making_figures.ipynb

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   "execution_count": null,
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   "source": [
    "# Run this to make sure you have the packages needed to run the notebook\n",
    "!pip install seaborn numpy pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anatomy of a matplotlib figure\n",
    "\n",
    "There are two components to a matplotlib figure:\n",
    "1. [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=figure#matplotlib.figure.Figure) object which is like a canvas\n",
    "2. [`Axes`](https://matplotlib.org/api/axes_api.html?highlight=axes#matplotlib.axes.Axes) object(s) that live within a  [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=figure#matplotlib.figure.Figure) object, and is where all the plotting is done.\n",
    "\n",
    "Some things that belong to the  [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=figure#matplotlib.figure.Figure) object:\n",
    "- All `Axes` objects\n",
    "- Main title\n",
    "- Legend\n",
    "\n",
    "Some things that belong to the [`Axes`](https://matplotlib.org/api/axes_api.html?highlight=axes#matplotlib.axes.Axes) object:\n",
    "- Plotting elements (e.g. lines, points)\n",
    "- Anything axis related:\n",
    "  - Range\n",
    "  - Tick marks\n",
    "  - Tick labels\n",
    "  - Labels\n",
    "  \n",
    "  \n",
    "You can create `Figure` and `axes` objects simultaneously using [`plt.subplots()`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=subplots#matplotlib.figure.Figure.subplots) function. Some useful keyword arguments to know:\n",
    "\n",
    "- `figsize` : 2-tuple of floats, Figure dimension (width, height) in inches. This affects the figure size when saving. \n",
    "- `nrows` : int, # of rows of subplots\n",
    "- `ncols` : int, # of cols of subplots\n",
    "- `sharex`, `sharey` : bool or string, sets the same axis (range, ticks, and scale) of `axes` objects. You can control which `axes` objects share axis.\n",
    "  - bool : sets all subplots with same axis\n",
    "  - `row` : sets all rows with same axis\n",
    "  - `col` : sets all cols with same axis\n",
    "- `tight_layout` : bool, Resizes `axes` objects to remove as much empty space as possible.\n",
    "- `constrained_layout` : bool, Just a better version of `tight_layout`. Highly recommonend using `constrained_layout` over `tight_layout`.\n",
    "- `gridspec_kw` : dict, Arguments for finer control of sizing of subplots.\n",
    "  - `width_ratios` : list, Defines the relative widths of the columns. The list does not need to sum to 1.\n",
    "  - `height_ratios` : list, Defines the relative height of the rows. The list does not need to sum to 1.\n",
    "  \n",
    "  \n",
    "`plt.subplots` returns a tuple of two objects in the following order:\n",
    "- `Figure` : matplotlib Figure object\n",
    "- `ax` : either `Axes` or array of `Axes objects. Return an array when you have more than one subplot with shape (nrows, ncols)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commonly used `Axes` methods\n",
    "\n",
    "- [`set_xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html?highlight=set_xlabel#matplotlib.axes.Axes.set_xlabel) : sets the x-axis label\n",
    "- [`set_ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html?highlight=set_ylabel#matplotlib.axes.Axes.set_ylabel) : sets the y-axis label\n",
    "- [`set_title`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_title.html?highlight=set_title#matplotlib.axes.Axes.set_title) : sets the title of an `Axes` object\n",
    "- [`set_xscale`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xscale.html#matplotlib.axes.Axes.set_xscale) : Set the x-axis scale (e.g. log, linear)\n",
    "- [`set_yscale`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_yscale.html#matplotlib.axes.Axes.set_yscale) : Set the y-axis scale (e.g. log, linear)\n",
    "- [`axhline`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.axhline.html#matplotlib.axes.Axes.axhline) : draws a horizontal line in a given `Axes` object.\n",
    "- [`axvline`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.axvline.html#matplotlib.axes.Axes.axvline) : draws a vertical line in a given `Axes` object.\n",
    "- [`get_legend_handles_labels`](https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.axes.Axes.get_legend_handles_labels.html) : returns the handles and labels for legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commonly used `Figure` methods\n",
    "\n",
    "- [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=suptitle#matplotlib.figure.Figure.suptitle) : Add a centered title to the figure. This title is different from `set_title` for `Axes` objects.\n",
    "- [`text`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=figure%20text#matplotlib.figure.Figure.text) : Add text to figure.\n",
    "- [`legend`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html?highlight=figure%20legend#matplotlib.figure.Figure.legend) : Place a legend on the figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving figures\n",
    "\n",
    "Some useful keyword arguments:\n",
    "- `fname` : str, Path to where you want the figure saved. Set the format by ending the string with `.png` for png or `.pdf` for pdf.\n",
    "- `bbox_inches` : str, I always set this as `tight`. This saves any elements that were drawn outside of the Figure.\n",
    "- `dpi` : scalar, The resolution in dots per inch. Ignored if output format is vector format (e.g. pdf, svg).\n",
    "- `transparent` : bool, If True, the axes patches will all be transparent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lineplot with custom legend\n",
    "\n",
    "This example shows how to make a line plot with user-defined colors and the legend outside of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample Size</th>\n",
       "      <th>Algorithm 1</th>\n",
       "      <th>Algorithm 2</th>\n",
       "      <th>Algorithm 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.951229</td>\n",
       "      <td>0.778801</td>\n",
       "      <td>0.998002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.904837</td>\n",
       "      <td>0.606531</td>\n",
       "      <td>0.996008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.860708</td>\n",
       "      <td>0.472367</td>\n",
       "      <td>0.994018</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.818731</td>\n",
       "      <td>0.367879</td>\n",
       "      <td>0.992032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.778801</td>\n",
       "      <td>0.286505</td>\n",
       "      <td>0.990050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   Sample Size  Algorithm 1  Algorithm 2  Algorithm 3\n",
       "0          5.0     0.951229     0.778801     0.998002\n",
       "1         10.0     0.904837     0.606531     0.996008\n",
       "2         15.0     0.860708     0.472367     0.994018\n",
       "3         20.0     0.818731     0.367879     0.992032\n",
       "4         25.0     0.778801     0.286505     0.990050"
      ]
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   "source": [
    "# Generate points to plot\n",
    "xrange = np.linspace(0, 5, 101)[1:]\n",
    "X = np.e ** (-xrange)\n",
    "Y = np.e ** (-5 * xrange)\n",
    "Z = np.e ** (-1/25 * xrange)\n",
    "data = np.array([xrange * 100, X, Y, Z]).T\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    data,\n",
    "    columns=['Sample Size', 'Algorithm 1', 'Algorithm 2', 'Algorithm 3']\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melting pandas `DataFrame`\n",
    "[Melting](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) means to \"unpivot\" a data frame in such a way that one columns is the identifier variables (in this case, sample size) and columns containing measurements are stacked into a single column."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample Size</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Algorithm 1</td>\n",
       "      <td>0.951229</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Algorithm 1</td>\n",
       "      <td>0.904837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</td>\n",
       "      <td>Algorithm 1</td>\n",
       "      <td>0.860708</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>Algorithm 1</td>\n",
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       "      <th>4</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Algorithm 1</td>\n",
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       "   Sample Size     variable     value\n",
       "0          5.0  Algorithm 1  0.951229\n",
       "1         10.0  Algorithm 1  0.904837\n",
       "2         15.0  Algorithm 1  0.860708\n",
       "3         20.0  Algorithm 1  0.818731\n",
       "4         25.0  Algorithm 1  0.778801"
      ]
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    "df_melted = df.melt(id_vars='Sample Size')\n",
    "df_melted.head()"
   ]
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    {
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sm7RsrW4gnr+uBVnp4aTF35qsFEWSllyv8pKu6Y5FRKSPUkIi/YUSkihKSHq3puYQpeGZwza3q7ZsDicv26rjm1wtkrR4CYu/NWmJDA9T0iIiIr2VEhLpL5SQRFFC0vc1NocorYpKVip3TGDime4YvKSltcISlbREhowVKWkREZEkUEIi/YUSkig9kZCEysqoffY5Ms84HV927IUPpXs1NLWwpaohnLBEVVgqvARmV5OWwbn+7Y33bZIWP4Nz1YgvIiKJpYRE+gvNr9qDXEMDG792BK6yEgv4yfrWt5Id0oDkT0theGEmwwszOzynoamF0qqG1lnCNseouEQnLWU1jZTVNPLhF1Ud3jM/3Ig/OKoBv7XiEt4C6UpaREREZGBRQtKDzO8ncOwx1P3+WWoXP62EpBfzp6VQUphJSSdJS2NziC2RhCWqwhJpxt/crqelvLaJ8tomPtzYcdKSm5HK4KgEJbJey+CoxCXLr/9sRUREpP/QkK0oPTFkq/6VV9j6ne8CUPzm66TusUe3PUuSr6k51Do8bEtlu2b88PCweGcPy/Kn7lBZiSQsg8NVl9yMNMys+96YiIgknYZsSX+hX7X2MP9RR+ErLiK0aTO1T/+W3B/OSHZI0o3SUn0MK8hgWEFGh+c0t3hJy5aoJCUyTCySzESv01LT0MwnW5r5ZEtNh/f0p/naJCyRPpbo1/mZ6fh8SlpEREQkuVQhidJTs2xV3HwL1b98gJQ996D49b/qN9myUy0hx7Zqr6dlS1TSsiVccYkkLs0tXf/vOTXFGJyzvY9lcLuelsG5foLZflJTNIOYiEhvpAqJ9BdKSKL0VELStGoVm/9rEgCDnnka/yGHdOvzZGAIhRzltY1tqiw7VF0q62loCnX5nj6DYLZXXRncLllp/T7Hj18ziImI9DglJNJfaMhWEqSNHUvaAeNp+te/qV2shEQSw+czCrP9FGb72XdYbsxznHNU1TdHJSwN3gxiVW2Tl+r6ZgBCDm84WVUDrO/42XmZaQzOaVtpGZwTbsgP788OpKoaKCIiIjtQhSRKTy6MWL1gIRXXXY9lZzPkn//Al9Fxj4FIT6ttaI6xVksDW6q2DxmLd62WjPSUmElLdEN+QVY6KeprERHpElVIpL9QQhKlJxOSlm1lbDxoAjQ1UXD/fWSeflq3P1MkkRqbQ5RWeUmKl7Q0tM4kFqsZvytSfMagHH9U4hKdwHjVlkEaIiYiAighkf5DQ7aSJKWwgMCk46j/4wvULl6shET6nPQuzCDWEnKU1TS29rNEqittp0JuoL6ppfX8TRVeoz5UdHjfvMgikznh/pactn0ug3M09bGIiEhfoQpJlJ6skADUvfgi26ZeBGYMWf4WKcOG9shzRXoT5xzV9c1RUx97iYtXedm+r7y2Ka77+tN8rUPCWoeGtRkiplnERKRvU4VE+gtVSJIo8F//hS8YJLR1K7XPPEPOFZcnOySRHmdm5GSkkZORxqii7A7P84aIRScpkZ6Wtv0tkSFiDU0hPt9Wy+fbajt5NhRmpbetsuT4w83425OYrID+qRQREekuqpBE6ekKCUD5DTdSM28eqaNHU/TqMg0xEdkNoZCjrLZxe4UlvEbLljbDxBqoaWiO676Z6Sk7JC2DooeJ5QQozE5XtUVEepQqJNJfKCGJkoyEpHHlSrac+HUABj//LOkHHthjzxYZqCKziEUSlbZDxbxkZmtVA3H04+MzKMz2x+xrGZwTee0ny6/pj0UkMZSQSH+hcQhJlvalL5G63740v/8BtU8tUkIi0gMy/ans6U9lz0FZHZ7TEnJsq27YnrhENeRHJzK1jV5DfshBaVUDpVUNvL+hssP7ZqSntM4k1pq4RHpdwq+D2X7SUlVtERGRgUEVkijJqJAAVP/6ISpuvAnLyWHIOyu0JolIH1JT3xxeWLKB0qrtDfnRX7dWx1dtASjISt+huhJJXAbleOu4aCYxkYFNFRLpL5SQRElWQtKybRsbJ0yExkYK7plN5lln9ujzRaR7NbeEKKtpbFddaZu4lFY1UF0fX29LeqqPQZF+ltYZxdr1ueQECKRr3RaR/kgJifQXSkiiJCshAdh26WXUPfsc6YcczOBnftvjzxeR5KtrbG7Tx7IlPKOYtwBl+PvqBppb4vt3OyeQ2tqE35q8hKsukf2FWWrKF+lrlJBIf6GEJEoyE5L61/7C1nPPA6Doz6+QNnp0j8cgIr1fKOQor21sk6RsCfeuRH9fVtMY130jTfltKi6ts4l5w8QG5/jJy9QwMZHeQgmJ9Bdqau8l/EccTsqIEbR89hm1v3mSvOuvS3ZIItIL+XxGYbafwmw/YzpZS7WxOcTW6qgKS2UDm8NDw6KTl7oYTfmdSUsxBuV4lZai3EgCs324WCRxyfTrfy8iItI1qpBESWaFBKDynnupuuvn+IJBhvx9OZaenpQ4RGRgcM5R09DcWm1pX2Up3Y1hYpn+FAaHE5fWWcVyAgzK3f46mO3Hn6b+FpFdpQqJ9BdKSKIkOyFp2fAFGw85FEIhCh/8FRnfOCkpcYiIRIseJhZzqFi48lJW00i8/0vJzUhrU12JTmAiQ8e06KRIbEpIpL9QTb0XSRk2FP+xx9Lwpz9R85vfKCERkV6hq8PEmlvCw8QiFZaoBv3S1qpLPZV122cTq6xrorKuiY83VXd4XzNvGuRIgtI+aYkMGyvISifFp/4WEZG+RhWSKMmukADUvfAC26ZNBzOK33qT1JKSpMUiItId6pta2FoVlaxURlVequpbj0X6W7oqxWcUhhOXQVH9LINy/N5QsXDTfn5mOj4lLtIPqEIi/YUqJL1M4Ljj8A0eTGjLFmqfWkTuD2ckOyQRkYQKpKVQUphJSWFmp+e19reE+1hiJS6lVQ00NIcAaAm58LHOG/NTfLY9aYmeWSw3QDA7XTOKiYj0MFVIovSGCglAxa23UT1nLiklJRS/+TqWoqZPEZFYnHNU13uJy+aoJGVLu6b80qr4G/PTUoxgdtthYdHDxYLhr7kZSlwkOVQhkf5CCUmU3pKQNK/5hE1HHgVA8PFHCRxzTFLjERHp65xzVNQ2URqeCnlrdWP4a6RJv4Gt4cSlJbQLiUubaksgKonZnsAocZFEU0Ii/YWGbPVCqaP2Iv2wQ2l882/UPP6EEhIRkd1kZuRnpZOflc7o4pwOzwuFHBV1TZRWhWcRq2xorbCURs0qtq26sTVxaWpxbCyvZ2N5facxRCcukepKrApMXkaaelxEZEBRQtJLZZ13Ho1v/o36F5fSsnEjKUOGJDskEZF+z+czCrLSKchKZ59O/tmNNRXy1qq2ycuuJi6p0UPFwslLdL9L5HWBmvNFpJ/QkK0ovWXIFoBraGDjVw8mtG0bOVf9UM3tIiJ9UJvEpbptxSU6gdm6C4tPRmYVi05YglH9LZEEpiBL67j0VxqyJf2FEpIovSkhge3N7b4hQxjy1ptYqgpaIiL90fahYt5aLZEqS6THZWvU66Y4E5fWdVyy/W2GjA1ql8gEs/2kpypx6UuUkEh/oYQkSm9LSJo//ZRNXzsCnKPwoQfJ+PrXkx2SiIgkkXOOykjiEl1piZG8RKZDjkdeZtqOiUtUAuNNi+wnI12/IOsNlJBIf6GEJEpvS0gASs//Lg3LXsF/1JEM+s0TyQ5HRET6gNbpkKu3Dw3bGtWYH/26Ns4FKAEy/SleohIzadmevGhmse6lhET6CyUkUXpjQlK39CW2TZkKQPFfXiN11F5JjkhERPqT2obYicvW6sbWyktpVQOVdU1x3zs91Udh9o7DxbYnLdv7XFLUoB83JSTSXyghidIbExLX0sKmww6nZf16si+eTt5Pbkh2SCIiMgA1Nodak5Popvz2X8tqGon3o4Uv3Oeyw1CxdsPFgtl+/GlaLDhCCYn0F0pIovTGhASg6r77qbzjTiw/j6F/fxvLyEh2SCIiIjE1t4Qor21qU12JlbzsSoM+QE4glcKoCkukr6V9EpMTSO33w8WUkEh/oYQkSm9NSFq2bGHjxEOgqYn82f9L1tlnJTskERGR3RJp0G8/NGxrVOKyLXyspqE57vu3GS4WVXUpjCQw4X2FfXhaZCUk0l8oIYnSWxMSgG2XXkbds8+RduCBFD3/bLLDERER6TF1jc2ticu26MSlXTJTXhv/cDGA/My07b0t0UlLaxXG25/pT+lVVRclJNJfKCGJ0psTkoY336T0zLMBGPziH0n/8peTHJGIiEjvEj1cLJK4eJWW7Y36kQrMrkyL7E/zhROW7UPFCrOjm/W9PpeeWoxSCYn0F3FPJG5mWcBhQDHwsnNuU8Kjkh2kH3ooqWPG0Pzhh9Q8/Ajpd92Z7JBERER6ldQUX+uCj51xzlHT0LxDkhIZJhb9uqJ2++xiDU0h1pfVsb6srtP7m0F+ZjoH7x3kpjPGJ+S9ifRncSUkZnYpcBuQCzjgeGCTmRUBnwJXOud+Hcf9/MBPgfOBAuBfwLXOuT914dpJwHXA/oAP+ACY7ZxbFM976ivMjKzvnk/FdddT98wS8mb9D76CgmSHJSIi0ueYGdmBNLIDaYwc3Pm5Tc0hymq8JCW6IT9WItMYrro4B2U1jdTuQu+LyEDU5YTEzM4A5gC/B54DHoocc85tNrMXgG8CXU5IgIXAGcA9wGrgQuCPZna0c+7NTmI5GXgWeAP4SXj3OcBTZpbjnJsXRwx9RuZZZ1J5+x246mpqfvMkOZddmuyQRERE+rW0VB9FeQGK8gKdnhdZjNJLVryEJT8zvYeiFOnbutxDYmZ/A2qcc8eZWRDYAkxyzi0LH78OmO6c27OL9zsYeAuY4Zy7J7wvAKwENjjnjurk2j8C44FRzrmG8D4/sAZY7Zw7uktvasf79toekojyG26kZt48UoYNo/jN17HUuEfdiYiISD+gHhLpL+LpuNofWNLJ8S+AojjudybQRNtKSz0wDzjCzIZ2cm0uUBZJRsLXNgBlQOcDO/u47CkXgBktGzZQ/+LSZIcjIiIiIrJb4klIWnZy/jCgJo77HQh84Jyrbrd/OWDAVzq59s/Al8zsZ2a2d3j7GTAGuDuOGPqc1L32InDccQBUz+uXI9NEREREZACJJyH5F3BirANm5gPOAt6O435D8aoq7UX2Devk2luARcC1eL0nq4EfAKc6517q6CIzK+9sA/LiiD9psqZNBaDxreU0rlyZ5GhERERERHZdPAnJL4CvhysRhZHrzWwssBj4EnBfHPfLABpi7K+POt6RBuDD8HPPBb4D/ANYZGYT44ihT/IfeQSpY8YAUDNvfpKjERERERHZdV1OSJxzTwG34lUl3g/vfgF4DzgNuMk598c4nl0HxJooPBB1vCP3AycB5zrnnnTOPQ5MAjbizdjV0XvI72wDKuKIP2nMjOypUwCo/d3vaSktTXJEIiIiIiK7Jq5lRJ1z1wFfBe4F/ggsxaucHOyc+2mcz/4Cb9hWe5F9G2JdZGbpwEXA88651mVWnXNN4ZgONrN+P/VUxhmnY/l50NhIzWOPJzscEREREZFdEldCAuCc+4dz7irn3Deccyc55/6fc+7vu/DsfwL7mll2u/2HhL/+q4Prgnjrp6TEOJYWPma7EE+f4svMJOvccwGoeeQRXGNjkiMSEREREYlflxMSM1tjZqd2cvxkM1sTx7OfxksgLoq6hx+YArzunNsQ3reHme0bdd1moBw43czSoq7NBk4BVoarJb2Sc46G6kaa6nd/9dasCy8An4/Qps3U/d//JSA6EREREZGeFU+FZCTQvpoRLQvo0qKIAM65t/Ca0u80szvM7GJgWfge10Sd+gjbe1ZwzrUAPwf2A940sx+Y2VV40wUPB27uagzJsOjy51j47UV8/Ne1u32v1OHDCUz2Jj6rfmgeXV3kUkRERESkt4h7yFYnioHaOK/5Ll4/ynfxZuhKA05yzr3e2UXOuVuAb+OtjfIT4GdAJXB6uPm+10rPTgegdlti1m/MvmgaAE3v/JPGv+/KyDkRERERkeTptPnbzI4CjonadbqZjY5xaiFwDl5fSJeFV2b/UXjr6JxjOtj/BPBEPM/rDTILvNmMaxKUkKQffDBpXzmApn/+i+oHfoV/Yr+f9VhERERE+pGdzUZ1LF4FAsABp8/niB8AACAASURBVIe3WFYDMxIUV7+VVeglJImqkJgZ2d/7HmWXXkb9i0tp+ngNaXuPSsi9RURERES6286GbN0D7AWMwpu56gfh19HbSGCQc27MLs62NaBkFmYCULst3tFtHcs46euk7LEHOEfNr3+dsPuKiIiIiHS3ThMS51yFc26dc24tXrXkN+HX0dunzrltPRJtPxAZslVblpgKCYClppI93ZusrGbxYi2UKCIiIiJ9Rjwrtf/ZObelO4MZCFqHbJXV4UKJmxUr81tnewsl1jdQ8/AjCbuviIiIiEh3imtF8/AK6N/EW7ywgB0TGuecm5ag2PqlzHBCEmpx1Fc2kJEfSMh9fVlZZJ1/PtX3/4KahQ+Tfdml+DIyEnJvEREREZHu0uWExMwKgVeAL+P1kzi2r4juovYpIelEJCEBr0qSqIQEIHvqFKp/9SChbduoW/w0Wd89P2H3FhERERHpDvGsQ3IzsC/eyup74yUgJ+ItUPgb4G0gmOgA+5tAjh9fqvdjr0lgYztASlERmWd4k6BVPfhrXEtLQu8vIiIiIpJo8SQk3wAecc4twFuEEKDFObfKOfcdoA64LdEB9jdm1i2N7RHZF08HoOWTT6hfujTh9xcRERERSaR4EpIheFUQgObw1+jxRr8DTk1EUP1dZoLXIomWNmYM/uOOA6D6l79K+P1FRERERBIpnoRkG5AV/r4KaAJGRB1vwmt0l52IzLSVqNXa28u59HsANK5YQcNbb3XLM0REREREEiGehORDYByAcy4EvANcaGZ+M8sEvgusSXyI/U/rkK1uSkjSDz2UtIMOAqDq/l90yzNERERERBIhnoRkKXCmmfnDr/8Xb/rfbcBm4KvA7MSG1z91x2rt0cyMnCuvAKDhlVdpfPfdbnmOiIiIiMjuiichuRUY4pxrAHDOLQLOBF4E/gic55ybl/gQ+59IhaS7hmwBBCYdR+p++wJQdf+cbnuOiIiIiMjuiGeldhdJRqL2PeOcO905d5Zz7qnEh9c/RXpI6soTu1p7NPP5yLnicgDq/+//aFq9ulueIyIiIiKyO+KpkHTKPFqJrwvar9beXTJOPpmUkXuCc1T/QlUSEREREel9djshCSci5wHvAQt3O6IBoP1q7d3FUlPJudyrktQ+s4Tmzz7rtmeJiIiIiOyKnSYkZnaEmf3ezN4zs7+a2feijp0IrAQeBYYBd3RfqP1Hd67W3l7mGafjGzIEWlqofkDrkoiIiIhI79JpQmJmhwN/Ak4B9gW+Bsw1sx+a2Sy8ZvahwM+Akc65Wd0cb79gZt26OGKbZ/n95Fzi5ZA1v3mSls2bu/V5IiIiIiLx2FmF5BqgAfhvIBv4CvAucB1wE/ArYJRz7kbnXFl3BtrftK5F0o1Dtlqf9e3z8BUUQEMD1b9+qNufJyIiIiLSVTtLSA4BfuWce845V+uc+zdwNZAPPOacu9Q5V97tUfZD3b1aezRfZiZZF03znvfwI4TKlDuKiIiISO+ws4QkCPyn3b7I698lPpyBo7sXR2wve8qFWE4OrqZGVRIRERER6TV2lpD4gMZ2+yKvqxIfzsDROmSrByokAL68PLKnTQWget58VUlEREREpFfoyrS/WWZWGNmAwvD+nOj9UcelC3pyyFZE9vSLvCpJdTXVD/66x54rIiIiItKRriQkDwBborYPwvufabd/C6ApnLooUiHpztXa2/Pl55Md7iWpnr+Alm2qkoiIiIhIcqXu5PjDPRLFANR+tfaM/ECPPDf7omlUz5uPq6yk+sEHyfvxNT3yXBERERGRWDpNSJxzU3oqkIEmK9zUDt7iiD2VkESqJFX/O5ua+QvIvvhiUgoLeuTZIiIiIiLt7axCIt3En5OOL9VHqDnUI2uRRMu+aBrVD83zqiS/+hV5//PjHn2+iIiI9A4rVqwIAN8AjgIykhyO9D+NwEfASxMmTHivo5OUkCRJZLX26s01PTbTVoQvL4/s6RdRdff/UrNgIdnfu5iUQs1HICIiMgDdmJaW9u1AIBBITdXHQkmsUCjk6uvrXXNz82UrVqy4acKECU/EOk9/85Ioq8BLSGp6aC2SaNnTplL964fCVZIHVSUREREZYFasWDEhJSXljKKiolBxcfE6M0t2SNIPNTc3+9avXz+0rKzsJytWrPhnrEpJV2bZkm4SaWyvLavv8Wf78vLIvng6ADXzF9CydWuPxyAiIiJJdWJ6enpeUVFRqZIR6S6pqamhkpKSL1JTU/OBSbHOUUKSRD29Wnt72dOmYnl5uNpaqn8xJykxiIiISNIUpaWl4fP5emb9ARmwUlNTQ4FAwIB9Yh1XQpJEPb1ae3u+3FxyLr0EgOqHH6F5/YakxCEiIiJJkZmSkqLSiPQIn89ngD/msR6ORaIkY7X2HWKYNhXf4MHQ0EDVPfckLQ4RERERGZjibmo3s33wyi1BYIes2jn3SALiGhAiPSSR1drN1/O/pPBlZpLzg+9Tce311D61iOxLLiFt71E9HoeIiIiIDExdrpCYWbGZvQB8ADyHt4r7wnbbgkQH2J9FhmxFVmtPlqzzziNljz2gpYWqn/88aXGIiIiI9KTnn38+x8wmPP/88znJeP59990XNLMJq1atSu/quW+88Ua/Wy8mniFbvwCOB34JnAUcG2P7r0QH2J+1X609WSw9ndyrfghA3bPP0bhyZdJiEREREUmk66+/vtjMJhx22GFjkh1LV9x5552D77vvvmCy4+hJ8SQkxwMPOOeucM4945z7c6ytuwLtjyKrtUPyGtsjMk77Jqljvf9OK++4M6mxiIiIiCTK4sWLg8OGDWtcvnx5zrp169KSHU+0yy67bGtNTc0/9tlnn8bIvvnz5w9+7LHHBiUzrp4WT0LiA/7VXYEMRJHV2gFqy5KbkFhKCrnXzASgYdkrNLz1VlLjEREREdldK1asCKxatSrj7rvv/jQjIyO0cOHCgmTHBFBZWekDSE1NJTMz0/l8A3ueqXje/V+AA7orkIEqqyAy01byhmxFBE44gbSDDgKg8rY7cE7TkouIiEjftXDhwmAwGGw+88wzKyZNmlS+ePHiLg2Fuu222wYPHz58/0AgcND++++/3wsvvJB98MEHjz344IPHRp+3fv361LPOOmtkQUHBAX6//6Bx48btt2DBgjZJT6RPZf78+QWXX355SVFR0fj8/PwDYccekpKSkv1XrVqV8fbbb2eb2QQzm9D+mXV1db6pU6eOKCgoOCAjI+PA448/fu8NGza0maiqpKRk/0mTJu29ZMmS3HHjxu0XCAQOGjdu3H6vvPJKJsD9998fHDly5Jf9fv9BEydOHPvBBx+06WF59913/SeeeOLegwYNOsDv9x9UXFw8/uSTTx61devWlK7+7OMRzyxbPwReMbNlzrnfdkcwA9H2xRGTWyEBr2KT9+NrKD37WzS+/Tb1L/+JjONjLqgpIiIi0ustWbKk8KSTTipLTU3lnHPO2XbuueeO/ve//+0fP358h7MJ3XHHHYNnzZq1x8SJE6svvfTSTWvXrk0/55xzRufm5jYPGTKkKXJedXW1HXXUUWM///xz/4UXXrh5xIgRjU8//XTh1KlTR9XV1X1y2WWXbYu+76233josEAiErrzyyo1VVVUxP9jffvvtn11zzTUjAoFA6Oqrr/4CYOjQoc3R51x55ZV75OfnN8+cOXPDJ5984p8/f37x9OnT3R/+8Ic10ed9/PHHgYsvvnjkBRdcsCU7O3vbfffdN+SMM87Y5/rrr19/7733Drngggu2VFZWpsydO3fIlClTRr755psfAtTX19vXv/71MSkpKe6SSy7ZGAwGWz777LP0pUuX5m3dujUlGAy2xP8n0bl4EpJfAtXAIjPbAKwB2gfknHPHJSq4gaC3DNmK8B/+NfxHH0XDn1+j8pZbCRx7DJYa9+zQIiIiIkn10ksvZa1fvz79vPPO2wZw2mmnVebm5rYsWLAgOHv27JirQdfX19vtt98+7IADDqh5/fXXV6WleS0n48ePr/v+978/MjohmT179uA1a9YEHnzwwTXTp08vA7jqqqu2HHjggfvdcMMNI6ZNm1bm9/tbh5s0Nzfb8uXLP8jMzOxwCMr5559ffssttwzLzc1taZ/QRASDweY///nPH0WGeYVCIRYuXFjcPllYu3ZtYNmyZe8fe+yxtQDDhw9vnD59+qgbb7xx+KpVq94dMmRIC0BTU5PNmTNnyOrVq9NGjx7d9I9//COwfv369FdfffX9o48+OnoIT7etoB3PkK1RQBrwKdAM7AHs1W7TAhZx6g2LI7aXd911YEbzRx9R+5snkx2OiIiISNweffTRYFFRUdMJJ5xQDeD3+93kyZPLnnnmmcKOrvnLX/6SWV5enjplypQtkWQE4JJLLtmam5vb5hfxL774Yl5RUVHTtGnTyiL7MjIy3LRp0zZv3bo19a9//Wtm9PnnnHNOaWfJSFdNnz59S3TPyVFHHVXd0tLC6tWr2wy7GjNmTF0kGQmfVwMwadKk8kgyAnDooYfWAHz00Ud+gMLCwhaAJUuW5NfX1/fIInldTkiccyOdc3vtbOvOYPujyFokvWHIVkTauP3I/NbZAFT+/G5C1dVJjkhERESk65qamnj++ecLDj300Kr33nvPv3LlSv/KlSv9hx56aM2nn37qj/RStLdmzRo/wNixY9sM6UpLS6OkpKTNvvXr16fvtdde9e0b0seNG1cfvlebBGGvvfZqJAHa3yeSQJSWlrYZBlZSUhLzvOHDh7fZn5+f3wKwdevWVIB999238aKLLtp07733Dg0Gg1859thjR991112DysrKuq3zfmC39PcC0UO2XKj3NJHnXn0VlpFBqLSU6rm/THY4IiIiIl22ZMmS3LKystRnn322cP/99/9yZPvhD3+4J3jVk56OKTMzM5SI+6SkpMT8wOica1PN8Pl8Mc9LSYndlx49mdGvf/3rz5cvX/6fK6644ouqqqqUH//4x3uOGzfuS5988km3TJscd3OAmeUCk9g+PGsN8JJzriqRgQ0UkcURXchRV1lPZn7vWHwzZehQsi/5HlWz76H6Vw+S9Z3vkDJsaLLDEhEREdmpxx9/PFhcXNx02223fdb+2GOPPVb43HPPFTQ3N+9wbNSoUQ0Aq1at8k+ePLl1iEhTUxPr16/3jx07tnVIS0lJSePq1aszQqEQ0VWS999/PxC+1y5VRMx6ZJTUTk2cOLF+4sSJG4GNy5YtyzruuOP2veeeewbfe++9Ce8liatCYmYXAZ8Bi4E7w9ti4HMzmxbvw83Mb2Z3mNkGM6szs7+ZWZeb4s3sPDNbbmY1ZrbNzP5sZgfHG0cyRYZsQe8atgWQfekl+AYPxtXXU3nXXckOR0RERGSnqqqqfC+//HL+pEmTyqdMmVLWfps+fXppaWlp2nPPPZfb/tojjzyyNj8/v3nBggWDm5pa+9d54IEHgpWVlW1KCyeeeGLFpk2b0qLXNqmvr7d58+YVBYPB5iOOOGKX1nTIzMxsaf+snrRt2zZf9HsH+OpXv1qXkpLi6uvru2V0VZcrJGZ2KvAgXkXkeuA/4UNfAq4EHjSzzc655+J4/kLgDOAeYDVwIfBHMzvaOffmTuK5GbgGeDQcVxbeOilD4nh+0kVWaw81h7yEpBdNC+DLyiL3R1dTPvMaahc/Tda0aaR/+UvJDktERESkQ0888UR+bW2t75RTTqmIdfzkk0+uDAQCoccee6xwypQpW6OPBQIB96Mf/WjDtddeu8fhhx8+9pvf/Oa2tWvX+hcvXhwcMWJEg5m1jmuaMWPGloULFw6+9NJL91q+fHlWZNrfDz74IGPOnDmfRM+wFY/x48fXPvzww0UzZ84cOnr06PohQ4Y0n3rqqT02EukPf/hD7tVXX73HN77xjbKxY8fWt7S08NRTTwXNjLPPPrts53eIXzxDtmYC7wOHOOeiu5z/ZGYLgL/hJQhdSkjClYxzgBnOuXvC+x4BVgJ3AEd1cu3XgFnAGc65JXG8h17HzMgqzKBqc02vWByxvcxvnU31vHk0r/qQyp/dTPDJJ3pNKVFERESkvaeeeqowEAiETjnllMpYx7Ozs91hhx1WtXTp0vxzzz13h6l1Z82atcU5Z3Pnzi2+6aabRowdO7Z20aJFq2fMmLFHdJKRnZ3tXn311VUzZswY/tRTTwVrampS9t577/p58+atmTp16i5/cL/55pu/WLdunX/u3LlDampqfBMnTqw+9dRTV+3q/eI1ceLE2qOPPrri5ZdfznviiScGBwKB0L777lv79NNPf3TcccfVdMczraurcZtZFfBT51zMsTtmNhO43jmX08X73Qn8ACiMTnDM7H+AW4AS59wXHVz7FDDSOXeImfmAzHZJ0i4xs/K8vLy88vLy3b1VXH438wU2rSrlq+eNZ8K3xvfos7uiftkrbD3/uwAEH3mYwHH/leSIREREJD8/n4qKigrnXP6uXL9ixYoHCwoKThs1atS6RMfW37S0tBAMBr8yefLksieffFI/r12wZs2aPcvKypZMmDDh4vbH4hkHtrNfi8dbljoQ+CBGIrE8/KyvdHLtccDbZnYrUAFUmdlaM/t2nDH0CllBr7G9urT3VUgA/Mceg//IIwGouOmnuMaEzFonIiIi0uvU1tbu8Jl3zpw5wYqKipSjjz5akzh1g3iGbP0LuNDM5jrn2pRrzCwbr//jX3HcbyiwPsb+SFVkWKyLzKwACOIN92rBGya2DbgceMzMajsaxmVmOyt95HUh7oTLLsoCoGpT71zvw8zI+8n1bD5hMs0ff0z1goXkfG+H5FZERESkz1u6dGn2tddeO/yUU04pCwaDze+8807WokWLBu2zzz51U6ZM6ZYeioEunoTkLuAZ4B9mdh/wXnh/pKl9NHB6HPfLABpi7K+POh5LdvhrEDjUOfcWgJktwWuMvwHoU30lucXeW+qtCQlA2n77kfXd86lZ+DBVs+8h8/TTSBk8ONlhiYiIiCTUmDFjGoqLi5seeuih4oqKipS8vLyW008/vXT27NnrA4FA71k0rh/pckLinPudmV2B13B+P9uHaBlQA1zhnPt9HM+uA/wx9geijnd0HcAnkWQkHF+DmT0N/D8zy47VU7KzMZbhCkqPV0lywglJdWktoZYQvpTeuV5l7tVXUfu73+PKy6m8/Q4K7v55skMSERERSahx48Y1Llu2bHWy4xhI4vrk65ybC4wAvgX8T3g7GxjunIt3Oe8v8IZttRfZ19GiK9vwKiubYhzbhJcgJWXo1a6KJCStU//2Ur6CAnJn/giA2qcW0fiveEboiYiIiIjsKO5fxTvnyp1zi51zd4a3p51zMed53ol/AvuG+0+iHRL+GvPTrnMuFL62JMbh4Xh9JTtM4dab5RRt/xFUbu69w7YAsr7zbVL32w+co+L6n+BCoWSHJCIiIiJ9WDLHBj0NpAEXRXaYmR+YArzunNsQ3reHme3b7trFwAgzOz7q2ly8as0bzrneW2aIITU9pXXF9t7cRwJgKSnk/+wmABpXrKDumT7VriMiIiIivUyHPSRmtgyvT+RE51xz+PXOOOfccV15sHPuLTNbDNxpZkOBj4ELgD3xZuyKeAQ4mrbTDv8SL5H5rZnNBsqAaUA+3jCyPienOJvasrpen5AA+A87jIyTT6bu+eepuPVWApNPxJfdvtAlIiIiIrJznVVIRgF7sT0RiLzubBsV5/O/C9wb/nofXsXkJOfc651d5JyrBY4Ffo83w9dteOuRTNrZtb1VTnFk6t9uWQAz4XKvvxYLBAht2kzVffcnOxwRERER6aM6rJA450Z29joRnHP1wI/CW0fnHNPB/o3A+YmOKVkifSSVfaBCApA6fDjZV1xO1c/vpvpXD5J55hmkjRmT7LBEREREpI/pnfPLDkB9YS2S9nIuvYSUkSOhuZnyWdfinKbmFhEREZH4dDkhMbMWMzuvk+PfMrOWxIQ18ESm/q3ZVktLU9/4MVogQP6tNwPQ+ObfqHv6t0mOSERERCQ+zz//fI6ZTXj++edzkvH8++67L2hmE1atWpXe1XPfeOONjhYQ75PiqZDYbh6XTkQSEhxUb+kbfSQAgaOPJuOUkwGo+NnNhMrKkhyRiIiIyHbXX399sZlNOOyww/rM2PI777xz8H333RdMdhztrVu3Lu2yyy4rOeSQQ8ZkZWUdmKhELpFDtvYAqhJ4vwEle1Am5vNyur7SRxKR95MbsKwsQlu3UnH7nckOR0RERKTV4sWLg8OGDWtcvnx5zrp169KSHU97l1122daampp/7LPPPo2RffPnzx/82GOPDUpmXLG8++67gV/+8pdDNm7cmD527NiELbPRaUJiZv9tZvPNbH5418WR1+223wE3AH9LVGADjS/FR/agTKBv9ZEApAwdSu6Prgag9vHHafzHO0mOSERERARWrFgRWLVqVcbdd9/9aUZGRmjhwoUFyY4porKy0geQmppKZmam8/l6f2v34YcfXvPFF1/8c926dStnzJixMVH33dk7/wremiAX4q1JclTU6+jtv4A3gCsSFdhAFBm2VbW57wzZisiaciFpX/oSOEf5/8zCNTcnOyQREREZ4BYuXBgMBoPNZ555ZsWkSZPKFy9e3OVhULfddtvg4cOH7x8IBA7af//993vhhReyDz744LEHH3zw2Ojz1q9fn3rWWWeNLCgoOMDv9x80bty4/RYsWNAm8Yn0qcyfP7/g8ssvLykqKhqfn59/IOzYQ1JSUrL/qlWrMt5+++1sM5tgZhPaP7Ours43derUEQUFBQdkZGQcePzxx++9YcOGNrPnlpSU7D9p0qS9lyxZkjtu3Lj9AoHAQePGjdvvlVdeyQS4//77gyNHjvyy3+8/aOLEiWM/+OCDnfawFBQUhIYMGZLwZucOp/0FcM7dBNwEYGYh4DvOuScSHYR4coqz4d1Nfa5CAmCpqeTfditb/vubNK1cSc3Dj5A9bWqywxIREZEBbMmSJYUnnXRSWWpqKuecc862c889d/S///1v//jx4xs6u+6OO+4YPGvWrD0mTpxYfemll25au3Zt+jnnnDM6Nze3eciQIU2R86qrq+2oo44a+/nnn/svvPDCzSNGjGh8+umnC6dOnTqqrq7uk8suu2xb9H1vvfXWYYFAIHTllVdurKqqSon17Ntvv/2za665ZkQgEAhdffXVXwAMHTq0zW96r7zyyj3y8/ObZ86cueGTTz7xz58/v3j69OnuD3/4w5ro8z7++OPAxRdfPPKCCy7Ykp2dve2+++4bcsYZZ+xz/fXXr7/33nuHXHDBBVsqKytT5s6dO2TKlCkj33zzzQ/j/RknQqcJSTt7AVu6KxDpm1P/RkufcBCZ551H7eOPU3nHnQQmn0hqSUmywxIREZEuck1NNH/66U5/U95TUvfYo9HSdq3t46WXXspav359+nnnnbcN4LTTTqvMzc1tWbBgQXD27NkbOrquvr7ebr/99mEHHHBAzeuvv74qLfz88ePH133/+98fGZ2QzJ49e/CaNWsCDz744Jrp06eXAVx11VVbDjzwwP1uuOGGEdOmTSvz+/2t6yI0Nzfb8uXLP8jMzOxwrYTzzz+//JZbbhmWm5vb0j6hiQgGg81//vOfP4oM8wqFQixcuLB469atKcFgsLWCsXbt2sCyZcveP/bYY2sBhg8f3jh9+vRRN9544/BVq1a9G6l2NDU12Zw5c4asXr06bfTo0U2xntmdupyQOOfWdWcgErU44ua+mZAA5M36MfUvvURo82bKfzyL4CMLMdMEbCIiIn1B86efpm8+6pj9kx1HRNFrr76btvfejTs/c0ePPvposKioqOmEE06oBvD7/W7y5MllzzzzTGFnCclf/vKXzPLy8tQbb7zx87SoZOiSSy7Zet11142IPvfFF1/MKyoqapo2bVrrNKMZGRlu2rRpm2fOnLnnX//618zjjjuudSz+OeecU9pZMtJV06dP3xLdc3LUUUdVz5s3r3j16tXpwWCwtdl8zJgxdZFkJHxeDcCkSZPKo4deHXrooTVz5szho48+8icjIYmre8bMCszsajNbbGYvm9mydtufuivQgSDSQ1Jf0UBTXY//XUgIX34++Tf/DICGZcuo+93vkhyRiIiIDDRNTU08//zzBYceemjVe++951+5cqV/5cqV/kMPPbTm008/9Uf6KGJZs2aNH2Ds2LFthnWlpaVRUlLSZt/69evT99prr/r2Denjxo2rD9+rTbVpr7322qXkqr329yksLGwBKC0tbTMMrKSkJOZ5w4cPb7M/Pz+/BWDr1q3xjJ5KmC4/1Mz2BF4HhgEVQC6wDSjAS2xKgb7Xjd2L5BRltX5ftbmGwj3zkxjNrsv4xkkEvj6Z+j++QMUNN+I/+mhSCguTHZaIiIjsROoeezQWvfbqu8mOIyJ1jz126QP8kiVLcsvKylKfffbZwmeffXaHDyGPPvpoMLpy0FMyMzNDibhPSkpKzCqLc67NsBSfzxfzvJSUmO0rOLfbxZtdEk8WdDOQDxwHvAtsBr6FN9XvtcA5wNGJDnAgySzIICXNR0tTiMpN1X02IQHIv/lnbHr9DULbtlHxkxspvP++ZIckIiIiO2FpaezqEKne5PHHHw8WFxc33XbbbZ+1P/bYY48VPvfccwXNzc2fpabu+FF41KhRDQCrVq3yT548uXUcfVNTE+vXr/dHr79RUlLSuHr16oxQKER0leT9998PhO+1Sz/LgTbcPZ4hW8cBv3bOvYI3BTCAOedqnXPX4iUpdyQ6wIHEfNbaR9JXG9sjUoYMIe/66wCoe2YJ9cteSXJEIiIiMhBUVVX5Xn755fxJkyaVT5kypaz9Nn369NLS0tK05557LjfW9UceeWRtfn5+84IFCwY3NW0fQv/AAw8EKysr25QWTjzxxIpNmzalRa9vUl9fb/PmzSsKBoPNRxxxxC5VYTIzM1vaP6s/i6dCEgRWwd4ADAAAIABJREFUhr+P/OlkRB1/CfhJIoIayHKKsylfX9nnExKAzHPPoXbJ72h84w3Kr/kxRa/8CV92drLDEhERkX7siSeeyK+trfWdcsopFbGOn3zyyZWBQCD02GOPFZ522mmV7Y8HAgH3ox/9aMO11167x+GHHz72m9/85ra1a9f6Fy9eHBwxYkSDmbWOa5oxY8aWhQsXDr700kv3Wr58eVZk2t8PPvggY86cOZ9Ez7AVj/Hjx9c+/PDDRTNnzhw6evTo+iFDhjSfeuqpVbtyr0SbOXPmUIAPPvggAPDII48Uvvbaa9n5+fnNs2bN2qUZeeNJSLYAkTF4VUA9MDLqeDptExTZBdsXR+z7CYmZUXDn7WyadDwtGzZQedvt5N9yc7LDEhERkX7sqaeeKgwEAqFTTjllh2QDIDs72x122GFVS5cuza+trY05NmrWrFlbnHM2d+7c4ptuumnE2LFjaxctWrR6xowZe0QnGdnZ2e7VV19dNWPGjOFPPfVUsKamJmXvvfeunzdv3pqpU6eWxbp3V9x8881frFu3zj937twhNTU1vokTJ1afeuqpq3b1fol01113DYt+vXjx4kEAw4YNa9zVhMS62rxiZi8CW5xz3wm/fhUoAibjDf36I1DtnJu4K4H0BmZWnpeXl1deXp60GP75zH946+F3KByZz1n3npy0OBKp6pcPUHnzLQAMWvQU/sO/luSIRERE+r78/HwqKioqnHO71HS6YsWKBwsKCk4bNWqUlnbogpaWFoLB4FcmT55c9uSTT+pnFqc1a9bsWVZWtmTChAkXtz8WTw/J74HDzCxSBfkpsA/wCfBx+Puf7W6wA932xRFrkjbTQaJlT7+ItAO/AkDZVVcTqu771R8RERHpv2JVTubMmROsqKhIOfroo3vF0Kn+JJ6FEecCc6NeLzOzw4DzgBZgiXPujcSHOLBEhmw11TXRUN1IIMef5Ih2n6WmUnDPbDafOJmWzz6j4qc3U3Dn7ckOS0RERCSmpUuXZl977bXDTznllLJgMNj8zjvvZC1atGjQPvvsUzdlypRdHoolse3W4ifOub8Df09QLML2hAS8mbb6Q0ICkDZ6NHnXXEPFTT+l9vHHyThpMoFjjkl2WCIiIiI7GDNmTENxcXHTQw89VFxRUZGSl5fXcvrpp5fOnj17fSAQ6B9DWHqReBZGLASGO+f+3cHx8cBnzjlljbvBn51OemYajbVNVG2qZvDoYLJDSpisi6ZR98ILNL61nLKrfkTxspfx5eUlOywRERGRNsaNG9e4bNmy1cmO4/+zd9/xVVf3H8df5+6beZObhEDYGxQQEQQFrIoCVlDU/kQrIiAOrFqqxVacbRVFLYqFWitDxYGLirgpCIoiClgXhD0MI3uvO87vj3sDlxBCbgh8k5vP8/H4Pu693/lJqvW+c1ZzEc4YkpnAwlqOLwBmnFA1AqXUoVaSwgiY+jeUMplI+PtTqKgo/AcOkP/AQ0aXJIQQQgghDBZOIDkfeK+W40uBYSdWjgAOL46YWWJwJQ3P0r49cfdNB6Dsrbco+/hjgysSQgghhBBGCieQtAL21HL8l+A54gTFpkQDTX+19mOJvn4c9iFDAMif9id82dkGVySEEEIIIYwSTiApAdrVcrwdUHFi5QgIWRwxQgOJUgrXU0+g4uLwZ2eT94e7I2aKYyGEEEIIEZ5wAsnXwHilVGz1A8F91wPrGqqw5ix0tXbtj8wv6pa0NFwzAoslVvz3v5S8+JLBFQkhhBBCCCOEE0ieBFoDXyqlrlJKdQ5uVwFfBo89cTKKbG6qFkf0efyU5pUZXM3JE3X55TivvBKAgr/+FU96usEVCSGEEEKIU63OgURrvRKYQmBF9sVAenBbHNz3O6318pNRZHNTNYYEAq0kkcz1yF8xt20L5RXk3nY7ukJ6/QkhhBBCNCfhtJCgtf4X0Am4G3guuP0B6KS1fq7hy2uerE4rjvjAgoiRNvVvdabYWBJmPwNmM95NmyiYISu4CyGEEOLUWbZsWaxSqt+yZcuOGpZwKsyePdutlOqXnp5uq+u5X375pfNU1HaqhBVIALTWGVrrWVrr24LbM1rrjJNRXHMWlxLZA9tD2fufRezv7wSg5N8vUL5qlcEVCSGEECJS3H///S2UUv0GDRrU1eha6mrmzJnJs2fPbnSrY69atSpq3LhxbTt16nSa0+ns27Jly16XXnppxx9//NF+IvcNO5CIU+PwTFuRtxZJTWLvuB1bv34A5P3+DzIVsBBCCCEaxJtvvulu1apV5bp162J3795tNbqe6qZMmZJTUlKyoUuXLpVV++bPn5+8aNGiJCPrqsmMGTNSP/roI9eQIUMKH3nkkb3jxo3LXrt2bezZZ5/dc8OGDY763tdyrANKqfmABm7SWvuCn49Ha60n1bcYcVhcaqDVsGB/ocGVnBrKYiHhH7PJvGg4/sxM8u64E/eil1EmycxCCCGEqJ/169c70tPTna+99tq2G2+8sePChQsTHnzwwUyj6wIoLCw0xcXF+S0WCxaLpUlMq3rXXXcdHDJkyE6Hw3Go3nHjxuWeddZZpz3yyCOpb7/99q763Le2b3s3BDdztc/H20QDSGgTD0DenoJms0aHpW1bXDMfB6Bi1WqK/zHH4IqEEEII0ZQtXLjQ7Xa7vVdddVXBsGHD8t988806d4OaMWNGcuvWrXs5HI4ze/Xq1eOjjz6KGTBgQLcBAwZ0Cz0vIyPD8pvf/KZ9QkJCH7vdfmbPnj17LFiwICH0nKpxKvPnz0+47bbb0lJSUnq7XK6+cPQYkrS0tF7p6enOb775JkYp1U8p1a/6M8vKykwTJ05sk5CQ0MfpdPa96KKLOu3bt++Ihoa0tLRew4YN67RkyZK4nj179nA4HGf27Nmzx8qVK6MAnn32WXf79u1Pt9vtZ/bv37/b5s2bjzuG5aKLLioJDSMAvXr1qujcuXPZ1q1bG76FRGttqu2zOLkS2gYCSUVxJWX55UQlRNTYpWOKumw0lWvXUvLSyxQ+8SS2Af2xDxxodFlCCCGEaIKWLFmSeMkll+RZLBbGjh2be80113T+/vvv7b179651Ws/HH388+d57723bv3//4ltvvfXgrl27bGPHju0cFxfnTU1N9VSdV1xcrIYOHdrtl19+sd9www2Zbdq0qXzrrbcSJ06c2LGsrGznlClTckPv++ijj7ZyOBz+22+//UBRUZH56CfDY489tveee+5p43A4/Hffffd+gJYtW3pDz7n99tvbulwu77Rp0/bt3LnTPn/+/BaTJ0/W77///o7Q87Zv3+646aab2o8fPz4rJiYmd/bs2alXXnlll/vvvz/jmWeeSR0/fnxWYWGhee7cuakTJkxo/9VXX20J93fs9/vJzs629ujRozTca6scr8vWv7TWXwc/DwU2aa2z6vswUXeutDhQgIa8vQXNJpAAxD/4AJXrN+D56Sdyb/sdKZ98jNnd6MZ1CSGEEKIR+/TTT6MzMjJs1157bS7AmDFjCuPi4nwLFixwz5o1a9+xrisvL1ePPfZYqz59+pSsWbMm3WoNDDvp3bt32R133NE+NJDMmjUreceOHY7nn39+x+TJk/MA7rrrrqy+ffv2eOCBB9pMmjQpz263H2pR8Hq9at26dZujoqKO2f1l3Lhx+Y888kiruLg4X/VAU8XtdntXrVq11RTs2u73+1m4cGGLnJwcs9vt9lWdt2vXLseKFSs2nX/++aUArVu3rpw8eXLHhx56qHV6evoPqampPgCPx6PmzJmTum3bNmvnzp09NT3zWJ577rnEzMxM63333ZcXznWhjtdlq1PI55XARfV9kAiPxW45tEBi3t4Cg6s5tZTDQeJz/0RFR+M/cJC8O+5E+/1GlyWEEEJEPJ/XT35Goa2xbD5v/f/7//LLL7tTUlI8F198cTGA3W7XI0aMyHvnnXcSa7vu888/j8rPz7dMmDAhqyqMANxyyy05cXFxvtBzP/744/iUlBTPpEmTDn0ZdzqdetKkSZk5OTmWL774Iir0/LFjx2bXFkbqavLkyVmmkHG2Q4cOLfb5fGzbtu2Iblddu3YtqwojwfNKAIYNG5ZfFUYABg4cWAKwdevWsGbL2rhxo+Oee+5pe+aZZxZPmTIlp54/zrFbSIBsoEXIZ1Xfh4j6SWgTT+GB4mYXSAAsHTvgeuJx8qb8jorPVlE8Zy6xt//O6LKEEEKIiFZ0sNi2eMrSXkbXUeXquaN/cKXFVR7/zCN5PB6WLVuWMGTIkMKff/750JfsgQMHlrzxxhtJK1eujAr9oh5qx44ddoBu3bod0a3LarWSlpZ2xL6MjAxbhw4dyk3VJuHp2bNnefBetgsvvPDQlKkdOnQI+2epSfX7JCYm+gCys7OP6AaWlpZW43mtW7c+Yr/L5fIB5OTk1JYNjrBnzx7L6NGjO8fFxfmWLFmy3WyusQdandT20C+B+5RSbYGq1HeFUqpzLddorfVf612NOEJCGxe7v8kgb0++0aUYIuqyy6j4ci2lixYFxpOc1Q/7oEFGlyWEEEKIRm7JkiVxeXl5lqVLlyYuXbr0qBaRl19+2X2sQHIyRUVFNUiXD7PZXGMri9b6iAYEk8lU43nHCg91nUgpJyfHPHz48K7FxcXmzz77bHPbtm29x7/q2GoLJL8HXgTu4NBoBq4IbseiAQkkDaRqYHtzbCGp4nroATwbNuD5+Wdyb5lCyocfYG7V0uiyhBBCiIgU2yKm8uq5o38wuo4qsS1i6tWi8Morr7hbtGjhmTFjxt7qxxYtWpT43nvvJXi93r0Wy9FfhTt27FgBkJ6ebh8xYsShFao9Hg8ZGRn2bt26lVXtS0tLq9y2bZvT7/cT2kqyadMmR/Be9apfqcbbMam0tFQNHz68865du+zLli3b0qdPn1onCKiL2mbZ2gWcp5SyAanALgIh5d0Tfaiom6qpf8sLKygrKMcZX+/Z1Jos5XSS+MLzZI78Nf7sbHJuvoXkt95A2U9oQVAhhBBC1MBsMVGfLlKNSVFRkWn58uWuMWPG5EyYMOGogdZOp9N/zTXXuN577724MWPGHLXg25AhQ0pdLpd3wYIFyVOmTMmpGkfy3HPPuQsLC49oWhg+fHjBmjVr4hYuXJgwceLEPAgMip83b16K2+32Dh48uF6tMFFRUb7qz2oMvF4vo0aN6vjdd99Fv/LKK9tDu6OdiOP2E9NaVwJ7lFIvAl9rrXc3xIPF8blaxx96n7e3oFkGEgBLu3Yk/uNZcq4fj2fDBgoefAjXYzOMLksIIYQQjdCrr77qKi0tNY0aNarGLiaXXnppocPh8C9atCixpkDicDj0H//4x33Tp09ve+6553a7/PLLc3ft2mV/88033W3atKlQSh3q1zR16tSshQsXJt96660d1q1bF1017e/mzZudc+bM2Rk6w1Y4evfuXfriiy+mTJs2rWXnzp3LU1NTvaNHjy6qz70a0k033dRmxYoVrvPPP78gJyfHPHfu3EPd4WJjY/3jxo2r1ziDOg9c0VpPqM8DRP1ZHRZiU6Ipyiwhb08+rU5vcfyLIpTjgvOJvesPFD35FCUvL8La9wyir77a6LKEEEII0cgsXrw40eFw+EeNGnVU2ACIiYnRgwYNKvrkk09cpaWlNfaNuvfee7O01mru3LktHn744TbdunUrfeONN7ZNnTq1bWjIiImJ0Z999ln61KlTWy9evNhdUlJi7tSpU/m8efN2VLWY1Mff/va3/bt377bPnTs3taSkxNS/f//i0aNHp9f3fg3lxx9/jAJYuXJl/MqVK+NDj7Vq1aqyvoFEHWvwSnDdEbTWq0M/H0/V+U2RUio/Pj4+Pj+/8Qwi//CvK9nzbQanXdKVwTcPMLocQ2m/n9yJkyj/dDnY7ST/5x1svXsbXZYQQghhCJfLRUFBQYHW2lWf69evX/98QkLCmI4dO0rvlzrw+Xy43e4zRowYkff666/L7yxMO3bsaJeXl7ekX79+N1U/VlsLyWeAVko5g922PiMwaP1Yqga+N7r+bk1ZQpt49nyb0awHtldRJhMJzzxN5iWX4tu1i9zJN5P84fuYE2udTlwIIYQQIiylpaWq+nohc+bMcRcUFJjPO+88w7tORZraAslEAgGjarVG6bJlgKqB7RJIAkzx8bjn/ZusS0fj++UXcm+6maRXX0HZbMe/WAghhBCiDj755JOY6dOntx41alSe2+32bty4MfqNN95I6tKlS1lNA+XFialtlq2F1T6/eNKrEUepCiRl+eWUF1bgiJPZpazdu5Mw6+/k3nIrlV+tJf/+B3E99mijniJPCCGEEE1H165dK1q0aOF54YUXWhQUFJjj4+N9V1xxRfasWbMyHA7HCa+0Lo5U50HtwhjVZ9pqeVqKgdU0Hs5RlxK7ZQtFf59F6aJFWHt0I+aGG4wuSwghhBARoGfPnpUrVqzYZnQdzYXp+KcEKKUGKKUmV9t3mVLqB6VUhlLq0XAfrpSyK6UeV0rtU0qVKaXWKqUurMd9PlBKaaXU0+Fe29jZoqzEJEcDkLe38Qy2bwxip/4exyWXAFDwwEOUr/7c4IqEEEIIIUS46hxIgAeB0VUflFJtgdcILJpYANyjlAp3nMlCYCqwCLgT8AMfKqUG1fUGSqlfA3WaAaypknEkNQsMcp+F9bTTwOcj95Zb8e7YaXRZQgghhBAiDOEEkj7AFyGfxxKYWesMrXVP4BPgqGm8jkUpNSB4j2la62la6+eBC4A9wON1vIcNmAXMrOtzm6JDgWSPBJLqTFFRJC6Yhyk5GV1QQM4NE/AXyO9JCCGEEKKpCCeQuIGDIZ+HA6u11hnBz0uBLmHc7yoCM3i9ULVDa10OzAMGK6Va1uEedwJO4MkwntvkSAtJ7SxpaSS+8G+w2fBu307uTbegKyuNLksIIYQQQtRBOIEkH2gBgbEfwEAgdBFETSAc1FVfYLPWurja/nUEW15qu1gplQrcD9yrtS4N47lNTkLbQCApzSujorjC4GoaJ/tZ/Uh48gkAKr74gvx7/sSxFv0UQgghhBCNRziB5DvgRqVUPwJBwAF8HHK8A0e2oBxPS2B/Dfur9rU6zvUzgHQC40/qRCmVX9sGxB/3JgY4cqatQgMradyirryC2LvvAqD0jTcpevoZgysSQgghhBDHE04g+SuBELEOuBdYrrX+NuT4pcDXYdzPCdT05/7ykOM1Co4/uR6YqpvBn8Ht0Tai3VGAzLR1PLG/v5Oo//sNAEVPPkXpm28ZXJEQQgghhKhNndch0Vp/qZQ6k8DYkQLg9apjSik3gUHtS8J4dhlQ0yp/jpDjR1GB1e+eAd7WWn9R0znHorV21Xa8MbeSJLSJpySnVAa2H4dSCtfMx/HtP0DF55+T98dpmFu2xD74XKNLE0IIIYQQNQinhQSt9Rat9bNa65e01pUh+3O01lO11qtru76a/QRaXKqr2rfvGNeNAQYA/1RKta/agsfigp/DGcvSJMjA9rpTViuJzz+HpXs38HjImXwTnvR0o8sSQgghRCO0bNmyWKVUv2XLlsUa8fzZs2e7lVL90tPTbXU998svv4yo77rhLIxoVkpFVdvnUkrdpZR6RCl1epjP/g7orpSKqbb/7ODr/45xXVsCda8AdoZsABOC788Ls5ZGr2pguwSSujHFxeF+6SVMLVLQhYXk/HYc3oyM418ohBBCiIhy//33t1BK9Rs0aFBXo2upq5kzZybPnj3bbXQd1a1evTrqoosu6tSqVateDofjzKSkpD5Dhgzp8umnn0afyH3DaSH5F4HxIwAopawE1iV5Avgz8I1SqtaZsap5C7ACN4bc004gVKzRWu8L7murlOoect17BFpJqm8Ay4LvN4RRR5NQ1UJSklNKRYlMaVsXlrRWuF96CRUbi2//fnKuvQ5fbq7RZQkhhBDiFHrzzTfdrVq1qly3bl3s7t27rUbXU92UKVNySkpKNnTp0uXQF7z58+cnL1q0KMnIumqSnp5u9/l86rrrrsueMWPGnttuu+1ATk6OZeTIkd2XLFkSV9/7hhNIBhNYa6TKVUBP4DbgHAIzbP2prjfTWn8NvAnMVEo9rpS6iUCrRzvgnpBTXwI2hVy3XWv9n+pb8HDVscwwfq4mIXSmrfxfpJWkrmynn4Z7/jyw2/Fu20bO9ePxl5QYXZYQQgghToH169c70tPTnU899dQep9PpX7hwYYLRNVUpLCw0AVgsFqKiorTJFNZICkNMnjw5b8WKFdtmzpy5f+rUqdkPP/zwwa+//jrd5XJ5Z8+enVLf+4bzk7fkcNcogF8DP2mt/6m1Xgs8DwwK8/nXExigfj0wm0CLySVa6zVh3ifiOWLtRCUExvtLt63w2M8ZROI/ngWTCc/G78idfJMsnCiEEEI0AwsXLnS73W7vVVddVTBs2LD8N998s87doGbMmJHcunXrXg6H48xevXr1+Oijj2IGDBjQbcCAAd1Cz8vIyLD85je/aZ+QkNDHbref2bNnzx4LFiw4IvhUjVOZP39+wm233ZaWkpLS2+Vy9YWjx5CkpaX1Sk9Pd37zzTcxSql+Sql+1Z9ZVlZmmjhxYpuEhIQ+Tqez70UXXdRp3759R0xWlZaW1mvYsGGdlixZEtezZ88eDofjzJ49e/ZYuXJlFMCzzz7rbt++/el2u/3M/v37d9u8efNxx7DUJDY21p+YmOgpLCw01+d6CGOWLQKLFYY+6FfAOyGf9wNhJaPgyux/DG7HOudXdbyXCufZTVFCGxeleQdkpq16cF4yEtdjM8ifdg8Vq1aT9/upJPzjWVQT+GuEEEIIIepnyZIliZdcckmexWJh7Nixuddcc03n77//3t67d+9aV5p+/PHHk++99962/fv3L7711lsP7tq1yzZ27NjOcXFx3tTUVE/VecXFxWro0KHdfvnlF/sNN9yQ2aZNm8q33norceLEiR3Lysp2Tpky5Yi+4o8++mgrh8Phv/322w8UFRXV+AX+scce23vPPfe0cTgc/rvvvns/QMuWLb2h59x+++1tXS6Xd9q0aft27txpnz9/fovJkyfr999/f0foedu3b3fcdNNN7cePH58VExOTO3v27NQrr7yyy/3335/xzDPPpI4fPz6rsLDQPHfu3NQJEya0/+qrr7bU5feal5dnKi8vN2VmZpr//e9/J23dutX5+9//vqb1BesknECyk8CUv88ppc4l0GKyMuR4KwLTAYuTJKFNPBnfHyB3j6xFUh/Rv70Wf04OhY/PpOzdpZhcLuIf+RuBmaSFEEII4fX5ycgrq9dfyk+GtARnpcVcvz8efvrpp9EZGRm2a6+9NhdgzJgxhXFxcb4FCxa4Z82adazZXCkvL1ePPfZYqz59+pSsWbMm3WoNDDvp3bt32R133NE+NJDMmjUreceOHY7nn39+x+TJk/MA7rrrrqy+ffv2eOCBB9pMmjQpz263H1ozz+v1qnXr1m2Oioo65jp648aNy3/kkUdaxcXF+aoHmiput9u7atWqrVXdvPx+PwsXLmyRk5Njdrvdvqrzdu3a5VixYsWm888/vxSgdevWlZMnT+740EMPtU5PT/8hNTXVB+DxeNScOXNSt23bZu3cubOnpmeGuuaaa9p//PHHCQBWq1Vfe+21WY8++ugpCSQLgL8rpX4E0oBMjlyp/Wxgc30LEceX0C6wjErOzjy01vJFuh5ibv8dvuwcSubNo+TFl1BOJ3H3TZffpRBCCAFk5JXZrn72i15G11Fl8e2Df2iXFF2vftYvv/yyOyUlxXPxxRcXA9jtdj1ixIi8d955J7G2QPL5559H5efnWx566KFfqsIIwC233JJz3333tQk99+OPP45PSUnxTJo0Ka9qn9Pp1JMmTcqcNm1auy+++CLqwgsvPDR4dezYsdm1hZG6mjx5clbomJOhQ4cWz5s3r8W2bdtsbrf70Fp+Xbt2LasKI8HzSgCGDRuWXxVGAAYOHFgyZ84ctm7daq9LIPnLX/6y/+abb87es2eP9bXXXnNXVlaaKisrldPprNfPFk7kfAZ4kMDq6huBMVrrUji0MOJA4IP6FCHqJqVLoNtjWX45xdmlxzlb1EQpRfxDDxB19f8BUPzcvyj6+yyDqxJCCCFEQ/J4PCxbtixh4MCBRT///LP9xx9/tP/444/2gQMHluzZs8deNY6iJjt27LADdOvW7YhuXVarlbS0tCP2ZWRk2Dp06FBefUB6z549y4P3OqK1qUOHDg0yiLX6fRITE30A2dnZR3QDS0tLq/G81q1bH7Hf5XL5AHJycurUWDFgwICyMWPGFN555505q1at2vr9999HXX311e3D/kGCwlmpXQN/DW7Vj+UQ5vgREb6Eti4sNjPeSh9ZW7KJTT6hKZ+bLWUy4XpiJrq8nLJ3l1L091koh4PY26YYXZoQQghhqLQEZ+Xi2wf/YHQdVdISnPX6Ar9kyZK4vLw8y9KlSxOXLl2aWP34yy+/7A5tOThVoqKi/A1xH7PZXGNLRPUx1SaTqcbzzOaax58Hvu6Hx26365EjR+bPnj27ZXFxsYqJiQn7JuF02RIGM1tMJHVK5MCmLDK35tDx3HZGl9RkKbOZhGeeRldUUP7RxxQ+OgPldBIzcYLRpQkhhBCGsZhN1LeLVGPyyiuvuFu0aOGZMWPG3urHFi1alPjee+8leL3evRbL0V+FO3bsWAGBNTdGjBhRXLXf4/GQkZFh79at26EuUWlpaZXbtm1z+v1+QltJNm3a5Ajeq16/y6bWlbysrMyktaagoMAcExPjPf4VRwo7kCilziIwXiSBo7t8aa31US0oouGkdE0KBpJso0tp8pTVSuLcOeRMupGKlZ9RcP8DKLud6N9ea3RpQgghhKinoqIi0/Lly11jxozJmTBhQl71406n03/NNde43nvvvbgxY8YUVj8+ZMiQUpfL5V2wYEHylClTcqrGkTz33HPu6lPbDh8+vGDNmjVxCxfyK6RjAAAgAElEQVQuTJg4cWIeBAbFz5s3L8XtdnsHDx5cr1aYqKgo34lMo3uy7Nu3z9KqVasjAkdubq5p2bJlCampqZVpaWlhhxEII5AopZwEpvm9mMAUwDr4Ssj7qm5d4iRJDo4jydqWi9/nx1TPmSdEgLLbcf/7ebKvv4HKL78kf9o9oBTR115jdGlCCCGEqIdXX33VVVpaaho1alSNs79eeumlhQ6Hw79o0aLEmgKJw+HQf/zjH/dNnz697bnnntvt8ssvz921a5f9zTffdLdp06ZCKXWoS9LUqVOzFi5cmHzrrbd2WLduXXTVtL+bN292zpkzZ2foDFvh6N27d+mLL76YMm3atJadO3cuT01N9Y4ePbqoPvdqSFdccUVHu93uP/vss0tSU1M9e/futb3++uvugwcP2p5//vkdx79DzcJpIXmAQBh5BPgvgSl/xxOYbevPgJPAAofiJErpmgSAt9xL3t4C3O0bzYKjTZZyOnEvnE/OuOup/Hod+X+cBj4f0eOuM7o0IYQQQoRp8eLFiQ6Hwz9q1KijwgZATEyMHjRoUNEnn3ziKi0trbFv1L333pultVZz585t8fDDD7fp1q1b6RtvvLFt6tSpbUNDRkxMjP7ss8/Sp06d2nrx4sXukpISc6dOncrnzZu3o6rFpD7+9re/7d+9e7d97ty5qSUlJab+/fsXjx49Or2+92soY8eOzXn11VeTXnjhhZTCwkJzbGysr2/fviUvvPDCzl//+tfFx79DzVRdB68opbYC67XWY4OzamUBw7TWK5RSFuAb4COt9Z/rW4zRlFL58fHx8fn5jXedD601L13/FuWFFZz3u4F0v6iz0SVFDH9JCTnXj6dy7dcAxD/yN2JuGG9wVUIIIUTNXC4XBQUFBVprV32uX79+/fMJCQljOnbsuLuha4tEPp8Pt9t9xogRI/Jef/11+Z2FaceOHe3y8vKW9OvX76bqx8Lp79MGWBV8XzVvsQ1Aa+0FXgPGnkih4viUUodaSWQcScMyRUfjfvklbOecA0DB9PsoXrDQ2KKEEEIIccrV1HIyZ84cd0FBgfm8884zvOtUpAmny1ZRyPlFgJ/A6uxVCoDUBqpL1CKli5s932aQuSXH6FIijikqCvdLC8m9YSIVX3xBwX33g89HzI2TjC5NCCGEEKfIJ598EjN9+vTWo0aNynO73d6NGzdGv/HGG0ldunQpq2mgvDgx4QSS7UBXAK21Tyn1E3AVMF8F5ia7AjhqajXR8JKDLSS5u/PxVHix2mX25oZkqhpTMnESFas/p+DBh9ClpcTc/rsmNw2fEEIIIcLXtWvXihYtWnheeOGFFgUFBeb4+HjfFVdckT1r1qwMh8NxwiutiyOF8012OTBRKfV7rbUP+BfwD6XUdgKza3UA7j0JNYpqUjoHZtrSfk329lxa9pQ1KRuacjpxz59Hzk03U7FiJYWPz8RfVETcvX+WUCKEEEJEuJ49e1auWLFim9F1NBfhjCF5DDif4FS/Wuu5wN0EumrlEQgjMxu6QHE0R5yduJaxAGTJOJKTRjmduOe9gPPSSwEonvtP8v90L9rnO86VQgghhBCirurcQqK1LgbSq+37O/D3hi5KHF9KFzeF+4tkHMlJpmw2Eub+AxUbQ+lrr1O6aBG6pJiEWX9HBRdKEkIIIYQQ9Ser6jVRh2ba2iItJCebMptxPTGT6Mk3AlC25D/kTr4Jf1mZwZUJIYQQJ6TI6/X6jS5CRD6tNV6vF6DGL0/HbCFRSg2t5wNX1+c6EZ6UroFxJEWZJZTll+N0OQyuKLIppYh/8AFMcXEUPfV3yj9dTs7Ya3EvnI8pQRanFEII0SSll5eXe7KyshKTk5NzjS5GRCa/368yMzOTysvLyzm8hMgRauuy9RmBwep1pYLnm8O4RtSTu0MiJrPC79Nkbsum3VmtjS4p4imliPvDVEwuFwUPPEjlt9+SdfkVuF95GUtr+f0LIYRocuZ7PJ7O+/fvvzYvL6+NxWKRnjOiQfl8Pl1ZWYnH4ynw+XxvAR/UdF5tgWTCySlNNASLzUxi+wSyt+eSuSVHAskpFDNxAubkZHLvuBPvtm1kXXY5SS+/jLVnD6NLE0IIIeqsX79+3vXr19/r8Xi2eTyebkCs0TWJiFMKZAIf9evXb8OxTjpmINFav3gyqhINJ6VrEtnbc8naKgPbTzXnqEtJcrvJmTgJ/4GDZF1xJe7587CfM8jo0oQQQog669evnxd43ug6RPMmTXNNWNU4ksyt2Wgta/ScavZzBpH8ztuYUlugi4rI/u11lL79jtFlCSGEEEI0KXUOJEqp25RSy2s5/olS6uaGKUvURUqXwExbFUWVFB4oNria5snaswfJS9/F0qULVFaSd8edFD75lAREIYQQQog6CqeF5AZgay3HtwATT6gaERZXWhxWZ2AtDJn+1ziWtDSS312CffBgAIpmPU3e725Hl5cbXJkQQgghROMXTiDpAvxQy/GfgueIU0SZFMldqrptyTgSI5ni43Eveomoa68BoOw/75J99TX4cuR/FyGEEEKI2oQTSKxAbYtdOI5zXJwEh8aRSAuJ4ZTVimvm48TdNx2UCkwLfOloPOnpRpcmhBBCCNFohRNItgAX1XL8YmD7iZUjwtUiuGJ79vZcPOVeg6sRSilib72FxH//C+Vw4Nuzh6xRl1H28cdGlyaEEEII0SiFE0heAy5WSv1VKWWr2qmUsiqlHiYQSF5t6AJF7Vqe1gJlUvi9fvb/dNDockSQc+RIkpa8jbllS3RJCbkTb6Rw1tNov9/o0oQQQgghGpVwAsksYDUwHdinlPpCKfUFsB+4H/gCeKrhSxS1scfYSAmOI/ll436DqxGhbL17k/zh+9j69weg6MmnyL3pZvzFMiOaEEIIIUSVOgcSrbWHQCvIn4BfgL7BbS8wDRimta48GUWK2qWd0RKAX/4ngaSxMScnk/TG60Rddx0A5R9+RNboy/Du3GlwZUIIIYQQjUNYCyNqrT1a65la6zO01tHBra/W+slgYBEGaNM3EEjy9hRQklNqcDWiOmWzkfD4DFwzHgWLBW/6FjJH/pqyjz4yujQhhBBCCMM1yErtSil7Q9xH1E9ylyRsUYH1SH75TlpJGqvo68eR9OZiTCkp6KIicidNpuCRR9FemYxACCGEEM1XOCu1j1RKPVRt3xSlVCFQopR6VSllbegCxfGZLSZa9WoBSCBp7OwDBpDy8YfYBp4NQPHcf5I99hp8mZkGVyaEEEIIYYxwWkj+CHSv+qCU6gE8A+wDPgWuBm5r0OpEnbUOGUei/drgakRtzCkpJC1+nZgptwJQ+dVaMoePpOKrrwyuTAghhBDi1AsnkPQAvg35fDVQBgzQWo8EFgPjG7A2EYbWfVsBUF5QQc6uPIOrEcejLBbip99L4vwXUHFx+DMzyf6/sRQ++ZR04RJCCCFEsxJOIEkAQpcDHwas0FoXBj9/BnRooLpEmOJSY4hNiQak21ZT4hw+nJQPlmHt1Qv8fopmPU32/12NN2Of0aUJIYQQQpwS4QSSbKAdgFIqFugPfB5y3AqYG640EQ6lFK2Ds23JeiRNi6VDB5LfXUL05BsBqPx6HZkXD5fV3YUQQgjRLIQTSL4CblFKXQU8DViAD0OOdyawSKIwSOszAt229v+ciadCuv00Jcpux/XQg7hfXIgpMRGdn0/uxBvJv3c6/rIyo8sTQgghhDhpwgkkDwbPfwOYALyktf4ZQCmlgDHAmgavUNRZWu8WKJPC7/Vz4CeZtakpcgy7kJRPPsI2aBAAJS++RNbFI6j87juDKxNCCCGEODnCWan9ZwID2y8DfqW1nhBy2AXMItByIgxij7GT3NkNyDiSpszcsiVJi18j7s9/AqsV744dZF02hsJZT8uAdyGEEEJEnHBXas/VWr+ntV5dbX+e1voZrfX/GrY8Ea5D0/9KIGnSlNlM7O9uI3nZUixduoDXS9GTT5E15kq8O3caXZ4QQgghRINpkJXaReNRNbA9d3c+JbmlBlcjTpTt9NNJ+fB9oidNAsCzYQOZwy6meN58tN9vcHVCCCGEECfumIFEKbVTKbW9avV1pdSOOmzbT13poiYpXZOwOq0AZHx3wOBqRENQTieuvzyE+7VXMaWmosvLKXjgQbKv+g3eHdJaIoQQQoimrbYWkt3AHqBq2e89wX21bXtOWqWiTswWE616tQBg73eylkUkcQwdQouV/yXq2muA4PTAF11M8fP/Rvt8BlcnhBBCCFE/Smt9/LOaCaVUfnx8fHx+fr7RpZyQH99PZ83z3+CMdzBu4ZUokzK6JNHAyj/7jPw/3oNvXyB02vr1w/XE41i7dTO4MiGEEKeKy+WioKCgQGvtMroWIU6EjCGJQG3ODKxHUlZQzoHNWQZXI04Gx69+RcqK5UT99rcAVK5fT+bwkRTOfAJdXm5wdUIIIYQQdWdoIFFK2ZVSjyul9imlypRSa5VSF9bhuiuUUouD41xKlVKblVJPKKXiT0XdjV18y1iSOiUCsG31LmOLESeNKTaWhJmPkbT4dczt24PHQ9Ezszk47GIq1nxpdHlCCCGEEHVSa5ctpdSKMO+ntdbHDRQh938NuJLA+iXbgBuAs4DztNZf1XJdNrAP+A+BcSu9gFuArcBZWut6/Yk4UrpsAfzvPz+zdsEGHHF2rltwJWaLNIZFMl1WRtHsZyma+08IrlUS9ZuriLtvOuakJIOrE0IIcTJIly0RKY4XSPyAB6is4/201jquTg9WagDwNTBVa/10cJ8D+BHYp7UeWsu1v9Jaf1Zt3/XAi8AErfXCOtZb/b4RE0iKs0t45cYloGHkA+fTtl+a0SWJU8CzeTP50/5E5fr1AKj4eOKm3U30uHEos9ng6oQQQjQkCSQiUhzvz+ZeQAHLgd8C8Vrr2Fq2OoWRoKsIhJ0XqnYEWzbmAYOVUi2PdWH1MBK0JPjaI4waIlZMUjQtT0sBpNtWc2Lt3p2k/7xD/KOPoOLj0QUFFEy/n6xLLqXi2/VGlyeEEEIIcZTjBZI04M9AZwJf+DOCYz4aYiqfvsBmrXVxtf3rCISgM8K8X2rwNftEC4sUnYe0B2DX2r14KrzGFiNOGWUyETP+elp8voqoa8YC4PnxR7Ivu5y8qX/Al5lpcIVCCCGEEIfVGki01lla66e01r2AQcC7wE3Az0qpr5RSNyqlYur57JbA/hr2V+1rFeb97gF8wDvHOkEplV/bBkTUoPiO57TDZFZ4yr3s+TbD6HLEKWZ2u0l48gmSl76LtVcvAErfeJODg4dS9I85MhuXEEIIIRqFOo901lqv01rfQiBIXA+UAP8C9iulrqvHs51ARQ37y0OO14lS6lpgEjBTay2rxQc54uy07hvIddtW7TK2GGEYW78zSX7/PVwzHsWUkIAuKaFwxmMcPP9Cyt7/AFmLSAghhBBGCnvqJa11udb6FeBB4L9ANNCxHs8uA+w17HeEHD8updQQAuNO3gfur+1crbWrtg0oCKP+JqGq29ae9RlUFNd1bgIRaZTZTPT142ix5nNibpoMFgu+PXvIvelmsq/6DZUbNxpdohBCCCGaqbACiVKqpVLqT0qpzcBqAgPIZwAL6vHs/QRaW6qr2revDvX0AZYC3wNXa6199agjorU/uzUWmxm/18/OtXuMLkcYzBQfT/yDD5Cy4r84Lr4IgMq1X5N16Whyb7kV786dBlcohBBCiObmuIFEKWVVSl2llHqfwJofDxEIAJcC7bTW07XWe+vx7O+A7jWMQTk7+Pq/49TVCfgIyAR+rbUuqUcNEc/qtNJuQGtAZtsSh1k7dcS9YD7u117FetppAJS9t4yDv7qA/Pvux5ctc0MIIYQQ4tSoNZAopWYTaMlYTGDGrbuAVlrr/9Naf6i19p/As98CrMCNIc+zAxOANVrrfcF9bZVS3avVlQp8AviB4Vpr+fZUi85D2wOw74eDlObVqSecaCYcQ4eQ/NEHJMx+BnPr1uD1UrJgIQfPGUzhk0/hLyw0ukQhhBBCRLi6LIxYRmDK3w11uJ/WWs+q88OVegO4HJgFbAfGA/2B87XWa4LnfEZg5XYVct13QB9gJvBDtdtur22V9+PUEzELI4byeXy8NP5tKksqOefGs+g1qvvxLxLNjq6ooHjhixTNfhYd/HdAueKJveUWoidNxBQVZXCFQgghQsnCiCJS1CWQhENrreu8HHRwZfa/AtcBCQS6gt2rtV4ecs5nHB1IapsW6EWt9Q1h1l1134gMJACr/rGWzZ9uI6VrEmOeGGF0OaIR8xcUUPyv5yl+YR66JNAT0pSUROzvbiP6ut+inHWeAE8IIcRJJIFERIrjBZLzwr2h1nrVCVVkoEgOJBnfH2DZ/YGcd/Xc0bjS4gyuSDR2vpwciufMpfjFF6E8MEO3KSWF2FtvIWrcdZgkmAghhKEkkIhIUWsgaW4iOZD4fX5ev+VdijJL6DmyK0NuGWB0SaKJ8B04QNGz/6DklVfB4wECLSYxN99E9PjrMUVHG1yhEEI0TxJIRKQIex0S0TSZzCZOD44d2fLf7ZQX1rQmpRBHM6em4nrkb7RY8wXRN4wHux1/djaFjzzKwbMHUTjrafwRGOKFEEIIcWpIIGlGug/rjC3airfSx88fbTG6HNHEWNJa4Xrkb6R++QXRN04Chx1/Xh5FTz7FgQEDKXj4L/j27Te6TCGEEEI0MRJImhFblJUeF3cB4KcP0vF5ZB1JET5zaiquhx8ide1XxEy5FRUTgy4pofj5f3PgnHPJu+tuPFu3Gl2mEEIIIZoICSTNzOmXdsNkVpTmlbN11S6jyxFNmDk5mfjp95K6bi1xf/4TpuRk8HgofX0xmb+6gOxx4yn//AtknJoQQgghaiOBpJmJSYqm4+B2APywdJN8WRQnzBQfT+zvbiN17Ze4HpuBuX3gn6+KFSvIGXsNmRcNp2TxG+gKGbckhBBCiKNJIGmG+lzeE4Dc3fn8slH6/IuGoRwOosddR4vVq0ic929sA88GwLtpE/l/uIsDAwZSOPMJfPvlnzkhhBBCHCaBpBlK6phIq14tAPj+3U0GVyMijTKbcY4YQfLbb5H8wTKcYy4Hsxl/djZFz8zmwNmDyL35VirWrpUWOiGEEEJIIGmuel/WA4BfvttPzq48g6sRkcrWpw+J/3iW1LVfEXvnHZiSksDno2zZMrKv/A2ZF11M8cKF+AsLjS5VCCGEEAaRhRFDRPLCiNVpv+aN371HfkYhXS/oyPl3nmN0SaIZ0BUVlC17n+IFC/Fs3Hhov3I6cV42mujrfov1jDNQShlYpRBCNA2yMKKIFNJC0kwpk6JXsJVk2+pdlOSUGlyRaA6U3U7UlVeQsmwpye+/R9Q1Y1FOJ7qsjNLXF5N16Wiyho+keMFC/HnScieEEEI0B9JCEqI5tZAAeCu8vDJ5CeUFFXQb1olf3T7I6JJEM+QvLKT0nSWULHoF76aQMU02G84Rw4kaezX2wYNRZrNxRQohRCMkLSQiUkggCdHcAgnATx9u4Yvn1oGCK54cSXJnt9EliWZKa41nw0ZKFi+m7N2l6OLiQ8fMLVvivPIKoq68AmvXrgZWKYQQjYcEEhEpJJCEaI6BxO/z8/bUD8jdnU9qj2RGz7hY+u8Lw/lLSyl//wNKFi+m8qu1Rxyz9upF1FVX4rz8MsxJSQZVKIQQxpNAIiKFBJIQzTGQAGR8f4Bl9y8H4MK7BtN5aHtjCxIihHfnTkrfepvSd5bg27Pn8AGzGfuQwThHj8Y5cgSmuDjjihRCCANIIBGRQgJJiOYaSAA+nrGKXWv3EpMUxf/NHY3VbjG6JCGOoLWm8ptvKH3rbcreW4YOnSrYbsdxwflEXXYZ9mEXYnI6jStUCCFOEQkkIlJIIAnRnANJ4YEiFt/2Hn6vn37X9Oassb2NLkmIY9Ll5ZSvWEnZu0spW/4plFccOqacTuwXXIDz0l/juPACTNHRBlYqhBAnjwQSESkkkIRozoEE4OuXNvLd2z9hsZm5eu5oYpLli5xo/PzFxZR//Aml7y6lYtUq8HoPH3TYcZx/Ps6RIwPhxCX/zRZCRA4JJCJSSCAJ0dwDSWWph8VT3qU0r5zOQ9tz4V2DjS5JiLD48/Mp+/gTyt7/gIrVq8HjOXzQYsE+aBCOERfjvHg45lYtjStUCCEagAQSESkkkIRo7oEEIP2/2/ls9lcAjHrkIlqd3sLgioSoH39BAeXL/0vZ++9TvmrVEd26AKy9e+EYNgzHsAux9uqFMsk6sUKIpkUCiYgUEkhCSCAB7dcsmfYRWVtziHZHcdXTv8YRZze6LCFOiL+0lIrVqyn/6GPKPl2OrvbvuKlFCo4LL8RxwfnYBw/GFBtrUKVCCFF3EkhEpJBAEkICSUD+LwW8fdeHeMu9tOufxvDpv5K1SUTE0F4vlV+vo3z5cso+XY5v584jT7BYsPXvj+P8X+E4/3wsPbrLP/9CiEZJAomIFBJIQkggOWzLih2sfOZLAAZN6kfv0T0MrkiIk8OzfQfly5dT/ulyKr/55shB8YAptQX2wUNwDB2CfchgzCkpBlUqhBBHkkAiIoUEkhASSI608pkv2bJiByaLicsfG05yF7fRJQlxUvmLiqhYs4bylauoWLkSX0bGUedYenTHMWQI9nPPxXb2AOneJYQwjAQSESkkkISQQHIkT5mHd+76kPyMQuJSY7hy1iXYomxGlyXEKaG1xrt1KxWrP6d89edUfvUVurT0yJPMZqy9e2M/9xzs556D7ayzMEVFGVOwEKLZkUAiIoUEkhASSI6WsyuPJXd/iM/jp9Pgdlx492DpTy+aJV1ZSeWGDVSs/pyKL9ZQ+d134PMdeZLFEggoA8/GPnAgtv5nYYqLM6ZgIUTEk0AiIoUEkhASSGr204db+OK5dYCMJxGiir+4mMqv11Hx5ZdUfLEGz08/QfX/PzWZsHbvjq3/WdgG9MfWvz+WtDRjChZCRBwJJCJSSCAJIYGkZlprlj/xOTvW7AHgvNsH0X1YJ4OrEqJx8efnU/HNt1R+/TUVa7/G8/33R7egAOZWrbD1OxNbv37YzjwT6+mnoewytbYQInwSSESkkEASQgLJsXkrvHzwlxXs/zETZVJceNdgOg1uZ3RZQjRa/pISKtdvoPLbb6lc9w2V69cfPQYFwGbDevrp2Pr2xda3D7YzzsDcvr10jRRCHJcEEhEpJJCEkEBSu8pSD+8/sJzMrTmYzIqL/3we7fq3NrosIZoE7fXi2bQpEE42bKByw0Z8e/bUeK5yxWPr0wdr797YevfG2qc35latJKQIIY4ggURECgkkISSQHF95UQXv3fcpubvyMVtNjHzgAtJ6pxpdlhBNki8zk8qNG6lcvwHPd/+j8n//QxcX13iuKTERa+9eWE8/Hetpp2E7/XTM7duhTKZTXLUQorGQQCIihQSSEBJI6qY0v4ylf/6Egn1FWBwWLnngAlqeJovFCXGitN+Pd8cOPBu/o/K776j8/gc8P/8E5RU1nq+io7H27In1tJ6B1x49sHTvJlMPC9FMSCARkUICSQgJJHVXnFXCu/d+QnFmCSaLiSG3ni0D3YU4CbTXi3fLVip/+B7P9z/g+fEnPD//XPN4FAClMLdvj7VHD6zdu2Ht1g1L925Y2rdHWSyntnghxEklgURECgkkISSQhKfwQBEf/nUl+b8UAtBrVHcGTjgTk1m6kAhxMmmfD++u3Xh+/BHPTz/h2bQJz8+b8B84cOyLbDasnTtj6doFS5cuWLt0Cbxv3x5ltZ664oUQDUYCiYgUEkhCSCAJX0VJJSv+voY932YA0PqMlgz742DsMTKNqRCnmi83F++mzXh+/hlPejqezel409OP3ZoCYLFgad8eS+dOWDp3xtKpE9ZOnbB06ojJJd9xhGjMJJCISCGBJIQEkvrx+/x8s+h/fPfOTwDEtYxl+L3nkdhW/v9RCKNpvx9fRsahcOLZug3v1i14t2xFl5XVeq0pMRFLx45YOnbA0iGwmTu0x9KuHabY2FPzAwghjkkCiYgUEkhCSCA5MVs/28mqf3yFz+PHZDFx5m9O54wrT8NsNRtdmhCiGu3349u3D++WrXi3b8ezbTve7dvwbt+BPzPzuNebkpKwtG+PuV07LO3aYmnXDnO7tljatsWUkiJTFAtxCkggEZFCAkkICSQnLnNrNsuf+IKig4GpSxPaxjP0toGkdk82uDIhRF35Cwrw7tyJd8dOvDt2BLedeHftQhcVHf8GDjuW1m0wt22DpXVrzG3bYk5LC7xv0xpTUpIEFiEagAQSESkkkISQQNIwPOVevn3tf/ywdDPar0HBaSO7MmDcGdiibEaXJ4SoJ601/txcvDt34du1KxBa9uzBt3sP3j178Gdl1e1GDjuWVmmY09Iwp7XC3KrV4ddWaZhbtZSpi4WoAwkkIlJIIAkhgaRhZW3LYfWctWTvyAPAEW+nz+U9OW1kV6xOmdVHiEjjLynBt2cP3r178e39JRBW9u7Fu2cvvowMdGFhne+l4uMxt0wNhJTU1CM2U4sWmFumYkpIkIUhRbMmgURECgkkISSQNDy/z8/3725i/Wvf4630AeCICwaTSySYCNGc+AsL8f2SgfeXX/BlZAS2ffvwZewLvD94EPz+ut/QasWcnBwIKC1SMKekYEoJviYnY05JxpScgjnJjbLLzH8i8kggEZFCAkkICSQnT0luKf9752d+/ngrvqpgEmvn9Eu70W1YJ2KSog2uUAhhNO314jt4EN/+A/j378dXte3bH9h/4EAgtFRWhn1vFR+POSkJU3IS5qRkTMlJmNzuwL4kN6akJEyJbsyJCaj4eGl5EU2CBBIRKSSQhJBAcvKV5pXx3Ts/semjrYdaTJRJ0bpvS7oP60y7/mkyK5cQ4pi01vjz8vDvP4Av8yC+zEz8BwKvvoMH8WdlB95nHoTyivo9xGzGlJiIyZ2IKSERU1yA22oAABa3SURBVGIi5sQETAkJgf0JCSGbK9B1LC5OQow45SSQiEghgSSEBJJTpzSvjB+Wbmbzf7dRXnD4S4Mj3k7noR1oP6A1qT1TMFvkP/BCiPBprdHFxfgOZuLPzgoElexs/FlZgdfsbPw5uYH3OTl1mz2sNiYTpvh4lMuFyeXC5IoPvMbHBzaXCxUfH9gfH48pLh4VH4cpLg4VEyOzjol6kUAiIoUEkhASSE49n8fH7m8zSP90G3s37g/MyhVki7bR5syWtB/QmjZntpLV34UQJ40uL8efm4cvNwd/Tg7+3Fz8ObmB93l5+HLzAvvycvHn5uHPz69X17EamUyouFhMsXGYYmMD7+PiULFxmGJjULGBz6aYmMCxmFhUbMyhVxUTiykmGmWTWQybGwkkIlJIIAkhgcRYxdklbFm5k51f7SF7e+6RBxW42yeQ2iOZ1B4ptDwthWi3TAsqhDCG1hpdWhroPpafjz83D11QEHhfteXl4S8sxJ9fEDgW3HRx8ckpymbDFB2Nio1FRUcH3sdEo6KiA4ElOvrw/uhoVHRU4FhUFCoqKvg5uDmjMEU5wW6X1ptGTAKJiBSGBhKllB34CzAOSAD+B0zXWv+3DtemAbOAiwETsAKYqrXeeQL1SCBpJEpyStn9zS/s/iaDjO8PHBoIHyomOZqkjgkktkvA3SGwxbWIQZnkP55CiMZLe73ooqJAQKkKLEVF+IsK0YVFgX2FhcF9RYF9RYX4C4vQxcWBrbz81BRrMh0OKVFOlNMZCCvOqveO4GtwczgOv1Z/H7JhtweudThQdnvg1WI5NT9TBJFAIiKF0YHkNeBK4GlgG3ADcBZwntb6q1quiwE2ALHA3wEvMBXQwBla67x61iOBpBHylHs5uDmLA5syOfBzFgfTs/BWHB1QACx2M/Gt4ohvGUtcy1jiWwVeY5OiiUp0yoB5IURE0B4PurgYf3ExuqgYf0kgqPiLitElJYePlZYG35egS4rRpWX4S0rQpSXoktLAuaWlpy7g1MZiCYST4IbdjnIc/qxswX12O8puQ9lsgfOCr8oW2FfzZ1vgeqv18LVWG9isKKs1cCz4Hmvw1WJp9K1DEkhEpDAskCilBgBfE2jVeDq4zwH8COzTWg+t5dppwGNAP631xuC+7sFrH9VaP1DPmiSQNAE+r5+cnblkbskhZ1ceuTvzyN2df2jWrmNSEOVyEO2OItodTVSCA6fLgTM+sDlcDhyxduzRNuwxNix2+WudEKJ50D4fuqzscEApLcNfWhrYV1Ya2FdWHvgc3O8vLYXycvxV+6vOLS8PbGVlwdfAZyrqOeuZkWy2QMuNzYqyhry3WA+/Wq0oqyX43nI41FgsWE8/ndjbppy08iSQiEhhZCCZCfweSNRaF4fs/zPwCJCmtd5/jGvXAV6t9TnV9n8MtNNad69nTRJImii/z0/hgWJy9+RTuK+Igv1FFOwvpHB/MSU5pfW6p9lmxh5twxZtxea0YnVasUYF3lscFqwOCxa7GYvdcnizmTBbzZhtZiw2MyarGbPVhNlixmQxYbaaMJlNmCyhrwqTxdTo/xInhBAnQvv9UFFxOLCUl6MrKgJb1fvycqioPPpYZSW6oiJwfdVWWYmuqITKikPHA58rA58rg589nsBnTyVUVMIp/N5jv+B8kl5+6aTdXwKJiBRG/gm4L7A5NIwErQMUcAZwVCBRSpmA3sDzNdxzHXCRUipKa33Ut1Cl1PGSRnxdCheNj8lswpUWhyst7qhj3govJbllFGeVUJJTSnF2KSU5pZTll1NWUBZ8raCy5MgZc3yVPkor/7+9e4+Tq6zvOP75JoFcIIRLLISbvMAAQVHDLUXoi2ChLZZCQLBUjYJFRIqWYtVXW61FrcUCBQRBqEgIVhugysW7SAJNUGyREOSimHIPkQQhhFx3d37943kmOTmZmSyZ2T3D8n2/XofZfc5zzjw7Pya7v3luq1j5wqpB+Rk0TGiYUoIyfNi679eVl77XMCEVvwaUHiXBMKF8XwRQOFevm4rXJ0P1h3qdelH5fKFswzxqw3obnm+ccDXMwxpW7V/C1lZe55zQrEICRuejwa/jEflodx/dWkDUoFZLSVKt+HWs+35dWWxYHlGok89tVC9S2Q67jmV8m801ey2oMiGZADzToLyehOzc5LrtgZE0SFZymfK9F7bbQBsaRowcwbgJYxk3YWzLen09fax5ee36Y8Ua1ry8lp6VPaxd2UPPql7WruqhZ2UPPat76V2TjvR1H71re+lb20dfT19+rL2idkYtiFpQ6wXYxPAzMzOrkIDh+WhhdLM/ZcysqMqEZDTQaEDp6sL5ZtexOdduqksz96C4l+Q1avgWwxmz3WjGbNfsf71XJmpBX08ftb4afT01ar01+npr9PX0EX1BrS+VrXusRSqv1dJjb42INMyhlpOVDY6g8HXkT+TIX0OtFuuGJkQtCEh1ovgIkf6TDgrfk++17gcqlMUGRfUTGxVG+Vz5NervyIl+VmxvJIaXQDezztp+d4+kMuuPKhOSVaSejrJRhfPNrmMzrzUbNBomT4w3MzMz24RhFT73s6ShVWX1skVNrvsdqXek2bVB4+FcZmZmZmbWZapMSOYD++Y9RYqm5Mf7G10UETXgAdJ+JWVTgEcbTWg3MzMzM7PuU2VCchOwBXB6vSDv3H4aMC8iFuWy3fMeI+Vrf1/S5MK1+wBvB24c6IabmZmZmVlnVDbAPSLukXQj8K+S6qtivR94PWnH9rqZwBFsuCDnFcAHge9Juoi0U/u5pKFaFw98683MzMzMrBOqnnH7PuBz+XE7YAHwjoiY1+qiiFguaSop+fg0qadnNnBORDw/oC02MzMzM7OOqWyn9m7kndrNzMzs1cI7tdtQUeUcEjMzMzMze41zQmJmZmZmZpVxQmJmZmZmZpVxQmJmZmZmZpVxQmJmZmZmZpXxKlsFkmqAxo0bV3VTzMzMzFpatmwZQESEP2C2VzUnJAWSekm9Ri916Jb1zGZZh+5nnePYdCfHpXs5Nt3JcelegxGbbYBaRFS9r5xZW5yQDCBJLwJ4ffDu49h0J8elezk23clx6V6OjVn/uYvPzMzMzMwq44TEzMzMzMwq44TEzMzMzMwq44TEzMzMzMwq44TEzMzMzMwq44TEzMzMzMwq44TEzMzMzMwq431IzMzMzMysMu4hMTMzMzOzyjghMTMzMzOzyjghMTMzMzOzyjghMTMzMzOzyjghGQCSRkr6oqRFklZJ+pmkP6y6XUOVpAmSzpc0W9JySSFpapO6x0n6haTVkp6U9BlJIxrU21bS1ZKWSFoh6Q5Jbx3wH2YIkXSwpC9Leii/hk9K+k9Jb2hQ922S5kpaKWmxpEsljWlQz++tNkk6SNK3JT2RX8PFkn4g6W0N6jouFZL0ifzv2fwG5xybQSJpao5Do2PfUl3HxWwzOCEZGDOAvwG+Dvw1UAO+L+nQKhs1hO0DfBLYFVjQrJKkY4Cbgd8BH8lf/yNwcaneMOC7wCnAZcAngB2BOZL2GoD2D1WfBE4Ebie9D64GpgL3SZpUr5QTvZ8Ao4Bzga8CHwJmNbjnDPzeatdewAjg34GzgQuA3wPuknR0vZLjUi1JOwGfAlY0OOfYVOMSYHrpWFQ/6biYtSEifHTwAA4BAjinUDYK+A1wV9XtG4oHMBbYIX89Lb/+UxvUexC4FxheKPs80AdMLJS9K99jWqHsdcALwMyqf95XywG8DdiyVDYRWA3MKJR9D3ga2LpQdnqOwdsLZX5vDVysxgCLge84Lt1xkP5gvQOYA8wvnXNsBjcWU8u/E5rUc1x8+NjMwz0knXcS0EP6ZASAiFgNXAMcLmlCVQ0bqiJieUQ836qOpP2A/YCrIqKvcOoKUk/hOwtlJ5E+9bql8BxLgBuAaZK26FTbh7KIuDsi1pbKHiUlhpMAJG0DHE1K9F4uVJ0JvExKDuv83hogEbESWAJsC45L1SQdAryX9Cl7+ZxjUyFJY5sM83VczNrghKTzJgOPlP5BAvg5IMDzEKoxOT/+b7EwIhaRPtGaXKp7b0SUdw39Oak3ZqM5ENY/kkQa/rY0F+1PGj5UjstaYD4bx8XvrQ7Jf1iNl7SPpC8AbyINNwHHpTL5PXIZcF1EbDR3BMemStcDLwGrJP1I0v6Fc46LWRuckHTeBODZBuX1sp0HsS22Xv0Tp2ax2blU1zEcGO8BdiH1NoHjUqVrSb0ijwAfA74CfCGfc1yq8z5Sb+6nmpx3bAbfWuAm0lyP44HzSMOu5kraO9dxXMzasFG3o7VtNLCmQfnqwnkbfPXXvVlsxpTqOoYdllej+TIwl/RJI2w6LsXX2nHprPOAq0iLQUwHRgJbkF5jx6UCksYC5wPnR0SjP1jBsRl0EXE3cHeh6FZJt5F6Qz5D+qDFcTFrgxOSzltF+sVeNqpw3gZf/XVvFptVpbqOYQflFYO+S1oY4OSIqOVTjktFIuIB4AEASV8n/XE1gzS+3XGpxqdIn8b/W4s6jk0XiIj7Jd0O1JfqdVzM2uAhW533LOu7bovqZYsanLOBV/+0sVlsFpXqOoYdImkc8H1gHPDHEbG4cNpx6QIR0UNaxOFESaNxXAZdnsh8DqkXcUdJe0jag/RH6pb5++1wbLrJU8D2+WvHxawNTkg6bz6wr6StS+VT8uP9g9weS+qTQw8qFkramTRkZX6p7oF5cmnRFNJqKb8ZqEYONZJGAbcBewPHRsSvSlV+CfSycVy2JE3sLMfF762BM5o0oXYsjksVdgS2BL4IPFY4ppBWpXuMtLePY9M99iTNwwLHxawtTkg67ybSOOzT6wWSRgKnAfPyqk42yCLiQdLk3TMkDS+c+jBpQ6r/KpTdRJpUeHy9QNJ44GTglvxpsm1Cfp1nAYeShmn9rFwnIpaRNk6cXvrlPB3YGrixUOb3VgdIel2Dsm1I/38/FRHPOS6VeAw4ocHxIPB4/nqmYzP4mrxnDgeOBH4I/rfMrF3aeGVTa5ekG0gb9F0MLATeDxwMHBkR86ps21Alqb4izSTg3cDXSL/gX4yIy3OdY4FbSZuNzSItc3o2aW+Sswr3Gk6aeP1G4ELSErVnAbsBB0aEe0j6QdIlpFVpbmP9qlp1L0fEzbneAaQJo78krcu/K2nVp9kR8Y7SPf3eapOkO0iTZ+8mbYa4G+kPoV2BUyLihlzPcekCkuYA20bEWwtljs0gyu+ZlaTXfCnpd8cZwDLg4Ih4MtdzXMw2V9U7Mw7FgzTm9wLSONHVpLXFj6q6XUP5IO162+h4vFRvGnBfjstTpJWGRjS433akXyhLgRXAbOCAqn/OV9NB2mG6v3E5HJhHmsz5W+BLwFYN7un3Vvtx+UCOzXOkzdmWkJLGIxrUdVyqj9ccSju1OzaDHoOPAvcAz+f3zDOkD712d1x8+OjM4R4SMzMzMzOrjOeQmJmZmZlZZZyQmJmZmZlZZZyQmJmZmZlZZZyQmJmZmZlZZZyQmJmZmZlZZZyQmJmZmZlZZZyQmJmZmZlZZZyQmJkNIElzJD1ecRtOlRSSplbZDjMzs0ackJhZ15G0p6SrJT0iaaWkFyQ9LOk6SUdW3b5uIGm4pOmS5kpaLGm1pKclzZb0WUkjq26jmZlZf3indjPrKpIOAu4EeoCZwIPAaGAi8EfADyPi7Opa+MpImgPsERF7dPi+s4B3AfOAW4AXgN2AA4CjgV0jYmmuOxzYAlgbEbVOtsPMzKxdI6pugJlZyWeAMcBbI+L+8klJOw1+k7qLpANJyci3I+LEBud3AF6qfx8RfUDf4LXQzMys/zxky8y6zUTg+UbJCEBELC5+L+nPJd0q6UlJayQtlXSzpDeXr5X0eJ7T8RZJt0t6WdJzki6SNELSKEkXSnomD4G6S9Kk0j3q8zGOkvRPkp7Iz7tA0in9/SElTZR0vaRnJa3NbbtA0lb9fI0A7mjyGj0fET0N2jy1UBYtjhmlth4l6UeSXsyvywJJZ/b3ZzUzM2vFPSRm1m0WAvtIOjEivtWP+mcDzwNXA4uBvYAzgHmSDoiIR0v1dwV+DMwCbiINAzsX6AXeSBoedj4wHvhb4GZJkxoMdfoisBVwRf7+NOCbkkZFxIxWDc49HHcALwJXAc8AbwE+Chwm6YhiQtHAwvx4sqT/iIgXWj1fE9MblP0pcArw20JbzwC+AvwM+GdgBWlI2JWS9oqIj2/Gc5uZma3jOSRm1lUkHUqaQ7IF8CgwF/gfYE5EPNyg/lYRsaJUNgmYD1wTEWcVyh8HXg+8KyJuLJTfC0wGbgOmRf6HUdJHgUuBP4mIH+ayU4FrgSeBN0fEslw+DlgAjAV2iYhVuXwOpTkkku4HRgIHR8TyQvkJwLeA0/qR1NwK/BmwErgbuCcfP4mIlaW69TYfGRFzmtyvPndnQa63WtIE4DHgWxHx7lL9S0nJ4MSI+L9WbTUzM2vFQ7bMrKtExE+BA4HrgHGknocrgIfyEKo9S/VXACjZRtJ4YAnwK2BKg6d4ppiMZHMBAZfFhp/S/Hd+nMjGrqwnI7kdy0g9CdsBU5v9fJL2B94MfAMYKWl8/cjtWEHqtdmUdwIfAX6Zn+8fgFuBxZI+1o/ri23aLV/7HHB8RKzOp04iJU7XFNuZ23ob6XfIUa/kuczMzMqckJhZ14mIByLi1IjYEdgDeD8pOfgD4BZJW9brSpos6TvAcmAZKRlZAuxPSg7KHmtQ9kKTc/XyHRpcs1FvDfBQftyzwbm6+pyU81jf1vrxHGkY2I4trgcgInoi4vKImAJsQ3pt/oWUWF0o6S82dQ8ASWOB7+TnPTYinmvQ1tsbtPXH+dwm22pmZtaK55CYWVeLiCeAmZKuJyUlhwGHAHMl7Q7cRVpR6nOkXpEVQACXAFs3uGWr1aaandPmtb7lvS4CftCkziuaE5KHh80lvSazgR8Bfwl8s2VD0nLAs4D9SMnIg03a+j7g2Sa38XAtMzNrixMSM3tViIiQdA8pIdklF59ASjqOi4jZxfp56ds1A9ikSaT9P4r2y4+t/kivT7Lvi4jbO96qNPkc1r9GrXwJOAY4qz5HpqTe1qUD1FYzMzMP2TKz7iLpaEkbfVgiaTTr51bUh0bVezRUqvtBYKD3K/lwnshef85xwJmklbPubHHdfaR5H2eW58Pk+4yQtH2rJ85LBr+hyelp+fGhJufr9zgHOAu4NCKubFLtBlJSd15+/cv3GCfvCG9mZm1yD4mZdZuLgR3yKlIPkFaR2g14N7A3MDMiHsh1v5/PXy/pctJQp8OAd5CWxh3If+OWAvdIujZ/fxqwO3B6eZWrotzTM5207O8CSV8j7UY/BngDcCLwd8CMFs/9FmCWpDuBOcDTpDkgU0gbJi4HPtvsYklvIg0ZWwz8QtJ7S1UWRsRPI+JpSR8Gvgo8nIfNPQG8jjRHZxqpV+jxFm01MzNryQmJmXWbc4HjgcNJK0ltS5qsvoC098eMesWIWCjpGOALwN+TekzmAUcAl5MmxA+UT5Imkv8VaWL3r4H3RMQ3NnVhRMyXNJmUeBxH6llZTvrDfgbwk03c4i7g46T9QD6Qn1/AU6TlfS+IiN+0uH48qYd8J9JqZmXXAT/Nbb1W0q9Je7J8iBSPpaT5Op8mJTVmZmabzfuQmJm9Av3Z08PMzMz6z3NIzMzMzMysMk5IzMzMzMysMk5IzMzMzMysMp5DYmZmZmZmlXEPiZmZmZmZVcYJiZmZmZmZVcYJiZmZmZmZVcYJiZmZmZmZVcYJiZmZmZmZVcYJiZmZmZmZVeb/AUCVIUHBjOTLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "sns.lineplot(\n",
    "    x='Sample Size', \n",
    "    y='value',\n",
    "    hue='variable',\n",
    "    data=df_melted,\n",
    "    ax=ax,\n",
    "    palette=['#e41a1c','#984ea3', '#377eb8'], # This sets custom colors,\n",
    ")\n",
    "\n",
    "# Set various labels\n",
    "ax.set_xlabel(\"Sample Size\")\n",
    "ax.set_ylabel(\"Misclassification Rate\")\n",
    "ax.set_title(\"Classification Task\")\n",
    "\n",
    "# Deal with legends\n",
    "handles, labels = ax.get_legend_handles_labels() \n",
    "labels[0] = \"Algorithms\" # Rename the title of legend\n",
    "ax.legend().remove()\n",
    "legend = fig.legend(\n",
    "    handles=handles,\n",
    "    labels=labels,\n",
    "    ncol = 1, \n",
    "    loc='center right',\n",
    "    bbox_to_anchor=(1.15, .5),\n",
    "    bbox_transform=plt.gcf().transFigure\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two heatmaps with shared colorbar\n",
    "\n",
    "This example shows how to use gridspec_kw to control the sizes the subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Plotting random matrices')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Data\n",
    "np.random.seed(1)\n",
    "X = np.random.uniform(-1, 1, size=(50, 50))\n",
    "Y = np.random.uniform(0, 1, size=(50, 50))\n",
    "\n",
    "sns.set_context('talk', font_scale=1.2)\n",
    "\n",
    "# Making our subplots\n",
    "fig, axes = plt.subplots(\n",
    "    ncols=3, \n",
    "    figsize=(10, 5), \n",
    "    constrained_layout=True, \n",
    "    gridspec_kw=dict(width_ratios=[1, 1, .05])\n",
    ")\n",
    "\n",
    "# Plot using seaborn heatmap function\n",
    "global_kwargs = dict(\n",
    "    center=0,\n",
    "    vmin=-1,\n",
    "    vmax=1,\n",
    "    cmap='RdBu_r',\n",
    "    square=True,\n",
    "    xticklabels=False,\n",
    "    yticklabels=False,\n",
    "    cbar_ax=axes[-1],\n",
    ")\n",
    "\n",
    "sns.heatmap(\n",
    "    X,\n",
    "    ax=axes[0],\n",
    "    **global_kwargs\n",
    ")\n",
    "\n",
    "sns.heatmap(\n",
    "    Y,\n",
    "    ax=axes[1],\n",
    "    **global_kwargs\n",
    ")\n",
    "\n",
    "axes[0].set_title(\"Unif(-1, 1)\")\n",
    "axes[1].set_title(\"Unif(0, 1)\")\n",
    "\n",
    "fig.suptitle(\"Plotting random matrices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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