Week5 - Scientific Packages II.ipynb

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    "<h1><center>Introduction to Scientific Packages II (Matplotlib and Pandas)</center></h1>\n",
    "\n",
    "> 1. Basics of Matplotlib\n",
    ">> - The basic plot\n",
    ">> - Subplots and Axis elements\n",
    ">> - GridSpec\n",
    ">> - Some examples of decorating your figure\n",
    "> 2. Basics of Pandas\n",
    ">> - The Series and DataFrame classes\n",
    ">> - Filtering by columns and rows\n",
    ">> - I/O functions\n",
    ">> - Looking at data\n",
    ">> - Summarizing data\n",
    ">> - GroupBy for exploring data\n",
    "\n",
    "\n",
    "\n",
    "<u>Main modules of interest:</u>\n",
    "> <b>Matplotlib</b> : High-quality 2D charting library that has functions taking data arrays as inputs and returning figures. Also, allows us to focus on visual effects like colors or spacing an an abstract level. (https://matplotlib.org)\n",
    ">\n",
    "> <b>Pandas</b> : Used for data analysis and making tabular data structures with mixed data types (DataFrames). Organizes everything visually into tables which can be useful if you get lost in what's stored where. (This will only be used behind the scenes in this lesson, https://pandas.pydata.org)\n",
    "\n",
    "<u>List of other included modules</u>:\n",
    "> <b>OS</b> : Standard input-output functions for accessing and saving files on your operating system. (https://docs.python.org/3/library/os.html)\n"
   ]
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   "source": [
    "## Import modules"
   ]
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   "source": [
    "# data handling module\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# data viz module\n",
    "import matplotlib.pyplot as plt \n",
    "# the percent sign below is something called Jupyter magic making the Jupyter Notebook interactive\n",
    "# - (In this case, just plotting figures inside the notebook.)\n",
    "%matplotlib inline \n",
    "\n",
    "# I/O + system module\n",
    "import os"
   ]
  },
  {
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   "id": "062ec150-a40b-4151-8a9e-da01f7ffdd6f",
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    "## Basics of Matplotlib\n",
    "\n",
    "Matplotlib is built around two Python objects:\n",
    "+ Figure objects\n",
    "+ Axes objects\n",
    "\n",
    "\n",
    "These two classes are your bread-and-butter interfaces to the plotting <i>backends</i>. <i>Backends</i> deal with how the plots are rendered into the different image formats (.jpg, .svg, .pdf, .png, etc). You don't need to do anything with the backends for the most part - just know that we structure the figure layout with Python code, and the backend renders it into an image that could be <i>rasterized</i> (made up of pixel dots with different properties), <i>vectorized</i> (expressed as scalable vectors having multiple layers to the image), etc.\n",
    "\n",
    "They also have very similar functionality and interact with each other. You will be using both constantly throughout the entire data science workflow - from exploring data to designing more elaborate and informative figures for publications/presentations.\n",
    "\n",
    "> <b>Note</b>: There are a number of visualization packages. Some examples include: <b>Matplotlib</b>, <b>Seaborn</b>, <b>Bokeh</b>, and <b>Plotly</b>.\n",
    "> \n",
    "> All packages are typically built around <b>Matplotlib</b>\n",
    "\n",
    "**Pros:**\n",
    "<ul> \n",
    "<li>Huge amount of functionality/options. (There are >70,000 lines of code associated with this library.)\n",
    "<li>Works with numpy arrays and python lists.\n",
    "<li>Comes with many prepackaged Python distros (anaconda, WinPython, etc.).\n",
    "<li>Easily saves plots to raster (.png, .bmp, etc.) and vector (.svg, .pdf, etc.) formats.\n",
    "<li>Has an excellent set of examples for coming up with figure layouts (with code) at http://matplotlib.org/gallery.\n",
    "<li>Shares many syntactic conventions with Matlab.\n",
    "</ul>\n",
    "\n",
    "\n",
    "**Cons:**\n",
    "<ul>\n",
    "<li>Slow for rapidly updating plots.\n",
    "<li>3D plotting support is not great.\n",
    "<li>Documentation is not always useful (it's actually very extensive, but often out-of-date or lacking in up-to-date examples)\n",
    "<li>Essentially has two primary interfaces.  One is intended to be close to Matlab, the other is object oriented.  You will find examples that assume one or the other, but rarely the one you are after. \n",
    "    - There actually several interfaces that work with several backends.\n",
    "<li>Shares many syntactic conventions with Matlab.\n",
    "</ul>"
   ]
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   "source": [
    "### The basic plot using the Figure Class"
   ]
  },
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     "text": [
      "Help on function figure in module matplotlib.pyplot:\n",
      "\n",
      "figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, **kwargs)\n",
      "    Create a new figure, or activate an existing figure.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    num : int or str, optional\n",
      "        A unique identifier for the figure.\n",
      "    \n",
      "        If a figure with that identifier already exists, this figure is made\n",
      "        active and returned. An integer refers to the ``Figure.number``\n",
      "        attribute, a string refers to the figure label.\n",
      "    \n",
      "        If there is no figure with the identifier or *num* is not given, a new\n",
      "        figure is created, made active and returned.  If *num* is an int, it\n",
      "        will be used for the ``Figure.number`` attribute, otherwise, an\n",
      "        auto-generated integer value is used (starting at 1 and incremented\n",
      "        for each new figure). If *num* is a string, the figure label and the\n",
      "        window title is set to this value.\n",
      "    \n",
      "    figsize : (float, float), default: :rc:`figure.figsize`\n",
      "        Width, height in inches.\n",
      "    \n",
      "    dpi : float, default: :rc:`figure.dpi`\n",
      "        The resolution of the figure in dots-per-inch.\n",
      "    \n",
      "    facecolor : color, default: :rc:`figure.facecolor`\n",
      "        The background color.\n",
      "    \n",
      "    edgecolor : color, default: :rc:`figure.edgecolor`\n",
      "        The border color.\n",
      "    \n",
      "    frameon : bool, default: True\n",
      "        If False, suppress drawing the figure frame.\n",
      "    \n",
      "    FigureClass : subclass of `~matplotlib.figure.Figure`\n",
      "        Optionally use a custom `.Figure` instance.\n",
      "    \n",
      "    clear : bool, default: False\n",
      "        If True and the figure already exists, then it is cleared.\n",
      "    \n",
      "    tight_layout : bool or dict, default: :rc:`figure.autolayout`\n",
      "        If ``False`` use *subplotpars*. If ``True`` adjust subplot\n",
      "        parameters using `.tight_layout` with default padding.\n",
      "        When providing a dict containing the keys ``pad``, ``w_pad``,\n",
      "        ``h_pad``, and ``rect``, the default `.tight_layout` paddings\n",
      "        will be overridden.\n",
      "    \n",
      "    constrained_layout : bool, default: :rc:`figure.constrained_layout.use`\n",
      "        If ``True`` use constrained layout to adjust positioning of plot\n",
      "        elements.  Like ``tight_layout``, but designed to be more\n",
      "        flexible.  See\n",
      "        :doc:`/tutorials/intermediate/constrainedlayout_guide`\n",
      "        for examples.  (Note: does not work with `add_subplot` or\n",
      "        `~.pyplot.subplot2grid`.)\n",
      "    \n",
      "    \n",
      "    **kwargs : optional\n",
      "        See `~.matplotlib.figure.Figure` for other possible arguments.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    `~matplotlib.figure.Figure`\n",
      "        The `.Figure` instance returned will also be passed to\n",
      "        new_figure_manager in the backends, which allows to hook custom\n",
      "        `.Figure` classes into the pyplot interface. Additional kwargs will be\n",
      "        passed to the `.Figure` init function.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    If you are creating many figures, make sure you explicitly call\n",
      "    `.pyplot.close` on the figures you are not using, because this will\n",
      "    enable pyplot to properly clean up the memory.\n",
      "    \n",
      "    `~matplotlib.rcParams` defines the default values, which can be modified\n",
      "    in the matplotlibrc file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.figure)"
   ]
  },
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   "id": "d1b8cede-e9e8-4aac-aee8-087c05fb0413",
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    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
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     "output_type": "display_data"
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   ],
   "source": [
    "# This is our interface\n",
    "fig = plt.figure() # the \"basic plot\" is class instance with a size and Axes attributes associated with it\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "1d7e56e6-57d2-4a87-a060-3ff9e802dc84",
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    {
     "data": {
      "text/plain": [
       "matplotlib.figure.Figure"
      ]
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     "execution_count": 4,
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   "source": [
    "type(fig) # this type is handled by the new_figure_manager behind the scenes so that w \n",
    "\n"
   ]
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     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fed64479810>]"
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lGXoJS1zSR9CDEHV4VMeqDkx3wLPKbObsmQ8oxEp2iZEt0YDfdDD6W71JVgZO5vESXlFOou9nfaYstxOsf97139Lk+s3LH2HGsk2+E3h4MMHLBwl60qaLD81YEZ3IgW4sE+84332gPvJAbQr+uBW1jg5/VD1xKv73L/PVXywxpI6gm+RmZbTV3uJESrZKh6DX1stPxILIJfqbJv7zQwz76XS5jHWJgeA1nwydk27lpt35Qz9kq8MSmTBNKgPXMhx6HNQzfBRsIkjQZUUgpvDkO2ErIRYo9Am6d5yPn/w2TrpnhrgsSgumsgptoEsinl+4XipdlASgCMFI80gdQQ8i2LS8jq+eOBW/f93PhajqNkXu5u6EFGnAg+8VRC7RZT81dw12KSrKlGXogoHqrzs73Wm/eh0//lfY2kQEWRO9sMgl99caQXfqJRM+QLaZeefSAoUdngu/zkKyAMW03Dwi8pfZ2W5kxDbxjseF66JDQ1BBXURtblvkEoVgHyR5WHbqCHoUkfLK3dZt92/p/vSmXymjahHg2jWzaFCeoAdsdKlgYroWGuatXKLvsb5dyEl7Ht723BIMummaQn34GUsTdJ6nqKXwCXkfBY/Tmc7234vvPPke91mIQ9fIP5dWvz1kh6EMh/742+FYL+pK+sKYLCWlqOqiafWw7ABSdwRdsK9CRNJz485pS63UgTVpmnMIuggqIhcXa7buRUU5QY92LSLTqnIqQuWl57NmLlcPf8vNV3JycB2LLBH0vBexQZGLiBA2r/CfjVoEU3RP4eKHMgT9wRnhMwbUxyPVWqBkxLKmxFisfDKRi0HUN1LpyaDap6Lwua4MPTjYfbLQwDsuLVJRip50zwyMuetVADnTtuqJU/HiIr/sT8ZskTW5ZM0WVSGqD6vM5Yy45GEZuj9v06hTELnIQlTXsJWLd2cn/5FJLAQHND1FY3Hoge8SKkUl8rYZXqGYi3H6CHpgVAQH+5CbX8A+jVNOgnA5A68LeJ6IMNI3K4/m0MMydFfkokc0ljohah8NipIY9u0yg0zIoVsapaxc/8zYrocdi6KDczHLoxQ7JDw76xXDQsSFUCmqkE8StGTHXj1TzlkfbcZHjCB1Iuhw0jLTydTG7j8KIZKTQPoIugR4tsNBrlSmT3/10vL87wKHHn6zkqMUFclC84Q3ARl6XMSZAENufgHVE6f6DjZ2obubcrlEVU7rb++sxvCfvYgVm3YL07Fk6DYRVooWfifF8cmMQ0qBzXuiTxHirYOPvbVKuj6U6o1lmR6Lt+MsvPvIGyvD5WciF3mEZOhJli3Y5hfs0OWdlfJWLiYqx4DL+c//ZKvUrsVoLBcGWLJ3WRlpsG4fO3byqoGSXnUONFi5aY8wnStDryxPZooEiYC3XVTkyCZkw1Hlbd0d7cXashlbPbdu+75838nUQ08pGj2jTO04WWEAMpFLkaA6+EWORZUOJxckMD4rl8CwdLlL7aiAnNe89duwcz8u+Z2cll00ka05ushy6JyEqseeuXP9r3PCYh0v3BCxJmXoQgRlxJrNnQQt2RnDe/a1ZZsw9pevSaUdest0rNc42s22DN2LuMfumUbqCHqc7UzcrdCB+gZUT5zK9B6U06z7r+PEcqmeGD5Ql4Vj73yFXRfFoGartoi9+nQhOx14BE59QuVa+7VlYksdV3cSJ2JjFNZs3Yt3V29jPvM7FlmrgjIozNbn7ufFlmhzVm7JlRuYK5t2HcCnjl1/0OZdZi6aIuiyjnFJIXUEPQjW4JLp0PqGRuVO3eAEexKJL0JyckF+BeuPYsZyUYvIaBrSMnTOfdU+lG3qugQ4r5PumYHP/99bAOT8BGQQh9hKxUmi/JrpiHuCzn5BBHevbgn/em8djr/7VbywcD1G3/kK3qzZnE8j08d+z+fC7/11DdivYFTBMuHMRC4KkLFllRlYQ25+AV//s+BgBkYW5RI2yaKiZTj0Zet34QMnwFV0Ybxy9JStxRiIXvLw6tJw8Kb9dQ349l/nYzUn7oeqbF/VeYZtZ2x2Aa5raMTm3X5FozaHbqAPS2lHEHUGgbvDWbhuR/6eqsjFO4SG3PwCRt7+kvBdb/vY8oPQhZRjESFkHID7AZQDeIRSenfgeV8AfwbQ3kkzkVIq70oYAyx+gWVNEYSO7Etk8ZBXmAbq4zc/Y8vQvQTi7N/MVK4Xb5FTceSo2bgL0xepRcMzAW/7PD13DU4b0s33/PXlmzBtwXosZdimA+pmi7K02FV2yeTOIvoq3OqN//gQsz7a7Lvnc/3XpNIbdu5Ht7ZVWu/yICtyMbXkyaowvFWS4tCpl6D7P0h0NGEQrDAIP/rnh8y0m3YdQJc2zaXz1kEky0kIKQfwEIBzAAwFMIEQMjSQ7CcAnqGUjgAwHsD/ma5ooT4x3lVIyxqzIgWZSzwXf7oTj7+9ypOPd2vnf0fHsUgGagdn5NKedd9M3Dt9mdmKSJeeA8tgJYp4qByWsTLCVNELNwqkrv3+cx+yDxp34d3WP/dhWCejrxQtvMjTn6QJUbsh1lMeI3P7c4vzv73WKaoKf28be6OFPj13Nf465xMm8/FWzWYc8/OX8YJkADBdyHDoowHUUEpXAgAh5CkAFwJY7ElDAbR1frcDkJy1fYI7nuAhBCzM+mhziNtyEZKT2rJDj2iT91ZvCw36Utg5srna3L0oM0MZnPar1zHu8O5SaUWn0sjESN+4S2yrPeTmF/K/mTHhvb8T6htZUZKJQ75lka8RDVyL3uEk8h6woRv8LAjvOGEdg+higSMSenf1Now7Qm4M6kBGht4LwBrP9Vrnnhe3AvgKIWQtgGkArmNlRAi5mhAyjxAyb9MmvXggxYyjJnSllzAh5EZbNPxVbim8XC92FHGlAG+bvOI5FKPw3Gx5sounuyVnEa8LHnzTd82qo5q7fjitX+QiDyPRFktgcXcha2VEKfDM3DU49/5ZUu94F1GWHNy1ruGV5aLUTi8ypRSdAOAxSmlvAOcCeIIQEsqbUjqZUjqKUjqqS5cuWgWFHTBKA1IWAoHrvB16xMtL13OODeOVIyUmKI2Wi6qr6VrKEvR8vQy2JS8dc1/i4yAVFgfplGHIWbnELEQRhXAbnEI9lb7xnx9i8Wc78bpE8DjvgskSuVw6ebZU/UpNKSpD0NcB6OO57u3c8+LrAJ4BAErp2wCqAHQ2UcFSgjAGs6TbtP86rBRlYdxvZkVn7s0XKqKc4saOjkLSJ/cEQTm/jZbByLjE6ETRoCMG2i1xbgD1EXS1Onn7S/c4PluQIehzAQwihPQnhDRDTuk5JZBmNYDTAYAQchhyBN1cjFUPQvFYSqQ9pcQmHKWorgyd91qptIkI7oSK5NBNi1xsLGAW2ttnJ61SFcudL7RDt1CeaynM+yzd/vQScRNhLUoFkQSdUloP4FoA0wEsQc6aZREh5DZCyAVOsu8D+AYh5AMATwK4giZ9bLoE4toP6/S7yGxRzRpFHSrfm5iLu4NCjI5AmwQa2fggSoFtPhDjxCLO/dv+u5jzRD6PqGc2YKv9o0QuIpQcYfNAyg7dsSmfFrh3i+f3YgAnmK0aB0WUEIg6Xkfk4ireTLiXz121Fdc//T6mf+9kLS6tvIwkKg9spBRlIKE2CfoHmOYLVFtaRj7OloGH7y1ctxOfbNmDfp1aRedpWNYTDLHMgrx+gV0hG1PTFvfs/YQmxKCnz1M0CFvKPdagXbuNfyYkj+7sOlAwceOaLSrXLpzf3c8vxdpt+7Dks51aURyTivvtgqdzDC4qXg9AEzDt5QmocZGn3PuaVDpdxyJWXfbWqp1FW0rIWxsZnub+HZAih156woc8UkfQi6nCYwXlcsGbdKN/XnDuCJktOhZPcTn0kOVPlN0iA0mLXBrzE9XfJnUBM7A/zIrmLFWgzKFrzt24jIYu18gqd5vkgRQvLwmbjTIKSFSGrntEoUr6KG/jUibgQaTuTNEgrEV1jTFAuGkC13kuISYt3V/XAFfZnrMqczl/fsb3vOD3Ci2eDN2PhhKzGpCpjeqB2zIw5fgCqIstjBAwQ8PJrbvpnbhfhi5OS2lxIyiqIHUE3caW2QSkJn7IyiVahv6z/y6KzDcYu0ZmPgaPYUta5MLTRwQ5dNNQD1gmIUO3sAY1alq5sBIXxVbaUJFRCktdcqBi5RISlWqUt4wTi8g0UidyCaK0+DkxwlYuub+iMfmnN1dplKMOk6fbyyDPoQcVxZaJT1LLVtyv8B9BpyBDZ9xTDWAWlX+SEgh3PPDNFvWgYuViwhfi2feCrjt2kD4OPXC9rwQUPoRhrcEEh0PX5TK4ZWpauSSJwiTx11UUQ8UEVHd4Gt0aCRkRiK7rv255xmFK5GJNpKomcgGAJZ/tzM2TGHWyPctSz6G/uDj5kK8s6Ji3FaItmutm6lFaqcgddQj6Gk6Mchm43x5ltmgayl8poxthLKCiNVWGY9aVY7NeU+XQRanFB1woFSMFW57CPpFLlFLU+eJz7p+Fs+5TD2/tz8suUkfQg7Tv2XftbGUe8URmk4GUUjTIoTPiocfF/roG/PbVGuX3dGTo59yvFpLAi7ynaOC+6qHPylCVoWtOQdF7MmIlv8glXrmqYizRYiIk9hbIVV7kEpFOdQH8k8cuP+rdUMiOEhb0pp6gJ/WuCXA9RTXzY33PH2at9BYoDR0OXSZmBg+U5mzMgxxPqR26KwNVJlKOoHtFLvHaRFXPHLWm8gigDWY6mGdwzOvO6WkLCnHJo79Xr4xiIHUEPc0IW7nk/ppcaPZoElmbhyGz0Egpnnxndei+bRm6KrTt0AXvySxaviQqHLoRkYvursQ8vI5FlFItI4EoyIpc8telNUR9SB1BjxNcySbNkjJvC1wHlaKDb3pesczwvXpFcze37KR3L42UXabt+NKq48e2SaJMmrhVUBW5iJLvrW3gtomNtvLW/Y2azfjMOajdxUMzxIdMyyCSoJcwAQ8idQS9VCEnQw+IXFwZukNkag3Ij0stnCcPvAXQulJUWYbu+W2IkMl8o675Just5eBTEemnLhAfr2cHFLv327FoK3hsc54H2qOUZ1jqCHqx5eA8SJm3cUQuJi0GvUrFUnZZ5tWszrJStBSGjzqBVUkbXymqb1lid7yJGJ443pyFnTI7g9KdRWGkjqA3JUQNJB3YJoim0EgpW/xhefZY8RRlVFp04LYMh+4XocdViioSdM0hZJt/qK0XEHTEdzKSPV+ghPmkjKCbgo7Zoo1Dor0iF506JQUejbFRnSN7t8v/Vpahy6RRrLQMgfUfQSeft207dNWyTYHSaHGiLmPk5sp93eB3vVmzGaPueNlaBMyMoBuCnGNR0Gwx95dAT2bKKtOrVCxlC0BKKXMC2SAKpSBm8UKVQ48L1bG1a79cdMYgbNtnRynMtcNQR1TbpHPTok93YvPuA6jZuNtYnl6kjqDHEU9YOX7MgQ43XDgkmhgTlXjN/lQmWNJBz4q1M1AXuUikUayDlG6DMn9qQZUg/UzhdCMvvMWYHk0UYpELEGenG2W2GLyOP3htMVupI+ilCqmteeDaG23RVFCqOp9SNDo9IcDslVuw5LOdRsqXRU6GHoYVLs8z09Unvfn6yI0Vr8hFvg6spJYtQRNDpFJUcxlx24z3vg3jAlshDVJH0E2s/KaDFTVQigN1DZHpggMjf0n0HGp+/3rYBtdnhy45aMZPnq1cdlzk7NDDvWlf5GLeDl39xBul5PFd/xPaDtkshVKKuvqIEnSD3EU8D5ILE81pywItfdEWY1J0SikG/HhadEIFzP9kG+Z/si267MC1V7uu41DzZs2W0D0vp1/qMnTmfQtleceMDcmSjTqbnO9JRVu0vW5EiSV1zX+j6m1j12irS1JH0OOimEQuJEP3iFxsONSUchAh3pbTBudCOL9l8MCrNejdoSXeXR29YMvDnlLUhJWLLnxiIgv5RxF0fZGLxzqBmUB4qQVbi2zqCHpcxWZxnW0CVi75aIvm7Me9HGgpc+g82KhynDg1Sz7bic89+AYAoG/Hlsw0NkQo3jRqIpcwEjuxiHJ+G8paKEOPUWDUmzZaL1OKGkCxvUzDVi65vyaVol7iZXLtMn0ABtezz65O1NIYMG/n7ed24zWKLQVcELZLEX1GHE9RZbNFA+1pi7FMHUGPMyE/2bK3qEKIYNnuVjjHoZupmbd5TA6apA40siEm8u7qbJquysKWUpRSynH9VytPFz5nKON5R49nbTt0p7Y8s8iVm/Zg9M9f1sydjYxDN4SNuw4UrexQLBdPr9qIMihDCN4LHDDNg+nwuryqWWFcbCtFVQm0jAzdK3JRyjuMohxBZwFRX6HtSxGR8Z/eXOWjGyZa05Z+K3UEPe58bChiNMJgJ3qVVabigHsHtcxWW9b+3IrIhdGbpRwngwfVKttwVhLl2yTMFhE9nvU5dDFsMAEZh94EwOPQKTUXNtaWUrS82AqIGIhj5WIDUvTVF8tFriMpJ++klKJ+Ra4Fq61Iymsn3yAvY+LTMsciBymmK1zX/9xvQ1Yu3vIM8kym252CE8vFbDEAgkpR8wPIxtzUNxgJp24yStGIZ3Fl6DzYOM0rU4oaQjEXBJ7IhYIa46ZJSqxceLBjh15aXICyDF1BKcpCchy6vXJ4Cl8vtKMtRlQ7mK2J77QVjiGFBL20JqcKeBw6peYUV97BZ3KC2ZChM+8bLSUH+56iarWe+/FW85VACYhcbOevSHil8414bmNXV1SRCyFkHCFkGSGkhhAykZPmS4SQxYSQRYSQv5mtZtOEj6Cb4tA9v03OY9ODOqcUZd83DR9Bt8AQqNb5Volohv7FOB126F7YKFH4GZxgb3L5Ji9yKZrrPyGkHMBDAM4EsBbAXELIFErpYk+aQQAmATiBUrqNENLVTnXjc1izPtosla59y0ps36sXF5qHkFLUuaaI3k7KwudYZCTHHGwoRdn1syty2X3AbJ/ago+cS4tceJ6iJmokUwHb2VsSuUQ8D+ZqxGyxiBz6aAA1lNKVlNJaAE8BuDCQ5hsAHqKUbgMASulGs9UsIC5Z+fG/Fhiphw5CB1zY4NB9Vi4lLHIBZYoCbHPoz8xbq50Pj6BYUYpq2KHnGIPw/eSUol7LHMN5R8yROErRqAZuamaLvQCs8Vyvde55MRjAYELIm4SQ2YSQcayMCCFXE0LmEULmbdq0Sa/GCcH2JAUCoW6NsTdeIbqhLAGURYwUHY6DRWiCd3jxU1Rg+/AOG04iOhz6Y2+uwpQP1oXu25ShX3/G4Pxv2+uGPRm6OOPg7tTEd9ryDTAVnKsCwCAApwLoDWAmIWQYpXS7NxGldDKAyQAwatQorS9K6mSdHfvMb82DH+wSNIrS59Cj5Ig67uwyHHopR4y0CZ0F8q7nlzLv2yTofiW857fhfqPOfxG10cvbTrZC2PLeleHQ1wHo47nu7dzzYi2AKZTSOkrpxwCWI0fgM3gQnKQNBSG6MeJbxplgcRElQ9cpis2h25Ch24V1zrSElaK+8WZ58bXGoUflGxhBJr7S1iIrQ9DnAhhECOlPCGkGYDyAKYE0/0aOOwchpDNyIpiV5qpZQHqNFsMDwWuHbkpJ4h18Rjn0CBm6Tv1ro06ggRmrlLQ6oy1ctwMTJs/Ggbp4Wk27HHpyjWsvOJcYNlwwiiZyoZTWE0KuBTAdQDmARymliwghtwGYRymd4jw7ixCyGEADgB9SSsPH6RzkELn+m3I0IHZE6MY5dErZMeCtKEUN5cPrI1v6lknPLsCCdTuwdH28814Ti+VC2b9N5R1htRiDQ1czWzTBfNlaZKVk6JTSaQCmBe7d4vlNAdzg/LOKtHJbOXBELjDHTdsKnxvV7qr1p6Bsgq6UixzSqRSleaV5XDtom/TclpksC6qiEel8I57bGD7FFLlkMISwHXpBKWrMxsWS679ppSjAjgFv+wg6G7AxN3NK49yC99c5q2PlpUs8vnh078g03mHxwZrt+d+mm4RG6Jl4sYFk8xbBBkE/cWBn85kihQQ9zRx6SIaeF7mYk6F7YVKGHkUUVInGjKWb8PKSDXGqJA1TY4Z7sLWNyIIohFR+30ModaBL0M87sgfGH9NHmMbbtLbPGrBnjBIhm7dAdKo7tzKeJ5BCgp5m8OzQTZot+sozmNeyDbuEz1UXj/teXs68H8zGzFwyMyF5X2hL52gqpLLuwl5GSGT7J8VgUUQI0WEvOJcNO3RbSB1BL7XIeSoIcnJ5kQuVD0mgUkaSB9WYKivWYb+cmWaK6Hy2Y79SuXHAs9PXgW4+MrJ7bhorDIpA5BKjvGLI0G0hdQS9GPS8W9vmRvIJzivXauLDtdvx5Dvx5KSsMmyGMw2Va4j4hByLDGRr3Q7dSp7s0Ag6iOLQTxjYiXk/R8hKh5oVzw7djwdn1OgVlADSR9CLAFN0MTixXHMyk5y0zXgaIpiS18fxFOVVwTaH9dJi87oASs2dMxu1MDSvKGfez7WbnnzZuKcoLZ7rf/AbbXiRm0LqCHrp8AvqCBF0yzKRJN3mzYlczL9rW0w3eaYVHzpjMvSoaIu81pERuSQ5HyMJryXX/yR3unGROoKeZgTnpw2C7h17tk5FYcGWe7lKtmmaeDIwdaB55OHKHMJNPP8vNmQMB2wdcMFzzLr9oiP0CrSI1BH0JF2NTSM4sWwQQZ8MPVEO3ZTIpWkRZV1QSlGXkMiFN6Vy4R5Kpz9EQ4Mihut/xJjjNV8pUqLUEfQkMbBra6P5BRWHtkQufTu2RM92VSm1cglcq3DoZqpQEjBq5aIZA0UmhgkvZ+PrMhWV5rr+2yGxvG+xcZJRXKSOoCfZhN4B/dx1J8bOLwmRi2/QJ0nQDX3LA698pP1uU2LuKQzaoWty6LnZZsedXitPQaZ1DY34ePMe4/kC/PYrQXqePoKeJNwV2NTgTELk4gYpIoQkepakqbLWbttnJJ+0Q8aqQxaRIhcO0S6TsHLhgSfGGNG3vVZ+gHiMvbVCPxZglGiSV66NKIxxkTqCXoqroiy48dBNloECT5Ukw2pLvKMiU29Kh2GY/JZopSj7voxIgRsOgZO+W5uqyDzZ+dnr3UgOnWsOW3rEKHUEPUmY5nCD5mO2Zdwm6//AhBHC57ZNMGXA+9ymROh1oKsUjeNYxJU7x6A4QqVojLEeabbIGT+ZDN0AknT993aYib5Lwg69kdI852ByPaqqEA8VW9YpByspjhPfO4go60cep2mDYMWxFbfGoUc858rQzVclNlJH0IsBSs0sJEkQ9Fxdc8TAFJG9+fyhaNVcHDq/BBh0LqL6btzh3ROqiTzimOEFEakU5d23QbEk8uzZji2WEY3npevFweNEuO8ldqA4F7zmi7PbsIUSrJIYSe5yTMvIguPRmpjCqbap3Du0rIychza+5cl3VmPb3lrp9LrrVwnunI1ur6JFLjzHIhscejRY4j0Ke1ZM67aLFfE80WUpBgpMH0FPsCyvFtuGyMWOlUsuT0IM52/4xCJZ7Fc4T1NXVl6K5o4U5hgKbTt0C9RB5ptaV7F3g8XShRQrRpAOUkfQk4TpDgtyStbMFj2/TSGKGykFpWhTgrcfo3D5mH7C57p26DZk6DKmfryxlmQoC1+53LDMpUfR00fQkxS5eAoz0Xe8Ay5MwlWKEhCjcu2o70/qIGIRdKtQirJQFUSKwzQ5dLNeGOKyfGkYiSi1abgoxrurtxWlXB2kfCjbhWnHgUSUot7fhogsIRJEowQ4dN0aVHHCxxYTKmdkRnGKUV3D48RtcKAyefJSFItn2LDT7tF6JpE6gp6oIiI/+KghK5fAtXUrF3P5Rk3ELz38trnCJBFccHVFWFXNSpCgG7Ks+t1rK3CgrkGcyIIdumJRjHL9oDh4TVhVILZFO8hheukIbn1tiClyIpfcb5Nb1CjGqhjcU1kgvAHVlLGWJocO6QEoknX/4oWlke/zXf+joy0q97vUN/kTefiqDBFIH4deDAbdULks13/TYp2c/XJuipraAJBk90XSCPbJ8Nte1MqnqrK0pkGz8jIlQhl3bPKVovJ5dG0jd0yj1KEZgSRlhDiORaVF0XkizcHdzEZpVUFpjeQSg89T1EB+QRFLIwXKbVB096dBtrkEFfrSMt4oQlBVWWIcOvH9KRqIQrRFUVcM79Pek6dMuezrEtC7S6GY7E/qCHqSTWW6LFb4XNOmYa7IhThcjQm4+TVVsDj0LpIcpy2oKEXj8gQmPEVliZhMnsGxVhAhZohC6gh6kjAtcmEp7Uxz6D6ZssF8S4mcE8McbAsGh17M0KgEUOq8uphH1YlPLBLD3f3ItpcM4Q9x6IQ4R9BlJD0KqSPoSXKKpoNcMQm66fACzl/CKU8Xpcihm6rSJUf3Dt3T3Tkd1qMt+nRsEas+hBR0ITLYW1sfqzxuPVTSSraXHIfOrkda6Hkxp0rqCHqSIIIrHbCUlDJckAq8R3El6PmfKEj+r5latWwWNvaKIwqLWy8CAkrlRS5x+1ls5WIWcnbo/jRljg1uSuh5UZE6gi4aDz8+d0hiZemAxTHb2Nq75sNmOXRjWfnAi6wngrsw1lv0BY/zvbFl2oZ9CHQhdaaoU09Zb1stDj1tLHoRkT6CLnjWp0NLo2UZj4fOYNGNW7l4kGQsF12cPLgL2res1Ho3ruxYhFgceszBQgJ/oxDXPHXjrv3iikhAWikqn6XvnTQ6FnXQHNdxIEXQCSHjCCHLCCE1hJCJgnSXEEIoIWSUuSrKwzQXaZ5DZ5ZithAUXPWbutmiTeius/KGfmKoRFuMuxOb+dFm5n2VRU0+TIF6GhsixCRweM92zPtXndjfWpmRBJ0QUg7gIQDnABgKYAIhZCgjXRsA3wUwx3Ql/eUIn3KfDOqqbuzvC86l/HYYiYlcMjOv2IglP44tciFK0RbjxtHhlaNE0KXTyTgWsbVXpSZF111gDu/V1mxFPJDh0EcDqKGUrqSU1gJ4CsCFjHS3A/gFAM7+zT5E409ngpomjEylqKVjvkjALT5WfoSkmkPXaobi0XNHxCBfaVuB0WxYuciFzw3mnevDYoXPVcWDXx6JLx7dG4O7tWE+t+l4JEPQewFY47le69zLgxAyEkAfSulUUUaEkKsJIfMIIfM2bdqkXFknF40neiID4pOhG7ByYUw8m/bOaZChl+pCUUwZOlylqGQ2sTl0nh26xHfkzWSlRS4yHDr7ndLiz/mL7sCurXHvF4ejnENdbY752EpRQkgZgF8D+H5UWkrpZErpKErpqC5dusQtmlUXs/l5/toSudgJUeraoZvN82BCHP8AExy6Sj62YtETBepgcniEzRZzxNPWQeS6KLHqAJAj6OsA9PFc93buuWgD4AgArxFCVgE4DsAUW4pR0TwzTXNc7tlUv/3faytC92xYuRRyTIdStBQnhu73EhJfjKa6yMeXobPLsyFykUkWnBKl6NQGlOa4lSHocwEMIoT0J4Q0AzAewBT3IaV0B6W0M6W0mlJaDWA2gAsopfOs1FgA8zJ0s2aLSeGDtTucM0XN5JfboaSoAQKIK25LorwgqHPylAxsydCLpRQNJilzRFClSEB1YHOBiiTolNJ6ANcCmA5gCYBnKKWLCCG3EUIusFYzDnTl5FqTWv2VkkJmtqiPosZyIa7Zolz62MrvGDL0fBaSSfXOFHVl6KVF0aNqU4wFSOqAC0rpNADTAvdu4aQ9NX619CBa/WMrRSPI+6Hd2mDHvjqs36lm5GOLUJo8U9SVyR9MiCMKM+FYlKTZIrceEhXIe4rajOXiij5Li55rM0w251L6PEU1heg6IoPCQIruOF3uIapWE0b31coXyDj0OIglcjFQdi58rlxOcQ8b55Vio891zhR1RS6lFm1RtzYlbeWSNIQiF9GzmCIXa5x0RMYdW+m5D5uPB2JvFJaa9QIQT+RiwGoRjRTYuqdWKr2Ns2kBNZGLNIcukybkWKRutti5NT+efUtTZ8iW3rBNH0EXQUQcdeaYysS0RZPiKCNNVSnnqGQoM0butqHTN3FqZcJZ7G9zVkunjWu2yKuumpWLbEL1JK5OQWVAnzKYbxZd7M1msR2LSgraZos6Vi4KDZ8LHqQ+saJKiKr2gC6tuM+MRls0llMY/Tvzv6FYKKaVi+r7xeTQCwdcyHLoEiKXoAw9X5Y8xAYSxSHpnVo1c8q3V0bqCLoIpm3UvflFdYItsUEkwRc8M1klGyEKXDx6xTF45PKixHPjIpbIJXbpajmcemjXmKWxy1PpcqPBuQL1ycW2KUHHooglhqfctYnUEXShJYthK5dExo8iiy77Hbv212NfXYNenaKrYRSdWjfHGUO72StAA7G2xbEdi9TSf+Ho3mjdXMpgTak8FU5WNqXUQsmzcpGujb6uTQWq9MFtz8zKRRKiwRKnEWVCmWprvGOm59Vr3fZ9WvVhlknsyv1sQ4e+Hl3dQbus2FYuGmXGIehx4BI1aU9RHZFLiZotqiIJ34bUEXThuCmm3ExysHVq1Qy//tLw/LWpwxBso6maLc66cWzo3m0XHo4fnHWodp5Jy9BNlBkXZkUufrjiPhUdlS6dUEE3xdO23MUsk6EbgG2uSXaoEULQ0VGOaNVDUwRzMIPH2Y3o2x59OoZPuRrYtXU8xyLtNzXLI/H2T6J3ZZvBoJELw2wxB5XwuWLRrBmMVdRdFD4rs3KRgnlP0dzfFpVydqsyW0JC/AM2rsglKdizw7eTrwx4it644qXYwbm0nODsNKS8B6g5Fp2VolTFLUcIDqsI1tmmYYGL4gjeigDRJOndoQXWbgvLnNu3rMQPzz4U5w7rYczKxTQBT0K2TeBR6JDSnVyqsCXTTFrkEnv3KSjQNIcuFcslJEN3RC4KAy8ps8U4Hug2kDoOXTsAl8Z7lALXjB0oZSctL3LxV0W1c4PlJMXhplmyw+sbE+Z6rDzjLrKst0WOMgBQZngmf254z1xdJBvDqB06Iw1V9PIQVWfHvjqFnPTLCcLto8zKRRMVZQTfPX0QABMy9AgrF8nRFvS6VJ38JgfDs98+HqcP8csB+3RsgaW3j/OXSQoD1/RgLOZCoUu4R/Rtj7k3nWG2Mh6wiGhlubiycRYR1puuU5q0mMCkUjTIoTt/1XaG6u3RpkpdYKHC7SchckkdQddVdqi05QkDOwGwI1ooI/5vKJYM+UfjhmBk3w4hrue5605CFUNnIEMwRPEzeJBt4mG92CeoxwFfhh6NLm0E32qhTyedexiOG9CRXRwxP45onqDLpTepFA1i1ZY9WL5ht1IkU5320DozQb0YqxZ36SPomiKK2NvgKBm6JGkqlQOXe7bPmVwF5ZLtWrCDgcnU+SfnHRa7Xiwc2q0N/n3NCUbzbFFZjutOH2g0T5s4pEtrPHX1GO7zWNwf41U3JG+ZJEU3arYYSGMpskEIOpZNSt60gb82kDqCLoIwOFdcRVXEc0oV5OjxquLPS2nLF74nq8rND0aLbcxDRTkxelRf26oKLLl9HI4/pDPzuS0OSnbBU1eKmleNu0RU1hjApDWMia9RyeHiEb0we9LpWkpylVeSiCGTOoKu2yZarv8KahhpGTpBLIoeUooqvMsiitJiJYmCtDgc5TfiY+f++iKUChw3oJNUOq0xbpZBzxNq2cOybXLottG/cyt0b1cV+5hK+XeUX5FG6gi6CMyGUlXucDOP97qLYD1UB0QwtdKWL0Yb5L3cPPc6t/Y7SCWh9EkDbCvgQ+mJ+YVRlkPP10GyBnJWLvGhE/dFZ/wKw41wlLsZQfcgCbtrm3bWwbgoUV8T1fkyg+MQJ8Sud/DlbXujX+eW88Oz/e7xxTyH0ySK4brvxeqtezXKjLFYM95VtXIxy6HHH0g6Nut6IhcNIbpFpI6gi2C6vbxjItpsUd6xyGe2GHvjEJ2BWzPW5JSpt5cDJIxFwYXM5B/SvU0ob9swvUCLqhz1PYf14HsW6iKmFI+JK0/oD8D8rmvjzgORaUyUqHLOqjuOZRXA/pd1XsmsXPLQtXIRDUwTE14llkuc7oxTVW8bNCtX63oW1xRsU53JL9P2vDQ9FYMjycLWdCMEuOy4ftbyNvXuqrvPw8mOI1NUn6oeEr3nQLT+wsQaomMZY9psMThuSeiHeaSOoIug7UUaAIuARL3fp2NLvVguEflGDTKp72LIQ89y4o9L1Rlek6tCJkGGxrS3YhRuPn+oZMrSiVVgZUdC7OkvpO3QDRTfvmUlbjr3MCMiF51TnGybLWaORQzoNonONlllSNw//ijpesh6il48opcRuXRwl/KFo3vnt5c8S54Xrz/Zd80KYRpS+nAa8nPDe+ZN9nQma1xyrH4QQbzy3D7ty4jkGLc7bzhzcMwc1CASQ/zpimPyv2W/S2Q5dsHwnvjGyQNkqyaEyjmreS9oy+KTgk+MPaSOoIthr6micu7RroVcPiErF37aqsrySAIoxaC7JmgKq8Pgbm1wxmGeU4QYrwYHM48D6diyEt3aVjGzMcm0iM5XTRKmdoosfP3E/px8YyhFRc8ED8cO6Sol0vSXZVf06UJJhu7USdZE0/duaelE00fQ1cz0+DK+K46vzv/mDSSfUtQQ5QkqsCIklCEiHEqvUC8S+AsAh3UXKenC3LgobIFb1aSsF4PldGtThTk/Pj2UzvzR3X4c3pPdhqx2qLfg9mjDsciFaSsXnYPUdaCyOBSsXCwT9ATmReoIuggqnBFvEtpGGQm4/kf0chRTrSBCzy9K3iJ/dM6QyPd5ilzeBFBVuEZB1oKovKw4B+X17iC3OwOA2nqFUxoY4A2XOPoLUevKb+qK0fJ8NKqIXNy/lkUuSYzOFBJ081L0YEeyuAi5UuVMAKVzo2GiqRM+1x3bLDpbWV6GrqJAU/ly8ix6HsG61TfkCmpWwR9WVp0qONRH9bT4WBYjEc937ovnpcoiCnHbVNQ8pjl0YT0Mcu9KBN2pu22lqAube5TUEXTVg6DdQRIm2tGwtT30K0UF5dPwINOZN+53uJNTKyg/617gZl1DjvtsrkDQdbmWqsqyUK0qcqEsE4fKN2zbWwtAP5CZjQVRtODJy8Zly5JMGBNeydZDXx4pTOv2n20rlHz2FtsghQRdr9GjCGfk+4b6Oii+iGu2qALVvPw6BFZ+/mtXPty8gn9kX5D46SyaL11/MmbdeFr+umWzXHm79tcxiaut+fP2pNOYMntRM293CHqHlvrnyobKi/m+qH1MeoACYusTk8Tea7YYKbZ0ZegRCY/sHQ7hXGrhLlJH0L0c61UBjX8xIgGqgkBewUpBQ4NRJziXO1F0zbNyilzWVt9/z+XQRSIXExjUrY0vHvkhXVoDADbvrjXSz1FZuN/do12LvPWObD69HHl7f02LHK6JbRxiGEPkIuNY5H2iYx+uA+/CITsmogj/MdXhePRu3rdfeLhs1awqhqVmHiFkHCFkGSGkhhAykfH8BkLIYkLIh4SQVwghdtzh4F9FT4o4lgvwELPA9BrVr0N0YQqu/7IIKq+idg5RXIPM4hCcdLxXgrJ077Bjc+hBgu7I0APCekIIWjXPcdHtW/rjrfdqH7bVDiKKWHVvV4VvnjwAD4wfwRa7GZg/3QWEO4hyjoaSgOC7pw/GU1cfh5F9JcYfJ4/QPUJifaNQKSopVxZ6Ynt+i+i5STJnw1OUKXZ0/vZsH60Yb+HsJG2KnSIJOiGkHMBDAM4BMBTABEJI0EXvPQCjKKVHAvgHgHtMV9SFL8BUsK6C94J9NcDh6ljPbCJoYiYiyBTRg0zGwmJwt9y3dmgl3uY/++3jmfd9VfAtcn4c2i0Xp+Wsw7shiLGHdsXtFx6On5xXGDqdWzfD1TEcSbz1mnTuYRjWu50w0JR8vuE8rjihWvr9ZoLj4ppVlEmH0U0KIhm6aSMXFYefOPDvBOSYIj079Nw7os/63hmD8Oy3jzcqZuNBhkMfDaCGUrqSUloL4CkAF3oTUEpnUErdEHGzAfQ2W80CdBodUCPabuf4+kjBmiSqHip1EX3vRUf1xFCJYE83nz8Uf/vGsTgsHxiLbTkj3DazOPTA6BncvTUW/uxsXDyiF+N9gsvGVOfl3QBw5tBuUpYFKlvUYG4nS+ziZCDqh7AZau5GRWCn0rWt+hF9wrIMQWy2GCFyCSjco9DQYF+G3qNdFU49tNDvUVVzH0emYzyX0XO2bFbu25EVlUMH0AvAGs/1WuceD18H8DzrASHkakLIPELIvE2bNsnX0gPvFjBkMSHoENMeamMGdMKAzupy0KA8OkrkErYKKaB/59aQQfPKchx/SGflAcvi3Pyu//4XyghB6+YVxpywZJFENEUVvYDbbq61z1eO64tFPztb68xVmboRxBNXiNpP1qVAtsdVOfQ/XD5KKT0AvD3pdKW2NuFYJBW11PmbGrNFQshXAIwCcC/rOaV0MqV0FKV0VJcuepyTsqVGvnIa73jAslXXERcEzxQVfQ5leIrq2KGHRFOcd3iLHiHsZ62b+09JJ4G/tsEkbpYK37mvDn//1hhuuV64feQS9DJC0Kq5+onyLPAWS1Vbe9+7AhIjfXCFrMhFINzOmaL6cebQsPhOFZFKbvev1thxRC4yKROYGDIEfR2APp7r3s49HwghZwC4CcAFlNLooMea8G59g4NNNPhU5OsuvJMkmKSRAuNH9xXkqlYWuwJ6zg68Mj/asBsAsPSzndp5eenGqH4dcM8lR3rKia6rP4nctym5cbPMFg2wRJt3yw9pd9zYsPaxsYiJ2icqb/dd2ZAGIoL+/bMO5T6Lg8h4SM7zeBy6/DtxFt8oyIy4uQAGEUL6E0KaARgPYIo3ASFkBICHkSPmG81XswBdF2dvp/75ytGx66HbKWFFbjwrFrkjvXJp5n2yDQDw7urtnLL4eVQ4C8uN4wqTjhCCLx1TWOtlYrlYF8dIiI2iwHqjqhnftj4Il2Y1c+zxeV/8xo/GqlUMZrm8135wKp755hghdzm4WxvB0wLqG+RCGvAU1GMP7RLa8R03IGwmaAOy5rysqhf4LSkeXTqlLiLJI6W0HsC1AKYDWALgGUrpIkLIbYSQC5xk9wJoDeDvhJD3CSFTONnFr7CXQ5eQoRdM9nJ/K8sJTgkoyoIdxbJCCBIi3UWWBuspFLmYOdZN2jmEUb6LsjKCVXefh6tPPkRQDnHyMbtNd+tx4VE9OUpOsc2xqt1vcBH45ReH43un88PW8ryQo/pOxRSyUFY4U12T2urOrTC6f0chhRnep73UwnNo97b4zaVHcZ9/5/RBAArhIYII3n3/ljONMF6AvMhF74ALh0grOCfaVIpKCfYopdMATAvcu8Xz+wzD9eKiXKAUFcFNWhmh5bni+Gr075yzjRa1O4vTkO0n2Qk4dkhXbasef3liRA0wnUOLvejjiQuu8zUugb1//IhAOSziFh/B5vjC0WpGW8EFgbcrMSFOSwK9O0T7ChAAF43ohe89/T7z+Q1nDsYNZw7Glx5+m/k8OAbbm/SkjaTorshFnEykW5Pnz+0idZ6i3s4RuZjzUCExiZjOG4HrOIusTynKSfOd0wfhguE9Q8QgSOCl6H2R6Eab5hV47GvH4GueUMVe2KhWsL2uOmmAMkcUl4OSfd+Y+CmuDD3BE514Y8EmZJs5TnAuNV8He+1tRvWeILwEjaUV96JPh5ae4Fy591gcus68cvvvmycP8Lmhy7wnY+XSxpEnBseYTl2j5fTqeUrlR4BTD+3KLUtV5MJ9LkhwyuAu+P1rK+QKki5RDHdyJxnnI5anaHL0HOcM68Gug8UyI8e/87ciQkHHPJqyxEQuqePQvatoVaWYQ3/y6uPyv93GrGB48ckdcBF8lns46dzDcNVJauaLMiIM9zuDXIONIPwy369WnitLZzyzvF0wkTvPEEP2/FXX+mmQ46Gr2mW3nD9UOaZ8HC47QXpeErj3C0f6rt3+KRd4+PKgJnKxv8CnjqB7t6nBMK3BCecNnOQ2ZpQMPVdGdD3ixBjyi1zYhVU6gysYS0NrWyj7Lu+xYpGyyaUHuIrZohGlqFLy0HdcMLwnVt19HrpoOhJdeWJ/zLtZXi0VdyNw4sDO2u+aWgxsmvIFh1nQnNTtvyhxLPOchLzrv7wtf2oci5JGkEOXmbgyBL2QXwHBTlGND8J7jzcZ3QBPIpm5tPWKG6tCcTGIO/CYSkuDTIqtHYAycYlcJ9XrlKS45vdfOTp2HqzqHi0TAC8BSOpEI+cHW+SiUI9SsXIpVURx6Lmb7rPcD9YqLOVtGRK5MIqS6Km6Bio1CFzR0PA+7dGmqgLd2lahZuPu8IEXMs48bp5lBLWihIYGmvxCY6Y838LL4tBVlaLO35k/HIt9dQ261YoFJUKB6G/8xSXDUN9IcdO/FoaetVCwsZfF984YhCN6tsNVj88znrdp5C3gNJxcVIh0qXiKlizCHDofrieblyDe9flhOHFgZ60VU5f21TU0SnHZLmferkUlFtx6dj4WcxwZelJmcroeuzzw2trWBHHHQ99OLXFodznHGhF06mmaQx/QpTX+59h+3OdnMyJkxkEupo98eqsSl0hP0dxfrwz9hIFyETHdN1TEekWPh16qCIpPRBxyfWP48IUJo/viL1cdy31HlJ+uzC93SHBh4PCIH0t5CzCsXiTKdMuITMtJoEpahPPH8prClqGrgTfh3EiR3dv5HYK+5XG28pYfh0gp+VhwEj/45RGFNBF5VHfSO3CD95FRBgtJIlLk4urXPJNrjGSIY5nwucFyMisXSbDayeVKHXouZ4cuMZt0ZehBDp0HnglVyGOV834vb8B9BU28L++Y4Q2ivlOWZllVmDELZN8+olc73D/+KNx9id9KYljvdvjn/7JjyQN6a5gqg86qcvsWBeecqDEdW1/ifOX7t5yJrxzXV90ZK0Fbm1Bb5HewZfw0YI/DPIcuqH7+2zKRizxW3nkus8G/PXYgrjyhP8aPzsUcCcaolkWwf3WtXIIydL5SlP1A1nN01o1jQ2XwB534Y5QdYATJ7ZstxmfRRckvPKpXKOYIAPRsn+Pazxra3ZOPWsEXDO+Jn34udwCIisglJ0NnWWCwf9tE+5bNcMdFw1BVWS5V5si+7a3XiWdyHESlZ1cs3V5OOhUGz+bSlWqlqBeEsAlW6+YVuOVzQ/FWzWYA8B2wEAVRw+tyjXWSQYwqOSIXX+gD8GlnWSAdUBh0fxWImby446Ij8IsXlioHSZKP5SKXLtqxyHQsF6XkAHLni37w07PQtio8pWQ+c/kd5/jEgd5XdOOoe/OwZTWjMwvuu3Q4fvfaCizfsFtJZOFF+5aV2L63jvvcO89lHYu8c4vVXkzXf4WAWwVuPpOhc+Geg0mpuFGPHdAJ3zx5AO4JOBUE0aNdlZQ5nMCgRoja+kYpQsbj0LU8RZ2XXILeP3QwBzvTfp1a4f/+52jlEAviaIuF33EHdr9OuRgjYw4pyDtNkC3d7X+7FpW+vlX5vKBttJegvDXxNK36eBsjIQY9UDy71ItH9Ma5jseojMiChT9+VXzwhTeOepRlmHtdwWCCXHz/THZwtvwrUlYu9nsh9QT92W8fj/suHY6yMvFBueVlBJPOPQxd2/Aj3N3keH26A0Akb48agP976iG4jXESeJBD53FOYXt5eXfy0YHTyfMceiOvTDscA3thjEbUoutiYNc2eGviafiGx1OXLfuMzuvWzxXOOjV1ML2bjc5Edl+5ZGTvyLjqhIi5R0DiKDnX8/mcIUr1lIENMhb8nh+e7Y+l7m3zY/t3zEd7ZKHAoXtl6P40g7q1Yduh5/VTpeFvm3qC3rtDS1w8IqeA0VVUuhh3RHeUlxGcObQbrjyhP376uQJBlpHD/cAToJ8AuHxMdShNfSONFBEAfA49KrbLijvPxVOekAfeNPkYI5xeNyXfls2FRzi/NKqPz/Vd1K0927fwTV5W2TKj4ooT+udD85reEuspRQk+vPUs/OKSYdrles8wtc0civIXtaYpgnjN2IHcZ2VlBDd4OOxgVVlhQUKH5xCOp6jHcuWBCSPwdGDu+dMin9YWmowMHTAXJa+yvAy3eDg2FljE6CvH9cP2vbX45YvLtQe4C54MPRgKIAjWQuAOOl7QqM6tm2Pz7tpE7NS9xFe0AC+7Yxz6T8pFbGYdOs3PP3xPlkAr7J6lIFPs988cjG0cWXDbqkr5cgJlvXj9yTiki9yZs9ZgcTjFEV8EuyVP0AXjP6d4ZtUj97eR5pTa7HeJL61NNC2CbnHbE+wLHjGSmcSio+1clJexvWCDg84rP+bBO+iAsKXMn68cjZnLN6FjK3MxqHPlikewiKB73/3eGfztskkQ0xQ9nzH/0XUCUYAKBnZtjZWb9+SvgycNuYv4Eb3aYuE6/hGEyuaSpSFpiIV8LJdyvsiFN5Z1dhiZY5EsYraTWJkXUIpGlCUSX3i5e95AGcLxUAye2DSke1u8qag0C3Lo3dpW4Yuj+nBSm4W35EY5gx8lbowpQ5d9N5/ezIRLSq5KAfz60qPwxNdHc7lMt1mmXHOilTro6ktUwFNMqmJwN//OhcWhhxSn4I0jeSudJEQuqSXoroWDF952Gt6nvdXyedt4mb6KkvU/MGEE19OOZUURGUQx8Fz3XFYT8NYlrs7DNPLWQJILjXS+CdiYtG5egZMGdcHMG8fi2W+HnZzcdo8S2SUNFe9J724mzlcM6d4W7958Js4/0h+b3S9D94PHUxQ49GgkYeWSSpHLglvPYkZNdAfFod3a4Klv8JUTPAhPPw+m5aS79Jg++O8Hn+LLx/aVKoepxBNUhOUXFWXBECQotiP5yZLphoQIuvQJQm76hMuNX06hoJ7tW6Cn10vYgYyHtAgPX3Y01m3bh9ueWyz9jhQBUyCIJsESL3rbqHng8ByuGbH7Q8WxKFOK+tGGoyxyOb4ubZpbiSDHKiuIbm2r8NINp0i/yxrzIs7VS4xdLj7SxT7wPLkgXWIkRvAk0xU8as1UzPUe7d0hTGBNQqa2UafxROHsw3MesEGCXirmejoImpW6eqvR/TuiRWCH3Ky8LMJsMRq2VDRepJKg81DoIL33ZR1iAHlb5WblZagN2J5H0QvRlt9L0C8/vl+ubpKecPlrw/T8rs8Pw7ufbFN+r9RELnl5qKHcLjqqFzq0bIZTHHNIW2jDCEUQBC/Ymylozzn3RwJD4bnrTsTHHsVxMMSKy6F3bxv2VckpTMVmi1E4f3gPPPveOhxlURycWhk6C0kGcZIt67UfnhrRgf6ZMLxPe5wxNBzKNCgv/8LRvfMenNEydH8C2Xgwspgwui/u/eLw/LVsN5hy4ImEZIVcjlqGQMqAEIJTD+1qVXZ6WI+2UvlHHexiY+qIalWe54r12kanSY/o1Q6f85gWFs4bzl27i14DY2BWlrMdF93qyzAnpw3phlV3n4eBXe2ZkzZJDt2GjFjVysVFz/Yt8PdvjcGCdTtQV9+IDq2aYb/n0IRgVf9zzQnC/PLRIz0ViJah+5HUaThRxZQah/7jcw/DyL4dpExBSwWyjEVcGbppXHlif2zYtR+nD+mGt1ZsKYrohuY5dMds0WkjVrwl3oKoG4vGFpoUh44AF2sTKlvYyvIyjOzbAccO6ITB3dqEbIRVwBpAkQQ9ZOVit4FchdJRfToI0zUmxKK7pYyKOBKtqrIcF43olYg1QtKojAgfYAOiZmzVvAJ3XDQMrZzdUDEIYp6g580Wc23E5tDZMvQvjsp5qZ9xGP+AkCQXqyZF0E8c1BlfPrYv7vq8XCyQOFANWOVFVWV5Pl6JKulwabF30JGIXrRBoESKvrZVlXjuuhPxwISjQs+8kyIpDt2VYT8wYUQi5ZUiZI9XUzWxjNuF+rL3+GM6L3Jxrt0Ti+oaaahePM/tw3u2w6q7z0Nfhhl1MdCkRC6V5WW482L12BenDO6Cv85ZzYxzzUPwPFNVuBEPh/RoixcXb4hM7w6+04Z0xdY9tbjutELsiiQPFAaAd246HS2bidvqiF7tIvNJSoZ+/RmD8ZXj+qEbQ9mVdsgSVNtK0biwMRR+c+lReGkJf24FOfTKPIfOFrnoctpJ+CG4aFIcui5uveBwvPGjseig4PoeFQEvCsdUd8TLN5yMa8cOZDpJ8dC2qhIvXn8KBnnENrzh8tUx/WLVkYeubaqUFj8vTIbP5eGV7/vNRsvKSJMi5i9876R8THtZIhMlQ79sTD/0bFflUxrGhczRh12cWO+igy7uvHgYzjisq+/ewK6t0bOduE8vGtELD315JPf5CQM7AwCqHeaqPC9DZ4tc0oAmxaHrorK8DL07RBPVP1w+CrX1jbjmb+/G5tCBXPhXAHj9h2NRPXGqdj48Dv3WCw73RYwsBfhFLnbK6OLEyGc1y+jqjvj8SPlgX6WIId3bYtn6XUrvRInd+nVqhbcmna5cl6AttxeuSK1Vc754srpzK7x4/ckYEIrRX8CXj+0bctRr0awcb006Pda8uXxMP4w7ont+sXd3MfUNjSGRKs/KpdSQEXQFnDm0W/7kozgydB24HAIzmiLXLZn4nj305ZGYuuBTG9XTgm0ZetA5BACe+dYYq2UmjVImMntrc9ZcUeK5OEYCXpx/ZA8MUIgwSYh/5+buYhoaaWgH6g3c9aNxQ3DusO4oRWQEXREH6nPytaBrcFyUEWBUP/5RbzeOG4IWleW48KgwdykrQz/vyB44LxC/ophgWROYQL2zZe4RsSVPM1Q8FE3irYmn4fi7X5VKu7e2HgDQyqLXdq/2LTB2SE7p/aBAvCID18qlvpGideAowWblZfm27tSqGfp14u8oiomMoCuibYtc2IHDerQ1mu/Ku84TPm/XohI/OZ8do73ETIylYYu77NiqGW6/8HCcLjAlSzuK1eXBODGdHL1T+5bhcBx7XA7dkKMWkDMKeHXpxvy1aqRREbyORUHnsgqvyEWy8Yc4NGJAl+SIv1RLE0LGAbgfQDmARyildweeNwfwOICjAWwBcCmldJXZqpYGju7XAX/5+rHKByfbRNJWLnHgtYe2KXK5jHFaVFOCSyRFsuckcMXx1WjbohKXjOwderbngHkO/fdfOTqfr2l4HYuCHHpFGckr8WVn2yUje+Hwnm2NM38iRBJ0Qkg5gIcAnAlgLYC5hJAplFJvlJ6vA9hGKR1ICBkP4BcALrVR4VLAiYM6F7sKPqSInqN18wp8/8zB+NVLyyNFLrNuHJuXw2bwo1f7Fnj8ytEYGeEsxcKcH58em8M/yZkDFeVl+BInlr5rwTLIkIwcyFmXNaswexCLi36dWuHI3u3w43MPQ4eWuTLGH9MH5x3ZA4QQbNi1HwDQuU1zUTZ5EEISJeaAHIc+GkANpXQlABBCngJwIQAvQb8QwK3O738AeJAQQmiSwVUOYrgWBsMkbL9LAZeN6YdfvbQc3zxlgO/+tO+clJ80ANCnY2k4a5QqTpYI+nXnxcOw8NMdvntxTTiX3j5OKpTA50f2Qre2VThhYDpCKTSrKMOUawsHgHz083NQUUby82v3/tzOoH+Jys8BgETRXELIFwCMo5Re5VxfBuBYSum1njQLnTRrnesVTprNgbyuBnA1APTt2/foTz75xOS3HNRYsHYH+nVuKX0OZYYMGdSwctNuTFvwGa4ZO7Co4SEIIfMppaNYzxK1lqeUTqaUjqKUjurSxW5I0YMNw3q3y4h5hgwWMaBLa1x72qCSjvUjQ9DXAfAKyXo795hpCCEVANohpxzNkCFDhgwJQYagzwUwiBDSnxDSDMB4AFMCaaYA+Krz+wsAXs3k5xkyZMiQLCKVopTSekLItQCmI2e2+CildBEh5DYA8yilUwD8EcAThJAaAFuRI/oZMmTIkCFBSNmhU0qnAZgWuHeL5/d+AF80W7UMGTJkyKCCdIQQy5AhQ4YMkcgIeoYMGTI0EWQEPUOGDBmaCDKCniFDhgxNBJGeotYKJmQTAF1X0c4ANkemalrIvvngQPbNBwfifHM/SinTM7NoBD0OCCHzeK6vTRXZNx8cyL754ICtb85ELhkyZMjQRJAR9AwZMmRoIkgrQZ9c7AoUAdk3HxzIvvnggJVvTqUMPUOGDBkyhJFWDj1DhgwZMgSQEfQMGTJkaCJIHUEnhIwjhCwjhNQQQiYWuz6mQAjpQwiZQQhZTAhZRAj5rnO/IyHkJULIR87fDs59Qgh5wGmHDwkhI4v7BXoghJQTQt4jhDznXPcnhMxxvutpJ2QzCCHNnesa53l1USuuCUJIe0LIPwghSwkhSwghYw6CPr7eGdMLCSFPEkKqmmI/E0IeJYRsdE5wc+8p9y0h5KtO+o8IIV9llcVDqgi658DqcwAMBTCBEDK0uLUyhnoA36eUDgVwHIBrnG+bCOAVSukgAK8410CuDQY5/64G8Lvkq2wE3wWwxHP9CwD3UUoHAtiG3AHkgOcgcgD3OenSiPsBvEApHQJgOHLf3mT7mBDSC8B3AIyilB6BXAhu9yD5ptbPjwEYF7in1LeEkI4AfgrgWOTOc/6puwhIgVKamn8AxgCY7rmeBGBSsetl6Vv/A+BMAMsA9HDu9QCwzPn9MIAJnvT5dGn5h9zpV68AOA3AcwAIct5zFcH+Ri4e/xjnd4WTjhT7GxS/tx2Aj4P1buJ93AvAGgAdnX57DsDZTbWfAVQDWKjbtwAmAHjYc9+XLupfqjh0FAaHi7XOvSYFZ5s5AsAcAN0opZ85j9YD6Ob8bgpt8RsANwJodK47AdhOKa13rr3flP9e5/kOJ32a0B/AJgB/csRMjxBCWqEJ9zGldB2AXwJYDeAz5PptPpp2P3uh2rex+jxtBL3JgxDSGsA/AXyPUrrT+4zmluwmYWdKCDkfwEZK6fxi1yVBVAAYCeB3lNIRAPagsAUH0LT6GAAcccGFyC1mPQG0QlgscVAgib5NG0GXObA6tSCEVCJHzP9KKX3Wub2BENLDed4DwEbnftrb4gQAFxBCVgF4Cjmxy/0A2jsHjQP+b2oKB5GvBbCWUjrHuf4HcgS+qfYxAJwB4GNK6SZKaR2AZ5Hr+6bcz16o9m2sPk8bQZc5sDqVIIQQ5M5mXUIp/bXnkfcA7q8iJ1t371/uaMuPA7DDs7UreVBKJ1FKe1NKq5Hrx1cppf8DYAZyB40D4e9N9UHklNL1ANYQQg51bp0OYDGaaB87WA3gOEJIS2eMu9/cZPs5ANW+nQ7gLEJIB2d3c5ZzTw7FViJoKB3OBbAcwAoANxW7Pga/60TktmMfAnjf+XcucvLDVwB8BOBlAB2d9AQ5i58VABYgZ0VQ9O/Q/PZTATzn/B4A4B0ANQD+DqC5c7/Kua5xng8odr01v/UoAPOcfv43gA5NvY8B/AzAUgALATwBoHlT7GcATyKnJ6hDbjf2dZ2+BXCl8/01AL6mUofM9T9DhgwZmgjSJnLJkCFDhgwcZAQ9Q4YMGZoIMoKeIUOGDE0EGUHPkCFDhiaCjKBnyJAhQxNBRtAzZMiQoYkgI+gZMmTI0ETw/yyb+DlpsIQsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at some \"data\" \n",
    "fake_data = np.random.random(1000)\n",
    "\n",
    "# we create a Figure instance\n",
    "fig = plt.figure()\n",
    "\n",
    "# we then call the plot method\n",
    "plt.plot(fake_data) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f50f2d0-18f6-4ef9-b6ac-48d0e70bf9d1",
   "metadata": {},
   "source": [
    "Notice that the \"plot\" method creates a 2D \"line\" plot. This line plot could be a timeseries, i.e., an time-ordered sequence of values, but can also be anything we want to be plotted using a line plot. One example could be plotting the curve of reconstruction errors for machine-learning models as we vary learning parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6de2ec74-201b-40ba-9d93-bb24776bba41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# can also clean our cell output by adding a semi-colon to \n",
    "# the end of the last line of the cell. These can get messy \n",
    "# the more elements are involved in the last line.\n",
    "plt.plot(fake_data);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2485bd-f7dc-4c35-a67e-ed01c9aa9994",
   "metadata": {},
   "source": [
    "> <b>Note</b>: Generally, we want to tell the interpreter when to render the final plot. This is done with matplotlib.pyplot.show(). However, since we're using a Jupyter Notebook and used Jupyter \"magic\" to render plots in-line, we aren't here. This should be done (in conjuction to \"closing\" plots via matplotlib.pyplot.close() ...) when you are running code in scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3c063b5-089c-499d-a452-1f57a8d40b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ... the bare minimum of the Figure instance\n",
    "fig # WHAT HAPPENED TO THE AXES??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5b2ac4-d4f3-43dc-b9ce-53f8ec028c00",
   "metadata": {},
   "source": [
    "## The Anatomy of a Figure\n",
    "\n",
    "See figures folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71f6433-bb00-43e7-b70e-bb710ab1d45f",
   "metadata": {},
   "source": [
    "### The Axes Class and Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f5846c0-f960-44b5-a8ca-b604b0e1a32d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots creates the entire layout and gives us access to the Axes of Figure\n",
    "fig, ax = plt.subplots() # <- returns a tuple\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13e5c025-bb7b-480c-8861-f6f67569ceae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:>, matplotlib.axes._subplots.AxesSubplot)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax, type(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1656c8e9-ad3e-4bee-85cc-84fec3b285a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fed6384bc10>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can essentially do the same thing as before\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(fake_data) # here we work directly with the Axes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09501283-50d5-4344-8eef-d5112210c560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVqklEQVR4nO3dX4ic9b3H8ffnZBsPBFqL5kLWhWTddHPSNBSdWHvTCr1IIiG50EKWgk1JCMUNvehNhQP9YymnvSpIpJLGoN4k6QlCV+uuSD1BeqHrpGjOLpKTNVmbLBY3WoTSuprley7mSZxM5l93n9nMPL/PCwbmmee3M78fH57PTObPE0UEZmZWfP92sydgZmYrw4VvZpYIF76ZWSJc+GZmiXDhm5klwoVvZpaIloUv6aik9yVNNdgvSY9LmpF0RtLd+U/T8uZci8vZWiPtvMJ/GtjeZP8OYEN2OQD8ZvnTshXwNM61qJ7G2VodLQs/Il4FPmwyZDfwbFS8Btwq6Y68Jmid4VyLy9laI3053Ec/cLFq+1J223u1AyUdoPKKgjVr1tyzcePGHB7elmrz5s3MzMwgaT4i1tbsdq49bPPmzUxNTS022N1Wts61O50+ffpyneO1LXkUftsi4jBwGKBUKkW5XF7Jh7cas7Oz7Ny5k+np6XeXcz/OtfvMzs6yfv36T5dzH861O0la8vGax7d05oCBqu07s9ustznX4nK2icqj8MeAh7NP/u8DPoqIG/7Zbz3HuRaXs01Uy7d0JB0D7gdul3QJ+AnwOYCIeBJ4EXgAmAH+AXyvU5O1/IyMjHDq1CkuX74MsEXSPpxrIVzNFrjFx6xVa1n4ETHSYn8Ao7nNyFbEsWPHrl2XdCYinqre71x719VsJf05Ikq1+51tuvxLWzOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0tEW4Uvabuks5JmJD1aZ/9eSfOS3swu+/OfquVtYmKC4eFhgM3OtTicqzXSsvAlrQKeAHYAm4ARSZvqDD0REV/NLkdynqflbHFxkdHRUcbHxwGmca6F4FytmXZe4d8LzETE+Yj4BDgO7O7stKzTJicnGRoaYnBwECBwroXgXK2Zdgq/H7hYtX0pu63Wg5LOSDopaaDeHUk6IKksqTw/P7+E6Vpe5ubmGBi4LibnWgDO1ZrJ60Pb54F1EbEFeBl4pt6giDgcEaWIKK1duzanh7YOcq7F5FwT1U7hzwHVrwDuzG67JiI+iIiFbPMIcE8+07NO6e/v5+LF6n+4OdcicK7WTDuF/wawQdJ6SauBPcBY9QBJd1Rt7gLezm+K1glbt27l3LlzXLhwAUA410JwrtZMX6sBEXFF0kHgJWAVcDQipiU9BpQjYgz4gaRdwBXgQ2BvB+dsOejr6+PQoUNs27YN4MvAz51r73Ou1owi4qY8cKlUinK5fFMe264n6XRElPK4L+faPZxrMS0nV//S1swsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLRVuFL2i7prKQZSY/W2X+LpBPZ/tclrct9ppa7iYkJhoeHATY71+JwrtZIy8KXtAp4AtgBbAJGJG2qGbYP+FtEDAG/Bn6V90QtX4uLi4yOjjI+Pg4wjXMtBOdqzbTzCv9eYCYizkfEJ8BxYHfNmN3AM9n1k8C3JCm/aVreJicnGRoaYnBwECBwroXgXK2ZvjbG9AMXq7YvAV9rNCYirkj6CLgNuFw9SNIB4EC2uSBpaimT7iK3U7PGHvJF4POS3gWGca7VnCuFzBV6O9urhpf6h+0Ufm4i4jBwGEBSOSJKK/n4eevlNUh6CNgeEfsllZdzX861ezjX5oqwjuXk2s5bOnPAQNX2ndltdcdI6gO+AHyw1EnZinCuxeRcraF2Cv8NYIOk9ZJWA3uAsZoxY8B3s+sPAa9EROQ3TeuAa7kCwrkWhXO1hloWfkRcAQ4CLwFvA7+LiGlJj0nalQ17CrhN0gzwQ+CGr4LVcXiJc+4mPbuGmlwHcK7VenYNzrWlIqxjyWuQn9jNzNLgX9qamSXChW9mloiOF34RTsvQxhr2SpqX9GZ22X8z5tmMpKOS3m/0XWpVPJ6t8Yyku1vcn3PtAs71Rs61iYjo2AVYBbwDDAKrgbeATTVjHgGezK7vAU50ck4dWsNe4NDNnmuLdXwDuBuYarD/AWCcyjc77gNed67O1bn2fq7Vl3bOpbOcZ5oinJahnTV0vYh4Ffjw6nadXHcDz0bFa8Ctko441+5WmyvckG29XO9ocMw61y5RL9cadXNtdb/tvKXzNLC9yf4dwIbscgD4TdW+eqdl6K/5++t+5g1c/Zl3t2hnDQAPZgfPSUkDdfZ3m6e5Ptfadf4T+A+ca6/lCtdnW2+d36b+Metce0e767xOO9/D78gzTcE8D6yLiC3Ay3z2CqhrtZHrWuAPzrW3coW2sv0maR+zPZlrHtr6Hn72wcwLEbG5zr4XgF9GxJ+y7T8CP4qIsqSvAz+NiG3Zvueo/JPrr2vWrLln48aN+a3E/mULCwvMzMzw8ccfXwaeA05FxDEASX8HvhMRv8+2nWsPWVhYYGpqapHKj6yqcz0L/AX4We0xC3wO59r1Tp8+Xe94PQvcHxHvNfvbTp88rfpn3nPAXcC2iJgulUpRLi/r3E62TLOzs+zcuZPp6el3qfzc/qCk41TOrvgpjc+v4ly73OzsLOvXr/+UG3P9CFho8GfOtQeocibUG3JtVfaQz9cyG56sKZqcliGHx7V8vQicB2aA3wKv4FyLoDbXR2hwzDrXnlIv15byKPwx4OHs2zr3UfNMExEvRsSXIuKuiPhFdtuPc3hcy1H2fu5oltNXgKM4155Xm2tElGlyzDrX3tAg15ZavqUj6RhwP3C7pEvAT6i810dEPEnlmeYBKs80/wC+t7Ql2EoaGRnh1KlTXL58GWCLpH0410K4mi1wi49Zq9ay8CNipMX+AEZzm5GtiGPHjl27LulMRDxVvd+59q6r2Ur6c9T5zz6cbbp8Lh0zs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLRFuFL2m7pLOSZiQ9Wmf/Xknzkt7MLvvzn6rlbWJiguHhYYDNzrU4nKs10rLwJa0CngB2AJuAEUmb6gw9ERFfzS5Hcp6n5WxxcZHR0VHGx8cBpnGuheBcrZl2XuHfC8xExPmI+AQ4Duzu7LSs0yYnJxkaGmJwcBAgcK6F4FytmXYKvx+4WLV9Kbut1oOSzkg6KWmg3h1JOiCpLKk8Pz+/hOlaXubm5hgYuC4m51oAztWayetD2+eBdRGxBXgZeKbeoIg4HBGliCitXbs2p4e2DnKuxeRcE9VO4c8B1a8A7sxuuyYiPoiIhWzzCHBPPtOzTunv7+fixep/uDnXInCu1kw7hf8GsEHSekmrgT3AWPUASXdUbe4C3s5vitYJW7du5dy5c1y4cAFAONdCcK7WTF+rARFxRdJB4CVgFXA0IqYlPQaUI2IM+IGkXcAV4ENgbwfnbDno6+vj0KFDbNu2DeDLwM+da+9zrtaMIuKmPHCpVIpyuXxTHtuuJ+l0RJTyuC/n2j2cazEtJ1f/0tbMLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS4cI3M0uEC9/MLBEufDOzRLjwzcwS0VbhS9ou6aykGUmP1tl/i6QT2f7XJa3LfaaWu4mJCYaHhwE2O9ficK7WSMvCl7QKeALYAWwCRiRtqhm2D/hbRAwBvwZ+lfdELV+Li4uMjo4yPj4OMI1zLQTnas208wr/XmAmIs5HxCfAcWB3zZjdwDPZ9ZPAtyQpv2la3iYnJxkaGmJwcBAgcK6F4Fytmb42xvQDF6u2LwFfazQmIq5I+gi4DbhcPUjSAeBAtrkgaWopk+4it1Ozxh7yReDzkt4FhnGu1ZwrhcwVejvbq4aX+oftFH5uIuIwcBhAUjkiSiv5+Hnr5TVIegjYHhH7JZWXc1/OtXs41+aKsI7l5NrOWzpzwEDV9p3ZbXXHSOoDvgB8sNRJ2YpwrsXkXK2hdgr/DWCDpPWSVgN7gLGaMWPAd7PrDwGvRETkN03rgGu5AsK5FoVztYZaFn5EXAEOAi8BbwO/i4hpSY9J2pUNewq4TdIM8EPghq+C1XF4iXPuJj27hppcB3Cu1Xp2Dc61pSKsY8lrkJ/YzczS4F/ampklwoVvZpaIjhd+EU7L0MYa9kqal/Rmdtl/M+bZjKSjkt5v9F1qVTyerfGMpLtb3J9z7QLO9UbOtYmI6NgFWAW8AwwCq4G3gE01Yx4Bnsyu7wFOdHJOHVrDXuDQzZ5ri3V8A7gbmGqw/wFgnMo3O+4DXneuztW59n6u1Zd2zqWznGeaIpyWoZ01dL2IeBX48Op2nVx3A89GxWvArZKOONfuVpsr3JBtvVzvaHDMOtcuUS/XGnVzbXW/7byl8zSwvcn+HcCG7HIA+E3VvnqnZeiv+fvrfuYNXP2Zd7doZw0AD2YHz0lJA3X2d5unuT7X2nX+E/gPnGuv5QrXZ1tvnd+m/jHrXHtHu+u8Tjvfw+/IM03BPA+si4gtwMt89gqoa7WR61rgD861t3KFtrL9Jmkfsz2Zax7a+h5+9sHMCxGxuc6+F4BfRsSfsu0/Aj+KiLKkrwM/jYht2b7nqPyT669r1qy5Z+PGjfmtxP5lCwsLzMzM8PHHH18GngNORcQxAEl/B74TEb/Ptp1rD1lYWGBqamqRyo+sqnM9C/wF+FntMQt8Dufa9U6fPl3veD0L3B8R7zX7206fPK36Z95zwF3AtoiYLpVKUS4v69xOtkyzs7Ps3LmT6enpd6n83P6gpONUzq74KY3Pr+Jcu9zs7Czr16//lBtz/QhYaPBnzrUHqHIm1BtybVX2kM/XMhuerCmanJYhh8e1fL0InAdmgN8Cr+Bci6A210docMw6155SL9eW8ij8MeDh7Ns691HzTBMRL0bElyLiroj4RXbbj3N4XMtR9n7uaJbTV4CjONeeV5trRJRpcsw6197QINeWWr6lI+kYcD9wu6RLwE+ovNdHRDxJ5ZnmASrPNP8Avre0JdhKGhkZ4dSpU1y+fBlgi6R9ONdCuJotcIuPWavWsvAjYqTF/gBGc5uRrYhjx45duy7pTEQ8Vb3fufauq9lK+nPU+c8+nG26fC4dM7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS4QL38wsES58M7NEuPDNzBLhwjczS0RbhS9pu6SzkmYkPVpn/15J85LezC7785+q5W1iYoLh4WGAzc61OJyrNdKy8CWtAp4AdgCbgBFJm+oMPRERX80uR3Kep+VscXGR0dFRxsfHAaZxroXgXK2Zdl7h3wvMRMT5iPgEOA7s7uy0rNMmJycZGhpicHAQIHCuheBcrZl2Cr8fuFi1fSm7rdaDks5IOilpoN4dSTogqSypPD8/v4TpWl7m5uYYGLguJudaAM7VmsnrQ9vngXURsQV4GXim3qCIOBwRpYgorV27NqeHtg5yrsXkXBPVTuHPAdWvAO7MbrsmIj6IiIVs8whwTz7Ts07p7+/n4sXqf7g51yJwrtZMO4X/BrBB0npJq4E9wFj1AEl3VG3uAt7Ob4rWCVu3buXcuXNcuHABQDjXQnCu1kxfqwERcUXSQeAlYBVwNCKmJT0GlCNiDPiBpF3AFeBDYG8H52w56Ovr49ChQ2zbtg3gy8DPnWvvc67WjCLipjxwqVSKcrl8Ux7brifpdESU8rgv59o9nGsxLSdX/9LWzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEuHCNzNLhAvfzCwRLnwzs0S48M3MEtFW4UvaLumspBlJj9bZf4ukE9n+1yWty32mlruJiQmGh4cBNjvX4nCu1kjLwpe0CngC2AFsAkYkbaoZtg/4W0QMAb8GfpX3RC1fi4uLjI6OMj4+DjCNcy0E52rNtPMK/15gJiLOR8QnwHFgd82Y3cAz2fWTwLckKb9pWt4mJycZGhpicHAQIHCuheBcrZm+Nsb0Axerti8BX2s0JiKuSPoIuA24XD1I0gHgQLa5IGlqKZPuIrdTs8Ye8kXg85LeBYZxrtWcK4XMFXo726uGl/qH7RR+biLiMHAYQFI5Ikor+fh56+U1SHoI2B4R+yWVl3NfzrV7ONfmirCO5eTazls6c8BA1fad2W11x0jqA74AfLDUSdmKcK7F5FytoXYK/w1gg6T1klYDe4CxmjFjwHez6w8Br0RE5DdN64BruQLCuRaFc7WGWhZ+RFwBDgIvAW8Dv4uIaUmPSdqVDXsKuE3SDPBD4IavgtVxeIlz7iY9u4aaXAdwrtV6dg3OtaUirGPJa5Cf2M3M0uBf2pqZJcKFb2aWiI4XfhFOy9DGGvZKmpf0ZnbZfzPm2Yyko5Leb/RdalU8nq3xjKS7W9yfc+0CzvVGzrWJiOjYBVgFvAMMAquBt4BNNWMeAZ7Mru8BTnRyTh1aw17g0M2ea4t1fAO4G5hqsP8BYJzKNzvuA153rs7VufZ+rtWXTr/CL8JpGdpZQ9eLiFeBD5sM2Q08GxWvAbdKuqPBWOfaJZzrDZxrE50u/HqnZehvNCYqXym7+jPvbtHOGgAezP5pdVLSQJ393a7ddbY71rl2B+fqXK/xh7b5eB5YFxFbgJf57BWQ9TbnWkzJ5trpwi/Cz7xbriEiPoiIhWzzCHDPCs0tT+1k9a+Mda7dwbk612s6XfhFOC1DyzXUvHe2i8ovknvNGPBw9un/fcBHEfFeg7HOtXc4V+f6mRX4tPkB4P+ofHL+n9ltjwG7suv/Dvw3MANMAoM3+xPyJazhv6j8ZxNvAf8DbLzZc66zhmPAe8CnVN7v2wd8H/h+tl9U/qObd4D/BUrO1bk612LkevXiUyuYmSXCH9qamSXChW9mlggXvplZIlz4ZmaJcOGbmSXChW9mlggXvplZIv4fsij07nbMvU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How are multiple axes stored?\n",
    "fig, axs = plt.subplots(nrows=3,ncols=3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9abef720-b326-453a-b328-6c9f0c334ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(axs) # as a numpy array\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89c59f10-5d95-41ca-bb03-f440ebad5bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],\n",
       "        [<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],\n",
       "        [<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]], dtype=object),\n",
       " (3, 3))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Axes object is a... of shape...?\n",
    "axs, axs.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6dcd59e-ded0-4ebc-b2a7-80503d7ebb58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.398529,0.657941;0.227941x0.222059)\n"
     ]
    }
   ],
   "source": [
    "# ... and can be indexed like any other array\n",
    "print(axs[0,1]) # note the address\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90604f50-5c35-45fc-8ab2-2049a9521581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.398529,0.125;0.227941x0.222059)\n"
     ]
    }
   ],
   "source": [
    "print(axs[2,1]) # third row, second col\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396b56e2-c54c-466e-be0b-434ddbcf138b",
   "metadata": {},
   "source": [
    "> <b>Note</b>: Generally, I like to collapse the 2D grid to use with non-nested for loops when I need to plot several subplots. This is a preference, and not necessarily universal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0902b7e6-f529-482c-943f-20d6659ed2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example of collapsing using Numpy\n",
    "fig, axs = plt.subplots(3,3,\n",
    "                       constrained_layout=True) # changes spacing\n",
    "# numpy.array function\n",
    "axs = axs.ravel() # unwraps the 3x3 array into a 1x9 array\n",
    "\n",
    "axs[4].plot(fake_data); # == axs[1,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cafa261-7f35-42b5-9daf-1fa4ccebb371",
   "metadata": {},
   "source": [
    "> Alternatively, you can use axs.flatten() for a similar effect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22789539-1036-4516-9f1a-0602c46f1185",
   "metadata": {},
   "source": [
    "### GridSpec\n",
    "> The GridSpec class gives us more control over the placement and span of each of the subplots\n",
    ">\n",
    "> If you need even MORE control, look into the <b>mpl_toolkits</b> Python library. Example. AxesGrid can give you even more control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46d4b8ff-bfc6-46cd-b539-1f278d132d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66c64359-9527-4a40-9740-f123d9a9cd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = GridSpec(nrows=3,ncols=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b61230d0-b20d-4c18-8428-54b56edb981d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matplotlib.gridspec.GridSpec, GridSpec(3, 3))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gs), gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dbb4bbf5-91af-46c6-ba11-97a7039a2d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the main power of grid spec is scaling the subplots\n",
    "fig = plt.figure()\n",
    "\n",
    "# Create grispec object and define each subplot\n",
    "gs = GridSpec(2, 2) # 2 x 2 is the shape\n",
    "\n",
    "# We can create each axis here\n",
    "ax0 = plt.subplot(gs[0, 0]) # Top left corner\n",
    "ax1 = plt.subplot(gs[0, 1]) # Top right corner\n",
    "ax2 = plt.subplot(gs[1, :]) # Bottom, span entire width\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9748fc92-7fb7-43fc-933b-4e8ae14e5fa9",
   "metadata": {},
   "source": [
    "Let's just make a figure and dissect the pieces to it. Matplotlib is definitely one of those packages you'll have to explore. The first Data Exercise should be very helpful for this! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3b0e588-1a9d-49b2-8c49-83bb1a32fff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and empty figure - DON'T SPECIFY ANY SHAPE DETAILS ABOUT THE FIGURE\n",
    "fig = plt.figure(figsize=(12,4)) # figsize sets the width by the height\n",
    "\n",
    "# Create grispec object and define each subplot - a different method\n",
    "ncols, nrows = 2,2\n",
    "spec = fig.add_gridspec(ncols=ncols, nrows=nrows, # we use the attribute method to create it\n",
    "                figure=fig) # here we tell grid spec what figure it belongs to\n",
    "\n",
    "# let's add some colors, too\n",
    "colors = [['r','orange'],['y']] # frequently used colors are specified using strings\n",
    "                                # and some can be specified using shorthand single characters \n",
    "\n",
    "# Now we directly add the subplots\n",
    "for ax_j in range(ncols):\n",
    "        ax = fig.add_subplot(spec[0,ax_j])\n",
    "\n",
    "        \n",
    "        ax.plot(fake_data,\n",
    "                color=colors[0][ax_j],\n",
    "                \n",
    "                # we're just going to throw in some example changes here\n",
    "                marker='o', # adding circles to the lines\n",
    "                alpha=0.2 # changes the opacity (values between 0 and 1)\n",
    "                )\n",
    "        \n",
    "        # we can decorate specific axes\n",
    "        ax.set_xlabel('X-axis')\n",
    "        ax.set_xlim([0,1000])\n",
    "        \n",
    "        if ax_j==0:\n",
    "            ax.set_ylabel('Y-axis',size='xx-large')\n",
    "            \n",
    "        \n",
    "        ax.set_title('The Same Fake Data')\n",
    "            \n",
    "        ax.set_yticks([0,0.5,1.0])\n",
    "        ax.set_yticklabels(['0','1/2','1'])\n",
    "        \n",
    "# bottom row       \n",
    "ax_bot = fig.add_subplot(spec[1,:])\n",
    "ax_bot.plot(fake_data,\n",
    "                color=colors[1][0],\n",
    "                \n",
    "                # we're just going to throw in some example changes here\n",
    "                marker='o', # adding circles to the lines\n",
    "                alpha=0.2 # changes the opacity (values between 0 and 1)\n",
    "                )\n",
    "\n",
    "ax_bot.set_xlabel('X-axis')\n",
    "ax_bot.set_yticks([0,0.5,1.0,1.5,2])\n",
    "ax_bot.set_yticklabels(['0','1/2','1','3/2','2'])\n",
    "ax_bot.set_ylim(top=2.05) \n",
    "\n",
    "        \n",
    "        \n",
    "plt.suptitle('A Somewhat Elaborate Plot')\n",
    "\n",
    "\n",
    "plt.tight_layout(); # similar to \"constrained_layout\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924281b3-1007-4b23-b49b-4b9a889da985",
   "metadata": {},
   "source": [
    "## Basic of Pandas\n",
    "\n",
    "<b>Pandas</b> is a Python library for high-level data structures and data manipulation.\n",
    "\n",
    "This includes:\n",
    "> + loading/saving data\n",
    "> + filtering, selecting, grouping functions\n",
    "> + basic data exploration\n",
    "> + plotting and visualization\n",
    "\n",
    "Pandas is built around two data structures: \n",
    "> + Series objects, and \n",
    "> + DataFrame objects\n",
    "\n",
    "Pandas is just not Zen (https://twitter.com/tymwol/status/1390281948564701184). So we will really just take a very brief glimpse at its functionality. You'll discover your own way of working with Pandas, though."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc141f6-cebc-4c53-aac8-6834f2964b63",
   "metadata": {},
   "source": [
    "### The Series and DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "01039332-018d-4a94-878f-81a2b0d8c848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order</th>\n",
       "      <th>alphabet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order alphabet\n",
       "0      0        a\n",
       "1      1        b\n",
       "2      2        c\n",
       "3     23        x\n",
       "4     24        y\n",
       "5     25        z"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = {'order' : [0,1,2,23,24,25],\n",
    "           'alphabet' : ['a','b','c','x','y','z']}\n",
    "\n",
    "df = pd.DataFrame(example) # notice that D and F are capitalize\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dbe70806-6ac8-4b73-8a1f-cb668bc2f180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # [0,6) by 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af999294-0126-4353-aabf-c9d562540b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order', 'alphabet'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # column is essentially a list so can have mixed built-in types, \n",
    "        #  here we just have strings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0225871f-be81-4876-bf58-c36e7519370a",
   "metadata": {},
   "source": [
    "### Filtering by columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d76b8d04-cd5e-4a05-8c9f-3c91cbf3b4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     0\n",
      "1     1\n",
      "2     2\n",
      "3    23\n",
      "4    24\n",
      "5    25\n",
      "Name: order, dtype: int64\n",
      "Type = <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# we can select columns\n",
    "print(df['order'])\n",
    "\n",
    "print('Type =',type(df.order)) # every column in a DataFrame is a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58b5a853-73e1-4c1f-ac03-9d7174a0b93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 23, 24, 25]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example['order']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "698d10bf-c27c-4577-ad87-ab028e8e7d26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     2\n",
       "3    23\n",
       "4    24\n",
       "5    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.Series(example['order'])\n",
    "\n",
    "series # this is the same as above, but has \n",
    "       # no Name associated with it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301cc956-b872-49bd-95df-6167ebb7a5de",
   "metadata": {},
   "source": [
    "### Columns (and rows) can be Numpy arrays or Lists depending on needs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e119de9b-bd48-4c54-9477-8a54fe027c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2, 23, 24, 25])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df['order'])# using Numpy for casting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2506446-2170-458a-8b75-cdb20b399593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2, 23, 24, 25])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order'].values # second is Pandas function and preferred\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6fb2a72-77c9-4484-b2c5-2b8e1ae66866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'x', 'y', 'z']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['alphabet']) # using built-in function for casting\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ec8892a3-109c-467c-a0b9-e0ccb8909932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'x', 'y', 'z']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['alphabet'].tolist() # using Pandas\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddbd024f-df7b-4dbc-a132-95e4bf7e730c",
   "metadata": {},
   "source": [
    "### Filtering rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f867d880-68b0-48c6-9656-c7fd5ec675b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order       1\n",
       "alphabet    b\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also select rows\n",
    "df.loc[1] # returns row values for row with index LABELED as 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a9b22435-856b-4da4-88a3-b3c0b0e1bf2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order       1\n",
       "alphabet    b\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1] # returns row values for row in index POSITION 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ea1d8f0-af77-40a1-ba5a-89463ea8137c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order</th>\n",
       "      <th>alphabet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order alphabet\n",
       "1      1        b"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index==1] # this one returns a view into the DataFrame\n",
    "                # for any and ALL indices satisfy this\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ac7463-e312-43ce-b8bf-686b3d009ab9",
   "metadata": {},
   "source": [
    "### Selecting values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "08af7718-290d-41a9-b196-19efc28ee265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a', 'a', 'a')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['alphabet'][0], df['alphabet'].iloc[0], df['alphabet'].loc[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6feeea7-1ec7-4468-b5ed-76221ae23213",
   "metadata": {},
   "source": [
    "### Loading data\n",
    "\n",
    "There are methods for reading several different data formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef3ee0a5-eca3-4bf7-9ca5-55d95799568d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BooleanDtype', 'Categorical', 'CategoricalDtype', 'CategoricalIndex', 'DataFrame', 'DateOffset', 'DatetimeIndex', 'DatetimeTZDtype', 'ExcelFile', 'ExcelWriter', 'Float64Index', 'Grouper', 'HDFStore', 'Index', 'IndexSlice', 'Int16Dtype', 'Int32Dtype', 'Int64Dtype', 'Int64Index', 'Int8Dtype', 'Interval', 'IntervalDtype', 'IntervalIndex', 'MultiIndex', 'NA', 'NaT', 'NamedAgg', 'Period', 'PeriodDtype', 'PeriodIndex', 'RangeIndex', 'Series', 'SparseDtype', 'StringDtype', 'Timedelta', 'TimedeltaIndex', 'Timestamp', 'UInt16Dtype', 'UInt32Dtype', 'UInt64Dtype', 'UInt64Index', 'UInt8Dtype', '__builtins__', '__cached__', '__doc__', '__docformat__', '__file__', '__getattr__', '__git_version__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_config', '_hashtable', '_is_numpy_dev', '_lib', '_libs', '_np_version_under1p16', '_np_version_under1p17', '_np_version_under1p18', '_testing', '_tslib', '_typing', '_version', 'api', 'array', 'arrays', 'bdate_range', 'compat', 'concat', 'core', 'crosstab', 'cut', 'date_range', 'describe_option', 'errors', 'eval', 'factorize', 'get_dummies', 'get_option', 'infer_freq', 'interval_range', 'io', 'isna', 'isnull', 'json_normalize', 'lreshape', 'melt', 'merge', 'merge_asof', 'merge_ordered', 'notna', 'notnull', 'offsets', 'option_context', 'options', 'pandas', 'period_range', 'pivot', 'pivot_table', 'plotting', 'qcut', 'read_clipboard', 'read_csv', 'read_excel', 'read_feather', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_orc', 'read_parquet', 'read_pickle', 'read_sas', 'read_spss', 'read_sql', 'read_sql_query', 'read_sql_table', 'read_stata', 'read_table', 'reset_option', 'set_eng_float_format', 'set_option', 'show_versions', 'test', 'testing', 'timedelta_range', 'to_datetime', 'to_numeric', 'to_pickle', 'to_timedelta', 'tseries', 'unique', 'util', 'value_counts', 'wide_to_long']\n"
     ]
    }
   ],
   "source": [
    "print(dir(pd)) # recall dir gives us everything in the namespace \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a52afd7e-99b5-458a-8b6e-4befd5a07b16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read_clipboard\n",
      "read_csv\n",
      "read_excel\n",
      "read_feather\n",
      "read_fwf\n",
      "read_gbq\n",
      "read_hdf\n",
      "read_html\n",
      "read_json\n",
      "read_orc\n",
      "read_parquet\n",
      "read_pickle\n",
      "read_sas\n",
      "read_spss\n",
      "read_sql\n",
      "read_sql_query\n",
      "read_sql_table\n",
      "read_stata\n",
      "read_table\n"
     ]
    }
   ],
   "source": [
    "# Examples of loading\n",
    "load_list = [x for x in dir(pd) if 'read' in x]\n",
    "\n",
    "for method in load_list:\n",
    "    print(method)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31140be-cd4a-42da-8042-e6393e764c1c",
   "metadata": {},
   "source": [
    "### Reading a specific file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3e530715-a6ed-43b5-8aeb-616e84023f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/vrhaynes/PythonDataCamp/exercise_data/pokemon_alopez247.csv\n"
     ]
    }
   ],
   "source": [
    "path_to_dir = os.getcwd() # get the current working directory (cwd)\n",
    "path_to_data = 'exercise_data'\n",
    "filename = 'pokemon_alopez247.csv'\n",
    "\n",
    "# join paths \n",
    "f = os.path.join(path_to_dir,path_to_data,filename)\n",
    "\n",
    "print(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d434a097-6726-4f4b-a356-70a7a9a24d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function read_csv in module pandas.io.parsers:\n",
      "\n",
      "read_csv(filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]], sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal: str = '.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n",
      "    Read a comma-separated values (csv) file into DataFrame.\n",
      "    \n",
      "    Also supports optionally iterating or breaking of the file\n",
      "    into chunks.\n",
      "    \n",
      "    Additional help can be found in the online docs for\n",
      "    `IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filepath_or_buffer : str, path object or file-like object\n",
      "        Any valid string path is acceptable. The string could be a URL. Valid\n",
      "        URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is\n",
      "        expected. A local file could be: file://localhost/path/to/table.csv.\n",
      "    \n",
      "        If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n",
      "    \n",
      "        By file-like object, we refer to objects with a ``read()`` method, such as\n",
      "        a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\n",
      "    sep : str, default ','\n",
      "        Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
      "        the separator, but the Python parsing engine can, meaning the latter will\n",
      "        be used and automatically detect the separator by Python's builtin sniffer\n",
      "        tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
      "        different from ``'\\s+'`` will be interpreted as regular expressions and\n",
      "        will also force the use of the Python parsing engine. Note that regex\n",
      "        delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n",
      "    delimiter : str, default ``None``\n",
      "        Alias for sep.\n",
      "    header : int, list of int, default 'infer'\n",
      "        Row number(s) to use as the column names, and the start of the\n",
      "        data.  Default behavior is to infer the column names: if no names\n",
      "        are passed the behavior is identical to ``header=0`` and column\n",
      "        names are inferred from the first line of the file, if column\n",
      "        names are passed explicitly then the behavior is identical to\n",
      "        ``header=None``. Explicitly pass ``header=0`` to be able to\n",
      "        replace existing names. The header can be a list of integers that\n",
      "        specify row locations for a multi-index on the columns\n",
      "        e.g. [0,1,3]. Intervening rows that are not specified will be\n",
      "        skipped (e.g. 2 in this example is skipped). Note that this\n",
      "        parameter ignores commented lines and empty lines if\n",
      "        ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n",
      "        data rather than the first line of the file.\n",
      "    names : array-like, optional\n",
      "        List of column names to use. If the file contains a header row,\n",
      "        then you should explicitly pass ``header=0`` to override the column names.\n",
      "        Duplicates in this list are not allowed.\n",
      "    index_col : int, str, sequence of int / str, or False, default ``None``\n",
      "      Column(s) to use as the row labels of the ``DataFrame``, either given as\n",
      "      string name or column index. If a sequence of int / str is given, a\n",
      "      MultiIndex is used.\n",
      "    \n",
      "      Note: ``index_col=False`` can be used to force pandas to *not* use the first\n",
      "      column as the index, e.g. when you have a malformed file with delimiters at\n",
      "      the end of each line.\n",
      "    usecols : list-like or callable, optional\n",
      "        Return a subset of the columns. If list-like, all elements must either\n",
      "        be positional (i.e. integer indices into the document columns) or strings\n",
      "        that correspond to column names provided either by the user in `names` or\n",
      "        inferred from the document header row(s). For example, a valid list-like\n",
      "        `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n",
      "        Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
      "        To instantiate a DataFrame from ``data`` with element order preserved use\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
      "        in ``['foo', 'bar']`` order or\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
      "        for ``['bar', 'foo']`` order.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the column\n",
      "        names, returning names where the callable function evaluates to True. An\n",
      "        example of a valid callable argument would be ``lambda x: x.upper() in\n",
      "        ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
      "        parsing time and lower memory usage.\n",
      "    squeeze : bool, default False\n",
      "        If the parsed data only contains one column then return a Series.\n",
      "    prefix : str, optional\n",
      "        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
      "    mangle_dupe_cols : bool, default True\n",
      "        Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
      "        'X'...'X'. Passing in False will cause data to be overwritten if there\n",
      "        are duplicate names in the columns.\n",
      "    dtype : Type name or dict of column -> type, optional\n",
      "        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n",
      "        'c': 'Int64'}\n",
      "        Use `str` or `object` together with suitable `na_values` settings\n",
      "        to preserve and not interpret dtype.\n",
      "        If converters are specified, they will be applied INSTEAD\n",
      "        of dtype conversion.\n",
      "    engine : {'c', 'python'}, optional\n",
      "        Parser engine to use. The C engine is faster while the python engine is\n",
      "        currently more feature-complete.\n",
      "    converters : dict, optional\n",
      "        Dict of functions for converting values in certain columns. Keys can either\n",
      "        be integers or column labels.\n",
      "    true_values : list, optional\n",
      "        Values to consider as True.\n",
      "    false_values : list, optional\n",
      "        Values to consider as False.\n",
      "    skipinitialspace : bool, default False\n",
      "        Skip spaces after delimiter.\n",
      "    skiprows : list-like, int or callable, optional\n",
      "        Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
      "        at the start of the file.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the row\n",
      "        indices, returning True if the row should be skipped and False otherwise.\n",
      "        An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
      "    skipfooter : int, default 0\n",
      "        Number of lines at bottom of file to skip (Unsupported with engine='c').\n",
      "    nrows : int, optional\n",
      "        Number of rows of file to read. Useful for reading pieces of large files.\n",
      "    na_values : scalar, str, list-like, or dict, optional\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values.  By default the following values are interpreted as\n",
      "        NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
      "        '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n",
      "        'nan', 'null'.\n",
      "    keep_default_na : bool, default True\n",
      "        Whether or not to include the default NaN values when parsing the data.\n",
      "        Depending on whether `na_values` is passed in, the behavior is as follows:\n",
      "    \n",
      "        * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
      "          is appended to the default NaN values used for parsing.\n",
      "        * If `keep_default_na` is True, and `na_values` are not specified, only\n",
      "          the default NaN values are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are specified, only\n",
      "          the NaN values specified `na_values` are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are not specified, no\n",
      "          strings will be parsed as NaN.\n",
      "    \n",
      "        Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
      "        `na_values` parameters will be ignored.\n",
      "    na_filter : bool, default True\n",
      "        Detect missing value markers (empty strings and the value of na_values). In\n",
      "        data without any NAs, passing na_filter=False can improve the performance\n",
      "        of reading a large file.\n",
      "    verbose : bool, default False\n",
      "        Indicate number of NA values placed in non-numeric columns.\n",
      "    skip_blank_lines : bool, default True\n",
      "        If True, skip over blank lines rather than interpreting as NaN values.\n",
      "    parse_dates : bool or list of int or names or list of lists or dict, default False\n",
      "        The behavior is as follows:\n",
      "    \n",
      "        * boolean. If True -> try parsing the index.\n",
      "        * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
      "          each as a separate date column.\n",
      "        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
      "          a single date column.\n",
      "        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n",
      "          result 'foo'\n",
      "    \n",
      "        If a column or index cannot be represented as an array of datetimes,\n",
      "        say because of an unparseable value or a mixture of timezones, the column\n",
      "        or index will be returned unaltered as an object data type. For\n",
      "        non-standard datetime parsing, use ``pd.to_datetime`` after\n",
      "        ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n",
      "        specify ``date_parser`` to be a partially-applied\n",
      "        :func:`pandas.to_datetime` with ``utc=True``. See\n",
      "        :ref:`io.csv.mixed_timezones` for more.\n",
      "    \n",
      "        Note: A fast-path exists for iso8601-formatted dates.\n",
      "    infer_datetime_format : bool, default False\n",
      "        If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
      "        format of the datetime strings in the columns, and if it can be inferred,\n",
      "        switch to a faster method of parsing them. In some cases this can increase\n",
      "        the parsing speed by 5-10x.\n",
      "    keep_date_col : bool, default False\n",
      "        If True and `parse_dates` specifies combining multiple columns then\n",
      "        keep the original columns.\n",
      "    date_parser : function, optional\n",
      "        Function to use for converting a sequence of string columns to an array of\n",
      "        datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
      "        conversion. Pandas will try to call `date_parser` in three different ways,\n",
      "        advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
      "        (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
      "        string values from the columns defined by `parse_dates` into a single array\n",
      "        and pass that; and 3) call `date_parser` once for each row using one or\n",
      "        more strings (corresponding to the columns defined by `parse_dates`) as\n",
      "        arguments.\n",
      "    dayfirst : bool, default False\n",
      "        DD/MM format dates, international and European format.\n",
      "    cache_dates : bool, default True\n",
      "        If True, use a cache of unique, converted dates to apply the datetime\n",
      "        conversion. May produce significant speed-up when parsing duplicate\n",
      "        date strings, especially ones with timezone offsets.\n",
      "    \n",
      "        .. versionadded:: 0.25.0\n",
      "    iterator : bool, default False\n",
      "        Return TextFileReader object for iteration or getting chunks with\n",
      "        ``get_chunk()``.\n",
      "    chunksize : int, optional\n",
      "        Return TextFileReader object for iteration.\n",
      "        See the `IO Tools docs\n",
      "        <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
      "        for more information on ``iterator`` and ``chunksize``.\n",
      "    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
      "        For on-the-fly decompression of on-disk data. If 'infer' and\n",
      "        `filepath_or_buffer` is path-like, then detect compression from the\n",
      "        following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
      "        decompression). If using 'zip', the ZIP file must contain only one data\n",
      "        file to be read in. Set to None for no decompression.\n",
      "    thousands : str, optional\n",
      "        Thousands separator.\n",
      "    decimal : str, default '.'\n",
      "        Character to recognize as decimal point (e.g. use ',' for European data).\n",
      "    lineterminator : str (length 1), optional\n",
      "        Character to break file into lines. Only valid with C parser.\n",
      "    quotechar : str (length 1), optional\n",
      "        The character used to denote the start and end of a quoted item. Quoted\n",
      "        items can include the delimiter and it will be ignored.\n",
      "    quoting : int or csv.QUOTE_* instance, default 0\n",
      "        Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
      "        QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
      "    doublequote : bool, default ``True``\n",
      "       When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
      "       whether or not to interpret two consecutive quotechar elements INSIDE a\n",
      "       field as a single ``quotechar`` element.\n",
      "    escapechar : str (length 1), optional\n",
      "        One-character string used to escape other characters.\n",
      "    comment : str, optional\n",
      "        Indicates remainder of line should not be parsed. If found at the beginning\n",
      "        of a line, the line will be ignored altogether. This parameter must be a\n",
      "        single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
      "        fully commented lines are ignored by the parameter `header` but not by\n",
      "        `skiprows`. For example, if ``comment='#'``, parsing\n",
      "        ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
      "        treated as the header.\n",
      "    encoding : str, optional\n",
      "        Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
      "        standard encodings\n",
      "        <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .\n",
      "    dialect : str or csv.Dialect, optional\n",
      "        If provided, this parameter will override values (default or not) for the\n",
      "        following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
      "        `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
      "        override values, a ParserWarning will be issued. See csv.Dialect\n",
      "        documentation for more details.\n",
      "    error_bad_lines : bool, default True\n",
      "        Lines with too many fields (e.g. a csv line with too many commas) will by\n",
      "        default cause an exception to be raised, and no DataFrame will be returned.\n",
      "        If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
      "        returned.\n",
      "    warn_bad_lines : bool, default True\n",
      "        If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
      "        \"bad line\" will be output.\n",
      "    delim_whitespace : bool, default False\n",
      "        Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be\n",
      "        used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
      "        is set to True, nothing should be passed in for the ``delimiter``\n",
      "        parameter.\n",
      "    low_memory : bool, default True\n",
      "        Internally process the file in chunks, resulting in lower memory use\n",
      "        while parsing, but possibly mixed type inference.  To ensure no mixed\n",
      "        types either set False, or specify the type with the `dtype` parameter.\n",
      "        Note that the entire file is read into a single DataFrame regardless,\n",
      "        use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
      "        (Only valid with C parser).\n",
      "    memory_map : bool, default False\n",
      "        If a filepath is provided for `filepath_or_buffer`, map the file object\n",
      "        directly onto memory and access the data directly from there. Using this\n",
      "        option can improve performance because there is no longer any I/O overhead.\n",
      "    float_precision : str, optional\n",
      "        Specifies which converter the C engine should use for floating-point\n",
      "        values. The options are `None` for the ordinary converter,\n",
      "        `high` for the high-precision converter, and `round_trip` for the\n",
      "        round-trip converter.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame or TextParser\n",
      "        A comma-separated values (csv) file is returned as two-dimensional\n",
      "        data structure with labeled axes.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.\n",
      "    read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
      "    read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> pd.read_csv('data.csv')  # doctest: +SKIP\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.read_csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "657460fd-1455-4d62-84d0-0585c45d5857",
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon_df = pd.read_csv(f) # data is read"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae48562-dd9c-411f-b6d5-34fdecdb18cb",
   "metadata": {},
   "source": [
    "### Let's take look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "75a11f5e-2bf4-4bc0-989a-d1345bf0366d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type_1</th>\n",
       "      <th>Type_2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>...</th>\n",
       "      <th>Color</th>\n",
       "      <th>hasGender</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>Egg_Group_1</th>\n",
       "      <th>Egg_Group_2</th>\n",
       "      <th>hasMegaEvolution</th>\n",
       "      <th>Height_m</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Catch_Rate</th>\n",
       "      <th>Body_Style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.71</td>\n",
       "      <td>6.9</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>True</td>\n",
       "      <td>2.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>Red</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>False</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.5</td>\n",
       "      <td>45</td>\n",
       "      <td>bipedal_tailed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Red</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>False</td>\n",
       "      <td>1.09</td>\n",
       "      <td>19.0</td>\n",
       "      <td>45</td>\n",
       "      <td>bipedal_tailed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number        Name Type_1  Type_2  Total  HP  Attack  Defense  Sp_Atk  \\\n",
       "0       1   Bulbasaur  Grass  Poison    318  45      49       49      65   \n",
       "1       2     Ivysaur  Grass  Poison    405  60      62       63      80   \n",
       "2       3    Venusaur  Grass  Poison    525  80      82       83     100   \n",
       "3       4  Charmander   Fire     NaN    309  39      52       43      60   \n",
       "4       5  Charmeleon   Fire     NaN    405  58      64       58      80   \n",
       "\n",
       "   Sp_Def  ...  Color  hasGender  Pr_Male Egg_Group_1  Egg_Group_2  \\\n",
       "0      65  ...  Green       True    0.875     Monster        Grass   \n",
       "1      80  ...  Green       True    0.875     Monster        Grass   \n",
       "2     100  ...  Green       True    0.875     Monster        Grass   \n",
       "3      50  ...    Red       True    0.875     Monster       Dragon   \n",
       "4      65  ...    Red       True    0.875     Monster       Dragon   \n",
       "\n",
       "   hasMegaEvolution Height_m Weight_kg  Catch_Rate      Body_Style  \n",
       "0             False     0.71       6.9          45       quadruped  \n",
       "1             False     0.99      13.0          45       quadruped  \n",
       "2              True     2.01     100.0          45       quadruped  \n",
       "3             False     0.61       8.5          45  bipedal_tailed  \n",
       "4             False     1.09      19.0          45  bipedal_tailed  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.head() # first 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "20ad5445-2ffd-4639-b79d-2a15cbb84b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>706</th>\n",
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       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>708</td>\n",
       "      <td>Phantump</td>\n",
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       "      <td>Grass</td>\n",
       "      <td>309</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
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       "      <th>708</th>\n",
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       "      <td>Grass</td>\n",
       "      <td>474</td>\n",
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       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Amorphous</td>\n",
       "      <td>False</td>\n",
       "      <td>1.50</td>\n",
       "      <td>71.0</td>\n",
       "      <td>60</td>\n",
       "      <td>several_limbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>710</td>\n",
       "      <td>Pumpkaboo</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>335</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>711</td>\n",
       "      <td>Gourgeist</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>494</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>Brown</td>\n",
       "      <td>True</td>\n",
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       "      <td>Amorphous</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.70</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60</td>\n",
       "      <td>head_base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>712</td>\n",
       "      <td>Bergmite</td>\n",
       "      <td>Ice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304</td>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "      <td>85</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>99.5</td>\n",
       "      <td>190</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>713</td>\n",
       "      <td>Avalugg</td>\n",
       "      <td>Ice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>514</td>\n",
       "      <td>95</td>\n",
       "      <td>117</td>\n",
       "      <td>184</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>Blue</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Monster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2.01</td>\n",
       "      <td>505.0</td>\n",
       "      <td>55</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>714</td>\n",
       "      <td>Noibat</td>\n",
       "      <td>Flying</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>245</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>Purple</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Flying</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.51</td>\n",
       "      <td>8.0</td>\n",
       "      <td>190</td>\n",
       "      <td>two_wings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>715</td>\n",
       "      <td>Noivern</td>\n",
       "      <td>Flying</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>535</td>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>Purple</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Flying</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.50</td>\n",
       "      <td>85.0</td>\n",
       "      <td>45</td>\n",
       "      <td>two_wings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>716</td>\n",
       "      <td>Xerneas</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>680</td>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "      <td>95</td>\n",
       "      <td>131</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>Blue</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>3.00</td>\n",
       "      <td>215.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>717</td>\n",
       "      <td>Yveltal</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Flying</td>\n",
       "      <td>680</td>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "      <td>95</td>\n",
       "      <td>131</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>Red</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>5.79</td>\n",
       "      <td>203.0</td>\n",
       "      <td>45</td>\n",
       "      <td>two_wings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>718</td>\n",
       "      <td>Zygarde</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Ground</td>\n",
       "      <td>600</td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>5.00</td>\n",
       "      <td>305.0</td>\n",
       "      <td>3</td>\n",
       "      <td>serpentine_body</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>...</td>\n",
       "      <td>Pink</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0.71</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3</td>\n",
       "      <td>head_arms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>720</td>\n",
       "      <td>Hoopa</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>Purple</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.51</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>head_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>Brown</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.70</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Number       Name   Type_1  Type_2  Total   HP  Attack  Defense  Sp_Atk  \\\n",
       "706     707     Klefki    Steel   Fairy    470   57      80       91      80   \n",
       "707     708   Phantump    Ghost   Grass    309   43      70       48      50   \n",
       "708     709  Trevenant    Ghost   Grass    474   85     110       76      65   \n",
       "709     710  Pumpkaboo    Ghost   Grass    335   49      66       70      44   \n",
       "710     711  Gourgeist    Ghost   Grass    494   65      90      122      58   \n",
       "711     712   Bergmite      Ice     NaN    304   55      69       85      32   \n",
       "712     713    Avalugg      Ice     NaN    514   95     117      184      44   \n",
       "713     714     Noibat   Flying  Dragon    245   40      30       35      45   \n",
       "714     715    Noivern   Flying  Dragon    535   85      70       80      97   \n",
       "715     716    Xerneas    Fairy     NaN    680  126     131       95     131   \n",
       "716     717    Yveltal     Dark  Flying    680  126     131       95     131   \n",
       "717     718    Zygarde   Dragon  Ground    600  108     100      121      81   \n",
       "718     719    Diancie     Rock   Fairy    600   50     100      150     100   \n",
       "719     720      Hoopa  Psychic   Ghost    600   80     110       60     150   \n",
       "720     721  Volcanion     Fire   Water    600   80     110      120     130   \n",
       "\n",
       "     Sp_Def  ...   Color  hasGender  Pr_Male   Egg_Group_1  Egg_Group_2  \\\n",
       "706      87  ...    Grey       True      0.5       Mineral          NaN   \n",
       "707      60  ...   Brown       True      0.5         Grass    Amorphous   \n",
       "708      82  ...   Brown       True      0.5         Grass    Amorphous   \n",
       "709      55  ...   Brown       True      0.5     Amorphous          NaN   \n",
       "710      75  ...   Brown       True      0.5     Amorphous          NaN   \n",
       "711      35  ...    Blue       True      0.5       Monster          NaN   \n",
       "712      46  ...    Blue       True      0.5       Monster          NaN   \n",
       "713      40  ...  Purple       True      0.5        Flying          NaN   \n",
       "714      80  ...  Purple       True      0.5        Flying          NaN   \n",
       "715      98  ...    Blue      False      NaN  Undiscovered          NaN   \n",
       "716      98  ...     Red      False      NaN  Undiscovered          NaN   \n",
       "717      95  ...   Green      False      NaN  Undiscovered          NaN   \n",
       "718     150  ...    Pink      False      NaN  Undiscovered          NaN   \n",
       "719     130  ...  Purple      False      NaN  Undiscovered          NaN   \n",
       "720      90  ...   Brown      False      NaN  Undiscovered          NaN   \n",
       "\n",
       "     hasMegaEvolution Height_m Weight_kg  Catch_Rate       Body_Style  \n",
       "706             False     0.20       3.0          75        head_only  \n",
       "707             False     0.41       7.0         120        head_arms  \n",
       "708             False     1.50      71.0          60    several_limbs  \n",
       "709             False     0.41       5.0         120        head_only  \n",
       "710             False     1.70      39.0          60        head_base  \n",
       "711             False     0.99      99.5         190        quadruped  \n",
       "712             False     2.01     505.0          55        quadruped  \n",
       "713             False     0.51       8.0         190        two_wings  \n",
       "714             False     1.50      85.0          45        two_wings  \n",
       "715             False     3.00     215.0          45        quadruped  \n",
       "716             False     5.79     203.0          45        two_wings  \n",
       "717             False     5.00     305.0           3  serpentine_body  \n",
       "718              True     0.71       8.8           3        head_arms  \n",
       "719             False     0.51       9.0           3        head_only  \n",
       "720             False     1.70     195.0           3        quadruped  \n",
       "\n",
       "[15 rows x 23 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.tail(15) # last 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "93053afa-4912-420a-ab1b-2a104e13373b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do we have missing data in one of our columns\n",
    "pokemon_df.isnull().values.any() # if so, normally these need to imputed OR\n",
    "                                 # removed. If removed, you need to report how many and why.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "927e2c01-c3eb-4ad3-b4d2-c66b4efdc319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Number',\n",
       " 'Name',\n",
       " 'Type_1',\n",
       " 'Type_2',\n",
       " 'Total',\n",
       " 'HP',\n",
       " 'Attack',\n",
       " 'Defense',\n",
       " 'Sp_Atk',\n",
       " 'Sp_Def',\n",
       " 'Speed',\n",
       " 'Generation',\n",
       " 'isLegendary',\n",
       " 'Color',\n",
       " 'hasGender',\n",
       " 'Pr_Male',\n",
       " 'Egg_Group_1',\n",
       " 'Egg_Group_2',\n",
       " 'hasMegaEvolution',\n",
       " 'Height_m',\n",
       " 'Weight_kg',\n",
       " 'Catch_Rate',\n",
       " 'Body_Style']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can grab the column values\n",
    "pokemon_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7a2d38ec-6812-4634-9c91-6584baf3c2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Missing = 901 \n",
      "Missing Cols :  ['Type_2', 'Egg_Group_2']\n"
     ]
    }
   ],
   "source": [
    "# How many and where?\n",
    "total = 0\n",
    "columns_with_nans = []\n",
    "\n",
    "for column in pokemon_df.columns.tolist():\n",
    "    \n",
    "    nan_count = len([val for val in pokemon_df[column].tolist() if val is np.nan])\n",
    "    \n",
    "    if bool(nan_count):\n",
    "        total += nan_count\n",
    "\n",
    "        columns_with_nans.append(column)\n",
    "    \n",
    "print('Total Missing =',total,'\\nMissing Cols : ',columns_with_nans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53855f91-e91a-45f9-a6e7-a2a021b5c967",
   "metadata": {},
   "source": [
    "### Quick summary of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0f67c408-29de-45af-ae9e-a1c4dadf2b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>Height_m</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Catch_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>721.00000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>361.00000</td>\n",
       "      <td>417.945908</td>\n",
       "      <td>68.380028</td>\n",
       "      <td>75.013870</td>\n",
       "      <td>70.808599</td>\n",
       "      <td>68.737864</td>\n",
       "      <td>69.291262</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>3.323162</td>\n",
       "      <td>0.553377</td>\n",
       "      <td>1.144979</td>\n",
       "      <td>56.773370</td>\n",
       "      <td>100.246879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>208.27906</td>\n",
       "      <td>109.663671</td>\n",
       "      <td>25.848272</td>\n",
       "      <td>28.984475</td>\n",
       "      <td>29.296558</td>\n",
       "      <td>28.788005</td>\n",
       "      <td>27.015860</td>\n",
       "      <td>27.277920</td>\n",
       "      <td>1.669873</td>\n",
       "      <td>0.199969</td>\n",
       "      <td>1.044369</td>\n",
       "      <td>89.095667</td>\n",
       "      <td>76.573513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>181.00000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>361.00000</td>\n",
       "      <td>424.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>541.00000</td>\n",
       "      <td>499.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>721.00000</td>\n",
       "      <td>720.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>255.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Number       Total          HP      Attack     Defense      Sp_Atk  \\\n",
       "count  721.00000  721.000000  721.000000  721.000000  721.000000  721.000000   \n",
       "mean   361.00000  417.945908   68.380028   75.013870   70.808599   68.737864   \n",
       "std    208.27906  109.663671   25.848272   28.984475   29.296558   28.788005   \n",
       "min      1.00000  180.000000    1.000000    5.000000    5.000000   10.000000   \n",
       "25%    181.00000  320.000000   50.000000   53.000000   50.000000   45.000000   \n",
       "50%    361.00000  424.000000   65.000000   74.000000   65.000000   65.000000   \n",
       "75%    541.00000  499.000000   80.000000   95.000000   85.000000   90.000000   \n",
       "max    721.00000  720.000000  255.000000  165.000000  230.000000  154.000000   \n",
       "\n",
       "           Sp_Def       Speed  Generation     Pr_Male    Height_m   Weight_kg  \\\n",
       "count  721.000000  721.000000  721.000000  644.000000  721.000000  721.000000   \n",
       "mean    69.291262   65.714286    3.323162    0.553377    1.144979   56.773370   \n",
       "std     27.015860   27.277920    1.669873    0.199969    1.044369   89.095667   \n",
       "min     20.000000    5.000000    1.000000    0.000000    0.100000    0.100000   \n",
       "25%     50.000000   45.000000    2.000000    0.500000    0.610000    9.400000   \n",
       "50%     65.000000   65.000000    3.000000    0.500000    0.990000   28.000000   \n",
       "75%     85.000000   85.000000    5.000000    0.500000    1.400000   61.000000   \n",
       "max    230.000000  160.000000    6.000000    1.000000   14.500000  950.000000   \n",
       "\n",
       "       Catch_Rate  \n",
       "count  721.000000  \n",
       "mean   100.246879  \n",
       "std     76.573513  \n",
       "min      3.000000  \n",
       "25%     45.000000  \n",
       "50%     65.000000  \n",
       "75%    180.000000  \n",
       "max    255.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa9f594-8e4d-43de-96b6-d0882acedb25",
   "metadata": {},
   "source": [
    "### Visualizing data is built into Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a84a49bc-38bc-4ce5-a148-d841953cdb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fed60580d50>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = pokemon_df['HP'].values\n",
    "x2 = pokemon_df['Defense'].values\n",
    "x3 = pokemon_df['Sp_Def'].values\n",
    "\n",
    "# Option 1 - Pyplot.plot\n",
    "plt.plot(x1,x2,marker='.',linestyle='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c952812c-ffbe-4891-87f1-62a2594f361a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fed6447e610>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Option 2 - Pyplot.scatter\n",
    "plt.scatter(x1,x2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8429ca1c-773d-45a9-bef7-bef4e6ecbc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sp_Def', ylabel='Defense'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Option 3 - Pandas\n",
    "pokemon_df.plot.scatter(x='Sp_Def',y='Defense')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1652ca00-4a9e-4b63-90fd-7584d432aca3",
   "metadata": {},
   "source": [
    "> <b>Note</b>: Some visualization packages utilize Pandas DataFrames!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2af0b9a6-a694-4857-9f0f-12b70a862232",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ... Option 4 - Seaborn\n",
    "import seaborn as sns # this is a Python joke, but has become standard\n",
    "                      # stands for Samuel Norman \"Sam\" Seaborn\n",
    "                      # A character in The West Wing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d1ed29fd-e395-413b-895c-91c473467df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sp_Def', ylabel='Defense'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Option 4 - using Seaborn\n",
    "sns.scatterplot(x='Sp_Def',y='Defense',data=pokemon_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb95b361-25e4-4988-8c32-7d6323623d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Rmt9o5fNK1rZ/3fA785gmN2GzPhI/jhzNL9845SExcKJOSTHGvnLvwu4ICuepRsPuL22cnMRlfVNbZa6EJmMXsNzwMPYFikPY8ur/El7TtDdIimOcKGr0WTLnwzzvt1HGDE+MowVnxV67O/r/C3zLVsulITuhT9N4+8Bs4EJLZIzHwOuUEodAC63PwdYDxRiK+9/Afg/P45NEM6I48ePM2vWLAwG2xQKDhZJv9YwGBS5Z0WzcGIOj187gidmjALgTx/t55rzBjqNMbCt7JduPMC1Y9LaVUkmMhmB4dChQ516vr6SV/nI9OGMSIn1ut1XGNFXXmVuSozkW/YC/HYX0Vp/Bvhalk70sr8GbvfXeNpL5j3vB3oIQjcmMjKSiooKHDmRn3/+OdgU9XstpwsHnu710ioTj2/Y53Hehkaz13CLI920ZejFF63JZLQ15Cm0nxkzZrBlyxYmTpzIxo0bz/h8fSWv0hEubE8YsbW8Ssm37PnIsl4QOsCSJUuYOnUqBw8e5Hvf+x72it8jgR6XvzhdOLAt4UJHlVjLsEpEWLBze0qskWvHpBFkgJzEaFJijVTWN7VJ6sLX+UUmw79YrVYeffRR9u/fz5IlS1q+nOTtGAd9Oa/S2/a25ED66pLha7vkVfYcxCAThA4wZswY/vOf/7Bv3z601gwePJjQ0NCGQI/LX/gKBzqquE73OpwKt7gabQsn5vDCpoMsnJjDqq+OcH1eOss+ds8ly0mKalPoxdv5JWzjf1atWsXbb7+N2Wympqam5cutpsX05bxKB64Gk9miue+dHRRVNHRKDqTkVfYsxCAThA7wxhtvMHnyZHJzc3nkkUf4+uuvASICPS5/0Vo4MDM+ksMVdacNF7YMqyRFG6kxNXP2gEjO6hfOxefEs2DVN84qMoBVXx3hqVmj+WD3UdLiwjEGB3GsttHrSt9gUEwamsTqeWNtvfpijPSLDOGLQxVu+4vHoHMZPHgwd999NyNHjuTKK690e+3BBx+UwqxW8GYwLZiQ46yebLmoaS9tWSgJ3QcxyAShAzz88MPMnDmTzz77jI0bN3LXXXfxj3/8I/D19X7CVzgwOcbIhl1H2Xe0uk3hQkdYJTM+0uNGtOyGcz08ZAsm5FBysoEXNhVy5YgUZ/J/Rnw4D08bQUiQchpVAB/sKfPwwLlWa04amuSxj3gMOocxY8Ywd+7cdknB9HZOZ/x7M5iWfXyAuZdk8dbXxVw7Jo39ZTavY0cWDpJX2bWc6WJPBEgEoQMEBdkEwt9//31uu+02fvjDH0Ivnk++qrssVrhzzVbW5BezYEJOm6u8vN2ImsxWpzHm2Lbs4wMoFLeOP8dpjKXEGrk+L515r+a7qZIfOeF5zpbVmrtKq6QS00/cfPPN/OAHP+C7774D2iYF05tpSzNwXwZTtDGI2WMzWPFZIT/729ftbiTuQNqPdR1n2vwdevENRBD8SWpqKj/96U9ZvXo1V111FY2NjYEekl9xhBvXLxjHqnkXsn7BOCbnJlNeY7uhlFaZePXzIuZeksX8Cdm8cssFrXqdWt6IUmKNmMwWrzenwuN1bpWY145J8zDc7lyzlbLqRuIiQrn9smzmT7D9xUWEulVrtiagKZwZIgXjTltkWHwZTGf1i/B6jbd34SDtx7qOzpDd6dszRhA6yJo1a9iwYQN33XUX/fr1o7S0FGzK4b0Wb1VcrqHM0ioTz35SgDHEwLXnprbqqnc9LiXWyOyxGXx7ot5r2NNstRIdfqoSUym8GlX1TWbmXJThITDreq62CmgK7acvSsG0RlvChd4KUR6/biTGYNUpoUaRw+g6OiM8LAaZIHSAiIgIpk2bRllZGUeOONUu+pybpaOVjZnxkTzzo3PZXlxFamw431U18MnechZMyPGosowMDeJ4TSMLJ+awdOMBosKCWDAxG0ckYO2WYirrm+gfEepVYHb+ZdkeAppSidn59DUpGF848ogMSp3W+PdlMB2uqPN6rEJx6FgtNaZmvqsykRIbTm5KDMHBBrf8peQYo8c+bTEKpOCl43SG7I4YZILQAZ5++mkWL15MUlKSM0QDZAdyTIHgTFbgTWbN8k2Fbgn8G3aWOqssxwzsx76yGp77TyH3/XAoY+Ij+MGwJLYWV/Gbf+zwkMZoslo9VqhxEaGMGtiPP//PGDL6i4CmP+lrUjDecK2ajIsIdS4iWjP+vXmefUnE/O793W7FLQ5l/6kjzuKjfeXO923pKf7dNSNI7x9OfGSYz+tdJDLOjM6Q3RGDTBA6wNKlS9m3bx/x8fHObUqp3QEcUsDwdkM5k+oyR9jTUWk256IMQoMN1DXaol9LN+738IKtm38JBoMiLyOWORdn0dBopl9kKMWV9dy2Mt/jBuMYr9lsZUfJSZtMhou3QWgfFRUVvPbaa+zduxeAoUOHctZZZwV4VF2P63VdWmVi5eYi5o3P4tyB/ciIj2yz8e+6cCiqqOObb0+ycnMR145J8/AC3/f2TjLjI53v622f3/5jB3MvyWLFZ4U+jSyRyDgzOmOxJ788gtABBg4cSGys935zvRWrVVN4rJbNB49TeKzWZ/XQmVSXKYXTG/Dp/nJu+V4mSzce4LaVW7jppS/59MBxfnLx2aS4NF82NVvZW1bDWdFGZp2fwa/f3Mbdb+1g13dVPPGvfT6TbM1mK29vK+H65Z/zs799zfXLN/P2thLMZvdxCa2zZ88ehg8fzpYtWxg0aBA5OTl89dVXDB8+HKBPJee1vK5Lq0ws21hAeGgQWQlR7bs52xc6xpAglm0soLTK5DN/8qjL+/rax7HdV6J5azlQQttw/J+NzRrQ7v9vEA+ZIHSIrKwsLr30Un74wx8SFhbm2Nxqm5ieTHvCGW1ZaUeEBnvNt7jw7P5MGppItDGEMelx3PTSlx7esHnjs7h2TBrPflLgPG5/WQ3JMUYeeGenc3+r9n5jKqu2JdnuKq3ivrd3engbchKjGDUwzg/fYu/k/vvvZ+nSpcyaNctt+9q1a5kxY0ZqgIYVEPzRvqvlOX3pAZ5uH21fD/lKNJfWY4FHPGSC0AHS09O54ooraGpqoqamxtEyptfOp/aUdPtaae8urWZnyUkKymqoqDOxeGquWzn+4qm5GBSU1TRitWqsWns9j1VDkP2bduSevZFf7OYlcOBNUiAi1KYh50sC42iVeATaw44dOzyMMYDrrrsOILzLBxRAzlRmwpsX2vWca7cUs3Ciu97fI9OHkxgT6tzubZ8FE3J46+ti53NvRpZIZAQe8ZAJQgdYtGgRAPX19URE2Dom9eY2Me0p6fa10v72RD3fnqh3U9t/atZoDpTXEhKkCA02MPeVU/ley2fneT2PQcGgxGjmT8hGa3j1c5sSf0qs+/s6bkyuyc0LJuTQbLG97ksCIzlWPALtITKy1Rt2n5K9OJM8ota80C1bjn3vnHhKq0wkxxrJTYnlq6ITrNxs0wFM7x/Oibom5l9mq0TOTozi8Q17KK0ytWpkScFL4BGDTBA6wObNm5k7dy61tbUcOXKEbdu2AfS51kmtrbRb9uczBhuobGjm1nFZgM1g+sWarcy9JItGM9z71g43D9z97+zgD9eN5Ndrt3tUVAK8+Gmh240rPjKURVfnsvi9XZiarVTWN5Eca+SuSYM42WBGa1idf4TJw5MByE2J4ZHpw51hS4e3ITelb+UGninl5eUsWbLEY7u2xchCunxA3QStwaDg0PE6myHlQ0bCbLayq7SKapO51VC/489RMBPTZCHaGILBYGsfFhpsP68GU7OFNfnFlFaZGJkaw68mDcFggNR+4eSmxJ7WyNLtawggdBJikAlCB/j5z3/Ov/71L6ZOnQrAqFGjAKIDOig/0p6SbteV9u7SavaU1rBhZynXX5DOcnuSvWsTZVclfVeKKhroHxnCuvmX8G1lPRGhwSTFhJHe3/ae6+0r+QGRYZjMFnZ+V80HO0t5fvZ5nKxvpn9EKEerG3jyg/3ERYQyMy+NX00agtY2b0RwsIHpo1LJSYziqIu3Qaos28dtt93mCNl741hXjiXQtJS9aCk/0TLv0lFYct/bO7l1XNZpvdC+vGiXD07kjgk5bouLhRNz+OeOUq4ckcJdb247be6nyF4EHqX9ZAorpf4KTAHKtdbD7dseBG7j1CT9jdZ6vf21e4G52FzcC7TW/zrde+Tl5en8/Hw/jB4y73nfL+f1F4cf+2Ggh9CnuPDCC/niiy8499xz+eabbwBQSjVorSMCOS5/zgnHyrwt4QzHvgeP1bKzpIqQIAPPfFLg4WGbNz4Li9VWGebweLV8fUhyjM+bgusN7eeX56A1bjfA31w5hPMy4thXVuumXSY3mq5BKbVFa51nf3yv1vr3XT0Gf86JlhQeq+WqZZ9iarZy+2XZrPjM85pe71Lcsu3bSq5f/jmmZivzJ2R7nQMOSZeyahMRocEsWPU1RRUNbvusnjfWeR7X7StvuYA5LoUx3sbgbeyn21foOK5zoiX+XAq+DEz2sv0prfVo+5/DGBsG3ADk2o/5s1IqyI9jE4QzYuDAgfz3v/9FKUVzczNPPvkk9HKl/raWdLvKXty2cgvPbyokOdbodfWf3j+Ct74u5r1tJSy62j3J/8Grc3kjv7jVfnCulZJmi/bQX3r0n3sxW7TTGHNsl4biAWFmoAfgb8raID/hKiPhWliydksxd00a7FHosvdojVNC5vrlm7k+L90p+zIyNYY/zBjFkRMNXt/rRH1Tm6UsRPYi8PgtZKm13qSUymzj7tOAVVrrRuCQUqoAuADY7K/xCcKZ8Je//IWFCxdSUlJCamoqkyZNAigK9Li6A94qMn31qYwMDea689KIjwghMTqU5398HvVNFo7XNHKyoQmAuZdksb/MFhJzeOUcHrhvKxv41aTBpPWPoKHJwq3jsli7xZY743jv0moTcRGhXDsmzRkeXbuluN19AYUzpte7I9siUeGad9mysCQuMoR547Ow2vPP4qNCmf/aN14FlD/dX86NF9h0924dl+X1vXwVriREeeZ+iuxF4AlEssR8pdR2pdRflVIOsZ9U4FuXfYrt2zxQSs1TSuUrpfLtvdIEocu4++67Afjkk0/4+9//TllZGeXl5fztb3+DPlZR5gtvK+01+cXcP2WY2+p/4cQcHlq3m037yjGGBHP7a99w00tf8Ys1WwkONtA/IoTZYzNY8VkhP/vb106BWbPZ6vTAzX/tG574YB+Fx2p5fMNeXvy0kNljM5weBGOIgbS4cOZcZDvPMx8X8OKnhcy5KIPkGLnRdDG9PlX8dBIVLfMuHYUlxhAD145J47f/2MmyjQWs3VKMxQoVtU3cOi7LQwg5yAC3jj+HxetsBSxrtxTbCmdavFe/iGCPMSycmOOUjfE1dl/jFfxLVyf1Pwc8jG1iPgz8EfhJe06gtV4OLAdbbkBnD1AQWmP9+vU89thj/P73v2fmzF4fgekQLVfaKbFGZualERUaxBMzRlFUUceogf0oLK+lsr6Je68ayq/tScdgu+Esfm8Xy2efxwPv7vYINa6eN9bDA7d046m2Sw4PgqNNjDE4yGvT8UnDkgPw7fRper2HrKV0RHKMkUnDkjlW6z3v0rWw5NtKW9gxJdbI7LEZLPvYXa7l1c+LnNIVE4ckOvcHW+jz1c9tshfDUqIZmhJDZnwkXxyqcMphKGWrnly5uYhz0/uROSCq1bGL7EXX06UGmda6zPFYKfUCsM7+tAQY6LJrmn2bIHQrJk+eTFxcHLW1tcTExKC1RinlKPE/t7VjfRS69AdWA5nAYWCW1rpSKaWApcBVQD1ws9b6a399rs7EtSLTW6XZggk5nKhr5C+bCpl7SRbgXQC2usHsdbsvQVfXas1hKdGsXzDOeVPytv+xWhPnJErIsgt5I9AD6Aq89XY9JzGKpiYLXx+p5Gi1ibNijfQLD6GsppGkGCMjUvsRbQxxesocxhi4hylXfFbIH2eOJsigiDW6d7sorTKx4rNCVs8bS2Z8JIcr6jAoRWV9k7OrBZwKQ/rqN9ty7J3J6Xrc9nW61CBTSqVorR3imdcAO+2P3wVeU0otAc4CcoAvu3JsgtAWnnjiCZ544gmmTZvGO++84/aaUuqb0xz+MvAMsNJl2z3ARq31Y0qpe+zP7wauxDYPcoALsXmXL+yUD9FBXH9MU2KNWKw4vQA1pmaO1zUSYwylyWxlcGIUb/7sIk7WN3Orvbk3nLq5PD/7PH46/mySY8JBwcKJ2U7dJLDfNGLCvOa09I8M9dka5trRKcw8P53jtU0cr20kKSrE77kxDh2pvt6gvLCwkIULF7J582YMBgMXXXQRQKjjda31o4EbXWBparKwYc9RCsprsWo4UFZDfGQoz/2nkMr6Jqd0xSPTh3PkRL3XBcSQ5Gjem38Je49WM+Mvm4mLCPUQPn5k+nCGJsW4SW+03GfJrNGkx0V0ucSFyGqcHr8ZZEqp14FLgQFKqWJgEXCpUmo0tpDlYeCnAFrrXUqpNcBuwAzcrrWWfByh2/LOO+9QVFTEgQMHuPzyy2loaIDT5GT6KHSZhm2eALwC/BubQTYNWKltrrfPlVL9WixouhRf+kqOx6u+OsL1eeluYZaFE3OIDA32enPZU1qN1vBzlx/nhRNzWLnZprq/aEouJZV1bkKvxhADi67O5WhVPYun5rLoXfftpZW1jD1nALe8/JVz+0NTh3PV8IQ2a6i1F1fZDdeb4vRRqX3OKPvRj37E7bffzj/+8Q8AVq1axbvvvpsV4GF1C3YfraaksoHlmwrdrvc5F2Xw+IZ9zlD80x8f4K5JQ7wuIA6U15ASa+RXb253eopXbi5i3vgshqbEkBJjJDIsmM8Kj7PvaDVxEaFu+5w7sB8Z8ZFO79np+s12NoF4z56GP6ssb/SyeUUr+/8O+J2/xiMInckLL7zA8uXLOXHiBAcPHqS4uBgguwOnSnIxso5yqkG5r0KXgBhkrj+m145Jc664HY/nXpLlEWZZuvEAT8wY5fXmcla/CI+8saUbD/DnH42htsnCyv8WMufiLP6yca9b/stf/lPAryYN4c//9tz+xHWj3DSXTM1WHnh3J5kDLvBbbow0KD9FfX09s2fPdj7/8Y9/zOzZs/uWVeqDalOz1zzGF2afx19vzqOx2UpVQzOPXTuCN776lvunDOPhdbudxtv9U4ax+ssj5CRGu1UMR4UFYbZo6hrNlFabeOyfeyiqaHDLOwOwWKHRfGoOtqcVWmcRiPfsCjozDCtK/YLQAZ599lm+/PJLLrzQFkXMycmBM5xPWmutlGp3oYpSah4wD2xNz/2BL30lx2NfmkuNZovbzSUjPpz7pwzDoJRbC6XSKps0BUCT2cIvrhjMibomrh6V6nw9JdbItWPSMFs1RRUNzrwYx/aymkavYyirbiS/6AQRoUGttoTpSOixtQblowb6OKiXcuWVV/LYY49xww03oJRi9erVAFX2PEm01icCO8LAYWq2elwncRGhHK1u5P533NX1vz8kEYOCp2aNZs/RaixWWL7pIDecn05EqMHDO+0QXG6Z/L/s4wMsnGgTS3b1XC+ZNZphKdFdLnHRG2U1OjsMKwaZIHSAsLAwQkOd6TGYzeaOnqrMEYpUSqUA5fbtbS506YrK49b0lVzL5Fv+2B46Xs+67SU8NWs0xhDFifpmDpTVeiT5b9hpa/Hyf6997dz+i8sH8d62EmaPzWDDzlImD09h2ccH3DSXXCvSfGkx7T1aw6/e3OYWEm2thU17Qo/SoPwUa9asAWD58uWAs5dlf2ALtjSVPhu+PHtApMd1MjMvzWmMwSmv2bzxWVx8Tjw3v/SV2/5LNx5g2fXneninvSX/P2s30tLiIvhVC0/0nWu28v4d4/wWxvdFe9qv9RQ6Owwr7mRB6ADf//73efTRR2loaODDDz90SGBUdeBU7wI32R/fBLzjsn2OsjEWqApU/hj41ldyPH5vWwm/uHyQm3H2i8sH8dbXxRRVNPDYhj2EhwZjtmhMzRanN8xxE5k3/hyPm8tTH+1nyshUm7E1/hznKt9Vc8m1Is2bFtOCCTm89XWx82Z37Zg054+mq1K/r9DjrtLW/0tddaQc79nXGpR/9dVXHD16lEOHDnHo0CEWLVrE8OHDufrqqwF2a63P1lp7NcbsepTlSqmdLtv6K6U+VEodsP8bZ9+ulFLLlFIFdi3LMV3zCc+ccxKi+ONMd42v9P4RXr2rVg3Harwr7FeZmr16ql33cVQbG0MMhAQpnxXGk3OTWb9gHKvmXcj6BeP8nlzvkNXoyvf0N53d3UA8ZILQAR577DFWrFjBiBEjeP7557nqqqt4++23W5Vq8VHo8hiwRik1F5vS/yz77uuxSV4UYJO9uMVPH8UDXzkRE3ISePUnF3C0upGU2DA2LLiEvWW1lFebuP+Hw6hvMrupjBvtnqWRqTFcf0G6c8Xv6hUbNygRpSDaGOxMQnZgarYyKDGKJ2aMIspocObORBuDGJIczfLZ51HXaOHFOXkUHq/luX8XOrWYhiRHs/dojTN84zifqzSGa+5KR0OP0qAcfvrTn/LRRx8BsGnTJu69916efvpptm7dCpBxmsNfpgdXHnvD1/y5cngyQ1PGUVRRxzffnqSs2uTVu2pQkNSiwtih5RcZFuysSHbs763a2Bhi4A/XjaR/ZCgZ8eFMGZnqvPbf21ZCiMHAjpKT1DdZ/CI/4es78LesRlfT2WFYvzUX7wqkufgppLl41+PoFJGQkAC03jS2qzjTOeErJ2JCTgLv7izlAZd8l4emDefCs+NYv+MoDc0WZwWZA2OIgfmXZZOVEOXm1ne81jJ88Zsrh1BtMmOyJx+/t62EKSNTWfFZIQ9NGw7aypr8b/nZ97M52dDMtyfqWZNfTGV9E4un5qK05qmNBfaQ5CjuXLPN4z0d4RxjiIH37xjHOYlRWK2aLw5VOKszXfdfPW9sn0vOby+jRo1i27ZtANx+++0kJCTw4IMPAqCUatBaR7R2vL3yeJ2LNt8+4FKXMP6/tdaDlVLP2x+/3nK/1s7flc3F25JTZLVqPt5XxoGyWpJijBRV1Dmv44UTc0iNCyetXzifF55g2ccHvGr5LZyYwz932ML8LWUvjtc2Ut9k4ZyESOIjg/juZDMPuFQkPzxtOGHBil+v3XHavKeO5FX2JXmLjnzW1u4T4iEThHagtWbx4sU888wzWK22m3dQUBB33HFHgEfWOfjKiXj1Jxc4jTHH9gfe2cmrP7mApRtt+VvePEwJUWHsPVrt9TXX7XERodQ1WdwSlBdNyeX1L4uc77XshnO5ZsxA5r/+jUcS86J3d/HkjFHMzEvDGBzE8ZpG7rxiEEs+3O92E1u5ucijfczhijr++MFeFk3Jdbaicdy4+lLosaNYLBbMZjPBwcFs3LjRmUNmpyN34B5ReeyNtuYUNZm1hyGV2s9IRGgw0WHBHD5RT7AB5l+WTUZ8pEcemKOCubiynvmXZWMyW9Eajtc28tg/9wG2BcXKWy7ggXfdx3P/OzuZNz7rtGPsaF5lX5K36OzuBmKQCUI7eOqpp/h//+//8dVXX3H22WcDNkHM//3f/wVIDOjgOgFfORFHq90rGFNijcy5KIP6ZltD78FJ0V5DLNHhIVwUHw5kY7U749dusXkDLC5v4y1BefG6Xcy9JItjtU1cOyaN0GCDs1rTsY9rEnNdk5mcxGhe2HSQq0amoDX8ceYorPYQjlXDLycN4lhNIxarZud31dQ2mimvbiS/qIomcxF/mDGKhiYz4aHBpMcZ+1TosaPceOONfP/732fAgAGEh4czbtw4AAoKCuAM+7t258pjb7RF2sGbwXLf2zt5/45xFB6vZePecqwaghSckxCBwnuu2IHyGpZtdFfgt3W+cK089hxPXEQoOYnR3HnFIM4eEEnJyXpqGy18d7LeLcToK68yo38E5w6M8zk3equ8hS86MwwrBpkgtINXX32VDz/8kAEDBji3ZWVl8be//Y3ExMQBrRzaI/CVE5HsktOSEmvkZ+OzqG+28NNXtzjlLBweppbCsbd8L9NDEDMxJoxlGw8438NXgnJYsMFZRamUdy+cUrYxRoYGs7+8huvGpBEaEuQUlM2ID+fuyUMpPFbLOYlR1JnM/H7DXuc4Tc0WjCEGtpdUs+D1b5yfef2CcX78pnsPv/3tb5k4cSKlpaVMmjQJpRyhOSvAkQ6cskdUHnujLTlF3gwWmwRGA1UNzShsi5bUfmGc1S+d707WeD3neen9nNtdPcCtVR47FlIOj5vDy/zethJSYo0883GBswrZ4GNOHjpeR9GJep+est4ob9FVyPJPENpBc3OzmzHmwJ5H1uMTJFyrKeFUrteIlFgemjbcWdlYUd/k5tEqqmjgL5sKeHLGKB6/bqRbab4jbAinwi2VdU3ccH66832C1Cn5DAfGEAOZAyLdBGe97WNQsHhqLvWNzbyRX0xFfZPTGEuJNXJ9Xjp3rtnKkx/s5xert1Jvr/J0eOXW5HtWZ/b0cvyuZuzYsVxzzTVERp76zgYNGgS2gpT20iMqj73ha/64XksRocFu17HDSJr7Sj53vbGd5zcVMntsBreNz2bRu7u8Xp8LJ+YQGmxg3vgs/nT9aH575VAiQoKorG9qtfJ4Zp53qYwpI1N5eN1uZxXy4xv2EG9vUeaKMcSAMTS41QrktnwHgnfEQyYI7cBVe8wL1tZe7Am0lhMxfeRZZA2IpLTKxL6yGo/Vc1FFA2EhBqobTl+aH20M4URdE/MvyyYpxki/iBB+cfkgnvroVM6XI/nYcfymfeUsmTWavUersWpb0v8dE3IY2D8cpaC00sRvrhpKZf2p8Kq3Rs2OzgKOsZVWmZzVmUrBuOwBJMWE8cWhik6rQJOmyt7pKZXHvpLbvf2/ni6nqMliYcGEHOd16ctI+vP/jHFenxt2ltrC6Y1mzk6IoKrBTEVdMxefE8+3FfWU1TYysF8YL87J42R9M8tnn8fhiroWlcdRhAYbvM5HV4FnxyLm8Q17PPIqF03J5cVNB1utQO7svKq+hBhkgtAOtm3bRkxMjMd2e7VyeJcPyA/4yokIDQ0iL7M/hcdqKSj3HkbZUVLF2Kx4nyKyjudHTjQ4qx3nXpJFRI2BNfnfurVDeuaTA/zqB7a+fnERoUwenuJWzXT/lGGs+rKImXnp/PnfBc6WMYun5pKXEUt+UVWbtJocNz3HeC7I7M/kpZ92WoVYX6o6ay8+WuwBTPSyrwZu9++IPPGV3D51xFl8tK/c6/9razlF8ZFhrM4/wtxLskiKDmVAVJjXa7TRHm6fed5AkmKMHCyv4ZO95R6VlQsn5rCr+CSp/VK4dWX+KePp6lx+MTGbv31xhCCD7Zz9IkJblcrQ2n0R48irNDWZGdg/guX/Ocj2kurTih/3NnmLrkJCloLQDiwWC9XV1R5/NTU1AF8HenxdQWZ8JCPSYp3isHBKhPWN/GKWfLDXGd50FZF17Hf/lGFEG4PIiA9nwYQcPt1fTnZiNDecn86Kzwp55uMCVnxWyLzx57D8PwdZMCGHmXmenq6H1+3mwqwE/vzvAn45aQjzJ2Rzx4RsquqbmDvuHBZOzCYqLMhnmNPb2B6/biT3v7PDo0LMVUS2vfiqOjuTcwpdh6/k9u3fVfH4hj3MvSSL+ROyuXVcFo9v2HPa/9fM+EjunjyUFZ8Vkp0Uzb6yGq/X6Im6Rv7v0mye+aSAX71pC2X+7NJsrz0xb77kbGeY3rF98Xu76BcZxvUXpLN8UyG/enM79729g0VTcj3m7brtJSycaBNRdhhvgDOv8tdrd7C5sIJhqf36pPhxVyEeMkEQ2oXBoJgwOInshCiGp8byxaETaI2bCGtkqIEnZ4yirsnMgKhQXv3JBXxXZeLgsVpn4vAj00fwwa4SJg9P4UBZDX//4ggLJ+aQFhdBfaOZ2IgQqkzNvPp5Eb+9aqiP0Gcw1+elOxuVOzwGD723m8r6Jh6eNpwnZ47irjdOvf6Lywdh1Zr7fjiUjPgIrhiaxPG6RhKjjVTUNVJU0eDxPmdSIdbXqs56G75Eg8trTFyfl+7WJ3LBhBxO1DU6/199haon5yaT+/NxFFU0EGQw8NSs0Ty24VRj8Ien2fTEWhpfviRkTtY1e90ebFBulcmOXM8lM0dxpLKBoSnRNDRZuGfyUI5WNfDg1cNIjg33qiloscKwlGhWzxvb58SPuwoxyARBaDcGgyJzQBRWDS9+6v7jPTMvjV++sd1t24KJ2R4/8ve9vYMX5+Tx27d38NDUXG65OINIY4izAiwjPpwHrx7OgbIaEqK9h1pyz4rhNnuYBtxzxJ79pID739nJipvy+NOs0dQ0mjGGBFFysp5VXx3hpZsvcN44s4l2O29nVohJ1VnPxle/0oSoMDfxYUfu1+p5Y4HWQ9VNTRbyD5/kt2+fEme9f8owakzN1DdZOHtAhFdD3iHh0nIs/SJDvG6va7J4zfVstFiJCDVQ02DGZLZwuKKe97aV8MotFwDw6DUj+M0/drgZmqvzj3DdmAvIjI+UfEg/ISauIAgdxltFVU5ilNcbibcV/LHaRm6/9ByOVjdS22RxruYdicX/+/ctPPrPvWz79qRHeHHhxBzQutUcMVOzlR0l1RyqqOP+d3Zyx+vf8KePDnDHhBzS4zwF5P1RISZVZz0bX/1KNd6v6fomm/Sar1B14bFaNhedcBpjjtceXreb+iYLaf0i2Fp0nDF2WQtX3ttWwsPThnvMg5c/O8Siq91DkYuuzvUZsj9aZSLIYOCuN7dx1xvbefHTQu6YkEPBsVomL/2Ul//fIZ6aNZoFE7OZe0kWq/OPcPfkoaTHRbBh11GuWvYpN77wBVct+5QNu45itfbcjj/dCfGQCYLQYbxVVNWazB6rdYesRcsV/KHjdVyUFc8tL3/lpvbfsjqyutHC2i3Fbkn/KzcXsejqYV4FaVNjw5k/IZv3tpUwNCXaqZcGp3KAzh0Y52yd5LrinzQ0ifWdWCEmVWc9G1/9So9U1nu9ppNibJ5PX6HqQxV17Cip8vpaamw4z/77AL+/diSF5bVu1ZjGEAPX56WTEB3Ki3PyqDI1kxAVRkOTmZTYcKLCgnjuf86lusFCv8gQvq2o49sT9dw/ZZhzoePwdgEeIsv3vX1KwX97STUPrdvNzLw08jL6cd2YVKdnrK+o8AcCvxlkSqm/AlOAcpceZf2B1UAmcBiYpbWuVDYlwaXYSprrgZu11n0iQVoQujOuxkpKrBGLFcprvIcqggyKE3VNlNeYeOUn51Ndb+ZEXRORxmCsFguPTB/uVqm26OpcDNpKZb0t/yU8xOC8wXmrjqysb+LZT9yVyY0hQfzp+tH8/p97aDJrj55/i6bk0tBkdjPYrh2ThlJQVtNARv8IPthT5rNSrrOQqrOeTXCwgVED49xkHhyez5bXTnpcBIXHajEo5dVgCw02+Aw9HqlsoKiigYraRo7WNPLethK3Rcjq/CM8MWMUh47X0S8ilEPHaimpMp1S9k+MwhgaxMufHWJYaj/S48LpFxnKwok51DVZnLmeM/PSvM6JnMRoUuzVk9eOScOqISIs2DnXJR/Sv/jTQ/Yy8Ayw0mXbPcBGrfVjSql77M/vBq4Ecux/FwLP2f8VBCFAuObAeGtwvGTWaCYNTeKDPWU8vmGPM8HZ276Lrs7lvIwY/nT9aHaXVhMaZCBIaSrqzXxX3UhGfDhRocHO1Ty437AcFZEty/3vfWsHlfVN3D9lGNkJkdz00lduq/fF63bxwuw8p3SGQ8Hc1GzlxU8NLJ+dJyt+oUN483ymx0Xw7wPlbC+uIiI0iEevGc7hinqnwRQfaTOi3ttW4uH9cvRldeQXvvLfQ/zv97N58L1dbtf83tJqnvtPIbdcnIFF49EFI0jB5BEpvJF/hGeLqpzzb9VXp6RhhibHeJ0TxhAD904egslsdWoCvvhpoXORIvmQ/kXZ9ZP8c3KlMoF1Lh6yfcClLi0x/q21HqyUet7++PWW+7V2/ry8PJ2fn++XsWfe875fzusvDj/2w0APoc+jlNqitc4L5Bg6c04UHqvlqmU2Pa7bL8tmxWeelVer543l+uWfs3BiDg3NFqwaBidFuzVDduy78icXMOevXzrPF2Sw3UziIkK5f8owHt+wh99cNYwms5XQYEWNyezmUXv8uhEcq2kkOiyEkqoG3sgvdlZ1GkMMLJ99HnP++pXH51gyaxRl1SYami0ehQULJma79QN0sGrehYzN6vGdsAJOb5sTp+Pw8Vr+ufOoW9sw1wb3i67OJTk2jL2lNaz66gizx2YwIMpI4fFa3si39XhdPDWXfuFBBAcFc7C8htom27zSGt762raPTejV+zx7YsYofvXmNv4wY5RbK7CXbj6fbcVVZPSPYMVnB5kwJBmT2fuc8FZluX7BODLjI0VT7wxpbU50dQ5ZkouRdRRIsj9OBb512a/Yvs3DIAtU01hB6Gu4hidahhAdYY5vKxuIiwglJjzE6b1aMDHba1jjRF0Tt47LIiosiIz+kVQ3NHPruCzWbimm9GQD1+els3DVN5iarTxz47k8/fEBt3DNkg/3M210KicbzDzzcYHH+atNZhZOzGZNC0OtX3gI8ZEh1DZ6Vpz5Ch11xopf1Pn7HmXVja22DVv83i6emDGKlZuLuHZMGicbzESHNnN+Rn9yEqOIDQ/haFUDpmYNZjPVjRaPax0c89HzejY1W53bG5rMbtuPnKjnTx/t597JQ1h4+WD2fFdFTmJ0mwtwyqptYUnJh/QfAUvq11prpVS73XOBahorCH2NluEJx+OWzYtn5qW5JQj7MnJ2l1azdksxcy7K4BcuK+wFE3JI6x/hNMYACo/XUVTR4JYzBnBOQhSFx2q9nn9PaQ0rPit0NlmurG9i4cQciirqMJltN6qWx723rYTHrxvJ3Wu3u634z7QCUtT5+yZ1LvmKSuHsmeqo+l27pRiwScNYtfvznMRoQoMM1DZa0FoxICqUGHuVZMtrPTI0iJRY7+HD5FgjGfHhhIfabu+OQpdoYwhPXT+aY9UmyqpNDOwfSVFFXZsLcJotGqtVSz6kH+lq2Ysye6gS+7/l9u0lgGtXrDT7NkEQAoSrXIOrqn3L5sVnD4h0+/Feu6WYX1w+yK0E/84rBvFGfrGzoXdL7aaQIMWt47KcCcVNFqvXcv3q+maMIUE8MGWYh9r4W18XO3XIfnPVUOaNzyIiJAiT2eqzifjdk4dy5bBkVs8by19+PIbV88YyaWjSGRtNos7fN8noH+m8vqLCgphzUYaz+8SLnxYy56IMSk/Ws2yj7fnNF2fyv9/PYvmmQh5dv4dvvj3JH/61j7vf2sHtr39DbEQov7lyiNs1+5srh2BQij99tI8HW0hdPHh1LkXHa7n90mzWbfvW2bh8+aZC/u/vX/OL1Vupa7Lwxw/284s1W0nrH8Gj14xwO0d8ZKiHzMeCCTnc/84OuX79TFd7yN4FbsLWPPYm4B2X7fOVUquwJfNXnS5/TBAE/9IyaTk5xsikYckcKD/VWLy0ykR0WLDHijoyLIh547OwajAoCA8JAnw3G//6yEle/LSQBRNy2LCzFGOwgSdmjCQsOIhmi5W6RjPxUaEs/egA20uqSYk1Ohsm7z1a69YlwBGusVjhL5sKue68NKenwmDAWaV24dn9OS89zmuV5bCUaEqrfIcaTxeOlGq0vsnZA05VXpotmmc+KXAzypduPMD8y7Kdz5/6aL9TasLbYmXRu7t4atZonpgxioYmM2cnRGKxam5+6SvmXpLFc/8pcAvrP/cfWxuxX7+5jRfn5GHVmnktJF9chZPvemMba392kXOu2s5RSGiw4g8zRrG/rMatC4dcv/7Fn7IXrwOXAgOUUsXAImyG2Bql1FygCJhl3309NsmLAmyyF7f4a1yCILQd1/CEwwgJDTY4c7UAqkzN3HnFIGe+zJyLMnjk/T0eIY/5l2VjMlu9hkO0PW9ldf4RWw/LTQf5ycVnU99scaus/MXlgzhW20RplYkVnxXy/OzzvBYbxEeF8cC7tuq0GLunomWFZlJMGEcq6716suaNz2LZxgKvoca2hCOlGq3v4ZgfGfHhrPzJBZRVN3rP8TJb3Z5HhAZx+2XZpMeFe93fbNXUN5qJCQ/GYrFypNLklIbxFtY/fLyOuIhQLFZNTaP5VJ6my4LFVTi54FgdEaFBvPLfU4sagILyGrf8Nbl+/Y/fQpZa6xu11ila6xCtdZrWeoXWukJrPVFrnaO1vlxrfcK+r9Za3661PkdrPUJr3TUlMYIgtAmHEXLVsk/5ycv5PL/JFn75nwvT+e0/dmKxauaNtzVZTouL8HpjSYwOY9O+cjdF8Yz4cJbMGk1YsIH5E7K54fx0Hl63mykjU6mob/LwGDz10X6uHZPmrFh7+bND3O8lfPncvw/whxmjmD8hm4H9I702ZLZYfXuygg0G5+OWoca2hCM7U53fatU2dfeDxyk8Viuq6N0Qx/x47J+72FlSzZy/fumzabirsEFGfDjRxhBWfFbItycbvO5/oLyGu9/awS/WbKO0upF+4cFu11XL/Q0K5lyUwU//toX5r33Di58WMntshjMdwHUMxhADB4/VUt9k8dgnL6O/dJfoYkSpXxCE0+LNCFm68QB/un40pmYrdU2nqsGevvFcn8nGN30vkzqTmXnjszgnIZLaRoubp+n+KcOIiwhFKd/VXkOSo1kycxR/+c9BtpdUc+E58W5hG0d45eJs2wrfV9XnsVqTT09WdmIUKbFGZ2Np11BNW8KRnaXOL8UBPQPH/Hh+9nnOrhBrtxR7aI0tmpLLXzbZ5onj+f+99rXP/R+YMoxVXx4BbAUCR6tMrPrqiLO3pDcts2ADLPnIM09z7iVZbkUvrtpn152X5rbPklmjuTgrvlM7VginRwwyQRDc8JYf5csIiQxzX62bmq2UnKz3KuJqDA2iqKKed7aWMGVkKsEGAw+v8+zn58hn8VXt1S8ihD3fVTEpN5nxgxNJ7RfBkx/spaiiwW2/ScOS+N45A6g2NXuVw3DcZFpWWS6YkMPjG/Zw7Zg0nv2kwCNU09ZwZGdUo0mrmp6BY35U1jW75Ve++nmRUzNs79EaXv+yiCkjU52LB5PZ4rb/hp2lLJk1mr1Hq7FY4flNB7k+L51jtU1uOWavfm6TzQgPMfDyLRdwoq6RqLBgDlfUERYU5GMhE8WfZo3myIk6rjsvzbl4qaxvcqYMjEyNceqNSTVl1yMGmSAITnx5ZHISo7waIUdPNrB4ai5//ncBv7h8EE99tJ9X/lvE/34/yy2pPzEmjMq6ZlJiwpyK/o7ela6tWwByEqN48oN9/OTisz0MuzuvGMQTG/Zy5YgUZ8K0McTAQ1OH8+y/DziVyJ/50bkcOdHg9jlc5TAc4ReDQXFWP6NXD5tS3kM1vlrm+COcI8UBPYOkGJvURGqckQUTs3FElTftKyfIAKHBilhjEJNyk505ZOu2l3BJ9ki3eTVuUKKbAQ44PVctZTTCQwwYlOLml750u8ZrtGcvWceCITzEgDE0iK+PVBIbHsLiqbk0NFsIDVLkZcSSkxTdKdeVaPB1DDHIBEFw4ssjs+q2sV69Xqn9w9lZXMWvJg3hu5P1PDVrNBarJjTYQEpsOHVNZgqP1/Hkv/ZTWd/Eo9eM4OO9tibhg5OiyYgPdxpojvM+Mn04D0wZRm2jmZzoKP50/WgMSnHwWC0v/b/DXqvRHnh3J6vnjaXZYiUkyHYzuum1Lz1CrK/ccgEJ0WFuN4j4yDCvhQHjsgdw7bmpHjeTrmwWLsUBPYP0uAjumjSY7cVVTpX7jPhwfvb9bBa/t4u4iFD+9/tZ1DY1ATbv7y+vGERIEG7zKsjgPUyvFCRHh/LI9FwammxVx5X1TU4jzRFaX7rxAC/OOY9Hpo/gvrd3OOfU4qm5rP6yiJzkGI857FikPDRtOGmx4Wf8XUiYveOIQSYI3QCl1GGgBrAAZq11nlKqP7AayAQOA7O01pX+HIcvj0zxyQZWbi5yrtQjQ4OwWDUVtU2k9Iug+GQ9XxysYHBKDFX1zWwvqcIYbHAr+wdYunE/88afw8PrdhMXEcoDU4Y5RWId73Xf2zu5a9IgKuvNFFU0cPaASEKDoa7JYv+uvN+06pssVNY3c+earU7vW8t9NNrDA+DL43V+Zn+fN5CuCud0pTdO6DhHKus5UF7r1nJoyshUFtv7UM65KIO6JotH38mkWCMpsUanN3n4WbFeDfC8jDhqG83sL6v18BjPuSiDxzfsA2zX+Ml6M6FBmpduPp/jtU3Ehgfzkr3ZuLfiFocExgPv7GRwUhSjBsad0XchYfaOIwaZIHQfLtNaH3d5fg+wUWv9mFLqHvvzu/05AF8emZQYI5X1TTz7SYFTqd81ZLj0hlEMSopmS1ElVm1TwF84cZCHUTRlZKpT1b+0ysSB8lqvhtOAKCNPfuCe1/XethJmj83AYPCeWxaklNuNoK2epa70eLWX7jw24RRl1SYiQoOYe0kWSTGhZMRH0tBk4fnZ57HnuxqGnRXN/e/s9DCGnp99HnERwVw2KIHqRjMWq5XfXTOC3/5jh9u1v6e02qMXq6nZypIP9/PEjFHOcRhDDPSPCsVssbL8PweZdm6as79rblo/n943x+OjVSZGDaRDOMKU+8tqvEptSJj99IhBJgjdl2nYtPwAXgH+jZ8NMl8emRFnxfLI9OHc9/ZON6V+sIVMjtc28/C6bW43kSijp2Bsy5CMQ5G/peFUeLzW7cbjyKNZ9rFNWNNb+PTrbyudx3irWGvNs9SdE5i789gEGymxRg4dD+Gj3Ue5bkw6D7yz0yMU76hodDVSKmqbqDU1U9d0yvOVER/OMz8aw97SahrsCfzXnZfmPMYVx/OUWKMz7PjCfw6y+dAJFk/NJSkm1G1++dIAdDxOjulYKNxbmNL180qYvW10deskQRC8o4EPlFJblFLz7NuSXDpWHAWSvB2olJqnlMpXSuUfO3bM68nbqmVlMCgmDU1yaSV0EZOGJhEaGsT0UamsnjeWIcnuDYmvHePey9JhQFks2k1zzBhiYGhKjJt2kmtLJsc+D03N5Q276KwDx0re1Gzl7AGRXDk8mffvGMdLN+cxb3wWKzcXUdtocZ7HUeE2b3wWz//4PNYvGHfGOSydpQcmumK9D4sVHlq3mzkXZ7F43S6mjEx1W7Q45sS1Y9KcxxhDDCREhXG8rolVXx1h7iU2Hb+rR6Xy8LpdDDsrhmD7VAlSp6qOXXHolN15xSBeuvl8Ptr9HcNSbZ6wRe/uwhgS5GyD5G2uLZxoaznm0PXrFxnSoc/vLUzp+Lz+CLP31jkkHjJB6B5corUuUUolAh8qpfa6vqi11kopr786WuvlwHKAvLw8j33ak2RrtWqvrYQm5yYTHGxg1MA4lFJuK23fOV1mms0W97YsnxS4ebcq65uICAlyqvhrDbERoVTWN7mdz7GSN4YYSIoxkt7fFrYrrzGxbKNN16mlV6yyvonwkCAGJ0eROeDMvEutfYdAmyvKJOG5d1JeY8u9bGg0uy0eXHEk7cMpAeO6RjPZiVFevWn7y2p4flMhD03NxdRsQSnl4Rn+xeWDePm/h+2Vw6PYUVLLoOR+zvcrrjRRVd/szP0EmH9ZNgnRYdSYmhnYP5KfX55DeGgwL246yNkDIsiIb/9c8ZV72lJGozPozXNIDDJB6AZorUvs/5Yrpf4BXACUKaVStNalSqkUoLwj525Pkm1r+2bGR3K4oo6q+iYemDKMh+xeMV96YXERoRiU4oF3d7u9x7HaJmdvvuKTDfxlU6GbPtidl+ew6OpcZ0K04wblEML85RtbuXvyUCbnJrvlvLl6xVJjwympaiAnKYr0/me+Mvf1vQxbOI7dpTVtvjlIwnPPxFXGITEqjAazheLKBjLiw2ls1pitGmOIgQi7Ll94iMFjTmTEh3NeRhyPXzuCiLBgXvlvIXVZCQxNjvbqTfvDjFH2CuJdzL0ki3XbS3hy5ij+/KMxHKtp5FhtIy//97Bz7hQeq+OBKcOoazIzf0I2720rITkmjILyGmcVsVNiBqgxmXnw3V1uc0+hKDxW224DqmXuaUqskZl5aYQEd34QrjfPITHIBCHAKKUiAYPWusb+eBLwEPAucBO2HrA3Ae905Pzt0bLytW9ZtYm9R08ZHnkZsbzykwtobLbQbLXy6DUj+E2LROTF63bxyLQRHjemyvomYsKDGRAVgsFgcHrDHEr91Q3NvP5FEX/+0Rj2l9cyNCWak3bJjMf/aROAdTUSXXPeKuubOCchitR+Rs4/u/8Zr8xPl6hcVt3YrpuD6Ir1PLx5ZBZOzOHLwgomDU9h8Xu7GJQYxaKrc3nlv4UsmpJLjanJzZuVER/O/12a7VTxd6j0v/5lEdFhwV6vicPH65yPgwzwPxdm8PB7u/nfy7JZZF+sODCGGMhKiHJWLDu0+YYnx1BlambhxBxWfXXEwxPnKnuxcGIOP1+91anT1x6Pk+s8jIsI9egd25kerN48h8QgE4TAkwT8Q9liCsHAa1rrDUqpr4A1Sqm5QBEwq0Mnb4eWla99I0KDuOXlr5yr7KtHpvJ1UaXbDefpG87lZEMzxpAgSk7W02TW7C+rYdGUXBavO+XtWjw1l7KqBn69dicZ8eG8dPP5bC6swGKFZz4ucCYBN1s1Vqt2u4ktmJDjbC7u+AH2VxViWxKV65rM7bo5iK5Yz8NX2zDXNknjBiXyl/8U8MtJQ6iubyStfyQPr9vtDBUOSorm129uczvH4nU2z1dqXLjXa6LRfCoRf1BiNKHBBv7nwgyOVZs8PMgPTxvO4xv2uJ3/gXd3kpcZx6U5iSTHGBk9sJ9zDrt+jhU35fHFoROs3Hyq4KC9HifXauBjNY3c9JK7BmBnerB68xySpH5BCDBa60Kt9Sj7X67W+nf27RVa64la6xyt9eVa6xMdOX97Gl372rfJYnX+AF47Js2j8XeTWVNwrJb739nJHa9/w58+OsCcizJI6x/BXzYVOBOW516SxZ//XUBUWAjGEAM3nJ/OtyfqSIkNZ8VnhU5D56GpuZRU1vtMjHb9AXZUIY7NGkBWQlSn5ZG0JVE5o3+k10RrXzeHzmw63ttRSh1WSu1QSm1VSuXbt/VXSn2olDpg//fMRLPagC+PzEmXNklKQVFFA/vLanj234VYrNopE/PMxwXsL6vxeg6loLzaxIIJvpPtH5gyjOKT9RQer+Xbkw08959CTtY38ecfjeGJGSN5atZoosKCuXpUqrM5uOP8RRV1/Gv3UT7YXcaRinqvY6gxmVm2scBpjDm2l9eYOB2uyfWHK+rIjI/EqrXPRUpn0JvnkHjIBKGX0x4tK1/7Hq6oc65KlYJgg/sK1Zt6/tKNB3hhTh5FFQ08+0mB2/s0WzVzL7FVR/788hyazRaWzBxFTaOZ5Fgj73xdzBXDU3wmRnfFD3BbEpWBdgm3iq5Yu+m22nz9IkM8JCWiwoKYc1EGT36wl7snD+HxDXtbzbPUGgbGR3D4WC1PzBhFfZOZGlMzVqvmgSnD2HO0mqc/LqCyvon7pwwjIiSI2WMzfKrtt/TgNjZbOXKinuWbCrl1XJZ3jcHYjnmcfCXXD06K9qsHqzfPIfGQCUIfoD1eJG/7tlyVZiWc8gylxBoZkhzN/12azdM3nss9Vw5m/oRs4iJCqWs0e/UgKaV49hPbjebIiQYeeHc3//faNyx6dxdfHqpk/a4yIkKDvB47cUgik4Ymcbiizi9l745Vv8FeTdry/R39/gwG5bw5rF8wjlXzLmyTvIa/PHp9hGnYNPmw/zvd32/ozSOzcGIOL392yCnr4pCU0BqWbrT1VB2cHMW88TbPsDEkyEMC5v4pw0iKCaWqvpklHx3gjtdt179VQ1ZiFHVNZmdPTFOzlYfX7WZAVJiH13jpRpvXtqUHd+HEHJRBOY03RxVyS89SbkpshzxOvpLrHQsmf3qweuscCoiHrLu0iREEoe2EBivmjc8ie0AkwQblrHy8Pi+dX73pLgr73rYSbvleJjUNzR75LouuzuXFTQedocln/23znrlWUy6aksvLnx3yKu6amxLrU5rjTH+YXVf9cRGhHjID3m4sItzqNxzafBp43i7v0mZtPmAeQHp6+hkNoqVHZkBkGCazhawBkaTHh7PqtrEUn2wg1hhEffOp0H55dZNTkgXgnisHM/eSLMKCDWQnRNJssRIXGe6h4L/kw/3MG5/Fso0FHnmLFu2716Xj8dCUaF66OY8tRSdpaLK4FaO8+rmt/VnuWdEMSY5xepY64nHy5UE+Wm3qtR4sfxPIkGXAXdGCILSNwxV1zH/tG0zNVl677UKqG5pZnX+EX04a4pGs7FDVX/Lhfu6aNIh3t37HkzNGYbFqQoIUJ+oauW38OWhg3bZv+eWkIXx3soFhKdE0mq38atIQlm86yPaSavaW1TJvfBbnDuxHRnykM3zqr7J313OXVplYubnI4/3lxtJl+E2bzxeu8haumnLejO7hqf2c+0fUN3HoeD1D7MLHcRGhJMWEsWBiNlZt08irbbSwbnsJ1+elc+cb25zFMHdPHkpBeS1NFqvTcHL1jDnm04rPCokN9+x+4Qh9Oh4nRIVy5ESDW2szV6NuxWeFrPzJBW6fuyOLitaS69tyPjcpkWgjQQaboPPptPx6M90ph6zL28QIgtA2yqpNDEqM4tbx51BR20jJyQauz0unoNx3srKp2Up0WAjbS6qZ//o3zJ+QzTMf2zwGro8HJfdj7ZZi/ufCdAYlRRETHsyUkSkcq22isr6JIckxfH9QovMH2p9l7y3PXVplE55dNe9C8YB1Mf7U5vNGewVHW3pT51yUwZv5RfzhupGUnGzglpe/Ii4ilJl5adx5xSBqG5t5eNpw5tkrM1NijTbjrEUV7+r8I04DC07lTT40dTgZ8eE8PG2406vmmkPmePzV4UqnMeY43tWou/OKQSxc1TF5C1fOpPG9LykRRy5cbxF6bS+ByiHza5sYQRA6l7P6Gbnxwgx+/eY2YsNDaGiysDr/CBedE+81z8qhqh8RFuy2zdvj+IgQbr44k2c+KWDeq18z79UtWDTc98OhbFjomZPlWJm3fM/OSBr257mFtqOUilRKRTseY9Pm28kpbT44A20+b/jyvB6uqPO5/+Mb9jD3kix+PjEHU7OFH45MpeRkA0s3HiAuIpTZYzNYvqmQX725ncc37ONodSNxEaEAHj1hHYbTPZOH8tbXp1qHGUMMZCdG8+y/D3Cy3kzmgAj+PvdCnrnxXH79g8FkJUTxm6uGOluImcxWrwuWYSnRzBufxUv/zyYme7rPdzo6kj/Z2nftmgt3JuPqyQTKILtEaz0GuBK4XSk13vVFrbXGZrR5oLVerrXO01rnJSQkdMFQBUE4WdfszAN76bNDZCVEcsP56TQ2WzySlRdMyGHd9hIWTsyhuLLeo4y/5ePk2HCe+mi/x4/zvrIarBqPH3h/lr335pL6HkYS8JlSahvwJfC+1noDNpHkK5RSB4DL7c87hdY8r96oqGvk+rx0VnxWyN1v7eDtrSVYNAyMi3AaaY1mi9MAMzVbeeCdnczMs/Wz9NVeydRscRNLXjAhh9+v30NRRQMb95Yz8y+f8z8rvuDbynpe+PQQP311C/vKatykK7wtKhKiwzosb+GLjibX+/quXXPhOksmoycRkJBlV7uiBUHwxFe+jLf9qhvN3DouC7Dlw7z9dQm3fT+L6gYzr39RxNM3nIvJbEGhKD5Zz7TRqaTFhQNw16RBpPeP5L4fDsWgFEerGrjuvDS0hpWbi/jtD4d6/XG2aryGIf1Z9t6bS+p7ElrrQmCUl+0VwER/vGd7BUdDgwyszrc1BY82BhFtDGHxe7uYN/4cZ6uilvlbpmYr5yREuRn8Ld/vaLXJTSzZVcYi1N4M07FomXtJllNSxnGulj1dHYuKxOiwbiOo6uu7dvWc90WvdJd7yALhihYEwR1HDsdVyz7lxhe+4Kpln7Jh11EP+QjHfretzOeZjwt48dNCZo/NYG9ZLT95OZ/+kaHsL6/ltle38MKmQqxA/4hQvnfOAF757yH2Hq3lkff3Mu/VLUSEBvGLNVtZvG4Pz3xcwFtfFzMzL43+dj0nV4whBgwKnz/K/ix7760l9ULrtNc72mSxOj1kNSYLD6/bzZSRqTxs7/EK7mLCjnMmRNmqdwcnRXH/lGFu73fnFYNIjjHy7Yk6wkOC3MSSF07MIcjlWnT1KDlkN4whBkqrTKzOP8JTs0bz4pzznKHE9P7dx/vrS0rE4Tnvq17pQHjI/NomRhCE09PWSkVfavXzxmeR3j+CIcmRPDR1OA+8u5PtJdX8+s1tPDQ1l7VbipgwJJlXPy8CbD+4jhtCy353b+QXc+cVg1jy4X63BN+cpKg++aMsBIb2ekdDgwxOL5Qj/BgWbPAZinNIvhgUPL5hHymxRuZclGEThG00c1ZcOPVNFp79+ACXDknkjfxiZ+slhzf5uvPSnOd1LFrA1h82IiSI+ZdlYzJb0RoeWrebB68e5jafu4v3t+V3nRBlq7I8N71fn/ZKd7lBFghXdF8g8573O3zs4cd+2IkjEXoCreXLOKQlyqpNGJQiLiLUI+9kRGoslw1KJDjYwFXDE8gccAHHahpJiA6jocnMNWPS+eMHe52r+yWzRnP2gEjOHhDp0e+utMrES//vMAsn5pCdGEWMMYSkmDDS+/vnR7mtoVqh7+FLrqHlNZMeF8GJuia3OZQRH05OYpTXUNzgpGjmXpLFX/5TwEPTbNWSU0amUtdkYV9ZDe9tK+FXk4Yw//VvbAuSywexfFOhMxyZEmtkZl4aqf3CmT8hm/e2lXD35KEMS4nm4nPiCTYY+OUbWymqaHB73+RYdw9zRzXz/DFnvI0lc0DfrmTuTrIXgiB0Eb5yOJJjjD7L0V0Ths9JiCI42BZuiAo3kpcRxoZdR5nz1y+dxz1+3UjuudJI/8gwtx/wrIQor/ISj2/Yx6p5F3JhVrzfPnd7pQ0EoeU1kxEfzh0Tcvj2RL1b3tb9U4bx2IY9HvlbCybk8Oj6Pc7502yxcvul2Tzw7imx5MVTc7FYzPzhuhEkxhhRWPnd9BH89u0dbt5k17k1aWgSwcEGMgdEYTZbuWNCDve9fUoO45Hpw8lNie30zy9zxn9I6yRB6IP4ypexWPFaju6oDPOV3+EttHn32u30jwzzmocVKHmJ9kobCELLa2bKyFTue3sna/JPtSIqrTJReKyWoooGpxr+EzNGMm98ljMpH2zXuMWincYY2K7BRe/uwhAUTNGJBha9u5PgoCC0trB89nn8/toRHn1i7167nSOV9c4xBgcbmD4qldXzxvL8j8ewet5Ypo9KdS6aOvPzy5zxH+IhE4Q+iK98mS8OVXgNZeamxPLSzXmk97eFHVsaWO0Vaz0TUUnoeAjFn6KyQs+irddQy2vGkS9WWmViw85S/jBjFA1NZtL7RziNs2c/KSAl1sit4852k7C4a9Jg9pXVer0G95XVOI+zajh8wkRZTRNJMUav++8vqwFwjjs42MCogXGMGti535PMma5DDDJB6KN4y+FIjPYeyjxQXsMznxQ4c8Fa4uu4hCjfVZIdTTA+kxBKe6UNhN5Je64hX9dMXEQok4enOFuHZcSH89DUXKf3KzRY0T8ylHnjs2x6egpCDIogg3e5C2OwwZnof9vKfOe4nrp+tNf9d5RU8/PVW/0ePpQ503VIyFIQBCdBBpzl83CqHD3IoFoNVfg+zvd7dVRe4kxCKCL8KkD7rqGW18x720p4ZPpwZua5K+0XVTRwsr6JeeOzmD8hm4enDefet3awbGMBz3xcwLKNBfx+w14MSvmcYzPz0jzCk4/9c4+HPMaCCTaJiK4IH8qc6TrEQyYIghNHQ21f5fa+QhW+jjs3vV+nV06dSQhFhF8FaN815O2aSY+L4KO9ZR7nsAIWqy2sWdVg9voeaXHhhIcE8fQN51LfZMFgULyw6SC3X5ZNiBfZjKKKBmpMzc65NTgp2q1IoDPCh62Fb2XOdB1ikAmC4CQpxkhlfZOz3B7apqDt6zh/hDXONITS0dJ/wZ2eLB/S3mvI2zWTHBvudo6UWCMxxhD+9JHNw7VwYrbX9yirNtFk1m6VmAsn5jAoKRqN93BmjcnCs58UYAwxMG98lpsMzZnOs7aEb2XOdA0SshQEwUlHFbS7MqwhIZTA09ZOD92VzriGghRufVxn5qXxkItK/5r8Yo/Q5J1XDKLZoj2aii/deIAaUzPpcRE8ft3IVuffyLTYTr32pYqy+yAeMkEQnHRUQbsrwxoSQgk8be300F3pjGuo6EQ9r39R5KyyjAwN9tDWW7m5iBfm5HGyvolgg4GjVQ3EGkO8hjKPVjdSfLKMJR/uY+4lWQQZ4IKz+5MaG+42/wDWd+K1L1WU3QcxyARBcKOjCtpdGdZoq6K6GGqdi+P73V9W0+Nv4u25Xr1dVykxRvaX17Lg9W8AmD/BM0RZWd9EZX0zCsXeo9WsyS/muvPSvIYl+0eG8uMVX2Bqtro1DF+/YBxjswa4jaczr32pouw+SMhSEIReQU8Po3V3XL/fnd9VB0TYNxD4uq5yk2N4aNpwt+rLh6YOdwsnLp6ayx8/2Msdr3/D85sKmXNRBpv2lXuEMh+ZPhyN9mnkdnSMbbn2JQWg+yAeMkEQegU9PYzW3XH9ftduKfZoEdRbb+K+rqv1C8YxfeRZZA2IdHqlkmJP6Y4NSormjx/sdfaXdOSKLZ99HrHGEL53TjylVSaSY43kpsRypLK+w56qM7n2JQWg+yAGmSB0c5RSk4GlQBDwotb6sQAPqVsiuTD+xfX7La0yOVsEjUyNIScputfexE93XeVl9ndu33zwOMs22sKN8ydkuzX7dhzX0GRh/KBEAEa6qOqfSfeKM732pYqyeyAGmSB0Y5RSQcCzwBVAMfCVUupdrfXuwI6s+yG5MP6l5fdbWmVixWeFrO/lHsj2XFct9/V2XHJs53evkGu/d9CrDbLMe94P9BAE4Uy5ACjQWhcCKKVWAdMAMchacKb9MYXW6avfb3s+t+u+a7fYZC8cyvuOXLHclFif79VRT1Vf/b/pbSite27Ca15ens7Pz/f5uhhk/ufwYz8M9BC6DUqpLVrrvE4+5wxgstb6Vvvz2cCFWuv5LvvMA+YBpKenn1dUVNSZQ+hROCrNJBfGP7T3+/XHnGgvp7tPtIX2fG7XfZOijdSYmt1yxYKD/VNLJ9d+z6C1OdHtPGSSLyMI7UNrvRxYDrabT4CHE1AkF8a/9NXvtz2f29u+rrli/qKv/t/0JrqV7IVLvsyVwDDgRqXUsMCOShACSgng+nOeZt8mCIIg9CK6m4dM8mV6GGcSFg5UuLOHjfkrIEcpdTY2Q+wG4EddPQhBEATBv3Q3gywV+NbleTFwoesOrvkyQK1Sap+Pcw0Ajnf6CLsnPfKzqsc7dFhAP+tpxpzR2e+ntTYrpeYD/8IWxv+r1nqXr/23bNlyXCnlK4msR14nHaSvfNbu/jk7fU60F5kTTvrKZ+3un9PnnOhuBtlpcc2XaQ2lVH6gk0m7CvmsvRut9XpgfRv3TfD1Wl/67vrKZ+0rn/NMkDlho6981p78ObtVDhmSLyMIgiAIQh+kuxlkznwZpVQotnyZdwM8JkEQBEEQBL/SrUKW7c2XOQ2nDWv2IuSzCm2hL313feWz9pXP6S/60vfXVz5rj/2cPVoYVhAEQRAEoTfQ3UKWgiAIgiAIfQ4xyARBEARBEAJMrzTIlFKTlVL7lFIFSql7Aj2eM0Ep9VelVLlSaqfLtv5KqQ+VUgfs/8bZtyul1DL7596ulBoTuJG3H6XUQKXUJ0qp3UqpXUqphfbtvfLzdiUyJ3rmNSJzwj/0pvkAMifs23v85+11Bpnqfe2XXgYmt9h2D7BRa50DbLQ/B9tnzrH/zQOe66IxdhZm4Jda62HAWOB2+/9db/28XYLMiR59jcic6GR64XwAmRO9Yk70OoMMl/ZLWusmwNF+qUeitd4EnGixeRrwiv3xK8B0l+0rtY3PgX5KqZQuGWgnoLUu1Vp/bX9cA+zB1r2hV37eLkTmRA+9RmRO+IVeNR9A5gS9ZE70RoPMW/ul1ACNxV8kaa1L7Y+PAkn2x73msyulMoFzgS/oA5/Xz/SF76nXXyMyJzqNvvId9fprpLfNid5okPUptE23pFdplyilooC1wM+11tWur/XGzyt0Lr3xGpE5IZwJvfEa6Y1zojcaZH2h/VKZw+Vq/7fcvr3Hf3alVAi2SfZ3rfVb9s299vN2EX3he+q114jMiU6nr3xHvfYa6a1zojcaZH2h/dK7wE32xzcB77hsn2OvKhkLVLm4cLs9SikFrAD2aK2XuLzUKz9vFyJzoodeIzIn/EJfmA/QS6+RXj0ntNa97g+4CtgPHAR+G+jxnOFneR0oBZqxxb7nAvHYqkgOAB8B/e37KmzVQweBHUBeoMffzs96CTY383Zgq/3vqt76ebv4u5U50QOvEZkTfvtee818sH8emRO94PNK6yRBEARBEIQA0xtDloIgCIIgCD0KMcgEQRAEQRACjBhkgiAIgiAIAUYMMkEQBEEQhAAjBpkgCIIgCEKAEYNMEIQ+i1KqtsXzm5VSz9gfP6iUKlFKbVVK7VRKTQ3MKAWh65A5ETjEIBMEQfDNU1rr0cBM4K9KKfnNFPo6Mif8hHyRgiAIp0FrvQcwAwMCPRZB6A7InOh8ggM9AEEQhAASrpTa6vK8P17a6CilLgSswLEuGpcgBAqZEwFCDDJBEPoyDfbwC2DLlwHyXF7/hVLqx0ANcL2W1iZC70fmRIAQg0wQBME3T2mtnwz0IAShGyFzwk9IDpkgCIIgCEKAEYNMEARBEAQhwCgJ/wqCIAiCIAQW8ZAJgiAIgiAEmB6d1D9gwACdmZkZ6GEIAgBbtmw5rrVOCOQYZE4I3QmZE4LgTmtzokcbZJmZmeTn5wd6GIIAgFKqKNBjkDkhdCdkTgiCO63NCQlZCoIgCIIgBBgxyARBEARBEAJMjw5ZtgWrVXO4oo6yahNJMUYy4yMxGFSghyUIgiAIQi/iTO2NXm2QWa2aDbuOcuearZiarRhDDCyZNZrJuclilAmCIAiC0Cl0hr3Rq0OWhyvqnF8OgKnZyp1rtnK4oi7AIxMEQRAEobfQGfaG3wwypdRApdQnSqndSqldSqmF9u0PKqVKlFJb7X9XuRxzr1KqQCm1Tyn1g46+t9WqKTxWy/6yGueX48DUbKW8xtThzyUIgiAIguBKWbXpjO0Nf4YszcAvtdZfK6WigS1KqQ/tr3k0J1VKDQNuAHKBs4CPlFKDtNaW9rypq9vw1nFZGEMMbl+SMcRAYrTxTD6XIAjtIPOe9zt87OHHftiJIxEEQfAPSTHGM7Y3/OYh01qXaq2/tj+uAfYAqa0cMg1YpbVu1FofAgqAC9r7vq5uw7VbilkwIQdjiO1jOmK6mfGR7f48giAIgiAI3siMj2TJrNFnZG90SVK/UioTOBf4AvgeMF8pNQfIx+ZFq8RmrH3uclgxXgw4pdQ8YB5Aenq6x3u5ug1Lq0y8+nkRcy/JYmRqDDlJ0VJlKQiCIAhCp2IwKCbnJjNkwTjKa0wkRre/ytLvSf1KqShgLfBzrXU18BxwDjAaKAX+2J7zaa2Xa63ztNZ5CQme3QccbkMHpVUmVnxWSE5SNFkJUWKMCYIgCILQ6RgMiqyEKMZmDeiQveFXg0wpFYLNGPu71votAK11mdbaorW2Ai9wKixZAgx0OTzNvq1ddIbbUBAEQRAEoSvxW8hSKaWAFcAerfUSl+0pWutS+9NrgJ32x+8CrymllmBL6s8Bvmzv+3aG21AQBEEQBKEr8WcO2feA2cAOpdRW+7bfADcqpUYDGjgM/BRAa71LKbUG2I2tQvP29lZYOnC4DbMSos7oAwiCIAiCIHQFfjPItNafAd7cUutbOeZ3wO/8NSZBEARBEITuSK9W6hcEQRAEQegJiEEmCF1EILtXCIIgCN2bXt1cXBC6GQHpXiEIgiB0f8RDJghdRKC6VwiCIAjdHzHIBCEAtOheAbbuFduVUn9VSsXZt6UC37oc5rN7hVIqXymVf+zYMX8OWxAEQfATYpAJQhfT1d0rBEEQhO6PGGSC0IUEonuFIAiC0P0Rg0wQuojWule47Naye8UNSqkwpdTZdLB7hSAIgtD9kSpLQeg6Ata9QhAEQejeiEEmCF2EdK8QBHeUUgOBlUAStgXJcq31UqVUf2A1kIltkTJLa11p9zIvBa4C6oGbHZXLQtuxWjWHK+ooqzaRFCP9nrsLYpAJgiAIgcKXNt/NwEat9WNKqXuAe4C7gSuxhe5zgAuxFcRcGJCR91CsVs2GXUe5c81WTM1WjCEGlswazeTcZDHKAozkkAmCIAgBoRVtvmnAK/bdXgGm2x9PA1ZqG58D/VrkYAqn4XBFndMYAzA1W7lzzVYOV9QFeGSCGGSCIAhCwGmhzZektS61v3QUW0gTRJvvjCmrNjmNMQemZivlNaYAjUhwIAaZIAiCEFC8aPM50VprbPllbUa0+XyTFGPEGOJ+6zeGGEiMNgZoRIIDMcgEQRCEgOFNmw8oc4Qi7f+W27eLNt8ZkhkfyZJZo51GmSOHLDM+MsAjEySpXxAEQQgIvrT5sGnw3QQ8Zv/3HZft85VSq7Al81e5hDaFNmAwKCbnJjNkwTjKa0wkRkuVZXdBDDJBEAQhUPjS5nsMWKOUmgsUAbPsr63HJnlRgE324pYuHW0vwWBQZCVEkZUQFeihCC6IQSYIgiAEhFa0+QAmetlfA7f7dVCCECAkh0wQBEEQBCHAiEEmCIIgCIIQYPxmkCmlBiqlPlFK7VZK7VJKLbRv76+U+lApdcD+b5x9u1JKLVNKFSiltiulxvhrbIIgCIIgCN0Jf3rIHC0xhgFjgduVUsOwtcDYqLXOATban4N7S4x52FpiCIIgCIIg9Hr8ZpBJSwxBEARBEIS20SU5ZNISQxAEQRAEwTd+N8ikJYYgCIIgCELr+NUgk5YYgiAIgiAIp8efVZana4kBni0x5tirLcfSS1piWK2awmO1bD54nMJjtVit7XIICoIgCILQB/CnUn+fb4lhtWo27DrKnWu2Ymq2Opu4Ts5Nlr5hgiAIgiA48ZtBJi0x4HBFndMYAzA1W7lzzVaGLBgnPcQEQRAEQXAiSv1+pKza5DTGHJiarZTXmAI0IiGQiFiyIAiC4AsxyPxIUowRY4j7V2wMMZAYbQzQiIQAI2LJgiAIglfEIPMjmfGRLJk12mmUOXLIMuMjAzwyIRCIWLIgCILgC38m9fd5DAbF5NxkhiwYR3mNicRoI5nxkZLQL5ypWLJb9bFSah42Dxrp6en+G7QgCILgN8Qg8zMGgyIrIUqS+AUnLcWSbQoxNrTWWinVbrFkYDlAXl6e6KoIgiD0QCRkKQhdiIglC4IgCN4Qg0wQuggRSxYEQRB8ISFLQeg6+rxYsiAIguAdMcjOAKtVc7iijrJqE0kxkrAvtI6IJQuCIAi+EIOsg0hbJEEQBEEQOgvJIesgvtoiHa6oC/DIBEEQBEHoaYhB1kH83RbJatUUHqtl88HjFB6rxWoVNQNBEARB6K1IyLKDONoiuRplndUWScKhgiAIwpnkKXdVjrPkUnceYpB1EEdbpJZGU2e0RfIVDh2yYJwIzAqCIPQBzmRh3lWLenEedC4SsuwgjrZI6xeMY9W8C1m/YFybJ8rpQpH+DocKgiAI3ZszyVPuqhxnyaXuXMRDdga0ty1SW1cT/gyHCoIgdBeUUn8FpgDlWuvh9m0PArcBx+y7/UZrvd7+2r3AXMACLNBa/6vLB91FuC7MU2KNXDsmDaXgWG3jacOCrS3qOzPK0p73aRnaTIkKY+fRao5WN5IcE8aIlFiMxr5tkvTtT9/FtDUU6c9wqCAIQjfiZeAZYGWL7U9prZ903aCUGgbcAOQCZwEfKaUGaa0tXTHQrsaxMI+LCGX22AyWfXwAU7OVFz8tPG1YsKsW9W19n5bOiEnDBnD50LN44N2dznvcQ1OHM3VESp82yiRk2YW0NRTZ0XCoIAhCT0JrvQk40cbdpwGrtNaNWutD2DpYXOC3wQUYx8J8Zl6a0xiDtoUFHccaQ2y3eH8t6tv6Pi2dEf8z9mynMeb4TA+8u5MdpVWdOr6eRt81RQNAe1Yt7Q2HCoIg9CLmK6XmAPnAL7XWlUAq8LnLPsX2bR4opeYB8wDS09P9PFT/4FiYt7xnwOnDj45jhywYR3mNicRo/1Q/tvV9WjojKuuavX6msurGTh1fT8NvHjKl1F+VUuVKqZ0u2x5USpUopbba/65yee1epVSBUmqfUuoH/hpXIOmqVYsgCEJXcejQoc4+5XPAOcBooBT4Y3tPoLVerrXO01rnJSQkdPLwug6DQZEZH+m8ZzhoS/jRsagfmzWArIQov0VY2vI+DmeEg/6RIV4/U1JMmF/G2FPwZ8jyZWCyl+1Paa1H2/8ciZquuQGTgT8rpYL8OLZOob3irV0RijSbrWz7tpINO0vZ9u1JzGbr6Q8SBEHoIDNmzABg4kSPdqwdQmtdprW2aK2twAucCkuWAANddk2zb+vV9IaFfMvP8LfPD/HQ1OFun+mhqcMZkRIbyGEGHL+FLLXWm5RSmW3c3ZkbABxSSjlyAzb7a3xnSkf1V/wZijSbrby9rYT73j6VKPnI9OFMH5VKcLCkCwqC0PlYrVYeffRR9u/fz5IlS1q+nNTe8ymlUrTWpfan1wCOKMu7wGtKqSXYkvpzgC87OOweQ1eFH/2Jt8+QEhXG2QMiKKtuJEmqLIHA5JCdUW5Ad6E7irfuKq1yGmOOMd339k5yEqMYNTAuIGMSBKF3s2rVKt5++23MZjM1NTUtX251JaiUeh24FBiglCoGFgGXKqVGAxo4DPwUQGu9Sym1BtgNmIHbe2uFpS90OzrodTcFfW/OiPPPjg/YeLojXW2QPQc8jG2iPYwtN+An7TlBd0nWPBP9FX9NjNIq72M6WmVi1EAfBwmCIJwBgwcP5u6772bkyJFceeWVbq89+OCDpT4OA0BrfaOXzSta2f93wO86NNAeSkeiMb1RQb+7GZj+oEvjWJ2RG9BdkjVbJilC6/orVy37lBtf+IKrln3Khl1H/dIsPCU23OuYkmNFUFYQBP8yZswY5s6d6zTKdu/eDTAgoIPqBXREDb+3Keh35X00kHSpQaaUSnF52jI34AalVJhS6mx6QG5AR/VX/DkxclNieGS6e6LkI9OHk9vHEyUFQfA/N998Mz/4wQ/47rvvABg0aBBAYkAH1UbaW6DV0WM6Qkda6QWy/Z4/vpfeZmD6wm8hy76QGxAarJg3PgurBoOyPW9JV7WwAAgONjB9VCo5iVEcrTKRHGskNyVWEvoFQfA7x48fZ9asWfz+978HIDi4ZyRod/eQYEdU9wPVfs9f30tX3kcDiT+rLHt1bsDhijrmv/aNxwW/vkVSf1dPjOBgA6MGxknOmCAIXUpkZCQVFRUoZbvxfv7552DrOdmt6UiBVlcWdXWklV6g2u/563vpK/2de8YSphvhSCzcX1bDreOyWLulmNIqmxvYm8UufSkFB9JIWejNLFmyhKlTp3Lw4EG+973vcezYMYAjgR7X6eiI96UrPTYdkb0IlFSGv76XvnIfFYOsHXhzxy6YkMOrnxdRWmXyarH3Bg0ZodN4GWmkLPRSxowZw3/+8x/27duH1prBgwcTGhraEOhxnY7uEhJ0rSJMjDYSZLBVzjsqCh3GR1m1zQHQ1vtIZ0ll+HrNbLayq7SK0ioTCVFhZMSHU1Rx6r+9MzxZfeU+KgZZO/Dmjl328QHmXpLFis8KfVrs0pdSgN4vliz0XSoqKnjttdfYu3cvAEOHDuWss84K8KjaRncICXpb7C+cmMPKzUVU1jexZNZoQoOVM00mIz6ch6eNICRIeZWA6Oy8OMDra5cPTuTdHd+5iZE/NG04z35ygKKKhk71ZPWF+2ibDDKl1ACt9XF/D6a74hqm9OaOHZkaw/oF43xa7B3RT+kLmiuCkz7fSFnouezZs4cJEybwgx/8gHPPPRetNV999RWPPvooQLdP8ukOIUFvi/2lG22L/Wc/KeDONVuZNz4LU7OVlFgj1+elM+/VfJ/GVmfnxQFeX/vb3As9xMgfeGcnf5t7IWartdd6svxFqwaZUupq4K+AWSllAWZprf/bJSPrJriuGm4dl+XVTZ2TFO3zIu/uFTxCwDljsWSt9XJgOUBeXp7fhHky73nfX6f2y3sefuyHnTgSwRf3338/S5cuZdasWW7b165dy4wZM7p1xxUHHfG+dKbHxlfulb0+AlOzFYd6xLVj0lj28QEP4yh13lhGpPbDYFCnzeXytuBv7Rit8S467uOYitpGfjA8BaF9nM5D9jtgnNZ6r1LqQuAPwPf9P6zug+uqYe2WYhZMyHFOhra4Y/1dweMav0+JDSc3JUZkLnoQWusyx2Ol1AvAOvvTPtlIWeh57NixgzfffNNj+3XXXQcQ3uUD6oH4yklz5H8ZQww41uJKeTeONu4tp+Skicm5ySTFGMmID2fKyFSnUffethISo40+F/yDk6JbzYvz9lqyj3GLGHnHON2d26y13gugtf4CiPb/kLoXrquG0ioTr35exNxLsnj+x2NYv2Dcab1WFXWNzL0ki/kTspk/IZuUWOMZi/o5hPe+OXKCf2wt4frln/Ozv33N9cs38/a2Esxmq7fTCt2Q3iSWLPRNIiNbzQ+SIpQ24E1ofOHEHN76uthpMI1Mi3V73RVjiAGLFadYanpcBHdMyGHFZ4U883EBL35ayB0TckiPi/C54A8y4FPs3JcQ+sizYkWMvBM5nYcsUSl1p6/nWusl/hlW96HlyqW0ysSKzwo99Ma8YbVqvjtp2z8uIpSZeWncecUgKmobSY4xuu3n6j5OifVdweO6uvnDjFHc/440E+8p9AWxZKHvUV5ezpIlnrcCbXPvhHT5gHogLXPSEqJsVZbnpvdz5mEBrF8wjhN1jeQkRnH32u0e1f6uC/eWuV33vb2TMelxPhf8R6tNrebF+XpNxMg7j9MZZC/g7hVr+bzXcybVNIcr6rh77XbiIkKZPTbDLdSZHh9Jen/bOby5j5/50bnOihrX93Rd3TQ0mqWZeA+it4slC32T2267jZqaGl8vH/P1Ql+ktWIt15w0RyrKyfpmwkOCMZutFFc1OI8bnRZHZnwEG/eWY7HiIb3UWpQlKcZIXkYscy7OoqHRTEx4MBW1jdQ3WThcUUdmfGSrzoaWMhoiRt55tGqQaa0Xd9VAuitnUk3jmBS+kjBbq155/45xrHd5T4er2bXSMyIsuFPi95KHJghCR1m0aJHP1x588MFSx2Ol1L1a6993yaC6IW0t1jKbrby9rcTp4cqID+f2y3J44J2dbsdNGppEyUmTT2eBryhLWmw4s87P4NdvbnMet+jqXP704X72l9d6HZMUmnUNrd51lVLLWvvrqkEGGsfKZWzWALISotp8ATrCnb6SMMtrTD5XMsdqTc73zIyP5IM9ZVy17FN2flftjNe/sOkgi6bknlH83jH5JQ9NEAQ/MzPQAwgkbW2Qvau0yi3cOGVkqtMYcz3uSGU9k3OTWb9gHKvmXeiW0+wr5yszPpI9ZdUe51v83i5uHX+Ox5gc+cpfHT7R6ti7qtF6b+d0IcstLo8XY8t5EdqIY1LsO1rd7uoVV2VjX5We20uqCf36CC/dfD5VDc2kdCB+33LySx6aIAh+ok+7UtraVqi0ysSgxChuHX8ODY1mEqLDeCsi1Nmir+Vx3qQ3fOWkfXGogsr6Jq/jaGgyu507LTac/x6q4OsjleQkRhPnYwyZ8ZHiPeskTheyfMXxWCn1c9fnvQF/i686JsWwlGgy4iP5zT92eLiWrVbN49eNdEvQbJmj5qvSc0RqDIOSos9o3KVVPhI87XloIlArCEIn0afdJq4FYimxRq4dk0aQAcJDgrFatfN3NSM+nBsvdA8pOlT7HQaRMcRAQlTrqSmOyE5Lg+mZH53r1QkQHhrsfJwUbeS9naX81uWe5W0MidHGLm203ttpT+ukXjWZuiombjAoMgdEkd4/ktED+7nloQF8sKeMJR/uY+4lWQQZIC+jPxdnxbuNISLUPVfMUem5+raxZ3zBp8SG+8xDk7wBQRA6kT79o+GImDy+YQ/X56U784qXbyp0+11tbNYsfm+Xh2r/vPFZLNtY4DSOgtoYCGlpMC3/z0EWXZ3rfA9HDtmLmw46f+NrTM1OY8zXGByOgy8OVbTJ8yecnj7by7IrrfqWTWMNyuY6DgkyOMfw7CcFgM0YWnFTHsnRRoKCFKVVJprMFg9B2gUTcjhW18jh47VYrDirZ3w1g/XWrNZgUOSmxPDI9OFuvcgceWiy8hEEoRN5I9ADCCQGg2LS0CQSo8P48YovPH5XB98xDqWg+GSDVwMnJzGa+ROy0RpWbi7i3PR+nBUTzvbvqjhabSIlxsiIs2IJDQ1yO7as2kRcRCjXjklzisR+sLOUlbdcQGV9E8kxRvpFhnD2gAiSY4xYrLDnaLXXMaT1C+elm/PIsGuTGQzKL43W+yqna51UwynPWIRSqtrxEqC11jH+HJw/aWs8/0xprWnszLw0r2PYXHiCFz8tdO53y8UZKAWLr84lIiyY4sp6Vn11hJnnDeRAWS1LNx7w8GCBp5xGy2a1k3OTCQ42+NSR6arvSBCEnk9hYSELFy5k8+bNGAwGLrroIoBQx+ta60cDN7rAY7VqPthTxl4fxs6eo9Xc9cY2nr7Be0gxLMjAMx+fWrif1c/I29u/c6u+fGjacKaPPMvNKEuOMTLnogy3+8TCiTkkRIdxQVa8c7+BcZGnbROYGGNkfE6CW4Sksxut92VadXpqraO11jH2v2CXx9E92RiDU/F8V/xh1ftqGnvtmDSs2rvisqNv2NKNB5hzUQYaxdKNB7j7rR386s1tAPzk4rMJMijnJHOc21H50tr7uu5ntWqOVNbTZLGSHGukvsnCkcp6rFbdZd+RIAg9nx/96EfMmjWLo0eP8t133zFz5kyArECPq7vg+E329bvvkDQyGGDhxBwP1f7goFP7Lpk1mvLqJo9qyQfe2cn276rczl1java4TyzdeIAaU7PbfoeOexaPuY7hd9eM4OKz4z3SVRy50t6qPYX20WfFplorC+4MHGXA+8tquHVcFiku2mCmZlvTWG8X/YIJtnYZjv3S4iJ46qP9HpMpMcZIQnQYcRGh3H5ZtrM1U1xEKGXVvuU0XJvVllWb2LDrKLe8/CVfHark+uWfc+MLX3DVsk/ZsOso6XERfv2OBEHoPdTX1zN79myCg4MJDg7mxz/+MfThe0xLHL/J3n73758yjDfybb/7x2uaWLm5yNlyb+4lWazcXITJbHUzeHw19i6rdm/L952Pwq3S6kanPIXZbHXTuHQtHnv2R+eyet5YprXwvLnSUWkowZ0+m0N2JoKvp8NbmNLR2sKhpqy17aJfnX+E52efR0VtE4cr6pz7gEMOw+J1Mh08VktOUrRXV3S0MdijEMBxPtdmtRGhQdzy8lfMvSTLq3Dt+3eMIzRYMW98FlYNBgWhwTLRBEHw5Morr+Sxxx7jhhtuQCnF6tWrAaqUUv0BtNYnAjtCd7q6gtwRcXA1doIMkJ0YzdGT9YQGK26/LJuIsGD+9/tZJEQbqW5oJiIsmBGpUSRGG2m2nPo9T/GSu5URH05CdBgbdpZyVr9wosOCiQn3fi/Y9V0VdY1mzj+7HwfL66kxNbNwYjZr8osprTK1q01gW5CK/dPTZw0ycG9V0Zl4Cxcu+/gAcy/JYsVnp3LDjCEGrs9L54F3dvLItOGUVZuorG8CTrmpo4zeJ1NWQhQGhVdXdF5GHMNSYj3i+q7vu2TWaJosVqfXzJvRd+REnbN9k+t7d9YEFQSh97BmzRoAli9fDjh7WfbHpmep6Ubhy0BUkLvmWjmMnQUTcvjjB3t5aFoud0zI4b63dxIXEcqcizJ41GVsD00dzi/f2EpRRYNzrBNyEnho2nBn2DIjPpzbL81hzl+/dPvN/+eOUhZOzHFbuDscBKn9wrDqTO5721PeorK+iUevGdEpERGp2G8bfjPIlFJ/BaYA5Vrr4fZt/YHVQCa2RsqztNaVSikFLAWuAuqBm7XWX3f0vQNhiTves6KukZP1zV4NnIz+4cy/LJushChm5qVhscKGnaXccH46zRZbztaKm/KwWjUnG8yUV5swhigevWaEm4bZggk5PL5hD3dNGuLxPnERoTSarby/o5SU2DDWzb+Y43XNDIgMw2S2cPaASFJijQxNimFPWQ3GEANx4SFejb7I0GAGJUYxblCiM9S5dkuxUwxQVjuCIHz11VcMHDiQQ4cOAfDKK6+wdu1aMjMzefrpp3drrc8N8BA9CEQFuWtUpqzaRERoEM0WK5OHJ6M1/PTVrzE121rttVxoP/DuTlv48JMC51jXLxjH9JFnkTUgkrJqEwnRYU5jzHHc0o0HnCHPJ2eMYm9ZDVrbel8C3DFxED99dYvHMU/MGIUChqfGdMrvulTstw1/esheBp4BVrpsuwfYqLV+TCl1j/353cCVQI7970LgOfu/7SYQlrjjPR36Mo1mi1cDp+hEA89+UsD8Cdk883EBKbFGfjY+i/pmC//32tdeVyi/mz6CyDADcy/JQimck6m0yoRBuav8p8TaqmluXZnvtrK6angCmwoq3b6TR6YPZ9WXRTx09TBCQoI8VlB3TRrMr9Zu42ffz+Yv/ylwrswWTswhJdYoqx1BEAD46U9/ykcffQTApk2buPfee3n66afZunUrQEZrxwZq4d7ZFeRtdQL4ispsPnjcOR5fEYtoYxC3X5btXBwfKK/lRF0jAyJDabZYOVbT6PW49P7hAOwtq+HFTwsxNVtZcNnZfC8nifIa79/DgfIaxqTHkdYvwrn9THoed9eK/e4WRvVbwqXWehPQMmdgGuBQ+38FmO6yfaW28TnQTymV0pH3bWu/sM7E8Z5TRqay7OMDrMn3TNr8/TUjiDUGMX9CNuEhBowhBq4dk0ZFfZPXsKOjGvK3b+/AbIEVnxXyzMcFPPtJgTMP7ciJeu68YpDzfWbmeV9Z7T7q+Z3c9/ZOLsxKIMoYwr1v7XBLIp03PouB/SMoqmhg8Xu7mDIy1W1sJ+uau/w7FgShe2KxWOjfvz8Aq1evZt68eVx33XU8/PDDAKcryX4ZmNxim2PhngNstD8H94X7PGwL9w7RmRXkjgX5Vcs+dSuKak8/x5bjaTm2jPhwoo0hzvvAi58WUlHbyC/f2MbmQ5X8fv0eQoINLJh4qsArJdZ2zpKTDcwem8HnB49x/5Rh3Pq9gaT1j+aml75kX1mt1+9h9MB+PPPxfj7YU4bVqs+453F3rNjvjP+3zqarK2CStNal9sdHgST741TgW5f9iu3bPFBKzVNK5Sul8o8dO+bxemuWuL+oqGtk7iVZpMeF26pXXJI250/IZvns8/jTxv08+s99vLethEGJ0TwyfThDU6KJCA3yvqqJC2f+hGxuHZdFeJiBR69xbyK+6OpclAJjsIElM0fx9I3nMiwlxkfVTaPPSs8ak9k55mc/KeCZjwtYtrGAk/ZcNtfKTMfz0gB8xyANbAWhO2KxWDCbbX0QN27cyIQJE1xfbtXdEKiFe2dW2bfFCXC63y7X8azdUuwhe7FoSi4Pr9vt9h4PrdvNlJGpLH5vF7dPyOFIRT3LN50y2OZclMG9k4fwRn4xH+89yi8nDSEs2MCk4amsyS/yWfH5yPThvPrfw+QXVTk/h6+ex7tK3SU2uuL77iwC4bw5HQFL6tdaa6VUu++oWuvlwHKAvLw8j+O7WjXYatV8d9KWoOkqpucwcIwhBgwqi6KKBlJijVyfl84dq75xhvoemDKMjPhwiioa3MZbUtXAMx8XOEuio8OCue+qIRytacKgoKnZwvrtpUwensKj/7T1PFs4MdvrZ4+PCuXFNwu9VnpGhHkvGnDta6ZdvmVjiMFnuyV/rnYkKVQQuic33ngj3//+9xkwYADh4eGMGzcOgIKCAgBLB07Z3oV7KS1QSs3D5kUjPT3d4w06s8r+dOE4b79dj14zgjHp/Ujvb3vPluNJijbyvXPiKa2yqezvKKnyKWNkarYSYlA8+cE+j0jL/MuySYgK5box6dzy8lfO9180JZcmcxHbS6qdzoMhyVHEhIfwx3/tY3tJtdvn8JUX7eh5fDr8qWrQUbpjGLWrPWRljhWN/d9y+/YSwPW/Nc2+rd10tSV+uKLO2Rjc22rj0WtGOPVlrh2T5iEv8dC63dx75VAPEUDHMaZmKw+v203BsVpiIsJ45uMCdnx7krMTIrlt/DkMT43horNt4YI1+Z4rq4em5nK8poFbx2XRaLYw56IMp4dt3fYSXthk62vW0vv24qaDZMSH89z/jCE8xMD8CdlkxIezZNZoclNiuny10x1XM4IgwG9/+1v++Mc/cvPNN/PZZ5+hlKN1mxXgyJmcW9tKNTu0cNda52mt8xISErzu01naWUkxRluFo4seZEZ8uHOB6iq4Crbfrt/8YwdvfVPiFiIzm62cqGvieG0TJ+qaGJIUwxXDkgkLMZAU6z3kNzgpmoz4cGfFvCumZivhIUHcOv4cFq9z7425eN0ubh1/jnPfIAOEBBkoPFbLlSNSPD5HSmy418+YHNv2RXh30yrrjmHUrvaQvQvcBDxm//cdl+3zlVKrsCXzV7mskNpFa5Z4RxL4TneMq5XtGqo8LyOW+MgwTtQ1MSsvjTX5xV6TNeMiQokIDWLx1FwiQoMJDzFw/zu7nFpkYDsm2GCgocnMZYMGcNXIs5hnr4wxhhh4eNpwAD7Zf5yVm4t4cU4elfXNJEaHcbKhkYWrtrntu3BiDq9/UcQj00cQEqRIjjHy97kXUmrvhxYfFUpOYiQlJ038799PFRs8ft1IJg1NIjjY0OWrne64mhEEwcbYsWM9tg0aNAhsyfftpUwplaK1LvXXwr0zSY+LcEpWuBZNpcdFYLVq9pR6tkqKiwglJzGavUerSe0XzuCEKN7dWerWBukPM0YSpAz88o2txEWEepWuePKDvdx+aTZhQQavUYuzB0RSY/Lu3TJbrM5CsJZalmu3FFNZ3+T2OW6/LMejTdPQpJ7bsKc7tnzyp+zF68ClwAClVDGwCJshtkYpNRcoAmbZd1+PrXKmANsEvuVM3ttbJUtHQl5tOaZliLS0ysS67SUMSori//7+udtFbtXaa1Wkq3F1/5RhHuKrxhADOYm2FcVPxmVxm72KEmwT6/53dvLCnDw+2X+cyvomvjx8gmUbC1gwMZvlmwo99p17SRb7y2uJMQYzIrWfs0J0yshU9pfVcH5Gf9LiwvnxCvcS6rvXbmdEaqxzdeMPDTdfSANbQegz+H3h3pkcqaz3ml81Jj0OgAPlNV5/93/1pm2hvHxTIY9eM4JnP3GPnhSU1zp/v0urTPxzRynLZ59HaZWJiNBgXth0kKKKBh54dxcvzD6Ph6cN534Xg2nBhBwWr9vFEzNGef3tTIsL57HrRniVvXBIbLh+Dm9tmvIy4nrsgrg7hlH9ZpBprW/08dJEL/tq4HZ/jQU6poPSlmO8WdkPTxvBvFfzPS7ypTecy/1ThjmTM71VRT68bjdPzRrNL1qo/D+2YQ83XZRJev8IpwQG2HTBSqtM1Jia3SQzAKzae/l0kAEemT6c3JRYDlfUOeU6HOFU2wpvBHERoR6eukB5pLrjaqa9BFKbTxC6I4FcuHcWrXnvtYbt3550M5a8/e7/5h87mH9ZNg0uRVSuBV8psUYmD09xW7wvmJDDsdomSqtMnGwwExqs3OSRgpWFP1w3irqmZhZNyXWGLR05ZHVNZozB3ovKXFvs7S+rITTYQFxEKNeOSTutJmV6XARHKuspqzaREmvEYsWWF9cNZCVa0tWOhdPRZ5T6OxLyau0Y14twWEo0798xjmO1Nivb13E7SqrYtK+cJTNHYQWCDcpnCPOZH51LeEgQZdWNlFY10GTWpMZFUGUys+KzQrdJuTr/CNHGEP78ozH89u2dbkaUt5XR+JwERqf1IzjYQFm1ySnX4b7C28G88Vks21jgdmxCVGA8Ut1xNdMBXiYA2nyC0F3pbgv3jtCa9z7IAJOGp/DMJwecrZIGJUZ7vT8kxxrdwp6uBV/e8o9du78EGxTRxjBWfGYTEP/N5Gyyk/rx/w4eJycxmk/2lfKHGaNoaDITHhrMyv8W8uvJQxkQFeZ17K4t9naUVBNrDPIa2kyK9tSkfGT6cJ7++ABNZu1xjBRitU6fafzakQQ+X8ckRBnd9EsmL/2UfWU1XJAZT1aCreeYt+PiI0KYOjqVO9/YxvzXvmFPabXbfg5X9gPv7mT/0VrmvpLPnWu28fTHBdzyvUxijcH81q7YD6cm5T2Th3L0ZAPbik86Wy8BXsunl8wazZj0OKegX0qskaHJ0dw6LsupXeM496CkaI9ig0MVgZOb6G5Joe0lUCX+giD4j9YKyU7WNbP4vV0UVdhEwZdtLHCGMF1x6Eq2LPh6eNoIjCEGwoINXo24cLsB9OGuUiLDbELgGfHhDIiJ5P9e+5o38os5UF7DVSPS+K6ynj99dIBfv7mNWXnpnBUX5nXsCyfm8NbXxc4F/1tfF9Nk0V71MmtMnpqU9729kykjU712HJBCrNbpMx6yjoS8fB0TZKDVUGaQAY8EzIUTc0iLi3BKXoCtKtJbCNNbs+8lH+7nyRmjvE7KRrOF43VNzipLx/tW1jcRGRrE6nljaWi2eHiVrFbN7tIa7nrzVNK/Qxqjsr6J9LhwZ2NxrXF2D5Belp2K30v8BUHwH615771pNq7JL+aR6cPdvGGPXjOCJ/61z20/U7OVkCDFC3Py0C3yj8FmPI3JiKOgrIZLhyZx4wtfclVuEn+4bhQ3vfQlcRGhzB6b4ZaK8vC04Zw9IJL3txWTHh9BWr8ot7EnRNm8emcPiOTQ8TosVs1156WREBXm9d7zXZX3aJBryLPla1KI5Zs+Y5B1JOTl7Zj0uAi2HKnk1nG2PrmOHC5bvlUD+8tqiAkP5p87Sp3x/MjQICxWTaPZyq3jspzHlFaZMKCdRk9qbLjzYvZ2Idc1mb1OyuLKBkxmW+Lnys1F/PlHYzhe20hKv3AKymoorTIxKCna4/M6cuRccwMazRZuuTiDtP6RNFqsbiFLB6ebUN2hHUV3GEN78Zc2nyAI/qVlLpJDCDbIoFg4MZs1+cX2lkNGZualERcZwt/mXkiT2UpEaBCNZotbdANsv+0WqyYs2MCxmkYWTMhxM64WTMihxmTm3W0lTByazEVn92fcoEQOV9RharZ6DXM6CsB2fFfN7Ivde71rbWvblN7f5qQ4cqLeubj3pXHp6AbgLeSplPeUGSnE8k2fMcigYwl8rsd4q7pcMCGHDTtLmXFeGl8cOoFVQ5CCGeel8dx/CgG4+eJMln5yaiL94vJBvL/9O6aOPotoYyiJMeE8tG4Xs/IGurmOW17I3ibl/VOG8Y+vv+XCLJvWTmV9E1uLT/JGfvFp4/dl1Savq6hHpo9g0tAkiirqvI5jQGSYz++rOwi4docxtIMeVeIvCELrePv9WTgxh3/uKOXKESluv8kPTR3OmvwimsyaRVfnsvi9U4n3d14xiIPltfz1v4e4Z/JQVucfcUvaX51/hGmjU7luTDprvz7CnZMGs+3bKkzNtl7Kvhb2Xx0+wazzMzAo+OpwBd+dNDm1NB2/lTmJUSz5cL9bNKdl1MemSRnrEUVyzSHzdkxPKsTqapTWPXdBnZeXp/Pz87vs/Q6W1/LDpz/1MFBe+cn5fF100iNEOXpgLI1mzc/+tsXjmKdvONdNsf931wzHoBRHq0ys+uqIR9Wja/XktWPSCDJAdmI0f/xgL7dfloOp2UxFbRPxkaE8959Crh2T5kz+dxvrLReQEB3mLEp4e2uJmzSG637GYAP5Ryp54l/73H4kLj4nnmH2Cs2WHqjCY7VctczzO+rKMGegxqCU2qK1zjvNPpnAOpcqyyeACpek/v5a618rpX4IzMdWVXYhsExrfcHpxuDPOZF5z/t+Oa+/OPzYDwM9hD5PW+aEv+nq+4Sv35+Xbj7fqZbvuv0PM0ax72gN67aXMPO8gWQnRrG7tJrwkCBnCsu67SVu94SM+HDunjyUgvJaLFYrI9NiCQ8J5vNDFUSEBqFQ1DeZed7Lb7ujEODJGaPYc7TG633i6RvP5baVW9w+V0qskd9NH87W4pNMHJLIiNR+bvqerlGkI5X1lNeYSI6xVVk6Ct56QqTC37Q2J/qUh+xM8CXwZ2q2UmeyeE14fHLGKBrNFq/HVLmI9ZmarRTZ+5A5wocGAzw5YxRBQQo0HK9tpLK+CVOzlRWf2dog/X79HkqrTDzwzk7mX5bN85sK+cXlgwB8ro4+LTjOi58WsmTWaCYNTfJZ8fNpwXHe21bC3T8YYstdM1tIjA6jxmSm2tTMx/vKmP/aN24rn8m5yd1CwNXXGMqqA5u70BtK/AVBsOGaFuEq79DQ7P03/3htk9ftDU1mlIImsyY6LJjQIANWDWlxEcRFhKIUZMVHMjo9lud/fB5mq5WK2iZnusnMvDRqGy1EhQVTXFHH+l1l3Dt5CENT+vHQ1FweePeU182RI2xqtoKCwUlRXscUFuwpNFtZ30R9k4XM+Ehiw0M8vg+Hb8dbJOqcRMkZawtikHnBW/7R4Yo6D4E/sK0m6pq8T8C6RjMp/bzH2PtHhLq/p10zzNED08FT14/iDxtsyZ5zL8liUFIU+8tqnT0pHe/lSLp86qP9zL0ky/k+3mL7pmYrj2/YQ2o/I4kxvsuebzg/nSKXPALHhP7Dv45ww/npTp0y16KG7iDgGhHqvT9nRGhQl43BG72hxF8QBPewZFxEqFt6yG+uHMyCidk4itEdqvfg/Tc5Osx2G77l4gzqmy3832unuqMsnJhDalwYA+PC+cnLNm3LuycPZunGA17TTR6amgvAX/97iDsm5PDsvwucchtDkmP4y78LnL2MI0KCqGuyeO2l3NBs9VqYFhps4Hfr91BZ3+Rc1H+wp6ynpId0e8Qga4HVqvl4Xxnbi6uc+WAj0mKJDQ9hTX4xd00a7Gzi6sjhCg/x3rbiWG0jZ/Uzer2wzS2kI4J8JEAeOl7HzLw03sgvJsgACVGhHDwG152XBsCmfeVcOiSRaGMwT994Li9sOkh4iIEgg+LJmSNBK+oazUQagwFNcWUD91w5mLDgIK5f/rnXlhz3TxlGjamZoSkxHirODu2bpRsP8IcZo9hfVgOcEgm8IDO+0wVc25ug32SxeE2AbbZYfR4jCILQVlxFw13lHVJijWiUMw3E8XsfERLEC/+fvTOPj6o6///7TJLJZE/ITkKCMWFLIIARsQVUsFYtAiqu/WlrsXQRwVpbra1ScF+qX1Fai7u2VrRYF0SqghZt3QDZt4RAMCEkIYTsk0lmzu+PmXuZ5U4WyDJJzvv14sUsd+49M7nn3uc8y+fZsN/nurT4klyONbawalMp91+ap7eqgxORFu9Q55Bws9+k/bvf2cnzPz6T1LgIvYpTW+Br4cp9lQ0smpHD79/aQU2TjcUzc3l6QxEl1c36tXL5+kIun5iuF5yZXMVpi9850dbv1te3sHL+5C4Lriv8owwyLw4da6SwosFnQk3NSSAtNpT4CDPzp2UxMjkKc5CJA0cbOOxwsHR2Hne/vUN3IWclRFLd0AJCEmEO8jmxg0wnDDDNY3b3zDEsdUlguLuXf33BCBZMz2bFhv2kxoR5jG3xJbk8/Z8ilrkm0+JLcp1NwD/Yy+VnZHgkiS6+JJc125yJpQ+t3aN75F7+vIT507IYOzSGNofkwbW7KaluZuGMbL8lzdZWB0WV9Ty1vkj/jVKiLd0u4HoyCfrxEaGGCbAX5qWc1BgU/ZNTyXlT+WeK9nBPi3BPD7lsYjqPf7TPx6hacF4228rqqGqw6ZEOIQTPbtjPj74znD/8YLTfBuHeoc5IS3C7SfvVDS0EmYzfG50axfxpWbz8+YkIy5LVO3l4bj4C2HOkXo++VDU4c5HHpEaREBnKLSu3+HRuKfcje6GkLU4OZZB5UVHXYpgPNjEjjl9fMIo73tzGzHFpRIYGsau83i3p8gCLZuQQHRai64ppFSfxkaEMiQyltKaJJpud2Agzf/vc2YQ8NNjE8IQIDh9vYkRKpIful6YHZg4O4g9vOXtQagaWNrYl7+7U+45pzx+/cjzXfyeL37r0xdy3fXhuPs0tbR6TqLzWyrJ1Ray47gy9ZRM4w6j+wpnOkmw8fqMLxjgNnu5sR3EyLa+Gx0dw+4Wj+3WbJYVCEbh4p2Zkxocxc1waGXFhHtJG4LxmWdtO9Dp+7rNi/ZpdkBnD0NgwqhpaSAg3Th9JivJ8PTjIuQDWqimN0kPCzEE8dc0Eio82YrM79LBpXHiIj5SRtrguyIwjf1gMceHBHG9uA+DdrWVcMGYCUZYQappsjEuL5sZpp2Nrs5MWG0abw+4h6+E+BkXXUQaZF422NkOLv87aCqBXuoxKydcNtyhLEDPHpZEeF643jNU+pxlSz31WzF0zxyAl/OmDvVx9ZobHKsUSYuKJqyeQHG3xMOjumjmGw8ebPDxT3mMLDTZ5PG9zSBwOaXhhkFIa5rVlxocRaQn20FdbtanUMPS3cuMh3XvnftyqBmu3J2+eTJHAAGmzpFAoAhR30fANeyv5+TnZHtEI7fqo5Wtplx73ivmCzBiuLMjkhhe/Ji7czC/OyfJJH1k4PYdVm0o8kvNLjjYQHhJEkMBDWFyT0Xjk37uZPirFp0o/PS6MoTFhhkbc6JRoHv33Xl9Zjtl5jE6OJjjYxF+vm8jh4y36Qt8S4uyJ+d+iKq4/O1MXDleL35NHGWR45ij56+1VVNnAmKHR+kludVXSpMZYiLKE8H8fFXLj1Kx2Q3z3rN7FvClZlFQ388S6Qr1XpDbx7l+zi5985zQWnJeNtc2BSUBdc6u+EtLG4j227KRIUmMs+uQvrKz32K/7hUEIwbJ1+1gyK5fFrgmeGR/GL8/N5ieuPAX3z63ceIhH5+ZTfLSRrIQIzCEmll09gYWvfePTM7Mn+lyebJFAoDWNVSgUAwf3RV9VfQs/euErw1zb5z5zVr4LJC/8uIBhceFY2+xkJUQQF2Hm+ue/0vPB7n9/D3HhZh6em09RZT12B6zdUc7UEUk0trTx4g2TqLO2Ygk2cdfbO7jijGEkm4P463VncLS+hWFDnA4Bo97ET6wrZPWCKWQadJ+5a+YY/vJJEVNHJPlEh+5+ewdnZMQhBIQFB7PkXc8ctyWrd/LYFfnsqajn7kvGMDQmjNzUaLX4PUkGvUHmnaOUGR/m09bi1u+N4IX/HuRHZw/XT0Ytjn/ZxHR9hQL+Q3xwwjhLjbFw2cR0RiRF8ecfTiDIZKL0WBO3Xziah1z5W1q4s765ldMTI/VcMe8V0cLpOTy0djd3Xjyaosp6XYdMO577hWHxzFye3bCfqSOS+LOr+kYIGJEc5RPeXLbeaTCGhQRxn0teQ/s+r8+fzNVnZvgUKgT1QGfUk2l5pVAoFL2FJkfkjrXVQcaQMOZNyeLF/x2kvNbKa/PPYnh8BDvLa5FAZX2LLnM0IimCR+bmc+BoIwJ4Y2MpgGHroyOtbdw4JYuGljZuf3O7/t7Dl4+jpLrZbyTlYHUjRVUNZAwJ470FUyivs1JnbePA0QaqGmx+P7f7SB23vbGVJZfkGr7f6pAeec3uOb5tbQ52lte6uhQ4jbXg4EHTQrvLDHqDzDtHqaS6mSfXF/K3eWdxtKGFIeFm/vJJEZdNTCctLkzPFbC12lkyK5ey4836ZzfsrfRRW3YP7VlCTFiCTT6TzH2byyamMyolkuiwEP7+xQEmZMRTfryZT/ZW8usLRmEOEh7J6pr3a29FPc9+ekKHTMPa6mBUSiQPz83n2Q372VZWx7SRSdjaTlR5eueUaZ8bkxrNknd3+SRyHq618v72cmc+mq2N2HAz3x5rZM+RehwSXRiws1WR7VVRqvCjQqEINNwX8jdPN24rVFXf4lHhmBARyltby/TF/orrz+D6szP9CoHbHVJ/TVvEf1vTxIjkKMJDgnR5DHBel/dXNbQbSWm1S5au3kVabChXFmRy9zs7PO5BJpPx5/ZV1GNtdRAeaiwnFCSEYY5vRly4x/fVnAxz8tOUUeaHQf+rGOUolVQ3s6Gwip//bTO/WbWVC/JSee6zYp7ZsJ+fn5PNc58V8+DavbS02hmRHKVPgqkjknj6P07P063fG8FjV45n5cZDerjQ6UUSPu7kZesLuWxiOuW1VlZvKyMmLIRNJTVcM2k449KjeXdbGWeeFs9v/7mV/VUNPPdZMU+tL2L5xyc0ZTR9scc/2sdlE9P172IJMREbbua3/9zKtrI6UmMs5KZG8+sLRhBscuaKHa5t1r+D++dSYyyG/dWGxli4aGwqv/3nVv7vo0Iqapups7ax43AdH+06wtvbDnPxsk+55pkvuXjZp6zdeQSHw7gjhHZha297Lfw4OSuBrMRIZYwpFIo+xX0hbxKCRTNyPIyhRTNyMAcJ/fljV47H2mbXjROAQ9WNPLGukKvPzMDaZufGqVksmJ5NXLiZJ9YVkhgVqhtj103O5LnPilm2rojb3tjKMQOv3OsbS7lr5hje3VrGwume41k4PYcH1+7msonpXP+dLN0YgxP3oDa79Pke9186VvfWPbNhP4tn5nq8v2RWLis27PcYh5bju7O81uP7ajnVO8tru/3vMVAY9B4yfzlKWgXhzHFpusfrgtwU/bF7zF8LcQrhNOa0VZG2qskYEkZkaDCHjjXpGjLuaKFMLZdrvkv7y7miGMst54/gxpc3eUz+174+xMxxaQSZYHRKNH/5pEjflxY61C4Mh4816sn4VxVk6JWU2kRdu6O8033KHrtyPFGWEH3b68/OpNFm113Wt184kt//a7vPimnkzVMNE/5PpopSoVAo+hL3hXyjzc6qTaUekYuXPy/h/kvzeG3+WbpX/4NdRzyu/ceb24gLNxMdFuKTyP/KFyWkuO5Nmt6YFt50Nu0O8rlv1TTZqGtuZensPJptdh6em8/Bo420tDn0SIoQ/iMimfHhmIODuO2CEaTGhDE6NRqTQF+Ubyurg69KeHRuPg4pSYgKxYRkX2WDx760HF/Ns+Z9nCO1VvKHoTBg0BtkRjlK7iFEIdAnQnpcOCOSIrlx2uk4HJLHr8ynqr6FjPgwHp2bT7jZc5JoJc7zp2Vhd8Dyj4tYNMPYvT1xWCxnDo/zEWL9w1vbefr/naGPYUi4mYaWNhbOyOH3//J0OVc12KhpsnFGZhy3fm+ELqeREhtOUqzkzotGs2ilp/Gj5Zi9/HkJz1xfQJvdQcaQCE5LiPAbLvzyQLW+D+/KUn8G56FjjYYGWSC0WlIoFIMPf6kSnRGi9l7I1zTZPDqsWEJMDIkw02SzYxJw4Ggj0ZZgPeVFCBiZHMVPp57mkYOsXZMfnZuPxexcUDe32n1U+TPjw3TtS/d7wDeHqskfFkudvY39lfU+chQjk6OICTMOPUoEP3tlE5YQEyvnT2Z4fASHjjVy/6VjudO1yN5X2UCttZUVG/ZTUt1MaozFRyz9/kvHYhKQHmdc0ZkSEziSGF0VHe9pBr1B5m10hIUEeVQQJkSE6G0x7rx4FNeclelR9vvAZWMpOdrMXS5R2N98f6RPM25LsIm//KdYF4D1zjO7a+YYfv/WDi4/I93QOHFIh0drDs3z5d66yD0Jv7iygWCT8Bjnohk5pMWG+fXO1TTZSI22kJ0c5fP7eFcrul+MmrxWW/7yDMLNxqdaILRaUigUgwt/gtOdbQXkvpBftanUJ8Jw75w8Fr72DbY2qV+7RyRFcsv5IzhwtBGHhKLKerKTjHtJNtnaqGkUfFVczRVnDuOKgnSPfLKZ49KorLOy4rozqG1upaS6iS3fVnP+6KEe1fKaxEZNk427Zo7h/jW7MQcLls7K88ghWzIrl39t+laXuhiZGOXRGmr+tCxOT4zk8PFmVn51SC/qSow0Ex/p7PaSGBnKoZomHvn3XmqabPzpivE8Mnccv/nnNo/fJTc1prf/3IacjOh4TzPoDTLwNDocDukhKpoSHcYtrscJEaHc+oZnNeKBo416uA4gWAgPVf7UGAvJMaFcMymDljYHz//vAFefmeHh3m60tlJea/XbPiksJNhQrFYTF9ReS48NIzTEREWt1bDs+dEr8g33bxI4hQbb7J36vdwvRkcbWzz2WXa8ybBVVHJ0aIf7cp8UqopSoVD0FP5SJTrbCsh7IZ8SbeGCMSlUNZxY1JdUN3PTedn6tfDSCWkcqbV6VCQ+ftV4w2tyybFmnvtsJ3fNHMPTn+zn+u8M98gn8y4AyE+PYdLwIVzvJb/xxLpC/nztRFraHPzF1ccSYPknJ1rfSQl//qSI2y4YxZi0WJZ/XMjI5Ej9d9CEwzXP2YSMWI/v++MXvna109vh8T1+/cYWVi+Ywsr5kzlSayUlxkJuakzAJPQHYrpMnxhkQoiDQD1gB9qklAVCiCHASmA4cBC4UkpZ0xfjG5kcxZ9/OJGI0GCqG06UJtvs0mc1ozUFB2eF5AOulkQaFpduV0iQICshitGpo6iotfLmZmdMPzM+jEfm5vPkNRNIjQllVEq0R47XvXPy/JZVCzcj3hJiIn1IOF8UVZKbFme4vd3h4L5Lx+o5Xpp3ztZqx9rm4GB1E+Hm4A7dtu4Xo2ONLQy7fBy/XeVcBf3jq0P8cdYYVlx3BscaWxkSEUJwEGQMMTawOlNFGWhuZYVC0b/xlyrRlVZARtGD05Mi+Xz/UWIsISy7ZhQOh+SRufm8uelbspMj+fpgjYf49oPv7+beOWP5w1snrslayoy19YR2ZUWd1SOfzNvo+ut1Z1BZ32LsbWu1c997uz2q5Uuqm9lX4Wx9p7G3ol5f4Bv9DnGudBQpnfe90xIi2H2kTr8XGR27qqGF5GgLTTY7UZaQHr1ud/U+EYjpMn3pITtPSnnU7fkdwDop5YNCiDtcz2/vzQEZuTBfmTdJdzk/MtfXw+Tu1TI6KePCzbrn6Ta3EOKvzh/Bh7vKufyMDF0cUNOZufOiUVQ22DAJaLbZGR4f7tezpT2+a+YYfvfmNn55bjYHjjb6KV9uZPW2Mp7+f2ewo6yW5lYHK786xIV5qR4rrs64bb29inlpMVTWW0mNsfDVgRruetuz1NnhkH73156IayC6lRUKRf/GX6pEasypp1AMjbX4pLY8dPlYjtS2eHjHNMPraIOV/7tyPC12B/sqGvQEfDix8H5947csviSX8tpmQyPiyHErERbjdJGkqFDMwYKbzsvWF/Hvbi3T9TG17bTnRr/DuLRofnFuNv/dfxSHdH7+9gtH69tpn/M+dqtdcvGyT3v82n0y94lATJcJDN+hk9nAS67HLwFzensARi7M7aW1usvZqOx3SLiZpbPzPF5z54qCdIoqG3xCjo9/tI+bZ4zw6U1519s7iI0I5an1RSxbV8R9a3YD+JQj3/q9EUzOGsLCGdlOeY2vDlFS3czid3YigdsuGOlT9vzm5lJKqpv5+d82kTs0muc+K2bqiCSfFdetr2/hYHVjp383d1mK402tujGm7e9USp39uZW7Mj6FQqFwR0uVcL9GuleWe7/elRSK442tPtf1/VWNPtfFZesLuaIgnXqrnaKqBopdkkbeHVAKMuP4zfdH8cGOckalRPncYywhJqoaWljxn/0svsTz/rR0Vh5jUiK4eXqOLpf07KfF3HReDl8WV+nbLZrhvD8Y/Q6pMRaumuSszl+2zvn5qwoyeGjtbuIjnPc/I6mNhy4fx11v+1bc98S1+2TuE/7Ogb5Ml+krD5kEPhBCSOCvUsoVQLKUstz1/hEg2eiDQoj5wHyAjIyMLh3U26XpLWBa3diix+kvm5hOcrSZ4fER3HJ+Dmmx4ZTVNJMYZeapaydgCQ6i0WZn75F6RqREsmhGDkLA3TPHsNRNST8rIZKiqga/qxqj15ttbR7PDx+38rKrGbmWd/bCfw9y98wxpMWE6UrL4PTIpceFc+BoI8uuHo9EsK201mfVZQkJYuX8yXxbY7ziMnLbdsYl7M/lf7KlzoHoVlYoFP2b9lIlOitEbXQ9BCgz8GIFm0yG17GMuHD+9OE+Lj8j3bB38N0zx7C7vI7mVjtXnpnB618f4lfnj+Dxj/Z5RFu0bgB8WcILPz6Tow02kqNDyU2JpKKh1UcP7O63d7By/mSaW+0kRloIMsGEjFjD36G91lBH6qzMGTeUrIQIappsvPKTSbTaJSkxzvtpSXWzz3fuiWv3QOl53FcG2RQpZZkQIgn4UAixx/1NKaV0GWs+uIy3FQAFBQXGaqMGGLVIunl6joeK8EOXj6MgM4bpo1JYv+cIl5+RwV1v7+Cqggxd2kHzNq3c6Kw0+cdXh1i1+VsWz8xlV3kdNU02bj0/h6ToMI42WIkMDfKbrD801rgsOC7M7PE8MSrUsKx695E6j56Va3eUc9HYVI+x3j1zDKu3lfmsupKjLWQlRhJlCemU27azLuFUP81rT7bUORDdygqFov/TUb9b6SrMOnC0kcp6z0VoW5uD/xVXs7HkGA4J+44c54Ypp1NdbyMp2rcfclZihOF17EidVb821zTZeOWLEwtvk4Ck6FCWrt5FXLiZKwrSmTUhjWMNznvM8IRI9lc14JCSy89wioG/u7WMz4ur9fvCQ5eNI8xsbAwea7RhCQlCCGeO7/CESEMj05+xE2SCpCgLwcFOiY9Wu4P4yFAPo6a3rt0Dpedxn4QspZRlrv8rgX8Bk4AKIUQqgOv/yu48prdLc+a4NJ9Vw+2rtvHrC0Zx8Ggdv/n+aJa8u5OZ49JYufEQ86Y4VZRvnJrFyo1OUdYn1hVy/dmZXFWQwS9f3cyjH+zjyfVFRFhC+NsXB6iztmGXEB9h9gk53jM7jyfW7fVx8y6akUNwEPrzu2eOoaq+maWz8ny20xSUtRXLL87N9gmNLl29i3tmj/Xrlu3IbetwSIqrGvj64LFOuYRzU6O5d47nWE+l1DkQ3coKhWJg4t455NbXt/L+jiP84EnPLiJtbQ7e21HO/Fc2smxdEV/sr2LG6FRueOFrFvzjGx5eu5ulszxDh0FC+FzrF8/M5Y1N3wJOQ2rxJbn6wvvZT4tJjrZwj8sYu25yJis2FPOrlVt55IO9xIabkbKV+Ainqr8Wivz5Odl8ssd567S2Orj9zW20tEnDMOc33x73+V5GXVOSoiyGny/IHEJGXLjfTiu9ee0eKPeJXveQCSEiAJOUst71+AJgKfAO8CPgQdf/b3fncb2tfH9VIbY2OwXDEzhY3Yi11UGUJcinz5jW98va6vARRnWvjBmVEoWtzcFf/lPM9Wdn8sjcfJpa2ogOC+FBVxPxsuMtzJuSRZAJspOieGDNbu6aOZonrxlPYWUDT64voqbJxl//30Re+ckkKupbiLaEcPuqbT49JlsdDsPvFBIkWOPHLdue29bdK3bj1KxOuYSDg03MyU8jJymyW0qdA9GtrFAoBibuC/fLJqb7LHA1aYzbXVXlqTEWbp4xgk0lzurJVZtK2VhSCxzSQ4cCnPpdroW9lnayavMhbv/+KOwSCivr+ceXJ7xjo5KjOHy8SZfO8M7zvfudnfz1ujNYtNJTSHzJuzuZNyXLqarveq3seJNPKFTTJ/P+XkaL7rWLpvpIEz10+Ti+kxXPoZqmdqUjeuvaPVDuE30RskwG/iWc5R7BwKtSyrVCiK+B14UQ84AS4MpuPagfl6b38zBzMHe/s1mvqEyL9TW4lq13arg4P2/3686NCA2myWanpsnGQ2v36u8vnJGtx9bLa616KHLB9GxqmmwkRoZy3fNfeez37nd2suzqCYSbg6izthr2mDQJYfidws1B7bpl/bltvb2KnXUJBwebyB8W123tMQLNrdwTBLoUjELR2/TFnHBfuPtbtGt5sueNSODicUP17irulZNlx1sAsDskt6zcYqgftnjmGBqsrZS6dL4A3ZBKjbHokQZ/4zje2Orzely4mVEpUSyYng04PW/1Vjtvbna2dhqdGkVceAgPvb9Hb8METgkO7/xfLZd6z5F6xqRG8d7NU6lqcBo7Wv71vop63RB1z1H2XqjLTicXnTwD4T7R6waZlLIYyDd4vRqY0VPH9RYgfXdrmd6D0j0nStNyeWaDs2KlxOUpc8fa6uBQdSOLZuQQG2acgzV+WCx//+IAEzPjfZIwx6RG+5WxWDg9h0Zbm8/EuKogg6tWfKHnvy2emcuS1SfU/pfOymXFf/YbJoW22j3H31ncL05GCadddQkrPbEOCTgpGIWij+nVOeG9cPcnjVGQGcP/O3s4N7262WexvmhGDgA3vPg1N07NwhJiorzWSrCw8+drJ7Kl9Dh2Bzy9YT9Xn5lBaLBJP45mBAWZwGRyFolpGmTe4xgSafZ4PTXGwvVnZ3rkEC+emcs/virR2/jNm5JFjCWIi8am+oh3a62O4sLN/PCsDJKjLZTWNLHk3V3UNNn0nGHAJ59YM0TLa636Qr2jvGN1P/Bl0Cj1G7k0M+LCmZgR5+Hi3FhyDEuIyblS+bKE31082nAyZMRH8ICrDYV7ry9LiFMTrNXuoCAzntTYcB79YI+Hq/rZT/dz18wxeg8z7TN1za289vUhll09weOY3mKAJdXNPL2hSFdaNgmICTezr7KBKq+k0OToUBIijVXyO8L94lRea+WVL0qYPy2LCcNiyYyP6NIEUnpiJ8Vs4FzX45eAT1AGmWJw06NzoqOWSJokxK8vGMXnxdWGi/VhceG65qT7QjY/I8GjWhHgiXVOA04rFPNOj/ndhaPIT4/xuV8smpHDt8caPdrwXVHgG2JdstoZwtxX2aAbTT88K4OnPi7y2O6JdYV8b3QyT107gcKKBsNm51ooEvAJU2pVl899Vqwv1NtTwh8eH6HuBwYMGoMMjF2a3s/HpETofb62ldXxpw/2cM/sPA+h04XTc3hgzQnl49jwYL1dkpTwlCvv69G5+ZQeb2L+tNN9jC+7w8HjV46nydZGybEm/TPuGjDayRpk8nVZa0rLz35azKIZOSxfX6hP2uUfn6i8XLp6F8//aNJJ/V7eXsWaJhujUqI5Z0RSlydNILapCDD6RApGoQhgen1OtNcSyT1Ud6zRhkMae9CCg4T+WnmtlbU7ynl4bj6V9SciDponTAhIjwvnmQ37+fUFo3QxWXBeIx9Yu4cXfnwmK78q9FjUv7+9nN9cOIo2u4OXfzKJmqZWIsxBvOHWTFzbx6iUKJZfO4GI0GCGxoYxJCJE74PsPo7qJhvDh0Sw4NVvDI2tNzeXUlXf4rdzzLi0aNa4jC2TSbQrRQG+Rp27sXYynjPN41bd2II5yESTzd7vPG+DyiDrDJFhFi7MS2R4wiSq6ltIjArlUHUDL/z4TL491kTp8WYPTS9nqNGk5wC4ExwkGD4kgiFRZp64agIOJPsrG3hq/YmeYqkxFh69YhxjUqNJdUuAd78omITw6JepHffM4XGckzOJpOhQJmTE0mp3eExabZxVDVZOTzI2etpzG3tfnDS9mi8PVHf5RFd6Yh3S61IwCkWA0ydzwl9LJHcv/yNz83Ux1GXrC3VZitMSIogJCyYzPoyZ49IIDTaRkxTJg2t363nJWtWkd6HYkeNNhtfIz4uruWhsKi9/XqIbUdefnclPX97o8fmVGw9x/dmZ+nbg6otZ3UiQSfiEKN/fXu7RpeXZT4t5dG6+4RhCg01cf3YmP3rhKz0M630/ykmO8vjN2pOi8Hc/qKizsudIfZc9Z9rf5qG1u328jP3J8xZISv19iibv8L+io2wqqeU3/9zKTa9+g93hoKqhlTve3EZQkCAsJEhPqLeEOBXzw8wmw7LgA0cbKT7ayPXPfcXP/raJX63cQpDXSVHTZOPLA8f4+d82c9WKL/hgd4XeZkhTv0+KCvWRzVg0I4ddh+u47vmv+PJADRPT4xgaE66rMS//uEiP5ydGGmuxuJd4e5csa2jjmDQ8nr0V9Vz4hP9t20ObnN6/kdITc9IXUjAKRSATaHPC3cv/zIb9/HxaNis3HuLOi0Zxz5w8APZXNVJW08TPz8l2hu8+3MevXt/CVQUZbCk5ytJZuVxR4NuPctn6QsamxxpeI+0OZ2jzigKn1phRaHLZ+kJdiknbTjPU3I0xbfsn1hUyf9rpPuMorKw3HENWQoS+Dy0M25HERHtSFP7uB+HmoJPqzKL9bWaOSzvlzjN9iTLI8DRMrn32S37x981cVZBBaoyFFf8pZmishdnj02hsaSMvLZpH5+bz0GVjWTQjh9AgE4+s3cNdM8f4GEyAYSNY9wnjrSdmdPJkDIkgJzmS+dOcWmjzp2URHhLEy587G9De/fYOth2uJcjk22Jp0Ywcgvz8lbvSbuJUWxgNFJ2YnkAIESGEiNIe45SC2cEJKRjoASkYhSJQCcQ54e7V2VZWxz++KuHOi8cQYQlhwaub9bZCEhNP/6fIx2CqbYHSYw2MSY029A412dp44FJPzUit5Z211cGEYbG8Nv8sxg+LNfy8Vo2ZmxrD0/9vIvOmZPHKFyU02oyVADCo3nx9Yyn3e41BS7FxD8NqArZ//X8TWbNwqqEHSouwrFk4ldfmn+Wxnb/7gc1uLN2khTk7+tv4q0jt6POBwqAKWfoLzxkZG1rcXJOk+Nm52VTVW7G1Odh9pF4PC2ru4Zun57By/mSO1FoJMwdx+6rtXH5GuuHJoU0sgeCWlVt8Yv7eYTyTSTB9ZDJZCZHsPFzHniP1PL2h2KPMuKLOSqvd4dNi6eXPS5iQEcvwBN+wYFfCiKcachwoOjE9RJ9IwSgUAUzAzQnvENy2sjp2Hq5lxYZi4sLNei5WaU0TV5+Z4SF1pBkLy9YfYNGMIONCsSERVNZbWXHdGWwsqcHuwKNycUiEmSabnfgI304AlhBnc3BLiImUmFCiLCHcsrIjySLf/dQ02ZgwLJaV8yfzaeFRMuIjOHy8iWhLCJnxYR5yTc99VsyaDnKA/UlR+LsfHKxu7LS8ktHfxv937R+RmEFjkLVX5efP2NA0Wj4/cIxLz2hh4T+2sGC60xXt/QcfNiSCrMRI8ofB1m+Pe4Q1vbfNjHduu7+ywVBPzCjEqJ3YNY02D1007TPJ0RaGRJgNWyz5Oxm70m6iO1oYDQSdmJ6gr6RgFIpAJRDnhHeRkyXExIikKMOcsLtmjiHV1S5Ok7HIGxpNZnwYpydG+lTm33/pWPZV1PPrN7YSF27m+rMzPXK+ls7OY+Fr31BS3UxmfJhhodnKjYe479Kx5KbGYDKJdqtFl87K4+G1u30kmRbNyGH/0QamnZ5IYWWDXmSgjWH5x4WUVDd3S4TD6H5g9Bt35jja5x5au/uU5Zn6EiF7Q7GthygoKJAbN27s1LbFVQ1cvOxTH4NijauM1+g9rYx36ew8Xv+6hI0ltYY9ML2TBtvaHLy1tYwn1xe2m2B48GgD7+844pNseVFeiqFHC8Bms/PWtsPc7TYZl87OY864oQQHm7pUStwVKQolW9ExQohNUsqCvhxDV+ZEVxl+x3s9st9A5OCDP+jrIQwIBtqc0KIsmldHSnh7a5lh0dWiGTlIicf1//5LxzI0NoQt39aTFhtOYWU9Zw4fgkNKXWAWnMVeVxSkkxYTRkqshbvf3uHRqDszPszV5q+R4QlOL1a91c65IxM4c3i871gjQ6lpbuVIrZWkqFAeWrubs7ISOS0hHEtIEKU1TbqAbE2TjZXzJ+u6l+7fSWtI3pMRDu/fuKtVlscaWwgJ4CrL9ubEoPGQVdRZPdzKABv2VlJV34JDSp65roA/vL1dt/4fuHQsseHBXDBmMqOToynIjPPRL3P/wx+sbtT/8MHBJmaNHcrw+AiONdl45SeTaLVLUmI8T47yWmuXQowAZnMQs/JSOS0+nCN1LaREhzI2NQaz2dkAsythwa6EEQd7yFGJGCoUg4POzHXpkjgKDoLTEyMNIyzD4yP4lVcqzJ3/2s6iGTk0t9rZc6SeNzeXctZpQ/j6YI3HPspd6v0LZ2QTHhrsYYyBU/aoqLLeo7rfEmLiyoI0tn5b46rGDCM2PNipki8EE4bFYcoUfH2wmumjUnyqPN/cfEIyw1u1Xxt/c6udyVkJp/wbt8fJRlIGQgRm0BhkWqmw5o3KjA/j5+dk60J9lhBnf660WAtDIkJ9JqGRa9VfeS7AR3srfd6bnBXvsc/kaEuXQozgvFisL6zy66nq6knZle0Hwgl/MijvoEIxOGhvroOvQv2iGTkMcynce3uT7A5paNQkRoXSaG1j1eZSrj87ky8PHPOraTYqJZpDfvKqCjKH6K9bQkz89bqJfH2wxiN6s/iSXP7xZQn7Khv072EOMhlWeWo505YQZzeC/pyL1V8ZNFWWWumwdoLNHJemKxyD86S8fdU2hkSEkpUY2eGNtr2qw85WJJ5M5eGpVjsquo76zRWKwUFXr+uvfX2IobFhPtXtC6fnUHq8yVDaIdIczNMbijl3VBJPrCvEIdE1zdz3cc/sPJ7+pIiXPi/x2f9jV47nO1nxHhWMsRazboxp41vy7k5unHa6x/do8lN1KcSJfWvi5KoqvncZNB4yd5Vk8N84trNVg+1VHUrZuX2fTBhQCaz2Puo3H3ycSr6cyj/rv3Tluq71GP5kXxVvbCzlkbn57K2o10W5AcPewgeqG7nz4lGAU9F/1aZSrpucycqNh5g3JYsgE4xKiebpT4r0ZuMvf17CSzdMQiI97hPuEYu1O8qNw4y2No/v4a9Aa2p2ApdNSNP3PZhTVPqKQWOQ+TsJT9Yl21HVYWf33dUwYHdUOyq6hvrNFYrBQVeu6/OmDOfRD/Zx49Qsapps7HW1snP/7MqNh3jiqvHsLK9jVEo0b3x9iLHDYgkSgkhLMJYQk67rdf3ZmQyLCycoSOBt9tQ02UiMCm33PpEaYxw6TYmxsGB6Nu9uLUMgMAn40xXj+fUbnmHZM4cP8TC4BmuKSl8yaEKW3uHBd7eWce+cvJN2ybYXbuxJEVQlsNr7qN9coRgcdPa6Pi4tmoRIi+7hWjg9xzDs+PNzsimpbmS0yxiblBXPig3F/PLVb7jr7R0snXXiHgRw2z+38ou/beaWlVu45qxMxqVFd/p6Mzo5iqWzPe9piy/J5U//3suznxbz83Oyue+9XVz4xKc4pINFM3J0oXFzsPJ8BQKDRvYCfMtptUax7n0ay2s7X0XXXnnuyZbunsz3UK7knqczv/lAK/H3ZjDJXpwKKmR5gv44JzpzXa+os/JFcTV/dcldaE26YyxBjEyNZntpLRnxETywZrcu7PrX687wkLYAp3zFn64YT0ubnXkvbfTxbr18wyQSonyLzIwormrgt//cwvXfyUI6JEEmwYoN+/WwpyblpCXuuwufaxJQyhvW8yjZCxdGLtisxEiGx0ecVBVdey7dnnT3Kldy76N+c4VicOA+140kMLISnR1TXt9YqueIacr1C6fncMeq7QDcefFoLj/D2SZv1aZSjhz3zU8rqW6mos6Kw0/ecU2TjUlZ8Xqv5fakOCrqrJQdb2HvkXoy4sL49ngzVQ02j/1pkk/WVgcZcWEsmJ6tj0/lxPY9g8og84e/yppRasWgUCgUg5L2JDCSo0OpabLxyhclPDI3n8LKer3VEcD1Z2fqHVU0eYzYCLNhjpcWpPKX/9VZ2R1vaSet2tO9/ZL7scpqm3lqfZE+vpRolRPb1wyaHLL2aK+yRqFQKBSDD/eFemqMhXlTsthzpI7tZbXkpUSxdFYeNU027l+zm7CQIJ77zNlf+IqCdA+JJWurgyfWFfLtsUYf+YrFl+SyYsN+ntmwn8Uzcz3eu3dOHrmpMZ2W3fGWdtL0xS6bmK4bXW9uLtUfv7Gx1GN8ds9boKIPUB4yVBWdQqFQKDzRFuqpMRaPXpUrNhTz2JXjmZmXzPCEcCrqWkiLtfCPn55FWY0Vc7AwXOBHWUJoamnj8SvHY7M7SIg089D7e/QcL74q4eG5+QggY0gYuakxBAebOi274y3tpG2XMSSMBec5Q5N/vGQMseFmblm5RVfl17ararByepKKCPUlAechE0JcKITYK4QoEkLc0RvHPNkqOi2u//n+oxRXNeBw9N8CCYVCoVCcuK43t9q586KR3PWD0Vjb7Nw4NYvUGIvuoSqvt5EQGUpCpJmYMDNx4WbiI80kR4cZCsIeOtbMktW7+dXrW9hX0cCxxlb2VTbo22wrq+O3/9xKaoyF/GFxBAc796E5DLz35+0w8LfdoWPNPPrBPp5YV0hOchSJUc5wa0f7U/Q+AeUhE0IEAcuB7wGlwNdCiHeklLt68rgnI4Kn2ukoFArFwML9uh4Xbub6szO59Y2thjlZu4/UcdsbnnliL39egjlYcO+cPI8WRtp77o/HpkWydFYed79zYruls/LIS4n2GJPmMPC+13g7DIy2cz+u+2c6sz9F7xNQBhkwCSiSUhYDCCFeA2YDPWqQQder6FQhgEKhUAws3K/rl030zQXTej4+91kx+yrqffLENCmJJ9cXsnL+ZJpb7bqk0oSMWI/HYSFB3LN6Jw/PzafZ1kaYOZiX/1dMwfA4sixd7+jivZ37sbw/o1T4A5NAM8jSgG/dnpcCZ7lvIISYD8wHyMjI6L2ReaHa6Sj6I0pLrOdRbZf6L+7XdX/t9YJMcP+lY3nk33t93tNkJUqqm2lutTM5K0F/f3hCpMfjz/cfZWNJLRtLvvHYj9E9pLMOA6Pt3I/b1f0pepeAyyHrCCnlCillgZSyIDExsc/G0dm4vkKhUCj6B97XdaNr/IxRSUzMiDXMw3KXlejoXqDuIQpvAs0gKwOGuT1Pd70WcKh2OgqFQjGwcL+ur9pU6iNT8diV4xmbFkvGEN/rv7usRGfuBeoeovAm0EKWXwM5QojTcBpiVwPX9u2QjDmZQgCFQqFQBC7e1/WUaAsXjEmhqsH3Gt/ZfK3OHkvdQxQBZZBJKduEEAuAfwNBwPNSyp19PCy/qDi8ojcQQlwIPIFzTjwrpXywj4ekUAxYjK7rRvpcnc3X6uqxFIOXgDLIAKSUa4A1fT0OhSIQ6CspGEXfoAoCFIrBS8AZZAqFwoM+k4JR9C/6qoJWGYIKRffQrw2yTZs2HRVClLi9lAAc7avxBNAYIDDGMdjGkNkD++ySFAzQIIRwr8cfbH+D9giEcQy4MYiH2n27J+ZEl1D3iXYJhHEMtjH4nRP92iCTUnroXgghNkopC/pqPIEyhkAZhxpD7yClXAGsMHovEL5/IIwhUMahxtD7qPtEYI9DjeEEgSZ7oVAoPOk3UjAKhUKhOHmUQaZQBDa6FIwQwoxTCuadPh6TQqFQKLqZfh2yNMAwbNPLBMIYIDDGocZwinSDFEwgfP9AGAMExjjUGPqeQPj+gTAGCIxxqDG4EFLr9aBQKBQKhUKh6BNUyFKhUCgUCoWij1EGmUKhUCgUCkUfM2AMMiHEhUKIvUKIIiHEHb143INCiO1CiC1CiI2u14YIIT4UQhS6/o/r5mM+L4SoFELscHvN8JjCyTLX77JNCDGxB8fwRyFEmeu32CKEuNjtvd+5xrBXCPH9bhrDMCHEx0KIXUKInUKIRa7Xe/W3CFTUnFBzQs0JT9ScUHMioOeElLLf/8OZ7LwfyALMwFZgTC8d+yCQ4PXaw8Adrsd3AA918zGnAROBHR0dE7gYeB8QwGTgyx4cwx+B2wy2HeP6m4QCp7n+VkHdMIZUYKLrcRSwz3WsXv0tAvGfmhNqTqg54fPbqDmh5kRAz4mB4iHT28tIKW2A1l6mr5gNvOR6/BIwpzt3LqXcABzr5DFnAy9LJ18AsUKI1B4agz9mA69JKVuklAeAIpx/s1MdQ7mUcrPrcT2wG6eyfa/+FgGKmhNqTqg54YmaE2pOBPScGCgGmVF7mbReOrYEPhBCbBLOFjYAyVLKctfjI0ByL4zD3zF7+7dZ4HLzPu/mgu/xMQghhgMTgC8JnN+iL1FzInDOAzUnAgM1JwLnPFBzwoCBYpD1JVOklBOBi4CbhBDT3N+UTh9or2qL9MUxXfwFOB0YD5QDf+qNgwohIoFVwC1Syjr39/rwtxjMqDlxAjUnFKDmhDtqTvhhoBhkfdZeRkpZ5vq/EvgXThdrhebidP1f2QtD8XfMXvttpJQVUkq7lNIBPMMJd3OPjUEIEYJzkv1dSvmm6+U+/y0CADUnAuA8UHMioFBzIgDOAzUn/DNQDLI+aS8jhIgQQkRpj4ELgB2uY//ItdmPgLd7eiztHPMd4HpX5chkoNbNTduteMXZL8X5W2hjuFoIESqEOA3IAb7qhuMJ4Dlgt5TyMbe3+vy3CADUnAiA80DNiYBCzYkAOA/UnGiHrlQABPI/nJUR+3BWZvy+l46ZhbMqZCuwUzsuEA+sAwqBj4Ah3Xzcf+B09bbijG/P83dMnJUiy12/y3agoAfH8IrrGNtwntSpbtv/3jWGvcBF3TSGKTjdzNuALa5/F/f2bxGo/9ScUHNCzQmf30fNCTUnAnZOqNZJCoVCoVAoFH3MQAlZKhQKhUKhUPRblEGmUCgUCoVC0ccog0yhUCgUCoWij1EGmUKhUCgUCkUfowwyhUKhUCgUij5GGWT9FCGEXQixRTi7128VQvxaCNHh31MI8YjrM4/0xjgVCoVC0fuoe0T/Q8le9FOEEA1SykjX4yTgVeC/UsrFHXyuFqfeir0XhqlQKBSKPkDdI/ofykM2AJDOdhzzcTZsFUKIINcq52tXA9efAQgh3gEigU1CiKuEEIlCiFWu7b4WQnzXtd0fhbPp6ydCiGIhxELX6xFCiPdcq60dQoirXK+fIYT4j3A2zv23lxKzQqFQKPoQdY/oHwT39QAU3YOUslgIEQQkAbNxtns4UwgRCvxXCPGBlHKWa9U0HkAI8SrwuJTyMyFEBvBvYLRrl6OA84AoYK8Q4i/AhcBhKeUPXJ+PEc4eYU8Cs6WUVa4JeB/wk9767gqFQqFoH3WPCHyUQTYwuQAYJ4SY63oeg7Mv2AGv7c4HxjhbfQEQLYSIdD1+T0rZArQIISqBZJxtJP4khHgIWC2l/FQIkQfkAR+69hOEs1WGQqFQKAITdY8IQJRBNkAQQmQBdpwd6wVws5Ty3x18zARMllJavfYF0OL2kh0IllLuE0JMxNkH7F4hxDrgX8BOKeXZ3fNNFAqFQtHdqHtE4KNyyAYAQohE4GngKems0vg38AuXqxghxAghRITBRz8Abnbbz/gOjjMUaJJS/g14BJiIswlsohDibNc2IUKI3FP/VgqFQqHoDtQ9on+gPGT9lzAhxBYgBGgDXgEec733LDAc2CycS5kqYI7BPhYCy4UQ23CeCxuAn7dzzLHAI0IIB9AK/EJKaXO5vZcJIWJc+/k/YOepfDmFQqFQnBLqHtHPULIXCoVCoVAoFH2MClkqFAqFQqFQ9DH9OmSZkJAghw8f3tfDUCgA2LRp01EpZWJfjkHNCUUgoeaEQuFJe3OiXxtkw4cPZ+PGjX09DIUCACFESV+PQc0JRSCh5oRC4Ul7c0KFLBUKhUKhUCj6GGWQKRQKhUKhUPQx/Tpkqeg5HA7JwepGKuqsJEdbGB4fgckkOv6gQqEYFKhrhELRvSiDTOGDwyFZu/MIt76+BWurA0uIiceuHM+FuSnqgqtQKNQ1QqHoAVTIUuHDwepG/UILYG11cOvrWzhY3djHI1MoFIGAukYoFN1PjxlkQohhQoiPhRC7hBA7hRCLXK//UQhRJoTY4vp3sdtnfieEKBJC7BVCfL+nxjbQcDgkxVUNfL7/KMVVDTgcpyb2W1Fn1S+0GtZWB5X1Vj+fUCgUgwl1jVAoup+eDFm2Ab+WUm4WQkQBm4QQH7ree1xK+aj7xkKIMcDVQC4wFPhICDFCSmnvwTH2e3oidJAcbcESYvK44FpCTCRFWbpr2AqFoh+jrhEw/I73TvqzBx/8QTeORDFQ6DEPmZSyXEq52fW4HtgNpLXzkdnAa1LKFinlAaAImNRT4xso9EToYHh8BI9dOR5LiPP00Iy84fFGvWcVCsVgQ10jFIrup1eS+oUQw4EJwJfAd4EFQojrgY04vWg1OI21L9w+VoqBASeEmA/MB8jIyOjZgfcD2gsdZCVGntQ+TSbBhbkpjFo4lcp6K0lRqoJKoVCcQF0jFIrup8eT+oUQkcAq4BYpZR3wF+B0YDxQDvypK/uTUq6QUhZIKQsSE/u0I0dAoIUO3OmO0IHJJMhKjGRyVgJZiZHqQqtQKDxQ1wiFonvpUYNMCBGC0xj7u5TyTQApZYWU0i6ldADPcCIsWQYMc/t4uus1RTuo0IFCoVAoFP2fHgtZCiEE8BywW0r5mNvrqVLKctfTS4EdrsfvAK8KIR7DmdSfA3zVU+MbKKjQgUKhUCgU/Z+ezCH7LnAdsF0IscX12p3ANUKI8YAEDgI/A5BS7hRCvA7swlmheZOqsOwcWujgZHPGFAqFQqFQ9C09ZpBJKT8DjNw0a9r5zH3AfT01JoVCoVAoFIpARCn1KxQKhUKhUPQxyiBTKBQKhUKh6GOUQaZQKBQKhULRxyiDTKFQKBQKhaKP6RWlfoVCoVAoFE5UH0yFEcpDplAoFIo+QQgxTAjxsRBilxBipxBikev1IUKID4UQha7/41yvCyHEMiFEkRBimxBiYt9+A4Wi+1AGmUKhUCj6ijac/YzHAJOBm4QQY4A7gHVSyhxgnes5wEU4RcNzcPY0/kvvD1mh6BmUQaZQKBSKPkFKWS6l3Ox6XA/sBtKA2cBLrs1eAua4Hs8GXpZOvgBihRCpvTtqhaJnUAaZQtFLDObwjMMhKa5q4PP9RymuasDhkP1q/4qeRwgxHJgAfAkku7XYOwIkux6nAd+6fazU9ZpC0e9RSf0KRe+hhWc2CyGigE1CiA+BH+MMzzwohLgDZ3jmdjzDM2fhDM+c1ScjPwUcDsnanUe49fUtWFsdWEJMPHbleC7MTemWnqs9vX9FzyOEiARWAbdIKeucrZCdSCmlEKJLFrYQYj7OkCYZGRndOVSFosdQHjKFopcYrOGZg9WNurEEYG11cOvrWzhY3dgv9q/oWYQQITiNsb9LKd90vVyhneuu/ytdr5cBw9w+nu56zQMp5QopZYGUsiAxMbHnBq9QdCPKIFMo+oDuDM8IIeYLITYKITZWVVX13KBPkoo6q24saVhbHVTWW/vF/hU9h3C6wp4DdkspH3N76x3gR67HPwLednv9elc4fzJQ6zZ3FIp+jTLIFIpexjs84/6elFICXQrPBLo3IDnagiXE81JjCTGRFGXpF/tX9CjfBa4Dpgshtrj+XQw8CHxPCFEInO96DrAGKAaKgGeAX/bBmBWKHkHlkCkUvUh74RkpZfnJhGcCneHxETx25XifHK/h8RH9Yv+KnkNK+RngL9FvhsH2EripRwelUPQRyiBTKHqJToRnHsQ3PLNACPEazmT+fhmeMZkEF+amMGrhVCrrrSRFWRgeH9FtCfc9vX+FQqHoDZRBplD0Hlp4ZrsQYovrtTtxGmKvCyHmASXAla731gAX4wzPNAE39OpouxGTSZCVGElWYmS/3L9CoVD0NMogUyh6CRWeUSgUCoU/VFK/QqFQKBQKRR+jPGQKhaLTOBySg9WNVNRZSY5WuVoKhULRXSiDTKFQdAqliK9QKBQ9R4+FLAdz3z6FYiCiFPEVCoWi5+jJHDKtb98YYDJwkxBiDM4+feuklDnAOtdz8OzbNx9n3z6FQhEgdEURXzX7VigUiq7RYyFLl15SuetxvRDCvW/fua7NXgI+wdlIWe/bB3whhIjVxDJ7aowKhaJzOBySNrvEEmLyMMqMFPFVaFOhUCi6Tq9UWQ6mvn0KxUDkYHUjf3h7Owun5+htiiwhJh66fJyPIr4KbSoUCkXX6fGkfu++fU6xcidSSimE6HLfPmAFQEFBgYqDKBS9QEWdlZLqZl75ooR5U7IQAqSEtFiLj9ervdCmEm5VKBQKY3rUIBsMffuUDIBiMKA18C6vtbL84yLA6SG7fKKPE1vftqPQpkKhUChO0JNVlh317QPfvn3Xu6otJ9MP+vZpuTIXL/uUa575kouXfcranUdUArNiwKE18HYPV/pr4N2VbRUKhULhpCc9ZAO+b5+/XJlRC6eq0IxiQNGVBt6q2bdCoVB0nZ6sshzwfftUroxiMNGVBt4Dudm3SlNQKBQ9gVLqPwVUroxCMbhQkh4KhaKnUM3FTwGVK6NQDC6UpIdCoegplIfsFFC5MgrF4EKlKSgUip5CGWSnyEDOlVEoFJ6oNAWFQtFTqJClQqHoFKo/pUpTUCgUPYfykCkUig5RyexOVJqCQqHoKZSHTKFQdIhKZj+BlqYwOSuBrMRIZYwpFIpuQRlkCoWiQ9pLZlcoFArFqaNCln5Q4o8KxQlUMrtCoVD0LMpDZoDqUalQeKKS2RUKhaJnUQaZASpfRqHwREtmX7NwKq/NP4s1C6cOuoR+RfcjhHheCFEphNjh9tofhRBlQogtrn8Xu733OyFEkRBirxDi+30zaoWiZ1AhSwMq6qzEhZu5bGI6wnW/WbWpVIk/Kk4JIcTzwEygUkqZ53rtj8BPgSrXZndKKde43vsdMA+wAwullP/u9UG74a65p0L67WO1trG9vJYjdS2kRIcyNjUGi0Vdbg14EXgKeNnr9cellI+6vyCEGANcDeQCQ4GPhBAjpJT23hioQtHTqCuEAakxFq4/O5Mn1hXqJf6LZuSQEq3yZRSnxIsMgJuPksBoH6u1jXe2l3P3Ozv032fprDxmjU1VRpkXUsoNQojhndx8NvCalLIFOCCEKAImAZ/31PgUit5EhSwNsDvQjTFwhiyfWFeI3dHBBxWKdpBSbgCOdXJz/eYjpTwAaDefPkeF9Ntne3mtboyB8/e5+50dbC+v7eOR9SsWCCG2uUKaca7X0oBv3bYpdb3mgxBivhBioxBiY1VVldEmCkXAoQwyAyrrjUv8qxr6R4m/UlTvd/Srm09/kMDoyzlwpK7F8PepqGvptTH0c/4CnA6MB8qBP3V1B1LKFVLKAillQWJiYjcPT6HoGZT/3ID+XOKvwkn9jr8A9wDS9f+fgJ90ZQdSyhXACoCCgoIetzwCfX709RxIiQk1/H2So0N7/NgDASllhfZYCPEMsNr1tAwY5rZpuus1hWJAoDxkBvTnEn8VTupfSCkrpJR2KaUDeIYTYcmAvfkE+vzo6zmQFBXKklm5Hr/Pklm5JCmDrFMIIVLdnl4KaBWY7wBXCyFChRCnATnAV709PoWip1AeMgP6c7+69sJJqkI08BBCpEopy11PvW8+rwohHsOZ1B8wNx9tfoy8eSqHjjUSbg728f60V4XZ1uZgZ3kt5bVWUmPCyE2NJji447VhZys7+3oOHD5uZe32cv563Rkcb2olNjyEFz87QGZ8OJnxag66I4T4B3AukCCEKAUWA+cKIcbj9BofBH4GIKXcKYR4HdgFtAE3BUKRi0LRXSiDzA/uJf79iUAPJw0UKioquPPOOzl8+DDvv/8+u3btAkho7zMD7eazt6LeMCwI+A0ZOhySt7aW8Ye3TlQg3jsnjzn5ae0aZV0JQ/b1HEiOtvD5gWN8vO+ox/HvuiS3V47fn5BSXmPw8nPtbH8fcF/PjUih6Dt6LGQ5kAX/OkoY7umE4vb2H+jhpIHCj3/8Y77//e9z+PBhAEaMGAGQ1N5npJTXSClTpZQhUsp0KeVzUsrrpJRjpZTjpJSz3LxlSCnvk1KeLqUcKaV8v0e/UBdpLyzY3ns7y2t1Y0x77w9v7WBnBxWIXQlD9vUc6OvjKxSK/klPesheZABoLnnT0Uq9pxOKO9p/fw639ieOHj3KlVdeyQMPPABAcPDgcja3FxaUEr/vHW9qNXzvSK2V/GH4pSthyL6eA319fIVC0T/pMQ/ZQNFc8qajlXpPJxR3Zv9auHVyVgJZiZHqRtADREREUF1djXC1cvjiiy/Aqag/KNDCgu5oYcH23kuNCTN8LyWm/XBie/s0oifmQFc832oOKhSKrtIXVZb9SnPJm440mHpao6k/aEANBh577DFmzZrF/v37+e53v8v1118PcKivx9VbtBeWM3rvqWsnICU0trTxlx9OJDM+TH/v3jl55KbGtHu8jLhw7p2T57HPe+fkkREX3oPf8gSaZ/riZZ9yzTNfcvGyT1m784jS+FMoFN1Gb8dZ+p3mkjf+EobDQoL4fP9Rws3BZMaHUVLd7PF+dyUU93XCssLJxIkT+c9//sPevXuRUjJy5EjMZnNzx58cGHQUlnN/LyXawq7yen7w5Kd6mP2BS8cSGx7MkIhQclNjOqyyPFTTxJPrC5k3JQshQEp4cn0hEzPiTqrwpqu9OP15pkctnNrvCn8UCkVg0qsesv6oueSN0er/3jl5LHztG6555kuuWvE5N0/P8fAAdGdCr0oYDgzeeOMNmpubyc3N5a233uKqq64C6B13TYDgHpYbHh/BwepGPZwH6O85JD7GzO/+tZ3M+Ejyh8V5GGP+woIVdVZsbSfWX0KArU2elGe4K96utjYHW7+tYVd5HTdOzSLVLbTaWc+06pyhUCg6Q696yPqj5pKG+4p6TGoU7908laoGK2EhQSx87RvdI6ZVja2cP5nmVnu3J/SqhOHA4J577uGKK67gs88+Y926ddx2223861//yujrcfUFHRWaHDluHGY/2uBsJaR5qTLiwvlgd4XhflJjLFx/dqbeY9YSYmLRjBxSorvuGe6st6utzeEj0bFweg6vfFFCea21U57pvu4aoFAo+g89KXvxD+BzYKQQolQIMQ94WAixXQixDTgP+BU4NZcATXNpLQGmueS9or7wiU/ZW1HPpOHxNNnsHuFJcF7gm1vtPZbQqxKG+56goCAA3nvvPX7605/ygx/8AAZp54uOCk1CgoVPQn5mfBilNc0eXqr3dpTz0NrdhvuxO9CNMe29J9YVYve08zpFZ/MwjSQ6lq0v5LKJ6Z32TPd11wCFQtF/6Mkqy0GhudTV6i/FwCAtLY2f/exnrFy5kosvvpiWlsHXOFoLxe2rqG83nFfT3MrC6TkeYfbbLxzNnf/a7jGnbl+1jdsuGGW4n8p6YyOqqqHrIcvOztnyWuNjjkmNYs3CqZ3ycqkiHIVC0VkG5Yq+q7R3Ue3OnC6Va9J/eP311/n+97/Pv//9b2JjYzl27Bg4q4MHBe5e45//bTPPflrMdZMzdWPK3cCJDzezcuMh5k3JYsH0bOZNyaK4qsFwThVW1hvupzsXPp2ds/4kOtLjwjrtmVYLNoVC0VkGl5rlSdJeZWN35XSpXJP+RXh4OLNnz6aiooJDh3S1i0Hj9jDyGi9zVUE+91mxh4EzdmgMN52Xw91vn8jFWn7tRH1OjUuL5sZpp9NsayM1xsKydfu4bGK6z34eu3K8z/w4mYVPZ+dsbmo0987J82nz1JFEhzua8dcd41YoFAMbZZB1go4uqt3R91KV1fcvnnzySZYsWUJycjImk+4Bye7LMfU07oUtza12Qw/XuLRo1iycSkZcuIesxKy8VLISIiivtSKBv31+kIXTc1i/5wiXT8zgt//cqs+txTNzSYkN5YIxk8lNjdENpQtGJ7Ny/mS9Kfno5Cj9GEmRoTS32Smtae5Uw3L3OetPAiM42MSssUMZHh/BkTorqdEWxg7tWKLD+ziqCEehUHSGThlkQogEKeXRjrfsX3RWi6g3LqodtYZpa3Ows7xWvxl1dMNR9CxPPPEEe/fuJT4+Xn9NCLGrD4fUo3h7cBfNyDb0GuckR5ERF87/iqvZWHIMh4R3t5Zx+4WjuTA3hQNHG3U9sj0VDTx+1Xh+/MJXHguRJat38vyPz+SqFV94NCz3rsC8d04eT64vpKS6Wa+6fPnzEmqabJ1qWG70vbybpH+0t/KUvdbdsWBTKBQDn3YNMiHEJcDzQJsQwg5cKaX8X6+MrIfpaoiwpy+q7YVFjcrvO3vDUfQMw4YNIyam86Gr/o63B/f1jaUsmpHjIUPx2JXjyYgL570d5dy+apuHVMRDa3czKiWKIBP65wAq/SxEDh9v9vASSwMtsz+8tYN5U7JY/nGRXnWpPf/DWzvISXLqnHXle7kfE3yPqbzWir5m+B3vnfRnDz74g24ciaK76chDdh8wVUq5RwhxFvAwcE7PD6vnCbQQYXth0e1lx3VjzD3f5ptva5jgJayp6B2ysrI499xz+cEPfkBoaKj2cnJfjqkn8fbgltdaefnzEl66YRISqXuND1Y36sYYeOaWaY3HX/68hEUzcogOC8FkEoYLEXNwkP75Y40tHKltMTTchDB+3lHDcs07vq+i/qSapCuDTKFQdDcdGWRtUso9AFLKL4UQUb0wpl6hoxBhT+EvTOodFk2JtmB3wJcHqmlzSOLCzSRGmrlmUqZHvo3ylPUNGRkZZGRkYLPZsNls2ssD7o+gna/NrXYWzcjm9Y2llNe65CyabFhCTDTZTkgG+ptXQSYICwmiutHGlQXp2B2Se1bvYkRSJItn5rJk9U6PHLJnN+wHoCAzBikh3BxkaLhJt0Jk9+ftNSy32ez870A1mw/VkJMU1W6rM9WmTKFQ9BYdGWRJQohb/T2XUj7WM8PqefqiJ6RRmPSpaydwWnwklfUnDLTh8RH6dnHhZq4oSOfXF4wgKSqU+a9s8gnbdCY0o+heFi9eDEBTUxPh4c6OSX/84x/L2/tMf8PofHXP01o6O0/vUqF5dEcmRxnOqwnDYj22vXdOHtZWB9vK6uCrEh6em0+zrY2MIeH86YM9bCuroyAzhivPzOS6578iLtzsEyJdOiuP5Z8U6sfQxtZeNWRbm4N3d5Tze5cGmmYAPr2hyON7dHdlp0KhUHRERwbZM0BUO8/7LX1Rju4dJo0LN1NY0cCCV7/xGMOY1CjdGLtucibL1jtvQg9dPtbQ+9BeaEbRM3z++efMmzePhoYGDh06xNatWwEGVOsko7D+E+sKeeLqCVhCTGwvreWS/DRWbXJ6zW59fQtrF031mVcPXDqW5R8XerQX+/ZYk264bSurY+E/vsESYuK9m6fy8NzxVNZbCTaZ+H/PfYm11aGHSOdPyyInKYo9R+p5fWMJy66eQHOrnYSIUKxtdrISIkiJsfhtWL6zvFY3xrSxLFm9k4fn5hMaJMhJjvLbJF1VSCoUip6kXYNMSrmktwbS2/RFObp3OOeyiek+7WBufX0Lz1xfgLXVwWUT03VjDCDcHGzoffAXmjGivcrSzladKuCWW27h3//+N7NmzQIgPz8fBshiRcMo/BgXbuZoQwv3rN5l2N9xz5F6j16vSVEWqhtb2FhS67Gf1zeWcv+lY7nzX9t1L/CIpCgcUtLSaud4Uyt2h/TJW1u2roiHLh/L8o+LSI2xYG11ICUIIYgMDSY6LIQoS4jPeaud29/WNBsuaooq65kzPs1vuoJUGs0KhaKH6ajKcll770spF3bvcHqX3i5H9w6TCmGcNHz4uDN04v3+Mxv2++TbdEWosqMSfyVM2zWGDfNxSw6o27ZRWP+KgnTdGANfQdjtZXXcsnKLz7njvZ+aJhsTM2JZu2gqmw8d19souYcef/+D0YYLkDBzsN5s/EcuyQzvcKr7eX3oWKN+jBunZhnuc2JGnI93XIk1KxSK3qSjJORNbv9meT3f1LND6x56qh3Ryex3eHwET107gYUzslkwPZtRKVGGbVWq6ltYOD2HIIHH+9vK6li1+RCv/GQST/+/iaycP7lLCf3t9eRUTZC7xrBhw/jf//6HEILW1lYeffRRGGBK/UYthjKGhPtN2l84PYc3N5f6nDv+WhVlDInAIfHpafnEOmcD7xX/2c/iS3I9Prf4EmfC/xUFvt5l7XPa8b+taeSzoqPsKKvTj7FqU6lPX837Lh3Ld06L9zGy1JxQKBS9SUchy5e0x0KIW9yf9wd6aoV7Kvu1tUlWbCjG2uqgIDOG+y4d65FgrIV/AK4/O9Ondcv0USnc9s+t3H7haMamxXbpe7RXWapK/LvG008/zaJFiygrKyMtLY0LLrgAoKSvx9WdmEzCRx3fJIwrD7OTonhgzW69AtP93PHeT3pcGA4p+WDXEYJMwq+cxbayOuJ3lPPSDZOoamghNdpCfKSZ0xLCabLZiQs3c9nEdF3qYtWmUv3x2acNofhoE9+4Kinjws2U11opr7XyyhclzJuSxZjUKNLjwhidHE1pbbNPqL6vKrEVCsXgpCutk/pdOKantMZOdr/un0uNsTB9VArL1u1j3pQsgkwwOiWav3xSpN/UnlhXyNpFU1k5fzLr9lRid6Dn6pzM9+ioslSV+HfM7bffzkMPPcTHH3/M3//+d4/3/v73v9v9fKxf4nBID3X8zPgwHrxsnJ77pS0SHrp8HH/6YI9+3oLnueO+n7hwM9efnal7txZfYhyWjA1zhiUnZcV7hCW1hc+hY40e+9FClgDj0qL5Qf5QfvG3TT7hTM0oe+6zYtYsnOpR0ex9jL6oxFYoFIOXAaeb5E57K9zu3G9qjIV5U7LYV1FPcVUDNpudrd/WsHZHOVu/PY7NZqe4qoF9FfXcODWL1BiLnrBfUt3M8o+LWLauiF+9voX5005nwfRsFs3I5v+uGk9VQwvVjTbe2FgKwOVnpLNgejZx4Wa/38NfONVf6EiT2vD3nuIEa9asQUrJAw880NdD6XG8FxBXFWRww4tf88i/9zJ/WhZPXTuB926eyg/yUrn9wtF+zx33/XgXsgyNDWfRDM8Q4qIZOQwbEmEYltRChnYHhiHLkCDBbd8fqXuV3d+7oiDdZ3zthSXVnFAoFL1JR0n99ZzwjIULIeq0twAppYzuycGdKj21wnXfb2qMxUOaQtNOuvvtE2HGpbPz+GjXYXKSYwkywd0zx1BS3WRoLO6rrOep9UVYQkz85vsjWfLuLn5xTha/OCeLo402HBKCBNz2/RG0tjkormrwqZRsL5zaXmWpKvHvmAsvvJC4uDgaGhqIjo5GSokQAuksw5vQ3meFEM8DM4FKKWWe67UhwEpgOHAQZ3uyGiGEAJ4ALgaagB9LKTd3x3fwrqbNiAvnUE2TR8gOoKq+hRunZgEQYQ7SDSCt2tESYmLl/Ml8XXKMkUmR/PPnZ3s099bOnYo6qx5ezIgL444LR5IcHUZdcyuWYBPvby9n3pQshACLKx/S1uYgd2h0l0PsydEWmmzGjc/TY8P4yw8nMiI5itMSToQlRyRFOrtftLQRHhrMMxv2s6+iHnA2NF/Tz+eEqp5WKPoHHeWQ9esy/p7SGnPfr7c0xfXfydKV9MF5I7j77R2suK6A+a9s1Mex/NqJhsai3fXU2urgkX/vZcF52aTFhbGvokHPPdO8CJV1Lfz0lU0eBldH4dT2KktVE+SOeeSRR3jkkUeYPXs2b7/9tsd7QohvOvj4i8BTwMtur90BrJNSPiiEuMP1/HbgIiDH9e8s4C+u/08JI4Pdu0n3U9dOwNYmPba5a+YYPQ9Lw9rqYN2eSt04M6pyNJmEXhH5xLpCPWT5KwOxWYCfT8uiusnG3op6RqV0XUV/z5EGgk1+wu/RFqacnuBRBDM01sI1Z3l2v1h8SS6VdVaPatH+OicCvVI0UBYpCkUgMKBDlppHaM3Cqbw2/yzWLJza6QtRe1WU7knKo1Oj9DAkQHNLG3HhZm46z1lJqYUXj9Q26x61eVOy2F9ZzwOXjvUIh2hVahrOvJ0Igkwmw/BMely4T+VXT4VpO0tbm8MjXNvW5uj4Q/2Qt99+m5KSEj766CMAmpuboYP5JKXcABzzenk2oBXLvATMcXv9ZenkCyBWCJF6quM2Mtj/8NYOZo5L059vK6312eae1bv0kJ+G9wLCu8pROyfdw4tG2ntaOPH6szNparWzYkMxT60v4rY3tvLzc7LJjA/Tj9deiF2bP1rjc/f37p0zltOTwn0qko83trLk3Z0e41ny7k4SIkMHRFVlP6gUfRG40Os1bZGSA6xzPQfPRcp8nIsUhWLA0JWk/n7JyXh9OlpVeic7u1dHxkaYDZONY8LNPuHNzPgwll87kd3ldYxNj+Hut3f4JEYHmwT11jZDI6uppU1/XFHnrPzqy0TktjYHb20t86gKHai9Np955hlWrFjBsWPH2L9/P6WlpQDZJ7GrZCml1nLpCCcalKcB37ptV+p6zac9kxBiPs4bFBkZ7TcL8Cf2OioligXTncMPNpkMz7eshAj93LKEmPjV+SN48X8HPbYRAj1HUgv7HWtq8QhZGu17TGo0YeYgfubVGuzp/xTx8OX5lNdaGRprISUmlC8PVJMcbeGc7CG8/JNJNNraiAgJpvR4M3dePJpnNuzn5c9L+Ot1Z1BRayU5xsLfvzhAfKSZ3YfrSYkJIyo0mCN1VmqabIbjqXebW/25qjLQK0WllBuEEMO9Xp4NnOt6/BLwCU6vsb5IAb4QQsQKIVLd5o9C0a8Z8AbZydBR2M/ofU0cs6ymydAD8Nvvj/QJb5ZUN3PTq5t57Mrx7C2v44dnZfLYh/t0Y+2OC0djszuI8NNYuaqhRX8cbg4C+qYllMbO8lqfZOqB2mtz+fLlfPXVV5x1ljOKmJOTA6c4n6SUUgjR5WpmKeUKYAVAQUFBu5/3Nti1cOJv3EJ2j1813vB8izAHM39aFg4JJgFhBhp6EeYgj0WHJcTEw5eP4xfnZHH/+3v8CrMmRIZyrNHTONIKCdyrLBdfkss/vixh0vAYRqTE8frGEi6fmMGS1ZtPbDMzl1WbD2EOMnG0wcbfvijhwrxUw6rL9sRntcf9uaqyn1aK9uoiRaEIFHrMIOvPuQH+vAjVDS0crG7E1uYwXHVmDAkDP8nGcRFmjje3GmontbTayUuPwW6XzJ+WRbg5iChLiJ5nkxkfxh8vyeWP7+70uDHVNttYNCOb0xMjaLNLPt9/lORoi0cicmKkhSATulfBXwJ3R0m/nUkMLq81Xo0PxF6boaGhmM1m/XlbW9vJ7qpCW+W7QpKVrtfLAPdfLd312inhbbAbVTI++P5uHrp8HLev2uaRQ3bvml0++Vzzp2V55JDZHZKnPi7y2N9vV21j/rQsrK0nhFndDbbFl+QSGWoiJizCw3jwXsBo4cSH5+aTFBXKj1/4iofn5vvkbC5ZvZPnflRAbaNTc8xoP0+scy6gNPHZJV5z69kN+wdEVWVfLtC6g95YpCgUgUJPeshepI8TmE8WIy/CDd8dznXPO1fqi2ZkG646Dx1rJtxsMnyvobmN3KExhJ0d5BPOjAoL5icvbuSX52azbF0RN52Xzf995OlJ+8t/injphkmUH28iOtzMknd36knY98zO49Y3tujPtfCqt8ZSZnwYN0/P8QgpGiVweyf9djYxODUmzPC7d6XXZn/hnHPO4f7776e5uZkPP/yQP//5zwC1HX3OgHeAHwEPuv5/2+31BUKI13DOhdruCM14V9oaVSSWVDeTFmthzcKpVNRZCTcHcaSuxcMYA6dho4U6terI4QkRhka5loLpLsw6IjkSIQTPbtjPkPBsvjcmhT9dMZ5fv+E8z4JM6HmX7osYKR1U1TuN/+YW43B+TVMrt72xXQ+jGm2jic/yZQkv3zCJmiYbKdEWYiNCOC0hvN9WVbrTFz17u4FeXaQoFIFCjxlk/Tk3wMiLoIUSAT1p2NuwevnzEq4/O9PwveQYC/sq6n28B0+sK2TRjBz9BmQJ8e1hCc6b5NGGFobGhnO9K4Sj7eOut3cwb0oWy1371sKrgEdodea4NJ+Q4rbSWr16U3vNW3S2s0K4uanRPp0FutJrsz/x4IMP8txzzzF27Fj++te/cvHFF/PWW2+1e3MQQvwD5/mfIIQoBRbjNMReF0LMw6n0f6Vr8zU4PcZFOL3GN3T3d5AS4iNCyYwPY+a4NIQrDBliEtRZ2xgSEcpZrpZCbQePGYcxQ4N5ar2z0fcVBekMiQgx3E67/2sh0mFx4UicfSZrra2kxFgwmQTfG5XE3+adxZE6K8lRofyvqMopoOzmUbtndh6nJ4Y7Q/WhwYbHCzGZdAFb7TXvbbSG4fsqG0iICqVg+BAOVjdy+Lin97i4qsHQM9xZOYm+lp3oh9XTvbpIGX7He6e6C4WiW+jtHLJ+kRugrSrHLJpKRV0L1Y1OPaZVm0p1pe+XPy/hkbn5CAEHjzYSHhJETZONRpudVZtKdV2l+PAQMuLDCQ4ykRkf4bEfcBo3Q2PCePKaCUhp566ZY6issxqrl4eHUFFvHBYUwvN5SXUjLV6hVSNDz+EWYnX3RBypayYjzlmV1tnE4OBgE3Py08hJiuRIrZWUGAu5qTEDLqEfwGQyMWfOHObMmUNiYiIA8+fPb/czUspr/Lw1w2BbCdx0quP0xtvbWZAZw03n5Xjo5i2akcPv/7XDQ74iNyWapbPyuPsdN329WXm8t7XUQ9biDYPFymNXjsccLMiMD+Mn3zmNplY7t7nlrD08dxzh5mD+V3SUljY7f3Tz/i6/diI3vbrZZwHy+JXjefjycbz0v2IWz8xlyeoTIccls3KJCQ/imU+LmDcli+jQIO6dM5Y/vOXbwFwbX0ZcuKEX2BwsWPDqNz6eYaBTXuNAl53oawJtkaJQ9CV9ltTfH3IDdpXXG1ZSltc6q7MKK+uZMSqJ297YSly4mXlTspg4LJZnPy1m+cdFjEuL5obvnsa+igaPG5T7fiwhJvZVNvDcZ8UsnZXLvzZ/y4zRKdw1cwz3rN7lkddy57+2c0l+Wrurfe35N98exyGNPQPuz7UG5nHhZo9k7Gc/PVEh2ZXE4OBgE/nD4gZczpiGlJIlS5bw1FNP4XA4f4+goCBuvvnmPh5Z5/D2dp6VlagbY+CZX7X84yIPb+vyTwr1hYaUzufLrp7A5Wdk8qMXvvLIj3x0bj4RoUFkuiQqABIjQ1m3p9LDI+uUhLHy23+eyFfTqjfLa61sLT1uuBjYfaSOyNBg7rxoDLUtNl6+YZJL2kWwYsN+9lU2eMyzzPgw/jbvLKobWkiNsRBlCWFCRqwewvPnBdZy39xfM/I++/Ma91T7toFCICxSFIpAobddFxWallKg5wb4q6S8bGK6vsIemRxFbmoMj105npomG8s/LuLPnxSydFYulhATN047nQPVjT5J08vWF/LDszI8tJOsrQ7ufmcnZ2Ul8tDavWw6UM0LPz6TJ68Zz0s3TOKbkqOUVDfrSdHuGktLZ+WyeluZ/vzumWP4ZE+lz7bvbi3j3jl5Hp8dEm7mV+eP4IoC38TnP7y1g53ltaqFjBuPP/44//3vf/n66685duwYx44d48svv+S///0vQFJfj68jNNV8TSdvVIqz8bbGuLRoHp6bT05SJE9eM4ERSZFU1lupqLPqbb6eWl/E8o+LsLVJV76WjZunZ/PzaVk891kxD76/l9v+uZWaplY9PGdtaaXJZicrIdJDt++HZ2V4pANYWx08/tE+fniW0/utLSrc0fTPHvtwH/W2NiprbVjb7Lz4vwMs+Mc3bCurIy7cjLXNzi0zclgwPRtbm6TN4eD7eamMGxbHaYmRTM5K0MWS/XmBHV5LPs0z3Fm9v77WBVQoFP2H3vaQ9UpuwMnmbLS1OdhZXkt5rZVgkzC8kI5IiuSRufnUNrUwLC6Mr0uOkZMYyT9uPIvS41aSo0PZUnKUv153BnXNrR4hQff9DBsSzpKZo4kOD+WWGTl6y5aMuDAenpsHUnDDi197hGGq6lv5eN9RPSl6VEokyVEW4iJM/HFWHt8cqsHugL9u2M9VBRm88kWJvu24tGhykqNIjwkjJymSb2uakRKe2bAfgPnTTjccp7NCsv3E4L7OkelNXnnlFT788EMSEhL017Kysvjb3/5GUlJSQjsfDQjcw4ve4bvESDPXTPJVrU+NsWB3eHpXtf24S1IsmpGjq/lbWx3c+a/tjB8WS0qkmdU7KjzCnZr3KtElwOqOtdVBYmQo4FxEeHuLtc9aWx202Bzc/uZ2MuPDWDwzlwNHG7G22Yk0B/PA2j2eeZztSD348wJ7n8bunuHOeI37qeyEQqHoA3pS9qJPcgNONmfDXdQ0LtzM3ZeMMbyQ7qtsYPW2Mhacl8OVK77wuanVNNlYMiuXFz87wE+mZukhQd+kY0F0TJhHvsziS3JptdsZFhvhI5C5+J2d/PW6M/h431HKa60891kx86Zk8Zt/bvPJswF0XbTlHxfx3GfFrFk41afqUru5OaSksLK+3QpJf4nBgy1HprW11cMY03DlkQX8F/bXlHv+tCyyk6J8JSTe3cnKn05mbHpsh3IZ7qFO7bXKeitHG1p0Y0x7XTs/IyzGSflasv6C83JIijbz0g1n8t/91dgdeIT7bXa7rlf2y1c3+zUOn1hXyHdPj/f7u/iThzAHC3183p7hzshJ9HfZCYVC0Xv0ZJVln+QGnGzOhruo6WUT03nw/d0+ekn3zM6jprGFBy8bp3uvtGO434z+/EkRD142joo6K2dkxnnoHGmCr4WVDdgdDn7yndN4ekMx5bVWlry7k8euyKemsdXQa3C8qRXAx0vgL89GCM/wor8w7JJLcvm/dYWG37ejCsnBliPjrj1mQMD3iar0UxSSFhOGwyGNvaR1VvJNgvNHnqiAjAsL4Y2NpT69LTOGhLFgejYb9lZy7qgkahpbsfvZ76iUSJKjQz2KALT5IQS8dMMk1m4v4663y/jdhaNIjbGw5N0TnrK7Zo7hvjV72tUZczcOj9S2cLi2XG9+7l5s4k8eAvDbXLwzchL9VHZCoVD0AQNOqf9kW4W4i5qGBpsoqW7Ww31CgCXYxLAhYUSEBiGRho2WT0sI546LRhJsMnmEGx+9Ip9FM3IYGmOhwWb3EHy9e2Yud1w4EpPJRNnxJiwhQcRFGGuZpUZbePKa8ew50sDaHeV6AnV+egwLZ2Tr+S6rNpVS02TjnJwELp+QRr21lQ92HSHITxg2MSqUmiabx/c1CSjIjOuwQjLQW7N0N1u3biU6OtrndeeagrBeH1AX8RdCO1TTzKiUKGMvabSFA1UNbCyp4S6vasyXPy/R54GmxffcZ8UsviSXp/9TxLLqIp66doLPfjPjw4gOC+GbkhrS48IMBZG1/MiLc9t4YO0eFs3IYf60LDKHhDM0Now73txGea21XZ0x9++xo7xWF7E1aullMgndCKuoc36n4fERfiUjOisn0Q9lJxQKRR8w4Ayyk83Z0ERN48LN5CRFYgkxUV5rZfnHRXq+zI9f+NrnZgRORfEgEyRGhdLU0sb97+/xWK3f9sZWHr9yPHaH5J7VztJ7Lcxyk1uYZeH0HJas3sm8757Gw5eP5bertnvcmP65qYSkmAhWbyvjqoIMlq0vJC7cTLQlWK9c08YWYQ4iMSqUjSU1uufPn6Dt3op63Tu2/OMi3auW2YmwymDLkbHb7X7fE0L0WXeJzmIUQrt3Th5Pri/k030hPqr1987J47jVxrq9lby9pUw32AFe+/qQsxjEZeS4e22XvLtT91B5q+Fnxofxy3Oz9bC8tjAJDTZx48sbPebO3e/s5Pkfn8mbW8ppbrUTbQlh2fpCfjrlNF2uA4zTAjQnlPd81QpWchIjyc840dLLPfweF27mioJ0RiRFMTo1mtMSlFdLoVD0LAPOIOsoZ8NfAromanroWBMPrvUMV15RkM5rXx/yuRktmZVLVUOLR8LxPbPzDL1nDikxB5/wUBmFWbS8mvvf38N9c3J5/sdncrShhcTIUP61+RCJ0RGs2lTKfXPy9HyZyyam+1SpPbGukMevHM+hY00eQrDtCdqagwUv/eRM2uySemsbQ2OdIayObkKBmCMzmIoMuopRCC0jLpyJGXFU1ltJjbGw8qeTOVLn1JGLDQ/h+//3Kbecn6MvAtwXEKNTo3jq2gnsLq/Xc7vAs9H41BFJNLW08fJPnGr4UZYQfuIW8td6ui6ZlWvo6aprbuXOi0Yyblgcb24q4abzcshJiuDr4ipeuOFMahtbue/Ssfz+XycWMHdeNIpx6bHkupqW375qu8+cLKttJp8TBpkWfveWgBnoeZEKhSIwGHAGWXs5G+0loGuiph/vq2TZuiKP8F3u0BgswUE+NyOHQ+rGGJwQrdT6+2lYQkw4JNjddMHaC7PEhZtpaZP6TUs7XmiwicRIM3XWE+1iQoNNhvvZX9WA1UsYVhO0/fO1E9n87XEizEGMHxbL+GGxDI218PXBGh+Vfe+wjhHmYOHRdNoc3Hc3rcFWZHAyGIXQ3J9nxkO+6/XP9x915pjFhusNyOHEAuJv885iSEQot72x1eNcs4QYNxpfOD2HYJPxuR9uNk7w31VeR4Q5mNtXbeWmc7PZeKCKkcmRRIWHcYPLa50ZH8ZT106kqLKBJlsbUZYQJJLv56Xy1YFqappsHsezhJhIiAj1eE0LvxstlgZyXqRCoQgMBp6EOiduOO46Q+A/Af1gdSPgFDU9PdEzXPnU+iLCQkyG3qxWr2Rldw2nl39yJktnj2HRjGzuuzQPc7CJwzXNPhpi7mgCr1cUpLPUy9Bbtr6Q7ORIp7bZ0Qb9s6clRBjuJ8PlofJ+r6bJRoPNzlPri3ho7V5uePFrkqMtHG9q9WmrpOmQtcfB6kYWvPoNy9Y5f6s3NpayrbSWT/ZVUlzVgMNbyKmH6ehvrOgaWkj6wNFGQyOq1e4gNjyYxZfk+mjjAYbzZmRqtOE5W1rTxF0zx3jsZ+H0HN7YWMrjH+3jijOGUXq8mQvyhmJtdVDd0KJrmtnaJNtKjxMXFoJDwv+t20erXbq+Q6jP+BZfkktyjKdBpn1Xf4slLa9MoVAoeoIB5yFrD+8EdK1V0L6KesAZfsuIC2fFdQVsLDmGQzp1kOqtxg2MI0OD9BX9uLRoXw2nmbn8t6iKcHMqy9YVcvfMXJaudubWRIYGGeorrdx4iJun5xger6XVgUBydtYQJmbEsunQcUKCBL+7cJSH5tLC6TkcPt6kC8O6eygWz8zlWZf2mLbfynorx5uMKzudOmSd+01TYyx9HuoZbEUGPY0Wkt57pM7Qe5UcbeHwcSv/+LKEh+fm02xrI8wcTNGR44xJizP8W2wvrWXJrFwWv3MiV00Lnf/oO5k8MjefvRX1SIlHGDQl2sKTHxf6eKt/df4ILMEmnzlwvNlZlTwsLoKhsY08OjefRlsbEeZgosKCGBZnLFHh77u22mWnwvgKhUJxMgwqg8w9Ad2f8eDdu+6hy8eRMSTc8AJdWFHP4pljWLJ6FzdOO91Xw2n1Th6em89v/7mVeVOyWLp6JzdPz9E9UZnxYSy/diIN1lZiI8wcb7KxZFYebXaH4fEcDsn9a/b4CHve+r0RLJqRQ6PNjpSwcuMhZo5Lo7zWyitflHDr+TmMGRpDk81O2fEmqhpsHvtNirL4DRdpiuqd+U0DIdQz2IoMToau5tiNTI4iyhJEZryzWbf7fDEJaG61M3NcKoeqG7G2uf72tjbiIsyGf4vmVgd//qSIR+fm45CSemsrbXbp9N622CmqrOfZT4t9PneopomZ49J8zrHHP9rn0+Jo2fpCXv7JJD7ff5TkaAvfzUrkUE0TlfVWEiMtBJngywPVJEc7c+gO1TRRUWd1dt8YGkVmfITHd104PYe73t7OCz+epJ/L7f2OKo9RoVB0lUFlkA2Pj+CpayewrbSWnKQon5wYo951t6/axvsLp3LP7Dyfkv8X/lfCL87J4vErx/s08tY+32xr03PDSqqbCQ02seyqCdil84JdeqzJY2X/8NxxpMZYfLxni2bkIEyCyyb6CnI+9qHzhvTU+iI9HPP0f5w5bOZgQWxEqF695i1iqyXgl1Q3Gib8R1lCOvxNtaR+f6Ge3vROBWKRQSDRlRw7720z48NYcV0BIUGCpCgLB6obuPCJT4kLN3P92Zk89XGRvs+ls/J4ZK2vlp97f8k9FU7D68/XTuTPnxRy2wUjSIsNp6m1zedcvHvmGJ5cX8TlZ6QbnmNGLY4+KzqqV4Bq39FbHDkzPsxjkaRtO2xImEffTm3M2rnc3u8Ivo3HH7p8HENjLcRHhCrjTKFQGDKoDDIAW5tkxYZibpyaZXhhT4txCltGhgbRZpdY2xxUNrQw6bRYVlx3BjWNrSRGhfLMhv1cNjGd0OAg/vJJEb//gbGyf5jL8yRdCf2FlQ1kDAknLTaUkcFRerUkOJP5y2qa+e0/tzEiKZJH5+Zjl5KQIMHTn+znp9NOJyMuzHDco1OieXjuOMJCgvh49xEeunwcRxtsJEeFct3zX3kYcE+sK+SlGyaRGHXi5nCkzpnw7667BjhDR+D3JuJeRFHV0GLo2ehN75QS4myfrgj5em9bUt3M/Fc2ssbVXFvzJBstEu5+ZwePXzmev3xSxCNz8ymsrPdR2ZeutmJHG1q486IxtNgd3PDi17qB94grBDo8IYKaplY9Mb89iQv31+yuTdy/I3g2BZ85Ls0nd/LW17ewcv7ZPPeZ/3O5vd/R+xjawm7eFGevT1VkolAojBhUBpn3RdTowl5W62zg7b7if3drGTedm+PRi2/JrFxqm2xYQkxcPSmDR/69m8Uzc1my+kRezOKZubz8v2I9N0zzDtQ02Xh4bj5Hjjd7HF+7scWFm7kwL5Xb3PLRFs3Iofx4E3UtdsNx7z5Sp3sDFl+Sy+2r/neqzAAAK+9JREFUtmFrk/z+4tHcODULcArGagK4EulxA06OtugN0rVwrruHor2biFZEESjeKSXE6Z+OcuzcQ23NrXa/OZdx4WZd3sWfZ7TR1sblE9N5c9O3XJCb6jE3tLlgCTFRerwZIQTRlmCsrQ7Ka608tHavvq8nrh7Pg+/v8ZhH3jlkYSEnxJS11xxSsmB6NuA89/dXNdDS5uDPP5xAW5uzwCUxKpQ3DWRqWu12HrtyPA+t3c3McWkEmeDMzCFkxIV3+DtKP/1rtd9JVWwqFAojBpVBVlFnJS7czGUT0w2T6rVQnveK/+ozMyg93uRh2Cx+Zye3np+DMJk4UtfI5NMT+WBnOX+97gwq61pIi3M2ZP7JlNMxCec+3FXNm21t1LW0eRhX2gXbXyuYBedlGybqe4teLnl3J4tm5CAlHkadu0Ho7bVyN6ZONhdMeacCn/Zy7LzDcO5CwkY5l+7nndE+Dx1rAuDSM4bxpw/2sOC8bNLiwjhwtFE/D93PyRd+fKbhfmLDQ/R8yCsK0slOiuSv153B9tJamlsdvPi/gwDMn5ZFWkwYcRFmjtQ26wLNJxY0zfzlP8V+m6u7dxyIDTMzPj2OVrvTu+W9MOkoV9HoPekKq/Z2GF+hUPQPBpVBpinuu/fNe/zK8TTa2kiIDOV3b273WfGnxliICQvxuIAvnJ7D2h3lRFpCPKoq75o5huONNh77cJ+h/pK2vysK0gk3BxNpDvKokNQakfvzOFjbHPqNad6ULEYkR2ISgvvX7PZZ4afHGetGOVvPRJAeE0ZxVYNH0rFmTO2rqDc8fkknkpSVdyqwac+L6e1BdhcSdjfSNU9Zc6ud3/9gNCv+s9+wYviVL5xVk5HmIC7JT8Pa5uCdb8q47jvDcUh8qih3Ha5jyaxc/vxJke6VGp8ey3tbSwGnR8sSHESzrY0dhxs9tP4Alq0rYsH0bGLCQny6ZWhiyUbhVa25uuZhXjQjB2ubnUM1Tboxpm2rLUw68gZ7v6f9HqCKTBQKhTED2iDzrnRyOKTHxbikuplfvb6FBedlk+zq56jhXjlopAn26Nx83fukvX7P6l08Ojff2U7GQH9p0QynUWZUIdncamdcegy3XTCSemtruyvs8lorz31WzLPXF7C19Lih6GVTi7FUx4jkKKy2Vt7fdcRw5a8ZUkbH/+bb4z5J0sr71b8w8mKmx4Sxvew439Y088jcfJ7ZsJ9tZXWU11r5qria535UwNF6m19P2T2z8xgSGaKLA0sJa3eUc8N3MokNN+t5ktqiRUrJu1vLKKlu1sdlCTFxrKmVnWXHWThjhIfq/l0zx5CVFK2f/yHBQUzNTuDtLb77mJqdQHVji+G5X2dt9bvYyUmKYsH0bKSElz8vISshguiwEN2jrnXoWLWpVPdutecN1t6rqLPSapfc9fZ2PXdOFZkoFAojBqQwLJyoELt42adc88yXXLzsU/YcMfb8WNscVNQ1c9+csVhCTKza5PQMtOetwqWof9N52dz6vRE8ec0Ebjk/h+AgZ4Niw8T71GjDCsnTEyIZOzSGOmsbr3xxkOToUO6ZnacLWWbGh/HnaycSFmJiwfRsMuPDWHxJLgePNhBiEj5is4svyXV6E0J8xTf3VdSTGB1muPLXxFO11b/7PhfNcAp0Gm2v6F+4CydnxIXzzvbDXLXiCxa8+g2/+edWrpmUybi0aMalRXPxuKHMe2kjhVVOMWKjcPZdb++g7FgzqTFhPPtpMW9uLuWisak02Ozc7dIa07a9Z/UutpbWctN5OWTGO3uxax6kNzeXkhoXoRtj7p/RjLEn1hXyy79v5rrnv+Kmcz33sXRWHvlDY0iPCzc896NdFcNG70ngqfVFLP+4iJomGykxFt2j/txnxTy1vohnP3WGO1OiLT6/o7sAtft7Z5+ewJTsBF748SRem38WaxZOVQsZhUJhyID1kBlVQRVW1ht6fs46bQhBJmhts7P82olsLT1OfEQIz1xXgESSGR/GzHFp+ir53a1lmINMPrkoC6fn8MD7u/n9xcYVl/4EZu1SUlLdSJPVxsOX51NRZyVjSBgv3zCJmmYbjS12Dy/DPbPzWLPtMLnpsZyREUtseAgFwydRWd9CUlQo728rIykmnKWz87jbTapDC5sMiwtvN7Hb3YtSUWclSAg2f1vD5WekexQGaNu3tTnYWV5Lea2V1JgwclOjO2y3pAgMdpbX+lQZLlm9k8evHA/Ar1xzSMtdtLbZDc+d6DAzj3+0lwXnZTMmNZpfvrrZp5JZC3WmxYRRVtPEnRePYefhWvLTY/nLJ4WU11oJ8tNWaWSyZ0WytdVZyfnw3Hz2uURkl39SSFZiBPERZkMJl6p6q77Ycn9v6aw8Xv5fMeCcp/fOySM3NYaSY02G4c0LxqR06TdWYXyFQtEZBqxBZlQF9frGUu6/1FPcctGMHH77z22YgwVLZuVx06ubGJEUyQ3fPY2vS44RHhKsV0+WVDfrHqiKWqvPxVprDn7fml0+zY4XTs+hrKbJ0FALDhJsLqnm/NGp/OiFr/Sy/yfWFeql8t4eifnTnAUGmUPC2VFeR1FlAw4JhRX1jM8cwlubyzjr9HiPMJKWPJ3SCfFUk8np6dtzpN4wF0YrDGhrc/DW1rKT6oGp6D3cw/epMRaqG2yU11kRGBtAEthzpE5/T8tdvPPi0YbnTvHRBkqqm3n0g30smJ6tv99eUcBdM8fwxsZSVmwo5uG5+Uw+vZ6cpCjD/VtbjQ3BfRX1PLX+RC6ZM0To8JBw0cKQd148mlvOzyEh0syyqybQ6nCQHhfGyMQoRqZEcqTW2VA9NzWG4GATlfXGlZRVDVZOT1LGlUKh6F4GrEFmVAVV02RjYkYsaxZOpaS6kW++Pa5XV910XjaVLiPupuk5lFQ3smJDsY8hUl5rZcm7O1lySa7hxVoTgB0SHswTV01gZ3mtrr8EGK7cI0KD+OHk0/jZK5uICzfzu4tHU1RZz41TnS2WjI6TMSScmLAQ6lvaKKtp9hjrohk5zD8ni1tf3+bjxbtr5hie/6zYp1LTKK/FyMuoFQaMSolmeHwE28uOG/bAzEmKJH9YXLf/XRVdx716ckRSJNeclcmSd52hxKeunWBoANkdEof0zCUsr7Xy6Ad7uG/OWH7/lmeOl7tRpO3DvSLYKNR5z+pdzJuSxfKPizh4tJGn1jslV4zmSEiQyXCcUnoeMznawpAIsy7h4v7eniP1+muWEBNr3KqG84fF+bQIU10fFApFbzJgDTItD8pdR+iMjDhSIi0crrfS0uYg3BzED8/KwNrmDIk4HG08dc0Ews1BPPbhPkPv13KXNll4qHGrIU0A1mQysf1wjc+N6uXPSzx69b38eQl3xI5C4MxJu25ypk/lZmZ8mE/ycmqMhZToMCrrmw3DKiuuO4PFl4whPS6M741O5mhjC612yW//uc2lkt6gexDOOm0I3z09wSevxZ/W0oRhsZwzIgmTSejhS+9tOuqBqeg93A1r7xZfK/6zn8WX5OoGmu7NPd7Eu1vLfAz3335/FCOSolh2tdOQ21NeR7211aOwZMPeSn2fr3xRwvxpWWQnRfpdwFhCTGQlOBcDNU02IsxBvPKTSVQ32ggNdrYoe9JVFONeIb14Zi67yutYMD3bqRV4Xg7jhjq9W95VjvfOyePJ9YV6lfOIpCikpN3elIGiq6dQKAYHA9YgM5kE549Mcpbmu4UOl87OY/nHhdjapE+7F2fJ/R5+eU624c0jNPhEknv58SbDtjArNx7i1u+NIDo0WJex8PbSea/U91U0MDlriGF15j2rd/ncFBbNyOG2N7ZR02Tj3jl5ukCn+1gbW+z84u+bPSoit5fV6jfO8loryz92Vkx+b3SS4U3Jn4cg062aLDUmzHCblA56YCo8EUIcBOoBO9AmpSwQQgwBVgLDgYPAlVLKmq7u292wbvaqvt1WVgdflvDCj8+kutHG7vJ63Zt73eRMVm48xLwpWc4FTWYc1Q02Lln+mcc5v3ZHucdcOHdUEk//p0g3+O0OqG5oMTxPTAIWTs/B7nDoVY5/+U8xd1w0CpMQLHl3B5dNTGfayCQAFpyXzciUKJpsnnmV9186lovHpGA2BwF4VEAmRlooPd7IDydlEGEJ8ZDn6EjwWOnqKRSK3qJPknyEEAeFENuFEFuEEBtdrw0RQnwohCh0/X/K8a7dFXU+FVt3v72DmePSDPWIFr+zk5nj0nTvlzuWEBPZSZFkxodx75w8hidEEhps4uG5+dxx0SgenZvPyJRIrj4zA0uwiTqrjdMSIvRqTW0f98zOI8YSxILp2Syakc3vLhzFm5tLOXyskawEYy9CcVUDf752Ik9cnc/8aVl6mFULD15RkO4zVs14dK+IzE2N5t45eR7j0RKYjTCqtvT2EHR1n4p2OU9KOV5KWeB6fgewTkqZA6xzPe8ymmENGJ7b5mBBkEkQExbCc58VU15r1XPGZo9PI29oNOPTYwk2CT3/Ek54jn867XRMJqextHBGNjlJUZRUN7P84yJWbXJW5ra0ObjXVcUMWlVkLmEhQazceIh9lY0eVY4JkWZCg02MTYtkZEoUw2LDSI8L55O9FSRGhvpUCd/5r+3srarH4Wpq6V4BKQTc/fZOUmLDdWNM+9ytr2/hwNFGiqsa+Hz/UYqrGvR9eO/Hu5JSoVAoupO+9JCdJ6U86vZcu/k8KIS4w/X89lM5gL9wWkZcmP7Y+z0hoOx4k6HQ5UNrd/PrC0YhHU7j7bKJ6dQ0txIWYuLbmiayEiMYMzSaoooGzMHBhJud4cCXbjiTqgYbSVGhHKhq4LGPPNu+AHxb20JcmMPQizBp+BBe+6qEoUMifEKg1lYHw+Mj9M9pnr4ws4k7LhpJQ4vdQztpTn4aOUm+CcxGdMZDEBxsYmZuKplDwjlS10JKdChj29mnokvMBs51PX4J+ISTmBPuobdnNniGKAsyY7iyIJPrn3cWk7iHBWuabKTHhvHcZ/s5KyuRjCHGfVT3upqEL5qRQ3qkhSCTU6rlijOGkRxtobSmiZf+V4I5WPDYleMprmpgdGo0f/v8IJ8fOMbSWXks/6QQOCHb8tD7ezAHC64s8AzhL52VR1OLcYL/3iP11DW3cXZWvMf5V93YwlUFGRRVGsve7D5Sx21vnDiG0thTKBR9QSCFLLvl5uPO0FjjcFpZbTMxlhC/OWAv/a+E3108yqNKS0vo31dRz9i0GJ9k+Vu/N4Ijx6266r5mxN235pDeNumKgnQ9+R6cN4PHP9rHvCnOismXvzjoEwa9b85YkmNCWbOzghunZhmOuaLOyoLzskmJtnCopon/+6hQb0vz7tYyrj87k6TIUMBpQBklMPujo5L9tjYHq3eWqyrLU0cCHwghJPBXKeUKIFlKWe56/wiQfDI7djesS6obKatp4rEr8qlvaWPYkHB+8uLXWFudXSBe/tyZ85WTFEV0WDBPrtvH9FEpLFtf6Pf803o3vvb1IX427XSe/6yYn0/LNuxdeevrW5g3JYubXt3MI3PzGZMWy0e7D/PI3Hwq61tAwgqXMO2yayZ45LtZW51SF3+fd5bhOEqONXP3Ozt56PJxXDJuqG5QmYNM7Y7fvTOF5jVTvSb7np4M4ysUgUhf3TG1m88mIcR812vdcvMBp5Gw9dsaqhut3D1zjEeY5M6LRhFtCSYzIdznvXtm5zElewi/ODcLc5BJF4Rc/nGRrrItJXx7rNFQ4LW6yeYTzpk5Lk1vPeOQJ8RkF0x3/osLNyOEU9vs5+dk6zk7C2dk85f/dwY/yE1hWJzTw6ElWXuHEHOSI7HZHfzh7R0sW1ekewbdj1/T3Ao4k5j9hWfc6ex2RjpWf3hrBzvLa0/2zzdYmSKlnAhcBNwkhJjm/qaUUuKcNz4IIeYLITYKITZWVVUZ7lwzrC0hQSz/pJid5fV8W9NMZZ2nqn15rZVl64qQSBqtbZyVlaifk+eNjOP+Sz3DjpqgK8DMcWksXb2Ls7ISdWMMTsyFyyam615ozbO2/OMiPth1lJpGG8EmwX1rdjN1RBILpmf7leQ42mjjgUvHGY7D2ursPekuWtxkc3rUtKpP98/df+lYXfDY/RiV9VYUAUGPhPEVikCkrzxkU6SUZUKIJOBDIcQe9zellNLlKfDBZcDNB8jIyPB531sXKzM+jMeuHE9RZQOjU6MoqW7i0Q/26e89dc0EGlrsFB9t4LEP91HTZOOBS8dS3dDCg5eN48DRBl7fWEpNk01vQnxFQbrhjcLbZnG/+YQGmzAHC0MZioTIUCYNj+OFzw7w0OXjaLVLrK12MmLDMJuDTng4UqI41tjCyvmTabLZ9Z6SACYh/IZgtapH7+bR/sIznd0O/IeFVZVl15BSlrn+rxRC/AuYBFQIIVKllOVCiFSg0s9nVwArAAoKCowtZxfe/Vyf/n8Tjb3INc0MiwvntIRw7rhwNA6Hjf2VLSz/xFltnJ8ew47DtR69KDVRV3/dLbSKSq0SWZOs0IpAgkye88O9ubn72LaV1rJ6Wxl/+eEZHG1o4WB1k8c4NINK83BpOXTufWCDTDBjVBIxYSGGrceUtEXA0u2RFIUiUOgTD5n7zQfwuPkAdHTzkVIWSCkLEhMTfd739tiUVDdz6+tbaGlzYA428egHez3e21ZWyx1vbtM9S3HhZo7UWbnnvd3csnILf91QzK3fG8GdF40C4PIz0nXxSncsISZGJTv74aW6Kgzdbz7DEyJos0sfz9o9q3exrbSW+a9s4uzsBI412Pjpyxv56cub+MFTn7F25xG9ND8rMZKC4fHkD4vj7NNPJBlrIq5GY9KOnxxtMdQVM2qB1Nnt4ESVpfdxVZVl5xFCRAghorTHwAXADuAd4EeuzX4EvH2qx7I78DgHy483+xSe/O7CUYQEmbjtn1u57Y1t/Or1LbTYg1j+SaGerL+j9BjpsWG6MWMJMTE+PdZjP+64V1Su3lbGohlOj5Z7EYglOMhjbFpzcyNPWEl1M7/4+yaGxlr0QgT3Y7kbVO7FKVof2FEp0YxNiyVjSMeFK4o+46QjKZ3xGisUgUave8hcNxyTlLLe7eazlBM3nwc5hZuPP4+NEFBV79t02CFPrOZTYyweoqxam6C73t7BgvOyefSDfQCMS4vW1fvdpSjuW7Nbz91aufEQVxVksHLjIRZOz+Hw8SZsdke7XqzHPtzH/GlZPoaQdz6Ld9P04fERhppJ2jiWzs5j3NAYNn1bY3h8zZug7dc9p8ZoO3e0KkvvHDJVZdklkoF/CWdvrmDgVSnlWiHE18DrQoh5QAlw5akeqLLe6tEwOyHKwpubvuXhuflIl+F/vNHGox/s8alifOb6AvYeqeN4cxsO4HhjMy/fMIkKV8HH1kNH9XPOOxdy6axchg0JZ9fhOq44w+k6/f0PRjMsLozRydEcqmmiqKrBJ3z6/vZyXrzhTKrqnYbfMxv2e3jCjjXafApwvA2qjopTlLRFwHLSkZSueI0VikChL0KWPXrz8U7k14QgRyVHkRgV6hMC0bTCjERZ3dX5tWbD5bVWpo5I4ukNTp2ljLgwymqbdSkKgGXrC3n2+gJ2HK5l5rg0Vm48xG0XjNJDNkZJ0eA/7OluCLUXTtRuLEdqrYQECY43t/KnK8YzbmgMZnMQSVHGumKJkRaP/fpLfjYK4wQHm7pUuanwRUpZDOQbvF4NzOjOY3mHLLWqxj99sEdvDfanK/INDfKKOqse7tcWIVu+raGuxc5v/umUoUiNsTBvynDSYsN5dG4+xUcbaXM4CA0xseI/+/l4n7Ow2hJi4r2bp3JaQoTHeefeNzYsxERUaDA/fuFrjzlZ1WA7kdOJ4Kn1RR5hyLFpsT4GVXvFKarXZGByKmF8haI/0ut3TSllsZQy3/UvV0p5n+v1ainlDClljpTyfCnlsZPZf1RosB7m0G4+KzYU88tXv+G2f25l8SW5HuGJrMQIlszKNRRl1RKRLSEmDh1r0vW+gkzooZtvjzfr4U4Np2RAKw++v5fnXBVnz2zYz33v7fYbgtGeey/MvQ2h9sKJ2o3lO9kJnHlaPN8bk0LB8CG6WGaQCZ/jL5qRQ5DJc79Gyc/thXG0ys3v56WSPyxOGWMBjHfI0trqYMm7Tv09cBadWEKCDEOOh441eXzuiXWF2OzS43wpr7WSEh3Gr17fwoJ/fMNjH+5j2boibl+1nctdnjF/592GvZX8fFq2Xkzz5PoiGm124sLN+jHd5+TS2c6m4N5hSOXd6v/0ZhhfoQgUAkn2ols4UmfVGwuPSoniN25l8yXVzTz9nyJnyb+1jdRYC3e9vYMYSwg/nXa6oVcgyITuKbt75hieuHo8SVGhHvIVRt6kKEuwrjz+9IYiZo5LY/nHRbz8eQkrrjuDemsbQggeWrtbX+3/6vwRDI094cUyMoT8tTMyCid6o8kaeDddnpARq0sXaNtpyc/j0qLJSY5SYZx+jhaOLvSjxZURF8aC6dnkpkazdPVOPeQYF27mioJ0Tk+MpLSmSfcSa5+ztjk8zpdRKVHgJ6nf7pAsmJ5NhDkIu0Oy50g9ceFmRiRFMnVEEhlxYRyubdY7T2hGn9ayTNvPmNQoVs6f7GwKnhxJea2VobFhRIUG8+WBaj2Mr87Xfk2vhfEHE8PveO+kP3vwwR9040gURgw4gyw52qI3Fl4w3bcFUkl1M7uO1PPUeuf7zh6RzeytqDc0rLKTonjAlRu243Adyz/2bIC8alOpYTPkNrvDQ8RVuO4NNU02ws3BxISZWfjaZj08IyW8+lUJy6+dyJp28llOpeGx+29j9Fn3/WpehzVKj6nf05lwdFltM6s2lTL8ghGUVDfzyhclLJqRQ3RYiI9AshbGdw+3a+fLvClZjE6NMjyGMAlWbSrlusknWpZlxofx83OyfXppasfQcizd9zM61dnY3jt0r1VB1zTZlLhrP6c3w/gKRaAw4GJLRu1+3HEPC2r5Y4BhmG7RjBzdGFs6K4/V28oAp1GV4jLKfvv9keQOjWbRjBwWTM9m/rQsUmIsHD7u2Qxcq3ZcMiuXpGgzuanR3Dw9Rw/PPPdZMTdPz2FUcnS7rVo6086oK7+N9tlT2a8isOkoHL1oRg6f7KnkdxeP5ttjTXrosaHF7tNqaNn6Qu68eDSZ8WEsnZWrzwlNwuXTfZV6w3L3Y2jhxcsmeqYGzByXphtj7se4bGK6/lltCrifk0ahe03vr72qYIVCoQhUBpyHzL2i6lhjCyOSIvmtq++dVu11vMmmh06Wzsrl7nd2Ul5rZeXGQ7pmmXYT+N1Fo0iMCuWDHYe5b85YappaiQ4L5k//3svUEUnY2hw8+u+93DjtdJptbYSZg3l2w37OHeVshqwJzkaHBfPX687gxc8OkBkfTsYQ/22MHA7JgaONlBxrJMIcTHJ0KBlDInSJi5OtClPVZoOTijrPykqt72ROUiQWcxB/+vdeLsxLpaiynjc2lurhSn96YoWV9Sw4L4evD1Rx/6Xj+PJANXaHU2H/qoIMXvmihH+4GpbXNreSGmNhdHI0BZlxPhW8HWmW/er8ERRkxvGd0+M9zkl/oXvNm9bZMH5XMKpuVvNDoVB0FwPOIIMTVVPpMWEcrj3C/GlZOCScNXwIf3h7uytM6SQzPowXfnwmNU029hyp557Vu/QcGUuIieXXTuSBNbvZV9nA9NGp7K2oZ1pOAvsqG9hWVseC6dnsq2xg4T++0fdpCTFx47TT9Ryypz4u1HPILCEm7rokFzBuY2RURbloRg45yZFMH5msG2UnWxWmqs0GH0aVlYtm5DAyJYrjTTbOHZWktxaqabLp+WAjk41Dj3YH3PX2DuZNyeLLA9UsW3ciBL5svTPn67nPijEJwQVjToQNsxIjdU+x9z69n49MjmLelCxe/aqE80cnc3qS5znpL3TvLjbbneKuXRFLVigUipNhwIUs3dl2uJbf/tMp+vrU+iIq6q0exhg4c8qONth4aO0eLMFBHkKXd80cw9LVOzl3VBKLZuRw4GgDT60voqXVrlcrajlk7uGZe+fk8acP9uihyKvPzNBFMDsKA/oLxWwrrVUhGMVJYVRZ+cS6QuwOpw5fxpBwj3Cmlmf46Ad7WDLLM/To3qIoyOTctzva63+8JJdH/r3b55z1rvR9d2uZT3hz0Ywc7l+zW587QQZXKaMQu7vYbHeH27silqxQKBQnw4D0kGkc8QprhAYHGa6qm1ra9ERm9wrEemsrJdXNpMWE8X/rCrn/0jxem38WxxpsHtWK4AwBnZYQwejUaDLiwpmYEUdlvZXESAtBJpiQEdupMKC/UIxD0u0hGMXgoLLe+JyqarBSVW+jos6q5425txYakRSF3WHnpRvO5L/7nWFJ94T+cemx3LN6p8d+LSEmCjLjOHK8mbLjLT7nrFGl7z++LOGlGyZR02SjqLIBcHbEcK8CHp7ged57h9+7Os+6yqlUNysUCkVnGNAGWapXWKPseJNPReTC6TkcbWzRb0haBaIlxMS8Kc6KtEM1zdQ02ciMjyArMZKt3x43rFZ87aeT9Yuzd+jP+4biD3+hGJNA9ddTnBTtVeaahODRD/boeWNateS9c/IICTbx4oZiHr1iPKNSoj3CdfdfOpbXvz7IVQUZHor898zO4/ZV26lpsjF/WpbPOeuv0jcxKpTEqFBuWbnFcJxGGIXYOzvPusqpVDcrFApFZxjQIcuxQ2NYOjtPD2v846tDpMWGMX9aFg9dPpb507J45YsS/vbFIZ/KM63nnva/ewjEHIxPmGXxJbmEhpz6itxfKGZceoyqeFScFO7nVGqMhYUzsnl0bj5SQm5KJDed62x3NG9KFgtnZLP82om8t/Uwi177hqsnZZIeG84Fo5NZcV0BC2dkM29KFmt3lHH+mKE+n1uz7bAuVzHCpV/nbyzQfyp9A3lsCoViYDAgPWTu1VAThsXy2k8nU3a8meRoC3kp0eSlxXCssUXPGbO2Oli58RArrisgJEgQbg7CZnfw3ex4Wu0OLsxL8QiBlFQ388GOcv563RnUNLYSFxHCi58dYEh4CKNTY09p7Hoo5uYpHK61Um9tIzk6lLzUGJU83If05wo77Zwas2gqmw8d585/bfdITP9BXhLDE8KprG8BCU98tI9tZXUA/OGtHUzMiANg/isbPTxExxqdrbkq6604HJ6fs4SYGJ0SbdjCqD9W+p5KdbNCoVB0hgFnkPmrhrooL/VEtZclkuHxERxvrtArME0CbHY7U7KTO7zIpseFMSkrnp+9ssmjai0tLqzbvsPmb4/7NOyek5+m2hL1AQOlwq62uZWD1Y3cODWLVZtKKa+1cuvrW3jv5qlMOi2ez/cf5ZpnvvT4jJYn5d7JQWNjSS1tDgcX5qayfm8F545KYtrIJIIEjE2P4bQEY+9Rf630DeSxKRSK/s+AM8j8VUON8lKcP1jdyIJXv/HJCemMMr0lOMiwau17o5O75TvsLK/VjTFt/394awc5SZHkD4vrlmMoOk9nz6lAxcigdFfD332kjtMSIjrMk2rvPVub1NuJaQarQqFQKDrPgHO3tFcNdTLbGVHV0GL42aONLSc5ak+0HBzv/R+p7Xhsiu7nVM6VQMDIoHRv0r2vop6D1Y0nnd+lJCEUCoXi1BlwHrLOVkOdak/Inqy4So0JM9x/Soyq6OoL+nuFnT+DMsiE7in7zunxZCVGcsHoZFbOn0x5rZVUV/eIjvK7OiMJ0Z9z8BQKhaI3GHAess5WQ3W0ncMhKa5q4PP9RymuasDhkF0+xsmSmxrNvXPyPPZ/75w8clNjumX/iq7R3yvskqIshj1dRyVH8coXzmbcSVEWHA7JB7sruGrFF/z8b5u5asUXfLC7Qj/3tRwq7z6r/vafGOk0WLWQ6cXLPuWaZ77k4mWfsnbnEY85pVAoFIOdAech62w1VHvbdZTE3dMVV8HBJr99LhW9T3+vsNPU8b1bJ5Uca6KmydZh6LGjXDl/+9cU9vt7Dp5CoVD0BgPOIIPOV0P5264zN5Cerrgy6nOp6Dv6c4WdkTr+y5+X8MdLxrBm4dQuhR67sn9NYX+gqNyrsKtCoehJBqRBdqoMlBuIQgH+1fFzkqM8zueTzZXzt3/tc/09Bw8GjvSJQqEIXJRBZsBAuIEoFBpaDpy3MeEvr7Kj7bq6/5PdbyChwq6Kwc7wO9476c8efPAH3TiSgYsyyAwYCDcQhUKjO/IqT2X//T0HD5TXXKFQ9DwBZ5AJIS4EngCCgGellA925/47kwcyEG4gCoU7p5pX2V37l71UWNnd+V7Ka65QKHqagDLIhBBBwHLge0Ap8LUQ4h0p5a7u2H9X8kD6cxK3QhFI9Hb+VU8cT3nNFQpFTxNoOgqTgCIpZbGU0ga8Bszurp0rRXGFovfp7XnXE8fTvOZrFk7ltflnsWbhVJXQr1AoupVAM8jSgG/dnpe6XtMRQswXQmwUQmysqqrq0s77ewscxeBECHGhEGKvEKJICHFHX4+nq/T2vOup4/kTxlUoFIruIKBClp1BSrkCWAFQUFDQpYwUlQei6G/0dBi/N+jteafmuUIRWKgKzc4RaB6yMsBdCjXd9Vq30N9b4CgGJT0axu8NenveqXmuUCj6I4HmIfsayBFCnIbTELsauLa7dq6qJxX9EKMw/lnuGwgh5gPzATIyMnpvZJ2kt+edmucKhaI/ElAGmZSyTQixAPg3TtmL56WUO7vzGKp6UjHQOJUwfm/R2/NOzfOBS09LIykUfUVAGWQAUso1wJq+HodCESD0aBhfoehPDIScSoXCHwFnkCkUCg96NIyvUPQz9JxKACGEllOpDDKFD6dSTHAqnGwhQr82yDZt2nRUCFHSy4dNAI728jH9EUhjATWezO7eYVfD+GpOBNRYILDG0xdj6e450WFOJXjmVQINQoi93TyOjhjsf/f26NJ4xEM9OJIe+m06GLPfOdGvDTIpZWJvH1MIsVFKWdDbxzUikMYCajw9RVfC+GpOBM5YILDGE0hj6Wnc8yr7gkD6rQNpLBBY4wmksUDgyV4oFAqFQuEPlVOpGLAog0yhUCgU/QU9p1IIYcaZU/lOH49JoegW+nXIso/oMze4AYE0FlDjGawE0u8cSGOBwBpPII3lpOgNaaRuIpB+60AaCwTWeAJpLAgpA1K2SKFQKBQKhWLQoEKWCoVCoVAoFH2MMsgUCoVCoVAo+hhlkLWDEOKgEGK7EGKLEGKj67UhQogPhRCFrv/jevD4zwshKoUQO9xeMzy+cLJMCFEkhNgmhJjYS+P5oxCizPUbbRFCXOz23u9c49krhPh+N49lmBDiYyHELiHETiHEItfrffb7DAbUnOjUeNScGESoOdGp8ag50RmklOqfn3/AQSDB67WHgTtcj+8AHurB408DJgI7Ojo+cDHwPiCAycCXvTSePwK3GWw7BtgKhAKnAfuBoG4cSyow0fU4CtjnOmaf/T6D4Z+aE50aj5oTg+ifmhOdGo+aE534pzxkXWc28JLr8UvAnJ46kJRyA3Csk8efDbwsnXwBxAohUnthPP6YDbwmpWyRUh4AinC2PemusZRLKTe7HtcDu3GqePfZ7zOIUXOic6g5MXhQc6JzqDnhhjLI2kcCHwghNglnKw6AZClluevxESC5l8fk7/hGLUXSemlMC1zu3efdXPO9Nh4hxHBgAvAlgfn7DCTUnOgcak4MHtSc6BxqTnSAMsjaZ4qUciJwEXCTEGKa+5vS6ePsM92Qvj6+i78ApwPjgXLgT715cCFEJLAKuEVKWef+XoD8PgMNNSc6Rs2JwYWaEx2j5kQnUAZZO0gpy1z/VwL/wulKrdBcmK7/K3t5WP6O3yctRaSUFVJKu5TSATzDCXdzj49HCBGCc5L9XUr5puvlgPp9BhpqTnSMmhODCzUnOkbNic6hDDI/CCEihBBR2mPgAmAHzjYdP3Jt9iPg7V4emr/jvwNc76oSmQzUurlkewyv+PqlOH8jbTxXCyFChRCnATnAV914XAE8B+yWUj7m9lZA/T4DCTUnOoeaE4MHNSc6h5oTnaQ7KgMG4j8gC2f1x1ZgJ/B71+vxwDqgEPgIGNKDY/gHTvduK85Y9jx/x8dZFbIcZ5XKdqCgl8bziut423CezKlu2//eNZ69wEXdPJYpON3M2/5/e3cMItUVxWH8+2OsIhgLC4tUKko0otmAWIgokmKxCKiIIbVgp51gk8bGRokhWNnKYkgpuEJiKiEkUdQtNAmYIjZuI9gIyrGYB442zuyO3n3r94PLG95w79w7zGEO985wgNtdm275/iz3ZkyMPB9j4gNpxsTI8zEmRmiWTpIkSWrMI0tJkqTGTMgkSZIaMyGTJElqzIRMkiSpMRMySZKkxkzIJEmSGjMhWwaSnE4y19UJu51k55j9v0vyf9f37yQ/J/lshH6buz63kqxf+AqkyTImJPWNCVnPJdkFHAC+qKptwH5eL446qnNVtb2qNgIzwC9J1r6lz9fAT1W1o6r+XcBrShNnTEjqIxOy/lsHzFfVM4Cqmq+qR0keJjmb5G6S35NsGHXAqpoBZoFvAJJMJfktyZ9JriVZl2QaOAEcT/Lr5JclLZgxIal3TMj6bxb4NMmDJD8m2TP03JOq+hz4ATg/5rh/AZu7wqwXgENVNQVcAs5U1VXgIoNdhL2LXoU0OcaEpN75qPUEtDhV9TTJFLAb2AvMJDnVPX156HpuzKHTXTcBW4HrgzqtrGBQp0xakowJSX1kQrYMVNUL4AZwI8ldXlWxHy5UOm7R0h3AHwy+hOaqatdi5ym9L8aEpL7xyLLnkmxKsnHo1nbgv+7xkaHrzTHGPAh8xWAX4T6wtvuhNElWJtmy2HlL74oxIamP3CHrv1XAhSSfAM+Bf4BjDP5ltibJHeAZcPQt45xM8i3wMXAP2FdVjwGSHAK+T7KawWfmPDA3+aVIE2FMSOqdVI27a68+SPIQ+LKq5lvPRVoKjAlJS5lHlpIkSY25Q/YBSXIaOPzG7StVdabFfKTWjAlJS4UJmSRJUmMeWUqSJDVmQiZJktSYCZkkSVJjJmSSJEmNvQQFe9OOGSpKagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now all together\n",
    "df = pokemon_df[['HP','Defense','Sp_Def']] # another way to filer\n",
    "\n",
    "fig, axs = plt.subplots(3,3,figsize=(10,10))\n",
    "axs = axs.ravel()\n",
    "columns = df.columns.tolist()\n",
    "col_x_col = [(col_1,col_2) for col_1 in columns for col_2 in columns]\n",
    "\n",
    "for i, (ax,col_pair) in enumerate(zip(axs,col_x_col)):\n",
    "    \n",
    "    if i in [0,4,8]:\n",
    "        vals = df[col_pair[0]].values\n",
    "        ax.hist(vals)\n",
    "    else:\n",
    "        sns.scatterplot(x=col_pair[0],y=col_pair[1],data=df,ax=ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "44217e3f-f9c2-481c-a38e-290fa06cb8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wow... this is way easier\n",
    "sns.pairplot(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2caecf50-50fd-4240-be4c-ee407c75e884",
   "metadata": {},
   "source": [
    "### GroupBy for quick data exploration\n",
    "\n",
    "The GroupBy instance method let's us partition a DataFrame by desireable characteristics (the column descriptors or categorical metadata stored in the columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9f5f6310-77e6-4224-8751-d8da6ebfeea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type_1</th>\n",
       "      <th>Type_2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>...</th>\n",
       "      <th>Color</th>\n",
       "      <th>hasGender</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>Egg_Group_1</th>\n",
       "      <th>Egg_Group_2</th>\n",
       "      <th>hasMegaEvolution</th>\n",
       "      <th>Height_m</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Catch_Rate</th>\n",
       "      <th>Body_Style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.71</td>\n",
       "      <td>6.9</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>True</td>\n",
       "      <td>2.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>Red</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>False</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.5</td>\n",
       "      <td>45</td>\n",
       "      <td>bipedal_tailed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Red</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>False</td>\n",
       "      <td>1.09</td>\n",
       "      <td>19.0</td>\n",
       "      <td>45</td>\n",
       "      <td>bipedal_tailed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number        Name Type_1  Type_2  Total  HP  Attack  Defense  Sp_Atk  \\\n",
       "0       1   Bulbasaur  Grass  Poison    318  45      49       49      65   \n",
       "1       2     Ivysaur  Grass  Poison    405  60      62       63      80   \n",
       "2       3    Venusaur  Grass  Poison    525  80      82       83     100   \n",
       "3       4  Charmander   Fire     NaN    309  39      52       43      60   \n",
       "4       5  Charmeleon   Fire     NaN    405  58      64       58      80   \n",
       "\n",
       "   Sp_Def  ...  Color  hasGender  Pr_Male Egg_Group_1  Egg_Group_2  \\\n",
       "0      65  ...  Green       True    0.875     Monster        Grass   \n",
       "1      80  ...  Green       True    0.875     Monster        Grass   \n",
       "2     100  ...  Green       True    0.875     Monster        Grass   \n",
       "3      50  ...    Red       True    0.875     Monster       Dragon   \n",
       "4      65  ...    Red       True    0.875     Monster       Dragon   \n",
       "\n",
       "   hasMegaEvolution Height_m Weight_kg  Catch_Rate      Body_Style  \n",
       "0             False     0.71       6.9          45       quadruped  \n",
       "1             False     0.99      13.0          45       quadruped  \n",
       "2              True     2.01     100.0          45       quadruped  \n",
       "3             False     0.61       8.5          45  bipedal_tailed  \n",
       "4             False     1.09      19.0          45  bipedal_tailed  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2c32a67a-399f-4ca0-bf56-8c9ebf972763",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type_1</th>\n",
       "      <th>Type_2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>...</th>\n",
       "      <th>Color</th>\n",
       "      <th>hasGender</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>Egg_Group_1</th>\n",
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       "      <th>Catch_Rate</th>\n",
       "      <th>Body_Style</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.71</td>\n",
       "      <td>6.9</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>0.875</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "      <td>True</td>\n",
       "      <td>2.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>45</td>\n",
       "      <td>quadruped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>Oddish</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>320</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>Blue</td>\n",
       "      <td>True</td>\n",
       "      <td>0.500</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.51</td>\n",
       "      <td>5.4</td>\n",
       "      <td>255</td>\n",
       "      <td>head_legs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Gloom</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>395</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>Blue</td>\n",
       "      <td>True</td>\n",
       "      <td>0.500</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.79</td>\n",
       "      <td>8.6</td>\n",
       "      <td>120</td>\n",
       "      <td>bipedal_tailless</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Number       Name Type_1  Type_2  Total  HP  Attack  Defense  Sp_Atk  \\\n",
       "0        1  Bulbasaur  Grass  Poison    318  45      49       49      65   \n",
       "1        2    Ivysaur  Grass  Poison    405  60      62       63      80   \n",
       "2        3   Venusaur  Grass  Poison    525  80      82       83     100   \n",
       "42      43     Oddish  Grass  Poison    320  45      50       55      75   \n",
       "43      44      Gloom  Grass  Poison    395  60      65       70      85   \n",
       "\n",
       "    Sp_Def  ...  Color  hasGender  Pr_Male Egg_Group_1  Egg_Group_2  \\\n",
       "0       65  ...  Green       True    0.875     Monster        Grass   \n",
       "1       80  ...  Green       True    0.875     Monster        Grass   \n",
       "2      100  ...  Green       True    0.875     Monster        Grass   \n",
       "42      65  ...   Blue       True    0.500       Grass          NaN   \n",
       "43      75  ...   Blue       True    0.500       Grass          NaN   \n",
       "\n",
       "    hasMegaEvolution Height_m Weight_kg  Catch_Rate        Body_Style  \n",
       "0              False     0.71       6.9          45         quadruped  \n",
       "1              False     0.99      13.0          45         quadruped  \n",
       "2               True     2.01     100.0          45         quadruped  \n",
       "42             False     0.51       5.4         255         head_legs  \n",
       "43             False     0.79       8.6         120  bipedal_tailless  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at grouping by common values (we snuck this in early)\n",
    "target_col = 'Type_1'\n",
    "grass_type_df = pokemon_df[pokemon_df['Type_1']=='Grass']\n",
    "\n",
    "grass_type_df.head() # contains only those rows where Type_1 is Grass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6b61c0c3-dabc-486d-8824-da6506d6a57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fecea858190>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use group by as a way of applying these to all common values\n",
    "grouped_df = pokemon_df.groupby('Type_1')\n",
    "\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f57cf528-d2f2-4de2-a2eb-95213f940a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can apply methods/functions for a grouping and columns of interests\n",
    "grouped_means_df = pokemon_df.groupby('Type_1')[['HP','Defense','Sp_Def']].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "687d3682-9d4e-4f38-9324-b3af8922c7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HP</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Def</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bug</th>\n",
       "      <td>55.952381</td>\n",
       "      <td>67.698413</td>\n",
       "      <td>62.158730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark</th>\n",
       "      <td>67.178571</td>\n",
       "      <td>67.928571</td>\n",
       "      <td>67.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dragon</th>\n",
       "      <td>78.041667</td>\n",
       "      <td>79.125000</td>\n",
       "      <td>83.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electric</th>\n",
       "      <td>59.500000</td>\n",
       "      <td>59.083333</td>\n",
       "      <td>67.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairy</th>\n",
       "      <td>74.117647</td>\n",
       "      <td>65.705882</td>\n",
       "      <td>84.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fighting</th>\n",
       "      <td>70.240000</td>\n",
       "      <td>64.280000</td>\n",
       "      <td>63.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fire</th>\n",
       "      <td>68.595745</td>\n",
       "      <td>64.893617</td>\n",
       "      <td>69.468085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghost</th>\n",
       "      <td>61.565217</td>\n",
       "      <td>75.956522</td>\n",
       "      <td>76.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grass</th>\n",
       "      <td>66.196970</td>\n",
       "      <td>69.363636</td>\n",
       "      <td>68.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground</th>\n",
       "      <td>72.400000</td>\n",
       "      <td>82.166667</td>\n",
       "      <td>61.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice</th>\n",
       "      <td>71.652174</td>\n",
       "      <td>71.043478</td>\n",
       "      <td>76.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>76.526882</td>\n",
       "      <td>57.795699</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>67.250000</td>\n",
       "      <td>68.821429</td>\n",
       "      <td>64.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>70.617021</td>\n",
       "      <td>65.042553</td>\n",
       "      <td>82.382979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock</th>\n",
       "      <td>64.536585</td>\n",
       "      <td>99.756098</td>\n",
       "      <td>73.073171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel</th>\n",
       "      <td>64.818182</td>\n",
       "      <td>119.409091</td>\n",
       "      <td>79.409091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>70.866667</td>\n",
       "      <td>70.485714</td>\n",
       "      <td>68.076190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 HP     Defense     Sp_Def\n",
       "Type_1                                    \n",
       "Bug       55.952381   67.698413  62.158730\n",
       "Dark      67.178571   67.928571  67.500000\n",
       "Dragon    78.041667   79.125000  83.041667\n",
       "Electric  59.500000   59.083333  67.722222\n",
       "Fairy     74.117647   65.705882  84.705882\n",
       "Fighting  70.240000   64.280000  63.680000\n",
       "Fire      68.595745   64.893617  69.468085\n",
       "Flying    68.000000   61.666667  66.666667\n",
       "Ghost     61.565217   75.956522  76.478261\n",
       "Grass     66.196970   69.363636  68.863636\n",
       "Ground    72.400000   82.166667  61.266667\n",
       "Ice       71.652174   71.043478  76.130435\n",
       "Normal    76.526882   57.795699  62.000000\n",
       "Poison    67.250000   68.821429  64.392857\n",
       "Psychic   70.617021   65.042553  82.382979\n",
       "Rock      64.536585   99.756098  73.073171\n",
       "Steel     64.818182  119.409091  79.409091\n",
       "Water     70.866667   70.485714  68.076190"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_means_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31f3dd8-1e6c-4f52-87e5-4ccbc1c94c6a",
   "metadata": {},
   "source": [
    "## Let's practice with the Week 5 Data Exercise I"
   ]
  }
 ],
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