Target_Brazil_Sales_Analysis (1).ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "- Target is one of the world’s most recognized brands and one of America’s leading retailers. Target makes itself a preferred shopping destination by offering outstanding value, inspiration, innovation and an exceptional guest experience that no other retailer can deliver.\n",
        "\n",
        "- This business case has information of 100k orders from 2016 to 2018 made at Target in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers."
      ],
      "metadata": {
        "id": "xsir88T8r2z-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data is available in 8 csv files:\n",
        "\n",
        "1. customers.csv\n",
        "\n",
        "2. geolocation.csv\n",
        "\n",
        "3. order_items.csv\n",
        "\n",
        "4. payments.csv\n",
        "\n",
        "5. reviews.csv\n",
        "\n",
        "6. orders.csv\n",
        "\n",
        "7. products.csv\n",
        "\n",
        "8. sellers.csv"
      ],
      "metadata": {
        "id": "-xJahI7nr4UF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Each feature or columns of different CSV files are described below:\n",
        "\n",
        "##### The customers.csv contain following features:\n",
        "\n",
        "- customer_id :\tId of the consumer who made the purchase.\n",
        "- customer_unique_id\t:Unique Id of the consumer.\n",
        "- customer_zip_code_prefix : Zip Code of the location of the - consumer.\n",
        "- customer_city :\tName of the City from where order is made.\n",
        "- customer_state :State Code from where order is made(Ex- sao paulo-SP).\n",
        "\n",
        "##### The sellers.csv contains following features:\n",
        "\n",
        "\n",
        "- seller_id : \tUnique Id of the seller registered\n",
        "- seller_zip_code_prefix :\tZip Code of the location of the seller.\n",
        "- seller_city\t : Name of the City of the seller.\n",
        "- seller_state\t: State Code (Ex- sao paulo-SP)\n",
        "\n",
        "\n",
        "##### The order_items.csv contain following features:\n",
        "\n",
        "- order_id:\tA unique id of order made by the consumers.\n",
        "- order_item_id:\tA Unique id given to each item ordered in the order.\n",
        "- product_id:\tA unique id given to each product available on the site.\n",
        "- seller_id:\tUnique Id of the seller registered in Target.\n",
        "- shipping_limit_date:\tThe date before which shipping of the ordered product must be completed.\n",
        "- price:\tActual price of the products ordered .\n",
        "- freight_value:\tPrice rate at which a product is delivered from one point to another.\n",
        "\n",
        "#####  The geolocations.csv contain following features:\n",
        "\n",
        "- geolocation_zip_code_prefix:\tfirst 5 digits of zip code\n",
        "- geolocation_lat:\tlatitude\n",
        "- geolocation_lng:\tlongitude\n",
        "- geolocation_city:\tcity name\n",
        "- geolocation_state:\tstate\n",
        "\n",
        "##### The payments.csv contain following features:\n",
        "\n",
        "- order_id:\tA unique id of order made by the consumers.\n",
        "- payment_sequential:\tsequences of the payments made in case of EMI.\n",
        "- payment_type:\tmode of payment used.(Ex-Credit Card)\n",
        "- payment_installments:\tnumber of installments in case of EMI purchase.\n",
        "- payment_value:\tTotal amount paid for the purchase order.\n",
        "\n",
        "##### The orders.csv contain following features:\n",
        "\n",
        "- order_id:\tA unique id of order made by the consumers.\n",
        "- customer_id\t:Id of the consumer who made the purchase.\n",
        "- order_status:\tstatus of the order made i.e delivered, shipped etc.\n",
        "- order_purchase_timestamp:\tTimestamp of the purchase.\n",
        "- order_delivered_carrier_date\t:delivery date at which carrier made the delivery.\n",
        "- order_delivered_customer_date:\tdate at which customer got the product.\n",
        "- order_estimated_delivery_date:\testimated delivery date of the products.\n",
        "\n",
        "##### The reviews.csv contain following features:\n",
        "\n",
        "- review_id:\tId of the review given on the product ordered by the order id.\n",
        "- order_id:\tA unique id of order made by the consumers.\n",
        "- review_score\t:review score given by the customer for each order on the scale of 1–5.\n",
        "- review_comment_title:\tTitle of the review\n",
        "- review_comment_message:\tReview comments posted by the consumer for each order.\n",
        "- review_creation_date\t:Timestamp of the review when it is created.\n",
        "- review_answer_timestamp:\tTimestamp of the review answered.\n",
        "\n",
        "##### The products.csv contain following features:\n",
        "\n",
        "- product_id:\tA unique identifier for the proposed project.\n",
        "- product_category_name:\tName of the product category\n",
        "- product_name_lenght:\tlength of the string which specifies the name given to the products ordered.\n",
        "- product_description_lenght:\tlength of the description written for each product ordered on the site.\n",
        "- product_photos_qty\t:Number of photos of each product ordered available on the shopping portal.\n",
        "- product_weight_g\t:Weight of the products ordered in grams.\n",
        "- product_length_cm:\tLength of the products ordered in centimeters.\n",
        "- product_height_cm:\tHeight of the products ordered in centimeters.\n",
        "- product_width_cm:\twidth of the product ordered in centimeters."
      ],
      "metadata": {
        "id": "--H-Y9Ytr8ux"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CRE3lNMFM7mK"
      },
      "outputs": [],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "project_id = 'targetbrazilsalesanalysis'"
      ],
      "metadata": {
        "id": "0etujGhSNbBC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import bigquery"
      ],
      "metadata": {
        "id": "vlH_nlDONa3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "client = bigquery.Client(project = project_id)"
      ],
      "metadata": {
        "id": "ImaIKDTkNasw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import figure\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import statsmodels.api as sm\n",
        "from scipy.stats import norm\n",
        "from scipy.stats import t"
      ],
      "metadata": {
        "id": "ce1NveYlNagS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Customer Table\n"
      ],
      "metadata": {
        "id": "5Ok9wvaOQ0mR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    * EXCEPT(is_generated, generation_expression, is_stored, is_updatable,is_hidden\n",
        "    ,is_system_defined,is_partitioning_column,\n",
        "    clustering_ordinal_position,collation_name,column_default,ordinal_position,table_catalog)\n",
        "  FROM\n",
        "    targetbrazilsalesanalysis.Customers.INFORMATION_SCHEMA.COLUMNS\n",
        "  WHERE\n",
        "    table_name = 'customers';\n",
        "    \n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "M3kcuL20P0mx",
        "outputId": "e4172ea1-3b88-4adb-a19b-8a9b0e202566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  table_schema table_name               column_name is_nullable data_type  \\\n",
              "0    Customers  customers               customer_id         YES    STRING   \n",
              "1    Customers  customers        customer_unique_id         YES    STRING   \n",
              "2    Customers  customers  customer_zip_code_prefix         YES     INT64   \n",
              "3    Customers  customers             customer_city         YES    STRING   \n",
              "4    Customers  customers            customer_state         YES    STRING   \n",
              "\n",
              "  rounding_mode  \n",
              "0          None  \n",
              "1          None  \n",
              "2          None  \n",
              "3          None  \n",
              "4          None  "
            ],
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Total Number of Customers data we have: 99441\n"
      ],
      "metadata": {
        "id": "sWCepKfeiy0P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "    \"\"\"\n",
        "    SELECT COUNT(distinct(customer_id)) from Customers.customers;\n",
        "    \"\"\"\n",
        "    ).to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzB8c8kyP0jL",
        "outputId": "5de51a03-1354-4c18-9c3a-d65c57e359ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99441"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "select count(distinct(customer_unique_id)) from `Customers.customers` ;\n",
        "\"\"\"\n",
        ").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOiaLjr6P0gN",
        "outputId": "a09092fc-9fa1-4b70-e673-1fbbecc09e11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96096"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "14994 unique zip code prefix"
      ],
      "metadata": {
        "id": "PUxlVwlglYgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "select count(distinct(customer_zip_code_prefix)) from `Customers.customers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-0x2lTmP0dF",
        "outputId": "639a6e80-052a-4550-e88e-3f1b00a541af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14994"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That means Target has customers from 14994 different locations"
      ],
      "metadata": {
        "id": "N9Zgl3ZjmPLf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "select count(distinct(customer_city)) from `Customers.customers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaviDXB0P1yj",
        "outputId": "1e0eb086-e91d-411f-e9ad-e5c523186b59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4119"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "select count(distinct(customer_state)) from `Customers.customers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCDo8abVP1vF",
        "outputId": "2e69a613-2c91-4380-b57e-d14393ee19b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customers are from different 4119 cities and 27 states from Brazil"
      ],
      "metadata": {
        "id": "rztdLT8rmn0U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting Data on Brazil states\n"
      ],
      "metadata": {
        "id": "LYEnrtcpnBIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "brazil = pd.read_html(\"https://brazil-help.com/brazilian_states.htm\")[2]\n",
        "new_header = brazil.iloc[1] #grab the first row for the header\n",
        "brazil = brazil[1:] #take the data less the header row\n",
        "brazil.columns = new_header #set the header row as the df header\n",
        "brazil.drop(1,axis = 0,inplace=True)\n",
        "brazil.reset_index(inplace=True)\n",
        "brazil.drop([\"index\"],axis = 1,inplace=True)\n",
        "brazil.columns = ['customer_state', 'State', 'Capitol City', 'Region',\n",
        "       'Size (in km²)', 'Population (2007 estimate)', '% Pop. Urban/Rural',\n",
        "       'Number of Municipal Districts', 'Per Capita GNP in Reais (R$)',\n",
        "       'Life Expectancy (2007 projection)']\n",
        "brazil.drop([\"Size (in km²)\",\"Life Expectancy (2007 projection)\"\n",
        "             ,\"Number of Municipal Districts\",\n",
        "            \"Population (2007 estimate)\",\n",
        "            \"% Pop. Urban/Rural\",\n",
        "            \"Per Capita GNP in Reais (R$)\",\n",
        "            \"Capitol City\"],axis = 1,inplace=True)"
      ],
      "metadata": {
        "id": "BON_L0hpP1rA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "brazil"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "-SpILUtryb9B",
        "outputId": "21437f0d-4e3a-45bd-ad1f-bd1daf7a99e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_state                State       Region\n",
              "0              AC                 Acre        North\n",
              "1              AL              Alagoas    Northeast\n",
              "2              AP                Amapá        North\n",
              "3              AM             Amazonas        North\n",
              "4              BA                Bahia    Northeast\n",
              "5              CE                Ceará    Northeast\n",
              "6              DF     Distrito Federal  Center West\n",
              "7              ES       Espírito Santo    Southeast\n",
              "8              GO                Goiás  Center West\n",
              "9              MA             Maranhão    Northeast\n",
              "10             MT           MatoGrosso  Center West\n",
              "11             MS    MatoGrosso do Sul  Center West\n",
              "12             MG         Minas Gerais    Southeast\n",
              "13             PA                 Pará        North\n",
              "14             PB              Paraíba    Northeast\n",
              "15             PR               Paraná        South\n",
              "16             PE           Pernambuco    Northeast\n",
              "17             PI                Piauí    Northeast\n",
              "18             RJ       Rio de Janeiro    Southeast\n",
              "19             RN  Rio Grande do Norte    Northeast\n",
              "20             RS    Rio Grande do Sul        South\n",
              "21             RO             Rondônia        North\n",
              "22             RR              Roraima        North\n",
              "23             SC       Santa Catarina        South\n",
              "24             SP            São Paulo    Southeast\n",
              "25             SE              Sergipe    Northeast\n",
              "26             TO            Tocantins        North"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d565ba99-3cc7-4087-b44c-3aa5aa7dbc88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_state</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AC</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Alagoas</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AP</td>\n",
              "      <td>Amapá</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AM</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BA</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CE</td>\n",
              "      <td>Ceará</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DF</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>Center West</td>\n",
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              "      <th>7</th>\n",
              "      <td>ES</td>\n",
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              "      <td>GO</td>\n",
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              "      <th>9</th>\n",
              "      <td>MA</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>MT</td>\n",
              "      <td>MatoGrosso</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>MS</td>\n",
              "      <td>MatoGrosso do Sul</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MG</td>\n",
              "      <td>Minas Gerais</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>PA</td>\n",
              "      <td>Pará</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>PB</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>PR</td>\n",
              "      <td>Paraná</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>PE</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PI</td>\n",
              "      <td>Piauí</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RJ</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>RN</td>\n",
              "      <td>Rio Grande do Norte</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>RS</td>\n",
              "      <td>Rio Grande do Sul</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>RO</td>\n",
              "      <td>Rondônia</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RR</td>\n",
              "      <td>Roraima</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>SC</td>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>SP</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>SE</td>\n",
              "      <td>Sergipe</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>TO</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d565ba99-3cc7-4087-b44c-3aa5aa7dbc88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d565ba99-3cc7-4087-b44c-3aa5aa7dbc88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\n",
        "\"\"\"\n",
        "\n",
        "select \n",
        "customer_state,\n",
        "COUNT(customer_id) as Number_of_customers\n",
        "\n",
        "from \n",
        "    `Customers.customers`\n",
        "GROUP BY\n",
        "    customer_state\n",
        "ORDER BY \n",
        "    Number_of_customers\n",
        ";\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()\n",
        "df.merge(brazil,on=\"customer_state\")"
      ],
      "metadata": {
        "id": "dGLkQMYSP1o0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "outputId": "c3a0c9c0-af5d-49af-f882-04f2209cdb82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_state  Number_of_customers                State       Region\n",
              "0              RR                   46              Roraima        North\n",
              "1              AP                   68                Amapá        North\n",
              "2              AC                   81                 Acre        North\n",
              "3              AM                  148             Amazonas        North\n",
              "4              RO                  253             Rondônia        North\n",
              "5              TO                  280            Tocantins        North\n",
              "6              SE                  350              Sergipe    Northeast\n",
              "7              AL                  413              Alagoas    Northeast\n",
              "8              RN                  485  Rio Grande do Norte    Northeast\n",
              "9              PI                  495                Piauí    Northeast\n",
              "10             PB                  536              Paraíba    Northeast\n",
              "11             MS                  715    MatoGrosso do Sul  Center West\n",
              "12             MA                  747             Maranhão    Northeast\n",
              "13             MT                  907           MatoGrosso  Center West\n",
              "14             PA                  975                 Pará        North\n",
              "15             CE                 1336                Ceará    Northeast\n",
              "16             PE                 1652           Pernambuco    Northeast\n",
              "17             GO                 2020                Goiás  Center West\n",
              "18             ES                 2033       Espírito Santo    Southeast\n",
              "19             DF                 2140     Distrito Federal  Center West\n",
              "20             BA                 3380                Bahia    Northeast\n",
              "21             SC                 3637       Santa Catarina        South\n",
              "22             PR                 5045               Paraná        South\n",
              "23             RS                 5466    Rio Grande do Sul        South\n",
              "24             MG                11635         Minas Gerais    Southeast\n",
              "25             RJ                12852       Rio de Janeiro    Southeast\n",
              "26             SP                41746            São Paulo    Southeast"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-924914ba-4b24-4f78-982e-a767377b1a90\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_state</th>\n",
              "      <th>Number_of_customers</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RR</td>\n",
              "      <td>46</td>\n",
              "      <td>Roraima</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AP</td>\n",
              "      <td>68</td>\n",
              "      <td>Amapá</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AC</td>\n",
              "      <td>81</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AM</td>\n",
              "      <td>148</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RO</td>\n",
              "      <td>253</td>\n",
              "      <td>Rondônia</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>TO</td>\n",
              "      <td>280</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SE</td>\n",
              "      <td>350</td>\n",
              "      <td>Sergipe</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AL</td>\n",
              "      <td>413</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RN</td>\n",
              "      <td>485</td>\n",
              "      <td>Rio Grande do Norte</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>PI</td>\n",
              "      <td>495</td>\n",
              "      <td>Piauí</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PB</td>\n",
              "      <td>536</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>MS</td>\n",
              "      <td>715</td>\n",
              "      <td>MatoGrosso do Sul</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MA</td>\n",
              "      <td>747</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MT</td>\n",
              "      <td>907</td>\n",
              "      <td>MatoGrosso</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>PA</td>\n",
              "      <td>975</td>\n",
              "      <td>Pará</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CE</td>\n",
              "      <td>1336</td>\n",
              "      <td>Ceará</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>PE</td>\n",
              "      <td>1652</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>GO</td>\n",
              "      <td>2020</td>\n",
              "      <td>Goiás</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ES</td>\n",
              "      <td>2033</td>\n",
              "      <td>Espírito Santo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>DF</td>\n",
              "      <td>2140</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BA</td>\n",
              "      <td>3380</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>SC</td>\n",
              "      <td>3637</td>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PR</td>\n",
              "      <td>5045</td>\n",
              "      <td>Paraná</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>RS</td>\n",
              "      <td>5466</td>\n",
              "      <td>Rio Grande do Sul</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>MG</td>\n",
              "      <td>11635</td>\n",
              "      <td>Minas Gerais</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>RJ</td>\n",
              "      <td>12852</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SP</td>\n",
              "      <td>41746</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "          const element = document.querySelector('#df-924914ba-4b24-4f78-982e-a767377b1a90');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "((df.merge(brazil, on = \"customer_state\").groupby(\"Region\")['Number_of_customers'].sum())/99441)*100"
      ],
      "metadata": {
        "id": "SW2kz6YwP1mN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f23a5e77-ad9c-4a2b-a509-bdea7d909b7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Center West     5.814503\n",
              "North           1.861405\n",
              "Northeast       9.446808\n",
              "South          14.227532\n",
              "Southeast      68.649752\n",
              "Name: Number_of_customers, dtype: Float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Total 99441 customers are there in the given data\n",
        "\n",
        "68% of customers are from SouthEast Brazil\n",
        "\n",
        "14% are from South Brazil\n",
        "\n",
        "And Rest are from other regions of Brazil"
      ],
      "metadata": {
        "id": "AR8LEcjzrhIi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Sellers Data:"
      ],
      "metadata": {
        "id": "ncVRxb_CsB6v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "   * EXCEPT(is_generated, generation_expression, is_stored, is_updatable, is_hidden, is_system_defined, is_partitioning_column, clustering_ordinal_position, collation_name, column_default, ordinal_position, table_catalog)\n",
        "  FROM \n",
        "     targetbrazilsalesanalysis.Customers.INFORMATION_SCHEMA.COLUMNS\n",
        "  WHERE\n",
        "     table_name = \"sellers\";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "ZkZxifyeP0Zz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "d8986fa1-5f31-4e93-82ff-d4c332696a92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  table_schema table_name             column_name is_nullable data_type  \\\n",
              "0    Customers    sellers               seller_id         YES    STRING   \n",
              "1    Customers    sellers  seller_zip_code_prefix         YES     INT64   \n",
              "2    Customers    sellers             seller_city         YES    STRING   \n",
              "3    Customers    sellers            seller_state         YES    STRING   \n",
              "\n",
              "  rounding_mode  \n",
              "0          None  \n",
              "1          None  \n",
              "2          None  \n",
              "3          None  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c5866f7-0215-42b7-88ca-04f1aefc0c7f\">\n",
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              "      <th>table_schema</th>\n",
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              "      <th>0</th>\n",
              "      <td>Customers</td>\n",
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              "      <td>YES</td>\n",
              "      <td>STRING</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Customers</td>\n",
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              "      <td>INT64</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Customers</td>\n",
              "      <td>sellers</td>\n",
              "      <td>seller_city</td>\n",
              "      <td>YES</td>\n",
              "      <td>STRING</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Customers</td>\n",
              "      <td>sellers</td>\n",
              "      <td>seller_state</td>\n",
              "      <td>YES</td>\n",
              "      <td>STRING</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c5866f7-0215-42b7-88ca-04f1aefc0c7f');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    COUNT(DISTINCT(seller_id)) \n",
        "FROM \n",
        "    `Customers.sellers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "id": "YD-uuMjVP0XZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e020cea-52ef-46ee-e18e-a9c2dad15051"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3095"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    COUNT(DISTINCT(seller_zip_code_prefix)) \n",
        "FROM \n",
        "    `Customers.sellers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "id": "M9iOQjgIP0Uy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "905bcc1d-e175-43f2-aaa8-4d48f7b5e825"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    COUNT(DISTINCT(seller_city)) \n",
        "FROM \n",
        "    `Customers.sellers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "id": "qqvB-i3iP0SF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fb7124b-0a32-49b1-d53d-201cd4083641"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "611"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    COUNT(DISTINCT(seller_state)) \n",
        "FROM \n",
        "    `Customers.sellers` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UyVBHuaytmPo",
        "outputId": "cb6ef61b-7aa0-4ae8-dd3c-0801228840c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 3095 seller's data present here.\n",
        "\n",
        "These sellers are from 2246 locations, 611 cities and 23 different states in Brazil."
      ],
      "metadata": {
        "id": "g6CcPrqbtrnJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "select \n",
        "    seller_state,\n",
        "    string_field_2 as seller_state_,\n",
        "    count(seller_id) as  number_of_sellers\n",
        "\n",
        "from `targetbrazilsalesanalysis.Customers.brazil_data` as bd\n",
        "join `targetbrazilsalesanalysis.Customers.sellers` as s \n",
        "on bd.string_field_1 = s.seller_state\n",
        "\n",
        "group by seller_state,string_field_2\n",
        "order by number_of_sellers desc\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "Sm_YUxlhtmMP",
        "outputId": "ce75c05b-5da5-4b49-ab1a-85be43bd6514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [seller_state, seller_state_, number_of_sellers]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef2e8615-9e1f-4331-8e85-1e526665e8f8\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>seller_state</th>\n",
              "      <th>seller_state_</th>\n",
              "      <th>number_of_sellers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef2e8615-9e1f-4331-8e85-1e526665e8f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef2e8615-9e1f-4331-8e85-1e526665e8f8');\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Products"
      ],
      "metadata": {
        "id": "5wTNuDDtHAL4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    * EXCEPT(is_generated, generation_expression, is_stored, is_updatable,is_hidden\n",
        "    ,is_system_defined,is_partitioning_column,\n",
        "    clustering_ordinal_position,collation_name,column_default,ordinal_position,table_catalog)\n",
        "  FROM\n",
        "    targetbrazilsalesanalysis.Customers.INFORMATION_SCHEMA.COLUMNS\n",
        "  WHERE\n",
        "    table_name = 'products';\n",
        "    \n",
        "    \n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "H_WW0AnEtmJf",
        "outputId": "4f7ff18f-2370-4f2b-c361-a6ce9ce85062"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  table_schema table_name                 column_name is_nullable data_type  \\\n",
              "0    Customers   products                  product_id         YES    STRING   \n",
              "1    Customers   products       product_category_name         YES    STRING   \n",
              "2    Customers   products         product_name_lenght         YES     INT64   \n",
              "3    Customers   products  product_description_lenght         YES     INT64   \n",
              "4    Customers   products          product_photos_qty         YES     INT64   \n",
              "5    Customers   products            product_weight_g         YES     INT64   \n",
              "6    Customers   products           product_length_cm         YES     INT64   \n",
              "7    Customers   products           product_height_cm         YES     INT64   \n",
              "8    Customers   products            product_width_cm         YES     INT64   \n",
              "\n",
              "  rounding_mode  \n",
              "0          None  \n",
              "1          None  \n",
              "2          None  \n",
              "3          None  \n",
              "4          None  \n",
              "5          None  \n",
              "6          None  \n",
              "7          None  \n",
              "8          None  "
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              "          const element = document.querySelector('#df-67412402-709f-44cf-a542-1144371dc9e5');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of Unique Products available in Target"
      ],
      "metadata": {
        "id": "7ORn9TowHPc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "SELECT COUNT(product_id)\n",
        "FROM `Customers.products`;\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDz7fVu2HVKR",
        "outputId": "b312545c-5c73-43ce-8de8-c2638a9f71db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32951"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Product Category Name"
      ],
      "metadata": {
        "id": "7vDinACGIFMf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "SELECT COUNT(DISTINCT(product_category_name))\n",
        "FROM `Customers.products`;\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2APs-qo1tmHc",
        "outputId": "dbb1af70-f15e-4d04-aa34-9bcab0e38687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "73"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    COUNT(product_id) \n",
        "FROM \n",
        "    `Customers.products` ;\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()[\"f0_\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjBkQheXtmFS",
        "outputId": "8bde0c94-0243-43ce-db31-905f66c9225d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32951"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are Total 32951 different Products available in Target\n",
        "\n",
        "And there are 73 different Product categories"
      ],
      "metadata": {
        "id": "5ZbuKVMONKF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# product_category_name\n",
        "client.query(\n",
        "\"\"\"\n",
        "\n",
        "SELECT  \n",
        "    product_category_name,\n",
        "    \n",
        "    COUNT(DISTINCT(product_id)) AS Number_of_product_per_category \n",
        "FROM \n",
        "    `Customers.products`\n",
        "GROUP BY product_category_name\n",
        "ORDER BY\n",
        "    COUNT(DISTINCT(product_id)) desc\n",
        ";\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "165O4c84tmCg",
        "outputId": "ac2ccb6b-71ce-40da-ba71-5c865437d070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product_category_name  Number_of_product_per_category\n",
              "0                 cama_mesa_banho                            3029\n",
              "1                   esporte_lazer                            2867\n",
              "2                moveis_decoracao                            2657\n",
              "3                    beleza_saude                            2444\n",
              "4           utilidades_domesticas                            2335\n",
              "..                            ...                             ...\n",
              "69  fashion_roupa_infanto_juvenil                               5\n",
              "70                casa_conforto_2                               5\n",
              "71                       pc_gamer                               3\n",
              "72             seguros_e_servicos                               2\n",
              "73              cds_dvds_musicais                               1\n",
              "\n",
              "[74 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>moveis_decoracao</td>\n",
              "      <td>2657</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>beleza_saude</td>\n",
              "      <td>2444</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>utilidades_domesticas</td>\n",
              "      <td>2335</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <th>69</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>pc_gamer</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>seguros_e_servicos</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>cds_dvds_musicais</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 2 columns</p>\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Order Items\n"
      ],
      "metadata": {
        "id": "_i7qQWkKNqop"
      }
    },
    {
      "cell_type": "code",
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        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    * EXCEPT(is_generated, generation_expression, is_stored, is_updatable,is_hidden\n",
        "    ,is_system_defined,is_partitioning_column,\n",
        "    clustering_ordinal_position,collation_name,column_default,ordinal_position,table_catalog)\n",
        "  FROM\n",
        "    targetbrazilsalesanalysis.Customers.INFORMATION_SCHEMA.COLUMNS\n",
        "  WHERE\n",
        "    table_name = 'orderitems';\n",
        "    \n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "dg54aK-otmAJ",
        "outputId": "43073055-478b-4628-a801-6b3f54d609f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  table_schema  table_name          column_name is_nullable  data_type  \\\n",
              "0    Customers  orderitems             order_id         YES     STRING   \n",
              "1    Customers  orderitems        order_item_id         YES      INT64   \n",
              "2    Customers  orderitems           product_id         YES     STRING   \n",
              "3    Customers  orderitems            seller_id         YES     STRING   \n",
              "4    Customers  orderitems  shipping_limit_date         YES  TIMESTAMP   \n",
              "5    Customers  orderitems                price         YES    FLOAT64   \n",
              "6    Customers  orderitems        freight_value         YES    FLOAT64   \n",
              "\n",
              "  rounding_mode  \n",
              "0          None  \n",
              "1          None  \n",
              "2          None  \n",
              "3          None  \n",
              "4          None  \n",
              "5          None  \n",
              "6          None  "
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              "      <td>freight_value</td>\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CREATED A VIEW HAVING ORDER QUANTITY AS PER ORDER_ITEM_ID FROM ORIGINAL ORDER_ITEM TABLEM\n",
        "\n",
        "ADDED A COLUMN CALLED TOTAL_AMT_TO_PAY\n",
        "WHICH IS\n",
        "\n",
        "total_amt_to_pay = ( price * quantity ) + freight_value"
      ],
      "metadata": {
        "id": "Jd2oFhQoPyce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CREATE VIEW `Customers.orderitems_SALES` AS\n",
        "\n",
        "SELECT *,\n",
        "ROUND(((x.price + x.freight_value) * x.quantity),2) as Total_Amt_to_Pay\n",
        "FROM\n",
        "(\n",
        "SELECT  \n",
        "    DISTINCT\n",
        "    order_id,\n",
        "    product_id,\n",
        "    seller_id,\n",
        "    shipping_limit_date,\n",
        "    price,\n",
        "    freight_value,\n",
        "    COUNT(product_id) OVER (PARTITION BY order_id, product_id) AS quantity\n",
        "FROM \n",
        "    `Customers.orderitems`\n",
        "\n",
        ") as x\n",
        ";"
      ],
      "metadata": {
        "id": "1jN8mRoXP8FV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Orders Table"
      ],
      "metadata": {
        "id": "jpshC_BETqUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    * EXCEPT(is_generated, generation_expression, is_stored, is_updatable,is_hidden\n",
        "    ,is_system_defined,is_partitioning_column,\n",
        "    clustering_ordinal_position,collation_name,column_default,ordinal_position,table_catalog)\n",
        "  FROM\n",
        "    targetbrazilsalesanalysis.Customers.INFORMATION_SCHEMA.COLUMNS\n",
        "  WHERE\n",
        "    table_name = 'orders';\n",
        "    \n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6VvFqg5HTqDq",
        "outputId": "0f2484f2-772c-4671-849f-e9b7edb68474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  table_schema table_name                    column_name is_nullable  \\\n",
              "0    Customers     orders                       order_id         YES   \n",
              "1    Customers     orders                    customer_id         YES   \n",
              "2    Customers     orders                   order_status         YES   \n",
              "3    Customers     orders       order_purchase_timestamp         YES   \n",
              "4    Customers     orders              order_approved_at         YES   \n",
              "5    Customers     orders   order_delivered_carrier_date         YES   \n",
              "6    Customers     orders  order_delivered_customer_date         YES   \n",
              "7    Customers     orders  order_estimated_delivery_date         YES   \n",
              "\n",
              "   data_type rounding_mode  \n",
              "0     STRING          None  \n",
              "1     STRING          None  \n",
              "2     STRING          None  \n",
              "3  TIMESTAMP          None  \n",
              "4  TIMESTAMP          None  \n",
              "5  TIMESTAMP          None  \n",
              "6  TIMESTAMP          None  \n",
              "7  TIMESTAMP          None  "
            ],
            "text/html": [
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              "      <td>STRING</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>orders</td>\n",
              "      <td>order_purchase_timestamp</td>\n",
              "      <td>YES</td>\n",
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              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Customers</td>\n",
              "      <td>orders</td>\n",
              "      <td>order_approved_at</td>\n",
              "      <td>YES</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Customers</td>\n",
              "      <td>orders</td>\n",
              "      <td>order_delivered_carrier_date</td>\n",
              "      <td>YES</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Customers</td>\n",
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              "      <td>order_delivered_customer_date</td>\n",
              "      <td>YES</td>\n",
              "      <td>TIMESTAMP</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Customers</td>\n",
              "      <td>orders</td>\n",
              "      <td>order_estimated_delivery_date</td>\n",
              "      <td>YES</td>\n",
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              "            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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time period for which Data is given"
      ],
      "metadata": {
        "id": "cWoa-psWT7Zk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "SELECT\n",
        "DATE_DIFF(MAX(DATE(order_delivered_customer_date)),MIN(DATE(order_purchase_timestamp)),month) AS time_period_in_months\n",
        "FROM Customers.orders;\n",
        "\"\"\"\n",
        ").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "LkqjwHbHTqAY",
        "outputId": "0f949122-cbd3-402b-b3ad-15e73c3fcbb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   time_period_in_months\n",
              "0                     25"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>time_period_in_months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b512e3e5-03a2-474c-b2f6-410c4b7dee24');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NUmber of order as per order status presented in data\n"
      ],
      "metadata": {
        "id": "nP6Qr9o0XmJa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "SELECT\n",
        "order_status,\n",
        "COUNT(1) AS Number_of_Orders\n",
        "FROM `Customers.orders`\n",
        "GROUP BY order_status\n",
        "\"\"\"\n",
        ").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QgQxFVgMTp-Y",
        "outputId": "6891c06a-7ccb-40f8-8df5-31937f2a8b1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  order_status  Number_of_Orders\n",
              "0      created                 5\n",
              "1      shipped              1107\n",
              "2     approved                 2\n",
              "3     canceled               625\n",
              "4     invoiced               314\n",
              "5    delivered             96478\n",
              "6   processing               301\n",
              "7  unavailable               609"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0cd4bf29-96df-473e-9c9e-0da616b4e9b2\">\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>order_status</th>\n",
              "      <th>Number_of_Orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>created</td>\n",
              "      <td>5</td>\n",
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              "      <td>1107</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>canceled</td>\n",
              "      <td>625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>invoiced</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>delivered</td>\n",
              "      <td>96478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>processing</td>\n",
              "      <td>301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>unavailable</td>\n",
              "      <td>609</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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-0cd4bf29-96df-473e-9c9e-0da616b4e9b2 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-0cd4bf29-96df-473e-9c9e-0da616b4e9b2');\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": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Is there a growing trend on e-Commerce in Brazil ? \n",
        "How can we describe a complete scenario ?\n",
        "Can we see some seasonality with peaks at specific months ?"
      ],
      "metadata": {
        "id": "ct0ZUVPEYKxt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Revenue Per Year"
      ],
      "metadata": {
        "id": "rC11d9VgYZxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\n",
        "\"\"\"\n",
        "SELECT\n",
        "EXTRACT(YEAR FROM order_purchase_timestamp) as _year,\n",
        "ROUND(SUM(Total_Amt_to_Pay), 2) AS revenue,\n",
        "COUNT(o.order_id) AS number_of_Orders\n",
        "FROM\n",
        "`targetbrazilsalesanalysis.Customers.orders` as o\n",
        "JOIN \n",
        "`targetbrazilsalesanalysis.Customers.order_items_SALES` AS ois\n",
        "ON o.order_id = ois.order_id\n",
        "GROUP BY _year\n",
        "ORDER BY _year;\n",
        "\"\"\"\n",
        ").to_dataframe()"
      ],
      "metadata": {
        "id": "FLRUEtabTp8j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " year\trevenue\t  number_of_orders\n",
        "\n",
        "\t2016  57183.21   332\n",
        "\n",
        "\t2017  7142672.43 46314\n",
        "\n",
        "\t2018  8643697.60 55779"
      ],
      "metadata": {
        "id": "bn6c12ZjdzBX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "compare to 2017 , revenue has increased in 2018 by 21%."
      ],
      "metadata": {
        "id": "-oAwrKPwfdts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "x.month,\n",
        "AVG(x.num_of_orders) AS avgerage_orders_per_month\n",
        "from\n",
        "(\n",
        "\n",
        "SELECT\n",
        "    extract(year from order_purchase_timestamp) as year,\n",
        "    extract(month from order_purchase_timestamp) as month,\n",
        "    count(order_id) as num_of_orders\n",
        "from \n",
        "    `targetbrazilsalesanalysis.Customers.orders`\n",
        "group by \n",
        "    year,month\n",
        "order by\n",
        "    year,month\n",
        ") as x\n",
        "group by x.month\n",
        "order by x.month\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "wWTqay7ATp5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "NZOBYpdpTp2A",
        "outputId": "0fc7ba03-36a4-402e-ca10-e8b2c49e4017"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    month  avgerage_orders_per_month\n",
              "0       1                     4034.5\n",
              "1       2                     4254.0\n",
              "2       3                     4946.5\n",
              "3       4                     4671.5\n",
              "4       5                     5286.5\n",
              "5       6                     4706.0\n",
              "6       7                     5159.0\n",
              "7       8                     5421.5\n",
              "8       9                     1435.0\n",
              "9      10                     1653.0\n",
              "10     11                     7544.0\n",
              "11     12                     2837.0"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = df[\"month\"],\n",
        "           y = df[\"avgerage_orders_per_month\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cSsqm-SrTpzf",
        "outputId": "018b76a9-cc20-4f43-d8de-d7dba90ef112"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1156b33d0>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "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": "markdown",
      "source": [
        "Average number of order are higher during November month.\n",
        "\n",
        "September and october month average orders are comparatively low.\n",
        "\n",
        "May, July and august have higher average orders compare to other months."
      ],
      "metadata": {
        "id": "Eznqdm4cfzFB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of Orders Per Week Trend"
      ],
      "metadata": {
        "id": "1-fimMd-f8YY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    extract(year from order_purchase_timestamp) as year,\n",
        "    extract(month from order_purchase_timestamp) as month,\n",
        "    extract(week from order_purchase_timestamp) as week,\n",
        "    count(order_id) as num_of_orders\n",
        "from \n",
        "    `targetbrazilsalesanalysis.Customers.orders`\n",
        "group by \n",
        "    year,month,week\n",
        "order by\n",
        "    year,month,week\n",
        "\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "uGXngZRfTpw6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "IQq56Sastl9p",
        "outputId": "822c8d7c-d7c2-4649-c35b-db6e9dd74a31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     year  month  week  num_of_orders\n",
              "0    2016      9    36              2\n",
              "1    2016      9    37              2\n",
              "2    2016     10    40            258\n",
              "3    2016     10    41             65\n",
              "4    2016     10    42              1\n",
              "..    ...    ...   ...            ...\n",
              "110  2018      9    36              4\n",
              "111  2018      9    37              2\n",
              "112  2018      9    38              3\n",
              "113  2018     10    39              2\n",
              "114  2018     10    41              2\n",
              "\n",
              "[115 rows x 4 columns]"
            ],
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>10</td>\n",
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              "      <th>110</th>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
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              "      <td>2018</td>\n",
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              "    </tr>\n",
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              "<p>115 rows × 4 columns</p>\n",
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              "          document.querySelector('#df-93c16091-a72d-443b-b59c-5a79ae727c57 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-93c16091-a72d-443b-b59c-5a79ae727c57');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"year\"] = df[\"year\"].astype(\"object\")\n",
        "df[\"month\"] = df[\"month\"].astype(\"object\")"
      ],
      "metadata": {
        "id": "mpeebEPutl68"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "sns.lineplot(data  = df,\n",
        "            x = \"week\",\n",
        "            y = \"num_of_orders\", hue = \"year\"\n",
        "            )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "uxD3Ulxhtl4n",
        "outputId": "eb68ba1e-7dec-4478-eff1-bae49dafba7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb114c8fa90>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Growing Number of Orders Trend as per Number of Orders"
      ],
      "metadata": {
        "id": "npEFXVMdgavD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "select\n",
        "    *,\n",
        "    round(avg(num_of_orders) over (order by year,month,week\n",
        "              rows between 2 preceding and 2 following),0) as rolling_avg_order\n",
        "from\n",
        "    (\n",
        "    SELECT\n",
        "        extract(year from order_purchase_timestamp) as year,\n",
        "        extract(month from order_purchase_timestamp) as month,\n",
        "        extract(week from order_purchase_timestamp) as week,\n",
        "        count(order_id) as num_of_orders\n",
        "    from \n",
        "        `targetbrazilsalesanalysis.Customers.orders`\n",
        "    group by year,month,week\n",
        "    order by year,month,week\n",
        "    ) as x\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "DIxAH15Qtlyn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"time\"] = df[\"year\"].astype(\"str\")+\"_\"+df[\"month\"].astype(\"str\")+\"_\"+df[\"week\"].astype(\"str\")\n",
        "df.reset_index(inplace=True)\n"
      ],
      "metadata": {
        "id": "c1yvkUo5gVwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "sns.lineplot(x = df[\"time\"],\n",
        "           y = df[\"rolling_avg_order\"])\n",
        "plt.title(\"number of orders per week trend line\")\n",
        "plt.xlabel(\"time\")\n",
        "plt.ylabel(\"rolling average orders per week (rolling +&- 2)\")\n",
        "plt.xlim(1,100)\n",
        "plt.xticks(rotation = 70)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "2Xue9zA5gVuA",
        "outputId": "d6e113dc-bc7d-4de8-b197-f010e78e0785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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/Q8bYz3TbMBF9nIgWiOgR233vJqJTRPSQeftZ22O/T0RHiOgJIrredv8N5n1HiOgd/j6eOLyL6Choqk6uVMAYcMEMD9DjqMoM+tBQkj7oEolkwPCVPEAG6JLufPsJoznRSy4zAnQlEZMZ9JAJU8txK4wGR518gDF2lXm7AwCI6HIArwJwhfmavyOiOBHFAXwQwMthNEl6tfncQGGMtUlcog53cNlvC9ClxGV4sHzQR2Bfk0gk0YTLW4Donot+4/YH8bn7nx70MM4Lvv3EIubGFKv5oZFBj+Z+ExVCC9AZY3cDWBF8+s8D+CxjTGWMHQNwBMDzzdsRxthTjLE6gM+azw0Ue/agrkc/kD2+bAToF9gkLtLFZXiQRaISyfnB+77yOD5134lBD8MRewY9igH6eqWBfz90Bt8/JhpmSHqloTdx95OLeMmlcyAyTDSURGzkrmG1ho5fveX7+NH8+qCHAiDcDLobv0lEh0wJzKR53y4AJ23PmTfvc7s/UOyzwNoIzAiPLVUwmU1iPGtY18sM+nBRkkWiEsl5wVd/fBbfenxh0MNwpF3iEr3rw6FTawAg5ZtbwMHjqyiqmiVvAQAlER+5a9hTi2V85/AS7j8+HJM+XwE6EX25z/f7EICLAFwF4AyA9/e5vTaI6K1EdJCIDi4uLgq/zn5yGgVN1fGlsiVvAYwMujyJDQea3rSaRkXxoiiRSMRRG7q1YjZscInLRDYZyQz6ITPLKZNP4XPnEwtIxgkvNBsUAabEZcSuYafWqgBaRg6Dxm8Gva/sNWPsHGNMZ4w1AXwUhoQFAE4B2GN76m7zPrf73bb/EcbY8xhjz5udnRUelz1rHsUTVSfHl8uWvAUwMujlujYyTZiiTNn0o0/FR295UCKRtKNqTWvFbNjgGfTpXCqSiamHT8oM+lbxrccXcM0F08grLWduJREbiXjJzmkzQB+WSbXfAP2H/bwZEe2w/fkLaNk1fgnAq4hIIaILABwA8AMA9wM4QEQXEFEKRiHpl/oZgxM12yww6kFTta7jzHqtLYOeScXRZNH/bKNAyawFmM6nUB+hzrUSiWQzqtYcWotbnkGfziuRDLSsDHpjOL/fUeHkSgVHFkpt8haAZ9Cjt994cWrIAnSvRkWbYIzdBABENM0YW/Z6LhF9BsCLAcwQ0TyAdwF4MRFdBaMT6XEAbzO3+2Mi+jyARwFoAH6DMaab2/lNGM2R4gA+zhj7sZ8xi1Cz2d1F8URl58RKu4MLAORScQDGUmA6GR/IuCQGPJs2nU/hzHoNdb0JJSF/E4lkFFE1PRIZ9HMbtQGPxh8LGzWcNccsu2SHy53cXvHSdlWCkohjWasPYkihEdkAnYiOAvh3AJ+CYaHoaXfIGHu1w923eDz/vQDe63D/HQDuEB1nL4ySxOX4UruDC2Bo0AGgUtcwlUsNZFwSg5JqZK2mcgoAI8MmA3SJZPTQmwwNnQ3Nxb6ToqpBScSQVxKRu+49bGbPL5zNyQA9ZL71+AL2T2dx4Wy+7X5lCDToepPh1GoVe6ezgWzv1GpENeiMsYsAHANwL4D3hTaiAWDPoA96h+uXY0sVAMD+mdYOmzEz6KOk1Ts0v4abP/ND6M1oSURK5sVk2pwoSR9ZiWQ04UGvqjXRGEKNd7HWwFg6iVQEtcSH5tcQjxGet29SWgiHSLWu496jy5vkLcBwNCr68qHTeNlffRtrlWAy+VyDXhySVS/XAJ2IvkZE+2x/Xwvg7TBkKa/YgrFtGaMkcTm+VMZMPoWxdNK6L6cYAXp5hAL02+49gX97+DSWS+qgh+KLkm1ZGRgN1yCJZBRgjOHDdx0N7JxiT/YMS0bOzkZNQyGdiKSW+OH5dRyYy2Mqp1iuWJLg+d7RJahaEy91CNCHoVHRU4tlNHSG9Wqj+5O7oGo6ForGsT8sdSNeGfQ5xtgJACCinwPwcQD/hTH2MQAXbMXgtor2RkXROlF1cmy5jP02eQsAZJIticsowBjD3YcNG82oWWxZEpc8z6BHa/wSyajy9EoF7/vK4/i3h08Hsj37dWVYMnJ2SjUNY+kElEQ8UokpxhgOza/hqj0TyKbiaOhsKFcoRoE7n1hANhXH8y+Y2vTYMGTQF83JdBATzLPrRk0DEYambsRLg64S0eth2BzeDODZjLHTRFQAkPN4XeQYtQz6iy5pL+bgGfRRkbg8ea6EcxvDNdMVpdiRQY9a5koiGVW4Vvz0ejAFk/bs4jCep9okLrrhKMW7RA4zJ1eqWKs0cOXuCSvpVKnrGM8Mou/iaPPtJxbxUxfPONZJDUMGfcGMA4KI27j+fN9U1pKiDhqvPfq1AP4TgJ0A/hzAx4nonQDuhOFhPjLUbD9ulAOmsqphoai2ObgAhg86MDoSl7ufbDWhiloGnfug24tEJRLJ4OHnEu7k0C92icuwZOTsFK0MuhEGROVc9NC84X9+5e5xywBhVJJPwwRjDPOrVTxjR8HxcSURH3wGvWhMpoOoHeTH/SXbxqyV7kHjmkFnjB0B8Gb+NxF9C8BPA/g9xtg3tmBsW4baGA0f9OPLpoNLR4CesU5iw3eR6IW7Dy8iHiPoTTaU2k4vSmoD2VTcmjRFfcVGIhkV+LnkTGABeuvYHkYnl84Ava43I2HDe+jkGpREDJduH8ORhRKA0ZFvDhN8/00nnfO4qUQMepNB05tIxAezesE140Fk8k+vGcH+gW15fO3RcwP9XBw/Li4/ZIz9RVSCcz/uHlziEqNoB0zHuYNLhwad+6CXh2TZph+qdR3fP7aCay80NHFRy6CXVA05xZ61itb4JZJRhZ9L+IW6X9oy6EMZoLckLkB0rn2H5tdx+c4CkvGY5VAWtetAFOBBb9rFBtg+sRsEzSbDUoAa9FNrFcyNKZjMGvLTYVAcCAfoRPSFMAcSNH5OiLVGE0RALpWIdMB0bMnIJtgtFgGbzeIIFCR+/9gy6loTNzzTaEobtRNzSdUxpiQsTd+gNXySFnc+voCPfeepQQ9DMiB4Bv1csRZI0WGbBn3IAnS9yVCu68grCaTi0ZG46E2GR06v41m7JwC05JtRuw5EAd5h3W1VhU/sBnUNW6s20NCNRGwQcdvptRp2TmQwljYUB8MwqfaTv78wtFGEQNNHC/VaQ0c6EY+kH6ydY0sVbCsoli6Pk4rHEI/RSCwD3v3kElKJmNXVLGqfqVRrIG9amwHRuCieL/zTA/P4qAzQz1t4kMdYy9GhH4bZxYVr4sds56IoXPuOLJRQqeu4cvc4AHuAPlzf7yjAlQVuEheeZBpUBn2h2DpGg8mgV7FrIoOcYsRPwzCp9gzQiWivedsHIElEe/h9WzS+nvHTv0bVmkgnY4ZtUAROUm4cd7BYBAAiQjYVHwmJy92HF3HNBVOYyRtFllH7TCVVQy5l131Ga/yjzEatITNx5zH23/50ADr0YZa4bNSMIrhCOtkKtCJw7XvYKhA1MujcQlgWiQYP77A+rBl07uASxBgYY0aAPplB3gzQh2FS7WWzCAD/AIABIAD7zL/JvO+l4Q6tP5o+NegKz6BH2E/1+FIZP3P5NsfHsql45E9ip9eqOLJQwquu3gMlEQNR9DInxZqGPVNZKMnBntwkmynWtMgfI5LesZ9LTq8HEaAPr8SlaMug80K4KAToh+bXMKYkcKFphMAthOXEOni6Z9AHm2RaLNoC9D4lLkulOupaEzvH01aAPgzHrGeAzhh7Cf8/Ef2QMTbUQbkdXxIXM4M+DL6evbJRa2C5XN9kscjJpRKoRFyDzu0Vr7tkFkRkfKaInZjLda1dgx6Bi+L5wkatAa3JUNeaVnZIMlgWiyr+8qtPoFTXkIwRkvEYkokYUvEYEjHCjokMbvqp/YH4d5dVHam4kaQJolDUfi0Ztgx60cygj6WT0FlwOt6wOTS/jmfuGkcsZvzeVpFoxK9tw4gVoLsUifJzZG1QGfS2AL2/MfAVs12TWeSHSIPeLYMeWXzE54YGPRlHMh7dDPrxJcNi0UniAhgnssoQ7HD9cPfhRWwvpHFgLg/AWBWIWga9VDNcXFLSxWXo4FnFal0/bwL0s+s1EAHbCulBD2UT5zZqeM1H78P8ahW7JzNWx0j+b62hQ9WaeM7eCTx772Tf71epa5jIJqE1WSBe6PzYLqQTvhqfFGsNfOw7x/CbL70YyZBs3njwMZZOWOYBw55BVzUdj53ZwE0vbDUyz46YhfAwwfvDKC4Sl0H75y8Ua1bdYL9j4Mf7zok0cqloBuh/E9ooQsBvkaiSjCMRo6E/SblxbMnZA51jBLPhBoNfPnQaf/vNw/jKb12HeCzYjnSa3sQ9h5dwwzO3W9mynJKIpAY9b28OEtEVm1GEZxUrDQ3jSA54NFvD//NPDyMeI9z6xucPeihtnF6r4jUfvQ+LRRW3vekax1bj65UGnvunX8d//PhsIAF6ua4jpySQU+IBadCNY3s6r6BUE2988p3DS/ibbx7GCy6axjUXTvc9DifsEhcrgz7kyanHzxTR0Jnl4AIAmaSUuIRFN4nLoIuLF4oqdo6ncXy5ElgGffdEFgzG8TAMzcX8+KDfGuI4AsdXkWijibS5bBrVjCb3QN83nXV8PJtKhJ5tfvT0Bp48V8KZAPSbnTw8v46NmobrLpm17ssko5VBVzUdDZ0hrySQiJHhuz/kF0Un/uyOx/COLxwa9DACxcjIGr/F+XSxXyyqOGcrthoG5lcruPEj92K5VMcn3/R8x+AcAMazSfzkRdP46iNnwfwsmbpQUTVkU3HsHM8EG6DnUr4SCRtVI5i3L+EHjV3iYtksDnmy4JCtgygnHiMoidh5dcxuFa0A3S2DzmWag9OgzxXSUBL9x23zq1XkUnEUMonouLhEGT8ZdFUzJC5Rtlk8vlzGzvG068G0FRl0vv2TK8EH6Hc9uYgYAS+8eMa6L6dEy5nGbm1GRFAS8Uhq0B84sYrPHTyJ+dXKoIcSGPaK/fOpULRS162AcBh4ermCG//+PqxXGrjtzdfgufucg3PO9Vdsx/HlCp48V+r7vct1w2Fp50QGZwLRoOsgAiayKRR9XOy5w0qYAfqG7VzEM6TDnix4eH4d07kUdk1k2u6PotQxCqhdXFyUAWfQF4sq5sYUI0Dvc3J52nRwITLqXJREbCgkLiMboPvJqNQaLZvFKAZMgCFxcSsQBXgGPewA3dihT4YQuN395CKu3D2BCbPLF2B+pggVB/EDnmvcjKLk6IyfU2voYAz43P0nBz2UwCjaJAjnUzaurGpDE6AfWyrjxo/ci3Jdw+1vuRZX7Zno+pr/fPk2EAH/8cjZvt+/UteRVeLYNZFBUdWsQLlXVK0JJRHDWDrhKxu3UTWea/d5DppiTUMqHjMSU/Fo2Cweml/DlbvHNxUEb8W17XzEalTkUo8zcA36Rg2zYwqUZP+JrtPrVey0TfzySkIG6GHiR+JS06Jvs3h8uVuAHn6WgZ8k51eCDdDXKnUcml9rk7cARgY9SoWv/IDnVeJRnRDypc/P3X8SWkSPl054UAREz7qzH0qqhqKqQfdzwgyBIwsl3Pj390LVmrj9zdfimbvGu78IwFwhjefsncRXf9x/gF5WWxl0oH8vdCNAjyOnxH1d7PnEYDFE6VHRbJgGIBIF62VVw5GFkuV/bmcYLIQffHp14GMIGlGJyyAmdmVVQ7muY24sHYg0+dRqtW1lJheVAJ2Iftvh9iYiumorBtgrvjuJRthmca1Sx1qlgQtcHFyALZa4rAYrcbnnyBKaDHjRJTNt90ctc2JJXEyNm5KMpqSq1mhiJp/CQlHFNx9fGPRwAsGeQR+1C60bmt5yPxh0QdTbbjuIJmP4zFuuxeU7C75ee8MV2/HomQ08vdxfYqBS15FJxbFzwnC06T9A16EkYsgrSX8BurmicS7kDPqYLVEADHcG/ZFT62gy4Fl7Nk/ctuLa5sXDJ9fw3//ue/jiQ6cGNoYwEG5UNID9hnugz40pUJL9JboqdQ2rlcamDHpUNOjPA/B2ALvM29sA3ADgo0T0uyGOrS98ZdAbTaSTcSiJeCQz6NzBpZvERdWaoWbKLIlLwBn0u59cRCGdaKveB4wTczlC2U5L4sID9Ihq0FVNx8su24bthTRu//7Tgx5OIGzU7Bn08yNAL9s+52WAL14AACAASURBVPqAZS4nV6r4xefuxqXbx3y/9vortgNA31n0Sl1HLhW3Mmmn+tShq40mlGQMeSWOutYUDoD5vrgQcgZ9rCODPswB+qH5dQBwzKBnBpxBv+WeYwAGfwwFTa2hIxknV0e21sRu6797Xp8xV1CMuK2PfddycJlsD9CHoZOoSIC+G8BzGGO/wxj7HQDPBTAH4DoAbwhxbH3hP4MehxLRIlHu4bl3ytnBBTCCWSDc5fuqlUEPLkBnjOHuJ5fwwgMzVsc7TuQy6B0Sl6i6BtUaTWSVOF559R7cfXgx8AnZIGjToEewLqAX7BmifvXW/dBsMtT1pmtDlG7snc7iGTsKAQToGrJKAjN5Bck4BSZx8duZcGtcXDSMKYaV6CAzoaI8PL+GXRMZzOSVTY8ZtUiDCabOrFdxx4/OAIDlJ98vmt7Eu7/0Y9x234lAttcrtYb3MTnI/YbXZ8zyItE+xsAn4m0Z9HRiKJJ/IgH6HAD7maIBYBtjrNpx/1DRFMwUM8agaqbNYgB2PYPAvtzjBu+4FmamgWfkzm2oln6tXw4vlHB2o4brDsxueiyXMmbOjYiseljNQWwSl2G+KLrBJ7SvunoPCMBn749+Ft2eLYlSXUM/2CfrgywU5ceA21K6CDdcsR0PPL3ac2GlcR5hyKXiiMUI28fTgUlc+IqZqMyF74vr1UZg59FOSmpL4pKIEYjCz6CXVQ2//fmHevqNDs2vO8pbAN6EbzDX7U/eewJNxhCjYAL0ht7EzZ/5IW793nF8+eHTAYywd2qaDsXFAx0YrDSKry7NjZk2i31896dWeZOiDg16RDLonwbwfSJ6FxG9C8B3AdxORDkAj4Y6uj4QTaDzi4OSjBttniMYMC0WVSTjhPGMe3OVnGJc/MohBujVum556gbRiQ8w5C0ANhWIAkDWvPBFJYvOD/iWxCV6AbqmN6E1GdKJOHZOZPCSS+fw+YPzkZkkuVE8DyUu9u6Wg8ygd2uIIsL1z9wGxoCvP3qup9fzyQrvTBmEF7rdxQUQD9A3ag0kTFnBYkhZdEODblwvDMvX8A0SfnB8Bf/84Cnce3TZ1+tWy3U8vVJxlLcAQDY5GA16pa7h9u8/jeuv2I5CJolan2NQNR2//ukH8ZVHzmIql8JaZbCSmVpDtwpBnUjEY4iRvwz6X339SfzO5x/ue2wLZswzmU327eJyeq2KeIywzZbgNFxcBn8d6HpGZIy9B4bufM28vZ0x9ieMsTJj7LVhD7BXRCUudq9PJRFDkyFyzhQLRRUzeQUxj+6dmSQPZsObFZbrGi6eywMAng5I9nDXk4u4eC7fNrvlbIVsJ0hKqgai1rijqEFvZTuNU8drrtmLxaKKbz7WW2A0LGzUGsimjHNAUEvVw459pWCQ+lnLzq2PDPql28awfzrbs90iT1zwRMauiQxOB6FBT8R9Z9A3qg2rnigsq8UNmwYdwJYkp44uGF71y6W6r9cdOsX1584Z9EH5oH/hwVNYrzbwphdegEwy3td5o9bQ8WufehBff/Qc/vi/XoHrr9iGlYq/7yloVNN+2gu/dXsPn1zD/cdX+h0aFosqZvOKNbnsT+JSxfZCuk1Cm1fiKKmDrykQTVk8COAfAfwLgAUi2hvekIKBQSzQ5hcHxZS4AMPfsKGTxaKKWQ95C9C68IQpcanUdVxmFnkFYbVYa+j4wbEVR3kL0Ap0o9KsqKRqyCsJy8c3ij7ondZbL750DjvG0/h0xItFedHc+dT0xB4w2m0mt5qWW0TvGXQiwvXP3I57jy5jvYfMI5+sWBn0iQzObtT6KqpXTYlA3keArulNlOs6Lp41Eh1hFIo2mwwlVUPBHqAn4qHLO4+YAfqqz8Dz0Mk1EAE/4WK9mVUSgU2qv/HoOfzZHY91lcg2mwyfuOcYnrV7HM/dN2l2te5tDLWGjrd88iC+9fgC3vsLz8TrX7AfE9kU1ir1QDrk9gqXMnrh9xpWa+iBrNYtFGtWzNNvJ9FTa9VNza/yShK1RnPgyVoRm8WbAZwD8HUAXwbw7+a/Q09NYFZlDzisooeIWS3y2aQXVjAbUoCu6YZLwd7pLFKJWCBWi4+d2YCqNXHNhc7dBHnDn6jY4pVqmqU/BxDJouRaRwY9HiPcePUefOfwUt82d4OEL/lHrfC4H+xFUEMhcemxSJRz/RXboTUZvvWE/9Wczgz6zokM9CbrK4PNJS5WgC6gaeVSqwPbjAD93EbwGfRyXQNjsCQuwNbI7XiAvlz2F6A/dnYD+6dzbeO1k03G0dBZIDK7f3noFD5y91P40F1HPZ/37ScX8NRSGTe98AIQEdLJeE/1ApW6hptuvR/3HFnCn//ilXjtNfsAAFPZFBo6G6gXd03rHqD7lUbVtCY2qg3hGkE3jKSkYYfarz32qdWqZa3KsSTBA07+iaQsfgvApYyxKxhjVzLGfoIxdmXYAwsCkcDNnr2xjPejlkEvdc+gc4lLNaTsIHe+yCsJ7J7MBOLssWQuhe4YTzs+nrV09dHIeJZUzVruBqIpcXFqXnHj1XsQI+AzES4W5b7Qg7Zs20rsF5+BSly6NEQR5ardE9hWUHqSuWzOoPfvhW65uKTFXVz4RGnvVBaJGIXi5MInAfn01iULGGM4smgE6Cs+JS6LRRXbCt0NEIKYWC+Z3/f7v/YEvntkyfV5t9xzDNsLafzsT+ywxuA3i19SNbzhE/fjvqeW8f5ffhZeefUe67GJrDEZGaQOvSYgcfEbHNfqOpqs/2v2QlHFXIFn0Hu/jupNhrMbNeyabM+gW3UjA44tRAL0kwDWwx5IGIjMaO3Zmyj4wXaiNxmWS6qngwvQmhGGlR3kQU0mFceeyWwgVotLJeNk6WStBbQy6FGRJJRUrf2iGEEXF3682IuHdoxn8NLLtuEfD54M/Ni57d7j+Nh3ngp0m04YEpfkwJuebCU8YJzOpQbq4sKTJF6OESLEYoTrr9iOu55c9D3JsjLo5jklCC90teHfxYVLjSayKczklVAD9DYNesgZ9OVy3Qo2V3xm0JdLdUx7rBBnA1xJXS7Xcd0ls7hoNo//8Zkf4sz65gnaY2c28N0jy3jdC/YhaeqWM0n/E/uP3v0U7j++gg/ceBX++3N2tz02lUsB8P9dBUmtoXdd1VISMai+MujGd9RPQqChN7FSrlsxjzG57O23XygaMrbOGrecj1WvMBE5Iz4F4NtE9Pv2bqJhDywIRC60jhKXCFktrpTraDJ0z6CHLHHhF/tcKoE9UxmcXOlf4rJsBuj8ZNVJVDXonCj6oLvphV97zV4sler4RoDForfddwJ/9MUf45P3hu8HXKwZmtxhaBu+VfBjdvt4uq1R01YTRJEo54YrtqPWaOIu0/1JFMvFxUxk7DAv2H1n0JMxK+gXCtDNDHohncC2ghKKxIV7/ndKXMJMTHF5SyGdwHLZ36RjuVzHtMs1AAjWLGCppGLvVAYf+pXnotYwXFU6v5eP33MMmWQcr3l+qxTPyKD7+/64ucPPX7Vr02MTWTNAH2ChqJgGPe4vg27GW/3UvPDEnaVB7yPRxS0WN2vQ/RV2h4VIgP40DP15CsCY7eYJEX2ciBaI6BHbfX9BRI8T0SEi+hcimjDv309EVSJ6yLx92Paa5xLRj4joCBH9LfEKOwFElpzsrhTcIjBKWU1uw9UtQG9lGUKSuHRk0Nerjb51rUulOsbSCdeTRMtmMSIZ9Fp7gB7FDLrqIke47pJZ7JrIBNZZ9MuHTuOdX3wEyThZJ+Qw2bAy6INrerLVlOs6sqk4JrLJgUpc1IA06ADw/AumMJFN+m5axM9fPNjLKwmMZ5KBSFziMUI2FRfKxvGVjEImidmxdCg2i24Z9K0I0K/eP+UrK1zXmlivNjCdC1/i0tCbWKs0MJNXcPFcHn/+S8/CD59ew5/d8Zj1nMWiii8+dBq/+NxdVhANGBl0vxr0al1DxuXaxpNSawMN0JtdV7V8a9DNYL6f843dA90YgyFx6aWglttBRzZAZ4z9sdNNYNu3Arih476vA3imqWF/EsDv2x47yhi7yry93Xb/hwC8BcAB89a5TVfENOitgIPvjFGSuNg7annBTwRhZZv5ZMjIoBsdTfvVoS+VVFd5i/Fe4cp2gqYzg85bFA+yUt8vbtlOXix6z5ElHF8q9/Ue3zm8iP/1uYfwvH2T+LUXX4xKXQ99ErZhZtAz55nEJackUEgnh0Li0o+LCycRj+Gnn7EN33zsnK/zeLlDgw4YtS/9Bei61cwlr4h1JrQy6Jkk5grhSFzsWXpO2E36jiyUkE3FccXOAtaqDWF3HO74Mp0XyaD3N34+ceBymp+7cgfe9MILcOv3juOLD50CAHzqvhOo60288acuaHttLxKXijlBdmKKZ9DLg2wgFo6LC9BfUfpCR2NGpQ/3PR6gd0pc/NSNhInrGZGI/tr899+I6Eudt24bZozdDWCl476vMcb4J74PwO5NL2wfww4ABcbYfcyIYj4J4L91e2+OkAadNypKxKDEoxegWxn0vHMhJSceI6ST4Xk88x2ZZ9AB9C1zMQJ0rxNzxBoVdWrQI2jr6RVM3Xj1HhABX+qjA97DJ9fwttsewEWzeXzs9Vdjt1m849c72Q+qpqOuNQ2bxR4utFGlXNeR5wH6MLi4BCBxAQyZy0ZNw31PiTfE6cygA0ZWrVcNut5kaOjMqtXIK4m2Zlhu8OcU0glsG0tjpVwP/HpkdTRuk7j487P2y9HFEi6azWM6r4AxcavFVh1S+BIXSzphe693vPwyXL1/Eu/4wo/wo/l1fOq+E3jpZXO4yLTB5PRSJFpt6Fb2v5OxdAIxGnwGXUiDLrh/NpvMem4/GXSrc3qhPUDvZTX69FoVE9lkm3kDAF+ytDDxSlncZv77lwDe73Drl5sAfMX29wVE9EMiuouI/pN53y4A87bnzJv3CSFywDhr0KMTMC3yE9iY+wmMk0slQstEVm0XuD1TRlA132eh6HKp7rm0mUrEkIzTwGe5IjBmWGZ12iwC0drfvCzxthXSmM0rlq7PL0cWSnjDJ36A6XwKn7zp+RjPJC370MUQZS6tJf/zr0g0m4pjPJscsA96sAH6Cw/MIJuK4z98yFzKdc08n7QuiTsneu8myrPRfFU2n06IubhUG4iRca7mAUjQ+76jxCXkRkVHFkq4eC7vu/iRT8y3okjU6b2S8Rg++JrnIKck8Mq/vxfL5Tpu6sieA8a+6ztAr+uuEpdYjDCZTQ2+SFRE4iK439ivc/2s2HHVAI8NFPM77MVq8dRqFTvHNzdBtFxchrVIlDH2gPnvXU63ft6UiP4AgAbg0+ZdZwDsZYw9G8BvA7idiAo9bPetRHSQiA4CYgcsX55R7DaLEQqYFosq8kqibWnWjUwqjkpIEhe7C8J4JokxJRGMxKXLxKOfBhFbSaWugzF02CxGz3e/Zuu868RMXulJM356rYrX3fJ9xGMx3HbTNZgrGCtCfGl7KaSW50B7wJJJJc6bDHrJkrgYjV4Gdd7r9Nbvl3QyjhdcNIP7fLSUr6i6JZnj7JzIYL3a6CmLxo9pfoznUgnBIlHDjz8WI2sJfyHgQtFirYF4jNqCwzBdXEqqhjPrNVw8l7eKPUVXxCzZiVCRaH/HrZtr2FwhjQ++5tmo601ctn0MP3Xx9KbXZpKGXNFPYysviQtgWC0OymZR05vQmkzAB128wZVd0dBfgK5iKpeyEqpKvHdzj9Nrmy0WgdZ1etDJP9eojoh+BKMhpyO9eqET0RsAvALAy0zZChhjKgDV/P8DRHQUwCUATqFdBrPbvM9tTB8B8BEAUHYcYGIZ9FbAEcVOoiJdRDlhZgd58WkmFQcRYc9UFk/3EaBrehOrFe/iIMA4kKJQJMovzu0SF3PmHyEnl1a20zmYmhlTfGf81ip1vO7jP0CxpuGzb7vWanMOtC6WSyFKXIqWJtfIoNd1o4OcvfXzKFKpa5jNKyhkDKnDRq3hWfMRFkE1KrKzraDgwadXhZ9frmubkhzcC/3MWhUHtnX1RWhDtaSTpsQlncC8wMrSRrWBQsYYxzZzkhq0Dp17/tv9FsIsEj1qFoheNJvHlDnh9itxESoS7VO+2cqgb54MXHPhND795mswN6bAyacikzLOFdWG3lZn5EWtoSPjkVibyg0ugy46aU75KBKt2a5z/bhGLWy020r3WjvIGMOptSp+8qLNE65kPAYlERu4xMVrT3pF0G9GRDcA+F0AL2KMVWz3zwJYYYzpRHQhjGLQpxhjK0S0QUTXAvg+gNcB+D+i79erD3qUAiZ/AXqi75OYGzzw537re6YyOLrYe7EgPzHNdHWniYdmHRkkVoDe4eICRGvFppsl3mxewZFzRV/b/MeD8ziyUMJn33otrtjZ3s7byqCHKHHh8o4x02YRMC72hREP0Muqjv3TxooXYASHgwnQm0jFY4jFhA26ujKWTqJYa4Ax5hhQdVJRdevcxeHODqfXaz0E6LxfQKtItKR2zxpu1BoomNrw8DLo2qYgMkybRe7gcvFc3ipMFe0mulyuIxEja9LiRFAOZUslFalErE2GaOfaCzcHchy+GlGtiwfolbqOjEcAPJFNBdLwrxdEZWeKj0ZFNdvz+tKgdzRm7FUqulHTUFK1TQ4uHOOYHdIAnTHWl/kwEX0GwIsBzBDRPIB3wXBtUQB83Txp3mc6tlwH4E+IqAGgCeDtjDFeYPrrMBxhMjA063bduici2WJVayJGQDJOkWxUtFhS8YwdYmqgbCpudcwLGh4k8yzYnsks7npyUfgC2YmlrfdY2gTMDHoENOhcy5aPvAa9fem+k5mxFJZKdV+/++n1KvJKwvECqCTiKKQToQbodl9ono2r1nUrUBpVyqarEP+cg7JarDX0vpsUdVLIJNDQjaI0EW27cwa9dy90K4OetAXoQjaLmqV/nc4riFEYGfRGW4EoEG4G/chiCYkYYd901rpPtJvocknFdD7leS4JyqFsqVTHTM77vdzg+5gfq8WKwz5nZyqbwqH5Nd9jCQLRVS0/GXS7dLAficviRg0XzbauFa2VaH/7L6+V6nRw4eTTQxygE1ER7RIXMv8mAIwx5hkVMsZe7XD3LS7P/QKAL7g8dhDAM73eywmCeJFoOmnIMpQoBuhFFdcdEJe4hKVp456uPAu2ZyqLWqOJxZJq+ZX6gS83dsugZ5LRzaBHsSiZd0d0u4jN5hXU9SY2qhrGs2IB7kKXVaCZsd507aLYNehB6VmjgFEkmrCyk4NqViRi5+YXHoBu1BpC267UN2fQ58YUxGPUW4DeaJe45JSEUAC5UWtgr2lTG48RpvOK5fscFBs1ra1AFAhXg35koYT9MzmrALeQTmBFsFlRN6MAwPielET/DmVLJdWzGNULa2Lvy3Kw6eriAgATuSRWy+KrQEEi2t3XVwbdpkzoNRnAGNsUU7Rqufz9/vy4dtKgA0bdyKA16F5FomOMsYLtNmb/dysH2QtEJOaDbvOqjVrAVGvoKNY0fxKXkPTa5Y4LHHdy6dVqsaU9FMigR12DHpLsKAy6dZfj+6IfHXo3mdZMXsFSMTwt5oZNg55JRqv5Va80m8y0WYxbGfRBeaHXGs3ACkQ5XEoh6k7DJyt2EvEYthfSlleyHzolLmPpBOp6s6t80tCgtya22woKzhWDl7gU0p0SF6P2IoyeDEcXSrjYZks4nVd8SVy8PNA5Rn1Vf8fsctnb1rfb+wPiTjKa3kRdb7q6uABGBr2uNweSLBCVuPjSoJvbnMj2buu6WmmgoTNHDbrfuO30Os+gOycQ82kxa9QwETorEtGziOg3zVtPxaFbTYxENeitJdCodRIV7SLKCbdItN3TlXuh92q1KJpBj4otnpfEJUpFyd2CKa5h9tMBcbHYXvTTyWxewZLP9uB+4Cfh/HmUQbcaiyktDfogJS5BFogCLZu0omAgUG1sdnEBem9W1CoS5S4uYjIMo2FWK0CfG0sHnkF3kriEdS6qa02cWKng4rlWgO6n+HG5rHZN0gA8+dRnBr1Y7zmDzmMI0Qw6rwXzcnGZzPqzpAwStUutEUdJxKE3GTSB/YZn2reNpXu2de30QOdjMMbsX+KSSsQw47JCI9pcLEy6BuhE9Fsw7BDnzNuniejmsAfWLzEiXxIXAJGTuCz4DtDDs5Cr1DXL3B8Adk/21020W8EOJ5dKhGYdGSSORaJWBj0a+xtgrDiJZND9SFIWNmpdMuipUG0WN2oN5JWE1ZIdGP0A3eqcqSTaXFwGQbdVmV7gAahoBqys6sg6nGsML3T/GeyWDzp3cTHG46VD15tGrwR7QeTcWPDdRItOEpeQklPHl8vQm6z3AL0kFjRnU/01GGOMmRn0HiUuPgP0mjlWL4nLpDkxGYTVouVu51JrxPEzseMJ07mC0nMywOqcbvudejX3OLVWxc7xtGtxumjdSJiIZNDfBOAaxtg7GWPvBHAtgLeEO6z+iYlKXBpNaycjIqNhQ0Qymq0uouIZ9HJdC2UZs9KRQc+k4pjJK31IXMQKdrJKfOCzXBGcJC5Rk1QB3bOdfjPoZVVDua571inM5BVs1LTQ3JXsAUurSHT496l+aE0Y41ASMaTisYE1K7Kfg4OilUEX+0yVuoaswyRh50QGZ9araPrwtwY2+6DnTfmfV9FZyeoiasugF9JYLqtCGUoRrIZpnRKXkByl7A4unOlcSkjiUqlrqNR1HxKX3s8PGzUNDZ31LHHh542a4Bj4WD0lLjljP1gZQDdRPxIXQCzJxCcv2wrpnvsu8NUk3icD6L2fyKm1qqv+HDBWF0sDTv6JnBUJgH2UunnfUBOLic1mOwuUUj6KHgYN1/nal3u8yKTiaLJwAkKnpgt7pjI42aPExWhSJJY5MZoABT/pCJKSqiEVbzXDAuwuLtHJ1naTuExkkkjESDiDbi1ZevzWPIMm2tzEL8aSvxGwcB3yqGfQW63tDT/sQqZ3XWi/dFuV6YWClUHv/pmaTWacvxwy6Lsm0mjozHeRcqfEJa+YGXSPAN2qhcjYJS4KGAuuD0C1oUNvss0uLvFwA/QLZ1u9DaZyKayW613P2ZbMsUuRKGBc2/rJoLs1KRLFbwa9dfx5NSriGfRBBOjeDek4VnNHoQy68Rx+ru/lfLPgcL3wMwY7p9ecu4hyxtJi1qhhIhKgfwLA94no3UT0bgD3wcWNZZiIEQlp0NWOgMMoeojGxXmxqCJG3k0c7OR8FrL4wanIas9ktucA3Y/2UG+yoV/1KNW0TS4RkfRBb+jWsr0TsRhhOp8SDmhEZFozIXuhF83ujUBwXQmHnU7JVSGTGKAGPfgiUT8ZdEuP7xAscQs2v4WinRIXfux7uULw799ewGl5oQdUKGp3LLIT1mrekYUSdk1k2q4NU7kUtCbr6hpkdREVyqAnUGn0vgLk1aRIBL8BOn+eZ6OiAWrQuzWk4/jJoNdsGXSgt6L0xaKKXCru0pFb/Jxd15pYKKreGfRUArVGM7DVq17w/PaJKAYjIH8jgBXz9kbG2F9vwdj6gkjsItuZvQmzYUPQLBZVTOUMKzAR+EkyDElItbE5g753KovTa7WedvClYl0om8EvqsOuQy+pWpu8Bei9uGWQ1AR8pWfyirDExanoZ9P2etC1+8FZ4jLc+1O/cMcLfqErpJMDc3FRu0z6eiGXSoBILEvHz4duGnQAvnXonRl0a8LgM4NudRMNqFCUryhsblRkZiFDCNDt8hagFQR3CzyXzcLwKYFETSYV7+sa0G8GPe3zvFEVkLgUMkkQGc4lW023hnSclgZdLNYCWpPOXhICC8XN9Uq9uLicXa+BMXcPdKAlR+3XX78fPAN0xlgTwAcZYw8yxv7WvP1wi8bWF36KRO36xzD9YINm0WFn9SLM4MOQuHRk0Kcy0JsMZ9b9Xdx4wY5QcZAS3qQjSEqqZi1zc6LYuVZt6F0Lh2bHFOEleZ4Z9NKg8xqLsKwW7a4WXIc8+hn09qzxeCY5MB/0MFxcYjFCXhGzSauo3TPoZ9Z9ZtA7NOh8IuSVQec1AO0adGPfD8pqccNB5w4glCZ9zSbDU0ubA/SWO4n3pIOfQ0SC5myyPw36Mrf17TeDLqxBNyeFHhKXeIwwkUlidSAZdF4kKqZBrwll0I3nzFoSF//nm4Xi5r4qvRQ4z68ZK/tuXUQBW93IAGMLkXXFbxLRL9JWO+X3SYxIqGDDbrMIGD92lDLofgJ0a5k1jABd1TZr0Ht0ctmoihfsREWSUKpp1gHP6bW4ZZCIOG7M5MUbCy0UVSTMC5HX9gB/3up+2LD5QifiMaQSsb6Wy6MADxStDHpmgBl0LXiJC2CuCvjJoDvIDQrpBPJKoneJixng8Iy1lyvEhtXRtjWOmbwCoiAz6N0kLsGdR0+tVVFrNDdn0HNiNSV+ZCf99sNYLNVB1JKV+CUZjyERE0sKAnaJi/e5dDKbwuoAi0RFGhUB4i4u6WQME9nebV0XiypmO1ZbE+Z372ff5Sti3gF6d+elsBE5K74NwD8CUIlog4iKRLQR8rj6JkY+OonaC/eSEQvQfSzJhdWEhTGGioPEZY/ZEc+vDp17XotJXLpnpoaBktlW3U4iRojRaPmgAzyDrgoV7vJJppvVFWBcxHKpeCgSF8bYJl/ofi3bosCmAD2dGGCjouCLRAEjCBXKoNe5J/zmMRARdk7490JXtSaIgGTc2K/5ecqzSLS6WeKSjMcwnUsFZrVYtCYBLj7oAV77nBxcAGBKVOJSUpFJxh0nTp1kUvG+Ookul1RMZlNIxHufKGaS4mMQkbgAhtXiIAJ0taGDCF3dlfxq0NPJ/hqjufXM8NPRFGh1Ed0+7r5ymxNwXgqbrnuj2Tk0xhhLRamTqD8fdJvEJSI2i7zlbS8Z9KCDj1qjCcY2Z6B2jKcRj5Fvq0XueS20tBkRzXBZ1SwvZA4RQUnEIyOpAsQcN2byCho6E8qQLHRpUmRtc0wJxcVF1Zpo6KwtIV+1SwAAIABJREFUo9jvcnkUKHfIOriLyyDckGohZtBFXFwsT3iXQLAXL3RVM6wj+cJzLEbIpeJdXFw0EGFT74fZsTQWt6pINMBrnxWgz3Zm0I0AvZvV4opgF1HAOGYbOkOjx/EvlcRMCbzIpOJCxhSAmIsLAExmk1gpD0KD3r7/uuHPxcVIhhZ6bIxWqWsoqc6d0/1Kk0+tVjE7pnhey6xVr2EM0Ilov9cLyWB30AMKCiIjcOzmX9tZ9BYVm8X1qtHy1k+Azk8GQUtc3PR0iXgMO8bTvjPoyz6q9y1t55AHVEV1s8QF4PvbcI/djpjExfjdRApFuzUpam1TXDbjB6swzxaw9GvZFgXKdQ1KImZlDMczSTR01lcWshcaehN6kwWuQQfEM+hVjww6AOwYz/jPoDf0NktVY/uJLhp0o2FW52rS3JiCcwEXibo2Kgrw2ndkoYTpXMpquMNJJ+PIpuJdM+hLZfHOnpk+pY7LJTFTgm5jEC4S9SFxGYzNotiqlh8HFb76mk7GkUrEfNssWh7oDvVKRqLLh8RlvepZIArYi0SHMEAH8BdE9AUieh0RXUFEc0S0l4heSkTvAfBdAM/YonH6JmbO/LxmVYwx1LVmm4NAKhEPNIsQFiL+0Z1wS6egm7B4ZQP2TGZ9a9D9VNS3NOhDLnGpbZa4AObSXEQy6Iwx4yQrUCQKiGnGl0oqZj0KRDkzPqwb/dDKKNolLv3pWaNAWdXarMpay85b+7lFG6L0wlg6IahB56sJzhn0XRNpLJfrwtlRoJVBt5NPJ7q6uHQWbwK8m2gwGfSSmaXv/Kx+tMSiHFks4aIOeQtHpJvoso+sdta6tvUYoPvI1rvhV+ISo9bEyI1JH11Xg0S0cLvVy0Nc4gKYRek+M+hOHujWOHxKk0+tVbFrwvu6Y8nShlGDzhj7ZQB/BOBSAB8E8B0AXwTwZgBPAHgpY+zrWzHIXuAButcBw3cq+/JqVGwWFwX8ozvJhVRQaW960onRrMinxMUs2JnMuhcOcizryCG2WdT0JqoNfZOLCxCtmgfLOq5LMGW5rnSRpGh6E8vlupjEJS/uDOMHpyX/TJ9dCaOAEaC3fkfeXn6rmxW1GqIEL3EZSycFNejejhotq0Xx85iqNTcV2OW7ZNCLNa1Nf87ZVkhjqVSH3mU1+NhSuWsyZMNMFHRm6YO2WWSMOVosckS6iS6X6j4C9P4SNUtFte8MejoZR1VwBYK7nnWTkExmU1C15pav6IkWbvtx/6naAnSj5sXfb+Vlyes30VWqaRj3MCYAWteEQUpcPKsvGGOPAviDLRpLoMTIaHlaqWuuPqpWpXKiQ+ISAds7np3sxWYx+ADd/QK3ZzKLxaLqqxBsqaRiSrBgJ6sMfwadZ+g6fdABREqDrgp2l7My6F0kLkulOhgT24en8wpWK3VoerOvQq5OnArzRJbfo065rrdlUfnFaqsLRVtuEeFJXBhjnoEQn9x7adABw/nhwlnngLMTVdsscckrCW8Xl2qjTWrFmSso0JuG9ayXHemvf/pBTGaTuP0t17o+p1jTHLP0QdssLpXqWK82NunPOVO5lOcKmx+rXaC/a1utoaOoakKuYZ5jSMaFnOMAI1jtJm8BgKmc8VutVOrYlfKWZASJuMRFvJeHvSnkeCbpW4POV5GcjDH8XkerAp8vN8wa9KhDZobAa1nSKXujRMRmkeux/AToqXgM8RgFHsx6SlxMJ5d5Hzr0paLqqzjIPoZhpNW10UGDHo/GhBCwN6/wPm2MZ5JIxqmrJMWPTGs2nwJjwXfVc8qgZ8+bDPpmictWdxNVBRui9MJYOgm92V1XX6kbsg+3/XpXLxl0B7ejnJLoWiTqlEG3uol66NDXKw08dmYDj58teo7LcCzaPAkI2mbRzcGFM5VTsOpR/FhUxa12gZYcoZfjtlXzFIAGXVjionV1cAGACdP2cau90GuNptCkuTWx89cUkhel+4Fb8k46WGEqPhOr1bre9ftPxmNQErGh1aBHGr6CV627B9uW/jGCNouLJRVKIrap4t8LIkI2FQ9cDtJN4gLAl5PLclm8YCfBD6IhzqDzrJmbxCUqGXSnFScniAjTue7dRK0mRQURDXo4XuhOtnOZZGL0i0Q7A3SeQR+UxKVLXUMvcNlON5lLWdXNzqPOWfZthTSI4MsL3dCgtx8nY90C9KqLBt08PryOpwefXgVgTGCXPY6RokctDBBcBv3IYrcAPWl1CnXCjwc6YM+g+78OLPfZRdQaQzIu/P6GxEUkg24G6FtcKFoTaEgH9KBBT7Q06L4z6BvulrxKUtzco6E3oTWZ0AQpr3jXjYTNCAfo3TXoTu1so2KzyP2j/faPCsPj2ZK4OGSIe/FCXyr50wNm+2zzHDZWBt1R4hIN1yDAn16Ye6F74aeOYmZMTNfuF/cM+vBO+IKgXNfbVnRaEpdRKhI1PlM3q8VKfXOTNTupRAxzY4qvbqKGxGVzBt3TxcUlu21l0D0KRQ+eWLH+f9jMXjtRVL0z6EEF6EcXSsil4tjh4jM9lVNQazRdjzMeNE/lxK4D/djtLvXZRZSTTsaFOmoC4hIXXoe1WtniiXOHu50bfvabWqNpfeZC2n+R6GLJ3ZLXj8RF1EEHMK7ZQ51BJ6I/6fg7TkSfDm9IwSBUJOoQcETFZtFvF1FOLpVAJWArNS+Jy2xeQToZ8+XkslzyV1GfTSWGO4PuIXFRIuIaBDivOLkh4rqyYPndd/+t+YRtKaCGLZxirQEiIJ9qD9CH3bazX8qq1rbixYO2rZa41ATrGnqh9Zm6ZNDrettqghN+vdDdXFzcMujNJkNJdZa48PO8l9Xi/cdXrWDYM0CvaZuaFAFG0zQif+3SvTiyYDi4uCWQLC90lwk3n4j7LxLtJUA33stP0z8nMqmYLxcXkQzu5IAkLmpHfxg3eLM9kf2matvmeCaJDbM+RBQvS14/UlFeJyASoOdS3nUjYSOSQd9DRL8PAESkAPhnAIdDHVUAtCQuXhp0hwx6IkIZ9B5OKJlUHJWAZ4RWgJ7cfJEjIuyezOJpwQC91tBRUjVfGfScMty+1Z4Slwj5oPvJds6OiUlcJrLJrpIZoBXEB221uFHTkE+1u1pkUnHUtWZX14woU+7obJuMx5BNxYWzWgsbwdj+tfapMBoVcYlLlwy66p1BB3iA7k+D7lQk2tCZYyBRVDUwBsciUSURx2Q26ZpBr2tNPHxyDS9/5g7klQSOnHPXoRsBuvN5Wgnw2ndkoeRaIAq0pBtuNSXLPrpJAzaJSw/n0qAy6Jmk+HVIVOLCV7YGInEROM8TkXDMVLP1BihkEtCbzFcixEhKOq/I+JGKWhl0EYmLx6R6KxA5K94E4CfMIP3fANzJGHt3qKMKAD5z9ywSdbRZjENvMmhDHqQvllRHu6FuhFEAx33V3WakeyYzwhr0lge63wz68Aa5JdUIEJwkLqmI2HoCzrakbszkjc6fXo3C3No2O5FXElASsa7WbH7ZqDU2ZS2j4q3fK4wZF8bOAKGQFivceuzMBp7/Z9/EwyfX+h6Lk8wwKFoSl24ZdM3VA52zczyNU2tV4YyfqumONouAs6+yk5uQnbmxtGuR6I9Pr0PVmrh6/yQunsu7ZtAZY2aRqPN7pAIySCjWGji7UXP1QAeAqXyXAN3Mars5sHWS7aPHx3Kpjmwq7uriI0omlUC1oQvtI7WGbvUl8SIRj2E8kxxIkaho8zAlERdKMhmF0y0NOiDuGtXoYsnrxx6bxz+iGvRBrs57dRJ9DhE9B8CzAfwNgBthZM7vNu8faoQ06C42i0CwDRuCpqE3sVKuYzbfvbiukzCasJTrOpJxsr67TvZMZYU16Hy50W8GPehVgSApmfr4vMMJOUo2i34y6DN5BVqTYc3jBLxQ9LaNs0NEhhd64BKXzRnFTJ9NT4Yd1Vwd6JR1iBZu8QBQdFXMi1aRaHgSl24BerWuO9bP2Nk5kYGqNYUniE4SF6vrsUO9TKujrUuAXlBwzmXfP3jcKBB97v5JHPAI0FWtiYbOHDPogNmkLwAXl6OLZQDuBaKATeLi8n2ulOsopBOu15ROeLDViwHCckncNUxkDCLn80pdR0Zw1Wgym8TKlmvQxSQugJjqQG8y1PWm9R35dY3iEza3pGQvGvS0iAa9izVq2Hj9Au+33d4HYBXA5ebffxn+0PqDr1h7ZYudlld5Z69hzmou9eCBzgkng657Zh/2TGZRrGlYFzjJLFvLjT783ZNDnkE3D3CnVuKRcnHxkUGftYo63QNqXpUvysyYEoqLS2fAEgXrzn4oWzUR7Z+7kBFrHnLGlHoE0YI8XImLWJFopye8EzxhIJrJdHJx4d93Ud08Hv69c+eZTubG0lh0kRUdPLGCfdNZzI2lcWBbHotF1fG34RMVJxkNEFxX424Wi4Bd4uJ8PC+VxD3QASAeI6ST4hrw9vcSdw3zggfcIhN7ozBZLGM/mUsFcqz5wU/fEhGjA7XDorfgM4Pu5YHeGoM/DXpW4PMZ1qiDuw647iGMsZds5UCCJiYgceE7ldJhswgMd4DeSxdRThhdEstdNJyW1eJqBePZcc9t9SJxySnD7bpRUhvIJOOODXb8+rcOElGbRaC9qPOSbWObHmeMeVblO24zl8Lp9WC0z5xiTcO2DpvHnDLqAbpzUXchncRZAW35GfM3WAsgqxdmo6JsKo54jLrKdkQ06H6LENXGZhcXL4lLUSCDvlhS0WyytnoJxhgOHl/Fiy6dBQAcmDOOtSMLJTxv/5Tje7hJXILqon1koYRknLDPdPByIq8kkIq7S9b8dBHl9Lo6vFRSsXvSfayicIlntaFjsstz7Y4m3ZjMpnAuoJoPERhjwj7ogGms0SWDzictnRIX0Qw6l3e5WfL2pEEX+P7H0glLojoIRFxcthHRLUT0FfPvy4noTeEPrT+IjCy6Z5Goi80iEFw1exj0E6DnQpC4VLpYRvGTn4iTSy8SF+PEPLzBVEnVHfXnAO9cO7z7mh3VZ5Eo4O5bvlHVUNea/jLo+e7WjX7xlLg0hnfS1w8llwy6qMTlLA/QA3B88VPX4BciMnyMu2rQu7u48GynqB5V1ZqbNehp921smGN0az8+N6agobNNxYLHlytYLtdxtRmM86y1k8zFyVLUTlDnoiMLJeyfznl2/CUiTOVSrisSRhdRfwG64UPeawa9f4kLPy92y+JrerNN7tGNyWwqkMmwKH6PSUOD7r3fdK6+8onohqB8ZKFLUzslEYcmWDvop0g0l0qg1mgOrCZR5Be4FcBXAew0/34SwP8Ma0BBkjWLNtxws1kERjdAD0vi4rVE7McLfamkIq8kfBWN5UJwpgmSkurcHAQwTix1renLbmpQ+PJB542FXHSz1pKlL4lLCitl78JTvxRrm5vD9GPZFgVafQs6JS5i3sTcDzwIZ4laQwdRKzESNGPp7gF6Nx90oLWqIiJf0MxGKK4SF68iUbcMulmrsdBxPB08bvifP2+fkbPdNZFBJhnH4XPuAbrbuSiogvWjiyVPeQtnKpfyLBL129mzlx4fzSbDStlf3w03eMDXbQzcaUbExQUwmjoF3UHZC9VnXYiI+09n/ZLfDPpi0dvVx0/tYKUunmiyJtUDkrmInBVnGGOfB9AEAMaYBiASV6500rv1rlPRW9Ad1cJg0Yd/dCfZVMIqEguKsqp5ZtDHM0kU0gkhJxe/HuiAOelo6IEGbqLc+fgCPnjnEc/nlGoNjwB9+IuSOX6KRAsZYwnbLYO+aGVExAudZ/IK9ObmLGKvMMaw4ZRBH3ENupsvfyFtdM3rdhxxiYtITUk3eHdBvw3XRBlLJz016HWzcFJU4iJS68KPZTeJi1eRqNtK27YCb1bUGaCvYjyTxEWmpWEsRqaTy2arxa2SuJxaq2Kvh7yFM5VLOUpc+DE+41vi4j/5tFqpo8n6t1gE2iUuXvjx4QaAiWwK1YbuKdcNEr/OSikB/XfntYPv53406FO5lGvRsNXRVKCHTc2HxIWfI0sDktCKBOhlIpoGwACAiK4FsB7qqAIik4pZB4MTNU1HPEZI2rI3POvRb8BUrDXwqftOhJIZXSypGM+I+Ud3EoaFXLWhI9dlZ987Lebk4reLKGBkAhlrnVi2kn96YB5/843DnktgZVXvGqAP84oNx+l4ccNwXUlhqegcTFtLlj6sQi1de0DdRKsNHXqTbQpY+ulKGAV4ENMp6yhkkmDM+2LU0JvWpCsIiUut0QxF3sIZSyc8l9Gt1YQuBXt+bPxatU3OEhcnTetG1Vhlizu0MQdsGfQOLfLBEyt43r7JNl36gbm8VahpR0Ti0u91T9ObqGvNrpIhwD2DvmYGzaIWi5xMDxl0PkHY0gy6D5s/oPU9bJUXeqvWSFTi4j+DHo8RxtIJcQ16l74vPBYSapjky2bRuDYMyslF5Bf4bQBfAnAREX0XwCcB3BzqqAIi0zWDvtkKy5K49Dlb/cojZ/GH//oIfnQq+LmMH//oTjIhBB+dXQmduGDG+aLRSS/FQbkBShIWiyrqehMnPPT1RVVzvWD5mfkPGsMbVzyYmhlz14z3JHHJd3eG8YNbwML35VHPoHfK0rizgldm/NxGDTznEJTEJQwPdE4hnfSUuJStyYpgBl1gqZsHCZ1Fdtw1wskVYqPWcHVXAVoTWXsGfaVcx9HFMp67v70k8eJteZxZr21aOehm5einG6MbfuQbU7kUVhwm2zxo9i9xSaDis26E27YGkUEX1aB7dd52YjJrNisqb40O3W93XxEXl5adauv6Idp3ATAteT2SOX6UD7406DyDPiAJbderLWPsQQAvAvACAG8DcAVj7JDIxono40S0QESP2O6bIqKvE9Fh899J834ior8loiNEdMjutU5Erzeff5iIXi/64boH6JsvDkH5oHPfzicdtID9YnTU6i1A5ztckLaE1bp3kSgAXL6jgPnVatcZ81JJxYzPz2YFVAPQifFs4pNn3bv3ldTNVn6c1sx/+INBv8HUbN69m+hiUUU6GcOYQKbN2t5YsN1EW0v+nUWio92oiNssbsqgW4Vb7scoLxDdNZEJRuLi4BceJIV0wnMZndeuiGbQRfYJfix3fq5YjFx9lTeqmxtm2Ukn4yikE20Z9AdOGP7nz9vX7tZid3KxY2nQPc5F/Upcqj7kG9O5FIqqtunc12tnT6NLtr/z6JI5GeilK3cnPODuJkVpuYgI2ixmB5NB91Mk6jeDDhjSV1GJy1KXmIcXZItcR6umw1LMZbXKzpi16jWkAToRZQG8A8D/ZIw9AmA/Eb1CcPu3Arih4753APgmY+wAgG+afwPAywEcMG9vBfAh8/2nALwLwDUAng/gXTyo70a6S1W3U0YwKA0693c97NF2uVcW+gjQM0nxC40oFQGJy+U7CwCMLoRuaHoTKz1oD1uTjq0/iHgG5gmP37lU8ygSHbCtpx8JVs3WCU4EL9cV3qTIj/Y4aInLhuULfb5KXDo06BmuC3U/jrj+/Bk7ClirNvqW8IWdQTeKRN2DANEMeioRQyJGQqsqVgbdQYKYU+LWBMnOhkOxcidzhXRbBv3giRWk4jFcubvduvaAi5NLsaYhZ1pPOhFEkaif7DDvJtqZGV7uwckLMM0C/Epceui74Ybo6rQfiQVg+KAD7l1Xg8ZPrRHw/7P33tGSZHeZ4HfDpX/e1Kt25V61k5DrFhJSt5AaicEJs4udFWJgEQNiFtg9yzgYwTCc2cPCzC7DoAUNaMWBETAMAg0IgaYFaiEJraRGtEx3ma6qbnWZ5026iAxz94+IGxkvM8y94TJfoe+cd6pevnyZ8TIi7v3d736/72PuP0ka9HFWnjd3gVLqqQai+5VEJC46B6HIMAwXm9ICHcC7AQwAvNr7/jqAf8Pz4pTSJwDsjjz8zQDe4/3/PQC+JfD4b1EXfwNgjhCyBuBrAXyIUrpLKd0D8CGMF/2hqGly7GrWTcuKYNAzF+juoHMx5wKdXaxpV/xFFB89Izm2+IE1t0D/4o3oAn2vZ4JSCDPoNQF2K0/0Bzba3o0bd567RrSN2yQ16M/cOsT9/+qDeG6ny/V8PSS+PA5LLbcJLKzpUDSkCHAZF0UiuUtcRsNhVFmCKhN/u/52Q8ewoEhkzDmFx1mBObg8sNaC7VD/+k8L3bQL8UBnaFVVdAwrciHBq0F3n8NXAEZp0AEvmTCsQO9bkSFFDCutyhE/7E9f28OL7pgZm8PuWqhDU6QxBt3dyYteBORhs8g+T0YExWGYJnr0fmaFaBk+6NsdA7JEMBeze8GLGrfEhV1z/DaLQD7BYDwQCaQDeCUu44sSXlvX/Z6JgR1vyTucR3lComzuxVFcdkEZ4DkDZymlvwDABABKaQ9Alpb7VUrpTe//twCsev+/A8CXAs97wXss6vExEELeRgj5NCHk01tbW67EJWZANULM+PPyQWcMet4Sl+7ARt+0p0biYnqerkkM+nKrguVWBV+MYdCHIUXizAlQvhUSO15Cos+zYdkY2E5sY5b7vPIL9Cef24duOri2wxfZbniOG7xYjnFdEQ0pAtzG08Wm5u9aZAXbXg0rWpLGjuOMrtcTMbp7wSNxuXmgo6HJuNNz6cgqczEE+xpE0aoqcGj0eMckEUlJogB/AehLXEIKnMgCnYNBXw0w6Lpp43MvHIyFEQFuA97Z5ebY7m2Y538Qebi4CDHoDff+H2WGdzoGJOK6l4igpsVLWsOw0xlgoaFxyR2SwKtBFwnKAYA5T4O+W5oGnT+QDuBrLu6HyGZ4NegbXr9S3Hwh0svVN1MU6FPMoA8IITUMXVzOAshlhqQurZGbzQml9NcppQ9RSh9aXl5OvGENyx5bJebGoHsT1/X9fq4nN4sHOjBkNnjcCHjQE9AcPrA2E8ugs63NNMxJ8FjKApssHzw5g6vb3dDVeyfBe9jfmpsAW3t1211U8HrIizpusJ2QMEnK5qGeqtE5z7CiOFeLtKmExwFdI1ySxhO/fXNfx9pcLcDqZSsawnYx8wRbfEXJXLq+J3zyMdQrMhexESdxaVbDC/S2bsVq0AG3QNlsG6CU4vPXDzCwHd//fBTrK81QiUtcgV66xCVCurHdHWC+rkVKcaJQV2WYNoUp0D+23TGE55soVBQJhCDWOQ4Ql7iosoRWVZmABj3PJtHxxGBeBv3mvlugn5yrRT5HhOjSE4IVg2gcgwL9ZwB8EMBdhJDfgasb/8kM77nhSVfg/bvpPX4dwF2B593pPRb1eCJqaoLEJYQR9AumjE2iu13Db4DLU4eetUDPO8a87w/IyQzUAydncGmzHTkJ+Ay64N82/JvKvYnY8b7m7BJsh+LK1rhUhLH60+iDfnXbZc55B580TaLAeFiRbto41K1U17BboOczUcX5QhcR6DUt6Ea4CrUqCghJKNAPdazNVn1WL2vRYBRss8hkI1FOLr4en2P8amgK165KVJMoe41RPavjUC8wK/4YllsVDCwHh30Ln7rmNoi+IqZAf2Gvf2RMbOsmmgVLXPoCkqHFiAJ9pyOeIgoEm7v579vtziD1XDoKQkiiMQUg7uICuDKXsgr0sADHOPAw6GHppDM1Fb2BnbigemHfldXdOR9doIuYLYgw6KosoaJI06tBp5T+BYBvA/B9AN4L4CFK6V9leM/3A2BOLG8F8MeBx7/Xc3N5FYADTwrz5wDeRAiZ95pD3+Q9loikbWrdHI9jzstmca9r4uHT7vZjWKpbWmRm0AUCN3jAJoCkJivAZdBNm0baLfoFeiOdi0uezjQ8YOfiNeeWAITr0Nue53G0Bp0x6JMo0D0GnfNzE2U7hwz60QI9TUiR/5o5M+gSQSibnMZT+bigOwgv0CWJoFWJ9w2/ddDHiZmqr9nN6oVeFoMetehgEy8Pg17Twhs8R+Fr0MMkLiHJpt2BBYcikUFfnXHvl422js88t4szS43I5sb1VbdR9NnNIWmQLHFx3TiyNP6KFJ+zNRUSCSvQB1gUnAPc92S7wyIFen4MOpDsHAeIS1wAt1F0LwfXJB6IBhVVFBm2Q2OzQMISg1nPS1LS7439PlSZxPugq/wMOo/rXBAtL8BtEuBxcfltuAX6s5TSP6GUbvO+OCHkvQA+AeBeQsgLhJAfAPB/AHgjIeQSgK/xvgeADwC4AuAygHcB+BEAoJTuAvg5AJ/yvv6191gimMQlasAJZ9CzM5qGZaNjWHjJnXOoKFKujaJbvh5LvLgBxAI3eCASusCcXKJ06NudAVSZJDZLjcIPXyr5JtpqGyAEePjUAhSJ4EKI1WKHIxwEKF+DbjsUz++KMuiCEpcIBp1Jg5YFQor812xp2OkMcgkAa3sJr2FOMnVNnogrUBlwGfTw+3UmxvrMtB1stg2szdV8ffBBRlYvbAzOE+y+S2LQ6xzjV4NT4xwrcakoY9dVlJvQKJgkbONQx6ef28NDp6LNzM55VovBRNFD3Ypl6fOY+0QKdEkimK+Pp4nudsXTpIPvKXLf7nQGuYQUMSQ5xwFugSiNFKtJWKir2JtiFxcg/rrRPdY6ONayeT5J5nJjv48Ts9XYPgERs4XeQIwUaFTGd73KAk8l9BsAHgHwHzz9+d8CeIJS+n8n/SKl9LsjfvRYyHMpgLdHvM5vAvhNjmM9gqoqw6HuhRM2WOphGnQ5uwad2UYttTScW2niIkdADy822waUDF3nrJDOq6EyKpUwDKcWG6iqkqtDf8X4z3c6BhYbFeHYb/9vKptB7xhYqGuoaTLOLDdCG0WHsepJLi7lHvv1vT5M2y1yeaVBosXUTFWBpkiRDHoaJ6LlZgUD293mn61nc144jNH91jQlc/E5regadmRRMluLbtxy9c/A2mzVZ7+ysnpFJ4mygjTqb+oOLNdCkaNYqmuKv6iNQ5zEhfmgU0r9cW7YrJzg4uIx6J94dgf7PXPM/zyIexbrUGVyRIfe1hNcXAIGCWlSqgExH3QgPKwoTZo0IO5Q1hv+fXT6AAAgAElEQVRY6Jt2LhaLDEnOce772qhr4cRAFObr2lhPQVEICxWKQ7BBM6qvtx8ij5zl6HkB3AL95Gy0vMU9BgGbRQGJC4DI7IIywCNx+UsAPw/gp+Ey2w8B+OGCjysXJEXvhvk6SxKBKpNMBTqzjVqoazi/2spdg77UrKTuOpclgqoqCXe7R4GxFTwDsiwR3HdiBl+8GZ6u6oYUiTMnkkRQ1+TcdgV4wc4FAKyvtkJ3SjoRoTAMIltzeeLK9nCw512s6SGuR3EgxN2W3Bor0L1doDQMOvNC72aXucQVLHUOJuy4ojuI9uWfqUY3bt3yLBZPzFahKRIampy9SbRwH/T4bfReRMNsGLhtFn0GPUSDXlFgOfTI/c4KFJ4mUQD4wOdcE7TRBNEgVFnC6aWGL68cWA4My4kNBsvDIKEn0JMEeAV6gBkeWA4OdctvIBWBqFnAdpv5recscUli0E1LSGIBeBKXEhl0RSJci1aAl0Efd2tiO0bJDLqOO2IaRIHAPMpR1/RNW0j/34hwXioDPBKXxwF8DMB3ArgA4GFK6X1FH1ge8IMDIk6aETE5uJHH2Rn0hYaGdS92mTfSNglbnfQhRQx5OlT0BRteHjjpOrmESRR2uum0h+77K6Uz6NuBc3HvagvP7/bGPld2YycliZYdVHRtu+u9P38DjHu/iLGdS63xNNHNtmujluZc+wV6DlaLhzGa3HrlNi7QDStScx0XHnKDuSl4bNZcXcN+P33RQKlbqBbrgx4vcekOLO5iUtgHPeTvCksm5JW4NCoKmhUF13Z6WGhoOLPUiH3++koLlz2JS1RqbhB5hPT1THdHgteBZbGpHfFB9z3QMzWJ8o1nbJGfp8SFx+qxL+DDzTBfV9Ed2KXstIoG0vH0UYUtxH0GPaY2smwHtw71WAcX9xjENOgif19rmgt0AE/BDSp6EYCvAPAiz3Zx6pHIoFvjTaKAO7DmwqA3NJxnWsCcGkW3MqSIMtRTRCJHQcQFAXAbRQ91C9e9zuwgttvptjYB9jeVz6Czc3F+Nfw8J9ksahOSuFzd7qJVUXDnfI1bs5mmoW+5qY25rmy1DSw2K8I2agD8HZY8nFzaMZrcegpP5eOC7iA6OCtO4nLLSxE9MetKLebqaiYGPczZIW/UVBmKRCJtFnuGzdXgDgD1Ch+xMfSRDndxAY4mEw4Z9OQxlLHor7hnPlEicW6lied3e9BNO2ApGu/iAmQr0PsDMXZylEFncrh0TaJiEhe2yE+zGIiC2yQa//n1BD8jYJgmmnXHigdh0t84DBn0JEOO0eTiZAZ9s23AdmhigS6SX6ObjtAOxiQ16DwSl5+glD4Kt1F0B26y6H7RB5YH4hh0x6EYWE6oplaTs/nBsq2ohYYWKNzykblkSRFlyNNCricgcQECjaIjfuiUUmx3B6m3G92mvvIKKj/RlTHoJ9zzPCpz6RoWCIneYRAJWMgTV7a7OLXU8IJTkj8326EwbSrc0LfUDGfQ03igA8OJOw8nl1iJy23qg27aDgaWE7mgjpO43DzQUddkf1HjFujpF0p+M1qBTaKEELSqSuSio2fa/Ay657OdNDcYlgOJAErIArQZwuizY0ti0IGhe9fDMfIWhvXVJhwKXNnqJu7kAfk0rPcGNlfDLcNCo4L9vgnbSxtmxXqaecA3C+CcB3b898qRQVflZB90AR9uBpY7MOp4UwR00xbqQWBzmB4zhxmWjdpI0T/UoEePs4zIuyPGYhFw73PXJjT+s7e8YEWRHYxpl7j8KCHk9+A2h34z3GbNryv6wPIAOwlhTRu+TjBkpchzouOw2x2AeElod87XUFNlXMihQLcdip1udt/WuqbkFmMuyqDfd6IFQsadXNqGhYHlpB4sGxU+j+K80DYsGJbjTyR3L9RDHXvahoVmTEOQIhFIpHwN+rWdLk4vNdxrgWPwGXb2i7Gdy60KdruGPwEDwGZbT30NLzQ0SCSvAj1a4uJmKDhwnGS3mCef3xMKR5kkugk9EXHexDcP+librfrXsitxSc/o+c1oBUpcAJc1jtagRzvajKJe4bPxMyy3wAm759lO2lEGPbl4ZmBWi6+IaRBlWA84uRzGeP4z5CG3E7WwW2xooHTop892n9M0bvoSF865jTHoafTuccdQjMTFPcYyvNBFswl4NOhhspKKIkGTpVgG/QYr0OeSXet4ApN8i0sRiUtEuFgZ4DkLVQD/DsB9lNKvoZT+LKX0wwUfVy7wGfTB+EmLY294jPfjsNsbYK6mQpYIJIlgfbWZi8RlrzeA7dCcJC452Sx6DDHvDV3XFJxeaowx6H6KaCYGvbybaNSPXpYIzq00cSFE4tKMmXgJIb7/cFkwLBvX9/o4vdTgZgdErbcYlpoVOPToxLKVgUGXJYKFhpa5QKeUhcNEMeh8sd2bbR3/wzs/jvd8/Fqm4ykLbJepGaVBj9Fs3zzQsRZwU5irqTjIsOWedtEnilaI9zhDd8DPoDf8AjD+fjEipJNAeHR4WzfR0GSuprx7FutoVhS86I6ZxOeeWqpDlggub3ZiU3MZeKQKSegJaPqBYXHMdp3ZPJClSZTXLGCnO0CrquS6QKxyBhWJS1w816RuCRIXwcZtnl3gMHkkIa6lcpwGnTHoawkuLu5xyIlEVxoP+oamQDedWJ/3osAjcflFSuknKaXHbr/X16CH3DBxZvyVjJHHu92BrxkDXCYjDy/0YcDLNElcxv1Nk/DA2swYg+6HFKVl0DUlN109D7Z9q8Dhyv7e1RYujnihd4xoxwwGTZEyB2OJ4Eu7PTgUOL3UQJOzGVJPqRdmCxh27doOxXZnkNrHH2CymWxMUndgw6HRBQvvdvnGgWs9+JGLW5mOpyz4wTwRRRSzrgxjtW4d6L7+HPAkLn0ztSe9aCBKWrgFeoTEZWBxu7j4IW8J44xhOqH6cyA8OvxQNxMdXBj+8evO4k/+yWu5JAgVRcapxToubXAW6HJ2uV0vBYMODOUmfhYGx27CKEQthLdS2jkmHQOfxEXs71sokUEX7TXi8c+PslOdqUVL6gCXQZ+rq1w2zjx1m+6RtUI2i1W261V+T1Kx1MWEwS6yMC2pHhNnmzXyeLc7OJJOdn61ic22kYltAgIBL1Pk4iKi4WR44OQMXtjrH7kxszbslM6gd8bPxfkTLdw61I/8XZ2IWPUgKjlEbIvgypbr4HJ6qYE6ZwNMFgYdGC7AdrvZd4HySBNtJ2z51zhTCdmW/Cev7h6L5NEkX/6ZiORNy3aw2dZxMlCgz9c12A5NnbIXNwbniZkYiUvXsH3pShIavo1fEoMereENdXHpW1z6c8At8E8luLcEsb7SwqXNduL1DgSs6jIGFQlF2DeOaqvTZmEA4hbCOx0jV4tFAKhpye/vSlzErnkWDFaG1aJoNsHQxSWuSTQ8Q2OmGh2MBrjOUUke6P5xqMnS5DQMOttt7EygJ+m2LtDZSQjXoEcXHFltFne7A18zBgwdPi5uZmPRR2UVaZErg25Ywtt1D6y527NPB1j0bW/gSdsAW7YtXti5uDekIbhjxMdrA2xgKa9Av+pZLLImUZ6FzdCZQrRAd+8D9nnlsQu01MwucUliFOuccgZWWAwsB5+8upPpmMoA22WK06AD49Znm20DDgVOBCZL1uSVlngoo0kUSNCgCzDovLsqbtBPPIPeHWPQxRljHqyvNnFtp+dfpzwMejYfdIu7HwkYZ9DTpogyiJBPO530tr5RqKkyLCe+kVhUBgS4pGGzomQOBuOBaCAdtw96yH02G5NcDHghRQkOLgw8Ehff1EIoqMgd5yYRVhRboBNCZELIX5Z1MHkjzmYxjr3JarO42zWPDDLrq00A4w4fomDFTdZtuZqWHKbAizR6ujAnl6wNO42SXTe22gbkkURXdp6DDcEdPVniUlGyXW+iuLbTxWJDw2xNRV2TufR1adlOtoBhBfVmhpAiBsagp5VWAMm+0DXOYowVPrJE8NFL26mPpyx0fIlL+D0b5axw07NYXDsiccm27e4v+kqQuIQVAY5D/VRHHjCmPZlBj9ag11UZhByd7A8TEj6z4NxKE7ZD8dQLB6iqEtQYnbuIl3QURJtEfQbd055vdweZkj1rAgFjaYPx4lCNkdUypHFxAVwdeikSlyI06FEMek31cwDCcH2vjzsTHFyCx8GrQRf5+1gT+SQaRWNnW0qpDcAhhMyWdDy5YqhBF2wSzWCz6DgUe72jDPodczU0NDlzo+hW20BDk7n0WHFoaC5rmqXAYUhToK+0qlhqVo7o0He6BubrKnd62SjqXiOHzeG6kQe2ve3RYKIrO89BHXqXW+JSHvt/Zct1cAGGUock5wMjpcSlWVFQUSTft3wzRLsviqVWBbrpZNoxOUzwha7HLO6D2O0OoEgErz6ziCeOgQ6dFZdJEpdRXehNL0V0bS4ocXGfm9abuTyJi4LOwBpz5GGTNbeLCzeDHi1xkSSChqYckQW5EpeCGHTPyeVvn99LXATk4eLSE0xpVGUJM1UFu55UbKdjYCmDq0qdk3yybAd7PTN/Bj1m1x5wbU5Nmwq7uACupCyuQN841PHDv/2ZTNanAPMsF5e4xDPo4d7qszUlUoN+qJtoGxZOcji4uMeR3MvFzovINRomSysLPGehA+BzhJDfIIT8Mvsq+sDyALsgQptEffYmhEHPUDC1dQu2Q48wwYQQnIuIghdBHimigDuIODQfa78023XAMFGUYbs9yLQzMJw8y7mJwgKjCCE4f6J1hEFv8zaJlixxYTrWekhwShjSNvQRQrAcSBP1JS4ZGXQgm9UiY1RnI6QFvLHhrCH80fNLuLTZ8QvZaYXfJBqTJAqMS1xYSNHaTMDFhRXoKa0W42SGeaJVVUHpuIa062c48CeJAkhsRo9rEgXcxdG4xKUYBv3McgMSiU/NZcgjqEi0SRRwLRV3Ai4uWWwPeeWbWfzWk94fiF7Y91MUiAzzdS1Wg/6+v72OP/v8LXz2S9liagzBJlHfPz+iOLZsB5ZDQ1+TadDDyEJmscgrceGZR5mjn2hQEZA8RxYBngL9DwH8NIAnAHwm8DX1IIR4fsbREpcwpiOLzeJuL9wm6vxKMweJS3r/6CAaCYOICNIw6ICrQ7+02fYng52ukU17WBELqciKrU54YNS9qy1/p4RSii6PBp3DvzUvdA0Lm23DZ9AZe5jUoZ6F7Qw2dW61jczWZuw6yVKgJyUr8saG73gN4Y+eXwYAfPTidMtcWChV1KKxpspQZTImCbmxr6Omyke00rM1lm6YTeJShosLMG4d6evxuTXoAhKXmAK9UZF9No5SisN+tN1nVlRVGXcv1AHEN4gC2VONbU97XVcFHUq8NNHewELftLNJXDgZdLajV4SLCxAtcWHHlkriUlf9+iIMjz+9ASB7mJFuhgc4RiFJGhXnADZbU2F5UrNRiBboQjaLQhp0j0GfNg06AFBK3wPg9wH8DaX0Peyr+EPLB1E3bBx7k8VmkW3VjRbo955oYbszyHTzhLG2aeCzpjmwzakL9JMzMG2Ky5tuMbvdycagNzgZz7wQdS7WV1vY6Q6w3THQN10rv2SJi5zJOUEErEH0DCvQeRn0DA19wTTRLCFFDGxhlMVqkbdJlEfistDQcO9qCyutCj5yaXIyF55mzd7AzS2ImqAIIaFporcO+1ibqx5x15jLS+ISU8zmAcZOj1otsrGCW4PObBYzSFwAoFlV/YUSs/ssqkkUAM55MpckGU0lI4POFi6i8wEr0LNmYQBD+WYS2OI+y2IgDEkadL9ATyNxaWjYj/BB3+sO8Jnn9gAMveTTIkqOEoWknRc9piiOakoHgOv77q7dHbwFOo+Li3dtiJACYdkFZYEnSfSbAHwWwAe9719KCHl/0QeWF2oRwQFxIRlZJAe73g00WqCvr4ZHwYvAjUhPr91lqOXNoKfQxDMnF6ZD387oSetPniXcRI7n5R12vMzJ5eKttr/iTm4SLc8H/drO0MEFCGzfJUxqWVIfl1tHGfSsPv55SFzauglZIpETJa/emElcCCF4ZH0ZH7u8XVofRBBPvbCPl/3cX/gL3ih0DNdlI87GLqxxyw0pOjr2qLLrLJG+QJ8wgz5gqap8719RJMgS4UgSjdfwNisyOl5BwnYqimLQgWHzetESl7Ts8CIr0HOQnfAy6MweNXebRZZeHnEMw0VhOokLS9wexV9e2AQbdnYykIBxcpQoJKVhs/MR1gwe1ZQOuAy6KhNuZzceYjVVUNE0F+gAfgbAKwHsAwCl9LMAzhR4TLmiqkoRDHp0wZGlSTSKQT/vDZKXUhboummjrVv5SFwqfEwQD3oDy2+oE8HppQaqqoQv3jj0/7Ysg2WjUh6Dvt83I728z58YOrmwRjCeibEsF5erngf6qUVRiUv61Mflpub7n+exyMxL4tKqRheqvBKXYObBo+eXsN8z8bnrB6mPKy2e98KnksaXLke0/UyI68nNfR0nZsaZrNmamkHikn7RJ4JWhLd7V5BBJ4SgribnLfBp0N33ZouGojToALC+0vTfNw6KREBIfLNfHNIWnwsNt/nRz8LI0LjJq0HfbjO2vpgm0UgG3RTrewiCOd7s98fvt8ef3sRyq4KVVgU7GcbFNIF0hJBYWXCcWiGqKR1wC/QTs9UjRgxx4JK4pAgqUmUJFUWaWg26SSkdnXHKzzxNiZqWxKCHSFzULAV6OIN+YqaKVkXBxZROLoz55LUcikNN5dNSJoFSir5g1z6DLBHcd2IGT9889GU/WbWHQD6ynSTE+dEvNyuYr6u4uNERYNCTB5a8cHW7i5OzVf/z4m2AyZL6uNyqwKEua5WHTEuVJczV1cwMepIntCyR2MnetB0c9E3/Xn9kfRmEYCJuLuxau+E1c0ahO7ATJVej6X5+SFGIm8J8Q03dJKpbNlSZQOacgNMiWoMuxqADXt5CUpNogsSlUVF8No5t7RfKoHsSlyQNOiEkUwZIlgLdtKk/x2VrEuWz293uGtA8B5k8kaxBFy8QGZhr0t6IzGVgOXji4hYeu28FS81KJhlt2l2tiiJH7gLHSdmGDHqIxGWvzy1vcY8heSe6b9rQvJ0wEbSqSupAtizgKdC/QAj5HgAyIWSdEPIfAHy84OPKDTU1fMsr7qLRZDdsIM1W9W7XQFWVxlgZQgjWV9M3il7wrPvuOzGT6veD4NXXJkE3HVCKVBIXwHNyuXnoF1q5aNBLiOP1C/SQ4yWE4Lzn2NP1C4DpCSq6utM9kkTY5JS4GH5TdbomUQB4bqeH3sDOLHFhr7mdQYN+qMenNxJCEtk4ZnnGGPSFhoYXnZzFRyegQ2cF3839eBeZrpEcJONKXIYT5laHhRSNF+hzNS1Tk6ho8FUaDAv0cAZdJFinrinJlqQJTaKtYIHOJC4FatDPrjSgKRLXfZel/yotO8wKctZcn0WDHkXIjWKn4wYipUksjYOvQY+UuKTT6QPAQkTuwKeu7aJtWHjs/lUsNjU/9C8N0vYaxTHocUU/u+6jGHTeBlGAbx7VTTvV4qjBmbidN3hm238C4EEABoD3AjgE8ONFHlSeqMZo0GWJhPpuZ9Hi7XZN/0YaxfnVFi4laESjcOFWG4pEfPeNLMhL4tLNMNgArg79oG/iqRfcDZpsCXLl2SxudVyWMooJPr/awsVbbV/Hy6VBL8kH/ep298g1xKvd1y0bFUVKNaEteZ8Ts9XMYrHov2bGNNEkBh1I9lRmTNVCYEv+0fNLePL5/dCmpyLB2OGbCQx6z7A5JC7qEU3ojf3xkCKG2bqaqUm0aA90YMhOj+rq0xRLdU32mfcoJGnQGYNOKfWvk6KCigB3UfHffvS1+J9edU/ic7UMu3lZGHTATdqua3Iq216GuirDtCnMBJlO1p6nKCRLXNK7uPjBYCMF+H9/egOaIuE15xax1MwocWFEjOB9GedExl4z7G+ejWgStWwHtw51MQbd2/2Jy3fpDaxUBXqzokyti0uPUvovATwG4PWU0n9JKY2fBaYIblJiOIMe5R6QrUA3sBBRaK6vtrDrOXyI4uJGG2eWG/6xZQFjOPoZi9m+oIZzFCxRlEkCeJtBwlCmBt1PdI0q0E+00DYsPLvlLsZ4NOhlMOh73QH2e+ZIgc4kLtm8nePAzisr0LOEFDEsBbyT08DVoMcXRUlsKUs/nG8MX+fR9WXYDsUnnt1JfWxpwBZYST7sHQ4GfXaEQfc90GfHJ8v5enqJi1ESg15RJGiyNCZxYde8SPBb0q6KZbthafEuLgpsh8KwHH8hVFRQEcO9J1pcf2cWsqCX0qGEac4vb3QykTQAfwIwY9DzRlx6efDxNEUiW8jsBRbElFI8/vQmXnN2EXVN8R1x0iKtxEVTpEgnsn4MK9+K0KBvtN1dOzEGPTkwqW86qRZHQVlameBxcXmYEPI5AE/BDSz6O0LIK4o/tHwQ6eISY8bv+3ra4gPVbs88kiIaBGsUDSZN8uKZW22c9xxCsqLBOYglISuDft+JFggBPu4VM3kw6GVo0Lc7A1QUCa2ICY85uTzp2V7x2CwOElb+eeCqp/EMFujMyYTHZjFtM5/PoN/Mk0Gv+E1ladDmCG5x5XHRn8uuL3EZ/j0vu3seDU0uXYfuS1wSNejJybYzNQUDy/Enaz9FNEbiMprSyQN3DC6eQSeEoFVVxli63sCCRMRkW0kaZ7bQTmoSBdxrkElcimTQRZBF4pLaZtEb99uGlTnZs+6TT/Fz23bHyD1FFEi2Wczi4sJsTYMSl2e3Onh+t4fH7l8F4M6hvYGdWr6aNjzM1aAnSVzG7wlZImhVlDEXF1EPdPcY4v3YAfe6SLM4ak1rgQ7gNwD8CKX0FKX0FIC3A3h3oUeVI6Jsl+IKjmEyVjoGfTGiyeXelFaLHcPCC3t93HcinwKdl2VIgs+YpCzQ65qC00sNdAwr89ZmRZEgkfI06MutSqTcgy3EPvO8W6DzSFyA9O4JvGAOLqMyqUZFSZQ7ZSnQG5qMmir7CatZdkoYllsVtA0rMlI7CYd6cjhMEls6lLgM73dNkfDqs0t44tJW4QuuIFgD08ahDivmOuoaHE2iI64nNw/ckKLZEKeRuboKhyJVA5UrcSmeQQe8Jq8QBj3JcnIUSdeESIHeNSwc6iZqqpzLzmgeyOIoxT4XkR0JAEfmy6i5kxdD+Wb09UgpxU5ngKVW/gy6LBFUFKkQiUtVlVHX5CMSl//+9CYA4LH7VwAMPz9mIymKtNkEaTXowHhTOjAs0O8IaUyPQoWjbtNN8aRbYLo16Dal9KPsG0rpXwMo/0hTIkqDHqcTzFIw7XVN3w5pFMutCmZrKi4K6tCZdVpeDPrQoSIfiYtIk9UomB961u1GQgh3SEVWJDmRzNU1rLQq2O+ZUGWSyNDxrPzzwLWdLmSJ4C4vWZChUeFh0NPrhQkhWGppGFgONM+BJSvYRJRGLuY4FB3DSpQV1BKKMRYIMj/y97zu/BK+tNvHczs94WNLC6aPdKiblxAFt0k0foIa1YXe8jzQwwpZpovlCUkahWikeBa0qmpIUJElPFm7DHpcgR7t+cwQ9FU+7FuFNoiKIovcLq0POis8gezzQJLEBHB7EQa2g6UCGHTA/fujfND7AxsScefgNJiva0fSRB9/egMPnpzx5WdsVyBtWFF6F5doBxVm3RhVb402pQPA9VQMOo/EJR2D3qxOL4P+EULIrxFCvpoQ8jpCyK8C+CtCyMsJIS8v+gCzIsrFxTDtyE7ltIlqhmWjY1iRLIDr8NEU9kJnjPu9OTHozKEiSXecBFbUpZW4AEMdeh4NO/UKX0hFVmy1jUQWmJ2rRiWZoeNZ+eeBK9td3DVfgzoyOTQ4rMniJGE8YOc3buchzettp5iIOgMLlCbLCniaROfq6lij+SPrywCAJ0p0c+kYrlwDiNah245ri8pjswgMdaE3D/qhDi4AMOc9N8ybOQllNYkC4Qx6j8NychSNihwvceFwO2oFC3SOnZwykU3i4sk3Muirs/qS1zkSpVkTZREMOhAtq2XHVRfctQlivqH6DDpLD33svhX/50wulFaHnjaboBLngx6TJAq4/RejDPr1vT7m66rQrjpbAMRZLfYG6eax5hRLXF4C4DyAd8ANLbofwMsA/BKAXyzsyHJCTXUtE0e7uuMmh7RNosyfNIpBB9xG0YsbHaHt72dutVFTZdw1X09+MieSig8esEEoS4F+/1p+BbrLoBdfoG93jMgGUQa225EkbwGGK/+inVyubh21WGRoVOTEwUePWdDygC1okj43XrDXSaNDZ4VasouLgp4Zo0HvDkIdm04tNXD3Qr1UHXpHt3CPFz7FXFdGwQrLpGuS7SwwXaibIhrOZA11seIMetZrSgRugT7OoIuOXTVNjh1jhhIXDgZd9wr0AkOKRBEnVUhCb2BDk6VQZ7QkMFIrq8SFJ2CMLeqL0KADrECPapgU37UJYr6u+fcaSw9l+nMA/q5AWoertIF0cS4urM6IKoxna+qYD7qoxSI7BiB+JzqtxKVZUaCbTqx8sAgkVg+U0teXcSBFIWh7FGQO4zx4NZkVTGIng+m+4gaZ8ytN/Oe+6Uaez/Dpqy5utHF+tcmdqMUDHj/fJDAGPot2/EG/QM/OZtQ4LNCywrQd7PYGyQy6SIGuFi9xodQNAvnKMwtjP2tUlETGRTedxII2DqygzsMDHRheL2kmojantV1SbPhO14gMVXn0/BLe9+R1V9ZTgr64Y1h48OQMrm53Ixl0/35NslkMSFzckCIjtEEUGEpc0nihZ+lrEMVMVY3UoIugobkNtJbthBaivsQlToNeHWYPtPXoHddJQJOlUE9qHvRTSIYY2H2UlajhyfjYySF3Iw7ViF17dlxpJBYM83UNz++60rnHn3HTQ198x6z/c8agp3W4ShtIF6tBt1xL69GdW4aZ0AJdx92LYoTkkOiKbxJNs8MzDPSzMVsvr19kOjpTCgS70EY1YXW+CHAAACAASURBVHEOApkZ9AgXF2DIrIokil641clNf87A4+ebBMZSZGEEllsVfO2Dq3jtueVMxwKgFA36bncASqM90BnOn2DpfXzWZkA6W09ebLYN9AY2zoQy6Mnbd1mLKbagya9AT88UcTPoakJQUdeMLNAfWV9Gd2DjSa9RuGi0dRMn52poaHKkk0uXk0GfDUhctjoGbIdGS1zq0VHdSdDNeL/wPNGqjhcBvYGVuFgZhZ+3EEFuGAl6W2DcxWWaGHTmKJUGrnwj3RgxHwj7yoJhHkb0fcvSdldzcJMKQy3C2pkdV5Yd54WGhr3uwE0PveCmhwaJu4Ymo6JI2SUugjtbbhp2dJJoXNPpTFUdyyi4sS+WIgoEzT2iz30/JYPOZGlto9x8i9u+QK8HGPQg4hwEhk2iYgyzz6DHsMHrgk4uOx0D2x0jN/05Q5IbAQ+GPujpBxxCCH7tLQ/hG75iLdOxAOVo0P0U0YRCc33FdXLh0bhqJTSJXt12HVxCJS5acpOoYWVz3GAMetLnxouqKqNVVRKj7cMwZNA5gopMO1KOttON9lL+qrOLkCVSSqoopRTdgY1WVcHaXA03IyQuw56R+L+75UtcTL/YPxnhpsCK+dH4cR6U2yTqyt+C6dDdgTiD7mucI/p3DA4N71EXl/hE27KRycUlZfEDBCQuefmgxxRpV7c7aFWVzIuByGOI0aCnLRAZ5upuMfuJKztoGxbeENCfA15DfrOSWeIiunDW5OjrJoncma2p6BiWLx851E20DStyzIkCl81iSqIpyKCXidILdELIvYSQzwa+DgkhP04I+RlCyPXA418f+J1/Tgi5TAi5QAj5WpH387u6R26YuMkhrc0ia96IY9CXmhrm6youbfIV6BdybhBlyEXi4mkOo7auykYZGnQ/pChhe7RRUXBmucFlKehvzWU8H3FgBXpYEm2joiTaU7p64fTnecigZw8pYnjonnn89aVtYTtDPxwmgbmsaQooHbJKQTgOxV5vEDnJt6oqXn73HJ64uC10bGmgm244TqOiYG22GilxYbskSUmiFUVGVZVwqFt+SNGJmXA2S5XdPIDUTaIlatABHEkD7BniGvSkxGIeiUtdk0EIc3FJTrQtE1ldXNKSNaw5NKvspMERwndtu4czS41cmtXDUI3ZecsqcWHjzX/9zAvQFAmvXV8KfU5aBt0wbRDBbADALeijrpskO1XmYsR2NtN4oAPJEhfboRhYTmoXFwDoTBuDTgj5dkJIy/v/TxFC/jCLewul9AKl9KWU0pcCeAWAHoD3eT/+9+xnlNIPeO/5AIDvAvAggH8A4FcJIdyfcDVCk8bVJCrYELDbHYCQoS4zDIQQrK+2cIEzrIiFGt07hRKXfoot4iKRx9+UhC2PmeCRavzW978S/+Lr7098Xhk2i1e3u9AUCSdDmv2YNCiu0M0qcWHblXfOiw26cXjD/at4freHZz1/d16IMOhAeDF2qJuwHYqFmEazR9eX8fkbB5mS/XjAtl2bXoEetavAFmE8fRGscYtNllEadACYraupbBbda6qcxb3v7R5oFO2mcHFJklDwNIkSQtDUFGy1DVgOnSqJSzYfdAt1Nd1i45tfehI/9Q33Z5bAseIrjum8uh3eLJ8XipS4sNriz79wy08PHcViU0tvs2i5idGii5dkBj36Pp8dcY26vpeyQFfjpaJDD3rxMafp1TmdKWTQf5pS2iaEvBbA18ANLnpnTu//GIBnKaXPxTznmwH8LqXUoJReBXAZwCt53yCKQY9rEk1bMO32BpirqZATmjkfWJvBM7faXAPhhY025upqbtIAhiSPZx50UzZcFIV6gsNCHuBl0AHgzvl6rKMPg+/fWnCBfmqxHtpo3KgocCKYYoaslngvvnMWv/e2V+GREMYnLdj27oef2RD6PaZ3TJIWxAV67fghRdGvce+JFigdMkJFoRPQ1K/N1rDdMUKvpWHyb3IRNVN1w0NuHeioqvHe9XN19Ui6IQ8s24Hl0FIlLgCONIqmcXFhBX10gZ7MoLPXYV7P0yRxyWKz2B+kl2+szdbwPz9yJjOrLUkEVTU6KEg3bdw46IfuJOaFmipFN4maNmoZTBWYa5RhOUfcW448JwODnpaIiWfQ419zdPHMxss7U7u4RO9eAEj1+Tcr7jF29GIJwFFwBRV5/34DgF+nlP4pgLzEW98F4L2B73+UEPIUIeQ3CSHz3mN3APhS4DkveI9xISq4wIjZdknbJLrbjd7yDuLVZxfR42wgu3CrjXtXW7lvx/F4Xychy4BcBOqV7H9TErbaBloVJde/uywNetSk1PDZgfDPjlKa2QcdAL7yzGKu1/EdczXcd6KFDz+zKfR7bd3iCpCK6l8BhnK2OAa9NZLIWRTYeWtWFJycq4JSN1F0FF0BBp2Fh9w8dC0W487bfF3DvuDfyMJLSmPQPZaO7Z4MLAemTYUZdHbfRzWj+z7oCX9Xs6r4+v6pCyrKYLOYhR3OC/WYue25nR4oDZf65fn+kRr0gY1ahmt+PkAIjOrPGZgGPU2ScVrrU02WYTs01IYwae6YrbNx0j1n1/d1qDIRljslSVzYrkYaiYufUFuyFzrPlXKdEPJrAL4TwAcIIRXO34sFIUQD8GYA/8V76J0AzgJ4KYCbcH3WRV/zbYSQTxNCPr215TZn1UImWduhGNhO5ARdSWmzKFKg8zSQUUpxcaOTu/4cyKdJtDewhCe4ItHQZJg2LZSJ3urEp4imQdLKPytsh+L5nV7ktm7DD/cIH3wGtgNKxa23ysAb7lvBp67tCbmItHUTraqauFiIkzMwBj3OIs9vtiyYdWGsjitxcVmnMNa+y6lBB5jExcLN/T5OJNjBztbEJS5pEwvTYvRc9PzdBEEG3dc4p5e4AC6DfmMaGXRPqpCmuOtNCWFTi9GAx/Xi5IWo9HKA7dqknzNZf9sDazOREpCFhgbDclLtJqfdKfXlJWEFesJrsuufjeE39vtYm60J20pXElxc+hkK9JbHoLensED/DgB/DuBrKaX7ABYA/O85vPfXAXiSUroBAJTSDUqpTSl1ALwLQxnLdQB3BX7vTu+xMVBKf51S+hCl9KHlZde2L4xBNxK8Potm0Ge8BrKPXopvILtxoKNjWLlbLALuwsWwnCOuBqLoZmx4yRv1hMkzD2y1jdz9c4v2Qb+x38fAdkItFoFkBl3nSEecFB67fwW2Q4VCgdq6xdWYN3TsGP9cdn0GPfp+ZxPPaEBO3mCTRrOq+M4Ht0IY9A6niwswTPe7daBjLcFNIY3ExS/QS2sSPXouWAEn7uISz6TxSlxaFcU/hmnSoFfU5Lj0KPQGlvDnWQTiQvji3KzyQk11rSrD5tasLi4LDQ1VVcI/eNGJyOcw0mA3hQ49Tvobh7g0bN2MrxNma+MSF1EHFyB5Hu35EhfxeWwqGXSvGfNJSukfUkovAQCl9Cal9C9yeO/vRkDeQggJ+ux9K4DPe/9/P4DvIoRUCCGnAawD+P9434TdDMGmjWGcbb4+6LsxvsijeGR9GZ+7Ht9AduHWIQDgvgIY9CTWlAdZuvaLQD1h+zkPbBfCoBerQb/is0bN0J8n6mpLZjtF8NK75jFfV4VkLrzx6nEMOleBXhvXPRcBX4NeUQMM+niB3htYqKlyYo8M4BaNe70BNmJCihjm6xoO+iYcgcW+zikFyQujGvS0GQ6JTaKci9ngLsbMNLm4yOkzGaZH4hLNoF/b7mKpqRW6a8EKwNFGUdN2ZVVZSK2qKuODP/Yofvirz0Y+x8+I6IpbLepWOgY9zlijb9r+wi8MbJwMMuiiDaLA8NqNKtCTEk3joMgSKoo0XQU6pdQGcIEQcneeb0oIaQB4I4A/DDz8C4SQzxFCngLwegA/4R3DFwD8PoAvAvgggLd7x8WFsCbRJAZdlggUiQhJDpJs10bxyPoSKAU+djmaRb9wyw0zWi+IQQeysc3dgYX6FElc6pXsi44kbLWPn8Tlms8ahSezMUY1iUGfxgJdlgi++t4V/NWFTe7dIH4GPdpTeaczQF2Tufyuiy7Q2YK0UZHRqCiYqSqhVosdw+aStwAuq9XWLS+kKH6ynK2pcKjY9u+kJC6MQWd6fN7Pg8HfVYmRuMgSSYy7Z01nwHQx6Gn7YWyHwrCcqZC41DUllkEvUt4CRBtTsO+zLmJOLTVirY0XsjLoaZpEfavg8evGSLBTrakyVJngsO8mF9861IVDigC3iJZj6jbd//zT1SytqjKVEpd5AF8ghDxOCHk/+8ryppTSLqV0kVJ6EHjsLZTSF1NKv4JS+mZK6c3Az36eUnqWUnovpfTPRN6r6ktchhdOEoMOiNtNsckszgM9iK+4cw4zVSV2a/7iRhtrs1V/CyhPsIkpiw49bWxuUWhwpMhlgW7aaOtW7gV6Wt99Xlzd7qJZUSI92VkhGeWFPox/nj6JC+Dq0Pd6Jj77Jb7UTleDnjxI12I8lXkW44osoa7JxUtc9KHEBXAdMcIY9K7B3zMSZBhPcjDoALAvIHNJIknyRkWRoSmS/1mJONoEUVUlEBLvg84jBWsGFgbT5IOeNtU4r+IzD7huXuHn5+pOF6cWiy3QqyGy2uD3RS9iWNjTTgoG3Ujp4jJk0MfnkCSbRULI0DXqUIdDxS0WGSqKFDmPZtGgA+5Oc9kMOs/I8NOFH0WBkCUCTTlqu8Sjf6wokpAOjydFdPS4Xru+hI96QSthDWvP3GoXoj8HgJrnV5tFDtJL4SNcJNhkW1Tal58imrMGXZEIJFKcBv2KxxpFNUUm6evK1guL4tHzy5Algsef3sQr7llIfL7LoHNIXNT4JtG4BlGGVlUpXuJiWNBkyWex1ubCw4pENMJBZ5ETCQU6s2Dc75m4Z5HvmIeR4uUt+oKR4mwxKqqZJoSgHtOEaFjR5gNBsMVUVZVSaX6LQlp551AyNPn5oBahQW/rJrbaBk4vF8ygh8hqgYAGuuBF6aLnLLWTwmpRNx2sprgn2TUfZtWbpEEHvKZ03fKJhTQMOjuOqLqtn/Hzb1aU6bNZpJR+BMA1AKr3/08BeLLg48oVNVUe0aAnszeiDDprkuJl0AFXh37rUMflzc7YzyzbwbObnUL050DAQi4D29wbWFOxpckw3BUo5iZi8clLrXwjogkhqChyqsYsHlxLCOZoHGOJC+AO7g+fmufWofNKXOJ80He7BpecrVVV/SChotDRLb/gA1wG/WZIWFHHsLglHUEGfS1B4uIX6AJOOmVLXABX680a0XwGPUXQWpydq2E6XAU3IzZ4FoplIq3Ehc0j07CjGqVBf26nBwA4XTCDXotY2PufUcFzZk2TUdfkVGFFae10ozTorkVvfJIoALRqLoOeNkWUoaLIkQw6kypWUzSJAu49GzVHFgWeJNEfBPAHAH7Ne+gOAH9U5EHlDdd2afjB8rhSiEYe73bdgX8xxhd5FCy05YkQN5drOz0MbKcwBt1nTVMW6MxHeBoGZIZhk2jRDHp+cfUMFVWKtIfKAsOy8cJeD6cXw/XnQLBJNIFBn1KJC+DKXJ651fbDX6JgOxQdg49BrygSJBK+iN3tDGI90BlaVcX39y0KHcM64m1+craK3e4glMHj3fFisrqKImE+JqQIGKYbikhcJrHoC+5mpHVxcX8njkG3uRpfW955mKYGUSB9w3qvpOKTB1E+6H6zfNEMeqQGvbxdhoWGhp1OiibRlOm+US4upk1hOzTxNVly8XW/QE83x7qBSRFSzYwMemsaC3QAbwfwGgCHAOC5uYQ75E8papqMfuDCYZrauGaIuOjaMOx6Epf5mGTBUdw5X8eZ5UaoH/rFjTYAFOKBDgwlLmH6Wh74bMAUSlzCbPHywJY34OWtQQfc660IicvV7S4cCpyLWehpigRVJpExxpNgO0XxhvvcVL0kFr3jp4gmX7eEEG+yP/q5UEqx0x3EpogytKpqKRr0YIG+5rFPoyx6R0SD7hXoa7PVRL/4udpQ4sKLSSz6gueCybnSMOg1TYmU0fFKXNh5mKYGUSBeSxyHXkn6ah648/348bNm+XsWCtagRwSclSVxAYDFZiW1xCVVkmgEg65z9prMVBUcegz6fF1N3chZiSFWj6MGnWd0NCil/pkmhCgA0ptnTwBV9agmzeCYHCqKXDiDDgCPri/jb67sjK36nrnVhkSAcyvh1nhZkWQXloSemS7oo0g0EhwWsoIx6Lx9BiKIi0rOgosbnhNQwnXUiNm2Lzv1MQ3OLjdwz2IdH356I/Z5TOLAa7PmTvZHP5fewIZhOdwMevEadPOIxIU1dY7q0LuG5TdSJ4F9Pkn6c2DItgsV6CU3iQLhDHqaHcBGyDXB4Bboya/Z9Bn0KSvQ5XQN60P5xuQJm7rqBtaZI8Xi1e0uTs5WC19EsDlRn5DEBQCWGlo6iUvKJtGhi8vRv5ktxJOcYWa95OK0FosMccqHvmlDk6VEh6UoNKvTyaB/hBDyLwDUCCFvhJv8+d+KPax8UdeOatANK3l71T3R/IXebtdAVZWEb/5H1pegmw4+fe2oA8XFW22cWmwUNoHVM0pcpmlLk2GoGS6IQW+7uuM4i6u0qChyIT7olzfchd6ZhG3dhhY9+PiD7BQ1s42CEII33LeCjz+7E9tXwQo0XueMMD3rLkeKKEOwMbEojEpcfAZ9xMmlZ4hLXE4m6M8B162mVVWEwoomEX7lFuhDDXpFSTdZ1zQ5hkHndXGZTgbdD3sR7IdJm8xaBOoRuQ5XE3px8kKSzWIZuwwLDS02YyUMlLpWmWkat6M06Gyhl8Raz3ga9OsZC3SXWI1IEh2kk+8wNKdU4vLPAGwB+ByAHwLwAQA/VeRB5Y3aSPRuEU2iu11TmD0HgFedWYQqEzwxInO5uFGcgwsQTN1Md8ExF4RpYEwYmFSjKA36dsfAUgHsOcC25vI/7osbHZxabHBEj8uRNovTHFQUxGP3rcKwHHz82ehsAVag8TbnhcWG84QUMcwEisKi0DXsowV6CINOKUVXwMWlWVVQVSXcHdO7EMRcXfWDRngwmSZR1V+g9TM4UDUiNM6A1yTKUQSwHY9p06CnDSoqs/hMQpQBQhke6EB0gV6+xMUApfxiB0ZcpvNBD9954ZWyzdZUmDbFtZ1eagcXdhxRuz96xhTXZkWBbjqwCjJzCAOPi4tDKX0XpfTbKaX/o/f/Yy1x4bH4ErVZ3O0aQvpzhkZFwSvumcdHLw6LCt20cW2ni/MF6c+B6E5zXkwTYxJEXVOK06AXEFLEINqUzItLm22srybLpOqaEmm5yZMbMA145ekFNDQZj8fo0NMw6KMTvV+gcyzWWlUFhuUUlhILeBr0wN9TVWXM11XcCGjQddOBQ8FdlMoSwft+5DX4gdee5nr+XE0T80H3d2XK1aD3BjZM20HXSJ96GZdUyStxmVYNelof9GnaUQ1LlN7rDnDQN0sp0KsRC4QyJS6LDQ2mTYWCdYwMjdtRDDpbpCRZ9DKp18ByshfoEddub5Bs9xgHds8WZeMchsjRkaV6Rn2VdoQ5oKaJ2yxWRBn0nsmlSQ3DI+vL+OLNQ1/jfHmzA4eiMItFwJ2Aq6qUQYM+PQNyEA1NLs7FpWPk7oHOELfyTwvDsnFtp4f1leTrqBnTAHMcmkQBd5J4ZH0Zf/nMZiRzxFhl/gJ9nC3dEZC4MKa+SBa9Y5i+KwjD2mwNNwOONmxrtinQFHn/2gz3TsNcXcWekAbdbaZMakDNE+ycd3QLvYGVvkCvJLi4cCw6WtOqQU9ps9ibIg26z2AHzpHv4FIigz7qolTmLoMfViSgQ88SSBetQecr+oNhjFklLlF1Wz+lvp6B3bNhtrlFETBxZ+IbAXwTgA96X//Q+/ozuDKXY4OaOhpUVITNooGFBDuyKDy6vgwA+Nhll0W/cMt1cClS4gJE21HxYBolLoCrP8zi7R4FSmmhDHqcdi4trm33YDuUk0GP1tXqlg1ZIoVo7/PGG+5fwc0DHU/fbB95vD+w8fN/+kW84/1fwF0LNe5JoBaqQWeOTXwMOoDCGkVN24FuOkckLoBrUxZ0cfFdSwq6X+fqmrDEpewFX/BcdAd26s8ibtzkdXFZalbwllfdgzfcN12GaKltFo3p2VGth5gFMAeXMjToqixBkUiIxMVygxNLGEeZ/E7EajFLIF0Ug84rcQkGo6W1WAQSbBbN9LtmAHy53//7sWtjPzssiICJ/NQopc9RSp8D8EZK6U9SSj/nff1TAG8q5GgKwqiOVLdsKBKJbRAStVnc66Zn0B88OYP5uoonLro69IsbbWiKhFOc+s+0qGvRuuMkTKvEpRET85wFHcOCbjoFFuj5S1yYVSc3gx4jcSkz8TELXn+vW/B8+Jmhm8vHL2/ja/+vJ/Cuj17Fdz58N/70f3mEuzish1i27XZNqDIZY63DwBjoogZwVng3q+MM+o0Ag87ObVHJv3M1VdAHPVvDVhoEz0VPILRpFK7pgAPbGd+l4Q0qkiSCn/uWFxVmo5sWwyRRQZtF04YqT8ciPsws4Op2F7JEcNd8sXOqfwyqjP5gRO4xcFBT5VJ2jZaa4mmiWbIJkjXofBIXIH2KKDuOSBeXQTYN+qvOLOKtr74H/+mvr+L3PvX8kZ8dCpATIuC5mwgh5DWBb76K8/emBlVtvEk06YIRcdUwLBsdw+LyRQ6DJBG8dn0ZT1zaBqUUz9xq49xyM7UdEC/itJRJmCbNYRC1DIuOOGx7W4VLBUlcRJuSeXBps8Pl4ALEe7xOgu1Mi+VWBS+5cxaPP7OJg56Jf/oHT+F7/tMnIRHgvT/4Kvzbb3uxkKwg3MXFdfPhmWiLZtDZ644y6GtzVRzqln9O2e7I6PPywrzXJOqEFK1hMDjSBfPGTE4MOmu0DfPa5g0qmlZkSRIto/mRB2FNold3urhzvub/fUWjGmLF2TfLS94eMuhii2YgncRFkQgICfNBF5O4qDLJNMfG2WP3zezX6E9/4wN4ZH0JP/VHn8cnr+z4jxfl1MVzJr4fwK8SQq4RQq4B+FXvsWODmuoW24zxcCeH+D9dxGZxz/NAT8ugA8Cj60vY7hh45lYbFzfapTArjYqSOoZ8mjSHQTRimh2zwE8RLVTiknOBvtHGPZxWnfVKtHY/bXjFpPCG+1bx2S/t42v+/UfwB0++gB963Rl88McfxavPLgq/Vl0bl0ztdvlSRIFggV4Mw8K05aOaemaPyGQuWYJ5eDBb1+BQ/oWIbtqpttKzgDVktnUTvQG/J/wofIY2ZEGrm3wSl2lF+iZRa2rmgrCMj6tb5Ti4MNTU8eby3iCbxEIErEBncjweZOk1IoSEstfMCz5Z4sKC0WqQpPQ7DJoSncjt2ixm+/wVWcKvfM/LcddCHT/8O0/iS7s9ABNi0AkhMoDXUUpfAuAlAF5CKX0ppfTJQo6mIPjBAd6J0007cRtShNHc8W6CtAw64DaKAsCfPnUTNw/0wvXnAHD3Qh3Xtnupfrc/sEDI9Dl7FKVBL7xAj9HOpcWlzU5iQBFDU1MwsJyxcA/AlYQdJ1bwTQ+6qaLLzQr++O2vwT//uvtTD8x1TzIVbDrd6Q64GkSB4dZtUQwLK7xHpSujVots0VoUg+6nifb5GDt30Ve2xMX9292dBTt1CjKTxoQlzLpNosdnMTuKKCY0CWUWn0nwNejefE8pxbWdLk4tllygjzaJlrjLUFVltCqKv/PLg6yBdJo8XhyLJIkC2eQtQHKSaB6f/2xNxW+89WHYDsUPvOdTaOtm+Rp0AKCU2gC+2/v/AaX0oJCjKBijvqQGx+QgYrOYB4N+YraK86tN/M4nnwMA3HuimATRIM4tN3F9v58qvrY7sFEvSU8ngqI06Fttl4ks1MUlRwZ9YDm4tt3lahAFhuEeYdeCMQG2MwvuX5vB4//r6/DHP/oavOiO2UyvVdNkUHp0y3+3O+BqEAWGBXphEhcjQuIyezSsqFtwEx+zmOV1ctFNO5XfchYEHXUyMeiqd6+MjDOWQ+HQcq0j8wYhbhNjKonL1BToR3c4NtsGegObS+qXF9wE4nHLwTI/o4WmWFhR1kC6iirHNInGv6YiS2hWFKxlaBAF3GO3HBrqVd7P2CQaxOmlBt75D1+OZ7e6+LHf/ayQg5UIeEaSjxFCfoUQ8ggh5OXsq5CjKQjs4mDMKo+mVlMkmDbl0lTmwaADLovOTvS9J2YyvRYPznns6pWtrvDv9gbpGagiUaQGXZYI5uvFBBXl7YN+bacLy6HcOzHNmGRZHknYtOHMcjOXhjUWBR9kS3cFGPRm0RKXCF/31Vl3IXmDMegFa9Bna+7nwdsoqk9Ag+4z6H3LY3zzZdCHQS/H614ZhajFMDBdDPpoxsdV5uBSMoOuT1DiArg2sDslSVwAxqCPFujJmTMM7/imB/D9r+HLXYgCu/fCyNX+wPY96vPAV51bws+8+UF8+JlN/McPX87tdYPgGUleCuBBAP8awC95X79YyNEUhNqoxIXDqzbKNigMe36yYDZ29ZH1JQDuJHpyNttKkgeMXb281U545jiy+AgXCaZBzztLa6ttYLGhZdLHxYE1Jed13Jc2OgCGi7AksGIljEE/Tk2ieWNo2eZ+LgPLQVu3uFJEATdvoKHJhTHoQ3/zo+RARZGx1KyEMOhF2Sy6789rtejuypRbyKqyhKoqYdPbDcvi4gKEFOgZGchpgZaiH6Zn2qhNiQZd8jI+2I751RI90BlqIe5PZTfSLjQqQk2iRsZAuooqwQhh0FU53jGP4dsfuivzjmdUD4XjUBiWk/vn/5ZX3YPvffU9uHWoJz85BRLvKErp6wt55xIxKnHhaXpjXqU8bgO73QEIOWq2nwZfeXoRmiLh/GqzFOnIPYsNKBLxizkRZE3lKgr1igyH5u8SsdUpzgMdCNhU5XTcFzfakAhwdplTgx4jcdFNhzuw5nZDbcQRYq/HFuP8OymtqlpYExFj0EdtFgHXT5gx6J2BBU2WPobfQwAAIABJREFUCnOxYDtLe5xb6pNa9LWqKja8yTSLDzow3iTqM+jHWOICMLmd2C5kf2BhbaZ4UokXQa/6a9tdaLKUKQBHFKEa9JIXMUtNDX/3wj7383n14lEIY9D7Jcsj/cAka/w4ABRSs/yrb3wAV7e7eC73V+Zg0Akhs4SQf0cI+bT39UuEkGzLnJIxuuXFZbOo8gc27PYGmK9rkDOyqzVNxo89to63vPqeTK/DC1WWcM9iHZc3xQv0/sAuzFM5CxohIRV5oMiQIiCw8hdszorC5c0O7l6oC/l9A+ExxpPwrJ4WjLKljJHilbgArrSiSA06IUMpThBrs1Xc8lxceoadmjHmAWvy2udciEyiSRRwj5M52+TOoH9Z4lLQEYkjmH1yZbuLexbrmednEVRDXVys0Pu0KCw0XA06r/VpVolLuAbdKbXXJMqPvcgUV0WW8O7vezj31wX4JC6/CaAN4Du8r0MA7y7kaAoC0x35TaIcmtqKz6AnF3q73QHmU6aIjuLtrz+Hb33Znbm8Fg/OrTRxeUu8QO9OqcRlWGjmWxBttY3CGkSB6IElLS5utHGOI6CIgS22whpsdet4NYnmidpIMcaarnibRAGvQE9pZ5qEjm6hqSmh0qu12doRm8UibfAUWUKrqmCft0nUug0Y9MEog367SFzEC/RpahIFvIAx7569tt0tJUE0iJom+QUvQ9mf0WKzAtuh3A4jInrxMFRCLA6NkskdtjgerdvYtVDUrn9RmTU8r3qWUvoOSukV7+tnAZwp5GgKAjspepBB57BZBDgZ9O4Aixn155PC+koLz+30Ug3I01mg58+gOw7FTtfAUqEMOtuay37cpu3g6nYX5zkdXIBAgR7l7TyFcqYyUPdDadzPhTVdiTHoaoEadDNyJ+vkXBUdw8KhbqJjWIU1iDLM1zX+JtGJSVyG1nNpx6/IJlHz9pC4aAIOZgy9KdtRrVcU9AY2bIfiuZ0ezpRdoEdKXMq75pea7hjFa7Wom8kJ63EIc77TrXKlsEFp8pHjKJBBLxI8Z6JPCHkt+8ZLFe3HPH/qUB9h0HkKDhHJgWu7djw1uudWmrAd1ydWBN0pCqYIou67keRXEB30TZg2LZZBV8MHljS4tu06uPBaLALDoiPMxeXLEpdhMTZsCOcv0GdqxRXoXcMO1Z8DR60W3QKq2Mlprq5ySVwopa7EZQKFbDBFNu34xcid0XtlqEE/XkXAKMK0xHFwHJqbx3ReqHsSkxv7fQxsp9QGUWBYoLOmf9N2YNq0dIkLAG6rxayBdC6DPi5xKXMhzuq6MQa9QA16keAZIf8xgP8YSBL9FQA/VOhR5YxxH/TkgkMTkBzsds3MDi6TAnP5ENWhT9uWJoMfw50jg77VKTakCEif4BeGS965XBeQuMQ1iRrHLEk0T4z2r7CG8DkBu01Xg16MxKUdw4yzsKIbB310DKtwhnO2pnJJXIZa7ckw6AxpFyySRDwJRYTE5ZgvZiuqGIPO5tVp2lFlAWO+xWLJBXp1JD+hSA10FNiu/k6Hz2rRlZ2lv3bDdl7c9M4SJS4RdVuvYIlLUeBxcfk7AC8hhMx43x8WflQ5ozrixKBzpL3x2iw6DsVeb5DZA31SOLvcBCHiBXpvYKcO+igSRWjQi04RBaK7z9Pg4kYbRMDBBXAHLkLGnSlsh2JgH+/48iyoj4wdO13xhvBWVSksSbSjm2Me6Axrc0EG3cKJgl025uuaH30dh6Gd24QL9Aw7gG4BeJtKXGQJh33+65UVP9NUoNc8DTrbGZ6ExAUYxsv7GuhSNeguibAj4KyUZfenoshjzLVu2YVL644eQ/hONFsg5emDXga4RxJK6eFxLM6BgAbddDVppk2TGXSZj9Fs6xZshx5bBr2mybhjriZUoDsOdW0Wp1DiwljCPDToumnjT566gV9+/BKAYgv04Y5N9uO+5Dm4iEwGhBA0NAUdY3TbPltn/3HHaE9DmobwmaqKgeWMNY3lgTht+WqrAokANw/66BrFa4R5JS5DO7dJS1zSX9N1TYmxWTze94omaLM4LD6nZz6oa66Ly5WtLhqaXOjYHfX+wLAwnMQihlmf8nqh8ySsx0GTx5uLddMp9X6IIrr0KVxE8mB67qgCocoSFImgN7C5rYR4bRbzShGdJM6tNH1ZBA/YBDvVDHpKDbrtUHz82W380d/ewJ9/4RY6hoXVmQp+5KvP4nSBSXRRK/80uLTRxjpnQFEQdU0e23nI2tl/3FFVJRACX86wk6IhvOWniVq5L3Q6enSBrsgSVlpV3DzQPYlLwRr0moqDvgnHobGBXv4YPIFC9qjEJRuDPm6zyFxcjve9wkLTeNEzWQjW9MwHzAf92o7r4FJGrkgQ1RFZbdEuImHQFAkzVYU7TTRr43ZFHU/DNkpujI10cTmmGvS/FwU6MGzaYBdQUsGhcdosDoNLjieDDgDnlpv4xLM7sB3KtXXPvLKnaUBm8Jv6Qvy842A7FP/nn1/Af33yBWy1DbQqCr7uRSfwrS+7A195ZrFwD1228s+qQWcOLo/dvyr8u82KMrawyeqNe9xBCDniqbzXHQhJh4BggW7mzuS1DSuySRQA1uaquHnQR29QvAZ9rq6BUuBQN2M1+vpEJS4ukSKRbIV0eIF+e/igi9os9iYg30gCS/K8ut3NnE6Z6v0DEhdg6AJV9i7DUrPCL3HJaH0azqCXmxicJHG57Qp0QkgdwP8G4G5K6Q8SQtYB3Esp/ZPCjy5HVDUZusnPoGucjCbbPloQaBqbNqyvNmFYDq7v9XH3Yj3x+dO4pcmQ1mbx8mYH/89HnsWrzyziZ9/8IN5w30rJ3ef5MOjP7XRh2jQdg14JY9D/fhfogFeMmUOJy8Onxe71VsUtCvN2cqGUomNYaMUU3idna/i7F/Zh2rTwHa85T/qz30sq0CcncWGLpYamZGJVGxVl7F5h8rTbQ+Ii0CTK5ANTNEY0NBmmTfGl3R7e/JKTpb8/W6zoE5S4AK4OnbtJNKPEJYxB75dspxqZJDq4fTXo7wZgAHi19/11AP+msCMqCDWvUYNb4sLpquEz6M3jW6D7Ti5bba7nsy3NaZS4yBJBVZXGQkSSsNl2w0t+4o3n8fUvXiu9IOXdsUnCpQ1XqnR+ld/BhaGhKWONb0O283izgllQ1xT0B7bfEC7igQ4clbjkid7ABqXxUo212Spe2HNdccvQoAPJaaKTXPQxBr2eUe4T3FVhGGrQj/e9IpokOiw+p4ewYeSRQ4FTBUoTI99/CiQuwDBNlAc8+TBx0GQZtkNhBYw1yk4MjurlmtTnnxW8QUW/AMAEAEppD0Dm/X7PtvFzhJDPEkI+7T22QAj5ECHkkvfvvPc4IYT8MiHkMiHkKULIy0Xfr+5tefEWHLyaYLZ9dJwZ9HPLbjHHirskMInLNG1pBlHXxqUaSdg8LN6pJQ55MegXNzrCDi4MYayg7lvHTee5LgOunMHCft+EQ8U80AHXBx1A7laL7FzFSVxOzA6dW8qQuABD0iIKujW5RV+QQc+ChheEE8TtUqBrigRDwGaRkSHTNB8EmerTy+UX6NUxictk5szFZoW7STQPDTowdL6jlJaeGBwncVFlArWgxM+iwHO0A0JIDQAFAELIWbiMeh54PaX0pZTSh7zv/xmAxyml6wAe974HgK8DsO59vQ3AO0XfqKrK6JvOsODIKUl0rztATZWnanASxWxdxVKzwu3kwgadaUqOC6KuycIadOZ1vjKpAp1tzWV0+ri02cZd82IOLgxhRcckG/qmBTVPb7zrN4RPB4PeZgV6nMTFs1oEshelSZjzFiIHCV7o+gSlILO1nBh0b9EWhGHZkDMkMU4LKp6WmIXsJKE/hQ4ZRwr0STDoU+DiAriJx3u9AWwn+Vy6AY7ZXFyAYc00sB1QWu5OWVyBfhxlmjxn4x0APgjgLkLI78AtnH+yoOP5ZgDv8f7/HgDfEnj8t6iLvwEwRwhZE3nhmipDD0hcki5EXh90N6To+LLnDOsrTVze4mTQGWMypRd8IwWDvtU2UNfkiS06RJJr43Bpo5NKfw64kqXOmK72yxKXul+gu4WneIHuFoWHOTPoHa/gj/JBB4ZhRUD6YB5eMAZ9P4lBnwINel3NyKBrsr+TyGCYt0degO9gxjkWTaUPuvc3zNVVzE9gfh5rEp2QxGKxocGhyfck4C4w82DQWXE8iWZwQkioTag+ZUm3vEgcTSilHwLwbQC+D8B7ATxEKf2rHN6bAvgLQshnCCFv8x5bpZTe9P5/CwCzorgDwJcCv/uC9xg3WFe37+KS1CTK6YO+2zVuiwL93EoTlzc6XKzJNDImQdQr4/rQJGy2jYnJWwCx5NooWLaDK9sdrKfQnwMeg/7lJtEx1FQlE4POGO68GfSOz6BHW7wGGfSiA0MYO72XwKAPA33Kv6bYZ5CdQVfQN92+BAbDuj0KdN65j2FS8o04MD38JPTnQIgGfUKf0ULTSxPl0KHrppNRg370upnUQjysh6I3pcnnSeBxcWF6b1Y4300ImQXwHKU0y4zzWkrpdULICoAPEUKeCf6QUkoJIXx7bMNjfRtcCQzuvvvuIz9zm3osX0KQdCEqsgRZIolNe7s9cyIr9LxxbqWJtmFhs21gNSFxsDflEpeGNi7VSMJWW5+YvAUAFIlAItk06Nd2eqkdXABPg+41QzIfa/3veVARAD/WnU1yoj7oskTQrCi5M+is4I8rvJeaFSgSgeXQwpv4ZIlgpqrgIKlJdILXlCJL7k5ZVg16QMLAxkGDI6H6OCDoYMaz1O8NLCgS8Qu0aQArxspOEB19/6HExYI8gc9oqREIK0pw3nU16FlcXJiDiu2/HlC+PNJNNB13cbktGXQAvwrgbwD8OoB3AfgEgP8C4AIh5E1p35hSet37dxPA+wC8EsAGk654/256T78O4K7Ar9/pPTb6mr9OKX2IUvrQ8vLykZ9VVRm66Qi5UoT5eo5it2sIuzpMI1hRx6NDn8amoCBqIYE7SZg0g04ICY1KFsHlTdeFJ42DC3C06GD4sotLQOLiNVvNpwgla1WVwhj0OImLLBF/wV1G5PZcXZtqiQsArM5UsZTRdcvPWwgQAYaVTcM7LeDtv2LoGi47WXYYUByYnOvUhAr0iuIGnOm+xMVBTS3/M1r0GfT4tkHLdmA5NLMPOjCcMyaVd1BRpLGd6H7JgUl5gWc0uQHgZV7h+woALwNwBcAbAfxCmjclhDQIIS32fwBvAvB5AO8H8FbvaW8F8Mfe/98P4Hs9N5dXATgISGG4UNMkz8WFn72pqMkF+l7X9CN1jzPOCRXo0+d7G8RiQ8M2Z+c6w1bbwEorfuegaPBcb3G46LnwnF1JNykxJjC4uPlyk6gnjxvY2OkO0KwoqVhSt0DPW4Puvl7STtbJOfe6zirr4MF8XU2UuEwyqAgA3vOPXomfeOP5TK8xzFsY3iu3jQZdsEDvD+ypkzuemKmiVVXw8KmFibw/CzgbSlysiRSITI6XZLWYh7PSqIuL7st6ype43C4adB5K5Tyl9AvsG0rpFwkh91FKr2RYDa4CeJ/3+wqA/0wp/SAh5FMAfp8Q8gMAngPwHd7zPwDg6wFcBtAD8I9E31DUBx1wV4RxkgPDstExLCweYw90huVWBa2qgkubyV7o3YEFTZGm1q1gdaaKna4B03a4bJV000ZbtybKoAPJ11sSLm12cNdCLbWUgbFOHcPCivfYpIupaQALKtrtDlL3m7SqamEMelLz54nZGoC9Uhj02brG5YMuS5OzPOMJY0sC+8yPMui3l8SFu0nUtKfKAx1wd3KeesebJsrqBwv03oQWMfN1FYQgkbDKo9eoIh/to5oUuRMWtNU3bcxUxXc+Jw2eu+oLhJB3Avhd7/vvBPBFQkgFnje6KCilVwC8JOTxHQCPhTxOAbw9zXsxsKaevt+gxCFxSQhsYKvS24FBJ4S4jaIcDPo0MiZBrM1WQakrW7kj0CQXha32ZD3QGcKS2ERwaaON9ZV08hZgaMMXLDqGlnjTuRgrA3VNge1Q3DrUUxfoM1VFeFcnCR3DhqZIiUXh6aWGx/wXfw7naiqe3+nGPsewnFLjv4tALYxBv82aRHkb1vsDayrZyUlLbqqqjP7A/Qx7E9JAK7KEuZrqN7hHIY9iepRBZ4uTsjM0Kuq4Br03sI9diijAJ3H5PrjM9Y97X1e8x0wAry/qwPIGuzlYAxMXg54Q2DAthV1eWF9p4vJm/OQKuBd70Z7KWbDqWcvdOuhzPZ+liE76PGbRoFu2gytbXayvpmsQBYZSiaDVom65RSBrGv37CDZ2XN/rp+43cRn0nCUuhokWByv+tkfP4I/e/lWlFCx8Epfj6UkcROM21qAPbRb5xqJJscPTjpom+4WvPkENNE9YEdspzeaDfjTLY1L9S64GfUTicrs2iVJK+5TSX6KUfqv39YuU0h6l1KGU8hlnTwFq3kVy0B9AlQlkjoKjosixDPqk0yfzxrmVJrY7RmKTV28wGT0dL07MsAKdL0+LLbQm6eICiEdsB/Hcbg8D28nGoFfCdbXHne3MClZ83DzoZ5C4FNAkqluxKaIMzYqCcxmuCxHM1jUc6mZsMIob/z294wcP2PgX9EK/bSQujEHnHIuOq4Vd0ZgGiQvg6tCTC/QcJC4jDLoxIbemSoTE5TguIhNnXkLIOiHkDwghXySEXGFfZRxcnmADyF7X5N7GCdMyBbE5JYVdXuBtFHUZ9Om92Fk4y01OBn1adkKSrrc4XPIaRM9nYdA1pkE/KnE57sVUVrCxw6HiHugMTIPOm87Ig45hlaIrF8FyUwOlwE4nenGsW/axZ5obEU2it4PbUdBmkQfTLnmcFFjfGzA5iQsALDW1RBcXw8ouZRyVRvlNoqUX6OPEav+YNonynI13A3gnAAuupOW3APx2kQdVBFiRsdcbcGui3Mjj6G0+Jo1Yat4mBfqyy7IlFujGdDMmszUVFUXCxqHO9fzNtgGJiPtb540weyheXNpwm3vPLmeXuPRGXFz+vhfowQa4LAz6wHYy9RiMoq1PX4HOgpFuHETfe4ZpH3tXoEibxWP+dwHiLi69/5+9846TrKj2+Pd093RPns2JTbBLDhKWDIIgQRAEBEHJCCgg6hPD4wliwvRUVFAUREUfoKioiAgKgiCIJEkLwpI2w4bZMLFneqbeH6eq+86d2z09s93bt5v7+3zmMzN9+946t+rUqVOnfnWqPxO6TaJhQL3dXA6O4lKZOprQlBzxoKJSJAMYnsWlQmkW64ZmcRkcNFW7aleMg95gjLkXEGPMYmPM54GjyytW6dHgcdCLjXKMlPZudYeeIpqsEQrAFuMbqK+Ljeygh9wgiwjT2+p5Y2PxFJeJzamiaE/lxKZw0Bet6mSLcQ2bdHiUiwoO4aDXSFRwU+CNDm7KJlGgpIcVhTGCPr3NOujr869e1YJONQbRwTIDNbFJdCxpFsMcsKkUGupi2Tzo3X2ZiqUlntiUYn13P5kC++lKQXHJRdCHnp5aEQ66R3fdwWjVqKPF1FxaRGLAIhH5iIgcD4w9TFchZCku3f1FK+FIae9WdaRrht4CeqjJVpOaeXn1yBSXsC9pTm2tH8Um0TSTQ7AKEsSdKxYvr+rMUpTGiqDUcb2ZKILuNexjTanaYlN8lZKH3pkujoO+OeFyrhd20Ktfp1zAZ3gEvfod9NFSXLr7BkJ7JkYl4eegV8pBdIdytRfYW1aKDZ25zcWVTbPoH0cdzahWKS4fAxqBjwJ7AKeRO0yoauAaZ313X9FGdKQ0i5U+fbIcmD+lOctnzofudPgddI2gF0dxWd2RZkpr5dtxJH3LB2MMS9u7mbuJ+Z0T8RipRGzYQUXVTkfYVDQNobiMTU/caZ8lddBDSHFpa6ijMRlnZQGKSy1M+uIxob4uNtRB7x/c7CnlygFH0ynGFhljqnYDXrnhUjtDZbO4OJtV6LCikqRZTPg56IMk45s/A1gqER+SxcW1Qc1F0EUkDpxsjOk0xiwzxpxtjHmvMeaRzSRfyeAap3+g+ONsk4lYwcMaVm/srfjpk6XG1lOaWb6+Z8jSrR/dfeGmuICmWnxzQ7qoTXmrOnpDEkEfnr+1GGzo6acjnWHWhFIcwJKgq28oxaXaN/RtKrzOx1jTLLY2uAh6iSkuIYugO3pZrVNcQCduzk4aY2qG4pI9qKgIul1v/yDG5Cg/EXJoqIvT2zdA/8Ag/QOmchQXG0EvlMmltwQZVxIxQWRoBL0S/XwYxaVCm1VLgYK1Z4wZAA7YTLKUFd7GKZqDXiCiaYxhdWc4Iq+lhKNJvLo6fz70aqC4TGutp29gcMQjjgcHDWs6+0LRjv7NLcViabs6QzPHl+aExK4oi8sQeCMv4zdhkyiULoLel9ENp8XkQd/cmDGuoeAm0VpZlWlIxum2fSUzaBg0tXGg12goLm6CEvbxoBJoSMbo6R/IrrJULA+6tVlrCmVWKgHFRUSGOMfpCq2UORlccM4dFlVzDrrFv0XkdhE5XUROcD9ll6zE8HaOUqRZXNfdT/+AqSkOOoycarEvM0hm0ITeILtUiyPRXNZ19zEwaEISQR8bB33pum4AZk0Y+dTUkdCUTAyhuKQz1bn7vZRwup5MxMacXjTHQS9NBN21UdgoLgAz2hpYOUIEvRaoIBpBV+fL9du3WhaX7irm95YbDXVxMoMm2+creVARFElx2cR2TMZzQc2evgo56D4uvJtEViPFpRjrXg+sBQ7xfGaA28oiUZkwNIJe7CbR/AcVuRSLtUZxmTOxiXhMWLSqI/C623AReopL9rCiXnac0Zb3e9lc9q2Vb8exUlyWtDsHvRwUl4G3/EFFbkI/sSk55tM4Sx1Bd5l2mq3jHyZMH1fP6s40fZnBwAxX6QotfZcaDcl4tq84zmst0MH8VIVC6K6S8aAScH6Gc4wrFdQa11BHTApTXLL6u4m2PlWXy0RWKSqbd4KZSsQ92WRq0EE3xpy9OQQpN7yNU6wRLZRm0Z0iGgZqRCmRTMSYO7ExbwS9q0qWNF26t5Ei6GE5pAhym0SNMaNyBJe2dzOusY7WEjhrjck4G3ujPOhexGJCQ118zCkWAZqTCURgY09pIujO0Q9rBN0YeHNjb+CksRY2iYLSwYZH0KvfQReRIZHQQogoLvnhIrYuB3mlVhliMRkxF3qvzUA01gCEgzfzXW+mMocDpTwUrRZyqwPVqKPFnCS6jYjcKyLP2f93EZHLyi9aaaHKp3+PJs1i38AggwHHVrvIaxioEaXG/CnNeR30bMQkhI6BF5Oak8REI+iFEKZ2TI0yvZnD0nU9zCoB/xzU4eseRnGpfqdjU9GY3DQHPRYTmpOJIZOfTUFnmCku9rCi5QE0l4FBoxv1a4AK0lBXmxQXKP5U454K86vDjOzZK85Br+Aqw8SmVOHTfUsUiNF9VLlNopWgsrk+6OToqdVNohbXA5cC/QDGmGeAU8opVDkgItkGGk2aRQhe6stSXGosgg7qoL++tjswgpKNmIRc2RPxGJNbUiM66GGKoI/ZQW/vZnYJ6C2gS9WO32yMiSLoFpOaU2wxbtM4/i31iRJSXDQSH7YsLqAUF4CVAecQ9Fbo8JJyQCPoluJSgqPSw4Ri6XY5iktkI/xoCAnFBfSAtZE46KXok96Vl0qd3pnd5OwOTHKbRKtQR4ux7o3GmEd9Sx+lS+a7GdGY1CXJYpUm5XHQ/fes2pimOZWoSe7d1lNaGBg0LF7bxdZTW+juy/DPV9Zy34uruO8/q4Fc2rgwY1pbw4gUl1UdvTQl45t0AmepkN3cMgoHfXDQsHxdD4fvOLUkMjSn4nTZQbd/QDNTRA463HDWgiH50MeC1oa6km0SDTvFBWDF+uF9r1Sb0cIAN55ALvdzLXDQoXAGMy+6q5g+UG7U2zpZ01lZigtoqsWFKzbmvV4qZ1o56LkIeiWSaPgDXW4SXY02pxjrvkZE5qEbQxGRE4GVZZWqTHANVOzyaqHd7Ktr7BRRL1wml2v//gprOvt45NW19GUGaUzG2X/+JD566Hz2mDO+wlKOjGmtqYLpIsEdUlT5DaIAKXdU8ihSLb7Z0UvfwGDJKC6NKY2gG2OyuXFrJSq4KShFCstiI+ifv30hU1pTXHjw/LzfcakwW0IYQW9IxhnfWBeYC703s+np3MKCxmSODlabFJeR7VBPNkNG+PSw0hhOcamgg96UHCHNYmlSn6bisWzkulKrr26S7J0oQHVSXIrpVRcB1wHbichy4DXg1LJKVSa4Bip2cCiUD3Z1DZ4i6rDV5CaS8Ri3PbmceZObOH2fObxj2ynsueX4qhqAprc18PArawt+Z1VHOhT8cxhuWIqBy4FeigwuoBHZzKChb2CwpqKdYUBLfV2WGlcIdzyzki3G1Rd00LMUlxBG0EH7XtBporWkU03JON39A9lDiqB2JrNFR9AdxaUG2rPUaPRtEq3kKsP0cQ109GZo7+oL3EvTW6K9Rqm6WHZ/TG//YIU2iVoOuqO49A+QiElgRqmwoxjrvtgY804RaQJixpjg/HtVADeDHc1JohAcQV/V0cvOM8eVTrgQoTGZ4PcX7U9zKsHsTTw+vpKY2lpPR2+GrnQmL4VlTUea7We0bmbJguE/KrkYuBSLpeKguzzfXemBrBy14EyFAS31CV5ZXTiC3ts/wJrONJnBwjrQ2ZtBJLzUghnjGlhm8/N70ZtN5xZOuUeDhmQCY9QRyVJcauC9YORTtB0qfQhPmJHjoKeH/F8J7DpLfZUnF6/jnTsMp0OWakNnMh7L9gXN1lTBNIsDLh97ZSYKpUAxtfeaiFwH7AMEp/aoEtSPMoLujG2wg167FBeAHWa0VrVzDjCtTdunEA99dZgi6E7fihgYHZa2dyMCM8aVhqbjsvN0pTM1taEvDCiG4rJsna6IrO/uZ313/k1dHekMzanEJqdFKxdmjKsPprjUEFe7KaX9tbsvk6PRAmtXAAAgAElEQVS41MB7wVBHqxB6+gaISe2sHJQSzt9Y113Zg4oA3jZzHImY8MSSdYHX0yXM4uLGr4pRXLIR9FwWl/oqnUAW06u2A+5BqS6vicg1InJAecUqD3IUl+LTLMJwTnBnOkN3X2U2QEQoHtNabS70PJlcevoG6EhnQpOJx7/7vBgsXdfNtNb6kkXuHGWiqy+TO/65RqKClUZLfR0be/qzR1AHYakn6rx47fAItENnbya09BZQistGu3rlhdPtWtApN5509w3UHsWlrvgIelMyvBPFSiKbB70zTTwmWX+iUrLsuEUbT7we7KD39g+W5EA6zYM+YDOAVebE4CAOes1G0I0x3caYW40xJwC7Aa3A38suWRkw5jSLvgj6qo21m2KxljCtLXeaaBBWhygHOowtzeKy9tLlQIccZaIrPZDdJBpRXEqDlnrl9/cWiEy6CDrA4vYCDno63A76jDypFnM6Vf2ObJNnMltzm0SLPKiopz8T0VvywPkbG3szNNTFKz6JWTBnPE8vWx/YrqU6PCyV0NPX0xXcDJ4bR9XWdPdlatdBBxCRg0TkB8ATQD3wvrJKVSY4Q1LsrC6vg+6Oh28JR/aPCMGYZrOz5KO4uA17Ydns6z9goRgsae8u2QZR8ETQI4pLydFiT3otlGpx2bpu6uI6kC9ekz8DUWc6E8oc6A65w4qG9r3eGtrX4MaT7r4BD7e+NvpKsVlcuvsGQrsPotLw6ngYJjF7zBlPOjPIcys2DLtWsjzo9oCrSmZO8Sf36OkfDEX9jwUjWngReR34N3Ar8CljTOG8dSFGdpPoKNMs+h2mnIMeDscuQjAaknHaGupGjKCHZaLlluaKzYOezgzwZkcvsyZs2gE6Xri8/l3pDAm7JFsLzlQY0God6o29Gabk2Ze8rL2HmeMb6ekb4PVCFJeQR9Cn29WrlT4eem1lcdH6704P1BwH3UVCR0JXeiBKsZgHcZs5xKUorjQW2NTITy5ex+6zh6ZJLlkedPu+lZyI+7O49PZVL8WlmJ61izEmf4b7KkKp0ixmKS4hcewi5Mf0tvoCEfTwnCIK+fc85MPydT0YQ0kpLjkO+gCphHKlowh6adBaZAR95vgG+jKDLGkvEEHvzWSd4DBiams9MWHYRtHcwF39OtWYjaBnshvSKskzLiWSRaZZ7OnPhML5DCsa6nSiEwYHcUprPbMmNPD46+s498Ch10q1odMfQa8sxSW3STQsY/xoUYyD3iciFwE7ovQWAIwx55RNqjJhtJtE/el6HFZ3pEkmYrQ2RJGDsGNqa33BCHo8JoF5YSuB0eZBX2r5yqXMttOYchz0DMa4PRuVH1xqAe5QoUKZXJat6+HwGW0MDhr+9uKqvN8LewS9Lh5jSks9Kzb4KS61s0m00UNxSWc013Kilhz0IjeJhlkPK42GujgbevpDQ7FYMGcCDy5agzEmy4nXPP6l2SSaSsQZGDTZXOiV6OdBDnoYJkhjQTEt8gtgGnAEujl0JlCVudBHmwc9X5pFd7hNpTd9RBgZ01oLRdB7mdiUJB4LRzv6l+ZGgsuBXp4IeiZ76mOtLNtXGjkOerCD3pXOsLarj5njG5g9sZHVHensMdV+aBaXurLJWgpMH1c/bJNobvNYdQ6YXrhNouqgD9YM/xxGl2axWp2fzQHnc4RllWH3OeNZ05nOHnAHnlNwSxRBB9hoVwkr0c8T8RjxmGT9tp6+yqR7LAWKsSjzjTGXA13GmBuBo4G9yytWeTDaPOjJRDDlYFVHb5TBpUowra2eNZ1p+gOiQas70qFqx3wrNvmwrL2bZCJW0r0QqUSMmKizmK4hvnAYkIugB1Nclls6yMzxDcyd2AQEp1ocHDR09mVoToW7XWaMa2DFsE2itbOZssFLccmU5qCXsCBVFyNdZAQ9LM5nGOEmL2GZxDge+uOL27OflXJfiOvXG3sq56A7OZzf1tNfvTpajJV0o8l6EdkJaAOmlE+k8mHvLSdw+A5TmVRkWj3HJxyeZrG2DymqJUxrq8eYHN/ci9Wd4TmkCLx50IuluHQzc1wDsRKuAIgITamEplmsITpCGNCS3SQa7KC7kzdnTWhkjqUtLV47nIeux8sT6iwuADPa9LAib9733swAyUSspDpbKbjj7bv79NTdWph0OKRsmsVCOftB3z3aJJofbhIXljraZmoLLakEjy/O5UMv5b6QbAS9J1OyZ44FKcuFB7vKU8MO+nUiMh64DLgdeB74+lgLFJFZInKfiDwvIgtF5GP288+LyHIRecr+HOW551IReVlEXhSRI8Za9k5btHHdGQuoK5InmC/N4urOdLRBtEpQKBe6TrTC046JmBCT4jnopU6x6NCcStg0i4PEhGzavwibBj3QJT/FxS07O4oLBEfQO+39oae4tDWQzgxmT1IEnXyWgusaBiTiMZKJWDYPei056MkiV/N6+qJNooXgIueNIYmgx2PCbnPGDzmwqJSBGNcHNlQ4gp5MKEXLGKMniYak/keLEad1xpgf2z8fALYqQZkZ4BJjzJMi0gI8ISJ/tdeuMsZ80/tlEdkBOAXdpDoDuEdEtjHGFH/c4hgRlGYxnRlgfXd/FEGvEmRzofsc9IFBw9quvlDt7hYRUol40Vlclrb3sOuscSWXozEZz+Z2rg/BARu1glhMaE4l8jroy9Z1k0rEsvtbJjYlA1MtdqZ18At9BN3mQl+xvie7EbtSx3+XC03JuE2zOFBTm6m9+6/yvZcxhu4qpg9sDjhdD1MEd4/Z4/nOvS+xoaeftoa6kh5I52helXbQ3TjqfLewUIxGi80+5TfGrDTGPGn/7gBeALYocMt7gF8aY9LGmNeAl4G9yi+pRkhiMjSCns2dHSLucoT8yHdYUXtXHwODJlQOOtgjtouIoG/s7WdDT39JN4g6NKcSdKYzJTtdLkIOrfV1eSkuS9t7mDm+ITshmj2xMZDi0pnWAbUl5Nkz3Gmi3lSLteagNyYTuU2iNbSZOt/qsRfpzCDGhMv5DBtyFJfw1NGCueMxBv69RKPoJaW4xP0R9MpSXLr73IFJ1dk3Kyq1iMwFdgP+ZT/6iIg8IyI/sbQaUOd9qee2ZRR26EsKf7qp6BTR6sK4xjpSiRhv+LJJrA7pYVPJeKwoisvS9hxfudRQp0MpLrVCRwgLWuoLRNDXdzPTM+GaO7GpMMWliiLoDnogSu3olK42ZWqOg57vDBAvnPMTFvpGGOEcwzDV0a6zxhGPCU8sdg56KSPoQ7O4VCpynarTcbTHnWgaognSaFAxiyIizcBvgY/bg5CuBeYBuwIrgW+N4Znni8jjIvL46tWrSyKn/0S1VRvDdbhNhMIQEaa11fPGxqGbRFd1aEQ9bO3oDMtIcA767DI46E2pBJ3pgZqLdoYBrfV1ebO4LFvXM+RU2DkTG1mxoWcY5SlLcQl5BH1iU5JkIsZKD72s1lZlGlOJbB702qK4jBxBdylAG0Ouh5VEQwgpLk2pBNtPbwlw0EuQB31YBL2yFJceF0EPySbd0WJEqUXkhICPNwDPGmPyn6RR+Jl1qHN+kzHmNgBjzJue69cDd9h/lwOzPLfPtJ8NgzHmOuA6gAULFhTefl4kkp50PQCrrWMXUVyqB9Na63nTx0FfHdKVkGI56G5DYTkoLk2peHaTaC2ljgsDWuoTgXn5O3r7Wd/dPySCPmdiI8ZoW8+f0uz5rtskGu5BR0SY3jb0sKLe/oGaygrUWGcj6JlBxjfWXgS90CZR5/xEHPT8qA8hxQWUh/7rJ5aRGRjMUlxKMcF0EXTnoFdqVSllT8J1k49a5qB/EPgxcKr9uR74DPCQiJw+2gJFCZY3AC8YY77t+Xy652vHA8/Zv28HThGRlIhsCWwNPDracscKP+VgVUeamMDEpshBrxZMa6tn5cahFBdHVQpdBD1R3AEhS9d101KfoK2x9Jk8mlKJbG7nWqIjhAEt9YlADvqydbkMLg5zbC70Je1DeejulL6wO+gAM9oahlFcaomr3ZSK1yYHPT5yytfuyEEfEdksLiGroz3mTqC7b4AXVnZkA0KliHYn47lNoqlErGIJBhwHvafKHfRiLHwC2N5FuEVkKvBz9LCiB9CTRkeD/YHTgWdF5Cn72f8A7xeRXQEDvA58CMAYs1BEbkXTO2aAizZHBhcHNxNzWLUxzcTmVGhOn4wwMjSCnh5yvPHqjjQtqUToIhupIo/YXtreXRZ6C2hmis50puainWFAS31dIAfdOeizfBx0gNfXDOWhOw56UxU46NPH1fPIK2uz//f2D4RuUrwpaLCbRDOD+bOdVCPcylnfQP6htstSXBrqwq+HlULYDipycAcWPbG43XPCegkoLo6D3tNfUSpbKhEn3e/ZJJqszslzMT1rlpd+Aqyyn7WLSDCZsgCMMf8AgrzbOwvccyVw5WjLKgWSCX8EvTd0GwsjFMa0tnr6BgZp7+pjoj2YaHVnOpSOQrLICPqS9m62ntJSFhmaUgl6+wfpSg8wtTUafEsJt0nUO1mE3J4CbwR9fGMdLanEsEwunekMqUQsS0MIM2a0NfBmR5rMwCAJuxpZSxz0JrtJ1JjaOB3VIRtBL8BBjyguI6MxZAcVOcwY18CMtnoeX7yOPedOAEoVQc9RXCrJMnDUZKej1WpzirEo94vIHSJypoicCfzBftYErC+veJXHsAh6R3SKaLUhKNXi6o3hdNCL4aAbY4ZtKCwlHHViXXdf1Rq2sKKlvo6BQZNdenVYtq6Hhrp4Nl84KId7zqRGFrcPjaB3pDPZU0nDjhnjGhgYNFlKma7K1I4j25DNg16bWVwKbxKNHPSRUB9SigvA7nPG88TidSWluLgIev+AqSg90lFcHAe9MWQTpGJRTA1eBPwMza6yK0pvucgY02WMeUcZZQsFkoEOerg2FkYoDHea6JteBz2kEfRUYuQsLqs70qQzg2VJsQg5Y7a2M3LQS43WBq1bP81l2bpuZk1oGMbZnBOQarErnakK/jkoxQVgpU1zWmuZgZqSCbr6lA5WSxuqgw7p8yOXIaN23rvUcLY0bBQXUJrLyg29vLZGV+hKMXFOek5pryjFxZ9mMYT1XwxGbBGj+I0x5r/sz2+MMSXJkFINSCXiWU7wwKBhbWc6yuBSZXAOujfd26qNveF00OviIx5UtKSMOdBBN76BZnCINomWFi31uqnXn2px6bqeIRlcHOZMaGRpezcZz76Ezt5M6HOgO8xoc7nQte/VWh70hmScQUPNRdBHlWaxSqOTmwMHbzuZ/zlqO3aY3lppUYZhgaW2/OPlNSRiQiJeCg56PPDvzQ3loHvSLNaqgy4iJ4jIIhHZICIbRaRDRDZuDuHCAG+axbVdaQZN+A63iVAYk5tTxIRsqsWudIauvoFQroQUc1DR0nXWQS9DikXQqKBDLW18CwMcNWVjQATdyz93mDuxicygGTK57KiiCLr3NFFjTM3lQW/yRI9ryUEviuLSH1FcRkJTKsH5b59HLIRJJbab1kJjMs7S9p6S9ckhEfQK9gd/Fpf6Kt0kWozU3wCONca0GWNajTEtxpjwTQfLhGQ8R3HJHVIUPscuQn4k4jEmt6SyTs7qkKZYBLc0V5iD7nKgBzl0pYA3O0gtOVNhQGv9cIrLhp5+OnozgROuORP1s9c9G0U7e6vHQW+p142uKzf00jegR8PXkk55D+mppclsMSeJ9vQNIFJbE5O3EhLxGLvNHgeUJoMLQF1ccCy9SlKfUok4mUFDZzpDTIZOHKoJxUj9pjHmhbJLElJ4OejZw20iikvVYVprfXaT6OpOd0hR+NqxmDzoS9u7mdKSKpuj4yguUDrDHUHhKC4be3IUl6AMLg4uF/rrHh56ZxVF0EF56MvX93gORKkdnfJGj2spD7qbbPQVCBZ09w3QWBevWK7rCJuOPWZrusVSTS5FJOsMVzJFr/fApMZkomp1tBgr/7iI/Ar4PZA9L92dAFrr8G7ac8fDh9Gxi1AY09rqeXW1RiFDHUFPxEmPkAd9SRlzoEMUQS8nWgIi6LlDioa3qU7EYizxRtDT1cNBB83ksnJDD+n+6k55FoShdLDacdCLOUm0u28gdOkDI4wOe2RTLJZOd53PVOksLgAbuiubj31TUUwNtgLdwOHAMfbn3eUUKkzwRtBzFJfwOXYRCsMbQV+1MbwTLadvhfZha4rFMjronkG3llLihQFBm0SXuT0FAWkzYzFh9oTGoRH03gzNqdKfIFsuTG9rYOX63pqMoDcM4aBXryPgRzEnifb0ZYastkWoPuw2exwipZ00JxPu4KPK6YabYK7v6avaQ4qgiAi6MebszSFIWDHEQe9IM66xrqYM8VsF09oa6OjN0JXOsLozTSImjG9MjnzjZoY3vVmQgesfGGTlhh5mlYl/Dn6KS6TrpURTMk5MhkfQm1MJ2hqCnW5NtagR9HRmgL6BwarJgw4wo62etV19bLC0nlrSqVqNoDsu8YgR9Bpqy7ciWuvr2HZqyxA93lS4flDpk0QB1nX1V7WO5m0VEfm0MeYbInI1MCycZ4z5aFklCwm8lINVHb1Mbg5f1DXCyJjWpu32xsZeVm1MM6k5Fcqd9SM56CvW9zBoypdiEYamTaslZyoMEBFa6uuGRdBnjh+eA91h7sRGHly0msFBQ1daaSLVxEGfMU4nk6/ZSUYt6VRDjXLQHZe4UBaXnv6BKINLDeAbJ+7CYAkTZ4fDQc9x0Cc1hy8QVywKWXm3MfTxzSFIWOGlHKzqiHKgVyum2tNE39zQG9pDiiCXOzbfwFjuHOgA8ZjQUBenp38g2iRaBrTUJ4ZF0IP45w6zJzbR2z/Iqo50Vi+qyUF3hxW9uroTqK2Nx97VplpbWU2OcGhaVzoT5UCvAewyc1xJn5fMOuiV56Cv7+4r24nbmwN5e5cx5o/2942bT5zwIeXZLLNqY5q9tpxQYYkijAXT7YEpKzdoBN0dXhQ2pBz3M0/2BJdisZwOOuhG0Z4aOx0xLGipr8vmQTfGsLS9m322mpj3+3M9qRYdtaWpihz0LVwEfU3tRdAb62qT4gJ29bjgQUUDTIxWlCP4EIoIui27q8ppWIUoLn8kgNriYIw5tiwShQzZzTKZQVZ3pEO5sTDCyJhmI+hvbNQI+i4z2yosUTDcMnm+gXHpum7q4pJ9n3KhKRVnTWdlU2XVKjSCrhSX9d39dPUNFJxwzbWpFpes7c7mRa8mDrqbDOeOFK8dnarVTaKgjlZEcYkwWrh+UOmDihwqmY99U1HIyn9zs0kRYjiHaXVHmr6BwdBSIyIURkMyTltDHSvW97A2xBQXt8fhpkeWcPm7tx/GS17S3s0W4xqIl5k/7zYN1RIdISxorU+wfL1mEsqlWMy/DDu9rZ5ETHh9bRcTLZ+ymiguqUScSc1JXlvtIui1o1PJREy52gODNcVBh6GnaAehuy9y0CMMRzIMEXSPg17NK3aFKC5/35yChBUugu4G0illjlxGKB+mtdazcMVGBk04UywC7DtvImftN5efPPQaTak4lxy+7ZDry9q7y05vgRy3tpqNW1ihm0Q7AF0RgcIOeiIeY9aERhav7WbbaS0AVZUHHXSj6DPLNgC1p1MNyTh9PYM1SHEZIYLeN0BDXXXpYYTyw/WDSkauk94IehXbm0IUl2cJprgIYIwxu5RNqhDBNfRy56CH1LGLMDKmttXzr1fXAuHNZS8ifO7dO9DTN8DVf3uZhmScCw+en72+dF0PR8woPz3HcZxrzZkKA7ybRJdlHfTCk645ExtZ3N6Vva+liiLooKsAzkGvtUhzUzLOhp7qPhAlCMlELG+aRWMM3X2ZKIIeYRhc/64k5ctbdjXraCEr/5Y5jKgQXEO7SFfkoFcvprfWZ7ndk1vCuxISiwlfOWFnejMDfOOuF2moi3P2/lvSmc7Q3rV5dqVHFJfyoaU+QWc6gzGGZet6aK3PnwPdYc6ERp54fR2daXXQqy2C7jZpQ+1N+lyksNYi6IXSLKYzgwya6ub3RigPHOsgDFlcoEYj6MaYxe5vEZkK7Gn/fdQYs6rcgoUFLoIeUVyqH1M9mVvCPtGKx4RvnvQ2evoG+MIfn6cxGc+mw5q9OSkuNbbxLQxora9jYNDQ3TfA0vbuEaPnoIcVdaQzLGnvJibVN+i4TC5QezrlVptqbZNooTSLPX3KTa/m6GSE8iAVgpNEvat09VWsoyNOcUTkfcCjwEnA+4B/iciJ5RYsLMg56N00JuNVtTkrwlBM9zjoYaW4eFEXj3H1B3bjoG0m89+3PcuP/v4KALOKcOg2FY3JiOJSLrTUa7S8ozfDsnU9Ra2IzJ2kbb5wxUaaU4m8hxqFFS4Xekz0lMpagpssJWssgl6Ig97dHznoEYIRjk2iubKrLZjhRTEW5bPAnsaYM40xZwB7AZeXV6zwwC3XLG3vCX3UNUJhuNSELfWJqnE8U4k4PzxtD/aaO4HfP7UCKH8OdIC2hjoSMam5ZfswwKVI3NjbP+IhRQ6zJ2iqxf+s3FiVQQJHcamvi1fd5GIkNKUS1MWl7JmVNjeSBRz0nj6lWkUHFUXwI7tJNCRZXGrdQY/5KC1ri7yvJuCWStaEODVfhOLgThOttnZsSMa54aw92W32OCY1JxnfWJivXAqcus9sfnzmAmI15nSEAc5Bf31NFz39AwUzuDjMmtCAiHJ/q41/DjmKS7VMjEeDhmS85ugtAMlEPG+axe6I4hIhD8J0kihU9z6JYiz9XSJyN3CL/f9k4M7yiRQuuAg6wJQQbyyMMDIcxaUaV0KaUwluOW8f1nX3bZYI5JSWeqZsG+l7OeAoLs+v3AgUR1lKJeLMaGtg+fqeqoygT25JkYhJRQ8vKReak4ma3ExdkOJiHfRqdn4ilAdh4KCLSHYFqJoj6AUtvagn8D10g+gB9uPrjDG/K7dgYYF3JlZtkdcIQzGusY5kIhbqDC6FUF8XH5INI0J1otVGwF+wDvrMIrPyzJnYqA56fflXUEqNeEyY2lpfk5Sps/afy0HbTq60GCVHoTSLuU2i1TdZjFBeONZBpTeDp2wWomqeRBbsXcYYIyJ3GmN2Bm7bTDKFCt6lyymtkYNezRARzj9wK3adNa7SokR4C8MfQS+Ggw6ayeXhV9ZWXQ50hxnj6ulM5z+Zslqx/fRWtp/eWmkxSo5kPEa6P9hB78py0KvX+YlQHhy983RiAm2bgYpZCKm6GB3p6uagF2PpnxSRPY0xj5VdmhDCuzM/orhUPz55xLYjfylChDLCcdCXtvcwvrGuaMrKnInqyFcjxQXg/LfPo8vmcY8QfqTqYqTzRNCzFJcqdn4ilAezJjRy/tvnVVqMUFBtNhXFWPq9gVNFZDHQxVv0JFGoTu5yhAgRwoXGZJx4TBgYNEVHzwHmOge9CjeJAhy2w9RKixBhFHAUgczAIIn4UGpSlAc9Qtjh6HTVrKPFWPojyi5FiDHEQY8oLhEiRNhEiAgt9QnWd/eP6lTYORM11WJTlUbQI1QXtrO0nYtv+TffOWXXIXTP7oiDHiHkcL5bNXPQR9yxY4xZHPSzOYQLA1IRxSVChAglhqO5jC6C3kRbQ102kh4hQjlx1M7Tuezo7fnzc29w7o2P092Xoyf19GUQqWwqvQgRCiFlqS3VTMOKetcISMQEsaffbY780xEiRKh9tKTUlhSTA92hIRnnkUsP5bhdtyiXWBEiDMG5B27FN967Cw+9vIbTb3iUDd39gEbQG2rw0KkItYNUCE403VRUjYMuIkeKyIsi8rKI/PdmLJdkPMbk5lRkjCJEiFASuAh6MTnQvWhIxqPDoyJsVrxvz1n84NTdeXbZBk6+7p+s7kjT3T9Q1dzeCLWPVCKGCFWd2rUqJBeROPB94F3ADsD7RWSHzVV+KhFjcmtEb4kQIUJp4FItjiaCHiFCpXDkTtO54awFLF7bzUk/fJhXV3dWNbc3Qu0jlYjRWOWrPFXhoAN7AS8bY141xvQBvwTes7kKTybiTG6ONohGiBChNHCHFW0ROegRqgQHbj2Z/zt3b9q7+njk1XYa66INohHCi1QiXvWTyGpx0LcAlnr+X2Y/2yzYb95EDtx60uYqLkKECDWOeVOa2WZqc5QFI0JVYY854/nVh/ZlUnOKic3JSosTIUJeTG5JVe2p4Q5ijKm0DCNCRE4EjjTGnGv/Px3Y2xjzEd/3zgfOB5g9e/Yeixe/ZZLNRIgQoYpgjGFg0AzLLx0hQjWgvauPzOBglNksQmjRmc7Q0zfA5M10fo2IPGGMWVDKZ1bL6LAcmOX5f6b9bAiMMdcZYxYYYxZMnjx5swkXIUKECKOBiETOeYSqxYSmZOScRwg1mlOJzeaclwvVMkI8BmwtIluKSBI4Bbi9wjJFiBAhQoQIESJEiFByVAUB0hiTEZGPAHcDceAnxpiFFRYrQoQIESJEiBAhQoSSoyocdABjzJ3AnZWWI0KECBEiRIgQIUKEcqJaKC4RIkSIECFChAgRIrwlEDnoESJEiBAhQoQIESKECJGDHiFChAgRIkSIECFCiBA56BEiRIgQIUKECBEihAiRgx4hQoQIESJEiBAhQogQOegRIkSIECFChAgRIoQIkYMeIUKECBEiRIgQIUKIIMaYSstQFohIB/BiwKVJwJo8t5X6Wq2WFRY5orKqq6ywyBGVVTtyRGXVjhxRWdVVVljkCEtZ2xpjWvJcGxuMMTX5Azw+ms/Lca1WywqLHFFZ1VVWWOSIyqodOaKyakeOqKzqKissclRDWWP9iSguESJEiBAhQoQIESKECJGDHiFChAgRIkSIECFCiFDLDvp1o/y8HNdqtaywyBGVVV1lhUWOqKzakSMqq3bkiMqqrrLCIkc1lDUm1Owm0QgRIkSIECFChAgRqhG1HEGPECFChAgRIkSIEKHqEDnoESJEiBAhQoQIESKECJGD/haHWBTzvbFcK+Z6qe6pZYS9PsIunxebU9Zi+1cxzymFPMU8s1Qyb/13Fw8AACAASURBVCrCIEOxEJFRj6WbYlPHUtZY7XC52yEs44N7Zrn7a5hk39xyhh2boovlqK+3jINeysoTkUYRSZbquWMx7va+cSIyMegZIpIQkbqAe4bIayzcNRFpEJHWoOLyyWE8GxmC6sOMYaPDaO8RkXih+hgrSvWcPM+WQv974W0j3z1FvbP3PhGZLCJbjnRPwDPy6VRyLG082vLz3F90HToYY8xYjOpY+rq3f40VIiIj9bGxIJ9chWQudX8o9Lyx1NtYbMBY+pD9PyUi21pZB0chY8zeY3yfz3ZyFHr3Uuuh7deB7+0fH0ZbbrGyleoeEYmJyFwRaRaRmSLSNNpnFuprxejUSP11hOePqY6LlH3IGOD/fr5njgXFvkeA3zIae1wyW+TKLaBXw+Ty92N/3efzpUYj91vGQfcYGb9CHO2Moh+2gncSkXG+Sx8BLvA+13PPdBHZV0TeKSIniMhs3/V6+51jRWRX+4zBfHKIyNkiMi/Pa10MfMo9w3ftQmB/EUnY5wxRQBGZZZ/9TRGZ5rn2UeBgEYnb7yVF5DJgWp46ereIXCUiJ7pnuDoWkXki8hkR2SfgvkLG4xQRmZavs4pIi4hsJyIXisjettyBQvUh6pTuLyK7i0h9nucGTS4G7bW4T8Y9ReR0ETklz7MC+5aI7GzrPubXnSAdtYPMp0XkHd7veFDonQ/0OBDe+/4L+IKINATcU+i9AnUK+JuIXCMiU+zn8SKfV6h+54jIISKywCffXiKytdiB1+9sF+jnh4rIx0TkChFp9hpVEdlKRI4SkZMD+mtgHdp2udj24/1EnYJsvYjIBGsDPiciOzmZCtkHEdnW2oHdfdV0m9Nzj8zNnvvy6doEEVkgIuMDrgX2MVu/XxWRQwLuEWurRNSp80/6AvtXvra07zLo+Z6zG9NE5KMisl+QDFLAZlO4P0wTkd1E5GDb3tMK3SMibSJyplj7FdD3rgJ+LSLrrE5OEZEJnvuTIrKFiBwuIlOd/MA5IvIPpxeez/8IfEZEznXvLiIHWDkOd/UX5DDnK8u2y6Ei8hUReYeIpLz1YZ+Xce3quRY4PojIebYOE3nq392f1Y8C9V7Q1kue8dfKMClP0d8BfgvcCXwSONP1p0J6aNvvYFv3HxSRuZ53FrEBjQCd2ltEdvM97gb7vuJ5hhtPjxO1KZM917J2YwQHO7DuRe3GkSLyYRG5SHz2SnQcbvDp7+9E5DciMsd+xzvmbC9qJ98ZUE8tIrKjiMy37z7Jc21nEZnlLdt37zDfB1gg+e35TqI28jTx2DuHAmNHXhnt9YTVrV0l51uNE5FzbF/ZQkRaPN8X4D2i/kZ2bLN9Jq/eEOBLeeUuBm+JLC4icjDwH2PMG57PXIP+GzjSGPOGVZL5QAJYBPwvsB8wANwDfBboA1bae/8KfMkY85KowzUoIr8DJgDP2O+22mf91BizWkRuAMYDK4BdbVm3AacDhwFvAo0eOW4FDjDGrBSRrYAFwFzgn8BNQBpYBnzeGPN329EMsATYxxizXET2su+xo32P3wB3A/8CJgJ/BzqA/YGzgV2MMatE5DDgBOADwOeAHwIzgPcDTwEvAzcDfwbeDVwO7AUstde+BLwAbG/vOQo9besJTzvsDvzbZ5hete+8whqynVydGmMWicgttv46gQ8DG4FbgHOAnjz1cQswB2i38n0a6LAGIUiGGHA+cJ8x5kXP53FjzICIPAwsBGah6ZUG7Hv+yxhzn4icAywHXgJWGGPS9v4XUZ17BXgC+D1wjNWJ+4wxiz1lzQd+bL/7DlSn3msnI+47SwrowCvAKvv7Ifus02w7vQpMB76ADmgDgAD/yPdewM8ZrlN7A4cCvwOWGGOuxINC9QRsW6B+/2TfYUvg48AbqG5dD3wNeA3tV88CRwO/tr8f89Wh65dPALcDM4FHgUFgH+Be4Awr1wogCVzqbIXVRX8dfgI41t67PXAQ8AvgHmPMnfa+3wKrbdvMAD5jjHm1kH0Afonq7yzgY0AGtREfNcZsY597EnAgsBb4hzHm3gK6djOwla3Do9EjqtuAxcCTDO9jOwLnAg/bdzrDtu3zwF3AfwN/NMY87anfhDEmIyK3WrmD+tcd9uvettzV1v9fgIeMMWvs8+ZZPVmE2qPjgd2Bl4wxT9nvPEV+m/0c+fvDLUDK8+yXgcesrP57tgKuRW39vsCfjTEf97z3ccCHjDHvEpEP2XfZwdbxY8CXgW/bd15u6/wbVrfvBnqtfnzfGPNvEWlEdXi1/W4bsAE41dbnI/bvRuD7wI3GmFc98nwvT1n3oP1vAB1fMsCDVrZfWf24xT7vCdFJw1J0THqE4ePDWVZvXhGR7YHdULv6sK23LY0xr3nkitnnJwPq/UfAi+Sx9aiD7R9/ZwI/N8bsb58/ATjZyrodcJEx5gCrR/uhNmM2cB/gHE6vHs61cn0bHTNfsM/rQce4/0X7+xHAVOBq1P4MWt2+H/UB7rVteCLwFftOr4vI29GxcZFth3/Y9nvTttUjwHtsHR1qv/O8MabDU4diy1oKHBxQ9yfZ5z9l22s724Y/MMY8KyKPoQG2X6ITl9eB+4EHgPXAV4wxK21Z81Fb9LSV+wPAzqhO3YPav51R+7sEHYOfRX2K/xA8tj1ojGkP8H2agLcB30VtjN+e32U/mwX8xNbXbvY725F/7LgR1aNhMtp6/AJqM5bZ5623338I2AXtd88AvzPGPGSf/RBwpTHmTtFJzTn2XXZHbU6Q3jwB7ObxpQ5B9fF24IfOThdCwVlwLUBEjkeN5X0i8gbq2P7LGNMpIsegjf6mnfl8Gu3E/wKagUZjzM4isiPwRXTQqkcV54fA+4CPisiV1oE+GZhkjHEz5KmoIuwNXCIiv0AN1O7oQNEPHIAOxjFUUeZ45FiODizrRCNt30UHkieAK9AB+gMi8ingAyKyxhizUDTausg6UtvY+/5s390N1OOMMZ8VkRmo8l6HKus4YCcRWQ58A1XyO9BBygBvR52bQ4B5wG3GmMtF5DXUiX8I2AP4H+BuY8yHReRSdDKxBvik/e4JwBbA48DTdtC6GTWYj1qDfQlqZGdY2Q8RkR+infNU63g5Q7I/6hzshhpUb328F2gxxuxrB96vAscZY34uGm14HHhGRO4CbjbGPIMa2u8Ay0VkBTqY/dIYs8Y6Sc1oJPoA+7xnUSNzmoj0o4bsZ8A64HkReQEd7B9AjdMzqNN9FGqgY8ADIrIWnfjdjU7aHkWN+oXANeiE5PuiS2dfAO41xpwdoAMx1GlsQI3x0cAfbD09DbwLOAU4HFhjB5gTgXZjzHkicqTvvT4FLA7QqUZ0sPk58GVbN59EndeTCjzvctu2T4tIJ+rgu/o9Dh2I90f7wRXoJMwZ8X1R43iArbfDgElov3lYRNpdHRpj1tv3GkQH1n3QAej/0MHxAmDAGHOIiExHbcXZwFdtHb6AOr5/9dThQahj+SVjTJ+IfBo1vJ8VkaPRyOoc1GCLrbsfiMjPrZy/Bf5mZXL24TqgzxjzbhE5wb7zIOqc9Nv2foeV7W/2uZeIRkSDdG171Mm7F+13l9uyF6F9/HHbx+YCV6KOmivv08CZVt8W2Tb4X3RC9SER6bO69UPrJL8X7Xd72Xbx9q/jgCmo3TjS05aT0b7eCxwlIq/b9ng7OsCtQJ2CG9AB/2t2cns98Kwtdy5DbXYjcL8x5oyA/nAS0GYd6knoKugkqzurrOyXuHtQ277QGPMJ0RWIH4nIB4wxN4tG1y5FHUzQwX2SMWZv0cj3FWifPhDtX1uiTluzdVLabHsfBPxURK4xxvxYRL6COo6PoA7WF6yMa4GXjTGzReTH9n1PEZHLjDG/tk5VUFnXoY7Zt9Ax7xOikcuLUYfwMdQZEeAOEdlo2+RiW1f+8WEvqztzRESsfixGnWxn9/4hIotRZ/AHaH9rtfX+fqtjzejYcT05W+/Vw3+iE8V4wPi7H3Cv6KpGCzphmGjbrsvWHcaYV6ze/EJEDkId8A5jzMFWJ7025SNosPLDdsLXa+XYiDrk44wxbxORPVHb9jZjzJN2LJlkjLnX6oEbmzptubeg9n0ROuZ/AHXOZ6Jj+w62zd6J2rSdbL0dLiIvA88ZY54FYiLydeBF65zP99R9HTo+HgV02/9n2meeJyKXW7kuQn2X71idmorasEOBu0Xkq6hTfCrwT2PMp60d+RY6mX8fatfWG2P2EJGZqI3ZCZ287IhO/HrQCYAb2w63crxOsO/zXdQXaGKoPR8P9BhjzhWRI2zZj6L2+PNWr4LGjlOsjjhfxivjdiLyG1veoagNarJypYHb7Vi1Azr+3yAi/0QnhkkXfLFt9BvUbu8JfA/4k09vvooGFVZZH+Mbti7/afXgEdRmFYYxpqZ/rNLdiDokV6BRkWvRGdDLaKcFXY74hv17B3SAuNrznPegjtLtaGT4fmBrdLB/HnVGDrZlNfhk2BKNSFwJ/D5ARmeQP+WT49eo8/MB1MB81X7egnbOX9v/Z1gFWAZ8Bo0YXI3OPI8F/sd+T4DL7Dv8H9oZzrXvJajh/D3qkB+EDkLPoA7st6yMp3rkftIq5u62Ps72XHsJ+IX9+y/AeZ5rP0M74Xm2LXZFnYxF6GD0HNrhVwFft/dsgQ4qX0edaPes/dBB/A5U+U8OqI9b0Ciku2cftLO32LZc7pPhITTichUaFTgNddCftfV2B7DWPuv76OoI6MB7NeqAXYPqx/noQP5zVP8uR43Tafb7P0I79DOosbrY1s+f0AHmR+jKgXvXO+3fF6M6+kHbdv53bkQdo98CF9p7voxGwh629d6C6tsSdAD+OPCjPO91vdUDv049YOW+1tOPrrf1eVaB5z2F6kw9qt/e+n0JWGa/+x3U+IIOPLfZd5oLxNHo3hPoisCfUYPsrcMv2bpvt8/4oUeOGKrDj3h0Yx7aH7dGoyn/CKjDVahe/Nx+9m3gRM/zPw98z/PMuJXhUnQS+ixQ57MPzwJ/sf9/F41ogg4uK+xzrwKO8dz3bTSyFKRr/0Ft0XqsTtp7dkQnio/Z/z8LfM3+fSPa9z6ORsu8/flxrD1EHe3bUefuIVvW8jz9637UsfC35W2og3iFbf8rrOxL0IHrLtT+XeJ57k9Qvbk7j81+E7gqj038IFZH7fUzbH3+1T5zR989rwD7er5/jKcOP2HroxXte+cAW3m++2PUjl5NbpV6Bjrp/o4t936rF+9CHYxPoQGzg8np5322Hq9FI7Kg9m9fNIp5LepkXMrQscqV9S60TzwD1PvGnDNRnfoEcKCnPtegTtZqPOODvT4H7VM/t/d/1n7eat/rr7YNd0f7zHp0nLofXa17ArUJV1kZVwIPBOjhFlYPbgwYf59G+/xjtt7O8XznV6iT+EVbR1M99b8M+FUem/I3e1/MfvZBdHLRgI4NX/OUcQE5/fsWak/3QB33P6GT2HvtOz4OvMdz79VoMO4KYC/72R22nRehtu8INLh1lf05D3UGH7Tt3eir+wPRqPt3fO07Hu0vl9r/zwGOtn+/iI6zr6H9/HTU6Xy3lX03+71fAhd7nvkXrI3ylbUrqmcfRPX+/eTGtkWo7f8xwb7PAlTHv8Jwe+7GlC8B13l8n6dR+xA0dvwWa+vzyJh9lv18L1v+PfYZu3uuia3Dw1Af40LUmXd6dBRqx26x3/Xqze0o+2EfrC/lee75eHyYQj81H0FHB691xphnRORxNGK1AzrrHg+sEpF1qELtb+95AR10uiG7xPQHETkTdaba0KXeRSJyLnAcOsO8D1WyO0WX6O8zxjxhjHnNRifWAy+JyINoZ/2NUT7S59FZ2JfRGfABVo6voTPyK9BZ5/vs551Wxkft/28YnfH+BjWoJ6JOzv2ogXnBvoOxM98HbB3cjzrd/zGqOZ12leFU1Bi/bJ91oJVxMnCX2KUk1Dh1okvfK9HIymRjzGp08J4tIj9CozirPW0yF42kPA28YnTp+lxb11egiv6c/c7vbHnLbTutBra0M9s/oR3vYdR4zAb2Nsb8SkQ+gxq2c62cD9uIqDHGPCK67PdB1KDebox5SkQ+ZHSJ7CTUgf4YcKsx5v9sNGRLtEP/FHhZRFbZcp9FH7xBRNx73WrfbZF9r31QJ+BTVu5DUWO2NWro/0NO76bY774LjZh+2dbbo0CX6ErNO1FH+BfoILQC8OrAh40x3xaRD6LR4Nmosbga+CYaIesQkWvQge7jts1n2/e627aBe69udND06tR41OHcFTXEWHkutPffWuB5T6POQBodLG+2kaG9UD3/on3eb1EdxRizTERWohPJn6CO4kQ0IrInsMEY84KIvILq6vaow30xsMxGW25AHV7HIVyEOnaOrvGKiPwedeCm2Da6Hvi6pw4vQweWtCi1owV1nEAd6n+j0Z06NDo/ICK32facb2X+mIjcb4x53NqHF9EIz1O2fv9iZXzOfvY2W8eni8hyY8yTaATnu6gD5te1jWj06g7UcVpon7dQRP5l28UtHx9sZb/MttOpaJ9pEpF6Y0wvSsdZaCOndxtj7rLvdwQ6GF3tqUNv/1pjnwnaH5fZv89Bo/IbbNtPR23SKtQJOtnW6x+d7UL7303AEXls9nOojgKs9NnEevvOP0GjZs22/EdQ5/tgY8z3rd34Ixq9fg/wT2s37gQ+YqOxB6ATh4322k1m6HL1ntgVESAuSrNaISJ/R525B1H9NLZuMqiTvS2q94+JLsNvgTo7V6Crmnug/f47Rldsd0YdzGXAGlFusresXWy7bw+cJSJ/tO2RQaOLE9Ex62LbXg2oU/hNVO/vt8/+D4AxZrEt40jbNi/Yzzfaz1+x9fSUMea9Vh8+igYlVqD9+CTgW1bGZ4ApNlo8k5werkDHL0d7innG3/vQ8ajOtv0Znnqfho6ZB6JO53IgY1cBOmwdg9olZwOWidLY9gHuERGDjtNutXo5dq+e1cNrRXnzh1qZH0bHyHeik/IXRVfKT7P1eYiIPG/75u5of+wArrLvMw8NeMxFKYIvishfgG1QGzMDHSuOR/vavSg15wX7Lv9EV70+IMp1/p0x5m/GmHW2TdzegoeA/xFdvf6Xrd+vAvsZYx7Ur3AV6ry/ZPX628Djnv5Xb+vhRtvOTxpjFtmxczWqU59EJ1t/QMe2b6ATlQF0lWWI72OMeVxEnkcnbleQs+d7o+Mo6Biw1rZXh7WHjj7oHzt+CiyyMt6FrhR6ZTTAnqIrUfejzvdv0UnmXsD5tu9ljFKSd0MnKv+LrpjPQf2knVHb8RV08nmP0xtjTI+1TxnUXnWj/kKjMabblvOYrUvnSwWjGC++Fn9QZ64LHYQbgPcCzfZaAjX229n/3Sx8LjpwLrGKFEdnTgnUkL6A8ufehc7UfoLO2m5DDctUe8+HrXK8gDpHl6CRvKOtHC0eOZ5FDcHbPZ+32Xun2P/jHlmOQCP6k6wy/beV9yXUObkNHRziqFFpIWeQP4R23qPRJbEXUdrItWjUZFdXH+jEwXGLp9rnXIl2spvRqMRhqLHcHu2UJ2GjT762qMMTUbSfPYd29BYra6Mtb4qtlwvs++xu209QY3UUalBdfRxu62OOry0n2bZ5w7ZlkAzPAke4OvZ8/op95xn2/W+1dfQD1BhN9dRT3PfMhbZ+z7R18nIBHW1GnUfv8+agA9ci33eDdMDddwIaHX3Fd0/CyYc6Dg/YutjClvMb/3vZzz+DDrYvoTp+GzDZV7+7M3T1Y+4I9ST++vU+z/7t1YFj0WjLv0bo59k6tPdPQA3u3WiE9mFPWQn7uwntD+uBCfazk7x1iC6hXmDregtPHS7JI0edradBtL9fiUb57yJnH7a0z3VL3Z9CnZunbBtehjqPN6NOnjeyl1fXvPpLzqZMQSf9R9j3FbQfvYI6PtNRu3AhOoF82PPcpKsr+/8zwDRfHQ7pX4Xa0qc3uwAbA74/H3ja83+9rZPGgPdyNjnGUBswE3X8/4tclDBrS+33vfe8gFIYXJm7oYGHhVYf3uar7yRqv2+2/7tnuXfbEnXQVto6duU1o5OZO8lRk36EOlCCUtFuRR3MPeyztsenawXK+gQ6zjyLTqBvQMef/ex334s6fhlge489yTc+fAzV32Xo5O+nqB67thxiS21bLUH7/u722eM89d6Ajplu/G3w6YbT3bno+LvUtsXenvd9my1jO1Sv346OYR9Dx8D98dmZAJuyj20HN1aMR53Eua497e9d7fu+gur5LmhQYYZtzzr7zDtsvV6N2htv5HZfe/1+rw0PsBu7YVf57f/7owGbJb663x+1Fbfbsn+NBoKmeHRjAWrjB7G+ja+suSjPOsh+bYeOyQnUf/mm/bkWjVw/TM4OnoGuUrzse0Yh3+dyirPnE9C+7N4r5rvuxuYL0AnGt9GVIifjNNQO/Lct7wNo/5tv9epRcn3vLjR45559BOpw/wcdG7+C9tlz0AmG0xOnN1Pts4N8qUl+Gxf4voUuVvuPrZyT8RlSe20/2xgHeRvZKvI78SyDe5RrmlXwB/MoR8r+TqCOzAJbzqlolGZ7hg86l6ID2em+Z81Cl4Xu9peFGtRHrJx+GdrQqOv+5IxXMzojPxId1BYE3PMl1BG4xHdtOrCn77MEuhx2jud/QQ3mZeiKwjaeeotbJX4A7YgHoAZ0J199bG/lOAm4zFfmgbbDLPC/s70+j9yyX6DSWxmzjgw6UN2Xpy0nOhl8Ms4CPunTjVno4PU5NGIw3y+D1Z2PAh/3fPbfnmclAsraktwSfsxT3idRA/I51Gjk7eQeHTgDNYafBt4Z8L3t0VWSw3wyfwgdYH5m9cc9L4VGSN6F8vO8dRyzOvUxdCByjoqrp++Rozn562lHdMlwH4bqfAzV9+vI0bEuQfVwOup07RdQh/PITRRiHtmPsu/7LZQvKb7vXIwOHPt4PjsDS/tg+GSujtxEdGtsn/DV77VYJxON2LwNtQ8fsbrgdTh2sW38TeAkj9zz0Qn2JHSpdL6r30K65pHxGHy2zXN9f3JLyTFbtz9FJ2TfRfed+N9rG/teXtld//ocutLonhf3/H0oOfqT18a2Aod75BXUiboA+JjX3vhkPwadtAzRG499OxOdQPnvexs5asUwuxKgTz9BB9vr0KjZRPsuW9rrs9GI6g4Bchxn2zTt6+N32ufdZ9t1b+zE0Fc/MU+9HIM6kwehdBf/e52NBku8ZbXaNryA4WPAO1CdP5bh9r4NXVF4Et2ECTqpm4/q7ymoPZ/DcJ2/wr73D3zPPNC+9wEB9XQMSmfZN+Daj4A/+OpjHjp2PYQ6xONsW0/N055ePTwFnXQcHFDWrra9d2GoLUqgwaD78vSjXVAHucnWzx6o8zvOo+cpVCcPcc/02VDXl2YCx3vaPYbau51tPd2EJg5w941HV2LehfoJu/naZEfgA365PXXZ6KkbJ0OTrafzPP/vjk5Kjkcn8dt5ZGhEV4I+YT87GDvx85Q3H3VcL7d60kTOns9C/aJ3ePsfueDTXwnu57ujkfu9bR1vbZ/3XtTG7o1Gr9sCdGIHNFAyEfU/Trbtdha5SaWTYxzajw5G91f4n3UgGkg5zFfHw3ypkX6K/mK1/TDU8P0VjyFFO/Tf0JnVs+ggcwraIdtsxe6CdiLXKO6eH6Gzv/meeyahDujeQQ1m738VdapvtspSjzowQXK8G51l/gTlid1OzoGbjxqva+y1/VCn50h7/92o8/MiPl6avf9lNKryMNqJmlEjej+5DVl/wON05Xmf+iK+M8xxJGccXX3chA4Y25OLyL5mO8CH7XslA2S/0iP7Jbbu/dG4ybZd3EaloDZ5zMpwIRoBuxiNOGWf5/l+3udhHTYr43SPDr4PXXZ9wtfOx6LG8x2o8U4F1VNQXaJOxcN5dGA6AbqI6u/fPW38e09beHXqddRRuxB1iHfwXdsPdQzfNULf8+rhdz3XtiW//m7tue8/ePTXI2u9r439fXk+alx3RR3VL6FUhckMd9zv99VHwif/jVaO7wa0c75JYFD7fx2NMl+Dza6EOlCHMdSuPGfr4GR8A9ooyvLrmnues1MX2nJbGTp4xIPeL089xX31FNReziYeElCWq+eGQnXpe9fZVkfmMryvzLdy/F+AHFt52vIld40Am41vkpZHjq+T48Z+HQ2u/AxdbXGTG79tS9qyXkPt16Oow9tiZbvB97xfoJH6G7ABBX8dofa3boSypqNR3W3QSOHF6LjmvcdtHjzX6kq+8cH1sedsnR4P7OyR50V01edKe21rNNJ5A8pDP9TKF9TPv+drS9cfhvRnpzPkItzOqbwC5Tk32Dq8y1OHv7DvNmxstvVyv9WNV9Fxz+m/8x+ux+c/2OuvozSLh9GIdjMaOJlt22zcSLo00lhpP28jYIxgaL98Fe2Xfh1xbfmQbcsWNJCx1ShlayPnX/j3MgTKZ6+5gGWQ7/P+PGWN1d54x4GF5DJX7eD5zs3oZOwNlIa2JdofxqMTkmFtxnCfoxWdPHyD4saVEX2pQj81m2bRcpnHG2M+LroDehd0qfs4tOIeMMb8l2iGkSPtZwejnWsQbczXUGd8MbpZYzza+XcLuCeNcpl67ecLjTGOh3o6Gl3wZtTYyt57P9rQl3ieeTy6HLKdMaZfRK5HeX3fF93pv6sx5igRuQjluj2JLkHNB+4wxlxk+cEu2nCTiLSh0Zbp6Kz8DjQieCA6q34AOMoof8pb3jh0o8uNeeq5GY2qfF1yKe3cb++1LNfK8tu+iDrlN9j6ONzKdS46eJ7iea+ErZOTAmR3qc1uRPnw2d3vInIVatx/atvFey2Jbi5ZFNAm7ahh7yrieQuN7m+Ioct6WxpjjrF85e9bvWiz9X4wqkdHogb+OHSQ/Y591gtoppQVVr5jjDG/DajzK9CIwxdRp2Sqp662tXL/xiPj81g6gH2XGwN06m3GmKM9OvVv+6x56DLfR+21021Z26IT2GOMMet88n0BU53ZogAAIABJREFUpWN9wvI/f486uTeJyJWo83lCHv39kzHmQo/+/t4ox7AJ5Qn/yZbhVkMuRwfeS0Tkc6gj8jA5PmsHOtBOsHX+iNE0mF8A3m6MeYd9nrc+vob2sSM9ctxudC9CC3CQMcalDvS+93tQ/X0/Ss/aydP+01Bn9X22Dv+NDnjePnspGiF6zcq/EDjZGNM1yrL8uubsFPY7tzG8P8TRVbyfecqIoc5Pg1E+dyPqQN5q6+mr6KaqIwLa6xp0EvqLgLJiwGnGmJ8HvJdLlZa1Hfbzq+zzbkXpCD8m11e+BOxkjDk+QI6vovp2nPcauhJ3NDpxemakuvDI9w90QvkaOtH8tTHmq1Y/v4+uoBzLcFu/FzpYv2F/O/u1Ezqp+iLq4P7L87xrUOf13345PO3jt6NbkUtluJ+tK2eHXJ926YadfPPQaOF/oQ7IdIaPDw+iUdmPozr1Gho1X4Tq9DfRdn4GHWv3Qx3xk1A9PxWdOLp+fqcx5oKA9roSdaqODxibX0LHKH8u8n+gKS8XWp592hhzoq1Dl93nFobr4RfQlYSj7XP8NnGBMebwAP/ht+gkyz8WbYOuNl1vjOm2NgpjNLe3/d2IjiE/c9c87zFE5+1nQXq/FO3bzehq/u+t7E8bY66xY/ZxqH32yvh22yafRieTvb56dDLm63s3o9z9ndHUjMtE03u+D53IOH9pmTFmqR3DjkL9JX9/2BcNUN5iNBOWs+cj2ZvdfHbZqzc72n5+Kaqrr6I+z4to4O9MO8adZmWeiOpvO7r63uOrhzhqY+ei9K8J5PrDSqtTrQwfVw4yxhwcoFMFfakgxIr9YhXiMFRpQI3SA8aYs1A+7BboEhXYZRFjzDnGmK1QZ/DPqLLtixrji1AHZwYahfDfI+jM7gF0M8Se6Kac89BGfD9qJOehPKWLUGM1iGb0+K73meTSCbkDfn6KKjbock3S/n0gyne8yBhzKOrsuY0769DB8QT73XNQx+Cn9v0PsXL8BHXo4ugA4S/vDDSiP+TgBNHDAWKoQ7TBljmYuyziu+ac87j9XjsabXD1kUA35ByBzrK97/WSbYsg2TvQSMaBtu73Qg8COQ8dHDrI8e68125GO9w81Kl2bdKHGqGdinze2fbarbauY9Yp/Tg6EJyIGqfn0SVp185nogP8s6j+ZHVNdAPSd9Eo67ADK1Cjc4ut3y18ddVHLsetk/8sdNl9AeoQgw4QhXTqQo9OveS5dpMxxkXXn7dy+3EQGp3DlvFPcnroUlR6y/Lq7/MwRH/fa9//fPseWQNq9egQ+z3QQeQpT78cRKMqN6OrB0k0Pd1XUR1rsM+LMbyPuQ306+3zj7f/n4suTQe1yzGoTb0AtQGu/evQiM85njq8wPPOC+39u6MTgbONMVuiGyb9hxaNVFaQrrn6iKHRQK9uON3+FepEZQ8tsfW7D7pci73+D089nUhuDHH19F77/4FoHw8q69eoLRpyQIoPWdthsS26d2E66uB5+8oH0Ul9kBwnoZNZ/7U5tp6mFlMX9u9WNNq9ELWLC60zLXYStRW5POV+W78S1b/jydmvm9AVlR1Q+/EX3/PmYfuKX9cK2NEnUGdiCurAeO3Q99ANa70++R5Hde1wdHXOb2O70b70S3I6dZbVqWWow/YtlJrQidIcp9n6PhZ1Jm/y9XO3aTnbz+2rnexpL++YOA+1y25TsLdNrgdetA7hRjRJg6vDqaiDGqSHH0IjoEE24GR0QyEM9x/iKKXGX0/Po9HkW+y7GY8D7vToVDTimr0mucNv/DoP6vT79f4C1PHdCptcwcp+lP37dPse/vHyBivjIaiO++Fk9MvhaGyv2Wu7ARfYvvceVAe9/tKHPWPYuQT3h2XoisR3PXVVjL3x22Wv3jTav3dHHfezrB1dgVL+fmev7wn0G82l/7+2Xg91L+udNNk6PBCd5Lt23mDfeQ+CxxW3mdavU6cT4EsVhBlj6D3MP+is5kz7d5JcijS3EepZW+kJNF+vu1aHRvOO8Xy2Nxql+yPqnB2Kzk7dhjxBl2/e7blnW1S5PoXmJj7YXhuPKvps1HH4OrrsNN8+020wOdw+82DPM3+NRpXvQ2fCMSub4/e6jTVneu6JoxOHg9Co2UH2WgyNeOyODthfQGfWEz3Xf4sq/R/QSCMM5fC61Ze73H32/4T/GkOXjFyd3Y3OTK+1vx9BncdWT1248v5GjqsnHtn/gk6exntk385T9xs87RIPahc04vhD2yb3oMuUs8gtZY7meeehA9Vf0Vn1XrZdjkAja5/0tfMBaGTKtfPe6PLZD1Bdcxw87wbVOLnl3ruw+xA89fpP4FifLp6BDmIbPGXFGVmn4uhk8RD72V7uWlC72N8Nth0dJ/FudBC8Gx287sXu+2AT9NdT1hH2uwk0QjjRI8uDDO2X09B+f7Kti1O99Wvr4zR0ADnN0xecHO8MksPTNrvb9l+DOl57ufay7X+J5zP3zk+R43Z7bVHKtsvB3vodoax8ujZaO5Wlu6C2aGtPW04sop4OQvvm0Z5rI5Ulvt9Zu0Kur0zwyOD6yo/QtIBnePTayXEYSmE7PUDGU1Gb7trS28+Hyee1X5668G4gnYfqn7veRs7W32PrqwVdVv8mOrj/BTjBfn8iQ23eNujhad6yR7KjrqzLUGf8QdQO7e2572qUwldIPq+NvQf4or13Ch6dsp95dXQ+6pA0o3bjPHL5v2P2p1A/Pwx1yA7Crpr66rxQf/C2y0RPmzyPrqS77zk9/AxqY4NswFmoH/B21H+4yFfGA6gd8Y9F2bYMai+/fN52zDOW5tP7z6K01048Y4SVfdiYzdCxvhgZA/ue59phaN/7ITp5O9YjQ74xzOv7DNE3j5xjtTfF6M1l5DbDH4vVc3v9h9g0zDCEruJts2s87XwaNguSvT6dkceVQF9qpJ+KO9Ob48enhPPwGD5fg8xHd98O4wyh0cd1Be4puAPb01j5Mmr4n/mM7/ocArJ3eK67XdZNATJsQKMo2bpAJxp/AF6z/zd47jkFNeS9ePIbe659BaVWfBClJLhrJ6E5XL+MRoX+FPSO6Mz/zkL14X+vEWTPW/fFXPPLMNbnef5PopEeb9ad5/+/vfOOl7Oo/v97chOKkAgI0gkkURBBCKL0Iv6ki0gRUIqCiFIUC6CCdJCmAlJFOkqTrgSCkkgXEZDeBZQiBAhV+BIyvz8+Z+7Ozj5l99m9NzfwnNdrX3fvM/vMfOacM2fqOUMjwkTgQ5acZ411LdXhCPOYDP4GHSjF2IlOZTxfD00uX0C3zra0N2QM+zGmetiB/u6KBmC5ZWXxgyTqR/LOp9BKW3pfQT8/MnD8GLWHqdjAM0mP5XJ1nvyTdtDE3+j51qiDe6WbsjJ0rcVOmSx/gY5A5Mly9kSWRXzql3NGWbPm6GHsJOoyZBnquyStOr+i8akIR6FNzGnnRf4zKb7Z0KAkxJyexeqR2pVM+5VRzlfRQOh6tCqYOua2ZUfJt0MLFLxTiJGMvi/hTWYEjzbaeYtMonquh/qc52nTBsQyMb1KeRjknGsD0vzte5iIxXy6BYUBPDjFh3Q+C1879rxU74uwJxjvQLbrPBKbYhhbcNDcLmdJMdJoe2Xjpbx+NrONUdHetKGjWeX1R39KdC5gHksjLHJum7XflvYree/mfcJ2wfuavB29cM7Nhs4eXWT/D0fbXth5py8AZ3idiQppzt5/G22TgGLbvocUKbzzXpyfl2RepBGndZhzbrr3/lI7Gzc5YPDeT/NBOxo4To8wTveKQXtEhHcEitXpLb/l0TnfNwMOL7rLOXcmGuAEHN7r5si3aWwN/5/lOxatIp2Cxel2ugnrCTT73QcNHh4xfvw2em9fwz0OGaunnG7Ou8Z7fzHq7N9Ds8lTjU+Z/LDv/fUqwd5P4fhD4L3JZQTa9iUn7TI0oJwcsqmSn21pDUfbZzdHsMYB13ndKhbO52fJeXGr6yR0K+fs6AjUe/bO3L5x3ntzGke4SHSgUBddIz53WzoV4XsPGc7D0DnIm4Ef25m/p+29EOv1Tad48qcZhiY9jHU0T3+trK8jAzq1oCzQ5PA0y7u/LWeU1YdWFbfw8rcIsnQxPwxHSBtj+d9kOL7qFE/4QctzlG/ExN4Cne8sk38mf60d7Y0GUlf3qKzYtr2X8PcQtLL+B+PvT9HZ7pi//wuytDwz+ZTam4yywo17e5luT0NnZA9zzl2IIr54p5tJg10Z5b1/zeq2aSTjYbFdLrJ7ZTbR0hY1vb4Cre76qJ1MT3D06xqA9/5ts3P/s///z5KabBuyf9NT++Wa/XPGot2Pi9HZ8u3s3c8659qyo6YbfYYjSzeeN/7l2t48jBn91BmWt7Nt+4vQoOakNM9O+qmkPRyG7NcdtGkDYpn4KM50hj3PtQH2+35b76Lxg+nEcGRL5kI7g+8AP3HO7eV1PnssWnk92ynO/NY09Ga0c+4o4BTn3CTMnkdtti29j3jVMkZA4xRvGOdEg/M7gK855x7zirkeZHWmYQznyUn6nPesXZ6WYvDl46WgG3ch3b7EdHQYilOf1rmSvYn1xr6nOhrGbYubTp2JVu3vMHsZ/F9i3m+BfApy22z4nzb6lVinQlsqJN/hiH5m/6CVpZE5aXNhYYZy0rPC+xW+U5BX09ZuO3miLaE5c/L7EIkXdZQ2H+ahnFengAOFATvEvn8ErVycaf//CjkBhS343wJnWdpRNM5T74bOpH0XnaM+hygiChZtoB1+lNQrxV4aDaKqTHqof3E4rSY5G++fQYb0L2hrciO0xbkA6mxDnVeO8yrjVQGejnTKMB5kafOiDijoyzymB0Eem9IcD7lID4vKmg8NoDopq2q7bOFHaBOG46OG6cwIx7kRjha5FMg/r86HGP65elhWCz8Cf+37qA75W6Q3LXIu0O1foxWw19DK6lFoIjmchl9MJoYo77zwiEX61pSGzqJORUfEAo4QK70tHJY2G3aMKHqWZ9ta7Bda2Djc/t8N+S6cTJd2tEQ3ivqiXBubpVNp/kS7OHk634beHGRlzd+JjhbJpIQ3RbrdNH6I8A0jakNW71+hVfqg85ej4xXzoHPZ0yJ9OxcLfdmN3mdhp2FThqF++yA0OA0Y34ow/g75t7Tb5xRFPEqPAFZuYyUyKWrnWXYvxvEgskHt4qg05ijCX/hepy+8Xz5FjRYZkW908l7WO5EQ+9PyBIpm0FmD0xYcaDa8T1pOVlpRfllY0GrKJtH/c1kjPR6dHQvXkTtLuxFtZ92GbjEDhbbcMsrjDCyGcUZ5bfGjqF7Rb3bOaEClcmkHQyf5ZeQzLAtjKhfkYLOyPVsWGdW7kEPPDTSupy7rfPt51Q7+NvQmzu9S7Iyf/f8pdAZ6UTSwOr1MznkY2ihrGRTRobCsMp2nMSBpt84nY34p9v+CaAcpON3+NksuEc/L5N91WZ3qWszfCGcV/rZlb4JuG/+XRcfg7keT+rtQp7gyjevB/4x2ki4qKDvWqSa73IG+hba3HzrH7wzH6ShqRxOOAr6HAenONM68t21XoncvQmddhyM7ekqUX1d2tEw3st7JSCvTqbz31qmoN6U2oBOZBIyU2MM2MM6GHP5SfONQX7g3GgRfac+XRRFKrkP2/EkafWlHep+Fowg7rTYl9NuHmk5dkYOxrT4nD0OahrWxjDrf026dU5nQYTsvwfGPTngftwcq9CvtfN63UVxsS6XlmXNuuG2vbI8cF+L04fbe17BjDiGf6L3t4vei5/3vRDTMtj7607xJyd7ts7+rIucHH/K0nzTlaWWF/Pq9rL3v37qHDE/w6L1t4/yi8obZ31VQRJdwZfhw7/1U5Mi2ktU7ROcYZmnHoEgBs6BBPDRungu0CGqEOHna91OEoS+LH9HzpjpHdYuxr+cbkWSGFcilib9lMjEdaPLCT/IblsXfGGYWxgw5nw88adtn96KB7y/RCsjq2HY2MMIl0S9Mt1t0IKpbJv48nQq8yEjbB10F3Wd8WRmtROyNjqOcl9SNgCNPr0NZKfaorNAuV0Fh0/rLcs4NS8tKym8qK5Y1OW0lI+3n6KrmgGNjdEZ4T7TK+TvD3xfnlZTVJP8gr0hHuyorLianrCx+7IMG/8Py+JvwpIxPFJR1PhqUDDPdfgqdfd8DRQ451Xt/G3LI+xgaPKyFBqiZGBK712SX8/QttudJ2inojPMww3EbOr8ccJxjv+/ne9QGQEcA+tCW+NUBQ/TbYKdWpeFkBom8EO/vQtvhR9HwM4Au7Kj9LM9WtuCL7F5/xK4s+5Xa2AIcu4bnVmReP5Xa7CIb8E3gvFjWGTIJR8ECDwPGXHselVVkH4K+hTYUjp88huzht5DszrLfP+i9P8h7/wXUxhfDjjeiIxa7tKv3CR9bxiP2tcV+RRinIgfp7dCE52z73QMJxtWBU40XTfYmlm+KIUmP00IbA7jde78Lioj1qtW5/1hURn0z7U0b/UqWTsU4/h7heKMMhz1vaUcV+5VyanckP7N8iDy2o2d9tK4kBg/hsOI6PC8t471wTevIjHeyyppIs3d2uBI3zLiOQ+cJR+dgDBFmwvNrorI+gzq40IiaPMGz8rPva9GIYBCcs44juukqee8a4JGMtFlRY38kLjP63Tokzno03zwZbx8FfhxPY7uvqc5Rmakzx3E0ZsWlckETipgfI7PyS95xJF74efwtkHP/NiEZOhWVNSzCPjvafsza5uxL3nORfsQybsFPjk6VpWWUNRoNKp5J21usazkYRqOt67SsuD3G741Gqz9PZ/BiONqanT1H/ksT3XYYleUq4FiQYr3vpH2VRTsqLKtE19rS34S/twJP5tjRTvmUVVaLblvaMBpt40PAzUnZfdH31O7FOpWb1kabDfgmGr4PIf+bk9Bga3ZaL2sJeY0BJmTwLZwJD3L5RZTfHGiVsV83kvyCE2ihHc2yCZbnigkPZyHRw6jtuPSdPBubx0Py7fkXi/QGhQNM22yRDXgcHYvKk0mw5zEP+yiwRfabuTMwxn3HsKz3kvqehOzhv3J0oF/nO9T7eUku96IRJSvm0zAyxgEJxhPQCvkjBRivz9LlpK30ty+S/sH+TszBMdw+x6HB8x0Zvymzy5XbeQ6O1QPvg5yj380T8T5uDytR3K+kY8FMueR93ldOos65PYGPOzkHfAdNsN72Ovi/kVN86hWdc+8ib+CX7L0t0dbUHM6536MD/C/F7wAvOuduQ44KU5ziqH4cWMw5d7Q9fynCsjqKkR3irq/mnLsfzaA+6ZxbBNjCOfc/JOSFganOueD8snqEY1sUg31d59wryPgE7IcjD/Lvou2TFMfKhmNpZNBfshnx4cDbzrm9vff/sN+uhEJw9TvTOOfGoLNyv0KKGDva4L1/xzl3XFaa0XTg+MAPZAD/6uT4Ng4Y7pxbFzkdnoY84j8LfL8A+yHAr51z/zHZzmI8/EGRXJxzqzvnAob7UKObyzm3menNYsDuxuPAi5Odc9cgQ7OA8eKNNvi7X46cbwE+5+QwdbhzbgGT85SE70sE7E6xfv9oPOnnlff+Fqv/KajhB4zvGcYmGQdZtqFTuWkZuhGcTfuAl51zHwWmJDpwOHIa2ht5/U+L8jsUHdv4r+XXV1DWNCvrOZPbTcgx7C3v/ave+2nOuYORE9qj9o6P6nWw8e0pS5slSusUx3Om9xs4504Fhc/z3k8sa1/OuY2Mvy22qGJZebrWjv5m8fc1tEoZVovm8N6/brqWyyfn3CltlrVEpIej0DYxXiuzYVV3FPCgc24d7/31PnLIMhxFdi9NC2X1txOvi2TGIOe6gD/G9669sxJqT5ejUGp7Aos458ajYzknAas653Ywnt/qnNsEHd95xT7eN1ar10SDiR9Zft9FK+PbA0845+5C8bebnEAptqP3o0HaN4JumAyCvjW9gy7/OcHqPA+KthEcIVcCfhjrKOr7LqGhUzjnlge+7JxbAQ2gFnbObQzMn2PP5wFey9Gbn6HLXxZxzv3AZJzbHmgMqi6zcr5v5QeZvIOCGyyKdoOD02PQm0xb5Gz8AHzI6XKi2HbM45x7xWtHaozJp+mCNsvfoSNKIb527NgZaASm8/ZeW3oPHIj6vUeMH8sivX8xYPTev5z0HU1lR/mtCOyPouoEHscYtwTusPZ+uDdHXKv/oWjwfr1zbjTNfWwf2uX4htPYZxFs7OO9f9z4Q6JvY9FxoCDv0d77p6yORfYms51H+WTp6Ou+4cSd4jgS2VJ86+7SwWhA/qjJeSSN9nAhOf1KxNu4v+xPKyXf5kh+qH9MyPegOJqHoO3JE5GBOAh1ODshJ41n0fGBC1Bs5jtNyGegM2UnobOAj6LLRTZFM+L/otW7z6FjHxsjb+Br0GUokyyfPnQxxYbIG/8StJ11NlqNGYOusT8GrVa9gQLfz4Zmbvsih6p/o63Vu9Gs/9P27HW0BbsVuiXrWKvTJcBXfDTTRcZ7c8MYLvwZa3nuaXgOQNuuv4zeCzO+vbD4zBGvFymQQ2aa8WNL4+cRxt8X0G1in0be5S8bxgsT7BMi7OPQudU4hNTFKOrFt0vk8hxyTNvRnl1nMnnMnh9mvL/A+LwXcnZ9GTlr3YE6g2tphGXK4m/AODZDzs9bHmegc7mXGo4tffPs/Cfo1jEsvztNB9aPeHUbjZsnA8a/W/43oEnI9yIZH4h0+WNRfqlOhRtc185IWyPRrb0sv7E06+EPaKw2jDOZBBwTacSJXRKthAVduwydA/y3yWK2qKx9rKzA29CGfo30/j/orOXd0TvnGG9vsro/GqVNsLIfRA5aT3SCI9LrIP8jkY0pa1/j7Hc7Ilv0DA1bNDbhbztl5ena6/b9SYr1N4u/cRja7ZDtCjx8PHon5tPRnZaVYSMWjr7PiwZA76IQZWehwedlKLrJw6jDfBYNnreM9C3WxUtRZIpRNLeTM+03dxXhQx3zMfZ9d9SmPo/a4e9o3Cq4IdK3o4xfIQ75MVb+wia7W+3ZKHRE4z40ULo05BfxIMsJNLWjoax/Wf77It34CfDL6J0N7Z1LkL4cj9r4ZRHGnZGOjrU8dkUX0dyFdPQhGjr6JNKNPyHd+CuKjLITzfb8YbS4kKc3JxkPgv7+FfXZ/0ETrSwbEMtkJ2T7g0yuMJzBnl+B+v9J9jdul7EtGkfz+OFq+9yC+o7L0c7HV9Dxjy9FMhkVYfxy4HuU3rKrn6XzJXo/EV3CBhpfbGY8fsDk1BfJ8lxgxyTfMoyLRN/Hoj5gD9RP7YP6qtORDgV8L5ocd7ayZ0307RIaY5/jSWLYI309g+aQlgdb2WV2+UTU3za186QdP0mzjk5AfcT45HebAb+IyjgGHXkJu5LjUBsYhcZnmyFfgWco7ld+jcVezxtLlY5rO/nxUP7Q2mhjQ3ovkcMTMqR7mgLeBPzcnu9mwv8uugjgPhrG+xtoprQncpo80p5/Cw26NjKlPB+debvO0sN17guZoryKBvjboca1npX3b7TFsjlqjF9GIanuQ+HHAvYfWplbIGO/vz3/IjKEW0e/3Ty8azg2R436EGSszkdX4p5jipa1BbQytmJh/48Ejk1+U5iGBhLXAz+Lnh+JBpGPGT82Rh3WD9Gq0VYR9pVpGKTDUYM/Hw0mJyKDuTuK83xcjlwmAjdF5W+KzgFeiwzO5+ydJ1DHuLWVsZ3xZkkshitaPTnA5JbF3wNRx3O+5RVu3DwHNdzVka49iBzBvklBhAZ0UdYJgb8Rr7aw/zeIMJ5gGKcjh99PoGu3z0FRd0agVYITcnTqFuCknLRjiSIw0NCN/YNsUQd2Jw1jeQCNSCGroo7y+/b/xuiq9Pi90aiDvwBzmE3a0X7AKfZsZzTJW8zeeQCYZGkbIKO6tcn/OhpxdTdHA5AR6AzmTejK7LZx2Pct0dGTIJdNKWlflnZUlN8haPKyBxpwDe+wrAPJ1rV10CR4T/L19xMZ/D0UuMQ3Oq7HrOwN0eDpTwV82qOTsux76Nj6bQf0Xzz1JcvzGPu8jPT6zzRs9heRzm5j/++Pru0OaX9BNzk7dNFUwLcPGkiU4dsUDQY+iwYCu0W/O9bqeV1i8xZCE4BpaOJyPGrnJyG7dyLSuXuQHVgc6caxoV4ZdjjPji5E4zboz0WY70eOlKsgOxiOZY5Gk4rv2fOXkS05HoXaPBJNFI5Ciw4Lor7vAOPZEcbHtH/7BdLHu1CbCjbqAMvnkhy9uSdK2w71g2Ms7SJg9Qwd3QQNtJZBA+64f/wbcGvUBzyBxgAbYhOTHFuUjh8eRyH61kcTrSkRn260369L4zjJgvZukGXQn5GxvPJ0vg2992gssACyKRPRzsAmqO3vhhYQv2l8O814X4Qxs9+mOZLQD6z83WlEEtreeLsYsquhXe6H+tugG99BR6oWQvr2GKajlr6c/T70AV+z3wyj3C7fS6Mf6G/nUd7r0KqjS6Cxwl3AMtFvP2d83cp4uBsaW/3GePg71DauQScgJqJJ0VYU9yt/ADZM+8us9p33eT85id4BzO6cWwYx+GTv/V+899egRvpp51xwzHkPbW3+Ggn8SXu+HPAD730wqCPQjBG0NXmz9/5YtF3yqD1fHPiW9/5P3vur0azys8B0pzinDwJ4759Fxu9mdJnPnsDZ3vtr0YD0GdQwRgGXeu8v897fjRRgnB0dAJ27msN7/we0Hfc/e34tUqTDnHOXOuc+ZtgmWPofvfeXoEHsRoZrjPd+AmrM66O4vasmfL3d/nrXcJJ9JPmNt7/bIYcel6Q9jRrAms65JZ3igF6FVlcOtfe2RUcpfmE82CjCfof3/lLDvobxfzwyYqd673/ivT8BGYPAj1Qu/0bbr0c5xXX/k/f+aSTHgw3D4sCe3vuTvfcXIFkujvRgF+/9Dmjidp7xPqzup/x9D205r4AM5tne+1vRatANaMCzHBpMfButkkxDjV9ME4awFfdXdO7+O8b7wKs/GC9GRhh3N4yIcOO5AAAgAElEQVTPohX7b3nvb0H6vAwa3LwIzJqjU2+i4z5ZafOjziPQ37y2CDdBk0OQnP8J/Nw5dykyfhdZnW5Bk42tnHN/tbJecM5tgPTkKK+tzX+gAcbWUVlXeO/fNf5Nt2MEPwNu8d4/be9MNRnvgjq2w7z3F5j8HwXmds59B+nXmd77d73396FVk7ljHEhf/pni8N5fbjhAtmMC0p3HvPeXk92+foO1LzRBnzuyRYvY35PRSs1aHZaVqWve++vRYH0J8vX3Wxn8nR3FsN8FTfYme+8vNh4+CHykQF5jM8r6XVpWUq9gK/rtildv5pFOfQ0Y573/ERoM/x7p5MftvWvRStWhpm9b0tDFa9Gk9FC0kvdUhO9INKko4gVI3z6CFmdupXENPagNX4X08ehg28zWX4kGMwsh+zoRtck50MRnRTSIn8N7/6TXtvdyhjl2VOxnGxl21Mq63+qxnZNj/+Vox2slwz4bsLfZvWe99/d7749DdudRpP+nGT9eR+egt0E3Oj6H+r5XjGfjkM5OczpOeDOyXZehgc85aLU12KiD0FnxOXL05nbUv22C+sQTvPdPWNoDJs9ULjegIzOnowFdfAxkYSyGPtL9Pb3315j+joR+Z9bUFr1O8/jhl97762z8EAIlBD4dh+4NWQ+1ybFex9CcyehV04kQjKLpKAyJzkfHPor0/kGr88XItv3Ge7+z9/5KdMxjVTS+uAxN+idaXkUY8/r0pZCtAunqJd77E7z3YdI5D7oAa2svh9LQLtdEfVgY+1xv1XrW9G0vKysc+/gn6o+eM7u8Purrp+fZ5Uhv5kHHWiBq59HY5zkSHfXe/8t7H05W7BLhmOS9f97qdiRqo5OMh++hCf0qaMyxr/H+JO/9hRT3K38l6i+997dZf9k2vZ/OoN+AlON0tDUSG9J5kELe4ZybimZi51nayzQG22GbFjR7mwvYwzm3BTI+4Z03ot+djJQh0FJolfcENOvcFjXCF9DZt9H2uyOAMU4RO96xhtKHnZ13zj2OGvKKyAg97HQ+8U4a57Wetf9BCni+c+5KtL25G1qx+RA0zlrZu7OhWWXw3p7bvo8EdrEzblMsbUO0EnOK997bOcNtof/84sKo074RrZrEUQNAE6FnnHPPI4O7LTIGr6KBxak0jucEPOOQEXrYztDF2HdDRzReQoPxG8IZLzT7vc5+e4rlG2hR1Lg+jYzEwyaTCagzehwZ3xeid5ZCk6XJzrm9nM5mbwis5uWHsKjVvS/BuAMaDL+BVsXG2Nm8vVEHtj/awv1PxN9FkN6G6DlxQ77D+HYYGiTcTkMHxgEvee/3TTB+yevceZiE3+q9X9k5tzea5LxHtk7Ng/Q5TrvMzjuOp3H5T3y2cXtsIoqM7LbYpR1ogPJUqIj3/iYg4Pg66ggOQVu7TzjnHkN6sQWN864xP/6OVky2RW33Qde4NGIYGhDOhzrUSREPx6E2uRla+Ynb2CbIwPfjQMZ2frSKdkCOXC6w3x9ueCC7fd2KDPxeaCA9kmZb9F+0GzAam+ymZ0cLysrUNePHOmiHbbJzbm/TjY3RokMfsnFEvwcdcVgRhVzzaMUp0BjDEcvrH2j17AvAAd77SVFZW6KVuWNcI/JBH7rRcmE0eLzR8u63K0bTzd68jKLabIpWDlczHjwU/S62e2+gtoxNcFObuB/w7TZ5ARqYbu2cuxrp42HOuWet/Cne+z845263vGNb/xyaVD/mvb/XOfcA0stwdfrRqK862cmn4mlkz2407GlHnmlHraxH0ET4H15+GH1IN0K0mKORPsd271WTwXVeZ5WnGs4F0MDoZjTACavioe8bbVh/bvnPa799Aw2YjkCLTZfS0P8l0Cpvlt4shWzHtmh1erRzbl60cv9ldEyqqe1576c658Kk+VrkuxVkcheNyCkn0ojWAZp0X2L59SW2aDxqt1njhznRzsZLzrlXrX5TkDw/j8YAID1an+b+ciM67y+z9H5zZMM+hQaMN0TvfBy1idto+E6MRjb4WDSJLMKYtr29adjsaajfDBT6qePQgLi/XVq5w+w3YezzjNNlSX1oEvG45TMMnccOPmxHop2Zx5xzXyDHLkd68xyNKDipDQjtPNbRe5wiqcyO+r8mHPb9UtRmLzW5vGzt4Rq0yPRP1OfEY46ifmULtGOXZVfaI9/BcvvM8DHGjEIG5Vk0iwtbKHMjAx/O+a4A3JCTzzomkE7eWYvIGxltJX4XrfD8As0Wj6Cx4nkCMspX07g4YEOkeBeZku1ueQ0jukYYrbb8hWZP476o3OMKeHQxanhh2/M21JgvpHF0py9JOwsZo4lR2rnojN+VSBlvQDPGcE4xbKGNQQPhPH6E2N8u1Ksd7NiKnT1flejsbJ5cimQSMMTvRP8vjTrx6+3/zItKMjC2yDn57W1oNeZl4OMx7xM8Y433uTpQhJHGtuoCwF5FOpWmmZwnoZX3VVAn3SRnk12Qc5Me0qyjeTi2NllMQQOOh/LKinTqmjzeog4+HBH6LHBbpGNFbSzgeMNkeFc7cilpX1l17rcrkQ48301Z5NiURDeeR232qjb4e30OD2M+3Y46wv0zynoV2Y6z43rRbDdWQR3mXRkYxiF9WgUNAv9sz2ehwO4VpXXKC6vrOKLoLPb/ujRfWFNk6+NoDsPQAC8cN3DofPMt6AhkmUyK7Ghqw2LdaAtfVJ9n0OC5pe+joc+roMH/lWhB5EqT9WpZ9pwcvYnKDPo7DU1an87hxwikR2OCXLJkktUHUG6LisYPIfJLrL8vogntl1D7jPvLdWi2h+32l7l6X2TbEowxjv/aO30ZaU19epG9oTEm6ovk34IvaQ8PoQXGF9Hi1NI5dR5j8imzyy32JscGpDr6b+Sn8TyyjcsW4JhAdntYMI/3FPQr3Xxm+IC6Vx+iEEHRs9BoR6UMt/SlsAsUMgTyOWCnNK3knZWw847J89nQjNVlYPyoCXpE8vxDqCNqqZelLwasUMCPfueajLQFgR9GPLoerRj8ypR5lKWNTdKupeE8tSQNp5XNUUP8G5qxnwjMY2mzoogFGyfYFkMGI730YjF0VqsI+4+CzKPnqwJfz5HL6kQXJ8UySfNJZJn1zkKRfNrGGMk5PmMc+Ls+MpL9vM/Q7W8B65bpQIKxRW/y2kqU36dpNtZLotXoxUyOk9BAoknO9tsmObfbbpOy7kGr+JtnlQX9YcT6y4p5m8re0sejFe2sC1ZmycAxFm1PLkLSJtL2hVa+U7nE7SstcxjN9iTowHh0ZrFKWaW6Zs+XBe6z71sW8Nelsizg4YdjHYiefwINguew/MMqbmo3JpnMLy3SJ+PTkkV63aHelfIixZFR91mNVx9OZDUu6Gny+7CYMFekC1n8uDbFQb4dHWf5ZNmw0ake0uiLMvsHNPgdFmEs7ftMxiugSWeWPV8hT29SPhk/7kP6245cvpjWIeSf/N9izzPaZdb4YSOaJ2LBHo4wed0Y4TsXDbpDfzmZhvNyW/1lnt6TMXaI2uWWZNuUDxuO47ExEAV9ekbeTfYGORU3jYlobpezJziWRAP6Rcmx5xHv4zpn2uUie1PS1kO/Mh7tImXZvdnItnsj7HlWeyjtV7q2U73IZKh9UobFwi14J/Oq2KK09Lkp65/QbG91awSx8QmxYkMnmBVnfdFECcLzhQrwpZ7g4yIcy2ERNaL0OaPvR9FwjvkEWjl4AjlBHBOlLYVWZJ5AR4m+hjneou2du1EnsiQ6tnJQxI/v2PNcfuTwcNUi7O3IK8lvHDoeU4ghI79FqmIskPPCCe+XMt4/afwdkZQ5HwUDE6I4wlllxW2jE52yOsZyvgN1/P1yTjAMzykrHXQ24UCTtW0yyvpIUVl5vC2RZX9c47SNWVqWXP4VySWWfzijX6qjWbxvQwfaLqtE11w3sszhYVZZH7aysur1FFrBPcN+EzAsiGxPWxjisgr4W6Rvo7rlBQ0b+3U00JnDygjy+qZ9T21D2FUJK35bo1W3EB3iTnSkqtSOFpVl/19Dw+71214au3Fbt4Mxer4EOmv8ebLvG/lw4HWBjSrTmywb0La9sb8tjpmp/pbYophPoe2NRUfeYr3ZwfCFfu9u5BMD0vlL6KC/rKL3CcbV0CA5bnsr0+jTv0pxn952n0Px2Gd507dYltsbr0YU1DndGUvtcqbeJDjG0NDRoI+xzLY1HLNEOI6nuT+fM8kzsz3k2MSOxm5ln0ovzUwfChotDSOZlTY8K63knXD+7gK0PXcMmoF/BHVCN5NhvCJBtkRByXue1CtN+3mE4/EIxzyG45bo/X2RM1D8/g5oFeMsNKCNlW4r1KF8A62QPYo6ggOj33wbzUwDjvPQUYxSfiTYYx62YG9TXjGGyShMUzsyKYpMUxVjk5wD75OyLjT+rh+/04Zut+hNL3QKGd5YzodHad8GTozfLcCQ2WG2UdbP2ikr5W1BOy/lE0mbSOVCvl4Xyr+orARj12Vl8aMbWXbAw58V8PBqtP17HwV2o0yf2tSpwrQqvKDZDgZbfztydg9y+RU6H/tChrwWILI3NFY0Yxx/orG1X2ZHi8o6Hh1jyLK9eWktGBPeHYMcJv+Gjk0cRSNk3XBkD9NFgnZtVNCbjuSSyCTo6G4kxwsosOd5NpHWvuMmFOHlNjSofYXmQeYV6GbTGF/b/WUVvSd7zHE2OnbTL8sIx1nk9+mFfQ45fWzCq3jMUcSrTvqOUr2Jnh1Nq45+MeL9cxm8/wf5NraoPXTUr1T5vJ+cRPspOsAP9HtMbw+8kqRRkOaTtGFI0YPnc5Z39pNoK2cFdO7qXeSZ/goS9L+9Av33Wf7BAbIPnbsLXt39ZdknK3JKiyd4hP0pw/Fp1DjeMRw7GI6nIxxHeYtYEDnijMJC5aFzmz5yWrsMzdJPQh7ic6Ozeoc55/ZDDkwhNOEYYAvv/a3mZLg5cvp5CQ0u+vnhGw4UAXvKw+3tnRj7dBpHHvLk8iTaZrzF6dKD5fNk4nUBQSrnLN7nYWzhr292DAkOKSHP07z377rGle/bowHXqyiizmre+7v6Be76ozqkdW7SGy9HsSDLbnXqcXN0+hxyaj0HOfJsRkPOjQyEsR+ffVraSsSbsrIed85tHsqKnF5DPrG8murVjiyzcGBtImqnTXJBjpSbe+9vM73ejDbkX1ZWlg50WlYRPwpkmcvfDIqxF8k55eF26Gzn6+i4yE9Rp5llN5ootm0RD1tsdkZaVnvYHjl+Fun1N4DfOufWNozTjX9xv/EvZAP2Rzbt/5lc5kbnXB9GTt2bo23zd5D9eijRjYBjLWRHJwB3J+0rtaObFZQVbOV45Cu1rXNuObQVvyfydclLCzY27qfia8xfRUdGP4NW0z8MXOScewr1YS8mfQVk2BQK9KZALtujyBvnRTpFKM/yC/ofO2ZWsgFW5ydp9B2Hob5ja5PTnGjAt7LTRUvhsqgTknp30l82UZH+Rj97kta+aHnkkB7L8ooIR16fntnnFIyJysYcebzqr3OevSmQSaENQBGDUh39JdKl/xm/Volkth06pnQBmmx/klYb2/EYIcWfVce2qOrIfih80AxnWXQ2Kt2Oa0pDqzfzFKVl5RelbYy2GF38PClzlAk0LitcmjOdRpzavpBf0Ll2yyqqVxUc0TsuA0eT01FSxsjk/49b/n+kEVt2FLBA9JtQr+ULcGRhnwc51UwHPh+/UyaXGEPC30xeDICcc3Uq5n2GnL+AzmHOGpddhJHqOrVaXFZOncN7k5GT2bVodaK/XmX4OtDftKzbMUetdnnRYxzxeyNJ9LqsfQ1yWe3obyf8LbKjnZTVVK8cu7EvvbXnue2hgBd/RFvgsRPraFqdR0ehNjMhwblaKhcaTtYere6ORhFKshzU/kqJHS0qy/4G+7pZhn3NTKPAxkbvzovOlKc2ZW17b+08u0d1vQly+Q+Kfd0iE8tvt4iHsUNvZRtARt+R8PBzNCKoxPLqtL/M1Hva09+WvijFmMg5s083XqyUYujAfpXiiHhV1d60g6NJR6PnaxuOtVOZMbA2tiWtk8/MvoJ+HA3nh3ecQi2Fy30ORLMn0Lbe7MAazrncNHSeMMyO3rGZ+vxo1rsfWnlYyTl3jpX5agBis7jXnHPzIQM+1d4P7z2PtlEC7QeMd7pi9nDn3HvIUL9sM+tQ1rmWXygrrvO8aHa6hnPuPq/Vh+GGY177TRGO1Zxz//Le/xv6VyPGonNiS6NZ8eveNM3qOQJ1TE+j1bBAjwK/8o3wjNi7r9l7wyIMFyHv50nxKmMBD49D4cJeAt5yCiG1vMmyUC4Bg9Ur8DFPJiT5pbyvIudUR4ejlY3/55y7FXjOa4VkjP0m8PQodOHKO04xs8ejEFuTCzBW1akTorKWRytB2zmFAbw+wjgWRRv5OdpeXxKdY0zbXpFM8vR3FrQdGvPjf1bWasAmzrmz0AUaj0R6U1SvKu1oVhQWMVMu3vsgn1ivXYH8B7uszDo751ZDbfY/UTsPsizib669IUfOTlfLP5WUNRxYJrY3Rv12w/B+lt7Z8wOBpZ1zK6MVy2mW9yecc3m8+Ai6yfhLTjsLhwNvGc51nXM/896/DLzmnFsTODHIxfh1J7osa5I9/zgaVKyCtt23QNEeHgW2dc4d4BUzGbRydzSyjUV2NK+sQA/b8xDL2kfyzEyjwMZG/cqUSEen0mzP7/IK5xlWGavoTZ6OXoQmDctnycR05lPOuc+gNnFim/ahsC8F3jJbH+r8SefckxEPJznnHFqhXYoonGygdvrLAr0/kAz9Nf3L64vWRLs7/RitvGHOuVXI79MrjaUKxhxroNXqLF6tQzV70w6OJh2N+HE7CjU8OeCI25hz7nX7W9nGRnm52O4FeVWiqiP7Gf0h2/s9eOb+Drg3SnsMxU8uSjsbhQKKI0g8gFZScr2zM3D1ez7T8JheADlGhUgGqSf10ejSnXUz0jqOnGLpqSd4iuPraEXiIDQbXBMp/c5o5vhImmZ5bZWXZunL5fAlrteJ6PKWloglGTwMzjejrLxYzh3LJU8mGRjb9nAvkHNexIq/oY7wyIiH+0fyWg8LGYkmi5ch/TwKzfpvSTFmYG9XpxZPyroTOZBdiTrh4yKMe1p+S6Kt+rhe3UQyuDqPH4b9djqPMlSlHeXisN8uW6LbWTo1mGVl1hm12bStB1kW8bfI3mTKuaCsw+z5yWTbjU3orT0/E626xpF49jV+Z/LC8tibVifFIse21Hl0FLqgKvwfnECD/Qr57Wnynxzh+CSN1fRMO1pUVtnzvDSKbWxeRJtY105FR1EGQm/WNR5eliOT89EALOR3MubASm/70lhvvm944z7xUar1l3l6n6e/LVGyaPRtMQ+zMOb16UX2vKjt5elGEY6q9qZbHPsgZ+pOxio9GyNU/ZSdNxzKNB4d7gdtHY5EndHJaNX73SjtTbSiUpR2NzKY34vy2wAFvl8WGc43kTF6CTjSOfdV59wIm5EFuhw5GoDOzN3pdUvVUWjl9F6kVHejVYBT7fkP0crld+ydUNZrwC+dc19Fjh9xnd9GDe1kNIv7XoTjfOSMkofjRDSjfwop3eb2/v+hFZ4F0Vmy/jTn3JeBH+elOee+j10EkUEBw5soksOH0FnL3Up4OB5dwPEacvoYiSLMnEw1uWTKxPiby/uKct4duNPeCzq1nuF8BRmkwMO50e2ee1rZdzrn5kSd9tteNzMeZnnMmmKkuk6NSMp6Eun9NKQbS0UYF7Hy90We9dDa9nJlQr7+kscPdHnUx6L8FkR6UyivkrSOcZhcLiObinRqMMvKrDMa6DxAc5sNsizib5G9yZRzQVlrI7u7Gdl24zf01p5PM0w7R/X6FjrrnckLw7ErsKRz7lH7/R+9bgZ8GMVqnxcIF0k945MLhbz3r3nvn7DfLIwGEUta3b6EJtgfRSvqTxjOgGMr4IsldrSorMLnBWlFNjavX4nt+a/Q5P5u59xO9FZvRqKLuNbPkck8wFTL7zf2+390YQPy6rwEOrP8BJqYrYr0dyfUJy5Atf4yT+/z9Heugr5osxKMeX36T8m350VtL083inBUtTfd4pgP6d73nHNfa3Os0ssxQiWamY+4/B3Y3Rrt41ijRTelTQZ2jdIu896/XZTmdAvVbkjQt6Gr4J8CcHKCCdtTj3jvN3fObYVmrS97768J2xre+xcjjK8iJ5ph3vuHnK6DvRAJ/DLvvXfOPezlrBPy60PbWc57/xCwZZR2E43O43Hgcu/9OzRufVzW8A7z3j9TgmMcmqE/iy7qGG+fxQ3f8d77s51u0voUjSMWRWk7ohXXrFsXX8WufDcebuuc2wE5szxewMNUzhPRWb89q8jF6Sa4LJl8voT3VeQ8Pzoq9AgNHX3O6RrkNZBRPxI5+CyPOqLAQ4dm6o/QcPJ71enYSZDtI977gHEvqunUy6hjDGXd67VFfTwyjgtmYPw8MMVk8gTNba9IJpn6W8KP8TRuwXusg3p13I7alEuubufo1GCWlVfn7YFrrM3OR6Otl/G3yN7kyTmvrEcszyPRKuqnUQjYYDdOB9bqoT1/Eh0rWMD4tD1aqS3iReD76TScFEud+WIyuUx3zo1El9/s6eT0uDZqt0cYHyYgG3wkmlgH+ZfZ0dyy0CA393lBWpGNzexXgh21bB+N7Pl30ZG5XunNMsb3Q9Bxi/8CB0cyWQq76RcYsL7UOXcSOnrzmHPuoyarldCqbDf9ZabeF+jvuugG26y+7eSKGP8f+fZ8MvltL083inAsRTV70y2O+ZG9Wcn4Po7e2tiyMUI18l0sv8/oDzqPtAmaeV2LzgZthsLifMnSPtNB2m0oBFea3y3AF6zM4CgwCzom8hawoj3Lu5iiD60w7IqOHexk740P7yT5jY/zS9LWKKjzejHGNnA0lecbWzjxLYhp/O+ytHAeLo3JHN9qGbZx90Bbo7k8jOS8qdX5AXT8oh25xPUK0QPa4kUO7zuVc3hvzxx5fSEpK+Xvhsjr/m00gN4NbT+uk1HnbnRqfE5Z10dlpRiXIFsPy2RSpL9l/Oi4XhXbUR6O+SvKv5s6B2ejXujv+AR3O/zNw9522yupV/9zemvPtwz8peFXVAVfi/N78l6ISBJsWvi7K7Br9LvAowPR4DwvvzI7mltWSEt0Y9f4vay0EhvboqMF9vy7aPI1oHqTyOSHMW96YAM2J9GbDBn1RXzfP8XXaV9Ktt6X6W9uf1kRY549L2t7qf1ySf5FODq1y23jyKhnHILzQLqz523b2G4/M3yQ3bOKFBjSKmlZz2OBR7/bEvO0LsCWF6Eh19u7KC0H42kd4JiAQlFtkJPnxZaWKm5R2hJoRroxkeHPwpDyo0MeTqYRbSFTLsjpbW0yIqCUyWQg5Jzw7+eokzo14/0laFxHHhr/R9Aq0jfQKtLHivSjG52KyvomOvO+U5sY220rWbrWET86rVcPcVyBtjeLYvYWyr+Dsi5CTmOzdau/qPNYE4vaUYW/CfZcOeeVZc+3QitiTWXlYcgqq0pah7y4CF0U19TJ0xhQNEU7in6TF21jfRq3FcfPP2E4NsjgYaEdLSirLFJTlcgphZF1Iv5kRv/qVm8sbQsaPgHxIHDeLOxd2oDDyIgwEuFrkpfVd0W66C/L9D4He0tf1AXGtux5Dr7jSNpDCY7ls+rcib3JSWvR0TZwdGXP29GpXnx6mtlgfZChXDRlJpqN9qdFDC5L2zDOj8Zq1bzoRtCmd+x7mGXdhmJ+ngV83J71hfeicsaircO1Uuwhv7isjLTcOneIY210tu1ktEI6BZ3FWgOFqLoenU9vOy3BlIVjzQy+jwWuK8G+Wg4PC+WCzopuaP/Phbarv4fimi5KY8Un5FfI+4pyXjPNz3h4YxEP0zon7+dh7KVOFcqZ4vZVJJO8snL5UZJfUb2q1LlMLp3Kv5uy7kI2ydGF/lpZkyryt8jGtrxXUNZXivQpT+fpzp5vFGO3Z2W8yHJSbHIoI9+u3GO6cTYaOIxFV5yn+X3aZH89irvcqR3tS58HHch45/OmG31FaSnviWys/X8urSEnfwysbulxiMhr6J3erNGGTHZIy+rCBgSMKX/z7OFWkSwr9Zcp74v0t6gvqoqxDXuem5Zjp76KjrKlur0V0qmq9qYMR6qjq6M2eS6ttmhCF/a8cIwwEJ8AYqYhO++zH7rE43nkBHC/9/4lSzsUXfXaVho6f7Uf2ib5Z7v5GZYN0Dm+L9rvpqFVzg0yMK6Ati03QWet7sko6xDk4JCFPTPNcGyEHALbwbE/cqrZwunSixeR88n6yPHiHu/9Dy3fBdpMO997/2unsEKnJTjuN2xN8kJbU0+ibddOePgAcqbLk/PiqBF+wilk0iXIkWNuZODPRt7hIb/70apPEX97ImfLYwRwjvf+nykP0Y2AcX4PGI4pbehvtzr1gKX/yDCe7b2/J8H4ENperNL2WnCU8KOsrI7bSkUc56MVlFSv29HRKmVdhc5Xjm9Tf9sp6yzv/b095G+WHuaV9S10Nvjz0Go3aNX5AbHnJbw4H3W+Hu0uLmu/fRUNQhZHA/FXMuTyDtp6XwftPE1DZ4c3N1xxfmNMVu+ijn5cB3b0EGRX0udPmyyfSNKWBk723l+ekd/SaIFm1gzerwA84L2f6JzbBDjYN8Ib7o6OYyxm5V2Izq/fj3b45kV2vRd6sz4wGxrwZclkUxTd5YicsqrYh7H2/HMRf/tQBOK039vesEyu0l+So/cUjEcsn6y+qCrGSvY8wnF40h7GodC4TyDH07ciHI8jR9Se2JsIR5aOLmr1DkeWZzEcO6JJ5NH0yJ5HOJb13t9Lj2lmHKBfSL4h3R8pye4dpK2DIoosjGZc7eY3GjmnnO69/5Fzbikk3FWRI8vTqGMP772LZnezVyirKG1xS28Xx6ooLNUt6DrkccbXpZDX8Y3e+50TnpelHYiux/0K2jKKcWyKBo6/RR1DwENd88YAABR1SURBVP44Ghh3gv1dFDd6vwJ+jEdnvhdC23c/9d5v45y7FM2Gn0erMu3m10s5f8qejw18T3g4D3La7ES3q+pvXloRxonAg8hY9qLtDWZZVXEciAZhL/ZIR8vK+j0a8H2P7vV3KMjyDLQN/nXv/R0Z9a2i81XaQxnfJwF/8XIoi50UZ0Md+7nI0e4HaIAa5HIs6sTvBE4xWayOrg0/D8WBntfyWh0NwJZGg4pfBge1NuzoqmhQ8Cha8FgyKmtfy5/onUPQiv1+hi8rrUh/F0Oh6f7kvd/J6WbTn6LB1xzG41nRAP1VFPFp9wryKpLLScix8fIMmeyNBu/HWh7D2yirLG0PdIZ47ojvG6NV2K2Aad4GTE4xxc+ken+Zp/dF9jyvL6qKsaoNWBzZp78Q2SnkBP0eCs87v/c+OC2vgiZzvbbni5Otozcg3RwOPO+9P8BwrInOoL9N7+x5kMvusf72imbGKC6xZ27caMs8pvPSwmUHWdEFyjywbwVONSP7ELp4omokg248wTvBMRUNANZF4bGCF/NDzrm3gEWdrsE9zSvMEG2kjUFGs9+zP8JxEDpz+Syt0WKq8HCZEn7sgDrb60224ZrdoqgkgyXnh1ADv9HKmeK9nxbx8FxksB+n0akPlP7mpRVhfBM4w3t/YY/a3mCWVRXHGBRl41Tn+j33u9HRsrLSqDrd6O8MlyVa7XsY2MU59x/gv17Ujc5XaQ9lfD/B+hTnFX1hIjDRaVX1Z2gguANwXSKXF4E/Awv61mgbi9uzKd77kN8VaFA7n+H4L7RlR7uJ1JSXVhbRpiXCCDqf/h800fgMipe9AgqJ2Ou2Nyc6noA9j2WyP/At7/3fe2gfxtMaYWQndKTnt8A+zrmgv7d22V/m6X2RPc/ri6pirGoD8iKNHY/GFJ9AdixEVbl1gOx5no7+2nDMhVbPA44bnHPH0Ft73nbUpUrkB+jszGB/6D76SG50gYJ3wjXA/Z72dBnJoCL2tnAgJ8KFgG+jK2xfRivp+6DVirB1eDE6p95O2nHA7T45i1XGj17zME2j86gkAypn4/0qaIXhijwe9kq3K75TCeNQL6tbHHSg172qM13q7xCR5c/QoOERCuzGQMu5DV78ncihLMHR5ESXIZc9glxoRNuYA912+TbNNm8kCvN2VRE/yNe3negiUlMV/aU4ss4XIr70uu39Gq24lspkgPRmmOEbjQae09DiVs/6y070npy+qAuMvbIBcXu4CcUGfwFNMm9BE4cBtec06+if0eTmCDS5ey3FQY/seVlaLz4z1UVFzrlZnHNrOp1/SqkPXe7xGZst+bI0p+vFV0RxP6ck7xXlNx040Xv/vLMrlr3306P0J4Btvfd3xdgrltVLHEegFaA1gSu99/Ogbdl50LmtOdA5rLO89x9BqwllaROQp33AE666nW51XhmtEs0f86MKDzvgxxR01iyEzPw92mb/nff++qSswZLzEej88APG8/kSHm5jur0RrdRr/c1LK8J4HXCUc24Dp6vBS/ENobKq4pgAbOEVl7gv6HWUZ6c6WloWgNO111fTnf4OBVl+GYWJ+zu6ke+jdKnzRWkV295d6HK2rYC9nHNTnHOXOF1Vjvf+X977cAmKT+RyrtVpF3Sd+XS0M3gVGjT8Dbguyu8I48Oz9nde2rCjVvD/ee9PR3cr3J08PwuzrxnvZKYZ5elvbEdf995f6XU86QfIKXEf4A/e++usnOlV5FUglyWQo+XtWTJBRykusXFAXE5RWWVp/XW2tOmG72p0XOIr3vu5aK9PLOsvM/W+xJ7n9UVVMVa1ASmfQnu4zOS2IvAT7/2Hray5uyir3b4+1tHn0Yr2NsAl3vtREY5e2/MsfqT62B35Ho72B/JDxQgjBWm50QWK8ovwNHn7Rhgn9aqsHuP4G3CzpW+Czkyuhmbi26DZr4vT7Le5aZa+iqU1eThH2FuixVTkYSk/yPb2Hiz+Fsn5QOCqlPdRfptXwNhrncrFaO/cMZOWVQlHpNsdRUfpsqyOouoMEf5mloXsxlVWr17pfJX2UMb3c4BfRP8vgI5P3ALskdqVRDZZunEE8KuM/MIlYEXyb7GjUfpaOc87jmhESUSbuM602tHTB1JvIh7+Pio7lsmBPdabzIhGNPR3RAa+bvrLPL1vJ9pRGmGkKsZKNqBAN7YpkGXP7XkZDjTxbRrjDIA9b4q6FI85evmZ4QPvtoGq0eYZ0usqpP0HOQV1mt8e5IR/ChiBZXtUVi9x3A88RqPT+Qry7gadK7wzKqfdtDWQM0wWjmMDduRU0S0Py/hxRpLfWqihhvw+NQPlPAWtfubx8JEKOtBrnSrCeB46kzkzllUVxxpo5bdXOtppWd3o7wyXJfAHFO/98pz6VtH5Ku2hjO+PIofNtL9ZCoWL+0mOXIJtODnRjWOAg4NuRPldixZJVszBkWdHP5JXlr27lfGyJRRhXhrF+ptn92J7vlwP5FUkl/OBh3Jk8gBwXo/tQ9p3rIlC9u0Y9V+96i/z9L7InufJpCrGqjYgC8daCY5P9aisKn1zjGO5DN730p73T+AH8jPTRHGxw/1vee/3T55XjT5yBvnRBap6YFeJZNBN5JS2cNiW2HfQUZMtgF967293zl2MVpB+gLZNv++cG+G1BVSWdjZyZnkSnauEZu/nzwD/Rk4tabSYKjws40eeR3rV/KpgbJGz8f5AdNTnp8Bd3vv3Eh4ui+LVHuS9f69NjD3TqTYwHoyctNa14xfvzQxldYljXWTUH0bHpZahok4NsbIGS5ZT0Lby7ZgDFRrwdqPzHbWHNvn+Ajq28RjwG29OdPb+7eha+hdpP9rGo+hGzTHAJ33jSMJ30I2EuyCbOB05gpbZ0bLIHvvT+6hFWXYv2PPbgN18F9G/2pDLJuic/ZUoYsmz0bvPIUe+b9gRBV8VR5SW1Xd8DK2qvov1Xz3qL/P0vsie58mkCsZubECnOAbKnpfhmIbp6EDY2ATHMbG8ek4DPQPo1QfNdM5B550WSNLupHHmuK00y28Kioe5AM1HGIryux3N4Mah7aP5kPD3QTOxN5DSLEDjEoeqZfUMB3IomdPeC9t6n0Vezw8BC9uzYR2mzYUuPxpnaQHHQchbeipwgKUNr8rDNvjxILBVgqGb/HomZ+P9HWh7bZjlF/NwCTrU7byyumgPRRgfQatavWp7g1ZWFzgeQuHLUr2upFNDqKzBlOXDyD5k2Y2Odb5Ke2iDF0tR4ERHtm3bB4WvewYNhOZDcaV/hlbQX0C2L83vjgp2NK+sfVDIveeBw+ydBaJ3itKKdCrP7hXZ817qzQQ0WfpajkzupId6U9B3/BjdVPkU8ENLW4Xu+8tMvafYnufJpCrGqjagCo6BsOdV+dEzGxvhCBd29fx4i/d+5giz6JwbiZwgLkUN937n3MPI83s0EtIpHaRNsLSXkWG8H3jYKQRWUX4LAXjvfx3BexGFfroVzcr2Qk5HIc+qZfUUR/Lejfb3dufcG8Bz3vtnIueYTtKmOuduiNJeNAz3Ac8hD+/vOefWA65wzlXhYTv8eN17f2HA0IP8einnhYDp3vubovwCD19AcbYvQzexDaT+ltU5C+PbVvcLO8xvqJRVBUfQ7T+6hkNWL3RqRpc1mLJ8A3jW7EOoVzc6X7U9FOH7EDpPehMKAbelc2474/kEtLX9tDPHNt8afnE7dOHUFOfctejiky+gIzB7eIWUa8rPyncJPzLtaFFZ3vtrXCME5K8s3//65lCELWklOpVp90rsea/1ZhwKabcmcGYik8loB2GRDsvquO+IeLgPWoEF7R5001/m6X2Z/mb2RRUxVrUBVXAMlL2pyo9e21iC/nobpfeaZpYoLiH6yAbIkLbrMZ2XVhRdoJIHNtUiGfTcE7wAR0ukCPv9jsC29t0leZWmOXmep5F1iqLFVMVexSO9an69lnMW3wMPn6Bz3R4oncrC+BIacHSa31ApqwqOHYFvOF1skUaM6kanZnRZgynLPNtRVeertoc8fBOAB733n7TvhznnVvPen+u93wdtfT9vclk3ForLiLaBbrVcDFgPrdjt45xbPcnv3wA+OtpAsR0d7EhNeXZvQKN/Bbk457ZBR4Q+juKrH5rI5GuGvVd6UxbNZDqtETq66S/z9L6baEedYqxqA6rgGCh7U4Ufg2Vje0t+AJble/mh2EO4Y29qCqILFOVn6Xke2AdmYaxaVq9xRO+tQg+3Ysj2cM6NFlMVewmv8rzEK+XXazkX8Z0Kul1UVgmf2orGM5Btb7DLqoKjQK+71akZWtZgynKo2PMSvv8Bcyiz/1OHsok5csmLtjG5JL/LO7SjgxqpqcDuDWj0rw5kciBaBW3BXhUHBRFGMmTUVb9Jjt5Trr+50Y46xZiHoU15dYSjalm9xjHY9rzXnwHNvCcAixvtZDr0pqY4ukBRfkUe2FUiGXSMvSqO6L3cTqKibLIi6xRFi6mKvYgfeV7iVfPrtZxz+U4F3R5AnWrBWAXfECurYxwFet2tTs3QsgZTliU2YzDteR4vhqGLnz5j//fZ34vROdXj0TXmv4jSglyyom0cgFZHjy7I7wdxWps6MJiRmvLs3oBF/+pQJo8CF4S0HuEoijDS0wgd5Og93UU76ghjHoY25dURji7K6imOkvbVc3ve68+QPuJiW4eTUGiccIHHRehc3BfRJQV/tbQR7aTZZwngBtuauKSN/DZG1zc/A7wZnUl8HjgUncubzzm3YoyxYlk9xxG9d0PgYy/kg5xP34z+f8Ew/BhYzTn32R5gL+PH0qhzDPrRbX69lHMu36vqdkFZ3epUE8aBaHuDXFbHOCJq0use6dQMK2swZVlEg2zPc/F5nck9GTkHQuOym6PRLcvroi3vN70dR/HeP++9PxitrM/vnFsxen4QsBGwhD3Pyu8M+318vCVQiw4UlHUw2rJfFBiV8U5mGu31D012j2J73lO9aUMmI9EtlaFOvcDR0ndEPFwt8L1bKtJ7yvW3pS+qgrGLttcxjgG2N1X4MZj2vLc0GLOAbj5Y9JHw3f52G32kKLpAFQ/sqpEMeu0JXhYpYq4ey6Ylsk6QF/nRYqpi78gjvYv8BkLOmXynum4PhE61YOwC31AqqyMceXrdA52aoWUNpiyHmD3vFN8FaHCRJ5eyaBtNUR1CfjHuDnRgMCM1Zdq9IC8GIPpXBZnMaXh6pTdFEUZ6GqGDYr2vGu2oI4wlGCr1iQV6P1D2pmN+MIj2vNefIR/FxWtm/Ub0Hd999JG86AJVPLCrRjLotSd4O5EipsZ5dkOuOLJOXrSYbrB36pFeJb+BknMm37vQ7V7rVCbGAWp7g11WRzja0OsqOjXDyxpMWRbRDLDnndq8HVGUkFlRDORt6CzaRhrVYUcUDx5aHfaKdGAwIzW1Y/ey7Plg6c2OwNze+zdcFEu7SxxlfUeWLCtTid5XiXbUMcYu2l7HOAaonXeMYzBt7ICQH4RZwEB8sIsb7Htft2ltvHMWMpK/ATaw59sBR6K4tIu1m2eX2HuGo0v+n2g4Tm0Hx0Bgt7Iftr8b9SC/QZNzr3W7S53qCOPMXlZJWkd6PbOUNRRkOZg4usFXIJc7UYzl36HIDn1l8hqgsq5Cq79Zdq8orUinzqIDu/c+0JuO+46B+JTob0cyGUAe9gxHl3rTMQ6GgI3tSjYDXcD74UOBR7I9GxyP3pkYR6+xDwQvhgp/68+M+Qym/GtdG5qfPLlQMVpMr8tiYKIWfaB0cWao71DBODPjGCrYu6r3jAYwM3woD8k1KB69MzOOXmMfCF4MFf7WnxnzGUz517o2ND95cqFipKYBKGsyvY9a9IHSxZmhvkMF48yMY6hg7+YzpKO4DAVyxR7Jg+bROzPj6DX2geDFUOFvTTOGBlP+ta4NTSqSCxWixQxAWT2PuPNB08WZob5DBePMjGOoYO+aZvQMYWb4UO6RPCgevTMzjl5jHwheDBX+1p8Z8xlM+de6NjQ/JXLpWbSYLsvqddSiD5Quzgz1HSoYZ2YcQwV7N596Bb0N8t5P9963eCQjL+V+j94aRz6OXmMfCF4MFf7WNGNoMOVf69rQpBK5POu9nxr9diDtTVFZLdEl2kzLxPhB08WZob5DBePMjGOoYO+GQszKmiqQc+5DKPzTM3H4pxpH+zh6jX0geDFU+FvTjKHBlH+ta0OT8uQymPamqKyqaVVwvF9pZqjvUME4M+MYKtjboXqAXlNNNdVUU0011VRTTUOIhvTyfk011VRTTTXVVFNNNX3QqB6g11RTTTXVVFNNNdVU0xCieoBeU0011VRTTTXVVFNNQ4jqAXpNNdVU0/uYnHNzOed2te8LOef+MKMx1VRTTTXVVEy1k2hNNdVU0/uYnHOLA3/03i8zg6HUVFNNNdXUJg2f0QBqqqmmmmoaUDoCGOucuxt4FPiE934Z59zXgU2BOYCPAccAswDbAe8AG3rvX3bOjQVOBOYD3gJ29t4/NPjVqKmmmmr64FB9xKWmmmqq6f1NPwYe994vD+yVpC0DbAZ8BjgMeMt7Px64FdjefvMbYA/v/aeBHwEnDQrqmmqqqaYPMNUr6DXVVFNNH1ya5L1/HXjdOfcqcJU9vxf4lHNuTmBV4GLnXHhn1sGHWVNNNdX0waJ6gF5TTTXV9MGld6Lv06P/p6P+YRgw1Vbfa6qppppqGiSqj7jUVFNNNb2/6XVgZJUXvfevAf9yzm0J4ETL9RJcTTXVVFNNrVQP0Guqqaaa3sfkvX8JuNk5dx9wdIUsvgbs5Jz7J3A/8KVe4quppppqqqmV6jCLNdVUU0011VRTTTXVNISoXkGvqaaaaqqppppqqqmmIUT1AL2mmmqqqaaaaqqpppqGENUD9JpqqqmmmmqqqaaaahpCVA/Qa6qppppqqqmmmmqqaQhRPUCvqaaaaqqppppqqqmmIUT1AL2mmmqqqaaaaqqpppqGENUD9JpqqqmmmmqqqaaaahpCVA/Qa6qppppqqqmmmmqqaQjR/wevswpbs5I2GwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "there is a increasing trend in orders , trend sustains during 2018.\n",
        "there a slight fall we can observe during october 2017 following with a great hike in november month and again a fall in end of december 2017 and january 2018."
      ],
      "metadata": {
        "id": "vmAewmD5j6M9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Revenue Trend for given time duration"
      ],
      "metadata": {
        "id": "Mi5MtOsnj8iy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "select\n",
        "year,\n",
        "month,\n",
        "week,\n",
        "revenue,\n",
        "avg(revenue) over (order by year,month,week\n",
        "                        rows between 4 preceding and 2 following) rolling_avg_revenue,\n",
        "avg(Number_of_orders) over (order by year,month,week\n",
        "                        rows between 4 preceding and 2 following) rolling_avg_orders                        \n",
        "                                        \n",
        "FROM\n",
        "(\n",
        "SELECT\n",
        "    EXTRACT(YEAR FROM order_purchase_timestamp) as year,\n",
        "    EXTRACT(MONTH FROM order_purchase_timestamp) as month,\n",
        "    EXTRACT(week from order_purchase_timestamp) as week,\n",
        "    ROUND(SUM(op.payment_value),2) AS revenue,\n",
        "    count(o.order_id) as Number_of_orders\n",
        "from \n",
        "    `targetbrazilsalesanalysis.Customers.orders` as o\n",
        "join \n",
        "    `targetbrazilsalesanalysis.Customers.orderpayments` as op\n",
        "on o.order_id = op.order_id\n",
        "group by year,month,week\n",
        "order by year, month, week\n",
        ");\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "k-dIo-BHgVrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2271
        },
        "id": "sA8vrqq2gVo5",
        "outputId": "d176128c-2cc7-4e4d-84d4-ab2e5dd10a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     year  month  week    revenue  rolling_avg_revenue  rolling_avg_orders\n",
              "0    2017      4    18    9437.00         90168.938571          580.857143\n",
              "1    2017     10    44   71244.76        151608.758571          944.000000\n",
              "2    2018      3    11  277672.64        230850.810000         1479.142857\n",
              "3    2017      6    23  127764.65        109514.142857          734.571429\n",
              "4    2017      2     7   78615.45         55335.327143          346.142857\n",
              "..    ...    ...   ...        ...                  ...                 ...\n",
              "110  2017      6    24  120145.26        104297.741429          691.142857\n",
              "111  2017     12    53   10190.93        179201.102857         1206.857143\n",
              "112  2018      1     2  270341.37        177635.568571         1220.857143\n",
              "113  2017      8    35  138430.55        127800.922857          825.285714\n",
              "114  2017     12    52  118253.87        151369.867143         1020.000000\n",
              "\n",
              "[115 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fac73efd-c7a6-47cc-8f20-211918b0c12d\">\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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>week</th>\n",
              "      <th>revenue</th>\n",
              "      <th>rolling_avg_revenue</th>\n",
              "      <th>rolling_avg_orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>9437.00</td>\n",
              "      <td>90168.938571</td>\n",
              "      <td>580.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>10</td>\n",
              "      <td>44</td>\n",
              "      <td>71244.76</td>\n",
              "      <td>151608.758571</td>\n",
              "      <td>944.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>277672.64</td>\n",
              "      <td>230850.810000</td>\n",
              "      <td>1479.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>23</td>\n",
              "      <td>127764.65</td>\n",
              "      <td>109514.142857</td>\n",
              "      <td>734.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>78615.45</td>\n",
              "      <td>55335.327143</td>\n",
              "      <td>346.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>24</td>\n",
              "      <td>120145.26</td>\n",
              "      <td>104297.741429</td>\n",
              "      <td>691.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>2017</td>\n",
              "      <td>12</td>\n",
              "      <td>53</td>\n",
              "      <td>10190.93</td>\n",
              "      <td>179201.102857</td>\n",
              "      <td>1206.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>270341.37</td>\n",
              "      <td>177635.568571</td>\n",
              "      <td>1220.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2017</td>\n",
              "      <td>8</td>\n",
              "      <td>35</td>\n",
              "      <td>138430.55</td>\n",
              "      <td>127800.922857</td>\n",
              "      <td>825.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2017</td>\n",
              "      <td>12</td>\n",
              "      <td>52</td>\n",
              "      <td>118253.87</td>\n",
              "      <td>151369.867143</td>\n",
              "      <td>1020.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fac73efd-c7a6-47cc-8f20-211918b0c12d')\"\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-fac73efd-c7a6-47cc-8f20-211918b0c12d 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-fac73efd-c7a6-47cc-8f20-211918b0c12d');\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": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"time\"] = df[\"year\"].astype(\"str\")+\"_\"+df[\"month\"].astype(\"str\")+\"_\"+df[\"week\"].astype(\"str\")\n",
        "df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "pzz9tVjYgVmV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"standardized_rolling_avg_revenue\"]=(df[\"rolling_avg_revenue\"]-df[\"rolling_avg_revenue\"].mean())/df[\"rolling_avg_revenue\"].std()"
      ],
      "metadata": {
        "id": "ZPeIsk4WgVkB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"standardized_rolling_avg_orders\"]=(df[\"rolling_avg_orders\"]-df[\"rolling_avg_orders\"].mean())/df[\"rolling_avg_orders\"].std()"
      ],
      "metadata": {
        "id": "BSAoaZaTgVhv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "sns.lineplot(x = df[\"index\"],\n",
        "           y = df[\"standardized_rolling_avg_revenue\"])\n",
        "sns.lineplot(x = df[\"index\"],\n",
        "           y = df[\"standardized_rolling_avg_orders\"])\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Trend of revenue and orderes over time\")\n",
        "plt.title(\"Trend line of Average revenue and orders per week time\")\n",
        "plt.legend([\"revenue trend-line\",\"orders trend-line\"])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "mCifkPUlgVfB",
        "outputId": "ba1fa69d-80e7-4007-8dd6-049bccdcf55d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "select\n",
        "\n",
        "FORMAT_TIMESTAMP(\"%a\", order_purchase_timestamp) as day,\n",
        "count(order_id) as number_or_orders \n",
        "from\n",
        "Customers.orders\n",
        "group by day\n",
        "\n",
        "\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "6Iu3Cn3egVcv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5bN8iFZggVaM",
        "outputId": "8f16d31c-18d0-49b8-abb4-9f9f33c181e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   day  number_or_orders\n",
              "0  Sat             10887\n",
              "1  Tue             15963\n",
              "2  Fri             14122\n",
              "3  Mon             16196\n",
              "4  Thu             14761\n",
              "5  Wed             15552\n",
              "6  Sun             11960"
            ],
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              "      <th>day</th>\n",
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              "  </thead>\n",
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              "      <td>Sat</td>\n",
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              "    [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-087cdfa8-df02-4fc7-b2c1-0b57348edf6b 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-087cdfa8-df02-4fc7-b2c1-0b57348edf6b');\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": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = df[\"day\"],\n",
        "           y = df[\"number_or_orders\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "QMrspfFwgVWi",
        "outputId": "8a686ddd-8f48-48ad-a01a-dec99ebcf037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1135912e0>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "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": "markdown",
      "source": [
        "Tuesday, monday and wednesdays have relatively higher number of orders."
      ],
      "metadata": {
        "id": "1IICFwF5l76g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. What time do Brazilian customers tend to buy (Dawn, Morning, Afternoon or Night)?"
      ],
      "metadata": {
        "id": "wL3KvnDUl-Mq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "orders_per_hour = client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    X.HOUR,\n",
        "    COUNT(X.order_id) as number_of_orders\n",
        "FROM\n",
        "            (\n",
        "            SELECT\n",
        "            order_id,\n",
        "            EXTRACT (HOUR FROM order_purchase_timestamp) AS HOUR\n",
        "            FROM\n",
        "            `Customers.orders`\n",
        "            )\n",
        "            AS X\n",
        "GROUP BY \n",
        "    X.HOUR\n",
        "\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "yr-t44qRgVT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "orders_per_hour"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "yNlgrY_XgVRX",
        "outputId": "a3c490ba-1945-4009-c756-581422168f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    HOUR  number_of_orders\n",
              "0     11              6578\n",
              "1      1              1170\n",
              "2     17              6150\n",
              "3     13              6518\n",
              "4     12              5995\n",
              "5     18              5769\n",
              "6     10              6177\n",
              "7     21              6217\n",
              "8     15              6454\n",
              "9     22              5816\n",
              "10    16              6675\n",
              "11    20              6193\n",
              "12     0              2394\n",
              "13    23              4123\n",
              "14     3               272\n",
              "15    14              6569\n",
              "16     9              4785\n",
              "17     7              1231\n",
              "18    19              5982\n",
              "19     4               206\n",
              "20     8              2967\n",
              "21     2               510\n",
              "22     6               502\n",
              "23     5               188"
            ],
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              "      <td>6193</td>\n",
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              "      <td>2394</td>\n",
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              "      <td>7</td>\n",
              "      <td>1231</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>5982</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8</td>\n",
              "      <td>2967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2</td>\n",
              "      <td>510</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6</td>\n",
              "      <td>502</td>\n",
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              "      <th>23</th>\n",
              "      <td>5</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-22e302cb-04c6-470a-ad21-cc2b6f15e448 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-22e302cb-04c6-470a-ad21-cc2b6f15e448');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = orders_per_hour[\"HOUR\"],\n",
        "           y = orders_per_hour[\"number_of_orders\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "eVhc-SwGqo9m",
        "outputId": "51e7de53-b6a6-4d05-d3d7-4beecf286019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb113d6a340>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "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": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    X.HOUR,\n",
        "    COUNT(X.order_id) as number_of_orders,\n",
        "    CASE\n",
        "        WHEN X.HOUR BETWEEN 5 AND 7 THEN 'Dawn'\n",
        "        WHEN X.HOUR BETWEEN 8 AND 11 THEN 'Morning'\n",
        "        WHEN X.hour BETWEEN 12 AND 17 THEN 'Afternoon'\n",
        "        WHEN X.hour BETWEEN 18 and 21 THEN 'Evening'\n",
        "        ELSE 'night'\n",
        "        END AS time\n",
        "FROM\n",
        "            (\n",
        "            SELECT\n",
        "            order_id,\n",
        "            EXTRACT (HOUR FROM order_purchase_timestamp) AS HOUR\n",
        "            FROM\n",
        "            `Customers.orders`\n",
        "            )\n",
        "            AS X\n",
        "GROUP BY \n",
        "    X.HOUR\n",
        "\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
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          "height": 802
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        "id": "x1xCYjR8qo6_",
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      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "    HOUR  number_of_orders       time\n",
              "0     11              6578    Morning\n",
              "1      1              1170      night\n",
              "2     17              6150  Afternoon\n",
              "3     13              6518  Afternoon\n",
              "4     12              5995  Afternoon\n",
              "5     18              5769    Evening\n",
              "6     10              6177    Morning\n",
              "7     21              6217    Evening\n",
              "8     15              6454  Afternoon\n",
              "9     22              5816      night\n",
              "10    16              6675  Afternoon\n",
              "11    20              6193    Evening\n",
              "12     0              2394      night\n",
              "13    23              4123      night\n",
              "14     3               272      night\n",
              "15    14              6569  Afternoon\n",
              "16     9              4785    Morning\n",
              "17     7              1231       Dawn\n",
              "18    19              5982    Evening\n",
              "19     4               206      night\n",
              "20     8              2967    Morning\n",
              "21     2               510      night\n",
              "22     6               502       Dawn\n",
              "23     5               188       Dawn"
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "WITH order_time \n",
        "AS\n",
        "(\n",
        "SELECT\n",
        "    X.HOUR,\n",
        "    COUNT(X.order_id) as number_of_orders,\n",
        "    CASE\n",
        "        WHEN X.HOUR BETWEEN 5 AND 7 THEN 'Dawn'\n",
        "        WHEN X.HOUR BETWEEN 8 AND 11 THEN 'Morning'\n",
        "        WHEN X.hour BETWEEN 12 AND 17 THEN 'Afternoon'\n",
        "        WHEN X.hour BETWEEN 18 and 21 THEN 'Evening'\n",
        "        ELSE 'night'\n",
        "        END AS time\n",
        "FROM\n",
        "            (\n",
        "            SELECT\n",
        "            order_id,\n",
        "            EXTRACT (HOUR FROM order_purchase_timestamp) AS HOUR\n",
        "            FROM\n",
        "            `Customers.orders`\n",
        "            )\n",
        "            AS X\n",
        "GROUP BY \n",
        "    X.HOUR\n",
        ")\n",
        "\n",
        "SELECT \n",
        "    time,\n",
        "    SUM(number_of_orders) AS total_order\n",
        "FROM\n",
        "    order_time\n",
        "GROUP BY time\n",
        "ORDER BY total_order DESC\n",
        "\n",
        ";\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "ZX-UbtCtqo4M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "-b5HElRAqo1S",
        "outputId": "9cb2d745-3a77-4a78-8a7f-3233416c8e21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        time  total_order\n",
              "0  Afternoon        38361\n",
              "1    Evening        24161\n",
              "2    Morning        20507\n",
              "3      night        14491\n",
              "4       Dawn         1921"
            ],
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              "                                                     [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",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = df[\"time\"],\n",
        "           y = df[\"total_order\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
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        "id": "LSQmSbu8qoyz",
        "outputId": "b44edb95-7cde-4e1f-e5e4-8344cdc1e351"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb113c7d340>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "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": "markdown",
      "source": [
        "Customers are purchasing during morning 8 am to evening 11 pm\n",
        "\n",
        "Afternoon Orders are very high \n",
        "\n",
        "Night orders are lowest"
      ],
      "metadata": {
        "id": "0h3tIMcyrEaK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NUmber of Orders per state\n"
      ],
      "metadata": {
        "id": "vaO5mZ7VrRQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "select \n",
        "    c.customer_state,\n",
        "    count(o.order_id) as number_of_orders_per_state\n",
        "\n",
        "from \n",
        "    `targetbrazilsalesanalysis.Customers.orders` as o\n",
        "    join `targetbrazilsalesanalysis.Customers.customers` as c\n",
        "    on o.customer_id= c.customer_id\n",
        "group by\n",
        "    c.customer_state\n",
        "order by number_of_orders_per_state desc\n",
        "\n",
        ";\n",
        "\"\"\").to_dataframe()\n",
        "df = df.merge(brazil,on=\"customer_state\")"
      ],
      "metadata": {
        "id": "gUs1A6JvqowN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "4FQAm4XNqor7",
        "outputId": "896cf2f5-dcbd-4297-fa37-c83024c303ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_state  number_of_orders_per_state                State  \\\n",
              "0              SP                       41746            São Paulo   \n",
              "1              RJ                       12852       Rio de Janeiro   \n",
              "2              MG                       11635         Minas Gerais   \n",
              "3              RS                        5466    Rio Grande do Sul   \n",
              "4              PR                        5045               Paraná   \n",
              "5              SC                        3637       Santa Catarina   \n",
              "6              BA                        3380                Bahia   \n",
              "7              DF                        2140     Distrito Federal   \n",
              "8              ES                        2033       Espírito Santo   \n",
              "9              GO                        2020                Goiás   \n",
              "10             PE                        1652           Pernambuco   \n",
              "11             CE                        1336                Ceará   \n",
              "12             PA                         975                 Pará   \n",
              "13             MT                         907           MatoGrosso   \n",
              "14             MA                         747             Maranhão   \n",
              "15             MS                         715    MatoGrosso do Sul   \n",
              "16             PB                         536              Paraíba   \n",
              "17             PI                         495                Piauí   \n",
              "18             RN                         485  Rio Grande do Norte   \n",
              "19             AL                         413              Alagoas   \n",
              "20             SE                         350              Sergipe   \n",
              "21             TO                         280            Tocantins   \n",
              "22             RO                         253             Rondônia   \n",
              "23             AM                         148             Amazonas   \n",
              "24             AC                          81                 Acre   \n",
              "25             AP                          68                Amapá   \n",
              "26             RR                          46              Roraima   \n",
              "\n",
              "         Region  \n",
              "0     Southeast  \n",
              "1     Southeast  \n",
              "2     Southeast  \n",
              "3         South  \n",
              "4         South  \n",
              "5         South  \n",
              "6     Northeast  \n",
              "7   Center West  \n",
              "8     Southeast  \n",
              "9   Center West  \n",
              "10    Northeast  \n",
              "11    Northeast  \n",
              "12        North  \n",
              "13  Center West  \n",
              "14    Northeast  \n",
              "15  Center West  \n",
              "16    Northeast  \n",
              "17    Northeast  \n",
              "18    Northeast  \n",
              "19    Northeast  \n",
              "20    Northeast  \n",
              "21        North  \n",
              "22        North  \n",
              "23        North  \n",
              "24        North  \n",
              "25        North  \n",
              "26        North  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2904664-92de-422e-a166-62aeb9337838\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_state</th>\n",
              "      <th>number_of_orders_per_state</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
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              "      <th>0</th>\n",
              "      <td>SP</td>\n",
              "      <td>41746</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RJ</td>\n",
              "      <td>12852</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG</td>\n",
              "      <td>11635</td>\n",
              "      <td>Minas Gerais</td>\n",
              "      <td>Southeast</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RS</td>\n",
              "      <td>5466</td>\n",
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              "      <td>South</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PR</td>\n",
              "      <td>5045</td>\n",
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              "      <td>South</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SC</td>\n",
              "      <td>3637</td>\n",
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              "      <td>South</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BA</td>\n",
              "      <td>3380</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DF</td>\n",
              "      <td>2140</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ES</td>\n",
              "      <td>2033</td>\n",
              "      <td>Espírito Santo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GO</td>\n",
              "      <td>2020</td>\n",
              "      <td>Goiás</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PE</td>\n",
              "      <td>1652</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CE</td>\n",
              "      <td>1336</td>\n",
              "      <td>Ceará</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>PA</td>\n",
              "      <td>975</td>\n",
              "      <td>Pará</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MT</td>\n",
              "      <td>907</td>\n",
              "      <td>MatoGrosso</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>MA</td>\n",
              "      <td>747</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>MS</td>\n",
              "      <td>715</td>\n",
              "      <td>MatoGrosso do Sul</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>PB</td>\n",
              "      <td>536</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PI</td>\n",
              "      <td>495</td>\n",
              "      <td>Piauí</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RN</td>\n",
              "      <td>485</td>\n",
              "      <td>Rio Grande do Norte</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>AL</td>\n",
              "      <td>413</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>SE</td>\n",
              "      <td>350</td>\n",
              "      <td>Sergipe</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>TO</td>\n",
              "      <td>280</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RO</td>\n",
              "      <td>253</td>\n",
              "      <td>Rondônia</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>AM</td>\n",
              "      <td>148</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>AC</td>\n",
              "      <td>81</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>AP</td>\n",
              "      <td>68</td>\n",
              "      <td>Amapá</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>RR</td>\n",
              "      <td>46</td>\n",
              "      <td>Roraima</td>\n",
              "      <td>North</td>\n",
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              "  </tbody>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df.groupby(\"Region\")[\"number_of_orders_per_state\"].sum()/df[\"number_of_orders_per_state\"].sum())*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvFMnsLpqonw",
        "outputId": "3f3a100b-dd0c-424f-caa3-42720aa2fde2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Center West     5.814503\n",
              "North           1.861405\n",
              "Northeast       9.446808\n",
              "South          14.227532\n",
              "Southeast      68.649752\n",
              "Name: number_of_orders_per_state, dtype: Float64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(\"\"\"\n",
        "\n",
        "select \n",
        "    c.customer_state,\n",
        "    sum(op.payment_value) as revenue_per_state,\n",
        "    avg(op.payment_value) as avg_sale_per_state\n",
        "from \n",
        "    `targetbrazilsalesanalysis.Customers.orders` as o\n",
        "    join `targetbrazilsalesanalysis.Customers.customers` as c\n",
        "    on o.customer_id= c.customer_id\n",
        "    join `targetbrazilsalesanalysis.Customers.orderpayments` as op\n",
        "    on o.order_id = op.order_id\n",
        "group by\n",
        "    c.customer_state\n",
        "order by revenue_per_state desc\n",
        "\n",
        ";\n",
        "\"\"\").to_dataframe()\n",
        "df = df.merge(brazil,on=\"customer_state\")"
      ],
      "metadata": {
        "id": "TDGl1UJ6qokz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "ASLZ_zi-qoiB",
        "outputId": "0364a0ae-0953-4207-d351-69ae1b87b9a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_state  revenue_per_state  avg_sale_per_state                State  \\\n",
              "0              SP         5998226.96          137.504630            São Paulo   \n",
              "1              RJ         2144379.69          158.525888       Rio de Janeiro   \n",
              "2              MG         1872257.26          154.706434         Minas Gerais   \n",
              "3              RS          890898.54          157.180406    Rio Grande do Sul   \n",
              "4              PR          811156.38          154.153626               Paraná   \n",
              "5              SC          623086.43          165.979337       Santa Catarina   \n",
              "6              BA          616645.82          170.816017                Bahia   \n",
              "7              DF          355141.08          161.134791     Distrito Federal   \n",
              "8              GO          350092.31          165.763404                Goiás   \n",
              "9              ES          325967.55          154.706953       Espírito Santo   \n",
              "10             PE          324850.44          187.992153           Pernambuco   \n",
              "11             CE          279464.03          199.902740                Ceará   \n",
              "12             PA          218295.85          215.920722                 Pará   \n",
              "13             MT          187029.29          195.228904           MatoGrosso   \n",
              "14             MA          152523.02          198.856610             Maranhão   \n",
              "15             PB          141545.72          248.325825              Paraíba   \n",
              "16             MS          137534.84          186.867989    MatoGrosso do Sul   \n",
              "17             PI          108523.97          207.106813                Piauí   \n",
              "18             RN          102718.13          196.778027  Rio Grande do Norte   \n",
              "19             AL           96962.06          227.077424              Alagoas   \n",
              "20             SE           75246.25          208.438366              Sergipe   \n",
              "21             TO           61485.33          204.270199            Tocantins   \n",
              "22             RO           60866.20          233.203831             Rondônia   \n",
              "23             AM           27966.93          181.603442             Amazonas   \n",
              "24             AC           19680.62          234.293095                 Acre   \n",
              "25             AP           16262.80          232.325714                Amapá   \n",
              "26             RR           10064.62          218.796087              Roraima   \n",
              "\n",
              "         Region  \n",
              "0     Southeast  \n",
              "1     Southeast  \n",
              "2     Southeast  \n",
              "3         South  \n",
              "4         South  \n",
              "5         South  \n",
              "6     Northeast  \n",
              "7   Center West  \n",
              "8   Center West  \n",
              "9     Southeast  \n",
              "10    Northeast  \n",
              "11    Northeast  \n",
              "12        North  \n",
              "13  Center West  \n",
              "14    Northeast  \n",
              "15    Northeast  \n",
              "16  Center West  \n",
              "17    Northeast  \n",
              "18    Northeast  \n",
              "19    Northeast  \n",
              "20    Northeast  \n",
              "21        North  \n",
              "22        North  \n",
              "23        North  \n",
              "24        North  \n",
              "25        North  \n",
              "26        North  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab665799-e545-4f49-830e-d9f7d55e9095\">\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SP</td>\n",
              "      <td>5998226.96</td>\n",
              "      <td>137.504630</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RJ</td>\n",
              "      <td>2144379.69</td>\n",
              "      <td>158.525888</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG</td>\n",
              "      <td>1872257.26</td>\n",
              "      <td>154.706434</td>\n",
              "      <td>Minas Gerais</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RS</td>\n",
              "      <td>890898.54</td>\n",
              "      <td>157.180406</td>\n",
              "      <td>Rio Grande do Sul</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PR</td>\n",
              "      <td>811156.38</td>\n",
              "      <td>154.153626</td>\n",
              "      <td>Paraná</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SC</td>\n",
              "      <td>623086.43</td>\n",
              "      <td>165.979337</td>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BA</td>\n",
              "      <td>616645.82</td>\n",
              "      <td>170.816017</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DF</td>\n",
              "      <td>355141.08</td>\n",
              "      <td>161.134791</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GO</td>\n",
              "      <td>350092.31</td>\n",
              "      <td>165.763404</td>\n",
              "      <td>Goiás</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ES</td>\n",
              "      <td>325967.55</td>\n",
              "      <td>154.706953</td>\n",
              "      <td>Espírito Santo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PE</td>\n",
              "      <td>324850.44</td>\n",
              "      <td>187.992153</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CE</td>\n",
              "      <td>279464.03</td>\n",
              "      <td>199.902740</td>\n",
              "      <td>Ceará</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>PA</td>\n",
              "      <td>218295.85</td>\n",
              "      <td>215.920722</td>\n",
              "      <td>Pará</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MT</td>\n",
              "      <td>187029.29</td>\n",
              "      <td>195.228904</td>\n",
              "      <td>MatoGrosso</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>MA</td>\n",
              "      <td>152523.02</td>\n",
              "      <td>198.856610</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>PB</td>\n",
              "      <td>141545.72</td>\n",
              "      <td>248.325825</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>MS</td>\n",
              "      <td>137534.84</td>\n",
              "      <td>186.867989</td>\n",
              "      <td>MatoGrosso do Sul</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PI</td>\n",
              "      <td>108523.97</td>\n",
              "      <td>207.106813</td>\n",
              "      <td>Piauí</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RN</td>\n",
              "      <td>102718.13</td>\n",
              "      <td>196.778027</td>\n",
              "      <td>Rio Grande do Norte</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>AL</td>\n",
              "      <td>96962.06</td>\n",
              "      <td>227.077424</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>SE</td>\n",
              "      <td>75246.25</td>\n",
              "      <td>208.438366</td>\n",
              "      <td>Sergipe</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>TO</td>\n",
              "      <td>61485.33</td>\n",
              "      <td>204.270199</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RO</td>\n",
              "      <td>60866.20</td>\n",
              "      <td>233.203831</td>\n",
              "      <td>Rondônia</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>AM</td>\n",
              "      <td>27966.93</td>\n",
              "      <td>181.603442</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>AC</td>\n",
              "      <td>19680.62</td>\n",
              "      <td>234.293095</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>AP</td>\n",
              "      <td>16262.80</td>\n",
              "      <td>232.325714</td>\n",
              "      <td>Amapá</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>RR</td>\n",
              "      <td>10064.62</td>\n",
              "      <td>218.796087</td>\n",
              "      <td>Roraima</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df.groupby(\"Region\")[\"revenue_per_state\"].sum()/df[\"revenue_per_state\"].sum())*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZgHepZAsYQW",
        "outputId": "8d86ca8d-b5df-49a8-b1f0-7c10aadd2e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Center West     6.432668\n",
              "North           2.589954\n",
              "Northeast      11.858921\n",
              "South          14.524080\n",
              "Southeast      64.594379\n",
              "Name: revenue_per_state, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "São Paulo ,Rio de Janeiro , Minas Gerais ,Rio Grande do Sul and Paraná are top 5 highest orders states and also generating highest revenue."
      ],
      "metadata": {
        "id": "uQBcI6BNseQU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "more than 80% of orders are coming from south, southeast and nothest Brazil.\n",
        "90% of the revenue is coming from south, southeast and nothest Brazil ."
      ],
      "metadata": {
        "id": "OHrUTwChsiHs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 20 Cities where highest number of orders coming from"
      ],
      "metadata": {
        "id": "PV4j0NZWsj-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT \n",
        "    c.customer_city,\n",
        "    count(distinct(o.order_id)) number_of_customers,\n",
        "    sum(op.payment_value) as revenue\n",
        "FROM\n",
        "    `targetbrazilsalesanalysis.Customers.customers` as c\n",
        "    left join  `targetbrazilsalesanalysis.Customers.orders` as o\n",
        "        on c.customer_id = o.customer_id\n",
        "    left join  `targetbrazilsalesanalysis.Customers.orderpayments` as op\n",
        "        on o.order_id = op.order_id\n",
        "\n",
        "group by \n",
        "    c.customer_city\n",
        "order by \n",
        "    number_of_customers desc,\n",
        "    revenue desc\n",
        "limit 20\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "kqE71Iy6som9",
        "outputId": "7a722a5c-994a-4f8f-efe8-c3d3f6ec1e06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            customer_city  number_of_customers     revenue\n",
              "0               sao paulo                15540  2203373.09\n",
              "1          rio de janeiro                 6882  1161927.36\n",
              "2          belo horizonte                 2773   421765.12\n",
              "3                brasilia                 2131   354216.78\n",
              "4                curitiba                 1521   247392.48\n",
              "5                campinas                 1444   216248.43\n",
              "6            porto alegre                 1379   224731.42\n",
              "7                salvador                 1245   218071.50\n",
              "8               guarulhos                 1189   165121.99\n",
              "9   sao bernardo do campo                  938   120434.84\n",
              "10                niteroi                  849   139996.99\n",
              "11            santo andre                  797   105627.09\n",
              "12                 osasco                  746    94358.72\n",
              "13                 santos                  713   112343.17\n",
              "14                goiania                  692   125494.60\n",
              "15    sao jose dos campos                  691    91637.75\n",
              "16              fortaleza                  654   119863.47\n",
              "17               sorocaba                  633    88337.50\n",
              "18                 recife                  613   110745.23\n",
              "19          florianopolis                  570   106512.16"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38c1a448-a48b-4924-9c81-c216eb98c873\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_city</th>\n",
              "      <th>number_of_customers</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sao paulo</td>\n",
              "      <td>15540</td>\n",
              "      <td>2203373.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>rio de janeiro</td>\n",
              "      <td>6882</td>\n",
              "      <td>1161927.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>belo horizonte</td>\n",
              "      <td>2773</td>\n",
              "      <td>421765.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>brasilia</td>\n",
              "      <td>2131</td>\n",
              "      <td>354216.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>curitiba</td>\n",
              "      <td>1521</td>\n",
              "      <td>247392.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>campinas</td>\n",
              "      <td>1444</td>\n",
              "      <td>216248.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>porto alegre</td>\n",
              "      <td>1379</td>\n",
              "      <td>224731.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>salvador</td>\n",
              "      <td>1245</td>\n",
              "      <td>218071.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>guarulhos</td>\n",
              "      <td>1189</td>\n",
              "      <td>165121.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sao bernardo do campo</td>\n",
              "      <td>938</td>\n",
              "      <td>120434.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>niteroi</td>\n",
              "      <td>849</td>\n",
              "      <td>139996.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>santo andre</td>\n",
              "      <td>797</td>\n",
              "      <td>105627.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>osasco</td>\n",
              "      <td>746</td>\n",
              "      <td>94358.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>santos</td>\n",
              "      <td>713</td>\n",
              "      <td>112343.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>goiania</td>\n",
              "      <td>692</td>\n",
              "      <td>125494.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>sao jose dos campos</td>\n",
              "      <td>691</td>\n",
              "      <td>91637.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>fortaleza</td>\n",
              "      <td>654</td>\n",
              "      <td>119863.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>sorocaba</td>\n",
              "      <td>633</td>\n",
              "      <td>88337.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>recife</td>\n",
              "      <td>613</td>\n",
              "      <td>110745.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>florianopolis</td>\n",
              "      <td>570</td>\n",
              "      <td>106512.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38c1a448-a48b-4924-9c81-c216eb98c873')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\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-38c1a448-a48b-4924-9c81-c216eb98c873');\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": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Product Analysis"
      ],
      "metadata": {
        "id": "Zc7a1nQxtvJe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top Selling product categories and How Many orders per placed per category"
      ],
      "metadata": {
        "id": "oMyQqhhYtxk5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\n",
        "\"\"\"\n",
        "SELECT\n",
        "    product_category_name,\n",
        "    count(distinct(op.product_id)) as Number_of_products,\n",
        "    count(distinct(op.order_id)) as Number_of_orders,\n",
        "    sum(payment_value) as revenue\n",
        "FROM\n",
        "    `Customers.orders` as o\n",
        "    join `Customers.orderpayments` as op\n",
        "    on o.order_id = op.order_id\n",
        "    join `Customers.products`  as p\n",
        "    on p.product_id = o.product_id ###unable to figure out\n",
        "GROUP BY\n",
        "    p.product_category_name\n",
        "ORDER BY \n",
        "    revenue desc,Number_of_orders desc\n",
        "LIMIT 20\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "SWXxTLZFsYNs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "health and beauty, Watches present, bed table bath, sport leisure, computer accessories, Furniture Decoration, housewares, Automotive are some of the top selling product categories.\n",
        "health and beauty products are top selling having highest orders.\n",
        "PCs and Musical Instruments category have relatively less number of products , but contributes in a high revenue."
      ],
      "metadata": {
        "id": "98XRQh2rzIFj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average price per product category along with average fright value :"
      ],
      "metadata": {
        "id": "jv9_S43HzOkA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "select \n",
        "    p.product_category,\n",
        "    min(ois.price) as min_price_product,\n",
        "    max(ois.price) as max_price_product,\n",
        "    avg(ois.price) as avg_price,\n",
        "    avg(ois.freight_value) as avg_fright_value\n",
        "    \n",
        "from target.orders as o\n",
        "    join target.order_items_SALES as ois\n",
        "    on o.order_id = ois.order_id\n",
        "    join target.products as p\n",
        "    on ois.product_id = p.product_id\n",
        "group by\n",
        "    p.product_category\n",
        "order by avg_price desc,avg_fright_value desc\n",
        "limit 15\n",
        "\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "gl8S3YkzsYIP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCs,house pastals oven and cafe,agro industry and commerce,musical instruments,Kitchen portable and food coach are having highest average product price categories."
      ],
      "metadata": {
        "id": "S4ybybSCzWxp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis delivery time"
      ],
      "metadata": {
        "id": "pWnjcKIDzl3F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating columns like, what is the time taken to complete delivery from purchase time.\n",
        "\n",
        "What the expected time for delivery ?\n",
        "\n",
        "How many days exceeded from expected delivery time ?\n",
        "\n",
        "Is the delivery was on time ?"
      ],
      "metadata": {
        "id": "Qt-86YVNzped"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "\n",
        "SELECT\n",
        "*,\n",
        "CASE WHEN days_exceeded_from_expected_delivery > 0 THEN 'delayed'\n",
        "    ELSE 'on_time_delivery'\n",
        "  END AS is_on_time\n",
        "FROM\n",
        "(\n",
        "SELECT \n",
        "    order_id,\n",
        "    date_diff(order_delivered_customer_date,order_estimated_delivery_date,day) as days_exceeded_from_expected_delivery,\n",
        "    date_diff(order_delivered_customer_date,order_purchase_timestamp,day) as days_taken_to_delivery,\n",
        "    date_diff(order_estimated_delivery_date,order_purchase_timestamp,day) as estimated_time_to_delivery\n",
        "\n",
        "FROM\n",
        "    Customers.orders\n",
        "WHERE \n",
        "    order_status = 'delivered'\n",
        ")\n",
        ";\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3WSd01MlsYF1",
        "outputId": "d57e9c61-5ba3-41b3-9dd6-aee4e5a0cc31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               order_id  days_exceeded_from_expected_delivery  \\\n",
              "0      635c894d068ac37e6e03dc54eccb6189                                    -1   \n",
              "1      3b97562c3aee8bdedcb5c2e45a50d5e1                                     0   \n",
              "2      68f47f50f04c4cb6774570cfde3a9aa7                                    -1   \n",
              "3      276e9ec344d3bf029ff83a161c6b3ce9                                     4   \n",
              "4      54e1a3c2b97fb0809da548a59f64c813                                     4   \n",
              "...                                 ...                                   ...   \n",
              "96473  ebca4856d6b3b849437fe99d11633d25                                     6   \n",
              "96474  a6f521d5e68e95961ac13d448a960fd7                                    -5   \n",
              "96475  f41397c4cf4c8a67f5f540472acbfe4f                                     5   \n",
              "96476  b674e463ea07d8a8fac9951be50283f1                                    10   \n",
              "96477  cd317fc34a4ea25ea8d1743f003712ac                                    11   \n",
              "\n",
              "       days_taken_to_delivery  estimated_time_to_delivery        is_on_time  \n",
              "0                          30                          32  on_time_delivery  \n",
              "1                          32                          33  on_time_delivery  \n",
              "2                          29                          31  on_time_delivery  \n",
              "3                          43                          39           delayed  \n",
              "4                          40                          36           delayed  \n",
              "...                       ...                         ...               ...  \n",
              "96473                      28                          22           delayed  \n",
              "96474                      28                          34  on_time_delivery  \n",
              "96475                      28                          22           delayed  \n",
              "96476                      28                          18           delayed  \n",
              "96477                      28                          16           delayed  \n",
              "\n",
              "[96478 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c68a12a-0a7d-4912-b624-9d173ae3a4cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>days_exceeded_from_expected_delivery</th>\n",
              "      <th>days_taken_to_delivery</th>\n",
              "      <th>estimated_time_to_delivery</th>\n",
              "      <th>is_on_time</th>\n",
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              "  <tbody>\n",
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              "      <td>on_time_delivery</td>\n",
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              "      <td>3b97562c3aee8bdedcb5c2e45a50d5e1</td>\n",
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              "      <td>33</td>\n",
              "      <td>on_time_delivery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68f47f50f04c4cb6774570cfde3a9aa7</td>\n",
              "      <td>-1</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>on_time_delivery</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>276e9ec344d3bf029ff83a161c6b3ce9</td>\n",
              "      <td>4</td>\n",
              "      <td>43</td>\n",
              "      <td>39</td>\n",
              "      <td>delayed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54e1a3c2b97fb0809da548a59f64c813</td>\n",
              "      <td>4</td>\n",
              "      <td>40</td>\n",
              "      <td>36</td>\n",
              "      <td>delayed</td>\n",
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              "    <tr>\n",
              "      <th>96473</th>\n",
              "      <td>ebca4856d6b3b849437fe99d11633d25</td>\n",
              "      <td>6</td>\n",
              "      <td>28</td>\n",
              "      <td>22</td>\n",
              "      <td>delayed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96474</th>\n",
              "      <td>a6f521d5e68e95961ac13d448a960fd7</td>\n",
              "      <td>-5</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>on_time_delivery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96475</th>\n",
              "      <td>f41397c4cf4c8a67f5f540472acbfe4f</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>22</td>\n",
              "      <td>delayed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96476</th>\n",
              "      <td>b674e463ea07d8a8fac9951be50283f1</td>\n",
              "      <td>10</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>delayed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96477</th>\n",
              "      <td>cd317fc34a4ea25ea8d1743f003712ac</td>\n",
              "      <td>11</td>\n",
              "      <td>28</td>\n",
              "      <td>16</td>\n",
              "      <td>delayed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96478 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c68a12a-0a7d-4912-b624-9d173ae3a4cd')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-4c68a12a-0a7d-4912-b624-9d173ae3a4cd 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-4c68a12a-0a7d-4912-b624-9d173ae3a4cd');\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": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client.query(\"\"\"\n",
        "\n",
        "SELECT\n",
        "    DISTINCT\n",
        "    x.delivery_on_time,\n",
        "    COUNT(*) OVER (PARTITION BY x.delivery_on_time) \n",
        "FROM\n",
        "        (\n",
        "        SELECT\n",
        "        *,\n",
        "\n",
        "        CASE\n",
        "            WHEN order_delivered_customer_date IS NULL  THEN 'not_yet_delivered'\n",
        "            WHEN (order_delivered_customer_date < order_estimated_delivery_date) THEN 'on_time_delivery'\n",
        "            ELSE 'delayed'\n",
        "            END AS delivery_on_time\n",
        "\n",
        "        FROM\n",
        "        Customers.orders\n",
        "        ) AS x\n",
        "\n",
        "\n",
        ";\n",
        "\"\"\").to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Mxm6_CeLsYDn",
        "outputId": "7d130792-5f07-4a3f-86e3-13d4a0ccf03e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    delivery_on_time    f0_\n",
              "0            delayed   7827\n",
              "1  not_yet_delivered   2965\n",
              "2   on_time_delivery  88649"
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtime = client.query(\"\"\"\n",
        "\n",
        "\n",
        "select \n",
        "    order_id,\n",
        "    order_status,\n",
        "    date_diff(order_approved_at,order_purchase_timestamp,day) as approval_time,\n",
        "    date_diff(order_delivered_carrier_date,order_approved_at,day) as time_taken_to_start_delivery_by_carrier,\n",
        "    date_diff(order_delivered_customer_date,order_purchase_timestamp,day) as time_taken_for_delivery,\n",
        "    date_diff(order_estimated_delivery_date,order_purchase_timestamp,day) as estimated_time_for_delivery,\n",
        "    date_diff(order_estimated_delivery_date,order_delivered_customer_date,day) as diff_estimated_delivery\n",
        "from\n",
        "    Customers.orders;\n",
        "    \"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "A87hVZ7TsYBW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtime.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "ef8a02e4-d135-4ef3-ed4c-ec69c7406c81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99441 entries, 0 to 99440\n",
            "Data columns (total 7 columns):\n",
            " #   Column                                   Non-Null Count  Dtype \n",
            "---  ------                                   --------------  ----- \n",
            " 0   order_id                                 99441 non-null  object\n",
            " 1   order_status                             99441 non-null  object\n",
            " 2   approval_time                            99281 non-null  Int64 \n",
            " 3   time_taken_to_start_delivery_by_carrier  97644 non-null  Int64 \n",
            " 4   time_taken_for_delivery                  96476 non-null  Int64 \n",
            " 5   estimated_time_for_delivery              99441 non-null  Int64 \n",
            " 6   diff_estimated_delivery                  96476 non-null  Int64 \n",
            "dtypes: Int64(5), object(2)\n",
            "memory usage: 5.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtime[\"approval_time\"].mean(),dtime[\"approval_time\"].median() # in days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2ct1tYasX79",
        "outputId": "f3529c64-5b76-42ce-c62b-d4c70719c4ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.2697998610005943, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtime[\"time_taken_to_start_delivery_by_carrier\"].mean(),dtime[\"time_taken_to_start_delivery_by_carrier\"].median() # in days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiLcSvrysX5S",
        "outputId": "79fa5415-e7f3-486a-d97f-e9a0830eb436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.315667117283192, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtime[\"time_taken_for_delivery\"].mean(),dtime[\"time_taken_for_delivery\"].median()  # in days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udaexICT0MdY",
        "outputId": "21104a92-1e73-4201-846a-c40fc259107e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12.094085575687217, 10.0)"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "dtime[\"estimated_time_for_delivery\"].mean(),dtime[\"estimated_time_for_delivery\"].median() # in days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woZ-_x6W0Mah",
        "outputId": "6ce79808-b147-4652-f1b8-8b29378f76fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23.403958125923914, 23.0)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution Of Delivery Time"
      ],
      "metadata": {
        "id": "ChzXbTgC0VDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(dtime[\"time_taken_for_delivery\"].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QhX8EL4G0MT5",
        "outputId": "7037f857-33ba-4f8d-ff16-6db849991a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1108e2400>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "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": [
        "sns.boxplot(dtime[\"time_taken_for_delivery\"].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "4wvFDGwb0MRI",
        "outputId": "a31f8f96-0ca6-4993-f5d9-838216c5f7fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb10d334dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "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": "markdown",
      "source": [
        "distibution of time difference between delivery time and estimated delivery time :"
      ],
      "metadata": {
        "id": "2cf45LvU0eGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(dtime[\"diff_estimated_delivery\"].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "GYWDJiWB0fKw",
        "outputId": "adc0284b-f0ed-42a0-ada1-84090579f917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb11357c250>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "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": [
        "sns.boxplot(dtime[\"diff_estimated_delivery\"].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "-PBLHAOv0MNx",
        "outputId": "caad8b38-be3c-4ba2-a4d4-17e12e464f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb11461c340>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "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": [
        "state_delivery_time_freight_val = client.query(\"\"\"\n",
        "\n",
        "\n",
        "/*Group data by state, take mean of freight_value, time_to_delivery, diff_estimated_delivery*/\n",
        "\n",
        "SELECT \n",
        "    c.customer_state,\n",
        "    avg(date_diff(order_approved_at,order_purchase_timestamp,day)) as mean_of_approval_time,\n",
        "    avg(date_diff(order_delivered_carrier_date,order_approved_at,day)) as mean_of_time_taken_to_start_delivery_by_carrier,\n",
        "    avg(date_diff(order_delivered_customer_date,order_purchase_timestamp,day)) as mean_of_time_taken_for_delivery,\n",
        "    avg(date_diff(order_estimated_delivery_date,order_delivered_customer_date,day)) as mean_of_diff_estimated_delivery,\n",
        "    avg(oi.freight_value) avg_fright_value\n",
        "FROM\n",
        "`targetbrazilsalesanalysis.Customers.orders` as o\n",
        "JOIN\n",
        "`targetbrazilsalesanalysis.Customers.customers` as c\n",
        "on o.customer_id = c.customer_id\n",
        "JOIN\n",
        "`targetbrazilsalesanalysis.Customers.orderitems` as oi\n",
        "on oi.order_id = o.order_id\n",
        "\n",
        "group by \n",
        "c.customer_state\n",
        "order by\n",
        "mean_of_time_taken_for_delivery\n",
        "\n",
        ";\n",
        "    \"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "GwpTr7cK0gGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "state_delivery_time_freight_val.merge(brazil,how=\"inner\",on=\"customer_state\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rKaY8PxH0gAM",
        "outputId": "5dadc658-2cb7-47c3-cb57-726ee670c916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_state  mean_of_approval_time  \\\n",
              "0              SP               0.251960   \n",
              "1              PR               0.295470   \n",
              "2              MG               0.275103   \n",
              "3              DF               0.285121   \n",
              "4              SC               0.296935   \n",
              "5              RJ               0.245541   \n",
              "6              RS               0.323600   \n",
              "7              GO               0.336477   \n",
              "8              MS               0.262515   \n",
              "9              ES               0.285018   \n",
              "10             TO               0.295238   \n",
              "11             MT               0.346919   \n",
              "12             PE               0.279623   \n",
              "13             BA               0.311924   \n",
              "14             RN               0.275992   \n",
              "15             PI               0.261993   \n",
              "16             RO               0.320144   \n",
              "17             PB               0.411960   \n",
              "18             AC               0.402174   \n",
              "19             CE               0.316181   \n",
              "20             SE               0.257143   \n",
              "21             MA               0.391252   \n",
              "22             PA               0.320370   \n",
              "23             AL               0.349099   \n",
              "24             AM               0.218182   \n",
              "25             AP               0.512195   \n",
              "26             RR               1.480769   \n",
              "\n",
              "    mean_of_time_taken_to_start_delivery_by_carrier  \\\n",
              "0                                          2.306220   \n",
              "1                                          2.378165   \n",
              "2                                          2.356214   \n",
              "3                                          2.392288   \n",
              "4                                          2.434067   \n",
              "5                                          2.502109   \n",
              "6                                          2.299627   \n",
              "7                                          2.086938   \n",
              "8                                          2.273956   \n",
              "9                                          2.506684   \n",
              "10                                         2.490446   \n",
              "11                                         2.183286   \n",
              "12                                         2.346950   \n",
              "13                                         2.394898   \n",
              "14                                         2.757576   \n",
              "15                                         2.309568   \n",
              "16                                         1.838828   \n",
              "17                                         2.547739   \n",
              "18                                         2.315217   \n",
              "19                                         2.433538   \n",
              "20                                         2.822917   \n",
              "21                                         2.790954   \n",
              "22                                         2.486512   \n",
              "23                                         2.635321   \n",
              "24                                         1.915152   \n",
              "25                                         2.320988   \n",
              "26                                         2.901961   \n",
              "\n",
              "    mean_of_time_taken_for_delivery  mean_of_diff_estimated_delivery  \\\n",
              "0                          8.259609                        10.265594   \n",
              "1                         11.480793                        12.533900   \n",
              "2                         11.515522                        12.397151   \n",
              "3                         12.501486                        11.274735   \n",
              "4                         14.520986                        10.668863   \n",
              "5                         14.689382                        11.144493   \n",
              "6                         14.708299                        13.203000   \n",
              "7                         14.948177                        11.372859   \n",
              "8                         15.107275                        10.337855   \n",
              "9                         15.192809                         9.768539   \n",
              "10                        17.003226                        11.461290   \n",
              "11                        17.508197                        13.639344   \n",
              "12                        17.792096                        12.552119   \n",
              "13                        18.774640                        10.119468   \n",
              "14                        18.873321                        13.055662   \n",
              "15                        18.931166                        10.682600   \n",
              "16                        19.282051                        19.080586   \n",
              "17                        20.119454                        12.150171   \n",
              "18                        20.329670                        20.010989   \n",
              "19                        20.537167                        10.256662   \n",
              "20                        20.978667                         9.165333   \n",
              "21                        21.203750                         9.110000   \n",
              "22                        23.301708                        13.374763   \n",
              "23                        23.992974                         7.976581   \n",
              "24                        25.963190                        18.975460   \n",
              "25                        27.753086                        17.444444   \n",
              "26                        27.826087                        17.434783   \n",
              "\n",
              "    avg_fright_value                State       Region  \n",
              "0          15.147275            São Paulo    Southeast  \n",
              "1          20.531652               Paraná        South  \n",
              "2          20.630167         Minas Gerais    Southeast  \n",
              "3          21.041355     Distrito Federal  Center West  \n",
              "4          21.470369       Santa Catarina        South  \n",
              "5          20.960924       Rio de Janeiro    Southeast  \n",
              "6          21.735804    Rio Grande do Sul        South  \n",
              "7          22.766815                Goiás  Center West  \n",
              "8          23.374884    MatoGrosso do Sul  Center West  \n",
              "9          22.058777       Espírito Santo    Southeast  \n",
              "10         37.246603            Tocantins        North  \n",
              "11         28.166284           MatoGrosso  Center West  \n",
              "12         32.917863           Pernambuco    Northeast  \n",
              "13         26.363959                Bahia    Northeast  \n",
              "14         35.652363  Rio Grande do Norte    Northeast  \n",
              "15         39.147970                Piauí    Northeast  \n",
              "16         41.069712             Rondônia        North  \n",
              "17         42.723804              Paraíba    Northeast  \n",
              "18         40.073370                 Acre        North  \n",
              "19         32.714202                Ceará    Northeast  \n",
              "20         36.653169              Sergipe    Northeast  \n",
              "21         38.257002             Maranhão    Northeast  \n",
              "22         35.832685                 Pará        North  \n",
              "23         35.843671              Alagoas    Northeast  \n",
              "24         33.205394             Amazonas        North  \n",
              "25         34.006098                Amapá        North  \n",
              "26         42.984423              Roraima        North  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b1848af-69d7-46a4-afc8-64ea301524ba\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_state</th>\n",
              "      <th>mean_of_approval_time</th>\n",
              "      <th>mean_of_time_taken_to_start_delivery_by_carrier</th>\n",
              "      <th>mean_of_time_taken_for_delivery</th>\n",
              "      <th>mean_of_diff_estimated_delivery</th>\n",
              "      <th>avg_fright_value</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SP</td>\n",
              "      <td>0.251960</td>\n",
              "      <td>2.306220</td>\n",
              "      <td>8.259609</td>\n",
              "      <td>10.265594</td>\n",
              "      <td>15.147275</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PR</td>\n",
              "      <td>0.295470</td>\n",
              "      <td>2.378165</td>\n",
              "      <td>11.480793</td>\n",
              "      <td>12.533900</td>\n",
              "      <td>20.531652</td>\n",
              "      <td>Paraná</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG</td>\n",
              "      <td>0.275103</td>\n",
              "      <td>2.356214</td>\n",
              "      <td>11.515522</td>\n",
              "      <td>12.397151</td>\n",
              "      <td>20.630167</td>\n",
              "      <td>Minas Gerais</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DF</td>\n",
              "      <td>0.285121</td>\n",
              "      <td>2.392288</td>\n",
              "      <td>12.501486</td>\n",
              "      <td>11.274735</td>\n",
              "      <td>21.041355</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SC</td>\n",
              "      <td>0.296935</td>\n",
              "      <td>2.434067</td>\n",
              "      <td>14.520986</td>\n",
              "      <td>10.668863</td>\n",
              "      <td>21.470369</td>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RJ</td>\n",
              "      <td>0.245541</td>\n",
              "      <td>2.502109</td>\n",
              "      <td>14.689382</td>\n",
              "      <td>11.144493</td>\n",
              "      <td>20.960924</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RS</td>\n",
              "      <td>0.323600</td>\n",
              "      <td>2.299627</td>\n",
              "      <td>14.708299</td>\n",
              "      <td>13.203000</td>\n",
              "      <td>21.735804</td>\n",
              "      <td>Rio Grande do Sul</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>GO</td>\n",
              "      <td>0.336477</td>\n",
              "      <td>2.086938</td>\n",
              "      <td>14.948177</td>\n",
              "      <td>11.372859</td>\n",
              "      <td>22.766815</td>\n",
              "      <td>Goiás</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MS</td>\n",
              "      <td>0.262515</td>\n",
              "      <td>2.273956</td>\n",
              "      <td>15.107275</td>\n",
              "      <td>10.337855</td>\n",
              "      <td>23.374884</td>\n",
              "      <td>MatoGrosso do Sul</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ES</td>\n",
              "      <td>0.285018</td>\n",
              "      <td>2.506684</td>\n",
              "      <td>15.192809</td>\n",
              "      <td>9.768539</td>\n",
              "      <td>22.058777</td>\n",
              "      <td>Espírito Santo</td>\n",
              "      <td>Southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>TO</td>\n",
              "      <td>0.295238</td>\n",
              "      <td>2.490446</td>\n",
              "      <td>17.003226</td>\n",
              "      <td>11.461290</td>\n",
              "      <td>37.246603</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>MT</td>\n",
              "      <td>0.346919</td>\n",
              "      <td>2.183286</td>\n",
              "      <td>17.508197</td>\n",
              "      <td>13.639344</td>\n",
              "      <td>28.166284</td>\n",
              "      <td>MatoGrosso</td>\n",
              "      <td>Center West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>PE</td>\n",
              "      <td>0.279623</td>\n",
              "      <td>2.346950</td>\n",
              "      <td>17.792096</td>\n",
              "      <td>12.552119</td>\n",
              "      <td>32.917863</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BA</td>\n",
              "      <td>0.311924</td>\n",
              "      <td>2.394898</td>\n",
              "      <td>18.774640</td>\n",
              "      <td>10.119468</td>\n",
              "      <td>26.363959</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>RN</td>\n",
              "      <td>0.275992</td>\n",
              "      <td>2.757576</td>\n",
              "      <td>18.873321</td>\n",
              "      <td>13.055662</td>\n",
              "      <td>35.652363</td>\n",
              "      <td>Rio Grande do Norte</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>PI</td>\n",
              "      <td>0.261993</td>\n",
              "      <td>2.309568</td>\n",
              "      <td>18.931166</td>\n",
              "      <td>10.682600</td>\n",
              "      <td>39.147970</td>\n",
              "      <td>Piauí</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>RO</td>\n",
              "      <td>0.320144</td>\n",
              "      <td>1.838828</td>\n",
              "      <td>19.282051</td>\n",
              "      <td>19.080586</td>\n",
              "      <td>41.069712</td>\n",
              "      <td>Rondônia</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PB</td>\n",
              "      <td>0.411960</td>\n",
              "      <td>2.547739</td>\n",
              "      <td>20.119454</td>\n",
              "      <td>12.150171</td>\n",
              "      <td>42.723804</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>AC</td>\n",
              "      <td>0.402174</td>\n",
              "      <td>2.315217</td>\n",
              "      <td>20.329670</td>\n",
              "      <td>20.010989</td>\n",
              "      <td>40.073370</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>CE</td>\n",
              "      <td>0.316181</td>\n",
              "      <td>2.433538</td>\n",
              "      <td>20.537167</td>\n",
              "      <td>10.256662</td>\n",
              "      <td>32.714202</td>\n",
              "      <td>Ceará</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>SE</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>2.822917</td>\n",
              "      <td>20.978667</td>\n",
              "      <td>9.165333</td>\n",
              "      <td>36.653169</td>\n",
              "      <td>Sergipe</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>MA</td>\n",
              "      <td>0.391252</td>\n",
              "      <td>2.790954</td>\n",
              "      <td>21.203750</td>\n",
              "      <td>9.110000</td>\n",
              "      <td>38.257002</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PA</td>\n",
              "      <td>0.320370</td>\n",
              "      <td>2.486512</td>\n",
              "      <td>23.301708</td>\n",
              "      <td>13.374763</td>\n",
              "      <td>35.832685</td>\n",
              "      <td>Pará</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>AL</td>\n",
              "      <td>0.349099</td>\n",
              "      <td>2.635321</td>\n",
              "      <td>23.992974</td>\n",
              "      <td>7.976581</td>\n",
              "      <td>35.843671</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>Northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>AM</td>\n",
              "      <td>0.218182</td>\n",
              "      <td>1.915152</td>\n",
              "      <td>25.963190</td>\n",
              "      <td>18.975460</td>\n",
              "      <td>33.205394</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>AP</td>\n",
              "      <td>0.512195</td>\n",
              "      <td>2.320988</td>\n",
              "      <td>27.753086</td>\n",
              "      <td>17.444444</td>\n",
              "      <td>34.006098</td>\n",
              "      <td>Amapá</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>RR</td>\n",
              "      <td>1.480769</td>\n",
              "      <td>2.901961</td>\n",
              "      <td>27.826087</td>\n",
              "      <td>17.434783</td>\n",
              "      <td>42.984423</td>\n",
              "      <td>Roraima</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.corrcoef(state_delivery_time_freight_val[\"mean_of_time_taken_for_delivery\"],\n",
        "           state_delivery_time_freight_val[\"avg_fright_value\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ujus300X0f9z",
        "outputId": "8f1a9f17-5c96-40f2-8c53-dcb8049a6613"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.78995771],\n",
              "       [0.78995771, 1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "delivery_time = client.query(\"\"\"\n",
        "\n",
        "\n",
        "SELECT \n",
        "    o.order_id,\n",
        "    c.customer_state,\n",
        "    (date_diff(order_approved_at,order_purchase_timestamp,day)) as approval_time,\n",
        "    (date_diff(order_delivered_carrier_date,order_approved_at,day)) as time_taken_to_start_delivery_by_carrier,\n",
        "    (date_diff(order_delivered_customer_date,order_purchase_timestamp,day)) as time_taken_for_delivery,\n",
        "    (date_diff(order_estimated_delivery_date,order_purchase_timestamp,day)) as estimated_time_for_delivery,\n",
        "    (date_diff(order_estimated_delivery_date,order_delivered_customer_date,day)) as diff_estimated_delivery\n",
        "FROM\n",
        "`targetbrazilsalesanalysis.Customers.orders` as o\n",
        "JOIN\n",
        "`targetbrazilsalesanalysis.Customers.customers` as c\n",
        "on o.customer_id = c.customer_id\n",
        "WHERE\n",
        "    o.order_delivered_customer_date is not null and\n",
        "    o.order_delivered_carrier_date is not null and\n",
        "    o.order_approved_at is not null\n",
        "ORDER BY \n",
        "  c.customer_state\n",
        "  ;\n",
        "    \"\"\").to_dataframe()"
      ],
      "metadata": {
        "id": "haMHQjVy0f4J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "delivery_time= delivery_time.merge(brazil,on=\"customer_state\")"
      ],
      "metadata": {
        "id": "rA4UyGF10fzx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating 95% confidence Interval of Delivery time for each states"
      ],
      "metadata": {
        "id": "4sDzcIxv2Jdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "delivery_time"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "XiJhzm1V2IuB",
        "outputId": "7c872128-9d04-46db-eeb4-405a7689f460"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               order_id customer_state  approval_time  \\\n",
              "0      c3ad507aba1f6b47354085e7e6bed0cf             AC              0   \n",
              "1      5b829c37201779df841d79b1875ca7ef             AC              0   \n",
              "2      48b4f1f96d5ae13b404fe7d041a8d393             AC              0   \n",
              "3      0c956783114b7ac9633e16c494c191ce             AC              0   \n",
              "4      e660da13091dbe48784500c1c3e2fb75             AC              0   \n",
              "...                                 ...            ...            ...   \n",
              "96456  29145ea9a79826e9eb23fd0684f6777e             TO              1   \n",
              "96457  334062cc253eacbccdb7825335f7b815             TO              0   \n",
              "96458  d37b53698d3839a94cb33273bb8c0241             TO              0   \n",
              "96459  abbf8270415671551b11b0e45c72eeb8             TO              0   \n",
              "96460  5c8b55fb57c1225d199343bbc1ab6ee0             TO              0   \n",
              "\n",
              "       time_taken_to_start_delivery_by_carrier  time_taken_for_delivery  \\\n",
              "0                                            3                       18   \n",
              "1                                            2                       16   \n",
              "2                                            0                       15   \n",
              "3                                            1                       22   \n",
              "4                                            3                       22   \n",
              "...                                        ...                      ...   \n",
              "96456                                        8                       30   \n",
              "96457                                        0                       15   \n",
              "96458                                        1                       12   \n",
              "96459                                        5                       29   \n",
              "96460                                        4                       34   \n",
              "\n",
              "       estimated_time_for_delivery  diff_estimated_delivery      State Region  \n",
              "0                               52                       33       Acre  North  \n",
              "1                               38                       21       Acre  North  \n",
              "2                               47                       32       Acre  North  \n",
              "3                               46                       23       Acre  North  \n",
              "4                               44                       21       Acre  North  \n",
              "...                            ...                      ...        ...    ...  \n",
              "96456                           29                        0  Tocantins  North  \n",
              "96457                           28                       12  Tocantins  North  \n",
              "96458                           28                       16  Tocantins  North  \n",
              "96459                           28                        0  Tocantins  North  \n",
              "96460                           29                       -5  Tocantins  North  \n",
              "\n",
              "[96461 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>customer_state</th>\n",
              "      <th>approval_time</th>\n",
              "      <th>time_taken_to_start_delivery_by_carrier</th>\n",
              "      <th>time_taken_for_delivery</th>\n",
              "      <th>estimated_time_for_delivery</th>\n",
              "      <th>diff_estimated_delivery</th>\n",
              "      <th>State</th>\n",
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              "      <td>15</td>\n",
              "      <td>47</td>\n",
              "      <td>32</td>\n",
              "      <td>Acre</td>\n",
              "      <td>North</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0c956783114b7ac9633e16c494c191ce</td>\n",
              "      <td>AC</td>\n",
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              "      <td>46</td>\n",
              "      <td>23</td>\n",
              "      <td>Acre</td>\n",
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              "      <th>4</th>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>96456</th>\n",
              "      <td>29145ea9a79826e9eb23fd0684f6777e</td>\n",
              "      <td>TO</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96457</th>\n",
              "      <td>334062cc253eacbccdb7825335f7b815</td>\n",
              "      <td>TO</td>\n",
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              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96458</th>\n",
              "      <td>d37b53698d3839a94cb33273bb8c0241</td>\n",
              "      <td>TO</td>\n",
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              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>28</td>\n",
              "      <td>16</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96459</th>\n",
              "      <td>abbf8270415671551b11b0e45c72eeb8</td>\n",
              "      <td>TO</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96460</th>\n",
              "      <td>5c8b55fb57c1225d199343bbc1ab6ee0</td>\n",
              "      <td>TO</td>\n",
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              "      <td>4</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "      <td>-5</td>\n",
              "      <td>Tocantins</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
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              "<p>96461 rows × 9 columns</p>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "delivery_time[\"State\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZRVtqx_2Iqf",
        "outputId": "25bebe68-77cd-453f-98f7-c5c5ab0ff3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Acre', 'Alagoas', 'Amazonas', 'Amapá', 'Bahia', 'Ceará',\n",
              "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
              "       'Minas Gerais', 'MatoGrosso do Sul', 'MatoGrosso', 'Pará',\n",
              "       'Paraíba', 'Pernambuco', 'Piauí', 'Paraná', 'Rio de Janeiro',\n",
              "       'Rio Grande do Norte', 'Rondônia', 'Roraima', 'Rio Grande do Sul',\n",
              "       'Santa Catarina', 'Sergipe', 'São Paulo', 'Tocantins'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "state_delivery_interval = {}\n",
        "for state in ['Acre', 'Alagoas', 'Amazonas', 'Amapá', 'Bahia', 'Ceará',\n",
        "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
        "       'Minas Gerais', 'MatoGrosso do Sul', 'MatoGrosso', 'Pará',\n",
        "       'Paraíba', 'Pernambuco', 'Piauí', 'Paraná', 'Rio de Janeiro',\n",
        "       'Rio Grande do Norte', 'Rondônia', 'Roraima', 'Rio Grande do Sul',\n",
        "       'Santa Catarina', 'Sergipe', 'São Paulo', 'Tocantins']:\n",
        "    btmeans = []\n",
        "    data = delivery_time[delivery_time[\"State\"]==state][\"time_taken_for_delivery\"]\n",
        "    for i in range(1000):\n",
        "        btsample = data.sample(50,replace=True)\n",
        "        btmeans.append(np.mean(btsample))\n",
        "        state_delivery_interval.update({state: [ np.mean(btmeans) - 1.96*(np.std(btmeans)),np.mean(btmeans) + 1.96*(np.std(btmeans))]})\n",
        "        "
      ],
      "metadata": {
        "id": "7kovKAda2InO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "state_delivery_interval"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWErnhj72IhL",
        "outputId": "14b4c2ce-56be-4034-cbc1-86f4c30cc691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Acre': [17.76811647725025, 23.52320352274975],\n",
              " 'Alagoas': [20.68473936720925, 27.33838063279075],\n",
              " 'Amazonas': [22.384117621379602, 29.6258023786204],\n",
              " 'Amapá': [20.813172263399093, 32.44830773660091],\n",
              " 'Bahia': [15.699294468764286, 22.003385531235715],\n",
              " 'Ceará': [17.180823885135098, 24.411936114864893],\n",
              " 'Distrito Federal': [10.576689732573056, 14.397870267426944],\n",
              " 'Espírito Santo': [12.353370001492593, 18.35902999850741],\n",
              " 'Goiás': [12.677837903922743, 17.54988209607726],\n",
              " 'Maranhão': [18.07977882427857, 24.341821175721428],\n",
              " 'Minas Gerais': [9.563582032499353, 13.475777967500646],\n",
              " 'MatoGrosso do Sul': [13.036210750593037, 17.312709249406964],\n",
              " 'MatoGrosso': [15.335641465818576, 19.882798534181426],\n",
              " 'Pará': [19.646141473965557, 27.050578526034442],\n",
              " 'Paraíba': [17.042399747985513, 22.94572025201449],\n",
              " 'Pernambuco': [14.992874901211154, 20.769005098788853],\n",
              " 'Piauí': [14.823356492723384, 23.012843507276614],\n",
              " 'Paraná': [9.637257474538835, 13.340622525461164],\n",
              " 'Rio de Janeiro': [11.612325778149566, 18.22891422185043],\n",
              " 'Rio Grande do Norte': [15.232626673917522, 22.53757332608248],\n",
              " 'Rondônia': [16.778565056541375, 21.003554943458617],\n",
              " 'Roraima': [21.42198572519841, 36.8983742748016],\n",
              " 'Rio Grande do Sul': [12.23546483630869, 17.39437516369131],\n",
              " 'Santa Catarina': [12.12770364423515, 16.899456355764848],\n",
              " 'Sergipe': [16.41415366764919, 25.500086332350808],\n",
              " 'São Paulo': [6.4720107696593505, 10.060949230340649],\n",
              " 'Tocantins': [15.208494673652751, 19.32414532634725]}"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confidence_interval(x, values,  color='#2187bb', horizontal_line_width=0.25,confidence=95):\n",
        "    \n",
        "    def CI(data,confidence, sample_size=75,trials = 3000):\n",
        "\n",
        "        bootstrapped_mean= np.empty(trials)\n",
        "\n",
        "        for i in range(trials):\n",
        "            btssample = data.sample(n=sample_size,replace=True)\n",
        "            bootstrapped_mean[i] = np.mean(btssample)\n",
        "        sample_mean = np.mean(bootstrapped_mean)\n",
        "        sample_std = np.std(data)\n",
        "        standard_error = sample_std/np.sqrt(sample_size)\n",
        "        talfa_by2 = t.ppf((1-((1-(confidence)/100)/2)),df = sample_size-1)\n",
        "        margin_of_error = talfa_by2*standard_error\n",
        "        \n",
        "        return margin_of_error,sample_mean+margin_of_error,sample_mean-margin_of_error\n",
        "        \n",
        "    \n",
        "\n",
        "    \n",
        "    error,bottom,top = CI(values,confidence)\n",
        "\n",
        "    left = x - horizontal_line_width / 2\n",
        "    top = np.mean(values) - error\n",
        "    right = x + horizontal_line_width / 2\n",
        "    bottom = np.mean(values) + error\n",
        "    \n",
        "    plt.plot([x, x], [top, bottom], color=color)\n",
        "    plt.plot([left, right], [top, top], color=color)\n",
        "    plt.plot([left, right], [bottom, bottom], color=color)\n",
        "    plt.plot(x, np.mean(values), 'o', color='#f44336')\n",
        "    "
      ],
      "metadata": {
        "id": "QglgMVbr2hcd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,12))\n",
        "plt.grid()\n",
        "i = 1\n",
        "for state in ['Acre', 'Alagoas', 'Amazonas', 'Amapá', 'Bahia', 'Ceará',\n",
        "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
        "       'Minas Gerais', 'MatoGrosso do Sul', 'MatoGrosso', 'Pará',\n",
        "       'Paraíba', 'Pernambuco', 'Piauí', 'Paraná', 'Rio de Janeiro',\n",
        "       'Rio Grande do Norte', 'Rondônia', 'Roraima', 'Rio Grande do Sul',\n",
        "       'Santa Catarina', 'Sergipe', 'São Paulo', 'Tocantins']:\n",
        "\n",
        "    plot_confidence_interval(x = i,values=delivery_time[delivery_time[\"State\"]==state][\"time_taken_for_delivery\"])\n",
        "    i += 1\n",
        "plt.xticks(list(range(1,28)),['Acre', 'Alagoas', 'Amazonas', 'Amapá', 'Bahia', 'Ceará',\n",
        "       'Distrito Federal', 'Espírito Santo', 'Goiás', 'Maranhão',\n",
        "       'Minas Gerais', 'MatoGrosso do Sul', 'MatoGrosso', 'Pará',\n",
        "       'Paraíba', 'Pernambuco', 'Piauí', 'Paraná', 'Rio de Janeiro',\n",
        "       'Rio Grande do Norte', 'Rondônia', 'Roraima', 'Rio Grande do Sul',\n",
        "       'Santa Catarina', 'Sergipe', 'São Paulo', 'Tocantins'])\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "id": "cAp5VvF82hXP",
        "outputId": "ad5f6de3-2044-460d-a5ef-847aee711de7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After purchasing , the average time for approving the order by seller is 0.26 days and median time is 0 , means with in a day.\n",
        "\n",
        "Average time taken for a carrier to start the delivery is 2 and a half day.\n",
        "average time taken to complete delivery is 12 days. and median of delivery time is 10 days.\n",
        "\n",
        "Estimated time delivery average is 23 days.\n",
        "\n",
        "There is a positive correlation between freight value and delivery time.\n",
        "long distance deliveries are having highers freight values and also takes more time for delivery.\n",
        "\n",
        "States São Paulo ,Paraná,Minas Gerais, Distrito Federal ,Santa Catarina and Rio de Janeiro are some of the states having faster delivery time relatively.\n",
        "\n",
        "Alagoas, Amazonas, Amapá ,Pará and Roraima are some states have very slow delivery time relatively."
      ],
      "metadata": {
        "id": "PcFS6oCL2ron"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights and Recommendations :\n",
        "\n",
        "- We have 99,441 customers of data available. \n",
        "- We have 96096 number of Unique Customers ids. \n",
        "- 14994 different locations of customers\n",
        "- Customers are from different 4119 cities and 27 states from Brazil.\n",
        "- total 99441 customers are there in given data.\n",
        "- from total 99441 orders , 1107 are shipped ,625 were canceled, 96478 are delivered.\n",
        "\n",
        "- 68% customers are from southeast Brazil , 14% are from south Brazil and rest are other other regions of Brazil .\n",
        "\n",
        "- Total 3095 sellers who are from 611 different cities and 23 states in Brazil and from 2246 different areas as per zip-code data.\n",
        "\n",
        "- São Paulo state has the highest numbers of sellers in country.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "g4KA1K8e3Ys1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis of sales and revenue as per time : \n",
        "      \n",
        "- Time period for which the data is given is 25 months. \n",
        "\n",
        "- compare to 2017 , revenue has increased in 2018 by 21%. \n",
        "\n",
        "- Average number of order are higher during November month , september and october month average orders are comparatively low , in may and july and august have higher average orders compare to other months.\n",
        "\n",
        "\n",
        "- Tuesday, monday and wednesdays have relatively higher number of orders."
      ],
      "metadata": {
        "id": "p_fQdxuB3kli"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Increasing trend  :\n",
        "\n",
        "- there is a increasing trend in orders , trend sustains during 2018. There a slight fall we can observe during october 2017 following with a great hike in november month and again a fall in end of december 2017 and january 2018.\n",
        "- we can observe the trend of increasing orders with time and also for revenue.\n",
        "\n",
        "- we can observe there's 815% growth increased in terms of orders and 707% growth increment in terms of revenue in January from 2017 to 2018. \n",
        "- growth rate for july and august in 2017 to 2018 is relatively very low!\n",
        "\n",
        "- 2017-february, 2017-march,2017-november were the highest growing sale month compare to its previous month."
      ],
      "metadata": {
        "id": "bIfum7-T3qj0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer_purchasing Behavior: \n",
        "\n",
        "- customers are purchasing during moring 8am to late evening 11pm.\n",
        "- afternoon and evening orders are very high  , compare to morning , and night time."
      ],
      "metadata": {
        "id": "RFiGhz1U3w5f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Product Category : \n",
        "\n",
        "- In products Data , total 32951 different products available in Target with 73 different product_category.\n",
        "\n",
        "\n",
        "- health and beauty, Watches present, bed table bath, sport leisure, computer accessories, Furniture Decoration, housewares, Automotive are some of the top selling product categories.\n",
        "- health and beauty products are top selling having highest orders.\n",
        "- PCs and Musical Instruments category have relatively less number of products , but contributes in a high revenue.\n",
        "\n",
        "\n",
        "- PCs,house pastals oven and cafe,agro industry and commerce,musical instruments,Kitchen portable and food coach are having highest average product price categories.\n",
        "\n",
        "\n",
        "- 61% orders are between price range 10-100. 33% are from 101-500 price range.\n",
        "\n",
        "\n",
        "- PCs/electronics, Furniture products,Kitchen Service Area Dinner and Garden equipments, Industry Commerce and Business , agro indsustrial commercial products,Bags Accessories, musical instruments, Construction Tools Illumination are some product categories having high average freighter value.\n",
        "\n",
        "Delivery time : \n",
        "\n",
        "\n",
        "- avg_estimated_delivery_time and delivery_time have a positive correlation with avg_freight_value.\n",
        "\n",
        "\n",
        "- after purchasing , the average time for approving the order by seller is 0.26 days and median time is 0 , means with in a day.\n",
        "- average time taken for a carrier to start the delivery is 2 and a half day.\n",
        "- average time taken to complete delivery is 12 days. and median of delivery time is 10 days.\n",
        "\n",
        "\n",
        "- estimated time delivery average is 23 days.\n",
        "- There is a positive correlation between freight value and delivery time.\n",
        "\n",
        "- states São Paulo ,Paraná, Minas Gerais, Distrito Federal ,Santa Catarina and Rio de Janeiro are some of the states having faster delivery time relatively.\n",
        "- Alagoas, Amazonas, Amapá ,Pará and Roraima are some states have very slow delivery time relatively.\n",
        "\n",
        "\n",
        "Region and State vise Analysis : \n",
        "\n",
        "\n",
        "- São Paulo ,Rio de Janeiro , Minas Gerais ,Rio Grande do Sul and Paraná are top 5 highest orders states and also generating highest revenue.\n",
        "- more than 80% of orders are coming from south, southeast and nothest Brazil. 90% of the revenue is coming from south, southeast and nothest Brazil .\n",
        "\n",
        "Recommendations :\n",
        "\n",
        "- from the distribution and statistical analysis we can observe the average time to complete the delivery is 12 days . which should be reduced to atleast half , as due to high competition in e-commerce market , its is vital to do so.\n",
        "- In order to reduce the delivery time, if we look at the average time for carrier to start the delivery itself takes atleast 2 and a half days. and order approaval  time is 0.26 days . These two should be optimized at as low as possible, that can result into delivering faster. \n",
        "- If we look at Top states where delivery is really slow compared to estimated date , they are all from north Brazil region. Delivering faster in the north states may create and increase new customers and revenue from north.\n",
        "- Increasing network in north brazil , having small towns can help increase the customer base. As north Brazil has the worlds largest river and most extesive rain forest, must be a good travel destination, introducing necessary survival/ camping/adventure products can help increase revenue and order from northen region . \n",
        "- top selling items are between 10-100 dollars,introducing new different more products from top selling categories can increase revenue more.\n",
        "- It was observed an increasing trend in revenue and orders over time , yet during october and january sales are decreasing probably after Festival Sales. Introducing possible discount on not so running product can help sell more products during those low going months."
      ],
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        "id": "FqB3MnjT31M8"
      }
    }
  ]
}