07_uebung_archaeologie_loesung.ipynb

{
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   "source": [
    "_Lizenz: Das folgende Lehrmaterial kann unter einer [CC-BY-SA 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) Lizenz frei verwendet, verbreitet und modifiziert werden._   \n",
    " _Authoren: Jana Lasser (jana.lasser@ds.mpg.de)_  \n",
    " _Das Lehrmaterial wurde im Zuge des Projektes \"Daten Lesen Lernen\", gefördert vom Stifterverband und der Heinz Nixdorf Stiftung erstellt._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lösung Übung 07 - Archäologie: Visualisierung über einer Landkarte\n",
    "### Praktische Hinweise\n",
    "$\\rightarrow$ Übungen sind dafür gedacht, dass ihr sie euch daheim anseht und versucht, ein paar Aufgaben selbst zu lösen. In den Tutorien könnt ihr euch weiter mit dem Lösungen der Aufgaben beschäftigen und dabei Hilfe von den Tutor*innen bekommen.  \n",
    "\n",
    "$\\rightarrow$ Wenn ein Fehler auftritt:\n",
    "1. Fehlermeldung _lesen_ und _verstehen_\n",
    "2. Versuchen, selbst eine Lösung zu finden ($\\rightarrow$ hier findet das Lernen statt!)\n",
    "3. Das Problem googlen (Stichwort: Stackoverflow) oder den/die Nachbar*in fragen\n",
    "4. Tutor*in fragen\n",
    "\n",
    "$\\rightarrow$ Unter <font color='green'>**HINWEIS**</font> werden Hinweise gegeben, die für die Lösung der Aufgabe hilfreich sind und oftmals auch weiterführende Informationen zur Aufgabe liefern.\n",
    "\n",
    "$\\rightarrow$ Mit **(Optional)** gekennzeichnete Übungsteile sind für besonders schnelle Menschen :-)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"top\"></a>Inhalt\n",
    "---\n",
    "In diesem Tutorium möchten wir uns damit beschäftigen, Daten denen eine Räumliche Information zugeordnet ist auf einer Landkarte zu visualisieren. Dafür beschäftigen wir uns zuerst mit sog. \"Scatterplots\", die es uns erlauben, Datenpunkte anhand ihrer x- und y-Koordinaten darzustellen. Dieses Wissen wenden wir dann an, um die Anzahl der Fundstücke im Mittelmehrraum graphisch darzustellen.\n",
    "\n",
    "Das Tutorial gliedert sich in\n",
    "* [Scatterplots und Orte](#spatterplots)\n",
    "* [Landkarten](#landkarten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"scatterplots\"></a>1. Scatterplots und Orte\n",
    "---\n",
    "Die Funktion ```plt.scatter(x,y)``` ([Dokumentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html)) erlaubt es uns, einen einfachen Scatterplot zu erstellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<matplotlib.collections.PathCollection at 0x7f4422af8550>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = [3,5,7,4]\n",
    "y = [1,3,2,4]\n",
    "\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A.** Mache dich mit den Verschiedenen Argumenten der Funktion ```scatter()``` vertraut. Du kannst der Funktion z.B. mit dem Argument ```s``` eine Liste an Größen und mit dem Argument ```c``` eine Liste an Farben übergeben, um die Größe und Farbe der einzelnen dargestellten Punkte zu modifizieren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f44236827b8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sizes = [10,20,40,80]\n",
    "colors = ['red', 'green', 'blue', 'orange']\n",
    "\n",
    "plt.scatter(x,y, s=sizes, c=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir laden den Fundstücke-Katalog und die Tabelle ```ÌCRATES_LOCATION.csv```, in der die Längen- und Breitengrade der verschiedenen Fundorte vermerkt sind:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jana/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (15,32,34) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\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>Location_ID</th>\n",
       "      <th>Location_simple</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Coordinates_uncertain</th>\n",
       "      <th>Harbour</th>\n",
       "      <th>Sea</th>\n",
       "      <th>River</th>\n",
       "      <th>River_navigability</th>\n",
       "      <th>Road</th>\n",
       "      <th>...</th>\n",
       "      <th>Modern_country</th>\n",
       "      <th>Late_Hellenistic_Area_Provincia</th>\n",
       "      <th>Early_Imperial_Province</th>\n",
       "      <th>Middle_Imperial_Province</th>\n",
       "      <th>Early_Byzantine_region</th>\n",
       "      <th>Comment_Remarks</th>\n",
       "      <th>BA_map_reference</th>\n",
       "      <th>BA_Rank</th>\n",
       "      <th>BA_rank_uncertain</th>\n",
       "      <th>Pleiades_URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>aazaz</td>\n",
       "      <td>37.004606</td>\n",
       "      <td>36.519218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Syria</td>\n",
       "      <td>Syria</td>\n",
       "      <td>Syria</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>none</td>\n",
       "      <td>not_available</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>not_available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>abdera</td>\n",
       "      <td>24.973047</td>\n",
       "      <td>40.933650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thracium/Macedonicum_Mare</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Greece</td>\n",
       "      <td>not_available</td>\n",
       "      <td>Thracia</td>\n",
       "      <td>Thracia_Rhodope</td>\n",
       "      <td>not_available</td>\n",
       "      <td>none</td>\n",
       "      <td>51_D3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pleiades.stoa.org/places/501323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>abu_jiffa</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Israel</td>\n",
       "      <td>not_available</td>\n",
       "      <td>Arabia</td>\n",
       "      <td>not_available</td>\n",
       "      <td>Palaistine_III</td>\n",
       "      <td>none</td>\n",
       "      <td>not_available</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>not_available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>adulis</td>\n",
       "      <td>39.663840</td>\n",
       "      <td>15.244510</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Red_Sea</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>Barbaricum</td>\n",
       "      <td>Barbaricum</td>\n",
       "      <td>Barbaricum</td>\n",
       "      <td>Barbaricum</td>\n",
       "      <td>Study of the coastal change since Antiquity su...</td>\n",
       "      <td>not_available</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>not_available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>agioi_pandes</td>\n",
       "      <td>21.679860</td>\n",
       "      <td>38.661422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Hellas</td>\n",
       "      <td>none</td>\n",
       "      <td>not_available</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>not_available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location_ID Location_simple  Longitude   Latitude  Coordinates_uncertain  \\\n",
       "0            1           aazaz  37.004606  36.519218                      0   \n",
       "1            2          abdera  24.973047  40.933650                      0   \n",
       "2            3       abu_jiffa   0.000000   0.000000                      1   \n",
       "3            6          adulis  39.663840  15.244510                      0   \n",
       "4           10    agioi_pandes  21.679860  38.661422                      0   \n",
       "\n",
       "   Harbour                        Sea          River River_navigability  \\\n",
       "0        0              not_available  not_available      not_available   \n",
       "1        1  Thracium/Macedonicum_Mare  not_available      not_available   \n",
       "2        0              not_available  not_available      not_available   \n",
       "3        1                    Red_Sea  not_available      not_available   \n",
       "4        0              not_available  not_available      not_available   \n",
       "\n",
       "            Road                   ...                    Modern_country  \\\n",
       "0  not_available                   ...                             Syria   \n",
       "1  not_available                   ...                            Greece   \n",
       "2  not_available                   ...                            Israel   \n",
       "3  not_available                   ...                           Eritrea   \n",
       "4  not_available                   ...                            Greece   \n",
       "\n",
       "  Late_Hellenistic_Area_Provincia Early_Imperial_Province  \\\n",
       "0                           Syria                   Syria   \n",
       "1                   not_available                 Thracia   \n",
       "2                   not_available                  Arabia   \n",
       "3                      Barbaricum              Barbaricum   \n",
       "4                          Achaia                  Achaia   \n",
       "\n",
       "  Middle_Imperial_Province Early_Byzantine_region  \\\n",
       "0            not_available          not_available   \n",
       "1          Thracia_Rhodope          not_available   \n",
       "2            not_available         Palaistine_III   \n",
       "3               Barbaricum             Barbaricum   \n",
       "4                   Achaia                 Hellas   \n",
       "\n",
       "                                     Comment_Remarks BA_map_reference BA_Rank  \\\n",
       "0                                               none    not_available       4   \n",
       "1                                               none            51_D3       4   \n",
       "2                                               none    not_available       0   \n",
       "3  Study of the coastal change since Antiquity su...    not_available       3   \n",
       "4                                               none    not_available       4   \n",
       "\n",
       "   BA_rank_uncertain                             Pleiades_URL  \n",
       "0                  0                            not_available  \n",
       "1                  0  https://pleiades.stoa.org/places/501323  \n",
       "2                  1                            not_available  \n",
       "3                  1                            not_available  \n",
       "4                  1                            not_available  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "catalogue = pd.read_csv(\"../daten/ICRATES/ICRATES_CATALOGUE.csv\", encoding='ISO-8859-1')\n",
    "location = pd.read_csv('../daten/ICRATES/ICRATES_LOCATION.csv', encoding = \"ISO-8859-1\", low_memory=False)\n",
    "location.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B.** Wieviele verschiedene Fundorte gibt es? In welchen (modernen) Ländergrenzen liegen sie?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Es gibt 275 verschiedene Fundorte\n",
      "Die Fundorte liegen in:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Syria', 'Greece', 'Israel', 'Eritrea', 'Turkey', 'Egypt', 'Italy',\n",
       "       'Cyprus', 'Jordan', 'Libya', 'India', 'Ethiopia', 'Iraq',\n",
       "       'Lebanon', 'Albania', 'Tunisia', 'United_Arab_Emirates', 'Sudan',\n",
       "       'Malta', 'Yemen', 'Macedonia', 'Oman'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Es gibt {} verschiedene Fundorte'.format(len(location)))\n",
    "print('Die Fundorte liegen in:')\n",
    "location.Modern_country.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**C.** Stelle die Fundorte als Scatterplot dar. In welchem Bereich (Längen- und Breitengrad) liegen die Datenpunkte? Was hat es mit dem Punkt bei (0,0) auf sich?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scherbenfundorte im Mittelmeerraum')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = location['Longitude']\n",
    "y = location['Latitude']\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel('Längengrad')\n",
    "plt.ylabel('Breitengrad')\n",
    "plt.title('Scherbenfundorte im Mittelmeerraum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Einträge mit Longitude = 0 und Latitude = 0 stellen offensichtlich Fundorte\n",
    "# dar, bei denen Längen- und Breitengrad unbekannt sind. Wir können alle Nullen\n",
    "# mit NaN ersetzen, damit wir im Folgenden einfacher mit diesen Spalten rechnen\n",
    "# können\n",
    "\n",
    "# importiere die Bibliothek NumPy, die uns den NaN Wert zur Verfügung stellt\n",
    "import numpy as np\n",
    "# ersezte alle Nullen mit np.nan (inplace=True gibt an, dass die\n",
    "# Ersetzung direkt (inplace) erfolgen soll, und keine Kopie des\n",
    "# DataFrame dabei erstellt werden soll).\n",
    "location['Longitude'].replace(0, np.nan, inplace=True)\n",
    "location['Latitude'].replace(0, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Es gibt 233 Fundorte mit bekannten Koordinaten\n",
      "Der Längengrad liegt zwischen 10.32 und 79.82\n",
      "Der Breitengrad liegt zwischen 10.15 und 45.56\n"
     ]
    }
   ],
   "source": [
    "# sind die fehlenden Einträge erst einmal herausgefiltert, lassen sich\n",
    "# z.B. der minimale und maximale Längengrad (und Breitengrad) sehr leicht herausfinden\n",
    "print('Es gibt {} Fundorte mit bekannten Koordinaten'.format(len(location.dropna())))\n",
    "print('Der Längengrad liegt zwischen {:1.2f} und {:1.2f}'\\\n",
    "      .format(location['Longitude'].min(), location['Longitude'].max()))\n",
    "print('Der Breitengrad liegt zwischen {:1.2f} und {:1.2f}'\\\n",
    "      .format(location['Latitude'].min(), location['Latitude'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um herauszufinden, wieviele Fundstücke es pro Fundort gibt, müssen wir den Katalog nach der ```Location_ID``` filtern. Um z.B. die Anzahl der Fundstücke mit der ID 1 zu finden, gehen wir wie folgt vor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Am Fundort aazaz (ID 1) wurden 9 Fundstücke gefunden.\n"
     ]
    }
   ],
   "source": [
    "# nach dieser ID suchen wir\n",
    "ID = 1\n",
    "\n",
    "# wir wollen auch direkt den Namen des Fundortes herausfinden, dafür\n",
    "# erstellen wir eine Maske \n",
    "name_mask = location['Location_ID'] == ID\n",
    "# und filtern dann dass \"location\" DataFrame entsprechend\n",
    "location_name = location[name_mask]['Location_simple'][0]\n",
    "\n",
    "# um alle Fundstücke, die an einem bestimmten Fundort mit einer\n",
    "# gegebenen ID gefunden wurden im Katalog zu finden, erstellen\n",
    "# wir ebenfalls eine Maske\n",
    "location_id_mask = catalogue['Location_ID'] == ID\n",
    "\n",
    "# wir filtern den Katalog mit Hilfe der Maske...\n",
    "filtered = catalogue[location_id_mask]\n",
    "# ...und zählen die Fundstücke\n",
    "count = len(filtered)\n",
    "\n",
    "print('Am Fundort {} (ID {}) wurden {} Fundstücke gefunden.'\\\n",
    "      .format(location_name, ID, count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**D.** Iteriere über alle Location IDs in der ```location``` Tabelle. Finde für jeden Fundort heraus, wieviele Fundstücke dort gefunden wurden. Speichere die Ergebnisse in einer neuen Liste, füge die Liste dem ```location``` DataFrame als neue Spalte hinzu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "sherd_count = []\n",
    "for ID in location['Location_ID']:\n",
    "    location_id_mask = catalogue['Location_ID'] == ID\n",
    "    filtered = catalogue[location_id_mask]\n",
    "    count = len(filtered)\n",
    "    sherd_count.append(count)\n",
    "    \n",
    "location['Sherd_Count'] = sherd_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"landkarten\"></a>2. Landkarten\n",
    "---\n",
    "Der in Aufgabe **1.C** erstellte Scatterplot der Fundorte ist schwer zu interpretieren. Insbesondere fehlt uns der Kontext der Geographie wie Ländergrenzen, Landmassen und Wasser. Um die Darstellung zu verbessern, ist es z.B. sinnvoll, im Hintergrund des Scatterplots eine Landkarte der Gegend darzustellen. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exkurs zu Geopandas\n",
    "Um Landkarten darzustellen, gibt es in ```Python``` die spezialisierte Bibliothek ```geopandas```, mit der sehr einfach Landkarten dargestellt werden können. Da die Bibliothek auf dem JupyterHub noch nicht vorinstalliert ist, müssen wir sie erst einmal selbst herunterladen und installieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in /home/jana/anaconda3/lib/python3.6/site-packages (0.5.0)\n",
      "Requirement already satisfied: pyproj in /home/jana/anaconda3/lib/python3.6/site-packages (from geopandas) (2.1.3)\n",
      "Requirement already satisfied: fiona in /home/jana/anaconda3/lib/python3.6/site-packages (from geopandas) (1.8.6)\n",
      "Requirement already satisfied: shapely in /home/jana/anaconda3/lib/python3.6/site-packages (from geopandas) (1.6.4.post2)\n",
      "Requirement already satisfied: pandas in /home/jana/anaconda3/lib/python3.6/site-packages (from geopandas) (0.23.0)\n",
      "Requirement already satisfied: click<8,>=4.0 in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (6.7)\n",
      "Requirement already satisfied: six>=1.7 in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (1.11.0)\n",
      "Requirement already satisfied: munch in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (2.3.2)\n",
      "Requirement already satisfied: attrs>=17 in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (18.1.0)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (0.5.0)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /home/jana/anaconda3/lib/python3.6/site-packages (from fiona->geopandas) (1.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /home/jana/anaconda3/lib/python3.6/site-packages (from pandas->geopandas) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in /home/jana/anaconda3/lib/python3.6/site-packages (from pandas->geopandas) (2018.4)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /home/jana/anaconda3/lib/python3.6/site-packages (from pandas->geopandas) (1.14.3)\n"
     ]
    }
   ],
   "source": [
    "# die nachfolgenden Kommandos musst du dir nicht merken. Falls du jemals eine\n",
    "# eigene Bibliothek nachinstallieren willst, ersezte \"geopandas\" mit dem\n",
    "# Namen der von dir benötigten Bibliothek\n",
    "\n",
    "# die Bibliothek \"sys\" gibt uns Zugriff auf Funktionen des Betriebssystems\n",
    "# mit dem Operator \"!\" führen wir Code nicht im Python-Interpreter sondern\n",
    "# auf der Kommandozeile aus. \n",
    "import sys\n",
    "!{sys.executable} -m pip install geopandas\n",
    "\n",
    "# importiere das frisch installierte geopandas unter dem Kürzel \"gpd\"\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um Landkarten darstellen zu können, brauchen wir erst einmal Informationen über den Verlauf der Ländergrezen. Diese sind standardisiert in sog. \"shape-files\" abgelegt. shape-files sind vielfach öffentlich zum Download verfügbar, z.B. auf [dieser](https://gadm.org/download_country_v3.html) Website. Die shape-files für Deutschland haben wir im Ordner ```gadm36_DEU_sh``` im StudIP hochgeladen. Der Ordner enthält eine Reihe von verschiedenen Dateien, die geopandas benötigt, um das shape-file korrekt einzulesen. Informationen liegen außerdem in verschiedenen \"Ebenen\" vor:\n",
    "* Ebene 0 ist die Ländergrenze von Deutschland\n",
    "* Ebene 1 ist enthält außerdem die Grenzen der Bundesländer\n",
    "* Ebene 2 enthält die Grenzen der Landkreise\n",
    "* Ebene 3 enthält die Grenzen der Gemeinden\n",
    "\n",
    "Wast du die Informationen über die Grenzen heruntergeladen, kannst du es sie geopandas einlesen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>GID_0</th>\n",
       "      <th>NAME_0</th>\n",
       "      <th>GID_1</th>\n",
       "      <th>NAME_1</th>\n",
       "      <th>VARNAME_1</th>\n",
       "      <th>NL_NAME_1</th>\n",
       "      <th>TYPE_1</th>\n",
       "      <th>ENGTYPE_1</th>\n",
       "      <th>CC_1</th>\n",
       "      <th>HASC_1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU.1_1</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Land</td>\n",
       "      <td>State</td>\n",
       "      <td>08</td>\n",
       "      <td>DE.BW</td>\n",
       "      <td>(POLYGON ((8.708021159999999 47.6895752, 8.707...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU.2_1</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Bavaria</td>\n",
       "      <td>None</td>\n",
       "      <td>Freistaat</td>\n",
       "      <td>None</td>\n",
       "      <td>09</td>\n",
       "      <td>DE.BY</td>\n",
       "      <td>POLYGON ((9.740663530000001 47.55353546, 9.740...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU.3_1</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Land</td>\n",
       "      <td>State</td>\n",
       "      <td>11</td>\n",
       "      <td>DE.BE</td>\n",
       "      <td>POLYGON ((13.17135715 52.3977623, 13.17135334 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU.4_1</td>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Land</td>\n",
       "      <td>State</td>\n",
       "      <td>12</td>\n",
       "      <td>DE.BR</td>\n",
       "      <td>(POLYGON ((12.26715565 52.2313118, 12.27047348...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU.5_1</td>\n",
       "      <td>Bremen</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Freie Hansestadt</td>\n",
       "      <td>State</td>\n",
       "      <td>04</td>\n",
       "      <td>DE.HB</td>\n",
       "      <td>(POLYGON ((8.711423870000001 53.04463196, 8.71...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GID_0   NAME_0    GID_1             NAME_1 VARNAME_1 NL_NAME_1  \\\n",
       "0   DEU  Germany  DEU.1_1  Baden-Württemberg      None      None   \n",
       "1   DEU  Germany  DEU.2_1             Bayern   Bavaria      None   \n",
       "2   DEU  Germany  DEU.3_1             Berlin      None      None   \n",
       "3   DEU  Germany  DEU.4_1        Brandenburg      None      None   \n",
       "4   DEU  Germany  DEU.5_1             Bremen      None      None   \n",
       "\n",
       "             TYPE_1 ENGTYPE_1 CC_1 HASC_1  \\\n",
       "0              Land     State   08  DE.BW   \n",
       "1         Freistaat      None   09  DE.BY   \n",
       "2              Land     State   11  DE.BE   \n",
       "3              Land     State   12  DE.BR   \n",
       "4  Freie Hansestadt     State   04  DE.HB   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((8.708021159999999 47.6895752, 8.707...  \n",
       "1  POLYGON ((9.740663530000001 47.55353546, 9.740...  \n",
       "2  POLYGON ((13.17135715 52.3977623, 13.17135334 ...  \n",
       "3  (POLYGON ((12.26715565 52.2313118, 12.27047348...  \n",
       "4  (POLYGON ((8.711423870000001 53.04463196, 8.71...  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lies das shape-file ein\n",
    "DEU_map = gpd.read_file('../daten/gadm36_DEU_shp/gadm36_DEU_1.shp')\n",
    "\n",
    "# das shape-file bzw. die darin enthaltene Karte verhält sich wie ein DataFrame\n",
    "DEU_map.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir können die Landkarte ganz einfach darstellen (dabei wird im Hintergrund auf die Funktionalität der Bibliothek ```matplotlib``` zurückgegriffen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4413335550>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DTWuq+NwrNrPv0ijfe6qTz718E6trbLz2G09xuj/AXz9/DW+9trUock45azMRQnyKTML7g8B4uWoj0EfGpHJgtteWc0q55Z6nO/mH+5eG/d4LNtTw8Az9YJP5yuu3Y9Ip/OiZbl6+vZ4bVlWBgBd9+bFZ8zBLkXW1Nt6wt5mHjg/gCSeosOo50u0jMta2sqfNzd+/ZD3bCiyvm7Wc0mxmlFLK6knHdDDLTKlMdlFVSa8vSo3deNku0Rv2ttDkMvPVP1zApFMwaDWc6g8QT6kIMpXCg4H4zCcuElZWWbAYtAyH4mxtdHCsx89sj82/+MHhia8fOT3IulobNqN2WQUkAIdJR7XdiNWo5alLozAtlj/T4eFot6/gQWmuLNiMMqejKjMjJ3r9vP27zzAUjLOl0cE/3r6BXdNyKzeuqeLGNRl79DMDAfa3e/BHkqgSDDoNekWDeaxy+JHTQ4V4GzPS4DJhMyicGVh4pfRSzR9diZoxuZSP/+zEFFmXyUgJh7u8vOXa1vwOboGUK7pLhBO9fl759SdJTGvg3Fhvp8llxqjTYNQpDARitI+EUYTg0ix9ZHqths+9fBN1DhMfve/YFE2kQrGu1rYsg0o++c7bdnPz2uqrH5gjytIlBaLXF+X8YBC9NiPsv6bGxuEuL4mUyuoaG7FkGr1WQ8085Tc+dt8xfrA/e422Wo3g7nfs5ViPj3/Oc/XyTGgEmHTKnOU7yswfl1nHw399U8F01ctuJjkikkhxYShIMq3yld+d50fPdPGn88Mc6vISTaQJxpKsr7OztsbGulobikawq9XNtasqqbIZqLQaGAw8m/OIJdNc7cHQ7YnwkwPZ3U1KqZJ3/O8zrK+z8fwNNVnpvq9zGLEtUAXSbdGzqcFBEWwSLVlC8RROc/blirNNufdtFpJplZ8e7GEkFMdtMWA3aamwGNg0tgWdViVvva51iiZ1IJbEF0lyfjBEMq1ydiCIJ5zAG0kSiCbRaQUOk4437m2ZeM2hTi+729zolNk/jR/6ydGc1B5FEmne+b8HeeNzmnng/dfyrSc6FuQft7fNPUV6dyHKlyOhBCMhD9ubnShCIKXkYJdv3ucpMztGnYK2BKL+sg5K/miSjpEw6thMRadoMOoyy64Kq4E79zQTiCW59+kufn9mkBO9GUF9o05BkEkcb210EkuqnOj1MxCY267PikrrhK/XXPy9xvW3c0EirfLtJzrYd8nD379kHfsujV5196raZiCaSLOuzsZQIM6xHh/RpMqWBsei62EOTwpEe9rc7G/3LOp8ZZ4lGEtxpNvH9mZXoYdyRZZNTimWTGPQahgIxPCEE6yvtaPRiAlh/pn4/r5OPv/Q6Yl6j2yhUwQ1diN/98K1vGxr/USzaTyV5uxAkAanaUol7nefaOdTv3i2XWKhgmpX4+3XtaFKle8+ObXAtbXCTI83SkqVbGl0cKrPz2yCiduanJzo9WdlZldOfmef1+1q4p/u2HiZVlM+KCe6JyGl5J6nu/jK785TbTfwvptWsaXRMas9Tq8vyod+fDRT85FDnGYd//fea1hVnUmA33nXPo50+3Bb9Hz7rbsn6kqklNx51z7C8RRaRUP7SDjrTiXjbKi384L1NYTiKX6wvwsV0CuCFrcFg07DMx3eWV+7ssqC1ajlaLd/wdd3mnX4IkmaXCa6i2BXcCly8p9eWBAHmLKe0iSEELzxOS28akcjQ8EYbose2yz+ZJ5wArdZT60j9+L0vkiSd999kIf+4gZO9wc40u2bGMPLv/YETW4TO5tdOM163nZdG//y6zOE4/EJ3Z9ccKovwKm+ADajltfuasSkV0AKXBYdd+/rotFlIp5SZ6yJqbQaeHqRy621NTZO9PrLAWkObGty0DEawReZ+wPKYdKhKfJe1WURlMYx6RVaKixXPOYrvzvPr07058T+ejJbGx2srLKys9WFQauhyW3m7nfswRNO8Jc/PAJkROa7PVHqHEZ0iiCSSBFPZZah2V5STicYS/GdsWXcqmor3Z4w8dSVZ9XnB4PsbHHS64sxsICq6p0tLjpHw+WygDmSTEuaXWbW1Chzzr35o0l+sL+Lt13XWrQa38sqKF2NWDLNvU93kcjhTGScjQ0OPnvHJlQpEUJQaTVww+qqGW8ut0XPY+dGkDJzI0YS+fV1uzAUYkdzJlckJUjkRE7JbdGTUlUC0RSeSBJPp48qq4EdzU4Od/uumPsy6zPOrxVWA0OBGAc7Z18alslQ6zDS5DLhjyYxajNLeW8kyY5mJ4fmuFv56V+e4rpVlTlR8swG5aA0iYOd3rwEJMh0tT9wpA+DVsOXXreNG9dUkUqr/PtvL7coOpnD3be5cqjLR4PTRFpKWtxmEqk0aQkjoTiecIqdLS5O9fmJJlWGQ3GqbHpuWFVJMJ6i1xtlaIbl3rpaG4e6fFwcnrnyvMxUVlRa6PNFZ5yFDgRi7G7NKHSKse9NOgWjVkFoMqYDZ/uDE+UaDx7rY23t2jy/g7lRDkqTmO7RlWtC8RShOLz77gPsbnUz4I8t2iUjl4zL7M70oTjY6aXZbabSqkeVjOXHMjtnOo1gR7OTtCo51R8gmZaYdAqjodwukZcKbZVm/NEU7SPhWZuT+3wx+nxXXjK3VZqfDUrH+/nzW1YVpftJOShNIpFSqbQaiCRSE8nAeCpNMp3bHcpYUuVP50tfYKHLE5kxsCdVObG00AiosOiptOrp8UaptOox6hSsBu2U7f9xXfORUOyquaxsoFPEpPodSToNiiIYCcZn7SHMB0adhnA8vegc5+5W15Sd04vDYU70+rOi9JltykFpEm+5tvWyTupvP97OA0f7JnbGyiwOVcJoOMHo2IdsPKmtaATbm5xoFYEqM260UmZaV4aDsVnrorKBWa/QWmGZMZ9n0AoanSZcFh0GrcKF4dC8drsWik7JmFBuaXQuqoC0yWXCadZPBCSNyGwoqKrkNycGy0GpFPFFk+WAlAfSquTwDH/nfn+MnS0uDnd5s26GsKvFhUYjGA7GOdU/c94unpL0+KL0jC1d19XasBt1DAVjNLvN+CJJhoLxRRe0bmqwY9QqaAScHgiyvtbOyT7/4gKS24TNoCOZVnGadbjMejSCiQA1EIjzvA017GkrrsBUDkpXYXdr4UvyNzc4ODeYaQIuUfntRXGw08uuFheH5hmYNAJ2tbhJplWkhEgyhUGrYNYrnB0McmABu31nBoJoNZBS4dxgJv9X5zAwGIizp82NL5rEF0lcVUxvV4uL0TFN9XA8zYneZ4Nird3A/o7Ft9d0e6LAs/Ve02d49U4j6+uKbweuHJSuQqPLnLO2jrmSTKusq7WRSKvYjDoG/DFiyTSBWJJYMj+7hYXmQKeXapuB1koL3kiC84NX3xCod5qy8uGezvSlZL8/E4DGC0d3tbhodpsRCHzRBA5TplD3SLePjfV2Umk5ERDbZ8hXDeRBHfR562v4l1dtnrWIuJCUg9IVUFXJX/3oSEEDEjyrqGjQatja6JhIJtfaDdTVGgHBSDhOtyfK5gYHF4dDOS+uLARDwThDwTh7Wt0ztqFYDVqklIQTaRSNmGi0zjczzcDMOg3NbjNHFtGCk03cFl3ROp0s2CFXCPEZ4A5ABYaAt0op+3I10EIgAbuxeOJ2PKVyoNM7USg3EIhPPFV1GoFRq+Fkn5+tTc4p3fZLjfHZT6YuBwQZqZP9HV4EmbxPPKXOOAspFJGkWlT1WIFofgtw58N8RN5ullJum9RQ90Up5RYp5Tbgl8A/Zn94hUXRCLY0FpfrqCozFdZ7pyUnk6oklsrknNLLJPH0TIeXdBpOjvXK7W51safNTftIqKgCUjHy6p2NhR7CrCx4GiClnLxdYYFZ67pKmjU1xZcIDMRSnBsMsqfNTSqtEogliSdVPOEEFoOWVI7rqoqJg11e9FoN/f7YhA5UpVVPg0lHnd1ISlU53uMnskxyb1ejwWniE7ev53kbago9lFlZsEMugBDic8CbAT9w80wvnGZGuegB55tsyMTmAm8kedl2scOkI5JIz7q9vVSZbqZgM2a2v4/2+EmmVVZVW0mkVaQq6fNFl2WA2tPm5iWbarlzT3NBtJTmw4IdcqWUj036/ccAo5Tyk1c6T6kZB0gpuf0/Hy+K3rMyc+dKipXNbjO1DiNDgRgdo/ltKyoE62ptfPNNO6+qjpEPsmocMJ7AllIOAfcDe6Ydci/wqvkOspiRUvLJB06WA1KBsRoUtjQ42Nxgp9pmQKcRWA2XP+l1Y/m/JrfpigWHXZ7IhF6UVV/cM4ZskEirRRGQ5sOCHXKFEKullOfHDnsZUHifniwhpeSffnGK7z3VefWDy+QMq0FhdY1tYifRpFdAQCieps5hpNltRgLJlMrpgQDHeua23R6KpzjeG8Bp1rHaacJl0ZNWJYmUikmvEI6n6PVFqbYZOD8YKulk6YA/hjecwGXRF3ooc2bBDrlCiJ8KIdaSKQnoBN6bu2Hml58f6eO7T3YUehjLCgHsanURT6pEkmnSqsQXSUwpbYhOqr2anNheKL5IcsY+NpNeIZpI44sk2d7knLH9pVT4+5esL4j07WK46millJeArTP8fEkt1ybzrccvFXoIy44VVZYr6n/nk8nBT6eUtjXix392go//7ARNbhNffPVWnrOiotBDuiql/RfPEQuRci2zOLpGI2wosj6sNTXWnLSpFIJuT5Q779rHm7+9n7v3dRJPFW/FfzkoTSOVVnOuz13mcpKqRJWSdUUk0Vpqy5658Ni5YT7xsxPc8dUnirbAtByUpjEYjC/LTvxi4MxAiG5PpGg610/3B2itmNmGKx9U2ww5szE/MxDkjq8+ziOnBnNzgUWw9B4FV+HXJwZ46Hg/6bEnczItec4KN4m0isusJxQr3p6g5UA4kebScIj1dTZO9wfRCAr2kEimZV7kYm1GLaurrWgVDQc6PGxrcqLVaBgMRNnZ4spZri0QS/Guuw9w67oaXre7iRvXVBaFPO6yMKOczJ/fe4gHj/UXdAxlro5Zp2FVjQ1fJJl37XSALY0OLg6Fcmr3tKraCoAnFMczi5rlnlYXF4bDeUkpOEw6Pn3HRu7Y1pCT82e1eHKpMOCPcc2KCiqtpVOzsVyJJFVMOqUgAQnApFNyGpA0IqNAcWEoNGtAAtjf4cUbTuRFbNAfTfIvvzpT8I2eZRWUzg8F+fjPTjBSYi4aTnNGiMtmUNjZ4mJvm5uKEiqGWygnev1srLcX5NrjWkxbGnKjEtHgNBGOzy3oSTKKCKuqLRP3Qq540aa6vLhDX4llFZSaXGY2Ncx+ky/GMNRp1qFXsp+V3NPqJhxPYTNqiSZVDnZ6ebrdQ8tVErCVVj3bmhxsrLezsqq02gzG2VjvIJJIL+r/ZaEc7fHT5DJxvM/P9iZn1oNjMi1JqfNrDHaZ9VTmWJjtt6cHCr77vKwS3a2VFn7xgevp8UaJJDK2NfsujXK0x8fzN9TQ5YnwzT/Ov3BSq8moQobjsL7ORp8vin+BIlrbmxwoGg0ajQCZ2QFKpiXJ9NTzXUlZsrXCTDylcqTbT2uFuWBLoMUSS6Xp80ULovyZSKkTypZnBoJU2rI7M21ym+adwB4/vtKqp60y+8WmL9lcy0detA53gWfhyyooAQghaHI/O8u4ZuWzFa7JtIovnORHB7rndc6UCvUOE4OBOKf7g+xpdXNuKLggK57O0QgVVsNVTSlTqqTBacKo05BWJZFEmganEb1W4Vivf6IqeTAQp8llotMTveL5ipFjPX7qnUYMWg3tI4ULrCuqLFlvzBZCsLbGitWowxOOU23LLJlUKTnS7WNLoxNVStJjciuxZJrQ2HJPEQJ/NLs2T+tqbfzXG3Zm9ZwLZdkFpSuhUzT8y6u3cKTbx9nB4FWPN2o1bG50IMd8ysbZ3+FB0Qg21dsxG7REE2mMOg2do5EZ7asn44kk8USSWA0KG+odHOz0zqgkeWGGoDXTuTc3OEq6Knnc9fVKciS5Jp4D07np72Vy0K22GejzRaf09gkBG+vtpFVJNJmecFLJFueHQgwFYxPBsZCUg9I0fntq8KoByaAVbG1ycaovMOsUOq1KTkx7um6stxOKJeckMhaKp9nf7mFns4uDXfObpitjNtmdo5GSDkg00sEIAAAgAElEQVSQydW1uM0FC0iQefjks15qpoeLlJllpEmnEIpnv5YurUq+/1Qnf/OCtVk/93xZVonuufD5h07P+rtV1Vb2tLlJq5kn3XxvDkUjEPPM2moXkDzfUGfnmQ7vVWdlpYAvkkSvLdxtWm0z0D4SLooq/7QqcxKQxvnpoV5GQoW/Z8ozpWn89fPW8K3HLyEl9Hgj6BTNhCTuTEumuVJp1XNhMJgXKdaBQIxdrS6iifSSEKl7psNbsOWb3aQjrcqc1iwVC72+KLs++wgtFWY2Nzj43Cs2T3jW5ZNyUBpj36VR3vf9g0QS6ZzkEFZWWSfMCudDLDn/D8NoKI4vkmBzjmps8s2WBseC/g7ZQKsRuC06RpdRk3bnaASNEKgFmh4u+6B0vMfPf/7+PPsujRLIYd/b+aEgO5qdaBUNFwaDV6zinUy3N8r25vn5uKkSdje7ONJdHPpEi0VFkiiA2L9BqyGaSNNZoiUVi0GniIKpVS7GjPKLwEuBBHAReJuUsiQk+ro9EaLJNMFYkr/84RF6vLnfLveEk3jCPlZUWWYMSDpFsKXBQTylklIlZweCbG5wcKzXv6BituFQnFqHiUqrAQGc7PMTLUEXD60GjDqFjgKUBMRTKgadBqtBmdiOXy685drWgl17rm4mHcAuKeXIpJ+9APi9lDIlhPgXACnlR650nmJoyB0JxXnJf/wpr0lgITKV2SOh+GUuqQatwKLXYTYoU4KjWa9kzXq72magwWUqOdfclVUWYkmVXl9ha6xWVFow6jSc6r96mchSodFl4tU7G3n/c1dlbaNhrg25izGjfHjSt/uAVy/0XPnixwe6+dyDp7NeeHY1pIRgLLOLtKXBQSKtEkmkMekVzg0GiUcSTF8hZCsgQWaLudFlytr5ssmmejsmvYIqM02qUoJGw4ShZp+v8IHg0kgYq0Gb6dgfCuOJJAoqqZIPerxRvvzIeV66tZ6VVda8XntRZpSTeDvwo6yOLAe8dlcTr9rRyEPH+/nSb89xKY/Ke4V+yl4aCaNTBMkicM/VamB7swuNEBzt8RErgWVlKJ5if4cXk15ha2OmJ8+o03C8N7O7WW0z0OgycWkkvKBK/mLk9XuaaCuAPdNcg9J1k80ohRBnxs0ohRD/AKSAe2Z6YbE55CoawUu31rOx3s5L//PxZbHVC5m8TFuFBZ1Ww4XBEJ5I4XaTUmqmtywQS5VEQJpMNJHm6CQrp3W1NhSN4FR/gKFgHINWw9paG1JKNCKjplmqvO26tkwPZp6ZU1CabEYphBg3o3xMCPEW4HbgVjlLcmpsVnUXZHJKWRn1AkmmVXSKhqFAjNf/975lE5AgoyU14I9l2hXq7CTS6YImb4/2+NnW5CzY9bPFmYGpM+B4SuXs2M90imBPmxvIeAke6PCWjIecThFoCiHPwOLMKF8EfAS4SUqZt62RcDzFtx9vx6RXeOcNK6547HAwztPtozx1cZSDnV56fVFuXlvNr08MkEiX1hM6W0gJJ/oCtFSYCcULu9XdPhJC0YgZe/uWAsm0nFLwOd4Lub/dg0WvkEipJIvwvVdaDXz8tvUTypj55qq7b0KIFWSsuuFZM8rPCSEuAAZgdOx3+6SUVzSkXOjum5SSfn+M7+/rpK3Sgkmv8E+/OIVFr5CWktXVNl68qZYb11TxmV+ewmrQEkmkeeh4P6ki/E8vBmodRmrsBgxahUg8dVmfXq7Z1uSk2xNZVkWJkDG6dJl1BKMpah3Gq6pB5Jvnra/hH25bT1tl9nNJWdt9u4IZ5aoFjm1eXBgK8e7vHaDTE7nsiTo89m+3J8rvzwxh1GlQhFhWy7KFMr6cAwoiAhdPpZddQIJMTmpcVmZDEaqHvnx7PXVl5ckrs6raymt2Nc1pih9LquWANE821NnxhvO/W2Qz6jDqiv72yynZLPvIFh+49zAf+emxgo6h6O8KfySJRLJ3LGFYJntUWvWcHwoWZCduf7uHNdXF4e9WKIaCMWrtuZW3XQhPXRwlECtcWUPRByWHWcf7n7uKv3vh2oJoNS9lRkIJau2Fm6of6/VP7E4tRSz6K3uo1dqNDAYKLxUynaFgnDu++gS/OTlQkOuXjO9bPJXmyQujPHFhhG893p7lkS1PGpwmbEYtWo1A0Ygp9Tf5YkWlhUAsiSIEybQ650blYmRrowODTiGaSGPQaTjV62d9vYPAWAeBSa/giySpsRvwRpKLksLJF7/4wPVsbsyO2kTO20zyjUGrcPO6ag52Lo3O90Jj0Ss0OI1EEmnODgYx6RXW1jy7nDLqNDkPUnUOA3qtZorlVbPbjGvMRujcYLAom4grrXpUKTHrtbjMegxaDRohONHru0wva6b7tcKix2bQYtRqiOVAJiebnB4IZC0ozZWSCUrjfOCWVYTiKb77ZEehh1LSCCEYDiVoH2u1SUZTBKLPFgLuaXOzZUylYLHsanERiqdwmHScGwzijSTZWG/DoFXwTstndXkidHlgY72tIC4m0zHpFFQp2dTgACkRQnC6P4AqJZ5wckEKE4e7M43Re9pc7G8vjoesImBHi4uOkQjDoThtlRZu21zHHdvq8z6WkgtKRp3Cp162kXqnkS/+5mxR9HKVInVXqJFpdpvo9kSmCNcvlO1NTg5Mmi3YjFrsRi0n+zIBcLY8YSSRpt5pymt/4nR2NDu5MBwiEE3nZIb+TIeXPa3unOqob2lwYNIrSCkJJ1L4Iyl6fFFWV1txW/T4o5nAKqXkmQ4vdQ4jrRVm/NEknkgCQZG2mRQj775xJZ2jEe55uqvQQyka3BY9KyotpKUkEE1SaTXgCScmgo9Rp2FTvQNvJHHFor1au2nBHxT9WNPvyioLeq2Goz1T5VKC04T0ZpsNtY9E2NXiomO0sPrYgQX6980FKWE0nLtE955WF/tnMLZodptn/f+f/CC69+ku/u9gD+trbbzpmlauW1VBhcWQc830kg1KAOvrCmPpXKxU2wwc6fYynqa4OBxmW5MTt0VHa4UFVTJl1jIb+zs8rKq2zjsRazdpsRq0CCG4MLz4Gc7BLi9Okw5vAZLfu1tdWTd7nAmXWQ9M/Vu5LXq84cSi+uT2tLk5PsvSez7mpImUytEeP0d/chTItKB88qUbeOnW3C3rSjooPVPi9kHZ5sxA8LIP05Gx/IUnPHeBN4NWoJ3WHV5jN5BMq9TYjQwF4oyGE9Q7jTS6zKTSKuF4moFAbMKnLRtICS0VFlZXa1DHplTnh4IkUyoVVgO9vigmXW56yPzRZF40k6KTtMd3t7oIxlIEYylq6myY9Qonev3EU/MbhNOso2MkPFE5nk1GQnH+dH64HJRmY3ODg58f6Sv0MArC5gYHkswOUCqtotVoiCRSjIYWVwi5s8XFsR7fRPd7rd1Ic4WZPl+UapuBU30BNjc4aHKbOdUfyGoQmonxoDqdiDeKWa8QTqRZX2fDbtQtyJhhNkLxFIoQE8EwV5zsC9BaYUaVTHmY9I69ba1GsLPZiaIR9Pli6LQaQrEUgVgCh0lPpVWPxaBFSkipKnqtwoA/llOr9j5fDFWVOZM1KdmgdLDTywNHl19AWlNjxWbQzdugcj7U2I30+2OkVUlzhZlzgxkL8vGdJm8kyWAwRqLA29njbRqn+4PoFMH2JufEztZi8YaT1DuNebE77xidPYCkVMnBGWSMNSIzaymEt9+ZgQDhRAqbMTf2S0Vf0T0TT14c4TXfeJJjBSj2KyRbGhxcGg7lNCAd7PTS443SVmlhe5MDVUpWTOsY7/RE2NLopAD6X7OSTEuO9PiyViG+rtaWl4C0UFRZODnekVCCO+/ax2AgN7PkkpspPXJqkL/60ZElrY88E0KAQaehpcJCpdVAOJEimkhfZkSQLa6W5N7f7kGQURjo98eKorm00mqgL0smA5FEGpNOU5TFm8XAyb4AT14c4RXbG7N+7pKaKXWNRnjfPQdzal1crMixnMPF4TBPt3s40RvAbdFTQEdrJJkdvnW1NrY1Oal3GAs2e9rT6kavaBjwZyconR0MFkzkrBRwW/TcuLoqJ+cuqZmSogiaXGauXVXBH84MF9x6p9AMBuIUg4DmoUk5j9XVVpJp9Yp5klywv8ODEJkl7rEe/6JlZ016Ba2mpJ7ZeeWalRVUWHOjcFBSf/UGp4mv/tkOau3GZStnO87GejvxZLpoNJ/dZh172tyYdEreA9I4UpK1+yKaSKPTChymknpu542b1uRmlgSLc8h9DfApYD2wR0qZU5fJbk+Er/zuPD891LMs80nrazP+aMmUSlJVMWg1DBZg52U2PJEkqirx5dlTbzqjoQS7Wl0IkQksZr0WCQwFYvMOls90eNnZ7MITSWDUKvT6IvhzWOFdKggBN6yuzNn55/MYuHmyQy5wAngl8M3sDilDLJlmf7uHa1ZW8F9/uMiXHjmXi8uUBK0VZi4MBUkUeZ+flPOrFs4FQ8GZt8nrnUb2tLo50esjkVZxmvUoQlwxsEsJx3t9VFgMXBoO0+A0UWs3IZE4TfoZVQGKHZtBS1rKRW1MXL+qkjpH7sxNF+OQexoy3ea5oN8f413fO8C6Whsn8yxqX2yMhBLUOIx0F+EWtVWvEE+pqFJysMvLjmbnlBxTsdDny1Sb67UaUmrmbzpugeSLJDg3OPNuo17R0OQ20+ePXZbDzFcrSjb56ht2sLraSo83yjf/eJHnrq0iEEvx8yO9s/4NJmPUafjnV27O6Riz5ZA7Kws1o2yrtPB3L1zL+cEQVTYjQ8HYsqtLgox0xsoq66yVzYWmym6gfSSCzaidKA8oZiYXfI5bIDW5TGxvcnKiz4/brGcwGMegFcRTkhq7kYvDM39YT/YF2NniQtEIDnR4ij6tsKLKggDSqmRPm3tKTdfbrmulzxfjrd/Zf0U5lg/esppGlzmn45yT8qQQon6yQy7wwUkOuY8CH5pLTmkxypMf/9lxvr+vtBQBVlVb6BqNLGrZtbPFycHO4gtIzW4TOkVDhdXAwU7vkvJuGxeZa6u0EIil5tSYvKHOzqn+4p/R6xUN775xBTtbXextc2PWPzsvuTQc4mVffWLWkpsNdXZ++cHrF9xeMlflyTntvk12yCXjAbdnQaNaBJ99+WYe+ZubcJhyU9qebfa0ubkwFGZj/cJU+wxaDXva3PQU4ZINIBxPYzVoSabUJRWQINNG440kOdTlmzUg6RTBhjo725qcbG92YjUU/y6dTsn08n3jjxf5j0fOX6ZF5o8m+d+37+EvbrncPe2WddXc8869ebHxXrBDbs5HNgM1dkNRtTZckbH/75FwnF0tLk72+a9YHWwzallbY0OIjL34dHfVYmM0nGA0nFh2LjN6rYYqm4FoPF0SM6NxVlZZ+Ml7ryWtSuwmLQbt5aYG25tdBGNJ3v7dZ6b8vNJq4Ftv3pWXgARzyynVAPePJbTHHXJ/LYR4BfCfQBXwoBDiiJTyhbka6F2PXSwJpckWt4kKq4FzQ5ku+25PlG5PFJdZh92kI5pMXyYctr3ZSZ83Oieto2JhXa0Nh0mXtbaOYkWnCCqsBuxGLRoh6PVFSabUgthSzQWbMeMOnVYlt22u47lrq7hpbRVVVsOcNqVsRh1P//2tHO328euTA5wfDPGybfV5C0iwOIfc+3nWzjunHOjw8MDRvqIPSAA1DtOMMxxvJIlBq0GdttTZ0+biQIe36JOkk9GN3aDZlAopVprdZnSKZkLKBS5XzywGblhdya4WN39+80pC8RRGnYJRd2WLp9kw6hT2rqhg74qKLI9ybpRERfeuVjd/+4K1hR7GjJh0GtbV2tjb5sZh0l1ReC6eUtnY4MA+qUo4mZYlFZAAahxGzg0Gr37gEsBp1jOQo274xTIuS1th0fOaXU385fNWo1U0OM36BQekYqD4s3Nj7G1z84N3PYd4Ks0nfn6iYDU7bZUWqqwGEJnt5XODwSlP0atxpNvHyioLDqOOREotgCz74ql3GBfk4lGKnB0IklYLWyD5yu0N1IwJ+t+yroZDXd6Jh2CPN4pRp1BlKz6n3YVSMmaUk7kwFOTOu55mJJT/NotsFsxtbXQUxAByMWxqsNM1GqHOacI5thN6rMc/RdZ1qdFSYUarETmTibkSekXDN960g1vW1eT92tlmyZlRTmZVtY2PvXgdT7eP0jEaydkulU4RbG92EUumUTQCRYgJn7RscH4oRIPTVFJqB6m0ZH2dnZSq8nS7B5tBYWODnQMlVtk8GZ0iWFMztXNgXa0NmzHz8RgNJfIWkLQawWdfvonPPXSaUDzFW69r5ea11Xm5drFQkkEJ4FU7G9nc6OBj9x3P2jl1GsH2ZidpVRJNqgwEojndlo8k0mysN5ZUUJq+VF1RZS3pgNRWacZu1OGJJHCYMkvqtbVWuj3ReS3LF4PTrOP2LXU8eXGU+99/HQ6Tjtu21NHjjS5Lx56SDUoPnxzgAz84nBWd6C2NDvSKhn5/bEafrGyzrtZGWpWMhhMMFHlbxtXwR5PUOow0u0yE4mmMOg1Hun0lkbzf3uwkEk9ztMePSadhc4OTHl+EHm/0MufeXPLnz13Fu25cgRxz4IXM1vz6utIoFM42JRuUrl1VyTUrKjg7ECSWSuNboDfYpno7fb7oFD/7XLKrxcXBTi8S0GrAUzgD2KxQ7zBxot8/JZjvbnUxEorTPpIfxYCdLS40AgSCA51z70ETMNEhEE2qOXWqHWdllYVQPMX6OjuKEAgBb72uNTOeHDW3lxolG5SsBi3feevuiaKuHz3TxUd+evWlnCJgZ6sbZMZDTAjyFpAANBoxIcxWYDOQReO26BkOxy8rBh3fCHCZdZh0Cn05mg3uaXNlZjpjBpw7mp3zmqEd6vJRadVT5zDQ78/Ppsmn79jEdatyp0W0FCiJOqXZmFxl+tpdTbznxhVXfY0k0yW9v8NDMJbieG/ptAoUCodJR4VFP/G9EBnlS61GcP4KchfeSBIhMrZQOUHCib7ARHAfDSXY0ezENsc+tCaXiUqrPqsByaxX+Jvnr+Frf7aDJz96C3956+qJ7frrVlWwttaWtWstVUp2pjSdYDzFY+eHqXMYp8hnKCKjtxyKZ7asVQnJsbs4lefEh6IRqKpkdY31ih/mYqOlwsyFwSC7WlxIJIP++Jw1rnp9MbY3OdjT5ubsQBD/FZQpTXqFZrcZk04zcdyVloDxadK3nZ4InZ4Iu1tdtI+EaavMWJWrqkSv1ZBMq3R5ItQ7TJj0CheGQvT5ogvWgHrD3mbaKi1sanDQ4DRNdNdPTk7/9fPX8IFbVtHni9LsNpeXaHOgJOuUZiKVVvnaoxd4+OQgVoOWkVAcq0FLry+KQadQ7zCSViXxlIrVoOXsmMFiodjb5i6JNg2LXkFKmRWFRadZx5oaG6m0OiUIVNsMtFaYOd47tWlZq4EdLW4EmdYOs15hNJyYKMtwW/QEookZl8GKgLl2JdXYDBh0ypxVM90WPXfubuLDL1o3twuUAZZ4ndJ0TvUFePTcEC6zHr1Ww/4OD9NjbW+RVSCfGQiyvcnB4e7iLp6Mp1RMuuys8n2R5ESJxfQi1FA8fZmKQkrlspKM8VkQZKrrD3bOnA+cT5vkuCTuyioLdqOO9XV2VlRZ+OZjlxieJpf7gg01/N0L17K6prwMyxUlHZSklAwEYnzygRMlJUtabTPgtuhJFvG+uduin1CSzEVLyWQhsXFd7dYKM1U2A4e7vLNuAgwFYhOKicks7xRcHA6jEXDjmireeUMmP/nA0b4JxdPbNtfxsZesy7ny4nKnZIPS05dG+cTPT8xJV7iYaKu0oFc0XBoOFZURQGYXykQ4nmIoGGdVtTWnhaPdnggusw4hwBNOsrPFRVqVaIRAp2hIzdJv1umJ5tRO22bUsaLKwkgozjtvWME1Kyv4wL2HkVLyV8/LvRRsmRIOSgOBWEkFpN2tLvp9MdpHwlgNSlEFJIBYUuXiUHAid5RrgbnMxkNm82FTvZ0jXd55LblyhT+a5C9/eITV1Va++aadbKx38OqdjRzs9JaXbHmiJEsCHj07xCcfOFnoYcwZq0FLNJmmZ6ydZHwnMN/YDFp0ysy7P2lVsqKqMDbVJ/oCRRGQJnN+KMT3nuoE4C3XtvL3L1lf4BEtHxZjRukGfgS0Ah3Aa6WUeUnsPHVptKA7Z/OhwWmiwWUin7uckJEOrneY0Gk1ICGeSnO8149OEdhNWuwGHbUOIwe7vEgJ0WR6SXf6L4RNDRl9datBy6rqwgTs5chizCg/CvxOSvkFIcRHx77/SFZHNwNpVfKLI325vkxW2NxgRwiRd61tIUAjBIfHbJn0isjUSEmIpyTxVIpANEWPL8q2JieKEPijCbq9hTWSLDY+9JOj3Heoh7vevKskjAGWCotZvt0B/O/Y1/8LvHzxw7k6aVXS5C6NZKNZr+XcVTrNnWYdG+tttFZk7z21VlhonvQ3SqTlrKYFR7p9HOzycmE4TDxVZGuoIuCpS6O8+D8e498ePntZeUCZ3DBX37d2wEumS+ObUsq7hBA+KaVz0jFeKaXrSufJVvHkb04O8P57DpWEtY/boseo09Dny1SZG7UaNtTb0SoahoOxKRXLG+rsWfOrr7Tq0Qgxo4V1mYVhN2p50aZa3rC3hbaxmqYyc2euxZMLNqMEHphLUJrmkLuzs7NzHm9jdr7+6EW+/UR7STy9WivM2Iw64qk0naMR4leor7EbtbgtejpGF7eU2tzg4HhvcRdmljJ3bKvnP+7cXuhhlBT5MKMcFELUjV2sDhia5bV3SSl3SSl3VVVVzXX8V+V9z13Jv7/2MpOVoqRjNMLxXj/nBkNXDEgAgVgK96Tm14VSCKng5cSpvgCfe/DUkreYKgRXDUpCCIsQwjb+NRkzyhPAA8Bbxg57C/DzXA1yNp6zooK/ff4aGpymfF86p2iVxVdq9Ptj1CwhMfli4/xQiOO9/pJ2DSlW5nL31wCPCyGOAvuBB6WUvwa+ADxfCHEeeP7Y93lFp2j44K2r+dOHb+ZdN7Txsq31+R5CTjjQ4WFX6xXTc3OiyW2m0rr4WVeZy9na5OSrf7YjK7PaMlNZjBnlKHBrLgY1XzQawT/ctgEpJXfubiIUT+EJJ/jP318oKf3rcVQJoVgKvSIWVfl9oNObkRuRGZvt+aBoRElsJBSKD79wLZXW8kw0Fyyp4gshBNeOqfr5Igk+++DpAo9o4ZwZCLKzxYUQLEqY/0i3j7W11suC0u5WFylVEowlMWgVOkfCbGxwoEpJjzdKk9vE/vbSaXLOFxvr7fzFravZ3eou9FCWLEsqKE3mzECQcKL47JXniqIR+CIJIot8DylVolem5j10iiCRVglGU5j0mgnBtqfbPWg1GcmQWoeRBpeJZEpFr9VgN+o41V9W6QzGUqypsU2405bJPkvuLyul5L5DPfztj49epqlUKlRa9Zj1CheHw4uWat3V6qJzmnhZMi052u1nNJy4LGCNbw4qIqOS6bboCMVTnOoPsK4s5UqXJ8Krv/4kH7vvOE9cGLn6C8rMmyU3U/JFknziZycIJ+bex6XTCLSKIKVK7EbdvPMv2WYklGB1tZVgLAsqCBI8097PlkYHRp2CAJLpmUsUDnRmlm79k0qd7KZysSBk8nM/2N9FMq2WTQBywJIKSsPBOP/8q9NzDkgNThONroy28tmBIFpFMBpOsLfNTa8vik7RZNURdz6kVJUKi35KgHRbdKyotBJJpFDl5caQ03GYdHROK8KstOrp8UYvC1Rz4eJQCJdZh7dEmqFzze9OD3LXYxd5940rCz2UJcWSCkp3PXaR+w71XvGYeqeReqeJS8NhPJHElN25cSOBce1so07D7lYXQgj6fVG68yip2z4S4dqVFZzpD9JWZeFgpxdPOEm1LcWZgeCcutb90STXraqgOWlGlRJBppBzIQFJEdBaaUGQUY3Ml3tsMeONJLnrsXbecf0KFE3ZECBbLKmgdLzHT5XNgMOky6gaktmRS6UlgViStCq5NBKe6EO7GrGkOiGzu63JwXAwTiyPZm2JlIonksDTmZm9BWPJicbaeCrNpno7Pb4odqNuRtH7tkoLybSkxxOZ0KFeKGkJZwcChOJp9kzaeaq2GWitNHOw07csSwhGQnH++kdHeNXORm5ak72OheXMkgpKdpMOfySZo344kdeABHC81z9hGTXd+aTbE6WbzMzNYdSxa6x8YMAfQ6doMOsVen3RrC4/x8XpkqrKhjo7OkUQTabZ3+5la6ODoz3Ls9fugaN92IzaclDKEksqKH3i9g184VVaBvwxfnakl3uf7poiUL9Q3BYd2gJMz+MplUaXCUWICdXKmUik05zuD2DQaVhVbcu5ftPhGTzSdFlojSllfnNygM++fFPZ1y0LLKk7qcltxm3Rs6Hezt+/ZD0/+/NrycY9Eoyl0BToZnumw0ud03jFY5pcFuqcJpwmfd4F5cY50OllQ93yLRkYCSW4/l/+wD/cf5yBHNmULxeWVFCazqpqGz9937XYjIubECbTksPdXjbV29nT5qat0pKlEV6d1goziassG/d3eLgwFOJSgXYKIWOuORhY3soEvb4o9zzdxYd+cpRYWVp4wSyp5dtMxJNqVm6QZFpyYqzyeVeLK2+lAtV2Iwc6it9JVwKRedSGLWUevzDCW7+zH5dZj82ondgtff9zV5W1vufAkrHtvhLHe/z8+2/P8oezw4s6jxCZ2qZgLJkVdci5sLLKwmAgVjAHlLmi1WQC6Fx3NpcjJp3Cv712Ky/ZXFfooRSErIq8lTqbGx3822u3saXRseBz6BTB7lY3/b5oXgKSUadhT5uLXm+06AMSwLo6O4E8BepSJZpM88XfnOXBY/2FHkpRsyyCEmS0sj/64nXsbXu2xubFm2p5w95m/vH2DTxnhfuKTZZGncLxHl/e/MmaXGb2t3vzXoawUILRZNmiaQ60j4T54A8O8cP9XYUeStGy5HNKk7l2ZSW/PNZPMq1yYe/nXvwAABJaSURBVCiEWa/lc6/YDMDbr29jNBTnVV9/ckZ97GAsRbPbjMui4+a11XjCCU72BTjYmRt5D1OJKRp2eqKsrrFOVI1fLTm/nFElfPS+4zx+YYTPvWIzjnJP4RTmHJSEEApwAOiVUt4uhLgF+FdADxwE3iGlLPr5++fHglAqrV72ZK+wGnj+hhpMei0aAY+cHiSSSOM06bg0EuaObfU8f0MNWxozfgmxZJpb/+2PORGSGy5Bje3zYzbq62qtnBkoHUv1QvHLY/3cuLqKV+5oIC0lekVTrnNiHoluIcTfALsAO/AyoBO4VUp5TgjxaaBTSvk/VzpHoRLd8yEQS2LRayd6mVRVotEIEmO6QtM53R/gPXcfxBtJEIxlJyavrLJwcbhw2/uLQa8INjU4ODRDgWWZmTHrFSKJNHvb3HzqZRtZX2cv9JByQlYT3UKIRuA24FtjP6oA4lLKc2Pf/xZ41UIGWmzYjbopzZWasa9nyzetr7Pz2Idv5ug/voCP37aerU1OXrG9gXW1Nj7yonXsbnVhM2pprTBP6RmbjTU1VnpK2Kk2kZZcHA5RadVnpXB1OTBeSvF0u4dX/teT/PRgT4FHVFjmunz7MvBhYLxkdwTQCSF2SSkPAK8GmnIwvpJBoxG884YVvPOGFVN+/r7nrsQfTeIw6VBVyR/PDbOvfZSfHuyd0QbJoteWvFOtP5pCkJnxOc36CW2mMlcnmkzzmQdPcce2+qy42pQiVw1KQojbgSEp5UEhxHMBpJRSCHEn8CUhhAF4GJhx7TLNjDJb4y4pxhOZGo3g5nXV3LyumvfdtJJ9lzwc7vYyHIzT642iSjmhSlDqSODCcBgIs73JyZnBINFyceWcCMVS3He4l5dtrccfTdI+EqbOYaSlIn+dBIXkqjklIcQ/A28iE3SMZHJK90kp3zjpmBcA75RSvvZK5yqFnFK+kVLyqQdO8r9PZcc5uFjZ0ujg2DJVEVgolVYDq6utPHVplCqbgffetJJ3XN9W6GEtmKzllKSUH5NSNkopW4E7gd9LKd84ZuHN2EzpI8A3FjnmZcmXfntuyQckyDz9dUo5yTQfRkJxnro0CmRUVT/74CkeOTUIsKR76xZTp/R3Y0s7DfB1KeXvszSmZcOpvgBf+f2FQg8jLwRjqQllzzILQ0p4z/cPYtRq2NLo5N537V2SJQTzyqRJKR+VUt4+9vXfSSnXSynXSim/nJvhLW3+5/H2Qg8hbzgtupJ1lykm0qoknEjz1KVR3nP3QeKppTdjWp7p/QJzz9OdvP6uffz00PLZ+rUallXzQF54+NQg9+zrIp9N9fmgfKfkkXA8xXP/9VH+f3v3Hh11eSZw/PvMTDK5zGRyv5CEQAjXhEu5KSjuKliQpdI9x/bQ1epqxe7xuFrP1u1aa7fb3e66usXd1bNY2y1Yi5eVStfqKiDUW6lIkItAgCAEEiBXkjC5TSYz7/4xk8glgSQzk9/8Mu/nnyRz43knzJP39/7e3/M0tXmItSOZeKsFV2IcrZ26E0o41bm7Rt0hnJ4pjZB2Tw9/u3E/De7YSEglWcl9zRsyHfGcbu5kXGaS3lAZRiIwPX/4lS+ilZ4pRZjfryg/2czfv3GQihhqe60ItCCaPz6dlo5uXIlxtHR49bpSGCnFqCy9q5NShP37tkr+c1ul0WGMuMY2D5NzIt/EINbVjGAvwpGiD98i6MDpVp7ZHnsJCYKXmuhDtZAtLc1h3ri0frvpJMRZuO+G4n6eZW56phRhsXy4YraaUNEkJcHGurvnM6PAhc0SaCd/vKGdn394nK2H6lg0MZMnb5tBnivR6FDDTielCPH5FbtMUPA/EkrHpODzK6qazFl+JRqc7+ohLSmur59epsNOpsPO/PHpHKt347DHkeu6custs9JJKUIq6938w+8OGR1GxOWnJpDlTMBus9Da2Y3PDwfPxM6CfqTcMCmL4qz+O5+UZI/u/no6KUXISztjowZzalI8e6t1Qbdw88fCvpEB6KQUAV1eHxtGeVJyJtgoyXKwRyekiLj7unFGh2AYffYtAhLirBSPYBddI3R29/Std2jhMyXXye8euJ7FU3OMDsUweqYUZp4eH09vraT2/Ojb1HahHn+gXfjssam6HncIspx2vD4/Oc4EVs0vZNW8sSTGx/ZZS52UwuzA6Vaee/9zo8MYMZ+eaqEgLXFUbuKLhBkFLpaW5lKUkUTpGBfjR/mMejh0UgojpRSvfFJtdBgjLi0pTielq7BahEeWTuav/mSC0aFEPZ2UwuinW47yWgx2ohD01u3+iMBPvzaTsnwX6cnxZDrsRodkCjophcnmg7U8+/vYqCJ5qYra86Qnx3OuvdvoUKLGlFwn31kyiWVluUaHYjqhdMhdDDxF4AxeG/CXSqnY/FQCP46BjZID8foU2U47rZ1efDG8v6bXAzeWcOfCIrKdo3PHdaQN5ZzuQ0DFBT+vBW5XSs0CXgJ+EM7AzORsa2dEWnebyeFad18rqVhXWe/GadfvxXANt0MuBErm9PYXdgFnwhuaecRbLTFf7tVht+Lu0lUlATYfrGPJmvfZGuw8og3NYGdKvR1y/Rfcdi/wfyJSQ6Av3BP9PVFE7hORchEpb2hoCCnYaJXhsLPp/oXYB2jtHQum5qXg9elDt16nWzpZ/atyjta5jQ7FdK76KbqwQ+4ldz0MLFdKFQDrgDX9PV8p9bxSaq5Sam5WVlbIAUerjm4flhgtIFSUnsgBfRFuv77/+me8Vl49KruORMpgjjmuA24VkeUEO+SKyFvAFKXUzuBjXgXeiVCMpvDw/+ylcxQ3CLySk+c6mZLr5HCtnhVcqvxkM+UnmznT0sVDSyYaHY4pDKtDLrAScInIpODDbubiRfCY8ts9pznRGLu1g7Icdr0d4CqefvcoOz5vNDoMUxjWIohSqgdYDfxGRPYRWFN6JJyBmUmD2xPTFSYb2zwUZSQZHUbU215Rb3QIpjCkU0ZKqfeA94LfbwI2hT8kc2n39PCzDyJ/rZvDbuOuhUXsrW5BEHZ83hg1rZrK8lPo6I7NQ9fBWj49l+/dMsXoMEwhts9jh4FfqYhuGFxWmst3l05iXEYyVouwr6aVgrREth+u54UdVVFR5bGmuZPC9NE5U7JaJOTfr9Nu4/EV03Spl0HS71KIzrV340iITG5fOCGDx78yjZJsJzarBRFhVmEqNouQl5KA1+dnWWkuP/izqYZebd7c4SWun24bZiYCc4vS+KevloX8Wsun53GyqYNtFXWs+8MJvD7/1Z8Uw/RMKURv7j9L9bnI7OZevaiY/NRAt4q68110eX2kJsXj6fGTlhzPM9+YzbsVdfzFNWMpzkrmnvXlAMwsTOW22fnB4v0dfHSskVNNHXSH+GEoy0+hx6f6zrLNG5eGRYSqxnZG2xalZaW5rL1jDruqzvHcHbOZkOXgeGM7mQ47Hx9v4qnNR/oeW5yZTEm2g7J8F+4uL7tPNnPwzHk8PYH3+9Xyal4t/6J6RMXZ8zx528wRH5NZiBrBFdq5c+eq8vLyEfv3RkK7p4cn3j7Mix+fDPtrWy3CtcXpTMpxcqy+jfKqZv7r9tncOCX7sscqpdh54hx5rgSKMi6fNTW3d+P1+VFAZV0bL35cxbaKenoGcWiSYLMwvcDF3uoWvD6F027DmWjjTEugkF2W006D2xPyeI3U32Haz745h6Wll19Q6/crnv39MbYcqqUgNYkfrywlO+WL69xONXVw34vlA26RiLda2Pn9xaQlx4d3EFFORHYrpeZe7XF6phSiZLuNH91aSmObhyN1bmwW4WhdW9/9KQk20pPjqWnuvCwBWC3ClwpTuXdRMWNSE/D0+HnyncPsqmoGAm2aEuNs3L1wPIXpiax9/3MWTMjoNw4R4dri/u8DLvoA5KQkcP3ETJraPGzcXcP6HVWcvaD9c5xVmD02Da/PH1xMV9ht1r743Z4e3J6evscPJiHZLILdZqF9hBfErRahKD0JT4+fpHgrcVYL9W4P4zOTWDVvLBNzHGQ47CTHW2nt9HK2tYvGNg+pifEkxve/umGxCA8unsiDi/vfdzQ2I4k7F4zjR28cvGx2KgJ/8+VJMZeQhkLPlMLE0+PjbEsX//rOYd4+UEu2086SaTn848oyrBbh2e2VbDlUR0FaIufau5lZmMotZXnMKky96HU6u3388XgjgtDt85PpsFOWn4LdFrkSqV1eHy/sqOIXH52gwe0hz2VnbHoyzR3dtHZ42bD6WsamJ7HgX7bR6fUFd68zpLN/MwpczClKY90fqgC4bU4BY1ITaXB38ea+sxcluXBJSbDx9nduYIwrAblkt71S6rLbwuknbx3ilV3VuLsuHleeK4H3H7mR+Bi8JGmwMyWdlMLsSK2btKS4i6bzZqGU4tHXP2PPqRZKx6QwNc/J7KI05hSlA9DR3UNLh5fGNg/15z089MqeQc98rivJ4MV7rqGmuZNun4/81KS+WtSd3T6ON7bxy4+qmFXoYnpBKmvfO8bmg0O7oNWVGEeeK4Hmjm7SkuK5riSTx1dMG9qbEEZdXh9rth7l+Q+O9912zfh0Xv32AsNiMpJOStqwDGUG8eb+MxypdfPM9quX0ZqU42DN12dRlu+66mP317TwbkU9r5VXX3RYeSVZTju//tY1TM6NvkaNB0638tInpzjV1MGiiZl8O0ZL4uqkpI2Yyjo3K575qO9s00B+uGIa91w/ftCve6qpg3t/teuiNToAu81CcZaDmnMd2OOs3LmgCFdiHHOK0gaV9IwS6UPGaKcXurURU1Hr7ktIyfFWOr2+y9abvjJzDN9cUDSk1x2bkcRNU3LIdNgRCdQCf+CmEuYUpVF3vounNh/h/j8ticrZUX9iOSENhZ4paSFravOwac9p0pPjWT49j/s3fMr2w19c5/XoLVO4c8G4mO9nFuv0TEkbMRkOO/cuKgYCGwMvTEiPLZ/K6huKjQpNMyGdlLSw8fr8/PzD48wqTOXLpTmsmjeWdL0fRxsinZS0sImzWljz9VlGh6GZXOzt4NI0LarppKRpWlQJpRnlh0Dvudhs4BOl1FcjEKOmaTFkKGtKvc0oUwCUUot67xCR3wD/G97QNE2LRaE0o+y9zwncBPw2vKFpmhaLQmlG2evPgW1KKePrsmqaZnqhNKPs9Q3g5Ss8f9R3yNU0LXwGM1PqbUZZBbwC3CQivwYQkQxgPvDWQE+OlQ65mqaFx7CaUSql7gje/TXgTaXU4OpLaJqmXUWoO7pXAU8M9sG7d+9uFJHhFrPOBEZLi1E9lugzWsYB0TuWQZWJGNEqAaEQkfLBXGFsBnos0We0jAPMPxa9o1vTtKiik5KmaVHFTEnpeaMDCCM9lugzWsYBJh+LadaUNE2LDWaaKWmaFgNMkZREJFVENorIYRGpEBFTNs4SkYdF5KCIHBCRl0XENM3hROSXIlIvIgcuuC1dRLaKSGXwa5qRMQ7WAGN5Kvj/a7+IbBKR1Cu9RrTobywX3PddEVEikmlEbMNliqQE/AfwjlJqCjCTQLUCUxGRfOBBYK5SqgywEtjnZRbrgWWX3PZ3BK57nAhsC/5sBuu5fCxbgTKl1AzgKPDoSAc1TOu5fCyISCFwM3BqpAMKVdQnJRFJAW4A/htAKdWtlGoxNqphswGJImIDkoAzBsczaEqpD4Bzl9y8Engh+P0LgCnqafU3FqXUFqVUb4/tj4GCEQ9sGAb4vQA8TeAietMtGkd9UgKKgQZgnYjsEZFfiEiy0UENlVLqNPBvBP5ynQValVJbjI0qZDlKqbMAwa/ZBscTLvcAbxsdxHCJyK0EijHuMzqW4TBDUrIBs4G1SqkvAe2Y5zChT3C9ZSUwHhgDJIvIHVd+ljbSROQxoAfYYHQswyEiScBjwA+NjmW4zJCUaoAapdTO4M8bCSQps1kCnFBKNSilvMDrwEKDYwpVnYjkAQS/1l/l8VFNRO4CVgC3K/PulZlA4A/fvmBljwLgUxHJNTSqIYj6pKSUqgWqRWRy8KbFwCEDQxquU8C1IpIkgf7NizHhgv0l3gDuCn5/FyYuiSwiy4DvAbcqpTqMjme4lFKfKaWylVLjgpU9aoDZwc+RKUR9Ugr6a2CDiOwHZgH/bHA8Qxac6W0EPgU+I/Dem2bnrYi8DPwRmCwiNSLyLQIVIm4WkUoCZ3oGXTHCSAOM5VkCjTC2isheEXnO0CAHaYCxmJre0a1pWlQxy0xJ07QYoZOSpmlRRSclTdOiik5KmqZFFZ2UNE2LKjopaZoWVXRS0jQtquikpGlaVPl/uer/0m5TKK4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Darstellen der Landkarte\n",
    "DEU_map.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um einen Punkt auf der Landkarte darzustellen, müssen wir uns das Achsen-Objekt, auf dem der aktuelle Plot dargestellt ist, von matplotlib geben lassen. Das machen wir mit der Funktion ```gca()``` (für \"get current axis\"). Mit diesem Achsen-Objekt können wir dann wie gewohlt die Funktion ```scatter()``` ausführen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f44134cd438>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Darstellen der Landkarte\n",
    "DEU_map.plot()\n",
    "# Achsenobjekt liefern lassen\n",
    "ax = plt.gca()\n",
    "# einen roten Punkt auf der Landkarte darstellen\n",
    "ax.scatter(8, 51, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Damit haben wir alle nötigen Zutaten, um die Fundorte der Fundstücke auf einer Landkarte darzustellen!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A.** Lade die shape-files von Griechenland herunter. Stelle die level 1 Karte (Bundesländer) wie oben für Deutschland gezeigt dar. Zeichne einen Punkt an der Position von Athen ein."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die shape-files finden wir [hier](https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_GRC_shp.zip), die Koordinaten von Athen finden wir z.B. [hier](https://de.wikipedia.org/wiki/Athen). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>GID_0</th>\n",
       "      <th>NAME_0</th>\n",
       "      <th>GID_1</th>\n",
       "      <th>NAME_1</th>\n",
       "      <th>VARNAME_1</th>\n",
       "      <th>NL_NAME_1</th>\n",
       "      <th>TYPE_1</th>\n",
       "      <th>ENGTYPE_1</th>\n",
       "      <th>CC_1</th>\n",
       "      <th>HASC_1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GRC</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC.1_1</td>\n",
       "      <td>Aegean</td>\n",
       "      <td>None</td>\n",
       "      <td>Αιγαίο</td>\n",
       "      <td>Apokentroménes dioikíseis</td>\n",
       "      <td>Decentralized administration</td>\n",
       "      <td>None</td>\n",
       "      <td>GR.AI</td>\n",
       "      <td>(POLYGON ((26.83235931 35.36346817, 26.8323593...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GRC</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC.2_1</td>\n",
       "      <td>Athos</td>\n",
       "      <td>None</td>\n",
       "      <td>Άθως</td>\n",
       "      <td>Apokentroménes dioikíseis</td>\n",
       "      <td>Decentralized administration</td>\n",
       "      <td>None</td>\n",
       "      <td>GR.MA</td>\n",
       "      <td>(POLYGON ((24.23402786 40.1870842, 24.23402786...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GRC</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC.3_1</td>\n",
       "      <td>Attica</td>\n",
       "      <td>None</td>\n",
       "      <td>Αττική</td>\n",
       "      <td>Apokentroménes dioikíseis</td>\n",
       "      <td>Decentralized administration</td>\n",
       "      <td>None</td>\n",
       "      <td>GR.AT</td>\n",
       "      <td>(POLYGON ((23.27013969 35.90597153, 23.2701396...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GRC</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC.4_1</td>\n",
       "      <td>Crete</td>\n",
       "      <td>None</td>\n",
       "      <td>Κρήτη</td>\n",
       "      <td>Apokentroménes dioikíseis</td>\n",
       "      <td>Decentralized administration</td>\n",
       "      <td>None</td>\n",
       "      <td>GR.CR</td>\n",
       "      <td>(POLYGON ((24.06568909 34.8726387, 24.06568909...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GRC</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC.5_1</td>\n",
       "      <td>Epirus and Western Macedonia</td>\n",
       "      <td>None</td>\n",
       "      <td>Ήπειρος και Δυτική Μακεδονία</td>\n",
       "      <td>Apokentroménes dioikíseis</td>\n",
       "      <td>Decentralized administration</td>\n",
       "      <td>None</td>\n",
       "      <td>GR.EM</td>\n",
       "      <td>(POLYGON ((20.91569328 38.98958206, 20.9156932...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GID_0  NAME_0    GID_1                        NAME_1 VARNAME_1  \\\n",
       "0   GRC  Greece  GRC.1_1                        Aegean      None   \n",
       "1   GRC  Greece  GRC.2_1                         Athos      None   \n",
       "2   GRC  Greece  GRC.3_1                        Attica      None   \n",
       "3   GRC  Greece  GRC.4_1                         Crete      None   \n",
       "4   GRC  Greece  GRC.5_1  Epirus and Western Macedonia      None   \n",
       "\n",
       "                      NL_NAME_1                     TYPE_1  \\\n",
       "0                        Αιγαίο  Apokentroménes dioikíseis   \n",
       "1                          Άθως  Apokentroménes dioikíseis   \n",
       "2                        Αττική  Apokentroménes dioikíseis   \n",
       "3                         Κρήτη  Apokentroménes dioikíseis   \n",
       "4  Ήπειρος και Δυτική Μακεδονία  Apokentroménes dioikíseis   \n",
       "\n",
       "                      ENGTYPE_1  CC_1 HASC_1  \\\n",
       "0  Decentralized administration  None  GR.AI   \n",
       "1  Decentralized administration  None  GR.MA   \n",
       "2  Decentralized administration  None  GR.AT   \n",
       "3  Decentralized administration  None  GR.CR   \n",
       "4  Decentralized administration  None  GR.EM   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((26.83235931 35.36346817, 26.8323593...  \n",
       "1  (POLYGON ((24.23402786 40.1870842, 24.23402786...  \n",
       "2  (POLYGON ((23.27013969 35.90597153, 23.2701396...  \n",
       "3  (POLYGON ((24.06568909 34.8726387, 24.06568909...  \n",
       "4  (POLYGON ((20.91569328 38.98958206, 20.9156932...  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lies das shape-file ein\n",
    "GRC_map = gpd.read_file('../daten/gadm36_GRC_shp/gadm36_GRC_1.shp')\n",
    "\n",
    "# das shape-file bzw. die darin enthaltene Karte verhält sich wie ein DataFrame\n",
    "GRC_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(23.833,38.083,'Athen')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wichtig hierbei: die Längen- und Breitengrade werden auf Wikipedia\n",
    "# in Graden, Minuten und Sekunden angegeben. Diese müssen wir erst in \n",
    "# Dezimalstellen konvertieren. Die hier angegebenen Werten sind bereits\n",
    "# konvertiert\n",
    "lat_athens = 37.983\n",
    "lon_athens = 23.733\n",
    "\n",
    "# stelle die Karte dar, mit dem Argument \"alpha=0.7\"\n",
    "# können wir die Karte etwas transparenter machen, um\n",
    "# andere Dinge auf der Darstellung leichter erkennbar\n",
    "# zu machen\n",
    "GRC_map.plot(alpha=0.7)\n",
    "ax = plt.gca()\n",
    "\n",
    "# roter Punkt an der Stelle von Athen\n",
    "ax.scatter(lon_athens, lat_athens, color='red') \n",
    "\n",
    "# außerdem können wir mit der Funktion \"text()\" noch\n",
    "# eine Beschriftung hinzufügen\n",
    "ax.text(lon_athens + 0.1, lat_athens + 0.1, 'Athen', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B.** Wir wollen im Folgenden nur die Fundorte betrachten, die in und um Griechenland liegen. Eine kurze Suche mit Google Maps zeigt  \n",
    "\n",
    "Ort | Beschreibung | Breitengrad | Längengrad\n",
    "--- | -------------| -----------| -----------  \n",
    "Othoni | westlichster Punkt | 39.851232 | 19.391932\n",
    "Trigono | nörtlichster Punkt | 41.746835 | 26.174643  \n",
    "Rodos | östlichster Punkt (Zypern ausgenommen) | 36.438004 | 28.241690\n",
    "Gavdos | südlichster Punkt | 34.803067 | 24.122037\n",
    "\n",
    "Filtere die Location-Tabelle, so dass nur die Orte innerhalb Griechenlands verbleiben (du  kannst davon ausgehen, dass die \"Ländergrenzen\" von Griechenland vom nördlichsten, südlichsten, östlichsten und westlichsten Punkt des Landes aufgespannt werden)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Location_ID</th>\n",
       "      <th>Location_simple</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Coordinates_uncertain</th>\n",
       "      <th>Harbour</th>\n",
       "      <th>Sea</th>\n",
       "      <th>River</th>\n",
       "      <th>River_navigability</th>\n",
       "      <th>Road</th>\n",
       "      <th>...</th>\n",
       "      <th>Late_Hellenistic_Area_Provincia</th>\n",
       "      <th>Early_Imperial_Province</th>\n",
       "      <th>Middle_Imperial_Province</th>\n",
       "      <th>Early_Byzantine_region</th>\n",
       "      <th>Comment_Remarks</th>\n",
       "      <th>BA_map_reference</th>\n",
       "      <th>BA_Rank</th>\n",
       "      <th>BA_rank_uncertain</th>\n",
       "      <th>Pleiades_URL</th>\n",
       "      <th>Sherd_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>abdera</td>\n",
       "      <td>24.973047</td>\n",
       "      <td>40.933650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thracium/Macedonicum_Mare</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>not_available</td>\n",
       "      <td>Thracia</td>\n",
       "      <td>Thracia_Rhodope</td>\n",
       "      <td>not_available</td>\n",
       "      <td>none</td>\n",
       "      <td>51_D3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pleiades.stoa.org/places/501323</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>agioi_pandes</td>\n",
       "      <td>21.679860</td>\n",
       "      <td>38.661422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Hellas</td>\n",
       "      <td>none</td>\n",
       "      <td>not_available</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>not_available</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>aigeira</td>\n",
       "      <td>22.377887</td>\n",
       "      <td>38.129793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Corinthian_Gulf</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Achaia</td>\n",
       "      <td>Hellas</td>\n",
       "      <td>none</td>\n",
       "      <td>58_C1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pleiades.stoa.org/places/570043</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>alexandria_troas</td>\n",
       "      <td>26.157736</td>\n",
       "      <td>39.748672</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Aegean_Sea</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>not_available</td>\n",
       "      <td>...</td>\n",
       "      <td>not_available</td>\n",
       "      <td>Hellespontus</td>\n",
       "      <td>Hellespontus</td>\n",
       "      <td>Hellespontos</td>\n",
       "      <td>Pre Roman name: Antigoneia\\r\\rRoman Colony: Co...</td>\n",
       "      <td>56_C2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pleiades.stoa.org/places/550434</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22</td>\n",
       "      <td>amphipolis</td>\n",
       "      <td>23.849103</td>\n",
       "      <td>40.820547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Thracium/Macedonicum_Mare</td>\n",
       "      <td>Strymon</td>\n",
       "      <td>Navigable</td>\n",
       "      <td>Via_Egnatia</td>\n",
       "      <td>...</td>\n",
       "      <td>Makedonia</td>\n",
       "      <td>Makedonia</td>\n",
       "      <td>Makedonia</td>\n",
       "      <td>Makedonia_I</td>\n",
       "      <td>none</td>\n",
       "      <td>51_B3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pleiades.stoa.org/places/501347</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location_ID   Location_simple  Longitude   Latitude  \\\n",
       "1             2            abdera  24.973047  40.933650   \n",
       "4            10      agioi_pandes  21.679860  38.661422   \n",
       "5            11           aigeira  22.377887  38.129793   \n",
       "11           17  alexandria_troas  26.157736  39.748672   \n",
       "16           22        amphipolis  23.849103  40.820547   \n",
       "\n",
       "    Coordinates_uncertain  Harbour                        Sea          River  \\\n",
       "1                       0        1  Thracium/Macedonicum_Mare  not_available   \n",
       "4                       0        0              not_available  not_available   \n",
       "5                       0        0            Corinthian_Gulf  not_available   \n",
       "11                      0        1                 Aegean_Sea  not_available   \n",
       "16                      0        0  Thracium/Macedonicum_Mare        Strymon   \n",
       "\n",
       "   River_navigability           Road     ...       \\\n",
       "1       not_available  not_available     ...        \n",
       "4       not_available  not_available     ...        \n",
       "5       not_available  not_available     ...        \n",
       "11      not_available  not_available     ...        \n",
       "16          Navigable    Via_Egnatia     ...        \n",
       "\n",
       "   Late_Hellenistic_Area_Provincia Early_Imperial_Province  \\\n",
       "1                    not_available                 Thracia   \n",
       "4                           Achaia                  Achaia   \n",
       "5                           Achaia                  Achaia   \n",
       "11                   not_available            Hellespontus   \n",
       "16                       Makedonia               Makedonia   \n",
       "\n",
       "   Middle_Imperial_Province Early_Byzantine_region  \\\n",
       "1           Thracia_Rhodope          not_available   \n",
       "4                    Achaia                 Hellas   \n",
       "5                    Achaia                 Hellas   \n",
       "11             Hellespontus           Hellespontos   \n",
       "16                Makedonia            Makedonia_I   \n",
       "\n",
       "                                      Comment_Remarks BA_map_reference  \\\n",
       "1                                                none            51_D3   \n",
       "4                                                none    not_available   \n",
       "5                                                none            58_C1   \n",
       "11  Pre Roman name: Antigoneia\\r\\rRoman Colony: Co...            56_C2   \n",
       "16                                               none            51_B3   \n",
       "\n",
       "   BA_Rank BA_rank_uncertain                             Pleiades_URL  \\\n",
       "1        4                 0  https://pleiades.stoa.org/places/501323   \n",
       "4        4                 1                            not_available   \n",
       "5        3                 0  https://pleiades.stoa.org/places/570043   \n",
       "11       3                 0  https://pleiades.stoa.org/places/550434   \n",
       "16       2                 0  https://pleiades.stoa.org/places/501347   \n",
       "\n",
       "    Sherd_Count  \n",
       "1           391  \n",
       "4             7  \n",
       "5            17  \n",
       "11            0  \n",
       "16          422  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# erstelle einen Filter mit den oben angegeben Koordinaten\n",
    "# (liegen schon als Dezimalzahlen vor)\n",
    "filter_greece = (location['Longitude'] > 19.39) & \\\n",
    "                (location['Longitude'] < 28.25) & \\\n",
    "                (location['Latitude'] > 34.8) & \\\n",
    "                (location['Latitude'] < 41.75)\n",
    "\n",
    "# filtere das location-DataFrame\n",
    "locations_greece = location[filter_greece]\n",
    "locations_greece.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**C.** Stelle alle Fundorte in Griechenland auf der Griechenland-Karte als Punkte dar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f44134763c8>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GRC_map.plot(alpha=0.7)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.scatter(locations_greece['Longitude'], locations_greece['Latitude'],\\\n",
    "          color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**D.** Als letzten Schritt skaliere nun die Größe der Punkte der Fundorte mit der Anzahl der gefundenen Fundstücke. Benutze dafür das Argument ```s``` der Funktion ```scatter()```, dem du eine Liste der Größen übergeben kannst. Dafür kannst du die in Aufgabe **1.D** erstellte Liste der Funstückanzahl je Fundort verwenden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f44130c4fd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JIiMen9l1M4f86KWXuO/aawG479pr+dG+fZBIcPnll1NVVYXFYmHLli3U19cP7fPII49w8OBBvvKVrwBw9OhRrr32WjZu3MhDDz3EsWPHhra96667sFgsrFu3jo6ODjKNmQm32EkmtdkhL08XUJkpWVnQ368F+nWTNsc1uRhIJBaF/benv5/nXn+dow0NiAhJw0AMg7fdeivOYQ5Eq9VKIpEA4NixY3z+85/nxRdfxJq6P+6//35+9rOfsXnzZr73ve/x/PPPD+07fJy5KNVrCuDFTmurtuFVV8/emMXF2pm3fbsWyCbzSiiW4NVzPTx9vJNVpR6sFuH2TRVkO60kDYXbbp1ZPKrNtihswI++/DIfvOEG/v2P/mho2fWf+Qy7h2mww/H7/dx333384Ac/YHgk1cDAAOXl5cTjcR566CEqK9Nu5DzrmAJ4sXP06ITZS9NiUJOuq4P162d3bJMp0dQb4gu/PDYkHzv7tWnohTe7ENFyc025l2VFHtZV5LCmbBqZanY7GAvfH/6jF1/ks/fcM2LZPZddxreeeYblY1ynP/vZz2hoaOCjH/3o0LLDhw/zxS9+kSuuuILa2lo2btzIwMD8NV5eFB0xtm/frsyC7GOQTMKDD2rn2Wzb8Pr7tVnj7W+f3XFNpkRfMMb/+/lRwrFkWtvvWlPC+6+omZoQPnVKm7Em0QSbmpqons03rdmgpQVuvBFWrZq3KZw4cYK1a9eOWCYiB5RS2yfb19SAFzP9/VpzyYQDJStLZ88ptSjsgxcr+dkOHnjHeg43+nh4X9OkdsnnT3bS3BdiSWE279xaicuehl8gP39x/8b5+fM9g2mzOFyfJmMTDmfuxrHZtHd8AZQenAkDkfEL7UXiyUXRZLfI4+SmtSXYLOn91mc6AjxzvIOEkeZ3y83VD/JFcC5GMDjf3Nz5nccMMAXwYkapzN40g0bGRYIvFOMnB5s50NCLLxTju3vq+IvHXmdffS9KKfojcZp6QwSiCf7l2dN8+n+P8NzJzvmedtp88MraKW0/0cNnBA6HNjelkY6+oB5Y0agWvtOJe58lZno+TBPEYsaWwZ9vULhn8hizzC+OtPLCm10AZDttBKM6FOnH+5r43p564kmDpKGwWYVEUt84rzf7uWntAu8Aga66VeydWq2GVl+Y8tw0OwjX1MDJkzpNfRzsDgcDAwMLpyRlf7+uBzxPDNYDdk1wziZj8dxdJheSk5M5DTUS0ba1BSqAlVL85mg7164qRoD+SJxY4rwnf1D4gnZkDWdQ+AKc6Qrw2rkerlhWmPE5z5Q3WqZW8+Bk+wDbagvS23jVKl0TZAKKCgvp7unBv0BqL9ja2xlYs4bkiROTb5whBjtiTJeFeXeZpIfbraucRSITai7TIhCYV8/yWBiG4pevt9LcF+Zk+wChaILfvtlJNGEQiCQmH2AMIrEkPznYQkNPiC01eXhdNoo9TmzWhWedc9utOO0WovH0QsaOtfYTTxrY0/kuRUVQUqJ/93HCGm02G2ULpV/cwACUlFB+1VWL2oFoCuDFzoYN8NpruqDObBKJwIoVsztmiqbeEE8cbcPtsHHHpnJ+9UYb9d1BXHYrsYTB2vIcSnNcrC338qO9TbT5wwA4rBbquoMjxuoJzNxJ2B2I8uSxdp481g7A7ZvLuWtLJfsb+ugJxKjrDrKyxMPO5YV4nPNzywSjCX59tD1t4QvQ4Y/Q0R+hKj+NZBoR2LoVnnpq9uPKM4HfD7fcsqiFL5gCePGzYoWu55tMzk4qMmgtKD8/7e640UQSfziOYUA8aZCf7RhXUAWiiRGJBXtOdxNPjhQqZzoDM5r+THn8SBsd/VGaekO0+3Xiw/76Xh7e10ix10VNQRYfuLIWi0CWY3ZuIaXUCLuqUopWfwRfKEYwmuSFU52Eoulp+blZdmIJA6/LTkH2FBxUNTXaoRWNLuzawIPzuwgaNZgCeLHj8cC2bboa2gxsUUMYBvT2wp13pqVddPZH+MIvjxOJn08UEBHcDitOm4Xq/CzKc11EEkkSScWZrsAIs/Vo4btQ2FfXC4AjGsYT8OMZ8OOIhjAsVpo8uTxw7CzJ/EJu3VTJLetKsUwSIhaMJnjlbA/rK3PwheJUF2RxtjNAR3+EffW9xBIG799Zi8tu5UxngKa+0JBDMV2sFuFDVy3hymWF9EfiZDtt6ZkfBrHb4dprdaH+hSzcOjrgLW9ZNBXcJsIUwBcDW7bAuXNacBak6XQZj9ZW2LgxbWEeiCZGCF/Q2lsomiAU1Q6w1xdhdcvsgJ/qxjcpbW8CwLBaSVptiDKwJhKIUkRdWbxUv5rXTq/lE7esIX+YtqmUoqM/yu4z3XQNRAnHEhxr7R9ab7UIVouMcBx+5YmT057v6jIv795RTW1hNgB5WdMMzVq5UteEbm/XNUEWGl1dsHSpnudFgCmALwbsdrjtNl1Qva9v+plBbW06HfXKK9PexR9OM9Z0FhHDQGWog4MYBpVNZ1h+7g0SNju+/CKQsbVIeyzKmpMH6W+t57+NCMtXVhNLJukaiHK0pX9EJMZokoYimW6ixDiU5rr44JW1ROMGGytzJ9XC00JEa8EPP7zwTBHRqH5Du+66RW/7HcQUwBcLublw113wxBNaiy0tTd8mHI3q17rly3Ve/RRe7bpnwQk2GfZYhMLuVoq628j19WBLxlEIEXc2ffnFdJVU4c8tQs0wJVsMg+VnjlDdeBpffjGGdeLbI+5w0ldQSlawH+uvfsGTl11P0JM3ozlMhVKvizVlGWik6fXC9ddrh1xV1ez5FmZCMqm18ltuWRxOwjQxBfDFRF4e3HMPHDgAhw/rGN7CwgsEajJp4AvHKbQaqN5eGnwRzq7dRrR8GVcnhBy7mjDQ/qXTXbx6roePXbec/fW9Gfs61kSC6saT1Da8CUDUmUUoOwfDagWlsCVilHS2UNl8jog7mzdXb6WvsGzax6toOUt142l6C0vH1XrHIpSdgyscYNORPezfcRNxxyyHBI5DSU4GtdOVK3WnjL17danT+SzYbhi66M7OnQsuNHKmmNXQLlZ8PnjzTXjjDUgkUErR3BemayBKVyBK50CUqupiWmrXsNvwjhAaa8q9JJKKpUXZbKrKI5JIUtcVxGKBwmwn//NaI/GkkdFM5axgPxveeAV3OIA/txBlmVgLc0TDeAZ8NFev5MzKTZNuPwKlKOlo4vJXnyTiziJptZO02Qlme0nY0xdyOb5uOspqOLVmW/rHngHrKnL41FtWZS4rTSl4+WXd6mq+NOFkUgvfrVu1aWyRmB4WTDU0EbEC+4EWpdTtIvLHwJ8Cy4FipVR3pudwSZKXB1dcATt2kPD5+a9fH6auvw+VL8RLHISyvMQcrjEv6JNtuj7qmc4ATx8fvy1LJoXv1oPPk7TY8OWXpLVPzOmm1+GksvkMtniMk+t2TGqSsCYS5Pe0U9N4kpWnjpAd7Cfi1k4sSX25vvxiegvKCGd5Jr35+3MLqGipo6lmFeEsb1rzngkF2Y7MpgSLwFVX6TeovXuhrGxubcLRqDY77NypI30WifCdCnNhgvgkcAIYNFbtAR4Hnp+DY5tYLJyN2Xgt4YHShW87syYSrD/6KkmLjXD2FIWYWOgrKKWsvZEBbz7NteO/rrrCQTa88TLZgX5idge2RILewrKRN7kyyOnvpaC3g+6iCtrLl0ws1MVC0mqltL2B+mUbpjb3adDujxCOJXE7MqiZisCOHfqB/sILWguei+iIri5terjllovO7DCcjBp2RKQKeDvwn4PLlFKHlFL1mTyuyXlePtvN1589Nd/TSJuqplNkBfunLnwHEcGXX8Syc2+QFewfcxNXOMiWgy/giEbwFZScvwlGa1hiIeL2EPDkUdjdRmXzGVA6bCwvy05elv2CEpHhLC/FXa3Tm/sUOdMZ4L/21GX+QCJaCN53n9aCGxvTqpw2LaJRPX5ZGbz73Re18IXMa8BfA/4cmPLdJCIfAz4GULOQg8IXML852sZPDrbMONxprrDFY9Q2vEl/btGMxjGsNgyrjYqWc5xZtWXEOmsiwYY3XkaUQdCrIxackSBqohAuEYKeXPL7OsGdxfpd21hdlkM0nuT7r9SPqLsbtzvI83VhTSRIZriQkcth5db1c1ibweuFt74VTp+G3bvPl4P0zoK5ZWBApxc7nTrJYuXKi9LkMJqMXSEicjvQqZQ6ICK7prq/Uuo/gP8A7YSb5eldlCQNxf76XrKdNmwW4dkTnYtG+AIU9LQjhqGjHGZIxOmmtv4E9UvXkbCfT0pYZ/i5Jl/IWr6G0hwnbb4IB5+ux5jEXmy3WbAVF3G9rZ/CfG0HFYHrVxXT1BsmHE/SG4yyrNhDVlaMAxaDTCZUO2wWrl1RNGup0GkzqA0vXao11UOHoKlJC86cHP1vOoJTKS3A+/v1vyUl2txQU3NRZLilSyZ/vauBd4jI2wAXkCMiP1RKvT+Dx7yoicST/PZkJy+e7qLE68JmEa5aUcS22nx6AlGeO9nJb462D21fkZdmLdgFQkFvBzHnLIRwKYOldcfJ9XdzbMNO2iuWku20cdPaEm4/VY/VUwtebQ9fWWonVpVHW0/7mEM57RY2VeVS4nHpRIeeHu0Yqq3FYbOyuiyH1alY3GTSwGq1QFaMvJtXcbQ3Rn13iGOt/ll9EC4rzmbHkgKuX12M0zZPMbp2u44bX7YMurt1X7nGRp2NOeidtdt1+NpguIxh6C4rg+Tm6nq+q1bpamyXgMY7mowJYKXU54DPAaQ04M+YwndsAtEEXQNRkoZBwlAEownqukMcaOhlVamX6vwsmvpCHG7yDZVd7OzXNrjXW/xsr83nUKOPeNLA47KS7bBjtegqX4uJXH+3jsyYMULC5iBpsWGP6mI6N68r5e3ldqwvd+m41mGsX12Ft6URKcyjzR8hEk9iKIXTbmFrdf7Ivmoej077HiM21mq1QCIBDgfLq4tYXqMFSiSe5OnjHTT0BKnIc9M1EKWpL0SbT8/NabeQSKaXGVddkMWnb1mdXq+3uUBEO+WKi+Hqq3ULK79fZ2T6fFrgJhI6Jt1u1868/Px572SxUJjzRAwR+QTaLlwGvC4iv1ZKfWSu57GQ+JdnT49bAWxQ0I6HYSj21p1PhghGk7jsNjp8i0v4AtjjMaLONEonToYI55avJ9enu0W77Fa21+Zj7WoeO6EgO5uafDcUZFGW68JutaCUQikuTO91OnW1uGhU12MezcCALg06TJtz2a3csfnCcqGJpIEC7FYLhqHoDkRxOaxE4wYvnOrimeMdI4oVFXoc7FxWuHCE71g4HOcFssmkzIkAVko9TyrsTCn1DeAbc3HcxUI0MXsVwZQCu1UXellM9l8AhSBKMdGsbRahJMdFbzBKZKLauKIF7y2bq9jytk3aVtoUGVMAx7OyaY1aSDT3UFSQQ5ZTC8UJ34iT47SJDwR0Gm8aDC/6bkl9LwBccO+2KspzXENRDvnZDr7wjg2ZDTkzmXPMVOR5picQZWCa3RzGo7UvTI7bTn8kwWKyqgU8uThjEaLWC7XgIo+TLdV5rCr1YLNaSCQNjrf1c6YzQEtfeITQdtksXLm8kHXKjXXrUhh0VFksF2SPdA1EeeZEB/GEl+qmUxzviVKV72Z5sYcc9zjOoPEKAQUC2hFVWTnNMzCSK5YV8OTxduJJxZ1bKkzhexFiCuB5ptDj5L7Lq/n+y/WEY+NoVVNEROgPx6nIc9MdjBGfRQ07k/QVlFDb8CZR10gBbLcKd2wux+s6LxBtVgubqvLYVJVHNJ6kcyBKdyBKSY6L8pyUw6w5MLJludutHUEpkkmDp4930B2IInlF5Pd14goHaAZqCsYxhQw6k0Z76pNJ7YC6665Z66NntQgP3LF+dqqcmSxIFl7jq0uQHUsK+INdy2fVCSwidPRHsAiUL5JoiN7CcqzJC98GSnNcE9o9nXYr1QVZbK3JpzLPrQVWIKALEQ0XwGVlWgtOmQ/OdQeHHJXKYqG5ajlKLDgiYfrHeysJBnXI1HAHUjIJzc06Y2yWtF/Qv6EpfC9uTAG8QFhfkUth9uzm2RsKonGDpJF5Ddhpt1BbmM2Na0uGioJPlYA3D39uIe7gwIjlzX1h/uPFczT1htIfrLcXLrtspKk691WBAAAgAElEQVQgKwtWr9brgFjSwCIMmWniTjfnlq9HiWDv943QloeIRGDJkmGTDuhiMam6GyYmU8EUwAuESDxJIDa7tuBBbHNUSvCebZW874pa3r9z+pmLp1duwRUNYRnl5Eoail8caSUYTaMAfE+PjkRYtuzCdevWaSFqGKwty+EPd63g/quXkJWyrxaWFFB82w14Vy3ToVS9vUPbD3Wf9nr1uuZmvfyd79TC9xKMYzWZGaYNeAGglKKhJ0RklmzAo/GHYzhslhHtb2aLdRU5VOdnsb4yh3XlOiHBabeypCib+lEdjNMhkJPP2eUbWX7mdXz5JSOK3yQNxQ9fbeS6lcXkZdspy3FdWA0sENBxpzfcMHbIWXGxbuF06BCW6moQweuyc9/l1fSHE8OSV0ohvEoXt+/o0MkG/f2webM+RmWlTiKoqEivTKNSpoA2uQBTAM8zhqH41Rtt/OxQSwaPoU0EsyWAB1NwDzX62LW6mG21I/vQue3WGbVvb6pZhS0eZUn9SfpzC0bU5I0mDPY19OKyWbhiWeFIc0d3t7bH3nGHDvgfb/I7d2pt9sQJLUBtNjxOOx7nKMea262zvcrK9Ng33KC7UE+1JGNzMzzxBOqKK5BNm6a2r8lFjSmA54FYwsBu1drQ6c5ARoUvQDiepMTlBDcMhGdm5rBaBKfdyj3bqnjH5ko8Ln0Jff/lehp6QiQMg1ZfeGa1gkWoW76RgDef1ScPYjH6CXjzSNq0gLz7ssqRwrK/X2ddVVXBrl0jHW9jYbGc327/fq2d5udrG/EgSukxAwFtcrj7bi2sp4hhKHa/dorQnpMcP5ug75yFZcXZXL+qmGXFC788qElmMTtizDFKKf7u1ydo9UXwOG2I6FjUuaLQ48AfjmMoLRymyvt31nLNyiJsFhnx+q+U4uF9TTwzQQH36eCIhilra6Sq6RS2RBxRBtesKNLCK3X8QF4hsnUL2SuXT711TiQC9fVw8KAW5IPfSSldGGbTJi14p2lHb+wJ8Z/PnybY2EzAkzdUGEhE+MZ7tsx9MR2TOSHdjhimAJ5DBoXvua6p20ZnExEQhGyXdcoacU1hFn99+7oxOzGEYgn+8alT07L9ToYYSbJCAdzhAE5l8OHrllFbXQx5ebTELeyr6+WurTMIAVMKQiFdu8Bi0WFmrtnp7RZNJNlzppvHDrYQjSdZWpTN2vIc7tpSaYaZXaQsmJZEJucRETrnUNsdD6UAmd6Dt7EnxPNvdnHtyqIRqbQAWQ4b926ror47yKEmH2fHqW8xHZTFStCTS9CTS2mui/9sFyqjEQqyerFYhKePtVPocbCtNh+33Tr1Vj0ikD298LnJcNqs3LimlKuWFxFPGiMSSkwubUwNeA7Zc6ab7+6pZ6Gcc4fNQpbDii+URmjXMNZV5PDJm1ZeIICHo5TisYMtPPFG20yniQi8bWM5BdkOctx2tlbnEYknOd42QF6Wna8/c5qEYRBN1YbwumysLPXyRzesmPGxTUymg2mCWGAcbfHzX3vq8E9R2GUWRXmem3A8iS+Y3rw8LhtfeufGtKIclFKc6QxwqNHH3vpe+oKxac1yTbmX/3PrmguW9wSiCLpY+mMHm6nrCtITjBFNGBiGotDj4OoVRbxjc0Vmm1eamIzCNEEsIGIJg289f5ZIPDNxvhOR19tBZcs5WiqX4SsY3b5GaPNFKM1JP6zqlnVlaYeYiQgrS72sLPXy9k3lvN7sZ399L2e7AkMFiGxW4YNXLiEvy87Bhj7OdAZo7guPGKelL4w/FCc3a+Sre6Hn/Lzvv2rp0P8NQ/HD1xp44c0ufnmklc1VeSwpyox5YU4wDB2P/MYb0Nmpa+46HDo8bsMGKC+ftpPQZH4xBfAc4LBZWFKUNdTufS5Zd3wvYijy+zrZfd2dI9YpoCzHSddA+prppqpJQrzGIdtp48rlhVy5vBCApt4QPznYwvWri9lSrWN211fkopTiaEs/57oDROMGXYEoBxv6+PsnT3L31gpWl+XgmcSGarEI7728BsNQvHS6m68/e5r3XlHDjiUFE+63IDlzBl55RdcZ9nh0SJzVquOd29vh7Fldge2qq8bO/DNZ0JgCeA7Yfbqb0x2Z7BA2Pn35pZS1NdBeVn3Bupp8NwkFZblO+oJxwvEkVhGSyoAxClmuq8ihZAra8kRUF2Rx55aKC2r/iggbq3LZOEzQv3iqix/tbeSnh9vYsSTClcsKKfY6JzQr2KwWbllfRosvzLmuIP/+wln21/fx4WuW4rAtEm3x0CHYs0cX/xmeWBKJ6I9SOnY5FIKf/EQ3s9y8ef7mazJlTBtwhlFK8eUnTs5qRMBUEMPAFQkScWWPSOu1WQW33cpAJEF+th1fKE6Rx0l3IEaJ10lHf5jRQvj/3r6OpbP4Kv9Gs58nj7Xz4WuWkp89cXua3ae7+e6wFuy5WXZ2LivknVsrsU/gDDQMxYO763j1XA8AH7tuGVcsK5ydL5BJTpyAZ5/VySXJpDY9dHbqGhSx1BvL8AdQIqG15Ouvhyuv1Nqwx0z0mC8WjA1YRKzAfqBFKXW7iCwFHgYKgIPAB5RS0/POLAJEhN5pOp9mA2WxEM4a2TZcKUWJ10VrqidZbzBOsddBIJpAKUVXIEqRx3VBT7mGnuCsCuDOgQgn2vr5zP8e4R1bKrhzy/hxvNesLMJuFf7jxXMA+ENxnjzazkunu3n/FTXjClWLRbh1fRkrSz1srMwdYTdesEQi8NJLWoC++aZudmkYOjXa7R6/DXxeHuzbp4Xxnj06jXrjRm0rNp2QC5K5MEF8EjgB5KT+/irwz0qph0Xk28CHgW/NwTwySiSepCWVglvscRI3DF4718vhpr5pe/8zgVKKslz3kPAFred2D7MDO20WfOEL53y8rZ9dq0tmbS65bjtZThuhaIJfHmnlspp8qscphB6JJ7msVq8fXpYyFE3Q6g+jlBrXJFFTmEVN4Sz0mpsrzpzRjT/9fl3cPS8vPSebw6E/Vqt2zLW2wunTugTnVVeNTLU2WRBkVACLSBXwduBLwKdE3yE3Au9NbfJ94AEWuQBOJA0++9jrQ579wS7cCw23w0qWw0ZHf2TC7cKxJOW5LhTQG4wNFfE50TZAbzBGwSTmgnTZvqQAfzjO/7zWiFLw9WdP87HrlrGyxHOBMLVZBJvVwls3lA1pwYMcbenn2pXFFM2xdquUIp5U49qUI/EkwWhialp3ezt8+9sQDp8vID8VsrO1Y66iQhekLyjQqdaNjbqY0NKlkw5hMndk2hvxNXQH5MEyXIWATyk1mP/aDMxeC4E5ZtB+/lpd74i+bgtR+CqlyHba6EmzVX2bP0K7P4ICyvN0Sm4omuDhfY30R2YvlvnGNSUsL9G2yr5gjK8+cZIHfnGMX7/Rlirqo0/mYNLHtpo8vvTODVy3qnhISPeFYvjDcxdfHU0kafWF2Vffy2t1Peyv7+W3JzsZSJ2XWMLg3184y5d/fYLwVEIP6+vhRz/SXTemW3/C5dL7D7cTl5Zqc8avfqUdewvxAr1EyZgGLCK3A51KqQMismtw8Ribjnk1iMjHgI8B1NRMv8B3JjnY2MeDu+uGMrAWJor8LAfRpEH3NNKg4wmDQDSBw6Zbtdd1B/nGM6dZVebhxjWlM9Y6gylte1lRNgXZDo62+DnW2k/zgWYeO9CM027hkzetYnWZtnvabFbKct186KolGEqx+3Q322sLWD6HlcVOtg3wjWdPX7D8eFs/91xWxeOvt7K3rpdda0rIz0rzbaG+XgvIvLyx291PldFdm91uXcN4zx4tgC+7bObHMJkxGYuCEJEvAx8AEoALbQP+KXArUKaUSojIlcADSqlbJxprIUdBfO4nr9PZP//1HcZDO9yc9ITi06p+Nshos4pFhJvXlfLuHReGt82Udn+E/Q29HGvtpzcQo7rATXmum9s2lJE9LAkkaSg6ByIUe5wTpkXPNr5QjH946k3ahtvRRcd72ywWgtEEl9Xmp58K3d6uw8hKSrTgfPFFbTqYLr29OhpiLEGeTEJTkw5ZW3NhdqHJ7DDvURBKqc8Bn0tNZhfwGaXU+0Tkf4F70ZEQHwJ+nqk5jKY/EqcnEJtVT/4nblrJV584Oeut5cfDIlCW60o1WBAMpUgaiq6B6JiOKBGhJxjDbrMQnYEAHv2cNpTiRFv/hM6v6VKW6+L2TRXcvqkidWz9HUcLWatFKM+d+4ajWQ7biOL2boeVP9i1nP5wgv986RwlOS4+fE2attZYDJ55Rmu+TqeuxgY66mE6JohkUj8NRndtHsRq1eaNF17QjrrJaiebZJT5iEj/C7RD7gzaJvxgJg5yoKGXxw40k0iev1FyXPapCd9o9MJXuVGU57p51/bqjEX52GxCRb6L0lwXoPC67LT7o7T5I7T6wrT7I3QORHE7rBTnuCjPdVHsdVKW66Iiz01FnpvSXBfuCboKT5em3hBfeeIkDT2ZLa8pInOq4U5GfziO1WJhQ2UuS4uy2bGkgOr8LPbX62afFbkTd3Eewf792mY7GFpmt+vY34FpZk0ODEB1tY6eGA+HQx/nhRfGbjxqMmdc1IkYE2lno793mz/C7tPd3H1ZJTZl6NfAU6e0xnDjjTqmchixhEFTX4jaVNjUJx4+NOu2YBEoz9XxujarLoCeSBoLzodit1p47xU17FxWuHiyzGZAImlgEUEEHj3QzG/f7OQvbltDsdfJPz99ijZ/hP/79nWU5U5ST7izEx599MK+cn4/7N6t+9dNle5uuOYanZ48GU1NcPPNsGrV1I9jMiHzboJYCAwK30Hn0bJiD9F4kqRSfPWJkwRjScKxJA6bhYGITkLYfaabneE23h06h7Wm5vwr4qAnOUXSUHz9mdNkO63kuh0ZccS5bNaheN1EUjGOv3LeiScNvv9yPYZSsxonvFAZro3fu62Ke7dVkUwa2GxW/uSmlZztDEwufEEX13G7L2zqmZOjha/PN35vu7Ho69N25HSEL+gwtf37YeVKM1Fjnrj41RW0IK5JaapOu5WeYIxWf4S+YIxIPEl/OD6kEScMg2XWONbsbH1RDjZgDIVGjGmz6jE7+6Oc7shMkZ1YwlhUGuXw5I5LBRH9ZmKzaSGa47KztSZ/8h2DQf2GNZazTUTXdHC5tBAOh8936xgPn08nWkyl6WdWlt6vY3bbSJmkz0WtAQ9nuNZSlefmvZfX8MNXG4aWicAf3bCCLdV5SEMhPP44fcpKNBCiNMeFjHJW7KvrpblvpFCeLdx2K163DasICh0VsBg40NDHu7ZXLSh77YLl7FntZBvP0Wa3ayfZb3+rTRJ2u1YGCgq0ycLpPN9GKRTS2uzWrdq+OxWysuDYMZ30YTLnXDICeDgiwtaaPETgh682oJS+lr2DIU5LlsDNN+M8eJiEx8tT5auo7YuS59ae/8NNPk53DmQs/tfrti3o0Lbx8IViHGn2s602DQ3wUmewjORYGIYWio2NOlQsEtFaan8/tLToz7JlWigXF+uawAUFF5oy0iEvT6c933CDWVN4HrgkBTBAXpaDXatL6A7E+M3RdpRS/NsLZ9lWm8+uVcVUrllD1po1ZKEDlwPRBIca+3h4byOJGYRzpYMvFMdusxBPLD4P9f76XlMAT4ZhaGdZyTj28s5OaGjQwlVEmyLy8rQpIhaDQEAvu/rq8QvzpMtgbeGBATMkbR645B95926r4s/espJlxdn4Q3EONfqwjqEJZDusWERIzoEfLJYwcFotC9TlNjH76nupy0BX5IuK/n4t9MbTOM+d0w7f0Y4xt1sLycpKLcRnM4TM75+9sUzS5pIXwKA7MXzy5lXkuO3EEsa4LXqOtfjmrKFmIJrAuYgccIMoBV/61Qke3F03omrZxYBhKH52qIVzXYGZXQcTxfgmEloYTla5TGT6scKjsVh0BIXJnHPJmiBG43HayHHZaO4LD4WsDSeeVNy6oZwVpV58oTjPnewkHMtcjzeXw4KMWTpj4aOU4uUz3Rxs7GNpYTb3bqua855skXgy/WSINDndGeCXR1rpHIjw0Wtn0P5nouSeqQj22VIGrNbzxXtM5pTFp2JlkMHwoT1ne4aKk4MWKA6bhdrCbG5YXUKWw4aRYU04EkuSM0nvs4VOJJbkRFs/X3ni5JwXpbdkIK41123nd7ZX85Frls0s/Xqia8dm0+aHcHj8bQbHmK2OFwu1fuolgCmAh3HbhjLu2FzBmY4B/vThQ/zw1QZ+friFF051DW1jKF2OsMSbRqD9jNC92ZwZSCGea+JJg2/+9gy+0OLWsspyXdy2oQyLZYbCfaJoBREd4RAYo4WVYZwPPfN4ppakMRHJ5Pi1I0wyimmCGIbLbuXOLRXctbWS3mCMH+9rYs+ZHtwOK1csLcTtsJI0FFl2K4Fo5ovvdA/EsFsFp91KdB5a2k+GNZEgv6edgt527Ik4cbuT7uIK+vKLUZaRQqa+O8g/PPUmf3TDiowX0DEMxaMHmnnvFQuzjCku18SZZ2Vl+tPRoYWszabtvadTJTCrq3W6cSo1fcZx18nkzKMpTKaFKYBHMfhqWZDt4A92LScQTfDK2R4ae0OsLvPisFl4y/oyCr1OvvncmYzPJ55UVHgdhONa4PeHEyQzHAY3KUpR1XSGJXXHsSYTxJwulFiwGEnKW8+RleflWO06GgtHlqps80V48KU6PnPr6lm3z4KOwFhT5mUgHOfK5SN7xJ3tCsxpzeAJyc2d+JXfaoUtW3Q0RF2dFpAdHQR7fbRELRwuzUed8mORfnKzHNyyrnR25mQy55gCeBI8ThtvGeMCX17sIdtpIzgHmnCr77w9sCzXNb+ZcUqx/PQRahpP4c8rJnlB1a1cNtfmcK+thZeyvDxmFBFLGkNJK3XdQf7650f5+PXLL3B0zoRILMFPD7Vwy7pSLqvNp2KU/TwT1eCmjdOpkzAiEa0Nj4XVqms0LF1Kwu8n1uun5cBR3ojY6PAUolKJOu39EVr6Qty2vozSHNfUzSODD4LZMmeYTAlTAE+TXLedL965gf/Zq1v0DNaVyHRd4O5AlCynlVB0fkwSpe0N1DSeorewFOT8q6/HaaOmIAuP00Z+nhvyiri2+U2uvXMNRmUVR1v9fG9PPf6wrsn8m2Pt/OGuNAuWT8DZrgDLirJxOWy89/IalhVnk+W48LKuyJv7usETUlOja0EME8CGoTjVMUChx0k8adAbjNEdiNLQEyIQNUgWr71gGKVgfUUOBdmO6dmmg0EoKhppAx5M9nA4zNb2GcYUwGkSiSdxWC0jLvLcLDt/sOt8mcp2f4S/+ukbGZuD7m7hpsUXnpcANTEMltadYCCnYITwBbhpbQm1haNCzfLy4OBBLNXVbKrK48/esooHfnEMgIMNPvac6ebqFUUXHiiZ1LGwoZB2PCWT5wuUWyxaO8zK4vUBxb/vaeDTb1nJsmIvGyoX0Wv0qlXw+usjFr1a18P++qnH4x5v7SfbaWN1qQfbVNORfT646Sb9f8OAgwfh8OHz53zJErj2WlMQZ4gJBbCIfGqi9Uqpf5rd6Sxc2vwRHt7byB/uWk7uOH2+8rLsrKvI4Xhrf0bmUJHnpnWehC9Arr8HVyRIX8GFJhlfKE5t4aiFOTnQ3Kxb5BQUUJ7rGmorr5Tih682sKHUQ248hPL5aDpZT+lAD05fL4lEkr5wnOLBnnO6BcjQ0A29IU7W9fKpbavoe7YNdflaJC9P2zKnUxNhriku1ppnIAAeD8FonHNdk2cQ5mXZcdks9ARjxFNpmf2RBM+e6ORYi587t1SmHzmTSGgH35Il+u99+3R5yooKvVwpaGuDxx+He+4xIyUywGQa8KBrdDWwA/hF6u87gBczNamFyNKibH5nexWnOgNsq8kf83XPZbfy6VtWc6Chj289f3aWs+Z0LLLVIvPmhMsK+jEsY9/chxr7KMt1EYolWFqU0pZESCiFv6WTgvx8bFYLf3LjCv7isTdwhgKUdDTR+a97cHis1HcHea2pn4LSAt6+YylWm5VXX2+j2p3FxsocrMM8/cmkwfG+DvKWeQiHwuT6ziB9jVo7ttt1KceVK9OvizsfiOjGmE8+CR4PIjKpY/LW9aWsKvXqNlOBKA+91jhifXt/lJdOd7N9ST556TQD7eqCjRu1qSES0ZpvZeX5B5iIflA0N+vCQKOaEpjMnAkFsFLqCwAi8hRwmVJqIPX3A8D/Znx2C4wVJV4SSYPjbX42VI7vtNhWm8+Hrqrl+y/XTyu+XSmFx2UjnlQYKGxiweOy0dATGtKC5wNbIoEaJ3yqP6KdYA6rhcqdbhyp+riGgl8ebOLmqhoC4Ri/eeogV505ybpoL0W5bpavrKU7Bn3JAdYVlrCyxIMhglWEm9aW8IvDrbxyrptrVxZT5HHgcdo41x1kebGHlSVacMXiSRgUXrEYHDgAe/dqzW7TJl3WcSFW+lq6VIeb9fWRlZ/P2zaW8UaLn9MdgQsSV9aU57Ci2EN3IMbpzgFOto2dhny8rZ8cl43Ll41+HRlFOKy13M2b9d8+n/53rLcHtxtaW00BnAHStQHXAMOviBiwZKIdRMSF1pKdqeM8qpT6vIjcCPwD4AAOAB9WSs1NR8tZwGa1TCh8B7l2ZTHluS4e2d9MU1MXOf5eEnYHvryiCWNAS7xOeoIxgiknm9UiJOR8S/nOQIS8LDu+0ATFuTNE1OHCYozv/IslDGIJgx/tbWJDZS7bavPpC8XpsRsc+M0rZL9xmBslxtrlZdgLNw4JxWInFHudqTGS2FPC2223sqkqj2dPdvDbk53kuG28a0cNm6pGnv9Y0sCREsDNwQQVpWVYBF1V7Oc/15rwtm2wevXCEsRWqy4D+cgj4PWS5bBxxdJC1pXncLYzSCieoL47REG2ndWlHl6r7x3XRiwC795eTasvwivnullTnkOOexyTgVJa+33b287XnLDbx0+RjsUmr01hMi3SFcD/DewVkZ+i++K8E/jBJPtEgRuVUgERsQO7ReRJ4PvATUqpUyLyN+jOyBlpzDnfrCjx8rmrKzj2r09z6M0WUAr75ZfzQv7SEV11B7FaBI/LRufA+VrAo80NDouF/vD8PK/8+cWIUqCMC5xwI7YLxznS5GNzWTalTuHThQOojg5kS/UFrdKTSWOEeWFQcwYdk7223Mvaci/13UFqC7PHNP14UiFnSin9EEga+nW+oEB/wmF47jk4eVILvIUUclVQADt3wp49OsFCBK/LzpYaPcerlkNHf5hnT3TRHRi/RrRS8FpdL9uX5OO2W3nsYDO/s70Kj3MMIdzWph9Gg7bfwXkUFV3YBimR0IJ5aZpdnk2mRFoCWCn1JRH5DXBNatHvKqUOTbKPAgbzKe2pTxKIKqVOpZY/jW5df1EKYAA5d44NRU5OxGup8Dq5ytbFxmt34YvD/vo+lpdkc7JtgEA0wXWrirhmeSH/9MyZMdscOWwWkoqM16EYj4g7m67iSnL9PQQ9E0ccrCn1UP/cKxAOsTwnB6mpQSlFImlwritAXXeQYDTBzevKyHWPL8wHE2OWphEzbBiKpUXZF9ZpcLt12FdPDzz8MFx9Ncm167DaFoizbtMmXY3sxAndEXnU/Etz3NyxuZyfH27FZbcyEImPGe5Y1x2kOxDFabMSiSc50TrAjqWjWh61t+s6xNddN/I4Ijoa4he/0OaG7GxtF47FdN3hsVonmcyYKXVFFpESYChwUSnVOMHmiIgVbWZYAXwT+CxQD9yjlNovIl9Ha8kbx9j3Y8DHAGpqarY1NDSM3mRxcPQoiedf4HuNCXJsiityoOYzf4zYbETjSZx2qxZMhsJm0f3FfKEYn37kyJjD2WxCicdFfyROIMMxx2ORHfBz2f7niLiziTnHjq11REMsO3uMFbYYte95J/12F08f76AnFMdulQtimN9zec2QCSLjxOPQ1kZfXhF5b7sFyV8gxeOTSXj+ea2lD3eEDcMw1NAbQE8gymt1vbT6wyPOp8NmYefSQjZX6wfk0MNoMKKhuBje+tYL3kSGCIV0t462Nh16tnLl9LozX+Kk2xU5LQEsIu8A/hGoADrRNuGTSqn1aU4mD/gp8CfoyIq/R9uGnwLerpTaOtH+021LvyCIRuFXv+LM0XNEEgb5t98KK1diKMjPdmAoRSSWpCTnfED+4SYf//Ls6YnHFSjIcsx5lTGAHH8Pm47sxmIkCXjyR2TD5fW0s67uKJ78HJa963aCdjdvtPg53OQbd7yyHCcbKnNZW54zsypjaRKMJnj06SMUO2DdB+9myY4pNLLMJIahnYcHDkB+ftr1GQYicQLRBAVZDmwWsI4W3oMtjVatguuvP99o1iRjzHZb+i8CO4FnlFJbReQG4D3pTkYp5ROR54HblFL/AFybmuQtwKp0x1mUOJ1w552s2OXnO6+1EAu6Ce1tpDcYQ0To8Eco9Di4cU0pt20o41irH6fNgtdlmzirTunkkLlmSVE2FGWz33Ezpe0NVDedwZqMY7NauCFfYQ3U43jLNcRrajnUF2dfXQOTRc2190cpz4udF76DSQAiEzeunCYWgXhuHvXBCL1f/yE5n3o/BZctACFssWh7cG0tPPusNgUUF08af+t12fGOVbo0mdStjwBuu01HMZjt5xcU6QrguFKqR0QsImJRSv1WRL460Q4iUpzazycibuBm4KsiUqKU6hQRJ/AXwJdm9hUWAVYrRl4+b7vSzTeeOX2BM6UnEOMXR1qIJpLsq++lLxhnY1Uu++p6JxzWZrFQ5LVhtwiGUnRksJGnCNxzWRW3rNcRBq+cLSGaWMfpll5q7AnW9DTB3r28ueMGzvTHqewIUZ7rwmW3EpqkcL09FqX9+FmSwVasvr6RtXAtFh3FUFSktcL8/BknWrT6InhcdjpjBv68QgKP/4YCu+iY2IVAeTm8611w5Ij+xGLaMZZuxbJwWCe/iMDatToCJHtuC+KbpEe6JohngLuALwNFaDPEDqXUVUdztIoAACAASURBVBPsswkd8WBF1x1+RCn1NyLy/wG3p5Z9Syn1tcmOv5hNEHXdQfac6WZpUTbff7k+A0kUivJcN7GkomcCL/lMsVqE61cXc93KYnqDMTr6I9QWZrO6zAunTtH12C95JezE43WRSCpKc11sqsylNxjjbFeAIo+TYDTBibZ+2lMPiqxgP4XdbeT6e0BBcVEOy6sLyMoeZp80DG3GiUa1R97p1JpcRcWUWrAnDUVdd4AVJV5iiSSxpEFdVxBfOM7rdT2sZ4D1H7yH4h2bZ/vUzYx4XCdBHDp0XpsFXUNi8M1g8BxBqr23V1dTW7ZsfFvveCilS1/6fPp47e3ahDEYoma365C08nIoLNSZh6Zwv4DZtgFnA2G00HwfkAs8pJTqmelE02GxCuDOgQhf+MVxIvEk25bk80azn1jCID/bQZbDSkvfzBMqCrLnxw4MWsH6eK2V7cdeIVZYjCN78pvdMBR7T7XT8toRCnraSNgcRFxZI16NV5d5qS5w47aP8YI2WCjGZtPRA+N1Fp4ApRRdA1FyXTYOt/g52OjDiEQpDfm487O/h33VyimPOSdEIrpGRl+fjuiIxbTAtNv1m0FBwfQEolI6ZvrYMV0CM5EyfaVqbmC1nv99DEOvD4fPNwV1uWDNGh3atlCcmvPMrAngVCTDk0qpm2drclNlMQrgWMLgsz95HX8qYeLOrZV4XTY6/BHetqmcuq4g35jM0ZYGWU4rwWhy0voQCijNcWK1CG2+2Sln6Q4NsGPvM6i8PBJOJ1ctK+JMZ4DN1bnUFGQRSRjYLGAfbjLo64ODB/H1h2jBybnusRt3OmzCihIveVkOovGk7tk3PLEgGtXCqKYG1q3TAnmKKKVo7A3htFk42TZAiQvW2GJY7rvv0hAk0SjU12vturdXa8vTMfHE4/qBkEjoCI4tW8aN5LhUmDUnnFIqKSIhEclVSpm9q9PkaKt/SPgCtPvDvG3D0qHuBesrcrBbLcSTM2stHoomqcxzEYnGcf3/7L13lFzped75+26onLs6B3Qj5xmkGcwMOJw84pBiFCWKkmXJCl5Z3pUl73qtXds6tlZnrbVXXu2xLIsKVCCpJUWKpMQcJxMTMQAGOXWjc6icq+693/7xVedudKHRAAaDfs6p042udKtQ9dz3e77nfd7BASzLYaKlE6npGJqgOehGE0ojHk2XCHgMTEOjVm8E8ZTy9F45jRQa/X07qXga63gSjsO2s29iGSYFDKjYfO/MOAD9iQKaUG3IfXE/P35Ph7rT1JQKfPH5iHS2EgFMQ+fc2GLPc9WS80KNPKbOQ5ubZqMm3W61QTU8rIhk377rJmEhxEyCW2vIozYBk0llB/vgB9+9BCKlIt5nn1VVdTSqmkBWC9NULdVSqpPi176mHvPxx1e1Qrmb0OgntgycFEJ8F5iJbJJS/k835ajucEgp+fbbYzP/dpsaW5oDVGs2liPxmDqGrnGwN8obAylqtnNDMxFH0mV6L79N36VTeF0GF8s58vceBCQjc6pdIQRe06BQmdWKd5/8EZ5yEeE4eEpFju9/uKHnbB+5TDQ1QbKpbcnrp6XuZKFCvlIjUCpQO/oKNa8P3xxdcmtrkJjfxZWpwjWD5ss1mxcvJoh4TbpjXqI+F7oQmE1NFEbGKBZeo7hrNx0xP+YqRvTMODBiMRgcVMvxvbfBGVEsKv01nZ7VdX0+tQkXidy4haxYhJdfVn7jlha1ublWEGL2OLNZ+Nu/hUOH1MlxPUltSTRKwF+vX9bRAIQQTM7ZEKtakkzZ4g+fu8T9fU08vLUZKSV7u8JsbQ2gaxp//uKVG3rOUCZJyR+kBHhSCc4vE9hTnVtxS4mvkMNfyFBxefEVlw54WQhvMcfmCyfIhFf+8mZKFqf6E+y8fIK0pXHmao7euGoVDntNwl6TeMBNzOfi2GBq3gljISo1m/GazXi2fhuhgvEzRZvApXMMj1fJtnRyT1eYh7ao5oG5zQsNo61ttjX4VkgRlqVI/9gxpcVOTymersBte/ZvfX3KrdHWdv32vIEBZW9zHCXd3ExLWiiktOg33lCNHU8+qcg+mVT6sc93d8g8K6DRVuS/vNkH8m5CuWbTHHBzf1+MtwbTHNgQ44GNMfb3RGc6vt4aTHNmNMvTO9toDXt4/vwkFyeWmITbIAb6drL7xMszvy+FgMdAn0tGQjDW2s1jP3iVVDTOD574qRWfZ0Z6MF1LjCNaGoOvnqCaGiftVTaquZLDo9tbCLgNNE1wYEMMt5HhytTKubgASGZknoI/SMdIP4VAhHt7emdusqopEdM7/WsoRZRrNolCFbehEQ/MqWJHRlRORS6nSKuzEwDLdihW7fm6t+OoDrVLl5QL5JFHGs+1OH1aPU9Ly/U7I1YLXVet1ZkMfPaz6vWVSurvtq2ue/TRu3ogaEPfICHESdQ+zlxkgNeB/+NWuSHuFExkK/zM/RvoafLxyLYWWoJuchWL1rBJMl+ZabS4pytCc117bA97boiAM5E4Lx95P8CiicTTyJct2sKzHXdNE8Pce+wFHE1wfM9DTLYu1gGjiTFaxwexDIPRjo0Es8lrSg8L4SnlaZocIb0gO0ITgt2dIQLu+R/BzS0BvC6dQsViJF2aCR0HRf5COiqTeEH1JjUdyzDpGL7MSxdbee/W5saDyZfCtBRx9izsaqjh85qo1Bw+9dwl0qUa/+Un70WT9a63N9+EWAyno5Nnz08wcTaN7UiShSoSiPpNPnRPJ25DU6+nqUldpnMt3vte5fW9Fk6ehOeeU6R9O6QAnw+OHlVE/Pjj6vhB7Ql87WvwEz9x10oUjUoQ30QF6Xyu/u9PAAJFwn+BCmhfRx09TbMbWa0hD+WaTTJfJeQxidWrn80tQabylZmKNFGo4jY1gm7zmqlX18JyxDvvNnN+b54cJpKepOp20zdwlrN7H5h32/jEEHtO/oiyx4dm27SP9COkJBdsfOkYS4xj68YiwnSk5OxYFgR0hL0zuq3H1GemF7eHvRy9nMCoVQlnpvAXsiAltm6SCTeRD0bmPW7F7cWfz3Chf5z+qQJHtjSzoz24+vbm5mY1IWL79huqgjPFGgPJAh+8t4OryaIi32efhXPnVBWoaUxky7w9vHiSSqpQ46+P9hPyGBze2IRlq1FY7bEYwrKUpFAuK511KZw7d3vJF1QbtG0r7/Brr6luv0hESRJDQ0oa2Xzj8wHvRDQqIj0kpfwtKeXJ+uV/B94rpfw9VsgFvttxZaqA5UjVwovyBmdKatk8dyn62PZmfut9O/C5F3/RhWDm/quFJgQtQTeVOe3LVzdsJdnUStXl4fLG3QhnviNjw8A58oEwRX+IfCiKr5gjPjlMzdXYRpBu1Ygmx3GEIJhJEEpP4stnEfVM4ZotyZUs3hpMc2WqoCpey+H8eI582SLkNTCtKm1jA/gKOSpuLxWPH0fTaEqMEkuMzx/vLgSOphNJTVCxHRYv2q4THo/atBodvaGHCftM9nSGObAhxkf2dalqcA75ArSFPPQunKlXh+1AvuIQD3rY0OQlU6xxYTyHNAwlWbz0khrwuRATE4qg29tvb4U5MaFkD7db/Xz99dkNRr9fNZrcpWi0Ag4IIe6XUr4CIIS4D5jOB7xjwtRvFU4OZehPFNjaGuQLrw/ygb3t7OtRVaOhafhc80m2ajkETA3bsdnTGeZqYrE39uGtzbgNbUnL1kpoC3vIlmrzcoYBioEIX/nYr+KulOgeOM/B177H64cen6mkNRyY4zCOpCYpeRsfzhhNjtMyPoTUNKQQSCEQ0kEKjXSkmVwoOqP3zjggBCDVjDlNQCg9hXDkvOQ1Rzcoe/0EcynywfC868peH02JMQobNrGzYw2GdPr9anhmV9cNPcxMFT48rEb/LBE7+YG97VxNFjk1kuHSgvlwHlPD1AU+t8mOjjlkahhqQ+7ZZxXRTuuptZoi33D4ujoGbwp0fbZpw+tV5Hv6tPIL2/aqPNzvFjRaAf8S8KdCiCtCiH7gT4FfrnfI/Z836+DuVEzlK3zr1BhdUQ8Bt8FfvNyPVXcfxPyuRTYpl6HR1xJiQ1OADU0+WufotKCKvM8eHaA7usCjKyWN+NckLJvHYJluCoEIFY+XktfPXMLd/MRD7PNZuMtFQqkJQrkk42098+6v2TbeYp5weopoYpxoYpxweopQepIt599CICl7/VQ8Pqr1CrZmumlKjBHKLJF1UX8549kSLX6TUDFLbUnrlap2Fzo3pKajOQ5GtUK2tAZTQyIRtUTOrIEFvlZTG2Gx2JIOBk0T9Mb9PLmzdZFBQdcEftcyROV2KxJ74YXZvx07pqxs74S5eB0dSiaZRjisNh/Hx9UK4y4eddSoC+I1YI8QIozqnpubLfiFm3JkdzDcpka5avOtt8f58XvauTCe59X+JPf1xmYaMeaiZjszWcAhr7moq83QBft7ovzUoW72dof5rz+4SNVy2H7mdfz5DG8efAy54AstBGxpDRJw65wby/NPjvSxpSXAufEcli15+dIUyXNX6Ll6jrM7DnJl0x68xRz73/gBFzffQ/vOjbzn0X24d3fg++6POPHScQY2bKPsCxD3m7gyKczBQYK5FBLmHXMkOUkkPY5ZrVLy+am5XNRMNyDQbQtvIQdIYskRiv4glrlUhSY4PZTi4bgfTyyC5UgypRq27RA2BamKQ7UoCfkMtvRFyZctBlNF8mV1ojEqJco1e/mxPI1CCFXBnT+vPK03gqtXVRv1CtW0y9B5z+Zmnr8wOfM3j6FdW8tublYnimRS2dpee23GUXHbEYsp98XkpDqhGYZaWTz3nJq23N5+u4/wtqFRF4Qb+BhK7zWmPwhSyv9w047sDkK6WOVHlxI8tasNXRMz89xODKX56P5Ovn1qnJNDGXa2h5acVluzHWo2GEKQLdVI5GezHXRN8ItHNnJffbJBT8zH//z0Nl6+OMXQeRc1l5vmoJst7WH8moO4coXR05fYsnsTzzy6lzw6piZmHAHTucO7OkKY8RKnRo4xgsX9e9txpVz4L9qEWjxcrjrYAFu2sGfjRqoTkxydstgfkmwaPYe7WuL1UpGCP7RoKe3PpRFSUjNd6LZNJDWJrZvkQhGC2RRafTmqFW18+QzZ6NKB3zWhM1a0afNWMd0uol4TY2IcCgW8breq7nqaIeTFCjhMZCvk1VFjFYpcmMjPy1leNeJxJUOsottuBlKqqjTcmCyysdk/j4ATxRpSymuTsGmqqRrj4+p53imdfLqu3rsrV1QHnmWp93HDBnXieCfN6bvFaPTT9FWU4+EN1Ky3dczBSLrMF98Y4vx4nojP5Pnz6osT8bkQQvBju9tIFaokC9UlCdg3Z2kZ9pp88v4evvTmEH1Nft6/u43uuI9KzcbQBF5TJx5ws39DlLOH7ufI7jYObIji0wV84xuQvoq9ycfk1ZP86Pcv8PmOfQTCfgxNw+vSubc7wnu2xBUx3befI/fsYrsl6huCXfDIbtB1bEfOeoYzGVpNSfPkMJuTWZpbo1ieEBE7w0SuvEgFyYWb8FSKhLJJLMPEFgLdsohNjWPUqhRCYUDgtgr4irllCRghGPGEKQ+OkXf50C2LzlwCVzCAP5vH8HjQ62b+ctWhL+4n6ncxUsiiOQ7nxnLs6QzfeBVsmko+yGRmLVTXi0JB2a5WqEqllBQrFs+dn5z3d8tyuJoszrROL4lYTDU+GMaNtRbfDBiGmq6xceMsATuOsvndd9/t16lvExol4C4p5Y/d1CO5Q2HZDm8NptneHmQsW+bE0Kw64zI0ilULx5H8mw8s3RyxEC0hD5tbgjy8dfkxMGGvRtgb5rd/fBcuo149DA6qS08Po6kS35jM4Jnoxy/jjMremfueH8txZarA+/e00xRw4fN4mNfPVq+a5jVsTE3RNTbAw3qOpu52dJeBDhzqjVK2bM6N5hlMLRWqM/sYtmFgazqecoFgJknZ46Pi8uBoGromlo3pLEdipEslAvkMjgalqo01Nkle17jUEiMwkuferggBj0HAY1B1HFoCLkaERr5icfRKkqd2tl77TW8U6fTqCTi9/ESQaeTKNb5/ZpyRdBlrzvth6oIP3dtJR2SFBgrTVDLHO3mApq7PVua6rsi4v19N67gL0SgBvyyE2COlPHlTj+YOxNmxHN8/M05v3M8ze9r4i5f6Z657cyDFxYk85ZrN735kDzH/ymf50FKTDZbBDPmC6r2vf7CPXk6oJDLTha+w2Fv62pUkJ4bSWLbkX/3YNja3LOhEklJFHZqmkhe+/W1Ip2nbuGGe3CCEUIFCjkPAraNrGmXLpma6EBIcoanHmr6PJsiEmvCVcjiGiRUMUfb48br0JefbSaBiQyneTj4YwVfIUnH7cDSNXChGxeMnnynzfGmS/T1Ron4XUa+LiqEhXOp99BhrtLx1uVQ27mo3jFKpFVt/C1WbiWxlhnzNahlH05Gai6YGPjtUq+oYV2rMeCchHFbSzJYtd+W0jkYJ+Ajw80KIKygJQqAGH78D5rjcXhyvV7yjmRL3dkdoDXsYnxMqM70T/99+eJHffGrrPLnhejGQKPDGQIoNTT56m/w0zW1pbWpC1mqcvJpkpJ4D4aqWyYaXrtgqNaXD/vfnLvPItmae3NmK29DVJtG3v628m36/yng9flxZnZb4ghiaxqHe2Ym5lybzlKpe2vQWvKMWyVwJSxe4DNXdJnWNYiCEu1qmWiqh+W1CIwPQ1o0lxbwxS6oIVwRe8fgWJbV5inn8hSzpaDMvXZpiV0eY9rCbzqiXZ+7bxGdPpzg7mqUr6mVDzIe+ipCeGfj9ykK2WpRKK+rHbSEPP3mom++dmWBqPMmW82/R1RbBePSRxrr6pqZmg9PvFAQCqhkjkVjbYKA7BI2ywftu6lHcwTi8sYnxTJlo3V62nCvsylSBT7/Uzz99eOOSTohGMJYp8/UTqilA1wRP72pjd6fK3s16w5z2tDP83CtEhEAgGW/tIRG/dstwqlDly28O893T43RFvWx87Xnai2kO3bcNM5mAP/ojRRwNaHRSSnqbfFCtYpmtaFPjNOsOetSHXU+Bsx1JplgFx0HYDoZtQ6VIoVzD73Vh6oKaLTF0gcdcujKeRjg9RTCfpuLxkTci5Mo1NkQ92LrGpG7SG/dzZarA106M0hx0c29XBF0XbGoOoInZ4jxZqM4/mS0Fr1dZp2q1m9rUEPG5+Nj+Tk5cNNGm/GzrikFzg97rROLObelNJtcJeDlIKQeEEEeALVLKT9fnvTXuyH8XY1NzgN98ahuW7WDoGltaAkzU07raIx4mspUZffPNgRR/89og/+jwhht+XtuRfOPkKN84OYpenwkn9W6ChwL4ijnKHj/ZUGyRPW055MsWZ0dzxPqHGPUGOPXSFXbkxmjrH6PsD9Mearr2MlhKRC6HPjEB6TRSSjANPNk01CqYfj+mYZIqV9CrFtlgDEfTqJpupprbQWjkKzaaAL9bp1R1rkm+AKmmVspeP3pTlAd7mvCZGlq5jNbSjN/rIVua1V0ncxW+W88r1jWBqQmqtoNL1zi8qWllAp6u/rPZ1enAfv/spIkVIITgni2tyN4PXt9Q0mRSde/daY0NXq/qNrwLdeBGbWi/DRwEtgGfBkzgM8BD17iPB3geNX7eAL4opfxtIcTjwH9CNYHkgZ+XUl68kRfxTsB0VfuzhzdwZEucquWwuzPMYLLIF98Y4u1hZeSfyq3ORJIuVvnrowNLXjezgSUEuVCMXCi25O0aQTLWRsv4IHkngnPqFEPCjVO0uHJxipDHpCPioTXkWZzSdfWq8nm6XNR8ASwpcYdCinTyeSiXcbyCYihGIqAjNR3h2IRySbKR2eN1JDM2vpVQdXupur3siodmTw7ZNM7OXcT8LnZ3halZDmfGsqQKs00ZtiNn3rOy5axMvnNRKKyOgFcRvSiup5qt1WaTxu60GW1+v1pd3IVo9FT5EWAf8CaAlHJECLFShlwFeExKmRdCmMCLQohvAn8EfEhKeUYI8c+AfwP8/KqO/h0Il6GxtXX2remO+fj1x7cwkimhawJ9lRsN3zk1TmmF6cJrgYtb9iKkw4b+s9i6wXDnJjpG+wHIFSv0J1OMVkp0mjZtfkN1Z2UyUK1SiUQZKzlMTWSwHUnIa+KTHtyVLDUzQC1VoOTVkSFFRlLT0e0ynnKJsleRhiYELkPD0MWKFfA0riYLbGz2q00otxst3gSa4J6uCLbtsL01yFePj5BYZnbeRLZM50oOg2msVmONRNSJau6m5FqiWFQk7PPdeGg7KDKXUj3ezYbbPTvj7i6zozVKwFUppRRCSJgZ0nlNSDVsbjpf0axfZP0y3R8ZBt71pz5NE3QtbCO+DrwxkOQ7p8dWvuEawDLdnNl1PyVvgK5gBOFIhOMQSk8RziQQjoPUNNK6QSoj6HZL4tkpdJcLMjlCppu8dGHVLCplyJkuIo6GWalQc7nxlgpUPD5qLtUgITWBq1rGFY2wrT2A29AJeQwsR/L9sxPY9jKi+hzkyhZj6SJttYLKF5jTgPBKfxK3rhHxmyQKVToiXkxNMJBUtjkhVHPLQtRsZ+nJGs4qR0j5fMoDnMk0nuF7PSiXVXW+Fg6IYhFefFER8IMP3vy8XiHUpVhcJ+Bl8AUhxB8DESHELwP/BJUHcU3UB3q+AWwG/lBK+YoQ4peAbwghSkAWOLzMfX8F+BWAnp6epW5yV+DkUIY/fu7yDY0sWg2iqQkqbi+xxBgtE0NYhkHV5V2kKWcmJ9BcJjGfDzfgLpUITY2SdnSymgptzwXCtORTKpbSMGiTFarhCJbt4PYKQqaF3uxjLF1hOF1iZ3uQC5OFhsh3Gt5yATpaVe5AHdPNJL1xP7s7w8A4B3qitIW9fOvtUS5O5HlgU3yeBDGQKPBqf5JEvsqH7u2gbXpWHChCWi0Bgzo5fO1rN4eAbVsd25zXf0OPZVnq9TaoW68J7jQHxxqg0U24/yyEeBJFmNuAfyel/G4D97OBe4UQEeDLQojdwG8Az9TJ+H8Bfh8V9rPwvp8CPgVqKnKjL+idiInsrEuiEbx6JUlbyMMPz03MdNXdanhLecxKhe6rF5Qf17N40aPZFma1Qgo32akCUZ8Ls1hBWmDXqkxHPJiWTcoTpFsW8ceCGNUqhFxqs8i2cWo1pkydtrDGWK5EolDlyOYmUsUqw6kyNdshuYx8AGBWK0jhUtOR5yzvdU1wf9+sXvv+PR1MTwF/eGsz93ZHaAvPSg9SSl64MDXzXF95a4RfPtKHodcfU4gba5vt7FQ7/en02pPw5KSyCq4kGTiOmr5hWer1+P2LJYtgEB54QN32Vo4NupGT2x2KRjfhfk9K+b8C313ibytCSpkWQjyLsrPdMx1rCXwe+Nb1HfKdhefPT/LZVwb4jSe2sr195WSqQsXi0y9doWrd3g+jP5+lbXSAstePr6iaOXTLwp9PUzNdlPwhNMeeWT7ajmQqX8FTrOCyHGzDJCJs3C4HO+7FiMcxklOzqWLT7aiA7TicHMpQqtl0R31sawviMXV8LoPOiI+a5XDs7X6sRIpUtBlHn/3YmtUKZq3C8+E9vN/tZiW37HQ163MZizzZQgg2Nwd4rZBEAjXL4cvHhmkPe9A0wS6nSvhGCFjX4bHH4POfV/7XtXIrFIvKfnatDrhaTW10Xb48m0w2PWeuowN6e+fnVNyOeW13YSZEo6/4ySX+dk1vsBCiuV75IoTwAk8AZ4CwEGLab/Jk/W/vSqSLVf7y5X4sW/JnL14hUw9UuRaG06XbTr5GrUrbaD8VtwdrDkm4y0W85QLBQgajWsVfyOArZPEVshiWchnUXC5wJJpj43aZaKUipqkjdIGMx9UXu1xWzojxcRgawhwf5xFXnsc7PNzTGcKzoOnANDR2OjliqXE85XrLs5R4izl02+Lypj2UvQGyDW7aXQt7u8N84lAXezoVGY1myrx5Nc2xqylsyY0H3MTj8NBDqqljLZbclYpqwHjPe5bXT69ehc98Bv7+71X1HY0qJ0cspn6fmFCh7kNDN348N4J3SnjQLcQ1T8FCiF8F/hmwUQhxYs5VQeClFR67HfjLug6sAV+QUn6triF/SQjhACmUnvyuhNfUVatufQn9W18+gaFp7O4M8eCmOLs6QovSrfrifn71kU0MpUqcGMowkGhwQOUaomvwApZuIjUNG43pTIeKx4urWlJTJ9ITaI6DkBLdquGvVSh5fFQ9fgqBEO5KifGSg+UOIC+PkC9qbGoN4XO7qVkWDI9gul04ug4eD3o+jyuZhGGXimtcYPXyb+4j6AmA48VTq2Hm86QjzYx09mKZbkrZCpmSRdR/Yw4An8vAa+oc2eImW6rNbtYhGEwVia2FK+CeexRxvvqqkiVW2zxRLCryffpplQx34sTi24yMwFe+goVA+vxoQ8PomjYbAalpqvK1LNXx6HKp6Mi5mJ7KfDMrVClv3bDQdxDEtSqyev5vFBW6/q/nXJWTUi6Rpn1zcPDgQfn666/fqqdbU5wZzfJnL14htYSG+ZtPbmVX57XjCS9O5PmrH/UznFp6zPxaQ7csHnjp6/gKOeKJEYq+AD1Xz1Fx+wC1ZI0lxwGJoxv4c5n6lAuBYVvkglFFqnNgVspkInHc0maHzGP7/YiODmQyBZOT6NEItLRg6zqyZiOKBfSe7vk5sY5DKZVBVKuYoQBnQx38MC1w5gT+mIbGLzzYO7+CrtVUPsLkpGpS6OhoWH+t1Gy+fWqc/kQBpORBb5W9v/2buLxrYPOSEk6dUm4Dj+f6vMVSqtdkmvDEEyr5rFKBT396Udra+Be+TPnts0y4Amr1JR18VpXwA4doCXspVS3yZYtIwIVeqeDVBTz8sCLb8XG4eFE1nxiGkil6e9e+265cVv9Pn/zk2j7ubYQQ4g0p5cGVbreSCCWllP1CiF9b4glit5KE71TsaA/xsf1d/OkLlxdd92cvXeGpna08Lxo1mwAAIABJREFUubNtfvrYHGxuCfCv37edf/H/vbVsYpi3mMdTKmCZphqYeQM+03BmCsOukQ9FaJ4aBqFRNT3oto2tG+i2hW7VqLmVjazi8eLLZ7FNExAYVpWqPr+SsQ2DjkqGiEsHw0BvblZf4tYWcKuKa3w0yfiECnd364JNhbOYhcKsFqlp6PE4ro29EI2yS9M4/fogo3NyN0JuA7ehcWkyrwZ7TleZuZyqrhIJlUm7a5cikhXgNnWe2dPGyeEMR08N8bol8KUq7FwLAhYCdu9WhPn88yrJzuNRssByS/FaTb0Gy1IZHQ88MFs1ut1q86xcBo+HgUSBb789yv5TlzFsbVb6EgLLUQl+DGVoDrqZzFeIB1zc1xtT3XSplKqujx9Xj9nUpJ7z4kX1/IcOra1cUCg09P/xbsRKBPw54AMoK9nCwQcS2HiTjutdhcMbYxi64O/fGmEkXcJj6vz6E1vY1Bzg2NUUz56b4PEdy0cm+lwGm1oCnF9iHlzz+BA7Tr+CAIQjGens48LWfQ23IC9EKJPAMkyKviA100S3LMoeH4F8RtnIYB7BW4aJZbowalVkfZ7bQjiahkhMUYpFcbXFsT0eHNvBcGwStkawo4tYVw+jgynGJ9Mc6AxgGijC2b9fkYDXi2vO80op2dcdoWolZxostreHsByH/qmCIuArV5TWPDdjwLZVaHlra0NLXkPXuKcrwsilIZxNm9jRXvfEjo+rFK/JSUVQ9967OgtYNAof/KB6nNOn4cKFWW14epNs+qfLpZ5ny5alq/jOThXt6PEwlCrhFEq4/F7ClQIltyBbdvCUC2TCTTP/h5O5Ck0BFxqCiVyFdtNUssXYmDoZTO8BGIZ6nZOTioQXyhQ3gnJ5bexzdyCuScBSyg/Uf76DA0bf+RBCcKg3xpaWAH/03CVsW9IZ8aJrgoO9K7cN1yybYsUi4DHmdYe5KmV2nHmVfDCKbZggJZ1Dl0jE20nEV/eBDmcSVF0epKYx2dxJx0g/FY+PUFYtdmzdUITq2Gp4pxAUfUF8hSyeShFniUreVamgVyo4Pj+0tKBrGjpQzmSpxtswDB3LcWiN+oiGPUQjXrUEnppSP5fQXYUQbG4N0p8oUqhYWI7EZ+pcnixwoLdeNQ8NQTiM7ThcTZZIFipsaAqo/ONUqmHNUdMET20KM3RgC8+en0QMDnLkzFGMUEC5Gaam4MtfVlrsasarC6EIraVFLf9zOeUWmfbgut1Kp/X7QQiklJwcSvPqlSSbWwI8sq1Ohh0d6uQCbG0N0kqVTaJPja8fHWUiW+KcJ046Esdj6uzqCBHymgTcc2ig4KiNujkulXnweBQ5ryUB32q72zsIjdrQBPAzQJ+U8neEED1Am5Ty1Zt6dO8yRHwu/sXjW+lPFBbt9F8Lhq7xjx/spS/u5/Rolr96eYCpfAVXpYSQKPIFtbw0XfgKORKrDJYyaxWc+lTkTKSF5skRdKvGzOJHCHKBCJFMAlt3sHUDgYNlukh7fDiajqtSRtZvL4TyC5f8AZxQhOh0ZV6poPs8VMMRrkzm6WsO0L6wHdjjUSTavHw4/a7OEI9tb6FUs/G7DYpVa5G97PRolv4ptZlm6hpxz/LtwPlKjULFpnXBKCNT1+nZ2MWPLhcpfOmbFKI+ClOCTXHQNI0d0SY8L72krGA3sjyf3hSrW8JKVZu3RzIUhot4zAphr8k33x7j1HCGza0Bfu6B3tn7dnfz9miOuC9PWyxAswjBBVS139pKs5Qkx3JEHUnU71o64N1xFPEutze01q3UhYKqtO/CJDRovBPuvwEO8BjwO0AO+BJwg1MK7z54XTo7GvADz4UQgo31SMJdHWF++4M7+cbJMV44VgEkmm3NeGONWvW6RscvhKPp6PUlsG0YDHVvYePF4ziImTbkqsdHStPxFzK4rSq2bpANxVSegxDoVm1m7puj6QRyKTzlAtlSDU++QtRjYJTLlPq2cGFC2e46Y1682oKPo2kqLfIaaK83UvjrVdw88u3pwbpwkasJJVG4TZ3NTV4o5pesuJKFCmdGcwQ8xnwCrtWUdh2N8Mk9Pib6AnwzpZHJlBmra9BHdY2f7tSIZLNrVs1lijX+03fOMpouz/u7x6XzoX2dvG93Wz0CVXLsapqo3wU7dpC4eJFwsA+v1zs7Tsk0EUKs7EUvl5W+XCgsnc1QqaiGj0Zg2+qEci3CTqWUN/ouDGOHxgn4finlfiHEMQApZUoIcXc1bb+D4HMZ/MSBLp7Y0cLfyQzh559HCoEmHcbbVs4AXg7CsXGVC7SOD5GOxslEWigEwoy19xHMpnFVSzMdcTWXm2qwnU3tISqWo9Le6kWTbZgscrhKsITOwGSe8XKB+I7NNMdC7PNYZEpVvKZB1XLQBKvOS16E3l7E+ARN/VdwXB72xNTqgN27VXW9ADG/m4c2uxd7tRMJ2LNHkZnj0BL28rObW/jhhQQXJ/NULYeozyDg1tYsy6Bcs/nP3znHaLqMpgn2dIbxuXSag27es6V53nSViuUQ8hrKNtfcwwdTgzC9wurrU7pyrIGEPNtWr7GtTVXxb76pZBq/f3YmXlvbtR0bjqM8zpcvq5OnYahj6OlZ/N5Myxx36QYcNE7AtbqfdzqMpxlVEa/jNiLic/GPf/EZPtfWyukzg1iGSTrSvKoNOOE47Dj9Gi0TI8QSYwTzGULZDFc3bGGypQtXtcTuEz+iZrpnqu1yzcGua68b436ShRohr0HFcsgUq5Rr01WwhtQ1NMBbLTIZaeFK0SR0MYEjHTQ0Ij4XlyfzmHWftKFrqgJb7Qw2AJcL/fD9uPUwO9w1PD6v0klXmEw8z5stJdVyBde2berfbjds3Yp+4QJP7GznYDHK5YFJeocvYEivcl3s3n1N2aQRPH9+El0TbGz284G9HdzTvbx1zmPq9Db5MXQNU0gqk2dxZ7PKG9zZOdv9tsRJZ+7rJJmE7dsVKba3K5fFhQuqSnW7lXuku3t5P7DjwMmTytERDs+6Jy5cULrxwuGbU1OqfXwt0tvuUDRKwP8v8GWgRQjxu8BPoGIk13GboWuCT77/AP+PO8jpkcXz3xpFMJeiZXyQ0c5eQrkUeX+IUGYSb7GDkj/IcNcWwukEbaMD1FwuaqYHR4JW13rjATcRr/py2Th0hD2cGMqgaXUCRiDLZYbiHVSDEfymRtWy6yRt83p/gragl02t/llLXqWimjKWwVi2TLPfde1RQ6bJvgf3zPzTcSS5Uo1wo5OSMxnOG2G++vIo//KpepfeAw8ob+zgIJFikf3Hjymnxr49pE9f4PW/e5aWj3+IREsX92+MrWoM1ZM7W3lqV+MrmelVw7aOCPqj74UvfUlVrh4PHDwIr7yiqthAYGa5bzsOpZqDbtXwFvOqSp1uZ06n4dw5Rb7To6lWkh6SyVnNfvokNu2eSCQUMU/P1KtU1G3uuee63pd3GxoqlaSUnwX+FaohYxT4sJTyb2/mga2jceia4BeP9BHwrD5bIJhNEsoksXWTbDCCu1oGoaE7s5aoq707SMTbKHv8eMpFXOUiE+n8zGMYusDQBW5dx5aSvW1+NrslbR6NWkcXEy1dlPxhbEdSqNhULYnX1Am4dTShMZot8crlJFXLVtXvtC92GcR8JrVlvNFLoVKz+fKxYa5MXkd3YS5HbvsuBhJFitN5zB6Pso599KMQDCLvuw/rqaehqQl3WzMT7iAnPvNVvvL6AN96e4yp/PWH8C/skGwUuiYUUe7fr6xyoDTphx5SVWkioS6ZDHomQ6J/mMmpLHLXLlXhapqy7h09qqrmeFwR5euvq4p1ISoVdftabdbLvNSxh8OqEpdSXcbH4b3vVSeEuxgrfmOFEBpwQkq5Gzh78w9pHatBxOfiJw50zZvK3DCkZNPFk7SN9eNoGhNtPWw//ToVt5uyZ3anPB8IUfb6KQQipKMt+PMZXLk8rYaj2lvnwHQcdI8HMx5B7t7OxFCK9gsXmGjpphhQeiZAe9hDzZFogM9tEPO7cAkglVYkcg05xWVcn9sgUaiSKdV46dIU5ZrN4U0ryBuVCpO2zl8PWjhS8Pz5ST50b4ciRyHUycFxKHZ08WZ/il3tQYo1SXdHjKDb4umH2mjum63gixULn3v1J8lrYVq3rtlSTcs+cEARXi4HwSAZw0344EG1uTY5qU5wmsaG/WHscGRmijSgNFxNm52s4fEoeeHSpVm3QqEAZ8+qHAlQt8/llpeMDEPJEY6jTgCbNq3Osvcuw4qfBimlI4Q4LoTokVJevRUHtY7V4f6+Jv7uzeGZScwNQwgSTe1c7dmKFBrucolEUxs108Qy5+hzQmOyuYvO4Uvkg1Ey0WaKRguRtgAdHm1eZoDudqvhnIkkx8+OMKl78Yeb6R68iLN7J5u39SFgEXEXcwVSQylce3YTWGNzfkfES8RnUs057O2+tg4MwOQkTQ8/xKO1CB0RH3u6FtynPq/NX9dgUyWLviY/XVEveCsQnh/hebPIF2YrZpchODeWI1+psf/RRxFf+QpFzWAwVYGYJOz3LxpZtOg0Nr15NhemqaZkgPp59Kgi01hM/Z/btpIfkknYu3fxibNSUc9bqagK+MiRu9b5MBeNfiLagVNCiFeBmfWblPKDN+Wo1rEquAyNg71RfnBm4rrve3r3/VzYto+qy41hVXGExs7Tr9E0NUo6OqvppWKtRFMTuMtFNdnCkhwbzhPa1kzAP+fjVCxCpYLW2sIWrx87J5nyeOiJ+/HXSuSGxhgv2YTCftqCHhLpAgNjKaTPR8/h/YQ3r30I/2SuwlimzOM7WlbWZVMpaG5G272Ljy/n6zUMNYHi7Fm65+ZWpFKqErxNzQXHB9N0Rdyk4nFiDz+M54c/pDvWjLdR73lzs+qGmysP5HJKIwaVrlarzZeHdF1Vta++qvTxuZ16Uqq/bd+u3psPfeiulx6m0SgB//ubehTrWDNUaqszp0hNo1rPd5iues/svI+t596kbayfbCiGZbqRmsZQ9xa2nD9G1XEjNR1HSk6PZDnUG0VIqTZwTBMOH0YbGiJULLK3K8ixwTReI0h89zZobSUyNISdL3BhPIcIRtlx+CBmUwyXa43DXuqo2g4f3tc504BQteylZQzLUkvs979/5aaKQ4fUkn5oaCZgHq9XheTcpgqvULXoiTcR8hiwezdarcbVz3+dcmsbm9sjyybG1WybbMmiqa1NEfDU1Kx04PfDxnrywOCgclgshM+nSHo61tLjmR0W2tqqHut975sfsnSXo9GJGM9N/y6EiAMJuVKw7ToaRs2yqdnyhpeop0eyvHxpiY2SVcI2DM7sPMRkcyfbzr2BmUtT8gYoe7yMtfXSNtpPIagqnalUngm9QqvPhA0bVF6BywW6TvnqECdOXKEn4qUlGoaeHoqGC8+uXQgp2VpfrtqOXDsP8BJYOHhzmnyzpRp+lz7rphgbg8OHG7PAeb3w4Q8rwkomVWXX3X1bZ5t96N7O+e/lvn1ssh3Ec8/jusYsXU1ouE1NEeWBA0qrTacV+ba0zKagOc7y2nw8rhwhwaDyDYdCaiWg6/CBD6j3Zh0zWCmO8jDwH4EkqgPur4E4yj3xc1LKWzLN4k6Oo2wEUkoqlnNd7clzka9YfOXYMD88e/3Sw3IwatV6AI9OPhBBcxyiiTG6hi8SzKbQbYvuwQsYtSpVl5eay02mqZknHt9PrGXB0rtYJD80SqZUo2NLD+JWTNpdAdOf+0Vug1RKVXIf+chtCwh3HIm2TDrejaBy9hz6c89hGPp8q9j14vhxVfUvVQVPTanN07Y2tRoYH1cE/tRTa5sf8Q7HWsVR/lfgf0NNL/4B8D4p5VEhxHbgb3iXjxO6VRBCrJp8Af7whxeXTEpbLdqHL7H54klEPXC95Atwavf9TLV2MdXahXBsPOUip3fdzz3HniMdbaHqUaR6IlXjkYXfM5+PwNZNvFNUv4FEgWfPTSIE/Mz9G2Z9x9Wq0q4bkR6uA28MJPnu6Ql+7oENS+cvLMDNIF+Ak+44x3vuZ8fF4zw4OKhkgdU0QfT1qYq/Upl//2xWrQDicaUZJ5Owb5+Sae6yaceNYqX1niGl/E7d8zsmpTwKIKVct6O9Q2DZDhfG1458I6lJtp99g4I/TCbaTDrWguY47D3+Uj2UB6SmU/IFmWjr4a39jxDIZ9DryV1N/nf2F01KyYsXpsiW1HioSq3u7a3VYHQUHn/8urvvilWLy5N5Tg6lSRUq8/5+7GqKTz1/mQvjOf7jN8+Sryw9Nmm5rOe1xJ7uCBv72vl+7wGGDz6k5IXh4Vl3Q6MIhZREMT0OKZFQP30+1bAxNqZkio99TPmP18l3WaxUAc/d0Vn4v3TNT4wQwgM8D7jrz/NFKeVvCyFeQI00AmgBXpVSfrjxQ17HXBi6RkfEy/hEBrNWpepyz6ajrQKdQxcpe/zYc2xIJV+AaHKcaGqCqeb5ExcmW7s5Y9fYcfp10tEWriaL5CsW+3uiuG+gqr9ZqFoO79/bTqZUo2o5eF26WiqPjMAjj6id/EJBVXYrDM2UUvKVt4b59tvjtIU9NAVc/A/vVZ1ep0Yy/Mnzl8nNiQ8tVCzOjGY5tCCC9I2BJCeGMvzCQzc39dVt6OzpCjOWLfMPpRq/8FOfwH11QOUaT00pqSASaWz0UEsLPPqokmzKZUXGpqn03nvvVZtxaz05412IlQj4HiFEFpVF6K3/Tv3f12gsB6ACPCalzAshTOBFIcQ3pZTvmb6BEOJLwFdXeezrAIr5IrsunWDT8ZMIVNvvcOdGBnp3ziPRRuEt5amZi5eljtAwq0t3dI11bMSoWWy5eJx+KblkGBiaxqG+BgJgbjHcpo7b1In46lVZraaqwCNH1MbR5z6nOrtMU1V599yzpFZaqVlcmixw7Gqamu0wmCyypTWAqWtcnMjxqecvz8tunsZ0iE65ZlOq2kT9Lu7piqgA+VuAeMDNT9/Xw1i6yNvJMht7txDdsQNGRym/+RZO/wA+U1MVrMulSFnXFSlPh9lXKvMzIUxT5V/s2HHXxkquFisFsq+6hKm7JKb7VM36ZaZqFkIEUfGWv7Da57jrISX5f/gmj9uTfDHWRFVqaLZNz9XzuMtFzuw+vOTdwj6TkMckXaySr1jzol+TsRY6hq9Qc80nYSEdiv7lowyHNmzFMg12nH6dTCTOyeEMOztCMzGR70hUq0p2ePRRCtE4yb/4LM29HXg6OxUxv/CCqoR37Jh3t2ShygvnJzg+lJk3q286C2NzS5A9nWF+dCkx736eemjR9O/Tur+ha7MnhBuAlLLhFua2iI+pQpXPvjLAA5uaOLChE3dHB3bNgiuX4R/+QW2gbdyonB6VitJ1BwbUyuDIEZUQF4ko3fcuHCm/Frip3456gtobwGbgD6WUr8y5+iPA96WUq0+QudsxPk5Lahy2b+RjnRX+7s0hKkAq2kLrxBBX8xkKAdW9Zeoaj2xr5siWOJ0R78wX9YdnJ/jM0YGZhxzp3ETHSD/+fIaCP4jmOIQySVKxVjXK5hoY69iIrZvsOP0qtUqJZKH1nUvAqZSSGp56CrZuxf/iixwvWHzzrXF+8qBLEWJrq4pknEPAjiP5D/9wisd2tHKwN0qqWKNYtXh0WwuPbZ9NQHtoc3wRAT+4uQkhBJbt3LDdrlyzKddsQh4TR0ouTRYYSZfY1xNpiMyzpRrZssXVZJFTI1m+c3qcf/nkNuXBPn9eSQzNzeryzDPqTpal7IXVqpIfrpWuto6GcFO/HVJKG7hXCBEBviyE2C2lfLt+9U8Df7rcfYUQvwL8CkBPz9p3Rb0rMDk5s1vfHHSzpzPM+fEc2bKFFEKRaJ2Ad3WE+MR9i9/Hy1Pzg2nK3gDH9j/Chv4zNE+OYOs6A73bGOzZ1pBtabK1m6I/xLYzr9cN+5tW1FJvKSxLbRI1Nyu3w/SGW7FIPBakPFTmq28N84lDPbhdLrWzX8dYpsRLF6d4elcbG5sDtIfcPLGjjVLVJuybr3f2xf385KFuvvDaIIauAvUf3BRnKl8hugbV7limTFvYg6YJNATb2oJsa1ve47sQIa+Jz9RJ5Kv43Qblqo1lO2TLVSarOr2JJF7TgK1bZ+9kGGqTch1rhlvyzZBSpoUQzwI/BrwthGgC7kNVwcvd51PAp0D5gG/Fcd5xWDA6ZkOTj6jPxXfPjIOUM6OFAMrWooh0LNthILE4GawQCHN692E1AWM6eOY6UAiEOXbgEYKVMf7R2CjC72+oLTdZqBBbpktrTTBd9R4+rJbPc08MfX1sPHuOsNfgYG9MBdpMTMwLjIkH3HzsgGokqNkOZr2KdRmLq1mPqXN/X4yxTJknd7bSEfGSK9d4oz/F4Y1N6K7l1T3HkXzt5ChTuQof3tc5oxvPrZx74/5l798odnaE+OC97ezqCLO5JTjjjT7XvYOu3k7wmjibt1Ct2Tdkk1zH8rhpBFwPba/VydcLPAH8Xv3qjwNfk1KWl32AdayM7m5FwPWRM+1hD18/eQXdqiGFRjo6uyHSF1+8yWPoGr/wUB/JQoXhVImvvjUy7/rVTlZW99V5ztvJex7cT9/J11Q13NJyTd/ptIa65qhU1GphYdU7F319GFs289NcxLZziJG8ilA8OOulnysbmA1ICBGfi597YAPFqsUrlxNsaPKztS3IX/7oClta1Sy7hbg0kWMqX2UoVWRne4iBRAFDF4Q85rJj2lYLTQg+dO9sWpsQAp/b4MDmVr43YFLKObz99bOEvSb7uiM8sq1lyZPNOlaPm1kBtwN/WdeBNeALUsqv1a/7BKrDbh03gkBAbYY8/zz4/WheL+21AulMljM7D81LMivVFlfAoJbKfXE/+3sko5kyr15JrukhvpYR9H34w2rk+htvqKoyGFTktqCyvp4GhHy5hs9lLH+f6QCYXE5plQ89pKYvLJBDBpNFWkMeXIYBTz+Na8+e2S6vG2wpLlVt/uSFyzy9qw0NaAm6EELwS0f6GFow561Ytfjs0aucGc1yeFMTv/KejYt0YnONyW/h41+cyPGd0+O8OZCaR/aW7TCcLlG1nXUCXmNcsxX5nYJ3eyvyDWNsDE6dgmSSQc3LZ3IBRt1hmoNu+usab0vIw289s52QZ3lvpu1I/uD7Fzg1nFn2NkLAjvaQytVGYjmSoVSJcnVpggf42cMbeHR7i7IxDQ/DW2+pwBbTVNXoWvpFazXVGGBZlFtayW3fTfP2TUt2tl0Yz/F/ffsce7vC/I+PbVm7Y6gjka9wNVlkR3sIt6HNcyhMOxYmc2XeGkxjO/CdU2O8f287j+9oXfNjWQmvXknyJy9cxqk3hChnRJSN8QAhr7HqgPi7FWvViryOOwFtbTPjYl57c4hf39WKJgSWhH/31bfJFOsdbCsEpema4OldrcsS8ObWAL/26OZFJH70coI/ef7yso979HKCI1vimLquDPo9PUqPPXcO3n5bbYyBkiemx+gIsbKtSkrVBFAsqp9CKKLdswe2baPo8nNhNEvzHPJN5CvomiDic+Fz6/zYrjYObIjw+985x2M7Wrn3GrPXVsLz5yf5/plxfvWRzfXGDDdNgaUll+nXdWEiz1uDaQ5vbOI3ntxKd+zW5WQUKhbHh9IUKjZvXk3RE/Mxki7xsf1dPLHz1p8E7kasE/C7DId6o5RqDn9/fIRfeKiPf/v+nfz5S1f42cMbFu3UL4Wd7SG2twc5Ozq/vVkI6Ax7l6yg93aFifldJAvVeX83dY3HtrfQHfMu1kyjUbUZduCAaonNZJQnd2REXYB51Cvl0puB0ajyqra3K09qJDJTUceABzcrHTyRr/CDsxPsaA/x5WPDRLwmJ4cz/M6Hd9MSdPPR/V03vNF0ZjTLUKrEH3z/PM/saefI5viKleODm+I8uOn2NC+cGMrwZy9c4bee2c6TdcK9Hi/xOm4c6wT8LkN3zE/VcjhSJ56o38UvvaePcIMbXEIIPrq/iy+8NsjFidl5b0/vauPjB5eOEvSaOh0R7yIC1jXBI9uaaQldwy9qmrN+02nHQa2mSLlYVLKF48xGIGqaqnJ9PqUjN6jRnh7N8q23x5jMV/jIvk7+5tWr2I7kal0Dvl5XQdVy+NapMV69kuCfP7qFtrCHjx/sxmVovHhhiu+cGudQb+wd7R7Y0OQjHnDTHp4NCJom33UivjVYJ+B3IVyGxpbWWU9oo+Q7jU3NgXmbLaau4TZ1pvIV4kssqaWEx3e0cLA3Om8mnUSublDoNCmvIUbSJfrifhxHcmWqwH19MTbGA+xov7Z39qWLU2xuCdC64CTyvTNqs+qfPryJtrC6LuZ38fMP9mLoGvmy9Y4mX1Ajmn7rme3zJmUMJotoAlpDHgz9xgi4WLE4M5bjwIbbMxnkTsA6Aa9jScztpmoJufGa+pLkC8q9sLcrQrlm8zevXp2ZylGpOUzmKmxouv0fs48f6EJbha2uL+7nh2cnSJdq/Oz9PdiOxNQ1drYHed/utkVVohCCn71/5cahZKFK1GfecJVZqlr8+Uv97GgP8ui2lut+vIVdc50RL6lilbNjOXZ1hFZ9fPlyjc+9epXuqI9i1Vp5BNRdinVPyTqWREd4tuL7uQd6eXjryjqlx9T53Q/vZm/X7EbWH3zvApO56x/LvtZYDfmCqhJ/8mA39/fFqNqSsM+Fz23QGw8sS05CiBWJK+Z3rZrcbEfyVy/388U3hhjLlmkOuDl2NX1DFsJ0sUq5ZqNpgv/7u+f5L989z1uD6VU9luNIXu9PMZAo8g8nRpaN4FzHegW8jmXQEpqtdrPlGm6jsbSuqN/NTxzs4tRIBtuRZEo1/u1X3uaBTU389H09t8RHutYTJTRNsK/n9i6jk4UqIY+BoWsUqhZuU+ObJ0cp1Wye2dVGyGuQKKz+RBfxuShWFVH+xhNbGUoV2d3ZwOToJSF577Zm9m2I8oXXBtek9frdinUCXscijGXK/PFzs7ay8cz1NSzGfCa7wkGEAAAb90lEQVQf2tfBvu4obw2m+dIbQ5wczvBzN6gpNoqbNVHidiLiNWdeV8hj8r497dgOtIbcNAXVyTLiu/427mLVQiDwuvQZmaA56KY5uPqW8OnVRthr8oG97dyi//Y7EusSxDoWoS3s4cP7OjnYG0PTBF87MXpd9//B2QkKFZuOiJfHtrega4J0scpnX7k6U2W9m+E4kh+em+A7p8ZYq0anhSeVkMfkk/f3zGvaWM2mn89lqFD6a2A4VWQ4XVrVwNf2iLch+edOaAi7GVivgNexJJ7Zo0aH908V+OIbQ9d13/fv7Zj5QnlMnV97dDMvX0oQ9ppcGM9zzw00O9wJ+IuX+/n5Bzdw9EqSv319iI8f7Lotlq6aZWMaN+7EGEqV+Hw91W1zc+DatsLrgJSSYtVW+coXpvhkA5uX7zasE/A6rokNTT52diwfxL4UFmqw93RH3vWkOxcf3d/JaKZCzOfiz164QkvIzSPblp4IfHEihxBizSdiSCnR1oj079/YxO7OMC9enOK/P3eZJ3a2rLp5RNazi587P8nxwTSF+gbdvd2Ru9J7vE7A67gmhBAz1XAjSKYL/Olffo+fiVXojHjVjLVNm1Y3ffcORchjcHo0y8WJPEKwrH2vXLP585f6SRWqPLOnnfdua75mVsf1QAiBrgsS+QrFqn3DLc5+t8GTO1p5eEszhi6oWtcXzFO1HN4aTPPd02NcnpwfgSqEmAmrv9uwTsDrWDtUKpz9k8/RfuICr7U20by3A9dzz8GJE/DjP65yHu4CaJrGg5vi3Ncb49FtLcuS37Rtb5rMbgYBuQyNqXx1TapLTRMr6sULIaXkb18f4gdnJ6jZS4eRhLwGezrvnhXSXKxvwq1j7XDiBAe9VdLxNq5aJp8+keC4DGCnM/Daa7f76G45DF1bsfIUQuA2dTKlGo4jqVorJCZdJ4Iek21tQXJli8oykaRrieOD6Rn9P1eu8ZmjA3z71Niy5Avwzx/dfNfGXK5XwOtYG0gJJ09SjDahi1EsKalYDs+dn2Qk7uXJM2cxHnroXTmqPFuuYWoC7w10e/lchgpzu0kWupB37d/3fMViNF1iMl9hx//f3rkHx12e9/7z7EW7knZ1v1i2bMu2bGwgYGNxJ8EYmkMIhSSQczohbZjTHia0k9Okk7Zpe9qk7emZps20J03b09JJTpgOTYEQaMItmCSEmAQbMDYO8RUbX2TJkiWt7ivt5ekf70+WLLS6r3YlP5+ZHe3+bvs8u6vvvvu8z/s8y6IEfXI+E6MlNsD/ee4QA1MswlhWGmZ15cXxy2giLs6vHWP+SaVgeJi+pJBMj6YUrasuZuuaKtLptCuyswQpCQfnXPdhJASRLQHOBqc6BzjZOUBsIMFgIkVRKMDa6mJEhOMdA1OKL8Anrl21qHyeb2wEbMwPgQBUV1M34IreHPcKwdeVFVITVCgtWdJddC/GCaRNdSVsqrswQ+Zcb5ydR87x4oGzk54bCvr41PUNrK+ZfiPRhSQ2MExp4dxrdUyFjYCN+aOpCX93jA9fUsndm5dz3doKQpJ2bYiamlwpSWPJoqq8cuQcz7zVcr4gUyY+tqWea9dW5mXsV1V5+q0WhpLprC8QyZr3IhIWkd0isk9E3haRP/W2i4j8hYgcFpEDIvI/s2WDscCsXg233oqvO8bqeIyr/ANcFhh2fesuuSTX1i1Z8mUVmYjwS5fWcv+NDVMee6JzIG/sHo+I8MnrVgMwNM+TouPJZghiCNiuqn0iEgR2ishzwCZgJbBRVdMiMnGGurE42bTJ5f22txNQhaqqJR16yAe6+ocoLSzAP41OzdkmEg5yU2MV//qzE6TSFwpsVSTEnVfWUVca5lzfcN6HbRainnPWBFjd19tIS4Wgd1PgQeATqq5Dmaq2ZcsGI0cUFMCKFbm24qKhIhLOq9HkYCL1HvHd2lDOpz+w7vwKyUYbdgFZjgGLiF9E9gJtwA5V3QWsA/6biLwuIs+JyITtaEXkAe+Y19vb27NpprHAzHeuq5E/qCpvnOi6YFskHODXrm9YklXq5kpWBVhVU6q6GagHrhGRy4EQEPdaNv8L8I0M5z6kqk2q2lQ9z+1pjNxSEPC5lLSeHtfrzZgz+fJzPpVWvv926/nHfp/wm9saiYQs4WoiFiRopKox4CXgduA08IS360ngioWwwcgjenuJP/IteOQRePbZ0bb0RtZR1WmHKzr7XZeMmRDw+/jA+mpE4LLlJfzhHZu4ZFl+pprlA9nMgqgWkTLvfiFwG3AQeArY7h12M3A4WzYYecrRo5w53c6LPUE4eRLOTp4zalzIXEI4Q8k0g8NTi+rgcIp/23ViVs9xQ2MVX/zly/jsbRtm3G36YiObvwvqgIdFxI8T+sdU9WkR2Qk8IiKfw03S/UYWbTDykeJiVkeDdPYNgAShsHDqc/KJZBK6u6G3160AFHETjyUlEI26x1kkmKHFxOBwcsrl0NOd2S8I+CgI+Ogfmnl350goYCGHaZLNLIi3gC0TbI8BH87W8xqLgMZG/Dd/gKbmZti4ESoqcm3R1KRS0NwMe/fCmTNOZNPp94ptMOhynjdudCl4WSBTvHcutSjG88Qbp3m3Y4CKYuvnlk3sa8pYeHw+2LzZ3fIdVTh+HF5+GQYG3Ci3ri7zqr5kEg4edCU46+vh/e+H8tw29JwNO4+e464rl+fN5N5SxQTYMDIxMAA7d8KRI1BdDZWVU58TCECt16etowMefRRuuAEuv3zRLMVu64mTVr2oi+QsFCbAxqLgrdMxwkE/G2oXaEa9txeefhr6+mDlytnFdSsrXbrdT34CsZhbkp3nIqyq/NOPj1FaGGTbJZb+mW3y+9NgXLT0xBPn06XSaeWfXz7Gy4cXaEHOwAB873swNATLls1tUi0YdKGI/fvhlVdcSCOPOdszxImOfj6zfb2FHxYAGwEbeYkPONLWx4baKD6fEAr4ONk5MOV5vfEEqbRSVjTLySNVF++NxaCoyMV/OzudGIMLMZSVQWkpRCLTa7Pk80F9Pbp3L1JXB42Ns7Mtyxxu7eWrPzxCOOgnkCHTwphfTICNvCGRShPwiSvofW6A3njifMhhfU2UX7T0THxiezt0dXGuvIb/9fw7JNNptq6u4MFt62ZuxO7d8PzzMDzsHou4hqJ+v7ufSDhxHlk8UlkJa9e6TA7/JOlaPh+ybBm89JIbVUfmtwvyfODzgeBqOfztjsP8zi9toDJDQ1FjfrAQhJE3BP1eY8pYjNPPvEhH22h/sdqSEIPDKZ588zR7TnbRE0/QF08w1BmDJ58k9dzz/Ogfv0UynUaV88dMm8FBeOEF+NrXIB53mQuVlU5Yi4tdRbdQyOUsl5W5FLPKSheueO01+NnP3NLqyQiH3Qh7z545vErZo7Emer4MY2t3nENne3Ns0dLHRsBG/tHSQt3h/XwrHiYmAYpDAXYd63SFsve1XHBoXTrO1t3v0psWQlUVfOLaVbTE4qysKJx+i/fTp2HHDpfnG4m4jIfpIOLEubjYTdbt3OlygNeuzRw3rq6GAwfgmmvyskzn2NfsBwfauLSuZPbhHGNKTICNvCPRuJ7HN23jnL+Ilw5NPvHW4guza30TZbF2musb0T3NXFIb5X31pbx0qI1UWrl1U23mC7zzjgs5VFVBf/+sQgMDw0nOxoVVZWX4Dx50I+hNmybOePD7R3OLN22a8XPNF4lkChEhMKaGcDKV5vUTnQDc2FjFPVvrKc1CM09jFBNgI+9IKBTUVkPH1JNuAO21K2mvXekeDKfYeyrG3lMxABqqitm+sWbiGf2TJ5341taSSKfpONlKX6QMX18/0XCASNhPQHx0DQ5TWVxAbDDJcDLFspLwBdcL+HwUh/z4/H4Xlnj3XSe0GzdObHAkklMBjvUPEU+kOdbRT89gktjAMB/dsoKCgI+rVpXTPZDgv1690pYTLwD2Cht5xytHOzg5TfGdDuPFV1VJ9PQhz79ALFxC89lBDh06QXVLL/2RzNMiIm7wur4mysa60XzkgoCPmmh49KDKSjeyrqiAmgkqjxcXQ2uru9gMUr2SqfQFI9bZEi4I8Mju4+w50cWtm2rZdkk1Ia/ew+UrSmmoKjbxXSDsVTbyjhMdrqOyP5mkuD9G2hegL1I6q3zc5q5Bvr7zONFwgJ7BBO929NPWHeeyA7spaWuht8TVoSjt7sc1bMnMSApvbHCI7sEwybQyOJykuCBAtDCAqquHm1JFQ0VE9u93S5ELRmOow8kUbb0JpK0bbe5Ai4qoLy+cVs5tS/cg1ZEQ4TnWfGiNuS+3L911GSsrit6z38R34bBX2sgrkilXLjGQGOKKvTuJ9nYhqjTXN3Jkw+YZi3AileanR89dsK2sq52K5hN0VYzGhn3pFC4Ja2rae4dp7508Nu33CU0lUH3sGH0N6zjQ0sPprkGauwZRoKL9DKf+5bvcVg4rV0RdXvGmTbB8ecZ0tlDQf34E3NodZzCRYs0syj02VEf5rVusRm8+YAJs5BUiwsqKIlJvnSXa20WsohY0zYrmd2ipa6CvZO6FbZY3v8NwqHCcmM/vwoNUWtndmaLwhdc4sFFJ+0f/1co7Wll7bD8VYT+hSAOkvZDE0aMubHH77S7VbRwjYY5n97cQDQe4pqGCc31DVFmu7qLFBNjIK/w+4SNbVnCkpYIzZ0pYvayMRDJNdSRBqKGMqg2rKCsq4Fh7Pz9v7gZcfdxj7f3Tun54sJ/q9mZiZReWikxPtohilqjPjy+ZItrTRXe5S20r7zhL/amjDIWKOB2tYtuaZRAKuFV35eXQ1QVPPQX33EOnL0RH3xC1pWGCPh/hoI8fH27nqTebuf+GBkJB//nYrbE4MQE28pL1113J+tNHQFJu1dmadVz5oS2utgJw1apy7t1aD7haEd95s5nuwQTV0RCxAbeK7VzfML8440S6Ohpy2Qr9bS7UKxdOZg2FwmgWVt8OB0OU9HTQXV6NP5lg+ZljxMNF+NMpGleUU1gwKqB9QwlC0RJoO8ueJ17kO5F1nOtzS6A/fEUdiVSaHb8467LYOvqpLy9kVaV1nFjMmAAb+UlFBdx7r/tZXlDgUrqCE+ek+nxyXozHoqok00o8kSLqLTBo/34bb58sQwJBegaT59unDxcUIsqMMxOmIhksoLjfrZCL9nQimsafTtEXKaWM0QyNdFo52THAT9/pID6UIBo7TveNVVDgwg7PvHXhApSOvuF5LcBu5AZ7B438paLCrRibJSJC0C8Ex6RuVfd2su2qNVBUxHAyxTP7WzjVOUja76enpJyigT6GQoUjF5irB6T9fgKDSQKJYUp6ukgEQxQMD9FTUsnZ1l784qOhqoizPXFePdbpPa8PEIr7e4gVTLxarjgUoDpqsd/FTtYEWETCwMu4NvQB4Nuq+kUR+SauGWe3d+j9qro3W3YYxgX097u+bUBBwM+dVyynZzBBPJlGGgoJ/XAHPW2t9A4M0V1a5WLF8yDEvnQS0TSSTpPyB+iLlpFOKftOx9h3OjbhOZKeOC1ORLh14wT5xcaiI5sj4CFgu6r2iUgQ2Ckiz3n7fldVv53F5zaMiRkXYgj6faMVv3wlMNhLSWUxb6b8lMXaGQqFGSyaW8qWAqIwFCqkuq2ZEw2bJp308wlECnwkgu+twSAi3HVlnXUbXiJksymn4roeAwS9W35XozaWPsGga7AZGP3o98YTREIBZGgIVqyAM2dcz03xEUwMMzibp/ELiZT7uItCWoRUJEppSRHRS9axIlqIKvQPJznXN0R1NMSaqmLqywopTg4Bday9oYlTsTh98SR7T3Vx84Ya1tcU01iTf6UsjdmR1Riw15L+DaAR+AdV3SUiDwJ/ISJ/AvwA+IKqDk1w7gPAAwCrVq3KppnGxURlJZw750pLeoxM0FFcDJEIfYUlFJ4+hfp9o/HgaVATDbOmuoigz0dZUZDBRApBiZ/1cdUNa4l0nSOwbSOXJRJQk6HiWioFZzvg9tvZsrqCLa46JB/fuoJgwFLOlhpZFWBVTQGbRaQMeFJELgf+AGgFCoCHgN8H/myCcx/y9tPU1GQjZyMj3QMJQkEf4enkxK5Y4YrwlJa+d19REVx9Nb3x19GzXRRGi1nTUEtvPMlwKo0gBPwQCQcRIBT0saykkKFEisKgn2DgwtS2ooIAxOMU1pRD7Bzcuh3WrHHtjpqbXWnKMcuU6elx3TeuvRbWXVhMfv+ZHhprItMvsWksChYkC0JVYyLyEnC7qn7F2zwkIv8f+PxC2GAsXUqLZiBKlZVulJmJigpW3f1BuOMW11q+tRXqJ+92EQpMUiAnFnNif8stcNllbtvdd8Pbb8O+fa7zhgik065wz513OpEex5aVZdajbQmSzSyIaiDhiW8hcBvwZRGpU9UWcZ+mjwA/z5YNxsVDIpXmyT3NJNJp7t1aTyjTz/XaWjfSHRq6IAzxHoJB2LwZTpyAQ4fcSDUSmX5GRCoF3d2uhdH997u29COEw3RufB+ddWtp8A0TQJ0t5ZmXWZv4Lk2yOQKuAx724sA+4DFVfVpEfuiJswB7gU9n0QbjIiHo97G2upgfHGxjOJnOLMB+P2zZ4loILV8++UX9ftfdorraiXBbmyuyXlzsBHm8KKZSTtgHBty5NTWuQ8ZY8fWoKC6gotg6TVzsZDML4i1gywTbt2frOY2Lm6aGCq5aVU5qqtbvjY2uj1s8Pr22QNEoNDW5HOIzZ5wQd3WN1qccEWKfzy0eWb/eddhobYUbbpibU8aSxlbCGUsKn0/wTVXZrKgIbr7ZdcNYtWr6YYXiYieu69e70e7goIvdgktrKxxTYa2lBS691F3fMDJgAmxcnKxbBxs2uPZBdXUzP9/vz9w/rqfHxXSvv35OJhpLH2tLb1yciMC2bS5O29o6f9ft6XEj4zvvdCNiw5gEE2Dj4qWgAD70IVi2DE6dcmUvZ4uqiw0nEvDRj7p0N8OYAgtBGBc3oRDccYfL+X31VTdqraiYWQGegQG3um7DBjfpVmx1GozpYQJsGCOpaatWwa5dLvfX73dtgcLhicV4eHg03FBW5kbSa9bMay1hY+ljAmwYI1RWutFwdzccPgzHjrm0s/GiqupGyg0NrpFmba0JrzErTIANYzylpXD11e6WSIyuaFN16WbRqE2wGfOCCbBhTEYw6BZVGEYWsCwIwzCMHGECbBiGkSNMgA3DMHKECbBhGEaOMAE2DMPIESbAhmEYOcIE2DAMI0eITlW8Og8QkXbgRA6eugo4l4PnzTZL0S/zaXGwFH2C9/q1WlUztL4eZVEIcK4QkddVtSnXdsw3S9Ev82lxsBR9gtn7ZSEIwzCMHGECbBiGkSNMgCfnoVwbkCWWol/m0+JgKfoEs/TLYsCGYRg5wkbAhmEYOcIE2DAMI0eYAHuIyEoR+ZGIHBCRt0Xkt73tFSKyQ0SOeH/Lc23rdJnEp78WkYMi8paIPCkiZbm2dbpk8mnM/s+LiIrIoiniO5lPIvIZETnkbf+rXNo5Uyb5/G0WkVdFZK+IvC4i1+Ta1ukiImER2S0i+zyf/tTbvkZEdnk68aiIFEzrgqpqNxcHrwOu8u5HgcPApcBfAV/wtn8B+HKubZ0Hnz4IBLztX14KPnmPVwLfxy3aqcq1rfPwPt0CvAiEvH01ubZ1nvx6AfiQt/0O4KVc2zoDnwSIePeDwC7gOuAx4Fe87f8EPDid69kI2ENVW1R1j3e/FzgArADuBh72DnsY+EhuLJw5mXxS1RdUdaQH+6tAfa5snCmTvE8Afwv8HrCoZpYn8elB4C9Vdcjb15Y7K2fOJH4pUOIdVgqcyY2FM0cdfd7DoHdTYDvwbW/7tHXCBHgCRKQB2IL7dqtV1RZwHyigJneWzZ5xPo3lvwPPLbQ988FYn0TkLqBZVffl1Kg5Mu592gC83/tp+2MRuTqXts2FcX59FvhrETkFfAX4g9xZNnNExC8ie4E2YAfwDhAbM6g5zeigYFJMgMchIhHgCeCzqtqTa3vmg0w+icgfAUngkVzZNlvG+oTz4Y+AP8mpUXNkgvcpAJTjfuL+LvCYyOJrvzyBXw8Cn1PVlcDngK/n0r6ZoqopVd2M++V4DbBposOmcy0T4DGISBD3QXlEVb/jbT4rInXe/jrct96iIYNPiMingDuB+9QLXC0WJvBpHbAG2Cci7+L+MfaIyLLcWTkzMrxPp4HveD97dwNpXNGXRUMGvz4FjNx/HCdiiw5VjQEv4b4gy0RkpMlxPdMMq5gAe3gji68DB1T1b8bs+i7uA4P39z8W2rbZksknEbkd+H3gLlUdyJV9s2Ein1R1v6rWqGqDqjbghOsqVW3NoanTZpLP3lO42CIisgEoYBFVEpvErzPAzd797cCRhbZttohI9UjWkIgUArfhYts/Au71Dpu2TthKOA8RuQn4CbAfN9IA+ENczOoxYBVwEvi4qnbmxMgZMolPfweEgA5v26uq+umFt3DmZPJJVZ8dc8y7QJOqLgqxmuR9ehH4BrAZGAY+r6o/zImRs2ASv3qAr+JCLHHgN1X1jZwYOUNE5ArcJJsfN4B9TFX/TETWAv8OVABvAp8cmTyd9HomwIZhGLnBQhCGYRg5wgTYMAwjR5gAG4Zh5AgTYMMwjBxhAmwYhpEjTICNvEBE+jJs/6mIlInIsxPtX0xk8tG4eLE0NCMvEJE+VY3k2o6ZIiJ+VU1N89hF6aORPQJTH2IYuUFE7sYl7o+sAPukqp4VkS/hFsas9f7+X1X9O++cPwbuA05557yhql8RkXXAPwDVwADwP1T1oIh8E7cwoAlYBvyeqn5bRHzA3+NWbB3H/Vr8hrfvXdwCiQ8Cfy8iUeABz86jwK+q6oCIrAH+Dfd/9nz2XiljsWIhCCOfeRm4TlW3AI/iSk2OsBH4L7g6Al8UkaCINAH34KpufQwnqiM8BHxGVbcCnwf+ccy+OuAmXG2Mv/S2fQxoAN4H/AZw/Tjb4qp6k6r+O65ew9WqeiVuWeqve8d8Ffh/qno1sCiWRRsLi42AjXymHnjUK4JUgBuJjvCMt9RzSETagFqciP6Hqg4CiMj3vL8R4Abg8THFxEJjrvWUqqaBX4hIrbftJuBxb3uriPxonG2Pjrl/uYj8b6AMiOCKwgPciPtCAPhXXPF7wziPCbCRz3wN+BtV/a6IbAO+NGbf2HX2KdxnOVOpRh+uXuvmDPvHXkvG/c1E/5j73wQ+oqr7ROR+YNuYfTbJYmTEQhBGPlMKNHv3PzXZgR47gV/2+nZFgA8DeDVoj4vIx8FV6RKRK6dxrXtExOeNirdNcmwUaPFKL943ZvsrwK949+97z1nGRY8JsJEvFInI6TG338GNeB8XkZ8wjTKMqvoarnzoPly92deBbm/3fcCvi8g+4G1cq6nJeAJX1vLnwD/jquJ1Zzj2j739O4CDY7b/NvBbIvIa7svEMC7A0tCMJYWIRFS1T0SKcJN4D4z0JZvDtSqB3cCNi6XGsLE4sBiwsdR4SEQuBcLAw7MVX4+nveLbBcCfm/ga842NgA3DMHKExYANwzByhAmwYRhGjjABNgzDyBEmwIZhGDnCBNgwDCNH/CcTVq3LWaZuTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GRC_map.plot(alpha=0.7)\n",
    "ax = plt.gca()\n",
    "ax.scatter(locations_greece['Longitude'], locations_greece['Latitude'],\\\n",
    "          s=locations_greece['Sherd_Count'],color='red',alpha=0.3,\\\n",
    "          label='Anzahl')\n",
    "ax.set_xlabel('Längengrad')\n",
    "ax.set_ylabel('Breitengrad')\n",
    "ax.set_title('Anzahl an Fundstücken in Griechenland')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E.** An welchen Orten wurden die meisten Fundstücke gefunden? Woran kann das deiner Meinung nach liegen?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F. (optional)** Darstellen der Fundorte im gesamten Mittelmeerraum:\n",
    "* Finde heraus, in welchen 10 (modernen) Ländern die meisten Fundstücke gefunden wurden\n",
    "* Lade die shape-files dieser Länder herunter.\n",
    "* Stelle die Landkarten dieser Länder in einer Darstellung dar  <font color='green'>**HINWEIS:** Du kannst der Funktion ```plot()``` mit dem Argument ```ax=``` eine spezifische Achse mitgeben, auf der die Darstellung geplottet werden soll. </font>.\n",
    "* Stelle alle Fundorte in diesen Ländern mit Kreuzen dar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# leere Liste\n",
    "mod_countries = []\n",
    "\n",
    "# wir iterieren über alle Location IDs\n",
    "for loc_ID in catalogue.Location_ID:\n",
    "    if pd.isna(loc_ID): # wenn keine ID verfuegbar ist, packen wir nan in die liste\n",
    "        mod_countries.append(np.nan)\n",
    "    else:           # sonst den passenden Wert aus der Formen Tabelle\n",
    "        mod_country = location[location['Location_ID'] == loc_ID]['Modern_country']\n",
    "        #print(mod_country.item())\n",
    "        mod_countries.append(mod_country.item())\n",
    "        \n",
    "catalogue['Modern_country'] = mod_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>Modern_country</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Greece</td>\n",
       "      <td>11726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>7247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Israel</td>\n",
       "      <td>4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Syria</td>\n",
       "      <td>3926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Libya</td>\n",
       "      <td>1281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>1127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Macedonia</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Modern_country  count\n",
       "1          Greece  11726\n",
       "4          Turkey   7247\n",
       "2          Israel   4179\n",
       "0           Syria   3926\n",
       "7          Cyprus   1867\n",
       "9           Libya   1281\n",
       "15        Tunisia   1127\n",
       "5           Egypt    724\n",
       "20      Macedonia    551\n",
       "8          Jordan    504"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_counts = []\n",
    "\n",
    "for country in location.Modern_country.unique():\n",
    "    country_mask = catalogue['Modern_country'] == country\n",
    "    count = len(catalogue[country_mask])\n",
    "    country_counts.append(count)\n",
    "    \n",
    "country_counts = pd.DataFrame({'Modern_country':location.Modern_country.unique(),\\\n",
    "                              'count':country_counts})\n",
    "\n",
    "# die 10 Länder mit den meisten Fundstücken sind Griechenland, \n",
    "# die Türkei und Israel\n",
    "country_counts.sort_values('count', ascending=False, inplace=True)\n",
    "country_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ['GRC', 'TUR', 'ISR', 'SYR', 'CYP', 'LBY', 'TUN', 'EGY', 'MKD', 'JOR']\n",
    "countries.extend([np.nan] * (len(country_counts) - len(countries)))\n",
    "country_counts['Country_code'] = countries\n",
    "country_dict = {}\n",
    "\n",
    "for c in country_counts['Country_code'][0:10]:\n",
    "    filepath = '../daten/gadm36_{}_shp/gadm36_{}_1.shp'.format(c,c)\n",
    "    country_dict.update({c: gpd.read_file(filepath)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GRC_map.plot(alpha=0.7)\n",
    "ax = plt.gca()\n",
    "\n",
    "for c in country_counts['Country_code'][1:10]:\n",
    "    country_dict[c].plot(ax = ax, alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location['Modern_country'][0] in list(country_counts['Modern_country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "for c in country_counts['Country_code'][0:10]:\n",
    "    country_dict[c].plot(ax = ax, alpha=0.7)\n",
    "\n",
    "for i,row in location.iterrows():\n",
    "    if row['Modern_country'] in list(country_counts['Modern_country'][0:10]):\n",
    "        ax.scatter(row['Longitude'], row['Latitude'], marker='x', s=10, color='red')\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}