02-Stochastic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from toggle_equations import toggle_rhs\n",
    "from attractor_stats import *\n",
    "from steady_states import *\n",
    "from sim_funcs import *\n",
    "from plotting_funcs import *\n",
    "\n",
    "from collections import OrderedDict,Counter\n",
    "#from networkx.drawing.nx_agraph import to_agraph\n",
    "from matplotlib import rc\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "#import networkx as nx\n",
    "import copy\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(style='white', context='notebook', \n",
    "        palette='muted', rc={'axes.labelsize': 16})\n",
    "\n",
    "plt.rc('font', size=15) #controls default text size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters for Euler-Marayama simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha1_A = 0.3\n",
    "alpha2_A = 0.8\n",
    "alpha1_AB = 0.3\n",
    "alpha2_AB = 0.3\n",
    "alpha1_B = 0.8\n",
    "alpha2_B = 0.3\n",
    "\n",
    "myPara = [[0.3,0.8], [0.3,0.3], [0.8,0.3]]\n",
    "a = 0.6 \n",
    "c = 0.6\n",
    "b = 0.4\n",
    "d = 0.4\n",
    "max1 = 1\n",
    "max2 = 1\n",
    "lambda1 = 1.25\n",
    "lambda2 = 1.25\n",
    "omega = 50\n",
    "dt = .01  # Time step.\n",
    "T = 100  # Total time.\n",
    "n_traj = 1000\n",
    "x_init = 0.6\n",
    "y_init = 0.6\n",
    "tolerance = 1e-8\n",
    "\n",
    "#For plotting\n",
    "n = int(T / dt)\n",
    "t = np.linspace(0., T, n)  # Vector of times.\n",
    "ss_colors = ['#38CAFF', '#ff9400', '#99dd44'] # Since we have a maximum of 3 attractors (stable ss)\n",
    "ss_unstable_colors = ['#ed1c24', '#fe6dbc', '#fca085'] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running EM simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "em_traj_A = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_A, alpha2_A, a, c, b, d, max1, max2, lambda1, lambda2, omega, seed = 20)\n",
    "em_traj_AB = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_AB, alpha2_AB, a, c, b, d, max1, max2, lambda1, lambda2, omega, seed = 20)\n",
    "em_traj_B = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_B, alpha2_B, a, c, b, d, max1, max2, lambda1, lambda2, omega, seed = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "em_traj_10 = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_A, alpha2_A, a, c, b, d, max1, max2, lambda1, lambda2, omega = 10, seed = 20)\n",
    "em_traj_100 = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_A, alpha2_A, a, c, b, d, max1, max2, lambda1, lambda2, omega = 100, seed = 20)\n",
    "em_traj_1000 = em_cle(n_traj, x_init, y_init, tolerance, dt , T, alpha1_A, alpha2_A, a, c, b, d, max1, max2, lambda1, lambda2, omega = 1000, seed = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the colormap and centre the colorbar\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "class MidpointNormalize(colors.Normalize):\n",
    "    \"\"\"\n",
    "    Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)\n",
    "\n",
    "    e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))\n",
    "    \"\"\"\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        colors.Normalize.__init__(self, vmin, vmax, clip)\n",
    "\n",
    "    def __call__(self, value, clip=None):\n",
    "        # I'm ignoring masked values and all kinds of edge cases to make a\n",
    "        # simple example...\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.collections as mcoll\n",
    "\n",
    "def colorline(\n",
    "        x, y, z=None, cmap='copper', norm=plt.Normalize(0.0, 1.0),\n",
    "        linewidth=3, alpha=1.0):\n",
    "    \"\"\"\n",
    "    http://nbviewer.ipython.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb\n",
    "    http://matplotlib.org/examples/pylab_examples/multicolored_line.html\n",
    "    Plot a colored line with coordinates x and y\n",
    "    Optionally specify colors in the array z\n",
    "    Optionally specify a colormap, a norm function and a line width\n",
    "    \"\"\"\n",
    "\n",
    "    # Default colors equally spaced on [0,1]:\n",
    "    if z is None:\n",
    "        z = np.linspace(0.0, 1.0, len(x))\n",
    "\n",
    "    # Special case if a single number:\n",
    "    # to check for numerical input -- this is a hack\n",
    "    if not hasattr(z, \"__iter__\"):\n",
    "        z = np.array([z])\n",
    "\n",
    "    z = np.asarray(z)\n",
    "\n",
    "    segments = make_segments(x, y)\n",
    "    lc = mcoll.LineCollection(segments, array=z, cmap=cmap, norm=None,\n",
    "                              linewidth=linewidth, alpha=alpha)\n",
    "\n",
    "    ax = plt.gca()\n",
    "    ax.add_collection(lc)\n",
    "\n",
    "    return lc\n",
    "\n",
    "def make_segments(x, y):\n",
    "    \"\"\"\n",
    "    Create list of line segments from x and y coordinates, in the correct format\n",
    "    for LineCollection: an array of the form numlines x (points per line) x 2 (x\n",
    "    and y) array\n",
    "    \"\"\"\n",
    "\n",
    "    points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
    "    segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "    return segments\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = 0\n",
    "end = 10000\n",
    "sample = 1\n",
    "path_id = 5\n",
    "\n",
    "x = em_traj_10[path_id][0][start:end:2] \n",
    "y = em_traj_10[path_id][1][start:end:2]\n",
    "fig, ax = plt.subplots(figsize = (8,6))\n",
    "\n",
    "lc = colorline(x, y, cmap=plt.cm.magma, alpha = 0.7)\n",
    "cbar = plt.colorbar(lc, ticks=[0, 1])\n",
    "cbar.ax.set_yticklabels(['Start', 'End'])\n",
    "ax.set_xlim([0,2])\n",
    "ax.set_ylim([0,2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2, 10000)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em_traj_10.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f18874c15f8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_start= 0\n",
    "time_max = 10000\n",
    "path_id =  9\n",
    "diverging_time = time_max/2\n",
    "\n",
    "plt.scatter(em_traj_10[path_id][0][time_start:time_max], em_traj_10[path_id][1][time_start:time_max], c = range(n), norm=MidpointNormalize(midpoint=diverging_time,vmin=time_start, vmax=time_max), cmap=plt.cm.PiYG, linewidths=0, marker='o', s=3)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f18873e6e10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD/CAYAAAAddgY2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3wUdf748dfMbEth0ysJhE5o0hTFCoiA0iwIP0BPBGwHenqecHceYDn9gpzoKR7q2Q8VORUPUEABC4IKCAiEThLSQxqp22bm98dCMCZkl5DCbj5PH/Nwdz+fmfkMPHjv7OfzmfdH0nVdRxAEQWgV5JZugCAIgtB8RNAXBEFoRUTQFwRBaEVE0BcEQWhFRNAXBEFoRUTQFwRBaEVE0BcEQWgkCxcuZOjQoXTr1o3Dhw9Xf56amsrEiRMZMWIEEydOJC0trUnL6qULgiAIjWL79u16dna2PmTIEP3QoUPVn99xxx36qlWrdF3X9VWrVul33HFHk5bVR9zpC4IgNJKBAwcSFxdX47PCwkJSUlIYPXo0AKNHjyYlJYWioqImKfPE0JgX3FA2m419+/YRFRWFoigt3RxBEC5iqqpy8uRJevXqhcViafBxSkpKKC8v96qurutIklTrc6vVitVqrXffnJwcYmJiqmOboihER0eTk5ODruuNXhYeHl5vey6KoL9v3z6mTJnS0s0QBMGHLF++nIEDBzZo35KSEgZdeRm4agfyupjNZux2e63PZ82axezZsxvUhpZyUQT9qKgowP2XGBsb28KtEQThYpabm8uUKVOq40ZDlJeXg0tC6lsJZq3+ynYZ++6645Onu3yAuLg48vLyUFUVRVFQVZX8/Hzi4uLQdb3Ryzy5KIL+mZ8osbGxJCQktHBrBEHwBY3RFSwH6EgB9dfRZR2VhseniIgIkpOTWbNmDePGjWPNmjUkJydXd8M0RVl9JF1v+SybmZmZDBs2jI0bN4qgLwhCvRojXpw5hmFwBVJA/SFQr5JwbQ3y6nxPP/00GzZsoKCggLCwMEJDQ1m7di3Hjh1j7ty5lJaWYrVaWbhwIR07dgRokrL6iKAvCIJPacygb7rKu6Dv2OJd0PcFF0X3jiAIQkuQFQnZQxTUFO8Ge32FCPqCILRakuzePNXxJyLoC4LQasmShOzpRr6O+fm+TAR9QRBardZ4p++zl5NedICn191BfllGSzdFEAQfJUkgy/Vvfnaj77tBv6A8m7SC/aTk/NDSTREEwUedudP3tPkTn+3e6Rx1CZ2i+hAf4nleqiAIQl0URcLjM15i9s7FISQgkj+PeLulmyEIgg8704VTH3/LRezTl/P5vrd4e9sTLd0MQRB8lCRJSLKHzc869X026P+Uto6Vu5aw88TGlm6KIAg+SvIwiCuLPv2Lh9USCUDP+CtauCWCIPiq1jhl02eDftfofkzo/wf6JQxp6aYIguCjFNk9mFsff+vT9xj0i4uLeeyxxzhx4gQmk4n27dvz5JNP1krhOXfuXLZu3UpYWBgAI0eO5P7772+aVgOyrFBQls3OE18xuvfMJjuPIAj+y5uBXE/lvsZj0JckiRkzZjBo0CDAvdr74sWLeeaZZ2rVveeee5g6dWrjt7IOOadS+frIf2kfniyCviAIDSNLnrtv/Czoe7yc0NDQ6oAP0LdvX7Kzs5u0Ud44cPB7dDQGmK9u6aYIguCjPA3ievNLwNecV5++pml88MEHDB06tM7yt956ixUrVpCYmMgf//hHOnXq1CiNrMvV3W4hakcAXYcOa7JzCILg3yTJc5oFP5uxeX5B/6mnniIwMLDOLpyHH36YqKgoZFlm1apVzJgxg6+++qpRljSrizE0kONXnySzZBWj2t7VJOcQBMG/yZLkuU/fz4K+1z9cFi5cSHp6Oi+88AJyHX9KMTEx1Z+PHz+eyspKcnNzG6+ldThweCtH0nc06TkEQfBfskFC8bDJBv+K+l4F/SVLlrBv3z6WLl2KyWSqs05eXl716++++w5ZlomJiWmcVtZB13Ti9lox5nhYyV4QBOEc3Pn0PW/+xGP3zpEjR1i2bBlJSUlMmjQJgISEBJYuXcq4ceN47bXXiImJYc6cORQWFiJJEsHBwfzrX//CYGi6xwAkWcJ2dSBlrqImO4cgCP6tNXbveIzKXbp04dChQ3WWffbZZ9Wv33777UZrlDdKqwr5MWc9/XXxRK4gCA0jy14E/dY8e+disunjd0hPLadI+4FZLd0YQRB8kuRF940k6c3Umubhs0GfkyqVlSq60dbSLREEwUe57/TrD/ruO33/Cfw+G/Qziw7TJtjAhJ6T0VUNSfGz32CCIDQ5gyxj8JB7R5V1QG2eBjUDn42UlpBoTqRXsvnQN7hOVbZ0cwRB8EGSLCN72CQ/69T32asJv6QHAMeLjnKkcm8Lt0YQBF90pnvH0+ZPfLZ7p6qsHICKUy4CLMEt3BpBEHyRLOFxIFcWA7kXhxwyADCYFcJMTfcQmCAI/su7gVz/utP32e6dQRviMSgSDqcLW0kpust/BloEQWgesqygKPVvstw0+cNais8G/Z/iUnGpOh06BmG2BFV/bneKKZyCIHinNfbp+2zQX5f1JQDpaZUYQwKQDAq/pG9nxN+T+enoty3cOkEQfEFrDPo+26cfHGyBMjAYJIyY2HF8C3/5YBrdE/rQPrLp8vgLguA/3AnV6r/3FQO5F4nhpl5UtC8gMcFK2akiVnz/OlV2OyP63EJMaNuWbp4gCD5AkjzfyUt+lmXTZ7t3VlVu5UR6JcdSi5BUnfuGziExogO7035q6aYJguAjRGplH1KOHYCqKg2no4qO7ZK5JnkkksdVjgVBENwMBgWDof7ZOU2YIb5F+GyEvN45AABZN7Im7UOm/3sYt10xjXuHz2nhlgmC4CskL+7yz7d7Z/PmzYwfP55x48YxZswYNmzYAEBqaioTJ05kxIgRTJw4kbS0tOp9GlrWED4b9Cs7lmEyQ0yCwrbUNYQGRqLIfvaVLAhCk5IlL2bvnEfQ13Wdxx57jEWLFvHZZ5/x3HPPMWfOHDRNY/78+UyePJn169czefJk5s2bV71fQ8sadM0XtHcLKiiswGGHinIX8SFteX7KR4QFRbZ0swRB8CGy7M20TXfd3NxcMjMza2ylpaV1HFOmrKwMgLKyMqKjoykuLiYlJYXRo0cDMHr0aFJSUigqKqKwsLBBZQ3ls7fGB+zpAJzMsxM/qCOaptW5YLsgCMK5yJLsxZRNd/mUKVNqlc2aNYvZs2dXv5ckiRdeeIEHHniAwMBAKioqePXVV8nJySEmJgZFcY8fKIpCdHQ0OTk56LreoLLw8PAGXbPPBv1gi5lT5U4CAhRcqpMV37/C/tydPD3hrZZumiAIPkLy4uEr6XT58uXLiY2NrVFmtVprvHe5XLz66qu88sorDBgwgJ07d/Lwww+zaNGixm34BfDZoH+JqSu5hp+JbxfAvoIfuSQyHIfL0dLNEgTBhxhkAwbF6KGO+/+xsbEkJCTUW/fAgQPk5+czYIB7osmAAQMICAjAbDaTl5eHqqooioKqquTn5xMXF4eu6w0qayif7Q9JyclGdcGxQ+Vk5RWzYuvbFJ861dLNEgTBh8iSguJhkyXvE67FxsaSm5vL8ePHATh27BgFBQW0b9+e5ORk1qxZA8CaNWtITk4mPDyciIiIBpU1lM/e6SfEhJGWm4utSsdg0AkJCOFg9i/8Z8s/mXrVgy3dPEEQfIAse86iKcua18eLiopiwYIFPPTQQ9VTPZ999llCQ0NZsGABc+fO5ZVXXsFqtbJw4cLq/Rpa1hA+G/TbBFqqX1dWOXA47EhIvP/d6yLoC4LgFfdAroegL51f2vaxY8cyduzYWp936tSJlStX1rlPQ8sawmeD/rFTuQBEx5qwBlsodtlQXRpG2dTCLRMEwVecWQfXUx1/4rNBP6+yGABVVQkJMTO01wgsantuHzyjhVsmCIKvMMhGjEr9N4oG2b+ybPrsV1iYKwQAo1GmotLF5zs/Z+X217hl8SByijNauHWCIPiCM/P0PW3+xGfv9A3xGqSD3Q4x4RGo9lPklRaDLmExBrR08wRB8AHeDeT613KJPhv0r+zUn/TsDRQXOTl8PBtVdxEbFsfvrnqEsGCRjkEQBM9kSUbxOJDrX3f6Hq+muLiYmTNnMmLECMaMGcOsWbPqzPtQVVXFH/7wB4YPH87IkSPZvHlzkzT4jEPF+3G6dCQJXJoTVJmcwhz+s2Vpk55XEAT/0Rq7dzxejSRJzJgxg/Xr17N69WoSExNZvHhxrXpvvPEGQUFBfPnllyxbtozHH3+cioqKJmk0wMlCB+juXNdto6NRUZFk6BTTucnOKQiCf5ElpbqL55zbeTyc5Qs8Bv3Q0FAGDRpU/b5v375kZ2fXqvfFF18wadIkAJKSkujVqxffftt0C5RP6jcBJFBVsLsqkSTQNSisKGiycwqC4F8Minv2Tn2bpzQNvua8frdomsYHH3zA0KFDa5VlZ2fTtu3ZtWnj4uLIzc298Baewzdpm9yBXocqu4Po8DBMFolyZ3GTnVMQBP/SGrt3zmsg96mnniIwMJCpU6c2VXu8diAts/q1ze6isrKEblE9uWvon1qwVYIg+BL3w1meZu/4V9D3+moWLlxIeno6L7zwQp1/CPHx8WRlZVW/z8nJqZWGtDEpv/q+CgkMJDIslKNFKQSYgprsnIIg+BcJd599fZtEK+vTB1iyZAn79u1j6dKlmEx1P702cuRIVqxYAUBaWhp79+7l6quvbryW/kbX2HbVr48dO8XJ4mISDZ0pqypvsnMKguBfzqRh8LT5E49Xc+TIEZYtW0Z+fj6TJk1i3Lhx/P73vwdg3Lhx5OXlATB9+nRKS0sZPnw49957L08++STBwcFN1vDjeTnVrwOt7sWLT1RmcDBzd5OdUxAE/6Kcnqdf/+ZfQd9jn36XLl04dOhQnWWfffZZ9evAwED++c9/Nl7LPFANldWvdVWiqlzj3n73Mlaund1OEAShLopiwuAh946iOJupNc3DZ7/CnJr7LyKwzdnPvsj8FFU7vzSogiC0XrIkeTF7p/7lFH2Nz6ZhcDrdme9sFWBQdCyBEvHRscjxgS3cMkEQfIUiKSgeZu94StPga3z2Tj8mNgj3F7CErUqjbUQCf+z6JJZQq6ddBUEQAJA8zNyRJQVJBP2Lg2Kyo5z+nRIYJHOyIotNJV+hubxf2kwQhNatNXbv+GzQLy5y4nKCOQCSOlpBh2WbF/OnNWIRFUEQvGOQTV5t/sRng36I1YLRBPYqnbTUMgJNFgyKkf25e7A5qlq6eYIg+AJJRjrdhXOuDT+bsumzV2NULMiyBEiUl6oYZTNO1UHXmB4YDf71zSwIQtPw1J9/ZvMnPjt7p6yyAqfD/VpTobC4jDZSMCOMwzyOxguCIADIyMge0izIvntvXCefvZrfDq6UVzpRFAOjhkw+72OlfvIjRfvEurqC0NrIXuTe8fSl4Gt89k4fSUOSwWgExQBmi8SI8OEYQrxfH7fg51TWjX0OW0EZEf2SuHnbU03YYEEQLjaSF903/jZl02eDvjU4kEzNhsMOsgsCgyQCO1o5ZaogEu9y/phCAglqH0lYj7b0+/P4Jm6xIAgXG0Uyokge0jBIrXgRlYtJQdHZbJqKAVRV560f3mDZ+mfQdd2rY1g7xVCSkkVZegHB7cRi6oLQ2rTGRVR89mqCgowYTn8BO+1gq9LoFtKTWyNvw1VUWf/Ov3LF83dQnllE8cEsz5UFQfAr7qDuqV/fZ8NknXy2e0eVXKius+9tlXCZejnRJaEYI7xfSKX7tOvoMvlKFLN//YQTBMGz1tin77NfYacKnfy2F+e9k/8l4ubeXu1fnlHIN9NfZc/iNfzy/NomaKEgCBc7Tw9mVT+g5Ud8NuiHhBkJCIJfL2qjuVRsqUWU7/A8/bIsNZ+jK7aR8eUvpP1vZxO2VBCEi9WZefr1bz4bJuvks907gUEKmkaNu/0+8X1w5pbiyDxF8MDEevcPbheJYjHQ7XfX0HnS4CZurSAIFyNFMnrMreNvs3d8NuijyWhqzaD/c+5OlP4RhF3W7tz7nWYOCyJheB+2PvQOpUfzGDDvVlw2B5Iso5h8949FEATveZNmwd/SMPjs7xbFoGEwScgyyApIEoS1CUZ3qkiy51SoppBAhv5nFl3vvIaI/kns/9cGVg95ii9vfb4ZWi8IwsWgNfbp++wtrSQrqC4wWSTsVe7b/cz8fAq/PYxxQDKmmLPrKGqaiqppGA1nf6Z998AbGALNXPGPO/j67mUcff972t9yGUmj+zf7tQiC0DJE7h0f4rTrBAS6k62dERUVjCW0DYawmksmulQXTpe9+qGtypwSXJV2dNW94EpwUhQA0Zd2whIeRNbGfc1zEYIgtKimeDjLbrczf/58brjhBsaMGcPf/vY3AFJTU5k4cSIjRoxg4sSJpKWlVe/T0LIGXfMF7d2CNE1DMcg1nr49ebKcTfYfkE01v7kNihHjr1a8X3P901RkFtHnkZsAiB3clcC4UNrf2Jef/rqCnU9+3DwXIQhCi1Jko8cFVBT5/AZyn3vuOcxmM+vXr2f16tU89NBDAMyfP5/Jkyezfv16Jk+ezLx586r3aWhZQ/hs0O+QEEdZiVrjAS0AyVHXcok6LtWFerryZc9MQjYofNTzUb4YvYig+DBGrnmMH+d+wLWv38PwlX9o+gsQBKHFSShebd6qqKhg1apVPPTQQ0inMwFHRkZSWFhISkoKo0ePBmD06NGkpKRQVFTU4LKG8tk+/az8/BpdO2cGdOVTzlp1ZVnBbLIgSRLpq3cSPagzgbGhfDF6IVlf7eWnx1dgCDCRtWkfA5+cQEB0SDNeiSAILeV8Zu/k5ubWKrNarVit1ur3GRkZhIaG8vLLL/Pjjz8SFBTEQw89hMViISYmBkVxH0tRFKKjo8nJyUHX9QaVhYeHN+iafTboKwYwmABdwmQGVYWE2ChGdb2pzvqyLFORV8I3M14D4I6cfzE18xW+vnsZkX2TKM8spOOtg4jo7Xm6pyAI/kE6nXvHUx2AKVOm1CqbNWsWs2fPrn7vcrnIyMigR48ezJkzhz179nDffffx4osvNm7DL4DPBv0qm4quuX8+2W3uZSwVWceZUwZ9a9fXdR05xMSAZ2/np0eWo2s6itnIsOWza1cWBKFVkJCRPPRynylfvnw5sbGxNcp+fZcPEB8fj8FgqO6OueSSSwgLC8NisZCXl4eqqiiKgqqq5OfnExcXh67rDSprKJ/t01cMOooCilFH03Wcdp2jaSeRdBnboZO16kuShMloQi13oNqclGcU4rI5yNq0vwVaLwjCxUHycoPY2FgSEhJqbL8N+uHh4QwaNIjvv/8ecM+8KSwsJCkpieTkZNasWQPAmjVrSE5OJjw8nIiIiAaVNZTP3unLuoTDoWO2SOiajq5DSHAguHTkNuZa9XVdx+ly0uWuqwnpEI0p3kr5yRK+uPH/GPP1PGIu7wLA7kX/o3B3GsPef7C5L0kQhGbWFPP0n3jiCf7yl7+wcOFCDAYDixYtwmq1smDBAubOncsrr7yC1Wpl4cKF1fs0tKwhvAr6CxcuZP369WRlZbF69Wq6du1aq85LL73E+++/T3R0NAD9+/dn/vz5F9S4+uhASLiCruvouo6tEkorbPwib+XquLur61XZKtDRkSV3XdlipP2YAaiqCykkmOveuo+oSztV11dtTlyVjiZrtyAIFxMZzx0e5xf0ExMTee+992p93qlTJ1auXFnnPg0tawivgv6wYcO488476xzI+LXx48czZ86cRmmYJwFmM067DVXVcdjcn2mahhJqrp4qBbh/memgOl24Sm1oVS4M7aPR0TEEmek0aXB1/eIDWYQmt2XAvFub5RoEQWhZ0un/PNXxJ159hQ0cOPCCBg6ags1px+nUcTp0NO30QK4i0dnRr7rOmV8Bsqyw/ZH3yfxiD6sG/JXcHw7jdDmx22w4nGfv6ve+8Dnf3L0M1eGq65SCIPgZCRlJ8rD57tBnnRq1T3/t2rVs2bKFqKgoZs+eTb9+/Tzv1ECqqiNJoGmABIoCISFmTJfUHOAwKAbsFTYufWEKaDphPRII69MO1e7kl2f/R99Hx0Kkewxg0MLJ9P7DjSLLpiC0GmcHauuv4z8a7Sts0qRJbNy4kdWrVzN9+nQeeOABiouLG+vwtcSEReCw6e4HtHT3FhwQwI7MFdV13DN2LCgGBQlQ7S7C+rVDNso48yuI6t8JuY2pOpWDOTSIsOS2TdZmQRAuLq1xEZVGu5qoqCiMRneOiiuvvJK4uDiOHDnSWIevpajsFJLkTqksy4AEJaVVlDorqHKeqlFXMRlxllRhlA0YDSYkScaSGErC2Dom9AuC0IrIXm7+o9GuJi8vr/r1gQMHyMrKokOHDo11+FqiQkJoE+puvq6DwSDhdLkotOez5eibNSvbNCSTQs6Wgxx95zu+mfoKiqQgSTJGo6nmwK8gCK2I5PE/f+ve8arz+umnn2bDhg0UFBQwbdo0QkNDWbt2LTNnzuTBBx+kd+/ePP/88+zfvx9ZljEajSxatIioqKgma3hRWQWWQCgrcadgMJlkrhrcjhBrAHrV2aRruq5jDLaQ+v4O7EUVWMICsReUo0gyBoOCLPvXt7ggCN47nydy/YVXQf/xxx/n8ccfr/X566+/Xv36Qh8YaAiXC8yBMqpTp6pKo6jISVRSe9pYQtEqnciBRhxOG5qmUbI3kx5/HIE5NIiuU64mf386bTpFERAQJO70BaHVEgO5PiMmPBLNpWOv1NB195TNn/dk8nPKDroe64ersBKH04EsKSiKkUueGI85JAgAe5WNjNU/I8kyh9//jtLU/Ba+GkEQWoK7A0f2sImgf1E4kZdDednpBVQkaNc+CKvVTEVABUpEAFJsAC7VgUtzVa98c2btXMVkoNfsUTjzyvlm+msc/+8PLXUZgiC0oMbOp+8LfHZCekRwOPnWSuxVOrIsoWk6NpuLtJMn2B26gfZbLifiig7IyLhcTmz5pUi6RGj7GGynKtDNEoEJ4dy+ZxHWjtEtfTmCILSA1tin77NXExJiwWSSQZdwOaC40MFNw3txSY8ElEAzx97diiwpqLoKuo45sg2m6GCytx7kv73ncOjVzbhK7dgKypCNBnRNo+RwTktfliAIzcjz3B3PaRp8jc/e6QcFGHE63SkYACorXWzbcZzR11xJ3163of9DRdNPL60lKeBwIpkVwvq34+aD/4fuVNn7wufsemYVA5+8nbRV2yn4OZVbtz9DRJ/2LXdhgiA0H0lyDwh6quNHfPZO34AFo1F3P417Wm5eGccyMyipzMZkDTjdl+/OuGYKCgCgKv8UORtTMJrN9Jh5PYFxYRx4fSPFR3IYuOA2QrvFt8j1CILQ/Frjnb7PBv0DGccxGJXq2VSSLNGnZxybt+3nrU8XItk1jAYzMjI6Ok6XA7PJQv4Pxzj67ncc/NdGguLD6HHvMOKv64msyOx45lMq8kpIXbWdlZf+GVthWctepCAITcrzzB3/S7jms1eTFBOP0SShmAAJdE1nb4q7T94QEULuD0dQFPfDV5pTBR00TSXuumRirupWfZz+f76ZQX+fhKu0Ct3uIv+Ho0gGGYPF6Hc/6wRB+C1vZu6I2TsXhcyCPCQk9DNZkE/HZ5PRxPQhj5Lz7m5ir+mGy+lCd2pUZBVj7RiDyRpAVP8ORPftCEB5ZiEfD36cDrcNIv6aHnS8dRCyIpM0ekDLXJggCM3G/WiWp3z6/sVng35omyAKT5XisGtIgNEsk9w1ksS2kbh0B93uG0Z5RiGH3vmGoPYRJI0ZgMFgABfEX5lcnX7h2H9/oCr3FPHX9aTH9KEte1GCIDSz1vdErs8GfYMsU1WhoanuXhhdg5y8clIOFvDA5Q50XcPSNpROd1zJzrkfERQTRtJN/ZFkjeJj2Rx6dTPRl3ai8+2D0Vwa3aZe3STtLDtRQMnhbBKv79MkxxcE4QKcTsvusY4f8dk+/fySU6iqiqyArLgzbQYGGlA1nX98+iyyrGA0Gtnz5Coue3EqMUO6u3fUdapOlnLwjU1se/Q/BMaG0OfBUbiqHOT9dLT6+Flf7+eNiOmc/Pn4BbVzy6Pv8r8bn6EwJfOCjiMIQuOT0JF0D5ufRX2fDfoul4q9yn2Hr+vulbSKi92L5QYag9B1DV3X6DBuILkbU5BkCe30pP7AUCuuSgeJI/vwzQNv8EHyw+x85lM+vXo+RQeyKNiTxi//XEd470QCokIuqJ3d77gWS3gbNJvzgq9ZEIRGpnm5+RGf7d5xuVygS+51cFVA0iktcyJJMO36+3GoNmRNIemWy9A0FVlWUFUXLtWF5nQhyRInd6Yy4G+3EhQbSve7hxAzqAvrJzxP6fE8zGHB9J87juDEiAtqZ4cxA5ie+zpHP/6BA+9+wzUv3AWApmrIis9+5wqCf9Bx3zV6quNHfDbqODUVp/PsV/CZVbTMRhPhlkhkFEwGM5qm4XQ60TQNg8GIK6+CVVfNQ9d0KnNKOP7JTwS3i2Tn3z9m68PvYDAbib6sM3dlLaPP7FGN1t68H4+Q+r/t6JpGWUYBr4dPI+XNTY12fEEQzp+ke7f5E58N+gEmEwbT2fdnptTbnQ4+3vE+AZYgFMVwelEXU/VsHcVsxBR6OsVyUTkndx6nPKuQwLhwnOU2Cvee4LL5Exq9vVcuuoM7j70MkkRlTjGdJ1xBRO+GpXtwlFbisjkauYWC0ArpXm5+xGeDfkWVHaft7PuAgLMPUOw/sRuXy4mu60iShKIo1QulVJ0sxZZf6q6oSEiKzM/PrAIg8cZ+DHnzPsJ7JbBr8WqOfrSN3G2HG9zGwn0ZfD/nP9UBWpIkjn38I/+98m90/X9XEnNpJwBUh4s9//ycyrwSnBU2bEXl9R53xcC5fHnHSw1ulyAIp+m6d5sf8dk+fbPRjGKw4zx9w1tRoWIwugN7j8R+OF3O0wG/5iW2aReJtVMMkkmh+GA2mtOFtVMMrko7+T8dZci/7yVt9U5+/MsHmCOCierfgdFr/9ygNmZ9s59fXvqC3vffgDXJnb45YVgvBi+aSuzlXarrFX8uHRgAACAASURBVO49wZY/votiMZH+xS6K9mdwx+F/nvO4/R4ZTVDChY01CIJAq5yy6bNBv21ELNlZZdgq3X8jsiJhMRsIMAXRt6QDmq7icDqwyEqN5RBlo4K1Ywzdpl1HcGI4nw15kp73DWfwoqkMXjQVgH2vbMAQZMZVYSdhWO8Gt7HP70fSbcrVmE93J2V+vZ/0L3Yx+NnJSLLMiQ17OLT8O4a+di8Tdy4kvGcibdpFUJFbUu9xe913AyVHc1kzdiFXPf87QjvHNriNgtCanZmy6amOP/HZ7h2jUcdhP/uXoWvu+foRYUFkmLZTvP0Exl93+p9mCDQzavVjHHxzE1v/+B6aU2XvS+s4sW43AD/N/4iCXWkgQYfxlxF/bY8LaueZgA+QuWkfKW9swlnp/nlSfDCLE+v3sOXR9wjpHIusyMRe3pWtj/2HXc+vrve4toIy8n46iq1AJIUThAYTffq+IyIknN/03FBR4SK5c3tCwuP45aEVuEqq6lz0fM+StWRt3E/BrjRks4IpPJjgdpFUnSylTfso2o28BFeFA0tkMNEDOjZamwc9cTvTMpdhCrYAcMmDN3LF3/8f+1//iorMQsD9pdRx/GVE9k2qtX/m5n0cfPcbAGIv78L03NdrdBMJgnCeWmHQ99nunYqqKmxVNT+TJNibksn0aVezO+t1qrKLsUS2qbWvWuUEXUd3uhdZ6XbPVXw5YQmB8WGM+fJxku8eQu4Phwnt2ri59SVJwmCp+eujx/ShdLzlMixhwYB7/d6hr91b5/6/LF1P/o5jdL/z2kZtlyC0Wt4M1IqB3ItDXFgcsrQX7TeTaNNzM1AkK93njSFn3T5Ul0pk/w416gz4680U7TtB+pqdxF/Xk8uenkTC0F4YT9+BA8QM6sKeJWtJGNqrzrvuxnQm4P+arbgc2aAgGxQMAe4viuvf/j2uSnuTtkUQWhNv5uGLefoXicCAYMwW9wCuJEnIMgQFmrhmQH8yUreQMONy9ACJL0b+H86yqlr7J989hEF//38MeeM+3u/yIFmb99fov3eUVPDTvI848sH3qE5Xrf2b2qdDFvDpkAW8Fvo7tsz5D28l3oezrApzSCCb73+d7C0Hmr1NguB3WmH3js8G/dS8YzjsOppLR9d1wsPNuFQXfTv0Jjg4iq33vs3elzfgLLPx85Of1No/YXgfej84CsViRHOonPh8V41yc1gwkw+9QMbX+/h8/HPn3T7V6UK/gJ+F/R8bT+/fjyB52hCcpZVU5pZQsCcNR1kVR1ZsJX/HcRzlNtLX7fJ8MEEQ6qbp3m1+xGe7d45lZeI8k8NMh5JTdgwGiYAqK3FR3Wn3j/64yu0Ufn+c6EGd6zxG8YFMig9kMWHX/9XqawcIbhtO8u+uq9Ht40n2lgOYrIF8futiEq7rydDX72vI5dFt8lUA9Jg2FF3T6HH3sOqHuWacfANZkdm1ZA1bH/sPE3cuJFIs5i4I503Ci+6dZmlJ8/HZoB8VFI/RWIDT4V4f1+WEHl1juSphHJaEUHSXRnBIMOFTzj0Yu/u51Rz54HvMUW2QdRj23izir+tZo07vB0YAoNqdHP/fDjqMGcBXv3uZpJsG1Dmg+uUdLxHSJY5uU68homdio1yrJMvEXNqJn55ciaO0iqsW3wlAj7uHEtIhWgR8QRC85rPdO/079sFglKufwpVlOHw8l/2V2yn69ihFW44iS/VfXnBSFB3GDaT7764lsm9SrZk+B9/9hpVX/BVnhY20tT+zYfKLHPrPdxQdzKYyr+4HqEav/jNDX7uXQfMn0Pm2yxvnYk8rzyik7ERB9XtzSCAdx18GQPaWg2z983I01c/ywApCU2rCNAwvv/wy3bp14/BhdyqX3bt3M3bsWEaMGMHdd99NYWFhdd2GljWEzwb9KoqQFXC5dIwmHZNFol1iG2yOTHZNfhtJknCq585hbyso4/C73+KssDPoqUmMWj2H8F7tatSRTQb3zBlJImnMAG5a9Sd2LlpFYHQI/f80rs7jRvRKrE650NiGvn4foz56pM6yjI172bfsS5zltjrLBUGoQxMN5O7fv5/du3cTH+/uadB1nT/96U/MmzeP9evXM3DgQBYvXnxBZQ3lMegvXLiQoUOH1vjG+i1VVXniiSe4/vrrGT58OCtXrrygRnlDdwSiazrouLt4JCgscPD5rh10emw4OR/vQYLqhVN+K++HI1RkFNLnoRvPeY6uk65k/MZ5fNj/Mf438u+0G9mPa5bcxWXzb6Mip/iiuqu+bN5t3JWxDHNIYEs3RRB8hqTpXm0Aubm5ZGZm1thKS0trHdPhcPDkk08yf/786odD9+7di9lsZuDAgQBMmjSJdevWXVBZQ3ns0x82bBh33nknU6ZMOWed1atXc+LECTZs2EBJSQnjx4/niiuuICEh4YIaV58fj26rHshtE2yke3IEu3fnk+o4hrmrFVe5HU1TcThtWMy1A+H3D7/jTqjm4YlbSZIwBVnI+f4QVSdLSbppABU5xbzbcRaX/u1WBv7llqa4vPMmSVL1k76CIHjpPBKu1RUDZ82axezZs2t89uKLLzJ27FgSE8+O6eXk5FTf9QOEh4ejaRolJSUNLgsNDfX+On/FY9A/8w1Tn88//5wJEyYgyzLh4eFcf/31rFu3jhkzZjSoUd7oFNONnT9nAVBa6uSXvXkoBonB3QcRf3t/irelUl+qpPCeiZzclYq9pAJzWNA5arlN+OlZ0tf+TO7WQ4R2iePHJ1fS9+Gb6DD20sa9KEEQmpc3UzJPly9fvpzY2JrJDa1Wa433u3btYu/evTz66KON2szG1Ch9+r/9NoqLiyM3N7cxDn1Ol3a4gpCwszn0NQ369+zK/YPn4MwpJ+yKDiCB4bcJek4bvuIh7IVl7F70mcdzyYrMvte+ZMuj71KWUUDmxn10nXI1Eb3OfpOXpuXzzew3PebCFwTh4uFeLVGvfztdNzY2loSEhBrbb4P+9u3bOX78OMOGDWPo0KHk5uYyffp00tPTyc7Orq5XVFSEJEmEhoYSFxfXoLKG8tmBXAMugkPPNl8xSHTrGE3G8Z84tnRz9YNRqqbWub9iNjLk7Qfo89BNXp1v+LuzufW7p0i6sT/3FL1VazpmwZ509r/2JaeO5TXwigRBaHaNvDD6Pffcw5YtW9i0aRObNm0iNjaWN954gxkzZmCz2dixYwcAH374IaNGuZdj7dWrV4PKGqpR5umf+Tbq06cPUPvOvynYsVGUdzagG006+44dp/N7O+GIihJooPcTtyLV82hFp/OYUmkJr50f54yiA1kkDu/DjII3MbUJ8PqYgiC0LF3T3RNCPNS5ULIss2jRIubPn4/dbqdt27Y899xzF1TWUI0S9EeOHMnKlSu54YYbKCkp4auvvmL58uWNcehzCggIxGwIoJIKjCZolxiC1SqTNOBy5HcsyIEGj/P0G0PVyVI+7Pso/f44hiuemdzk5xMEofHoqobuYRaep/L6bNq0qfp1//79Wb267nUyGlrWEB6j4tNPP80111xDbm4u06ZN46ab3N0hM2fOZO/evQCMGzeOhIQEbrjhBm6//XZ+//vf1xi5bgrlzlLsTgeSLKEoEpKsEx4YSXbgPkq+ykBKa56eq4AoK9e9MpPku4c2y/kEQWhEuhd5d1pbauXHH3+cxx9/vNbnr7/+evVrRVF44oknGrdlHsTSiaoqd/ZLxSgRFGjh9qRHyOj+M5UrColJ6w79mqctPaaLgC8IvujMYK2nOv7EZwdyzYZAOiVFERSkEBtv5lSJg+Wpz1DlqiTyxq5wlc9eWoNtvu81/nvV35rk2AW/pLPi8j9TdCCTshMFVOafapLzCEKzauSBXF/gswnXQkqi6NI1hIhYiWPHC6kosyM5FfquLiF7yw7ix/aDp1q6lc0rsk97jE00kKy5VJwVNjSnyifDFmBNiubmL+c1ybkEobmIO30fUqJnUVhSRmFJKcFtTn93qRC4y0KnL66nw8PXtGwDW0Cv+28g7qpuOEora5Ud/eQHlvd5hKqTtR8b90Z0/45M3buEyD7tuWbJNAYtuN2r/XY89xlvdXoAV9XZFb/87R+R4MNaYT59nw36FYGnCAqw4HRqXJLUh6sGt6N791iMXYLRAlyUm1rffPmCPel8futi9r+5qVaZIcCMJaINkuHC/8o7jB5A/JXdPdZzVtgI7RRL3ODuyEb3F/NX9yzj3R4PXXAbBKEx6JpePYPnnJsI+hcHh82BS7VTValxsjyP8nI7OhLa1SBLCm2MsZ4P4mei+iYx5n9zSZ42lKOf/EDRgczqsqRR/bjm+bs4sWFPo52v+HA2O579hLKMglplB5d/x7LwuwjrHs/I9x5ENrifnk64pgedbx7UaG0QhAtxZp6+p82f+GzQV8oCMZuNJCWGERIUzO5f8kg9UQj7NQK+CcWl2Ukv+xGHWnt9XH+l6zpBiRG8ETuddZNe4Kcna2Y7/fn51WycuazRulf2vfol2/72IRt+91KtspiBHek5YxjBCRE1Pu8+9RqufObcyfsEoVmJgVzfYYyHqiwHhafK0JwGTCaZ/t0vIX7CAKKt3Ukt2wrolDpziFTqz6TpD0rTT/LF7f+gNO0kcVd0I/f7Q/S6ZzgAqWt/Ztvj7zPqw0e4fMHt1ele63L0kx/I3Lyf616a7vGcg56YSESvdkT1TapVFtatLUOXzmzw9QhCs/BiINff5un77J2+Q7cRFuS+iyytLEdC4pMvN5NRkEKlq5hgQxQWOZQQY9Omg7hY5G47RP7Px0ka1Y9bNi3gnpJ3aHtdT97v9yjf/fEtJFnCHBZESKf6u732vPwFe/+1nqJDWfXWc1bY2LlwFYnDehPVr8M56x3+aCsfXPoY9pKKWmW6prHx3mUc+2y7dxcpCI1NPJzlO3SHRlaBO5Nnm4AQwhLjCCgJQDK5SM3eSmCYOwtdiSODqIAuLdnUZtF10lUkDutDQJSVNbcsInZQVypyi6nILsJWXMGoFY9gCQ9mzS2L6Hzr5XSfUvfspmGv3kfK25sJ8bD6V8nhHHb836eEdokl+c7rOPzRVhKG9CQwKqRGPdmgoFhMda4urTpcpH7+M0Hx4XQaJ9JUC82vqdMwXIx8Nuif3J+P3enAaFAwGhRCrIGMunEo2lEnkREdqQwrRpYUDJIFTXchSz57qV4LiLKSt/0oeT8eJaJHImlrd2IrLGfUikfofPMgVLuT0rSTVOWfe9pm5tf7Sf7ddShmY73niurXgWlp/yK4bTglR3P44v8t4YonJ9F+RF9cNgdtr0oGoPMtg+h8S90DtwaLiRkZrzX8ggXhQnnTZ+9fMd93u3fa9e6Ky6XhdKmU2YsYmNwTg25BSbAQkBgO6Oi6Rm7VPors6S3d3CZR8Es6VYVlNT4rO1GAanfS4+6hjP7kserPwJ1OevLPz9Hv4dF1Hq+qoJSvZ/2blDdqT/msS3DbcABCO8dx66YF9H3wRtbd8SJrbrmwLICC0Fx03YvZO37WveOzQb/KUUp4aBAul8bJgio27NyAs8KF/c1iivVUZElBkiTaBvUl1NS0yd9agup0sWLQXL5/7L0anwdEWhm37nEcpVVoqsbt256hzwMjvDpmQKSVKb88z2XzJ3jdDltxOVUFpSRc2xNTmwDCeySgWOr/lfBb2+Z/yIF3vzmvfQShUbhXUfGwtXQjG5fP9nnkajmoqoYiydhtLg4eLMIxuBK5g4Kh0IIjohKz3IZgY0yzpFhuTqrdyfHVO7jhvQeJ6NWOwv0ZrLl5EUP/dQ+f3/4PZKNCUGwostHApO0Lz+vYYd3Ob+D7w4FzKM8s5J6Tb2KyBjJ65aOozroXrjmXo5/8SMyATiTfee157ScIF6q58ulfTHw26EcGxVN0qgJJgcAgGXuFwtrvv+GGL3qhdXEhRyo4tAo03YksmVu6uY0qdc1O1k1awuhPHyO8e1v+e+08KvNKMAaZ6XL7YGyFZQyaNwFN1Sg+lH3egfx89LhrCJlf70MJMAEgyTIG8/l9yU7d8w8k2b++mAUfcR5r5PoLn/2XdiDrR0LaBKCqOqoKlgAJa2oAwbYIQi9th4xCQlB/FMnU0k1tdB3GDmT0qjm0H+XOHd3n9yO5Zsk0Yi/vytB/3cONH/2RiF7tyPjyF/7T8w+UHM1l473L+N+YZ2sd61RqPh8Pmc/J3WkNastlf7uNWzYuQDE2/P5BBPyGsZVUsO/NjahOl9f76LpO0cFMzxVbCV3zkIJB1dA1/xrJ9dk7/TJHCeEhwZwqrcRkkggPDeCqH3sS/3Y/qrRTWAyhZFbsJC6oFyGmti3d3EalGA10GD2g+n3X2wfXKC8+lE1AlJXOE65ANipYO0QT0iEGU5Cl1rHUKgflGQU4ylrPk8v+wFFexTePvEXK25sJ69aWtl7kQgI48N43rL/rJSZu+Tvxg73bx5+JKZs+xOQ0oygyZpMJo1GjfVwsYd2SaBMUS6WzEJduJ9gQRbCh/vnm/kbXdZb3eRhLRBtmZP+bvqcXfh849+Y664f3SOB3R5de0DmLD2Xx4aA/c8M7s8V8+2Zy9NMfSXl7M9cumeZ1wAdIGtmPq56dSnS/DjjKqsj+/iBJI5tptaGLUGvs0/fZ39V6kIbN7sDudOJ0qaTmZOHo4sCeW4ZRDgQkzEobFPn8ZpL4OkmSCIgKwVZY3mznNIcH0254H6ztI5vtnK1d9/93NbdtXEC/B2/yWDf9qz18+6d30HWdwOgQLp1zM4YAM3v+tZ5Pb3yagv0nmqHFFyeRZdOXuCRyTp5dvcnhUKn8IYfsZ3ZhkC2Aju5vc628dOfhl5ie2XwPPQVGhXDTykeJ6nvudAznkvVtCj8983GDzuuyO9k8+9/k/nSkQfs3Bs2l8l7fR9i+8NMan+9/exM/Pv3fJjuvbFBIHNK73jxK9tJKynOKydi4l31vbsJV5ahR3vue4Yz55DEie7ZrsnZe9DTNu82P+GzQrzh1ioiQIIyKgqLIXNvtOiI6dCLyua5UVhUC4NJsLdzKlmEMNBMQZW3pZnjl6Kc/snPRZ7jszvPe115UTspbm8n54XATtMw7kiwR1i2eoLiwGp+nb9jN4f9ubaFWuW2Y9jLL+z/Klc9M4Z6s1zEG1pzFZgkNovP41p3mWneqaB42/TynIF/sfLZPf3DEGL6p+oQAyyl0ICDYRND14eTbjqPrGhFSe+JD+7R0MwUPrn7uTi6ffzsGD2kfDn+0lW3zP2TCt09V5/cJigvj3oK3MFhaboaWJMuMXvForc9HLX+4xWd99HvoJjrfPAhJklr0z+hidqZ7x1Mdf+Kzd/oZ+iEqHeW0DW9H2/D2JEZ2QGujoeoONFyUOnOR/OyhLH8kGxROpebz78R72PfmRr7/6/s4K+216plDAglOiKg1NfS3waxg3wk23PUytuLmG9OoiyRJyIriVd3iw9l8++g7OCs8/zItPpzt9RTNhGt6kjxVPPBWL9G94zs6hfZDliWQJMKDIzCZFMpd7m4dg2ShfZvLWriFgrfW3/lPXJUOTh3PY+eiVRQfzq4uS3l7MwV702k/oi+3fjkfc2hQvcc68eUeDn+0lcq8U/XWawy6prFm4mJS3v0agA13L+Wre5ed93Gyvkvh5xfX1LkCGcDmh95g4/2vciotn7eTH2Tnc59dSLMb5MgnP5C99WCzn7epiZWzfEjX4AHcMmAGdqed7KJM0guOYpItBMqhhJiiKXVmez6IcFHofMvlAHS9/UqGLJ3Jisv/TGFKBi6bg033v8bul77g7S6z2Pr4+2yb/yE/PrWy1jF0XWfbghVsmfMfekwbQnj3pn82Q9d0ig9mUZ5dBIAh0IQxyLunv102B7v+uZYPLp9Lwb4M7st7k/DuCXWfx6WhOVWs7SK59h930XXSlY12Dd7a/OAbDR5wPxdNVfnp2U8oTMlo1OOeD/Fwlg8xxrVBK9EpqShE01XKM0voFN0TqykGg2xB11XKHfkEm1rXPH1f1GXCFeT+eISCPWkERFnpPvUaguLCMFhMTN37PIGxoXz7yDtE9GzHwfe/pfhAFr1mXF9j8NRRWsnO5z6j/ci+9HvI8zTGxiAbFO7Ys6T6/dCXvV8p7MB73/D1H94k4bpehHSMwRLepladT298mrKsQqZsX4Rico959P9D3RlSm9qkrc9gDLLw85LVZG87xOiPzo5jlGUU8MHlc7nuxel0ve0Kr49ZmXeKrfM+QFc1Inq0TFJEXfMc1EXQv0jsSd/MF8feQtPdI+sxlgRcmpNSRz7h5gSQdOxaJcEt3E7Bs8he7bh53eO8FnM3QXFhOMqqcJyqxBIWTGjnOACuf+0+d2UJ1k15kZ/+/jFDXp5RfQxzSBB37FuCrmrV+zSH42t3sPH+15iw6Qmvz3sqLZ+v7l1Gn3tvYOjSmedMQ3EqNY+KnGI0p1od9FuKtV0UAI6yKmy/SedtDDIT3b9jrRlMngTHhzM9dRlBsaGN1s7zpbs0j7NzdJd/BX2f7d4xGSTahrTDYnT38WZXplFZ6cCl26r79lvpNH2fddvXT+Ior6Iy7xSysfYg6Mlf0ml7bQ8iL0mi3fCaM7Mc5VV8NvpZ3uv9yHnlorlQR1Zuozyz8LwGjq3to7jhzd9z2V9vqzfv0OTtiwiKDSPlna8boaWN4/J5t3PbxidqfGYJb8P41X85ryeDz2iTEIFs8G7Auyl47NrxIk2Dr/HZO/12Qck42pWzZo87n3ywuQ2WAAWzHIxZDgSgjeja8SnhyQmM/mQOaBrBbSNqlJWm5fN+v0cJ7RxLadpJyjMLa5RX5JRw6mgu3aZcfUHJ385H8ZFsdF3nhrdmkbFpH18//BYTv33aYwI5SZLoeddQj8eXjQbCk9ue9x20cB686N7xt9k7Xv3rSE1NZe7cuZSUlBAaGsrChQtJSkqqUeell17i/fffJzraHWj79+/P/PnzG73BZzh0B5nFx6rfhwbEYJCNGGQjRsWdWMyp2TEpgU3WBqHxRfVpX+fnbdpF0nHspcRf2Z0e04bgKK2kIq+EoBh310BYlzjuK36n1gNIv1Z0MPOcg6UNcXJ3Ggff/44Bj4wle+tBAiKtUM8TsufLYDYy9tO5jXY8oQ6aDmrrSq3sVdCfP38+kydPZty4cXz22WfMmzePd999t1a98ePHM2fOnEZvZF0O5P7AnuyzTzw6NRsaKk7Njq7rSJKEQy0nyCjukvyBJMuM+fSx6vcfDHyMoLgwhi67t/qL4tcBP/WLn9k063VueHMWCVcnk75hD5/e+DRjP51Dp3HeTect2HeC4//bzsA542vMud+2YAVtr06m64TBdLixP8YgC1GXJHHJ/SMb6WqF5uKekikezqqhsLCQlJQURo92zxoYPXo0KSkpFBUVNXnj6tM9biDDkm+hZ9uBWC3hJIR3JlAJo40x6nQNiRBz0y0eIrSsIUtnUlVYxobfvVRnuTkkkIBIK/8dMo/dS7/g+Ort9J55PW2v6VHvcW1FZdXrDh/95Ae2LVhB1cmzC8mrDie7/rmWY//bDoCxjnTVgu8Qffp1yMnJISYmBuX0nY6iKERHR5OTk0N4eHiNumvXrmXLli1ERUUxe/Zs+vVrupStZksgP+77igqH+x9occVJ2hgj0Tg7iCfReD+1hYtLhxv70yYx4pzl8YO7M/G7p9n5/GoSh/Zm698+ZMAjY7CEnZ3Ppakqa25bTFBMKMOW3QvAJ6OepjQtnzv2PM9lf7nVPTU09uyvRcVkZMaJVzEEiLQG/kB3ec6to7v8K/dOo83emTRpEhs3bmT16tVMnz6dBx54gOLi4sY6fC05JZnVAR9A01TcmTXPfCvrIg2Dn4vs3Z7I3jXHAPJ3pfJa3Ay2zV/Bu73+QO8Z1xPZsx335r7BZX+5lW0LVlCwz51KOG3dbo599hOFh86uJNVp3GVUnSwlb8cxZINCcHzNGxsAU3CA1ykWhIvbme6d+jfvu3eKi4uZOXMmI0aMYMyYMcyaNau6V2T37t2MHTuWESNGcPfdd1NYeHYyQkPLGsJjVIyLiyMvLw9VdX/bqapKfn4+cXE15yRHRUVhNLrnEl955ZXExcVx5EjTpbzdlb+hxvs+CVej4kTizD9GEfBbg/T1u/noqr+S88Nh/p14DwW/pBHdrwPBbcMJ6RiDbHL/mDVYTFTmlfDTMx9zfM0OANrfcAk3rfgjt6ybV328QX+5lXty3qDj6IEtcj1C82rs7h1JkpgxYwbr169n9erVJCYmsnjxYnRd509/+hPz5s1j/fr1DBw4kMWLF7vb0MCyhvIYGSMiIkhOTmbNmjUArFmzhuTk5FpdO3l5edWvDxw4QFZWFh06nH9+dW/FhiTVeB9kDEVC+dWdvoau+1dfnFCb6nDhKLdhCDQT0bMdEb3aMf7zv9L7nuHcsm4eZuvZ2VvBbSOYkfEalz42HnAvO9l1wuAaGT4/vfFpdr/0ebNfh9AydN3TXf75xZHQ0FAGDTqbrrpv375kZ2ezd+9ezGYzAwe6byYmTZrEunXrABpc1lBezd5ZsGABc+fO5ZVXXsFqtbJw4UIAZs6cyYMPPkjv3r15/vnn2b9/P7IsYzQaWbRoEVFRUR6O3HBXJI0lxBLJwbztFJRno0pOZDkATdNRMKPhIKdiL/HBlzRZG4SW13HMQDqOcf+DuHnd4x7rn5nieS7msCBMbQIapW2CD1C9mLJ5ujw3N7dWkdVqxWqte+0KTdP44IMPGDp0KDk5OcTHn51YEh4ejqZplJSUNLgsNLRhTzJ7FfQ7derEypW1k1y9/vrr1a/PfBE0F0UzYJCNJIR2IuvUMRyuChQpAkmWiA/oRb7tMKcc2cQjgr7gvRuXP9zSTRCake5U0Rz1P8F9ZqB3ypQptcpmzZrF7Nmz69zvqaeeIjAwkKlTp/Lll19eeGMbic8+kZt6ag+f7V2G2RCAqqkokgnQ0XQXeVWHCDW3pcieRpWrbCNoSgAADOxJREFUlACDb6wiJQhC8/Kmz/5M+fLly4mNja1Rdq67/IULF5Kens6yZcuQZZm4uDiys89m/i0qKkKSJEJDQxtc1lA+O9rZNrQro3vOJDn2Ulyag/TCQ0iSjISCU6+gyJaGqjvIKNvR0k0VBOEipauaO+lafdvpoB8bG0tCQkKNra6gv2TJEvbt28fSpUsxmdxTe3v16oXNZmPHDnc8+vDDDxk1atQFlTWUz97pS5JMl+i+JIZ1IcQSRefo3gAospFQYwcK7EcBCVV34tSqMMqin1YQhJrO507fG0eOHGHZsmUkJSUxadIkABISEli6dCmLFi1i/vz52O122rZty3PPPQeALMsNKmso3w36uo5dK0eXVZLjByCj4MKB1RhLTFB3qtQSKlwF7tk8/vUUtSAIjURXdY+pk3VPA72/0qVLFw4dOlRnWf/+/Vm9enWjljWEzwZ9XQMJA+AeZFEkA+BePlHXNcIs7aksLyEusCdGRdzlC4JQ25kuHE91/InPBn2zwYpLtSPpOrquIcsGNE2j3JmHaumMS7Wh46qRlkEQBOHXNIeKJtWfZkFz+FcaBp8N+na9FF3XUCQjSO4n1wIN4URYOmKQzUQEdMBqjsMoi4RYgiCcg6p77rM/j+4dX+CzQV9TXbh0G7JuQNNVNFRkzOga/7+9u4uNomzDAHzPbrc/UkotKbj7KX/mA6v1L5oYQ2rQLbTS3VYbfgxINE01RvRAE40noA0R5cRECB5oIkowURpDE7FBgoRgTUQPyLfGFiNVAtjSFlYsUMrOzvt8B9tuW1s6Q+3uzOzeF1nY3bzQmzZ5On3nmWeSPUks+EQ0meHuHbM1mcS1LZvDdycyJA41tK8/qP5C18D/IJJZ35mJKDVM2zUt7Pm7jWuP9HVjcOjZ2AKvywAG4n9hhm/8dEQiotFEWWjZzMbbJTrR8GA1maAf86r+N4s+EZmSuEA0s+6dzNo5cG3Rz/HmImZM3JlTxBuiE5EFKhaHUpN3+Kl4ZnUAurboa6N+jRzta5iRU4rcnBm2ZiMidxBDjRrHfv01mcS1RV83BiFQ0DQteeLWq3lR4OVwNSKyyBDTos+WTYdI3CFr7I9dShSKcnkzdCKyRuLmN0nhkb5DeD0+KKUjR8uFaEBMDUAg8Hhc24VKRGkmhoWiz+4dZ8jR8hDDFUDTABjweXLh4ssOiMgGlsYwCMcwOMKsvP9AXdOhq0EAAk1LFHylFI/2icgSMSy0bGbYxZ6urY4FOcXIQSEMiUGg4ENikua5gQhixoDN6YjIDW7kJiqZwrVFX1dXYWhXkO8tggc+xGQAIgpxDCJ6rdPueETkAhzD4CI5Wj7ytGIMqCg8Wg7iogMANAAqw34cI6IUUeYncmGy/eM2ri36mqbhb/0sBAbyPIUj+24akOflxVlEZE4MMe/e0TLrINK1RR8ACn1zcEnvxjV1eegdbeh31+5aEVEaxZWBuEl3Ttyku8dtXF308z0zcQndydcaNChRmOHj7B0iMqdEwTA50ldmV+y6jKuLfn+sBwBQgNm4pl0EAHg0D3I0n52xiMglFATKrOhze8c5dEm0ZsaR+NODXMwtKIPXk2tnLCJyCSVi2viRaY0hrt78Ls3/L3yemxDHNQCAQgxK6TanIiK3UKIsPTKJq4v+lfh56GoAHoxs51yO99qYiIjcRCwUfNOWTpexVPT/+OMPrF27FlVVVVi7di1OnTo1bo1hGGhqakJlZSWWL1+O5ubm6c46TqJLR8NMnz/5Xsy4mvKPS0SZIa5UooNn0kcWFv0333wT69atwzfffIN169Zh8+bN49Z89dVXOH36NA4ePIgvvvgCO3bswNmzZ6c98GilNy3GoqIKlBTMgwe5ALzI9czAZZ1H+0Rkzhjq3jF7ZBLTon/hwgW0t7cjFAoBAEKhENrb2xGNRsesa21txerVq+HxeFBSUoLKykocOHAgNaknMDtvIQADV4xenB88mXE/khHR9JOhE7mTPbJu4Fp3dzfmzp0Lr9cLAPB6vZgzZw66u7vHrQsERm5g4vf7ce7cuWmOe335OWPvmGV6NxwiynoKFk7kZlgtcXXL5miGjNxFa3beIni0jPmvEVGKWOnOybTuHdPK6Pf70dPTA8Mw4PV6YRgGent74ff7x63r6urCPffcA2D8kX+q5XpvwoKZS9P28YjI/YyhE7mTrsmwgWum2zuzZ89GWVkZ9u/fDwDYv38/ysrKUFJSMmZddXU1mpuboZRCNBrFoUOHUFVVlZrURETTgCdyr+Ott97Cnj17UFVVhT179qCpqQkA8Nxzz+Hnn38GANTV1eHWW2/FihUrsGbNGmzcuBG33XZb6pITEf1L2XhxlqWN79tvv33CvvuPPvoo+dzr9Sa/GRARuYFYGMOQad07PNtJRFkr0ZZpdiKXRZ+IKCMMt2yarckkrp69Q0T0b8SVAd3kYdbd809WxtbYiUWfiLLW8PbO5I8b296xMrbGTo7Y3jGMxHfSdF7BS0TuNFwnhuvGv3HJGzMt6le8+piPO1pRURGKikamAQyPrdm1axeAxNiaLVu2IBqNjmtzt4sjin5fXx8AYP369TYnISK36Ovrw/z586f0dwsLCzFr1izsQ6el9Xl5eRPWp5deegkvv/xy8vVkY2tY9EcpLy/HZ599htLS0uQni4hoIoZhoK+vD+Xl5VP+N4qLi3Hw4EFcvnzZ0noRgaZp494ffZTvFo4o+vn5+XjwwQftjkFELjHVI/zRiouLUVxcPA1pRlgdW2MnnsglIpomVsfW2EmTTLvcjIjIRp2dnXjjjTfQ39+PoqIibNu2DYsWLbI7VhKLPhFRFuH2DhFRFmHRJyLKIiz6RERZhEWfiCiLOL7oWxleZBgGmpqaUFlZieXLl084+9/ujG1tbaivr0d5eTm2bdvmuHw7d+5ETU0NamtrUV9fj++++85R+b788kuEw2HU1dUhHA5j9+7djso37Pfff8e9997ryK/xjh078PDDD6Ourg51dXVpvf+F1c9ha2srwuEwQqEQwuEwzp8/n7aMWUMcbsOGDdLS0iIiIi0tLbJhw4Zxa/bt2ycNDQ1iGIZcuHBBKioq5MyZM47KeOrUKfnll1/kvffek3fffTdt2azmO3r0qAwMDIiISEdHhzzwwANy9epVx+S7dOmSKKWSz5ctWyYdHR2OySciEo/H5emnn5ZXX33VkV/j7du3pz3XMCv5IpGIPP7449Lb2ysiIv39/TI4OJjWnNnA0Uf6w8OLQqEQgMTwovb2dkSj0THrWltbsXr1ang8HpSUlKCyshIHDhxwVMb58+fjzjvvRE5Oei+CtpqvoqICBQUFAIAlS5ZARHDx4kXH5CssLExeBj84OAhd1ye8LN6ufADw4YcfYtmyZViwYEHKc001ox2s5vvkk0/Q0NCA0tJSAMDMmTORl5eX9ryZztFFf7LhRf9cFwgEkq/9fn/aJnZazWiXqeRraWnBvHnzcMsttzgq37fffouamho8+uijaGxsxJIlSxyT78SJE2hra8Ozzz6b8kxTzQgAX3/9NcLhMBoaGnD8+HFH5evs7MSZM2ewfv16PPnkk/jggw8y7laFTuCI2TvkHD/++CPef/99fPzxx3ZHGScYDCIYDKKrqwsbN27EI4884ogrHXVdx6ZNm/DOO+84emDgU089hRdeeAE+nw/ff/89XnzxRbS2tuLmm2+2OxqAxLm5X3/9Fbt27UIsFkNjYyMCgQCeeOIJu6NlFEcf6Y8eXgTgusOL/H4/urq6kq+7u7vTcpR6IxntciP5jh8/jtdeew07d+5MWzGdyucvEAjg7rvvxpEjRxyRr6+vD6dPn8bzzz+Pxx57DJ9++in27t2LTZs2pTyf1YwAUFpaCp/PBwBYunQp/H4/fvvtN8fkCwQCqK6uRm5uLgoLCxEMBhGJRFKeL9s4uuhbHV5UXV2N5uZmKKUQjUZx6NAhVFVVOSqjXazmi0QieOWVV7B9+3bcddddjsvX2Tky9zwajeLYsWNYvHixI/IFAgEcO3YMhw8fxuHDh/HMM89gzZo12LJlS8rzWc0IAD09PcnnHR0d+PPPP7Fw4ULH5AuFQmhra4OIQNd1/PDDD7jjjjtSni/r2Hwi2dTJkydl1apVsmLFClm1apV0dnaKiEhjY6NEIhERSXRNbN68WYLBoASDQfn8888dl/Gnn36SiooKuf/+++W+++6TiooKOXr0qGPy1dfXy0MPPSS1tbXJx4kTJxyT7+2335aVK1dKbW2thMNh2b17d1qyWc03mh1dMlYyvv7661JTUyPhcFjq6+vlyJEjjspnGIZs3bpVqqurZeXKlbJ161YxDCNtGbMFB64REWURR2/vEBHR9GLRJyLKIiz6RERZhEWfiCiLsOgTEWURFn0ioizCok9ElEVY9ImIssj/AZo4nRJpF75LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(em_traj_100[path_id][0][time_start:time_max], em_traj_10[path_id][1][time_start:time_max], c = range(n), norm=MidpointNormalize(midpoint=diverging_time,vmin=time_start, vmax=time_max), cmap=plt.cm.PiYG, linewidths=0, marker='o', s=3)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot -  EM trajectories (10 trajectories)\n",
    "\n",
    "The bolded black line represents the gene expression level of an 'averaged' path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7kAAAGLCAYAAAACilVPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydeYAU1bX/v1U9K+uwDDAsLqhAlIjEgcGoaAATIyo+XzT6XCIkaIhi5Kc+NYJKxIUY44IrKIlJcOe57xqQRWXfdxjW2beetdeq+/ujqrpr7a7u6e7qbs7nD+iqunXvqZrue++559xzOMYYA0EQBEEQBEEQBEFkAbzTAhAEQRAEQRAEQRBEoiAllyAIgiAIgiAIgsgaSMklCIIgCIIgCIIgsgZScgmCIAiCIAiCIIisgZRcgiAIgiAIgiAIImsgJZcgCIIgCIIgCILIGkjJJYgUsGDBAtx1111OiwEAGD58OA4fPpzUNsrLy3HFFVdg9OjR+Oc//5nUtgiCIAgiXmh8JojshJRcIqtZv349rrnmGpx99tkYO3YsrrnmGmzdutVpsVLGDTfcgHfeeSfl7b7yyisYO3YsNm3ahBtvvNG0zOrVq3HDDTdg9OjRKCsrw5QpU7Bw4UL4fL6Uyvqf//wH5557Ltxud+jc119/jfPPPx+tra0plYUgCOJ4gcbn9B2fnebOO+/Efffdpzm3du1alJWVoba21iGpiEyDlFwia2lra8Pvf/97XH/99Vi7di1WrFiB2267DXl5eU6LlvVUVlbitNNOs7z+2Wef4fbbb8dll12GZcuWYc2aNXjqqadQU1ODqqqqFEoKTJgwAePGjcNjjz0GAGhpacFDDz2Ehx56CN27d0+pLARBEMcDND47R7TxOR2YPXs2VqxYgdWrVwMAfD4f5syZg3vuuQf9+vVzWDoiUyAll8haDh48CAC49NJL4XK5UFBQgPPOOw8jRowAABw5cgQ33ngjysrKUFZWhjvvvBMtLS2h+ydMmIBXXnkFl112Gc466yz86U9/Qn19PX73u99h9OjRuOmmm9Dc3AwAOHbsGIYPH4633noL5513Hs477zwsXrzYUrbNmzfjmmuuQWlpKS6//HKsWbPGsuyECRPw8ssv45JLLsGYMWNw3333haydzc3NuOWWWzBu3DiMGTMGt9xyC6qrqwEATz31FNavX48///nPGD16NP785z+H6vzuu+/w85//HGPGjMHcuXPBGAMAHD58GNdffz3OPvtslJWV4Y477rCU65tvvsHkyZNRWlqKG264AQcOHAAA3HjjjVizZk2oXeXvoMAYw+OPP45bb70VV199NYqKigAAQ4cOxZw5c3DSSScBAERRxMKFCzFp0iSUlZXhj3/8Y8jaqrzv9957DxdeeCHKysrw4osvhtqIdK8Z999/P1asWIGVK1fisccew9ixYzFx4kTL8gRBEET80PicnuMzABw9ehTXXXdd6D3OnTtX484d6f3ccMMNePrpp3HNNddg9OjRmDZtGhobG2N+t7169cLs2bMxZ84cdHR04LnnnsOQIUNw5ZVXWj4zQRhgBJGltLa2srFjx7L//d//ZcuXL2dut1tz/dChQ2zVqlXM5/OxhoYG9j//8z9s3rx5oes/+9nP2FVXXcXq6upYdXU1GzduHLviiivYjh07mM/nYzfccANbsGABY4yxo0ePsmHDhrFZs2ax9vZ2tnv3blZWVsZWr17NGGPs2WefZXfeeSdjjLHq6mo2duxYtnz5ciYIAlu1ahUbO3Ysa2hoMH2On/3sZ2zy5MmssrKSNTU1sV//+tfsb3/7G2OMscbGRvb555+zjo4O1traymbOnMlmzJgRuvf6669nb7/9tqa+YcOGsZtvvpk1NzeziooKVlZWxr799lvGGGOzZs1iL7zwAhMEgXm9XrZu3TpTmcrLy9moUaPYqlWrmN/vZwsXLmSTJk1iPp/Psl2F/fv3s2HDhrGjR4+aXlf4+9//zq666ipWVVXFfD4fmzNnDps1a5bmfd9///3M4/GwXbt2sTPOOIPt378/6r1WfPTRR2zs2LGsrKzM8m9BEARBdB4an9NzfGaMsauvvpo9/vjjzOfzsXXr1rHRo0fbfj/XX389mzhxIisvL2cej4ddf/317Iknnojr3TLG2MyZM9ktt9zCxo4dyyoqKizLEYQZZMklspZu3brh9ddfB8dxmDNnDs455xz8/ve/R319PQDgxBNPxLnnnou8vDz07t0bU6dOxbp16zR1XH/99ejbty/69++P0tJSnHnmmTj99NORl5eHiy66CDt37tSUv/XWW9GlSxcMHz4cV155JT7++GODXB988AHGjx+PCy64ADzP49xzz8XIkSPx7bffWj7Lddddh5KSEhQVFWHGjBn45JNPAEirnb/4xS9QWFiIbt26YcaMGYZnMGP69Ono0aMHBg4ciLKyMuzevRsAkJOTg8rKStTW1iI/Px+lpaWm93/66ae44IILcO655yI3Nxe//e1v4fV6sWnTpqhtNzU1AQCKi4tD52bNmoXS0lKMGjUK77//PgDgrbfewqxZszBgwADk5eXhtttuwxdffIFgMBi677bbbkNBQQFGjBiBESNGhJ7Dzr16Ro0ahba2Npx77rno3bt31OcgCIIg4oPGZ2ucHJ8rKyuxbds23H777cjLy0NpaSkmTJgQ0/u58sorcfLJJ6OgoAAXX3wxdu3aFfe7feCBB7BmzRr84Q9/wMCBA6PKTxBqcpwWgCCSySmnnILHH38cAHDgwAHcfffdePTRR/G3v/0NDQ0NmDdvHtavX4/29nYwxtCjRw/N/X379g19zs/P1xwXFBSgo6NDU76kpCT0edCgQdi7d69BpsrKSnz++edYtmxZ6FwwGERZWZnlc6jrHThwYCjwgsfjwWOPPYaVK1eGXLPa29shCAJcLpdlfWoFs7CwEO3t7QCAu+++G8888wx+9atfoWfPnpg6dSp+9atfGe6vra3VDDg8z6OkpAQ1NTWWbSr06tUrVMeQIUMASK5bAHDttddCFEUA0nu69dZbwfPhtTie59HQ0BA6Vv89CgsLQ3+PSPf279/fVK4HHngAU6ZMwVdffYWNGzfiJz/5SdRnIQiCIOKDxmdznByfa2tr0bNnTxQWFmqeT4mVYef96OVXj8uxvtu+ffuiV69eab+HmEhPSMkljhtOOeUUXHnllXjrrbcAAE8++SQ4jsOHH36IXr164euvv9bsi4mHqqoqnHLKKQCkDt0sQEJJSQmmTJmCefPmxVSvgrrexYsX4+DBg3j77bdRXFyMXbt24Yorrgjt4YmV4uLikFzr16/H1KlTMWbMGJx44omacv369dNMEBhjqKqqslQg1QwdOhT9+/fHV199hWnTplmWGzBgAB599FGcffbZhmvHjh2L2Eake8145513UFVVhZdffhkjR47EnDlz8N5771EQFIIgiBRA43N0UjE+FxcXo7m5GR6PJ6Toqp8vnveTiHsJIh7IXZnIWg4cOIDFixeHAj1UVVXh448/xqhRowBIK6pdunRBjx49UFNTg1deeaXTbb7wwgvweDzYt28f/u///g+XXHKJoczll1+OZcuWYeXKlRAEAT6fD2vWrAnJacbrr7+O6upquN3uUJAL5Rny8/PRo0cPuN1uPPfcc5r7+vbti6NHj9qW/7PPPgvJ0bNnT3Acp7GGKvzyl7/Et99+i++//x6BQACLFy9GXl4eRo8eHbUNjuNw77334rnnnsPbb7+N5uZmMMZw6NAhjZX22muvxdNPP42KigoAQGNjI77++mtbzxHLvTU1NXjiiScwb9485OXl4dprr0VRURFeeuklW20RBEEQsUHjc3qOz4MGDcLIkSOxYMEC+P1+bNq0SWN5jef9JOJegogHUnKJrKVbt27YsmULrrrqKpx11lm4+uqrMWzYMNx7770ApP2cO3fuRGlpKW6++Wb8/Oc/73SbY8eOxUUXXYSbbroJ06ZNw3nnnWcoU1JSghdeeAEvv/wyzjnnHFxwwQV49dVXQ266Zlx66aWYNm0aJk2ahCFDhmDGjBkAgN/85jfw+XwYN24cfv3rX+P888/X3HfjjTfiiy++wJgxY2ytnm7btg1XXXUVRo8ejRkzZuD+++8PuRSrGTp0KJ544gk8/PDDGDduHJYtW4aXXnrJtuXzkksuwdNPP40PP/wQF154IcaNG4c77rgDV199NS6++OKQ7BMmTMC0adMwevRoXH311bZzKMZy79y5c3HJJZeE9jdxHIeHH34Yr732Gvbt22erPYIgCMI+ND6n7/j817/+FZs3b0ZZWRmefvppXHLJJaF743k/Cp25lyDigWPx+k0QBBHi2LFjmDhxInbs2IGcnMTuApgwYQLmzZuHn/70pwmtlyAIgiCyHRqfO8cdd9yBoUOH4vbbb3daFIKICbLkEgRBEARBEASBrVu34siRIxBFEStWrMA333yDSZMmOS0WQcQMBZ4iCIIgCIIgCAL19fWYOXMm3G43BgwYgIceeginn36602IRRMyQuzJBEARBEARBEASRNZC7MkEQBEEQBEEQBJE1ZJ27stfrxfbt21FcXBwx2TZBEARB2EEQBNTV1WHkyJEoKChwWpyMhMZmgiAIIpFEG5uzTsndvn07rrvuOqfFIAiCILKMJUuWhFJNEbFBYzNBEASRDKzG5qxTcouLiwFIDzxgwACHpSEIgiAynerqalx33XWh8YWIHRqbCYIgiEQSbWzOOiVXcYMaMGAABg8e7LA0BEEQRLZAbrbxQ2MzQRAEkQysxmYKPEUQBEEQBEEQBEFkDaTkEgRBEARBEARBEFkDKbkEQRAEQRAEQRBE1kBKLkEQBEEQBEEQBJE1kJJLEARBEARBEARBZA2k5BIEQRAEQRAEQRBZAym5BEEQBEEQBEEQRNZASi5BEARBEARBEASRNZCSSxAEQRAEQRAEQWQNpOQSBEEQBEEQBEEQWUNKldz58+djwoQJGD58OPbu3WtZ7tNPP8Vll12GSy+9FJdddhnq6+tTKCVBEARBEARBEASRqeSksrGJEyfixhtvxHXXXWdZZtu2bXjuuefw2muvobi4GK2trcjLy0uhlKmFiSKaV61C0fjxTotCEARBEARBEEQciJ5WBA9tQd6PznNaFAIptuSWlpaipKQkYpl//OMfmDZtGoqLiwEA3bt3R35+firEcwR/dTWqFi92WgyCIAiCIAiCIOLEt/ETtL/3mNNiEDIpteTa4cCBAxg8eDCuu+46dHR04KKLLsKMGTPAcZzToiUFzuVyWgSCIAiCIAiCIDoDc1oAQk3aKbmCIGDPnj34+9//Dr/fj9/97ncYOHAgrrjiCqdFSw48xf4iCIIgCIIgiIyGiU5LQKhIOw1r4MCBuPjii5GXl4du3bph4sSJ2Lp1q9NiEQRBEARBEARBEBlA2im5l156KVatWgXGGAKBAH744QeMGDHCabGShuKGHWxrc1gSgiAIgiAIgiCIzCelSu68efMwfvx4VFdXY+rUqZg8eTIAYPr06di2bRsAYPLkyejTpw8uueQSXHHFFTj11FPxq1/9KpViphZZyT361786LAhBEARBEARBEETmk9I9ubNnz8bs2bMN5xctWhT6zPM87rvvPtx3332pFM1xhNZWp0UgCIIgCIIgCCIeGEWeSifSzl2ZIAiCIAiCIAgisyAlN50gJTdNoJ8FQRAEQRAEQRBE5yElN10gFweCIAiCIAiCyExoLp9WkJLrMEyUc2qJlFuLIAiCIAiCIAiis5CSmyYE3W6nRSAIgiAIgiAIIh44UqvSCfprOA25NhAEQRAEQRBEZiOnBSXSA1JynYaUXIIgCIIgCILIbHhSq9IJ+ms4DCMllyAIgiAIgiAyG9ldmXnbHRaEAEjJdR5ScgmCIAiCIAgio+Fkd2XfjmUOS0IApOQ6Dym5BEEQBEEQBJHhyHtyaW6fFpCS6zT0QyAIgiAIgiCIzIYCT6UVpOQ6DO3JJQiCIAiCIIgMJ5RCiJTddICUXKchJZcgCIIgCIIgMpuQJZeBiYKjohCk5DoPKbkEQRCEw8yfPx8TJkzA8OHDsXfv3ohly8vLMWrUKMyfPz9F0hEEQWQAsiXX8+XLaH7mOoeFIUjJdRpScgmCIAiHmThxIpYsWYJBgwZFLCcIAh588EFMmjQpRZIRBEFkCGpLrqfVUVEIIMdpAY53aE8uQRAE4TSlpaW2yi1cuBAXXnghOjo60NHRkWSpCIIgMggKPJVWkCU3TSi64AKnRSAIgiAIS3bv3o1Vq1bhpptucloUgiAIgogIWXKdRrbkMlF0WBCCIAiCMCcQCGDOnDl47LHH4HK5nBaHIAgi/aC5fFpBSq7TKO7K9MMgCIIg0pS6ujocOXIEN998MwCgpaUFjDG0tbXh4Ycfdlg6giCINIC2IKYVpOQ6jfKDoB8GQRAEkaYMHDgQa9asCR0vWLAAHR0duOeeexyUiiAIIp0gg1U6QXtyHYaRuzJBEAThMPPmzcP48eNRXV2NqVOnYvLkyQCA6dOnY9u2bQ5LRxAEkf54vn7FaREIFWTJdRqy5BIJoDXQipZgMwYVDnZalJhYsPcpnNP3pyjtXea0KARxXDN79mzMnj3bcH7RokWm5WfOnJlskQiCIAgibsiS6zRkySUSwGuHXsWjO+c6LUbM7G7diU1NG50WgyAIgiAIgsgiSMl1GrLkEgkgKAacFoEgCIIgCCJjYEIAzEf5vrMVUnIdhpGSSyQALgMTkDf5G50WgSAIgiCI4xTPf/4O95NXOS0GkSRIyXUaclcmEkLmKblVnkoAAJeBshMEQRAEkdkwT6vTIhBJhJRcp6E8ucRxCs/J3Q/puARBEARBpJoke8F1/GcxxDbyWnMKUnLTBEbuykQnyERrqCLzpqYNDktCEARBEMRxR4Ln3nlnTtIc+35YisD+dQltg7BPypXc+fPnY8KECRg+fDj27t0bsWx5eTlGjRqF+fPnp0i61BNyUyZLLnGcEbLkghZ5CIIgCIJIPrtuuglN334LABCaKhNbOc1l0oqUK7kTJ07EkiVLMGjQoIjlBEHAgw8+iEmTJkUsl/HIPwihvd1hQYhMZk/rLqdFiBm19fnF/QsclIQgCIJIV7x+AbsO0xyJSBze8nLpQ9Cf2IqZicEqAwODZgspV3JLS0tRUlIStdzChQtx4YUX4qSTTkq+UE7CGMDz8B48iKDbjYqXX0bQ7XZaKoJIOpzKkruvbY+DkhAEQRDpynur6vH/XtrvtBhEFuE5cCA5FZtZcsm66xhpuSd39+7dWLVqFW666SanRUk+jIHjpT+D6Pej5fvv0RHFjZsgsgEX51Id0UonQRAEYSQokJJAJJZgc3NS6mVMBLi0VK2OS9LuLxEIBDBnzhzMnTsXLpcr+g0ZDmMMXE6O02IQRMpRq7V+0eeYHARBEET6QkquswQObUHrWw86LUZCYcGg/CnBC+wMAK9Trchd2THSTruqq6vDkSNHcPPNNwMAWlpawBhDW1sbHn74YYelSwKyu3LoM0EcJ4ig7ztBEAQRmYaWgNMiHNcE9nyH4IH1TouRUJggJKliEeBcAIJRixLJJ+2U3IEDB2LNmjWh4wULFqCjowP33HOPg1IlEVEEp1isSckljiMoojJBEAQRDcUQxhgDR1ax1JON7zxJGU0Cu1clpV4iPlLurjxv3jyMHz8e1dXVmDp1KiZPngwAmD59OrZt25ZqcRyHMQahtTX0mSBiJVO/N4wsuQRBEIRNKNOiU2Sfkpvbr5/8ieYh2UzKLbmzZ8/G7NmzDecXLVpkWn7mzJnJFslZVAqKv6bGQUGITCVTlUWmC7V/sL0cJ3cd6pA0BEEQRDqTmSNd9pBNlvQeY8YAAFz9h0KoKU/cs+Xkg8vNA/O0qk5mxzvLRNIu8NRxh0rJDW2Ez1DLHOEMolletgyAgeHUbsNCx3XeWgelIQiCINIRZUok0tzIUYSq7Mv8wffsL31I0DwqZ8Cp4Lr2Nr3m/f5dtCy+PSHtEPYgJddpGEP+4MGhz9J/1JET9tnXlpkDDwMDp1rh5LNkhZggCIJIHH175gIgd2XHkMdmFszCAGDKfDtB827GBHAucyfZwP51EKqTlJ+XMIWUXIdhjCG3uFg5cFYYIiOp82WmBVTUuQdx1B0RBEEQOoaWFAIgAwCRBBQLbqI84kQR0Cu5yjSHFvJTDs0qnUYUQ3lymbJMScuVRCcRmYigmN4h7BkYeJUlN1v2+hAEQRCJQ3FTFknHdYZsXFzQW3ATNe8WBXCuXF1biamaiB1Scp2GMXA5OcgpKgLkvF20WknEgplq+GHFe/jjphkplyUWGBN1llxScgmCIAgtCfYoJWImm/8AyrMlSMllRktu8Mg2ND06OTH1EzFBSq7DMMbA8Txyi4tDgadYIAv3PRBJoyi3l+FcheeYA5LEhrQnN9wFFboKHZSGIAiCSEfCxrZsVLIIJ2ChGDii5v9ICPVH4F3/ceR6RQFcjtaSG6zaH6eURGchJddBmCii6pVX0Lx6NTieB5MtudX/+IezghEZRQ6f8kxgCUEJPDWkywkAgHw+32GJCIIgiHRDEMldmUgSMbgre39YCs+XL1pXFfBCrD8C6N2VCccgJddB3N9+Gz5wucIphAgiBgQmhD67/W4AmZE7V5TdlY92HAFA21YIgiAII2F35dSNEkGB4Zf3bcGm/a3RC2c7GeCm7Dl0CMG2tthvDAWesvGMvCtyVULQvJzgj10uIiGQkusg1a+9FvrM8TwpuUTM3LlpJva27gkdH+441Ok6KzzHsLFpfafriYYUeIrXnCEIgiAINWHX0tS1uXGfpNzur/CkrlEibg499BBq/vUv2+VbN2yQPoRWUATrwjKcXa853RdV7Gi2LReRWEjJTRdIySXiwCt6Ud4WzrsW3lcS/2zgzcP/xqvlL3dSsugwOYXQj3qcIR0nvUWCIAgi0xCY8j+NEk4gNCgxPtLr/XsPH0bb1q2o++ADAEDLmjW27/VXVkoZTVgMgacs8t+GkOviu/fRnlfyC1MGiZRDSm6awJG7MhEnIhPxox6nAwA4rvM/6fL21CQrF+U9uS5ZZjuBHwiCIIjjCycsuTVNkotpeql1zhCs2uu0CKYcfPBBHP3b31D/3ntx3c+CwbByayeFkImSy7ztaHp0Mlgw7JJcUPZf2kJidCsxkRxIyU0TyF2ZsOJw+0E8vuthy+siE8BD2gMSjhSYEtE6hZJCSImwnAEiEzHS4GvArRumOy0GQRAZTCzbJhNFU2sg9Y2mK74OpyVIDowhWLlX/hhdyfX9sNRwTmxrBAAINeUAE8EVdgffo1jXDi3gOwUpuSmEBQMQGiulz4JuZYfnIfp8DkiV3TTXteOFmZ84LUan2NWyMxScyQwRYsgL5rVDr8pn039gVvbkXjpwinxMA0G20eCvd1oEgiAyHMVNWUyhwvnGslq5zZQ1mf6ku8LPx6bSMFGEUCnHNLFjyTWvRfqP4+WP1i7JQv3RONsg4oWU3BTiXf0mWl6SrBq7f/tbzbXWdevQ9M03ToiV1XS0Zv7CgRAlIAJjDIWuLgAAv5g5LlZKCqHBXYYAAN49+pbDEhGJJxO+iQRBpDOxbJtMeNupb5KIlwiKKmMMHfv2aSN0q8vHq8Art3Gc9AWNsO+WtTfF1wYRN6TkphDfps+cFuG4g+czf6O/T4ysqIsQDTlmMyGFkBJ4SqHCcyxCacKK6oNNqDua3tEbRXLXIggiThT947dP7oa7LbXbuqjryhxyevWyvNa6di0OP/IIxI6w67Va4fXvXhX6LLbUgQW8lnWFUgUBaFs6T7qnuRYAo+BSaQYpuSmEHadhxFs3bIDn4EFH2uZidF9JR76p+dL0fEugBQBQ7a0Cz0XO32aXJYdei14oQTCI4CK49sTLrpad+LZ2WcLrTVf+72/f4f1nf3BaDFOUScRnVR87LAkRjfnz52PChAkYPnw49u41DzTz/PPPY/Lkybj88stx5ZVXYuXKlSmWkjgeEVQ+ww0tgZS2ncrcvETn6P6Tn1heC7ZI8yWNS7MoIufEUQAA77f/DJ1ufu4mdHy10Loh1cqH2Fgh1X90h7waQ0puOpH5GkAGIugSVvedMsUhSVLDsQULULlokRSuPcVkgY5riU8MrzQe69Dv9YhvYN7YtKETEsWGqLLkury5GPrRBQmp971j7+Dto68npK5MId0Xj2u9NU6LQERh4sSJWLJkCQYNGmRZ5swzz8S7776LDz/8EI8++ihmzZoFr9fa4kEQiUCtZz76+qGE1fvL+7agsiGyp5RAOm7GEGlBwnT+KYqAKwcBnwsBn9ZQ4N/8hXVDJnW5ik+EZMnN4klnBkJ/DQfYe9ttmuOi8eMdkiR1+CsrsXvatNQ3LM/+s3E1Vv1I5e37E1NnCoM/vXHkX1jT8D0AIL+5OwobrF2NiMhwaarlstD/2ff7yzZKS0tRUlISscz555+PwsJCAMDw4cPBGIPb7U6FeMRxjHr8rmzwRyhpj/lvHsbmA60AgOb2yO7PIkWeCpPuvtuRDClKsFdVGcYYwBgaDvdGw+HeAAD/jm+jt2PyHvie/cA8bWBtDTGJTCSXKJmNiUSSe1oZAvtMklWn6QQ1GbBgEFxO6r92TGTgXNn2niOsWsY5LquDXOn3zCYadVsnf5b9Cz3JJN33nu9v2+e0CESCef/993HCCSdgwIABTotCZDmJtqYu3+LG8i3GxZm/vXsEoghcOKoo3DYpuWHS3FhgyFqiviYrt5oyomhQWNs/+Ev0dkTB6JTMGAIH1oePOR5cfiGYtz1qfUTyICU3Hchmn1odvooKFJx4ouF8pacCAwpKwCfa1UPulAWBgU/MttW0IXLU5fgGI42SK0c/JtIfT1vnrRvJQf79McoBnk2sXbsWzzzzDBYvXuy0KMRxgN5wlqwF2K82SNFvv9kUjoIrkL9ymAy25DZ/9x0ArZLLRNGouLtyASHKvm+zdpgIrqBr6LDXfR+BBQNw/+WK6HITSeP40a7SmGwIjmQXz4EDpucf2fkQNjatN73WGZS+SAymeeccB5GU3HiHZbVbqRN5a4P+yOmSiMxCseBm43aB45VNmzbh7j3DBJ8AACAASURBVLvvxvPPP4+hQ4c6LQ5xHKDvPxIZ3iOaqvzeasr1HSLN+/FIcV/8lZXSB33aIJ3injt0tI2GjPMUJopwFZ8Evld4yweXk4vcEedFr49IGsePdpXGcK4sMzFGoG3rVstr/iipcuJBGRyzcZIdaZ9jIvZAig68M0FIv8WIF2Z+gprD5vsOX5j5CYSAc4p5W7DVsbbtoERVpj252cHWrVsxa9YsPPvsszjjjDOcFoc4TtAbU4UEjk3pGs8gHUmneVTDp58azrm6dIl6n9FdWfdM8iHfs1+ESizmKUwE17VIc4or7B5VJiJ5kJKbSqw6UwtLrr+6Oq06lUQQSaFPxpMyUVFyk1C5w6gVh2tPuKHT9a2sW25Zf6oQ09Q1rOFYi+FcY5WkYAYd9BJ4Zs+TjrUdC3m6PM5E+jFv3jyMHz8e1dXVmDp1KiZPngwAmD59OrZt2wYAmDt3LrxeLx544AFMmTIFU6ZMwZ49e5wUmzgOsLLkHqz2xFxXPIGkOnzkYQQgZe7K/h3LowaAqn377bjqjuquLM97coedY12JicWYy80HE0Vwui13eSPO1RbMobEwldCe3DRA765ceOqpAIAD996LIXfdhW4jRzohVlKIGHTKpiba0eKFu7YdA0/tE7VsVltyVc+Ux+ehT15f5PF5ylVN2TUN3+PNI0vw1OjnLOt7/9hSfQsJktSIlau1mIaWXAAQTBTZ/RurpA8OfrUCB3OdazwGxvQuc1oEIgqzZ8/G7NmzDecXLVoU+rx0qb6PIIjkox++FS+jPzyzF0sfGoku+fa94eKZClz95+34+JFRsd+YZQSP7UTesHFJb6f9gycAAHlnxJZWUD/P23XTTTj1ySeR2yc8V9RYchkDs9r2ZWaUyisE/B4wM2WfMQCiIYWQq592SwdX2C3iMxCJhSy5KUX7ozl57lzprE7x8+zfj6blywEAoi/xLrxOUhhhD5fdsWfFOzvw/jM/2Cob6ouyQMk92F6uOdZbWn/WfxKGdR9ueW80d3B90K9kuis/vecJ0/NN1W2m550mGDQbCJ1bQAmIAexv3Yt+X5yV8rbtIqomAuQSSBBEvOgjHKuPY4l+XOv2xzWupenaa8rIHzMFAOD7Ic0XuUwsrMFW7Zae9u3bwecIyOtfbGrJDY3nJmNWjqyw+jd9bmybyZGa9Z6ZumPW2gCxpS7akxAJgpTclKL9MeUNHIiT584F53Ihf8gQzbXqf/xDviXzlTM1fDfjKlbMkVflV8IYw4HNVZGLhiy5wNvzV2LtJ3tjayuNaA9qFUD1aqLABPDgIcov50CbNm+unSjJ+jLJdFdWFPZpJ9+sPb+tJmltdgYhYBw8lZ+mE3kUV9evwFN7zRcK0oUqT2Xos+nKN0EQhA0MllzR/HM0fjN/F9xt2vmGE7EnMg4n3lEcmTYiBZ5SqH3zTemDyyUHnrJ6NuOcSbH6er9/x3DN+8NSQBQATutVoHdfBoDAwc1R5SQSAym5CcL97A1gAS+EusMQ3NW27uFzc8PpdKwiLGd4Bxxs01nmTDqhjyo+AGBfqVKUCiYyfPHqxoiWNKYqW3+sBYd31tpqIx3RP6f6fe1u3Qme4yAwQbNo8Ny+pwBEjyAJXalCV2FSLZSK7PoBwMwtOB0wexXKd2vn6iMplgYIRkwflR7ku8J7j0QKPEUQRBy424KGhUT1cayLjAbLL3VNNkj9S+K69Y79JrOB2mz/LAB/ZTXad+4EmAi+IC98MYIlN9J8PHhosy1LrlQ4XVP+ZR8pV3Lnz5+PCRMmYPjw4di719yq9vzzz2Py5Mm4/PLLceWVV2LlypUpljJ2WFsjvN+9g5ZFf0Drv++1KGWtamSrO1+wsVFzbJase0/rLuWqrTqVdEAv3fGZdFeE2xRFrWJ/g3ScwYndRV1KHwagT15fAEBA9IPnXPiufiVu3zgjVGZXy04A5quJetRfQQ5cSgJP8brfRCL25FZ4jkW8/ucdcwyu39GINN7tXhO5vaSQAYtfxzqOhj6LTIRfpIGdIAgjn65pgN9igfPaR3bg83XaeYQghsf2Fz6swBvL7HsAGeZa2Tn1SiyOWHLj+MPYNevLVUtKriptYsAbuX3dexCaqnSXTfI3m9TDfO1ggezaipiupFzJnThxIpYsWYJBgwZZljnzzDPx7rvv4sMPP8Sjjz6KWbNmwev1WpZ3GtEj+fx7V8tuEBaueSxo/aW2jDqcAZPZSNR/+KHm2EzJrfVKA5TdJ9Vb+1gExUh5fd/8cwsAZ1xLE4XBkstEFOVJ4eoL+ELwEX7OsQ4XHPiU5MnVDwgnnzkgaW01+RshsCBqvNU40LrP1j1Hdsl7Z0zHu7CXQKrJhG/xovIXQ59FJmDWpluxr3UPbt0wHU3+xgh3EgRxPLHg/WPYd8w6UrLHrx2LRMZCY/uq7c14f3X0PY4HKs3rJx03OkLtIadFsIWZu7K5C7M8djcc1M7XBSF0Taw7AubXfWf0HlRCQHssGgNP6d2XAcCz/DW0LPy92SMQCSblSm5paSlKSkoiljn//PNRWFgIABg+fDgYY3C7zfNUpgOsvUl7wspqFkFh7ffrX5vfksis5w7Q6tG9GxMlV6Gi4yg6gu1YtXRnRLfVin0NmuNIiuumrw7YEzQDUCy5ATGAWzdMBwMDJ/+Ee+X1Bh9h5dPOnlxNeY5LUUAlrVw8n5wpx/I3tuK5V/6NuzbfIbVjc7+Pp01amIrkadHaGHsai86TCWpuGMVducorrXzP3naPk+IQBJGh9CvKhShq/YzsjG/z/n0IgNFdedaL+01KE2rE1gwOlGQyhxaCLrhyBeSzY2CiCNEreRkxMQgwoKO5ADXLd8Oz/J/a+6p180l93cxEybXYiig2Z+7WuUwi7ffkvv/++zjhhBMwYEDyLDydxsZGcwDgu/S0rKLLsGHmFzLcknvAo4sIbKLkKsPVqvoVeOvo69i6/KDlPkczxSuSklt9UKtkZ/KqrfLsSvodSckNXQ0pvGbYcVdWvx0OXEr2UerdlZOlWO/87ij67DolFGHartKvLByYlc7GtFSdwS/68cxe87y9XkFaCMjk3x9BEMnDrneqi+dkd+XwOTtro0r9ZnYD6sutCVbtQ87gM1LWntAgbf+xO1YMmTULAJA3YIDW9Vj5Q5sG1ABcuQLAAe014RgmnuWvQWyqgLc1H/6OfK37sgn6gIr+bV8bldo4AmgRiSOt3/7atWvxzDPP4MknzSdO6QITdHvNrHrr3ILQx/x+3W1Wntmdr6j7hplZptU5U7e7twEAao+YW+7NXEMjuYuOPP9E7YkM3vts2JMr7/84s+dZ+HHRWRGtk/aiK0v86UcPgk+ZJVdLrE12BNvRGmiNXlCH3T3wnDx74pJkYc4mWgLN2Nu62/TausY1AKzzIxMEQdiB5zk0tATwzSbVlgcb3bPShZtFU87waVbSEFsb0Pr3O0IuvXkjJyS9zZaXb5E+2ByjNcFbTcJuW81jOE5yeRc94fm7f/MXEN3h/d1+d0fkxnVKbmDfWhOjF80dnCRtldxNmzbh7rvvxvPPP4+hEXKrpgVBnV++1ZdanTfS6s3r9uZm+gqjqN+OYGLJDaqiAXtFyeIjCubPHevr6DOoh+Y4k/sbRXRlr6wIERx43HLqrTip68ngTfZ+hO+18+BSma453eTAU6nYk6vLzRtjdOVn9j6JB7bfF3O7kfYvq4mo3Dr403QH0m/7hp3vWGb3ZgRBJAuz3mN/hVHJ4Hng3RW1eHppOOBfU2sQlfWRA/nk5kp9fku7MWWh1bzitEGFEevMekTZa4yJ4PsMBt+zXwobtzlZU1lO1X9GxaDiq6gw1qyqWvS2W1Zd/dWeyG2bRW62cE/OO/OiyHURSSEtldytW7di1qxZePbZZ3HGGalzk4gb/eZzSyWXqYoYy3C5ueh6+unakxm+J7etq84dNcKeXDvEasm1s0jgbfej7mhzp+RKJIwxvDDzE5O9ntK7FENuy6JGsdC7/mrulL9vbUFrq6dSl2QRTk10Zb1itOyNrTHd3xxwh1yQrVB/PwrrijD8jUtsr64qe4TNyhd2zzecSxXLa79xrO1OkeGLdgRBJAmTLvm9VfWGcy6eQ9BkEfyB1w7aauaeRcY4HSJjWLHVuHB41qndbNWZrYQCL4kiwLvSuv/O7dNHO1+WZfWWm2VSUD0Hs2/54Aq6ao596z4wlPHvWG56r2vAqfGlRSI6RcqV3Hnz5mH8+PGorq7G1KlTMXnyZADA9OnTsW2b5Ko6d+5ceL1ePPDAA5gyZQqmTJmCPXuirKg4CBN1iptVP6B2bTCZNA977jkM/N3vEidYGtCiMqS6ThjUeSU3RlcjO0ruird34J2/rOqMWAmltUEaWFYt3YH2ZvWeEDnqn/w9EpigUb4iuSsrCvDL+5+3LKNUxXOcHHgqHuljQ/8r8HuMq+x6mmraEPQL8v3RuzD1nu3C+l7I8eXhzSNL7MmnvBSTcdDlCp9srIrdZTqR9OjbxdH2JexYctN3kkQQRPqT6+IQCBr7kYoollwFszXxY3U+PPbGYcP5SMEcMx3GmGFPqZ7W1+8HAAR2r5KnH/H334Ej22O7we6r5zic9uyzKDr/fM1pxZLbbdQoAADfRT1GypUzwO/JtS2Sfj4ZPLoDAOAa/KOo9/Ld+6DH1Kdtt0UkhpxUNzh79mzMnj3bcH7RokWhz0uXLk2lSJ1H0E/MLToCvTKsg8/PBwvq6spwS67ST7373y7cIIwE/Mbn4cGH95sy3Y06juw0i/IXoeO18fqEYHrtExTlv/nBrTXod0IR0Fc+L3ewjf56+VjQWnItlNxKTwU8cuCfdsHcNafOV4vmgGTN5sCDT7G7stjDB77FnmX0jXnf4icXnYJxl49AS9DcAt/VFV6BV+feLVkzKjb5IlhyV7+3K/TZ5wkYrqeSPgNt7vFPIg9st8oPHkaMMqkiCOL4xGzIN9s/m5fLwxeIvR+JpDMt/KQy5voyHc9XC+HfuQJFd1gv+KozhzBfW1Sl2LKeYABt/74Hvf70SQx32YybwXFwde8O8Dxa16+H0N4OV9euIetHKA6Mfi6t6Lmx6O26OkLTVZcNRZlDZu+Xy1DS0l0502h7c46tcppVIJvf9Uzfk1uU30f6wAFwuUwtuTm8qoOQH3foKPNo2g0mFrNIr0g/SJr2MWn2itUiB/zhRQ8RIgJiAH/Z/ah0rHNXtrJqtgfb0OCX0i7VeKtNyzyy46HQZ57j5Dy5yXsx/fKlvT2hfcZcePCo9FREDVDk90a2+KoV/s7kRg7tyU3TsUnMiW75Tie65HSNXoggiOMOs4VEv8pi+6f/ORFvzj4DORaW3M5gpkwD9qI2ZyrByj1gHW4Ej+2KXhjygnTc81HpvqZHJ9u+Q3Sbz1UMhLytpP/9NXLgKEVWJQCVSkGN9hT+jjzzC/rnV47tKLmSlmujHJFISMlNBlYdgWYVzKKMrqM3WHYzDF7e75DPFwA8Z55CSB2Qi1lbzqzOR9qTa+qbpL8/aokUoxJIFBiK8/thcOEQfFn1Ge7Y9IfQNb27ssvSXZmLcE0iwMKWSEXBVay/ySSspEv/DxszCI/sfAi7WyIPvLEs/lgFMcsmMmUtLNr3kCAIQmHvsXDgqfxcHj275iAQFHGoJnJql1ixmiYM6VdgfiELECqlLYBt7z5sq7zYUgffD3F6WabAg0eZCwkd0ncmNEfQKbuh8nISRibq0xhGaMTiObgcO5ZcDlx+OmwrOr6gGUciMEzcoiu5XfKMEd+kurQ/uPoPP+yEYM7T5AsHjmAu3pBCqLGqFcX14TQ/Lq/ksmod9t14zqpTanN7Ta7FUIFDqC2ojDGITES+qwA1Pu3KZofQYctdeWPTevyoh/0AbjzHo8Ffj/m75sUouX2UJ1TcpzkGtA6qRvEJUi7p5kCTxZ0W9UX4G6rdleMm2ldEd31n83YsPGC9/zlhhMZvMW28PvQuyS5V1G+B3JUJgjChqdW45cOvcktWxv54rbg/PaOn5bXtB8238Yw4oUvE+7KDFFgX1VbURI9TOksudO7JzCyVEIP02AwGJTcQaY8uY8g9bSzyzvoFACD/bPuWaYADl5uP/LMvjeEeorOQkpsAuELdfjiL33DwsBRYi+MY8gadYKtuoaWlM6LFhHvFioRbjjn5XQhMgMgxQFf/pwvXo9dHI0PHue1SyP7Y+kFj4aryRvxzzjeGDtVs/216qAYq1JZcUUSDv16jKCj8UL9aExjDyl15Rd2yiOmF9NhNr9MZFEVeUYi6uLoBHCDK+9Y/qHgv4v36AFW3bbzZsuxbj62IX055iT/awKy/vKlpA7a4N8fdbqzscG/DVzWfp6y9SDAwjaLr4sKhHyhPLkEQapS+de6/DhmujTolHFtBGen4GHyIa93+UP0frA4vuBd1ixyOpnuhNF7yHIdpF5dgYB8L99VsIBX7RDUKZoIXOvVKroKi7KosuQUnn2xyv/YwmiU3Z9CPwOUVgokC+MIeEQqbi2mdP5RIBvS2E0DBuP/WHIvNNYYyzS9OB/NICisDwALm7jZ6d9xeF6Uut1bV4sXwVdvcB2ETZatlkAXwWe1nBkuuvkcRcyXlxcoF2ZVr/Mqa7c8M+ATTehqr2gxl292JdX3qLOpXUuOVvktmSq5P9AEWltyTu2pzS0fLYaquP1KU5mTBGAPjGAJiQD6OPBDu22A/UIi33SQolM2VjbDLU+Ry+vFVn/832XSvGIBjHUdT2qYVjDFslRX8UUWjUZxfHLpGgacIglATKbam+prSx7qidK13L9yPbQfbsHq7G7+Zvwvf75TmXW3e8AJbgck8Qs31k6SYIDwntatMI9bubkGd2x9ZgAxDHVwqGnzfE6MXMmtD3e8neAxQ5szqubPg8WD/XXfJzcleToyBz1MWK1TbvHJikIdJqZR8a99H89P/AyYHnS2caCcripK+gtSuVEJvOxHwfNQk2WKTdlLOcRazZt1sObdv306JFjOdTPGjh5cfs60bB5GPnieXE6SvpJXlrO8g48rZvvWVCLrdKJ8TDgCmpHexYxGuP5Y6a7kdNIq5/ABmexlrfTXY4t4UOuY1Cq9WKY6m5KotbNHK2qXeV4dbN0w3v2iI38AAnoX+XiOLzsTh9kMJkcMMTrT3jOFtPbHZ+5Xak225DBaEJ1xiCqJh24GBoVuOZIE5t+/5uP6km0LXNrs3pESGmkNNeGFmLJE8CYJwAqugT8ZrSh73yH339oPtWLu7BR//IAVbbDRxg87NibLoKw+fHMeB58LjwIOvHcTiz6si3pvNuIrteSAa0Ci5yXFXbtu6NVR/xQsvhL0SGZNWSzjOVMEs6G7PyBGaA8hzMeZtA0SpjWjzfzVCjVneXiJZkJKbCJQfkAzXozhCYcTmH5viFEL1H32U0Po4EdjwEx5t3SUlV69EtzRogxvxAWk/hJVSYXZ6yI+K4auogO+oypLFKUpu2jkjR0UTDVgOxGVmydWjVmy9uqBRsURKtgr6FSstAdlzwebfgHEs9Oz1vnr8ZfcjCZHDvDF7XZ+VR0HAH015lYNgJFnJFfMCYJw9a3OqYBA139c8Puzqt7c1NfnO964//lKCEEQmEik2pGBiybVjCBMZsPmA5LXlDxgbiDTEvf6n0+HiFYUacs74cB3HdRaYeOdT6nmsen9uMADBbfR8DBVtqbe8FkL+gwRVW/uCbne4DVGUPAh5Hpz6y6MsXtj9ezJRUnDVXwBRQP7Y/wLHR56fcfldwfcZLMl2eKvNBolEQEpuImAMULt7dusVsWhox7sZ+h40xUpa6/r1CVUMORbS0yRLrqqDq9zfaCjfd8epACJ4tFjJpntviqLmrjUPKJEurKhbjo8rP9CcUytWtWulVUYedpTc8M9ZnypoyeHXOiNmXCh/kQX7njJcMyjdDAAXTk4fFCPvDT/5zP6m5xV32GhKvX1LrpJrT5eKSleu5pBbe11xoUp2UA/GgfGya36aaLkiYxBlWWq9NQl/B4vLF+LWDdPRGkgvDwyCIGInYtBA0ahcRrPkAkBzmyr1nkn9kfb19uqeG1JyOU5SdDXKdrakgcm1l5dewTXgFMS9kmrhruxd9TpaXphmeZvoNaaMDAuk81bjw16AnPoaY2ABnzQma2KYyBous/n3ZLIxS+1Vx5hh1SXnxFGGW4vufBuuXiX22iESCim5CYAxUbe6Y2199TQXKDfZrDs1E1d1Ox27dyesXp5Jyi0AiBzg9YeVzvZmlZuI3Lz7lKMGebRympwTmYmSK/0fLZ+q03xa+RE+q/pYc453GTtdW5Zc1c85yNLnufe0WqcDClk6GQfGsdDf3S/6ItbJWUxSmOyyK0axoNpWcuVJVuUB7YJM3dFmzfF379vLNZhoOMZByEufvzWgBJ6S3n+vvN6WAdHiRXHRr/BYRKgnCCJjiBT8fv1eo5LjUvX9Y0f0wI0/HxA6Xr3dLdepnSj4AtpGOryRx4ewksuB55NjyW33Cpj2hDPjBgBbAZBcJcNCn/PHTInfW0j1/tT7c0VPBCVWd58lyoKyWrHVzcd9Gz4FhIDFBBLI6dULofiIVn9fxqR3poqnY5j7Awge3hJdZiJlkJKbCHTuynozpFB3OPw5KL1yy47SKUuuyo24+bvvElYtJ4Y9Q0UeaPI2hK6pB45uFf1wxj+uQI43z3BNjbJ/dsS4wThrohRcSTRRchUrcbq7K5utCrtyjD9LO8GgEuVmrKY50By9UBwozyMyEUxk8LcKcPly0VwvLYJUeSO7m1o9q7JqH2mfFwCc+t6kiNdbmzxSMCy5miM76zTXN38j7auxUrYV1NbVtQ0/oCNo7lnQGmhBlScOF1vGwVPcaGjLSZS0V4Diqmwul0+IvJBhWX+aPCdBEJ0nWl+tYBZEd0hxPq79WdirZ9V28/Gq3SPNbwYXS9bLWrdJMEIVir6kBJ5KxjSiuT2IqkY//vVVYoN92sZWACiVcp9bEHfQKLViG5u7bvQXr8wFCk48UWlM1zaD6PMAHEPP885T3ShVzwB0O/PM6Eo/E8HAw1NeDiGgTGpFY5DJvMKoMhOpg5TcRKD46oeOdT8yrzGir+3OIkV7coOtUVbU4mTsOhE5yv5/XeAptQsoJ8gh+/3SntzVS3ea1rfm4/CevuFjB4XqUTq6lnXrNOViUfzamjzRCyUY87y/xo7djiXXTplYafIbXcoTgVq593ZIE47uFQNwaG2D1S0arNzNFOUqWhCmHF9+xAWQfz3wH5RvqQ6VOfUnWlej9hZJQVN/h3c0bw99Dtcdvv7aoVexrnGtaXuvli/EvJ0PRpTZDI5xaBxx0NBWqlG/SwYGESI4cBjR43TLiMobm9Z3ui0rjut9cwSRQdiNdamMGerftstiHNBbcpVAlCOGdLHV1g45by7PS4Gn1NUlum/ZfCA5c6+o2MkAkKj5p2oMYG32oznHMqR1HztW+qA3Ooki/I2tYCKPnuecY7ivw12Iti1bwLnM8+MyOa2h2NaEQJuA6q93o+5gH+Wi4T0WlF4eUc6u/z3b5hMRiYCU3ETARKh9HAxpckw6E77XQNOq9EpZqiyRnCvxCpJC7wbZusYDvkBYkTR7NE7eH6GkALKC4zj0GdgDJ57RT+Ou7K/SRj4UI/lC6Wiu77BdNlGYvYMWEzmsFNhxfX4a+qx3C33kx3/pnHAAntj9KNqCcQ7CEWYDihKax+fFtUxuZUEVIYKHK6q7MhDdIuhrD8jfLWN7tYfdhvJ7W9Vu/vJeXl0TObx5fsa4A1SJHIKFPoiuYKq372tQv0sGEYfbD+HkrqeA53gMKNQuEPTNkwLz+WSXdLff+C7ttmUNabkEkQkcq7MX3TZkyVWd06cTWr5F6ksCQfMYCif2L7DVlrs9GGozWYGn0tzJTEK9QMlFiCUTDfWcOMc857B3w8fwbf1aezLSu1ZeIK94R4aDjarn0UwUUb98m7VoQReCbndowdrQpJwmCLzL+LcXBUAOOpVfdqUsc+QvCFfQVf5A6lcqoLecALyr3gBf2D10zPfoo7murAQxEaFN7jmDRtirPFV7clXLqV2GDYtQ0pygGMTc7doVqhW1ywCo9uTygEu1yb/NbWI5tRkEQFE6eJ4zdVdWOLjVOnJfOsBMLI4bvzpgOKdPCaTgFbyqMtp3UJRnHQBNTwFvPfj7xTjzApp8dzuC7bIbsPTco3udHVfVHMeFcuqq2dWyAyIEWy5wZu9ec112V87JdUEMmpftWhR+b1pFz3gOsM4Ta5Yiyg5SYDcRvJADBJxT7DY2rQt9Zozho8r3Ud6+37TsqF6jNcf3b7sbFZ5jttuyo+QW9e9quz6CIJxjyTfSGH3mUO1v1mqBX73g6PGb96dqy6u7LRjqMboX2lvMV5QkXg48FSkCdLwozxfDOnxiCUTfLqKM03ln/aJzftuq+/iiAaoLqr/lFy+i4/MXdPfZqFMfdtuwfZCBBY2LyIrK3mviRBRfdRXAGAI+F9oaddZ+UQBjDIF9a41BZVWW3C4TfxtBWFW7ipJPSm5KoLecIIJHw66KnH6lSlZyG48Vob1J+gGJbRZuoE7tyVWttLm6dYv5dp/oQ62vRqN4vHX0dQDQRFfuwYfrXvvx3tDnvNbYJqUlp0idDcdzGktu07JlMcvuJK0mVlKztDVWlly1otaZAD+n9xyJ/xp8Vdz3myGajFB3b7kDW9ybQtd4jjeU6lLTx3CfHo4Hvq39j+H8q+UvA4iuwALR94IxSBMRVw4PQTAvW1Rs9b01L//mkX+bno/H1bxPXh8U8F3Qp0B+X0HnunN1ECizv7sa5VlrvGGvC59gz5pjl2497VlsCIJwFr+8gNi9i9bL5VCNtk8IR1cOn3t/tUWKGVXfXpDHhxTK0ad1Ny8v87+/PkHTBsdx4DhpIV1MsKariLjnaOo9yCQBbGjX+jzFcSu5aiWTWXw2O46OsiDh6iLN2Ah+zQAAIABJREFUrQ2elIyZL5goe3KVaMyMwdNcAH+Hdv7OAj4wTys8X74IsV07b2dMNOa0imbJzZXHpihph4jEQEpuMjD8yKTjgC/cifs3fWZ+r95dOUV7cpndjTEWKPtlzJQLjSX3cA1Er3FCO2D9SABAvy32LNzDx0g5xzjZkqt0dKI/stWxoJu5q0wy2d2yC3dt/mPoePvKw6gqbzSk+VEw64+tAk+pUwt1xo1KZCJ65/XG82cvMlyLN2WClctwS6BZq9TpHjinPbqCsvuHY/B0WP+tpQi/0nfxxDP6Ia+H8f1FtQgyacEhJ5eHYGHJvWjqaNPzttsIEfs7njr0ZuRx+WC8JBun84Io31KNhsrU7PdSBxaJtsWCl591df1KfF+/GkB0xdii1TjuIQginVCmHoZuQz5WLLzK+Pb9zuipw9T6aPdCV0Td7MJRRaHPShvdu8gxQjhJh2EsvM/XZ5J3Nx6SYR1OOMlwV444PujGwUhJkXX1cDk56DFuHCCK8Ozfry2n/O02fa6uQGpPFMHxvOW4xVRRmdvqdYvaZoGnoo3lSuomOwmfiU5Db7kTMMZCrsia80IQ7Z8+Gz4hl+Hi6SBSZMlt27y5U/crk3kz61g4urL041cn6rZi2JhBttrleQ6Vr7yKpZ88AgAoOv/8yHI6MLIcbD8AjyCt1gZ8Qax4ezvWfLTHNM3PF4s3GtLTAMDuFvNAXGplsTP5+xhETQoiNSvrvrVVR8AvaFa71a657aqowgySovPAGQ9Lx7o/ictvbyHim6NfWV7jwYcWXJjIwOcZ342V63BITtld2RXBXblL93y0ldSZXlPqsEOskbH75hejq6urVL+s5Fat0UZu/vyVDfhsyQ8x1RsvaiU1mhWdU0XW3t8meXPwcXx3M2GOSBBEZBSF0qqvPKGftOhZkGc9XT1bZ6FVz0NEFh5jinsagwupu971e6RFwSt+Why6xnOSW3R1o7SoauXVEyvPvnc0IfUkArG51vyCZozkbI9nhmrUvXWcEZptwXGoXLhQ2zZjYLJPeMdnC8zv43lAFJFz0lm6C0yav8sy+9p1uYX1ltz8LsgZNDyyiIqnp4nuQCQeUnI7gefrRXA/ro2klnPyaIjuKvg3fxE6x5SN63bmcQ65K9e8/nr4IA7rcUihMJngntpDss4qhiY71um8AvMAPXo4noMYCKK4zt57ciKlkFr5XHRX+HthNks/sKnKeBJAlcf8fKKUXJExzYrkn04PR/r9ovpTW3UsuvNz/PBhOPiSOsLx2obvQ58ZGAQmhqzT+oUHX8/OWx95jtekE+Jzje8mmpWVyW5OObk8hIgbp0wWdpS2bbhNxwUDAMlVn/FSW/luoyueJ5CaiOHq31W0IFrKYgoDww8NSrqy2L+7kRYpPl243iAXQRDpx8/OkrYe6deflUOe4zDzvwbjpP7WqVkiTZs8PhEtHUH07p6jWUzMz1X23YbP1bdI261yVdeUezbtb5PlTEyfsvOwQ27KJvh3rjC/oDyrKHYqRaF/x3Lz6jsiW+U5i0CN1uVNVBqr+SYH5J9zlfRssruyq/jE0OW8Ln4AHJggoPXf/2u4nTHAt/Z9zZev153vIPeU0shCKpbcZCr7RAhScjtBsGpf6DNfpORq4yA26RSSGDoHQ0fiwCRNrYTWLl2Kw489Fv2eCPlJu+RK+3CFGL5tdve/8DwHBg6Cje0Nv5k30RFLrpnyqeTxtYs+qJSqcgMje/44Yl1mE3/JkhuubFDh4JjkU1DyxwKAYHB1UrcWthzrxRFzjRZuUyIEKZOU3LAl126qJjU/fLgHe9YcgyvH2pILAN2q+gEAvqn5EgfbpKBhigLNbA5ku1p22CqnwMDAc1ollzP5gTEhNcGo1AsGm5s2Rixr5nofzwTKjis46bgEkd4wxtCtwAWPT9CmIlMFz71kbB/kuOz3Eerf/b++rsaMZ/aajAHGlETNbdLYkxMKbBnenxtMgAX3g9V16bnwZtX/KnNBISAHnopPMROOqTIPMBHM74XobUOwUjqv8XyU4fsMRkR/HY5Dt9GjDedigS/oLrVgphwz+R8xCLHRmMOey++qKmcfLkdxV6Y9uamAlNxOEf52c3lyRDazH1kn3BJStSdX26hkwerYtw9tmzahY8+eqLco7opq68qgY9Ln4IFDUrXyt810tU0vgk1llOM5MI4LB7fyeCwHkbyCHGeUXJPvREHX3Bj2a9qz0ro4adWzj5yiRc+MU28HB860Xb0lN17ahoT3GUdK48NMLLk5P5XctPV7SzVyanIr8zjpU8k9fUjhCZpyftGPuzbfDr/ok5Rck/Fkx5HdxpMqgn4BNYfccOVaB54CAMaFv/N/3fM4RBZ23k2WJTe0PUBkoTY40fiQ8WYm6gxKaiArTJXceNyVbUy4nPi9ZyLz58/HhAkTMHz4cOzdu9e0jCAImDt3LiZNmoSLLroI77zzToqlJLIRkUnz/a3l7Vi1PbxVRxnH9T3D2BE9Qp9zc8z7DdM4Q7px+EcndMHQkgLNlK3dK3WYSh52juNCC8xB2ZtnYB+dy6pNBIHhpY8rM2MvroKyUCzIAUXjjTslqhauGUPbu39G87M3hE6pPR/DSIGuFIXYWCnD4JkzNac6dH1X/pAhpnNoV25QijvFJF92TsoVZfrFYUHzRXe/2y8Xj+2lcDmyyzxFV04J9JY7AfO2G0+aKbmdCerkkCU3UFuLw488YntlTBmQ1ApUV/n1uAZIli5OvmTniWzvZZQtuYqU7m+/tWyA47m0GWD8nmBMSq6aMb3LQp8PtR8Mfe6e2x0PnPEw/nvIVXhi1DOm90pKrrHT39kSjg7+w0e7cWh7fKmXBFd4QPis6mOc/toU5HRoA0mVtx2QXIgVJVeZzHSJ/gXxtoWDTbkCOeha2welvceaKpMiROxp2Q0mMgwc38Vw/a3db9l6pqbqNnuLTbLc6nQ60fb9xguTv/WMMYicvOff7L2lSMm1CoymcFq34Ti1m5SazGzvdzz25oDJnnY9DZXRg9QQwMSJE7FkyRIMGmQdC+Gjjz7CkSNH8OWXX+Ktt97CggULcOyY/dRPBGGGyFjIclrfHM7OoFZ41QzsE47ZYNVvmHmU6cvOmzoUz9w6TKP8XnpOXwBaw57yWcm92yU/PgucT/YGStSe3oRiMU6F5mGykiu6zbdNRcPVb6i6UojuaiAouQNbITYchXf9x5JCvOBGnbjywq7OYBKo1e4t7jFmDJq+MsbuCP3JGQunHFIijCmXIIlnFZS18VgRvK358c/RyV05JZCS2wnE+iOhz4yJKLzoFuSNOM9QTrOKFSNMFOE5dCi1Fl1RjNmlRlGclEl9nS/c2RSeOxaAahJu41kEmwsDiruyVhZzQumGdOR3MQajSCRmVipXbmw/Pa1CHK5PH6G5f8EAuLgcdMkxKnWAdq9qqG752CtI+zc3fnkAn7683nBva6MHtUfMg4Z5ZOWTE8PP1b42FxzjkNuqlWVD0zqIEAzuyjmnyRZAlSU30veQk33Ue+T0tFwweOfoG5KV2mzx3aZm5esIQIwwMeFkF4W+205D8ebh6BA8gIlnQ6JgjKHJ3whOdlcWOLl/YRx8HdrcwakaR9V/p23NWwzX7xh+Fy7sNwGAlUIcu5obsJG/edVS84BthJbS0lKUlJRELPPpp5/iqquuAs/z6N27NyZNmoTPP/884j0EEQ3GYOqKvK/CfM8qr8ohZLUGb27J1R67XBxyXFxIwQYkt2hAuz1IUYIDgvWWLDvsOiyt+h+oSk2chFgQmy2CJyqW3GAQgf3rjFvxbJJ7whnhKtU5c3v01RYMar2A/Js+heiuMeb0FaWAT8FqbS72/BO0Hl3gedNsHgCkCakyF1X2Xqv/tkz2exOs5++dskGl4VpHNkJKbgIpGHM5+K5Fxgvyj4TJCgBfWAC+Zz97lTKGQw89hH1//GP0snGij3bMmEptlDv4yldfjViHEkBGUXa3NG1Cvlf6FRee9WMU5RYhKMcQsJOuqMFrb8+qYsnVpl4z7z04Lv7ogJ2hMwGhABg6w3hr4wAEmdGCHD6O/G6+eHUD3n1item1v98nr5aqFNSh7pFyuxzePfqmprzIwqH3mchQ1K8ruEIR7f3qNe8rkrW7x+GBAIBRvUZbh/+HNE6bT4bM7/nPEqOSJgRF7LcICtbRV/qu9tt4Ovpt/hEAKfoxkBx3Za8oTZI4OfCU0kbXmr549Z4vNWXFltR08eq/09GOIxZlJMzc4uPZkxvNegwA1eVNMddLmFNVVYWBAweGjktKSlBdbZ4GjSDsIjKt4qqgdOn6vkFb1Lzf2H7I6GVn1cfcdPGA0GeX3KUU5vGY8lOtAuYLyAv5cbqD7auQ+u3/9+L+KCVTj9BgEelZWSUVAmD+2JXzpkcnQ6g/olEUg4c2Q/m75RqiGRsR3cY+hslpf1oX/xFCYwUEt+R51vOcc0Jlek2YYFpfUUkzevSXgltKGVLCllz1PKLL6SMlMSMouRwH+Lcvi/oMBvkZnNlLdBxCSm6C6HbFvdIHTm2FkjqI8qeWIOALu7h0HX4K+J79YcXA3/8+fCD/6ITW5OW7NCidogj3qlUAwgNDy5o1Eev4uPID6dbwyIResu7MgcfDP56P1h4cWrvBYMkVeZMfu1zPvvUVqNzfYNkux0W35A4e1kcRCUryb4WAXwhbv5KlAJsMrky0SFCuLwcGMK2irChQsaK0pldC1NbGNR9b77+OeYHAZV1eZAwulbuytFjBQgnawzJbK4l9dp0CAMjn8yMqw4wxuFxGFzPOIlrZ7h+MLpitjR58uTgcUKl770J0LZLcsOvPlPYBKX+jYx1Hsb9VOpcMS66SG1lkouRtZfKOju2pT3i7kbDjeq/8vV3gcVH/X2iuxbMQVOiyjrZKEER60+GTxn1RZHCZKLnKsPlL2bqqMGxwF0OZ0wabey6Z1aene2E4gi8fCjjF4feXaV33Q0punNMEJ8KrRKPg3GukD1aGB0VoUYw/6FT9UU1cGt+6D0J/DO/qN03vcZWcFrlSxkJ+5C0v3YyWF6ZJ51Xuy67u3U3jvxR09yOvULtHmOM4cDwP9zJJYeXy8lAy815wvCuqUUZojn1rV82+YnhbY4scTcQHKbkJwtQyq+rVWNewK5jo90fsMHqOGxe+z4lIfKIIX6UcTU7ujDgTJcEMBhF+OfBMgzw28RwPnuPh4lzwFGqfqXhID/iKjAq84gL61WubsfyNbQAAIWDS2QSkfR2a8UuufvAIaSWWUwWRkAIESgWaatpQvkVlnetEiPxI7GjeZjjHGCDY2TDJMU0gpr+e9Sx+WXJp6PjSgVOiVjGsu5S3rX+BtLDiETqwaunOUFoctZK04QvzVeZVdStsrQFogkbx1ntsRQjgVO7KkqcQk1yIVHUYXcOMlXU0BCyVLAamsXaq6bNrqMkd9jj5zP44a4L5/T80rMbu1l0AYldy7fzem/yS5VgQBYBDaE+umo6WyMGfFG7dMB2tAXv7VkUm4tYN0xEQAybXwjKc0s18gqK4p3Mcn5AgZ9FSFRGJpaSkBJWV4SijVVVVGDBgQIQ7CMKa/35oO+YtOaRxVz5Q6YEoMlTW+zDhrF7oXujC4GLtXpPzf1yEzx4bBQA4bZC00HXDJGujgYKJHm0gJ0Ihr19Rco19dFBgCESIwA84NJeLhhzh1zVwmEUBhpyTzkLemRM7ZwTQBV+N5vbc7ddzI15XLLl6fOoYAUowqYhyiWDBILicHENZKRgVcPDRZ+Fty7OoAEDQOB7ageX0ius+IjZIyU0UZr8l1Q+bqVZ7gu6WqB0elyOv8kRZ/qt48UXL6G/RCLa0yAq3cY9mdyU0u/LDt6kAHm4/hFmbbgMAiIpyKb8cgQlgHBd6phPP6Icxl1h0riYRdl/+f8b9X4G6WjCOgyjyEBC2DAJAnxIpbyin3sOj2pe78p0d+OafYddUPoYUBbGwt9UYxZfBngLEQkqfJFuhq1DjpmkWxEfN/FFP4Q+n/hHPn70IxfnSQkxADGDr8oNob5L2qtiR440j/4p5jOvYZS2bqIquHAwIEIJMUkg5bQAlxZIbDAgI+IIw+6Exn/UEgjERTGT4tv4bwzWzlDt2YSwsiv6rqrxnILyAYHeCoyjrAhNwsL3ctMyX1Z9JdQsieI4z/ft1tNpTcgGgNWjPS2TpsbcBhPduqykpCLuxKumn+hdoFSBFseU5XpOuKl4ECtyRUi6++GK88847EEURjY2N+Prrr/GLX/wi+o0EYcHq7c34xxdVOFwjjUXfbGrCim1u/PbJ3XDxHEafZsz9rWbqxZLxwM52h+rG6Hv4XRHmAB6f4q5svHb/4gO4/fl9xgsq0jHelGL55Aq7mV9nIrr+173IP+sXyDvzIsPlwOFtcD95dcQmgsd2RdzXaipWl56RCwgCIBteuK5hZbF55cpwGSWYFKRoyj36ace53FNLAcYkS63LZT7HleeOAW+O5fyHK4z8HbUkhyy5qYCU3DgxTFrNLBNWkzCOj+76If/gfFGiV7asWYNgm0l4dRvsu/12VL/2msbFo2DoUCk5tnxO2bQvdthLXK52hW3vCriLwgNQAV8Ixocj4213b0WV15h/DADqPLWm5/Uoe3Ib6n+GjV0mYG/+aDTXtcOVw6NrT8mdVD0AigJDe7NRAehd0i3pgQA03xmRGZQTUdAeHzt/PcBLllzLNLlRBvduOd2Qy2sDa31a9ZFGHiuX4NL/z953x2lS1Ok/1f3myTO7OzObI2xeck7CEpQlGADBgCh4CnfqiXc/FTzkQBTTqZjXAIoBEIFbsgtKTktYdtmc04TdyeGN3fX7ozpUV1eH953ZAc5++PDZeburq6qru6vqm55vwzHO/ofx0/JJ/8NDp9QS0O/97nPo2z9kCHic9Ahba/7wL1Zh+Zcew/7dcsZNL0uubqTD6tfc1kqquu97oCdk3BEVneTlsHL1cv2TCbyL6pYY5XRotIS/7rkH393gkZ/aTJdluHnLLAvP37c+RO/M6sI9s390MkVBT6EHgz1ZdOywY/mr4/ZCv3WQbfYumPRBx/WmYEtAXJbcjRJFUBBkKapeX7kVj/7q1bLr+mfHzTffjFNOOQXt7e244oorcO655wIArrrqKqxZwzxRLrjgAkyePBlnnXUWLr74YlxzzTWYMmXK29ntCP8HMJx3zsPDOdstOMj66pU/vjrl9jwbaWaFrGnJlVS0aU8WO9o9CI4ArNrYjz896e3Wqo1x2gd7DTIXEw92Zd3mzlAaWqHUOkOltPYtoHlJlhEAuZfvBwDkX74PhTfdDMcjgZclV4RX+Hb6tMsRnzyPjYNRl6w+85Kh7ip0bB7nOt/XXovh3OTyOh9hTBGpEiqFK/etadbhNrO6Jt8+EgQHaBiTd3ZLCJKCEbiRZLdssfrStGwZ9OFhB5Ozng9vEQKAv3U8anRJByjQV0uszW1STUInA9A1mw22u9iFVI9bazdc9BaqjznXtv4SAlAQDCr1ACEYVBvw/P0bAN5TRXgIB/b2o3ZcxjFuyXT8oLsTlQqcZZ9Sxya9pJcgZkTpm7UHrS8uCS04hsVAcQANAHLDRdTBFiTFu79i5lVY9erLjj4HQZrjVnKMgllyu/bZ2lXmfktBdOIoBwDdbazcQz9/BZnapMMdl0DxtuRCZ5sSQqHFi1CLtsA/ONHNKLnjTfdGpGVGA9q3OwmMqEMWd7bdnmvjyrlZlnXoUOHchJmutzql+Efnk5ZAKYMplOq6zoRcaDiwYDPGvRUQxxRQX1jcuuFmnPnsJ7FvSzeuvo0JQ5RSxEgMJVrC3uwe45h8jiPGfzzu23MPljafVVY/ZBbsTa/sdbxTEcLh+uuvx/XXX+86vnz5cutvVVVx443+boQRIowWNI9Y3TAI4gj42eedHmSJGMFlZ/i7POfytvAtIsiQLDIqi4JySav8XitBcSMjCiUqEwGU6kZXGX2oj6UOMq29ihIqN7mJ7Ep77tA65F5JlYJqmiWU0iFvckFrlKmT7EOpm4D+h1civ2cPqhYvBgiBxhmLaMGw+jueifz59K1rw0TpmQC8U/JZ/h9HZMmtFC5LrqSMhyBLQlhyw2ipRgOFjg5LqB133nnMbUPXUeo3LF8VxqlqVBPDK6FAASVA1hBgCQioEHMKALve85JcWDJw1Dn2Zp51j1G9m9i9fj8IuFhc4XqZpYcoZExTEjPCILtBjWre+fMkY3H9/Mo3m6Zw8PxfWVyubigkxg/4ayRDWXJ17+fGC6IULE+uxsUxvdn3BnPP5tmVZfkOxc0AZcLsvi1dEI3SC+sWo6gVQQnFxg8zN181rqB/6j6U0m7Nu2xzpMTk9xTGRU6DxsaY65jsnsw8xfuye7DDw03Zatd0XScZEIWwMABldF7evcN7Qrmvi5s0Ch1L6g8P3U4YZuQgyGJyXe9GhAgR3lUw52BdpyhnG3TaEjuzhRZoQ3DOEw/ctBiXnOYv5PqyKwdMv4mY80ZKwlo/1vG6dIh54ZBEGvH5p4AkUq4yfT+8jDELWyFrimtPq3VuP+h9lULXQX0MISaKHUxprZVU51Y2kbG9JH2swpWw/oeFVojCbcYCYyrk3nrrrTj99NNx6KGHYtOmTdIymqbhxhtvxNKlS3HmmWfinnvuGcsuhodrIyizYDnLWHG2jP2orOaqDwumWq8UWn8/4hMmQEkmmbZO09B5113sZBlEOCKShrHNjJ0jhIAqQKnE5UMl7vr1mIbamCQVkwQmuzIVN81m3jMAs4+ciDlHuXVtezYJrM0HeaERhUTekqtDd5w/5P1Ms0oVUwngfL9a0+x+KmGlHdKYxpIC+MUXHoEOHeP2T8XD333DUY7FwHL9DUEPWaj1dp03Ny9NiSaOhEgoJLArm4oAfhPguoYS6KC4/4cvonqvc6MyJTMNg4VBgFDbPZmyWGciEcj7Ov1TT5hjwqfZ0uPueKOjGxl53M6h7fjFFx5BiWNo9LMyfHvDLdLjffuH8NN/ewg//beHoO1lJBhVarWtaJZ8R6HBDcMt62+0BG4/iJsypppwjqcu3Kc5joQQ9Bbk+ZaD0M+RZMnclSMhN0KEdzfMqaVQokiWkU+eJ6jKBQgQdVXhiDR5mEJuJbG1VYL7dJ5T7sZUgnwxXKXrdw2NimuzOX8nFi8FIUEWWk7IFeb9whpvj6MRIy5Lbs9ANc2dOxdALZdCCLqOwTVu0k8AiM+w99PUyLkrw8EURItDEXHiWGBMhdwzzjgDf/jDHzBp0iTPMitWrMCuXbvw+OOP46677sJtt92GPQFxqW8HChuedR4wNnDq+Gn2Mc6leaiHo7hXSLBAJezks1u3ulyHrY3mCIUzLZezNFlEVR3uymHqzmlOi5iisdxjZ/1Nw/SdFJkYY0AkYIY+c0IlVL7hX9SwGCnCaRZ99q0KAfKKO5VIqaBZQ9g8vQFnXu5vZWI5dH2L+EIraoHa2Gf/us7x2+HCSp1CbqLO+DQNc/jktNvKevn0T+GEcSdX3ueSqZnWoOruRb+7zSmw9h8IjlfVE26mQUsYNO63REuWJU8USkTLvvmuZAds0hB33kTv/McULM0OFYVARXAzMFDIuReeRNKO6lj+pceMfsH6RoebncqSOdWHWgLY1oGtAIBSsQSVqFCg+qZFAuSKi/4ue+z1HvasqE6t8XPd3wgQKrWVhKzO3W1nmYlpe96XCahhwF+nScbxYGreI0SIMAYwpo0wLrz85y4Kkl545JtLUF8dDy4ooM0grqokT25VyrnV/s0jdkhLSaP46q+3hqrniz/bguUPyXlMyoK591DjUuHVAdU0zoTgkhlFkJi3kDvw6qvQim7xpe7EE8NVrtjvitbf77LkkgRTJNMAxuxKYO6vtVzkrjwWGFMh96ijjkJra6tvmYcffhgXXXQRFEVBY2Mjli5dikcfdbPqvt0YXvF95wFjtlXqJkBtNeI9uAmhMJywNoaEKE5BUgZhs6YNDGD/ffc5y4ySkAtek6UozpxpRt1VCxeGru7K5SU0PGC7BKuETZKnTDidyRWmLxEl0s25y9Dtc3uEAO3xGfJzXM67IJAQegc//OKLj2LbanfSch5i/lXehVWnmnPxNHLMmkJfSpIT9Jim41AVqyq/s9T5r6ZriPe42RUrcqGiMgI29o9upEwq6baQK302fAohj4efr7EFcFVRLGVJPJvCpGeOsKui1DAz8q7S5rgqEi8Ed3snfWi+69hA17BNFEaAIuf6vHlwo+VKe2Td0QCA+1c+Do1qiCkxKVGUswey85wlW7Gt20QheF/reb6WXK/nOFBksauvdr/iOF6JoEhBrZRQJhKKM+1CXdz2zpCldCoXckuu83d1Q5RLN0KEdxPM2Uqn5cWpnn6YOyVLTbp8i20QKlkWRYv0o684151tbd6kVSIeeH4UcqAbcycx0+z47EeJKRAqinXdQYUpVCvez87yNBTAr139L70knOT/tp9Hfs8elyVXiZevBAmLgVfZ3rh26ui/mxHceMfF5La1tWHiRNu1tLW1Fe3t/sLDOwP8F2RsQkVyKofwWJ6QCwDFLsG91piYAgXmAPBMdURVUeqzGWytDXIZwTGJIkD22qQ+Zh1HNhwFnQD5LBuHqo5x0AvymEt+Y94rcSG1yvqsgeaEx6cGqmlMO/rEVzTSuJiB7pDMvAZEMiKeXVk3NYjM3I3jmk7AkQ1Hj6h/Fgwh0syTO9yfR91Ls9zFKhgPmQuw+W2UjO9hSBtCQWdacTUuTPSCsOYVk7vn1FXW7yd/uQ61j7JcwFX7xqN+61T7elDoOnUqUyi13JV7C07SCpm7azzlXvB2bziA1X/fbnkxxLPOmCZTyH32Obag9Q0zN9uZfzwDpVL5GwXHMBCTeIq94+dOPB8tGW/l4SNtD0qPm7G/B/L7hePBcVZud2VaVlqgynPcEqSUFI5uPFYaO9yx3XaDTqZjiCejjUQddFPnAAAgAElEQVSECO8mmFOLjxcpAKA6raK5wVakycL8x9WNvrAiU1JmfdxaSxrFLzysr+87tmnU+lUW+D2jEs5Cy9yax8D6SIw5W6ss/6yJQns74o02oZbJ20JS1bZwb57j/k7NmIEp114bup3MoYcGlnGQuRqkVu/EtMn/F/GOE3LfrZBaP/yEzyDiKVl94sbSqH/wzTcD++cLSu0PXlHQ88QTznMjhBWLBwJtsBWP3G0LrXmOzPYT31iKD3zxBGZMzgJDfcHazd5+Pw0kcfwLcK5Gbhl3xCjXAqaVNBxWdyTq4/XQKUsdYyI/pGFm1WwoioLmVxfgsNoj8cmZnx55J+FmQB444DHOFTx6s+4tg3bOQFPwffbAUyBFFfPvOJ9rQ3inRXdlqUsqUMrYfS4Ml1Czm+VkrdvhdOvWQRmLoYe7spjKpnk6swYc9V6OqdjjGyjkStg4IEnVQ5kQFycJKCuZ8Lk/z1JiqbkECtkiqJHaqBLQQbe7ssppvfdudirDeovy+NeS8b6JwqKXUOzoQwh35YyagQwELFdzJdChIaWmkVEzuHfP3f71EBKOLC1ChAjvGJieLL95tA3d/d75Ve/5r4Woq/JPEBL3IA0cCcq1KXT1F7FnvzxLxVgTTpnQemx36dBuyJzFV+vrhJ4tl8U+5LMw94uZcLwsfhjeuNF1rPbKH7P6VXvNLHTaKSurDzsM6Zkzy25j/Sc+gYHXXpOW2fDJT6Kw31AmW888WpvGAu84Ibe1tRX79tlar7a2NrS0tLyNPaoAlipSQ6nXvcGkQEWWXK92On7/+/L6J6DQ1gZtgE1Y/Ic/EiT75InXM/1CnAUnfGRqk2iZ0QBCCAoHKO643ha2iwW55ae729siVCywBZJ3iR3qZcKRa3oZhQ1xfli+4U57bPZX396J0l3NUIgKneooaTp0hd3P37sfR17PIR1Lo37bFAz2+gv8Pf/4B2jJ3hBofvSSxm2On8xSN3m5z1a0ABsC7f9s/LZ1KNVTh/hABv3FfqiFOAjn0uxqQiSekllyidvN3ZOAi1IWB6tQXD3788jUJlHdkAYlOojuvipVxTT/x7zvEEydz3ICxlPOjZSZCorq1CJV41GzqxU61RFXuOv4eyIUj7U/jM+//lnrWFPCzsGX1yUbIm4css+lrPbNdzvGfbdiLmGv+FdTqVK5VZXrnuGufE7L+6xjM6tnS8v2F/tRohUKuVSHShS82sNcrAdK7vzHJqLw3AgR3oXg5spVm72/bxGyNUBkNR4N+BE/bd2XLWvdrA8Q0g8W6DA3rkExuSY4i2//T67A0F9uLq/RsBOyKeTGEwEFg+EIsyNA5tzPW7l+CeeSrA1zTM0BY6HW1nqey+3Y4XlOGxpy1h8pYMcE7zgh95xzzsE999wDXdfR3d2NlStX4uyzz367u+WJxOHv8z5JdcYC5zqOQCFXqknysOSOFJ13320J46awa0G3mZHLQXJQvond2TABgG3JkWYSkbTlJYD2+VlyiduSa1dIMfe4yVxZz2pCI10jn5S9mAtzuyloewIDxQHcs/vPyA3nLSumHtOwN7sHqVgqVP/ab7/dmmBzQwX84guPeJY126ABGsVKZFwiicltfnUBpj1+AnSqIdnnjP3NZ+33pDU1kfUlwJLL8kP5d46UjBx6oOzdIRQNiQZ8+Kun4ENfOhEwLMZiHKmiKpi2kL2jyz57DK6+7VyoqoJPf/8cu0/GwCx+zwzoYnJjAGohziy5ir2I1uxpsYaZ6hR7hnc7hMtMzFaErO9/y1Wn7FkM9ecsF3nekvviAxusv7VYCS90Pee+GMBrPcyV2oyV9kNJd96nzF2ZAGhJB2cM3Dq4BUXd20IT1I+CXrDaV4m3Uo6MQghChAgRRh+Fove67YjMKCMEQlwjLzxxnMOS+5llFWUzdcFPNvnX2zZh425nahu/OeiIOTUV9aF7YGSuvPqg7e1D1BhoSW6UcICoDjM2X0eYeTYx/5Sy+qg0+PP3BKFqwQIoScGowvUzwRvP+P4H7Kur5rs5Ovb+9KfhOzZaXDoRQmFMhdybb74Zp5xyCtrb23HFFVfg3HPPBQBcddVVWGNQfV9wwQWYPHkyzjrrLFx88cW45pprMGXKlLHs5ijAjsmVC6I08EOadPXVmPKlLzmOmQHrFkYg5MoszADQ/dhjjt9mgmw/gToo8boNglyWxUsOlgxhWrJHXTvgdr8eyWZVtkwOdGdRVWfHUY7GhjiZkcf/iNa+lhkGQYYhzBVpAWv6VuPxX662hESqsGtMS51MybDru9/F8ObNVk5js/d+Mcx8u2YM8Ggy8zZsnob+A+78dcRI85Pss7WgezYewL3ftQWwuBJn7srcE5NZcof78piY9GZoB4Azm5jyiRpCs1lvqiqBVFXCyA1PXONKdQpFMtYxMXYYwMwlLVIr6KTnjrDclU1UdYyzhVyqu74Z02WYlJRALyazzxtfsonMVN22CPAEZn5bxNW9rwMAZlXP8SnF8Eavc+4RHwulLO+x+exixNtCQY3//NCZ68SNa69zHX+84xEMlAYsBYLfNzvWua8jRIgQDhv3eOc45b/ZctbkTFLB9R+ZhqvPZ2uDQgjOPc72kAlDQOmF/7jY5nkIYlcekjD0y9BQE8PCGW7CxzDYsrc8/g8R2l5bEYp4Eggj5CqKI2OI80EF70UVPvNICFSd/6XgQmIbVRwRp6Kg5fLLEU8ZBhUCkEyddbr5wx9GrMHci9n30vf882W32//yy9bf2a1brbhbHtZeg9rEkREOPsZUyL3++uvx9NNPY926dXjuuefw0EMPAQCWL1+ORYsWAQBUVcWNN96IlStXYuXKlbjkkkvGsotlQ+/a7T5oGch0SxBNz5xunc7t2BX4givJJNSM3M3VamYEH0lJtNgGwU/IDdkPXnagvUwwq13iLjesu4W0nWs73QUDYLIdJ9Ju4fOB215ETzuXImeE7MpeGCwNokidE55JhCVqqfOG9btuehL5+n7GTmvlQXXXPbR2LQZeew1D64zURMYN/O32142fHjdkCbnsfK7ksWDyWs/e8IuxOe7FjL2RoYQygZAzHg/3O91ydaq73JVlltxCrmQpAbxwZvM5OKvlvdCpDqWkMsstP4hC7K/VHqWBuVYtLjaVSN2VAWYd5S25rElj3CXXmELf/DvPR+1O2+LQlnUTlhACHNN4HFTOFW/odbcQXkrmLfdxPzQkGDmHX3zryvbHXT3moUMHMf4DgKkZ7w1NnASTwewY2obOvPub3zm0AwAwLjnB6IWPkEsQacsjRHgH4p6nvNdz3h24nJhaQghOXFhvk1URYAqXO3d3Z3gGYxH8khDkZSryCnoV7xmQe7NoOsVA1t/T5YY7gskBwyJUtg/YLMuWZ5pjQxfu+vSZ/xKqPwBAYgnwG58w+8z09OmY8+MfmxcgVluLeMoIW2toQXzOsXY7sRiUFDN0xCdMsI5bgq93Bz1PUUqx46ab0P24vV4OrmV554c3MMWCdRfR0jQmeMe5K7/bUNrtdi203l5GfwoASM+23Y/Vqky4QP8gRmPTlThWflxHubG3fpNg+HQg9uTw6KM/Yn/EJF+6YFlMpmN448lt8n7VdkiPA7YQJ8NgT86R8oeMNIcQIJ20ZNaoINfv9CU90Aztoy0Uegms1KUhNF1YZXG8V878jC1sGav1M51PS6vODtlCT3zIPxULVXQcWMjIpno6mPKgUMspKwhQ1Au2YCm5nbgSh5kX2KrXYyXQUwHurpQJtVRnLtSUOK2H1CCeenDfA+jM2e8Q1Wmga7i52CqKgkmS/MUAXO7KAKDmmWVXK+mW8P78gWfY/XDzQXzQVm7dvO4GR5usj0xBMm1hs5UiRyaY909rg6KryHT4M3iaMbvSWGADu7O7HL+9LLkmFB834mlV0wPdEO/Y8WvpcdNybo6fr0V4hLmvI0SIcHBQ8lmbB4ZtKTGmlr9FNT1xFOIUTl9YHz6+VwQfVhVkyRVjdoey5XEe3PvMflz837J95UGCoobbj5plLWuuPbjZp0LwwhCC5FHLQGoCGKVj3LrJLcabPvsZDLz+emAzsWqmkFerTcU8ex6ZM6507r0Isfa1tUceaR1OTbWt9iKmfvnLaDzjDFQtXiw937ViBWuRC1Ns+81vAAAdf/qT0Z3IkjuWiITckUL2onLEU7b/vX1ayYQTcmXCUN+LL9o/zBRCpfLj24bekk+iXkH10thisxvGvUyvmuE613L55Y7fsThzk+4t9YESHduG3EnQqeIcUzWuesbk0hrvnHHluCcRVJbkPQjDGrNmkmJ4pcITHbYW0HQ99n1djPdEVETI7n5Cstl6F3esYcKd7kFS9fhvXsMti7/D6jIFT48h1eMaCtVDzvp0gkyd4bJLKCamJ3ECrLuiT8+6xkhpS/CNRd/GhGSz1OoJhFsgCIit6BAUJwvrFgEUWNv3Jl7sst2ThgfygYLRplf2sioJsHzrz6RlNKohJlgsU90m0ZdutfGHnb9j91OGWpdqAAoKqE7R2MIWctm7blq7E/3+uZR/u/1XVp9D98EVk2tYco3d4JbBTd7XglrMyyePPy10m3wfw4Q1jYbeKkKECKMPPyH3bs7KW4GM67AN8HuokdBu8CEsQdsEUcj9tx9v9ijphDmndvWNLN62bDgEV6MvYvpLWVluQPMv3hvcDlGY1XhAzE0vNFHPxcpy2gU9l8eeH/7Q8zp+TZr9ve+h9ROfcDafEjwjFQUolRwEVNZxD2QOPRTp2bMx9YtfRJXhfVo2zH1aRDw1JoiE3BHD50WluiV4ODaFAcm3HeUE7Pv5z+3qR7CD63/lFXmTXlZhj/7u3dxlbTwVSYBtw3veY/1dHatG6xATrOJFFif5Vv8a7Fz6PJZ9/ihQSnHgoYdcAgmllQmgfH5cP7TObMD2NR14+SHvjbkfOnYYuVZ9mkv22QQTja1u11+ZO7DCfZ5ez7r70UftH2FcjghxuenOznjneVPCThGUgqpGeoES6yvRFahxI8aYUExMT0Zzii1gjetnQis5+1sbr0XMcImqTzSwGF0v5ucAoZBlxSLQSzpoQgNVKVQuTpQo9jiUOPKoZ/+yzmHhlyE7WLDq6CnKF2yd6ogpzm+J6GwsSgVnTC5LJ6SjKVFGzkSNsYET0/VdptAxvyOJW7YM/Fhf8+pV+P2O273LGv0/sIdZR3RKQUj4TLkfmf5xTMvMwAWT3h/yCgZbEDe04T5eJGpsjPI6RogQoSyIguDEJjlpYyVxtKaXiE4Fj9qya+Lq5N2VA/YiT70p5zsJgjlVjTkrPFFcQq7evVdeVlGsfYbeLc/96wWlykgJpHp7HtZ97k7UXPLfXN/KaYFzc29qslyRcwMG94owsIQQFLu6QIvhlQrE+SKEuqbU7dwjWMaIaG0aE0RCbgUIjKc1WOFKezdwVl3+GhLKkptobvYvMALiKS/rb+2xx0qPe1lyH/jRi+jdxayVc2vn+bbJM6Ee9wK1LLaDkzvRPKMeu7/3Pey/5x5UDQoWSQJn/Cx/zqe9oNhKExd8/ngATNitBM/ea8bEhit/4gcXuI5Nf/Qk1zF+QtV1in1buvHHm/6Blb97Q1qvaMlVJOkTCIhL6KlWvONtiyaJRoCgpECxnmcWhvWaEqgcYROlOkgrc6FufWWRpRyYMK0eH/36e8wOWm0pUKB5MP+GsXwSEBSKBcuiWR2zLZoxRbXaWde3NrAuZ73Gvz5DQil1xZ4u7jgZAPDKX7Y5hLMiLUAHRVwJnzKhuCaNgZ6stQk0/83XcrH2hpDrxSsmEk6JAuOLHqzMANCRZYqAu299xrpW4WJyvTApPRlzag5FWs3gP+d9FWk1g2UTL/C9hofJ8uz19GNx+50P+/1HiBBhbFEsCd5aKpHG347oE6aAqjjX0ErBW569Uu6ZeG6tncLtkZe7fEo6ccMd27G9LTt2Qm6SWTaJorozQChyzzOiqJ5W3qC8ufH5p7I/fIRcpboBJMV5HknTbxjVVAnrpcd+WNeMGF/RQiv8rj/tNHk5zw5Ulm4zYcT/RjLu2CAScitBgIBadcF/AACyT/6G09rA2hWHjf900Z+7ujH6Qm7jWWeV3Zau65hZNRuL6w8LbtfYBFMojt23QhTLhbq+TxByPVa6gl5A84C31jQlToIeUBSC+SdOxfgpdcGFJTBdYv0Er3SXXbeonVbyccRzKfESh8BAdYr7f/gCejuHsOmVvdj8hS+4G9FElyN3fwgImtbNchwrFbz7HcYSptESdEpxWOPhAIBXkv8AAKS7Guw+EIqCXnBIhmbV8aSK2qaMVY5YGm3imX6Jj6OVQddZTO7TnU9BIxqumvlZZGI886Ltgj054x2DI0PrrEaj3/ZifeolCx1lKNyW3N63mMZ4oDPr2ihRquPYpuND9yH/bBXeenYnFMOfz8zlm68bRKo+ZvSBYXzSmWdcoyWs738LM6qcacq8XMNlSPU4vxVqWnIDdmhfnX+Dq925Nf7KMR7HjTsBAJBU2Nw4UHJurPhhVRRlxLmvI0SIMLrYsz+PzQI7sIzRHnAKqWFhVsW8S+zjI5kJ/vaqbY0Tt0LiGjmZI7v60X17EBarNg3g6h+F9yYrx0uFeQsZ+5S8oYQ2BVmJu7KXkAtFBTQPwqy96z3bJ7Xj7ZSOPkKu+0Kf518QGLqDxkOsS/idnjPHfdyneUV0cw4LU4guY72NUDkiIbcSBLycjBXOgMRdOdbY4Ll5l2Hy5z5n/Z2YyOV6G4EqyEvI9SSk8iWecpLOhGqfECQLOlrajFQ5nOCfoU7hNF1t/65vtgWVjlw7Fuz1dpnhUwQFQVGIL1GVF/52x+vYv5tpbv0eadNa7zQt1fsmSI8Th7uy85wjBZRxsveZZxxlinm3xpUQgnFvOfui5bw7Xh1jbtZ12+QES4Ch2aaARux3Ss0ZKV6MqlM9dfjN9l8C62237fUvMGZyhxKDt+QSxTsmN2jLYhJyUQIQ6sqnWpeo49opbyP14M9eRvOMeocSJVXtfGd1UM80OpRSx31RyspPy0z3uR/3oe62QWx9vQ0AkK4zFlyFQjOVHYa2oFp1WupXtj+OH2/+AVZ2sHRhRzUcY/Q5/JyUq3OSuJTDrixiXHI8qmNybwJxI1cXr8N7JizFFTOvBAA8sOdez/LJTDzSlkeI8A7DVd/f4Dr2yXNa8cUPulNFViLkmqCiu/II5gLe8iwqKEU92qmL6z3rqUn7W//OPLIhdD/LuZ8NV1yBDVdcAQAY/Ost7KC5JkrclT2hxAxSCEl/8t5pjeJTufhVtQzhsOhNhuhatgM4JUQLraiQNX83LVtmHUvWVybIVsKTE+HgIBJyK0E5swvPkKIomPOjH2HS1Z8pS4uTnmVb3pKTmbCR37sXpb4+r0uC4eF+LBNy03PmBBBPyXOLymBbcgkUSjFpr+FSyb2KJzzpTCGUqbE1o/EE5/46Aku2iWSaCSKKSipyZ+JTG/lpVpMD3i7BVW3jpMf5SdjPImW2O7BqFQAgZoxRqeCeaGXupGGE+/rt3rmqmcBGrPjQ9IEGzP0zy4Gtii7T+93eCfx9Musn767s/YznntHieU43GIiJzmK/RSVMJp7hUvqU99x1jUINYETJaVmXYG2iMKxhw8A663dWy4JSHXUJY3Mk6U4+5x83FE8YSgX2ZQEAilVs00GF58tbP2tjdagyBMxyLLk0JngNCHlyL5v28dB1+eWoFsmwNg9sQk+h21K+mO/K2md24r7/eR6UArXjmFfAey5bhJHZbyJEiHCwMXl8EsfMrcVph7nDhVo9YnXDgFJA5daW3sHKBQ/eldptyXX+FuONeYwPEJrqqvytnLycVunMVtpupBgcZory4paXkF+1wlnIY29FFO90Q55kVa46KnPzFZGsFgTgEVpyk1PYHidWXVnuYh4my7IUUUzumCIScitBiM2g2jILqZMudRFPxWproaarQmvOpnzpSw7G48whhwAAtl13HfaY+cAAFLu7yxL6vMrKhNzWT3wChX37vCc36A6Coq7G4PZzpAoFJWXN1LzwRcQcnNxkwGvj/ATv8//1WFTV+bt7KwrBmVccYdRLPFmG/eBM7VLZpFWsHpYe9yKeSqSFhdB8LsakPXMJE/5ES27Xvn4c2O6ObaYlHwHau9t289BAABCVlVbz9kIeE4RcdaG7/WLe3nwsaTgc5zQuw+O/fQ0KkcQKAeifxqz3C8/xti7DTGlDARDqSmlDFGLlkPUjL/KC6EIvaoUHS4OIKeG0wEOlQeiUIqV4p2l64nerfetQTSGeUOs97J7H0m7p1QVsGbQZPrPaMHedalnFvQTNp/f/w/r7tiN+Yfxl329X/oAhXBNrHFpSrb795UGgeFrmeVIwAFjTtxpv9L5m/TbTND1991q0bethwrbxbJLpyJIbIcI7HQmfXLixkOSRMlA43ZWv+0h47xIR6aS9frgsucK678ccHRTOwWMo597f8E1XMrfJ5vjSDsna4rXHlbk2G8i/9FfHb1LTxCy/AuKzjwnuqNjHgnP9VuMa0rVCisSg/W9ATG5qyhTMu/12Z5mAnLh+oLruzkJAqfXgorVpbBAJuZWA+5hIlZysKDZlIbSigtzOncY1XD5TNQZoWqiYiuqFCz0nRpq3NVlbvvhF9D3nTRQjwrQIW/1tNCRTiZBrUqwPrVvnOgewj9lK4K1TNPkyxBtkP02HcL8Eqn9xEeFYeBWV4LWeV/HQvv/F1o41AICm5BpXK5naJBaeMh1X3LLUsyf1zdWWIKyoCkpD2bLZWB2Ljk6Rzxbxi39/pKw6co1yizwhBHXjmXs2v5CKb8O+X/4SANB86aVWPwBg6xtOluAVP3kZ//ip21Us1+OtLNj0shBTJBmefdl9ACU2RwTXwap6wWU85V6I2rf1WH8nY0kUuim2vNYGhRCpdXF4PHvB/FzkzZhcQhkhlsj7q3DsypUw8IbZrMR8csXyKNACS8Fj1lnGvi6ZYd/mCRfMx44znwclFEXTVYoA1VNj2Kytx/9s/LZ1zf78futvhdgCppe78l27/uAoD8DxHtz01g3QqW4oqsp3ASfwdj/fm3W+f0vqD3cQVR1aM9ehJAEFTv/oEpz9qSPA8iBGO4kIEd7JmNjkrYwuN5SEh8mwb2JCfeVW4U+911baibpwt9DrXU9M4n79y3+3sxvw9ysKyyyu1vm7XJS6utC7r8Z5UGZZDZNCSIDWud3xmw50ITbJuDduHSfV5RN85oecz850RVcaJ3PH5AOvGmtkkLuyFCN5/zQNg6sFBQIXGx1JuWODSMitBI6X0+NFJQo6Hn4R7YZmiHIBIoQQJkx6BPD7grNeipOcNjQklvZE1fz5jt/j389SeUhjco1+67mc+xwMd2XDGpM05O6gzzeXY5OTIp2XnBPLvi221HzEmbPwSNsKPNy2AmsMF5vFB9yCW201oA/0I13jvYDqum5Z5BSFYP9DD6H/hRcCeu4Ev4mmOkV2IO9KjRMM+353Ln2BO0owfaERryumoJL1RdPQ++yzlkAsc1eWoev1Aki9vKzFHM23I7x3vYVeKJpquSvzAktNo5CbTvdfNPhbU6BAh+7DrO09fbE+GoIsoXhBYAq24nVRXiyqrJ+y3wCsmFxS6++18fsdv7W+Idb58P0wrZb19TW49qx/Y69SnmMYJsRVn865ACtEsTYHokJhZpWToIwHn4aqSAugcLor+z0bV12EWKzJIuriToKr6li15ap8XNOJyMSqnN8bAVpmNGDWYa1jn4ojQoQIZePfP+QdCnPusWWkVRNAqc3OPLM15StMB6G10b7Wte8Slg8/67NsezVlgq0I5nMEi7Xo3P2wfnj31ws7brkFuUFB8SyZKCnVodSOR93nfu/sw1BPqH2rSW5V2s3IRFPHX2SfNITqun+9PXzHxXs11nWlbrx1SGuTk3ZZdxdm0fa+WtKnYF4QfdjppVfo7ESphyn1I96psUEk5FYAh8bI6z0XYxd03flRqXFAKz/pN18nb8kFUJbWSXT1rTuJpbCR0aebgq/W3+86B9jEUwQEx7/A6vXqSb8xoSdjjFq/asg9gAq8J9HpC5uhGZrEJW+4Z4lqjU0g+37+c2z50pdc52OOmF4KxbjfWFyFRuJlxzk73JUpIN55UEoVdiGnvU3Zz1RMIRSE4U2b0ParX4HqFGpcsViAw0AZF1LhQtxu2al+I4ZFtctYEBYChfpPOYTYLq/EIJ760zeekvfZKFfT6HbzpbotyFJC0VvocZzn8+TqVC8rHtXsp3DAVebAz5gVftP5f/Otq6QXmcs/Z5k+b+KFYTti/dmcakHtLsFNmCPystrjXID9LLliPGxNzA6bcAvOzJL77M27kOqqC/fec50s0iLe7HWnxvrzrjsxWLKVHDrVLddslSiuPjqei0+sb4QIEd4ZSEpS3ZmYN63K81wQNJ1a3kU/+dyhqA4gfQqCSRolWmpFS67fNuyDJ8tJJnl4TVmDWc1BclXJzGYKWDwSC97jLqjrIKlqKNXCHiI/jMKmFz3rH/zLzRh+/BfIPuUUjtUJ062/TXZlkgz/bHVBOW7ajGIts5E67XIAgNa+1eNqg/dF3NseZEuu7EEOrFqF9jvuAAAMdlaudIkQHpGQWwmMl7fqwi97q2PEj0OYGUksAVoqhG4yNWMGq0YUbPk6Q9fmFnL9XDfMyUH3SJpNqQ6FMGbVlNzYa6FnEhNIEnlvq5UaYFnryDM33GSeuzjJyHQGVeYKk29rkyb5Vjgtq65x8XtVcWiI+cb5ysDPY8/85S3X+Xm1C3BIzVzHsVXdLzt+Ew/rJu9iG0YGo4WCUZZCUZWyNL1hybF1oruE3Ke+t9VRByV2Z9c8vVOsIKAf9nnVh10ZABSiIpZQ0dDiJoqglLkoN2yeilRvrSsHLVFgCX+re1/HHTt+7dsvv34CQJ1BdtTQai/c2gArU4T/d95V6GLpLrjnrYe/AcAAACAASURBVA6nEBsKZgcXP1ul5IyBkmWu5ce0pJewP8+sB6K7lyhADpV4i7qw6QALx9ByFOkD9WXFnpk97Mx1uJ73+v512Dq4xdF300qsEtVV3qFHVIkjzVOECBHeWbj7awugcmvyKYsqS+MnQ7FEvfg1K8Lya+fim1fODHRPXr/L26PuhAXB92cpHYX1e98B596vEv0dkaS9UZtnugtS3R3DCkCpm4DY1IXu8gCSx30QxU0vIL/qf5Ff9b/enTDjdMtYI8xct3b/wJagRApqPVPsUq/URgXjJRDdlUPkw609pHJPAhe9N/x5ZCIcHERCbiWgOki6Fon5J8NLn0YEqYHKLLllCLkzbrgBAHDg/vsdxxMtNsNsx5/+FKqu4Y0bsf+ee1xxuZ4wfWy8iKcoY3Qlq7dg1lb/mVcZZsLGhOIuJHU54RIAzM+6tYWW666B7TPtMX7r0iechT0mUJOUCTDiNlU7hpCClE39LlqKxGbjSgynjndqSp/d/7RQibyvvMvn8IC3ckOEbpDvhLVipVuV8DMBoZ6LK4kZJxTvdvuLcm8Aqw4CbHuDpcUBVaDLvi+zGSj4xDeW4pxPHek4nalNghoxuQ0bpwMAGhNOjTQhBNPS063f2we3+fYLALYu+7ujnzwaW2tw9W3nIplyx31Rn/EAgOObTkROzzoEw+3L85hznx1PPnmunIHbxV4tgChud2XenbyrcACbBjYCYB4Z9XE7/YVI+sRbenl35fhABj0rFXT+1Tjm8T579tEQcu/b+xfcvv1XAIATxp1sneeVPZ35DqsfClGhU83h5cAzhceTMVx2/all9SVChAhjh5qMUwn1wVOCLZ1BaKo1mOYpUJNR8ZEzmkdcJ8CYj+syMbclV5BG39wWPmxMhu1tzFLgcouWkRiVCZni30y9ox3YZR/TNUDCKaHUTvCUrknI1ECJeSchdcpHUZ5Zxkb/K6+AUsUgKGWMDlafJYhXq0b/hPsJIWSnmjJQVPbAG84803kygOiKSoTcQHKsCKOOSMitBJyWK730KqSXXuUuQxTnZKDrDvdiOnAAWm/HiLtCYuVbKgbXMKImk7V5xk03+bdhJa+WT246Za6WsR3t0vM8lO0snk4BxbjSPk95qLm023UsnophbZ9NMnX4694Thtf0dfpHlmD8FHbfzF3ZcI1VCCiXqzc0hOJ/+O9/OH6zhPTOz2yg5BT0PjjpYo+q7ft7lrMSB83NVKdQY4q1CA715ZAddCpUOo6wY20T9YqrztlHTIQUhHovrpaM4z2G3TlfVjKAEOSzTLhSqOKIHxXbUYiCRCrmcEEHgKq6JHuMhEDR2PfxwSmXOKtQiEMYMwXMqvoU3nPZYmnX9ETJVV6EGpdMqT7jAQCZGLP+WjZXApSy1GGV3bPhgLy9ACG3lNWhFIQcvl65h6mOBk4ZIB17q7D9Z/XeZgytimHICo0vU8jlxnLn0A7fGrYPbcPjbYzYjVAFXYUu3LHtN551R5bcCBHemWhucCsEM8mRp5c5Yg7bY1BQqArBR5d6p5orF4pCsKMjh8/8YKN1zCuSqChwc4gs0hM80gmZ6YrEZdb1O0R/XTAq6dpVj8GujONYcTsXLqLLLbkgxOVWVn3ZLfIOQojFNaBUNyJ90qVluh7af+79yU+MvgCFN1cGXlo71RjnSmJyOR1xzRFHBJfnoetua7EwRiNKAxohFCIhtxJQavlmJhedgdQxkvg5UbB5wx1vJlKuVwKZ+0nwRYZgZ2i2UlOcxA/TrrsOrVdeCYBZiqVkVABKg8x9UdepI91NGDA7mw6iA99a/D0AQN0ppwR0m2BNrzyVSn2cuSmr9dvNwp71nP7RwwAAw/15h5BbIKlQOWPDopAtOmIITbTn2hy/62O29czO/8km144dLJed6CIcHz8eXtB1ytyydaBr3wDuuP4JPPAjp2Wcj/2lGnXNBFX1drzIhFdtkjJFUbzjg4nwr6xIgJXPQTylqcj2uDXPA5OZMoVnojztskXYc/IqxFMqTFZd3lE3IbgrK4Q4+qKA4N7dd6OqPon65uBYIdFd2YRWlAiQAWtpTssZxYx3UedW1oDXURTwRQy1FTH1H86UDYvrD5OW5V2BAbe7sgM+zzFApneXl+Vu5tk4hW85r7Px6vxOFbYe2IYNfXLW9wgRIrxzMWW8f0yilxAYFgcjHN+c9nd22HFZuk6RjLvnsFWbBhy/TeH7ktP8rdWvb2H7KtFyKzJNj+T+irk4clm2zsUms/U9+7dfWOcp1UFk2QFE4w3ALdruDpGUd85ZEk+h/kv3huovlawRhMDBa6NOPNRVBgDUhBGTK+xjw7grw0xDaDXI9SkE8ZSSTguHBFf3QnhvzgiVIRJyK4HMDUEEISj2cm4rEl98qpdAC9myYnNdzQiWXL8Pb2j9euT27LE+bi8rcGbOHNQbRFSF9nZLoyfW3fM0c7u1Ughx5weUOt/9uU6Yu8ncjRQ18Vrf/pgoUjZOR6zSXBPtmS3nsDoSxpj7PB8+rQ1RjbFQgO5YK9ZsG51E5bmhAn71n48bOWS9P7MJ0+qdeYC5c5RS9HSwBa9Y4N4fQpCaPt2zTjsml+K1x1k8Y3Ywj/wwJzBykkj/Jg0o2i1ffdu5lmART8Uwfs0h3HVugZvrltEBz64FurLyAo2iq3jjf7oc5w95fxMK9WxMFNjPav7xU3HE8XPxyW+fablqi2mDHO0oBHqW9XXcm3NAoODJzr+hoBe8U3ZxN+Yl5LZvZ8QeU54MnwvwhQOM+dlstvm1BdYYZjpYTND0RXKXOxnploljG4+XHq/jXJJ5mARyJjSqIatlrd9za9hm6AuH/Iczr7X4TCt0V3ZU4XiJnOdnVtusz3qJgujRMhYhwrsNMhZijVtbRpI+CDhIQq5k3tep/DhvuT1hQR2+cuk0AMAnzm7Fpac349LT/d2oxf7XVTn3Jk+/2Ysf3Ov2eAuLkhEtprbMdp/UNQ9LLiAu8PoAW6Op4C6s1Ae7iZOEvRdTJ831LujxLCnVuYHiSEDzwyjuWsv6122koRPuh4ZyHebWOdHYE8Zd2UD1YYeZB0O0GWE0Ee0OKgClejBTTwgtUaz1EPT95JPo+/mnQ7U74eKLkZw2zXHMpY3y+fB23Xor9v74x1bfMnPmYPp//Vdgu17uyqawo+mUxbRygvyqqrPQrzjjIF94wE7106eOAzGsNZpJsx4wabzWuwqdfbtwwgs6UoqTlEclCqrfPwBl/CbMmvsKil1dHrUI1kLLQ5T9MTA8soXVhCmU6pQJDnWtGeTq3a4p9ROqHN4/hJPzKSjO+RRzkSlxQi5bZ9yTpTmpmm7Y1AxZgel84H1vWkcMC0+23y1T0OPjoHcuZXlYv/bml+WVmGPpIXTU7GwFEQkkfEC3Z1zHiMJvgpx1fWjKJVCJygRxSnGg180kaWJ4II+hTcxq2vzaAnst89Nf8e+NMZbie9gyg3kU1O7ycPeWQIe3xTSNKjz405exY00HJklIME764ALH784zmafD/kUbXW7yJu7ZLY/d1w0CORO9xV6s61tr/Y4rzLIyJTPVselofcnp3h1krRfBj/eBwn6rLyY2D9iugTOrZuP05rOs37RfxYxH7PjdCBEivDugSoRcXjAIY2jzw8FgVuf7ZNav6VSa/5ZfMKpSChJcKMvHz2zBOUf7kxqJ3dd0p/X7wRcP4LFVAeE/YWDclDp+OrQDu5F76T7mkixbPySWXJMtWYyJJZkySMSUGGov/15Z3QaAqvOutX9w+8fc83dj8M7/x/ph7kuEFypWI+QLloF7rOL149//fjR/9KPSy9SaGjZORp8Ix2tTd7K9XkXs/wcfkZBbCajuuYG0EHA+NvMIxCbPA832g/bvD9VsrL4eyVYxRYjgQhFCu2RZqlQV6ZkSZj0R5sct1F3IGfk1dR0KFCvW18QbGSfpy+srbYp3DTGDOMAmQwijWUttY7GJec1J40ygQJ1VAFSKrtIuX4GZt9SZltx1z+8y6gFW/307BnsDaKIDQDWTIVFnFkVKpW6rikKcltEY56YJgkmHMMKhxlbnhCwdK2PC1DlLLn+OF3LFuFkiGNHNIRrut92aC9XD0KEhr9meB9JJmjs0db7tVj3178ci3SW3Iprgc+KKbt69p29A00JbqPTS9CuEoH1bDzqWe7u76Wa8lCGQmYIZ5TUDAqjKW9PZP4c1HImPTLvcOnzmJw6XXitTcLjBKi1U2YRsOTqMXevZ/DBrSQumznO6qadrnG7YNMn6WKgZdo2faTH2wtP7n3Qde/7AM1zvTJK2gLmvbEuuuz7ekruy4zHrbx0aVCh440lGFJbbA8SHva3ZESJEeGdCJhfyS6GYf7ZcHAzxgV9zHnyRKdJ1XS6w88vT5We1us4HQRM8pkxCSRO5AhsgPrduuWBEm4bX1pxjkHv5fmSf+BXocB8gy10uicmFSTglshsTBbFZRyI2bUlwRwKs9tRjTYlPX4L43BOAWMLRL9OqzO9PRCE1DJ8NiSXtrgl9TM+ahcalS90XmWV13bVPo5qG7JYt3IGIiOpgIxJyK0EId2VeCI4l3JMFzQ2B5rOu475QJPEQZVhyxWu8Ym15NJ5zjjU5iAJNZzcnVBHF1bYuSk4cWMSkSZMrF6Jl6NPkwoJCCCjVzRzhvnBYco1FY6DbfBYUz/11HTa8VLkbEAA8/8B6AMDmwY0glKCvPYv+6fsw2OpckIgg5NIaJvBXqVX4wqEsz++iU6Zh/gnOuGnpWBnHqBmTS22BnlI43h214BQAieok6TEF4r2bbIs40RWouQTSB2xBle+7+crzlryjzpnjaKd+m3AfAA5fyilauGcTb3Y+yPbx2xzCuJcbOFEIXnhgA2jOe3rb9iYjfavbzhjG27MGaZodbu9CKWML/LvW7TeK687UPx5EUPuOZxbWbGOvdewzs/4VS+ptodisZ2ii/Y7wygiiEJx66SJHvbG48xs2XbQJnOPTsGGGZcE9q+W90jy8q3vfsJiWPz3ragBAkbrnLgWKryDbskqeYsILMqFZJMdqz7bhpa7nsWNoOxSiWERcSml0wgsiRIgwtkhKSPoKHKdBR8/I4hVDpJYvG7xgvq8rb7TDCK78YDI+lwNxq6frYvtsfF4TYn/LwYYrr0T3I4/YB/jNkWz8ZJbcGLu3vZ1OVmlCVMQmzEDNR24J7kiA4tRkOJZeqsYRn3W0IDAafeSPVeAakF56JWqbyxvfiZ/+NIjCjAyWkGuMa3bLFhTa2qDW1TmORzh4iITcShDGXZl/eSXvsbZvI4bu/1ZZzRJCUOzqwr7ly+1jPnEGO9/qlOZutWJyQ3z0vCAsptcZXMNcGd/oeg3PH3i2rEmEEkY8BXCW1RBCrrm3bhA8UQkU6NChE4CUoRwzhdzaJuYau6+LSVG8e3Al6NxpCDMUGOpk49Y3bS92nv28s98KsWJytXgRijGGrelJGJccb5UR42CprqP+tNMcx4Y3GqlgNF1qyeWRoU5ypVKniqPfOweXfY3VKZt7TcF42hPHswmcOtMJqTXAUPMBhwBUXR+c6zWe4oRrvj2IlP9O4cfLoijG1Gbe507pUMyzZzLpOeYObuYqdng6+MCMb2bu6JxngMelphW48/D10BX296L6JWhMuF3WGjZP526G+5MQ1DT4Wy0twZYSy70YAFpeWWgJ0ZTSQGusamgTNJmQGzapchk4tskZP0yFHdb/7rsPv9vxWwCMqMv8Zkgk5EaI8K7EJae5YzYnBZBRlYWxisnVKdSDsJPetMeZYtG05H7oFKc3T99weWkPRfSvWsX9MtYIXYdS5ya3JMSdmjA2ncWbvrXNaYCg5QhwAWXjyRJUibHIgqJg+JHbMHjfrUbjxnFds6weYYw6rmpT1ai9nAnpCdGL0gMkFjMs3py7smA11kxWZY+0RxFGD5GQWwnEnLcycAIf1UdJW0MIslu2oO+55xzHXH0zsPrJbVjz1A5nFfw1AR99csoUVC20rTJdK1Y4zmtZ5tI7VBoCBUWs3t8V1QnbkmvHkvpLp1qyAHMo63s44qRa05JLQ1ly+TnatFgef+E8R5nXHt+KkcBk2SU6sYUCSccIR+REKMGCOjbezakWrgxxkFNlBwvQdYqaw+WusVTToajEcZ/5bAnFvD2hHtV0rOu6WEJF/YQqu2NivYr9fL6z4Rb8ZvsvncI3AYabuzApyay17UetRXVDGlffdq60nwBw7meOxuGny13mB4qCBpVQl/Ajg9h1me7FbXFlF3Vky3P9YpZcuy6vuOfcuD589JZTMTi5A4quWoswX1pGwESou+66U9hzuPzmM1zlLTIuSnDyeC5cgBLkDFbiF7ued7TVkGhEQXfmYVYNZk2eYXlieqJnP0cKkZ1dtOSu7n3dLksU6IYvoxKRTo0atm/fjksuuQRnn302LrnkEuzYscNVpqurC5/+9Kdx3nnn4ZxzzsHXv/51lMrMLR4hAgApI3FN2hYGZrYGK0j9oMtCaUYILxdrk0TrR/86x/p7pPGWyx/a5/itaRQKAT713oloabTDVHa0jyy0KrfNzhFvk0fqgCJjV3a7KxNFhdo6B0XqFOTe3D6MN7cNIhQC9tNK42SXNTd5BLevIAQ0N4TiekaGqg+yWOXSrrX2alWh1TQ1dToazz4bajpkWIzp6SmJyXW5SI/UJz9CIKIdQgUY+P1/Qu/eG1CKYyktBmuQaBjffEJcO/aht5yWWiphcXZ3LZwld+ZNN6Fq3jzP8yatu2kFazj1VM+ysmuJuQiY/wYIuZ2Hr0c+xdqatssuu6+VWJbcMEKuLO1Kpsads28ksPLSUgWqoeaN5dyLNh+TS0Fx2oSl+J/Df4xLpl5mlWGWXOd1W3sbPS3npcEhQ8j1GYgyWI5NZMfb5vOdwzuwsX+D1caB+VuY2oJQZJQqjJ9Sh/++/P/5tgEADS3VUHmXW67dTX0bHGUpvHO8Ovou7ERk9yK+A6aLNYE7XtkPVLDk+qG6OsOs0YpukXPxLZkCvJkiyazf6qN5Xwb5lhp3v8eWuzIVFCWcom2g1I+SYaGtidWAUh0vHhA8DIx/D+Tt/LznTryAnSOk7DRBQRA5Dih0xIncxU8hqhXzXg6RWQR/3HDDDbjsssvw2GOP4bLLLsN/SUgJf/7zn2PWrFlYsWIFVqxYgbfeeguPP/7429DbCO92xGP+8+aCacGp3MYavCXX/It3V07EFKQTB2dO0imsdlQCLJg+uuNDklWw7op6GHJkKYQAxGYehcWxzY5jFASdvSFdzgPW0MTh73OpVhNLzuQud455cd1TAIDBP3/NuqVKLLkAoKbTaL70Us/zEz78YcfvWEMDs3jrurUfrz32WMSampCaMYOVqa+HmtBAZXHPEUYV0Q6hDPTcci4G7roBNNsfXLhc//8wsbSE+FpuXb89Jg5SRkxuQIfYP5QgM0RRGggfuzCgNsKOmzCEPEFAP3JoJRoSNrMqjemWYLzgLePaieOx8kzVtuQqgBKgQZW5HCkS4ogghNHUEp3YQm5WcMVqKIIoBJqhzVNLcSiEIKEkLUsaIHdXLmrEU0lBZcRTrn45f1ctELSzsuGQHKM6hZIAOo5ZC0IIzmp9L1IkJS0LANmmHsw9brL1W/YsTExMTXYeIDSUkKuVRE2zu42Js5zM3+NXH4pMe5PxSoaX4HTRkuuzWJsWfapqlnA28LcEpjzBrOqW4MgpIPj4ZsshwPxXZlWoyxvXCUK8yR5uWMfN342JJlBQ6BAIMox/s5rtMqcQBR2mG/5BsObyWN37BiamJ0nPERArV3OyLwRDZoRAdHV1Yd26dVi2bBkAYNmyZVi3bh26u53srYQQDA0NQdd1FAoFFItFNDcHpwqJEMHEpKYEbr1qFuqrveNUf33tXHx6mfz7D4uDEpPLLbk6p5u3hE/F9qCKjdCHWafARTeuxfqdQ3jvV1Yz7hPF7AeRWpUrRfKYC52WW90rJM9tyQWAbe15jCO9jmN6GT4/gSFCMuJOvn8+ITSEAHNu+9Eo7HflaDzTFrbn/vrXyBxyiMWfQylF7fHHo3rxYmRmzbIXbUVh9xNZcg86IiG3TJS2rgouBIDmQrppWBeEsFAFTASxhgaHy+9Qn+3GUmi3rUOOD20EUBqYoEAowcd/p6Hzz38u63oCiqGMLSwOvPqq43yt3oOJfWyTRS60iYF4lKABhLfkVmZlCpUYXMC21faYehEOEZ1ANRYPnnRo0wceh/qhduaKXPJOiwOwBa1UdCoASl0HUNgvZ+WmlDLBWhiHOUdO5Mo4zw29JTAPBrxrM1ecitq1Uw3PHNMKSpBQ49CpLHU7Qy1pQOtMW8D0cu/N1CYxp0pI7k7csZoy7NsibszdZcR2x62dg3Fr5oC9leVZch1s3SFWdarolpA7tF5B7e5WqEQFMVyNa/Y2O8qasCzGhiVXpiDINCQwPK4buVZ5Ci3e/Zj1l0CnOoZLw9LyPAZ7c7j3u88hN1QYdUbj13tedR2bV7tAUtLos7HDrGp3x41FKB9tbW1obm6GamwEVVXFhAkT0NbW5ih39dVXY/v27TjppJOs/4888si3o8sR3qXQKDAugIipOq1K8+iWA6987iMB77VjukPzwmdM5Y6P0F1Z1ykGcxq2tmWN+uw5X1WIi33ZD4mWFv8ChhuyPmR4a1EPBkYPS+72jjwUYeNVTwZC8VuweuUCaPLIZYjPPQnQdffa6mQQ9a0+VlMbrh+VgGvbEqQ5d2ViayYAw5DTevnlzGtMZKSOMOqIhNyDhNzTdyKeKoa/IKwl12/iVJwMx3w6lq1ftnObjpYld11uFgCghtQhxglqQ+70plIw11BI7+mtUw0B2hA4UgnmTqwIRZMxttkmhGDf8F6nu3IZMRh+FkUvZAdsV5xESs4k3fLyYqy8lbndUi49ULF2GFq64LDkAu7YRIDdmyjkElCU+uRM07rBrkx16rDmOiy7Qpx4erqzjtpx/g8x3dWAxpcOQfv2HoeGlRDCXEk9xj5O4775ep19FMcinCVXxJDmJp6SLr7EcOv1mRX7pu8RekQdz0y8t95ZuyTtUFSBWSDNMIX3ti6Tmh6o6vbMKPU7f/O4aMql2H3e8yg0ypVslLPYfnb253DRlEuhU4q+Yq+rpAjzGzmwJ4QnS5nQOeF7Yz9jJo8p8m9KgduzIcLY4NFHH8Whhx6KZ599Fk8//TRWrVqFRx999O3uVoR3EXih0AujwW13MKYI3jhrbrX+/WdbsK2NGRRiqoLhvI6u/iJjQx7BfZhLYdHYW/2/5Vst6+2OjhwO9JWxvwwSuA3htbjxeaO4Jn0IxY3PobRztev4YE6HLoRATVU7wvfPY7+QOfuzqP7AVxjpKREv4S25lStEWj7+8YqvZU1LuDRMgi7uJSh0dKDbCO2IjxuHUl5F7zP+af0ijByRkHsQYc4roYTdMCxrRu4tz9OK4rDkzj5iorRcsafHKl8ptKyd/qj22VmOc8MhhVyAEUXJ7mnHwiqrDMDFSQpzdTyWxM2LbsW2wa3oyLeDEmDe+vJXt0rclbVi8DPjU+bcsvg7jnMULHetzrnXSidMhVhEVtYxUOv5iVrawayC7rZBvPa3rY7xcggGwhDVzHO+C7OPaMX7/uUo7xszsOHF3dYsMjE9CYQAezd3ebpK60XqGGvxdh19FMJ59JjmENLCQpUQaHiuiZT4brD2nOb05AhiY77iXy5wHYvlUji+8xx2vfEgmlMt8k0Zd8wUMgdfZsKfrNmEkkBVrMrq08z31gnVGUzeVMPCukUYn5zAPCC4MieOOwXTMtNxZMPRrnsFYFlRRxO8B8OPNn8fABDziMklUA5KH/6Z0draio6ODmiGpUHTNHR2dqJVYBS98847cf7550NRFNTU1OD000/HSy+99HZ0OcK7FLxFUoZpzSmkJbwZ7wTwllyZJTVmdHvvgTze3DaIOZMyuPfr5aVUE+svcPuDksFFkIwriHt4j8kQGFplpGDkLvDcHxbeesr6W500FwBQ1IAdunu/GUb2rPrgdah6/5d9y1Bdr9hd2Q/zbr8dDaefXtG1vjDz5HL7g9z27dZpMzSv9x9PSS+PMHqIhNyDCUOzFU8HC7mhiaf8TgtCrtd3b7EkjyRHlwfB1daZBPe/PzjJNgC0x6ejIdEIUIrcbmdeWs1ML2SMy7qBtcZvZx1EUdCQaLSIdBynJfV6wc+66IU3OebqTK089UG+1o5Tdgl00KFwltzeWbvkllwFWPvMTsexHnWCpSGsXuJOtt63fwiFnNMVhhcMFEUB5QJzG44QYjgJceTN9cLWN9pBCHBC00k4vIFzW/RYU3P9JWGD4xwUi2maALseEyywBOgpMAXNzKrZgX0zoUo+hAUnT3Mdq9nTguRAtdtdwAe6EHkkui57RSXlesTvh2D5tW6LWKrHFlLNd5SkqOM3D4Uo6Cv2oaAzDYEiCPjZElNOmW7LZn7pKRlbGXPZtI8hE6vChJQz1tIiSAshYG58eU9gGR6aZP6LebiwTc5MCbZMRCgLTU1NmDdvHh588EEAwIMPPoh58+ahsdEZuz558mQ8/TRjMC0UCnjhhRcwZ84cV30RInhB1/3J+n7+hUNH7KoMHBx2Zb7bsmnQFDwLRR13P9WJjbuHkUlWJrAXDYG2Z8Bex821c86kdHlWYkoRr/cOMSFEcYbM+WQQoXl7Xa6+6AYAQKEExOB2vQ3zCBKHnoD4tMW+ZaimWfvZWLKEmvGDjv7pA/LwnLcLVo5cL3N+lB93zBAJuQcJ8bknWX+br3P6kEO8LwjhruxneVXSaZe7cjEX4O8/gkXA68rhDLEYkIMwoDYChCC/bx+2f+1rAICphlv1stkXAwBqdMON0pBuXfKH6XptssoKp816RdQ0Oif8StyVB7qZwHDBvx2LCz53nLQM5TqcqXWyK5taPtOS2zVvq4slkPXNfawvNh7QNBzys59hwkUX2XWK3t6S4gAAIABJREFU7XtYcuua09hy4RPWb5m1c/9uuTu0C8QW7uz2fEivuLFOVTtZrc3r0zVJ6MPuZ/JwG1PQXDs3mLnZxPrBda5jfvlmy1EKizlnHemxFYIDhQOO8jcs+AYo0TFugdG+VV4+XhNfOMxRHwAoaVMRILH6C1+AKrw7Zqqg13tWGeUV6KCojgUTONlk6MHzxhO/d7u0+UFMYQSwjddZLe91HJteNYOlEIosuaOOr3/967jzzjtx9tln484778SNN94IALjqqquwZs0aAMBXv/pVvPrqqzjvvPNw4YUXYvr06bj44ovfzm5HeBdg9VY7fIJZcg9+mwdDD8bvE2RzUNwQzsudnhpr3ArlI+a452TC/VHW/ek6MpMbvM8LsbalveulC2HqxA8jdfJH7MuSzG2vqAMJcnDiS/ViEV0rViAWL7B9IAVLJ8T1j+fKEdcnZbxboX2wUezoQO+TT7K+SF725OTJkqsiHAyEnmr++Mc/ep4rFArWghiEd2suvtKe9WWVTx1zAaCyDXxs+iIAQN3xx1vnqz96q6N8cZubeCUMEhMNFxHC2HZ5huIda/1zfo5kDfDKaRuQmcaBjNaPUlcXdn//+9axeGMj5t1+OxY0MOtkguaMeuXuyk3nMLfPkebulFnFivlw79zE2U1IVfmnIDLzz/L91A13Zc3Q2GrJoqclVwYtm4WaToPEYkjPZpbNDamj5YVhL8rv+5ejMO3wcSjU2RpZWbt8zDEr46GR5iyW5gKzf7d33KbKBTaJygUrJdGefqhFf3KSsCgnJRAr739+/6KNaD+abfopS4Zln+TvhxCUdObFMa92PgAgqSYx2LqfU1yEt44OGkoVW8h1lxGJy2jJWWh3lnk2dOY7jfLMknvP7j8Ftm9ZcsdIvlSIgupYtfOY8Q7yOZ//2fHhD38Y999/PwqFkOk6PDBr1izcc889eOyxx3DPPfdg5kyWv3r58uVYtIitYVOnTsVvf/tbrFixAg8//DBuuOEGxMTcjxEicOgbKuHLv7LzzgdZckcLB0XI5S25whbokW8usViWy1XCXfeR6Y7fl5w2AVv2MjJA3l15MMvmPYLyiLUopSB+oT4C30tx/TPemgijXGzGESAq+/bT+jBaFLc1daS5ggFgx803sy4CiCVKdje5/iWP/xDXqHCfWhmxy6OIfHu7QZglj9mNMDYILeTedNNNuOaaa9Db6yQo2bRpEz7wgQ/g/vvvD1XPuzUXX6ltc3AhHopqfYwkbljw+ElD+Pjzrz8SWKXus4khsZjLkisvyAlZQ25CnrDID8snDkYkxf5unOhvHZpeeMt1TLRWE+EP0ZJbfRizdFkMvxXOqbJJZ9OqfZKSkmv9rMBGf0yrb0a1c9uZC8C653YhnlJRrBmWsytzfWsp7rD+TnHaQFPZsT8mpl2wB6S7jblO54aKUARXUPE34I5T/vwhX3SVAdgrJeu3FypxDR8Ryn0pArrXeeR6dC1gmzbq466sl3RkYux5f2z6JwEYrtMKdZGjxd5yuoXKYMbTWu7KkndWVFaogmLirl1/cPw2Wcn7isxq/59zr5O2/Z0lP7De11JhbARMBYrL5dB0PR/qzTmO6+o/L0tlPB7Hl7/8ZZx88sn45je/ia1btwZfFCHCGEEUdFjKnYPbZlNtfNTzyALOOXdbW1Zynv2bK5THHSHO5MmEgu4B95y2vZ3Ne2u2D2FvVxlKLUqhD8izMbAOKG7hULKm0/wQcs/+ESAKai69yTp+QfLgxZbmdxqhWsQYX1ML7QgN4tY58X3rDrePG20QQkCLb4+AHcFG6Klm+fLlWL16NS644AKLaOJ3v/sdLrroIiQSCfz1r38NrOPdnIuPSNw5Zcjv3cuInRTV+tiUVMKowx5ufcDpxqjtcbtUiqA+1mwlHmeWXO4DT9ewONFd6+WT2/CGDYFtmiCitt7Pkmt2IUC2KJKU65ifIA/IY3KBkVtyZdBHIYeZ2S8zd+vHpn+CO0uRHWBumrEkuw+ZhpsXCqcW7LzBsUa3YCQm7+Hn+8EetkDWjc+44lS9UhfxGJeUp2sp9tvWTC/t8pyjbFKKSki+RgZ5n7ziqMVNWX+xH5sHNrnKaVRj6ZJ4d2WhzPSq6QBg5T02LZGarmN4IG/lTi4MBb9rirEzNIVcGUR397p4vbTc7GoWOqEQxcFYPc3or4hMrMoalzeeGBshik0lznGRKWPs0m/Hu/X24/e//z0efvhhXHjhhbj//vuxbNkyfOxjH8PDDz+MYrTJivAOg0ZpReFB5eDOr8zHR5cGpM0ZIXqH3Psxc/m+9S4Jq74fhOFQufEZFas3pVDS3kYHrWsPSrude1BZ6FRpn7H/CKnUHs2okvwQ20dbVfJ9ULyF3FGh6q4A+fZ2dN59N3qeYGFhjeecA6gqqgyvmAhjg9BP/6STTsIDDzyA2bNn44orrsAHPvABfOtb38Kll16Ku+66CzNmzAis412diy9EEMnOb34T2667Drt/8AMQJQaqAxNObEGqdRwAQcg9UOYkCA8h1/igLUsu565sush27ZW7jjade27otid/4QtCs/LZa0HDYnxo8qUAAN1DENZj5n2460hwbJ47p0oEPo9Jc6RCrux+REZjL/xhxx3IasOWUoGHmnO6MVcZlr0LJn0Al077mEUoZZEKSd2V+XvjYnznzuVvwDgbPA61TRnXAiYTcg89xhk3QgjBxovk6ULM+ry8k6q5GNiDvcER4eV+LMYDW+UFwepb62/CDzZ9x1VOpzpKtIgY4RRAQlvm88yoLHZJIQrUfAIb2zZjmMtjHWYfY44bSXrvHFxu55IH0pxswecPudYqHzYvsOmC5+XFcTAgfpcmoZYI8/tvmngQ8yG+gzFz5kx85StfwTPPPINvfvOb0DQN1157LU499VR897vfxe6QBHwRIhxs6LrT7ffdijinUEsnDAVkhQKpKMiWMz6TxsmVtTyorjtC2UQU1z3lTg0kTzBvdNCpbPxN7nx5u6Mo5FKdAATQS6rRPT5MiF/3nI2S1Ohb9MOg1OV0347V1oLEYog3Nb0t/flnRVkqjqamJlx55ZWIxWJYt24d5s2bh6uvvhrx+OjEzpl4R+biMz5updE7YHx4I9Ny0WLRcB3WQNvW2hOCY9IofzL0suSSRAKJSZNAVBVU19G9ciW6HnvMvs5DnZacNAk73+oMlQon2dKCGP9xelg5m1MtmJWZbbQrr2vPKavkJ+CcuNpbuTEyXb+9hFzzukonVclsXCqFc8t8vutZ7B7ejStuWQrA6aYdyzsXIHMzvqhuCZpTtqbZIhWSCJu8kEu4fsoWVBog5FzwueNQVZdyCUOymNyYK40DQakq5yrHzjhjct0d4/rtozCS3dMp186SlAyPQxMLpMeXffYYj044f7pzyDJQ6CjpJUc+V7H/cSWO/5x7nfVcFaIgs78R9O/jXOzXgTBd9qu8lS+umFzucTSvYnHBMSVmlSOEgIDguKYTcHTjsUJzgleA0Wx/F3PTW/exB3Bg/pby7qFM6NAx7s051ne9fUhuRe67YB1Ou2wRLvy8nADunwWJRAIXXnghrrvuOhx11FHo7u7Gr371K5x99tn43Oc+h/37fVwWI0QYA+hjYMkdCxw9lynUDp2cwTc+NXNU6+YtuQ+95M8cHGooKYXa7G2ISiw5C6RmnPOgZC+i7TW8/0pOosBD1Z3SekeV4ZrCWgcoJY7+aftsDzeaHf087qMCVQUtlUaUujNC+Qg92pqm4fvf/z4+9alP4bjjjsP3vvc9tLe348ILL8SqVd5CC493dS4+44NSG1sDCsJQVbKYXEJgzUK9T/FxC+V//KKQO/1rX8OUz38ec374Q0z67GetmNyOP/0JnX/6k1TgEN2BH/r5K9j48t7gxgViAq+UR0RRrHQgXuRUxSp3LIsMeYmC0kvIXVzHiKoqXjp1HdPyTncdPn9t4OVGSpYrv30Wzr/GQ3iCbfEUhSE/BbBTyxvw3hhl3/ORxUjXJNDf7RxrU+FhCTlx9luVuIG6Uh4Zz394gnvRbc/5x73EErYg6OtSKiNTMr4fk4Rofm15eQcP0eTlEx6pHRLVzuM1HszDOqUo0ZLTkisB7wLMKxPu/+GLdiHJfdeNFxJOG49eafRWvogWAd5KO27tIf+fve8OkKO40v+qeuLmoF2tVmGVs0AIEUUQySJawGFjMJh8HGAOG2M47COY32GO45wA24cBy4ex72xzmCAwAo7oE0EWQQgJ5RxXq9XG2Z3prvr9UZ3T9IRNUn//7Ex3dVdNb3d1vfe+9z3HGETXBOWRcovTxQ3m+aSkIg4ucYAWTukHgAR1ql1PLJ8MxjiGfzQDRPF/Vcl1nZh+3JhAZa8OVvT09OCZZ57BRRddhIsuugj79+/H97//fbz77ru499578fHHH+O2224b6GGGOMRgX4YI4amBGUsxsWGnWo6N8YJLHtnftVIOF2hbcy+WLMtSQsdU95ZILuXammYhOmYmImNMVNqAKXpqB44tP01dEqRoSFYY4qq27kwXjWeMdS2X+z9FQ6q01qOvOO440IQ1HY8AgmmpjnvYrDgSY+waKiGKjcBG7te+9jUsWrQIt99+O371q1/hnHPOwXPPPYdx48bhiiuuwMMPP5z1HEO6Fp9m1GlCMeOz0KdNE4SWFJ/ZZ+ThWiaToEOw0U2SEyYg1tAAKZk0cnLNs4pN3AYAZFv+MwCkg6gI20StPJX9KEVDXCyWvSY4ri6Ms03j13zVdE9pWgOmbl89w7jGY0pVmfg8HYecc4xPfw7KjWshB6QrA8BruwXTIJaMwu+XadExzdCoGCYMmZ4O74lZMRnbQV99lcNKUFVfhuat1jJAzGbk0lLxPS45PQp2gSiNxluy10m3Wd0uHARe98URp49H08x60afPgsBtz39s/xkAIEpi6thzW1BUxN0prMRD/cTuHJo37CT9c5QYrBUOBoUrrg4CL3i2dblsRy6Y5N4m5pOTG2BKtztYKKGQueIazbd0b66zrP4Pa1YXFmXXcOaIczAyaWXJDE80GHWTuXVso6YaUYcppzaA+SmHHuRYs2YN7rvvPpx44om499570djYiEWLFuGll17C5Zdfjrq6Onz1q1/FD37wA3z00UcDPdwQhxjss5UoITT0rdw124T6scx4TkapG+yHSzmW1v2vN/0raSjd3frnyvoOx36RXqdYPRI5vGeZS+WFLp7ss1Jv9kiuRYRKKi6zNAjs79TuNWvAeqystzYtUEeNQEcx1KdD+COwkdvZ2Yk//vGPuPLKK/VtdXV1ePLJJ/Gd73wHTzzxRKDzDNVafDyt3rCcA4Si7Kv3eLZN794NQiVwRkAIh9KyHQBQffrpepvISJFPmUu+QLyx0b+B3RBVXy8fLl7jaNpg+j8GNQy1B7KnO+NNcSZEF9exR3LHTKvDKdfOgNSr5UL6d6x7HodtRG+lmJjNRm5UNlFgbbfysIULfc89/fgxmHyUyYumjjXGDRpOLiqyX3QYJaa8cj3FOK205Bnzxoi+er0X6dr8WSPv8mxjh9za6loCSYtOawZNZkQHWFUvYtRlzGrHE+f433f7ZhqiTF5zdiQqoWaEiIhSSnD4Kf45/HPOMIwnJZ7B51c+h9bMfnXs/iuAK+8/zfLda0Hltd2eo2qmI5vBOAfL0cj1ytlSXFgDXs4AkgxOV3Z7xMp/cQR6THm1BBQKV7Lmk3EXqjxVclyNeeCMhgW4dvwNju3dKjv5gsavWLZzxRhLb6dsEc861LBw4UK8/vrruOKKK/DGG2/g4YcfxrHHOmnbTU1NmD17tssZQoToG9zzm4149Dmx/uGc4/WP9oPzoZ+TW19lGFKbd/dYjNzpTSVuh/hi3IgkaiuMc+YaGfa7nr07d4r1jdbGrS2VkNm4HPK2lca2HASbZO5sy0GgFMHINSsUM5lqJ3c1wkmyAqxtj+0EfW9IElvKplswSekUtaJ1ujIh6N25u8/HdqgjMLfr2WefRTLppJQBwNVXX43jTDVg/aDV4rPj8ccf1z9rtfgGFdQHKrN+mfrdfwLo3bsPEAVEoewSRkDJtGlGA82AiwsKJu/JXs6nLIsqm1YnlxAi1rbqs+3mTSs1iRYFEZ4hJrryr+94FbW1xq0TMRuGLS169MUeyeWcozRWChYJZjxqk0Fq6ifgEfV6mYZ6zH4j8qPTb9XvtWedhX3PP+957iMXTLQPTvwxbappLMe+7W2Il8RQXuN+77uBUoJJcxuxzqUEUQ/rUccZ/AVC1d8+PLPNW1jKNuHvf/VVdHU52Q+KmkutXa/VR7+JcaXjESGHOc+pXpMvXXUEvnTVEZ5eRzmeRnVMsDHaW7pd25iHSCjFUWdPtigua5h2/BjES6K+ZYayGZXJsjhKKuLobhf3JfWI2HoZuX55ROZnJVskd+aJTb7jNGPbF/sc2xw1hNW/0nAZk7/pvpDSnBeTy6e47o92ivs40ysjUSJezJQQKFwOEMn13d0nULMAMKVsKmBKJzXPabF45JCO5P7sZz/D6aefrgs6emHChAn47W9/20+jChEC+HCNETVkHPjRn4QA2lCvE6rNzdo7MWPSKDlueiVWbRHvwYf+PhjTRaIE37+0Cbf+h9A4kJXcDDO/yLhmXGlLh0jMhbnHFKDX+u7O5X+kuDieGQjkIhi58cZGZJqbxVpaFZ1ynFVdK9Lakej47e22nX1v5Db90z9ZUgFLZ85E18qV1kZ6TVFxXZlCAIVBbm9HpOLQFEzsDwReaXsZuBqmmQ24gxC0wpqUn20CULrME4aqimouw6MujCNNs/TPBUOlK3MALVID9mx2F8wBYOXDBJkDKLV4xFpazBOlcS16d+3Sm9kjuZxxEAKkKzoDdAhXT92ZrxgGckPSMJIMkRwtcdH71n5jz+tY22Etn2SM1egzGpPwxwf/ipceWxZsvCbY7w8titnSK4wZBpuhT4AbJt6c5Vwc3OO+s2/t2bDe9cVXP0aUlNEpvwRQ4G6oRWISEqWGh9LznqdcN5A2fOwdbdajf5QglojoYzFj3KzhOO3y2VlylP2nLUKJJRJcO9I9p9bLkLarK0dNUW6zkcs49zVyc1nH7d3ifFYd49MdMRyJGvc+tWvTVDLWfIiOyo1O4TxKco/kduwPllefG5wT0YwyNZ+aWcdmVm6felKjp0bAoYAFCxZkNXBDhBhwHCTMzAtPqMO4BpFv+dE6YcTLZlaZaaqaOa4s8HmnNZXiqgUN6jmCvTwumCfWpX51h7W1jRr6AJWc/4jMOhfNG5ec3OT8K1z7yKjxsnZmOF85SM7Gun6sLOvj7vxUqD4TybR+5gQkYqRY8S71/emmIO3BxComorW1iJu0hez5uADQeO21AIzgjf46HeIOn8GOUOYrKHLNoFcfLEKA6IxT1c+mm1m90WNT5+VEC/EDoRRdHTI+ic3DipITs7bVwBTunWOrH0CcK2YAlMuQETU8eYyBcw5CCXq6MhZapBDiInhwzo8BAJXz5mXvEwFtcPUanjvzG5Zj3fA/2/+An639kXWjm0iXek18r43aTW1smHcbAMecJyJr48uE8aXll2qIxiTMrHSJppr6oMkEEqwLtfJOxOFvZBBw1wi+FpE2R5IVrrjWH41EJVz9r1/y7QcQOdZaJG3EBGftXg0r39ksfkcQKpbt/9dUYtCbvWulmo83PtaNrvRu5wI7s0EyXSvzbcI487x2YgwF5mnZVi5615x7LoKInZPmqBlo+aMfo7jk5DrUlV2ekS4XEbJ84cYo2fCByDVr2yXu93NGiFIVZpu2rCZZXBXPECFCFB0dqeDpP4MZ153TiJSaXvTPizYBAEbXG0ZNIXVtNce0l9E6qs6qndHaKVuOc0PvdkEX16bXqtucTEpEnZoc7MAex7b4USINLDbLmhIkq+u/JZnjcVXHPWp3FPFofmvbL667Ds3PPgu53V0pmXOAxBLWDQCguGib9IORa4fb2jY6TKwR5QPCINfX4MVQ5wrhidDIDQrOclObMy18aam6yDZHF/W6ojynxXD1aad576QUOzZ3oFWqDzA+o88PFq/B+y8683atzQmUlNOwKldaAULwfulZAIBWuRRMZpBUQ6brgJF8rxm/JXFhaI05xV+8i6jRiYxXiqttcqiP16N05kz8/KaIp0y7wt1FttxqyK1aKmoZ+/17JPVtdHjVEZbtXurJNbFazK6ag6qYM4rpCY3lAgIJDJN7PoI9dluzYAHqL77Y6A8czMeLao6G7kztyCmvVMP6hW+I4UkMN0/6NgBg1BQxkVc3OD3YvSlx7f2oyBrcxJE0SDk6hbzoyl6wG3NeVFgGxTeSW1rhXr9w9LQ6/wFoTg3TdRozvQ6jJteq/XpXQya6Y8hJvwcATpzbhfCU7KidbIebs6e3ps2lZX5wM3J3rBNGtLZw1J4bzhhGTRmG8bMbIBF6SNOVQ4QYCtjV0pu90RBBzGS8xaMUpQmz0Gj+59Vye70M5a+fNtzyXcvd9XulNj/7LAAIYSnAdW2U2uVk16W/+D/nydRj5W2fWzbH4mKRpoAiDcH+mkC36deCc47PN2dPydPBOXo2b0a7R1WVRLn1XqI1gtWn/UYLBiBSGqs31uDxJjVtSV3Pti1dCgCIVgjj26sKSYjiIDRyg4Kz3DxCphs3GhcPpFRqiExpi9Fo02G+1Fo7Gi6/HOVz57ruI5Si054HAKB2pJPvTyQJS58zxJL2bc+yWCUEUBS9FrCG8WkhFpZWS4C83z4DX3ywXc8jNYMxIwJ14yPnYHhTla+MIJEkJCdPxvDdHFNXMzx0uFDZJWXu+Yj3zLwfERoBp8TObtRx92ffc92+9aGHxIeoYVHv2aRRSL0nycaJWuTSukD3mlejNIrrJhgCO0ECUB++rAk7qUqz4I7jnn96I5qrraVyskbn9bPyvIxcTsQ9zihDnKpeVXVgX739BM/jghidB/ZaX7pmIzdQJNd8bI4qJ3ZjyytKqEVy7deutDKOxok1mH2ae+3EbO/cL98k6tWa/3/n3nA0quo1xwGH3z0JAN1Kt9HUdQwmNUpQMK6AZjmn2/2kRITjom504TlFmnPBrZQRy4ixza4SjjGmcBx//jScec2RIITqJbxChAgxOJGMHzzLzfEjjCii/XVWiE3ltxT84TXjccJMq3M8pq6z/NSdtdKTJWOHo27+DNcBNr+71rEteepVzpOp72F2wCqYVF0p1n91pFV3wRIYy+Btzb247bHCaqrHyoBIXERqo3F7upd64RRnEGMg8r/N5T6HnXeeGIetjRQT64Y9Tz/dX8M6JHHwzDp9DM5YTrruZu8MTVZg6qJF7snlnGeNoNgRH+leW6tj+XJXVbd4SQTJiVahJVpSgnXLDWGkQHRlWKXoASDCnfSQnq6MHuE0r7Lb93U7mNluuQuW/dEojv2A4fTXFZREhHHLO73FjTTl3X/8+B9c9x/ItAIAxpVaDZDebUIQg2cClFNSUT+mEkefI2qP2g2jujFWimwhE61upJlya+x2177t7djyuVFGgIAj3RP8t+Rj5IKqUUHKEKPCezvjhDGYf8ksSFHv8wUxOu2iXRs61xnHBymTk3/FZIdSrzlHNy4JJ8iwWB0Y5+hlvY5rd9k9p+DL3zzG05iXshj5Gs0846HuzTnPmpe8dN+7elvrwc62HXI7Pj3wiWMest+z9lPVxGp1JbgLv2PQs06+eCbGTM8SrXaB9gy51czNqCwA7XczxnXaOwUN6cohQgxyFFpmZzDB/Fu6bZURCgnMSR7rhJvPH4UjJpbrkduTZon1hbYk9aNIayw1GiEoHdcAEokgMW6cZ3sNlhxYfaP7e2dliVBzT5JeaObcB/JMXV057+nZdDHLRnAfB4Iq5tTqFPssVjpgLog1GI5aXYvHPnh1XB3Ll/fXsA5JhEZuUHAFJE9uf3TCkd5GDiG5P4Re7j6PmWTnuv14da8135NGoxbDNusiUR0/sRn6SeakoKx4a5MeydXsBc45utt70d1mpZmMuummQP0CQPe6dZZd8TFjXJo7r7MbHWRT10bX7iqUFpQqbaisM5V28vjXiYW2+jtt+/ZMcXpG3aDRuv3cv4buFEPNWWfBS4CKe3wLovKbXyRXE/niujhTSUUC0493/l/MCJST64O8DPIcoBlbKSWFXamdFqN3fKlwFlFCsT/doo7H+jxKUck3Wi1ly1NSn0VP9WfwwEZ80wxr6gJhWgkGl/z6LOe053iPKx3vSq0urfJ3XHmhOlaDMSUe96r6L9AWc5xxnfZOQ7pyiBCDHsVQ2h0s8PNZv7Isf52CaU2lGDnMmeZiX/JdfoYQOdLnQz/lEkMJFKASCKUYd8892Qfj8p71Wse2ExFhbufGuomZSgjl6+PX1m7JCRNAqKApR5POwEr88DMgjfIQv82BKVks0LjxP9To4faAzlBXGB8qCI3coGAMPOWeBO8GmkiASmrkLe5Or626/TmQWDK3XF8Alccfn7UOrB0yN/oomSJEkMzrXJ5FBU+vWRu1F9o2jnuz3KhlaSx6xf7mbeLaNYyvthxdOn06ygLWbrSLEJRMcpbIcVuo92zaFOj8ADCzZynmdr+GiMkQ8ZqKmGmhbY+YrTjwieW713w2QzNAfZwMRhvhFSTcm646elodpqaW6UqKAFAxrAR1Yypx7HnuZWWA7IrFbuCU6X+jNHsBds34CZKT64cgOblbV4t6Mzc+ck7WtnPPNFgO8ug2/X/57LY/4l9W3QPFRIUdFhc5x5RQrGxbIT5nqdtrh+RC5dfQOLHGqDTgpf7MWWAjt2FcNc76eyO9gXDtfnW29WKU/Gj2I+IY2yL1srFX4JT609VjxXlLqxJqubFAw7MgKSVxx7R/xlXjr8W3Jn/Xsk/zM7Tu7IKSUYSDyZS/diirK4cIMRRQjJqpgwV+kdN0Jv/fOaExiSe+Y5R3POkwYTxmZOs5NQEqbRibdvfAE5qWNphXAAAgAElEQVSTn7PcAio5MBcVdf3ITCYFA9XFjvN942vMwcT48eAdLSitTqF2tLMSQXT8HJR95R4g5lIFZpAYk2bDF4Dlf9H92q/AOoon4hjCQGjkBgTr8inH4wZFca9HZgKJqIZBjgZGrK4OdRdcEKjthMQO50ZdwjyHyViL5Eas0WxPARzbAl3JKObTWNtGfCLkltxBK9zEoszlXtgV5zjO4YXSGTP0Pih4oGgjNy207Z5UuxHS2+2i+gehYJwNs04cK/rgDESSBF3Z68pzjhhP2TZxfOW7J2DOlya6H4PcoqN7Z69GurRbp6pqxm42LLh6DoDcc2TtCELv37s14PNKjGjn1++ej9SZm3R6cpqLunfm/+2Fo76Khw7/KSgo0qxXHU9uv+ew+VaqmD3yqT2XXvcgR3a6slmN2jI8bnU+mWE/5+gSEZFPSAnLuDTEaBxRGM6NC289HpfdewoIJbnNLTbUxesxqVykAWglrDQb9n8efA8r3t4MphjPKEEYyQ0RYrCjrTN4+sxgxymzvYUji5k6ccYcERSwG7kaKkoivvsBQCozaTnkEtXMIfji5sDo4TH9WuRjZ3Z/8QVaXnxRDMUUXCk599uu7QmhQFpb+5g7HFgzx85+1GG6vr3Lnkdmw9/6aUSHFkIjNyB63n4qp/ZcURAdPxuVt/wue+Mieprsxk85calJa6L8GcdlgXqM3Gs31tyPJAQYNqrCcCL61ATTosT2vGF7++2PPKJ/brrzTpRMnepobokojlKVCLNM7PtffRWZ1lbXMQFAy84Oy772fd3o7ui1UCazGbkdrT6e1iyIqAIFhGUxcrkqrQ+uRnuN7W64aty1+udcjNzm2Wuw7iuvgms5uVIwA2OYKoBWqJEbZKyJUi9JbitufPgclFQKI66yrhQkSsB0gS81/9MUJYzQCEoipdjZswMdmQ7nCQOgqr7U8r12hFHH11zuyEx5zjDjuWPIHsm11/rVEO1U89rtlYUUgo/ubcXH/7tB3zaz8jD8/MjHjXOajpl0pFCzbBhXjWRZTP8sSdSr2lhemHXSWADAjjWG1kCmVwFnzKCBEerIow4RIsTggnwQacM11Lgr5wPAgSIa8wlVrEtW3Oe31VuNdLHejHubmrPOQsWxxwKMORzEuyPeJf+Ij5EbnXiU5budin5pxw/Ri7gjJ9etrKEXzOJNteeeC2m40FGR6jxSWsxrS7NhOcCRXI056YB9XVqE3OGWJUsKcjAfjAiN3CIiUm1QcbmigESioKUBSsUUKWeg4rjjsDZhLcuTMOWtNn3/+wBMZUZyeRjUtlv+3agvmyzdBzniU6aGElM+rObRc5lw1N8/5vbbHbu8omQlU6Z4lgnKdqwde37/e6R3WgUL/CK5T//gTSz+xYeqWrTY5lxk2wV/Cp94OLjqFXSnK3Nu1Ds205XHH+5UqwWAuTXH6J+DUm6/P/1eoz89khvst2nXKteSPnbYRcPcEEsEz5833yeC+qpdQwEvA2pF2yeu27PBrjwejYtrP3rqMBx//jQ9H5yZFjbtmXb8+xcP4OPW5aIUV7ZyP6b7zfz7qjeo84HtX0Yz4nq999wX3uc0O8XU8487rAFXPXCGpR0hhUVyLX2q59m6stm81UpXBgUHD1/uIUIMYvylgFzVoQR7LdtCMKNJvAuG17g7bc2rAC8Dsv2994SeCWeOtWaD7BQqNU7u/Y6JTjza8l3r2+5814xcbWj55mVLJSWAJAIYkRGTUHXni/4HEIryax7RPw8k7EzFxNixYrvdiVAEO2Dvf/0XeMadNXioIjRyiwizwBFXlMA5DdqClWcKqyOXcBFiig6r1T/TpDVfwVxHNVt0TcvF5aZbhgN4+2QfFV2J6n3oeYYu3bQvW+a9sxDkoJSsIVJVhbIjjkA65X9splcGUziIpDkMrIbQuk6r8FTW9XeQ3864yMkFB/eYEDnnqoFrdGgR0bKhPi6oukFzchuThrK3OSc3CIhq3AbJya1pLPfcd1hV9hzu0VOHBRoTYBL/gojAcz2Sqyo2+lBhK6M51DvW+7Ne63iJeLYIISCUoHp4GW585BxUDxc0s94KwcbY1LURnx74WNCVs0RyFRhhE0V2jj8fg9B8zNyznPnwGkQkt1hGrvH5W5Nv07dZ6MqEgICElOUQIQYxPlgdXNNkKEPL1/32343O+xzRiDG3Lb7/MJw0y+M9Y1o3PPiHra5NMq2tIirKeSCDLzr5WMe5XTq2fJPVdV4C1jWs5qfVjOBcIrl2SPVjjd5dfod5DSY1TEREjfwONoGnxuuvFx/sRm6B4+QO2mQIIDRyiwvzzaUo3lx8O1SDpfu1x4o+JNZpUCr1h50QMMYhm0qUZDM8CCFIjB1rUfXNcNl3qU0jxLnAdnuQFe8i5W7tx9xxh+9YjUPFsZu7NwemM1bNnw+eyWD/Ln8qak9nGpwx9HBBQ862wM6lnI8fMvv3CzPMrTsuom3xEQ05Cz0EEY5ywFRCKFBzSgIJQQHAl648wrHtyOqjXFq648gzJ+HK+08P1DZZHseld50MQItC2koI+dw7tbHgxrQbTvvG4boTwv5qqqwrhXzUXmw/eZm+bdn+D/DpgY/RJbukIZhQKnk7NtwRLAddyx+uafB2QiBP4Sk3mB1xk8oN2pc5VQAIKcshQoQYHNCWLGMb8lOZB4ATZ1bhpzcKR6JEiaehZt7s5USoOvFE1C5YADDFYeQuTzhTvmjVCO3s3gO07dKM2dNiyyzbNaNWUdfGhVDWSxbciKrv/Ml7SFEjgl5+6Q/1gdJh+TsbCkHDVVeh6Z//2bJt2m9+g/gIcX1Zd5tlX66lRB1QjVy3aiKHMkIjNyDic89Dcv4V/o1skdzARq6aY5j+ZEm+w/NE+3vvmfoxZqaPX99gaRckTzK9Z48lkssyZb5LY0miOuXy+Yfftw/BCVdVKue20mkeUvF2NNaDJpP4w5ansabDm4Zp6Y5SdK1c6blfM9ozGQbGgC4mjGElywK780DKd38QcEKQaVEpXy6UUA7hZ6GRCIpmZfiOp+8mUzeP7+FVTsPXC5JEUVIRnDZWVS+ipkLESKMdiD9+/9tCyxlNOWqU8cXFA7tmxlL01Lbh7eY3AACVUZGz+5mq7OyGu2bch2snGHWiNdE3Mxw5uSyYkRvEK04IyV53OyDMlO0eVbyNcytdGdAoy+HLPUSIEAMLLZJbSF1gSSKYMtq9KocZ9h4W3uXyXmBMsApt6srfbbgZv69a4NJevC94r7M8pIbMZms/ikd1Dp2urE7NcpYqHn4gUsSzUonYHwWtEqlZmqhr1a1/QOl538m7z0JQffLJKHHTmVHBe2zXt0AjVxdiDY1cC0IjNygYA6L+njk7XTnXSG4hWPS917Fyu9sEYMrNU/vpWrkS+7ZbvX5t+7qz9sFSKUvOBc+UebZta+6GFKGOSK7fIjlbjm2uIBBFuYUIU/bJlSaTvv+LvVvb8Ni3/6J/54zhp+seAgAw7u+i1ESXCgNBx7Jl0KWu7D+Ji7xEGotCIfnVdA6C+2c9hHtm3G/MHn1gT2sRvI6Ru/VtfV0fF3AvR+NnPAUpZ+SFY9SSTptW7BH9+FzHDBNKz7Or5qj9el+LhsQIlEWMSOverW2ONg46MQ9g5AYU5yS0eIwps7Pji/e36eOwR3IJoUVVNQ0RIkThGF4dTABwKOKBa921IWJRzcjt+zHUVlgZWGkXlWWuKCCUWsT6AEAhEXCX91dknHAmK3s3e/ZrN4A1Y/bpnrP1bZQYEd7uXrE+KoSunA9IotSoYjLIYB4XY6RoRm4YybUiNHIDgnPmqzYHAGAMNQsWID5yJHp37AheZ6wALn5bcxc+WLwGqY5efL7CSmGslJutnj51gos1NDjouDUjfOiHJnDbLdPjEyxLdaXxl8eXY+uqvaYT5DbJFWz4EqJVugEAVEetaoKWxT4hen8TapzUn56utP6ZyQw9XRldfCkbVTIXISQvWBb1cNYt5eo2Go1A4n1XrqEqVoX6RH2fnR8wHoldx32qb2sqHYuRyVEeRxSpX1XESHxWc3J9/re0AMNbyynNaGkDPs9GbbxONFG/52Lwuy0s/vTgu5bvidbsThhmMyy9UFThKfPYufHXLZIb0pVDhBhc4JzjvivHZW84BHH4eHcn/y0XCnpsoVUEgmBsQwI3LRzp24arVRmy1cmtuuN5AEBkpKAwS7U+57WtWWWF45HRj+LlzAn6NsaBlz8QzLOfPy9KWeYrPBUYQ8jRqew22JSd+0qKl5MbGrkWhEZuULgo09nBenpQOmsWaCKB/a+8gt7t2/t8WGs+3IHlS9a77ovzFJh5UlMfovTu3WjdbTWINYXXbLCr520a7/1g7tkkapVu+myPcXw/zkEcqigNB365/hHs6N4ODq4buowzdHz0kd6eSJJ+jejODY7zRaIu/381L9Wck7unx4g+0ihw+X2nYtrxTlEwM4JMb2avK5GclNAtK/eqkdwY4rxwenQ2PHT4z7TR9FkfmhMhKZWgOlaD702/p8/6AqzCU9CFp7xv2kKiy4kSEeXQRLLcevnW5O+KD1xTrxR/czKuXR462VZuomq9//0pThOMrsw5x57NOdYV94CZrmwYztwlJzcUngoRYrCBc2BMff65qYMZXnPhyGHC89+XNq68fRV+c8IynHVULeYfXu3fWFHE2jXLGpZIEUSnnQSSKEXZ1/4folPnebbVRJ30LhhHdZl3xPRAh6y3yxlBg0UA3MVKBj+6D5QUzugMI7muCI3coGCGFyx5+rWWXZxzpPeKaCWRJGMxFtCi0+px0uoRWVq6HuwLZioNUww68NaYreZXgEXv5381VP/cjOnqM85wbMvl/Hac2SDEjRhnSO/di3gPoHAFv9r4CwBcz5npZb3YYaq9C0L0iaYx4zRypYhz7FpeqjmK9ODqf7G0Ka9OFuzVPebsCajlRkScUuoaLeMcoLFIYLOzEJ9DSUTQ41ms+JL1+r9d/XvrFGd5qb4AIQQyk9GrGCqRfhHCfI3cS++ajynHiKi09n90+39OKp+M0ckxUFQ6vPY3l36zTUNzq48GYdnnBs6DRXL3bnHSoxWZQfGo9agh1dHrYJi4RaGZIsZhLf1EfQXCQoQI0f9gnBerQmIIE3qWvYjYp3+GJBGUJf3fBXokl/lHcgGg7II7QKiE6Pg5IJIPzTdmrdShMO5Lz+7sUd9feeTk0lhwyvuQLiNXgMM8tWkT1t1yi/gSGrkWhNNPUDDFKfmtov2997BBrfEqaCEq3TESkKKq0YinnZTzsLJFVpITJgAAIjytWw5ShZOaGFRmfVfM8OANz2wBABxZFzxi7VYjterkk70PKGDS2tK1CUpHBxYsERPsvt5mU5TOqZorJZO6I4C6mH9utXO1GrGa8bGxcz16mUlGP6C1me1Xzj5pNGKUoe7CC8VpPcR9GOPBc8GLgO1Xv4V0hbdARaHgkriuQUscFQoCgme2/wG3fvJN/V/3fsv/oalkHL7UcJajfb5GblV9qe740G9Dr5uAADLXPOHi74hEcIdYeU3Sd//IklHBjFwWrPyTBjO9/7Fv/wWPfesvPq2B1/7zE/z3D9+xbGMK10ssafc7U5jDaUQhhZHcECEGGVraZWQyHEdMNKi9k0b6z0dDDcm419zZl3Rl/xVD6w/PQWarENDkASO5QUGHjUF07OGWbQpz0rO1Mkj2dq0dGazY6F8dwIyGyy4L3JZ37AvcdqARnXyc5XshbOWuVav0z2Ek14rQyA0KdYKIzZiP6ARrIWy5zYhcEEp1T4rS4V+GRkPpl28TtcnyiET4PRhl7IB+w0tc1hvHGhpy7seOGEuhWhY05BFlhRk58YYGDPvyl133dSxfnvP5tDqqL+16AQBgDjRyDuxPiwLoil0sykRXNiOWFM4K12tty8n90ZoH7Q1yHL07uKr6o9UrJtQosG7G/p0dSIz0z9EpJjQjv9iobRSOmH85QlxP2k9TFSUE+3qbHdvn15+KhSMvdGlfuENh5ORa3/3burfi41bxHMhcRnW0BrOr5wQ+/7Tjx+C0bxzuub9jJRDpzk4pDEpX1vwRuze1Bh4jAHTsd4rfMYVj7gKhUPnhS6L2tKI4I8qUkDAnN0SIQYiMwi1Kww9/c/IAjqb4OGZqMUQlc4SH839iY1JX7WX7tultdeEpH2dx0Cho5d//EpFGK6NPVrhlfZSMU8yd7NR5kRWOJ/+yC3c87mTKeaHi+OMDtx1SsL1LCwlCW1iaoZFrQWjkBoQ2QZQu/K4zId90s5JoVDcs07t3IwhoxTBERk7Nj26nlb51ibCMSa/Rx8LhXWsNANYu24Ed61p8u6o991z98/Gpl/E/N+xU+zZuIy3qkgtIJKJHKIuBptKxnvs4GDg4qMKx/bp/tOxL79wJpctqsM88sQnplIieuU5C6iX1jCIVy5mrKiNq7ABCCVp3dyCdymDlX7dYmlbMEeqIlaVDd7IjlOCGh89GMi6Mr/6L5Jrynk3/PK/+i1G2Ztys4eJcPi+5XT3iWdMcM7lcj0RJ1FqqyIY9zzMk91dlPQ9nHBW1yawe54lHNAYemxltzW5GLkMkLu55Zo7k2lgVBzIH8Opu/0hxiBAh+g+a0URJ/4gwDTaUJvruncUzPa7b4zGKAz/+qviiXnK90gdX/CmxBVhZCjPUWr40twbP3jsLVy0YgYoSydEu12ByUJbh0IPtdxXgpDWvwcNIrhWhkeuDjtaU4d1isvcEoT6Ekaoq8TmfyYLQvDww+7a1q4c7JwICgOt5cMSVqnL29XP1z1otWy/UX3SR8SVqyvWVzHm/xjgqhmWv89bf4ABGJ8dA8qj40/auoTo7cnItSiqD1Vr1iiIFnZ6zteOMAYSgUvVqEkLw55+8h7f/sBLv/MFa11f7HwxlIxdQRcPUK0P6aara3r3NPAD9o92o/LYqCLWqzbumci446qxJmHPGhKztSiOlYGAWAzxfpDrT2RuZkE7JKK1M4IaHz/FtJ6kCbcVYnCgyQ8yWx9/d3uu6aH6n+a2C+wsRIkRxoNVEHTksjqhLqs/BghIXnZHn7puF6vK+K10jb/rYdbt1WhRfOpYtQ+fKlWJ9aVoDLphrqzShCRfJMlgmN50Nc66tNoRohCARo57tgiA5aVJO7QE48oUHLQgwrGm/8b0AJ0PMzN4LjVwLQiPXB7+9+w1s+0Jw/DNrlkLe5r6g1RdzlKq5D3ncrFrORI4YMVFMVJ6OUq5FcmGZ4Bon1WDWSU1onGBMdBOOyEH4yryANX2uNBm2X79rfvDz9QMaEiPAOcf1E2+ylBXSEKmtRWafkdOx8OZjMW6WoHbv2XzAl87jVieXJoDKWu9awjnBHslVr/m65TsdTbV96R19r+7d19AfrX7y5nYp7rlCdrp0ebS4FLWjzp6MphnZyzJVRCuFgnYRItuL7nzNsa2s2nuB8MHiNVj93jbP/Rq0+2/fDmcZrlyhZBREbeW3tq5qLnpN7RAhQhQXssIRj1JQSnDJqcMHejh9gv/41hRce7Zz3RR3q8RQTDB3L/3lB36qf+ZM1tcsvVu2AOD+wlOqcbTt4Yex8c47cxqOWTVZ+0QpgV1rMEgJoV4TAzK1bl1O4xhaICDmdK8c1v9dq1dDSRkVNGjUcKiEkVwrwpVCFqR7jHqjzCupXV1wEUpBCEGmNbdcNHGwBDAFrMupTOoL9RmxR3KJZtzK2mRopSuXViZQP7YKNGLcAmVVwaX+zb2ZF5xf/uYxGDO9DsNGVYBQgkiU5mY8e6D8yCMLPkd1rAYAR5TGMLVsmmN/2axZulCXhopaseh//4UvfH0XissEddyto3DBLce5tHY5PovyrN0L6/eu6k9qGC9InzkIVIOd5RZ1zBfmfiz3uO2Ca4JT/UWj1vCXXYvVSG7f9FvT4O+UUeTsL1C9xvGG/f4NA0CWGaJxp4CfmwhciMKwadMmXHzxxViwYAEuvvhibN682bXdyy+/jPPOOw/nnnsuzjvvPOzbN3TEXkL0H2TG9QhuRWn/iSH2J5qGJ5AMWH6xPzBK3qR/Ti35JTo/FhFf+qWFYIpiCUjItqiqZhz1bN5scfZnA+ccCjOM2/mHi9QXSgg6U4peKxcIVkKo/YMPAvd9MKF3b7BrzjIZbH3wQay94QYA4npteeABU4PQyDUjoPxvCAAAzXK5CAEIAesWuWXx0aODn5tQZDZ/gt7li1H9vZdyHprZgJ3U8xGGySLCl96zB0iOBVfHprVlCockUUgmteNP39yEeRdOD9ZhTxqA8B6Z1Zqj8QjOuf4oI+pJCJjCkCiN4oJv5y8goHQ78/X8cFjlbJRGSgG8pW8jIGAiOxkSd1kkEwJaWmrdpBqMhPrT0N1yMyfWTUQ8EoyypGT8JyauRnL1Or4+hmxEpQjZaxr3DfrWyNVouQNTGsC4fpKHkVtIndx8ISK5+f9vm2bWY8vKva773Er25AptLqodWXi0e/fGVtd7/VDM8etr3HPPPbj00kuxcOFCPP/887j77rvx1FNPWdp89tlnePTRR/Gf//mfqKurQ0dHB2I5lPgoBjZu3Ihly5bh6KOPxrhx4/q17xDBIcsckmrkRtV1RnV5uOQsJjhTQNSqH0k483TlA6Je+QPP7sV3JvZgrMlR7jA48zSONP/7+fPqMKoujiMmCsEpSoHeDMMjzxmMMkUJIF5YqJE2VMoI2a5D7/Y9cEp1OWGPsnd9/rnlexjJtSKM5OYA4mGwaJFMIkmW/NTqU04Jfm5KwbvzoPfpxqSxKcFTSHCNymClFZfNno2K444DkxmoRHMqCeKF2jPPtHwnlOilgiglUGSGEy6agerh+VN3684/P6f248smoCRiNViFIept5FYedxx6thgiTr0rXgPbJ2r8Ekog2wzRCQsMqrfCFUdeblIKlhsy6+SxmH3aeP9Gjkiu9/9Nq+dbn9mKcYf1LU1sQtkkNCb6Ts1ZM3Jr48P6rI8gaM9Yn02JqLTxfp5Cjx92Yt6R3ClHi//TSV+ZoW9ra7YKrbEivCC1d/fwpuxiVm7Q2DNaCaIWF9pzMeatEAZaWlqwatUqnKuKC5577rlYtWoV9u+3RuN/85vf4Oqrr0ZdXR0AoLy8HPF4MN2CYmD9+vU488wzceutt+KMM87A+vXr+63vYiPN0ge1IrjMOCKakauWk0lnceaGyA3dLz+sf74x+UfHfi29iYFAkRULBczu0MzXOJKZUM8eXh3DOccY72k3R6zCAuqPHBKwqysH+92OKLvtOrPeXoQwEBq5WWC5faQsXkhKQZOGYVN96qk5dETBldyS/QHg//68GoCVVj3yhn9wtJNJDCAEo7/1LVTPnw9FYZBUqvLUY72VV83QKLWNaav8O4lEcFznYpx/k5NSnO6RsX1tS94iNInxwvgrmTIFFcceG/g4Qqh10uAcClfUMiiA5FIXtGTyZMQbDWXY7sU/Re87/6meD3jrvz6ztB99guF3Y5w5orlBxYFOvGgGjj/fSZ82wx7J7djvrq4IGMI/w5TdOOu6uY79G++6C61vvBFobNlwzfjrcef0u4tyLjdo900xhJZy7tv0+bMDn1r2GZHc/p1CmXoP5xPJPfGiGbjkn09GJGbMY7//f29Z2hRlzV2gAbp7UyuYwvDqIpVqF3Fe41RH+CIvJnbt2oXhw4dDUp20kiShvr4eu3btsrTbsGEDtm3bhq9//eu44IIL8Itf/KJfWRavv/46ulQF/FQqhSVLlvRb38XGtz++CU9s/OVAD6PoaGnP4Kw7P4WsGEau9rer51AxYPoH6RWv659riIszUH2eGaEg4AA1gjBa5smWqFpSUvFQ48wChXFEXOZ8yeXVaKdIu4HLctY2ByPkPZvzOk7ptOqIbH/4YY+WhyZCIzcHEMmDeqoJ/ezcCZqvV5tSIC2ir9xDVMB3bKbPUiKBEVddhZE33wwJxoTR1mIYRiKSq1JBA753utvEwjLKbfmRlCLBU2gYV+16HJMZlEx+E6jZOJZsVGI/UFAwE5VWUkTeLAcHAYXkYTRFa6yKg1AFpQghaG+xUqbXdq7RPzPOwGyLvWJK32f27w9ckkpyMQrM6N22zUFxyReU0H7PSx0I2H+jkZPbv3RlhSt5R3JjySiqh5chWWbQS+32SXEiuYXd91KEYse6FmxfIzzWlBJMnzfG0ibT6z6fdGQKF7sK4Q1FUbBmzRosWrQIv/3tb/HOO+/g+eef77f+UyaxFQBYsWJFv/XdF/j0wCcDPYSiY1+bcNYrLkZuiL4DcUsdUudiBip0WoiTrvyzYZcgUlWVt7NKsdVB1uCWUvLCe/uylqDrXmOsq2KN+ZSjGyp0ZetXZVfurJStP/4xWI814MFyTO072HHwr06LiMjoGVnbkDzzk+RtJqNDzl1kxzw/ldcmUXXyySiZMgXD5B04oltE7YhkpapolOKgk5tnOy3aJnkv+D95Y2OgPuww58jWX3wxJv7oR8GOI8RCBaNMi7aK3yBx660/6lvfAgBUn3GG9USarD7nDovgtT2v6J8ZWJ9Sz1pefBGAUNGb+OMf+7YNYmR0LF8OAJhTPRfTK2YWPsA+xE0Tb0EkWz58n8C4jnHJ6rzSjdx+nkIPpFuRZr19pjbNcyzx4IZChxaJSZZHrbK+FFOPCcY2eeiLB7I3CuHAiBEjsGfPHijqfKcoCvbu3YsRI6yigY2NjTjzzDMRi8VQVlaG0047rV8Nzb/97W+W74sXL+63vkMEQ0u7MHIzJiNXM4KmjBp8ZQUPaqgpTgqICJ6YjVzzXE8plLQstuU4gSvMyL22dO1ymk83uFcvMEOj45ZMmYKqE0/MaSxDC4W/w7uGuJOvP9DvRu5QU3DUpgFSUoVI0+HWfYzhgKmuKpB/FIP3GLlxPFOYkmxNQ7k+FgKgXBFqz8REVVHMkdygRq6ew+ERsfQp6+FXmsQPI6+/HhMeekicPhZDtLY20HEUFO80v59QWSEAACAASURBVKl/lxRBeeVcGMDUZORK5eUonz0bAJAcOxYTHnpI70feYtBU/S5TSklB5n1Hs+EmKpEj2lwAvjzyAtw06Zaina8vML1y4I3wr4z+muX7QKkrr+tcC6DvcoH3bm1Dx/5U9oY+0OYDbV7R/saS3o4K8xzEGbess+pGVwZWU25Jh0q/+aC2thbTpk3TjcbFixdj2rRpqLHNNeeeey7++te/gnOOTCaD999/H1OnTu23cb711lv91leI/PD4S0L00hzJ1dBQ078iZQcjgq7XaF2T/pkRivru9brGCCCM02vOGoGxDQkQScJPntmCO57YAKUttwofsuJOTc5XHFBpF2ycpjvvRO1ZZ+V1jqEAQgioJHRiAABS+Gz0BfrdyNUUHJcsWYJLL70Ud9/tzOfTFBx//etfY/Hixfj973+P8vIgumPFBeccskYh4IrDiJPb2rDrySfRvXZtEToz0e/kIuWb6fmMeif6J2bKyWUBozeGxpWtvbYi9THwjzh9guc+P0ilpYipIie5oLlXqMf2VIiJY2x7BTZ2rVfzZgkiPj+ZUJqz+EGn3IGH1waLMucDnmO+jFRWpPq8hzDMOdZJyRqB0Izb/ogwx6i4hyeUTTT1X/xI7rwLRF74314pvDbhhNkN+nzR0yUiO34jNpcmEvOR0ZpKNFRT7gfce++9ePrpp7FgwQI8/fTT+MEPfgAAuO666/DZZ0KP4JxzzkFtbS3OPvtsnH/++Zg4cSIuuuiifhvjxRdf3G99hcgPGg02o7jnaoYoFO6LF2rbzpq3GA5HbVt7s75fYRzRCBECVJRi9eZOfL7ZKkQYBApzOjMAb2mGbDa6WdcmLwwRtjIgAuu1Y4QCtjQqO1PUDfa1YbypyaPloYl+NXKHioJjd3c3rr76ajzw3A34px/dh87OTkH1oFY6rjaBtL/3nmV7yTR/ESE3ZNZ9qH/mmeIaudpTb2bTKoqTrlyatU6uaFcjW3NDteP9otj9Xdfyrb0iiisrIip++h+Ne4yAoDtjTOYONTpJAk9bt8np7Ebm9tS2fIebFaUzc4tmVp92GoCBKr1zcGBVu0ghuGmid6Q7Svre+/qPk24FYF0c9EUkt6RSPP/FeFY3fLIbS1VRPA1eebSA1dHGGbP4yygloZpyP2DChAn405/+hCVLluBPf/oTxquif48//jhmzZoFAKCU4s4778Rf/vIXvPTSS7jzzjtBfRg8xcbZZ59t+T5x4kSPliEGCpqRqygckYj1ua0JSwgVDg8HvATr/BqddpL+/v9JqXDAx44wKmEoDIhGKBgXjn2SZ7qVovCcyrzZtUvsSI7PUmniIEOmR63UkOcrrnfHDsv3ssMOQ8fy5YdsvWE7+tXIHSoKjq+99hqWLFkCzhmWfbEKL7zwAsCYhe4LwJeeWwh4Hjm5Gs676Rj9s17ayMW1ZRae0haY5miKG7avEUW9k9yWVxEg6llMEaYg0KJwPQn7dlFCaEenQdvhaev1JpSCp63J/DvXWx0xZpRH+p5lEB8xIid1aY1unUtR9xBWtKb3Y1rFDEyryM/DWiyMLBH5qGZqdF/QpCMxMb99/tetaN6WG2XNDZ2tgvbMVFX2kkpvR6UlkutSqzc0ckMAcDC6qqvdxQ5DDBy0x9ceyf2v70/HVWeO8DgqRGC4rIX//fqJGCk1Wzcy2aKYnI6UgUSNBZGscMTUSC6hFFKeIdBiR3ILV1ceIo59dU0ciYvfy7Yvz+s0dRdcoH8uO/xw9O7Yge2PPIIdvzz4lNvzwaAUnhpoBccdNs/I+vXrgUyvI5Lrhe7Vq7M38kMWurKcVrB/V4frPkvem82wzKSNyYMxDkmN5M67cDpO/8ZssCxGbusep2hAVbQKUmkpKrMIBBzYmzsNphBc0nQ5AOCjOdb/mcIVEAL0yIYCnV0sjEhSTk6V26bemb1RgeCK4ivsZUf5kaKcE+nHKMvBiKNqjvZ00IwrHY9ZVYf1+Ri0qK3Ux0rOkahxr2xasce1TXlN7lQyzWjV6je7QTaprzOFO7zaWlTg2ocW5Nx/iIMH0ai1woF8iJYbGczQBI1kW05uVVkU0SzK/yECgFsjtpxzzBhrrTyRXHADuJxGZ7dWlpKAglnWsEynKwOgFAT5RXK1Orl22N+bx0yrQNPwRD8YuUMF4vrEkjLipb152+ZmunJywgR0fvRRMQZ30KBfZ5yhouBYUmLNv+vcux0AtyjTFRuJEy7RP7Nu/zIY7z7zOf77h++4KhaXV5tCl7acXDN9UAhPid9TOawE4w9vyBrJ1YSnOAgQF4ZhQkqCSBIar7nG99h0T+41gAtBfXw4AGDtVOf/jICCmn6qPX+VSJIx4UQTqB9T6dtXbWyY7/5iwG7kegl5ReNqGxfjVm5vR8+2vqNUHwyw5952yd7Omdum3okvNfS9MIYWta2JFU9wzA2RqHF/eTl5zrzWWQs7GzSmiF+e+5oPtxt9M+5YCOmsFErC/NxDGDGbQzKdLkykMUTxodVCVRR31d0QBUKdHBMnfh0AoOxyaigQKQowBX9+RzgrpYgCwmQQyQiCKIwjKlFBH6bUkdMbFArjrsJTdsyeUIYte3qy0pULMXJpRR2k6nzKDvU/4nPPA0mItSeRnO88L1BbKU3Le7WfGZNDAf1q5A4VBcekLfG9u10khvcVPRkAImNnG19kf4NQi4raoy1n//1clFQYRq7dk9bTbZxXCE+ZxV0IFJlh5TubPfttmlEvzguAVAvDL2gJlb4qeeKFbPRoYppQOOeWRT2JRsEVddJhCsYeNjznvu6deX9O480GLstAxHhBef28i757gu1A43ft/NWvsOmuu4o6roMN98/6N3x78nf172k28ItoorqpItSjTnceGDXF6ZiRTEauRjG2o9SHcuwFzkRqhF9WQ2erqYY34w4jO9MjFj6UEpz0tZk46WJrjvqNPnnTIQ4e2CO5mUz/Ok9DZMeYerEGySgc0dDILTpYSrD4pPpxAID0yjedjagEcIZ0r4wvSseAUIBwBTAJJWp0ZYUBIBQ075xcuEZyzUjGKVK94vxZI7k5imyaUXHdL1D+jYfyPr4/EWmcjIrrH0Ni/hVqSmHAZ8V+fUzfM62txRvgQYJ+544MBQVHu5Gb6hH0YWKP5OaowOsHWlqlf1b27/BpCezaoOaHOsId9pNax2teuDKT8BRg5Ly986fP4QUqEZRWxxHjvYhqC+6Az2U2712xQX0GRkBAubF/9bADWLrPKAVloQVz5mugE49+6uL1OYw2ABjzpB5r0dsbHzkH1cNVz6CtjAsAsDDqkRVxKY6J5ZP17wrP/4VbLGiR3MMqD8/SMjhGTxVGbt3oCoyZLgT+zHTlj1+3skS0+yhZHszIjScjqFMZEEzhkKL+KQCr/s/IkX/lieUOxffKOhFhJ5Rg+nFjMPMEq4Lk5PIpgcYVYmjDbuSGkdzBh6OmiLzpXfvTfRkXOGTBU4Lpp61Tev/2AgCAmZygRIqINQNnmBNdAwCgXAFMaxtDeIqjd/MmHNu9Mq/xMOYuPGUZMzeWo31JVybxEpD40KnFTEurkDz+q2IdHXCJrEVux9xxB0qmTtWvV+nMmUht2NBHIx266PcpaCgoONrpyqmeHtd2do9T1Ukn5d2nVDtK/9zzzm8DiU+5CbRYoBo61aeeCsBc59ZKVxZNs1urTOEoqTM8gWWRMowuGZP1OO3Y/oTDIWHZRxDhFF3qv/nVBRJa0i2Odr2dMYApFtEbuwJ1hFjVImM0hpmVxc/T9MvJHTO9Xjda7Gh9/XXjSxGdMocK+CASsaiNF48WP3qaMGw5h+4YMdOVzeCcC+E1ElxA7rBTxmOM2gdTOCIRmtMcwG1zG1Vz+by6jxYxyh1i8MJOVx6qkdxVbYZBsbHz4FqY9mTEe2bRK7vw7meFC9iFsEHLq41YHY6UZaxtOMfw3r3Qq2uAWtZFijknF8CxKeOeJDlUM1G4N135zz8QjJueNNOjvdn0TvpaZLa9vR1vvvkmWlqca76BQqRhfGC6sv4PIwTg3KArc45oVZX3cYcoQj+bC5yRXKvBKbe1oXvdOgsXvnzuXIy4+uriDSLAHW8XgrIvQLXvsYYGxymF8FRuVCLGOLq4oEpHKivxw8MewmVNVwY6tnLY4PKuSZyipZbg0ZujACGuxowiG3mAGk64cDr2HLHKOI9NDKg6VoMbJt5c9PH6GbkLrp5jUdU2Y/+SJfpnpbvbtU0Ib+xK7RzoIegoZtmgsuokYskIOOO6ARmJuZ9/3/Z2PP/w+zkppJubMsZAIzSnxcu2NftQVp3Edf8uhKYkieIrt5/Q7yrtIQYXDhbhqbaMYfz9aM2/DuBIio9n3mnO3ihE/mAiuBIZMcm7DY2Ac4Yj2j8zDrMJp2rCU4rJoRiPBgy3Ws7jXS4oETP61IzcbLGZspkzUTZnTuD+c8HatWsxbdo0XHbZZTjttNN0kdkXX3wRt9xyCzZv3twn/WYDHT4RHc3l2P273wU+hhACzpgebOOMIbPfuwrIoYrQyHVBImGN1vXYaqbufvppbLn/fkskt+Nvfyu43/JrHwWtGal+yz7JpFMBX/CqcTTpSJGQzzlXSwi5//u9FqNMEfUrF10Vwaibb4ZEIsFLmfTz4nRkciSOrT3esT2aFr8tyiWYGMuuv1mKCifG53/dom+jEQoWNa67/fcHzVHOFay310I1iiX86w3aFaMBIL3TMNjs4gUh3FER9Rcd608U8xGiRBN44pBUI1fyiORqgnS5lPHZvakVy5esByD6iUQpejrTgQ3dz97ejM7WFKJx4z6vGz14/hchBgaDka78myW78MkG92oHXnh514t9NJoQBz0YgzRiki8tl0gSOLMyDRXunN8lSiyMwLmTy/U+gkLxUFe2Y1ileHazvgE4R+n06YH7D4rOzk6ccsop+vfm5mbce++9eOCBB/AP//APeOaZZzBv3jzsHwBDsWfTZgBA+3vvue7nsozVV14JAIiPHo3hl14q+N+cg3Wp4picg+YQgT9UEBq5LnAYub3Gi7Rr9WrIWnJ3AQnybojUjzPqmOVB2fBaBFedcAIOP2Uc4iVikmnepuZ0eExMXuV+OONIxpLoKiOQSoJFZq99aAFOvexwTzptXyFG47h87FWWbVThuP4xGZxzUE7BTHe/PZIbS6ZBqNjW1mxEQCklaJ2yCesXvgHAmZPbF/VLAVHLVzIxDM676WjUN3lTU7T8XZpIYN8LL2Ddrbda94cRsUBYOPLCgR6CDorilRAilIAzbsmnkrI4vXIRNW6aXo/h48T9yRjXqdByuu9ynG+b8k8Ynmjos/OHGHgMRuGpP7y1F8++Gyx6+dj6n0NmMva7pMccjHCrnxqiQDDFs9JHB1ed11RyGKq9zPhf7GvLYO+BjDByTUufLXtFal4urBvGeKB3wzHTKtRz+7fjPvojheBrX/uaY9vLL7+MRx991LJt1qxZ6PFIUewraL9X6exEyiWazHqNQBtLpZCcPFmP5La+IdainDGUH3WU5bjMgQN9N+ghgtDIdYHdyE21GzfK1gcfRGq9GqHwMHIjhfDiNUpJDp60WSeP9dw37Te/AY3HhbqpmhOXbf7wMoAY44hKUTSVjAs8tlgigqnHjPLM9+tPaGWDlK4uRDixRHK3d2+1NvYQAqASAZc4eqvbTQ1N+/vokeKyDGJa4JVUJFBR6+9oqJo/H8MWLkTzs89CtnsnQyM3EOJ0cHhGrxp3Lapj1fjOlDtw1/T7Cj4fIQScA5wZz7uX00vPpc3hnimpjKNEFanijCOWjCKejGQtU1YISiKl4HkqhIYYGhiMkVwg+Ot6RdsnWNPxRd8OZhAhSIQvRG7gTLHUuzXjOeUM8YFGHDclNZUPuvxfRcoVUx2dGrY3q8ZUDkauwvz/zxMahXNeIgSUAks/F1T9zpT7+tkvNasQfPzxx4HbuhnEfQliqpyx+d57Hfu7Vovynwfefhvp3bvFO1uN5JpROW+e5XvPpk3FH+wQQ2jkusBu5PZm3GnBZiN3zB13GMePG4dIbW1+nasWqJ/gjaaGqkFbe/b60JcJJWDqpBeNR3xLgXS2plw9eUzhIBSQ+rBecF+iRA3I8kwGcRKzRHK/6FjtcoRz4v6g1Z1Ooh/RR8YjVxTLRAgA4w4bjhETvGun0kQCcpuH8Edo5AbCYIl4z605BpRQjC+biIbkiOwHZIEWyeWcozclomHUI+qiiUDlUp9WilDdoNXmDSJRT7G82sZynPL1w7LS8P1AQPtdxT1E/8KtTm5fC9UEQS733edtKzxV+Q82hOrKxQVnCqDIIC5GLikfhmFcMASIFNHn7RTUtR51zq0y4+45srlGcn3eDT+5YSL+/INZIMRqd6e8WD2KYimXOBBYtmwZ1q1z1h/uM7g8KKy3FynVSJXU9LJdixYZ7Sm1XFAajSJSVoaEKuYL+NemP1QQTkEucBi5MkOkyVm+w2zklk6bpn8eddNNmPCv+YlJkJhKSfW5ObUIyayTx2LCESP0hfiWz/d6HkMpBVcjMpxzRGLek8gLj36AneucdCrGGEC5r3LxYMY3nlKdAIzhrOFnWyK5dhDiXpz7g9alAIAEFfeIvbzQ1u4tjmOKAZ7JOIzcSUc24oJvHed5zP5XXsH+V15x3zlIjLcQAwNCCRjn4Ixj3XKRq00IQXlN0tFWKz2WyYFqLEUomKzVReSglIKqhrVX+5qGsoJuS0oIGMKX+sEMSinitryz/qYWuiGXteTbzW8OKtX2YmPeDCN3PhJGcouKjkXfRuef7tPpypHRM4ydTEEX1wxag668m4mAC3NxrFBC3B2PuURyOTzVlQFRpigRow5D2O2Z6friC7QtXdondOVcMX/+/P5zoNkuRtv772PfCy9gs1piVSovt7YnRGVjGeNrUHN2zZCSzvd5UHSmlEHhQCwUA38nDUI4jFyFoSUdx2vLbZRPD7oyiURAo3mWtNBWeT43l3lSqh1Zrgcc/W5IKhmTGWM862Jy/+5OxzaucID2HSW3L9CYGOnYxhlDkiQskVw7aFm1O105IwzN44edAACQSP94HN0iuYVgsEQoBytunvTtgR5Cn4KodHzGOGbMM8qAXf6DUx1tNbqyl4HqBmckl6gpE+7WgKKoAlim+3Lk5NzYMJRQsJCufNDDoZkxCIxc5SBYDBYL5qh2rhUcio0DP74YvPfgqSqg7NkAyL06XZkkhfHDOQeYgk6mPhuE6lG8CiI0VqKs13E+SlzUjtU5OLVxo6O9G4LUydX7M6253NgPbe++K4ZQZLryAw88kNdxS5cuLeo4vCCVlVm+7/yP/0DLSy8ZG2y2BiEE9tB4rM7K8KSJBKiLAGk2vLPiAH7w1CZ85b6VB4VS+tCxVvoRjpeozJHYsRw/fmabZXtfyHUrzWok0GexphmzisxAKdUNltrGCs9jiFlFj2dXSm3e5qS57uzaCUL7Tlypr/C7r9uMQ7W2mF8kV5QVEg3mVLyrb05XdGJc6XgcUX0kAIO6fcPEfyzqmO3gslxUI1c+cOCg8NL1FaZWTMfPj3x8oIfRZxAvSWGAjpleb2Hm243LXFSVNZiNXBHJJaCSe63cjtYUWna0g0rWMkO59ktBwcNI7kEPeyR3MOTlrtrsLtZ4KIExju89uQFmP1YsMrBGLu/pBOvYN6Bj6BPo1qJ2fYWRu50NV/dHsPvDFACgMiMCFjGWcj2V2d9dmjAcjZvvuw9dq1a5HmOGwjiC+jLszDcHtPm/iEbuwoULHeJSQXGHKQ2xL8GyzGF22rFUWann5EaqqjD8ssscx9DS0rzWeIuW7ML7q4XmzK9f2YX1O4a2k2hoWSv9BEmSHAIXYE4Vx52PPQYAiNbXF61v3qHShP2MXC0iqzCR66ZOHFOOdkYtNfR0pbF3ixDQEpFc/8lGKyuiIc3SeGfvW+hQ2rHGNX91cOIrY76Gs2d+3bKNyzJ2P/UUpq7xngBSu7vQtks4DSaUGBM9JxwtvS06ZZuqdXIPpIXidk0Lx5YHHyzqbwDc6cqFQh5ExdBD9D8oIWCygrKqBG58+Bx9u72mtb0edxBIEYq9W9uw4ZNdePP3n2HbF82glLgauetVurQUoTj2y1P17d3tzsiDH0gYyT0kYDdye3tzu0/6AjmQHA5abNydwsfrOyGbrNx/vGD0AI5IgGcG/v4oNjQ6r9Q4GQCQWfMeeG8XNiij8NycJyysm3S3eF6oiwMwEacWZeSuHmaxerf+279lNcD86uTaYW7mZn9pRlkxI7l/K6C856YiCTe1t7dj7dq1YIyBc44dO3agq8twjHWvzrKmNkVyaTKJSFmZTleOjxqF2PDhRlv1GkZratD+4Yc5j1UTCtPQ4SEQNlQQGrkecEZzvRdPmb3eubC5IjpFVUfz8cBoE5giM1BC9IhJJOY9MXz6xibsWCuMmo79Kezf5azrt+CaOahuELQJuxoy5xyEUWxMbQj+YwYBJpdPxbEqtVjDxu99DzwH778E00NOONrlNlAQHFVzLOYNOxEAMKakCQAweivLPmHlgWIqDiYnqUXkB0HeS4iBA6EEikolNmPe3xl5Xl1tPVj659zvZ+2c29fsQ6qjF0zhlpQJM/bvEkY0lShmntBkbN+ZW+1RGgpPHRIYjDm5IYCln4voT0/aWCsdPqHMq3n/4WB0fKnO9cRxXwEIRdfz/wYAUCBhd2safjS1TbuMiO6ImjjsGSTclru75u//HsznGQtaJxcwjOGSOPXNYy9WTm6hbLWyssLuX0VR8Lvf/Q7Tpk3DKaecgtGjR2PUqFE4+uijMW/ePKxcuRIAEGvwLn2X3rPH8jtYSv3/EYL07t3oWrnS9XrJra1ofe01ND//fE5jjtsCXENduypc5XrAmZebPY+1ONAoxd53lp5bqy5QV7wlvE1+Ru6plx2OhvHVAIA3nv7Utc2E2SOQUGvp2iO5DAyEE4AMvTu+0AmTEGDeNFFiiBPV0wiKK8ddgzMazgQAjCkVi3PeR09UMXNydWP5EMrLlWUZjz/+OJ7PccIfzEj1Kvi8AJokoQRMZg4jNxqTACKcaV45tNmgvZNjSYMRQyj1VXuUbNTGXGtrU0JD4alDAIMxkgtkX1AfKiyDVVsMeqOfIFFfo6urCx/t7ERLS/HTygYaXBb3PCFErBUVIaqpgOKdFQfw4ntGLmU0YWUhtnc7I3NvlIr0qwvb3nC9j3t37/Yci8jJDTZubckRj1H350WL5BZprfPRRx8VdPxJJ50EAGhra0Mq5U739oKiKDjrrLNw++23u+5vbm7GD3/4QwBA47XXep5n/2uvuZYrJSZ1ZcUUFdavoZqPu+/Pf85p3HYMdcdxaOR6wC2SG4/2w+VSJyu/eo/aPSdycgkmHdkIAJDUN0rPshfQ9uiVlmOSZTHE4mLi8CoVAgBE3ecwcjkDGAGnQ++GzzUCyjNOr+WwjFrbkPj/fs2Buue//zunPrOOKY9Ibny0B1VMe9McQkZuU1MT7r33Xtx4440YOXJkzi+swYj/ebcZtz22Pu/jhW6Fu2iIoBazvIMgmnFcO8JQhaRq5NjRVn1RU9uKuLqh3NHWD5QQyC5pJSEOLgymSO6BTuN+y+YPUrhzofrV0ZeiKlpd7GENCNzeJgMlcNjR0YGzzz4b3/jzepx80RVYu3btgIyjryBvdDfeuLqkX7vdeL9RybgxezwU8nuIeKZO6P7UXW3Zxzkp1JWD/Z9TvWoZS4n4ly4qUiR3xYoVBR3/8ssv46677sL06dMxceJEvPeefwlJM1577TV8/vnnvm3efvttAEBy4kT/k7lc/0xrq/453Ww4NdIaszRP49Su/B5Gcg9SbNtmFZlKy24C7MWHbtz6qSurb9PNn+0BocQRiZG3rQRrt6qiSREKWRYTXKrDm6q7Z7PI25VsdGXGFRBG9UjmwYyMywuEtwtPpvb7vQRutEB3z9atxR2UouQsxpAYN851e6bASXCo4dVXX3Vsu/TSSwdgJMWFV83ZoNDmDTeBJ6oK1eVL9+ImJXf9nJJ7CaF1f9up7re+juLJ3Lz5BBQZnsEnrR/hpuXX5TrkEEMEgyWSu2lXCpfcb+g1KFmeR5k7HTBjS8ehPJqbMydfsI4WsO72Pjt/tt/fn1i8eDHWrxcOwI7OLtxzzz0DPKL+xcZd7o6fxxbvxPNLnYq5zCQmSl3KSrhFEvVjleB0ZQ3RiHvN9K4vRDChWKlZP/nJTwo+x69//Wv980UXXYQf//jHkGU563E333xzTv1EYu7nzDQ3Y+tDDzm284xpPjH9f1i3YFIUq0bu+6udIrRDCaGRGxBf7Ev1i7hEyalXAwDSK9/0bGNeKBJKsHbZDmsDl6LfUpRCyTAc2OstItPzwbNQMkxvb4bCBV25Ml6BBQ1nZ/0dQxld/3O/YxvRJn7dyHWHpM41PMAkmAvyieRWn3KK5Xt81CgAqnBBTc3Qd9EFxNVXX+3Y9uGHHw75hU+hURLteNdIbkQoIbsJRQWBZhub5xvOoesHuMHOMJlzxoSc+pTUPLXtqW1qf4Nn0R2ieEjaaj8GMXJ/vu5nyBQ5yr99n7VfOcuzIjPnOyFCI67b+wJtj3wDnb//Xp+dfzAZubfddpvl+zvvvDNAIxl4lFQZUd2uHgXvrRKOjlNnGwyCZcnp+mc3I5f5PGMKD05X1hCNuEdylXYxtmIZuS05imtOnz49a5sf/ehHaGpqyvp+6e4Opkq8X63SIkXdHQnp5mbXgAQ1zYMl06Y59mf27AnUvx1dNqGpV5ZZ6f5K89Yh9W4NjdyAyDDeL5O4VCdyO5U93gJP6R7jpegWhSGS08iNRCUoMsOBPd45fKn/fdIYh71wNxTUrZiCVIuMYfE6+6FDFgmacN1eUd+B8umGCA5RI7caHdlO6QCAU+tPx5FlRwAAIhXe5ZzyAVeUnHOLvV4ULJUSynyHiJHrNSE/8cQTgTyygxHPP/88Oh4gJQAAIABJREFU/unKI7HiqbMxZ84c7MnjheYbyZUoFIVByTMnV1No/uhVYx7bu+UAPnrNe16zR3LtbJJsiKg1q1cc+ASA+zMaYugjn0juqvaVaM8UN4ppn1a8mBXL9y/DyztfhMytc83lY69CiVSClNJ/JTqUvZugHMhv8Zv13Hk6xEL0LeKlhnPHfM9+db5RFUQm/nOtn9OeseB0ZQ0RStxzPYuorqx4RJ+PO+44z2NuuOGGwOd/7bXXch6TGzo6hMBiNOF+jaltvtOQaDLWp/HGxqKMBXDP2bbsf/wGyFs/K1p/fY3QyPXAmDFjLN+jsWRWT20xITVO8dynUYoBj2iOi5Gr1a30+gWs20pJsC98NdGMyo2jhlydXD9URqsAAFESde6MJVFxo6Cq2CO5bp61vxt9MRol8eLwU8vLB37CU7t378Yll1yCP9sEBswvimhdnW7UZlpakGlpQWrduqKOcbBi4cKFnvv27Rt6NRTXrVuHG2+8Uf++Z88ezJkzJ+fz+KVma+V+8hWeSpa7v5iDjOew+e40++zHixPsSG0HEBq5BytyzcndmRJMJ8n03vp4fQfSmeI6+TbvcR/H4p3P46VdL2BHysq4IiCI0mjRI8zZoDRv6ZPzbtg19HUOhgqkUc6IY2SUoYofZOprGm44+LMl4/nRlRUPXQc/CD0InwZFyMn1erc/88wznsf4GcB2LFq0CIqi4Mknn8Qdd9yBjRs3WvaPUplz2dDZKdhOXpFcqaTEdbs56CGVlgbqKwi+2GZ1us2d7JJOIQ98bfKgOHislSJj8uTJlu9PtJ/m2bZs9uyi9h2degKkqmBGEpUIrn1oAc6/xXg4iStdWYKSUTwXreyAVT1Pti0ANNEMwiloH942S5cuxfXXX4+HHnqoXwRFNDVWx4KYALS8To+KE42mrNOV3d8i2oRUYrt/CgHnHOmdO11zcrdt24YjjzwS77zzDr75zW/immuuMX6CqX2mudnxVulen79o0VDCzJkzPfcVGsldsmQJTj/9dFxyySXYWuw8bA/Mnz/fdbtdRyAbtNx8N0dZd3svPly8xpdenCsmzW3E5KO8Pc7aOPza5IJOuRMr24aOxzlEMOQaydXmamIycr/35Eb878etXocEgv2xefylna7t9vaKyOkv1z9s2U6JeJd6u577Cn3T34qNVpbYLRcGW+QXGwcOHHDdvnPnzpzpq4MVsaknuGw0jNZcE1lYtsCFX04u4zmraK/bkcKyNU5mRfncubmdyAdPPfVUTu0rKiowYsSIwO2XLl2K7373u7j77rvx9NNP48QTT9TXrJxzbN++PdB5NCO3Yp7L/xRAxbHHuh/YTyUgzc4QHUMo0DV0RtrP+OCDDyzf925dDmKK3kVqaoydxeanU+pZQsgu3EIIQSwRQeNE03hc6coUcoaJ8iAuIDGrt+j/nv3/7H13eBRV//2Z2d30Agk9CYQWivReRBCkiYBIBxUQQcoLKsirL4KASlGKiCAdkaIgSid0BKQXKUFCIEASEgiBNFI22d2Z+f0xO7vTZzbZRPh9c56Hh52ZO3dmszP33k8756Zgm0v5pExWR+2bu5Geno7+/ftj7969WLx4MZYvX14k1+EQNmmS43vZGBssNN87xdhVzu1GriiSW9azPORgLM3WubgzFZjTqJNL4WklGgAPHDjg3DBItY4FkDHwIiMjERISgrFjxzpYb18kmM1mpKSkCO5dbWFjtapHURiGwb59+7BkyRIkJQkjMWazGe+99x6io6Nx8uRJ9OjRo3A3rwNq9+tup9Cje+kFrskFAE8fZ3bEqAVdQZKkzv7cQ/E39fonEsOiBC8+XDVyuSydbKtQd9nd6bV3kuQjmbX9lev8CIJUVVIoEhRBPd3bc29K9tUKlY9AFTVWrVolu7958+Zo0KABQkJCHCmiLxKYPJ4TQcYxSRg9HJ87PrskOX7LVkU5k08rkqviDH6cXrBMhD3nhJFWxmZjnfFQjxzrxYoVKyT7fv75Z8X2c+fOdal/m82GrVu3Cvb16dMHAPDbb7/p7uett94CABCkAd5VpEa2Yuq2/RnwqV1b97W0cPm21PEgW4bxAilzlBi5ChAvGEkQjrpMACDtEkOhEyei/Ntvu/XaBGkAQ8u/5PeuCSOusmkiCunKuc/yFeuGbMnqqasU2PtJq3Vf4BF3FRTNwKoQHRITBC1YsKDA1xGj9po1kn1+DRoIWJIpXs0UQxPIuXnTGcl1GLnsf0qMmI7B2Y0G4u2xY3E5NRWN27dHjRo1NEk0uOikOL2ZsQhTTMQTSWxsLEaNYllpd+/ejTAlCaLnCAcOHED//v1x7tw53L17FzVq1EDjxo0RFhaG3Nxc2Gw22cmOg9iZJcbmzZsxevRofPPNN+jWrZtAekhck5OWloYLFy4U7gtpYP/+/YrHLBZpCpHVasWaNWsQEhKCkJAQlwhYMp/kFDhdGRCOTQaTAaSB0GXkevt5aLYpwf9dPHsmXIjdsjOyymH85VG4nH4RALD+vnAOeJReuJQ7pQzN5LR8QWmTklOYBBfJLXojl6F4RkoRLFBTnwkNnR8nRqBqRW+F1kWL77//XrPN8OHDi/5G3AwmX6qFKoDRhJoh7N88zCqtu6ZBIt+iEDzRMAXUjM7IC6k4eMl1LWKxk+nxr78iLz4ewa+/Di9euWBycjLu3lXmcpBDfn6+rNP3tddeUzynRYsWAIDZs6XEo3px/fp1fPTRR5g0aZJL5+Xk5ACkARV7SaO5DxWcNlzmU15cnGC/qQyrL+/XsKFL9wAA0366L9n3HPHJFQglRq4CBg4cKNgO9/EQsM5Z7dEh3/r14VG24ERMS5cuRUhICFq2bOl8kQlS0Uh6GMteN9w7xt5UxqNHspMqv86W0701Z1tgNJHoN6Wt4Jzc3QsBAAPHyafZWi32SZJkcCVd6iXUi5V7k9D/yxuyx4rSQFCqZ+VSxbxIb1AMDUvMGQCAxewBW3q6IxoqTldWhH0ycGckl2EYfPr338iwC5IPHjxYld1u/fr1AKQeQDFDIp+4AAAmT54s6evf1KDUwpYtWzBy5EicOXMGffv2dQi3c/jkk09QRfQdxeDE2JXw6aefOj6npaVh586dju29e/dK2vfp0wd3ClnrnJ2djfnz52POnDmSKPS+ffsUz+vSpYvjc1RUFEJCQhAeHi5gkR48eLBLEfros4moXLdg41tYHed5BAFEn32AE1uk6cMmLyPa9XfWk/kHeWPcD0UfFS/Biwn+OwgAGzduVG3/IJetQX2YJ8zE2P6XVErFJSgYiyPm38LO086+1TKfSIJ08F0UJfiSgnT6oyK/nqs1msWNc+fOvZCZSg7IPDNerfvhu3E1AQCEzPEUJgiXbstHsLUiuTaFFHAOnP6tFkZ2d0YqxRw33DUIDw9HvemePXvQunVrvPLKK5g+fbquawCijDY7KmmQM/n7s4GLQYMG6a6nlcO2bdtk9weokJFu3rxZkNWhtF6VA62wRhNkmxYQ3ZoHCQh3HcG3EnblFx81ROLMQVQ6KljZ9IrSnTo5ai9dZbzl48yZM44UicTERIwePZo9QBoU05XFkVi5yST/MrsQph47vTKckcswDMqGBaJc5VKy/fuXZidkD5FGZV426/XODE9CusV1rx2Huw/NyLfqf0G0qMrNZjN27dqFS5cKZnhz/ZtIIyhbnkM+yLd0LjyrVAEMbKqbM4qvfj+cx9OSnKzazhXkyKQKXb16VTH15uuvvwYAECb23jm9XJpHae/XuDFMwcGC8+T+hsVVZ1oQyBnlfOzatUuzj1Kl5N8DJfDF5ZUMTqWaWb2YNGkSFi9ejGXLlqFv376CY5mZ2pp1DMOgW7duiscvX76s+16Sbj+FNc+GPh+3Rss3lMnw5ND4tWqOz2pyRxWrlkZAsHtSG/uHDXZLPyV4ftFQFKEgNeZgjsW1ig87Dn4R9Rk8Agtp4EIayQ0OcKbn5/IW/UrPPkEQIAl5ltn4nDhMi/pU5qyCga+4YD6y2m39KuE5t3EBQJCV8yJA8JTIrA9J70AHy7FYBijw4y1YlyckYExPT8eECRMQG/kxMuL/Ur32kz/+UD1utelb05mMzgdDbOR629fcFp5SwKeffurIUFq3bh2Sda6r5Erdxo8f7/hcvry03MzHvqb38vLC0qVLdV3HFZw5c0bRUJ81axa77qdpVJ01C9W//VZ3v6RIUq3i++8jRIMl2mw2Y+vWrdi/f7/qGjvQ1yiMt9k3BJkhzzlKjFwF+IgYzTzNaXg1h10cVnjnHadxW4jUn8GDhQsyR9oVQQIK6coPbrGGdnCQnUxDZjZxpLXwJjZOnoOh5c9xtAODdv1fQuU65QT7bfb7yQ965mAkdgUURWHfvn14EH1KUoOUm5uLkJAQ2fPUmPBOnTqFGjVqYNy4cejdu7dLdRAhdnbaNyr1xhuVeoMkSKQvG+E4TpAMG5W1p2aL05WVwNWuPFG5b1fxV0qKZN9nn32GqVPlNQ87duwIwBnJrTxliqRN9pUrSBOl21avLtUl1VpAFjfy8/Nx8uRJ/P33327pr3Pnzi6116sPV5AF1PHjxzFx4kSB8Xznzh3cv+90VonHJTEoisJff6kvWObLCMuLwaUMm7MteHQvHRWrBaFp1xoaZwmhR8c39WEWEqKfSDRyC4pSBRibSvBioX///oLtrl27qrbnSlLSLE8Rl2xGqiUVnsGFl9HhDwUtavljSCfnwpk/xSpFap/mPwEBUlAyw+Fezt1COZMlKMIaujyLdK3yvEdyAf06ps8N+IaFi5E00tsfNggDF4sXL8b27duR+zQGD04tQAovIhggYhkOfuMNAMBnq+/i+DUnYVtCimuZXh5G53qiRohwLuMCBKbSTv1esVP34sWLsv0yDIOoqCgH+aLcuoWL1ALAzJkzJccNvMw3Ly95acnCoHTp0hgzZgzWrl0r34AkAYaCV5UqMLkQhRWnkvvWro2Ali1V05Vr1KiBSZMm4f3338e8efMU25kMIiec3Q4gjDJqJM8pnq8V7HMEseB8dGYmGufddmw7fvZCTB5yzK5paWkgSFIx3fXZU3ZgLh1oT2tQmUy4tGUOAcHeOLElSvUc0BRsFgqxfwuZIvmpPa2C2yifr4CPPvoIo0ePxunfpyFq4xuO/sxmM2rWrKl43scffyzYfvr0KebOnYvWrVtLUso//vhj3ROXpz0dpXWZtuhe8Q2QeWZQvL8LAdZg5Rbquo1cNxAmCPpjGMTKkGTcuCGf8g2wjL9t27bF7G++AcMwjoguAIR/8QVq2r2UubxaNqvVKlv3Uhze7vz8fIwePRpVq1bFO++8o0gKQlEUevfujcGDB6Nnz55uubar6dhcaqSYhEqMRYsWudRvXFwchg4dij9kPOb8NGSubkgJlStXxogRI1TbnD59WvN+6r0SrtlGC76B2guFx/Hsgsldi+LC8AWU4MVAWVF5kJIzJSWPdQ5SdiMzy5aFsd+zc7hf2G3Zc1wBf/FXyt8kMGz5t8Rn4i/nWR59QlkjPduWDZIgwYBBlogUi3AT+ZrzZosuNbfPDOlc9Jz5RmWhJyvmuQLNrhc9XuoAj/odJYf5PC6M/fmx0TR+tb2EevXq4fqG13Fr5yjkPrmFrIdXsIbHU8LQNuznzWnlB8tnxFy7l41vtjizu77aFOfSV/CwR3L7tC2DdvUDRV+AfU92X7vm4JAQY8yYMbL9Tpw4Ed26dcPLL7+MrVu3ahq5r732moBJ+eDBg4K2YglRQD3gogV+iYWiU84eyVVDqY4yv7sMFwcA+DdqJJuy/Msvvwi2ly5dKpveDQCX72ThwEWns42xP4OE579DKlcQvABD0b8DMQ19XI6QHp+y037riVa4gnfffVc1XTm8PustrhCYab++Smeig89SWYMl6bYy22ze+R2y5DA0aFj82L9Bti1b5aJSpKWlYfv27YJ9XN0CVz+qBH7kLCEhAQ0bNsTSpUsV02jV2PP4EKeZk3m5oPl/LoIRMAoSBHB/sHrKDuA+I/fx48eYM2cOQkNDsctFaRiANZpWrFiBBXl5IE0meDdsiK1xcfh4zhyclklXVYr+KQ1+7sSBAwewb98+WCwWHDt2TFJzx+HYsWOIinKvJIyYxEYPTp48qWls/vjjjy71uXDhQsVjfMeNFhs04J466gbtwwvdB59dWQncWFOYsg8+ip2ptgTFDrEDOjIyEpTMuLs1YTMAp/wdC/Z58y77EIShcMRTV+4458FbCTn4frtTMoTkzb38SG6XCt3RJpjlw+Brs392XUhU424jV4nIsrAQR3Fb1GbrDt3s69UNVyThhg0bVoR34n5wKaKezXqC9JMaL6SPs+bzoYl1BK26cwezV25GejrrTLQ8S0Ls/km4f+RzyflpfM4OcaRYIXLsamlm04gA9GlbBgYDISGeMpUpA5phMMOFNSEAxMTEONaXNpsNixcvFkRlOfj5+Tk++/j44Pz587hy5Qru378vkRkMDAwUn47w8HDV+1JD48aNHZ8JgkCdOnUkbWhG/T2ttWKFy/ZG1ZkzYeAZ9/fu3cMUmcy+kSNHyjq/oxNEQSP7/TGW55erRYwSI1cBaoyNAMBoyBYUFFeuXGG9cAoPOxfx4CKLuYfXKC/sCuC9tUafBCj2u/EHE8pGgSHZ/poFqS/wOTx8+BAhISGoX7++5NikSZPw+++/O+pH1fDOO+8gOTnZQc+uBq3+TFwthmgQJBkGFG8AMZYNAy0yKHI9DXi7ynCMrCbvTQSApzt2aN6jFhiGQZMmTbBs2bJC93Xw1Cnk5eVh+/nzWH3nDvbs348hQ4bgsShCGx0dLXv+4sWLi9zQFQ+6n332mWy7EydOuP3aYucLH//884/sfnGZQVHfBx+HDh0q9LX4E64SjKZimhrsQ4yedGWr1YqYmBhVx4TQoCmBK7h//z4GDhyIrl27YuDAgYgTsXbyce/ePTRs2BDffPNN8d2gHXIp++LoBOB0QKdZnE5dj0CnbElAtZsCUhVXEXmB7bd13QAkpAjXA3zujGDPMo7PrYLbwMfoCwDwMSpHQ9ztPFdymhcWYsKh9g24coHiJ6a5ceMGqlWrpt3Qjri4OMS+SFrx3JpQJlvFs3lvEB5O50+d/DgAwHYXODXKtGoFknu3RMRHWUqcJ/Y14tie8uVmYpTyM2L0GyHIs9BIShU5mWgaD3Vk4nGBjIsXLyIkJMRRnsUhISFBViuZH8kF2PTkcuXKwcNDns2f73h+++23XdLQFcMo+nuOs5fL8fEsN1/1PSW9vBDUtSsqvPuuYL+YQJQPwmBwBF6uX7+Odu3aKbYdMGAAzGnCbL4G1XyFjezPYPbvXyn287yhxMhVwLuiB8ndePr0qeKxa3GPFB92p4SQXbf2abyiQcylFrgKOivVfr5zorLZaDAkgxp+ETCR2hGap5lWdO+nbAwCwIcffqjrfo4dO4alS5fqJh1QA81F4EWRIwMD0Lxd/kPnOAbwgPeX4dvXWdbpRqUbo0nppoW+DzXs3r3brf1dvnwZC0WR2k28Ok8A+PPPPxXPHzlypFvvR4wcUZaEEoqqPpjvzLl9+zY++eQTLFiwQDUl3J24cuWKZhsumuuOWuQrV66gb9++yLcqp6JzNfzFBdUSCrAG7qBBg9CxY0fUqVMHtWrVktXDtDEvDiHG84YZM2ZgyJAhOHjwIIYMGYIvvvhCth1FUZgxY4aqHEdRQm5RKucY497rPMoZdQjt7NS1JIwWRUk9V/DfgdJFJt94NhHOBS4hiPAqX/tIMps+uSLWTQQ4NAUyKBQwemq3LQQ6Ni6NHydGILSs+2sa1bB161Z07dpVNqKvBrUMmucO9nWeoVxVwe6AcWvh/ap6iYoeBIaEwNOeIkx4CR0wfDIoPrhgbPPa8pKKSth7LhWHRLJDDE3Dt0kTzXM///xzmM1mvPnmm4pt5JwXWnwWYgwaNAjnzp3DsWPH3O7M40eVOWTn2wRr+SpTp8JTJOPoUa4cSotTltXWRSQJhqKQkZGB7t27a97Xnb0TBNvdWwSjcQ32XhlrHp6ts6/ZLS8OaVuJkasAMfuanwuU3lq4deuWal3cyr2nHA/7lSN3cT9KZoCxDy4MA0UjV3G/BugsdvDhLwBomgZD0oL6IjXEJZvxNF4/i6sWfvrpJ7f049ugAQCpvA5JM6AJYVEVTVP4NX4T4nxseEZlAwwDW3bRk1UsWbLErf0NGDBAsi9WFA07e/asah8vtNwCD+JUR8BpQFqtVvTt2xe//vorvvvuO5e17sSIiorSlV48ceJEzTZcupm7cO7cORyJUq4x0jI6C4ImnVlis5SETJzecRM/TnASbGnV5F68eBHnzp1zbGdnZ2PWrFmSGi2xkWsk3Ddu//+M1NRU3Lx5E2/YCWbeeOMN3Lx5E2lpUvKjVatWoUOHDoVK3ysM9PIEcHOVUqZTfno5CcOrKzDasw+8PKTLKL7tTClcX+2JT7WwTvAbmddVWrkAmmYXw7aiyUDj49/Qxy3oWK3mUI6Pj8eqVauQmJio2KZYQVMwhNaVkP4YSlWQJQLSS5LIYcOGDY7sv1sPciXERRYeAabFxrZLTmOjsaQbMg+yLl3CU50qGWL1Ez0oiJM8LCwMtWo5VQX+85//uNyHHNq2bSvZ13nSt4LsS5+ICH2cPyptCIMBoCiXU/MDfNj1sa+XwTGW5V87DCbbjWR4xYQSI1cB4pz8bJtN1fOqF+vXr0enTp1k0xU4HLkcjfiHrGF7dtct/LVNmjZpi79m/0RI0pIJLzbFIPvXabDcOe/yPVofsV4wfm0ubWONXH4dkRqKgsxRTRaFj6FDh8rW5tRZvx7lBw1iNyTpygAlqMklwGTn4HTKcSyKYb149a/TSPzwkwLduyvQG9ksDO4okDspwRXZGT0wm8344osv0KxZM9njcn8DVydtOaxbt06yL9se3V+8eLHsor6g6NatG8LDwzX7vHfvnmZfRUGScjXuL9goZSM8oIx7ySVCIsogJCIYV4/exbVjwkwCLaP65s2bsvuPHTsm2LYVMHvl/zoePXqE8uXLO2rZuFS+R4+Emqq3bt3CqVOnMHz48H/hLlnwF50c5NJUaXvqulJ0nyBpl4lz+Ojbrize7VJB9lg+mY7xl0cBkE+hH1P9P2hb9hXJfjH0OpU1+2FoCRElH5aYM6Bz1LVQnxfExsbitddeQ0REBJYuXapZh9u6cT3V43LzyqVLl9CmTRvMmjULLVu2lNVEL24wNKX6G4phdXG+5HM/UDSDMBHp571p0xyfe08XcmP4eeu/LyVkX7uG/CIs5pars3UVkydPxieffIJOnTrJ1v3qha+vr2SfjaLx8IlwreBYr6pArbSBS1d2VWKzc1O25tvDSDqCXS8qqWOx3/WLUvcjlxJ12x79WrEnCb4q9Nxq+PxzacG/HDp+OM/hsc5OV/ZcMyBAPRXWXRirOlM+/rl4A4/SdHhvDTzj1R794B7urFwbjjw8DIZk8GZoX7mzBYiPj0evV6WF9YXFA50ETMePH3dqDovAiWxL05UZAbsyByNv/iyXUjx1Rnq/p7ugx3jkUoMsFgtSU1MLHdndtGkT1q5dK1lEc/jggw8k+5QMHb0YOnQoXn75Zcn+zZs34+bNm1i8eHGB+46MjFQ8Vr9+fZw/77qziQ/O6C/IxLpp0ybFYzcTlSc/jsndXchKz0XS7VQBWUlerj0SoGHkyjF/A+zf5dChQ5gyZQreffddRF1mF19ty7AGhJJ8Swlch9VqxfTp0zFr1qxCLfAKCzG7MiBv5Caa2QhcoEl+cUuQFK7EukaiyAfNMBCXkvedyT5/+6+xc/L4y6MQn3tffCrql2oIb4Mw4mmmijAFkKZkazk55PwxG3nntIkVxfjlWOGlmFzFkiVLEB0djZycHMydOxdVVGoSAWDbHzuwa3BtxeMPHz6U7OvdW6gpKzcfFTtoShdt9VC7lFWGAuuuEnr06OGITkTdl74XjCgriatJB9iInyswynAwBLZtC3mBoMKjZs2aCHJBlkcJHh4e+Pjjj7FhwwYkJCQgNjZWM4tAiRFaDocuCQNavnXrwiCT2iyAWkSJJJHvAhkbAGQ9uorXW5TGhs/qsKpG3HxteDGzoordyH1R6n7kkG4fNHadeYpzt1yfHOPj411qzwlacyQwuiNZvIXd+Zgs7D2rzKbMwVjJ6R03VGQlfRiK7WfAV/8gx5oNkDTCfKTU6mKsXLlS333KICIiQtEzpUQEJAcxJTwHh5HLW6QxNAWShpBd2W7EtT9u1wWjGdS55bqRm5uSgsWTJ6Nfv35YunRpsURp9aB3x46OdCS9bLz3799H586d0aBBAwwdOtQh0q6EnJwcJCUlyT63cjp1fMjVCPPTVQuCSZMmgSRJiQbewoULXdbL5eP8+fNoqOH0euuttxSPNW2qXeP95MkTPHjwAMHBwYL9QRHadTZq/ZPli1ZGY9Dnr+D1D9hoPW3j11iwuLCXlXLRIp46LNJ05mPEiBH45ZdfcPToUSyZvBS2PBt8DGwUmpbRIC2BFBUrVsTjx48dNY0URSElJUVAtvLkyRMkJCRg9OjR6NixI37++Wf89ttvmD59erHf7xfj3xdsHzlyRNLGTLFOmpR8qc44wBq5hQHDSKMouRwRE2/Ie5CbgOp+2umVcTlSY9htYGhWtYHbtL+DtDkLTJ59TipA+tUtHvvqHzPVI6bugpzMmhK6RQQBBInqQV7oXTtYto1eRmZ3cIIUCjQFENrGZDWfOPz48AmWxMk/90rYsWOH4xnYdOSxRAOXESXY/7Cj4Gncnw2q7GDi5pB5+jTOxMQUuE81HD16tEj69fb2xuTJkxUlBVu1aoVpvAi4FtZEnpLsK//22/BTq1VWcXwQBIH+J0/qvj4A3D88FbOm/xdlAz1AALj70AyGpmBLdJKTejR+3aU+/00Uq5H7ItX9yMFEkkgwsV5LgwNyAAAgAElEQVSyJ8ZSGq2F2L17N9q0cU1f9uQJliwotDbrueYiq217OIvRGRDCKCy7kwcC1+7pMMh5C09r7CXB9dgNEgypz8jTK+Mjh99//10xCusOODRj+QODzSKJ5HI6xbVj2O9c56b2d+cWDqYyTjbNyWPHYv6WLTh79izmzp2L5s2bu0yOURTYdeyYg3VPr+E9YcIEB6HDyZMnsX//fsW2Bw4cQEREBFq0aIHQ0FDcuXOn8DetgIiICITadY/VwNXZBwQEaLR0DXqurQY9i6xRo0ahVatWSEkRLlzK1tVmHFf7vrsi5VmdGYZBix4s2VphmJaDKvgjvB77dw+rUxb+Qd6yCZhaEkKlSukbb3Myc5B80bXFXQmA4OBg1KlTx5GWuXfvXtSpU0cQ/ahUqRLOnz+PY8eO4dixYxg2bBgGDBiAr74qfqZNPz9pup+rDkSCLJwDhKadduHCMSIjlhA+5Z6kNhHT7SynooNeBQO9sN67AuoRTxvY7gh/tnIMMn4oGMnmkwwLYh86o88+nv9edF8J3gY45vr3mpSTbaP3uWnatCkuXiyqWKMOMLRmJPfGjRsYMKAftv9zFeceuGYwVqlSReDo+OC7GITPngsyoi57eTeUC3EwGAhZ/aHWwfKOiMJfr+ifTbEj+/vvv8cff/yhGLRp3769ZF9ymlQ5ILBVK4Qp8HaUHzoU5fqzuttpaWkOfeGIiAhYLBb8/fffyHUxkgsAe3dvR1paGq7dy4bZQsNy/Qgs152OZndJ/hUHivVOX6S6HwCo1S5CsJ1rs2FxmSEAgEj/tohwQQtTK3Ilh6tXr7If7IMBQzEwmEjUa8UnxSIA2gbGkgf6mZ2xWZCixyAjW5v8huFFPLg6IM7I9aOs8Esq55AQKiqsXLkSwcHBskRJcvj2229x4cIFxeNygzL3cvIjudZ7f8MgiuSSomhfjbs6vrvdMPYoXx4gCJw6dQq7RdHHzMxM7Nq1y3mPNAU61zmwFWQiKWgab+dly5CVlaXKrMyHmAV49erVim3FjMwdOnQAACQlJSlG2fkQR1vlos29evXCJ598gsjISJw/fx4HDx7EjBkzFL8PN9mIDcXCgPtehUFh6m1NPmVRvfuiQl2fq0kG2N+Hcxos3fAtGIYBaXTPNGEwEKBpRsDazkErXbmJDtZNDsZsk+O3ruqrX1Lk/zpmzpyJTZs2oWvXrti0aRNmzZoFgHWwuFufurB4qba0LldONkQNBYnkpuQ9xn8us05YmmFgsD+34nTNoHrCcT/bps2BcCjZ6TQk3bw0yzu5EQBgCGHTdhkz+84zuRmA1T62uhjJvfWg6EkYxXB1fmSbs9+remlP+Mg47NSULsRQYvSlKAo/rV6Fd995R1HnvbDQU5M7bNiwAhujJElKDLLvT1pwOcH9PAcGkoAcsfmOYi7VcidGjBjhYHCuVasWevXqpdp+6NChBb6W1WqFxWJBUOfO8KnJZl7ypTpzcnKwbNky9OzZs8DXOHDgAKz27CvGLBy/ikp3uyjw3Jnjz0vdDwAEBQhz+LN5NQk0YQDhpZ9F8LECBbsarDY2HZQbC2iaYReDPPpuAjRAUcg7uw2ZSzkGNefoQYJGj5bOyCIAhNR0esvyLuxC5opRAvIqfwO76KZt7IPcPO8pytysAYIq+OOipEXGYdeuXY4If9WqVVXbcmjevDlCQpT12ZSYbeusX+8wcn/44Qc06zcav3xzGSnPnOm3BhHdvEHHO81FRj3DwkAYDBg4cKBsuwkTJjhSr/Mv7UHmYqfu6saNG7UvJEJAQABWrFjh8nnZFgt+++03fPTRRy6fC7ALDoqiMH/+fIcH8S+RVBEf586dQ4sWLfDee+9p9p2Xlycw/uTYhZcvX46PP/7YwZhcr149jB49GhEREZK2RYUffvjB8VnLOfP06VPZBYgaL4E6CBAGD/iWra1Ys8pBro6Rw6BBgxw11sOGDXNENg6fiMS9x//AaHLPOEwaCNCUvLPI2199fHClTv3c8ovIy2V5CCbV+lT/Df4fR/Xq1bFt2zYcPHgQ27Ztc9S5rl69WlbrfMKECfj003/n7yvHJpvFI9MTv2diwsTMOw2BAhi5j/OSeU5gp11oEDlpvMsK6zwblmqMDyP0kxYWGfuD3QGe9bO0jpBQ5XuWIs9S/KUAW7du1W7EwytVAgQkk2fflz7H/DHcVdA0jc6dO6Ny5cqYNnMWjh47hvHjx2Ps2LEF7lPlYgBpAMMwyMjIkPBizJw5s1Ap1VarFf7NmuGuh3NNdeJ6BggwiM/ORpfDhxD9+zDY8gpf4mIgCYlGNcMwMBcg6qiFX3/91e19yqFJkyY4ceIEfvvtN+zfv19zzask6aPlpDh+/DgaNWqEmjVr4qeffsLDhw8xSIagasGCBfpvXganTp1CFzsBVeYzMxiGwZrLj9FjUzQ+WrGjSMgwiwLFauS+aHU/dcrXFWxniowmvULyhUlPjX/iTDnhjNy8C6ynsFvYIVTyjAdD28CYndFA622nF5kEI8lw8fB2FpBbY8+DTnsIUDZ4NOoKAAj3vg1fwzPQVhuOX0uHn50d0u+RfLqPHqil+cyePVvAsuvp6alZ4wg4dc+UJHfy89UJt+Lj4zFv3jykpmfgwZ1MzNl0S7GtHiMXNA3CaES5AQPAaAzWM2bMwOXLl5GbKsxi+N///qd4zoYNGyT7li9fDoIg0LNnT6xatQr169eXlclRglJNvLj2Uw5Xr17F1q1bBYRNgwYNUiQ66ttXm7SMDy56xzCMxHjWIhsRQ292gKvgsyRGR0ertAQaNmyIzp07C+p3smRYrvVLHTAOz7uXl5eqU1CNdOPKlSto3Lgx1q5dK/kOW84sQXJ2nM77kceNGzcQEhKCmnWq45vfPkJWujM9sFQ5X7wyrCZ69e7pcJQMGDAAY8eORVJSEu7du4fY2FicdLGuaFZfNgpJvqCMkCVQR0ilipJ9fEeYuBa7qSj9l6FJEAYKQf6ukanwI10MwzikU7ReWQIEIvyl0WcAqOSl7Kh1B2wPeWnKdsOIzpAxhlyM5N5JKt5IbkpKCiZPnqy7fa1gL7xaNRAEQcKzKetAFzsjAODMmTMCw0JvjS4AnD59WjaTyt1a9wBw6+591Jr4I0JDQ/HSSy8hLCwMqaks38rGjRtVM6vEmDJlioTjIjExEZ5t2mBZsHCuvORRHSPtEoPW3Ce4+dtgFBYkKVw/UxSFqVzmoouYO3eu6nE5ssmiQqVKldC2bVt4emrrUZMkKbums8acUT1v1qxZyMjIgM1mw7Rp09C8eXPV4IIchgwZotnm9u3bMJnY94Wx5CEmNQ+Lzz1CfGY+Ii/dKlRZYnGiWFcAL1rdT+nSpQXbZpGxqtfIlVvI8tGpUyfFY6djIp2auBQNwkCCyWcnlzJBFDsvqTAnEmAkpQ/8idoWx0oRUY/vgvQOsB8HDIQNtNWKo3/r1+c8ffo0WrSQryUqU6YM3uz3tuwxORmZHTt2aF6PM3L79u0r6+GN0SAx2LZtm2A7McWs+JsSvDVT7JQpyJBZdDMUBcJk0lWvcPbsWfTq1Qv13/8amXnak2rjxo3RsWNH3L17F3/++Sdu376NpKQk9OrVy/E89OjRAwcOHMCoUaM0+9OCmo4zH1OmTJHsc1d0Jy8vD1arFRMnTsTHIjkDk0ldykqs+/v11187Prdr184t9wcIMxT0pHRGR0dj/fr1jm05L3N8fDzeflv+XVGDnDONe0def12dKOLp06eKDo/Is1tk9//55594//33HcZpSEgIqlevLtAxzczMRNeuXR3bWeYMrN+7BDn5WdhxYRW+/306BrzzJq5du+Zoc/r0aezevRstWrRAu3btZGuXtJCZmoVBoQVPByvB841A0dwMiIxce8SyW4UeAAAP0oTATHZuom0mMLQBVSqYJOQ3WkjNd5I4UgzA2UxastJKMkYAMCGicHrcWqDTeVFlN7GNZ2TbsEcHoaU78dNPP+lqN3v2bKztXR2/9IuAiSO0s6f5ejaTT98MDQ3Fe++9hw8++EDTgco3gpXGTD5omkZ2dnaha1q7j5bOq2vWrAEAfPbZZ7r6uB/3AIfP3MZHH30kK2PTpUsX+PPkgDxNJLb8IXUsFPa7GEhCoHz5/bff4qJM2rjJKxALFy5U5fRQk9C5e/dugfRxiws+PlKZvti7sarn3L59W/W4FgwGA2bPng3fCg1U20VHR8PHaEVwgAkklYdN154IjhdG9YZhGIFkVVGi2H/9F6nuRzwIZEkiufr6UaoB7Nq1K/bt24cNGzYoasCGl63tZELk0pW5FKkK1QEAeae2OLywVEqc4HwSDB6ksBHNPh+1ZncqjAkMLw2aIRlsPb0BFf92RnaevnQHjMIkuWXLFgwYMECWZe748eMAgBEfyE/mfjIU6Z6enpqGFn+AePnll1G9enXB8d69ezv+rjabTWIEyEW+4gLrSCdChkF5nnyQ9ckTPJOpBWZoWlDrqxdt197ArVu3FFOvp/fujW3btoEgCHh5eSEiIsLxbDIMg4yF/QU1Ejdu3HD5HviYOXMm/P39C9WHu3Dw4EFs3y4lR+IIsJRQuXJlJCYm4u7du0hMTBS8y1q1MkqQM0j5E2yfPtokUADwI6+WX470hCRJ3VJjWvjyyy8BKNeS6cHdJGF0l4vMvv322xLysby8PNSoUcNBJijHannt7jmcvhWJm4mXkPj0HjKfFU3a06AmJUbu/68gjCY0D2ss2McnsOSM3EAPlrDMQBiQ8ISd3x4ceBsMbUDFMgbdjmoOh5MPOD4zDOOoJdeqKVfLKAgwSQ3ti2mFY5IXgHdtz+a9VRrqR26ecC7d8Jn7JQPFUMrYEmPYsGFoGeoPTyMJmOzcDnYj16NBZ4xUIKA6ePCgLj1cvvNciVUXYNcc9+/fR1hYGGrVqoXQ0FDNectV6P2bAOy87mEiUbcKOxfKGVhxcXHIszrXeLVKy6c/0zYnR8b+ua7LaZKEMF154dKlsu1qdpkJosKr8PHxUYxUKxncc+fOlXB7PG+oW7euZN/pK8ocK+4gLV20aBFMJhOqvTYbtfv+jPajldnK69WrBx8vEowlD49zpOV/BXF23L9/H6GhoahZsyaqVatW5ESsxW7kvkh1P+JBYE9iIq5veB0m2gaCYWCj9P3ASvTla9euRaNGjQAAC5ctwPjp4yRtjAYPWO9eQvaOeaApBgRJwKP2yyADyoLwZg0R24MbyL+0BwBgPsNGNH16st630uQzXLnLRpIrVg9ChyH10by7fM0iWcpJaJVlCQbOCrXlbN75YLKk3tvjx4+rphFVrszKDvn6yut9KXlOw8PDUStYeZASD2BydYlRUVH47LPPULVqVbRo0UJAnpSTKY1SPwh9GT5dhLpmcnxbNpkaUVBOHbvHZtc0D5Wi+S3r1sWbbdoopyBzTgebBVRqIqyxFwvNHsxFgvUQRBU1CqNPyDkFxJ5euYlFD9q1a4cJEyYAAFq0aCFZsBQk20Tp2XcXA3SH19i6nxo1amDZsmUFiopyePToETZu3CiIzCqBI9pTyqa4eLdoJB3E6NOnj1tZQUvwnICm4eshHBP5kdyzqawUh9EuuZJuSQdpYCNwVJ4vGJoECBour694Qwm/JtfbU7iUsuWK5joG6P6/a3iQoi7Xdjsrxv3PK4+syLOBU5LRliQsz1FyYOtB2UD1+sPCIDMzU5V7A2Ad5ePHj8eVK1cE470xjB3rOcImwuSFtm0Ll77KEUfm5eWpMjPXqlVLkirbvn173ZG4hw8folGjRo4sGSWoGdp8iLODlFJqObKh9LtHsWnBcNk29w5Jy6ro/HxkicgplWAgnTQwakaOsVQtrD/IGtqvv/66bFRWqbZVTv9YCzTN4Emma/rChUFgoFTDe+byTY7AkBhymXOuokePHniSaQVBGuDhWxYwSiP6HCwWCwwkAcOT2zj7QJqRerUAKeb8dyI/Px+bN292uQ9X8PzG8Z8DyHm6AKDUtQ2oY8l0eKIOHz6MZcuWyQ42K1asEKRK8nFi6w389TtLQLTz0R9Iah6H1q1bC9ocvr4Vj/KrwBr9lzOSy9AwhNaRTVFmGGD5xWTU6f0+Nl17gvamv9GpsTO1q27rygiuJB+l82zBRnsuhMp/bzl2ZYZhMGLECNn2jn55g2mpah0lx5XSTRiGxvKe1dGkovQlPDJMv6GyceNG0DSN5ORkLFy40LF/+eq10n5l9BYJmTWHTx2p55qL5NpsNgw9JdU7Kwhm9O6tnv5snykYmxXPVn6A7N9mympL640M8mnw69UrHt3D4gbnWHIFw4YNA0EQ+Oyzz5CUlIQdO3ZIHA9ZVm+cPXtWVROXA1cHJl4k9bfLAQCsx9UVyLFKv7fIqc395ptv4pdffnGpTw4xMTFo1qyZ7rQ4Tsty3bp1BbqeFiJqVNduBODChQs4f/58kdxDCf49kH6l4OshnKe4SG6OLQfbHrAp9gawc4+NsSL3cWXkPmblvhjKABAUbAqRXJpmhBJ6cm0YxpGm7G/nufAP/wfV+i2FJVPIaRBt97+euK7OAP397QXItArb6GFmVoWBvTdT7baC3Vk/ixzTOo3rDYeTcSLKeY8Nqikvkt0BJSMGYBf9SUlJiImJwdSpU1GunDBK69PJrqfsMHI9EXO1cDJAy5Ytc5RmqEFJf16JkFKMxYsX48mTJ5rt9GQmLVy4UDJfqaX5AsCD0wsVj5lTpYZ6xl9/IfH77zXvBWCZ9rn1syvqEHLlcKVLl3Y4n/lISEjQ3S+Hnw8l49156twaxYGhQ4ciJCREYOxarVaXidf46P/mmzh9+jS8vb1BEEAgkYUAIgvp2erOLQNJwJAp7zBQIjp7dukS8nU6X1auXKmrXUFRYuSqQMnIPRr1O3xoChTFYNu2bRg+fDjmzJmDFi1aoEqVKli5ciXy8/ORkpKiGN158OABbp5OQNSJOADAxTR2ISauJeSDoWmQBhIMZQNhMMnSyc/bcRrLLiQjPz8f804lYektb1y6rW+SJAgSIA24UUE+asiQjMCwTkhIwPLly2Gx6PN8EQRQrr6UBU4JTFYayvmasOGtmljdqzoIAN5GEjsG1UIFv4J5jrXkcnbu3IkZM2YI6grlIrnZMh4sa2oqQBCq+rGuwnL2LDLUDGZ7mnLeKafxwrFU8/HBBx/oMprEdejFBa0JVww+WZ2rYBgGd+/eVfx7rFy5Et9//z0qV66M5s2bY+XKlZgzZ45mvx98F4N8Qxn88MMP2LBhg2rdcP/+/TFv3jyJ0cg3egcOHIieI5UXGo06ClmqIyIiHCnVzZs3x8+7rsmdpknUIYeCRKkTExOLrO5mxRL9DgBXCc9K8AKAIMB4CccAzpmRYXFGdBcsM8OU5QNP0gs5iTWR/Jc9bZ82gCFsoBSysT5fdw//WyvNDOIzEDOMNE3ZI9CeMi3yjB6OZI3ruw/NmPHzfUm/3So4a+bFpFmfXpukO7p75coVXL9+XXjP9nWCV3P1kgXCSz7Tig+aZvDrscfYcIhd3EaEemPqkHBd91ZQxMfHKx6TUwbgR6QNZe2ZMtxayWByW11yQZGSkqKL3EpvhEsPo7IS8eD7778v2UdZ9OkGW7IfI+HkNxg1ahRbM6vyjCYnJ2PUqFF45ZVXEBISgoG92iM7g1UcOXDggOw5vuVecnxOfMI6DMRkWQBwOylfll24ICU/9x65loFX1OC+w+rIh9j3l7qCghbmz5qF8PBwAOxPtcD3O8zxWaZ5Xn72Eyw590j2mFK9c9LSpUiWIdWSQ1HzLZUYuSp49kwqzMzB28Bg395dkkHWZrPhyy+/xPTp0xEZGSl7bo3qNQtUDE9TDEgDwdbOEgQoBjj3IAuxqc4Xc/3Ry4JzVhw9i7hk9RQpDvHx8bj5OAeyAmYAQNKgrWx976NHj9C5c2fMnj1b9/0TBAGvwFBUCglz7NMjJwMArcP8ETW+ES5+0AA1g+WN8LCwMNn9chCTTvGxZs0aQcRLLpJrlSFJiJ89G7a0NIwZM0Z6QgHhazSCVjMU7BN2/qU98GjMerxNJhOio6PRpk0bdO/eHZcuXYLBYECfPn3QPlw9DdZdabKuYPLkyS7XKhWm1uarTXH4bnsyBg4cKOsQ6NatG/r164ezZ89i586dsm34YBgG3f/HGpTmfPb36NSpE/78809HTawclsrUIUVGRiIzx4b4x+w7G2+tg5cGbYN/SHNJW4PBhN5thPJgr7zyCpKSkrBz505F6Z+C6PPp1VLmo2XLli6foxc1q4UjNjYWf//9NxITE1HXrhWoBLW0whK8eCBAgBJpNXLkZV4G59jQLD0XEX90gZE0opSfEWUDWccTQxvAELRiJPf6/Wxcvyd9ZvhG7uN0i4CQuGUjBoE17Y4lyaTBNjwX/QwXbknXFe3KdnB8tsloUDI6RIW+/vprvPHGG+jevTu+++4757kUa1AxFnYeMYQq1M9S2oYXv14TAOpV9UOgr2sM1UUOOQ1PLpJrMGJI/TLS48WMKlWqICQkBMOHD5dNTXXneGUwGNCqVSvZY/zMIQ73Dk/V1e+t7SOQEXcCkZGRGDNmjMARs337dlSpUgW9e/dGWloa+vfvj8jISEdJ2ePkhzi0YhAYhlGsgyZNziDTqEUxyMqVfz4jz6fKOucrVaqk63vw4ePlOqdKYaHm4I+Li4PZbMb2v55gzW5l8i0tLGjaFDSPU8hmY+BL5MGf0H7OIpcOwKrL8hKoanZMZmYeNh4WOmAePZIay0Ut+Vhi5KpAjn2Ow4PkKHz+qbK+6ObNmxXTVR7EK2s+1n17uOIxc1Y+0pOzYT60ApbrRzBqye94f/ddvLU1BpG3lVmQr2+QMqseOHAA3bt3x7q/H4NmGOyNSUO7du0w4LdbOLhOPn2EIRgkJT7Anj178M033yA7O1vxmhzKlK/s+My9yzv2HUeN7ouwZftBDS+Oa7VJq1at0tWOpmlNbVg+a6JcJBdgI+tFiUWLFmlGOPmEU5YrbASZYWgEBARg27ZtWLNmjSPq6eHhgWU9qqn2pyb1VBSoWLEiRowYAS8vL5eo/vVqKcvh7M1nOPUPS3YkZsds3749jEbXFm38TAmunom7x5EjR+pKX+bQsWNHLPr9AcYsdtayGjx8Ef6qlMUzM8+ARjXY6Mtnq6VeXlLh2fm39ccLix7VyoDOfQZvb2+UL18eBEFg15o1+FyG04HDoUOHivEOS1DUYBga3h4KZTW8ecNkH6L/eZCG1nUDMPBVNp2VoUkwhA3PcqQLZ4pmoDS0NyzlLHWIup+DjGzn+bS3k6+CkPOM8pCVa3M4xgDASDrHnJNPjgEA/IzOyCqtEX20Wq1Yvny5Y5uvkWk+ai/LsV/Dq6XCeCRnHIpw5h8hSZxSJPxfhcz3cGS9GQwwkAQih+onyvrPf/7jrjuT4PDhwxg6dCjOnBHKxhT2mvObNkXFihXh6emJWbNmKTqv5QxBc6oywagSbt26hQ4TJ+K1w4cREhKCCRMmwGaz4dKlS6hfvz7u3bsne97x48cVHdyBlYXp9QO++gfVusyTtKMZRkJe6uPjo7p2ilWQvzqpUU5QFBCXKIrBrVUz0qWBFb1oFBQEhmfkWu3vLWN3vpWrry+FXozExETFY/TjJPxyTGgcyzn2izp7sMTIVcGrr76qeGz38fma51eoUEG+33r2Scb+DkZlOCe7wPBQSfvM3FSYKW88iHE+5FGPc3Hywt8A2MDrfw/H45ODcYr3wq/tOHv2LEaOHInr169j0dlH+OlKCuadSnIQAPxzOhlZZunLbr1D4JU3BmDMmDGqkVA+nj521kUwDFAzxBsAAZ+ytRFWRV9dnV7IEZfJIV2ONEoGqXadXR+LvFFwR6YORAmbX34Zy5Zpp4ZwGNevn0PblVTTvZWZ0C3XDuu+jhhiD3JRkgLUqFEDf/75p2OQW7Jkie56JVf+lnKgaeDrTXEICQnBtGnTEBQUhCZNmmDePOkkqoXUZ87JQy6t8IcfftDdV1xcHPLt0ZIcHoMpQRpg8i0raEt5VkJLuwTKtXtShxPnZP1xt3Qi4i+IxdCjkfxv4tMWVZG1VvjuUVlZeLVCBXgoeJZfdMO+BCIQBEKC5B12fCOXW+Y+NWeApp2OH4YywkbbEJ0gXeymZylHNMt5Ced0i9V5rZsJzjnTpOEne5YrYvonnCf8mcKSsvEN20xrJtItaVCCWYbokFOrYOzZV45oprfU4PHqMEyXkZubJzR+iprTTY1Uafr06fIH5DwU3PtPGkGWrYLKpTzxRoS+xbXaOtBdEEdUC+OUKx8YiMZBQbh06RLu3bunypmilMYctVE9e0kOaRpSmXJQk8rzLS/lXfGr0AB+FZxsznX6bUJcch4sNuDbb7+FyWSCh4eHaklOltmGCUvVo6L3izFteezYsarH9+7di5Qb25B0XnvNw1duANjARnx8PEzBwQIjl6uHpu0jZIXGw9Cw3wpUbOaa/OS0adPw6NEj2fR7TxnZNL58IgelslB3ocTIVUFh//hK6c6Nw1mdTm4CfpznDOkbPaU/ydID/8PFx34oExqI6o0q4FBsBgb/Li38PxCr7IXip6v069dPcOy7s4+QIZIE2H1JShiTePICbDpSmpSQm08hy0w5DAGlVDEOnP6r4nGbkNKcIAhddRj//POPZhsA+OU+Wzs1ZEO+7HGKZxCaY2ORT1GYr9B3eW9vdHJBn3WQt7djUCrTW1n2Qe5vRD1RrmECgO+7hyse6y26VocOHQTb3bt3x7FjxzTZLrVw8uRJnDhxQiBVVL58eV11wwcPHixwWjWfOfG0PSoxduxYREVFYc+ePQ4mcFfg5eF8Z5UeaS2dWg6tWrVyjAv9ZgmloMrUcf423sE14Fexkaq3mjsmp2fZq1cv2RS2Bg0aSGr6/m3UrFnTkVXzVaNG8PWSfudEu4d4X0cpsR1QuPT2Ejx/IL384GUSzq5L39IAACAASURBVM8csQ7f0UTYPz+5/CpiEnNBkkCzCH+QJgsSaPY5v3Ff6CBSSg1maAYZ/7BjcuqzfElbW54z88tUKhmP/uqJh8f7oG2ZVyR9cY6slAx2PDISUqu4K69Od/Ht+ZgW5VSZoCgKq1evxqRJk3Dx4kVZXoxu3bqBYRgncaF9PCBlam8J0gCG1p7b80Xpyi5SKbgMNVIaJeZ9Rs5YJzgj1wAygHUWzuusrocLsJqkRb0Idzd+L+Iax+JARERtePjLrzGqdZmLBu9GosG7kTD5BOF+ch5+OvAIQ4cOxfXr13H9+nXJGpcPjsx515knirXu45bchsVWPLXbejLYkv/+CZYs+bpYDrVq1UKvXr2QkJCAmJgYJCYm4v79+zAwFAz+voJ0ZS7jzItw7vMJCkfZuvpkEPlo1qwZqlSpolreqQZX+VhcRYmRqwKCIEAYC/4DcDVCYpBc6gw36fDInAiDvAt43I7DWLhsLmw0hUkqEVslKKVOKyHuyS1kmTNgseUhOSMBs7ePxombl7VPFKHHwP+g+/+uITPHhjmb45GcZoGHif2+3GDDMAyyzNIJ1hon//fj8Gy1VHJpyJAh6Kiw0OUwePBgXfe+64FyWrkYWdeuYfeDBzgoQ1v/gb1eMHHyZE3tXw6W5GTcsxvsQSqSLeajayT7rHHqtO7NKikTjMhNDnFxcVi6dCnWrVuH1atXo1atWg4Reg6ukhlpMVMqYeHChYVifRYzJ743Pxpbj8vXm+hFuVLaJGhKWR1ifPnll7h6V74MoGzdtxDeaRZCW3+Ial2+YYniVPBUQwpBTjs3Li5O132K0bNRFQzoot+Jo4VevXohPj4eCQkJOH78OG7fvo0jnTujddmy8rwx9sGEIAisa9NGclgu0lWCFxueJm8YeGm+ZrMZ2dnZspFc67MyiEvOc0RySZPTcXleVCPLtmFQvpWQUyPzaQ4e/MGe9+U2dowVrJFp5/vIkDaQHnnIexqCIVXekdz7z3bipqeZ7CLTQEgzDUjePjHD8saNGzFz5kxs3boVb775pqJe6u7du0H4sA5BY3l75FskvQTSyEZ5degpGQ3C9dCuMwVPodSDtWulCggcFBfHckau/YciCAK2eKcTLykpSZZ8acWKFUhISICvr68kFbY4UBAFAA6lAgLg17ixdkM7xCU7/zZCQkKwYs1Gl4yfpFT2vQwICBA4zuXBPgsr9jxE3GPldXG+pXiMXA+PgstvcYRbr7/+OpYvXw6CIGAwGODn5+f4++Wd+wN0Sqwgkqsmf1pQLo1GjRoJtMoB4CVDLHKPrkVcXBxOuUlxxFWUGLkaeH1TZzSt1qFA527ZskX1OPcO89OSrDnKL+ju/X9gyebi89It2f9fzN89EWuPyUsg6UGCB0uGlJFtQ+Oa7GRRvrQHCAJITs9HdHwOVu17iAFfSiOgxpBaqn3T6VKDslSpUti4cWOB71eMMykpjs/+5eTTcaKHD0fmqVNYeUc+BaYmL+oZHh6O4RrEP772mlCrPcVcbbCnczMl+4zl1OtVvYzKr32pUqUk+0wmE/r06YOuXbs67qV+/fpYvHgxOnXqhGnTpuHtt9+W9ay7O02Uv+Bwh57kozQLrsZq15arITrBGdGXY04FgNGjR2v28+mnnyrrIdsRENIcQTW7wmDyRngFYXTyjqjOiB9hloNcLZCr3liCIPDN5A8wt21p/K+a9FnUQr169XD48GFcv34dSUlJjn/Lly+H0WiUfX4Yxvk+ZJw6hQRR9L+yry+qBgijL67IVJTgxQBBEPDzEupMPn78WDAumERRWQNJgCAA2uZkPufX0QOs4UkYbPANFdcROp+7+8ms00QwBInJG+zPqVzdapKdLZYzGkkZh1Woj7N0yUILHVbijCUlY3DcuHGgktl6fcKTfSdIP2GKaqn/bgf1JB75V7RVAcoEKjPGFwRZWVm4efMmvv76a0yfPl3AFHz5sutOdQCskUuQSL5dFua7HFcB7zewCf+Wchr1Pbp2dYw9ess3dg2ujWDvgpNwzZkzB507d0Z4eHiB9EcB4MPatUHn54NwYd599913C3StosKPP/4I/1Kulczwa+O1YOP5QNRKE6wUg5w8yi3rjKJAvZZvYOHChUhKSnIEH+RApz8CQTBgeCnFYiPXBCveIVnZP73cNmLk5+ejRYsWOHDgABiGweZ795BzdTkif12HV199VXcpmrtRYuRqwORjxGst3SdB0asZj03YbjBwkgEtg1qDIClUaa9MinDjzt8Fvna+RdtTW1js3HsEdQduRUTvVWg9+oDDKLLYaESE+uCtdmyqEMMA835NwKQVsdh5WsEbrKNGqKjxBS8a7+ElHRDPnDmDRTdvYre9/kkO1Xk1MdHDh2PGlClY1aoVDu3ZI9u+pqYnkgeZsJYWIZaHQTvFVQsEQaB///7YsGEDxo4dC5IkZTVUN23aJNmnRcR048YNxWOcEfhk1y7cGjECdyZN0nW/alCKnOrFmkj1NCKAZf5W0zNMTEzExIkTFY//MEHKHszWtzsxUVRntPgPZVIIQJ28S06PUIx169Zhx8AI9LCnfXpVeUnjDCGmTJmCgwcPom7dui7VAXNrDjo3E88uXECOTHp1eW9PwXZBU6lK8HxDbOROnz5dIsHDB0kAH74Vhvde5RaEjGTBN29LPDijiO+A5oZGz/QAh73LHy4JQlSvSrOGxu1EaUlJrj1K5GlyLsGm1p0haBPhX1vxe7iCzDzhvEUYhYYqQRpgif4LoITlPxy6/+8aLsWw7484XVkNd+7cwaefforFixfLZlLs3LkTtWvXRufOnbF8+XKsW7dOIPelNl6qRXhBUyD82fHE8pjL0nH+xoEfb4GpdlsYq7LRzpdffhk1atRwHJ9Quzae8uZmrdKY4cOHI2pcQ1QP8sKHrYSyVj/99BNWr16NixcvambJLFu2DDdv3oTVKv878LFz507Z/T3DwmB9+tSZoq4DWo5VgGU6rqijnTvQpEkTTY1qMeSWPDTNwCLzvFK8vh+lyZeicX32m3VDttzH3Zg6VR+jNR8VaqqnOdse3UH6nB6w3DjGGrn25yr/+mFU3io0OMd18sYrhouoW8UHZcoUnH3cbDbjv59+js5HjuCnu3ex+0o0Ptwfpygz6qqqRkFQYuTqgCXAPXTuvr6+qF+ZrYNjaAa0PeffSrMPn5E0gTDQaPFBB7dcTwylGhZ3okJIVRg9/eEVGAoPXsSQYdjBxeDCE8forP9l8nLk63DcBMq+qjZ6Cu8n2WxmqfGTkrAoWllA3F80STI2G6r5+6OWQsputg4NPQ6EOPUMgKl6E/VzCEJ2AaGV5q0FDw8PARlIvXr10K5dOwm5xciRI1X7UWPb47Rn0+zaerY0ZTIWMeSYVAGgQ0Np9LoosHz5cmzfvl2wb/Dgwbhz546mc6FGJR80rMZGsXu2YhdwcuzJU2W0PZWgFmXvqpIiDwCnT59G165dUSPYG7CwC1iCMOiqqeagxXCuCHuELP/KfsWIxZvVnItNo9GIUaNcI9QowYsBvqQPAJw4cQJPn7BOUzLfacy9lsM6okiSQHCACd1qNmMPkLSEUTU3n4J3OZbwyMarU019yGbyeKUFgiDZ+YYkCDzITcCh5P3wUsg6nLRCupCrGMQ2tlLORXg5z/LqX9YOPaoGfHxzSpm8iYPPa6OcWrIymL6ezVCxiKLeLWrLG4C5ubno0KEDNm3ahPnz50scoIsWLcL48eMl58XFxaFKlSqa2q/dunVTPMbQlLSUg3fbpLc/jJVqwXb/CrtNktizZw/mzZuHTZs2oXdYGNIPO8kbCYJAR5Vyk6+++soxfvesVRrjhw1Gy5YtMXv2bHTp0gWvv/46KlWqBJPJhN27d6t+L71o3ry5JJo/x56inHH8uJNoSyfUHKwA4FW6Kj4tRJmQKyBJEjaKgYdCqWDdKtIaaUom2vrrscfo/YU0+DD3FydnydKdyu/G33fY912O1NHdeP/99zF16lS8WVueCEwOt+PT8cNOZUe2gJuFgKMml0qWMl3TYJ+Xb0epl5HVCQxUPQ4AqU+1dZs56HGwFBYlRq4GavpFAAzQuKqUPMJVrF//s+NzSoIztY9LVXqaToMgKWiU2hUYhw/tF7AsFwX4XnErxaCp8Sa8kccauRRgJIUDF6m2tqdsMIY3gv+wharXzFg0AHl//SLYVzlQf52DlmQMV5vLzmPO7/eTTi+U2BPMpY0wFIWFQztI2sfyWAq9eR5mOZiqN5Pss8acRfqcHpL9jN0YgaePbA2XnGxB9PDhSD9xQvUe+Pjggw+wfv16fPvtt9i+fTsIgkBUVBQWL16Mt956C1u2bNFVb1Snjnw2AydmThegxnLg1/KkYAbVh9C9aNmyJaKionD06FEkJiZiwYIFmsQmO2axiwvOAz2uN5vGyNm4YeWcUcsrhUy95ozU7t27q7bjfgchmGJJSXKmKxOKEYuXKwbju67heLdZZfzxxx+oVk1dOqsELx4q9zuPTLPUybVj6w6AAer86hwDvRnWKM3NdzpDPUgPEARLhsjHsxwKFdruAwDYeCGiB7Fs6Yop2xuwR20rBnvgSPJB7ErajmohwuwBNRmh8qXZ+cnGMxpNpDQVmITUWFFKS1TC7ph0AOpjHBlYHsYwbSOGFkW9G9eQr1fllAE4/P7774i2O4LbtGmDhQuV53SbzYamTZvKHjty5Igq4zLASibRmfYIroN0S9jGEv2XYDsgIADvvPOOg0mZtnOY3J06FZTZjKn162NN69bY+O67SEpKwt27d3Hjxg0kJSUJtEJNBhLjK2di+/btGD58uOTeamroebuCcePG4fLly5gzZw527dqFFrwInCuRXEA7Zbl8/YF4SYeBU1hw90HRznKbV+oLr/soTRoVFD+XALDpqDzXRoxMZoUcvvvjgf1eij5d2dPTE+PHj8fidZuxsGu4rnN8ytZB5HnlKDP1JM7xmSAZXk2u8vch7QG3E7w1X/eapTCsUVkc3bgUIwrIpfJvosTI1cBHtaaAYAi8+pI669j0z7+Ap1GdwbN6VecDQvMmz1DvMHjagnA+OtvuIS66gvcxY8YUWd9eQdUFnt5sM4XJ3pvQ2eMcAC6SK5ptVOZexpoPwsMLxhDttC0qVUgS9X13/TqqJ06cwK5du9BgdF2UayxN1fgxJgb5dlIOL/98eFaujFybDUc1vM0cgkSTg8V+HkNR6NFIneExTyPFiUqJg2dL4bNpvXNetq3VTrhBegfAZDKhh6gWSYlwID8hQXa/HAiCQOfOnTF06FCBznT//v3xww8/oJ1OhmklKZ+yZcvK7i8MClpyE3U/G8t3a0dJxAgKCkLt2rV1p4Yb7ekPSpPtf3pLZcf0YsYMYYokJ2XhbrKVld9/L9iuXq3gk6Xj96IpULnsgsXkJUzxy8vyQuvSlfBu6Vpo1kzqCCrBi48bns9gkIk+rlmy1hHtF6Msz/lpIAwgeHW0DMPgxPV0VOXVult4BXxx/qyRZgnMRuhrvwFgs0AupV8AAAzoIJw7aEroPK0U7Lz2n1fZ6LFVgQDmzRCWAFCcrWHTwYAsh7seYejWrRsGDhyI2NhY+L+7QNiANOgqDxKPQUpj55UrVyT7XnvtNdy+fRvx8ers/2pQcn7yYb3vLOl6uGKF/ZNoqasSteaQun8/LA8fOrKFwv38UPERmxHg5eWlmHFEp0m5QjgUVBVACRUqVMCwYcMkYxytI+WZj4oVK6oeN/mWA0EQONK5s8v36Ao4Bw5FMw5HkFGU/idXR5uUahHoTgNArTB9rNjZZgpXYtnAgqdJOm54a3BbuBOmKg3QpXogprVXn9MJgyc8/MpJ9meuGgs6j3V0G0o7uWD46cpyUTTC7gTk5MZq1KiBG+Mb4cb4RpjfJRxT2oagcsXyaFDEmrZFgRIjVw8YAt4evoqHY2Nj8faQYci3qTMYly7Fe0B4kwMNBmkpgWAoA2vkEgx6DtDHAOwqzp07VyT9AkBIi7Gw8mjX/cAuQMsQGaBoBhTNgLTXg3ZtFoSerYJlaykcsOUDRnag25inLsFCPRRKKtUM9sax4fWwefNm3L4tlVvigyRJNGvWDNXeCEebWfK1iJdTUwEPb5SqmIWwDz/EtzpliABpWmieXZqIysmRLybhgdFIXbZEHYUl6pjssWfrPxbtsdeAZyTDcus0vp0+HWOaNsXHH3/s0FR8XtCkiXzKtTvSW8b2FEoTHLuqTzdZjH3nUrH7bMHYRbPNlKoXlsOsYVUd5DRighyO1VnMeMqhXrgvmtdSr+8ePXo0Ll++jL179yIhIcERlVBjqOQWmpabJwX7bQ/+AUNTEvbrq1evonP79qhtd/YYSCNmzBQa13rgV7cqDN4meDa1s0JTNuTao0PG0tKaXpuCvnUJ/v9AGcYLner1l+ynaRq+j7TrygyEQUAWdehyGub9muBgPAYAK8/wy9nDOn5oIzsmG7yzhY4qUvh+mpOFDsxGNaTvlPiddoLdL67LzbAWbKzq/d1uREVF4dSpU+jWrRuMoew7nE0ZMGrUKHz5/Urk5ivXJ3IQy/656iAsjObsvXvSNEtZWPNhfuaMqptjYyVaRwSpTRCVsnUrAPcQHLoTclqjYlifupf12jMwDL8HdXFrn3Lg5neKZkDaAyIuBqUdSElXVxfgsOXPx5i69h4Sn+Qh3yr9rZvXcq9jQgvHrc0wsL5y6ULpGl1Qf+gO2WP00wQ8W82WARCezrUSQQD5D5Ud8gTHPWBVtmGICrVhJElMrO0eroDiQomRqwOEglcYAC5evAgPowc2TD+q2Q+/mP6bX+Mcn620BbAYUeGRPwgDBa8nvqhW1jUSl38DXqXCUanFWJRrMBjVusyFb7m6gkhuKZIlq3hEl4GNYo1cLl25SnkvxyDGQTyZMDYrCLuRu9/qLLInS1eS3ItD8J6Hcr5GdOjQAb6+voiMjJQc51C+vHYtVEJODvzfngdD+eqAwYBTPNZlNYwaNQqEiCKesUeFE+bNgy0hSlKHMdXV2heFiKDY8OcjZ/scePv6ol9QED755BNFUXigeCd5i41GltkGkiQlk/nevXvdco0AH6nx80RDbkcOSn8XPfp6p//JkK2nEYvQ8zMfPHhe5m1f1MOADqwnV8nIBYB79v5y8ijciMuW/Z4VKlRA48aNBc4YDw8PRWmJPXZSFpvM82W9fQ779u1Dnz594Ovri507d6Js2bIgKArfNWuGIS9/hNGvzXAwmprvy7NRy8H2IBqg8wED+z7xdT3Nj6TEUpY8ZcdkCV58NL0Sg5oV68sey0uQN2L4Q6WBMKCUn3MJlJLOGrf89GWrTRrdJCg7625DYcorzYjbCt/L4V2kdZ1KRu7T/Kc4fjUdA8uLMq+owpdWmM1mMAwDmmHQasVlREZGYt3WHXh5tvzCmQPDMKBEQ9vrLfXXEBYWnp6e2o3syEx2GibZ//wjmSMNZVk9dDpbmdOB4MqY3Dz/zZ8/3+VzoqZ0wPERLyHm8G/orCOaan3suiyekgxgrTdXgSAIZBhYJ01Z0e9Qs2ZNTJkyBSRBIMBkwoKmTTG5bl3F6/zyyy+Kx7joOJ+/pZSfskOiW3Pl5y9dJ+PyoUvsMzBqUYyu9oUFwzB4mKrsUFqd3xc5hC+++VI6/5arPxBhbdS5LJgsu4PD5PydbBYDMv48zm7wHHfW0uGCfZbYC8j+/Wsw+cKU7ttUZVB2kr9mLpBEqmGohsqIu1Bi5OqB3citVFqYAvvBa7NQqVIlh/Hau5kyoc4vv/wChmbg4cW+sHxvMcVQCEcp1EgoBYKkEXasDvwelcPe9/pjQkt9+ppiBNeS1mTqwe7duzFk2iBdbSN6/YgytXuiQqN34FehIQDAYqURUoZ9uWpWZieaLMaXjeRSznRlk4GQsFompAhffMaWD8Lo4RiEbAbWMxU4drWzjf3lZMzPQOcICUQIb6fnvGHDhrLf4X/tQnRNnhW6dAFBGsFQNt30/F9//TVmzpwJk0iWx2BP4+Xqfsa3qICIYDZFrn358mivU1PV0V8F9bpdDmJb2JbB/r2oHCexGvUkQSJLlHFMPlJcFOg9PcohJ9W5c2ds3rwZ48aNw7Zt29CYp/1nVDHKtWCQMQqf5VD/j73vDpOiyrs+t0KnyTkCM+QhiShZERQjGDCBiIgB17DmdXfFnOMm17SGNaGsomsOuLpGUFlBCZIZ8jADzDCpp1NV3e+PWzl09wzIvu7HeR4epqtvV1VXV937i+dgWSdZlr9c4S6Zc+3j7lJSZth70zXYJYjMDuyt51Xj+d+yDExmkNefJS8nV6EUja1soV9e244b/rYRj/wzOeOyGZdccgk+nzUQ3zx4Gb65eDC+++47bN++3ZRNdxp/VIpBEAQ8+uijWLduHYYPH87ORZIgchyqiwcgP5MFlVoWLcLmO+5AohPkYQSyXorH51cgJ0n5u2K3yA/ifwpVzXG019RiRHlfx3v/fOA5LN+yCFv3GIEYYnNWOPAIBY1nx0wCpUiqjJu2vpgC1ETVw82stJK8PVPLSmOn95iJjEQ39XMSdq14FYEvL8InD16OnmXWlqaExz0qcgIeeHUr3l1kfTZW/5RaCuuJyan7z5cvX45l9VZDtiMuofE+b0bj+r1xR7lywOdcC+U09HZ/TmjMyvprQYA94OA//GQAQMsj5yGx4T8eO+pcQCFw5LkgWYUQ+49NOm7KlCkWFulU6JGfAdLRjMKQCDHmDOa1L1uGuqeftmwzr+npYubMmbjnnnv019WHT8PgGe+iqordT4IaVLzIlMkjhOCW4mJcc801+HDGDLw6bhyG5uejX5Ky7KOOOsrzveJiFrhdtaUDkRh7NgKiu5uSncEjFEhtizW1Ji/dtvfk27G3vXOl36nw48Z2XPTwmqRjeDmKaccfaQn0lx56PkoOmeH5GUfQ3fQcmmX3zISufkHVj1YDdJEFTyCx7htH+59MOSz4nlWelewnsig3DpifAwed3HSg3jtnj/k1epYMREAM4ehBZ6Awm/UxyGrmpqbSnSwBYA+2olDwInsotVsumOWDTGX42pijxZvKVwkozhrYNTrvwppTMXR459hyq6qqMHDgQGyrTZ2lLBp4Bg7r4yy/ikkKRJUVr08Z+05R+CDLFJJC0Wvz6wi//RB4njj6kRw9h1ICEHyY9xmLSkZDxSCqxh+Xr2ZzTRlcaiu1CIyxkl8sXrzY8vrNaf1w7hBnj+eU2ac5tt39+OOISgrCkQiIIKAyBVlQTk4OLjBJB5mh2OjUy4vy8O4D12PdihW4efBg8J1YXEkwizFjpgH7HBhUSQTqVZkfJdqO1qcvQ8d7fwIARLdZJ7r/BsaPH4+bbroJY8aMsWwPmoiEOptpdiOauvm5Wvz+mY1JRdLtMGtGmh3NehdiDDu0o9zyXC1u+vtGrN4SRkdMRmbQumibn4mcDEHvUzLDizhr1RbDiF28ptVxnpbzoRRKq5WUTmlpQGGGiNzYbmT5eVRWVlrLM91K7WMRx3MIWMvutQqONlUHM12SlGhbAFJMBJUkiDVHofnH9ZaAUyAriqKhPmQVsWBFuj3PB/FLBQEVZBw+yEmaQxUF7y55Hi99+TCWb1kEABChoCDbeGZ5wmPKkWw9URSKL5YZQdLwNkYQlFANRdnkjBLZer8W+q1ryNjCI3HLECYJ0rThE9T/8AIWb96JXz/9PqKt1n5Nr0xum8R6BBXb3PbHB/7kOt6MI3ukLq9csmQJvi12lg7f9qk1yGauPlMUpvk7bYKzF9CM/alT3xXQNmsbiK+w0Mn9YerJbX/tduOzpnlK62GkkgQxRbUXCWbBf9gk+IceD76gW9KxwWDQlfjRCy+dZiRXpDpntrFl4UK0LFyY9v6SYdasWbpW+eHHXQrC8bhUbe/R1HiOKSrCVf3748zTT8dLzz+P8lAIlFKERBGiOpf37IwMoglaK8xzH+3EpvooBlVnYPxQ9z7QaeNLdJZyM+6ft8WSKX3g1c71gF85xdoP+8wHO7FhR3pkVekgGk8dfA2SONpevAHHHnssFi7ZgMm/+RTFg6eCuPSS67JedgkwU6UTMbdSmHWi1SBeaNt6q31oehELi8irDetSiZ2xT71Qc+ZcdO/efZ/3kw4OOrlpoMTPnNkMfxbOGXs1rj/5zxg3dDLiahnqxh8ZkRDv0edx0gjWX0vVnlQFAKeauYQQyFRGoI5l9/o1GTcQBwX5XRAXz+9zAvzZlbjgqntSjp3UJw/XXXcdzj33XDz77LNoiXBoElMvkkOGnYr1Lg/+K5826Fmqwiz2Pw8FksJKnUobvkL8p8+hUIrFSzajkBg9Rg5SCzWTO6iKXZuVw25EziUsWp459U42xmxQqw31WnbXQqEOoKKiQp/AV14xFH0KrBGpK/uwHtbTXZxcAOg7/EgMf/BjPPSnP2F7R/JJ78orr/R8T7H1Pon9jwAfzISP4zpvmAs+ENHvWsJtR2LtIstrTs1gJ1TG7dYnmLNM1UlQk1FIxfD83wBNJJAzlkXMU/Ut2+Hm6Gli8p1xcrNMDulIk5SGm7SPHdoa8v26Nixd347rntyA6fesciQP0hG5TybLdckkdl989B+WEXLLYgOAVLsELY/OsmxrfZxVpshN7r08bhJfHQseR8sTlzjHqgbjSUObMOMOFnzjVUOo0+XwigIpIqPp8+/10n8AyCoMI9C9GoGsGDgfl4pQ9iB+4RC6DQAhCnIzkgeC313yPCLxMPyKjG5FRtUOT3gc2psFK79a2YxdzYaRyAnsvpI0J9fUgqBlcsV21nOryRgV+AqRI7KqHU3/dse3VmcmY8/7qDIRW3k5ue0JFqjZ2WgNmC3+ZrHbcB3PPfccAODw8uSl+t9++y127nL2bb65ugkx0/p06V+sTpWsUF0asHuxewXUXXfdlfTYXcFHqmRcOuBybA4pz+uSQtv+/GfEd+1ykO9o5ZlRW8YTEQAAIABJREFUF1IsKklINDSAS5bBUhS2T45z1a53g11Ozo6awiDmndnXYgPSqDNDG3MpTS5NwZacDoJ+do209WW7yIIbhBCc1rcv/nj//RinrsFQlLTKul+4+mrWI+2C6v4j4Pf78ebXRrD1oUt665WBdnAEOHFEAUbWGGtvS1jCF8ubLYSQy2s7l9U+aYSzHPfKR1NXZ6WLZOaB21r4w4Z2R5WjGVoFGpWsc4W+PnOChemdSqZ9qYHqwkUfQTEH70wB7NZdThLKx48y2kSyC3ri9bOd1TRuOP/88zH9xncghg5cm8NBJzcNHHac08gfcEJ/xEUBrXs6ICUMQ+uQHk6B5pF9WQ+FolAQQkBhvfAylSHnsH3krTfIKhKqszjjMPfI6f3334/K8YZzU9nvWJw27V5UjmaaZ/5gCPPOTH7z3T6hG66//no8+OCD6N+/PyilKBrcC0LAmND7ntULlb2N6NbxvXJxcegdtHY4yzy27oqhPSLj+d/WYFRfZkBwULBjT8wSFV69pQP3hB7DI5lGfwqNtEPavsp40NVMrpb5oUIIJMAWbz6vDPAFEfvBtPhpn1Mf7vjyT9hmRYbSlprkp3/2AFRn9IQgChAC3sGFvz72WMp9JdMylPdYo/lE8IMqkonivROQEgAvsn8pEF/+L9ftuqRRpFU9P7WkVcuw/ReIN5I5mx0bNkCJRuGvVO/JFORddiRzCjsjF2DuPz/tiEJMVXtkoy4C9Ha4XdJYQnFEeX0ubI92uF0r7RmqLPLbtrvvo+OTp93fAHQdXMcCrLjfr7Td+axp95hPiSIz18ZC34nfL5AZBVUUEB+73+VWo3yPFxVIW1ci48ybQXxBcIHUz8RB/HIRPO5SEIU4NVFd8O26BRgf2YV/3GtIYzTE6rEhzMqZm1qtAZvsbiyj+dzHdWiPyKhdpjLig4Io7Jks4/rq2wCgJnsAjitl0lsCT0BdnJ1PPv4A/SpDiLXtxI5vH8P8lx5Bq+kevmnA7QCAY0uZDuzCn9xbIszQ2m1GVWZi4sSJAIDv65Ib9u+//74u6WOHmb9im8m4VlTySJ4Dbp9ZjZvOrXL9vJf8jxvunNANi7/+MuW4wYPde6/NkHdthtK6B0L1obY3ZAAE0TYf2n/8EeHVq50ZMXVus/NnAEZQunjqVGR5MLVTqoBwzJl2+93dMHz4cFx33XWu753cLw+vnd0Xg0uYDSVUHWL534yYyTEX1NaovH3UvAfg0Klt4w1nhwgCqCyjZRELnFNZhmyzeewERUuWLEHFqlXY/fbbrsfLGnE7ANZaY8eDl/TCvRdZy/AJIeA5gu9WG8/PX/7Jqs9iaazB/y0kC4Lbl8LdLXFUFvnxUubNOEJwMpYDpu9qc3JjPy7Q/9acXKW9CYk1pqy/qT831u6DogCN23It97Db9NrXV4qvLhyE4y5+BiecfA36F6Vmst6ydTvuvfde8P7clGP3Jw46uWmg/0gnnbcgcuATEube8RmW/dtg/Zs07Dw89ue/YcyYMRjZ51hcdeKDUFRj2MjkEmSrBmJHawwKlZEocpY4LstmvZmTBrlHqk866SQcdvUhOP2u0zB37lzkjbgGtT5Dg5TjoE+SbujVcyiCtn4HQgh8Oe0YeckNmDDwdIy8bjhqZvTF9X+9FrPOORsXDSvGbRMqMVjw1oit3xtHSZ5Pr/PnoOCZD3ZajHFZoZBsGoCtnz6Hthdv0MuHogv/gfiyj7GlXs3W2ueGeATRr142Xiua/qzV+I599yZa/jpTz/Cadyb2HWUZyxEer257BcN/b1soO4mxY619OZypvDm2bqnlPSL6AUXWe2S9oLQ1QglbjR4qJ0B4EYh3vZzGLlOkOynqZEw76UTuD0Ri3n0yW+6+Gx1r18JfwcqoaBo9YGYHzSubCbByvHQRNy2k/btlYNbxrOIjmYOuKBRxSdENYzvqbJkbLWuS9DxcskGar27PWksepFg0ZiW8kne7yEaZF8Rl/0J8GQua+EdMsZBcuO5fdXLNVQwhVTOy9Tur5BVVFOx55x3jtSwj0dQEwSeBExTVElBllex6yYoEsdsAgAJye2q22IP45UIo6YnsVYzgZtrYq5OOXbSOBUObG8JYvXo1Ro8ejbenfIjaNbXIUBJ4+j2W+ekba0GOEoEMtoas3taOVz6tR916Nic2lDejvXYA2rb0xaFqu46sPhetiRbE2mKIx+PgOIIVL012nEdLSwuoomDtmxehcd37+OD1pzHnppv1csgyfymuKLsAfTMNB+G3/eck/W5ffvklXj6jD/52ci+dJXVUZWoJsO3b3fvzvXRoG/bGMf+L3eA4gpE12ehe7C6ZuLATpbPlWT6UBIFFixYllYdrm3cLwu8lL9VufeYKtDx6PuI/WrO+VJYBnx/NOxlxDiHEQdmb2LLMGGvDzr//HcTnA+f3e681igJwHKgsQdqyArGlHyY9V4CpOlx//fW4+eabLduvGVWG+yb2sFR1kQD7PVOVQhedfjqQJmdIKlxxWiXuvaina7UB4XlQWUaDWppOZaf9ckq3bnhk+HDcccgh+OiYY1Cqco2EUyg5uPmAg6szcaiJnfz6s7rh6EOdZczfrGIO74pN3kGeUpcSZzu6yuicDu5+ebPne3b28q0NUextk8ATBZcH57t+Rus5ppJh9yrRdsjb1f59RdKvaWKT4Sg3bstFpEnBK3PYHNq6KwuxNj8SERHt824yDqCe0vj27y3HzQsKiEjZuDXj7wCAW22yR++dOBbX1tRg2rAhmH3uHN0u2rY7uQrN/sZBJ7cLOO3qURBETieyaN9r/GiEEJx21mTMnz8fEwefhaxgLvwhEZRSbNwRASEEIij6xY3ok0xlcOpPEc8wnJWGbHZndit0L5PJy8sD4QnKR5Wgtvs6R6mrFjE6+1h3jd/fHeF03mWZIlSyDdk9whjT7wSUH1ECQgjyinNxxw1X4trR5cj2u2c5tfIWDdqCwKu6v0vWtekzWEKiiFEt08KuY0UTK8WSthiToNKyS3cIUua0NCPcVEaZ2LpSZ4GNfPGivt3o7bXKyfBq2MpNL7czGDXK6jz3uvde5J94IginIBa2OQSiH5AlbFFJH0SPxb7liYvR9tJvrBvlBCCI4JIsfpRSh9wL4AwG2KHdT9FOMOB2FpGY7Mr4m05GlSoK+IyMtJxwM7+LVw8r4FxkksHsLNuzw7U2lmQNL3/agDPvWImWNJkf06led3PMNZ9etDm53l/P1HvX3oTWpy9zDjE5udHFBhNraOLFyJ79eNJz1KsFTEYiEdnzv+u11yxjW/e2Y7daykcpRcMrr2DDddexCpi8UvZ7q1aI3GztIwbhQDjuvxKYOYj/HnxCavLAhWs+wOvfPoGJEydi69atoDLFnGm3YNjun9A7wXpg+yXaUJUwGchEQVyikNSAFiVAorkI/YpKoagOJYUCRVZw51H34cJxF6O6utpC4GPH3OetlUBv/vMNvRwytvRDlD1/D+KmIFOPDNaT2bq1zbGv6dOno7KyEoeUZrB5TQ30Xj8mdftKW5tzf4A3y+7cT1hZrEYItD/QvygI4g+hR48e+PHHH13HDCwOQtq0VK/MSoZomw/166zrZ8e6dRD7mwjqOA4gVkcw/MY9CH/4mE7WUzjFsJloIgEqyyA+XxInVwYIj9iS9yDXrUXHR4+CxtMz5mfNmqVr1I8bNw7nD3Wu/4k1CwFfEHx+8t81e9Qo1Dz7bFrHTYXiXB8O7Z2Fw90kdHje2ibkMd8eOngwxhYXQ7B5jb+xsS//+rDx+t/bd6cOTk4clq/zVwzokZ4ergatlSCZvN6N5/TwfG9foQUN3FQY7AFySoE/veHNjTJSWIEn1QCdOZPb8sep1oFqJje65H3jPCIi2ndShJ9/0vhcg/pbm2xojmfneUqblU2eKgBPjXFnDyrEkl8NwbSpD+LzSROQLQqYVFmJ4wcORCjg1206My/CgcBBJzdNrD73XctrUeQRSFFeOn4aK7EJN0fx05Ywvnx2MVp2OyNMy7evxN64yiAcMibGDjVQKhPgxilPOj6naVpuj2zD0ubvHe9rWZzyCncB9ajP6chpRr4osImGqg3r9szTh3FGBFSYI+K0sWw/4wbbyhDUBZcj7CHZ2y4hEmf7MZeTlJJG5GQI0JPKplIJrrC7nqlKafBrE63pAW2f+zsk1rLIcuyb142xchzZVzyH4PjzLbvg1cVvX0hrDjvsMP230SDk5qJk6lT4gs57hvgCoHICvnK2gFXdeit6P/Sgc8dS3MIgTSll35UXkHn6jci+9GkI1U59WRptR/itB/TXGaezzIDSnkJzcT9FhJPhsbd3YOb9zrI5L2JcyVTaJ7exoEn7smXug01ISAoCqqi7ogDnq3IedrbTaCcMuMP6ZGGEulBq98uYgSxbcMUj7vJNC75vREKieG5BfVrHqChIbbwXmAiwNGf75JuXAwAG9LD25hXlui8wXI7REmHP6upQnVylfS8Utd99S5BJnfG5JkbwkDPCrkQi4AIBizEUfu8vrof5YQ2rVki0tWPznXdi76eqPBsFPt3TDeFVq3QjlcZtjOyRVrUv7v+WtuVB/LyozO+Vcsznq97C2jpnyd+S2s9RKhn3vGSaugmhIAR6SxIFC7Ye0jMbslqppCgK3plizR4+/rh30Kf+x5cd26QIm9dptB0Kpfj37eeho9HoA2xa24x///orx+fuvvtu6wbVCbPzTXQW0ajTQVu7nQXgNZZVN7z44oue77khNyC4kumY8coZqfv9Oj5i1zsRcwbgmz//HJH1RuWZ1NzsmqqL//ABEo3suwkmRYTCk08GFAWcKFrmLxoNQ2lRSTqpmsmNGfZdYr21QsULwWAQ//znP7F27VrMmzcPouCeRMj7zevgsp0OcKDaIKYiHp/dF9irgYS8PNBo1OLkKi73CwDPPubxpaU4prQU+T4fTqqowKRc4x7Ypjq5x7hkarNDPM4cZ70Gh/QynNUeJe7VBWZIkoIHL+mFOdOrPMdsrHOugXvb9i/L8u02JYV7Xt6MhSvZ2idRNYjrYY5obUhXB+chA+zaUzkJ4aX6E8qwZrETHUQngPRCINu6xraVsfZNKcGjX8Taw+4XOAR5gjgE45iUBw8ZrbvZszJxWD4mj8xHYuvKpMfdXzjo5KYJRTQieAUV2UmzQRoGjDXYwxQF8HmVKDbVg2SwG8DM3EhVoXqZI+AIh8uOuxv9yg/FlMOqsXZtak0vniPwDzsJHO8+8W2m3fFJfIT+WpYp1qsL2d69aoZTjQBRSi1eZoPCmvOvO7Mb/CKHECI4a+21OMv3sXH+KpmDlskFjExSYY4IH2GT5NkTSvEn4Q5IomqQUwWJjd9D6HkY+DEzsLk+vYioLidky1BqpT4AIDezaDSV4uCCWY4FdnWrIdHwn/94SAvYMKqwEPPmzUNNTQ1GjRqFhx9+2HswAQSfhFBeh94nQcQgaPte5E+ciGCfPhCyssCH/AhkRdFX7f+NfP4C+7xZv0yRALUPiPhD4PPLETzahdHZ1iMk9hmZ1vfSFkwukHrh6Cq86Pu9Sn7XX3WV/rdYWAi5vR07n07ST2ran7ZY1++NYdoERk4i8MTC0Lh0g3t2ww0KpehuW1QXmXro3EgkGlvTy+C+cfsgnH9cKco9SDfMKM41zj/ot97Pdi3qUhd2ZgCQdzBJg8jnLyCxbpHrGO35annEkDFoaDW+ozhAlYbo2Iub/15r+WxY7f8zZ1hp3N2Z/mwpy85+Nv9rSxUBpQRZchhyS4veKJRodpbpH8zk/v+Dxhom47MvQckfN3+NXFN/uUYKKcf9AFGwbVcMUlx1aAl7n+d4KFRBLBbDi5P+sQ/fgGHV/Om47y8v4ZpH5mLI48sw+52N2PD+1SgMs5aATR84CZHKysr0flyhJ+uD1bSjvSTK0oV97TM7D25z2HfffYebb74ZN954Y9rH0KSOUvWwpmNrJWqdhnrxOecgU5UO3P3GG/r23a+/bmFXNoOIIjIPOQScyVnU5hKtD1VD+1sPoOWxC9g8T1XiKXOGuJP3ZGZmpnbATn3Ocv4/Z52t6RhyOIxa02+tBZ/tPc1e5xPgedw4eDBeO+ooXDdggM7KbMZvznay7756yyBcdKI1m60Fmkf0z8aJSXRzo3EFS9a1oaVDRmGOqAe93eC2TnbsxwoGgBFKmfH1yhY9a/vHyAz8IPXD+5+sAYHzuH6Rs2yXm+pAI948MFn9qwAALUs2p3VuVqZl63vtFSzo1LglH2ftdcpL+pEANd37CiXgoSDw0sWgUgIKpQgoHQi/kZoYd3/goJPbBfiDIuKd7PdqT6K1RQmFpsUbbDIiiL2bmBGoECCeFUZ+ZjEuH3cK7hpTYEyI1j1ZXh0xOAdS/UZM8X3metygCCjgdGP8k6VNeHj+NkiRENp39EQ8M6xHYyioZfIVwCb7HiUBnDK6EM9k3YUQIhgrGlk1quqtcqaHMdvPjnXZpBIUcyyTOOHQAgSUMPxRNUKsyGh/9TZItUvQ1m4YwqkzuVq5svVa06jx8MdXqg+lRtiUBOXlqUu+AOCqmhqMGzcOn3zyCd544w307esdeVZkAlniwHHU4MnaW4fEhsWglBpkSlRGblmb3ssbXfSac2cu34HTeneKWXSXUmopM/WPOA1EC3pICYTUsiF512bH7jm1nNQrSvtzoKKALS7plCv7Tb+P1Nqq66e6ISFT+DiKu2b2wIRDWIT4guNLMXV8Cfp3N8qdepalnwGRZJpUqH5f/KyQn9cd8c6AUoqn3nPvqQO8I8MaEhu/h7TDI4CmOAMSI8RV+m9lDhgtWW8NFrR89RW7j8xOrstPvPbyyzFtCWOk3WvXN6QEMcLuDyWZDAPHgcoyMnpXIHtAhfe4g/ifwpwpf+vS56IJm16sQBDkgki054BwCjY3RNG6h42hhOntSoqEj+rfR0+TlNm+4tEHf4/3Flozzf9+g/WhbvvM+Uzv3LnTeKE9m4qsy+v5k3APpMK0adMs/ftKkvl48+bNOOOMM3Rm53QxtnuWtnP2X6QNfqHz1UPStlWujqG/ogK8aieZGX2zR43yJCujiQSIKCJg+l2bv/ySZX45zjp/tasaxqqDSwixnkcaTq60c70rSz0AZM1MEiy3nPSBq1rJHjkSeccc43xDvS79/vY3dP/d7/TNHWsMPdhOs+inif7dM/DKnAGoKPDhyffqXMdQSjHlthW4+blatJsC65dOdrfxjh/uZFh+fsFOl5E/DyTwECHh2rY7MEow2vfm3cQqp0Se6DZ4JV+P1idno/0VZ+8+yWC2Dk+YDSxLaT5f1PVPAIBA3e9XKc72PVJYAQpifJAS5CdaWBuBIoFSoGzLUsTafv5KQeCgk9sptFQZRA3+Tkr7vPH8cse2ggpW/04Ugo4dRslJi8psrCZyoYSywcnshhib+zHgkZnlA9bF2idwkOvWIo9rw6iTLneM9/MUMjg9U6qxwhJOAQWP9WcabLwUFMRvONY8UUmlCEG+qcZec1zZjtgkP4CvxVjhR2STdvCqmDldbTjeAuc9+TXvMK55U1uKLJi60Es7kghtU2oQUCUpkxJVY3r27NQatH1POCHlGA2JiA9U4XTdMkqB+K69iHeIgCwbup/aOSaJ6lI5ASJYnVySmYfAkeeCy1UdJEWyLKLBYy4GAHBFPUDlOPKOOQaZw4Yh/NGj1n1TRTcQflaYFj5ZodjRGIfAk7QIoMxR4/VXXYVd8+Z5jo3HJFywfR6K3n4SPrUu/uzxJRgzMMdiI7j1yXhBUZh27Yf3ORkvAUA+wCWzI2uyEY4qeHOhVRrk7KOMUuRUwQOlfa978EfwAYqE+E+fO97Ssu7SNu/yo0DPnsgdP96WYXUagYpJmiujo8nyHqVAB8cyV/UfuJdYCT0PY9kDRUF4ww6HJvVB/I+Bmsh5COmyoxsGp2dvKEcREjIAygGEoiUsoU0LOFOCY4bm4vM1X4F2on9/XyClI5FmcnKVJmboP3KSYVPwPI+M0iGdOu6XXxsEUmZ/beaxpZZxjz76aFIH5t1333FsW3jRIINlVv2ssrcOUwdaM3FnDLC+ppQirlif6baXbjCVDZvuB553lQQKVFW5U8aCcQcQQYC/rEzfFq+rA+F5nWxJH2teo9X9hU4w2VlpsH63PXcN4is+de4TAFdQiezZT7AXopFJXz1rlk7gJ4fDDuLInxMVl12GgpNOcmyXIxEIBQUghCCjpsbJUk1IWs54UxdLgvOyRFdCyXsu7ImSPB922rTrtZ/x1LHW0ufLT/EOiqbTK7y/IIGV+AJWm1oLqssKhQ9sXgjB+7xoWP2sKhuUiKTXD0spQSKm2qLUel2lcmdgjyrAns35oBToQ7eBjyug6udyw+3o3czseCpLkBWK6mUL0FS7/0vr3XDQye0EmvptRmN/VoYnilYHaezpA1w+AXCaplzC2YtL1DKc7v8ehY46Y0EKq8+5whH4DjkWwTNvwq5JS7H7pK8R4CO6I5SwSXj0mPwcnrjaPYs4/OgZCBUZvbn3nXsWeKJABuckoyEUUDj4YTg5lFLwuUZmSVQfsJwMdi58hcEG+cdLWc2+xvY2UvwJVwRfwx2hJ8GXqbILpiZ5t3IM/T2TQ//ix/UIR41FQOhulRXQFoiO9917/XSoGdBkJW4+jk3St912G5544gn8zhSdNOMfRx6JslmzXN9j2VmP70ZYdxelQMNnq9G0PZeRW6i/re6YumTPdMjOTC7hRQSPnI7QCVeoYyRLibP2nYngB6Q4W7glCfJ2W1+sLKXFWryvWLzWyPhp8jkCT9LK5PK2fp9EU5PruHmfNWDvby5DRbTOElnWYC4xjico3v9uD7anwQAoyTRpKZ1b9uNoD2F7M9wE7lPh/ONKnT3xKi44wTDYUl1XGt4LLtN5jsQXBFUUbF5orQr5T2KA/j11Q9MF0dpaxOrq9Ig/VWS0NngTfwCAbAvmUYVDI5zfMbukFQXdTVkVU09uZHuKvvOD+EVD8dlaUwjBtZP+gOxg53QYd1MF9XvZmlQgyxCIAKpwyB+8CKXDDGevoi4b2/+zDb3eOhrhnZ3T3+wq3n333aTvt8+/A9JWlu0xBzTHds/GoydVo3DAFPzt7y+jsMZd/90L5507DQBw7jElluzXlCOsXB6p2noOHTwQdx/dDbkBHjeNq8DKK4YixyzRp/Y2gxdxwaHFKC5i+8/PycbFw6zVLH9Yez+u/eEKtCTclQjCe42qHCIIiLkwSO95+20Qn3sLjhKPO5yxYN++IBzncHKVPSo5mIkIT+zlLjGUDBZ904ThsHDBLHCFrLIrMPpMNlY9N1kNBkotxtolFDizjwcK2x5+GNQUUHQrU07Hnpj/hfcakgr23mEAyAjwaNgbxzMfWDO8mt0KGNVjAHDyaG/C0Uiy6qEuwi04lBngIUOAoCaSpvqNFsDmP52DQfwGJGQKkbC5T0s4AYB/2CTLvgKjzwIA+AaO79R5tdRnoXFLPsRRM+Abfob1zZ79XL6HXvKJaEMAhAJcMXOG8zvakKeWUsdjcbSE02vZ2l846OR2Ah1le1A/imVkC4qtBnbTzjYMPqrK8Zl+w1lkSHTpx20RWXmu2BEEkZ1ZxYaicgSPuRgKz4HLkxErYBOa2I+RPmnEF2ZUlbLz8tv0NTOVVvQ+8Q+Yd9lUrLxiKPIzQhA4CoVyMNvpmT1WgffFQCmHsfxM9MuqwYllkzGiwMoWrJVKaE4Tb2IpLtGMdFvZcDYJQ96pEvKYy34S3pGojID1uvxo6pnMnHq7/jeXU5LUIRT7sN5jpW0P5D1b9ciW53iOOY+EEJxyyim4ytQLquGtY8eiMEm/avtrd0BusPYmZqlMgJp/3dEchBJTmWclycjkanTwSb4TlVT5IBdwKnu0tO0ntD5zheN9IoigiTjruzUtPsFjf6UfnyoKQjUsMBKrcy8D2p+IqYuITyApy2rd4KUz/OLHyUmeBlaxXvC8TAG3vbAJj761A69/uTvpZwDmMNoX1qev62963/mZ1VuZYZwZ8K4iOLxvcufPDdMmlKTFWphO8CD2H6eOIY11ALKETTut1SLtNIi6xjjmfdbgfUxVPzGyfr2+qEub3ZlUzYj7MxzbMhRnHy8vUIgBdT4SA7bg1UECqv9l7D5kLXIka+VCyJ+FK0+8v1P7yQnvgk+da0s7JPCER7fCDAQLdyLUk5UQxxJR1O5YgTaVJKrpwX3re00Xv/71r123DxvGSAYT6xfr26Qt1oqxXeVnoPzw2Tj88JHIrhjepeMTYq2iCviMuSsej2ODqRTYjmeffRY0EcNpNQX4+qLBOGewNXPG5Zej9anLEP32DRCOR1GGiI/nPYt58+bhw8fvRLccg5NA7DsKm8JsPZ2z/AbEV31lkU1xnLcHcaJilx0zof755x2SZmJ+PiNh5DhEN23StWF12TRTJldbd9kLd/O67ZWb0LHgCWODydGxfx9COIBw8Gl8B+para91ps/6CvdNESIZBvQI4UJTsNQNspmt2y2B4NG/08xl4vVsputr55DoDNz60DUiRk1aSENWyAiyPHqVd3uZ2Y4+emguGlsTXS67ppTqzvZlJzN7+ceN7fp7ANCt2I+An0N5cUhPJFn2EWnFDP8HSCQUhAgLxF8XNBHZ2aoTuTz2m3FZ1gDIl4nkEpmaAohv0DFo/GCB5T2/rZd596Y8/TZkZcrqNXOxNZ56ZyvEd15Keuz9jYNObheRX8oM0dwy9v/e+naMPX0Apt9ylGXcYcf19tzH8pGmm0cxbs5cJY4vhjaivUwAF8iETGWInA8tPJvYhB6sPFLyqI0HnJPFuVt+DwAooMyx5qCgb6gRBVwLVm01ItLFw9WeVYVDua8nrup7HSaXn4pCv3VxEohx7MjnL1hKbvKzmLFNbeLUQWJ2LI0Jr32eVSfOjF9/Yb1+Fs02E8kDl1eWvLQ3yr4jF8qB3OgT6qa4AAAgAElEQVSuDwgAjwx7AkcUjsPsXk4JlbvuugscISAAbhhbjl59wiiq9maaBAClhTlLNB5B28s3IrqLBSp0cW4TlWd8zSKd0Ti+7hv2uZSZ3OQlH+2v3ub+huBDfMUn6Hj/T1BM5XCB4aeABLNB5QRi27bpkWL7wr+/kJNhnL+WpS/J8yXVmgXcWSTtZENSezsitbUYEnFnOtbQrSgAgScWYom0pHsU6njONNZDwJ08Syubao+6/64F2QJG9HeRbEgD6XCOuDneUsPGND6YgLR5GUaIqyybY/Dh139dhxc/rscKyX2uW6ca6UJuLiDL2HzvvWh462PLGOpSkpnd5JRO6OCcQSXBxz6b/aunEJpk1UulndA9/v8dmzZtwtSpU3H88cdj6tSp2OxSBvnYY49h0qRJOOWUU3D66afjq6+cjL8HEpRXEFLSJ4vzQlu0BZUSC+DEpSg+ue8zbHj3B8gqq/IueTsefvcqzP/mMTzy4W+xvXEjlm76Yp+Puy948kmn4kJ8zULLmvBWnDkPe1oTIByPSVe86rqv8ePHex7HK+D43HPPodrE7OuGE044wZKdtEMrrY5+96bu+Alv3oZx48YhY9Hz1vOwBYzDb92fVDvXzcn1lVkdNf/oMxEYc3bS7xCvr2dkdqpjqcmbkSCbp6kiu06+4dfvdmwDWIAvvu5bY4M5GeASfM+78V1dOkgrU9baMMxrXtmFFyb9HvuCP1zaB2eZ2l7cSpbN0K59wcknsw2UemZyc5V2NPPMjvYJBP26hfDA7NRs6XZwtoDzOROK0yIt08a4BZ41dYJTxxQiO0PAjPtWYel6b4KnZFAo8MZXzB4cN4RVJGkqI5o/uG1XDHtaEkhQAVW8ew9wd74ePYpFXFTwtcuXsX4HnY/G5PzGqYAno2emd9IuhlDAlkCTE4Lh2JqWW4eGPYCWtiiGR5xqGj8nDjq5+4jMIvYQUMoM3txiax8jL1ovsRQwJjHFb0xuh8aMsroQlUEVDgm+Fds6tqpOLnMct+aKIGp/huzh5N42swq3zKhyfe/fCZbRJKDoHV6Kan4HwhEZi/Z8hQ28qQeXEqNnxgVaJhfwIEUCjGykG8zRy44Wz2FxlfJc65eKmqSHLAsLxzuynhlnmASt1airEmkzyqNcwBMB5/Q4D1UZzsX7wgsvxIJZg/HxzAE4f2gxCAF4UYHicv5amXL4DbbQKe1NkLYs1/1wPfJl6neI7till/noQQOPhYERVnhnclOBCD7El38C2r4H8m7mTGRdyMq8aaQVkU+fRet33+lZuNiOHYjV1aH1e6dU1b7AXH7+zjcsI7N+RyQlXX+vB10klmxObsOLL2LznXdiVvP7zrEmXDK5HG/cPghlprKldCLKkkyTspimypoee5izLPiF3w1w1yVMA+kQXbn1One8620oaiDZReCLnGyXe6lxrn+NTMNjkbOxXnbXbM4ZMwZUURBZtw5tyxm5FRdilSdu5Gbdtzr7bt2uNiewL84XVIALWrPgvP/gEpcubrvtNkyfPh0LFizA9OnTceuttzrGDBkyBK+//jreeecd3Hvvvbj22mtd5WYOJKrjK3FIx+eWbTnVndPO/Oynf0IBwdLaL/DQO1dh3Wcb8PkzX+LdMxaAUoqn377TMn7ewhQtMTaM7ma9L5d9sG+MzN3H3YhwZgeuXPIry3Zp+ypAlvBWbLxl+xfLWPZ5W1sWBMG5ZlxzzTWex3Jru3jqqadw883ewWkzUumxA6xNQiu59jwPl3YIeQczmMNNQbTUW20vIggI9rI6S+Z5Jvf37yA4fhakbT+Zz0T/q/ruuyEWFSG6eTMIz+ufTexh65Q+F8UjKWWQNEQWsiADbTOqDyL//rvxfZpZ1VHmOe4O8rorWFWWFhSMbTMCgV5yPT8LXJx6X0mJ431zMFpz0N1A1OteXuCHolBdy7YzsK/F8z7bldY6rlVjzTnXqY2r3fo8T/SgdUesa21c5rXXLGcIOG2FgT3dW480nFe+AoOLXa6n7T7ky9XSYlPga2eiAAABn52GnaEoyB03DjGTQok9kwsYdqw5yS01OhNAIjnwqgcH3AL4JUaLvdCUnY3dA1ipTmm1e69dRo418xDLSTPqTDm0Bdfg/tV3YV3bWoiELUwxnkNdgE1wSze69yCOqsnBob3ZoppzzSuW9+oUlpHlQBGvGoXVxSehOM+Hl7e8iFrB1G9HOVetMA0n+L5BwKXhnTPpZVI5Dr6oyvK+LufjYZG/HTsKzX4n451GCGSWSyEcD04tkyYcD6rI1nIf0wOvNLMFMv7Dh+h478+e3ysVykIcyrJsWmNrv3EOtGWxwbFJRoqpv6NaDkIVYxKOtyYYWcbit6A0qWyaLg753nsnoe25a9D27FWQd2/u2hcR2HcgBIg3dbC2oqAx6cVX/ht5Rx/NNAIBtH3/PRpeeQU7Hn3UdXddxdBexuS5p8X47d7/zjlBbr77bhBBQM7YsayEzIaOdUbGtu3HH9G6eLFjjBt4jsAnWMv20yEmlRWql0O5oa7R+Xwcd7hx3heeUG4hrfrg3iFpRZ698OUK9z41ALjp3B44emheUpbUZOCzi6G0W+ebbXIxPogfAVFg59yOEOqVAgubuhlZhx/ufO5Vgzt9yR/n+RMCcPmV7sesPLDC879UNDY2YtWqVZg8eTIAYPLkyVi1ahWabH3uRx55JIKqMd2vXz9QStHc7H3fHQj4aRS5ym6EAluQqP4RGyd/htEzB+Gee9KXqNjeuBHtioQPXTRsN76z2bEtLqXv2I+oyMR1o8vQKy+A/KCAOyd0Q3ag66QrhYMLkFt1JLaEt0AxPWtcQTdAleV6O34U5oSNNpW3FxlOleQSfPbKyFKq4LC+WXjyGmYsnz4qC3LTDtxxxx3pn7CUSIuEyQ7fkGNTjtEc33BzEJFWq5NHRBFikbUCTdprJBMIx4MQYnNyCXwhtnYHKisR6se+t9TcjIxBg1zPgUbDlu+XddFfPc83+oWhI+zm/BNfUDs5z30ARrmy+ft5lWf/HCA8b9ESBqx6vcFevSAWFho90YSg7qmnvPcHijEDcyDJlPWbCp1fBzW/W/tst6LU8nvs1Nh4e+XVkJ4ZOGdCCY47PB+iycnt6hotqWtvdWlAd3LjWibXtvydOi55aXhpKOHaZ0tM1Y0Zp/0OYvdByL3xPUsQ5tOYKhuqHlTI8F4jqSwj0dgIMcHmu2Dv3q7SS6EpKrMzTX5tBm03KgJzuh0YUsgD7uT+UqPFPs6Hflk1KAkYTtxCJQtLA59gb5/NKOpfnOTTBiiv4KdZb+GnWW8BAKK5rWirtPYM7hCCoIrx0/yr4SNs7WAsgasnnoCH9ryIaFzBM0utAvSAs5GdC+VYXvfk2aRDQEESEShCCPEEhUDsiy5JWf74j7NdSnXNJcNSArARPOjlE+o4vqI/fIeeqL/fRkNQHOdiTED27ydUsp5Rec8WhOffibYXf2O8acrsKk3eJcr7DJcoLu1otW2wzmLBLHY/mxmrqcKBCAIin5h0XxUZ8h5n2ea+gqhOri5jlOAdpc+anMLPiZCpRKhEzdYP65OJCS4ETZENG0AlCXkTJ7rvzHRvbP9z5wMZ5sUrHd3N1rDsyuio4ePvnUEoyVRub7dJ9kXrEwBG2DLAhTnGb3fEoFxUlwWwfY/heMu7NoMmYlCi3uVXF7Tdhszp90La/hPCb1r7HLcrJZDBW36rXK4NvXh3CSM3/dq2iFp2l0jgxBuXuX3Mgg0+ZzYZAHIudWfVFfwHluTil4qdO3eipKQEvHpT8jyP4uJiq0yNDW+99Ra6d++O0tJSzzE/N8ahF/YUAA0lBDkF/wFXXItoYQuqCwoxa9YsTJkyRR+bHUxO+rb+hxdct698Nr3yuqP+MMaxrSgk4K8nVaOmKIS3p/fHlxcOwukDCgBQHFrm7DlPB0N+NQC5pBXxz62SPUqjsU4o4LBZcWeKHXzms5bXubm5KCwsxFFVzsxOz4IODK7ORI+SAKpLAzgx+haan0itOAAAorp2UDkBvrgaOVdary/JSk6UFF/+r6Tvy6bpkroY11wggLILL0TJjBmO99xKZxU14BzvMILYhaecYpyvfX5WA3Ry4zZLNZpQ4i4rpWVpNUibfnAOUjRugeQOmt6Taz6nA6CRaz6WZAtumW2FyiuuQM/77kObiZSsY7X3c0QohcABLWEJcgpCx3TQpyKIi08q71T/rN3JfWB2bxzSKxPXntENgsnJ7epl1rK1GvFSz7IAHnh1K+r2xByZ3FSVAZRS954qkw2nBVEIIQDhkVnC1toWRS2zV58BPuD9haiiIPzTT+BUQrucsWMREJ3nxuWUqueV9LTRq87gDEjRabffcECd3F9ytPihQ/6Cqd2nW8ieiMBuorqxP+Lql3Zg7bYOr4/raO1uJfBp6l+LRMiaMY0RnskXmMCpEZrFzWzSaIsk9OOboSB5KcWZ/k/V/Skgchycz494QtH3DwA7PmNsaqk0Q4nPWRKmtOzSJxYqJ1zHAIazSniRMQCrqOR3QSHOh0ibBLz6g5RmRnwj1xskGPGVnyc9//0H55Mt7dpk3WAz7sUg++1kwUYWYZtBqSyh9alLu3RWwQkX6H8L3QbpmmkAHKzMiswh0Wp1dpREQtfKdTu3/QFtLaOU6tmGgmwRiSRSPm79uAB0TeGun4uxaMQSyTOLskyxoS6Cbbu8S7C0agozJNMN7BP27/Uc0d96PC37ouHZD3daBOhbn7kC0YX/AG11kmxlnHkzrm2/HjH4IVa5SyTVK4U4dUwhqkqMQFYRMTIljqyxifVYQw7Xhr18JjbdfQ9KEsn727dklmCrrxQd05xEamaYnWVeOHCyD/8/YfHixfjLX/6CP/zhD//V8ziNPxQbJhfg7dN49F9DMXyxlRDx0UcfxYoVK/DyY2/jyhMfwPlHX4+HHnoIF198sWNf29YvcGzrDAJ5fvzhiYf1Yx933HH47IJByPC5GawE9xxtDdjMO7MP5p3ZB78dm1yfPbt7Fsq53eBl70yIBG8jOaeoEnPnzgXAbLCffmKZzOf+/nfH2I1LDPmfx6/uh1zSips+3Zr0/DS899577A85AQgiuCzrWkc4Yx7ny73Jf8yo4IxKmNuOM64TlZ1zKRFFcD4fhJwcx3tuHABmfgx9H8myo+oaqrTu8R5jQuvjF1leSzudhF1UlkACmUaZqQe2aUFck7N+QDO5JlugQi2hNh+fCIKn7ZAzdqxzfwC+XNGCFz6uR2uH7MqUnArxhLG2PPLrvhjRP7tTEsLJeECE/ZDJXbeN2fkaiVvtTpboaGiOW9bKvpVBVweWKzC1AVHFlbA1YQqcWNRHeB5SlB1js8KyxFr5PYc48rvtRaBHBXLLba139mAQIfD7nOemBV3M7Oa548Y5xpnhy/gfzOT+UqPFACBwAgQiYk9sN9a3qf1kgvEjFQ3/ly6B4oWvg0XY23+zZRvlFQR2WSOaFLBkcgEgU7D2m5z/4EpE6p09BAnFPXOhkSTMix0PAMjwc/BBAieIiEkK4orxwFQWsclJY521g6qlNOG3H2RMxY6TUPclxb0dI12rlkNi3SJ9c5wKaAw7H6LfTWPf1R7x8h9+CgJHnusYnzHlRgRGG9TnXHaRY8x+g4uYOxewOhyKplemOsTaHBavr7eU/TgcuLTLOF1gmijF3sORe/Vc4y01UqwNad6Rg9o51qoKGo9ZorMdqxjpUOOHH3b9nGxYXsucroRpgfGLHGIJ7wXHfI3MWV1NY3XLAw907WRMt50XMZSGmOqEJzyiLiE/59qTm5CpTnDh60JJVjLYMw12ZvLTj3Q+A4q94kAFjXVgF7wzLZTwmHHT1eB5gm0muaX/SAP1v8NR2RJJJxznkPSQYiJkwkPe24Q+8a3YK2TiX5kjII8xftcfqxih1S5ZZQxPY9n6T5BVeASGn5py7EEAZWVlaGhogKwaNbIsY9euXSgrc5bN/fDDD7jhhhvw2GOPoWdP96zVAYMgIkA4SCJBXAQkl/hXfn4+jjx5GI6/cBiqSwZg+vTpuPBnIOkp2tEbE444HB/O6I/5t16MZ5991nswIeie68fH5w3AxBMvwb9mDsDgkgwMLsnABZPG4bxD3NergRcw9nbZJ2FLnpFxfHNgDrYUmaqCkjwjrWEZEyZMwI4dO/C3vxkVEP5+o52nSWV88MEHWL9+PQDg1Hvm4t21qWW5Lr30UgxSy3s9VQBMtgGXkSzLTuA75DgAgJIGW3r2yJFsn1om2cS63/tPf1LPyVizheph6mHYvjPyw/q86OU4SuF2KLtZdR3xBSH2dmeujq9O0mpnb2kCy+7yxdWuVT1mIh9NrkfLxvWYMwecXZv2Z4TFoVWvs1njXEO337DKOvP3KZ/NKgHaygyiwqsvHqr/3RKWuuTkvvgvlinvKrd+uk5uMq6aZNBsHTvmPFuLDxYbAd7D+2a7JodIwLDHo1/Odb1/5O0GMSSfY6ou5XhkF7F7+sbdL6B3bKsRcJY64AtKqLr9LgQybRrUS6y8GEQUXcuVtecp0sISY9mDuqcmQot7t0PuT/yfZuX4vxIt1sCrGcY/r3sYAHDEUCPTmdVjLX7/zEa9d9QNmTnOSYjyCkLN1jKh8kI/Th1jLX/m7dlNTgFVOHQ0dEPDd8fpm7eEbRlEMNZREsjEoOoMFBdlg8spwYD8MEi4Uc3kWh9uLVvt1heRc92ryDAxmLY+5WQh1hzY+JqFkOs9mFu1fhTC68zHACOaCits0lx/muGUDe+XjbEDcxyOg1DaC8Ejpzv3zwsWx5YEu0bmkw7ctHCpLdgQ+/Z1AEBRT1a1oLErA9DJMTi/AM4uSZSMXRkA/N7ZS0t/shfrHtHKwK2/dUtDJtqWLLV8TpvI2n5wKbPqIrSoZkKiqCjwobLID5/IJX2OzBHi4rOdzJjJyqKSobrUuPaled4Gw+6WON5eyKL3mUEXBk+BoHdF0JXkSZap7kDva3lyZzGsdxYO7Z3p+X7GTGOeJbyoB0DcSr74rHz4fCIEjuDDxUYlToL4kODZPRlLUGsk2OP7yurcVio1ohAtWB7sDaHJIJk5RGC91horY4KmzlisCDADKjDqjBQjDwIACgoKUFNTo2ff3nvvPdTU1CDf1vu+fPlyXHvttXjkkUcwcOBAt10dUBDeh9Et7J5ePILD1u7uJg3Pc+gxsFjPmPTo4QwQ7ytKavuBxjpQme1HTeP34JJVvqjPQnm2D3/uudjC85A14wFcOdI9qN99AitBpmICy8uMuX9JZQaWFDMPf7ucunUqXa3KFV/Pw+zZszF+/HhcdtllWLMnuWG6ceNGrF+/Hrfccoux0UXPPXDEOQiY1u3Eeit7v7amBidejLw57yE4fhYAYKeS2sHOOfJI9oemOa+uW9kjR0LMywOfk2MhQcrUCCrVaS6QGUdio1pmaws6Zw4bhowhQ7D+CkPWqWPB44zwywXJlByi38xnf6hJA0opogv/4akS4UbORxUFGQMHItQ3vUz4foNpLteyum4cGGYyqoAtIMabbKTyQVZm/q5kS93s1aqSAI4/3DqHjR3ozOxXlQTQr9LblhJ4YiHJ7Ap6V3hXRq6oNWxgQSCuOs7yjjWW10rEPUDtBsLxlnzT5U1vsO0ih4w89kwTlz7wPe+8Y3mdM3IkMgM8OogfWUWG026vjND2qSGrsGuM1PsDB9TJ/cVGi1XYHc3V250l1H+cn6R/kndOXkLCGbEdVZONPuXWB47aYlK+7CZwQgJU4RCr64fG5awfKCS4ZF8JoOytw0OX9MbkEXkAL0LZvQVKSwN4n99RmjllHIuqupVTcoFMvZ/TC1SWGK2+FAcNuyxKhDOkcWyGwEfxMYhTtiByNscsIVO9xCMVhPJ+Fs264DEX6frC+wzBB/+I05BxOmu2jy35wDnGlN1V2hpNx2a/Y/alTvkHJS47/ACzs8y79PoIpd4SVRbWa9u9q6glqqJfhlu8U4vItX73HXKPsspiSU3uhGddgeZ0JSSKYX2zMHlkAfwCSVoubM4um6PXgepqNC7oesnhlacZ5EUa1b8bZt6/Wo8aHzXEyYL49l1DUFHo14kmzEj8FyVtfCKxSHDVrytCvM6Yr1oye2KzzOZisd9oPWK9dL1BlrdRZkZ24IhzAMDRk5xQOJ14KiErUExZFC+tZarOAWM6VoBSgly+FXQN6935PGMYvgocbxm/ti51CfJP/p7o2QnioYMAbr/9dsydOxfHH3885s6dq5MLzZ49GytWMObbO+64A9FoFLfeeitOPfVUnHrqqVi7du1/76QFEYXq7ZDwAb4kFSAcT3RJqT07WnHjFOcc3FX0KhkEQgnk9vRKV70cGS63FOB4hFz63gDAl83mu2CBs/pNq7bdpFRg3GCnIX9o70yd0X2nCykegKQZ7ndsBq8ZzzzzDGpraxEIBBCytY1QOeGoUAqOmwH/oKPdd8YLgCyBL+8HoZxlrknIO0jtCGCr1U9aEFHLdlZcxgLycksL6l8w+oOJL4CM036nk0OCUGP9thE/ZtTUWPVgAUCRLYF6AMg89z51X2lI2BSpARf1nvDqI3XTgaeS5CR3OACwZLiTBHP0smaOgxKNIuuww/T3It37e3wKXcrkavryI2uM+4HjCIaqNsZ9FzP7qVuxs9/5iWv6obzQuw/aL3Ko3ckct3S05t3gFhDXsMS0xmrrrd1e9Y+YYnkd+/YNx37E3iqplL3h1aPHt6S6AYIvtfOuBSiIIIAQgptLL0dGXgQFPZogFhY4nFwuyK5lsDezTzPyrU5vICv681ZXms/lgBxFxS81WqzB7uQO6eN8KNYH38Tczc9btu3IZAuOvWJn51cnw7fL6E8YeTLrwyCEYHfCuojtiVmN7oqjX0fegMWAwiMryKNl3TBUZVQjoThLGMykCJFPnrKQMPk5WWd409CvpMjC+moHyXQy2+rwBQE5gfiPTAfT1bHkOH0RMUdAFwpHYi/NQkyVDbJPJYvXtGLevxu8j20+RKa1/EmsOgT+YZP016GTrkprP24gvIjQxNnw9Vd/O5dsKzU5uZEvXtR7JbQ1Tyg0+itidXUgogBQio5/WQ0vxUQ6lXX+HwEAfLGJCZNL0r1vYm+0MkgC8m6jzDwZOU/OqFHInTDBsi2xZw+oLKNpHxxK/TzUBSMhKUhIFKLAQRQ5izPW8u23aPiHIbnhRYaldHRg17x5XT6XZHIDTR6SRgHXfjsmZ+BW/pRK//fnhE/gHMGD2GaWJfWPOgMvfFyPv0amAWDlyBrMbRjZJIwL2m6DfyhzPM2yDd2K/VDAgUBBtyJWIaIHRDgOod7WgAwnyMgqbEe2ZER5FZmzGIYClRGLW5+vhaudDPWL17SircO4jynh4K9wJ945CHf06tUL8+fPx4IFCzB//nw9uPz0009j8GDW3/XGG2/g22+/xdtvv63/69cvef/gzwnCi6ByAn0zBiAQAQb+lKTNgSNQFApKKXZvbQHXBcZfL0wZcQn8rVkIf/CIvk1zVmo7+mHezsvBFZmyxy4tLgAg9B2tO2crrxiK/L6GHumIoy7CQLU1oO0wZ2ChNcjWgieiZ8HnIsFiLrMM+t3nrRyX/tVUuPLKK3HiiSfC7/dwEiRJN358A8dbWGEzzrTJEPECGyslAEXSjXWvqhcFwB6782An33Q5r/ZlVpI734BxoLIMXpQg+mUk1n0LSilohxGkp7IEIgiIbtrkdhjrMVXHm5iytF6QNf4OzY7wqN6yS/C0L18OyPIB7cXVYXJstethz9QCsDjg8bo6vfw1b+JENFQMtgw1Z2K74uROHV8CjgBzpldZtq9TuXIKszVFic7v2ycQbGlg1/+2FzYlrTTzwpxna1MPArBykxowMd0zYu8RiP3gbBMLTrRyC/iHn4LMs29H5pm3WAeqTq4Y8LBjxkzV/9Z0533lRs+71OKUyZSJAI6joJJkC8BQQyd58mQUnHic47OBzFhaAaD9gQNervyLjBarsDu5fhdbO9DtJ3zTuNCybYPMJlkh18quF2nogQ07jAjH4HFVAJixfUSxsznfcT7BMKjC4bjD83HC8Hz4OD/iLk4uX8ZKWaLf/dPxHucPISZRBCQWVZlRfDV6ZFQlPa7Y3Z1KH4QDCWSCJqKgUWaI6pT4ZlBTpDTGJiDfkIk44vLfgILTM7nma5MK2Rc/lnqQKbqlSQ91FlnnPYTMGfdZtrkyN5scTKV1NxKrvmTH5Slqnn/eMjS+cyc4n3ozxa0R4fD7fwHEADLOuAlEEOEffqqVoTZZSZwpW5BYYxUOpx1GFYIma6Qh+7Jn9L99ZWWORZTPykK8oQEN8+aBKgrksPWcOwPN6UtIVHVyCfw2Z6zuySfR9JHBJO5mtABAvCG9AEhne4oppTj33lVYuDJ98jtzDw8AfL2yGZRSSDLF6AHWzENeloCrT3eXwNmf8AnWTC4A7N3OMtHS1hWISwp20kLETpiDSTet0M+/rtGYUyJcBl68rEIvTfvadE227YpBBgdOjmPb7ii+W9OKtqVLAbCMu11yIr+iBRn5EYRka3XGjRkGAQ4BRSRqndMUm1pu7m/fwm0vbMI73+yxEHhs2NGBOc96tEscxP8GVIfoot4Xo2h38gCSZtw27wojI9dZDthVzJx3LPyqdn1BWIKkCPihdTQSaj/m8jbWI6r1cLIX7o7MnjCH3c3G/V456tcYfN77GDzjXZRUj8ax67xLFDfn+dCm9suJAsGbd1idCIVSvSzyk6VNroZ6sAtaqyVmfVQT9t47CfFVX4EqRk9uxqk3IOPUG/Qxvr6jrS03hFMDFxJzKlPowEscwbawTRXA5lBmjxqF6rvddWfNiId9kBNsX4kNiyFt/B7SZsMZljb94MhIWwLOZmiZMy2Q4pG51yA31Zmyx+5jqerkauew7Y9/RMf69a4kWj83tAxtt+uvR2I3S8CUnuvkRtFtB0LAZzOkDJAAACAASURBVGYi/zjm8JTOmIEGzhpQueN841oKXfDb+3UL4f17ncmZi05kzppCtZ7azu+b563VZdt3d53Q8Ei1ysKrdchedi1UDUVg/ExXp5CIQXybMNnjvAix93CIvQ63jFu7I3kFJJfPrlHgqJmIl2bi86wz4DPJUwVdAhg8lQBCITW32IJGRK/2zBo6FMVTp4MrtJLs+UIJwFX1fv/jgDu5v8RosQbBlvJfn2UXdHdfZCV1ohta5eyzCZoWO5+qnVfepwBZgrM8Z0rlWZbXhLC+3L6VIVx9ejf4OJ+rk0tU5y7yqZMIQ8kpRzyhQCJRhHf2wGEVXevtCE6YhdzfvQUi+NH6t18h8jkrB/IPO8k5WJF1zU3f4GPYNt6H3EwB5x9XqjNDaiWh6YAzZUa9YO45IF3kLxe6DYBQ0ivpGKluLZSWXRD7sUCFeaE0o59JN45ogqv2W0hOgAtlg1ePGTr2Eou8AEni5Pq70I+4ZvZs8HlG+wDtaHY4ucRUIrz3s890gfquQONtSmj6eDyBTyCIS0kyMp2MXL+Ya70Hd736aqc+f9IcVjp798tbUow0HaMlgWc+MKox7nl5C1rCMiSZOogrXpkzECcMTy6nkS7unOVhdAHwiUbwwGwIRkgIwQkX4qsVLQAImgqGWj7394+M7/FP6QTsuPUm7Hj8cQDApnpj8czO4HUHtIbfhIUrm006ji5Gvccax3FAYQ82P3CgEGyfVdTn+O2scfhg3PUgqpQHzxFLifh/1rZZ2KQP4n8PRBBB5Tg48JDTnNJfu/8rBDP3D0nP6TedA9F0I2+N9EGLlI814UMR/kklVPSzFiJelbsDACrFrWz3KhqWL8YNT1kDM4QQ1lMHBcEk8yIAPDOcMRjH4tRBEFNR6NfJJF//cje+X+usiNi+3buH1AvFxd49wB0fPcr68pNIomSedZv+d2D4qSxwIScYgaX5c4QDbysVXVSVgdIVyc+PcBwClamDiJFWFqjQ1tr2125H9FO2Rhd0Zxldi5NLjSysYHMq9IC6tj67aBOb0frkbKPH0iuTG48jUF2NyquMKrSmjz5CeEWKC/BzQF2DwytXwq/2t7tVWGn2CSEEof799fJVABAFDg8WnofflzD7IWi6X/dVQsh6qmxfWtVRVxKI9vPpDGuzHVkhdm/cPtN9rb7iVGsCJmv6PRCKq/VraWlbE314JDpdbzPysmv/9r6aACDuJ+4bxKr1gmOn4ou9JzPiOpNtWXruuai6/XbH57Rr2bLQmtgjAasDnzXjAUimZ5nj6YHycf9vE0/9XwNno+WXiC06whmRHrMR6VfLiEbbGt6njC1EnWCNnF7+10noXlMEgROA9Sfo23PEHBxdbNMHJRSgHLoXs8nZz/ktLMkAUB/diagtU2J2PH0iQSu/FRLfhj1Lx8PHdW3xF3odzsTVbQ8Z51HanFj3DfiyPvDVHAEAkLYyRyI3UwDtwt2fSleMDTLtN0WEeF/Q9vx1iHz6DIiY/FqaF0yp3cpMmDn9XvYHVVR2SmOsHhgA4Btg7Zc1Izh2mud7/sNPBgBk/8qqL2rv+5Hr1jii11JjI2rnsH5kjXHZC42tCc8eFkqpLrsVlxRIMoUgEIgCSSoh1Bm8nHM8Ei66y11Furp7C1ca5T1adpHnWeaa/xln3eH9sjG4OgOH9HJGic3BA3Ov7B45GyRojH/oNSdj+qbsY9G0PQfbYwZreMu33wIAjgz/gIpEA3qI7VDUJeWG0FycOKJA74/L7O7sXeZcOAo0CH4ZCzJHYlFoiN7j+3XhGFxwQhnGDGL7CnNBfLLe2AdvKhEP+rhOBckO4hcKNZPLEx6bq9J7sGRJwfwHWWXLuJpTUoxOjpzcPAim+e2b5mMhEPZsdaxhpZmUU9caUwVOx/t/AQk6Jcb68NswYlst2qUsRINW5/GejMeR6aLg0G5yDhoz2Vz37x+dXBiXnVwBn2isgW5+xMyZM50bUyAry/k9NNBoO2LLPkZ8xaeeYyzcGRNmgbY3Ir7qCyjN9ZbqK9+AcVBs57w7wzq3/+vqvl0m9ONF5lxSl1Jywilof+12KG1G25jZTgmMPN06XgtEq+1EUZV4Mhk0YiHq4uRGamux9cEHEd20yVNC70BCu8Zifj5yj2A2nKuTa8rkwqbtmh3iUS8WIs4529PE/Syv99wN/fWeW9IF+9Lh5LoktL5d1QJKKa54ZC1e+NhbMUbbk0/k8MdLnZwqnuzNglp9YuLEIYIfPUoCWJhQA9OmRM7Ln9bjxBuXgVIKWfVdEhF3m9TNfjYnE/jMTASrqvTXQ3qyYBnxcJrtmWQulI2+V7BnZGeO9rz/j2Zyf8lINXmWjzcawc16tdqUSWxGnShwyMj0drbq6oySqpsH3OHoIeIECYSTUVnEHl7RJZN710+34q0d1gZ1xdSjG/Lz2NNDJWFQun476A+JPcsmsHPLmskYqbPONxhc/cNPAfEz41pRWQhHD+h8T5AduoMI1j9sJ30CkLIMKl0kzZamyhab7yfNUFI3WbRJTb1JAOA/jPUWi31HWfqbHLs3lYFlTr3D8l5w4mzkXDUXfEHyCDcXyrRMdnnHHGN5304x///Yu+7wKMr1e6ZtSzY9IQmEhBJ6lW5Big079oLKVRG9iKLXil25oIhdUUGv6MVeURERRSwoCkiHQEISCKT3ZPvOzO+Pb+rObEkIqPeX8zw87M7Ozs5uZr7ve9/3vOeEYtqC3fj0Z3MRp637SZXNKvXgBoICOIYGx9Jt6l2NP+64sK/RENHIhF+EtRUBXsTgHubWWpHeAwAQSZAbqfe3I/D49b0w/1oTkTINhVpwqVUcRgyCYtTJr7LeyAYJuG3wuy0oF1QKU/krryAnw4qpzevwr9p3cHXBEsgXsAV+UBTQ+MMPoGgBSUP02enMPjUkmxsBq53H4zCXARYCtnfric/YkYiz0WhyS57ZIf1uDE0pitahglid+N8ExXLgq0tAASjPpuBq262Jsfmnmm5PcqSZbtfi1P5DQfE0uJCx6pCX3HsBgcx9AyWlZDa7D8BpaNIm9nB+kYzz73rOBGzxGD8o+tj1+MTw9oqrFgzFS7f0wUXj00HTFIb01CS/KOIpXdukJrz69QsvBhQOvXpFZjbxYZSHldOQ5jbtHBXYv0l6TZ2nLSddATFkDRZiCoBaphnW3FzY8/OjnncoEiefibj8XB1TSnOWAAD/gULNhxu/gwIpEKEYjqgm/6xqRSTPXak8th2vugMobDST66L00Uc1n/Un9OCGwLVzJwCATUlRzsc0+JbPlabJOK2pDoYKm/bpemQ+95GQmaL+Tddti67QHYryEKG2UPtYAHjkv6Vo9fIorvDiyw2RPd9lJDvV3ywtUbJiktaClsGTwA1QvWbZrrJmj+bvzwfxr4tzUJAu9b1qEiRyb299SxC8WajHMHBMmR323HQswZBr7okZUnCuuf+6TJumPFb0ajTgm0g1OZSZdbTRGeS2EROlampRyz7Da7YU1fZC61cbFGmcfuMo2Ow0MtjusEsWGxaWQoNbQIPVvA+G5tTFpqlqMoD47uqga6Ut2NOsTiitkqDLTzXrdO+RJw7LsNPR6lOrvOeOOwK1MyljyWuEkgCA4ixIvHkZWImqRWu8u4JlewxWO4lxrKHn7qZz2tY/qw0QE//5OlFOREiGtp105VBY8seA6TbA/EUTUSjndaowiVnShLOZ2BGF+Awq72uDcAqbO0T3nKIZgziXGfy71uomL84am4F3Q0sAB6rItfX6KvOs5jvfkUHPbiWWQXJPLsuovaOeYr1YQ7/XXjMcJ3vGDGTfcIP5+VMsHrrrJFSwsdGBB/eII1ZbSeT3DhVqCgRFpTL9/M3hF1Ly+wEowm68QALe9vrsxQqapkwDaZI8ENDiCaLs6WeU7dbyAMBGTvrIOjahSah/DFD75hlNLxkNAfsOeeDeuxeiQENwNaD14/apHdOUAB9rgUAxsHI0NpWQ66qJJgt22VN56VflqG0mC/ZWz7GdSDvxZ0GqKPECZvW7HXEutIlLyLHGgKZnl4G44ZSHTPYmuPHGG3HfzdfjkuGjQfG0oR5R7CGBYpmXBH+MpJRsnTQDSXd8qOxnpuXwR0DywQUNW0MxTrCHt0NzlmYjb9WJpq8dl68Gsz2z7Epf4oSh6pgv2wg1awTbKIrCtONjbw2b0jcdXbt2BV9dqkuetwVUQhript6rVH+43qPASnOqttrdCML6OSFNXfinuNX7/MXZHCiKBpeUhLz77mvzedSu+QWuQvOWFEpi6bXsLlW2Ve/XJEJCKmGUotQMIGDsh0y8/X0k3fWpLsiVe3KFpmrD/rpj/wUqubxkZ2Tv2VMJgMzOSxcAi6JuzTNltH5OZhgKeV06rlc+HE4bGUE4NQzk5PApx5H7J3RdIK8JvvuDBNCxzj/a4Ds/xGLI0vd4xEtrVwCIm3oP0Z7RBJ9CSy2sHK2sMd5cXYHrF0lWe9I5UxSUINei8cG1du0Kpmv4e1229rP37h22wKfd3Cwxu8IeT9IkSKckHY9o9pgdhM4gt41I4Eiv7DP7noy4X1DUWMiIIrrmp6LSW4nq4EF4eDJY01K1wR3mb+1raFvQyVAs/mjYpFCW3z/4tmGfyngWd3XbBwGAZeBEfBxU1Q0TIniuhiKUc68EjSEG1ZTFrkiFJ89dqaMmBQ9sVQI1JlvtBT5rjD6Lfu7x5LndGv1ypVP1vbkUa1GzrBoxqPb25BrAWsAf2g3RxJjbsNii6LD9vPFDBoNmBDCsiYgEHzTQq53Tn4bjjDb0wrbh+2qFLKwDT9Jljv1bvozpGE9/VIYbnyVicaFCSzKcDnJcuQIX4EmQy7EUCkqb4T10CIG6kIyoSQ8yY7cjYdw4088IUixyMmzoe84ksMnqAs8dRshu4Q29cfeluahtCsDl5Q2TVZAnAlkPXZWH/AiZ5xvOUpUJv5UmPlEUwfPisRIVNIB4/Qm45NFd8JWpC+yAxxL2frDK0a1UXUiK1++X9O5zYT/vq9/Uv12wZAsCe3+BTUN5otMie5W+d/9AOO0MbIwAQSTnYbPQaGXi8ETaVSi2EhbC1IfUnrRdpe0XQevE3w90quQbywfRLZ5cT10bbBAFAf6a8DZgWgz5h15I8cIxM8GxVtx3wRLT/R944AHcMH0aHCyFzM2DcChOvzht5UkiKChVZX9fTRZ3Ai+Comhdu0kovvSTAI6lyLgTbzEmPU+TxKfSSjIQV2VScaZ4jOob3Rf+lS+Ipdes5/UJ+wXvrsZbU3vjmqHpOKtP+ETosqm9sfBsEpQ3vzYLTc+qvrextnUARCtDblsCADAcRG8r2JxBuqrq8ipC+b0i9yplW8Y49TEABMTIva8AYf5wJn3EzpEjSc9oyPic2acGDEu+T9Ko/rrXlLxeKN1TThqKoo5+LFdxFStGDfVU8bUPRhE1+gtUcuU1AZuYGJHhSNE0cu64gzwRBF1UFNozDgAPXp2H52a1vQofK566sTfOP6HtxRxZDEq2Adp1wIXtxa3wBwRMuXcbDteSv9mrX5J7yhGyVuU1bI+rT9UzL0L9ecMxvSiaAZORB7b7IEWsjbI6YJW0NujkLKzZb8HhOj8272tRWr54QQQvecvzmiow43CASekKy7DTjR8GoMlH7r1QC0kt7KfOVB4nn0IKgGyKeRKBohnYnF6kJpAg137ajWGP25HoDHI7GE2FQxBHJyCoqVDyAuHZLz+wDACQxJGeMnmADK1cygjHdw8Hmc78U80P5FwC5GIak6IGABUJZPB98PRsaQJRPyMpLvYsWvyV83XPY+qJDQGbOwxsDrGIohPUSYerKTTd//lZ+YZFdpugzRx1UJArV1jlCrboU3tr/Vu/BuVUs5VxobLuGmRdex1yrtZT5+yTriW+iSF0ZUDyAY4z9jmGwjmd2A6ZGX2HQ7lGEEsUgkqGtuusWWHbKJqW/lOZzA/V+LBpn0qH/XW3uSLoUIk6R1NAkIdEV6bAMRQGVG1Gyf33K+q8MsIJbckTrb13b/RdsgQ9JNV2+d7qet5ZyH9GrV4efPpp8y8CEkcLIjBveSmmLdBT7VxeIh6VkRy537pXth1dpH08PnKjCwpdOeJbjxq4CBTeO14rBQD0yNSPAfL9JlODSytj86kGgFzqMADA4vAr9x7rVI8v9wEnZjYrlRKbUz0+y0iJD1pQxOjkoLuKM6eT/ryjfdWkTvw9QVE0EXAK+pUE2NS3W3Houeew/847o7yboNc5PXDtxPtw/9wHcO0n18Ai9b5dNnc8zh2p940980xJz4KmwcWTQNJ52FxdWACtLC4BIChVW2wnGVVoFUj7j2GI5dvAHgm4+9Lu+PSRwVgXIB6j40sIQyuzVZ9YPS6JeGRSDG9QUTeD1hpsxfoavPrlYen9LI7LjsedJ3bFE6fmYsXl5hTmkdnxoMPNo/7YnREMYDj4Nn+JYNlO3eYit8rqmfV7E8YljwVv0VfiEzmSYNjTvBstAfN5J+WUU8ClGpk9XFoaHP37I1KvoCNHHxxXFUkBU8jvQNEMrCPOhsgH4fl+Wdjj6dZNfPQAHYjeNncskCIFNLrJLExiw9a9O0SfD+7CQoPq07VnZCErRZ1Ls1Ks6NPt6NGWB+TGtcueSGaQ984mCa23vqnEv98pVTQuZj6jT5q7ffrk1J6DavI1Ic78nklwsJh/XU+cPDTyus4+fhqSbiOit5bBp8DKUXD7BDhmLEGTSJgPr3x5WBkDeF5UtDL2UmoxKXvmTFCsBXEhdpqpXcm4JrSScUbwmN/LSXd9qmi7AFDUmBXbwFAwLJKyWsDZgqBTc2DpMzbi9+wodAa5bUSOw1h9qNuhBpF128aDBoOgJqMoiPo+vCy7XtK8z4SeOON61SRbBu8nA3jZajVD+tKIpSj5xDwD0hIkg/rnhz8FAOxvLQKgpzoHNefBZvdBf45w5xv3DW1TFYTt0kvXWxJJPTEUtpOJuAXXe5Q6YGsGyOChXWZvA8NQaGwNYveBSOcZfnKXrXeo+BRQjiPv/QWgZGID+whVI5SSqe2ZteSPNry92623knOyWvV9uJBM4qXftb0TG5vdF87pz0TfUYNmTZ8tFfQrmeOEUaPA9DTxPQbAVx8gapgAHvlvSUyfI9NpMpItCAoi9h3ygGNpsCwFq9Rb3vzrr2069/SLLgJtscCWm4sGOh4VYYIh0Rc+Wy73CsmiWFpct6gAJZXeqBMly9CoavCD50X0zyWTdm1TAB6/gBF9nMhJD288f7QQLrimGQGHJaP7EwfpJ9h0qU+ouYm8HpWGpbmPhwtk4o9PUX/HpHwLPs0ejwbBiW0S7die4ENcskd6rAa5FkqA1y+ADwSVSdos+6/FtuJONeX/byAKywEd4yTUBzUSujqyUX/bFtScfBj1dK2yXRBEDMoZg35dSc9/l6wcPP88aTdpef0WUC6yL+PV38t2C5n797qG4YNKtdLBB+R7R71HqrNOIu+RKiIDW8k+ghTsUgyLCcOSYbPQoELmtuY6vRNCUu0k8h6aR4ozfBL3xEHGue+VL8vx2fpaPPn+QVAUBSZHrW73SomQ/A6TPHV98ZR+t/jY1eMpk9aJCk+58ti78XNkNbgQxyWAKSKB+X+vIt+3xkeq9y8WPoOPysxV9CmOMwgsAgAEIaJbAQDTFbPPxUHwmMwnNAOhpRZ+E39TU0h0ZctwE0eKvxgcstuJvC6haTAO8+CUomkIXi8EtxuiX5+YufjkDPznzv6m7/srYWQfJ0bkOzH5uBQleQ0ReHdteNtCrW97UgTtHVnEatyARAzv7TT0KpuBohk4//EsKJaDhaPR6uFx7gN6lW256iy38wCAfcLpSJ40Cb0WLgSXrGdpiIIIvzeIpHTyd/QVk/jBOWqU+TmwFv26VLp3LFlZpvvbNEKoXG/zYx4NdAa5bUQ/p/6GnN5tNpr2qgGqw0qjRWhArY9MgLwgQhD0SoaXd78Ks/Nvh0fKpGZnx6PnUKN4hBi0ovijm5HM6DPFfbJVKpKNVieg3+pIQKClSgNAQPCD7XkcvCyF4lT9hMUzcsBI4bj8IxDoMQly7affZLorm0kou9qgTyuSZBliLgYiS8DLvUSGU0jrDrb7ENPXACil86Rb/tuuyrMp5AyudOxg6Vb9OZnYRGgRN1CqZHOckTJKMxA94X0RYwWbHdkWKv38s/UbJFWFrqfnQAx4QXMc+rz8MgCAiTefyPgAo9CtYvWQCwRFXDohA8nxLH7dRSpwHEuBY2ic4CaLVC5NDVLl3yoSmHiVRv9YlxloZPS0vdwYerXkycGjqXSsWqBPQIR62YVC9vmrbQ4o323O4kJ4/QLG9EvEktvbLvBypJAnpDM4Ve4/uVsjBJ7Gv2qW4+mKZzC2q/7emnoiyc5mHyJ/j8PVkSs0T1c+q1gcjK+UAg0NI+Xw4XqIDI2dfC+UCCqlOy7FjYxetUiYLKnd0ixoKdHBUUGFthgtyAWAAblHrxLQib8gJIVlWmNtZs0hbSuu3bvhLipSto87z3jfDUwmc0aVrxKg1Ws1NdsJiqJw4Zgbcd8FS/Dim6t1PrJy0Jm9ISQ5yZnPLX4vubfoRHU+zzjregAA12M4drSoC78khiSGhOYahfrLgsf2IKFyXrNR38bRcqAP6AAZ67qd+i6KmXWm5wBE1riQVZndiXp7kzlj9QvXJedKbTeiedJLTvrKiOY0oIOJKOS83WqPtGcNcQQQKUD4fB3ZJv1Z5FYwANjU8Lvp4SmO06nLy6hdsQKNP/wQ+3lK8LVasf+hx40v0Ax8G1TRT8c5t0c8juBtBdO1Hxxn/FO3XdSoHPVdssRABc9/8cU2n/ORgmJZ5D34oDKn9P/Pf0DbwiRD/gL06iNFjyw75klijlUNUgLezeOTMKKaAHTiU3JBy6xWcdrIFJw8JAlj+kdvMdCCzSJjgdUkKBYEUXFd8foFMJLY0ykXjEbm1VfDYkLXX/HCBrx252rs30pcCeTEcqi/fShkMVJbXh4yLrsMGZdearofk6TGOBaNoNbRRmeQ20aEVtRGddEHVXKF54VCQodsbJUmNk2Um2pNQ7+E/vjkpxrDa1rI2dhQOxB/UEAf23AAwHldVen6YUmqyqx2IPQJftRxAuZNzsKOEDriPi8JyhwWFr2y224EL0MO0CirWjWmTEQ9yAv6yy7hptfgOFUVDrIdfwlaTVQvZcVUUQS2F7fi/Ad3YMq9asY+8YaXETclQp/qkZibRQHtDJOpjiLoI1OBKZbVUZ3JQVmA6Rg/x0hwDDBPDFjyhkKURDMYaXFnzzRPhIgCBTHgQXFF7DS1QFBQhKYq6klgzLEUKApIEMhvEaglyaJlJ9yPlJsjLxIAgImLLK9qNri3B1wUHyCZVjt94R70664/J0uUAPlo42rbSgQ4Bk1pDqUlwimQv5vFrU+qHB9iezbGracQmsEWwbpqQ00KTrP8ioDI4j2f2gtEUcQ7j0kmgS9ldYDiA3jHORfxlBupKWQMVHqEI0BrPxFLUNyJvzcohlRytZAD3oMLF+LAvHnK9sEn5xneH8+Zjxmhc72Vi3zfnv3P0eg5NBOtLvNrjpd65LTHTXSSOZJOzsLOVjXIrUsnCWDPt0sR2P0jAOAEbhtaaTL+xsfpVf2dufuUBTRrd2NrMHz1MCWBQ05GZCYJdeJ07OdV5f1rhqXjhhFd0C3BgsdP6Y7jc6R5IEQJmK89CL7RaN3VJuaUYJ7EDkXcdzuQWUXGr3/0vAHdW0gyVxD159Tob9BRl2mOM1QU5Xkm2NgY8TO9Pxl1TtxNZG7UalkARgcHa4RebABwf74I/OECw3VXuXy5eu4WC9hE/W+pTe4cS9h7GhX8zaANfq0x+BX/ryBR01rHa6ztQnHWmDTcc3lkfYpIMHMSKK/zY6WkiTFncSGaxeiy8+VFepqxrGQejUVIS2tDiqKQesYZcA4bZr6jpv+cTgqvCN/R6FwBdDCyUtXJw8t7Y3KCCucoEmcjGbA1m/WS5yWVXkxNvxbPDl+M8RkTle1jUwmVNNWShvW1PwEABiQMgl/wodYamWbYsHukUiltF6QB3XnNImWTGe0IgEGkiUnO0lVymZRs5Fw8x/A++fxEAHcv3a8o3O0v92BvmVuRXg8HLn804i5+MPp3aQPkSq1ooqBoHT1VoSvHaQzvtaBoGv2XLSPH8Opp2BTDAHwAdHK2yTs7EOEoWpxNoSADgCgK8K5XKWCcVV1YttY5IHrdBhETGVuKWgzb/EERVo4GQ1NKfxhNUaZ/x+3FLsx+0fzYMvq99pqBghMKSloQxA0aFHG/aIgWqNqtavY61Cf4aFsIRYIFUnU0wMNHc4bMsjXB6K+rxcXN4f0uZTQXNaCuUU36MKy66DyP/Q69mUMIgoUIGne6bg15t5TxttohStdeCtWMfj2TcfKQJF3QGo4yrs0//AXa1zpxtMFyhn5Gz/79YXZV78tBJ+Vi5tNnYFya3u6iZMqPuOrRSYb3RvPu7No7BcXbwnszN9e5IYoiRFEEm0uqv5QtDvbTbzKwePbuVasnQqu6+OxKEXpknT3MQpKPLeAxW2xrEeApfOU/ARhIks0cQ+OWsVn4+qoBOLuvRlgmpJLbvOQmNC++znC8uAhWJVqIwSB8W9YA0DsRhMI6Ziq6f6/6eWfHdYPz4wEYuOx81Pv1Ve5Hdt2Pe7b/S3luRlcuksWRAPDl+v7KhH++rj7h/bDY/XCmGdsi6tes0W/QzKsBgcXi2SsRDbJIpwxRFNH4/fe6bVxysirmhL+G2nIkaIMkMy/dvxvOGBWbOnO8zbgGsMSQpJVRX9ECrys2N4tocMGBeSnPhn1dNFlziTGGh5ZM84BVZq7I6DCx1zaiM8jtQGTasuC0qxf2v7bORr2vQelrC4VcJdl90Nj7BwCDe6gLzuXfVioKbgDxt+Vo/XHznX2RwCZicNJQuHkSMDkYB/yCD2xqd8Pxz5G5VwAAIABJREFUfbwPqZY01O0YB5ebbbe3JJ2ao1zATFp3OORG9jCVXKZLTzCZkb31TN8nnd+mvfpq080v7MOcxYX4fW9kai/FcLDkj2nz50Y8JsvBdvwlEAPGwch+8lWgbfFInrvStB83FJx8bnIGmGZJ8NxR1OowCCsPb7EqgQYA+Ld/B4oiapNkB8CZTiZ7n8uKwnseNTsMAOCZj8oM2/wBUsndsKcJxRVqkiBcrsLMv1V3viGTfVaKBS/fqqdqM3Y7Mi65BEcKWwxK3zIaWtQF1YgjaQnoADyapvo1+mgL9iLEY1hTmbnvSpJdtgo+VLL6iT3/2fATZs1HHyFQrf4+DGfiBwrytzos6FsxlIoPZ1MSRw7Ki965Sbjn8lxdJdcWZsHwR6G6AO2Mcf8fgOHg3/UD+PpyZPSOrqh88V1EyTe5SzwYjgFL6edRd5d6OJNJdaL7gHQIiXHYZUmM2qJAMTT6jQ1fqVr9+h9Y8fwGfPLML4i/9BE4r34SFEXDNuJs0/3pZIkirKlselJJL2RLvZ4xU/nLFHKtM+oYGVrR1EKbhDNDkBfwa3CoQZQm6Y6PEX+ZOs6LXhca5p8V8VgAFDXYaDi4aBGqitLhbbGgteBg2P1so87TPacEdez3C/p5IvQ5ZbEodOVgSwtqPv1U93ri7DdhG09Um23HX6qjWVIUkJLTBJo1/ra+8nL9821q0BsUSfIhmuq00Ky/frXaEb0WLlQex2n8jKP2Ef+FQP8PBLmxsImsHKWzGZr9IhFTvetS4zrcDMEAj/fm/4iPFq2PvnMMyEji8NSN4VWred54PVdaesBHRRejTTzhBPR99VXD9tfuXI2GSnONjD3+UizZvzjqsTsCf5+742+ABC7BECh+uekQ6v31OOQuQx9nP9zUW5007ruCLCLDZVX/eW5XPDAtDwDw9ndVWLmhVtnfzE7HxthwZvbZ8PBurDj8CQCgzl+HPc27sdSyxbD/i4XPINOWhUAzWcC2R3Uu6e7PkDjzFd02uX8uXCWXScxAwrXhs7RaXH+m2gskV8++3miu3vaneWOyViDo01Vz2V4jw5jKRzoO+b0o2UiesxLV0GiWAkeIcFYbFGuD6Fe/E18XEqhSoiIYFA5P3UhMw4f0NFYI/UERFpaGyysAooh7mt5DeiKL7FQrfnIYKxWxZlBlVDb4TRdztrw8XZ+TGU4wEWfRfn4s4hAyCjQCVqGm8scSfGMluvvUKkWfuFLU5+qrWA5OxLWTU+EQPIoI1YKqxcgM6u85bZ9OyunmFgShKBeMImBaX0TL8DNh6Xs8kueuBMXZAOna60LXtaknV4uj7UnciT8fQn05fBtXoPmVGTFV7tNzyL0t8DJ7JPw1dfZNo+Ed0hPFFmfUIJemKfQZSfpdT7zQ3Du9vKgeVSWNoFhO8YINC+nLyG0sVFwyRl96BZzXPIXSHargDR1gAJHGK1/qg6wn9sxDOEQik2zb34oNe5rRNdViqDxRFpvBcz3sZ2jt/PjYKMjuggIAQGNFIireeBMAEBDUJKFFonzDog+a/S71C2n3NwsqaU0lt3D2bNSuWKF/3ZkG+4lyy4X0ftnqULIAZPOGG47b9NNP+g2ai5GX7KSa64zzZdyF9xu2yRC86vyrDWYplu2QZO2xBvUnUas7EpHWySP7kCR2VqrVdD06vHdsSe4f3iNtQc215gWwtiLa2l5updCiks3FL/HnmOytB0VRoK3ma11Xk57heP/p2SjtmoEtDZuxrdEYkxwNdAa57cC03OkAgJm9SP/nvH+ovQksTSGZUoUdNlRsR9ZJn2PBnkexr6UAHKVmHGmawk3ndMW0U8wtCBiG0vXEybLksp+oGTy8RxGgAqB45pqhwlsBXuQhCtIA3g4aZWjfCQDQiVLfYxtsa8JBu6jlWNpUGVLG7gMug0n3sQBfWQTvLx8gULQRAOA4aw6clz7S5uPIYliOcwm1irJLUu5RzOGPFPLkmX3jjYpgC0AquYFiVWlZK6KRlleHbrffBQBgOHUwP4FVB67nZuVjQC7pBfluS4POKw4gveVWjsaVk7vAwQEZ7gp4CwuRBDf6Z6uToUvKJqY4Y88CVzX4IYrmtjkUwxj6p0KxfqfRimbaKe3rI4nXsDsqolSjOwJiMAhPcbFhu/eXD8jr0p8hgXajtftAvJeo9r/TAo8Re77AvKpXUL50KYKtxkxsryeeAAB0ueIKZFx2Gbpcfjn6L1umKIWHAw313sylCa2ztMqLxFlvgOt3AhwTpyuv8+V7ESj8DQAQT3kg+qSeYc24d9KQRIwNI9Yxui9ZTMge253434XoVvsotUGuvVdktpB8H9BRlkHfbSHtQqF9+Fm91NaIjFyS9OnWNw0zn5mCIRN64IoHJoQ9ZrTFKxGilL6MNI9SDAvQDNiuevGsxP058NYb1xCHPEb2jIwLTyJz9O0X5Rhee/mLw3jzm0ocrpPGKo6Mv2z3wdJ5xDYOM8lasar26WGIgoA3S1W6MCvRfERaz9rZ+JlKT3fzLrDSOosXeeQ68nT7hlVXNoP027PdiOAoX0U+R4jLM+zKhPTKUpoeRJ9AfsPmWqMzhKWv6s5hO/EK3Wu6IDckkNC+9ndBWHGqKNjweUHUdrRjhUjrZHmuL630YslKknQKbVeKBa7G9ltwmWnrRGu1OLS3NuxrbfG8DsXnL/5m2FacIKLeH8Zm6CigM8htB3Ic3WFn7BiSRKpNI6TsjZNNAMtQsItqlSN50AZYEtSeWiZkcD73+DTYLJGpQxlJZFKR+06DvBjW8/KQWz+xTe6iF3CSA3SAKBF6eA9EURrI20lXNkC+JzogyM1O1Q/skSo5q36vx/kP7gj7+tECmzsETNd+cH0qKSxGoInFAloKbilbdLGAjoDzOEJZdQ4bhgRJLj7vwQchuJqAgFdnZg+Q3mbWIoBLIpVNPqBev9fYvgQAnDAw0eB3V9MUQiMLkGSNhaVwUQ3pVzqwYAHKly4FpaHovZFMsokllbEP/HKyw/Sapih4Cs29mM0waRhZzMrX3iUnxyZedekEsl9bvGU7Ak2//ILSR02o49JkFfBKYmcUQDM0NtlVxXgxGATdRHramtav1/1OzRlEcdXShSyoU047DalnnKG8zkbph86k1YmtH6PaTNGJGYi/YK7hevf++qHyWHA3SecsCfvdnI9bpubgoav1KrAyHryqB76YNwRXnXrsBC468ddCtKqRqCiexjbvhc65XSeqfbsX3aEyIhhpQZmUEYfUbPPKTWujBwIvmC4gB4/PRTDAQ6gn9jh0YgZEUSRU1oAXVaV6caTsDcOQ6G5bS4u8Zgny0RewtuNJxdB+2swoe+phGUT0QiyDJxt6TWNFwbXXYlflJuU5I0+tAX2SsqlKTRoU/VQNG0OCqVu33IQD7lLdvhTLQgwGTX97a/cQOqmUeGZCHQpMKMJJJ+hZMUxGnvKYu4SsDX75rEDZVrdqFRrWrdMfJGTtsP+ee5THrFN/LR1J8PFngImLaxe1WhRF/LFmP1rrj8B7uQOhZWvOntoNX85TmQ3yemNs/wRMkPxuY/GsDgUdRdgyEi6baFyfRFvbr379j7CvmVV5o8Gsx1fG2u4c9rbsafMx24vOILcd6ObIwaJherrtXf3m4rLuV+KH7Y347Qt1sKMZfYBAtaNLrNlNjlHfGkCQF8EyVNiJ+cR0VZq7f8JAHJes96Mal3YCaKgT4gF3CSDIQW6bT80Ucl9dm+m6IVi1YKiB3rFT4+Uba7BxtEHHpxzxd9VBmui0meCjCYpl0X/ZMpJlleT+bT16KHRzoaFCtz+TRmj2lImidDxFJqJkE5/GTftadBOzPyjAwtLgRWCYVw2mXDt3gtOo/BZbSZ/br7ubTbO5W4paUN+sz8z/vIMERawJ48F74IBhW2g/1dMSzRoA7pT6aORenFDF4XCYfnoWBubFofDwsZ2cG0KESmT4t30DAKgvU4NRUQRsVvVvJfK8ToVcpg8CwMaRVyoiaaZow6KLo8iYNv+62BQ6Kas+O+2wRR6sGIbquKRdJ/6W4N1q4CP4Y2dQJHLmlhkye0pOuIheN6beNg4X3nGC6f5A+Fvis+c24JU5q7BljVqBdCRYcc28yeg5LAtBP6/oVoh8APCTMYSKT0HRlnLD8U701CifZ2mMBxWMbWlnVmUaP1j//WVPSyYtJACMNj9JXz7unNtjmsvCsWvSa9RzlN3ItJXYrcNoZHRXx+Syr91oDYT3y6ZomrB5TKq5XULtT2gaXpcf9LhrdH7zsqKxJTMTKaefDtpuR91XX+neaj9pmvLYL3mm1h1W57Xq999HZch4KsaoLE12/vsEuT3nz0f+c8+16728lLBe/sj3f4lqrraQm+BglKCXZSi4vGRe65JswbptjahvCSBg0u8aDQ1V5PpNSGu7HV5GknqvyW1XkVr5Qn/TEy4cgPNvHas897S0vcUq4CPXcXyyOm/vbNre5uN0BDqD3A5CblwPOFhSiQj4wg/odb7wtIBwkJVnB/eIj0hVBoA+TpXK1Ds+XydOdVXeP6T/p+veQ3PkIm4PXdkMTCqhayu05Q6EVnzoH2dk4bHp5pWcYwqGQbBUtTI64snnKAtNRYK/ktBIKYpS+p5a3rlPo4hNwTpmKuLOvwf10nVujdcPgnMu6IZrTlOpakN6kvvipRWH8cM2tQrhD4iwcBTe/rYSvhDxl4TKIpjBb5IVnft6Ma5csFu3LSWBBG5m/e6JUsZdDrhFUUTx3LkQNCIf/XPjlAquDDloaovVVjTbkaMB5wji2x1oUBkkZuIwz3suw87SViRqjOrFQECXiKhfvVqhi08ea27yLoMy8UNM71FnsifB3Zd1N+3VNgPXWxWM++CBgTqGx/J7B+C9+wciIY6B3UrHLO7Rif8NcH31QWZczy5gU1LgHKr61/IuI0003DD92GC95+ktU0mSTb7/m54llFImIxdZPVPQJTe8j2S0alvpLrUVhQ8KYFganJVBwBdEwrXPwzrqPAQP7oRHsq4RwGLbWpUF4U9Q2Q/JvA/+plTkf3YK0rf3VbYfcJXq+lS1SI5nsVTy7JY1B+y8H5T2vCXlaq23fNK9XyDpXyrTwhxtmwfD0W+ttEpxpZ3JSJi5ROdzm+DMwP6tlRgyIU89v8LIliwyZTl+uL63NtSLnc3qg2Vzv8V/7v5GV72VexB7zp+PLpdfjvSLLoK9d2/o30zG1cQ578Drjo0eHa7ibZZcjKYr8VeCNTu73SrQfq/627XUdUyP6pHg7e/UfnitNgdFkUQ8oCbEthS2IBBo+3rQ6iDXTnucGLRsx2mTCetK9vYNhcAL+PwFvac1TVNITFfHlf8+ZJ40j4SC3w8BAFobPAj6yXUaKjSVH9/H8L6jgc4g9xgjxdI28RwtWJrCK18chsfX9szQjb1uxpgU0vsxOnWs7rWsriRL3JGVj+S5K8N7x3YgRvZNwJlj1M8Z1OPYUHy1oEIo6EcS5CbMWAwmu2/0HY8S+GY108z1HgkAEFtq4ZOqgNbR54F2JMIy4CT4AwL8IovkbPU9ogiM6Z+g60O99gzVAqlCGmwFvx++AOnJ7ZsTPlvppshi4uKTyeQfiIE64/UL2FvmxhmjUkwZD4zDQShrgQAEnw+81HdafP/92DN9urKfWRV41YKhbbpP5Orv49eTqkyX5KNfnZeDTd6kn5YPqEN+hZCGFjePx2eovYtiMGiglB3m0vFKygVIcERepGj7uWVolZXXB/SCNROGJsecWKPsKqPDGXIeqQkcEuNYLL9nAD58cBAmDotMm+7E/xbiL5yrPGZ7jUTKkBTkP/20ErBYsrIguI2L43As5dAeXXnBKve1MZJgVCwU3EEn5qL/uBwkZZjPS5XFaiJKDXJZxX7DX7AegT0/wff7Z6BTc1B9SA3WR5zeG5Zm9TknCij/nvjnUkEyBnxU9j4WFvwbj+16wPDZHzw4UMdKuWVqN+TbeVR8sR3dgprfy0Q0iqLoqJYgYhvbdniPB2yqcc3Q19kP49Mn4pq863BF3j/ApHYlVVhpnBuRRJJ6riY1SZm6x8gQ0SYcKI6D4PfrAuse8/RCXclzV4LreZxa6dJcMPZevUCxrDJWcqmpBo9dWmK00Y5E+D3qbxiJysl26UlspjRVbUPwLEOI/vuue28HPK1HXwfiaMLnVn+Ltx9d9+ediAS9Tox6TWinMvlSa/Hw+GUXYX7IbYexILs3iRMaq43JOTMMylPHF+35xUVgPB3YVY1X5qwy+ONSNAWGa1+hRRRFfP3aZp2q8pJ/fY3DhXXgQ+zGkizHZp7uDHI7GPdenhvWdhQAejvbn73w+AV8+0dD9B0liFImdf6QRRicNFS34M+xq9WOZKmSE2tv0l8Ns8/vhuX3DsCdl3RH4SFP2KzVUUPI7yYHh+0Bk56r+zskzHoDceff3e7jtRVyvyVAFjIyPKtfBgA4TpmhbGtoDWJNgCRMMnrVQgAFiID4/Pm6Y2oHd5eHh2vvXuy94QYUlLlh4SiM7JOAfZbI1bdrz8hGagILXwxZ0akP7cDXG+uxJ4w1F0AMzIONjdg7cyYKZxMPx0CIyvQR+UZLkBfGVo5CQhyD8UNiozofEaTrp2UT6WUTNR6iNSXqIvKEoWm46dyuOnqTyPPwFOmr6El8C4JgovbvhRs/6gTynXlNmwSdHqXSYtcLSsUyNnEs3WFslE78PcFm9oboIZ7cjr590e+NNwCKQtW7xDrLW1YG165dOP/WcRg0Pk95n1acKPRakxW65QWtVfI+RwwtKoNPzsPEK4bg8vtPRv7IyH7nfFAAzdKw2jm0NpLgS2xRmV+ULU53bmPONiZD6aAUeNLkXv2++lsAxGUhFE47C4qikJGsmf+loJnRBISRgnlepBVRJefVi2Add5GSkOLyhsMaxh7JDILXC8ZuR7ymAg8AvZZtxpAHv8EI6yAMTCT+5mIgAGt2NgIjT4N10CjDsbr01I+zNGj4BB+e27eIPOc4FN12G9x7NL2BUSqj2tx13MCB6Pfaa8rzls2bEajVs/QoqwPJc4nWBB/kMXQiYZ3Vh7FVAQC22wA0fPcdCq6/XqnUhlOuFWMIcnevP4iK/cdO5OdowOeJUSTsGOHySeoaSa7kXjGpC66crOo/JMaR+5ClKbz0Oemtf2VO7MULgReRNyhDemz+d26qcWHde0SD5vHreykijFprvUhWYT+8r9ev6T4gnfzrnw6bQx+Qf/NGbErItYeaUbytErt+1tt/rXh+g2Hfsanh2zw6Ep1BbgejZ5YdWWGqNd0dkRd20VDdGFvwNiqFUPvkzGUiZ1xYT9IIUmX4wxjL/wURrocvNYEDTRHBoUUfhvfXOxoQg+ognHTnx+0W2TADk5gBy4Dx0XfsIITS6yhr+Mr4Ha8WgQPJsr7kvwR+ioUoGoMMbQXw459q8Pp7KrXYytIY2is8ZbWeUa9djqXhl/pzig67UVHvQ7OLfH7XNONC4EBVeMEn2mZDw9q1YV8HgOmnZ+KJGZEVWqNBVgO2WWi8f/8gXVX7aKFpveStJ2Xbggd3mu53xWndFfVrGYJJn1oG3wgWfLuYHgf5TCzxXoD3faeB1tAXaVtkmjKtUWZ1Xr2ozZ/bif+nYDgES7boRKX85eVw7ST3QMkDD+Dgk08iu3cKOI3gY5o1/Jj9626pv5+hiACUqwHWEWfrkoDRQFEUTr50MKY9NNHwWjDAQxBEiKIImqZgsbPgA4KhiiM014KKkMTJDbjQ109YNSIlwF6TDAjq/jubtmNVxZeG91lYGl/+m7AsREkzZIBfVZenEzOUYC0UW5pPwCdV1wIgCsSOif9QokE6PhmO028Ke76hEDwe0DYbbHl5AIBMDasGACrfekt5LAYCAMvh572JePfFXQCA7v3T4RwloDWrGt7UJjwz/CUAQO/4PrDQFjT467GvZS9EUQTVDr9WPhj+t08YMybsawDQVOsGZ2URn2wz1eO0DFMF/OQko9wzHJbmGyXIrSj+ewe3Mr5989hYzcSKC05Uxwo58XXVqZm4+OQMhcGWk0HWI+u2q9X9SAFnU40L5UVqIioY4JE3mATTAb8x+bLr5wN4+9F12L2erHUZhlJaf7S2X5HapVob9Ouj407thbNvGo2EVCOzrugPow6AGeReXDPkx/XFBd0uVp73S+gfdt+ORGeQ28GwW2m4/eaDzznZ55tuj4ZX5vTFPZd3R5MrNlECjiZBtqwwaAatN+DYPl3QI7N90u7HGpF8xhKk7Jnbe4x7VTTqwxTX/t9RFEVMuXcbpty7LfrORwshEydljSx8cFAg2ctTx3VDQBPkejd+ruzDMBReva0v7IIXJ7q2oKpBpZVZOBoDcuPQM84YkGbMuR3L005XvKKtHA2/RFee/WIhrn2yAF4p6PWbWEfNvSJ8Uom22VD/9demr8n9ezZ3IwblHJmgmDzhtNXf9UjgKyMK6/LiiK8j/TGJc95T9qFowbSHNtjQACaeTJY5d9yhbL/7vHTTREIkpD36CO5x34IdfD5W+CfATpG/seOyeXCcFdluKP4CDQW127GZDDvx94V1LKHpMmkSZd7EW1zbitC6U5/4CaXSaSGrxDM0hUDRRnh/frddugkWG4uENAeGn6pPnPncAXhb/YBIgmFWogrKlGXlnBu8+OTpX3TbUnqqzAyLKKB7gIxd6Tv6oufKk5G8Tx0DXy56AV+W6z1hZTA0hcWzV6K/jwS3TXRsQWBD8vGGbdqkb6xwBV3gXS7Qdjus2SQRmHTiibp9mn//XXlcsK0OX9eO0L3ef1wO0k+lwfgs8H2TCIu0Dipq3Yf42jQs/oj47voEn06vQIZBWVmCM4VoMAjOTFBhknNyYB5OPKtwUzk2fV0Ie7wFQb+geNPLivSOKTcriYRgE/kbBL1+7LSNhb+6Gk2BRpS49LZwjn79wKWHT858+gyxkqwsiZ3991eEmbfwnwmt8HGowObZY8n9mJNO1oG7SmOjG69ZtgWfPadWO/mgAJZjEJdoRcBnHJt2aiqlq//zB+orWlCwmzAJWBo4p/UQ7jozHSxD4epTM7HsruhzaGgC7apHJ4XZMzyEELYXo+lZDr6fiUxbFi7sdglu7XNH6FuPGjqD3A6G3UrD6xPwzPCXkOPoGAGU3C42dE+3oaEltiD3zKyzcVn3K3FyhjFrLEMb5I7qm4DFt/55faBtxYpHB+ONO/sZtud3JZNRcYU3pt7NDkM7ZPHNIPduAEBpG+xyOhIGClQET8QzR6dibWA0AOC4gRlISHQoDgj+naRKKvIBiAIPjqUwyLsfFzSvQ3ZApQVbOQquPXsQJ9llaMHtfB/PJjyNcX1IcGVhKQNduUCiJHs0iSWZKpSTHj4oo+3hxaMEnw+e4mIU3XEH9s6Ygfo1a3D4lVcQqAsvogQAgqcFnh+XQ/SrAbu2knusYO3WDbaePZV+MyYpE2zPERA1VZ3krk2gHEbBHMHtVnp54wcNUrZnjhlu2DcaUrrqx78ivjtaBAesPYeDSeka5l0EdEKnv20nYodj0rVIuv0DWPqdACo+BaI3PCUUAA6/+KLueVAkc2uPOCNzY3i+GtjwNaUAEPX4kTDuXP3cdWBnNYq3Veq2ZfVKgc8dgF3THuIX1fHsuoWnAQB6D1CD3BTBDy5E7Clz42DD58/aPMOwTVZzlVHNxpasrT3cYtzIty3IbQk0465tc9DSXAMmPh7OUaPQ58UXDRXMtPPOU8+3xpyls7t5J+z1+nHtpPQJcGzOQdbvpFrtCrog+oxJkHAtEbKFys+f7EXinHeQMPNVwz5MHGHENG/cGO5r4rhTe6GmrBmtDR6lSisHxRRFKd7mMoXa3eRBDZeDBk8d5m6/E4sKFuiOlzBqFHo/+WTYz5Ox9TujZ/rfDdm9UzDyjHwAwGPfzceiRW9g1dJNUd5F4OW9aPQ3Rt8xRlAUhcW39MGqBUORnqhnbcpsJzPBy4jnGCJMxgcEMBwN1sIi4Aui0lOh2IPWlDXpVLr3b6nAe/N/ROKOYvTzNWH7WvL3TvB6QFEULp/UBZzXh8WzzdkYMlIy9QUkZ7JxjfTq7atQX2FyzwPgAzy2rlWvtcweyZjxlMpQcNQmw8bYManLqejjPHbxRmeQ28GwcTQ8fgFllTxu6PXPDjuumapsOCRbUnBS+gQwVHhhiGw7WWTa6NiVYv8qsHA0MlOMAYyWprFsdaXh9aN2Pn2PR/yljxzxcea9rVrb3PTcvnYd48Zn92J7cfsXYFyI16ltzNSw++495MZlEzNQefZzsHQfCM5uhShVAPiKQjTMPwuNT5wP14onwdAUhrNk8p7k2gwAOKXlN1g4GrUrSHUh5/bbAQCZV18NNiUF/MGtAAAxQBYkdisNj59XKMoAsOBd8puZSeTLlX0zyIIh8uIk67rrlNcavv1W5zNb9fbbaN6wAQejLCianrkM3p/fRaBQzcjK962VO3ZDLW2zgXU6FY9b0e+BAAu8BzU0ftZqKhwTrhJB22Jb9GZIFhzJkyfr/AQBYIV/Avjr3zJ7mzksf7+xqRN/HmSfZbG1HsFKdbEV2uMJAMmnnKJ7zktB7h397jHsq+3g8K4j1UBEEV1qCw7srsaPH+gryxX76/HLp7thHXkOuHySSBQ19oNWOxlnR5waRpRIgi/JfEHaHGjWPV+7XM8eygp6cGLv2O8/UQTqvyX9v5QjAVRceMVpAFhXvRZ3bZ0DAFh+gPymAZ8btMUCiqYVNolzLNF8aDhjsE793l9m3pK0v7UIvgQy/8mqrhSgE3o+7ClTHidPnozu99yDzKuvNj1ewM/D3Uw+t+iPclA0Aya1m2E/OUAuf9UYAAOA3WlF3zHkfTt/LIXg94OOi1PU7EVBQOmjj+qC5HcWkSBuzQlHrjPgajq2Xu0djZ5DMzHiNJKASv1sKBwHMlCyvSrKuwjeOfAW7ttxZ8yfJYoiiloir796ZJnfGxfACsfFAAAgAElEQVSelIHFt/RBSgKHaZNjZ0j6XKQVsaWeJO2DAR4MS6OpxoU1y7bg31sew4I9j+Ljsvfx4cKfwx4nP9CCDV/sBQAUbjys9PYX/EbYXOHoxOfMGq0oOkcCHxDw3vwfTV+rLGnAwd1qAeOcm8cY1KEDwrEXQesMcjsY8h/1hU8PYctOCq7yPOU1NkYKkBm0VMGnbow8scWCTFtkO5C/I7SBxCc/60WE3v62Ei9+duiofTbXayQSb17WrveKohjRxywS3lpTiTkvFWLtlgY8/FYJDlR5cffS/dHfGAapZ52FfE2Vw3rcmcrjOA2FFAD2l3vw9cZ69B/SW+l9C6QZKS6BPT8hPZHDKFpv83Nm6y+wsrSyirTn56P/smVInjQJvZ/S9GFK6p5OO4tWD4+1W83pVzKVWRZcc9rDUwpl/9fUc89Ft1tvReLxKu0u1DNXOX5lbIkT14onFcuer34j1d+jEeSKoqgoeu6ZPh1BSRlbDATQun073AUFEINBtHy8ELU/7MbBhQuV96bcukR3LFtP0usuBINgk5PR/a67AAC9Fi5E76eeivmcUqdMQf9ly5B51VUAgHfvG6C89vq/+qFHZuwL57gpN8Mx5eaY9+9EJ2T4fv1IedxNEpbTou5LfW9qpi0rrIe9qVh+G/pxzXDZfeMx8+kzYIvj0Ps4Y59+QpoDdeUt+P2rIsRdeD8oRxLiLp1n2E+mGCZnmtNofYnmQW6DX9+vGSpukygEMNQXufrVUq+yjbY3jULV8uUAgIotHGznPRz2fb/WrseHZe/Cxbtw0HVA8c/07y4EZdFXxhaM2owXZ3NoaG5F+eoflO3FzeYuFZfkXIGSM8ki/NfPC3B13rWYlHYanOWqWJAIoo4MAJlXXYW4fv2QPMmcmrnxK32wIwcLbYWnxQe/JKB0aF8dyp56CoLLBTEYRP3XX6PgWtLXfOgl0kfspdRxsiq9bS1QvBhEa7AVVruaiHnz/u/add7HAs21bribzYNw+bocfHKeqeLvO4+tw8E9NYbtWrQEmyO+HorGQAOe2fckHt/9WJveBxDGlhwA98iyo6QytuRCeg7RHpHtevgAoSszHI3aQ81I20XW/D8cNA8wzVBf0YK3HiB/d7mav+Vb8n+o9kpOP3Pa+8ATVTZWJI9ivyeAvRv1bDxWoiqfM0vtV9danB4rdAa5Rwn+oIBFH5ahasMUAMAdfe85Il8orSVLqFhMe9E/YSAGJxmz3H9nhOs9WP5dFVb+Vge3SX9DR6G9glNnzt2Oix9VM/kfP0xooiUxUJY3FjRj7yE3nvzgIH7bow7m329tgMvLo7xOT8tqcQeVYNAMFMOAjTcumNheI8H3GIv95R4crFYH7tNH6hcbzb9vAptrvKaEmgNoqTH2U5c+cB/ce0nmUVstbHn9FuVx08szwDdUIN5Go8XNI2DCanBYaQSCIgRBRLOL/I051ji8Bct2QXCptHAuORnO4cNBMQzyHn4YANC6davhfQAQN9hI/dOCyQ5/f7fH7y4aXLt2oXDOHEU5Vg5yhUAAXWfNAgAUXH89qvenwe9RF499XnoJXJK+0pJ7zz3ocuWVaFy7FsGGBnBphC5sycgAZ2LrESuSNB682W3s6bUMnADr8Cnt/uxO/P+E7aQrwXYfhIb5ZyFQtBEUy6LvK68Y9vOUloL3kDH2gm6XKEJFoUhxGpPTR+pEkJLpBMMxyOmfrtBhtZCrkJtXF4GiGSTNeRuUjYyfx5+vznGCz4dL7jkJk69SxSPzvX8oj5OKc/BQtycMx19Y8G/d89xBXdBjSBfkDcpQgqNo1jNaQaAKG1nIVrzxBgDAWxY+Ibj8wDLl8RMF83DzCwFM/C6I4NZdCo03FOU7hmBD3BR4Skux7fsS030AYHTqGPA2ct47fijFmNRxCFbog6Pvqr6J2bO1WfJlnXgloTprK96zNs/QUb8Tx48Hl5FhOIYcHDhTVH2LRheZm8RgENXvv48gWKyPOxvrnESYx+NQj5NcmKtUoqP5LgPAewfext3bboMt3oLL5hLRSq3v6dGAHFi3B+/MW4fPnt9g2hdfVkD6TOX7jXfq1zON1S58tvZrrKs2BvGLChZg/u5HEBRIklwQhbB+0TI8vBv37yBuFmWeg/ji8KcR9w/4gmF7nu1Wdf0xul+C6T4yDu1TW6EO7KrG4cI60AwFXtIa4bkAEkq6ot97Z4Y7RFhoad2bvibsruqD6hoop1/41qCTL1XXPOs/3qU8Dh2z3n7sBxRs0CeAREqEh/cgp18askeRNSVlIkx6tNEZ5B4lKBkcgcFd/e5Dj/heRzwxnjMu9YjVXrW4OX8Opve4LvqOfyPIyYDe2eYVoxb3kQe5JRUeTLl3GwoOxiYq0BZMGpYMh6TC988YKMvhLqmF7x/ERY/sxHWLCrCzRJ18LnlsF25bXNimc0qeuxLOSx/BlfN34+YX9mHmM3vR2Eomi4vG6yd1a06OrvoLAEEfg4Z160yP7ddUTbX3B1+tWcgEfWh++Xr04vdixS+1uoQPQLxnLRxRXnZJomMv3WIMOEVRQMt/70LTc1co2xy91N5Qe14eLNnm6sdsSgpcO3ZEXGTItOpjBbl3tn71agBAieTz6y8vh83Es1aGTNHWgrZYwDjURRjtiCw41olO/FVBORIgSDZCrR88TLZp1HSTJ08GAJQ+/DAq3yRUWZqiwYVhWqUlcli1QErcsXKipv1zuWvXLuyZPh2B2lpwVhY/vLfDsI9Mbe2aryaYvn93u+41MRjE3pkzES82K+JIANA1sB8TWj5EjyGkevnRvF+xcOizoHj9OWvHMm+rH9n5qThz5iicdAlJssYn2SJWb1iNOrUzlYwpjT+Qaqs8fhS27IUQg1/uwN3kcxqleUIURbhb1PE0AAtEioG/shLrPyGMIDtrDFjsjH7cEgRRJ6hj9duRYc1QPHZDUVnSoKN0WqwkGO4/Vj+ear+Th/cgKATx9c40HKo3JvICkoCY3akmGjfFnUYs9yT85JwKv6Z1bCs9Wnmcvr0veq0g+irbm8wTsFr8Uvcz0rf0Q0NDMywSrb2pxnytUtC8x7RHu634rmoN7t52W7veK/AimltacduWWco2URTRXOvGylf0Pc5Mi/H3ZQtS8GHZe4btJa5iHPYcQrGLMNu+qVyFOVsitxDuad6je/515VcR99++rtQgBidDa+XzyDU9Ih5HC/k7a4XnAnEe5PxgtMoKh35jVEq9Ga1b62MbybcZACZfNRSclcGOH9V2uldvW6Xbx9NiXPu8feAt3LH1FszaPAOpUwBwwp9Cm+8Mco8BcuPyOuQ4/zy3myIT3glzOKTsWVG5B7yJr2ezOzbxrkj45/Mk+PxUokRPuXcbthSZ08KiwRuixH3npXqxnvrmQMTqc+Hh6NXej3/S03mKK9o30GjFnR5+sxQ0pc9Wpp5zDiwZGZDTznyQQuW+dNQeSEHNV+t0x6LZticbRhW9hANVXjz/6SGMG5CAL+aR7HpVgx8WloI/KMLtI+eYmWK08fL9pmZl+y9bhrxrT0XLqzeg9SOVlpSlUWDNueMOOPr3R5dp05BxIVFv9R0KT1cTPa2Iv1ylFDbMPwun9Qrv1XukEEzEU5RzCdNbGwnaQMCsmt9eaH2SO9GJow3a5gRfFdKyIfXg23PSkXq26t3qKdZ7QkeCKIqAXG06goR16zZSDWz65RdYbKxSFTnjelUteOw5RJjlcGGdUlGtLydzjD2ejG1lzz4LgCS3LFL19bLreoACCcEz9n+rHO/N29ZhwH/PQ6/PVFpumfsgvGVlEAUBrQ0eOJPtaPDX4zMHCfxLdlThqwcISyTQ0KAoAsuw2Dkcd2ovdOuTioNFLQhCrY4efuEFAMCz+xbh88OfRP1NGph0eKg4OPoROmPR5nIsm0vOf+Ay1ZWCTVQt5ZIEUv266fkzcdNzamL1pRFLFU/iQ3tr4XWpFeluRYPxa916dLn0UmRdf72y/YCrFMsf/R6fPP0Llt65Gk2BRnz81HoU/HYIYyWhsMEn5yn7HyyqRNcfyd/rjq234IOyd+ANMNhtH2tIhPq9QcQl2UBRFKY9rIqBllgGmv4WjmFGkT9bI/neDia2imzGtn6ggyx2ubeh7OTfkZxlfF9LoAVrq9YAQNQKZzRUeGKzmAkHPiDoKrnLH/4eyx/5Honp5snW4ikqbbc1uzqmz/ii/LOIrweEAF4vNjI+IsHvDf+7hROb5MWg4RqxOjiMO19P5dUmrhi/Oof646KvKWQGghaDTlKV1n/5TA3m5URIOPQd3c1U4dnV5AUf4NFYra/g9xqehfhkOzbUrVe27W8tAgI03g3Tz3s00RnkHgWYLbA7cWxAURSenEmq3UXlagDYRfIulqmsHYEfd6iUj6fa4c0rCKLyvgXX98TS21XFuRMGkUntygW7seCdA6bvjxUj+hCam3ZgjZShlxE0SRLI2HvIDStHg9FkyQPV1WjZvBlI6Ql60FTUFIenwaTn6XvCMi65RHns3aT2yznOnqM8bhDVBU6TK6goGaY4WVQ3BjBz4VZU7C9B/+4OpRquhWftf3TPRT+5PgL7VKEoWy6ZCJInTUL8oEHoftstcOZnwN6b9MSUPPCA6fcRBR5iax3YbgOQeJuaVZ5ebeyjiwUiH4R3n7HCI9sbAUDd558bXpfBpaWZ9iJGQtDEVqMjoPVJ7kQnjjZEUQBfvle3rfFpMr7YsQ8UNBWS6lrw1aURj+ff9QMa5p8F1ycL4K5jIYoAnZQZ+RwEwWBTJEO2m6n55BNwVlZZzNIaoTaKojB4PBmL3rh3jelxXJrjN3y1EmNbVyKwVx0zhGpj4GFrTMCNNKFj/ljxLUoeeAA7rr0Rpbuq4Uyx4/vq73DAUwLX8WTOOdiciPcf/wlFt92G/XfqxXv2b6lAU40LY88j85ab1rejVL37Liw+ET8cVK3agq2tuPmFAPq9OwUDl50Pe4MdjUwqtjomYEP8mci64QYAwPpvyHfTBrgA4GtSF9SBpCzltwq1P5G9Pt1NXnhdAfQZJQlt/k6CX0v3HMWiqCnQhIUF/0ZzjRQYiMCSJ1agqpT0JNeUkXleFj5aPHslvnp+C5KK1epuYYt6vVW89pruXPzeICw2MgbuEregNZMEZQet/XXVXBlp0/5B/g/oexy7WDPRFGhU6Leh+PzF37B49kp0Ke+pbNvj3Ql3RgMaKlz4+Kn1WKdhDdyz/XbsaibPyz3t6zV+o2Qptjb8ATdPfrtowW5LvQfNdW4Ubi5HiasYP1auAwD4bWQubgo0KvsBhGEweHwuGv2N2Nao0uO9aepcFV+egYHLzsfi2SuJcrUowh0Mz7KTReZCsb819oSXDFscWVfK66ni1v34966HyWtSAUBbCACAW/64CT/VrFOeN1a74HMHwLL6NUtqdgIuuP14MIkCsjeo7Qj+hFbsmk4C9r4nZSkMgZnPTEHf0eQ6P/OGkeg/Ts8+0DIvvFLibNrDExUqfih8vA9P7JmH1mAraJPWrzfv/w5vPbgW7zz2A9JzEuBMsWPKjBE4/drjcNnDevsvj3R9BLxBLL3j62Na0e0Mco8CKuvVzOFF49vXp9mJ9kPuodqwh0xOgiCisTWIgXlxberJXbO5XicI1eIO4twHtuv2ufgRMhnXNQcx/53SqMfkBRGNrQE0tATw1Idl+HknOcdhvZzophGYuFtT0T1UY16x2xOGLr04hKrrD4hw+3hU1KnXZW1zwNRbVjlPXsQ592+HJ8Lv5bDpB2XZAmH/3feh/BNzBcCU4dLCJHTk0dgweb55GQDADTgZbBZZQLHdBiKTrlX2keP1t+8dgOX3EnGj+x2vI/eb2xGnOS9RFCC41V5l2zjS8+T66nn4fjf228jCJwljCF3M/d3raH37XnBJ4f2ZAUBwNaFyXzrchfvhLa/WidXccpoxGy0Gg0a7Js2xSm+dhpL5T8G/X+2vE7xe7Js1S0lWaC2N0s4/H/Z8YrHg6NsXtNUK54gRSD9TnWySJ01C70UaQa8QOMeMCfvakeD+aXl4YXb+UTl2JzoRCjZHrZBR8SkQBR7wuZHZpwYWexCt/70d8WlqsNT82izwtarirtBaDzHgQ8vbc+HbsgquFUSwzV+wHs3VTrCn3A/raNXORkagrg6eEtJm4S0tRVmYe03Q9J2WF9YqC/pQj8k+o1XKYajFUChqPv4YdtGN+lUqjZAVzatMP/1nL/q8dwbwNJknf3aeB4hA4I/1iH+PVFqaKZXiqLUrWfH7K5i1eYZCd3T2rUNwIwnCd9nHAQB2DCZjef3q1bhhSRA3LJF6Iv1+FN58M4JgwfgI7bTnitOxxaFWl9/5z2/4tuobuCvMAxFXg1qhOu/W48N6eR53GklKrn17O9Yu3wZbnKZaJQC7ivZh2/clcDV58WvtzwhxXkJclZqgHTqRUE2tccbiBS0tob0FaiKvaT2pYPFuNwS/HwXf7lB+r+UHlqF+nFrhipt8luGYTY3ku+f692B0Hhnju+QlocpXiTdKluLWLTcZ3lPpqcChAyR4TvtGDVo2NmxA0EGur6rSRuxeb0zGxx/KwJIvlmNVhZpc3n/gIBbPXmmgsrqavLrK+Kb637G0+GW0SGrdr+4372sHAJ4X8N+H1mL5w99jzbIteGHDi9j5b7KGYT1WJBbl4L4td+OdeeuU9/g8QdSUNWNt9Ros2b8Yh04kzgwiY56Af+vBtXj5lq/wTcVqZdt1PWfq9vHy6npKm/jfWK8mux8ZNB9dNg4E44tc5ZSTKx43Cdqe2vs4yr0kOSHTleVCQFlBDQ5WkdfeL3sHgijg02d/xTuPke+rrdweP5X03Wf2SEZlrr69zNqorkV+bvwB8Ulk3ciwtHI+FjuHiVcMwYxFp8ORQO614yR/bu13Tkh1KErtoShs3YuD7gO4e9ttEDQ9uHKfN6D27deUNeOM60egxxCS/Ctu1TNpdjZth6UXua4DPv6YCqF1BrlHAZnJ6mDYUSJRnYgdyfFkwumZZce/3y7FWfdthy9Acqbz3zmAPwpbIgZ4Mp7+qAwPvKFaUVQ1+hXRoxvPIRnhVq8aBP60o0mpfu4+4MKlj+3EY8tLdcf8+KcaXP7v3bhi/u6wKsGAXjQpKIhY+Zsa4BVXeFDd6MftL5PM4wcPDMTTN/XGzed3xYShSeiRZceqBUNx0fh0pCVyWPpVOS58eCeue6pA8W+75ok9OO/BHWjxmC8mvNLv4/YJ4AURfxSqdOz0RDIo1jXrF1Gy2JEZ2ORkZM2YgS63LkCPh43VUNpqhdDaANdXzyvb4s+/C3RaDuIveRiWYacCACYOTcQ060pckUj6YFISOPg2fYF3nHPRiyHZaKuGJuTf+T2anr0cQlM1KEcirKPI4tS/VZ0EASCwfxNEgUewlPQ8ud69C6LfA/8WsmhsfOpisFbyfQON9RBFEb6tq+Hd9AWCDRUIVJJF8sGFC3HgscdQvV9dJE10Fug+q3nTJhRcfz3Kly7VbT/86qsINjWh6bkr4G0lE1f580/At4N4DssiOc2//YbGn34CACRNmED+P+kk5M6di36vvYbce+8FQAJ8ep8ayKdNnaoISpmBkbyDc25rX29VOHRJtqB3dmePbyeODZhEohPAdh8MsbUejY+fq3tdbK1HfIoHaVIAUbkvHc1LboQoqbg3PX8VXCufQ+OWfWj+7GX43WS8E4JkXKn+6BNQmixd04YN2DN9Oor+9S+UPkKs5Fp3kAqZrJYrw19djUpJnMnRty96M+r8Igoigi0tKF+6FIG6OvAlqibD16+Rxf2JF/RHoK4OoiCE1Q8AiAiSTfRgQsuHusWzDM5rQzOjF5Rr/PQD5Owk43zPvfqFL9e9B/bYRuLwuyRp/+7/tXfn0U2V6QPHv0ma7qE7baELtEAtO7SCyCY7siuCjoogm4ggzMjI8gMZBRRcBlRwY5RlXMcRioAIIzAoDAiIIFsBWQu0dN9omzbJ/f1x26QhocVhoCU+n3M4h+QmN29vcu9zn3ddoI699fnwn+StV8ctlmh9UIADbRxvKw9m7uNEeSttru76Ff9F5y18t8t5Bamft4mTybYkV+/r7XQtTwC9u30FbOppW6yN2tqBH946za41x1g1eyupxak0W6W2GF87QWBJwmXc66mxUKdzcrtcqgUFInfYxtAWh8SQcfoCJydO5NSUKaTv+gkviy1+jlxti7nHU73ZXj7ZlMlL/duS3tytPvbREBGm4+GZXSgtMeF7MRTDebWSuMhk3x115bm/URB8nW671zQWZxjTyTTaup5Hf3cv9Xe15ejfsvnXqp8pMhWx+XX192sssb8/WDV7Kx/N+BepZ7IxFpehLU8Cr5qvWvd95Xyu07kr3p9qP45TX2CLCTqTnoidCTT9+yByr9hX3vd4ohVGs5pE5jVKoev8KF5s/jJpdx8mvZV9bK1w+VU1sVva9gPaBiQSfKgJXunq8oiXylutPzi9jEkH1N+kyWJiT9Z/rO/xI4Dgo42J3N/2usvuWMwWigvUJG/byX/bb1Ms1hZcD72W7NQC1i/by/drbY0k2cYsUk/berQdDfoRgKLgbJb7vUKhqZADOfvxuWx/vuiLvViWsJz0VskoTfMIbRCA3tMNs2KiebcoOj+kVvIdzz/G1CNPM2pBTya+3d+azF47RveZn8ax9NQSLIqFFWeX89cT6kR1+7P3Ov27ywKcN67oyu9ZC8ryefvUXx22e/jbn5OV19S9lW57P7KzZ88yY8YMcnNz8ff3Z9GiRTQo775TwWw2M3/+fH744Qc0Gg3jx49n2LBht7uo/7V3p8bxwFz1IlHRVVTcPl4eOvreHUheocnaUgpw5Jx6cv7fR+rJtemVVmTkluLrpUOr1dgt82Iur8FMTimitMyCu17Lht3qTVF8lDe9EwJ5b71j15z0nFLqBXuwbN1F8ovM/OdoHgXFJgzlY6ZWfJvq8J6745z/Rkb1CWPl5jRKSi0sTbpEi4a+RNX15Jm37CekMni7ER/lRnyUD/3b25KYMffXY8tP9t2Ci68ZAzz8paN46LV8MqspbjoNQ144zDcvt7SOFX78FXWSjwCDWv6Vz8dzMaOE2SscZ7j0uuY8rtBo8WK79Xc9G8Ry7UhirZcXeW89bn1cZ6x6c6jRaNA3uhtTilqOcWeeAXfgHMBIAIz77Mfa7Kr0nZceU1sm8pY9CRotGq9rFjwf+QYFq56j8Iu51ueCG2px01vIff0hu9cGR+eSdjKEX6f+ibo9WqBN2Ya5TEvG2a8c/mbFosH/+SRyXx3isB7tpfIlmvJ376b+U2ots7moiPzduyn66Qe8/Gwt+sV5Xpx5YzXhnb6hsFjtvni50kyxHhe/pP6YydYZkBWdjqvfvIV3z3EYD25BowX/enmEvfRltTOKaj08iF+5ssrXCNflSrE5YNZGLHnp6nl/HW7utmuhYoGTT43FYoLQRlB8cCeFmYEUltctunuVWmcoL8vIIG31agLvvx99UJDd+WjdX3lrbcG+fVx6/318W7QgbfVqLCW2bnpFJ06g5QSUJznhQSZOlQ8xqGgNNHj3oEBnm8HevPo1flWKCerfn9LLlwkaOJCs9esdPr/e6NHkff89GsBYvnRN+8JNHKw7CGORrXKyIsECWy406e0yLJzmgHe09bO35CSCHjBBj281VLQr6xX7GZj3NmlBoeE4H45xY8yHtuTAY5ot2ddc22x6jajt9j1K+oxpS2zrcP4x7XNOnFGvYZ0K1qHRVz3TbN+xCdbKgdg24fR7KpFVs7diuBRq97pTP10iAjV57DQziu8XqF21gwab+T5gLxtT9TwaPcLaapvT6DwBv6rXYn2hN0HHY+z2t6ckgT1LDpOgDaBOcQ6phhh8zbkYiwqIO67+5nzMeVzV+XE2xXYsTj70HYHHYwjbr078ZSjKRuvpiYe3npy0QqLT1JbyY4+vZ/q+55hVdwFrl+zmrvYRpMRfoG6AD0a/AoKPNsbNQ8ehPzjGJYBFW9+gODQbw7l6PNptKDtRK2i904M4lX6ZfTl78EftSWa8Wopn+fqplScXWrtYTcTj6U9JQB6XOh3AXecLWoWvVu4itn1dej+WyLJfl3A8/xhTmkxzKIe+SI1zZn0ZujLH1kStVoPFovB14Zd24zvr6P0I9gihT7+OfHp+NXUPqWNZ8yNTqZNiWxqzS3A3NBoNZaVmQn9uSlCmgbOaE2w9sp/jpkzS71ErBUrMJexI34Znpj9TOk7ip82/snejeo/lcyqc5dM2Ywj0ot1z9Vj5/ae8MuAlcrIL+Gzu99ZW0szsbIrNxWDRgFbhSkkaoR7lv6nmfta1ZfOPWmh2dAhn+u9g6ye2ScRyGp/jaOpBEia151iemlxWTOTVkK52x6ViOoDugxO4VHSRf7i/BVFaVp+9wP6cvbzV5T3Mipl/panDBPLL8nHXuuOp88TL4E5+oVrhEt3Mljwfzz/KC4dnklOm3i+aFTP7sn+0bg9+rJQmgY1ZnvcmR48m0Qz7YQQAPuUtyjN+ec763Ntt32dHxnb+mfI5od31mE/piG0TzuEd5/jP2uO07h7jsJ//tdue5M6dO5dHH32UwYMHs27dOl544QVWr15t95r169dz4cIFtmzZQm5uLkOGDKFDhw5ERDguwl0bebpr+fPwKF77xwXcnfRlF7fezsN5fLvPPsF7/49xPLXYNnZm3F+T7boCLxoXS3j5eOrPt9tqRdfuyqBDUz82788msYmBeU+qJ+bQziF89UMGnZr7WZPpMW8k8/W8FhjLbMFr049ZeLrr6NDMcRr5/u2DmDTE+e/64ftCCfDVs/grNQg9tfhEtVPRX+vTWc0Y8H/2Xaw3LmhJ/0rPGcssPPTiEYZ3VVtA+s36hcb17WvIcwpMuLtpCA1wJ+9q1ZMa6QwGfFu2JOyJJ1DKytDdwCRGOl8fKqff2pBou+3aYMfZgnMWDq9r4fsAABr0SURBVKLO6Ldwi2pBaa6tdjJYX0TOy/3RN26H6bRt+nwUCxqdG96DnqPs+E4UYxHawPoO+3XT21cEuMUm2u8HSN96mNDGkHHWcXkdz4YNKTl7lst/+5D8kyHoL3xM1HNBuDdu5/Da83OmUsfrFGkn1YBjKnVzutRS6s5MINPheY0W3L1sN61lJ3dTenCzXUu1p2/pDS+ZIX6/XC02ayot6eY94I+4x3fCfOUsBavVG26fh+bAouVgNlNmdKNiqGPOJT+7JbcAh8c527aRs22b089N/+oru3V483fvJn/37irLGmC6QsYnHzs836roe3YabDeUuvLJebI2bgQgqE8fa5Lb6PXX+XWaLZmIW76cE+PG0So8l31nfPFWCrn3yqd2iW2FzgVrMemgYj5ALQqJRVs50LcRebvsJ0JKS1N7w7S9utWaGJe02Inn4U7kXK1P09V3cXRkEslxGkIyFNxz/bmsb4iHUkyD0mRKNR6ElKXgZbnKBQ/bZDuB0V+RfX6o9XHFuMOJrdUuvQXFWmvfQz2l1a5W4R9q60XXoks0eg/n18CI7xMByIo/zbuXkmCU/fY9WbvYk7WLriHdeGReP+b8msTlTj/TbOUQGn1t6y6dG5OCT1oQ+iK1hfInn550LlAn3fKyFHJm4mR6lb/27qItfNG1KaEHbF3rH4wexpaLtol59GbQuLnhU8d+RuGmHw8EYEd9tSEl+ceLRF3sgFuxB/nRasV71F0hHKr0njP9vkdj1tJwcydiNnWhzLsIfZE3O/+dwrX8T9uGSn3y0r8Z/dceaM32s+tW5pnjR+z6bnbPpZzIYMXHa7hCCc32DmFlpy+IIMHuNfV2t8HkXsqJR7/haWbw/Upbq2xms1/JapeM2WRGybKvFPHXq8vfmctP2DpBXlzVFJITdxadUU9RWBYhv8SR9msORMPq2eoEZlkpBdShHsoFuEQuRg8PfPyCedP9DQwftCaW+9iwwfk4+oLsYpLWbiVyf3s2mHZwYava/bwo30iZdzG5R/KZ4f48zb4czJn7v2fxpWXM7Psc3p45tKvnzc/eervKpZiNXcnCVkF0+d7yHmSafIeu2Bd67OGhyxO494F48jOLrLVRRnMJOzN3gA7QmdmfoybHzx6YYPf+meVJ57KE5fgGe7Dxpy0E0IDYYQZ+LTiJBg0KijXBBfixvFW7uV9LjuT9wg79N+wowHr+nR643fqdH390Aw94Pcai0y8x7a6Zdp+t1WjpFNyFf6Z8zmXzRUYtGENRgZHDO845Pc63wm2988nKyuLYsWOsKO+uM2DAAObNm0d2djaBgbbaym+++YZhw4ah1WoJDAykZ8+efPvtt4ytNBtebde9TQDd2wRU/0JxSzSJ9OLAKfVCNKhDMHuT84mq60lYgDtpOerF5dqxrtOXn3bYD8DKzWms3KzWXesqTQ4ytl89xvarh9mssO9kPi+uPqd+3hw1+LwwogEv/f0cK8rfWzEbcwXrshRVCA+yv7nam2y/sHmvhKp/YzqthtmPRdMk0pvkC0UUGy1otRrax9fhzOViMvJsF95/7LAl9s5mbTaUTyB0veWZAOqNH49ndDQe9cuTRw/na6MaRr6BZcVzaDRqN8Cyr2eoL08YgPGnDQ43MFovA76PzKPw8zlo/UOx5F4Bi5n8v9m6SOv7P0fZxjd4y1Od7KnsVHl3G50bmCu1KjTvjkdz281JnQnLKd72EWUnHW9EvbqPxvOeoZiunEHr6UvhS89QmKXePF05Zd+NyD3IQNScebj5+pI8diz5e9QxPmUlek4veAd4x/pan8CrXM32oSgllyKcd99r8v57nHxqgtNtAIYQ9fddvOU9tN5+XE1yXA/TZ+hs9DGOs3UKUZkrxmaNRoPflE/Ie/MxdEERaPSe6Orbkir3JvcQ/+E9HB81iuwU23W0IqF1CwzAg4sU53lhMd94ZXVF0qkzGDAXOJ91P2bhQs7MUK95EaUn8TdnUHTCvmeQR2QkpKTga86hUKeWT4t9BaPO1xdDQgKlmZnog4OJnjkTc5HalVVbPlu656F/Ee1u+7ujjMlkuYVxVWdbK9sNE1wz/cIH490o9ThP13+HkKl3rMTwsaix6GBrLWdb5hJ/GDzy1Qq6ZquGkAqkAlQasXXWw7bu5n0FXxIQG45y8hDBT03ibV0Svim7id7awekxw8sARgvRxuPOt18jMMxA10dacFf7CGtXyujmdTl/JJ1fB23DGJhvndgqtf0vZMerPbxa+LXicN4hh/3tyNjOjozt1iQjpKk3GcfUY+0RqeA/oJTgNWFkVpoD4gfDgwDcVWK/FM4vM7uQlbvXmuRGjzTRPbQ/Je1LOHzyCgOS1eEo5rw8NFoNw57vRJ0gbz6cvsW6j+xLtnHlFa3T/uHeGIGwGNvveWb8C7zCSyxs8QZ/36zutyIRr8xYp8D6/VX20Z9++/jJ0lwF9noSjjo+OGKnmuDmxl7A/3QUWfGnCToei1upeq7Ft21AdNO6zN4/C0WjYPYyquOkr5k/sr5XBAa9WtF/b3BnGhviCIgPodh8lSXn93AuQu3qHvJLHPqkBqxP/hHjdYZkhf+o3n9ZdM7nHWnYMtSua69hfwMAa4JbQdEo+J+OslYOxGxSx6x+9q/d9AD2rFRbhVO67rXr1g6Q/MhGNBat9TeVXKD+todFPmJdFsnsZaRL+bJegeG27yfDeGOzSld45qdxNDs7hADUv+Ptc45dihv7NuFU4Uk+Oa9Wbj7daDKvJy/k7FX7e+OSoPL5brQWLO4mvjKvAjP8+dAUAF5r9SaW8mYLvVZPkHsw99VV77m8Deo9YUKfRr+p/P+t29rMmJqaSmhoKLryNcp0Oh1169YlNTXV4XX1Ko03CQ8PJy2t6okXhKjsxZG2bhBPD6rPiufVgfwf/fkuNixoaR1TCzDr0WjrbMaVPTcskoTGtovK6L7hzHmsgcPrdDoN98T7OSStMeFevDPFNglURXJdP8id2Y877scZZwllv3a21sMRvaqe5ROgY3N/Qvzc6dzCn96J6g3rX55oyOoZTVk9Pf6GygGwZKI6eVDFuKU2jRxbaP3uvdeW4FZB6xeKVqt2vdGVt5y6RTbDs+PDeA9y7NoEoI9pC4B332fwn2E/s7DhySX4tOzm8B7Prk8QMH1dlWXRBdbD96HZ+D37MR5t++EW2RzDk0sImLURz3vUlgW30Bi0fnUJmzSLwK7269UFxWkJbZRBzOtvoff3R+PmRvg4de1B/y5dHD4PwNu/mIBG9pffkJgsQjo2pNHLL3DXihXoPDyJ++ADu+2hjTLwDbpKaKMMfAKKwUO9Wamc4Faekdo9rgMava37sxDOuGps1vr44z9zPW7lya1Go8Hv2b/j96cvnL4+KFptzWgwZw6N3vgrhuAigqOzCYnJwhBSQINZ6szEFRPUAURMnaouNxYXZ7eveuWJv3d8PHGVxt9HTJ2KR5jtut3YeIgQk5rg6uvWJWbhQgDCx6hr2EeU2mZ91Vbq7xL1/PPq9smTiSkfC+wdF4ehja1Sy7dNG9wwEVN6BP9u3Qh58EFiSw/TruhfNGpkXwFZeE9jGn74Hg1ffJHsV0ZS6qFe533C99Ct4EseX9SF+Ha27qBu5Qn3w1P+hkVvIqWLfSJXlU4tjGiADn8aRtvpY7irndpTpzDyCvGj/K0TDD1Q3zZkpHG7SOpaLtOw1HlrmzPNOkZZE1yA2Fbqcf+/LrYWJ7/2MO3hp3kgYhjxdZoxLtY2sdOSNraKycpeb/0WD4ztwqgFPQGICApnYuNnuXeIGk8jS+3Hip5uYp9IPRI3mrfbvYuxy494WQrol6COG+9XfwDTZ4zGI0iN06XpaiITEumHh7f+uhNtVRg2rA/jXu9Dq24NSQxUE6oI70iWJSzH4F6HpxbfT8eh6kSN9RsHodVquH+82pJ9X49Eei1oxJOv9mDotI6kjnI+NrrC2b4/cK6X4xqxpwf82+nrC/uf5FLnAxwdmURae7UhwNNHz7KE5eg0OgxeBp5p/Qxz736RwfUftL5vRvwcXm/9FomB7RgaYVuFwU3rRrhXPTy99QQY/BkXY/vecmPUFuqUZLX3U69R16/o1ZodV2Lw8fPg/nGJPLXkfgrDnSeTJg+1keRK4o39HotCs8hsdgqvMB0JcwM5028HZs8yTN7qfir/zffV7cGyhOW81HwhTzd61un+/hA1wvr/kQ3GMDRiOEPqD7V7TYB7oN3jgvqOa+ZWNqXJNMbEqBXr94erS6092XAszerYKqfGxExgadsP6LWgMccfd1zhYXD9B/F288bXzXZ/+FKLV2jgY1sr+Ok3+9F+QJzDe28FjeJshPgtcuTIEaZPn87G8q42AP369eO1116jWTNbt42BAweyYMECWrZUa4GWL1/OlStXmD17drWfcfHiRXr06MHWrVtrZRcqIYQQdxZXjysSm4UQQtxpqosrt7UlNzw8nCtXrmA2q7VaZrOZ9PR0wsPDHV53+bKt605qaiphYdW3WAkhhBDit5HYLIQQwtXc1iQ3KCiI+Ph4NpRPyrBhwwbi4+PtxvwA9O3bly+//BKLxUJ2djbfffcdffr0uZ1FFUIIIX4XJDYLIYRwNbd96t+//OUvfPzxx/Tp04ePP/6YF8vHkowbN47D5WvLDR48mIiICHr37s3w4cN55plniIx0nF1VCCGEEDdPYrMQQghXctvXlYiNjeXLL790eH55pYkZdDqdNcAKIYQQ4taS2CyEEMKVyCKuQgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchiS5QgghhBBCCCFchltNF+B/zWw2A5CWllbDJRFCCOEKKuJJRXwRv53EZiGEEP9L1cVml0tyMzIyAHjsscdquCRCCCFcSUZGBtHR0TVdjDuSxGYhhBC3wvVis0ZRFKUGynPLlJSUcOTIEUJCQtDpdDVdHCGEEHc4s9lMRkYGzZs3x9PTs6aLc0eS2CyEEOJ/qbrY7HJJrhBCCCGEEEKI3y+ZeEoIIYQQQgghhMuQJFcIIYQQQgghhMuQJFcIIYQQQgghhMuQJFcIIYQQQgghhMuQJFcIIYQQQgghhMuQJFcIIYQQQgghhMuQJFcIIYQQQgghhMuQJPc6zp49y8MPP0yfPn14+OGHOXfuXE0Xqcbl5OQwbtw4+vTpw8CBA5k0aRLZ2dkAHDx4kEGDBtGnTx9Gjx5NVlZWDZe2dli6dClxcXGcPHkSkON0LaPRyNy5c+nduzcDBw5kzpw5gJx/lW3fvp0hQ4YwePBgBg4cyJYtWwA5RosWLaJ79+525xdUfVx+78fMFch36Ehi828nsblqEpurJ7HZuVoVmxXh1IgRI5SkpCRFURQlKSlJGTFiRA2XqObl5OQoe/bssT5euHChMnPmTMVisSg9e/ZU9u3bpyiKoixbtkyZMWNGTRWz1jhy5IgyZswY5b777lNOnDghx8mJefPmKQsWLFAsFouiKIqSkZGhKIqcfxUsFouSmJionDhxQlEURTl+/LjSunVrxWw2/+6P0b59+5TLly8r3bp1sx4fRan6t/N7P2auQL5DRxKbfxuJzdWT2Fw1ic3XV5tisyS5TmRmZioJCQmKyWRSFEVRTCaTkpCQoGRlZdVwyWqXb7/9Vhk5cqRy6NAhpX///tbns7KylNatW9dgyWqe0WhUhg8frly4cMF6ostxsldYWKgkJCQohYWFds/L+WdjsViUdu3aKfv371cURVH27t2r9O7dW45RJZUDaVXHRY7ZnU++wxsjsfn6JDZXT2Jz9SQ2V682xGa3/017sGtJTU0lNDQUnU4HgE6no27duqSmphIYGFjDpasdLBYLn332Gd27dyc1NZV69epZtwUGBmKxWMjNzcXf378GS1lz3nzzTQYNGkRkZKT1OTlO9lJSUvD392fp0qX8+OOP+Pj4MGXKFDw9PeX8K6fRaFiyZAkTJ07E29ubq1ev8v7778s16jqqOi6Kosgxu8PJ7756EpurJrG5ehKbqyex+bepqdgsY3LFf2XevHl4e3vz+OOP13RRap2ff/6Zw4cP8+ijj9Z0UWo1k8lESkoKTZs2Zc2aNUybNo3JkydTVFRU00WrNUwmE++//z7vvPMO27dv59133+WPf/yjHCMhhFMSm69PYvONkdhcPYnNdwZpyXUiPDycK1euYDab0el0mM1m0tPTCQ8Pr+mi1QqLFi3i/PnzvPfee2i1WsLDw7l8+bJ1e3Z2NhqN5ndZAwqwb98+zpw5Q48ePQBIS0tjzJgxjBgxQo5TJfXq1cPNzY0BAwYA0KpVKwICAvD09JTzr9zx48dJT08nISEBgISEBLy8vPDw8JBj5ERV125FUeSY3eEkNldNYnPVJDbfGInN1ZPY/NvUVGyWllwngoKCiI+PZ8OGDQBs2LCB+Pj433VXgwqLFy/myJEjLFu2DHd3dwCaN29OSUkJ+/fvB+Dzzz/n/vvvr8li1qjx48ezc+dOtm3bxrZt2wgLC+PDDz9k7NixcpwqCQwMpH379uzatQtQZ9fLysqiQYMGcv6VCwsLIy0tjTNnzgBw+vRpMjMziY6OlmPkRFXXbrmu3/nkO7w+ic3Vk9h8YyQ2V09i829TU7FZoyiKctN7cUGnT59mxowZ5OfnU6dOHRYtWkRMTExNF6tGnTp1igEDBtCgQQM8PT0BiIiIYNmyZRw4cIC5c+diNBqpX78+r732GsHBwTVc4tqhe/fuvPfeezRp0kSO0zVSUlKYNWsWubm5uLm5MXXqVLp27SrnXyVff/01y5cvR6PRAPDss8/Ss2fP3/0xmj9/Plu2bCEzM5OAgAD8/f3ZuHFjlcfl937MXIF8h44kNv93JDZfn8Tm6klsdq42xWZJcoUQQgghhBBCuAzpriyEEEIIIYQQwmVIkiuEEEIIIYQQwmVIkiuEEEIIIYQQwmVIkiuEEEIIIYQQwmVIkiuEEEIIIYQQwmVIkivELRQXF1ftv+7duwMwY8YM6/9ri3nz5jFhwgSn29atW0dcXBwPPPCA0+1r1qwhLi6O8+fPO90eFxfH4sWLrY/nz5/P+PHjb77QQgghRBUkNktsFq7PraYLIIQr++KLL+weT5o0ibi4OCZPnmx9zt3dHYCJEyfyxBNP3NbyVeXChQt88cUXfPbZZ063JyUlAXDs2DFOnDhBXFzcTX3e+PHj6dmzJ7t376ZDhw43tS8hhBDieiQ23ziJzeJOJUmuELdQ69at7R67u7sTEBDg8DxAVFTU7SrWDVm1ahVxcXG0aNHCYVtaWhp79uyha9eu7Nixg6SkJKZPn35Tn1e3bl26devGRx99JIFUCCHELSOx+cZJbBZ3KumuLEQtcW2XqIsXLxIXF8dnn33GG2+8QceOHWnTpg3Tpk2juLiY8+fPM2bMGNq0aUOvXr1Yu3atwz6Tk5OZMGECd999Ny1btuSRRx5h//791ZaltLSUr7/+moEDBzrdvm7dOiwWC5MmTaJt27asX78es9n83//x5fr378/OnTtJTU296X0JIYQQN0tis8RmcWeSJFeIWu6DDz4gPT2dhQsXMmXKFDZt2sTcuXOZNGkSXbt2ZenSpcTFxTFz5kxOnTplfd/Ro0d55JFHyMvLY968ebz99tv4+/szatQojhw5UuVnHjx4kPz8fBISEpxuT0pKIjY2lpYtWzJkyBAyMjLYuXPnTf+tiYmJWCwWdu3addP7EkIIIW4Vic1C1G6S5ApRy0VGRrJo0SI6d+7MqFGj6NatG+vWrWPs2LE88cQTdOzYkQULFqDVatm8ebP1fa+++irh4eGsWrWKvn370rVrV5YtW0ZkZCTvvPNOlZ958OBBNBqN07E8hw4d4syZMwwePBiAfv364eHhYR0HdDMCAwMJCwvj0KFDN70vIYQQ4laR2CxE7SZJrhC1XJcuXewex8TEANC5c2frc35+fgQGBlq7EpWUlLBv3z769u2LVqvFZDJhMplQFIV777232m5R6enp+Pr6WifeqGzt2rVotVoGDRoEgMFgoEePHmzdupWCgoKb+ltBDabp6ek3vR8hhBDiVpHYLETtJhNPCVHL+fn52T3W6/UA1KlTx+55d3d3jEYjAHl5eZjNZt55553r1gxbLBa0Wuf1XKWlpU6DaGlpKZs2baJ169b4+PiQn58PQK9evfjmm2/YtGkTw4cPB0Cn0wE4HQ9U8Zybm+MlyMPDg5KSEqflEkIIIWoDic1C1G6S5ArhggwGA1qtlscee8zadela1wuiAP7+/tYgWdm2bdvIzc3lwIED3H333Q7b165daw2kQUFBgFrzXFHDXaGiNrjiNZXl5eXd9JIHQgghRG0jsVmI20eSXCFckLe3N4mJiSQnJzNr1qwqg6YzMTExlJWVkZaWRlhYmPX5pKQkvLy8ePfddx32mZSUxJo1a7hw4QJRUVG0atUKT09PtmzZwj333GP32orxSe3bt7d73mw2k5aWRt++fX9TeYUQQojaTmKzELePJLlCuKgZM2bw+OOPM2bMGB566CFCQkLIycnh2LFjmM1mpk2bdt33JiYmAvDLL79YA2lWVhY//PADgwYNcrpWXnBwMGvWrCEpKYlnn30Wg8HAhAkTWLJkCaWlpXTr1g29Xs++fftYsWIFDz74ILGxsXb7OHXqFEVFRU5rooUQQog7ncRmIW4PSXKFcFHNmjXjn//8J0uXLmX+/PkUFBQQGBhI06ZN+cMf/lDleyMiImjZsiXbt2+nd+/eAKxfvx6TycTQoUOdvic2NpY2bdqQlJTE5MmT0Wg0PP3004SGhvLJJ5+wfv16LBYLUVFRTJ06lSeffNJhH9u3byckJIR27drd/AEQQgghahmJzULcHhpFUZSaLoQQovZZs2YNCxYsYOfOnXh5ed2Wz+zXrx+9e/dm6tSpt+XzhBBCiDuJxGYhbowsISSEcGrw4MGEhoby6aef3pbP++6778jKymL06NG35fOEEEKIO43EZiFujCS5QgindDodL7/8Mp6enrfl84xGI6+++qrD8gtCCCGEUElsFuLGSHdlIYQQQgghhBAuQ1pyhRBCCCGEEEK4DElyhRBCCCGEEEK4DElyhRBCCCGEEEK4DElyhRBCCCGEEEK4DElyhRBCCCGEEEK4DElyhRBCCCGEEEK4jP8HBUNKSHgPgxwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16,6))\n",
    "ax0, ax1 = axes.flatten()\n",
    "\n",
    "for i in range(n_traj-5):\n",
    "    ax0.plot(t,em_traj_AB[i][0][:], lw=1)\n",
    "    ax1.plot(t,em_traj_AB[i][1][:], lw=1)\n",
    "ax0.plot(t, em_traj_AB[:,0,:].mean(axis=0), '-', lw=4, \n",
    "           color=\"k\")\n",
    "ax1.plot(t, em_traj_AB[:,1,:].mean(axis=0), '-', lw=4, \n",
    "           color=\"k\")\n",
    "ax0.set_title(\"Sample paths of Gene X\")\n",
    "ax1.set_title(\"Sample paths of gene Y\")\n",
    "ax0.set_xlabel('Time (AU)')\n",
    "ax1.set_xlabel('Time (AU)')\n",
    "ax0.set_ylabel('x')\n",
    "ax1.set_ylabel('y')\n",
    "\n",
    "#ax2.scatter(x_interpolate, y_interpolate, c = range(n * k), cmap=plt.cm.jet, linewidths=0, marker='o', s=3)\n",
    "#ax2.set_title(\"Phase portrait of X and Y with simulated trajectory\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attractor statistics for stochastic paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_entry_x = dist_entry_y = 0.1\n",
    "dist_exit_x = dist_exit_y = 0.4\n",
    "myPara =[[0.3,0.8],[0.3,0.3],[0.8,0.3]]  #Alpha1 and Alpha2 parameter values for EM_traj A,AB,B respectively.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steady states and classifying stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/porcupine/miniconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:175: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
      "  improvement from the last five Jacobian evaluations.\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "path_colors = [[] for i in range(len(myPara))]\n",
    "x_coordinate_ss, y_coordinate_ss = steady_states (alpha1_A, alpha2_A, a, c, b, d, lambda1, lambda2, myPara)\n",
    "stable_steady_states, unstable_steady_states = stability (alpha1_A, alpha2_A, a, c, b, d, lambda1, lambda2, x_coordinate_ss, y_coordinate_ss)\n",
    "grouped_stable_ss, grouped_unstable_ss = group_steady_states(stable_steady_states, unstable_steady_states, myPara)\n",
    "\n",
    "x_coordinate_ss_A, y_coordinate_ss_A = steady_states (alpha1_A, alpha2_A, a, c, b, d, lambda1, lambda2)\n",
    "stable_steady_states_A, unstable_steady_states_A = stability (alpha1_A, alpha2_A, a, c, b, d, lambda1, lambda2, x_coordinate_ss_A, y_coordinate_ss_A)\n",
    "\n",
    "x_coordinate_ss_AB, y_coordinate_ss_AB = steady_states (alpha1_AB, alpha2_AB, a, c, b, d, lambda1, lambda2)\n",
    "stable_steady_states_AB, unstable_steady_states_AB = stability (alpha1_AB, alpha2_AB, a, c, b, d, lambda1, lambda2, x_coordinate_ss_AB, y_coordinate_ss_AB)\n",
    "\n",
    "x_coordinate_ss_B, y_coordinate_ss_B = steady_states (alpha1_B, alpha2_B, a, c, b, d, lambda1, lambda2)\n",
    "stable_steady_states_B, unstable_steady_states_B = stability (alpha1_B, alpha2_B, a, c, b, d, lambda1, lambda2, x_coordinate_ss_B, y_coordinate_ss_B)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "1/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "2/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "3/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "4/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "5/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "6/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "7/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "8/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "9/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "10/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "11/1000\n",
      "12/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "13/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "14/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "15/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "16/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "17/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "18/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "19/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "20/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "21/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "22/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "23/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "24/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "25/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "26/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "27/1000\n",
      "28/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "29/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "30/1000\n",
      "31/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "32/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "33/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "34/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "35/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "36/1000\n",
      "37/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "38/1000\n",
      "39/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "40/1000\n",
      "41/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "42/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "43/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "44/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "45/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "46/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "47/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "48/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "49/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "50/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "51/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "52/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "53/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "54/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "55/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "56/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "57/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "58/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "59/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "60/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "61/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "62/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "63/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "64/1000\n",
      "65/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "66/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "67/1000\n",
      "68/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "69/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "70/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "71/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "72/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "73/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "74/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "75/1000\n",
      "76/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "77/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "78/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "79/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "81/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "82/1000\n",
      "83/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "84/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "85/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "86/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "87/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "88/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "89/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "90/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "91/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "92/1000\n",
      "93/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "94/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "95/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "96/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "97/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "98/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "99/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "100/1000\n",
      "101/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "102/1000\n",
      "103/1000\n",
      "104/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "105/1000\n",
      "106/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "107/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "108/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "109/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "110/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "111/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "112/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "113/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "114/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "115/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "116/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "117/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "118/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "119/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "120/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "121/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "122/1000\n",
      "123/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "124/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "125/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "126/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "127/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "128/1000\n",
      "129/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "130/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "131/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "132/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "136/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "137/1000\n",
      "138/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "139/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "140/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "141/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "142/1000\n",
      "143/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "144/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "145/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "146/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "147/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "148/1000\n",
      "149/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "150/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "151/1000\n",
      "152/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "153/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "154/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "155/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "156/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "157/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "158/1000\n",
      "159/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "160/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "161/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "162/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "163/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "164/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "165/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "167/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "168/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "169/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "170/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "171/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "172/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "176/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "177/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "178/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "179/1000\n",
      "180/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "181/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "182/1000\n",
      "183/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "184/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "185/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "186/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "187/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "188/1000\n",
      "189/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "190/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "191/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "192/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "193/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "194/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "195/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "196/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "197/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "198/1000\n",
      "199/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "200/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "201/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "202/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "203/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "204/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "205/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "206/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "207/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "208/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "209/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "210/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "211/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "212/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "213/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "214/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "215/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "216/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "217/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "218/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "219/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "223/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "224/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "225/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "226/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "227/1000\n",
      "228/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "229/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "230/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "231/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "232/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "233/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "237/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "238/1000\n",
      "239/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "240/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "241/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "242/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "243/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "247/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "248/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "250/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "251/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "252/1000\n",
      "253/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "254/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "255/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "256/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "257/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "258/1000\n",
      "259/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "260/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "261/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "262/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "263/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "264/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "265/1000\n",
      "266/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "267/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "268/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "269/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "270/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "271/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "272/1000\n",
      "273/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "274/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "275/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "276/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "277/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "278/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "279/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "280/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "281/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "282/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "283/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "284/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "285/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "286/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "287/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "288/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "289/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "290/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "291/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "292/1000\n",
      "293/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "294/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "295/1000\n",
      "296/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "297/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "298/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "299/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "300/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "301/1000\n",
      "302/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "303/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "304/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "305/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "306/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "307/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "308/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "309/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "310/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "311/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "312/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "313/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "314/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "315/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "316/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "317/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "318/1000\n",
      "319/1000\n",
      "320/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "321/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "322/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "323/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "324/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "325/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "326/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "327/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "328/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "329/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "330/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "331/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "332/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "333/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "334/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "335/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "336/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "337/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "338/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "339/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "340/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "341/1000\n",
      "342/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "343/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "344/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "345/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "346/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "347/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "348/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "349/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "350/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "351/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "352/1000\n",
      "353/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "354/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "355/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "356/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "357/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "358/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "359/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "360/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "361/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "362/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "363/1000\n",
      "364/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "365/1000\n",
      "366/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "367/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "368/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "369/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "373/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "374/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "375/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "376/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "377/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "378/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "379/1000\n",
      "380/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "381/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "382/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "383/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "387/1000\n",
      "388/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "389/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "390/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "391/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "392/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "393/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "394/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "395/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "396/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "397/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "398/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "399/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "400/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "401/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "402/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "403/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "404/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "406/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "407/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "408/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "409/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "410/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "411/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "412/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "413/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "414/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "415/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "416/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "417/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "418/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "419/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "420/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "421/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "422/1000\n",
      "423/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "424/1000\n",
      "425/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "426/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "427/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "428/1000\n",
      "429/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "430/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "431/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "432/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "436/1000\n",
      "437/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "438/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "439/1000\n",
      "440/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "441/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "442/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "443/1000\n",
      "444/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "445/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "446/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "447/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "448/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "449/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "455/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "456/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "457/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "458/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "459/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "460/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "461/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "462/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "463/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "464/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "465/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "466/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "467/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "468/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "469/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "470/1000\n",
      "471/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "472/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "473/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "474/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "475/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "476/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "477/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "478/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "479/1000\n",
      "480/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "481/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "482/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "483/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "484/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "485/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "486/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "487/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "488/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "489/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "490/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "491/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "493/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "494/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "495/1000\n",
      "496/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "497/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "498/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "499/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "500/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "501/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "502/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "503/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "504/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "505/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "506/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "507/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "508/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "509/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "510/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "511/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "512/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "513/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "514/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "515/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "516/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "517/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "518/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "519/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "520/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "524/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "525/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "526/1000\n",
      "527/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "528/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "529/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "530/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "531/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "532/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "533/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "534/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "535/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "536/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "537/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "538/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "539/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "540/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "541/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "542/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "543/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "544/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "545/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "546/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "547/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "548/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "549/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "550/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "551/1000\n",
      "552/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "553/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "554/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "555/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "556/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "557/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "558/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "559/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "560/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "561/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "562/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "563/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "564/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "565/1000\n",
      "566/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "567/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "568/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "569/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "570/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "571/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "572/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "573/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "574/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "575/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "576/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "577/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "578/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "579/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "580/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "581/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "582/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "583/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "584/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "585/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "586/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "587/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "591/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "592/1000\n",
      "593/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "594/1000\n",
      "595/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "596/1000\n",
      "597/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "598/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "599/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "600/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "601/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "602/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "603/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "604/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "605/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "606/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "607/1000\n",
      "608/1000\n",
      "609/1000\n",
      "610/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "611/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "612/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "613/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "614/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "615/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "616/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "617/1000\n",
      "618/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "619/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "620/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "621/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "622/1000\n",
      "623/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "624/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "625/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "626/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "627/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "628/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "629/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "630/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "631/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "632/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "633/1000\n",
      "634/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "635/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "636/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "637/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "638/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "639/1000\n",
      "640/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "641/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "642/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "643/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "644/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "645/1000\n",
      "646/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "647/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "648/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "649/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "650/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "651/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "652/1000\n",
      "653/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "654/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "655/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "656/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "657/1000\n",
      "658/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "659/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "660/1000\n",
      "661/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "662/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "663/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "664/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "665/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "666/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "667/1000\n",
      "668/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "669/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "670/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "671/1000\n",
      "672/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "673/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "674/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "675/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "676/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "677/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "678/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "679/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "680/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "681/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "682/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "683/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "684/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "685/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "686/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "687/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "688/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "689/1000\n",
      "690/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "691/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "692/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "693/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "694/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "695/1000\n",
      "696/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "697/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "698/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "699/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "700/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "701/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "702/1000\n",
      "703/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "704/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "705/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "706/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "707/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "708/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "709/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "710/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "711/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "712/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "713/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "714/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "715/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "716/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "717/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "718/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "719/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "720/1000\n",
      "721/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "722/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "723/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "724/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "725/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "726/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "727/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "728/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "729/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "730/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "731/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "732/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "733/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "734/1000\n",
      "735/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "736/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "737/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "738/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "739/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "740/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "741/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "742/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "743/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "744/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "745/1000\n",
      "746/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "747/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "748/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "749/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "750/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "751/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "752/1000\n",
      "753/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "754/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "755/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "756/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "757/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "758/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "759/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "760/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "761/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "762/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "763/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "764/1000\n",
      "765/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "766/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "767/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "768/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "769/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "770/1000\n",
      "771/1000\n",
      "772/1000\n",
      "773/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "774/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "775/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "776/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "777/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "778/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "779/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "780/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "781/1000\n",
      "782/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "783/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "784/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "785/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "786/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "787/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "788/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "789/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "790/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "791/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "792/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "793/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "794/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "795/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "796/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "797/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "798/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "799/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "800/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "801/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "802/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "803/1000\n",
      "804/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "805/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "806/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "807/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "808/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "809/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "810/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "811/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "812/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "813/1000\n",
      "814/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "815/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "816/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "817/1000\n",
      "818/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "819/1000\n",
      "820/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "821/1000\n",
      "822/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "823/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "824/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "825/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "826/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "827/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "828/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "832/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "833/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "834/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "835/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "836/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "837/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "838/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "839/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "840/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "841/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "842/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "843/1000\n",
      "844/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "845/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "846/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "847/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "848/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "849/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "850/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "851/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "852/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "853/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "854/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "855/1000\n",
      "856/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "857/1000\n",
      "858/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "859/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "860/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "861/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "862/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "863/1000\n",
      "864/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "865/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "866/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "867/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "868/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "869/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "870/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "871/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "872/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "873/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "874/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "875/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "876/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "877/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "878/1000\n",
      "879/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "880/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "881/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "882/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "883/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "884/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "885/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "886/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "887/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "888/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "889/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "890/1000\n",
      "891/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "892/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "893/1000\n",
      "894/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "895/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "896/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "897/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "898/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "903/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "904/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "905/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "906/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "907/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "908/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "909/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "910/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "911/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "912/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "913/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "914/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "915/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "916/1000\n",
      "917/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "918/1000\n",
      "919/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "920/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "921/1000\n",
      "922/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "923/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "924/1000\n",
      "925/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "926/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "927/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "928/1000\n",
      "929/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "930/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "931/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "932/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "933/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "934/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "935/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "936/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "937/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "938/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "942/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "943/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "944/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "945/1000\n",
      "946/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "947/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "948/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "949/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "950/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "951/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "952/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "953/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "954/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "955/1000\n",
      "956/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "957/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "958/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "959/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "960/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "961/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "962/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "963/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "964/1000\n",
      "965/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "966/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "967/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "968/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "969/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "970/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "971/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "972/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "973/1000\n",
      "974/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "975/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "976/1000\n",
      "977/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "978/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "979/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "980/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "981/1000\n",
      "982/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "983/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "984/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "985/1000\n",
      "986/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "987/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "988/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "989/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "990/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "991/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "992/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "993/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "994/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "995/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "996/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "997/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "998/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "999/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "0/1000\n",
      "1/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "2/1000\n",
      "3/1000\n",
      "4/1000\n",
      "5/1000\n",
      "6/1000\n",
      "7/1000\n",
      "8/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "9/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "10/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "11/1000\n",
      "12/1000\n",
      "13/1000\n",
      "14/1000\n",
      "15/1000\n",
      "16/1000\n",
      "17/1000\n",
      "18/1000\n",
      "19/1000\n",
      "20/1000\n",
      "21/1000\n",
      "22/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "23/1000\n",
      "24/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "25/1000\n",
      "26/1000\n",
      "27/1000\n",
      "28/1000\n",
      "29/1000\n",
      "30/1000\n",
      "31/1000\n",
      "32/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "33/1000\n",
      "34/1000\n",
      "35/1000\n",
      "36/1000\n",
      "37/1000\n",
      "38/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "39/1000\n",
      "40/1000\n",
      "41/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "42/1000\n",
      "43/1000\n",
      "44/1000\n",
      "45/1000\n",
      "46/1000\n",
      "47/1000\n",
      "48/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "49/1000\n",
      "50/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "51/1000\n",
      "52/1000\n",
      "53/1000\n",
      "54/1000\n",
      "55/1000\n",
      "56/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "57/1000\n",
      "58/1000\n",
      "59/1000\n",
      "60/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "61/1000\n",
      "62/1000\n",
      "63/1000\n",
      "64/1000\n",
      "65/1000\n",
      "66/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "67/1000\n",
      "68/1000\n",
      "69/1000\n",
      "70/1000\n",
      "71/1000\n",
      "72/1000\n",
      "73/1000\n",
      "74/1000\n",
      "75/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "76/1000\n",
      "77/1000\n",
      "78/1000\n",
      "79/1000\n",
      "80/1000\n",
      "81/1000\n",
      "82/1000\n",
      "83/1000\n",
      "84/1000\n",
      "85/1000\n",
      "86/1000\n",
      "87/1000\n",
      "88/1000\n",
      "89/1000\n",
      "90/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "91/1000\n",
      "92/1000\n",
      "93/1000\n",
      "94/1000\n",
      "95/1000\n",
      "96/1000\n",
      "97/1000\n",
      "98/1000\n",
      "99/1000\n",
      "100/1000\n",
      "101/1000\n",
      "102/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "103/1000\n",
      "104/1000\n",
      "105/1000\n",
      "106/1000\n",
      "107/1000\n",
      "108/1000\n",
      "109/1000\n",
      "110/1000\n",
      "111/1000\n",
      "112/1000\n",
      "113/1000\n",
      "114/1000\n",
      "115/1000\n",
      "116/1000\n",
      "117/1000\n",
      "118/1000\n",
      "119/1000\n",
      "120/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "121/1000\n",
      "122/1000\n",
      "123/1000\n",
      "124/1000\n",
      "125/1000\n",
      "126/1000\n",
      "127/1000\n",
      "128/1000\n",
      "129/1000\n",
      "130/1000\n",
      "131/1000\n",
      "132/1000\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "136/1000\n",
      "137/1000\n",
      "138/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "139/1000\n",
      "140/1000\n",
      "141/1000\n",
      "142/1000\n",
      "143/1000\n",
      "144/1000\n",
      "145/1000\n",
      "146/1000\n",
      "147/1000\n",
      "148/1000\n",
      "149/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "150/1000\n",
      "151/1000\n",
      "152/1000\n",
      "153/1000\n",
      "154/1000\n",
      "155/1000\n",
      "156/1000\n",
      "157/1000\n",
      "158/1000\n",
      "159/1000\n",
      "160/1000\n",
      "161/1000\n",
      "162/1000\n",
      "163/1000\n",
      "164/1000\n",
      "165/1000\n",
      "166/1000\n",
      "167/1000\n",
      "168/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "169/1000\n",
      "170/1000\n",
      "171/1000\n",
      "172/1000\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "176/1000\n",
      "177/1000\n",
      "178/1000\n",
      "179/1000\n",
      "180/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "181/1000\n",
      "182/1000\n",
      "183/1000\n",
      "184/1000\n",
      "185/1000\n",
      "186/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "187/1000\n",
      "188/1000\n",
      "189/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "190/1000\n",
      "191/1000\n",
      "192/1000\n",
      "193/1000\n",
      "194/1000\n",
      "195/1000\n",
      "196/1000\n",
      "197/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "198/1000\n",
      "199/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "200/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "201/1000\n",
      "202/1000\n",
      "203/1000\n",
      "204/1000\n",
      "205/1000\n",
      "206/1000\n",
      "207/1000\n",
      "208/1000\n",
      "209/1000\n",
      "210/1000\n",
      "211/1000\n",
      "212/1000\n",
      "213/1000\n",
      "214/1000\n",
      "215/1000\n",
      "216/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "217/1000\n",
      "218/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "219/1000\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "223/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "224/1000\n",
      "225/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "226/1000\n",
      "227/1000\n",
      "228/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "229/1000\n",
      "230/1000\n",
      "231/1000\n",
      "232/1000\n",
      "233/1000\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "237/1000\n",
      "238/1000\n",
      "239/1000\n",
      "240/1000\n",
      "241/1000\n",
      "242/1000\n",
      "243/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "247/1000\n",
      "248/1000\n",
      "249/1000\n",
      "250/1000\n",
      "251/1000\n",
      "252/1000\n",
      "253/1000\n",
      "254/1000\n",
      "255/1000\n",
      "256/1000\n",
      "257/1000\n",
      "258/1000\n",
      "259/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "260/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "261/1000\n",
      "262/1000\n",
      "263/1000\n",
      "264/1000\n",
      "265/1000\n",
      "266/1000\n",
      "267/1000\n",
      "268/1000\n",
      "269/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "270/1000\n",
      "271/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "272/1000\n",
      "273/1000\n",
      "274/1000\n",
      "275/1000\n",
      "276/1000\n",
      "277/1000\n",
      "278/1000\n",
      "279/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "280/1000\n",
      "281/1000\n",
      "282/1000\n",
      "283/1000\n",
      "284/1000\n",
      "285/1000\n",
      "286/1000\n",
      "287/1000\n",
      "288/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "289/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "290/1000\n",
      "291/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "292/1000\n",
      "293/1000\n",
      "294/1000\n",
      "295/1000\n",
      "296/1000\n",
      "297/1000\n",
      "298/1000\n",
      "299/1000\n",
      "300/1000\n",
      "301/1000\n",
      "302/1000\n",
      "303/1000\n",
      "304/1000\n",
      "305/1000\n",
      "306/1000\n",
      "307/1000\n",
      "308/1000\n",
      "309/1000\n",
      "310/1000\n",
      "311/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "312/1000\n",
      "313/1000\n",
      "314/1000\n",
      "315/1000\n",
      "316/1000\n",
      "317/1000\n",
      "318/1000\n",
      "319/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "320/1000\n",
      "321/1000\n",
      "322/1000\n",
      "323/1000\n",
      "324/1000\n",
      "325/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "326/1000\n",
      "327/1000\n",
      "328/1000\n",
      "329/1000\n",
      "330/1000\n",
      "331/1000\n",
      "332/1000\n",
      "333/1000\n",
      "334/1000\n",
      "335/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "336/1000\n",
      "337/1000\n",
      "338/1000\n",
      "339/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "340/1000\n",
      "341/1000\n",
      "342/1000\n",
      "343/1000\n",
      "344/1000\n",
      "345/1000\n",
      "346/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "347/1000\n",
      "348/1000\n",
      "349/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "350/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "351/1000\n",
      "352/1000\n",
      "353/1000\n",
      "354/1000\n",
      "355/1000\n",
      "356/1000\n",
      "357/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "358/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "359/1000\n",
      "360/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "361/1000\n",
      "362/1000\n",
      "363/1000\n",
      "364/1000\n",
      "365/1000\n",
      "366/1000\n",
      "367/1000\n",
      "368/1000\n",
      "369/1000\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "373/1000\n",
      "374/1000\n",
      "375/1000\n",
      "376/1000\n",
      "377/1000\n",
      "378/1000\n",
      "379/1000\n",
      "380/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "381/1000\n",
      "382/1000\n",
      "383/1000\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "387/1000\n",
      "388/1000\n",
      "389/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "390/1000\n",
      "391/1000\n",
      "392/1000\n",
      "393/1000\n",
      "394/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "395/1000\n",
      "396/1000\n",
      "397/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "398/1000\n",
      "399/1000\n",
      "400/1000\n",
      "401/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "402/1000\n",
      "403/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "404/1000\n",
      "405/1000\n",
      "406/1000\n",
      "407/1000\n",
      "408/1000\n",
      "409/1000\n",
      "410/1000\n",
      "411/1000\n",
      "412/1000\n",
      "413/1000\n",
      "414/1000\n",
      "415/1000\n",
      "416/1000\n",
      "417/1000\n",
      "418/1000\n",
      "419/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "420/1000\n",
      "421/1000\n",
      "422/1000\n",
      "423/1000\n",
      "424/1000\n",
      "425/1000\n",
      "426/1000\n",
      "427/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "428/1000\n",
      "429/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "430/1000\n",
      "431/1000\n",
      "432/1000\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "436/1000\n",
      "437/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "438/1000\n",
      "439/1000\n",
      "440/1000\n",
      "441/1000\n",
      "442/1000\n",
      "443/1000\n",
      "444/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "445/1000\n",
      "446/1000\n",
      "447/1000\n",
      "448/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "449/1000\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "455/1000\n",
      "456/1000\n",
      "457/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "458/1000\n",
      "459/1000\n",
      "460/1000\n",
      "461/1000\n",
      "462/1000\n",
      "463/1000\n",
      "464/1000\n",
      "465/1000\n",
      "466/1000\n",
      "467/1000\n",
      "468/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "469/1000\n",
      "470/1000\n",
      "471/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "472/1000\n",
      "473/1000\n",
      "474/1000\n",
      "475/1000\n",
      "476/1000\n",
      "477/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "478/1000\n",
      "479/1000\n",
      "480/1000\n",
      "481/1000\n",
      "482/1000\n",
      "483/1000\n",
      "484/1000\n",
      "485/1000\n",
      "486/1000\n",
      "487/1000\n",
      "488/1000\n",
      "489/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "490/1000\n",
      "491/1000\n",
      "492/1000\n",
      "493/1000\n",
      "494/1000\n",
      "495/1000\n",
      "496/1000\n",
      "497/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "498/1000\n",
      "499/1000\n",
      "500/1000\n",
      "501/1000\n",
      "502/1000\n",
      "503/1000\n",
      "504/1000\n",
      "505/1000\n",
      "506/1000\n",
      "507/1000\n",
      "508/1000\n",
      "509/1000\n",
      "510/1000\n",
      "511/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "512/1000\n",
      "513/1000\n",
      "514/1000\n",
      "515/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "516/1000\n",
      "517/1000\n",
      "518/1000\n",
      "519/1000\n",
      "520/1000\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "524/1000\n",
      "525/1000\n",
      "526/1000\n",
      "527/1000\n",
      "528/1000\n",
      "529/1000\n",
      "530/1000\n",
      "531/1000\n",
      "532/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "533/1000\n",
      "534/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "535/1000\n",
      "536/1000\n",
      "537/1000\n",
      "538/1000\n",
      "539/1000\n",
      "540/1000\n",
      "541/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "542/1000\n",
      "543/1000\n",
      "544/1000\n",
      "545/1000\n",
      "546/1000\n",
      "547/1000\n",
      "548/1000\n",
      "549/1000\n",
      "550/1000\n",
      "551/1000\n",
      "552/1000\n",
      "553/1000\n",
      "554/1000\n",
      "555/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "556/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "557/1000\n",
      "558/1000\n",
      "559/1000\n",
      "560/1000\n",
      "561/1000\n",
      "562/1000\n",
      "563/1000\n",
      "564/1000\n",
      "565/1000\n",
      "566/1000\n",
      "567/1000\n",
      "568/1000\n",
      "569/1000\n",
      "570/1000\n",
      "571/1000\n",
      "572/1000\n",
      "573/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "574/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "575/1000\n",
      "576/1000\n",
      "577/1000\n",
      "578/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "579/1000\n",
      "580/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "581/1000\n",
      "582/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "583/1000\n",
      "584/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "585/1000\n",
      "586/1000\n",
      "587/1000\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "591/1000\n",
      "592/1000\n",
      "593/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "594/1000\n",
      "595/1000\n",
      "596/1000\n",
      "597/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "598/1000\n",
      "599/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "600/1000\n",
      "601/1000\n",
      "602/1000\n",
      "603/1000\n",
      "604/1000\n",
      "605/1000\n",
      "606/1000\n",
      "607/1000\n",
      "608/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "609/1000\n",
      "610/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "611/1000\n",
      "612/1000\n",
      "613/1000\n",
      "614/1000\n",
      "615/1000\n",
      "616/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "617/1000\n",
      "618/1000\n",
      "619/1000\n",
      "620/1000\n",
      "621/1000\n",
      "622/1000\n",
      "623/1000\n",
      "624/1000\n",
      "625/1000\n",
      "626/1000\n",
      "627/1000\n",
      "628/1000\n",
      "629/1000\n",
      "630/1000\n",
      "631/1000\n",
      "632/1000\n",
      "633/1000\n",
      "634/1000\n",
      "635/1000\n",
      "636/1000\n",
      "637/1000\n",
      "638/1000\n",
      "639/1000\n",
      "640/1000\n",
      "641/1000\n",
      "642/1000\n",
      "643/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "644/1000\n",
      "645/1000\n",
      "646/1000\n",
      "647/1000\n",
      "648/1000\n",
      "649/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "650/1000\n",
      "651/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "652/1000\n",
      "653/1000\n",
      "654/1000\n",
      "655/1000\n",
      "656/1000\n",
      "657/1000\n",
      "658/1000\n",
      "659/1000\n",
      "660/1000\n",
      "661/1000\n",
      "662/1000\n",
      "663/1000\n",
      "664/1000\n",
      "665/1000\n",
      "666/1000\n",
      "667/1000\n",
      "668/1000\n",
      "669/1000\n",
      "670/1000\n",
      "671/1000\n",
      "672/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "673/1000\n",
      "674/1000\n",
      "675/1000\n",
      "676/1000\n",
      "677/1000\n",
      "678/1000\n",
      "679/1000\n",
      "680/1000\n",
      "681/1000\n",
      "682/1000\n",
      "683/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "684/1000\n",
      "685/1000\n",
      "686/1000\n",
      "687/1000\n",
      "688/1000\n",
      "689/1000\n",
      "690/1000\n",
      "691/1000\n",
      "692/1000\n",
      "693/1000\n",
      "694/1000\n",
      "695/1000\n",
      "696/1000\n",
      "697/1000\n",
      "698/1000\n",
      "699/1000\n",
      "700/1000\n",
      "701/1000\n",
      "702/1000\n",
      "703/1000\n",
      "704/1000\n",
      "705/1000\n",
      "706/1000\n",
      "707/1000\n",
      "708/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "709/1000\n",
      "710/1000\n",
      "711/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "712/1000\n",
      "713/1000\n",
      "714/1000\n",
      "715/1000\n",
      "716/1000\n",
      "717/1000\n",
      "718/1000\n",
      "719/1000\n",
      "720/1000\n",
      "721/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "722/1000\n",
      "723/1000\n",
      "724/1000\n",
      "725/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "726/1000\n",
      "727/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "728/1000\n",
      "729/1000\n",
      "730/1000\n",
      "731/1000\n",
      "732/1000\n",
      "733/1000\n",
      "734/1000\n",
      "735/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "736/1000\n",
      "737/1000\n",
      "738/1000\n",
      "739/1000\n",
      "740/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "741/1000\n",
      "742/1000\n",
      "743/1000\n",
      "744/1000\n",
      "745/1000\n",
      "746/1000\n",
      "747/1000\n",
      "748/1000\n",
      "749/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "750/1000\n",
      "751/1000\n",
      "752/1000\n",
      "753/1000\n",
      "754/1000\n",
      "755/1000\n",
      "756/1000\n",
      "757/1000\n",
      "758/1000\n",
      "759/1000\n",
      "760/1000\n",
      "761/1000\n",
      "762/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "763/1000\n",
      "764/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "765/1000\n",
      "766/1000\n",
      "767/1000\n",
      "768/1000\n",
      "769/1000\n",
      "770/1000\n",
      "771/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "772/1000\n",
      "773/1000\n",
      "774/1000\n",
      "775/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "776/1000\n",
      "777/1000\n",
      "778/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "779/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "780/1000\n",
      "781/1000\n",
      "782/1000\n",
      "783/1000\n",
      "784/1000\n",
      "785/1000\n",
      "786/1000\n",
      "787/1000\n",
      "788/1000\n",
      "789/1000\n",
      "790/1000\n",
      "791/1000\n",
      "792/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "793/1000\n",
      "794/1000\n",
      "795/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "796/1000\n",
      "797/1000\n",
      "798/1000\n",
      "799/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "800/1000\n",
      "801/1000\n",
      "802/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "803/1000\n",
      "804/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "805/1000\n",
      "806/1000\n",
      "807/1000\n",
      "808/1000\n",
      "809/1000\n",
      "810/1000\n",
      "811/1000\n",
      "812/1000\n",
      "813/1000\n",
      "814/1000\n",
      "815/1000\n",
      "816/1000\n",
      "817/1000\n",
      "818/1000\n",
      "819/1000\n",
      "820/1000\n",
      "821/1000\n",
      "822/1000\n",
      "823/1000\n",
      "824/1000\n",
      "825/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "826/1000\n",
      "827/1000\n",
      "828/1000\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "832/1000\n",
      "833/1000\n",
      "834/1000\n",
      "835/1000\n",
      "836/1000\n",
      "837/1000\n",
      "838/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "839/1000\n",
      "840/1000\n",
      "841/1000\n",
      "842/1000\n",
      "843/1000\n",
      "844/1000\n",
      "845/1000\n",
      "846/1000\n",
      "847/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "848/1000\n",
      "849/1000\n",
      "850/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "851/1000\n",
      "852/1000\n",
      "853/1000\n",
      "854/1000\n",
      "855/1000\n",
      "856/1000\n",
      "857/1000\n",
      "858/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "859/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "860/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "861/1000\n",
      "862/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "863/1000\n",
      "864/1000\n",
      "865/1000\n",
      "866/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "867/1000\n",
      "868/1000\n",
      "869/1000\n",
      "870/1000\n",
      "871/1000\n",
      "872/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "873/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "874/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "875/1000\n",
      "876/1000\n",
      "877/1000\n",
      "878/1000\n",
      "879/1000\n",
      "880/1000\n",
      "881/1000\n",
      "882/1000\n",
      "883/1000\n",
      "884/1000\n",
      "885/1000\n",
      "886/1000\n",
      "887/1000\n",
      "888/1000\n",
      "889/1000\n",
      "890/1000\n",
      "891/1000\n",
      "892/1000\n",
      "893/1000\n",
      "894/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "895/1000\n",
      "896/1000\n",
      "897/1000\n",
      "898/1000\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "903/1000\n",
      "904/1000\n",
      "905/1000\n",
      "906/1000\n",
      "907/1000\n",
      "908/1000\n",
      "909/1000\n",
      "910/1000\n",
      "911/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "912/1000\n",
      "913/1000\n",
      "914/1000\n",
      "915/1000\n",
      "916/1000\n",
      "917/1000\n",
      "918/1000\n",
      "919/1000\n",
      "920/1000\n",
      "921/1000\n",
      "922/1000\n",
      "923/1000\n",
      "924/1000\n",
      "925/1000\n",
      "926/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "927/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "928/1000\n",
      "929/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "930/1000\n",
      "931/1000\n",
      "932/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "933/1000\n",
      "934/1000\n",
      "935/1000\n",
      "936/1000\n",
      "937/1000\n",
      "938/1000\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "942/1000\n",
      "943/1000\n",
      "944/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "945/1000\n",
      "946/1000\n",
      "947/1000\n",
      "948/1000\n",
      "949/1000\n",
      "950/1000\n",
      "951/1000\n",
      "952/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "953/1000\n",
      "954/1000\n",
      "955/1000\n",
      "956/1000\n",
      "957/1000\n",
      "958/1000\n",
      "959/1000\n",
      "960/1000\n",
      "961/1000\n",
      "962/1000\n",
      "963/1000\n",
      "964/1000\n",
      "965/1000\n",
      "966/1000\n",
      "967/1000\n",
      "968/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "969/1000\n",
      "970/1000\n",
      "971/1000\n",
      "972/1000\n",
      "973/1000\n",
      "974/1000\n",
      "975/1000\n",
      "976/1000\n",
      "977/1000\n",
      "978/1000\n",
      "979/1000\n",
      "980/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "981/1000\n",
      "982/1000\n",
      "983/1000\n",
      "984/1000\n",
      "985/1000\n",
      "986/1000\n",
      "987/1000\n",
      "988/1000\n",
      "989/1000\n",
      "Fe_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fe time to take the last value\n",
      "990/1000\n",
      "991/1000\n",
      "992/1000\n",
      "993/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "994/1000\n",
      "995/1000\n",
      "996/1000\n",
      "997/1000\n",
      "998/1000\n",
      "999/1000\n",
      "0/1000\n",
      "1/1000\n",
      "2/1000\n",
      "3/1000\n",
      "4/1000\n",
      "5/1000\n",
      "6/1000\n",
      "7/1000\n",
      "8/1000\n",
      "9/1000\n",
      "10/1000\n",
      "11/1000\n",
      "12/1000\n",
      "13/1000\n",
      "14/1000\n",
      "15/1000\n",
      "16/1000\n",
      "17/1000\n",
      "18/1000\n",
      "19/1000\n",
      "20/1000\n",
      "21/1000\n",
      "22/1000\n",
      "23/1000\n",
      "24/1000\n",
      "25/1000\n",
      "26/1000\n",
      "27/1000\n",
      "28/1000\n",
      "29/1000\n",
      "30/1000\n",
      "31/1000\n",
      "32/1000\n",
      "33/1000\n",
      "34/1000\n",
      "35/1000\n",
      "36/1000\n",
      "37/1000\n",
      "38/1000\n",
      "39/1000\n",
      "40/1000\n",
      "41/1000\n",
      "42/1000\n",
      "43/1000\n",
      "44/1000\n",
      "45/1000\n",
      "46/1000\n",
      "47/1000\n",
      "48/1000\n",
      "49/1000\n",
      "50/1000\n",
      "51/1000\n",
      "52/1000\n",
      "53/1000\n",
      "54/1000\n",
      "55/1000\n",
      "56/1000\n",
      "57/1000\n",
      "58/1000\n",
      "59/1000\n",
      "60/1000\n",
      "61/1000\n",
      "62/1000\n",
      "63/1000\n",
      "64/1000\n",
      "65/1000\n",
      "66/1000\n",
      "67/1000\n",
      "68/1000\n",
      "69/1000\n",
      "70/1000\n",
      "71/1000\n",
      "72/1000\n",
      "73/1000\n",
      "74/1000\n",
      "75/1000\n",
      "76/1000\n",
      "77/1000\n",
      "78/1000\n",
      "79/1000\n",
      "80/1000\n",
      "81/1000\n",
      "82/1000\n",
      "83/1000\n",
      "84/1000\n",
      "85/1000\n",
      "86/1000\n",
      "87/1000\n",
      "88/1000\n",
      "89/1000\n",
      "90/1000\n",
      "91/1000\n",
      "92/1000\n",
      "93/1000\n",
      "94/1000\n",
      "95/1000\n",
      "96/1000\n",
      "97/1000\n",
      "98/1000\n",
      "99/1000\n",
      "100/1000\n",
      "101/1000\n",
      "102/1000\n",
      "103/1000\n",
      "104/1000\n",
      "105/1000\n",
      "106/1000\n",
      "107/1000\n",
      "108/1000\n",
      "109/1000\n",
      "110/1000\n",
      "111/1000\n",
      "112/1000\n",
      "113/1000\n",
      "114/1000\n",
      "115/1000\n",
      "116/1000\n",
      "117/1000\n",
      "118/1000\n",
      "119/1000\n",
      "120/1000\n",
      "121/1000\n",
      "122/1000\n",
      "123/1000\n",
      "124/1000\n",
      "125/1000\n",
      "126/1000\n",
      "127/1000\n",
      "128/1000\n",
      "129/1000\n",
      "130/1000\n",
      "131/1000\n",
      "132/1000\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "136/1000\n",
      "137/1000\n",
      "138/1000\n",
      "139/1000\n",
      "140/1000\n",
      "141/1000\n",
      "142/1000\n",
      "143/1000\n",
      "144/1000\n",
      "145/1000\n",
      "146/1000\n",
      "147/1000\n",
      "148/1000\n",
      "149/1000\n",
      "150/1000\n",
      "151/1000\n",
      "152/1000\n",
      "153/1000\n",
      "154/1000\n",
      "155/1000\n",
      "156/1000\n",
      "157/1000\n",
      "158/1000\n",
      "159/1000\n",
      "160/1000\n",
      "161/1000\n",
      "162/1000\n",
      "163/1000\n",
      "164/1000\n",
      "165/1000\n",
      "166/1000\n",
      "167/1000\n",
      "168/1000\n",
      "169/1000\n",
      "170/1000\n",
      "171/1000\n",
      "172/1000\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "176/1000\n",
      "177/1000\n",
      "178/1000\n",
      "179/1000\n",
      "180/1000\n",
      "181/1000\n",
      "182/1000\n",
      "183/1000\n",
      "184/1000\n",
      "185/1000\n",
      "186/1000\n",
      "187/1000\n",
      "188/1000\n",
      "189/1000\n",
      "190/1000\n",
      "191/1000\n",
      "192/1000\n",
      "193/1000\n",
      "194/1000\n",
      "195/1000\n",
      "196/1000\n",
      "197/1000\n",
      "198/1000\n",
      "199/1000\n",
      "200/1000\n",
      "201/1000\n",
      "202/1000\n",
      "203/1000\n",
      "204/1000\n",
      "205/1000\n",
      "206/1000\n",
      "207/1000\n",
      "208/1000\n",
      "209/1000\n",
      "210/1000\n",
      "211/1000\n",
      "212/1000\n",
      "213/1000\n",
      "214/1000\n",
      "215/1000\n",
      "216/1000\n",
      "217/1000\n",
      "218/1000\n",
      "219/1000\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "223/1000\n",
      "224/1000\n",
      "225/1000\n",
      "226/1000\n",
      "227/1000\n",
      "228/1000\n",
      "229/1000\n",
      "230/1000\n",
      "231/1000\n",
      "232/1000\n",
      "233/1000\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "237/1000\n",
      "238/1000\n",
      "239/1000\n",
      "240/1000\n",
      "241/1000\n",
      "242/1000\n",
      "243/1000\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "247/1000\n",
      "248/1000\n",
      "249/1000\n",
      "250/1000\n",
      "251/1000\n",
      "252/1000\n",
      "253/1000\n",
      "254/1000\n",
      "255/1000\n",
      "256/1000\n",
      "257/1000\n",
      "258/1000\n",
      "259/1000\n",
      "260/1000\n",
      "261/1000\n",
      "262/1000\n",
      "263/1000\n",
      "264/1000\n",
      "265/1000\n",
      "266/1000\n",
      "267/1000\n",
      "268/1000\n",
      "269/1000\n",
      "270/1000\n",
      "271/1000\n",
      "272/1000\n",
      "273/1000\n",
      "274/1000\n",
      "275/1000\n",
      "276/1000\n",
      "277/1000\n",
      "278/1000\n",
      "279/1000\n",
      "280/1000\n",
      "281/1000\n",
      "282/1000\n",
      "283/1000\n",
      "284/1000\n",
      "285/1000\n",
      "286/1000\n",
      "287/1000\n",
      "288/1000\n",
      "289/1000\n",
      "290/1000\n",
      "291/1000\n",
      "292/1000\n",
      "293/1000\n",
      "294/1000\n",
      "295/1000\n",
      "296/1000\n",
      "297/1000\n",
      "298/1000\n",
      "299/1000\n",
      "300/1000\n",
      "301/1000\n",
      "302/1000\n",
      "303/1000\n",
      "304/1000\n",
      "305/1000\n",
      "306/1000\n",
      "307/1000\n",
      "308/1000\n",
      "309/1000\n",
      "310/1000\n",
      "311/1000\n",
      "312/1000\n",
      "313/1000\n",
      "314/1000\n",
      "315/1000\n",
      "316/1000\n",
      "317/1000\n",
      "318/1000\n",
      "319/1000\n",
      "320/1000\n",
      "321/1000\n",
      "322/1000\n",
      "323/1000\n",
      "324/1000\n",
      "325/1000\n",
      "326/1000\n",
      "327/1000\n",
      "328/1000\n",
      "329/1000\n",
      "330/1000\n",
      "331/1000\n",
      "332/1000\n",
      "333/1000\n",
      "334/1000\n",
      "335/1000\n",
      "336/1000\n",
      "337/1000\n",
      "338/1000\n",
      "339/1000\n",
      "340/1000\n",
      "341/1000\n",
      "342/1000\n",
      "343/1000\n",
      "344/1000\n",
      "345/1000\n",
      "346/1000\n",
      "347/1000\n",
      "348/1000\n",
      "349/1000\n",
      "350/1000\n",
      "351/1000\n",
      "352/1000\n",
      "353/1000\n",
      "354/1000\n",
      "355/1000\n",
      "356/1000\n",
      "357/1000\n",
      "358/1000\n",
      "359/1000\n",
      "360/1000\n",
      "361/1000\n",
      "362/1000\n",
      "363/1000\n",
      "364/1000\n",
      "365/1000\n",
      "366/1000\n",
      "367/1000\n",
      "368/1000\n",
      "369/1000\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "373/1000\n",
      "374/1000\n",
      "375/1000\n",
      "376/1000\n",
      "377/1000\n",
      "378/1000\n",
      "379/1000\n",
      "380/1000\n",
      "381/1000\n",
      "382/1000\n",
      "383/1000\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "387/1000\n",
      "388/1000\n",
      "389/1000\n",
      "390/1000\n",
      "391/1000\n",
      "392/1000\n",
      "393/1000\n",
      "394/1000\n",
      "395/1000\n",
      "396/1000\n",
      "397/1000\n",
      "398/1000\n",
      "399/1000\n",
      "400/1000\n",
      "401/1000\n",
      "402/1000\n",
      "403/1000\n",
      "404/1000\n",
      "405/1000\n",
      "406/1000\n",
      "407/1000\n",
      "408/1000\n",
      "409/1000\n",
      "410/1000\n",
      "411/1000\n",
      "412/1000\n",
      "413/1000\n",
      "414/1000\n",
      "415/1000\n",
      "416/1000\n",
      "417/1000\n",
      "418/1000\n",
      "419/1000\n",
      "420/1000\n",
      "421/1000\n",
      "422/1000\n",
      "423/1000\n",
      "424/1000\n",
      "425/1000\n",
      "426/1000\n",
      "427/1000\n",
      "428/1000\n",
      "429/1000\n",
      "430/1000\n",
      "431/1000\n",
      "432/1000\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "436/1000\n",
      "437/1000\n",
      "438/1000\n",
      "439/1000\n",
      "440/1000\n",
      "441/1000\n",
      "442/1000\n",
      "443/1000\n",
      "444/1000\n",
      "445/1000\n",
      "446/1000\n",
      "447/1000\n",
      "448/1000\n",
      "449/1000\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "455/1000\n",
      "456/1000\n",
      "457/1000\n",
      "458/1000\n",
      "459/1000\n",
      "460/1000\n",
      "461/1000\n",
      "462/1000\n",
      "463/1000\n",
      "464/1000\n",
      "465/1000\n",
      "466/1000\n",
      "467/1000\n",
      "468/1000\n",
      "469/1000\n",
      "470/1000\n",
      "471/1000\n",
      "472/1000\n",
      "473/1000\n",
      "474/1000\n",
      "475/1000\n",
      "476/1000\n",
      "477/1000\n",
      "478/1000\n",
      "479/1000\n",
      "480/1000\n",
      "481/1000\n",
      "482/1000\n",
      "483/1000\n",
      "484/1000\n",
      "485/1000\n",
      "486/1000\n",
      "487/1000\n",
      "488/1000\n",
      "489/1000\n",
      "490/1000\n",
      "491/1000\n",
      "492/1000\n",
      "493/1000\n",
      "494/1000\n",
      "495/1000\n",
      "496/1000\n",
      "497/1000\n",
      "498/1000\n",
      "499/1000\n",
      "500/1000\n",
      "501/1000\n",
      "502/1000\n",
      "503/1000\n",
      "504/1000\n",
      "505/1000\n",
      "506/1000\n",
      "507/1000\n",
      "508/1000\n",
      "509/1000\n",
      "510/1000\n",
      "511/1000\n",
      "512/1000\n",
      "513/1000\n",
      "514/1000\n",
      "515/1000\n",
      "516/1000\n",
      "517/1000\n",
      "518/1000\n",
      "519/1000\n",
      "520/1000\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "524/1000\n",
      "525/1000\n",
      "526/1000\n",
      "527/1000\n",
      "528/1000\n",
      "529/1000\n",
      "530/1000\n",
      "531/1000\n",
      "532/1000\n",
      "533/1000\n",
      "534/1000\n",
      "535/1000\n",
      "536/1000\n",
      "537/1000\n",
      "538/1000\n",
      "539/1000\n",
      "540/1000\n",
      "541/1000\n",
      "542/1000\n",
      "543/1000\n",
      "544/1000\n",
      "545/1000\n",
      "546/1000\n",
      "547/1000\n",
      "548/1000\n",
      "549/1000\n",
      "550/1000\n",
      "551/1000\n",
      "552/1000\n",
      "553/1000\n",
      "554/1000\n",
      "555/1000\n",
      "556/1000\n",
      "557/1000\n",
      "558/1000\n",
      "559/1000\n",
      "560/1000\n",
      "561/1000\n",
      "562/1000\n",
      "563/1000\n",
      "564/1000\n",
      "565/1000\n",
      "566/1000\n",
      "567/1000\n",
      "568/1000\n",
      "569/1000\n",
      "570/1000\n",
      "571/1000\n",
      "572/1000\n",
      "573/1000\n",
      "574/1000\n",
      "575/1000\n",
      "576/1000\n",
      "577/1000\n",
      "578/1000\n",
      "579/1000\n",
      "580/1000\n",
      "581/1000\n",
      "582/1000\n",
      "583/1000\n",
      "584/1000\n",
      "585/1000\n",
      "586/1000\n",
      "587/1000\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "591/1000\n",
      "592/1000\n",
      "593/1000\n",
      "594/1000\n",
      "595/1000\n",
      "596/1000\n",
      "597/1000\n",
      "598/1000\n",
      "599/1000\n",
      "600/1000\n",
      "601/1000\n",
      "602/1000\n",
      "603/1000\n",
      "604/1000\n",
      "605/1000\n",
      "606/1000\n",
      "607/1000\n",
      "608/1000\n",
      "609/1000\n",
      "610/1000\n",
      "611/1000\n",
      "612/1000\n",
      "613/1000\n",
      "614/1000\n",
      "615/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "616/1000\n",
      "617/1000\n",
      "618/1000\n",
      "619/1000\n",
      "620/1000\n",
      "621/1000\n",
      "622/1000\n",
      "623/1000\n",
      "624/1000\n",
      "625/1000\n",
      "626/1000\n",
      "627/1000\n",
      "628/1000\n",
      "629/1000\n",
      "630/1000\n",
      "631/1000\n",
      "632/1000\n",
      "633/1000\n",
      "634/1000\n",
      "635/1000\n",
      "636/1000\n",
      "637/1000\n",
      "638/1000\n",
      "639/1000\n",
      "640/1000\n",
      "641/1000\n",
      "642/1000\n",
      "643/1000\n",
      "644/1000\n",
      "645/1000\n",
      "646/1000\n",
      "647/1000\n",
      "648/1000\n",
      "649/1000\n",
      "650/1000\n",
      "651/1000\n",
      "652/1000\n",
      "653/1000\n",
      "654/1000\n",
      "655/1000\n",
      "656/1000\n",
      "657/1000\n",
      "658/1000\n",
      "659/1000\n",
      "660/1000\n",
      "661/1000\n",
      "662/1000\n",
      "663/1000\n",
      "664/1000\n",
      "665/1000\n",
      "666/1000\n",
      "667/1000\n",
      "668/1000\n",
      "669/1000\n",
      "670/1000\n",
      "671/1000\n",
      "672/1000\n",
      "673/1000\n",
      "674/1000\n",
      "675/1000\n",
      "676/1000\n",
      "677/1000\n",
      "678/1000\n",
      "679/1000\n",
      "680/1000\n",
      "681/1000\n",
      "682/1000\n",
      "683/1000\n",
      "684/1000\n",
      "685/1000\n",
      "686/1000\n",
      "687/1000\n",
      "688/1000\n",
      "689/1000\n",
      "690/1000\n",
      "691/1000\n",
      "692/1000\n",
      "693/1000\n",
      "694/1000\n",
      "695/1000\n",
      "696/1000\n",
      "697/1000\n",
      "698/1000\n",
      "699/1000\n",
      "700/1000\n",
      "701/1000\n",
      "702/1000\n",
      "703/1000\n",
      "704/1000\n",
      "705/1000\n",
      "706/1000\n",
      "707/1000\n",
      "708/1000\n",
      "709/1000\n",
      "710/1000\n",
      "711/1000\n",
      "712/1000\n",
      "713/1000\n",
      "714/1000\n",
      "715/1000\n",
      "716/1000\n",
      "717/1000\n",
      "718/1000\n",
      "719/1000\n",
      "720/1000\n",
      "721/1000\n",
      "722/1000\n",
      "723/1000\n",
      "724/1000\n",
      "725/1000\n",
      "726/1000\n",
      "727/1000\n",
      "728/1000\n",
      "729/1000\n",
      "730/1000\n",
      "731/1000\n",
      "732/1000\n",
      "733/1000\n",
      "734/1000\n",
      "735/1000\n",
      "736/1000\n",
      "737/1000\n",
      "738/1000\n",
      "739/1000\n",
      "740/1000\n",
      "741/1000\n",
      "742/1000\n",
      "743/1000\n",
      "744/1000\n",
      "745/1000\n",
      "746/1000\n",
      "747/1000\n",
      "748/1000\n",
      "749/1000\n",
      "Fp_coord at 0 with multiple identical coordinates.\n",
      "Adjusted fp_time to take the first value\n",
      "750/1000\n",
      "751/1000\n",
      "752/1000\n",
      "753/1000\n",
      "754/1000\n",
      "755/1000\n",
      "756/1000\n",
      "757/1000\n",
      "758/1000\n",
      "759/1000\n",
      "760/1000\n",
      "761/1000\n",
      "762/1000\n",
      "763/1000\n",
      "764/1000\n",
      "765/1000\n",
      "766/1000\n",
      "767/1000\n",
      "768/1000\n",
      "769/1000\n",
      "770/1000\n",
      "771/1000\n",
      "772/1000\n",
      "773/1000\n",
      "774/1000\n",
      "775/1000\n",
      "776/1000\n",
      "777/1000\n",
      "778/1000\n",
      "779/1000\n",
      "780/1000\n",
      "781/1000\n",
      "782/1000\n",
      "783/1000\n",
      "784/1000\n",
      "785/1000\n",
      "786/1000\n",
      "787/1000\n",
      "788/1000\n",
      "789/1000\n",
      "790/1000\n",
      "791/1000\n",
      "792/1000\n",
      "793/1000\n",
      "794/1000\n",
      "795/1000\n",
      "796/1000\n",
      "797/1000\n",
      "798/1000\n",
      "799/1000\n",
      "800/1000\n",
      "801/1000\n",
      "802/1000\n",
      "803/1000\n",
      "804/1000\n",
      "805/1000\n",
      "806/1000\n",
      "807/1000\n",
      "808/1000\n",
      "809/1000\n",
      "810/1000\n",
      "811/1000\n",
      "812/1000\n",
      "813/1000\n",
      "814/1000\n",
      "815/1000\n",
      "816/1000\n",
      "817/1000\n",
      "818/1000\n",
      "819/1000\n",
      "820/1000\n",
      "821/1000\n",
      "822/1000\n",
      "823/1000\n",
      "824/1000\n",
      "825/1000\n",
      "826/1000\n",
      "827/1000\n",
      "828/1000\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "832/1000\n",
      "833/1000\n",
      "834/1000\n",
      "835/1000\n",
      "836/1000\n",
      "837/1000\n",
      "838/1000\n",
      "839/1000\n",
      "840/1000\n",
      "841/1000\n",
      "842/1000\n",
      "843/1000\n",
      "844/1000\n",
      "845/1000\n",
      "846/1000\n",
      "847/1000\n",
      "848/1000\n",
      "849/1000\n",
      "850/1000\n",
      "851/1000\n",
      "852/1000\n",
      "853/1000\n",
      "854/1000\n",
      "855/1000\n",
      "856/1000\n",
      "857/1000\n",
      "858/1000\n",
      "859/1000\n",
      "860/1000\n",
      "861/1000\n",
      "862/1000\n",
      "863/1000\n",
      "864/1000\n",
      "865/1000\n",
      "866/1000\n",
      "867/1000\n",
      "868/1000\n",
      "869/1000\n",
      "870/1000\n",
      "871/1000\n",
      "872/1000\n",
      "873/1000\n",
      "874/1000\n",
      "875/1000\n",
      "876/1000\n",
      "877/1000\n",
      "878/1000\n",
      "879/1000\n",
      "880/1000\n",
      "881/1000\n",
      "882/1000\n",
      "883/1000\n",
      "884/1000\n",
      "885/1000\n",
      "886/1000\n",
      "887/1000\n",
      "888/1000\n",
      "889/1000\n",
      "890/1000\n",
      "891/1000\n",
      "892/1000\n",
      "893/1000\n",
      "894/1000\n",
      "895/1000\n",
      "896/1000\n",
      "897/1000\n",
      "898/1000\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "903/1000\n",
      "904/1000\n",
      "905/1000\n",
      "906/1000\n",
      "907/1000\n",
      "908/1000\n",
      "909/1000\n",
      "910/1000\n",
      "911/1000\n",
      "912/1000\n",
      "913/1000\n",
      "914/1000\n",
      "915/1000\n",
      "916/1000\n",
      "917/1000\n",
      "918/1000\n",
      "919/1000\n",
      "920/1000\n",
      "921/1000\n",
      "922/1000\n",
      "923/1000\n",
      "924/1000\n",
      "925/1000\n",
      "926/1000\n",
      "927/1000\n",
      "928/1000\n",
      "929/1000\n",
      "930/1000\n",
      "931/1000\n",
      "932/1000\n",
      "933/1000\n",
      "934/1000\n",
      "935/1000\n",
      "936/1000\n",
      "937/1000\n",
      "938/1000\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "942/1000\n",
      "943/1000\n",
      "944/1000\n",
      "945/1000\n",
      "946/1000\n",
      "947/1000\n",
      "948/1000\n",
      "949/1000\n",
      "950/1000\n",
      "951/1000\n",
      "952/1000\n",
      "953/1000\n",
      "954/1000\n",
      "955/1000\n",
      "956/1000\n",
      "957/1000\n",
      "958/1000\n",
      "959/1000\n",
      "960/1000\n",
      "961/1000\n",
      "962/1000\n",
      "963/1000\n",
      "964/1000\n",
      "965/1000\n",
      "966/1000\n",
      "967/1000\n",
      "968/1000\n",
      "969/1000\n",
      "970/1000\n",
      "971/1000\n",
      "972/1000\n",
      "973/1000\n",
      "974/1000\n",
      "975/1000\n",
      "976/1000\n",
      "977/1000\n",
      "978/1000\n",
      "979/1000\n",
      "980/1000\n",
      "981/1000\n",
      "982/1000\n",
      "983/1000\n",
      "984/1000\n",
      "985/1000\n",
      "986/1000\n",
      "987/1000\n",
      "988/1000\n",
      "989/1000\n",
      "990/1000\n",
      "991/1000\n",
      "992/1000\n",
      "993/1000\n",
      "994/1000\n",
      "995/1000\n",
      "996/1000\n",
      "997/1000\n",
      "998/1000\n",
      "999/1000\n"
     ]
    }
   ],
   "source": [
    "switching_A = switch_times(em_traj_A, dist_entry_x, dist_entry_y, dist_exit_x, dist_exit_y, stable_steady_states_A)\n",
    "switching_AB = switch_times(em_traj_AB, dist_entry_x, dist_entry_y, dist_exit_x, dist_exit_y, stable_steady_states_AB)\n",
    "switching_B = switch_times(em_traj_B, dist_entry_x, dist_entry_y, dist_exit_x, dist_exit_y, stable_steady_states_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/1000\n",
      "1/1000\n",
      "2/1000\n",
      "3/1000\n",
      "4/1000\n",
      "5/1000\n",
      "6/1000\n",
      "7/1000\n",
      "8/1000\n",
      "9/1000\n",
      "10/1000\n",
      "11/1000\n",
      "12/1000\n",
      "13/1000\n",
      "14/1000\n",
      "15/1000\n",
      "16/1000\n",
      "17/1000\n",
      "18/1000\n",
      "19/1000\n",
      "20/1000\n",
      "21/1000\n",
      "22/1000\n",
      "23/1000\n",
      "24/1000\n",
      "25/1000\n",
      "26/1000\n",
      "27/1000\n",
      "28/1000\n",
      "29/1000\n",
      "30/1000\n",
      "31/1000\n",
      "32/1000\n",
      "33/1000\n",
      "34/1000\n",
      "35/1000\n",
      "36/1000\n",
      "37/1000\n",
      "38/1000\n",
      "39/1000\n",
      "40/1000\n",
      "41/1000\n",
      "42/1000\n",
      "43/1000\n",
      "44/1000\n",
      "45/1000\n",
      "46/1000\n",
      "47/1000\n",
      "48/1000\n",
      "49/1000\n",
      "50/1000\n",
      "51/1000\n",
      "52/1000\n",
      "53/1000\n",
      "54/1000\n",
      "55/1000\n",
      "56/1000\n",
      "57/1000\n",
      "58/1000\n",
      "59/1000\n",
      "60/1000\n",
      "61/1000\n",
      "62/1000\n",
      "63/1000\n",
      "64/1000\n",
      "65/1000\n",
      "66/1000\n",
      "67/1000\n",
      "68/1000\n",
      "69/1000\n",
      "70/1000\n",
      "71/1000\n",
      "72/1000\n",
      "73/1000\n",
      "74/1000\n",
      "75/1000\n",
      "76/1000\n",
      "77/1000\n",
      "78/1000\n",
      "79/1000\n",
      "80/1000\n",
      "81/1000\n",
      "82/1000\n",
      "83/1000\n",
      "84/1000\n",
      "85/1000\n",
      "86/1000\n",
      "87/1000\n",
      "88/1000\n",
      "89/1000\n",
      "90/1000\n",
      "91/1000\n",
      "92/1000\n",
      "93/1000\n",
      "94/1000\n",
      "95/1000\n",
      "96/1000\n",
      "97/1000\n",
      "98/1000\n",
      "99/1000\n",
      "100/1000\n",
      "101/1000\n",
      "102/1000\n",
      "103/1000\n",
      "104/1000\n",
      "105/1000\n",
      "106/1000\n",
      "107/1000\n",
      "108/1000\n",
      "109/1000\n",
      "110/1000\n",
      "111/1000\n",
      "112/1000\n",
      "113/1000\n",
      "114/1000\n",
      "115/1000\n",
      "116/1000\n",
      "117/1000\n",
      "118/1000\n",
      "119/1000\n",
      "120/1000\n",
      "121/1000\n",
      "122/1000\n",
      "123/1000\n",
      "124/1000\n",
      "125/1000\n",
      "126/1000\n",
      "127/1000\n",
      "128/1000\n",
      "129/1000\n",
      "130/1000\n",
      "131/1000\n",
      "132/1000\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "136/1000\n",
      "137/1000\n",
      "138/1000\n",
      "139/1000\n",
      "140/1000\n",
      "141/1000\n",
      "142/1000\n",
      "143/1000\n",
      "144/1000\n",
      "145/1000\n",
      "146/1000\n",
      "147/1000\n",
      "148/1000\n",
      "149/1000\n",
      "150/1000\n",
      "151/1000\n",
      "152/1000\n",
      "153/1000\n",
      "154/1000\n",
      "155/1000\n",
      "156/1000\n",
      "157/1000\n",
      "158/1000\n",
      "159/1000\n",
      "160/1000\n",
      "161/1000\n",
      "162/1000\n",
      "163/1000\n",
      "164/1000\n",
      "165/1000\n",
      "166/1000\n",
      "167/1000\n",
      "168/1000\n",
      "169/1000\n",
      "170/1000\n",
      "171/1000\n",
      "172/1000\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "176/1000\n",
      "177/1000\n",
      "178/1000\n",
      "179/1000\n",
      "180/1000\n",
      "181/1000\n",
      "182/1000\n",
      "183/1000\n",
      "184/1000\n",
      "185/1000\n",
      "186/1000\n",
      "187/1000\n",
      "188/1000\n",
      "189/1000\n",
      "190/1000\n",
      "191/1000\n",
      "192/1000\n",
      "193/1000\n",
      "194/1000\n",
      "195/1000\n",
      "196/1000\n",
      "197/1000\n",
      "198/1000\n",
      "199/1000\n",
      "200/1000\n",
      "201/1000\n",
      "202/1000\n",
      "203/1000\n",
      "204/1000\n",
      "205/1000\n",
      "206/1000\n",
      "207/1000\n",
      "208/1000\n",
      "209/1000\n",
      "210/1000\n",
      "211/1000\n",
      "212/1000\n",
      "213/1000\n",
      "214/1000\n",
      "215/1000\n",
      "216/1000\n",
      "217/1000\n",
      "218/1000\n",
      "219/1000\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "223/1000\n",
      "224/1000\n",
      "225/1000\n",
      "226/1000\n",
      "227/1000\n",
      "228/1000\n",
      "229/1000\n",
      "230/1000\n",
      "231/1000\n",
      "232/1000\n",
      "233/1000\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "237/1000\n",
      "238/1000\n",
      "239/1000\n",
      "240/1000\n",
      "241/1000\n",
      "242/1000\n",
      "243/1000\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "247/1000\n",
      "248/1000\n",
      "249/1000\n",
      "250/1000\n",
      "251/1000\n",
      "252/1000\n",
      "253/1000\n",
      "254/1000\n",
      "255/1000\n",
      "256/1000\n",
      "257/1000\n",
      "258/1000\n",
      "259/1000\n",
      "260/1000\n",
      "261/1000\n",
      "262/1000\n",
      "263/1000\n",
      "264/1000\n",
      "265/1000\n",
      "266/1000\n",
      "267/1000\n",
      "268/1000\n",
      "269/1000\n",
      "270/1000\n",
      "271/1000\n",
      "272/1000\n",
      "273/1000\n",
      "274/1000\n",
      "275/1000\n",
      "276/1000\n",
      "277/1000\n",
      "278/1000\n",
      "279/1000\n",
      "280/1000\n",
      "281/1000\n",
      "282/1000\n",
      "283/1000\n",
      "284/1000\n",
      "285/1000\n",
      "286/1000\n",
      "287/1000\n",
      "288/1000\n",
      "289/1000\n",
      "290/1000\n",
      "291/1000\n",
      "292/1000\n",
      "293/1000\n",
      "294/1000\n",
      "295/1000\n",
      "296/1000\n",
      "297/1000\n",
      "298/1000\n",
      "299/1000\n",
      "300/1000\n",
      "301/1000\n",
      "302/1000\n",
      "303/1000\n",
      "304/1000\n",
      "305/1000\n",
      "306/1000\n",
      "307/1000\n",
      "308/1000\n",
      "309/1000\n",
      "310/1000\n",
      "311/1000\n",
      "312/1000\n",
      "313/1000\n",
      "314/1000\n",
      "315/1000\n",
      "316/1000\n",
      "317/1000\n",
      "318/1000\n",
      "319/1000\n",
      "320/1000\n",
      "321/1000\n",
      "322/1000\n",
      "323/1000\n",
      "324/1000\n",
      "325/1000\n",
      "326/1000\n",
      "327/1000\n",
      "328/1000\n",
      "329/1000\n",
      "330/1000\n",
      "331/1000\n",
      "332/1000\n",
      "333/1000\n",
      "334/1000\n",
      "335/1000\n",
      "336/1000\n",
      "337/1000\n",
      "338/1000\n",
      "339/1000\n",
      "340/1000\n",
      "341/1000\n",
      "342/1000\n",
      "343/1000\n",
      "344/1000\n",
      "345/1000\n",
      "346/1000\n",
      "347/1000\n",
      "348/1000\n",
      "349/1000\n",
      "350/1000\n",
      "351/1000\n",
      "352/1000\n",
      "353/1000\n",
      "354/1000\n",
      "355/1000\n",
      "356/1000\n",
      "357/1000\n",
      "358/1000\n",
      "359/1000\n",
      "360/1000\n",
      "361/1000\n",
      "362/1000\n",
      "363/1000\n",
      "364/1000\n",
      "365/1000\n",
      "366/1000\n",
      "367/1000\n",
      "368/1000\n",
      "369/1000\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "373/1000\n",
      "374/1000\n",
      "375/1000\n",
      "376/1000\n",
      "377/1000\n",
      "378/1000\n",
      "379/1000\n",
      "380/1000\n",
      "381/1000\n",
      "382/1000\n",
      "383/1000\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "387/1000\n",
      "388/1000\n",
      "389/1000\n",
      "390/1000\n",
      "391/1000\n",
      "392/1000\n",
      "393/1000\n",
      "394/1000\n",
      "395/1000\n",
      "396/1000\n",
      "397/1000\n",
      "398/1000\n",
      "399/1000\n",
      "400/1000\n",
      "401/1000\n",
      "402/1000\n",
      "403/1000\n",
      "404/1000\n",
      "405/1000\n",
      "406/1000\n",
      "407/1000\n",
      "408/1000\n",
      "409/1000\n",
      "410/1000\n",
      "411/1000\n",
      "412/1000\n",
      "413/1000\n",
      "414/1000\n",
      "415/1000\n",
      "416/1000\n",
      "417/1000\n",
      "418/1000\n",
      "419/1000\n",
      "420/1000\n",
      "421/1000\n",
      "422/1000\n",
      "423/1000\n",
      "424/1000\n",
      "425/1000\n",
      "426/1000\n",
      "427/1000\n",
      "428/1000\n",
      "429/1000\n",
      "430/1000\n",
      "431/1000\n",
      "432/1000\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "436/1000\n",
      "437/1000\n",
      "438/1000\n",
      "439/1000\n",
      "440/1000\n",
      "441/1000\n",
      "442/1000\n",
      "443/1000\n",
      "444/1000\n",
      "445/1000\n",
      "446/1000\n",
      "447/1000\n",
      "448/1000\n",
      "449/1000\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "455/1000\n",
      "456/1000\n",
      "457/1000\n",
      "458/1000\n",
      "459/1000\n",
      "460/1000\n",
      "461/1000\n",
      "462/1000\n",
      "463/1000\n",
      "464/1000\n",
      "465/1000\n",
      "466/1000\n",
      "467/1000\n",
      "468/1000\n",
      "469/1000\n",
      "470/1000\n",
      "471/1000\n",
      "472/1000\n",
      "473/1000\n",
      "474/1000\n",
      "475/1000\n",
      "476/1000\n",
      "477/1000\n",
      "478/1000\n",
      "479/1000\n",
      "480/1000\n",
      "481/1000\n",
      "482/1000\n",
      "483/1000\n",
      "484/1000\n",
      "485/1000\n",
      "486/1000\n",
      "487/1000\n",
      "488/1000\n",
      "489/1000\n",
      "490/1000\n",
      "491/1000\n",
      "492/1000\n",
      "493/1000\n",
      "494/1000\n",
      "495/1000\n",
      "496/1000\n",
      "497/1000\n",
      "498/1000\n",
      "499/1000\n",
      "500/1000\n",
      "501/1000\n",
      "502/1000\n",
      "503/1000\n",
      "504/1000\n",
      "505/1000\n",
      "506/1000\n",
      "507/1000\n",
      "508/1000\n",
      "509/1000\n",
      "510/1000\n",
      "511/1000\n",
      "512/1000\n",
      "513/1000\n",
      "514/1000\n",
      "515/1000\n",
      "516/1000\n",
      "517/1000\n",
      "518/1000\n",
      "519/1000\n",
      "520/1000\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "524/1000\n",
      "525/1000\n",
      "526/1000\n",
      "527/1000\n",
      "528/1000\n",
      "529/1000\n",
      "530/1000\n",
      "531/1000\n",
      "532/1000\n",
      "533/1000\n",
      "534/1000\n",
      "535/1000\n",
      "536/1000\n",
      "537/1000\n",
      "538/1000\n",
      "539/1000\n",
      "540/1000\n",
      "541/1000\n",
      "542/1000\n",
      "543/1000\n",
      "544/1000\n",
      "545/1000\n",
      "546/1000\n",
      "547/1000\n",
      "548/1000\n",
      "549/1000\n",
      "550/1000\n",
      "551/1000\n",
      "552/1000\n",
      "553/1000\n",
      "554/1000\n",
      "555/1000\n",
      "556/1000\n",
      "557/1000\n",
      "558/1000\n",
      "559/1000\n",
      "560/1000\n",
      "561/1000\n",
      "562/1000\n",
      "563/1000\n",
      "564/1000\n",
      "565/1000\n",
      "566/1000\n",
      "567/1000\n",
      "568/1000\n",
      "569/1000\n",
      "570/1000\n",
      "571/1000\n",
      "572/1000\n",
      "573/1000\n",
      "574/1000\n",
      "575/1000\n",
      "576/1000\n",
      "577/1000\n",
      "578/1000\n",
      "579/1000\n",
      "580/1000\n",
      "581/1000\n",
      "582/1000\n",
      "583/1000\n",
      "584/1000\n",
      "585/1000\n",
      "586/1000\n",
      "587/1000\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "591/1000\n",
      "592/1000\n",
      "593/1000\n",
      "594/1000\n",
      "595/1000\n",
      "596/1000\n",
      "597/1000\n",
      "598/1000\n",
      "599/1000\n",
      "600/1000\n",
      "601/1000\n",
      "602/1000\n",
      "603/1000\n",
      "604/1000\n",
      "605/1000\n",
      "606/1000\n",
      "607/1000\n",
      "608/1000\n",
      "609/1000\n",
      "610/1000\n",
      "611/1000\n",
      "612/1000\n",
      "613/1000\n",
      "614/1000\n",
      "615/1000\n",
      "616/1000\n",
      "617/1000\n",
      "618/1000\n",
      "619/1000\n",
      "620/1000\n",
      "621/1000\n",
      "622/1000\n",
      "623/1000\n",
      "624/1000\n",
      "625/1000\n",
      "626/1000\n",
      "627/1000\n",
      "628/1000\n",
      "629/1000\n",
      "630/1000\n",
      "631/1000\n",
      "632/1000\n",
      "633/1000\n",
      "634/1000\n",
      "635/1000\n",
      "636/1000\n",
      "637/1000\n",
      "638/1000\n",
      "639/1000\n",
      "640/1000\n",
      "641/1000\n",
      "642/1000\n",
      "643/1000\n",
      "644/1000\n",
      "645/1000\n",
      "646/1000\n",
      "647/1000\n",
      "648/1000\n",
      "649/1000\n",
      "650/1000\n",
      "651/1000\n",
      "652/1000\n",
      "653/1000\n",
      "654/1000\n",
      "655/1000\n",
      "656/1000\n",
      "657/1000\n",
      "658/1000\n",
      "659/1000\n",
      "660/1000\n",
      "661/1000\n",
      "662/1000\n",
      "663/1000\n",
      "664/1000\n",
      "665/1000\n",
      "666/1000\n",
      "667/1000\n",
      "668/1000\n",
      "669/1000\n",
      "670/1000\n",
      "671/1000\n",
      "672/1000\n",
      "673/1000\n",
      "674/1000\n",
      "675/1000\n",
      "676/1000\n",
      "677/1000\n",
      "678/1000\n",
      "679/1000\n",
      "680/1000\n",
      "681/1000\n",
      "682/1000\n",
      "683/1000\n",
      "684/1000\n",
      "685/1000\n",
      "686/1000\n",
      "687/1000\n",
      "688/1000\n",
      "689/1000\n",
      "690/1000\n",
      "691/1000\n",
      "692/1000\n",
      "693/1000\n",
      "694/1000\n",
      "695/1000\n",
      "696/1000\n",
      "697/1000\n",
      "698/1000\n",
      "699/1000\n",
      "700/1000\n",
      "701/1000\n",
      "702/1000\n",
      "703/1000\n",
      "704/1000\n",
      "705/1000\n",
      "706/1000\n",
      "707/1000\n",
      "708/1000\n",
      "709/1000\n",
      "710/1000\n",
      "711/1000\n",
      "712/1000\n",
      "713/1000\n",
      "714/1000\n",
      "715/1000\n",
      "716/1000\n",
      "717/1000\n",
      "718/1000\n",
      "719/1000\n",
      "720/1000\n",
      "721/1000\n",
      "722/1000\n",
      "723/1000\n",
      "724/1000\n",
      "725/1000\n",
      "726/1000\n",
      "727/1000\n",
      "728/1000\n",
      "729/1000\n",
      "730/1000\n",
      "731/1000\n",
      "732/1000\n",
      "733/1000\n",
      "734/1000\n",
      "735/1000\n",
      "736/1000\n",
      "737/1000\n",
      "738/1000\n",
      "739/1000\n",
      "740/1000\n",
      "741/1000\n",
      "742/1000\n",
      "743/1000\n",
      "744/1000\n",
      "745/1000\n",
      "746/1000\n",
      "747/1000\n",
      "748/1000\n",
      "749/1000\n",
      "750/1000\n",
      "751/1000\n",
      "752/1000\n",
      "753/1000\n",
      "754/1000\n",
      "755/1000\n",
      "756/1000\n",
      "757/1000\n",
      "758/1000\n",
      "759/1000\n",
      "760/1000\n",
      "761/1000\n",
      "762/1000\n",
      "763/1000\n",
      "764/1000\n",
      "765/1000\n",
      "766/1000\n",
      "767/1000\n",
      "768/1000\n",
      "769/1000\n",
      "770/1000\n",
      "771/1000\n",
      "772/1000\n",
      "773/1000\n",
      "774/1000\n",
      "775/1000\n",
      "776/1000\n",
      "777/1000\n",
      "778/1000\n",
      "779/1000\n",
      "780/1000\n",
      "781/1000\n",
      "782/1000\n",
      "783/1000\n",
      "784/1000\n",
      "785/1000\n",
      "786/1000\n",
      "787/1000\n",
      "788/1000\n",
      "789/1000\n",
      "790/1000\n",
      "791/1000\n",
      "792/1000\n",
      "793/1000\n",
      "794/1000\n",
      "795/1000\n",
      "796/1000\n",
      "797/1000\n",
      "798/1000\n",
      "799/1000\n",
      "800/1000\n",
      "801/1000\n",
      "802/1000\n",
      "803/1000\n",
      "804/1000\n",
      "805/1000\n",
      "806/1000\n",
      "807/1000\n",
      "808/1000\n",
      "809/1000\n",
      "810/1000\n",
      "811/1000\n",
      "812/1000\n",
      "813/1000\n",
      "814/1000\n",
      "815/1000\n",
      "816/1000\n",
      "817/1000\n",
      "818/1000\n",
      "819/1000\n",
      "820/1000\n",
      "821/1000\n",
      "822/1000\n",
      "823/1000\n",
      "824/1000\n",
      "825/1000\n",
      "826/1000\n",
      "827/1000\n",
      "828/1000\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "832/1000\n",
      "833/1000\n",
      "834/1000\n",
      "835/1000\n",
      "836/1000\n",
      "837/1000\n",
      "838/1000\n",
      "839/1000\n",
      "840/1000\n",
      "841/1000\n",
      "842/1000\n",
      "843/1000\n",
      "844/1000\n",
      "845/1000\n",
      "846/1000\n",
      "847/1000\n",
      "848/1000\n",
      "849/1000\n",
      "850/1000\n",
      "851/1000\n",
      "852/1000\n",
      "853/1000\n",
      "854/1000\n",
      "855/1000\n",
      "856/1000\n",
      "857/1000\n",
      "858/1000\n",
      "859/1000\n",
      "860/1000\n",
      "861/1000\n",
      "862/1000\n",
      "863/1000\n",
      "864/1000\n",
      "865/1000\n",
      "866/1000\n",
      "867/1000\n",
      "868/1000\n",
      "869/1000\n",
      "870/1000\n",
      "871/1000\n",
      "872/1000\n",
      "873/1000\n",
      "874/1000\n",
      "875/1000\n",
      "876/1000\n",
      "877/1000\n",
      "878/1000\n",
      "879/1000\n",
      "880/1000\n",
      "881/1000\n",
      "882/1000\n",
      "883/1000\n",
      "884/1000\n",
      "885/1000\n",
      "886/1000\n",
      "887/1000\n",
      "888/1000\n",
      "889/1000\n",
      "890/1000\n",
      "891/1000\n",
      "892/1000\n",
      "893/1000\n",
      "894/1000\n",
      "895/1000\n",
      "896/1000\n",
      "897/1000\n",
      "898/1000\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "903/1000\n",
      "904/1000\n",
      "905/1000\n",
      "906/1000\n",
      "907/1000\n",
      "908/1000\n",
      "909/1000\n",
      "910/1000\n",
      "911/1000\n",
      "912/1000\n",
      "913/1000\n",
      "914/1000\n",
      "915/1000\n",
      "916/1000\n",
      "917/1000\n",
      "918/1000\n",
      "919/1000\n",
      "920/1000\n",
      "921/1000\n",
      "922/1000\n",
      "923/1000\n",
      "924/1000\n",
      "925/1000\n",
      "926/1000\n",
      "927/1000\n",
      "928/1000\n",
      "929/1000\n",
      "930/1000\n",
      "931/1000\n",
      "932/1000\n",
      "933/1000\n",
      "934/1000\n",
      "935/1000\n",
      "936/1000\n",
      "937/1000\n",
      "938/1000\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "942/1000\n",
      "943/1000\n",
      "944/1000\n",
      "945/1000\n",
      "946/1000\n",
      "947/1000\n",
      "948/1000\n",
      "949/1000\n",
      "950/1000\n",
      "951/1000\n",
      "952/1000\n",
      "953/1000\n",
      "954/1000\n",
      "955/1000\n",
      "956/1000\n",
      "957/1000\n",
      "958/1000\n",
      "959/1000\n",
      "960/1000\n",
      "961/1000\n",
      "962/1000\n",
      "963/1000\n",
      "964/1000\n",
      "965/1000\n",
      "966/1000\n",
      "967/1000\n",
      "968/1000\n",
      "969/1000\n",
      "970/1000\n",
      "971/1000\n",
      "972/1000\n",
      "973/1000\n",
      "974/1000\n",
      "975/1000\n",
      "976/1000\n",
      "977/1000\n",
      "978/1000\n",
      "979/1000\n",
      "980/1000\n",
      "981/1000\n",
      "982/1000\n",
      "983/1000\n",
      "984/1000\n",
      "985/1000\n",
      "986/1000\n",
      "987/1000\n",
      "988/1000\n",
      "989/1000\n",
      "990/1000\n",
      "991/1000\n",
      "992/1000\n",
      "993/1000\n",
      "994/1000\n",
      "995/1000\n",
      "996/1000\n",
      "997/1000\n",
      "998/1000\n",
      "999/1000\n",
      "0/1000\n",
      "1/1000\n",
      "2/1000\n",
      "3/1000\n",
      "4/1000\n",
      "5/1000\n",
      "6/1000\n",
      "7/1000\n",
      "8/1000\n",
      "9/1000\n",
      "10/1000\n",
      "11/1000\n",
      "12/1000\n",
      "13/1000\n",
      "14/1000\n",
      "15/1000\n",
      "16/1000\n",
      "17/1000\n",
      "18/1000\n",
      "19/1000\n",
      "20/1000\n",
      "21/1000\n",
      "22/1000\n",
      "23/1000\n",
      "24/1000\n",
      "25/1000\n",
      "26/1000\n",
      "27/1000\n",
      "28/1000\n",
      "29/1000\n",
      "30/1000\n",
      "31/1000\n",
      "32/1000\n",
      "33/1000\n",
      "34/1000\n",
      "35/1000\n",
      "36/1000\n",
      "37/1000\n",
      "38/1000\n",
      "39/1000\n",
      "40/1000\n",
      "41/1000\n",
      "42/1000\n",
      "43/1000\n",
      "44/1000\n",
      "45/1000\n",
      "46/1000\n",
      "47/1000\n",
      "48/1000\n",
      "49/1000\n",
      "50/1000\n",
      "51/1000\n",
      "52/1000\n",
      "53/1000\n",
      "54/1000\n",
      "55/1000\n",
      "56/1000\n",
      "57/1000\n",
      "58/1000\n",
      "59/1000\n",
      "60/1000\n",
      "61/1000\n",
      "62/1000\n",
      "63/1000\n",
      "64/1000\n",
      "65/1000\n",
      "66/1000\n",
      "67/1000\n",
      "68/1000\n",
      "69/1000\n",
      "70/1000\n",
      "71/1000\n",
      "72/1000\n",
      "73/1000\n",
      "74/1000\n",
      "75/1000\n",
      "76/1000\n",
      "77/1000\n",
      "78/1000\n",
      "79/1000\n",
      "80/1000\n",
      "81/1000\n",
      "82/1000\n",
      "83/1000\n",
      "84/1000\n",
      "85/1000\n",
      "86/1000\n",
      "87/1000\n",
      "88/1000\n",
      "89/1000\n",
      "90/1000\n",
      "91/1000\n",
      "92/1000\n",
      "93/1000\n",
      "94/1000\n",
      "95/1000\n",
      "96/1000\n",
      "97/1000\n",
      "98/1000\n",
      "99/1000\n",
      "100/1000\n",
      "101/1000\n",
      "102/1000\n",
      "103/1000\n",
      "104/1000\n",
      "105/1000\n",
      "106/1000\n",
      "107/1000\n",
      "108/1000\n",
      "109/1000\n",
      "110/1000\n",
      "111/1000\n",
      "112/1000\n",
      "113/1000\n",
      "114/1000\n",
      "115/1000\n",
      "116/1000\n",
      "117/1000\n",
      "118/1000\n",
      "119/1000\n",
      "120/1000\n",
      "121/1000\n",
      "122/1000\n",
      "123/1000\n",
      "124/1000\n",
      "125/1000\n",
      "126/1000\n",
      "127/1000\n",
      "128/1000\n",
      "129/1000\n",
      "130/1000\n",
      "131/1000\n",
      "132/1000\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "136/1000\n",
      "137/1000\n",
      "138/1000\n",
      "139/1000\n",
      "140/1000\n",
      "141/1000\n",
      "142/1000\n",
      "143/1000\n",
      "144/1000\n",
      "145/1000\n",
      "146/1000\n",
      "147/1000\n",
      "148/1000\n",
      "149/1000\n",
      "150/1000\n",
      "151/1000\n",
      "152/1000\n",
      "153/1000\n",
      "154/1000\n",
      "155/1000\n",
      "156/1000\n",
      "157/1000\n",
      "158/1000\n",
      "159/1000\n",
      "160/1000\n",
      "161/1000\n",
      "162/1000\n",
      "163/1000\n",
      "164/1000\n",
      "165/1000\n",
      "166/1000\n",
      "167/1000\n",
      "168/1000\n",
      "169/1000\n",
      "170/1000\n",
      "171/1000\n",
      "172/1000\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "176/1000\n",
      "177/1000\n",
      "178/1000\n",
      "179/1000\n",
      "180/1000\n",
      "181/1000\n",
      "182/1000\n",
      "183/1000\n",
      "184/1000\n",
      "185/1000\n",
      "186/1000\n",
      "187/1000\n",
      "188/1000\n",
      "189/1000\n",
      "190/1000\n",
      "191/1000\n",
      "192/1000\n",
      "193/1000\n",
      "194/1000\n",
      "195/1000\n",
      "196/1000\n",
      "197/1000\n",
      "198/1000\n",
      "199/1000\n",
      "200/1000\n",
      "201/1000\n",
      "202/1000\n",
      "203/1000\n",
      "204/1000\n",
      "205/1000\n",
      "206/1000\n",
      "207/1000\n",
      "208/1000\n",
      "209/1000\n",
      "210/1000\n",
      "211/1000\n",
      "212/1000\n",
      "213/1000\n",
      "214/1000\n",
      "215/1000\n",
      "216/1000\n",
      "217/1000\n",
      "218/1000\n",
      "219/1000\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "223/1000\n",
      "224/1000\n",
      "225/1000\n",
      "226/1000\n",
      "227/1000\n",
      "228/1000\n",
      "229/1000\n",
      "230/1000\n",
      "231/1000\n",
      "232/1000\n",
      "233/1000\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "237/1000\n",
      "238/1000\n",
      "239/1000\n",
      "240/1000\n",
      "241/1000\n",
      "242/1000\n",
      "243/1000\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "247/1000\n",
      "248/1000\n",
      "249/1000\n",
      "250/1000\n",
      "251/1000\n",
      "252/1000\n",
      "253/1000\n",
      "254/1000\n",
      "255/1000\n",
      "256/1000\n",
      "257/1000\n",
      "258/1000\n",
      "259/1000\n",
      "260/1000\n",
      "261/1000\n",
      "262/1000\n",
      "263/1000\n",
      "264/1000\n",
      "265/1000\n",
      "266/1000\n",
      "267/1000\n",
      "268/1000\n",
      "269/1000\n",
      "270/1000\n",
      "271/1000\n",
      "272/1000\n",
      "273/1000\n",
      "274/1000\n",
      "275/1000\n",
      "276/1000\n",
      "277/1000\n",
      "278/1000\n",
      "279/1000\n",
      "280/1000\n",
      "281/1000\n",
      "282/1000\n",
      "283/1000\n",
      "284/1000\n",
      "285/1000\n",
      "286/1000\n",
      "287/1000\n",
      "288/1000\n",
      "289/1000\n",
      "290/1000\n",
      "291/1000\n",
      "292/1000\n",
      "293/1000\n",
      "294/1000\n",
      "295/1000\n",
      "296/1000\n",
      "297/1000\n",
      "298/1000\n",
      "299/1000\n",
      "300/1000\n",
      "301/1000\n",
      "302/1000\n",
      "303/1000\n",
      "304/1000\n",
      "305/1000\n",
      "306/1000\n",
      "307/1000\n",
      "308/1000\n",
      "309/1000\n",
      "310/1000\n",
      "311/1000\n",
      "312/1000\n",
      "313/1000\n",
      "314/1000\n",
      "315/1000\n",
      "316/1000\n",
      "317/1000\n",
      "318/1000\n",
      "319/1000\n",
      "320/1000\n",
      "321/1000\n",
      "322/1000\n",
      "323/1000\n",
      "324/1000\n",
      "325/1000\n",
      "326/1000\n",
      "327/1000\n",
      "328/1000\n",
      "329/1000\n",
      "330/1000\n",
      "331/1000\n",
      "332/1000\n",
      "333/1000\n",
      "334/1000\n",
      "335/1000\n",
      "336/1000\n",
      "337/1000\n",
      "338/1000\n",
      "339/1000\n",
      "340/1000\n",
      "341/1000\n",
      "342/1000\n",
      "343/1000\n",
      "344/1000\n",
      "345/1000\n",
      "346/1000\n",
      "347/1000\n",
      "348/1000\n",
      "349/1000\n",
      "350/1000\n",
      "351/1000\n",
      "352/1000\n",
      "353/1000\n",
      "354/1000\n",
      "355/1000\n",
      "356/1000\n",
      "357/1000\n",
      "358/1000\n",
      "359/1000\n",
      "360/1000\n",
      "361/1000\n",
      "362/1000\n",
      "363/1000\n",
      "364/1000\n",
      "365/1000\n",
      "366/1000\n",
      "367/1000\n",
      "368/1000\n",
      "369/1000\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "373/1000\n",
      "374/1000\n",
      "375/1000\n",
      "376/1000\n",
      "377/1000\n",
      "378/1000\n",
      "379/1000\n",
      "380/1000\n",
      "381/1000\n",
      "382/1000\n",
      "383/1000\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "387/1000\n",
      "388/1000\n",
      "389/1000\n",
      "390/1000\n",
      "391/1000\n",
      "392/1000\n",
      "393/1000\n",
      "394/1000\n",
      "395/1000\n",
      "396/1000\n",
      "397/1000\n",
      "398/1000\n",
      "399/1000\n",
      "400/1000\n",
      "401/1000\n",
      "402/1000\n",
      "403/1000\n",
      "404/1000\n",
      "405/1000\n",
      "406/1000\n",
      "407/1000\n",
      "408/1000\n",
      "409/1000\n",
      "410/1000\n",
      "411/1000\n",
      "412/1000\n",
      "413/1000\n",
      "414/1000\n",
      "415/1000\n",
      "416/1000\n",
      "417/1000\n",
      "418/1000\n",
      "419/1000\n",
      "420/1000\n",
      "421/1000\n",
      "422/1000\n",
      "423/1000\n",
      "424/1000\n",
      "425/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "426/1000\n",
      "427/1000\n",
      "428/1000\n",
      "429/1000\n",
      "430/1000\n",
      "431/1000\n",
      "432/1000\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "436/1000\n",
      "437/1000\n",
      "438/1000\n",
      "439/1000\n",
      "440/1000\n",
      "441/1000\n",
      "442/1000\n",
      "443/1000\n",
      "444/1000\n",
      "445/1000\n",
      "446/1000\n",
      "447/1000\n",
      "448/1000\n",
      "449/1000\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "455/1000\n",
      "456/1000\n",
      "457/1000\n",
      "458/1000\n",
      "459/1000\n",
      "460/1000\n",
      "461/1000\n",
      "462/1000\n",
      "463/1000\n",
      "464/1000\n",
      "465/1000\n",
      "466/1000\n",
      "467/1000\n",
      "468/1000\n",
      "469/1000\n",
      "470/1000\n",
      "471/1000\n",
      "472/1000\n",
      "473/1000\n",
      "474/1000\n",
      "475/1000\n",
      "476/1000\n",
      "477/1000\n",
      "478/1000\n",
      "479/1000\n",
      "480/1000\n",
      "481/1000\n",
      "482/1000\n",
      "483/1000\n",
      "484/1000\n",
      "485/1000\n",
      "486/1000\n",
      "487/1000\n",
      "488/1000\n",
      "489/1000\n",
      "490/1000\n",
      "491/1000\n",
      "492/1000\n",
      "493/1000\n",
      "494/1000\n",
      "495/1000\n",
      "496/1000\n",
      "497/1000\n",
      "498/1000\n",
      "499/1000\n",
      "500/1000\n",
      "501/1000\n",
      "502/1000\n",
      "503/1000\n",
      "504/1000\n",
      "505/1000\n",
      "506/1000\n",
      "507/1000\n",
      "508/1000\n",
      "509/1000\n",
      "510/1000\n",
      "511/1000\n",
      "512/1000\n",
      "513/1000\n",
      "514/1000\n",
      "515/1000\n",
      "516/1000\n",
      "517/1000\n",
      "518/1000\n",
      "519/1000\n",
      "520/1000\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "524/1000\n",
      "525/1000\n",
      "526/1000\n",
      "527/1000\n",
      "528/1000\n",
      "529/1000\n",
      "530/1000\n",
      "531/1000\n",
      "532/1000\n",
      "533/1000\n",
      "534/1000\n",
      "535/1000\n",
      "536/1000\n",
      "537/1000\n",
      "538/1000\n",
      "539/1000\n",
      "540/1000\n",
      "541/1000\n",
      "542/1000\n",
      "543/1000\n",
      "544/1000\n",
      "545/1000\n",
      "546/1000\n",
      "547/1000\n",
      "548/1000\n",
      "549/1000\n",
      "550/1000\n",
      "551/1000\n",
      "552/1000\n",
      "553/1000\n",
      "554/1000\n",
      "555/1000\n",
      "556/1000\n",
      "557/1000\n",
      "558/1000\n",
      "559/1000\n",
      "560/1000\n",
      "561/1000\n",
      "562/1000\n",
      "563/1000\n",
      "564/1000\n",
      "565/1000\n",
      "566/1000\n",
      "567/1000\n",
      "568/1000\n",
      "569/1000\n",
      "570/1000\n",
      "571/1000\n",
      "572/1000\n",
      "573/1000\n",
      "574/1000\n",
      "575/1000\n",
      "576/1000\n",
      "577/1000\n",
      "578/1000\n",
      "579/1000\n",
      "580/1000\n",
      "581/1000\n",
      "582/1000\n",
      "583/1000\n",
      "584/1000\n",
      "585/1000\n",
      "586/1000\n",
      "587/1000\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "591/1000\n",
      "592/1000\n",
      "593/1000\n",
      "594/1000\n",
      "595/1000\n",
      "596/1000\n",
      "597/1000\n",
      "598/1000\n",
      "599/1000\n",
      "600/1000\n",
      "601/1000\n",
      "602/1000\n",
      "603/1000\n",
      "604/1000\n",
      "605/1000\n",
      "606/1000\n",
      "607/1000\n",
      "608/1000\n",
      "609/1000\n",
      "610/1000\n",
      "611/1000\n",
      "612/1000\n",
      "613/1000\n",
      "614/1000\n",
      "615/1000\n",
      "616/1000\n",
      "617/1000\n",
      "618/1000\n",
      "619/1000\n",
      "620/1000\n",
      "621/1000\n",
      "622/1000\n",
      "623/1000\n",
      "624/1000\n",
      "625/1000\n",
      "626/1000\n",
      "627/1000\n",
      "628/1000\n",
      "629/1000\n",
      "630/1000\n",
      "631/1000\n",
      "632/1000\n",
      "633/1000\n",
      "634/1000\n",
      "635/1000\n",
      "636/1000\n",
      "637/1000\n",
      "638/1000\n",
      "639/1000\n",
      "640/1000\n",
      "641/1000\n",
      "642/1000\n",
      "643/1000\n",
      "644/1000\n",
      "645/1000\n",
      "646/1000\n",
      "647/1000\n",
      "648/1000\n",
      "649/1000\n",
      "650/1000\n",
      "651/1000\n",
      "652/1000\n",
      "653/1000\n",
      "654/1000\n",
      "655/1000\n",
      "656/1000\n",
      "657/1000\n",
      "658/1000\n",
      "659/1000\n",
      "660/1000\n",
      "661/1000\n",
      "662/1000\n",
      "663/1000\n",
      "664/1000\n",
      "665/1000\n",
      "666/1000\n",
      "667/1000\n",
      "668/1000\n",
      "669/1000\n",
      "670/1000\n",
      "671/1000\n",
      "672/1000\n",
      "673/1000\n",
      "674/1000\n",
      "675/1000\n",
      "676/1000\n",
      "677/1000\n",
      "678/1000\n",
      "679/1000\n",
      "680/1000\n",
      "681/1000\n",
      "682/1000\n",
      "683/1000\n",
      "684/1000\n",
      "685/1000\n",
      "686/1000\n",
      "687/1000\n",
      "688/1000\n",
      "689/1000\n",
      "690/1000\n",
      "691/1000\n",
      "692/1000\n",
      "693/1000\n",
      "694/1000\n",
      "695/1000\n",
      "696/1000\n",
      "697/1000\n",
      "698/1000\n",
      "699/1000\n",
      "700/1000\n",
      "701/1000\n",
      "702/1000\n",
      "703/1000\n",
      "704/1000\n",
      "705/1000\n",
      "706/1000\n",
      "707/1000\n",
      "708/1000\n",
      "709/1000\n",
      "710/1000\n",
      "711/1000\n",
      "712/1000\n",
      "713/1000\n",
      "714/1000\n",
      "715/1000\n",
      "716/1000\n",
      "717/1000\n",
      "718/1000\n",
      "719/1000\n",
      "720/1000\n",
      "721/1000\n",
      "722/1000\n",
      "723/1000\n",
      "724/1000\n",
      "725/1000\n",
      "726/1000\n",
      "727/1000\n",
      "728/1000\n",
      "729/1000\n",
      "730/1000\n",
      "731/1000\n",
      "732/1000\n",
      "733/1000\n",
      "734/1000\n",
      "735/1000\n",
      "736/1000\n",
      "737/1000\n",
      "738/1000\n",
      "739/1000\n",
      "740/1000\n",
      "741/1000\n",
      "742/1000\n",
      "743/1000\n",
      "744/1000\n",
      "745/1000\n",
      "746/1000\n",
      "747/1000\n",
      "748/1000\n",
      "749/1000\n",
      "750/1000\n",
      "751/1000\n",
      "752/1000\n",
      "753/1000\n",
      "754/1000\n",
      "755/1000\n",
      "756/1000\n",
      "757/1000\n",
      "758/1000\n",
      "759/1000\n",
      "760/1000\n",
      "761/1000\n",
      "762/1000\n",
      "763/1000\n",
      "764/1000\n",
      "765/1000\n",
      "766/1000\n",
      "767/1000\n",
      "768/1000\n",
      "769/1000\n",
      "770/1000\n",
      "771/1000\n",
      "772/1000\n",
      "773/1000\n",
      "774/1000\n",
      "775/1000\n",
      "776/1000\n",
      "777/1000\n",
      "778/1000\n",
      "779/1000\n",
      "780/1000\n",
      "781/1000\n",
      "782/1000\n",
      "783/1000\n",
      "784/1000\n",
      "785/1000\n",
      "786/1000\n",
      "787/1000\n",
      "788/1000\n",
      "789/1000\n",
      "790/1000\n",
      "791/1000\n",
      "792/1000\n",
      "793/1000\n",
      "794/1000\n",
      "795/1000\n",
      "796/1000\n",
      "797/1000\n",
      "798/1000\n",
      "799/1000\n",
      "800/1000\n",
      "801/1000\n",
      "802/1000\n",
      "803/1000\n",
      "804/1000\n",
      "805/1000\n",
      "806/1000\n",
      "807/1000\n",
      "808/1000\n",
      "809/1000\n",
      "810/1000\n",
      "811/1000\n",
      "812/1000\n",
      "813/1000\n",
      "814/1000\n",
      "815/1000\n",
      "816/1000\n",
      "817/1000\n",
      "818/1000\n",
      "819/1000\n",
      "820/1000\n",
      "821/1000\n",
      "822/1000\n",
      "823/1000\n",
      "824/1000\n",
      "825/1000\n",
      "826/1000\n",
      "827/1000\n",
      "828/1000\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "832/1000\n",
      "833/1000\n",
      "834/1000\n",
      "835/1000\n",
      "836/1000\n",
      "837/1000\n",
      "838/1000\n",
      "839/1000\n",
      "840/1000\n",
      "841/1000\n",
      "842/1000\n",
      "843/1000\n",
      "844/1000\n",
      "845/1000\n",
      "846/1000\n",
      "847/1000\n",
      "848/1000\n",
      "849/1000\n",
      "850/1000\n",
      "851/1000\n",
      "852/1000\n",
      "853/1000\n",
      "854/1000\n",
      "855/1000\n",
      "856/1000\n",
      "857/1000\n",
      "858/1000\n",
      "859/1000\n",
      "860/1000\n",
      "861/1000\n",
      "862/1000\n",
      "863/1000\n",
      "864/1000\n",
      "865/1000\n",
      "866/1000\n",
      "867/1000\n",
      "868/1000\n",
      "869/1000\n",
      "870/1000\n",
      "871/1000\n",
      "872/1000\n",
      "873/1000\n",
      "874/1000\n",
      "875/1000\n",
      "876/1000\n",
      "877/1000\n",
      "878/1000\n",
      "879/1000\n",
      "880/1000\n",
      "881/1000\n",
      "882/1000\n",
      "883/1000\n",
      "884/1000\n",
      "885/1000\n",
      "886/1000\n",
      "887/1000\n",
      "888/1000\n",
      "889/1000\n",
      "890/1000\n",
      "891/1000\n",
      "892/1000\n",
      "893/1000\n",
      "894/1000\n",
      "895/1000\n",
      "896/1000\n",
      "897/1000\n",
      "898/1000\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "903/1000\n",
      "904/1000\n",
      "905/1000\n",
      "906/1000\n",
      "907/1000\n",
      "908/1000\n",
      "909/1000\n",
      "910/1000\n",
      "911/1000\n",
      "912/1000\n",
      "913/1000\n",
      "914/1000\n",
      "915/1000\n",
      "916/1000\n",
      "917/1000\n",
      "918/1000\n",
      "919/1000\n",
      "920/1000\n",
      "921/1000\n",
      "922/1000\n",
      "923/1000\n",
      "924/1000\n",
      "925/1000\n",
      "926/1000\n",
      "927/1000\n",
      "928/1000\n",
      "929/1000\n",
      "930/1000\n",
      "931/1000\n",
      "932/1000\n",
      "933/1000\n",
      "934/1000\n",
      "935/1000\n",
      "936/1000\n",
      "937/1000\n",
      "938/1000\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "942/1000\n",
      "943/1000\n",
      "944/1000\n",
      "945/1000\n",
      "946/1000\n",
      "947/1000\n",
      "948/1000\n",
      "949/1000\n",
      "950/1000\n",
      "951/1000\n",
      "952/1000\n",
      "953/1000\n",
      "954/1000\n",
      "955/1000\n",
      "956/1000\n",
      "957/1000\n",
      "958/1000\n",
      "959/1000\n",
      "960/1000\n",
      "961/1000\n",
      "962/1000\n",
      "963/1000\n",
      "964/1000\n",
      "965/1000\n",
      "966/1000\n",
      "967/1000\n",
      "968/1000\n",
      "969/1000\n",
      "970/1000\n",
      "971/1000\n",
      "972/1000\n",
      "973/1000\n",
      "974/1000\n",
      "975/1000\n",
      "976/1000\n",
      "977/1000\n",
      "978/1000\n",
      "979/1000\n",
      "980/1000\n",
      "981/1000\n",
      "982/1000\n",
      "983/1000\n",
      "984/1000\n",
      "985/1000\n",
      "986/1000\n",
      "987/1000\n",
      "988/1000\n",
      "989/1000\n",
      "990/1000\n",
      "991/1000\n",
      "992/1000\n",
      "993/1000\n",
      "994/1000\n",
      "995/1000\n",
      "996/1000\n",
      "997/1000\n",
      "998/1000\n",
      "999/1000\n",
      "0/1000\n",
      "1/1000\n",
      "2/1000\n",
      "3/1000\n",
      "4/1000\n",
      "5/1000\n",
      "6/1000\n",
      "7/1000\n",
      "8/1000\n",
      "9/1000\n",
      "10/1000\n",
      "11/1000\n",
      "12/1000\n",
      "13/1000\n",
      "14/1000\n",
      "15/1000\n",
      "16/1000\n",
      "17/1000\n",
      "18/1000\n",
      "19/1000\n",
      "20/1000\n",
      "21/1000\n",
      "22/1000\n",
      "23/1000\n",
      "24/1000\n",
      "25/1000\n",
      "26/1000\n",
      "27/1000\n",
      "28/1000\n",
      "29/1000\n",
      "30/1000\n",
      "31/1000\n",
      "32/1000\n",
      "33/1000\n",
      "34/1000\n",
      "35/1000\n",
      "36/1000\n",
      "37/1000\n",
      "38/1000\n",
      "39/1000\n",
      "40/1000\n",
      "41/1000\n",
      "42/1000\n",
      "43/1000\n",
      "44/1000\n",
      "45/1000\n",
      "46/1000\n",
      "47/1000\n",
      "48/1000\n",
      "49/1000\n",
      "50/1000\n",
      "51/1000\n",
      "52/1000\n",
      "53/1000\n",
      "54/1000\n",
      "55/1000\n",
      "56/1000\n",
      "57/1000\n",
      "58/1000\n",
      "59/1000\n",
      "60/1000\n",
      "61/1000\n",
      "62/1000\n",
      "63/1000\n",
      "64/1000\n",
      "65/1000\n",
      "66/1000\n",
      "67/1000\n",
      "68/1000\n",
      "69/1000\n",
      "70/1000\n",
      "71/1000\n",
      "72/1000\n",
      "73/1000\n",
      "74/1000\n",
      "75/1000\n",
      "76/1000\n",
      "77/1000\n",
      "78/1000\n",
      "79/1000\n",
      "80/1000\n",
      "81/1000\n",
      "82/1000\n",
      "83/1000\n",
      "84/1000\n",
      "85/1000\n",
      "86/1000\n",
      "87/1000\n",
      "88/1000\n",
      "89/1000\n",
      "90/1000\n",
      "91/1000\n",
      "92/1000\n",
      "93/1000\n",
      "94/1000\n",
      "95/1000\n",
      "96/1000\n",
      "97/1000\n",
      "98/1000\n",
      "99/1000\n",
      "100/1000\n",
      "101/1000\n",
      "102/1000\n",
      "103/1000\n",
      "104/1000\n",
      "105/1000\n",
      "106/1000\n",
      "107/1000\n",
      "108/1000\n",
      "109/1000\n",
      "110/1000\n",
      "111/1000\n",
      "112/1000\n",
      "113/1000\n",
      "114/1000\n",
      "115/1000\n",
      "116/1000\n",
      "117/1000\n",
      "118/1000\n",
      "119/1000\n",
      "120/1000\n",
      "121/1000\n",
      "122/1000\n",
      "123/1000\n",
      "124/1000\n",
      "125/1000\n",
      "126/1000\n",
      "127/1000\n",
      "128/1000\n",
      "129/1000\n",
      "130/1000\n",
      "131/1000\n",
      "132/1000\n",
      "133/1000\n",
      "134/1000\n",
      "135/1000\n",
      "136/1000\n",
      "137/1000\n",
      "138/1000\n",
      "139/1000\n",
      "140/1000\n",
      "141/1000\n",
      "142/1000\n",
      "143/1000\n",
      "144/1000\n",
      "145/1000\n",
      "146/1000\n",
      "147/1000\n",
      "148/1000\n",
      "149/1000\n",
      "150/1000\n",
      "151/1000\n",
      "152/1000\n",
      "153/1000\n",
      "154/1000\n",
      "155/1000\n",
      "156/1000\n",
      "157/1000\n",
      "158/1000\n",
      "159/1000\n",
      "160/1000\n",
      "161/1000\n",
      "162/1000\n",
      "163/1000\n",
      "164/1000\n",
      "165/1000\n",
      "166/1000\n",
      "167/1000\n",
      "168/1000\n",
      "169/1000\n",
      "170/1000\n",
      "171/1000\n",
      "172/1000\n",
      "173/1000\n",
      "174/1000\n",
      "175/1000\n",
      "176/1000\n",
      "177/1000\n",
      "178/1000\n",
      "179/1000\n",
      "180/1000\n",
      "181/1000\n",
      "182/1000\n",
      "183/1000\n",
      "184/1000\n",
      "185/1000\n",
      "186/1000\n",
      "187/1000\n",
      "188/1000\n",
      "189/1000\n",
      "190/1000\n",
      "191/1000\n",
      "192/1000\n",
      "193/1000\n",
      "194/1000\n",
      "195/1000\n",
      "196/1000\n",
      "197/1000\n",
      "198/1000\n",
      "199/1000\n",
      "200/1000\n",
      "201/1000\n",
      "202/1000\n",
      "203/1000\n",
      "204/1000\n",
      "205/1000\n",
      "206/1000\n",
      "207/1000\n",
      "208/1000\n",
      "209/1000\n",
      "210/1000\n",
      "211/1000\n",
      "212/1000\n",
      "213/1000\n",
      "214/1000\n",
      "215/1000\n",
      "216/1000\n",
      "217/1000\n",
      "218/1000\n",
      "219/1000\n",
      "220/1000\n",
      "221/1000\n",
      "222/1000\n",
      "223/1000\n",
      "224/1000\n",
      "225/1000\n",
      "226/1000\n",
      "227/1000\n",
      "228/1000\n",
      "229/1000\n",
      "230/1000\n",
      "231/1000\n",
      "232/1000\n",
      "233/1000\n",
      "234/1000\n",
      "235/1000\n",
      "236/1000\n",
      "237/1000\n",
      "238/1000\n",
      "239/1000\n",
      "240/1000\n",
      "241/1000\n",
      "242/1000\n",
      "243/1000\n",
      "244/1000\n",
      "245/1000\n",
      "246/1000\n",
      "247/1000\n",
      "248/1000\n",
      "249/1000\n",
      "250/1000\n",
      "251/1000\n",
      "252/1000\n",
      "253/1000\n",
      "254/1000\n",
      "255/1000\n",
      "256/1000\n",
      "257/1000\n",
      "258/1000\n",
      "259/1000\n",
      "260/1000\n",
      "261/1000\n",
      "262/1000\n",
      "263/1000\n",
      "264/1000\n",
      "265/1000\n",
      "266/1000\n",
      "267/1000\n",
      "268/1000\n",
      "269/1000\n",
      "270/1000\n",
      "271/1000\n",
      "272/1000\n",
      "273/1000\n",
      "274/1000\n",
      "275/1000\n",
      "276/1000\n",
      "277/1000\n",
      "278/1000\n",
      "279/1000\n",
      "280/1000\n",
      "281/1000\n",
      "282/1000\n",
      "283/1000\n",
      "284/1000\n",
      "285/1000\n",
      "286/1000\n",
      "287/1000\n",
      "288/1000\n",
      "289/1000\n",
      "290/1000\n",
      "291/1000\n",
      "292/1000\n",
      "293/1000\n",
      "294/1000\n",
      "295/1000\n",
      "296/1000\n",
      "297/1000\n",
      "298/1000\n",
      "299/1000\n",
      "300/1000\n",
      "301/1000\n",
      "302/1000\n",
      "303/1000\n",
      "304/1000\n",
      "305/1000\n",
      "306/1000\n",
      "307/1000\n",
      "308/1000\n",
      "309/1000\n",
      "310/1000\n",
      "311/1000\n",
      "312/1000\n",
      "313/1000\n",
      "314/1000\n",
      "315/1000\n",
      "316/1000\n",
      "317/1000\n",
      "318/1000\n",
      "319/1000\n",
      "320/1000\n",
      "321/1000\n",
      "322/1000\n",
      "323/1000\n",
      "324/1000\n",
      "325/1000\n",
      "326/1000\n",
      "327/1000\n",
      "328/1000\n",
      "329/1000\n",
      "330/1000\n",
      "331/1000\n",
      "332/1000\n",
      "333/1000\n",
      "334/1000\n",
      "335/1000\n",
      "336/1000\n",
      "337/1000\n",
      "338/1000\n",
      "339/1000\n",
      "340/1000\n",
      "341/1000\n",
      "342/1000\n",
      "343/1000\n",
      "344/1000\n",
      "345/1000\n",
      "346/1000\n",
      "347/1000\n",
      "348/1000\n",
      "349/1000\n",
      "350/1000\n",
      "351/1000\n",
      "352/1000\n",
      "353/1000\n",
      "354/1000\n",
      "355/1000\n",
      "356/1000\n",
      "357/1000\n",
      "358/1000\n",
      "359/1000\n",
      "360/1000\n",
      "361/1000\n",
      "362/1000\n",
      "363/1000\n",
      "364/1000\n",
      "365/1000\n",
      "366/1000\n",
      "367/1000\n",
      "368/1000\n",
      "369/1000\n",
      "370/1000\n",
      "371/1000\n",
      "372/1000\n",
      "373/1000\n",
      "374/1000\n",
      "375/1000\n",
      "376/1000\n",
      "377/1000\n",
      "378/1000\n",
      "379/1000\n",
      "380/1000\n",
      "381/1000\n",
      "382/1000\n",
      "383/1000\n",
      "384/1000\n",
      "385/1000\n",
      "386/1000\n",
      "387/1000\n",
      "388/1000\n",
      "389/1000\n",
      "390/1000\n",
      "391/1000\n",
      "392/1000\n",
      "393/1000\n",
      "394/1000\n",
      "395/1000\n",
      "396/1000\n",
      "397/1000\n",
      "398/1000\n",
      "399/1000\n",
      "400/1000\n",
      "401/1000\n",
      "402/1000\n",
      "403/1000\n",
      "404/1000\n",
      "405/1000\n",
      "406/1000\n",
      "407/1000\n",
      "408/1000\n",
      "409/1000\n",
      "410/1000\n",
      "411/1000\n",
      "412/1000\n",
      "413/1000\n",
      "414/1000\n",
      "415/1000\n",
      "416/1000\n",
      "417/1000\n",
      "418/1000\n",
      "419/1000\n",
      "420/1000\n",
      "421/1000\n",
      "422/1000\n",
      "423/1000\n",
      "424/1000\n",
      "425/1000\n",
      "426/1000\n",
      "427/1000\n",
      "428/1000\n",
      "429/1000\n",
      "430/1000\n",
      "431/1000\n",
      "432/1000\n",
      "433/1000\n",
      "434/1000\n",
      "435/1000\n",
      "436/1000\n",
      "437/1000\n",
      "438/1000\n",
      "439/1000\n",
      "440/1000\n",
      "441/1000\n",
      "442/1000\n",
      "443/1000\n",
      "444/1000\n",
      "445/1000\n",
      "446/1000\n",
      "447/1000\n",
      "448/1000\n",
      "449/1000\n",
      "450/1000\n",
      "451/1000\n",
      "452/1000\n",
      "453/1000\n",
      "454/1000\n",
      "455/1000\n",
      "456/1000\n",
      "457/1000\n",
      "458/1000\n",
      "459/1000\n",
      "460/1000\n",
      "461/1000\n",
      "462/1000\n",
      "463/1000\n",
      "464/1000\n",
      "465/1000\n",
      "466/1000\n",
      "467/1000\n",
      "468/1000\n",
      "469/1000\n",
      "470/1000\n",
      "471/1000\n",
      "472/1000\n",
      "473/1000\n",
      "474/1000\n",
      "475/1000\n",
      "476/1000\n",
      "477/1000\n",
      "478/1000\n",
      "479/1000\n",
      "480/1000\n",
      "481/1000\n",
      "482/1000\n",
      "483/1000\n",
      "484/1000\n",
      "485/1000\n",
      "486/1000\n",
      "487/1000\n",
      "488/1000\n",
      "489/1000\n",
      "490/1000\n",
      "491/1000\n",
      "492/1000\n",
      "493/1000\n",
      "494/1000\n",
      "495/1000\n",
      "496/1000\n",
      "497/1000\n",
      "498/1000\n",
      "499/1000\n",
      "500/1000\n",
      "501/1000\n",
      "502/1000\n",
      "503/1000\n",
      "504/1000\n",
      "505/1000\n",
      "506/1000\n",
      "507/1000\n",
      "508/1000\n",
      "509/1000\n",
      "510/1000\n",
      "511/1000\n",
      "512/1000\n",
      "513/1000\n",
      "514/1000\n",
      "515/1000\n",
      "516/1000\n",
      "517/1000\n",
      "518/1000\n",
      "519/1000\n",
      "520/1000\n",
      "521/1000\n",
      "522/1000\n",
      "523/1000\n",
      "524/1000\n",
      "525/1000\n",
      "526/1000\n",
      "527/1000\n",
      "528/1000\n",
      "529/1000\n",
      "530/1000\n",
      "531/1000\n",
      "532/1000\n",
      "533/1000\n",
      "534/1000\n",
      "535/1000\n",
      "536/1000\n",
      "537/1000\n",
      "538/1000\n",
      "539/1000\n",
      "540/1000\n",
      "541/1000\n",
      "542/1000\n",
      "543/1000\n",
      "544/1000\n",
      "545/1000\n",
      "546/1000\n",
      "547/1000\n",
      "548/1000\n",
      "549/1000\n",
      "550/1000\n",
      "551/1000\n",
      "552/1000\n",
      "553/1000\n",
      "554/1000\n",
      "555/1000\n",
      "556/1000\n",
      "557/1000\n",
      "558/1000\n",
      "559/1000\n",
      "560/1000\n",
      "561/1000\n",
      "562/1000\n",
      "563/1000\n",
      "564/1000\n",
      "565/1000\n",
      "566/1000\n",
      "567/1000\n",
      "568/1000\n",
      "569/1000\n",
      "570/1000\n",
      "571/1000\n",
      "572/1000\n",
      "573/1000\n",
      "574/1000\n",
      "575/1000\n",
      "576/1000\n",
      "577/1000\n",
      "578/1000\n",
      "579/1000\n",
      "580/1000\n",
      "581/1000\n",
      "582/1000\n",
      "583/1000\n",
      "584/1000\n",
      "585/1000\n",
      "586/1000\n",
      "587/1000\n",
      "588/1000\n",
      "589/1000\n",
      "590/1000\n",
      "591/1000\n",
      "592/1000\n",
      "593/1000\n",
      "594/1000\n",
      "595/1000\n",
      "596/1000\n",
      "597/1000\n",
      "598/1000\n",
      "599/1000\n",
      "600/1000\n",
      "601/1000\n",
      "602/1000\n",
      "603/1000\n",
      "604/1000\n",
      "605/1000\n",
      "606/1000\n",
      "607/1000\n",
      "608/1000\n",
      "609/1000\n",
      "610/1000\n",
      "611/1000\n",
      "612/1000\n",
      "613/1000\n",
      "614/1000\n",
      "615/1000\n",
      "616/1000\n",
      "617/1000\n",
      "618/1000\n",
      "619/1000\n",
      "620/1000\n",
      "621/1000\n",
      "622/1000\n",
      "623/1000\n",
      "624/1000\n",
      "625/1000\n",
      "626/1000\n",
      "627/1000\n",
      "628/1000\n",
      "629/1000\n",
      "630/1000\n",
      "631/1000\n",
      "632/1000\n",
      "633/1000\n",
      "634/1000\n",
      "635/1000\n",
      "636/1000\n",
      "637/1000\n",
      "638/1000\n",
      "639/1000\n",
      "640/1000\n",
      "641/1000\n",
      "642/1000\n",
      "643/1000\n",
      "644/1000\n",
      "645/1000\n",
      "646/1000\n",
      "647/1000\n",
      "648/1000\n",
      "649/1000\n",
      "650/1000\n",
      "651/1000\n",
      "652/1000\n",
      "653/1000\n",
      "654/1000\n",
      "655/1000\n",
      "656/1000\n",
      "657/1000\n",
      "658/1000\n",
      "659/1000\n",
      "660/1000\n",
      "661/1000\n",
      "662/1000\n",
      "663/1000\n",
      "664/1000\n",
      "665/1000\n",
      "666/1000\n",
      "667/1000\n",
      "668/1000\n",
      "669/1000\n",
      "670/1000\n",
      "671/1000\n",
      "672/1000\n",
      "673/1000\n",
      "674/1000\n",
      "675/1000\n",
      "676/1000\n",
      "677/1000\n",
      "678/1000\n",
      "679/1000\n",
      "680/1000\n",
      "681/1000\n",
      "682/1000\n",
      "683/1000\n",
      "684/1000\n",
      "685/1000\n",
      "686/1000\n",
      "687/1000\n",
      "688/1000\n",
      "689/1000\n",
      "690/1000\n",
      "691/1000\n",
      "692/1000\n",
      "693/1000\n",
      "694/1000\n",
      "695/1000\n",
      "696/1000\n",
      "697/1000\n",
      "698/1000\n",
      "699/1000\n",
      "700/1000\n",
      "701/1000\n",
      "702/1000\n",
      "703/1000\n",
      "704/1000\n",
      "705/1000\n",
      "706/1000\n",
      "707/1000\n",
      "708/1000\n",
      "709/1000\n",
      "710/1000\n",
      "711/1000\n",
      "712/1000\n",
      "713/1000\n",
      "714/1000\n",
      "715/1000\n",
      "716/1000\n",
      "717/1000\n",
      "718/1000\n",
      "719/1000\n",
      "720/1000\n",
      "721/1000\n",
      "722/1000\n",
      "723/1000\n",
      "724/1000\n",
      "725/1000\n",
      "726/1000\n",
      "727/1000\n",
      "728/1000\n",
      "729/1000\n",
      "730/1000\n",
      "731/1000\n",
      "732/1000\n",
      "733/1000\n",
      "734/1000\n",
      "735/1000\n",
      "736/1000\n",
      "737/1000\n",
      "738/1000\n",
      "739/1000\n",
      "740/1000\n",
      "741/1000\n",
      "742/1000\n",
      "743/1000\n",
      "744/1000\n",
      "745/1000\n",
      "746/1000\n",
      "747/1000\n",
      "748/1000\n",
      "749/1000\n",
      "750/1000\n",
      "751/1000\n",
      "752/1000\n",
      "753/1000\n",
      "754/1000\n",
      "755/1000\n",
      "756/1000\n",
      "757/1000\n",
      "758/1000\n",
      "759/1000\n",
      "760/1000\n",
      "761/1000\n",
      "762/1000\n",
      "763/1000\n",
      "764/1000\n",
      "765/1000\n",
      "766/1000\n",
      "767/1000\n",
      "768/1000\n",
      "769/1000\n",
      "770/1000\n",
      "771/1000\n",
      "772/1000\n",
      "773/1000\n",
      "774/1000\n",
      "775/1000\n",
      "776/1000\n",
      "777/1000\n",
      "778/1000\n",
      "779/1000\n",
      "780/1000\n",
      "781/1000\n",
      "782/1000\n",
      "783/1000\n",
      "784/1000\n",
      "785/1000\n",
      "786/1000\n",
      "787/1000\n",
      "788/1000\n",
      "789/1000\n",
      "790/1000\n",
      "791/1000\n",
      "792/1000\n",
      "793/1000\n",
      "794/1000\n",
      "795/1000\n",
      "796/1000\n",
      "797/1000\n",
      "798/1000\n",
      "799/1000\n",
      "800/1000\n",
      "801/1000\n",
      "802/1000\n",
      "803/1000\n",
      "804/1000\n",
      "805/1000\n",
      "806/1000\n",
      "807/1000\n",
      "808/1000\n",
      "809/1000\n",
      "810/1000\n",
      "811/1000\n",
      "812/1000\n",
      "813/1000\n",
      "814/1000\n",
      "815/1000\n",
      "816/1000\n",
      "817/1000\n",
      "818/1000\n",
      "819/1000\n",
      "820/1000\n",
      "821/1000\n",
      "822/1000\n",
      "823/1000\n",
      "824/1000\n",
      "825/1000\n",
      "826/1000\n",
      "827/1000\n",
      "828/1000\n",
      "829/1000\n",
      "830/1000\n",
      "831/1000\n",
      "832/1000\n",
      "833/1000\n",
      "834/1000\n",
      "835/1000\n",
      "836/1000\n",
      "837/1000\n",
      "838/1000\n",
      "839/1000\n",
      "840/1000\n",
      "841/1000\n",
      "842/1000\n",
      "843/1000\n",
      "844/1000\n",
      "845/1000\n",
      "846/1000\n",
      "847/1000\n",
      "848/1000\n",
      "849/1000\n",
      "850/1000\n",
      "851/1000\n",
      "852/1000\n",
      "853/1000\n",
      "854/1000\n",
      "855/1000\n",
      "856/1000\n",
      "857/1000\n",
      "858/1000\n",
      "859/1000\n",
      "860/1000\n",
      "861/1000\n",
      "862/1000\n",
      "863/1000\n",
      "864/1000\n",
      "865/1000\n",
      "866/1000\n",
      "867/1000\n",
      "868/1000\n",
      "869/1000\n",
      "870/1000\n",
      "871/1000\n",
      "872/1000\n",
      "873/1000\n",
      "874/1000\n",
      "875/1000\n",
      "876/1000\n",
      "877/1000\n",
      "878/1000\n",
      "879/1000\n",
      "880/1000\n",
      "881/1000\n",
      "882/1000\n",
      "883/1000\n",
      "884/1000\n",
      "885/1000\n",
      "886/1000\n",
      "887/1000\n",
      "888/1000\n",
      "889/1000\n",
      "890/1000\n",
      "891/1000\n",
      "892/1000\n",
      "893/1000\n",
      "894/1000\n",
      "895/1000\n",
      "896/1000\n",
      "897/1000\n",
      "898/1000\n",
      "899/1000\n",
      "900/1000\n",
      "901/1000\n",
      "902/1000\n",
      "903/1000\n",
      "904/1000\n",
      "905/1000\n",
      "906/1000\n",
      "907/1000\n",
      "908/1000\n",
      "909/1000\n",
      "910/1000\n",
      "911/1000\n",
      "912/1000\n",
      "913/1000\n",
      "914/1000\n",
      "915/1000\n",
      "916/1000\n",
      "917/1000\n",
      "918/1000\n",
      "919/1000\n",
      "920/1000\n",
      "921/1000\n",
      "922/1000\n",
      "923/1000\n",
      "924/1000\n",
      "925/1000\n",
      "926/1000\n",
      "927/1000\n",
      "928/1000\n",
      "929/1000\n",
      "930/1000\n",
      "931/1000\n",
      "932/1000\n",
      "933/1000\n",
      "934/1000\n",
      "935/1000\n",
      "936/1000\n",
      "937/1000\n",
      "938/1000\n",
      "939/1000\n",
      "940/1000\n",
      "941/1000\n",
      "942/1000\n",
      "943/1000\n",
      "944/1000\n",
      "945/1000\n",
      "946/1000\n",
      "947/1000\n",
      "948/1000\n",
      "949/1000\n",
      "950/1000\n",
      "951/1000\n",
      "952/1000\n",
      "953/1000\n",
      "954/1000\n",
      "955/1000\n",
      "956/1000\n",
      "957/1000\n",
      "958/1000\n",
      "959/1000\n",
      "960/1000\n",
      "961/1000\n",
      "962/1000\n",
      "963/1000\n",
      "964/1000\n",
      "965/1000\n",
      "966/1000\n",
      "967/1000\n",
      "968/1000\n",
      "969/1000\n",
      "970/1000\n",
      "971/1000\n",
      "972/1000\n",
      "973/1000\n",
      "974/1000\n",
      "975/1000\n",
      "976/1000\n",
      "977/1000\n",
      "978/1000\n",
      "979/1000\n",
      "980/1000\n",
      "981/1000\n",
      "982/1000\n",
      "983/1000\n",
      "984/1000\n",
      "985/1000\n",
      "986/1000\n",
      "987/1000\n",
      "988/1000\n",
      "989/1000\n",
      "990/1000\n",
      "991/1000\n",
      "992/1000\n",
      "993/1000\n",
      "994/1000\n",
      "995/1000\n",
      "996/1000\n",
      "997/1000\n",
      "998/1000\n",
      "999/1000\n"
     ]
    }
   ],
   "source": [
    "entry_times_A, attractor_indices_A, transition_mat_A, counts_A = switch_stats(switching_A, stable_steady_states_A)\n",
    "entry_times_AB, attractor_indices_AB, transition_mat_AB, counts_AB = switch_stats(switching_AB, stable_steady_states_AB)\n",
    "entry_times_B, attractor_indices_B, transition_mat_B, counts_B = switch_stats(switching_B, stable_steady_states_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_attractors_A = final_attractor(em_traj_A, dist_exit_x, dist_exit_y, stable_steady_states_A)\n",
    "final_attractors_AB = final_attractor(em_traj_AB, dist_exit_x, dist_exit_y, stable_steady_states_AB)\n",
    "final_attractors_B = final_attractor(em_traj_B, dist_exit_x, dist_exit_y, stable_steady_states_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 2, 10000)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em_traj_A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_1000_A = em_traj_A[:,:,-100:]\n",
    "last_1000_AB = em_traj_AB[:,:,-100:]\n",
    "last_1000_B = em_traj_B[:,:,-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vals_A = last_1000_A[:,0,:].flatten()\n",
    "y_vals_A = last_1000_A[:,1,:].flatten()\n",
    "x_vals_AB = last_1000_AB[:,0,:].flatten()\n",
    "y_vals_AB = last_1000_AB[:,1,:].flatten()\n",
    "x_vals_B = last_1000_B[:,0,:].flatten()\n",
    "y_vals_B = last_1000_B[:,1,:].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_hm_A = pd.DataFrame({\"x\" : x_vals_A, \"y\" : y_vals_A})\n",
    "traj_hm_AB = pd.DataFrame({\"x\" : x_vals_AB, \"y\" : y_vals_AB})\n",
    "traj_hm_B = pd.DataFrame({\"x\" : x_vals_B, \"y\" : y_vals_B})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_attractors = [final_attractors_A, final_attractors_AB, final_attractors_B]\n",
    "stable_steady_states = [stable_steady_states_A, stable_steady_states_AB, stable_steady_states_B]\n",
    "stable_steady_states = np.array([np.array(xi) for xi in stable_steady_states])\n",
    "unstable_steady_states = [unstable_steady_states_A, unstable_steady_states_AB, unstable_steady_states_B]\n",
    "unstable_steady_states = np.array([np.array(xi) for xi in unstable_steady_states])\n",
    "traj_hm = [traj_hm_A, traj_hm_AB, traj_hm_B]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 2)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj_hm_A.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot - Density of final attractors with inset attractor counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1884101dd8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_para = 1\n",
    "fig, ax = plt.subplots(1, 3, figsize=(20,8))\n",
    "plt.rcParams.update({'font.size':20})\n",
    "fig.text(0.5, 0.04, 'x', ha ='center', fontsize = 25)\n",
    "fig.text(0.04, 0.5, 'y', va='center', rotation='vertical', fontsize = 25)\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i].set_xlim([-0.1,2])\n",
    "    ax[i].set_ylim([-0.1,2])\n",
    "    ax[i].set_xticks(np.arange(0, 2.5, 0.5))\n",
    "    ax[i].set_yticks(np.arange(0, 2.5, 0.5))\n",
    "    \n",
    "    x_ss_stable = stable_steady_states[i][:,0][:,2]\n",
    "    y_ss_stable = stable_steady_states[i][:,1][:,2]\n",
    "    for ss_index in range(len(x_ss_stable)):\n",
    "        ax[i].plot(x_ss_stable[ss_index], y_ss_stable[ss_index], '.', color = ss_colors[ss_index], markersize= 20 )\n",
    "        ax[i].plot(x_init, y_init, '+', color = 'k', markersize= 20 )\n",
    "        ax[i].annotate('{}'.format(ss_index), xy=(stable_steady_states[i][ss_index][0][2], stable_steady_states[i][ss_index][1][2]),  xycoords='data',\n",
    "            xytext=(stable_steady_states[i][ss_index][0][2] + 0.01, stable_steady_states[i][ss_index][1][2] + 0.02), color = ss_colors[ss_index])\n",
    "        ax[i].annotate('initial', xy=(x_init, y_init),  xycoords='data',\n",
    "            xytext=(x_init + 0.01, y_init + 0.02), color = 'k')\n",
    "        ax[i].tick_params(axis='both', which='major', labelsize=15)\n",
    "    unstable_index = np.array([x for x in range(len(grouped_unstable_ss)) if grouped_unstable_ss[x].any() and (myPara[i][0] == grouped_unstable_ss[x][:,0][:,0][0] and myPara[i][1] == grouped_unstable_ss[x][:,0][:,1][0])])\n",
    "    if unstable_index.size>0: \n",
    "        x_ss_unstable = grouped_unstable_ss[unstable_index[0]][:,0][:,2]\n",
    "        y_ss_unstable = grouped_unstable_ss[unstable_index[0]][:,1][:,2]\n",
    "        for ss_index in range(len(x_ss_unstable)):\n",
    "            ax[i].plot(x_ss_unstable[ss_index], y_ss_unstable[ss_index], '.', color = \"#6600cc\", mfc='none',markeredgewidth = 2, markersize= 20 )\n",
    "    \n",
    "    ax[i].set_title(\"{} = {}, {} = {}\".format(r'$\\alpha_{1}$', stable_steady_states[i][:,0][0][0], r'$\\alpha_{2}$',stable_steady_states[i][:,0][0][1]), fontsize = 20)\n",
    "    ins = ax[i].inset_axes([0.66,0.66,0.3,0.3])\n",
    "    attractor_count_dict = OrderedDict(sorted(Counter(final_attractors[i]).items(), key=lambda t: t[0]))\n",
    "    barplot_col = ['#992DBA', '#38CAFF', '#ff9400', '#99dd44']\n",
    "    ins.bar(attractor_count_dict.keys(), attractor_count_dict.values(), align='center', alpha=0.5, color = barplot_col)\n",
    "    ins.set_ylabel('Trajectory counts')\n",
    "    ins.set_xlabel('Attractor')\n",
    "    ins.set_xticks(np.arange(-1, len(x_ss_stable),1))#ax.plot.xticks([-1,0,1,2])\n",
    "    labels = ins.get_xticks().tolist()\n",
    "    labels[0] = \"Transient\"\n",
    "    ins.set_xticklabels(labels, fontsize = 8 )\n",
    "\n",
    "sns.kdeplot(traj_hm_A.x, traj_hm_A.y, cmap=plt.cm.magma, shade=True, bw=.15, gridsize=100, ax = ax[0], shade_lowest = False)\n",
    "sns.kdeplot(traj_hm_AB.x, traj_hm_AB.y, cmap=plt.cm.magma, shade=True, bw=.15, gridsize=100, ax = ax[1], shade_lowest = False)\n",
    "sns.kdeplot(traj_hm_B.x, traj_hm_B.y, cmap=plt.cm.magma, shade=True, bw=.15, gridsize=100, ax = ax[2], shade_lowest = False)\n",
    "\n",
    "#fig.savefig('plots_stochastic/01_density_insets.png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "303.825px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}