Stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# report stats, monitor change\n",
    "\n",
    "record properties of ``treedb.sqlite3``\n",
    "loaded from [Glottolog](https://glottolog.org)\n",
    "[master repo](https://github.com/glottolog/glottolog) commit\n",
    "and monitor changes by diffing this\n",
    "\n",
    "<div style=\"text-align: right\">\n",
    "  <a href=\"https://nbviewer.jupyter.org/github/glottolog/treedb/blob/master/Stats.ipynb\">\n",
    "      latest version from GitHub in nbviewer\n",
    "  </a>\n",
    "</div>\n",
    "\n",
    "### contents\n",
    "1. [clone/checkout](#clone/checkout)\n",
    "2. [load](#load)\n",
    "3. [treedb.sqlite3](#treedb.sqlite3)\n",
    "4. [\\_\\_dataset\\_\\_](#__dataset__)\n",
    "5. [\\_\\_producer\\_\\_](#__producer__)\n",
    "6. [treedb.raw](#treedb.raw)\n",
    "7. [languoid](#languoid)\n",
    "8. [macroarea](#macroarea)\n",
    "9. [country](#country)\n",
    "10. [altname](#altname)\n",
    "11. [source](#source)\n",
    "12. [link](#link)\n",
    "13. [endangerment](#endangerment)\n",
    "14. [denormalized](#denormalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import collections\n",
    "\n",
    "import pandas as pd\n",
    "import sqlalchemy as sa\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import treedb\n",
    "import treedb.raw\n",
    "\n",
    "treedb.configure_logging(log_sql=False)\n",
    "\n",
    "treedb.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clone/checkout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8.52 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " CompletedProcess(args=['git', 'checkout', '-B', 'treedb', 'v4.2.1'], returncode=0))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "TARGET = '../glottolog/'\n",
    "\n",
    "TAG = 'v4.2.1'\n",
    "\n",
    "treedb.checkout_or_clone(TAG, target=TARGET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<treedb.proxies.PathProxy path='../glottolog/languoids/tree' inode=28991922601764532>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treedb.set_root(TARGET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('abin1243',), <DirEntry 'md.ini'>, <treedb.files.ConfigParser at 0x54de130>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(treedb.iterfiles())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('abin1243',),\n",
       " {'id': 'abin1243',\n",
       "  'parent_id': None,\n",
       "  'level': 'language',\n",
       "  'name': 'Abinomn',\n",
       "  'hid': 'bsa',\n",
       "  'iso639_3': 'bsa',\n",
       "  'latitude': -2.92281,\n",
       "  'longitude': 138.891,\n",
       "  'macroareas': ['Papunesia'],\n",
       "  'countries': [{'name': 'Indonesia', 'id': 'ID'}],\n",
       "  'links': [{'url': 'http://endangeredlanguages.com/lang/1763',\n",
       "    'title': 'Abinomn',\n",
       "    'scheme': 'http'},\n",
       "   {'url': 'https://www.wikidata.org/entity/Q56648',\n",
       "    'title': None,\n",
       "    'scheme': 'https'},\n",
       "   {'url': 'https://en.wikipedia.org/wiki/Abinomn_language',\n",
       "    'title': None,\n",
       "    'scheme': 'https'}],\n",
       "  'sources': {'glottolog': [{'bibfile': 'hh',\n",
       "     'bibkey': 'e:Lagerberg:Moegip',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'h:SilzerClouse:Index',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'h:SilzerHeikkinen:Irian',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'hv:Foley:Northwest-New-Guinea',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'hvtyp:DonohueMusgrave:Melanesia',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'w:Fiwei:Abinomn',\n",
       "     'pages': None,\n",
       "     'trigger': None}]},\n",
       "  'altnames': {'multitree': [{'name': '\"Baso\"', 'lang': None},\n",
       "    {'name': 'Abinomn', 'lang': None},\n",
       "    {'name': 'Avinomen', 'lang': None},\n",
       "    {'name': 'Foja', 'lang': None},\n",
       "    {'name': 'Foya', 'lang': None}],\n",
       "   'lexvo': [{'name': 'Abinomn', 'lang': 'en'},\n",
       "    {'name': 'Abinomn language', 'lang': 'en'},\n",
       "    {'name': 'Abinomneg', 'lang': 'br'},\n",
       "    {'name': 'Lingua abinomn', 'lang': 'gl'},\n",
       "    {'name': 'Llingua Abinomn', 'lang': 'ast'}],\n",
       "   'hhbib_lgcode': [{'name': 'Baso', 'lang': None}],\n",
       "   'elcat': [{'name': '\"Baso\"', 'lang': None},\n",
       "    {'name': 'Abinomn', 'lang': None},\n",
       "    {'name': 'Avinomen', 'lang': None},\n",
       "    {'name': 'Foja', 'lang': None},\n",
       "    {'name': 'Foya', 'lang': None}]},\n",
       "  'triggers': {'lgcode': ['macrohistory', 'moegip']},\n",
       "  'identifier': {'multitree': 'bsa', 'endangeredlanguages': '1763'},\n",
       "  'classification': {'familyrefs': [{'bibfile': 'hh',\n",
       "     'bibkey': 'h:SilzerClouse:Index',\n",
       "     'pages': None,\n",
       "     'trigger': None},\n",
       "    {'bibfile': 'hh',\n",
       "     'bibkey': 'hvtyp:DonohueMusgrave:Melanesia',\n",
       "     'pages': None,\n",
       "     'trigger': None}]},\n",
       "  'endangerment': {'status': 'shifting',\n",
       "   'source': {'name': 'ElCat', 'bibfile': None, 'bibkey': None, 'pages': None},\n",
       "   'date': datetime.datetime(2017, 8, 19, 8, 16, 16),\n",
       "   'comment': 'Abinomn (1763-bsa) = Endangered (20 percent certain, based on the evidence available)'},\n",
       "  'hh_ethnologue_comment': None,\n",
       "  'iso_retirement': None})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(treedb.iterlanguoids())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<treedb.proxies.SQLiteEngineProxy filename='treedb.sqlite3' size=None>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treedb.set_engine('treedb.sqlite3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:02:03.956089\n",
      "Wall time: 2min 4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<treedb.proxies.SQLiteEngineProxy filename='treedb.sqlite3' size=68775936>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "engine = treedb.load(rebuild=False, exclude_raw=False)\n",
    "engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 6, 11, 19, 24, 42, 215165)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.file_mtime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.58984375"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.file_size(as_megabytes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 526 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'23beb0a953017442928646fcedad304648111496791914014f60c4ebc2db9a0e'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time engine.file_sha256()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 407 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'strong:sha256:03ae265f46c79a5fd9ae44ada3ed50840dbdb897384b7ac57456ba12b6206a71'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.raw.checksum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.13 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'weak:sha256:9cf661e51d8cd6d8ef1f5e93dbbf4612a8a7e06712c56747d7a280b2d83f503b'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.raw.checksum(weak=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid_glottocodeCheck: OK\n",
      "valid_iso639_3Check: OK\n",
      "valid_hidCheck: OK\n",
      "clean_nameCheck: OK\n",
      "family_parentCheck: OK\n",
      "language_parentCheck: OK\n",
      "dialect_parentCheck: OK\n",
      "family_childrenCheck: OK\n",
      "family_languagesCheck: OK\n",
      "bookkeeping_no_childrenCheck: OK\n",
      "no_empty_filesCheck: OK\n",
      "Wall time: 2.51 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'path_json:id:sha256:1b85081a5fc1ddedf91f7099524a12c0d8941d6ccf3ff9feb889aec46b863e43'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.checksum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'path_json:id:sha256:1b85081a5fc1ddedf91f7099524a12c0d8941d6ccf3ff9feb889aec46b863e43'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.checksum(source='raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## treedb.sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO@treedb.logging_] treedb version: 1.3.2\n",
      "SELECT sqlite_version() AS sqlite_version_1\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3.31.1'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treedb import count, select, text\n",
    "\n",
    "treedb.configure_logging(level='INFO', log_sql=True)\n",
    "    \n",
    "select([sa.func.sqlite_version()]).scalar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRAGMA application_id\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1122"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_id = text('PRAGMA application_id').scalar()\n",
    "\n",
    "assert application_id == 1122 == 0x462\n",
    "assert application_id == sum(ord(c) for c in treedb.Dataset.__tablename__)\n",
    "assert treedb.Dataset.__tablename__ == '__dataset__'\n",
    "\n",
    "application_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.name \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name NOT LIKE ? ORDER BY sqlite_master.name\n",
      "('table', 'sqlite_%')\n",
      "[INFO@treedb.shortcuts] pandas version: 1.0.4\n",
      "SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM __dataset__) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM __producer__) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM _file) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM _option) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM _value) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM altname) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM altnameprovider) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM bibfile) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM bibitem) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM classificationcomment) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM classificationref) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM country) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM endangerment) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM endangerment_source) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM ethnologuecomment) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM identifier) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM identifiersite) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM isoretirement) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM isoretirement_changeto) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM languoid) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM languoid_country) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM languoid_macroarea) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM link) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM macroarea) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM source) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM sourceprovider) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM timespan) AS n_rows UNION ALL SELECT ? AS table_name, (SELECT count(*) AS n \n",
      "FROM \"trigger\") AS n_rows\n",
      "('__dataset__', '__producer__', '_file', '_option', '_value', 'altname', 'altnameprovider', 'bibfile', 'bibitem', 'classificationcomment', 'classificationref', 'country', 'endangerment', 'endangerment_source', 'ethnologuecomment', 'identifier', 'identifiersite', 'isoretirement', 'isoretirement_changeto', 'languoid', 'languoid_country', 'languoid_macroarea', 'link', 'macroarea', 'source', 'sourceprovider', 'timespan', 'trigger')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_rows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>__dataset__</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>__producer__</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_file</th>\n",
       "      <td>25228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_option</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_value</th>\n",
       "      <td>600096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altname</th>\n",
       "      <td>149531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altnameprovider</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bibfile</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bibitem</th>\n",
       "      <td>143220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classificationcomment</th>\n",
       "      <td>11791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classificationref</th>\n",
       "      <td>16043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endangerment</th>\n",
       "      <td>8165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endangerment_source</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnologuecomment</th>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identifier</th>\n",
       "      <td>20867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identifiersite</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isoretirement</th>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isoretirement_changeto</th>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>languoid</th>\n",
       "      <td>25228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>languoid_country</th>\n",
       "      <td>10988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>languoid_macroarea</th>\n",
       "      <td>20700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>link</th>\n",
       "      <td>28949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macroarea</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <td>188938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sourceprovider</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timespan</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trigger</th>\n",
       "      <td>30276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        n_rows\n",
       "table_name                    \n",
       "__dataset__                  1\n",
       "__producer__                 1\n",
       "_file                    25228\n",
       "_option                     53\n",
       "_value                  600096\n",
       "altname                 149531\n",
       "altnameprovider             11\n",
       "bibfile                     37\n",
       "bibitem                 143220\n",
       "classificationcomment    11791\n",
       "classificationref        16043\n",
       "country                    226\n",
       "endangerment              8165\n",
       "endangerment_source         11\n",
       "ethnologuecomment          620\n",
       "identifier               20867\n",
       "identifiersite               4\n",
       "isoretirement              344\n",
       "isoretirement_changeto     438\n",
       "languoid                 25228\n",
       "languoid_country         10988\n",
       "languoid_macroarea       20700\n",
       "link                     28949\n",
       "macroarea                    6\n",
       "source                  188938\n",
       "sourceprovider               1\n",
       "timespan                   134\n",
       "trigger                  30276"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treedb import pd_read_sql as read_sql\n",
    "\n",
    "read_sql(treedb.select_tables_nrows(), index_col='table_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.name \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name NOT LIKE ? ORDER BY sqlite_master.name\n",
      "('view', 'sqlite_%')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example\n",
      "path_json\n",
      "stats\n"
     ]
    }
   ],
   "source": [
    "treedb.print_rows(treedb.sqlite_master.select_views(), format_='{name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'sqlite_master')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM sqlite_master\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    }
   ],
   "source": [
    "from treedb import print_table_sql as print_sql\n",
    "\n",
    "print_sql('sqlite_master')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \\_\\_dataset\\_\\_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', '__dataset__')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE __dataset__ (\n",
      "\tid INTEGER NOT NULL CHECK (id = 1), \n",
      "\ttitle TEXT NOT NULL CHECK (title != ''), \n",
      "\tgit_commit VARCHAR(40) NOT NULL CHECK (length(git_commit) = 40), \n",
      "\tgit_describe TEXT NOT NULL CHECK (git_describe != ''), \n",
      "\tclean BOOLEAN NOT NULL, \n",
      "\texclude_raw BOOLEAN NOT NULL, \n",
      "\tPRIMARY KEY (id), \n",
      "\tUNIQUE (git_commit), \n",
      "\tUNIQUE (git_describe), \n",
      "\tCHECK (clean IN (0, 1)), \n",
      "\tCHECK (exclude_raw IN (0, 1))\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM __dataset__\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "from treedb import Dataset\n",
    "\n",
    "print_sql(Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT __dataset__.id, __dataset__.title, __dataset__.git_commit, __dataset__.git_describe, __dataset__.clean, __dataset__.exclude_raw \n",
      "FROM __dataset__\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__dataset__</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>Glottolog treedb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>git_commit</th>\n",
       "      <td>e49bc948c1195a8a5c5a85e1ea9293ea7412602e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>git_describe</th>\n",
       "      <td>v4.2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exclude_raw</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           __dataset__\n",
       "id                                                   1\n",
       "title                                 Glottolog treedb\n",
       "git_commit    e49bc948c1195a8a5c5a85e1ea9293ea7412602e\n",
       "git_describe                                    v4.2.1\n",
       "clean                                             True\n",
       "exclude_raw                                      False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset, = select([Dataset]).execute()\n",
    "\n",
    "pd.DataFrame.from_dict(dataset, orient='index', columns=['__dataset__'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \\_\\_producer\\_\\_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', '__producer__')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE __producer__ (\n",
      "\tid INTEGER NOT NULL CHECK (id = 1), \n",
      "\tname TEXT NOT NULL CHECK (name != ''), \n",
      "\tversion TEXT NOT NULL CHECK (version != ''), \n",
      "\tPRIMARY KEY (id), \n",
      "\tUNIQUE (name)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM __producer__\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "from treedb import Producer\n",
    "\n",
    "print_sql(Producer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT __producer__.id, __producer__.name, __producer__.version \n",
      "FROM __producer__\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__producer__</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>treedb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>1.3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        __producer__\n",
       "id                 1\n",
       "name          treedb\n",
       "version        1.3.2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "producer, = select([Producer]).execute()\n",
    "\n",
    "pd.DataFrame.from_dict(producer, orient='index', columns=['__producer__'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treedb.raw`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', '_file')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE _file (\n",
      "\tid INTEGER NOT NULL, \n",
      "\tglottocode VARCHAR(8) NOT NULL CHECK (length(glottocode) = 8), \n",
      "\tpath TEXT NOT NULL CHECK (length(path) >= 8 AND (length(path) + 1) % 9 = 0), \n",
      "\tsize INTEGER NOT NULL CHECK (size > 0), \n",
      "\tsha256 VARCHAR(64) NOT NULL CHECK (length(sha256) = 64), \n",
      "\tPRIMARY KEY (id), \n",
      "\tCHECK (substr(path, -length(glottocode)) = glottocode), \n",
      "\tUNIQUE (glottocode), \n",
      "\tUNIQUE (path), \n",
      "\tUNIQUE (sha256)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM _file\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25228\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', '_option')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE _option (\n",
      "\tid INTEGER NOT NULL, \n",
      "\tsection TEXT NOT NULL CHECK (section != ''), \n",
      "\toption TEXT NOT NULL CHECK (option != ''), \n",
      "\tis_lines BOOLEAN, \n",
      "\tPRIMARY KEY (id), \n",
      "\tUNIQUE (section, option), \n",
      "\tCHECK (is_lines IN (0, 1))\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM _option\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', '_value')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE _value (\n",
      "\tfile_id INTEGER NOT NULL, \n",
      "\toption_id INTEGER NOT NULL, \n",
      "\tline INTEGER NOT NULL CHECK (line > 0), \n",
      "\tvalue TEXT NOT NULL CHECK (value != ''), \n",
      "\tPRIMARY KEY (file_id, option_id, line), \n",
      "\tUNIQUE (file_id, line), \n",
      "\tFOREIGN KEY(file_id) REFERENCES _file (id), \n",
      "\tFOREIGN KEY(option_id) REFERENCES _option (id)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM _value\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600096\n"
     ]
    }
   ],
   "source": [
    "from treedb.raw import File, Option, Value\n",
    "\n",
    "for model in (File, Option, Value):\n",
    "    print_sql(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT _file.id, _file.glottocode, _file.path, _file.size, _file.sha256 \n",
      "FROM _file\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>glottocode</th>\n",
       "      <th>path</th>\n",
       "      <th>size</th>\n",
       "      <th>sha256</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abin1243</td>\n",
       "      <td>abin1243</td>\n",
       "      <td>1229</td>\n",
       "      <td>b7c19c6bc411908419022568fa021e39d64b1e9ab2e57c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abis1238</td>\n",
       "      <td>abis1238</td>\n",
       "      <td>1708</td>\n",
       "      <td>b0d8c9aca4d8dcca721e8dac7ff33c4f51f116ad300f35...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abkh1242</td>\n",
       "      <td>abkh1242</td>\n",
       "      <td>306</td>\n",
       "      <td>5dd28ae1265a0967b1a236d06c7fd6e7ac3b9e32d866dd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abkh1243</td>\n",
       "      <td>abkh1242/abkh1243</td>\n",
       "      <td>257</td>\n",
       "      <td>a5900355c69af22d94eaa0a147b95fc291270634bc033c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abaz1241</td>\n",
       "      <td>abkh1242/abkh1243/abaz1241</td>\n",
       "      <td>2450</td>\n",
       "      <td>1f9258cffd448ed9eb5b243d7252bb183aa89558904f8e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   glottocode                        path  size  \\\n",
       "id                                                \n",
       "1    abin1243                    abin1243  1229   \n",
       "2    abis1238                    abis1238  1708   \n",
       "3    abkh1242                    abkh1242   306   \n",
       "4    abkh1243           abkh1242/abkh1243   257   \n",
       "5    abaz1241  abkh1242/abkh1243/abaz1241  2450   \n",
       "\n",
       "                                               sha256  \n",
       "id                                                     \n",
       "1   b7c19c6bc411908419022568fa021e39d64b1e9ab2e57c...  \n",
       "2   b0d8c9aca4d8dcca721e8dac7ff33c4f51f116ad300f35...  \n",
       "3   5dd28ae1265a0967b1a236d06c7fd6e7ac3b9e32d866dd...  \n",
       "4   a5900355c69af22d94eaa0a147b95fc291270634bc033c...  \n",
       "5   1f9258cffd448ed9eb5b243d7252bb183aa89558904f8e...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([File]).limit(5), index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT _option.id, _option.section, _option.option, _option.is_lines \n",
      "FROM _option\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>section</th>\n",
       "      <th>option</th>\n",
       "      <th>is_lines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>core</td>\n",
       "      <td>name</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>core</td>\n",
       "      <td>hid</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>core</td>\n",
       "      <td>level</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>core</td>\n",
       "      <td>iso639-3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>core</td>\n",
       "      <td>latitude</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   section    option  is_lines\n",
       "id                            \n",
       "1     core      name     False\n",
       "2     core       hid     False\n",
       "3     core     level     False\n",
       "4     core  iso639-3     False\n",
       "5     core  latitude     False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([Option]).limit(5), index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT _value.file_id, _value.option_id, _value.line, _value.value \n",
      "FROM _value\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>line</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file_id</th>\n",
       "      <th>option_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Abinomn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>bsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>language</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>bsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-2.92281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   line     value\n",
       "file_id option_id                \n",
       "1       1             1   Abinomn\n",
       "        2             2       bsa\n",
       "        3             3  language\n",
       "        4             4       bsa\n",
       "        5             5  -2.92281"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([Value]).limit(5), index_col=['file_id', 'option_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT _option.section, _option.option, _value.line, _value.value \n",
      "FROM _file JOIN _value ON _file.id = _value.file_id JOIN _option ON _option.id = _value.option_id \n",
      "WHERE _file.glottocode = ?\n",
      "('abin1243',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>section</th>\n",
       "      <th>option</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">core</th>\n",
       "      <th>name</th>\n",
       "      <th>1</th>\n",
       "      <td>Abinomn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hid</th>\n",
       "      <th>2</th>\n",
       "      <td>bsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <th>3</th>\n",
       "      <td>language</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso639-3</th>\n",
       "      <th>4</th>\n",
       "      <td>bsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <th>5</th>\n",
       "      <td>-2.92281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <th>6</th>\n",
       "      <td>138.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macroareas</th>\n",
       "      <th>7</th>\n",
       "      <td>Papunesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countries</th>\n",
       "      <th>8</th>\n",
       "      <td>Indonesia (ID)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">links</th>\n",
       "      <th>9</th>\n",
       "      <td>[Abinomn](http://endangeredlanguages.com/lang/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>https://www.wikidata.org/entity/Q56648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Abinomn_language</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">sources</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">glottolog</th>\n",
       "      <th>12</th>\n",
       "      <td>**hh:e:Lagerberg:Moegip**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>**hh:h:SilzerClouse:Index**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>**hh:h:SilzerHeikkinen:Irian**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>**hh:hv:Foley:Northwest-New-Guinea**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>**hh:hvtyp:DonohueMusgrave:Melanesia**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>**hh:w:Fiwei:Abinomn**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">altnames</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">multitree</th>\n",
       "      <th>18</th>\n",
       "      <td>\"Baso\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Abinomn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Avinomen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Foja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Foya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">lexvo</th>\n",
       "      <th>23</th>\n",
       "      <td>Abinomn [en]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Abinomn language [en]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Abinomneg [br]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Lingua abinomn [gl]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Llingua Abinomn [ast]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hhbib_lgcode</th>\n",
       "      <th>28</th>\n",
       "      <td>Baso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">elcat</th>\n",
       "      <th>29</th>\n",
       "      <td>\"Baso\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Abinomn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Avinomen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Foja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Foya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">triggers</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">lgcode</th>\n",
       "      <th>34</th>\n",
       "      <td>macrohistory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>moegip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">identifier</th>\n",
       "      <th>multitree</th>\n",
       "      <th>36</th>\n",
       "      <td>bsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endangeredlanguages</th>\n",
       "      <th>37</th>\n",
       "      <td>1763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">classification</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">familyrefs</th>\n",
       "      <th>38</th>\n",
       "      <td>**hh:h:SilzerClouse:Index**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>**hh:hvtyp:DonohueMusgrave:Melanesia**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">endangerment</th>\n",
       "      <th>status</th>\n",
       "      <th>40</th>\n",
       "      <td>shifting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th>41</th>\n",
       "      <td>ElCat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>42</th>\n",
       "      <td>2017-08-19T08:16:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comment</th>\n",
       "      <th>43</th>\n",
       "      <td>Abinomn (1763-bsa) = Endangered (20 percent ce...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                     value\n",
       "section        option              line                                                   \n",
       "core           name                1                                               Abinomn\n",
       "               hid                 2                                                   bsa\n",
       "               level               3                                              language\n",
       "               iso639-3            4                                                   bsa\n",
       "               latitude            5                                              -2.92281\n",
       "               longitude           6                                               138.891\n",
       "               macroareas          7                                             Papunesia\n",
       "               countries           8                                        Indonesia (ID)\n",
       "               links               9     [Abinomn](http://endangeredlanguages.com/lang/...\n",
       "                                   10               https://www.wikidata.org/entity/Q56648\n",
       "                                   11       https://en.wikipedia.org/wiki/Abinomn_language\n",
       "sources        glottolog           12                            **hh:e:Lagerberg:Moegip**\n",
       "                                   13                          **hh:h:SilzerClouse:Index**\n",
       "                                   14                       **hh:h:SilzerHeikkinen:Irian**\n",
       "                                   15                 **hh:hv:Foley:Northwest-New-Guinea**\n",
       "                                   16               **hh:hvtyp:DonohueMusgrave:Melanesia**\n",
       "                                   17                               **hh:w:Fiwei:Abinomn**\n",
       "altnames       multitree           18                                               \"Baso\"\n",
       "                                   19                                              Abinomn\n",
       "                                   20                                             Avinomen\n",
       "                                   21                                                 Foja\n",
       "                                   22                                                 Foya\n",
       "               lexvo               23                                         Abinomn [en]\n",
       "                                   24                                Abinomn language [en]\n",
       "                                   25                                       Abinomneg [br]\n",
       "                                   26                                  Lingua abinomn [gl]\n",
       "                                   27                                Llingua Abinomn [ast]\n",
       "               hhbib_lgcode        28                                                 Baso\n",
       "               elcat               29                                               \"Baso\"\n",
       "                                   30                                              Abinomn\n",
       "                                   31                                             Avinomen\n",
       "                                   32                                                 Foja\n",
       "                                   33                                                 Foya\n",
       "triggers       lgcode              34                                         macrohistory\n",
       "                                   35                                               moegip\n",
       "identifier     multitree           36                                                  bsa\n",
       "               endangeredlanguages 37                                                 1763\n",
       "classification familyrefs          38                          **hh:h:SilzerClouse:Index**\n",
       "                                   39               **hh:hvtyp:DonohueMusgrave:Melanesia**\n",
       "endangerment   status              40                                             shifting\n",
       "               source              41                                                ElCat\n",
       "               date                42                                  2017-08-19T08:16:16\n",
       "               comment             43    Abinomn (1763-bsa) = Endangered (20 percent ce..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_file_values = select([Option.section, Option.option, Value.line, Value.value])\\\n",
    "                     .select_from(sa.join(File, Value).join(Option))\\\n",
    "                     .where(File.glottocode == sa.bindparam('glottocode'))\n",
    "\n",
    "read_sql(select_file_values, params={'glottocode': 'abin1243'},\n",
    "         index_col=['section', 'option', 'line'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT (length(_file.path) + ?) / ? AS path_depth, languoid.level, count(*) AS n_files \n",
      "FROM _file JOIN languoid ON _file.glottocode = languoid.id GROUP BY (length(_file.path) + ?) / ?, languoid.level ORDER BY path_depth, languoid.level\n",
      "(1, 9, 1, 9)\n",
      "[INFO@numexpr.utils] NumExpr defaulting to 4 threads.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_depth = File.path_depth()\n",
    "\n",
    "select_path_depths = select([path_depth,\n",
    "                             treedb.Languoid.level, count().label('n_files')])\\\n",
    "                     .select_from(sa.join(File, treedb.Languoid,\n",
    "                                          File.glottocode == treedb.Languoid.id))\\\n",
    "                     .group_by(path_depth, treedb.Languoid.level)\\\n",
    "                     .order_by('path_depth', 'level')\n",
    "\n",
    "_ = read_sql(select_path_depths, index_col=['path_depth', 'level'])\\\n",
    "    .unstack(fill_value=0).droplevel(0, axis='columns')[list(treedb.LEVEL)]\n",
    "\n",
    "_.plot.bar(stacked=True, figsize=(12, 3))\n",
    "\n",
    "(100 * _.div(_.sum(axis='columns'), axis='rows')).plot.bar(stacked=True, figsize=(12, 3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT _file.size AS file_size, count(*) AS n_files \n",
      "FROM _file GROUP BY _file.size ORDER BY file_size\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>file_size</th>\n",
       "      <td>3224.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>46101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count   min      max\n",
       "file_size  3224.0  65.0  46101.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_size = File.size.label('file_size')\n",
    "\n",
    "select_file_sizes = select([file_size, count().label('n_files')])\\\n",
    "                    .group_by(file_size)\\\n",
    "                    .order_by('file_size')\n",
    "\n",
    "_ = read_sql(select_file_sizes, index_col='file_size')\n",
    "\n",
    "_.plot.area(figsize=(12, 3), logx=True).xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "_.index.to_series().describe().to_frame().transpose()[['count', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT n_values, count(*) AS n_files \n",
      "FROM (SELECT _file.glottocode AS glottocode, count(DISTINCT _value.option_id) AS n_values \n",
      "FROM _file JOIN _value ON _file.id = _value.file_id GROUP BY _file.glottocode) GROUP BY n_values ORDER BY n_values\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_values</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  min   max\n",
       "n_values   35.0  2.0  36.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EoNQ5FsySJI0SFrnS6OSUDEmSJKmCZ5glSVIlz3xre2fBLEnbOIsZSRpaFsySJLXJr+yuzz/wtC2yYFYt/ucgSZK2NxbMkjQCPMsmSdsOC2ZJaoHvrkjS9mvYC+aImA+cB4wDLsvMJcM9BkmdZTEpSRrLhrVgjohxwIXAfwN6gRURcWNmPjyc49DoMJqLrE6+Xe5b7/V08rgYzceYpK3nv3ENl+E+w3w48GhmPg4QEdcAC4CWC2b/cWgg28txYSEvSdLwi8wcvgeLeC8wPzM/Wu5/APi9zPzTfvstBBaWu28CHhkkeh/g6Q4Ns5NZnc4zy6yhzjPLrKHOM2tsZHU6zyyzhjqvlazXZ+bkgTYM9xnmGKBts4o9My8BLmk5NKI7M7u2ZmBDkdXpPLPMGuo8s8wa6jyzxkZWp/PMMmuo87Y2a4dODKKGXuCApvtTgR8P8xgkSZKklg13wbwCmBER0yNiR+Ak4MZhHoMkSZLUsmGdkpGZL0XEnwK30ris3Fcyc3UHoluevjHMWZ3OM8usoc4zy6yhzjNrbGR1Os8ss4Y6b6uyhvVDf5IkSdK2ZrinZEiSJEnbFAtmSZIkqYIFsyRJklTBglmSJEmqsE0WzBFxUEQcExGT+rXPbyPr8IiYW9YPjog/i4jjOjTOpR3KeXsZ17Ft9v+9iNitrO8cEZ+OiH+JiM9FxO41s06PiAMG37OlrB0j4uSIeHe5/76I+FJELIqICW3kHRgRfxER50XE/4mIj9V9fpK0tSLitSM9hoFExN4jPQaNbWP52N/mCuaIOB24ATgNeCgiFjRt/oeaWZ8EzgcujojPAl8CJgGLI+ITNbNu7Lf8C/DfX75fM2t50/qpZVy7Ap+MiMV1soqvAL8q6+cBuwOfK21X1Mz6O+DeiPhuRPzPiBjwKyRbdAVwPHBGRHwVOBG4F5gLXFYnqBwX/xeYWPrvTONLcu6OiHdtxRjVprH8wjlWRcTuEbEkItZGxMayrClte3T4sW6puf9uEfHZiPhqRLyv37aLambtFxEXR8SFEbF3RHwqIlZFxLKI2L9m1l79lr2B5RGxZ0TsVTNrftP67hFxeUSsjIirI2LfmllLImKfst4VEY/TeO1+IiLeWTPr/oj464g4sE6/LWR1RcSdEfG1iDggIm6PiGciYkVEHNJG3qSI+ExErC45fRFxT0R8sI2sYTn+PfYHzBqVx/6rZOY2tQCrgEllfRrQDZxR7j/QRtY4YBfgl8BupX1nYGXNrPuBrwHvAt5ZbjeU9XfWzHqgaX0FMLmsvwZY1cbPbE3zOPtte7Du2Gj8oXUscDnQB3wLOAXYtWbWynI7HvgpMK7cjzZ+/qua+u8C3FXWf6eN42J3YAmwFthYljWlbY8OH8+31Nx/N+CzwFeB9/XbdlHNrP2Ai4ELgb2BT5Wf4zJg/5pZe/Vb9gZ6gD2BvWpmze/3u7gcWAlcDexbM2sJsE9Z7wIeBx4Fnmjj3+X9wF8DB3bg994F3FleMw4AbgeeKf/eD2kjbxLwGWB1yekD7gE+WDPnVuBsYL9+x8nZwO1tjOvQLSyHARtqZl1Xfp8n0PjCq+uAnV7+3dTM+haNky6Ly7F1dnmtOA24oWbWb4D1/ZYXy+3jdY+xpvXLgL8HXg+cCVxfM2tV0/qdwNyy/kagu2bWeuAfgSeB5WU8r2vz2F8O/D7wP4CngPeW9mOAu9vIuwH4II1vDv4z4G+AGcBVwD+M1PHvsT82jv1X5bbbcaQW4OF+9yeVA+ALtFH8DbRe7tfN2qH8Ym8H5pS2WgdMU9YPaRQae/f/5fYfZ4t5/wx8qKxfAXQ1HTwramb1L7gnAH8IfB3oq5n1ELBjea7PUgorGmeJ19TMWtX0ArIncF/z49TMsmjwhXOgrDFfNACPtLOtos8m4NvlZ99/eb5m1oP97n8C+D6N18m6x37za/+TVY/TQtZflH9Lb24+Vto8Lu7f0jjaGNdaYHxZv6fftlonXvqN678CFwE/Kb/HhR382bfz/9sP+91fUW53ANbWzOrY8e+xPzaO/Vf1bbfjSC3lAJzTr208sBTYVDPrXmCXsr5DU/vudQ/Cpr5TaRSoX+p/MNbI6KFxJmx9ud2vtE+qe+A0PZ8rgcfKc36x5P4/4K01s7b4ggbsXDPrzDKOJ4DTgTuAS2kUv5+smXUGjaLvkvKP5eU/ECYD36mZZdHgC+dg4xqTRQNwG/CXNJ3JB/al8cfUv7YxroeAGVvY9lTNrDU0vU6XtlNonFV/ot2fF/D3W3NclD4vv+5/gcb0uXZPlvTS+IPnz8trYzRtq/uu22nl93k0jXePvgi8A/g08NWaWZu9ttB4d3Y+cEXNrLtpvEN5Io3X/hNK+ztp4+wf8APg7WX9D4Bbm7bVLXI7dvxvo8d+rWOs9BnTx/6rctvtOFJL+eXst4Vtb6uZtdMW2veh6T/+Nsd5PDXfDmohcxdg+lb03xV4K40zm7Xe2m7KeGOHn9PrKGfpgD2A9wKHt5k1q/Q/aCvHZNFg0TBQ1pgvGmi8O/M5Gn9o/Dvw83LMfY6aU2tK3nuBN21h2wk1s/438O4B2ucD62pmfYYyta9f++8C17Zz3Db97O8BftJm/0/2W16ejrcfsLSNvHcB36AxlW4VcDOwEJhQM+eadn8mA2S9lca7eLcAB9H4XM0vymvYkW3mLS8Z33v5eKNxsuT0mlkdO/499sfGsf+qzHY7uriM1aXfi+bP+71o7tlGni+c9foP1wvn+Jo5o7loeEu/ouGNpb2douEg4N39jw2a5pa3kXdMJ/Iqsn5/tIyLxmdgZg/BcxxLWTM7ldWU15FjFjic307dmkXjD/fj2hxXc9bBNE4EjOWsN9P4nMdoG9dW/R5fydyazi4u29tCmeoxGvNGU1a/omHUjGt7y6qbR2Nq1CPA9TSmhi1o2lZ7mlon82i8WzAas0brcxzN41rb4WOsI3k0/kC/h8bFBD5LY5rg3wLfAT6xlVnf3g6yRuvPq+1xvSq33Y4uLtvjQpvz0ocjzyyztjaPDl6FqNN5Zpk1TGPr1JWzzBoDWc3LeCS9SkSs3NImGnOZRyzPLLOGOG9cZv4HQGb2lGuYXxsRry9ZdXUyzyyzhjrvpczcBPwqIh7LzF+W3Ocj4jdmbZdZr7Bglja3LzCPxoc+mgWND1eNZJ5ZZg1l3k8iYk5mPgiQmf8REe+h8eVHb25jXJ3MM8usoc77z4jYJTN/RePD8UDjizRoXDrTrO0v67faPTXt4jJWFxpflPH2LWy7eiTzzDJrKPPo4FWIOp1nllnDMLaOXTnLrLGR1bxECZEkSZI0gB1GegCSJEnSaGbBLEmSJFWwYJYkSZIqWDBL0hgVEVdGxHtHehyStK2zYJYkSZIqWDBL0giKiGkRsSYiLo2I1RFxW0TsPMB+MyNieb9+K8v630bEioh4KCIuiYjNvrAhInoiYp+y3hURd5X110TEV0r/ByJiQWmfFRHLI+LBiFgZETOG6EcgSaOeBbMkjbwZwIWZOQv4BfBH/XfIzDXAjhHxhtL0J8Cysv6lzJybmbNpfP3re2o89ieAb2fmXOAo4PMR8RrgY8B5mTkH6AJ623hekjQmWDBL0shbn+WbyoD7gGlb2G8Z8Mdl/U+Ab5T1oyLi3ohYBRwNzKrx2McCiyPiQeAuYCLwO8DdwF9FxNnA6zPz+RqZkjSm+NXYkjTyXmha30TjLPFAvgH8c0R8E8jMXBcRE4GLgK7MfCoiPkWj6O3vJX57kqR5ewB/lJmP9Nt/TUTcCxwP3BoRH83Mb9d6VpI0RniGWZK2EZn5GI2C+m/47dnll4vfpyNiErClq2L0AIeV9eYpH7cCp7087zkiDim3bwAez8zzgRuBt3ToaUjSNseCWZK2Ld8A3k+Zv5yZvwAuBVYB1wMrttDv08B5EfFdGkX3y/4OmACsjIiHyn1oTPl4qEzVOAhY2uHnIUnbjMjMkR6DJEmSNGp5hlmSJEmq4If+JGmUiYgLgbf1az4vM68YifFI0vbOKRmSJElSBadkSJIkSRUsmCVJkqQKFsySJElSBQtmSZIkqcL/B+nmQPvAgTUaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_nvalues = select([File.glottocode,\n",
    "                       count(Value.option_id.distinct()).label('n_values')])\\\n",
    "               .select_from(sa.join(File, Value))\\\n",
    "               .group_by(File.glottocode).c.n_values\n",
    "\n",
    "select_nvalues = select([file_nvalues, count().label('n_files')])\\\n",
    "                 .group_by(file_nvalues)\\\n",
    "                 .order_by(file_nvalues)\n",
    "\n",
    "_ = read_sql(select_nvalues, index_col='n_values')\n",
    "\n",
    "_.plot.bar(figsize=(12, 3))\n",
    "_.index.to_series().describe().to_frame().transpose()[['count', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT length(_value.value) AS value_length, count(*) AS n_values \n",
      "FROM _value GROUP BY length(_value.value) ORDER BY value_length\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value_length</th>\n",
       "      <td>632.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6215.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count  min     max\n",
       "value_length  632.0  1.0  6215.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_length = sa.func.length(Value.value).label('value_length')\n",
    "\n",
    "select_value_length = select([value_length, count().label('n_values')])\\\n",
    "                      .group_by(value_length)\\\n",
    "                      .order_by('value_length')\n",
    "\n",
    "_ = read_sql(select_value_length, index_col='value_length')\n",
    "\n",
    "_.plot.area(figsize=(12, 3), logx=True).xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "_.index.to_series().describe().to_frame().transpose()[['count', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO@treedb.raw.values] fetch statistics\n",
      "SELECT _option.section, _option.option, count(*) AS n \n",
      "FROM _option JOIN _value ON _option.id = _value.option_id GROUP BY _option.section, _option.option ORDER BY _option.section, n DESC\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "altnames               multitree              54,614\n",
      "altnames               hhbib_lgcode           29,050\n",
      "altnames               lexvo                  26,027\n",
      "altnames               elcat                  23,424\n",
      "altnames               aiatsis                6,542\n",
      "altnames               wals                   2,441\n",
      "altnames               wals other             2,097\n",
      "altnames               moseley & asher (1994) 2,089\n",
      "altnames               ruhlen (1987)          2,007\n",
      "altnames               glottolog              1,237\n",
      "altnames               ethnologue             3\n",
      "classification         subrefs                15,344\n",
      "classification         sub                    11,584\n",
      "classification         familyrefs             699\n",
      "classification         family                 207\n",
      "core                   links                  28,949\n",
      "core                   name                   25,228\n",
      "core                   level                  25,228\n",
      "core                   macroareas             20,700\n",
      "core                   countries              10,988\n",
      "core                   hid                    8,733\n",
      "core                   longitude              8,402\n",
      "core                   latitude               8,402\n",
      "core                   iso639-3               8,032\n",
      "core                   timespan               134\n",
      "core                   name_comment           14\n",
      "core                   speakers               1\n",
      "core                   name_pronunciation     1\n",
      "core                   location               1\n",
      "core                   comment                1\n",
      "endangerment           status                 8,165\n",
      "endangerment           source                 8,165\n",
      "endangerment           date                   8,165\n",
      "endangerment           comment                8,165\n",
      "hh_ethnologue_comment  isohid                 620\n",
      "hh_ethnologue_comment  ethnologue_versions    620\n",
      "hh_ethnologue_comment  comment_type           620\n",
      "hh_ethnologue_comment  comment                620\n",
      "identifier             multitree              14,906\n",
      "identifier             endangeredlanguages    2,999\n",
      "identifier             wals                   2,620\n",
      "identifier             languagelandscape      342\n",
      "iso_retirement         change_to              438\n",
      "iso_retirement         reason                 344\n",
      "iso_retirement         name                   344\n",
      "iso_retirement         effective              344\n",
      "iso_retirement         code                   344\n",
      "iso_retirement         change_request         335\n",
      "iso_retirement         remedy                 315\n",
      "iso_retirement         comment                232\n",
      "sources                glottolog              188,938\n",
      "triggers               lgcode                 30,089\n",
      "triggers               inlg                   187\n",
      "Wall time: 1.53 s\n"
     ]
    }
   ],
   "source": [
    "%time treedb.raw.print_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## languoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'languoid')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE languoid (\n",
      "\tid VARCHAR(8) NOT NULL CHECK (length(id) = 8), \n",
      "\tname VARCHAR NOT NULL CHECK (name != ''), \n",
      "\tlevel VARCHAR(8) NOT NULL, \n",
      "\tparent_id VARCHAR(8), \n",
      "\thid TEXT CHECK (length(hid) >= 3), \n",
      "\tiso639_3 VARCHAR(3) CHECK (length(iso639_3) = 3), \n",
      "\tlatitude FLOAT CHECK (latitude BETWEEN -90 AND 90), \n",
      "\tlongitude FLOAT CHECK (longitude BETWEEN -180 AND 180), \n",
      "\tPRIMARY KEY (id), \n",
      "\tCHECK ((latitude IS NULL) = (longitude IS NULL)), \n",
      "\tUNIQUE (name), \n",
      "\tCHECK (level IN ('family', 'language', 'dialect')), \n",
      "\tFOREIGN KEY(parent_id) REFERENCES languoid (id) DEFERRABLE INITIALLY DEFERRED, \n",
      "\tUNIQUE (hid), \n",
      "\tUNIQUE (iso639_3)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM languoid\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25228\n"
     ]
    }
   ],
   "source": [
    "from treedb import LEVEL, Languoid\n",
    "\n",
    "FAMILY, LANGUAGE, DIALECT = LEVEL\n",
    "\n",
    "print_sql(Languoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH RECURSIVE tree(parent_id, child_id) AS \n",
      "(SELECT root.id AS parent_id, root.id AS child_id \n",
      "FROM languoid AS root \n",
      "WHERE root.parent_id IS NULL UNION ALL SELECT tree.parent_id AS parent_id, child.id AS child_id \n",
      "FROM tree JOIN languoid AS child ON tree.child_id = child.parent_id)\n",
      " SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.level = ? AND languoid.parent_id IS NULL UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.level = ? AND languoid.parent_id IS NULL UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.parent_id IS NULL UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.level = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.level = ? AND languoid.parent_id IS NOT NULL UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM languoid \n",
      "WHERE languoid.level = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name NOT IN (?, ?, ?, ?, ?, ?, ?, ?) UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name != ? UNION ALL SELECT ? AS kind, count(*) AS n \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? AND root.name = ?\n",
      "('languoids', 'families', 'family', 'isolates', 'language', 'roots', 'languages', 'language', 'subfamilies', 'family', 'dialects', 'dialect', 'Spoken L1 Languages', 'language', 'Sign Language', 'Unclassifiable', 'Pidgin', 'Unattested', 'Artificial Language', 'Mixed Language', 'Speech Register', 'Bookkeeping', 'Sign Language', 'language', 'Sign Language', 'Unclassifiable', 'language', 'Unclassifiable', 'Pidgin', 'language', 'Pidgin', 'Unattested', 'language', 'Unattested', 'Artificial Language', 'language', 'Artificial Language', 'Mixed Language', 'language', 'Mixed Language', 'Speech Register', 'language', 'Speech Register', 'All', 'language', 'Bookkeeping', 'Bookkeeping', 'language', 'Bookkeeping')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25,228 languoids\n",
      "   242 families\n",
      "   187 isolates\n",
      "   429 roots\n",
      " 8,515 languages\n",
      " 4,234 subfamilies\n",
      "12,237 dialects\n",
      " 7,604 Spoken L1 Languages\n",
      "   196 Sign Language\n",
      "   123 Unclassifiable\n",
      "    81 Pidgin\n",
      "    66 Unattested\n",
      "    28 Artificial Language\n",
      "    14 Mixed Language\n",
      "    10 Speech Register\n",
      " 8,122 All\n",
      "   393 Bookkeeping\n",
      "Wall time: 1.62 s\n"
     ]
    }
   ],
   "source": [
    "%time treedb.print_languoid_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.id, languoid.name, languoid.level, languoid.parent_id, languoid.hid, languoid.iso639_3, languoid.latitude, languoid.longitude \n",
      "FROM languoid\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>level</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>hid</th>\n",
       "      <th>iso639_3</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3adt1234</th>\n",
       "      <td>3Ad-Tekles</td>\n",
       "      <td>dialect</td>\n",
       "      <td>nort3292</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aala1237</th>\n",
       "      <td>Aalawa</td>\n",
       "      <td>dialect</td>\n",
       "      <td>ramo1244</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aant1238</th>\n",
       "      <td>Aantantara</td>\n",
       "      <td>dialect</td>\n",
       "      <td>nort2920</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aari1238</th>\n",
       "      <td>Aari-Gayil</td>\n",
       "      <td>family</td>\n",
       "      <td>ahkk1235</td>\n",
       "      <td>aiz</td>\n",
       "      <td>aiz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aari1239</th>\n",
       "      <td>Aari</td>\n",
       "      <td>language</td>\n",
       "      <td>aari1238</td>\n",
       "      <td>aiw</td>\n",
       "      <td>aiw</td>\n",
       "      <td>5.95034</td>\n",
       "      <td>36.5721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name     level parent_id   hid iso639_3  latitude  longitude\n",
       "id                                                                          \n",
       "3adt1234  3Ad-Tekles   dialect  nort3292  None     None       NaN        NaN\n",
       "aala1237      Aalawa   dialect  ramo1244  None     None       NaN        NaN\n",
       "aant1238  Aantantara   dialect  nort2920  None     None       NaN        NaN\n",
       "aari1238  Aari-Gayil    family  ahkk1235   aiz      aiz       NaN        NaN\n",
       "aari1239        Aari  language  aari1238   aiw      aiw   5.95034    36.5721"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([Languoid]).limit(5), index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.id, languoid.name, languoid.level, languoid.parent_id, languoid.hid, languoid.iso639_3, languoid.latitude, languoid.longitude \n",
      "FROM languoid ORDER BY languoid.id\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>level</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>hid</th>\n",
       "      <th>iso639_3</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3adt1234</th>\n",
       "      <td>3Ad-Tekles</td>\n",
       "      <td>dialect</td>\n",
       "      <td>nort3292</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aala1237</th>\n",
       "      <td>Aalawa</td>\n",
       "      <td>dialect</td>\n",
       "      <td>ramo1244</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aant1238</th>\n",
       "      <td>Aantantara</td>\n",
       "      <td>dialect</td>\n",
       "      <td>nort2920</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aari1238</th>\n",
       "      <td>Aari-Gayil</td>\n",
       "      <td>family</td>\n",
       "      <td>ahkk1235</td>\n",
       "      <td>aiz</td>\n",
       "      <td>aiz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aari1239</th>\n",
       "      <td>Aari</td>\n",
       "      <td>language</td>\n",
       "      <td>aari1238</td>\n",
       "      <td>aiw</td>\n",
       "      <td>aiw</td>\n",
       "      <td>5.95034</td>\n",
       "      <td>36.5721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name     level parent_id   hid iso639_3  latitude  longitude\n",
       "id                                                                          \n",
       "3adt1234  3Ad-Tekles   dialect  nort3292  None     None       NaN        NaN\n",
       "aala1237      Aalawa   dialect  ramo1244  None     None       NaN        NaN\n",
       "aant1238  Aantantara   dialect  nort2920  None     None       NaN        NaN\n",
       "aari1238  Aari-Gayil    family  ahkk1235   aiz      aiz       NaN        NaN\n",
       "aari1239        Aari  language  aari1238   aiw      aiw   5.95034    36.5721"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([Languoid]).order_by('id').limit(5), index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.id, languoid.name, languoid.level, languoid.parent_id, languoid.hid, languoid.iso639_3, languoid.latitude, languoid.longitude \n",
      "FROM languoid ORDER BY languoid.name\n",
      " LIMIT ? OFFSET ?\n",
      "(5, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>level</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>hid</th>\n",
       "      <th>iso639_3</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gang1269</th>\n",
       "      <td>!Gã!nge</td>\n",
       "      <td>dialect</td>\n",
       "      <td>sero1239</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kwii1241</th>\n",
       "      <td>!Kwi</td>\n",
       "      <td>family</td>\n",
       "      <td>tuuu1241</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oung1238</th>\n",
       "      <td>!O!ung</td>\n",
       "      <td>language</td>\n",
       "      <td>book1242</td>\n",
       "      <td>oun</td>\n",
       "      <td>oun</td>\n",
       "      <td>-15.3</td>\n",
       "      <td>14.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abda1238</th>\n",
       "      <td>'Abd Al-Kuri</td>\n",
       "      <td>dialect</td>\n",
       "      <td>soqo1240</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aden1242</th>\n",
       "      <td>'Aden</td>\n",
       "      <td>dialect</td>\n",
       "      <td>jude1267</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name     level parent_id   hid iso639_3  latitude  longitude\n",
       "id                                                                            \n",
       "gang1269       !Gã!nge   dialect  sero1239  None     None       NaN        NaN\n",
       "kwii1241          !Kwi    family  tuuu1241  None     None       NaN        NaN\n",
       "oung1238        !O!ung  language  book1242   oun      oun     -15.3      14.35\n",
       "abda1238  'Abd Al-Kuri   dialect  soqo1240  None     None       NaN        NaN\n",
       "aden1242         'Aden   dialect  jude1267  None     None       NaN        NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_sql(select([Languoid]).order_by('name').limit(5), index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT child.level AS child_level, parent.level AS parent_level, count(*) AS n_languoids \n",
      "FROM languoid AS child LEFT OUTER JOIN languoid AS parent ON child.parent_id = parent.id GROUP BY child.level, parent.level ORDER BY child_level, parent_level\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">n_languoids</th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parent_level</th>\n",
       "      <th>NaN</th>\n",
       "      <th>dialect</th>\n",
       "      <th>family</th>\n",
       "      <th>language</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>0</td>\n",
       "      <td>1781</td>\n",
       "      <td>0</td>\n",
       "      <td>10456</td>\n",
       "      <td>12237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>242</td>\n",
       "      <td>0</td>\n",
       "      <td>4234</td>\n",
       "      <td>0</td>\n",
       "      <td>4476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>8328</td>\n",
       "      <td>0</td>\n",
       "      <td>8515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>429</td>\n",
       "      <td>1781</td>\n",
       "      <td>12562</td>\n",
       "      <td>10456</td>\n",
       "      <td>25228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             n_languoids                            all\n",
       "parent_level         NaN dialect family language       \n",
       "child_level                                            \n",
       "dialect                0    1781      0    10456  12237\n",
       "family               242       0   4234        0   4476\n",
       "language             187       0   8328        0   8515\n",
       "all                  429    1781  12562    10456  25228"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Child, Parent = (sa.orm.aliased(Languoid, name=n) for n in ('child', 'parent'))\n",
    "\n",
    "select_parent_levels = select([Child.level.label('child_level'),\n",
    "                               Parent.level.label('parent_level'),\n",
    "                               count().label('n_languoids')])\\\n",
    "                       .select_from(sa.outerjoin(Child, Parent,\n",
    "                                                 Child.parent_id == Parent.id))\\\n",
    "                       .group_by(Child.level, Parent.level)\\\n",
    "                       .order_by('child_level', 'parent_level')\n",
    "\n",
    "read_sql(select_parent_levels, index_col=['child_level', 'parent_level'])\\\n",
    "    .unstack(fill_value=0).assign(all=lambda x: x.sum(axis='columns'))\\\n",
    "    .pipe(lambda x: x.append(x.sum().rename('all')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.level AS level, count(*) AS n_languoids, count(languoid.iso639_3) AS n_isos \n",
      "FROM languoid GROUP BY languoid.level ORDER BY level\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_languoids</th>\n",
       "      <th>n_isos</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>12237</td>\n",
       "      <td>177</td>\n",
       "      <td>1.446433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>4476</td>\n",
       "      <td>45</td>\n",
       "      <td>1.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>8515</td>\n",
       "      <td>7810</td>\n",
       "      <td>91.720493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n_languoids  n_isos      ratio\n",
       "level                                   \n",
       "dialect         12237     177   1.446433\n",
       "family           4476      45   1.005362\n",
       "language         8515    7810  91.720493"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_nisos = select([Languoid.level.label('level'),\n",
    "                       count().label('n_languoids'),\n",
    "                       count(Languoid.iso639_3).label('n_isos')])\\\n",
    "               .group_by(Languoid.level)\\\n",
    "               .order_by('level')\n",
    "\n",
    "read_sql(select_nisos, index_col='level')\\\n",
    "    .assign(ratio=lambda x: 100 * x['n_isos'] / x['n_languoids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.level AS level, count(*) AS n_languoids, count(languoid.latitude) AS n_locations \n",
      "FROM languoid GROUP BY languoid.level ORDER BY level\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_languoids</th>\n",
       "      <th>n_locations</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>12237</td>\n",
       "      <td>260</td>\n",
       "      <td>2.124704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>4476</td>\n",
       "      <td>17</td>\n",
       "      <td>0.379803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>8515</td>\n",
       "      <td>8125</td>\n",
       "      <td>95.419847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n_languoids  n_locations      ratio\n",
       "level                                        \n",
       "dialect         12237          260   2.124704\n",
       "family           4476           17   0.379803\n",
       "language         8515         8125  95.419847"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_nlocations = select([Languoid.level.label('level'),\n",
    "                            count().label('n_languoids'),\n",
    "                            count(Languoid.latitude).label('n_locations')])\\\n",
    "                    .group_by(Languoid.level)\\\n",
    "                    .order_by('level')\n",
    "\n",
    "read_sql(select_nlocations, index_col='level')\\\n",
    "    .assign(ratio=lambda x: 100 * x['n_locations'] / x['n_languoids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.latitude, languoid.longitude \n",
      "FROM languoid \n",
      "WHERE languoid.level = ?\n",
      "('language',)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_latlon = select([Languoid.latitude, Languoid.longitude])\\\n",
    "                .where(Languoid.level == LANGUAGE)\n",
    "\n",
    "latitudes, longitudes = zip(*select_latlon.execute())\n",
    "    \n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.axis([-180, 180, -90, 90])\n",
    "plt.xticks(range(-180, 181, 60))\n",
    "plt.yticks(range(-90, 91, 30))\n",
    "plt.scatter(longitudes, latitudes, 1, 'black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH RECURSIVE tree(parent_id, child_id) AS \n",
      "(SELECT root.id AS parent_id, child.id AS child_id \n",
      "FROM languoid AS root LEFT OUTER JOIN languoid AS child ON root.id = child.parent_id \n",
      "WHERE root.parent_id IS NULL AND root.level = ? UNION ALL SELECT tree.parent_id AS parent_id, child.id AS child_id \n",
      "FROM tree JOIN languoid AS child ON tree.child_id = child.parent_id)\n",
      " SELECT root.id AS family_id, count(child.id) AS n_languages \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id LEFT OUTER JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? GROUP BY root.id \n",
      "HAVING count(child.id) >= ? ORDER BY n_languages\n",
      "('family', 'language', 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Family, Child, family_child = treedb.Languoid.parent_descendant(parent_root=True,\n",
    "                                                                parent_level=FAMILY)\n",
    "\n",
    "n_languages = count(Child.id).label('n_languages')\n",
    "\n",
    "select_family_nlanguages = select([Family.id.label('family_id'), n_languages])\\\n",
    "                           .select_from(family_child)\\\n",
    "                           .where(Child.level == LANGUAGE)\\\n",
    "                           .group_by(Family.id)\n",
    "\n",
    "select_top_families = select_family_nlanguages\\\n",
    "    .having(n_languages >= 100)\\\n",
    "    .order_by('n_languages')\n",
    "\n",
    "read_sql(select_top_families, index_col='family_id').plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH RECURSIVE tree(parent_id, child_id) AS \n",
      "(SELECT root.id AS parent_id, child.id AS child_id \n",
      "FROM languoid AS root LEFT OUTER JOIN languoid AS child ON root.id = child.parent_id \n",
      "WHERE root.parent_id IS NULL AND root.level = ? UNION ALL SELECT tree.parent_id AS parent_id, child.id AS child_id \n",
      "FROM tree JOIN languoid AS child ON tree.child_id = child.parent_id)\n",
      " SELECT n_languages AS family_size, count(*) AS n_families \n",
      "FROM (SELECT root.id AS family_id, count(child.id) AS n_languages \n",
      "FROM tree JOIN languoid AS root ON tree.parent_id = root.id LEFT OUTER JOIN languoid AS child ON tree.child_id = child.id \n",
      "WHERE child.level = ? GROUP BY root.id) GROUP BY n_languages ORDER BY family_size\n",
      "('family', 'language')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>family_size</th>\n",
       "      <td>56.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1433.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count  min     max\n",
       "family_size   56.0  2.0  1433.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "family_size = select_family_nlanguages.c.n_languages.label('family_size')\n",
    "\n",
    "select_family_sizes = select([family_size, count().label('n_families')])\\\n",
    "                      .group_by(family_size)\\\n",
    "                      .order_by('family_size')\n",
    "\n",
    "_ = read_sql(select_family_sizes, index_col='family_size')\n",
    "\n",
    "_.plot.area(figsize=(12, 3), logx=True).xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "_.index.to_series().describe().to_frame().transpose()[['count', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH levels AS \n",
      "(SELECT ? AS level UNION ALL SELECT ? AS level UNION ALL SELECT ? AS level)\n",
      " SELECT parent.id AS parent_id, parent.level AS parent_level, levels.level AS child_level, count(child.id) AS n_children \n",
      "FROM languoid AS parent JOIN levels ON 1 LEFT OUTER JOIN languoid AS child ON parent.id = child.parent_id AND child.level = levels.level GROUP BY parent.id, parent.level, levels.level\n",
      "('family', 'language', 'dialect')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"3\" halign=\"left\">max</th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_level</th>\n",
       "      <th>dialect</th>\n",
       "      <th>family</th>\n",
       "      <th>language</th>\n",
       "      <th>dialect</th>\n",
       "      <th>family</th>\n",
       "      <th>language</th>\n",
       "      <th>dialect</th>\n",
       "      <th>family</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parent_level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>1781</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>0</td>\n",
       "      <td>4234</td>\n",
       "      <td>8328</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>393</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.945934</td>\n",
       "      <td>1.86059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>10456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.227951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sum                     max                      mean  \\\n",
       "child_level  dialect family language dialect family language   dialect   \n",
       "parent_level                                                             \n",
       "dialect         1781      0        0      22      0        0  0.145542   \n",
       "family             0   4234     8328       0     45      393  0.000000   \n",
       "language       10456      0        0      32      0        0  1.227951   \n",
       "\n",
       "                                 \n",
       "child_level     family language  \n",
       "parent_level                     \n",
       "dialect       0.000000  0.00000  \n",
       "family        0.945934  1.86059  \n",
       "language      0.000000  0.00000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levels = sa.union_all(*[select([sa.literal(l).label('level')]) for l in LEVEL])\\\n",
    "         .cte(name='levels')\n",
    "\n",
    "select_nchildren = select([Parent.id.label('parent_id'),\n",
    "                           Parent.level.label('parent_level'),\n",
    "                           levels.c.level.label('child_level'),\n",
    "                           count(Child.id).label('n_children')])\\\n",
    "                   .select_from(sa.join(Parent, levels, sa.true())\n",
    "                        .outerjoin(Child, sa.and_(Parent.id == Child.parent_id, \n",
    "                                                  Child.level == levels.c.level)))\\\n",
    "                   .group_by(Parent.id, Parent.level, levels.c.level)\n",
    "\n",
    "read_sql(select_nchildren)\\\n",
    "    .pivot_table(index='parent_level', columns='child_level', values='n_children',\n",
    "                 aggfunc=['sum', 'max', 'mean'], fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH RECURSIVE tree(parent_id, child_id) AS \n",
      "(SELECT parent.id AS parent_id, child.id AS child_id \n",
      "FROM languoid AS parent LEFT OUTER JOIN languoid AS child ON parent.id = child.parent_id UNION ALL SELECT tree.parent_id AS parent_id, child.id AS child_id \n",
      "FROM tree JOIN languoid AS child ON tree.child_id = child.parent_id)\n",
      " SELECT anon_1.parent_level, min(anon_1.n_descendants) AS min, max(anon_1.n_descendants) AS max, CAST(sum(anon_1.n_descendants) AS FLOAT) / count(anon_1.n_descendants) AS mean \n",
      "FROM (SELECT parent.id AS parent_id, parent.level AS parent_level, count(child.id) AS n_descendants \n",
      "FROM tree JOIN languoid AS parent ON tree.parent_id = parent.id LEFT OUTER JOIN languoid AS child ON tree.child_id = child.id GROUP BY parent.id, parent.level) AS anon_1 GROUP BY parent_level ORDER BY parent_level\n",
      "()\n",
      "WITH RECURSIVE tree(parent_id, child_id) AS \n",
      "(SELECT parent.id AS parent_id, child.id AS child_id \n",
      "FROM languoid AS parent LEFT OUTER JOIN languoid AS child ON parent.id = child.parent_id UNION ALL SELECT tree.parent_id AS parent_id, child.id AS child_id \n",
      "FROM tree JOIN languoid AS child ON tree.child_id = child.parent_id)\n",
      " SELECT ? AS parent_level, min(anon_1.n_descendants) AS min, max(anon_1.n_descendants) AS max, CAST(sum(anon_1.n_descendants) AS FLOAT) / count(anon_1.n_descendants) AS mean \n",
      "FROM (SELECT parent.id AS parent_id, parent.level AS parent_level, count(child.id) AS n_descendants \n",
      "FROM tree JOIN languoid AS parent ON tree.parent_id = parent.id LEFT OUTER JOIN languoid AS child ON tree.child_id = child.id GROUP BY parent.id, parent.level) AS anon_1\n",
      "('total',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">n_descendants</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parent_level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0.204462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>1</td>\n",
       "      <td>4750</td>\n",
       "      <td>33.029267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1.437111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>0</td>\n",
       "      <td>4750</td>\n",
       "      <td>6.444348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             n_descendants                 \n",
       "                       min   max       mean\n",
       "parent_level                               \n",
       "dialect                  0    57   0.204462\n",
       "family                   1  4750  33.029267\n",
       "language                 0   125   1.437111\n",
       "total                    0  4750   6.444348"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Parent, Child, parent_child = treedb.Languoid.parent_descendant()\n",
    "\n",
    "select_ndescendants = select([Parent.id.label('parent_id'),\n",
    "                              Parent.level.label('parent_level'),\n",
    "                              count(Child.id).label('n_descendants')])\\\n",
    "                      .select_from(parent_child)\\\n",
    "                      .group_by(Parent.id, Parent.level)\\\n",
    "                      .alias()\n",
    "\n",
    "n_descendants = select_ndescendants.c.n_descendants\n",
    "d_min = sa.func.min(n_descendants).label('min')\n",
    "d_max = sa.func.max(n_descendants).label('max')\n",
    "d_mean = (sa.func.sum(n_descendants).cast(sa.Float) / count(n_descendants)).label('mean')\n",
    "\n",
    "select_level_mean_descendants = select([select_ndescendants.c.parent_level,\n",
    "                                        d_min, d_max, d_mean])\\\n",
    "                                .group_by(select_nchildren.c.parent_level)\\\n",
    "                                .order_by(select_nchildren.c.parent_level)\n",
    "\n",
    "select_total_mean_descendants = select([sa.literal('total').label('parent_level'),\n",
    "                                        d_min, d_max, d_mean])\n",
    "\n",
    "select_mean_descendants = [select_level_mean_descendants, select_total_mean_descendants]\n",
    "\n",
    "_ = pd.concat([read_sql(q, index_col='parent_level') for q in select_mean_descendants])\n",
    "_.columns = pd.MultiIndex.from_product([['n_descendants'], _.columns])\n",
    "_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## macroarea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'macroarea')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE macroarea (\n",
      "\tname VARCHAR(13) NOT NULL, \n",
      "\tPRIMARY KEY (name), \n",
      "\tCHECK (name IN ('Africa', 'Australia', 'Eurasia', 'North America', 'Papunesia', 'South America'))\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM macroarea\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'languoid_macroarea')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE languoid_macroarea (\n",
      "\tlanguoid_id VARCHAR(8) NOT NULL, \n",
      "\tmacroarea_name VARCHAR(13) NOT NULL, \n",
      "\tPRIMARY KEY (languoid_id, macroarea_name), \n",
      "\tFOREIGN KEY(languoid_id) REFERENCES languoid (id), \n",
      "\tFOREIGN KEY(macroarea_name) REFERENCES macroarea (name)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM languoid_macroarea\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20700\n"
     ]
    }
   ],
   "source": [
    "from treedb.models import Macroarea, languoid_macroarea\n",
    "\n",
    "print_sql(Macroarea)\n",
    "print_sql(languoid_macroarea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT macroarea.name AS macroarea, count(*) AS n_languages \n",
      "FROM macroarea LEFT OUTER JOIN (languoid_macroarea JOIN languoid ON languoid.id = languoid_macroarea.languoid_id) ON macroarea.name = languoid_macroarea.macroarea_name \n",
      "WHERE languoid.level = ? GROUP BY macroarea.name ORDER BY n_languages DESC\n",
      "('language',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_languages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macroarea</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>2347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papunesia</th>\n",
       "      <td>2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eurasia</th>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               n_languages\n",
       "macroarea                 \n",
       "Africa                2347\n",
       "Papunesia             2210\n",
       "Eurasia               1970\n",
       "North America          787\n",
       "South America          712\n",
       "Australia              386"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bEmTEd8II+9Iv0Kb4SwSuruG4nKWE2QNtS1WUj9YAcNOkyZx1wSXV1tngWM2MWV8QERlJcXER/3l3NvaICHZs38qDk27hvc9/4PM5HzPi1PH84a778Hg8lBQX4fJ4w8VipdDpiQGOvvPOO9lt2rTxFBYWyoABA1Kuvfba3LZt2+qpYoDRgnWyGH6As4E2gTalNohg6yvbt6xUvZICbUtV1GVKOPaMs4mINCJBu10unn5kChvXO7BarezYZhwk79t/AI/d9yfcbjfjJpxLUp/U4/X35pd2a17g3PHMM8/Y58+f3wxg3759YevXr49o27btMbPem6Zu6CnhyZARfxHwPSEmVmWMsqwLyVT0VquNskgKTueJ/sWRkVHHr99+7RVaJrTmo69/5t35P+ByGWdGBw0fyesfz6d123b85c9/5LOP3z9eR6Hk0y+/6bLgp4Utly9fnrVx48YNycnJxTpMTHCg/wh1xQgF8wHVpMoKdoZbNoTkFKd9x05kOlYD8N3nn1VZrjA/n4TWbbBYLMyb9QEej/F2c3bvpEVCKy69Op2Lr7yOzHVrflcvtnnL8GPesFarVq2KWLNmTXRl7WsaHj0lrAsZ8WcAs4CQjneUZNnl98l7f44hmE3FNawRY0/j7v/L4La7p/DY/Xfx2ovPkTpgcJX1L0+/mXtvvZ5v5n/KkBGjiIwydGf5L4uYMf0FbGFhREVF8+Tz00+oN3LsaXz09uuMGT4osWvXri369++vp4JBgj44Wlsy4k8FPgeiaioa7HgVR7s53600UUJjPzhaG9rFR+5oFWsP+ulzUzg4qqeEtcFYYJ9HIxArAIvQLFEO5gTajmBnb15x58OFzhaBtkOjBct/MuIHA18AjSrSwXDLht2BtiEUyDla3PXIsVKdtivAaMHyh4z4fsBXBEm0BTMZaVlXY8hgDShgT25xt6NFpY3uMxBKaMGqiYz4BIxDoY1ySnCKbAnag6PBhkLJrtzi7oVOd6NYEghFtGBVR0a8FXgfaLTxozrI4XaBtiGUUErJriNF3V0eb4OHttb4IVgi4hGR1SKyTkQ+EpGg/HYRkcX10OxTwGn10G7QEIa7UxQletu+Frg83vCdh4u66R32hsefc1jFSqlTAETkHYxMxM/Vq1V1QCk1wtQGM+IvAR4wtc0gRATLAMuW7Yu8fftWVy7s9DRT+3V9+0uNZfp3bM51f5jEfY8+CcDM6S9SVHSs0vjtVfHrLz8TFhbGKYOHAfDI5DsYc/oEzji35kRF330xj3tuvY45Pyyla49eJ7x2rNQdtzevpH37ZpEntcv6008/Rb3++ustZ8yYsetk2mkq1HZKuBDoISLni8hSEVklIt+KSBsAEckQkbdE5HsR2Swif/DdHysi88oaEZGXROQG33W2iDwuIitFxCEiSb770SLyuoj86uvnQt/9PiKyzDfqWysiPX33C30/Y0Tku3Lt1T6FVkZ8EjCj1vVClFEWx5FA21AZ4XY733352fHIDLXF7Xaz/JefWbN8WZ3qfzl3FgOGDOfLuZ9U+vqhQme7vKLSGhOzVoXL5WLMmDFFWqz8x2/BEhEbcDbgAH4GhiulBmCs8UwpV7QfcC6QBjwqIu39aP6QUmogRtC7+3z3/gJ8r5QaAowD/iEi0RgjvH/5Rn2DgYrb8iXAxb72xgHTpDbZATLiYzGcmWP9rhPiDLVkBdqESrFabUy8Op23//vy717L2b2TP1x5IRPPGMkfrryQvXuM//lHJt/BPx7/Czdffj5T7riJj95+g7dee4XLJ4xm5VJj1WDF0sVcf9GZnDPyFL6Z/2mlfRcdK2TVr0vJePbFEwTr119+5qaJ53L/7Tdy/pjB3HP/lO7/fvmV1qmpqcm9evVKWb9+vR0gJyfHNmHChO59+/ZN7tu3b/LXX38dDXDPPfe0v+qqqzqPHDmy5yWXXNJ13rx5sePGjesBkJeXZ5k4cWKXXr16pfTq1StlxowZzQCuueaaTn379k3u0aNHn8mTJ/vz/9Ro8WdKGCkiq33XC4H/Ab2BD0SkHYZ7yvZy5T9VShUDxSLyAzAUOFpDH2WfiBVAWfyQM4ELRKRMwCIwFr9/Af4iIonAJ0qpzRXaEuBvIjIG8AIdMJyT9/nxXgHeAII2gkF90FP2BO0O6BXpt3DZmaO44fa7Trj/9CNTOP/SK7ngsquY/f7bPPPogzz/v3cA2LF9C6++Nwer1XpCglWA2e+/zaED+5nxyZds37KJP990daXTw++/ms/IsafRpVsP4ps1I9OxhuTU/gBsylzH7H8vJb5Zc84ZOcBiD7+27Zq1ax1PPflkq2nTprV+/fXXd/3xj3/seM899+yfMGFC4ebNm8MnTJjQc9u2besB1q5dG7V06dKsmJgYNW/evONfjA8++GC7uLg4z6ZNmzYAHDx40Arw3HPP7WnTpo3H7XYzYsSI3kuXLo0cNmzY79KMNQVqtYZVhoi8CDynlJorImOBjHIvV1yJVICbE0dzFR2Gy5IBeMrZJMClSqmNFcpmishSjFHcVyJyi1Lq+3KvXwO0AgYppVwikl1Jf5WTEX8TcKlfZRsRsRR1AaWgjon76pGY2DjOu/RK3n39VSIifvszrl3xK8+9+hYA5116Bc//7bHjr5157kVYrVVv4o2bcA4Wi4XuvZI4fOhgpWW+/HQW19x8OwATLriULz79+Lhg9ek/kFZt2gLQsXMXho0ZH7Y7t7hj//79c3/88cdYgEWLFsVt3rw5sqy9wsJCa25urgXgrLPOOhoTE/O7Ffuffvop7v33399W9rxVq1YegJkzZ7aYMWNGgtvtloMHD4atWbMmQgtW7YgH9viu0yu8dqGIPA1EA2OBBzGyG6eIiB1DPE7DmFZWx1fAn0TkT8pIBjdAKbVKRLoB25RSL/iu+2GEeClv2wGfWI0DOvv1jjLi2wPT/CrbyBAhprvk7NiqOvj3u2pgrr35dq4851QuvPyaKsuUn/VHRlW/kR0e/tvRs8p2+o7mHmHZooVs2ZiJiODxeBARJv/lCQDCwn/zebdYLISH2zlaVNrKhaXE4/FIWbvLly/PrEyYoqOjvZXZpZSi4upFVlZW+EsvvdRmxYoVma1atfJceumlXUpKSprscaS6vvEM4CMRWQhUdApdBswHlgB/VUrlKKV2AR8Ca4F3gFV+9PFXjCQOa0Vkne85wBXAOt80NQl4s0K9d4DBIrIcY7Tl7wLNdKDJul6MsKzfW3OpwBDfvDlnnncRs99/6/i9/oOG8uXcWQB8PvsjThlSeUKNqOgYjh0rrFV/38z/lPMmXsGXSxx88ctavl62ng4dO7NqWfU7mwWltClTp1GjRuU/88wzx8NQL168OLKKascZO3Zs/nPPPXe8zsGDB625ubnWyMhIb4sWLTy7du2yLViwoEmftK9xhKWU+p3vnFLqU6Dy1UrYpJS6tZI6Uzhxcb7sfpdy18sxRmX41sH+WEn5p4Gnq7JTKXUIY8HffzLirwLOr1WdRsZIy/qStzxnVvm6P8cQ6pPrb72T92e8dvz5A088w2P33cnM6S/SvGUCT0x7qdJ6p55xFvf9MZ0FX3/Og08841dfX346i5vuuPuEe6edcwGfz/mYCdWEaPYowpVYvQCvvvrqrltuuaVTr169UjwejwwbNqxgxIgRO6vr9+mnn9574403durZs2cfi8WiHnrooZz09PSjffv2LerZs2efTp06OQcNGlQ79W1kmBpeRkQygEKl1LOmNVrfZMTHAxsJ0aihZrFHtVw20vni0LLnOrxM3bCIeHu2iVlnt1ldDd13UwgvY2oAP6VUhpntNRBP0sTFCqANuR0CbUNjwKuUZU9ucadurWK2BtqWxkiTXbwDICN+IHB7oM0IBmzi7RBPYU3HTzR+UOh0N9NRHeqHpi1Y8BLGDqYGGGzZmB1oGxoLe/NKOnm9KuiOiYQ6TVewMuLPoraL842cUZZ1eYG2obHg8njD9+WX6EgYJtN0BQseq7lI02KIZWNT/jyYzuFjpW1K3V6d6MVEmuYHNCP+TKDygztNmK6yt1WgbWhMKKUsBwpK2gbajsZEU1V/PbqqhCicXa143B6sv/tcLHxypal9jX54YI1l/vvCs3z+6SysFgsWi4WHp/6TftWk9aqK+gox4w9Hi1yt2sR594VZLe6qyugQM/7T9AQrI/40wNzYWY0EEexJsnPLetW1R6BtWbNiGT999zUffL6AcLud3COHcZWW1qmt5b/8TFRU9HHBqg3lQ8zUJg5XGV6lLAfynW06NI/cU9nrZSFmxowZo2Pr+0FTnBLq0VU1jLSs3x9oGwAOHthPsxYtCLcbfn/NW7SkdVtjDXvpzz9y+VljuPT0ETx6752UOg3f+bPT+h2PnbV+zSpuvuw89uzaWe8hZp5/OoP5sz/k6vNO49LTR7Ar2whecuTwIe659XomjB3ZVoeYMYemJVgZ8eOA0TWWa8KkWdY3+AntyhgxZhz7c/Zw/pjBPPXQvSz/ZREAzpISHrnnDv7+8uvM+nYxHo+bD996vcp2OnTsxGXX3sh1t9zOh18tZOAwY3BdFmLmxTfe519PP15p3cpCzJSxKXMdUzKeZtY3i5g360N2bNvKu/O+45Krrue9Ga8C8PfHHuTaW27n3fnf8/q7H+XddtttXcrqr127Nuqrr77a8tlnn5UPzXRCiJlNmzZtOPfccwvACDGzbt26zKysrPWLFi2KXbp0aY2+iY2RpiVYRlBATTX0sWQHReDCqOgY3vt8AY8+8zzNWyYwZdJNfPrhu2Rv20yHjp3p0s2YtV4w8SpWLK19OH9/Q8yc5fMdLAsxU0ZZiJlwu52OnbuQNmYcAD2SUsjZZbgMLvn5R6Y+MoXLJ4zm+qsub+tviJnJkycfKHtePsRMSkpKckpKSsrmzZsj1qxZ41/IpEZG01nDyojvCowPtBnBTkvygyZDkNVqZUjaKIakjaJnUgpzP36P3n1Sqylvw+s1Irc4nSXVtl0fIWbKrt0eY31deb28OedrIiIjASSxedSu5tHhXtAhZupKU3rT12MEBdRUg1VUq9bkVj7kaECyt25mx/bf3PE2rnfQrkNHunbvSc7unezcbsS5mzfrAwYPHwlA+46dyHQYwXG/+/yz43UbMsRMedLGjOP9mf89/vynxUtrPEiqQ8xUT9MYYWXEC4ZgafxgqCVrJ6SccCbLn2MIZlJ07BhTH51CQX4+VquVjl268egzz2OPiOCJaf/mvttvwON206f/QC679kYAbrt7Co/dfxevvfgcqeWOPzRkiJnyPPDEM/ztL/cz8YyReDweBg5Lizxr7Ijw6uroEDPVY2p4maAlI34M8GOgzQgV3nePXdDynMfG6vAy5tM61r6nbXykv/kFakVTCC/TVKaENwTagFBioGVzWKBtaKzkFbuDNuFHKND4BSsjPgqYGGgzQolOcqDJxwerL5xuT2Sxy2OvuaSmMkwTLBH5s4jEicH/fIlMq46523BcShPKMWgGdlxdLNIU1goCw9FjpXqUVUfMHGHdpJTKx8gn2Aq4EZhqYvt1pWJWH00NiGCzi6dK3zfNyZFX4tKCVUfMFKyyIwPnAG8opdYQ6GMEGfFx+JJaaGqHXdyVnhPSnDylbm9EUam7SR78PFnMFKwVIvI1hmB9JSKxGJmXA8mp6IiidSIMT6BNaNQcLdKjrLpg5jmsm4FTMJKcFolIS4xpYSA5LcD9hyw2XJby3zbf3Hutqe2fMe1tv8qdbHiX77+cT+du3eneK6lW9T798F3Wr13FQ0/+gw/fep3IyCjOn3hlrfuvivxiV/P2zSJzTGuwiWDmCEsBKcBdvufR+Jsivv7Qrjh1xKI8QTEyLR/epS788NV8tm3eWOlrbrd/y3SXX3eTqWIFUOrxRrg8OhppbTFTsF7GiJF+le95AfBvE9uvHRnxrYG+Aes/xBHltURavQGN3FBZeJdff/mZO2+44niZvz18P59++C4Azz+dwcXjhzPxjJFM++sjrF6+lAXffMFzTz3K5RNGsyt7Ozdfdh4vTH2Cmyaeyzv/m86Cb77gmvNP5/KzxnDrVRdx+OCB39nxynNTmTn9RQBmvTuTq88dz2VnjuKeW6+nuLjuYawKne7oOlduomGIzVEAACAASURBVJip8MOUUgNFZBWAUipXRKp1Q6hnxhHoRf8QJ9ZSWlrsiQjYIdLqwrtUJC83l++/nM+nC5YhIuTn5REXH8/YM87+XXTRgvw8Xv94PgD5R4/y9txvEBE+ee9N3njlBe579Mkq+znt7PO59Gpj4/mlvz/J7Pff5uobf5fo3C+KnJ6Y5lHoxB+1wEzBcomIFWNqiIi0IrCL7nr96iSJtpR6Azmr//LTWVxzs5E2siy8y+jTJlRaNjo2FrvdTsb9dzH6tDM5tYpyABPO/80XcP/ePdx/x00cOrAPl8tFh46dq7VpS1YmL/3jSQry8ygqOsaIU+u+6lDscsfUuXITxUzBegGYDbQWkacwTpc/bGL7tUWvX50kdlUaME+IqsK7jD3jbJT3t+/BsmijNpuNdz77jqWLfuTLuZ/w/oz/8toHcyttOzIq6vj11Ecf4Lo/3MHYM8/h119+Zvpz1R8dfOTeO3j+tbfpnZLKpx++y/Jffq7zeyxxeaMqCyejqRrTBEsp9Y6IrMAY2QhwkVIq06z2a0VGfCLQPSB9NyJs3pKATQfLwrs8OvX54/dumnguANs2b6TU6cTpLGHpoh8ZMGQ4RccKKS4uZvT4M+k3YAjnjTaiS0TFxHCssOrgBgUF+bRua0QcnvvRezXaVVRYSELrtrhcLj6f8xGt29Q99aBXKUtRqScq2m7T8dz9xDTBEpEWwAHgvXL3wpRSgVi4PSUAfTY6xOMKt4nyupVY/D2GYBbVhXc587yLmHjmKDp17UZSn34AHCss5M83X0OpswSlFPc/9jcAzrrgEp544G7efeM/TJs+83f93D75Qe67/QZat2lHv4GDydm1o1q7Jt33ENdecDrtO3SkR1IKRdWIoT8cc7qjtWD5j2nhZUQkG+gI5GKMsJoBezFE7A9KqRWmdOQPGfEPEBxuQSFL5oQPSe7cml2WxOJcd1iTjB/eEMRFhB3pkhC9veaSNaPDy9SOL4FzlFIJSqmWwNnAh8AdGEceGhJ9nMEkYiyl+sh7PVLs8uijDbXATMEarJT6quyJUuprYIxSagnQ0OE0+jRwf42WCHEG2oRGjcvjtXuV0qvufmKmYB0RkQdEpLPvMQXI9R11aOjjDbX34dBUQKGUItzrDIoT740Zl9t70psbXq9XCLzvbr1jpmBdDSQCc4BPgU6+e1bgchP7qZ6M+DYYbkGakyAibxuHj7kRd4ldf/3XL6Ue70kdsPZ6vXLw4MF4YJ1JJgUtZh5rOAT8qYqXt5jVjx/o4wwmkLjyGXbzAAfju1lyxe12eq3a762eKN5vaRlhk5M5oesF1rnd7lvMsilYMfNYQytgCsb60fFfvlKqoQ9w6swJJhBWepSuS/4PgPdc6b++6ZmQFmCTGjPPZE8999lAGxEKmDklfAfIAroCjwPZwK8mtu8v1ftWaGrNSMt6vfJevyQG2oBQwUzBaqmU+h/gUkr9qJS6CRhuYvv+0iwAfTZqUi3b9Zpg/aIFy09MdX72/dwrIucCOQTmD6EdSk2mNbkdAm1DI6djoA0IFcwUrCdFJB64F3gRiAMmm9i+v2jBMhmbeNvHU3g0jxg9eq0f9AjLT8zcJZznu8zDiEUVKLRg1QNDLVnZ33gHax/N+qF1oA0IFczcJXwDXyys8vjWshoSLVj1wCjLurxvvIMDbUZjxdblwfmSPfVcnQuyBsycEs4rdx0BXIyxjtXQaMGqBwZbNuoT7/VLGFAaaCOCHTOnhLPKPxeR94BvzWq/FmjBqge6yL6EQNvQyLGhBatG6jOiZE8M95yGRgtWPRCFs6sVnQ26HglYsMRQwsw1rAKMNSzx/dwHPGBW+7WgoSNDNFrc4F4WGZE1Oyb60OLIiBb3f/XdlkJ7XJwq3Rgnqqgb9fuF16QolTCBcwNtRtBj5pQw1qy2ThIdvfEk2BQWtn1ObPSub6Oi7Htt1mREjscW673j62N7ukwuzWs2bqjyFh50O9du8JZuCFPe/D7oke1JYVeuRh9pwQzMHGENrOR2HrBDKdWQUwmdNqkWHLZYDn0WE71pfky0d3N4WDePSFcM96rfsaONlA5d/c8xy4b8ZdGx6PYjwyJHtCJyBEp5Sr2uLSvdztWFyr23C3gDsRQQ6ujpth+YuUv4MjAQWIsxLUwF1gAtReQ2X0C/hiC/gfoJSUpEin+IitwwJya6cGWEvV2JSE9ERvhTNytRwodtVDJk+dPDfhn2+DJnRIuhACLWcGt474HW8N4AeN0Htnmcq3d5XFuaoUr6YO7nrLGiF9z9wMwPUjZws1JqPYCIpAD3A38FPgG0YAUAL3hX2e0bP4mN3r8wKjI+12JJQWRQXdrK6igJABbltQ1f9ni/xcOfWu0Kj/ndYVKLrXU3i+3MbmGcifIWH/WUrt/gca5DeY8kA81P8i01RvLv/WCeHmH5gZmClVQmVgBKqQ0iMkApta2B8641ecHaabPtnhMbvf2r6CjbLputtxJJBpJPtt0dremswCNgtXrdEWlLH+2+KO1vGzy2iJSq6oglspktYvAIW8RglPJ6vO7stZ6S1ble965E8OjYZQaHA21AqGCmYG0UkVeA933PrwA2iYid3xyjG4ImJ1h5FsvRL6Kjsj6LiS7dYA/v7BbpTD34p7mtEu62sj3MY6xx2TzO2LSlj7ZdnPbUVq8lrEbxEbFYrWHd+lnDjJBlXk/uLo9z9XZP6cZoVFFfmu4O75FAGxAqmClYN2BkyLkbYw3rZ+A+DLFqSN/CRr/oXgqli6IiM2fHROcui4xodUwkCZEGCeWTG8OB1nm/LcqHu461GL70cecvwx7frSzWWomkxdq8oyVqXMewqHEoVXrMU5q1xuNcW6o8B3uCamO+9UGLHmH5iZnHGoqBab5HRU4u22TtaJQjrHXh4Ztnx0bn/BAVGX3Qak1GpH8g7NjRWkpa553o8hbhzG03dPlTO5YOefgAYqmTI69IeLTN3m+ozd4PpZRS7j2ZbufqA17X9tbgSsL4Emys6BGWn5h5rGEkkIER8fN4u0qphg5Z3Ci+rfZZrfs+jY3e8kV0lGV7WFgPr0hPDO+BgLIxUcKGbP69j2500f7Og1c+u2n5wPvDETmpMDQiIhKWmBwelpgMoLwF+93OtZs9pZnhGGe+GltAwYOBNiBUMHNK+D+M+FcrgEAm39wYwL7rzDGRwm+iozZ8GhNdstZu71Bqke5A20DbVZGsRGlZ1WtxBTt6nbL2JcfqfneGIWKaqIgltk1Y5Mg2YZEjUcrt9Lo2r3A71xYq997u4G0MsaSy/S0oIhdj7LonK6WyatuRiFwEbFJKbahlvRswco/eKSK3AUVKqTdr2//JYqZg5SmlvjCxvbqSGWgD/MEDnqURERtmx0YfWRwZ0SzfOG4wNNB21UR2Gzop8EoVbjktcrNS+2x4Y8X6lBtTETmp9FWVIWKzW8OTB1nDjU1Pr3v/Vo9z9W6Pa0tzlLMPRlq5UGNbLcpehbE+fCXGjKa2XIQRWeV3giUiNn8OeSulptehX1MQpcwJwSMiUzE+LJ8Ax5MWKKVWmtJBbciIPwRUORIIFFvCwrbPNtxeInJs1t4YEVpDjnefce+0eat3bN/dYcwvm3pcPhQjkW6DoLzFuZ7SdZnGma/cPkCo/H773fvBPEdNhUQkBmMGMQ6Yq5RKEpGxwH1KqfN8ZV4CliulZvj+Jy/AOEX/Ncb/5jyMjak84FKMmdFiYCQwF9gEPAyEYyyvXKOU2l9hhJUBFCqlnhWRPwC3+spvAa5TStWbe5yZI6xhvp/lo7wpoKHTfIExyhoVgH5P4LDFcmh+TPSmeTHRnk01uL2EEkej2Z9QUL1gJe75Ka00LHZhdpdzRjeUXWKJbG6LGDLCFjEEpbxur2v7Go9z9VGve3dH8ARr+jcvsNnPshcBXyqlNonIkSrc4QAQkRYYMemSlFJKRJoppY6KyFxgnlLqY185gGZKqVN9z5sDw311bsFI3XdvNTZ9opT6r6/uk8DNGCHS6wUzdwkDGRa5IgERLKdQ8kNU1Po5MdEFKyPs7YpFevnr9hJK7GolRQkFNY/Mu2XPH10aHvdjTvtRpzaAWScgYrFZw7v3t4Ybx8O8niM7Pc412Z7SjTG+M1+mT1fryI57P5hX4mfZq4Dnfdfv+57Pr6JsPlACvCYi8zkxwGZFPih3nQh8ICLtMH5H22uwqa9PqJphOMB/VUP5k8JUHy9ftpyKiVSfMLMPP6nVgmJdUaBW2u0b58RG7/spKjLuyEm4vYQSGxPFNmCbf0sJSZveO7U0PHbBoYT+Y+vXquqxWFt0skSN6+Q781XoKc1c5XGudSnPod6gWgXQNL8+qyLSEmO20ldEFMbyi8KYxpVfT4wAUEq5xVgTPQ1jvetOqp7tHCt3/SLwnFJqrm+6mVGDaTOAi5RSa3zTxrH+vJ+6YuaxhulAFMb8+jVgIrDMrPZrSb0tvJe5vXwdHWXbabi9JAFJ9dVfMJLVUWrlD9hv3atjVwy456e8+O5j6sum2iASHmOz9x9ms/dHKaW87t0bPM5VB72uHW18Z74akuV+lpsIvKmU+mPZDRH50XeZ4vMoicAQqJ99611RSqnPRWQJxvoSQAFQXSioeGCP7zrdD7tiMVL7hQHXlKtbL5g5whqhlOonImuVUo+LyDSMRb5AYJpg5Vkk78vo6KzPYqKd6+vR7SWU2NaWTgqU1OIw58BVz41eOuThRUXR7UbWp221RUTEGtYxxRpmpAZU3vy9bufaLZ7STDvegr4YX8L1yRI/y10FTK1wbxZwNfAhRpSUzcAq32uxwKciEoHxdypLufc+8F8RuQtDBCuSAXwkInt8ttW05voIsBTYATioXgxPGjN3CZcqpYb51PwSjB2GdUqphj/smBEvwCGgRW2rlkLp4qjIDZ/EROcti4xI8Lm9hOJWeb3y3lT3HquiVglWvWJx/zLsiZXOiOZBf3wDQCl3ibd00zq3c22R8uztDsrshLIKaHHvB/OOmtxuo8VMwXoEY/57GvBvjD/Ga0qpR0zpoLZkxM8BLvSn6Ppybi8HDLeXxnaS2nSmv+he3qKQWuf98lhsJYvTnspyhf0+LE2w43Xv2+x2rs7xura28J35OtkQ0Vn3fjDvpKNoNCXM3CX8q+9ylojMAyKUUoF0RF5AFYK1z2rdNzcmessXMVGyLYjcXkKJ3QlyrEVh7b/srF53RNqSR7svSvtbpscWEVL/rBZb257htrN6Aihv0WGPc12Wp3SdRXmPplC3M1/+Tgc1Pk5asETkkmpeQykVqHWsBWUXx0QKv42Oyvw0Jrpojd2eGKxuL6HExkQs/bLrVtcIS/NY60VpT25TlrBgPR9VLWKJammLHDrSFjnUd+Zr22qPc3We172nE3j8PWv3S70a2QgxY4R1fjWvKQK38L7msYQWn38bFdnB5/YyJEB2NEoyO0qLShJ9+024q7Dl8KVPlC4ZllHrsDTBhnHmq8cp1vAeAHg9h7M9ztU7PKWb4lDFfak6hVcg8naGNKatYdXYkUi6Umpmg3TmI3Vm6vsYgQQ1JhNZovJn/tMTd7LtHItqm710yF+iEUsgz0LVG0o58z3OzA2e0rUe48wXZQlpN9/7wbxegbQtFGnIvHJ/bsC+yggGZ+xGSXGExHmEfSfbTnTRvi6DVk47QmDXO+sNEXucLeKU4fa460fam01uERZz6XpLWPcfxRL3Qc21NRVpSMEKRAC2LzmZeYumWgqiyDGjnfiC7N791/57J/XoNBsMiIjFGta5T3jMhafa42/5seYamoo0pGA1uHA40h37MeJzaeqB3S3FtEiyLXMzU/tkvrEBpZpCuqs8QAtWHWjsIyyAtwPUb6Nncwdz/6ZtDqwY3HPLxytQqrFnQZ4/afr4hkzM0mgw05fQjhFfpwsnhkguc35eZFZfteRd4B9UvVOjqSOZHaXZxb+YO3DuuGdBmissZmF2l7MbLCxNAHgr0AaEKmaOsD7FOKjpxvD+LnsAoJS608S+/MaR7jiIXnyvF7a0k3o5jtAte97odnsXLaiPtoOAvcA3gTYiVDHT+TlRKXWWie2ZyUyMyIsaEymMkuZe4aBFYfqRhOSN744tDYtbcDghdazZbQeYdyZNHx/InAchjZkjrMUikmpie2YyD51KqV4ojKi/cCL9100fG5e3/af6aj9ANOhZxMaGmYI1ClghIhtFZK2IOERkrYnt1xlHuqOU3zJSa0xkT8v6zQM5aNW0UVFF+xbXZx8NyMpJ08evC7QRoYyZgnU2hgPxmRjuOudRvdtOQ6O/2eqBze3rd/NXUJahvz41xO48+mu9dtQwvBJoA0Id0wRLKbWjsodZ7Z8sjnTHMgIXAbXRktWx/jP/WJQ3bPjSjL4217E19d1XPXIAfcTmpGnIc1jBwN8CbUBjY3N7MTuoXaVYva7IEUse7Wr1OEMi72QlvDxp+nh/k01oqqCpCdZcQK8hmEhejCR4jeiy9Y7NUxKXtuSxVuJ11ZTJJdgoAV4OtBGNgSYlWI50hwKeDrQdjY1jEexuqL7CXQUJw5c9YRevp16THZjMm5Omjz8YaCMaA01KsHx8AGwNtBGNib0t6nensCKRJUfaD1n+dCnKGwoi4AaeDbQRjYUmJ1iOdIeH32cf0ZwEm9tLgzu2xxTt7Tpo1XOhEJbmjUnTx/ub2VlTA01OsHy8CQ03jWnsZHWUek3tVBXx+dt793e8vAOligPRvx+UAI8H2ojGRJMULN9B0r/WWFDjF5s7NMxOYWW0PLKhX0rWzPUoFYzRD16aNH18KK21BT1NUrB8vIaOlWUKR2KltYKA5dZru//XwT22zvo1yMLS5KE3eEynyQqWI93hBSahI5KaQpGdXYHsv9PuH0Z03vl1oEIYVcbUSdPHa/9Vk2myggXgSHcsBV4PtB2NgX3NAzfCKqP79rmj2+39ZUGg7QA2As8F2ojGSJMWLB8PArmBNiLU2dJegmI6lrzx7bEtD69bEGAz/jRp+vimEOq5wWnyguVIdxwCHg60HaFOZkeJCbQNZfR3vDI2Nj97YYC6f2fS9PHVBugTkb+IyHpfVJPVIjKsLh2JyFgRGVHu+QwRmehn3YtFRIlIUl369rOPwSLygpltNnnB8jEdWBVoI0KZze2lfaBtKM/glc+OjCza39BhaQ4Dk6srICJpGJFMBiql+gGnQ53X/8YCI2oqVAVXAT8DV9axfrWIiE0ptVwpdZeZ7WrB4vgC/E2AM9C2hCoHm0k7BQWBtqMMQVmG/frkkHDn0eUN2O1dfrjgtAMOKaWcAEqpQ0qpHAAROU1EVvliyb3uy5OAiGSLSILverCILBCRLsBtwGTfKK0sBv4YEVksItuqGm2JSAwwEriZcoLlG7H9KCIfisgmEZkqIteIyDKfTd195VqJyCwR+dX3GOm7nyEir4rI18CbvvbmlfUpIm+UxckTkUt9918RkeW+EWeNZ9a0YPlwpDtWAw8E2o5QpjicnYG2oTwW5Q1LW5rRx+Y61hCBJN+ZNH38u36U+xro6BOEl0XkVAARiQBmAFcopVIxwpffXlUjSqlsjJnBP5VSpyilyqbA7TCCaZ5H1R4dFwFfKqU2AUdEZGC51/pjJD1OBa4DeimlhmIcA/qTr8y/fP0OwUg881q5+oOAC5VSV1fo8xEgTymV6htZfu+7/xel1GCgH3CqiPSr6j2DFqwTcKQ7/oURTllTBw40C77NC6vXFZm25LHOFo8zqx672Q7c4U9BpVQhxj/1rcBB4AMRuQHoDWz3iQgYASfH1MGWOUopr1JqA9CmijJX8VsE3vd9z8v4VSm11zcC3IohsAAOjIxYYExjXxKR1RgRUOJEjns7zFWVex6cDvy77IlSquyzcrmIrMRYkukDpFT35sxMQtFYuBFYAwTVmkwosLWdeLocCL5jbWGe4vi0JY+VLk57cruy2Lqa3LwHuHbS9PF+O4ArpTzAAmCBiDiAdGB1NVXc/Da4iKih+fLLGr8LBysiLYHxQF8RUYAVUCIypZL63nLPvfymFxYgraIwiQiUy5RVsWsqnHkUka7AfcAQpVSuiMyghvenR1gV8O0aXovxB9LUgsyOEhVoG6rC7ipoNXzZE+EoT47JTf910vTxfi/ui0hvEelZ7tYpwA4gC+giIj1896/jt+zQ2RijMjCmYGUUALX145wIvKmU6qyU6qKU6ogxQhxViza+Bo6n7RORU+pQpzkQhyFweSLSBiPMerVowaoER7rjB7RbRa3Z1EHaBdqG6ogsOdxh6PKpTpT3kElNfgk8Wcs6McBMEdngS9KSAmQopUowRvcf+UZdXow1KjAcqP8lIgsxRnRlfAZcXGHRvSauAmZXuDcLqLjmVB13AYN9i+cbMBb/a+JJoLmIrBORNcA4pdQajKngeowD3DV6KohSwTeEDwZSZ6bagG+BUwNtS8iglPpgqqdIIDrQplTH0bhumSsH3NMBkbiTaGYjMGzS9PHBHt6mUaFHWFXgSHe4gYuBUI0h3vCISElYcO0UVkaz/G3J/RyvbD+JsDRHgQu0WDU8WrCqwZHuyMWYV+8LtC2hwsFmoZGwNuHI+v7JWW+uq0NYGg9wxaTp4zfVWFJjOlqwasCR7tgBnAsUBtqWUGBbW3EH2gZ/abd/2ZAeW2fXNizN5EnTx39dczFNfaAFyw8c6Y6VwOUY28uaasjsKJGBtqE2dNr93YhOu7792c/iT0yaPv7FejVIUy1asPzEke74gmpOHmsMNnWQtoG2obb02DZnTNt9SxfUUOxfk6aPf6wh7NFUjRasWuBId7yG4WKgqYKcliQqI5Z5SJGS9ebYFkc2LKji5RnU4NSsaRi0YNUSR7rjSeD+QNsRrCgRizMEdgor45S1/x4bW7CjYliaj4FbJk0fr8//BAFasOqAI93xLPBH9Gn4SjkUh1kHMxucwSv+MTKy6MAvvqfvAFdNmj7eU10dTcOhBauOONIdr2K4T+iF+ApsbyPBmMHGL3xhaQbFFOx6Grh+0vTx+u8bROiT7idJ6szUC4APAXugbQkWzljpXfqHr7x1iqIZJDyVnJWpo9AGIXqEdZI40h1zMc5pVeWl3uTYmCitA21DHfECf9ZiFbxowTIBR7rjO4xQtVsDbUswsDuBjgpCLQnDUeC85KxMU2OQa8xFC5ZJONIda4HBwPxA2xJovBaxuWwhtVO4ARianJX5RaAN0VSPFiwTcaQ7jgLnAxk08QSth2OpKbZ5sDAHGJ6clbk50IZoakYLlsk40h3Kke54HCOmdtCFDG4osttIsE8JvcBjwCXJWZlBkzxDUz1asOoJR7rjc4wp4ppA2xIIMjtKeKBtqIZsYFxyVuYTyVmZTXokHGpowapHHOmObcAw4BlOjBTZ6AnincLXgX7JWZk/BdoQTe3R57AaiNSZqUMxfNKSA2xKg2D1KNe7f/eIBE+ikwPAH5KzMucG2hBN3dEjrAbCke5YBgzAiG0d7Os7J43HKmEua9DsFL4L9NViFfroEVYASJ2ZmgT8h7rlnQsZXnzFvaTNUYYH0ITVwJ+SszL9jXelCXL0CCsAONIdWY50x6kY6cS2Bdqe+iK7tThrLlUvHMaIXTZIi1XjolEJloh4fCmPyh4P1nN/n4tIs7rWd6Q73gGSMLIGm50vL+BsTJSwBu7ShZFduFdyVub05KxMHU2jkdGopoQiUqiUiqljXZtSKmCe+akzUyOBScCDQMtA2WEmPfaoTX9709OrAbpyY6R2fzI5KzO7AfrTBIhGNcKqChHJFpEE3/VgEVngu84QkVdF5GvgTRHpIiILRWSl7zHCV66diPzkG7WtK0taWaHdOSKyQkTWi8ittbXRke4o9sXZ6opxoNHv1OfByo42dFL1e5zDibEW2Ds5K/MWLVaNn2DZcjaLSBFZXe7500qpD2qoMwgYpZQqFpEo4AylVIkvnfh7GIc/rwa+Uko9JSJWoLKU7DcppY6ISCTwq4jMUkodru0bcKQ7CoAnUmem/gsj3tZtQJ/athMMuGwS4bawI8xLZ5ObPohxnupfyVmZe01uWxPENDbBKlZKnVLLOnPVbwk1w4CXROQUjJFB2XTmV+B1EQkD5iilVlfSzl0icrHvuiPQE2Pxt0440h15wEvAS6kzU0djLCJfCgTzCfLfcTSG/a3yTROsHzHSt3+SnJXZ6I+GaH5PYxOsqnDz2/Q3osJr5eNYTQb2A/195UsAlFI/icgYjLhXb4nIP5RSb5ZVEpGxwOlAmlKqyDflrNhPnXGkOxYCC1NnprYCbsIIz9zVrPbrk52tpbhV/kmtkx4G3gReTc7KzDLHKk2o0iTWsDB8xwb5ri+tplw8sFcZiTWvA6wAItIZOKCU+i/wP2BgJfVyfWKVBPVz9siR7jjoSHc8A3QH0oCnMUKjBC0bO0hdvhT3YYykzgTaJmdl3lNbsRIRJSLTyj2/T0QyatnG2LJ1TN/zGSIy0c+6F/tsSKpNn7W0b7CINKn4XY1thFVxDetLpdSDwOPA/0TkIWBpNfVfBmaJyGXAD/w2+hoL3C8iLowM0NdXqPclcJuIrAU2AktO+p1UgyPdoXx9LAEeSp2Z2h24wPcYjU9og4GsjuLvjucW4FPgE+AXE5ySncAlIvK0UqrWSTFExIbxdy8EFteh/6uAn4ErMcINmYpvV3s5sNzstoOZRnWsQQOpM1NbABMwRnlDMNyBTJue1pZwlyp661lPpICUu62A9cBC4CdgYXJW5h4z+xWRQuApIEYp9RcRuc93neEbMb8OtMJYwL9RKbVTRGYARzB+Z0eAkRhrmQeBPwE3Y+zeDgbaAlOUUh9X0ncMxhfXOIw10iTf/bEYX577gVMwxNkB/BmIBC5SSm0VkVYYI8xOvibvVkot8o0Q2wNdgEPAq8B9SqnzfH2+6LNNAY8rpWaJyCsYn4NI4GOlVEgng21sI6wmjyPdcQRjd/M9gNSZMSZoigAAAw9JREFUqTYgFeNDOwQYirHr2CCjsNIwsZfaWGR3sx/jn3MV8HNyVuaRBuj+38BaEfl7hfsvAW8qpWaKyE3AC8BFvtd6AacrpTw+gShUSj0LICI3A+2AURgHfudi5C2syEUYo/tNInJERAYqpVb6XuuP4QB/BMPL4TWl1FAR+TOGKN4N/Av4p1LqZxHpBHzFb07z5Xe1x5br8xEgTymV6rO1ue/+X3y711bgOxHpp5Ra6+8vMNjQgtXIcaQ73BgisQrjG7lMxNpj7GZ28v0s/2iNkQUoDGNXMsz3KI8T45+ussdeYDOwCdh6yrrMgKT9Ukrli8ibwF1AcbmX0oBLfNdvAeUF7SOlVHVnx+b41jg3iEibKspcBTzvu37f97xMsH5VSu0FEJGtwNe++w6MERkYGzgpIscHpXEiEuu7Lr+rXZ7TMaafACilyoJHXu47F2jDENsUQAuWJnTwidhO32ORv/VSZ6aWiZfXke6o7J8mGHkeQyzeqKZM+XWRmrIflfePlIovikhLYDzQV0QUxkhWiciUSup7yz338tv/owVjx/mE37FPwKqyTyq8D0SkK3AfMEQpleub8gZsecAMmsouocYEHOmOUke641gIiRVKqSMYeSNvLnd7Mb+NRq7BWByvjAIgtorXqmIixnSzs1Kqi1KqI7AdYxrpL18Dd5Y98Z0LrG2d5kAchsDl+UaDZ9fChqBEC5amKTANSCj3/C7gRt+u7nUYi96V8Rlwsc8la7SffV0FzK5wbxaGt4S/3AUMFpG1IrIBw9uhJp4Emvtcx9YA45RSazCWAtZjbDL4PZoOVvQuoUajCRn0CEuj0YQMWrA0Gk3IoAVLo9GEDFqwNBpNyKAFS6PRhAxasDQaTcigBUuj0YQMWrA0Gk3IoAVLo9GEDFqwNBpNyKAFS6PRhAxasDQaTcigBUuj0YQMWrA0Gk3IoAVLo9GEDFqwNBpNyKAFS6PRhAxasDQaTcigBUuj0YQMWrA0Gk3IoAVLo9GEDFqwNBpNyKAFS6PRhAxasDQaTcigBUuj0YQM/w+V0rUGFqw1IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_macroarea_nlanguages = select([Macroarea.name.label('macroarea'),\n",
    "                                      count().label('n_languages')])\\\n",
    "                              .select_from(sa.outerjoin(Macroarea,\n",
    "                                    sa.join(languoid_macroarea, Languoid)))\\\n",
    "                              .where(Languoid.level == LANGUAGE)\\\n",
    "                              .group_by(Macroarea.name)\\\n",
    "                              .order_by(sa.desc('n_languages'))\n",
    "\n",
    "_ = read_sql(select_macroarea_nlanguages, index_col='macroarea')\n",
    "_.plot.pie(y='n_languages')\n",
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT languoid.id, languoid.name, group_concat(macroarea.name, ?) AS macroareas \n",
      "FROM languoid JOIN languoid_macroarea ON languoid.id = languoid_macroarea.languoid_id JOIN macroarea ON macroarea.name = languoid_macroarea.macroarea_name \n",
      "WHERE languoid.level = ? GROUP BY languoid.id \n",
      "HAVING count(*) > ? ORDER BY languoid.id\n",
      "(', ', 'language', 1)\n"
     ]
    }
   ],
   "source": [
    "macroareas = sa.func.group_concat(Macroarea.name, ', ').label('macroareas')\n",
    "\n",
    "select_multiarea_languages = select([Languoid.id, Languoid.name, macroareas])\\\n",
    "                             .select_from(sa.join(Languoid, languoid_macroarea).join(Macroarea))\\\n",
    "                             .where(Languoid.level == LANGUAGE)\\\n",
    "                             .group_by(Languoid.id)\\\n",
    "                             .having(count() > 1)\\\n",
    "                             .order_by('id')\n",
    "    \n",
    "assert read_sql(select_multiarea_languages).empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'country')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE country (\n",
      "\tid VARCHAR(2) NOT NULL CHECK (length(id) = 2), \n",
      "\tname TEXT NOT NULL CHECK (name != ''), \n",
      "\tPRIMARY KEY (id), \n",
      "\tUNIQUE (name)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM country\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "226\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'languoid_country')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE languoid_country (\n",
      "\tlanguoid_id VARCHAR(8) NOT NULL, \n",
      "\tcountry_id VARCHAR(2) NOT NULL, \n",
      "\tPRIMARY KEY (languoid_id, country_id), \n",
      "\tFOREIGN KEY(languoid_id) REFERENCES languoid (id), \n",
      "\tFOREIGN KEY(country_id) REFERENCES country (id)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM languoid_country\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10988\n"
     ]
    }
   ],
   "source": [
    "from treedb.models import Country, languoid_country\n",
    "\n",
    "print_sql(Country)\n",
    "print_sql(languoid_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT country.name AS country, count(*) AS n_languages \n",
      "FROM country LEFT OUTER JOIN (languoid_country JOIN languoid ON languoid.id = languoid_country.languoid_id) ON country.id = languoid_country.country_id \n",
      "WHERE languoid.level = ? GROUP BY country.id ORDER BY n_languages DESC\n",
      " LIMIT ? OFFSET ?\n",
      "('language', 10, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_languages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_languages\n",
       "country                      \n",
       "Papua New Guinea          897\n",
       "Indonesia                 744\n",
       "Nigeria                   572\n",
       "India                     498\n",
       "China                     398\n",
       "Australia                 393\n",
       "Mexico                    328\n",
       "Brazil                    324\n",
       "United States             321\n",
       "Cameroon                  313"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_country_nlanguages = select([Country.name.label('country'),\n",
    "                                    count().label('n_languages')])\\\n",
    "                            .select_from(sa.outerjoin(Country,\n",
    "                                                      sa.join(languoid_country, Languoid)))\\\n",
    "                            .where(Languoid.level == LANGUAGE)\\\n",
    "                            .group_by(Country.id)\\\n",
    "                            .order_by(sa.desc('n_languages'))\\\n",
    "                            .limit(10)\n",
    "\n",
    "read_sql(select_country_nlanguages, index_col='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT id, name, count(*) AS n_countries, group_concat(country, ?) AS countries \n",
      "FROM (SELECT languoid.id AS id, languoid.name AS name, country.id AS country \n",
      "FROM languoid JOIN languoid_country ON languoid.id = languoid_country.languoid_id JOIN country ON country.id = languoid_country.country_id \n",
      "WHERE languoid.level = ? ORDER BY languoid.id, country) GROUP BY id, name \n",
      "HAVING count(*) > ? ORDER BY n_countries DESC, id\n",
      " LIMIT ? OFFSET ?\n",
      "(', ', 'language', 1, 10, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>n_countries</th>\n",
       "      <th>countries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stan1288</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>30</td>\n",
       "      <td>AD, AR, BO, BR, BZ, CL, CO, CR, CU, DO, EC, ES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stan1293</th>\n",
       "      <td>English</td>\n",
       "      <td>26</td>\n",
       "      <td>AU, BM, BR, BZ, CA, CK, CU, DO, FK, GB, GI, GP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stan1318</th>\n",
       "      <td>Standard Arabic</td>\n",
       "      <td>25</td>\n",
       "      <td>AE, BH, DJ, DZ, EG, ER, IL, IQ, JO, KM, KW, LB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amer1248</th>\n",
       "      <td>American Sign Language</td>\n",
       "      <td>22</td>\n",
       "      <td>BB, BF, BJ, BO, CA, CD, CF, CI, CN, GA, GH, JM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east2295</th>\n",
       "      <td>Eastern Yiddish</td>\n",
       "      <td>22</td>\n",
       "      <td>AR, AU, BE, BY, CA, CR, EE, HU, IL, LT, LV, MD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>russ1263</th>\n",
       "      <td>Russian</td>\n",
       "      <td>19</td>\n",
       "      <td>BY, CN, EE, FI, GE, KG, KP, KZ, LT, LV, MD, MN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stan1295</th>\n",
       "      <td>German</td>\n",
       "      <td>16</td>\n",
       "      <td>AT, BE, CH, CZ, DE, DK, FR, HU, IT, LI, LU, NL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port1283</th>\n",
       "      <td>Portuguese</td>\n",
       "      <td>15</td>\n",
       "      <td>AD, AR, BO, BR, CO, ES, FR, GF, GY, PE, PT, PY...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sint1235</th>\n",
       "      <td>Sinte Romani</td>\n",
       "      <td>13</td>\n",
       "      <td>AT, CH, CZ, DE, FR, HR, IT, KZ, ME, NL, PL, RS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nucl1301</th>\n",
       "      <td>Turkish</td>\n",
       "      <td>12</td>\n",
       "      <td>AL, AM, AZ, BG, CY, GE, GR, IQ, IR, MK, SY, TR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  n_countries  \\\n",
       "id                                              \n",
       "stan1288                 Spanish           30   \n",
       "stan1293                 English           26   \n",
       "stan1318         Standard Arabic           25   \n",
       "amer1248  American Sign Language           22   \n",
       "east2295         Eastern Yiddish           22   \n",
       "russ1263                 Russian           19   \n",
       "stan1295                  German           16   \n",
       "port1283              Portuguese           15   \n",
       "sint1235            Sinte Romani           13   \n",
       "nucl1301                 Turkish           12   \n",
       "\n",
       "                                                  countries  \n",
       "id                                                           \n",
       "stan1288  AD, AR, BO, BR, BZ, CL, CO, CR, CU, DO, EC, ES...  \n",
       "stan1293  AU, BM, BR, BZ, CA, CK, CU, DO, FK, GB, GI, GP...  \n",
       "stan1318  AE, BH, DJ, DZ, EG, ER, IL, IQ, JO, KM, KW, LB...  \n",
       "amer1248  BB, BF, BJ, BO, CA, CD, CF, CI, CN, GA, GH, JM...  \n",
       "east2295  AR, AU, BE, BY, CA, CR, EE, HU, IL, LT, LV, MD...  \n",
       "russ1263  BY, CN, EE, FI, GE, KG, KP, KZ, LT, LV, MD, MN...  \n",
       "stan1295  AT, BE, CH, CZ, DE, DK, FR, HU, IT, LI, LU, NL...  \n",
       "port1283  AD, AR, BO, BR, CO, ES, FR, GF, GY, PE, PT, PY...  \n",
       "sint1235  AT, CH, CZ, DE, FR, HR, IT, KZ, ME, NL, PL, RS...  \n",
       "nucl1301     AL, AM, AZ, BG, CY, GE, GR, IQ, IR, MK, SY, TR  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_lc = select([Languoid.id, Languoid.name, Country.id.label('country')])\\\n",
    "            .select_from(sa.join(Languoid, languoid_country).join(Country))\\\n",
    "            .where(Languoid.level == LANGUAGE)\\\n",
    "            .order_by(Languoid.id, 'country')\n",
    "\n",
    "countries = sa.func.group_concat(select_lc.c.country, ', ').label('countries')\n",
    "\n",
    "select_multicountry_languages = select([select_lc.c.id,\n",
    "                                        select_lc.c.name,\n",
    "                                        count().label('n_countries'),\n",
    "                                        countries])\\\n",
    "                                .group_by(select_lc.c.id, select_lc.c.name)\\\n",
    "                                .having(count() > 1)\\\n",
    "                                .order_by(sa.desc('n_countries'), select_lc.c.id)\\\n",
    "                                .limit(10)\n",
    "    \n",
    "read_sql(select_multicountry_languages, index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT n_countries, count(*) AS n_languages \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_countries \n",
      "FROM languoid LEFT OUTER JOIN languoid_country ON languoid.id = languoid_country.languoid_id \n",
      "WHERE languoid.level = ? GROUP BY languoid.id) GROUP BY n_countries ORDER BY n_countries\n",
      "('language',)\n",
      "SELECT ? AS value, count(*) AS count, sum(n_countries) AS sum, min(n_countries) AS min, max(n_countries) AS max, avg(n_countries) AS mean \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_countries \n",
      "FROM languoid LEFT OUTER JOIN languoid_country ON languoid.id = languoid_country.languoid_id \n",
      "WHERE languoid.level = ? GROUP BY languoid.id)\n",
      "('n_countries', 'language')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_countries</th>\n",
       "      <td>8515</td>\n",
       "      <td>10937</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1.284439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count    sum  min  max      mean\n",
       "n_countries   8515  10937    1   30  1.284439"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_lc = select([Languoid.id, count().label('n_countries')])\\\n",
    "            .select_from(sa.outerjoin(Languoid, languoid_country))\\\n",
    "            .where(Languoid.level == LANGUAGE)\\\n",
    "            .group_by(Languoid.id)\n",
    "\n",
    "select_lc_dist = select([select_lc.c.n_countries, count().label('n_languages')])\\\n",
    "                 .group_by(select_lc.c.n_countries)\\\n",
    "                 .order_by('n_countries')\n",
    "\n",
    "_ = read_sql(select_lc_dist, index_col='n_countries')\n",
    "\n",
    "_.plot.bar(figsize=(12, 3))\n",
    "\n",
    "read_sql(select([sa.literal('n_countries').label('value'),\n",
    "                 count().label('count'),\n",
    "                 sa.func.sum(select_lc.c.n_countries).label('sum'),\n",
    "                 sa.func.min(select_lc.c.n_countries).label('min'),\n",
    "                 sa.func.max(select_lc.c.n_countries).label('max'),\n",
    "                 sa.func.avg(select_lc.c.n_countries).label('mean')]),\n",
    "        index_col='value').rename_axis(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## altname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT altnameprovider.name AS altname_provider, count(DISTINCT altname.languoid_id) AS n_languoids \n",
      "FROM altnameprovider JOIN altname ON altnameprovider.id = altname.provider_id GROUP BY altnameprovider.name ORDER BY n_languoids, altname_provider\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from treedb.models import Altname, AltnameProvider\n",
    "\n",
    "select_provider_nlanguoids = select([\n",
    "        AltnameProvider.name.label('altname_provider'),\n",
    "        count(sa.distinct(Altname.languoid_id)).label('n_languoids')\n",
    "    ]).select_from(sa.join(AltnameProvider, Altname))\\\n",
    "    .group_by(AltnameProvider.name)\\\n",
    "    .order_by('n_languoids', 'altname_provider')\n",
    "\n",
    "read_sql(select_provider_nlanguoids, index_col='altname_provider').plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT n_altnames, count(*) AS n_languoids \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_altnames \n",
      "FROM languoid LEFT OUTER JOIN altname ON languoid.id = altname.languoid_id GROUP BY languoid.id) GROUP BY n_altnames ORDER BY n_altnames\n",
      "()\n",
      "SELECT ? AS value, count(*) AS count, sum(n_altnames) AS sum, min(n_altnames) AS min, max(n_altnames) AS max, avg(n_altnames) AS mean \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_altnames \n",
      "FROM languoid LEFT OUTER JOIN altname ON languoid.id = altname.languoid_id GROUP BY languoid.id)\n",
      "('n_altnames',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_altnames</th>\n",
       "      <td>25228</td>\n",
       "      <td>158497</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>6.282583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count     sum  min  max      mean\n",
       "n_altnames  25228  158497    1  246  6.282583"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_la = select([Languoid.id, count().label('n_altnames')])\\\n",
    "            .select_from(sa.outerjoin(Languoid, Altname))\\\n",
    "            .group_by(Languoid.id)\n",
    "\n",
    "select_la_dist = select([select_la.c.n_altnames, count().label('n_languoids')])\\\n",
    "                 .group_by(select_la.c.n_altnames)\\\n",
    "                 .order_by(select_la.c.n_altnames)\n",
    "\n",
    "_ = read_sql(select_la_dist, index_col='n_altnames')\n",
    "\n",
    "_.plot.area(figsize=(12, 3))\n",
    "\n",
    "read_sql(select([sa.literal('n_altnames').label('value'),\n",
    "                 count().label('count'),\n",
    "                 sa.func.sum(select_la.c.n_altnames).label('sum'),\n",
    "                 sa.func.min(select_la.c.n_altnames).label('min'),\n",
    "                 sa.func.max(select_la.c.n_altnames).label('max'),\n",
    "                 sa.func.avg(select_la.c.n_altnames).label('mean')]),\n",
    "        index_col='value').rename_axis(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sourceprovider.name AS provider, languoid.level, count(*) AS n_sources \n",
      "FROM sourceprovider JOIN source ON sourceprovider.id = source.provider_id JOIN languoid ON languoid.id = source.languoid_id GROUP BY sourceprovider.name, languoid.level ORDER BY provider, n_sources DESC\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n_sources</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>provider</th>\n",
       "      <th>level</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">glottolog</th>\n",
       "      <th>language</th>\n",
       "      <td>184938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>2387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dialect</th>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n_sources\n",
       "provider  level              \n",
       "glottolog language     184938\n",
       "          family         2387\n",
       "          dialect        1613"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treedb.models import Source, SourceProvider\n",
    "\n",
    "select_provider_nsources = select([SourceProvider.name.label('provider'),\n",
    "                                   Languoid.level,\n",
    "                                   count().label('n_sources')])\\\n",
    "                           .select_from(sa.join(SourceProvider, Source).join(Languoid))\\\n",
    "                           .group_by(SourceProvider.name, Languoid.level)\\\n",
    "                           .order_by('provider', sa.desc('n_sources'))\n",
    "\n",
    "read_sql(select_provider_nsources, index_col=['provider', 'level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT n_sources, count(*) AS n_languoids \n",
      "FROM (SELECT languoid.id AS id, count(source.languoid_id) AS n_sources \n",
      "FROM languoid LEFT OUTER JOIN source ON languoid.id = source.languoid_id GROUP BY languoid.id) GROUP BY n_sources ORDER BY n_languoids\n",
      "()\n",
      "SELECT ? AS value, count(*) AS count, sum(n_sources) AS sum, min(n_sources) AS min, max(n_sources) AS max, avg(n_sources) AS mean \n",
      "FROM (SELECT languoid.id AS id, count(source.languoid_id) AS n_sources \n",
      "FROM languoid LEFT OUTER JOIN source ON languoid.id = source.languoid_id GROUP BY languoid.id)\n",
      "('n_sources',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_sources</th>\n",
       "      <td>25228</td>\n",
       "      <td>188938</td>\n",
       "      <td>0</td>\n",
       "      <td>1548</td>\n",
       "      <td>7.489218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     sum  min   max      mean\n",
       "n_sources  25228  188938    0  1548  7.489218"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_lang_nsources = select([Languoid.id,\n",
    "                               count(Source.languoid_id).label('n_sources')])\\\n",
    "                       .select_from(sa.outerjoin(Languoid, Source))\\\n",
    "                       .group_by(Languoid.id)\n",
    "\n",
    "select_nsources_nlangs = select([select_lang_nsources.c.n_sources,\n",
    "                                 count().label('n_languoids')])\\\n",
    "                         .group_by(select_lang_nsources.c.n_sources)\\\n",
    "                         .order_by('n_languoids')\n",
    "\n",
    "_ = read_sql(select_nsources_nlangs, index_col='n_sources')\n",
    "_.groupby(_.index != 0).sum().rename_axis('n_sources')\\\n",
    "    .rename(index={False: '= 0', True: '> 0'}).plot.bar(figsize=(4, 3))\n",
    "_.drop(0).plot.area(figsize=(8, 3), logx=True)\\\n",
    "    .xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "\n",
    "read_sql(select([sa.literal('n_sources').label('value'),\n",
    "                 count().label('count'),\n",
    "                 sa.func.sum(select_lang_nsources.c.n_sources).label('sum'),\n",
    "                 sa.func.min(select_lang_nsources.c.n_sources).label('min'),\n",
    "                 sa.func.max(select_lang_nsources.c.n_sources).label('max'),\n",
    "                 sa.func.avg(select_lang_nsources.c.n_sources).label('mean')]),\n",
    "        index_col='value').rename_axis(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT level, n_sources, count(*) AS n_languoids \n",
      "FROM (SELECT languoid.id AS id, languoid.level AS level, count(source.languoid_id) AS n_sources \n",
      "FROM languoid LEFT OUTER JOIN source ON languoid.id = source.languoid_id GROUP BY languoid.id, languoid.level) GROUP BY level, n_sources ORDER BY n_languoids\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_ll_nsources = select([Languoid.id, Languoid.level,\n",
    "                             count(Source.languoid_id).label('n_sources')])\\\n",
    "                     .select_from(sa.outerjoin(Languoid, Source))\\\n",
    "                     .group_by(Languoid.id, Languoid.level)\n",
    "\n",
    "select_ll_nlangs = select([select_ll_nsources.c.level,\n",
    "                           select_ll_nsources.c.n_sources,\n",
    "                           sa.func.count().label('n_languoids')])\\\n",
    "                   .group_by(select_ll_nsources.c.level, select_ll_nsources.c.n_sources)\\\n",
    "                    .order_by('n_languoids')\n",
    "\n",
    "_ = read_sql(select_ll_nlangs).pivot(index='n_sources', columns='level', values='n_languoids')\n",
    "_.groupby(_.index != 0).sum()[list(treedb.LEVEL)].rename_axis('n_sources')\\\n",
    "    .rename(index={False: '= 0', True: '> 0'}).plot.bar(figsize=(4, 3))\n",
    "_.drop(0)[list(treedb.LEVEL)].plot.area(figsize=(8, 3), logx=True)\\\n",
    "    .xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT bibfile.name AS bibfile, count(*) AS n_used \n",
      "FROM bibfile JOIN bibitem ON bibfile.id = bibitem.bibfile_id GROUP BY bibfile.name ORDER BY n_used DESC\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from treedb.models import Bibfile, Bibitem\n",
    "\n",
    "select_bibfile_nused = select([Bibfile.name.label('bibfile'), count().label('n_used')])\\\n",
    "                       .select_from(sa.join(Bibfile, Bibitem))\\\n",
    "                       .group_by(Bibfile.name)\\\n",
    "                       .order_by(sa.desc('n_used'))\n",
    "\n",
    "read_sql(select_bibfile_nused, index_col='bibfile').plot.bar(figsize=(12, 3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT classificationref.kind, languoid.level, count(*) AS n_classificationrefs \n",
      "FROM classificationref JOIN languoid ON languoid.id = classificationref.languoid_id GROUP BY classificationref.kind, languoid.level ORDER BY classificationref.kind, languoid.level\n",
      "()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">n_classificationrefs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <th>dialect</th>\n",
       "      <th>family</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>8</td>\n",
       "      <td>435</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub</th>\n",
       "      <td>64</td>\n",
       "      <td>5678</td>\n",
       "      <td>9602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       n_classificationrefs                \n",
       "level               dialect family language\n",
       "kind                                       \n",
       "family                    8    435      256\n",
       "sub                      64   5678     9602"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treedb.models import ClassificationRef\n",
    "\n",
    "select_levels = select([ClassificationRef.kind, Languoid.level,\n",
    "                        count().label('n_classificationrefs')])\\\n",
    "                .select_from(sa.join(ClassificationRef, Languoid))\\\n",
    "                .group_by(ClassificationRef.kind, Languoid.level)\\\n",
    "                .order_by('kind', 'level')\n",
    "\n",
    "read_sql(select_levels, index_col=['kind', 'level']).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT n_crefs, count(*) AS n_languoids \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_crefs \n",
      "FROM languoid LEFT OUTER JOIN classificationref ON languoid.id = classificationref.languoid_id GROUP BY languoid.id) GROUP BY n_crefs ORDER BY n_crefs\n",
      "()\n",
      "SELECT ? AS value, count(*) AS count, sum(n_crefs) AS sum, min(n_crefs) AS min, max(n_crefs) AS max, avg(n_crefs) AS mean \n",
      "FROM (SELECT languoid.id AS id, count(*) AS n_crefs \n",
      "FROM languoid LEFT OUTER JOIN classificationref ON languoid.id = classificationref.languoid_id GROUP BY languoid.id)\n",
      "('n_crefs',)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_crefs</th>\n",
       "      <td>25228</td>\n",
       "      <td>29417</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1.166046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count    sum  min  max      mean\n",
       "n_crefs  25228  29417    1   11  1.166046"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_lcr = select([Languoid.id, count().label('n_crefs')])\\\n",
    "             .select_from(sa.outerjoin(Languoid, ClassificationRef))\\\n",
    "             .group_by(Languoid.id)\n",
    "\n",
    "select_lcr_dist = select([select_lcr.c.n_crefs, count().label('n_languoids')])\\\n",
    "                  .group_by(select_lcr.c.n_crefs)\\\n",
    "                  .order_by(select_lcr.c.n_crefs)\n",
    "\n",
    "_ = read_sql(select_lcr_dist, index_col='n_crefs')\n",
    "\n",
    "_.plot.area(figsize=(12, 3))\n",
    "\n",
    "read_sql(select([sa.literal('n_crefs').label('value'),\n",
    "                 count().label('count'),\n",
    "                 sa.func.sum(select_lcr.c.n_crefs).label('sum'),\n",
    "                 sa.func.min(select_lcr.c.n_crefs).label('min'),\n",
    "                 sa.func.max(select_lcr.c.n_crefs).label('max'),\n",
    "                 sa.func.avg(select_lcr.c.n_crefs).label('mean')]),\n",
    "        index_col='value').rename_axis(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'link')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE link (\n",
      "\tlanguoid_id VARCHAR(8) NOT NULL, \n",
      "\tord INTEGER NOT NULL CHECK (ord >= 1), \n",
      "\turl TEXT NOT NULL CHECK (url != ''), \n",
      "\ttitle TEXT CHECK (title != ''), \n",
      "\tscheme TEXT, \n",
      "\tPRIMARY KEY (languoid_id, ord), \n",
      "\tUNIQUE (languoid_id, url), \n",
      "\tCHECK (substr(url, 1, length(scheme) + 3) = scheme || '://'), \n",
      "\tFOREIGN KEY(languoid_id) REFERENCES languoid (id), \n",
      "\tCHECK (scheme IN ('http', 'https'))\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM link\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28949\n"
     ]
    }
   ],
   "source": [
    "from treedb.models import Link\n",
    "\n",
    "print_sql(Link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT link.scheme AS link_scheme, count(*) AS n_links \n",
      "FROM link GROUP BY link.scheme ORDER BY n_links DESC\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_scheme_nlinks = select([Link.scheme.label('link_scheme'), count().label('n_links')])\\\n",
    "                       .group_by(Link.scheme)\\\n",
    "                       .order_by(sa.desc('n_links'))\n",
    "\n",
    "read_sql(select_scheme_nlinks, index_col='link_scheme').plot.pie(y='n_links');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT link.url \n",
      "FROM link\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from urllib.parse import urlparse\n",
    "\n",
    "hosts = collections.Counter(urlparse(u).hostname for u, in select([Link.url]).execute())\n",
    "\n",
    "pd.DataFrame.from_dict(hosts, orient='index', columns=['n_links'])\\\n",
    "    .sort_values(by='n_links').plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## endangerment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'endangerment')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE endangerment (\n",
      "\tlanguoid_id VARCHAR(8) NOT NULL, \n",
      "\tstatus VARCHAR(14) NOT NULL, \n",
      "\tsource_id INTEGER NOT NULL, \n",
      "\tdate DATETIME NOT NULL, \n",
      "\tcomment TEXT NOT NULL CHECK (comment != ''), \n",
      "\tPRIMARY KEY (languoid_id), \n",
      "\tFOREIGN KEY(languoid_id) REFERENCES languoid (id), \n",
      "\tCHECK (status IN ('not endangered', 'threatened', 'shifting', 'moribund', 'nearly extinct', 'extinct')), \n",
      "\tFOREIGN KEY(source_id) REFERENCES endangerment_source (id)\n",
      ") WITHOUT ROWID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM endangerment\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8165\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT sqlite_master.sql \n",
      "FROM sqlite_master \n",
      "WHERE sqlite_master.type = ? AND sqlite_master.name = ?\n",
      "('table', 'endangerment_source')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE endangerment_source (\n",
      "\tid INTEGER NOT NULL, \n",
      "\tname TEXT NOT NULL CHECK (name != ''), \n",
      "\tbibitem_id INTEGER, \n",
      "\tpages TEXT CHECK (pages != ''), \n",
      "\tPRIMARY KEY (id), \n",
      "\tUNIQUE (bibitem_id, pages), \n",
      "\tCHECK ((bibitem_id IS NULL) = (pages IS NULL)), \n",
      "\tUNIQUE (name), \n",
      "\tFOREIGN KEY(bibitem_id) REFERENCES bibitem (id)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT count(*) AS n_rows \n",
      "FROM endangerment_source\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "from treedb.models import Endangerment, EndangermentSource\n",
    "\n",
    "print_sql(Endangerment)\n",
    "print_sql(EndangermentSource)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SELECT endangerment_source.name AS source, count(*) AS n_endangerments \n",
      "FROM endangerment JOIN endangerment_source ON endangerment_source.id = endangerment.source_id GROUP BY endangerment_source.name ORDER BY n_endangerments\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_source = EndangermentSource.name.label('source')\n",
    "\n",
    "select_source_nendangerments = select([e_source, count().label('n_endangerments')])\\\n",
    "                               .select_from(sa.join(Endangerment, EndangermentSource))\\\n",
    "                               .group_by(e_source)\\\n",
    "                               .order_by('n_endangerments')\n",
    "\n",
    "read_sql(select_source_nendangerments, index_col='source').plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## denormalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WITH RECURSIVE tree(child_id, parent_id, steps, terminal) AS \n",
      "(SELECT child.id AS child_id, child.id AS parent_id, ? AS steps, child.parent_id IS NULL AS terminal \n",
      "FROM languoid AS child UNION ALL SELECT tree.child_id AS child_id, parent.parent_id AS parent_id, tree.steps + ? AS steps, grandparent.parent_id IS NULL AS terminal \n",
      "FROM tree JOIN languoid AS parent ON tree.parent_id = parent.id AND parent.parent_id IS NOT NULL LEFT OUTER JOIN languoid AS grandparent ON parent.parent_id = grandparent.id)\n",
      " SELECT languoid.id, languoid.name, languoid.level, languoid.parent_id, (SELECT group_concat(path_part, ?) AS path \n",
      "FROM (SELECT tree.parent_id AS path_part \n",
      "FROM tree \n",
      "WHERE tree.child_id = languoid.id ORDER BY tree.steps DESC)) AS path, (SELECT tree.parent_id \n",
      "FROM tree \n",
      "WHERE tree.child_id = languoid.id AND tree.steps > ? AND tree.terminal = 1) AS family_id, (SELECT tree.parent_id \n",
      "FROM tree \n",
      "WHERE tree.child_id = languoid.id AND languoid.level = ? AND (EXISTS (SELECT * \n",
      "FROM languoid AS ancestor \n",
      "WHERE tree.parent_id = ancestor.id AND ancestor.level = ?))) AS dialect_language_id, languoid.hid, languoid.iso639_3, languoid.latitude, languoid.longitude, (SELECT group_concat(macroarea_name, ?) AS macroareas \n",
      "FROM (SELECT languoid_macroarea.macroarea_name AS macroarea_name \n",
      "FROM languoid_macroarea \n",
      "WHERE languoid_macroarea.languoid_id = languoid.id ORDER BY languoid_macroarea.macroarea_name)) AS macroareas, (SELECT group_concat(country_id, ?) AS countries \n",
      "FROM (SELECT languoid_country.country_id AS country_id \n",
      "FROM languoid_country \n",
      "WHERE languoid_country.languoid_id = languoid.id ORDER BY languoid_country.country_id)) AS countries, (SELECT group_concat(printf, ?) AS links \n",
      "FROM (SELECT CASE WHEN (link.title IS NOT NULL) THEN printf(?, link.title, link.url) ELSE link.url END AS printf \n",
      "FROM link \n",
      "WHERE link.languoid_id = languoid.id ORDER BY link.ord)) AS links, (SELECT group_concat(printf, ?) AS sources_glottolog \n",
      "FROM (SELECT CASE WHEN (source_glottolog.pages IS NOT NULL AND source_glottolog.\"trigger\" IS NOT NULL) THEN printf(?, source_bibfile.name, source_bibitem.bibkey, source_glottolog.pages, source_glottolog.\"trigger\") WHEN (source_glottolog.pages IS NOT NULL) THEN printf(?, source_bibfile.name, source_bibitem.bibkey, source_glottolog.pages) WHEN (source_glottolog.\"trigger\" IS NOT NULL) THEN printf(?, source_bibfile.name, source_bibitem.bibkey, source_glottolog.\"trigger\") ELSE printf(?, source_bibfile.name, source_bibitem.bibkey) END AS printf \n",
      "FROM source AS source_glottolog, bibfile AS source_bibfile, bibitem AS source_bibitem, sourceprovider AS source_provider \n",
      "WHERE source_glottolog.languoid_id = languoid.id AND source_glottolog.provider_id = source_provider.id AND source_provider.name = ? AND source_glottolog.bibitem_id = source_bibitem.id AND source_bibitem.bibfile_id = source_bibfile.id ORDER BY source_bibfile.name, source_bibitem.bibkey)) AS sources_glottolog, (SELECT group_concat(printf, ?) AS altnames_aiatsis \n",
      "FROM (SELECT CASE WHEN (altname_aiatsis.lang = ?) THEN altname_aiatsis.name ELSE printf(?, altname_aiatsis.name, altname_aiatsis.lang) END AS printf \n",
      "FROM altname AS altname_aiatsis, altnameprovider AS altname_aiatsis_provider \n",
      "WHERE altname_aiatsis.languoid_id = languoid.id AND altname_aiatsis.provider_id = altname_aiatsis_provider.id AND altname_aiatsis_provider.name = ? ORDER BY altname_aiatsis.name, altname_aiatsis.lang)) AS altnames_aiatsis, (SELECT group_concat(printf, ?) AS altnames_elcat \n",
      "FROM (SELECT CASE WHEN (altname_elcat.lang = ?) THEN altname_elcat.name ELSE printf(?, altname_elcat.name, altname_elcat.lang) END AS printf \n",
      "FROM altname AS altname_elcat, altnameprovider AS altname_elcat_provider \n",
      "WHERE altname_elcat.languoid_id = languoid.id AND altname_elcat.provider_id = altname_elcat_provider.id AND altname_elcat_provider.name = ? ORDER BY altname_elcat.name, altname_elcat.lang)) AS altnames_elcat, (SELECT group_concat(printf, ?) AS altnames_ethnologue \n",
      "FROM (SELECT CASE WHEN (altname_ethnologue.lang = ?) THEN altname_ethnologue.name ELSE printf(?, altname_ethnologue.name, altname_ethnologue.lang) END AS printf \n",
      "FROM altname AS altname_ethnologue, altnameprovider AS altname_ethnologue_provider \n",
      "WHERE altname_ethnologue.languoid_id = languoid.id AND altname_ethnologue.provider_id = altname_ethnologue_provider.id AND altname_ethnologue_provider.name = ? ORDER BY altname_ethnologue.name, altname_ethnologue.lang)) AS altnames_ethnologue, (SELECT group_concat(printf, ?) AS altnames_glottolog \n",
      "FROM (SELECT CASE WHEN (altname_glottolog.lang = ?) THEN altname_glottolog.name ELSE printf(?, altname_glottolog.name, altname_glottolog.lang) END AS printf \n",
      "FROM altname AS altname_glottolog, altnameprovider AS altname_glottolog_provider \n",
      "WHERE altname_glottolog.languoid_id = languoid.id AND altname_glottolog.provider_id = altname_glottolog_provider.id AND altname_glottolog_provider.name = ? ORDER BY altname_glottolog.name, altname_glottolog.lang)) AS altnames_glottolog, (SELECT group_concat(printf, ?) AS altnames_hhbib_lgcode \n",
      "FROM (SELECT CASE WHEN (altname_hhbib_lgcode.lang = ?) THEN altname_hhbib_lgcode.name ELSE printf(?, altname_hhbib_lgcode.name, altname_hhbib_lgcode.lang) END AS printf \n",
      "FROM altname AS altname_hhbib_lgcode, altnameprovider AS altname_hhbib_lgcode_provider \n",
      "WHERE altname_hhbib_lgcode.languoid_id = languoid.id AND altname_hhbib_lgcode.provider_id = altname_hhbib_lgcode_provider.id AND altname_hhbib_lgcode_provider.name = ? ORDER BY altname_hhbib_lgcode.name, altname_hhbib_lgcode.lang)) AS altnames_hhbib_lgcode, (SELECT group_concat(printf, ?) AS altnames_lexvo \n",
      "FROM (SELECT CASE WHEN (altname_lexvo.lang = ?) THEN altname_lexvo.name ELSE printf(?, altname_lexvo.name, altname_lexvo.lang) END AS printf \n",
      "FROM altname AS altname_lexvo, altnameprovider AS altname_lexvo_provider \n",
      "WHERE altname_lexvo.languoid_id = languoid.id AND altname_lexvo.provider_id = altname_lexvo_provider.id AND altname_lexvo_provider.name = ? ORDER BY altname_lexvo.name, altname_lexvo.lang)) AS altnames_lexvo, (SELECT group_concat(printf, ?) AS \"altnames_moseley & asher (1994)\" \n",
      "FROM (SELECT CASE WHEN (\"altname_moseley & asher (1994)\".lang = ?) THEN \"altname_moseley & asher (1994)\".name ELSE printf(?, \"altname_moseley & asher (1994)\".name, \"altname_moseley & asher (1994)\".lang) END AS printf \n",
      "FROM altname AS \"altname_moseley & asher (1994)\", altnameprovider AS \"altname_moseley & asher (1994)_provider\" \n",
      "WHERE \"altname_moseley & asher (1994)\".languoid_id = languoid.id AND \"altname_moseley & asher (1994)\".provider_id = \"altname_moseley & asher (1994)_provider\".id AND \"altname_moseley & asher (1994)_provider\".name = ? ORDER BY \"altname_moseley & asher (1994)\".name, \"altname_moseley & asher (1994)\".lang)) AS \"altnames_moseley & asher (1994)\", (SELECT group_concat(printf, ?) AS altnames_multitree \n",
      "FROM (SELECT CASE WHEN (altname_multitree.lang = ?) THEN altname_multitree.name ELSE printf(?, altname_multitree.name, altname_multitree.lang) END AS printf \n",
      "FROM altname AS altname_multitree, altnameprovider AS altname_multitree_provider \n",
      "WHERE altname_multitree.languoid_id = languoid.id AND altname_multitree.provider_id = altname_multitree_provider.id AND altname_multitree_provider.name = ? ORDER BY altname_multitree.name, altname_multitree.lang)) AS altnames_multitree, (SELECT group_concat(printf, ?) AS \"altnames_ruhlen (1987)\" \n",
      "FROM (SELECT CASE WHEN (\"altname_ruhlen (1987)\".lang = ?) THEN \"altname_ruhlen (1987)\".name ELSE printf(?, \"altname_ruhlen (1987)\".name, \"altname_ruhlen (1987)\".lang) END AS printf \n",
      "FROM altname AS \"altname_ruhlen (1987)\", altnameprovider AS \"altname_ruhlen (1987)_provider\" \n",
      "WHERE \"altname_ruhlen (1987)\".languoid_id = languoid.id AND \"altname_ruhlen (1987)\".provider_id = \"altname_ruhlen (1987)_provider\".id AND \"altname_ruhlen (1987)_provider\".name = ? ORDER BY \"altname_ruhlen (1987)\".name, \"altname_ruhlen (1987)\".lang)) AS \"altnames_ruhlen (1987)\", (SELECT group_concat(printf, ?) AS altnames_wals \n",
      "FROM (SELECT CASE WHEN (altname_wals.lang = ?) THEN altname_wals.name ELSE printf(?, altname_wals.name, altname_wals.lang) END AS printf \n",
      "FROM altname AS altname_wals, altnameprovider AS altname_wals_provider \n",
      "WHERE altname_wals.languoid_id = languoid.id AND altname_wals.provider_id = altname_wals_provider.id AND altname_wals_provider.name = ? ORDER BY altname_wals.name, altname_wals.lang)) AS altnames_wals, (SELECT group_concat(printf, ?) AS \"altnames_wals other\" \n",
      "FROM (SELECT CASE WHEN (\"altname_wals other\".lang = ?) THEN \"altname_wals other\".name ELSE printf(?, \"altname_wals other\".name, \"altname_wals other\".lang) END AS printf \n",
      "FROM altname AS \"altname_wals other\", altnameprovider AS \"altname_wals other_provider\" \n",
      "WHERE \"altname_wals other\".languoid_id = languoid.id AND \"altname_wals other\".provider_id = \"altname_wals other_provider\".id AND \"altname_wals other_provider\".name = ? ORDER BY \"altname_wals other\".name, \"altname_wals other\".lang)) AS \"altnames_wals other\", (SELECT group_concat(\"trigger\", ?) AS triggers_lgcode \n",
      "FROM (SELECT trigger_lgcode.\"trigger\" AS \"trigger\" \n",
      "FROM \"trigger\" AS trigger_lgcode \n",
      "WHERE trigger_lgcode.field = ? AND trigger_lgcode.languoid_id = languoid.id ORDER BY trigger_lgcode.ord)) AS triggers_lgcode, (SELECT group_concat(\"trigger\", ?) AS triggers_inlg \n",
      "FROM (SELECT trigger_inlg.\"trigger\" AS \"trigger\" \n",
      "FROM \"trigger\" AS trigger_inlg \n",
      "WHERE trigger_inlg.field = ? AND trigger_inlg.languoid_id = languoid.id ORDER BY trigger_inlg.ord)) AS triggers_inlg, ident_endangeredlanguages.identifier AS identifier_endangeredlanguages, ident_languagelandscape.identifier AS identifier_languagelandscape, ident_multitree.identifier AS identifier_multitree, ident_wals.identifier AS identifier_wals, cc_sub.comment AS classification_sub, (SELECT group_concat(printf, ?) AS classification_subrefs \n",
      "FROM (SELECT printf(CASE WHEN (cr_sub.pages IS NOT NULL) THEN ? ELSE ? END, bibfile_cr_sub.name, bibitem_cr_sub.bibkey, cr_sub.pages) AS printf \n",
      "FROM classificationref AS cr_sub, bibfile AS bibfile_cr_sub, bibitem AS bibitem_cr_sub \n",
      "WHERE cr_sub.kind = ? AND cr_sub.languoid_id = languoid.id AND cr_sub.bibitem_id = bibitem_cr_sub.id AND bibitem_cr_sub.bibfile_id = bibfile_cr_sub.id ORDER BY cr_sub.ord)) AS classification_subrefs, cc_fam.comment AS classification_family, (SELECT group_concat(printf, ?) AS classification_familyrefs \n",
      "FROM (SELECT printf(CASE WHEN (cr_family.pages IS NOT NULL) THEN ? ELSE ? END, bibfile_cr_family.name, bibitem_cr_family.bibkey, cr_family.pages) AS printf \n",
      "FROM classificationref AS cr_family, bibfile AS bibfile_cr_family, bibitem AS bibitem_cr_family \n",
      "WHERE cr_family.kind = ? AND cr_family.languoid_id = languoid.id AND cr_family.bibitem_id = bibitem_cr_family.id AND bibitem_cr_family.bibfile_id = bibfile_cr_family.id ORDER BY cr_family.ord)) AS classification_familyrefs, endangerment.status AS endangerment_status, endangerment.date AS endangerment_date, endangerment.comment AS endangerment_comment, CASE WHEN (endangerment_source.bibitem_id IS NULL) THEN endangerment_source.name ELSE printf(?, bibfile_e.name, bibitem_e.bibkey, endangerment_source.pages) END AS endangerment_source, ethnologuecomment.isohid AS elcomment_isohid, ethnologuecomment.comment_type AS elcomment_comment_type, ethnologuecomment.ethnologue_versions AS elcomment_ethnologue_versions, ethnologuecomment.comment AS elcomment_comment, isoretirement.code AS iso_retirement_code, isoretirement.name AS iso_retirement_name, isoretirement.change_request AS iso_retirement_change_request, isoretirement.effective AS iso_retirement_effective, isoretirement.reason AS iso_retirement_reason, isoretirement.remedy AS iso_retirement_remedy, isoretirement.comment AS iso_retirement_comment, (SELECT group_concat(code, ?) AS iso_retirement_change_to \n",
      "FROM (SELECT isoretirement_changeto.code AS code \n",
      "FROM isoretirement_changeto \n",
      "WHERE isoretirement_changeto.languoid_id = languoid.id ORDER BY isoretirement_changeto.ord)) AS iso_retirement_change_to \n",
      "FROM languoid LEFT OUTER JOIN (identifier AS ident_endangeredlanguages JOIN identifiersite AS ident_endangeredlanguages_site ON ident_endangeredlanguages.site_id = ident_endangeredlanguages_site.id) ON ident_endangeredlanguages_site.name = ? AND ident_endangeredlanguages.languoid_id = languoid.id LEFT OUTER JOIN (identifier AS ident_languagelandscape JOIN identifiersite AS ident_languagelandscape_site ON ident_languagelandscape.site_id = ident_languagelandscape_site.id) ON ident_languagelandscape_site.name = ? AND ident_languagelandscape.languoid_id = languoid.id LEFT OUTER JOIN (identifier AS ident_multitree JOIN identifiersite AS ident_multitree_site ON ident_multitree.site_id = ident_multitree_site.id) ON ident_multitree_site.name = ? AND ident_multitree.languoid_id = languoid.id LEFT OUTER JOIN (identifier AS ident_wals JOIN identifiersite AS ident_wals_site ON ident_wals.site_id = ident_wals_site.id) ON ident_wals_site.name = ? AND ident_wals.languoid_id = languoid.id LEFT OUTER JOIN classificationcomment AS cc_sub ON cc_sub.kind = ? AND cc_sub.languoid_id = languoid.id LEFT OUTER JOIN classificationcomment AS cc_fam ON cc_fam.kind = ? AND cc_fam.languoid_id = languoid.id LEFT OUTER JOIN (endangerment JOIN endangerment_source ON endangerment_source.id = endangerment.source_id LEFT OUTER JOIN (bibitem AS bibitem_e JOIN bibfile AS bibfile_e ON bibfile_e.id = bibitem_e.bibfile_id) ON bibitem_e.id = endangerment_source.bibitem_id) ON languoid.id = endangerment.languoid_id LEFT OUTER JOIN ethnologuecomment ON languoid.id = ethnologuecomment.languoid_id LEFT OUTER JOIN isoretirement ON languoid.id = isoretirement.languoid_id ORDER BY languoid.id\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(0, 1, '/', 0, 'dialect', 'language', ', ', ', ', ', ', '[%s](%s)', ', ', '**%s:%s**:%s<trigger \"%s\">', '**%s:%s**:%s', '**%s:%s**<trigger \"%s\">', '**%s:%s**', 'glottolog', ', ', '', '%s [%s]', 'aiatsis', ', ', '', '%s [%s]', 'elcat', ', ', '', '%s [%s]', 'ethnologue', ', ', '', '%s [%s]', 'glottolog', ', ', '', '%s [%s]', 'hhbib_lgcode', ', ', '', '%s [%s]', 'lexvo', ', ', '', '%s [%s]', 'moseley & asher (1994)', ', ', '', '%s [%s]', 'multitree', ', ', '', '%s [%s]', 'ruhlen (1987)', ', ', '', '%s [%s]', 'wals', ', ', '', '%s [%s]', 'wals other', ', ', 'lgcode', ', ', 'inlg', ', ', '**%s:%s**:%s', '**%s:%s**', 'sub', ', ', '**%s:%s**:%s', '**%s:%s**', 'family', '**%s:%s**:%s', ', ', 'endangeredlanguages', 'languagelandscape', 'multitree', 'wals', 'sub', 'family')\n",
      "[INFO@treedb.queries] hash rows with 'sha256', csv header: ['id', 'name', 'level', 'parent_id', 'path', 'family_id', 'dialect_language_id', 'hid', 'iso639_3', 'latitude', 'longitude', 'macroareas', 'countries', 'links', 'sources_glottolog', 'altnames_aiatsis', 'altnames_elcat', 'altnames_ethnologue', 'altnames_glottolog', 'altnames_hhbib_lgcode', 'altnames_lexvo', 'altnames_moseley & asher (1994)', 'altnames_multitree', 'altnames_ruhlen (1987)', 'altnames_wals', 'altnames_wals other', 'triggers_lgcode', 'triggers_inlg', 'identifier_endangeredlanguages', 'identifier_languagelandscape', 'identifier_multitree', 'identifier_wals', 'classification_sub', 'classification_subrefs', 'classification_family', 'classification_familyrefs', 'endangerment_status', 'endangerment_date', 'endangerment_comment', 'endangerment_source', 'elcomment_isohid', 'elcomment_comment_type', 'elcomment_ethnologue_versions', 'elcomment_comment', 'iso_retirement_code', 'iso_retirement_name', 'iso_retirement_change_request', 'iso_retirement_effective', 'iso_retirement_reason', 'iso_retirement_remedy', 'iso_retirement_comment', 'iso_retirement_change_to']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 10.9 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'25222b4feb2d89b4edaeecc546280a059ae6ba69da961d56ee4a387ba3b81fc0'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time treedb.hash_csv(treedb.get_query())"
   ]
  }
 ],
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