{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d91e383b",
   "metadata": {},
   "source": [
    "# 💻 Overlay\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GMGI221-2024/forelesninger/blob/main/07_overlay.ipynb)\n",
    "\n",
    "Overlay-analyser er GIS-operasjoner der to eller flere vektorlag er\n",
    "kombinert for å produsere nye geometrier. Typiske overlay-operasjoner inkluderer *union*,\n",
    "*intersection*, og *difference* - navngitt etter resultatet av kombinasjonen av to lag. I denne notebooken (inspirert av [Python for Geographic Data Analysis](https://pythongis.org/part2/chapter-06/nb/08-overlay-analysis-with-vector-data.html)) ser vi nærmere på hvordan vi kan utføre overlay operasjoner med vektor data.\n",
    "\n",
    "Den grunnleggende ideen med vektor-overlay-operasjoner er demonstrert i figuren under, hvor de grønne områdene representerer områdene som utgjør resultatet etter overlay-operasjonen. Det er viktig å huske at overlays opererer på GeoDataFrame-nivå, ikke på individuelle geometrier, og egenskapene fra begge beholdes (ofte, men ikke alltid). I praksis utføres denne operasjonen for hver form i den venstre GeoDataFrame mot hver annen form i den høyre GeoDataFrame.\n",
    "\n",
    "![Fire paneler som viser union, intersection, symmetric difference og difference av to geometrier.](https://pythongis.org/_images/vector_overlay_processes.png)\n",
    "\n",
    "\n",
    "Romlig overlay med to inputvektorlag (rektangel, sirkel). Det resulterende vektorlaget vises i grønt. *Kilde: [QGIS dokumentasjon](https://docs.qgis.org/latest/en/docs/gentle_gis_introduction/vector_spatial_analysis_buffers.html#figure-overlay-operations)*\n",
    ":::\n",
    "\n",
    "For å demonstrere hvordan disse overlay-operasjonene fungerer i praksis, vil vi utføre vektor-overlay-operasjoner mellom to polygon-datasett som representerer 1) bydelene i Oslo og 2) en 5000-meter buffer rundt Oslo S. La oss starte med å lese inn datasettene og forberede dem for analysen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "78145314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 6)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas\n",
    "import pathlib \n",
    "\n",
    "NOTEBOOK_PATH = pathlib.Path().resolve()\n",
    "DATA_MAPPE = NOTEBOOK_PATH / \"data\"\n",
    "\n",
    "bydeler = geopandas.read_file(DATA_MAPPE / \"oslo_bydeler\" / \"oslo_bydeler_befolkning_2024.geojson\")\n",
    "oslo_s = geopandas.read_file(DATA_MAPPE / \"oslo_bydeler\" / \"oslo_s.geojson\")\n",
    "bydeler.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "018bf967",
   "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>navn</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POINT (10.75040 59.91170)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     navn                   geometry\n",
       "0  Oslo S  POINT (10.75040 59.91170)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oslo_s.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cb0580",
   "metadata": {},
   "source": [
    "Her kan vi se at bydelene inneholder MultiPolygon-geometrier som representerer til sammen 17 bydeler, mens `oslo_s` representerer et enkelt punkt for Oslo S. Siden vektor-overlay-operasjoner skjer mellom to geografiske datasett, er det nødvendig å sikre at de begge deler samme koordinatsystem og i vårt tilfelle et projisert koordinatsystem, siden vi skal bruke en metrisk buffersone.\n",
    "La oss derfor først sjekke crs-attributtene og gjøre nødvendige reprojiseringer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2ae1d28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "print(bydeler.crs)\n",
    "print(oslo_s.crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf584d62",
   "metadata": {},
   "source": [
    "Begge datasettene våre er i `EPSG:4326`, la oss reprojisere til `EPSG:25832` som egner seg for norske data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c1349a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reprojiser bydeler\n",
    "bydeler = bydeler.to_crs(\"EPSG:25832\")\n",
    "\n",
    "# Reprojiser oslo_s\n",
    "oslo_s = oslo_s.to_crs(bydeler.crs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ba7476",
   "metadata": {},
   "source": [
    "La oss for sikkerhetsskyld dobbeltsjekke at lagene har samme crs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "15e0379c",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert bydeler.crs == oslo_s.crs, \"Ulike koordinatsystemer!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828e8c85",
   "metadata": {},
   "source": [
    "Supert, koordinatsystemet (CRS) stemmer overens mellom lagene. Derfor kan vi fortsette og lage en 5 kilometer buffer rundt Oslo S som vi vil bruke i våre vektor-overlay-operasjoner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ea92f5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "oslo_s_buffer = oslo_s.copy()\n",
    "oslo_s_buffer[\"geometry\"] = oslo_s_buffer.buffer(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "632d6534",
   "metadata": {},
   "source": [
    "Her har vi først lagd en kopi av `oslo_s`-laget vårt, og brukt `.buffer()`-metoden til å lage en buffer Polygon på 5000 meters radius rundt Oslo S. Vi kan nå lage et enkelt plot for å se hvordan lagene overlapper hverandre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a8dec5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = bydeler.plot(facecolor=\"blue\")\n",
    "oslo_s_buffer.plot(ax=ax, facecolor=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751f8365",
   "metadata": {},
   "source": [
    "## Intersection\n",
    "\n",
    "Nå kan vi utføre en overlay-analyse mellom disse to lagene. Vi skal lage et nytt lag som `intersect`(\"skjærer\") med bydelslaget. Vi bruker da metoden `.overlay()` i GeoPandas til å gjøre analysen mellom laget `bydeler` og `oslo_s_buffer`. Parameteren `how` i `overlay()`-metoden angir hvilken type overlay-analyse som utføres. De ulike metodene man kan velge mellom er `intersection`, `union`, `symmetric_difference`, `difference` og `identity`.\n",
    "\n",
    "Vi begynner med å bruke `intersection` som overlay-metode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "00251672",
   "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>fid</th>\n",
       "      <th>bydelsnr</th>\n",
       "      <th>bynavn</th>\n",
       "      <th>bydel</th>\n",
       "      <th>befolkning_2024</th>\n",
       "      <th>navn</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>030101</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Gamle Oslo</td>\n",
       "      <td>63721</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((594299.813 6640496.194, 595053.786 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>030102</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Grünerløkka</td>\n",
       "      <td>65532</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((598106.885 6643240.277, 598165.757 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>030103</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Sagene</td>\n",
       "      <td>47627</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((599000.023 6645042.516, 598989.131 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>030104</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>St.Hanshaugen</td>\n",
       "      <td>41571</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((597929.403 6643709.551, 597936.146 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>030105</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Frogner</td>\n",
       "      <td>60727</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((593123.834 6643198.372, 593664.254 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>030106</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Ullern</td>\n",
       "      <td>35440</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((592894.411 6643050.452, 592909.701 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>030107</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Vestre Aker</td>\n",
       "      <td>52590</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((595764.880 6647391.522, 595759.932 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>030108</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Nordre Aker</td>\n",
       "      <td>55056</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((599513.141 6646064.846, 599498.522 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>030109</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Bjerke</td>\n",
       "      <td>36450</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((601049.467 6644203.445, 601046.649 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>030112</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Alna</td>\n",
       "      <td>50358</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((601433.698 6642738.788, 601426.332 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>030113</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Østensjø</td>\n",
       "      <td>51895</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((601656.376 6639614.618, 601620.452 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "      <td>030114</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Nordstrand</td>\n",
       "      <td>54027</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((596941.033 6638803.261, 596901.883 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>030116</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Sentrum</td>\n",
       "      <td>0</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>POLYGON ((597652.231 6642995.353, 597659.927 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fid bydelsnr bynavn          bydel  befolkning_2024    navn  \\\n",
       "0     1   030101   Oslo     Gamle Oslo            63721  Oslo S   \n",
       "1     2   030102   Oslo    Grünerløkka            65532  Oslo S   \n",
       "2     3   030103   Oslo         Sagene            47627  Oslo S   \n",
       "3     4   030104   Oslo  St.Hanshaugen            41571  Oslo S   \n",
       "4     5   030105   Oslo        Frogner            60727  Oslo S   \n",
       "5     6   030106   Oslo         Ullern            35440  Oslo S   \n",
       "6     7   030107   Oslo    Vestre Aker            52590  Oslo S   \n",
       "7     8   030108   Oslo    Nordre Aker            55056  Oslo S   \n",
       "8     9   030109   Oslo         Bjerke            36450  Oslo S   \n",
       "9    12   030112   Oslo           Alna            50358  Oslo S   \n",
       "10   13   030113   Oslo       Østensjø            51895  Oslo S   \n",
       "11   14   030114   Oslo     Nordstrand            54027  Oslo S   \n",
       "12   16   030116   Oslo        Sentrum                0  Oslo S   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((594299.813 6640496.194, 595053.786 6...  \n",
       "1   POLYGON ((598106.885 6643240.277, 598165.757 6...  \n",
       "2   POLYGON ((599000.023 6645042.516, 598989.131 6...  \n",
       "3   POLYGON ((597929.403 6643709.551, 597936.146 6...  \n",
       "4   POLYGON ((593123.834 6643198.372, 593664.254 6...  \n",
       "5   POLYGON ((592894.411 6643050.452, 592909.701 6...  \n",
       "6   POLYGON ((595764.880 6647391.522, 595759.932 6...  \n",
       "7   POLYGON ((599513.141 6646064.846, 599498.522 6...  \n",
       "8   POLYGON ((601049.467 6644203.445, 601046.649 6...  \n",
       "9   POLYGON ((601433.698 6642738.788, 601426.332 6...  \n",
       "10  POLYGON ((601656.376 6639614.618, 601620.452 6...  \n",
       "11  POLYGON ((596941.033 6638803.261, 596901.883 6...  \n",
       "12  POLYGON ((597652.231 6642995.353, 597659.927 6...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intersection\n",
    "intersection = bydeler.overlay(oslo_s_buffer, how=\"intersection\")\n",
    "intersection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ace528",
   "metadata": {},
   "source": [
    "Vi kan nå se at vi har en GeoDataFrame med 13 rader for alle bydelene som intersecter med bufferlaget vårt. Vi kan også se at vi har med kolonner fra begge datasettene, og dermed at overaly-metoden fungerer litt likt `sjoin()` som vi brukte i en [tidligere forelesning](#05_romlig_kobling).\n",
    "\n",
    "For å lettere illustrere hvordan de ulike overlay metodene fungerer, lager vi nå en funksjon som plotter et kart før og etter overlay-analysen ved siden av hverandre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6c76cc32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_overlay(gdf1, gdf2, result, title):\n",
    "    \"\"\"\n",
    "    Creates two maps next to each other based on `gdf1`, `gdf2` and the\n",
    "    `result` GeoDataFrames.\n",
    "\n",
    "    Source: https://pythongis.org/part2/chapter-06/nb/08-overlay-analysis-with-vector-data.html#intersection\n",
    "    \"\"\"\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))\n",
    "\n",
    "    ax1 = gdf1.plot(ax=ax1)\n",
    "    ax1 = gdf2.plot(ax=ax1, color=\"red\", alpha=0.3)\n",
    "\n",
    "    result.plot(ax=ax2)\n",
    "\n",
    "    # Fetch bounds and apply to axis 2\n",
    "    xmin, ymin, xmax, ymax = gdf1.total_bounds\n",
    "\n",
    "    ax2.set_xlim(xmin, xmax)\n",
    "    ax2.set_ylim(ymin, ymax)\n",
    "\n",
    "    fig.suptitle(title, fontsize=16)\n",
    "    # Add an arrow between the plots\n",
    "    fig.text(0.49, 0.5, \"⇨\", fontsize=30, color=\"red\")\n",
    "    ax1.axis(\"off\")\n",
    "    ax2.axis(\"off\")\n",
    "    plt.tight_layout()\n",
    "    return fig, ax1, ax2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9787ec7c",
   "metadata": {},
   "source": [
    "Med denne funksjonen kan vi nå bruke `intersection`-laget vi nettopp lagde og plotte før- og etter-resultatet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "01d7dcd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1, ax2 = plot_overlay(\n",
    "    gdf1=bydeler, gdf2=oslo_s_buffer, result=intersection, title=\"Intersection\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37db3cea",
   "metadata": {},
   "source": [
    "Som vi kan se, beholder \"intersection\" operasjonen bydelene som krysser sirkelen og beholder alle disse geometriene i resultatet. En viktig ting å merke seg er at med `overlay()` vil den kryssende GeoDataFramen (`oslo_s_buffer`) også modifisere inputgeometriene ved å kutte dem i grenseområdene der de krysser. Dette er en av de viktigste forskjellene mellom `.overlay()` og `sjoin()` metodene, da `sjoin()` ikke vil modifisere inputgeometriene. Som nevnt tidligere, beholdes attributtdata fra begge GeoDataFramene for objektene som er en del av resultatet. I det følgende vil vi vise én etter én hvordan forskjellige overlay-operasjoner (dvs. union, difference, symmetric difference, identity) påvirker resultatene.\n",
    "\n",
    "## Union\n",
    "\n",
    "La oss bruke de samme inputdatasettene og utføre en `union`-overlay:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "02409539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Union\n",
    "union = bydeler.overlay(oslo_s_buffer, how=\"union\")\n",
    "\n",
    "fig, ax1, ax2 = plot_overlay(\n",
    "    gdf1=bydeler, gdf2=oslo_s_buffer, result=union, title=\"Union\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bea917e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 7)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "union.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379df877",
   "metadata": {},
   "source": [
    "Når man bruker `\"union\"` overlay-operasjonen, beholdes geometriene fra begge GeoDataFrames i resultatet. Som du kan se, har antall rader økt ganske betydelig fra 17 til 22 rader. Dette skjer fordi bydelene igjen blir modifisert av `oslo_s_buffer` i områdene der geometriene krysser hverandre: bydelene blir delt i to i områder der buffergeometrien krysser bydeler-geometriene. Dette vil derfor øke antall rader i det endelige resultatet."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8994dbe",
   "metadata": {},
   "source": [
    "## Difference og symmetric difference\n",
    "\n",
    "Noen ganger kan det være nyttig å fokusere på å trekke ut geometrier som ligger utenfor et gitt lag. Dette kan oppnås ved å bruke .overlay() med \"difference\" operatoren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dedea261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Difference\n",
    "difference = bydeler.overlay(oslo_s_buffer, how=\"difference\")\n",
    "\n",
    "fig, ax1, ax2 = plot_overlay(\n",
    "    gdf1=bydeler, gdf2=oslo_s_buffer, result=difference, title=\"Difference\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a13c3df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 6)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992f564b",
   "metadata": {},
   "source": [
    "Symmetric differance-overlay-operasjonen er en interessant en. Den vil beholde geometriene og attributtene utenfor `oslo_s_buffer`-laget, samt opprette en geometri innenfor `oslo_s_buffer` som inkluderer områder som er innenfor `oslo_s_buffer`-ringen, men utenfor `bydeler`-GeoDataFramen. Det vil si, i vårt tilfelle inneholder den et lite stykke av Oslofjorden, som vist nedenfor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f6f09426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ydu1a5Ofno6mpiZeV5+mMbnp94oknYjoxO/300yGVSvHWW2/hwIEDKY6OECI0SqYJyXLPPPMMlixZgsceewzDw8PjbrNYLLjtttvwzDPPABjZcBjJaKnHnXfeCZ/Ph7POOgtGozG1gafBnXfeCZlMht/85jf4wx/+MCk57u3txX333RdesR01upKfqk4Lv/nNbwCMTPq7//774ff7w7e5XC48/vjj4U1vyTp48CB+/OMf44wzzkAwGMRll12Gyy67jJdjT+fcc8/FEUccgX379uHMM8/EwYMHx93u9Xrx5ptvRuzgEo1cLscdd9wBALjwwgvx6quvTjqh3LVrF372s59NWy8fi3POOQfLly/HwMAATjvtNOzfv3/c7U1NTXj44YfD/y4tLcWNN94Iv9+PU045ZVLCz3EcNm7ciGuvvTZqZxtCSHagDYiEZDmGYbBjxw5cddVVuOqqq1BTU4OCggKYzWa0t7eHSzt+8pOf4Nxzz414jOXLl2PRokXYsWMHgOwv8Ri1ZMkSPPfcc7jssstw66234te//jXmzJkDqVSK3t7e8Oawn/3sZ+Me9/Wvfx0PPvgg7rrrLrz66qsoLi4GwzC45ZZbpmwVGI+zzz4bv//97/Hzn/8cP/zhD3Hbbbehvr4eVqsVbW1t8Pv9+Oijj6bsZjGVCy+8EDKZDADgdDrR09ODgYEBAIBUKsX//d//4Ze//GXS8ceKZVm88sorOP300/H++++jvr4es2bNQl5eHux2Ow4ePAifz4eioqK4j33ttdeio6MDf/jDH3DeeefBaDSirq4OwWAQbW1t4RXk4447LunvQyQS4eWXX8bJJ5+MDRs2YM6cOWhoaIBGo0FbWxuGh4dxzDHH4Nprrw0/5re//S16enrwzDPP4LjjjkNxcTEqKyvh9XrR0tISLvH54Q9/mHR8hBBhUTJNSJa77rrrsGjRIvzvf//DF198ga6uLmzfvh1isRhVVVVYvXo1vve9703ZomvUFVdcgZtuuilqb+lsdO6552LPnj3485//jHfeeQf79++HSCRCWVkZzj33XJxzzjnhnsSj1q5di2effRZ/+ctfsHv37nD7Nj5PMm655RYce+yx+POf/4zPPvsMTU1NMBqNWLFiBc4991wsW7Ys7mNu3rwZwEgSq9VqkZeXhzVr1mDt2rW47LLLwi0E06mkpATr1q3DE088geeffx47d+5ER0cHioqKsHLlSpx00km48MILEzr273//e5x55pl48MEHwz9DtVqN8vJynHPOOTj//PNxwgkn8PJ9VFZWYsuWLXjggQfw4osvorm5GaFQCGVlZTjjjDMm9a0Wi8V4+umncckll+Cxxx7D+vXrsW3bNhgMBjQ0NGD16tW44IIL0NDQwEt8hBDhMFw6t7ETQjLWLbfcgrvuugs/+clPcPfddwsdDiGEEJIVKJkmhMDv96Oqqgq9vb3YvXt3uBsBIYQQQqKjDYiEENx///3o7e3FMcccQ4k0IYQQEgdamSZkhurr68PFF1+M4eFh7Nq1CyzL4tNPP8WaNWuEDo0QQgjJGrQyTcgM5fF48Mknn2D//v2YP38+XnjhBUqkCSGEkDjRyjQhhBBCCCEJopVpQgghhBBCEkTJNCGEEEIIIQmiZJoQQgghhJAEUTJNCCGEEEJIgiiZJoQQQgghJEGUTBNCCCGEEJIgSqYJIYQQQghJECXThBBCCCGEJIiSaUIIIYQQQhJEyTQhhBBCCCEJomSaEEIIIYSQBFEyTQghhBBCSIIomSaEEEIIISRBlEwTQgghhBCSIEqmCSGEEEIISRAl04QQQgghhCSIkmlCCCGEEEISRMk0IYQQQgghCaJkmhBCCCGEkASJhQ6AEEIImSkCwRAODDhwaNCB1kEnWoacaBl0oGXICRHLoNKoRIVRicrD/1Ud/neJTg6xiNa/CMlEDMdxnNBBEEIIIbkoFOKwvcuCDS0mbGgdxuY2MxzeQNzHEbMMygyKcLJdblCgWCtHoUaOIq0MRTo5tHJJTMfiOA4uXxA2jx9Wtx82d+Dw/x7+9+Gvj95mG/O1S1dV4vvHzQLDMHF/D4TkKkqmCSGEkBRp6rTg7Ae/SMtzFWpkaCjSYFahGgqpaEKCfDgpPvzvQCjxj/5zl5bh9+cthFwi4jF6QrIXJdOEEEJIivzpvWbc/8EBocPgXWOVAY9e3oh8tUzoUAgRHCXThBBCCM9CIQ5PfNGKu9/ZD28gJHQ4KVFuUODv31qB2cUaoUMhRFCUTBNCCCE84jgOt76yE89v6hQ6lJRTy8R44BtLcdzsQqFDIUQwtDWYEEII4dF9HxyYEYk0ADi8AXznyU34xxetoLU5MlPRyjQhhBDCk+c3duCWV3YKHYYgLl1VidvPmg8JtfAjMwwl04QQQggP3tvTj6uf3owkGmVkvTWz8vDgN5ZBr5QKHQohaUPJNCGEEJKkLe1mXPr4enj8ubnZMB4lOjlevGY1yg1KoUMhJC0omSaEEEKScHDAgQsf+RJml1/oUDLGqfOL8fBlywAgpQNeOk0u7Oy2YsDmwfwyHZZXGWigDEk7SqYJIYSQBPVZPTj/4S/RbXELHUrGMSglWFWTh4tWlINhGGjlEtTmq2BQJV8CEgpx+M5Tm/DR/sFxXz9xbiHuu3gpVDJx0s9BSKwomSaEEEISYHX7cdEj67C/3y50KFnjm6urcMfZC5I+zu4eK06///OIt80u0uC176+BQkoTGkl60JZbQgghJE4efxBX/XMzJdJx2tVtTfoYFpcPf/+sdcrb9/fbcesrO7Cjy4J+myfp5yNkOnQdhBBCCIlDKMThxy80YUOrSehQsk4wyWvhW9rN+P6/tqJvmiT5te09eG17D0Qsg3duPBqzCtXJPTEhUdDKNCGEEBIjjuPwmzf34s2dvUKHkpXmJjh6fMjhxV1v78NFj66bNpEeKxji8J/t3TA7fQk9LyGxoJppQgghJEZ/+/QQfvfWPqHDyFpPXLEcx88pinqfYYcXT37ZBjHLIhgKweL245Wt3XB4Awk/r0Ymxu/OW4gzF5cmfAxCpkJlHoQQQkgMXt3WRYl0kvzT1Hk4vQFc9fQWbGk38/q8dm8Av/rPbpyxqIRa5xHeUTJNCCGETOPT5kHc/OIOocPIer98bReKtXIsrtCP+/rBAQec3gDkEhHvifQok9OHXqsHpXpFSo5PZi4q88gAHn8QrUNOtA87oZZJUGlUokQvh0REJe2EECK0Xd1WfP3RdXD6gkKHkhNYBrj6mDrIxSK0m5xo6rTg0KATACARMdOuXifj0csbccr84pQdn8xMlEynmcnpw8ZWEza1mdDcb0fLoBM9VjcmvgoilkGpXo4qowoVRiUqJ/ynU0p4j80fDMHm9sPq9sPmCYz8r9uPxioDnckTQmakjmEXznv4Cww5aANbLqjKU+LuCxZjZY1R6FBIDqFkOk2+ODiEX/93N5r7HbwcTy0To1ArQ6FGhiKtHFVGJeqLNGgo0kAjF8Pm8cPqGp8UW8OJ8si/be5A+N9Wtx+uKVZd8tUyPPbNRiytNPASOyGEZINhhxcXPLIOrUNOoUMhPLviyGr89NTZUEqp2pUkj5LpNLnx+W14bXuP0GEkTCZmcc+Fi2knNCFkRnD5ArjksQ1o6rQIHQpJkQqjAnedtwhHzsoXOhSS5SiZToN3dvfhpn9vz4l6ux+d2IAbTphFu6EJITnLHwzhe//cjI/3DwodCkmDi1dU4NbT5kKn4L98kswMlEyn2D3v7MdfPzoodBi8OmtxKf54wSLIJSKhQyGEEF5xHIefv7oTz23sFDoUkkaFGhnuPGcBbU4kCaFkOoWe+rINv/rPbqHDSImllXr87fLlKNDIhA6FEEJ402ly4Zi7P0KIPhlnpNMWFuP2s+ajUCMXOhSSRaj3Woq8vasXt/83NxNpANjWYcE5D36BfX02oUMhhBDeVBiVOG9ZudBhEIG8tbMPJ//5U7yytQu01khiRcl0CmxsNeGG57dPaneXa7otbpz/0Jd4d3ef0KEQQghvfnhCPSQi2hcyU1lcftz0QhPeoc82EiNKpnnW3G/Hd5/aBF8gJHQoaeH0BXHV01vw/MYOoUMhhBBeVBiVuKCRVqdnsptPmY36Io3QYZAsQQ0WedRjceNbT2yEzRMQOpS0u/ud/SjQyFBboEZNvorXY+/qtuKNHb3Y02uDiAHOWVqGsxaXUkcRQkjK3HBCPV7d1g2Pf2YsjJCvrK7Nw1VH19IUYhIz2oDIE4vLhwsfWYcDA/wMZclWxVo56ovUkIpYaBUS3HnOAqhliZ+zdZpcOPrujyaVzFyyshK3nzUPMjF1FCGEpMa97+7HAx/mVjcmEp1WLsb7Nx2DQi1tQCSxo9MuHnj8QXznqc0zPpEGgD6bB58dGMIH+wbw6rZubG4zJXW8Tw8MRqw9f25jB+7/4EBSxyaEkGiuPqaOOhbNICwDPPCNZZRIk7hRMp2kYIjDDc9tw5Z2s9ChZCSr25/Q47yBIG7/z27832u7przPQx8fwgn3foxvPbERFpcv0RAJISQitUyMm0+eLXQYJE1+ftpcHNNQIHQYJAtRMp0EjuNw+3924909/UKHkrESWdXZ22vDhY+sw5NftkXt9cpxwKFBJz5pHsSXh4apjREhhHfnN5ZjfqlW6DBIil3QWI7vHFUjdBgkS1HNdBIe/Ogg7n5nv9BhZCy9UoLNvzgR4iibODpNLmxoNSF0+Nfwg739eGd3/CcnYpbB0ko9/nTRElQYlQnHTAghE607NIxLHlsvdBgkRRaV6/DC1atpqi9JGHXzSNALmzspkZ6GUiJCkOOm/CXrt3lw8d/Wo9viTvq5AiEOm9rMuPfd/fjLxUuTPh4hhIxaXZeHU+YXJXSiTzJbnkqKRy5rpESaJIXKPBLw4b5+3PrKTqHDyHg9Vg9+8uIO2D1f1U2HQhy2dZjx5aEhfLRvgJdEeqxdPTSRkRDCv1u+NpcGueSgP399CUr1CqHDIFmOVqbjtK3DjOv+tRXBaMW8JOy/TT3Y22vDsko9eq0e7O21Y8jhTdnzHRp0wO7xQyOXpOw5CCEzT02+ClccWY3HPmsVOhTCk1U1RhxNGw4JD2hlOg6tQ05856nN1MQ/TgcHHHhhcxc+OzCU0kQaGNmUePaDX1B3FUII7645pg4yMX1s5opzlpYJHQLJEfSuEKNBuxffemIjTE5qwZbpWgaduOCRL/GbN/bA7QsKHQ4hJEfkqWU4n8aM54zF5XqhQyA5gpLpGDi8AXz7yY3oMLmEDoXEiOOAxz9vxdfu+xTrW4aFDocQkiOofVruKNbRcBbCD0qmp+EPhnDdv7ZiVzdtbMtGbcMuXPy39fj5qzth8yQ2QIYQQkbVFahx/JxCocMgSWIYQKegvTWEH5RMT+Mv7zfj0+ZBocMgSXp2QwdO/tOneJ8G7BBCknT10bVCh0CSpFNIIGKpOwvhByXT0zh2Nq1A5Io+mwff/edm/PD5bRhO8UZIQkjuWlljxOIKvdBhkCSopNTMjPCHkulprKg24tjZ1Donl7y+vQen3/85XL6A0KEQQrIQwzC0Op3lBu1eOLz0GUD4Qcl0DG48sUHoEAiPpCIW1xxTC3+AeoUTQhJzyvxiVBqVQodBEuQLhvDdpzbhb58eEjoUkgMomY7Bkgo9TplfJHQYhCeXHVGFK9bUQKekzSeEkMSIWIY6e2S59S0m3P3OfjzwwQFY3bRBnSSO4TiOludi0DbkxEl//gT+IP24stncEi1e//4aSGnwAiEkSVa3H8vufI8m4uaI1bV5eOa7q2hjIokbZRQxqs5X4Vurq4UOgyTBoJTgb5c3UiJNCOGFTiHBimqD0GEQnqxrGcYGmktAEkBZRRx+cEI9DFQakJXELIOHL2tEBdU4EkJ4dMIcKgHMJT96YTu1UCVxo2Q6DjqFhDYjZqlfnTkPR9TmCR0GISTHHD+X2qfmkn6bFw9/QpsSSXwomY7TN1ZVoq5AJXQYJA4XNpbjsiOqhA6DEJKD6grUqMmnz4RcsqXdjKZOi9BhkCxCyXScJCIWvzx9ntBhkBgtKNPiznMWgGFoQwkhJDVovHjueeyzFqFDIFmEkukEHDu7AGvr84UOg8Tgl6fPg1wiEjoMQkgOO4GS6Zzzv1196LW6hQ6DZAlKphPAMAx+esococMg06gtUFGdNCEk5ZZXG6GR0XjqXBIMcXhhU5fQYZAsQcl0ghaW63BErVHoMEgUSyr0QodACJkBpGIWq+jzIOe8uq0LNIqDxIKS6SR8b22t0CGQKEp0cqFDIITMEJRz5Z62YRd2dFmFDoNkAUqmk3Dc7ELUUmePjGVQSoUOgRAyQ9i9AaFDICnwafOg0CGQLEDJdBJYlsFVtDqdsfLUlEwTQtKD+gXlps8ODgkdAskClEwn6ZylZchXy4QOgxBCiICUUuoalIu2tJth8/iFDoNkOEqmkySXiPDtNdVCh0Ei+HDfIMxOn9BhEEJmAI1cInQIJAWCIQ6fNdPqNImOkmkeXLqqEgrqZZxx/tvUg6V3voeNrSYEQ7Q7iBCSOho5tcbLVf9t6qGuHiQqSqZ5oFdKcfaSUqHDIFO46NF1+MZj6/E4TbQihKSIVkEr07nq7d19WPvHj7Ctwyx0KCRDUTLNk5PmFQkdAoliQ6sJf/jfPhwcsAsdCiEkB9HKdG7rMrtx2+u76SoniYiSaZ6smZUPmZh+nJksEOJw80s70DbkFDoUQkiOoZrp3Lez24o3d/YKHQbJQJT98UQuEWHNrHyhwyDT2NZhwWNU7kEI4ZmWVqZnhH980Sp0CCQDUTLNoxPmFgodAonBq9u6YXFRlw9CCH90VDM9I2zrsKBj2CV0GCTDUDLNo+PnUDKdDVy+IP61oUPoMAghOaREpxA6BJIm/2nqFjoEkmEomeZRiU6BeSVaocMgMXh6XTsCwZDQYRBCckSJXi50CCRNnt/UKXQIJMNQMs2zE6nUIyv02Tw0JpYQwhuNTAwVTUGcEbrMbjR1WoQOg2QQSqZ5NqtII3QIJEavbqVLdYQQfjAMg3KDUugwSJrs67MJHQLJIJRM88zhCQgdAonRB3v74adSD0IIT85cXCJ0CCRNDg44hA6BZBBKpnlGyVn2cPqCdKmOEMKby46ogpJKPWaEJ75ow2cHBoUOg2QISqZ5JpfQjzSbfNJMb4aEEH7olVJctLxC6DBIGgRDHO58Y4/QYZAMQZkfz9Qy6jWaTT7aPyB0CISQHPKdo2rAMkJHQdKhnvZIkcMomeaZVkFTsLLJrm4bWgap9o0Qwo8KoxKnLigWOgySBu/u7sPOLqvQYZAMQMk0zzRyWpnONqfe9xn+9O5+ocMghOSI7xxVK3QIJA38QQ4/ebEJ/TaP0KEQgVEyzTONnFams40vEMJfPzqI3T20wkAISQ7HcagwKFCbrxI6FJIG+/vtOOuvn8Pk9AkdChEQZX48o2Q6O4U44P4PDuDRy5cLHQohJAs4vAG0DjpxaNCBliEnWoecaB1yoHXQCacvKHR4JI36bV68saMH31xdLXQoRCCU+fFMS2UeWevdPf3os3pQrKOxwISQ8UIhDne/ux9b281oG3ai3+YVOiSSQZ7d0IFzlpZRDjBDUZkHz+QSEdQyOkfJRhwHvL2rV+gwCCEZ6LHPWvDwx4ewodVEiTSZZF+fHe/s6hM6DCIQSqZToIRWNrPWsxs7wHGc0GEQQjLI1g4z7n6HNimT6HZ2076bmYqS6RSgMoHs1dzvwKFBp9BhEEIyhNXlxw+e3YZAiE6ySXS+AE1AnqkomU6BcoNC6BBIEj6nEbGEEIx05vjZyzvQbXELHQrJAh/sG0AP/a7MSJRMp8DR9QVCh0CSQB+chBAAeGZDB97eTXWwJDaDdi9e3tIldBhEAJRMp8DJ84tRnacUOgySoH+ua8fWDrPQYRBCBLS314Y739gjdBgky7y3tx9Ob0DoMEiaUTKdAiKWwXfW0gSsbOUNhHAPbTYiZMZyegP4/rNbqQaWxG1HlxVPr28XOgySZpRMp8gFy8phUFK/yWxFm0gJmblue303WmgjMknQn95rxpNftKK53w6Ly0cdomYAhqNXOWXufXc/HvjwoNBhkAQwDPDSNavRWGUUOhRCSBq9tq0bN/57u9BhkBxSoJHhrMWluPbYOuSrZUKHQ1KAkukUGrB7cNQfPoIvSJcKs1GRVoZHL1+OJRV6oUMhhKRBy6ADZz7wOY0DJymhkYlRV6hGVZ4SyyoNmF2swcpqI1iWETo0kiRKpnnEcRyGHD60DjnROuRAy6ATL2/twpDDJ3RoJEH5ainW3XoCJCKqiCIkl3n8QZz30JfY02sTOhQyQzAMUKiR4evLK/CdtbXQKUZKQ0MhDgwD3PtuM3b3WGFy+qCQinDdsbNwdAN1C8tElEwn6OCAA3t7bWgdcqJl0IGWISdaB52w0y7enPPUlStxDL2BEZLTbnt9F/65jjaOEWFIRAzOXFSKyjwl/tvUgzKDEp82j595cNHycvzxgsUCRUiiEQsdQDZq6rTggke+hD9I5yEzwc9f2YnPf3YcGIYuxRGSi97d3UeJNBGUP8jhlW3d4X9HmsRbV6BOZ0gkDnTtOk42jx/XP7eVEukZpNvipkEuhOSobosbN7+0Q+gwCJlWoZY2L2YqSqbjwHEcbn15JzpNlFjNNA4q3yEk5/iDIfzg2a2wuv1Ch0LItEp1CqFDIFOgZDoOz27swJs7e4UOgwjg9e09QodACOHZve82Y2uHRegwCIlJqZ6S6UxFyXSM9vXZcMd/abTsTPX3z1oRoBaHhOSMT5oH8cgnh4QOg5CYSMUsDRPLYJRMx8DtC+IHz26Dl0bLzli+YAj/3twpdBiEEB4M2Dy4iQazkCwyr0RLLVozGL0yMfjVf3bhwIBD6DCIwO55Zz+GHF6hwyCEJCEY4vCjF7Zj2En9/0n2WFyuEzoEEgW1xpvG69u78cLmLqHDIBnA7PLj7L9+ga8tKEaxTo66QjVW1+ZBLhEJHRohJEZ//fAgvjg4LHQYhMRlMU3izWiUTEfRPuzEL17dJXQYJIN0W9x4/PPW8L8LNTL88MR6lOkVWFppgEIiglRMF3wIyUTrW4Zx3wfNQodBSNwWleuFDoFEQRMQp+ANjIyW3d1Do2VJbPRKCXyBEL65uhonzy/C0gr9uEEvHMdhd48NXWY3PP4g5pZoMbtYI2DEhMwcww4vvnbfZxiwU6kWyS4KiQi7f30KWJYGh2UqWpmewu/f2keJNImLxTXSq/aRTw7hkU8OobHKgMuPqMLsYg3e39MPsYjFXW/vC99fxDLYe8eptJJNSIqFQhx+/GITJdIkKxVoZJRIZzhKpiN4b08/nvyyTegwSJbb0m7GlnbzlLfnq6WUSBOSBn/7rAUf7x8UOgxCEiKX0OdEpqNXaIIeixs3v9QkdBhkBijSUs9QQlJtS7sZ97yzX+gwCEmYmKVULdPRKzRGIBjCD5/fFr5cT0gq0WhYQlLL6vLjhue2IRCirUEke/XZPLB7KC/JZJRMj/Hn95uxqW3qy/KE8KlETyvThKQKx3H42cs70G1xCx0KIUkxOX1Y32ISOgwSBSXTh312YBAPfUyjZUn61OarhA6BkJz19Pp2vL27T+gwCOEF1U1nNnp1AAzYPfjRv7eDmgSSdKK+oYSkxq5uK37zxl6hwyCENyaa2JnRZnwyHQxxuOnfTRhy0C8qSR+piMWcEuoxTQjf7B4/rn92K3zBkNChEMIbKlfKbDM+mX7oo4P4/OCQ0GGQGWZuiQYyMY0hJ4Rvv3xtF9qGXUKHQQivhuy04JfJZnQyvbHVhD+/T6NlSfpRiQch/HtpSxde394jdBiE8C4YoistmWzGJtMmpw83PLcN1DGJCKEqTyl0CITklIMDdvzfa7uEDoOQlKCypcw2I5PpUIjDj1/Yjj6bR+hQyAylkdPwUUL44vEHcf2z2+D2B4UOhZCUkEuoLDCTzchk+u+ft+IjGi1LBCSiiVaE8ObX/92DfX12ocMgJGU6TbQBMZPNuE/07Z0W3PX2PqHDIDNcx7BT6BAIyQmvb+/Gcxs7hA6DkJRy+QJCh0CimFHJtM3jxw+e20qjZYng3t3TL3QIhGS91iEnfv7KTqHDICTlqDVeZpsxyTTHcbjl5R10qYRkBIloxvzpEZIS3kAQ1z+7FU4f1UmT3Nc+7MKg3St0GGQKM+YT/ZkNHXhrJ42WJZmBplkRkpzfv7UPu3tsQodBSNrs66Pf90w1I5Lp3T1W3PnGHqHDICSsx+qGn1odEZKQ9/b048kv24QOg5C0ClKJasbK+WTa5QvgB89tgy9AiQvJHBwHBIL0xkhIvHosbtz8UpPQYRCSduUGmk+QqXI+mb7t9d1oGaTOCSTzWNxU6kFIPPzBEK5/dissLr/QoRCSVhq5GLX5KqHDIFPI6WT6la1deGlLl9BhEBKRRi4ROgRCssq97zZja4dF6DAISbv6QjVYlhE6DDKFnE2mDw06aLQsyWjDDtqZTUisPt4/gEc+OSR0GIQIwhcMgeOoNDBT5WQy7fEH8f1/UcskktnoUjUhsem3efDjF6hOmsxcbUMu+GjTesbKyWT61//dTaNlScbrtXqEDoGQjBcMcbjx+e0YpnaSZAZz+gJoH3YJHQaZQs4l02/s6MFzGzuFDoOQaW1pNwkdAiEZ7/4PDmBdy7DQYRAiKI4D3t5FszIylVjoAPjUMezCrS/TaFmSHQ5RlxlCovry0BDu//CA0GEQIqjGKgP+74x5WFyuEzoUMoWcSaZ9gRB+8Pw22L0BoUMhJCYsQzuzCZnKsMOLG5/fDtpzRWayugIVnrpyJdSynEnXclLOlHn88e19aOq0CB0GITGrL1ILHQIhGSkU4vDjF5swYKeON2Rmu+v8RZRIZ4GcSKbf39OPxz9vFToMQuKysIwu2RESyWOfteDj/YNCh0GIoEp1cjRWGYQOg8Qg65PpHosbP6HRsiQLlRsUQodASMbZ2mHG3e/sFzoMQgR3RF0eGCoHzApZnUwHgiHc8Nw26tdLshKVJREyntXlxw+e3YZAiAqlCVFJqbwjW2R1Mv3n95uxud0sdBiEJIT65hLyFY7jcMsrO9BtcQsdCiEZ4aP9AwjSiWVWyNpk+tPmQTz0MY2WJdnrXxs6hA6BkIzxzIYO/I/66BIS1mV2Y1e3VegwSAyy8hrCgM2DH/2bWiaR7HXtsXW47IgqocMgJCPs6bHhzjf2CB0GIRlHxFLNdDbIupXpYIjDj16g0bIke914Yj1+duoclOlpAyIhTm8A1z+3Fb5ASOhQCMk4m9toUm42yLpk+uGPD+KLgzRalmSnSqMS1x83S+gwCMkIHMfhl6/tQgtNAyUkorUNBUKHQGKQVcn0xlYT/vRes9BhEJKwk+cVQSzKqj87QlLmxc1deHVbt9BhEJKxKOfJDlnzqW5y+nDDc9tAG1tJNltUoRc6BEIywoF+O277zy6hwyAko50wp1DoEEgMsiKZ5jgOP3mxCX02j9ChEJKUQRqPTAjcviC+/+xWePxUJ03IVAo0Mpy2sEToMEgMsiKZ/vvnrfhw34DQYRCStFe3dQkdAiGCu+ON3WjudwgdBiEZrVAjg0ycFWnajJfxr9KOLgvuenuf0GEQwotBuxcc9XQkM9h/m3rw3MZOocMgJOMpJCIaJ54lMjqZtnv8+MFz2+APUvJBcsOQw0dtHcmM1T7sxK2v7BQ6DEKygsnpo8WXLJGxyfTIaNmdaB92CR0KIbwp1cuhlIqEDoOQtPMGgrj+2W1weANCh0JIVmgZcuLAAJVDZYOMTab/taEDb+7oFToMQnjVaXJj3SHqk05mnrv+tx87aTQyIXF5eQvts8kGGZlM7+mx4Q4aLUtykEzMorHKIHQYhKTVe3v68cQXrUKHQUjWeXFLV9SrOU983ooT7v0Yu+hEVVAZl0zTaFmSy06aVwS9Uip0GISkTY/FjZtfahI6DEKyksnpw9Pr2iPe1jbkxB1v7MGhQSeufnoL5U0Cyrhk+rbXd9NoWZKz6grUQodASNoEgiHc8Nw2WFx+oUMhJGs99PFBDDkmzygYOz202+LGhlYqIRRKRiXTr2ztwstbqT6I5K5AiFYOyMzxp/easbndLHQYhGQ1uyeABz44MOnr7+/tH/fv/X32dIVEJsiYZPrggAO/fI1Gy5LctreX3uzIzPBp8yAe+viQ0GEQkhOeWteOT5oHw/8OhTjsm5A87+6xpTssclhGJNMefxDXP7sVLl9Q6FAISanPDgxiwOYROgxCUmrI4cVNL1CdNCF8+vkrO2FxfTWnYOI4lzd39GJ3D21EFEJGJNO/eXPPpDMsQnKRP8jhP009U97e1GnB45+1UKN+krVCIQ4/+vf2iDWehJDEdVvceOSTFgCAPxRCIDT+c8IXDOHiR9ejy0zzOdJN8GT6fzt78cz6DqHDICRtHv20BS7f5FZHdo8f33hsPX7z5l488OFBASIjJHmPftqCzw4MCR0GITnp75+34Ki7PkSnKXLCbPcG8MAH9PmRboIm0x3DLvz05R1ChkBI2g3avfjbpy2Tvv7i5i44D5c6/f3zVlqdJllnS7sJ97y7X+gwCMlZ/iCHLrMbZ/31iynv0zJEUxPTTbBk2hcI4QfPbYXdQ6Nlyczzrw0dk1YWPh6zucTq9qPb4k53WIQkzOLy4YbntiMYopNAQlIt2h4zyqvST7Bk+o9v70NTFxXKk5lp0O7Fra/sRGhM4rFnwk5smmhFsgXHcfjpSzvoBJCQDCBiJ25NJKkmSDL98f4BPP45jZYlM9vnB4fw782d4X9PfP/703vNMDt9ICTT/eOLNry7p3/6OxJCUm7ATpt/0y3tyXS/zUMtkwg57K6392FHlwUcx8HhHX9prrnfgWue2UK10ySj7eiy4Pf/2yt0GISQwwbtXphoISat0ppMB0Mcbnx+O73IhBxmcflx1l+/wH0fHIDHP7kGbkOrCQcGaDMJyVw/eG4b/EE64SMkk2zvpMmj6ZTWZPqvHx7EuhaaHU/IRH95/wCm2rc1YKNLdiRzUXUmIZnno32D09+J8CZtyfT6lmHc90Fzup6OkJwRojIPksHOWVomdAiEkAnahp1ChzCjpCWZHnZ4ccNz26ZceSOETC3SgBdCMsW1x9bhhDmFQodBCBljkDYhplXKk+lQiMNNLzTR7lJCEnRokFYYSOaSiUV4+LJGnDiXEmpCMsWxs+nvMZ1Snkz/7bMWfNJMtTuEJKqp0yJ0CIREJRWzePDSZTh5XpHQoRBCALy+vRuHBmnzerqkPJk2u6hzByHJ+OLgEPzBkNBhEBKVTCzCPRctRn2hWuhQCJnxeq0eXPTIOmzroK4e6ZDyZPr8ZeWThlEQQmLn9AWpTIpkBa1cgme/dwTqClRCh0LIjDfs9OE3b+6lWQVpkPJkuqFIg3svWkzjLQlJgtNLmxBJdijQyPCv7x6B6jyl0KEQMuNtaTdjY6tJ6DByXlq6eZy7tBx/umgxrVATkgCDUoLqPFrpI9mjWCfH81etRqFGJnQohMx4rUO0iT3V0tZn+uwlZfjjBYvT9XSE5Ayzy4/r/rWFWuSRrFKsk+Pp76yCRiYWOhRCZrQNtDKdcmmdgHhBYzmuO7YunU9JSE54f+8Avv+vrUKHQUhcZhdr8Mx3VyFfLRU6FEJmrD09NqFDyHlpTaYB4OZTZuN7a2vS/bSEZL2P9g9iSzutMJDssrhCj5evPRLFWrnQoRAyI/XbPQjS1LyUSnsyzTAMbv3aXFy5hhJqQuL14b4BoUMgJG5VeSo8/Z2VVPJBiAAsLj92dluFDiOnpT2ZBgCWZfCL0+fi9IUlQjw9IVnr0ABtJCFx2L9f6AjC6os0+NPXl4ChjeiEpN3BARrgkkqCJNMAIGIZ/Onri2liFiFx2NRGZR4kDs89B/zsZ0JHEXbSvCL8/GtzhQ6DkBmnx+Ke8ra/vN+M7ii3k+kJlkwDIxOzHrmsERctLxcyDEKyhsXth9sXFDoMkk3++Efgl78UOoqw766twWkLi4UOg5AZxe7xT/qa2xfE/722C395/wCuf3Yr1VUnQdBkGhgp+fj9eYtwEq1QEzKtYIjD+pZhocMg2ea3vwV+/WuhowAwsm/m9+cuwpxijdChEDJjqGWScf/e02PD0Xd/hKfXtwMAtnVY8NSXbQJElhsET6aBwyUfFy3GrEK10KEQkvEODU5d+0ZjY8mUbr8d+N3vhI4CAKBTSvC3y5fThkRC0qRE91U3nff39OPch77AoN077j6/fWsvnjmcXJP4ZEQyDQAauQT/uGIFDErJ9HcmZAbrtXoifv3lLV349X/3pDkakhUWLgRYFvjFL4B77hE6GgBAZZ4SD122jDYkEpIGMslX6Z7d64c3EJp0n2CIwy9f24Wn17WlMbLckDHJNABUGJW450KakkhINBPr2qxuP3731l78+MUmPPllGz5pHhQoMpKUW24BGIbf/0ZLO047DXj00ZGv3Xwz8Je/CPqtjlpbX4Afn9QgdBiE5Lw9PTa8vr0bADDs8EW97/+9vhtb2s3pCCtnZFQyDQAnzC3CL06j3d6ETMWo+mqaXNuQE994bD3+9mlL+Gs/f2UnWoeohR6Z4LvfBe6/f+T//+hHwEMPCRvPYdcdOwunzqcNiYSk0qOftuCHz2/HT19qimnfza2v7IA3QJvdY5VxyTQAfOeoGupBTcgUNPKv6kzveXc/dk8YFdttceMbj62nhDpb/eIXgNvN73+/+c3Isa+/fqS7x+j/f+wx4b7Pw1iWwUOXLsOaWXkx3V8qYnHq/GL8+qz5WFltTHF0hOSWFzZ34f290w//au534A//24deq5v24sQgI3d/sCyDP16wCAcG7Gjup0bjhIwlE4tgdvpgUEkhYiMXnPZaPbjwkXV4+8a1yFfL0hwhSYpYDMhTOHr75ptHEuxf/Qq4+uqR5/v2t1P3fDFgWQZ/+fpSnPHAZ+i3eWFQSnDc7EIcVZ+PAo0M7+7ux7t7+vCrM+fjqPp8aOUje2u+saoSL2/pwn0fHJhyLwEhJDH/+KIN//iiDTqFBOcuLUOBRoaafBW+tqAYDG12GIfhMviUo3XIibMe+Bx2b0DoUAjJGAwDaGRi3HPhYjz48SE0dVqmvO+FjeW4m/YhZIdbbgHuumskyb399tQ/3623An/4w8jGxKeeAi67LPXPOY29vTa4fEEsqdBPOlEcsHtQqIl8kuENBPHi5i48/PEhGj5BSIo9851VOKo+X+gwMkpGlnmMqslX4bmrjoBENP0ZkFTE4pT5RTh/GQ2AIbmN4wCbJ4Crnt4SNZEGgBe3dOHtXb3UjJ9khbklWjRWGSJecZkqkQZGrtZcdkQVPr75WNx1/kJo5Rl50ZWQnNDcbxc6hIyT8e84C8p0+M05C/Czl3eCZYDGKgOOnV2ImnwVPjswiA/3DeDXZ83HEbV50CtHNmZdvLICf3x7Hza10W5UQq55ZitkYhaLK/Q4e0kp7J4ArjiyGnKJSOjQiFDuuGNkVZphgMcfz4hVaT5IRCy+vqISW9st+PfmTqHDISQnPfZZCy5fXQWJKKPXY9Mq45NpAPj6ikqU6BRYVK4LJ8wAcNrCEpicvnHdDQBgRbURL1y9Gl8eGsb9HxzAhlZTukMmJKN4AyFsbDVh4+G/hbYhJ/5w/iKBoyIRBQKAh+f6X7F45D8AuPvukVIShhlplydwvXQqrG3InzKZLtHJceHyCtz/wYE0R0VIbui1erCr24qllQahQ8kYWXNacXRDwbhEetTERHoUwzBYMysf/756NV64ejUKNbQJi5BRu3qstEM7U/32t4BCwe9/v/zlyLH/+lfgpz/96v9/73vCfZ8pdNSsfETam6uRifGPb6/ATSc14GsLqB0fIYnaTFf+x8maZDoZK2uMOHl+kdBhEJIxdnXbaLjLTPP3vwM33DDy/++7D7juOmHjSSG9Uoo1s8ZvkGIY4MFLl2FOsRYAcOvX5kImnhEfgYTw7un17djVbRU6jIwxY95J1tYXRPx6qU6Ot25Yi7oCVZojIkRY6w5N37ifpNEf/jCyu5TP/371q5Fjv/UWcNVVI1+7556vkuoc9uCly3D6opF5BXqlBPdfvBRHN3z1OVCZp8T1x80SKjxCslqHyYVzH/oCj33aQlc5kSU103w4si4PYpZBYExXg5FLfisxu1iDn582F995arOAERKSXl8cGkIgGIKYNpHkvp07R/73978HfvxjYWNJE61cgr9eshRnLipBY5URBRFK/b5/3CxU56vwp/eaacgRIXHyBzn89q29sLr9+PHJDTO69/SM+RTVyCW49bS54To6uYTFn7++BLOLNQCA4+cU4sS5hQJGSEh67eq24cJH16F9mJKIGeGOO0Z6Wc8gDMPg1AUlERNpYGRYzJmLS/Huj47G785diCIt7a0hJF5//eggXt3WLXQYgsrooS2psLHVhKfWteHmk2ejOn98aUev1Y1fvroLH+ybftQmIblCIxfjX99dhUXleqFDIXy7/Xbg178Gbrtt5H9JVG5fENc/u5U+AwiJk1Elxb++uwpzS7RChyKIGZdMx2Jzmwl/fHs/NrZRSz0yM6ikIvzvh0ejMk8pdCiET7ffDvh8wO9+J3QkWcMXCOG213fh+U3Up5qQeHx9eQXuumBmtlydMWUe8VhebcS/rz4Cf/3GUkhptzeZAZy+IH76chNcvoDQoRA+XXopJdJxkopZ/O7chVheRT10CYlHYAZP2qVMcQoMw+CMRaV47nuroJHNmH2aZAZb32LCO7v7hA6D8Km+XugIshLLMvjTRUtQplcIHQohWeOT5gFs7TBjY6sJbl9Q6HDSiso8YvDO7j5c/fQWocMgJOV+d+5CfGNVpdBhEJIRDvTbcdGj62B2+YUOhZCsUqCR4eqja3Hs7ALMKtSk5Dlah5y4/T+7IRGxqDAqUGVUYlGFHksr9GnvLELJdIzuensfHv74kNBhEJJSiyv0uGxVJZRSMU6eXwQJtc0jM9ynzYO45pktcM2wlTZC+HLD8bNw44kNYCONJU2Qxx/ExY+uw/auyYNjTltYjIcubeTtuWJByXSMQiEOv3htJ57bSJtSyMxQW6DCGQtLcMmqSpTo+LvcvafHimue2QqXLxheTVhZk4czFpdAK5fw9jyE8GXdoWFc9fRm2D20p4CQRCyvMuBnX5uDFdVGXo734EcH8b9dvdjVbZt0W7FWji9vOZ7X5H06lEzHweMP4sbnt+NtqislM0iBRoa/XrIUq2rzkj5WMMThB89uxVu7Jv8N3XH2fHxzdXXSz0FIKmztMOOap7dgwO4VOhRCstYlKytw62lzk1o4GbB7cMI9n2BuqRYbWyN3XbvnwsW4oLE84eeIF13DjYNcIsL9lyzFOUtKhQ6FkLQZtHvx9b+tx/f+uRndFndSx3ppSycGHJGTkY+oty/JYMsqDXj52iNz4v2/wqDAqhojinVyoUMhM8xzGztxyp8/xY4uS8LHeOCDg7B7A3BEuVL05/eaEUpjdxFKpuMkFbO496Il+OEJ9ZBlYdu8wikmgREynff29OPkP32C5zZ2JPR4ly+Au9/Zj36bJ+LtH+0fxPqW4WRCJCSlKoxK/OXipfjOUTVCh5IUrUKCDa0miGbw+GcinF6rBxc8vA5/fHsfdnRZMOzwYmKRBMdxODTowP929sIfDAEAHN4Abnt9F57Z0A4AaO63w6iSRnyObosbm9vNqf1GxqAyjyTs6bHh0sfXZ81O73KDAgM2D4p1cgw6fDOudQ3hzwWN5bj1a3OQp458csZxHLyBEOQSEQDA7vHjl6/twuvbewAARRoZ+iNcLj9rcSnuv2Rp6gInhAehEIernt6M9/dm39UUnUICq3vkM0smZuANUApAhGdUSVFuUEAiYuH0BtA27ITHP5JEl+kVyFNLsb/PDm8gFH6MTMxiUbkOm9oiJ82XrqrAb89NzxAZSqaTtKXdhEsf3xB+0TPZympjeKpjoUZGtX8kKSKWQaVRiTK9ArMK1VDJRPD6Q2g3ubCnxwaLy4cLl1cgGOLw3p5+9I1ZkV5VY8SGCLVuSqkIn//sOBhVdAWFZDaXL4Dr/rUVH+8fFDqUuDRWGrClYyT5KDco0GVOrnSLEKEYlBLoFBK0Dbsi3r5mVh6euGIFZGJRymOhZJoHb+7oxY9e2A5fILMT6gqDAp2H3ziVUhG1eiKCkIlZzC3WYvsUNXN/vmgJzl1Wlt6gCEkAx3H4xWu78OyGxEqfhFBfqMaBAQcAoFQvh14hgUomhsXtBwOgud8hbICERCERMfAHv0pbS3Ry9Fojlw4Wa2W4/vhZuOyI6pTHlX1Fvxno9EUleOXaI6GSpv7sJ1GzizXhRFrEANV5KjRWGbCoXIcS2oRCUoxlgNHyTG8ghBCmPof/uHkgrRtHCEkUwzC4+eTZqDQqhQ4lJksq9OFEGgB6LB7s6bVjU5sZB/odaB10YiVPrcsI4VtVnhLLKg3hf6tlYgxFucLeZ/Pi9e09sHlSX4pLyTRPFpTpcOOJDUKHEZFcwmLsNpMgB+zptWFLuxk7uqxweANYWqEXKjwyAyyrNIQ3O7EMMDRFRw8A6DS58PLWrnSFRkhSDCopXrxmNfKn2D+QSaba/DvKH+Kwsc2EKqMSNfnZcYJAZgYxyyAU4uD0jXTwWFVjRIlODn+UhReNXIwdXVbc+vLOlMdHyTSPvnd0Lb69plroMCapzVdjX599ytvtngC2dVowp1gDjUycxshIJmEZpKRDzaJyHVy+IAIhDksr9SjVK9BjmfpDnQNw2+u7MUg1/SRLFGnluHRVpdBhRKWVi9E3xeXwidpNLlhcfpTp+RvWRHLTyhoDFpZpUVegQn2ROmXPU6KXo9PshscfgkTEYG+fbdxVlokKNTJUGJTwBkJ4c2cv3tzRm7LYAEqmeXfNMXUQp3HqTiw08tgS5H19dpTq5ZBmYcs/kriV1QboFGKEOGBOiYb34yskIrh8X/UDnWrDU55KihXVBmzrsMDtD+KmF7YjSOUeJEtcuaYG1x1bh0ztNleVp4pSXDWZ2eWP+bODzFxWVwA7u204NOjEgX4HqvNSc0VDrxhpgadXSlBboIbNHbnH9LwSDRaX62B2+bCn96vpiLe8vCPcxSYVKGviWZFWjo9vPhZzivlPShLl8ce+0XB/vwNzijIndpJ6Tl8Q1sNvTM19dl6TARHLwOzyoW3YhZU1RhyMsJJQqJFhfqkWw07fuBZHnx0Ywnt7+vkLhpAU0ikl+Ompc/D15RVChxKRMoE9PR3Dzow9OSCZweLyjft3qsqd7Ifrnje3mdEc4Uq7iAFW1hixp9eOpi7ruE2KAGD3BvBOCqdXUzKdAuUGJe65cDEyYYF6XokW7abIbWOmIpOw0CkSH/VJsovJ+VU5hdsfQjmPl3bnFGvC3QE2tppgnzCxanG5Dm5/ELt7bJEejjd29PAWCyHpcPMpsyd9TSTwh8HK6sitKKfj8ocwu0iT0ZvriXCUUtGkeQFsCs6+GorU49rfTbzCopGJUFugnnK0+KhnN3Sk7GonJdMpMqtQPe4FP3Z2AdbW56c9wXZ6/bDEOVRmU5sZswpVKYqIZBKjSoJe6/g3Q70y8kSpREhFU//CN1Ya0NRlnZRgj/Xmzl5s77TwFg8hqRbkOEjG/N5ff9wsvP3DtbjhhHqsrc8Pr/QqJOlLUMeWWcVrX58d80q1PEZDckVDhKvYwRC/LYKLdXJo5VMv7jEAygzKqPXTo7Z3WvD69m4eoxsTB/WZTp0nv2jFu3v6UZWnwp1nz4dYxMIXCOHDfQN4e1cvVtQYwYBBv82DAbsX6w4NTdl8PBEyMTtuWlC8Fpfr0NRl5S0eknmWVxkmjVxtKFIjxI28ScXyBhWJRMRgdrEG+/vsky63AUC5XoEBhzem3uynLSzGQ5c2JhQHIULoGHbhkwODUMtEOHdp+bjb2oed2NFlxRmLSuDwBjBg96Lf5sGnzUN45JNDKYmnTC9Hd5RNv7FYXKFDUyd9HpCvNFbqsaXDEv63TMxgfqkOnWY3CtQydFvcSdUp56ulYBkm6oC5lTXGaVekx1pYpsNr31/D+9UiSqYzSLfFjbMe+BzDTt/0d46BRDTSSiZCLhMTtUwElUyMfht1VchV80q06Bh2wnF4gM+KagM2tZnRWKWHP8hhRxwnU0aVFFVGJSxuP+QSFnt7p+4gM7tIg/39U98+0Zs3HIX5pbqY709ItuE4Dj95cUdK2kLG+/cWiUYmgkIqpsm5JKw6TwlvIIheqxdGpRQ1BSrs77NDqxCj3+rB/DJdXJ8hALCsUj/SBg9Ay6ATpij5UJFGBrPbH/fAvL9d3oiT5xfH9ZjpUJlHBinTK/DQpct46wbCMAwkosRfYoc3iDwa65zT9vTaoFNJsbzKgAqDAv02DxaUacGAgcsbiLksqSZfBbmExbZOC1qHnFET6ZU1xrg/2P+9qTOu+xOSbRiGwW/PXYDF5fyfNHoDyU+7tXuDKNLS5wH5StuwC05vEHOKNWAYIBAKobZAhX6bFwqpKK5Wq8VaOVbVGLG1w4KNbWZsbjNHTaSNSim0CklCk6efT8HnCSXTGWZVbV7EDSyJkIpYeJIccb6n10bN+3Nct9kNuycApzeADpMbcvFILefBQWfUyW7s4d3TRpUUrUPOqL2jRy2r1Md1SW7US1u64A/yW4tHSKaRS0R4+LJG3jf8BRK9PDnBzm4bTcwl49g8AQQOb+pr6rTi0IADdQUqOLzBmGYFzC7SYFahGn02T8ybZMv1CoDhEi5D/HDfAAbsyZU9TUTJdAa68qga1Bcm3/zc4Q1AJk5+lTvTJnutrDFMfycBrKw2jtt4lA2q85QwKCVoGXLAdHij6uZ2c7iOukAz+bUv1MiwqsaIBWU6bGw1RV09GGtFtQFbx9TXxcPlC6Izzq40hGSjUr0CPzihntdjahT89YvO5NHptfkqzCvJ/M2SC8t0KM3ykxIRy6BUL4dOIUGnyRkuT3X6guEOTm3DLhhVkzcPsszIBMMV1QYcGLBHbJk6FZYBpGIWJmdyPaMP9ieWiE+FkukMJBGx+NWZ83k5li+Q/IrElnYzFBnSGqlYK8feXnvKGsOPpVNI0FCkhkIiQqFGhsYqA5ZU6MOrRrOLR5rDLzi8033Q4YWYZVCbnz2dUAJBDkVaecRNgsDoCdn4twlvIASPPxhXLVyhRoZdU7S/i1W0KZ6E5JJvr6lGDY/vI3ytTAPAtg4z1Bk6KTdPLUVIgG1g2jHDbUr1clQYFCgcsxBRX6jGymojFh0u4VFKRTC7fFhZY0x7rHyRiVkwYFCdr4R3ijxDKmIi/h6LRSxCHIdNbWbE26luebURLUPOREIeZ+xAFz5QMp2hjqrPx6kJFsiPro4WamRxTbyayvxSLdy+5GvukqVTSDDs8MLuCSAvRavlDDOygjq/VAur24/mfgfc/iAG7F5saTdje6cFBYcTa51cgqYuK1QyMUp0ctg9frj9IQzYPagryI6E2uULRE1S9/baUTvhe7G6/XF/YBVr5Un/DsW6Ak5ItpOJRbjtzHlChxFRpVEJhzfxVnupsqrGiE1tZhwadKRl0ExNvhIra4yoMCpg8wRQqJGhTC9Hr8WDTrMbA3YvVtUYoVdIoJCKsLHNBIVEFP58dvtD2NhqwqosTaiVUhG6Le6oHV58QQ47Oq1YVqkf//VACAzif5FKdHLeWqXyvZE2M08vCQDgF6fPxUf7B+JqbycRMVhaoUeQG1lBSFaFQTHlqmW6VBgUKNUr4PAGwsM9UjEEIV8tRZFWNm4KXyRtw65xLQx3dVvhHJMoSkRsRpx8TMWokqDSqIJExGBr+/S/I3t77eEuH6PiqcVfVZPYwIiJEtloQki2Om52IU6YU4gP9g0k9PjGKgO6zC4YVdKoG4LjNdKHPvmVQT6IWAZlegUkIgY7uywAAH+QQ5FWlrIuVCKWQWOVARtbTWgd+upzIFJytqHVBAbAnp6RhLPb4oZRKcX2TvO4xykkIrjjmFQsNImIgVYuwZBj+gUOf4hDr9WDQo1s/M8ozo9wmZiBSiZGr5WfWme+P08omc5gFUYlTl1QjNe3Tz8FzqCUoDpPCbGIxZZ2M7QKSdyXTyLRKiRTTqdLh9oCFfQKyaRkzBllZYRlgGWVBnSYXDGffdYWqNBjdsf05jCRc0LibHb5MatajR6e/uj5UqyTQ6+QwO0Lxn12P/Hn0hfj97a0QsdLIg0AZhetTJOZ5ZavzYkrma4tUMHlDaIqT4lDgw4UaeW8vn/PL9VO6ksvpDK9Ah0R9lIUauQpSabrC9UIhri4NlFzAEbzti6ze9LtGrkYww4GSbRjTpt8tRQqqRg9FndcpRa9Vg+WVujHfR4H41ykm1Oi5bXPOd9XV6jMI8OdNK9o2vtUGpXQyiXY1mnFpjYzgtxIQhevxeU6LK3QY1aBKnzS2G8TLiFcWqFHy6Az4qa1Q1E2LMwtGXnDLzfEPhabZZikO5+M5cnAVQYGQI/VHfd4eWCkB+4orVwcdWohMNKjfGWNETu7+fsgj7QZkpBcVn+400EsJCIGOrkYJqcXPZaRhYFkEumFZVpIxlwBlLAMmpPsVc2XIq0MC8u0GJqiI4Nymj0+i8t1qMlXwaCcerJeJHqlhJd63bF2dFmz4r1tTrEGEhGDdpML/gRW6kITik6HHLGd7IjZkc39fA8MopXpGebo+nw0FKrhC4bCrcHELAOlTAy5RAS3L4B9ffzsSrW4fGg3jZw5F2hkqMlXJdTGjC+SKD0qPYEQqvOUyFPLsGXCSsno5hjX4RVjiYiJWqpiVElg43FZIE8l5TWJ5Euv1YPGSgO2JFD+Y3H7w5fppiv70SnEkIhY3n935mbBLn1C+HbmohK8tr0HLIPw3IBAiBvZy2HzQMyyaChSw+r2Y9vhhKMzwgpoPFRSEZr7Hagv0kAtE6PH6kaI42Jqf5kO1XmqqFe87J4A5hRr0G1xTzrxV8vE2NVtRZAb6aphdsWWpOmVEgT5uNw7QaLvyekmE7MwJ9FBg5uQu8ZS1rKkXI8+uwcb2/jPQ/huYkDJdIbTKqQo1ivwafNgyp9rbE/IQbs3ph6RqTRdSdVo7fKsAhWMaik2to68IY32vDw4YMfsIjXs3gBYhoHdE8DsIg04cPAFQghyHDz+EIxKKa9/rGw6dr8kSJxg6z6Ly49FZToM2L1w+4MoNygiXrIEgFmFmkknOMkSsQwaKzOzJSIhqbS2oQB/fv/AlLf7g0E0xTllbjpOXxBLKnTYPmY1MJv6S492aphTrEaPxQNvIBTee6SUisKX+BUSEcQsc7gsZvKKs0zMIhDiEAxxmFWgTkmJS6Lvyem2u8cGhVSU8BXcfX021Beqw72htfLo0zRnFaqx/XAdfCocOSuf1+NRMp0FTppbmJZkuipfxctGlaUVenBA0rtuY20YcXDQicpgCKtqjOAAbD6cGAdCwP7DvSTrClRwMUxKznAnSsXqBV82tJowu1iD/Qm0mRu7EbZQI4uYTDdWGnhPpIGRn+mmNhNW1ebxfmxCMtmScj3y1bKYL4vzZeKiAF8bv9Jp9KptY9VX70uVecpwErexzQSJiEG32Y1SvRwauQRKCQurO4BhpxdWdwCNlQYEuBAODvLblzjbKKUiaBWSaUv8puILcuMKPYwqKRDhBAYYuZqc6g3nfLewpZrpLHBBYwWOnV2Q8udR8dQ7tMfixp4e68gfSxIm1lhF02FyY0OrCRtbTRE3Xh4adMKUpg1sGbwwDWCk/jmRxZD9/XaUGxRYWW1Ej2VyIj2rQIVdPfyukI21rmU4ZccmJFOxLIOT5hWm9TmNSgmvyfPyKgNWVhsgTkEXplg099uxtEKPJRV6tEzYb+MPcvAEQhCxDA7027Gt04qWISes7pGkcUuHGU2dVlgS2IcUi8xdehnP5gmgy+xOqpWfmP2qdW+0NafF5fqIG0v59ObOXl6PR8l0FlBIRXj8m8txy9fmTLuxIhl2d/K7W4u1cji8AfiCXEy9lqN9PwL03ueFxeXL6N6hCokIiXY77DK7sbHNhL4JO+WNSikGHb642jjGSybOjMFBhKTbzafMCQ/8SIdinYLXZNruCWBjmxlLKvS8HI+L88PB7glgW6cF2zst4UmvE3Wa3Lx0wIpXhq+9jMMyQCCY+Hv8vj4HNDIJVlQb4AlErpleWW1IS8eYzdO0wI0XJdNZQixicc0xdSjVx96hIl4MM9KmZ2FZYhu9SvVyVBqV4VZx2zst0CkijxJdXmVAiU4edYU0mT9aIQU5YG9f5m1AHMVxI68Bn0wuH/Rx7oyP1z/XtWVklxRCUs2okuKFq1fj6qNrJ00kTQW1jL8TV41MHG5r2WFyQS5JPv4sXWeJqMfixpxijdBhxKRUr8CWCN214mFy+bCpzYxdETbpLyrXYVOaWi/qeP68omQ6i+zttcU1wz5eLYMOgOMSPjsv0sixbUwzen+Qw+xiDRaWjV9RWVSux+Z2M3qtHjQUTf0mImaz99ezJi9zJyDu6LZG/bknYlWNEe3Dqb0s12v14L9N0/dcJyQXySUi3HraXN7/diM6XKtWph8/FjsRdYXqcI3ygN2LBaVTr7DPKlRjcbkOUhGD2VESzEzelxKvTrMbKml2bF/rt6V2uu9Id5r0rNX/Z3sPr/sQsjdbmYGCIQ5H1KaufMAX5ODwBsEywLwSLSoMCsjEDDQyEQo1MlQaFagvVGNltRFyCYvFYy47zi/VYlunZVLbtJEa5q++trLGOG5jYrQ2a4FQdq5MA4A0xtWjMr0CCxK8EpCMWOOLlcuXnvHCj3/WmlMfpITEY9Duxb40XPXa2GrC4nIdQlwIg0kkHPNKNJP2Vww7fRCxzKQV6lU1RhwccKCpywqJiB1Z3JlCrr0FpGKibyr4g1y4W1YqFOvkaZu47PAG8OQXbbwdj5LpLLKgTIfnr1qNhy5dhrIUlXtwAHZ22yBiATCAN8DB7g1iwO5Fh8mNAwMONHWZoZaJsbfXhiqjAo1VenSZp16VNDl9WF5lQGOlAdsmXMLZ0W3F/NLJyeS8Eg0OZfHuaSbGSji9UpLWWmCNTIyllXrs5LGV1vxSbdqmZO7vt2OPgBM5CRHSl4eGUprMjNXUZUW5QYkV1SMLOJVGJZZW6NB4eDPhyhojVtYYsbRSD2BkMXvs5uvaAhW6LO5J7c9ah5zQysVYVK4Pf21ZpX5c32ilVBz1++RyqtAD6LLEflWvrkAlaPI9lKKWuVqFmNex97F4YXMnbwtB2XFtgYxz2sIS7O214YEPD6bsOaINHfEGOHgPj5duN7nDg16m0mv1RN3MMvGyjkTEwOr2w+HN5vrY2N7s5WIRhtPQz1shFYEBUGZQYEenhbePogqDAkMOb1pXivb22rAwjZuxCMkUZy8pw8oaI37zxl7euxFEsqnNDAYjEweVUlF4KMxEK2sM6Ld6Rjo4MSPv3wwA2xSb2s0uP/yBEAo1MrARJisOOryYU6zBvggtPI0qacoSOqHEM7E4Ty2FRi4e1wM8neaVarGJ5817AFBfoE66HjteA3YvXtvWg2+sqkz6WLQynaVOmV+cVU30p1KuV0waOLCs0oDuDJm0lbjYVg5Eh8ezauWpPa9dUKqFXCzCvj57wp08Ihl2eNFvS+8H2zu7+9L6fIRkkhKdAn/9xlL84byFaXk+DkC/zRsxsR21sdWMdpMb2zqt2NZhQcugM+IQlLG2HS73c/sCERdOpirnKtHJcuDzYTxFHJsy/QFOkM5GOsVIF47uCG1Rk5WvlmJPArMP+PDFoSFejkPJdJZaUKbDNcfUCR1G0ryBULjvpFTMYmWNMeqY2KwR41U4jhu5YMnXqOxF5TrML9VgdrEGUjELMctAImLAcUhJn21Dkr3EE5GOwTuEZDKGYTAvQnlcthmwe8P9nCeK9N6ytFKP3T3CJF2pFM+VPW8gBLcvfVdtG4rUWF5lQLlBjk1t5pSMlC/SytP6PY3VMeyKu9ViJJRMZ7Ej6/LCiWi2GnR4UaZXYHGFDhIWONjP7xslGwpCGvBD7vdA5vdCHAykpYF1rG39RqeMtQ45kx72opSK0DLgwO4eO/b32eELhKBXSiBiGDSlaCxrvlqGMr0Cap4G/sTC7QtiW0d62icRkqkWlunw89PmCB1GyuzuHn/FsrZAlfKOQUKJ562/0+zCwQE7Ly0Gx9IrJRHzCZZhsLndnNKTGL4GxiViZ7cVW3n4PKGa6SxWX6TBb89diJ++tEPoUJLi8QfR1ulCqV4e36AAjoPS74HK54HC74HK74bC54Ui4IU4FIQoFAQzRXVwiGERZFj4xBK4JHI4JQq4pTI4JQq4pHL4Rcn1oIx1FOroGfGA3YtVNUbs7bXBluC4VpcviHklWuzp/arefciR2qmPTV1WFKilqMlTwuUPYtDhnbJOki+BEIdn1negwqiEVi7hvTMJIdmAYRh868hq3PX2/pzscDOrUI2mrpFJuvlqKbpMLrj82dvhKRq9Uhpz3bSYZWD3hLCyxoCNrfwsKiyp0GF7pxUFahmq8pSwewOwuvzQKsTjunGlikegVelRAzyUKlIyneWMyvRfZufb6DS9CoMy+iUkjoPG54LebYfRZYXe44A4lFjixnIhsFwIEl8AKp8bBRj/puSUKmBWaGFSaGFRaOJOrnutHuSppBh2Rk9mx+5Y39BqgogBllTo0RNhF3ws+m3pqyVkGWB5tREH+u3YOabDxrwSLRiGS+lKxps7e7CtwwyjSoonr1yZ1pVxQjKFTCzCqfOL07IZMd1kYhFKdXKwLAOJiOU1kWYZQCUVQyUTQykTQS0TQy4RwR8MwekNwOkNwukLwOkNpKVVm04R+/vX6PwFPsstGIaBVMRg0OENt0KUiRn0pfHzZF6JFnv7bIJMPn5uUydOXVAMJonLw/QJlOX+tyt3NmNFOgNmuBDyXFYU2YeR77ImnDzHS+VzQ+Vzo9zaDwBwSJXoVxvRp8mDRzL9xs/aAlVMA3YmDqYJciOTIxdX6BJKptPVMknMAnOKtdgYob59dGW8oUgNqYjFrhS0svP4Q2gZcqJlyInv/2srHv/WckhEtEJNZp57L1qMvb02tAxF3/CXbTa3m6BVSGBx+VGqi78VrFTMor5QjZp8FWrzVagpUKEmX43qPCV0CknMiZPHH0SHyYWWQSdah5xoHXKgdciJ5n4HrO7Yu3BEE8+AstH5C11mN1bVGNHcb4+rG0gk2zosWFFtGNelwxtIX1a7o9uKVdUG1OWPDITptXrCk5TT4dPmQQzYvfAFQqgwKhM6BiXTWW52sVroEHhjG/PGpPU4UGIfQpF9GJI0JdDRqH0uqE0u1Jm6YJFr0K/JQ7/aOOWKdduwK6Y3OH+EwTQiJvHyjEKtLKEkPF7Lqqa/xNjcP3IyMdoXdfTffPukeRC/fHUX/nD+wqRWFgjJRjIxK2jNaaqEOMBy+D304OD0V7mkIhZLK/U4ojYPq+vysKRCD7kk+a4XcokIDUWaSZMnQyEOzQN2rD80jPUtJmxoHU44qY30ORAJw4xvo7eh1QSWGen1b/cE0GFKpqZcmPfOSqMCeoUUm9rN4Y2YLDOymd7tC+JACqc+j3XtM1vQ1GXFT0+ZjasTaO7AcHxsYySC6TK7cPy9n8Rco5vJSnUyBLu6UW3uhdqX+RtNQgyLPk0e2vSlcEvHr1bPKlTB5g5Mm9jKJSw8ES5flunlyNfI0BRnL9ElFfpxEyYn0sjFsCdYkz2qQC2D2eWLaXiETMxiUbkO2zss8Ke4rvOmkxpwwwn1KX0OQjLNvj4bTv3LZ0KHkTJidiSxjvT2YVRJceaiEpyyoBjLKg28JM+JGk2uP9g7gNe2dcecBIpZYFmVMeJVvkhUUlHEVduV1cakOh0trzJgc3t6N3YvKtNhf78d3ij5y/xSLYKhEPb1pW+I2x8vWISLllfE9RhKpnPAh/v6cfc7zdjbm52T4RguhFLbEE6W2tHRzU/Px3TiwKBfbUS7oRQO2VeXiOoKVNP2WpWJWYQ4bsq6vFmFKgSCHKRidtqVXYVEhMUVOnQMu1CglUEuHqkB3NphQV2BCmKWxYEBOxqKIg9DiNXKmtjf+JdW6KYc9MC3fLUU7/3oGEHa9REiFJPThxW/fT8nNyECI5PxKgzK8JRVuYTFyfOKcc7SUqytL8jI8i6O47Cn14bXt/fg9e3dUXvx1xepcSCOq3ZzSzQRJwWurDZgYxLDVCaWeaRapVGBPps35oXAuSUacByS+uyKlZhl8MQVK3B0Q0HMj6FkOgd4A0F896nN+OxAliWiHIdS+yBqh7uh4vwQsww8Wb7CPqTUozm/KrxSPd2o7QVlWuzutk07kXBhmXbKqZQqqQgLynTY12eb1LNVzAKzCkdGs48m7PG+eY9VppdjyOGLupIwammFPjyYIdWOaSjA785biDJ9/LWVhGS7Cx/5Mq2JULqtqjFi0O7FVUfX4ozFpVm14TgY4vDloSE8/lkrPmkenHR7oUaGAo0s6ufEWFMlvY1VeohZFnt6bLB747/6uLRSj21pnEA43WfjRHqlBA2FamweUw6SSiqpCC9csxrzS2ObtkvJdA7w+IO45pkt+Hj/5D/UTKX1ODB7sA1a78jKbZlekZLJSkIIMSw69MVoNZRiSXUeBh1eOL1BmCZ09ohl5XpUtFWD6d4ElZLxO+HrC9UJ1aHlq6VQSEToNE//OpXq5bC6/CndRLKkQo8ygwLrDw1j8y9PpHppMmN9cXAIt76yM8ma2cy0oEyLH55Qj+PnFKVtg3Wq7Oq24uFPDuGtnb3julawDLCgVIcd3dNfxZtqZXpUY5UBDEY29G+NIzk+piEfTm8QVrcfXRY3fIFQ+GpHTb4S+WoZOA68lILU5KvQGseG2UqjEnaPP+mNlvEq1Miw8RcnxnTf7Dm9I1OSS0Q4YU5hViTTkqAfdcNdKLMNjPu6QsBaN76xXAjV5h4U24fR7KrCoNqAFdUGmJw+VBgUKNLKYXX70RnHB59viiEw80q02NUV/Q14YkspqTi+DySpiMHiCj2a+x0YcsR2wtNj8Uzqec0nmZjFv68+Ar5ACP02LyXSZEZbMysfH//kWMz71dsR92Bko5XVRlx//Cysrc/Pmb/vBWU6PPiNZWgdcuLRTw7hpS1dCIQ4hDiEW9JFIxExUEmjp21bDie7EhET04JNbYEKWrkYnzSPv7JtVEpg8wTQWGXAhlYTWodcELNAdZ4SbUkOz9EpYm81K2IZyMQsOtKcSItYBpesrIz5/pRM54h5MV6KEJLRZcX8/kOQBif/UXj8wjZtn6hQIwOApDpjyANeLOprhkmbB23tCqysMWJHpyWmld2JeqfoKaqUiuJuqm92+SERMTH1T22sMqB1yJHgJeTUXfSSiFgM2LyoMCqhkSc3YIeQXPB6U3dOJNKlOjluO3MeTpmfXN/fTFaTr8Ifzl+E7xxVg9te3411LcPotXqwoFQbtZXo7CJNzCvD/iAX9b0xXy1FiU4+ZfmgyeXHymojNozZHxMIjUy9TSaZjrecZPnhZD5dGGZkk/3DlzWiscoQ8+Myr3KfJGRZpR4FhxPATMNwIdQNdWJpz76IiTQAyKXCr0wrJSI0FGqQr5JiwO7FgN2LArUUxdrp+0pHc4TCC/eHH2Hf3o6Ea8Lz1ZE31bHMSG/qePRYPFhSoY96H6mIwbJKPba0m2Fyxr8iUJWnTOnO+mCIm3YgDiEzyVmLy1CZYI/cTCAVsfj+cXV4/8fH4NQFJTmbSI9VX6TBs99bhfsvWYoirQwsw2BVjRHzSsa34WMYoNygiLtszuqe+j2yJl81ZSI9KlJ3kGQmIjZWGrArhlKWUXkqKba0pyeRritQ4bXvr8ExDQU4bWFJXIk0QMl0zmAYBgvLMm91Wub3Yln3PlRbeqLfUeDK/XK9At5AEM0DdgyNSdIGHT5YXL6kNrZZnD4oAl40du9FhSWxITtjVxgMSglW1RihkYkS3r3tn6JsBBhZla8wKuOqtxtlVEkxp1iNYYc3ocfHyu0P4uYXm+KquyMkl4lYBlV52ZlMr63Px9s3rsXNp8yBcpoyhlzDMAzOWlyKD358LI6oy8PmdjPUh9/vK4wK1BeqIWEZdJndcb3flekVU64g1+SrorZQjSbaZ8dU5pZoMK9Eiy0d5rgmSg47fVhWZYz7+RJx+qJSLKnQ4x9XrMDtZ82P+/GUTOeQReWZlUzr3HYc0bkLes/0rWxi6VnMl2qjCuV6BSqNSpToFChQy6CUiqZc4fUEQknVdI922GC5EBqG2rGw9wAYLr43pA2tJiwq12FRmQ5ObwAbWk2wexMrjTEqJVO2F5pXooU/GIp5Y+REJqcPA3YfHAnGFo8DAw5c9vgGuNM4KYuQTLaiOj2JB1+kIha/Pms+/nnlStQW5M4AskSoZWL8/LS5eOHq1SjWyNBYZUCfxYMDAw74EhhprpGLpxzNHQiFEpoqCQCKOK4iF2lkI2PCe+0J75/Zk4IJupF4D5eaJnpFhJLpHBJPUX+qFTjMWNazL+bx3/5gCA1FGtTmqxL+I5+OQjwyyard5ESXxQ2HJ4B+qxuDDi+ap+lu0WV2JTwfKhAKQSb+6k+t0GnC0u79EAfja1+0o8uKHd3WhN5YR4lZoFinmFRbqZSwWFltxJ5eW9I7pmsPj4RNhyeuWBHXmzshueykeUVChxCz6jwlXrnuSHzryOoZUdIRq8YqA+44ZwGMKmlSg66Go0zRLVDL4A0ktgixsdWMvBh6+c8v1cLlDya1Cb3CoMCcCSUvqbK3z45kmttRMp1DMmUjVqltAAv7DoCNY/W12+JGc78dLUNOyKX8/VoalSPt3BqKNAhxHJr77eGKEpPLh1gj9ARCKE2w1KMqTzWpL7PBY0Nj917IAumt+11SYZj05rakQg+lTJzU9KyxDg46UGFIfb/ni5aXo7YgfYk7IZluTrEGUnHmf6yfubgU//3BUViQgaWJmUCvlOJvlzfiV2fOg0SU2InGoMM7ruxHKRVhVY0RRqUU2zst6IsySCYauZiFxR19wWVltRF7e20JT9utylNiXokWNo8fm9PUP/3T5kH85f0DCQ8/yvy/OhKzTBgpXm3qxtyBVjBJFEEPJdFBY6I8tRTB0EgS7U2g1mssVYKDAhxTNNBX+1xo7NoDhS9ypw6+6ZWScTvBS3QyLCzTYXunBUNRVjHiZXH5YfcGUlp2dOzsAtx8ypyMnH5GiFAYhsGiDE5QWQa48+z5uP/iJRmz+JOpGIbBt9fU4OVrj0y4uUAgyGFVjRErqg0QMSPlgiaXL6mhJ/PLdFMmnAoJi2WVemxsMyX1HIN2L/ZHGEKWavd9cAB3vrEnocfSJ1GO4DgOL23pFDSGSnMv6kxdSR3DqJTCluDZbL5KOinhDXLclD2a4+VMYKpUbb4qanu90Y2JMj9/JxBTGX1DFrMjE8VMTj92xrGzOh4Wlx97emzQyPgtwZCKWHx7TTUevrQxY7vXECKkfHVm/l1IxSwevqwRl6+mso54LCrX4+VrjkR1AptLuy1ubGg1YVObOeE9NmMVamRTboKcXaSBUSXjZeO5yxdEfVF6yjvGmlWoxuWrqxJ6LCXTOeIfX7SltHvCdEpsg6gf7kj6OEZVYqsVRVoZPIEQCtUyGMfUc3UMuyHn6bJnj8UNuTj25FDMMrBNczkMAGRBH5b2xl9DHS+FmMWiMh1KdApsaDXFNBI8GUaVdNJzSFgG80s1WFFtQKlupOWgWiZGbYEKy6sMmF+qnfJ4+WoZXvv+GvzqzPlUJ03IFDKxzEMjF+PpK1filPnFQoeSlSrzlHjp2iMF7dillokgE7OTJvlqZCIsrzZgf7+d1ynGDDOy8JMuLANccWQ16hLcCJt5f3UkIU980SrYc+c7zZg7wM/zJ7KIXKZXwOryw+ENoHXYGZ4QpVNIkK+Wopyn3qscgBLd9D2nS7TykXZGInZcm71oVD43Fvc2gw3x25lCIWGxoFQ7shLt8mNHtzWhoTGJGLB74QtyUEhFKNLKUKCRIQRgd48dm9rM6LGOlLc4vAG0DDqxud0M6RRlGyU6OV6+djXmRUm2CSFAWRr2K8SjUCPDi9esxqraPKFDyWr5ahmeu+oIHDUrX5Dnn1eqm/TZ0Vilh0jEpqSueW+vHXNLtFClaeFkcYUeFzSWJ/x4SqZzAMdxU7bA4cPyagPyVFI0FE0+Y9N6HFjQdzCpGulREpaJ+8y2RCvHkN07bhhKp9mFCoMChRoZ+mweBJPofjEpxilOleViFg2FahSoZei1jbQzcsc51VHvsWNh30Ek+2LOKlRhZY0Rs4s18Ac57OqxYUOrCV1pSqIncvuC6Ld5MWj3Tru5Y1+/fVJPb41cjGe+uwpVebTZkJBoOI7DGzum6emfRiMnwUdiTjGdBPNBLRPjiStW4MS5hWl/bovrq4WhCqMCc4o12NJugSWFY753dtuglIlRnsSch1ict6wML19zZFKDxmZWd/Qc1T7s4vXyykQmhw/DTh9mFY5PpiVBPxb1HoAozp7JU/GHOCilLMoNKrTE0Jy+UCODzeOPuLFw7Bk0ny2svRMSZDHLoLZAjY5h57Tt9WKR77KgztSFQ3kVcT2OwUhLpV6bBwcHnACyc5iJ2xeEifNheZUBNo8fzf0O3H3B4oQvvREyk3ywdwCdJmFOmifSKST455UrUZHFUxkzkVTM4q/fWIbL/74Bm9LU6QIYmSEgFTFYWmnAlnZz2mZDlOkV6LdNvUlfImJQX6iBSiZCMMTB7g2gy+SC2z99XlKqk+P/zhgZXc+yydXxUzKdA/b18dvUvDZfBZVMDIYB5BIRNraOtEzb1mHGyhojnN4A2gYdqO9pgSzIb2s3ly8YU6lHoUYGpzcQ03jVJP9GxumzesAyDEIcB6NSApZl0Nw//VCaeFSbe2BWaGFSxlYfV6ZXQCEVjevUkc3c/mD4e/nJyQ04dQHVWRIyHZPTh1te2SF0GAAAuYTFE1esEGQT2Uwgl4jw+DdX4MJHv0Rzf/KLONORiRnUFajROuTEhtb0jPceJRWzKNHJkaeWwuEJIBDiIJeIoFdIEAhx2Ndni9jLulQvh8nhG3fVeqw5xRo8891VvG3YpWQ6B+zpsaE2X4WCwwmmVMwiGOLQ1BV/pwaJiAHLMhG7PPiCXDixnucewHyxB4kNx47O7Y++EU+vkEAtE0ftkjEWnxvHvcEQyvQK+ANByCSilNUfz+8/hA0VC+ETR9+QOadYg06TC92W3JsCuGZWHq45pk7oMAjJCm3DTl5bXE4lXy1FiONgcka+vC9iGTz4jWVorDKkPJaZTKeU4J9XrsL5D3/J+5VpBkBdgQpGtQwOTwAtQ460J9GjNib4vD0WD2ryVRG7j8wv1eJf310FvXL64TOxomQ6B5hdfrQMOaOWRkjFbNQ+1HOKNVBKRdjVbcXBacoVtB4Hirvb4FaIkaeSYjjGTXaxyFdJp0ySZWIW5QYFWged0zaNH4fnLkwGpQQHB7yAJ4DaAhVaEhy9HY006Mf8/kPYVjp7yrOBUr0cnSZXTKvz6bSyZmSkcTDEweLyIU8tQ1OnGd5A7JcFWQa4+4LFEFMfaUJi0mdNT7/6Mr0CTV1WrKg2RCwz+P25C3HC3OyZxJjNinVyPHXlSpz/8JewxvOZGIFRKUFtoRr+QAiHBp04OOgEUvDZlk5qmQgSEQP/mH1TJTo5nvz2Sl4TaYCS6Zxw86mzsa/PNu6NLV8thVTMokgjh90bAMdxyFNJsXHMfUQsg2WVevTbPNjXF1upAsOFML//EBhwsLr9YADU5KngD4bQxcPZsVElw5DThyqjEjKJCC5vADIJC48/BLc/iEMJ/HHzmUuX6hTY32cPj3ltGXRiVoEa7cMu+EP8tpozuq0ot/ajSx+5zEGvkKDHkp4P0FhIRAwWlOomrSQcGnSiTK+A2xeAKcbNKsurjQlPnCRkJtqShjIvhgF6DyftkRZnvrGqEhetiG+/B0nOrEI17rlwMb73z80JPb5ML0eRVo6mLmvapg2my85uG/LVUtQWqNFtdqHP5sUDlyxNyYwCSqZzgFYuwR8vWIwzH/g8PG3P4QnAEwiNS7Y6hl1YXKGD1x+CVMQiEAqhz+aJa8NKtbkXSv9Xx+QAtA6PJLilOgXkEhatQ86Ee3vYPH7kq6Xot3mmrHWKF8NTOi1mWTi9gXAiPergoANFWjkCwRCvq/QAMGu4CwPqvEnlHnOKNdjTy2+tdjK0CjFKtAps67REvL3b4sbKGmPMl+xOnkcrW4TEY2+EutFkiVgGVXlKKCQiBIIctApxeNGmqcuKVTVGWN1+9Nk8KNbKcdsZ83iPgUzvpHlFuHJNTVwtchkAK2qM2NZhRncGLcrwbcjhw5DDBDHL4M6zF2B5tTElz0PJdI6oyVfh6qNrce97zQAQMRH1hzg0dSY+8U7u96DaPHXbpR7rSFKep5LCoJSibdiFQByrtVVGJZQSEXqsbt4SaT5V5SmmXBnvt3kgYRk0FGnQMujgbaeziAti1nAn9hTVjvu6MoOGlpToZGBZFvun2Yg5trVSNBIRQ5sOCYmD2xdAIBiCXilBfaEaHDeSCA87fdOW7UVTlaeMWsY2Wkerlorw4DeWJdVajCTnlq/NweZ2E3bEsFdKJh75rEq0Hjkbnb6oBJesTN1VE0qmc0iqa2cbhjrAxtAGb9g50kpPKmJRYVBCIRXB6vah3zb1hsECtQxDDi+cviBq81Vw+YKTVoATxdcGxOlWuP0hDs39duSppFDLxGg3uXh53hL7ILq1BbAqRnbGy8UsdvXwvwqViFU1RjT322F2Tb8ZdDDGDaNXH12HcgO10yIkVg5vEBvbzChQy8aV+8nEDIwq6aSpddOZX6qFVMTi4EBsV79uP3sB6gqpfaWQpGIWD1yyFGfc/zns3uib+GcXa2NKuoW0tEIPXzAEmZiFmGXBgcPmNnNCV71LdHL85pwFKR1jT7t7ckQgGMLT69pSdvw8pwUFzvjqqXzBEDrNLjT329Fv86L48GRA0YRfaAYjmwtHTwZahpwwqKQw8rRBgI8/oCqjEi2Dsa3wDDt9aDe5UJunglbOz/nqnMG28DCX2cWaqJtJ06XCoEBTlwXmGOugzS4/VlRH3+FflafE9cfP4iM8QmaMfLUU3z+uDoOO8Ses3gAHhzcAnWKkTKw6T4kFZVrIJZM/+nUKCVbVGFGql2N3jw3bOi2we6dfoDlzcWlSk+MIf6ryVLjznAVR77O82pDxifSqGiO2dVqwt9eGrR0WbGwzYVObGSV6OWry4x/edebiUmjk0TtjJYuS6Ryxr8+e0pXpWlNX0sfoOzwZsNyggJj96levJl81afPigN0Lty+IKh4a/vunaVw9+kEzlTK9Ar0WD+JNX1uGneC4kbKXZKl9rvDJjHSKKYzpwmDkza7T7IYnhsb4YzV1WVFlnHpj4XeOqqFLxYTEiWEY/OTk2TimoWDc19VSEeaVaBEIhtBQpEaX2YVd3TYsKtdDKRVhRbUBNflKiFgGMjGLDa2muDY1a2Ri/N8Zc/n+dkgSzl5SiiPrIo9ul4mZpMp+Uo3BSLI/Wj408eJ0j8UDpzcAUZzrY+nYg0PJdI54aUvyye5UjC4rtF7+WuS0m1zIV0tRk69CnkoasQ8kALgDQXSYXKhMMqGOtoorZhkEQhzK9YpxSa9czKK2QIVZBWr0WNzwJdipw+4NwBcIoYCHxvA1h+vVY10JTpVygyLhnqO+QAgcGBRqZKjJV2FBmRaNlXqsrDZizaw8nLW4lOdoCZkZGIbBvRctHnc1zOELYnunBU5fEM39Doy+Fe7psUIjG9lM2DHsQl1B/Kt9AHDjSQ0o1Mj5CJ/whGEY3HH2fIgjTCtbWKZP6fjvZCgkLBaU6abtKDJg96KhOPZhQHkqKZZWpr7nOSXTOcDtC+Kd3akYnzIi2qbDRPXZPGg9XM4RrQaKw8hkL40s8XIJUZQRiGV6JZzeALosbri8AVQYFJCKWWjlErQMOnFw0JFwZ5JRdm8ATl8A+UmuUGu8ThhdVpicsdUep0ySVTMdJhcG7F60Djmxq9uGLYcv45XoFLz3/iRkJslXy3DK/Ok37zq8QfQf3sMQ5IDmfkfMQ7BGzSnW4FurqxKKk6TWrEINrjyqZtLXk+1FnQpyMYt8tRSlekXEYXGRqOPIB648qiZqDsAXSqaznMcfxI3/3hbu/ck3rccBgzs1m93ELINQDJsMHd4ADEkkorIoZRFjy6ndgRC6zG4YlFIMOPhLWI0qKXQKKSTi5MsXqk09kPNwnESsqDagscoAjSw1tWeXrKxMyXEJmSk8/iA+2j+Qlue64+wFNFQpg91wQj2KtF9dES3SyHAgA0s8FpTp4PAG4pohwcW4wtVQpMb31tZOf0ce0F9CFus0ufDdpzbjnd39KXuOKnNvyo5dYVBGndo4VofJhYYEd4tH+8PjJtzIYaTNHZ9kYha9Vjd6rckPtTF4bKhkUz8yOBK7J4At7WbsSUE/29p8FZZV6nk/LiEzySfNg2kZKX7m4tLwpFOSmdQyMW792lf17JV5mdchaV6JBm5/MO69N1s6zJhbMn2px51nL0jbHiNqjZel/tvUgx8+v21SgT6fJEF/3B084hFvk43WIRcKNbK4L0faPH40FGnAcRwCQQ6BUAi+IAeXN8Bb+7poHJ7obYriVW4fBBB5g0kqRVvhT1apXpHStkWEzARfHBxKy/P8gDruZIUzF5fiL+83j8x8CKYwWUjAyhojNrWZYl5lHovjRoaxaBVi2NyRP1/nlWjTesJHyXSWaijSpDSRBoAihwlM0hXDkUlFLLrM8a3U+kOhmMpCJrK4/XEn4HyyH67F7ozz+52KwToERmkAx6T3wpIshV02ZlGPWkKS9tq27pQ/x0nzitBQFPsGMCIcEcvg6mPqcOsrO8ND1TLFtg5zQon0qEG7F4vLdei1eqBViKGUiCEVs2BZBuCAi1aUp3WBhso8slSpXp7yyxdF9uGUHbsyTwnfNC3rIhly+lBtjG/nuS8Qgkzg2r4huxclWn52vYuCAeQ7098ndGJJDJ/W1uen7NiEzAQf7x+AjeerYJFcd2xdyp+D8Oe8ZWUo1EgFXVCKhI+Pk6YuKwbsXhwccGJHtxWb283Y2GrCzm5rTBtx+UTJdBYKBEP47lObUzq4Q+73QO+JbfpVvPJU0qQ2TJrdPqjiHKet5ml4SqLcgRDMLh+KeUioGYZBkSN1JzpTSbA7YExqC2hlmpBEdZlduPmlHSl/ntW1eWlpM0b4IxOL8N2jalNapherJRV6rKoxYmWNEYEUXlo/Kw1DWiYS/qdL4iZimZRfFi9O0aq0UiKCRMTCOc2402isbj9K9FMP/ohEr0zvH1YknkAIvkAIbAKXnlRSEWYVqNFQpIHNPVLLLgqldnz8WBUGRUJXEmLBMECBJvk+3ITMRPv77Djrr19gMA0rj9fSqnRWuvSIKpTF+ZmZCsNOLza0mrAxwTkFsbp4ZUVKjx8J1UxnIYZh8H9nzMO7e/pT9gZqdKWmY8Og3Ys+HrplxFtykEgCmwomlw8VBgUsLj8KtTKIWBaBUAjBIABwEIsYiFgWHMchGOIgYhkEQxw6TC4cHDPOnAWgd9sxrNLzHiMDQCpm4A189TMWsUzMPUDjtbhcH1ffUEIIYPf4cesrO/H2rr6UrvKNqjAqqBwrS6lkYhxZlx9X+7lUkLDpWb9dUKZLy/OMRZ9gWaqp0wJPisaHs6EgdF7++lFKxSxKdYqY2+DFos/qBQPEvD0ylSUxsVLLxMhXy6CRidBldsM+mFx9I9/JdJleAY1cjPZhF2ryVeNa4KWy6X0qa7EJyVUWlx/v7E5PIg0A5ywpo447WezcZWV4en27oDEY1VJe84BIlFJRxOmPqUZlHlmI4zj89OUdsCdRKhGNzuMAy/GXfFYalGgb5vcPyOkLIF8d+yCXdPWanKhIK8esAjWMKikc3gDahp3wBkK89EgxuvldKS7Vy7Gvzw63P4g9vTbUFqiwtEKHVTXGKUe+J0smZnHTybNTcmxCclmFUYkT5hSl7fnOXlKWtuci/FtaoUeV0L2m03Ded+LcIkFO+mhlOktZXKkbC2pw87fxcFaBGi2DqZm6pJFLMBjjgIJ0r0xXGpUAOHSY3Jg4UsfLUywarwuiUBBBNrmWdSurDRhy+rCpbXxP8ZY0XBKsNCpxTENByp+HkFxjcvqwo8uSludaWKaj9pVZjmEYnL2kDPd/cECwGIJpuIoyr1Sb8ueIhFamsxDDMLj6mFocUWtMyeV3PQ/JdE2eCmV6BSwuH1KVxnr8oZi6eqTrkg8DoK5AhSKtHB0mFzpMkft6JrP5cvzzcUm/VlVGBTa2mdOSOEdSrOOnXSAhM81NL2xHTxJdkeJxzlJalc4F5ywpFfT501GSNLKQlX6UTGep646dheevWo1fnj53+jvHSeNLbipgQ5EGrcNOdFvcGHLyO9q2wqCE5vBmtR6rGyzDoGKaXco1+aqUTjqUiljUF6phUEpwaNA57TjyYacPcp7KTtRJvFYSEYM8tbBdNKrz4usZTggZ8bNT56StfO2MRSVpeR6SWrUFaswXaOV2RbUBdk/qrqiPqjBQMk0SoJLyW6kjDfghDk2/ctpQpEFDoRqqCV0YqowqHOhPTX9qnUKCocPdS8oPJ9A6hQQufxDR1p5bh5wo0Skwa5pexvGuYKukYjQUaSAWMTgw4IApxtIbDkAlT0mkypfYypRMzKBQI8fWDgsvcSQqX+BknpBsNbdEi8tWVaX8eUavtpHccGRdXkqPL2aBcsPkBa4t7Wa0DaduUQsYyQeEOlmgZDrLKWX8jnhW+KMnZ1q5GA2FGrQMOtA84IBCwoabwesVEnSYnCnbY6CVi+EOBGH3BtBr9UAlEyPIjaz01kcZbxsIcei1unFo0IG6gvFJbEORBrX5KtQVqFCslU9KypnD95loVoEaHDg099vhSqCrSnO/fVIsiZju9ZqKN8DBn6K+0fFYVqUXOgRCslKPxY3PDw6m/HmOqE1t8kXSK1Wvp1TEoKFIDZZl0WV2Y07xV5+bI+1WU59u+gIhhATqDkXJdJY7YU4RrzXBKn/kOt9RRVo5mgfs4dqnIYcP+WoZyvQKqGTilCXSDUUadJq/ii3IcSjVydFrHflac78dDYVTJ9TAyIpw65ALFWPOmoccXvgCIRwadKLL4kb9hGNUGVVo7rfDoPyqc0i+SoqDg46EkuhRNXkqXuqUVb7or1c0Monwf/40+ZCQxNz7bjOa+1OzuXus1SleySTptaLGiFRsI6orVKO53xHe7G9y+rC4XIcyvRzLKg3w+FO/ePOzU2dDLBLmc034T1OSFIVUhNV1eZhVqOZlXKjCF3kIjEEhxawCFQ4MTH7z7ra4w/+lgl4hwaEIzzsxluYBO2qnWe0NcRyG7D4UHp64Z3b6xm3iPDRoR4luJNmWiVl4AyMJc4FmJJku1MigV0qilpXEot/ugVEVe2u/qUhCAYiD8W9onFWoxpCd33r2eGnkYpTQ5WNCEtJtSe0l81GraiiZziVauYTXoSaLy3VYWqFH34TNsAN2L5q6rOi2eLClwzzFo/mTp5LiijU1KX+eqVAynQOe/PZKvH/TMbj5lOT79coD45NplVSM+kI1bB4fDgrU8UGrECMY46Ubs3P6umVvMBSeuFdpVI7bnBjkAJc3gCKtDEalFL2HNxOaHD7U5qtg9wRwcDD5UhaXLwiOw6Sa80TIA/EnxQoJC7c/fePIIynWyuEPCV9qQkg2OrqhgJcFlGhmFapRoKF9DbmGr1KPWYVqNHVZsa3TAnMK2/XGQqeQCPr8lEznABHLwO4ZmYaVLHFoJMGSilg0FGkQDIVwYMCBoIBD6vwBbmTD4+H65mjMLl+420ckGpkYpTp5eApTpL6XVo8f/TZvOJEGgCGnDy1DTl4TUJPLB51ckvSIVdHh16xUH/sqr1zCb619ImweP8RpGi9LSK657thZ2HvHqXj52tWQiFLT/nNRefrHMpPUW1yu5+U4fFxd5cvs4uhlnqlGn2Q5Ym+vfdLQjUSIQiFUGZVQSkVo7rfDkwFjuHttHjT323Gg3w53DHXKJXpFxDKMugIV1DIxlDIxNDIxqvNU6EpRaUqseqxu1ExzgjCd5RValOrk6Ld5UR3jhKttHeaIO67T6ZKVlSkdU05IrmNZBo1VxpR1xamjPQ05KdnPHI1MhMZKAza2mniKKHlqHq7yJoOS6RxRqpeDZYCV1caEm5brFBLMKxope7C4hb1kE0mpXoFBR+Sa7rGa++0o1MqQf/is2aiUoMqoxKFBZzgxd/qCkKZoNSde03XVKIwyNr2hUI0dbcPosXoQDHEIchwUEhYSlsHsIg0WlkVuExQIAUUaYeuVqd0WIfxw8DQIaqJkky6Smarzk+vFXGZQpKUOOh4LBb6KQsl0jig3KPHiNavx23MX4L2bjsZz3zsirjHNSyv1YBjgUI81hVEmJxTiYFBKUWVUTrsBsN/mhdUdgEYmhs0TnDS0ZaR9TmYk0ybX1DXPeSophp1+1EcY5VuuV6Bt2BUuzQGATpMbc0q0KDUosL/fHnXc+pYOMxqKhFt5yoyfPiHZb0mFPiXHpWQ6NymlYpQkMX02HZ054jWvRJj+0qOEXRcnvGqsMob//+q6PMgkLD5pjt6H1KiSolyvwLbDwztYLvP+SEaFMLJhr23YCaNKComIjTpt0B8Kwe+d+vuxuKZf5U41qYiFNcpVAKVUhGGnDwcGHKjNV4VrjANcCG1DLoQ4DqIJr9m2MYNYLC4fZhdpsH+KQTqpqrWMxYoa4/R3IoRMa219Pj47MMT7cWlCae6qyVehN8Fx9Jkwo2Aiodus0sp0DltaoY86GGRBmRYcx2FH91er0SEmc38l+m0etA0f3jgY5GBNcvew3StsNwsA8AVDqJniA2tib+2WISeaB+xoHrCjZdAZbk4fjPKaefwhHBiwY9WYxLXKqMDyKgNK9XIopWLBNpGUTTMGnhASm9MWlmBljRHfWl3F2wlyiU4OhVT4jcokNRK96qBTiFEocIngRKU6ueCbIWllOocxDIPzG8vxx7f3j/u6RMRgaYUBG9smbx4IZkl3Bb1SMql0Ix4lWvm4bh1CGnZ6UayVo+9wPAyA+iINmmMcyx5go3/ghThgU5sJxzTkw+L2o3XQiXbTSJLeYxHmZ2BQSjKiowghuaDcoMQLV68GAJhcfvy3qSfpYxZSS7yclmjLQ48/iF0ZVg4ql4jg9AZ4aTWbqOzInEjCLmyswPFzCsP/XlyuQ75aGjGRBoAAk/kJTolOnlQiDQAaubA9KceyeQIwO30o1spRm69CgUYWcyINAMFpkmlgJKHut3nR1GmFzZOazUrxoESaEP5t77Tg8wP8jBhXSmmtLZeNdr9QS0XIj7LJfSJvgMOsCHt4hOQNhKAU+CoK/bXkuAKNDE9csQL7+mx4dWs3HvusBRFaK4dlw8q0XJz8H82QIzNWpcUsE+6+0mV2o88Wfy1atDKPsYRuHTQWJdOE8O/17d28Dc8QcpWPpJ5BJcXCMh3299tRoZFhKMpm9YkybQPiZUdUgWGE3dKe+ZkT4cWcYi1uPW0ulldH3/Q1XclAJhAnWRMoFbEwCTitScwwqM5Toa5ABZZl0DLkRMuQE74EN3XEsjINjPSkzRSzi4RtsE9ILhq9dN9QpIZUlNzHe14GDeQg/BOzDHZ2W+ELhLC3d/y+Go1cHLWmunXIicUVmTPQJ56BZalCp54zzPKq6I3WPeLMr5NLdhhjkVY2bmNfuswqVIPjOHSbv9pIyQePOPqH3tJKPUQMg+EYenSnS10hdQkghG/fXF2NAZsX3zqyGmKWwX939OCd3f1o6rTEfAyFRISFZdqkFy1IZpt4pXJTmwkLSrUIhDjs67NP27p0wOaFTMzCmwGD3TJh+BetTM8wK6ZZmXZLMz+ZdiZZ86sQoBawJl+FgwMOHBp0whPgr4uInxUjIIr+/bAMsLndjEOD/CXwyaowJDc0gBAymVomxu1nzUdNvgoVRiWuO3YW7rlgUcyPn1+qhVYuxsY2s+A1qCS1JpbxhDhgV48N+/pG9uvYvQEYlVPvLeq1ejJmdToTrnRSMj3DLKs0IFppkVOS2e3KxCyDAXtyK6ymNK7QqqSikRZ3ptSshDul079eww4fMuDEfZxEp3QSQuJTX6TB4mmmw0lEDFZWG7G7x4b+w++vGdhKmPAoGG3zFACOG9nbMnGKLoORRgaNVQZ0mtyCf7ZIxWxGbIikZHqG0SklUc/i3BLha4+iMSolCHKJF3qU6RUYcsa+0SJZDMOg2+xCIJSaT6ZYXi+L258Rl8HGqqBkmpC0Ob+xfMrbyg0KlBuUkzo8uXzCd/0hqRPLCPoeqwc7u21YXZcHlVSEReU6FGnlaOqyYku7GcEQB0mStfnJKtTIBN98CFAyPSM1VhmmvM0nliCYwe3xlNLEW9pJRSy8/vQOainUyFCkTd0JiksyfVmOxeUX/A1vLBHLJDXKlhASnzMXlU4a5sIywIpqA4bsXrQOTS4BiyXZItkrnpOlTpMLDMNgR5c1PA8BAAbsXsFrpg3KzNgomzmfsCRtpqubtssyd9VQKk78DLQyT5m2VWmNTIyGIg3ah11oGXKiNsokymQ4YnyttBnUV7tII4M4g5J7QnKdQSXFi9cciVPnF4NhgHklGlQYlNjUZoZnimTI5RN+QixJHUccE4C7zG4UaDIjac1U9Ik2Ay2vnnplGgAsCuGL+ccq1MhQX6hGXYEKEhGDKqMKijj7FNfkjWwATBUxw6BEK0ddgQpVRhUc3gCa++3hkpQesxt6Bb8JLQcGFnlsr1WFMXNq4al/LSHpt6RCj0cub8QHNx2DmnzVtIOvaGU6tznjeH3z1VJ0pGjfT7KEHiM+ij7VZqAyvQIlOjl6rZEHl5gUWlSbkx9Hmwgxy6BEp4BCKoLHH8SgzYMBuzfipsN8lRR6pRRgAJvbj0G7N2LbPK1cjH779ENaJCyDqjwlWIYJJ8Giw0NsOI4DxwFmlw/DE1a3NTIxSnQKNA9MPbXQEwihwqiExc1ff2u7TDltJ49RwleUfYUGthAinNoCNX515ny8ubMv6v0cGTAplaROPMm0RMROu2FRKNNtrk0XSqZnIIZh0FhlwBs7eiPebpWrEWJYsFx6aqGqjErIJCK4fQH0WT3oNMc2KnzI6RtXtiEXsyjSysPH6rd54Q+GoFNIpz1miVYOi9uPg9O0jxOzIwNXRgdFWpx+mFw+2KMk0qP4fjMyK7TT3wmAmEVGjBAfJZfQBTFChFSolaMqT4n24anfF3usmbkSSfjRHcOshQWlWohFLIKh0JSLb0KrysuMmQWUTM9QK6qNUybTIVYEm0wFvWf6BJEPQw4vnDzU53kCoXGXLhkAOoVk2kRazDIIhDi4Y9icGAhxCQ9c6TS5IROx8PLUcyrWZDoQArrNLkhEDPxB4VcXaGWaEOEtrzJGTaYtLj/MTh8MGXIZnfCrJcKm04k8gRAO9tjSEE3iMqUzFC0RzVDT1U2blOm5dKKRiXlJpCPhgJjKKqrzVBhMQ+9pfyiEYp66WIQYNq7adrs3iCKtfNKOfiHQmGJChLdims8AILaEi2SnSB1cJhpyeCEVZ3aamCn7gTL7p0RSZk6xdtI40bH61HlpiaNEnxl/COnC1xvTkFKPIBvfCm+X2Q0xK/yffOkMe80JyUTTLagAQBsl0znJ7PTBGsNCk9XlR2kGtzGVilgUaTIjPuE/WYkgRCyDZVH6TbulcljlqZsqpJKKUVugQnN/ekpJorG40jvEhQ99mvyEHrewTPjNGjPtBIqQTFRXoIYhyrhoILbVS5J9Yr3iwAEZ3ca0zKAAmyEDyTL3p0RSbkWUZBpIPGGbzqwCFQAOLdNs9kuVIq0c9YfHjzKIrRQkkwRYMYZUCSbFGfC+U6bPjJUEQmaykY3o0WcOpLKdKBHOoTheV6U0c/e4lBsyZ2GGkukZbPk0w1v61UZwPGZfOoUEVUYlDg46U1YnPRWpiEVDkQY6hQT9Ng8ODDhQbVQhTyVFII0tf0I8PFe/2giOSexP1xcICV43TWUehGSG6eqmN7ebwXHCb1om/Nrcbpr+Todl8obxTNp/Q8n0DLakQg9xlEskfpEEQyo9L89VV6CC1x+cdlBAqugVYjT328fVidm9ftg9gag/A77ZPMmvgvdoCxJ+7MEYWvilWomOkmlCMsF0CypDDi8OCXQFkaTO+pbYk2lXBg/vyaREn5LpGUwhFWH+NDW0bYbSpJ5DKRGhNl+FQ4POKcfWpsOAw4f8CWexw04fqvJU0GTQqO3pWOQa2JKoZQ+GOEHb46llYmjl1JGTkEywoEw77abo9S3DaYqGpEO3xY2OOBa17Bk0o2AiSqZJxpiubtomV8Msj62f8USVRiVELJMx7ZWMatmkrzUP2GFO4wbEaB1UYpHsyY3bH4J+mk1HqVSql/O2CZMQkhyZWIQl5fqo96FkOrdsiPP1LNJlRkvVSCiZJhkjpvZIxvgSOAnLoqFQjQ6TC/YMukQUSyugVEtmZ7RDqsQwD2U3EgF3Z1OJByGZZbrPgPUtJqqbziHxnhxtbDVhXkliC2qplknTdDMnEiKI6XZzAyMDXOyy2EZ2Fmvl0CnEaM7AXeD9No/gJQa2JBL61iRXpUcJuWmDNh8SkllWxFA3vbdX+L0WJHmhEIfPDgzF/bhd3VYsq9TzH1CSCjSTrzYLhZLpGa5AI0NN/vSJcouxLOrtLMOgoUiDAZsHQ870lU3ESsQAswrUsAlc/9Vn86BIG39rOKdUgQH19Cc+06nOUyaV0Ccrk1oZEUKAZZUGTFd59XpTd3qCISm1qc2EXqsn7scFOWTEwK+JMmlxJvN+OiTtGqepmwaAIZUBQ0p9xNuKtHIUaGRo7rdDuC2GU9PIxCjWKXBwMDNWyxXi+Ou89hVUY9pPvBi0DbsE3VAyuyj2EeiEkNTTKSVoKIz+d/mf7T28tPUkwnpte+InRT0WN4+R8KM0g8oGKZkm0/YaHdWcX4XQmP7GYvar1eh+W/xnu+lQrJWDZRl0Z9Abwf+3d9/xdd313cA/5+69tLdky9uWt7IcxwkJDw2BQii0pMBDCAmUkdHQ0LQUnr4oJTRJ6UMYJYRNSQghzOzhkG1bnrJleWnL2ndJd4/TP+Qla13de+4951593q+XX8Eav/OTkI++93e+o8sdwNKS1NJmgMnR7l6jdDlrqyrly39bUc5gmkhp5subHvCFsasz9XZqpDyReAJPHRpI+/P7vaGctpFNRaWCBoAxmKZ5e42eFdIZ0O2oADD5itBh1OL40DiUel6xpNgMdyCqiMLDi/W6QyhPId0jIahxsrhW0muHczww5yyTTo0qBT2WI6JJ8+VNA8Dv9jPVI5/tbB/JKM1RJSCnA87mYzVoFNXWlsE0YUmxGa4Ui9IGS6tQV12C076QInOjz1pWakHHaADRhBITT4BoIonxSBylM7Tru9CpompENNIWDAZj8gTT5fbJpwREpCypdHV6unUAYZnuHZS5TF8MJUTgkobM63akoqQUD4DBNAEQBCGlvOlVFVbYLUb8RiyRdMy41CrtRnTkwdSuQCSOQDSB0lkqkj1GG3rtZZJec321HSdl6rRy2hti3iWRAlU5jKiwz/2kbDwSx5P7eDqdj3rdQbxwdCjjdZIKapGopBQPgME0nTFX3rRZp8aWeieODoxjwBeGz2idt7uHXEw6NQLROBIK+kc/l0A0jmA0AYdx6uOqmEqDI2VLJSk6vFBIxpOlcCyJoXFl5tYTLWapHqh8/9VTiCv0aR/N7gevdSAhwUGGkgZuVSgsZZDBNAGYPW96XZUdZr0GLV2eKW/vclbCbZx7FLkcSix6ReZIz2UiEp82GfFweaPk6R0A4DTJ12MaADrz4IkB0WKUSt5091gQTx8ezMFuSCoj4xH8ak+vJGsp6YWU0upvGEwTAGBtpR16zfkfB5tBg821DrT2+zA8Hpn+CYKAI2VLEVUrpwBgeZkV3e6g3NtIS583hDqXCcCZFyqm7LxQkburSecYg2kiJUolbxoAvrvzJCci5pEfvdGJSFyaIFhJJ9NM8yBF0mlU+OZfb8CSYjM21DigUQnY2+Od83OiGi0OlS+b0i5PLjVOE04M5feULhHAmMmR1RQafzgmdebIguRDLjvRYrSy3DbtCdlM2gfHsfPYcA52RJnyh2P4xVvdkq3nVlDTgQoWIJJSXb+uAs/fuR0fubQO5hTHbvuMVhwua5S1INFm0MAdjCq2RV+qDgcEeFatg5jFFyeBcBx2o3xPE7pGGUwTKZFaJWBTCnnTAPDNF05IkoNL2fWDVzswHpFuSFeRRd40wQvVnnmSqxQMpmkKjUaFv9pcjZf+fge+9v6181Z4A8CIxYn20vrsb24GKgBWgxYBCW8YcghqjThQuQJF9uzeIDRqAd6gfDnlTPMgUq6tKQbTrf0+PLanJ8u7oUx0jQbw/T93SLqmSiFdvKx6TUqxSS4xmKYZ6TQq/O0lddj5hR34yntWo3iefsinbaU45arO0e7Oayy1yJ4HnKmIWod9lSsQU2uxv9e7oOmIC75WXN7TpJ6xoKKKWIjovM0p5k0DwP3PHVPUY386TxRF/Osfj0g+Z6HXE0ClAoLYZWUWReVvAwymaR4GrRo3X9GA1+65Gvf+xUo4TbOnCHS5qtDpzF3LvCXFZhyXqWeyVMIaPfZVrUREO/liRRQnizyyeZuQcyRsPCmiz5PfL36ICtXZeplUeIMx3P9ce5Z3ROl48egwdh4bkXzdAV8EpSlM7s22FeVWubcwDYNpSolRp8anrlqK1754De6+bjmss+RUdxRV41hJfdb3YzdoMeDL757FAZ0RLdWrEdRNLaQ4OTyB5ixOmtJp5P1nz1QPImUy6TRYU5V6J6HH9vTiQK83exuiBQvHEvjXPx7JytrFFh3aBvxZWXshlpZY5N7CNAymaUEseg0+/45leP2ea/C5qxth1qmnfUyfvSzrXT5sRo2sA0gy5TVY0VK1etZe0ru73FhTacv4OioBWFluxdoqG5qq7dhY65C9UJNFiETKlWreNACsr3bgvmfaOWZcQf7zheNZe/pXX2RGVKI2e5kYDyuvRorBNKXFbtLiC/9nBV6952rctn3JlB7VADBiceFAxXLEVal1BVmI5WVWDPgicMk8gCRdI2Yn9leuQFw9+/dGFCd7QjvmSKtJxaZaJ9oHx3G4349DfT7s7/EiFJX3Fx+7ABApVyr9pl1mLTbUOHCg14u3O8bw9aeP5mBnNJ+dx4bx8KvSFh2eVWHXY79CnkJ0KfDppvSRDi0qRRY9/un6VfjktgZ895VT+OWunnNFDx6THbtq1mDd4EnYInP/8KsEAVaDBla9BlajBja9Flaj9szftbAZNJPvN2hh1ath1GkgCALuffLQzENlFEiEgJNFNehxlKc0JtwbjKG53oXdXe60ryllWySpbKxN/eSLiHJrc93cKWab65w4PjQ+Jb3jp29147KlRXjX2oos745mM+gL4+7HD2Zt/TKbEQM+Zfyu7VTg000G0ySJUpsB/++9a3Dr9iX49ssn8MTePpj1GhSZzVA1lqPWfxpL/IOw6bWwGSeDYotBA5thMlA26TRQpVEYV1tkzotgOqzRo7W8EX7DwnK9DvV7YdGpMZHmaXLHiLIKNHVqFdZWZZ6+QkTZUWLVo6HYPC1gKbHqUW7TY2+3Z8bPu+eJQ1hTaUeNwvr/LgbxRBK3P7Y/q91V2gf8KLcZMOiXv1apczQAURQV1dGDwTRJqsphxNdvbMLX3rduenA8NAQcOABEpfsHX19kQksGJ7e5MGJ2oq10yZxpHbMJx5IZnU5X2I3oUdCI9bVVNug10/PsiUg5ttQ5pwTTW+qdOHraj9b+2Q8u/OE4Pv/ofvz605dBq2YGaS596+WT2N2Z3d+D4XgSlQ5lBNPj4TjcgSiK5mnZm0v8iaesmPGUuawM2LEDqK2V7Dr1ruz1ZM5URK1Da1kjDlUsTyuQPqvfl34xiSiKaG5wQcZueFNsYooHkeJ96d2r8flrGrG0xIzVFTa0dHkQSOHp2IFeL+59shWiyLqIXPnTodN46OUTObmWRkEvkpSW6qGc7wwtDno9sH49sG0bYE+9BdNsaouU90hRhIAuRyXeqmvCsLUo4/X6PSFsXkCF/YV6PSEIAJRS85fu10FEuWM3aXH3O1fg15++HFcuK4ZBm3qo8MTePvzHc8eyuDs6642To/j/L51Arl67KOlFEoNpIgBwOoErrwTWrQN06XflMOs1KLUq51GP22jH27XrcKq4BgmVdOkMJ4cn0u7sIef48Is11Tjk3gIRpchl1uHe61fh1Xuuxscvr4cuxZPJ771yCj98vTPLu1vcDvf7cNvPWmZsT5stSspRZjBNdJYgAPX1wLXXAmvXAkbjvJ8yk9oi+VM9hs0u7Kleg/1VK6cNYZGCLxRDQxpf58YaB44NjUu+n3QN5Pnod6LFqNQ6WWD+yj/swIeba1OakvjVP7Xh9wf6c7C7xadrNICP/3g3AtEEDvT6sKIsN0NMlBNKM5gmmk6tBhoagGuuATZsACwLuzHUy5TqIULAgLUEb9U2obVi2YI7dSzU/l4vlhSnFlCXWfVYWW7FKYV189iV5SIZIsqeSocRX79xHV6+ewc+sKl63lqMux8/iOeODOZmc4tErzuIj/5oF0YnzhfyRxOiYupicoXBNNFsVCqgpmaySPGKK4C6OkA7f2pDrosQ/XozjhXX4fX6jWgrW5KVk+jZlKSY0lLpMKJ9cBx+hU2KYjBNlP9qi0x48EPr8fxdV+E96ytn/bh4UsTf/WIvHt3dk8PdFa4jp3248Xtvotc99Qlf52gAW+vn7g8uBQWlTKNrLICkUoqBwNZ4pESCALhck3/WrgWGh4G+PmBkBIhPDw5zUYQY1BoxZHFhwFqMkM6Q9evNZiKFISwNxWbFTKq62N4uN+KJpKKqwokoPY2lFjz04Y34zI6l+OYLx/F829C0j0mKwL1PtmJ0PILPXdOoqLzbfPLWqTHc9rOWWQdx7e5yY02lDUdO+7O2BxHKCV7DsSSGxsOosOfuMGsuDKZJ2VQqoLx88k8yCfh8wNjY5B+3G4jHzxUhSjm8Jag1wGO0wWO0wmu0IaJRxujy7rH5e0aXWHWKewR2ViCawJHTfqxnISJRwVhVYcPDH9uCQ31e/OcLx/HKsZFpH/PgC8cxMhHBV96zBurFlpOQoWdaB3DHYwfOTReeiSgCp70huMw6SYa3aFTAsjIrIvEk4gkR8UQS7YPKqb8BgF53iME00YKpVJNdQJxOoLFxMriemAACAehPJDDQ2gtjLAxTLAx1MgG1OMeNBwISKjWiag2CWsPkH50RQa0eAZ0JUU16nTOybSISh9OkhWeODh1z3G8VYVfnGINpogLUVO3AT25uxt5uNx58/jjePDU25f0/e6sbg74w7v/getiN0txjQ9EExgIRjE1EMRaIYHQiirGJKJKiiM9e3SjJNeSSTIr4wWsduO/Z9pRSLDzBGNZX2yUJpjfUONEyy7RLpagvVk5rXAbTlL9UKsBmA2w2lG9eh7bhi27Oogi1mIQmmYA6mUBSmAygE4IKSQnb1uVakUU/ZzCt9EOfF9uGcdv2pXJvg4iyZHOdC7+89VK8eXIUD75wfMoI8ufbhtD2rdfw7Zs2YcMML6qj8SQ8wShGJ84HyJP/jWLszNtGA1G4z7w9OMswGUEAPn55Pcz6/Axz3IEo7n78AHbOcMo/l4N9PiwtMePUSGZPJ0cnpHvSmw01LiNKrfKlXF4sP3/KiC7SVDXDABhBQEJQS9rvWQmc8/SbVik8J3F3lxt9niCqnco5VSAi6V3eWIzLlhbhleMjePjPHUiKIootehRZdHjj5CjiiSQeea3zXMA8OhGRrGhaFIG2AX9OCvOktqtjDLc/th9D/vQCWodJByCzYLrPo+w2pkqbpstgmgrCmpmC6QI1XwHP2ceBgqCs6usL/eHgaXxmR34/giWi+QmCgKtXlOLqFaUzvt8TjOEfnjiYleFSrX2+vAqm44kkvvfKKXzzxeMZTa1t7ffBatBgPIMXJo2lFsXlSF9IadN0WVJPBcFu1KJOgaPFs2G+c+doIonmBifWVNig1EPq3+8/LfcWiEgBrltdhpfv3oEPN9dIfr863O+TdsEs2tvtxnu//QYefCGzQBqYTJVZVWHLaI2R8Qi21Dmh1ygzTOTJNFGWrK2yp9TtIt+pVcCyMgt6xgJYWmJFUhSh06jgOFPQMx6JY3enB1vqnIo9mT42NI6jA/6Mb/hElP9cZh2+fmMT/nprLb78+8M41CdNENyaB8H06EQE9z3Tjif29km6bvuAH1a9GuORmXPK5zMWiKK2yIRIXHkV7UatGivLrXJvYwplvuQgSsO6RZLqEUuIODE0gVKbAW0DfrQPjuNQnw+vnhjFqydGsb/HC2CyTZKSPd7SK/cWiEhBNtQ48NvPXIGvvX8tHPPUhqTi1MgEglFlDa46K55I4qdvduHqB16RPJAGAH84jhXlmR1WpDI2Xg5N1XbFzSpQ1m6IMjBjEWIBip95BnjxFKyLjUnQHimbfv5WN44OZG/AABHlH7VKwN9eUofXv3gN/vn6VShNcerrTJIi0JbFISbpCMcS+PlbXdjxwCv4yh+OZJTXPJ+Wbg/WVqYXUFc6DOcOZpQmFEvvtD2bGExTwSjEIsRqhxGrK2wwnMlbEwCoU0wsjCm84XQ8KeKLvzmEuML3SUS5Z9FrcOv2JXjti1fj39+/Lu2aGKlSRjI1Ho7he6+cwrZv7MS//P5IzrpleEILK+w8exhtNWjPHdwozeF+H8bD0hesZoLBNBWMQitC3FzrRJ83hLYBP+wmLdZX21Fk0aXcSF+h98EpDvX58OM3uuTeBhEplF6jxk2X1OKlv78K375pI65aXrKgXvpyFyEeHfDj358+isvvexnfeLY95/2b+z2hBeUXb65zotppxDEFd/JIipjSu1wJWIBIBaWQihAvvOkO+SNp9xxVugdfOIZ3rilDXZFZ7q0QkUJp1Crc0FSJG5oqMTIewZ8Oncbv9vfj4Dwnz3IUIfZ7Q/jDgcn9HRuSPyhNddpkpcOAfT1eJPLgJGZXpxs7Zmm3KAcG01RQ1lXZ8dShAbm3kTGXSQutRsDyMguOD02kt4ZZC3dAWY/CZhKOJXHvk634n09eMm8PbSKiEqseN1/RgJuvaEDHyASePTKItzvcaOlyT5uIeLYI0aTLXrgTTyRx5LQfb3eM4eX2YezqdGftWumYbUrkxSodRpz2hrO8G2nsVtj3mME0FZRCKELUqQUEognoIwnUuNJPWymxGvIimAaAN0+N4dctffjQ1hq5t0JEeWRJiQWf2dGIz+yYrBM51OfD2x1jeLtjDG2n/RgLRNF22o8tEg5vCUTiODE8gd2dY3jr1Bj2dHkwEVFm1xAg9c5OpxU+9fBCB3u9CEUTMOqUMeGYwTQVlEIoQrQatBgLRDHkD0OjTv+k1qSQm0yq/u2pNuxYUYJSm0HurRBRHtKqVdhc58TmOic+e/XkhFVfMIbHW3rwi7e70VBsQX2xCS6zDiadBha9BiadGha9BkadGtFEEoFI/MyfBAKROPzhOHrdQXSMBtA5OoHO0UDepdyNBaLQqgTE5knfUGrB4UziSRF7uz3YtqxY7q0AYDBNBeZsEWI+501b9BqMBaJIivO3v5tLql0/lMIfjuMbzx7Dgx9aL/dWiKhA2E1aOM16/O7A4p66WuEwoGee3yf5dgDzzOEBxQTT7OZBBWdtnp9Od7uDaCy1ZLyO0lvjzeS3+/vQMZJejjgR0UwWy0CvuRRZ5u/XnW/B9FOtA4gqZEIjg2kqOIVw43RKMP0r1aITJUmKwA9e65B7G0RUQJaWmGHQLu5wR6ua/+vXavLre+QNxvDq8RG5twGAwTQVoEIoQjzc78v45u9bYLN+pfjjwQHFjgAmovyjUauwuiKz0dr5br6sP41qMr+8ud6J9dV2xY4Sv9jvDvTLvQUADKapAOV7EaLdqMG6Kge06sz+earz5GZ4sYlIHM+0Dsq9DSIqIIXwxDJTposOaPQaAVaDGs31TmysdcJp0mF3lwc6jSpvihFfPDqkiE4qDKap4OT7JMRyuxG7u9wYD2d2g8ikrZ7cftXSK/cWiKiArKt2yL0F2em0ajQ3uGDUqmHQqhCJiwhFE9jf68WeLg/293oBAD3u/CngD8eSePaw/IcvDKapIOVLEaJJO/n4cUmJGVvqnGhucOGERBOz8vNcetLuTjc6RwNyb4OICsRiP5lOiiK8wRh2d7oRiiUQjk0W7sWTQCxx/hRaqxLyrvXft18+gXBM3hohBtNUkJRy46x0GGbM1VtXZcP6ajuqXSa0DfjRMRJAS7cHuzvdkOrpWiSmjCrndP2ap9NEJJHFXoSYFFP7xWLU51dHDwDoGgvimy8el3UPi/cniwqaEoLp5nrXmbY9529iG2scMGhVaO3342CfL+1R4akYGs+PsbCz+c2+PsTzsL0fESnPYilCbK53YkudE5tqHahyGGA3arCp1oGTw6k96dPk2XyCsx55rROH+32yXZ/BNBWktZXyBtPNDS4c7PNCr1GjbeB82kb3WODc47Vscwfy61HdxYb8Eew8poy2R0SU/5RwyJJNFp0ae7o9aOn2YF+PF/3eMHyhOPb1eFPu7qTK08L1RFLEPU8ckm2+AoNpKkh2kxa1MhbgeYNRROLJaePA3cEYSqzzN8+XQjQuYnOdIyfXypbHmepBRBLJl1qadE1EExm3hk3mSRePmbQN+GWbU8BgmgqWXKcQW+qc59I3ym2Gae8P5WiYigigL4Nx5Eqgz7MhAkSkXE0F3NHDZdKiud6FRIbBsDsYy+v77n+9eEKWKbr5+x0jmoccpxDrq+1oHzyf1jEyHoH2gtPpKocxpz0x8/iQAQAwNhGVewtEVCAKuQjRbtJid5cbWrUAozazIsJy+/RDoHwRjSfxj0+25vyEvTB/qoiQ+5PpzbVOtA34pwTLHaMBuEy6c3+vdOT2JpXPJwwAsLfbA0+AATURZa6QixDP5kSPRxIIZdgmzqrXSLEl2ezudOPRPT05vWZ+/6YlmkMug+kKux57ezxT+nWeZT5zYzJoVBj05bbDhjcUzeuTmGgiKVsOHBEVnkIsQlQJgCc4GUyfHM48xUGT4fRdJbjv6facHsTk98sPojnYTVpcusQFbzAGnVoFrUaASpj8A0z23YwnRcQSSUTjSYSiCQSjCQQicYTjqVcEr6uy4dTIzG2Hal0mJJJJLC0xn+ns4Zfka0vV6go7WrrdOb2m1B55rRMfbq7N64mORKQMhViEmBQn266enWCYKbk6YkhpPBLHD17rwD3vWpmT6zGYpoJWZNbj7Y6FB5NqlQCLXgOjVo1YIgkRQEORGa39XkQvOn1OikBwlqLCXk8QKkHIuCgkXQlRzPu86Wgiif/Z1YN//Ivc3BSJqHCtqy68YBqApL9jclnXk00/ebMLn9q+FHaTNuvXyv+zfKI5pHsKkUiK8IViGPSHMRaIwh2IYm+PByvKrTBelDYxV16yKEp7k1uo8XBqvUWV7uSwNCPWiWhxayyx5HXq22wO9fskq8kplDqVYDSBPxzsz8m1Cu8niugCTRKfQrT2+1FmM2DDBS2WlHzym6s2fNn2+slRjE7k9xAaIpJfIRchVjqMGa+hUQH+cGGcTAPA4y19ObkOg2kqaNmYhNg1FoQ7OPnKfVmZRdYRpvOJKznSX4BwjIWIRCSNQitCXFdlw4oyK1q6PBmvFU8CK8utEuxKGVr7fWg7nf1aJQbTVNDsJi0aSy2SrzsyEUGdy4jxcFzRAasmT0fDzuR3+/vx6vERROKFcdpORPJYl+fDW1xmHVxm3ZS3HRuSLhXOZiiscrpcTNJlME0F75GPbUGVBI+/LhRPJDE0Hsl5q7uFyufm+xcb8kfwsR/txmf/Zx9EUbkvYIhI2fL5ZHpLnRPuQBSJpIgtdU4sL7OgtV/ak1dBKJxDGAD43YH+rB/CMJimgldfbMavP30ZlpSYJVszlhARjim/fZC7QApJLvTi0WG8fnJU7m0QUZ5S6iTENZU2bK5zTnmbViWgqcqOrfVObK13nmt/5wvF0NLtwfEh6UdnRxbQGjYfeIMxvNA2lNVrKO+niSgLKh1GPP6pywq28GQmFXb9rP2v891v9+WmQpuICo8SixDXV9txfGh8So/naqcROo0Kh/p92NPlwZ4uT066Qw35lf3ENR3ZLkRkME2LRrFFj0dvuxRbLnrlX6gGfBHUnhl0srLciksaXNhU64BWPfMjPN0FLf6KLboZP0YpXj0xwlQPIkqbUlI9dGoBG2rsONjnw/IyKw71nS9o7/OEUGbLfaqeO1B4nZNeOzGCXncwa+szmKZFxW7U4me3NOPKZcVybyUnLHoNttY70T44jl2dbqhVAmIJERtrHFhSbEZjqQVb651wmbSw6jVobnCisdSCuiJlTxscnYiiY7QwT92JKPuUMgnRadLh4JkAOhSbntdbJMPBRjwhornelfPrZpMoAr/c3ZO19QurZJMoBSadBo/83y2487EDeObwoNzbyaqLx5eLmBw7e6jPi8QMB7tjnZM51pc0KPdGKgiAzaDFqeEJLC2RvlMLERW+JoV09KhwGDE0PnkSbNFND8nkeP6WEIEDvZm32VOaaBZzwRlM06Kk16jx0Ic34h+fbMUTe3PT1F0JUu1DemJ4AhqVoLi2fzq1Ck/dvg3LygqnDyoR5d7ZIkQ5C8mb613Y3eU+93eDTj3tY3xBeabYKu3eL4Vs5pszzYMWLY1ahf/4QBNuvqJe7q0ojjsQlbydoBRuuqSWgTQRZUzuIsSt9U60dLunvG1PlxvGC7qM6DUCusbkSWfTqQsvPDzY583a2oX33SJaAJVKwJdvWI073rFM7q0ojl5hraOKLXrceS3/fyIiachVhLi13ol9PV5cfFAqikDygsLqNZV2xGbKx8uB2QrV89n+Hi9a+7IzsVhZvy2JZCAIAu66bjm+9O5Vcm9FUfQa5dweVALw009shcOk7C4jRJQ/NtQ4UF9kQqlVD8sMKRZSEwA0VdlxYnhi1pSDGtfkPIQyqx7dY9nrPjEfm1GLygIa+nXWN55tz8q6zJkmOuOTVy5BY6kFX/1TW8H2Z16IowN+GDQq2E1aDPmz3yqpymHEhhoH/OEY+jwhGLVqeIJRDPjCuHFTNdZUKqP6nogKw6pKG7ouCljNOjUsBg2MWjX0WjV0ahV0ahVUKkB1ZjJgQhSRSIiIJZKIJpIIxRJQAdCqVZiIxDE4y/1ydaUNh/pnPxldV2WHRi3gkgYXOkYDGJNx6FaF3YiW7sIrQnz95Ch+f6Aff7mhStJ1GUwTXWDHilJc0ViMT/98L15qH5Z7O7KKJ4F4MommInPWgmmtWkCVwwitWoVf3nopSqz6Ke/3h2P4zs6TuPnyhqxcn4gWr8YSy7QixEA0gUA0s9HTzQ0u7O50T3t758gEisy6WYPk1jkC7VwLZvg9ULJ93R7Jg2lB5OQDomkGfCH89fffRk8Wm7wrXZ3LCLtRN+dJSjoEYfJxp82oxe8/ewWqHEYkRBF6TfYfsxIRXejG776BfT1eydfdWu/Enou6JzVV2SW/n2ZLuU0/6wl7vltZbsUzd1wJQZAuL5wn00QzqLAbcd8H1uGmH+ySeyuyKbUZpv0ySIdGJeCypUXYUufChloHmqrs8IZiqLAbYNBOBtC8ERGRHNZV2bMSTO/p8qCh2Ay9RoX2wXEAM7e+U6rgDANkCkX74DhODE9guYSdoZRTYUSkMJcvLcaXb1gt9zZkI0WD+1KrHr/61GX4+S2X4I5rl+Gq5SVwmnVoKDafC6SJiOSSzUmInaOBc10xqhwGtJ32z/MZyhHL4oATJfivF49Luh4PhIjm8IltDdjT5Z4yKfFL716FrfUutHR78ELbIN7umJ4btxBlNj2Wl1mhEgT8+fhIpluWTCTDm+nqCht++onmaXnQRERKke1JiO5gDA6TFiadBv3ecFavRal78egwjg74sbzMCrUq83QP5kwTzWNkPILL73sJsYSI61aX4eGPbj6XaxWMxnHnYwfwfNtQyutV2A24ZVsD3rW2HC6zDqYzI2TjiSTe9903cLhfGacXeo2ADTVOtA+Owxda+BSu5+7cjhXlHLBCRMoliiK+s/MkHnhe2pPKs5wmLVSCIGtnjnSsrbThcB6dpKdrTaUNj952KWwGbUbrMM2DaB4lVj3qi8zQaVT40rtXTSlaMOk0ePhjW/DMHVfiyzesRmOpZc61mutdePbO7fjklUtQ7TSdC6SByYlc993YlLWvY6EicRG7Ot0otaV3sjzgC0m8IyIiaQmCgM9dswz/+t41WVnfE4zlXSANAPpFkoZ35LQf3//zqYzX4ck0UQp2d7pRatWjvtg858fFE0m8fnIUiaSIztEARBHoGgvAG4yh2mXEXdcunzdX+KM/3IXXToxKuf2M1LqM6HGnHhivLLdiU50Tn96+FLVFpizujIhIOr/Z24d/eOLgtMmEi43DpEUomsg41S9fmHVq7PvydRl1lGLONFEKmhtcKX2cRq3CjhWlGV1rRZlVUcG0JxCFVi2kNNb2jncsw13XLc/BroiIpPWBzdUw6zW4/dH9iCYWRyA5k6Qo4uK7feWZ7ksdo4U30CwQTaCly4MrGovTXoNpHkQKY9Yr6zXueCSBtSlOHzxbuU5ElI/etbYcP/z4FhgXSZrDTPyhOJqq7DBoVWiud6HUqseALzzrSXVTlR2b65wQBMCkU6O53oX6PHsq+dapsYw+n8E0kcKsrrTJvYVpDvR6sanWgaUlc6e5RFM4vSYiUrIrl5XgF59shtWgrIONXDrc70O104jdXW4Mj0ewtMSMfm8ItS4Tqh1GbK5zYkudE0atCof6fdjb7YHNoEE8kUT3WACjE/mVJ76rk8E0UUG5ZmUpii3KaicnAtjX48WpkQCa6ydTXpwmLbbWO7Gx1nHul0610yjjLomIpLG5zoXHbrsUxRad3FuRRTiexMnh8ykdwxMRbKyxo8cdRJ83hL3dHrR0exC6YBS7LxRHNCFieDwCTZ48pdSpVbikwYVa19wHRfNhASKRAt33TDv+W4IK42ypLzKhxx08V6hTYtHj+nXluPf6VRzGQkQFo2NkAh95ZBdO+9gjeiEuaXBhV2dmMxhy4Ts3bcK7myoyXofBNJECDY+H8Ur7CMYCUbgDEbgDsTP/jWIsEIUnEEUgqpxxr3qNCi9/YQeqHDyZJqLC0u8N4SOP7EJnARbfZUtTtR2H+nxyb2NOa6ts+OPntk1pd5suBtNEeWoiEsfzRwbhCcbw7OEB7OnynHtflcOI7cuL0ecJ5aQzyN3XLcfn37Es69chIpLDyHgEH/3hLrQPjsu9lbywstyq6O+VIAC/+bvLsanWKc16DKaJ8t/oRATPHB5EjdOIKocRjaUWCIKAb710Av/5QnYmewGAy6zDAx9swjUry7J2DSIiJfAFY7j5J7uxr8cr91YUr77IhK6xoNzbmNW/vW8tPnJpnWTrMZgmKmDf2XkS9z93LCtr/8sNq/GhLdWwZjiGlYgoXwQicfzbU0fxqz09i364y1y0agFLSyywGjRTnprmikWvgSAA4+H4tPetLLfi6duvhEolXZEkg2miAna434cbHnpd8nWXl1nw/F1XSb4uEVE+aDvtx+ce3YeOEeZRz6fSbshpAefHL6/Hl29YDZVKwNhEBN5QDMUWPd7uGMNXfn8ED3xwPbYtS39Ay0wYTBMVuB3371zQ4zaNSsB711fisqVFODkygc6RAFq6PXAHzvcNvWxJER697dJsbJeIKC+8enwEn/jJHsR5RD0jg0aFddV2dI5mv+/0J7c14Ib1lTg+OI4PbqmetagwGk9CqxYkKTq8EINpogL3/T+fwtefaZ/zY3RqFb76vjW4emUpLHoNTLqpwwrCsQSeOzKIne3D+N2B03jgg+vxV5urs7ltIiLF++mbXfjKH47IvQ1FqnEZ0esOSb6uw6TF5lonyuwGiKKIGpcJf3fVUskD5IVgME1U4AZ9YXz8x7tnrayudZnwT9evwrvWls+7liiK+NZLJ3H7OxplvXERESlBIinilp/uwSvHRuTeiuI0FJslaydo0WvwhXcux5Z6F9ZU2hT3+4fBNNEiEIjEcc8Th/BU68CUt29fXoKH/mYj7CYWERIRpcMfjuFD//3WtAOLUqsewWgCE5HpRXCLwdpKGw6f9me8zrJSC773kc1oLLVIsKvsYDBNtEgkkyJ+8mYXfvBaBwZ8YVy7qhQPfXgTjDpOLCQiykRrnw9/+Z3Xz3X4qLAb8NTtV8KkU6NjJIBnDg/goZdPpr2+WiXAZdahwm5AQ7EZy8usCMcSGa2ZbZtqHRm3EdxS58Qvb70UOo1Kmk1lCYNpokVGFEWcGgnAYdKi2KKXeztERAXh3icP4dHdvQCAn9/SjCuXlZx7nyiK+NEbXXjguWMIxVKfXms1aHDLtgZ87LJ6uMy6Ke8TRRG3/qwFLx4dluYLkJjNqMHKchuOnvZjPM3T+Yc/uhnvXDN/CqLclB3qE5HkBEFAY6mFgTQRkYTWVTkAANetLsO2xqmt1wRBwC3bGrD7n9+BRz62BR9uroFmnj7H5TYDfvuZy3HntcunBdJn1/zq+9ZCr9BTW38ojt2dbqyssKa9xkAOW+plQjP/hxARERHRXN6zvgI6jQo3NFXMWiBnNWhx7eoyXLu6DJ++ainaTvvhDcUw5A9DFIFTIxPwhWKwGbX40rtXocJunPOaFXYj3rehCr9q6c3GlySJdDoHVjmMWF9jR1O1XfoNZQGDaSIiIqIMWQ3aBbUMrSsyo67InPF111bZ8KuWjJfJmsgC0lqAyem6n7iiXnEdO+aizGcDRERERDQvq0HZ3ZjaBvwwLaDQvc5lyqtAGmAwTURERJS3Sm3Krn9JikCZzYB1VamlbAz68yNP+kIMpomIiIjy1CUNRahyzJ1bLbfO0QBa+32odhqxttJ27u1atYBNtQ7YDOezjldf8P58wWCaiIiIKE+pVQI+tKVG7m2kpM8TwtEBPzbWOtBc70SZ1YB9PV4YtWqsKLeiymFEsVnZJ+0zYZ9pIiIiojzW7w1h+3/sRCKd1hkKIQB4/q7tWFaWfis9uTCYJiIiIspzyaQIXyiGsUAUnmAUYxNRuKf87wjGAlG0dHkWNDgmV/72klp87f3r5N5GWtgaj4iIiCjPqVQCnGYdnDMMeLlQIBJH+6AfiSTwkUd2IZpIAgCKLTrsWFGKK5cV41CfDz98vTMX2wYArCiz4s5rl+fselJjME1ERES0SJj1GmyucwEAnr5jGyLxJIotepRa9eda0o1ORHOyF41KwK3bl+AL71wB9TwTIZWMwTQRERHRItRYOnN+ci5GlH9gUzXuum4Zqp2mrF8r2xhMExEREdE525eVQBCAbFbV/f07lyu+pV+qGEwTERER0Tm1RSY0VTtwsNe74M+16DW4cVMVVIKAowN+9LiDGPBNH8TiNCl7cuNCMJgmIiIioin+alNVysH0NStL8f6NVbAbtdhQ64DtghHngUgcA74Qfvh6J14/OYpedwjNDS6YdIUTgrI1HhERERFN4QvFcNX9O+ENxub8uL9YW47v3LQJqhQKCEVRxN88/Dbu+0ATGorNUm1VdgymiYiIiGiaXncQn/r5XrQN+Ke9r9iix/0fbMKO5SXnuoCk4rQ3hMoCyZU+i8E0EREREc0oHEvg7l8fxFOHBs69rdSqx49v3oo1lXYZd6YcDKaJiIiIaFaiKOKV4yN4/sgQ3u4Yw8Mf3ZyXY7+zhcE0EREREVGast+Vm4iIiIioQDGYJiIiIiJKE4NpIiIiIqI0MZgmIiIiIkoTg2kiIiIiojQxmCYiIiIiShODaSIiIiKiNDGYJiIiIiJKE4NpIiIiIqI0/S8KlILxsXt9UgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Symmetric Difference\n",
    "symmetric_difference = bydeler.overlay(oslo_s_buffer, how=\"symmetric_difference\")\n",
    "\n",
    "fig, ax1, ax2 = plot_overlay(\n",
    "    gdf1=bydeler,\n",
    "    gdf2=oslo_s_buffer,\n",
    "    result=symmetric_difference,\n",
    "    title=\"Symmetric Difference\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "57e3127a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 7)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symmetric_difference.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a94341e8",
   "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>fid</th>\n",
       "      <th>bydelsnr</th>\n",
       "      <th>bynavn</th>\n",
       "      <th>bydel</th>\n",
       "      <th>befolkning_2024</th>\n",
       "      <th>navn</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.0</td>\n",
       "      <td>030113</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Østensjø</td>\n",
       "      <td>51895.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((603544.866 6638682.313, 603551.763 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.0</td>\n",
       "      <td>030114</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Nordstrand</td>\n",
       "      <td>54027.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((600631.693 6635249.786, 600624.152 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.0</td>\n",
       "      <td>030115</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Søndre Nordstrand</td>\n",
       "      <td>39124.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((603567.643 6634836.359, 603565...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.0</td>\n",
       "      <td>030117</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Marka</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((603567.643 6634836.359, 603567...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oslo S</td>\n",
       "      <td>MULTIPOLYGON (((596434.202 6638086.898, 595972...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     fid bydelsnr bynavn              bydel  befolkning_2024    navn  \\\n",
       "9   13.0   030113   Oslo           Østensjø          51895.0     NaN   \n",
       "10  14.0   030114   Oslo         Nordstrand          54027.0     NaN   \n",
       "11  15.0   030115   Oslo  Søndre Nordstrand          39124.0     NaN   \n",
       "12  17.0   030117   Oslo              Marka              0.0     NaN   \n",
       "13   NaN      NaN    NaN                NaN              NaN  Oslo S   \n",
       "\n",
       "                                             geometry  \n",
       "9   POLYGON ((603544.866 6638682.313, 603551.763 6...  \n",
       "10  POLYGON ((600631.693 6635249.786, 600624.152 6...  \n",
       "11  MULTIPOLYGON (((603567.643 6634836.359, 603565...  \n",
       "12  MULTIPOLYGON (((603567.643 6634836.359, 603567...  \n",
       "13  MULTIPOLYGON (((596434.202 6638086.898, 595972...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symmetric_difference.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce70092",
   "metadata": {},
   "source": [
    "Som vi kan se i tabellen, så inneholder tabellen attributter fra bydelene og en rad med data fra `oslo_s_buffer` i den siste raden."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dbbf8f",
   "metadata": {},
   "source": [
    "## Overlay med befolkningsdata\n",
    "\n",
    "I denne notebooken vil vi utføre en overlay-analyse for å velge de polygon-cellene i et grid-datasett som ligger innenfor Oslo. I denne\n",
    "øvelsen, bruker vi to input-datasett: et grid av statistiske polygoner med\n",
    "befolkningen i Oslo og et polygon-datasett av norske kommuner\n",
    "(`kommuner.geojson`), hvor vi skal velge bare Oslo kommune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "52082ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib \n",
    "NOTEBOOK_PATH = pathlib.Path().resolve()\n",
    "DATA_MAPPE = NOTEBOOK_PATH / \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "18ff58b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>gml_id</th>\n",
       "      <th>lokalId</th>\n",
       "      <th>navnerom</th>\n",
       "      <th>versjonId</th>\n",
       "      <th>oppdateringsdato</th>\n",
       "      <th>gyldigFra</th>\n",
       "      <th>datauttaksdato</th>\n",
       "      <th>navn</th>\n",
       "      <th>språk</th>\n",
       "      <th>kommunenummer</th>\n",
       "      <th>kommunenavn</th>\n",
       "      <th>samiskForvaltningsområde</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kommune.1</td>\n",
       "      <td>0466e7c3-6a23-4b70-97d4-cf81f949490b</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 17:47:39</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ibestad</td>\n",
       "      <td>nor</td>\n",
       "      <td>5514</td>\n",
       "      <td>Ibestad</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((589669.263 7630312.979, 589669...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kommune.2</td>\n",
       "      <td>0f821a2d-a307-41ec-9b64-f01c80e39357</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-11-07 23:33:28</td>\n",
       "      <td>2023-11-07</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Rendalen</td>\n",
       "      <td>nor</td>\n",
       "      <td>3424</td>\n",
       "      <td>Rendalen</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((327391.890 6869419.570, 327363...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kommune.3</td>\n",
       "      <td>251a3fdd-ac96-4ef2-badb-a9b75e6ab2e6</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 15:42:05</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Rakkestad</td>\n",
       "      <td>nor</td>\n",
       "      <td>3120</td>\n",
       "      <td>Rakkestad</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((284560.990 6591094.550, 284575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kommune.4</td>\n",
       "      <td>2758a7b8-edba-4d6a-936e-4754ce03ca8f</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 15:56:10</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Nordre Follo</td>\n",
       "      <td>nor</td>\n",
       "      <td>3207</td>\n",
       "      <td>Nordre Follo</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((269615.270 6638265.470, 269615...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kommune.5</td>\n",
       "      <td>340fff7f-b153-4a87-814d-12ca4f2d1682</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-11-07 23:33:28</td>\n",
       "      <td>2023-11-07</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Overhalla</td>\n",
       "      <td>nor</td>\n",
       "      <td>5047</td>\n",
       "      <td>Overhalla</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((359415.100 7152572.930, 359412...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gml_id                               lokalId  \\\n",
       "0  kommune.1  0466e7c3-6a23-4b70-97d4-cf81f949490b   \n",
       "1  kommune.2  0f821a2d-a307-41ec-9b64-f01c80e39357   \n",
       "2  kommune.3  251a3fdd-ac96-4ef2-badb-a9b75e6ab2e6   \n",
       "3  kommune.4  2758a7b8-edba-4d6a-936e-4754ce03ca8f   \n",
       "4  kommune.5  340fff7f-b153-4a87-814d-12ca4f2d1682   \n",
       "\n",
       "                                            navnerom  versjonId  \\\n",
       "0  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "1  https://data.geonorge.no/sosi/inndelinger/innd...          4   \n",
       "2  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "3  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "4  https://data.geonorge.no/sosi/inndelinger/innd...          4   \n",
       "\n",
       "     oppdateringsdato   gyldigFra      datauttaksdato          navn språk  \\\n",
       "0 2023-11-28 17:47:39  2024-01-01 2024-03-04 12:27:57       Ibestad   nor   \n",
       "1 2023-11-07 23:33:28  2023-11-07 2024-03-04 12:27:57      Rendalen   nor   \n",
       "2 2023-11-28 15:42:05  2024-01-01 2024-03-04 12:27:57     Rakkestad   nor   \n",
       "3 2023-11-28 15:56:10  2024-01-01 2024-03-04 12:27:57  Nordre Follo   nor   \n",
       "4 2023-11-07 23:33:28  2023-11-07 2024-03-04 12:27:57     Overhalla   nor   \n",
       "\n",
       "   kommunenummer   kommunenavn  samiskForvaltningsområde  \\\n",
       "0           5514       Ibestad                     False   \n",
       "1           3424      Rendalen                     False   \n",
       "2           3120     Rakkestad                     False   \n",
       "3           3207  Nordre Follo                     False   \n",
       "4           5047     Overhalla                     False   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((589669.263 7630312.979, 589669...  \n",
       "1  MULTIPOLYGON (((327391.890 6869419.570, 327363...  \n",
       "2  MULTIPOLYGON (((284560.990 6591094.550, 284575...  \n",
       "3  MULTIPOLYGON (((269615.270 6638265.470, 269615...  \n",
       "4  MULTIPOLYGON (((359415.100 7152572.930, 359412...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas\n",
    "\n",
    "rutenett = geopandas.read_file(DATA_MAPPE / \"ssb_rutenett\" / \"befolkning_250m_2023_akershus.shp\")\n",
    "\n",
    "kommuner = geopandas.read_file(\n",
    "    DATA_MAPPE / \"kommuner\" / \"kommuner.geojson\"\n",
    ")\n",
    "\n",
    "display(kommuner.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b036ee-9e99-4cf1-9710-bd68e7c71a97",
   "metadata": {},
   "source": [
    "Vi kan nå velge bare Ås kommune fra kommune-datasettet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0cc5e4ac-5eff-4810-afb5-9ba1f1c181f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>gml_id</th>\n",
       "      <th>lokalId</th>\n",
       "      <th>navnerom</th>\n",
       "      <th>versjonId</th>\n",
       "      <th>oppdateringsdato</th>\n",
       "      <th>gyldigFra</th>\n",
       "      <th>datauttaksdato</th>\n",
       "      <th>navn</th>\n",
       "      <th>språk</th>\n",
       "      <th>kommunenummer</th>\n",
       "      <th>kommunenavn</th>\n",
       "      <th>samiskForvaltningsområde</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>MULTIPOLYGON (((266871.580 6614231.500, 266871...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gml_id                               lokalId  \\\n",
       "27  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "\n",
       "                                             navnerom  versjonId  \\\n",
       "27  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "\n",
       "      oppdateringsdato   gyldigFra      datauttaksdato navn språk  \\\n",
       "27 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "\n",
       "    kommunenummer kommunenavn  samiskForvaltningsområde  \\\n",
       "27           3218          Ås                     False   \n",
       "\n",
       "                                             geometry  \n",
       "27  MULTIPOLYGON (((266871.580 6614231.500, 266871...  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aas = kommuner.loc[kommuner[\"navn\"] == \"Ås\"]\n",
    "aas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4604e487-d4e1-4a95-99ae-7ade3ef935a7",
   "metadata": {},
   "source": [
    "Før vi fortsetter er det en god ide å sørge for at begge lagene har samme koordinatsystem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "153a3b3a-79f3-44a2-8443-fd9dbec0562b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sjekke at lagene har samme crs\n",
    "rutenett.crs == aas.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c4fcbc",
   "metadata": {},
   "source": [
    "La oss gjøre en rask overleggsvisualisering av de to lagene:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d3bb7a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plott lagene\n",
    "ax = rutenett.plot(color=\"gray\")\n",
    "aas.plot(ax=ax, facecolor=\"None\", edgecolor=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b14f53",
   "metadata": {},
   "source": [
    "Her er alle de grå rutene gridceller med befolkningsdata for Akershus fylke. Datasettet inneholder 17376 ruter.\n",
    "Det blå omrisset representerer Ås kommune. Vårt mål er nå å utføre en overlay-analyse og velge de geometriene fra rutenettet som krysser polygonet for Ås kommune.\n",
    "\n",
    "Vi vil lage et nytt lag basert på rutenett-polygoner som `intersect`-er med Ås-laget vårt. Vi kan bruke metoden `overlay()` med en `GeoDataFrame` for å utføre overlay-analysen som tar som en input 1) andre GeoDataFrames, og 2)\n",
    "parameteren `how` som kan brukes til å kontrollere hvordan overlay-analysen blir\n",
    "utført (mulige verdier er `'intersection'`, `'union'`,`'symmetric_difference'`, `'difference'`, og `'identity'`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4ae9debf",
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection = rutenett.overlay(aas, how=\"intersection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22abba7",
   "metadata": {},
   "source": [
    "La oss plotte dataene våre og se hva vi har:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4a0bb477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersection.plot(color=\"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12aec719",
   "metadata": {},
   "source": [
    "Som et resultat har vi nå bare de rutenett cellene som krysser med Helsingfors\n",
    "grenser. Hvis du ser nøye etter, kan du også observere at **rutenett cellene er\n",
    "klippet basert på grensen.**\n",
    "\n",
    "- Hva med dataattributter? La oss se hva vi har:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "57716634",
   "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>ru250m</th>\n",
       "      <th>pop_tot</th>\n",
       "      <th>gml_id</th>\n",
       "      <th>lokalId</th>\n",
       "      <th>navnerom</th>\n",
       "      <th>versjonId</th>\n",
       "      <th>oppdateringsdato</th>\n",
       "      <th>gyldigFra</th>\n",
       "      <th>datauttaksdato</th>\n",
       "      <th>navn</th>\n",
       "      <th>språk</th>\n",
       "      <th>kommunenummer</th>\n",
       "      <th>kommunenavn</th>\n",
       "      <th>samiskForvaltningsområde</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22605006625250</td>\n",
       "      <td>10</td>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((260500.000 6625250.000, 260500.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22617506625250</td>\n",
       "      <td>3</td>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((261750.000 6625250.000, 261750.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22620006625250</td>\n",
       "      <td>23</td>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((262000.000 6625250.000, 262000.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22622506625250</td>\n",
       "      <td>10</td>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((262250.000 6625250.000, 262250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22625006625250</td>\n",
       "      <td>14</td>\n",
       "      <td>kommune.28</td>\n",
       "      <td>d285e9c4-8da0-4dfc-b927-31ba7ee17e6a</td>\n",
       "      <td>https://data.geonorge.no/sosi/inndelinger/innd...</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-28 16:00:22</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024-03-04 12:27:57</td>\n",
       "      <td>Ås</td>\n",
       "      <td>nor</td>\n",
       "      <td>3218</td>\n",
       "      <td>Ås</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((262500.000 6625250.000, 262500.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ru250m  pop_tot      gml_id                               lokalId  \\\n",
       "0  22605006625250       10  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "1  22617506625250        3  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "2  22620006625250       23  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "3  22622506625250       10  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "4  22625006625250       14  kommune.28  d285e9c4-8da0-4dfc-b927-31ba7ee17e6a   \n",
       "\n",
       "                                            navnerom  versjonId  \\\n",
       "0  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "1  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "2  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "3  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "4  https://data.geonorge.no/sosi/inndelinger/innd...          3   \n",
       "\n",
       "     oppdateringsdato   gyldigFra      datauttaksdato navn språk  \\\n",
       "0 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "1 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "2 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "3 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "4 2023-11-28 16:00:22  2024-01-01 2024-03-04 12:27:57   Ås   nor   \n",
       "\n",
       "   kommunenummer kommunenavn  samiskForvaltningsområde  \\\n",
       "0           3218          Ås                     False   \n",
       "1           3218          Ås                     False   \n",
       "2           3218          Ås                     False   \n",
       "3           3218          Ås                     False   \n",
       "4           3218          Ås                     False   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((260500.000 6625250.000, 260500.000 6...  \n",
       "1  POLYGON ((261750.000 6625250.000, 261750.000 6...  \n",
       "2  POLYGON ((262000.000 6625250.000, 262000.000 6...  \n",
       "3  POLYGON ((262250.000 6625250.000, 262250.000 6...  \n",
       "4  POLYGON ((262500.000 6625250.000, 262500.000 6...  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c68ee5",
   "metadata": {},
   "source": [
    "Som vi kan se, på grunn av overleggsanalysen, inneholder datasettet attributtene\n",
    "fra begge input lagene.\n",
    "\n",
    "La oss lagre resultatrutenettet vårt som en GeoPackage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0a83f86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection.to_file(DATA_MAPPE / \"intersection.geojson\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ad917d",
   "metadata": {},
   "source": [
    "Det er mange flere eksempler på forskjellige typer overleggsanalyse i\n",
    "[Geopandas dokumentasjon](http://geopandas.org/set_operations.html) hvor du\n",
    "kan gå og lære mer."
   ]
  }
 ],
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