maps.ipynb

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    "# Mapping in Python\n",
    "\n",
    "Based on the QuantEcon open source code for economic modeling\n",
    "https://datascience.quantecon.org/applications/\n",
    "\n",
    "\n",
    "**Outcomes**\n",
    "\n",
    "- Use geopandas to create maps  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "- [Mapping in Python](#Mapping-in-Python)  \n",
    "  - [Mapping in Python](#Mapping-in-Python)  \n",
    "  - [Case Study: Voting in Wisconsin](#Case-Study:-Voting-in-Wisconsin)  "
   ]
  },
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   "source": [
    "# Uncomment following line to install on colab\n",
    "#! pip install qeds fiona geopandas xgboost gensim folium pyLDAvis descartes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from shapely.geometry import Point\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "qeds.themes.mpl_style();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping in Python\n",
    "\n",
    "In this lecture, we will use a new package, `geopandas`, to create maps.\n",
    "\n",
    "Maps are really quite complicated… We are trying to project a spherical surface onto a flat figure, which is an\n",
    "inherently complicated endeavor.\n",
    "\n",
    "Luckily, `geopandas` will do most of the heavy lifting for us.\n",
    "\n",
    "Let’s start with a DataFrame that has the latitude and longitude coordinates of various South American cities.\n",
    "\n",
    "Our goal is to turn them into something we can plot – in this case, a `GeoDataFrame`.\n",
    "\n",
    "\n",
    "\"A geographic information system **(GIS)** is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and 3D scenes.\" from https://www.esri.com/en-us/what-is-gis/history-of-gis\n",
    "\n",
    "\"The **latitude** specifies a location's distance north or south of the equator. The **longitude** specifies the location's distance east or west from an imaginary line connecting the North and South Poles, called the Prime Meridian.\""
   ]
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   "source": [
    "df = pd.DataFrame({\n",
    "    'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
    "    'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
    "    'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n",
    "    'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]\n",
    "})"
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       "      <th></th>\n",
       "      <th>City</th>\n",
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       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "           City    Country  Latitude  Longitude\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66\n",
       "1      Brasilia     Brazil    -15.78     -47.91\n",
       "2      Santiago      Chile    -33.45     -70.66\n",
       "3        Bogota   Colombia      4.60     -74.08\n",
       "4       Caracas  Venezuela     10.48     -66.86"
      ]
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   "source": [
    "df.head()"
   ]
  },
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   "source": [
    "In order to map the cities, we need tuples of coordinates.\n",
    "\n",
    "We generate them by zipping the latitude and longitude together to store them in a new column named `Coordinates`."
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>(-58.66, -34.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>(-47.91, -15.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>(-70.66, -33.45)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>(-74.08, 4.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>(-66.86, 10.48)</td>\n",
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       "           City    Country  Latitude  Longitude       Coordinates\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66  (-58.66, -34.58)\n",
       "1      Brasilia     Brazil    -15.78     -47.91  (-47.91, -15.78)\n",
       "2      Santiago      Chile    -33.45     -70.66  (-70.66, -33.45)\n",
       "3        Bogota   Colombia      4.60     -74.08     (-74.08, 4.6)\n",
       "4       Caracas  Venezuela     10.48     -66.86   (-66.86, 10.48)"
      ]
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   "source": [
    "df[\"Coordinates\"] = list(zip(df.Longitude, df.Latitude))\n",
    "df.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Our next step is to turn the tuple into a `Shapely` `Point` object.\n",
    "\n",
    "We will do this by applying Shapely’s `Point` method to the `Coordinates` column."
   ]
  },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
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       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POINT (-58.66 -34.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POINT (-47.91 -15.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>POINT (-70.66 -33.45)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>POINT (-74.08 4.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>POINT (-66.86 10.48)</td>\n",
       "    </tr>\n",
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       "           City    Country  Latitude  Longitude            Coordinates\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66 -34.58)\n",
       "1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91 -15.78)\n",
       "2      Santiago      Chile    -33.45     -70.66  POINT (-70.66 -33.45)\n",
       "3        Bogota   Colombia      4.60     -74.08     POINT (-74.08 4.6)\n",
       "4       Caracas  Venezuela     10.48     -66.86   POINT (-66.86 10.48)"
      ]
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   "source": [
    "df[\"Coordinates\"] = df[\"Coordinates\"].apply(Point)\n",
    "df.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will convert our DataFrame into a GeoDataFrame by calling the\n",
    "geopandas.DataFrame method.\n",
    "\n",
    "Conveniently, a GeoDataFrame is a data structure with the convenience of a\n",
    "normal DataFrame but also an understanding of how to plot maps.\n",
    "\n",
    "In the code below, we must specify the column that contains the geometry data.\n",
    "\n",
    "See this excerpt from the docs\n",
    "\n",
    "> The most important property of a GeoDataFrame is that it always has one GeoSeries column that\n",
    "holds a special status. This GeoSeries is referred to as the GeoDataFrame’s “geometry”. When a\n",
    "spatial method is applied to a GeoDataFrame (or a spatial attribute like area is called), this\n",
    "commands will always act on the “geometry” column."
   ]
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   "execution_count": 7,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POINT (-58.66 -34.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POINT (-47.91 -15.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>POINT (-70.66 -33.45)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>POINT (-74.08 4.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>POINT (-66.86 10.48)</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           City    Country  Latitude  Longitude            Coordinates\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66 -34.58)\n",
       "1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91 -15.78)\n",
       "2      Santiago      Chile    -33.45     -70.66  POINT (-70.66 -33.45)\n",
       "3        Bogota   Colombia      4.60     -74.08     POINT (-74.08 4.6)\n",
       "4       Caracas  Venezuela     10.48     -66.86   POINT (-66.86 10.48)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame(df, geometry=\"Coordinates\")\n",
    "gdf.head()"
   ]
  },
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   "execution_count": 8,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gdf is of type: <class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "\n",
      "The geometry column is: Coordinates\n"
     ]
    }
   ],
   "source": [
    "# Doesn't look different than a vanilla DataFrame...let's make sure we have what we want\n",
    "print('gdf is of type:', type(gdf))\n",
    "\n",
    "# And how can we tell which column is the geometry column?\n",
    "print('\\nThe geometry column is:', gdf.geometry.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a Map\n",
    "\n",
    "Great, now we have our points in the GeoDataFrame.\n",
    "\n",
    "Let’s plot the locations on a map.\n",
    "\n",
    "This will require 3 steps\n",
    "\n",
    "1. Get the map  \n",
    "1. Plot the map  \n",
    "1. Plot the points (our cities) on the map  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Get the map\n",
    "\n",
    "An organization called [Natural Earth](https://www.naturalearthdata.com/) compiled the map data\n",
    "that we use here.\n",
    "\n",
    "The file provides the outlines of countries, over which we’ll plot the city locations\n",
    "from our GeoDataFrame.\n",
    "\n",
    "Luckily, `geopandas` already comes bundled with this data, so we don’t\n",
    "have to hunt it down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pop_est', 'continent', 'name', 'iso_a3', 'gdp_md_est', 'geometry'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab low resolution world file\n",
    "world = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\"))\n",
    "world.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`world` is a GeoDataFrame with the following columns:\n",
    "\n",
    "- `pop_est`: Contains a population estimate for the country  \n",
    "- `continent`: The country’s continent  \n",
    "- `name`: The country’s name  \n",
    "- `iso_a3`: The country’s 3 letter abbreviation (we made this the index)  \n",
    "- `gdp_md_est`: An estimate of country’s GDP  \n",
    "- `geometry`: A `POLYGON` for each country (we will learn more about these soon)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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       "    <tr>\n",
       "      <th>FJI</th>\n",
       "      <td>920938</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>(POLYGON ((180 -16.06713266364245, 180 -16.555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TZA</th>\n",
       "      <td>53950935</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371119710453 -0.950000000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESH</th>\n",
       "      <td>603253</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.665589565454809 27.65642588959236...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAN</th>\n",
       "      <td>35623680</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>(POLYGON ((-122.84 49.00000000000011, -122.974...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>326625791</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>(POLYGON ((-122.84 49.00000000000011, -120 49....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pop_est      continent                      name  gdp_md_est  \\\n",
       "iso_a3                                                                   \n",
       "FJI        920938        Oceania                      Fiji      8374.0   \n",
       "TZA      53950935         Africa                  Tanzania    150600.0   \n",
       "ESH        603253         Africa                 W. Sahara       906.5   \n",
       "CAN      35623680  North America                    Canada   1674000.0   \n",
       "USA     326625791  North America  United States of America  18560000.0   \n",
       "\n",
       "                                                 geometry  \n",
       "iso_a3                                                     \n",
       "FJI     (POLYGON ((180 -16.06713266364245, 180 -16.555...  \n",
       "TZA     POLYGON ((33.90371119710453 -0.950000000000000...  \n",
       "ESH     POLYGON ((-8.665589565454809 27.65642588959236...  \n",
       "CAN     (POLYGON ((-122.84 49.00000000000011, -122.974...  \n",
       "USA     (POLYGON ((-122.84 49.00000000000011, -120 49....  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = world.set_index(\"iso_a3\")\n",
    "\n",
    "world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'geometry'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.geometry.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the geometry for this GeoDataFrame is stored in the `geometry` column.\n",
    "\n",
    "**A quick note about polygons**\n",
    "\n",
    "Instead of points (as our cities are), the geometry objects are now polygons.\n",
    "\n",
    "A polygon is what you already likely think it is – a collection of ordered points\n",
    "connected by straight lines.\n",
    "\n",
    "The smaller the distance between the points, the more readily the polygon can approximate\n",
    "non-linear shapes.\n",
    "\n",
    "Let’s see an example of a polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"19.181956129643527 39.50246767837671 1.9606141764401386 3.308309692396122\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,82.31324504914954)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.06616619384792244\" opacity=\"0.6\" d=\"M 21.0200403174764,40.84272695572588 L 20.999989861747224,40.58000397395398 L 20.674996779063633,40.43499990494303 L 20.615000441172754,40.11000682225938 L 20.15001590341052,39.62499766698397 L 19.980000441170148,39.69499339452341 L 19.960001661873207,39.91500580500605 L 19.406081984136733,40.250773423822466 L 19.319058872157143,40.72723012955356 L 19.40354983895429,41.40956574153546 L 19.540027296637106,41.71998607031276 L 19.37176883309496,41.877547512370654 L 19.37176816334725,41.877550679783496 L 19.304486118250793,42.19574514420782 L 19.73805138517963,42.68824738216557 L 19.801613396898688,42.50009349219084 L 20.070700000000045,42.58863000000008 L 20.283754510181893,42.32025950781508 L 20.522950000000037,42.21787000000006 L 20.590246546680227,41.855408919283626 L 20.59024743010491,41.855404161133606 L 20.463175083099202,41.51508901627534 L 20.605181919037364,41.086226304685226 L 21.0200403174764,40.84272695572588 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f4fb01aacc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.loc[\"ALB\", 'geometry']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that it displayed the country of Albania."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Points in the exterior of Albania: 24\n"
     ]
    }
   ],
   "source": [
    "# Returns two arrays that hold the x and y coordinates of the points that define the polygon's exterior.\n",
    "x, y = world.loc[\"ALB\", \"geometry\"].exterior.coords.xy\n",
    "\n",
    "# How many points?\n",
    "print('Points in the exterior of Albania:', len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"59.94324588403841 28.733388576771137 15.799965820375682 10.338076985718448\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,67.80485413926073)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.31599931640751366\" opacity=\"0.6\" d=\"M 66.51860680528867,37.36278432875879 L 67.07578209825962,37.35614390720929 L 67.82999962755952,37.144994004864685 L 68.13556237170138,37.02311513930431 L 68.85944583524594,37.344335842430596 L 69.19627282092438,37.15114350030743 L 69.51878543485796,37.60899669041342 L 70.11657840361033,37.58822276463209 L 70.27057417184014,37.735164699854025 L 70.3763041523093,38.13839590102752 L 70.80682050973289,38.486281643216415 L 71.34813113799026,38.25890534113216 L 71.23940392444817,37.953265082341886 L 71.54191775908478,37.905774441065645 L 71.44869347523024,37.06564484308052 L 71.8446382994506,36.73817129164692 L 72.1930408059624,36.948287665345674 L 72.63688968291729,37.047558091778356 L 73.26005577992501,37.495256862939 L 73.9486959166465,37.4215662704908 L 74.98000247589542,37.419990139305895 L 75.15802778514092,37.13303091078912 L 74.57589277537298,37.02084137628346 L 74.06755171091783,36.83617564548845 L 72.92002485544447,36.72000702569632 L 71.84629194528392,36.50994232842986 L 71.26234826038575,36.074387518857804 L 71.49876793812109,35.650563259416 L 71.61307620635071,35.153203436822864 L 71.11501875192164,34.733125718722235 L 71.15677330921346,34.34891144463215 L 70.8818030129884,33.98885590263852 L 69.9305432473596,34.02012014417511 L 70.3235941913716,33.35853261975839 L 69.68714725126486,33.105498969041236 L 69.26252200712256,32.5019440780883 L 69.31776411324256,31.901412258424443 L 68.92667687365767,31.620189113892067 L 68.55693200060932,31.713310044882018 L 67.79268924344478,31.58293040620963 L 67.68339358914747,31.30315420178142 L 66.93889122911847,31.304911200479353 L 66.38145755398602,30.738899237586452 L 66.34647260932442,29.887943427036177 L 65.0468620136161,29.472180691031905 L 64.35041873561852,29.560030625928093 L 64.14800215033125,29.340819200145972 L 63.55026085801117,29.468330796826166 L 62.54985680527278,29.31857249604431 L 60.87424848820879,29.829238999952608 L 61.781221551363444,30.735850328081238 L 61.69931440618083,31.379506130492672 L 60.94194461451113,31.548074652628753 L 60.863654819588966,32.18291962333443 L 60.536077915290775,32.98126882581157 L 60.963700392506006,33.52883230237626 L 60.52842980331158,33.676446031218006 L 60.80319339380745,34.40410187431986 L 61.210817091725744,35.650072333309225 L 62.230651483005886,35.270663967422294 L 62.98466230657661,35.40404083916762 L 63.19353844590035,35.857165635718914 L 63.98289594915871,36.0079574651466 L 64.5464791197339,36.31207326918427 L 64.7461051776774,37.111817735333304 L 65.58894778835784,37.30521678318564 L 65.74563073106683,37.66116404881207 L 66.21738488145934,37.39379018813392 L 66.51860680528867,37.36278432875879 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f4fb01a5ba8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.loc[\"AFG\", \"geometry\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Points in the exterior of Afghanistan: 69\n"
     ]
    }
   ],
   "source": [
    "# Returns two arrays that hold the x and y coordinates of the points that define the polygon's exterior.\n",
    "x, y = world.loc[\"AFG\", 'geometry'].exterior.coords.xy\n",
    "\n",
    "# How many points?\n",
    "print('Points in the exterior of Afghanistan:', len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we’ve now displayed Afghanistan.\n",
    "\n",
    "This is a more complex shape than Albania and thus required more points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Plotting the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GIUOGMHTo0DeOFgVBKBpRALWcnZ0djRo1YteuXVJHAaBNmzZivjsN+vbbb+nQoQOTJk2icuXKVK1aFT09PQYOHMjt27dxcnKibdu2bNu2TeqoglDqiWuAWi4nJ4fExESUSqXUUQDYuHEjLi4uUsfQORkZGbi7u/P48WNCQkJwc3MjKioqzzImJib4+/vj4+NDu3btMDY2LtMDjgtCcYkCqIWio6OZMGECDRo0IDU1FTMzM/r06SN1LAB2797NuHHjpI6hc2bOnElqaioREREF3v7QsGFDDhw4QKdOnTA2NqZt27YllFIQdIs4BaqFDh06RGpqKoaGhkRHR/P7779rTQ/AFy9elPgtGGVBSEgI/fr1e+97/5o2bcrOnTsZMWIEp06d0nA6QdBNogBqodDQUPr378/s2bPZuXMn9vb2UkfKZWFhwfLly7l165bUUXTK48eP8513MT/u7u4EBAQwaNAgLl++rKFkgqC7RAHUMiqViuPHj9O+fXupo7zV2rVruXXrFs2aNRMdMdSoadOmjB07lvj4+EKt1759e3766SemTp2qoWSCoLtEAdQymZmZ5OTkvNEBQlt88sknnDt3juXLlzNs2DCeP3/+1uVycnKIjY0lIiKC06dP57uc8Ld9+/ZhYWFB+/btSUtLK9S6zZo1Iy4uTkPJBEF3iU4wWqZChQqsWLGC3r17c+nSJapWrSp1pLcaPHgw69evp2PHjjg4OBATE8OLFy+Ii4sjMTGRjIwMypcvj6GhIRUqVCAxMREzMzPc3Nxo06YNvXr1kvotaBU9PT2Cg4P54IMPWLBgAf7+/u+9rpmZGcnJyRpMJwi6SRwBaqHPPvuMHj168OWXX5KTkyN1nHwdOHAAZ2dnKlasmDsh77p167hy5Qrp6emkp6cTHx/P8+fPSU5OZv369VStWpXly5fj7u6uNbd2SO3s2bP4+vpib2+PXC7Hz8+vUOubmZmRkJDAnj17OHbsmNaePRAEbSMGw9aQ4g5Am52djbe3Nx07dmT69OlqTCY9pVKJs7MzW7ZsoVGjRujplb2/wzZs2JA7A0ROTg4eHh706dOHPn365Pb4fd/BwlUqFRMmTODx48ckJCRw/fp1Vq9ezSeffKLptyE5XR7oWV10eR8VdzBsUQA1RB0/dA8fPqRdu3bExsZqzW0Q6pKSksLt27fZtWsXs2fPljpOiXNwcKBFixb4+fnh5OT01j8CijpbxvHjx+nZsyc//vgj3t7e6oirtXT5w11ddHkfFbcAlr0/vUuRWrVqoaenp5OntExNTalcuTJLliwhKChI6jglrnz58rRo0YLGjRur/Qj4008/5dChQ0ybNo01a9aotW1B0CWiAGoxmUyGo6Ojzs4WXq5cOVatWsXgwYML9R5VKhWRkZGcP3+e0noGw9DQkNTUVI217+zsTGhoKCtXruSHH34otftJEDRJ9ALVco0aNeLKlSs6O+Zjnz59OHPmDB07dmTr1q2cOXOGvXv3EhMTQ+XKlalWrRrVqlXDysqKnJwcwsLCuHr1KiqVCn19fT744APmzp2Ll5dXqbmWmJCQwMOHDzEyMtLodurUqUNYWBhdunQhKiqKRYsWUa5cOY1uUxBKE1EAtZyjoyPBwcFSx9CoJUuWEBERgY+PD7Vq1eKzzz6jQYMGREVFERUVRXR0NGfOnAH+no1i6dKlNGnShOzsbKZNm8aXX37JypUr6d+/v8TvpGBJSUk0atSI1q1bM3z4cI1vr1q1ahw7doyePXvyxRdfsGzZMmxsbN65TlZWFrGxscTGxube3hITE0NWVhYff/wxrq6uGBoaajy7IGia6ASjIeq68BwREUG/fv24d++eGlJpl6J28nhdRkYGlpaW3Lhxgzp16qgpmeYMHz6c7du3ExUVVeARoDr2zz+ys7OpV68ekZGRVKlSBQcHB+rVq4eDgwMODg7Ur18fCwsL7t69i1wux9DQMM/Rt5WVFTKZjJMnT3Lz5k1cXFxo1aoV7u7ukhZEXe7goS66vI+K2wlGHAFquRs3buhcD1B1WrZsGbVq1SoVxQ9g6dKl/PHHH/j6+rJ58+YS+94aGBiQmZnJ3r17cXR05ObNm9y6dYvr168TGBhIeHg4JiYm6Ovr89133zFp0qR820pOTiYsLIyjR48ya9as3II4ZswYPvvssxJ5P4KgDqIAarHr168zdepUQkJCpI6itTZt2sSgQYOkjvHeDA0NuXjxIg0aNGDq1KnMmzevxLYdFxeHs7MzH3zwATY2NnmKlUql4vHjxzx69Ag3N7d3tlOxYkU+++yz3PUTExNxdnYu9DimgiA1UQC1WHBwMD179sTZ2VnqKFrp/Pnz3L9/v9QNq1atWjVOnDjBxx9/jI2NDWPGjNH4Nm/cuAGAtbX1W1+XyWTY2tpia2tb6LYDAwOpXr16qfs+CELp6DZXRjVr1oxLly5JHUMrKZVKevToweTJk7Gzs5M6TqHVr1+f3bt3880337B7926Nb+/AgQPUr19f7adcY2NjmT59OgsWLCg1vXAF4R/iCFCLNWvWjOvXr5OamoqJiYnUcbTK5MmTMTY25r///a/UUYqsbdu2/PLLL3z55Zd88cUXxMTE8Pz5cxITE1m8eDFNmjRR27ZOnTpFs2bN1NbeP7755hu6d++Oo6Oj2tsWBE0Tf7JpMWNjYxwdHTl9+rTUUbTOiRMnGD58eKm/r23AgAGMGTOGoKAgHBwcGDhwIAMHDqR3796kp6erZRtnz57lzJkzuLi4qKW9f5w+fZo//viDKVOmqLVdQSgp4ghQy7Vs2ZJjx45p7QS5UklISKBmzZpSx1CLOXPmsG7dOpo0aUK/fv0ASE9PJykpqchtKpVKVq5cyc8//8zz588ZNGgQvXv3VldkcnJyGDVqFDNnzsTc3Fxt7QpCSRJHgFquVatWHDt2TOoYWiclJUVt98hJTaFQkJ6eTo0aNXKfu3LlChUqVChSe0lJSVStWpW5c+fi5+fH8+fPWbFihVrvgY2OjiYyMhIfHx+1tSkIJU0cAWq55s2bc/HiRTIzMylfvrzUcbRGSkqKThwBHj16lJ49e7J48WLatWuX+/yVK1eKfHO5sbExqamp3LhxI09RVScrKytSU1PJzMwscqEWBKmJI0At9+jRI6pVq1bqr3WpU2ZmJmlpaRr7cC8J4eHhzJgxg65duzJnzhx8fX1zX1MqlcTGxpKens6dO3cK3baBgQFmZmZFWvd96evrY21tzbNnzzS2DUHQNFEAtdzevXvp1q2bGA3mNXp6elSoUKHUffhGREQwc+ZM6tSpQ7Nmzdi/fz9r165l/PjxeZbT09Pj/Pnz5OTk4OLiwvLlywu1nUuXLpGamsrHH3+szvhvqFmzJk+ePNHoNgRBk0QB1HL79u3jiy++kDqGVjEwMMDOzo59+/ZJHaVAt2/f5vvvv6du3bq4urqyb98+vv76axISErhw4QI9e/Z863qNGzemSpUqbNy4kf/+97+kpaW99zb9/f3p1q2bxsfntLW1VXsBTEpK4tq1a7k37guCJokCqMXu3bvH8+fPCxyaqizq0qULW7duVWubKpWKNWvWYG9vj4uLC8OGDeOXX34hNDSUuLi4AtdPT08nLCyMhQsX4uXlRbVq1WjatCl79uxh0qRJuUVv1KhRGBi83+X3Hj16ULlyZVauXPne7+PMmTMMGTLkvZcvKg8PDzZs2EBOTk6x21KpVPTv35/69eszZswYvLy8SExMVENKQcifKIBazMTEBGtra7p06cKjR4+kjqNVJkyYwPnz54t1q8C/xcXFMXnyZKKjo2ndujVxcXGsXLmSXr16UaNGDWxsbPj66685c+YMSqUyd73k5GS6deuGubk53bp1Y/PmzVhbW7Np0yaSkpK4ePEio0ePfu+i928//PADs2bNIisrq8BllUolaWlpJTI7g6+vLwYGBqxatarYbW3dupXHjx+TkJDA9evX6dChA5s2bVJDSkHInyiAWqxGjRocOXIEV1dXGjduzIULF6SOpDWsra2pVq0a27dvV1ublStX5uHDhwwePJjVq1ejr6/PtWvXePbsGampqcyZM4czZ87QuXNnqlSpgq+vL9u3b6dx48bcu3ePp0+fEhMTw6VLl1i5ciUdO3ZUy/Bg27Ztw8rKCn19/QKX1dPTY9CgQQwdOpTs7Oxib7ugba1du5aff/6ZBw8eFLmdmJgYpk+fztq1a3M7e02ePJnffvtN4+9BKNtEAdRy5cqVw9nZGWNj41I55qUmzZ49m6+++orDhw+rrU1zc3OWLFnCpUuXCAoKIiMjA/j7uuOAAQM4ceIEcXFxBAQE8PTpU/z8/HB1deXq1atUqVJFbTn+sXz5chQKBX/88cd7F9Nly5aRkJDAihUr1J7n3+zt7fn2228ZN25cnqPiwpgyZQqDBw/OM/Sbq6srdnZ27NmzR11RBeENYkJcDVHXJJTR0dF4eHjwv//9jzZt2qghmfZQx4Svy5YtY+rUqRw9elStY12mpqZiYWHxXqcdNSUqKor69esTEBBAly5dCrXu/v376dOnD3fu3NH47SI5OTl88MEHbN26tdAzlxw8eBB/f3+uXbv2xgTBQUFB/PLLL+zatSvf9XV5sld10eV9VNwJccURoBZTKpWMGjWKoUOH6lzxU5dx48bRsmVLNm7cqNZ2ExISJL/BOysri5ycHDp37lzodT09PXF2dmbs2LEaSJbX9evXKVeuHI0aNSrUeomJiXz99desXr36jeIH0LlzZy5duiTmGRQ0RhRALfbrr7+SkZHBjBkzpI6i9R4+fKjW9hITEyUfeScjI4MGDRoU+R7Q3bt3o1Ao0NfXR19fnx9++EHNCf+2cuVKBgwY8F7XKF/n7++Pp6cnn3766VtfNzExoW3btvz555/qiCkIbxBDoWmpu3fvsmjRIs6dO1fk3oNlweDBg7l48SJhYWFqbTchIUHyApiZmUmLFi2KvH6VKlWIi4sjOzubefPmsWvXLqZOnarGhJCdnU1gYGChZyyJjIxk//793L9//53L9ejRg4CAAPr06VOcmILwVuIIUAvl5OQwZswYZsyYQZ06daSOo7X69+/P/v37OX36NA4ODmpt28LCIrcDjFRycnJwdXUtVhsGBgYYGhoyZMgQrl27pvZrmhEREVSrVq3Q1xnXrFnDwIEDC5xJwtPTkxMnTpCSklKcmILwVqIAaqEVK1ZgYmLC6NGjpY6itfr06UNwcDBnzpzho48+Unv7Dg4OZGZmcvPmTbW3/b6USmWhO5Xkx9bWFlNTUy5fvqyW9v5x6dKlQk+Gm56eTkBAwHv9fFtYWODm5kZISEhRIwpCvkQB1BKvXr3i3r177N27l2XLlrF27Vq13EOmq44fP0716tXVNmnsv+np6dGkSRMCAwM10n5BMjMzUSqVfPjhh2pr09bWVq33TcLfk+0WtgDu3r2bpk2bUrdu3fda3sfHp1QMeyeUPuITVmKPHj2iefPm1KpVi169erFp0yZWrlxJ7dq1pY6m1R4+fEijRo34+OOPmT17tka20aZNG44cOaKRtgtSvnx59PX12bx5s9ra/O2331ixYgXnzp0r9LppaWlvDHn28OFDtm7dSrdu3QrV1po1awrVO9Xb25vDhw9Lfkpa0D3iPkANeZ97b1QqFV988QWtW7dm2rRpZa6zizruA7x27RqtWrVi06ZNfP7552pK9rfjx4/TsWNHLCwsMDU1xczMjCpVqmBra0uHDh3yHchaXe7evUvLli158uSJ2m7JmDhxIkFBQURERGBiYpLntcTERGrXrk21atXw8PCgYcOGXLx4kRMnTvDo0SPMzc0ZPHgwQ4cOxcHBAW9vbxwdHZk8eXJuGyqV6p29VgMCAvj555+5fft2oXqNfvLJJ4wePZrPPvssz/O6fI+buujyPhL3AZZiW7duJTY2lm+//bbMFT91cXJyYvHixQwYMKBYw3G9zaeffsrdu3fZtWsX8+fPZ/Dgwbi4uJCcnEy/fv1ISEhQ6/b+zdjYGGNjY1avXq22NhcuXIipqSlfffXVG6/t3r2bihUrMmHCBKKiovj9999JSUlh+vTpxMfH8/vvv3Py5ElcXV0pX748oaGhDB8+PHf9gIAAatasSfO0p0doAAAgAElEQVTmzfnf//73RvsBAQHMnj2b/fv3F/qWiR49erB///7Cv2FBeAdxBKgh7/NXV/fu3Rk3blyhTyHpCnUcAf6jc+fOGBgYsHfvXrW0lx+lUolCocDHx4effvqJESNGaGxbUVFR7Nmzh59//rnA2wUK48mTJzRo0IAtW7bQtWvX3Of79OlDdHR0gad9MzIyWLp0KcHBwURERDB69GiSkpLYtm0be/bsIT4+Hh8fHy5dukTFihVJSkoiKCiIRYsWERISUqQeu48ePaJJkyZERETk+WNRl49u1EWX95E4AizFtKGrfWmWkZHBwoULSU9PZ8GCBYSEhPDq1SuNbOvp06fMmjWLGjVq8OWXX9K1a1e+/PJLjWzrdb179+bRo0dqHRS6Zs2azJ07lwEDBhAdHZ37/LRp0zh//nyB1x0NDQ35z3/+w+HDh9m3bx9Xrlzh/PnznD59GicnJz799FPatWtH+/btqVevHg0aNGDbtm0cPny4yLer2NraYmdnx6lTp4q0viC8jTjvJqGqVavm+QAS3l9WVhbe3t4cO3aMI0eOsGfPHszNzTl69Ogb14mKa+DAgWzbtg17e3t+/vln+vXrp9b238XS0hJjY2MePnxY5B6hFy9epFevXpQrV46GDRvi7OxM3bp1efXqFV26dMHX15d69erx0UcfUa9ePS5fvvze77FJkyYEBQW98fyqVau4dOkS9vb2WFtbF3k0m9d1796dpUuXUrFiRRo3bqyWNoWyTRRACURHR3P58mXu3r2LpaWl1HEko1QqOXPmDPr6+ujp6eUO2VXQl56eHkOHDiUiIoLIyEgaN27M+PHjcXFxYceOHWotgE+fPmXbtm3cv38fa2trtbVbGObm5uzYsYNvvvmm0OsGBgYydOhQhgwZgqOjI+fOnePPP/8kJiaGDz/8kHLlyrFixQri4uJISEhAJpMRGhpa7MxmZmZ4eHgUu53XjR49mvj4eEaMGEFqaiqdOnVi2rRpat2GULaIAljCEhMTadiwIe7u7rRo0YL+/ftLHUkyKSkptGvXDlNTU1QqVe6XUqks8LGVlRVXr17FwsKC06dP06hRI+bPn4+/vz+rVq1S29GBQqGgZs2akhU/+HtKpAEDBnDr1i1Wrlz51oGjX5eRkcHOnTtZtmwZN2/eZN26dbk9Vl/vtPI2SqVSa+8/tbCwYMGCBSxYsICIiAhmzZpFWloalSpVkjqaUEqJAljCzM3NcXd3Z/jw4fTt21fqOJJr3rw5R48eLVYbtWvXpl69erx8+ZKcnBwuXbpE06ZNi53t9u3b/PLLL7Rs2bLYbRWHl5cXt27d4pNPPsHFxYX9+/e/cTo0ODiYo0ePcvPmTY4dO4aFhQU+Pj4EBwcXquOXtha/f3NwcEAul1NWOvEJmlE6ftp1SE5ODvb29vj5+bFmzRqp40hKJpOpbWzKwYMHs3nzZhwcHNi9e3eR24mPj2fjxo00b96cJk2aoK+vz4IFC9SSsTisra25c+cOzs7OuLi48Ntvv3H69GkiIyO5fv063bp149y5c9jY2PDnn3/y+PFjFi1aVGK9nqVgbGwsCqBQLKIAlqCwsDDatm1LREQE3377LV9//TUODg6cP39e6miS+ffoIkU1YsQInj59SnZ2NqtWreL58+fvvW5UVBQrVqygVatW1KhRA39/f1q3bk10dDSnTp2iWrVqaslYXHp6emzdupXly5fz448/0q1bNxo1akTjxo3x9PTkyJEj/PLLL7Rq1UrqqCVCFEChuMQp0BLw+PFj/P39uXjxIj/++CM9e/ZEJpPh6+vL3Llz+fTTT3Fzc2Pr1q1UqVJF6rglRiaTER4eTvfu3XFzc6Nz5840bNiwSG2VL18eJycnHjx4gK2tLc2bN+fUqVPUrFkz33WePn1Kp06duHPnDnZ2dnTt2pVdu3ZpTcHLz8CBAxk4cGDuY22+bqdJRkZGogAKxVL2fmtK0NmzZxk7diweHh44OTkRHh5Or169cjtomJqaMnfuXMLDwzE3N6d27dqMHj1arfd8aTMjIyPmzZtHlSpVCAgIyB0TtahOnz5NdHQ058+fx8XFhWbNmvHXX3/lu/zw4cMxNDTk5cuXhIeH8+OPP2p98Xubslj8MjMzmTdvHuXKlZM6ilCKiSNANcvJyWHVqlV89tlnjBs3Dl9fX37++WesrKzyXadWrVrs3LmTsLAwRowYQfXq1TE2NqZPnz7Mnz+/BNOXLH19fUaOHJn7WKFQMGTIELW0vWfPHnr37k3z5s0JCQmhYsWKxMTE8OLFC168eEF4eDgnTpwgMjISY2NjtWxTKBlKpZL+/ftjaGiosyOcCCVDDIWmZi9fvqRevXrcuXOH2rVrF7o7vlKpJCQkhGfPnjF69Ghu3779ztN4pdnjx4+5cuUKu3fv5siRI9SpU4eoqCgiIiLUtg1fX182b95MhQoVMDIywsjICBMTE0xNTfn666/p0aOH2ralbuocKk5XqFQqxo0bx9WrVwkKCiIrK0sUwQKIodDyJwqgBtjb23Px4kVsbW2L1U7fvn2JjIwkLCxMTcm0R69evZg8eTLdunXDxcWFTp068csvv+Dk5PTWgZTLIlEA3/T999+zfft29u3bh7m5uU5/uKuLLu+jMlEA5XL5Z8ASQB9YrVAo5hW2jZIsgN7e3qxbt446deoUq52nT59ib29P48aNcXJywt3dHblcnnud6tKlSwwZMgQLCwv27t1bqrq8GxgYcP/+/WJd89N1ogDm9euvv/LTTz9x8ODB3EsKuvzhri66vI90fjBsuVyuD6wAOgENgD5yubyBtKnyl5KSwv3799UyEom1tTUhISF06NCBZ8+eMX36dD744APMzc2xtbWldevWtGvXDkNDQ2rVqsWWLVvU8A5KhkqlKvSUOELZtXv37tyjv3ddTxeEwigNnWCaA/cUCsUDALlcHgh4A7ckTZWPhQsX0qZNG8qXL6+W9tzc3HBzc8t9nJ2dzV9//UV4eDjOzs7UqlULlUrFli1bGDVqFKtXr2bHjh1aP8ZoaTjzIGiPv/76i4oVKxY4DJwgFIbWHwECHwCPX3v85P+e0zoPHjxg48aN/PjjjxrbhoGBAfb29nh5eeWePpTJZPTr14/w8HBMTEyoVasWo0aNIjs7m+TkZEJCQtQ24oq6iAIoFMaECRMYMGAAnp6eXL58mZcvX6JUKqWOJZRyWn8NUC6XfwF0VCgUvv/3uD/QXKFQjHttmeHAO0f5XbNmTVNAo1OoxMfHU6FCBUxNTcnJyZHsFN+rV69ITExEqVSiVCqRyWTo6+tjaWmpFTPPq1Qqnj17hpWVlTgN+g5S/gxpq5SUFNLS0nJ/tqtUqUJCQkLubCJ6enpv/SrL+1GlUuns1FE2NjbFemPSfxoW7Alg89rjmsDT1xdQKBSrgFXvauSfTjCauhh87Ngx/vOf/3Djxg3Kly8veQcGlUpFrVq1GDhwIP7+/vTu3Zs///yTZcuWMWjQIEl/IVQqFYMHD2bevHlYWFhQt25dybJoM6l/hrRdZmYmz58/z/2DKjo6mujoaJ4/f05MTEzu4wcPHvDTTz/Ru3dvqSNLQpc7wRRXaSiA5wF7uVxeG4gCegNaN43Cjh07GDt2rNqu/RWXTCajY8eO3LhxAwMDA7Zv386OHTvw9fVlz549rFu3TrJpZGQyGX/88Qd37tyhSZMmbNmyBU9PT0myCKVX+fLl0dfXp0mTJu9cbvz48cTFxZVQKqE00fprgAqFIhsYCwQD4cA2hUJxU9pUeSmVSg4dOkSXLl2kjpKHq6srd+/ezX3co0cP7t+/z5MnT3ByciIpKUmybAYGBpibm7N48WJ69erFsWPHJMsi6DYjIyPS09PzPJeTk8OaNWvYtWsXCQkJEiUTpFYajgBRKBQHgYNS58jP1atXqVSpktadynN2diYmJibPc5aWlly4cAFHR0fGjh3Lxo0bJUr3t8GDB3Py5EkWL16s9hnEBQH+njUiOTk593FaWhojRozg5cuXWFhYMH78eBo1akS7du2Qy+U4Ojrq7DUzIS+tPwIsDf7880+tO/oDcHR0JD4+noyMjDdeCw4OZvfu3Rw8KP3fFV26dOHy5ctSxxB01OtHgDExMXTt2hULCwuOHTvGwYMHiY6Oxt/fn6SkJHx9falfvz5jxowRR4dlgCiARaRSqTh58iT9+/dn9erV9OvXT+pIb8jOzkZfX59nz5698Zq1tTUzZsygf//+eU6TSqFt27Y8f/5cfNgIGmFkZERGRgYRERF06NCBrl27snHjxtzr9UZGRnTs2JElS5Zw584dwsLCcHNzIygoCCcnJ3x8fAgKCiItLU3idyKomyiARXTt2jX69u1Lp06diIyMxMXFRepIb1i+fDl2dnbUrl37ra9PnDgRuVyOs7MzY8aMkayjgIWFBQ4ODqxZs0aS7Qu6zdjYmHPnzuHt7c3MmTOZMWPGO09x1q1bl7Fjx3Lw4EGeP3+Or68vO3bsoGHDhowdO5awsDBxD6KOEAWwiP4Z6HrkyJGYmppKnOZNaWlpLFiwoMDplAIDAzl//jxhYWHUqlWLoKCgEkqYV/Xq1d+4XikI6mBubs7jx4/ZunVrnomE34exsXHuLUS3bt2icePGTJkyBRcXF+bMmcO9e/c0lFooCaIAFlGlSpWwsLB454SrUlq1ahWWlpZ4e3sXuGzDhg25cuUKixYtwtfXl/j4+BJImFdCQgJVqlQp8e0Kus/Ly4t79+4Vu5NVjRo1mDx5MtevX2fv3r3k5OTg6elJhw4dWLNmjSS/N0LxiAJYDI0aNeLKlStSx3hDWloas2fP5vvvvy/Uer6+vtja2jJz5kwNJctfUlJSqZyNXdB+5cqVo3Llympt09nZmYULF/LkyRNmzJjBuXPnaNy4MQMGDJD8mrrw/kQBLIZGjRppXe/F5ORk2rZtS/Xq1YvUMWfLli2sWrWK+/fvayBd/p49e0aDBlo7yYcgvJWBgQGdO3dm69atREZG0rJlS3r27ElsbKzU0YT3IApgMTRq1IirV69KHSNXQkICrVu3JjU1tchHpk5OTrRu3Zrx48erOV3+4uLiSE9Pp2nTpiW2TUFQNwsLC6ZMmULv3r0ZOHAgmZmZUkcSCiAKYDE0bNhQawpgbGwsbm5uGBgYcPXq1WINeh0QEMDx48c5efKkGhPmLzw8HEtLS/T0xI+jUPrNmTOHqlWrMmnSJDHriZYTnzjFEBgYSKNGjaSOwbNnz2jevDmVK1fm3LlzxS4klpaWDBgwgJEjR5ZId+9bt26p/RqNIEhFT0+PzZs3c+3aNX755Rep4wjvIApgEZ05c4ZNmzaxdu3aN147derUG2MPasrjx49p3rw59vb2nDx5Um1HUUuWLCE6OpqAgIB8l0lKSmLu3LlYWlri4eGR54gxLi6Oc+fOERQUVOD1kGvXrmFnZ6eW3IKgDUxNTVm6dCmbNm2SOorwDqIAFpFMJkOpVLJu3bo8R0n379+nW7duJCYmkpiYqNEMDx48oFmzZri6uhIcHKzWtg0MDJg9ezaDBw+mbt26dO/endmzZ7N7926uX7/OtGnTsLa2Zv369SxbtoyaNWvSuXNnbGxsMDU1pUaNGnh6ejJp0iTq1q3L6tWr8z2avHTpEs2aNVNrfkGQ2unTp/nkk0/eeF6lUhEeHs66des4fvz4W4cqFEqG1k+Iqy7/zAdoZmamtjafPHnCiBEjMDQ0ZPPmzZiYmODm5sagQYPo1KkTO3fuZPr06Wrb3usiIiJo3bo1HTp0YMuWLRrZBvw9duKhQ4c4fvw4169fzx2yzMbGhsWLF9OuXbvcZTMyMti+fTsuLi7Ur18/92g0ICCAr776ChsbG9avX4+TkxPw/+e7q1y5Mnv27MHd3V1j76M0EvMBFkyb95G7uzt+fn60b9+eJ0+eEBoayvHjxzl+/DjGxsa4u7tz584dbt68SYsWLfDw8KBNmzbUr19frYNx6/J8gBYWFsXaUaIAFlN2djYLFixg48aN1K1bl48++oiff/6ZhIQEmjdvzl9//aW2kWJUKhXnzp1j69atrF69ml69evH777+rpW1Ny87OZtCgQezatYuhQ4cyZ84ckpKSqFixIlWqVCEtLU0rZqvXJtr84a4ttHUfvXz5kmrVqtG3b1/Onj1LfHw8bdq0QS6X065dO+rUqZO7bHx8PEePHiU4OJhDhw6RkZGBh4dH7peVlVWxsogCmD9RANUkLCyMnTt38sMPP1C+fHliY2Px8PDg9u3bmJiYFLldpVLJ6dOnCQwMJDAwkOzsbJycnPDz86Nbt25qfAclIzw8HA8PD5o1a8Zvv/3GkydP8PLyIjo6WupoWkdbP9y1ibbuo4cPHzJ+/Hjc3d2Ry+U4OTm99/X5+/fvc+jQIQ4dOsSxY8eoWbMmHh4etG3blhYtWmBkZFSoLKIA5k8UQA14+fIlCQkJxMfH0759+0Kvn5OTw8mTJwkICGDbtm3IZDKcnZ0ZP348Xbt2LdW3Czx//hx7e3t27txJgwYNOHToEAsWLODWrVtSR9M62vrhrk10fR9lZ2dz/vx5goODUSgUXLt2jWbNmlGlShVevXpFVlYWr169IjMzM/fr9ef/mazbzMxMK8csLq7iFkBxzknNVCoV48eP58cffyzSjd3p6ek0adKE2NhYGjduzObNm+ncubMGkkrD29ubLl26IJfLiYqK4vr167kDiwuCkJeBgQFubm64ubkxY8YMEhMTCQ0NJTExkfLly1OhQoXcr9cf//P/tLQ0lEolzZo1Y+rUqXz55Zfo6+tL/ba0hiiAarZhwwaePHlS5CPNiRMnkp2dTXR0dKk+0nubzZs3ExERwf79+3Ofu3TpEq1atZIwlSCUHubm5nTt2rVQ60RFRbFv3z4mTJjAqlWrmDVrFm3atNFQwtJFtz5hJXb79m3mzJlDQEBAkXpx/fnnn2zevJlDhw7pXPHLyMhg/PjxLFu2jKpVq+Y+Hx4eTtu2bSVMJgi6z9XVldDQUGbNmsV//vMfevbsSXh4uNSxJKdbn7ISunnzJp6enowcOZL69esXev3Y2Fj69u3LzJkz853AtjT74osvqF+/Pv379899TqlUEhcXR+vWrSVMJghlg0wmo1u3bty8eZNOnTrh7e3NpEmTePHihdTRJCMKoBrs2LGDbt26UbNmTSIiIgq9vkqlYsCAAdSrV4+JEydqIKG0QkNDOXLkCOvXr89zZJydnY2FhQXly5eXMJ0glC3ly5dnwoQJREREULFiRdzc3Fi4cGGJjV6lTUQBLIbs7Gy+++475s6di0Kh4JdffiE4OLjQ42euWbOGM2fOqH00F20xYMAA6taty4EDB3j58mXu89nZ2VhaWkqYTBDKLktLSxYvXsyZM2e4ceMGrVq14vbt21LHKlGiABZRbGws3bt3586dO5w/fx5nZ2du3rxJenp6oeYCe/LkCX5+fqxbt65EbtGQwoABA7CysmLx4sW4u7vnDhGXnZ2NjY2NxOkEoWz78MMP2blzJ/7+/nTt2pWwsDCpI5UYUQCL4NatW7Rt2xZ3d3cOHjyIpaUl48ePZ8KECezatatQM5vv2rULOzs7vL29NZhYWt9//z0KhYL79+9jbGxM+/btSUtLIysriyZNmkgdTxAEYODAgQQEBDB48GB27NghdZwSIQpgEUyfPp1JkyYxd+7c3HtqNmzYwN69e/H09CxUWzt37qRjx46aiKl19PT0OHv2LMnJyXTq1ImsrCw8PDykjiUIwv9p164dhw8fZubMmSxZskTn5zMU9wEWUnh4ODdv3uTAgQN5nq9QoQKmpqbk5ORw//799xquKCMjg9OnT7N+/XoNpZVWdnY2ffr04cSJE5iYmGBmZoaZmRl2dnaEhoaiVCpFARQELePo6MipU6fo3Lkzjx8/Zt68eTo7Tq9uvisNWrlyJaNGjaJChQp5nq9QoQI9evQgOjoaQ0NDQkJC6NKlS25XYwsLizfaOnHiBJaWltSqVauk4mtcamoqZ8+eJTQ0lN27d/PixQs2bdpEbGwssbGxvHz5kpcvX1KxYkWMjIwwNjaWOrIgCP9Ss2ZNTp48SY8ePRgwYAC///57scY01laiABbCixcv2Lt3L3fu3HnjtYULFxIVFUW3bt2wtbXl8ePH9O/fn19//ZVjx45x5MgRypUrl7t8RkYGS5YsoUGDBiX5FjTizJkz/O9//yMkJIR79+5RuXJl7Ozs6Nq1a77FH/4eoUIQBO1kZmbGwYMH8fX1xcvLi4CAgEL1bygNxGDYhTB//nxevnzJqlWrClz2n0F6MzIy+PDDD+nSpQu//fYb8HcnGm9vb5RKJcePH6dmzZpFziSl58+fM2HCBPbt24ebmxteXl58+eWX731rg64PZFxcYv8UTOyjghV3H6lUKvz9/dm0aRPbtm3D3t5ejemKRwyGXYLi4uKoVKlSodYxNDTk1KlTODk54eTkhIGBAZMmTcLHx4e1a9eWyiHPsrKyWLp0Kf7+/jg5OXHv3j2qV68udSxBEDRAJpPx/fffU6tWLTw9PRk2bBi9evXSiVuYRAEshDFjxuDh4cHkyZPzjGdZEFtbW3bu3EmXLl0wMjIiMDCw0L1FtcXRo0fx9fUlIyODbdu26dRMFYIg5G/o0KE0bdqU3377DQ8PD5ycnOjduzeenp6l9vqgOAVaSJMnT6ZSpUosWLDgncu97bTD+fPnsbe3z/eamDZ78uQJ48aN4/Dhw4waNYoffvih2Eev4vTVu4n9UzCxjwqmiX2UkZHB3r17Wb9+PadPn8bd3Z1KlSphYmKS56tGjRp4enoWaXKA9yEmxH1P6iqAUVFRtG7dmvDwcKysrN65nK78Yq5YsYJvvvkGV1dXtm/fTpUqVdTSri7tI00Q+6dgYh8VTNP76OnTp4SGhpKcnExqaiopKSkkJyeTkpJCWFgYH330EcuWLXuj57w6iAL4ntQ5I/yUKVMwMjJi0aJF+S6jK7+YKpUKExMTAgMD8fLyUmvburKPNEXsn4KJfVQwKfdReno6X375JTExMWzatKnQfSgKUtwCWPp6YGgBPz8/NmzYwNOnT6WOonHR0dEAai9+giDoPiMjI4KCgmjQoAFTp06VOs4bRAEsgurVq9O3b1/mz58vdRSNi4iIKJXXLAVB0A76+vp8++23HDlyROuGVhMFsAhUKhXR0dG544DqssDAQHGKSRCEYrGzs8PMzIybN29KHSUPcRtEEQQGBhIeHs7GjRuljqJR+/btY8uWLVy7dk3qKIIglHLt27fn+PHjNGrUSOooucQRYCHdu3eP6dOnExgY+F4DXpdWsbGx9OvXjx9//JHatWtLHUcQhFJOLpdz/PhxqWPkIQpgIV24cAGlUklYWBg5OTlSx9GYuLg4VCoVo0aNkjqKIAg6oE2bNpw5c4bMzEypo+QSBbCQevfuzc6dO9m0aRMuLi4cPnxY6kgaUbduXV69esWjR4+kjiIIgg6wtLSkXr16nD9/XuoouUQBLAJnZ2f27dvH5MmT8fX1pWvXrty7d0/qWGqlr6+Pvb09e/fulTqKIAg6on379hw7dkzqGLlEASwimUyGl5cXp0+fpmnTprRs2ZJDhw5JHUttUlJSqFSpEqGhoVJHEQRBR2jbdUBRAIvJ0NAQPz8/1q9fT//+/Vm+fLnUkYolLi6O6dOnU6NGDZ49e8bYsWOljiQIgo5o2bIl4eHhJCYmSh0FEAVQbVq2bElwcDDLly9nzJgxWnfD5/uYO3cuNjY27Nixg23btnHv3j0++eQTqWMJgqAjDA0NadmypdacWRIFUI3s7OwIDg7m9u3bxMXFkZCQIHWkQpk/fz5r167l5s2bdOrUSeo4giDooI4dO3L06FGpYwCiAKqdubk5gYGB6Ovr06JFi9yxNEsDmUxGvXr1pI4hCIIO69Chg9YMiyYKoAYYGBhgZmZGy5YtWbx4sdRx3ktWVpZO39coCIJ2aNiwIVlZWTx48EDqKKIAatKYMWNYs2YN6enpUkd5p9u3b9OkSRMsLCxwcHCQOo4gCDpMJpNhYmJCWlqa1FFEAdSkunXr4uLiQmBgoNRR3mnkyJHo6ekRGRmJoaGh1HEEQdBh0dHRxMTE0KBBA6mjiAKoacOGDWPJkiVacb47PyNGjCAmJgY9PfHjIAiCZp04cYIWLVpoxWw64hNPw9q2bUtKSgphYWFSR8mXj48PAKtWrZI4iSAIui40NBQ3NzepYwCiAGqcnp4e48ePZ/To0Vpz8+e/GRgYMG3aNObNmyd1FEEQdJwogGVMv379aNGiBd7e3rx69UrqOG81ZMgQYmNj+eOPP6SOIgiCjkpISOD+/fs0btxY6iiAKIAlQiaT8cMPP2BhYUH//v1RKpVSR3qDsbExfn5+/Oc//5E6iiAIOurUqVM0bdqU8uXLSx0FEAWwxOjr67Ny5UqePn3K3LlzpY7zVl999RX379/XmmGKBEHQLcePH6dFixZSx8glCmAJMjQ0ZMmSJSxevJjk5GSp47yhSpUqzJs3j65duxIeHi51HEEQdExoaCgtW7aUOkYuUQBLWN26dfn0009ZuXKl1FHe6quvvmLkyJG0bt2ap0+fSh1HEAQdkZaWxvXr13F1dZU6Si5RACXg5+fHokWLyMjIkDrKW82bNw9PT09cXV1JSkqSOo4gCDrg7NmzNGzYEGNjY6mj5BIFUAKOjo44OjqyYcMGqaO8lUwmY/Xq1bi4uNCkSRMyMzOljiQIQil39OhRrbr+B6IASsbPz4958+aRnZ0tdZS3MjAwICAggMjISM6dOyd1HEEQSrE7d+6wcuXK3EE3tIUogBJxc3PD2tqabdu2SR0lX9u2bcPKygp3d3epowiCUEqlpqbSvXt3pk6diqOjo9Rx8hAFUEJ+fn7MnTtXK+8LzMrK4rvvvmPatGlSRxEEoZRSqVT4+vri7OzMoIghvtQAACAASURBVEGDpI7zBlEAJdS+fXv09fU5cOCA1FHecOfOHeLi4vDz88PCwgJra2s+/PBD/vvf/xa6rYyMDNq2bcvnn3+ugaSCIGirpUuXcuvWLX766SdkMpnUcd5gIHUAuVz+BTADqA80VygUF157bSowFMgBvlIoFMGShNQQmUyGn58fc+bMwdPTU6t+QBo2bEhGRgZJSUnExMQQHR3NX3/9xciRI2nTpg3t2rV7r3ZCQ0Px8fEhOzubDz74QMOpBUHQFocPH2bOnDkcOnQIIyMjqeO8leQFELgBdAd+e/1JuVzeAOgNNASsgRC5XP6RQqHQqWnLvby8mDNnDsePH8fDw0PqOHnIZDLMzc0xNzfH3t4ed3d3Xr58Se/evYmMjCywO/OYMWNYv34933//PQABAQElEfsN2dnZXL58mbNnz3Lt2jXu3r1LTEwMCQkJZGVlERMTI0kuQdBVISEh9O3bl3Xr1mFnZyd1nHxJfgpUoVCEKxSK2295yRsIVCgUrxQKxV/APaB5yabTPH19fcaPH6+1w6P92/jx42nYsCGdO3fOd5knT57g4ODAgQMHOHnyJJMmTSItLY0KFSqUSEalUkn//v2pVasWlSpVwtDQELlczqpVq4iNjcXNzY2JEycyZ84crR2cXBBKq+DgYPr27cv69etp1aqV1HHeSaYtE7XK5fJjwOR/ToHK5fLlwBmFQrH5/x6vAf5QKBTb37LucGD4u9pfs2ZNU6DETjOqVKr33pZKpeLFixdYWlpqzSCx75KTk/PGBLqvv9ecnByMjIwwMzPLXSYpKYmsrCwqV66cZzl1T4qZlZVFXFwcMpkMMzMz9PX1MTAweOv3IiMjg8TERKysrNSaQV00sX90jdhHBSvJfZSRkUFCQgIWFhYl8llmY2NTrA/0EjkFKpfLQ4Dqb3lpmkKh2JPPam97Y2+t1gqFYhXwztlcHz16pAKoWLHiuxZTm+Tk5EJta8uWLVy4cIEdO3ZoMJX6yGQy7ty5Q0ZGBq9evcrz74cffvjGcEezZs3ixYsXed5fVFSUWq8Lzp49m3nz5jFq1CjmzJlT4C/g8uXL+f3337l69araMqiTuvePLhL7qGAltY/279/PkCFD2LJlC3Xr1tX49tShRAqgQqFoX4TVngA2rz2uCejs4JQDBgxg4cKFhIeHU79+fanjFMjGxgYbG5uCF/w/hf2DoDA2btzIzJkzSUpKYt++fbRp0+a91ouIiMDW1lYjmQShLNm7dy/Dhg0jICCApk2bSh3nvUl+DfAd9gK95XJ5BblcXhuwB3R2SBITExNGjRqFv7+/1FE0IikpCTMzM7W1p1QqmTNnDtbW1kycOJExY8bw4MGD9y5+ADdu3NC6G3MFobTZtWsXvr6+BAYGlqriB1pQAOVyeTe5XP4EcAMOyOXyYACFQnET2AbcAv4ExuhaD9B/GzFiBCdOnODixYtSR1G75ORkLCwsit1ORkYG48aNo1q1aqxZs4Z58+bx7NkzJk6cWOgjTHNzc6KiooqdSRDKqh07djBy5EiCgoJwcXGROk6hSX4bhEKh2AXsyue1Of+PvbuOq/L8/zj+OoCoYGAhIoKKibIZwy7UI8xitk5FnV1zfu2Y7dQ5Z23m1IUyZ86ceguzOxE7UBSwRek4nN8fxk8ndThxn7iej4ePwTl3vDmD8znXfV8BzDRsIvnY29szcuRIxowZw/79++WOo1MxMTFaF8C4uDhKlSqFi4sLv/32G82bN9eqU1Pnzp0ZO3asVpkEwVJt2LCBr7/+mk2bNpnslRTZW4DCh/z9/bl7967ZFcDY2FgKFCiQ5nMXLlxgzZo1XL16NcNp4ZRKJZUqVeL06dO0aNFC6x69LVq04OHDhzx8+FCr4wiCpQkICDD54gdG0AIUPpQjRw7Gjx/P2LFjOX36tFHNDqMNlUrFtGnTOHbsGD179uThw4eULVuWatWq8eLFC2xtbUlNTSU5OZk8efKQJ08e7O3tUalUJCcnk5ycTGJiIlevXv1g+IU28ubNS2pqqs6OJwiWYMuWLYwYMYItW7bg4eEhdxytiAJohL744gsWL17Mpk2b6NChg9xxdGLfvn3s2bOH3bt307x5c3LlykVQUBABAQHUqVOHihUr0qtXL0aMGMH9+/e5f/8+jx49ImfOnNjb22Nvb0+FChUoUqSIzjI9fvyYHDly4OjoqLNjCoI5i46OZsiQIaxdu9bkix8Y0UB4fXs7DlCXPREzom23/3///ZfRo0dz5coVcuTIocNkxuP98Ul///03X331FQ8fPjTYZABvL6Ua61RoYoxb5sRrlDldvkYTJkzgzp07LF26VCfH05aDg4NWl8jEtR8j1ahRI5ydnVm1apXcUQzCz88PNzc3vv76a4Od8969ewabGEEQTF1YWBjLli3L1oowxkoUQCOlUCiYPHky06dPJy4uTu44eqdQKFi0aBEBAQG8evXKIOd89OgRCQkJJCUlGeR8gmDKxo4dS58+fcyqxS0KoBGrVq0aXl5eLFy4UO4oBlG/fn1q1qxJjx49DHK+Ll264OrqSpkyZXjw4IFBzikIpujkyZMcPHiQoUOHyh1Fp0QBNHITJ07kxx9/5Pnz53JHMYj58+ezb98+wsLC9H6uggULcujQIXx8fPD09CQoKEjv5xQEU6NWqxk+fDgTJkwgT548csfRKVEAjVyZMmVo2bIls2bNkjuKQVSuXBk/Pz+6d+9ukPPlyJGDlStXMmPGDFq3bs28efMMcl5BMBUbN24kLi6Ozp07yx1F50QBNAGjR49m9erVFnOZbvbs2Zw+fZoLFy4Y7JyDBw9mx44dTJs2zSz/0AUhO+Lj4xk9ejTTp083y/Gy5vcTmaFixYrh7+/PmDFj5I5iEK6urvTt25eePXsa9Lze3t6cO3eOkydPUqVKFYvofCQI6UlNTaVXr17UrFmT+vXryx1HL0QBNBEjR47k2LFj/PPPP3JHMYhJkyZx69YtJEky6Hnd3d25ePEiRYoUwd3dnXXr1omp0gSLNHnyZO7cucOCBQvkjqI3ogCaCHt7e+bPn8+AAQOIjo6WO47eFSpUiLFjx8rS6yxfvnzs2bOH3r178+233+Lq6krevHkpXrw4n3zyCS1btmT8+PHs3LmThIQEg+cTBH377bffWLt2LevWrSN37txyx9EbUQBNSKNGjahXrx7jxo2TO4pB3Lx5U7ZeZ9bW1syYMYM7d+6QkJDA5cuX+e233xg4cCClSpXi2LFj9OnThxIlSnD79m1ZMgqCPhw8eJBRo0bx559/6nTqQWMk5gI1MTNmzKBOnTp8+eWX1KlTR+44enPw4EG2bNnC1atX5Y6ClZUVrq6uuLq60rRp03ePv+0e7uXlxaFDh6hcubKMKQVBezdu3KBjx46sWLGCChUqyB1H70QL0MQUKFCAWbNm0bt3bxITE+WOozeTJk2ia9euuLi4yB0lXQqFgvnz5zNkyBDq1q3LL7/8QkpKityxBEFjarWayMhIWrRowYQJE2jUqJHckQxCFEAT5OfnR5kyZZgxY4bcUfQmMjKSevXqyR0jUwqFgmnTpjF79mymT59O/vz58fDwYMSIEQYZzC8I2tq8eTO5c+embNmytGnTBn9/f7kjGYwogCZIoVDw/fffs2zZMoKDg+WOoxdRUVG4urrKHSPLBg4cyL1797h06RIDBw7k9OnTlC1blpo1axIVFSV3PEFI1/z581m5ciUPHjxg/PjxcscxKFEATVSxYsWYOHEivXv3RqVSyR1H56KjoylZsqTcMTRWunRphg4dyqFDhwgPD6dIkSKULl2azZs3yx1NED5y8+ZNbty4ga+vr9xRZCEKoAnz9/cnd+7c/PTTT3JH0am3q78b8/2/rChcuDA7duxg3rx59OzZk8KFC1OoUCEKFCiAg4MDhQsXpk+fPmIohSCbNWvW0KFDB7NdczQzoheoCVMoFBQqVMjsZix5+fIlOXPmNIuplxQKBb169aJFixY8fPgQGxubd//u3r3L+PHjcXJy4ssvv2TBggUGWwxYEFQqFb///jt//fWX3FFkIwqgCTt+/Dhnz55l3bp1ckfRqcTERGxszOtX09HREUdHxw8eK126NCdOnCAoKIgRI0ZQtGhRrl+//tF2gqAPQUFBODo6UqlSJbmjyMb0P2JbsIiICNzc3LCzs5M7ik4lJSWZResvqxo3bsy5c+ewt7fn+PHjcscRLERERATly5eXO4asLOddxgz5+fkRERHBgQMH5I6iU0lJSVhbW8sdw6AUCgVFihQRQycEg7GzsyM+Pl7uGLISBdCE2djYMHLkSCZPnix3FJ2ytBbgW0WKFOH+/ftyxxAsRO7cuUUBlDuAoJ0OHTrw4MEDs2oFWmoBdHR0tJg1HwX5iRagKIAm720rcNKkSajVarnj6MSDBw8s7hIoQPv27dm1axdPnz6VO4pgAUQBFAXQLHTo0IGIiAiDr52nDw8fPuSrr76if//+ckcxuC+++ILGjRvTsmVLuaMIFkBcAhUF0CzY2Ngwa9YsunbtyoYNG+SOk23Jycm0bt2aTz/9lClTpsgdRxYzZszg+vXrcscQLIBoAYpxgGZDqVSyadMm/P39OXv2LN99953JXUYcOXIkkZGRhIaGyh1FNvny5RMrSggGYWdnZ3aTaGhKtADNyKeffkpQUBAnTpzg888/5/nz53JHyjK1Ws2KFStYsGCB2Q2Cz6obN24QHBxslnO7CsYnd+7cFj8NnyiAZqZQoUJs3ryZsmXL4uXlZTKrRSgUCvr168esWbPkjiKLhw8fUr58eXr37k3p0qXljiNYANECFAXQLNnY2DBz5kzGjBlDkyZN+OGHH0hOTpY7VqbGjh3LlStXLPIemJOTE6VKlWLq1KmEhITIHUewADlz5iQ5OdmirziIAmjGOnbsyJ49e9i9ezdVq1blyJEjckfKUFhYGFZWVhY7IXT37t355Zdf5I4hWAiFQmHxPUFFATRz7u7ubN68mZEjR9KpUyd69erFkydP5I71kbi4ODp06EC/fv0oVaqU3HFk0alTJ65duyY6wQgGY+k9QUUBtAAKhYIvvviC48ePY2dnR6VKlVi+fDmpqalyR3vnn3/+ITY2lh9//FHuKLLx8PCgYMGC/Prrr3JHESyElZWVSdwe0RdRAC1Ivnz5mDlzJps3b2bNmjXUqlWL8+fPyx0LAB8fH1QqFZs2bZI7iqy6d+/OypUr5Y4hWIDnz58THx+Pk5OT3FFkIwqgBfL09GT37t34+/vj4+PD0KFDefnypayZ8uTJw7fffsuoUaNkzSG3Tp06cfXqVaNqnQvmKTg4GA8PD4ucd/cty/3JLZyVlRXdunXjxIkTREdHU7FiRQICAmSdT3TQoEG8fPmStWvXypZBbpUrVyZ//vz88ccfckcRzFxwcLBFL4YLYiYYi1ewYEEWLFjAqVOnGDlyJL/88gtLliyhQoUKBs+SO3dupk6dyvjx4+nWrZvBz28MFAoF3bt3Z9iwYQQFBdG4cWOsra2xsbHB2tr6g69z5MiBra0tOXLkIGfOnNja2tK3b1+aNGki948hmICLFy/i4eEhdwxZiQIoAFCjRg2CgoJYtWoV9erVo0OHDkyYMAEXFxeD5ujXrx/Tpk1jxYoV9OvXz6DnNhbffvsttWrVIl++fEyePJnk5GSSkpLS/G9CQgJJSUkkJCSwe/duEhMTRQEUsiQ4OJgOHTrIHUNWogAK79jY2NC/f3/atWvH4sWL+fTTT+nSpQvjx4/H2dnZIBly5sxJ//79Wbx4scUWwNy5c9O6dWvCw8P58ssvs7yfq6sr7du312MywVyoVCquXLli8S1AcQ9Q+EjhwoWZOnUqx48fB17fl/rmm294+PCh3s8dGxvLsmXLGDRokN7PZU4iIyN58uSJxX+iF7Lm1q1bODo6ki9fPrmjyEoUQCFdjo6OzJgxg2PHjpGYmIiHhwcjRozg8ePHejvnjBkzKFSoEAMHDtTbOczRgQMHKFasmMXOoiNoRnSAeU0UQCFTTk5OzJo1iyNHjrzrMTp69Gidr1x++/ZtFi1axLp163R6XEtw7949HBwc5I4hmIgLFy6IAogogIIGnJ2d+f777zl48CDPnj2jfPnyjB8/XmfLLg0ePJj69evz2Wef6eR4lqRhw4aEhYXJHUMwEaIF+JoogILGXFxcmDdvHv/++y8RERGULVuWb7/9lhcvXmT7mPv27eP48eOsX79eh0kth5eXF/Hx8UY/4blgHC5dumTxHWBAFEBBC66urixYsICgoCDu3r1L2bJl2b59u8bHSU5Opl+/fgwZMkRcxssmGxsbxowZQ7NmzfD29ubevXtyRxKMVExMDI8fP7bYSeffJwqgoDU3NzcWL17MjBkzsjWR86JFi0hJSWH69Om6D2dBJk2aREhICM7OzlSsWBEfHx+LX/Fb+Nj169dxd3fH2tpa7iiyEwVQ0JlatWpx6tQpjfZ58eIFU6ZMoW/fvhY9J6GulC5dmnXr1nH+/HnCw8P5+uuv5Y4kGJkrV65Qvnx5uWMYBfGOI+iMm5sbiYmJGl0GnTVrFrly5eLHH38kPDxcj+ksS/ny5ZkyZQo7d+6UO4pgZK5cuUK5cuXkjmEURAEUdEahULBy5UpGjBiBr68vV69ezXB7lUrFzz//zM6dO/Hy8qJDhw6oVCoDpTV/rVq1Ijo6mgMHDsgdRTAS8fHx7Ny5E09PT7mjGAVRAAWdatSoEUePHqVhw4Y0aNCAoUOHpjlMQqVS8erVKzw8PKhZsya7d+/m9u3bzJkzR4bU5ilnzpx0796dKVOmyB1FMAJqtZr+/ftTrlw5fH195Y5jFEQBFHTO1taWgQMHcuLECeLj46lQoQKLFy8mOTmZlJQU/vjjD0qWLEliYiK///77u33+/vtvZs6cqfF9RCF9vXr14uLFi3LHEIzAokWLOH/+PIsWLUKhUMgdxygo5Fz/zZDCwsLUgMHmvouOjiZv3rwGOZexu3LlChMmTCAiIoLY2FhSUlIYMWIEXbp0+Wi1iV69enH37l3+/fdfmdIaj/DwcIoXL67VMQIDA+nevTsRERE6SmVcdPEambvw8HCKFi1Kvnz5OHbsGCVLlpQ7ks44ODhoVclFC1DQOw8PD7Zs2UKVKlWoWrUqkZGRjBo1Ks1Pof369ePChQsypDRPR44cwdXVVe4Ygszu379PoUKFzKr46YIogIJBKBQKlEoluXLlyvDyS82aNYmPj9frhNuWJCgoCG9vb7ljCDK7c+eOKH5pEAVQMJgyZcpw48aNDLexsrKiUKFC4r6VDqSmpnLmzBm6du0qdxRBZnfu3MHNzU3uGEZHFEDBYEqXLs2tW7dITU3NcLtixYqJy6A6cO3aNXLkyEHlypXljiLITLQA06ZRAVS+tkqpVO548/1nSqWysX6iCeYmX7585M2bN9MOGTVq1GDVqlU8evTIQMnMU0BAACVKlJA7hmAERAswbVkugEqlciiwFLgJNHjzcDwwQw+5BDOVlcugixYtwsnJCU9PT86cOWOgZOblxo0bzJ8/n19++UXuKIIREC3AtNlosO03QBNJku4qlcoxbx67BohJ5YQsK126NDdu3KBx4/QvHNjY2HDgwAFGjx5Nw4YNWbZsGd27dzdgSnmFhobyzz//0LhxY2bOnImtre0H//LkyUObNm3SXf1drVbTo0cPmjVrRs2aNQ2cXjBGoaGhogCmQZMCmBe4/+brt4MHcwBJOk0kmDV3d3euX7+epW2///57ateujb+/Pw4ODrRq1UrP6QzrbaE7evQo169f59GjR7x48QKVSoW7uzt169Zl9+7dqFQqUlJS3k0k8PLlSwYOHMiIESOYMGHCR8cNCgri+vXrYiylALzuDJWQkEDhwoXljmJ0NCmAh4CxwMz3Hvsa0OqvTKlUzgVa8bqQ3gZ6SZIU9ea5cUBvQAV8LUnSXm3OJcivTJkyBAQEZHl7Pz8/rK2tKVasmB5T6VdqaiqrV6/mwIEDXLt27aNCV6VKFbp06UKlSpWoVKkSLi4uKBQKwsPDOXr06EfHU6vV/P333wwdOpQlS5bw448/0qlTp3fP58uXD2tra3LlymXIH1MwUikpKZQsWVLM/pIGTTrBDAXaKJXKu0BepVJ5HegA/E/LDBJQWZKkT4AbwDgApVLpAXQGKgG+wBKlUikWsDJx7u7umd4DfN/WrVuxtbWlevXqekylX71792bcuHGo1Wq6dOnCypUruXLlCnFxcYSEhLB27dp3E4iXKFEi0zcqhUJBmzZtCA0NZfz48fTv35+KFSty/PhxACpWrEhUVBRJSeLijKVTqVRER0fz+eefyx3FKGW5AEqSFAl4AZ2AL4EeQE1Jkh5qE0CSpH2SJKW8+fYE8HZuLD9gvSRJiZIkhQK3gBranEuQn4uLCw8ePMjy9j/99BOdO3c22U+vT58+ZePGjezcuZN169a9K3Surq5a/0w5cuRg8ODBhIWF0aZNG5o2bUqzZs3IkycPBQsW5ODBgzr6KQRTNXnyZNRqNWPGjMl8YwukySVQJElSAyff/NOHr4C/3nxdnNcF8a0Hbx4TTJidnR2JiYlZXvYoODiYGTNMt6Nxt27daNy4sV47o+TLl4/vvvuOgQMH4ubmxvPnz7G3t+fkyZMolUq9nVcwbrt27WLNmjX07dsXGxuN3uotRoaTYSuVyvv8f4eXdEmSlOFkg0qlcj/glMZTEyRJ2vZmmwnAZ0BbSZLUSqXyZ+C4JElr3zy/CtgtSdLmNI7fD+iXUYZVq1ZVBwzWklCr1SbbatG3R48eUbRoUdRqNdbW6V/VTkhI4MWLFzg5OZnka5mSksKTJ08oUqRItt6AVCpVhq9PWiIiIlAoFFhbW1O4cGGsrMx7rovsvEaWICUlhadPn+Lg4ECOHDlM8u8nK0qUKKHVD5bZX2W397724vVlz0XAPcANGAL8ntlJJElqmtHzSqWyB9CS18Ms3hbcB8D7o3hdgDRHUEuStAJYkdE53q4GYagVGsRqEOn77LPPuHDhAqmpqRnO5O/j40PJkiVZvny5AdPpzmeffUb16tWznT87Kx00atSIMmXK8M8//2TrnKZGrAbxsfj4eGrXrk2XLl3o37+/eC/KQIYFUJKkdzcR3rTIfCRJCn/vsX+APcC87AZQKpW+wBigoSRJce89tR0IUCqVPwLOQFlALBRnBuzt7YmNjSV37twZbnfp0iUGDx5soFS69bbH5+7duw163tmzZ9O/f39SUlLEZS8LpFarGTRoEGXLlqVfvwwviglo1gvUGYj5z2MxaH9f7idejzGUlErlBaVSuQxAkqTLwAbgCq+L7GBJkrJ240gwavb29sTE/PdX6WMVKlRgw4YNBkike19//TX/+9//cHR0NOh527ZtS9GiRRk1apRBzysYh5UrV3LixAnmz59vtpc9dUmTj4jbge1KpXIG/395ctybx7NNkqQyGTw3kw/HHQpmwM7OjtjYWIoUKZLhditXrsTT05OwsDCTW9MuKiqK+vXrG/y8CoWCRYsW0bZtW2bOnImdnZ3BMwjyOHPmDBMmTGD37t3kyZNH7jgmQZMW4ADgOLAMOPfmvyffPC4IWfb2Emhm3g4S/+677wyQSreSk5NlexNq0qQJBQoUYNOmTbKcXzC8Z8+e0b59e3744QfKli0rdxyTkeUWoCRJCbyeCWas/uIIliCrl0ABli1bRq1atZg6dSpFixbVczLdkbMAwut7Qfnz55ft/IJhTZw4EV9fX/z8/OSOYlKyXAAzWvZIkqQg3cQRLEFWW4AAn3zyCRUqVOCHH35g7ty5ek6mG2/nXsyXL59sGWJjYw1+/1GQz9mzZ5k+fbrcMUyOJvcAV/3n+yKALa/vB5bWWSLB7NnZ2WW5BQiwePFimjVrxvjx4ylQoIAek+nGn3/+Sb58+WS9bxkfHy8KoIVQq9VcvXqV8uXFwjya0uQSaKn3v38zL+dEIFrXoQTzpkkLEKBu3bq4ubmxaNEiJk+erMdkurF06VK6du0qWy88tVpNQkKCSV0yFrLv/v375MmTBwcHB7mjmJxsTxPxZkjCTGC07uIIliAhISHdtezSM2/ePObNm0d8fLyeUulGamoqISEhdO7cWbYMiYmJKBQK0RPQQly5coUKFSrIHcMkaTtPkhJI1UUQwXLcuXOHMmXSHf2Sps8//5w8efIY/QTPmzZtImfOnFSrVk22DDExMRp/wBBM15UrVyhXrpzcMUySJp1g/jsvqB2QCzDNqToE2YSGhmpcAAE8PT3ZsWMHvr6+Gu0XHh6Ora1tpuMOdeGnn36S9fInvJ6GL0eOHLKdXzCsy5cvU6lSJbljmCRNWoDdgO7v/fMFnCVJ+k0fwQTzlJycTHh4OCVLltR43549e7Jt2zaN9jl+/Djly5enRIkSFCxYkIYNGzJmzBg2bNjA9evXs7wqRWJiIqmpGV/sSE1N5dKlSx8sTiuHmJgYUQAtREJCAmfOnBEdYLJJkwLoJUnSwff+nZEk6ZVSqdR2QVzBgty/fx9nZ+dsXaLr0KEDL1684M6dO1naPiQkBF9fX8aOHUtcXBw7duygbt26nD59mvHjx1OjRg3s7Ozw9PSkd+/e7N27N83jBAUFkSdPHqytrbGxscHOzg4HBwecnJwoWbIkFStWpGHDhqxYsQIbGxtq1JB32cro6GhUKhUHDhzIsGinpqayfPlyjT9UCPJTqVSsWbOGcuXK4erqKusld1OmyTCIScAPaTw+EfhRN3EEc5ed+39v2djY4Obmxj///JPpJNmhoaE0atQIf39/Jk6cCLzuTVq3bt0PtgsLC2P79u38+++/tGvXjt27d9OgQYMPthkzZgx9+/blp59+IiYmhmfPnvHixQuioqJ48eIFr1694pdffmHYsGH0799f9jkY374htmvXjtjYWAoWLIijoyOVKlXC29ub5s2bssbFDAAAIABJREFUs2LFCpYuXYqNjQ3x8fHY2trSunVrZs+eTcGCBWXNL6RPrVazfft2xo0bR4ECBVi5cqVe15o0dxmuBwgfDIDfwesli97/6y4NfCtJkpt+4unO2+WQDDU4WSxBkrYVK1YQGhrK0qVLs7WUzbBhwwgJCSEwMDDdbR49ekT16tWpV68e69evz/KxZ82axffff8+FCxdwc3v9K33o0CFatWrFo0ePyJUrV7r7pqSkULx4cTw8PNi5cyf29vZZ/6HSoYulfp49e0ZwcDDBwcGcOnWKs2fPcvv2bUqWLMmMGTNo3749qamp7Ny5k4ULF3Lq1Ck8PDzYuHEjpUqVyvwEMrOk5ZBOnz7NsGHDePXqFd9++y3NmjXL0octc34vcnBw0OrTZlYKYOibL12BsPeeUgMPgdmSJGk1IbYhiAJoHMaNG0eZMmUYMWJEtt68bt++TaVKlYiKikqzIEVFRVGzZk1KlCjB/v37Nc73xRdfcO3aNc6ePYu9vT3169enTJkyrFmzJtN94+LiqFatGra2tuzfv1/rgej6enNXqVRYWVml+eZ59+5dRo4cyZUrV7hy5YrOz61rllIAVSoVJUuWZOTIkXTr1k2jRYDN+b1I2wKY6SXQtwPglUrl75Ik+WtzMkEIDQ3VuBfn+9zd3XFwcODw4cMolcoPnouPj6dZs2bkyZOHffv2Zev4W7ZswcPDgy+//JJx48Zx8eLFLC8ua2dnx5UrV2jUqBHVqlXjwIED2b7cq08ZvXmWLFmSX3/9ldKlSzNv3jxGjBhhwGRCevbt24ejoyM9evSQO4pZyXInGFH8BF3Q5h7gW5UqVWLnzp0APH/+nMDAQObNm0e9evV49uwZJ0+exMoqe0NcraysOHHiBMeOHaNFixY0b95cowHlVlZWHDp0iEaNGuHl5cXp06ezlUNOefLkYdasWdleyV7QvV9++YVu3brJHcPsZNgCVCqVVyVJqvjm6/+OA3xHkiTTWqxNkIVKpeL+/fuULq3d1LE9evRgwIABBAQE8OrVKwoXLoyTkxNJSUk4OjpqvRK6g4MDe/fupUOHDixbtixbx1i7di0TJkzA29ubv/76ixYtWmiVydDKlSun0Xytgv48fvyYwMBA5s+fL3cUs5PZO0Xf974WHz8ErYSHh1OoUCFy586t1XG6detGREQEXl5eNGzY8F3Bu337Nh4eHqSmpma7BfhWtWrVuH37tlbHmDlzJqVKlaJjx47Mnz+ffv36aXU8Q3J2diYuLk7uGGYrMjKSQ4cOcfDgQQ4ePMjNmzcB3t2XVSgU775OTU3F399f1tVFzFWGBVCSpCPvfesoSdLG/26jVCrb6zyVYJayOwPMf1lZWTF27MfLUt65cwcHBweti58u9enThxIlStChQwfu37/PtGnTZB8mkRXFihUjJiZGJx8mhNfDbd4Wu4MHD/Ls2TNq165NnTp1+Omnn6hYseK73wu1Ws3bzolv/5tRD2Qh+zRdDumjAgisAMTS00KmdHH/LyPr1q3Dy8tLb8fPLh8fH44dO0ajRo24e/cuq1evNvqZWuzs7MiZMyehoaG4u7vLHcfkPHjwgL1793Lw4EEOHTpEXFwcderUoU6dOvTs2RMPDw/xwcIIZFoAlUrl2xs2VkqlshQfjwNM0EcwwfzoswCmpKSwZcuWd51jjE3lypUJCQnBy8sLX19ftm3bZvSrNRQuXJjg4GBRADWQlJTEnDlzWLhwId7e3tSpU4chQ4ZQtmxZk2j5W5qstABv8brziwL4702Rh8AUHWcSzNTdu3dp2LChXo598OBBcuXK9dEsLsbEycmJ69ev4+XlRa1atQgMDDTqNfucnZ25fPkybdq0kTuKSTh69Ch9+/bFzc2Nf//9lxIlSsgdSchEVsYBWgEolcqDkiTp591LsAi3b9/WWwswICCA6tWr6+XYumRnZ8elS5do2rQpVatW5eDBg5QtW1buWGmqV68ee/fufTeVnJC2qKgoxowZw44dO5g1axatW7cWrT0Tock4QFH8hGxTqVSEhobqZd0ylUrFpk2bGDVqlM6PrQ9WVlYEBQWhVCrx8vJi06ZNREZGktmsTIbWpk0brl69+sFjDx48oF27dvj6+nLv3j2ZkslLrVZz7do1fv75Z9q2bUvp0qVJSUnh+PHj+Pn5ieJnQjRZD9AGGAQ0BArz3r1ASZKM97qTYBTCwsJwdHTEzs5O58fev38/tra2NG7cOPONjchvv/3GtGnT+Oabb3j+/Dk2NjZ4enpSq1atd3OZyqlmzZqkpqZSqlQpChQoQEpKCjdv3sTX15eCBQtSsWJFateuzT///GP2C/Dev3+fwMBAAgMDCQoKwsrKigYNGuDj48PMmTMpVqyY3BGFbNCkF+h8oDGve33OBCYAA4GszzYsWKybN2/qfM2yc+fOMXPmTP755x/69u2b+Q5GaNKkSUyaNInU1FTOnj3Lrl27OHr0KNu3b+fPP/9k/fr1DB8+XJZeo9bW1ly4cIFz584RFhZGQkICX375JS4uLgDcunWLOnXqsH79evz9zXOiqMDAQIYOHcqTJ0+oV68eDRo0YPjw4ZQqVUq09MyAJgWwLVBbkqQwpVI5VZKkhUqlci+wHNERRsiErgpgamoqe/bsYfr06YSEhODt7c21a9dwdTXtyYisrKzw8vL6YBjHnTt3WLRoEcuXL2f16tV660CUEVdX13Rf2zJlylCoUCGju3SrCzExMYwePZrt27czb948lEqlGLZghjT5P2oH3H/zdbxSqbSTJOkaUFX3sQRzc+vWLSpWrJjt/RMTE1m9ejXu7u74+/tTtWpVHj16xPbt202++KUnZ86chIWF0bZtW1q2bEmHDh14+PCh3LHM3uHDh6lSpQpRUVEcPXoUHx8fUfzMlCb/V68Cbz+engGmKJXKiUC4zlMJZufmzZtUqFAhW/sGBwdTrFgxpkyZwqBBg3j8+DFLlizRy/1EY2NlZcXcuXO5ffs2ERERlC1bloULF5KSkiJ3NLMTHx/P8OHD6dixI9OmTWPJkiXkz59f7liCHmlSAIcByW++/h9QjdcL5JrmzRfBoLS5BBocHEyhQoUICwtj1KhRFvlp3NHRkaNHj/Lnn38yZ84cKlWqxLFjx+SOxZMnT8xivNvJkyepUqUKYWFhHDlyhObNm8sdSTCAzFaD+Khb3XuPfffmv+bd/UvQ2suXL4mLi8PZ2Tlb+xv7tGGG1LJlSx48eMA333xDs2bNqFWrFoMHD6ZFixYG74l5//594uPjadSokUHPq0uJiYlMmTKFVatWMWfOHDHo38Jk1glmVRaOoeb1lGiCkKabN29Srly5bPeay5EjB6mpqTpOZbqsrKxYtGgREydOZPz48QwePJgePXrQo0cP+vXrh6enp0FyHD16FCcnJ5NtkScnJ9OyZUtsbGw4fPiwUc/KI+hHZqtBlDJUEMF8aXP/D0QBTI+joyO//PIL8Lq7/tSpU6lTpw4uLi4MGTKELl26ULBgQb2d/9ChQ1p1bJKTWq1m0KBBKBQK/vjjD63XkBRMk2l+dBNMyo0bN7QaAiEKYOaaNGnCoUOHePbsGf7+/ixcuJBy5cqRnJyc+c7ZtGfPHr744gu9HV+ffvjhB44fP86qVatE8bNgogAKeqftEAhRALPO1taWcePGcePGDaytrTlw4IBeznP16lWePHlCz5499XJ8fdq6dSvz58/nzz//JG/evHLHEWQkCqCgd9oOghcFMHuqV6/O+vX6majpzz//pGLFitjY2BAeHk5CgmmsinbmzBn69evHunXrzKL3qqAdUQAFvUpJSeHevXtarXhgY2ODSqXSYSrL0K5dO3bt2qXz46rVatasWYO7uzu1atWiZMmSNG7cmKSkJJ2fS5fu37+Pn58f8+fPp2pVMX+HIAqgoGfh4eE4OjqSO3fubB9DtAA1l5qayvTp0/UyR+rVq1d5/vw5hw8fpnbt2kRGRhIREUGPHj2Mdlq06OhoWrRowYABA2jZsqXccQQjIe7+Cnr18OFDrWfKz5Ejh9G+sRqrESNGYGVlpZe1/E6fPk3x4sW5cePGu8fOnDlDhQoVmDRpEtOnT9f5ObU1fPhwqlatypAhQ+SOIhgRUQAFvXr8+DFOTk5aHUO0ADVz8+ZNVqxYQWBgIDlz5tT58c+cOfPRwsaFCxfmwIED1KlTh9KlS9OrVy+dn1cbQUFBrF+/XqzgIHxAXAIV9OrRo0daF0BAFEANtGrVil69elGrVi29HP/kyZPUrVv3o8crV67M+vXr6d+/v16HX2jqxYsXPHnyBHd3d7mjCEZGFEBBr3TRApw+fbp488qiGTNmEBUVxZw5c/RyfLVazeXLl2ndunWaz/v6+pKcnIy1tbVezp8dFy5coHLlykaVSTAO4hKooFdPnjyhZs2a2d5/z5497N69+4P7TULaIiIimDNnDlu2bMHe3l4v53jw4AFAutOtJSQkYGVlZVTTo507d45PPvlE7hiCETKe31LBLGnbAjxz5gz58uXD0dFRh6nMU8uWLfHz80OpVOrtHKdPn6ZQoULpPv/q1Sujm1nl7NmzfPrpp3LHEIyQKICCXmlbAEeNGkWBAgVo27atDlOZnyVLlnD37l0WL16st3MEBgbSs2dPOnXqlO42sbGxRlcAra2tTWagvmBYogAKeqVtJ5icOXOyY8cOgoKC3k38LHwoKiqK8ePHs3LlSgoUKKCXcyxfvpzWrVszbdo05s6dm+520dHRRrd8VcuWLdm7d6/cMQQjJAqgoDdqtZrHjx9rvczMjRs3UKvVvHjxQkfJzEurVq2oV68e7dq10/mxVSoVw4YNY/To0fz9999888036W67d+9efHx89Nb7NLt8fX05fvw4sbGxckcRjIwogILevHr1ily5cmk1C8zq1atp06YNc+bMYdSoUTpMZx5CQkI4evQorq6uHD9+nJSUFJ0dOyYmhubNm7NhwwbOnTuX7r3FhIQEWrZsSdu2bRk3bpxepl/TRv78+fnss884ePCg3FEEIyMKoKA3jx49ynbrT61WM2XKFIYNG8bGjRsZNGiQjtOZBw8PD7777jvOnj1Lq1atyJ8/P59//jlLly7l1q1b2Z5B5/79+1SvXp3w8HBu3ryZ7jCUzZs34+LiwosXL7h06RLffPONUQ43cHFx4enTp3LHEIyMcd2tFsxKdjvAJCcn06dPH3bu3MnRo0dFF/YMWFlZMXbsWMaOHQtAcHAwy5YtY/HixYwaNQo7Ozs8PDzw8PCgfPnyuLu74+7uTqlSpciVK9e74zx79oyzZ89y+vRpDh8+zJEjR2jSpAlbt25Nc0hDTEwMfn5+nDp1innz5tG3b1+jnmXl4sWLJrl0k6BfogAKepPdFmDXrl05duwYly9f1sksMpbkk08+YcmSJcDr2XP27t3L4cOHuXLlCidOnODZs2dER0fz6tUr8ufPj6urK5GRkURFRVG4cGFcXFyoWbMmEydOpF69emmeY968eUyfPh0vLy+uXr2Ki4uLIX9EjSUlJXHz5k2TXb1e0B9RAAW9uXz5MiVLltRon4CAAPbt28etW7coXLiwfoJZCCsrKz7//HM+//zzj55LSkri9OnTnD59mvLly6NUKjMdvnDu3Dk6derEq1ev+O2332jdurVRt/reunbtGiVKlNDqXrRgnkQBFPTi1atX/PHHHxw/fjzL+4SFhTFgwACWLl0qip+e2draUrdu3TTn9PyvlJQUOnTowN69exk6dCiTJ0/Gzs7OACl148KFC+nOXCNYNlEABb1YvXo1SqXyo1UD0pOamkrnzp2pU6cOXbt21XM6IauePn1KjRo1KFCgAOfPn6d8+fJyR9LYhQsXqFSpktwxBCMkCqCgc/Hx8SxdupTAwMAs7/Pjjz9y+/Zt7t+/r8dkgiZCQkLw9vbG29ubP/74Qy9LKxnCxYsXGTBggNwxBCMkhkEIOvfHH39Qq1YtKleunKXtL126xOTJk9m4cSO2trZ6Tidkxfbt26lTpw4DBgzgr7/+MtniFx0dzfnz58VcoEKaRAtQ0KmkpCR++uknNm3alKXtExMTadu2LZ06daJBgwZ6Tidkxdy5c5kyZQpLliyhR48ecsfRyvLly/H29haTqQtpEgVQ0KkNGzZQrlw5atSokaXtly5dSmJiopjn00j06tWLLVu2sGvXLho1aiR3HK0kJSWxYMECAgIC5I4iGClRAAWdUalULFiwgJUrV2Zpe7VazcKFCxk1apRRrR9niVJSUvD29ubOnTucPHmSChUqyB1Ja8uWLaN8+fJiIgUhXaIACjqzbds2ihQpkuWWQ3JyMlFRUQwePFi/wYQMRUVF4eXlRd68eblw4QJFihSRO5JWVCoVEyZMICAggPXr18sdRzBiogAKOrNnzx6++uqrLA+Ojo2NpW7duqL1J6NXr15RuXJlqlSpwsaNG01+sPjTp0/p0qULycnJBAUFifGkQobEO4+gMwUKFCAmJiZL27569YqEhARmz56t51RCemJiYvD09KRq1aps3brV5IvfuXPn+Oyzz/Dw8GDTpk2i+AmZEgVQ0JmiRYsSERGRpW0DAgKwsrLK8lAJQbfi4uLw9PSkYsWKbN682egWsdXUxo0b8fX1Zdq0aUydOtXoVqUXjJMogILOODk58fDhwyxtu2jRIpOaTsucJCQk8Mknn1C6dGm2bdtm8mMvw8PDGTRoEJs3b6Z169ZyxxFMiCiAgs4ULVqUyMjILG374MGDD5bjEQwjKSmJTz75hOLFi7Nr1y6THeD+viFDhtC7d28x36egMdmvEyiVyumAH5AKPAZ6SpIU8ea5cUBvQAV8LUnSXtmCCplydHTMcgEsUqQIycnJek4kvC8lJYUqVapQpEgR9uzZYxYfQOLj47l8+TLLli2TO4pggoyhBThXkqRPJEmqAuwEJgEolUoPoDNQCfAFliiVSuNbalp4R5NLoM7OzqhUKj0nEt5KSUmhatWq5M2bl3379pl8hxd43ZHq1atXzJ8/3yxasoLhyV4AJUl69d639oD6zdd+wHpJkhIlSQoFbgFZm15EkEWhQoWIjo4mMTEx021dXFxISUkxQCohNTUVLy8vbG1t2b9/P/b29nJH0okJEya8W9ZJELJD9gIIoFQqZyqVyvtAV960AIHiwPtLAzx485hgpKysrChSpAiPHj3KdFs3NzfRAjSA1NRUatSoQWpqKv/++y+5cuUiPj5e7lhaO3nyJBs3biRv3rxyRxFMmEKtVme+lZaUSuV+wCmNpyZIkrTtve3GAbkkSZqsVCp/Bo5LkrT2zXOrgN2SJG1O4/j9gH4ZZVi1alV1wGArWKvVapNYLVvXHj9+TOHChTPthh4bG4utra3Jd7/XJ5VKhbW1dlf9nzx5QnJyMra2tqhUKlQqFQqFgoIFC5rsZUO1Ws2TJ0+wt7cnV65cFvl3pglzfi8qUaKEVj+YQTrBSJLUNIubBgC7gMm8bvGVeO85FyDNQWaSJK0AVmR04LCwMDVgsE+M0dHRFvfpdP/+/cyZM4fTp09nuu2mTZsoXbo01apVM0Ay0xQeHk7x4tm/6HH06FG8vb2pXbs2n376KQ0aNKBp06b8+uuvTJw4kZEjRzJp0iSTm4ln9uzZ7N+/n40bNxITE2Nxf2eassT3oqwyhl6gZSVJuvnm29bAtTdfbwcClErlj4AzUBY4JUNEIYs2btyIv79/lrbdvHkzY8eO1XMiy/bjjz/SsWNH1q5d+8Hj33zzDQ0aNODzzz/n0KFDbNy4kUKFCsmUUjO3b9/mhx9+IDAw0GxbNYLhGMNHv9lKpTJEqVQGA82AYQCSJF0GNgBXgD3AYEmSxE0jIxUTE8PevXvp3LlzptseOXKEnTt3UqBAAQMks1wnTpygY8eOaT5XrVo1QkNDiYuLo1KlSpw5c8bA6TSnVqsZMGAAX3/9NW5ubnLHEcyA7C1ASZLaZfDcTGCmAeMI2bRz507q1q2b6UoCKSkp9OrViz59+mh9f0tI382bN3n+/DlKpTLdbezs7Dhx4gSjRo2iUaNGzJ07lwEDBhhtyyogIIBHjx4xcOBAuaMIZkL2AiiYhw0bNtC/f/9Mt/v555+JjY1l3rx5WR40L2hu7ty5NGrUKEvj/ebOnUvTpk3p3LkzO3fupESJEjx8+BBnZ2emT59uFJdHnz17xogRI1i3bp3oOCXojDFcAhXMwPnz52nYsGGG2zx69IiJEyeyYsUKk+t4YWoCAwPp0qVLlrf38fHh+vXr2NjY8OTJE4oXL86JEydwd3fn999/xxC9xdOTlJREnz59+OKLL6hevbpsOQTzI1qAgk6UKlWKu3fv4uSU1miX14YPH46npyctW7Y0YDLLk5CQQHh4OJ9//rlG+zk6OrJt27YPHvv999/53//+x9KlS/n1118pX768LqNmKiYmhrZt22JjY8PkyZMNem7B/ImP4YJOlC1bluvXr6f7/NGjR9m+fTubNm0yYCrLtHbtWlxcXHSysru/vz8PHjzAycmJatWq8e2335KQkKCDlJl7/PgxjRo1wsnJid9//90spm8TjIsogIJOlClThmvXrqX7/OzZs2nevDnOzs4GTGWZ/vrrL5o3b66z4+XKlYutW7dy4MAB1q5dS9myZQkKCtLZ8dNy584d6tati7e3NwsXLhTr+wl6IQqgoBOZFcCqVavy4MEDAyayXFevXsXHx0fnx/Xy8iI0NBR/f3/8/Pzo06cPcXFxOj/P+fPnqV+/Pv3792fChAlG2ytVMH2iAAo6kdklUB8fH27dumXARJbp+fPnPHnyhPr16+vtHDNnzuTy5cscOXKEihUrcvbsWZ0dOygoCB8fH7777jv69Omjs+MKQlpEARR0wt3dndDQ0HRXeKhRowbR0dEZFklBe6tWraJChQrky5dPr+dxdXXl2rVr+Pn50aBBA2bMmKHV5OYqlYo5c+bQqVMnVq9ejZ+fnw7TCkLaRAEUdCJ37tw4Ojpy9+7dNJ/PkSMHderUYcmSJYYNZmG2bdtGixYtDHa+RYsWERQUxM8//0ytWrXS/f+fkdu3b9OgQQN27txJYGAg9erV031QQUiDKICCzmR2GdTPz0/vnScsXVhYmMELSM2aNbl//z5OTk5UrVqVmzdvZr4Tr6c2W758OTVr1qR58+Zs27YNV1dXPacVhP8nCqCgM2XKlMmwADZt2pR79+6RmppqwFSWRa1Wy7LMkY2NDTt27KBVq1Y0adKE58+fZ7i9SqWidevWLFu2jJ07dzJ48GAxOYJgcOI3TtCZMmXKcPXq1XSfr1ixIjY2Nvz7778GTGVZFAqFrAsN//777zg7O9OiRQuSkpLS3W7jxo08evSIvXv3UqFCBQMmFIT/JwqgoDOpqakZtu4UCgXNmjVj1apVBkxlWRQKhewt7EOHDhEZGUnv3r3TnEItNTWVmTNnMnLkSGxtbWVIKAiviQIo6ExgYCC+vr4ZbtOiRQtOnRLLOuqLlZWVrC1AAFtbW06dOsXu3buZPXv2R8/v2rULa2vrDFeqEARDEAVQ0ImEhASOHz9O06ZNM9yuadOmhIeHZ3h5TNCO3C1AeD2vaI8ePZgxYwbJycnvHler1cyYMYPhw4eLAe6C7EQBFHTi2LFjeHp6ZrrIbbFixXBycuLPP/80UDLLYgwtQIA///yT5cuXs23btg+WLwoKCuLFixdiQnTBKIgCKOjE/v37M738+VaLFi1YuXKlnhNZnri4OF6+fCn7QsN79+6lT58+/PHHHx9dEZg5cybDhw+XPaMggCiAgo4EBgZmeQLmwYMHc/PmTR4/fszTp0/1nMwyJCQkULlyZapVq6aXeUA1ydGuXTsWLFhA27ZtP3hu+/bt3Llzh/bt28uUThA+JAqgoLWwsDCioqKoWrVqlravWLEiN27cwMrKip49e+o3nIVYsGAB1tbW7NixQ9aelVZWVlhbW1O2bNkPHl++fDl9+/Zl+fLlYkV3wWiIAihoTZIkmjVrptFA5vz582Nra2uwteXM3aVLl6hVq5Ysg+DfZ2trS/v27Rk/fjzwukPOmDFjmDt3Lrt376ZmzZqy5hOE94lFtgStBQUF0a1bt2ztK3oC6sa9e/dkvfT5vsWLF+Pk5ERgYCDLli3jwYMH7N27l0KFCskdTRA+IFqAglYSExM5cuSIGNMls2fPnuHm5iZ3DADs7Oxo3bo1TZs2Ra1Ws3XrVlH8BKMkWoCCVk6cOEGFChUoXLhwtvYXLUDdePXqFUWLFpU7xju5c+emTZs2rFixQszxKRgt8ZspaGXt2rV07Ngx2/uLAqgbiYmJDBgwgNu3b8sdhd27d7Nv3z7mz58vip9g1MRvp5BtkZGRBAYG0rt372ztr1arRQHUgcTERKKjoxkyZAj169fn/PnzsmV59uwZffv25eeffyZ//vyy5RCErBCXQIVsW716NV26dMHBwUHjfRMTE4mPj6dhw4Z6SGZZ7t27h4uLC71798bR0REfHx/Wr19P48aNDZpDrVYzYMAAvvjiC7GorWASRAEUsiUhIYHffvuNw4cPZ2v/+fPn06xZM8aNG6fjZJbnwYMHWFlZ8fLlS1q1akWBAgXo3LkzXbp0Qa1WExMT8+5fuXLlGD58uM47zFy8eJGpU6dy7do1Fi1apNNjC4K+iEugQrZs2bKFatWqUb58eY33jYyMZMaMGdlqOQofq1OnDk2aNKFevXoEBQVRr149/v77b/LmzYuzszNVq1bF19cXf39/1Go11apVo2vXrgQHB2t97rNnz+Ln54ePjw/Vq1dHkiRy586tg59KEPRPkdZ6XeYoLCxMDZAvXz6DnC86Opq8efMa5FyGplaradSoEXPmzMny/J/v69KlC2FhYWzYsIHixYvrIaF5CA8P1+j1kSSJPn360KRJE6ZOnZru79/Lly9Zs2YNy5cvp0qVKowdO5YGDRqgUChISUnh2rXinWmOAAAgAElEQVRrhISEUL58eT799NM0O7KcPHmSadOmcf78eYYNG4a/v78shc+c/850xZxfIwcHB606EYgCqCfm/Et39OhRhg8fzrVr1zTu5XfixAmaNm3KjRs3UKvVogBmQNMCCK+L2/DhwwkKCmLx4sXUr18/3W0TEhL466+/WLx4MQ4ODtjY2HDp0iWcnZ2pUKEC169fJyoqiiZNmtCsWTOUSiVhYWFMnTqVK1euMGzYMLp160auXLm0/VGzzZz/znTFnF8jUQCzSBRA3fH396dZs2YMGTJEo/1SU1P55JNPqF+/PkuXLs3WG7wl0eb12bVrF/3796d58+ZMnjwZe3v7dLdVqVQEBQWRO3duPD09P+i9GRYWxoEDBzhw4ACHDh3C3t6e4cOH06VLF9mnXQPz/jvTFXN+jUQBzCJRAHUjLCwMb29v7t69q/HPt3r1asaOHcvDhw+xsrISBTAT2r4+z58/5+uvv+bEiRMsXryY2rVra5Xn7UK7xjS2z1z/znTJnF8jbQug8fwmCybhl19+wd/fP1t/UFu3bqVdu3ZG9QZqzgoWLMjatWuZO3cuX331FXPnztXqeFZWVuL/nWBWxG+zkGWxsbEEBAQwdOjQbO1va2uLpVxxMCZt2rRh/fr1bNu2Te4ogmBURAEUsmzTpk3Url2b0qVLZ2v/3LlzEx8fr+NUQlZcv36dTz/9VO4YgmBURAEUsmzdunX0798/2/vnypVLFECZnDlzRhRAQfgPUQCFLLl+/ToPHjzI1ri/t3Lnzk1iYqIOUwlZdfbsWVEABeE/RAEUsmTdunV069YNG5vsz55nZ2cnVoCXQXJyMiEhIZQrV07uKIJgVEQBFDKVkpLChg0b+Oqrr7J9jFOnTrFy5Upq1qypw2RCVtjY2PDFF1/QsmVLLl26JHccQTAaogAKmdq/fz+lSpWiQoUK2dp/69ateHt7M3jwYKZNm6bjdEJmFAoF69evZ/To0bRt25ZFixahUqnkjiUIshMFUMhUQEBAtlp/arWauXPn0r17d5YvX8706dP1kE7ICoVCQY8ePTh9+jSSJOHn58ejR4/kjiUIshIFUMjQ06dPOXToEJ06ddJov5SUFPr168esWbOQJIlu3brpKaGgiZIlS3LgwAHy58/Pzp075Y4jCLIS6wEKGdq4cSOtWrXSaAo5tVpNixYtCAkJITg4GBcXFz0mFDRlbW2NjY0Njo6OckcRBFmJAiikS61WExAQkK0FTvft20dsbCx2dnZ6SCZoKzw8nGLFiskdQxBkJS6BCukKDg4mOjqahg0bZmt/UfyMU0xMDKGhoTg7O8sdRRBkJVqAwkdSUlJ48eIFq1atomfPnhpPgKxQvJ6gPTU1VUyebGTUajUDBw7Ex8dHFEDB4okCKHygadOmXLx4EQcHB4oXL87MmTOzfSxRAI3PmjVrOH36NIGBgXJHEQTZiQIofODly5cEBwdTsWJFrY6jUCjerR8nGIfLly8zZswYtm/fnuECuYJgKcTHc+EDBQoU4Pnz5zo5liiAxiM2Npb27dszdepUrT/cCIK5EAVQ+EDBggV1UgCtra11VkgF7Q0aNIgqVarw5Zdfyh1FEIyGKIDCBwoUKMCzZ8+0Po6XlxcLFy7UQSJBW2vWrOHkyZP88MMPckcRBKMiCqDwAV0VwC+//JLt27frIJGgjcuXLzNq1ChWr14t7vsJwn+IAih8oGDBgjopgH5+fty7d08sfySj9+/7eXh4yB1HEIyOKIDCB3TVAixRogRubm4sW7ZMB6kETYWFhdGjRw9x308QMiAKoPCBQoUK6WyVgM6dO7N27VqdHEvI3MuXL1m1ahWNGjWiatWq5M+fn7lz576bmEAQhA+JAih8oHr16hw9elQn68W1bduWmzdviuEQepScnMyOHTvo2LEjrq6u/P333/Tu3ZsrV64wb9488uTJI3dEQTBaogAKHyhRogRFixbl1KlTWh/LxsaGuLg4JEnSQTLhLbVazcmTJxkyZAjFixfnu+++o1atWly4cIE//viDVq1akTNnTrljCoLREzPBCB9RKpXs2LGD2rVrZ2v/2NhYJk+ezNKlS/Hz86NJkyY6TmgZ1Go1d+7cITw8nPDwcCIiIggLC2P37t2o1Wo6duzIvn37KFmypNxRBcEkiQIofMTHx4cxY8bw3Xffabzv1q1b6d+/PwUKFODo0aNUqVJFDwmN2+PHj2nfvj358+fHycmJokWLUqxYMYoWLfruezs7Ox4+fPiuuL1f5MLDw4mMjGTPnj20b9+eYsWKUaxYMZycnChWrBhLliyhevXq4t6eIGhJoVar5c5gEGFhYWpAo4VdtREdHU3evHkNci5dU6lUlC9fnvPnz1OiRIks73fhwgXq1avHjBkz+OabbzLdPjw8nOLFi2sT1SglJSXh5OTEmDFjsLW15fHjxx/8e/LkCbGxse8K2tv//vfrnDlzmuzvkKGY8t+ZoZjza+Tg4KDVp0DRAhQ+Ym1tTZMmTdi1axcDBgzI8n5bt27Fw8MjS8XPnNna2tK+fXsSEhLo379/to8THR2tw1SCIPyX6AQjpKlhw4bs3btXo302btxI586d9ZTItHTt2pXNmzfLHUMQhAyIAih8RKVS8fvvv+Pt7Z3lfSIiIggNDdWoxWjO6tevz4sXL7h27ZrcUQRBSIcogMJHli5dSs6cORkyZEiW99m1axclSpTAzs5Oj8lMh5WVFQ4ODsTExMgdRRCEdBjNPUClUjkSmAsUkSTp6ZvHxgG9ARXwtSRJml2TEzR248YNFi5cyMmTJzVazX3Dhg00bdpUj8lMS2RkJOHh4VStWlXuKIIgpMMoWoBKpbIEoATC3nvMA+gMVAJ8gSVKpdJanoSWISUlhUGDBjF16lRKly6d5f0SEhI4fPgwI0aM0GM607Jv3z4aNGiAtbX4lRUEY2UUBRCYD4wG3h+T4QeslyQpUZKkUOAWUEOOcJZi9erV5MuXT+P7eGfOnCFfvny4u7vrKZnp2bt3r0b3UAVBMDzZL4EqlcrWQLgkSReVSuX7TxUHTrz3/YM3j6V1jH5Av4zOs2rVKsBwXcvVarXJdWP39fWlbdu2REZGarRf4cKF2bdvH+Hh4Rrtp1KpNN7HFKjVakaMGEGhQoW0+h0wxd8hQxOvUebM+TVycHDQan+DFEClUrkfcErjqQnAeKBZGs+lNcAxzVH7kiStAFZklOHtQHhDDQg1xcGnderU4cCBAxoPTm/atCmenp7Mnz9fo/3MdSB8REQEjRs3JjQ0VKP7qP9lir9DhiZeo8yJ1yh9BimAkiSl2TtCqVR6AqWAt60/F+CcUqmswesW3/vTkLgAEXqOarFevHjBy5cvKVWqlEb7vZ2Yefbs2XpKZnqcnZ1xc3Pj+PHj1K1bV+44giCkQ9ZLoJIkXQIc336vVCrvAp9JkvRUqVRuBwKUSuWPgDNQFtB+iQIhTSEhIXh6emrcYrlx4wYKhYLPPvtMT8lMU/fu3Vm/fr0ogIJgxIylE8xHJEm6DGwArgB7gMGSJGm/SJ2QppCQED799FON9zt69CjFihXTQyLT1rVrV3bs2EFcXJzcUQRBSIfsnWDeJ0lSyf98PxOYKU8ayxISEkLDhg013u/ff//NVuE0VgcOHEClUuHt7Z3t+3cXLlxgyJAhuLm5kZycrOOEgiDoitG2AAXDunz5crYK2a1bt6hcubIeEhnWy5cv+eqrr+jZsyf/+9//KFWqFN9++y137tzJ8jGePXvGwIEDadasGe3atSMoKIj8+fPrMbUgCNowqhagII/k5GRu3LiRrUJWrlw5k5/v8saNGyiVSho3bszhw4fJmzcv/9fevcfnWP8PHH/d2GbMxphEphgzvtIYcug74lNsxKRval9COVR+cyoVRWUtSslXco4olmOlRBdJmTnNYaMvvhhjjjNsTjvevz92b21hm+0+7b7ez8fDw31fh8/1vj+P6977/nyu6/p84uLi+Oabb2jTpg1NmjRh0KBBPP3007i5uXH9+nXi4+M5duwYx48f5+jRoxw/fpzdu3cTEhLCjh07qFatmq0/lhCiCJIABUeOHOGBBx6gcuXK97yvn58fa9assUBU1rN//358fX0LPMbRrFkzJk+ezPvvv8+GDRtYunQpI0eOpGLFily5cgVvb28eeugh6tWrx4MPPkiHDh2YMmUK3t7eNvwkQoh7IQlQcODAgRLP3O7j40NSUpKZI7IuHx8fzpy58xM2zs7O9OjRgx49enDx4kXS09O5//77S/V8nxDCPkgCFCW+AxSgQYMGXL161cwRWVeDBg2Ij4/HaDRiMNx9gmkvLy8rRiWEsDT5GSs4ePBgiVuADRo0ICUlhezsbDNHZT3u7u64ublx7tw5W4cihLAiSYCiVC1Ad3d3XF1diY2NNXNU1lW/fn3i4+NtHYYQwookAercmTNnMBqNpRqTs169emzdutWMUVmfj4+PJEAhdEYSoM7t3r2bNm3aFHrtqygPPvgg+/btM2NU1ufj48PBgwcL3WbHjh0cOXLEShEJISxNEqDO7dq1i0cffbTAsgULFhAYGMiSJUu4efNmoft26tSJzZs3l/m575566inWrVtH165d+eGHH8jK+mvUvYSEBAYMGEBoaCiDBw8usE4IUXZJAtS53bt3065duwLLVq5cSdOmTVm8eDEPPPAAw4cPv+0a37Jly+jYsSP3338/iYmJhIaGWjNss/P39+fo0aO89tprzJo1i4CAAGbPnk14eDidOnXC39+fhIQEqlSpwjfffGPrcIUQZiAJUMfS09OJi4ujVatWecsyMzPZtm0bY8aMYcWKFWzevJlKlSoRGBjI999/n7edu7s71atXZ+nSpbi7u9sifLOrUKECffr0ITo6mqVLl7Jnzx4uXLjAvn37mDhxIpUqVWL69OlERESQkpJi63CFEKUkCVDHDh48yEMPPVRgssw9e/ZQt25dqlevDoC3tzfjxo3D09OT+vXr523XsmVLLl68WKYffyhM27ZtWb58OYsXL6Zu3b+mpcxN/LNmzbJhdEIIc5AEqGN3uv63efPm2+awO336NCkpKTRt2jRvWa1atahcuTK///67VWK1pWPHjhEREcEjjzxChw4daN++Pb169bJ1WEKIUpIEqGMxMTG0bdu2wLLffvuNDh06FFi2detWAgMDbxv+q2XLlgW6RR1JWloaU6dOpWXLlrRt25bjx48THh7On3/+yUcffYSvr6+tQxRClJIMhaZju3bt4t133817n3v97/PPPy+w3datW+94l+djjz3GTz/9ZOkwre7UqVP06dOHqlWrMmHCBNq3b0+FCvJVEcLRSAtQpy5evEhycnKBlszfr//lioqKumMCDAgI4NSpUxaP1Zo2btxI69atCQoKIjIyksDAQEl+Qjgo+Wbr1OHDh8nOzubVV1+lXbt2tG3bll9//fW2638JCQncuHEDPz+/28po2bIlSUlJZGZmlvkkkZ2dTUREBDNnzmTevHm3dQMLIRyPtAB1qn379qxatYq6deuyevVqOnbsyLhx4wgMDCywXVRUFIGBgXccKcbLywt3d3dWrFhhrbAt4vLly/To0YOffvqJX3/9VZKfEDpRtn+2ixIzGAy0atWqwDOAFy5coEaNGgW2++OPPwod5SU8PJzBgwdz5coVXn75ZYvFa0lPPvkk/v7+LFq0CCcnJ1uHI4SwEkmAIk/NmjVvWxYVFcWECRPuus+QIUPw9vbmmWeeYf/+/cyePduSIZpdeno6sbGx/Pjjj5L8hNAZSYDirk6ePEl6enqRt/x37dqVHTt20L59e1xcXJg+fbqVIiy9+Ph46tSpg7Ozs61DEUJYmVwDFHeV+/xfcWaKOH/+PBkZGfTt29cKkZnPkSNHaNCgga3DEELYgCRAnTpy5AiRkZFcvnz5rtsUdf0v16lTp+jduzcTJ0687cF6e/e///2vwBBvQgj9kASoU/Pnz+ezzz6jefPm9OrVi7lz55KYmJi33mg03vUB+L/r378/Tk5ODBw40JIhW4S0AIXQL0mAOrVv3z7mzp3LuXPnGDFiBHFxcfzzn/+kS5cufPrpp2zatIns7GwaNmxYZFnh4eE0b94cb29v2rdvz+HDh63wCcxDEqAQ+iUJUIcyMjI4ePAg/v7+VKpUiZCQEJYsWcK5c+eYPHkySUlJjBgxgieeeKJY1//at2+PpmnExsZSo0aNMnMd0Gg0SgIUQsfkLlAdOnToEN7e3gWmQQJwcnKiS5cudOnS5bbxQIvDx8eH8PBwOnToQHZ29m2DZ9ub33//nUqVKhWY7kgIoR/2/RdKWMSePXsKPAB/J+XKlStRArv//vtJT09n06ZNJQ3Paj799FOGDRtm94laCGEZ8s3XoX379hWZAO/VwYMH6d+/P97e3tSvX59GjRqZtXxzO3r0KNu2bSsz3bVCCPOTLlAd2rt3L0OHDjVrmV27dqVWrVrs2rWrwMS5tpKZmcm0adP4448/MBqNef8g59pfQkIC/fr1o1KlSjaOVAhhK5IAdebWrVscOXKE5s2bm7XcBg0a0LZtW7tIfvv372fgwIFUrVqVgQMHUr58eYC8G3oMBgPlypXjn//8py3DFELYmCRAnTlw4ACNGjXC1dXVrOU++uijbN++3axl3qu0tDTCw8OZM2cOEydO5Pnnny/WXaxCCH2Sa4A6s3fvXgICAsxerr+/v00nx925cyctWrRg7969bNmyhdDQUEl+QohCSQtQZ/bu3XvbnH/m4OPjU+iwapa0c+dOunfvzuTJkwkJCZHEJ4QoFmkB6ojRaCQmJsbsd4ACxMTE4OXlZfZyi2P27NkMHz6c3r17S/ITQhSbtAB1IjMzk5EjR+Lp6UmzZs3MXv53331nk5tKrl27xpo1a2x+/VEIUfZIAtSBW7duMXjwYNLS0ti4caPZJ37NzMxky5YtREVFmbXc4li5ciVt27blvvvus/qxhRBlm3SBOrjU1FSeffZZKlasyNq1a6lcubLZj7Fz505cXV155JFHzF52URYsWEBoaKjVjyuEKPskATqw5ORkevXqRaNGjYiMjMTFxcUix1m/fn2xZo0wt2PHjhEbG4tSyurHFkKUfZIAHVRiYiJBQUEopZg7d27ew+CW8N1339G7d2+LlX837u7u+Pj48OKLL3LlyhWrH18IUbZJAnRAx44dIzg4mEGDBjF58mSL3hl5+fJlDh8+zODBgy12jLvx8vJi27Zt1K9fn8DAQHbt2mX1GIQQZZckQAf09ttvc+HCBXbt2kVERATr16/n/PnzFjnWpk2bqFWrFlWrVrVI+UVxcXFh+vTpTJ8+ndDQUGbMmEF2drZNYhFClC1yF6gDWrJkCUePHiU2NpbY2Fg2bNhAbGxs3o0qLVq0oEWLFvj7+/Pggw+WqoW4du1aWrRoYcboS6ZXr174+/vz7LPPEhUVxcyZM6levbqtwxJC2DFD7gj5ji4hIcEIOdeNrCE1NfW2CWdtyWg0curUqbykGBcXR3R0NM7Ozhw7dqxEsSYmJuLr68u6detK9AxgYmIiderUuef9CpORkcG4ceOIjIzkP//5D4899hgVKpTN33n2dg7ZI6mjojlyHVWtWrVU13fK5l8Gcc8MBgPe3t54e3sTHBzMvHnz2L17N9OmTSvxl2PYsGEEBATY1awKTk5OfPzxx3Tq1InXXnuNhIQEHn744bxWb0BAAHXr1pURY4QQkgD1Jjk5mbCwMM6ePUt0dDQ+Pj4lKuenn37i999/5+TJk2aO0DyCgoIICgri6tWrxMTEsH37dn744QfefvttMjIyGDBgAOPHj7d1mEIIG5IEqCNRUVEMGzaMf/3rX6xevRpnZ+cSlXP9+nVefPFFxo8fb7ObX4rLw8ODxx9/nMcffzxvWVhYGDdv3rRhVEIIeyAJUAeysrKYOnUqixYt4ssvv6Rbt26lKm/ChAm4u7szduxYM0VoPZmZmaxatYoVK1bYOhQhhI1JAnRwZ8+eZfDgwTg7OxMTE0Pt2rVLVV5cXByzZ88mOjraTBFa1y+//ML9999PkyZNbB2KEMLG5DlAB/fWW28REBCApmmlTn6QM/VQixYtePjhh80QnfUtWLCAf//737YOQwhhByQBOrCEhAS2bt1KeHi42YZCa968OZcuXTJLWdZ28eJFNm3aREhIiK1DEULYAUmADmzOnDkMGDAANzc3s5XZvn17zpw5Y7byrGnx4sV07doVDw8PW4cihLADcg3QQaWkpBAZGcnevXvNWq6fnx8ZGRnExsaWmW7Q/fv3ExERwa+//sry5cttHY4Qwk5IC9BBffPNN3Tu3Blvb2+zlnvhwgUAs0+qawnR0dF0796drl278o9//IM9e/bg7+9v67CEEHZCWoAOKCsri7lz57Js2TKzl/3hhx/SpEkT/Pz8zF62ORiNRjZt2kR4eDgnTpwgLCyM+fPnU7FiRVuHJoSwM5IAHdC6deu47777ePTRR81a7rlz55g3bx5bt241a7nmYDQa+f777/nggw9ITU1l5MiRPP3002WipSqEsA1JgA4oOjraInc65rb+7GH2h7+bOXMmM2bM4J133iEoKIhy5aR3XwhROEmADqhq1aqkpKSYrbzs7Gy++eYb5s6dS1RUlNnKNZerV68yadIkVq1axT/+8Q9bhyOEKCMkATogT09Pjh49apayoqOjGTp0KGfPnmXGjBl22fr78MMPeeKJJyT5CSHuiSRAB+Tp6UlSUlKpyjh16hSjR4/m559/5vnnn+eLL76wy3n1EhISmDdvHn/88YetQxFClDFyocQBeXp6lmq0lq+//prGjRtz4cIFjh49yty5c+0y+QGMGzeOF1980SzDvAkh9MU+/6qJUqlevXqpEuDPP/9McHCw3T80HhMTw8aNG9m1a5etQxFClEE2T4BKqXeBwcBF06JxmqatM617C3gRyALCNE3bYJMgy5hq1aqRnJxc4v1r165NbGysGSMyP6PRyJgxY3jzzTdLPKO9EELfbJ4ATaZpmjY1/wKlVBOgL9AUqA1sVEo10jQtyxYBliW51wCNRiMGg+Ge969Vqxa//fab+QMzox9//JHz58/LzA5CiBKz52uAPYFITdPSNE2LB44CrW0cU5lQqVIlypUrx40bN0q0v5eXF9euXTNzVOZhNBqJjo5m9OjRvPfee3Z7bVIIYf/s5a/HcKVUf2A3MEbTtMtAHWB7vm1Om5aJYsi9EaZy5cr3vK+Xl1eJk6elpKens3z5cj777DMuX77Myy+/jFLK1mEJIcowqyRApdRGoNYdVo0HZgGTAKPp/0+AQcCd+u6Mdyl/CDCksBgWLFgAQGpqanHDLhWj0Wi1Y93J2rVryc7OJjEx8Z73bdy4MatXry7RvvciKyuryGNkZWVx48YNrl+/TrNmzYiMjMTFxQXAblup5mLrc6gskDoqmiPXUdWqVUu1v1USoKZpXYqznVJqHvCj6e1poG6+1Q8Ad5yITtO0ucDcwspOSEgwAla7YSI1NdWmN2f079+fcePGlaiVdPLkSTp27GjxL01iYiJ16ty5UW80GnnnnXeYOXMmvXr1YsiQITRo0MCi8dgbW59DZYHUUdGkju7O5tcAlVL353sbAhwwvf4B6KuUclFKPQQ0BHZaO76yqmbNmsTExJR43/T0dNasWWPmqIpv2rRprF69ml27djFt2jS7nX1CCFF22TwBAh8ppeKUUrFAJ2AUgKZpB4HlwJ/AeuBVuQO0+F5//XU++eQT9uzZc8/7urq68uWXX9KvXz9+/vlnC0RXuJUrV/LJJ5/w7bffUqNGDasfXwihDwaj8Y6X1RxObheou7u7VY5nD90Oa9asITw8nJiYmBL1lX/55ZeEhYXRr18/Ll68yNmzZ7l69SqpqalMmjSJ/v37lyq+O3WBbt++naeeeoqVK1eWmRnnLcUeziF7J3VUNEeuo6pVq977c1752EMLUFhISEgInTt3ZuDAgZTkh86gQYOYOXMm8fHxVKtWjW7dujFq1Ci8vLwsNvrK+vXr6dmzp+6TnxDC8uzlMQhhIZMmTaJbt2589tlnjBo16p73f+GFF3jhhRcKLFu+fDmurq7mCrGAoKAg+vXrZ5GyhRAiP2kBOjgXFxfCw8P5+OOPzVZmWloaFStWNFt5+QUEBHDt2jX+97//WaR8R3Pt2jUiIiJYsWIF2dnZtg5HiDJFEqAOrFmzxqytqlu3blGpUiWzlZdfuXLl6NGjB+vWrbNI+WWB0Wjkt99+49q1a0RFRZGRkXHH7f744w8ee+wxTp8+zZw5c3jiiSfYvn37HbcVQtxOEqCDO3v2LKtWrWLMmDFmKzM9Pb1EI8wUV0hIiC4TYG7iCwoKYuzYsRiNRiZMmICvry+DBg0iMjKSpKQkrl27xuuvv84rr7zCzJkzWbp0KTt37mTUqFEMGTKEgQMHcuLECVt/HCHsniRABzdz5kz69etHzZo1zVamn58fixcvNlt5f9epUyeOHDnC+fPnLXYMe/L3xDd8+HD++9//4uHhQUxMDAcOHCA4OJj169fTsmVLHn74YTIyMoiLiyMoKAjIaTmHhoZy6NAhWrZsSZcuXXj33XelW1SIQshjEBZiD7ceG41G6taty/79+806ikpycjK+vr6MHj2at956q8TlFDYSzLPPPku7du1uuwHHkRiNRrZs2cKUKVO4dOkSEydOpG/fvpQvXx64c/2kpaVx6tQpfHx8Ci37gw8+YNGiRWzbtg0nJyeLfQZbs4fvmb1z5DqSxyDEXRkMBtq3b8+GDeadRtHT05Ply5fzwQcfcPjwYbOWnat379589dVXpKSkWKR8W9u2bdttLb7Q0NC85Ac5CfLvY6W6uLgUmfzWr1/PjBkzWL58uUMnPyFKSxKgg3v99deZPHkyaWlpZi23U6dODB06lCeffJLk5GSzd7X16dOHNm3aEBQUZPFBuW3hzcHje/sAABKiSURBVDff5MSJExw8ePCOiW/NmjVcvHiRRo0a8eGHHxZav7du3SIqKoqpU6fSu3dvQkNDWbRoEQ899JA1PooQZZZ0gVqIPXU79OnThz59+jBs2DCzlpuenk5gYCC7d+8mOzsbFxcXnJ2dcXJywtnZGWdnZypWrIiLiwuurq64ubnh4eFBtWrV8PT0ZNCgQezYsYP77rsPHx+f27ppjUYjH3/8Mf/5z39YtmwZzZo1M2v8tnT58mWef/556tevz8KFC3F2dgbg119/5c033yQtLY1Vq1aRlpbGwIEDqVWrFosXL8bDw4OEhASio6PZtm0b0dHR/Pnnn/j6+hIQEEBAQADt2rXjgQcesPEntA57+p7ZK0euo9J2gUoCtBB7Oul27tzJ4MGDOXz4sMWe30tLSyM1NZWUlBRSUlK4evVq3uvcf1euXOHy5ctcunSJK1eu8PHHH9O3b19SU1O5dOkS5cuXx9PTk5o1a+Ln50f79u0JCgpix44dDB8+nFmzZtG5c2eLxG8LN2/e5KWXXiI9PZ23336b8PBwjh8/zrhx4wgJCeH69etUqVIlb/26devIzs4mKyuL1q1b07JlS1q1aoW/v7/FHkuxd/b0PbNXjlxHkgCLSc8JEGD48OFs3ryZsLAwhg0bhoeHh61DIjExEWdnZyZPnszs2bO5devWHbv6qlSpQvny5UlNTWXp0qU88cQTNojWMjIzM3njjTfQNI2RI0fSr1+/vOt2fz+H9uzZQ/Xq1fH29sZgKNX33mHY2/fMHjlyHZU2AcpQaDrx+eefExcXx+eff079+vUZOHAgo0aNuutdmJaWlJRESkoKHTp0oHfv3mzcuJEjR45Qr149fHx8cHV1JTs7m8zMTK5cuUJiYiKnTp2iYcOGNonXUipUqMAnn3xSrG1btGhh4WiE0Be5CUZHmjVrxpw5c9i8eTM3btygWbNmDBgwgIMHD1rl+LnPuz333HP4+PiQlZXF4sWLMRgMBAcHs2zZMsLCwmjYsCEBAQH069ePJUuW4OHhQUBAACEhIXJjhxDCbCQB6kx2djZ169YlIiKCmJgY6tatS+fOnQkODmbLli0lmjWiKElJSUydOhVfX19eeeUVHnnkEebOnUt2djYDBgygRo0aHDhwgPXr1xMbG0tqaiobNmxg2LBhvPHGG/Tr188icQkh9E0SoM6MHTuWpk2bMnToUH766Sf69OnDvn37UErRsWNHOnXqxPHjx81yLKPRyEsvvYSPjw8xMTHMmDGD9957jxUrVjBu3DgqVqzIiRMniIiIoHbt2nn7VahQgUaNGtGrVy9cXV05fvw4n332mVliEkKIXHINUGfeffddLl68yPLlyzl37hzh4eG4uLjw2GOPsWbNGvbt20fr1q0JCwtj7NixpbprNDExke+//559+/ZRtWpVLl26RKtWrViwYAFPPfUU586dK9aYokuXLuWZZ57B19c3b+gvIYQoLWkB6oybmxuLFi1i/PjxHDt2jO+//57169cTEBDAmDFj2LBhA++//z67du2iWbNm/PLLLyU+1oEDB2jWrFnebPRz5szh6aefJiQkpMCD30WpXbs2q1evZsSIEZw+fbrE8QghRH6SAHXIYDDw2muv8dFHH9G9e3d27tzJyJEjOXz4MGFhYcyaNYuTJ08SHBzM0KFDeeaZZ0qUeA4cOEDjxo2BnFuxFy5cyBtvvFGimHNbpSNHjpTrgUIIs5AEqGNBQUH88MMPvP/++3mzxT/33HPExsYSERHBjh07MBgM3Lhxg+bNmzN16tS7zk13J7Gxsfj5+QGwaNEiOnfuXOQ4loV58803SU5O5uuvvy5xGUIIkUsSoM75+fmxceNG9u/fT7du3UhOTsZgMNC9e3e2bdvGwoULycjIwMnJiYULF1KnTh369+/Pt99+y+XLlwst+8CBA/j5+XH9+nW++OKLUs0cAeDk5MRXX33F+++/L12hQohSkwQoqFatGsuXL8fX15fWrVvnPRdoMBgIDAzkl19+4ccff8TX15esrCxOnDjBp59+Sr169ejQoQMRERHs27evQNdkVlYWhw4dokaNGvTs2ZOePXvSvHnzUsfarFkz6QoVQpiFDIVmIWV1+KHIyEjeeecd5s+fT8+ePW9bf+jQIVauXImmacTExPDggw/i7u7O6dOnyczMJCAggBs3bpCcnExSUhLOzs707duXSZMm3TZ8V2HzAeaqVKkS//3vf6lXr17esoyMDNq0aUPr1q0ZPXq0WSf7tSdl9RyyJqmjojlyHcl8gMKs+vbtS2RkJK+++ioDBw4kISGhwPrGjRvz9ttvs2XLFs6dO8eUKVNo1aoVbm5u3Lx5k3PnzuHj40Pv3r25efMmr732GuHh4WYdu9LJyYm1a9diMBho06YNo0eP5tixY2YrXwihD5IAxW1atmxJVFQUnp6e+Pv7M2LECC5cuHDbdm5ubgQHBzN9+nT+/PNPDhw4wCuvvEJKSgpLlixhwYIFZp+CKVedOnWYNWsWhw8fpk6dOnTt2pUXXniBmJgYixxPCOF4pAvUQhyl2+H8+fN8+umnrFy5kpdffpnXX3/dbDNJlLQL9E6uXbvG/PnzmTZtGt7e3oSFhdGlS5cyPWuCo5xDliR1VDRHriPpAhUWdd999zFlyhQ2b97M8ePHadiwIR999BE3b960dWgFuLm5MXLkSI4ePcqwYcOYNGkSHTp0YPHixRw6dIjMzExbhyiEsDPSArQQR/3VdejQISIiIti5cycvv/wyr7zyCl5eXiUqy5wtwL8zGo388ssvzJ8/n3379pGYmEjDhg1p0qQJTZs2zfu/Zs2adttKdNRzyJykjormyHUk8wEKq2rcuHFeq2r27Nk0atSIPn36MGrUKJo0aWLr8PIYDAaefPJJnnzySQCuX7/OwYMHiYuLY//+/WzcuJG4uDjKlStH06ZNadGiBePHj7+nIdqEEGWbtAAtxJF/deV38eJFFi5cyIIFC/D392fMmDHFvvZW3Bbgiy++mDee6N9VqFABDw8PPD098fT0pHbt2vj7+xfr+EajkTNnzvDcc8+RnZ2dd2epvdDLOVQaUkdFc+Q6khagsCkvLy/Gjh1LWFgYq1atYuTIkRgMBsaMGcNzzz1XqtkkAEJDQwudsDczM5Pr169z/fp1kpKSSE5Oplu3bnz11VfUqFGj0LINBgNff/01ly5dYt26dXaV/IQQlictQAtx5F9dhTEajWzZsoUvvviC7du3U79+fXx8fGjQoEGB/x944AHOnj1bZAuwuG7evEmHDh0ICgriypUrrF69mnnz5hU6fdJXX33FhAkT+PnnnwvMR2gv9HoO3Qupo6I5ch2VtgUoCdBCHPmkK64rV64QHx9PfHw8x48f58SJE5w4cYL4+HiSk5PZsWMHx48fp3v37lSoUPLOiKysLP7973+TnZ3N7NmzMRgMbN26lVdffZXWrVtTrVo1UlJSSE1NJTU1tcDrtWvX4uvra8ZPbT5yDhVN6qhojlxHkgCLSRKgfcnttuzXrx9nzpxhyJAhDB48mFq1ahW7jNjYWJYsWcKyZcto2LAhS5cuxdXVNW/91atX+fbbb6lQoQJVqlTBzc0Nd3d33NzcqFKlCjVr1sTNzc0SH88s5BwqmtRR0Ry5juQaoCiTKleuTHZ2Nhs2bCA2NpYvv/wSPz8/2rRpg6+vb153af369alRowYeHh44OTmRmJjI0qVLWbJkCZcvX+aZZ55h5cqVefMO5ufh4cGQIUNs8OmEEGWBtAAtxJF/dZnL3+vo6tWrbN26Na/bND4+noSEBC5fvszVq1dxdXXFycmJ4OBgnn32Wdq1a0e5co47loOcQ0WTOiqaI9eRtACFw/Dw8CA4OPiO64xGI6mpqbi4uODi4mLlyIQQjkgSoCgTDAaD1VrvQgh9cNz+IyGEEKIQkgCFEELokiRAIYQQuiQJUAghhC5JAhRCCKFLkgCFEELokiRAIYQQuiQJUAghhC5JAhRCCKFLkgCFEELokiRAIYQQuiQJUAghhC5JAhRCCKFLkgCFEELokiRAIYQQuiQJUAghhC5JAhRCCKFLkgCFEELokiRAIYQQuiQJUAghhC5JAhRCCKFLBqPRaOsYrCIhIUEfH1QIIXTE29vbUNJ9pQUohBBCl3TTArQ2pdRuTdMCbB2HPZM6KpzUT9GkjoomdXR30gIUQgihS5IAhRBC6JIkQCGEELokCVAIIYQuSQIUQgihS5IAhRBC6JIkQCGEELokCVAIIYQuSQK0nLm2DqAMkDoqnNRP0aSOiiZ1dBcyEowQQghdkhagEEIIXZIEKIQQQpcq2DoAR6OUegSYDVQEMoFXNE3baVr3FvAikAWEaZq2wWaB2ohS6lvA1/S2KnBF07RHTOt0Xz+5lFL/Bwwn5xz6SdO0sabluq8jpdS7wGDgomnROE3T1pnW6b5+8lNKvQZ8DHhpmpZkWiZ1ZCItQPP7CHjP9Ed9guk9SqkmQF+gKdAV+EIpVd5mUdqIpmnPapr2iKl+VgGrQeonP6VUJ6An8LCmaU2BqablUkd/mZZ7HuVLflI/+Sil6gIKSMi3TOooH0mA5mcE3E2vPYAzptc9gUhN09I0TYsHjgKtbRCfXVBKGYB/ActMi6R+/vIyMFnTtDQATdMumJZLHRVO6qegacBYcv4m5ZI6ykcSoPmNBD5WSp0i55f7W6bldYBT+bY7bVqmV48B5zVN+5/pvdTPXxoBjymldiiltiilWpmWSx39ZbhSKlYp9aVSqpppmdSPiVLqKSBR07T9f1sldZSPXAMsAaXURqDWHVaNBzoDozRNW6WU+hewAOgCGO6wvUM+g1JY/Wia9r3p9XP81foDHdUPFHkOVQCqAY8CrYDlSqn66KiOiqifWcAkcj77JOATYBA6qh8oso7GAU/cYZ2u6qgokgBLQNO0Lndbp5RaDIwwvV0BzDe9Pg3UzbfpA/zVPepQCqsfAKVUBaA30DLfYt3UDxR5Dr0MrNY0zQjsVEplAzXQUR0VdQ7lUkrNA340vdVN/cDd60gp1Qx4CNivlIKcetijlGqNzuqoKJIAze8MEAj8BjwO5Hbx/QAsVUp9CtQGGgI7bRGgHegCHNI07XS+ZVI/f/mOnHPnN6VUI8AZSELqCACl1P2app01vQ0BDpheS/0AmqbFATVz3yulTgABmqYlKaWkjvKRa4DmNxj4RCm1H4gAhgBomnYQWA78CawHXtU0LctmUdpWXwp2f0r9FPQlUF8pdQCIBF7QNM0odZTnI6VUnFIqFugEjAI5h4pD6qggGQpNCCGELkkLUAghhC5JAhRCCKFLkgCFEELokiRAIYQQuiQJUAghhC5JAhTCCpRSJ5RSxXq4uxTHuGYaMcZc5RmVUj7mKk8IeyMPwgvhIDRNc8t9rZRaBJzWNO1tmwUkhJ2TFqAQQghdkhagEFaklHIBppAzFRTkjMrxhqZpaUqpjsDX5Exj8wY5E5aO0zRtoWnf6sAicobaOwxsADpqmtbBtN5IztBWjwOhgFEpNRLYrGlaj9z1mqYdNW2/iHytRKXU68BocgZHLtByNMX9gSluF2ANOYO+3zRj9QhhVdICFMK6xpMzy8MjQHNy5mLLn2xqkTOPZB1yZu2emW+6n5nAddM2L5j+3UbTtLnAN8BHmqa5aZrWo6iglFJdgdfImUC1ITnjteY3hZxpmh4BfEzxTSiqXCHsmSRAIawrFHhf07QLmqZdBN4D+uVbn2Fan2Ga6fwa4GuatftpYKKmaTc0TfsT+MqMcf0LWKhp2gFN064D7+auME1ePJicFl+ypmmp5Ixz29eMxxfC6qQLVAjrqg2czPf+pGlZrkuapmXme38DcAO8yPm+5p/MNP9rc8QV87e4cnkBlYAY0/Q6kDOvXHkzHl8Iq5MEKIR1nQHqAQdN770p3nxsF4FMcuZvO2JaVvfum99xktMb5CSyXLXImR8O4OzfyvPO9zoJuAk01TQtsRixClEmSBeoENa1DHhbKeWllKpBznW0r4vayTRlzWrgXaVUJaVUY6B/IbucB/7+TOA+4HmlVHnTNb/AfOuWAwOUUk2UUpWAifmOnQ3MA6YppWoCKKXqKKWeLCpuIeyZJEAhrCsc2A3EAnHAHtOy4hhOzg0y54Al5CTTtLtsuwBoopS6opT6zrRsBNADuELOtcjc5Wia9jPwGfArcNT0f35vmJZvV0qlABsB32LGLYRdkvkAhSijlFJTgFqapt3xblAhROHkGqAQZYSp29OZnJZjK3Iek3jJpkEJUYZJAhSi7KhCTrdnbeAC8AnwvU0jEqIMky5QIYQQuiQ3wQghhNAlSYBCCCF0SRKgEEIIXZIEKIQQQpckAQohhNAlSYBCCCF06f8Bn9m6rmpNACkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "# By only plotting rows in which the continent is 'South America' we only plot SA.\n",
    "world.query(\"continent == 'South America'\").plot(ax=gax, edgecolor='black',color='white')\n",
    "\n",
    "# By the way, if you haven't read the book 'longitude' by Dava Sobel, you should...\n",
    "gax.set_xlabel('longitude')\n",
    "gax.set_ylabel('latitude')\n",
    "\n",
    "gax.spines['top'].set_visible(False)\n",
    "gax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating this map may have been easier than you expected!\n",
    "\n",
    "In reality, a lot of heavy lifting is going on behind the scenes.\n",
    "\n",
    "Entire university classes (and even majors!) focus on the theory and thought\n",
    "that goes into creating maps, but, for now, we are happy to rely on the work done by the\n",
    "experts behind `geopandas` and its related libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Plot the cities\n",
    "\n",
    "In the code below, we run the same commands as before to plot the South American countries, but\n",
    ", now, we also plot the data in `gdf`, which contains the location of South American cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3: Plot the cities onto the map\n",
    "# We mostly use the code from before --- we still want the country borders plotted --- and we\n",
    "# add a command to plot the cities\n",
    "fig, gax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "# By only plotting rows in which the continent is 'South America' we only plot, well,\n",
    "# South America.\n",
    "world.query(\"continent == 'South America'\").plot(ax = gax, edgecolor='black', color='white')\n",
    "\n",
    "# This plots the cities. It's the same syntax, but we are plotting from a different GeoDataFrame.\n",
    "# I want the cities as pale red dots.\n",
    "gdf.plot(ax=gax, color='red', alpha = 0.5)\n",
    "\n",
    "gax.set_xlabel('longitude')\n",
    "gax.set_ylabel('latitude')\n",
    "gax.set_title('South America')\n",
    "\n",
    "gax.spines['top'].set_visible(False)\n",
    "gax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding labels to points**.\n",
    "\n",
    "Finally, we might want to consider annotating the cities so we know which cities are which."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3: Plot the cities onto the map\n",
    "# We mostly use the code from before --- we still want the country borders plotted --- and we add a command to plot the cities\n",
    "fig, gax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "# By only plotting rows in which the continent is 'South America' we only plot, well, South America.\n",
    "world.query(\"continent == 'South America'\").plot(ax = gax, edgecolor='black', color='white')\n",
    "\n",
    "# This plot the cities. It's the same syntax, but we are plotting from a different GeoDataFrame. I want the\n",
    "# cities as pale red dots.\n",
    "gdf.plot(ax=gax, color='red', alpha = 0.5)\n",
    "\n",
    "gax.set_xlabel('longitude')\n",
    "gax.set_ylabel('latitude')\n",
    "gax.set_title('South America')\n",
    "\n",
    "# Kill the spines...\n",
    "gax.spines['top'].set_visible(False)\n",
    "gax.spines['right'].set_visible(False)\n",
    "\n",
    "# ...or get rid of all the axis. Is it important to know the lat and long?\n",
    "# plt.axis('off')\n",
    "\n",
    "\n",
    "# Label the cities\n",
    "for x, y, label in zip(gdf['Coordinates'].x, gdf['Coordinates'].y, gdf['City']):\n",
    "    gax.annotate(label, xy=(x,y), xytext=(4,4), textcoords='offset points')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study: Voting in Wisconsin\n",
    "\n",
    "In the example that follows, we will demonstrate how each county in Wisconsin voted during the 2016\n",
    "Presidential Election.\n",
    "\n",
    "Along the way, we will learn a couple of valuable lessons:\n",
    "\n",
    "1. Where to find shape files for US states and counties  \n",
    "1. How to match census style data to shape files  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find and Plot State Border\n",
    "\n",
    "Our first step will be to find the border for the state of interest. This can be found on the [US\n",
    "Census’s website here](https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html).\n",
    "\n",
    "You can download the `cb_2016_us_state_5m.zip` by hand, or simply allow `geopandas` to extract\n",
    "the relevant information from the zip file online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide-output": false
   },
   "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>STATEFP</th>\n",
       "      <th>STATENS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>STUSPS</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>01779775</td>\n",
       "      <td>0400000US01</td>\n",
       "      <td>01</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>00</td>\n",
       "      <td>131173688951</td>\n",
       "      <td>4593686489</td>\n",
       "      <td>(POLYGON ((-88.04374299999999 30.517423, -88.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>01785533</td>\n",
       "      <td>0400000US02</td>\n",
       "      <td>02</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>00</td>\n",
       "      <td>1477946266785</td>\n",
       "      <td>245390495931</td>\n",
       "      <td>(POLYGON ((-133.655819 55.625617, -133.624921 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04</td>\n",
       "      <td>01779777</td>\n",
       "      <td>0400000US04</td>\n",
       "      <td>04</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>00</td>\n",
       "      <td>294198560125</td>\n",
       "      <td>1027346486</td>\n",
       "      <td>POLYGON ((-114.799683 32.593621, -114.809393 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08</td>\n",
       "      <td>01779779</td>\n",
       "      <td>0400000US08</td>\n",
       "      <td>08</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>00</td>\n",
       "      <td>268429343790</td>\n",
       "      <td>1175112870</td>\n",
       "      <td>POLYGON ((-109.060253 38.599328, -109.059541 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09</td>\n",
       "      <td>01779780</td>\n",
       "      <td>0400000US09</td>\n",
       "      <td>09</td>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>00</td>\n",
       "      <td>12542638347</td>\n",
       "      <td>1815476291</td>\n",
       "      <td>POLYGON ((-73.72777499999999 41.100696, -73.69...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP   STATENS     AFFGEOID GEOID STUSPS         NAME LSAD  \\\n",
       "0      01  01779775  0400000US01    01     AL      Alabama   00   \n",
       "1      02  01785533  0400000US02    02     AK       Alaska   00   \n",
       "2      04  01779777  0400000US04    04     AZ      Arizona   00   \n",
       "3      08  01779779  0400000US08    08     CO     Colorado   00   \n",
       "4      09  01779780  0400000US09    09     CT  Connecticut   00   \n",
       "\n",
       "           ALAND        AWATER  \\\n",
       "0   131173688951    4593686489   \n",
       "1  1477946266785  245390495931   \n",
       "2   294198560125    1027346486   \n",
       "3   268429343790    1175112870   \n",
       "4    12542638347    1815476291   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((-88.04374299999999 30.517423, -88.0...  \n",
       "1  (POLYGON ((-133.655819 55.625617, -133.624921 ...  \n",
       "2  POLYGON ((-114.799683 32.593621, -114.809393 3...  \n",
       "3  POLYGON ((-109.060253 38.599328, -109.059541 3...  \n",
       "4  POLYGON ((-73.72777499999999 41.100696, -73.69...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df = gpd.read_file(\"http://www2.census.gov/geo/tiger/GENZ2016/shp/cb_2016_us_state_5m.zip\")\n",
    "state_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['STATEFP', 'STATENS', 'AFFGEOID', 'GEOID', 'STUSPS', 'NAME', 'LSAD',\n",
      "       'ALAND', 'AWATER', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(state_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have various columns, but, most importantly, we can find the right geometry by filtering by name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize=(10, 10))\n",
    "state_df.query(\"NAME == 'Wisconsin'\").plot(ax=gax, edgecolor=\"black\", color=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoPandas uses shape files (https://en.wikipedia.org/wiki/Shapefile)\n",
    "\n",
    "* .shp- Feature Geometry\n",
    "* .shx - Feature Index\n",
    "* .dbf - Feature Attributes\n",
    "* .prj - Projection information and attributes for drawing the map\n",
    "\n",
    "\n",
    "These files are published by the US Census"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find and Plot County Borders\n",
    "\n",
    "Next, we will add the county borders to our map.\n",
    "\n",
    "The county (zipcode, block, etc.) shape files (for the entire US) can be found on the [Census\n",
    "site](https://www2.census.gov/geo/tiger/TIGER2019/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hide-output": false
   },
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04</td>\n",
       "      <td>015</td>\n",
       "      <td>00025445</td>\n",
       "      <td>0500000US04015</td>\n",
       "      <td>04015</td>\n",
       "      <td>Mohave</td>\n",
       "      <td>06</td>\n",
       "      <td>34475567011</td>\n",
       "      <td>387344307</td>\n",
       "      <td>POLYGON ((-114.755618 36.087166, -114.753638 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>035</td>\n",
       "      <td>00308547</td>\n",
       "      <td>0500000US12035</td>\n",
       "      <td>12035</td>\n",
       "      <td>Flagler</td>\n",
       "      <td>06</td>\n",
       "      <td>1257365642</td>\n",
       "      <td>221047161</td>\n",
       "      <td>POLYGON ((-81.52365999999999 29.622432, -81.32...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>129</td>\n",
       "      <td>00485135</td>\n",
       "      <td>0500000US20129</td>\n",
       "      <td>20129</td>\n",
       "      <td>Morton</td>\n",
       "      <td>06</td>\n",
       "      <td>1889993251</td>\n",
       "      <td>507796</td>\n",
       "      <td>POLYGON ((-102.041952 37.024742, -102.04195 37...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>093</td>\n",
       "      <td>00695770</td>\n",
       "      <td>0500000US28093</td>\n",
       "      <td>28093</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>06</td>\n",
       "      <td>1828989833</td>\n",
       "      <td>9195190</td>\n",
       "      <td>POLYGON ((-89.7243244282036 34.9952117286505, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>510</td>\n",
       "      <td>00767557</td>\n",
       "      <td>0500000US29510</td>\n",
       "      <td>29510</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>25</td>\n",
       "      <td>160458044</td>\n",
       "      <td>10670040</td>\n",
       "      <td>POLYGON ((-90.318212 38.600017, -90.301828 38....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS        AFFGEOID  GEOID       NAME LSAD  \\\n",
       "0      04      015  00025445  0500000US04015  04015     Mohave   06   \n",
       "1      12      035  00308547  0500000US12035  12035    Flagler   06   \n",
       "2      20      129  00485135  0500000US20129  20129     Morton   06   \n",
       "3      28      093  00695770  0500000US28093  28093   Marshall   06   \n",
       "4      29      510  00767557  0500000US29510  29510  St. Louis   25   \n",
       "\n",
       "         ALAND     AWATER                                           geometry  \n",
       "0  34475567011  387344307  POLYGON ((-114.755618 36.087166, -114.753638 3...  \n",
       "1   1257365642  221047161  POLYGON ((-81.52365999999999 29.622432, -81.32...  \n",
       "2   1889993251     507796  POLYGON ((-102.041952 37.024742, -102.04195 37...  \n",
       "3   1828989833    9195190  POLYGON ((-89.7243244282036 34.9952117286505, ...  \n",
       "4    160458044   10670040  POLYGON ((-90.318212 38.600017, -90.301828 38....  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_df = gpd.read_file(\"http://www2.census.gov/geo/tiger/GENZ2016/shp/cb_2016_us_county_5m.zip\")\n",
    "county_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['STATEFP', 'COUNTYFP', 'COUNTYNS', 'AFFGEOID', 'GEOID', 'NAME', 'LSAD',\n",
      "       'ALAND', 'AWATER', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(county_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wisconsin’s FIPS code is 55 so we will make sure that we only keep those counties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "county_df = county_df.query(\"STATEFP == '55'\") #FOR YOUR PROJECTS google the name of your state + FIPS. \n",
    "# for example the FIPS code for NY is 36"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot all counties in Wisconsin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "state_df.query(\"NAME == 'Wisconsin'\").plot(ax=gax, edgecolor=\"black\", color=\"white\")\n",
    "county_df.plot(ax=gax, edgecolor=\"black\", color=\"white\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Vote Data\n",
    "\n",
    "The final step is to get the vote data, which can be found online on [this site](https://elections.wi.gov/elections-voting/results/2016/fall-general).\n",
    "\n",
    "Our friend Kim says,\n",
    "\n",
    "> Go ahead and open up the file. It’s a mess! I saved a cleaned up version of the file to\n",
    "`results.csv` which we can use to save the hassle with cleaning the data. For fun, you should\n",
    "load the raw data and try beating it into shape. That’s what you normally would have to do…\n",
    "and it’s fun.\n",
    "\n",
    "\n",
    "We’d like to add that such an exercise is also “good for you” (similar to how vegetables are good for you).\n",
    "\n",
    "But, for the example in class, we’ll simply start with his cleaned data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hide-output": false
   },
   "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>county</th>\n",
       "      <th>total</th>\n",
       "      <th>trump</th>\n",
       "      <th>clinton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADAMS</td>\n",
       "      <td>10130</td>\n",
       "      <td>5966</td>\n",
       "      <td>3745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASHLAND</td>\n",
       "      <td>8032</td>\n",
       "      <td>3303</td>\n",
       "      <td>4226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BARRON</td>\n",
       "      <td>22671</td>\n",
       "      <td>13614</td>\n",
       "      <td>7889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BAYFIELD</td>\n",
       "      <td>9612</td>\n",
       "      <td>4124</td>\n",
       "      <td>4953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BROWN</td>\n",
       "      <td>129011</td>\n",
       "      <td>67210</td>\n",
       "      <td>53382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      county   total  trump  clinton\n",
       "0     ADAMS    10130   5966     3745\n",
       "1   ASHLAND     8032   3303     4226\n",
       "2    BARRON    22671  13614     7889\n",
       "3  BAYFIELD     9612   4124     4953\n",
       "4     BROWN   129011  67210    53382"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.read_csv(\"https://datascience.quantecon.org/assets/data/ruhl_cleaned_results.csv\", thousands=\",\")\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this is NOT a GeoDataFrame; it has no geographical information.\n",
    "\n",
    "But it does have the names of each county.\n",
    "\n",
    "We will be able to use this to match to the counties from `county_df`.\n",
    "\n",
    "First, we need to finish up the data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "results[\"county\"] = results[\"county\"].str.title()\n",
    "results[\"county\"] = results[\"county\"].str.strip()\n",
    "county_df[\"NAME\"] = county_df[\"NAME\"].str.title()\n",
    "county_df[\"NAME\"] = county_df[\"NAME\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str.strip(\"abc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can merge election results with the county data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "res_w_states = county_df.merge(results, left_on=\"NAME\", right_on=\"county\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we’ll create a new variable called `trump_share`, which will denote the percentage of votes that\n",
    "Donald Trump won during the election."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "      <th>county</th>\n",
       "      <th>total</th>\n",
       "      <th>trump</th>\n",
       "      <th>clinton</th>\n",
       "      <th>trump_share</th>\n",
       "      <th>rel_trump_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55</td>\n",
       "      <td>035</td>\n",
       "      <td>01581077</td>\n",
       "      <td>0500000US55035</td>\n",
       "      <td>55035</td>\n",
       "      <td>Eau Claire</td>\n",
       "      <td>06</td>\n",
       "      <td>1652211310</td>\n",
       "      <td>18848512</td>\n",
       "      <td>POLYGON ((-91.65045499999999 44.855951, -90.92...</td>\n",
       "      <td>Eau Claire</td>\n",
       "      <td>55025</td>\n",
       "      <td>23331</td>\n",
       "      <td>27340</td>\n",
       "      <td>0.424007</td>\n",
       "      <td>0.460441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>113</td>\n",
       "      <td>01581116</td>\n",
       "      <td>0500000US55113</td>\n",
       "      <td>55113</td>\n",
       "      <td>Sawyer</td>\n",
       "      <td>06</td>\n",
       "      <td>3256410077</td>\n",
       "      <td>240690443</td>\n",
       "      <td>POLYGON ((-91.551282 46.157045, -91.2383809999...</td>\n",
       "      <td>Sawyer</td>\n",
       "      <td>9137</td>\n",
       "      <td>5185</td>\n",
       "      <td>3503</td>\n",
       "      <td>0.567473</td>\n",
       "      <td>0.596800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>101</td>\n",
       "      <td>01581111</td>\n",
       "      <td>0500000US55101</td>\n",
       "      <td>55101</td>\n",
       "      <td>Racine</td>\n",
       "      <td>06</td>\n",
       "      <td>861267826</td>\n",
       "      <td>1190381762</td>\n",
       "      <td>POLYGON ((-88.30638399999999 42.842095, -88.06...</td>\n",
       "      <td>Racine</td>\n",
       "      <td>94302</td>\n",
       "      <td>46681</td>\n",
       "      <td>42641</td>\n",
       "      <td>0.495016</td>\n",
       "      <td>0.522615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>097</td>\n",
       "      <td>01581109</td>\n",
       "      <td>0500000US55097</td>\n",
       "      <td>55097</td>\n",
       "      <td>Portage</td>\n",
       "      <td>06</td>\n",
       "      <td>2074100548</td>\n",
       "      <td>56938133</td>\n",
       "      <td>POLYGON ((-89.844931 44.684945, -89.345917 44....</td>\n",
       "      <td>Portage</td>\n",
       "      <td>38589</td>\n",
       "      <td>17305</td>\n",
       "      <td>18529</td>\n",
       "      <td>0.448444</td>\n",
       "      <td>0.482921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>135</td>\n",
       "      <td>01581127</td>\n",
       "      <td>0500000US55135</td>\n",
       "      <td>55135</td>\n",
       "      <td>Waupaca</td>\n",
       "      <td>06</td>\n",
       "      <td>1936525696</td>\n",
       "      <td>45266211</td>\n",
       "      <td>POLYGON ((-89.22374499999999 44.681365, -88.60...</td>\n",
       "      <td>Waupaca</td>\n",
       "      <td>26095</td>\n",
       "      <td>16209</td>\n",
       "      <td>8451</td>\n",
       "      <td>0.621153</td>\n",
       "      <td>0.657299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTYNS        AFFGEOID  GEOID        NAME LSAD  \\\n",
       "0      55      035  01581077  0500000US55035  55035  Eau Claire   06   \n",
       "1      55      113  01581116  0500000US55113  55113      Sawyer   06   \n",
       "2      55      101  01581111  0500000US55101  55101      Racine   06   \n",
       "3      55      097  01581109  0500000US55097  55097     Portage   06   \n",
       "4      55      135  01581127  0500000US55135  55135     Waupaca   06   \n",
       "\n",
       "        ALAND      AWATER                                           geometry  \\\n",
       "0  1652211310    18848512  POLYGON ((-91.65045499999999 44.855951, -90.92...   \n",
       "1  3256410077   240690443  POLYGON ((-91.551282 46.157045, -91.2383809999...   \n",
       "2   861267826  1190381762  POLYGON ((-88.30638399999999 42.842095, -88.06...   \n",
       "3  2074100548    56938133  POLYGON ((-89.844931 44.684945, -89.345917 44....   \n",
       "4  1936525696    45266211  POLYGON ((-89.22374499999999 44.681365, -88.60...   \n",
       "\n",
       "       county  total  trump  clinton  trump_share  rel_trump_share  \n",
       "0  Eau Claire  55025  23331    27340     0.424007         0.460441  \n",
       "1      Sawyer   9137   5185     3503     0.567473         0.596800  \n",
       "2      Racine  94302  46681    42641     0.495016         0.522615  \n",
       "3     Portage  38589  17305    18529     0.448444         0.482921  \n",
       "4     Waupaca  26095  16209     8451     0.621153         0.657299  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_w_states[\"trump_share\"] = res_w_states[\"trump\"] / (res_w_states[\"total\"])\n",
    "res_w_states[\"rel_trump_share\"] = res_w_states[\"trump\"] / (res_w_states[\"trump\"]+res_w_states[\"clinton\"])\n",
    "res_w_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can create our map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize = (10,8))\n",
    "\n",
    "# Plot the state\n",
    "state_df[state_df['NAME'] == 'Wisconsin'].plot(ax = gax, edgecolor='black',color='white')\n",
    "\n",
    "# Plot the counties and pass 'rel_trump_share' as the data to color\n",
    "res_w_states.plot(\n",
    "    ax=gax, edgecolor='black', column='rel_trump_share', legend=True, cmap='RdBu_r',\n",
    "    vmin=0.2, vmax=0.8 #range of your column value for the color legend\n",
    ")\n",
    "\n",
    "# Add text to let people know what we are plotting\n",
    "gax.annotate('Republican vote share',xy=(0.76, 0.06),  xycoords='figure fraction')\n",
    "\n",
    "# I don't want the axis with long and lat\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see from this map?\n",
    "\n",
    "How many counties did Trump win? How many did Clinton win?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_w_states.eval(\"trump > clinton\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_w_states.eval(\"clinton > trump\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Who had more votes? Do you think a comparison in counties won or votes won is more reasonable? Why\n",
    "do you think they diverge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1405284"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_w_states[\"trump\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1382536"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_w_states[\"clinton\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What story could you tell about this divergence?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactivity\n",
    "\n",
    "Multiple Python libraries can help create interactive figures.\n",
    "\n",
    "Here, we will see an example using bokeh.\n",
    "\n",
    "In the another lecture, we will see an example with folium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook\n",
    "from bokeh.plotting import figure, ColumnDataSource\n",
    "from bokeh.io import output_notebook, show, output_file\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar, HoverTool\n",
    "from bokeh.palettes import brewer\n",
    "output_notebook()\n",
    "import json\n",
    "res_w_states[\"clinton_share\"] = res_w_states[\"clinton\"] / res_w_states[\"total\"]\n",
    "#Convert data to geojson for bokeh\n",
    "wi_geojson=GeoJSONDataSource(geojson=res_w_states.to_json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hide-output": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"7e8fe595-ee4e-4fa3-b3e5-bb9bd038d4b7\" data-root-id=\"1007\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"78e3ea3a-5e10-418d-b0d6-29382fb1d62b\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"id\":\"1004\",\"type\":\"ColorBar\"}],\"left\":[{\"id\":\"1022\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"id\":\"1021\",\"type\":\"Grid\"},{\"id\":\"1022\",\"type\":\"LinearAxis\"},{\"id\":\"1026\",\"type\":\"Grid\"},{\"id\":\"1032\",\"type\":\"GlyphRenderer\"},{\"id\":\"1004\",\"type\":\"ColorBar\"}],\"title\":{\"id\":\"1006\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1027\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1009\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1013\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1011\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1015\",\"type\":\"LinearScale\"}},\"id\":\"1007\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1037\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"formatter\":{\"id\":\"1035\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1007\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1039\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"GeoJSONDataSource\"},\"glyph\":{\"id\":\"1030\",\"type\":\"Patches\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1031\",\"type\":\"Patches\"},\"selection_glyph\":null,\"view\":{\"id\":\"1033\",\"type\":\"CDSView\"}},\"id\":\"1032\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":null,\"text\":\"Wisconsin Voting in 2016 Presidential Election\"},\"id\":\"1006\",\"type\":\"Title\"},{\"attributes\":{\"high\":1,\"low\":0,\"palette\":[\"#053061\",\"#2166ac\",\"#4393c3\",\"#92c5de\",\"#d1e5f0\",\"#fddbc7\",\"#f4a582\",\"#d6604d\",\"#b2182b\",\"#67001f\"]},\"id\":\"1003\",\"type\":\"LinearColorMapper\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"1031\",\"type\":\"Patches\"},{\"attributes\":{\"callback\":null,\"geojson\":\"{\\\"type\\\": \\\"FeatureCollection\\\", \\\"features\\\": [{\\\"id\\\": \\\"0\\\", \\\"type\\\": \\\"Feature\\\", \\\"properties\\\": {\\\"AFFGEOID\\\": \\\"0500000US55035\\\", \\\"ALAND\\\": 1652211310, \\\"AWATER\\\": 18848512, \\\"COUNTYFP\\\": \\\"035\\\", \\\"COUNTYNS\\\": \\\"01581077\\\", \\\"GEOID\\\": \\\"55035\\\", \\\"LSAD\\\": \\\"06\\\", \\\"NAME\\\": \\\"Eau Claire\\\", \\\"STATEFP\\\": \\\"55\\\", \\\"clinton\\\": 27340, \\\"clinton_share\\\": 0.4968650613357565, \\\"county\\\": \\\"Eau Claire\\\", \\\"rel_trump_share\\\": 0.46044088334550337, \\\"total\\\": 55025, \\\"trump\\\": 23331, \\\"trump_share\\\": 0.42400726942298955}, \\\"geometry\\\": {\\\"type\\\": \\\"Polygon\\\", \\\"coordinates\\\": [[[-91.650455, 44.855951], [-90.922247, 44.857309], [-90.92235, 44.596292999999996], [-91.043815, 44.59664], [-91.16561899999999, 44.596987], [-91.529101, 44.596197], [-91.65024799999999, 44.596649], [-91.65036099999999, 44.683633], [-91.650455, 44.855951]]]}}, {\\\"id\\\": \\\"1\\\", \\\"type\\\": \\\"Feature\\\", \\\"properties\\\": {\\\"AFFGEOID\\\": \\\"0500000US55113\\\", \\\"ALAND\\\": 3256410077, \\\"AWATER\\\": 240690443, \\\"COUNTYFP\\\": \\\"113\\\", \\\"COUNTYNS\\\": \\\"01581116\\\", \\\"GEOID\\\": \\\"55113\\\", \\\"LSAD\\\": \\\"06\\\", \\\"NAME\\\": \\\"Sawyer\\\", \\\"STATEFP\\\": \\\"55\\\", \\\"clinton\\\": 3503, \\\"clinton_share\\\": 0.3833862318047499, 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       "  var render_items = [{\"docid\":\"78e3ea3a-5e10-418d-b0d6-29382fb1d62b\",\"roots\":{\"1007\":\"7e8fe595-ee4e-4fa3-b3e5-bb9bd038d4b7\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "  if (root.Bokeh !== undefined) {\n",
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       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
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       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
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       "    }, 10, root)\n",
       "  }\n",
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     "output_type": "display_data"
    }
   ],
   "source": [
    "color_mapper = LinearColorMapper(palette = brewer['RdBu'][10], low = 0, high = 1)\n",
    "color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,\n",
    "                     border_line_color=None,location = (0,0), orientation = 'horizontal')\n",
    "hover = HoverTool(tooltips = [ ('County','@county'),('Portion Trump', '@trump_share'),\n",
    "                               ('Portion Clinton','@clinton_share'),\n",
    "                               ('Total','@total')])\n",
    "p = figure(title=\"Wisconsin Voting in 2016 Presidential Election\", tools=[hover])\n",
    "p.patches(\"xs\",\"ys\",source=wi_geojson,\n",
    "          fill_color = {'field' :'rel_trump_share', 'transform' : color_mapper})\n",
    "p.add_layout(color_bar, 'below')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "**Exercise 1**\n",
    "\n",
    "The following data contains the lattitude and longitude of capital cities in Canada. As you can see there are no decimal points for lat lon values which means that the locations are less accurate (check your the lat lon in your own datasets and see how many digits you have after the dot!). \n",
    "\n",
    "\n",
    "\n",
    "Now, go back to the begining of this notebook and use this data to do the following: \n",
    "1. Plot a map of Canada \n",
    "2. Add cities \n",
    "3. Add city names\n",
    "\n",
    "Take a screenshot of your code and your **final** plot (you don't need to show each step as we did before, just show us the last plot which has the location of the cities and names) and upload one screenshot of both code and figure on Crowdmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_c = pd.DataFrame({\n",
    "'Province':['British Columbia', 'Yukon','Alberta', 'Northwest Territories', 'Saskatchewan', 'Manitoba','Nunavut','Ontario',\n",
    "'Québec','New Brunswick','Nova Scotia','Prince Edward Island','Newfoundland and Labrador'],\n",
    "'City' : ['Victoria', 'Whitehorse', 'Edmonton' ,'Yellowknife' ,'Regina', 'Winnipeg', 'Iqaluit', 'Toronto' ,'Québec'\n",
    "          ,'Fredericton', 'Halifax' ,'Charlottetown' ,'St. John’s'],\n",
    "'Latitude': [48, 61, 54, 62, 50,50, 64, 44, 47, 46, 45, 53, 48],\n",
    "'Longitude': [-123, -135, -113, -114,-105, -97, -69,-79,-71, -67,-64,-56, -53] \n",
    "})\n",
    "#notice I called this data df_c for Canada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2**\n",
    "\n",
    "For this exercise, please submit a screenshot of your code for creating a map of your data at any stage. The deadline is a few days before your second project's due date, so by then, you should have a clean draft. However, this is to get you started on finishing this challenging task. This exercise will be marked for submission and not content; however, the quality of your submission will be a good predictor of your second project's mark. \n",
    "\n",
    "\n",
    "1. In your project datasets, you should know the geographic level that you are working with. Is is Zip code? Is it county? Is it state? Is it country? Check the column that contains this information. Is it nice and clean? What is the format? Are there any missings? \n",
    "\n",
    "2. Go to the \"useful links\" column on the spreadsheet where you chose your preferred dataset. I have added the spreadsheet to this Module for your convenience as well. Check the column titled \"useful links.\" Check the sample code and see if there is code for a map. You can also go to the original data link, click on the \"Notebooks\" tab and find hundreds of example notebooks on your dataset! When you click on the \"Data link\" column of the excel file, you are directed to a Kaggle page. There click on the \"Notebooks\" tab. You can then search for keywords to find the sample code that you want. Make sure the sample notbook is in Python and not in R.\n",
    "\n",
    "3. Familiarize yourself with the U.S. Census Shapefiles. Look at the Wisconsin voting example above (in this notebook). I have provided the Census Shapefile links in the spreadsheet in the 4th and 10th row. You can use these links as in the Wisconsin example and make your plots. \n",
    "\n",
    "4. If you do not want to plot all of your observations as dots on the map, you should create a new data frame with the unique location names. All of your datasets are in panel format, which means that you have more than one row for each location. Therefore you need to first create a dataset with the unique values for each geographic level (whatever value you want to plot) and the unique names or IDs for the goegraphic levels (zip codes, cities, etc.), put it in a data frame, and then map it. For example, if you have created an average measure of SAT grades for each zip code. You want a data frame with these columns: (zipcode, average SAT for each zip). You can then merge this with your zip code shapefile (like we merged county with poll results in the example above) and plot. \n",
    "\n",
    "5. There will be more complications. For instance, the name of the city or county, or state may have extra strings, may be in lower case or upper case, and therefore your merge with the shapefile can fail. Always check the values in both your dataset (containing the original lat/long) and your shapefile. Leave 3-4 days for errors and for figuring out the sample code.\n",
    "\n",
    "6. Make sure to take advantage of the existing notebooks on Kaggle. Feel free to read, understand, and use their code. Look for a map and try to understand the code first and then apply it to your notebook. This is not cheating; it's how you learn to code. Simplying copy pasting someone's code is cheating.\n",
    "\n",
    "7. You do NOT need to use the same libraries as we used here necessarily. What I showed you was a popular library but there are many more. Get creative and explore. You can also look at other suggested notebooks for other datasets in the Excel spreadsheet to get inspired.\n",
    "\n",
    "\n",
    "**Note that creating your first map with a real-world data can take days or weeks. Start ASAP and get your questions answered. Producing a map of your data is one part in your second project.**\n",
    "\n",
    "\n",
    "Enjoy being a creator!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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