causal_structure_stock_prices.ipynb

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    "## Problem 2: \n",
    "### Anita Mezzetti\n",
    "\n",
    "See PDF file for details\n",
    "\n",
    "This problem is similar to the last assignment. However, while there data were given in the form _(node, sample, time)_, here we have _(time, sample, node)_.  We can proceed in two ways: we can change the algorithm from the last assignment to adapt it to the new form our we can resample our data in order to use the same algorithm. Both ways give the same result.\n",
    "\n",
    "Some function are equal in both ways. We rewrite them in both cases to make steps clearer :)\n",
    "\n",
    "index:\n",
    "1. [Changing the algorithm](#one)\n",
    "2. [Resampling data](#22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import networkx as nx\n",
    "import math\n",
    "from scipy.linalg import inv, det\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "\n",
    "np.set_printoptions(precision=4, linewidth=100)\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "warnings.simplefilter(action='ignore', category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Changing the algorithm <a name=\"one\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(7, 190, 12)"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "XX = loadmat(\"market.mat\")['DI']\n",
    "threshold = 0.45\n",
    "\n",
    "XX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_companies = list(XX.shape)[2] # num companies\n",
    "T = list(XX.shape)[0]\n",
    "\n",
    "n_sample = list(XX.shape)[1]\n",
    "\n",
    "nodes = set(range(num_companies))\n",
    "\n",
    "all_stocks = {1:\"Apple Inc\", 2:\"Cisco Systems\", 3:\"Dell inc.\", 4:\"EMC Corporation\", 5:\"Google Inc.\", 6:\"Hewlett-Packard\", 7:\"Intel\", 8:\"Microsoft\", 9:\"Oracle\", 10:\"International Business Machines\", 11:\"Texas Instruments\", 12:\"Xerox\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our nodes/companies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "set(all_stocks.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted function to find $|\\Sigma_{x_{0}^{x_{t0}}y_{0}^{y_{t0}}z_{0}^{z_{t0}}}|$ with data in the form _(time, sample, node)_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def det_cov_adapted(y, y_to, z, z_to, x=-1, x_to=0):\n",
    "    #print(f\"\\n y:{y} ytime:{y_to} z:{z} ztime:{z_to} x:{x} xtime:{x_to}\")\n",
    "    \n",
    "    data = XX[ :y_to+1, :,y]\n",
    "    #print(f\"shape data y: {data.shape}\")\n",
    "    \n",
    "    if x >= 0:\n",
    "        data = np.concatenate((XX[:x_to+1, :, x], data))\n",
    "        #print(f\"shape data x: {data.shape}\")\n",
    "    \n",
    "    z = XX[:z_to+1, :, sorted(z)]\n",
    "    #print(f\"shape data z1     : {z.shape}\")\n",
    "    \n",
    "    # right dimension\n",
    "    ash = z.shape\n",
    "    Z = z[:,:,0]\n",
    "    for deep in range(ash[2])[1:]:\n",
    "        Z = np.concatenate((Z, z[:,:,deep]))\n",
    "    \n",
    "    #print(f\"shape data z     : {Z.shape}\")\n",
    "    \n",
    "    data = np.concatenate((data, Z))\n",
    "    #print(f\"shape data: {data.shape}\")\n",
    "    \n",
    "    return  (det(np.cov(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to calculate each addend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_part(x, y, z, t):\n",
    "    A = det_cov_adapted(y, t, z, t-1) * 10**50\n",
    "    B = det_cov_adapted(y, t-1, z, t-1, x, t-1) * 10**50\n",
    "    C = det_cov_adapted(y, t-1, z, t-1) * 10**50\n",
    "    D = det_cov_adapted(y, t, z, t-1, x, t-1) * 10**50\n",
    "    #print(f\"A {A}, B {B}, C {C}, D {D}\") \n",
    "    return np.log(A*B / (C*D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to compute $I(x \\rightarrow y|z)$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direct_information(x, y, z):\n",
    "    return 0.5 * sum(log_part(x, y, z, t) for t in range(1, T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALL_IDX = set(range(num_companies))\n",
    "G = nx.DiGraph()\n",
    "G.add_nodes_from(list(all_stocks.keys()))\n",
    "\n",
    "di_matrix = np.zeros((num_companies, num_companies))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute learn the causal structure of the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in ALL_IDX:\n",
    "    for y in ALL_IDX:\n",
    "        if x == y:\n",
    "            continue\n",
    "\n",
    "    #if x==1 and y==2:\n",
    "        di = direct_information(x, y, ALL_IDX - {x, y})\n",
    "        di_matrix[x, y] = di\n",
    "        #print(f\"I({x}->{y}||{ALL_IDX - {x, y}}) = {di}\")\n",
    "        if di > threshold:\n",
    "            #print(\"add an edge between \", x, \"and\", y)\n",
    "            G.add_edge(x+1, y+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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     "data": {
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     },
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     "execution_count": 10
    }
   ],
   "source": [
    "di_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('outfile.txt','wb') as f:\n",
    "    for line in di_matrix:\n",
    "        np.savetxt(f, line, fmt='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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j7NKlC1999RUXLlwgPj6ejz/+mM6dO2M0GnO8dseOHXz33XdERUUBcOrUKVauXGmJw9vbm/DwcMsvAb6+vjRs2JCPPvqI5ORkjhw5wpw5c+jatSsAffr0Yfjw4Zw9exalFEeOHOHWrVuW9sqWLcvGjRv5+uuvmTFjRo7xOTk5sWnTJmbPnv3AuYSEBHQ6HZ6engDMnTs3wyoB2enSpQtjx47l2WeftSTWffv25YsvvuD48eMA3Llzh6VLl1qu8fb2zvI9B+mfwa+//pqIiAiio6MZN26c5VxqaiopKSl4enpiNBpZu3ZthuXUvL29uXXrFnfu3LEci4uLw9XVFWdnZ06dOsW3336bq+cmhBBFlSSqBWTIkCF8+eWXTJgwAS8vL7y9vXnrrbcYP358tr0zM2bMYMSIEbi4uDB69Gg6deqU6zZHjBiBj48PFSpU4Nlnn6VDhw7Y2dkBYDAY+P333wkLC6NChQqUKlWKPn36ZPhSzMmkSZMICgqiTp06lChRgqFDh6JpGt27d8fPz4+nnnqKgICABxKtDz74AFtbW7y9venRo4cloQGIjY3ljTfewMPDAz8/P0qWLMmgQYNyvBeOjo4MGzaMp59+Gnd3d/76668MbZYsWZJVq1YxefJkSpYsyYQJE1i1ahWlSpXK9fO9y97ens2bNxMQEMALL7yAq6srVatWZd++ffz8889ZXteqVSvatGlDlSpV8PPzw97e/oGfoV9++WWWLFmCh4cHCxYsYNmyZdjY2OQ5xt69e/Paa6/RuHFjKlSogL29PdOmTcvVte7u7qxcuZKgoCCcnZ1p3bo17dq1Y8iQIQB07NgRSL+nd9c1/emnnwgPD6ds2bK0a9eOTz/9lOeeew6AAQMG0KlTJ1q2bImrqyuvv/46SUlJGdosV64cGzduZPz48ZkmoPerXbt2psMJAgICGDhwIA0aNMDb25ujR4/y9NNP5+p5A/To0YMRI0bQvHlzwsPDadeuHUOHDuXVV1/F1dWVwMBA1q5dayk/atQoevTogbu7e6av/RtvvEGrVq2oUaMGoaGhtG/f3nLOxcWFr7/+mk6dOuHh4cGiRYto27at5Xy1atXo0qULFStWxN3dnatXrzJp0iQWLVqEi4sLb7zxBp07d871cxNCiKJIp+R3o2Lj22+/ZfHixWzdutXaoYhMjBo1inPnzvHjjz9aOxQhxH3S0tKIiIggOTk5x7IxKomt2hnCuUWcSsFFZ0d5StJUXwU3nUMBRCtE0WFvb4+Pj0+WnTI5/w4oiqxr167x999/06BBA86ePcvkyZN59913rR2WEEIUOREREbi4uFC+fPksx8SfSI5g9q1NrI47hFmfPjRGjw4NhQ4dc9nDi66hvF6iGQH2suuYEEopbt26RURERJabt0ii+gRLTU3lrbfe4sKFC7i7u/Pqq6/Sr18/a4clhBBFTnJycrZJ6i8xexh2fTFmpeFqcMCge3BknVlprLizn1WxB/m89Kt0cK+X32ELUajpdDpKlizJjRs3si4jP/0LIYQQ2Tt58iT+/v6ZnvslZg8fXl+Ek84OW33O/T+pmokElcK40v8nyaoQZP/5kslUQgghxEM6kRzBsOuLc52kAtjqjTjp7Bh2fTEnkiNyvuAfvXv3xsvLi8DAwDzF2L9/f5566qkMa3Y/jPLly+dp3d6mTZvmuBqKEDmRRFUIIYR4SLNvbcKstFwnqXfZ6o2Ylcac25tzfU3Pnj1Zt25dntrRNI3ly5fj6+vLtm3b8nStEIWBJKpCCCHEQ7htimd13CFcDQ83k9/V4MCq2INEmzLfYe5+jRs3pkSJEnlqY/PmzQQGBvL222/z008/WY6PGjWK1157jebNm1O5cmW+++47ALZs2ULjxo1p164dAQEB9O3bN9Oe2B9//JG6desSEhLCW2+9hdlszjYOZ2dnhg0bRo0aNahfv75ll7zIyEjatWtHjRo1qFGjBrt27crT8xNPPklUhRBCiIewKvYAZrRMJ07lhkGnR0OxKvbgI8Uxc+ZMZs6cmem5n376iS5dutCuXTtWrVpFWlqa5dyRI0dYvXo1u3fvZvTo0Zbtmvfu3cvkyZM5evQo58+fZ9myZRnqPHnyJEuWLGHnzp2EhYVhMBhYuHBhtjEmJCRQv359Dh8+TOPGjS2J8XvvvUeTJk04fPgwBw8epHr16o9yK8QTSGb9CyGEEA/hdMq1R65DoTjziPX07ds30+OpqamsWbOGr776ChcXF+rVq8f69et54YUXgPQNRhwcHHBwcKBZs2bs3bsXd3d36tatS8WKFYH0Hdt27NhBhw4dLPVu3LiRAwcOUKdOHQCSkpLw8vLKNkZbW1vLToC1atXizz//BGDTpk3Mnz8fSN+Ixs3N7RHuhHgSSaIqhBBCPIRYLQk9mS9XlVt6dMRoiY8poozWrVvHnTt3CAoKAiAxMRFHR0dLonr/Ult3H2d1/C6lFD169OCLL77IdSw2NjaWegwGAyaTKW9PRhRb8tO/EEII8RBc9Q5oPNoKjxoKd73jY4ooo59++onZs2cTHh5OeHg4Fy5cYP369SQmpifGK1asIDk5mVu3brFlyxZLD+nevXu5cOECmqaxZMkSGjVqlKHeFi1a8MsvvxAVFQXA7du3uXjx4kPF2KJFC7799lsAzGYzsbGxD/t0xRNKElUhhBDiIVS1K/PIdejQUSWX9XTp0oUGDRpw+vRpfHx8mDNnDpD5GNXExET++OMPS+8pgJOTE40aNeL3338HoG7durzwwgvUr1+f4cOHU7ZsWQAaNGjAhx9+SGBgIBUqVKBdu3YZ6g4ICOCzzz6jZcuWBAcH89xzz3Ht2sMNX5g6dSqbN28mKCiIWrVqcfz48YeqRzy5ZMF/IYQQIgeZLUh+2xRPg3PDcdLbPdSEKrPSSNBS+OtfY/AwOj+uUHNl1KhRODs7M2jQoAzHt2zZwqRJk1i1alWBxiOKN1nwXwghhHjMShidecGlJrHmpIe6PtacxIuuoQWepApRlEiPqhBCCJGDrHp8TiRH0C58MvY6mzwt+p+qmUhWaSwvP5AAe5/HGaoQRY70qAohhBD5IMDeh89Lv0qCSiFVy91M9lTNRIJK4fPSr0qSKkQOZHkqIYQQ4hF0cK8HwLDri0kwpeBqcMh0zKpZacSakzDo9Iwr/X+W64QQWZNEVQghhHhEHdzrEWD/FHNub2ZV7EE0FAqFHh0aCh069Oh42a02r5doJj2pQuSSjFEVQgghcpDdGLr7RZviWRV7kDMp14jREnHXO1LFroxMnBIiC9l9vqRHVQghhHiMPIzOvFaisbXDEOKJIJOphBBCCCFEoSSJqhBCCCGEKJQkURVCCCGEEIWSJKpCCCGEEKJQkkRVCCGEEEIUSpKoCiGEEEKIQkkSVSGEEEIIUShJoiqEEEIIIQolSVSFEEIIIUShJImqEEIIIYQolGQLVSGEEOIxMkUnELfuKCnnotBik9G72mP3Ly9c2wRjcHe0dnhCFCmSqAohhBCPQfKpa0TP30Xc+mMoswIU6HWgKXQ6HTcmr8elVXU8XmuIfbUy1g5XiCJBElUhhBDiEd1ZcYjIMStRZoXexQ694cGRdcqsEbvmKHF/HMN7eFvcXq5phUiFKFokURVCCCEewZ0Vh7g+agV6Rxv0tll/reoMegzuDqhUE9dHrQCQZFWIHMhkKiGEEOIhJZ+6RuSYlegdbdBlk6TeS2drRO9oQ+SYlSSfuparay5fvkyzZs3w9/enevXqTJ06NVfX7d27l8aNG1O1alWqVatGnz59SExMZOXKlYwbNy5XdeTVX3/9Rb169QgJCcHf359Ro0bluY6wsDDWrFnz+IMTRY70qAohsnQhLooZf6/kQlo4SboEHJQTFWzK827Fl/Fz8bR2eEJYXfT8Xek/9+cySb1LZ2tES0wj+sfdlPmsfY7ljUYjkydPJjQ0lLi4OGrVqsVzzz1HQEBAltdERkbSsWNHFi9eTIMGDVBK8euvvxIXF0fbtm1p27ZtnmLOrR49evDzzz9To0YNzGYzp0+fznMdYWFh7N+/n+effz4fIhRFifSoCiEesC4ijLb7B9ElqgvbXRZx2WMnN90Oc9ljJ9udF9E5qgsv7x/Muogwa4cqhNWYohOIW38MvYvdQ12vd7Ejbt0xzDGJOZYtU6YMoaGhALi4uODv78+VK1eyveabb76hR48eNGjQAACdTkeHDh3w9vZm3rx5vPvuuwAsXbqUwMBAatSoQePGjQEwm80MGjSIoKAggoODmTZtGgAbN26kZs2aBAUF0bt3b1JSUh5oNyoqijJl0ieLGQwGAgIC0DSNypUrc+PGDQA0TeNf//oXN2/efKD91NRURowYwZIlSwgJCWHJkiUkJCTQu3dv6tSpQ82aNVmxIn3oxLx58/j3v//NSy+9RIUKFZg+fTpffvklNWvWpH79+ty+fRuAr7/+moCAAIKDg3n11VdzvN+i8JBEVQiRwafHFjIqZSBRHgfQUm3Qku1RKQ6o1PT/ain2aKlGIj32MyplIJ8eW2jtkIWwirh1R1FmhS6TiVO5oTPoQdOIXXc0T9eFh4dz6NAh6tWrB8DMmTOZOXPmA+WOHTtGrVq1cqxv9OjR/PHHHxw+fJiVK1cCMGvWLC5cuMChQ4c4cuQIXbt2JTk5mZ49e7JkyRKOHj2KyWTi22+/faC+Dz74gKpVq9KuXTv++9//kpycjF6vp1u3bixcmP7/iw0bNlCjRg1KlSr1QPu2traMHj2azp07ExYWRufOnfn8889p3rw5+/btY/PmzQwePJiEhATL81y0aBF79+5l2LBhODo6cujQIRo0aMD8+fMBGDdunOW5ZHavROEliaoQwuLTYwv5esgH7Avcwp5/beFIq+0Zzl/7/m/2h6xjX8Af7K+5gzt74ljnOEeSVVEspZyLAtQj1aGU+qee3ImPj+eVV15hypQpuLq6AtC3b1/69u370DE8/fTT9OzZk++++w6z2QykJ5J9+/bFaEwf0lCiRAlOnz5NhQoVqFKlCpD+E/+2bdseqG/EiBHs37+fli1bsmjRIlq3bg1A7969LYnj999/T69evbJs/37r169n3LhxhISE0LRpU5KTk7l06RIAzZo1w8XFBU9PT9zc3HjppZcACAoKIjw8HIDg4GC6du3Kjz/+aHlOomiQRFUIAaT/3L/W4Xtsyjji1a089pWcMpxPuhBPxOTTeHXxo+buFtj5OXHmjYOYU21Y6zCXP67IMABRvGixyenrpD4KvQ4tNilXRdPS0njllVfo2rUr7dvnPK61evXqHDhwIMdyM2fO5LPPPuPy5cuEhIRw69YtlEpf+/VeSuU+Ka9UqRJvv/02Gzdu5PDhw9y6dQtfX1+8vb3ZtGkTe/bsoU2bNlm2f7+742vDwsIICwvj0qVL+Pv7A2Bn97+hF3q93vJYr9djMpkAWL16Ne+88w4HDhygVq1aluOi8JNEVQgBwIzrPwIa5QYG4PtBVYyuNhnOX515Dr2jgXJD/TG62lJ5Ri20RDMxm28BZmZck15VUbzoXe1Be7QeVTSF3tUhx2JKKV5//XX8/f0ZMGBArqp+9913+eGHH9izZ4/l2I8//sj169czlDt//jz16tVj9OjRlCpVisuXL9OyZUtmzpxpSehu375NtWrVCA8P59y5cwAsWLCAJk2aPNDu6tWrLUnt2bNnMRgMuLu7A9CnTx+6detGp06dMBgMWbbv4uJCXFycpc5WrVoxbdo0S72HDh3K1T2A9PGwd1dNmDBhAjExMcTHx+f6emFdkqgKIbgQF0WUxyG01KwnhSSdjce2jL3lsW1JO3S2emL33kZLtSPS4xAX424URLhCFAp2//ICHq1HVafT/VNP9nbu3MmCBQvYtGkTISEhhISEWJZvymqMqre3N4sXL2bQoEFUrVoVf39/tm/fbhkycNfgwYMJCgoiMDCQxo0bU6NGDfr06UO5cuUIDg6mRo0aLFq0CHt7e+bOnUvHjh0JCgpCr9dnOuRgwYIFVK1alZCQEF577TUWLlxoSUrbtm1LfHy85Wf/rNpv1qwZJ06csEymGj58OGlpaQQHBxMYGMjw4cNzfY/NZjPdunUjKCiImjVr8sEHH1gSZ1H46WFBXAEAACAASURBVFRe+vKFEE+kwYdns91lEVry/xLRk912k3YzleB16T0mR57fitHNhoCfGlrKHKi7nhKtylBhTBB6u2Qax3dlQo3XCzx+IfLbyZMnLT8132WKTuDv5yahc7R9qAlVyqyhEtOotGEQBnfHxxVqobZ//34++OADtm/fnnNhUWxk9vm6S3pUhRBcSAvPcfyZ3t6AOSHjRAeVpjD8M0RAobiQFp5fIQpR6Bg9nHBpGYgW9+ASTbmhxaXg0jqw2CSp48aN45VXXuGLL76wdiiiCJFEVQhBki4BVPY/YTpUdib1+v8mfaTeSkGlarjWLZF+QOlIQsZ9ieLFo3tDdAYdKjVvk3NUqgmdQYdHtwb5FFnh8+GHH3Lx4kUaNWpk7VBEESKJqhACB+UEuvQeVS3ZhCk2FaWB0hSm2FS0ZBNl36yElmDm0sRTmGJTOffOAfSOBtyb/DO+TqdwwNmKz0KIgmdfrQzew9uiJablOllVqSa0xDS8h7fFvlqZfI5QiKJNFhMTQlDBpjwRul0o4PRb+4nfH205d6jBRpxre+D/Q32eGlCFq9POEjnvAgYXI1Vm1raU06Gjgk35gg9eCCtze7kmAJFjVqIlpqF3sct0zKoya2hxyegMekqPetlynRAiazKZSgjBhbgoukR1QUu1AfUQP7ToNPS2JpZ4/YSfi+fjD1AIK8tussddyaeuEf3jbuLWHQNNSx/3rdeB9s+apHo9Lq0D8ejWQHpShbhHdp8v6VEVQlDBxQuv0zXTt01NyXlNx/vpbVPwjq6NXyVJUkXxZV+tDGU+a4/XoNbErjtKyrkotNgk9K4O2P3LC9fWQcVm4pQQj4skqkIIAPqV7saolEOgN4NmyP2FejNgoF+ZrvkWmxBFicHdEY9X61k7DCGeCDKZSggBQGufENok9cZgm/ZP8pkLejMG2zTaJPWi1VMh+RugEEKIYkcSVSGExcjArrROfB29jQm9XRLotMwL6jT0dknobcy0TnydkYHSmyqEEOLxk0RVCJHByMCuDDeOJ3avO3qbNPR2yejsktDZpv9Xb5eMzpiK+XhZPrWfJEmqEEKIfCNjVIUQD9g3dxWXJv3J8WsX+ObvlVxICyeJeBxwpoJNeUofSmPoG/+hRdIP1g5VCCHEE0yWpxJCPMDb25sXXniB77//PtPzmqbh7OzMxx9/zCeffFLA0QlR8HKzPJUQ4uFk9/mSn/6FEBls27aNGzduMGHChCzL6PV6OnbsyPTp0wswMiGEEMWNJKpCiAyGDh1KSEgIpUqVyrbcxIkTiYqKYvfu3QUUmRBCiOJGElUhhEVsbCx79uzh888/z7Gsl5cXQUFBDB48uAAiE0IIURxJoiqEsBg2bBju7u60adMmV+XHjBnD7t27iY+Pz+fIhBBCFEeSqAohLObPn0+vXr1yXb5t27a4uLgwfPjwfIxKCCFEcSWJqhACgOXLlxMfH8+YMWPydF2PHj2YN29e/gQlRBGUakolIuoSpy+d4PiFI5y+dIKIqEukmVKtHZoQRY4sTyWEACA4OBgPDw+2bt2ap+vi4+Nxc3NjxYoVvPjii/kUnRDWlZvlqeISY7kcdZGomEhQinu/XHUAOh1e7qXx9SqHi6NrfoYrRJGS3edLFvwXQnD9+nWOHTvGnj178nyts7MzDRo04JNPPpFEVRRb125d4fTlkyilMBqM6HS6B8oopYiMvkZUzHWq+vpTpuRTVohUiKJFfvoXQjBw4EBKly5NnTp1Hur6iRMncuTIEaKioh5zZEIUftduXeHUpRPodXpsjDaZJqkAOp0OG6MNep2eU5dOcO3WlQKOVIiiRxJVIYo5TdNYtmwZ//nPfx66jgYNGuDl5SVLVYliJy4xltOXT2LQG9Drc/eVqtfrMegNnL58krjE2Fxdk5ycTN26dalRowbVq1dn5MiROV4zb948PD09qVmzJpUrV6ZVq1bs2rUrx+tGjRrFpEmTAOjZsye//PLLA2VGjBjBhg0bchW7EI9CElUhirnZs2djMpkeOcns168fS5cuRdO0xxSZEIXf5aiLKKVynaTepdfrUUpx+calXJW3s7Nj06ZNHD58mLCwMNatW8dff/2V43WdO3fm0KFDnD17lg8//JD27dtz8uTJPMWamdGjR/Pss88+cj1C5EQSVSGKufHjx9OqVSuMxkcbsv7xxx+TlpbGDz/88JgiE6JwSzWlEhUTidHwcJ8do8FIVPT1XK0GoNPpcHZ2BiAtLY20tLQshxhkpVmzZrz55pvMmjULgPPnz9O6dWtq1arFM888w6lTp3Jd1709reXLl2fkyJGEhoYSFBRkqSc+Pp5evXoRFBREcHAwv/76a57iFQIkURWiWDt79ix///03X3311SPXZTQaefbZZxk7duxjiEyIwi/q9nVQKs8J4106nQ6UIjL6eq7Km81mQkJC8PLy4rnnnqNevXpA+s/wK1euzFUdoaGhlkTyzTffZNq0aRw4cIBJkybRr1+/h3oeAKVKleLgwYO8/fbblmEDY8aMwc3NjaNHj3LkyBGaN2/+0PWL4ktm/QtRjH3wwQdUrFiRypUrP5b6vvzySwICAjh//jyVKlV6LHUKUVglJMfzqOs7KiAhOSFXZQ0GA2FhYcTExNCuXTuOHTtGYGAgo0ePzn17/6xIGR8fz65du+jYsaPlXEpKSp5iv1f79u0BqFWrFsuWLQNgw4YNLF682FLGw8PjoesXxZckqkIUUyaTiT/++INvv/32sdXp7+9P+fLlGThwIL/99ttjq1eIwshkNj2eekxpeSrv7u5O06ZNWbduHYGBgXm69tChQ/j7+6NpGu7u7oSFheXp+qzY2dkB6cm0yZR+X9Qj9DYLcZf89C9EMTVx4kRsbGzo3bv3Y613yJAhrFmzxvJlJcST6mHHpj5Qj9EmxzI3btwgJiYGgKSkJDZs2EC1atXy1M7WrVuZNWsWb7zxBq6urlSoUIGlS5cC6Unl4cOH8x58Nlq2bMn06dMtj6Ojox9r/aJ4kERViGJq2rRptG/fPs+zlXPy1ltvYTAY+PLLLx9rvUIUNk72zjxqf6EOcLJ3yrHctWvXaNasGcHBwdSpU4fnnnvOssFGdmNUlyxZQkhICFWqVGHs2LH8+uuvlh2AFi5cyJw5cyxLXq1YseIRn01Gn3zyCdHR0QQGBlKjRg02b94MQJ8+fdi/f/9jbUs8uWQLVSGKoT179tCgQQOuXr1K6dKlH3v9Xbp0Ydu2bVy5IguaiydDZls8pppS2XVsGwa94aF+4lZKYdbMPB3YGBuj7eMKVYgiJ7stVKVHVYhiaPDgwQQGBuZLkgowefJkrl27Jr0m4olma7TFy937oceqmswmvDxKS5IqRDYkURWimElMTGTnzp2MGTMm39ooW7Ys/v7+DB48mB07dsgwAPHE8vXyQ6fT5XmjC03T0Ol0+HqWy6fIhHgySKIqRDEzfPhwnJ2defnll/OtjdTUVAICAtiyZQvPPPMMQ4cOzbe2hLAmF0dXqvr6Y9bMuU5WNU3DrJmp6uuPi6NrPkcoRNEmy1MJUczMnTuX7t2752sbvr6+REVFWR4/6q5XQhRmZUo+BcDpyycxm8wYDcZMx6wqpTCZTeh0OqqVC7BcJ4TImvSoClGMrF27lpiYGD7//PN8befrr7/O8EXt4OCQr+0JYW1lSj5FrSp18S5RBrNmxmRKI+2ePyZTGmbNjHeJMtSqUleSVCFySWb9C1GMhIaGYm9vz65du/K9rT179tCoUSNMJhNeXl5ERkbme5tC5JfsZiXfL82USmT0dRKSEzCZ0jAabXCyd8JbJk4JkansPl/ye5wQxcTNmzcJCwtj27ZtBdJevXr1OH/+PBUrViyQ9oQoLGyMtvjIJCkhHgtJVEWREnU5mu2zd5B45jrEJ4OzPY5VStP4jUZ4+sg+0tkZMmQInp6eNGrUqMDaLFeuHKmpqQBcj4lj/dHTxCbEoSkTep0RVycXWgVXw9vNucBiEkIIUXRIoiqKhMNbznB46gZKHwrHVYErCo1/BlnvOsuhH3YQGepH8HvPUqNpFStHWzgtWbKEwYMHF3i7B8Ovsev0SVxtEtDrwMUGNAV6HajUWNbuu0JsmhMNq/pTu6KM2xNCCPE/MkZVFHq/T/wDuxmb0GkaiTZGNP2Ds2n1msIxzYTS60np15yXBreyQqSF1/z58+nduzeJiYnY2hbcGLklf4WRFH8RHZBkgsz+Z6MDHIzp5xyc/ehcP6TA4hMit/IyRlUIkTcyRlUUWb9P/AOH6RtJMuox2diQaE5mcPg8ItNi0FDY6oy092hAp5KNiLezwWjWcJi+kd9BktV7fPbZZ7Ro0aLAk9SUhIukmsH8T4b6Sc83iY66gVIKg8FA7aaN6TGoP4kmMOhAn3CRJX8hyaoQQghAElVRiB3ecga7GZvSk1RD+kpqqcqMh8GZd7yep4q9D0tvb2fx7e0EOpQjwLEcJoOeJMBuxiaO1K1AcBMZBnDx4kXOnTvHsmXLCqzN/X9fISk+Y5IK0PGt16kSHISDkyPH9u7n21Gf41+zBnVbNMWsINkEWvxFDlzwpFYFGQYghBDFnayjKgqtw1M3oNM0S5IK4G504jPfbgQ4lsOo19OlVBNsdAb2Jpy1lDEZ9Og0jcNTN1gj7ELngw8+wNfXl8DAwAJrc9fpk+jImKQC1GhQDwcnRwB0+vTX9eK5c5bzZpU+FGDnqZMFFKkQQojCTHpURaEUdTma0ofCSbTJ/i16MeUGacpMkGP5DMcTbYx4H7zIjYjoYr0agKZprF69mkmTJhVYm9dj4nC1SSDJlPn5MW/9h2uXLgNgY2tLy46vZDifZAJXmwQi78TLagBCCFHMSY+qKJS2z96BTpHpxKm7krVUhkf8yFM2JanlVCnDOU2vQ6cU22fvyO9QC7UpU6ag0+l45513CqzN9UdPp8/oz+L88P9O4+sVP9O535tUDKiGg2PGXasUoNPB+qOn8j1WIYQQhZskqqJQSjxznaxTHTBpGu9d/A49OiaW65lFKUXCmeK9G9JXX31F27Zt0esL7qMemxCXzSuXzmhrS5OXnufOrdvMmzQl0zJ34uMef3BCCCGKFPnpXxRO8cloWZzSNI33Ls4iSUthhl9f7PWZz2TXAOKS8yvCQi8sLIwrV67w5ZdfFmi7mjKh5XLRO03TuHHteiZ1gKbMjzkyIYQQRY0kqqJwcrbPsrv//UuziTHH8035vrgYHbOsQg/gYp8f0RUJAwcOpGrVqvj4+BRou3qdkcxGbFy7eIntq/+g1asdcHJ24s9lK4i6cpVWnV/JpA7Q6wwFEK0Qj58pMYaYM5tIvnUBc3I8Bntn7EtWwL1qC4wObtYOT4giRRJVUSg5VikNu84+cPxkUgQRabcAeP3CNMvxVq41ecu79X2ldThV8c7PMAut5ORktm7dysKFCwu8bVcnF0iNffCETsdfGzez5ffVABhtbKjbvCkv93wt03rcnF3yMUohHr+kqLPcOPgzMWe2gDKjFOh0epTS0Ol0XN32Le5VmuEZ2hEHr8rWDleIIkF2phKFUtTlaMKe+YIkoyHbCVVZ0WsKB5NGzR0fFstZ/x9//DHTpk0jLq7gx3lej4lj3f5NJGexE1VOdIC9EdrUaSGz/kWhkdPOVLePryVi45egmdHbOaPTP/iLgNLMaCnxoDfg02IAJaq3yc+QhSgysvt8yWQqUSh5+XpwvWZ5HNOyWOMoB45pJiJD/Yplkgowa9YsunTpYpW2S7u7EJvmhMND/l7jYITYNCdJUkWRcfv4WiI2TERntMXg4JZpkgqg0xvSzxttidgwkdvH1xZwpEIUPZKoikKrRv9nUXo9RnNW06oyZzRrKL2eGv2fzafICrdNmzZx+/ZtJkyYYLUYGlb1R5G+LWpeGP5Z1urparKnuigakqLOErHxS3Q2DugNuduiWG+wRWfjQMTGL0mKenCIU3bMZjM1a9bkxRdfzLHsvHnz8PT0JCQkxPLnxIkThIeHo9PpGD58uKXszZs3sbGx4d1337Ucmz9/PoGBgVSvXp2AgIAs12PObbmCFhMTw4wZMyyPr169SocOHawYkXgYkqiKQqtG0yqk9GuOg0nLdbJqNGs4mDRS+jUvttunfvjhh4SGhuLu7m61GGpXfAoHZz/sjblPVg269J/8HZz9ZPtUUWTcOPhz+s/9uUxS79IbbEEzc+PgL3m6burUqdkOQbhf586dCQsLs/wJCAgAoGLFiqxatcpSbunSpVSvXt3yeO3atUyZMoX169dz/PhxDh48iJvbgxPBclsuKybTw/1qlpvr709Uy5Ytyy+/5O1+C+uTRFUUai8NbkXSuy2w0xTOKWnos1j3SP/PeTtNkfRuC14a3KqAIy0cYmJi2L9/P+PHj7d2KHSuH4Kdkx+2BnA0po89zYwOcLQBWwPYOfnRuX5IQYYpxENLn92/Bb3dww1T0ds5E3NmE6akO7kqHxERwerVq+nTp89DtXcvBwcH/P392b9/PwBLliyhU6dOlvNffPEFkyZNomzZsgDY29vzxhtvPFBPduXCwsKoX78+wcHBtGvXjujoaACaNm3Kxx9/TJMmTZg6dSo9e/akb9++PPPMM1SpUsWSQCcnJ9OrVy+CgoKoWbMmmzdvBtJ7ijt27MhLL71Ey5YtiY+Pp0WLFoSGhhIUFMSKFSuA9H+0nz9/npCQEAYPHkx4eLhlK+ns6m7fvj2tW7emcuXKDBky5JHvtXg0MutfFHovDW7FkboVODx1A94HL6JTClBo3P2Xlg4zir1eRl6a1KvY9qQCfPTRR5QoUYIWLVpYOxQgPVk9cMGTnadO4mqTgO6fbFVT6UtQoUBTiuhkJ54JCJCeVFGkxJzZBMqc5ZjUnOj0BlAaMac3USqkXY7l33//fSZMmPDAJMkRI0ZQu3Zt2rZt+8A1S5YsYceO/+3Qt3v3bsvfX331VRYvXkzp0qUxGAyULVuWq1evAnDs2DFq1aqVY0zZlevevTvTpk2jSZMmjBgxgk8//ZQpU9I3+IiJiWHr1q0A9OzZk/DwcLZu3cr58+dp1qwZ586d45tvvgHg6NGjnDp1ipYtW3LmzBnL8zhy5AglSpTAZDKxfPlyXF1duXnzJvXr16dt27aMGzeOY8eOERYWBkB4eLgltuzqDgsL49ChQ9jZ2VG1alX+85//4Ovrm+O9EPlDElVRJAQ3qUJwkyrciIhm++wd6TtOxSWDiz1OVbx5feLrxFy6wa8f/MHevXsxGovnW3vhwoUFul1qbtSq8BS1KjxF5J141h89xZ34ODRlRq8z4Obswie9utG2ZQvef+E5a4cqRJ4k37rAo66bo5Qi+VZ4juVWrVqFl5cXtWrVYsuWLRnOjR49OsvrOnfuzPTp0zM917p1a4YPH463tzedO3fOS9g5unPnDjExMTRp0gSAHj160LFjxwxx3atTp07o9XoqV65MxYoVOXXqFDt27OA///kPANWqVcPPz8+STD733HOUKFECSL+HH3/8Mdu2bUOv13PlyhUiI7PflTC7ulu0aGEZvhAQEMDFixclUbWi4vltLoosTx8P2o966YHjHUbfBODQoUP4+Phw5swZXF1dCzo8q/rll19ITExk5MiR1g4lU95uzrzWqPYDx4/8+yVmzZpl6eEQoqgwJ8ej0z3aCDqdTo85Jedl5Hbu3MnKlStZs2YNycnJxMbG0q1bN3788ceHbtvW1pZatWoxefJkjh8/zu+//245V716dQ4cOEDz5s2zrSO35e7n5OSU4bFOp3vgcXarZ957/cKFC7lx4wYHDhzAxsaG8uXLk5yc/a6E2dVtZ2dn+bvBYHjkcbTi0cgYVfFEuPd/OpGRkbmaEfukGTlyJI0bN8bevmjtxvXpp58SHx/Pr7/+au1QhMgTg70zSuVtVZL7KaVhsMt5c4svvviCiIgIwsPDWbx4Mc2bN3+kJPWugQMHMn78eEqWLJnh+EcffcSQIUO4fj19i+OUlBS+/vrrB67PqpybmxseHh5s374dgAULFlh6VzOzdOlSNE3j/Pnz/P3331StWpXGjRtbNi05c+YMly5domrVqg9ce+fOHby8vLCxsWHz5s1cvHgRABcXlyzXks5t3cL6pEdVFHmJiYkZHvfo0aPA97e3toiICE6ePMmCBQusHUqeOTo68swzzzBq1CheeeXB7VSFKKzsS1ZAl/f9SDLQ6XTYlyz/SHXkZYzqjBkzLBOfIL1H9N7Z/nc9//zzREZG8uyzz6KUQqfT0bt37zyV++GHH+jbty+JiYlUrFiRuXPnZvkcqlatSpMmTYiMjGTmzJnY29vTr18/+vbtS1BQEEajkXnz5mXo7byra9euvPTSS9SuXZuQkBCqVasGQMmSJXn66acJDAykTZs2GYZF5bZuYX2yM5Uo8kwmE61bt6ZXr168/fbbvP3224Vi1ntBevXVV9mxYwcRERHWDuWh7N+/n7p16xIREZHhS1SIwiKznXNMiTGcmN0RvY3DQ02oUpoZLS2JgDd+weiQ+yWdnjQ9e/bkxRdflDVOizHZmUo80YxGIxs2bKBr165069aN2bNnWzukAqVpGr/99hv9+/e3digPrXbt2pQuXZpBgwZZOxQhcs3o6I57labp26I+BC0lHvcqzYt1kipETiRRFU+UcePGER0dzZ9//mntUArMzJkz0TSNgQMHWjuUR/Lee++xfPlyNO3RxvwJUZA8QzuB3oBmTs3TdZo5FfQGPEOlF3HevHnSmyqyJImqeKK4urpSp04dPvroI2uHUmAmTJjACy+8gF5ftD/OgwYNwmw2891331k7FCFyzcGrMj4tBqDSknKdrGrmVFRaEj4tBuDgVTmfIxSiaCva32xCZGL8+PEcPHiQ27dvWzuUfHfy5EkuXbr0REweMxqNtGrVqtiNLxZFX4nqbfB5djDKlIo56Q5KM2daTmnm9POmVHyeHUyJ6m0KOFIhih6ZTCWeSJ6enrRr145Zs2ZZO5R81aZNG86fP29ZqLqoO3v2LFWqVOHUqVOyVIwoVLKb7HFXUtRZbhz85Z8dq7R/ZsHrUUpLXydUp8e9SnM8QztIT6oQ98ju8yWJqngiDRs2jKlTpxIf/3CTHIoCk8mEg4MDs2fPpkePHtYO57GpVKkS1apVY/Xq1dYORQiL3CSqd5mS7hBzehPJt8Ixp8RhsHPBvmR53KvKxCkhMiOJqih2UlNTcXR0ZMGCBXTp0sXa4eSL0aNHM378eBISEqwdymM1Z84c+vbtS1JSUrHdClcUPnlJVIUQeSPLU4lix9bWlqZNm2a7B3ZRN2PGjAx7Zz8pevXqhY2NjYxVFUIIIYmqeHJ9+eWXnD59mkuXLlk7lMdu586dREVFMWHCBGuH8tjp9Xo6dOjA9OnTrR2KEEIIK5NEVTyxgoOD8fHxKfLri2ZmyJAhBAcH4+XlZe1Q8sWkSZOIjIxk9+7d1g5FCCGEFUmiKp5oAwYM4Pfff3+iFpGPj4/nr7/+4rPPPrN2KPnGy8uLwMBAhgwZYu1QhBBCWJEkquKJ9t5776GU4ptvvrF2KI/NsGHDcHV15cUXX7R2KPlqzJgx7Nq1i8TERGuHIoQQwkokURVPNL1ez4svvsikSZOsHcpj88MPP9CrVy9rh5HvXn75ZVxcXPjkk0+sHYoQQggrkURVPPG+/PJLLl++zLFjx6wdyiNbuXIlcXFxT/TP/vfq3r07c+fOtXYYQgghrEQSVfHE8/Pzo3Llyk/EpKrhw4fTsGFDHB0drR1KgRg7diyxsbGsWbPG2qEIIYSwAklURbEwfPhwNm7cSGpqqrVDeWhRUVEcPXr0iRrGkBNnZ2fq16/PsGHDrB2KEEIIK5BEVRQL3bp1w97evkj+ZD558mSmTZvGwIED8fb2pl69etYOqUBNmDCBw4cPExUVZe1QhBBCFDDZQlUUG3369GHlypVFLuEpUaIE0dHRAPj7+7Nu3TrKlStn5agKlre3N23atGHevHnWDkUUU3nZQvV2YiJrjp/i7I1bxCYn42pvT2XPkrxQ3R8PR4d8jlSIoie7z5ckqqLYuH37NqVKlWLLli00btzY2uHkmp+fX4bdtYxGI2lpaVaMqOB9+umnTJgwgYSEBGuHIoqp3CSqJ69HMXfPftaePI1ZaaBAr9OhKYVOp0Ov09HGvyq96tXGv/STuVmHEA8ju8+X/PQvio0SJUpQs2ZNhg4dau1Q8qRUqVIZHs+cOdNKkVjPRx99RGpqKj/88IO1QxEiU8sOH6PTvIWsOn4SJ1sbPBwc8HB0wM3BHg9HB9wd7HGytWHV8ZN0mreQZYeL/iokQhQESVRFsTJ27Fj27NlDbGystUPJNWdnZyB9TdjNmzfz+uuvWzmigmdra0uLFi34/PPPrR2KEA9YdvgYn6z+A3ujEQ9HBwz6zL9aDXo9Ho4O2BuNfLLmD0lWhcgFSVRFsdKqVSvc3d2L1CxyGxsb9Ho9p06domnTptYOx2omT57M2bNnuXDhgrVDEcLi5PUoRq79EydbW2yNhlxdY2s04GRjy8i1f3Lyeu7HzJcvX56goCBCQkKoXbt2rq7Zu3cvTZs2pXLlyoSGhvLCCy9w9OjRXLeZG+Hh4QQGBubpmrv/ABciJ0ZrByCKh3MRN5k450/OnIkgIT4JJ2cHqlTxYWifllR8qmSBxvL6668za9Yspk2bVqDt5iTyRhRbNy4j/sbfkJoAtk44e1Zk0eJFeJWS8WzVq1fHz8+PAQMGsHz5cmuHIwQAc/fsx6xpuU5S77I1GkhITWXe3v2Mb/t8rq/bvHnzA8OBshIZGUmnTp1YtGgRDRs2BGDHjh2cP3+eoKCgPMUrhLVIj6rIVyu3HqfBK5/TqOkHrJi/mhO7w7h47AwndoexYv5qGjZ5n4YdxrJy6/ECi+nTTz8lPj6+0CQ7YUcO8v1X77Fn+r9xPDkPzxvbKHXn7szQAwAAIABJREFUIJ43tuFw8gf+mvZvvv+qP2FHDlo7VKsbNGgQa9asQdM0a4ciBLcTE1l78jSu9nYPdb2rvR1rTpwmOjHpMUeWbvr06fTo0cOSpAI0atSIf//73wBcvHiRFi1aEBwcTIsWLSyTNrM6fv78eerXr0+dOnUYMWJEpr2iZrOZwYMHU6dOHYKDg/nvf/+bbYxbtmyhadOmdOjQgWrVqtG1a1fuzvHet28fDRs2pEaNGtStW5e4uLjHcl9E0SKJqsg3gyct583Xx/H3oRMogwFsbdHZ2aH757/Y2qIMBs4fPM6br49j8KSCSRwdHR1p1KgRI0eOLJD2svPbbz9w6dd38YzeS5KyJV45koAjiTiQgCMJyoEkZYtn9B4u/fouv/1WvCcT9evXD51Ox1dffWXtUIRgzfFTmJWW5ZjUnBj0ejSlWHPiVK7K63Q6WrZsSa1atZg1a5bl+MyZMzOdZHn8+HFCQ0OzrO/dd9+le/fuHDlyhK5du/Lee+9le7x///7079+fffv2UbZs2UzrnDNnDm5ubuzbt499+/bx3Xff5Thc59ChQ0yZMoUTJ07w999/s3PnTlJTU+ncuTNTp07l8OHDbNiwAYf/Z+/O46Kq9z+Ov86wLyLiEqnhLiqIKCouKOa+kgtZZrlr1k0tcfd6c6nccas0bTHTn0tuUTeXXAktFxQJDAXFPRdA1mGAmXN+f1DcTATEmTks3+fjwQOZOed83+Bj4DPf813sxNJe5ZEoVAWTmLpsD9+s3YWi0eQWp3/+Ik+99yv3Yr7mXsxX3IvZiDY5BsnGBkWj4Zu1u8xWrC5btoyoqCju3r1rlvbys3fv11ieX0uWYkkG9ih/ezlGXkliyIKjTFz9CwoaMrAnS7HE8vzacl2sajQa+vfvLwpVoUSIfZAIz7jAo6IoxD5IKNKxJ06c4Ny5c+zbt49PPvmE0NBQAMaPH8/48eMLPd/X15fGjRszadIkAH755Rdee+01AN544w3CwsIKffzll18GyHv+nw4ePMimTZvw9vbG19eXxMREYmNjC8zVunVratasiUajwdvbm2vXrnHp0iWef/55WrVqBYCTkxOWlmK0YnkkClXB6EKOR/PNut0oFhZIFv8bt5WeGEnmwxgquLahasPhVKrVByu7qgBIFhYoFhZ8s24335thGECrVq1wdXUlKCjI5G3lJyLyHJrzn5GpWGPIZ6j4mj3R2Fk/OubNgCWZijWa858R8Vv5HQawbNky7ty5w7lz5fdnIJQMqTodGkl6pmtoJIkUna5Ix/7Vi1mtWjUGDBjA6dOnCzzew8PjkdfJqVOnWLBgASkpKfkeLz3he3nS4/lRFIU1a9YQERFBREQE8fHxdO/evcBzbGz+N3TCwsICvV6P8ufas4IgClXB6Bau3osiy48UqQDaxAvYOtXD3tkdjUaDtV1VrP8sVOHPYlWWWbjmO7PknDhxIrt371ZlvOO5wxuRFDnfInXj/stYW1pQ2zWf8V9YIiky5w6V317VmjVr0qhRI6ZMmaJ2FKGcc7K1RX7GPXNkRaGirW2hx2VkZOSN0czIyODgwYOFzrT/17/+xcaNGzl58mTeY1qtNu/f7dq1Y9u2bQBs2bIFPz+/Ah9v06YNu3btAsh7/p969OjB2rVr8zYluXz5crE26mjUqBF37tzhzJkzAKSlpaHX65/6OkLpJwpVwajibiUQfyEGrKweeVyW9SiyHtmQyb2YjdyL2UjC1T3I+n/0JFhZcTXid67eTjR51ilTpmAwGPj8889N3tbf3XtwnyrJZ9Hy+B+nxFQdh8Lv8F6gxxPP12JLleQz3E8oXVvBGtPcuXMJDQ1FV8SeKEEwhQZVK8MzdvpJkkSDqoXP4r937x5+fn55E4v69OlDz549gSePUXV1dWX79u3MnDmT+vXr065dO3bu3Mk777wDwOrVq/nqq6/w8vLim2++YdWqVQU+vnLlSoKDg2ndujV//PEHFStWfKzNMWPG0KRJE1q0aIGnpydvvvlmsQpMa2trtm/fzoQJE2jWrBndunVDp9Nx584devcu+ioJQukntlAVjOrNeVv5btN/wdr6kcezMxN5eD0EJAtcavVBkixJuv49ljaVcKnV59GLZGfTf3hf1v3nVZPn7devHxcvXuTKlSsmb+svO7atw/73jaQr9o89N2P9GZwdrZnxWjPmf32OhJQsVk9s+9hxDlImuiYjePmVN80RuUSqUKEC77zzDgsXLlQ7ilAO5LfFY5JWi//qz3CwtirWhCqDLJORnUPoxPFUsi/5E4W0Wi12dnZIksS2bdvYunUr331nnjtgQtkmtlAVzOby5Vvk995HY5FbuNo61cXKtjKWNhWxq9iQHN3jPaeKonDp8i2TZwUIDg4mPj6+0MH+xpT+4Cr5vT08GX2P2wkZvFtAb2oeRSH9/lXjhytFhg4dyoYNG9SOIZRjLvb29GrsTqouq1jnp+qy6N3EvVQUqQDh4eF4e3vj5eXFp59+yvLly9WOJJQDolAVjCojPRPyGQBvaV2B3HtkRbhPJklkpGsLP84IGjRoQJ06dXjvvffM0h4A2Rko+fwcfom+j96gMGrJz7z2wVF+v5HCgxQdwxcef+xYBQklO90caUusRYsWkZSUxOHDh9WOIpRjI31bYqHRkK03PNV52XoDFhoNI1oXbYepkqBDhw5cuHCByMhIQkNDqV+/vtqRhHJAFKqCUTk42pFvdyFgZVcNXepV9LqH6LPTyEy5jJVtPrtSKQoOjo/fFjeVmTNncuDAAfMN1Ld2QMpnTZuxfd1ZNK4lC8fmftR5vgKVKljz0ZjH/5BJKEjW5XsLQmdnZ3x8fJgxY4baUYRyrLFrNeb16kZGTnaRi9VsvYGMnGzm9epGY1ex65wgFEQUqoJRNWxY84lLiji/0B0LK0cSr+0l8epONFaOONfo+thxkiTh3rCmqaPmGTVqFFZWVixdutQs7TlWrZtfpzNO9tbUeq5C3oetlQZLjYYaVR0eP1iScKxW1/RhS7hFixYRHh5OcnKy2lGEcmxgM08+6N0DnV7PQ20mhiesJGKQZZK0mej0ej7o3YOBzQqetS8IgphMJRhZ3K0E/Dq9l7uGajEmFyiyjGQwcPL4SurWyKe31USGDRvGoUOHuHPnjsnbuvfgPqc+7k+mYv3IIv9FJSFjJ2XTZsJeqlURvTFVqlQhMDAw31nPgmAsBU32yDvm7n02nj7LjxcvISsKiqKgkSTkP9cE1UgSvZu4M6J1S9GTKgh/U9DrS2zzIBhV/ZpVqNOsEVfPXwSbYux/nZND3RYeZi1SIXcReVdXV86cOZO3E4qpPFe1GgnOLan68DQZPP0QB3t0PHD2FUXqn8aNG8eaNWtEoSqorrFrNRYH9GZG1xf58WIMsQ8SSNHpqGhrS4OqVejdpFGpmTglCCWF6FEVjC7keDTjRi/K3T71H4v+F0QxGMBg4KPFoxkd+KIJE+bPy8sLZ2fnvG0JTSki8hw3dr1DlmKZ76L/T2KBHhtJj1vgx3g3ffIe3uWJTqfD0dGRLVu28Morr6gdRyijitKjKghC8YjlqQSzCvD34I3xA5EMhtziswgUgwHJYMDBVcNbr/UgJCTExCkft2DBAk6cOPHIzi2m4u3VArn5m9hJ2VhQtElcFuixk7KRm78pitS/sbW1xd/fn7lz56odRRAEQTAyUagKJrF0ygDeeGsQkiyjZGWhPGFygfLn85Is88Zbg7h6YhMjRoygf//+zJo1y6yZX3rpJSpUqMCcOXPM0l7//sPRN38LG0mPA1ok8v8ZScg4oMVG0qNv/hb9+w83S77SZPny5Vy6dIlbt8yz/q4gCIJgHuLWv2Ay169fp3HbvlSp1YHsB2nw5+QCJAn+nFyAJFHXuzEzJ7xEP///LXT/1VdfMXbsWPz9/Tlw4ACWluYZTj1x4kQ2b95MUlKSWdoDiPjtHOcOfU2V5DO5y1YpCgpS7r8lCQWJBOdWtOg6XPSkFqBmzZq0a9eOHTt2qB1FKIPErX9BMJ2CXl+iUBVM4sSJE/j7+2MwGGjbti2bv/2eJV/8xKXLt8hI1+LgaI97w5pMG93tiROnIiIi6NChAxUrVuTs2bO4urqaPHd6ejoVK1bkhx9+oFevXiZv7+/uJ9zn+OE9pN+/ipKdjmTtiGO1uvh3GSAmThXB8uXLmT17NlqtFk0xVpwQhIKIQlUQTEcUqoJZbdmyhTfeeCNvK1UvLy8uXLhQrGulpqbSsmVLbt68yYEDB+jYsaMxo+arXbt26HQ6zp07Z/K2BOORZRk7OzuWL1/OO++8o3YcoYwRhaogmI6YTCWY1ciRI/n7+5/ExMRiX8vJyYmYmBj69u3Liy++aJa9pZcsWUJERAQJCQkmb0swHo1GQ+/evVm2bJnaUQRBEAQjEYWqYHSxsbF4ePxvvGl6+rPtSa/RaPj2229ZsmQJ06ZNY9CgQchPmJxlDH5+flStWpVp06aZrA3BNIKDg7lx4wbR0dFqRxEEQRCMQBSqgtHVqlWL5ORkBg8ezIoVK5gyZYpRrhsUFMTRo0f58ccfadSoEampqUa5bn7efvtttm/fbrLrC6ZRp04d6tevz+TJk9WOIgiCIBiBGKMqGN25c+do2bIlt27donr16ka//t27d2nZsiUpKSn8/PPPeHt7G72N7OxsHBwc+OKLLxg2bJjRry+YzqZNmxg1ahRarRZra2u14whlhBijKgimI8aoCmYVFBRE48aNTVKkAri6unLjxg18fX3x8fHhiy++MHob1tbWdOnShQ8++MDo1xZMa9iwYdjY2PDRRx+pHUUQBEF4RqJHVTAqnU6Hg4MD27dvJzAw0OTtzZ49m4ULFzJ69Gg2bNhg1GtHR0fTtGlTrly5Qp06dYx6bcG0Ro4cyY8//si9e/fUjiKUEU/To/rwYRoHD57mStwdUtO0OFWwp1796vTo4Yuzs6OJkwpC6SOWpxLMZvr06axdu9ak40f/KSQkhMDAQDw9PTl58iS2trZGu3bt2rVp0aIFu3fvNto1BdNLSEigWrVqhIaG4ufnp3YcoQwoSqF66dINNn9zkJ9+OotBlkEBjUZClhUkKXdiaLfuLXn99e64u7uZKbkglHyiUBXMpnLlyrzyyit8+umnZm03Pj6eVq1aIcsyp06dokGDBka57scff0xQUBCZmZliEflSpnnz5tjZ2XHy5Em1owhlQGGFakhIGB99uBmDwUCFCg5YWDz++8JgkElLy8DCwoJZs18nIEC8iRIEEGNUBTP56aefePjwIYsWLTJ723Xq1OHOnTvUq1ePJk2asGfPHqNc9+2330aSJFauXGmU6wnm8+GHH3Lq1KlnXh5NEAoTEhLGgvmbsLGxwtm5Qr5FKoCFhQZn5wrY2FixYMEmQkLCzJxUEEofUagKRjNz5kxatWqFk5OTKu1bW1tz5swZxowZw6BBg5g+ffozX1Oj0RAQEEBwcLAREgrm1Lt3b5ycnJg1a5baUYQy7NKlG3z04Wbs7W2wtrYq0jnW1lbY29nw0YebuXTpRpHbSk5OJjAwkEaNGtG4cWN++eWXAo8/duwYffv2feSxESNGsHPnziK3mZ+5c+cWurHG3r17uXjxYt7XGzdu5M6dO/kee+3aNezs7PD29qZJkyaMHz++WGtlG+N7+ztHRzGeuCQQhapgFElJSZw7d47FixerHYW1a9eyceNGli9fTqdOndDr9c90veDgYO7cuUNERISREgrmMmrUKDZt2qR2DKEM2/zNQQwGQ5GL1L9YW1thMBjYsuWnIp8zadIkevbsSUxMDBcuXCjRy2U9TaEKUK9ePSIiIoiMjOTixYvs3bvXHDHzPOvfCcF0RKEqGMWMGTOoXLkynTp1UjsKkLtE0blz5zh37hxubm4F/oIsTM2aNWnUqBFBQUFGTCiYw4IFC0hLSzPaUBBB+LuHD9P46aezVKjgUKzzK1Rw4OCBMyQnFz48JTU1ldDQUEaPHg3k3kFydnYuVrt/CQ8Px9/fHx8fH3r06MEff/zB/fv38fHxAeDChQtIksSNG7m9vvXq1UOr1T5yjStXrtCzZ098fHzo0KEDMTExnDx5kpCQEKZOnYq3tzeLFy/m7NmzDB06FG9vbzIzM5+YydLSknbt2hEXF0d6ejpdunShRYsWNG3alO+++y7vuE2bNuHl5UWzZs144403HrvOnDlzGDFiBLIsM3/+fFq1aoWnpyfjxo3L2+K7U6dOzJo1C39/f1atWkV8fDxt27alVatWzJkz55l+toLxiEJVMIqtW7cybtw4tWM8wsvLi1u3buHk5ETdunU5cuRIsa81d+5cjh8/jk6nM2JCwdTs7e1p374977//vtpRhDLo4MHTGGT5iWNSC2NhoUGWZQ4ePF3osVevXqVq1aqMHDmS5s2bM2bMGDIyMgBYt24d69aty/e8vzZF+esjJCQEgJycHCZMmMDOnTsJDw9n1KhRzJ49m2rVqqHT6UhNTeXnn3+mZcuW/Pzzz1y/fp1q1aphb2//yPXHjRvHmjVrCA8PZ9myZbz99tu0a9eOgIAAli5dSkREBNOnT6dly5Zs2bKFiIgI7Ozsnvh9arVaDh8+TNOmTbG1tWXPnj2cO3eOo0ePEhQUhKIoREdH8+GHH3LkyBEuXLjAqlWrHrnGtGnTuH//Pl999RUajYZ33nmHM2fOEBUVRWZmJj/88EPescnJyRw/fpygoCAmTZrEW2+9xZkzZ3B1dS30/0QwD1GoCs9s69atZGZmlshiwMnJiYsXL9K/f3+6du3KkiVLinWdwYMHY2dnx/z5842cUDC1pUuXEhUVxd27d9WOIpQxV+LuwDOum6Mof16nEHq9nnPnzvHWW29x/vx5HBwc8iaujh8/nvHjx+d7XocOHYiIiMj7CAgIAODSpUtERUXRrVs3vL29+eCDD7h16xYA7dq148SJE4SGhjJr1ixCQ0P5+eef6dChwyPXTk9P5+TJk7z88st4e3vz5ptv8scffxTr53DlyhW8vb1p3749ffr0oVevXiiKwqxZs/Dy8qJr167cvn2be/fuceTIEQIDA6lSpQoALi4ueddZsGABycnJfPbZZ0iSBMDRo0fx9fWladOmHDlyhOjo6LzjX3nllbx/nzhxgiFDhgDk20srqMNS7QBC6Td//nw6depUYrer1Gg0bNu2jTZt2hAUFMTJkyfZvXv3Uy83NXToUNavXy92PCplfH19ee6555gyZQqbN29WO45QhqSmadFopGe6hkYjkZqaUehxNWvWpGbNmvj6+gIQGBj4TCusKIqCh4dHvhOyOnTokNeL+tJLL7F48WIkSXpsYpYsyzg7Oz/1+P1Tp07x5ptvArl/P7y8vPLGqP7dli1bePDgAeHh4VhZWVG7dm10Oh2KouQVof/UqlUrwsPDSUpKwsXFBZ1Ox9tvv83Zs2d54YUXmDt37iN3xhwcHh228aTrCuoRParCM7lx4waXLl0qFbPi3333XUJDQzlw4AANGjQgOTn5qc5ftGgRSUlJzzSEQFDHO++8w+7du4s1k1gQnsSpgj2y/GxdqrKs4ORU+BhXV1dXXnjhBS5dugTA4cOHadKkSbHbdXd358GDB3mFak5OTl5PY8eOHdm8eTMNGjRAo9Hg4uLCjz/+SPv27R+5hpOTE3Xq1OHbb78FcovfCxcuAFChQgXS0tLyjv37176+vo/18OYnJSWFatWqYWVlxdGjR7l+/ToAXbp0YceOHSQmJgK5k3n/0rNnT2bMmEGfPn1IS0vLK0qrVKlCenp6gasCtG/fnm3btgG5RbJQMohCVXgmQUFB1KxZEy8vL7WjFEn79u25fv06OTk51KxZk7Nnzxb5XGdnZ3x8fJgxY4YJEwqmMH36dHJycvjyyy/VjiKUIfXqV4dn7ICTpD+vUwRr1qxh6NCheHl5ERERkbf0WkFjVJ/E2tqanTt3Mn36dJo1a4a3t3fe5hi1a9cGcgtWAD8/P5ydnalUqdJj19myZQtffPEFzZo1w8PDI2/C06uvvsrSpUtp3rw5V65cYcSIEYwfP77QyVR/N3ToUM6ePZs3vrVRo0YAeHh4MHv2bPz9/WnWrBmTJ09+5LyXX36ZsWPHEhAQgI2NDWPHjqVp06b079+fVq1aPbG9VatW8cknn9CqVStSUlKKlFEwPbEzlVBssixjb2/PokWLePfdd9WO81RkWaZXr14cOnSItWvXFnki2OHDh+nWrRtJSUnPPONWMK8+ffoQExPDlStX1I4ilEL57Zzz8GEavXpOxd7etlgTqgwGGa1Wx/4Dy3B2Fmt2CuWX2JlKMIlPPvkERVGYOHGi2lGemkaj4cCBA8yePZvx48czatSoIp3XpUsXXFxcRK9qKRQcHMzVq1eJjY1VO4pQRlSqVIFu3VqSllb4GNP8pKVl0L1HK1GkCkIBRKEqFNuyZcvo27fvU09KKknmz59PSEgImzdvxtvb+7E1AvMzduxYMX6pFHJ3d6dOnTqP3SYUhGfx+hvdsbCwIDs756nOy87OwcLCgqFDu5komSCUDaW3whBUFRUVxc2bN0vFJKrC9O3bl9jYWG7fvk2NGjXyJis8yfvvv09mZiY7duwwU0LBWGbMmMH+/fvFLjSC0bi7uzFr9utoM7OKXKxmZ+egzcxi1uzXcXd3M3FCQSjdRKEqFEtQUBANGjSgVq1aakcxilq1anH79m3c3d3x9PQscGaora0t/v7+zJ0713wBBaMYM2YMlpaWLF26VO0oQhkSEODHnDnDyMrKITk5DYMh/9UlDAaZ5OQ0srJymDNnGAEBfmZOKgiljyhUhaeWnZ3N4cOHy9wWc9bW1vz666+MHz+ewYMHM3Xq1Lzn/trN5S/Lly8nJiYmb4FsoXTQaDQMHDiQNWvWqB1FKGMCAvz4etMsevVug1arIyUlneTkdFJTM0hOTiclJR1tpo5evdvw9aZZokgVhCISs/6Fp/af//yH4OBg0tML35+6tNq8eTMjRoygXbt21KpVi82bN7Nx40aGDx+ed0zNmjXx8/PLW3dPKB3u3r1L9erVOXXqVIFL1QjC3xU0K/mfkpPTOXjwNFfi7pCamoGTkwP16lene/fWYuKUIOSjoNeXKFSFp1atWjUCAgL4/PPP1Y5iUlFRUbRs2ZKsrCwg9/u+d+9e3vPLly9n9uzZ3Lx5j8gTv2NISkGj1yNbWmLhUpHmHTxwqVxBrfhCAZo2bYqLiwvHjx9XO4pQSjxNoSoIwtMp6PUltlAVnkpoaCgJCQksWbJE7Sgml52dTXZ2dt7X9+/fJywsDD+/3Ft2vXq+jP0Da9L+70fqSxKykrvttwRI6Q9J3nKNcDsnXmjflEZNXlDnmxDyNX/+fAIDA9Fqtdjb26sdRxAEQXgCMUZVeCrTp0+nefPmuLi4qB3F5Hbt2sU/bzgMGjQIgOP7zmJ3+CS9nq9ChiKRLEukKhJpSu7nFFkiQ4YGmSnYHDrB8X1F3wFLML0BAwbg6OjI+++/r3YUQRAEoQCiUBWKLDU1lVOnTvHRRx+pHcUsPvzwQwwGA/v372fs2LF5e0Uf33eW2rFxZMqQomiQn7CHooxEiqwhU4basXGiWC1hXn/9db744gu1YwiCIAgFEGNUhSKbMGECW7ZsISkpSe0oqom5eBObQyfIlCHnbwXquau/E/RlMA8z0pAkiXquNdn87ofYWlsDYIWCrQZyurXHvbEYBlASpKam4uzszH//+1969eqldhyhhBNjVAXBdMQWqoJRbNq0idGjR6sdQ1U3TvyGhPJIkQoQ9GUwDrb2hH74OduDFnHjwV1mb/k47/kcJDQo3Aj7zdyRhSdwcnLC19eX2bNnqx1FEARBeAJRqApFsmvXLtLT05k3b57aUVSTmJhG/cxU0uTHb/Wn6zLp27IDTvaONKxRi/rPv8DVu4+usZomS9TLTCUpMc1ckYVCLF26lIiICBISEtSOIgiCIORDFKpCkcydOxc/P79yPUM64udoLCDfMaldvFrz/dlQHqanEn3jKrF/3MDf0+eRY2QkNMD5sGjzBBYK5efnR5UqVZg2bZraUQRBEIR8iOWphELdvXuX6OhoTp06pXYUVRmSUpCfMKK7t48fx6LO8uKccQDUrPwcE/sMefxABQyJqSZMKTyt8ePHExwczJdffql2FEEQBOEfRI+qUKigoCBcXV3L/S4+Gr2e/OpUvV7Pu18so1kdd8IWfsl3s1aQma3jjZWPbzErAxp9jsmzCkX373//G51Ox+bNm9WOIgiCIPyDKFSFAsmyzO7du5k4caLaUVQnW1rmuxDVrcR7yIrM3FffxNHWnlpVnyegtT+X/7j+2LEaQLa0MnlWoeisra3p3LkzCxYsUDuKIAiC8A/i1r9QoM8//xyDwcCUKVPUjqI6C5eKaNIf8s9u1drP1cBCo2HB9g0EjwoiMT2F78+EUqWC8+MXkcCispN5AgtFFhwcjJeXF9evX6dWrVpqxxFKuaycZG4kHiNFe40cfTpWlo5UtK+NW+UXsbGqqHY8QShVxDqqQoHq1atHkyZN+P7779WOorrExDRStvyXDPnxCVX7z5/kgx2fk67TIiFRtWIl1r45k3rP/2/NVA0KDhpwHtoHl8oVzB1fKISbmxutWrVi165dakcRSqCirKP6MCOOy3/s5GbicRRF/nNLZQkFJXdrZUnDC5U70fD5QVRyqG+W3IJQGhT0+hI9qsITxcbGEh8fz/79+9WOUiJUrlyBcDsnGmamkPKPJap6Nm9Hz+btCjy/gkYh1q4i3USRWiIFBQUxbdo0ZFlGoxGjooSnE3//AOHxK5EVGSsLBzSSxWPHyIqB6wlHuJl4DJ8671KnWg8VkgpC6SJ+GwtP9N5771GnTh0aNGigdpQSw619UxQkrPKdVvVkVijISLj5NTVRMuFZTZgwAUmSWLlypdpRhFIm/v4Bzl5djkaywcbSKd8tyEMdAAAgAElEQVQiFUAjWfz5vA1nrwYTf/+AmZMKQukjClUhX3q9ngMHDjBz5ky1o5QojZq8wI0G9XGQlCIXq1YoOGgUbjaoL7ZPLcE0Gg39+vVjxYoVakcRSpGHGXGEx6/EQmOPhaZoEyUtNFZYaOwIj1/Jw4y4Ip1z6dIlvL298z6cnJwKfVN17Ngx+vbtW+AxycnJfPrpp0XK4OjoWKTjBMGYRKEq5Gvx4sVYWVkxatQotaOUOBWe0/Dv0FBsJYWKGhnNEwpWDbnP20oK/z5+HEunLDMnFZ7W8uXLuX37NhEREWpHEUqJy3/sRFbkIhepf7HQWCErMpf/KNqYaHd3dyIiIoiIiCA8PBx7e3sGDBhQnMiPeJpCVRDUIApVIV8ff/wxAwcOFGP1/iY5OZmXXnoJHx8ftuz5jJzufsTaVcRBAxU1ChUlhQpS7ueKmtyJU7F2Fcnp7ke6fI/OnTsTGxur9rchFMDNzQ13d3exyoVQJFk5ydxMPI6VhUOxzreycOBm4jGyclKe6rzDhw9Tr169p1qhYu7cuYwaNYpOnTpRt25dVq9eDcCMGTO4cuUK3t7eTJ06FcjdWrhVq1Z4eXnx/vvvP1U2QTA2MZlKeMypU6e4d+8ey5YtUztKiaDT6XjjjTfYtWsXfy2S0alTJ9wbv4B74xdISkzjfFg0hsRUNPocZEsrLCo70dzPI2/i1J49e/D19aVFixZcuXKFatWqqfktCQX4z3/+wxtvvEF2djbW1tZqxxFKsBuJx1AU+YljUgujkSzQKzI3E49R3/WlIp+3bds2hgz5385369atA3J3WStITEwMR48eJS0tDXd3d9566y0WLVpEVFRU3l2EgwcPEhsby+nTp1EUhYCAAEJDQ+nYsWMxvkNBeHaiUBUeM3XqVDw9PXF1dVU7SokQEhLCzp07H3msZ8+eef92qVyBLi+1KfQ6v/zyCw0aNKBp06bEx8djb29v9KzCsxsyZAhjx45l3rx5fPjhh2rHEUqwFO21p5xW+TgFhRTttSIfn52dTUhICAsXLsx7rLAC9S99+vTBxsYGGxsbqlWrxr179x475uDBgxw8eJDmzZsDkJ6eTmxsrChUBdWI+7rCI7RaLSdOnBC79PzN4MGD+eCDDx55rHfv3k99HY1Gw2+//YaiKHh5eSHLsrEiCkY2ZMgQ1q9fr3YMoYTL0acj5btfXdFJaMjWpxX5+H379tGiRQuee+65p27LxsYm798WFhbo9frHjlEUhZkzZ+aNh42Li2P06NFP3ZYgGIsoVIVHzJkzhwoVKvDSS0W/DVUezJ8/H41Gg4uLC5Ik0aRJk2Jdx97enqioKO7evUv79u2NnFIwlsWLF5OYmMixY8fUjiKUYFaWjijP2KeqIGNtWfS1lbdu3frIbf9nVaFCBdLS/lco9+jRgy+//JL09HQAbt++zf37943WniA8LVGoCo/46quvGDZsmNoxShQfHx+ys7M5efIkDx48IDo6+pkmmVWrVo3w8HDOnTvHoEGDjJhUMBYXFxdatGjB9OnT1Y4ilGAV7Ws/Y39q7s5VFe1rF+lYrVbLTz/9xMCBAx95fN26dXnjVJ9W5cqVad++PZ6enkydOpXu3bvz2muv0bZtW5o2bUpgYOAjhawgmJvYQlXI8+OPP9KvXz9SUlLEenl/WrlyJe+99x5jxoxhw4YNRr32iRMn6NixI//617/yZuAKJcdPP/1Ejx49SEpKwtnZWe04gsry2+IxKyeZ788NwUJjV6wJVbJiwCBn0q/FNmysKhorqiCUOgVtoSp6VIU8s2fPxtfXVxSpf7p79y7vvfcerq6uRi9SAdq3b8/27dv5+OOPWbJkidGvLzybbt26UalSJbHphfBENlbOvFDZnxxDRrHOzzFk8ELlTqJIFYQCiEJVACAhIYELFy6Igulv6tWrhyRJ3Lx502RtBAYGsmLFCmbMmMGWLVtM1o5QPGPGjBH/L0KBGj4fiEbSYJBznuo8g5yDRtLQ8Hkx/EcQCiIKVQHIXZKqatWq+Pn5qR2lRPD390er1bJv3z4sLU27itukSZOYMmUKw4YN48iRIyZtS3g68+bNIyMj47HlyQThL5Uc6uNT510McmaRi1WDnINBzsSnzrtUcqhv4oSCULqJQlUAYMeOHbz99ttqxygRNm3aRGhoKIGBgfTo0cMsbS5ZsoRXXnmFHj16EBUVZZY2hcLZ2trSsWNHsTuPUKA61XrQsu5kZCWLLH0qsmLI9zhZMZClT0FWsmhZdzJ1qpnn94sglGZiMpXApk2bGD16NBkZGeV+J57k5GQqVapEpUqVSEpKMnv7/v7+nDlzhri4OKpXr2729oXHnT17ltatW3Pr1i3xf1KOFTTZ4y8PM+K4/Mcubv65Y5WCgoQGBRkJCUnS8ELlTjR8fpDoSRWEvyno9SUKVYGGDRtSt25d9u/fr3YU1VWsWJG0tDRSU1NVmVQmyzIeHh7cv3+f69evi4ltJUSNGjXo2LEjW7duVTuKoJKiFKp/ycpJ4WbiMVK018jWp2FtWYGK9rXFxClBeIKCXl9iC9VyLj4+nri4OPbs2aN2FNW99NJLpKamsn37dtUKRI1Gw4ULF6hduzaenp7ExcWZfIysULhJkybx/vvvI8vyM62hK5QPNlYVqe8qNk0RBGMQv3HLuaCgINzc3PDw8FA7iqq+++47QkJC6NatG4MHD1Y1i7W1NVFRUTx8+JDWrVuLrVZLgMmTJ2MwGPjss8/UjiIIglCuiEK1HJNlmf/+979MmTJF7Siq0ul0DBgwAEdHRw4ePKh2HCB3Z6SIiAguXrxI37591Y5T7llaWtKrVy+xfJsgCIKZiUK1HFuxYgWSJJX72f5ubm4oikJ8fLzaUR5Rp04dwsLCOHjwIGPHjlU7TrkXHBzMtWvX+P3339WOIgiCUG6IQrUcW7FiBQEBAeV6zN2wYcN48OABa9eupUqVKmrHeUzLli0JCQnhyy+/ZN68eWrHKdfq1atH/fr1mTx5stpRBEEQyo3yW6GUcxEREdy5c4fg4GC1o6gmNDSUb775hjZt2jB+/Hi14zxR7969+eyzz5g3bx5ffPGF2nHKtVmzZnHo0CH0er3aUQRBEMoFUaiWU0FBQTRq1IiaNWuqHUUVer2eF198EVtbW3755Re14xRqzJgx/Oc//2HcuHH8+OOPascpt4YPH46VlRUfffSR2lEEQRDKBVGolkM6nY7jx48zd+5ctaOopm7dusiyzKVLl9SOUmRz585l5MiRBAQEcPbsWbXjlEsajYaXX36ZTz/9VO0ogiAI5YIoVMuh+fPnY2dnp/oyTGqZNGkSN2/eZOHChbi5uakd56l8/vnndO3aFT8/P65fv652nHJp6dKl3L9/v1T0xAuCIJR2YmeqcqhKlSoEBgaybt06taOYXWRkJM2aNcPT05PffvtN7TjFIssyLVq04Nq1a1y7dg1nZ2e1I5U7zZo1o0KFCoSFhakdRTCTp9mZShCEpyO2UBXyHDlyhK5du5KUlFQuCxwrKyskSSI7O1vtKM9Er9dTt25d9Ho9165dw9raWu1I5UpISAgDBgwgJSVFbHNbTohCVRBMp6DXl7j1X85Mnz4dHx+fclmkuru7o9frOX/+vNpRnpmlpSVRUVHodDq8vb3F7lVmFhAQQIUKFZgzZ47aUYQSSNFlocRcRfn1AkromdzPMVdRdKX7DbIgqEFsIl6OJCcnEx4ezk8//aR2FLObN28ely9fZurUqWVmu1gnJyciIyNp2LAhXbp04ejRo2pHKldGjhzJV199xYoVK9SOIpQQSlIKXIyD63cgv5uV4dEotapDk/pILhXNH1AQSiFx678cGT9+PDt37iQhIUHtKGYVFxdHgwYNqF27donbfcoYoqKiaN68OYMHD2bLli1qxyk30tPTqVixInv27CEgIEDtOIKJFXbrX4m7DqciQZHByho00uMHyQrkZIOkAV8vpPq1TJhYEEoPcetfAGDLli3lcitODw8PLCwsiI2NVTuKSXh6erJ//362bdvG9OnT1Y5Tbjg6OtK2bVtx+1/ILVJ/vQAWGrCxyb9IhdzHbWxyj/v1Qu55giAUSBSq5cSOHTvIzMzk/fffVzuKWfn4+JCdnc2JEyewtDTNSJc7yfdZf2w9C3+YwQchE1n4wwzWH1vPH8kPTNJefrp06cLXX3/N0qVLWbNmjdnaLe+WLl3Kb7/9xv3799WOIqhESUrJ7Um1tAALi6KdZGGRe/ypyNzzi2jFihV4eHjg6enJkCFD0Ol0BR6/ceNGJEni8OHDeY/t2bMHSZLYuXMnkLuZyMWLF4ucwVhWr15N48aNGTp0KMeOHePkyZNmzyCUDqJQLSfmzp2Lv78/tra2akcxmzVr1nDu3DnGjBmDr6+v0a//65XzfBjyHv93IpA07TdYSWHYWJzHSgojTfsNW04E8uH3k/n1inkmb73++ussWrSISZMmsWvXLrO0Wd61bduWatWqMXXqVLWjCGq5GJd7u7+oRepfLCxyz/v9SpEOv337NqtXr+bs2bNERUVhMBjYtm1boec1bdqUrVu35n29bds2mjVrlvf1559/TpMmTYqUwZhbB3/66af8+OOPbNmyRRSqQoFEoVoO3Lp1i5iYGJYvX652FLO5e/cuEydOxNXVlQ0bNhj9+l+HfUPYpUnYWJ4hS29NZo4dOr09Wfrcz5k5dmTprbCxOE3YpUl8HfaN0TPkZ9q0abzzzju88sornDhxwixtlndvv/023377rVh5oRxSdFm5E6esirk8nJU1XLtd5NUA9Ho9mZmZ6PV6tFot1atXL/ScDh06cPr0aXJyckhPTycuLg5vb++85zt16pS3093+/ftp0aIFzZo1o0uXLkBuJ8e4cePo3r07w4YNQ6fTMXLkSJo2bUrz5s3zJnFGR0fTunVrvL298fLyyhtqFRwcjKenJ56enqxcuRLInS9x9epVAgICWLFiBevWrWPFihV4e3vz888/F/3nJ5QLYtZ/OTBlyhSqV6/+yC+nsq5evXpIksTNmzeNfu2vw74hIXU92XorZOXJLyEFDbocezSSnoTU9XwdBsP93jB6nn9avXo1N2/epHPnzkRGRuLu7m7yNsuzWbNmsWDBAr7++mtGjhypdhzBnK7dzp3d/6QxqYXRSLnnX78F7nULPLRGjRpMmTIFNzc37Ozs6N69O927dwfgP//5Dy1btsx3Up8kSXTt2pUDBw6QkpJCQEBAvpNKHzx4wNixYwkNDaVOnTokJSXlPRceHk5YWBh2dnZ5HR6//fYbMTExdO/encuXL7Nu3TomTZrE0KFDyc7OxmAwEB4ezldffcWpU6dQFAVfX1/8/f1Zt24d+/fv5+jRo1SpUiVvPeIpU6YU7+colGmiR7WMk2WZvXv38t5776kdxWz8/f3RarXs27fP6ONSf71yngepG8j6W5E6p/9Pj318POl/22vKiiVZeisepH7OqavmGQawZ88evL298fHxEeMnTczS0pKuXbvy0UcfqR1FMLfkNLNd5+HDh3z33XfEx8dz584dMjIy2Lx5M5C7LXZBK0+8+uqrbNu2jW3btjFkyJB8j/n111/p2LEjderUAcDFxSXvuYCAAOzs7AAICwvjjTdy33A3atSIWrVqcfnyZdq2bctHH33E4sWLuX79OnZ2doSFhTFgwAAcHBxwdHRk4MCBosdUeGqiUC3jPv30UxRFKTeF6qZNmwgNDSUwMJAePXoY/fqHozcB8iM9qQv2dsv7mL7RHwCfbjUeOS/3eAOHos0zBADgl19+wdXVFU9PT7RardnaLY+Cg4OJi4vjypWijTcUyghj7XCXVfh1Dh06RJ06dahatSpWVlYMHDiwyOM6W7duTVRUFAkJCTRs2DDfYxRFQZLy7xl2cHB45Lj8vPbaa4SEhGBnZ0ePHj04cuTIE48VhKchCtUybtmyZfTu3RuNpuz/V6enpzN8+HCcnZ359ttvjX79O8n3sbEIJyvnyRPSDm2JQ2Mh4du75mPPZeXYYqMJN9tqABqNhsjISAC8vLzEGEoTaty4MbVr12by5MlqRxHMyVhbF9sUfh03Nzd+/fVXtFotiqJw+PDhp9rSdeHChQX2+rdt25bjx4/nDQv4+63/v+vYsWPees2XL1/mxo0buLu7c/XqVerWrcvEiRMJCAggMjKSjh07snfvXrRaLRkZGezZs4cOHTo8ds0KFSqQlmak3mmhzCn71Us5Fh0dzfXr1wkODlY7ilnUrFnTZONSAX6I2IskKSgFvGyiT97DrVHFfN8YKGiQJJnvI/aaJF9+7O3tiYqK4u7du7Rv395s7ZZH06ZNY9++fUadGS2UcM4VzHYdX19fAgMDadGiBU2bNkWWZcaNGwfkjlENCQkp8PxevXrx4osvPvH5qlWrsn79egYOHEizZs145ZVX8j3u7bffxmAw0LRpU1555RU2btyIjY0N27dvx9PTE29vb2JiYhg2bBgtWrRgxIgRtG7dGl9fX8aMGUPz5s0fu2a/fv3yhiuJoQHCP4mdqcqwnj17cvXqVS5fvqx2FJN76aWXCAkJYevWrbz66qsmaWPhDzOwksLQ6e3zff5GzEM2zDjL6A9bUtujUr7H2Fpq0SsdmdHXvOMZL126hJeXF3369GH37t1mbbu8kGUZBwcH5s2bx7Rp09SOIxhZfjvnKLos2HUQLC2LN6FKVkCvh0E9kGyN1DsrCKWQ2JmqHMrOzubw4cP8+9//VjuKyX333XeEhITQrVs3kxWpAAZZi8KT/xgd/r+r2NhbPLFIBVCQ0MvppohXIHd3d44cOUJISAgTJ040e/vlgUajoX///qxatUrtKIKZSLY2UKt67raoxZGTDbVriCJVEAogCtUyauHChVhbWzNs2DC1o5iUTqdjwIABODo6cvDgQZO2ZaGxR+LJNyCuRT+kSZtqBV5DQsFS42jsaEXSvn17tm/fzscff8ySJUtUyVDWLV++nD/++CNvXUqhHGhSHyQNGAxPd57BkHte43qmySUIZYQoVMuoTz/9lMGDB6sdw+Tc3NxQFCXfdQGNrbJj3SfOij1z4CayQaHr6/ULvIYkSbg41jFFvCIZNGgQq1atYsaMGXkTIgTjqV69Oo0bNxY7VZUjkktF8PUCvaHoxarBkHu8r1fu+YIgPJEoVMugsLAwHjx4wNKlS9WOYlLDhg3jwYMHrF27lipVqpi8vb7e/VEUCYnHZ8+f/P4GlarZ4eTy5BUBJGQURUM/7/6mjFmoCRMmMGXKFIYNG8aRI0dUzVIWzZs3j9DQ0EL3YRfKDql+LWjTDAwyZGXljj3Nj6zkPm+QoU2z3PMEQSiQmExVBrVr147MzEzOnzfP4vJqCA0Nxd/fnzZt2vDLL78UfoKRfBjyHjaWZ9Dl5D+hqiC2VlqyDK2Z3a9krMLw2muv8e2333L+/Hk8PT3VjlOmODk58dZbb7F48WK1owhGUtBkj78oSSnw+5X/7Vj1T5IEtWtA43qiJ1UQ/qag15coVMuY9PR0KlasyPfff0/v3r3VjmMSer0eGxsbrK2tyczMNGvbv145T9ilSWTrLQvcPvWfNJIea0sDHRqtxLfu48uzqKVTp06cPn2auLi4Iu0bLhTNW2+9xY4dO0hMTFQ7imAkRSlU/6LosnO3RU1Oy13M38Y6dwmqWjXFxClByIeY9V+OzJo1CycnpzJbpALUrVsXWZa5dOmS2dtuU685VZ3GYmOZg0Yq2nqZGkmPjWUOVZ3GlKgiFeDIkSPUqlULT09PUlNT1Y5TZixcuJCHDx/y008/qR1FUIFka43kXhfJtxlSx1a5n93riiJVEIpBFKplzKZNmxg5cqTaMUxm0qRJ3Lx5k48++gg3NzeztavVapk1axa2traM7jSSKk7jsLbUY2ulzXfMKuSOSbW10mJtaaCK0ziG+71htrxFpdFouHDhAra2tnh6eorF6o3E2dmZVq1aMXPmTLWjCIIglGri1n8Z8t133zFw4EDS0tKwt3/6MZQlXWRkJM2aNcPDw4OoqCiztBkfH0+/fv2Ijo7Oe6xGjRrcunWLU1fPcyj6G2w04UiSjKIoKEhI5O6ZrSgasmQfunq8UeJ6Uv8pKSmJOnXqULduXcLDw8vFlrumduzYMTp37kxCQgIuLi5qxxGe0dPc+hcE4emIMarlhJeXF87OzoSGhqodxSSsrKyQJIns7GIurl0MP/zwA/369XvksYULFzJjxoy8r/9IfsD3EXtJSo9HL6djqXHExbEO/bz787xzVbNlfVbXr1/H3d2dF198kX379qkdp0yoWrUq/fv3Z8OGDWpHEZ6RKFQFwXREoVoO3L17l+rVq/PLL7/g6+urdhyjc3d35/Lly0RFReHh4WHWtt99991HdhtKSUnBycnJrBnM5ezZs7Rp04aRI0eK4soIZs+ezapVq0hPN/9uZIJxiUJVEExHTKYqB6ZNm8Zzzz1XJovUefPmcfnyZaZOnWr2IjU0NJQ1a9bQr18/JEnC2dm5zBapAC1btiQkJIQvv/ySuXPnqh2n1Hv//ffR6XRs3bpV7SiCIAilkuhRLQNkWcbR0ZHZs2cze/ZsteMYVVxcHA0aNKB27dpm2X3q76KiomjRogX9+/dnx44dREdHk56eXibfDPzT559/zrhx41i/fj1jxoxRO06p1rVrV27fvs3vv/+udhThGYgeVUEwnYJeX0VfCFIosb788ktycnKYPn262lGMztPTEwsLC2JjY83a7vXr12nVqhV+fn7s2LEDwOy9uWoaM2YMt27d4s0336R69eplerkzUwsODsbb25sbN26YdaUKQRCEskDc+i8DFi5cSPfu3bG0LFvvO3x8fMjKyuLnn3826/eWkJCAl5cXDRs25NChQ2Zrt6SZO3cuI0eOJCAggLNnz6odp9Ty8vKiRo0aBAUFqR1FEASh1BGFaikXGxvL1atXCQ4uGdtyGsuaNWs4d+4co0ePpm3btmZrV6vV4uHhgYuLi1imidwhAF27dsXPz4/r16+rHafUeu+99wgJCUGW819zVxAEQcifGKNayv21xufVq1fVjmI0d+/e5fnnn+e5557j7t27ZmtXr9fTsGFDMjIyiI+PL5Nr0RaHLMv4+Phw9epV4uPjxZqgxSDLMnZ2dixZsoRJkyapHUcoBjFGVRBMR8z6L6P0ej0HDhwoc2NT69WrhyRJ3Lp1y2xtyrJMy5YtSUxMJDo6WhSpf6PRaDhz5gzOzs54enqadR3bskKj0dC3b1+WL1+udhTBDGRtDpln75O+/zppe6+Svv86mWfvI2vFzm+C8LTK1qDGcmbZsmVYWFgwduxYtaMYTefOndFqtezbt8+s41K7devGpUuXiImJoUqVKmZrt7SwtLTkt99+o3bt2nh7exMVFVXuh0U8reDgYOrUqUNUVBSenp5qxxFMQH9PS+bpe2THJKPIf96slAAl95P26G2sG1XCrnU1LJ8Tb4YFoSjEX5pSbPXq1QwcOLDMFAybNm3i6NGjBAYG0rNnT7O1++qrrxIaGsqpU6eoVauW2dotbZycnIiMjOTatWt06dJF7TilTq1atWjQoAGTJ09WO4pgArrIRFI2XSLr4kOw0qCxs8z9sM39LNlZgpWGrItJpGy6hC4yUe3IglAqlI0Kpxw6e/Ysd+/eLTO3EtPT0xk+fDjOzs58++23Zmt3woQJfPvttxw+fBgvLy+ztVta1axZk9OnTxMWFsZrr72mdpxSZ86cORw5ckQMnyhjdJGJpO+7DhZSblGqkfI9TtLkPo+FRPq+66JYFYQiEIVqKTVlyhQ8PDxwdXVVO4pR1KxZE0mSuHnzptnanD9/Pp988gk7d+6kY8eOZmu3tPP09OTAgQNs3769zI2PNrXXX38dW1tbPvjgA7WjCEaiv6cl48ANJCsNkmXR/qRKlhokKw0ZB26gv6ctclurVq3C09MTDw8PVq5cWaRzwsLCaN26NY0aNaJRo0asX7++yO0VplOnTmLpOsHkRKFaCmm1Wn7++Wfmz5+vdhSjGDBgACkpKfzf//0fjo6OZmnzs88+Y+7cuaxbt44BAwaYpc2ypHPnzmzatImlS5eyZs0ateOUKoMHD2bdunVqxxCMJPP0PRRZKXKR+hfJUoMiK2SeuV+k46OiotiwYQOnT5/mwoUL/PDDD4VuhHL37l1ee+011q1bR0xMDGFhYXz22Wf897//fexYvV5M9BJKJlGolkLvv/8+jo6OZaLA+uGHH9i7dy9du3bl1VdfNUubO3fu5K233mLBggWMGzfOLG2WRUOHDmXRokVMmjSJXbt2qR2n1FiyZAkJCQmEhoaqHUV4RrI2h+yYZCQbi2KdL9lYkP37wyKtBvD777/Tpk0b7O3tsbS0xN/fnz179hR4zieffMKIESNo0aIFAFWqVGHJkiUsWrQIgBEjRjB58mRefPFFpk+fzunTp2nXrh3NmzenXbt2XLp0CQCDwcCUKVNo2rQpXl5e+b45PXjwIG3btqVFixa8/PLLpKenP+2PQxDyJQrVUuiLL77g9ddfVzvGM9PpdAQEBODg4MBPP/1kljaPHTvGK6+8woQJE5g9e7ZZ2izLpk2bxjvvvMPgwYM5ceKE2nFKhSpVquDt7c20adPUjiI8o6yLD3N7U58wJrUwkkYCWSHr96RCj/X09CQ0NJTExES0Wi0//vhj3lCpdevW5dtLHx0djY+PzyOPtWzZkujo6LyvL1++zKFDh1i+fDmNGjUiNDSU8+fPM3/+fGbNmgXA+vXriY+P5/z580RGRjJ06NBHrpmQkMAHH3zAoUOHOHfuHC1btixzm9AI6hHLU5UyBw4cIDk5mYULF6od5Zm5ubmhKArXrl0zS3sRERF069aNwYMHs2rVKrO0WR6sXr2aW7du0blzZyIjI3F3d1c7Uon34Ycf0qdPH1JTU3FyclI7jlBMhoTMZ76GAhgSdHWeFNwAACAASURBVIUe17hxY6ZPn063bt1wdHSkWbNmeUv4jR8/Pv9rKwqS9HgR/ffHXn75ZSwscnuEU1JSGD58OLGxsUiSRE5ODgCHDh1i/Pjxee39c9OPX3/9lYsXL9K+fXsAsrOzzbqjoFC2iR7VUmbWrFm0bt261P9xGzZsGA8ePGDt2rVmWbc0Pj6etm3b4u/vz9atW03eXnmze/dumjdvjo+PD/fvF23MXXnWq1cvnJ2dmTlzptpRhGeg6Ay5C6Q+CwkUXdHGh44ePZpz584RGhqKi4sLDRo0KPB4Dw+PxyY7hYeH06RJk7yvHRwc8v49Z84cXnzxRaKiovj+++/R6XIL6CcVvH9RFIVu3boRERFBREQEFy9e5IsvvijS9yQIhRGFaimSlJTE+fPnWbJkidpRnklYWBjffPMNvr6+T+wJMKaEhASaNWtG48aNOXjwoMnbK69OnjyJq6srnp6eaLVFn8lcXo0ePZrNmzerHUN4BpKtRW6X6LNQQLIt2s3Nv94E3rhxg927dzNkyJACj//Xv/7Fxo0biYiIACAxMZHp06c/cdhJSkoKNWrUAGDjxo15j3fv3p1169blTbhKSnp0qEKbNm04ceIEcXFxQO6E38uXLxfpexKEwohCtRSZNm0aVapUKdVLKen1evz9/bG1teXXX381eXtarZYmTZpQtWpVTp8+XWY2RyiJNBoNkZGRAHh5eSHLssqJSrZ58+aRnp4uJqKVYhZV7J75GhJgUcW2SMcOGjSIJk2a0K9fPz755BMqVaoEPHmM6vPPP8/mzZsZO3YsjRo1ol27dowaNYp+/frle/1p06Yxc+ZM2rdvj8FgyHt8zJgxuLm54eXlRbNmzfi///u/R86rWrUqGzduZMiQIXh5edGmTRtiYmKK+BMQhIJJiqI86/tBwUwcHR2ZPHlyqV6Wys3NjZs3bxIfH0/t2rVN2pZer6d+/frodDquXr2Kvb3YstAc7t+/T926dfH09DTLm5HSzN/fn6SkJH777Te1owiF+P3332ncuPEjj8naHB5+EgVWmmJNqFJkBXJkKv2rKRp7MWVEKL/ye339RXQvlRKbN29Gp9Px73//W+0oxTZp0iRu3rzJBx98YPIiVZZlWrRoQXJyMhcvXhRFqhlVq1aN8PBwzp8/z8CBA9WOU6ItW7aM6Oho7t69q3YUoRg09lZYN3JGyTIUfnA+lCwD1o0riSJVEAogCtVSYsGCBXTu3Blra2u1oxRLZGQkq1evpkmTJmZZFqpz587ExcURGRn52AxVwfTc3d05cuQIISEhTJw4Ue04JVarVq1wdXUlKChI7ShCMdm1fg5JI6Hon26oi6KXkTQSdq2qmSiZIJQNolAtBa5fv05sbGypXpfOx8cHKyurR9bvM5XAwEBOnjzJ6dOncXNzM3l7Qv7at2/P9u3b+fjjj/MWGBceN2HCBHbv3i3G9JZSls/Z49DDDSVHLnKxquhllBwZhx5uWD4n7vYIQkFEoVoKTJ48mZo1a+Lp6al2lGJxd3dHr9dz/vx5k7f19ttvs3fvXo4cOVJqf15lyaBBg1i1ahWzZs0SM9yfYOrUqRgMBjZs2KB2FKGYbL0q49irFhgU5Ex97tjTfChy7vMYFBx71cLWq7KZkwpC6SMGxpRwsizzww8/lNolqebNm8fly5eZMmUKHh4eJm1r7ty5fPbZZ+zevRs/Pz+TtiUU3YQJE7h16xbDhw+nevXqdO7cWe1IJYqlpSU9evRg8eLFvPnmm2rHEYrJ1qsyls/ZkXnmPtm/5+5YpUDutH7lz+VWNRI2Hi7YtaomelIFoYjErP8SbuXKlcyYMQOtVlvqllaKi4ujQYMG1K5dm/j4eJO29emnn/LOO++wYcMGRo8ebdK2hOIZOnQoO3bs4Pz586K3+x9iY2Np2LAhMTExYmevEqqgWcn/JGv1ZP2ehCFBh6LTI9laYlHFFpvGLmLilCDko6DXlyhUSzg3t/9n7z7Dorr2NozfM7QR0GA0oISIyLEcugr2AigWbNhijL72WKLR2FuCNbFrNHo0xsQSezR2Yq8gEVGRgIiIICoqiAEEHGDY837wyIkRpM0woOt3XYky7L3Ww+DAf/ZepQaNGzfm119/1XWUIlMoFKhUKpRKZe7We9qwe/duPvnkE7799lumTZumtX7yk5iYTtDxGLIeZ0CWBIZyDC2MadzOhqofmBTcwDvE3d2doKAgbt26hZWVla7jlCm2trbUq1ePI0eO6DqKkIeiFKqCIBTNm15f4q1dGRYaGsr9+/fx9/fXdZQic3NzIzMzk4sXL2q1SD116hR9+/blyy+/LPUiNfz6Y24di+GjJBXV//uYxH8Hfj/OIjb0Gheq6FOnvQ32zhalmq2sejl22MnJidjY2HK/FbAmTZ8+nVGjRqFSqbT6mhEEQShPyte95HfMxIkTqVu3brmbuf79998THBzM0KFDadq0qdb6uXr1Kh06dKBv376lviLC8b0RKLdHYZ2k4jlqnv33v/S//f05aqyTVCi3R3F8b0Sp5iur5HI5ISEhKBQKHBwccrdkFGDIkCEYGBiwaNEiXUcRBEEoM8St/zIqKysLY2NjfvnllwL3cy5LHj16RPXq1bGwsNDqIubR0dHY29vTunVrjh07prV+8nJ8bwQfBCWRgZrClFn6gDEyEhtVoV1PcesQIDk5GWtra2rVqsWVK1fK3fhrbenfvz+nTp3i4cOHuo4i/IO49S8I2iN2piqH5syZg0KhKFdFKrwYZyeTybh//77W+khISMDFxQUHBwd+//13rfWTl/Drj6mSR5EaGB1A55XeuC9uRZulHvxw9n/7bquADNRUCUoiPPRxqeYtq8zMzAgNDSUiIoJOnTrpOk6ZsXTpUh4/fsylS5d0HUUQBKFMEIVqGbV+/fpyV6R6enqSkZGBn5+f1sbYpaWlYWdnR7Vq1QgKCir1K3G3jsUgg1eK1ExVJl/v+xonK2eOTzjJ8FYj2BG0naCYoNxjVLxYnubWMe2uflCeWFtb4+/vz4kTJxg2bJiu45QJ1apVw8HBgcmTJ+s6iiAIQpkgCtUy6OzZsyQlJZWrsWpbtmzhzJkz9OrViw4dOmilj6ysLOzs7DAwMODPP/8s9SI1MTGdj5JUpPPqaJlLdy6hklTM7/4NhvqG9Gn0CZUUlfjl4uZXjktHzUdPVDxJTC/N2GWaq6srBw8eZOPGjcyePVvXccqEefPmERAQQEZGhq6jCIIg6JwoVMugqVOn0qBBg3KzR31aWhoDBw7EzMxMa8toSZJEgwYNSE1NJSIiAoVCoZV+3iToeAxyXszsf4X69W0T1ah5kBz/ymMvVwQIOiGuqv6dt7c3P/zwA3PnzmXDhg26jqNz3bp1o2LFinz11Ve6jiIIgqBzolAtY1JTU7l8+TILFizQdZRCs7KyQiaTce/ePa314e7uzp07dwgLC8PMzExr/bxJ1uO8r3A1smmCnkyP6XunosxSsjXwF54pn6HKyc7zeOXj59qMWS4NGzYMX19fRowYweHDh3UdR+cGDBjAxo0bdR1DEARB50ShWsZMmzaNypUr4+XlpesohdK9e3dSUlLYvn07pqamWumjR48e/PHHH1y+fFm3i8RnSa9fTQUUhgq+7jKLsAdhdPyuPbsv76Lae9WoVOH1NUIlQJ6Zo/Wo5dHs2bMZMmQIPj4+BAcH6zqOTn377bekpqbi5+en6yiCIAg6JVaVLmO2bt3KqFGjdB2jUA4fPsz+/ftp27Ytn3zyiVb6GDlyJAcPHuTcuXPY29trpY9CM5Tn+87OvZ477vXccz/uuKI9zf/V/LXj5IBkpKeVeG+DH3/8kfv379OiRQsiIiKwsbHRdSSdMDU1pXHjxsycORNvb29dxxEEQdAZUaiWIXv27CE9PZ05c+boOkqBlEolXbt2xcTEhBMnTmilj6+//poff/yRAwcO0Lz560VfaTO0MIbHWXl+7uzNszSs2ZAcKYeFfgvIUmXxpdeEPI9VWFTQZsxy78iRIzRs2BAXFxdiYmLKzVhtTVu8eDGtWrXiyZMnVK1aVddxhCJIlVIIyDpHnHSXdHUaJjJTasitaW7oTiW52I1NEIpCLPhfhtjb22Nubs6ZM2d0HaVA5ubmJCYmkpiYqJVfot9//z3jxo3jp59+YvDgwRpvvzgSE9O5u/Qaz1G/NgRgxJbhRD66CcB7Fd7j6y6zcK3p+soxcqACMmpOqk/VD0xKJ3Q5pVKpsLW1JTs7m9jYWAwNDXUdSScsLCzw9vYW41XLgMIs+B+TE80h5T4uZp/n5UAhGTLUqJEBcuQ0M2hFZ0V3bPRsSyG1IJQPb3p9iSuqZUR8fDwRERFs3ry54IN1bMCAASQmJrJ27VqtFKm7du1i3LhxLFy4sMwUqQAffGCCfxV9rJNUPPvHElU/DFhf4PkmyLhbVR9XUaQWSF9fnz///BMbGxucnZ0JDw9/J3evGjVqFEuWLBGFajlwJvMEPzz/Hkmdg7HMFD3Z60N8ctQ5XMg6Q0D2eUZU+AIPo/IxF0EQdOnd+8lfRk2cOJHq1avj6upa8ME65O/vzy+//EKjRo0YOXKkxts/ceIEn376KRMnTmTKlCkab7+kbNp8hJqiv8PTB9RAnfbv5pjL4qhUqRJ//vknd+/exdPTU9dxdGLGjBlkZWWxZcsWXUcR3uBM5gnWPl+JIUZUlL+XZ5EKoCfTo6L8PQwxYu3zlZzJ1M6wKUF4m4hCtQyQJIn9+/czbtw4XUd5I5VKRevWrVEoFFrZ4jE4OBhvb2/69+/PkiVLNN5+USmVSjZu3Ej37t0xNzdHJpPRwO0jkhpVwRhZoYtVfcAYGUmNqmDvZKHNyG8dS0tLgoKCCAgI4NNPP9V1nFJnaGhImzZtmD9/vq6jCPmIyYnmh+ffo6ACBjKDQp1jIDNAQQXWP/+emJzoQvc1ZMgQzM3NcXBweOXxp0+f4uXlRe3atfHy8uKvv/4qsK3CrNLy3XfflcrGE7GxsWzfvj334+DgYMaOHavxfmrWrMmTJ0/yfNzR0REXFxccHR05cOBAsdo/ePAgCxcuLGnMItm0aRMymYxTp07lPrZv3z5kMhl79uwpcntnz56lc+fOeX5u2LBh3Lhxo9hZi0sUqmXADz/8QE5ODhMm5D35pqywtbVFkiQiIiI03nZ0dDQtWrSgbdu2ZWb4w5gxYxgyZAj79+8nMTERgFatWtGu579JbFQFBTIqIsv3RSQHKiJDgYzERlVo1/PN49uEvDk4OHDs2DF27dpVJq+ya9uyZcuIiooiJkZsFFEWHVLuQ1LnFLpIfclAZkCOOofDyv2FPmfQoEEcPXr0tccXLlxImzZtiIqKok2bNhorlopTqObkFH35vX8Wqq6urqxatarI7ZTEmTNnCAkJYc+ePcUukrt27cq0adM0nKxgjo6O7NixI/fjnTt34uzsrPF+NmzYgJ2dncbbLYgoVMuAxYsX07FjR/T1y+6Q4S+//JK4uDjmz59PzZo1Ndr2o0ePcHZ2xsnJid9//12jbZfEd999h5GR0SuP7dy5E4B2Pf+Nol9t7lbVp8J/C9aKyDD5298r/HdMqqJfbVGklpCnpydbtmxh6dKlrFy5UtdxSpW9vT3W1tZl/o3suyhVSuFi9nmMZcVbQ9pYZsrF7HOkSqmFOr5Vq1Z5roJx4MABBg4cCMDAgQPZv7/wxe/Zs2dxd3enV69e1KtXj379+qFWq1m1ahXx8fF4eHjg4eEBwPHjx2natCkNGjSgd+/epKWlAS+uSM6dO5cWLVrw66+/4u7uztSpU2nUqBF16tThwoULwIuCtGXLljRo0IAGDRpw8eJF4MX64RcuXMDFxYUVK1a8clXv6dOn+Pj44OTkRJMmTQgNDQX+t+6yu7s7tWrVeqWw9fHxoWHDhtjb27N+fcHzB/4uNTWVypUr5+b9+9XrpUuX5m71vGrVKuzs7HBycspdnnHTpk2MGTMGePGmYuzYsTRr1oxatWq9cnVzyZIluLm54eTkxKxZswBIT0+nU6dOODs74+DgwK5du3Kfm5f9TJo0Kc/MLVu2JCgoiOzsbNLS0rh9+zYuLi65n587dy5ubm44ODgwfPhwXs6hv337Nm3btsXZ2ZkGDRoQHf3i6n5aWtpr/x7gxcY7L9e4NjU1ZebMmTg7O9OkSRMeP34MQGJiIj179sTNzQ03NzcCAgIAOHfuHC4uLri4uFC/fn2ePXtW6O9J2a2M3hERERHcvXuXkydP6jpKvkJDQ1m5ciV2dnbMnDlTo22npqbi4OCApaUlf/zxh0bbLqnVq1eTmZmZ+3HDhg2pVq1a7sf2ThbYO1nwJDGdoBMxKB8/R56Zg2Skh8KiAo28bMTEKQ3q168fDx48YPz48VhZWdGzZ09dRyo1kyZNYuLEiUiS9E5OKiurArLOISHlOya1IHoyPSS1xMWsc3RQdCl2jsePH1O9enUAqlevTkJCAvDiFvq6desK3Jr42rVrhIeHY2lpSfPmzQkICGDs2LEsX76cM2fOULVqVZ48ecL8+fM5efIkJiYmLFq0iOXLl+Pr6wuAQqHA398fgHXr1qFSqQgKCsLPz485c+Zw8uRJzM3NOXHiBAqFgqioKPr27UtwcDALFy5k6dKlubvSnT17NjfbrFmzqF+/Pvv37+f06dMMGDCAkJAQAG7evMmZM2d49uwZdevWZdSoURgYGPDzzz/z/vvv8/z5c9zc3OjZsydVqlR543Pg4eGBWq3mzp077N69u8DnfOHChcTExGBkZERycnKexzx8+BB/f39u3rxJ165d6dWrF8ePHycqKoqgoCDUajVdu3bl/PnzJCYmYmlpyZEjRwBISUnh6dOn7Nu3j5s3byKTyfLtRyaT0bZtW44dO0ZKSgpdu3Z95Q7MmDFjcr9P//d//8fhw4fp0qUL/fr1Y9q0aXTv3h2lUokkSdy7dy/Pfw8tWrR4pc/09HSaNGnCN998w5QpU/jxxx/56quvGDduHOPHj6dFixbExcXRvn17IiIiWLp0KWvWrKF58+akpaUVaRt0Uajq2MSJE7G1tcXWtuwuVdKwYUP09fUJDw/XaLtZWVk4ODhgaGhIaGhomfkFLEkSPXr04NChQ6xYsQILCwv69++f7zvzqh+Y4P2pQ56fEzRrypQp3L9/nz59+nDu3Lkysb5uafj888+ZNGkSK1asYOLEibqOI/xXnHS3xG2ogXsaaCcvrq6uBRapAI0aNcrd9c/FxYXY2NjXCpM//viDGzdu5L7msrKyaNq0ae7n+/Tp88rxPXr0AF78/oiNjQUgOzubMWPGEBISgp6eHrdu3Sowm7+/P3v37gVe3FlJSkoiJSUFgE6dOmFkZISRkRHm5uY8fvwYKysrVq1axb59+wC4d+8eUVFRBRaqLwvy6Oho2rRpg7u7+xuPd3Jyol+/fvj4+ODj45PnMT4+Psjlcuzs7HKvOB4/fpzjx49Tv3594MXVy6ioKFq2bMmkSZOYOnUqnTt3pmXLlqhUKhQKBcOGDaNTp075jh0F+OSTT1i1ahUpKSksW7aMb7/99pWvbfHixWRkZPD06VPs7e1xd3fnwYMHdO/eHeCVwrEw/x4MDQ1z8zRs2DB3PfWTJ0++Mo41NTWVZ8+e0bx5cyZMmEC/fv3o0aNHkXaZFIWqDqlUKk6cOFHkWxOlqV69eqhUKsLCwjTariRJuLi4kJaWxp07d4r07kqbkpOTadiwIfHx8Zw7dy73xdmnT58yU0i/61atWsX9+/fx9PQkNDSUunXr6jqS1snlcrp27SoK1TImXZ2GDFmJ2pAh45k6rURtWFhY8PDhQ6pXr87Dhw8xNzcv0vl/H+Kkp6eHSqV67Ri1Wo2Xl9crYyH/zsTk1btHL9v8e3sv3/hfv34dSZIK9XM/r6XeZTJZvrnPnj3LyZMnCQwMxNjYGHd3d5RKZYH9vGRra4uFhQU3btzA0tISSfrfqtl/b+fIkSOcP3+egwcPMm/evDwv5Pw938uvQ61WM336dEaMGPHa8VeuXMHPz4/p06fTrl07fH19CQoK4tSpU+zcuZPVq1dz+vTpPHM3atSIsLAwKlSoQJ06dV7J/PnnnxMcHMxHH33E7NmzUSqVeT6veeXO79+DgYFB7vfh78dIkkRgYCAVKry6sc20adPo1KkTfn5+NGnShJMnT1KvXr18M/yd+M2rQ99++y0GBga5Y4vKmnnz5hEZGcmkSZM0vn1pq1atuHv3LmFhYZiZmWm07eIKDg7GysqK7Oxs7t69+8o7SFGkli2//fYb9evXp2HDhrm3Od92y5cvJz4+Pve2p6B7JjJT1JRszxw1aioWc4zrS127ds2dhLp582a6detWovZeqlixYu5YwiZNmhAQEMDt27cByMjIKNQV0b9LSUmhevXqyOVyfvnll9yJV3/v559atWrFtm3bgBdDAqpWrUqlSvnv7pWSkkLlypUxNjbm5s2bRR5SlpCQQExMDNbW1lhYWJCQkEBSUhKZmZm5QxNe3iL38PBg8eLFJCcn547XLUj79u35+eefc49/8OABCQkJxMfHY2xsTP/+/Zk0aRJXr14lLS2NlJQUvL29+e677wp87S9YsOCVK6nwv+K6atWqpKWl5Y6VrVSpElZWVrnjmTMzMzWywkO7du1YvXp17scvM0dHR+Po6MjUqVNxdXXl5s2bhW5TXFHVof/85z/07t27TBZBt2/fxtfXF2tra40vFeXj48Ply5cJCQnB0tJSo20X1/r16xk1ahRt27bl999/L5PfE+FVFy9epE6dOjg4OBAbG4uxsbGuI2mVlZUV9erVY+LEia8sRSPoTg25dYnbkAEfFbKdvn37cvbsWZ48eYKVlRVz5sxh6NChTJs2jY8//piffvqJGjVq8OuvvwKFH6Oan+HDh9OxY0eqV6/OmTNn2LRpE3379s0duz9//vxXrt4V5PPPP6dnz578+uuveHh45F6FdXJyQl9fH2dnZwYNGpR7WxxeTJoaPHgwTk5OGBsbF7gqTIcOHVi3bh1OTk7UrVuXJk2aFCqbh4cHenp6ZGdns3DhQiwsXiwl6OvrS+PGjbGxscm9ApiTk0P//v1JSUlBrVYzfvz4Ql9wadeuHREREbnDJkxNTdm6dSu3b99m8uTJyOVyDAwMWLt2Lc+ePaNbt265V0BXrFjxxrY7duz42mNmZmZ89tlnODo6UrNmTdzc3HI/98svvzBixAh8fX0xMDDI/XdTEqtWrWL06NE4OTmhUqlo1aoV69at47vvvuPMmTPo6elhZ2eXZ9b8iC1UdSQwMJDmzZvz6NGjIt+mKQ0KhQKVSoVSqdToagSfffYZGzduJCAggMaNG2us3ZIYPHgwmzdv5quvvmLu3Lm6jiMUQUZGBjVr1qRSpUrcunXrrX+DsXv3bj799NMiT0YQSi6vLR5TpRSGp/4fCioUa0JVjjqHTJ7zQ6WtVJLnf5VQEN52b9pC9e3+qV6GTZ48GUdHxzJZpLq5uZGZmcmFCxc0WqTOnDmTn3/+mYMHD5aJIjUjIwNnZ2e2bdvGwYMHRZFaDhkbGxMWFsajR49o1qyZruNo3ccff0yFChXEv9UyopL8PZoZtCKjmGNMM9RpNDNoLYpUQXgDUajqQFpaGoGBgcybN0/XUV7z/fffExwczNChQ1+Z0VlSK1euZMGCBWzcuBFvb2+NtVtckZGRfPjhh8THxxMVFfXG2ZRC2WZubs6VK1e4du1a7gzWt1m/fv3K9ATMd00XRXfkMj2y1dlFOi9bnY2eTI/OirxnjAuC8IIoVHXg66+/pmLFinTt2lXXUV7x5MkTxo4di4WFRbHHNOVl27ZtjB8/niVLljBgwACNtVtce/bswcHBgTp16vDgwQOsrUs+zkzQrbp163L69GkOHTrEF198oes4WrVw4UKePn0qxqmWETZ6toyo8AVKnhe6WM1WZ6PkOcMrfIGNXtldmlAQygJRqOrApk2bGDx4sK5jvKZmzZrIZDLu37+vsTaPHTvGgAEDmDx5cplYVmfixIl8/PHHjBw5kkuXLmFoaKjrSIKGNG/enF27drFmzZpS32+7NJmZmdGwYUOdbNUo5M3DyItRFcaRRSbPpBRy1HlvI5qjzuGZlEI2mYyqMA4PI69STioI5Y+Y9V/KDh8+TGpqapm77e/p6Ul6ejq///67xsalXr58mU6dOjFgwAAWLVqkkTaLS6VS4eHhQWBgIFu2bKF///46zSNoR8+ePVm1ahVjx47Fysrqrf0+L1y4EC8vL5KTk8vM8m7vOg8jL2rq1+Kwcj8Xs88hqSXUvFgnVY0aGSBHj5aGnnRW+IgrqYJQSGLWfylzcXHB1NQ0d6u5smDLli0MHDiQnj17vrIfcUlERUXh4OCAl5dX7tpzunL//n3c3NzIyMjg4sWLGl8TVih7pk6dytKlSzl+/Dht2rTRdRytqFq1Kr169WLdunW6jvJOeNOs5H9KlVK5mHWOe9JdnqnTqCgz5SO5Nc0MxcQpQcjLm15folAtRQkJCVSrVo2AgACNTlQqibS0NCpVqsR7773HX3/9pZE24+Pjc9e3LOpiy5p24sQJOnfujK2tLUFBQZialmxhbaH86NevH7t37+bKlSs4OTnpOo7GTZ8+ndWrV+e7ULqgWUUpVAVBKBqxPFUZMXnyZMzNzctMkQrw0UcfAS/2Q9aE1NRUHB0dsbKy4uLFixpps7i++eYb2rdvT8+ePblx44YoUt8x27Zto0WLFjRp0kSj467LilmzZvH8+XN2796t6yiCIAhaIwrVUiJJEr/++iuff/65rqPk6t69O8nJyWzfvl0jRVxWVhZ2dnZUqFCB0NBQnS2+LkkSnTt3xtfXl1WrVrF9+3ad5BB079SpU9SsWRMnJydSU1N1HUejFAoFrVu3Zvbs2bqOIgiCoDWiUC0lmzdvJjs7mxkzZug6CvBiUtf+/ftp06YN/I86VAAAIABJREFUn3zySYnbkyQJZ2dnMjIyuHHjhs5m0z99+hRbW1tOnz6Nv78/Y8aM0UkOoWyQy+WEhISgUChwcHBApVLpOpJGLVu2jJs3b76VV4wFQRBAFKql5ttvv6Vt27Ya3empuJRKJV27dsXExISTJ09qpM3mzZtz7949wsLCqFRJN5MFLl++zEcffYQkScTFxZWpIRaC7hgaGnLjxg1SU1NxdXVFkiRdR9IYFxcXLC0tmTBhgq6jCIIgaIUoVEtBdHQ00dHRLF++XNdRAKhRowZqtZrY2FiNtNe5c2euXr3KlStXsLS01EibRbV27VqaNGlC69atiYmJoWrVqjrJIZRNZmZmXL9+ncjIyDKxM5omjR8/noMHD75VBbggCMJLolAtBRMnTsTa2rpMzBgdNGgQiYmJrF27ViPF3JAhQzh69Cj+/v7UrVtXAwmLbsCAAYwePRpfX1/8/Px0NjZWKNusra0JCAjg5MmTDBkyRNdxNGb8+PGo1Wr+85//6DqKIAiCxonf6FomSRJ+fn5MmTJF11Hw9/dn8+bNuLm5MXLkyBK3N336dDZv3syRI0dwc3PTQMKiycjIwMnJiV27dnHkyBFmzZpV6hmE8qVBgwYcPHiQzZs34+vrq+s4GiGXy/H29mbp0qW6jiIIgqBxolDVsmXLlqGnp8eIESN0mkOlUtG6dWuMjIwICgoqcXvLly9n0aJFbNmyhfbt22sgYdFERERgaWnJ48ePiYqKomPHjqWeQSifvL29Wb9+PfPnz2f9+vW6jqMRy5cv5+7du4SHh+s6iiAIgkaJQlXLvvvuO3x8fHR+O9rW1hZJkrh582aJ29qyZQuTJk1i+fLl9OvXTwPpimbXrl04OjpiZ2fHvXv3qFGjRqlnEMq3oUOHMnv2bEaNGqXzndM0wcbGhtq1azNx4kRdRxEEQdAoUahqUXBwMA8fPmTZsmU6zfHll18SFxfH/PnzqVmzZona8vPzY/DgwUybNo0vv/xSMwGLYPz48fTt25fRo0dz8eJFnS2DJZR/vr6+DBkyBB8fHy5fvqzrOCX21VdfcerUKbKysnQdRRAEQWPEFqpa5OHhQUJCgk5vx4WGhuLs7IydnV2Jc1y6dInmzZszePBgfvzxRw0lLJysrCzc3d0JCgpi27Zt9OnTp1T7F95e3t7enD59moiICGxsbHQdp0RMTEyYMmWKGK+tBUXZQjU7PZWkUH8yHt9D9TwN/QqmGFt8RBWnFhiY6Gb5PkEoy970+hKFqpYolUpMTEzYtWsXvXr10lkOAwMDALKzs0vUTmRkJE5OTnTo0IEDBw5oIlqhxcXF4ebmRmZmJoGBgWVi9QTh7SFJEq6urkRHRxMTE8P777+v60jFNnjwYPz8/Hj8+LGuo7x1ClOopsfH8ND/EEl/XkQt5bx4UCYDtRpkMmQyOVUcm1G9RRdMLMv3myJB0KQ3vb7ErX8tmTVrFiYmJjotUuvVq4dKpSIkJKRE7cTHx9OgQQNcXV1LvUg9duwYtWvXxtzcnPj4eFGkChonl8sJCgqicuXKODg4oFQqdR2p2JYsWUJiYiL+/v66jvLOSbhymrC103ly/QJyQyP0jSu++K+Cae6fckMjnly/QNja6SRcOa3ryIJQLohCVUs2bNhA//79ddb/vHnziIyMZMKECdjb2xe7neTkZBwcHLC2tubChQsaTFiwuXPn0rFjR/r06cOff/6JsbFxqfYvvDv09fUJCwsjMzOT+vXrl9vF86tWrYqzs3OZWA7vXZJw5TR3fluLTN8AfeOKyOR6eR4nk+u9+Ly+AXd+WyuKVUEoBHHrXwtOnDhB+/btSU5O1sl2orGxsdjY2GBtbV2i3aeUSiW1atVCT0+P6OjoUpu4JEkSnTt35tixY6xevZpRo0aVSr/FFZ/0F4fOBZD09DE52ZnoGRhR5X0LurZuTvUqlXUdTyiC+Ph4ateujaurK+fOndN1nGLx8/OjS5cupKSkYGpqqus4b438bk2mx8cQtnY6Mn0D5PoGhW5PUmWjVmXjMGpBoYcBDBkyhMOHD2Nubk5YWFju47/++iuzZ88mIiKCoKAgXF1d39hO48aNyczM5OnTpzx//pwPP/wQgP3795d4wm1hbdiwgbCwML777rsinSdJEosXL2batGlaSlZ0p0+fxtjYmCZNmug6Srklbv2XsunTp+Pm5qazPe/r1auHnp4et2/fLnYbkiTh7OxMZmYm4eHhpVakPnnyhFq1anHu3DkuXrxYpovUP8JvMW/dOrZuWkJqTAD6KVEYZtxDPyWK1JgAftm0hPk/rOOP8Fu6jioUkqWlJUFBQQQGBvLpp5/qOk6xeHt7U6lSJWbMmKHrKO+Eh/6HUEs5RSpSAeT6BqilHB4GFH55tEGDBnH06NHXHndwcOC3336jVatWhWrn0qVLhISEMHfuXPr06UNISAghISGlVqSWhCRJLFy4MM/PqdVqndwNOX36NH/88Uep9/uuEIWqhj19+pSrV6+yaNEinfT/ctLRhQsX0NfXL3Y7TZs25cGDB4SHh5dawR0YGEiNGjWQyWTcu3ePxo0bl0q/xbHpyHHOH92EIv0umWo5z9X6KNUGZP73z+dqfTLVcozS7nL+6CY2HTmu68hCIdnb23P06FF27dpVbm+hDx48mC1btug6xlsvOz2VpD8voqco3rAkPYUxSaEBZKenFur4Vq1a5TnZ79///rfGtrD+/fffadq0KQ0aNKBPnz6kp6eTnJxMnTp1ci9+9O7dm40bNwIwfPhwXF1dsbe3Z+7cubntTJ48GTs7O5ycnJg6deob++zfvz/jxo2jWbNm1KpVi3379gHw4MEDWrRogYuLCw4ODly8eJFp06bx7NkzXFxcGDBgALdv38bBwYGRI0fSoEED7t27h5mZWW7bO3fuZNiwYbn9jB49Gg8PD2xtbTl//jwDBw6kXr16DB069I3PAYCVlRWzZ8+mfv36ODk5cevWLaKjo9mwYQNLlizBxcWFixcvsnPnThwcHHB2dsbDw0MD35V3myhUNWzq1KlUqVIFd3f3Uu/7+++/Jzg4mMGDB9O0adNit+Pt7c3169e5du0a1apV02DC/K1evZoWLVrg6elJdHR0mZ55venIcZ5EniFLLeO5Wh81sjyPU/Pi81lqGYmRZ0SxWo54enqyZcsWli5dysqVK3Udp8jmz5/Ps2fPSn3y47smKdQftZST75jUgsjkeqjVEkmhARpO9kJ8fDze3t6FPj4hIYGFCxdy6tQprl69ipOTEytXrsTMzIxVq1YxaNAgtm3bRkZGBoMHDwZg4cKFBAcHc/36dU6cOMGNGzd4/Pgxfn5+hIeHExoayvTp0wvVd0BAAPv37889fuvWrXTp0oWQkBCuX7+Ok5MTCxcupGLFioSEhOS+Gbtx4wZDhw7l2rVrucMY8pOSksKZM2dYvHgxXbp0YerUqdy4cYMrV64QFhaW73PwkoWFBdeuXWPYsGEsX74cW1tbhg0bxuTJkwkJCaFZs2bMmTOHU6dOcf369dyiWyi+4l9yE/K0Y8cOxo0bV+r9PnnyhLFjx2JhYcHPP/9c7HYGDhzIiRMnCAwMpHbt2hpMmL9+/fqxY8cO5s2bx8yZM0ulz+L6I/wWCTfPsmXLfm5FRJOdnU3lKpX5ctpoAK5cusqJI6d5/vzFzPGK71Wk3+A+fPihOQk3z3LJpiaN7ero8CsQCqtfv37Ex8czfvx4PvzwQ52u4FFUxsbGNG/enK+//ppu3brpOs5bK+PxvZI3olaTkaCBdvJgaWmJn59foY+/ePEiN27coFmzZsCL9atbtGgBQIcOHdi9ezfjxo0jNDQ095wdO3bw008/oVKpiI+P58aNG3Tr1g25XM5nn31Gp06d6Ny5c4F9+/j4IJPJcHJy4sGDB8CLO4QjRoxAqVTi4+ODs7MzKpXqtXNtbW1xc3Mr1NfYpUsXABwdHbG0tMTOzg4AOzs7YmNjuX37dr7PAUCPHj0AaNiwYb7PbfPmzRkwYAC9e/fOPV4oPnFFVYN27NiBUqnUyWLbNWvWRCaTcf/+/WK3MWXKFLZu3Yqfn1+Bg/E1IS0tDXt7e/bs2cOxY8fKfJEKcOLCaWSoMatcmcYt3PjAvOorn3+WmoaLqxMTZo5lyqwJGBjos2X9NiTkyFBz4sIZHSUXimPy5Ml88cUX9OnTp9wt+bRkyRLCwsJ49OiRrqO8tVTP016sk1oSMhmqjDTNBCohtVpNhw4dcses3rhxg/Xr1wOQk5PDzZs3qVChAn/99RcAUVFRrFy5ktOnTxMaGkqHDh1QKpUYGBgQHByMj48Pe/fupVOnTgX2bWRk9EoOeHFn4+zZs1SvXp1+/fqxbdu2PM81MTHJ/btcLufvc8T/udzcy37kcvkrfcrlclQq1Rufg7+fr6enl2fRDPDjjz8yZ84cYmNjcXZ2zn2+hOIRhaoGzZ07F3d391Lf1tPT05P09HT8/PyKPS51yZIlLF26lK1bt+Ll5aXhhK8LDw/nww8/JCkpiejo6FLps6Tik/7CKD0OpVqPdp3b4OXticJY8cox7l6t6NC1He+ZVcLE1Bh3r1ZkZDwHQKnWwzDtLg+TxA+t8mTlypX4+Pjg6elJZGSkruMUWuPGjbGwsCi342zLA/0Kpi8W8y8JtRp947KxOkOzZs04d+4cd+7cASA9PZ2oqCgAli5diouLC1u2bGHw4MGoVCpSU1OpWLEilSpV4uHDhxw7dgyAZ8+ekZqaSufOnVmxYgXXrl0rVp67d+9SrVo1hg8fzqBBg7h27Vru77j8ikS5XE7lypWJiopCkqQi33p/03OQn4oVK/Ls2bPcj+/cuUOTJk2YN28elStXzr1CLBSPKFQ1JC4ujsjISJYvX16q/W7fvp0zZ87Qs2dPOnToUKw2Nm/ezNSpU1m5ciV9+/bVcMLXbdu2DWdnZ5ydnbl//z5WVlZa71MTDp0LQIY63zGpebkRGoGh4YvZwGpkyFBz6PxFbUUUtGTv3r00bNiQhg0bkpCQoOs4hTZmzBj27NlTbteFLeuMLT4qeSMyGcbmhWunb9++NG3alMjISKysrPjpp58A2LdvH1ZWVgQGBtKpUyfat28PFH2MqoWFBT/99BN9+vTB2dmZZs2acevWLSIiIti8eTOLFy/Gw8ODJk2asGDBAho0aICdnR0ODg589tlnNG/eHHgxDrRTp044Ozvj6elZ7N+Lp06dwtnZmfr163PgwAG++OILAIYOHYqTkxMDBgzI87xFixbRoUMH2rRpU+TfL/k9B2/SrVs3du/eTf369bl48SLjx4/H0dERR0dH2rZti4ODQ5EyCK8S66hqSO/evbl06RJxcXGl1mdaWhqVKlXivffeK/athcOHD9OtWzdmzJjBvHnzNJzwdV988QVr1qxh/PjxLFu2TOv9adK3G35CPyUKpfp/y9BsWLOJtGfpuWNU/y78+g12b/0N727tadzixfgphSybnPfqMH3YkFLLLWiGJEnUq1eP5ORkYmNjy8UGFCqVigoVKrB27drcmc9C8eS1zmN2eipXFw5HbmhUrAlVaikHKSuTBtPWY2Cim+UMBaEsEOuoapkkSRw6dIgJEyaUar8fffTiXfi9e8UbiB8YGIiPjw+fffaZ1ovUrKwsmjRpwrp169i5c2e5K1IBcrIzC301NToqhl+37cO1cf3cIhVeXFVVZZffLTrfZXK5nNDQUGQyGY6OjuXiKqW+vj7t2rVjwYIFuo7yVjIwqUQVx2bkKDOKdX6OMoMqTs1FkSoIbyAKVQ1Ys2YNarWasWPHllqfPXr0IDk5me3btxdr95mIiAhat25N165dWbdunRYS/s/du3exsrIiMjKSsLAwPv74Y632py16BkbIKPgGRFxsHL9s2I6Dsx1der06iUCGGn0DRT5nCmWdQqEgPDychISEEi0BV5qWL1/OnTt3ChxnJxRP9RZdkMn1kFTZRTpPUmUjk+tRvXnBM+IF4V0mClUNWLp0KZ07d0YuL52n8/Dhw+zbt482bdrwySefFPn8+/fv07BhQxo3bsxvv/2mhYT/4+fnR+3atalevToPHjzQ2KLUulDlfQtk/72imp2l4nmGErWkRq2WeJ6hJDtLxYN78Wxcu5V/1bGlV7/ur7UhQ0aVKhalHV3QoKpVq3L16lVCQkLw8fHRdZwC1a1bl1q1apX6HZ93hYmlDTY+w5GylIUuViVVNlKWEhuf4YXePlUQ3lVijGoJhYWF4eTkRExMDNbW1lrvT6lUYmxsjLGxMWlpRV/SJDk5GRsbGz788ENCQ0O1Wlz7+voyf/58Bg4cmLuLSXkWn/QXWzctIVMt56e1v3D3zqvjka1r1UCGjNg7d187d86Sr5Chxkgm8X+DJlO9SuXSii1oSWBgIC1btmTEiBGsWbNG13HeaP369YwZM4aMjIwS7Vj3LnvTGDqAhCunidm/HrWUg57COM8xq2ophxxlBjK5HjY+wzFv6KnNyIJQbrzp9SUK1RJq164dd+/eLbVla8zNzUlMTCQxMZGqVasWfMLfKJVKbGxsMDAw4Pbt21pbRkuSJDp27MjJkydZu3Ytw4cP10o/ujBv3ToU6Xd5ri76L/sKMhWZpjX5asQILSQTdGHfvn307NmTb7/9lmnTpuk6Tr4kScLExIRZs2aV6ZxlWUGFKkB6fAwPAw6TFBqAWi29WLpKJsv9UyaTU8WpOdWbdxZXUgXhb970+hJvrUsgKyuL06dPs2nTplLpb9CgQSQmJrJ69eoiF6mSJOHo6Eh2djZRUVFaK1ITEhJwdXXl6dOnXLp0qVQ2DihNXi09OX90E3IkpCKMnJEjoUaGV0ux7/PbpHv37qxatYqxY8diZWVF//79dR0pT3K5nB49erBq1SpRqGqRiaUN/+r9BdbeA0kKDSAj4R6qjDT0jU0xNv9ITJwShGIQhWoJzJ8/H4VCUSq/nPz9/dm8eTNubm6MHv36UkhvIkkSjRo14tGjR0RHRxdr8lVhBAQE4OXlRfXq1bl//z5mZmZa6UeXmtjX4WasO4mRZ8iUKFSxKkfCSC7xQV0PsX3qW2jMmDE8ePCAgQMHYmFhUWY3r1i2bBmWlpZcvny50NtNCsVjYFKJak076jqGILwVxGSqEli3bl2pzGBXqVS0bt0aIyMjgoKCiny+t7c3YWFhhISEYG5uroWEL3bvadWqFe3btycqKuqtLFJfGtSpHR/U9cBQpqaCTJXvSgAyXnzeUKbmg7oeDOrUrpSTCqVlwYIF9O3bF29v71f2QS9LqlWrhr29PZMmTdJ1FEEQhEITV1SL6fz58zx58oTFixdrvS9bW1skSeLmzZtFPrd///6cOnWKS5cuYWtrq/FskiTx6aefsnv37jI/Tk+TBnVqxyWbmpy4cAajtLv/3bFKnbv71Mv/Z5rWxKuluJL6Lti6dSvx8fE0adKEW7dulckd1+bOnUuvXr3IyMgoFxsWCIIgiEK1mKZOnUr9+vWLPFa0qL788kvi4uKYP38+NWvWLNK5EydOZMeOHRw/fpwGDRpoPFtaWhpubm7ExMRw8uRJPD3frRmsje3q0NiuDg+T/uLQ+YskJT1Gla1E30BBlSoWdGnVTMzuf8ecPHkSR0dHnJyciI2NpVKlsjUesXv37piamjJr1iyWLFmi6ziCIAgFErP+iyE1NRUzMzN+//333D2VtSE0NBRnZ2fs7OwIDw8v0rkLFy5kxowZ7Ny5UyvDE8LCwmjWrBmmpqYEBwdjaWmp8T4EoTzKysrCxsYGPT097ty5U+aWgxo9ejQ7duzg6dOnuo5SrhRm1r8gCMUjtlDVsJkzZ2JmZqbVIhWgYcOG6OvrF7lI/emnn5gxYwarV6/WSpG6ZcsWXFxcaNCgAXFxcaJIFYS/MTQ0JDw8nNTUVBo2bFjmtlpdsGABycnJHDt2TNdRBEEQCiQK1WLYsmULQ4cO1WofdnZ2qFQqQkJCinTegQMHGD58OL6+vnz++ecazzVq1CgGDRrEhAkTOHv2bJm7WiQIZYGZmRnXr1/n1q1bdOxYtmZ/V6pUicaNGzNjxgxdRxEEQSiQKFSLaO/evaSlpTFnzhyt9TFv3jwiIiKYMGEC9vb2hT7P39+fnj17MmLECGbPnq3RTFlZWbi5ubFhwwZ+/fXXUplEJgjlmbW1NQEBAZw+fZohQ4boOs4rlixZwrVr18Ttf0EQyjwxRrWIHB0def/99zl37pxW2o+NjcXGxoYaNWpw9+7rW3HmJywsjAYNGtC1a1f27Nmj0UwxMTG4ubkhSRKXLl2idu3aGm1fEN5mv//+O507d2bmzJnMnTtX13FymZub07VrVzZs2KDrKOWCGKMqCNojxqhqyKNHjwgPD2fp0qVa66NevXro6ekRHR1d6HPi4uJo1KgRzZo103iRevDgQerWrUuNGjWIj48XRaogFFHHjh1Zv3498+fPZ/369bqOk2vkyJHs3LlT1zEEQRDeSBSqRTBx4kSqVaumtV1d3NzcyMzM5MKFC4Ue+/n06VOcnJz417/+xenTpzWa5+uvv8bHx4cBAwZw9epVFAqFRtsXhHfF0KFDmT17NqNGjeLw4cO6jgPAV199hVKpZOvWrbqOIgiCkC8xE6aQJEnit99+Y9asWVppf82aNQQHBzN48GCaNm1aqHOUSiV2dnaYmZlx9epV5HLNvO+QJIl27dpx5swZfvzxR61PHBOEd4Gvry/379/Hx8eHwMBAnW9jamhoiKenJ/PmzSuVbaDfJUplOnF3wkn5K4HsLCUGhgreq2yOdS0HjBRiowVBKAoxRrWQ1q9fz5gxY8jIyND4TPcnT57wwQcfYG5uzuPHjwt1jkqlom7dujx79ozY2FiN7TLz6NEjXF1dSU5O5vz581rZKEAQ3mWdOnXi1KlTREREYGNjo9MsYWFhODk5ERMTg7W1tU6zlHWFGaP6V9IjboYFci/2Bmq1BGqQyWSo1WqQgUwmp4aNHXXtm1K5SrVSSi4IZd+bXl+iUC0kW1tb7OzsOHTokMbbNjU1JSMjg4yMjELdXpckCVdXV27fvs2dO3c0tjvW+fPnad++PVZWVly+fBkzMzONtCsIwv9IkoSbmxu3b98mJiaG999/X6d5rK2tcXV1Ze/evTrNUdYVVKjeuRVCcOAR1JKEgaEizztckiSRnaVEJpfj2rQTteq4aDOyIJQbYjJVCUVGRhITE8Py5cs13ranpyfp6en4+fkVegxo+/btiYiI4Pr16xorUpcvX46Hhwfe3t5ERkaKIlUQtEQul3Pp0iUqV66Mvb09SqVSp3kmTZrE4cOHy9zGBOXJnVshXA44hJ6ePkYK43yHYcnlcowUxujp6XP54iHu3CraOtmC8C4ShWohTJgwARsbG43PeN++fTtnzpyhZ8+edOjQoVDnfPrpp5w9e5bAwECN3DaUJIlevXoxefJkFi9ezN69ezU21lUQhLzp6+sTFhZGVlYW9evX12mROHr0aGQyGatWrdJZhvLsr6RHBAceQd/AED29wg0L09PTR1/fkODAI/yV9KjQfQ0ZMgRzc3McHBxeeXzy5MnUq1cPJycnunfvTnJy8hvbefbsGba2tkRFRQGQnZ2No6Mjly5dKnQWQSgtoiIpgEql4vjx40yfPl2j7aalpdG/f3/MzMwKvaTUuHHj2LVrFydOnMDFpeS3jFJTU6lXrx5Hjhzh1KlTTJw4scRtCoJQOKampvz555/ExcXh4eGhsxxyuZwuXbpo5Y7Ru+BmWCBqSSp0kfqSnp4+akkiMvyPQp8zaNAgjh49+trjXl5ehIWFERoaSp06dViwYMEb26lYsSILFixg9OjRACxdupRmzZrRuHHjQmfJyckp9LGCUBKiUC3AokWLMDAw0PjOMh999BEA9+7dK9Tx33zzDd9//z27du3C3d29xP2HhITw4YcfkpGRQUxMjEbaFAShaCwtLQkKCiIwMJC+ffvqLMeyZcu4f/8+oaGhOstQHimV6dyLvYGBYfGW7jMwVBAXE06mMqNQx7dq1SrPMc3t2rXLneTbpEkT7t+/X2BbH3/8MXK5nMWLF7Nu3bpXitutW7fSqFEjXFxcGDFiRG5Rampqiq+vL40bNyYwMJBTp05Rv359HB0dGTJkCJmZmaSkpFC3bl0iIyMB6Nu3Lz/++GOhvj5ByIsoVAuwevVqevToodHb4T169CA5OZmtW7diampa4PHr16/n66+/Zs2aNfTq1avE/W/cuBFXV1caNWpEbGws1aqJ2aeCoCv29vYcP36c3bt3M3nyZJ1kqFGjBnXr1hV3VYoo7k44arVU7N8PcrkctVoiLiZMY5l+/vlnOnbsCEB8fDze3t75Hvvdd98xdepUvvrqq9wCOCIigl27dhEQEEBISAh6enps27YNgPT0dBwcHLh06RKurq4MGjSIXbt28eeff6JSqVi7di3vvfceq1evZtCgQezcuZO//vqLzz77TGNfn/DuEYXqG1y6dInHjx9rdCeqw4cPs2/fPtq0acOnn35a4PH79u1j5MiRuYuFl9Tw4cMZOnQoU6ZM4dSpUxpfaksQhKJzd3dn69atLFu2jJUrV+okg6+vL2fOnCErK0sn/ZdHKX8lQEnXzVFDyl+JGsnzzTffoK+vT79+/YAXV+z9/PzyPf7o0aNUr16dsLD/FcqnTp3iypUruLm54eLiwqlTp7hz5w4Aenp69OzZE3gxydjGxoY6deoAMHDgQM6fPw+8GIrg6OjI6NGjxRa9QomJQvUNJk+ejIODg8auOCqVSrp27YqJiQknT54s8Pjz58/Tu3dvRo8eja+vb4n7btiwIZs2bWLfvn18++23JWpPEATN6tu3L0uWLGH8+PHs3r1bJ/0rFArmzJlT6n2XV9lZSmQyWYnakMlkZGU+L3GWzZs3c/jwYbZt21aoTPHx8axatYqgoCD8/Pxyh32o1WoGDhxISEgIISFg+/ipAAAgAElEQVQhREZGMnv2bAAUCgV6enq5x+VHkiQiIiKoUKECT58+LfHXJrzbRKGaj4yMDAICApg3b57G2rS2tkatVhMbG1vgsWFhYbRt25ZevXrx/fffl6jf6OhoPvzwQ2JjY4mIiKBbt24lak8QBO2YOHEi48aNo2/fvvj7+5d6/3379mX9+vWl3m95ZWCoeGPBVhhqtRpDowolauPo0aMsWrSIgwcPFnrzl/HjxzNjxgysrKxYvnw5o0ePRq1W06ZNG/bs2UNCQgLwYpvuu3fvvnZ+vXr1iI2N5fbt2wD88ssvtG7dGoAVK1bw73//mx07djBkyBCys7NL9PUJ7zZRqObj66+/pmLFihor6oYMGUJCQgKrV68ucO3Tu3fv4ubmRosWLdi5c2eJ+j1w4AD16tXDxsaGBw8eYGtrW6L2BEHQrhUrVtC9e3c8PT2JiIgo1b4XLVpEUlISZ8+eLdV+y6v3KptDyS6oggzeq/xBoQ7t27cvTZs2JTIyEisrK3766ScAxowZw7Nnz/Dy8sLFxYWRI0cC+Y9RPXHiBHFxcbnbY3fp0oXKlSuzZcsW7OzsmD9/Pu3atcPJyQkvLy8ePnz4WhsKhYKNGzfSu3dvHB0dkcvljBw5klu3brFhwwaWLVtGy5YtadWqFfPnzy/usyMIYmeq/Lz//vv0799fI2sL+vv707JlS9zc3AgKCnrjsU+ePMHW1hYbGxuuXr1aoklc06dPZ9GiRQwfPpx169YVux1BEEpfs2bNCA0N5c6dO5ibm5dav66urujp6Yk1Nf8hr51zlMp0Du76Dn19w2L9rJYkCZUqi259xmOk0Mw22IJQHomdqYrIz8+PlJQUjYzjVKlUtG7dGiMjowKL1IyMDOzt7alSpQrBwcHFLlJVKhWenp4sWbKEjRs3iiJVEMohf39/LC0tcXBwICOjcMsXacKCBQu4fPkyqamppdZneaVQmPBRTTuys4q3u1h2lpIaNvaiSBWENxCFah5mzpxJ48aNC7V0VEFsbW2RJImbN2++8TiVSpW720hYWFixZ+M/evQIa2trLl++zNWrVxk4cGCx2hEEQbfkcjmhoaHIZDIcHR1RqVSl0q+XlxeVK1dm2rRppdJfeVfPoSkyuZycnKJ9f3JyVMjkcuraN9FSMkF4O4hC9R+ePHnC9evXWbx4cYnbmjhxInFxccyfP5+aNWvme5wkSbi6upKUlER4eHihB8P/09mzZ6lZsyampqY8ePAAJyenYiYXBKEsUCgUhIeHk5CQQLNmzUqt32HDhrF169ZS6688q1ylGq5NO6FSZRW6WM3JUaFSZeHatBOVq4h1rAXhTUSh+g+TJ0/mgw8+oEWLFiVqJzQ0lOXLl/Pvf/+bmTNnvvFYLy8vIiMjCQ0NLXCiVX6WLFmCp6cnXbt2JSIigkqVKhWrHUEQypaqVaty9epVrl+/jo+PT6n0OWfOHNLT09m7d2+p9Fde5Delo1YdF9yadSEnR0WmMgNJkvI8TpIkMpUZ5OSocGvWhVp1Sr4VtiCUdwVNlRKTqf7BxMSEKVOmMGvWrBK1Y2BgAFDgshyffPIJv/32G8HBwcW6AipJEr169eLAgQMsW7aML7/8slh5BUEo2wIDA2nZsiUjRoxgzZo1Wu/Pw8ODxMTEVxaDf5fFxMRQsWJFqlSpku86pX8lPSIy/A/iYl7sWIX6xTqparUaZCCTyalhY09d+ybiSqog8KJITUpK4tmzZ9jY2OR5jChU/2bLli0MHTqU9PR0DA0Ni92OnZ0dERERXL9+/Y3F5xdffMF//vMfzpw5Q6tWrYrcT3JyMq6urjx48IATJ06U+CqwIAhl24EDB+jevTvffPMN06dP12pfwcHBNGrUiPv372NpaanVvsqD7Oxs7t+/j1JZ8MQpVXYmTxPv8jw9GZUqC319QyqYmPH+B9boGxiVQlpBKD8UCgVWVla5F/j+SRSqf1OnTh1q1arF0aNHi93GvHnz8PX1ZcKECSxbtizf4+bOncucOXPYs2cP3bt3L3I/V69epVWrVlSuXJkrV66U6vI1wrvjUUIip48c4dnDu6gzM5AZGVOxujVtO3XG3Lx4w1SEkvnPf/7DmDFj2LRpEwMGDNBqX1ZWVrRs2ZIdO3ZotR9BEIT8iEL1v2JiYrC1teXPP//E3t6+WG3ExsZiY2NDjRo18tzJ46UffviBUaNGsW7dOoYPH17kfjZs2MCIESNo06YNR48eLdFaq4KQl6tXQwk6tJPKj/9EppZQA2pkyFAjk8mQkJFs4USjLn1o0EBM2ittM2bMYNGiRRw9ehQvLy+t9bNkyRJ8fX1JT08XP2cEQdAJUaj+V48ePbh69WqhtjfNj0KhIDs7m8zMzHyXl9qzZw8ff/wx8+bNK3CSVV6GDBnCpk2bmDlzpka3dxWEl/bs2EXOha0gSTyXGaKWvV6gyNQSFdRZIJej17I/vfr20UHSd9uAAQPYsWMHV65c0doKH5IkoVAoWLlyJaNGjdJKH4IgCG8iClVe/DCuUKECy5YtY8yYMcVqw83NjeDgYC5cuJDvWNGzZ8/Spk0bxowZw8qVK4vUvlKppGnTpoSHh/Pbb7/RuXPnYuUUhDfZs2MX6gtbeK42IEemV+DxeuocKsiykbUcIIpVHWjTpg2BgYHcunULKysrrfTh4+PD9evXiYmJ0Ur7giAIbyIKVWDZsmXMnDmTjIyMYt3eWrNmDWPGjGHw4MH8/PPPeR4TEhKCm5sbvXv3Zvv27UVqPyoqisaNGyOXy7l8+XK+M+MEoSSuXg0lasNMlh27wvW4BLJUOZhXMmFhv/YAKLNUzNh5nNSMTCS1mk+aOdLOuTZ66hyMZDnU+ewb6tcXwwBKkyRJODs78+DBA2JjY7WyLF10dDS1a9cmPDw83y0OBUEQtEUMOgJWrFhB165di1WkPnnyhDFjxmBubp5vkRoTE0PTpk1xd3cvcpG6d+9e7Ozs+Ne//kV8fLwoUgWtCTq0EySJKpVMaetoi2Xliq8dY/NBZfq2cOLvi/PkyPRAkrh0aFfphRWAF7tXXblyBWNjYxwcHLSye5WtrS22trZMnDhR420LgiAU5J0vVENCQoiPj2f58uXFOr/m/7d371FRlXsfwL97gAEmIiT0eAULSUVUvKIiryZekPKYhKappPZm2qokWHpM0wMIoiZWYpYszVKRo2/l3VQOppmiSGLKxRhRREEbCW/DbRj2fv+gSAS56ADbme/nH+XZz/Os316LNfNl7/08u2NHCILw0MVT+fn56NmzJ7p27YqDBw82aO65c+di/PjxmDlzJpKSkh5ryyyi2tzQ3ESL38+jWFBiwsDu8B/gBpVl1a1CrJTmeG/0QHi7OQMPbCNZLChhd+McNJr8JqyaAECpVCI1NRV3795Fnz59HrrZ/ONYsGAB4uPjm+w1rkREfzH5oBocHIwuXbo80vNdw4cPR2FhIfbv3w8rK6tqx7VaLVxdXdGyZUskJSXV+4qtXq/HkCFD8Mknn2DTpk1Nsrk3mbbD+/ZVrO6vYeFUfUiCAgpISNi/z8CVUX3Y2dnh3LlzyMzMxOjRow0+/xtvvAELCwssW7bM4HMTEdXGpINqSUkJjh49ipCQkAaP3bp1KxISEjBu3Dj4+PhUO67X6+Hm5gaFQoHz588/dBeAB+Xl5aFDhw5ISUnB2bNnMWXKlAbXRtRQ965fweM+rC5JEu7m5RikHmo4R0dHHD9+HIcPH8b06dMNOrdCocD48eOxZs0ag85LRFQXkw6qYWFhsLa2xoQJExo0TqvVYsqUKbCzs8P3339f7bgoiujduzdu376N9PR0qFSqes2bkJCA559/HnZ2dsjLy4Obm1uD6iJ6VFJpEaQH7+c3dA4IkEq1BqqIHkXv3r2xe/dubNq0CYsXLzbo3B9//DE0Gg0SExMNOi8RUW1MOqjGxMRg8uTJDR7XoUMHAMDVq1drPD5s2DBcvHgR586dg729fb3mjIyMxIgRI+Dn54eMjAzY2Ng0uC6iRyVYqiA85jVVARIES/7eNrfRo0dj/fr1CA8PR0xMjMHmbdWqFXr06IG5c+cabE4iorrU7360ETp8+DAKCgoa/MyVn58fbt++jdjY2BrD5KuvvooTJ04gJSUFjo6Odc4niiLGjRuHvXv34rPPPsN7773XoHqIDOHpNk4QriYBAHR6PfR6EaIkQZQkFJXoYG6ugNLcHEU6HfDnWh1deTmKSnSwUppDoVBAEATYtq37d54a3/Tp05Gbm4vZs2ejdevW+Oc//2mQecPDwzF27FhotVr+MU1ETcJk91Ht378/JEnC6dOn6z1m7969GDNmDLy9vfHf//632vF33nkHMTExOHLkyEM3/b9fQUEB+vXrh+vXryM+Ph6enp4NOgciQ7mhuYmjITNQLJkjctcxZF7/o8rxF9o8i/mvDMH/frkD4gMfGfPH/g86t7GHlaDHsJCNaNXKoSlLp1rMmjUL69evR2JiIvr162eQOVu0aIFp06bhk08+Mch8RES1Mcmgevv2bdjb2yM+Ph7e3t71GlNSUgKVSgWVSgWttvpzeCEhIViyZAl27NhRr6sXycnJGDp0KOzt7XHmzBk4OPDLnZrXl6EL0OL6ryhSVN/Boi4qsQS32rpj1uKIRqiMHsdLL72EhIQEZGRkGGQf5g8++ABff/01bt26ZYDqiIhqZ5LPqM6fPx/29vb1DqkA4OTkBEmSkJ2dXe3YmjVrEBYWhpiYmHqF1HXr1sHDwwODBw9GdnY2QyrJQv8xEwGFAmZSeYPGmUnlgEIBjzF8haoc7dmzB25ubnB3d0dBQcFjz7dkyRLcvXsXe/fuNUB1RES1M8mgGhsbi7feeqve/WfMmAGNRoM1a9ZUC5Xbt2/H+++/j6VLl+LNN9+sc65p06Zh9uzZWLx4MQ4cOPBIb8Miagy9e/eAmdcUWAtl9Q6rZlJ5RX+vKXx9qkwpFAqcPHkS9vb26NatG0pKSh5rPhsbGwwcOBAfffSRgSokIno4k7v1v337drz++uvQarU1btL/oMTERAwaNAj9+vVDUlJSlWMJCQkYOXIk5syZU+ebrYqKijBgwABcuHABu3btapRNuYkM4du4bSg/tgUQRRQLyhpfAiBIIlSSDpJCATOvKfCfxKupcqfVatGxY0c4ODggPT39sf5ITkxMhKenJ27cuIFWrVoZsEoioqpMLqi6urqiTZs2SEhIqLOvXq+HpaUlLCwsql2FOHPmDDw8PPDaa69hy5Yttc7z22+/YcCAAbCwsMDp06fh5OT0WOdA1NhSUs7h1J5tsLtxDgpIkCQJEoSKLagEASIE3G7dAx5jXuOV1CdIXl4eXFxc0KdPH/z000+PNVebNm0wcuRIfPPNNwaqjoioOpMKqteuXYOjoyPOnDkDd3f3Ovs7OTkhJycHly9fRseOHSvbs7Ky0K1bNwwZMgQHDx6sdY7t27dj8uTJ6NevH44cOQKlUvm4p0HUZDSafCTs34e7eTmQSrUQLG1g29YR3r4vcXX/EyotLQ29evXCq6++iri4uEeeJzw8HEuXLoVWq+UjTETUaEwqqE6cOBE///wzrl27Vmff4OBgrFq1CmFhYVi0aFFlu0ajgbOzMzp37oykpKRaP6CDgoLw6aef4t1338Xq1asNcg5ERI/ryJEj8Pb2RmBgIKKioh5pDr1eD2tra8TExBj8la1ERH8xmaAqiiJUKhUiIiIQHBxca9+0tDS4ubmha9euSE9Pr2z/6xmvFi1a4LfffntoSNXr9Rg6dChOnjyJzZs3Y9KkSQY9FyKixxUXF4fJkydj1apVCAwMfKQ5fH19oVaroVarDVwdEVEFkwmqa9euxQcffIDi4uI6b1NZWFgAAMrKyirbdDodOnXqhLKyMly+fPmhC7GuXbuGvn37ori4GCdOnEC3bt0MdxJERAYUFRWFuXPn4j//+Q8mTJjQ4PEZGRno1q0b1Go1nJ2dG6FCIjJ1JvNg0YoVK+Dr61tnSHV1dYVer8cvv/xS2SaKInr16oV79+4hIyPjoSE1Pj4ezs7OcHBwQG5uLkMqEclacHAwAgMDMWnSJPz8888NHt+1a1c4OTnVeZeKiOhRmURQTUtLw5UrV+rcQmrJkiXIyMhAUFAQevT4eyXzkCFDcPnyZZw/fx52dnY1jg0PD8eoUaMwfvx4pKam8j3YRPREWLVqFfz8/DBs2DBkZGQ0ePy8efOwf/9+iKLYCNURkakziVv/Pj4+uHTpEjIzMx/aJzs7G8899xwcHR1x5cqVyvZx48Zh3759SElJqfEKqSiKGDNmDA4cOIDo6Gi88847jXIORESNydPTE7/++isuXryI1q1b13ucKIp46qmnEBYWhrlz5zZihURkiow+qOp0Ojz11FPYsGEDAgICHtrPysoKZWVlKC0thbm5OQBg5syZ+Oqrr3Ds2DEMHDiw2piCggL07t0bGo0GP/74Izw8PBrtPIiIGpMoiujSpQsKCgqQk5MDlUpV77GTJk3CsWPH6rWjChFRQxj9rf/IyEgolcpaQ2r//v1RWlqKo0ePVobURYsWYcOGDdi5c2eNIfXUqVPo0KEDACAnJ4chlYieaAqFAufOnYO5uTm6d+8OvV5f77FRUVHIy8vDmTNnGrFCIjJFRh9U165dW+tq1s8//xynT5/G9OnTMXjwYABAdHQ0IiIi8NVXX+Hll1+ucc5BgwZh6NChuHTpEhwcuPE5ET35rKyskJqaCo1Gg0GDBtV7XNu2beHq6spFVURkcEZ96//48ePw8vKCRqOpMUzm5+ejZcuWaNmyJTQaDYC/9xZcvnx5jc9bBQQEYMuWLQgLC8NHH33U6OdARNTUsrKy4OrqCh8fH+zatateY7777jtMmDABhYWFD90ZhYiooYw6qHp6eqKoqAgpKSk1HrexsUFRURGKiopgZWWF+Ph4+Pj4IDg4GCtWrKjSV6vVYsCAAVCr1di9ezdGjRrVFKdARNQsTp06BU9PT8ycORNr166t1xhbW1vMnj0by5cvb+TqiMhUGO2tf61Wi5MnTyIiIqLG48OHD0dhYSH2798PKysrJCcnw9fXF1OmTKkWUtPS0tC+fXvcvHkTWVlZDKlEZPQ8PDzw3Xff4csvv0RkZGS9xkyZMgXr169v5MqIyJQYbVBdsGABbG1t4evrW+3Y1q1bkZCQgHHjxsHHxwdqtRqDBw/GiBEj8M0331TpGxcXh549e8LNzQ25ublo3759U50CEVGzGjt2LD7//HMsXLiw2mdjTZYtW4Zbt24hPj6+CaojIlNgtLf+7ezsMGPGjGqb/Gu1Wtja2uKZZ57BrVu3cOPGDXTq1Amurq5ISkqq0nfOnDmIjo5GYGBgnS8LICIyVgsXLsSyZctw4MABjBgxota+Hh4eKC8vR3JychNVR0TGzCiD6q5du+Dn54d79+5V2wuwRYsWuHPnDu7evQtRFPHcc8/BwcEBGRkZla9X1el0GDJkCJKTkxEbG/tI78AmIjImb7zxBrZu3Ypffvmlypv7HnTkyBEMGzYM+fn5sLe3b8IKicgYGWVQ7dGjB+zs7PDTTz9Vaffz88OOHTsQGxsLf39/ODs7o7y8HJcuXapcpZqTk4O+ffuirKwMJ06cQNeuXZvjFIiIZGf48OE4ceIEMjMza30MqlWrVnjllVcQExPThNURkTEyumdUb9y4gdTUVHz88cdV2g8cOIAdO3bA29sbEydORM+ePVFYWIj09PTKkPrDDz/AxcUFrVu3Rm5uLkMqEdF9Dh06hE6dOqF79+64e/fuQ/u9/fbbiIuLa8LKiMhYGd0V1YCAAMTHx+P69euVbXq9HkqlEiqVClqtFp6enjh79izUajXatm0LAAgNDUVoaCimTp1ar0UDRESmSKfT4fnnn4cgCMjKyoJSqayxj0qlwubNmzFp0qRmqJKIjIVRXVEVRRHffvst3n333Srt7du3hyRJyM7OxtixY5GcnIwzZ86gbdu2EEURo0ePRlhYGL744guGVCKiWiiVSqSmpkKr1aJv374QRbHGPi+++CLCwsKaoUIiMiZGdUV1w4YNmDVrFoqLi2Fubg4AmDFjBjZu3Ig1a9YgJSUFX3/9NY4fPw4PDw/k5+ejT58+yM/Px5EjR9CvX79mPgMioidDTk4OOnfuDC8vLxw6dKja8XPnzsHd3R3Z2dlwdHRshgqJyBgY1RXVyMhIjBw5sjKkJiYmYuPGjejbty9yc3OxceNG7N69Gx4eHkhMTISjoyPMzMxw9epVhlQiogZwdHREYmIifvzxR0ybNq3a8R49eqB9+/YIDg5u+uKIyGgYTVBVq9XIysqq3O9Ur9fDy8sLlpaWmDx5MpYtW4aNGzfC19cX0dHRlRv8X7x4kVuoEBE9And3d+zduxebN2/GokWLqh0PCgrCzp074eLiUrlolYioIYwmqAYFBcHJyQn+/v44ePAgOnXqhPLyckRGRiIoKAgrV65EQEAAXn/9dcyZMwfh4eHYtWtX5d6pRETUcKNGjcL69esRERGBdevWVbZHRUVh/vz50Ov1uHjxIkpLS5uxSiJ6UhnFM6p6vR4qlQpBQUFYvnx5ZfvUqVMRGxuLefPmYeHChejfvz8uXbqEffv2wdvbuxkrJiIyLhEREVi8eDHi4uIwb9485Ofno7CwsPK4QqFAeXl5M1ZIRE8i8+YuwBBWrlwJMzOzatukbN68GVOnTsXkyZPRrl07qFQqXLp0qXJLKiIiMoyFCxciMzMTr732WmVbQEAANm3aBACVaweIiBrCKO57r169Gn5+fjh58mS1Y926dYO7uzvc3d1x9epVhlQiokZw5coVbNu2rUqbr68vIiMjAaDGbayIiOryRP2Jq77zByLSDyO9OA9asRg2Cmu0LrbG76IOUVFRVV7p17JlS/j5+eHDDz9EcHBwtTdVERGR4aSlpaGsrKxK27p163D48GGYmZnh5MmTUGtuI/LQKVz4XQNtqQ42lkp0+UcrLBzlAeeWds1UORHJ2RPxjOr32Rn4d9ZeZFqpAUiQAEACIPz5LwDHgvbIifgO+tOpcHZ2RosWLZCSkoK4uDj4+/s3W+1ERKZCFEVER0dj+fLluH79euVzqTvPXkTogWO4+EduRb/7vnYUggAAcHm2HRb7eOEV907NUjsRyZPsg+r7p/dgnW4PREhQlFlAkIRqfSRBgmihgyAKsPm/u1BsTYAoikhMTETnzp2boWoiItOWlpaGXbt2QfPCAGw4dQIVXzVmEIQaPsMlCUA5BEHAmx6D8Kn/sCavl4jkSdZB9f3Te7CubDdQZgZBNKuzv6Qoh2iuR6u95cj69xfct4+IqBkFfnsYG04dhyQpIAh1L4mQJBGCIDGsElEl2T6j+n12Btbp9gD6v0NqWchBiKeuAKV6oK0tLLdMqTJGEM2g0AP5L5vhh98vY5xT1+YonYjI5O08e/HPK6lVQ6p2dRigK/m7o8IMNkFLAACCoIAkidhw6gSGujjilZ58DIDI1Mk2qP47ay9EKwlm919Jbf00FH7dIZ7IBspq3o9PEM1QDh1C1PsYVImImknogWOQJAmCUP1umLlrL1j5jq9xXEVY1WPJgWMMqkQkz+2p1Hf+QKaVGooyiyrtFrMGweKtgRBsLGsdryizwAXrTGTdKWjMMomITFpycjJWrFhRbespteb2nwun6n5kq2ZmyMzPRdbN249dIxE92WQZVCPSDwOQalw4VR8V4ySEpx82aF1ERPS3lStX4l//+hesra0RGhpaGVgjD50CgBoXTgGAPj0F2pULoP0sBKXJP1c7/te4pX/OQ0SmS5a3/tOL8yBZVuw+9agkAD9cSMS4FbGGKouIiO6TlJQEANDpdAgJCUFoaCgCAwNxoV0viJIERQ0f4pYv+sLsuRcAcyV0R/ej7Mh+mLVqA3NH5yr9REnChRs3m+I0iEjGZBlUtWJx5f6oj0wCSsz0yM7ONkRJRET0gMLCwio/S5KErKwsaB26PXSMRfe+lf+3GuUHrTodZSknqwVVALhXWmq4YonoiSTLoGqjsH68y6kAIACd2znjVMpnBqmJiIiqmjhxIrZt2wZBEODv74/169fD1tYWg6I213+SWj7rn7asfT0CERk/WQZVV+u2+FVMrdYu6vSATg+IEiBKELUlgNIcCmX10xAAuKraNkG1RESmKSAgAJaWloiOjoatrW1le5d/tML5G1eq9Rfv3EJZ+llY9PIAzM2h+/EHoLgIFj36VuurEAR0ad2yUesnIvmT5Yb/6jt/wC31Q6DMvMqCKl3gDkjnrlfpK/RoA+Wn46q0SYIEWOiR7rYMzs/YN0nNRERUQa25jd4fr/lzD9W/P8PFP35H0ea1gL6sosFCCYsBQ2HpMbTK+IptrUScnfcunFvaNWHlRCQ3sryi6vLMs3ihxAUXrDJhplNWtj8YSB9GtCiDa3FnhlQiombg0soOnZ5tB3X+Ndz/NaN49h+wCQytxwzleMGhPUMqEclzeyoACHV+GQoIkBQ1b+z/MJKiHAooEOLyUiNVRkREdfm3jxcEQYAkiXV3vk/Fa1QFLPLxaqTKiOhJItug6texK95WjgEsyusdViVFOWBRjreVL/OtVEREzegV905402MQBEGqd1itCKkS3vQYxLdSEREAmT6jer/3T+/BOt0eiJCgKLOo8SUAkiBBtCiDAgq8rXwZq/uNaYZKiYjoQYHfHsaGUydQ8VVjVuNLACqOlUMQBLzpMQif+g9r8jqJSJ5kH1QBYMeVDISo9+GCdSYAqWKLVQmA8NfOJgK6FL+AEJeXeCWViEhmdv56EUsOHENmfi6Ais38/6L4M7i+4NAOi3y8eCWViKp4IoLqX7LuFCA8/TDSi/JwTyrG04I1XFVt8ZHrMC6cIiKSuaybt7H00ClcuHET90pL8bSlJbq0bokFIz24cIqIavREBVUiIiIiMh2yXUxFRERERKaNQWSLXG8AAAENSURBVJWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGSJQZWIiIiIZIlBlYiIiIhkiUGViIiIiGTp/wHIMFiCTrCFMwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# colors \n",
    "colors = []\n",
    "for i in range(num_companies):\n",
    "    colors.append('#%06X' % randint(0, 0xFFFFFF))\n",
    "\n",
    "#for the legend we need inverse dictionary\n",
    "inv_map = {v: k for k, v in all_stocks.items()}\n",
    "nodeColorDict = dict(zip(inv_map, colors))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(8,8))\n",
    "\n",
    "if nx.check_planarity(G, counterexample=False)[0] == True: # if planar\n",
    "    pos = nx.planar_layout(G)\n",
    "    nx.draw(G, pos=pos, with_labels=True)\n",
    "else: # if not planar\n",
    "    pos = nx.draw_networkx(G)\n",
    "    \n",
    "for nt in inv_map:\n",
    "    nlist = inv_map[nt]\n",
    "    ncolor = nodeColorDict[nt] # node color\n",
    "    nx.draw_networkx_nodes(G, \n",
    "                           pos=pos,\n",
    "                           nodelist= [nlist],\n",
    "                           ax=ax, \n",
    "                           node_color=ncolor,\n",
    "                           label='{num}: {nt}'.format(nt=nt, num=nlist),\n",
    "                           alpha=0.8)\n",
    "h2 = nx.draw_networkx_edges(G, pos=pos)\n",
    "h3 = nx.draw_networkx_labels(G, pos=pos)\n",
    "ax.legend(scatterpoints=1, bbox_to_anchor=(1,1.025),loc=\"upper left\", labelspacing=2)\n",
    "plt.title(\"Granger causation Graph of Stock Market data\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(7, 190, 12)\n"
    }
   ],
   "source": [
    "XX = loadmat(\"market.mat\")['DI']\n",
    "\n",
    "print(XX.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Resampling Data <a name=\"22\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(12, 190, 7)\n"
    }
   ],
   "source": [
    "XX = loadmat(\"market.mat\")['DI']\n",
    "\n",
    "XX = np.transpose(XX, axes=[2, 1, 0])\n",
    "\n",
    "print(XX.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def det_cov(y, y_to, z, z_to, x=-1, x_to=0):\n",
    "    \n",
    "    data = XX[y, :, :y_to+1].T # note: we write y_to+1 otherwise the :y_to would stop to y_to-1\n",
    "    if x >= 0:\n",
    "        data = np.concatenate((XX[x, :, :x_to+1].T, data))\n",
    "        \n",
    "    Z = np.transpose(XX[sorted(z), :, :z_to+1], axes=[0, 2, 1]).reshape(-1, n_sample)\n",
    "    data = np.concatenate((data, Z))\n",
    "\n",
    "    return det(np.cov(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a method that gets the log part of the equation that for convenience we rename $log \\frac{A \\cdot B}{C \\cdot D}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_part(x, y, z, t):\n",
    "    A = det_cov(y, t, z, t-1) * 10**50\n",
    "    B = det_cov(y, t-1, z, t-1, x, t-1) * 10**50\n",
    "    C = det_cov(y, t-1, z, t-1) * 10**50\n",
    "    D = det_cov(y, t, z, t-1, x, t-1) * 10**50\n",
    "    #print(f\"A {A}, B {B}, C {C}, D {D}\") \n",
    "    return np.log(A*B / (C*D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a method that computes the direct information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direct_information(x, y, z):\n",
    "    return 0.5 * sum(log_part(x, y, z, t) for t in range(1, T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALL_IDX = set(range(num_companies))\n",
    "G = nx.DiGraph()\n",
    "G.add_nodes_from(list(all_stocks.keys()))\n",
    "\n",
    "di_matrix = np.zeros((num_companies, num_companies))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute learn the causal structure of the graph. We add an edge if $I(X \\to Y || Z) > 0.6$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "I(1->2||{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.28222781013114623\nI(1->3||{1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.25001480525211295\nI(1->4||{1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.289779713169844\nI(1->5||{1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.4662251983692426\nI(1->6||{1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.4994128736397288\nI(1->7||{1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.6374652463552616\nI(1->8||{1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.6782549359085784\nI(1->9||{1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.5520297393663178\nI(1->10||{1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.38308353450587107\nI(1->11||{1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.3804028154372441\nI(1->12||{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.17506559830107105\nI(2->1||{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.3369562400403786\nI(2->3||{3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.2505020781345785\nI(2->4||{2, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.42764069939660976\nI(2->5||{2, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.3191155932698274\nI(2->6||{2, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.25526359265529114\nI(2->7||{2, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.08660567130507539\nI(2->8||{2, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.29054143088287787\nI(2->9||{2, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.2434451031489452\nI(2->10||{2, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.1913007712909507\nI(2->11||{2, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.17433474915377561\nI(2->12||{2, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.15930556659344655\nI(3->1||{1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.21173330991317077\nI(3->2||{3, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.38404674274817474\nI(3->4||{1, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.16129766531771897\nI(3->5||{1, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.5707090302090754\nI(3->6||{1, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.1426508632892905\nI(3->7||{1, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.23366248767317246\nI(3->8||{1, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.39982249109685514\nI(3->9||{1, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.24913003975123088\nI(3->10||{1, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.43640359715650084\nI(3->11||{1, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.3451359379252906\nI(3->12||{1, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.2761357396212384\nI(4->1||{1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.20028576283262328\nI(4->2||{2, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.18118151192947962\nI(4->3||{1, 4, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.3183653499961384\nI(4->5||{1, 2, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.27679109380127276\nI(4->6||{1, 2, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.15862577592598875\nI(4->7||{1, 2, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.238342184437849\nI(4->8||{1, 2, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.30862762670625304\nI(4->9||{1, 2, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.42021784999124867\nI(4->10||{1, 2, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.25240934517649244\nI(4->11||{1, 2, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.20675709151689473\nI(4->12||{1, 2, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.2774576146093891\nI(5->1||{1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.3437874595322749\nI(5->2||{2, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.2742897571726648\nI(5->3||{1, 3, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.26614142415622105\nI(5->4||{1, 2, 5, 6, 7, 8, 9, 10, 11, 12}) = 0.3364382417744441\nI(5->6||{1, 2, 3, 6, 7, 8, 9, 10, 11, 12}) = 0.264824799692955\nI(5->7||{1, 2, 3, 5, 7, 8, 9, 10, 11, 12}) = 0.31651898782230087\nI(5->8||{1, 2, 3, 5, 6, 8, 9, 10, 11, 12}) = 0.2990187287804947\nI(5->9||{1, 2, 3, 5, 6, 7, 9, 10, 11, 12}) = 0.33741943305962296\nI(5->10||{1, 2, 3, 5, 6, 7, 8, 10, 11, 12}) = 0.3986290611425678\nI(5->11||{1, 2, 3, 5, 6, 7, 8, 9, 11, 12}) = 0.3648528663709845\nI(5->12||{1, 2, 3, 5, 6, 7, 8, 9, 10, 12}) = 0.2471263054673164\nI(6->1||{1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.3499771543561786\nI(6->2||{2, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.4153761263809883\nI(6->3||{1, 3, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.2004910806911112\nI(6->4||{1, 2, 4, 6, 7, 8, 9, 10, 11, 12}) = 0.34020750865703003\nI(6->5||{1, 2, 3, 6, 7, 8, 9, 10, 11, 12}) = 0.30044045609393955\nI(6->7||{1, 2, 3, 4, 7, 8, 9, 10, 11, 12}) = 0.19748385708225705\nI(6->8||{1, 2, 3, 4, 6, 8, 9, 10, 11, 12}) = 0.6353074403719894\nI(6->9||{1, 2, 3, 4, 6, 7, 9, 10, 11, 12}) = 0.22372199630940698\nI(6->10||{1, 2, 3, 4, 6, 7, 8, 10, 11, 12}) = 0.4245407021329696\nI(6->11||{1, 2, 3, 4, 6, 7, 8, 9, 11, 12}) = 0.24797238475311886\nI(6->12||{1, 2, 3, 4, 6, 7, 8, 9, 10, 12}) = 0.32717430878918136\nI(7->1||{1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.1481541301434593\nI(7->2||{2, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.3366040216408168\nI(7->3||{1, 3, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.3126506329448736\nI(7->4||{1, 2, 4, 5, 7, 8, 9, 10, 11, 12}) = 0.3229471134689934\nI(7->5||{1, 2, 3, 5, 7, 8, 9, 10, 11, 12}) = 0.13173647342580924\nI(7->6||{1, 2, 3, 4, 7, 8, 9, 10, 11, 12}) = 0.2978305392417704\nI(7->8||{1, 2, 3, 4, 5, 8, 9, 10, 11, 12}) = 0.17909464861427574\nI(7->9||{1, 2, 3, 4, 5, 7, 9, 10, 11, 12}) = 0.19822788060540392\nI(7->10||{1, 2, 3, 4, 5, 7, 8, 10, 11, 12}) = 0.21373636933296844\nI(7->11||{1, 2, 3, 4, 5, 7, 8, 9, 11, 12}) = 0.3504590521976382\nI(7->12||{1, 2, 3, 4, 5, 7, 8, 9, 10, 12}) = 0.24534904079547426\nI(8->1||{1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.4802957402625416\nI(8->2||{2, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.5482169578107634\nI(8->3||{1, 3, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.3398709056852989\nI(8->4||{1, 2, 4, 5, 6, 8, 9, 10, 11, 12}) = 0.35998916922066804\nI(8->5||{1, 2, 3, 5, 6, 8, 9, 10, 11, 12}) = 0.5420271016206872\nI(8->6||{1, 2, 3, 4, 6, 8, 9, 10, 11, 12}) = 0.2101260662229043\nI(8->7||{1, 2, 3, 4, 5, 8, 9, 10, 11, 12}) = 0.36802304309869965\nI(8->9||{1, 2, 3, 4, 5, 6, 9, 10, 11, 12}) = 0.2047594485708149\nI(8->10||{1, 2, 3, 4, 5, 6, 8, 10, 11, 12}) = 0.28430185318484974\nI(8->11||{1, 2, 3, 4, 5, 6, 8, 9, 11, 12}) = 0.3815341967748424\nI(8->12||{1, 2, 3, 4, 5, 6, 8, 9, 10, 12}) = 0.40815771100871384\nI(9->1||{1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.43629977023952576\nI(9->2||{2, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.34185275565481643\nI(9->3||{1, 3, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.24986096882308356\nI(9->4||{1, 2, 4, 5, 6, 7, 9, 10, 11, 12}) = 0.09962783594450282\nI(9->5||{1, 2, 3, 5, 6, 7, 9, 10, 11, 12}) = 0.31873530662931865\nI(9->6||{1, 2, 3, 4, 6, 7, 9, 10, 11, 12}) = 0.1314946233003314\nI(9->7||{1, 2, 3, 4, 5, 7, 9, 10, 11, 12}) = 0.3176928879747025\nI(9->8||{1, 2, 3, 4, 5, 6, 9, 10, 11, 12}) = 0.19858516009069768\nI(9->10||{1, 2, 3, 4, 5, 6, 7, 10, 11, 12}) = 0.26848412206976435\nI(9->11||{1, 2, 3, 4, 5, 6, 7, 9, 11, 12}) = 0.2729240283284304\nI(9->12||{1, 2, 3, 4, 5, 6, 7, 9, 10, 12}) = 0.25678255099926833\nI(10->1||{1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.29048866954554686\nI(10->2||{2, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.41240692666565315\nI(10->3||{1, 3, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.4733143388235993\nI(10->4||{1, 2, 4, 5, 6, 7, 8, 10, 11, 12}) = 0.47691673269407225\nI(10->5||{1, 2, 3, 5, 6, 7, 8, 10, 11, 12}) = 0.30338111470370427\nI(10->6||{1, 2, 3, 4, 6, 7, 8, 10, 11, 12}) = 0.27016899758699625\nI(10->7||{1, 2, 3, 4, 5, 7, 8, 10, 11, 12}) = 0.2172165139620362\nI(10->8||{1, 2, 3, 4, 5, 6, 8, 10, 11, 12}) = 0.42317877349124144\nI(10->9||{1, 2, 3, 4, 5, 6, 7, 10, 11, 12}) = 0.319241935822337\nI(10->11||{1, 2, 3, 4, 5, 6, 7, 8, 11, 12}) = 0.1841270069950075\nI(10->12||{1, 2, 3, 4, 5, 6, 7, 8, 10, 12}) = 0.2124111440094603\nI(11->1||{1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.23038157582806665\nI(11->2||{2, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.2595557875569462\nI(11->3||{1, 3, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.15201884170876848\nI(11->4||{1, 2, 4, 5, 6, 7, 8, 9, 11, 12}) = 0.1638231501862747\nI(11->5||{1, 2, 3, 5, 6, 7, 8, 9, 11, 12}) = 0.24397986519084724\nI(11->6||{1, 2, 3, 4, 6, 7, 8, 9, 11, 12}) = 0.2421836331067228\nI(11->7||{1, 2, 3, 4, 5, 7, 8, 9, 11, 12}) = 0.13614083125783588\nI(11->8||{1, 2, 3, 4, 5, 6, 8, 9, 11, 12}) = 0.12083837133824554\nI(11->9||{1, 2, 3, 4, 5, 6, 7, 9, 11, 12}) = 0.08679172359751722\nI(11->10||{1, 2, 3, 4, 5, 6, 7, 8, 11, 12}) = 0.16873180516253133\nI(11->12||{1, 2, 3, 4, 5, 6, 7, 8, 9, 12}) = 0.18431818665578176\nI(12->1||{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.11099503040279066\nI(12->2||{2, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.1451719078989247\nI(12->3||{1, 3, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.22995965610916527\nI(12->4||{1, 2, 4, 5, 6, 7, 8, 9, 10, 12}) = 0.23958642013039136\nI(12->5||{1, 2, 3, 5, 6, 7, 8, 9, 10, 12}) = 0.10684640938911869\nI(12->6||{1, 2, 3, 4, 6, 7, 8, 9, 10, 12}) = 0.1655735012634574\nI(12->7||{1, 2, 3, 4, 5, 7, 8, 9, 10, 12}) = 0.17097798009839923\nI(12->8||{1, 2, 3, 4, 5, 6, 8, 9, 10, 12}) = 0.15503788816506853\nI(12->9||{1, 2, 3, 4, 5, 6, 7, 9, 10, 12}) = 0.23699507817760856\nI(12->10||{1, 2, 3, 4, 5, 6, 7, 8, 10, 12}) = 0.24618710561604984\nI(12->11||{1, 2, 3, 4, 5, 6, 7, 8, 9, 12}) = 0.2919907155840491\n"
    }
   ],
   "source": [
    "for x in ALL_IDX:\n",
    "    for y in ALL_IDX:\n",
    "        if x == y:\n",
    "            continue\n",
    "        di = direct_information(x, y, ALL_IDX - {x, y})\n",
    "        di_matrix[x, y] = di\n",
    "        print(f\"I({x+1}->{y+1}||{set([x+1 for x in ALL_IDX]) - {x, y}}) = {di}\")\n",
    "        if di > threshold:\n",
    "            #print(\"add an edge between \", x, \"and\", y)\n",
    "            G.add_edge(x+1, y+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[0.    , 0.2822, 0.25  , 0.2898, 0.4662, 0.4994, 0.6375, 0.6783, 0.552 , 0.3831, 0.3804,\n        0.1751],\n       [0.337 , 0.    , 0.2505, 0.4276, 0.3191, 0.2553, 0.0866, 0.2905, 0.2434, 0.1913, 0.1743,\n        0.1593],\n       [0.2117, 0.384 , 0.    , 0.1613, 0.5707, 0.1427, 0.2337, 0.3998, 0.2491, 0.4364, 0.3451,\n        0.2761],\n       [0.2003, 0.1812, 0.3184, 0.    , 0.2768, 0.1586, 0.2383, 0.3086, 0.4202, 0.2524, 0.2068,\n        0.2775],\n       [0.3438, 0.2743, 0.2661, 0.3364, 0.    , 0.2648, 0.3165, 0.299 , 0.3374, 0.3986, 0.3649,\n        0.2471],\n       [0.35  , 0.4154, 0.2005, 0.3402, 0.3004, 0.    , 0.1975, 0.6353, 0.2237, 0.4245, 0.248 ,\n        0.3272],\n       [0.1482, 0.3366, 0.3127, 0.3229, 0.1317, 0.2978, 0.    , 0.1791, 0.1982, 0.2137, 0.3505,\n        0.2453],\n       [0.4803, 0.5482, 0.3399, 0.36  , 0.542 , 0.2101, 0.368 , 0.    , 0.2048, 0.2843, 0.3815,\n        0.4082],\n       [0.4363, 0.3419, 0.2499, 0.0996, 0.3187, 0.1315, 0.3177, 0.1986, 0.    , 0.2685, 0.2729,\n        0.2568],\n       [0.2905, 0.4124, 0.4733, 0.4769, 0.3034, 0.2702, 0.2172, 0.4232, 0.3192, 0.    , 0.1841,\n        0.2124],\n       [0.2304, 0.2596, 0.152 , 0.1638, 0.244 , 0.2422, 0.1361, 0.1208, 0.0868, 0.1687, 0.    ,\n        0.1843],\n       [0.111 , 0.1452, 0.23  , 0.2396, 0.1068, 0.1656, 0.171 , 0.155 , 0.237 , 0.2462, 0.292 ,\n        0.    ]])"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "di_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"matrix.csv\", di_matrix, delimiter=\",\",fmt='%.3f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# colors \n",
    "colors = []\n",
    "for i in range(num_companies):\n",
    "    colors.append('#%06X' % randint(0, 0xFFFFFF))\n",
    "\n",
    "#for the legend we need inverse dictionary\n",
    "inv_map = {v: k for k, v in all_stocks.items()}\n",
    "nodeColorDict = dict(zip(inv_map, colors))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(8,8))\n",
    "\n",
    "if nx.check_planarity(G, counterexample=False): # if planar\n",
    "    pos = nx.planar_layout(G)\n",
    "    nx.draw(G, pos=pos, with_labels=True)\n",
    "    \n",
    "else: # if not planar\n",
    "    pos = nx.draw_networkx(G)\n",
    "for nt in inv_map:\n",
    "    nlist = inv_map[nt]\n",
    "    ncolor = nodeColorDict[nt] # node color\n",
    "    nx.draw_networkx_nodes(G, \n",
    "                           pos=pos,\n",
    "                           nodelist= [nlist],\n",
    "                           ax=ax, \n",
    "                           node_color=ncolor,\n",
    "                           label='{num}: {nt}'.format(nt=nt, num=nlist),\n",
    "                           alpha=0.8)\n",
    "h2 = nx.draw_networkx_edges(G, pos=pos)\n",
    "h3 = nx.draw_networkx_labels(G, pos=pos)\n",
    "ax.legend(scatterpoints=1, bbox_to_anchor=(1,1.025),loc=\"upper left\", labelspacing=2)\n",
    "plt.title(\"Granger causation Graph of Stock Market data\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}