Data Analysis using SQL.ipynb

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    "# <span style='color:Green'> Data Analysis using SQL </span>"
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    "## Motivation\n",
    "\n",
    "I am undertaking this project with the goal of enhancing my understanding of data analysis and showcasing my proficiency in SQL. By utilizing the European Soccer Database, I aim to provide a comprehensive introduction to data analysis using SQL—an indispensable tool for every data scientist. The logic and principles behind SQL closely align with other popular data analysis tools like Excel and Pandas, making it an intuitive and essential skill for anyone working with data.\n",
    "\n",
    "Through this project, I want to emphasize the practical application of SQL in the field of data science. I will leverage the real-world context of soccer-related data to demonstrate how SQL can be used to access, manipulate, and derive insights from complex datasets. By showcasing how SQL queries mirror the logic employed in other data analysis tools, I hope to make the learning process accessible and relatable to individuals familiar with those tools.\n",
    "\n",
    "Furthermore, I believe that understanding SQL and its capabilities is pivotal for effective data exploration and analysis. By working on this project, I will be able to strengthen my logical thinking skills and deepen my knowledge of querying databases. This project not only provides an opportunity for hands-on learning but also enables me to build a valuable portfolio piece that highlights my data analysis and SQL proficiency.\n",
    "\n",
    "Ultimately, my aim is to create an engaging and informative resource that introduces others to the power of SQL for data analysis. I am excited to embark on this journey of exploration and learning, and I look forward to sharing the insights and knowledge I gain along the way."
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    "## Acknowledgments\n",
    "\n",
    "In this endeavor, I am delving into the realm of crucial data science concepts, with a focus on SQL—a conceptual language tailored for seamlessly interacting with databases. Specifically, I am utilizing SQLite, a distinct implementation of SQL. It's noteworthy that most SQL languages share the capabilities discussed in this documentation. Discrepancies primarily lie in performance, advanced analytical functionalities, and, of course, pricing models.\n",
    "\n",
    "The crux of our exploration lies in employing the SQL language to craft queries that fetch, manipulate, sort, and extract data residing within databases. Central to this pursuit are the tables within the database. These tables serve as repositories for the entirety of the stored data. A fundamental principle is to distribute data across multiple tables, ensuring a well-designed data structure. Much of this project's focus revolves around navigating and working with these tables.\n",
    "\n",
    "Aside from tables, the database landscape is enriched with additional valuable concepts and features, albeit beyond the scope of this endeavor. A few notable areas that won't be covered include:\n",
    "\n",
    "Table Creation: Establishing the blueprint for data organization within tables.\n",
    "\n",
    "Data Manipulation: Inserting and updating data within the database.\n",
    "\n",
    "Functions: Utilizing functions to process input values and return manipulated outputs. A simple example could involve a function that trims whitespace from text.\n",
    "\n",
    "In summary, my aim is to equip myself with a profound understanding of these essential data science components. By focusing on SQL and its practical implications, I aspire to demonstrate the power of data manipulation, analysis, and extraction—a skillset that holds great significance in the data science landscape. Through this project, I intend to not only bolster my own knowledge but also share insights that may benefit others keen on exploring the fascinating world of data analysis."
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   "source": [
    "### Data Import Essentials"
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    "#Improts \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "path = \"../input/\"  #Insert path here\n",
    "database = path + 'database.sqlite'"
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    "### Connecting to DataBase\n",
    "\n",
    "\"The fundamental outline of the query is straightforward: I determine the desired output by specifying the elements to display following the SELECT statement. The asterisk '*' represents the inclusion of all available columns. Next, I identify the specific table of interest by employing the FROM statement. To refine the data selection, I incorporate conditions after WHERE, enabling me to extract the relevant information from the table(s).\n",
    "\n",
    "The arrangement and sequence of these components bear significance, shaping the overall query. Additionally, the presence of spaces, new lines, capitalized terms, and indentation collectively contribute to enhancing code readability. By adhering to this structured approach, I ensure that the code is not only functional but also easy to comprehend. This practice forms an integral part of my efforts to cultivate effective data analysis skills.\""
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>League</td>\n",
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       "      <td>CREATE TABLE `Country` (\\n\\t`id`\\tINTEGER PRIM...</td>\n",
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       "    type               name           tbl_name  rootpage  \\\n",
       "0  table    sqlite_sequence    sqlite_sequence         4   \n",
       "1  table  Player_Attributes  Player_Attributes        11   \n",
       "2  table             Player             Player        14   \n",
       "3  table              Match              Match        18   \n",
       "4  table             League             League        24   \n",
       "5  table            Country            Country        26   \n",
       "6  table               Team               Team        29   \n",
       "7  table    Team_Attributes    Team_Attributes         2   \n",
       "\n",
       "                                                 sql  \n",
       "0             CREATE TABLE sqlite_sequence(name,seq)  \n",
       "1  CREATE TABLE \"Player_Attributes\" (\\n\\t`id`\\tIN...  \n",
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       "3  CREATE TABLE `Match` (\\n\\t`id`\\tINTEGER PRIMAR...  \n",
       "4  CREATE TABLE `League` (\\n\\t`id`\\tINTEGER PRIMA...  \n",
       "5  CREATE TABLE `Country` (\\n\\t`id`\\tINTEGER PRIM...  \n",
       "6  CREATE TABLE \"Team\" (\\n\\t`id`\\tINTEGER PRIMARY...  \n",
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    "conn = sqlite3.connect(database)\n",
    "\n",
    "tables = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM sqlite_master\n",
    "                        WHERE type='table';\"\"\", conn)\n",
    "tables"
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    "### List of countries\n",
    "\n",
    "\"At its core, this represents the simplest form of a query. The essential elements that comprise a query are the SELECT and FROM clauses. These components are indispensable when retrieving data from a table. This minimalist structure forms the foundation upon which more intricate queries can be built, allowing for seamless data extraction and analysis.\""
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       "      <th>0</th>\n",
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       "      <td>Belgium</td>\n",
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       "      <th>1</th>\n",
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       "      <td>England</td>\n",
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       "      <th>2</th>\n",
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       "      <td>7809</td>\n",
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       "      <td>Italy</td>\n",
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       "       id         name\n",
       "0       1      Belgium\n",
       "1    1729      England\n",
       "2    4769       France\n",
       "3    7809      Germany\n",
       "4   10257        Italy\n",
       "5   13274  Netherlands\n",
       "6   15722       Poland\n",
       "7   17642     Portugal\n",
       "8   19694     Scotland\n",
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       "10  24558  Switzerland"
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    "countries = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM Country;\"\"\", conn)\n",
    "countries"
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    "### List of leagues and their country \n",
    "\n",
    "Embarking on this journey, I delve into the realm of data connectivity, guided by the essential concept of JOIN in SQL. JOIN serves as the pivotal bridge that connects two tables, forging a meaningful relationship between them. This mechanism comes into play when a common key exists within both tables, enabling a seamless fusion of their contents.\n",
    "\n",
    "Understanding the profound significance of keys is paramount in establishing these connections. A key serves as a unique identifier for each record (row) within a table. It can be a single value, often an ID, or a combination of values that hold uniqueness within the table.\n",
    "\n",
    "In the process of joining disparate tables, certain crucial steps must be followed:\n",
    "\n",
    "1. **Choose the Join Type:** The choice of join type determines the nature of the connection. Common options include:\n",
    "   - **INNER JOIN:** Retains only the records that satisfy the condition specified in the ON clause for both tables. Non-matching records are excluded.\n",
    "   - **LEFT JOIN:** Preserves all values from the left table while combining them with matching rows from the right table. Columns from the right table lacking matching values on the left are populated with NULL.\n",
    "\n",
    "2. **Specify the Common Value:** A shared value acts as the linchpin connecting the tables. In our case, it could be the unique identifier of a country, for instance.\n",
    "\n",
    "3. **Ensure Key Existence:** At least one of the values used for joining must be a key within its respective table. In our context, the Country.id stands as a unique key, while the League.country_id might not be unique due to the possibility of multiple leagues existing within a single country.\n",
    "\n",
    "JOIN operations, while immensely powerful, can also be fraught with pitfalls. Misapplying JOINs or misjudging their application is a prevalent and potentially perilous misstep when crafting intricate queries.\n",
    "\n",
    "JOINs, and using them incorrectly, is the most common and dangerious mistake when writing complicated queries"
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Belgium Jupiler League</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
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       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
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       "      <td>1729</td>\n",
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       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>4769</td>\n",
       "      <td>France</td>\n",
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       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  country_id                      name     id         name\n",
       "0       1           1    Belgium Jupiler League      1      Belgium\n",
       "1    1729        1729    England Premier League   1729      England\n",
       "2    4769        4769            France Ligue 1   4769       France\n",
       "3    7809        7809     Germany 1. Bundesliga   7809      Germany\n",
       "4   10257       10257             Italy Serie A  10257        Italy\n",
       "5   13274       13274    Netherlands Eredivisie  13274  Netherlands\n",
       "6   15722       15722        Poland Ekstraklasa  15722       Poland\n",
       "7   17642       17642  Portugal Liga ZON Sagres  17642     Portugal\n",
       "8   19694       19694   Scotland Premier League  19694     Scotland\n",
       "9   21518       21518           Spain LIGA BBVA  21518        Spain\n",
       "10  24558       24558  Switzerland Super League  24558  Switzerland"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leagues = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM League\n",
    "                        JOIN Country ON Country.id = League.country_id;\"\"\", conn)\n",
    "leagues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c791a87-1a24-49b8-9e8a-59d6383bdd56",
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   "source": [
    "### List of teams\n",
    "\n",
    "In SQL queries, precision in organizing and restricting output is achieved through the potent tools of ORDER BY and LIMIT. These commands play a pivotal role in shaping the final presentation of data, ensuring that insights are not only accurate but also focused.\n",
    "\n",
    "The command ORDER BY takes center stage, dictating the arrangement of output rows. It empowers me to choose between ascending or descending sorting, ushering forth a logical and coherent display of information. Whether it's arranging records in alphabetical order, numerical progression, or other custom criteria, ORDER BY wields the power to bring clarity to the dataset.\n",
    "\n",
    "Complementing ORDER BY is LIMIT, a directive that governs the quantity of rows making their way into the output. By skillfully applying LIMIT, I can exercise control over the extent of information presented, effectively curating the output to emphasize the most pertinent results.\n",
    "\n",
    "These commands, when skillfully wielded, facilitate the art of precision data presentation. As venture into the intricacies of ORDER BY and LIMIT, equipped not only to harness their capabilities but also to strategically utilize them for a refined and insightful data analysis experience."
   ]
  },
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16848</td>\n",
       "      <td>8350</td>\n",
       "      <td>29</td>\n",
       "      <td>1. FC Kaiserslautern</td>\n",
       "      <td>KAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15624</td>\n",
       "      <td>8722</td>\n",
       "      <td>31</td>\n",
       "      <td>1. FC Köln</td>\n",
       "      <td>FCK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16239</td>\n",
       "      <td>8165</td>\n",
       "      <td>171</td>\n",
       "      <td>1. FC Nürnberg</td>\n",
       "      <td>NUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16243</td>\n",
       "      <td>9905</td>\n",
       "      <td>169</td>\n",
       "      <td>1. FSV Mainz 05</td>\n",
       "      <td>MAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11817</td>\n",
       "      <td>8576</td>\n",
       "      <td>614</td>\n",
       "      <td>AC Ajaccio</td>\n",
       "      <td>AJA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11074</td>\n",
       "      <td>108893</td>\n",
       "      <td>111989</td>\n",
       "      <td>AC Arles-Avignon</td>\n",
       "      <td>ARL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49116</td>\n",
       "      <td>6493</td>\n",
       "      <td>1714</td>\n",
       "      <td>AC Bellinzona</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26560</td>\n",
       "      <td>10217</td>\n",
       "      <td>650</td>\n",
       "      <td>ADO Den Haag</td>\n",
       "      <td>HAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9537</td>\n",
       "      <td>8583</td>\n",
       "      <td>57</td>\n",
       "      <td>AJ Auxerre</td>\n",
       "      <td>AUX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9547</td>\n",
       "      <td>9829</td>\n",
       "      <td>69</td>\n",
       "      <td>AS Monaco</td>\n",
       "      <td>MON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  team_api_id  team_fifa_api_id        team_long_name team_short_name\n",
       "0  16848         8350                29  1. FC Kaiserslautern             KAI\n",
       "1  15624         8722                31            1. FC Köln             FCK\n",
       "2  16239         8165               171        1. FC Nürnberg             NUR\n",
       "3  16243         9905               169       1. FSV Mainz 05             MAI\n",
       "4  11817         8576               614            AC Ajaccio             AJA\n",
       "5  11074       108893            111989      AC Arles-Avignon             ARL\n",
       "6  49116         6493              1714         AC Bellinzona             BEL\n",
       "7  26560        10217               650          ADO Den Haag             HAA\n",
       "8   9537         8583                57            AJ Auxerre             AUX\n",
       "9   9547         9829                69             AS Monaco             MON"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = pd.read_sql(\"\"\"SELECT *\n",
    "                        FROM Team\n",
    "                        ORDER BY team_long_name\n",
    "                        LIMIT 10;\"\"\", conn)\n",
    "teams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "347e4255-340f-434b-9215-91e7fa60019d",
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   },
   "source": [
    "### List of matches\n",
    "\n",
    "In this illustrative example, our focus sharpens as we selectively unveil the columns of significance, casting aside the catch-all asterisk in favor of precise naming conventions. By employing this approach, we gain mastery over the specific elements that command our attention and align with our analytical pursuits.\n",
    "\n",
    "A unique challenge emerges as we navigate cells that share identical names, notably Country.name and League.name. To circumvent ambiguity, we deftly wield the AS operator to bestow distinct aliases upon them, fostering clarity and precision within our data landscape.\n",
    "\n",
    "The complexity of this query is apparent, an intricate web of joins interlacing the tables. The rationale lies in the database's star structure—a strategic design choice. The Match table houses the core \"performance\" data, enriched by keys and IDs, while descriptive particulars find their abode in ancillary tables such as Country, League, and Team.\n",
    "\n",
    "Notably, the Team table takes center stage twice, a nuance that demands careful consideration. Though the table name remains constant, we introduce two separate instances (distinguished by AS) to cater to distinct values—home_team_api_id and away_team_api_id. This conscious duality underscores the necessity of differentiating these values, ensuring accurate representation.\n",
    "\n",
    "A deliberate choice emerges in the form of a left join for the Team tables. This decision hinges on preserving match data within the output, even in scenarios where one of the teams remains absent from the Team table—an assertion of data integrity that echoes throughout the query.\n",
    "\n",
    "As our narrative unfolds, ORDER takes its place, orchestrating the arrangement of the output. This pivotal command precedes LIMIT, dictating the confines of our insight, while following in the footsteps of WHERE—another essential component that shapes our data narrative."
   ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-30 00:00:00</td>\n",
       "      <td>Valencia CF</td>\n",
       "      <td>RCD Mallorca</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21525</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-30 00:00:00</td>\n",
       "      <td>RCD Espanyol</td>\n",
       "      <td>Real Valladolid</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21519</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>CA Osasuna</td>\n",
       "      <td>Villarreal CF</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21520</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>RC Deportivo de La Coruña</td>\n",
       "      <td>Real Madrid CF</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21521</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>CD Numancia</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21522</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>Racing Santander</td>\n",
       "      <td>Sevilla FC</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21523</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>Real Sporting de Gijón</td>\n",
       "      <td>Getafe CF</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21524</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>Real Betis Balompié</td>\n",
       "      <td>RC Recreativo</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21526</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>Athletic Club de Bilbao</td>\n",
       "      <td>UD Almería</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21527</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-31 00:00:00</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>Málaga CF</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id country_name      league_name     season  stage                 date  \\\n",
       "0  21518        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-30 00:00:00   \n",
       "1  21525        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-30 00:00:00   \n",
       "2  21519        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "3  21520        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "4  21521        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "5  21522        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "6  21523        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "7  21524        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "8  21526        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "9  21527        Spain  Spain LIGA BBVA  2008/2009      1  2008-08-31 00:00:00   \n",
       "\n",
       "                   home_team        away_team  home_team_goal  away_team_goal  \n",
       "0                Valencia CF     RCD Mallorca               3               0  \n",
       "1               RCD Espanyol  Real Valladolid               1               0  \n",
       "2                 CA Osasuna    Villarreal CF               1               1  \n",
       "3  RC Deportivo de La Coruña   Real Madrid CF               2               1  \n",
       "4                CD Numancia     FC Barcelona               1               0  \n",
       "5           Racing Santander       Sevilla FC               1               1  \n",
       "6     Real Sporting de Gijón        Getafe CF               1               2  \n",
       "7        Real Betis Balompié    RC Recreativo               0               1  \n",
       "8    Athletic Club de Bilbao       UD Almería               1               3  \n",
       "9            Atlético Madrid        Málaga CF               4               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "detailed_matches = pd.read_sql(\"\"\"SELECT Match.id, \n",
    "                                        Country.name AS country_name, \n",
    "                                        League.name AS league_name, \n",
    "                                        season, \n",
    "                                        stage, \n",
    "                                        date,\n",
    "                                        HT.team_long_name AS  home_team,\n",
    "                                        AT.team_long_name AS away_team,\n",
    "                                        home_team_goal, \n",
    "                                        away_team_goal                                        \n",
    "                                FROM Match\n",
    "                                JOIN Country on Country.id = Match.country_id\n",
    "                                JOIN League on League.id = Match.league_id\n",
    "                                LEFT JOIN Team AS HT on HT.team_api_id = Match.home_team_api_id\n",
    "                                LEFT JOIN Team AS AT on AT.team_api_id = Match.away_team_api_id\n",
    "                                WHERE country_name = 'Spain'\n",
    "                                ORDER by date\n",
    "                                LIMIT 10;\"\"\", conn)\n",
    "detailed_matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "63b4a413-41f5-434c-9ae1-e4b7cc6df717",
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   },
   "source": [
    "### Basic Analytics\n",
    "\n",
    "Data aggregation—an integral step toward unraveling insights from a broader perspective. The shift from scrutinizing raw data to grouping it into distinct levels marks a pivotal transition in my analytical journey. Through this transformation, aim to extract meaningful patterns and trends by examining data at higher granularities.\n",
    "\n",
    "In the context of this illustration, extend the groundwork laid by previous queries. However, this time, the focus is on magnifying the analysis to the country-league-season level. This shift in perspective brings forth the powerful functionality of GROUP BY. This command, positioned between WHERE and ORDER, serves as a cornerstone for dissecting data into manageable segments.\n",
    "\n",
    "Upon determining the level of analysis, the SELECT statement bifurcates into two crucial components:\n",
    "\n",
    "1. **Dimensions:** These represent the descriptors that define the segments under scrutiny—essentially, the attributes by which we intend to group the data later.\n",
    "\n",
    "2. **Metrics:** Metrics encompass numerical measures that offer a quantitative perspective on the data. The catch here is that metrics demand aggregation through functions like sum(), count(), avg(), and more.\n",
    "\n",
    "It's imperative to maintain consistency between the dimensions chosen for the SELECT statement and those used in the GROUP BY clause. This alignment ensures accurate results, guarding against erroneous outcomes.\n",
    "\n",
    "Beyond aggregation and grouping, the HAVING functionality emerges as a valuable tool. Positioned after grouping, HAVING introduces an additional layer of data filtering—this time, operating on the output of the grouped data. Often employed to refine results, HAVING contributes to a cleaner and more refined analytical outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "_uuid": "57b770e270081fb69e56e896660228ea70a25921",
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     "iopub.execute_input": "2022-03-21T19:34:59.401336Z",
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     "iopub.status.idle": "2022-03-21T19:35:12.830560Z",
     "shell.execute_reply": "2022-03-21T19:35:12.829919Z",
     "shell.execute_reply.started": "2022-03-21T19:34:59.401276Z"
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   "outputs": [
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     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>season</th>\n",
       "      <th>number_of_stages</th>\n",
       "      <th>number_of_teams</th>\n",
       "      <th>avg_home_team_scors</th>\n",
       "      <th>avg_away_team_goals</th>\n",
       "      <th>avg_goal_dif</th>\n",
       "      <th>avg_goals</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.492105</td>\n",
       "      <td>1.207895</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.473684</td>\n",
       "      <td>1.092105</td>\n",
       "      <td>0.381579</td>\n",
       "      <td>2.565789</td>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.573684</td>\n",
       "      <td>1.194737</td>\n",
       "      <td>0.378947</td>\n",
       "      <td>2.768421</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.557895</td>\n",
       "      <td>1.239474</td>\n",
       "      <td>0.318421</td>\n",
       "      <td>2.797368</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.589474</td>\n",
       "      <td>1.215789</td>\n",
       "      <td>0.373684</td>\n",
       "      <td>2.805263</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.623684</td>\n",
       "      <td>1.173684</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>2.797368</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.697368</td>\n",
       "      <td>1.073684</td>\n",
       "      <td>0.623684</td>\n",
       "      <td>2.771053</td>\n",
       "      <td>1053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.078947</td>\n",
       "      <td>0.321053</td>\n",
       "      <td>2.478947</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.436842</td>\n",
       "      <td>1.089474</td>\n",
       "      <td>0.347368</td>\n",
       "      <td>2.526316</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.410526</td>\n",
       "      <td>1.081579</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>2.492105</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.415789</td>\n",
       "      <td>1.039474</td>\n",
       "      <td>0.376316</td>\n",
       "      <td>2.455263</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.468421</td>\n",
       "      <td>1.076316</td>\n",
       "      <td>0.392105</td>\n",
       "      <td>2.544737</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.473684</td>\n",
       "      <td>1.042105</td>\n",
       "      <td>0.431579</td>\n",
       "      <td>2.515789</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.342105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>2.342105</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.389474</td>\n",
       "      <td>1.021053</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>2.410526</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>France</td>\n",
       "      <td>France Ligue 1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.286842</td>\n",
       "      <td>0.971053</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>2.257895</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.565359</td>\n",
       "      <td>1.264706</td>\n",
       "      <td>0.300654</td>\n",
       "      <td>2.830065</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.588235</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>2.754902</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.748366</td>\n",
       "      <td>1.411765</td>\n",
       "      <td>0.336601</td>\n",
       "      <td>3.160131</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.591503</td>\n",
       "      <td>1.343137</td>\n",
       "      <td>0.248366</td>\n",
       "      <td>2.934641</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.660131</td>\n",
       "      <td>1.199346</td>\n",
       "      <td>0.460784</td>\n",
       "      <td>2.859477</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.647059</td>\n",
       "      <td>1.274510</td>\n",
       "      <td>0.372549</td>\n",
       "      <td>2.921569</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.513072</td>\n",
       "      <td>1.316993</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>2.830065</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.699346</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.477124</td>\n",
       "      <td>2.921569</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.471053</td>\n",
       "      <td>1.105263</td>\n",
       "      <td>0.365789</td>\n",
       "      <td>2.576316</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.498681</td>\n",
       "      <td>1.187335</td>\n",
       "      <td>0.311346</td>\n",
       "      <td>2.686016</td>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.536842</td>\n",
       "      <td>1.186842</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.723684</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.494737</td>\n",
       "      <td>1.144737</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.639474</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.511173</td>\n",
       "      <td>1.072626</td>\n",
       "      <td>0.438547</td>\n",
       "      <td>2.583799</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.431579</td>\n",
       "      <td>1.081579</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.513158</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.542105</td>\n",
       "      <td>1.068421</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>2.610526</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.521053</td>\n",
       "      <td>1.078947</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.618421</td>\n",
       "      <td>1.126316</td>\n",
       "      <td>0.492105</td>\n",
       "      <td>2.744737</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.536842</td>\n",
       "      <td>1.118421</td>\n",
       "      <td>0.418421</td>\n",
       "      <td>2.655263</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.631579</td>\n",
       "      <td>1.118421</td>\n",
       "      <td>0.513158</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.686842</td>\n",
       "      <td>1.184211</td>\n",
       "      <td>0.502632</td>\n",
       "      <td>2.871053</td>\n",
       "      <td>1091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.678947</td>\n",
       "      <td>1.084211</td>\n",
       "      <td>0.594737</td>\n",
       "      <td>2.763158</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.636842</td>\n",
       "      <td>1.105263</td>\n",
       "      <td>0.531579</td>\n",
       "      <td>2.742105</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.113158</td>\n",
       "      <td>0.486842</td>\n",
       "      <td>2.713158</td>\n",
       "      <td>1031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>1.660526</td>\n",
       "      <td>1.236842</td>\n",
       "      <td>0.423684</td>\n",
       "      <td>2.897368</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country_name             league_name     season  number_of_stages  \\\n",
       "0       England  England Premier League  2015/2016                38   \n",
       "1       England  England Premier League  2014/2015                38   \n",
       "2       England  England Premier League  2013/2014                38   \n",
       "3       England  England Premier League  2012/2013                38   \n",
       "4       England  England Premier League  2011/2012                38   \n",
       "5       England  England Premier League  2010/2011                38   \n",
       "6       England  England Premier League  2009/2010                38   \n",
       "7       England  England Premier League  2008/2009                38   \n",
       "8        France          France Ligue 1  2015/2016                38   \n",
       "9        France          France Ligue 1  2014/2015                38   \n",
       "10       France          France Ligue 1  2013/2014                38   \n",
       "11       France          France Ligue 1  2012/2013                38   \n",
       "12       France          France Ligue 1  2011/2012                38   \n",
       "13       France          France Ligue 1  2010/2011                38   \n",
       "14       France          France Ligue 1  2009/2010                38   \n",
       "15       France          France Ligue 1  2008/2009                38   \n",
       "16      Germany   Germany 1. Bundesliga  2015/2016                34   \n",
       "17      Germany   Germany 1. Bundesliga  2014/2015                34   \n",
       "18      Germany   Germany 1. Bundesliga  2013/2014                34   \n",
       "19      Germany   Germany 1. Bundesliga  2012/2013                34   \n",
       "20      Germany   Germany 1. Bundesliga  2011/2012                34   \n",
       "21      Germany   Germany 1. Bundesliga  2010/2011                34   \n",
       "22      Germany   Germany 1. Bundesliga  2009/2010                34   \n",
       "23      Germany   Germany 1. Bundesliga  2008/2009                34   \n",
       "24        Italy           Italy Serie A  2015/2016                38   \n",
       "25        Italy           Italy Serie A  2014/2015                38   \n",
       "26        Italy           Italy Serie A  2013/2014                38   \n",
       "27        Italy           Italy Serie A  2012/2013                38   \n",
       "28        Italy           Italy Serie A  2011/2012                38   \n",
       "29        Italy           Italy Serie A  2010/2011                38   \n",
       "30        Italy           Italy Serie A  2009/2010                38   \n",
       "31        Italy           Italy Serie A  2008/2009                38   \n",
       "32        Spain         Spain LIGA BBVA  2015/2016                38   \n",
       "33        Spain         Spain LIGA BBVA  2014/2015                38   \n",
       "34        Spain         Spain LIGA BBVA  2013/2014                38   \n",
       "35        Spain         Spain LIGA BBVA  2012/2013                38   \n",
       "36        Spain         Spain LIGA BBVA  2011/2012                38   \n",
       "37        Spain         Spain LIGA BBVA  2010/2011                38   \n",
       "38        Spain         Spain LIGA BBVA  2009/2010                38   \n",
       "39        Spain         Spain LIGA BBVA  2008/2009                38   \n",
       "\n",
       "    number_of_teams  avg_home_team_scors  avg_away_team_goals  avg_goal_dif  \\\n",
       "0                20             1.492105             1.207895      0.284211   \n",
       "1                20             1.473684             1.092105      0.381579   \n",
       "2                20             1.573684             1.194737      0.378947   \n",
       "3                20             1.557895             1.239474      0.318421   \n",
       "4                20             1.589474             1.215789      0.373684   \n",
       "5                20             1.623684             1.173684      0.450000   \n",
       "6                20             1.697368             1.073684      0.623684   \n",
       "7                20             1.400000             1.078947      0.321053   \n",
       "8                20             1.436842             1.089474      0.347368   \n",
       "9                20             1.410526             1.081579      0.328947   \n",
       "10               20             1.415789             1.039474      0.376316   \n",
       "11               20             1.468421             1.076316      0.392105   \n",
       "12               20             1.473684             1.042105      0.431579   \n",
       "13               20             1.342105             1.000000      0.342105   \n",
       "14               20             1.389474             1.021053      0.368421   \n",
       "15               20             1.286842             0.971053      0.315789   \n",
       "16               18             1.565359             1.264706      0.300654   \n",
       "17               18             1.588235             1.166667      0.421569   \n",
       "18               18             1.748366             1.411765      0.336601   \n",
       "19               18             1.591503             1.343137      0.248366   \n",
       "20               18             1.660131             1.199346      0.460784   \n",
       "21               18             1.647059             1.274510      0.372549   \n",
       "22               18             1.513072             1.316993      0.196078   \n",
       "23               18             1.699346             1.222222      0.477124   \n",
       "24               20             1.471053             1.105263      0.365789   \n",
       "25               20             1.498681             1.187335      0.311346   \n",
       "26               20             1.536842             1.186842      0.350000   \n",
       "27               20             1.494737             1.144737      0.350000   \n",
       "28               20             1.511173             1.072626      0.438547   \n",
       "29               20             1.431579             1.081579      0.350000   \n",
       "30               20             1.542105             1.068421      0.473684   \n",
       "31               20             1.521053             1.078947      0.442105   \n",
       "32               20             1.618421             1.126316      0.492105   \n",
       "33               20             1.536842             1.118421      0.418421   \n",
       "34               20             1.631579             1.118421      0.513158   \n",
       "35               20             1.686842             1.184211      0.502632   \n",
       "36               20             1.678947             1.084211      0.594737   \n",
       "37               20             1.636842             1.105263      0.531579   \n",
       "38               20             1.600000             1.113158      0.486842   \n",
       "39               20             1.660526             1.236842      0.423684   \n",
       "\n",
       "    avg_goals  total_goals  \n",
       "0    2.700000         1026  \n",
       "1    2.565789          975  \n",
       "2    2.768421         1052  \n",
       "3    2.797368         1063  \n",
       "4    2.805263         1066  \n",
       "5    2.797368         1063  \n",
       "6    2.771053         1053  \n",
       "7    2.478947          942  \n",
       "8    2.526316          960  \n",
       "9    2.492105          947  \n",
       "10   2.455263          933  \n",
       "11   2.544737          967  \n",
       "12   2.515789          956  \n",
       "13   2.342105          890  \n",
       "14   2.410526          916  \n",
       "15   2.257895          858  \n",
       "16   2.830065          866  \n",
       "17   2.754902          843  \n",
       "18   3.160131          967  \n",
       "19   2.934641          898  \n",
       "20   2.859477          875  \n",
       "21   2.921569          894  \n",
       "22   2.830065          866  \n",
       "23   2.921569          894  \n",
       "24   2.576316          979  \n",
       "25   2.686016         1018  \n",
       "26   2.723684         1035  \n",
       "27   2.639474         1003  \n",
       "28   2.583799          925  \n",
       "29   2.513158          955  \n",
       "30   2.610526          992  \n",
       "31   2.600000          988  \n",
       "32   2.744737         1043  \n",
       "33   2.655263         1009  \n",
       "34   2.750000         1045  \n",
       "35   2.871053         1091  \n",
       "36   2.763158         1050  \n",
       "37   2.742105         1042  \n",
       "38   2.713158         1031  \n",
       "39   2.897368         1101  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leages_by_season = pd.read_sql(\"\"\"SELECT Country.name AS country_name, \n",
    "                                        League.name AS league_name, \n",
    "                                        season,\n",
    "                                        count(distinct stage) AS number_of_stages,\n",
    "                                        count(distinct HT.team_long_name) AS number_of_teams,\n",
    "                                        avg(home_team_goal) AS avg_home_team_scors, \n",
    "                                        avg(away_team_goal) AS avg_away_team_goals, \n",
    "                                        avg(home_team_goal-away_team_goal) AS avg_goal_dif, \n",
    "                                        avg(home_team_goal+away_team_goal) AS avg_goals, \n",
    "                                        sum(home_team_goal+away_team_goal) AS total_goals                                       \n",
    "                                FROM Match\n",
    "                                JOIN Country on Country.id = Match.country_id\n",
    "                                JOIN League on League.id = Match.league_id\n",
    "                                LEFT JOIN Team AS HT on HT.team_api_id = Match.home_team_api_id\n",
    "                                LEFT JOIN Team AS AT on AT.team_api_id = Match.away_team_api_id\n",
    "                                WHERE country_name in ('Spain', 'Germany', 'France', 'Italy', 'England')\n",
    "                                GROUP BY Country.name, League.name, season\n",
    "                                HAVING count(distinct stage) > 10\n",
    "                                ORDER BY Country.name, League.name, season DESC\n",
    "                                ;\"\"\", conn)\n",
    "leages_by_season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "6f8b19b2-8559-4656-b182-9b11f59989e8",
    "_kg_hide-input": true,
    "_uuid": "5bbd68e2a116450427b48743f0da824df9cb8b79",
    "execution": {
     "iopub.execute_input": "2022-03-21T19:35:12.831713Z",
     "iopub.status.busy": "2022-03-21T19:35:12.831484Z",
     "iopub.status.idle": "2022-03-21T19:35:13.124595Z",
     "shell.execute_reply": "2022-03-21T19:35:13.123756Z",
     "shell.execute_reply.started": "2022-03-21T19:35:12.831674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32ab696b70>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WCEfYkreFF9Je4NIhl3LTqJscXU6PMshnEO9c9g6/G/871h5Yy7X/vpYdhTsc\nXZYQQohe6nRajOuBi7TW44Fo4DKl1ORW6/waKNNaDweeA5a0b5lCOEZeVR73f3s/4T7hPJbwGEop\nR5fU4zibnPl99O9557J3ALj5y5v5545/0mg9+6EQhRBCiLNxymCsDU3nN11st9ZjvP0CeMd2/2Ng\nuuqmCcLLywuAnJwcPvjgg1Oun5OTw5gxYzq6LOEADZYG7t1wLw3WBp678Dn6uPRxdEk9WnRQNB9f\n8TEzh87klZ2vcMuXt3Ck8oijyxJCCNGLnFYfY6WUk1JqB1AIfKW13tpqlVDgCIDWuhGoAPzbs9DO\ndrrBWPRcT257kh9KfmDR1EWE9w13dDm9gperF4umLeKp857iQPkBrl1zLWv2r8FR460LIYToXU4r\nGGutLVrraGAgMFEp1bqJtK3W4Z/8JVNK/UYplaKUSikqKjrzajvR/PnzSUpKIjo6mueee46cnBwS\nExOJjY0lNjaWTZs2/eQ1iYmJ7NjxY//IqVOnsmvXrs4sW7ST1Vmr+Xjfx/x6zK+ZHiZjRne2y8Iv\nY9WsVUT5R/FQ8kP8eeOfqaivcHRZQgghejjnM1lZa12ulNoAXAb80OypXGAQkKuUcgb6AqVtvP41\n4DUwZr472b7yn3iC+ozMMynvlNyiIun/4IOnte7ixYt5+umnWbt2LQA1NTV89dVXuLu7k5WVxdy5\nc2k9c99tt93G22+/zfPPP8++ffuor69n3Lhx7foeRMfbXbKbx7c8zuSQydwVc5ejy+m1QrxCeGPG\nG7y1+y1eTn+ZHYU7eDLxSRkVRAghRIc5nVEpApVSvrb7HsDFQOvEuga42Xb/WuAb3cPOfZrNZm6/\n/XbGjh3L7Nmz2bNnz0/WmT17NmvXrsVsNvPmm29yyy23dH6h4pyU15Vz7/p78fPwY8l5S3AyOTm6\npF7NyeTEbWNv4/2fvY+7szu//u+veT71ecwWs6NLE0II0QOdTotxCPCOUsoJI0iv1FqvVUo9BqRo\nrdcAbwDvKaWyMVqKrz/Xwk63ZbezPPfccwQHB7Nz506sVivu7u4/WadPnz5ccsklfPbZZ6xcufIn\nLcqia7NYLTyQ9ABFtUW8e/m7+Ln7ObokYTM6YDQrZ67k79v/zhs/vMGmY5tYct4S6fsthBCiXZ0y\nGGutdwExbSx/pNn9OmB2+5bmWN7e3lRWVtofV1RUMHDgQEwmE++88w4Wi6XN1912221cccUVJCYm\n4ucnwao7eXnHy2w6tomFUxYyJkBGGulq+rj0YWHCQhIHJrJw00LmrJ3Dnyf8mWsjrpVh9IQQQrQL\nmfnuBMaNG4ezszPjx4/nuee6IMTgAAAgAElEQVSe4/e//z3vvPMOkydPZt++fXh6erb5uri4OHx8\nfLj11ls7uWJxLtYfXs/S75dydcTVXDPiGkeXI05i+uDprJq1iujAaB7b/Bj/t/7/KKsrc3RZQggh\negDlqK7A8fHxunVXg4yMDKKiohxST3s5duwYF1xwAZmZmZhMjvne0ROOY2c6dPwQ16+9nsE+g3n3\n8ndxc3JzdEniNFi1lff3vM/zac/T160vi6YuIiE0wdFlCSGE6IKUUqla6/hTrSctxu3o3XffZdKk\nSSxatMhhoVicmRpzDXevvxtnkzPPXfCchOJuxKRM3DT6Jpb/fDm+br7c8fUdLNm2hHpLvaNLE0II\n0U1JemtHN910E0eOHGH27B7V3brH0lqzcPNCDlQcYMl5SxjgNcDRJYmzMNJvJMt/vpwbIm/g/Yz3\nmfv5XLLKshxdlhBCiG5IgrHotZZlLOOLg19wV8xdJAyQU/DdmbuzOwsmLeDl6S9TUlvC9WuvZ1nG\nMpkxTwghxBmRYCx6pdSCVJ5JeYYLB13Ir8b8ytHliHZy3sDz+GTWJ0wZMIXF2xbzu//9juLaYkeX\nJYQQopuQYCx6naKaIv707Z8I9Q5l0bRFmJT8N+hJ/D38efGiF3l40sOk5Kdw9WdXs+HIBkeXJYQQ\nohuQRCB6FbPFzH3f3ke1uZrnLngOb1dvR5ckOoBSijmRc1g5cyXBnsHc9c1dPL7lcWobax1dmhBC\niC5MgnEbFi1axOjRoxk3bhzR0dFs3br1jLexZs0aFi9e3AHViXPxTOozpBem81jCY0T0i3B0OaKD\nDfUdyrKfLeOW0bewYu8K5qydQ0ZJhqPLEkII0UWdzpTQvcrmzZtZu3YtaWlpuLm5UVxcTENDwxlv\nZ9asWcyaNasDKhRn6/MDn7MsYxm/HPVLLgu/zNHliE7i6uTKffH3MTV0Kg8lPcQN/7mBP8b8kZtH\n3yzdaIQQQrQgfxVaycvLIyAgADc3YzzbgIAABgwYwJAhQ3jggQeYOHEiEydOJDs7G4B///vfTJo0\niZiYGC6++GIKCgoAePvtt/nDH/4AwC233MIf//hHEhISGDp0KB9//LFj3lwvtrd0Lws3LSQuOI57\n4u5xdDnCASaHTGbVrFVcOOhCnk19lt+s+w351fmOLksIIUQX0mVbjJNW7qP4SFW7bjNgkBeJ1404\n6TozZszgscceY8SIEVx88cXMmTOH888/HwAfHx+2bdvGu+++y913383atWuZNm0aW7ZsQSnF66+/\nzt///neeeeaZn2w3Ly+P5ORkMjMzmTVrFtdee227vjdxYscbjnPPhnvwdvXm6fOfxsXk4uiShIP4\nuvvyzPnP8Gn2pzy57UmuWXMNj0x5hEvCLpHWYyGEEF03GDuKl5cXqampJCUlsX79eubMmWPvKzx3\n7lz7v/fcY7Q65ubmMmfOHPLy8mhoaCA8PLzN7V555ZWYTCZGjRplb1UWHc+qrTyY9CB5VXm8ddlb\nBHgEOLok4WBKKa6KuIrY4FgWJC3gT9/+ib5ufYkJjCE2OJaYoBhG+4/GxUm+QAkhRG/TZYPxqVp2\nO5KTkxMXXHABF1xwAWPHjuWdd94BjD+oTZru33XXXdx7773MmjWLDRs2sHDhwja32dQ1A5BJBzrR\n0l1L+Tb3WxZMXEB0ULSjyxFdSJhPGO9c/g5fHvyS7fnbSS9MZ0PuBgDcndwZGziWmKAY4oLiGB80\nHk8XT8cWLIQQosN12WDsKHv37sVkMhERYYxYsGPHDsLCwvj+++9ZsWIF8+fPZ8WKFUyZMgWAiooK\nQkNDAewBWnQNyUeTeXnHy8wcOpO5kXMdXY7oglxMLlwx7AquGHYFAMW1xewo3EFqQSpphWm8/v3r\nvKZfw6RMjOw3ktjgWGKDYokNjpWzD0II0QNJMG6lqqqKu+66i/LycpydnRk+fDivvfYaa9eupb6+\nnkmTJmG1Wlm+fDkACxcuZPbs2YSGhjJ58mQOHjzo4HcgAHIrc3lg4wNE9IvgkSmPtGjtF+JEAjwC\nuDjsYi4OuxiAanM1O4t2kl6YTlpBGqv2rWJZxjIABnsPbhGUB3sPls+Z6FANlgayyrPIKMngWNUx\nooOimdB/Ah7OHo4uTYiTqqivYNOxTWzN28rDkx/G2dR146dy1Gn9+Ph4nZKS0mJZRkYGUVFRDqnn\nVIYMGUJKSgoBAV2/lagrH8fOUNdYx01f3ERuZS4rZq5gkM8gR5ckegizxUxGaQZpBWmkFaaRXphO\neX05AP7u/vagHBMcw8h+I7v0L3/RtdU21rKvbB8ZJRlklGaQUZJBVnkWjdbGFuu5Obkxof8EEkMT\nSQxNlN93okvQWpNZmknS0SSScpPYVbwLq7bi6+bLe5e/x5C+Qzq9JqVUqtY6/lTryW9t0aNorXl8\ny+NklGbw0kUvyR8J0a5cnFwYFziOcYHjuIVbsGorBysOklaYRlqBEZS/OvQVAH2c+xAdFG30Uw6O\nY0zAGGnZE22qaqgiszTTHoAzSjM4UHEAq7YC4Ovmyyj/Udw86mai/KMY5TeKwD6BpBWkGcHjaBJP\nbnuSJ3mSIT5DmBY6jcSBicQHx+Pq5Orgdyd6i8qGSjYf20zS0SSSjyZTXFsMwGj/0dw+9nYSByYy\nxn8MTiYnB1d6ctJi3AP15uO4cu9K/rblb/x2/G+5M/pOR5cjeqH86nx7i3JaYRrZZdloNM4mZ0b5\njyIuKI6YoBhigmLwdfd1dLmik5XXlRsBuFkIPnT8kP35II8govyjjJtfFKP8RxHcJ/iU3XQOHz9s\nD8nb87bTYG3Aw9mDSf0nkTgwkWmh0xjgNaCj357oRbTWZJVnkZRrBOEdhTto1I14u3qTMCCBxNBE\npoZO7TLXY5xui7EE4x6otx7HXUW7uPnLm5kUMomXL3q5y38rFb1DRX0FO4t2klqQSnphOj8U/4DZ\nagZgWN9hRvcLWxcMCS49S3FtMRklGewp2WMPwseqj9mfH+A5oEUAjvKPapcQUdtYy/b87WzM3Ujy\n0WSOVh0FYLjvcKM1OTSRmKAYGZJQnLFqczVb8raQfDSZpNwkCmqM4WdH9htJ4kCjO8+4wHFdshtZ\ntw3GkZGRcgHLOdBak5mZ2euCcUltCXPWzsHZ5MyKmSvo69bX0SUJ0aZ6Sz0/FP9gb1XeUbiDKrMx\nmVF/z/72IeJigmMY7jtcJh7pBrTWFNQUtAjAGSUZFNYW2tcJ8wkjyu/HluAov6hOOWOgtebg8YMk\n5RqtyakFqTRaG/F08WRKyBQSByYydcBUgj2DO7wW0f30pM9PtwzGBw8exNvbG39/fwnHZ0FrTUlJ\nCZWVlSecaKQnarQ2csdXd7CzaCfvXf4eUf6960uB6N4sVgtZ5Vk/dr8oSKOotggAH1cfe7eLuOA4\nRvmPkj6jDqa1Jrcylz2le1pcGFdWXwaASZkY2ndoixAc6ReJl6uXgys3VJur2Zq31X5RVOsWv2mh\n0xgfOL5LtviJznGyMw6JoYkkDkwkOjD6rM44NNQ24urhmM9WtwzGZrOZ3Nxc6urqHFJTT+Du7s7A\ngQNxcek9p8ieTX2Wt354i8enPs4vhv/C0eUIcU601uRW5dov5kstSCXneA5gjEAwJmCMfYi46MDo\nLhO4eiKL1cKh44dahODMkkwqzZUAOJucifCN+LEV2D+KEf1GdJuLLJv6iDadFk8vTMeiLV22j6jo\nOPY+6rlJbM9v1kc9ZBKJoefeR72+xsz2z3PI3JzH3Ecm4enrduoXtbNuGYyFOFNfHfqKezfcy5yR\nc3h48sOOLkeIDlFaV0p6QTqphamkF6STUZqBRVswKRMj+o2wB+XYoFgC+wQ6utxuyWw1c6D8QIuL\n4jJLM6ltrAXA1eTKSL+RP7YE+0cR4RvRo1rwKxsq2ZK3xX7avGlUgVH+o+wthd1hVAFxavWWelLy\nU4wvRUeT7BeAtveoJlarJuO7Y2xdc4DaKjOjEkKYfNUwPLw6//+NBGPR4x0oP8Dcz+cy3Hc4b132\nVo/6AyXEydSYa9hVvMvoflGQxq7iXfYAN8h7kL3rRWxQLGE+YdI1rZV6Sz3ZZdk/tgSXZLCvbB8N\n1gYAPJw9WvYH9o8ivG84LqbecybOqq3sLd1rH3prZ9FO+zi0U0OnMi10GlMHTKWfez9HlypO09Gq\noyTnGkF4W/42ahtrO3Qc7GNZ5SSt3EfxkSpChvUlcc4IAgd7t9v2z5QEY9GjVZurmfv5XCrqK1gx\ncwX9Pfs7uiQhHMZsNZNZktliPOWmPq9+7n7GpCO2sDzSr3dNPFJjrmFf2b4WF8btL99PozYmyvB2\n9WaU36gWIXiw92BpFW2laeaypNwkvjv2HaV1pSgUYwPH2kNVlH+UXCzahZgtZtIK0+xnAA5UHABg\noNdA+wgSE/pPwN3ZvV33W1lax+ZPsslKKcSrnxsJVw9neHyQw7+gSzAWPZbWmvu+vY9vDn/D0hlL\nmdB/gqNLEqJLabqSvHk/5aYLaDycPRgfON7e9WJc4Lhu0yf2VCobKskszWwRgnOO59gnyvBz92sx\nNFqUXxShXqEO/4Pd3Vi1lT0le+yB64fiH9Bo/Nz97Kfhp4RMkdGBHCC/Ot/eZ3xL3hZqGmtwMbkQ\nHxxvv7hyiM+QDvnMmxsspK87TPp/D6GBmBmDiZ0Rhotb1/iSKcFY9Fhv//A2z6Q+w31x93HLmFsc\nXY4Q3UJBdYE9JKcXprOvbJ8x8YgyJh6JCYohNthoWe4Op8fL6spa9AfOKMngcOVh+/NBfYJ+0hJ8\nOhNliDNXWlfKd0e/I+loEt8d/Y7jDcdxUk6MDxxvb5kc0W+EHPsOYLaa2Vm4097lZV/ZPgBCPEPs\n/cIn9p9IH5c+HVaD1prs1EI2fZJNVWk9w2KDSLh6GD4BXesLtwRj0SNty9vG7V/dzvTB03nm/Gfk\nF60QZ+l4w3F2Fu60d7/4vvh7+8QjQ/sO/bGfcnAsAzwHOPT/WlFNERmltokybEE4rzrP/nyoV6jR\nCmwLwJF+kTKSgoNYrBa+L/7ePsJBRmkGYMzoN22gMbnI5JDJMprKOSiqKSL5aDLJR5PZfGwzleZK\nnJUzscGx9glchvkO65T/s8W5lSStyOJYVjn+oV4kXhdB6Miu+cVagrHocfKr85mzdg6+br588PMP\n8HTxdHRJohtrqG0kK6WAIxll9B/qw/C4YLz6df4QQl1FvaWe3cW77UF5R+EO+7BkQX2C7JOOxAbF\nEtEvokP6kmqtyavOM2aLazZEWtPoCAplTJThH2VvDY70i5RT9l1YcW2x/dR+6xDXNAxYZ4W47qor\nftmorWxg65oD7Ek+hlsfFyb9Yiijpg3AZOq6P0cJxqJHabA0cOuXt7K/Yj8f/PwDhvYd6uiSRDek\nrZpjWeVkbMpjf1ohjWYrHt4u1FaaQUHIsL4MjwtmWGwgnn17b0gGox9pVlkWaYVp9qHiCmuMmdy8\nXbyJDoq291MeEzDmjEeFsWpri4kymvoFV9RXAD9OlNG6JVi+EHdfpzrtPy10GpNCJnXoaf/uonn3\nlE3HNlFRX4FJmYgOjHZo9xSLxcoPG46y/fODNNRZGHt+KBNmhuPu2fVHbJFgLHqUv23+Gyv3reS5\nC57j4rCLHV2O6GYqS+vI3JxH5uY8jhfX4eruRMSEYCITQgge4kNFYS1ZKQVkpxZSeqwapWDACF97\nSHbEmJtdjdaaY9XHSCtIs/dTbrrK3dXkakw8YgvK0UHReLv+OCyTxWoh53hOi4viMksz7VNhN02U\n0TwER/SL6DEXBYq2nepCscTQxF4z3GB3uKDx8J4SkldmUZZfw6CofkybPQK/Ad3ni6oEY9FjfJr9\nKX/57i/cOuZW7o2719HliG6i0Wzh4I5iMjYd40hmGWgIHdmPqIQQhsYE4uLa9pXSJceqyE4pJDu1\nkPKCGpRJMTCyH8PjghgaHdgtWkY6S1ldGemF6fbprDNKMmjUjSgUI/qNYES/ERyuPMy+sn32cZbd\nnNzsE2U0BeHhvsPPanpZ0XM4amgxRzrlEHgDE4nyc/wQeOWFNXz3cTY5u4rxCfRg2rXDGTIuoNt9\nYZFgLHqEjJIMfvnFL4kOjOZfl/yrV42/Ks6c1pqiw5VkbMoja3sB9TWNePm5ETUlhMgpIWd0lbTW\nmuLcppBcwPHiOkxOikGj/IiICyJ8fCCuHvJ5bK7GXMP3xd/b+ylnl2cz2Htwi+HRwvuGy/9jcUrN\nJ6PYmreVOktdy8koBiYyyLv9JqPoDFprMkszu82kKQ11jaR+kcOO/x3ByclE/M+GMP6iQTi5dM+x\nqiUYi26vvK6c6z+/nkZrIytmrsDfw9/RJYkuqraygX3bCsjYdIySo9U4uZgYGh1I1NQQBo7ohzrH\nC0K01hQeqiTb1t2iqqweJ2cTg0f7EREfzJBxAV1mrE4hepqTTV/cNDZve0xf3BGaT7OdfDSZotoi\nAEb7j7Z3kehq02xrq2bv1nw2r95PzfEGIif3Z/KVw/D07d7XXUgwFt2axWrhzv/dybb8bbxz2TuM\nDRzr6JJEF2O1WDm8u5SMzXnk7CrGatEEhXkTNXUAEfFBuPXpmFPz2qrJP3jcCMlphdRUNODsYiJs\nbAAR8UGEjfHH+QTdNIQQ5+7Q8UP2kLw9bzsN1gY8nD2YFDLJPgtfiFeIQ2rTWpNdnm0fQWJH4Q4a\ndSPert4kDEggMTSRqaFTu+xwgvkHK0hakUVhznGChviQOCeC/uE9Y9QXCcaiW3sp/SVe3fUqj0x5\nhNkjZju6HNGFlOVX2y6ky6fmeAMe3i6MmNSfqCkh+Id27tioVqsmL7uc7JRC9qcXUltpxsXNiSHj\njJA8eJR/tz3tKER3UNtYy/b87WzM3Ujy0WT7DI/DfYfbu1xEB0XjYuq4Puw15hqjVdgWhgtqCgAY\n2W+kvX/0uMBxXboLUXV5PZtX72fv1nz6+Lgy5ephjJzY/5zPtnUlEoxFt/XtkW/5wzd/4MrhV/JY\nwmPdroO/aH8NtY1kpxaSsSmP/AMVKJMibIw/UQkhhI3xx8nZ8eHTarFydF852SkF7N9RRH11I64e\nzgwdH8Dw+GAGRvXDycnxdQrRUzVNhd50AV9qQSqN1kY8XTyZEjLF3u0iqE9Qu+0n+WgyqQWpmK3m\nFvuZOmAqwZ7B7fTOOk6j2cLO/x0h5YtDWC1WoqcPJu7yMFzdu26IP1sSjEW3dPj4Ya5fez0DvQfy\n7uXv9qgrkMWZ0brVmMMNVvr170NkQggjJ/Xv0uMMWyxWcjPKyE4p4MDOYhpqG3HzdGZYdCDD44MJ\nHeGLSUKyEB2q2lzN1rytP2nJjfSLtM8Qd7otuU0t002hu82W6cDobjO6itaagzuL+e7jLI4X1xE+\nPoCp1w6nb2DPHUNagrHodmrMNdz4xY0U1hSyYuYKQr1CHV2ScIDK0jr2bskjY3M+x4tqcXF3IiI+\nmKiEEILDfbrdGQSL2crhPSVkpRSSs6sYc70FD28XhsUEMTw+iJDhvl16tighegKtNVnlWfZxk9ML\n07FoC96u3kwdYBsRolXf38PHDxuh+gR9maeFTmOA1wAHvquzU3KsiuSVWeRmltEvxJPE2REMGuXn\n6LI6nATjNjRaG3k+9XnGB40nJiimy3Z+74201sxPms8XB7/gXxf/i4TQBEeXJDpRo9nCwZ3FZGzK\n40hGqW3MYV+ipoQwNDbohGMOdzeNDRYO/WCE5EPfF9NottKnryvDY4MYHh9M/3CfHtWnT4iu6njD\ncbYc22K/iK9p2vHR/qMZ6TeS1ILUFqNfNI0gERcch5tT1z1bdTJ11Wa2rT3ID98exdXdiQkzwxlz\nfmiv6eIlwbgNORU5XPvva6m31APGhz0mKMY+W9Mg70HdrjWqp1iWsYzF2xZzV8xd/GbcbxxdjugE\nWmuKj1SR8d0x9jWNOdzPjUjbmMN9A3v2rGcNdY0c+r6ErJQCDu8uxdJoxaufG8PjjJAcFOYtv4+E\n6ARWbWVv6V77+MJZZVlEB0XbR7gY5NO9xktuzWqxsif5GFvXHKS+xszoxFAmzgrvdTN6SjA+AbPF\nzO6S3aQVppFekE5aYRrHG44DEOARQGxQrD0oj+g3okuNLdhTpRem86svf8W00Gm8cNELDp/lR3Ss\n2qoG9m0tIGNTHiVHq3ByNjE0JpCoKSGERvbrld0KGmobObiziKzUQo7sKcVq0fgEuDM8Lpjh8UEE\nDPSSkCyEOGNH95aRtDKLkqNVDIjwJXFOBAEDvU/9wh5IgvFpsmorB8oPGDM12WZryqvOA8DTxZPo\nwGhig2OJCYphbMBYuRisnRXXFnPdv6/Dw9mD5TOX4+Pq4+iSRAewWqwc3lNK5qY8DjYbczhySggR\nE4JlmuVm6qrNHNhRRHZqIbmZZWirxje4j60lOQj/AZ07JJ0Qovs5XlzLpk+y2Z9WhLefOwnXDGdY\nbGCv/oItwfgc5FXl2UNyWqExrSmAs8mZ0f6jiQ2OJS4ojuigaPq69YyBrx3BbDVz239vI6M0g/d/\n9j4j+o1wdEminZUX1JCxKY/MLXnUVDTg7uXCyEn9iUro/DGHu6PaygYO7CgiK6WQY/vK0Br8Bngy\nPC6IiPhgfIN77hXkQogzZ663kPbfQ6R/dRgFxF4WRswlg2XSISQYt6uK+grSC9PtYXl3yW4arY2A\nMVRLXHAcMUExxAXH0d+zv4Or7T6WbFvC+xnvsyRxCT8b+jNHlyPaSUOdMeZw5qY88vZXoBSEjfEn\nMiGEIWMDusSYw91RdUU9B9KLyEopIC+7AoCAQV5GS3JccI/vky2EODGtNVnbC9j0yX6qy+uJmBDM\nlKuG4e0nZ7mbtFswVkoNAt4F+gNW4DWt9Qut1ukLvA8MBpyBp7XWb51su90pGLdW11jH98Xfk1aQ\nRnphOjuKdlBtrgYgxDPE3kc5NiiWob5Dpc9sG/5z4D88kPQA86LmMX/ifEeXI86R1pq87AoyNh0j\nO62IxnoLvsF9iEoIYeTkrj3mcHdUVVZHdmoh2amFFBw0rpEICvNmeHwww+OC5I+hEL1I4aHjJK/M\nIm9/BYGDvZl2XQQDhvs6uqwupz2DcQgQorVOU0p5A6nAlVrrPc3WeRDoq7V+QCkVCOwF+mutG060\n3e4cjFtrtDaSVZZFWmEaqQWppBWkUVJXAkBft77EBNpGvgiOZZTfqG4zAHhHySrLYt5/5hHlF8Xr\nl77eoVN1io5VVVZH5pZ8MjflUVFUi4ubExHxQUQmDKD/0O435nB3dLy4luy0QrJTCik6XAlA/6F9\nGR4fxPDYIDx9jS8lWmsarRqzxYq5UVNvsWC2aMyNVhosVhoarZjt/2oaLBYaGnWzZcatvun5nyxr\n/XorjRYrzk4mXJ1MuDqbcHFSuNjuuzqZ7PddnIzn3JxbLzPZl7k4Kfty+zLbdlydTLg4K1ydTDiZ\nlHzuRK9Qc7yBLZ/tJ2NTHh5eLkz+xTAiE0J65QXMp6PDulIopT4DXtJaf9Vs2QJgEHAnMAT4Chih\ntbaeaDs9KRi3prXmSOWRFv2Um8ZDdHdyZ2zgWKPrRVAc44PG4+ni6eCKO09lQyVzP59LtbmalTNX\nEtgn0NEliTNkMVs5uKuYjE3HOLKnFK1hQIQvUVNDGBYThItb7+zLprU2gmbzkNksKDYPkG0FzbZf\n1zJoNrQKoA3Ngq3ZYsW5xkJwhZXQSk2/BtBo8lw0e10tZDg3Ut3Ofy9Nip+G1WbB1tmkaLRqGhot\nLd5jQ7P3YG3n3nxKYdTTLDi7ONsCebPaXJsH62bP/7jsp0HetdV7dG32fNuBX/3kdS5OEtzFubE0\nWtm1PpeUzw/S2GBl3EUDif95OG4ePW8a5/bUIcFYKTUE2AiM0Vofb7bcG1gDRALewByt9edtvP43\nwG8ABg8eHHfo0KHT3nd3V1xbbPRTtgXlzNJMrNqKSZkY2W+kvZ9ybHBsj514xKqt/N/6/yM5N5k3\nLn2D2OBYR5ckzkDR4UoyNuexb1s+9dXNxxzu3y2mEW20WCmtbqCoqp7iqgaKKusprqqntLqBerPF\nFjxPElBtLa0nayFtb04mZQtaCldnJyOINQtfLs5NAVD9ZJlHnRXvogY88utxrrKgAYLcMIV54jbE\nC1dPF1ybtcL+NBSeuhXXqR1apixW/ZOwfKLW6RN9ObAva2r9tv2sGiwW279Ny37cj9Fq3npZyy8z\nDRYrlvZO7nAaLegtfy5tBXI3Fyf69XHF39MVP09X/LxcCfB0w8/LFU9XJwnfPVTO98V893E25QU1\nDB7tz7TZw+nXv/c0rp2Ldg/GSikv4Ftgkdb6k1bPXQtMBe4FhmG0GI9vHp5b68ktxqej2lzNzqKd\n9n7Ku4p2UWepAyDMJ8wIybYxlQd7D+4Rv+SW7lrKP9L/wfyJ85kXNc/R5YjTUFdlZt/2fDI25VF8\npAqTs2JodCBRCSEMjPRz+Ck7i1VTUl1PcaUt8NrCblPobRGAaxpo69edm7MJdxenZuFEtWgBbBlO\n2rcFsc3tN3tNewRPrTWlx6rJSikgO6WQiqJaTCbFwKh+DI8LZmh0AG59pDvTiVhs3U9aBOvGE7Xg\ntzxD0Fbgt7f0n8UZArNF27+U1ZotNDS2/WXM1dlkD8z+Xm4/hmdPVwK8XPHzdDOeswVqbzfnHvE3\npicry6/mu4+zOfRDCb7BfZh67XCGjO2ZjWgdpV2DsVLKBVgL/Fdr/Wwbz38OLNZaJ9kefwPM11pv\nO9E2e3swbs1sMbOndA/pBemkFqaSXphORb1x5bm/u/+PF/QFGxOPOJu61ymTTUc38duvf8vl4Zez\nOHGx/BLuwqxWzZE9pWRsyuPgriKsjZrAwd5EJXTOmMMWqzZadu3htu2gW1xVT0l122HX3cVEgJcb\ngd5uBHi52e8Herm2XO7t1qta15pmG2wKyZWldZicFYNH+TM8Lojw8QG4unev3y29WU1DIyVVDZRU\nN1BaXU9JVQOl1cZj47J1nRkAACAASURBVH59i8e1Zkub23F1MtmDs7+X7V9PN/v9phDt72UEah93\nCdKdpb62kZTPD7Lrm1ycXU3E/zyccRcOlNF9zkJ7XnyngHeAUq313SdY5xWgQGu9UCkVDKRhtBgX\nn2i7EoxPzqqtHKw4SGpBqr0LxrHqY4Ax8cj4wPH2oNzVJx45WnWUOWvnENQniPcvf58+Ll3/tHtv\nVF5QQ+bmPDK35FNdXo+7pwsjJgUTlRByzjMlNYXdkwXd5l0b2jp77eZsajvoersRaAu5Tct7U9g9\nW1prCnKOk51ijG5RXV6Pk4uJsDFGSB4yLgAXGfu0R6ltsFDSKiyXVhtfMEttAbspZJdWNVDd0HaQ\ndnFSRjeOVq3RzcPzjwHblb4eLvL/8QxZrZrMzXls+XQ/tVVmohJCmPyLYfTx6V3TOLen9gzG04Ak\n4HuM4doAHsQYmg2t9b+UUgOAt4EQQGG0Hr9/su1KMD5z+dX59j7KaYVpZJdlo9E4m5wZ5T+KuKA4\n+yx9XWXikXpLPTd9cRNHjh/hw5kfMthnsKNLEs001DWyP62IjE3HyMs2xhwePNqfqKYxh11O3Cph\nsf4/e/cdFud55nv8Ox2GoRdRRJEE6kIdFVu2ih3bUooTO+s023Gc+GSz2WzO2ewmm+YkTrK72RTn\npOwmZ+UiJ94UlzQpbpJc1HtBDZDoIDoM08v7nD/eoQrJsgSaAe7PdXFpNDMMD0jAb+65n/tRdHkC\nQ4PuoJaGtv7r9V+0lwu7fZXbTIeNzETriJXeDIcVh7zcO2aUpmi+0KOH5COteJ0BzFYjRaUZlCyd\nQsH8NMwWCcmTjS8YHhSa9UCtP8G9tBrd6Q7g8odGfByz0UBqgnVYS4dt5EAdCdLRbtOKpuaqbt76\nXSVtdb1kT09mzX0lZBXKqbDXSw74mAR6/D0cbzvePyKuvKN8yMEjfZv5lmYtJceRc8PXp5Ti0T2P\n8mLVi/xk/U9Ym7/2hq9BXEopRfP5Hs7uaabycCshf5jkrHjmrM5hZlk2AathxEru4KDb7vLT4Ro5\n7FrNxv4KbqbDOkLI1YNuRqJNehtjkKYpmiq7qTrUwvmjbfhcQSxxJqYt1ENy/tw0eRlXjMgXDNPl\nCYzY3jFSoO71jRykTUYDqXYL6ZFe6DSHdcSe6b4/U+zWUenHjzZXl489L5yn8mALCSk2Vt8zg5Jl\nU+Rn5CiRYDwJ+UI+ytvLOdqq9ykfbz2OK+gCIDshu//QkSVTljAjZcaYHzzyXMVzfHPvN3mk9BH+\nfvHfj+nHEpenaYpub5CGxl6qDrbQdqKDUE8QZTLQm2WlPsVIjQrSHvllNdIu/P6we4Wgm5lok7A7\nwWhhjYZzXVQdauXCsTb8nhA2u5lpizIpWZpF3uxUTCYJyeLa+ENhutzBy1ejh/RM+3FeJkgbDZBq\nv0w1eoSe6dQYC9KhQJhjr9Vx+KValAaL31XAkjsKJ+3oy7EiwVgQ1sJUdlcO6VNu87YBkGRN6q8o\nL8lawrz0eaN68MjJtpM8+NKDlGWX8bMNP8NklG/w0dQXdttHaFsYsjnN6SelO8Rcn4lpISNGDNSb\nwpy0hqmJVyQnjhR0L+3blc02IhzSqD/TqYfk420EfWHiEixMX6KH5NyZqZP65W8x9oJhja7+8Hzl\n9o5Od4BuT3DExzEYICXeMqR9Y3BLR0G6nbUzM8f8Z55SigtH29j9fBW9HT5mLM5k9T3FJGXI8e5j\nQYKxuIRSiobehv4e5SMtR6hx1gBgM9lYkBE5eGTKUhZmLsRhdVzTx+n0dXLfX+7DZDDxm02/ISVO\njqa8HnUdHn751nkaurz9obfDFSA0QmXXYjKQ6bAxzWxhhttARkcIU1BhsJtInJ1C/pJMphYkkZko\nYVdcu1AwTN2pTqoOtVB9soOQP0x8kpXixZkUL5tCzoxkDBKSRZQFwxpdnkgrhytAuztAp2toeO4Y\n1D/d7Q32T7n5cFkB3757/phVltsbXOz6XQWNFd2k5yVw89/MZOqs1DH5WEInwVhclQ5vh15NjgTl\ns51nCatw/8Ej/X3KU5Ze1cEjIS3Ep1/7NEdbjvLMxmeYmz73BnwWE1MorPHE7mp++GoFBgwUZzkG\nKrojbFBLMhi5WN7B2b0Xaavr1WcOL8xk9uoc8udEf+awmJiCgTC1JzuoOtRCTXkH4aBGQoqN4iVZ\nTF+cSWJ6HDa7GYtNJoWI2BYKa3R7gzyxq5qfv36ejQuy+dF9i7CZR+8VT58ryP4/XeDUW41Y7WZW\nvnc6c2/OxSgtSWNOgrG4Jp6gRz94pPUIR1uOcqL9BN6QF4D8xHyWZC3pP6WvMKnwkl90jx9+nM3l\nm/nW6m/x/pL3R+NTmBDKG3v40gsnKG90ctucLB67ez45yZe+vKZpioYz+szhC8f1mcMZ+Q59I93y\nbOIccnCDuHECvhA1J9qpPNRK3ekOtNDA7xej0YDVbsZmN2OzW4gbdLn/z4Th1+mXrXESqsWN9d9v\nXeDbW89wU3E6v7h/GQ7b9c331sIa5W82cuDP1QR8YebfmkfZu6eN+Vx4MUCCsRgVQS3I2Y6zHGk9\n0t+r3O3vBiAtLq1/M9+SrCU0uBr4whtf4IMzP8jXV309yisfn7yBMI9vr+C/36om1W7lm++dx8YF\n2ZeEgu5WfebwuX0XcXX5sSWYmVWWzezVOWTmX9/MYSFGg98TpLGiG58riM8TxO8JRd4il92DrvOG\nUFc4etlgIBKqLxOoB1+XYCZu0HXWOLO0dYhr8vzhBv75+RPMz03iyYfKSEu4thnC9Wc62fX7Sjqb\n3EydncrNHywhPe/aWhXFtZNgLMaEUorqnur+1osjrUdodDX2374gYwFP3fkUVpMMIX+ndle18y8v\nnKSu08N9y/L58sY5JA86qjfoD3P+SCtn9jTTVNmNwQD5c/WZw9NKrzxzWIhYppQi6AtfJkAPuhz5\n0zf4ds9VhOr4kavTcVcK2HYztngJ1ZPda6db+Ltnj5CXGs8zD68gL+XqN8b1tHnY/VwV1cfbScqI\n46Z7S5i2MENe/YgSCcbihrnovsjR1qOc6zzHh2d/mCkJU6K9pHGlyx3gO9vO8NzhBqZlJPDd9y9g\n1Yx0QB/j03CuiwtH26g63ErQHyY5M545N+Uwa0UOjlRblFcvRHQppQj6wwPB2a1XoAcHZ787GAnT\nl4ZsLXyF34EGsF0Sqke+HDesFcQab5a+/gniQHUnDz99EIfNzDMPl1GcdeVX5QK+EIdfquXYa3UY\nTUaW3VXIwg35ckhOlEkwvoz2BhfpuQlSBRBRp5Tizyea+eafTtHjDfK/bp3O368vIeQKUVveTs2J\ndhrOdhEKalhsJmYszWLOqhxyipOl4iDEKFBKEQpoQ8Kyzz08QF++Yj24h3okA5Xqkds/RqxYJ+iV\natmMFVtONzl54IkDhDWNJx8qY1H+pdOWlKaoOHCRPS+ex9MTYNaKbFa9fwYJKVLAiAUSjEfQ2+lj\ny5f3EJ9oIX9OGgXz0imYm0Z8orzsL26sxm4vX33xJDvPtbEwL4kvryqGZi81J9ppr9cPZUlMj6Oo\nNIOiBenklaRKq4QQMUQpRSiojdjqoYfnYdcNq2SHg9oVH98SZxq2UXFolTouwUx8khV7olX/M8mK\nNe76NoiJK6vtcPOxzfvpcAX45f3LuLlkYFJTS7WTt35XQUu1k6yiJNb8TQnZ05OjuFoxnATjEQR8\nIaqPt1N3qoO60534XEEwQFZBYn9InjItSZ6pizET1hRP76nh8ZfPkR8w8J6MVGxtATzOAAYDZE9P\npqg0g8IF6aTlJEhlWIgJKhQMj9hDfWnLx6UbFkOXCdVmqxF7JCTHJ1r7L9uT+sKzDXuShfhECdHX\nqtXp44EnDnC+zcWPP7SYWwvS2Pfiec7uu4g9ycqq989g1opseVU6BkkwfhtKU7TV9+oh+VQnFy/0\noJT+0lf+nNRIUE6XHk4xao6ebWPz785ga/VTFDZhVGCNM1EwL52i0gwK5qUR75BXL4QQVxYOavg8\nQTzOAF6n/sTa4wzg6Q3g6Qng7R24zucOwgi/5s02E/ZESyQ42yLVZwv2ZNuQKrQ9ySpHEw/T4w3y\nqScPYKh0cUvQihFYtCGfpXcVyROOGHa1wXjS/gsajAayCpPIKkxi2cZp+NxBGs52RYJyB+eP6Ecn\np+clUDA3nYJ5aeTMSJGXs8VV0zRFa42TqmNtHN3XhNEZYg5gTopj3vIpFC3IIKckBZO8QiGEeAdM\nFiMJyTYSkt++cKOFNbyuIJ4ePTgPCdKRt+5WD01V+mi9kZhtJj0kJw6vQFsvqVBP9BCtlKKzopt3\nNxno9VqoNIcovj2Ple+dIa/wTRCTtmJ8JUopOpvc1Eaqyc1V3WhhhdlmYuqsVArn6f3Jcp65GC7g\nDVF3upPak+3UnurA2xtEAxpMYezTHDx4zxyKpskR2UKI2BMOa/h6g0NCszdShfYMqkJ7+yrRI7DY\nTMQnWUnoC8+JVuzJI7d2WKzjK0R3NrnZ9Vwl9ac7Sc22s+reYn5yqoEXjjbyiZum8dVNc2QSSQyT\nivF1MBgMpOc5SM9zsORdhQR8IRrPdVF3qpPaUx3UnGgHIGWKnYK5ekjOnZky7r7JxejoadM3zdWc\nbKepUn8SZbWb6U4ysT0cIJBp5Zv3LmJNSeY7elwVCBBsaSHY1Ey4s4O4OXOwFF562qAQQowGk8lI\nQortqqYohMMaXmdwUNuGPxKag/rl3gBdFz00VnThd4dGfAxLnOmyVejhQdocxd+vPneQg1urOfl6\nIxabiZs/WML8tXmYTEa+PzedFLuVJ3ZX0+UJ8L17S7HIq4DjmlSM3yGlFD2t3v5qcmNFF+Gghsls\nJHdmCgVz0yicn07KFLsEmAlKC2tcvODsD8NdFz0ApGbbKVyQTovDyL8drKbd7edTa6bz+dtmEj/s\nh7pSCs3pJNjcTLCpmWBTE8HmJkLNzQQbmwg2NxNqa4Nh35/mnBwSVq2KvK3EnJGBEELEsnBIw9sb\nHAjPw6rPg3ukrxii+4Jy4ghBetD1oxWiNU1xelcT+/90Ab87yNw1eax4z7RLJlkppfj56+f5j5fP\nsX52Fj/7yJJLfuaL6JPNdzdIKBCmqbKbulOd1J3u6A9JiWlxFERaLqbOSsUaL8X58cznDlJ/upOa\nSIuE3x3CaDKQW5JC0YIMikrT8VqNfO2P5bx6uoUF2Qn86y05TFeuIcE32NxMqKmJYFMzmts95GMY\nLBbMuTlYcnKx5OZiycnBkpuDJTcXU3Iy3hMncO/Zi3v/frSeHgBsM2fqIXn1KuzLlmFMSIjGl0cI\nIUaFHqIDl23nGByk/Z6RQ7Q1zjS0B/qSdg4b8Un6xsPLHbrRWNHFW7+rpKPBRW5JCjf/TQmZ+Vc+\n2OPX+2v56h/KWVqQyuaPLyc53nLF+4sbS4JxlDjbvdSd7qTuVAcNZ7sI+sMYjQayZyRTME+vJqfn\nOaSaPA50XXRTc7KD2pPtNFX1oDRFnMNC4fx0Cmc6yE7yYuhsIdDYxLHDZ6k8WUWGu5Ppmou4nk4I\nh4c8niklRQ++ubl6+M2JXM7NwZKTgyk9HYPx7V+CU+EwvtNncO/di3vvHryHj6ACATCbiV+0kISV\nelCOX7AAg0V+MAshJqZwUNPD8vAgPTxUXylEx5sjrRuWyDg7K+5uPxeOteFIs3HTPSXMWJJ51b+z\nt55o5vO/PcqMTAdbPlFGVlLcaH7K4jpIMI4B4ZDGxQs9/b3JHQ36wQ32JGt/NTl/ThpxCRJeYkE4\nrNFc1UPN8TZqjrfS0xEAIDk+QLaplUx3JY7mU4Sam/ortn1CBiOuxDTSpheQWDgVc86gym9uDpbs\n7DGr5mo+H96jR/Vq8t69+E6dAqUw2u3Yy8pIWK23XliLi+UJmRBiUuoL0f3B+TJVaG9vAC2sWPyu\nAhbdXnBNe4d2VbbzyDOHyHDYeObhMgrT5ZW8WCDBOAa5u/16Nfl0B/WnO/F7QhgMkFWURMG8dArn\npZNZmCi7WseY5vNFenubcNdepL7KRUObhZZAGiGDFYMWIrXrHBkd5aR3lBPv78TocAy0N+TlYpiS\nzc4uI7+u8eNOyeQz96zgnmUFMRE8w93duPcfwL13D+69ewnW1gFgysyI9CavJmHVSizZ2VFeqRBC\nxB6l1HX/LD9W381DTx7AZDSy5RNlzM1NGqXViWslwfgygmEtJnaM9s24rTvVQe2pTlprnaAgLsFC\n/tw0CualkT8n7armVIoBSinCXV2Rvt5GfTNbpKc32NxMoKkJp89GR/p82jMW0JM0DQxGrCEXU7RG\nchy95OVbiMubEml1yMOSm4MpcaC37HBtJ198/iRVrS7euzCXr79nLhmO2P13CjY24t63r7+iHO7s\nBMA6bdpAf3JZGaYk+cEthBCjpaq1l/s3H8DlD7H5weWUTUuL9pImNQnGI+j2BLjth29w1/wcHlhV\nSMmUKzfS30heV4D6M536Jr7I/FuAjHxHfzV5yvSkSX8YRP8Is8jkhmBzE8GmJkKR4Btsbkb5fEPf\nx+6gt6iMjoz5tJgL8Gj6/Om0dCNFc1OZtiKfKdNT3/YIz15fkO+9dI5f7a8lNzmeb989n3Wzs8bs\ncx0LStPwV1ZGQvIePAcPobxeMBqJWzC/v6Icv3gRRqucwieEENejqdvL/Zv309Dl5ecfXcKGOVOi\nvaRJS4LxCC72+PiPl8/x5xNNBEIaq2ek8+DqIjbMzsIcQ4FTaYr2Bhd1pzuoLe/g4gUnSlNY40xM\nnZPWPzs5MW1iNfUPHWHWV+UdGnxHGmFmysgYaHOIbGgLpeVw0ZdCQ7OBhioXQX8Yk8VI/uxUChdk\nULQgHUfq1X/9Xj3dwtf+UE5Lr4+HVk/jH981kwTb+J80ogIBvMePRzby7cN74gSEwxji4rAvW9Y/\nFs42e/ZVbQwUQggxVKc7wENPHqC8ycn37inlnqVTo72kSUmC8RV0uPz89lA9v9pbS1OPj7yUeD66\nsoAPLS8gLSH2qmR+b4iGswPVZFeXH4DUnAR90sXcdHJKki87diZWqFCIUGvryME3Ms/3siPM+iY5\nDBtjZs7OxmizoZSio9FFzYkOak6201Kjt6YkpNgoWpBO0YIM8manvuONFK1OH9/48ym2nbzI7OxE\n/u2eUhblT9yT68IuF54DB/snXgSqzgNgSk3FvnJFpPViNdap8oNdCCGulssf4n89c4jdVR18ddMc\nPrlmerSXNOlIML4KobDG9rOtbNlbw+6qDqxmI+9dmMuDq4pYMDU5qmu7HKUUXc2e/mpyU1U3Wkhh\nthrJm5VKwdx0CualkZJlv+Fr0zwego2Nw4LvoMMrWlrffoTZ8Pm9aWmXrVSGAmEaznVRe1IPw31P\nGLIKEykqzaBoQQYZ+dc2Gk8pxW8P1vOdbWfwhzT+YUMJj9wyPSb602+kYEsrnn17+/uTQ62tAFjy\n8wf6k1eswJyaGuWVCiFEbPOHwvzv3x5j28mL/N26GXzhXbNiYsP2ZCHB+B2qbOlly95anj/SgCcQ\nZnFBCg+uKmLjghys5tgNQ0F/mMaKrv5qck+bF4CkzHgKIy0XebNSsdjGppocdjrpfW07zq1bce/b\nNzT4ms1YpkzpH1lmzhkcgPW2B6P9nQV4d7efmpPt1JzsoOFsJ6GAhtlmIn92KkWlGRTOT7/uDYsX\n2lz8ywsn2V/dyYppafzrBxYwPdNxXY85ESilCFRX94dkz/79aC4XGAzY5szu70+2L12CMT4+2ssV\nQoiYE9YUX/tjOc/ur+PDZQV8++75mGQS1Q0hwfgaOX1BXjjcwJa9tVxod5PhsPGRsnw+sqKQ7OTY\n7+ntbvVQ33fAyLkuQgENo9lAbnEKBfP0anJaTsJ1PUvVvF5cO3fSs20b7jfeRAWDWKZOJemuO7HN\nnt0ffM2ZmRhM1xfIlaZoq++NHL/cQVtdLwCONBvTFmRQVJpB7syUUWkjCYY1fvnmBX68vRKb2chX\nNs7hb5bly/i8y1ChEL7ycr3tYs9ePMeOQTCIwWIhfsmS/opy3Lx51/3/QAghJgqlFD94pYKf7qxi\n44JsfnTfImxm+Rk51iQYXydNU+yqamfL3hq2n23FaDBw57xsHlhVSNm0tHHx8kcoGKa5qoe6Ux3U\nne6ks0nv33Wk2vo38E2dk4btKo6r1gIB3Lt249y6ld6dO1EeD+bMTJI23kXSxo3ElZaO2tck6A/T\ncLZTD8PlHXh6AmCA7GnJFJXq/cJpudcX7oc7Vt/Nl54/wdmLvWxckM033jNPTix6hzSPB8/hI5H+\n5L34z5wBwJiYiH1FWX9F2TqtaFx8/wghxFjavKuax/5ympuK0/nF/ctwTIAN3bFMgvEoqu/08My+\nWn57sJ4eb5DZ2Yk8uLqI9y3KxW4dP/+Rezt9/dXk+jOdBHxhDEYD2dMHDhjJmOroH1umQiE8Bw7Q\ns3Urva++huZ0YkpOJvGOO0jatAn7sqWjVgns7fRRe7Kd6hMdNJ7rIhzSsMSZKJibTlGpvrb4xNHf\nGOn2h/j+K+d4ak8NUxLjeOzu+dw+V8bpjIZQZyeeffv0oLx7D8GmJgDM2dn91eSElSsxZ2ZGeaVC\nCBEdLxxp4J+eO8G83CSeeqgsJgcATBQSjMeANxDmT8cbeWpPLWeanSTFmfmbZfncv6pw3B35GA5r\ntFxw9leT+1oU4hMt5GZDWvsp7LtfwNRaj9FuJ/H220jauJGE1asxWK7/CGulKVpqnP39wn3HZSdl\nxOkb50ozyC1OwTSG/d07z7Xy1RfLaerx8rEVhfzznbNIjJPjuceCUopgfT3uvXpQ9uzdSzhyrLat\npETfxLdqFfZlyzE5xtf3khBCXI/tZ1r4zK+PkJcazzMPryAvRfZojAUJxmNIKcXh2i6e2lPDS+UX\nCSvF2pmZPLi6iFtKMsddT6pSiu7D5VT+6QD1VW464osIWhyAIj0VisryKVo4hayipOv63AK+EPVn\n9BaJ2nL9EBOD0UDOjGQKF6QzrTSDlCn2MX+Zvd3l51t/Ps2fjjdRnOXg3+9ZwNJCOZHoRlKahu/M\nGTx9/cmHD6P8fjCbiS8t7a8ox5eWjsoTMSGEiGUHqjt5+OmDOGxmnnm4jOKs2DmAbKKQYHyDtDh9\nPLu/jmcP1NHW66co3c79q4q4d+lUkuNj+xe6/8IFnH/ZinPbNgI1NWA2k3DTahI3bsJbUkZjtZe6\nUx20VDtRCmx2M/lz9OOqC+amk5Dy9tMfnO1evSp8op3Gim60sMJmN1MwL52iBekUzEsnLuHGfJ2U\nUjx/pJFvbz2N2x/i79YV87drZ8imhxig+f14jx7tn3jhO3UKNA2j3Y59+fL+irKtpET6k4UQE9Lp\nJicPPnmAUFjjyYfKJvTM/GiQYHyDBUIaL526yJY9NRyq7SLeYuL9S/J4cFURs7Jj55lfoKER51+3\n4dy6Df/Zs2AwYC8rI2njRhLfdfuI82h97qB+XHWkP9nTEwAgfaqDwkhIzp6RjMlsRNMUFy/09PcL\ndzXrG/5Sptgjs4Uj973B84BrO9x85cVydlW1s7QwlX/7wIKYOhJcDBXu6cF94EB/RTlQUwPopxwm\nrNJ7kxNWr8KSkxPdhQohxCiq7XBz/+YDtLv8/PL+ZdxckhHtJU0YEoyjqLyxhy17a/jjsSb8IY2V\n09N4cFURt8+dEpWjp0NtbTj/+hLObdvwHjsGQPzChSRt2kjiHXdimZJ11Y/Vd8Jc39zk5qoeNE1h\nsZnIKkqivaEXvzuE0WggpySFaZHZwilTbvyBI6Af4rJ5VzU/eq0Cs9HIF++azUfLCsZdu8tkF2xq\n6u9Pdu/dS7ijAwBrUVF/NTmhrAxTcmwezCOEEFer1enjgScOcL7NxeP3LWZTqRQARoME4xjQ5Q7w\nu0P1PLOvloYuLznJcXx0RQEfKisgw3F9h1C8nXB3N85XXsG57a94DhwATcM2axZJmzaRtPGuUTvS\nN+AL0XC2i7rTnVw830PGVAeFkRaJqxkDN5bKG3v44vMnONXk5Pa5U/jW++aRkyybGsY7pRT+ysr+\narL74EGUxwNGI3Hz50fGwq0kfvFijLax/T4TQoix0OMN8smnD3KotovH3jefj60sjPaSxj0JxjEk\nrCl2RI6efquyHavJyLtLc3hgddGo9hCFXW5cO3fg/MtWXLt3QyiEpbCA5E2bSNq4EVtx8ah9rFjm\nDYT50WsVbN5VTVqClW+9dx53zs+W3tQJSgUCeE+e7O9P9h4/DuEwBpsN+9Kl/RXluDlzLnu8uBBC\nxBpvIMxnnz3C9rOtfOFdM/m7dcXye+w6SDCOUVWtLp7ZW8NzhxtwB8IsnJrMA6uK2FSaQ9w1nN6m\n+f243ngD57a/4nr9dZTPhzk7m6SNG/WDN+bNnVTfSLsq2/nyiyep6/Tw4bJ8vnTXnJjfBClGV9jl\nxnPoYH9F2V9ZCYApJQX7ypX9Ey+s+flRXqkQQlxZMKzxxedO8MLRRh66qYivbZorrYDXSIJxjOv1\nBXnxaCNP76nhfJub9AQrHyrL56MrCsl9mxmGKhjEvXcvzq3b6H3tNTS3G1NaGkl33knSpo3EL148\n6SpjXe4A3956huePNDA9I4HvfmABK6enR3tZIgYEW1vx7N/fX1EOXbwIgGXqVBJWrcS+ciVxc+di\nLSiQo6uFEDFH0xTf3nqGJ3ZX8/7FeXzv3lIsUdivNN5JMB4nlFLsOd/BU3tq2H6mBYB3zc3mgdWF\nrJqe3l/tVZqG59AhPQy//DLh7m6MiYkk3n47SZs2krBiBQbz+DmFb7QopfjT8Sa+9efT9HiDfPrW\nGXx2ffE1Vd/FxKeUIlBdg3vvHv1Uvn370Xr1w20McXHYZszANnOm/lZSgm1mCebMzEn1qosQIvYo\npfj56+f5j5fPsX52Fj/7yBLirfJ77p2QYDwO1Xd6+PX+On5zsI5uT5CZWQl8OjtA2YVDeF95mVBr\nK4b4eBLXrSPptgXqkQAAIABJREFU3ZtIuPlmjNbJe3xkQ5eHr7xYzhsVbSzMT+Hf71nA7OykaC9L\njCMqFMJ37hz+cxX4K/Q3X2UF4bb2/vuYUlIGwvLMEuJmzsRaXCIn9Akhbrhn99fxlT+cZGlBKps/\nvlxaBd8BCcbjmPPUGY4+/XtMr79KurOdoNFE65wlFN57NzPedydGe3RGn8WKsKZ4ak8NP3jlHAD/\ndMcsHlhVhEn6rsQoCXV1DYTlysr+PzWPp/8+lqlTI1XlQYG5qEhO6hNCjKltJ5v5/G+OMT0zgS2f\nKCMrKS7aSxoXJBiPM4HaWpzbtuHctg1/ZRUYjdhXrqRr5Vp+bZnGH6qcBMOKW2dm8uDqQtbOzJqU\nDfhnmp186fkTHG/oYd2sTB67ez5TUyf3EwVxYyhNI9jU1F9Z9ldU4KuoIFBdA+EwAAaLBev06UPC\nsq2kBHNOjrRjCCFGza7Kdh555hDpDiu/engFhenyCtbbkWA8DgQvXtQP3ti6FV95OQDxS5eStGkj\nSXfcgTl9YPNYa6+P3xyo59f7a2lx+ilIs3P/ykL+Zlk+yfaJX6HyBcP83+2V/PLNCyTHW3j0vfN4\nT6mEDRF9WiBAoLp6SFj2V1QSam7uv48xMbG/Z9k2c6YemGfOxJQkrT9CiGtzvL6bjz95AJPRyJZP\nlDE3V36eXIkE4xgV6uyk9+WXcW7dhufwYVCKuHnzIuPV7nrbI26DYY2XT11ky55aDtR0Emcx8v7F\nedy/smjCflPsPd/Bl188SXW7m3uXTuUrG+eQmjB5e6vF+BB2OvvbMHz9LRmVaE5n/33M2dl6WC4p\n6Q/L1hkzJvXeASHE1atqdXH/5v24/CE2P7icsmlp0V5SzJJgHEPCvb30vrYd59atuPfuhXAY64wZ\nemX4rruwTZt2TY97usnJlr01/OFYI76gRllRGg+sLuSOedkTYpRLjyfIv/71DL85WE9Bmp3vvn+B\nnBsvxjWlFKGWlkuqy4Hz51HBoH4nkwlrUdFAK0bkzZKXN+nGMAoh3l5Tt5f7N++nocvLzz6yhNvm\nTon2kmKSBOMo07xeXK+/jnPbNlxvvIkKBLDk5elHMm/aiG3mzFFrA+j2BPj9oQa27KuhvtPLlCQb\nH11RyIfK8slKHH9N+Uoptp28yKN/OkWXJ8An10zj8xtmymgaMWGpYJBAbS3+ysr+sOyvqCBYX99/\nH4Pdjq24+JLAbE6TCpEQoyVQV4f32DEwmjDG2TDY9DejzYYhLg6D1YoxLm7gOpstJkaldroDPPTk\nAcqbnHzvnlLuWTo12kuKOaMWjA0GQz6wBcgGNOCXSqkfj3C/tcDjgAVoV0rdeqXHnYjBWAUCuHbt\nxrltG707dqA8HsyZmSTedSfJmzYRV1o6pj2xYU3xRkUrT+2p5c2KNiwmAxsX5PDAqiKWFKSMi37c\n5h4vX/tDOa+daWV+XhL/9oFS5uclR3tZQkSF5nbjr6oaCMuR1oxwZ2f/fUwZGcTNLMFWMrO/h9lW\nXIwx/soHBQkh9E21vhMn6N2xk94d2wlUnX/nD2I2Y7Ra9eA8ODDH2TBaI4HaZsVoi9weZ8Ngjdxu\ns2GwRW6Pi7vkeqMt8rhW26CgHrl+2AQclz/Ep585zK6qdr66aQ6fXDN9lL5KE8NoBuMcIEcpdcRg\nMCQCh4G7lVKnB90nBdgD3KmUqjMYDFlKqdYrPe5ECcYqHMZz4AA9W7fS+8qraE4npuRkEu+4g6SN\nG7EvXxaV07QutLl4Zl8tzx1qoNcfYn5eEg+uKuI9C3Nj8vALTVP8an8t33vpHCFN4x9vn8VDNxVh\nngAtIUKMtlB7+5BWDH9FBf6qKpTPp9/BYMBSkB+ZijFQXbYWyul+Qmg+H+69e3Ht2EHvztcJt7eD\nyYR9+XIS168nYdVKMJlQPh+a34+KvGk+PyrgR/P5UP4Ayh+53Re5PTDoct/7+XxogQDK5xtyvRa5\nDU279k/EZBoSxI02G1it1Lk12vyKKZnJTM9L7Q/kVwzqgyvjl1TJB+5jtNnAYhkXhbbhxqyVwmAw\n/BH4qVLq1UHXfQbIVUp99WofZzwHY6VpeI8dx7l1K86XXybc3o7Rbifx9ttI2riRhFWrMMTI5hm3\nP8SLRxvZsreGihYXqXYL9y0v4GMrC2JmzFllSy9feuEkh2u7WFOSwXfuXkBBemysTYjxQoXDBOvr\n8fXNXY4E5kBtbf8vX4PNhnXGdOIGhWXbzJmYs+R0PzGxhTo7ce18nd6dO3Dv3oPyejE6HDhuWYNj\n3Xoct6zBlHzjX51UwSBaJGQPDt96KB8WvkcK5SPcJ+z1caGpi+6uXrLiDEyxGSKPORDKCYWufdFG\nox6Sr7FKnnb//VGZyDMmwdhgMBQBbwLzlVLOQdf3tVDMAxKBHyultozw/o8AjwAUFBQsra2tveqP\nHW1KKfxnzuDcto2ebdsINTVjsFpxrF1L0qZNOG69BWNc7PbzKqXYe6GDLXtqeeX0RQBumzOFB1cX\nsXpGelR+KfpDYX6+8zw/f70Kh83M1949l/cvzpNf0EKMIs3nw3/+/EBlORKcQ60DL+qZkpOHBOW+\nSRkmhyOKKxfi+vgvVOPauYPe7TvwHj0KSmHOySFx3TocG9aTsHx5zBSxRptSih+8UsFPd1Zx1/xs\nHv/QImzmgVeLVCgUqXKPUM0eqSIeiIR2vx/N33f7oMs+31VVzFUwSPEbr2OZcuM3CI56MDYYDA7g\nDeA7SqkXht32U2AZsAGIB/YCm5RSFZd7vPFSMfZfqNYrw9u2EaiuBrOZhJtWk7xxI44NG8blL47G\nbi/P7q/lfw7U0+kOMCMzgQdXF/GBJVNx2G7MJoJDNZ186YWTVLW6uHtRLl9791zSHbYb8rGFEJHT\n/SIj5PoPLamsRHO7++9jyc29NDBPmyan+4mYpMJhvMeP07t9O64dO/Xf2YBt7hwS128gcf06bHPm\nTKriy+Zd1Tz2l9OsnpHOLx9YdsN+x1+OCof1inMU/g1GNRgbDAYL8BfgZaXUD0e4/UtAnFLqG5G/\nbwZeUkr9/nKPGcvBONjYSM+2bTi3/RX/mTNgMGAvKyNp40YS33U75tTUaC9xVPiCYbaeaObpvTWc\naOjBYTNz79Kp3L+qkBmZYxP4nb4g33vpLL/aV0deSjzfef981s7KGpOPJYR4Z5RSBBub8FdWDA3M\n1dUDL71aLNimTdODcuTQkriZMzHn5k6qwCFig+bx4N6zh94dO3G9/rq+MdViIaGsDMf6dSSuX/+2\n5wNMdC8caeCfnjvBvNwknvz48klbhBrNzXcG4GmgUyn1+cvcZw7wU+AOwAocAD6klCq/3OPGWjAO\ntbXhfOllnFu36qNagLiFpSRv2kTiHXdimTKxw9ux+m6e3lPD1hPNBMIaa0oyeGBVEetnZ2EapaOn\nXz51ka//sZy2Xj8P3TSN/3P7TBKi/OxVCPH2VCCAv7pmSGXZX1FBsKmp/z5GhyMSlGcOGSkXjb5N\nMbGF2troff11XNt34N67F+X3Y0xKwnHLLSRuWE/CzTdjSkyM9jJjyvYzLXzm10fIS43nmYdXkJcy\n+abWjGYwvhl4CziJPq4N4MtAAYBS6r8i9/sn4KHIff5bKfX4lR43FoJxuLsb56uv4ty2Dc/+A6Bp\n2GbN0k+h27QR69TJNwewrdfPbw/W8at9dVx0+piaGt9/9PS1njbX4vTx6B9P8dKpi8zOTuTf7yll\nYX7KKK9cCHGjhV2uoZXligp8lZVoPT3997EWFhK/aBHxixcRv2gRtpISmYwh3hGlFIHz5+ndvgPX\njh14T5wApbDk5eHYsJ7E9euxL10qLT5v42BNJ5946iAOm5lnHi6jOGtyPXmQAz4uQ3O76d2xA+fW\nbbh274ZgEEthAcmbNpG0cSO24uIbvqZYFAprvHq6haf21LC/uhOb2cj7FuXywKqiq54rrGmK3xys\n51//egZ/SOPzt5XwqTXTJ8SpfEKIkSmlCLW24q+oxHf2DN7jx/EeO66PxAKMdjtxpaXEL1qoB+aF\nCydMe5oYPSoUwnPkCK4dO+ndsYNgXR0AcQsWkLh+HY71G7DNLJH2nXfoTLOTB544QCis8eRDZSya\nREUqCcYj8J8/T/U996J8PszZ2XpleONG4ubNlW+uKzh70cmWvbW8eKQRbzDM0sJUHlxdxJ3zsrGa\nRw6559tc/MsLJzlQ3cmq6el89wMLmJaRcINXLoSIBXrvciPeo8fwHtPffGfPQjgMSFVZ6MIuN+5d\nu3Dt3IHr9TcI9/RgsFiwr1pJ4voNONatjco0g4mmtsPN/ZsP0O7y84v7l7KmJDPaS7ohJBiPQGka\nbT/8IY5164hfvBiDUSqX70SPN8hzhxt4Zm8NNR0eMhNtfKSsgI+sKGBKkj6qLhDS+OWb5/m/O6qI\nMxv56qa5fHDZVHniIYQYQvN68ZWX4zl2DO+x43iPHSPc0QFIVXkyCba04Nq5k97tO/Ds24cKBjEl\nJ+NYuxbH+vUk3HQTJocUVUZbq9PHA08c4Hybi8fvW8ym0om/QVGCsRgzmqZ4o7KNLXtq2HmuDbPR\nwF0LcrhtThY/33mecy29bCrN4dH3zCUrMXZnOwshYodSimBDg15RjlSWfefODVSVi4r0kBypLNuK\ni6WqPA4ppfBXVPSPVPOV63v0LQUFJK5fT+KG9Xrhyiwbs8dajzfIJ58+yKHaLh5733w+trIw2ksa\nUxKMxQ1R0+7mmX21/O5QPb2+EDnJcTz2vvncNlde7hJCXB/N48FbXt5fUfYeO6aP4wKMCQnElS4g\nftEi7JGqsill8vRLjicqGMRz6JA+Um3HDoKNjWAwEF9aimODPl/YOmOGvLIYBd5AmM8+e4TtZ1v5\nx9tn8tn1xRP230GCsbihPIEQB2u6WFKQQmKc7AwWQoy+t60qT5tG/MKFUlWOAeHeXtxvvaVPknjz\nTbTeXgw2GwmrV+vzhdeuxZw5OXpbY10wrPHF50/wwpFGHrqpiK9tmotxlMa0xhIJxkIIISY8qSrH\njmBTU39V2H3wIASDmNLScKxdq88XXrUKo90e7WWKEWia4jvbzrB5VzXvX5zH9+4tnXATpCQYCyGE\nmHSUUgTr6/tDsufYMfznKoZWlft6lRctwlY8Q6rK10gphe/06f6Rav4zZwD9a5y4YT2O9euJX7hQ\nvr7jhFKKn79+nv94+RzrZ2fxs48sId46cf7tJBgLIYQQRKrKJ8v7w7L32DHCXV2AXlWOX1g6EJZL\nS6WqfAUqEMB94CCuHdvp3bGT0MWLer/wkiX6fOF167FNnxbtZYrr8Oz+Or7yh5MsLUhl84PLSbZP\njPZICcZCCPF2ehqh4SDYHJCQBY4ssGeASXbET2RKKYJ1df0VZe+x4/jPnQNNP9xVqspDhXt6cL35\nJr07duB+8y00txtDfDwJN63W5wuvvRVzWlq0lylG0baTzXz+N8eYnpnAlk+UkZU0/idMSTAWQojh\nQn6o2wdVr0LVdmg9PcKdDGBP10OyI2sgMPdfzgTHFP1yQgYYJ29gmkg0t3toVfn48YGqssNBfKRX\nuW+usin56k4AHa8CDQ24duzQ5wsfOgThMKaMDBLXrcOxfp3eLxw3/sOSuLzdVe08suUQaQ4rz3xi\nBUXj/JAuCcZCCAHQWQ1Vr+lBuPpNCLrBaIHC1VB8GxTdBOEguFrB1QLutsjlVnBHrnO1Qcg7woMb\n9HA8JDBnRoL0sMv2dAnR48jbVpWnT48E5YWRqnLxuD40SmkavlOn+ucL+ysqALCVFONYp88Xjluw\nYFx/juKdO17fzcefPIDJaOTpTyxnXu74fUIowVgIMTkFPFC7Gypf1QNx53n9+pRCKLkdim+Hopv1\n9omrpRQEXMMC8xUuh3yXPobBqLdpOLIigXmKHqYTsi69bE+TEB2DLqkqHztGuLsb6Ksqlw4cbV1a\nGvNVZc3vx7Nvnz5JYudOQq2tYDRiX7oUx4b1JK5fj7WgINrLFFFW1erigc376fWF+O8Hl7Fienq0\nl3RNJBgLISYHpaC9IlIVfg1qdkPYD+Z4mLZGrwoX3wZp0+FGDK5XCvy9kcpzSyQwj3S5TQ/SVwzR\ngwPzZVo77OkgVbyoUEoRrK2NVJQjVeWKioGq8owZkbnKsVNVDnV14XrjDVzbd+DavRvl8WC020lY\ns4bE9etIuOUWOX5bXKKp28v9m/fT0OXlZx9ZMi4P8ZJgPJJwEM78GZJy9TdHNpitN3YNQojr53NC\n9RsDLRI99fr1GbMiVeENULAaLDHeA6kU+J0DIXlwYB5yOfIW9l/6GAbToHaOrGEV6WGX49MkRI+x\nsMuNr/zkwCEkx49HvaocqK3VD9rYsQPPkSOgaZizsvSDNjZswL5iBUar/C4UV9bpDvDQkwcob3Ly\n7/eUcu/SqdFe0jsiwXgkXTXw44WDrjDovzT6gnLfW+Kwv1vHd8O5EOOeUnDx5EAQrt8HWgisiTD9\n1khVeAOkTOCXfZUCX8+gHuiWy1yOtHSEA5c+hsEUCcrDWjj6NhMOvhyfKiF6FCilCNTUDDmAxF9Z\nObSqHKko2xct0o9Gvs6vu9I0vMeP988XDpzX24lss2bp84XXrSdu/rwJe/SvGDsuf4hPP3OYXVXt\nfHXTHD65Znq0l3TVJBiPJBSAjkpwNoOzEZxN0Nuk/9l3na/70vezJQ8KyjmQlAeJkT/7ro9PvTEv\n0woxWXg64fwOPQif366HP4DsBXqfcPFtkF8GpokxY3NU9YXot+2HjrR1aMFLH8No1kP05TYTDr4c\nlyIh+h24pKp87Bjhnh4AjImJA1XlRYuIX1iKKSnpbR9T83px791H747tuHa+TrijA8xm7MuXkbhO\nP2zDOjVvrD81MQn4Q2H+92+Pse3kRT6zdgb/dMescfEkS4LxtQp4oLcvOEf+7G2OhOfIm6sFGPZ1\nM8cNCss5kcCcN/Q6xxTZUCPE5WhhaDo60CvceBiUpj/pnLFeD8Iz1kNidrRXOrEopRcE+kLy4MA8\n5HKkIn2lED28Bzp5Kiz4IMTF9ia0aHvbqnLxjCHHWvdVlUMdHbhef53eHTtx796N8vkwOhw4blmD\nY/0GHGtujvkNgGJ8CmuKr/2xnGf31/Hhsny+ffcCTMbYDscSjMdSOKj/ohgclodXnnubL30p02DS\nw/GVWjcSc2K/L1KI0dLboleDq17Tq8PeLsAAU5cNbJrLXSxPKGOFUvq/0ZVaOAb3Rmshva/51n+G\nZQ/Lno53IOxy4zt5on9cnO/Y8SFVZUtenj4+TinMOTkkrl+vzxdevhyD9AuLG0ApxQ9freAnO6q4\nc142j39oEXGW2P1ZLcE42pQCT8dlKs991zVBoPfS97WnDwrLg1o2BlefbUnSuiHGn3AQ6g8MVIUv\nntCvT8ga6BOesV4fVybGN02Di8fhtW/AhdchtQg2fB3mfUB+dl0DpRSB6pr+inKgrg77smUkbliP\nbfbscfFStpiYnthVzbf+cprVM9L55QPLcNhi8+RQCcbjhc95aatGb9PQv3vaL30/qyMSlHOHtm/0\nB+o8GeMkYkN3/UAQvvCG/mTQaIb8lXoQLr4NpsyX/6sTlVL6qwKvPgot5ZC7BN71mD5LWggxIbx4\ntIEv/P4E83KTePLjy0l32KK9pEtIMJ5IQv4RwnPz0MpzbzOo8ND3M1r0wDx8ysbgAJ2YLZuXxOgK\n+qBuj75pruo1aDurX5+cPxCEp90ifaeTjRaGE7+FHd/Wf3bNvBNu+wZkzYn2yoQQo2DH2Rb+9ldH\nyEuN55mHV5CXEh/tJQ0hwXiy0cJ6r9+QsDys8uxsGuFYW4O+SWZItXn45I0cGVknrqzj/EBVuPot\n/f+ZyaYft9zXK5wxU15CFxD0wv7/grd+qJ8muPhjsPbL+s8ZIcS4drCmk088dZAEq5lnHi6jZEpi\ntJfUT4KxuFTf7vPBmwSvdmRdXPKgsDy4fWPQdTKybvIIuPUA3BeGu6r169NmDAThopvkCZW4PHcH\nvPV9OPD/9Naa1Z+F1Z+DuLcfTSaEiF1nmp088MQBgmGNJz++nMUFsXGSogRjce2ueWRdPCTn6WE5\nOV8f1ZQ8Vb8uOV+/3mqPyqckrpNSektE5at6EK7bq09dsSTobRHFG/S3tPEz7F3EiM5q2PEYlD+v\nH4O99kuw9OPS4iXEOFbX4eFjm/fT7vLzi/uXsqYkM9pLkmAsxthII+ucjfpbTwP0RML08PAcnxYJ\ny/mDQvPUgeCcmC2juWKFt3voscvORv36rLmRIHw7FKwEc+xtshDjUONheOXrULtLf+Xhtkdhznvl\nVSghxqnWXh8PPnGQqtZefnTfIt5dmhvV9UgwFtEXDg4E5p4G6KkfCM09Dfqbv2fo+xjNeq/zkNA8\nFZKmDlyOS5ZflmNB0/TxaVWvRo5dPqBv6LQlw4y1kQM2Nuj/LkKMBaWg8hV49ev6KxRTy/QJFgUr\no70yIcQ16PEG+dTThzhY28m33jef+1cWRm0tEozF+OBzDgvOg0KzMxKih5+0ZXUMatMYFpr7Wjmk\ninl13O1wfmfkgI3t+gZOgJxFehAuuR3yloEpNudSigkqHILjz8KO74DrIsx+tz7BIqMk2isTQrxD\nvmCYzz57hNfOtPKPt8/ks+uLozJ3W4KxmBg0TT9Bq6dxUMW5LzRH3vrC3GCOKZFe58u0bdgzJufc\n3HBIf8m6b9Nc01FA6TOvZ2wYOHbZEf1+MCEIuGHfz2HXjyHo0XuP135Jn6QjhBg3gmGNLz5/gj8c\nbWTr59YwJ+fGb7KVYCwmj6BvUNW5IXJ5WNtG0D30fUzWYcF5hLYNmyM6n89oczYPOnZ5pz51xGCE\nqcv1PuHiDXqFeDI+URDjg6sN3vweHHpCHwN40z/Aqr+bON+jQkwCmqY43tAdtSkVEoyF6KMUeLuG\nhufhbRsjHZASl3L50Jw8VR9TF4stBqEA1O8b2DTXUq5fn5gzcMDG9LX6eD0hxpOO87D9m3D6j/qr\nQmv/BRbfH5vfh0KImCLBWIh3IhzSexl7Gi5962vb8HYNfR+DUQ+b/aE5b1jbRv6Nm+3cVTsQhKvf\n0A9OMFqgcNXAXOGsubJpUUwM9Qfgla/pTwAzZsJt34RZd8n/byHEZUkwFmK0Bdwj9DoPa9sI+4e+\nj8U+KDQPb9vI1w9GsVzDsZlBL9TsHugV7qjUr08piLRH3AbT1oAtdk4dEmJUKQXntsGrj+r//wtW\n6xMspr7t7z0hxCQkwViIG00pfcpDT/3l2zZcFy99P3vG0Ckbw9s2HFP0SlhH1UAQrtkFIR+Y46Bo\nzUBVOH2GVM3E5BIOwdEtsPNf9Y26c++GDV/XvxeEECJCgrEQsSgU0I/gHqllo+8t0Dv0fYwW/Zhc\nT4f+94yZkSC8AQpvuraKsxATjd8Fe36iv4X9sOxhuPWfISEj2isTQsQACcZCjFe+nkETNeoHRtLl\nLdFHqqVGb0C6EDGv9yK8/m9wZIveynTz52HlZ+Q4eiEmOQnGQgghJq+2c/DaN+HcVn2T7LqvwKKP\nyJHzQkxSVxuMZXCpEEKIiSdzFnz4WXjor/rm1z99Fv7rZqh4Rd8PIIQQI5BgLIQQYuIqXA2ffA0+\n+LS+YfXZD8LT74HGI9FemRAiBkkwFkIIMbEZDDDvbvjMfrjrP6D1NPy/dfDcw9BVE+3VCSFiiARj\nIYQQk4PZCisegc8dgzVfgLNb4afL4aUvg6cz2qsTQsQACcZCCCEml7gk2PA1+NwRKL0P9v8n/HgR\n7HpcPzxHCDFpSTAWQggxOSXlwvt+Cp/eDQUr4bVH4SfL4Nj/gBaO9uqEEFEgwVgIIcTkNmUufPR3\n8OCf9QNB/vBp+MWtULU92isTQtxgEoyFEEIIgGm3wKd2wj2bwe+EX30AttwNzSeivTIhxA0iwVgI\nIYToYzTCgnvhswfhjn+F5mPwi1vghf8F3XXRXp0QYoxJMBZCCCGGM9tg1Wf0CRY3/QOcelHvP37l\na+DtivbqxGgL+aH+IOz7T/3fuPVMtFckouRtj4Q2GAz5wBYgG9CAXyqlfnyZ+y4H9gH3KaWeu9Lj\nypHQQgghxo3uetj5XTj+PxCXDLf8E5R9Sg/QYnxRCjovQONhaDgEjYfg4kkIB/TbDUZQGsx+N9zy\nBchdHN31ilFxtUdCX00wzgFylFJHDAZDInAYuFspdXrY/UzAq4APeEKCsRBCiAnn4kl49VE4vx1S\nCmD912H+PXoLhohN3q5ICD6sh+CGQ+CNzK222PXgm7cUpi6DvGVgidcrxwd+Ab4emLFBD8iFq6P7\neYjrMmrBeIQH/iPwU6XUq8Ou/zwQBJYDf5FgLIQQYsI6vwNe/boelHMWwu2PwfRbo70qEQpAS/nQ\nanBHVeRGA2TO0sPv1Mhb5hwwmUd+LJ8TDv437P0ZeNqhYDXc8o96UDYYbtinJEbHmARjg8FQBLwJ\nzFdKOQddnwc8C6wHNnOZYGwwGB4BHgEoKChYWltbe9UfWwghhIgpmgYnfwfbHwNnAxTfDrd/E6bM\ni/bKJgel9A2RjYcGqsHNxyHk029PyIpUgSPV4Nwl+uEu71TAA0e2wJ7/C85GyFmkV5BnbZJXCsaR\nUQ/GBoPBAbwBfEcp9cKw234P/EAptc9gMDyFVIyFEEJMFkGf/rL7mz/Qx7wt+iis+zIk50V7ZROL\nzwlNRyKV4EhF2N2q32aO0yv3ectg6lL9z5SC0a3shgJ6j/muH0FXtV5tXvN/YN4HLl91FjFjVIOx\nwWCwAH8BXlZK/XCE26uBvv99GYAHeEQp9YfLPaYEYyGEEBOKpxPe+gEc+CUYTPpUi5v+Qd+sJ96Z\ncAjazujht68lou0cEMks6cVDWyKmzAeT5cat7dSL+r912xlInQY3fx4Wflg2Y8aw0dx8ZwCeBjqV\nUp+/ig/8FFIxFkIIMVl11cCOb8PJ34M9HW79Iix9CMzWaK8sdjmboOHgQDW46SgEPfpt8amRELxc\nrwbnLgH6fyiNAAAIaUlEQVR7WnTXC3orzblt8Nb39fUm5sJNn4MlD4LVHu3ViWFGMxjfDLwFnEQf\n1wbwZaAAQCn1X8Pu/xQSjIUQQkx2TUf1mbg1b+lVxdsehbl3y8atgFv/2vRVghsOQ2+TfpvRAjml\nA9XgvKWQNj22v2ZK6Zsx3/oB1O4Ge4b+asHyT11bT7MYE2M2lWK0SDAWQggx4SkFVa/pEyxaT+tB\n7/bHoOimaK/sxtA0aD83NAS3ntLnBAOkFg0KwcsgewFY4qK65OtSu0cPyFWvgS0ZVjwCK/4WEtKj\nvbJJT4KxEEIIESu0sL5xa8d39OrorI1w2zf08WETiat1UAg+CI1HIdCr32ZLhrwlAyF46jJIyIju\nesdK01F48/tw9i9gSYBlD8Hqv4fE7GivbNKSYCyEEELEmoAH9v8nvPUjCLphyQOw9l/GZ2AKeqH5\nRCQAR6rBPXX6bQaTPrZuaqQ3OG+ZvmFuso03az0Db/0Qyp/T20QWf0zfkJlaGO2VTToSjIUQQohY\n5W6HN/9DP0DCZNWriav/HmyJ0V7ZyDQNOs8PqgYf0g/S0EL67cn5Q0+Py1koG9AG67wAux6HY8/q\nbSSl9+mj3jJKor2ySUOCsRBCCBHrOs7Djsf08V8JmbD2S/pUgxs1euxyPJ1DQ3DjYfB167dZHfox\nyoNbIsZjxTsaehphz0/g8FP6QSRz3wdr/lHfcCjGlARjIYQQYrxoOKRPsKjbo7cc3PYNmP3uGzON\nIeSHi+UDIbjhoH6ABYDBqB9kMXXpQEtE5iwwmsZ+XROZqw32/Vx/xcDvhJI79NP08suivbIJS4Kx\nEEIIMZ4oBRUvwauP6pMc8lfoEywKVozux+iqHjhCueEQXDwB4YB+uyN74NCMvGV6ZdjmGL2PL4by\ndsOB/6eHZG8nFK3RA/K0W2N7RN04JMFYCCGEGI/CITj2K9j5XXC1wJz3wIZvQEbxO38sb7feBtF3\nhHLjIfB06LeZ4yMtEUsHWiKS8iSQRYPfpbdX7PkJuC7q/x63fAFm3in/HqNEgrEQQggxngXcsPdn\nsPvH+gSIZQ/pp+g5ska+fzgILacGJkQ0HISOyoHbM2YNHJoxdRlkzY1+L7MYKuiDY7+G3Y9Dd51+\n1PWa/6MfDCPtK9dFgrEQQggxEbha4Y1/h0NPgiVeH/e16u/0DXL9fcGHoPk4hLz6+9gzhm6Oy1sC\nccnR/TzE1QsH4eRzsOuH0F6h953f/L/1aRbyZOaaSDAWQgghJpL2Stj+TTjzZ30mrhbUrzfZ9KkG\nU5cPVINTCuUl+IlAC8OZP+mn6V08CckFcNPnYPH94/uEwCiQYCyEEEJMRHX7oPx5SC/R+4OnLACz\nNdqrEmNJKah8Fd76PtTvB8cUWPVZWPYJ2Rx5lSQYCyGEEEJMJEpBzS79cJjqNyA+FVb8Lax4RL8s\nLutqg/EkO5tRCCGEEGKcMhhg2hp48E/wye2QvxJe/y78aIE+5s/VGu0VjnsSjIUQQgghxpupy+Aj\nv4FP74aS2/XpJY8vgG3/DD0N0V7duCXBWAghhBBivMqeDx98Ej57CObfC4c2w48XwR8/qx85Lt4R\nCcZCCCGEEONdRjHc/TP43FFY+nE48Tv46TJ47mFoOR3t1Y0bEoyFEEIIISaKlALY9H34/El9ckXF\nS/Cfq+B//n97dxeaZRmAcfz/zKXp0GA5ggmGdlBBlqzhEtPRDLVSsVSa2YIGqTCkiR5osAMZeCCF\nGqZOzSRZmARD+0APXFSIDDZpMCQiQYsYsY2+GKToHg+eIxPn127v1/f9/2Bng+fiOrp4eN77fiO7\nAVHDchhLkiTlm/GPwLzmbCBXb4QLp2BfDXy6BM6fip0uZzmMJUmS8tW4UnhhE6zrgRc3wx89cPBl\nOLAgOxs50rG9ucphLEmSlO/GjIfnG7M3yC9thb9+g9ZlsLcazh6DoaHYCXOCw1iSJKlQPDAWqlZn\nP9JbvBMu/gtH6rLvkLs/hyuXYyeMymEsSZJUaIpHQ0Vddszb0o8hKYK2VbDzWej8BC5fjJ0wCoex\nJElSoSoaBdOWZReF1H4GY0vhq8bsLOTTu+DSYOyE95TDWJIkqdAVFcETr8A77VDXBqVT4cSm7Da9\n79+H//6OnfCecBhLkiQpkyTwWA28/TXUn4DyCmhvhm3T4GQzDA7EThiUw1iSJEnXm/wcvPkFrPoO\nplbDDx/A9qfg+HvwT2/sdEE4jCVJknRj5dPh9UPQ0AFPLoaOPbDjafiyEf48HzvdiHIYS5Ik6ebK\nHofXWmBtF0xfCT+2wocV0LYG+n6OnW5EOIwlSZJ060qnwKLt8G43VK2Bs0fhoxlw5C3o7Y6d7q44\njCVJknT7JpTDgi3ZbXqz18O5b6FlDrQuh187Yqe7Iw5jSZIk3bmSiTC3Cdb1QE0T/N4FB+bBwYXZ\nWE7T2AlvmcNYkiRJd+/Bh2DOhuwN8vwtMPALHFoC++fCT9/cFwPZYSxJkqSRM7oEZjZk3yAv3AaD\n/XB4BeyeBQPnYqcblsNYkiRJI694DFTWw9oz8GoLlDwMEybFTjWs4tgBJEmSlMdGFcMztdlfjvON\nsSRJkoTDWJIkSQIcxpIkSRLgMJYkSZIAh7EkSZIEOIwlSZIkwGEsSZIkAQ5jSZIkCXAYS5IkSYDD\nWJIkSQIgSdM0zoOTpA+4EOXhMBHoj/TsfGe34dhtOHYbhr2GY7fh2G04Mbt9NE3Tspv9U7RhHFOS\nJJ1pmlbGzpGP7DYcuw3HbsOw13DsNhy7Ded+6NZPKSRJkiQcxpIkSRJQuMN4b+wAecxuw7HbcOw2\nDHsNx27Dsdtwcr7bgvzGWJIkSfq/Qn1jLEmSJF3DYSxJkiThMJYkSZIAh7EkSZIEOIwlSZIkAK4C\n7jw5ngruQhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3308320240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(index=np.sort(leages_by_season['season'].unique()), columns=leages_by_season['country_name'].unique())\n",
    "\n",
    "df.loc[:,'Germany'] = list(leages_by_season.loc[leages_by_season['country_name']=='Germany','avg_goals'])\n",
    "df.loc[:,'Spain']   = list(leages_by_season.loc[leages_by_season['country_name']=='Spain','avg_goals'])\n",
    "df.loc[:,'France']   = list(leages_by_season.loc[leages_by_season['country_name']=='France','avg_goals'])\n",
    "df.loc[:,'Italy']   = list(leages_by_season.loc[leages_by_season['country_name']=='Italy','avg_goals'])\n",
    "df.loc[:,'England']   = list(leages_by_season.loc[leages_by_season['country_name']=='England','avg_goals'])\n",
    "\n",
    "df.plot(figsize=(12,5),title='Average Goals per Game Over Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "f2a75e9a-6653-48b0-9197-cd6359ab6da8",
    "_kg_hide-input": true,
    "_uuid": "c3c47ee74d74659d2b9b23ad00e0bbef9a0ea5c5",
    "execution": {
     "iopub.execute_input": "2022-03-21T19:35:13.125922Z",
     "iopub.status.busy": "2022-03-21T19:35:13.125685Z",
     "iopub.status.idle": "2022-03-21T19:35:13.306897Z",
     "shell.execute_reply": "2022-03-21T19:35:13.306168Z",
     "shell.execute_reply.started": "2022-03-21T19:35:13.125882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32ab574748>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TnJwclFI0Njae2n/ChAl4eHgAEBMTQ0FBAX379mX8+PGsWLGC6OhoGhsbT51D\nCCHao7nRxI7VB9n2VT7YKUbeHI5xXBCGbtQX6eLmSMLVIQyaGExBhnlO5B8/P8DWL/Ppn9CbuHFB\n+IW522SCJU6XWVzFgwu3E93HnX/NHGzT/btXx/ozwK8n//o+h8nxARi6yKcu4nQ2mxifr7J7ucTG\nxp6q2gK8/vrrVFRUkJiYSHBwMK+++ipXX331afukpqbi6np6L5uT00/Lo9rZ2Z363s7OjqamJgD+\n/Oc/M27cOJYuXUp+fj4pKSln3N9gMJza55577uG5554jKiqK2bNnd8xFCyGuCEVZR1i7KJtjh07S\nf7Avo26JwM2z+053ZmenCIv3JSzel6NlNWSuLSZrUyk5Ww/h09cNY0oQEUl+ODjKuBBbVHKslrvf\n30ovFwfeuysJVyebTVkA8+vtkQkRPLhwOyvSS7hhkFSNuyLbfetlJePHj6euro4333zz1G0nT54E\n4Oqrr+bNN988VdnNzs6mpqbmos9VVVVFYKD5B2f+/Pnt2mfYsGEUFhaycOFCZs6cedHnFkJcOWqq\n6vn2vd0sf3knpmYT1z8UzzX3Grt1UtyWp78ro2dEcufzIxl72wBMzZo1H2bx/uMb2PBJDlXlJ60d\nomjlRF0jv5q/lZP1zbw3Owk/967xWr12oD+Rfm7867sc6TXuomz77ZcVKKVYtmwZjz76KHPnzsXX\n1xdXV1deeOEFpk+fTn5+PgkJCWit8fX1ZdmyZRd9rscee4w777yTl156ifHjx7d7v1tuuYWdO3fi\n6WkbU9UIIWyTyaTZva6Yzcv309TYTOJ1oQy5JgT7K7hC6uhsz8AxgcSODqA09xjpa4rZ9X0RO78r\nJCTWG2NKEMExXl1m8GF31Nhs4sGFO8g9XM382UOJ8u86S4G3VI0fWriDLzNKmRIfYO2QxAVSWlvn\nHU1iYqJOS0s77ba9e/cSHR1tlXi6kuuvv55HH32UCRMmnPF+eRyFEIcLjpO6YB/lB08QFOXJ2JkD\n6OVn3cUQbFX10Xp2ry9mzw8lnDzegLuvCwPHBBI9og/Org7WDu+KorXmic8y+GhrIXNviuOWpL7W\nDumCmUyaq19eB8A3vx2DnbzJsglKqW1a68TzbScV4y7k2LFjDB06lPj4+LMmxUKIK1v9yUY2L99P\n5rpievR0ZNLdsYQn9paBZufg5unEsMn9SLw2lP07y8lILWLjp7ls+Xw/EUP9MI4Nwje4p7XDvCK8\nuTaPj7YW8tC48C6ZFMNPVeOHF+3gq8xSro+TqnFXIolxF9KrVy+ys7OtHYYQwgZprcnecogNn+RQ\nV91IXEoQQ6f0w0lWgWs3g730hePeAAAgAElEQVQdEYl+RCT6UVF0gozUYrK3lLF3Qyn+/Twwjguk\n/+DeGOxleM7l8PmuEuZ+vY8bBgXw+0mR1g7nklxn7MMr3+Xwr+9yuG5gH6kadyHyG1MIIbq4I6U1\nrPtoH8X7jtE71J3JDw+SCucl8gnqybjbo0ie1p+sTaVkri3m2//sYb17LrGjAogdHYibp9P5DyTa\nZWv+Ef5n8S6Ghnox9+a4Lv8Jh8FO8fD4cH7z0U5WZpbxizjbWalPnJskxkII0UU1NjST9lU+O789\niIOTgbG3DSBmVIBUpzqQs6sDg64KJn58Xw7uPUJmahFpK/PZ9nUB/eJ9MKYEERDZq8sncta0v7ya\nOR+kEeTlwtu/HIKTffcYHHp9XAD/slSNrx3oLz+XXYQkxkII0QXlp1ew7uNsTlTWMWC4PyNuDJdV\n3S4jZacIifUmJNab4xW1ZK4tZs/GEvJ2lOMV4IpxbCCRw/xxdJY/qxeisrqe2fO3YlCK+XcNpVeP\n7vMaNlh6jX/z0U6+3l3GdUapGncF8hMshBBdyIkjdfzwcTYHdlXg2ceVqb8bTGCkTN3Ymdx9XBhx\nUzhDJ4eRk3aIjNRi1i7KZuPSPKKS+2AcG4inv+v5D3SFq2tsZs4HaZRV1bHo18MJ9u5+s6ZcHxdw\nqtf4mlipGncFkhi34ebmRnV1Nfn5+WzcuJHbbrvtnNvn5+dz/fXXk5mZ2UkRCiGuRM3NJnatLmTr\nlwcASJ7Wn/gJfWUgmBXZOxqIHhFAVHIfDh04TsbaInb/UEzGmiKCojwxpgQRavTGzoaXMbYWk0nz\n+8W72FF4jDduSyAhuHu+uTPYKR4ZH8FvP97Jqj1lXDNQqsa2ThLjs8jPz2fhwoXnTYyFEOJyK8k5\nSurCbI6W1hAW78OoWyJw93axdljCQimFfz8P/Pt5MPKmCPZsKGH3umJW/jsDN08nBo4NJGZkAC49\nu0+bwKV64Zssvswo5U/XRXNtN28xmBxv7jV+eXUOk2Kkamzr5G3sWTz++OP88MMPDBo0iH/+85/k\n5+czevRoEhISSEhIYOPGjT/bZ/To0ezcufPU9yNHjiQ9Pb0zwxZCdCO1Jxr4bv4elv5jB031zVz3\nQBzX3R8nSbEN6+HuSOK1odzx12SuvddIL78ebF62n/lPbGD1vD2UHajCWgtr2YoFPxbw1tr93DE8\nhHtGh1k7nMvOYKd4aHw4WWUnWLXnkLXDEedhsxXjsueeo35vVoce0yk6Cv8nn2zXts8//zx///vf\nWbFiBQAnT57k22+/xdnZmZycHGbOnEnblfvuuece5s+fz8svv0x2djb19fXExcV16DUIIbo/bdLs\nXl/C5mV5NNY3k3BNCInXheJwBS/l3NXYGezoN9iXfoN9OVJaQ+baYrI2l7LvxzJ6h/Rk4NggIhJ7\nX3HLc6/Zd5inlu9mfFRvnp4cc8XM5jEl/qcZKq6O9btirrsrkopxOzU2NjJnzhyMRiPTp09nz549\nP9tm+vTprFixgsbGRt577z3uuuuuzg9UCNGllR88wSdzt7F24T58gtyY8b9DSZ7aX5LiLsyrjytj\nbo3krudHMubWSBobTHz/wV7mP7GBjZ/lcryi1tohdordJVU8tGA7Uf49eXXmYOyvoN5re4MdD4+P\nYE/pcb6VqrFNs9mKcXsru53ln//8J35+fuzatQuTyYSzs/PPtunRowcTJ05k+fLlLF68+GcVZSGE\nOJuG2iZ+/Hw/GalFOLs5cNXsGCKHSmWpO3F0tseYEsTAsYEUZx8jM7WInasL2fHtQUKNPhjHBtI3\n2gvVDXtQS6tq+dX8rXi4OPDeXUm4Otls+nHZ3DAogFe/z+GV73KYGCM/27bqyntltlPPnj05ceLE\nqe+rqqoICgrCzs6O999/n+bm5jPud8899zB58mRGjx6Nl5dXZ4UrhOiitNbkbjvM+iU5nDzewMAx\ngQyb0g9nVwdrhyYuE6UUQQM8CRrgSfXROnb/UMLuH4rJT6/Ao7cLxrFBRCX749Sje7wGTtQ1Mnve\nVmrqm/nk/mT83H9eWLoS2BvseHBcOH/4JJ3Vew8zMcbP2iGJM5DE+Czi4uKwt7cnPj6eu+66iwce\neICbbrqJJUuWMG7cOFxdzzxH5ZAhQ3B3d2f27NmdHLEQoqs5dugk6z7aR+Heo/gG9+S6++PwC3W3\ndliiE7l5OjNsSj8Srw0lb8dhMlKLWb8kh83L84gc5o9xbBA+QW7WDvOiNTabeHDhDnIPVzNvdhJR\n/lf263va4EBeW5PLK99lc1V0b6ka2yBlrdGxiYmJum2rwd69e4mOjrZKPB2lpKSElJQUsrKysLOz\nTv9Ud3gchejOmhqa2fZ1AdtXFWBvb8ewG/ozcGygTOMkAHOfecbaIrK3HKK50USfcA+MKUH0G+yL\noQv15WqteXJpBou2FPLCTUZmJAVbOySbsDitkMc+SefdXyZylVSNO41SapvWOvF820nFuAN98MEH\n/OlPf+Kll16yWlIshLBtBbsrWfdRNsfLa4lI8mPkzeG4ejhZOyxhQ3yDezL+jmhG3BjO3o2lZK4t\nYtW7u+nh4UjsqABiRwfi2sv2XzP/XrufRVsKeWhcuCTFrUwbHHiq13iCVI1tjlSMuyF5HIWwPdVH\n61i/JIe87eX08uvBmJmR9I2ScQji/LRJU7C7kozUYg7uqcROKcLifQgb5EtIrDfObrbXi/zFrhIe\nXrSDKfEBvHLrIEn+2li8tZDHPk3nvbsSGR8lVePOIBVjIYSwAaZmE+lritjyxQFMJs2wKf0YPDEY\ng4N8qiTaR9kpQo0+hBp9qCo/SebaYvb9WEbejnKUAv/+Hqfu9+zTw+pJ6Nb8I/x+yS6Ghnrx4vQ4\nq8dji6YlBPLqGvNqeOMGSNXYlkhiLIQQl0lpXhVrF+6jsriakIHejJ4RiYevrFonLp6Hbw9G3hzB\niBvDOVxwgvyMCvIzKti0NI9NS/Nw93EmxOhDqNGbwAjPTn8DdqCihjkfpBHUy4W37hiCk73Mv30m\nDgY7HkwJ5/HPMkjdV864qN7WDklYSGIshBAdrK66kY1Lc9m7oRQ3TyeuvddI2CAfqQqJDqPsFH5h\n7viFuTNsSj+qj9ZRkFlJfnoFe9aXkLGmCHsnA8HRXoQYvQkZ6H3Ze9mP1DQwe94W7JRi3uwkPF0d\nL+v5urobE4J4bU0uL3+XQ8oAX/n9YCMkMRZCiA6iTZq9m0rZ9FkeDbVNDJ4YTOIvQnF0ll+14vJy\n83QmdnQgsaMDaWxopnjfUfIzKinIqGD/znIAeof0JDTO3HLh09etQxOxusZm5nyQRmlVHQvnDCfE\n+8xTmoqfONqb5zV+4rMM1maXkzJAqsa2QH5bn8Hf/vY3Fi5ciMFgwM7Ojrfeeothw4Zd0DE+//xz\n9uzZw+OPP36ZohRC2JKKomrWLtxH2f4q+oR7MHbmALwDu+78s6LrcnA0nOo51jqSyuJq8tMryc+o\nYMuKA2z54gCuHo6EWJLkoCjPS1py3GTS/H7JLrYfPMobtyUwJMSzA6+me7spIYjXvs/l5dU5jI2U\nqrEtkMS4jU2bNrFixQq2b9+Ok5MTFRUVNDQ0XPBxpkyZwpQpUy5DhEIIW9JQ18SWFQdI/74IJxd7\nxv8yiqjhfbrlsr6i61FK4RPUE5+gniReF8rJ4w0UZJoryTlbDrHnhxIMDnYEDfAk1OhNiNGHnl4X\ntjLd3G/28WV6KU9eF8W1xj6X6Uq6J0d7Ox4Y158/Lc1kXU4FYyN9rR3SFU8S4zZKS0vx8fHBycnc\ni+Xj4wNAaGgoM2bMYM2aNQAsXLiQ8PBwvvjiC/7617/S0NCAt7c3CxYswM/Pj/nz55OWlsZrr73G\nXXfdhbu7O2lpaZSVlTF37lxuvvlmq12jEOLSaa3Zv6OcHxbnUHOsnphRASRP7W+TU2cJ0aKHuyPR\nI/oQPaIPzU0mSnKOmQfwpVdQkFkJi7LxDnIj1OhNqNGH3qHu51x4ZsGPBfx7bR63Dw9mzuh+nXgl\n3cf0IX15/ftcXlmdzZgIGYtgbTabGP+wOJuKwuoOPaZPXzdG3xJ5zm0mTZrEs88+S2RkJFdddRUz\nZsxg7NixALi7u7NlyxY++OADfvvb37JixQpGjRrF5s2bUUrx7rvvMnfuXP7xj3/87LilpaWsX7+e\nrKwspkyZIomxEF1YVflJ1n2UzcHdR/AOcuOaXw/Ev5+HtcMS4oIY7O3oG+1F32gvRk2P4Nihk6da\nLrZ/c5BtKwtw6elASKy5khwc44Wjy09pQ+q+wzy1fDfjBvjyzORYSegukrlqHM7/Lsvkh5wKxkjV\n2KpsNjG2Fjc3N7Zt28YPP/zAmjVrmDFjBs8//zwAM2fOPPX/o48+CkBRUREzZsygtLSUhoYGwsLC\nznjcqVOnYmdnR0xMDIcOHeqcixFCdKjmRhPbVxWw7esC7OwUo6ZHYEwJxK4LLdMrxJkopfD0d8XT\n35XBk4Kpq2nk4J5K8tMrOZBeQdbmMuwMioCIXoQafWjyc+LBxduJ8u/Ja7clYC8/A5dkemIQr6/J\n5ZXvchgtVWOrstnE+HyV3cvJYDCQkpJCSkoKRqOR999/H+C0F2rL1w8//DC/+93vmDJlCqmpqTzz\nzDNnPGZLawaYP4IV4kJok5aeVSsr3HuEtYv2UXW4lv4JvRk1PQI3T9tflleIi+Hs6kBkkj+RSf6Y\nmk2U7T9umTO5kvVLcgCYZe9AUpQvxwpO4NzfA4MkxxfNyd7AA+PC+fOyTDbkVjIqwsfaIV2xbDYx\ntpZ9+/ZhZ2dHREQEADt37iQkJISMjAw+/vhjHn/8cT7++GOSk5MBqKqqIjAwEOBUAi1ER6mvbWLz\nsjx2/1CCwV7h7OqAUw97nHo4mL92tce5h+V/VwfL7fY4uTqc2tbBySDVh0tQU1XPhiU55KQdxsPX\nhckPxxMc623tsIToNHYGOwIiehEQ0QvjdSHc+a+NOJY3cENvT3I3lJK9rgSnHvYEx3gRYvSx2WWq\nbd0tiUG8sSaXl1dnMzLcW35vW4kkxm1UV1fz8MMPc+zYMezt7QkPD+ftt99mxYoV1NfXM2zYMEwm\nE4sWLQLgmWeeYfr06QQGBjJ8+HAOHDhg5SsQ3cGpgV0fZ1NzvIGo5D4497Cn7mQT9TWN1NU0cuzw\nSepqGqmvaaK5yXTWY9nZqTMnzq0TaldLst3qNkcX+3MOuunuTM0mMtYW8+Pn+zE1aZKuDyPh6mDs\nHWQlL3Flamw28eDCHew6VsO8OUmMifSloa6Jwr1HTs2ZnJN22CaXqe4KnOwN3J/Sn6eW72ZjXiUj\nw6VqbA3KWh/rJyYm6rS0tNNu27t3L9HR0VaJ53xCQ0NJS0s7NUuFLbPlx1Gc34kjdaz7KJv89Aq8\ng9wYd3sUfqHuZ91ea01To4n6mibqTzaeSpbr2nxtTqjN27Tc1ljXfPZAFDi5tCTRLQm0+WsnSzW6\n7W0tVWqDfdf+SPXQgeOkLsyiorCavjFejJkRSS+/HtYOSwir0Vrz5NIMFm0p5Pkbjdw6NPjn25j0\nactUtwygt/Yy1V1JXWMzKS+mEuzVg4/vHS5vKDqQUmqb1jrxfNtJxVgIG2EyaTLWFLH58/2gNSNu\nDCd+QtB5B3YppXBwNODgaLjgntfm5tYJtaUafbJ1Mt1kTq4t91eV15pvP9kE53hP7eBkaFWlbmn3\nsFSrf9YG8lMV297Bzqp/COpqGs2tK+tL6OHuyKR7Ygkf0lv+OIkr3r/X7mfRlkIeHNf/jEkx2OYy\n1V2Ns4O5avz057vZtL+SEf1tvxjX3Uhi3E75+fnWDkF0Y+UHT5C6IIvDBScIjvVm7MxI3H1cLvt5\nDQY7erg70sPd8YL20yZNfW0T9SfbVKlbJdHmJNv8/5Gyk6daQEzNZ8+o7ezVGZLon1o/Wr4+Va22\n3ObobH9JgxO11uzbXMbGz3Kpq24kflxfhk4OO21qKiGuVCvSS3jh6yymxAfw+4kD2r2ftZep7qpm\nJPXljVTzaniSGHe+dv3WV0pdA7wCGIB3tdbPt7n/LuBFoNhy02ta63c7ME4huqXG+ma2fLGfXd8X\n4exq32UqlMrOPBDQ2dUBaH8Cr7WmqcH08wS6prFVkv1T5frEkToqCs0JdlP92ds+lAKnswxCbKlY\ntx2U2PL/0UMnWbcom5KcY/iFuTP5kUH49u3ZAY+SEF1fWv4Rfrd4F0mhnrw4Pe6ixx109jLVXZmz\ng4H7xvbnL1/sYVNeJcn9ZbBvZzpvYqyUMgCvAxOBImCrUupzrfWeNpt+rLV+6FID0lrbfFJgy2Qq\nuK6jILOStQv3ceJInXnVtGn9LYlm96WUwsHJgIOT4YKXnW1uNJ1q42hdjW5Jqs1Va/PtddUNHDts\nrlLX15677QPAqYc9KbMGEDMyQKbFE8LiQEUNcz5II7CXC2/fkYiTfcckqmdapvrgbnOSnLO1Y5ap\n7upmDg3mzdQ8Xvkum+T+ydYO54rSnorxUCBXa70fQCn1EXAD0DYxvmTOzs5UVlbi7S3TlFwMrTWV\nlZU4O19Zv0C6mpqqetYvySE37TCe/j2Y9vsEAiJ6WTssm2dwsMPVw+mCexJNJk1DbdNZByICDBwT\niEvPC2snEaI7O1LTwOx5W1BKMe+uJDxdL9/PRw93R6KS+xCVfI5lqgMty1THnX+Z6u6gpWr87Io9\nbN5fyfB+UjXuLOedlUIpdTNwjdb6Hsv3dwDDWleHLa0U/weUA9nAo1rrwjMc69fArwGCg4OHFBQU\nnHZ/Y2MjRUVF1NXVXco1XdGcnZ0JCgrCwaF7Vx67Im3S7NlQwqaleTQ2NJN4bSgJk0JkhLYQwqbU\nNTYz690fySiuYtGc4QwJ8bRKHFrr05apLs2rQpv0OZep7k7qGpsZPXcN4b5uLPr1cGuH0+V15KwU\nZ3pb1jab/gJYpLWuV0rdB7wPjP/ZTlq/DbwN5una2t7v4OBw1iWVhejKjpTUkLowi9LcKgIjezH2\ntgF4+rtaOywhhDiNyaT5nyW72FZwlDdmJVgtKYYzL1NduOcIB9IrzrhMdYjRm169u8+0ii1V4/+3\nYg8/7q9kmFSNO0V7KsbJwDNa66st3z8BoLX+v7NsbwCOaK09znXcM81jLER309TYzLaVBWz/pgAH\nZwMjbwonKrmPtAoJIWzSC19n8WZqHk9eF8Wvx/S3djhn1XaZ6qOlNQD08uthmeXCG/9usEx1XWMz\no15YQ6SfGwvnSNX4UnRkxXgrEKGUCsM868StwG1tTtZHa11q+XYKsPcC4+00zc2mLv+DIrqGon1H\nSV2QRdXhWiKH+THq5gjpYxVC2KyFPx7kzdQ8Zg0LZs7oftYO55xaL1M94sZwqsprKcg0J8npawrZ\n+e3BbrFMtblq3I+/frmXrflHSAr1snZI3V67Vr5TSl0HvIx5urb3tNZ/U0o9C6RprT9XSv0f5oS4\nCTgC3K+1zjrXMa1RMT5aVsOyl3YQNz4IY0oQjs7dsy9JWFdddSMbPs0ha1MZ7j7OpNwWRd8Y+WUm\nhLBdqfsOc/f7aYyO8OHdXyZi34ULSA11TRTtPWquJmdWUnu84bRlqkOM3nj1ce0yn9zVNjQzeu73\nRPm78997hlk7nC6rvRVjm1oS+nI7UlrDhk9yOLj7CE6u9sSP70vcuCCcenS9d5HC9mityf6xjPWf\n5NJwsolBE4NJ/EXoFTsXpxCia9hTcpzp/95IiLcri+9Lxs2p+xSNzrZMdU9vZ/O8ynFdY5nqd9bt\n529f7eWT+5JJlKrxRZHE+BwOHThO2sp88tMrcHSxJ25cEPET+nb7OWTF5VNVfpLUBfsoyjqKX5g7\n426PwjvQzdphCSHEOZVW1TLt9Y0oBUsfGIm/R/ee7vPUMtUZlRTtPUJTo6lLLFN9sqGJ0S+sISbA\nnQ/vlqrxxZDEuB3KD54gbWU++3eU4+BkwJgSxKCr+kofqGi35mYTO789yNYv8zEYFMOn9id2TGC3\nn2NTCNH1Vdc3Mf3fmyg8cpIl9yUT3cfd2iF1qqaGZopaLVNdfbQeAL8wdwaOCSQ8sTf2Drbzid/b\n6/J47qssPr1/hFVnC+mqJDG+AJXF1WxbmU/OtsPYO9gROyaQwRODbfJdo7AdZfurWPPfLI6U1NBv\nsC+jb4nEzVNeM0II29fUbOLu99NYn1vBvLuSGBPpa+2QrEprfWqZ6uwtZRwtO4mzqwMxo/oQOzoQ\ndx8Xa4d4qmocG+jBB78aau1wuhxJjC/C0bIatq0sIHtLGXYGO2JGB5AwKRg3z+790ZK4MPW1TWxe\nmkfmD8W49XJizK2RhMVf2X9UhBBdh9aaJ5dmsmjLQf7vRiMzhwZbOySborWmeN9RMtYWc2BnORoI\nNfpgTAmkb5SXVZeN//faPJ5fmcVnD4wgIViqxhdCEuNLcOzwSbZ/XcC+zWVgB9EjAki4Ohh3b+u/\nYxTWo7Vm/45y1n2cTe3xBozjghg2pZ/MbiKE6FJakqsHUvrz2DVR1g7Hpp04UsfuH4rZs76E2hON\nePR2wTg2iKhkf6sM3K+pb2L03DUYAz14X6rGF0QS4w5wvKKW7d8UsHdjKWgYMNyfIdeG4OHbfVbW\nEe1z4kgd6z7KJj+9Ap++boy7PYreIVdWP54QoutbkV7CQwt3MDk+gFdmDJLxEO3U3Ggid/thMtcW\nUbb/OPaOdgwY5o8xJajTB1q/mZrHC19nsfSBEQyWqnG7SWLcgaqP1rF91UH2/FCCyaSJTPJjyLUh\nsqTvFcBk0mSsKWLz5/tBa4Ze34/4CUHYdeE5PoUQV6ZtBUeY+c6PxAd58OHdw3C2oYFlXUn5wRNk\npBaRvfUQzY0mAiJ6MXBsIP0G+3bKAmI19U2MeuF74vv2Yv7srlM1rqitoLK2kgFeA6xyfkmML4Oa\nqnp2fHuQ3euKaWo0ETGkN0OuDZVpubqp8oMnWPPfLMoPniA41puxMyNtYgCGEEJcqPyKGqa9sYFe\nPRz57P4ReLrK7EuXqq66kb0bS8lcV8Txijp6eDgSOzqQ2NEBl33w/utrcnnxm30se3Akg/r2uqzn\n6ijPbnqW5bnLWT19NZ7OnV/plsT4Mjp5vIFd3x0kI7WYxvpm+g32JfG6UHz79rR2aKIDNNQ1sWXF\nAdK/K8S5pyOjb4kgfEjvLrNKkhBCtHakpoEb39hAVW0jSx8YSaiPfNp5NrqxkYaiIhyDglAO7esh\nNpk0B3dXkpFazMHdldjZKfol+GIcG0SfcI/L8rej2lI1Tgj25L27kjr8+B0tvyqfqcunMj1yOn8a\n/ierxNDexFhGDV2EHu6OJE8LZ/DEEHZ9X0j694Xs31FOaJwPideF4hcqvaddVX5GBesWZXPiSB0x\nowNIntpfFn4RQnRZdY3N/PqDNEqq6lg0Z5gkxefQePgwxQ8/Qu2uXSgnJ5xjYnA2DsTFGIdLnBGH\n4OAzJrl2dsq8ip7Rh2OHT5K5rpisjaXkph3GO9ANY0ogkUP9cXDquNYVNyd75ozux4vf7CO96Bhx\nQbZdNX51x6s4Ghy5N/5ea4dyXlIx7gD1JxtJX1PEru8KqT/ZRHCsF4nXhdGnv4e1QxPtVFNVz/rF\nOeRuO4ynfw9Sbo8iINy2f9EIIcS5mEyaRz7awYr0Ul6/LYFfxPWxdkg2qzZzN0UPPkjz8eP4PHA/\nzRWV1GZmUrd7N7quDgA7Dw9cBg7EOc5oTpaNA7H3PfNUnY0NzeRsOUR6ahGVRdU4utgTndyHgWMD\n6eXXMQP4T9Q1MuqFNSSGePIfG64aZ1ZkMvPLmdwbdy8PDX7IanFIK4UVNNQ2kbG2iJ2rC6mrbiQo\nypPE60IJjJRRo7ZKmzR7NpSw8bM8mhqbSbw2lIRJIRgcZHCdEKJrm/t1Fm+k5vHEtVHcO7a/tcOx\nWVVffknpk3/C4O1F39dfxzk6+tR9uqmJ+txcatPTqcvIpDYjg/qcHGhuBsC+Tx9cjEZc4ow4DzTi\nPDAWg9tP44601pTlVZGxtpi8bYcxmTTBMV4MTAkiZKD3Jc8K8tr3Ofx9VTZfPDQKY5DtFeO01sxZ\nNYfso9l8deNXuDlab0yWJMZW1FjfzO4fitm+6iC1xxvoE+5B0i/CCIrylD5VG3KkpIbUBVmU5lUR\nGNmLlFlRHfZOXgghrGnRloM88VkGs4YF89epA+Vvzxlok4nyV/5F5Vtv4TJkCEH/egV7b+/z7meq\nraVu715zspyeQW1GBo2FheY7lcKxfz9cjHGn2jCcB0SiHB2pqapnz/oSdq8rpqaqgZ7ezgwcE0jM\nyACc3S6uZa+lapwU6sW7d5435+t0G4s3cu/qe3ks6THuiLnDqrFIYmwDmhqa2bOhhO3fHKTmWD1+\nYe4kXhdKyEBv+SVlRU2NzWxbWcD2bwpwcDYw8qYIopL95TkRQnQLa7PL+dX8rYyO8OHdXyZiL9NL\n/kxzdQ0lf/wj1d99h8fNN9HnqadQjhc/U0fT0aPUZWb+VFlOT6f5yBEAlIMDTtHRpyrLjjFGiqt6\nkLG2hJKcYxgc7IhI7I0xJeii5sf/13c5vPRtNiseHsXAQNupGpu0iVtX3EpVfRVfTPsCR4N1Z0KR\nxNiGNDea2LuplG1f51N9pB7f4J4kXhdKWLyPJGOdrGjfUVIXZFF1uJYBw/wZeXM4Lj1l2iIhRPew\np+Q4t7y1ib5ePVhyXzJuTjLGvq2GoiKK7n+A+v378Xv8cTxvn9Xhf4u11jSVlFCbYa4o16VnULt7\nN/rkSQDs3NxwNg6kIXIo+YYI9hfZ09Rgwi/MHePYQMKH+LW7pe94XSOjnv+eYf28eeeXtlM1Xnlg\nJY+te4y/jfobU/pPsXY4khjbouYmE/t+LGPbynyOV9ThHehG4nWh9B/sa9W1168EddWNbPg0h6xN\nZbj7upAycwB9Y7ysHZYQQnSYsqo6pr6+AYBlD47E38PZyhHZnpoft1D8m9+gtSbony/hOmJEp51b\nNzfTsH8/tekZ1GaY21K4YUYAACAASURBVDDqsrOhqYkmgzOHw6+iqM8oqumJszNEJ/tjnNiPnl7n\nfx5fWZ3DP1fbTtW4sbmRG5bfgLO9M0uuX4LBzvqLyUhibMNMzSZyth4ibWUBxw6dxLOPK4nXhRA+\nxE+W5+xgWmuyfyxj/Se5NJxsYtD/Z++sw6M4uz58bzbuIcSVEIIGdwtWJFiLtfBWaEsLFHft91Zw\nLUFaXuotheJFiye4BA0JIUYS4u6yNt8fgxYoCdkQYe7ryrVkMzPPmWV39jfnOc/59XSlla87uvoV\n/yGVkKjOBKUG8VPwT3zQ8AOa2DSp6HCqPXnFKoZ+d557GQVsH9OO+g5S29B/krl1K0kLFqLv6orL\nhvXou7tXdEhoiospvn37vlgWf5KzDYhz8iHN2hsAB71U6tWV497JC8P69dExeNo8JLtQScelJ2jn\nYc3/KkHWeGvoVhZeXMj67uvp7Ny5osMBJGFcJdBoBCKvpBB4KJqMhHws7Yxp0dsNr9Z2kuWwFshK\nKSDgjzvEhWZiV8ucru/Wk1wKJSReAbmKXIbsHUJCfgIAA2oPYEqLKdQ0qlnBkVVPVGoNo34N5HR4\nGj+ObIWP17NbiL2uCEolSYsWkbVlKyY+nXFasQK5WeU15FJnZ1N46xbpV0K5c1tBtNIZpa4JxvlJ\nOCWfxd08AzPvehg1ul+z7OGBTC5n9dEw1hwP5+DETjRwrLgbowJlAb67fHEzd+Pn3j9XmpJRSRhX\nIQSNQNT1VC4fjCY9Lg/zmoa06O1O3bb2yHUlgVxa1CoN147GEngwGrlcRts3a9Oos5NUriIh8YqY\nfXo2f9/9m297fMulpEv8EvwL+nJ9xjQew3/q/wc9uWSaoy0EQWD+nltsvhjL4kHeDG/tWtEhVSpU\nmZnET5pMwaVLWI/6GJspU5DJq9aMoVKhIuxkBEH+8aRnypALShxSA3GMPo5pQSI6xsYYNmqErH4D\nvooAq2ZNWTauZ4UJ0o03NrLu+jp+6/MbTW2bVkgMz0ISxlUQQRCIDkon8MBdUmJyMa1hQPOebtTv\n4ICuXtX6IFcUiZHZ+G8OJSMhn9rNbOj0thcmluXrWS8hIfGIA1EHmH16Np81/YyxTcYCEJsTy7LL\nywiIC8Dd3J1ZrWfR0aljBUdaPdgYEMniQ6GM7VKbWb3rVXQ4lYqisDDiPhuHKiUFhwVfYzGg4heA\nlZXk6Bxu+ccRHpiMWiVgV0NFLSECy8hTKG+HICiV4oZWVpg2afKEGYncsvxNqzKLMumzqw+t7Vvj\n182v3McrDZIwrsIIgkBsSAaBB+6SFJWDiYU+zXq60bCTo1Qb+xyKC1Vc2B3JrdPxmFoa0Hl4XWo1\nlqZtJSReJQl5CQzeOxhPS09+6v0TujpPdkQ4FXeKZZeXEZMTQxfnLsxoNQNXcynD+bIcuJnIuD+u\n0q+xA37vNJPWqDxG7okTJEyfgY6JCc7r1mLUpHrVuRfmKbh9NpFbAfHkZhRhYmlAg/Z22Ftks2bj\nHtqrkmman4AiKgru6zw9V9dHZiTe3mK9spGRVuNadnkZm29vZteAXdS2rFymMpIwrgYIgkDcnUwC\nD0STEJ6Fkbk+zXq40rCzI/qGUgseEF+jyKupnN4WRmGOgsZdXWg9oJb0+khIvGLUGjUfHf6IO5l3\n2N5/Oy5mLs/cTqlW8vvt3/nuxncoNUpGNhzJKO9RGOtJ5jql4UpMBsM3XaSxkwW/j2qDoTSrCIjf\nCekb/0fqmjUYNmyI8/p16NnZVXRY5YZGIxBzK50g/zjuhWSgI5ehdDBka1YWP0xrj5eZnKJbwQ+7\nYBTeuoUqMVHcWS7HwMvrkc1148YY1K6NTPflvj8T8hLot7sffT368nWHr7V4ltpBEsbVjITwTC4f\niCYuNBNDEz2avuGCt48z+kavrwDMzSji1JY7RAelU9PFlK7v1nup5ugSEhJlZ9PNTfhd8ytxz9LU\nglS+ufoNeyP3Ymtky9SWU/Gt5VtpFupUZq7EZPLJr4FYGOmxc2x7aphIvdhBdKRLnDefnIMHMe/X\nD4cFX6Nj+Pq0rMtKLiAoII7b5xJRFqkpNJHjO6gOdVrZoffYbLMyJeVJM5KgIDQ5OQDIjIwwbNDg\nicyynrNziT6X887M4++7f3Ng0AHsTezL7TxfFkkYV1OSorIJPBhNzK10DIx1adzNhcZdnTE0eX0W\ns2g0AkEn47iwV5wiat3fgybdnKVOHhISFcSttFu8d/A9urt1Z3nn5aUSt9dTrrP40mJC0kNobtuc\nOW3mUK+GVCv7LG7GZbH6aBgn76RiY2bA9tHtcK9pUtFhVQqUSUnEjRtPUUgINlOmYP3JqNf2JktR\npGL9zzfJvpmBjUYHA2Nd6rV3wNvHCQubp2dmBEFAGRPzhBlJUUgIgkIBgNzS8mGtsmhz7f2UdXZY\nZhhD9g7h/QbvM73V9FdynqVFEsbVnJSYHAIPRnP3Rhr6hnK8uzrTtLvrS/utVxVSY3M5+XsoqbG5\nuDWypvM7XpjX1G6NlISERMkpUBYwbP8wilRF7BywEwuD0psLaAQNeyL2sObqGrKKsxhSZwjjm43H\nytCqHCKuetyKz+abY+Ecu52MpbEen3b24IN27phIrnYAFF6/zr0JExDyC3BcsQKzbl0rOqQKJzNf\nQaelJ+njYEUPXWOirqWiEQRcG1jj3cUJt4bW/9qpSVAqKQ4Pf8KMpDgyEjQaAPScnDD09n6YWZ6X\n8gMXsm9waNAhLA3Lf5HfyyAJ49eEtLhcAg9GE3ktFV19Od4+TjTt4YqxefWaWlMUqbi0/y43j9/D\n0EyfTsPq4NnC9rXNCEhIVBa+OPcFu8J38UOvH2hl36pMx8pR5PDt9W/ZEroFEz0Txjcbz1CvoU8t\n4ntdCE3K4Zuj4fwdnIS5oS6fdPJgZAd3zAyrdwKkNGTt2UPS5/+Hrr09LhvWY1CnTkWHVGlYfjiU\nDf6RHJncGUcDfYJPxxN8OoGCHAXmNQ1p5ONM/fYOJZ5x1uTnUxQSIorlW2JmWRkfL/5NBoXO1ji2\n9nmYWTb08kKmV3neq5Iwfs3ISMgn8FA0EYHJyHV1aNjZiWY9XTGxqPqtyqKD0gjYcoe8jGIadnKk\n3Vu1MTCuPB82CYnXleOxx5l8cjIfNvqQqS2mau24EZkRLLm8hIuJF6ljVYc5reeUWXRXJcKSc1lz\nLJwDQYmYGejyUcdafNSxFhZG0nXvAYJaTcrKVWT8+CPGbdrg9M1qdK2kGYbHycxX0HHpCbrVt2Pt\n8GaA2Oc/6noqQf5xJEZkI9fTwau1Hd4+zti4lt70RJmWxsKfP8Q0IpHBqqYobwWjzsoCQGZggGH9\n+vfLMMQfPTe3CktoScL4NSUruYArh6K5cykZHR0ZDTo40KyXW4m81isb+dnFnP4znMirKVg5mNDl\nP3Vx9KycUzQSEq8bKQUpDN47GAcTBzb7bta6aYcgCByPPc7yy8tJyE+gl3svprWYhoOpg1bHqUxE\npOThdzycfTcTMNaT81HHWnzcsRaWxtVrBrCsqHNziZ86jfzTp7EaMQK7ObMrVWayMrHs71C+DRCz\nxnXsnhS+aXF5BAXEEXYxCZVCg72HOd5dnKnd3LbE5mInY08y8eREPm/7OcPqDhPrlePjKbp586HN\ndVFICEJhIQByCws8/U9qvU1cSZCE8WtOdmohV/+OJvR8EsigXnsHWvRyqxL1uIJGIPhMAud3R6JW\namjp60aznm6SC6CERCVBI2gYe2wsV5Ov8mf/P/Gw8Ci3sYpURfwU/BM/BP2ADBkfe3/Mh40+xEBe\n9WfDHnA3LR+/4+H8dT0eQz05H7R355NOHlK3iWdQfPcucZ+NQ3HvHvbz52P1ztsVHVKlJuN+1rhH\nfTv87meN/0lxgZLQ80kEBcSRnVKIkZkeDTs50bCTI6ZWz0+qqTVqhuwbglKjZPfA3ejpPPvmRFCp\nKI6MpPDmTZT34rCdOkUr51ZaJGEsAUBOeiHXDscSci4BQQN129rTorcblraVs2doekIe/r/fISkq\nG6e6lnQZUQ9Lu8oZq4TE68rvIb+z9PJS5reZz9v1nhYm6rx80r/7lvwLF6nx3ruY9++PTKdsN7YJ\neQmsDFzJkZgjOJk6MaPVDLq5dKvS6wxi0wvwOxHO7mvx6MllvN/OnU87e1DTtPqIfm2Sd+Ys8VOn\nItPRwclvDSatW1d0SFWCJYdC2XgqkqNTfPC0NX3udoJG4F5oBkH+8UQHpSGTyfBoUpNGXZxx8rJ8\n6rO2J2IPn5/9nBU+K+jl3qu8T6PMSMJY4gnyMou5djSG4NMJaFQa6rSyo0Ufd2o4VI5WPyqlmsCD\n0Vw7EoueoZwOg+tQr519lf7Sk5CojoRlhjF8/3DaObZjbbe1T3xGBUEgZ98+UpavQJWaip6LC8p7\n9zBq2hS7z+dj1LBhmce/lHiJxZcWE5EVQTuHdsxuPRsPy/LLWJcH9zIKWH8ygh1X4pDryHi3rRuj\nfTywNat6JW+vAkEQyPztN5KXLMXA0xPnDevRd3au6LCqDOl5xXRcepKeDe1Y886zs8b/JCetkFun\n4rl9NpGifCVWDiZ4+zhRt609+oa6FKuL6be7H9aG1mzpu6VKfFdLwljimeRnF3P92D1uBcShUmrw\nbG5LS193rJ2efxdZ3sSFZuC/+Q7ZqYXUbWNPhyGeGJlJU4gSEpWNYnUx7+x/h4yiDHYN2IW10aNe\npoXBwSQvWEjhtWsYentjP38eht7eZO/eTcrKVagzM7EcOhSbKZPLvEhKpVGx7c421l1fR6GykOH1\nhzO2yVjM9Eu/eOhVkpBVyLqTEWwPvIcMGSPauDK2S23szCVB/Dw0CgVJX35J9s5dmPbojtPSpeiY\nVI6ETlVi8aHbbDoVxdGpPtS2Kfn3vUqhJuJKCkH+caTE5KJnKKdeWwcinS6zKmoJm3puoq1D23KM\nXHtIwljiXynMVXD9+D2C/ONQFqnxaGpDS1/3l1qV+tIx5Ck4tyOC0AtJmNsY0WVEXVzq13hl40tI\nSJSOpZeW8vvt39nQfQOdnDsBoMrMJPWbNWRt24bcygrbqVOwGDToidIJdU4OaevXk/H7ZnRMTLCZ\nOBGrd95+aevZB2QUZbD22lp2hu3EytCKyc0nM9BzIDqyyrUeISm7iA3+EWy9dA8BgbdbuTCuqycO\nFpV/zUdFokpLI27iJAqvXqXmZ2OpOX58mUtyXlceZI17N7Jn9dtNX+oYyXdzCPKPIzwwGY1aINcm\niWGD3sC9sXWVMNiShLFEiSjKV3LjxD1unohDUajCzdualr7u2NcqfZP+kiIIAncuJnF2ewSKQhXN\nerrS0tcd3ccsKyUkJCoX5+LPMfrYaIbXG87cNnMRVCoy//yTVL+1aPLysPrPCGzGjUNenABRAXA3\nAHISoONkaPAmyGQUR0SQtHAhBecvYODlhd38eVqpEw1JD2HxxcVcT71OI+tGzG4zmyY2TbRw1mUj\nJbeIb/0j2XwxFo1GYGhLF8Z1rY2zlbRu4kUUhYRwb9x41JmZOC5ehHmfPhUdUpVn0cHbfH86imNT\nffAoRdb4n/idXU9gQASdcwZSnKXB1MqAhp2daNDBsVJ7KEjCWKJUFBeqCDoZx/XjsRTnq3BpUIOW\nvu5ab4+WlVJAwB93iAvNxN7DnC7/qVehZRwSEhIvJrMok0F7B2Ghb8HWflvRXAsiacFCiu/cwbhF\nE+yGtcJQdRvunoK8ZHEnK3eQG0DaHajlA32WgW09BEEg98hRkpcuQZWQiLlvH2xnzEDPoWxt2ARB\n4MDdA6wKXEVqYSoDag9gSosp1DSqWfYXoJSk5RXznX8kv1+MQakWGNzciQnd6uBSQxLEJSHn779J\nmDMXuYUFzuvXaaU2XUJ8X3ZcegLfRg6sesmscWpBKn1398XH2YelHZcSHZROkH8ccaGZ6OjK8Gxh\ni7ePM3a1zCtd3bEkjCVeCkWRilsB8Vw/FkthrhKnupa09K31zBWppUGt0nDtaCyBB6ORy2W0G+RJ\nw46O/2pJKSEhUfEIgsCkk5M4E3+Gza38MP92KzmHj6NraYhdGzVmljHIZICJjSiAPXzERys3UKvg\nyk9w4mtQ5EObMeAzCwzN0RQWkv79D6R//z3o6FBz9KfU+PBDdAzK1pEhX5nPppub+CXkFwzkBoxt\nMpYR9UZovc/ys8jIV7DxVCS/nouhWKXmrWbOTOjmiXtNqSa2JAgaDWnr1pO2YQNGTZvivG4tujVf\n/Y1NdWbhgRB+OHOX49O6UOsl3pcLLixgZ9hO/nrzL1zNXR8+n5mUT1BAPKHnE1EWqbFxNcO7ixN1\nWtpVmtlgSRhLlAmlQk3I6QSuHomhIFuBg6cFLX3dcalfo9QCOTEyG//NoWQk5FO7uQ2dhnlhYim1\nI5KQqArsCNnMwvOLWXrDBtcTSaARsK6fh3UT0PHsAB5dRCFsWx+ed23IT4PjX8LV38DUDnp+Dd5D\nQSZDERdPytKl5B49ip6LC3ZzZmPatWuZs00xOTEsv7ycgLgA3M3dmdV6Fh2dOpbpmM8jq0DBptNR\n/Hw2mgKlmoFNHJnYvU6ZpqtfNzT5+STMnkPu0aNYvPUW9l9+gY5+5Z2Wr6qk5hbTadkJ+no7snJY\n6cqNYnJieHPPmwz2Gsz8tvOfuY2iSEXYxSSCAuLJSMjHwESXBu0daeTjVOE+CpIwltAKKqWa22cT\nuXo4hrzMYuxqmdPS1x23RtYv/OIqLlByfk8UwafiMbUywGd4XdwbS3f/EhKVGrUS4gLhbgDRUcdY\nGJnAh8c0WGXLMPMywfYDX/Rb9wPHZlDaLGzcFTg4HRKugms78F0O9t4A5J87R9LCRSgiIzHp1Am7\nOXMw8KhV5tM5FXeKZZeXEZMTQxeXLsxsORMXc5cyHxcgu0DJD2ei+PFsNPkKFX29HZjcow6etpW7\nO0ZlQxEXT9y4cRSHh2M7cwY1Pvig0k3DVycW7A/hp3PRHJ/qU6rZjOkB0zkVd4qDgw6+sERJEAQS\nwrIICogj6noagiDg3siaRl2cca1fo0JmiyVhLKFV1EoNoRcSufJ3DLnpRdi4mtGyjzu1mtR8+AYv\nVBVipGuEIAhEXk3l9J9hFOYqaNzNhdb9a6FvWLYV6BISEuWARgMpweKCuSh/iDkHynzyc3Q5FmqP\nV5QGHScbnP7vC0x9umlnvOu/w7EvoDATWo2CrnPByApBqSRj82bS1q1HU1xMjfffo+bYz5Cblq0U\nQalW8tvt39h4YyNKjZKRDUcyynsUxnovV/ObU6TkpzPRfH8mitwiFb7e9kzq7kVde0kQl5aCwEDi\nJkxEUKlwWrUK007lk9WXeERKbhGdlp6kfxNHVgwtWdY4OD2Yd/a/wyfenzCx+cRSjZeXWUTw6QSC\nzySgLFIxckkHDIxfvYW3JIwlygW1WkPYxSSuHIohO7UQaycTmvV25QBb+DnkZwbYDqZZWB+SbudR\n08WUru/Ww9bNvKLDlpCQeIAgQObdR50j7p6CgnTxb9aeqB07kn65mNR9pymSa1CMfJO2E75Cpqfl\nL7LCTDi5CC5/D0Y1oMcX0PQ/oKODKi2NlFWryd61C7lNTeymT8d8wIAyZxFTClL45so37Ivah62x\nLdNaTKNPrT4lPm5esYpfzkXzv1NRZBcq6dnAjsk9vGjgKF3jXobMbdtI+upr9J2dcf52Awa1yj5D\nIFEyvtoXwi/nozkxzQc36xffeH5y5BNCM0I5OOjgS/cLV6s0pN3Lw65WxXxeJGEsUa5o1BrCA1O4\ndDCKnOQiMo2SUDpnYhXpAcjQbZPByLcHYmooLTqRkKhw8lJEARzlLwri7FjxeTOHhwvmBPfO5Jy+\nRsqy5ahSU/H3lpHxYV/m+S4v39gSb8LBGXDvAji1AN8V4NQcgMIbN0hasJCioCCMmjXDbv48rXQo\nuJ5ynUUXF3E74zbNbZszp80c6tWo99zt84tV/Ho+hv+diiSzQEn3erZM7uGFt3P5tbWszghKJclL\nlpK5eTMmHTvitGolcnPp5uJVkpJTRKdlJxnQxJHlL8gan084z6dHP2VGyxm83/D9VxSh9pGEsUS5\nE5YZxqTjkzG6Z0uv9BGo0nSxq2/ClToH2J+6GxsjGyY0m1ApG+5LSFRrinLEkogofzErnBIiPm9o\nAe6dHi2Yq1kHZDKKQkJIWrCQwqtX0WtYn6Ud04l3M2F7/+2Y6L2Cm1tBgJvb4Ojnoohv/j50/y+Y\nWCNoNOXinqfWqNkTsYc1V9eQrchmqNdQxjcdj6XhoxaVhQo1v1+I4buASNLzFXSpa8PkHl40ddFu\nG8vXCXVWFnGTp1Bw4QI1Ro7EdsZ0ZPLK0bXgdePLfcH8ej6Gk9O64Gr97LIijaBh+IHhZBZlsu+t\nfRjIq+7CeUkYS5QrR6KPMP/sfEz1TFnVZRVNajYhL6sYUysDZDIZ11Ous/zycm6m3aR+jfrMaDWD\nVvatKjpsCYnqiaoY7l0SRXBUAMRfAUENuobg2vZRGzWHpqDzSISoMjNJXbOGrD8fudYtrnmJw7FH\n+LXPrzS2afxqz6MoBwKWwoVvwcAMun8OLT4EHfmT7nmmpthMnIDV22V3z8suzua7G9+xJXQLJnom\nTGg2gX613uLPywl86x9JWl4xnerUZHIPL1q4lU2Mv+4UR0Rw77NxqBITsf/ySywHvVXRIb3WJN/P\nGr/V1ImlQ579Wf87+m9mBMxgQYcFDPQc+Ioj1C6SMJYoF9QaNeuvr2dT0Caa2DRhVZdV2BrbPnNb\nQRA4dPcQq6+uJik/iW4u3Zjacipu5m6vOGoJiWqGRg1JNx8tmIu9AKpCkOmAY/NHvYRd2oCe4VO7\nCyoVmdu2kbrGT3StGzECmwnjOZR2ijmn5zCu6TjGNBnz6s/rASm34dBMsfzD3ht8V4JrGwCKw8NJ\nWriIggsXMKhbF7t5c7XinheRGcGii4u5nHwJmcKR/MR+tHFoxZQ3vGjlLlnVl5XckydJmD4DmZER\nzmv9MG7WrKJDkgC+2BvM7xdiODm9y1MGNEqNkjf3vIm+XJ8d/Xcg16namX1JGEtonRxFDrNPzeZ0\n/GkG1xnM3DZz0Ze/uM9kkaqI30J+4/ug71FoFAyvN5zRjUdjYSDV50lIlAhBgPSIR6URd09DUZb4\nN5v6j4SwewexXOJfKLh8maSFiygODcW4TRvs5s3F0MuL+Lx4huwdgqelJz/1/gldnQruIiMIELIH\nDs+DnHhoMhx6fAlmds9wz/PFduYM9OztX2oohUrDtsB7rDsZTpomEDPHQ6h0Mujt3ptpLadhb/Jy\nx5UQEyTp339P6qrVGNavj/P6dWV2OZTQHknZRXRefpJBzZxYMvjJrPG2O9v4+sLXrO22li4uXSom\nQC0iCWMJrRKZFcmkk5OIz41nTps5DPUaWuoV4mmFaay7to5d4bswNzBnbJOxDKs7DD2dV9+2RUKi\n0pOT+Kg04m6AKA4BLFwec5jrDGYlE23KpCRSlq8g58ABdB0dsJs5C7NePZHJZKg1aj46/BF3Mu+w\no/8OnM2cy/HESokiH06tgHNrQc8IusyB1p+AXO8Z7nmjqfHhyBK75ynVGnZeiWPtiQjiswpp4WbF\n1De8aOZmzC/Bv/DDrR+QIWOU9yhGNhpZpesrKwJNURGJn/8fOfv2Ye7bB4eFC9ExqliTB4mn+e9f\nt9h8MfaJrHGBsoC+u/viYubCL71/qRZ9pSVhLKE1jsceZ+7puRjpGrGqyyqa2zUv0/HuZNxh+eXl\nXEy6iLu5O9NbTqezc+dq8cGTkHhpCjMh+swjIZwWJj5vVEMUwA+ywjU8nu8w9ww0CgUZP/1M2saN\noFJhPWoU1p+MekKg/O/m/1h7bS2LOi6if+3+2j4z7ZAWAX/PgohjYpbcd5n4usCT7nmurtjNno1p\n1y7Pvaao1Bp2XYtn7Ylw7mUU0tTFkilveNG5Ts0n9knIS2BF4AqOxhzFydSJGa1m0M2lm3StKgHK\n5GTixk+gKCgIm8mTsB49WnrdKimJ2YX4LPNncAtnFg8SDXc23dyE3zU/fu3zK81sq0fZiySMJcqM\nRtDw7Y1v+e7GdzSybsTqrqu1NqUoCAIBcQGsDFxJdE40bR3aMr3ldOrWqKuV40tIVHqUhWJt8IOs\ncOJ1EDSgZwxu7R9lhe28QeflurrknjxJ8uIlKGNjMe3RHbvZs9F3fjIbfCvtFu8dfI8ebj1Y1nlZ\n5RYvggB3DokCOSsWGg6CngvAwgmAvLNnSV60+JF73tw5T/TGVak17L2RgN/xcKLTC/B2smDqG150\nqWvzr+d9MfEiSy4tISIrgnYO7ZjdejYelh7lfrpVlcKbN4kbNx51fj5Oy5dh1r17RYck8QL+769b\n/HExFv8ZXTA1UtBnVx9a2rVkbfe1FR2a1pCEsUSZyFPkMefMHPzv+TOw9kA+b/d5uUwjKjVKtt3Z\nxobrG8hT5vGW51uMbzb+hXaTEhJVDrVKFL9RJ0UhfO8SqItBRxecWoot1Dx8xH/rvrh2/99QREeT\ntHgx+QGn0PfwwG7uXEw7dnhquwJlAcP2D6NYXcyO/juqTt2/shDOroEzq0EmB58Z0PYz0DV4yj3P\n+oP3sRo9hoOR2aw5Hk5Uaj4NHMyZ8oYXPerblvhGQKVR8eedP1l/fT2FykKG1x/O2CZjX9rsoLqS\nvW8fifPmo2tjg/OGDRjW9arokCRKwIOs8ZCWztRwPsyvIb+yc8BO6ljVqejQtIZWhbFMJusNrAHk\nwPeCICx5znZDgO1AK0EQ/lX1SsK48nI3+y6TTk4iNieWma1mMrze8HLPIj1ombQ1dCv6cn0+afwJ\n79Z/F0Pdp1fUS0hUCQQBUkMflUZEn4HiHPFvdt6PSiPc2omtybSAJj+ftO++I/3nX9DR16fmuHHU\nePc/yPSfLbS/OPcFu8J38UOvH6pmO8XMaHFxXuh+sPaEPkvBswcAqtRUkleuImfPHrKNLdhY35fE\nlj5MfqMuPRvYG2wHGQAAIABJREFUoaPzcte0jKIM/K76sSt8F1aGVkxuPlnq1Q4IajWp33xD+qbv\nMW7VCie/NWXuNS3xapm/J4g/r93EzHMVfWr1ZmHHhRUdklbRmjCWyWRyIAx4A4gDLgPDBUEI+cd2\nZsABQB8YLwnjqknAvQBmn56Nno4eK7usfOVfltHZ0ay6soqT907iYOLAlBZT6O3eu3JP70pIPCDr\n3v3SCH+x1Vhesvi8lfv90oguYl2siXZnRARBIGf/AVKWL0eVkoLFm29iO20qujY2z93neMxxJvtP\n5qNGHzGlxRStxvPKCT8mtnfLiIR6/dD0XMiRBANWHw2H27eYcnsvbqkxGDZrhr2W3POC04NZfHEx\nN1Jv0Mi6EXPazHn1fZ8rCeq8PBKmTScvIADLd97Gft487VuIS5Q7CVmFdP91LPoWNzg0+ACOpo4V\nHZJW0aYwbgd8IQhCr/u/zwEQBGHxP7b7BjgGTAemS8K4aqERNGy6uYn119dTr0Y91nRdg4NpxbXU\nuZh4kRWBKwjNCKWxTWNmtppJE5t/t62UkHjl5KdD9KlHWeGMKPF5E5vHOkf4gFX59e4uun1bdK27\ncgXDhg2xmz/vhT1iUwpSGLx3MA4mDmz23YyevBqIGFUxwvn1aPyXoVKrWa8cwBGrYYzt0Yi+jezJ\n27OblFWrRfe8YcOwmTypzBlNQRDYH7Wf1VdWk1qYysDaA5ncYvJrVQqmiInh3mfjUERHYz9/HlbD\nh1d0SBIvSURmBG/tHYQqoyPHP1yJo2X16iCiTWE8BOgtCMKo+7+/B7QRBGH8Y9s0A+YLgjBYJpP5\n8xxhLJPJPgU+BXB1dW0RExNTilOSKC/ylfnMPzOfY7HH6OfRj/+2+2+lKGFQa9TsjdyL3zU/0grT\n6OPeh8ktJle7u1iJKoQiH2LOw11/MSucdAsQQN9M7CH8QAzbNihV54iXQZWZSaqfn+haZ2GBzdQp\nWA4ejOwFC/U0goYxR8dwLeUaf/b/Ew+Lqr+ITBAETt5JYfXRcFLjo1hk8ifd1GcQLN2Q9V4CdfuA\nTIY6J4fUdevI3PyHVt3z8pX5/O/m//g15FcM5AaMbTKWEfVGVI8bjn8h//x54iZPQQY4rVmDSds2\nFR2SRBmYcGIClxIvkxE6jXda1OfrNxtVdEhaRZvCeCjQ6x/CuLUgCBPu/64DnABGCoIQ/W/C+HGk\njHHlIDYnloknJhKdE83UFlN5r8F7la5soUBZwI+3fuTn4J8RBIH3G77PKO9RmOiZVHRoEtUdtVK0\nV47yF7PCcZdBowS5vugq90AIOzaDVySCBLWarG3bSP1mDeoHrnXjxyG3KNnCud9CfmPZ5WV83vZz\nhtUdVs7Rli+CIHAqPI1VR8O4cS8LlxpGTOxWh7eaOaEbewYOzhDrvD3fEOuPrWsDUBQWRvLCRRRc\nvIhB3brYz5+Hcauyl43F5MSw7PIyTsWdwt3cndmtZ9PB6elFj1UdQRDI3PwHyYsXY+BRC+cNG9B3\ncanosCTKwLWUa7x/6H0mNJtAdGQ7dgTGETCzCw4W1Sdr/MpKKWQymQUQCeTd38UeyAAG/Js4loRx\nxXMm/gwzT81ELpOz3Gc5bR3aVnRI/0pSfhLfXP2GA1EHsDa0ZkKzCbzp+WaVt6mUqERoNJAS/Kg0\nIuYcKPIAGTg0eVQa4doO9I1feDhtUxAYSNKChY9c6+bOLdWq/zsZdxh+YDgdHDvg182v0t0ElxRB\nEDgbkc7qY2FcicnEydKIid09GdTcGT35YxlztRIubQL/xaAqgvYToNM00DcR3fMOHyF52VKtuOc9\nzqm4Uyy9tJTY3Fi6uHRhZquZuJhVD+EoKBQkfb2ArO3bMe3WDcdly5CbSkmKqowgCIz8eySxubEc\neOsAGXnQdYU/w1u78tXA6pM11qYw1kVcfNcdiEdcfDdCEITg52zvj5QxrtQIgsAPt37A76ofXlZe\nfNP1m8rldPUCglKDWB64nGsp16hjVYcZLWfQzrFdRYclURXRqCErRlwoF+UvWi0XpIl/s/a8v1jO\nB9w7gnGNCgtTmZxMyrLlomudgwN2s2Zi1qtXqYRtsbqYd/a/Q2ZRJrsG7qKGYcWdT1k4H5nO6qNh\nXIrOwMHCkPHdPBnawgV93X8pIclNhmP/hRtbwNwJei2EBm+CTCa65236XnTPk8upOWaM6J73nE4e\nJUWhVvD77d/ZeGMjKo2KDxp+wCjvURjrvfobKm2hysggbuJECgOvYD16NDaTJr6wdEei8hNwL4Dx\nJ8Yzv8183q73NgBzdt1k55V4Ts3sir1FxZdWagNtt2vzBb5BbNf2oyAIC2Uy2VdAoCAIe/+xrT+S\nMK60FCgL+L9z/8fh6MP0du/Nl+2/rJIXakEQOBJzhNVXVhOfF4+Psw9TW06tFvWSElqiOA9yEyEn\nQXzMTRRtlnMT7j8mQm4SCGpxezOHJxfM3TeNqEg0CgUZP/9C2nff3Xet+xjrTz55KVvdpZeW8vvt\n39nQfQOdnDuVQ7Tly6W7Gaw+Gsb5qHTszA0Y19WTt1u5YKBbihmj2AtwcDokBYndQfosB9t6ACji\n4u675x0T3fPmzMasa9cyx51SkMLqK6vZH7UfW2NbprecXiU77RSFhhL32ThU6ek4LFyIRb++FR2S\nhBZQa9QM2TcEhVrBnjf3oKcjloTdyyig6wp/3m3rxhcDyt7FpTIgGXxIPMW93HtMOjmJyKxIJjef\nzMiGI6vcxfmfFKuL+T3kdzYFbaJYVcywusMY22QsloaWFR2aRHmhUUNeypMC9ynxm/ioZ/DjGJiL\nAtjcAcwcxUcLF3DrADXrlPuCudKQ6+9P8uLFKGPuu9bNmvXSdZxn488y5tgYRtQbwZw2c7Qcafly\nJSaT1UfDOBORRk1TAz7rUpsRbVwx1HvJEiqNGgJ/hBNfi4sp24wBn1lgaA7cd89buAhFVBQmPp2x\nmz37Cfe8l+VayjUWX1zM7YzbNLdtztw2c6uM02fOkSMkzJ6D3MwM53XrMPKuPtPrrzt7I/cy78w8\nlndeTu9avZ/42+ydN9l1LZ7TM7tiZ171s8aSMJZ4gvMJ55lxagaCILC883LaO7Wv6JC0SnphOhuu\nb2BH+A5M9EwY03gMw+sNr/arwqsdRTliFveh6P2n+E0SewM/yPI+QEcXTO3vC177R6L38UczezAw\nrZjzKgWK6GiSFy8hLyAA/Vq1sJs375mudSUloyiDwXsHY2lgyZa+WypFx5mScP1eFquPhhEQloq1\niT5ju9TmP23cMNLX0pqC/DQ4/hVc/RVMbeGNr6HxMJDJRPe83zeTtm4dGoUC65EfYD16TJlradUa\nNbsjduN31Y9sRTZDvYYyvun4SnsjLwgCaRs2kLZ2HYZNGuO8di16trYVHZaEllCoFfTf3R8LAwu2\n9tv6lElNdcsaS8JYAhAvbL8E/8Lqq6vxsPDAr6sfLubVYxHIswjPDGdF4ArOJZzD1cyVqS2n0s2l\nW5XPjFd51CpR0D4ucJ8lfhV5T+9raPEMofsP8WtiA1W81lF0rdtIxs8/IyuBa11JEASBSScncSb+\nDFv6bqkSGcpb8dmsPhrG8dAUrIz1GO1Tm/fbuWGsX7aWas8l/orYvSL+irio0nc52HsDonteyqrV\nZO/eja6NDbYzZ2Der1+ZryfZxdl8e+NbtoZuxUTPhAnNJjDUa2ilWkisKSggYe48cv/+G4uBA7D/\n6it0DAwqOiwJLfKgQ83GNzbS3vHZybKZO27w1/UETs/sim0VzxpLwliCQlUhX5z7goN3D/KG2xss\n6LCgStYTlxZBEDgTf4YVgSuIyo6ilX0rZrScQX3r+hUdWvVDEMSShScE7oPHx8RvfgoImif31dG7\nL3Adns7umjuIz5s5VEj3h1fJs1zrbKZO0UpmbnvYdr46/xXTW07ng4YfaCHa8iM4IZtvjoVzNCQZ\nCyM9Pu3swQft3TE1KCdB/DgaDVz/HY59AYWZ0GoUdJ0LRqIBSOH16yQtWEjRrVsYNW+O/fx5GDZo\nUOZhwzPDWXJpCZeSLlHXqi6zW8+mpf0Lv7fLHWVCAvfGjac4NBTb6dOp8dGHUnKhmpGnyMN3ly9e\nNbz4vuf3z90uNr2Ariv9eb+dG//tX7WzxpIwfs1JyEtg0slJ3Mm4w4RmExjlPeq1u7CpNCp2hO1g\n/fX1ZBdnM9BzIBOaTcDWWJoKLBFq5X1x+4LSBmX+0/saWT0qX3im6HUEY+sqn+UtK0WhoSQtWEBh\nYMld60pKdHY0w/YPo4lNEza+sfGpadLKwp2kXL45FsahW0mYGerySScPPuzgjplhBZRBFWbCyUVw\n+XvxPdzjC2j6LujoIGg0ZO/a9cg97+1h2EzSjnvesdhjLL+8nMT8RPq492Fqy6nYm5S9bdzLUHD1\nKnETJiIUF+O0cgWmPj4VEodE+bLu2jo23tzI1r5baVjz3wXvjO032Huj6meNJWH8GnMp8RLTA6aj\n0qhY0nkJnZ07l+t4xSo1605EsOl0FCq1gFxHhp5c5/6jDLmODF0dHXTlMnT/8W+5jgxduY74/INH\nHdn9v+s8/LdcR+fhsR4eW0d8/vFjPTmuuI9Ck09AylbOpOxGLtOll9Nwerm8jYme0WNjP4pN/ozx\nH4+5yt9gCAIUZT0/u/swy5sK/OP6INd/uozBzB7MHR/L/DqAXvVpCl8ePNO1btAgZHLtTKUr1Ure\nPfQu8Xnx7Oy/EzsTO60cV5tEpOTyzbFwDgQlYqKvy0cda/Fxx1pYGFWCdQFJQWJ5Rex5cGwOfVeA\nUwsA0T1v7Toy/7jvnjdpIlbDhpXZPa9QVchPt37ix1s/oiPTYZT3KD5o+AEG8ldXvpC1cyeJX3yJ\nnqMDLhs2YFC79isbW+LVkVaYhu8uXzo5dWJll5Uv3D4mPZ9uKwMY2d6dz/uVfaakopCE8WuIIAhs\nvr2ZFYErcDN3w6+bH27mbuU65pWYDGbuuElkaj59Gzvgbm2MSi2g0gio1Jr7j/d/12gePq/WCCjV\nwv3H+79rBNQazVP7P9pW88SxlOrSvXdleukY2B5Cz/wWGqUFxSm9UeU0AUqXSZM/EOFPifHHRfUj\nMS1u+4+//cvNwD+FvVxHhqmBLr0b2eNs9YKyApUC8pJeXNqgKnx6X6Ma/xC4zxC/xtaVqnNDVUNQ\nq8navp3U1d+8lGtdSVlzdQ3fB33P6i6r6eHWQ6vHLiuRqXn4HQ9n740EjPXkfNihFqM61cLSuGx9\ng7WOIMDNbXD0c7ELSvP3oft/wcQa+Id7Xr162M+bqxX3vPi8eFYGruRozFGcTJ2Y2WomXV26lusN\nuaBSkbJ8ORm//IpJ+/Y4rV6l9fekROVh4YWFbA/bzl9v/lVijTB9+w323Ujg9Kyu2JpVzayxJIxf\nM4pURXx94Wv2Ru6lm0s3FnZciKl++a3Azy9WsfzwHX45H42jhRGLBnnj42VTbuM9D/UDwf2YmH4o\nstUCSo3mSfGtFgjJuM7WyHXE5ofhalKX/s5jcDVpeF+Eax4K9qeE/YPfNY+e/+ex1RrNw7Ef3+7x\nGB/fR/WM+J84tkaD+BEVsJLl0c8d3vKU08SiAHneM7K8D8wpHkdu8GTN7rPEr6k96FXNi11VoeDK\nFdG17vZtjFu3xm7evFK51pWUwKRAPjr8EW96vslXHb7S+vFflui0fPxOhLPnWjwGunI+aO/Op509\nqGFSyQTxPynKgYClcPE70DeFbvOh5UegI3/aPa9vX2xnTNeKe96FxAssubiEyOxI2ju2Z1arWXhY\nar9Puzo7m/gpU8k/dw6r99/DbubMMme/JSovsTmxDNwzkEF1BvF5u89LvF90Wj7dVwXwYXt35lfR\nrLEkjF8jkvKTmHxyMsHpwXzW9DNGNx5drvWE/ndSmLf7FgnZhXzQzp0Zvepi8ioWyGgRjaBhf9R+\n1lxZQ0phCj3dejK5xeTKZduanwZ3DiHc3odw9xQ6z8jyqo2skVs4Pr1g7aH4dRRrJaUsb4WhTE4m\nZcVKcvbte2nXupKSo8hh8N7B6Ovos73/9kqx2PZeRgFrT4Sz82o8enIZ77V1Y7RPbWqaVrEOBymh\ncGiG6JJo7w2+K8C1LcCT7nm6utQcPVor7nlKjZJtd7ax/tp6ClWFjKg/gjFNxmCmb6aNM6I4Koq4\nsZ+hSEjA4b//h+WQIVo5rkTlZWbATPzj/Dnw1gFsjEuXzJq67ToHgxI5PbMbNmZV7POLJIxfGwKT\nApkWMI1idTGLOy6mq2vZnZqeR2a+gq8PhLDrajy1bUxYNqQxLdyqpq3sAwqUBfwS/As/Bf+ESqPi\n3frv8knjT7T2xVNqMqMh9ID4E3te7ORg4QpePaFGbVSm9gSmG7AtTM2BuxqU6NGtni0j2rji42WL\nXEcSwJUFjUJBxi+/kPbtY651o0ahY1x+YnXmqZkciT7Cr31+pbFN43IbpyTcScrl53PRbA+8h46O\njHfbuDGmi0eVnYYFxPKKkL/g8DzIiYMmw6HHl2Am1nAr4uJIXrKEvGPHteqel1GUgd9VP3aF78LK\n0IrJzScz0HNgmRIgeadOET91GjJ9fZzX+mHcokWZ45So3ISkh/D2/rf5xPsTJjafWOr976bl032l\nP6M6eTDXt+p1eZKEcTVHEAS23tnKskvLcDZzZk23NeVmhywIAgeCEvlibzBZBUo+61Kbcd08S2fF\nWslJzk/G75ofeyP3UsOwBuOajmNQnUHo6pRzJlwQIDkYQvfD7f2QHCQ+b9sQ6veDen3BvvEzM773\nMgrYcimWbYFxpOUV42RpxNutXHi7lUu1cCmqyuQFBJC8aDGKmBhMu3fHbvbLu9aVlP1R+5lzeg7j\nmo5jTJMx5TrW80jLK2bv9QR2XYvjVnwO+nIdhrd24bOuntXrPanIh9Mr4dxasVSp6xxo/SncNxTK\nO3OW5EWP3PPs58xB3929zMMGpwez+OJibqTeoIlNEzb02IC5vnmpjiEIAhk//UzKihUY1K2Ly/p1\n6Dk6ljk2icrP6KOjCU4P5tCgQy+d/Jn653UO3krkzKxuVW7WRxLG1RiFWsGCCwvYHbEbH2cfFnda\nXG4ZzqTsIubvucWx28k0drZg6eDG1Hco3YW4KhGcHszyy8u5knwFT0tPprecTgenl3cdeyYaNdy7\neD8zvF/MEiMDlzaPxHCNkt/kKFQajt1O5o+LsZyJSEOuI6P7/Sxypzo2Uhb5FaKIiRFd6/z9Rde6\nuXMx7dSx3MeNz4tnyN4h1LGqw4+9fiz/G7rHKFKqOXY7mV1X4wkIS0WtEWjsbMGgZk70b+KIdRX7\n8iwV6ZFwaBZEHAWbeqI5SC2xC5CgUIjueevXP3TPqzlmDDomZXPP0wga9kbu5cvzX9LCrgXf9vgW\nPZ2SdfLQFBeT9H//JfuvvzDr1QvHxYvKdQZDovJwMfEio46MKnNP86jUPHqsCuCTTh7MqWJZY0kY\nV1OS85OZ6j+Vm2k3Gd14NJ81/axc6okFQWDr5XssOnAbpUbDtDfq8mEHd3TllbMXqjYRBIETsSdY\neWUl93Lv0cGpAzNazqC2ZRlaFymL4G6AKITvHBJbocn1waOLKITr+oq2tGUkOi2fLZdj2REYR3q+\nAidLI4a3dmFYS5cq3X+ysqPJzydt4//I+OknZHp6omvde++WybWupKg1aj46/BF3Mu+wo/8OnM2c\ny31MQRAIjMlk19U49t9MJLdIhb25IW81d2JQMyfq2FVQKVJFIAgQ9rcokLNioOFb0HMhWDgB993z\nVq4ie88edG1tsZ0xA/N+fctcY/5XxF/MPzufwXUG8992/33h8ZQpKcRPmEjhjRvUnDCemmPHInvN\n+4g/gSCI7pzJwaIDZ+1uYFA93seCIDDiwAjSitLY/9b+MrcAnLz1GoeDkzkzq2uVuvGVhPFzENRq\nrfUKfdVcS7nGVP+pFCgLWNhxYbm1YYpOy2fOriDOR6XTzsOaJYO9cbMuW5ajKqJQK9gSuoWNNzZS\noCpgiNcQPmv6GTUMS1hXXZQN4Ufh9j6IOCZebPXNxHrhev3AswcYlk/2vVil5kiwmEU+H5WOro6M\nHvXtGNHGlY6eNdGRsshaQRAEcg4cFF3rkpOxGDgQm2lTteJaV1L+d/N/rL22lkUdF9G/dv9yHSsm\nPZ9dV+PZfS2e2IwCjPXl9G5kz+DmzrT1sH69ZyeUhXDWD86sApkOdJ4B7caBrigcCq5dI3nBQoqC\ng7Xmnud31Y9NQZuY2mIqHzb68LnbFQbdIm78eNQ5OTguWYJ5r55lGrfKU5QDqaGiCE4JgeQQ8bEw\n49E2uobg1Ru8h0KdNx7+P1ZFjkQfYVrANL5q/xVv1XmrzMeLSMmj5+oAPunswZw+VSdrLAnjZ1Ac\ndZeY997D3NcXiwEDMGzUsMqYNWwP286ii4twNHFkTdc1eFp5an0MlVrDj2fvsvJIGPq6Oszzrc/b\nrVyqzGtUXmQWZbLh+ga2h23HSNeITxt/yn/q/wd9+TOygblJcOegWC989xRolGBiC/V8oV5/qNXp\nlV9go1Lz2HIplh1X4sgsUOJaw5h3WrswtIVLlVxZXFl4wrWuQQPs5s/HuLl2XOtKSlBqEO8deo+e\nbj1Z2nlpuXxWswuVHLiZyK6rcQTGZCKTQYfaNRnU3IleDe2rXEeaciczBg7PFWeHatSGPsugjpjE\nEDQasnbuJHXVatTZ2VgOG1om9zyNoGHWqVkcjj7Mqi6rnpksyT5wgMS585Bb18BlwwYM69Ur0+lV\nKVQKSA9/JHwfiODs2Efb6JuCbX2wbSD+2DUAmRyCd4s/BWlgYAEN+kOjIWKpjE7VSa6pNCre+ust\n5DI5OwfsRK6l2CdtvcbRkGTOzOpW+Vsu3kcSxs+gODyc1LXryDt5EkGpRN/DA4sB/THv1x99Z6dX\nGktJUaqVLL60mO1h2+ng1IGlnZZiYaD9xushCTnM2nmToPhs3mhgx4I3G1WvxTJaICorihWBKzgd\nfxonUyemtpjKG25vIMuIErPCoQcg7jIgiDXC9fqJP86tKoX1cZFSzeHgJDZfjOXS3Qz05DJ6NrBn\nRBtX2nlYS1nkEqLOyiLVz4/MrX+KrnVTJmM5ePArn4kqUBYwdN9QFBoFOwfsLPUirH9DqdZwOjyV\nnVfjORqSjEKlwdPWlMHNnXmzmSMOFpKz4QuJOAYHZ0JGJNTtC70XgZU7IPYOTl23/kn3vLfffqn3\nUJGqiI+PfExYRhg/9f6JRjUbAaIIT13jR/rGjRi1bIHzmjXoWltr8wwrD4IAWbH3hW8wpNwW/50W\nBhqVuI2OLljXEYWvbQOwaygKYgvX51+f1Sq46w9BO8RkhyIXTO2g4SDwHiK6IVbyxNH2sO18df4r\n1nRdQzfXblo7bkRKLm+sPsUYn9rM6l01brYkYfwvqLOzyTl8mOy9eykMvAKAccuWmA/oj3nv3sjN\nK8fistSCVKb6T+V66nU+bvQxE5pN0Nrd3gMe2Dl/6x+JpbEeXw5ohK+3/WufJf43zsWfZfn5BUTk\nx9FcrcPMpAQaKhTg0ETMCtfrK15wK/FrGJGSyx8X77HzahzZhUrcrY0Z3tqVIS2cq1TN2KvkKde6\n4cOxmTC+whzCvjj3BbvCd/FDrx9oZV92xzVBEAhOyGHn1Tj23UggLU9BDRN9BjRxZHBzZxo5mUvX\nhdKiKoYLGyBgOQhq6DgFOkx6aJledCeM5EWPuefNn4dxyxd+bz9FWmEa7x58l2J1MX/4/oEt5iTM\nnEneiRNYDh2C/eefv5J691dCQcZjJRAPRPBtUbQ+wMJVvAbbNRA7/Ng1EEWxbhleA2UhhB2GWzsg\n7Aioi8UbnUZDxHIL28onDgtVhfTd1RcnUyd+7fOr1j+/E7dc49jtqpM1loRxCVHExZOzfx/Zf+1F\ncfcuMn19TLt2xWJAf0w7daqwi8nN1JtMOTmFXGUuX3X4it7uvbU+RmB0BrN2inbOg5s7M79vfayq\nwJu7QlCrIObswx7Dqpw4dpuZsc7amgyZhv7O3ZjYdg72JmV3vHqVFCnVHAxK5I+LsQTGZKIv16FX\nI3tGtHalrUcNSQjd51W51pWU4zHHmew/mY8bfczkFpPLdKyk7CL2XI9n19U4wpLz0Jfr0KOBLYOa\nOeNT1wa912DBbbmTHS9aS9/aCZau0HuJuOBWJrvvnneY5KXLUCXed8+bOQM9O7tSDRGZFcm7B9+l\nQbE1s3cIqKJjsJs9G6t3/1M1P8eKArEO+EH294EYzkt+tI2R1SPha1tf/Ldt/XJbu/GQomwxgxy0\nXVxULWjArpGYRW40WPw/rgR8H/Q9a66u4efeP9PCTvt9qh9kjcf61GZmFcgaS8K4lAiCQNGtYLL3\n7iXnwAHUGRnILSww8+2DxYABGDVt+souLrvDd/P1ha+xNbZlTdc11K1RV6vHzytWsfzvUH69EFOh\nds6VHkUBRJ4QawXD/obCTHFBRu3uYlbYqzd5egZ8H/Q9v4X8ho5Mh5GNRvJhww8rheNYaQlLzuWP\ni7HsvBpHbpEKDxsTRrR2ZXBz59f2hkmZnELKihWia529veha17t3hQqNlIIUBu0dhKOJI5t9N6Mn\nL1mrrscpUKg4HJzErqvxnIlIQxCghZsVg5o70c/bEQvj0h9TogTcPQ0HZ0DqbfB8A/osBWux243o\nnreJ9O9/EN3zxoyhxsgPSuWed+nAjzB/OXoyXWr7bcC8Y6fyOhPtoVFDRtQj4fugDjgjCrivT3QN\nwabu0yLYzL7iZ+byUsRa5KAdEHdJfM6lrSiSG7wJphXz3ZpdnE2fnX1obtecdd3Xlds44/+4ysnQ\nFM7M6lbpvyckYVwGBKWS/HPnyN67j9zjxxGKitBzdcWif38sBvRH382tXMZVapQsu7SMrXe20s6h\nHcs6L8PS0FKrY1QHO+dypSBDnC4L3Q8Rx0FVCIYW4NVH7DFcuxvoP92hIy43jjVX1/B39N/YGNkw\nsflEBtQ15qseAAAgAElEQVQeUK7W3OVFoULN/psJbLkUy9XYLPR1dfBtZM+INm60creqmtmnUvJP\n17oaH39EzU8+qfCerxpBw+ijo7mecp1t/bdRy6JWyffVCFyISmfn1Xj+vpVIvkKNs5URg5o781Yz\nJ2rVfP06z1QIaiVc2gT+i0FVBO3GQ+fpD68rinv3SF66VHTPc3PFbs4czLp0eeFhM7dsIWnhIooc\nrJjhm0G39iOY22ZuOZ9MKRAEcXFySvCTi+FS74ivAwAycX3G4yUQtg3E56rCgrfMaHFWIGiHeG4y\nudiS03uomEwp70z2Y6wKXMXPwT+zY8AOvKzKb3YrLDmXXt+cYlwXT6b30m4ST9tIwlhLqPPyyD1y\nlOx9eym4cBEEAaMmTTAfOADzPn1eejXxP0kvTGdawDSuJF9hZMORTGo+SatN+jPzFXy9P4Rd1+Lx\ntDVl6eDGtHDTTuxVnuy4R2Yb0WfFWkAzR/FCVr8fuHV46Gj1Iq6nXGfZ5WUEpQVRv0Z9ZrSaoZX6\nz4ridmIOWy7FsvtqPLnFKjxtTR9mkatrVjHv1CmSFy56pa51JeXX4F9ZHricz9t+zrC6/9/efcdV\nVf8PHH8d9sYBLhD3QHGhgjlzb1z5LTVbts3ZcFTf7NvSLEem1bc0v5nWrxwJzsqtCQ5cKJqTJQiI\nXDbccX5/HFdlicLlMN7Px6NHgPee8/YIl/f9nPfn/f5XoZ5zLjmLdUfiWReZwGVDHu6OdgxsWZPh\ngb60q1NZNl3qJfMK/DoLjq0CDx/o867WA/n6G8+svfu48t57FFy8iFu3blSfMf2O0/NUo5Gk994j\n/fv/w61bN2p9NJf5p7/gf6f+x/Sg6YzxH1Oyfy/QSg1ulkDclgTnXrv1GLcatxLfG90gvJqAQ9m7\n23ZHV05qCXLUam1joJ0TNO6r1SQ36gP21tvcnpSdxMC1A+lXrx/vdX7Paue5YfyqSHadSWHvtO5U\ncim9q8aSGFuBMSmJjA0bMKwPJf/sWbCzw61rVzxDQnDr/iA2jve3aelk6kkm7ZiEId/ArI6zGFh/\nYLHFrKoqG45r45wNueVznPM9U1VtleJ0mFYnlnhU+7pXk1uT52oF3vctOotqYfPFzSyIXEBSdhI9\n/Xoyte1U/DxKR93Z/cgpMLHhWCIrD8RyLC4dRzsbBrasyZhgPwL9yscq8h+m1tWtS/XXZ+LWpfTc\nij6TdoZRG0fRyacTn3T/5B+veVp2ARuOX2ZNZALH4tKxtVHo2siL4YG+9G5WHSf7CvzzX9rERsCm\nVyDpuNYKrP/cmxu5bp+epxYUUOWJJ/B6/rmb0/NM166RMHESOQcPUvXpcXhPmYJia4vZYmbKzins\nit/Foh6L6Orb1Tqxmwq0zg+3l0AknwJD3K3HOLj/cSNcNX+tI4RLIfvBl3WqqnUrOvGjVnKRnQKO\nHuAfAi1GQN2uYFu8d23/ve/fbLiwgQ3DNlDLzfrjvs8kaavGE3o05OU+pXfVWBJjK8s7fRpDaBgZ\nYWGYUlKwcXfHo19frR65bdtCTxQKPR/K27+9jZezFwu6L8C/avE1y65o45z/kcUCCYe0VeHoDVoL\nJQCfdteT4UHg1ahYT5lrymXFqRV8deIrjBYjo5uO5tmWz1ql3V5JOnnZwKqIWNYfvUxWvokm1d0Z\nHezH0DY+eDqXvVVkS06ONrVu2bISn1pXWHmmPEZtHMW1vGusHbL2jkNm8k1mdpxOZm1kAjvOJGM0\nqzSr6cHwQB9CWteimru0Xyy1LGY4/DVse0cbBBT8PHSbdvPWuzE5mZR58/8wPc+xcSPiXxyPKSWF\nmu++g2dIyB8OmWPM4YktT3Ap4xIr+q8o2l4Vi0Xr/Xvl1G2lENFaj+Db26F5Nb61+nsjCa7kp38d\ncGlhNmmb9aLWaC0+8zO0PvfNh2nlFr7tinytzqefZ3jocEY3Hc20oGnFFPjdvbjyMHt+T2VPKV41\nlsS4hKhmM9nh4WSEhpHxyy+oOTnY16qFx/V6ZMcGdx4jbLQYmXdoHt9Gf0tQjSA+6vYRlZ2Kp7TB\nYtHGOX+wqeKNc/4DUwFc2q0lwmc2abuZbeygbhctGW4yEDxqWj2MlJwUPj36KevOrsPT0ZMXWr3A\nyCYjsbf55yTSkpNDzuFIciLCyTt1CtVktnqs98JsUUnNzic5I5+sfBM2Cni5OVLNwwn3MlS3XnDx\nIqaUFDyHhOD98sslOrWusGYfmM3K6JV81uszOvt0vvl1VVU5EpfO2sh4wo4lYsg14u3uyLA2Pgxr\n41Nx3wiXVdlXYft/4PD/tBHxvf8DLR++mSzdPj0PGxvsqlbFd/GnOLdsecfDJeckM2rjKBQUVg1c\nRTWXQnxvZ6fetvp7PQlOOa0l7DdU8rutBKK59v+qDYvWDq2iMebB2Z+1leTft2rt3yrVud7Z4iHt\nzcV9mLR9EhFJEWwevrnYcorCOJ2UQb8Fe5jYoyFTS+mqsSTGd5KfCUkntB9q95rFXsxvyckhc9s2\nDKFhZO/bBxYLTs2b4zkkBI8BA7Dz8gK0SWqv7HqFA0kHeNT/UV5u93Kx1RNfSs1m+trjhF9Iq5jj\nnPMztcb60Ru0F538DLB31SZPNR2k1XY5F++GxsI6nXaauQfnciDpAPU86/FKu1fo4tPl5i1xS34+\nuUePkRMRTnbEAXKPHwejEeztcWra9L5LdUpCdoGZKxl5XM0uwGJRcXawpbqHI1XdHLEr5atFNu7u\nVH3mmRKfWldYexP28sKvLzC66WhmBM8AIC4th5+OJLD2SAIXU7Nxsrehb/MaDA/0pVODqhXvTXB5\nkxCpda9IOKR1OBgwF2pqya9qNpO+di054RGFaut2Ou00j21+jLoedVneb/mtjjkFOVp3jBurvzeS\n4OzkW092rnIr8b1RAuHdtEQ3kVUIeQZtn8uJ1XBhp7bPpVpzrdQi4CGoXLgN/0eTjzJ281jGtx7P\n862et27Md/DCt4fZezaVvdN6lMo9KJIY38mlfbB8gPaxjR141NLeoXnWhkq1tYT5xscevkV692tK\nSSFj0yYM60PJO3UKbG1x7dSR7B7teNXyI4nmNGZ1nMXgBoOL5a9mMltYuvci836pgOOcs1K0FeHT\nG7UXFXM+uFSFJv21gRv1u91sqK83VVXZGbeTjw9/TPy1SwwxBjAqJwDn4+fJjTyCmp8PNjY4BQTg\nGhyES3AHXALb6N4NobAy84ysP3qZVRGxnErMwNnelpBWtRgd7EdLX8+K8f1YjNLy0hi+fjiVnSrz\nZc9v2H46nTWH44m4mAZAh/pVGB7oS/+AGrg7lb5fRKIILBY4ulLboJebBu3GQY/Xtd6998JsYvfp\nH5lw6AO6OddivrkKtinRkHaRP7ZDa/rXJNitupRBlLSsFDj1k7aSHBehfc03SCu1aD5Uu5NwB6qq\n8uTWJ7lkuMSm4Zt0aRkanZhB/4V7mNSzEVN669fn/e9IYnwnuenaO/D0OG1zQHrsrY8zLnPzRQIA\nRVtV/nPCXMlPm6pTqXahk638c+cwhIZxZd0P2KWkk+eg4NizG3VGjsUlOLjIo2RvH+fcp1l13qkI\n45yvXdJWhU9vhLhwrcG6p9+teuHawcW+oaGoVLOZvNOnyQmPICsinMwDEdjmFQBwrXYlanXtTdVO\n3XFp3w5bd3edoy0aVVU5Fm9gVUQMYccSyTWaaV7Lg9HBfgxp7YNbGSq10IuqqkzYPpF9CfsItHuL\nfdEO5Bkt1PNyZUSgD0Na+1C7Stl4wySKIPca7PgADn6pJcU934I2Y/86xlhVtd9jt6/+Jp+ElN/B\nnM9KDzdmV63CE/k2vOzZ8lYSXL25NsGtLLRDq2iuxdzW/u0kKDZa+7eAh7TfdU639qvsjt/N+G3j\nmRk8k1FNR+kW8vMrDrPv/PVV41K250QS43tlKoCMhL8mzOmx2n8ZCbc2Gdzg6n3nhPlGIn39dpPJ\nYmJh5EL+F/U1IZmNGJfQgIJfd2HJysKuWjU8Bg/CMyQEpyb3VpeTZ9TGOX++Sxvn/J8hAfQPKKfj\nnFVVK4O50VbtSpT29eoBWiLcdCDUaFGqVjdUVaXg3DmywyPIORBB9oGDWAwGABzq18clOAilbUu+\ncz7ONwk/4WjnyNMtnmZss7E42pbesol7lZFn5KcjCayKiOV0UiauDraEtPZhTLAfAT5leyOitUQn\nZvDhvq85lP0VeVcG4pLbg5BWtRge6EPr2pXK58+4+GdJJ2DTaxD7m9Y1p8tUbbjE7d0g8tJvPd69\n5h9Wf1Vvf96PCeP7sz/y7wf+zcjGI/X7u4h7d+WU1vrtxGpIjwFbR639W4uHsDTszcgtY8kx5hA6\nNPS+hv4Ul1OXMxjwyR4m92rE5F6la9VYEuPiZjFDZuJtCXPMH5NnQ/xtTcqvc/LEUMmXV11U9qtZ\nPOLZnNcaPox9lbpYnGqQFXEUQ2gYWXv2gMmEY5MmeIaE4DFo4F3rxirEOGeLGWLDtUT49AbtOqOA\nX4dbyXCVwg84sDZVVTHGxmqJcEQE2RERmK9eBcDe1xeX4CBcO3TAJSgY++p/vB120XCReYfnsTNu\nJ7VcazGl7RT61u1brhIgVVWJjE3nuwOxbDh+mTyjhZa+nowO8mNwq1oVftBMcmYeoUe1Fmunr57H\ntd4nVLJpxMzA+fT0r16xWywKjapqt9h/fuPWaGRHj+uT4G5shLv+8R3aoZksJiZsn8D+y/tZ0msJ\nHWt1LOG/gCgyVYX4Q7e1f0smrFJVZlZ2ZU7jsQwImqr73dJnvzlE+IWr7J3eA49SVOIliXFJU1Wt\nP+GNFWZDHGdSTzIp/RDJqpE307MYlp72x+fYu0Kl2pjsa5Fx0QbDsVTyLiSDouDaPhCPocNx79MX\nW7dbm+fK/ThnY55WJ3w6DM5shpyrYOug3T5qOgiaDNBtxOadGBMT/5AImxITAbDz9salQwdcOwTj\nEhyMg69voY4XnhjORwc/4sy1M7TybsVr7V+jpfedd5yXZYYcI+uOxLMyIpazyVm4OdoxtE0tRgfV\noVmtirOxJ89o5udTV1gbGc+es6mYLSotfV3J8VpIrprCuiFrC9dJQFQs+ZmQcFibCOdZ+57ulGUV\nZPHYlsdIzErk2wHf0qDSnTsniTLAbKLgwnZC9s/EIz+b7xMuY+PqrbV/C3gIagfpchc1KsHAoEV7\nmdq7MRN7Fm8b1KKQxFhnWy9t5c19b+Ju78787vNp6dVCqxX7u1INQxzkXqMg0xbDJRcMl5wxZtuh\n2IF7Y3c8OzQguW5Dvj2rEJXtyQOBrXl2YCdcnctBLXFuOpz9RUuGz/4KxmxtFaRRH21VuFFvcCwd\nNbem1FSyIyLIiThAdkQ4xphYAGwrV8YlKOh6ItwBh3p173u112wxs/78ej6J/ISreVfpX68/UwKn\nUNPN+q3lSpqqqhyOucaqiFg2nEikwGShde1KjA72Y3DLWjg7lL9VUotF5VDMNdYcjmfTiUQy803U\n8nRiWKAPw9r4siHuK5ZGLWXBgwvoWaen3uGKcigxK5HRm0bjaOvIygErqepcVe+QxH1aGb2S2Qdm\n80X3RXTMzrzV/s2Up5V1BozQNu5Vb16icT3zzSEiStmqsSTGOjFbzCw6soilUUtp7d2a+d3n4+Xs\nVbgn52feTJjVazHkHjmCYd8pMo6nYMlXsXU041EnF8+6uThVNqLY2IKnz19rm298XMTOGlaVkQhn\nNmo1wxd3a/XbbtW1FWH/Qdo0oFIQuzk9neyDB8mJOEBORDj5Z88BYOPmhkv79loi3KEDjo0aFXqo\nS2FlG7NZemIp35z6BoAnA57kqYCncLYrHR02ilt6TgFrIhNYFRHD+ZRs3J3sGN7Gh9HBdWhSo3S8\nMSqKS6nZrD2SwLoj8cSl5eLqYEv/FjUZHuhDh3pVsbFROJh0kHFbxzGs0TDe7vi23iGLciwqNYon\ntzxJ4yqNWdpnKU525WCRpYLJNmYzYO0AGlVqxJd9vry1GJOXof1ujVoN53do7d+8/bUeyS0e0jZb\nWtmNVeOXezdmQilZNZbEWAeGfAPT9kxjX8I+RjYeyYygGUUqgr8xzvnddUdpdOkET+econp0JBhN\nONSqimdbHzybOGBvc72EIzNR685wkwLuNf6+q4Zn7ZKdS596TlsVPr1RG5EJ2q3ApoPAf7A2ha6Y\nk8t7Zc7KJvfwIbIjDpATHk5edDSoKoqzMy6Bgbh0CMa1Qwec/P1R7EqmjisxK5F5h+ex5dIWarrW\n5JV2r9C7Tu9yVX98O1VVOXAxjVUHYtl8IokCs4W2dSozOsiPgS1rlqlxxuk5BWw4nsjayHgiY9NR\nFOjc0IsRgb70aV4dF4db30MZBRmMCB2Bg40DPw7+UZd2S6Ji+TXmV6bsnEK/uv2Y03UONor0vy5L\nlhxdwmfHPuO7gd8R4BVw5wfdbP+2WuvgBODb/nr7t2F/2/6tODz9v0McvJTG3mndS0U7SUmMS9i5\na+eYtGMSl7MvMzN4ZpF3/P7dOGezwUDGlq0YwkLJPXQYAJf27fEIGYxHrx7Ykvn3pRqG+L921nDx\n+tNqs98fE2mnInQNUFW4HKklwtEbIPWM9vWarW+1VfNuqmsnCUteHrlHjmjlEeER5J44AWYzir09\nzq1b30yEnVu00H1E8KGkQ3xw4AN+v/Y7QTWCmB40nUaVS8c7cWtJyy5gzeF4vjsQy4XUbDyd7Rke\n6MPoID8aVS+dq8hGs4WdZ1JYGxnPtuhkCswWGld3Y0SgL0Na+1DD868rc6qqMm33NH6O+ZkV/VfQ\nwruFDpGLimhZ1DLmH57Psy2fZUKbCXqHIwopNTeVAWsH0NmnM/MenFe4J6XH3mr/diVKa/9Wr5u2\niuw/uGi/7+/gRLyBwZ/u5ZU+jXmph/6/qyQxLkG/xvzK63tfx8XehfkPzqd1tdb3fax7GedcEB9P\nRlgYhvWhFFy6hOLggFv37ngOCcGtc+e/JnIWM2Qm/TVhvj2RvkNnjb8t1fD003Y+357Ymo0Qs+/W\nGOaMBFBsoU5H7QevyQDt+TpRCwrIPXHiViJ85Aiq0Qi2tjgHBNzcMOfcpg02TqXv1qLJYmL176tZ\ndGQR2cZsHm7yMC+2fhFPx/Ld9kxVVfZfuMqqiFi2nkzCaFZpX7cyo4P96B+g/yqyqqqcSDCwNjKB\n0GOXScsuoKqrA0Na+zA80IfmtTz+cYU/7HwYM/fO5KXWL/Fcq+dKMHJR0amqyqz9s1h7di3vdnqX\nIQ2H6B2SKIT3I97nhzM/8NOQn6jrWffeD5AcrSXIUau1uQC2jtp+nhYjtTZwxTQUa9zygxyOvcbe\naT10718viXEJsKgWlhxdwhfHv6ClV0vmPTiP6q7/3Gbtn9zvOGdVVcmLisKwPpSMTZswp6VhW6kS\nHgP6a/2RW7Uq3G33m5014sAQe4eV5zgoyPzjc+xdbiXMDi5wYZfWS9POCRr01FaGG/e7Y+ugkqCa\nzeSdOqV1jQiPIOfwYdTcXFAUHP2b4hp8PRFu2xZbNzddYrwf6XnpfHr0U378/Uc8HDyYGDiR4Q2H\nY1sBmvSnZuWz+voqcszVHCq52DMi0JdRQX40rFay/4aJhlzWHUlgbWQC55KzcLCzobd/dYYH+tC1\nsTf2hRjNHJ8Zz8iwkTSu3JhlfZdViH9DUboYLUZe+OUFDicf5r+9/0v7Gu31Dkn8g7jMOEJ+CmFo\nw6G89cBbRTuYqmodTk6s1laTs5PBwV373R3wkNYRqgjt347HpxPy6T5e7duE8d0bFi3WIpLE2Moy\nCzKZsWcGu+J3MazhMN7o8AYOtvd3q/3P45zfGOjPv9rd3zhn1Wgka98+MkLDyNy2DTU/H/s6fngO\nDsEzZDAOfn73FaN2cFVLev+uVCPnGtTtpHWSaNADHO6e1Bc31WIh/+zZW4nwwYNYMrVk3qFhA1yD\nO2jlEe3bY1upUonHV9xOp53mg4gPiEyOxL+KPzOCZ9CmWhu9wyoRFovKb+evsupADD+fvILJohJc\nrwqjg/3oF1DDan1/s/NNbD2ZxNrIBPadT0VVoV2dygwP9GVgi5p4uhS+ls5kMfHU1qc4e+0sq0NW\n4+PmY5WYhbgbQ76BsZvHcjX3KisHrLy/VUhRIqbtnsb22O1sHL6xeNs5WsxwaY/W2eJUGOQbtHLL\n5kO1lWTfoPvaB/TU8oNEloJVY0mMreiC4QKTtk8iPjOeaUHTeLjJw/e9Ecqa45zNWVlkbv0ZQ1gY\nORERoKo4t26N55AQ3Pv1w65y5WI5j55UVaXg4iVtstz1fsLma9cAsPfzwzU4WEuEg4Kw8y49/Y+L\nk6qqbLm0hY8OfURyTjKD6g9iStspFar/bXJmHj8eiuf7g7HEpeVSxdWBh9pqq8j1vIr+Bs1sUQm/\ncJU1kfFsiUoip8CMXxUXhrXRSiUKc2fnTr449gWfHv2U9zu/z+AGg4scpxBFEZcZx5iNY3B3cGfl\ngJVUcir7iwflzem004wMG8m4gHFMbjvZeicy5WttVKNWazMFTHnaneGAEVpNcvWAQu8POhaXzpDF\n+3itXxNefFC/VWNJjK1kZ9xOpu+ZjqOtIx93+5h2Ne56je+opMc5GxMTydi4EcP6UPLPngV7e9y6\ndsUzJAS3B7th41h2RhAbExLIDo8gOyKcnPAITMnJANjVqHE9Ee6Aa3AQ9rVq6Rxpycox5vDVia9Y\nfnI5djZ2PNfyOcY2G3vfdzLKIotFZe+5VFZFxPJL9BXMFpWODaoyOtiPPs1q4GB3b6sdZ69ksiYy\ngfVHE0g05OHuZMeglrUYEehD2zqVi/TzejzlOI9tfow+dfswp8uccttlRJQtR5OPMm7rOAK8Aviy\nz5cV6vWjLHj+1+c5kXKCzSM24+FQQsOQ8jPh9CZtJfn89uvt35pqpRYtRmjdpe7iia8PcCwunb3T\neug25VQS42JmUS18cfwLlhxdQrOqzVjYfSE1XGvc17H+PM75zUH+VHIpmRcfVVXJP30aQ2gYGRs2\nYEpJwcbDA4++ffEMGYxz27bF3o+3qIzJyTcHauSER2CMjwfAtkqVmwM1XIODsK9TR5ILIC4jjrmH\n5rIjbgd+7n5MC5pGV9+ueodV4pIz8vjhUBzfHYgjIT0XLzcHHmpbm1FBtf9xhfdqVj6hxy6zNjKB\nEwkGbG0UHmzszfBAX3r6VyuWjX45xhxGho3EaDGyOmR1yf2CE6IQNl3YxLQ90whpEMK7nd6V19VS\n4mDSQZ7a+hRT207lyYAn9QkiO/VW+7fY/drXfNreav/mfue86EjsNYYt+Y3p/ZvyfDd9pi1KYlyM\nso3ZzNwzk+1x2wlpEMKbHd68r2bopW2cs2o2kx0eTkZoKBm//Iqak4N9rVp4hAzGMyQEx/p3fxdo\nDaZr18g5cJCciHCywyMouHABABsPD1yC2mt1wsFB2lANecH+W/sS9jH7wGwuZVyii08XXmv/WoWs\nGzRbVHafTWFVRCzboq9gUaFLIy9GB/nRq1l17G1tyDeZ2RadzNrIeHaeScFkUWley4Phgb6EtKqF\nt3vx3lF567e3WHd2HUv7LpWNTqJU+uzYZyw5ukQ6pZQSqqoyZtMYknOS2TBsQ+kYyJIep23Yi1oN\nSSe09m91u9xq/+b8x3LNx5cd4ESCgT2vdddl1VgS42ISkxHDpO2TuJRxiVfavcIY/zH3lYztOJPM\n62tPkJiRx+MP1OXVvk10u51wJ5acHDK3bcOwPpTs334DiwWngAA8Q0LwGDgAu6rWGxlqzswk59Ah\ncsIjyI6IIP/0aQAUFxdc2rW9nggH4+TfFMVWduzfC6PZyKrTq/js2Gfkm/MZ22wsz7V8Dlf7kt8Y\nWRokGfL4v4Nx/N/BWC4b8vB2dyS4XhV2/55CRp6Jau6ODGvjw7BAH5rWsM4q7o2hCk+3eJpJgZOs\ncg4hikpVVWbuncmGCxv4sOuH9K/XX++QKrQbrxtvd3yb4Y2G6x3OX6Wc0VaRT/wI1y6CrQM07K0l\nyY37gYMLx+LSORxzjdHBfrq02JTEuBjsjt/N9N3TsbWx5eNuHxNUM+iej3Etu4B3Npxi7ZEEGlZz\nY86IlrStU7o3vZlSUjBs3IghNJT8U9Fga4tr5054Dg7BvWcPbJyL1t/QkptLTmTkzUQ4LyoKLBYU\nBwecAwO18oigYJxbBKDY6z8tpzxIzU1lweEFrD+/Hm9nb6a0ncKg+oMq7Iq72aKy80wyqyJiORx7\n7WapRKeGXtjaWO+aXMm+woiwEfi4+fBt/2+LNBlTCGsrMBfwzM/PEJUaxdK+S4vUo1/cP5PFxLD1\nw1AUhbUha7GzKT2Lan9xY7DXidUQtRayksDBTetW1WLk9fZv+rzuSWJcBKqqsjRqKZ9EfkKTKk1Y\n0H3BPbdRujHOeVboSQy5Rl58sAHjezS0Wgspa8k/exZDaBiGDRswJSZi4+qKe58+eIYMxiUoqFAr\nuJaCAvKOHbu5YS732HEwGsHODueWLW8lwm1al6lNgGXR8ZTjfBDxAVFXo2jl3YoZwTNoXrW53mFV\nCBbVwnO/PMexlGP836D/o55nPb1DEuKuruVdY8ymMWQbs/l2wLfUdtdvQFNFteb3NczaP4sF3RfQ\n06+n3uEUnsUMl/ZqpRan1kOeAVyqwoRIcC75jieSGN+nHGMOb+x7g19ifmFAvQHM6jgLZ7t7WyG9\nfZxzK19PZl8f51yWqRYLOQcPYQhdT+bWn7FkZWFXvTqegwfhMTgEpyaNbz3WZCLv5Mnr7dPCyYk8\ngpqXB4qCU/PmuAQH4dqhAy6Bgdi4Vsxb+nqyqBbWn1vPgsgFXMu7xvBGw5kYOJEqTvoMYako/nfy\nf3x06CP+/cC/izwyXoiSdNFwkUc3PYqXsxcrBqyQzaIlKM+Ux8B1A6npWpMV/VeU3bt8pnw4t00b\nJtLzTV1CkMT4PsRlxDFxx0QuGC4wte1UHmv22D19E/55nPMrfZrwZKd6Vr01qwdLXh5ZO3ZgWB9K\n1t4nW7wAABh1SURBVN69YDLh2LQpbl26kP/77+QcOoQlOxsAx8aNtT7CwcG4tGuHrWf5Hl1clmQW\nZPL5sc9ZFb0KZztnxrcZz7+a/At7G7m9X9zOpJ1h1MZRdPbpzMLuC8vuLzdRYR1MOsizPz9Luxrt\nWNJribxOlJBlUcuYf3g+X/f9+r7bwwqNJMb36LeE33h196soisKHXT+kY62O9/T8i6nZTF9znIiL\n9zbOuawzpaWRsWkzhrBQ8o4dx6FuXVyCg6+XRwRZddOeKB4X0i8w+8Bs9ifup2GlhkwLmkaHmh30\nDqvcyDPlMWrjKNLz01kTskZW5kWZ9dO5n3hz35uMaDSCtx54S97gWZkh30D/tf1p7d2aJb2W6B1O\nmVfYxLgUV3CXDFVVWX5yOQsiF9CgUgMWdl94TzVUfx7nPGdEi/se51wW2VWpQpVHx1Dl0TFYcnOL\nvDFPlLz6lerzRe8v2B63nbkH5/LMz8/Qu05vXm73sowoLgbzD8/nXPo5Pu/1uSTFokwb2nAosRmx\nfHniS+p61OWJgCf0DqlcWxq1lKyCLOleU8IqdGKca8rlrX1vsfnSZvrW7ct/Ov4HF3uXQj/fmuOc\nyyJJissuRVHo6deTTrU68b+T/+OrE1+xO343TwU8xZMBT95znb3Q7Infw6rTqxjjP4ZOPp30DkeI\nInupzUvEZMQw7/A8arvXpmedMrQZrAxJyk5iVfQqBtYfSJMqTfQOp0KpsKUUCVkJTN4xmTNpZ5gU\nOImnAp4q9CpvntHMou1n+WLXhRIZ5yxESUvKTuLjQx+z5dIWarrW5NX2r9LLr5d8j9+Dq7lXGRE6\ngspOlfl+0Pc42krHFVE+5JnyGLd1HL9f+53l/ZdLZxsrmPXbLNafX0/Y0DB83X31DqdcKGwpRaFm\n/yqK0k9RlDOKopxTFGX6Hf78eUVRTiiKclRRlL2KojS7n6BLSnhiOI9seISErASW9FrCuBbjCv0L\n/9ClNAZ8sofFO84ztI0Pv07txoAWNSVhEOVKDdcazO02l2V9l+Hm4MbUnVN55udnOHvtrN6hlQmq\nqjLrt1lkFGQwu8tsSYpFueJk58TCHgup4lSFCdsmkJSdpHdI5coFwwXWnVvHw00elqRYB3dNjBVF\nsQUWA/2BZsCoOyS+q1RVbaGqamvgQ2BesUdaDFRVZcWpFTz/y/N4OXvx/cDv6ezTuVDPzco38db6\nKEZ+sZ98o4Vvngrio5GtqOTiYOWohdBP+xrt+WHQD7we/DrRadGMDBvJ7AOzMeQb9A6tVPvx9x/Z\nGb+TyYGT5TaoKJe8nL1Y3HMxuaZcxm8bT7YxW++Qyo1FkYtwsnXimRbP6B1KhVSYFeMg4JyqqhdU\nVS0AvgeG3P4AVVUzbvvUFdCnPuMudsXv4sODH9K9dne+HfAtfh5+hXrejjPJ9Jm3i2/CY3j8gbr8\nPKUrXRt7WzlaIUoHOxs7Hmn6CBuHbWREoxGsil7F4HWDWf37aswWs97hlToXDBeYe3AuD9R8gEeb\nPap3OEJYTcPKDfmo20ecTz/Pq7texWQx6R1SmXc85Ti/xv7KE82foKqzdHXSw11rjBVFeQjop6rq\n09c/HwsEq6r60p8eNx6YCjgAPVRV/cs9V0VRngWeBfDz82sbExNTLH+JwlJVlV9ifqFXnV7YKHd/\nT5B2fZzzujI0zlkIa4u+Gs3sA7OJTI7Ev4o/M4NnyqjY64xmI2M2jSExO5E1IWuo5lJN75CEsLof\nzvzAO+HvMLrpaGYEz9A7nDJLVVXG/TyO8+nn2TR8E6725b/la0kqzhrjOxXP/iWbVlV1saqqDYBp\nwBt3OpCqqv9VVbWdqqrtvL1LfsVVURT61O1z16RYVVXCjl2m97xdhB27zMSejdg4sbMkxUIA/lX9\nWd5vOXO6zOFq3lXGbh7LjD0zSM5J1js03S0+upjotGhmPTBLkmJRYfyryb94rNljrDq9ipXRK/UO\np8zad3mfNkil5bOSFOuoMO3a4oHbG/v6Apf/4fHfA58VJSg9JRpyefOnKH6NTqaVryffPh1c5sc5\nC1HcFEVhQP0BPFj7Qb468RXLTy5nW+w2nmv5HGObjcXBtuLV3h9MOsiyqGWMaDRCWliJCmdq26nE\nZsby4cEPqe1em66+XfUOqUyxqBYWHF6Aj5sP/2r8L73DqdAKs2J8EGikKEo9RVEcgEeA0NsfoChK\no9s+HQiUua3rFovKqohY+szbzd5zqbwx0J+1L3aSpFiIf+Bi78LEwImsH7Ke4JrBLIhcwPDQ4eyO\n3613aCXKkG9g5t6Z1HavzWvtX9M7HCFKnK2NLXO6zKFJ5Sa8uutVzqSd0TukMmXzxc2cuXaGCW0m\nYG8r47b1dNfEWFVVE/ASsBWIBn5QVfWkoij/URQl5PrDXlIU5aSiKEfR6owft1rEVnAxNZtRX4Yz\nc90JWvh6snVyV57uUh9bG2nBJkRh1PaozaIei/is12coKIzfNp7x28YTk1Gy+wj0oKoq74a/S2pO\nKnO6zrmnIUFClCcu9i4s6rEINwc3xm8bT0pOit4hlQlGs5FPj3xK0ypN6V+vv97hVHgVdsAHaOOc\nv9p7kfnXxzm/MdC/Qo1zFsIajGYjK6NX8vnxz8k35/NYs8fKdc1c2PkwZu6dyYQ2E3i25bN6hyOE\n7qKvRvP4lsep51mPr/t+LW8W72JV9Co+OPABn/X6rNAtZMW9K+zmuwqbGJ+8bGDamuNEJWTIOGch\nrCA1N5UFhxew/vx6vJ29mdJ2CoPqDypXbzzjM+N5KOwhmlRuwrK+y7C1sdU7JCFKhZ1xO5m0YxLd\na3dn3oPzCtUJqiLKNmYzYO0AGlRqwNI+S8vV62NpU6yT78qTPKOZuVtPE/LpPpIM+SwZE8gXY9tK\nUixEMfNy9uLdzu/y7YBvqeZSjZl7Z/LY5sc4dfWU3qEVC5PFxIw9M1BQeL/L+5IUC3GbB2s/yKvt\nXmVb7DYWHF6gdzil1jcnvyEtL43JgZMlKS4lCtOVotw4ednAhO+OcCElm4fa+vLGQH+ZXCeElbXy\nbsWqgatYf249CyIX8MiGRxjReAQT2kygilMVvcO7b1+d+IqjKUf5oMsH+Lj56B2OEKXOGP8xXMq4\nxNcnv8bPw4+HGj+kd0ilytXcqyw/uZxefr1o6d1S73DEdRVqxdjDyR57GxsZ5yxECbNRbBjWaBhh\nw8J4tNmj/HT2JwatG8TK6JVlclrW8ZTjfH7sc/rX68+g+oP0DkeIUklRFKYHTaeTTyfeDX+X/Zf3\n6x1SqfLliS/JM+cxIXCC3qGI21S4GmNVVeV2hRA6O59+ntkHZhOeGE7DSg2ZHjSd4JrBeodVKNnG\nbEaGjcRkMbE6ZDUeDtLSUYh/klWQxdjNY7mSfYUVA1bQoFIDvUPSXXxmPIN/GsyQBkOY1XGW3uFU\nCFJj/DckKRZCfw0qNeC/vf/Lgu4LyDXl8vTPTzN151QuZ/3T7KDSYc6BOcRnxvN+5/clKRaiENwc\n3FjcczEOtg6M3zaeq7lX9Q5Jd4uPLsZWseWFVi/oHYr4kwqXGAshSgdFUejp15OfhvzE+Nbj2RO/\nh5CfQvjs6GfkmfL0Du+Ofon5hXXn1vF0i6dpV+OuCw9CiOtqudViUY9FpOamMmnHJPLN+XqHpJsz\naWfYeGEjo/1HU921ut7hiD+RxFgIoSsnOyeeb/U8oUNDebD2gyw5toQhPw3hl5hf0KvU606uZF9h\n1m+zaF61OS+0llUeIe5VC+8WvN/5fY6lHOONvW9gUS16h6SLhZELcXNwY1zAOL1DEXcgibEQolSo\n6VaTj7p9xLK+y3B1cGXqzqk88/MznLt2Tu/QsKgWXt/3OkaLkdldZmNvIyNbhbgffer2YXLgZLZc\n2sKSo0v0DqfEHUo6xJ6EPTzd4mk8HT31DkfcgSTGQohSpX2N9vww6AdmBs8kOi2ah8IeYvaB2WQU\nZOgW04pTK4hIjOC19q9R17OubnEIUR48FfAUwxoO44vjXxB6PlTvcEqMqqrMj5xPNZdqjG46Wu9w\nxN+QxFgIUerY2dgxqukoNgzbwIhGI1gVvYpBawex5vc1mC3mEo3lTNoZFkYupHvt7oxoNKJEzy1E\neaQoCm92eJOgGkG89dtbHErSbwpuSdoet53jKcd5sdWLONnJULHSqsK1axNClD3RV6OZfWA2kcmR\nNKvajBlBM2hdrbXVz5tnyuORDY9gKDCwNmQtlZ0qW/2cQlQUhnwDj256lGv511g5YCV1POroHZLV\nmCwmRoSOQEVlbcha7Gwq1Hy1UkHatQkhyg3/qv4s77ecOV3mkJqTytjNY5m5ZyYpOSlWPe+8w/M4\nbzjPu53elaRYiGLm6ejJkp5LUFAYv208hnyD3iFZTdj5MC4YLjCxzURJiks5SYyFEGWCoigMqD+A\nsGFhPN3iabZc2sKgdYNYFrWMAnNBsZ9vT/wevjv9HY/6P0onn07FfnwhBNT2qM3C7gu5nHWZyTsm\nYzQb9Q6p2OWZ8lh8dDEtvVrS06+n3uGIu5DEWAhRprjYuzApcBI/DfmJoBpBzD88n+Ghw9kTv6fY\nznE19ypv7nuThpUaMrnt5GI7rhDirwKrB/JOp3c4dOUQs/bPKlVtGovDd6e/40rOFSa3nSxDxsoA\nSYyFEGWSn4cfi3ouunkr9sVtL/LStpeIzYgt0nFVVeWt394isyCTOV3n4GjrWEwRCyH+zsD6A3mx\n1YuEng/lqxNf6R1OsckoyOCrE1/RyacT7Wu01zscUQiSGAshyrQuvl1YG7KWl9u+zMGkgwxdP5QF\nhxeQY8y5r+P9+PuP7IrfxeS2k2lcuXExRyuE+DvPt3qeAfUG8MmRT9hycYve4RSLZSeWkVGQweRA\nufNUVkhiLIQo8+xt7Xki4Ak2DNtA/3r9WRq1lMHrBrPhwoZ7ui17wXCBuQfn0rFWR8b4j7FixEKI\nP1MUhf90+g9tqrXh9b2vczT5qN4hFUlyTjIro1cyoN4AmlZpqnc4opAkMRZClBveLt681/k9VvRf\ngZeLFzP2zODxLY9z6uqpuz7XaDYyffd0nOyceLfTu9go8vIoRElztHVkYfeFVHOpxqQdk4jPjNc7\npPv22bHPMKkmXmrzkt6hiHsgr/xCiHKndbXWfDfwO97u+DYxGTE8suER3t7/Nml5aX/7nE+Pfkp0\nWjSzOs7C28W7BKMVQtyuslNlFvdajNFi5KVtL+k69fJ+XTRcZN3ZdYxsPJLa7rX1DkfcA0mMhRDl\nko1iw/BGwwkbFsYY/zGsO7uOQesGsTJ6JSaL6Q+PPZh0kK+jvmZEoxHSTkmIUqC+Z33mPzifmIwY\nXtn5CkZL2WrjtujIIhxtHXmu5XN6hyLukSTGQohyzcPBg2lB01gTsoZmVZsx+8BsRoaN5EDiAUCb\nvjVjzwz8PPx4rf1rOkcrhLghuGYw/37g3+xP3M8HER+UmTZuUalR/BLzC483f5yqzlX1DkfcIxm/\nIoSoEBpUasCXvb9ke+x25h6ay7ifx9GnTh+MFiNXc6+yYsAKXOxd9A5TCHGbYY2GEZMRw9KopdTx\nqMPjzR/XO6R/pKoqCw4voIpTlVIfq7gzSYyFEBWGoij0rNOTTj6dWH5yOUtPLCXPnMfENhMJ8ArQ\nOzwhxB1MDJxIbGYsHx/6GF9331Jd7rT/8n4ikiKYHjQdV3tXvcMR90HR69ZEu3bt1EOHDulybiGE\nAEjMSiQiKYLB9Qdja2OrdzhCiL+RZ8rjqa1PcS79HF/3+5rmVZvrHdJfWFQLj2x4hIyCDEKHhuJg\n66B3SOI2iqIcVlW13d0eJzXGQogKq6ZbTYY2HCpJsRClnJOdE5/0+IRKjpWYsG0CSdlJeof0F1sv\nbSU6LZrxrcdLUlyGSWIshBBCiFLPy9mLxT0Xk2PK4aVtL5FtzNY7pJuMZiOLjiyiceXGDKw/UO9w\nRBFIYiyEEEKIMqFR5UZ83O1jzqWf47Xdr2G2mPUOCYA1Z9cQlxnHpMBJMhyojJN/PSGEEEKUGZ18\nOjEjaAa743cz99BcvcMhx5jDZ8c+o231tnTx6aJ3OKKIpCuFEEIIIcqUh5s+zKWMS3wb/S1+7n6M\n9h+tWyzfnPqGtLw0PunxCYqi6BaHKB6SGAshhBCizHml3SvEZ8Yz5+AcfN196erbtcRjSMtLY/nJ\n5fSo3YNW3q1K/Pyi+EkphRBCCCHKHFsbW+Z0nUPjyo15ddernEk7U+IxfHn8S3JNuUwKnFTi5xbW\nIYmxEEIIIcokF3sXFvVYhJu9Gy9tf4mUnJQSO/flrMv835n/Y2jDodSvVL/EziusSxJjIYQQQpRZ\nNVxrsKjnIgz5BiZsn0CuKbdEzrv46GJsFBteaPVCiZxPlAxJjIUQQghRpjWr2ow5XeZw6uopZu6Z\niUW1WPV8v1/7nbDzYYxuOpoarjWsei5RsiQxFkIIIUSZ192vO6+0e4VfY39lQeQCq57rk8hPcHNw\nY1yLcVY9jyh50pVCCCGEEOXC2GZjicmI4euor6njXocRjUcU+zkir0SyK34XkwIn4enoWezHF/qS\nxFgIIYQQ5YKiKMwInkF8Vjzvhr+Lj7sPHWp2KLbjq6rK/MPzqeZcjTH+Y4rtuKL0kFIKIYQQQpQb\ndjZ2fNTtI+p61mXqjqlcSL9QbMfeGbeToylHeb718zjbORfbcUXpIYmxEEIIIcoVdwd3Pu35Kfa2\n9ry47UXS8tKKfEyzxcwnRz6hrkddhjUcVgxRitJIEmMhhBBClDs+bj4s6rGI1NxUJm2fRL45v0jH\nC7sQxrn0c0xoMwE7G6lELa8kMRZCCCFEudTSuyXvdX6PoylHeXPfm6iqel/HyTfns/joYgKqBtC7\nTu9ijlKUJpIYCyGEEKLc6lu3L5MCJ7H54maWHFtyX8f4/vT3JGUnMbntZBRFKeYIRWki9wKEEEII\nUa6NCxjHJcMlPj/2OX7ufgxuMLjQz80syOTLE1/SsVZHgmsGWzFKURpIYiyEEEKIck1RFN564C0u\nZ1/mrd/eopZbLdpWb1uo534d9TWGfAOTAydbOUpRGkgphRBCCCHKPXtbe+Y/OB8fNx8m7ZhEbEbs\nXZ+TkpPCilMr6F+vP/5V/UsgSqE3SYyFEEIIUSF4OnqyuOdiFBTGbxuPId/wj4///NjnmCwmJrSe\nUEIRCr1JYiyEEEKICsPPw48F3ReQkJXAlJ1TMJqNd3xcTEYMa86u4aHGD1Hbo3YJRyn0UqjEWFGU\nfoqinFEU5ZyiKNPv8OdTFUU5pSjKcUVRtimKUqf4QxVCCCGEKLq21dvydse3OZh0kFn7Z92xjdui\nI4twsHXguVbP6RCh0MtdE2NFUWyBxUB/oBkwSlGUZn962BGgnaqqLYHVwIfFHagQQgghRHEZ3GAw\nz7d6ntDzoSyNWvqHPzt59SRbL23lsWaP4eXspVOEQg+FWTEOAs6pqnpBVdUC4HtgyO0PUFV1h6qq\nOdc/DQd8izdMIYQQQoji9WKrF+lfrz8LIxey5dKWm19fcHgBlR0r80TzJ/QLTuiiMO3afIC42z6P\nB/6pkd84YHNRghJCCCGEsDZFUXin0zskZiXy+p7XqelakxxjDuGJ4bzW/jXcHNz0DlGUsMIkxnca\n8XLHmYqKojwKtAO6/c2fPws8C+Dn51fIEIUQQgghrMPR1pGFPRYyZuMYJm6fSBWnKtRyrcXDTR7W\nOzShg8KUUsQDt2/H9AUu//lBiqL0Al4HQlRVzb/TgVRV/a+qqu1UVW3n7e19P/EKIYQQQhSrKk5V\nWNxrMUazkXPp5xjfZjwOtg56hyV0UJgV44NAI0VR6gEJwCPA6NsfoChKG+ALoJ+qqsnFHqUQQggh\nhBXV96zPkl5L2Bm3k4H1BuodjtDJXRNjVVVNiqK8BGwFbIFlqqqeVBTlP8AhVVVDgbmAG/CjoigA\nsaqqhlgxbiGEEEKIYtW6WmtaV2utdxhCR4VZMUZV1U3Apj997d+3fdyrmOMSQgghhBCiRMnkOyGE\nEEIIIZDEWAghhBBCCEASYyGEEEIIIQBJjIUQQgghhAAkMRZCCCGEEAKQxFgIIYQQQghAEmMhhBBC\nCCEASYyFEEIIIYQAJDEWQgghhBACAEVVVX1OrCgpQIwuJwcvIFWnc5dncl2tR66t9ci1tR65ttYj\n19Z65Npaj57Xto6qqt53e5BuibGeFEU5pKpqO73jKG/kulqPXFvrkWtrPXJtrUeurfXItbWesnBt\npZRCCCGEEEIIJDEWQgghhBACqLiJ8X/1DqCckutqPXJtrUeurfXItbUeubbWI9fWekr9ta2QNcZC\nCCGEEEL8WUVdMRZCCCGEEOIPJDEWQgghhBACSYyFEEIIIYQAJDEWQgghhBACkMRYCCGEEEIIAP4f\nkrem72n/rpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32ab578c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(index=np.sort(leages_by_season['season'].unique()), columns=leages_by_season['country_name'].unique())\n",
    "\n",
    "df.loc[:,'Germany'] = list(leages_by_season.loc[leages_by_season['country_name']=='Germany','avg_goal_dif'])\n",
    "df.loc[:,'Spain']   = list(leages_by_season.loc[leages_by_season['country_name']=='Spain','avg_goal_dif'])\n",
    "df.loc[:,'France']   = list(leages_by_season.loc[leages_by_season['country_name']=='France','avg_goal_dif'])\n",
    "df.loc[:,'Italy']   = list(leages_by_season.loc[leages_by_season['country_name']=='Italy','avg_goal_dif'])\n",
    "df.loc[:,'England']   = list(leages_by_season.loc[leages_by_season['country_name']=='England','avg_goal_dif'])\n",
    "\n",
    "df.plot(figsize=(12,5),title='Average Goals Difference Home vs Out')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dc398357-9a0c-46db-ba76-1f9d07b93b41",
    "_uuid": "dbd4ef350896497e1f11d0023fce9810300a45b2"
   },
   "source": [
    "### Query Run Order\n",
    "\n",
    "Stepping further into the query intricacies, unravel the sequence that governs the execution of SQL code—a fundamental understanding crucial for crafting effective and insightful queries.\n",
    "\n",
    "In this pivotal phase of our exploration, I acquaint myself with the sequential order through which code operates, outlining a structured flow that ensures precision and meaningful analysis. The sequence unfolds as follows:\n",
    "\n",
    "1. **SELECT:** Initiating the process, this stage lays the groundwork for selecting the specific columns or data attributes to be displayed.\n",
    "\n",
    "2. **FROM:** Setting the stage for data retrieval, this step involves specifying the tables to be used and establishing connections between them. The foundation for data exploration takes shape here.\n",
    "\n",
    "3. **JOIN:** Paving the way for comprehensive insights, the JOIN operation steps in, facilitating the merging of disparate tables through shared keys.\n",
    "\n",
    "4. **WHERE:** Delving into data filtration, this phase selectively retains rows that align with specified conditions, refining the dataset.\n",
    "\n",
    "5. **GROUP BY:** Elevating analysis to a higher plane, this component groups data based on chosen dimensions, offering a broader perspective on patterns and trends.\n",
    "\n",
    "6. **HAVING:** Following grouping, HAVING contributes an additional layer of data filtering, honing in on specific aggregated conditions post-grouping.\n",
    "\n",
    "7. **ORDER BY:** Shaping the presentation of insights, ORDER BY orchestrates the arrangement of output rows, enhancing readability and comprehension.\n",
    "\n",
    "8. **LIMIT:** Concluding the journey, LIMIT governs the final scope of the output by capping the number of displayed rows, harmonizing with sorting and prior filtering.\n",
    "\n",
    "This intricate process can be likened to a two-part symphony, where a new temporary table is crafted in memory, and subsequently, the desired information is gleaned from it. The first part encompasses:\n",
    "\n",
    "- Choosing tables, establishing connections, and applying initial conditions (FROM + JOIN + WHERE)\n",
    "- Grouping data based on desired levels (GROUP BY)\n",
    "\n",
    "The second part revolves around the refined table and entails:\n",
    "\n",
    "- Ordering the output for clarity (ORDER BY)\n",
    "- Imposing additional post-grouping conditions (HAVING)\n",
    "- Limiting the display to a specified number of rows, adhering to prior sorting and filtering (LIMIT)"
   ]
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    "### Sub Queries and Functions \n",
    "\n",
    "Leveraging the power of subqueries within SQL opens up a realm of advanced data manipulation, eliminating the reliance on external scripts. This tool is particularly indispensable when confronted with intricately structured tables that defy direct joins.\n",
    "\n",
    "Our journey takes us to a scenario where we seek to unite two tables—one holding players' fundamental particulars, and the other housing a plethora of attributes. However, a roadblock emerges: the mismatched keys. While the former table boasts a single row for each player, the latter is keyed by player and season, resulting in a perilous cartesian product. This risks distorting averages and compromising the integrity of our analysis.\n",
    "\n",
    "A remedy arises in the form of a subquery—an elegant solution to our conundrum. The pivotal step involves transforming the attributes table by grouping it to the player level, sans the seasonal distinction. Herein lies the importance of crafting a strategy for consolidating attributes—be it through averaging, selecting maximum values, or other pertinent methods.\n",
    "\n",
    "With this synchronization achieved, the tables harmoniously merge through a join, guided by the subquery as a temporary entity. This strategic maneuver ensures the elimination of duplicate rows post-join, aligning the data for accurate and insightful analysis.\n",
    "\n",
    "Furthermore, our exploration unveils the utilization of two distinct functions:\n",
    "\n",
    "1. **Conditional Function:** A cornerstone of data manipulation, this tool ushers in conditional logic akin to IF statements in other languages. In SQLite, this functionality is orchestrated through a combination of CASE, WHEN, and ELSE statements. The dynamic nature of these conditions yields varying results based on input data, offering versatility and adaptability in analysis.\n",
    "\n",
    "2. **ROUND Function:** Presenting a straightforward application, ROUND encapsulates the simplicity of rounding numerical values. This is just one of many invaluable functions that SQL languages inherently provide, enhancing the analytical toolkit at one's disposal."
   ]
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       "<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>calc_height</th>\n",
       "      <th>distribution</th>\n",
       "      <th>avg_overall_rating</th>\n",
       "      <th>avg_potential</th>\n",
       "      <th>avg_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165.0</td>\n",
       "      <td>74</td>\n",
       "      <td>67.365543</td>\n",
       "      <td>73.327754</td>\n",
       "      <td>139.459459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168.0</td>\n",
       "      <td>118</td>\n",
       "      <td>67.500518</td>\n",
       "      <td>73.124182</td>\n",
       "      <td>144.127119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170.0</td>\n",
       "      <td>403</td>\n",
       "      <td>67.726903</td>\n",
       "      <td>73.379056</td>\n",
       "      <td>147.799007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173.0</td>\n",
       "      <td>530</td>\n",
       "      <td>66.980272</td>\n",
       "      <td>72.848746</td>\n",
       "      <td>152.824528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>175.0</td>\n",
       "      <td>1188</td>\n",
       "      <td>66.805204</td>\n",
       "      <td>72.258774</td>\n",
       "      <td>156.111953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>178.0</td>\n",
       "      <td>1489</td>\n",
       "      <td>66.367212</td>\n",
       "      <td>71.943339</td>\n",
       "      <td>160.665547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>180.0</td>\n",
       "      <td>1388</td>\n",
       "      <td>66.419053</td>\n",
       "      <td>71.846394</td>\n",
       "      <td>165.261527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>183.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>66.634380</td>\n",
       "      <td>71.754555</td>\n",
       "      <td>170.167861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>185.0</td>\n",
       "      <td>1278</td>\n",
       "      <td>66.928964</td>\n",
       "      <td>71.833475</td>\n",
       "      <td>174.636933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>188.0</td>\n",
       "      <td>1305</td>\n",
       "      <td>67.094253</td>\n",
       "      <td>72.151949</td>\n",
       "      <td>179.278161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>191.0</td>\n",
       "      <td>652</td>\n",
       "      <td>66.997649</td>\n",
       "      <td>71.846159</td>\n",
       "      <td>184.791411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>193.0</td>\n",
       "      <td>470</td>\n",
       "      <td>67.485141</td>\n",
       "      <td>72.459225</td>\n",
       "      <td>188.795745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>195.0</td>\n",
       "      <td>211</td>\n",
       "      <td>67.425619</td>\n",
       "      <td>72.615373</td>\n",
       "      <td>196.464455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    calc_height  distribution  avg_overall_rating  avg_potential  avg_weight\n",
       "0         165.0            74           67.365543      73.327754  139.459459\n",
       "1         168.0           118           67.500518      73.124182  144.127119\n",
       "2         170.0           403           67.726903      73.379056  147.799007\n",
       "3         173.0           530           66.980272      72.848746  152.824528\n",
       "4         175.0          1188           66.805204      72.258774  156.111953\n",
       "5         178.0          1489           66.367212      71.943339  160.665547\n",
       "6         180.0          1388           66.419053      71.846394  165.261527\n",
       "7         183.0          1954           66.634380      71.754555  170.167861\n",
       "8         185.0          1278           66.928964      71.833475  174.636933\n",
       "9         188.0          1305           67.094253      72.151949  179.278161\n",
       "10        191.0           652           66.997649      71.846159  184.791411\n",
       "11        193.0           470           67.485141      72.459225  188.795745\n",
       "12        195.0           211           67.425619      72.615373  196.464455"
      ]
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   ],
   "source": [
    "players_height = pd.read_sql(\"\"\"SELECT CASE\n",
    "                                        WHEN ROUND(height)<165 then 165\n",
    "                                        WHEN ROUND(height)>195 then 195\n",
    "                                        ELSE ROUND(height)\n",
    "                                        END AS calc_height, \n",
    "                                        COUNT(height) AS distribution, \n",
    "                                        (avg(PA_Grouped.avg_overall_rating)) AS avg_overall_rating,\n",
    "                                        (avg(PA_Grouped.avg_potential)) AS avg_potential,\n",
    "                                        AVG(weight) AS avg_weight \n",
    "                            FROM PLAYER\n",
    "                            LEFT JOIN (SELECT Player_Attributes.player_api_id, \n",
    "                                        avg(Player_Attributes.overall_rating) AS avg_overall_rating,\n",
    "                                        avg(Player_Attributes.potential) AS avg_potential  \n",
    "                                        FROM Player_Attributes\n",
    "                                        GROUP BY Player_Attributes.player_api_id) \n",
    "                                        AS PA_Grouped ON PLAYER.player_api_id = PA_Grouped.player_api_id\n",
    "                            GROUP BY calc_height\n",
    "                            ORDER BY calc_height\n",
    "                                ;\"\"\", conn)\n",
    "players_height"
   ]
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      "/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  series.name = label\n"
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mLstk3tZcDDAooXw0eWBCOO4aTa4XfL08uLVXFG/O3cHOA8eIDvFzOpKIiMhZ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\niIhE8f9g9YZ8UvhznQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32ab6627f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "players_height.plot(x=['calc_height'],y=['avg_overall_rating'],figsize=(12,5),title='Potential vs Height')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Analysis using SQL\n",
    "\n",
    "By : Gokul Raja \n",
    "\n",
    "Feel free to delve into the database schema, experiment with different query optimizations, and contribute to enhancing the database's efficiency and performance.\n",
    "\n",
    "Contact me :<a href=\"https://github.com/Gokul-Raja84\">Github</a> or <a href = \"mailto: gokulraja840@gmail.com\">Send Email</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}