn_id, cluster)\n",
    "glimpse(links)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61111c3b",
   "metadata": {},
   "source": [
    "Now let's look up data for the largest cluster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26449dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "100003920"
      ],
      "text/latex": [
       "100003920"
      ],
      "text/markdown": [
       "100003920"
      ],
      "text/plain": [
       "[1] 100003920"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_id = big$cluster[1]\n",
    "big_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a97cfdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table (query)\n",
      "?? rows x 7 columns\n",
      "$ isbn_id     \u001b[3m\u001b[90m<int32>\u001b[39m\u001b[23m 41407804, 42062628, 11502887, 14397303, 19141493, 40937273…\n",
      "$ isbn \u001b[3m\u001b[90m<large_string>\u001b[39m\u001b[23m \"0000744395\", \"000074445X\", \"0001004735\", \"0001004743\", \"0…\n",
      "$ LOC        \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…\n",
      "$ OL         \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 3…\n",
      "$ GR          \u001b[3m\u001b[90m<int64>\u001b[39m\u001b[23m 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1…\n",
      "$ AZ14       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\n",
      "$ AZ18       \u001b[3m\u001b[90m<uint32>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …\n",
      "Call `print()` for query details\n"
     ]
    }
   ],
   "source": [
    "bl = links %>% filter(cluster == big_id)\n",
    "bl = semi_join(isbns, bl) %>% arrange(isbn)\n",
    "bl %>% glimpse()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3067397c",
   "metadata": {},
   "source": [
    "What are the things with the highest record count?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfa2583f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A rowwise_df: 51481 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>isbn_id</th><th scope=col>isbn</th><th scope=col>LOC</th><th scope=col>OL</th><th scope=col>GR</th><th scope=col>AZ14</th><th scope=col>AZ18</th><th scope=col>btot</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>41407804</td><td>0000744395</td><td>0</td><td>0</td><td>1</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>42062628</td><td>000074445X</td><td>0</td><td>0</td><td>1</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>11502887</td><td>0001004735</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>14397303</td><td>0001004743</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>19141493</td><td>0001034375</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>40937273</td><td>0001046403</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td> 6693084</td><td>0001049283</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>30197848</td><td>0001054783</td><td>0</td><td>1</td><td>0</td><td>0</td><td>  75</td><td>  76</td></tr>\n",
       "\t<tr><td>33321586</td><td>0001385208</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>31969676</td><td>0001660047</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>15553922</td><td>0001660926</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>30770041</td><td>0001661019</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>17792203</td><td>0001847694</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>26965565</td><td>0001848259</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td> 8473513</td><td>0001849123</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>34007959</td><td>0001956671</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>19235257</td><td>0001956795</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>41619337</td><td>0002034840</td><td>0</td><td>0</td><td>1</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td> 2678626</td><td>0002219336</td><td>1</td><td>2</td><td>0</td><td>0</td><td>   0</td><td>   3</td></tr>\n",
       "\t<tr><td> 9935945</td><td>0002315394</td><td>1</td><td>3</td><td>1</td><td>0</td><td>   0</td><td>   5</td></tr>\n",
       "\t<tr><td>36299108</td><td>0003302245</td><td>0</td><td>1</td><td>0</td><td>0</td><td>3900</td><td>3901</td></tr>\n",
       "\t<tr><td>19681411</td><td>0003395359</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>30397902</td><td>0003700860</td><td>0</td><td>2</td><td>1</td><td>0</td><td>   0</td><td>   3</td></tr>\n",
       "\t<tr><td>30858214</td><td>0003702936</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>12786661</td><td>0003708853</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>37344922</td><td>0004214072</td><td>0</td><td>2</td><td>0</td><td>0</td><td>   0</td><td>   2</td></tr>\n",
       "\t<tr><td>11125651</td><td>0004214501</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>12746265</td><td>0004244702</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>29211569</td><td>0004247256</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td> 6837553</td><td>0004247264</td><td>0</td><td>1</td><td>0</td><td>0</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>42697277</td><td>B01N1A58F4</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42017610</td><td>B01N5JX21M</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42503286</td><td>B01N7ZJ4MT</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41610140</td><td>B01NAIYB6S</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41309603</td><td>B01NBKD6ZI</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41945885</td><td>B06VVSS7QH</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42699618</td><td>B06WP1M6KT</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42159285</td><td>B06X9HW4Z6</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41317162</td><td>B06XFPGRC7</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41405924</td><td>B06XQ4F351</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42593673</td><td>B06XQ64QV8</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42278150</td><td>B06XY3R81W</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41450793</td><td>B0716WRPFV</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42002127</td><td>B071RSDB49</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41347557</td><td>B0728MXM3Q</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41464214</td><td>B072F1WKW1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41970572</td><td>B072L78PHK</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42475729</td><td>B072MQ8FL7</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41421625</td><td>B072S79HNS</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41927152</td><td>B072Z2KK5V</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41707133</td><td>B07381C14X</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41349067</td><td>B073GF59RD</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41928917</td><td>B073GGNYFL</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42029193</td><td>B073MZ23QX</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41452617</td><td>B073QM98F5</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41474679</td><td>B073SM1DVP</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41529678</td><td>B073SMQ7TV</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>42266578</td><td>B074P9CM5T</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41546978</td><td>B074WFRH82</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>41637353</td><td>B215071972</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A rowwise\\_df: 51481 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " isbn\\_id & isbn & LOC & OL & GR & AZ14 & AZ18 & btot\\\\\n",
       " <int> & <chr> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 41407804 & 0000744395 & 0 & 0 & 1 & 0 &    0 &    1\\\\\n",
       "\t 42062628 & 000074445X & 0 & 0 & 1 & 0 &    0 &    1\\\\\n",
       "\t 11502887 & 0001004735 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 14397303 & 0001004743 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 19141493 & 0001034375 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 40937273 & 0001046403 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t  6693084 & 0001049283 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 30197848 & 0001054783 & 0 & 1 & 0 & 0 &   75 &   76\\\\\n",
       "\t 33321586 & 0001385208 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 31969676 & 0001660047 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 15553922 & 0001660926 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 30770041 & 0001661019 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 17792203 & 0001847694 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 26965565 & 0001848259 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t  8473513 & 0001849123 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 34007959 & 0001956671 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 19235257 & 0001956795 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 41619337 & 0002034840 & 0 & 0 & 1 & 0 &    0 &    1\\\\\n",
       "\t  2678626 & 0002219336 & 1 & 2 & 0 & 0 &    0 &    3\\\\\n",
       "\t  9935945 & 0002315394 & 1 & 3 & 1 & 0 &    0 &    5\\\\\n",
       "\t 36299108 & 0003302245 & 0 & 1 & 0 & 0 & 3900 & 3901\\\\\n",
       "\t 19681411 & 0003395359 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 30397902 & 0003700860 & 0 & 2 & 1 & 0 &    0 &    3\\\\\n",
       "\t 30858214 & 0003702936 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 12786661 & 0003708853 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 37344922 & 0004214072 & 0 & 2 & 0 & 0 &    0 &    2\\\\\n",
       "\t 11125651 & 0004214501 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 12746265 & 0004244702 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t 29211569 & 0004247256 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t  6837553 & 0004247264 & 0 & 1 & 0 & 0 &    0 &    1\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 42697277 & B01N1A58F4 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42017610 & B01N5JX21M & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42503286 & B01N7ZJ4MT & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41610140 & B01NAIYB6S & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41309603 & B01NBKD6ZI & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41945885 & B06VVSS7QH & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42699618 & B06WP1M6KT & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42159285 & B06X9HW4Z6 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41317162 & B06XFPGRC7 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41405924 & B06XQ4F351 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42593673 & B06XQ64QV8 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42278150 & B06XY3R81W & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41450793 & B0716WRPFV & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42002127 & B071RSDB49 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41347557 & B0728MXM3Q & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41464214 & B072F1WKW1 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41970572 & B072L78PHK & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42475729 & B072MQ8FL7 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41421625 & B072S79HNS & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41927152 & B072Z2KK5V & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41707133 & B07381C14X & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41349067 & B073GF59RD & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41928917 & B073GGNYFL & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42029193 & B073MZ23QX & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41452617 & B073QM98F5 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41474679 & B073SM1DVP & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41529678 & B073SMQ7TV & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 42266578 & B074P9CM5T & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41546978 & B074WFRH82 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\t 41637353 & B215071972 & 0 & 0 & 1 & 0 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A rowwise_df: 51481 × 8\n",
       "\n",
       "| isbn_id &lt;int&gt; | isbn &lt;chr&gt; | LOC &lt;int&gt; | OL &lt;int&gt; | GR &lt;int&gt; | AZ14 &lt;int&gt; | AZ18 &lt;int&gt; | btot &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 41407804 | 0000744395 | 0 | 0 | 1 | 0 |    0 |    1 |\n",
       "| 42062628 | 000074445X | 0 | 0 | 1 | 0 |    0 |    1 |\n",
       "| 11502887 | 0001004735 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 14397303 | 0001004743 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 19141493 | 0001034375 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 40937273 | 0001046403 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "|  6693084 | 0001049283 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 30197848 | 0001054783 | 0 | 1 | 0 | 0 |   75 |   76 |\n",
       "| 33321586 | 0001385208 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 31969676 | 0001660047 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 15553922 | 0001660926 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 30770041 | 0001661019 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 17792203 | 0001847694 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 26965565 | 0001848259 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "|  8473513 | 0001849123 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 34007959 | 0001956671 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 19235257 | 0001956795 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 41619337 | 0002034840 | 0 | 0 | 1 | 0 |    0 |    1 |\n",
       "|  2678626 | 0002219336 | 1 | 2 | 0 | 0 |    0 |    3 |\n",
       "|  9935945 | 0002315394 | 1 | 3 | 1 | 0 |    0 |    5 |\n",
       "| 36299108 | 0003302245 | 0 | 1 | 0 | 0 | 3900 | 3901 |\n",
       "| 19681411 | 0003395359 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 30397902 | 0003700860 | 0 | 2 | 1 | 0 |    0 |    3 |\n",
       "| 30858214 | 0003702936 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 12786661 | 0003708853 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 37344922 | 0004214072 | 0 | 2 | 0 | 0 |    0 |    2 |\n",
       "| 11125651 | 0004214501 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 12746265 | 0004244702 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| 29211569 | 0004247256 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "|  6837553 | 0004247264 | 0 | 1 | 0 | 0 |    0 |    1 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 42697277 | B01N1A58F4 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42017610 | B01N5JX21M | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42503286 | B01N7ZJ4MT | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41610140 | B01NAIYB6S | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41309603 | B01NBKD6ZI | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41945885 | B06VVSS7QH | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42699618 | B06WP1M6KT | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42159285 | B06X9HW4Z6 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41317162 | B06XFPGRC7 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41405924 | B06XQ4F351 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42593673 | B06XQ64QV8 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42278150 | B06XY3R81W | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41450793 | B0716WRPFV | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42002127 | B071RSDB49 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41347557 | B0728MXM3Q | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41464214 | B072F1WKW1 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41970572 | B072L78PHK | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42475729 | B072MQ8FL7 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41421625 | B072S79HNS | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41927152 | B072Z2KK5V | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41707133 | B07381C14X | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41349067 | B073GF59RD | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41928917 | B073GGNYFL | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42029193 | B073MZ23QX | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41452617 | B073QM98F5 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41474679 | B073SM1DVP | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41529678 | B073SMQ7TV | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 42266578 | B074P9CM5T | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41546978 | B074WFRH82 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "| 41637353 | B215071972 | 0 | 0 | 1 | 0 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "      isbn_id  isbn       LOC OL GR AZ14 AZ18 btot\n",
       "1     41407804 0000744395 0   0  1  0       0    1\n",
       "2     42062628 000074445X 0   0  1  0       0    1\n",
       "3     11502887 0001004735 0   1  0  0       0    1\n",
       "4     14397303 0001004743 0   1  0  0       0    1\n",
       "5     19141493 0001034375 0   1  0  0       0    1\n",
       "6     40937273 0001046403 0   1  0  0       0    1\n",
       "7      6693084 0001049283 0   1  0  0       0    1\n",
       "8     30197848 0001054783 0   1  0  0      75   76\n",
       "9     33321586 0001385208 0   1  0  0       0    1\n",
       "10    31969676 0001660047 0   1  0  0       0    1\n",
       "11    15553922 0001660926 0   1  0  0       0    1\n",
       "12    30770041 0001661019 0   1  0  0       0    1\n",
       "13    17792203 0001847694 0   1  0  0       0    1\n",
       "14    26965565 0001848259 0   1  0  0       0    1\n",
       "15     8473513 0001849123 0   1  0  0       0    1\n",
       "16    34007959 0001956671 0   1  0  0       0    1\n",
       "17    19235257 0001956795 0   1  0  0       0    1\n",
       "18    41619337 0002034840 0   0  1  0       0    1\n",
       "19     2678626 0002219336 1   2  0  0       0    3\n",
       "20     9935945 0002315394 1   3  1  0       0    5\n",
       "21    36299108 0003302245 0   1  0  0    3900 3901\n",
       "22    19681411 0003395359 0   1  0  0       0    1\n",
       "23    30397902 0003700860 0   2  1  0       0    3\n",
       "24    30858214 0003702936 0   1  0  0       0    1\n",
       "25    12786661 0003708853 0   1  0  0       0    1\n",
       "26    37344922 0004214072 0   2  0  0       0    2\n",
       "27    11125651 0004214501 0   1  0  0       0    1\n",
       "28    12746265 0004244702 0   1  0  0       0    1\n",
       "29    29211569 0004247256 0   1  0  0       0    1\n",
       "30     6837553 0004247264 0   1  0  0       0    1\n",
       "⋮     ⋮        ⋮          ⋮   ⋮  ⋮  ⋮    ⋮    ⋮   \n",
       "51452 42697277 B01N1A58F4 0   0  1  0    0    1   \n",
       "51453 42017610 B01N5JX21M 0   0  1  0    0    1   \n",
       "51454 42503286 B01N7ZJ4MT 0   0  1  0    0    1   \n",
       "51455 41610140 B01NAIYB6S 0   0  1  0    0    1   \n",
       "51456 41309603 B01NBKD6ZI 0   0  1  0    0    1   \n",
       "51457 41945885 B06VVSS7QH 0   0  1  0    0    1   \n",
       "51458 42699618 B06WP1M6KT 0   0  1  0    0    1   \n",
       "51459 42159285 B06X9HW4Z6 0   0  1  0    0    1   \n",
       "51460 41317162 B06XFPGRC7 0   0  1  0    0    1   \n",
       "51461 41405924 B06XQ4F351 0   0  1  0    0    1   \n",
       "51462 42593673 B06XQ64QV8 0   0  1  0    0    1   \n",
       "51463 42278150 B06XY3R81W 0   0  1  0    0    1   \n",
       "51464 41450793 B0716WRPFV 0   0  1  0    0    1   \n",
       "51465 42002127 B071RSDB49 0   0  1  0    0    1   \n",
       "51466 41347557 B0728MXM3Q 0   0  1  0    0    1   \n",
       "51467 41464214 B072F1WKW1 0   0  1  0    0    1   \n",
       "51468 41970572 B072L78PHK 0   0  1  0    0    1   \n",
       "51469 42475729 B072MQ8FL7 0   0  1  0    0    1   \n",
       "51470 41421625 B072S79HNS 0   0  1  0    0    1   \n",
       "51471 41927152 B072Z2KK5V 0   0  1  0    0    1   \n",
       "51472 41707133 B07381C14X 0   0  1  0    0    1   \n",
       "51473 41349067 B073GF59RD 0   0  1  0    0    1   \n",
       "51474 41928917 B073GGNYFL 0   0  1  0    0    1   \n",
       "51475 42029193 B073MZ23QX 0   0  1  0    0    1   \n",
       "51476 41452617 B073QM98F5 0   0  1  0    0    1   \n",
       "51477 41474679 B073SM1DVP 0   0  1  0    0    1   \n",
       "51478 41529678 B073SMQ7TV 0   0  1  0    0    1   \n",
       "51479 42266578 B074P9CM5T 0   0  1  0    0    1   \n",
       "51480 41546978 B074WFRH82 0   0  1  0    0    1   \n",
       "51481 41637353 B215071972 0   0  1  0    0    1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bl %>% collect() %>% rowwise() %>% mutate(\n",
    "  btot = sum(c_across(!starts_with(\"isbn\")))\n",
    ") %>% slice_max(btot, n=20)"
   ]
  }
 ],
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