TecMty_kmeans_target A00544748.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PosgradoMNA/Zayra-Annette-Lopez-Regalado-A00544748-Actividades-de-aprendizaje/blob/main/TecMty_kmeans_target%20A00544748.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Maestria: Inteligencia Artificial Aplicada\n",
        "\n",
        "# Materia: Ciencia de Datos\n",
        "\n",
        "# Alumna: Zayra Annette Lopez Regalado\n",
        "\n",
        "# Matricula: A00544748\n",
        "\n",
        "# Profesor: Mary Paz \n",
        "\n",
        "# Notebook 2. K Means. "
      ],
      "metadata": {
        "id": "9GdIqgO1UGze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este notebook se basa en información de target\n",
        "![imagen.png](https://www.america-retail.com/static//2020/12/target01.jpg)\n",
        "\n",
        "\n",
        "Ahora imagina que somos parte del equipo de data science de la empresa Target, una de las tiendas con mayor presencia en Estados Unidos. El departamento de logistica acude a nosotros para saber donde le conviene poner sus almacenes, para que se optimice el gasto de gasolina, los tiempos de entrega de los productos y se disminuyan costos. Para ello, nos pasan los datos de latitud y longitud de cada una de las tiendas.\n",
        "\n",
        "https://www.kaggle.com/datasets/saejinmahlauheinert/target-store-locations?select=target-locations.csv\n",
        "\n",
        "Si quieres saber un poco más de graficas geográficas consulta el siguiente notebook\n",
        "https://colab.research.google.com/github/QuantEcon/quantecon-notebooks-datascience/blob/master/applications/maps.ipynb#scrollTo=uo2oPtSCeAOz"
      ],
      "metadata": {
        "id": "6mUW5QNwhz5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install qeds fiona geopandas xgboost gensim folium pyLDAvis descartes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwkfEqinwESD",
        "outputId": "013a9be4-6671-41f5-a1ea-d20dc25ba102"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import geopandas"
      ],
      "metadata": {
        "id": "WBpdGjbquQii"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importa la base de datos"
      ],
      "metadata": {
        "id": "KNTIoZlGvPum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url=\"https://raw.githubusercontent.com/marypazrf/bdd/main/target-locations.csv\"\n",
        "df=pd.read_csv(url)"
      ],
      "metadata": {
        "id": "PAJWHRVN781V"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploremos los datos."
      ],
      "metadata": {
        "id": "sH4hvPAYvUGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DuzNp7KseyBg",
        "outputId": "f650df92-cd35-4a5f-b18e-48be2c592627"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name   latitude  longitude  \\\n",
              "0  Alabaster  33.224225 -86.804174   \n",
              "1   Bessemer  33.334550 -86.989778   \n",
              "2     Daphne  30.602875 -87.895932   \n",
              "3    Decatur  34.560148 -86.971559   \n",
              "4     Dothan  31.266061 -85.446422   \n",
              "\n",
              "                                             address         phone  \\\n",
              "0        250 S Colonial Dr, Alabaster, AL 35007-4657  205-564-2608   \n",
              "1       4889 Promenade Pkwy, Bessemer, AL 35022-7305  205-565-3760   \n",
              "2          1698 US Highway 98, Daphne, AL 36526-4252  251-621-3540   \n",
              "3  1235 Point Mallard Pkwy SE, Decatur, AL 35601-...  256-898-3036   \n",
              "4         4601 Montgomery Hwy, Dothan, AL 36303-1522  334-340-1112   \n",
              "\n",
              "                                    website  \n",
              "0  https://www.target.com/sl/alabaster/2276  \n",
              "1   https://www.target.com/sl/bessemer/2375  \n",
              "2     https://www.target.com/sl/daphne/1274  \n",
              "3    https://www.target.com/sl/decatur/2084  \n",
              "4     https://www.target.com/sl/dothan/1468  "
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              "      <td>Alabaster</td>\n",
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              "      <td>-86.804174</td>\n",
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              "      <th>1</th>\n",
              "      <td>Bessemer</td>\n",
              "      <td>33.334550</td>\n",
              "      <td>-86.989778</td>\n",
              "      <td>4889 Promenade Pkwy, Bessemer, AL 35022-7305</td>\n",
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              "      <td>-87.895932</td>\n",
              "      <td>1698 US Highway 98, Daphne, AL 36526-4252</td>\n",
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              "      <td>-86.971559</td>\n",
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              "      <th>4</th>\n",
              "      <td>Dothan</td>\n",
              "      <td>31.266061</td>\n",
              "      <td>-85.446422</td>\n",
              "      <td>4601 Montgomery Hwy, Dothan, AL 36303-1522</td>\n",
              "      <td>334-340-1112</td>\n",
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          "metadata": {},
          "execution_count": 26
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    {
      "cell_type": "code",
      "source": [
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      "metadata": {
        "colab": {
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        },
        "id": "MxbhdLkavl7Q",
        "outputId": "d37231c4-9e3b-484d-a211-0881305a2d29"
      },
      "execution_count": 27,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1839 entries, 0 to 1838\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   name       1839 non-null   object \n",
            " 1   latitude   1839 non-null   float64\n",
            " 2   longitude  1839 non-null   float64\n",
            " 3   address    1839 non-null   object \n",
            " 4   phone      1839 non-null   object \n",
            " 5   website    1839 non-null   object \n",
            "dtypes: float64(2), object(4)\n",
            "memory usage: 86.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Definición de Latitud y Longitud**\n",
        "\n",
        "**Latitud** Es la distancia en grados, minutos y segundos que hay con respecto al paralelo principal, que es el ecuador (0º). La latitud puede ser norte y sur.\n",
        "\n",
        "**Longitud**: Es la distancia en grados, minutos y segundos que hay con respecto al meridiano principal, que es el meridiano de Greenwich (0º).La longitud puede ser este y oeste."
      ],
      "metadata": {
        "id": "cpfqKT4qxAZz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "latlong=df[[\"latitude\",\"longitude\"]]"
      ],
      "metadata": {
        "id": "oPzX3DGju0As"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n4QKgxn4e6V0"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "¡Visualizemos los datos!, para empezar a notar algún patron. \n",
        "\n",
        "A simple vista pudieramos pensar que tenemos algunos datos atípicos u outliers, pero .... no es así, simplemente esta grafica no nos está dando toda la información."
      ],
      "metadata": {
        "id": "gMk6UVE2e67y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#extrae los datos interesantes\n",
        "latlong.plot.scatter( \"longitude\",\"latitude\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "-QZRqKiDfj2J",
        "outputId": "05abf631-1521-40d1-f26d-86c8d621d90c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd769174190>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "latlong.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "O6b3QguyyjM_",
        "outputId": "82f70bdb-50d3-47bd-8138-6b96c5fd4c12"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          latitude    longitude\n",
              "count  1839.000000  1839.000000\n",
              "mean     37.791238   -91.986881\n",
              "std       5.272299    16.108046\n",
              "min      19.647855  -159.376962\n",
              "25%      33.882605   -98.268828\n",
              "50%      38.955432   -87.746346\n",
              "75%      41.658341   -80.084833\n",
              "max      61.577919   -68.742331"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2a851f72-5e20-4b5a-a300-f539a1f4a6ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1839.000000</td>\n",
              "      <td>1839.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.791238</td>\n",
              "      <td>-91.986881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.272299</td>\n",
              "      <td>16.108046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>19.647855</td>\n",
              "      <td>-159.376962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.882605</td>\n",
              "      <td>-98.268828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.955432</td>\n",
              "      <td>-87.746346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.658341</td>\n",
              "      <td>-80.084833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>61.577919</td>\n",
              "      <td>-68.742331</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a851f72-5e20-4b5a-a300-f539a1f4a6ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a851f72-5e20-4b5a-a300-f539a1f4a6ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para entender un poco más, nos auxiliaremos de una librería para graficar datos geográficos. Esto nos ayudara a tener un mejor entendimiento de ellos.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hkPMyAyIDunz"
      }
    },
    {
      "cell_type": "code",
      "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();"
      ],
      "metadata": {
        "id": "_blyM4esyJTu"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Coordinates\"] = list(zip(df.longitude, df.latitude))\n",
        "df[\"Coordinates\"] = df[\"Coordinates\"].apply(Point)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "01i0TciKfPco",
        "outputId": "90ee548c-7a7f-4057-8924-1c86763c7640"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name   latitude  longitude  \\\n",
              "0  Alabaster  33.224225 -86.804174   \n",
              "1   Bessemer  33.334550 -86.989778   \n",
              "2     Daphne  30.602875 -87.895932   \n",
              "3    Decatur  34.560148 -86.971559   \n",
              "4     Dothan  31.266061 -85.446422   \n",
              "\n",
              "                                             address         phone  \\\n",
              "0        250 S Colonial Dr, Alabaster, AL 35007-4657  205-564-2608   \n",
              "1       4889 Promenade Pkwy, Bessemer, AL 35022-7305  205-565-3760   \n",
              "2          1698 US Highway 98, Daphne, AL 36526-4252  251-621-3540   \n",
              "3  1235 Point Mallard Pkwy SE, Decatur, AL 35601-...  256-898-3036   \n",
              "4         4601 Montgomery Hwy, Dothan, AL 36303-1522  334-340-1112   \n",
              "\n",
              "                                    website  \\\n",
              "0  https://www.target.com/sl/alabaster/2276   \n",
              "1   https://www.target.com/sl/bessemer/2375   \n",
              "2     https://www.target.com/sl/daphne/1274   \n",
              "3    https://www.target.com/sl/decatur/2084   \n",
              "4     https://www.target.com/sl/dothan/1468   \n",
              "\n",
              "                             Coordinates  \n",
              "0  POINT (-86.80417369999999 33.2242254)  \n",
              "1  POINT (-86.98977789999999 33.3345501)  \n",
              "2  POINT (-87.89593169999999 30.6028747)  \n",
              "3         POINT (-86.9715595 34.5601477)  \n",
              "4         POINT (-85.4464222 31.2660613)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d830402e-8991-4f43-b3dd-90dc0773e3d2\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>address</th>\n",
              "      <th>phone</th>\n",
              "      <th>website</th>\n",
              "      <th>Coordinates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabaster</td>\n",
              "      <td>33.224225</td>\n",
              "      <td>-86.804174</td>\n",
              "      <td>250 S Colonial Dr, Alabaster, AL 35007-4657</td>\n",
              "      <td>205-564-2608</td>\n",
              "      <td>https://www.target.com/sl/alabaster/2276</td>\n",
              "      <td>POINT (-86.80417369999999 33.2242254)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bessemer</td>\n",
              "      <td>33.334550</td>\n",
              "      <td>-86.989778</td>\n",
              "      <td>4889 Promenade Pkwy, Bessemer, AL 35022-7305</td>\n",
              "      <td>205-565-3760</td>\n",
              "      <td>https://www.target.com/sl/bessemer/2375</td>\n",
              "      <td>POINT (-86.98977789999999 33.3345501)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Daphne</td>\n",
              "      <td>30.602875</td>\n",
              "      <td>-87.895932</td>\n",
              "      <td>1698 US Highway 98, Daphne, AL 36526-4252</td>\n",
              "      <td>251-621-3540</td>\n",
              "      <td>https://www.target.com/sl/daphne/1274</td>\n",
              "      <td>POINT (-87.89593169999999 30.6028747)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decatur</td>\n",
              "      <td>34.560148</td>\n",
              "      <td>-86.971559</td>\n",
              "      <td>1235 Point Mallard Pkwy SE, Decatur, AL 35601-...</td>\n",
              "      <td>256-898-3036</td>\n",
              "      <td>https://www.target.com/sl/decatur/2084</td>\n",
              "      <td>POINT (-86.9715595 34.5601477)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dothan</td>\n",
              "      <td>31.266061</td>\n",
              "      <td>-85.446422</td>\n",
              "      <td>4601 Montgomery Hwy, Dothan, AL 36303-1522</td>\n",
              "      <td>334-340-1112</td>\n",
              "      <td>https://www.target.com/sl/dothan/1468</td>\n",
              "      <td>POINT (-85.4464222 31.2660613)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d830402e-8991-4f43-b3dd-90dc0773e3d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d830402e-8991-4f43-b3dd-90dc0773e3d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf = gpd.GeoDataFrame(df, geometry=\"Coordinates\")\n",
        "gdf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SBTBrsvlfIU_",
        "outputId": "d0360d12-773a-4e2a-a011-4f590edd0fa1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name   latitude  longitude  \\\n",
              "0  Alabaster  33.224225 -86.804174   \n",
              "1   Bessemer  33.334550 -86.989778   \n",
              "2     Daphne  30.602875 -87.895932   \n",
              "3    Decatur  34.560148 -86.971559   \n",
              "4     Dothan  31.266061 -85.446422   \n",
              "\n",
              "                                             address         phone  \\\n",
              "0        250 S Colonial Dr, Alabaster, AL 35007-4657  205-564-2608   \n",
              "1       4889 Promenade Pkwy, Bessemer, AL 35022-7305  205-565-3760   \n",
              "2          1698 US Highway 98, Daphne, AL 36526-4252  251-621-3540   \n",
              "3  1235 Point Mallard Pkwy SE, Decatur, AL 35601-...  256-898-3036   \n",
              "4         4601 Montgomery Hwy, Dothan, AL 36303-1522  334-340-1112   \n",
              "\n",
              "                                    website                 Coordinates  \n",
              "0  https://www.target.com/sl/alabaster/2276  POINT (-86.80417 33.22423)  \n",
              "1   https://www.target.com/sl/bessemer/2375  POINT (-86.98978 33.33455)  \n",
              "2     https://www.target.com/sl/daphne/1274  POINT (-87.89593 30.60287)  \n",
              "3    https://www.target.com/sl/decatur/2084  POINT (-86.97156 34.56015)  \n",
              "4     https://www.target.com/sl/dothan/1468  POINT (-85.44642 31.26606)  "
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>address</th>\n",
              "      <th>phone</th>\n",
              "      <th>website</th>\n",
              "      <th>Coordinates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabaster</td>\n",
              "      <td>33.224225</td>\n",
              "      <td>-86.804174</td>\n",
              "      <td>250 S Colonial Dr, Alabaster, AL 35007-4657</td>\n",
              "      <td>205-564-2608</td>\n",
              "      <td>https://www.target.com/sl/alabaster/2276</td>\n",
              "      <td>POINT (-86.80417 33.22423)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bessemer</td>\n",
              "      <td>33.334550</td>\n",
              "      <td>-86.989778</td>\n",
              "      <td>4889 Promenade Pkwy, Bessemer, AL 35022-7305</td>\n",
              "      <td>205-565-3760</td>\n",
              "      <td>https://www.target.com/sl/bessemer/2375</td>\n",
              "      <td>POINT (-86.98978 33.33455)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Daphne</td>\n",
              "      <td>30.602875</td>\n",
              "      <td>-87.895932</td>\n",
              "      <td>1698 US Highway 98, Daphne, AL 36526-4252</td>\n",
              "      <td>251-621-3540</td>\n",
              "      <td>https://www.target.com/sl/daphne/1274</td>\n",
              "      <td>POINT (-87.89593 30.60287)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decatur</td>\n",
              "      <td>34.560148</td>\n",
              "      <td>-86.971559</td>\n",
              "      <td>1235 Point Mallard Pkwy SE, Decatur, AL 35601-...</td>\n",
              "      <td>256-898-3036</td>\n",
              "      <td>https://www.target.com/sl/decatur/2084</td>\n",
              "      <td>POINT (-86.97156 34.56015)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dothan</td>\n",
              "      <td>31.266061</td>\n",
              "      <td>-85.446422</td>\n",
              "      <td>4601 Montgomery Hwy, Dothan, AL 36303-1522</td>\n",
              "      <td>334-340-1112</td>\n",
              "      <td>https://www.target.com/sl/dothan/1468</td>\n",
              "      <td>POINT (-85.44642 31.26606)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93dfe2ca-99fd-41ab-9a9e-a1f019c79366')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mapa\n",
        "\n",
        "world = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\"))\n",
        "world = world.set_index(\"iso_a3\")\n",
        "\n",
        "world.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "wB6Jkrvbfef1",
        "outputId": "529a44f8-4081-4d82-dc7d-44b6d3033b86"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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     MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
              "TZA     POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
              "ESH     POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
              "CAN     MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
              "USA     MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
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              "      <th>pop_est</th>\n",
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              "      <th>name</th>\n",
              "      <th>gdp_md_est</th>\n",
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              "      <td>Oceania</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>8374.0</td>\n",
              "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
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              "    <tr>\n",
              "      <th>TZA</th>\n",
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              "      <td>Tanzania</td>\n",
              "      <td>150600.0</td>\n",
              "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESH</th>\n",
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              "      <td>W. Sahara</td>\n",
              "      <td>906.5</td>\n",
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              "    </tr>\n",
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              "      <th>CAN</th>\n",
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              "      <td>North America</td>\n",
              "      <td>Canada</td>\n",
              "      <td>1674000.0</td>\n",
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              "      <td>United States of America</td>\n",
              "      <td>18560000.0</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-f59d7883-993c-42a1-a2e7-de3311864596');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#graficar el mapa\n",
        "world.name.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qkBCMSUflw_",
        "outputId": "fe8c244e-a3a2-4d67-b6e0-59835d4e5c80"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fiji', 'Tanzania', 'W. Sahara', 'Canada',\n",
              "       'United States of America', 'Kazakhstan', 'Uzbekistan',\n",
              "       'Papua New Guinea', 'Indonesia', 'Argentina', 'Chile',\n",
              "       'Dem. Rep. Congo', 'Somalia', 'Kenya', 'Sudan', 'Chad', 'Haiti',\n",
              "       'Dominican Rep.', 'Russia', 'Bahamas', 'Falkland Is.', 'Norway',\n",
              "       'Greenland', 'Fr. S. Antarctic Lands', 'Timor-Leste',\n",
              "       'South Africa', 'Lesotho', 'Mexico', 'Uruguay', 'Brazil',\n",
              "       'Bolivia', 'Peru', 'Colombia', 'Panama', 'Costa Rica', 'Nicaragua',\n",
              "       'Honduras', 'El Salvador', 'Guatemala', 'Belize', 'Venezuela',\n",
              "       'Guyana', 'Suriname', 'France', 'Ecuador', 'Puerto Rico',\n",
              "       'Jamaica', 'Cuba', 'Zimbabwe', 'Botswana', 'Namibia', 'Senegal',\n",
              "       'Mali', 'Mauritania', 'Benin', 'Niger', 'Nigeria', 'Cameroon',\n",
              "       'Togo', 'Ghana', \"Côte d'Ivoire\", 'Guinea', 'Guinea-Bissau',\n",
              "       'Liberia', 'Sierra Leone', 'Burkina Faso', 'Central African Rep.',\n",
              "       'Congo', 'Gabon', 'Eq. Guinea', 'Zambia', 'Malawi', 'Mozambique',\n",
              "       'eSwatini', 'Angola', 'Burundi', 'Israel', 'Lebanon', 'Madagascar',\n",
              "       'Palestine', 'Gambia', 'Tunisia', 'Algeria', 'Jordan',\n",
              "       'United Arab Emirates', 'Qatar', 'Kuwait', 'Iraq', 'Oman',\n",
              "       'Vanuatu', 'Cambodia', 'Thailand', 'Laos', 'Myanmar', 'Vietnam',\n",
              "       'North Korea', 'South Korea', 'Mongolia', 'India', 'Bangladesh',\n",
              "       'Bhutan', 'Nepal', 'Pakistan', 'Afghanistan', 'Tajikistan',\n",
              "       'Kyrgyzstan', 'Turkmenistan', 'Iran', 'Syria', 'Armenia', 'Sweden',\n",
              "       'Belarus', 'Ukraine', 'Poland', 'Austria', 'Hungary', 'Moldova',\n",
              "       'Romania', 'Lithuania', 'Latvia', 'Estonia', 'Germany', 'Bulgaria',\n",
              "       'Greece', 'Turkey', 'Albania', 'Croatia', 'Switzerland',\n",
              "       'Luxembourg', 'Belgium', 'Netherlands', 'Portugal', 'Spain',\n",
              "       'Ireland', 'New Caledonia', 'Solomon Is.', 'New Zealand',\n",
              "       'Australia', 'Sri Lanka', 'China', 'Taiwan', 'Italy', 'Denmark',\n",
              "       'United Kingdom', 'Iceland', 'Azerbaijan', 'Georgia',\n",
              "       'Philippines', 'Malaysia', 'Brunei', 'Slovenia', 'Finland',\n",
              "       'Slovakia', 'Czechia', 'Eritrea', 'Japan', 'Paraguay', 'Yemen',\n",
              "       'Saudi Arabia', 'Antarctica', 'N. Cyprus', 'Cyprus', 'Morocco',\n",
              "       'Egypt', 'Libya', 'Ethiopia', 'Djibouti', 'Somaliland', 'Uganda',\n",
              "       'Rwanda', 'Bosnia and Herz.', 'Macedonia', 'Serbia', 'Montenegro',\n",
              "       'Kosovo', 'Trinidad and Tobago', 'S. Sudan'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "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(\"name == 'United States of 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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "swDogh7dfusl",
        "outputId": "3ba65725-3c5c-40a2-ebc7-ce69088168f4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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kyZN5OY+IiEiBMMQpOS0tLbi6usLV1RUAIJVK8fDhQ1y6dAkymQxdunSBqakp6tWrB11dXXkQS01Nxd69ezFixAjExcXB19cXY8eOLXTubdu2oVq1atDX14dEIvmstVmJiIioYoiys7OrzG1vKSkpAABdXV2BK1EcMpkMiYmJMDU1/ei2qKgorFq1Ctra2jh27Fih7QkJCR89jsret99+iy5duqBv376Fvp+RkQEdHR2BqiK+/sJjGwiPbVC+qlWrVuQ29sRVcSKRqMggJhKJ0L59ezg5OcHGxgZisRh6enoVXCEBnGKEiIg+xBsb6JO0tbXh4uKC8PBwoUup0jgmkYiI/o0hrhxcvHgRderUwZYtW5CVlQUAePv2Lc6dO4fLly8rXa9KWloaZDIZzpw5I3QpRERE9H8Y4spBeHg42rRpgzNnzqBZs2bo3LkzrK2t4evri/Hjx8Pd3R2hoaGQSqVCl1pIXFwczp8/L59+JD8/H35+fnByckKjRo3w66+/ClwhERERvccxceUgMTER/fr1w7hx4/DkyRM8fvwYZmZmqFmzJmrVqoXAwEAsXrwYvr6+GDBgAJo2bYqmTZvC2NhYsEtm4eHhGDduHBo2bIiYmBhoa2tDU1MTlpaWiIyMxFdffSVIXURERPRxDHHloF27dpg1axbWrFmDlJQUZGVlwdDQENnZ2fj111/x448/wsvLC9HR0QgNDYWfnx9u3LgBFRUVNGvWDMuXL0eDBg3KvC6ZTIa0tDQYGBgUCot+fn5YuXIlDh8+DDc3N8hkMsTHx+Ply5dwdHTkWCwiIiIFxBBXDr799lt07twZSUlJMDY2hr6+PlRUVBAbG4vx48dj69atGD9+PAYNGoR27doBAG7duoU5c+YgMjISWlpaZVbL7t27ERwcjLi4OPzzzz9QVVVFzZo14enpiZ49e+LQoUOIiorCuXPnYGFhAeDdAHozMzOYmZmVWR30ZZRtHCUREZU/jokrJzVr1oSdnR1q1qwJFZV3L7OlpSWOHj2K3377DaGhoTAzM8Pw4cPh7OyMrl27wsbGBufOnYOhoWGZ1JCUlIRff/0Vo0ePxr59+5CcnIz09HSEhIRAX18fkyZNQnx8PC5cuCAPcKS42CNKRET/xp64CqaiooIOHTqgQ4cOSExMxMGDB+Hu7o5OnTpBTa1sm+OPP/7AkCFDPpgg9v0YvPnz55fp4xEREVHFYYgTUN26dTFx4sRyOffLly/h7++PO3fulMv5iYiISFi8nFoJZWdn44cffsDQoUNRt25docshIiKicsAQV8lkZWXB29sbtWvXxrJly4Quh4iIiMoJQ1wlkpGRgQEDBqBBgwbYvXs31NXVhS6JiIiIyglDXCUhFovRt29f2NnZYdu2bVBVVRW6JCpDnGKEiIj+izc2VBJLly6FjY0NNm3axKkoKim2KxER/RtDXCWQmpoKf39/3L59m7/oiYiIqgheTq0ENm3aBC8vL5iamgpdChEREVUQ9sQpuYyMDGzduhXnz58XuhQiIiKqQOyJU3Lp6elQVVXF/v37OfidiIioCmGIU3KmpqaIiIhAYGAg+vTpA7FYLHRJCikvLw9Lly6Ft7c38vPzhS6nxBjQiYjovxjiKgFTU1OEhISgTp06aNWqFWJjY4UuSaGcP38ejo6OOHHiBBISEjB79myhSyoV3rRCRET/xhBXSWhqamLZsmVwdXXF3r17hS5HIbx+/RqjR49Gv3798OOPP2LkyJGQyWTYvn17qXvjpFIpcnJyyrhSIiKikmOIq2Rq164NqVQqdBmCkslk2LVrF+zt7ZGcnIx+/fph/vz5WLNmDb7//ns8ffoUamqff0/P8+fPsW3bNnh7e8PExATGxsb49ddfkZaWVo7PgoiIqHgMcZWMSCSq8uOnfvnlFyxZsgQrVqzAmTNnkJ2djaCgIFy6dAk+Pj7Q0tL6rPPs2bMHtra2aNasGYKDg9G6dWuEhYUhKioKz549g7W1NXx9fTkOkYiIBMEpRioZFRUVJCQkQCqVQkWl6mX0sLAw7Nq1C9HR0QgMDISHhwf8/PxKda7bt2+jdevWWLZs2QfLmP3xxx+IjY3FsmXLYGVlhcmTJ2PChAnQ09Mri6dBRET0SYKGuNevX2P79u24evUqsrOzYWxsjB9++AFfffUVgHeXxfz9/XHixAlkZmaiUaNGGDt2LBo0aCBk2YKSSqV48OABzp07h0ePHqFGjRqwsbGBjY0NrK2t0atXL4wfPx4ODg747bff0Lt37yoT5lJSUjBs2DCsX78etWrVQmhoKCZOnFjq8zk7O+PPP/8sch1aS0tLbNiwAQ8fPsTy5ctRv359tGvXDv3794enpyf09fVL/dgfIxaLIRaLUb169RJdDiYiospJlJ2dLci1t8zMTEyePBl2dnbo0aMHdHV1kZycjJo1a6J+/foAgAMHDmDfvn2YNGkS6tWrB39/f9y/fx/r16+HtrZ2iR8zJSUFAKCrq1umz6UinDp1Clu3bsXFixeho6ODdu3awd7eHpmZmbh37x7u3buHzMxMXL9+HQBw4sQJLF26FCoqKti3bx8sLCzKvKaEhASFWSVCJpOhe/fusLGxwdy5c/H69Ws0b94cL168KNX/FeDd82vWrBkePXr0WXeGisViHD9+HMHBwThz5gzatGmD/v37o1evXjAwMChVDe/NmjULW7duxdu3b5GVlQU1NTXo6uri1KlTePXqFRwcHL7o/FQ6GRkZ0NHREbqMKo1tIDy2QfmqVq1akdsEC3E7duzAnTt3sHTp0o9ul8lk8PHxQffu3fHtt98CAHJzczFkyBAMHz4cXbt2LfFjKnOICw4OxoQJE9C/f3/8+eef0NTULLRdJpPB0NAQ0dHRMDExkX9v06ZNWLlyJbZv3w4PD48yrUlRQpxMJsOSJUtw4MABHDt2DOrq6ti1axdOnTqFgwcPftG5TU1NcfTo0RL3/qanp+P48eMICQlBVFQU3NzcMGDAAHTr1g0SiQQpKSnyj+TkZCQnJ0NDQwMeHh5wdXWFurp6sc83NzcXSUlJyMnJgbu7O2rXrg0fHx94eXmhRo0aX/Sc6fPxl5fw2AbCYxuUL4UMcePGjYOjoyNSU1Nx+/Zt1KxZE126dEH37t0hEomQlJSEUaNGYcWKFWjUqJH8OF9fX+jq6mLKlCmFznf8+HGcOHGi2MecOXMmAOWdb6ugoADp6ekoKChAjRo1oKWlJX8uMpkMr169Qo0aNT4IeBKJBG/evEH16tVRo0YNiEQiFBQUIC8vDxKJBHl5ecjPz0f16tVRvXr1z359CgoKirzUWFGkUinevHmD/Px8GBgYyOtJS0uDlpZWiXvhpFKp/DXJy8tDbm4u9PX1P3hNS0ImkyEnJwc5OTmQSCQQiURQUVGBqqoqVFRU5B/vHzs/Px+ampqoVq0aNDU1i32NCwoKoKKigtzcXLx9+xYSiQQGBgbFhkAqOzKZTGnfTyoLtoHw2Ablq06dOkVuE2xgTVJSEo4ePYpevXqhX79+ePr0KTZu3AgA6NGjh3z6hv+OK9LX10dqauoH5/Pw8PhkT9P7njhl/otBT08PJ0+exMaNG3Hnzh0MGTIEb9++xf79+2FjY4NVq1YVCr3vSSQS+Pj4QCKRIDU1FZmZmWjevDkcHR3RvHlz1KtXD99//z0sLS2xdevWz3qNhO6JCw8Px/Dhw9GjRw/MmTNHftepWCzG119/jefPn3+y1zU7OxsXLlxAREQETp06hVu3bsHBwQFOTk6wtbVF48aNYWFh8cXjCkvS+5uSkoKIiAiEhYXh9OnTaNCgAbp164bu3bvD2dm5UKj7bxscOHAAXbt2xaZNm9C+ffsS1/no0SMEBwdj3Lhxn30Xb1XGHgjhsQ2ExzYQjmAhTiaTwcrKCj4+PgDeDRJPTEzEkSNH0KNHD6HKUngikQju7u5wd3fHkydPsGvXLtSqVQuRkZEwMzMr8jgTExOEhIQgMjISjRo1grm5+Qd/OR09ehQ///wznJyccObMGdSuXbu8n06pZGdn45dffsGBAwewdu1afPPNN4W2//PPP6hbt26xwSkzMxMrVqzAmjVrYGVlhTZt2mDGjBlwcnIq9Ri6smJoaAhvb2/5EmFXrlxBWFgYxowZg8TERHTs2BFdu3aFu7v7B8f269cPhoaG6N+/P+bNmycfivAply5dwtq1a3Hp0iWYmZkhOTm5yKEORESkGAQLcQYGBvIbGN6rV68eXr58Kd8OAG/evIGhoaF8nzdv3nzxIPHKwsLCAnPmzPns/d+PuSpKtWrVsHLlSjRu3BivX79WyBB348YNDBo0CDY2Njh79uxH/y9YWVkhLi4OeXl5H1xWzMvLw+bNmzF//ny4uroiMjISDRs2rKDqS05NTQ2tW7dG69atMWfOHLx48QKRkZEICQnBtGnTcOrUKWRkZKBx48byY9q2bYvIyEh069YN2dnZGDZsWJHnDw8Px/Lly5GSkoKpU6ciICAAEokEDg4OaN++Pbp161YBz5KIiEpDsBBna2uLhISEQt9LTEyUBzYjIyMYGBjgxo0b8suDEokEd+/exfDhwyu83qri/PnzMDU1/eglWaHt2LED06ZNw/z58/Htt98WOQZDS0sLpqamePToEezs7FBQUICrV6/ixIkT2LVrF0xMTLB79240b968gp/BlzMxMcHgwYMxePBg5OfnIzU1FW5ubliwYAHGjBkjf03s7e0RERGB9u3bQ0tL64MeuYKCAixYsACHDx/GsmXL4OXlJb9Mq62tjT179qBPnz5wcHBA3bp1K/x5EhHRpwkW4nr16oUZM2YgICAAbdq0wZMnTxASEoKhQ4cCeHfZ0NPTE/v370e9evVgamqKgIAAaGlpoV27dkKVXelFRETA09NT6DI+Sk9PDyKRCJaWlp8cRGtra4sVK1bgzZs3OHXqFOrWrYv27dtj1apVcHV1rRSDcNXU1KCtrY0jR45g9OjROHbsGLZs2SLvQbWyssLJkyfRsWNHaGtro2fPngDejRkcM2YMsrOzceXKlY8OmnV1dcXEiRPh7e2NvXv3yu94JiIixSFYiGvUqBFmz56NHTt2ICAgAHXq1MHgwYMLXb7p27cvJBIJNmzYIJ/sd968eYKPWarMdHR0kJub+9FtUqkU/v7+kEgkgvSG9urVC+rq6hg8eDD8/PyKDfOenp4IDw9H586dsWDBgkodQmxsbHDy5EksXLgQzZo1w7Zt29ClSxcAgJ2dHY4cOQIPDw9oaWmhYcOGGDx4MDp27Ig1a9YUexfr7Nmzoaqqii5dumDPnj3ySbiJiEgxCDbFiBCUeZ64irJnzx5cuHABO3fuLPT98+fPY9KkSSgoKEBcXBxu3bqF/Px8+Pn5Yd68eZ9192Z6ejr2798PQ0NDea9QaURFRaF///5YvXp1lR+z9d+7wqKiojB+/Hj0798fixYtks8vdP78efTq1QsqKiqYN28exowZ89mPsW/fPkyYMAHr1q1D586dy/w5KDPelSc8toHw2Ablq7h54qrGekz02UxMTJCYmCj/Oi4uDt9++y0GDBiA77//HuHh4Rg0aBDmzZuH9PR0rF+/Hj/++CNksv//b4EdO3Zg0aJFKCgoQEFBAcLCwjBo0CDUr18f33//faHzl0bbtm3RtWtX7Nq164vOUxm1a9cO0dHRiI2NhZOTE+7evQvg3eXR0NBQBAcHlyjAAcCAAQMQGBiIyZMnY86cOUX21BIRUcViTxwVEhsbCxcXF9SsWRPAu5tJxo4diwkTJqB69eoA3q1526pVK5w8eRLVq1fHgAEDMGDAAPy///f/sHr1aqxcuRJmZmaQSCRITExEnTp14O3tjZycHISEhODixYtfNEnw77//jt27dyM0NLTK36lc1F/AMpkMu3fvhq+vL+bMmYMJEyZ88TjAly9fYtSoUYiNjcWGDRtgb2//ReerDNgDITy2gfDYBuVLIVdsEAJD3OdJS0tDXl4egHd3Kn5sGafdu3ejS5cuqFOnDpKSktC1a1c0a9YMt2/fxuHDh1G3bl0cPHgQTZo0gb29PdLS0uDi4oIjR46gZcuWpa5t/fr1WLZsGY4ePQpjY+NSn6ey+NSbZ2xsLEaPHg0dHR2MHiVgTZcAACAASURBVD0anp6eX7Qsl0wmw7Zt2/Dzzz9j8uTJ+OGHH754ImRlxl9ewmMbCI9tUL4Y4v4PQ1zZ+vcPbmxsLObPn4/Fixd/NFzNmDEDampq+PPPP0v9eHv37sXUqVNx9OhRhZ7brSJ9zptnXl4eDh48iEOHDuHSpUvo2rUrBg0aBHd391IvJ/b06VP0798fPXr0wOTJk0t1jsqAv7yExzYQHtugfDHE/R+GuLJVkh9cBwcHnDx5stTzz4WEhGDkyJE4fPgwL+P9S0nfPF+9eoWgoCAcOnQIMTExaNKkCdTV1aGhoQENDY1Cn2toaKB27dowMTGBiYkJjI2N0bhxY9SqVQsA8OTJEzg5OX1ytZDKjL+8hMc2EB7boHwxxP0fhriy9bk/uG/fvoWlpSUyMjKgplayWW0ePXqEn3/+GVeuXMHWrVvRqlUr+bbc3Fyoq6vzcl4p3zwTEhLw9OlT5OXlQSKRIC8vT/55fn4+cnJy8Pr1a6SkpCApKQnJyclISkpCbGysfGqShQsX4uzZs9izZ0+lmHuvpPjLS3hsA+GxDcpXcSFOsHniqOp4/PgxLC0tSxTgXr58CV9fX+zduxfjx4/HunXroKWlBalUiujoaGzbtg1Hjx6FmpoaGjZsCCsrK0ycOBFOTk7l+EwqF1NTU5iampbomK5duyI0NBR9+vQBAPz000/YuXMnjh49iu7du5dHmUREVISq24VBFebBgwewtbX9rH3T0tKwcOFC2NnZIS8vDxcvXsSUKVOgpaWF6OhodOjQAXPmzEHXrl2RmpqKV69eYe/evfD09MR3332Ha9eulfOzqdp8fHywYcMG+dcaGhpYv349Zs6ciczMTAErIyKqehjiqNzdu3fvkyHu/v37+OGHH2BhYYFbt27h+PHjWLJkCWrXro2YmBgMHDgQU6ZMwezZs3Hz5k2MHTsWurq6qF69Opo1a4ZRo0Zhy5YtGDhwIB4/flxBz6zq6dWrF/7++288ffpU/r1vvvkG7dq1w+LFiwWsjIio6mGIo3KTm5uL+fPnY8+ePejdu/dH9zl79iy6dOmC9u3bQ1dXFxcuXMDGjRthaWkJ4N2Yq549e6JLly64d+8e+vfv/9GxV/n5+YiKioKWllaVHJtVUbS0tDBgwAD4+fkV+v7KlStx4MAB3Lx5U6DKiIiqHoY4Khe3b99Gp06d8OjRI9y8eROOjo4f7JObm4u+ffuiZ8+euHXrFmbOnPnB9CQ3btzA2rVrMXXq1CKnw0hKSkLHjh1x48YNnDp1Sh4AqXz4+Phg27Zt8rkEAaBOnTpYsmQJJk+ejPz8fAGrIyKqOhjiqMxFRETAy8sLP/30E4KCgoqclDcoKAiNGzfGkCFDigxozs7OuHz5cpGPdebMGbRs2RIuLi4ICAiQrzRB5cfGxgaWlpbYunVroe8PHToUBgYG2Lhxo0CVERFVLbw7lcrUtWvX8MMPP+DgwYNo27Ztkfvl5+fjzz//xJAhQ4o9n4uLC+bOnQupVFpoKpHY2FjMnj0bUVFRWL16Ndzd3cvsOdCnLVmyBN9++y2ys7Plk/2KRCJs2rQJLi4u6NmzZ5WdO46IqKKwJ47KhFgsxvTp0zF48GD8+eefRQa4nJwcbNiwAY0aNYJUKkWPHj2KPW/Lli2hrq4ODw8PvHjxAi9fvpRPJWJpaYkrV64wwAnA3t4eR48exYYNGzB9+nRIpVIAgJWVFaZOnYpp06ZBJqsyU1ASEQmCIY6+iEwmQ0BAAJydnaGmpoZ79+6hX79+H+wnFouxaNEimJubIygoCOvWrUNwcHCxkxgC7yY5DA0NhaOjI5o3bw5bW1v51CM//fTTF60DSl/GzMwMx44dw7lz5zBo0CDk5uYCeDd33IsXL3Do0CGBKyQiqty4YgOVWlpaGnx8fJCRkYENGzbA2dn5g32SkpKwatUqbN68GZ06dcKPP/5Y6mWz7t69i+rVq3Pd1H9RhJnSs7OzMWbMGGRkZCAwMBB6enq4ePEievfujYiIiBJPKKxMFOH1r+rYBsJjG5Sv4jo72BNHJSaVSrFw4UK8fv0affv2xZUrVz4a4LZu3Qo7OzukpaXh1KlT2Lhx4xete2pvb88Ap4C0tLSwbds2WFtb4+uvv0ZCQgJcXFwwbdo0tG3bFr6+vnjz5o3QZRIRVTrsiaMSW7hwIaKjo7F///4iB69nZ2fDwsICAQEBaNq0aQVXWHUo0l/AMpkMCxcuxMOHDxEcHAwAeP78OX777TcEBQVh3LhxGDNmDLS1tQWutOwo0utfVbENhMc2KF/siaMys3fvXhw8eBAhISFQVVUtcr9t27bBwcGBAa4KEYlEGDlyJM6dOye/0aFevXrYvHkzzpw5g7t376Jly5bYunVroTnmiIiodDjFCBUpMzMTcXFxiIuLwz///INnz57h0KFDOH36NAwNDZGQkPDR42JjY7F06VJs2rSpgismoZmYmEBHR+eD9XIbN26MAwcO4OrVq5g5cybWrVuHmTNnwsvLq9DUMURE9PkY4qiQvLw8LFmyBNu3b0dWVhYaNGgAc3NzmJubo1GjRggPD4ednd1Hj42Pj8f8+fNx6NAhTJgwAU5OThVcPSkCJycnnD9//qPr5bZs2RJhYWGIiIjApEmTkJycjPHjxwtQJRGR8mOII7m4uDiMGjUKBgYGuHz5Mho2bPjRdUiTk5NRq1atQl///vvv2LVrF4YOHYorV67AwMCgIksnBeLk5ISzZ89i5MiRRe7TsWNHDB06FPHx8RVYGRFR5cLrGAQAOHz4MDp37owBAwbg2LFjMDc3LxTgJBIJ9uzZg9atW8Pa2ho6OjpISUlBz549YWtri9zcXJw/fx5z585lgKvinJ2dcf78+U/uV61aNUgkkgqoiIiocmJPXBWXlZWFWbNm4fz58zh69ChatmxZaPuLFy+wfv16+Pn5wcbGBhMmTIC7uzskEglycnLg5eWF33//HfXr1xfoGZCisbOzg0wmQ/fu3TF//nw4Ojp+dL9q1arJJwgmIqKSY09cFfXy5UssWbJE/gv22rVrhQLckydPMHbsWNjb2yMxMRGHDx9GYGAgunfvDjU1NWhra0NdXR19+vRhgKNCVFVVcebMGbRr1w49evRAnz59cPv27Q/2y8vLY4gjIvoCDHFVzIMHDzB58mQ4OzsjNTUVUVFR2L59u3yOnzt37mDw4MFo1aoVqlevjkuXLmH58uVo3LixwJWTMtHU1MTo0aNx9epVtGjRAh07dsSSJUvk2588eYL58+dj8ODBAlZJRKTceDm1ikhJScHcuXNx6tQp/PDDD3jw4AHq1Kkj3x4XF4cZM2bg9OnTGDt2LK5duwY9PT0BK6bKQFtbGxMmTICXlxd69+6NnJwcTJs2DZ6enpg6dSq+/vproUskIlJa7Imr5AoKCuDn5wc3NzeYmpri0aNHmDt3rjzAZWRkYNasWXB0dISFhQWuXbuGKVOmMMBRmapbty5CQ0Oxb98+6OjooEWLFhg1apTQZRERKTX2xFViV69exU8//QQdHR2cPn1avm7p27dvcenSJURFRcHPzw9t2rRBdHR0pV6onIRnaGiI4OBgbNq0CVOnTv3o9DVERPT5uHZqJRUQEIDZs2dj1apV8PDwwPnz5xEdHY2zZ8/izp07aNKkCZydndG7d+8i7x78FK6XJzy2gbD4+guPbSA8tkH5Km7tVIa4Surhw4fo1q0b6tSpg8TERLRq1QouLi5o3bo1mjdvXiaLkPMHV3hsA2Hx9Rce20B4bIPyVVyI4+XUSqpRo0Y4ePAgRCIR7OzsoKbGpiYiIqpM+Ju9EmvWrJnQJRAREVE54d2pREREREqIIY6IiIhICTHEERERESkhhjgiIiIiJcQQR0RERKSEGOKIiIiIlBBDHBEREZESYogjIiIiUkIMcURERERKiCGOiIiISAkxxBEREREpIYY4IiIiIiXEEEdERESkhBjiiIiIiJQQQxwRERGREmKIIyIiIlJCDHFERERESoghjoiIiEgJMcQRERERKSGGOCIiIiIlxBBHREREpIQY4oiIiIiUEEMcERERkRJiiCMiIiJSQmpCPfCePXvg7+9f6Hv6+vrYuXMnAEAmk8Hf3x8nTpxAZmYmGjVqhLFjx6JBgwZClEtERESkUAQLcQBgamqKRYsWyb9WUfn/OwYPHjyIwMBATJo0CfXq1YO/vz/mzJmD9evXQ1tbW4hyiYiIiBSGoJdTVVVVYWBgIP/Q09MD8K4XLjg4GH379oWbmxsaNGiAKVOmIDs7G1FRUUKWTERERKQQBO2JS0pKgo+PD9TU1GBjY4OhQ4fC2NgYycnJSEtLQ/PmzeX7ampqwt7eHjExMejatauAVRMREREJT7AQ16hRI0yePBn16tWDWCxGQEAAfvrpJ6xbtw5paWkA3o2R+zd9fX2kpqZ+9HzHjx/HiRMnin3MmTNnAgAyMjLK4BmQTCbjaykwtoGw+PoLj20gPLZB+apWrVqR2wQLcS1btiz0tY2NDUaNGoXIyEjY2NiU+HweHh7w8PAodp+UlBQAgI6OTonPTx/KyMjgaykwtoGw+PoLj20gPLaBcBRmihEtLS2YmZkhMTERBgYGAIA3b94U2ufNmzfybURERERVmcKEOIlEgufPn8PAwABGRkYwMDDAjRs3Cm2/e/cuGjduLGCVRERUVcTHx+PKlSuIi4sTuhSijxLscuqWLVvg5OSEOnXqQCwWY+/evcjJyUHHjh0hEong6emJ/fv3o169ejA1NUVAQAC0tLTQrl07oUomIqIqxNvbGwUFBcjNzcW1a9eELofoA6UOcW/fvsWDBw8gFovRrFmzEl/mTE1NxfLly5Geng5dXV3Y2Nhg+fLlMDQ0BAD07dsXEokEGzZskE/2O2/ePM4RR0REFUIikSAoKAhubm5ISkqCsbGx0CURFVKqELdv3z7s378fubm5EIlEmDdvHgwMDCAWizFixAh8//33n5wGZMaMGcVuF4lEGDRoEAYNGlSaEomIPtvVq1exa9cuaGlplei4CRMm4Pfffy+nqqgoMplM/vnEiROxcOHCIreXhEgkKvR1SkoKVFRU0Lp1a1y+fBmenp6lOi9ReSlxiDt27Bh27dqFLl26wMHBAUuXLpVv09PTg4uLC86ePcu53IhIafzyyy+QSCQYMWJEiY57P8clVbz3gUtNTQ22trZFbv9cHwt+v//+OywsLODq6orTp08zxJHCKXGICwkJgZubGyZMmID09PQPtltYWCA4OLhMiiMiqggjR45EeHg4Jk+eXKLjEhISSnwMla2EhARMmjSpXB9j0KBB6NSpE2bMmIGBAwfCzMwMtWrVKnFQJCprJb47NSkpCc2aNStye40aNTjpHxEplU6dOiEiIgISiUToUkgBmZub4+rVq5BKpZg+fTqcnZ1Rr149dOjQATNmzMC+ffvw7NmzUl/GJSqtEoe4GjVqQCwWF7n9n3/+4VxuRKQwpFIpLl26VGxAq1OnDqytrXH27NkKrIyUiYGBATZv3oxr164hNTUVL168wB9//AEbGxucOHEC3bt3h42NDSZNmoQHDx4IXS5VESUOcS1btsTx48c/2tv29OlTnDhxAi4uLmVSHBHRlwoKCkL//v1ha2uL8ePHIyws7KOBrlOnTggNDRWgQlJGurq6cHNzw7Rp03Dw4EE8f/4cV65cgYWFBTw9PeHt7Y1z587Je+eSk5MREhKCRYsW4ciRI8jKyir2/NnZ2di2bRsGDx6MsLAw9vLRR4mys7NL9D8jLS0N06ZNQ0FBAVq2bInw8HC0bdsWBQUFuHjxImrXro3ly5dDV1e3vGoutffLbilibcqIS60Ij23waSNGjECnTp3kc0/u378fDx48gIeHB3r16oX27dtDQ0MDf//9NyZPnozbt29/9rkTEhJgampajtXTpyhiG2RnZ2Pnzp1Yvnw5NDU1kZmZCbFYDBcXFzRt2hRXrlzBlStX4OLiAnd3d7i7u6NevXoA3v2e2rx5M7Zt2wZnZ2f06NEDq1atgomJCXx9fYsdziQUvg+Vr+LWTi1xiAMAsViMnTt34vz588jMzATwbtksNzc3+Pj4QE9Pr/TVliOGuLLFH1zhsQ0+be/evdi/fz8iIiLk33v+/DkOHDiAgIAAiMVinD17FtnZ2bCyskJWVhZUVD7vIoUiBoiqRpHbQCqVIjo6GkZGRrCxsSn0/0osFuPEiRMIDg7G8ePHUbt2bairq+P58+cYMGAApk6dKr/zOT8/H35+fpg3bx7atm2L2bNnw8zMTKin9QG+D5WvMg9x/yYWiyGVSqGnp/fZb3xCYYgrW/zBFR7b4NMkEgmaNWuGEydOoGnTpoW2yWQyODg4wNfXF+3bt4etrS0uX76M+vXrf9a5FTlAVBWVoQ0KCgrkPcANGjQoclx5RkYGli1bhnXr1mHo0KGYMmWKQvw+4/tQ+SouxH1x6tLT04OBgYHCBzgiqpo0NDQwcuRI/PHHHx9sE4lEGDNmDLZv3w7g3V2IsbGxFV0iVXGqqqpwcHCAg4NDsTcG6ujoYN68ebh16xbevHmDVq1aYevWrcjPz6/AakmRfHKeOH9//xKfVCQSYeDAgaUqiIiorPXu3Rt9+vSBTCb7YG6vwYMHY9asWUhJSUHDhg0RGxuL9u3bC1Mo0WcwNTXFtm3bcOPGDUydOhV+fn5YuHAhOnToIHRpVMFKFeLevwn+924ZkUgkf5NkiCMiRWFpaQmZTIZHjx6hUaNGhbbp6emhT58+8Pf3h7m5OR4/fixQlUQl4+DggIiICISEhMjH0C1YsADm5uZCl6a0Nm7ciBs3bmDt2rVQVVUFADx58gS1atVSyPH+nwxx/119ITU1Fb6+vjA3N0fPnj1Rt25dAO/GJYSGhuLZs2eYM2dO+VRLRFQKIpEIbdu2RURExAchDgDGjBmDwYMHw8bGhr1wpFREIhE8PT3h7u6OlStXonPnzhg6dCimTp2KGjVqCF2eUrl+/TpWrFgBa2trzJgxA9bW1ti/fz/i4uKgr6+PgwcPokGDBkKXWUiJB7KtX78epqammDJlCqysrKCtrQ1tbW1YW1tjypQpMDExwYYNG8qjViKiUnsf4j7G2dkZ2tra+Oeff7iMFiklTU1NzJw5E7du3UJKSgqcnZ2xb98+zi/3mbKysjBq1CisXbsWR48eRUJCAu7du4fFixcjOTkZkyZNQrdu3RATEyN0qYWUOMTdunULX331VZHbmzZtips3b35RUUREZa1t27Y4ffo0pFLpB9tEIhG2bNmCw4cPQ0NDQ4DqiMpG3bp1sWvXLuzfvx8bN25E165d+Tv5M8yaNQtubm4YMGAA9PT0cPz4cezYsQOdO3eGqqoqJk6ciOHDh2PLli1Cl1pIiUOchoZGsUn0/v37fBMkIoVTt25dGBgY4N69ex/d7uTkBGtr6wquiqh8uLq64vLlyxg5ciR69+6NvLw8oUtSWCEhITh37hzWrl1b7H5169ZVuJ7NEoe4du3aISoqChs3bkR8fDwKCgpQUFCA+Ph4bNiwAdHR0WjXrl151EpE9EXMzMwQHx8vdBlEFUJVVRWjRo1CgwYNcPHiRaHLUUhisRjTpk3Drl27lHKuu0/e2PBfw4YNQ3p6Oo4cOYKjR48W2iaTydC2bVsMGzasrOojIiozRkZGSEpKEroMogo1ZcoUDBs2DH/++SeqV68OIyMj9jr/n9OnT6NFixZKu+Z7iUOcuro6pk2bBi8vL1y9ehUvX74EANSpUwctW7bkrc1EpLAMDQ3x4sULocsgqlA+Pj44cOAABg4cCBUVFYwfPx7z5s0TuiyFcOrUKXh4eAhdRqmVOMS9Z25uzsBGRErF2NgYCQkJQpdBVOECAwPx7NkznDt3DqtWrUJubi40NTWFLktQMpkMkZGR+Pnnn4UupdS4VhYRVRnsiaOqSlVVFZaWlhg8eDDMzc3h5uYGLy+vKj284NGjRwCAxo0bC1xJ6ZW4J87T0/ODZWs+JigoqFQFERGVF46Jo6pOVVUVAQEBuH79OtavX48lS5Zg1apVQpcliMjISHTp0uWzMo2iKnGIGzhw4AdPuKCgACkpKbh48SJMTU3h5ORUZgUSEZUVhjiid2PbnZycMHXq1Cq7QkleXh42b96MTZs2CV3KFylxiBs0aFCR216/fo3p06fLl+IiIlIkhoaGDHFE/+fXX3/F6NGj8fTpU8yfPx81a9YUuqQKs23bNlhaWqJDhw6ffYy2tjYePXoEqVQKFRXFGI1WplXUrFkTXbt2RUBAQFmeloioTOjo6KCgoABZWVlCl0IkOHd3d9y5cwe1atWCq6srDh48qHCT2ZaH169fY8WKFVi2bFmJjvP29kZeXh6WLl1aTpWVXJlHSU1NTSQnJ5f1aYmIvphUKkV+fn6VvyuP6D0dHR3873//Q2BgIFatWgVvb29kZ2cLXVa5SElJwerVq+Hs7IxRo0ahadOmJTpeU1MThw8fhr+/P4KDg8upypIp0xAXFxeH0NBQXk4lIoX06tUr1KxZE2pqpZ5diahScnFxwbVr15CcnIyrV68KXU6Zkslk6NatG5ydnfHgwQNER0djwYIFpTqXkZERDh8+jOnTp+POnTtlXGnJlfidbOTIkR+9kyMrKwtv376FpqYmZs+eXSbFERGVpZSUFBgZGQldBpFC0tDQQKtWrRATE4M2bdoIXY5cZmYmXr16BalUCplMBqlUKv94/3Vx4uPj8eLFC6SmppbJWDZHR0esWLECEydOxKlTp774fF+ixCGuSZMmH4Q4kUiEGjVqwNjYGG3btlXK9ceIqPJLSkqCsbGx0GUQKSx7e3vcvn1b6DIAvLuDdOvWrVi+fDlq1KgBFRUVqKioQCQSFfr8/Udxxo4dW6Y3IwwePBizZ8/GnTt30KRJkzI7b0mVOMRNmTKlPOogIip3KSkpMDExEboMIoVlZ2eHAwcOCF0GkpOTMWDAANSpUwenT5+Gvb290CUVkpOTAysrK4SGhgoa4kocS9esWYMHDx4Uuf3hw4dYs2bNFxVFRFQekpOT2RNHVAx7e3vExMQIWkNCQgJ69OiBvn37IiwsTOECXGRkJJo2bYpatWrh+++/F7SWEoe4iIiIYpetSU5ORmRk5BcVRURUHlJSUhjiiIphYmKCvLw8vHr1SpDH/+eff9CjRw+MHj0ac+bMUajVFNLS0jBy5Ej4+PhgwYIF8PPzQ+3atQWtqcynGElPT+edX0SkkHg5lah4IpEIdnZ2xV5xKy+xsbHo0aMHpk6dip9++qnCH78o+fn52LlzJ5o0aQJVVVWcO3cOHh4eQpcF4DPHxN25c6fQQMcLFy58tDcuMzMTZ86cgbm5edlVSERURng5lejTbG1t8eDBA7i5uVXYY8bExKBv377w9fUV/BLle7m5ufjrr7+wZMkSmJiYYMuWLXBxcRG6rEI+K8Tdvn0b/v7+AN6l9AsXLuDChQsf3dfMzAyjR48uuwqJiMoIe+KIPs3e3r5Ce+Ju3LiBgQMHYtmyZRgyZEiFPW5RMjMzsXHjRqxcuRL29vZYu3YtXF1dhS7roz4rxHl5eaF79+6QyWQYMmQIxo0b98ETEolE0NTUhIaGRrkUSkT0pdgTR/RpdnZ2CAkJqZDHkkgkGD16tHy1CCFdu3YNGzduxL59+9C+fXv4+/uXeFWHivZZIU5TU1O+TM3mzZuhq6uLatWqlWthRERlKSsrC3l5edDV1RW6FCKF9v5yakXw8/ODhYUFBg4cWCGP918ZGRnYs2cPNm7ciNTUVAwZMgTnzp1TmpWnSnwHgqGhYXnUQURUrh48eAALCwuFutuNSBHVr18f6enpEIvF0NPTK/Hxubm52L17NyIiIlBQUCBfVUEmk32w77Vr13Du3DlBfi7z8/PRrl07GBsbY9asWfjmm2+gqqpa4XV8iU+GuFmzZkEkEmHevHlQVVX97CW1Fi5c+MXFERGVlZMnTyrMHWVEiiw3NxdqamrIz88v8bGXLl3C999/D3t7e4wcORLVqlUrdmUFExMT2NrallXpJbJ69Wro6upi9+7dSvvH3SdD3H+Ts1QqVdonS0RVV1hYGJYvXy50GUQKLzQ0FI6OjqhVq1aJj1VVVYVEIsH+/fsVegnOZ8+eYfHixThx4oRSZ5pPhrhFixYV+zURkaJLSUnBkydP8PXXXwtdCpHC27NnD/r27VuqY1u2bAlHR0cEBQXhu+++K+PKyoZMJsO4ceMwduxYWFpaCl3OFynxZL937tyBWCwucrtYLMadO3e+qCgiorIUHh6Ojh07Ql1dXehSiBTamzdvEBkZiR49epT6HK6urrh8+XIZVlW2QkJC8PTpU/z4449Cl/LFShziZs+ejevXrxe5/datW589bo6IqCKcPHnyi34pEVUVq1atgru7e6luaHhPX18fT58+LcOqyo5MJsPMmTMxd+7cSjElWonvTv3Y3SX/lpeXp9TXl4mo8omOjsbatWuFLoNIocXGxmLdunU4ffp0qY5/8uQJFi5ciAsXLsDPz69siysjGRkZiIuLg7u7u9CllInPCnFv375FZmam/OuMjAykpKR8sF9WVhaioqJKNRiSiKi8WFpa4uHDh6hXr57QpRApJJlMhokTJ2LChAkl/jmRSqWYMWMGDh06hMmTJ2PHjh2oXr16OVX6ZfLz86Gurl5pOps+K8QFBQVh7969AN6tzLB582Zs3rz5o/vKZDL4+PiUXYVERF+oc+fOCA0NRYcOHYQuhUghhYSEIDY2Fn/99ddnH5ORkYF169ahf//+0NbWRkxMDGrXrl1+RZaBgoICqKmV+CKkwvqsZ9K8eXNUq1YNMpkMf/31F9q2bfvBHR3vl92ytraGlZVVuRRLRFQaXbp0wZgxY7By5UqhSyFSODKZDL6+vsWOE5PJZHj27BmuXLmCv//+G9euXcO9e/fQu3dv1K5dG6tXcoGJPgAAIABJREFUr67gqktHJBJBIpGgoKBA6Sb2/ZjPCnGNGzdG48aNAbybBNDV1RUNGjQo18KIiMpK06ZNIRaL8fjxY/6RSfQfZ8+ehVgsLnIy7MuXL2PSpEnIyMiAs7MzWrduje+++w4tWrSAtrY2EhISKrji0qtduzbs7Oxw9OhR9OzZE3/99RcGDhyotEuJlrhPUegFaomISkpFRQVdunTBkSNHMGnSJKHLIVIoO3bswPDhw6GiUnjCiqysLCxYsABBQUH43//+h379+glUYdmaMmUK1qxZg549e2Lx4sUIDw/Htm3blHIKolJfGL5//z4eP36Mt2/fQiqVFtomEokEW8yWiOhjbG1t8eTJE6HLIFI4+vr6ePDgAWQymXzA/+nTpzFlyhR8/fXXuH37dqW6YdHLywvTp0/HiRMnYGNjgyNHjmD//v0YNGiQ0KWVWIlDXGZmJubNm1eowd9PO/L+c4Y4IlI0GhoayM3NFboMIoXzyy+/wMPDA3369MGIESMQFhaGqKgobNiwAd26dRO6vDKnpqaG33//Hb/99pt8pg1TU1OBqyqdEoe4v/76C7GxsZg6dSoaN26MUaNGwdfXF0ZGRjh06BAeP34MX1/f8qiViKjUNDU1kZOTI3QZRAqnVq1auHDhAlavXo1Dhw7BysoKd+7cga6urtCllan8/HxkZ2ejRo0a6N69OyQSCfbu3Yvr16/DwcFB6PJKpcQrNly5cgXu7u5o164dtLS03p1ERQV169bFhAkTUKtWrSKnHyEiEgp74oiKpqamhunTpyMwMBDLly+vNAEuLy8Px48fx4gRI2BkZARjY2Ooq6ujfv36CAwMhLe3N27cuPFFK1QIqcQ9cRkZGWjYsOG7g/9vrpV//3XbokUL7Nq1q2yqIyIqI9WqVWNPHFElJZPJcOvWLTx69AhxcXGIjY3F/fv3cfPmTVhZWaFXr144ffo06tevj/z8fEil0v+vvTsPi6rs/wf+HhZZBUEF2QMBF0REQjH0UVESxFLTTDNAzMylJBM1zeUbiktuuBsu+Yi5oqCmgZqW5kKRO0ruC4iQOIAgMjgzvz98nF+TCMh2OMz7dV1dV5xzz5n3nNvRD/e5z30087FbZmZmyMvLAwAYGhrCwMAA6enp6NixI4DnRd6/b3QgIhKarq4uR+KI6hmlUomkpCR88803uH//PoyNjZGbmwtdXV3Mnj0b3t7esLS0VHuNxi32+08tWrRAamoq3n//fQDPR97i4+Nhbm4OpVKJPXv2qNaUIyKqK9LT00U7eZmIXvbTTz9hxowZSElJAQAYGBhAIpGgffv2kMvlWLp0KbZv3y5wypr12nPigoKCYG1tDZlMBgAYPnw4GjZsiCVLliA6OhomJiYYOXJktQclIqqKS5cuwdPTU+gYRFQNFAoFVq1aBQ8PDyxfvhy//vor7t+/j/3796NTp07IycnBX3/9hTNnzggdtUa99kicm5sb3NzcVD83adIEK1euxJ07d6ClpQVbW9t68SgLIqpfUlNT8emnnwodg4iqKCoqCjExMfj555+hVCpx9+5d7Nq1C6GhoVAoFAgICMBXX30FX1/fejHvrSzlFnEv1lApj5GREQAgJycHAGBhYVGFWERE1efZs2e4cuUK3N3dhY5CRFWQmpqKadOmAXj+TGSJRILGjRvj7bffRmxsLNzc3FQLFmuCcou4ESNGVOqE7Nmz57Xa79y5E5s2bUJQUBBGjRoF4PmExa1btyIpKQkFBQVwdXXFqFGj+NxWInot169fh5WVFRo2bCh0FCKqgo0bN8LAwABffvklIiIihI4juHKLuHHjxtV4VZuWlobExETV0iUv7Nq1CwkJCQgPD4etrS22bt2KGTNmYPXq1TA0NKzRTERUf2RkZMDQ0BAKheKl50MSkXgsWLAAJiYmSE9PFzpKnVBuEdezZ88aDVBYWIhFixYhPDwcW7duVW1XKpXYu3cvBgwYAF9fXwDPH1obHByMX3/9FYGBgTWai4jqj27dumHBggVYtmwZvvjiC6HjEFEVXLhwocZrE7EQ/FfSFStWwNfXF23btlXbnpWVBalUqnY3mZ6eHtzc3JCWllbbMYlIxLS1tbFq1SrMnj0bV65cEToOEVXBH3/8AS8vL6Fj1AmCrniXlJSEzMxMTJgw4aV9UqkUANCoUSO17Y0aNVLdPPFPiYmJSEpKKvP9pkyZAuD5gsRUdUqlkudSYOyDimvatClOnDiBoqIipKenV8s0EblcjoyMjGpIR5XFPhBebfaBXC5HXFwcLC0tNebvPn19/VfuE6yIS09Px6ZNmzB//vxqWT05ICAAAQEBZbZ5cactJzdXj8ePH/NcCox98HqMjY3h5+eHhQsXokePHlU+XkZGBhcQFhj7QHi12QexsbH473//i7i4uFp5v7pOsCIuLS0N+fn5GDt2rGqbQqFAamoqfvrpJ6xcuRIAkJubq7ZcSW5uLszMzGo9LxGJn0QiQatWrXDnzh2hoxBRGU6ePAkvLy/o6emptuXk5GDy5MlYv369gMnqFsGKOB8fH7i4uKhti46OhrW1NQYNGgQbGxuYmZnh3LlzcHV1BQDIZDKkpqYiLCxMiMhEVA/Y2dnh9u3bQscgojJMnjwZFhYW2LFjh+oBAp999hn69euHTp06CZyu7hCsiDM2NoaxsbHaNn19fTRs2FC1Dty7776LnTt3wtbWFjY2Nti+fTsMDAzQtWtXISITUT1ga2uretYiEdVNPXr0wDfffIN+/fph5syZuHfvHv744w8cO3ZM6Gh1iqA3NpRnwIABkMlkWLNmjWqx38jISK4RR0SVZmdnh927dwsdg4j+R6lUolOnTti6dSscHR0BAL169cLmzZvh4eGBvn37QqlUYv369fz3/1/qVBE3d+5ctZ8lEgk+/PBDfPjhhwIlIqL6xtbWFjdu3ODCv0R1RGpqKpKTk3H9+nVVEeft7Y2cnBwEBQVh+fLlGDp0KC+jloJ/gxGRRnFycoKVlRVWr14tdBQiAhAfHw8AuH//vmqbjo4OFi9ejPfffx/z589XPS+V1LGIIyKNoqWlhWXLlmHmzJm4e/eu0HGINN7Jkyfh6OiIzMxMte1hYWGq0TdeRi0dizgi0jgtWrTAqFGjMHLkSCiVSqHjEGm0Nm3aQCqVqo3EvWBiYoLi4mIBUokDizgi0kjh4eG4f/8+YmNjhY5CpNH+85//IDc3t9Qi7vHjx/xFqwws4ohII+nq6mL58uWYOHEiHjx4IHQcIo3TqVMn9O7dG9bW1gCAI0eO4Pjx46r9Fy5cwNGjR9G3b1+hItZ5LOKISGN5eHjgww8/REhICGQymdBxiDRK37598dNPP+HNN98EAEyYMAEDBgzAn3/+CaVSiXHjxmHy5Ml8SlMZWMQRkUabOnUqdHV18dFHH0Eulwsdh0hjfPXVV8jJycGQIUMAAF27dkV0dDSCgoLwww8/IDs7G8OGDRM2ZB3HIo6INJquri7Wr1+PrKwsTJo0Seg4RBrF3NwcW7ZsQUhICE6dOgVfX188efIEc+bMwaRJk6CjU6eWs61zWMQRkcbT19fHmDFjcOnSJaGjEGmkxo0bo7i4GElJSSgqKgLw/NGbVDYWcUREAAoLC1UP2iai2qWvr4+nT59i3759ePbsGSIiIvhElQrgGSIiAtCzZ0+cPn0aGRkZQkch0jgGBgbIzc3Fjz/+CAsLC96RWkEs4oiI8HxR0YEDB2LNmjVCRyHSOPr6+rhw4QIAICoqiqPiFcQijojof0aMGIG1a9dyhXiiWqavr4+WLVsiOTkZAwYMEDqOaLCIIyL6H1dXV7Ru3Rq7du0SOgqRRnFwcMDu3buxfft2oaOICos4IqJ/GDFiBJYtWyZ0DCKN8u677+LkyZPYvHkz7t27J3Qc0WARR0T0D7169UJmZiZSUlKEjkKkUVxdXeHj44M///xT6CiiwSKOiOgftLW1MXz4cKxYsULoKEQax9PTk+s1vgYWcURE/xIcHIw9e/bg4cOHQkch0ih2dnZ48OCB0DFEg0UcEdG/mJubIygoCN99953QUYg0ipWVFbKysoSOIRos4oiIShEeHo7o6GjcvHlT6ChEGoNF3OthEUdEVAoXFxeEh4dj2LBhUCgUQsch0gjW1ta8nPoaWMQREb3C6NGjIZfLER0dLXQUIo3QtGlT5OXlQSaTCR1FFFjEERG9gra2NlasWIG5c+fiypUrQschqve0tLTQtGlTZGdnCx1FFFjEERGVwdHREVOmTEFISAiePXsmdByieu3WrVt4+vQp9PX1hY4iCiziiIjKERYWBhMTE8ydO1foKET1Wnh4OEaPHo0mTZoIHUUUWMQREZVDIpFg2bJlWL58OVJTU4WOQ1Qv/fjjj7h8+TI+//xzoaOIBos4IqIKsLGxQd++fXHo0CGhoxDVO0VFRfj888/x7bffQk9PT+g4osEijoioglxcXJCWliZ0DKJ6RalUYvbs2fDw8ICfn5/QcURFR+gARERi4erqigMHDggdg6heKCgowNatW7F69Wrk5eVhz549QkcSHY7EERFVUIsWLXD+/HncuHFD6ChEopWamooxY8bA3t4ee/fuxddff40//vgDtra2QkcTHRZxREQVZGVlhenTp6Nz584YPnw4CgoKkJCQgCtXrqCkpEToeER13u+//45u3brB1NQUx48fR2xsLHr06AEtLZYjlcHLqUREr2H48OHw9vZGSkoK5HI51qxZg2vXrsHIyAi//PILzM3NhY5IVCdlZ2dj4MCBiI6ORlBQkNBx6gWWvkREr8nd3R1hYWFo2LAhtm3bhpSUFHTt2hW9e/fGjRs3uCgw0b88e/YMgwYNwqBBg1jAVSMWcUREVSSRSBAZGQkvLy9069YNhoaGaN26NR4+fCh0NCLByWQyREREQEdHB1OmTBE6Tr3CIo6IqBpIJBJERUXh4sWLSE9Ph52dHdeUI422bt06eHh4oFGjRjhx4gTWrl0LbW1toWPVK5wTR0RUzRo0aIDAwEDMmDEDOTk5GDx4MB8jRBrlu+++Q1RUFGJiYtC2bVsYGhoKHale4kgcEVENCAkJwdy5c3Hs2DG4ubnh2rVrQkciqhUbN27ErFmzkJCQAB8fHxZwNYgjcURENUBLSws9e/ZEz549sWHDBvTr1w/JyckwNjYWOhpRjcnPz0d4eDgOHToEJycnoePUexyJIyKqYWFhYfDy8sKwYcOgVCqFjkNUY5KSktChQwe4uroKHUUjsIgjIqphEokE3377LW7duoX58+cLHYeoRuTm5mLx4sUIDAwUOorGYBFHRFQL9PX1sWnTJsydOxc5OTlCxyGqVtevX0fHjh3h6emJkJAQoeNoDBZxRES1xMbGBl5eXjh16pTQUYiqTXFxMTp37oxRo0Zhzpw50NHhdPvawjNNRFSLOnTogOPHj6NPnz6qbTKZDA8ePMC9e/dw584d3LlzB7dv38adO3dw9+5dzJ49G++9956AqYlKt3btWrRv3x4xMTH4z3/+I3QcjcMijoioFvn4+GDMmDFISUnBgwcP8ODBAzx+/BhNmzaFjY0N7OzsYGtrCxcXF/j5+eGLL77gY7yozlEqlYiIiMDevXtx8OBBNG/eXOhIGolFHBFRLfL19cWsWbNgZmYGCwsLNGvWDObm5tDSKn12y+jRo/HZZ59h06ZNmDRpErp06QKJRFLLqYnUzZ07F0ePHsWhQ4f4FAYBSYqKijTmfvfs7GwAgImJicBJ6ofHjx+jYcOGQsfQaOwDYdXW+S8qKsK2bduwcuVKmJubY9KkSejfvz//8QSQkZEBGxsboWNolPj4eHz++ec4ePAgrK2t+fdQDdPX13/lPt7YQERUxxkYGCAsLAzJyckYN24cFixYABcXF6xYsQKFhYVCxyMNcvbsWYwcORKxsbGwtrYWOo7GYxFHRCQS2tra6NOnD5KSkrBq1SokJibCwcEBkydPxr1794SOR/VcSUkJPvzwQ8yZMweenp5CxyFwThwRkSj5+PjAx8cHt27dQkxMDDw8PODv74/x48fDx8enWt5DqVTit99+g0wme2UbXV1dvPXWW1xWQgPExMTA0tISAwcOFDoK/Q/nxFGlcR6E8NgHwqpL5z8vLw9btmxBTEwMLCwsMH78eAwYMAC6urqVPua1a9fg4eGBDh06vLJNeno63nvvPXz77beVfp+q4Jy42pGXl4cWLVpg586dcHd3V9tXl74H9VFZc+L4qxMRUT1gamqK0aNHY+TIkUhMTMTKlSsxceJEjB07FiNHjoS5uflrH/PZs2ewtbVFQkLCK9s8fPgQXbt2hb+/P/z9/avyEagOmzNnDvz9/V8q4EhYLOKIiOoRbW1tBAUFISgoCBcuXMCaNWvQvHlzfPDBB/D29oZSqYRSqYRCoSj1/5XK/39x5sGDB+UuZ9KkSROsWrUKoaGhSEpK4j/y9ZBcLsfKlSvxxx9/CB2F/oVFHBFRPdW2bVusWrUKWVlZ2LRpE44ePQqJRAItLS1IJJKX/v/Ff//06aeflvs+Xbt2RWRkJHr06IFt27bBz8+vpj4SCeD27dto3LgxrKyshI5C/8IijoionrO0tMTEiRNr9D0GDhwIKysrDB48GMuXL8cHH3xQo+9Hteevv/6Cs7Oz0DGoFCziiIioWvj6+iI+Ph7vvfce9PX10bdvX6EjUTVgEVd3sYgjIqJq4+bmhu3bt2PQoEEwNDTkzQ71wJUrV+Di4iJ0DCoFF/slIqJq1a5dO/z3v//F0KFDcfbsWaHjUBVJpVKYmpoKHYNKIdhI3P79+5GYmIisrCwAgL29veruKeD5IpNbt25FUlISCgoK4OrqilGjRsHBwUGoyEREVEGdOnVCREQEIiMjER8fL3QcqgI3Nzf89ddfQsegUghWxDVu3BihoaGwtraGUqnEzz//jKioKCxZsgSOjo7YtWsXEhISEB4eDltbW2zduhUzZszA6tWrYWhoKFRsIiKqoKFDh2LBggW4ceMGmjdvLnScOk8mk2HXrl24dOkSbt26hVu3bqGgoACtW7eGu7s72rZtCxcXF9y/fx9paWm4fPky0tLS8Ndff8He3h4LFy7EW2+9Ve25PDw8EBMTU+3HpaqrU09sGDJkCEJCQhAQEIDQ0FAEBQWp7nAqLi5GcHAwwsLCEBgYWKnj84kN1YurdAuPfSAsnv/yzZo1C8XFxVixYkWNHL++PLEhPj4eERERsLe3h4+PDxwcHGBvbw8jIyNVwXb58mXcvHkTVlZWcHFxgaurK1xcXODi4oJTp05h9uzZ6NSpExYtWgR7e/tqy3bjxg10794dFy5cKHU/vwc1q84/sUEul+PEiRN4+vQpWrVqhaysLEilUrUH7Orp6cHNzQ1paWmVLuKIiKh2ffLJJ/Dx8cG3337LqyhlmDNnDoYOHYovv/zypX0eHh7lvt7BwQHvvvsuFi9eDH9/f/z5558wNjZWa/P06VNERUXh0qVL+Oabb9C2bdsKZXN0dIRUKkVubi4aNWpUsQ9EtULQkbjbt29j4sSJkMlkMDAwwIQJE+Dt7Y0rV65g0qRJWL9+PSwsLFTtly5dipycHERGRr50rMTERCQlJZX5flOmTAGAclcgp4pRKpU8lwJjHwiL579iHj16BCMjIxgYGFT7seVyObS1tav9uLWtqKgIhYWFlXo82r/l5eUBAMzMzFTbiouLkZeXBx0dHejq6qKwsBDGxsYvFXqv8vDhQxgZGUFPT++lffwe1KymTZu+cp+gI3E2NjZYunQpnjx5ghMnTmDJkiWYO3dupY4VEBCAgICAMtu8uJzKYd/qwSF04bEPhMXzXzHbtm3DxYsXsWnTpjLb3bt3D/PmzUNhYSGKiorw9OlTtGzZErNmzUKDBg1KfY0Ql1Pv3buH48ePq+ak2draIjw8HI6OjpU+plQqhZeXF+7cuVPlfBKJBH5+fpg5cyaCgoIwadIk7N+/H/Pnz0efPn0AAJmZmejcuTMOHjyodtXrVTZt2oTbt29j/vz5L+3j90A4gi4xoqurC2trazg7OyM0NBROTk7Ys2eP6reH3Nxctfa5ublqv1kQEVHdFxQUhP3790Mmk5XZbty4ccjLy4O3tzf8/f0xaNAgpKamolu3bsjMzKyltKUrKSnB7t27ERAQgHbt2mHHjh2QyWSqwQNvb28MGjQIhw4dwubNmxEeHg5fX180atQICxYsKPf4xcXFpY5yVYaxsTHWr1+P8PBwODs7Qy6X4+TJk6oCDgCsrKwQGRmJYcOGoaSkpNxj9urVC0ePHq2WfFR96sScuBeUSiVKSkpgaWkJMzMznDt3Dq6urgCe37WTmpqKsLAwgVMSEdHrsLa2xn/+8x8EBQVhx44dpf4y/uuvv+LMmTNITk5Wm8gdGBiIhQsXwtvbG3FxcfDx8anN6ACAtWvXYsaMGXByckJwcDA2bNigNr9v4MCBmDBhAjZv3owpU6bA3t4e7dq1w1dffQULCwsMHToUAMp89JlMJoNCoUBJSQl0dXWrnNnd3R1nz57FgwcPXrlQ7+DBg7F7927MmzcP06dPL/N47dq1Q25uLu7evVutN01Q1WhPmzbt/4R4440bN0JXVxdKpRIPHz7Enj178MsvvyA0NBQ2NjaQy+WIi4uDjY0NFAoF1q9fD6lUirFjx1b6D3hhYSEAVNtvO5pOJpPxXAqMfSAsnv+K69OnD86dO4evv/4a/v7+aNKkiWqfQqHAgAEDMHHixJcm20skEvj6+qoKqL59+6q99vHjxzW64sCuXbswefJk7Ny5E1988QXatGlT6r9Benp68Pb2RkhICPr27YuOHTvCwcEBTZo0QVBQECZMmICSkpKXlgBRKBSIi4uDtbU1zp49i9TUVHTv3r1asuvp6aFx48av3C+RSPDWW29hzJgx6N27t9oc9NLanj17FiUlJWjXrp3aPn4PapaOzqvH2wQr4o4ePYqEhATExcXht99+g0KhwOjRo+Hl5QUAaNWqFUpKSrBt2zbs27cPhoaGmDRpUpkT/MrDIq568YsrPPaBsHj+K05LSws9e/ZEgwYNEBoailu3bqFdu3YwMTFBbGwsUlJSMGfOnFdOkH9xWTAuLg4DBw5Uba/JIu706dMYOnQotm/fDnd390ofx8TERFXI3b9/HwUFBcjMzERmZibCwsJw4MABLFq0CA0aNIBSqUS/fv2q8VOUn83ExASzZs3C8OHDoaX16llWhYWF2L9/P/r376+2nd+DmlVWEVen1omraVwnrnpxMqvw2AfC4vmvnJycHCxbtgyxsbH46KOPEB8fj/Xr16NDhw5lvq6wsBBeXl44ePCgasSupm5suHHjBjp37ozo6Gj06tWrWo6Znp6Ob7/9Fo8ePUJeXh7y8/PRp08fjB8/HtnZ2UhISMDHH39c6wWRUqnEO++8g08++QShoaGvbCeVSuHk5IRTp06hWbNmqu38HtSsstaJYxFHlcYvrvDYB8Li+a+arKwsREdHQyKRYM6cORV6zerVq3H69Gns2bMHQM0UcY8ePUKnTp3wySefYMSIEdV67LoqISEBsbGx+Pnnn8tsN2bMGBgaGmLatGmqbfwe1CwWcf/DIq568YsrPPaBsHj+a9/Tp0/h5eWFhIQEeHt7V3sRl5mZiffffx+enp6YNWtWtR23risqKoKbmxsuXrxY5vm8evUqfH19cf78edXNHfwe1KyyijhBlxghIiJ6Hfr6+ggPD8e8efOq9bhyuRzLly9H27Zt0bFjR3zzzTfVevy6zsDAAH369MGWLVvKbOfq6gofHx9s3769lpJRWVjEERGRqAwZMgRHjx5Fenp6tRwvJSUFHTp0wPbt27Fv3z5Mnz69zAn+9ZWHhwdSU1PLbffll19i/fr1tZCIyqN5f0qJiEjUGjZsiIEDByImJqZKx1EqlZg0aRKCgoIwYsQI7Nu3Dy1btqymlOJz/vz5cm8uAYA2bdrgwYMHtZCIysMijoiIRCcsLAzr1q2DUln5ad1Lly5FYmIiTp06hSFDhmj88z9TUlIqtJiylpYWFApFLSSi8tSpJzYQERFVRKtWreDu7o6CgoJKvf7nn3/G3LlzcejQoWp56LzY5eXl4e7duxVaD09XVxfFxcV88H0dwJE4IiISpejoaBQWFuLYsWOv9brbt29j6NChWLduHR8h9T8XL16Eu7t7hZ6IZGJiAisrK1y6dKkWklFZWMQREZEoWVlZwdTUFB999BFycnIq/Lp58+YhODgYXbp0qcF04mJjY4M7d+5U+PJ0YGAgDh06VMOpqDws4oiISLT09PTwzjvv4OOPP65QAVJcXIy4uDiEhITUQjrxcHR0hJ6eXoXuTgWAoKAgHD58uIZTUXlYxBERkajNnDkTycnJuHnzZrltf/rpJ7Rq1Qp2dna1kExcevbsifj4+Aq17dq1Ky5duoTc3NwaTkVlYRFHRESi1qBBA3Ts2BGnTp0qt+2xY8fg5+dXC6nEJyQkBN999x1KSkrKbWtgYIDOnTvj6NGjtZCMXoVFHBERid6bb75ZoSLu2bNnZT7GSJO5u7vDwcEBCQkJFWofFBTEeXECYxFHRESi5+3tXaEiTqFQQFtbuxYSidPIkSOxdOnSCrUNDAzkvDiBsYgjIiLR8/DwwF9//VXuunFyuZxrm5UhKCgId+7cwdmzZ8tt6+TkBFNTUzx79qwWklFpWMQREZHo6evro3PnztixY0eZ7TgSVzYdHR0MHz68wqNxJiYmVXpqBlUNizgiIqoXRowYgeXLl5dZVOjr6+Pp06e1mEp8QkNDsWfPHmRnZ5fb9unTpxzZFBCLOCIiqhd69OiB/Px8HD9+/JVtGjdujEePHtViKvExNzdH3759sWbNmnLbFhUVsYgTEIs4IiKqF7S0tDB9+nT0798fkydPRn5+/kttGjduzLXNKmDkyJFYvXo1ZDJZme2Ki4trKRGVhkUcERE3q34wAAAWtklEQVTVG/369cNvv/2GjIwMtGjRAmvWrFFb96x58+a4ePGigAnFoXXr1mjZsmW5cwwLCgo4EicgFnFERFSvWFlZYcWKFdi+fTu2b98OJycnzJ07F48ePULPnj1x7949XLlyReiYdd7IkSMRHR39yjmGMpkMT548gZYWSwmh8MwTEVG91LZtW8THx2PLli24ePEimjdvDn9/fzx58gSJiYlCx6vzevXqhdzc3FfOjXv48CHMzc1rORX9k47QAYiIiGqSu7s7Vq1ahaysLFy6dAlWVlZwdnYWOladp6WlhW3btmHw4MG4c+cO5s6dq3bpNCUlBa1atRIwIbGIIyIijWBpaQlLS0uhY4iKs7MzkpKS0KVLFwQHB8PNzU21b//+/ejZs6eA6YiXU4mIiOiVGjduDD8/P7WH3SuVSiQmJsLf31/AZMQijoiIiMrUuXNnHDhwQPWIrfPnzwMAXF1dhYyl8VjEERERUZn8/f2Rk5MDGxsbBAcHw9/fH59//jmXFxEY58QRERFRmRo3boykpCTcunULiYmJGDt2LFq2bCl0LI3HIo6IiIgqxNHREaNHjxY6Bv0PL6cSERERiRCLOCIiIiIRYhFHREREJEIs4oiIiIhEiEUcERERkQixiCMiIiISIRZxRERERCLEIo6IiIhIhFjEEREREYkQizgiIiIiEWIRR0RERCRCLOKIiIiIRIhFHBEREZEIsYgjIiIiEiEWcUREREQixCKOiIiISIRYxBERERGJEIs4qjSFQoHevXvDzMwM165dEzoOERGRRmERR5VWUFCA3NxcDBs2DPHx8ULHISIi0igs4qjSjIyMoKOjg71796JJkyZCxyEiItIoOkIHIPHS1tZGUlISDhw4gP79+7+0/+nTp8jOzoadnR0kEokACYmIiOovjsRRlRgYGGDAgAHQ0lL/o5SRkYGuXbvCw8MDmzdvFigdERFR/cUijmpEfn4+rl69ij/++AORkZF4+PCh0JGIiIjqFRZxVCNatWqFzp07Y+nSpSgqKsLChQuFjkRERFSvsIijGrNs2TK4uLjgvffeg5+fH/bt2wdLS0u0b98eY8eOhUKhEDoiERGRaPHGBqoxjo6OGDdunOrnhQsXQk9PD7du3cKtW7fQokUL9OnTB05OTgKmJCIiEieOxFGtiYiIwN27d/Ho0SMMHDgQu3fvRmBgIMaMGYOnT58KHY+IiEhUWMRRrZNIJOjbty/+/vtvnD9/HikpKVi0aJHQsYiIiESFRRwJok+fPujevTt69eqF7OxsuLq6Ch2JiIhIVASbE7dz506cPHkSGRkZ0NXVRYsWLRAaGgoHBwdVG6VSia1btyIpKQkFBQVwdXXFqFGj1NqQeEVGRuLgwYPo3r07LC0thY5DREQkKoKNxF28eBFBQUFYsGABoqKioK2tjWnTpuHx48eqNrt27UJCQgJGjhyJxYsXw9TUFDNmzMCTJ0+Eik3VyNzcHIMHD4alpSXOnDmDEydOCB2JiIhINAQr4iIjI9GzZ084ODjgjTfewJdffon8/HxcuXIFwPNRuL1792LAgAHw9fWFg4MDxo8fj6KiIvz6669CxaYaMm7cOPTp0weffPIJsrOzhY5DRERU59WZOXFFRUVQKBQwMjICAGRlZUEqlcLT01PVRk9PD25ubkhLSxMqJtUQe3t7bNiwAW+88Qa6dOmCX375RehIREREdVqdWScuJiYGTk5OaNmyJQBAKpUCABo1aqTWrlGjRsjJyXnp9YmJiUhKSirzPaZMmQIAapdsqfKUSmW1nEu5XI5Zs2bB3Nwcb7/9NkaPHg2pVAqpVAodnTrzR7ROqq4+oMrh+Rce+0B47IOapa+v/8p9deJfyHXr1uHKlSuYP38+tLW1K3WMgIAABAQElNnmxWW6hg0bVuo9SN3jx4+r5VxGRUWhuLgYy5YtU22bN28ekpOT8f3331f5+PVZdfUBVQ7Pv/DYB8JjHwhH8Mupa9euxbFjxzB79mw0a9ZMtd3MzAwAkJubq9Y+NzdXtY/qByMjI5SUlKht+/zzz3H69GlcvHhRoFRERER1m6BFXExMDI4dO4aoqCjY2dmp7bO0tISZmRnOnTun2iaTyZCamqq65Er1Q58+fZCQkKD2LFWZTAYLCwv8/vvvAiYjIiKquwQr4lavXo3Dhw8jIiICxsbGqjlQRUVFAJ6v6v/uu+9i165dOHnyJO7cuYPo6GgYGBiga9euQsWmGuDs7AxdXV3cuHEDAPDnn3/Cy8sLvr6+CAkJETgdERFR3STYnLgDBw4AAKZNm6a2fciQIfjwww8BAAMGDIBMJsOaNWtUi/1GRkbC0NCw1vNSzSouLoaxsTFmz56NpUuXYsGCBejXr5/QsYiIiOosSVFRkVLoELXlxY0NJiYmAiepH6pzMquvry/s7Oxw9+5d7Ny5EzY2NtVy3PqOE4qFxfMvPPaB8NgHNausu1MFv7GBCAD27t0LIyMjrFixggUcERFRBdSJJUaIGjdujPXr1wsdg4iISDQ4EkdEREQkQiziiIiIiESIRRwRERGRCLGIIyIiIhIhFnFEREREIsQijoiIiEiEWMQRERERiRCLOCIiIiIRYhFHREREJEIs4oiIiIhEiEUcERERkQixiCMiIiISIRZxRERERCLEIo6IiIhIhFjEEREREYmQpKioSCl0iNqSnZ0tdAQiIiKi12JhYVHqdo7EEREREYmQRo3EUfUaP348lixZInQMjcY+EBbPv/DYB8JjHwiHI3FEREREIsQijoiIiEiEWMQRERERiRCLOCIiIiIRYhFHREREJEIs4oiIiIhEiEUcERERkQixiCMiIiISIe1p06b9n9AhSLycnZ2FjqDx2AfC4vkXHvtAeOwDYfCJDUREREQixMupRERERCLEIo6IiIhIhFjEEREREYmQjtABqG5LTEzEsWPHcPPmTRQWFmLdunWwtLR8qd2ZM2ewZcsW3Lp1C7q6umjevDmioqJU+7Ozs7FmzRpcuHABDRo0QNeuXTF8+HDo6urW5scRpYr2AQDIZDJMmDABt2/fxuLFi+Hi4qLaxz6ovPL6ICsrC9u3b8eFCxcglUphZmaGLl26YPDgwdDT01O1Yx9UXkW+BwUFBfjuu+/w+++/AwA6dOiATz/9FMbGxqo2t2/fxpo1a3Dt2jUYGxsjICAAgwcPhkQiqdXPUx9IpVJs2LAB586dw5MnT2BtbY0BAwagW7duqjYV6ROqPBZxVKbi4mJ4enqiY8eOWLduXaltTp06haVLlyI4OBhffPEFlEolbty4odovl8sRGRmJhg0bYt68eXj8+DGio6MBAJ9++mmtfA4xq0gfvLBhwwY0adIEt2/fVtvOPqia8vogPT0dcrkcY8aMgbW1Ne7du4cVK1bg8ePH+OyzzwCwD6qqIt+DBQsW4O+//8Y333wDAFi2bBkWL16MGTNmAACePHmC6dOnw83NDYsXL0Z6ejqWLl0KfX199O/fv9Y+S32xePFiFBQUYNq0aTA1NcWpU6ewePFiNGnSBG3atAFQfp9Q1bCIozL17dsXAHDt2rVS98vlcsTExCAsLAy9evVSbbezs1P9/9mzZ3H37l2sX78eTZs2BQAMGzYMy5cvR3BwMAwNDWvwE4hfeX3wwunTp3HhwgVMmTIFKSkpavvYB1VTXh94eXnBy8tL9XOzZs0waNAg/PDDD6oijn1QNeX1wb1793DmzBnMnz8fLVu2BACMHTsWX331FdLT02Fra4tffvkFxcXFGD9+PPT09ODg4ID09HQkJCSgX79+HI17TWlpafj000/RokULAED//v2xb98+XL16FW3atKlQn1DVcE4cVcmNGzfw8OFD6OjoIDw8HMHBwZg+fbraSFxaWhpsbW1V/3ABQPv27VFSUoLr168LEbveefjwIVavXo2JEyeiQYMGL+1nH9S+J0+eqF0yYh/UrLS0NBgYGKBVq1aqba1bt4a+vj7S0tJUbdzc3NQucXt6euLRo0fIysqq9cxi17p1axw/fhz5+flQKBQ4ffo08vLy0K5dOwAV6xOqGhZxVCUPHjwAAPzwww8YNGgQZs6ciSZNmmDq1Kl49OgRACA3NxdmZmZqrzMxMYGWlhakUmmtZ65v5HI5Fi5ciH79+sHR0bHUNuyD2pWdnY34+Hj07t1btY19ULOkUilMTEzURtMkEglMTU1V51cqlaJRo0Zqr3vxc25ubu2FrScmT54MiUSCoUOH4r333sOiRYswceJEODk5AahYn1DV8HKqBoqNjcWOHTvKbDNnzhy4u7uXeyyFQgEAGDRoEHx9fQEAn332Gc6dO4cjR45g4MCBVQ9cD1VnH+zcuRO6urro169fdcXTCNXZB/8klUoxc+ZMeHp6qi4BUulqqg+o8l6nT2JjY5Gfn4/Zs2fDxMQEp0+fxpIlSzBv3rxX/kJJ1YtFnAZ699131e4eKs0/L/mUxdzcHID6HDhtbW1YW1vj77//BvD8N93Lly+rve7F8Pu/RyY0RXX2wfnz53H58uWXiriIiAh06dIFERER7INSVGcfvCCVSvH111/DwcEBX375pdoIBPvgZdXZB2ZmZsjPz4dSqVSdd6VSiby8PNX5NTMze2nE7cXP/x6h01QV7ZPMzEz8+OOPWLZsmapgc3R0RGpqKvbt24dx48ZVqE+oaljEaSBTU1OYmppWy7GcnZ2hq6uLjIwMuLm5AXg+OvfgwQO0b98eANCyZUvs2LEDDx8+RJMmTQA8n+Stq6ursc/bq84+CA8PR3FxsernnJwczJw5ExEREaq5KOyDl1VnHwDAo0ePMHXqVNjb22PixInQ1tZW288+eFl19kHLli1RVFSEtLQ01Z/7tLQ0PH36VDWpvmXLlti4cSNkMplq7ui5c+dgbm7+ymV7NE1F++TF3zlaWuqzsrS0tKBUPn+aZ0X6hKqGc+KoTFKpFDdv3kRGRgaA53eA3bx5E48fPwYAGBoaIjAwEFu2bMGZM2eQnp6OtWvXoqCgAN27dwfwfOKwvb09lixZghs3buDcuXP4/vvv0atXL96RVwHl9UGzZs3g4OCg+s/Gxka1/UWxwD6omvL6ICcnB1OmTIGZmRk++eQT5OfnQyqVQiqVQi6XA2AfVFV5fWBnZ4f27dtj5cqVSEtLQ1paGlauXAlvb2/VXZBdu3aFnp4eoqOjcefOHZw8eRJxcXG8M7USbG1tYWVlhdWrV+Pq1avIzMxEfHw8zp07h06dOgGoWJ9Q1UiKioqUQoegumvLli3YunXrS9vDw8PRs2dPAMCzZ88QGxuLI0eOoLi4GM2bN8fHH3+sNrqQnZ2N1atX48KFC9DT0+Mip6+hIn3wT1lZWRgxYkSpi/2yDyqnvD44fPgwli5dWupr/7koLfug8iryPXixsGxycjIAoGPHjq9c7Pfq1aswNjZGYGAgF/utpPv372Pjxo24cuUKioqKYGVlhX79+qFHjx6qNhXpE6o8FnFEREREIsTLqUREREQixCKOiIiISIRYxBERERGJEIs4IiIiIhFiEUdEREQkQiziiIiIiESIRRwRic7hw4fxzjvvICsrS+go2LJlC955551ae78pU6ZgypQptfZ+RFR3sYgjIqpm+/fvx+HDh4WOQUT1HIs4IqIq+OCDD7Br1y61bQcOHMDPP/8sUCIi0hQ6QgcgIhIzbW3tlx52T0RUG1jEEVG9cOLECcTFxeHu3bvQ09ODp6cnQkNDYWFhoWqzZMkSHD9+HGvXrsXq1atx/vx5NGjQAH5+fhg2bJhaMZafn4+1a9ciOTkZWlpa6NixI/r27Yvw8HC153W+eKbnvn37AAAff/wxsrOzAUA1V87CwgLr169XPWP1n89TBYCLFy9i6tSpmDNnDtzd3VXbExMTsWvXLjx69AgODg74+OOPS/3sJSUliIuLw9GjR/H333/DxMQEnTt3RnBwMPT19avpDBNRXcMijohE7+jRo1i8eDGcnZ0REhKCvLw8/Pjjj7h8+TKio6NhamqqaqtUKjFz5ky4urpi+PDhOHfuHBISEmBlZYXevXsDABQKBWbNmoWrV68iMDAQdnZ2SE5OxpIlS8rNMmLECMTExEBfXx+DBg0CgEoVUgcPHsTKlSvRqlUrvPPOO/j7778xe/ZsGBsbo0mTJmqfJyoqCpcuXUKvXr1gZ2eHe/fu4cCBA7h79y4iIyP5cHeieopFHBGJ2rNnz7BhwwbY2dlh3rx50NPTAwB4enpi6tSpiIuLUxvBevbsGXx9fTFkyBAAQGBgIMLDw3Ho0CFVEXf69GmkpaVh+PDh6N+/v6rd9OnTy83TqVMnbN68GSYmJujevXulP1NsbCycnJwQFRUFXV1dAIC9vT2WLVumVsT9+uuvOHPmDKKiotRG8VxcXLBo0SKcPXsW7du3r1QOIqrbeGMDEYna9evXkZubi8DAQFUBBwDu7u5wdnZGSkrKS6/p1auX2s9ubm548OCB6uczZ85AS0sLAQEBqm1aWloICgqqgU/wshef6e2331YVcADg5+cHIyMjtba//fYbrK2tYW9vj7y8PNV/bdq0gUQiwcWLF2slMxHVPo7EEZGovZh/ZmNj89I+W1tbnDx5Um2bjo4OzM3N1bYZGxujoKBA7ZiNGjWCgYGBWjsrK6vqil2mF5/J2tpabbu2trbaXDoAuH//PjIyMvDRRx+Veqy8vLyaCUlEgmMRR0QaRcj5Ya96b4VCUeljKhQK2NnZYeTIkaXu/3fBSkT1B4s4IhK1F3efZmRkvDT3Kz09Xe3u1Nc55vnz51FUVKQ2GpeZmVmh17+qWDM2NgYAFBYWqm1/MfL2z/cHno+yeXp6qrbL5XJkZWXB0dFRtc3KygrXr1+Hh4cHb2Ag0jCcE0dEoubs7IxGjRohMTERMplMtT01NRXXr1+Ht7f3ax/T09MTCoUCiYmJqm0KhQL79++v0Ov19fXVLs++0KxZMwDApUuXVNvkcjmSkpLU2jk7O8PU1BQHDx5ESUmJavuRI0deKgC7dOmC3NxcHDhw4KX3KykpwZMnTyqUmYjEhyNxRCRqOjo6CAsLw5IlSzB58mR0794deXl52LdvHxo3bowBAwa89jF9fHzg6uqKjRs3IisrS7XEyIvCrLwRL2dnZxw4cABbt26FtbU1DAwM0KFDBzg4OKBFixbYtGkTCgoKYGxsjOPHj0Mul7/0mT766COsXLkSX3/9Nbp06YLs7GwcPnxYVQi+0K1bN5w4cQJr1qzBpUuX0Lp1ayiVSmRkZOC3337DV199pXbXKhHVHyziiEj0/Pz8oKenh7i4OGzcuBF6enp48803MWzYMLU14ipKW1sbM2bMwNq1a3HkyBFoaWnBx8cHgwcPxuTJk9GgQYMyXz948GA8fPgQCQkJePLkCSwsLNChQwcAQEREBFauXIm4uDgYGRnB398fbdu2fWn5koCAACgUCuzevRvff/89HBwcMG3aNGzevFmtnZaWFqZOnYo9e/bgyJEjSE5ORoMGDdCsWTP07t0bb7zxxmt/fiISB0lRUZFS6BBERGJw6tQpzJkzB/Pnz0fr1q2FjkNEGo5z4oiISlFcXKz2s1wux48//ghDQ0M0b95coFRERP8fL6cSEZXiu+++g0wmQ8uWLVFSUoJTp07hypUrCAkJUVtUmIhIKLycSkRUil9++QUJCQnIzMyETCZTPVu1T58+QkcjIgLAIo6IiIhIlDgnjoiIiEiEWMQRERERiRCLOCIiIiIRYhFHREREJEIs4oiIiIhEiEUcERERkQj9P3zG3FB7xEq6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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(\"name == 'United States of 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.\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('Target en Estados Unidos')\n",
        "\n",
        "gax.spines['top'].set_visible(False)\n",
        "gax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "OR0ydTOvf_Xp",
        "outputId": "8d208f6b-eff7-40bc-d869-8af8678346e7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿qué tal ahora?, tiene mayor sentido verdad, entonces los datos lejanos no eran atípicos, de aquí la importancia de ver los datos con el tipo de gráfica correcta.\n",
        "\n",
        "Ahora sí, implementa K means a los datos de latitud y longitud :) y encuentra donde colocar los almacenes. \n",
        "\n",
        "Nota: si te llama la atención implementar alguna otra visualización con otra librería, lo puedes hacer, no hay restricciones."
      ],
      "metadata": {
        "id": "SMkonhsqfgSf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster es una agrupacion de elementos,en el ejemplo actual representa los puntos que representan las tiendas, para este caso podemos visualizar una clusterizacion como una region."
      ],
      "metadata": {
        "id": "icyFERMvYB9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tu codigo aquí\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "K_clusters = range(1,10)\n",
        "kmeans = [KMeans(n_clusters=i) for i in K_clusters] \n",
        "\n",
        "Y_axis = latlong[['latitude']]\n",
        "X_axis = latlong[['longitude']]\n",
        "score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))] \n",
        "\n",
        "\n",
        "\n",
        "plt.plot(K_clusters, score)\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Score')\n",
        "plt.title('Elbow Curve')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IfXq2ieSgD2e",
        "outputId": "b3881451-b108-48f8-86d7-97c2cbc083b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA es para reducir dimensiones, la variabilidad de tus datos, para trabajar con menos variables de entrada y KMeans es para hacer clusteres, aqui solo los agrupas y la variabilidad no nos importa"
      ],
      "metadata": {
        "id": "95ujRgapYVBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Se utilizaran 3 clusters\n",
        "kmeans = KMeans(n_clusters = 3, init ='k-means++') \n",
        "\n",
        "kmeans.fit(latlong[latlong.columns[0:2]])\n",
        "\n",
        "labels = kmeans.labels_\n",
        "labels"
      ],
      "metadata": {
        "id": "Km8kp3oNYXQB",
        "outputId": "7f245610-5433-41f5-f7a2-d302940b97b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 2, 0, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[[\"longitude\",\"latitude\"]]\n",
        "\n",
        "kmeans = KMeans(n_clusters=3).fit(X) \n",
        "centroids = kmeans.cluster_centers_ \n",
        "labels = kmeans.predict(X) \n",
        "\n",
        "C = kmeans.cluster_centers_\n",
        "\n",
        "C_DF = pd.DataFrame(C) \n",
        "C_DF[\"Coordinates\"] = list(zip(C_DF[0], C_DF[1])) \n",
        "C_DF[\"Coordinates\"] = C_DF[\"Coordinates\"].apply(Point) \n",
        "\n",
        "\n",
        "gdf_C = gpd.GeoDataFrame(C_DF, geometry=\"Coordinates\")  \n",
        "gdf_C\n"
      ],
      "metadata": {
        "id": "rOhDWa6kYXMo",
        "outputId": "f7155732-027a-4315-d147-e3c3c68f5b5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0          1                  Coordinates\n",
              "0 -118.624473  37.487342  POINT (-118.62447 37.48734)\n",
              "1  -78.569908  37.789554   POINT (-78.56991 37.78955)\n",
              "2  -93.327172  37.980063   POINT (-93.32717 37.98006)"
            ],
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              "      <th>0</th>\n",
              "      <td>-118.624473</td>\n",
              "      <td>37.487342</td>\n",
              "      <td>POINT (-118.62447 37.48734)</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-78.569908</td>\n",
              "      <td>37.789554</td>\n",
              "      <td>POINT (-78.56991 37.78955)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-93.327172</td>\n",
              "      <td>37.980063</td>\n",
              "      <td>POINT (-93.32717 37.98006)</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6062480-66dc-476a-a3a2-2d33fa5b2a84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6062480-66dc-476a-a3a2-2d33fa5b2a84 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6062480-66dc-476a-a3a2-2d33fa5b2a84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, gax = plt.subplots(figsize=(15,10))\n",
        "\n",
        "world.query(\"name == 'United States of America'\").plot(ax = gax, edgecolor='black', color='white')\n",
        "\n",
        "gdf.plot(ax=gax, color='red', alpha = 0.5)\n",
        "gdf_C.plot(ax=gax, color='black', alpha = 1, markersize = 300) \n",
        "\n",
        "gax.set_xlabel('longitude')\n",
        "gax.set_ylabel('latitude')\n",
        "gax.set_title('Target en Estados Unidos')\n",
        "\n",
        "gax.spines['top'].set_visible(False)\n",
        "gax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0hG6FGHkYXIT",
        "outputId": "ab4ca420-f0da-4b5d-e963-51a7e8bc7423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conteo de tiendas por cluster"
      ],
      "metadata": {
        "id": "R9j4fBMkY9JT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gdf_C"
      ],
      "metadata": {
        "id": "C9vILvmAYXAY",
        "outputId": "447124ee-8041-4ac0-91fa-94669f146640",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0          1                  Coordinates\n",
              "0 -118.624473  37.487342  POINT (-118.62447 37.48734)\n",
              "1  -78.569908  37.789554   POINT (-78.56991 37.78955)\n",
              "2  -93.327172  37.980063   POINT (-93.32717 37.98006)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f49cabc0-a623-4f4d-8920-31564e89d92c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Coordinates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-118.624473</td>\n",
              "      <td>37.487342</td>\n",
              "      <td>POINT (-118.62447 37.48734)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-78.569908</td>\n",
              "      <td>37.789554</td>\n",
              "      <td>POINT (-78.56991 37.78955)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-93.327172</td>\n",
              "      <td>37.980063</td>\n",
              "      <td>POINT (-93.32717 37.98006)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f49cabc0-a623-4f4d-8920-31564e89d92c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f49cabc0-a623-4f4d-8920-31564e89d92c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f49cabc0-a623-4f4d-8920-31564e89d92c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿Que ciudad es?\n",
        "\n",
        "Las siguientes coordenadas, nos ayudaran para posteriormente investigar una libreria con mapas y la alimentamos\n",
        "\n"
      ],
      "metadata": {
        "id": "oDOjXjeGZiFe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.internals.concat import concat_arrays\n",
        "\n",
        "Location1 = str(gdf_C[1][0]) + \", \" + str(gdf_C[0][0]) \n",
        "print(Location1)\n",
        "Location2 = str(gdf_C[1][1]) + \", \" + str(gdf_C[0][1])\n",
        "print(Location2)\n",
        "Location3 = str(gdf_C[1][2]) + \", \" + str(gdf_C[0][2])\n",
        "print(Location3)"
      ],
      "metadata": {
        "id": "ffi48TLzYW2F",
        "outputId": "51678cb0-54aa-40c8-c674-f87f7a2c6ece",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37.48734203064935, -118.62447331844157\n",
            "37.789554004474006, -78.56990807484885\n",
            "37.98006260590112, -93.32717230430622\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿ A que distancia estaran?\n"
      ],
      "metadata": {
        "id": "G6KIcjKEZ-qU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from geopy.geocoders.yandex import Location\n",
        "from geopy.geocoders import Nominatim \n",
        "from geopy.distance import geodesic\n",
        "\n",
        "geolocator = Nominatim(user_agent=\"my-application\") \n",
        "Locations = [Location1, Location2, Location3] \n",
        "for i in Locations:\n",
        "  location = geolocator.reverse(i)  \n",
        "  print('almacen en ---', location.address)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MCAG6VlCZ66e",
        "outputId": "c72ca26e-d2cf-46b9-dab2-c6269af9dbf8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "almacen en --- Paradise Estates, Mono County, California, United States\n",
            "almacen en --- Langhorne Road, Totier Hills, Albemarle County, Virginia, 22946, United States\n",
            "almacen en --- Hickory County, Missouri, United States\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distancia entre los 3 almacenes"
      ],
      "metadata": {
        "id": "pyRI_l0DaWn9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "distancia1 = str(geodesic(Location1, Location2).miles)\n",
        "print(\"\\nDistancia entre el primer y segundo almacén : \", distancia1, \" ft2 \\n\") \n",
        "distancia2 = str(geodesic(Location2, Location3).miles)\n",
        "print(\"Distancia entre el segundo y tercer almacén : \", distancia2, \" ml \\n\")\n",
        "\n",
        "print (2179.65/1.16)\n",
        "print(1381.42/1.16)"
      ],
      "metadata": {
        "id": "3yFlH5f-Z6z9",
        "outputId": "0ca7f61f-4038-487d-cdb1-bbdfaf0c88bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Distancia entre el primer y segundo almacén :  2179.654449831999  ft2 \n",
            "\n",
            "Distancia entre el segundo y tercer almacén :  805.9209470497035  ml \n",
            "\n",
            "1879.0086206896553\n",
            "1190.8793103448277\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j0aLo4lgZ6tU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}