{
"cells": [
{
"cell_type": "markdown",
"id": "2533ac08-4579-4f44-b4e9-04cdf6b2ec0d",
"metadata": {},
"source": [
"# Animations with Matplotlib\n",
"\n",
"This notebook shows how to use the `FuncAnimation` function from the `matplotlib.animation` module to create animated plots. "
]
},
{
"cell_type": "markdown",
"id": "a25dfb9e-c13a-4368-b78e-99b21e8719d5",
"metadata": {},
"source": [
"## Animation Basics\n",
"\n",
"Before we dive into more complex example. it is helpful to understand the basics of matplotlib animation."
]
},
{
"cell_type": "markdown",
"id": "59f6a24c-aa0b-428e-987c-b8c6a5303344",
"metadata": {},
"source": [
"Let's define 3 positions and we will create an animation of a point moving between them."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7352f24a-9346-472e-b0ff-82b5dd143f2d",
"metadata": {},
"outputs": [],
"source": [
"points = [(0.1, 0.5), (0.5, 0.5), (0.9, 0.5)]"
]
},
{
"cell_type": "markdown",
"id": "dc5700d6-31c8-47ee-a839-ea33dde9776b",
"metadata": {},
"source": [
"Then we use the `FuncAnimation` class which makes an animation by repeatedly calling a function and saving the output as a frame in the animation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f525f5cb-52a4-411d-b80f-0a93f48ccb0b",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.animation import FuncAnimation"
]
},
{
"cell_type": "markdown",
"id": "ee2b1f7e-7bea-49d9-97ea-f8624c1534fa",
"metadata": {},
"source": [
"We need to define a function that takes the frame number and generates a plot from it. Here we define a function `animation` that takes the frame index and creates a plot from the point at the same index in the `points` list. So at frame 0, it will display the first point, frame 1 the second point and so on."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bccd980f-bcc8-40b7-978b-522460120499",
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"fig.set_size_inches(5,5)\n",
"fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
"\n",
"def animate(i):\n",
" ax.clear()\n",
" # Get the point from the points list at index i\n",
" point = points[i]\n",
" # Plot that point using the x and y coordinates\n",
" ax.plot(point[0], point[1], color='green', \n",
" label='original', marker='o')\n",
" # Set the x and y axis to display a fixed range\n",
" ax.set_xlim([0, 1])\n",
" ax.set_ylim([0, 1])\n",
"ani = FuncAnimation(fig, animate, frames=len(points),\n",
" interval=500, repeat=False)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"id": "3dbd4619-6dd3-4056-a4ed-75ae94fa6c68",
"metadata": {},
"source": [
"The animation is now contained in the `ani` object. We can call `save()` and save the result as an animated GIF. We need to specify a `writer` that supports the output format."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fe9cf429-a1cd-4615-95c5-bed9acdc3896",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.animation import PillowWriter\n",
"# Save the animation as an animated GIF\n",
"ani.save(\"simple_animation.gif\", dpi=300,\n",
" writer=PillowWriter(fps=1))"
]
},
{
"cell_type": "markdown",
"id": "91491d39-2a07-4b37-8ee3-d229286efa9e",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "728bd5c8-ddbc-46de-84ba-a05255341551",
"metadata": {},
"source": [
"We can also use the `to_jshtml()` function to create an HTML representation of the animation and display in a Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "065d909b-2c7a-441f-a9f7-cdfabc6be6cc",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "41c2324c-54c5-4c89-b27c-c783bc2d399a",
"metadata": {},
"source": [
"### Visualizing Douglas-Peucker Algorithm\n",
"\n",
"The Douglas-Peucker algorithm successively removes vertices from a segment using the distance between the original segment and simplified segment. It removes points whose distance is less than the specified threshold **e** [Learn more](https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm)."
]
},
{
"cell_type": "markdown",
"id": "d673308d-e89b-4ec4-9ec5-2531bcd5e5d4",
"metadata": {},
"source": [
"Below is a python implementation of this algorithm. We use the `shapely` library to calculate distance between a point and a line segment. This implementation uses a recursive approach to achieve the result. The implementation is adapted from https://github.com/fhirschmann/rdp"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e29b3ee6-3aab-4bfd-bce1-966381cfaef0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"e=1\n",
"original=[(0, 3), (1, 0), (2, 1), (3, 2), (5, 3), (7, 4), (8, 4), (10, 3), (11, 1), (12, 1), (15, 2), (17, 3), (18, 3), (20, 2)]\n",
"result=LINESTRING (0 3, 1 0, 7 4, 10 3, 11 1, 18 3, 20 2)\n"
]
}
],
"source": [
"from shapely.geometry import Point, LineString\n",
"\n",
"def douglas_peuker_recursive(point_list, e):\n",
" dmax = 0\n",
" index = -1\n",
" \n",
" for i in range(1, len(point_list)):\n",
" point = Point(point_list[i])\n",
" line = LineString([point_list[0], point_list[-1]])\n",
" d = point.distance(line)\n",
" if d > dmax:\n",
" index = i\n",
" dmax = d\n",
" if dmax > e:\n",
" r1 = douglas_peuker_recursive(point_list[:index+1], e)\n",
" r2 = douglas_peuker_recursive(point_list[index:], e)\n",
" return r1[:-1] + r2\n",
" else:\n",
" return [point_list[0], point_list[-1]]\n",
"e = 1\n",
"simplified_list = douglas_peuker_recursive(point_list, e)\n",
"print('e={}'.format(e))\n",
"print('original={}'.format(point_list))\n",
"print('result={}'.format(simplified))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8c556442-1741-4814-84f5-02ff52f6ae41",
"metadata": {},
"outputs": [
{
"data": {
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AodiS9h8TSnp5njffOXcNVu3aELgN2AMMB17zPG9VvMcfc841wJKejbBk7AqgDZb4/E8COeZzZjrn6gK9seQgWOK5PtA25uOD8T8vAS9hQ8HaA49gPVanxcTeK4HHd8MSs1WBJljybxPQFfgkZoBbivM87y3n3FxscF9tLLl+ABuENoj/thGJTTo3B3oCd2PJuGnAXST8uvA87zPn3AqgC5Zgbo5935YBnychxiHOuePYz0dsEnl9cmO/AO9iSe+G2MWEG7Gk6F4saf4N8M35DudLAfOBV4HXgMexiw8zgB6e5/2eloF4nrfQOVcZ+3m+Gbu4cAhLFr982uOOOOduAN7EfibqAOux19AP+3mK/9wrYy4idcEqrrPG3JVoAjnm87o655ZgX5v7sEGE67DfxXc9zztxni9XRERExBfOv+NOERERETmbmGTldUBuz/OO+B2PpG/OuXpYVW4vz/Ne8TUYEREREUkz6oEsIiIi4iPnXHbnXJ4EtrfDqil/VPJYRERERET8ohYWIiIiIv4qivXqnYYtu88IVMFaJOwHnvUvNBERERERSe+UQBYRERHx105gGDaErT6QBdgBDAb6eJ63zsfYREREREQknVMPZBERERERERERERFJkG8VyHnz5vWKFy/u1+5FREREREREREREJMaiRYv2eJ6XL/523xLIxYsXZ+HChX7tXkRERERERERERERiOOc2JbQ9LK0DEREREREREREREZHgoASyiIiIiIiIiIiIiCRICWQRERERERERERERSZBvPZBFREREREREREREzuXkyZNERERw7Ngxv0MJCVmzZqVIkSJkypQpSY9XAllEREREREREREQCVkREBLly5aJ48eI45/wOJ6h5nsfevXuJiIigRIkSSfoctbAQERERERERERGRgHXs2DEuvfRSJY9TgHOOSy+9NFnV3Eogi4iIiIiIiIiISEBT8jjlJPdrqQSyiIiIiIiIiIiIiCQoyQlk51wG59wS59zkBO5zzrkPnHNrnXPLnHNVUzZMERERCXTDOs2heMYIwlw0xTNGMKzTHL9DSjvDhkHx4hAWZrfDhvkdkYiIiIiIpLEmTZqwf//+sz6mZ8+eTJ8+/byef+bMmdx6663n9bkXIjkVyE8BKxO572bgypi3DsAnFxiXiIiIBJFhnebQ4ZMqbDpVBI8wNp0qQodPqqSPJPKwYdChA2zaBJ5ntx06KIksIiIiIuKXNC7w8DyP6OhovvvuO/LkyXPWx7766qs0bNgwVeNJaRmT8iDnXBHgFqAP8EwCD2kGDPU8zwN+c87lcc4V9Dxve8qFKiIiIoGqx6DiRJLjP9siyUGPT4vStkx/n6JKI716QWTkf7dFRkKPHtC2rT8xiYiIiIikV7EFHrHH6LEFHnBBx+f9+vXjiy++AKB9+/Y0b96cm2++mfr16zNv3jzGjx9P3bp1WbhwIXnz5uW1115j2LBhXH755eTNm5dq1arRpUsX2rVrx6233kqrVq0oXrw4999/P5MmTeLkyZOMGjWKsmXLsmDBAjp37szRo0fJli0bgwcPpkyZMhf6lTlvSUogA+8DzwO5Erm/MLDltI8jYrb9J4HsnOuAVShTtGjR5MQpIiIigSoqis2nCiV41+boItC5c9rGEyg2b/Y7AhERERGR0NO5Myxdmvj9v/0Gx4//d1tkJDz0EHz2WcKfU7kyvP9+ok+5aNEiBg8ezPz58/E8j+uuu466deuyatUqBg8ezIABA/7z+IULFzJmzBiWLFlCVFQUVatWpVq1agk+d968eVm8eDEDBgzgnXfe4fPPP6ds2bL88ssvZMyYkenTp/PCCy8wZsyYxF9zKjtnAtk5dyuwy/O8Rc65eok9LIFt3hkbPG8QMAggPDz8jPtFREQkiERH4438li+fXgq8nuBDLs+wDXbvS9Ow0lzFihARceb2HDlg61YoXDjtYxIRERERSa/iJ4/PtT0J5syZw+23306OHLbqskWLFsyePZtixYpRvXr1BB/frFkzsmXLBsBtt92W6HO3aNECgGrVqjF27FgADhw4wP3338+aNWtwznHy5Mnzjj0lJKUCuRbQ1DnXBMgKXOSc+9rzvHtOe0wEcPlpHxcBtqVcmCIiIhIwPA8mTWJ393488teTjONNyuTZyeb9F3GUbP95aP6iWTmY4WIuusinWNPCm2/+d4kcQMaM9vEVV0CnTtCtG+TL51+MIiIiIiKh4iyVwoD1PN606cztxYrBzJnntUvr2num2IRyUh+fkCxZsgCQIUMGoqKiAHjppZeoX78+48aNY+PGjdSrVy95Aaewcw7R8zyvu+d5RTzPKw7cDcyIlzwGmAjc50x14ID6H4uIiISgn36CGjX4qVl/yq8azZSMzXi7bzR/7rmMzzouoliGCBzRFM0QQZsa61myOS916kDMcVBoatsWBg2yA1Ln7HbIEFi3Du6+2w5wS5aEl16Cc0xkFhERERGRC9SnD2TP/t9t2bPb9vN0/fXXM378eCIjIzly5Ajjxo2jTp06iT6+du3aTJo0iWPHjnH48GGmTJmSrP0dOHCAwjErGYcMGXLecaeUcyaQE+Oce9Q592jMh98B64G1wGdApxSITURERALFvHlwww3QsCFs28ZFPZ7k8kqXsnBxBro8F0aGDNB2QG02RhUh2gtjU1QRhv1akp9+gmeftYLckNa2LWzcCNHRdtu2rVU+DB4Mf/4JTZpA796WSH7jDThyxOeARURERERCVEIFHoMGXdAAvapVq9KuXTuuvfZarrvuOtq3b8/FF1+c6OOvueYamjZtSqVKlWjRogXh4eHkzp07yft7/vnn6d69O7Vq1eLUqVPnHXdKcckpqU5J4eHh3sKFC33Zt4iIiCTR0qXw4oswZQq/XtyEGTVe5MWxVSFLFjzPjseS6ttvLQ/dty9kypRqEQeupUutCnnyZLjsMnjhBXjkEYhZsiYiIiIiIglbuXIl5cqV8zuMZDl8+DA5c+YkMjKS66+/nkGDBlG1alW/w/pXQl9T59wiz/PC4z/2vCuQRUREJIT9/TfceSdUqcKJub/To95c6hyYzBcra3DwuCU8k5M8BliyxLo53HADbE+Pja4qV4ZJk+DXX+Gqq+Cpp+DKK+Hzz8HnoRgiIiIiIpKyOnToQOXKlalatSotW7YMqORxcimBLCIiInE2bIB27eDqq+H77/nzkQ+oXnQrr8+sSbt2jqVLOe+BeG+8Ad98A4sXQ9WqMGdOSgYeRGrUgBkzYPp0KFQIHn7YEsrffGMtMEREREREJOh98803LF26lL///pvu3bv7Hc4FUQJZREREYNs2eOwxKFMGRoyAp58mcsV66o15gojtGRk/Hv73v/NPHsdq3Rrmz4dcuaB+fSt0TrcaNLCeHhMn2lCPtm2hUiWYMAF8ajEmIiIiIiISnxLIIiIi6dmePfDcc1CqlA2WeOghdv62Ae/td8heLB/DhsHy5dCsWcrtsnx5+P13GDAAypa1bek2X+oc3Hab9fcYMQJOnIDmzeG66+DHH9PxF0ZERERERAKFEsgiIiLp0YED8PLLULIk9OsHd96J9/cqvq71CaXrFmTwYHtY48Y27y2l5c5tnRvAZstdcw2sWpXy+wkaYWFw113w559W6r1zJ9x4o5Vpz53rd3QiIiIiIpKOKYEsIiKSnkRGQt++ljh+9VVLUi5fzt5+X3Jnt5Lcey9UqAB166ZdSAcPwqZNlkQeOzbt9huQMmaEBx+E1avhww+tx0ft2tCkiTWPFhERERERSWNKIIuIiKQHx4/DRx9Zq4quXaF6dVi0CEaNYvq2q6hQwVrvvvEGzJplD0sr119vudFy5aBlSwsvKirt9h+QsmSBxx+H9evhrbescXS1atCqFfz1l9/RiYiIiIike+3bt+evFDo2z5kzJwDbtm2jVatW/25v3bo1FStW5L333qNnz55Mnz49yc+5ceNGypcvnyLxZUyRZxEREZHAFBUFQ4dCr16webOVFo8eDbVq/fuQY8fg0kvhu++gcmV/wrz8cvjlF+jc2QqkixWDTp38iSWgZM8Ozz8PjzwC770H774L48bBPffEtSAREREREZH/GDYMevSwU6CiRaFPH5tZnZI+//zzlH1CoFChQowePRqAHTt28Ouvv7Jp06YU309yqQJZREQkFEVH21C2q6+Ghx6yRsY//gg//wy1ajF/vs3MA7j1Vpvh5lfyOFaWLPDJJzBpUlx/5OPH/Y0pYOTODa+8Ahs2wDPPwLffQpky0LEjbN3qd3QiIiIiIgFj2DDo0MHa5Hme3XboYNvP15EjR7jllluoVKkS5cuXZ+TIkdSrV4+FCxcCVkHctWtXqlWrRsOGDVmwYAH16tWjZMmSTJw4EYAhQ4bQrFkzbrrpJsqUKUOvXr3O2M/pVcONGzdm165dVK5cmdmzZ9OuXbt/k8uLFi2ibt26VKtWjRtvvJHt27f/u71SpUrUqFGDjz/++PxfcDxKIIuIiIQSz7MMbJUq0Lo1ZM4M48dbC4RGjTgZ5ejZ0wqQ3347LkGbMYDWJN16K2TKBLt2wVVXwYAB9rIEyJvXvnHr1tlR8P/+B1dcAc8+C7t3+x2diIiIiEiaqFfvzLcBA+y+7t1t9MvpIiPhqafs/T17zvzcc5k6dSqFChXijz/+YMWKFdx0003/uf/IkSPUq1ePRYsWkStXLl588UWmTZvGuHHj6Nmz57+PW7BgAcOGDWPp0qWMGjXq3wR0QiZOnEipUqVYunQpderU+Xf7yZMneeKJJxg9ejSLFi3iwQcfpEePHgA88MADfPDBB8ybN+/cLyoZlEAWEREJFT/9BDVqQNOmdoT0zTfwxx/QrBk4x99/292vvWYdEBYutKrfQJUpk/VFfuwxuO++Mw8C07VCheDjj2HVKrj7bnj/fWtn8dJLsH+/39GJiIiIiPgmIiLh7Xv3nv9zVqhQgenTp9O1a1dmz55N7ty5/3N/5syZ/00qV6hQgbp165IpUyYqVKjAxo0b/31co0aNuPTSS8mWLRstWrRgzpw5yY5l1apVrFixgkaNGlG5cmV69+5NREQEBw4cYP/+/dSNmYh+7733nv8LjieA6o1ERETkvMybZw2+fv7Zmgl/9hncf79lYGPs2wfXXmsFyaNH27C6QHfxxTBxIrz+OvTsCcuWwZgxVnArMUqUgMGDbfJgz57Qu7cllp97Dp58EnLk8DtCEREREZEUN3Nm4vcVLWptK+IrVsxu8+Y9++cnpHTp0ixatIjvvvuO7t2707hx4//cnylTJpxzAISFhZElplInLCyMqNMmhMc+JrGPk8LzPK6++uozqoz3799/Xs+XFKpAFhERCVZLl1q/h5o14c8/oX9/WL0a2rf/N3l88KA99JJLrOfx8uXBkTyOFRYGL74I339vlQTdu/sdUYAqW9b6Ii9ebD8PL7wApUrBBx+okbSIiIiIpCt9+tgs6tNlz27bz9e2bdvInj0799xzD126dGHx4sXn9TzTpk1j3759HD16lPHjx1PrtOHmSVWmTBl27979bwL55MmT/Pnnn+TJk4fcuXP/W9U87EKaPsejBLKIiEiw+ftvuOsu63M8d66V6K5fbxWnWbP++7ARI6B4cZg61T6++24oWNCfkC/UjTfCokU2ZA/gn3/g1Cl/YwpIVarA5Mn2c1GunDV6u/JK+PxzOK3yQUREREQkVLVta8UzxYqBc3Y7aJBtP1/Lly/n2muvpXLlyvTp04cXX3zxvJ6ndu3a3HvvvVSuXJmWLVsSHh6e7OfInDkzo0ePpmvXrlSqVInKlSvz66+/AjB48GAee+wxatSoQbZs2c4rxoQ4z6epNOHh4d7ZGkWLiIhIPBs3Qq9eMHSoXULv3NmGp+XJ85+H7dtnfYNHjIDq1e3hV17pR8Cp49QpqFvXvgTffGNL0CQBngczZlh7k/nzrfdHr152JSFMNQQiIiIiEjxWrlxJuXLl/A7jggwZMoSFCxfy0Ucf+R0KkPDX1Dm3yPO8M7LaOnsQEREJdNu2WUa4dGkYPtwSx+vX2zS8eMnj6dOhQgXrc9y7N8yeHVrJY4AMGaBdO/jlF6hWDX7/3e+IApRz0KCB9cieONEy7m3bQqVKMGGCJZhFRERERETOQQlkERGRQLVnjw1DK1XK1lw99BCsWwfvvgv58iX4KevWQe7c8NtvVniaMUTH5bZvb10anIPatW1uoPKhiXAObrsNliyxCxDHj0Pz5laePm2avnAiIiIiImmgXbt2AVN9nFxKIIuIiASaAwfg5ZehZElLFt9xB6xaZQ2ACxc+4+ELF8K4cfZ+hw42R61atTSO2QfVqllf5Pr1oW9fOHrU74gCXFiYta/46y/43/9gxw5o3Ni+gHPn+h2diIiIiMhZ+dWGNxQl92upBLKIiEigiIy0TGjJkvDqq5bcW7HCmhiXLHnGw6Oi7GE1asCLL1pvYOf+M0cv5F16KUyZAjNnWoeGY8dg0ya/owpwGTPCgw/C6tXw4Yc2lLF2bWjSxK4+iIiIiIgEmKxZs7J3714lkVOA53ns3buXrMk4cdQQPREREb8dP249GPr0sarQm2+2BsZVqyb6KatXw3332Wy0tm3ho4/OaIecLnXubPn2YcPsyyhJcOSI/QC99Rb88w+0bGlXJq66yu/IREREREQAOHnyJBERERw7dszvUEJC1qxZKVKkCJkyZfrP9sSG6CmBLCIi4peoKMt29uoFmzfD9ddbErl27bN+2pYtULYsZMkCAwfCnXemUbxBYN06y38uW2ZdQF56yTo3SBIcOAD9+tlbZCTcc09cKxUREREREQl5iSWQdUolIiKS1qKjYcQIuPpqG4yXPz/88IP1YThL8jj2Yvvll1ux6PLlSh7HV6oU/Por3HsvvPKKzY7bt8/vqIJE7tx2MWPDBnjmGfj2WyhTBjp2hK1b/Y5ORERERER8ogSyiIhIWvE8mDQJqlSB1q0hc2abfrdggfU7di7RTx01CkqUgCVL7OPHH09wnp5gvZCHDLGZg/Pnw+7dfkcUZPLmhbfftnLuhx+2gXtXXAHPPqsvpoiIiIhIOqQEsoiISFr46Sebdte0qbUHGDYMli6F5s3Pmjjev986Cdx5p1Ue58iRVgEHN+fg0UetmLZMGcvdz5rld1RBplAhGDAAVq2Cu+6C99+3dhY9e1q7CxERERERSRfOmUB2zmV1zi1wzv3hnPvTOdcrgcfUc84dcM4tjXnrmTrhioiIBJl58+CGG6BhQ2sD8Nln8Ndf0KYNZMhw1k+dMQMqVLBuF6+8AnPnQunSaRN2qMiVy27HjoV69SypfPy4ryEFnxIlrKR7xQqbTPjaa7btzTdtAJ+IiIiIiIS0pFQgHwdu8DyvElAZuMk5Vz2Bx832PK9yzNurKRmkiIhI0Fm6FG69FWrWhD//tOrNNWugfXuIN+k2MTNnWjuGefNsllkSP00S0KwZdOsGn34KderYzEJJpnLlrC/y4sX2c929uzWd/uADZeVFRERERELYORPInjkc82GmmDcvVaMSEREJVn//bcv9q1SxkuHXX7desk89BVmznvPTlyyBX36x9196yT6+5ppUjjkdyJgR3njDKpH//huqVrWuInIeqlSByZPt57tcOfvZvvJK+PxziIryOzoREREREUlhSeqB7JzL4JxbCuwCpnmeNz+Bh9WIaXPxvXPu6kSep4NzbqFzbuFuDWEREZFQsnEjPPAAXH01TJkCL75oDXi7d4ecOc/56VFRlmu+7jro0sV69mbKZBXIknJuvx0WLrT2viqavUA1a1qflWnToGBBG7hXrhwMHw7R0X5HJyIiIiIiKSRJCWTP8055nlcZKAJc65wrH+8hi4FiMW0uPgTGJ/I8gzzPC/c8LzxfvnznH7WIiEig2LYNHnvMmhMPHw6dO1vi+LXXIE+eJD3F2rVw/fXQowe0aAFTp551rp5coNKlrQtDkyb28fjxNqxQzoNz1t/7t99gwgTIls36e1eubB97WrQmIiIiIhLskpRAjuV53n5gJnBTvO0HY9tceJ73HZDJOZc3hWIUEREJPHv2wHPPWQ/YQYPgwQctE/zuu5CMi6QrV1qubeVK+OYbG5h3ySWpF7aYjBntdvt2aN3a2oQsX+5vTEHNOWja1Hp/Dx8Ox45B8+ZQvbpVKCuRLCIiIiIStM6ZQHbO5XPO5Yl5PxvQEPg73mMKOGe1Us65a2Oed2+KRysiIuK3gwfhlVegZElLFt9xhzXVHTgQihRJ8tPEtootWxaeeQaWLbNEpqStggUtv3nkiLUPGTbM74iCXFgY3H03/PWX9UTesQMaN4b69a1nsoiIiIiIBJ2kVCAXBH52zi0Dfsd6IE92zj3qnHs05jGtgBXOuT+AD4C7PU+lJiIiEkIiI6FvXyhRAnr1sqTYihUwdKhVISfD2LGWON60yQo3X30VLr88leKWc6pd21paXHMN3HMPPP203xGFgIwZ4aGHYPVq+OADu8hSu7b1DVm82O/oREREREQkGZxfed7w8HBv4cKFvuxbREQkyY4fh88+gz59rJryppugd2+oVi3ZT3XgADz1FHz5JVStau0qrrwyFWKW83LyJHTrBpddBs8/73c0IebIEfjoI3jrLfjnH2jZ0q6cXHWV35GJiIiIiEgM59wiz/PCz9iuBLKIiEgCoqKsurhXL9i82abc9eljVZTnYdYsuO8+iIiAF16Al16CzJlTOGZJUdOmQaZMUK+e35GEkAMHoF8/e4uMtJLvl1+2ljAiIiIiIuKrxBLIyRqiJyIiEvKio2HkSLj6aluCnz8//PADzJx53sljsFx05szWBva115Q8DnSeZ62uGzaEd97RDLgUkzu3XZRZv956hXz7LZQpAx07wtatfkcnIiIiIiIJUAJZREQELEM4aZL1lrj7bsvwjhsHCxZYv2ObFZssy5bB8uX2/vvvw9KlUL16ikYtqcQ5mDoVbr8dnnvOZiUePOh3VCEkXz7LzK9bBw8/bAP3rrgCnn0Wdu/2OzoRERERETmNEsgiIiI//QQ1akDTpnD4MAwbZtne5s3PK3F86pS1eg0Pt3wYQK5ckCNHikYtqSxXLiuQffddGD8err0W9u71O6oQU6gQDBhgw/buusuutJQsCT17WrsLERERERHxnRLIIiKSfs2bBzfcYH0Ktm6FQYNg5Upo0wYyZDivp9ywwXrmdutm+ehvvknZkCVtOQfPPGPXGG66CS65xO+IQlSJEjBkCKxYYV/o116zbW++aQP4RERERETEN0ogi4hI+rN0Kdx6K9SsCX/+aVWPa9bYUvpMmc77aRctgooVrXXF0KEwahTkzZtiUYuP6ta1HxPnYNUqu0Bw8qTfUYWgcuXsF2fxYlsV0L07lCoFH3wAx4/7HZ2IiIiISLqkBLKIiKQff/9ty+SrVLFpdq+/bj1Yn3oKsmY976eNHbBWoQLcd5/1Pb733vPqfiFBYOJEa1Fyww2wfbvf0YSoKlVgyhT7PS1Xzn5Hr7zSeiVHRfkdnYiIiIhIuqIEsoiIhL6NG+GBB+Dqqy0p1aOH9Zro3h1y5rygp544Ea65Bv75x+buffwxFC2aMmFLYHruOWuTvXixzVycM8fviEJYzZowYwZMmwYFC9oqgXLlYPhwiI72OzoRERERkXRBCWQREQld27fDY49B6dKWcHrqKVi/Hnr3hjx5LuipDx2C9u2hWTMriPznn5QJWYJDmzbw2292/aF+fZg+3e+IQphz1qf8t99gwgTIls2+AZUr28exSwBERERERCRVKIEsIiKhZ88eKxMtWdIG4z34IKxdC/36Qf78F/z0c+ZApUoweLAVMS9YYLuS9KVCBVi40Ibs1a7tdzTpgHM2mXLpUptOeewYNG8O1atbhbISySIiIiIiqUIJZBERCR0HD8Irr1g299134Y47rO/xwIFQpEiK7eb11y2X9csv9n7mzCn21BJkcue2fshZs8KBA5bPXLXK76hCXFgYtG4Nf/1lPZG3b4fGja0UfO5cv6MTEREREQk5SiCLiEjwi4yEvn2hRAno1QsaNbJJdkOHQqlSKbKLFStgyxZ7f8gQK4KsVStFnlpCxNq1lr+85hoYN87vaNKBjBnhoYdgzRr44AO7WFS7NtxyCyxZ4nd0IiIiIiIhQwlkEREJXsePw0cfWZK4a1e49lrrKTBmjA3MSwHR0VbMXK0adOli2/Lnh1y5UuTpJYRUqwaLFkHZstCiBXTrZv2xJZVlyQJPPAHr1sEbb8C8eTbd8I47YOVKv6MTEREREQl6SiCLiEjwiYqCL76w4XhPPGG3v/wC339vWbwUsmkT3HCDJY6bNLFctcjZFC0Ks2fDI49Ya4uuXf2OKB3JkcOy9uvXw0svwdSpUL483H+/bRMRERERkfOiBLKIiASP6GgYOdKqix96yEqBf/gBZs6EOnVSdFezZ0PFirB4sQ3LGzsW8uVL0V1IiMqSxdpuf/WVDdgDzXdLU3nywKuvWtL46afh22+hTBno2BG2bvU7OhERERGRoKMEsoiIBD7Pg0mTbFn63XdDpkyW0V2wwIZnOZfiu6xYEW69FZYtg3btUmUXEuLuuQcKF4ZTp6BZM/jkEyWS01S+fPDOO9ac+uGHbeDeFVfYkoI9e/yOTkREREQkaCiBLCIige2nn6BmTWjaFA4fhq+/hj/+gNtvT/Gs7pQpcOON1lo5d24YNgyKF0/RXUg6dPSoJZE7dbKLEZGRfkeUzhQuDAMGwKpVcOed8N57NnCzZ084cMDv6EREREREAp4SyCIiEpjmzbMGxA0bQkQEDBpkA7HatoUMGVJ0V4cPw6OPWsXx9u2wc2eKPr2kczlzWgF9r17W1qJGDZv3JmmsZEn48ktYsQJuuglee80SyW++CUeO+B2diIiIiEjAUgJZREQCy9KlcNttVnX855/w/vuwZo0tQc+UKcV399tvUKWK5aeffx5+/90GoYmkpLAwK3j97jvYssUK6KOj/Y4qnSpXDkaNgkWLLJvfvTuUKgUffGDLD0RERERE5D+UQBYRkcDw999w112WzZ0zB/r0sTLNp56CrFlTZZeeB088AVFRNofvrbdsAJpIarnpJhvMOGSIJZWjoqy9hfigalXrWzNnDpQta39rrrzSeiVHRfkdnYiIiIhIwFACWURE/LVxIzzwAFx9tSVzevSADRvghRds7X8qWLkS9u+3Fsrffmstla+/PlV2JXKG4sUtdwlW/NqkiWa6+apWLfj5Z/jxRyhQwFY7lCsHw4erTFxEREREBCWQRUTEL9u3w2OPQenSlqh56ilYvx5694Y8eVJll9HR1hGjShXLT4O1QL3oolTZncg5lS0Ls2ZBtWqwcKHf0aRjzkGjRjB/Powfb6se2rSBypVhwgRbriAiIiIikk4pgSwiImlr715rNlyqlDUefvBBWLsW+vWD/PlTbbdbtlh+6Omn7fbll1NtVyJJ9tBD1kEBrBD2s8+Uq/SVc9CsmS1L+OYbOHYMmjeH6tVh2jR9c0REREQkXVICWURE0sbBg/DKK1by+8470LKl9T0eOBCKFEnVXU+fDhUqWHHhZ5/BxIlw2WWpukuRJAsPt3lu9erBk0/C5s1+RySEhUHr1vDXX9YTeft2aNwY6teHuXP9jk5EREREJE2dM4HsnMvqnFvgnPvDOfenc65XAo9xzrkPnHNrnXPLnHNVUyfc4Das0xyKZ4wgzEVTPGMEwzrN8TskEZHUMWyYNXoNC4OiReHuuy1x3KuXlf8uXw5ffWVVyGmgbFmr7vzjD2jf3ooMRQJJ3rzw3XeWmyxWzLbt3etvTAJkzGhl4mvWQP/+1kC9dm245RZYsuS/f+uKF7ePRUREQkC6zl/o/7vIGZx3jqV4zjkH5PA877BzLhMwB3jK87zfTntME+AJoAlwHdDf87zrzva84eHh3sJ01OxvWKc5dPikCpHk+Hdbdo4wqOMS2g6o7WNkIiIpbNgw6NABIiP/u71iRfjiC2v2mgamTrVQvvzSjv1EgslXX0HnzvYzfNNNfkcj/zpyBD78EPr2hX/+gQwZ4NSpuPuzZ7fWPG3b+hejiIjIBUrX+YuEzmX0/13SEefcIs/zws/Yfq4EcrwnyY4lkDt6njf/tO2fAjM9zxse8/EqoJ7nedsTe670lkAunjGCTafOXKJdLEMEG6NSd+m2iEiaKlYs4TX4xYrBxo2pvvsjR6zF8oABcNVVMGOG2lVI8Fm71rq8LF9unV9efFEXQgLK/v1WkXTgwJn3pdHfOhERkdRSLGMEmxPKX7CJjWVD/Mr22rUQFXXmdv1/l3QisQRyxiR+cgZgEXAF8PHpyeMYhYEtp30cEbPtPwlk51wHoANA0aJFkxx8KNh8qlCytouIBJ3oaBg1KvEGrmnQ2HX+fLj3Xjvue+YZ6NMHsmZN9d2KpLgrroB58+DRR23g4/z58PXXcPHFfkcmAOTJY33dE6Im1iIiEsT69j7B5lOFE7xvM5czMPsz3FpoMUWy70vjyNLI338nvH3zZjvf0RV9SaeSlED2PO8UUNk5lwcY55wr73neitMeklA3yTNKmz3PGwQMAqtATn64watohm0JViAXzbANUAWyiAQxz4PJk+Gll6zJcKZMcPLkmY9L5QuHUVHQpo3t+qefbNaVSDDLnt1asNSoAU89BTNnwu23+x2V/KtoUdi0KeHtIiIiQeLPP2HMGOj+zHEyDfmMo28dJRuPcZTsZzy2QNhuOi5+mE5LoE4dmzfbsiXky+dD4Kll/vyE/797nrXi690bmjTRUBVJd5J16cTzvP3ATCD+moUI4PLTPi4CbLuQwEJNnw4byc6R/2zLzhH6dNjoT0AiIilhxgyoWROaNoXDh61E8n//s8zX6bJnt3LgVLBmDRw/brOuxo2zJf9KHkuocA46drSq+tjk8Zo1/sYkMfr0OfNvXbZsqfa3TkREJKWsXw+vv24jSsqXh16vRLP4ijvhiSd4uepkPms+JcH8xduPrGHVKmuvtWuXHaMULAhzQmm+XkL/37Nnt2VhBw/CrbfaQN2ZM30JT8Qv50wgO+fyxVQe45zLBjQE4tf0TwTuc6Y6cOBs/Y/To7YDajOo4xKKZYggtji7e4P5od+AXkRC02+/QYMG9hYRYUMlVq60wRL33msfFytm2a9ixVJl6ITnwUcfQaVK8MYbtq1iRcidO0V3IxIQYotaV6ywE72OHe3Cifiobdv//q0DaNZMA3ZERCQgxY6/WrQISpWCHj0g1/HdfJjvVbZ6hbju8m3www8wcyZtx93xb/7CEU2xDBH/DtArXRp69oS//oKlS6FrVwiP6Zbaty+0amUVzUeP+vZSL0z8/++x5zKffGLtLQYOtArl+vWhUSOrWBZJB845RM85VxH4EsiAJZy/9TzvVefcowCe5w10zjngI6wyORJ4wPO8s07IS29D9E63d80+Lit9EV1rzqbPXJXJiUgQWbrUWlVMnmxr1V54wa7Gp3Gj4a1b4cEH4ccf4eabrei5YME0DUHEF1FRNlDvrbfg2mth9Gi4/PJzf56kgUaNbAnEhg1WiSwiIuKzffssmTt8OFSpAu++C9GnPD545E9un9uFYn//YFemX3vNLoJeYFuGvn2hXz/YuRNy5YLmzeGee6Bx45R5PQHj6FFLJL/+OuzZY6sxX3vNqllEglxiQ/TOmUBOLek5gQwwo2oXrjk0g1yrF6l3jogEvlWrrNTg229teNRzz8GTT0LOnGkeytSp1uv4+HE7CH7kEf0ZlfRn7Fho1w6yZIERI2wxgPhs5kyrRvroI3jsMb+jERGRdGzsWPjiCysojoqCK6+0f01Plf/JSo/nz7cy5FdfhbvuggwZUmzfUVH2L3HECEteX389TJhg9y1aZInskJlDd+gQ9O8P77xj7S3uugt69YLSpf2OTOS8JZZADpVf26BzQ/uS5Fq7xJZ8i4gEqo0b4YEH4KqrYMoUO+Bcv94qj31IHgMULmwX95cuteJnJY8lPWrRAn7/HfLnt1bkEgDq1rWe8H37JjxIVEREJJUcOwbffx/XpmLSJJtt3bmzJW1XDZnHUxNugIYNbSlfbPu5Nm1SNHkMNpekYUP4/HPYscM6P4CdVoSH28qpZ56x4xif6hlTTq5ctjRs/Xro1g0mTrTzpvbtYfNmv6MTSVGqQPaJt3Ub7xbpR/4WdbhvTDO/wxER+a/t222AxKBBViLQqZMdFOXP70s406fDtGm2bF9E4hw5Yh1kMmSwE8XixdUH3FfffQe33GJlXw884Hc0IiISwqKi4KefrNJ37FgrgF2yBCpXhgMHLLcZtmypJTinTLHj+BdesOV7adx+DiAy0vKrw4dbsvvkSbjiChg2zNpyhYSdO204S2zW/JFH7GteoIC/cYkkg1pYBKCauZZz/FRGFkWW8zsUERGzd69laT/6yI7qHnzQeh4XKeJLOJGRlrf+8EMoW9Zm9yk5JnKm48dteWqWLHYSWaGC3xGlU54H1arB4cNW2ZXCVV0iIiIAixfDTTfB7t1w0UW2Munuu62lVcaM2LC3l1+Oaz/3/PPwxBO+rSCM759/YNw4GDkSvv7aRquMG2dh3303lCjhd4QXaMsW64n8xReQObO1/nv+ebjkEr8jEzkntbAIQC3q7mXx0XJsnL3F71BEJL07eBBeecWO1t55B1q2tCO4Tz/1LXm8cKHlYT780I65Fi9W8lgkMVmywDffWN6yenV7X3zgnLX6WbPGJhyKiIhcIM+z4+Dnn7e2EGCFFY0a2UXjnTth8GC48UbIGLHRVsBcfXVc+7kNG6B794BJHgNcfLHVqfzwgyWPAX7+2Yp1S5a0Y5n+/W1RZFC6/PK4NiG3327trUqUsJ7Thw75HZ3IeVEFso/WzdjEFQ2K0a/pTJ6eUM/vcEQkPYqMhI8/hjfftDHNLVrYgc3VV/seVvHidsF+yBDroyYi57Z9u81vmT3bCo3efRcyZfI7qnQmOtom2mfKZM3a1ahdRETOw99/W3uK4cNh9WqrLH78cXjvvQQevH079O4Nn30WEO3nztfGjVaVPHy4teaqXh3mzbP7IiMhe3Zfwzt/y5fbQPLx4+HSS+1789hjkC2b35GJnEEtLAJU5WyryJnpOHMOVvQ7FBFJT06csAPM3r1tusWNN9r74Wf8n0hTmzdbwXNYmCXAype3CgURSbqTJ6FrV1i71s5TQmbSeTD56iu47z5r9njbbX5HIyIiQWLXrric7803W4VuvXrW1qFlS8s9/seePXHt56Ki4KGHrOexTysIU9LKlbB/P9SoYT2dL78crr8eWreGZs0CqqA66X7/3b4/P/4IBQtaq8CHHrKqGZEAoRYWAequWhHkOrSVk5uDdW2GiASVqChb41a6tJUwXHkl/PILTJ3qa/LY82zWRLlyMHCgbatTR8ljkfORKRP062fLWsPC7MLMrFl+R5XOtG5tS1X79AmBEfMiIpKadu60/G+tWpZT3LrVtr/7LkREwIwZ0KFDvORxbPu5kiXtgXfcYSXLAweGRPIY7LygRg17/8QJm0f3xx9wzz2WZL/rLvjrL39jTLZrrrGrAjNn2veuUycoUwa+/BJOnfI7OpGzUgLZZ937F+B7mpDpuwl+hyIioSw62taDlS9vDcfy5rWk8axZlqn10fbtcMstdvxUu7ZVFIjIhcuY0W67d7ehOu++q1xmmsmY0crA58+3M38REZF4/vwTGjeGQoWs7dShQzZ3LUsWu/+qq+y+/4iMjOun26uXPcGKFTB0KJQqleavIa3kywdvvw2bNlntS7t29u81tkvU4sVW1BsV5WuYSVe3ri23/O47G6zXrp2dp40aZedtIgFICWS/XXUVlC7NgZFT/Y5EREKR58HkyVC1qq19y5jRyhJ//93aVvjcm3PyZDtWmjnTKi+mToXChX0NSSTkfPIJNG8OXbrAnXdqdkuaadfOzvz79PE7EhERCQBHjlg9R+x1xYsvtp6/3btbDnjZMhsilzdvAp98/LgdLJcqZRcor73WJk6PHm05hXQiLMxqXwYMsCKUcuVs+wcf2KlN4cK2yHLu3CDIwzpnfUpiv4/O2YFaeLgNQNRVfwkwSiD7zTkGlniTy2aOYN+6f/yORkRCyYwZULOm9d88dAi+/trWfd1+u++J41jZssEVV8CSJTZHIkDCEgkpF11kBS3vvAPjxtk554YNfkeVDmTJYln7n3+GX3/1OxoREfHBiRMwaRK0aQOXXWb1HJ99ZvcVKgSrVtkYkkTnV0dFwRdfWPu5J56w29mz4fvvoVq1NHsdgSh2pRVY546xY62w93//s1WN9er5FlryOGcNrpcvt0ryAwfg1lvtRcyc6Xd0Iv9SAjkAVGtdhuNkZfJbf/odioiEgt9+s/XqDRrAli3w6afWE61tW8iQwe/o+PnnuOnRDRpYuGXK+BuTSKhzDp59FqZPhwIFEqlukpTXoYN9sVWFLCKSbpxeONq4MTRtam1v27a14+Cvv467P9Hiidj2c1dfbUPW8ueP651bu3Zqhh+Usma1Gplvv7VBhF9/bV82sBx87drWMnrVKl/DPLsMGeDee+N6WW/cCPXrQ6NGsGCB39GJKIEcCMLvLUeRDNsYOymT36GISDD74w+rNq5Rw65gv/cerF1rCYxM/v99OXYMnnkGbrjBKi+OHbPtqjoWSTv16tnihFy5rI3i22/DyZN+RxXCcuSAzp2tx+GSJX5HIyIiqcTzrCjiqadsRvWRI7b9ueesG8GOHVbTUa/eOeo5PM9KlmPbz2XObMuHFiywbLQOnM8pVy5L1t9/v328a5edCr36KpQta1/avn3texKQMmWyiYFr19oAi6VL4brrbFDMsmV+RyfpmBLIAcCFOVqUX8MPOypyeMdhv8MRkWCzapUdYFauDHPmWKXb+vWWtMia1e/oAMubhIdbTrtTJ2vBHCChiaQ7seeeY8fC88/bSoCAPYkKBY89Zn1EXn/d70hERCSFbdliPYxLlrQajk8/hUqVYN8+u/+WW6BJkyTWcvz0k7Wfa9oUDh+GYcMsedi8uRLHF6BQIav83rLFzkUyZbI20itX2v3bt8POnf7GmKBs2az6Zv16m644a5ad77VuDatX+x2dpENKIAeIFg/k5hjZmPr2cr9DEZFgsWkTPPigDc6YPNmmbqxfb7c5c/od3b/27YPrr7fbqVPh44+tKE9E/HXPPbbEc+FCq8aZM8fviEJUnjw20WfMmLizVRERCVpr18KaNfb+vn22mqdsWfjyS0tEjhkDl1+ejCecN8+W6DVsCBERMGiQ/b9o0yYg2s+FisKFrb5m/nz7Hl5/vW1/+21LMjduDIMHw/79fkaZgFy54MUX7TyvWzeYONHO/9q3h82b/Y5O0hHn+TTZMTw83Fu4cKEv+w5Ep06c4pNLe9Cy4UEKjhvgdzgiEsi2b7cq40GDbBRxx45W+pA/v9+R/cfu3ZAvn70/aZIVVFx6qb8xiciZli2z2S0bN9rJb5s2fkcUgnbvhuLFoVUr+yKLiEhQiYiwlsQjRtiF1/vvhyFDrOPE3r3nOVtg6VJ46SUrBMmf34pAHnlEy/TS2MqVdkF9xAjL0WbObIs7A/bf9c6dtqpp4ED7+JFH7GenQAF/45KQ4Zxb5HleePztqkAOEBkyZ+Dxu/dS8Kev4fhxv8MRkUC0d6+tNy9VytbHPfCAXT5/772ASh57nvU4LlHCWraBtWZW8lgkMFWsaG1lWre2FnuSCvLls370w4bBhg1+RyMiIsnQujUULQpduthx7jvvQO/edp9z55E8XrUK7roLqlSx5T+vvw7r1lkDZSWP01y5clabs3atVSd36hR3auV59n2fNAlOnPA3zn9ddhn0729l8PfdBwMG2Plht25xvVNEUoEqkAPI8Yk/MLLZMMq99wjXdK7ldzgiEigOHrQk8bvvWj+0tm1tjHCpUn5HdoadO2011eTJthJvyJBkLuETEd95np2DPPQQlC7tdzQhZOtWa5L54IPwySd+RyMiIgk4eBDGj4cffoChQ62DxLvv2lC8u+++wP+LGzdCr172xNmyWT+FLl2s1ZEEpO3boXx5y8tefLGt2GrdGurWDaDuImvW2Lnh8OHW7uLZZ+Hpp+19kfOQWAWyEsgB5PjB4+TPfYw7Si/j81V1/A5HRPwWGWkNg996y6qPb7/dxgeXL+93ZAmaNMnyIocPw5tvwhNPWIcNEQkuGzfCNdfYgqgvv7Q/PZJCHn3UGixu2GANF0VExHdHj8KUKZZ/mzLF/v8VK2aD10qUSIEdbN9uJcuffWYHx5062ZXaAFpBKIk7cQKmTbMWF+PH27nOyJFw5512X6ZMATLjcPly6NnTgrz0Umtx2KmTXawQSQa1sAgCWS7Kwq3FVjBhTTmijkX5HY6I+OXECUscX3GFtawID7f15WPHBmzyGGzgRNGisGiRrcBT8lgkOBUvbr/HZctCixZ2jhulw5KU0bUrnDpl5WwiIuKbkyet2hhg1iy44w6YO9e6Df36q13nu+Dk8ent5wYNskqLtWuhXz8lj4NI5sxwyy3w1Ve22vLbb6FJE7uvf39bXNS9u82U8Kk+01SoYP0D58+36chdutj55CefBFD/DQlmOr0PMC3uCGOPl5c5A1f4HYqIpLWoKKtMK10aHn/c/uH/8gtMnWpJ5AA0a5a19AS45x47XrnqKn9jEpELV7QozJ5tc1neest+vyUFlChhUwoHDoQ9e/yORkQkXYmOtmPXRx+FggWt9TBAgwYwfbp1GvrgA6hR4wIrSg8etJYCJUpYw+RWreDvv+1vf5EiKfFSxCfZs9vFhpw57eOrroIyZeDtt6FSJav1eeMNnxPJ114LP/4IM2faz2CnTlYV8OWXdhFb5DwpgRxgbnquIlk5ytjB+/0ORUTSSnS0XcouX94qE/LmtaTxrFlQJzDb2Rw/Ds89B/XrQ9++diziHGTM6HdkIpJSsmSxc93Bg+Hhh/2OJoR0727rpfv39zsSEZF046WXbC5HvXpWSdq4sb2BtSBo0CAFetpGRtqBcYkS1uu4USNrKzB0aEDOLpELd8stdtq2bZvNsrv0Uqv/ib0AMXIkRET4FFzdulYN8N131sC5XTs73xw1ys4/RZJJPZAD0O2FfuPYvqN8H1lXa8BFQpnnWaO1F1+EP/6wS9ivvWYNRwOikVbCli2zasTly6068Z134q7Ci0hoe/NNyJ3bqrcC+M9U4GvVysrdNm2yL6iIiKSoP/+En36CJ5+0j++/39qttW4Nt90GOXKk4M6OH7f+xn36wI4dcNNN1vO4WrUU3IkEi5Mn7aLE7t1QoICd8tWpYz97rVpZrVCa8zxrh/jSS7ByJVSpYj+jN9+sAzo5g3ogB5FvXlvP98dvsJ6nIhKaZsyAWrXsCPbQISuFWLbMGo4G8D/xiAhbFbV7t+W+Bw5U8lgkvYiOtr6QnTpZEUtkpN8RBbEePeDAAStXEhGRFLFhg7UPqFjRCi2ffhq2bLH7hgyBCRPg7rtTMHkc236uTBmbHl26tFV8fv+9ksfpWKZMdpsvn+VqX3kFdu2Cjh2tdcqYMT4E5Ry0bGkVQF9+aVdTbrkFate2VhciSaAEcgDK1uJmyJgRb8xYv0MRkZT222+2Rq5BA9i8GT791Hqi3XNPCqybSz2HDtltkSKWNF6+PG54hIikD2FhNti7Vy+75lWjBqxb53dUQapKFav66dcPjhzxOxoRkaA3aZINM3vhBciVCz780HoaX3653Z+i9RnR0dabILb9XL588MMPloirXTsFdyTBrnRp6NkT/voLliyBZ5+F6tXtvpEjrSJ5zBjrbJUmMmSA++6z889PPoGNG60nYaNGsGBBGgUhweqcCWTn3OXOuZ+dcyudc386555K4DH1nHMHnHNLY956pk646cTFF/NmiYFU++A+vGg/u6+LSIr54w9o2tQyLsuXw3vv2RTmDh3iLlMHIM+zwoqiRWHePNvWrp1PS69ExHdhYXYiNGWKVXXVqBF3gUmSqUcPG6T32Wd+RyIiElT27bM/nQ0awEcf2bbrr7c2Sxs2wNy5No+6QIEU3rHnWaa6alUrZc6UCcaNs8Rb48YBvYpQ/OUcVK5sP6OFC9u2AwdgzhxLIl92meV1v/8+jQbwZc5s/cjWroV334WlS+G666BZM1sVK5KApFQgRwHPep5XDqgOPOacuyqBx832PK9yzNurKRplOnTxNVey5PjVrBi3xu9QRORCrFplB5iVK9tEhd69Yf166NwZsmb1O7qz2rXL2jE/+KBNFS5Y0O+IRCRQ3HwzLFpk0+pz5bJtvk4cD0a1atmAm7fftv6ZIiJyViNGWPe3AgWsBmPLFsie3e7LnRu6doXixVNp5zNmQM2aVhBy+DAMG2ZJt+bNlTiW89Khg7UHnDYN7rzTrk106xb34/T332kw6y5bNnjmGTs/fe01G+JeuTK0aQNrlIuS/zpnAtnzvO2e5y2Oef8QsBIonNqBpXfNupbFEc3Yj7b5HYqInI9NmyzzetVVMHmyrafbsMEqzoKgafDkyVChgl0Ff+cdO2ZOtQNyEQlKJUrY9TGw1hZNmsDevb6GFHx69LDR7V9+6XckIiIB5/hx670f69NPLWf71FN2EXPVKjvcTlWnt5+LiIBBg6yxbZs2Ad1+ToJDxozQsCF8/rnNXxwb08U0MhLCw20V6LPPwsKFqXyhPlcuG+y+fr1lsSdMgHLloH17a7soQjJ7IDvnigNVgPkJ3F3DOfeHc+5759zViXx+B+fcQufcwt27dyc/2nSkQMX81LpoOWN/U8mfSFDZvt3WzF15JXzzjY1+Xr/epjJffLHf0SXZihVWcbxwoR20hKljvoicxf79dqGpWjU7qZckatgQrrkG3nrLhjGJiKRzUVHw44+WGL7sMmtNEZs6GDnSajTeftu6SKRq4e/SpVbuXKOGHRi//75VZD78cEC3n5PglSULlCpl72fIYG1aqlWzft7XXGP9lH/4IZWDuOQSeP11O3997DEbenHllXZOu2NHKu9cAl2SUwLOuZzAGKCz53kH4929GCjmeV4l4ENgfELP4XneIM/zwj3PC8+XL995hpx+tKj/D8uOlWHtT5v8DkVEzmXvXls3V6qUTZl74AE7yHzvPcif3+/okmTuXDtgB3juOZg/36qQRUTOpV076+PnedaZ4fPP/Y4oSDhnVcjr19vabBGRdOzHH60/7I032mCx22+3nvuxNRj586dBUcOqVXDXXTbsdM4cS6atW2dlzwHefk5CR5Ys0Lq1FQLv3GnHVcWLx51WzpsHb7xhC1xTxWWXQf/+dj57330wYICd53brZk3IJV1yXhLq4J1zmYDJwA+e5/VLwuM3AuGe5+1J7DHh4eHewoULkxFq+rP51wgG1Pqax1/ITZE+Hf0OR0QScvCgJYn79bNJUm3awCuvwBVX+B1Zkp04AS+/DH37wrXX2lJBtXITkfOxZw+0bWtJgJkzrcWvnEN0tDWaj462Iata8iEi6YDnWZHv8OFQr561QdqwweoxWre2Xvtpmq/duBF69YKhQ60vbOfO0KUL5MmThkGIJE3v3vDSS/Z+9erWUuzOO1NxZs2aNXaOO3y4tbvo0sV+R2IHYUhIcc4t8jwv/Izt50ogO+cc8CWwz/O8zok8pgCw0/M8zzl3LTAaq0hO9MmVQE6i8HBbIjNvnt+RiMjpjh6Fjz+2Ubp791qJxKuvQvnyfkeWLCtWwL332gF8+/aWB9dxgIhciFOnYOLEuLlCJ07YsG85ixEjLGMyZgy0aOF3NCIiqWbVKstBjRhh72fMaHmpHj18Cmj7dms1N2iQXcDr1MmqLINkBaGkXxs3WluX4cPhjz+sen/zZvsxPnkylTqtLF8OPXva8Iu8ee13pVMnu+giIeNCEsi1gdnAciB2BuQLQFEAz/MGOuceBzoCUcBR4BnP835N4On+pQRy0px87U1+7jmDyku/JH8l9UMW8d2JE7aGqHdvO+C88UZ7P/yMv68Bb+VKG7KbO7e9pKZN/Y5IRELNihVWVfbZZ/bnUhJx6pQNq8mVy5rPaxmIiISQAwfseNPzbL70qlVWdXz33dCyJVx6qQ9B7d1r/ec/+siybQ89ZEPEihTxIRiRC/PXX5ZQbtLEFjRdcQVcfbVdm27aNBVmuC9YYL8v06ZBoUL2/kMPqWIgRCSWQD7nGjnP8+Z4nuc8z6voeV7lmLfvPM8b6HnewJjHfOR53tWe51XyPK/6uZLHknRrq97JjfzIqD6r/Q5FJH2LioIhQ6BMGRsoUKoUzJoFU6cGXfL4+HG7LVvWct8rVih5LCKpI1s26115883w2mt2UiMJyJDBqngWL06DCTkiIqlv507Lzdaubb1bjx+3a2NDhkBEhA1e7dDBh+TxwYNW8lyiBLzzDrRqBX//bTNMlDyWIHXVVZY8BoiMtMVMS5daW7H8+a2td4rWb157bVy/shIlrAq5bFn48ku7KC4hSU3WAly5W0pSNvM6xk7XmnIRX0RHw7ffWmuKBx6wo9zvv4dffrGx0EHE86ytW4kS1sbKORuWpxV6IpJaSpWyLlxt29qKx6ZN4Z9//I4qQN1zD1x+uS2lFhEJUvPnQ+PGVpT4xBNWfdyliy3iA7juOrsvzUVGwttv24Fwr17QqJEtxx861P5ZiYSInDnt2simTXbK2q6dXbDZtcvu37jRCoejolJgZ3XrwuzZ8N131i+8XTubwj56tKoGQpASyEGgRfgWZv1TkT2r9vodikj64Xk29rlaNbtkmyGD9ab8/Xe46aagW168Zw/ccQfcf78dI6dKTywRkQRkz27n5x9/bMUq/fv7HVGAypwZnn8e5syxMz4RkSAQGWm1FsuX28eeB+vW2aKK5cvtrUcPH2dsnDhh/4BKlbK/sddea6WYY8bYGn+REBUWBnXqwIABcZ0XAf73P7vIU7gwPP44zJ17gble52yp2cKFMGqUbbvjDlul+9139kdBQoISyEGgRafLOEVGJr31l9+hiKQPP/8MtWrBrbfaMrevvoJly2wtUJAljsH+b1eoYEOt3nrLVhoVL+53VCKSnjhnqxvnz4cXXrBt+/b5G1NAeughuOwyVSGLSEA7cQImTfrv8vgvv7T7rrsO1q61P2O+zpaOioLBg6F0acuSlS5tF+e+/94KRETSkYwZrR4K7DhszBhbTPu//1mbmauvToHOE2Fh1hJm+XL7g7B/P9xyi+1g5swLfHIJBEogB4GqrctSLEME037QEgCRVDV/PjRsCDfcYCNsP/3UeqLdc0/cf9wg9N13kC+fFU8//3xQvxQRCXJVqlih7T//2Pl7x45xfdkFaxr9zDNWqv37735HIyJyBs+zwoSmTW0USNu2Vnvx1lt2v3M+11tER8PIkZa9fvBBOwj+4QdLYNWp42NgIoEhWzarixo1ytpafPWVdWqMPUe86y5rE75q1XnuIEMGuO8+O4/+5BPrmVG/vrWNWbAghV6F+MF5PpWTh4eHewtTtIt3aNv40Gtc/tXrZNi7y8f1PyIhatkymxw7aZIdZHbvblmNrFn9juy8/fabtamoVg2OHrULwlmy+B2ViIiJirI/u2+9ZauJR4+29r8CHDoExYpZadD48X5HIyLpmOdZfcXw4bY6fc6cuEF4+fJZPihzZr+jjBHbfu7FF+GPP6yk8rXXoHnzoFxBKOKHyEgrGp41y36lqlSB1q2hTRtreXFejh61RPIbb1hfxWbN7HezQoUUjV1SjnNuked54fG3qwI5SBR/oD4ZTh6zUkIRSRmrV9t/xEqVbElb796wfj08/XTQJo9PnoSXXrIOHN2727Zs2ZQ8FpHAkjEjvPmmLaFcuRKqVoWffvI7qgCRKxc8+SRMmBDXVFREJA2tX2/L3EuVgho1bFFegQI2EA9sTtYttwRQ8njGDKhZE267DQ4fhq+/tiTy7bcreSySDNmz24qCLVugXz8rSHr+eSviB/sbEDuML8liV1etX2+J45kz7fy7TRub7C5BQwnkYFGjBq/meIvOL6n6WOSCbdpkfSavusqqjl94ATZssAkfOXP6Hd15W7kSqle3PPh991lFn4hIIGvRwjo15M9vCWXNWYnx5JP2/+iNN/yORETSibVrYccOe3/ZMujbF8qUsWrjnTvtgl+ePH5GmIDffoMGDewtIgIGDbID4rZt1bNN5AIULmw1VfPn29+GO+6w7UOHQqFCNpBv8GBrc5xkuXLZCoH166FrV7tQXq4ctG9v7SMl4KmFRRB55KpfGLayKnv+yUjWPMFZHSniqx07bKLHp59aT4eOHa1MN39+vyO7YL//bqudc+a0Y+fbb/c7IhGRpDt8GI4dg7x5Yfduq2rLndvvqHz2/PPw7rvWhPCKK/yORkRC0Nat1i44tkXFK6/Ayy/bkLwDB6xNRUD64w9LRE2ebMfxL7wAjzwStCsIRYLF6tU2H2/4cKu/ypzZ5s6PHGmry5Jlxw67UD5woH38yCP2u1ygQIrHLcmjFhYhoOW9OThCTqa9u8zvUESCy969dpWzZEnrv/TAA7Zc5r33gj55HDstt0oVeOopW+2s5LGIBJucOS157HlWOBYeru4NPPOMrR19802/IxGREBMdDY0bW+/5Z5+1v71vv20L9MCSQgGZPF61Cu6+GypXtobMffrAunV2EKzksUiqK1067tdu/nzo1MkSx7HJ43feses6J04k4ckKFID+/e28/L77YMAA65vTvTvs25eqr0POjyqQg8iJwye47KJImpdazuA1miArck4HD8L771sF16FD1mfplVdCopLL8+zK76uvwuzZAXqQLyJyHmbPhjvvtD/hn31mf7rTrccft2Ul69ZpyqCInLdDh2wm59KldlgMtjz94ostH1u6tJ/RJcGmTdCrl5U+ZssGnTtDly4B2FNDJP06ftxmAO/cCZdcAi1b2t+XunWT2FFmzRo7Vx8+3NpddOliv+u51MY1rakCOQRkzpmZ20qsYOK6qzkZedLvcEQC19GjdvmzZElbh9eggTVz+/rrkEge79tn/4zbtrV/zkeP+h2RiEjKqVMHFi+2wXpt21o74CRVsoSi55+PKw0UEUmGo0etb3GrVrbg7r77YOxYSyaDLcTr2TPAk8fbt9uFtCuvhG++sUrj9ett4IeSxyIBJUsWa2U8aRLcfLP9yjZoYPVcYCtnz1q/euWVMGyYtaipX9/+QJUsaVe9dMIbEJRADjJt2obR1JvAwam/+h2KSOA5cSJu6ctzz9ka6N9/t6Pl8uX9ji5F/PADVKhgL6lPH/jlFyha1O+oRERSVsGCMGOGVchNmmQ9ktOlokUt6/PZZ1bSIyJyFidPWj95sGLdVq2s08PDD8Ovv1ruNSiK+fbutQtopUrZ7JIHH7RJXv36BX37OZFQFtsT+euvYdcu+PZbW1UGdv5aqpS1OT5rm7IKFWzJxPz51qexSxcrAhs4MB1XFAQGJZCDzE3dKjM4++NcOn2k36GIBI6oKBsRXaYMPPaY/WeaNQumTrUkcggZONAKLubPt3++yR5WICISJDJlslzB0qW22uLECViwwO+ofNCtm734997zOxIRCUDR0XbY27GjXXwbOtS233knTJsGERHwwQdQowY452+s53TwoLWqKFHCVhO2agV//20HwEWK+B2diCRD9uxwxx1xHbjy5rUVD337QsWKVt/Vu/dZcsLXXgs//ggzZ9rfhI4doWxZ+yMXOwhI0pQSyMEme3a8m25m+bcriY6K9jsaEX9FR8OoUXaV8oEH4NJL4fvvrSz3+uv9ji7FLFhg7S8BvvjCpmRXrepvTCIiaSV3brt9802oWdNWMvo0wsMfV15pmaABA+Cff/yORkQCxKlTVphXtCjUq2c5lUaN4Oqr7f5LLoGGDYOk2CAy0lr1lCxpPVAbNbISxaFDrTBERIJe/fpW37VtG3z8sfVg//prKxgAyxVv3ZrAJ9atawMyvvvOKqnuv9/O/0ePtnyApBklkIPQuKJPUnHvz8z/YoXfoYj4w/NgyhSoVs1OqsPCrMnb77/DTTcFQXlF0pw8acfQNWvaMFqwf7TZsvkaloiILzp3hmbNLGFy551xfTzThRdesBf84Yd+RyIiPvrrL+srCjaUasECOxwePtyWiw8fDrVq+Rtjspw4YZmkUqWsZcU111ilxJgxcZlwEQkp+fNDp06WE1682E7dT56Eu+6yauV69axzzZ49p32Sc9ZYeeFCKyADK28OD7fEcrqqLPCPEshBqMHTlcjECcZ+ts/vUETS3s8/25HxrbfaMrevvrIBeS1ahEziGGDVKnuZvXpB69YwaJDfEYmI+Ouii6zYpG9f66N37bW2sjldqFABmjaF/v3TcUNokfRpwwZ44w2oVMlyqg8/bAW7YCu7J0yw4co5cvgaZvJERcHgwbae/fHH7faXX2wlYbVqfkcnImkke3a7zZTJWjS+/DLs2AGPPmoteT75JN4nhIVZa5vly63R+/79cMstNoF51qy0Dj/dUQI5COUumpuG+f5g7NISeNG60iLpxPz5tg7vhhtsvOunn1rm4J57rAQjhMyaZfMC1q2zC6xffaVB0yIiYNcJn3sOpk+H48ctB5Fu9OgB+/ZZL1ARSRc+/dS6OrzwgiWIP/jAjg9jky5hwXY2Hx1tU7XKl7fBePny2YTomTMtASQi6Vbp0pZAXrkSliyBZ5+1YgGAefOs4HjMGDh6FDv/v+8+ywd88oldaatXDxo3TqcDM9JGsP3LkRgtbj7G+qhi/DFqtd+hiKSuZctszXL16vZ+v342hblDh7iGSSEiduVNeLj9P1yxwi6wiojIf9WvD6tXWw7C8ywfEfLJ5GuvtQup774Lx475HY2IpLB//oHPP7df80mTbFuDBtb/fcMG+PVXeOIJKFDA3zjPi+fB5Mk2xOOuu6wx89ixluhp3DikVhGKyIVxDipXtr99sQsSIiKs5UWrVnDZZdYG+fvv4VSGzFauvHatHR8tWQLXXQfNm1uVsqQoJZCDVNOu5QjjFGM/3u53KCKpY/Vq691QubKV5PbuDevXw9NPQ9asfkeX4kaOtJYVkZFWYTJwoC3bERGRhMUOhvr1V8tHNGxoyx5DWo8e9iK/+MLvSEQkBURFWU/j226zpMjDD9tCu+PH7f4rroCuXaF4cV/DvDAzZthAj9tusxY8X38Nf/wBt9+uxLGIJMkdd1gSedo0e3/CBFuIfOqU3b95dzaiOz9j+YJXX7W2l5UqQZs2sGaNv8GHEOf51Gw6PDzcW7hwoS/7DhUzKj/Dtcdnk3Pl736HIpJyNm2yP/pffmmJ4qeesolJF1/sd2Sp4p9/rPXbN99YcdmYMVCkiN9RiYgEl2HDLPGSJ4+1/gmqIVLJ4XlQu7adRa1dG3IrcUTSg+PHbdZFxYr2K12ihCVB7rrLaieqVg2RvOpvv9lFrxkz7OC2Z09o105/t0Tkgh0/bt0rKlWyv6NXXGHbYv+OViuxD/fO29b35/hxeOABeOklKFrU79CDgnNuked54fG3qwI5iN3wUAly/r3QjkBEgt2OHbYur3Rpq0x4/HFr8tanT8gmj6dPt7lII0fasLy5c5U8FhE5H23bWq4iR4646d0hyTlLyGzebP8rRSQoREVZ5dyDD1qlcYMGcPKk/UrPmmX1E++8Y8u1gz55/McfNvSzRg3rx/b++1YB+PDDSh6LSIrIksWSx2Ct1Xv3totvH34I11wDpatfwrdV3rB8wmOPwdChcOWVVpy2c6e/wQcxJZCDmNesOW/Sla97qg+yBLF9+6BbN5sQ8skn1tBo7Vo72LzsMr+jSzWeZ4UYuXJZ0qNnz7jl2CIiknwVK8Lvv9sw7qDsEZpUN99sk1bfeCNu7aaIBKyRI6FwYWv1O2aMteb8+uu4AXjFigXhMLyErFoFd99t7edmz7YikHXrLGETgu3nRCQwZMhgVccTJ1pN2uef29/VzJmBAgVY/1R/3nx6Jxtvfxo+/tjyDt27Wx5CkkUtLILcdTlX4OFYcPhqv0MRSZ6DBy1J/O67cOiQ9Sd65RVbfxLCFi2yf2h588LWrVZcHTtJW0RELpznxVXwDRsWU4lS2t+YUtzo0dYEcMQIW68pIgHB82DpUvvVvPtuu9YzZ46tom7d2q7/hFwuddMmW0r35ZeQLRt07mzt5/Lk8TsyERE+/9wWQABUr3yU1mHfcsfi7hS86Ij9rerc2aq65F/n3cLCOXe5c+5n59xK59yfzrmnEniMc8594Jxb65xb5pyrmlKBy9m1qLOH349czeZ5W/0ORSRpjh61NXolS8LLL9savmXLrBQjhJPHUVG2tKZ6dXjxRdtWuLCSxyIiKS02eXwk5rwgPBzGjfM3phTXogWULQuvv24ZKxHx1apVVgdRrpwto+7XDxYssPtq14Zvv7WZcSGVPN6+3VrOXXmlDfN46ikbYNW7t5LHIhIw2reHDRts4dZRLxtPLb6fklkiOFznZujZk+gSpayo7ehRv0MNeElZLBMFPOt5XjmgOvCYc+6qeI+5Gbgy5q0D8EmKRimJur1zMQDGv7PW50hEEjBsmI2NDguzstsHHoBSpeC556zJ24IFMHYslC/vd6Spas0aqFPH+vbfcYf98xIRkdSVIwfMnw9lyli+tVs3u5gXEsLC4IUX7ALs5Ml+RyOSLsXmGk6csEHIr74KBQtaD/bt2+GRR/yNL0Wdfkx/+eVw2212TP/pp9bYee1ay5rnz+93pCIiZyhe3I4Dly6FP/+Ezz4PI+fkETB/Pg2jf6RplysZXuhZjvT/nGGP/ELxjBGEuWiKZ4xgWKc5focfMJLdwsI5NwH4yPO8aadt+xSY6Xne8JiPVwH1PM/bntjzqIVFyqmQdQ2XZj3CzP2V/Q5FJM6wYdChA0RG/nd76dLw2Wdw/fX+xJXGpk6Fli2tB9Mnn9hyRhERSTvHjllh3KBB0KgRfP+99csLelFR9j81f36YNy8EJm+JBL5du6yDzPDh8M8/sHy5/er98IPVQxQu7HeEqSCxY/pataxtRalS/sQlInKBoqOha1cY8eUxInZnJRPHiCYjp4gbTpSdIwzquIS2A2r7GGnaOu8WFvGepDhQBZgf767CwJbTPo6I2SZp4O6am8l5YCuntu/yOxSROD16nHmgCXYmn06Sx2C975o2tRMMJY9FRNJe1qxWJPfFF9Y1KSSSx2CTV7t2tTLrGTP8jkYkpM2YATfeCIUKwWOPWfK4deu4VQ033hiiyePDh+0KXELH9BERSh6LSFALC4O334ZNO7LyyyyPLET9J3kMEEkOegwq7k+AASbJCWTnXE5gDNDZ87yD8e9O4FPOKG12znVwzi10zi3cvXt38iKVRPV4Ly+TuZUMUyb6HYpInM2bE96+ZUvC20NI7ITtU6fgssusSqVIEb+jEhFJ3x54wPKtANOnw8CBIdA+uF07y2j16eN3JCIhJTLS+hZvjRkzs2uXtSTr2tWKAlassFqJTJn8jTNVHDtmjePvvNNWOOzdm/DjEjvWFxEJMmFhUOd6xxESHlC0+VShNI4oMCUpgeycy4Qlj4d5njc2gYdEAJef9nERYFv8B3meN8jzvHDP88Lz5ct3PvFKQipWhJIlOTByqt+RiMS56KKEtxctmrZxpKEDB+C++6BVKyvKSOx4W0RE/DV0KHTsaPnXhArrgkaWLDYp8OefrY2FiJy3EyespXjbtpY3vesuSyKDzbBYt86u1YTk6I6oKOvD8cADVv3QogXMnBn3cUJC+JheRNKnohnOSGOedXt6c84EsnPOAf8DVnqe1y+Rh00E7nOmOnDgbP2PJYU5x4fF3+Wy6V9zYPMBv6MRsanwBw6cuU44e/aQrZKaOdOu5XzzDfTsaefxmiMiIhKYhgyBl1+Gr76CmjUtMRS0OnSAvHlD9v+rSFo4ejRuNtzUqZZEnjEDnnzS7s+QIQTbjEdHw5w51pOjUCG46SYbbt2ihSWTt22Djz+Gd9+1Y/jThfAxvYikX306bCQ7R/6zLTtH6NNhoz8BBZikVCDXAu4FbnDOLY15a+Kce9Q592jMY74D1gNrgc+ATqkTriQm/K5SHCcrU95a4Xcokt69/76t6WvbFgYPhmLF7Ii7WDGbYNS2rd8RprioKDt/z5IF5s6FXr1CdEmjiEiICAuDV16BKVNsFXZ4eBAnkXPkgM6d7cUsXep3NCIBz/Pgt9/s16ZNG9uWLRs8+6xVIG/fbn3T69cPoZ7psTwPFi+G556D4sWhTh07Xq9f39pW7NxpHzdubH3WwY7dBw1KF8f0IpK+tR1Qm0Edl1AsQwSOaIpliEh3A/TOxnk+NX8LDw/3Fi5c6Mu+Q1F0VDRFsuyiZsENjI6o4Xc4kl59+ik8+ii0bAkjRsQdeIaoZcvgyivtpGP1ahuekiOH31GJiEhybNgAn38OvXsHcYXh/v2W1Lnxxrg19yLyH6tWwZdf2iHqhg2QObNVHI8cGYKJ4vj+/tuGcowYYQetGTNaxfHdd9u051y5/I5QREQChHNuked54fG3J3mIngS2sIxh3H7VKr7fWpHIPcHczE+CVmxDySZNrI9DCCePT52CN96wirXevW1b6dJKHouIBKMSJWwltnNWhXzXXUHYwz5PHnj8cRg92hJFIgLA2rVw+LC9/9130LevHbMNHmyD8UaPDuHk8aZN9oKrVIFy5eC116zaYdAgqzSeNMmqiJU8FhGRJFACOYS0vD8XkeTgx3eX+x2KpDejRtmQjRtugDFjrKQjRK1fD3XrwgsvQPPm8MwzfkckIiIpZelSGD8eqlWDRYv8jiaZOne2JTFvvOF3JCK+2roV+vWDa66xlWJjY0bAP/igtfWdOtUGaObO7WuYqWPnTvjoI6hVy1pUdO1qPdbef9++MDNmwMMPwyWX+B2piIgEGSWQQ8j1j1Xg4+zPUX3t136HIunJxInWQK5mTZgwAbJm9TuiVDNxIlSqBCtWwNdf25LHSy/1OyoREUkpLVvaTKnoaMu/fPGF3xElQ7581pB/2DBbny+Szhw+DPXq2TC8Z5+13+O334aGDe3+3LlDdMDx/v32x6pRIxuG98QTcOiQDbVet84aPj/1FBQs6HekIiISxNQDOdQ88ICVzuzcGdJVoBIgfvzRmsdVqgTTp8NFF/kdUapavdoqjgcMgKJF/Y5GRERSy5490Lq1/WsbPNiqFYPC1q1QsqSVWn7yid/RiKSqQ4fiTnu6dLFtrVpBhQr2+1u6tK/hpa4jR6wFxYgR8P33cOKE/e63bm1vV1/td4QiIhKkEuuBrARyiDk6egoj7hhNhQ86EP6EhulJKpo1C26+2Y7OZ8wI2aVwEybYUscBA4J4uJKIiCTbqVO2EvzhhyF7dvC8IPk/8MgjMGSIVSEXKuR3NCIp6tgxmDLF5sFNmWIfX3UVLF8OYaG+tvbECfjhB3vxEydaErlQIWvc3rq1DecIij9SIiISyDREL51wDRvwBB/y2YCTfocioey33+DWW6232o8/hmTy+OBBK+Bq3hzmz7ePRUQk/ciQwVZ9Z89ulY7XX2+5m4DXtatlv9991+9IRFLEyZP2Iw3w6qtWZTx7NrRvD3Pnhnjy+NQp+Okne7GXXQZNm9qx9z33wM8/w+bNcQ2flTwWEZFUFKr/atOtrHmycsvlyxi/qhynTpzyOxwJRYsXw0032UHs9Okh2Uzul1+sK8eXX0KPHpYvD8lBKyIikiT798OBA7bwpndv660asEqWtNkEAwdaLw6RIBQdbcdjHTtake1PP9n29u1h2jTr1vLhhzaCI+SSx54H8+bBk09C4cLWxHnkSCvemDIFtm+33+969exKl4iISBoItX+3ArRsAbu8fPw6aIXfoUio+fNPaNzYsqk//RSSS2MjI+GOO+x4fPZsSxSonbiISPp2+eV2MbFtW3jpJWjWzJLKAat7dzh6FPr39zsSkWQ5dMgG4BUtCnXr2sX8Bg3ihhaXLGn51IwZ/Y0zxXke/PGH/e6WLGmZ8UGDbJrnqFGwaxd89RU0aQKZMvkdrYiIpEPqgRyCDm07RL7CmehYZT7vLa7rdzgSKlavtvW7YWGWWS1Vyu+IUtSqVXDFFZY4XrgQypaFnDn9jkpERAKJ51lP/Kefhttvt6LAgNWqla0U2rRJy2gkoP31l/2Y3nyzdWwoVQoqVrS2vrfdFuLHY2vXWk/j4cNh5Uo7EG3UyF588+YhP6BaREQCj4bopTNNC1jT1olHGqoflly4jRuhTh04ftyG55Ur53dEKebUKWsd9+KL0Lev9bsUERE5m3nzbGV50aLWnzUgCwKXLIGqVeH1162qUSSAbNhgF2CGD4dly6zKf9MmO20J2N+plBIRYS9+xAirWgAr0rj7brvwky+fv/GJiEi6piF66cy3r61m4tHG1q9W5EJERMANN9ik52nTQip5vHEj1K8Pzz8Pt9xiLSNFRETOpUYNSx5HR0OLFtCpk11jDShVqlhJ53vvWX8mkQDRq5d1aejeHXLkgA8+gAUL4mpeQjJ5vHu39S2uW9f+eHTpYksa3nkHtmyxAo2OHZU8FhGRgKUEcojK2qIJZMiAN2as36FIMNu50xrP7dljo+crVfI7ohQzZowtj1y6FIYMsY91zC4iIsnheXZd9ZNPrIBwyxa/I4qnRw9LXH32md+RSDr1zz/w+efWt3jZMtvWsCG88YZVIf/6KzzxBBQo4G+cqeLgQRg61C7kFCxoCeLduy2Dvnq1VR8/+ywUKeJ3pCIiIuekFhYh7LVSg5mw7RoWHi3vdygSjPbutenO69db8rh2bb8jSlFz59ogpC++gOLF/Y5GRESC2Zgx8MADkCWLrUpv0MDviE5Tr571WV23zgIUSWXHj8PYsdaeYupUa0lxxRXWP7xRI7+jS2VHj8KUKfbip0yxL0axYtaeonVrq15Qe0EREQlgamGRDl16TUkWHSvPysnr/A5Fgs3+/dC4MaxZAxMnhkzyePJkeO01e79WLfjpJyWPRUTkwrVsCb//Dvnzw/33w7Fjfkd0mh49YOtWq4QUSSXHj9s1CrCE8UMPWRvuJ5+0QtvVq0M4eXzyJHz3Hdx7r/0RuOMOq1To0MFKrDdsgDfftJV8Sh6LiEiQUgVyCNu2eAeFqxWgd8OZ9JhWz+9wJFgcPmzJ44ULYfx4aNLE74gu2OHD8MwztoK3cmUbfpQ1q99RiYhIqDl82HJFFSrYkNYjR+Cii3wOyvPguutsZdGqVZAxo88BSaiIioKff7Zi27Fjra9x7PiVv/+G0qUhLFTLlaKjYfZse/GjR9vvV548djWpdWur/M+Qwe8oRUREkk0VyOlQoaoFqJFzOWPnXuZ3KBIsjh6F226zSSYjRoRE8njuXCv4+Pxz6NoVfvtNyWMREUkdOXNa8higZ08ID4cVK/yNCeesCnn9evvfLpICPvkEChe2moPRo6FZM3j9dbteAVC2bAgmjz3Plho88wxcfrklib/6yr4IEyfCjh12wNmggZLHIiISckLt37rE06LuXhYfLceG2RF+hyKB7vhxGyU/a5Ytc23Rwu+ILti+fXDTTVYkMmuWrR5U+0cREUkLN90Ehw5Z8e/w4T4Hc9ttUL68TS6LjvY5GAk2nmftKLp2he3bbVvu3DY4cswYm7n85Zf2Mx+SHRr+/BNefBGuvBKuvRY+/hiuucZ+sXftgm++sd8xHWSKiEgI0xq2ENfquRLsn9KbLD/lhzod/A5HAtXJk3DXXTbp5H//gzZt/I7ogmzZYgOtL7nEunBcey3kyuV3VCIikp7UqWPL+e+80/6t/vYbvP02ZM7sQzBhYfDCCxbI+PEhcZFYUt/q1ZYjHT48rvtJ9epw++32oxTkh4tnt349jBxpL375cvsduuEG+z26/Xa4+GK/IxQREUlT6oGcHlSubNmz2bP9jkQC0alT0LatHSR/+CE8/rjfEZ236Gjo3x+6d4chQ2zgtYiIiJ9OnoTnn4cBA6xDVKVKPgVy6pT1FbjoIptzEJKlonKhTp6ETJmsqrhgQdt2/fXW1rdlS8ib19/4UtX27fDtt5Y0nj/fttWsaS++VSsoUMDf+ERERNKAeiCnYyeatuL7ObnY8+dOv0ORQBMdDe3bW/K4b9+gTh5v3gwNG1pbuhtvtCIRERERv2XKBO+9Z0PFYpPH69f7EEiGDHaFdfFi+OEHHwKQQLV7t13gqFPHimsBLrsMhg2zVV0zZ8Ijj4Ro8njfPpuyfMMN1tS5c2dr6/bmm7Bxow3TePxxJY9FRCTdUwI5HVhV+S6a8B1jXl/ldygSSDzPDoiHDIFXXoHnnvM7ovM2apQNLfr9d+vAMX485M/vd1QiIiJxSpSw2wkToHRp6NcvbuBYmrnnHhv+1adPGu9YAtHkyda3uGBBeOwx+OcfSyLHat3acqoh5/Bhy47feqtlyjt0gIgIeOkl+OuvuIbPxYr5HamIiEjAUAI5HSjf/AquzLSBMT/k8DsUCRSeB1262Ajtrl1tVHwQCwuDihXhjz/gwQe1KldERAJX/frQtCk8+6yNHzh0KA13njmz9dOYMwd++SUNdyyBIDLSLrpHRtrHy5dbn+OuXWHZMlixwt4PSceOWYXBXXdZlcE999iBY+fOsGiRNXnu1QvKlfM7UhERkYCkHsjpRLfqM3l3fi12rjvCJSXz+B2O+K1nT3jtNXjiCWsaHIQZ1++/t2WVHWJmQ0ZHWyJZREQk0HkevPMOdOsGZcrA2LHWnjhNHD1q5dCVKqmVRTpw4gT8+COMGGHV74cPw+jR1s/4+HG7phCEh4FJExUFM2ZYT+Nx4+DAAciXD+64wwZl1Kqlg0cREZF41AM5nWvxSD6iyMTkt/70OxTx2xtvWPK4fXt4//2gO2s4cgQ6doQmTWDQIJsJBDr+FxGR4OGcdY6aNg327LEWTGkmWzYbGPDjj2m8Y0lrW7dae4rbboPvvrOWFD/9BM2b2/1ZsgTdYeC5RUdbhf1jj0GhQjYYY+xYe9FTp8K2bfDxx9arQwePIiIiSXbOCmTn3BfArcAuz/PKJ3B/PWACsCFm01jP8149145VgZy2oqOiKZZ1BzcUWsWXm+v7HY745f334emnoW1b+PJLG6gTRH77De69F9ats3Pf3r0ha1a/oxIRETl/+/dDnjz2/oIFULUqZMyYyjs9dMj6u9ata5WZEvQ8z35+hg+3Y6M337Rtzz5r8+EaN7Zq45Dkeda3eMQIe9uyxb4It91mWfObb9YBo4iISBIlVoGclMPTIcBHwNCzPGa253m3nmdskgbCMoYx+55BFB3RF47shhzqh5zufPqpJY9btrTBeUGWPI6IgOuvt0qaGTOgXj2/IxIREblwscnjiAjL5153HYwcabO9Uk2uXPDkk9bzdcUKKH9GjYgEib/+gq+/trzphg2WJG7b1u5zzoY1hqxVqyxjPny4NXPOmNEqjt94wxqN58rld4QiIiIh45zrdjzP+wXYlwaxSCor3q4eYceP2vItSV+GDo3r+/DNN2lQ2pRy9uyx2yJFbGD2smVKHouISOgpUsRaM8VWIf/6ayrv8MknIWdOS7ZJUFm/3jo1AAwcCH37wpVXwuDBsGsXfPGFv/Glqs2b7QVXqWKNw199FQoXtkKJHTtg8mTLoCt5LCIikqJSqvFTDefcH865751zVyf2IOdcB+fcQufcwt27d6fQriXJatemZ7a3ebpHdr8jkbQ0ahQ88ICtXxwzJmjWL0ZHwwcf2ArbmTNt2x13QO7cvoYlIiKSau6919o1Zctm1cgffZSKO7vkEru4PGIErF2bijuSlLB1K7z3Hlx7LZQqBXPn2vZu3ayt7w8/QLt2IXqctHOn/TLUrm0Hhl272vHse+9Z6f6MGTZV+dJL/Y5UREQkZKVEAnkxUMzzvErAh8D4xB7oed4gz/PCPc8Lz5cvXwrsWpIlY0a2XX4d/1tVi+MHj/sdjaSFSZOgTRuoWdNGbwdJ/7eICFuB+NRTVm2cZpPpRUREfFaxIixcaG1bN21K5Z098wxkygRvvZXKO5LzFRFhx0KXX27frlOn4iqOwebE5c/va4ipY/9+K6lu3Nhe5BNPwIED0KePXfCYPx86d7b7REREJNVdcALZ87yDnucdjnn/OyCTcy7vBUcmqaJFm6wc4iJ+em+Z36FIaps2DVq1siV+U6YETd/rb7+FChVg3jxbjTh5MhQo4HdUIiIiaSdPHhg/Pq67xKJFsGZNKuyoQAFo394G627Zkgo7kOQ6dMh6Gn/9tX182WVw8iS8/DL8/bf9LDz3XIgeG0VGWgPw5s3thT/4oE1O7tYNli+3txdesBJsERERSVMXnEB2zhVwzrmY96+Nec69F/q8kjoaPF2RXBxk7DfH/A5FUtMvv0CzZlCunPW8vugivyNKsh07LOylS201ov11ERERSV/CwmxkgedZjjc83BYTpbjnn7edvP12Kjy5JMWxYzB2rLXqyp/fWpl8/rndlymTtat4+WUoU8bfOFPFiRNxK+by54e777ZG4J06WZXx2rVWdaxBjyIiIr5ynued/QHODQfqAXmBncDLQCYAz/MGOuceBzoCUcBR4BnP88459iM8PNxbuHDhBQUv56dN8V+Ztrk02yPzkDFr8AxTkySaPx8aNrS1jjNnBsW6xh9/hKNHLecdHW1vQTTnT0REJFVt2mSLihYutGLM115L4f+TDz4Iw4fDxo1W+SmpLioq7nt4771WcZw/vyWRW7eGGjXsIkJIOnUKZs2yn7kxY+Cff6wnd6tW9uLr1IEMGfyOUkREJF1yzi3yPC/8jO3nSiCnFiWQ/TPlhbmMfeNv3plYhotvq+13OJKSliyxYXmXXmpVyAHeFy4y0gqfPv4YatWC2bNVcSwiIpKQY8dsNsCgQfavfty4FFxgtHq1Lf957jl4880UelKJLzoa5syxvOno0fDrr9bLeNEi2LcP6tcP4QvonmdFDsOHW7+yHTsgZ05rV9G6tRU/BMmgZxERkVCWWAI5VA9R5Cxu6VGZW95rCNMeBiWQQ8eff0KjRnY2+dNPAZ88/v13uOceO2ft3Blef13JYxERkcRkzWqzAapXh4kTU3i0QenScOedMGAAdO0KF1+cgk8uu3bZ4LuRI20oXrZs0LSpVSEDVKvmb3ypxvOsb/Hw4TBihFW4Z8kCTZpY0viWWyB7dr+jFBERkSRQBXI65TW/nWXzDlNx2w+4DKG6Pi4dWb0arr/e1jrOnh3ww0VWrrRBeQULwpAh0KCB3xGJiIgED8+zi65bt9qogwcfTIGLsMuXQ8WK0KsX9OyZInGmZytXwsGDcN111qGhaFGoV8/ypk2bWvFtyFq7Ni5p/Ndf1o6iYUN78c2bQ+7cfkcoIiIiiUisAlmZw3RqVJGnqbxrGr8PXel3KHKhNm60DGx0tFUeB3Dy+PBhuy1XDj76yM5VlTwWERFJnthk8Ycf2oC9Bx6wWQIXpEIFy2z27x/3D1uSZeNG6wBSqRJcdRU8+6xtv/hi2LkzblZcSCaPIyKgXz+45hrry9Gzp7VUGzAAtm+3Kx3336/ksYiISJBSAjmdavRsRTJykrGf7vY7FLkQW7daI8QjR2DaNMvMBiDPs/OHokWt0wbAo49Cnjy+hiUiIhLUXn8dXn4Zhg6FmjVh/foLfMIePawZ78CBKRJfevLYY1CiBHTvbl0Z+ve3PsexQrJTw5499rNSt64d5D37rB30vf02bN5s8zg6doR8+fyOVERERC6QWlikYzfmXcj6g3lZfawYLkzNZ4POzp12wL5tm1UeX3ON3xElaNs2W1r7ww9w443wxRcB355ZREQkqHz3HbRta+9PnWptE85bo0awYgVs2GCNl+UM//wDY8faLLhvvrFC2zFjYM0auOsuSySHrIMHYfx4a1ExbRqcOgVly1p7irvvtn7aIiIiErTUwkLO0OLGSNaeLM6fE9b6HYok1969doK3ZYudNQZo8njUKFsR+8sv8PHH8P33Sh6LiIiktCZNYNEiazNbtuwFPlmPHrBjh13xlX9FRlrOtGlTuOwyax2ybp3l2QFatoRu3UI0eXz0qGXIW7WyF3///dbkuUsXWLrU+hz37KnksYiISAhTAjkda9a1LI5oxn641e9QJDn277dS3tWrbQx77dp+R5So33+3lsxLlkCnTikw4EdEREQSVLKkXbjNnRuOHYNnnrHrzclWt671w+jbF06eTPE4g8nx49a+F+y2TRtYvBieeMKOcdasgfAz6nNCxMmTduX/vvssadyqFcyZAw8/DHPnWuY8tuGzDvBERERCnlpYpHMzKjzFdcwnx/Lf/A5FkuLwYWjcGBYutOWDTZr4HdEZfvrJVrzWqgUnTtg5RaZMfkclIiKSfsyaZYcLBQta4Wi1asl8gu++g1tugcGDoV271AgxYEVFwc8/w4gR1qbihhvsawh2+FW1KoSFaglOdDTMnm2l1qNH2xWI3LmtvLp1a6hXDzJm9DtKERERSUVqYSEJuuHB4uRYMd/W4ElgO3oUbrsNFiyws5oASx4fPQqdO9vy2ddes22ZMyt5LCIiktbq1rVi0ehou6Cb7G4UN98MVarAG29Yj9t04s03oXBhS76PGmXtKjp2jLs/PDwEk8eeZ+XUzz5rg/Dq1YOvvrJWaRMm2MyN//3PDvCUPBYREUm3Qu0QSJIputnt9OEFvum50u9Q5GyOH4cWLaykaOhQez+ALF5s1U39+8Pjj1vFjoiIiPjnmmvs/3OdOvDQQ9CnTzI+2Tl44QVrlxVbfhtiPM/a9774oh1mgSXc69Sx4tudO+HLLy1vGpL+/BNeesn6Fl97LXz4oR3MffMN7NoV1/A5Sxa/IxUREZEAoBYWQrXsK8mSIYpfD1XwOxRJyMmTcOed1rLif/+DBx/0O6L/WLDAqpvy57eVro0b+x2RiIiIxDp1ylYGtWmTzBln0dFw9dW2nGjp0pDpc7t6teVGR4yAv/+GDBmsa0ONGn5HlgY2bLAXPnw4LF9u5dT161t7ihYt4OKL/Y5QREREfKYWFpKoFrV2Mu9wBbYt3uF3KBLfqVM2vGT8eKsMCaDk8YkTdhseDq+8YuchSh6LiIgElgwZ7P906dJWdfv44/DDD0n4xLAw6N4dli2DyZNTO8xUFR1tt4sXQ5ky0KuXzYUbOBB27Ajx5PH27bZErEYNm7T4wguQMyd88AFs3QrTp1uJupLHIiIichZKIAstnrocgPF9V/kcifxHdDS0b2+VIn372hlfAPA8+PRTuOIK2LbNzi979IBLLvE7MhERETmb/futG9bNN0Pv3nGJ1US1bg3Fi1v/C59WLZ6v3bthwAC4/np46inbVrmybduyBWbOhEcegbx5/YwylezbB59/Dg0aQJEiNqTi6FFr8rxhA/z6KzzxBBQo4HekIiIiEiSUQBbK3VqKspnXMfbHXH6HIrE8zw7shwyxsqHnnvM7IsCqdG69FR59FMqW9TsaERERSY6LL4bffrN2Fi+9BM2aWVI5UZkyQbduMH8+zJiRVmFekFGj4KaboGBBeOwx2Ls3rnVHWJgNxStc2N8YU8Xhw9a/+LbbLDH88MOwebNd5f/rL2tD0rWrXRAQERERSSYlkAWA1tdtIMc/EUTv3ut3KOJ5ljAeMMAO9Hv29DsiwGbolC9v548ffABTp0KhQn5HJSIiIsmRIwd89RV89JG1smjc+BzFxe3a2T/8ZE3hSzuRkTBhQtxr+PFHWLUKnn8e/vgDVqywa/Ih6fhxa3N21102jKJtW0sUP/kkLFxoDZ9ffRXKlfM7UhEREQlyGqInZtEia2Y7eLCdKIh/eva0aTdPPGE96wJkaE2bNnYe8tVXOg8REREJBfPmWeFqo0aWgE30kOO99+CZZ6z1QQA0DD5xAqZNs1lwEybYa1iwAK65xt7PkSNgDp9SXlSUXc0fMQLGjoUDB6wPxx13WMuRWrWs1FpERETkPCQ2RE8JZDGeB8WLs/+qmuT5frjf0aRfb7xhw03at7dGwz6fAMycaUNmypWDQ4cga1ZbzSoiIiKh5a23rOPBe+9B5szx7jxyxFofXHed7wP1Fi+2hPe+fdaSo2VLy5vWrWsDA0NSdLRl+4cPtx4du3ZBrlxw++324hs00AGaiIiIpIjEEsi6PC3GOd4r3p+CU7/g0LZDfkeTPvXvb8njtm1tLLiPyeNjx6BLF7jhBnj5ZduWK5fOTURERELV/v1xQ+ciIuLdmSOHDWKbMsVaJKQRz7P2y08/DR9/bNvKlYOmTWHSJJvN8NlndrwScsljz4MlS6wXR4kSULs2/O9/9g0aM8aSyF9+aQ2fdYAmIiIiqUwJZPlX+B0lOEY2vuu7wu9Q0p9Bg+zErGVLG5zn41nQ0qW2BPTdd21Y3uDBvoUiIiIiaeSNN2D0aPjzT6haNYGZeY89BhddBK+/nuqxrFhhs9+uuAKqV7fE9rp1dl+2bHZscuutCVRKh4JVq2yAcrly9o147z0bQvHVV5Y0HjUKWrSwZWEiIiIiaUQtLORfp06conC2vdQtvI6Rm/3vb5dufPUV3H8/3HwzjBvn69nQzz/DjTfCpZfCF19YSCIiIpJ+/P235SfXrYP166Fw4dPu7NHDMs1//QVly6bofrdtixvO27QpfPeddWZo3RqaN4c8eVJ0d4Fl82YYOdJaVCxZYg2c69a1F9+ihfU4FhEREUkD6oEsSfLoVb/w9cqq7PknI1nzqLIh1Y0aBXffDfXrW09Bn6pJTp2youdjx+DFF6FbN52riIiIpFeHD9tF5dtus49PnozpkrB7t/VCvuMOWzF1gbZtg2+/tbzp77/Dxo1QtKgN7c2TB/Lnv+BdBK6dO63ke/hwmDvXtl17rR0X3nlnvMy9iIiISNpQD2RJkhb35uAIOZn27jK/Qwl9kyZBmzZQs6aNEPcheex51k6vatW4IXnvvKPksYiISHqWM2dc8njKFKhQwdpKkC8fdOgAX39t2d7z9Ndfdu28SBHrb3zypA3xy5HD7i9dOkSTx/v3W/+Nxo2t3Prxx21b796wdm1cw2clj0VERCTAKIEs/1HviQoMyPYs16352u9QQtu0adCqFVSpYmdmsWdMaWjXLlsS2r69taw4ciTNQxAREZEAd9FFcOAAXHedFcvSpYstW+rbN8nPceiQ5Zx//NE+zpvXiplfftlaZixeDM89Z8cjIScy0tpT3H47XHYZPPigJYu7dYNly+IaPpcq5XekIiIiIolSCws50333WVJzxw5NdU4Nv/xiE7NLl7YJNZdckuYhTJgADz8MBw9aK8OnnoIwXU4SERGRBGzfbl0V5syxY4a3j3Qk01eDrUlybOPieI4dsz7Gw4dbl65jx6w7w/DhaRy8H06csGz58OF20HXkCBQsaF/E1q2tVYVzfkcpIiIicobzbmHhnPvCObfLObcikfudc+4D59xa59wy51zVlAhY/BN5yx38b19zFv9vid+hhIZhw6xfYFiYnTw0bmwf//ijL8ljz4P337fVkYsW2UpJJY9FREQkMQUL2jXvzp2hf38YV/EVhh1vSfEiUYS5aIpnjGBYpzmcXpfSpAm0bAmzZsFDD1nyedgw315Cyjr92K54cfv41Cn7Ij38MBQoYD1Avv/e2pXNmAFbttgB2HXXKXksIiIiQeecFcjOueuBw8BQz/PKJ3B/E+AJoAlwHdDf87zrzrVjVSAHrsg9keTL59Gu/EI+Xl7X73CC27Bh1iswMjJum3PwwQfW9y4NzZljqyMLFrRlo7lzQ+bMaRqCiIiIBLlff4UNX82hw8AqRBLXgisjJ7koxyk2bMvKRRfFtau44QbImNGnYFNDQsd2GTNC9uy2tCtHDusR1ro1NGqkgy0REREJKolVICephYVzrjgwOZEE8qfATM/zhsd8vAqo53ne9rM9pxLIga1VkXn8ur0EEcfzE5ZR5annrXhx2LTpzO3Fil3Q8JnkOH4cevaEt9+2fseDBqXJbkVERCREFc8YwaZTRc7Ynp0j/H3rc1yefa8PUaWRyZP/mzyOlS0bDBkCt95qyWQRERGRIJRYAjkl6gEKA1tO+zgiZtsZCWTnXAegA0DRokVTYNeSWlo0i2bMgALM/2I5NTpU8Duc4LV5c/K2p7Dly+Gee2xGy8MPQ79+abJbERERCWGbTyXc9/go2bh87c9pHE0aSyh5DNbk+c470zYWERERkTSSEgnkhJp4JVjW7HneIGAQWAVyCuxbUsktXcuTacAJxn62lxod/I4miBUqBFu3nrk9DS6gTJkCLVpAnjwwaZIVxIiIiIhcqKIZtiVYgVw0wzZYudKHiNJQYqvLVBwjIiIiISwlehNEAJef9nERYFsKPK/4KHfR3DTM9wfr/joOSWhzIgnYutX6R8SXPTv06ZNqu439dtWoAffdBytWKHksIiIiKadPh41k58h/tmXnCH06bPQnoLTUp8+ZLSpS+dhORERExG8pkUCeCNznTHXgwLn6H0twGNdrOWMjb7L+B5I8O3dCgwaWQH71Vet57JzdDhoEbdum+C49z1rvNWwIJ0/CJZfAZ59BvnwpvisRERFJx9oOqM2gjksoliECRzTFMkQwqOMS2g6o7Xdoqa9tWzuWS4NjOxEREZFAcc4hes654UA9IC+wE3gZyATged5A55wDPgJuAiKBBzzPO+d0PA3RCwK7dkHBgng9XsS92svvaILH3r1Qvz6sWwc//AC1U/9kavdueOQRGDcOrr8eRo9W4lhERERERERERJLuvIfoeZ7X+hz3e8BjFxCbBKr8+Xn58v8x9Z2qzH/V72CCxIEDcOONsHq1NSFOg+Tx5MnQvj388w+8/TY8/TRkyJDquxURERERERERkXQgJVpYSAi7tFoJFhytyOofNvgdSuA7fBiaNLGWH2PHWguLVBYVBd26wWWXwe+/Q5cuSh6LiIiIiIiIiEjKUQJZzur2568EYNz7CUybljhHj0LTpjB/PgwfbonkVDR/Phw6BBkzWqHzggVQsWKq7lJERERERERERNIhJZDlrC6/rhDX5PiTsbPz+h1K4Dp+HFq0gJkzYehQaNky1XZ14gT06AE1a8YN+y5WDLJkSbVdioiIiIiIiIhIOqYEspxTi9q7WXCkPFvmb/M7lMBz8iTcfTdMnQqffw5t2qTarv76C6pXh9dfh/vvhxdeSLVdiYiIiIiIiIiIAEogSxLc0aUYL/EqmaZ/73cogeXUKbjvPhg/Hj78EB58MNV2NWYMVK0KERG2uy++gIsuSrXdiYiIiIiIiIiIAEogSxKUaliCV6/+lgLTv/Y7lMARHQ3t28OIEdC3Lzz+eKrurlo1uP12WL4cmjVL1V2JiIiIiIiIiIj8SwlkSZLjTe9gyqyc7F21x+9Q/Od58MQTMGQIvPIKPPdcquzi66/hnnvs/eLFbTbfZZel+K5EREREREREREQSpQSyJMnKSndzqzeJca+v9DsUf3meJYwHDIDnn4eePVN8F3v3wl13wb33wsaNcPBgiu9CREREREREREQkSZRAliSpdEdpSmTczNjvs/odir9eeQXefddaVrz5JjiXok8/dSpUqGB9jt94A2bNgty5U3QXIiIiIiIiIiIiSaYEsiSJC3O0qLyB6bsrcWDzAb/D8cebb8Krr8JDD0H//imePD561J76kktgwQLo1g0yZEjRXYiIiIiIiIiIiCSLEsiSZC0evoSTZGZK3z/9DiXt9e8P3btDmzbw6acQlnK/OkuWwMmTkC0b/Pjj/9u792grq3KP498HNgKiqATeQMhMj3kDC1CPRl5QETxS0EUkb5VGx8zOyMzK1GOjUjuaYZli6UE0KBCEIeiOvGQew0RA8IK3BAUJ8YaANzbM88e71N12LVh7s9Z+N5vvZ4w19lrvnO+7HsaYzrX2z3fPCbNnQ58+Fbu8JEmSJEmS1GQGyCrbwV/Zl13a/JO7pr+ddynNa8wY+Pa3YfhwGDu2YrcFr12bLaHcr1+WTwPsuy902MJXCZEkSZIkSVLLUZN3Adp8tKlpw6yRv6LHpKvgzZdg663zLqn6xo2DUaNg8GD4/e+hpjL/ySxcCF/+Mjz8MJxyCpxxRkUuK0mSJEmSJFWUdyCrUXqeegRt3lqTrbXQ2k2cCKedBkceCbfeClttVZHLjh8PBx4IixbBpEnZTc1ulCdJkiRJkqSWyABZjTNgAD/ocCXf+UH7vCuprttvz9Y7/vd/h6lTK7quxF57wTHHwIIF2aoYkiRJkiRJUktlgKzGadeOF7v344aFh/Du6nfzrqY6Zs7Mkt0DD4Tp06FTp02+5IQJcN552fNPfSrLpHfZZZMvK0mSJEmSJFWVAbIabfiIrXg9bc+9Vy/Iu5TKu+8+GDoU9t4b7rwTOnfepMu99hqMGJE9/vpXeHsL239QkiRJkiRJmzcDZDXa0d85gE6sZvK4NXmXUlkPPghDhkCvXtldyF26bNLlZs6E/ffP1jn+8Y+zALmCK2FIkiRJkiRJVWeArEbrsH0Hhuw2n9ue3Jt1767Lu5zKmDsXBg2CnXaCu+6CHXfcpMu99lq2Csa228KsWXDBBVBTU6FaJUmSJEmSpGZigKwmOfnEOk5Yfxur7/573qVsuscey3a169w5C4933bXJl3rySUgJdtgBamthzpxszWNJkiRJkiRpc2SArCY5/kcHMmars9nuTxPzLmXTPP00DBwI7dpl4XGvXk26TF0dXHIJ7LcfjBuXHTvkEOjYsYK1SpIkSZIkSc3MAFlNs+22pKOPYe6EJ0nrU97VNM2iRXDUUbBuXRYef/zjTbrMU0/BYYfBRRfBl74EJ5xQ2TIlSZIkSZKkvBggq8nGdz+XTy6bztzxC/MupfGWLs3C41Wrst3uPvGJJl1m3Djo0ycLkf/wB7j5Zth++4pWKkmSJEmSJOXGAFlNdux39qMtdUz+zfK8S2mc5cuz8HjFimyh4t69m3ypLl1gwABYsAC++MUK1ihJkiRJkiS1AGUFyBExKCKejIhnIuL8Iu2HR8TKiJhXeFxY+VLV0nxkr4/wmR3mM/mh3fIupXyvvAJHHw0vvAAzZkD//o2+xMSJ8ItfZM+HDIE77oDu3StcpyRJkiRJktQCbDRAjoi2wK+B44B9gBERsU+Rrn9NKfUpPC6pcJ1qoYYNXMUT7+7BE7c/m3cpG7dyJRx7bLbexNSp2cLFjfD66/DlL2d3Gk+alG2cBxBR+VIlSZIkSZKklqCcO5D7A8+klP6RUnoXmAAMrW5Z2lx89ry9AJgy+oWcK9mI1ath8GCYPx9uvRUGDmzU6XffDfvvDxMmwMUXw733Qk1NVSqVJEmSJEmSWoxyIrDuQP10cAlwUJF+h0TEI8CLwLkppccadoiIM4EzAXr27Nn4atXidO+7C3d/4iwOfnke8H95l1PcW2/BCSfAgw9mO90NGdKo05cuhUGDYPfd4YEHmrTqhSRJkiRJkrRZKucO5GJ/oJ8avJ4D9Eop9QauBm4rdqGU0piUUt+UUt9u3bo1qlC1XEec/lE6zn0AFi/Ou5QPe+cdGDYsu2V47FgYPrzsU5csyX52756teDF3ruGxJEmSJEmStizlBMhLgPq7pPUgu8v4fSmlN1JKqwvPZwDtIqJrxapUi7Zu6DAu4UeM/9HjeZfyr9auhREj4M474frrYeTIsk6rq4Of/hQ+9jGorc2OHXccbL11FWuVJEmSJEmSWqBylrB4CNgzInYHlgInAifV7xAROwPLU0opIvqTBdOvVLpYtUxt99qDyR1G0nnq24zIu5j3rFsHp54KU6bA6NHw1a+Wddqzz8LJJ8Pf/pZtltevX5XrlCRJkiRJklqwjd6BnFKqA74J1AJPAH9MKT0WEaMiYlSh2+eBRwtrII8GTkwpNVzmQq3YsEOWcf8b+/PPBSvyLgXWr4czzoDx4+Gyy+Dss8s6bexY6N0bHn8cbrkl2zCvS5cq1ypJkiRJkiS1YOUsYUFKaUZKaa+U0h4ppZ8Ujl2bUrq28PxXKaV9U0q9U0oHp5QeqGbRanmGnd2dRBumXrYw30JSygLjG2+Eiy6C884r+9S1a+Hgg2HBAjjpJIhiq39LkiRJkiRJW5DI60bhvn37ptmzZ+fy3qq8tD6xV4fFfKzzK9S+/Kmcikjw3e/CFVdkPy+7bKMp8JQp8Pbb2VLJKWWPNmX9bxVJkiRJkiSp9YiIh1NKfRseNypTRUSb4KR+z7DNq4tZ/+rr+RRx8cVZeHzWWRsNj1euhNNOg2HDYMyYLDiOMDyWJEmSJEmS6jMuU8X89y86c2saTpsZtzf/m196KVxySbZZ3ujRGwyP//KXbK3jcePgggugttblKiRJkiRJkqRiDJBVOX37Qo8evDahtnnfd/Ro+P73s4WLr7tug7cRL1wIRxwB7drB/ffDj38MW23VjLVKkiRJkiRJmxEDZFVOmzb8vNfVdJ9+HWteWtM873n99XDOOdlaFGPHQtu2Rbu98kr2c++9szuP582DQw5pnhIlSZIkSZKkzZUBsiqq3+c/yltszZ0/n1/9N7v5Zvj612HwYBg/HmpqPtRl3bpsOeRevWDOnOzYyJHQqVP1y5MkSZIkSZI2dwbIqqjDRu1H13iZyRPXV/eNJk2CU0/N1qOYNKnoOhTPPQeHHw7nnw+DBkHPntUtSZIkSZIkSWptDJBVUTUdahi65xPcvng/3nnjneq8ye23w4gR2RoUU6dCx44f6jJ2LBxwAMyfDzfdBBMnQteu1SlHkiRJkiRJaq0MkFVxw07qwBtsx91XVWEZi5kzYfhw6NMHpk+HbbYp2u2557I9/ebPh5NPhojKlyJJkiRJkiS1dgbIqrij/usArm1/Dv2e/n1lL3zffTB0aLYTXm0tbLfdvzRPmwZ//nP2/IIL4K67srWPJUmSJEmSJDWNAbIqrn3n9nx92Aq63nlztotdJTz4IAwZkiXCM2dCly7vN61aBV/7WpYtX3lldqymBto4uiVJkiRJkqRNYsSmqlgz+AuMeflzzLtx7qZfbN68bBe8nXbKbiveccf3m+6/H3r3hhtvzDbLmzJl099OkiRJkiRJUsYAWVWRBh7NOfySG361ZtMu9PjjcPTRsO22WXi8667vN/397zBgQLa+8X33wc9+Bu3bb2LhkiRJkiRJkt5ngKyq2GbnbTh25/lMfnQv1q9LTbvI00/DUUdBu3Zw993vL2i8ppBJ9+sHV12V3aB86KEVKVuSJEmSJElSPQbIqpph/7GWpet2Yfa4Jxp/8qJFWXhcV5ftjPfxj7N+PVxxBey+e9YcAd/6VnZzsiRJkiRJkqTKM0BW1Rz/vX2pYS1TrnupcScuXZqFx6tWZRvm7bMPixfDkUfCuedmdxt36lSdmiVJkiRJkiR9wABZVdNljx048iOP8NyjayCVuYzF8uVZeLxiBdTWQp8+3HQTHHAAzJkDN9wAkydDt27VrV2SJEmSJEmSAbKqbNpFc5iw+vhsM7yNefXVbMO855+H6dOhf38g2yCvd2945BE4/fRs6QpJkiRJkiRJ1WeArKpq/4UTIIL1kyZvuOPKlXDssfDUUzBtGjNWfZp587Kmq6+Ge+7J1j6WJEmSJEmS1HwMkFVdO+/MD7v/L4dePrR0n9WrYfBgeOQRVt98G6MmDWTIEPjZz7Lmjh2hbdvmKVeSJEmSJEnSBwyQVXUf+WQvZr15AM/e8/yHG996C4YOhVmz+NvFtfQ5fxBjxsB3vws33dT8tUqSJEmSJEn6gAGyqu5z5+4BwJQrn/vXhnfegeHD4Z57uOu8Wg770RHU1cG998Lll0P79s1fqyRJkiRJkqQPGCCr6nb/dA8O7LiQKX/Z4YODa9fCiBG8e8ef4brrGHDJQC68EObPhwED8qtVkiRJkiRJ0gcMkNUshh/6Tx5YdQDL5i2HdetYf8ppXDWlJ/t0fYnXPn8G7drBRRdB5855VypJkiRJkiTpPTXldIqIQcAvgbbAb1NKlzZoj0L7YOBN4LSU0pwK16rNWIf269mO1+l+YDd2jWVsl77P4+zH8QdDXV3e1UmSJEmSJEkqZqN3IEdEW+DXwHHAPsCIiNinQbfjgD0LjzOB31S4Tm3GbvnP+7lw+kGsZHsSbViauvM4+/LVzzzNtGnQrVveFUqSJEmSJEkqppwlLPoDz6SU/pFSeheYAAxt0GcocFPKzAK2j4hdKlyrNlM/HPNR3qRTg6PBn+/vSEQuJUmSJEmSJEkqQzkBcnfghXqvlxSONbYPEXFmRMyOiNkrVqxobK3aTD2/btdGHZckSZIkSZLUMpQTIBe7RzQ1oQ8ppTEppb4ppb7dXLdgi9Gz7YuNOi5JkiRJkiSpZSgnQF4C7FbvdQ+gYfJXTh9toX5y5iK2Zs2/HNuaNfzkzEX5FCRJkiRJkiSpLOUEyA8Be0bE7hGxFXAiMK1Bn2nAKZE5GFiZUlpW4Vq1mRp5zWGM+cZcerVdQrCeXm2XMOYbcxl5zWF5lyZJkiRJkiRpA2o21iGlVBcR3wRqgbbADSmlxyJiVKH9WmAGMBh4BngTOL16JWtzNPKawxh5zXuvehQekiRJkiRJklqyjQbIACmlGWQhcf1j19Z7noCzKluaJEmSJEmSJClP5SxhIUmSJEmSJEnaAhkgS5IkSZIkSZKKMkCWJEmSJEmSJBVlgCxJkiRJkiRJKsoAWZIkSZIkSZJUlAGyJEmSJEmSJKkoA2RJkiRJkiRJUlGRUsrnjSNWAItzefP8dQVezrsIbXEcd8qD407NzTGnPDjulAfHnZqbY055cNwpD1vyuOuVUurW8GBuAfKWLCJmp5T65l2HtiyOO+XBcafm5phTHhx3yoPjTs3NMac8OO6UB8fdh7mEhSRJkiRJkiSpKANkSZIkSZIkSVJRBsj5GJN3AdoiOe6UB8edmptjTnlw3CkPjjs1N8ec8uC4Ux4cdw24BrIkSZIkSZIkqSjvQJYkSZIkSZIkFWWALEmSJEmSJEkqygC5iiJiUEQ8GRHPRMT5RdojIkYX2udHxCfzqFOtR0TsFhH3RMQTEfFYRJxTpM/hEbEyIuYVHhfmUataj4hYFBELCuNpdpF25zpVVET8W705bF5EvBER327Qx7lOmywiboiIlyLi0XrHukTEzIh4uvBzhxLnbvB7oFRMiTH384hYWPgMnRIR25c4d4Ofx1IpJcbdxRGxtN7n6OAS5zrXqUlKjLs/1BtziyJiXolzne/UaKXyEr/blcc1kKskItoCTwFHA0uAh4ARKaXH6/UZDJwNDAYOAn6ZUjooh3LVSkTELsAuKaU5EbEt8DDw2Qbj7nDg3JTS8flUqdYmIhYBfVNKL5dod65T1RQ+b5cCB6WUFtc7fjjOddpEETEAWA3clFLar3DscuDVlNKlhV8edkgpfa/BeRv9HigVU2LMHQPcnVKqi4jLABqOuUK/RWzg81gqpcS4uxhYnVL6nw2c51ynJis27hq0XwGsTCldUqRtEc53aqRSeQlwGn632yjvQK6e/sAzKaV/pJTeBSYAQxv0GUo2WaaU0ixg+8KAlpokpbQspTSn8HwV8ATQPd+qJOc6VdVRwLP1w2OpUlJK9wGvNjg8FBhbeD6W7BePhsr5Hih9SLExl1L6U0qprvByFtCj2QtTq1ZiriuHc52abEPjLiIC+CIwvlmLUqu2gbzE73ZlMECunu7AC/VeL+HDQV45faQmiYiPAgcCDxZpPiQiHomIOyJi3+atTK1QAv4UEQ9HxJlF2p3rVE0nUvqXC+c6VcNOKaVlkP0iAuxYpI/znqrlK8AdJdo29nksNdY3C0un3FDiT7qd61QtnwaWp5SeLtHufKdN0iAv8btdGQyQqyeKHGu4Xkg5faRGi4htgFuBb6eU3mjQPAfolVLqDVwN3NbM5an1OTSl9EngOOCswp+j1edcp6qIiK2AE4CJRZqd65Qn5z1VXET8EKgDbinRZWOfx1Jj/AbYA+gDLAOuKNLHuU7VMoIN333sfKcm20heUvK0Ise2qPnOALl6lgC71XvdA3ixCX2kRomIdmST4S0ppckN21NKb6SUVheezwDaRUTXZi5TrUhK6cXCz5eAKWR/3lOfc52q5ThgTkppecMG5zpV0fL3luEp/HypSB/nPVVURJwKHA+MTCU2sSnj81gqW0ppeUppXUppPXA9xceTc50qLiJqgGHAH0r1cb5TU5XIS/xuVwYD5Op5CNgzInYv3CF1IjCtQZ9pwCmROZhsgfhlzV2oWo/CWlG/A55IKV1Zos/OhX5ERH+yeeCV5qtSrUlEdCpsQEBEdAKOAR5t0M25TtVS8u4U5zpV0TTg1MLzU4GpRfqU8z1QKktEDAK+B5yQUnqzRJ9yPo+lsjXYr+JzFB9PznWqhoHAwpTSkmKNzndqqg3kJX63K0NN3gW0VoVdkr8J1AJtgRtSSo9FxKhC+7XADGAw8AzwJnB6XvWq1TgUOBlYEBHzCsd+APSE98fd54FvREQd8BZwYqk7WaQy7ARMKeR0NcDvU0p3Otep2iJia7JdkL9e71j9cedcp00WEeOBw4GuEbEEuAi4FPhjRHwVeB74QqHvrsBvU0qDS30PzOPfoM1LiTH3faA9MLPweTsrpTSq/pijxOdxDv8EbYZKjLvDI6IP2Z9oL6Lweetcp0opNu5SSr+jyP4WzneqkFJ5id/tyhD+LiVJkiRJkiRJKsYlLCRJkiRJkiRJRRkgS5IkSZIkSZKKMkCWJEmSJEmSJBVlgCxJkiRJkiRJKsoAWZIkSZIkSZJUlAGyJEmSJEmSJKkoA2RJkiRJkiRJUlH/D2itr0PW8EiBAAAAAElFTkSuQmCC\n",
"text/plain": [
"
"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a89772c1-58b4-40ec-a589-d4bec31ae677",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"