ipynb/3. german_cities.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bd07183-3ae3-4104-b9f7-5c208a39848a",
   "metadata": {},
   "source": [
    "# German Cities\n",
    "\n",
    "Date: 2024/01/14 - 2024/01/15, 2024/03/19 - 2024/03/20, 2024/04/19(SQLite)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b8499f-5da1-4120-a3c7-23c9fdd9b4ff",
   "metadata": {},
   "source": [
    "The final list I want to create:\n",
    "\n",
    "```\n",
    "+-------+----------+-----------+--------+-------+\n",
    "| place | latitude | longitude | counts | order |\n",
    "+-------+----------+-----------+--------+-------+\n",
    "|       |          |           |        |       |\n",
    "                :       :\n",
    "|       |          |           |        |       |\n",
    "+-------+----------+-----------+--------+-------+\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "850f4607-f620-4a1b-871f-58e03ca02eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n",
      "  warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'NoneType' object has no attribute 'cadam32bit_grad_fp32'\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import spacy\n",
    "from spacy import displacy\n",
    "\n",
    "nlp = spacy.load('en_core_web_trf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c0ea50f-57ff-4d02-b9a3-50187102ca63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "\n",
    "with open('../data/pg35041-paragraphs.yaml', 'r', encoding='utf-8') as f:\n",
    "    paragraphs = yaml.safe_load(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2efd78-c109-4a84-80ae-b06ea6514e57",
   "metadata": {},
   "source": [
    "## Listing up locations in the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03b02eea-068c-4a6e-bc99-bd71bf23cf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pressburg',\n",
       " 'Hungary',\n",
       " 'Thuringia',\n",
       " 'Wechmar',\n",
       " 'Gotha',\n",
       " 'Thuringia',\n",
       " 'Schwarzburg-Arnstadt',\n",
       " 'Italy',\n",
       " 'Thuringia',\n",
       " 'Germany',\n",
       " 'Erfurt',\n",
       " 'Eisenach',\n",
       " 'Arnstadt',\n",
       " 'Thuringia',\n",
       " 'Franconia',\n",
       " 'Germany',\n",
       " 'Vienna',\n",
       " 'Eisenach',\n",
       " 'Arnstadt',\n",
       " 'Lüneburg',\n",
       " 'Danzig',\n",
       " 'Lüeburg',\n",
       " 'Hamburg',\n",
       " 'Celle',\n",
       " 'Lüneburg',\n",
       " 'Weimar',\n",
       " 'Arnstadt',\n",
       " 'Weimar',\n",
       " 'Arnstadt',\n",
       " 'Lübeck',\n",
       " 'Arnstadt',\n",
       " 'Mühlhausen',\n",
       " 'Weimar',\n",
       " 'Weimar',\n",
       " 'Weimar',\n",
       " 'Halle',\n",
       " 'Halleand',\n",
       " 'Dresden',\n",
       " 'Dresden',\n",
       " 'Weimar',\n",
       " 'Dresden',\n",
       " 'Dresden',\n",
       " 'Weimar',\n",
       " 'Anhalt-Cöthen',\n",
       " 'visitedHamburg',\n",
       " 'Leipzig',\n",
       " 'Anhalt-Cöthen',\n",
       " 'Weissenfels',\n",
       " 'Prussia',\n",
       " 'Potsdam',\n",
       " 'Potsdam',\n",
       " 'Potsdam',\n",
       " 'Potsdam',\n",
       " 'Leipzig',\n",
       " 'Potsdam',\n",
       " 'London',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Dresden',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Saxony',\n",
       " 'Sicily',\n",
       " 'Spain',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Leipzig',\n",
       " 'Dresden',\n",
       " 'Erfurt',\n",
       " 'Mainz',\n",
       " 'Erfurt',\n",
       " 'Berlin',\n",
       " 'Hamburg',\n",
       " 'Leipzig',\n",
       " 'Cöthen',\n",
       " 'Leipzig',\n",
       " 'Poland',\n",
       " 'Saxony',\n",
       " 'Leipzig',\n",
       " 'Weimar',\n",
       " 'Weimar',\n",
       " 'Brunswick',\n",
       " 'Leipzig',\n",
       " 'Dresden',\n",
       " 'Berlin',\n",
       " 'Leipzig',\n",
       " 'London',\n",
       " 'Halle',\n",
       " 'Cöthen',\n",
       " 'England',\n",
       " 'Leipzig',\n",
       " 'Halle',\n",
       " 'Leipzig',\n",
       " 'Germany',\n",
       " 'Leipzig',\n",
       " 'Dresden',\n",
       " 'Dresden',\n",
       " 'Leipzig',\n",
       " 'Dresden',\n",
       " 'Cöthen',\n",
       " 'Weimar',\n",
       " 'Weissenfels',\n",
       " 'Berlin',\n",
       " 'Dresden',\n",
       " 'Leipzig',\n",
       " 'MS',\n",
       " 'Prussia',\n",
       " 'Berlin',\n",
       " 'Germany']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places = []\n",
    "for doc in nlp.pipe(paragraphs):\n",
    "    for ent in doc.ents:\n",
    "        if ent.label_ == 'GPE':\n",
    "            places.append(ent.text)\n",
    "places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3aa44c2c-fc66-4d86-b148-878a92a581f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Leipzig': 29,\n",
       "         'Dresden': 11,\n",
       "         'Weimar': 10,\n",
       "         'Arnstadt': 5,\n",
       "         'Potsdam': 5,\n",
       "         'Thuringia': 4,\n",
       "         'Germany': 4,\n",
       "         'Berlin': 4,\n",
       "         'Erfurt': 3,\n",
       "         'Halle': 3,\n",
       "         'Cöthen': 3,\n",
       "         'Eisenach': 2,\n",
       "         'Lüneburg': 2,\n",
       "         'Hamburg': 2,\n",
       "         'Anhalt-Cöthen': 2,\n",
       "         'Weissenfels': 2,\n",
       "         'Prussia': 2,\n",
       "         'London': 2,\n",
       "         'Saxony': 2,\n",
       "         'Pressburg': 1,\n",
       "         'Hungary': 1,\n",
       "         'Wechmar': 1,\n",
       "         'Gotha': 1,\n",
       "         'Schwarzburg-Arnstadt': 1,\n",
       "         'Italy': 1,\n",
       "         'Franconia': 1,\n",
       "         'Vienna': 1,\n",
       "         'Danzig': 1,\n",
       "         'Lüeburg': 1,\n",
       "         'Celle': 1,\n",
       "         'Lübeck': 1,\n",
       "         'Mühlhausen': 1,\n",
       "         'Halleand': 1,\n",
       "         'visitedHamburg': 1,\n",
       "         'Sicily': 1,\n",
       "         'Spain': 1,\n",
       "         'Mainz': 1,\n",
       "         'Poland': 1,\n",
       "         'Brunswick': 1,\n",
       "         'England': 1,\n",
       "         'MS': 1})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "place_freq = collections.Counter(places)\n",
    "place_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57164e3d-c306-4e19-9bdf-17f0d833c4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Leipzig', 29),\n",
       " ('Dresden', 11),\n",
       " ('Weimar', 10),\n",
       " ('Arnstadt', 5),\n",
       " ('Potsdam', 5),\n",
       " ('Thuringia', 4),\n",
       " ('Germany', 4),\n",
       " ('Berlin', 4),\n",
       " ('Erfurt', 3),\n",
       " ('Halle', 3),\n",
       " ('Cöthen', 3),\n",
       " ('Eisenach', 2),\n",
       " ('Lüneburg', 2),\n",
       " ('Hamburg', 2),\n",
       " ('Anhalt-Cöthen', 2),\n",
       " ('Weissenfels', 2),\n",
       " ('Prussia', 2),\n",
       " ('London', 2),\n",
       " ('Saxony', 2),\n",
       " ('Pressburg', 1),\n",
       " ('Hungary', 1),\n",
       " ('Wechmar', 1),\n",
       " ('Gotha', 1),\n",
       " ('Schwarzburg-Arnstadt', 1),\n",
       " ('Italy', 1),\n",
       " ('Franconia', 1),\n",
       " ('Vienna', 1),\n",
       " ('Danzig', 1),\n",
       " ('Lüeburg', 1),\n",
       " ('Celle', 1),\n",
       " ('Lübeck', 1),\n",
       " ('Mühlhausen', 1),\n",
       " ('Halleand', 1),\n",
       " ('visitedHamburg', 1),\n",
       " ('Sicily', 1),\n",
       " ('Spain', 1),\n",
       " ('Mainz', 1),\n",
       " ('Poland', 1),\n",
       " ('Brunswick', 1),\n",
       " ('England', 1),\n",
       " ('MS', 1)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_freq_sorted = place_freq.most_common()\n",
    "place_freq_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e29ef89c-b4e1-4976-9d13-63f36132b299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "place_keys = [v[0] for v in place_freq_sorted]\n",
    "place_values = [v[1] for v in place_freq_sorted]\n",
    "fig, ax = plt.subplots(figsize=(12,3))\n",
    "ax.bar(place_keys, place_values)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e627fe54-fc94-42ea-befe-48b73e42dbbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schwarzburg-Arnstadt\n",
      "Lüeburg\n",
      "Halleand\n",
      "visitedHamburg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Leipzig', 51.3406321, 12.3747329],\n",
       " ['Dresden', 51.0493286, 13.7381437],\n",
       " ['Weimar', 50.9810486, 11.3296637],\n",
       " ['Arnstadt', 50.8349097, 10.9461479],\n",
       " ['Potsdam', 52.4009309, 13.0591397],\n",
       " ['Thuringia', 50.9014721, 11.0377839],\n",
       " ['Germany', 51.1638175, 10.4478313],\n",
       " ['Berlin', 52.5170365, 13.3888599],\n",
       " ['Erfurt', 50.9777974, 11.0287364],\n",
       " ['Halle', 51.4825041, 11.9705452],\n",
       " ['Cöthen', 52.793057, 13.9355243],\n",
       " ['Eisenach', 50.9747134, 10.3193565],\n",
       " ['Lüneburg', 53.248706, 10.407855],\n",
       " ['Hamburg', 53.550341, 10.000654],\n",
       " ['Anhalt-Cöthen', 51.751033, 11.973698],\n",
       " ['Weissenfels', 51.1999793, 11.9667857],\n",
       " ['Prussia', 41.3738765, -94.506357],\n",
       " ['London', 51.4893335, -0.14405508452768728],\n",
       " ['Saxony', 50.9295798, 13.4585052],\n",
       " ['Pressburg', 48.15926025, 17.139658691421687],\n",
       " ['Hungary', 47.1817585, 19.5060937],\n",
       " ['Wechmar', 50.8916042, 10.7779129],\n",
       " ['Gotha', 50.9494849, 10.7014435],\n",
       " ['Italy', 42.6384261, 12.674297],\n",
       " ['Franconia', 44.2273919, -71.7478468],\n",
       " ['Vienna', 48.2083537, 16.3725042],\n",
       " ['Danzig', 54.42880315, 18.79832701217957],\n",
       " ['Celle', 52.624056, 10.081052],\n",
       " ['Lübeck', 53.866444, 10.684738],\n",
       " ['Mühlhausen', 51.2094255, 10.4589044],\n",
       " ['Sicily', 37.587794, 14.155048],\n",
       " ['Spain', 39.3260685, -4.8379791],\n",
       " ['Mainz', 50.0012314, 8.2762513],\n",
       " ['Poland', 52.215933, 19.134422],\n",
       " ['Brunswick', 34.0718195, -78.1914215],\n",
       " ['England', 52.5310214, -1.2649062],\n",
       " ['MS', 16.7417041, -62.1916844]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reference: https://stackoverflow.com/questions/13686001/python-module-for-getting-latitude-and-longitude-from-the-name-of-a-us-city\n",
    "from geopy.geocoders import Nominatim\n",
    "geolocator = Nominatim(user_agent=\"bach_network(1)\")\n",
    "place_geolocation = []\n",
    "for place in place_keys:\n",
    "    address=geolocator.geocode(place)\n",
    "    if address is None:\n",
    "        print(place)\n",
    "    else:\n",
    "        place_geolocation.append([place, address.latitude, address.longitude])\n",
    "\n",
    "place_geolocation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24222714-cccc-467a-9c0a-70b2bd3cf9f0",
   "metadata": {},
   "source": [
    "- \"Lüeburg\" is misspelled, and it must be Lüeneburg\n",
    "- \"Halleand\" must be \"Halle and\"\n",
    "- \"visitedHamburug\" must be \"visited Hamburug\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78292e36-c8d6-4e90-a250-d5d187d32a4a",
   "metadata": {},
   "source": [
    "## Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5880877-caf5-47e9-8ec8-c4ddd55bb12f",
   "metadata": {},
   "source": [
    "- Anhalt-Cöthen and Cöthen is identical.\n",
    "- Schwarzburg-Arnstadt and Arnstadt is identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbbb90e0-7309-4d9a-98ba-05ea651cd0d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "GERMAN_CITIES_REPLACE = {\n",
    "    \"Anhalt-Cöthen\": \"Anhalt-Köthen\",\n",
    "    \"Arnstadt\": \"Schwarzburg-Arnstadt\",\n",
    "    \"Lüeburg\": \"Lüneburg\",\n",
    "    \"Halleand\": \"Halle\",\n",
    "    \"visitedHamburg\": \"Hamburg\",\n",
    "    \"Brunswick\": \"Braunschweig\",  # Replace it with its German name\n",
    "    \"Cöthen\": \"Anhalt-Köthen\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "964547c1-edde-4d38-afd7-d96d0a230711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Leipzig',\n",
       " 'Dresden',\n",
       " 'Weimar',\n",
       " 'Arnstadt',\n",
       " 'Potsdam',\n",
       " 'Berlin',\n",
       " 'Erfurt',\n",
       " 'Halle',\n",
       " 'Cöthen',\n",
       " 'Eisenach',\n",
       " 'Lüneburg',\n",
       " 'Hamburg',\n",
       " 'Anhalt-Cöthen',\n",
       " 'Weissenfels',\n",
       " 'Wechmar',\n",
       " 'Gotha',\n",
       " 'Schwarzburg-Arnstadt',\n",
       " 'Lüeburg',\n",
       " 'Celle',\n",
       " 'Lübeck',\n",
       " 'Mühlhausen',\n",
       " 'Halleand',\n",
       " 'visitedHamburg',\n",
       " 'Mainz',\n",
       " 'Brunswick']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_cities = {'Leipzig': 29,\n",
    "         'Dresden': 11,\n",
    "         'Weimar': 10,\n",
    "         'Arnstadt': 5,\n",
    "         'Potsdam': 5,\n",
    "         'Berlin': 4,\n",
    "         'Erfurt': 3,\n",
    "         'Halle': 3,\n",
    "         'Cöthen': 3,\n",
    "         'Eisenach': 2,\n",
    "         'Lüneburg': 2,\n",
    "         'Hamburg': 2,\n",
    "         'Anhalt-Cöthen': 2,\n",
    "         'Weissenfels': 2,\n",
    "         'Wechmar': 1,\n",
    "         'Gotha': 1,\n",
    "         'Schwarzburg-Arnstadt': 1,\n",
    "         'Lüeburg': 1,\n",
    "         'Celle': 1,\n",
    "         'Lübeck': 1,\n",
    "         'Mühlhausen': 1,\n",
    "         'Halleand': 1,\n",
    "         'visitedHamburg': 1,\n",
    "         'Mainz': 1,\n",
    "         'Brunswick': 1}\n",
    "\n",
    "GERMAN_CITIES = german_cities.keys()\n",
    "list(GERMAN_CITIES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73bdc288-1f69-463a-863c-ae1f7043fafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Leipzig': 29,\n",
       " 'Dresden': 11,\n",
       " 'Weimar': 10,\n",
       " 'Potsdam': 5,\n",
       " 'Berlin': 4,\n",
       " 'Erfurt': 3,\n",
       " 'Halle': 4,\n",
       " 'Eisenach': 2,\n",
       " 'Lüneburg': 3,\n",
       " 'Hamburg': 3,\n",
       " 'Weissenfels': 2,\n",
       " 'Wechmar': 1,\n",
       " 'Gotha': 1,\n",
       " 'Schwarzburg-Arnstadt': 6,\n",
       " 'Celle': 1,\n",
       " 'Lübeck': 1,\n",
       " 'Mühlhausen': 1,\n",
       " 'Mainz': 1,\n",
       " 'Anhalt-Köthen': 5,\n",
       " 'Braunschweig': 1}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "german_cities_ = copy.copy(german_cities)\n",
    "\n",
    "for k, v in german_cities.items():\n",
    "    if k in GERMAN_CITIES_REPLACE:\n",
    "        kk = GERMAN_CITIES_REPLACE[k]\n",
    "        if kk in german_cities_:\n",
    "            german_cities_[kk] += german_cities[k]\n",
    "        else:\n",
    "            german_cities_[kk] = german_cities[k]\n",
    "            \n",
    "        del german_cities_[k]\n",
    "\n",
    "german_cities_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb02e016-05d4-4d8f-a1a4-eadac9d9708e",
   "metadata": {},
   "outputs": [
    {
     "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>place</th>\n",
       "      <th>counts</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Leipzig</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dresden</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Weimar</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Schwarzburg-Arnstadt</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Potsdam</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anhalt-Köthen</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Halle</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Erfurt</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lüneburg</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hamburg</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Weissenfels</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eisenach</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gotha</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wechmar</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Celle</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lübeck</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mühlhausen</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mainz</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Braunschweig</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   place  counts  latitude  longitude\n",
       "0                Leipzig      29       NaN        NaN\n",
       "1                Dresden      11       NaN        NaN\n",
       "2                 Weimar      10       NaN        NaN\n",
       "13  Schwarzburg-Arnstadt       6       NaN        NaN\n",
       "3                Potsdam       5       NaN        NaN\n",
       "18         Anhalt-Köthen       5       NaN        NaN\n",
       "4                 Berlin       4       NaN        NaN\n",
       "6                  Halle       4       NaN        NaN\n",
       "5                 Erfurt       3       NaN        NaN\n",
       "8               Lüneburg       3       NaN        NaN\n",
       "9                Hamburg       3       NaN        NaN\n",
       "10           Weissenfels       2       NaN        NaN\n",
       "7               Eisenach       2       NaN        NaN\n",
       "12                 Gotha       1       NaN        NaN\n",
       "11               Wechmar       1       NaN        NaN\n",
       "14                 Celle       1       NaN        NaN\n",
       "15                Lübeck       1       NaN        NaN\n",
       "16            Mühlhausen       1       NaN        NaN\n",
       "17                 Mainz       1       NaN        NaN\n",
       "19          Braunschweig       1       NaN        NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(list(german_cities_.items()), columns=['place', 'counts']).sort_values('counts', ascending=False)\n",
    "df['latitude'] = np.nan\n",
    "df['longitude'] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79ad90a0-a0f1-4e94-89bf-f146d8805b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,3))\n",
    "ax.bar(df.place, df.counts)\n",
    "ax.set_ylabel('Counts')\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Bach Cities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90179b0c-16d2-4f3b-bba9-7e6937d8bdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "geolocator = Nominatim(user_agent=\"bach_network(10)\")\n",
    "\n",
    "for place in df.place:\n",
    "    address=geolocator.geocode(place)\n",
    "    if address is not None:\n",
    "        df.loc[df.place == place, 'latitude'] = address.latitude\n",
    "        df.loc[df.place == place, 'longitude'] = address.longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e1e94c5a-e583-4317-803e-4ff7bc9161bf",
   "metadata": {},
   "outputs": [
    {
     "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>place</th>\n",
       "      <th>counts</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Leipzig</td>\n",
       "      <td>29</td>\n",
       "      <td>51.340632</td>\n",
       "      <td>12.374733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dresden</td>\n",
       "      <td>11</td>\n",
       "      <td>51.049329</td>\n",
       "      <td>13.738144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Weimar</td>\n",
       "      <td>10</td>\n",
       "      <td>50.981049</td>\n",
       "      <td>11.329664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Schwarzburg-Arnstadt</td>\n",
       "      <td>6</td>\n",
       "      <td>50.834210</td>\n",
       "      <td>10.946181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Potsdam</td>\n",
       "      <td>5</td>\n",
       "      <td>52.400931</td>\n",
       "      <td>13.059140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anhalt-Köthen</td>\n",
       "      <td>5</td>\n",
       "      <td>51.751033</td>\n",
       "      <td>11.973698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>4</td>\n",
       "      <td>52.517037</td>\n",
       "      <td>13.388860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Halle</td>\n",
       "      <td>4</td>\n",
       "      <td>51.482504</td>\n",
       "      <td>11.970545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Erfurt</td>\n",
       "      <td>3</td>\n",
       "      <td>50.977797</td>\n",
       "      <td>11.028736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lüneburg</td>\n",
       "      <td>3</td>\n",
       "      <td>53.248706</td>\n",
       "      <td>10.407855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hamburg</td>\n",
       "      <td>3</td>\n",
       "      <td>53.550341</td>\n",
       "      <td>10.000654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Weissenfels</td>\n",
       "      <td>2</td>\n",
       "      <td>51.199979</td>\n",
       "      <td>11.966786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eisenach</td>\n",
       "      <td>2</td>\n",
       "      <td>50.974713</td>\n",
       "      <td>10.319356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gotha</td>\n",
       "      <td>1</td>\n",
       "      <td>50.949485</td>\n",
       "      <td>10.701443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wechmar</td>\n",
       "      <td>1</td>\n",
       "      <td>50.891604</td>\n",
       "      <td>10.777913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Celle</td>\n",
       "      <td>1</td>\n",
       "      <td>52.624056</td>\n",
       "      <td>10.081052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lübeck</td>\n",
       "      <td>1</td>\n",
       "      <td>53.866444</td>\n",
       "      <td>10.684738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mühlhausen</td>\n",
       "      <td>1</td>\n",
       "      <td>51.209426</td>\n",
       "      <td>10.458904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mainz</td>\n",
       "      <td>1</td>\n",
       "      <td>50.001231</td>\n",
       "      <td>8.276251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Braunschweig</td>\n",
       "      <td>1</td>\n",
       "      <td>52.264658</td>\n",
       "      <td>10.523607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   place  counts   latitude  longitude\n",
       "0                Leipzig      29  51.340632  12.374733\n",
       "1                Dresden      11  51.049329  13.738144\n",
       "2                 Weimar      10  50.981049  11.329664\n",
       "13  Schwarzburg-Arnstadt       6  50.834210  10.946181\n",
       "3                Potsdam       5  52.400931  13.059140\n",
       "18         Anhalt-Köthen       5  51.751033  11.973698\n",
       "4                 Berlin       4  52.517037  13.388860\n",
       "6                  Halle       4  51.482504  11.970545\n",
       "5                 Erfurt       3  50.977797  11.028736\n",
       "8               Lüneburg       3  53.248706  10.407855\n",
       "9                Hamburg       3  53.550341  10.000654\n",
       "10           Weissenfels       2  51.199979  11.966786\n",
       "7               Eisenach       2  50.974713  10.319356\n",
       "12                 Gotha       1  50.949485  10.701443\n",
       "11               Wechmar       1  50.891604  10.777913\n",
       "14                 Celle       1  52.624056  10.081052\n",
       "15                Lübeck       1  53.866444  10.684738\n",
       "16            Mühlhausen       1  51.209426  10.458904\n",
       "17                 Mainz       1  50.001231   8.276251\n",
       "19          Braunschweig       1  52.264658  10.523607"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correcting the results from Nominatim\n",
    "df.loc[df.place=='Schwarzburg-Arnstadt', 'latitude'] = 50.83421021524258\n",
    "df.loc[df.place=='Schwarzburg-Arnstadt', 'longitude'] = 10.946181282168581\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc5ece9c-88a8-4938-95f3-52fbc383d81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "places_data = [[row['place'], row['counts'], row['latitude'], row['longitude']] for _, row in df.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57530e97-18e5-4c20-861d-24e0ca894841",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../docs/data/places.js', 'w', encoding='utf-8') as f:\n",
    "    f.write(f'const places = {places_data};')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70ee760-3374-466e-8518-9622b560110f",
   "metadata": {},
   "source": [
    "## Place Appearance Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5ed74f4-d661-4829-95fd-5f7e17d766c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wechmar',\n",
       " 'Gotha',\n",
       " 'Schwarzburg-Arnstadt',\n",
       " 'Erfurt',\n",
       " 'Eisenach',\n",
       " 'Lüneburg',\n",
       " 'Hamburg',\n",
       " 'Celle',\n",
       " 'Weimar',\n",
       " 'Lübeck',\n",
       " 'Mühlhausen',\n",
       " 'Halle',\n",
       " 'Dresden',\n",
       " 'Leipzig',\n",
       " 'Weissenfels',\n",
       " 'Potsdam',\n",
       " 'Mainz',\n",
       " 'Berlin']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 0\n",
    "place_order = {}\n",
    "for p in places:\n",
    "    if p in GERMAN_CITIES_REPLACE:\n",
    "        p = GERMAN_CITIES_REPLACE[p]\n",
    "    if p in german_cities and p not in place_order:\n",
    "        place_order[p] = idx\n",
    "    idx += 1\n",
    "    \n",
    "place_order = dict(sorted(place_order.items(), key=lambda item: item[1]))\n",
    "list(place_order.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0df3a2-d156-426a-a390-6f98396b3ba4",
   "metadata": {},
   "source": [
    "## Export data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "649555bb-247c-43e0-b436-82889930bdb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "\n",
    "cities = pd.Series(list(GERMAN_CITIES), name='name')\n",
    "cities_replace = pd.DataFrame({'original': GERMAN_CITIES_REPLACE.keys(), 'replace': GERMAN_CITIES_REPLACE.values()})\n",
    "with sqlite3.connect('../data/bach.db') as conn:\n",
    "    cities.to_sql('cities', conn, if_exists='replace')    \n",
    "    cities_replace.to_sql('cities_replace', conn, if_exists='replace')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}