{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58577e7f",
   "metadata": {},
   "source": [
    "# Comparing the CSI-RS results with Matlab\n",
    "Compare the results of this notebook with the Matlab file ``CSI-RS.mlx`` in\n",
    "the ``MatlabFiles`` directory.\n",
    "\n",
    "The \".mat\" files in the ``MatlabFiles`` directory were created by running the ``CSI-RS.mlx`` file. If you want to recreate these files, follow the instructions in the Matlab file. [Here](MatlabFiles/CSI-RS.html) is the execution results of this code in Matlab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f93cec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io\n",
    "\n",
    "matlabPath = \"./MatlabFiles\"\n",
    "\n",
    "from neoradium import Carrier, CsiRsConfig, CsiRsSet, CdlChannel, Grid, AntennaPanel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f595bebb",
   "metadata": {},
   "source": [
    "## Carrier Configuration\n",
    "Create an instance of `Carrier` object. We use 15KHz subcarrier spacing, with a single Bandwidth Part starting at Resource Block 0, and 25 total Resource Blocks. We then call the `print` method to show the carrier information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49a0c95c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Carrier Properties:\n",
      "  startRb: 0\n",
      "  numRbs: 25\n",
      "  Cell Id: 1\n",
      "  Active Bandwidth Part: 0\n",
      "  Bandwidth Parts: 1\n",
      "  Bandwidth Part 0:\n",
      "    Resource Blocks: 25 RBs starting at 0 (300 subcarriers)\n",
      "    Subcarrier Spacing: 15 KHz\n",
      "    CP Type: normal\n",
      "    bandwidth: 4500000 Hz\n",
      "    symbolsPerSlot: 14\n",
      "    slotsPerSubFrame: 1\n",
      "    nFFT: 2048\n",
      "\n"
     ]
    }
   ],
   "source": [
    "carrier = Carrier(startRb=0, numRbs=25, spacing=15, nFFT=2048)\n",
    "\n",
    "carrier.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1836a1c3",
   "metadata": {},
   "source": [
    "## CSI-RS Configuration\n",
    "Now we create a CSI-RS condiguration object ``CsiRsConfig`` with two CSI-RS resource sets: One for Non-Zero Power (NZP) and one for Zero-Power CSI-RS resoueces. The following code then creates one CSI-RS resource in each one of the two CSI-RS resource sets.\n",
    "\n",
    "The ``print`` function is then called to print all the configuration properties in the ``CsiRsConfig`` object. \n",
    "\n",
    "Please note that to test the timing functionality of CSI-RS implementation, we are setting the ``period`` to 5, and ``offset`` to one. This means that the CSI-RS allocation starts at slot number one and continues every other 5 slots. We also set the current slot number of our ``Carrier`` object to 1 so that the first time a grid is populated the results will have CSI-RS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b255b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CSI-RS Configuration: (2 Resource Sets)\n",
      "  CSI-RS Resource Set:(1 NZP Resources)\n",
      "    Resource Set ID: 0\n",
      "    Resource Type: periodic\n",
      "    Resource Blocks: 25 RBs starting at 0\n",
      "    Slot Period: 5\n",
      "    Bandwidth Part:\n",
      "      Resource Blocks: 25 RBs starting at 0 (300 subcarriers)\n",
      "      Subcarrier Spacing: 15 KHz\n",
      "      CP Type: normal\n",
      "      bandwidth: 4500000 Hz\n",
      "      symbolsPerSlot: 14\n",
      "      slotsPerSubFrame: 1\n",
      "      nFFT: 2048\n",
      "    CSI-RS:\n",
      "      resourceId: 0\n",
      "      numPorts: 2\n",
      "      cdmSize: 2 (fd-CDM2)\n",
      "      density: 1\n",
      "      RE Indexes: 6\n",
      "      Symbol Indexes: 1\n",
      "      Table Row: 3\n",
      "      Slot Offset: 1\n",
      "      Power: 0 db\n",
      "      scramblingID: 0\n",
      "  CSI-RS Resource Set:(1 ZP Resources)\n",
      "    Resource Set ID: 0\n",
      "    Resource Type: periodic\n",
      "    Resource Blocks: 25 RBs starting at 0\n",
      "    Slot Period: 5\n",
      "    Bandwidth Part:\n",
      "      Resource Blocks: 25 RBs starting at 0 (300 subcarriers)\n",
      "      Subcarrier Spacing: 15 KHz\n",
      "      CP Type: normal\n",
      "      bandwidth: 4500000 Hz\n",
      "      symbolsPerSlot: 14\n",
      "      slotsPerSubFrame: 1\n",
      "      nFFT: 2048\n",
      "    CSI-RS:\n",
      "      resourceId: 0\n",
      "      numPorts: 4\n",
      "      cdmSize: 2 (fd-CDM2)\n",
      "      density: 1\n",
      "      RE Indexes: 4\n",
      "      Symbol Indexes: 6\n",
      "      Table Row: 5\n",
      "      Slot Offset: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set current slot number in the carrier object to 1. This is to test the CSI-RS 'offset' functionality.\n",
    "# Note that the CSI-RS power `powerDb` is 0 db by default.\n",
    "bwp = carrier.bwps[0]\n",
    "carrier.slotNo = 1\n",
    "csiRsConfig = CsiRsConfig([CsiRsSet(\"NZP\", bwp, symbols=[1], numPorts=2, freqMap='001000', period=5, offset=1),\n",
    "                           CsiRsSet(\"ZP\", bwp, symbols=[6], numPorts=4, freqMap='000100', period=5, offset=1)])\n",
    "csiRsConfig.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dcc1544",
   "metadata": {},
   "source": [
    "## Create a resource grid and populate it with CSI-RS\n",
    "Here we use the ``createGrid`` method of the bandwidth part object ``bwp`` to create a resource grid with the number of ports set based on the CSI-RS configuration.\n",
    "\n",
    "Then we can use the ``populateGrid`` method of the ``CsiRsConfig`` object to poulate the grid with CSI-RS information. The ``getStats`` function of the ``Grid`` object returns a dictionary containing some statistics about the resource grid allocation.\n",
    "\n",
    "After printing the statistics, the ``drawMap`` method is called to visualize the allocation of CSI-RS resources for all 4 ports of the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9669a30",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resource Grid Properties:\n",
      "  startRb: 0\n",
      "  numRbs: 25\n",
      "  numSlots: 1\n",
      "  Data Contents: DATA\n",
      "  Size: 16800\n",
      "  Shape: (4, 14, 300)\n",
      "  Bandwidth Part:\n",
      "    Resource Blocks: 25 RBs starting at 0 (300 subcarriers)\n",
      "    Subcarrier Spacing: 15 KHz\n",
      "    CP Type: normal\n",
      "    bandwidth: 4500000 Hz\n",
      "    symbolsPerSlot: 14\n",
      "    slotsPerSubFrame: 1\n",
      "    nFFT: 2048\n",
      "\n",
      "Grid Allocation Stats:\n",
      "  GridSize: 16800\n",
      "  UNASSIGNED: 16500\n",
      "  CSIRS_NZP: 100\n",
      "  CSIRS_ZP: 200\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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awM/PD+PHj0dCQkKRlrthwwY8fPgQLi4u+V4c+NFHH8HZ2RnZ2dlYv369Jf6cQrVr1w41a9ZETEwM9u/fDwAQQsjXJ4waNcrsdVSqVAmNGjWCEAJ//PFHvu0GDRqEOnXq6I2vUKGCfPfAmjVrzM7zOO0yn3vuOdSvX19vuoeHByZMmAAA2LlzJ1JSUgwuZ9iwYQgICDApg7azyfv37yMtLc2kZZjD1dUV7733nsFpU6dOBQDcvXsXe/bskcfv378f0dHRAID//e9/cHJyskgWe3t79OrVCwBw+PDhfNvVq1cPffv2LXR5vXr1QpMmTYqcIyMjQ76+auLEifm2Gz58OADgzz//zHe/MGbMmHwv/tf+72/fvm2xu8Py9k9UpkyZfNvlnWZOn0b50b63AgIC8PLLLxtsM2PGDABAUlKSzvZl64QQuHnzJgDAx8enxNdf0H7Xx8cHzz77LADD19Zs2rQJSUlJcHV1xbBhw3SmaTtEfOONN6BWqw2uu1mzZqhXrx6ysrLkz5XHmfq+NMTb2xu+vr7w8vKSxwUFBeHrr79Gp06dCp2/fPnyAFDoNVfFSdGFkYODAz7++GPcuHEDy5Ytw8svv4xGjRrJO/3ExET873//Q/369XH8+HGjl3vy5EkAQIsWLeDp6WmwjZeXF5o3b67TviRoL8L++eefATy6myU2NhZ16tRB69atjVpGbm4uVq5ciX79+qFy5cpwdXXVuRhT+1oVdBFc165dC512584d+cPYErKysnD27FkAQEhISL7tunfvDuDR33n69GmDbdq1a2dyjpYtW6J8+fK4efMmWrVqhW+//RYXL17M9wJGS2vevHm+22WNGjXkgi/vdqnd2fr5+cnbbVEcOnQII0aMQO3ateHu7q6zvXz22WcACt5ejH29Tf2/nDp1Sr7guUePHvDz8zM41KtXT54nNja2yBm6desGFxcXnDlzBh06dMBPP/1k0W3cmrTbS5cuXWBnZ3jXX6dOHVSqVEmnfWmQnJyMhw8fAnj0wV0czNnvar9sbtu2Ta+g/+GHHwAAzz//vM4TAnJycuROgadNm5bve8LPzw+XLl0CYNp7oqhWrlyJW7du4e7du9BoNNiwYQNcXFzwzDPPoG/fvgXe/AP89/+5ffu2xTIVlaILIy21Wo0XX3wRP/zwA6KiopCSkoI9e/bI31CTkpLw7LPPGn2nSGJiIgDIO4D8aD+AtO1LwvDhw2Fvb4+NGzdCo9HId6lpC6bCpKenIyQkBKGhodi2bRvi4uKQm5srV/Hau2wAFHg0pKDXJu80S742d+/elb+lF7T+vEeC8lt/hQoVTM5RtmxZrFq1Cj4+Pjh//jzeeust1KlTB15eXujXrx+WL1+O7Oxsk5dfmMK2S+30vH/7rVu3ADz6ZlZUEydORMeOHbFkyRJcunQJGRkZ8PLykrcXNzc3AAVvL8a+3qb+X/J+e0xISChw0MqvB92CMlSrVg0//vgj3N3dceTIEbz88suoWrUqKlSogMGDB2PLli1FLpA9PDwKzfT4tLzzWIqS93vFLe9ng7Ozs8WXb+5+t2PHjqhbty6ys7N17ky+evWqfJTntdde05nn7t27cn8/9+7dK/A9od1fmfKeMIeHhwcGDhyIo0ePIjAwEDt27Ci0R3HtHeZFvfPTkmyiMHqci4sLQkJCsHXrVoSFhQF4VIXv2rXLysnM5+/vj549e+LBgwdYsGABNm3aBHt7e71DqPmZOXMm9u/fD1dXV/zvf/9DbGwsMjIycOfOHdy6dQu3bt2S+5soqSMg1mDurb8hISGIjo7G0qVLERYWhho1aiAlJQXbtm3DsGHD0KRJE9y4ccNCac1n6uNh9uzZIx8RevPNN3Hu3DlkZmbi7t278vbyzjvvACh4ezH29Tb1/5L3tNaDBw8gHl0fWeCgvf29qBlCQ0MRGxuLBQsWYPDgwQgMDMTt27exdu1aDBgwAJ06dSrSI4j8/f3lnwvaZvJOyzsPma9cuXLyz/fu3bP48i2x39UeNfrxxx/lNtqf69evr9f3T973xM6dO416T+RXlBR3Vwlly5aVv9xrj4DlR9sNSt7/WUmzycIor1dffVX+WXu4sDDa6rigUwN5pxdXNZ0f7QY0ZcoUPHjwAL179za6B9nVq1cDeHQtyrhx4+RetfPSHl0oiLE7cEu+Nt7e3vIbtKD/Td5pxfm/cXNzw7Bhw7B48WJcvnwZ//77L2bPng0XFxf5SFJeDg6P+kst6JtOftdE5VVYwaWdnvdv124f+R0qz492e+nZsyfmzZuH+vXr6+0kjdleilve7b+of6MpvL298dprr2H16tW4fv06rl69ig8++AAqlQqHDh0q0nO0ypUrJ+f/66+/8m2nnWZnZ2fw+j5zKX2/V5y0/ZAB0Ot/zBIssd8dPnw4ypQpg2vXrmHfvn3Izs6W+3d7/GgR8Gi70u5zSuI9YS7tkUqNRlPg0Ujt/8ca14Jp2XxhlPciSmMPkea9dii/D6rk5GSda5Hy0p6fL64jLv369UO5cuWQlZUFoGgXXcfFxQFAvhfSxcTE4OrVq4UuJ7+L9PJO8/b2LrSzyaJwcnJCw4YNAaDAR0L89ttvAB79H5o2bWqx9RemUqVKmDBhAt59910A0Ls4VXuxofZ/YIgxT/M+efJkvufhr169Kn9w5b2WqG3btgAe7XyLcm1IYduLEAL79u0zennFpUWLFvK1hdoe8UtStWrVEBERIXdAV9QLk7XXxe3evTvf/Yb2iHf79u2L3GGtMfsk7fayf/9+5ObmGmxz8eJFufAubY/MqFu3LgAU2EGsqSyx31Wr1Rg6dCiARxdha683cnV1NdhTt6OjI1q2bAnAOu+Josr7uhf05APtNX3F8eXAWIotjKKjo3H58uVC22nv2AJg9Ifks88+CwcHB2RkZGD27NkG23z66afIzMyEo6OjfMeAlvabR3JyslHrKyonJyfMnTsX7777LiZMmICnn37a6Hm1dyb8+eefBqcb20X/unXrDB6BS0pKwvfffw8ABnvwNZe2R9r169cb/Hadmpoqn/p56qmn8r0TwxyFPadH+6H1+AWs2mf2nThxwmBxdOHCBaOe//TgwQPMmTPH4LRPPvkEwKOiVPthCzy6oLZq1aoAgHfeeUcuqgtT2PayYMGCYvkgKSo3Nze5KJk9e3aBvXEDph8VMPV/XxjtKf9r165h3bp1etOPHTsmf+HQ3llXFMbsk7TvrRs3buDHH3802EZ712P58uULvAHCFnXs2BEAinSjjrEstd/Vnk7bvHmzvJ97/KLrvLRnTHbs2IEdO3YUuOziOFKmpb2wPT+3bt2Sr51q2bJlvndnRkdHyxddG3MHW7Ep7v4ATLVt2zZhZ2cnnnrqKbFkyRKdHjizsrLE6dOnxYgRI+Q+Elq2bCl3TqdlbAePU6dOlTt4vHfvnvjoo4/k5Rrq4PGHH34QgPlPiH+8HyNjFdSPkbafGw8PD7FhwwaRnZ0thBDin3/+EUOHDhUqlUrukdXQ66JdrlqtFlWqVBF79uyR+1Q6fvy4aNSokbx8Q/0gGfs3G9PBY0BAgNixY4f8fz179qzcu3JhHTw+3it1UUyfPl306tVLLF26VKcvpIyMDLFmzRq5V9mhQ4fqzJecnCz3qNy6dWu5I7OsrCyxefNm4e/vL/cBVVgHj3Z2duLTTz+Ve3+9ffu2GDt2rPz3/e9//9Obf8eOHXIHj+3bt9fr4HH//v0iNDRUp4PHH3/8UV7mxx9/LFJTU4UQj94HM2fOFPb29qJcuXL5bqfGdiyo7S+lsP5oCuvg0d/fXwCPOpxcunSpTu+4iYmJYv369WLAgAF6/RDl7ceooN58X375ZTFo0CCxfv16nT5f7t+/L+bPny/3N5Nf/2cF6d27t/z/Xbt2rfy/+e233+S/q0GDBuLhw4dFXvbkyZMFAFG9enXx77//5tsubweP33zzjU4Hjy+//LL8GpnawePdu3fF7du35UHbd9D777+vMz6/fqTytrl9+7ac55tvvtEZb0rHgzt27BDAo97BC3qNtet8PLOhQdtfmLn73byaNWum0//PH3/8kW/bhw8fipCQEPl/OmPGDJ3OR1NTU8W+ffvEm2++KdRqtd78xr4vC9OqVSsxffp0ERUVpfO0iHv37only5fLfc+pVCqDfTVprVq1SgAQvr6+ZuUxl2ILo127dul1EOXk5CS8vb11uq8HIJo2bWqwJ9rCHgny/PPPy8sw5pEgWnfv3hU+Pj5yu/Lly4ugoCARFBQkjhw5YvTfWByFUUxMjPwoDODR4xnydg//6aefFvi6aNvlfSTI44/DcHZ2Ftu3by9S5sf/5oI6QTx37pyoVKmSvD4XFxfh6emps/5169YZnNcShVF4eLjO9uXq6qq33dWpU0enh1utH3/8Uaedh4eH/GHaunVr8e233xZaGOV9JIi9vb3eI0GGDx+u9yVAa8mSJcLZ2VnntSrokSBZWVmiQ4cO8jTtDlz7PujTp4/8RcHahZEQQvz999/yowu071tvb2/h5uam8z8LCQnRmc/YwihvJ5vAox6BtY9H0A7t27eXC8iiuHv3rmjSpInOdp33USNBQUEiJiamyMsVQojLly/Lj4Kxs7MTvr6+8j4pb3GfnJys0yln3se3aMeZ80gQ7f+5sCG/978x8xqzvRmSmZkp77cLesyUsRmA/3pYN3e/m1feLyuP93RtSEpKinj66ad1cnl6euo96sXBwUFvXksVRnn/7/b29sLb21vvsSTu7u5i6dKlBS5n6NChAjDcS3dJUuyptJ49e+LKlSuYO3eu3Nmgs7MzkpOTUaZMGdSoUQPPP/88Vq9ejRMnThT5Lg4nJyesWbMG69evR+/evVGuXDncv38f5cqVQ+/evbFx40asXLlSvsUyLy8vLxw8eBBDhgxBpUqVkJKSgtjYWPlOBGsKCgrCyZMn8dJLL8mviYuLC55++mns3r0bkyZNMmo5wcHBOHPmDEaPHg0fHx9kZWWhQoUKGDp0KM6cOYM+ffoU299Qv359nD9/HtOmTUPjxo3h4OCAzMxMVKtWDa+//jrOnz+P5557rtjW/+qrr2LhwoUYOnQo6tevjzJlykCj0cDLywsdOnTAV199hdOnTxu8IP6ll17CL7/8gq5du8LT0xMPHz5EzZo1MWvWLERGRsq3vhdm1apV+O6779CkSRM8fPgQbm5uaNOmDZYuXYolS5bkeypn+PDhuHjxIsaNG4e6devCwcEBDx48QFBQEAYMGIBly5bpnLt3dHTEr7/+ivDwcNSsWROOjo4QQqBly5aYP38+tm7dWux3rBRFnTp1cPbsWXz//ffo0aMHypcvD41GAyEEqlevjkGDBmHhwoVYu3atScufMmUKvv76azzzzDOoXbs2HBwckJqaigoVKqB79+74+eefceDAAaP/j3l5eXnh6NGjmDNnDpo1awZHR0eoVCrUr18fU6dOxdmzZ03qbgF41L/V/v370a9fP/j4+ODOnTvyPinvaQ61Wo29e/fip59+QufOneHh4YHU1FT4+fnh2Wefxf79+/H555+blEHpnJyc5BtbVqxYYdFlW2q/Czzq3FZ74bahi64f5+npiW3btmHHjh0YPHgwKleujMzMTKSnp6NSpUro0aMHIiIijL45yRRLlizB5MmT0bFjRwQEBCAjIwMPHjxAhQoV0KlTJ8ycOROXL18u8O7q1NRUbNmyBYBxf3dxUglRiu/ZpiLTviH379+f7+3ORES26J9//kHNmjVRpkwZ3Lx506QCt7ht2LABzz33HFxdXREfH5/v9UWljbZrlC5dulj9hg/FHjEiIiKypKpVq+Kll17C/fv3MW/ePGvHMeibb74BAAwdOvSJKYpyc3Pli80//fRTK6dhYURERE+Qjz/+GO7u7pgzZ45VnoVYkIULFyIyMhJ2dnYYP368teOUmHXr1uH8+fMYNGiQ0Y++Kk4O1g5ARERUUnx9fbFs2TJERUUhJiZG5/l61nD06FEMGTIEKSkpcncLb775ptVzlaTs7GyEh4cb/eir4sZrjEgHrzEiIio5Bw4cQJcuXWBvb4+goCCEhYXhww8/lHu1ppLHwoiIiIhIwmuMiIiIiCRP7LG63NxcxMfHw8PDw+QnkxMRET2JhBC4f/8+/P39i/yIHKV7Yguj+Ph4BAYGWjsGERGRzYqLi0NAQIC1Y1hU6SrzisDDw8PaEYiIiGxaafwsfWKPGOU9faa068+ZreiUmgtgNlMpNZtScwHMZiqlZlNqLuC/bKXxUpQn9ogRERER0eNYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSRRZGB08eBB9+/aFv78/VCoVNm/erDN92rRpqF27Ntzc3ODl5YWQkBAcO3bMOmGJiIio1FBkYZSWloZGjRph3rx5BqfXrFkT3377Lc6dO4fDhw+jSpUq6NGjB27fvl3CSYmIiKg0UQmlPYDlMSqVCps2bcKAAQPybaPRaKBWq/Hbb7+hW7duRi1XOw+g3GfQAMxmLKXmApjNVErNptRcALOZSqnZlJoL+C9bSkoKPD09rZzGsmz+IbJZWVlYuHAh1Go1GjVqlG+7zMxMZGZmyr9rNJqSiEdEREQ2RJGn0oyxfft2uLu7w8XFBf/73/+wZ88elC9fPt/2ERERUKvV8hAYGFiCaYmIiMgW2Gxh1KVLF0RFReGPP/5Ar1698PzzzyMxMTHf9pMmTUJKSoo8xMXFlWBaIiIisgU2Wxi5ubmhevXqaN26NX766Sc4ODjgp59+yre9s7MzPD09dQYiIiKivGy2MHpcbm6uzjVEREREREWlyIuvU1NTcfXqVfn36OhoREVFwdvbG+XKlcPMmTPRr18/VKxYEUlJSZg3bx5u3LiBQYMGWTE1ERER2TpFFkYnT55Ely5d5N/Hjx8PAAgLC8OCBQtw8eJFLFmyBElJSShXrhxatGiBQ4cOoV69etaKTERERKWA4vsxKi7sx8g0Ss2m1FwAs5lKqdmUmgtgNlMpNZtScwGlux+jUnONEREREZG5WBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUkU+UiQkpa3d1GlYbaiU2ougNlMpdRsSs0FMJuplJpNqblKIx4xIiIiIpKwMCIiIiKS8FQalPtwPoDZjKXUXACzmUqp2ZSaC2A2Uyk1m1JzAaX71B6PGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUkUWRgdPHgQffv2hb+/P1QqFTZv3ixPy87OxsSJE9GgQQO4ubnB398fw4cPR3x8vPUCExERUamgyMIoLS0NjRo1wrx58/Smpaen4/Tp05gyZQpOnz6NjRs34tKlS+jXr58VkhIREVFpohJKewDLY1QqFTZt2oQBAwbk2+bEiRNo2bIlYmNjUblyZaOWq9FooFarASj7GTTMZhyl5gKYzVRKzabUXACzmUqp2ZSaC/gvW0pKCjw9Pa2cxrJKxUNkU1JSoFKpULZs2XzbZGZmIjMzU/5do9GUQDIiIiKyJYo8lVYUGRkZmDhxIoYOHVpg1RoREQG1Wi0PgYGBJZiSiIiIbIFNF0bZ2dl4/vnnIYTA/PnzC2w7adIkpKSkyENcXFwJpSQiIiJbYbOn0rRFUWxsLPbt21foOU5nZ2c4OzuXUDoiIiKyRTZZGGmLoitXrmD//v0oV66ctSMRERFRKaDIwig1NRVXr16Vf4+OjkZUVBS8vb1RsWJFPPfcczh9+jS2b9+OnJwc3Lp1CwDg7e0NJycna8UmIiIiG6fI2/UPHDiALl266I0PCwvDtGnTEBwcbHC+/fv3o3Pnzkatg7frm0ap2ZSaC2A2Uyk1m1JzAcxmKqVmU2ougLfrl7jOnTsXuBEobQMhIiKi0sGm70ojIiIisiQWRkREREQSFkZEREREEhZGRERERBIWRkREREQSFkZEREREEhZGRERERBIWRkREREQSFkZEREREEkX2fF3S8na7rjTMVnRKzQUwm6mUmk2puQBmM5VSsyk1V2nEI0ZEREREEhZGRERERBKeSoPyHkprC09UBpSVTam5AGYzlVKzKTUXwGymUmo2peYCSvepPR4xIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikiiyMDp48CD69u0Lf39/qFQqbN68WWf6xo0b0aNHD5QrVw4qlQpRUVFWyUlERESliyILo7S0NDRq1Ajz5s3Ld3r79u0xe/bsEk5GREREpZkin5XWu3dv9O7dO9/pw4YNAwDExMSUUCIiIiJ6EiiyMCoOmZmZyMzMlH/XaDRWTENERERKpMhTacUhIiICarVaHgIDA60diYiIiBTmiSmMJk2ahJSUFHmIi4uzdiQiIiJSmCfmVJqzszOcnZ2tHYOIiIgU7Ik5YkRERERUGEUeMUpNTcXVq1fl36OjoxEVFQVvb29UrlwZd+/exfXr1xEfHw8AuHTpEgDAz88Pfn5+VslMREREtk8lhBDWDvG4AwcOoEuXLnrjw8LCsHjxYixevBgjR47Umx4eHo5p06YZtQ6NRgO1Wg0AUNpLoFKp5J+ZzThKzQUwm6mUmk2puQBmM5VSsyk1F/BftpSUFHh6elo5jWUpsjAqCSyMTKPUbErNBTCbqZSaTam5AGYzlVKzKTUXULoLI15jRERERCRhYUREREQkYWFEREREJGFhRERERCRhYUREREQkYWFEREREJGFhRERERCRhYUREREQkYWFEREREJFHks9JKWt7eRZWG2YpOqbkAZjOVUrMpNRfAbKZSajal5iqNeMSIiIiISMLCiIiIiEjCU2lQ7sP5AGYzllJzAcxmKqVmU2ougNlMpdRsSs0FlO5TezxiRERERCRhYUREREQkYWFEREREJGFhRERERCRhYUREREQkYWFEREREJGFhRERERCRhYUREREQkYWFEREREJFFkYXTw4EH07dsX/v7+UKlU2Lx5s850IQSmTp2KihUrwtXVFSEhIbhy5Yp1whIREVGpocjCKC0tDY0aNcK8efMMTv/ss8/w9ddfY8GCBTh27Bjc3NzQs2dPZGRklHBSIiIiKk1UQmkPYHmMSqXCpk2bMGDAAACPjhb5+/vj3XffxXvvvQcASElJga+vLxYvXowhQ4YYtVyNRgO1Wi0vU0ls5fk4Ssqm1FwAs5lKqdmUmgtgNlMpNZtScwH/ZUtJSYGnp6eV01iWIo8YFSQ6Ohq3bt1CSEiIPE6tVqNVq1Y4cuRIvvNlZmZCo9HoDERERER52VxhdOvWLQCAr6+vznhfX195miERERFQq9XyEBgYWKw5iYiIyPbYXGFkqkmTJiElJUUe4uLirB2JiIiIFMbmCiM/Pz8AQEJCgs74hIQEeZohzs7O8PT01BmIiIiI8rK5wig4OBh+fn7Yu3evPE6j0eDYsWNo06aNFZMRERGRrXOwdgBDUlNTcfXqVfn36OhoREVFwdvbG5UrV8a4cePwySefoEaNGggODsaUKVPg7+8v37lGREREZApFFkYnT55Ely5d5N/Hjx8PAAgLC8PixYsxYcIEpKWl4dVXX0VycjLat2+PXbt2wcXFxVqRiYiIqBRQfD9GxYX9GJlGqdmUmgtgNlMpNZtScwHMZiqlZlNqLoD9GBERERE9EVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJFPlIkJKWt3dRpWG2olNqLoDZTKXUbErNBTCbqZSaTam5SiMeMSIiIiKSsDAiIiIikvBUGpT7cD6A2Yyl1FwAs5lKqdmUmgtgNlMpNZtScwGl+9QejxgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJWBgRERERSVgYEREREUlYGBERERFJbLYwun//PsaNG4egoCC4urqibdu2OHHihLVjERERkQ2z2cLo5Zdfxp49e7Bs2TKcO3cOPXr0QEhICG7cuGHtaERERGSjVEJpD2AxwoMHD+Dh4YEtW7agT58+8vhmzZqhd+/e+OSTTwpdhkajgVqtBqDsZ9Awm3GUmgtgNlMpNZtScwHMZiqlZlNqLuC/bCkpKfD09LRyGsuyyYfIPnz4EDk5OXBxcdEZ7+rqisOHDxucJzMzE5mZmfLvGo2mWDMSERGR7bHJU2keHh5o06YNZsyYgfj4eOTk5GD58uU4cuQIbt68aXCeiIgIqNVqeQgMDCzh1ERERKR0NnkqDQCuXbuGUaNG4eDBg7C3t0fTpk1Rs2ZNnDp1ChcuXNBrb+iIkbY4UtpLYAuHTwFlZVNqLoDZTKXUbErNBTCbqZSaTam5AJ5KU6Rq1aohMjISaWlp0Gg0qFixIgYPHoyqVasabO/s7AxnZ+cSTklERES2xCZPpeXl5uaGihUr4t69e9i9ezf69+9v7UhERERko2z2iNHu3bshhECtWrVw9epVvP/++6hduzZGjhxp7WhERERko2z2iFFKSgpGjx6N2rVrY/jw4Wjfvj12794NR0dHa0cjIiIiG2WzF1+bi/0YmUap2ZSaC2A2Uyk1m1JzAcxmKqVmU2ouoHRffG2zR4yIiIiILI2FEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRxGaflWZJeXsXVRpmKzql5gKYzVRKzabUXACzmUqp2ZSaqzTiESMiIiIiCQsjIiIiIglPpUG5D+cDmM1YSs0FMJuplJpNqbkAZjOVUrMpNRdQuk/t8YgRERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRxCYLo5ycHEyZMgXBwcFwdXVFtWrVMGPGDMX1DEpERES2xSYfCTJ79mzMnz8fS5YsQb169XDy5EmMHDkSarUaY8eOtXY8IiIislE2WRj98ccf6N+/P/r06QMAqFKlClatWoXjx49bORkRERHZMps8lda2bVvs3bsXly9fBgD8+eefOHz4MHr37p3vPJmZmdBoNDoDERERUV42ecTogw8+gEajQe3atWFvb4+cnBzMnDkToaGh+c4TERGB6dOnl2BKIiIisjU2ecRo7dq1WLFiBVauXInTp09jyZIlmDNnDpYsWZLvPJMmTUJKSoo8xMXFlWBiIiIisgUqYYO3cgUGBuKDDz7A6NGj5XGffPIJli9fjosXLxq1DI1GA7VaDQCKu5tNpVLJPzObcZSaC2A2Uyk1m1JzAcxmKqVmU2ou4L9sKSkp8PT0tHIay7LJI0bp6emws9ONbm9vj9zcXCslIiIiotLAJq8x6tu3L2bOnInKlSujXr16OHPmDL788kuMGjXK2tGIiIjIhtnkqbT79+9jypQp2LRpExITE+Hv74+hQ4di6tSpcHJyMmoZPJVmGqVmU2ougNlMpdRsSs0FMJuplJpNqbmA0n0qzSYLI0tgYWQapWZTai6A2Uyl1GxKzQUwm6mUmk2puYDSXRgV26m06OhonD17FkFBQWjcuHFxrYaIiIjIYsy6+Hrr1q0YOHCgXo/Tn3/+OWrWrImBAweiWbNmvPaHiIiIbIJZhdHSpUuxa9cu1KlTRx538eJFfPDBBxBCoFGjRihTpgyWLFmCbdu2mR2WiIiIqDiZVRidOXMGjRo1goeHhzxuxYoVAIDvvvsOp0+fxokTJ2Bvb4+FCxeal5SIiIiomJlVGCUlJaFSpUo64w4cOABXV1eMGDECAFC7dm20b98e58+fN2dVRERERMXOrMIoIyMD9vb28u85OTk4ffo0WrVqpXPbvL+/P27dumXOqoiIiIiKnVmFUYUKFXDlyhX596NHj+LBgwdo166dTrsHDx7Azc3NnFURERERFTuzCqO2bdvizz//xOrVq5GSkoJPP/0UKpUKISEhOu0uXLgAf39/s4ISERERFTezCqOJEyfCwcEBoaGh8Pb2xs6dO9G0aVN07NhRbhMXF4eLFy+iRYsWZoclIiIiKk5mdfDYtGlT7NixAzNnzkRiYiJatmyJiIgInTZr166FWq1Gt27dzApanPL2Lqo0zFZ0Ss0FMJuplJpNqbkAZjOVUrMpNVdpxEeCEBERkUlK4yNBzDqVRkRERFSaFNuz0myJ0g6a2cKDAwFlZVNqLuCxbCusGMQAVeh/Pys6m4L+pzazrTGb0ZSaTam5gNJ9aq9IhVHXrl1NXpFKpcLevXtNnp+IiIiouBWpMDpw4IDJKyrN1SURERGVDkUqjPbv319cOYiIiIisrkiFUadOnYorBxEREZHV8a40IiIiIonF7krLysrCqVOncOPGDQBApUqV0KxZM52HyRIREREpmdmF0cOHDzF9+nR88803uH//vs40Dw8PjB07FlOnToWDA3sGICIiImUzq1rJzc1Fv379sHv3bggh4OXlheDgYABAdHQ07t27h5kzZ+LUqVPYtm0b7Ox45o6IiIiUy6xK5ccff8SuXbsQFBSE9evX486dOzh58iROnjyJO3fuYMOGDQgKCsKuXbvw008/WSozERERUbEwqzBaunQpXF1dsW/fPgwcOFBv+jPPPIO9e/fC2dkZS5YsMWdVeqpUqQKVSqU3jB492qLrISIioieHWafS/vrrL3Tu3BlVqlTJt01wcDC6du2Kw4cPm7MqPSdOnEBOTo5Olu7du2PQoEEWXQ8RERE9OcwqjDIzM416Qr2HhwcyMzPNWZUeHx8fnd9nzZqFatWqsa8lIiIiMplZhVFgYCCOHDmCnJwc2NvbG2yTk5ODo0ePIiAgwJxVFSgrKwvLly/H+PHj8330SGZmpk5xptFoii0PERER2SazrjHq2bMnrl+/jrfffhvZ2dl607OysjB27Fhcv34dvXv3NmdVBdq8eTOSk5MxYsSIfNtERERArVbLQ2BgYLHlISIiItukEkIIU2e+ceMGGjZsiOTkZPj7+2PIkCHy7fr//PMP1qxZg/j4eHh7eyMqKgqVKlWyWPC8evbsCScnJ2zbti3fNoaOGGmLIzNegmKR96gXsxlHqbmAx7KtsGIQA1Sh//2s6GwK+p/azLbGbEZTajal5gL+y5aSkgJPT08rp7Ess06lVapUCbt27cKgQYNw/fp1fPnllzrThRCoXLky1q9fX2xFUWxsLH777Tds3LixwHbOzs5wdnYulgxERERUOpjdHXWLFi1w+fJlrFu3DgcOHNB5JEjnzp0xaNCgYn0syKJFi1ChQgX06dOn2NZBRERETwaLPKfDyckJoaGhCA0NLbyxBeXm5mLRokUICwvjI0eIiIjIbDb9jI7ffvsN169fx6hRo6wdhYiIiEoBixxmyc7OxoYNG3DgwAH8+++/EEIgICAAnTt3xrPPPltsp9J69OihuAvSiIiIyHaZXRgdO3YMQ4cORWxsrF6R8uOPP+LDDz/EypUr0aZNG3NXRURERFSszCqMLl26hO7duyM1NRWVKlXC4MGD5dv1Y2JisGbNGsTGxqJnz544fvw4ateubZHQRERERMXBrMJo6tSpSE1NxdixY/HZZ5/pnTL79NNPMWHCBMydOxfh4eFYs2aNWWGJiIiIipNZHTz6+PigbNmyuHz5cr6P4hBCoGbNmkhOTsbt27dNDmppGo1Gfs6b0q5TsoVOvQBlZVNqLoAdPJqKHTwWHbOZRqnZlJoLKN0dPJp1V1paWhpatmyZb1EEPHrxWrRogbS0NHNWRURERFTszCqMqlatiqSkpELb3blzR772iIiIiEipzCqMhg8fjsjISJw/fz7fNufPn8eBAwcwbNgwc1ZFREREVOzMKozeffddPP300+jatSu+/fZbaDQaeZpGo8G8efPQrVs39O3bFxMmTDA7LBEREVFxKtLF11WrVjU4PiYmRr7OqGzZsgCA5ORkeXrlypVhZ2eHa9eumZ7UwnjxtWmUmk2puQBefG0qXnxddMxmGqVmU2ouoHRffF2kwsjOzvQDTCqVCjk5OSbPb2l5CyMiIiIqutJYGBWpH6Po6OjiykFERERkdUUqjIKCgoorBxEREZHVWeQhsrZOqeduAWYzllJzAcxmKqVmU2ougNlMpdRsSs0FoMD+C22dWXelEREREZUmZhdG//zzD1577TVUr14dZcqUgb29vcHBwYEHp4iIiEjZzKpWzpw5g86dOyM1NbXQw3xKOwxIRERE9DizjhhNnDgR9+/fx8CBA3Hq1CloNBrk5ubmOxAREREpWZH6MXqch4cHAgIC8Pfff9vchVjs4NE0Ss2m1FwAs5lKqdmUmgtgNlMpNZtScwGlu4NHs44YOTo6onHjxjZXFBEREREZYlZh1LRpU/z777+WykJERERkVWYVRu+99x6OHDmCAwcOWCgOERERkfWYdVdar169MHfuXPTv3x9vvvkmevfuLT8w1pDKlSubszoiIiKiYmXWxdcAcOTIEYwaNQqXL18ueEUqFR4+fGjOqnTcuHEDEydOxM6dO5Geno7q1atj0aJFaN68uVHz8+Jr0yg1m1JzAcxmKqVmU2ougNlMpdRsSs0FlO6Lr806YnT48GH06NEDGRkZAIBy5crB3d3dIsEKcu/ePbRr1w5dunTBzp074ePjgytXrsDLy6vY101ERESll1mF0UcffYSMjAyMHz8ekydPLrHCZPbs2QgMDMSiRYvkccHBwSWybiIiIiq9zDqV5unpiRo1auDUqVOWzFSounXromfPnvj3338RGRmJSpUq4c0338Qrr7yS7zyZmZnIzMyUf9doNAgMDASg3EOUALMZS6m5AGYzlVKzKTUXwGymUmo2peYCSvepNLPuSnNyckKtWrUslcVo//zzD+bPn48aNWpg9+7deOONNzB27FgsWbIk33kiIiKgVqvlQVsUEREREWmZdcSoT58+SEhIwMmTJy2ZqVBOTk5o3rw5/vjjD3nc2LFjceLECRw5csTgPDxiZBlKzabUXACzmUqp2ZSaC2A2Uyk1m1JzATxilK8pU6bg7NmzWLVqlaXyGKVixYqoW7euzrg6derg+vXr+c7j7OwMT09PnYGIiIgoL7Muvs7KysK4ceMwbNgwbN26tdB+jDp27GjO6mTt2rXDpUuXdMZdvnwZQUFBFlk+ERERPZnMOpVmZ2cHlUoFIUShz0uzZD9GJ06cQNu2bTF9+nQ8//zzOH78OF555RUsXLgQoaGhRi2D/RiZRqnZlJoLYDZTKTWbUnMBzGYqpWZTai6gdJ9KM+uIUceOHa3yANkWLVpg06ZNmDRpEj7++GMEBwfjq6++MrooIiIiIjLE7J6vbRWPGJlGqdmUmgtgNlMpNZtScwHMZiqlZlNqLqB0HzEy6+JrIiIiotLErMJowYIFSE9Pt1QWIiIiIqsyqzB68803ERAQgHfeeQdXrlyxVCYiIiIiqzCrMOrVqxdSUlIwd+5c1KlTB71798Yvv/xiqWxEREREJcqswmjHjh24cuUKxo8fD7Vajd27d6Nfv36oVq0avvjiC9y7d89SOYmIiIiKncXuSsvIyMCKFSswb948REVFQaVSwcXFBS+88AJGjx6Nxo0bW2I1FsO70kyj1GxKzQUwm6mUmk2puQBmM5VSsyk1F1C670orltv1jxw5gm+//RYbNmxAdnY2AKBt27Z466238Oyzz8Le3t7SqywyFkamUWo2peYCmM1USs2m1FwAs5lKqdmUmgso3YVRsdyu36ZNG3zzzTcYM2YMhBAQQuD333/H0KFDUaNGDaxfv744VktERERkFosXRmfOnMFLL72EwMBA/O9//4OdnR0GDBiA+fPno02bNoiJicHgwYPx008/WXrVREREROYRFpCVlSVWrFgh2rRpI+zs7IRKpRJeXl7ivffeEzExMTptd+zYIVxdXUXt2rUtsWqTpaSkCAAcOHDgwIEDBxOHlJQUq36WFweznpV248YNLFiwAD/++CMSExMhhECdOnXw1ltvYfjw4ShTpozePL1798ZTTz2Fbdu2mbNqIiIiIoszqzAKDg5GTk4OgEcFz9tvv43u3bsXOl/ZsmXli7KJiIiIlMKsu9I8PT0xcuRIvPXWW6hevbrR8929exf3799HUFCQqas2G+9KM41Ssyk1F8BsplJqNqXmApjNVErNptRcQOm+K83sU2keHh5Fns/b2xve3t7mrJqIiIjI4sy6K+3xoujhw4f44osv0KFDB9SpUwfdu3fHzz//bFZAIiIiopJSpMJo48aNqFChAiZPnqw3LTc3F3369MGECRPw+++/49KlS9i7dy9eeeUVjBgxwlJ5iYiIiIpNkQqj/fv3486dO3juuef0pv3www/Ys2cPhBDo168fvv32W0yYMAGurq5YtmwZfv31V4uFJiIiIioORbrG6NixY6hYsSKaNGmiN+3777+HSqXCkCFDsGLFCnl8y5Yt8dxzz2HZsmXo0aOH+YmJiIiIikmRjhjdvHnT4MNgk5KSEBUVBQB4//33daYNHDgQVapUwbFjx0wOSURERFQSilQYJSUlwcvLS2/8iRMnAAA+Pj4GC6e6desiPj7etIREREREJaRIhZG9vT1u376tN/706dMAgKZNmxqcr2zZsnj48KEJ8YiIiIhKTpEKo6CgIJw+fRpZWVk64/fu3QuVSoVWrVoZnC8pKQm+vr6mpyQiIiIqAUUqjLp06YI7d+5gypQp8rj9+/cjMjISANCnTx+D8505cwb+/v5mxNQ3bdo0qFQqnaF27doWXQcRERE9WYpUGI0bNw5OTk6YM2cOAgMD0bRpU/Ts2RMA0KpVKzRv3lxvniNHjuD27dv5Hk0yR7169XDz5k15OHz4sMXXQURERE+OIt2uX716daxYsQIjRozAjRs3cOPGDQBApUqVsGTJEoPzfP/99wCAbt26mRlVn4ODA/z8/Cy+XCIiInoyFflZaQMHDkT79u2xfft2JCQkoHLlyhgwYADc3NwMtm/ZsiWaNGmCrl27mh32cVeuXIG/vz9cXFzQpk0bREREoHLlygbbZmZmIjMzU/5do9FYPA8RERHZNpVQ2iN7jbRz506kpqaiVq1auHnzJqZPn44bN27gr7/+Mvhg22nTpmH69OkGl6W0l8AWnqgMKCubUnMBzGYqpWZTai6A2Uyl1GxKzQX8ly0lJQWenp5WTmNZNlsYPS45ORlBQUH48ssv8dJLL+lNN3TEKDAwEIByNziA2Yyl1FwAs5lKqdmUmgtgNlMpNZtScwGluzAq8qk0pSpbtixq1qyJq1evGpzu7OwMZ2fnEk5FREREtqRId6UpWWpqKq5du4aKFStaOwoRERHZKJstjN577z1ERkYiJiYGf/zxB5555hnY29tj6NCh1o5GRERENspmT6X9+++/GDp0KO7cuQMfHx+0b98eR48ehY+Pj7WjERERkY0qNRdfF5VGo4FarQag3IvaAGYzllJzAcxmKqVmU2ougNlMpdRsSs0FlO6Lr232VBoRERGRpbEwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKS2OwjQSwpb++iSsNsRafUXACzmUqp2ZSaC2A2Uyk1m1JzlUY8YkREREQkYWFEREREJOGpNCj34XwAsxlLqbkAZjOVUrMpNRfAbKZSajal5gJK96k9HjEiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSlIrCaNasWVCpVBg3bpy1oxAREZENs/nC6MSJE/j+++/RsGFDa0chIiIiG2fThVFqaipCQ0Pxww8/wMvLy9pxiIiIyMbZdGE0evRo9OnTByEhIYW2zczMhEaj0RmIiIiI8nKwdgBTrV69GqdPn8aJEyeMah8REYHp06cXcyoiIiKyZTZ5xCguLg5vv/02VqxYARcXF6PmmTRpElJSUuQhLi6umFMSERGRrVEJIYS1QxTV5s2b8cwzz8De3l4el5OTA5VKBTs7O2RmZupMM0Sj0UCtVgMAlPYSqFQq+WdmM45ScwHMZiqlZlNqLoDZTKXUbErNBfyXLSUlBZ6enlZOY1k2eSqtW7duOHfunM64kSNHonbt2pg4cWKhRRERERGRITZZGHl4eKB+/fo649zc3FCuXDm98URERETGsslrjIiIiIiKg00eMTLkwIED1o5ARERENo5HjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCSlpudrc+R9grHSMFvRKTUXwGymUmo2peYCmM1USs2m1FylEY8YEREREUlYGBERERFJeCoNgBDC2hF05D1kymzGUWougNlMpdRsSs0FMJuplJpNqbmA0n1qj0eMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJDZbGM2fPx8NGzaEp6cnPD090aZNG+zcudPasYiIiMiG2WxhFBAQgFmzZuHUqVM4efIkunbtiv79++P8+fPWjkZEREQ2SiWU9gAWM3h7e+Pzzz/HSy+9VGhbjUYDtVoNQNnPoGE24yg1F8BsplJqNqXmApjNVErNptRcwH/ZUlJS4OnpaeU0llUqHiKbk5ODdevWIS0tDW3atDHYJjMzE5mZmfLvGo2mpOIRERGRjbDZU2kAcO7cObi7u8PZ2Rmvv/46Nm3ahLp16xpsGxERAbVaLQ+BgYElnJaIiIiUzqZPpWVlZeH69etISUnB+vXr8eOPPyIyMtJgcWToiJG2OFLaS2ALh08BZWVTai6A2Uyl1GxKzQUwm6mUmk2puYDSfSrNpgujx4WEhKBatWr4/vvvC23La4xMo9RsSs0FMJuplJpNqbkAZjOVUrMpNRdQugsjmz6V9rjc3Fydo0JERERERWGzF19PmjQJvXv3RuXKlXH//n2sXLkSBw4cwO7du60djYiIiGyUzRZGiYmJGD58OG7evAm1Wo2GDRti9+7d6N69u7WjERERkY0qVdcYFQWvMTKNUrMpNRfAbKZSajal5gKYzVRKzabUXACvMSIiIiJ6IrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKS2OwjQSwpb++iSsNsRafUXACzmUqp2ZSaC2A2Uyk1m1JzlUY8YkREREQkYWFEREREJOGpNCj34XwAsxlLqbkAZjOVUrMpNRfAbKZSajal5gJK96k9HjEiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKS2GxhFBERgRYtWsDDwwMVKlTAgAEDcOnSJWvHIiIiIhtms4VRZGQkRo8ejaNHj2LPnj3Izs5Gjx49kJaWZu1oREREZKNUQmkPYDHR7du3UaFCBURGRqJjx46FttdoNFCr1QCU/QwaZjOOUnMBzGYqpWZTai6A2Uyl1GxKzQX8ly0lJQWenp5WTmNZpeYhsikpKQAAb29vg9MzMzORmZkp/67RaEokFxEREdkOmz2Vlldubi7GjRuHdu3aoX79+gbbREREQK1Wy0NgYGAJpyQiIiKlKxWn0t544w3s3LkThw8fRkBAgME2ho4YaYsjpb0EtnD4FFBWNqXmApjNVErNptRcALOZSqnZlJoL4Kk0RRszZgy2b9+OgwcP5lsUAYCzszOcnZ1LMBkRERHZGpstjIQQeOutt7Bp0yYcOHAAwcHB1o5ERERENs5mC6PRo0dj5cqV2LJlCzw8PHDr1i0AgFqthqurq5XTERERkS2y2WuM8p57zWvRokUYMWJEofPzdn3TKDWbUnMBzGYqpWZTai6A2Uyl1GxKzQXwGiNFUtpGQkRERLavVNyuT0RERGQJLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQ22/O1JeX3eBElYLaiU2ougNlMpdRsSs0FMJuplJpNqblKIx4xIiIiIpKwMCIiIiKS8FQalPdAWlt4ojKgrGxKzQUwm6mUmk2puQBmM5VSsyk1F1C6T+3xiBERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZHEZgujgwcPom/fvvD394dKpcLmzZutHYmIiIhsnM0WRmlpaWjUqBHmzZtn7ShERERUStjss9J69+6N3r17WzsGERERlSI2WxgVVWZmJjIzM+XfNRqNFdMQERGREtnsqbSiioiIgFqtlofAwEBrRyIiIiKFeWIKo0mTJiElJUUe4uLirB2JiIiIFOaJOZXm7OwMZ2dna8cgIiIiBXtijhgRERERFcZmjxilpqbi6tWr8u/R0dGIioqCt7c3KleubMVkREREZKtUQghh7RCmOHDgALp06aI3PiwsDIsXLy50fo1GA7VaDQBQ2kugUqnkn5nNOErNBTCbqZSaTam5AGYzlVKzKTUX8F+2lJQUeHp6WjmNZdnsEaPOnTsrbkMhIiIi28ZrjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQsjIiIiIgkLIyIiIiIJCyMiIiIiCQ22/O1JeXtdl1pmK3olJoLYDZTKTWbUnMBzGYqpWZTaq7SiEeMiIiIiCQsjIiIiIgkPJUG5T61GGA2Yyk1F8BsplJqNqXmApjNVErNptRcQOk+tccjRkREREQSFkZEREREEhZGRERERBIWRkREREQSFkZEREREEhZGRERERBIWRkREREQSFkZEREREEhZGRERERBKbLozmzZuHKlWqwMXFBa1atcLx48etHYmIiIhsmM0WRmvWrMH48eMRHh6O06dPo1GjRujZsycSExOtHY2IiIhslEoo7QEsRmrVqhVatGiBb7/9FgCQm5uLwMBAvPXWW/jggw8KnV+j0UCtVgNQ9jNomM04Ss0FMJuplJpNqbkAZjOVUrMpNRfwX7aUlBR4enpaOY1l2eQRo6ysLJw6dQohISHyODs7O4SEhODIkSMG58nMzIRGo9EZiIiIiPKyycIoKSkJOTk58PX11Rnv6+uLW7duGZwnIiICarVaHgIDA0siKhEREdkQmyyMTDFp0iSkpKTIQ1xcnLUjERERkcI4WDuAKcqXLw97e3skJCTojE9ISICfn5/BeZydneHs7FwS8YiIiMhG2eQRIycnJzRr1gx79+6Vx+Xm5mLv3r1o06aNFZMRERGRLbPJI0YAMH78eISFhaF58+Zo2bIlvvrqK6SlpWHkyJHWjkZEREQ2ymYLo8GDB+P27duYOnUqbt26hcaNG2PXrl16F2QTERERGctm+zEyF/sxMo1Ssyk1F8BsplJqNqXmApjNVErNptRcAPsxIiIiInoisDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpKwMCIiIiKSsDAiIiIikrAwIiIiIpLY7CNBzJW3F9G8vYsqDbMVnVJzAcxmKqVmU2ougNlMpdRsSs2ltB65LeGJPWJ0//59a0cgIiKyaaXxs/SJfVZabm4u4uPj4eHhYXYlrtFoEBgYiLi4OMU9M4bZTKPUbErNBTCbqZSaTam5AGYzlSWzCSFw//59+Pv7w86udB1jeWJPpdnZ2SEgIMCiy/T09FTcG0GL2Uyj1GxKzQUwm6mUmk2puQBmM5WlsmkfxF7alK4yj4iIiMgMLIyIiIiIJCyMLMDZ2Rnh4eFwdna2dhQ9zGYapWZTai6A2Uyl1GxKzQUwm6mUnE1JntiLr4mIiIgexyNGRERERBIWRkREREQSFkZEREREEhZGRERERBIWRhYwb948VKlSBS4uLmjVqhWOHz9u7Ug4ePAg+vbtC39/f6hUKmzevNnakWQRERFo0aIFPDw8UKFCBQwYMACXLl2ydizMnz8fDRs2lDs/a9OmDXbu3GntWAbNmjULKpUK48aNs3YUTJs2DSqVSmeoXbu2tWMBAG7cuIEXX3wR5cqVg6urKxo0aICTJ09aOxaqVKmi95qpVCqMHj3a2tGQk5ODKVOmIDg4GK6urqhWrRpmzJihmGdi3b9/H+PGjUNQUBBcXV3Rtm1bnDhxosRzFLaPFUJg6tSpqFixIlxdXRESEoIrV65YPdfGjRvRo0cPlCtXDiqVClFRUcWeydawMDLTmjVrMH78eISHh+P06dNo1KgRevbsicTERKvmSktLQ6NGjTBv3jyr5jAkMjISo0ePxtGjR7Fnzx5kZ2ejR48eSEtLs2qugIAAzJo1C6dOncLJkyfRtWtX9O/fH+fPn7dqrsedOHEC33//PRo2bGjtKLJ69erh5s2b8nD48GFrR8K9e/fQrl07ODo6YufOnfj777/xxRdfwMvLy9rRcOLECZ3Xa8+ePQCAQYMGWTkZMHv2bMyfPx/ffvstLly4gNmzZ+Ozzz7DN998Y+1oAICXX34Ze/bswbJly3Du3Dn06NEDISEhuHHjRonmKGwf+9lnn+Hrr7/GggULcOzYMbi5uaFnz57IyMiwaq60tDS0b98es2fPLtYcNk2QWVq2bClGjx4t/56TkyP8/f1FRESEFVPpAiA2bdpk7Rj5SkxMFABEZGSktaPo8fLyEj/++KO1Y8ju378vatSoIfbs2SM6deok3n77bWtHEuHh4aJRo0bWjqFn4sSJon379taOYZS3335bVKtWTeTm5lo7iujTp48YNWqUzriBAweK0NBQKyX6T3p6urC3txfbt2/XGd+0aVMxefJkK6XS38fm5uYKPz8/8fnnn8vjkpOThbOzs1i1apXVcuUVHR0tAIgzZ86UWB5bwSNGZsjKysKpU6cQEhIij7Ozs0NISAiOHDlixWS2JSUlBQDg7e1t5ST/ycnJwerVq5GWloY2bdpYO45s9OjR6NOnj842pwRXrlyBv78/qlatitDQUFy/ft3akbB161Y0b94cgwYNQoUKFdCkSRP88MMP1o6lJysrC8uXL8eoUaPMfqC1JbRt2xZ79+7F5cuXAQB//vknDh8+jN69e1s5GfDw4UPk5OTAxcVFZ7yrq6sijlJqRUdH49atWzrvU7VajVatWvGzwQY8sQ+RtYSkpCTk5OTA19dXZ7yvry8uXrxopVS2JTc3F+PGjUO7du1Qv359a8fBuXPn0KZNG2RkZMDd3R2bNm1C3bp1rR0LALB69WqcPn3aKtdTFKRVq1ZYvHgxatWqhZs3b2L69Ono0KED/vrrL3h4eFgt1z///IP58+dj/Pjx+PDDD3HixAmMHTsWTk5OCAsLs1qux23evBnJyckYMWKEtaMAAD744ANoNBrUrl0b9vb2yMnJwcyZMxEaGmrtaPDw8ECbNm0wY8YM1KlTB76+vli1ahWOHDmC6tWrWzue7NatWwBg8LNBO42Ui4URWdXo0aPx119/KebbXq1atRAVFYWUlBSsX78eYWFhiIyMtHpxFBcXh7fffht79uzR+7ZsbXmPJDRs2BCtWrVCUFAQ1q5di5deeslquXJzc9G8eXN8+umnAIAmTZrgr7/+woIFCxRVGP3000/o3bs3/P39rR0FALB27VqsWLECK1euRL169RAVFYVx48bB399fEa/bsmXLMGrUKFSqVAn29vZo2rQphg4dilOnTlk7GpUSPJVmhvLly8Pe3h4JCQk64xMSEuDn52elVLZjzJgx2L59O/bv34+AgABrxwEAODk5oXr16mjWrBkiIiLQqFEjzJ0719qxcOrUKSQmJqJp06ZwcHCAg4MDIiMj8fXXX8PBwQE5OTnWjigrW7YsatasiatXr1o1R8WKFfUK2jp16ijiNJ9WbGwsfvvtN7z88svWjiJ7//338cEHH2DIkCFo0KABhg0bhnfeeQcRERHWjgYAqFatGiIjI5Gamoq4uDgcP34c2dnZqFq1qrWjybT7f3422CYWRmZwcnJCs2bNsHfvXnlcbm4u9u7dq6jrUpRGCIExY8Zg06ZN2LdvH4KDg60dKV+5ubnIzMy0dgx069YN586dQ1RUlDw0b94coaGhiIqKgr29vbUjylJTU3Ht2jVUrFjRqjnatWun1w3E5cuXERQUZKVE+hYtWoQKFSqgT58+1o4iS09Ph52d7keDvb09cnNzrZTIMDc3N1SsWBH37t3D7t270b9/f2tHkgUHB8PPz0/ns0Gj0eDYsWP8bLABPJVmpvHjxyMsLAzNmzdHy5Yt8dVXXyEtLQ0jR460aq7U1FSdb+zR0dGIioqCt7c3KleubMVkj06frVy5Elu2bIGHh4d8zl2tVsPV1dVquSZNmoTevXujcuXKuH//PlauXIkDBw5g9+7dVsuk5eHhoXcNlpubG8qVK2f1a7Pee+899O3bF0FBQYiPj0d4eDjs7e0xdOhQq+Z655130LZtW3z66ad4/vnncfz4cSxcuBALFy60ai6t3NxcLFq0CGFhYXBwUM6uuG/fvpg5cyYqV66MevXq4cyZM/jyyy8xatQoa0cDAOzevRtCCNSqVQtXr17F+++/j9q1a5f4Prewfey4cePwySefoEaNGggODsaUKVPg7++PAQMGWDXX3bt3cf36dcTHxwOA/OXBz8+PR7O0rH1bXGnwzTffiMqVKwsnJyfRsmVLcfToUWtHEvv37xcA9IawsDBrRzOYC4BYtGiRVXONGjVKBAUFCScnJ+Hj4yO6desmfv31V6tmKohSbtcfPHiwqFixonBychKVKlUSgwcPFlevXrV2LCGEENu2bRP169cXzs7Oonbt2mLhwoXWjiTbvXu3ACAuXbpk7Sg6NBqNePvtt0XlypWFi4uLqFq1qpg8ebLIzMy0djQhhBBr1qwRVatWFU5OTsLPz0+MHj1aJCcnl3iOwvaxubm5YsqUKcLX11c4OzuLbt26lcj/urBcixYtMjg9PDy82LPZCpUQCunOlIiIiMjKeI0RERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRGRjbhy5QrGjBmDunXrws3NDS4uLggICECLFi0wZswYbNiwwSLr6dy5M1QqFQ4cOGCR5SnVtGnToFKpMG3aNGtHISIFUc4DeogoXxs3bsQLL7yAzMxMlCtXDu3atYOPjw/u3buHqKgozJs3D6tXr8azzz5r7ahERDaNhRGRwiUkJCAsLAyZmZl499138cknn8DFxUWnzalTp7B+/XorJbRNY8aMwZAhQ1C+fHlrRyEiBWFhRKRw27dvR2pqKvz9/TFnzhyDbZo1a4ZmzZqVcDLbVr58eRZFRKSH1xgRKVxCQgIAwMfHp0jzqVQqqFSqfKcbcy1RZGQkevToAW9vb5QpUwYtW7bEsmXLClzvvn37MGjQIAQEBMDZ2Rk+Pj5o0aIFwsPDcefOHblddnY2li9fjtDQUNSuXRuenp5wdXVFrVq1MHbsWMTHxxea+9ChQ+jbty98fHxgZ2eHxYsXAwCqVKkClUqFmJgYbNmyBV27doW3t7fO31vYNUaXL1/Ga6+9hmrVqsHFxQVqtRodO3bE8uXLTc6Vm5uLhQsXol27dihbtiwcHR1RoUIFNGrUCG+99RZiYmIKfG2JqPixMCJSuMqVKwMA/vrrL+zdu7fE1rtp0yZ07doVN27cQM+ePdGiRQucOnUKw4cPx7vvvmtwnrFjx6Jbt25Yv349fHx8MHDgQLRo0QJ3797Fxx9/jHPnzsltExISMGzYMPzyyy/w8vJCr1690LVrV6SmpuKbb75B48aNcfXq1XzzrVu3Dp07d8Y///yDkJAQdO/eHc7OzjptvvjiCwwYMAD3799Hr1690KlTJ9jb2xf6t69btw6NGjXCwoUL4eTkhKeeegrNmzfH6dOnMWzYMIwaNcqkXC+//DJee+01nD59Gi1atMCgQYPQtGlTPHjwAN9++y2ioqIKzUZExUwQkaLdv39fVKpUSQAQKpVKdO7cWcyYMUP88ssvIjExMd/5AIiC3uKdOnUSAMT+/fsNjgcgPv30U51pBw4cEK6urgKA2LVrl860r7/+WgAQ5cqVE/v27dNb37Fjx8T169fl3zUajdiyZYvIzMzUaZeVlSUmTZokAIinnnoq39wAxLx58wz+bUFBQQKAsLe3F1u2bDHYJjw8XAAQ4eHhOuPPnj0rnJ2dhYuLi9iwYYPOtJiYGNGgQQMBQCxZsqRIuWJjYwUAERAQIG7evKk3/e+//xaxsbEGsxJRyWFhRGQDLl68KFq1aiV/8OYdGjduLObPny8ePnyoM4+5hVGTJk0Mzvfuu+8KAKJ79+7yuOzsbOHj4yMA6BUTpvL39xd2dnZCo9EYzNe1a9d859UWRqNGjcq3TX6F0eDBgwUAMWfOHIPzHT9+XAAQzZo1K1Iu7Xz9+vXLNxMRWR8vviayAbVq1cLRo0dx/Phx/PLLLzh27BhOnz6N27dvIyoqCm+88QY2bNiAX375BU5OThZZ5/Dhww2ODwsLwxdffIHDhw8jJycH9vb2OHXqFG7fvo3y5cvjmWeeKdJ6/vzzT+zduxfR0dFIS0tDbm4uAODhw4fIzc3F1atX0aRJE735nnvuuUKXbUybvHJzc7Fz504AwODBgw22ad68Odzd3XHmzBlkZGTo3SGY3zpr164NDw8P7NixAzNnzsQLL7yA4ODgIuUjouLHwojIhrRs2RItW7YEAAghcObMGXz++edYvXo1fvvtN8ydOxfvv/++RdaV34e2dvyDBw9w584dVKhQAbGxsQAeFXAFXfCdV1paGoYNG4ZNmzYV2E6j0RgcX6VKlULXYUybvO7cuSOvLzAw0Kj2lSpVMmqdHh4eWLRoEUaOHImPPvoIH330ESpWrIjWrVujV69eeOGFF+Du7l6kvERkeSyMiGyUSqVC06ZNsWrVKqSnp2Pr1q3YvHmz0YWR9siMOYQQJs87adIkbNq0CbVr18asWbPQokULlC9fXj7i1bZtWxw5ciTfdbi6uha6DmPa5JX3NQkLCyu0/eMXexe2zmeffRYhISHYunUrDh06hN9//x2bNm3Cpk2bMHXqVOzZswcNGjQoUmYisiwWRkSlQI8ePbB161YkJSXJ4xwdHZGdnY379+/Dw8NDbx7tUZ78REdHGxyvvaXcxcUF5cqVA/DfnXOXL1+GEMKoo0Zr164FAKxZswYNGzbUm37lypVCl2Fp5cuXh6urKx48eIA5c+YUSz9HarUaw4YNw7BhwwAAcXFxeOutt7BlyxaMGTMGkZGRFl8nERmPt+sTKZwxR2WuX78OAAgICJDHaU/xXLhwQa/92bNnERcXV+Ay8+uvZ+nSpQCA9u3bw8Hh0Xer5s2bo3z58rh9+zY2b95caF4AuHv3LgAgKChIb9ru3bt1irySYm9vj+7duwP4r3ArboGBgZg+fToA8HZ9IgVgYUSkcN999x3CwsLwxx9/6E0TQmDjxo349ttvAQBDhgyRp4WEhAAApk+fjszMTHl8TEwMwsLCCi24Tp06hc8++0xn3OHDhzFv3jwAwDvvvCOPd3BwwOTJkwEAr776Kg4ePKi3vBMnTuDff/+Vf69Tpw4A4JtvvtFpd+nSJbz++usFZitO4eHhcHJywvvvv48lS5YYPOX4119/YePGjUVa7pkzZ7BmzRo8ePBAb9q2bdsAGC4Siahk8VQakcJlZ2dj6dKlWLp0KXx8fNCkSROUL18eycnJ+Pvvv+VTWy+++CJeeukleb4PP/wQ69evx44dO1CzZk20aNECt2/fxokTJ9CuXTu0bdvWYLGlNXbsWEyaNAlLly5Fw4YNER8fj0OHDiE3Nxdvv/02nnrqKZ32b7/9Ni5duoQFCxagU6dOaNKkCWrVqgWNRoOLFy/in3/+wf79++WjWuHh4XjuuecwZcoUrF27FvXq1UNiYiIOHTqEDh06wN/fv8B8xaVp06ZYvnw5RowYgREjRuCjjz5C3bp14ePjg7t37+LcuXP4999/MXjwYAwcONDo5cbGxmLIkCFwdXVF06ZNERgYiIcPH+LcuXO4dOkSnJyc9ApRIip5PGJEpHAvvfQSNm/ejLfeegvBwcH4+++/sW7dOuzfvx/29vYYOnQodu7ciWXLlsHO7r+3dHBwMP744w8MHDgQ9+/fx/bt25GQkIDJkydjx44dcHR0LHC9zzzzDPbs2QM/Pz/s2LEDx48fR9OmTbF48WJ89dVXeu1VKhXmz5+PnTt3on///oiPj8eGDRtw4sQJlC9fHtOnT9e5lmjgwIGIjIxEt27dcPPmTWzduhWJiYmYNm0adu7cWWi+4jRo0CCcP38e77zzDsqWLYvff/8dGzZswN9//43q1atj1qxZmDlzZpGW2bp1a8yaNQtdunRBfHw8tm7dil9//RX29vYYPXo0zp49i169ehXTX0RExlIJc24rISIiIipFeMSIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjCwoiIiIhIwsKIiIiISMLCiIiIiEjiYO0AtiI7Oxs5OTnWjkFERERF4OjoCHt7e6PbszAqhEajQVJSEjIzM60dhYiIiIpIpVJBrVbDz88PKpWq0PYsjAqg0Whw48YNuLu7o3z58nB0dDTqRSUiIiLrE0IgLS0Nt2/fhqurK8qWLVvoPCyMCpCUlAR3d3cEBASwICIiIrJBrq6uyMzMRGJiItRqdaGf57z4Oh/Z2dnIzMw06kUkIiIi5fL09EROTo5R1wqzMMqH9sVzdHS0chIiIiIyh4PDoxNkDx8+LLQtC6NC8GgRERGRbSvKZzkLIyIiIiIJCyMiIiIiCe9KM9H169eRlJRk1Qzly5dH5cqVrZqBnjzc9slWcFslkwgy6MGDB+Lvv/8WDx480JsWGxsrypQpIwBYdShTpoyIjY016e8LDw8XAMTt27cNTq9Xr57o1KmTEEKI6OhoeZ3r168v8rIGDRokAIgJEybkmyc6OlqMGDFCVK1aVTg7OwtfX1/RoUMHMXXqVJ12OTk5YsmSJaJly5bCy8tLuLu7ixo1aohhw4aJI0eOyO32798vAIh169bpreuff/4Ro0ePFjVq1BCurq7C1dVV1KlTR7z55pvizz//NPi3VahQQaSlpektKygoSPTp00dnXEH/s9dee01uFxYWpjPNzc1NBAcHi2effVasX79e5OTk5Pt6WUtsbKwo4+Ji/W3fxcXkbV/r6tWr4tVXXxXBwcHC2dlZeHh4iLZt24qvvvpKpKenCyGEyMzMFF999ZVo3Lix8PDwEGq1WtStW1e88sor4sKFC/KyFi1aJACIEydOyOO02452cHBwEEFBQeKtt94S9+7d08tj7LqMoV3nnDlz9KY9njXv+7ugYdGiRQbb29nZicDAQDFgwABx5syZIuUsTrGxsaKMqwK2VVfzt1UhSu/2+vh+0NAQFBSUb3sPDw/RsGFDMWfOHJGRkZHvegr6TH8cjxiZICkpCenp6Vi+fDnq1KljlQwXLlzAiy++iKSkpBL9NvLxxx9j4MCBRl/IptFosG3bNlSpUgWrVq3CrFmz9Oa9evUqWrRoAVdXV4waNQpVqlTBzZs3cfr0acyePRvTp0+X244dOxbz5s1D//79ERoaCgcHB1y6dAk7d+5E1apV0bp16wLzbN++HYMHD4aDgwNCQ0PRqFEj2NnZ4eLFi9i4cSPmz5+P6OhoBAUF6cyXmJiI+fPn49133zXq7+7evTuGDx+uN75mzZo6vzs7O+PHH38EADx48ACxsbHYtm0bnnvuOXTu3BlbtmyBp6enUessCUlJSUjPyMByANbZ8oELAF7MyDBr2//ll18waNAgODs7Y/jw4ahfvz6ysrJw+PBhvP/++zh//jwWLlyIZ599Fjt37sTQoUPxyiuvIDs7GxcvXsT27dvRtm1b1K5du9B1zZ8/H+7u7khLS8PevXvxzTff4PTp0zh8+LBOO0us63Gff/453njjDZQpUybfNj4+Pli2bJnBaTk5ORg/fjxSU1PRpEkTnWlDhw7FU089hZycHFy4cAHz58/Hzp07cfToUTRu3LjIWS0tKSkJ6Q8ysPxNoI6/dTJciAde/M68bRUo3dvra6+9hpCQEIPT9u7di8WLF+vt1/PuN5OTk7Fhwwa89957OHHiBFavXm3UegtkdFn3hCmoujx16pQAIE6dOmWFZJbJYMoRo8aNGwsAYsOGDUYv6+effxaOjo5i3759AoA4cOCAXps333xTODg4iJiYGL1pCQkJ8s+3bt0SKpVKvPLKK3rtcnNzddoaOmJ09epV4ebmJurUqSPi4+P1lpGdnS3mzp0rrl+/rve3NW7cWPj6+srfzLTyO2I0evRoveU/LiwsTLi5uRmcFhERIQCI559/vtDllCR5uwOEsNJwSvqmaOq2/88//wh3d3dRu3Ztg9vBlStXxFdffSWOHz8uAIiZM2fqtXn48KFISkqSfy/oG/jj74vBgwcLAOLYsWPyuKKsyxh5369ffPGFzjRDWfMzefJkvWVo9weff/65TtutW7cKAOLVV18tUtbiIm+rn0CIFdYZTn1i3rYqxJOxvRoSHx8vfHx8RFBQkLh796483tB+MycnRzRv3lwAEDdu3DC4vKIcMeLF12S0IUOGoGbNmvj4448hhDBqnhUrVqB79+7o0qUL6tSpgxUrVui1uXbtGgICAvSO0gBAhQoV5J+jo6MhhEC7du302qlUKp22hnz22WdIS0vDokWLULFiRb3pDg4OGDt2LAIDA/WmTZ06FQkJCZg/f36B67CUDz74AD169MC6detw+fLlElnnk+Kzzz5DamoqfvrpJ4PbQfXq1fH222/j2rVrAGBwe7O3t0e5cuVMWn+HDh0AQF5+3p8tua527dqha9eu+Oyzz/DgwYMiz793715ERETgqaeewjvvvFNo+65duwJ49D4ly3lStte8cnNzERoainv37mHlypXw8vIqsL2dnR06d+4MAIiJiTFr3QDvSqMisLe3x0cffYQ///wTmzZtKrR9fHw89u/fj6FDhwJ4dOh9/fr1yMrK0mkXFBSEuLg47Nu3r8DlaQundevWIT09vcj5t2/fjurVq6NVq1ZFnrdDhw5F+pDJkE71PD48/rcXZNiwYRBCYM+ePUXOS/nbtm0bqlatirZt2xbYTru9rVixwqhO4Yyl3XHn3dkX17qmTZtmUkGfkJCA0NBQ+Pn5YcmSJUadOtd+WJr7oUi6nqTtVWvGjBnYv38/pk+fXujfrWXJ7Y+FERXJCy+8gBo1ahh11GjVqlVwdnZG//79ATw64nTv3j3s2LFDp93YsWPh5OSEbt26oUmTJhg3bhy2bNmiV/xUrFgRw4cPxy+//IKAgAAMHDgQX3zxBS5evFhobo1Gg/j4eNSvX19vWnJysk7xkl/hEx4ejoSEBCxYsKDQ9f3000/w8fHRGzZu3FjovFrarHm/qZF5tA+GbtCgQaFtW7dujU6dOuGHH35AQEAAXnjhBXz33Xe4fv16kdZ59+5dJCUlITY2FosWLcK8efPg4+ODjh07Wnxdj+vQoQO6dOmCzz//3OijRrm5uRg2bBhu376NFStWoHz58gbbpaenIykpCQkJCYiMjMSIESMAAIMGDTIrM/3nSdteASAyMhIzZsxAt27d8MEHH+TbTru/vnbtGiIiIrB582Y0bNgQtWrVMjsDCyMqkrxHjTZv3lxg2xUrVqBPnz7w8PAAANSoUQPNmjXTO51Wr149REVF4cUXX0RMTAzmzp2LAQMGwNfXFz/88INO20WLFuHbb79FcHAwNm3ahPfeew916tRBt27dcOPGjXyzaDQaAIC7u7vetM6dO+sUL/PmzTO4jI4dO6JLly5GHTXq378/9uzZozd06dKlwPny0ma9f/++0fNQwbTbgXabLIhKpcLu3bvxySefwMvLC6tWrcLo0aMRFBSEwYMHIzk52ah11qpVCz4+PqhSpQpGjRqF6tWrY+fOnToXRFtqXYZMmzYNt27dMqqgB4BZs2Zhz549mDx5snx6wpDw8HD4+PjAz88PnTt3xrVr1zB79mwMHDjQ5Kyk60nbXpOSkvDCCy+gXLlyWL58OezsDJcoaWlp8v66evXq+PDDD9GmTRujzmQYg4UR5Su/w+ehoaGoXr16gUeNLly4gDNnzqBdu3a4evWqPHTu3Bnbt2+X3/BaNWvWxLJly5CUlISzZ8/i008/hYODA1599VX89ttvcjs7OzuMHj0ap06dQlJSErZs2YLevXtj3759GDJkSL5/i3bHkpqaqjft+++/x549e7B8+fJCXxNjP2QCAgIQEhKiN/j6+ha6Di1tVmN2imQc7R1+xhabzs7OmDx5Mi5cuID4+HisWrUKrVu3xtq1azFmzBijlrFhwwbs2bMHK1euROvWrZGYmAhXV9diWZchRSnof//9d4SHh6NDhw4IDw8vsO2rr76KPXv2YO/evTh16hQSExMxYcIEk3OSvidpexVCYPjw4bh58yaWLl0KPz+/fNu6uLjIXzYPHjyIuLg4/P7776hatWqR12sIC6MnlIuLCwDku6NMT0+X2zxOe9QoKioKW7ZsMdhGW2S88847qFGjhjx88cUXyMjIwIYNG/JddoMGDTBp0iS5+jd0wTbw6Fxyv379sGPHDnTq1AmHDx9GbGyswbZqtRoVK1bEX3/9pTetVatWCAkJMXgh4eM6duyIzp07m3xBa1Fos1avXr1Y1/Mk8fT0hL+/v8HtoDAVK1bEkCFDcPDgQdSoUQNr16416vqKjh07IiQkBEOHDsWePXvg6uqK0NBQ5ObmWnxd+QkPD8etW7fw/fff59vm7t27GDp0KDw9PbFy5UrY29sXuMwaNWogJCQEXbt2RdOmTeHs7GxyPjLsSdpe58yZg507d+K9995Dz549C2xrb28vf9ns0KEDAgICirSuwrAwekJpL567dOmS3rT09HTExcUZvEtM68UXX0T16tUxffp0vaNGQgisXLkSXbp0wbp16/SGhg0b5lvs5NW8eXMAwM2bNy3Stk+fPrh69SqOHz9e6PIKoj1qVNCHjCUsW7YMKpUK3bt3L9b1PGmefvppXLt2DUeOHDFpfkdHRzRs2BDZ2dlF7lXZ3d0d4eHhiIqKwtq1a4t1XXl16tQJnTt3xuzZs/Mt6EeMGIG4uDgsWrTI4h80ZLonYXs9duwYJk+ejFatWmHmzJlFylgcWBg9obp16wYnJyfMnz9f75vAwoUL8fDhQ/Tu3Tvf+fMeNdq6davOtN9//x0xMTEYOXIknnvuOb1h8ODB2L9/P+Lj4wEAhw4dQnZ2tt46tBdpay+mu3XrFv7++2+9dllZWdi7dy/s7OwKPLoyYcIElClTBqNGjUJCQoLedGO7IMj7IZORkWHUPEU1a9Ys/Prrrxg8eDBq1KhRLOt4Uk2YMAFubm54+eWXDW4H165dw9y5c3HlyhWDF5MmJyfjyJEj8PLygo+PT5HXHxoaioCAAMyePVseV1zryktb0C9cuFBv2ldffYVt27bhrbfeQr9+/cxaD1lWad9ek5OTMWTIEJQpUwarVq2Co6NjkTNaGnu+NsOFCxdsdt0VKlTA1KlT8dFHH6Fjx47o168fypQpgz/++AOrVq1Cjx490Ldv3wKXERoaihkzZiAqKkpn/IoVK2Bvb48+ffoYnK9fv36YPHkyVq9ejfHjx2P27Nk4deoUBg4ciIYNGwIATp8+jaVLl8Lb2xvjxo0DAPz7779o2bIlunbtim7dusHPzw+JiYlYtWoV/vzzT4wbNy7fO2iAR4f+V65ciaFDh6JWrVpyz9dCCERHR2PlypWws7Mz6ttyeHh4gRdSX7582eA1S76+vjpHgB4+fCi3y8jIQGxsLLZu3YqzZ8+iS5cuBj/ElMB6W775665WrRpWrlyJwYMHo06dOjo9Cf/xxx9Yt24dRowYgUqVKuGFF15A79690aFDB3h7e+PGjRtYsmQJ4uPj8dVXXxV6uskQR0dHvP3223j//fexa9cu9OrVC3/++WexrCuvTp06oVOnToiMjNQZf/bsWUycOBHu7u5o1KhRvtfaNWzYUH5/2pIL8ba97tK+vb7++uuIiYnB4MGD8fvvv+P333832O7FF18scnaTmdIj5ZOgtD8rTWv58uWidevWws3NTTg7O4vatWuL6dOn6zxzJr+eboX4rwdVSD2mZmVliXLlyokOHToUuN7g4GDRpEkTIYQQv//+uxg9erSoX7++UKvVwtHRUVSuXFmMGDFCXLt2TZ5Ho9GIuXPnip49e4qAgADh6OgoPDw8RJs2bcQPP/wgcnNz5bYFPSvt6tWr4o033hDVq1cXLi4uwtXVVdSuXVu8/vrrIioqSqdtQb16d+rUSQAo0rPStL2JC6H/zJ8yZcqIKlWq8Flpxmz7FnhW2uXLl8Urr7wiqlSpIpycnISHh4do166d+Oabb0RGRoZISEgQs2bNEp06dRIVK1YUDg4OwsvLS3Tt2lXvmYFF6UlYCCFSUlKEWq2Wt4eirMsYgOHe17Xvi7xZ876HCxrCw8OFEAXvD5SktD0rrbRur0FBQUa9jloFPTGgIEXp+VolhJHnD54wGRkZiI6ORnBwsMGLkPnUZnpScdsnW8FtlbQK+0zPi4VRPoryIhIREZFyFeUzndcYERHZgJycHNy+fbvANu7u7gY7MSUqaba8vbIwIiKyAXFxcQgODi6wTXh4OKZNm1YygYgKYMvbKwsjIiIb4OfnV+gDhS3V8y+RuWx5e2VhRERkA1xcXBASEmLtGERGseXtlR08EhEREUlYGBWCN+0RERHZtqJ8lrMwyoejoyNUKhXS0tKsHYWIiIjMkJ6eDgBGPXKE1xjlw97eHmq1Grdv30ZmZiY8PT3h4OAAlUpl7WhERERkBCEE0tPTkZiYiLJlyxr1qBJ28FgAIQRSUlKQmJiInJwca8chIiIiE5QtWxZ+fn5GHdxgYWQEIQRycnLw8OFDa0chIiKiInB0dCzSA3RZGBERERFJePE1ERERkYSFEREREZGEhRERERGRhIURERERkYSFEREREZGEhRERERGRhIURERERkeT/HXz1pcUehh4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = bwp.createGrid(csiRsConfig.numPorts)\n",
    "grid.print()\n",
    "\n",
    "csiRsConfig.populateGrid(grid)\n",
    "\n",
    "gridStats = grid.getStats()\n",
    "print(\"Grid Allocation Stats:\")\n",
    "for key, value in gridStats.items():\n",
    "    print(\"  %s: %d\"%(key, value))\n",
    "    \n",
    "grid.drawMap(ports=[0,1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ce95ec",
   "metadata": {},
   "source": [
    "## Comparing CSI-RS values\n",
    "Now we want to compare the CSI-RS values generated by our `CsiRsConfig` object with Matlab's results. The ``getReValues`` method of the ``Grid`` class returns all the complex values allocated for the specified type. We call this function 2 times with ``CSIRS_ZP``, and ``CSIRS_NZP`` to get the allocated values for Zero-Power and Non-Zero Power CSI-RS resources correspondingly.\n",
    "\n",
    "We then concatenates these values to get all CSI-RS resources in a single array ``csirsSymbols``.\n",
    "\n",
    "The results are compared with the values generated by Matlab's ``nrCSIRS`` function in the 5G toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0b88e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSI-RS NZP Values:\n",
      " [ 0.7071+0.7071j -0.7071+0.7071j  0.7071+0.7071j  0.7071+0.7071j\n",
      "  0.7071+0.7071j  0.7071+0.7071j  0.7071-0.7071j -0.7071+0.7071j\n",
      "  0.7071+0.7071j  0.7071+0.7071j]\n",
      "Matlab-Generated CSI-RS NZP Values:\n",
      " [ 0.7071+0.7071j -0.7071+0.7071j  0.7071+0.7071j  0.7071+0.7071j\n",
      "  0.7071+0.7071j  0.7071+0.7071j  0.7071-0.7071j -0.7071+0.7071j\n",
      "  0.7071+0.7071j  0.7071+0.7071j]\n",
      "Maximum Difference: 1.5700924586837752e-16\n"
     ]
    }
   ],
   "source": [
    "csirsZpSymbols = grid.getReValues(\"CSIRS_ZP\")     # These are all Zeros (ZP: Zero Power)\n",
    "csirsNzpSymbols = grid.getReValues(\"CSIRS_NZP\")\n",
    "csirsSymbols = np.concatenate((csirsZpSymbols,csirsNzpSymbols))\n",
    "\n",
    "# Load Matlab-generated CSI-RS symbols:\n",
    "csirsSymbolsMatlab = scipy.io.loadmat(matlabPath + '/csirsSym.mat')['csirsSym'].T.flatten()\n",
    "maxDiff = np.abs(csirsSymbols-csirsSymbolsMatlab).max()\n",
    "assert maxDiff<1e-10, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "# Print the first 10 CSI-RS NZP Symbols:\n",
    "print(\"CSI-RS NZP Values:\\n\",np.round(csirsSymbols[200:210],4))\n",
    "print(\"Matlab-Generated CSI-RS NZP Values:\\n\",np.round(csirsSymbolsMatlab[200:210],4))\n",
    "print(\"Maximum Difference:\", maxDiff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2151f1",
   "metadata": {},
   "source": [
    "We can also compare the whole grid with the one grid created by Matlab's function ``nrResourceGrid``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d48dd38d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid:\n",
      " [ 0.    +0.j     -0.7071-0.7071j  0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j      0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j      0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j    ]\n",
      "Matlab-Generated Grid:\n",
      " [ 0.    +0.j     -0.7071-0.7071j  0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j      0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j      0.    +0.j      0.    +0.j\n",
      "  0.    +0.j      0.    +0.j    ]\n",
      "Maximum Difference: 1.5700924586837752e-16\n"
     ]
    }
   ],
   "source": [
    "gridMatlab = scipy.io.loadmat(matlabPath + '/txGrid.mat')['txGrid']\n",
    "gridMatlab = np.transpose(gridMatlab, (2,1,0)) # Matlab uses a different order\n",
    "maxDiff = np.abs(gridMatlab-grid.grid).max()\n",
    "assert maxDiff<1e-10, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "# Print the first 10 Data Symbols:\n",
    "print(\"Grid:\\n\", np.round(grid[0,:,127],4))\n",
    "print(\"Matlab-Generated Grid:\\n\", np.round(gridMatlab[0,:,127],4))\n",
    "print(\"Maximum Difference:\", maxDiff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b791e56d",
   "metadata": {},
   "source": [
    "## OFDM Modulation\n",
    "To ger a time-domain Now we want to compare the ``ofdmModulate`` method of the ``Grid`` class. This waveform is then compared with the one created by Matlab's ``nrOFDMModulate`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2818bf70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waveform Shape: (4, 30720)\n",
      "Waveform Data:\n",
      " [0.0017-0.0022j 0.0027-0.0026j 0.0038-0.0027j 0.0048-0.0026j\n",
      " 0.0054-0.0023j 0.0055-0.0019j 0.0051-0.0015j 0.0042-0.0013j\n",
      " 0.0028-0.0012j 0.0012-0.0013j]\n",
      "Matlab-Generated Waveform Data:\n",
      " [0.0017-0.0022j 0.0027-0.0026j 0.0038-0.0027j 0.0048-0.0026j\n",
      " 0.0054-0.0023j 0.0055-0.0019j 0.0051-0.0015j 0.0042-0.0013j\n",
      " 0.0028-0.0012j 0.0012-0.0013j]\n",
      "Maximum Difference: 3.878959614448864e-18\n"
     ]
    }
   ],
   "source": [
    "waveForm = grid.ofdmModulate()\n",
    "\n",
    "waveformMatlab = scipy.io.loadmat(matlabPath + '/txWaveform.mat')['txWaveform'].T\n",
    "maxDiff = np.abs(waveForm[:]-waveformMatlab).max()\n",
    "assert maxDiff<1e-10, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "# print the first 10 samples of the waveForm for first TX antenna\n",
    "print(\"Waveform Shape:\", waveForm.shape)\n",
    "print(\"Waveform Data:\\n\", np.round(waveForm[0,3000:3010],4))\n",
    "print(\"Matlab-Generated Waveform Data:\\n\", np.round(waveformMatlab[0,3000:3010],4))\n",
    "print(\"Maximum Difference:\", maxDiff)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55080db",
   "metadata": {},
   "source": [
    "## CDL Channel Model\n",
    "Now we want to create a CDL Channel object (``CdlChannel``) and apply it to the time-domain waveform. \n",
    "\n",
    "Since CDL is a statistical model, there is always a randomness with the way the phases are initialized and the way rays are coupled. The ``getMatlabRandomInit`` helper function can be used to create the same random initial phases and ray couplings that are generated by the Matlab code.\n",
    "\n",
    "**Note 2:**\n",
    "The NeoRadium's implementation of FIR filters used by the CDL channel is slightly different from Matlab. To compensate for this difference we need to modify the ``stopBandAttenuation`` parameter. See the documentation of ``ChannelFilter`` class for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c59ed11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CDL-D Channel Properties:\n",
      "  delaySpread: 10 ns\n",
      "  dopplerShift: 10 Hz\n",
      "  carrierFreq: 4000000000.0 Hz\n",
      "  normalizeGains: True\n",
      "  normalizeOutput: True\n",
      "  txDir: Downlink\n",
      "  timing method: Matlab\n",
      "  coherenceTime: 0.042314 (Sec.)\n",
      "  ueDirAZ: 0°, 90°\n",
      "  Angle Scaling:\n",
      "    Means: 130° 70° 80° 110°\n",
      "    RMS Spreads: 5° 11° 3° 3°\n",
      "  pathDelays (ns): 0.0000 0.0000 0.3500 6.1200 13.630 14.050 18.040 25.960 17.750 40.420\n",
      "                   79.370 94.240 97.080 125.25\n",
      "  pathPowers (db): -0.200 -13.50 -18.80 -21.00 -22.80 -17.90 -20.10 -21.90 -22.90 -27.80\n",
      "                   -23.60 -24.80 -30.00 -27.70\n",
      "  AODs (Degree):    0    0   89   89   89   13   13   13   35  -64  -33   53 -132   77\n",
      "  AOAs (Degree): -180 -180   89   89   89  163  163  163 -137   74  128 -120   -9  -84\n",
      "  ZODs (Degree):   98   98   86   86   86   98   98   98   98   88   91  104   80   86\n",
      "  ZOAs (Degree):   82   82   87   87   87   79   79   79   78   74   78   87   71   73\n",
      "  hasLOS: True\n",
      "  Cross Pol. Power: 11 db\n",
      "  angleSpreads: 5° 8° 3° 3°\n",
      "  TX Antenna:\n",
      "    Total Elements: 4\n",
      "    spacing: 0.5𝜆, 0.5𝜆\n",
      "    shape: 1 rows x 2 columns\n",
      "    polarization: x\n",
      "    taper: 1.0\n",
      "  TX Antenna Orientation (𝛼,𝛃,𝛄): 10° 20° 30°\n",
      "  RX Antenna:\n",
      "    Total Elements: 4\n",
      "    spacing: 0.5𝜆, 0.5𝜆\n",
      "    shape: 1 rows x 2 columns\n",
      "    polarization: +\n",
      "    taper: 1.0\n",
      "  Channel Filter:\n",
      "    filterDelay (samples): 7\n",
      "    numTxAntenna: 4\n",
      "    numPaths: 14\n",
      "    pathDelays (ns): 0.0000 0.0000 0.3500 6.1200 13.630 14.050 18.040 25.960 17.750 40.420\n",
      "                     79.370 94.240 97.080 125.25\n",
      "    filterLen: 16\n",
      "    numInterpol: 50\n",
      "    normalize: True\n",
      "    stopBandAtten: 70.151\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cdlModel = 'D'\n",
    "seed = 123\n",
    "phiInit, coupling = CdlChannel.getMatlabRandomInit(cdlModel, seed)\n",
    "\n",
    "stopBandAttenuation = 70\n",
    "# Our calculation of beta is different from Matlab (See the ChannelFilter.getMultiRateFIR method)\n",
    "# The following code compensates for the difference:\n",
    "stopBandAttenuation += 8.861-8.71   # We use \"8.861\"; Matlab uses \"8.71\". See Note 2 above.\n",
    "\n",
    "# Create the channel model\n",
    "channel = CdlChannel('D', delaySpread=10, carrierFreq=4e9, dopplerShift=10,\n",
    "                     initialPhases = phiInit, rayCoupling = coupling,\n",
    "                     txAntenna = AntennaPanel([1,2], polarization=\"x\", matlabOrder=True),\n",
    "                     rxAntenna = AntennaPanel([1,2], polarization=\"+\", matlabOrder=True),\n",
    "                     txOrientation = [10, 20, 30],\n",
    "                     angleScaling = ([130,70,80,110], [5,11,3,3]),\n",
    "                     stopBandAtten = stopBandAttenuation,\n",
    "                     timing = 'Matlab')   # Use Matlab timing only when comparing results with Matlab\n",
    "channel.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c88dda2",
   "metadata": {},
   "source": [
    "## Comparing the channel matrix with Matlab\n",
    "NeoRadium supports different methods for creating a channel matrix from a CDL channel model. The method that matches Matlab's ``nrPerfectChannelEstimate`` function is called ``TimeDomain2``. Note that this is **NOT** the default method used by NeoRadium. See the documentation of ``getChannelMatrix`` method for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52c43fbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hActual Shape: (14, 300, 4, 4)\n",
      "Maximum Difference: 6.687402477901097e-12\n",
      "Channel:\n",
      " [-0.0038-0.0173j -0.0038-0.0173j -0.0038-0.0173j -0.0038-0.0173j\n",
      " -0.0038-0.0173j -0.0038-0.0173j -0.0034-0.0171j -0.0034-0.0171j\n",
      " -0.0034-0.0171j -0.0034-0.0171j -0.0034-0.0171j -0.0034-0.0171j\n",
      " -0.0034-0.0171j -0.0034-0.0171j]\n",
      "Matlab-Generated Channel:\n",
      " [-0.0038-0.0173j -0.0038-0.0173j -0.0038-0.0173j -0.0038-0.0173j\n",
      " -0.0038-0.0173j -0.0038-0.0173j -0.0034-0.0171j -0.0034-0.0171j\n",
      " -0.0034-0.0171j -0.0034-0.0171j -0.0034-0.0171j -0.0034-0.0171j\n",
      " -0.0034-0.0171j -0.0034-0.0171j]\n"
     ]
    }
   ],
   "source": [
    "hActual = channel.getChannelMatrix(bwp, numSlots=1, timeDomain=True)\n",
    "\n",
    "# Compare with Matlabmatlab results\n",
    "hActualMatlab = np.transpose(scipy.io.loadmat(matlabPath + '/H_actual.mat')['H_actual'], (1,0,2,3))\n",
    "maxDiff = np.abs(hActual-hActualMatlab).max()\n",
    "assert maxDiff<1e-8, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "print(\"hActual Shape:\", hActual.shape)\n",
    "print(\"Maximum Difference:\", maxDiff)\n",
    "\n",
    "print(\"Channel:\\n\", np.round(hActual[:,0,0,0],4))\n",
    "print(\"Matlab-Generated Channel:\\n\", np.round(hActualMatlab[:,0,0,0],4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0028863",
   "metadata": {},
   "source": [
    "## Applying the channel to the waveform\n",
    "Now we can apply our CDL channel to the waveform. Since the channel has some propagation delay, to make sure the whole waveform goes through the channel we need to append zeros to the end of the waveform. \n",
    "\n",
    "The number of these zero paddings is equal to the channel delay which can be obtained using the ``getMaxDelay`` function.\n",
    "\n",
    "The ``applyToSignal`` method is then used to apply the channel to the waveform. It returns the waveform transformed by the channel (``rxWaveform``).\n",
    "\n",
    "This ``rxWaveform`` is then compared the waveform created by Matlab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ba00f76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max. Channel Delay (Samples): 11\n",
      "RX Waveform Sahpe: (4, 30731)\n",
      "RX Waveform Data:\n",
      " [ 9.28855165e-05-2.17075417e-05j  8.57030299e-05-2.02737737e-05j\n",
      "  7.42472190e-05-1.58492709e-05j  5.84693723e-05-1.02529180e-05j\n",
      "  3.89449786e-05-5.42511598e-06j  1.68494399e-05-3.08190658e-06j\n",
      " -6.18506472e-06-4.40357150e-06j -2.83048719e-05-9.80838147e-06j\n",
      " -4.77166484e-05-1.88491000e-05j -6.29606231e-05-3.02508414e-05j]\n",
      "Matlab-Generated Waveform Data:\n",
      " [ 9.28855165e-05-2.17075417e-05j  8.57030299e-05-2.02737737e-05j\n",
      "  7.42472190e-05-1.58492708e-05j  5.84693723e-05-1.02529180e-05j\n",
      "  3.89449786e-05-5.42511597e-06j  1.68494399e-05-3.08190658e-06j\n",
      " -6.18506471e-06-4.40357151e-06j -2.83048719e-05-9.80838148e-06j\n",
      " -4.77166484e-05-1.88491000e-05j -6.29606231e-05-3.02508414e-05j]\n",
      "Maximum Difference: 7.457436639872327e-14\n"
     ]
    }
   ],
   "source": [
    "maxDelay = channel.getMaxDelay()\n",
    "print(\"Max. Channel Delay (Samples):\",maxDelay)\n",
    "# Append to the waveForm before passing it through the channel\n",
    "txWaveform = waveForm.pad(maxDelay)\n",
    "\n",
    "# Now apply the channel to the waveform\n",
    "rxWaveform = channel.applyToSignal(txWaveform)\n",
    "\n",
    "# Compare results with Matlab:\n",
    "rxWaveformMatlab = scipy.io.loadmat(matlabPath+'/rxWaveform.mat')['rxWaveform']\n",
    "maxDiff = np.abs(rxWaveform[:].T-rxWaveformMatlab).max()\n",
    "assert maxDiff<1e-10, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "# print the first 10 samples of the waveForm for first TX antenna\n",
    "print(\"RX Waveform Sahpe:\", rxWaveform.shape)\n",
    "print(\"RX Waveform Data:\\n\", rxWaveform[0,3000:3010])\n",
    "print(\"Matlab-Generated Waveform Data:\\n\", rxWaveformMatlab[3000:3010,0])\n",
    "print(\"Maximum Difference:\", maxDiff)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683b8dd7",
   "metadata": {},
   "source": [
    "## Adding AWGN\n",
    "Again to make sure we have deterministic results, instead of generating random noise we read the noise values from the file ``noise.mat`` which was generated by Matlab . Now we have the noisy received time-domain waveform ``noisyWaveForm``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af697aad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noise Varriance: 1.2203149233307698e-09 (50.00 dB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4, 30731)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add noise: (SNR=50 dB)\n",
    "noise = scipy.io.loadmat(matlabPath+'/noise.mat')['noise']\n",
    "print(\"Noise Varriance:\", noise.var(), \"(%.2f dB)\"%(10*np.log10(1/(noise.var()*channel.nrNt[0]*bwp.nFFT))) )\n",
    "noisyWaveForm = rxWaveform.addNoise(noise=noise.T)\n",
    "noisyWaveForm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6793b2d7",
   "metadata": {},
   "source": [
    "## Synchronization\n",
    "To synchronize the received noise waveform, we need to calculate the offset value which is the number of samples to skip at the begining of the received signal. This can be obtained directly from the channel model using the ``getTimingOffset`` method. \n",
    "\n",
    "But in practice, the channel information is not available and we need to estimate this value. The method ``estimateTimingOffset`` of the ``Grid`` class does exactly that.\n",
    "\n",
    "The following code uses both methods and compares the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4cc6a26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Offset from the Channel Model: 7\n",
      "Estimated Offset: 7\n"
     ]
    }
   ],
   "source": [
    "# We can get channel delay from the channel (This is cheating because we don't know the channel)\n",
    "chOffset = channel.getTimingOffset(txWaveform.shape[1])\n",
    "print(\"Channel Offset from the Channel Model:\", chOffset)\n",
    "\n",
    "# Or estimate the offset using a resource grid with NZP CSI-RS and the recived signal\n",
    "rxCsiRsConfig = CsiRsConfig([CsiRsSet(\"NZP\", bwp, symbols=[1], numPorts=2, freqMap='001000', period=5, offset=1)])\n",
    "csiRsGrid = bwp.createGrid(rxCsiRsConfig.numPorts)\n",
    "rxCsiRsConfig.populateGrid(csiRsGrid)\n",
    "offset = csiRsGrid.estimateTimingOffset(noisyWaveForm)\n",
    "print(\"Estimated Offset:\", offset)\n",
    "\n",
    "# Now apply the offset to the received waveform\n",
    "syncedWaveForm = noisyWaveForm.sync(offset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606dc414",
   "metadata": {},
   "source": [
    "## OFDM Demodulation\n",
    "Now we can use the ``ofdmDemodulate`` method of the ``Grid`` class to calculate the received grid ``rxGrid`` in frequency domain. This results are then compared with the grid created by Matlab's ``nrOFDMDemodulate`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83626aa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RX Grid Sahpe: (4, 14, 300)\n",
      "RX Grid:\n",
      " [-2.61575139e-03-0.00175339j -1.06114113e-03-0.0013182j\n",
      " -8.51931543e-05-0.00099349j  8.12506622e-04+0.00099399j\n",
      "  4.40631448e-04-0.00146395j -1.26437220e-03+0.0011728j\n",
      " -9.79090970e-04+0.00045581j -3.08299457e-04+0.00019759j\n",
      "  8.56087026e-04-0.00012872j -1.62260526e-03-0.00062589j\n",
      "  1.10153725e-03+0.00029551j -5.56870049e-04-0.00037998j\n",
      "  3.54244624e-04-0.00114511j -7.91488916e-04-0.00068177j]\n",
      "Matlab-Generated RX Grid:\n",
      " [-2.61575139e-03-0.00175339j -1.06114113e-03-0.0013182j\n",
      " -8.51931543e-05-0.00099349j  8.12506622e-04+0.00099399j\n",
      "  4.40631448e-04-0.00146395j -1.26437220e-03+0.0011728j\n",
      " -9.79090970e-04+0.00045581j -3.08299457e-04+0.00019759j\n",
      "  8.56087026e-04-0.00012872j -1.62260526e-03-0.00062589j\n",
      "  1.10153725e-03+0.00029551j -5.56870049e-04-0.00037998j\n",
      "  3.54244624e-04-0.00114511j -7.91488916e-04-0.00068177j]\n",
      "Maximum Difference: 8.538668235349703e-12\n"
     ]
    }
   ],
   "source": [
    "rxGrid = Grid.ofdmDemodulate(bwp, syncedWaveForm)\n",
    "\n",
    "# Compare results with Matlab:\n",
    "rxGridMatlab = scipy.io.loadmat(matlabPath + '/rxGrid.mat')['rxGrid']\n",
    "rxGridMatlab = np.transpose(rxGridMatlab, (2,1,0)) # Matlab uses a different order\n",
    "\n",
    "maxDiff = np.abs(rxGridMatlab-rxGrid.grid).max()\n",
    "assert maxDiff<1e-10, \"MISMATCH WITH MATLAB!!! (max Diff: %f)\"%(maxDiff)\n",
    "\n",
    "# Print the first 10 Data Symbols:\n",
    "print(\"RX Grid Sahpe:\", rxGrid.shape)\n",
    "print(\"RX Grid:\\n\", rxGrid[0,:,120])\n",
    "print(\"Matlab-Generated RX Grid:\\n\", rxGridMatlab[0,:,120])\n",
    "print(\"Maximum Difference:\", maxDiff)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f84ec63",
   "metadata": {},
   "source": [
    "## Channel Estimation\n",
    "Now we use the ``estimateChannelLS`` method of the ``Grid`` class to estimate the channel using the received grid and the CSI-RS information. We then compare this estimated channel with the actual channel obtained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af05bca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Absolute Error: 2.4569771845362033e-05\n",
      "Max Absolute Error: 0.0027963260779751394\n",
      "Mean Absolute Error: 0.0008693920418179478\n",
      "MSE: 9.67645612042719e-07\n",
      "Actual Channel:\n",
      " [-0.00383678-0.01727704j -0.00383678-0.01727704j -0.00383678-0.01727704j\n",
      " -0.00383678-0.01727704j -0.00383678-0.01727704j -0.00383678-0.01727704j\n",
      " -0.00337909-0.01709973j -0.00337909-0.01709973j -0.00337909-0.01709973j\n",
      " -0.00337909-0.01709973j -0.00337909-0.01709973j -0.00337909-0.01709973j\n",
      " -0.00337909-0.01709973j -0.00337909-0.01709973j]\n",
      "Estimated Channel:\n",
      " [-0.00469164-0.0179458j -0.00469164-0.0179458j -0.00469164-0.0179458j\n",
      " -0.00469164-0.0179458j -0.00469164-0.0179458j -0.00469164-0.0179458j\n",
      " -0.00469164-0.0179458j -0.00469164-0.0179458j -0.00469164-0.0179458j\n",
      " -0.00469164-0.0179458j -0.00469164-0.0179458j -0.00469164-0.0179458j\n",
      " -0.00469164-0.0179458j -0.00469164-0.0179458j]\n"
     ]
    }
   ],
   "source": [
    "# Least Squares Channel Estimation\n",
    "hEst, _ = rxGrid.estimateChannelLS(rxCsiRsConfig) \n",
    "\n",
    "hActual = hActual[:,:,:,:2] # Ignore the port related to ZP CSI-RS\n",
    "print(\"Min Absolute Error:\", np.abs(hActual-hEst).min())\n",
    "print(\"Max Absolute Error:\", np.abs(hActual-hEst).max())\n",
    "print(\"Mean Absolute Error:\", np.abs(hActual-hEst).mean())\n",
    "print(\"MSE:\", np.square(np.abs(hActual-hEst)).mean())\n",
    "\n",
    "# Print symbol values for the first subcarrier for the first pair of antennas\n",
    "print(\"Actual Channel:\\n\", hActual[:,0,0,0])\n",
    "print(\"Estimated Channel:\\n\", hEst[:,0,0,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe90fab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+PnXqFIA7QWJlioqKKn19VZ0/LCzM4Hj79u3Fxzt16iQeDwgIkFV+Zby8vAzalbH2V1RUBB8fH6NlmNv+KmPstb3wwgv49ttv0b17d7Rt2xaRkZF44okn0KtXr2qdq2nTpgaLmO677z4AwNmzZ9GjRw8AwPr167F8+XKcOHECt2/frrKuxpiaT247MNX27dvxyiuvIDs7G7du3RKPV2zvgGl9UmVOnToFQRDQrl07o49XvC2kefPmBnVo0KABjhw5cs96GesbcnNzsXDhQmzbts2gvsb6aSJzMEgls6jVajRt2hTHjh2T9TxXV1ejx/UD0RkzZmDdunWYNWsWwsPDodFooFKpMGrUKKMjZ6aUaWo+nU6Hhx9+GM8//7zRvOV/VOWq7Nz3qtOZM2cwYMAABAYG4o033oC/vz88PDywY8cOvPnmm9XaVsjU62YqY3+EgTv3YlZ2rqqUv7bXX3+90nsRbbFBvqVGUU25BjqdDj4+PtiwYYPRx+9136dcxl5b+/btcfLkSWzfvh07d+7E559/jnfeeQcLFy4Ut9iylk8++QTjx49HTEwM5syZAx8fH7i6uiIpKQlnzpwxqQxL/b7M8cMPP2Do0KHo27cv3nnnHTRp0gTu7u5Yt24dNm7caJC/Ou9BnU4HlUqFr776ymg5Fd8bcs51r7xlZWV4+OGHUVhYiBdeeAGBgYGoU6cO/vrrL4wfP96i252Rc2OQSmYbPHgw1q5di8zMTISHh1us3C1btiAuLg7Lly8Xj928edMmm1m3adMG165dk+wxqaQvvvgCt27dwrZt2ySjGxWn8lq2bAngzuIw/ZGkv//+26Ijo5UFosCd0RZjv6M///wTrVu3ltT12LFjEARBUt7JkyclzyufBlWr1Rb9fbRs2dLgXABw4sQJ8XFzVHVtTNWmTRt8++236NWrl6LBVp06dRAbG4vY2FiUlJRg2LBhePXVVzFv3jzxm6zkysvLM9gS6vfffwcA8duwtmzZgtatWyM1NVVyjoSEhOq9ICOs0Q4+//xzeHl54euvv4anp6d4fN26dWbXs7Jr3aZNGwiCgICAALM/PJvr6NGj+P3337F+/XrJYrL09HSb1oNqPt6TSmZ7/vnnUadOHTz11FMoKCgwePzMmTN46623ZJfr6upq8Ol+5cqVKCsrM7uupho5ciQyMzPx9ddfGzx25coVyb2itlA+olHxloeKf/QGDBgANzc3rFmzRnJ81apVFq1PeYBhLBht06YNfv75Z5SUlIjHtm/fjnPnzknyPfLII8jLy8OWLVvEY9evXzf40ofQ0FC0adMGy5Ytw7Vr1wzOd/HiRbNewyOPPIL9+/cjMzNTPFZcXIy1a9eiVatW6NChg1nllu86UZ0PUyNHjkRZWRkWL15s8FhpaalNPqj9/fffkp89PDzQoUMHCIIgTr9X1Q4qU1painfffVf8uaSkBO+++y4aN26M0NBQAMbb+759+yS/K0uxRjtwdXWFSqWS9FVnz55FWlqa2fWs7FoPGzYMrq6uWLRokUF/KQiCwe/Rkoz9ngRBMKu/J6oKR1LJbG3atMHGjRsRGxuL9u3bS75xau/evUhJScH48eNllzt48GB8/PHH0Gg06NChAzIzM/Htt9+K92ha05w5c7Bt2zYMHjxY3J6quLgYR48exZYtW3D27FlxGxhbiIyMhIeHB4YMGYIpU6bg2rVreO+99+Dj44Pz58+L+Xx9fTFz5kwsX74cQ4cOxcCBA/HLL7/gq6++QqNGjSwyygfc+Z3Xr18fycnJqFevHurUqYOwsDAEBATgqaeewpYtWzBw4ECMHDkSZ86cwSeffGKwDdKkSZOwatUqjBs3DocOHUKTJk3w8ccfG2wt5uLigv/7v//DoEGD0LFjR0yYMAHNmjXDX3/9he+++w5qtRpffPGF7Ncwd+5cfPrppxg0aBCeeeYZNGzYEOvXr0dOTg4+//xzszfqr1WrFjp06IDNmzfjvvvuQ8OGDdGpUydZ9zX269cPU6ZMQVJSErKzsxEZGQl3d3ecOnUKKSkpeOuttyQLzqwhMjISfn5+6NWrF3x9ffHbb79h1apViI6OFhdKlgeVL774IkaNGgV3d3cMGTKkyo3zmzZtiiVLluDs2bO47777sHnzZmRnZ2Pt2rXi/ZODBw9GamoqHnvsMURHRyMnJwfJycno0KGD0Q8q1WGNdhAdHY033ngDAwcOxBNPPIELFy5g9erVaNu2rdF7P01R2bVu06YNXnnlFcybNw9nz55FTEwM6tWrh5ycHGzduhWTJ0+22jdkBQYGok2bNnjuuefw119/Qa1W4/PPP7f6/ezkfBikUrUMHToUR44cweuvv47//ve/WLNmDTw9PdG5c2csX74ckyZNkl3mW2+9BVdXV2zYsAE3b95Er1698O233yIqKsoKr0Cqdu3a+P777/Gf//wHKSkp+Oijj6BWq3Hfffdh0aJF4oInW7n//vuxZcsWLFiwAM899xz8/PwwdepUNG7c2GBngCVLlqB27dp477338O233yI8PBzffPMNevfubdK385jC3d0d69evx7x58/D000+jtLQU69atQ0BAAKKiorB8+XK88cYbmDVrFrp27Yrt27fj2WeflZRRu3ZtZGRkYMaMGVi5ciVq166NMWPGYNCgQQbfid6/f39kZmZi8eLFWLVqFa5duwY/Pz+EhYVJVorL4evri7179+KFF17AypUrcfPmTXTu3BlffPGFwT6Wcv3f//0fZsyYgdmzZ6OkpAQJCQmyF98kJycjNDQU7777LubPnw83Nze0atUK//rXv6q9eMkUU6ZMwYYNG/DGG2/g2rVraN68OZ555hksWLBAzNOtWzcsXrwYycnJ2LlzJ3Q6HXJycqoMUhs0aID169djxowZeO+99+Dr64tVq1ZJ+ojx48cjPz8f7777Lr7++mt06NABn3zyCVJSUrB7926Lvk5rtIOHHnoI77//Pl577TXMmjULAQEBYmBubpBa1bWeO3cu7rvvPrz55pvi/cL+/v6IjIzE0KFDzTqfKdzd3fHFF1/gmWeeQVJSEry8vPDYY49h+vTpCA4Ottp5yfmoBHNXSRCR3bty5QoaNGiAV155BS+++KLS1SEn1b9/f1y6dEn2Qksicm68J5Wohrhx44bBsfJvyOrfv79tK0NERFRNnO4nqiE2b96MDz/8EI888gjq1q2LH3/8EZ9++ikiIyNtMk1MRERkSQxSiWqIzp07w83NDUuXLoVWqxUXU73yyitKV42IiEg23pNKRERERHaH96QSERERkd1hkEpEREREdodBKhERERHZHQapRERERGR3GKQSERERkd1hkEpEREREdodBKhERERHZHQapRERERGR3GKQSERERkd1hkEpEREREdodBKhERERHZHQapRERERGR3GKQSERERkd1hkEpEREREdodBKhERERHZHQapRERERGR3GKQSERERkd1xU7oCRCTfzZs3UVJSYrHyPDw84OXlZbHyiIjoLkv22c7UXzNIJXIwN2/eREBALeTnW65MPz8/5OTkOE3HR0RkK5bus52pv2aQSuRgSkpKkJ8PnDsHqNXVL0+rBfz981FSUuIUnR4RkS1Zss92tv6a96SSAZVKhcTERIuW2b9/f3Tq1MmiZSqpf//+6N+/v6J1UNezXCKie7OH972lnT17FiqVCh9++KFFy23VqhUGDx5s0TKV1KpVK4wfP75aZbC/lo9BqpW98847UKlUCAsLM7uMvLw8JCYmIjs723IVsxCtVotFixYhODgYdevWRa1atdCpUye88MILyMvLU7p6NZtgwUTkwD788EOoVKpK088//2xyWcePH0diYiLOnj1rvQqb4Z133rF4IGmOgoICPPfccwgMDETt2rVRp04dhIaG4pVXXsGVK1eUrp59Y38tG6f7rWzDhg1o1aoV9u/fj9OnT6Nt27ayy8jLy8OiRYvQqlUrhISEWL6SZvrjjz8QERGB3NxcjBgxApMnT4aHhweOHDmC999/H1u3bsXvv/+udDWJyEm8/PLLCAgIMDgup989fvw4Fi1ahP79+6NVq1aSx7755pvqVtFs77zzDho1alTt0bzqOHDgAB555BFcu3YN//rXvxAaGgoAOHjwIF577TXs2bNH0WtENQ+DVCvKycnB3r17kZqaiilTpmDDhg1ISEhQuloWUVpaimHDhqGgoAC7d+9G7969JY+/+uqrWLJkiUK1cxKW+lTtZJ/MqeYaNGgQunbtarXyPTw8rFa2vbty5Qoee+wxuLq6IisrC4GBgZLHX331Vbz33nsK1c5BWKLPdrL+mtP9VrRhwwY0aNAA0dHRePzxx7Fhwwaj+a5cuYLZs2ejVatW8PT0RPPmzTFu3DhcunQJu3fvRrdu3QAAEyZMEKevyqd9KrtPpuK9UyUlJVi4cCFCQ0Oh0WhQp04d9OnTB999951Zr+3zzz/HL7/8ghdffNEgQAUAtVqNV1991eD48ePH8eCDD6J27dpo1qwZli5dKnnc1HqW30e1bNkyrF27Fm3atIGnpye6deuGAwcOSPKOHz8edevWxV9//YWYmBjUrVsXjRs3xnPPPYeysjJJXp1OhxUrVqBjx47w8vKCr68vpkyZgsuXL5t1nYjIvmzatAmhoaGoV68e1Go1goKC8NZbbwG4c9vAiBEjAAAPPvig2N/u3r0bgGG/unv3bqhUKnz22WdYtGgRmjVrhnr16uHxxx9HUVERbt26hVmzZsHHxwd169bFhAkTcOvWLUl91q1bh4ceegg+Pj7w9PREhw4dsGbNGkmeVq1a4ddff8X3338v1km/HleuXMGsWbPg7+8PT09PtG3bFkuWLIFOp5OUc+XKFYwfPx4ajQb169dHXFycyVP07777Lv766y+88cYbBgEqAPj6+mLBggUGx3/88Ud0794dXl5eaN26NT766CPJ44WFhXjuuecQFBSEunXrQq1WY9CgQfjll18k+fSv9auvvormzZvDy8sLAwYMwOnTpyV5y9dA3OvvDQDcunULCQkJaNu2LTw9PeHv74/nn3/e4PdEyuBIqhVt2LABw4YNg4eHB0aPHo01a9bgwIEDYtAJANeuXUOfPn3w22+/4cknn8QDDzyAS5cuYdu2bfjf//6H9u3b4+WXX8bChQsxefJk9OnTBwDQs2dPWXXRarX4v//7P4wePRqTJk3C1atX8f777yMqKgr79++XfRvBtm3bAABjx441+TmXL1/GwIEDMWzYMIwcORJbtmzBCy+8gKCgIAwaNMisem7cuBFXr17FlClToFKpsHTpUgwbNgx//PEH3N3dxXxlZWWIiopCWFgYli1bhm+//RbLly9HmzZtMHXqVDHflClT8OGHH2LChAl45plnkJOTg1WrViErKws//fSTpEzFcSSVSKKoqAiXLl2SHFOpVPD29gYApKenY/To0RgwYIA40/Pbb7/hp59+wsyZM9G3b18888wzePvttzF//ny0b98eAMR/K5OUlIRatWph7ty5OH36NFauXAl3d3e4uLjg8uXLSExMxM8//4wPP/wQAQEBWLhwofjcNWvWoGPHjhg6dCjc3NzwxRdf4N///jd0Oh2mTZsGAFixYgVmzJiBunXr4sUXXwRwJygEgOvXr6Nfv37466+/MGXKFLRo0QJ79+7FvHnzcP78eaxYsQIAIAgCHn30Ufz44494+umn0b59e2zduhVxcXEmXdtt27ahVq1aePzxx03KDwCnT5/G448/jokTJyIuLg4ffPABxo8fj9DQUHTs2BHAndvG0tLSMGLECAQEBKCgoADvvvsu+vXrh+PHj6Np06aSMl977TW4uLjgueeeQ1FREZYuXYoxY8Zg3759knym/L3R6XQYOnQofvzxR0yePBnt27fH0aNH8eabb+L3339HWlqaya/VJBxJlU8gqzh48KAAQEhPTxcEQRB0Op3QvHlzYebMmZJ8CxcuFAAIqampBmXodDpBEAThwIEDAgBh3bp1BnlatmwpxMXFGRzv16+f0K9fP/Hn0tJS4datW5I8ly9fFnx9fYUnn3xSchyAkJCQUOXr69Kli6DRaKrMU7E+AISPPvpIPHbr1i3Bz89PGD58uOx65uTkCAAEb29vobCwUDz+3//+VwAgfPHFF+KxuLg4AYDw8ssvG7yG0NBQ8ecffvhBACBs2LBBkm/nzp0GxyteX1sqKioSAAhFlyAIJdVPRZfudJ1FRUWKvB6i6lq3bl2ly0w8PT3FfDNnzhTUarVQWlpaaVkpKSkCAOG7774zeKzi+/67774TAAidOnUSSkpKxOOjR48WVCqVMGjQIMnzw8PDhZYtW0qOXb9+3eA8UVFRQuvWrSXHOnbsaLTPWbx4sVCnTh3h999/lxyfO3eu4OrqKuTm5gqCIAhpaWkCAGHp0qVintLSUqFPnz6V/n3R16BBAyE4OLjKPPpatmwpABD27NkjHrtw4YLg6ekpPPvss+KxmzdvCmVlZZLn5uTkCJ6enpI+u/xat2/fXvI34q233hIACEePHhWPmfr35uOPPxZcXFyEH374QXL+5ORkAYDw008/SV6Psb+1prBkn+1s/TWn+61kw4YN8PX1xYMPPgjgzqf52NhYbNq0STLF/PnnnyM4OBiPPfaYQRkqlcpi9XF1dRXvp9LpdCgsLERpaSm6du2Kw4cPyy5Pq9WiXj15e2HUrVsX//rXv8SfPTw80L17d/zxxx9m1zM2NhYNGjQQfy4fadYvs9zTTz8t+blPnz6SfCkpKdBoNHj44Ydx6dIlMYWGhqJu3bpm3xpBRLaxevVqpKenS9JXX30lPl6/fn0UFxcjPT3doucdN26cZJYlLCwMgiDgySeflOQLCwvDuXPnUFpaKh6rVauW+P/ykeB+/frhjz/+QFFR0T3PnZKSgj59+qBBgwaSfisiIgJlZWXYs2cPAGDHjh1wc3OTzBy5urpixowZJr1Gc/r8Dh06iH0yADRu3Bj333+/pN/19PSEi8udUKSsrAx///036tati/vvv99onz9hwgTJvcGV9fmm/L1JSUlB+/btERgYKLl2Dz30EACwz7cDnO63grKyMmzatAkPPvggcnJyxONhYWFYvnw5MjIyEBkZCQA4c+YMhg8fbpN6rV+/HsuXL8eJEydw+/Zt8bix1bD3olarjQaCVWnevLlB4N2gQQMcOXLE7Hq2aNHCoDwABveQenl5oXHjxgZ59fOdOnUKRUVF8PHxMVr/CxcuVPbSlMHpfiKJ7t27V7lw6t///jc+++wzDBo0CM2aNUNkZCRGjhyJgQMHVuu8FfshjUYDAPD39zc4rtPpUFRUJN6C8NNPPyEhIQGZmZm4fv26JH9RUZFYVmVOnTqFI0eOGPRv5cr7rT///BNNmjRB3bp1JY/ff//993h1d6jValy9etWkvOUqXhfAsN/V6XR466238M477yAnJ0cyiFN+jaoqs7I+35S/N6dOncJvv/12z2tnMZzul41BqhXs2rUL58+fx6ZNm7Bp0yaDxzds2CAGqdVV2WhrWVkZXF1dxZ8/+eQTjB8/HjExMZgzZw58fHzg6uqKpKQknDlzRvZ5AwMDkZWVhXPnzhl0xJXRr48+Qbj7rpNbT1PKrCqfPp1OBx8fn0oXuFXWkSmGQSqRLD4+PsjOzsbXX3+Nr776Cl999RXWrVuHcePGYf369WaXW1n/cq/+6cyZMxgwYAACAwPxxhtvwN/fHx4eHtixYwfefPNNg4VPxuh0Ojz88MN4/vnnjT5+3333mfgqqhYYGIjs7GyUlJSYvMuBKf3zf/7zH7z00kt48sknsXjxYjRs2BAuLi6YNWuW0ddf3T5fP59Op0NQUBDeeOMNo3lN/dtmMgapsjFItYINGzbAx8cHq1evNngsNTUVW7duRXJyMmrVqoU2bdrg2LFjVZZX1bR/gwYNjK7O/PPPP9G6dWvx5y1btqB169ZITU2VlGfullhDhgzBp59+ik8++QTz5s0zqwxjLF1POdq0aYNvv/0WvXr1kkzBEVHN4eHhgSFDhmDIkCHQ6XT497//jXfffRcvvfQS2rZta9HbrO7liy++wK1bt7Bt2zbJCKGxaebK6tWmTRtcu3YNERERVZ6rZcuWyMjIwLVr1ySjqSdPnjSprkOGDEFmZiY+//xzjB492qTnmGLLli148MEH8f7770uOX7lyBY0aNbLYeYxp06YNfvnlFwwYMMCmv3cyHe9JtbAbN24gNTUVgwcPxuOPP26Qpk+fjqtXr4qr44cPH45ffvkFW7duNSir/BNfnTp1AMBoMNqmTRv8/PPPKCkpEY9t374d586dk+Qr/1Sp/yly3759yMzMNOt1Pv744wgKCsKrr75qtIyrV6+Kq1DlsHQ95Rg5ciTKysqwePFig8dKS0vt79tU+I1TRLL8/fffkp9dXFzQuXNnABC3HKqqv7U0Y/1dUVER1q1bZ5C3Tp06Rus0cuRIZGZm4uuvvzZ47MqVK+L9r4888ghKS0sl21uVlZVh5cqVJtX16aefRpMmTfDss88a/ZKWCxcu4JVXXjGpLH2urq4Go6ApKSn466+/ZJcl18iRI/HXX38Z3d/1xo0bKC4utuwJ2V/LxpFUC9u2bRuuXr2KoUOHGn28R48eaNy4MTZs2IDY2FjMmTMHW7ZswYgRI/Dkk08iNDQUhYWF2LZtG5KTkxEcHIw2bdqgfv36SE5ORr169VCnTh2EhYUhICAATz31FLZs2YKBAwdi5MiROHPmDD755BO0adNGct7BgwcjNTUVjz32GKKjo5GTk4Pk5GR06NAB165dk/063d3dkZqaioiICPTt2xcjR45Er1694O7ujl9//RUbN25EgwYNjO6VWhVL11OOfv36YcqUKUhKSkJ2djYiIyPh7u6OU6dOISUlBW+99Zas7VesjtP9RBJfffUVTpw4YXC8Z8+eaN26NZ566ikUFhbioYceQvPmzfHnn39i5cqVCAkJEbeZCgkJgaurK5YsWYKioiJ4enqK+5haWmRkpDiyO2XKFFy7dg3vvfcefHx8cP78eUne0NBQrFmzBq+88gratm0LHx8fPPTQQ5gzZw62bduGwYMHi9s7FRcX4+jRo9iyZQvOnj2LRo0aYciQIejVqxfmzp2Ls2fPokOHDkhNTTVpcRZwZ9Zu69ateOSRRxASEiL5xqnDhw/j008/RXh4uOxrMHjwYLz88suYMGECevbsiaNHj2LDhg2SmUBrGTt2LD777DM8/fTT+O6779CrVy+UlZXhxIkT+Oyzz/D1119b9sshON0vnzKbCtRcQ4YMEby8vITi4uJK84wfP15wd3cXLl26JAiCIPz999/C9OnThWbNmgkeHh5C8+bNhbi4OPFxQbiztVKHDh0ENzc3g+1Cli9fLjRr1kzw9PQUevXqJRw8eNBgqxSdTif85z//EVq2bCl4enoKXbp0EbZv3y7ExcUZbIkCE7agKnf58mVh4cKFQlBQkFC7dm3By8tL6NSpkzBv3jzh/PnzYr5+/foJHTt2NHh+xfObWs/yLahef/11gzIr1j8uLk6oU6eOQb6EhATB2Ftg7dq1QmhoqFCrVi2hXr16QlBQkPD8888LeXl5ktej+BZU+RCE69VPRfnOtaUJ1TxVbUGl319u2bJFiIyMFHx8fAQPDw+hRYsWwpQpUyR9lSAIwnvvvSe0bt1acHV1lWxHVdkWVCkpKUbrc+DAAcnx8j7n4sWL4rFt27YJnTt3Fry8vIRWrVoJS5YsET744AMBgJCTkyPmy8/PF6Kjo4V69eoJACT1uHr1qjBv3jyhbdu2goeHh9CoUSOhZ8+ewrJlyyRbY/3999/C2LFjBbVaLWg0GmHs2LFCVlaWSVtQlcvLyxNmz54t3HfffYKXl5dQu3ZtITQ0VHj11VclfUjLli2F6Ohog+dXvIY3b94Unn32WaFJkyZCrVq1hF69egmZmZkmX+vyvwX69Tf1740gCEJJSYmwZMkSoWPHjoKnp6fQoEEDITQ0VFi0aJHB66n2FlQW6LOdrb9WCYLgbHE5kUPTarXQaDQoOg+o1ZYoD9A0uTPNqLZEgUREJLJkn+1s/TWn+4kcFaf7iYgcB6f7ZePCKSIiIiKyOxxJJXJUHEklInIcHEmVjUEqkaNikEpE5DgYpMrG6X4iIiIisjscScWdr0bLy8tDvXr1+K0TpBhBEHD16lU0bdoULi4mfH7kSCo5IfbXZA9k99cAR1LNwCAVQF5enuW/o5fITOfOnUPz5s3vnZFBKjkh9tdkT0zurwEGqWZgkAqgXr16AIBzKkDND+bSN4GTvSGUpAXgj7vtkYgMif11bfbXpBytAPhfZ39tbQxSAXHKSO3CTg9A1UEqg1br+efamjyFqeBI6urVq/H6668jPz8fwcHBWLlyJbp3715p/pSUFLz00ks4e/Ys2rVrhyVLluCRRx4RH09NTUVycjIOHTqEwsJCZGVlISQkRFJGfn4+5syZg/T0dFy9ehX3338/XnzxRQwfPlz+CyCHJfbX/yQiJcm65YQjqbJx4ZQ+FVOV14HXyDbX385t3rwZ8fHxSEhIwOHDhxEcHIyoqChcuHDBaP69e/di9OjRmDhxIrKyshATE4OYmBgcO3ZMzFNcXIzevXtjyZIllZ533LhxOHnyJLZt24ajR49i2LBhGDlyJLKysiz+GomISHn8WlTofWWZG0dSJTjtb1NaAdDo7v11d2J7PWvBr0VtdefeKv3zenp6wtPT0yB/WFgYunXrhlWrVgG4s5DF398fM2bMwNy5cw3yx8bGori4GNu3bxeP9ejRAyEhIUhOTpbkPXv2LAICAoyOpNatWxdr1qzB2LFjxWPe3t5YsmQJnnrqKXNeOjkgsf3XYn9NytEKgOaGaV9Pask+u7y/dpavReVIqj6lR9GYmOQQLJgA+Pv7Q6PRiCkpKcnglCUlJTh06BAiIiLEYy4uLoiIiEBmZqbRamZmZkryA0BUVFSl+SvTs2dPbN68GYWFhdDpdNi0aRNu3ryJ/v37yyqHaghLtn8mJnOSXEqd14HxnlR95gQKNZUAXgsnY2wktaJLly6hrKwMvr6+kuO+vr44ceKE0XLz8/ON5s/Pz5dVv88++wyxsbHw9vaGm5sbateuja1bt6Jt27ayyiEiIsfAIFUfg9S7VHC6T2wOyYK/I7VabdfTRy+99BKuXLmCb7/9Fo0aNUJaWhpGjhyJH374AUFBQUpXj2yN/RMpydz2x3YrC4NUfQxSyZFYaupHRhmNGjWCq6srCgoKJMcLCgrg5+dn9Dl+fn6y8htz5swZrFq1CseOHUPHjh0BAMHBwfjhhx+wevVqg3tbyQnwjz0pqTrT/bY+rwPjPan6lL4fkYnJznl4eCA0NBQZGRniMZ1Oh4yMDISHhxt9Tnh4uCQ/AKSnp1ea35jr168DgME3u7i6ukKn05lcDtUgSt+PyMREVseRVH0OEijYhABeC3tnqY5SZhnx8fGIi4tD165d0b17d6xYsQLFxcWYMGECgDtbRTVr1kxceDVz5kz069cPy5cvR3R0NDZt2oSDBw9i7dq1YpmFhYXIzc1FXl4eAODkyZMA7ozC+vn5ITAwEG3btsWUKVOwbNkyeHt7Iy0tDenp6ZJdA4iI7JYl+mwnC44ZpOpjkGocA1b7pFCQGhsbi4sXL2LhwoXIz89HSEgIdu7cKS6Oys3NlYx49uzZExs3bsSCBQswf/58tGvXDmlpaejUqZOYZ9u2bWKQCwCjRo0CACQkJCAxMRHu7u7YsWMH5s6diyFDhuDatWto27Yt1q9fL/lSAHIiTvbHmuyMOe2PQaps3CcVenuY1eW+eyKhkv+T1WgFQHNdxj6ppwG1Bb6RT3sV0LR1nn33yLGJ7d+V/TUpRysAmjKZ+6RaoM92tv6aI6n6OJJKjkShkVQiIjIDR1JlY5Cqj0GqcSo43RvDITBIJWfGdktK4nS/TTBI1ccg9a6KgSkDVSKyJ+yPSElsfzbBIFUfg1RyJBxJJWfGdktK4kiqTTBI1ccglRwJg1QiIsfBIFU2BqkVMUglInIMTvYHm8jZMEjVx5HUu7g3qv3jSCoRkePgSKpsDFL1MUi9iwulbE/u9WaQSs7MBeyvSTkCALnfyMwgVTYGqfoYpFat/No42ZvEZtj2iEznDr5nSDkCgFtKV6LmY5Cqz+Wf5KwqBp+VjabyD4N94EgqOTMPsC8i5ZgTpHIkVTYGqfqcfSTVWFDKaX/bcea2RySXO5x7UIGUJXeqn8zCIFWfswepAINSJcltexxJJWfGIJWUZE6QypFU2Rik6mOQSkpikEpkOgappCQGqTbBIFUfg1TjOLpqG2x7RKZjkEpK4nS/TTBI1ccglZTEkVQi07kBcFW6EuS0ysx4DkdSZWOQqo9B6h1cQKUMBqlEpvMEg1RSDoNUm2CQqo9B6l3G9kTltSEie+EOBqmkHN5qYhMMUvUxSDXEEVTb4Ugqkek8wL9gpJxSM57DkVTZ+BbX5+yb+QPGbwZn4G4bDFKJTOcO/gUj5Zjzd5FBqmx8i+vjSGrlQTpXMhKRPeFIKinJ2Qe0bIRvcX0MUiuf3ucb0v5wJJWcmQt4Tyoph/uk2gSDVDJkLFDnvanWZ+70EZEz4u1ZpCRz2x77bFkYpOpTgZ2evoqfFJ19lNnaeH2JTKcDb0Mi5bDt2QSDVH2c7pcqn0rjJz/bkHudOd1PzkwH8/aqJLIETvfbBMcN9amYjCYXO6iDsyQ5BAsmIkejY2JSOMmlYH+9evVqtGrVCl5eXggLC8P+/furzJ+SkoLAwEB4eXkhKCgIO3bskDyempqKyMhIeHt7Q6VSITs726CM/Px8jB07Fn5+fqhTpw4eeOABfP7557LqzSBVnwtTpfd5KR28OUsiItOUMTEpnBzE5s2bER8fj4SEBBw+fBjBwcGIiorChQsXjObfu3cvRo8ejYkTJyIrKwsxMTGIiYnBsWPHxDzFxcXo3bs3lixZUul5x40bh5MnT2Lbtm04evQohg0bhpEjRyIrK8vkuqsEQXD6cRStVguNRoOiQEDN1aJ3OX3LsC1tGaA5CRQVFUGtVleer7y9HgDUdS1w3muAptu9z0tkD8T23wdQ84Y1Uoi2FND8YFq/ack+25z+OiwsDN26dcOqVasAADqdDv7+/pgxYwbmzp1rkD82NhbFxcXYvn27eKxHjx4ICQlBcnKyJO/Zs2cREBCArKwshISESB6rW7cu1qxZg7Fjx4rHvL29sWTJEjz11FMm1Z1vcX0czSIlmTvdX138MEKOyNwpVyJLqM50f3X883ytVis57OnpCU9PT4PsJSUlOHToEObNmycec3FxQUREBDIzM42eIjMzE/Hx8ZJjUVFRSEtLk1XVnj17YvPmzYiOjkb9+vXx2Wef4ebNm+jfv7/JZTBIpcoxYLctXm8i05WB7xlSjsLT/f7+/pKfExISkJiYaJDv0qVLKCsrg6+vr+S4r68vTpw4YbTs/Px8o/nz8/Nl1fGzzz5DbGwsvL294ebmhtq1a2Pr1q1o27atyWUwSNXHffeM42iFfeJIKjkzLvojJZnT9iw4knru3DnJdL+xUVSlvfTSS7hy5Qq+/fZbNGrUCGlpaRg5ciR++OEHBAUFmVQGg1R9DFLv0n8jVbxPl38YrINbUBGZjiOppCRzRlItGKSq1WqT7klt1KgRXF1dUVBQIDleUFAAPz8/o8/x8/OTld+YM2fOYNWqVTh27Bg6duwIAAgODsYPP/yA1atXG9zbWhmGZGRcVSvQld55oCYnIjKNJbdgY2IyJzkADw8PhIaGIiMjQzym0+mQkZGB8PBwo88JDw+X5AeA9PT0SvMbc/36dQB37n/V5+rqCp3O9OlZjqTqY6BgHKf77ZOlOkoH6WyJJHRQ/L5AcmIKL5ySIz4+HnFxcejatSu6d++OFStWoLi4GBMmTABwZ6uoZs2aISkpCQAwc+ZM9OvXD8uXL0d0dDQ2bdqEgwcPYu3atWKZhYWFyM3NRV5eHgDg5MmTAO6Mwvr5+SEwMBBt27bFlClTsGzZMnh7eyMtLQ3p6emSXQPuhSGZPqVH0ZROlY2cutpB3ZwlOQhbbwx99uxZqFQqoyklJcXSL48cgdKjaExMDiI2NhbLli3DwoULERISguzsbOzcuVNcHJWbm4vz58+L+Xv27ImNGzdi7dq1CA4OxpYtW5CWloZOnTqJebZt24YuXbogOjoaADBq1Ch06dJFnMZ3d3fHjh070LhxYwwZMgSdO3fGRx99hPXr1+ORRx4xue7cJxV6e5h14T6p9+T0rcV6tGWAJlvGPql7LbhPak/T993bvHkzxo0bh+TkZISFhWHFihVISUnByZMn4ePjY5B/79696Nu3L5KSkjB48GBs3LgRS5YsweHDh8VO7+OPP0ZOTg6aNm2KSZMmGey5V1ZWhosXL0rKXbt2LV5//XWcP38edeta4EKQQxDbfxD7a1KOtgzQHJW5T6oF+my5/bWjY5AKvQYUyk5PxCl+m9OWAZosGUHqTxYMUnsZXy1qbMWoUhtDV9SlSxc88MADeP/99+W8XHJwDFLJHpgVpFqgzy7vr50lSHWgCUYbUfprMe0luYLT/E423e/v7w+NRiOm8vuT9JVvDB0RESEeM2VjaP38wJ2NoSvLb4pDhw4hOzsbEydONLsMcnBKf287ExNZHRdO6eM3Thkqvx5OP95uhyx1X9Q/ZZiy756SG0Pre//999G+fXv07NnT7DLIwenA/pqUY06Qaok+28n+FjNI1WcHo1l2o+IbkH8MrE/hr0U1dd89pd24cQMbN27ESy+9pHRVSEkOtniFahhz2h6DVNkYpOrjSOpd5fd6OdkbQlEO8AFJqY2h9W3ZsgXXr1/HuHHjzHo+1RAMUklJbHs24QB/Fm1I6ftA7TGVjy4rXQ9nSXIosJWKUhtD63v//fcxdOhQNG7c2KznUw2h9P2ITExyOdnWV5agaJCalJSEbt26oV69evDx8UFMTIy4IWy5/v37G+yL+PTTT0vy5ObmIjo6GrVr14aPjw/mzJmD0tJS+RVSetGM0qkqSgdvzpLkUGi/v/j4eLz33ntYv349fvvtN0ydOtVgY+h58+aJ+WfOnImdO3di+fLlOHHiBBITE3Hw4EFMnz5dzFNYWIjs7GwcP34cwJ2NobOzsw3uWz19+jT27NmDp556Sl6lqdrsrr9Weo9MJia5lDqvA1N0uv/777/HtGnT0K1bN5SWlmL+/PmIjIzE8ePHUadOHTHfpEmT8PLLL4s/165dW/x/WVkZoqOj4efnh7179+L8+fMYN24c3N3d8Z///EdehcwJFGoSTvEry0HmNWJjY3Hx4kUsXLgQ+fn5CAkJMdgYWv+r8Mo3hl6wYAHmz5+Pdu3aGd0YujzIBe5sDA0ACQkJSExMFI9/8MEHaN68OSIjI638Kqkiu+uvnfAPNtkRtj2bsKt9Ui9evAgfHx98//336Nu3L4A7n8xDQkKwYsUKo8/56quvMHjwYOTl5Yl/JJOTk/HCCy/g4sWL8PDwuOd5xT3MegNq3qV7l920DOegLQU0P8nYJ3W3BfdJ7e88++6RZSjeX7cG1A7ywY5qHq0O0Pwhc5/U3RbaJ7W/8/TXdvUWLyoqAgA0bNhQcnzDhg1o1KgROnXqhHnz5uH69eviY5mZmQgKCpJscRMVFQWtVotff/3V6Hlu3boFrVYrSQCUn+q1t8T7UW2f5FBy2oqcnuL9tdJTvUxMcil1XgdmN+OGOp0Os2bNQq9evSTTgE888QRatmyJpk2b4siRI3jhhRdw8uRJpKamAqh8D8byx4xJSkrCokWLDB/gFlR36d8ULjd4IvPwOpODsIv+ugxO9web7Ig5C6dINrsJUqdNm4Zjx47hxx9/lByfPHmy+P+goCA0adIEAwYMwJkzZ9CmTRuzzjVv3jzEx8eLP2u1Wvj7+5s3mlVT8f5U25P7AclSn6r5OyaZ7KK/JnI0luiznay/tosgdfr06di+fTv27NmD5s2bV5k3LCwMwJ1Vvm3atIGfnx/2798vyVO+J2Nl+zBW9p3kDFKNKL8eTvbGUIS50/3Vxd8tyWA3/TVHskhJ1dmCqjqcrL9WdHJbEARMnz4dW7duxa5duxAQEHDP52RnZwMAmjRpAuDOHoxHjx7FhQsXxDzp6elQq9Xo0KGDvAopvQWU0on3pyqfiOyU3fXXRFTjKTqSOm3aNGzcuBH//e9/Ua9ePfGeJI1Gg1q1auHMmTPYuHEjHnnkEXh7e+PIkSOYPXs2+vbti86dOwMAIiMj0aFDB4wdOxZLly5Ffn4+FixYgGnTphn/9F0VZw8UTBk1debrY23mfGR0sk/VpBy766+5hoAcEftsWRQNUtesWQMA6N+/v+T4unXrMH78eHh4eODbb7/FihUrUFxcDH9/fwwfPhwLFiwQ87q6umL79u2YOnUqwsPDUadOHcTFxUn26ZOFQRin+JXC6X6yY3bXX7uBQSoph9P9NqFokHqvLVr9/f3x/fff37Ocli1bYseOHZaqFpVjwE5E/7C7/toVdxd4EtlamdIVcA52sXDKbrDTu8vJPq3ZBa7uJzKdG9hfk3LMGcThSKpsDFL1Ofs9qfr0r4OTvSkUw7ZHZDoGqaQk9tc2wSBVH4NU43iPqm3wnlQi0zFIJSVxJNUmGKTq42rROyp7EzCAty5O9xOZzhX8C0aOhUGqbHyL6+NI6h0cOVUG2x6R6biGgJTEv482wSCVKsegybY43U9kOjfwLxg5Fo6kysa3uD5O91eNX0NoXxikkjNjkEqOhkGqbHyL62OQKlXxzcCpNevihwAi0zFIJarx+BbXx3tSpbgNlW1xup/IdFw4RUoyp9/kSKpsfIvr40jqHcbeBAzerY+r+4lMx5FUUhKDVJvgW1wfR1Lv4Op+ZbDtEZmOgwqkJLY9m2CQSpVj0GRbnO4nMp37P4nIUXAkVTYGqfr4ybxyXNRjfxikkjPjPqmkJHP+JjJIlY1Bqj4VGKRW9gbgHwPrK1O6AkQOhAunSEkcuLEJvsX18Z5Uw9fvZJ/aFMXpfiLTceEUKYkjqTbBt7g+Z57ur6zhGwucnOxNYjMMUolMx3tSSUlc3W8TDFL1OfNIqpxg1FmvkbU56wckInPwnlRSEtueTTBIpcoxGLUtjqQSmY7T/aQkTvfbBN/i+px5uv9eeJO4/WGQSs6MC6dISeYsdGWQKhvf4vqcPUitqvHrT2042ZvEZpy57RHJxel+UhLbnk0wSNXnzPekAtLXXlUg6szXyJr4tahEpuN0PymJI6k2wbe4PmcPUvWZGrCS5bDtEZmOQSopifta2wTf4vqcfbq/XMWglMGTbXAklch0buAWVKQcjqTaBINUMsSgVBlc3U9kOo6kkpLMaXsMUmXjW1wfR1Irx9X9REREZEMMyfS5OHlSVZFc9ZLS9azJSS7BAskMq1evRqtWreDl5YWwsDDs37+/yvwpKSkIDAyEl5cXgoKCsGPHDsnjqampiIyMhLe3N1QqFbKzs42Wk5mZiYceegh16tSBWq1G3759cePGDfNeBDk2HROTwskcCvTXjsycP4s1W1WBWk1PpgasSgdyNTnJYYkA1YyOb/PmzYiPj0dCQgIOHz6M4OBgREVF4cKFC0bz7927F6NHj8bEiRORlZWFmJgYxMTE4NixY2Ke4uJi9O7dG0uWLKn0vJmZmRg4cCAiIyOxf/9+HDhwANOnT4eLC7sxp1TGxKRwkkuB/trRqQRBcLKXbEir1UKj0aDoaUDtqXRt7IzTtw7b0d4CNO8CRUVFUKvVlecrb68pgLq2Bc57HdCMAM6dOyc5r6enJzw9Dd8QYWFh6NatG1atWgUA0Ol08Pf3x4wZMzB37lyD/LGxsSguLsb27dvFYz169EBISAiSk5Mlec+ePYuAgABkZWUhJCRE8liPHj3w8MMPY/HixdV5ueTgxPY/h/01KUd7C9C8fu/+GrBsn13eX5ty3pqAQxD6lB7JtMdkysgqk+WSHBYeSfX394dGoxFTUlKSwSlLSkpw6NAhREREiMdcXFwQERGBzMxMo9XMzMyU5AeAqKioSvMbc+HCBezbtw8+Pj7o2bMnfH190a9fP/z4448ml0E1jNKjaExMcnEkVTYunNJnTqDgLHhdrM/c6f7q+qcMYyOpFV26dAllZWXw9fWVHPf19cWJEyeMFp+fn280f35+vslV/OOPPwAAiYmJWLZsGUJCQvDRRx9hwIABOHbsGNq1a2dyWVRDlILf+kPKKTXjOVzdLxuDVH3mLl6p6cy9QZwcilqtttvpI53uTiOcMmUKJkyYAADo0qULMjIy8MEHHxgd9aUajkEqKcmcIJVkY5Cqz9mD1Mo+obmakIeqT+GRVFM0atQIrq6uKCgokBwvKCiAn5+f0ef4+fnJym9MkyZNAAAdOnSQHG/fvj1yc3NNLodqkDIwUCDlVGe6vzqc7G8wg1R9zj7dX/7aq3oTOPP1sTYHCFI9PDwQGhqKjIwMxMTEALgzypmRkYHp06cbfU54eDgyMjIwa9Ys8Vh6ejrCw8NNPm+rVq3QtGlTnDx5UnL8999/x6BBg0x/AVRzlMK5BxVIWZzutwkGqfqcPUgtZ0qwSpbnIG0vPj4ecXFx6Nq1K7p3744VK1aguLhYnIYfN24cmjVrJk7Bz5w5E/369cPy5csRHR2NTZs24eDBg1i7dq1YZmFhIXJzc5GXlwcAYjDq5+cHPz8/qFQqzJkzBwkJCQgODkZISAjWr1+PEydOYMuWLTa+AmQXqrNXJVF1se3ZBINUfQxSpVRgoGpL5q7ury6ZZcTGxuLixYtYuHAh8vPzERISgp07d4qLo3JzcyV7l/bs2RMbN27EggULMH/+fLRr1w5paWno1KmTmGfbtm1ikAsAo0aNAgAkJCQgMTERADBr1izcvHkTs2fPRmFhIYKDg5Geno42bdqY+cLJoZm7wprIEjjdbxPcJxV6e5jFc9+9Kjl9S7Eu7S1A86aMfVI3WHCf1DHOs+8eOTax/T8BqD2Urg05K20JoNkoc59UC/TZztZfcyRVn7MvnKqo4nQGR5mti9eXyHQcSSUlse3ZBINUfZzulypf1c8RVNtwgIVTRHaDq/tJSZzutwkGqfo4kmr8DaC6x+NkGQ5yTyqRXXDCb98hO2JO22OQKhuDVH0cSb33yn5nvz7W5OwfkIjkcAU38yflcHW/TTBIrYhB2B1c2W97HEklMp0K/GBHyjEnVuBIqmwMUvVxJFWKgap9Y5BKzowjqaQkc9oeg1TZGKTqY5BqiBv72w7bHpHpGKSSktj2bIJBqj4GqZXjdbE+ru4nMh2DVFISR1JtgkGqPq7uN443iBORvWGQSkpi27MJBqn6GKQa/5TG/VJtgyOpRKZzA/+CkXLMGbzhSKpsfIvr43R/1fegOvu1sTYGqUSmcwFHs0g55gxoMUiVTdFxw6SkJHTr1g316tWDj48PYmJicPLkSUmemzdvYtq0afD29kbdunUxfPhwFBQUSPLk5uYiOjoatWvXho+PD+bMmYPSUjO+ikTFJCYXO6iDMyYiO8X+mompQiKrU3Qk9fvvv8e0adPQrVs3lJaWYv78+YiMjMTx48dRp04dAMDs2bPx5ZdfIiUlBRqNBtOnT8ewYcPw008/AQDKysoQHR0NPz8/7N27F+fPn8e4cePg7u6O//znP/IqxIYnpYLTfWpTlNy2x5FUsiG766853U9K4tei2oRKEAS7eckXL16Ej48Pvv/+e/Tt2xdFRUVo3LgxNm7ciMcffxwAcOLECbRv3x6ZmZno0aMHvvrqKwwePBh5eXnw9fUFACQnJ+OFF17AxYsX4eHhcc/zarVaaDQaFC0G1F5WfYmOyW5aSM2mvQloFgJFRUVQq9WV5ytvr/8HqGtb4LzXAc1T9z4vkT7F++vZgNrTqi+RqFLaW4DmTdP6TUv22c7WX9vVMqGioiIAQMOGDQEAhw4dwu3btxERESHmCQwMRIsWLZCZmQkAyMzMRFBQkNjhAUBUVBS0Wi1+/fVXo+e5desWtFqtJAG4u3DK2VPFKY3KjjNx+oicluL9tRsTk8KJrM5uLrNOp8OsWbPQq1cvdOrUCQCQn58PDw8P1K9fX5LX19cX+fn5Yh79Dq/88fLHjElKSsKiRYsMH2CgcEf5Nag4gsprY11cOEUOwi76a25BRUriPqk2YTdB6rRp03Ds2DH8+OOPVj/XvHnzEB8fL/6s1Wrh7+/PILWiyoJVsg7ek0oOwi76a67uJyVxdb9N2EWQOn36dGzfvh179uxB8+bNxeN+fn4oKSnBlStXJJ/OCwoK4OfnJ+bZv3+/pLzy1aTleSry9PSEp6eRm5m4T2rlW0852RtDEfyARA7AbvprDiqQktj2bELRkEwQBEyfPh1bt27Frl27EBAQIHk8NDQU7u7uyMjIEI+dPHkSubm5CA8PBwCEh4fj6NGjuHDhgpgnPT0darUaHTp0kF8ppe9JVDpVdu8p70m1TZJLsEAiMoFd9tdEjkah/nr16tVo1aoVvLy8EBYWZvBhsaKUlBQEBgbCy8sLQUFB2LFjh+Tx1NRUREZGwtvbGyqVCtnZ2UbLyczMxEMPPYQ6depArVajb9++uHHjhsn1VjRInTZtGj755BNs3LgR9erVQ35+PvLz88UXoNFoMHHiRMTHx+O7777DoUOHMGHCBISHh6NHjx4AgMjISHTo0AFjx47FL7/8gq+//hoLFizAtGnTjH/6rorSAYq9pKoCVQar1k1yWCJAZaBKJrK7/prI0SjUX2/evBnx8fFISEjA4cOHERwcjKioKMmHRX179+7F6NGjMXHiRGRlZSEmJgYxMTE4duyYmKe4uBi9e/fGkiVLKj1vZmYmBg4ciMjISOzfvx8HDhzA9OnT4eJieuip6BZUKpXxv8rr1q3D+PHjAdzZHPrZZ5/Fp59+ilu3biEqKgrvvPOOZGrozz//xNSpU7F7927UqVMHcXFxeO211+DmZtrdDOL2EEsBda1qvywis2hvAJrnZWxB9a5l2qv2BqCZ4jxbmpB57K6/folbBpJytDcBzWKZW1BZoM82p78OCwtDt27dsGrVKgB3Fj76+/tjxowZmDt3rkH+2NhYFBcXY/v27eKxHj16ICQkBMnJyZK8Z8+eRUBAALKyshASEiJ5rEePHnj44YexePFima/yLkXvSTUlPvby8sLq1auxevXqSvO0bNnSYCjaLLwn1ZA5309MtsGFU2RD7K+J9Ci8cErciu0fld27XVJSgkOHDmHevHniMRcXF0RERIhbw1WUmZkpWawI3NkqLi0tzeRqXrhwAfv27cOYMWPQs2dPnDlzBoGBgXj11VfRu3dvk8vhW1yf0vuT2kvSn3521UtK18sZkhyc7idnpvStOUxMclmwv/b394dGoxFTUlKS0VNeunQJZWVlRrd+q2zbt8q2iqssvzF//PEHACAxMRGTJk3Czp078cADD2DAgAE4deqUyeXYxep+u2Fuw6tpyq9BxeCF18a65AapRM6M/TUpSeG2d+7cOcl0v73d063T3ZmGnTJlCiZMmAAA6NKlCzIyMvDBBx9UGlRXxCBVHzs9KRU4ymZLctsep/vJmbG/JiVVZyS1Ov55vlqtNume1EaNGsHV1VXc6q2c/tZwFfn5+cnKb0yTJk0AwGDXjvbt2yM3N9fkcjh2o0/pqQMmJjk43U/OTOn3KhOTXAr01x4eHggNDZVsDafT6ZCRkSFuDVdReHi4JD9wZ6u4yvIb06pVKzRt2hQnT56UHP/999/RsmVLk8vhSKo+cxteTaYCgxhbYdsjMh37a1KSA7W9+Ph4xMXFoWvXrujevTtWrFiB4uJicRp+3LhxaNasmTgFP3PmTPTr1w/Lly9HdHQ0Nm3ahIMHD2Lt2rVimYWFhcjNzUVeXh4AiMGon58f/Pz8oFKpMGfOHCQkJCA4OBghISFYv349Tpw4gS1btphcdwap+sxZvFLTMCBVjtxOj9P95MwYpJKSqjOSWh1mPD82NhYXL17EwoULkZ+fj5CQEOzcuVNcHJWbmyvZu7Rnz57YuHEjFixYgPnz56Ndu3ZIS0tDp06dxDzbtm0Tg1wAGDVqFAAgISEBiYmJAIBZs2bh5s2bmD17NgoLCxEcHIz09HS0adPG5Loruk+qvRD3MFvNfVIBVP4mcPqWYl3aG4Bmhox9UldZcJ/U6dwnlRyD2P7/w31SSTnam4Bmvsx9Ui3QZztbf82RVH38ZH5H+TXg6n7bMncLqurihw9yRJz5IiWZ0/YUGkl1ZAxS9TFIlaosWCXr4HQ/EVHNxSBVNgap+vjJ3OneAHaFH5CITMedKUhJbHs2wSBVH0dSja/m5wp/2+BIKhFRzcWRVNmcfdyQjFF67zlnTnIouE/q6tWr0apVK3h5eSEsLAz79++vMn9KSgoCAwPh5eWFoKAgg+9uT01NRWRkJLy9vaFSqZCdnW1QRv/+/aFSqSTp6aefll95qhks2f6ZmMxJcil1XgfGIFWfCsp/d7u9JGPXhsm+glSFbN68GfHx8UhISMDhw4cRHByMqKgoXLhwwWj+vXv3YvTo0Zg4cSKysrIQExODmJgYHDt2TMxTXFyM3r17Y8mSJVWee9KkSTh//ryYli5datHXRg5E6QCFiYmsjtP9+hwoULA6Vxh/E+psXRGqlKU6yn/K0Gq1ksOenp5Gvw/6jTfewKRJk8Q98pKTk/Hll1/igw8+wNy5cw3yv/XWWxg4cCDmzJkDAFi8eDHS09OxatUqJCcnAwDGjh0LADh79myVVa1du7asr+YjIrIbluiznSw45kiqPqVH0ewtuRg55spktWTuFlQWGhHw9/eHRqMRU/m3j+grKSnBoUOHEBERIR5zcXFBREQEMjMzjVYzMzNTkh8AoqKiKs1flQ0bNqBRo0bo1KkT5s2bh+vXr8sug2oIpUfRmJjkUuq8DsyskdQbN25AEATUrl0bAPDnn39i69at6NChAyIjIy1aQZvi6n7DN4DKyDGyDoVH8c+dOyfZHNrYKOqlS5dQVlYmflNJOV9fX5w4ccJoufn5+Ubz5+fny6rfE088gZYtW6Jp06Y4cuQIXnjhBZw8eRKpqamyynFGNbbPJqIazawg9dFHH8WwYcPw9NNP48qVKwgLC4O7uzsuXbqEN954A1OnTrV0PW2D0/3Gg1IGqrYht+1Z6lP1P2Wo1Wq7/gaTyZMni/8PCgpCkyZNMGDAAJw5c0bW1+w5oxrZZzvhqBLZkeqMpNr6vA7MrHHDw4cPo0+fPgCALVu2wNfXF3/++Sc++ugjvP322xatoE0pPb1uL8nYND+TbZIcCkxbNWrUCK6urigoKJAcLygoqPReUT8/P1n5TRUWFgYAOH36dLXKcQY1ts8mciSc7pfNrCD1+vXrqFevHgDgm2++wbBhw+Di4oIePXrgzz//tGgFbUrpAIWJyc55eHggNDQUGRkZ4jGdToeMjAyEh4cbfU54eLgkPwCkp6dXmt9U5dtUNWnSpFrlOIMa2WcrfT8iExNZnVlBatu2bZGWloZz587h66+/Fu9punDhgl1PF96T0gEKE5McCnW28fHxeO+997B+/Xr89ttvmDp1KoqLi8XV/uPGjcO8efPE/DNnzsTOnTuxfPlynDhxAomJiTh48CCmT58u5iksLER2djaOHz8OADh58iSys7PF+1bPnDmDxYsX49ChQzh79iy2bduGcePGoW/fvujcubO8F+CEamyfTeRIGBzLZtY9qQsXLsQTTzyB2bNnY8CAAeKIyDfffIMuXbpYtII2ZU6g4CxUcLo3h82ZG6RWl8wyYmNjcfHiRSxcuBD5+fkICQnBzp07xcVRubm5cHG5+/m3Z8+e2LhxIxYsWID58+ejXbt2SEtLQ6dOncQ827ZtE4NcABg1ahQAICEhAYmJifDw8MC3336LFStWoLi4GP7+/hg+fDgWLFhQjRfuPGpkn+2Ef7DJjpjT9izRZp2szasEQTDrJefn5+P8+fMIDg4W/yDt378farUagYGBFq2ktWm1Wmg0GhRtANS1la6NHTHWMpzsDWJL2uuAZixQVFRU5eiW2F6XAupaFjjvDUDz/L3PS46tpvTZYvtfBKi9lK4NOSvtTUCTYFq/ack+29n6a9kjqbdv30atWrWQnZ1t8Am8e/fuFquYIpx9C6qKAaix0VOONFuPufukVhc/eNRoNbbP5swXKcmctseRVNlkB6nu7u5o0aIFysrKrFEfZTl7p1dZUOpkbwrFmNvpEVWhxvbZzt5fk7LMbXvss2Uxa9zwxRdfxPz581FYWGjp+ihL6UUz9pC4/ZSyicgKamSfrfR7lYmJrM6shVOrVq3C6dOn0bRpU7Rs2RJ16tSRPH748GGLVI4UwjefMuRed073k4lqZJ/NQIGUZE7b43S/bGYFqTExMRauhp1w9ntSy+kq/Mw/BLbBIJWspEb22eyvSUnmtD0GqbKZFaQmJCRYuh72gZ/M76j45qsYtBKRQ6mRfTb7a1IS255NmBWkAsCVK1ewZcsWnDlzBnPmzEHDhg1x+PBh+Pr6olmzZpaso22x4d25Bvqf1lzhdJ/eFMHV/WRFNa7PZpBKSuJ0v02YFaQeOXIEERER0Gg0OHv2LCZNmoSGDRsiNTUVubm5+OijjyxdT9tgp3dXxUC14s9keWx7ZCU1ss9WgdP9pBz21zZh1ls8Pj4e48ePx6lTp+DldXc35UceeQR79uyxWOVsTumVgkzOneTid1CTiWpsn03kSNhfy2bWSOqBAwfw7rvvGhxv1qyZ+F3bDsncYIHIEuS2PU73k4lqZJ/NhVOkJC6csgmzglRPT09otVqD47///jsaN25c7UophkEqKYltj6ykRvbZDFJJSWx7NmHWZR46dChefvll3L59GwCgUqmQm5uLF154AcOHD7doBW1K6eleJiY5ON1PJqqRfbbS71UmJrnYX8tmVpC6fPlyXLt2DT4+Prhx4wb69euHtm3bol69enj11VctXUfbUbrBMzHJwSCVTFQj+2yl36tMTHKxv5bNrOl+jUaD9PR0/Pjjjzhy5AiuXbuGBx54ABEREZaun22Z2/CILIFtj6ykRvbZ7K9JSWx7NmH2PqkA0Lt3b/Tu3dtSdVEeO72qqeB0n+JsSm7b48IpkqlG9dnsr0lJ1RlJrQ4n669NDlLffvttTJ48GV5eXnj77berzPvMM89Uu2KK4I34994blYGq9TBIJQuq8X22E059kh0xp+0xSJXN5CD1zTffxJgxY+Dl5YU333yz0nwqlcoxOzyAn8xJWWx7ZEE1vs9mkEpKYtuzCZOD1JycHKP/r1EYpN6bAF4ja+FIKllQje+zGaSSkjiSahOyJ7dv376NNm3a4LfffrNGfUhpFVcuVvUzk7KrRbm6n0xQY/tsS7Z/JiZzklxKndeByV445e7ujps3b1qjLmRvOGJqW7zeZAXss4nIUZm1TGjatGlYsmQJSktLLV0fZbkwSVJFSo8yOkOSQ8kRAXIoNbLPVnoUjYlJLqXO68DM2oLqwIEDyMjIwDfffIOgoCDUqVNH8nhqaqpFKmdzzry631jDr3gtdLaoCJnMUh2Wk3V6zqhG9tlO+Aeb7Eh1glRbn9eBmRWk1q9f33G/Sq8q5oxm1RTlr1uocEz/Z2cN4G2F15espEb22TrwgzMph23PJmQHqaWlpXjwwQcRGRkJPz8/a9RJWc4apJLyzJ3ury4n+2TubGp8n03kKDiSKpvsINXNzQ1PP/10zVspCjj3SCpwp/GrqviZrItBKllBje2zOd1PSuJ0v02YNcHYvXt3ZGVlVfvke/bswZAhQ9C0aVOoVCqkpaVJHh8/fjxUKpUkDRw4UJKnsLAQY8aMgVqtRv369TFx4kRcu3at2nVzSvda0KP0oiJnSERWUGP7bKUXzjA5byKbMOue1H//+9949tln8b///Q+hoaEGN+F37tzZpHKKi4sRHByMJ598EsOGDTOaZ+DAgVi3bp34s6enp+TxMWPG4Pz580hPT8ft27cxYcIETJ48GRs3bpT5qsBAQZ8AXgt7Z6nOkh1ujVcj+2wGC6Qkc9qeJdqsk7V5s4LUUaNGAZB+37NKpYIgCFCpVCgrKzOpnEGDBmHQoEFV5vH09Kz0PqrffvsNO3fuxIEDB9C1a1cAwMqVK/HII49g2bJlaNq0qdHn3bp1C7du3RJ/1mq1/7wIMDAzhgGrbfAak5U4cp9daX9NRDWeWUGqLb9ib/fu3fDx8UGDBg3w0EMP4ZVXXoG3tzcAIDMzE/Xr1xc7OwCIiIiAi4sL9u3bh8cee8xomUlJSVi0aJHhAwxSSUm8J5WsxJH77Er7axcArtZ6FUT3YM7NkhxJlc2sILVly5aWrodRAwcOxLBhwxAQEIAzZ85g/vz5GDRoEDIzM+Hq6or8/Hz4+PhInuPm5oaGDRsiPz+/0nLnzZuH+Ph48WetVgt/f38GqaQsc9qek3VYZB5H7rMr7a89cScRKcHcLajYZ8tiVpBa7vjx48jNzUVJSYnk+NChQ6tVqXLlU1QAEBQUhM6dO6NNmzbYvXs3BgwYYHa5np6eBvdJAWCQSspi2yMrc8Q+u9L+mkEqKYn7pNqEWUHqH3/8gcceewxHjx4V72sC7tzjBMDk+5vkat26NRo1aoTTp09jwIAB8PPzw4ULFyR5SktLUVhYaN5+gAxSSUmc7icrqZF9tgcYpJJyzHnLcLpfNrO2oJo5cyYCAgJw4cIF1K5dG7/++iv27NmDrl27Yvfu3Rau4l3/+9//8Pfff6NJkyYAgPDwcFy5cgWHDh0S8+zatQs6nQ5hYWHyT6D09kP2klDJ/xnA2xcFt1NZvXo1WrVqBS8vL4SFhWH//v1V5k9JSUFgYCC8vLwQFBSEHTt2SB5PTU1FZGQkvL29oVKpkJ2dXfnLFgQMGjTI6BZIZFyN7LM9mJgUTnJx+yvZzBpJzczMxK5du9CoUSO4uLjAxcUFvXv3RlJSEp555hmT9+O7du0aTp8+Lf6ck5OD7OxsNGzYEA0bNsSiRYswfPhw+Pn54cyZM3j++efRtm1bREVFAQDat2+PgQMHYtKkSUhOTsbt27cxffp0jBo1qtKV/VViIFb1Sn6u8rcuB7m2mzdvRnx8PJKTkxEWFoYVK1YgKioKJ0+eNLjfEAD27t2L0aNHIykpCYMHD8bGjRsRExODw4cPo1OnTgDubG3Uu3dvjBw5EpMmTary/CtWrBBHAMk0NbLPdv8nESmBbc8mzApSy8rKUK9ePQBAo0aNkJeXh/vvvx8tW7bEyZMnTS7n4MGDePDBB8Wfy2+Oj4uLw5o1a3DkyBGsX78eV65cQdOmTREZGYnFixdL7k/asGEDpk+fjgEDBsDFxQXDhw/H22+/bc7LYpBaMQjV/5kBqvU5yHT/G2+8gUmTJmHChAkAgOTkZHz55Zf44IMPMHfuXIP8b731FgYOHIg5c+YAABYvXoz09HSsWrUKycnJAICxY8cCAM6ePVvlubOzs7F8+XIcPHhQHJ2je6uRfbYHON1Pyik14zmc7pfNrCC1U6dO+OWXXxAQEICwsDAsXboUHh4eWLt2LVq3bm1yOf379xfvjTLm66+/vmcZDRs2NG/jfjLEIFRZCgepFfefNLZgpaSkBIcOHcK8efPEYy4uLoiIiEBmZqbR4jMzMyWrswEgKipK9lT99evX8cQTT2D16tX8DnqZamSfzYVTpCQGqTZhVpC6YMECFBcXAwBefvllDB48GH369IG3tzc2b95s0QralAvMvEu3BjC2UpFBq20pfL39/f0lPyckJCAxMVFy7NKlSygrK4Ovr6/kuK+vL06cOGG03Pz8fKP5q9omzpjZs2ejZ8+eePTRR2U9j2pon80glZRkTpBKspkVpJbfXwQAbdu2xYkTJ1BYWIgGDRrwXjFHZSw41w9c+Wu1PoVHUs+dOwe1Wi0eNrrtj0K2bduGXbt2WeT7551RjeyzOd1PSrptxnM4kiqbWUFqUVERysrK0LBhQ/FYw4YNUVhYCDc3N8kfOofi7Pek6hNwN3DlfnD2ycJBqlqtvud7t1GjRnB1dUVBQYHkeEFBQaVT8H5+frLyG7Nr1y6cOXMG9evXlxwfPnw4+vTpY9UV6jVBjeyzOZJKSmKQahNmTW6PGjUKmzZtMjj+2WefSTZzdjhKb/1kLwkV/u8C574VgkQeHh4IDQ1FRkaGeEyn0yEjIwPh4eFGnxMeHi7JDwDp6emV5jdm7ty5OHLkCLKzs8UEAG+++SbWrVsn/4U4mRrZZ7tD+S2ImJw3cXW/TZg1krpv3z688cYbBsf79++PF198sdqVUgxHUqumP7pKlif32iq0uj8+Ph5xcXHo2rUrunfvjhUrVqC4uFhc7T9u3Dg0a9YMSUlJAO7s0dmvXz8sX74c0dHR2LRpEw4ePIi1a9eKZRYWFiI3Nxd5eXkAIK449/Pzk6SKWrRogYCAAHNetVOpkX12ebBApARz2h5HUmUzK0i9desWSksN7xq+ffs2bty4Ue1KKYZBauXbTnELKutzkC2oYmNjcfHiRSxcuBD5+fkICQnBzp07xcVRubm5cHG5G3H37NkTGzduxIIFCzB//ny0a9cOaWlp4h6pwJ17TsuDXODu12saW7xF8tXIPpv7pJKSzGl7DFJlUwlV7SdSiQcffBCdOnXCypUrJcenTZuGI0eO4IcffrBYBW1Bq9VCo9Gg6BCgrqt0beyIUOFfsirtNUDT7c79g1XdIyi211mA2gL35GlvAZoV9z4vOa6a1GeL7f80oK6ndG3IWWmvApq2pvWbluyzna2/Nmsk9ZVXXkFERAR++eUXDBgwAACQkZGBAwcO4JtvvrFoBW2KI6l3ceTU9hxkJJUcT43ss0vBbYBIOdwn1SbMClJ79eqFzMxMLF26FJ999hlq1aqFzp074/3330e7du0sXUfbYmBWNSd7g9g1BqlkohrZZzNIJSUxSLUJs4JUAAgJCal53/TEkdQ7eF+qMnhtyYpqXJ9d9k8iUgLbnk2YHaSWlZVh69at+O233wAAHTp0wKOPPgo3N7OLVB6DVCkGqLbF6X6yohrXZzNIJSWZ0/Y4kiqbWb3Tr7/+iqFDhyI/Px/3338/AGDJkiVo3LgxvvjiC8mqXYfi7EFqeeOveA0YoBI5tBrZZ+vAIJWUwy+5sQmzgtSnnnoKHTt2xMGDB9GgQQMAwOXLlzF+/HhMnjwZe/futWglyUb0A9HywLRigOpkn+LsGkdSyUQ1ss/mSCopiSOpNmFWkJqdnS3p7ACgQYMGePXVV9GtWzeLVc7mnH0ktVxlI6ccUbUuTveTldTIPptBKimJQapNmBWk3nfffSgoKEDHjh0lxy9cuIC2bdtapGKKYJAqxXtSbYvXl6ykRvbZXN1PSmLbswmTg1StViv+PykpCc888wwSExPRo0cPAMDPP/+Ml19+GUuWLLF8LW2FQarxYJQBqm1wJJUsqMb32QxSSUncgsomTA5S69evD5Xq7l9RQRAwcuRI8Vj5F1cNGTIEZWUOOgfj7EGqKSOnTvYGsWsMUqkKNb7P5sIpUpI5C6cYpMpmcpD63XffWbMeZA/udR8qR1Sti9eWLKjG99kcSSUlse3ZhMlBar9+/axZD/vg7COp5Yyt7GeAan2c7icLqvF9tg7cBoiUw5FUmzBr4dSePXuqfLxv375mVYbsRFUBqpO9Qewag1QyUY3ss7m6n5TE1f02YVaQ2r9/f4Nj+vc+OeT9TSTFr0W1PV5bspIa2WdzJJWUxLZnEy7mPOny5cuSdOHCBezcuRPdunXDN998Y+k62o4Lk0GL0L8FQsVk9SSXYIFENV6N7LPLmJgUTuZQqL9evXo1WrVqBS8vL4SFhWH//v1V5k9JSUFgYCC8vLwQFBSEHTt2SB5PTU1FZGQkvL29oVKpkJ2dXWlZgiBg0KBBUKlUSEtLk1Vvs0ZSNRqNwbGHH34YHh4eiI+Px6FDh8wpVnm8J/XOm6A8UNX/pKiqkIeUx+l+MlGN7LM5kkpKcqB7Ujdv3oz4+HgkJycjLCwMK1asQFRUFE6ePAkfHx+D/Hv37sXo0aORlJSEwYMHY+PGjYiJicHhw4fFr1AuLi5G7969MXLkSEyaNKnK869YsUIycyOHWUFqZXx9fXHy5ElLFmlbzh6kVpzaNxasAs59jayJ15VszKH7bAEMUkk5Cn+4198HGQA8PT3h6elpNO8bb7yBSZMmYcKECQCA5ORkfPnll/jggw8wd+5cg/xvvfUWBg4ciDlz5gAAFi9ejPT0dKxatQrJyckAgLFjxwIAzp49W2U9s7OzsXz5chw8eBBNmjSR9RoBM4PUI0eOSH4WBAHnz5/Ha6+9hpCQEHOKJHtQ2X2oLuAfA3vEkVQyUY3sszmSSkpSeCTV399fcjghIQGJiYkG2UtKSnDo0CHMmzdPPObi4oKIiAhkZmYaPUVmZibi4+Mlx6KiomRP1V+/fh1PPPEEVq9eDT8/P1nPLWdWkBoSEgKVSiVuBl2uR48e+OCDD8yqiF1w9pFUwPhCqapGVUk5DFLJRDWyz+Y91aQkc9qeBYPUc+fOQa1Wi4crG0W9dOkSysrK4OvrKznu6+uLEydOGH1Ofn6+0fz5+fmyqjp79mz07NkTjz76qKzn6TMrSM3JyZH87OLigsaNG8PLy8vsipCdMBagGrsFgCyP15aspEb22RxJJSUp3PbUarUkSLU327Ztw65du5CVlVWtcmT9WczMzMT27dvRsmVLMX3//ffo27cvWrRogcmTJ+PWrVvVqpCilF7ZbS8JFf6t+H8m6157U1liZT9Ho2q0Gt1n65iYFE5yKdBfN2rUCK6urigoKJAcLygoqHQK3s/PT1Z+Y3bt2oUzZ86gfv36cHNzg5vbnTHR4cOHG90SrzKygtSXX34Zv/76q/jz0aNHMXHiRERERGDu3Ln44osvkJSUJKdI+6J0gGIPydh1gN6/ZD8YpNI91Og+25Ltn4nJnCSXAuf18PBAaGgoMjIyxGM6nQ4ZGRkIDw83+pzw8HBJfgBIT0+vNL8xc+fOxZEjR5CdnS0mAHjzzTexbt06k8uRNd2fnZ2NxYsXiz9v2rQJYWFheO+99wDcuZG3spt3yUGUB6PCP/+v+C9ZD68vWViN7rPNHc0isgQHanvx8fGIi4tD165d0b17d6xYsQLFxcXiav9x48ahWbNm4gfWmTNnol+/fli+fDmio6OxadMmHDx4EGvXrhXLLCwsRG5uLvLy8gBA3CXEz89Pkipq0aIFAgICTK67rCD18uXLkptpv//+ewwaNEj8uVu3bjh37pycIu2LOVOuNREDU2WYO91fXZYog+xSje6zGaSSkqoz3V8dZjw/NjYWFy9exMKFC5Gfn4+QkBDs3LlT7Btyc3Ph4nJ3Yr1nz57YuHEjFixYgPnz56Ndu3ZIS0sT90gF7txzWh7kAsCoUaMAVL7LgLlkBam+vr7IycmBv78/SkpKcPjwYSxatEh8/OrVq3B3d7dY5WyOQarxwFQ/aCX7wSCV7qFG99kMUklJDhSkAsD06dMxffp0o4/t3r3b4NiIESMwYsSISssbP348xo8fL6sOFXcXMYWsIPWRRx7B3LlzsWTJEqSlpaF27dro06eP+PiRI0fQpk0b2ZUgO8LpfuXw+pKF1eg+21If0ojMwbZnE7KC1MWLF2PYsGHo168f6tati/Xr18PDw0N8/IMPPkBkZKTFK2lTzh4olL/xjAWofFPaF46k0j3U6D6bQSopqToLp2x9XgcmK0ht1KgR9uzZg6KiItStWxeurq6Sx1NSUlC3bl2LVpBsjCOpyuH1JQur0X22C7i3MCmHbc8mzNrMX6PRGD3esGHDalVGcbwn9Y6qAlQn+xRn1ziSSiaqkX226z+JSAnmtD2OpMpmVpBaYzl7kGrKVL8zXx9r4+p+ItO5gX/BSDnmtD0GqbLxLU53mTrV72RvEiKyQwxSSUlsezbBy6zP2UdSgarvPeVoqnVxJJXIdAxSSUkcSbUJvsVJquLUPu9LtV8MUsmZuYD3pJJyzFk4xSBVNq5P01fd772vCQkV/q9/bXidrH/tHcTq1avRqlUreHl5ISwsDPv3768yf0pKCgIDA+Hl5YWgoCDs2LFD8nhqaioiIyPh7e0NlUolfs+zvilTpqBNmzaoVasWGjdujEcffRQnTpyw5MsiR+LGxKRwIqtjkEpS9wpOyX4IFkwybN68GfHx8UhISMDhw4cRHByMqKgoXLhwwWj+vXv3YvTo0Zg4cSKysrIQExODmJgYHDt2TMxTXFyM3r17Y8mSJZWeNzQ0FOvWrcNvv/2Gr7/+GoIgIDIyEmVlZfJeANUMrkxMCie5FOivHZ1KMOd7qmoYrVYLjUaDov8BarXStVGYUOH/FX8mq9FqAU0roKioCOoqGqLYXkcDao9Ks5l+3hJA8+m9z1suLCwM3bp1w6pVqwAAOp0O/v7+mDFjBubOnWuQPzY2FsXFxdi+fbt4rEePHggJCUFycrIk79mzZxEQEICsrCyEhIRUWY8jR44gODgYp0+fdtxvTSLZxPa/E1DXUbo25Ky0xYBmoGn9piX7bLn9taPjgDVJlY+WGrsHVQUGqjWYVquV/Ozp6QlPT0/JsZKSEhw6dAjz5s0Tj7m4uCAiIgKZmZlGy83MzER8fLzkWFRUFNLS0syua3FxMdatW4eAgAD4+/ubXQ45MPd/EpES2PZsgkGqPge8N9DijC2a0n/M2a+PNcm9thZeOFUx2EtISEBiYqLk2KVLl1BWVgZfX1/JcV9f30rvD83PzzeaPz8/X3ZV33nnHTz//PMoLi7G/fffj/T0dMnXfJITcQX/gpFyqjPdXx1ONlDEtzhJVRxJrWxTf1KehYPUc+fOSaaPKo6i2oMxY8bg4Ycfxvnz57Fs2TKMHDkSP/30E7y8vJSuGtmaufcFElkCg1SbYJBKxlUWkHIk1XoUvrZqtfqe9zg1atQIrq6uKCgokBwvKCiAn5+f0ef4+fnJyl8VjUYDjUaDdu3aoUePHmjQoAG2bt2K0aNHyy6LHBxXWJOS2PZsQtHV/Xv27MGQIUPQtGlTqFQqg3vUBEHAwoUL0aRJE9SqVQsRERE4deqUJE9hYSHGjBkDtVqN+vXrY+LEibh27ZoNX0UNV9W2VKQsBVb3e3h4IDQ0FBkZGeIxnU6HjIwMhIeHG31OeHi4JD8ApKenV5rfVIIgQBAE3Lp1q1rlkOnsqs9WemU3E5NcXN0vm6JBanFxMYKDg7F69Wqjjy9duhRvv/02kpOTsW/fPtSpUwdRUVG4efOmmGfMmDH49ddfkZ6eju3bt2PPnj2YPHmyeRVyYZJQVfi3/P9M9rNXqgIdXnx8PN577z2sX78ev/32G6ZOnYri4mJMmDABADBu3DjJwqqZM2di586dWL58OU6cOIHExEQcPHgQ06dPF/MUFhYiOzsbx48fBwCcPHkS2dnZ4n2rf/zxB5KSknDo0CHk5uZi7969GDFiBGrVqoVHHnlE/osgs9hVn610gMLEZA4GqLIoOmA9aNAgDBo0yOhjgiBgxYoVWLBgAR599FEAwEcffQRfX1+kpaVh1KhR+O2337Bz504cOHAAXbt2BQCsXLkSjzzyCJYtW4amTZsaLfvWrVuS0ZeKq5qdmgsAnd7PvB+VKoiNjcXFixexcOFC5OfnIyQkBDt37hQXR+Xm5sLF5e4nnp49e2Ljxo1YsGAB5s+fj3bt2iEtLQ2dOnUS82zbtk0McgFg1KhRAO4u3vLy8sIPP/yAFStW4PLly/D19UXfvn2xd+9e+Pj42OiVkxJ9dqX9Nb9xipTEXeZtwm7vqsjJyUF+fj4iIiLEYxqNBmFhYcjMzMSoUaOQmZmJ+vXri50dAERERMDFxQX79u3DY489ZrTspKQkLFq0yPABZ5/SLg9CqwpU9fORsiy8cEqO6dOnS0ZC9e3evdvg2IgRIzBixIhKyxs/fjzGjx9f6eNNmzY1+JYqsi/W6rMr7a+rM5pFVF3mtD0unJLNboPU8mm+qrauyc/PNxhFcXNzQ8OGDavc3mbevHmSfRu1Wi33WgSkQWhlgap+PrIshbegIqoOa/XZlfbXDFJJSQxSbcJug1RrMrZJOQCOpFbcI7WqQJWIyAYq7a8ZpJKS2PZswm6D1PLtaQoKCtCkSRPxeEFBgfh1iX5+fgbfF15aWorCwkKztrdxesb2SK0sD1keR1LJgdm8z2aQSkriSKpN2O2tvwEBAfDz85NsXaPVarFv3z5x65rw8HBcuXIFhw4dEvPs2rULOp0OYWFhNq9zjWS3LYSI7InN+2ylV3YzMZHVKTqSeu3aNZw+fVr8OScnB9nZ2WjYsCFatGiBWbNm4ZVXXkG7du0QEBCAl156CU2bNkVMTAwAoH379hg4cCAmTZqE5ORk3L59G9OnT8eoUaMqXdlfJWef7geMf8NUxWl/sg8cSSUbs6s+m6v7SUnmDOBwJFU2RYPUgwcP4sEHHxR/Lr85Pi4uDh9++KH4Hd2TJ0/GlStX0Lt3b+zcuVPyFYgbNmzA9OnTMWDAALi4uGD48OF4++23bf5aagxuOeU4GKSSjdlVn83RLFKSOW2PQapsKkEQnOwlG9JqtdBoNCi6ANzjWyGdh/6bqfxfjqZalVYLaJoBRUVFVX49qdheHwPU7hY4721As/Xe5yWyB2L7/4v9NSnH1P76Tl7L9dnO1l/b7cIpRXC6X/opzdi0P8Bg1V5wJJWcmRv4F4yUY07b40iqbHyLk1TFwNTY9D8XU1mH3OvKIJWcGYNUUhKDVJvgW1yfs4+kVtwnlfelEpG94j2ppCS2PZtgkEp3VbU3Kr8W1f5wJJWcmbMPKpCyzGl7HEmVjUEqSVU1za+fhyyPm/kTEdVcDFJlY5Cqz9k/mVec7tfHqX8isieW+pBGZA62PZtgkEp3VQxETRlVJeVwJJWcGYNUUpI5bY8jqbIxSCWpewWkzjzSbG2c7icyHYNUUhKDVJtgkKrPmaf7jY2g6v+fo6hEZE8YpJKS2PZsgkEq3WFs5T4DU/vGkVRyZgxSSUkcSbUJBqkkVdWiKQat9oVBKjkzBqmkJAapNsEglYzjPam2x2tLZDoGqaQktj2bYJCqj/ekGuLoqf3iSCo5MwappCSOpNoEg1S6Q/+eVGPT+wxW7Q+DVHJmDFJJSQxSbYJBKpmOgSoR2QsGqaQktj2bYJBKUhxFdRwcSSVnpvsnESnBnLbHkVTZGKRS1Yx9CxVZBzfzJyKquRikysYgVZ8zL5wCqh455WgqEdkTTveTktj2bIJBKt1lbNSUb0T7xZFUcmac7iclcbrfJhikEhGR42GQSkpi27MJBqkkxdFTx8LfFTkrAQwUSDnm9r3ss2VhkErGGVvdzwDWvnC6n5xZ2T+JSAnmtD1O98vGIJWIiBwPp/tJSWx7NsEgVR9X999R1R6pHE21HxxJJWdW+k8iUoI5bY8jqbIxSKW7TA1AnTmQtybuk0pkOm5BRUri16LaBINUMg1HUInInnC6n5TEtmcTLkpXgOwYR0ztm2DBJNPq1avRqlUreHl5ISwsDPv3768yf0pKCgIDA+Hl5YWgoCDs2LFD8nhqaioiIyPh7e0NlUqF7OxsyeOFhYWYMWMG7r//ftSqVQstWrTAM888g6KiIvmVp5pBx8SkcJJLof7akTFIJXJUCgWpmzdvRnx8PBISEnD48GEEBwcjKioKFy5cMJp/7969GD16NCZOnIisrCzExMQgJiYGx44dE/MUFxejd+/eWLJkidEy8vLykJeXh2XLluHYsWP48MMPsXPnTkycOFFe5anmUDpAYWKSi0GqbCpBEJzsJRvSarXQaDQoKgLUaqVrYwf03wgV/09Wo9UCmkZAUVER1FU0RLG99gPUFrhhR1sKaL6/93nLhYWFoVu3bli1ahUAQKfTwd/fHzNmzMDcuXMN8sfGxqK4uBjbt28Xj/Xo0QMhISFITk6W5D179iwCAgKQlZWFkJCQKuuRkpKCf/3rXyguLoabG+9cchZi+z8EqOsqXRtyVtprgCbUtH7Tkn223P7a0bFnJ3JUFl44pdVqJYc9PT3h6ekpOVZSUoJDhw5h3rx54jEXFxdEREQgMzPTaPGZmZmIj4+XHIuKikJaWlq1ql3eSTNAdVLmjmYRWUJ1RlKrw8kGi9i7k3FcKGX/LByk+vv7Sw4nJCQgMTFRcuzSpUsoKyuDr6+v5Livry9OnDhhtPj8/Hyj+fPz882u8qVLl7B48WJMnjzZ7DLIwXELKlISt6CyCQapRAQAOHfunGT6qOIoqr3QarWIjo5Ghw4dDIJoIiKqORikEjkqC4+kqtXqe97j1KhRI7i6uqKgoEByvKCgAH5+fkaf4+fnJyt/Va5evYqBAweiXr162Lp1K9zd3WWXQUSkCI6kysbV/USOSoHV/R4eHggNDUVGRoZ4TKfTISMjA+Hh4UafEx4eLskPAOnp6ZXmr4xWq0VkZCQ8PDywbds2eHl5yXo+1TAuTEwKJ7m4ul82jqQSkSzx8fGIi4tD165d0b17d6xYsQLFxcWYMGECAGDcuHFo1qwZkpKSAAAzZ85Ev379sHz5ckRHR2PTpk04ePAg1q5dK5ZZWFiI3Nxc5OXlAQBOnjwJ4M4orJ+fnxigXr9+HZ988gm0Wq240Ktx48ZwdXW15SUge+DsX2NNymLbswkGqUSOysLT/aaKjY3FxYsXsXDhQuTn5yMkJAQ7d+4UF0fl5ubCxeXuMEPPnj2xceNGLFiwAPPnz0e7du2QlpaGTp06iXm2bdsmBrkAMGrUKAB3F28dPnwY+/btAwC0bdtWUp+cnBy0atVK3osgx2fuaBaRJVRnJLU6nGwklfukgvukGqi4Ryr3SbUJ2fukhltwn9RM59l3jxwb90kle2DWPqkW6LOdrb/mSCoRETkeTveTktj2bIJBKpGjUmi6n8huMFAgR8LpftkYpBIRkePhPamkJLY9m2CQSuSoOJJKzozT/aQkc9oeR1JlY5BKxjnZG8EhMUglZ8YglZTEINUmGKQSEZHjYZBKSmLbswkGqWQaoZL/k+XIva4cSSVnxiCVlMSRVJtgkEqVM/ZmcLI3iF1jkErOjAunSEnczN8m+BYnKSd7AxAREdV0q1evRqtWreDl5YWwsDDs37+/yvwpKSkIDAyEl5cXgoKCsGPHDsnjqampiIyMhLe3N1QqFbKzsyWPFxYWYsaMGbj//vtRq1YttGjRAs888wyKiopk1ZtBKlWNQav9EiyYiByNKxOTwkkuhfrrzZs3Iz4+HgkJCTh8+DCCg4MRFRWFCxcuGM2/d+9ejB49GhMnTkRWVhZiYmIQExODY8eOiXmKi4vRu3dvLFmyxGgZeXl5yMvLw7Jly3Ds2DF8+OGH2LlzJyZOnCir7nYdpCYmJkKlUklSYGCg+PjNmzcxbdo0eHt7o27duhg+fDgKCgoUrHENx2DG/jBAJTti0z5bxcSkcDKHAv31G2+8gUmTJmHChAno0KEDkpOTUbt2bXzwwQdG87/11lsYOHAg5syZg/bt22Px4sV44IEHsGrVKjHP2LFjsXDhQkRERBgto1OnTvj8888xZMgQtGnTBg899BBeffVVfPHFFygtLTW57nYdpAJAx44dcf78eTH9+OOP4mOzZ8/GF198gZSUFHz//ffIy8vDsGHDFKxtDcVAhohMZLM+24WJSeGkIK1WK0m3bt0ymq+kpASHDh2SBJMuLi6IiIhAZmam0edkZmYaBJ9RUVGV5jdVUVER1Go13NxMXw5l9wun3Nzc4OfnZ3C8qKgI77//PjZu3IiHHnoIALBu3Tq0b98eP//8M3r06FFpmbdu3ZL8QrVareUr7oiECv+SfePCKbJDlu6zK+2v7SBQICdmTtuz4MIpf39/yeGEhAQkJiYaZL906RLKysrg6+srOe7r64sTJ04YPUV+fr7R/Pn5+WZX+9KlS1i8eDEmT54s63l2/xY/deoUmjZtitatW2PMmDHIzc0FABw6dAi3b9+WRPuBgYFo0aLFPaP9pKQkaDQaMVX8ZTslBiqOh/ekkh2ydJ9daX+tgvIjaUzOm8yZ7rdgf33u3DkUFRWJad68eWZUyDa0Wi2io6PRoUMHo4F0VVysUyXLCAsLE2+2XbNmDXJyctCnTx9cvXoV+fn58PDwQP369SXPMSXanzdvnuSXe+7cuTsPWPKPvqOliioe0x9lZbLd74HIgVijz660v1Y6SGFiUpBarZYkT09Po/kaNWoEV1dXg3u/CwoKjM54AICfn5+s/FW5evUqBg4ciHr16mHr1q1wd3eX9Xy7nu4fNGiQ+P/OnTsjLCwMLVu2xGeffYZatWqZXa6np2elv1CnJ1Tyf7I/lgps+XsmC7FGn11pf20HgQI5MXPaniX6bJnP9/DwQGhoKDIyMhATEwMA0Ol0yMjIwPTp040+Jzw8HBkZGZg1a5Z4LD09HeHh4bLOrdVqERUVBU9PT2zbtg1eXl7yKg87D1Irql+/Pu677z6cPn0aDz/8MEpKSnDlyhXJJ3Nzo32qBINW+8UgleycVftsBqmkJAcJUgEgPj4ecXFx6Nq1K7p3744VK1aguLgYEyZMAACMGzcOzZo1Q1JSEgBg5syZ6NevH5YvX47o6Ghs2rQJBw8exNq1a8UyCwsLkZubi7y8PADAyZMnAdwZhfXz84NWq0VkZCSuX7+OTz75RFzgBQCNGzeGq6tpe3g51Fv82rVrOHPmDJo0aYLQ0FC4u7sjIyNDfPzkyZPIzc2VHe3TPTCIISIzWLXPVnqql4nJQcTGxmLZsmVYuHAhQkJCkJ2djZ07d4qLo3Jzc3H+/Hkxf8+ePbFx40asXbsWwcHB2LJlC9LS0tCpUycxz7Zt29ClSxdER0cDAEaNGoUuXbogOTkZAHD48GHs27cPR48eRdu2bdGkSRMxibfsmEAlCILdhiDPPfcchgwZgpYtWyIvLw8JCQnIzs7G8ePH0bhxY0ydOhU7duzAhx9+CLVajRkzZgC4sxGtHFqtFhqNBkVXALXaCi/EUQhG/hWMHCer0GoBjc/dbToqz/dPe+0MqM3ZULpieWWA5si9z0t0L7bos8X2n+/k/TUpSqsFNH6m9ZuW7LOdrb+26+n+//3vfxg9ejT+/vtvNG7cGL1798bPP/+Mxo0bAwDefPNNuLi4YPjw4bh16xaioqLwzjvvmH9CLl65q6qFU2Qdcq8tp/vJzti0z3aw0SyqYcxpewpN9zsyux5JtRXxU85lfjIHYHwElUGq1Wm1gMZXxkhqkAVHUo86zydzcmxi+/+b/TUpR6sFNN4yR1It0Gc7W39t1yOppABjI6gMUO0TR1LJmXEklZTEkVSbYJBKd5kajDrZm8RmeF2JiIhEDFLpjsruQa34MwMp+8GRVHJmun8SkRLMaXscSZWNQao+LpySqng9eG3sC4NUcmZl/yQiJZjT9hikysYglaSMvQGc7E1BRA6AI6mkJLY9m2CQSndVNaXPEVXr4xZURKbjSCopiSOpNsEgVR+n+++q7Drw+tgPBqnkzDiSSkriPak2wSCVpCqOmHILKiKyRwxSSUlsezbBIJUMVTXdzzem9ci9thxJJWemA6f7STkcSbUJBqn6ON1/l7FrwQDVvjBIJWfGkVRSEoNUm2CQSlJVfeMUEZG94MIpUhLbnk0wSCVDxoJSjljYH46kkjPjSCopiSOpNsFvPqa7uM2UYxEsmGRavXo1WrVqBS8vL4SFhWH//v1V5k9JSUFgYCC8vLwQFBSEHTt2SB5PTU1FZGQkvL29oVKpkJ2dbVDG2rVr0b9/f6jVaqhUKly5ckV+xanm0DExKZzkUqi/dmQMUvVZ8o++Iybo/at/Pcx5M1KNtXnzZsTHxyMhIQGHDx9GcHAwoqKicOHCBaP59+7di9GjR2PixInIyspCTEwMYmJicOzYMTFPcXExevfujSVLllR63uvXr2PgwIGYP3++xV8TOSClAxQmJrI6lSAIThaXG9JqtdBoNCi6CKjVStdGYRUD1opBqtO3FuvRagFNc6CoqAjqKhqi2F7bAGpXC5y3DNCcufd5y4WFhaFbt25YtWoVAECn08Hf3x8zZszA3LlzDfLHxsaiuLgY27dvF4/16NEDISEhSE5OluQ9e/YsAgICkJWVhZCQEKPn3717Nx588EFcvnwZ9evXN/2FUo0gtv9fAHU9pWtDzkp7FdAEm9ZvWrLPlttfOzrek6rPCYfSK8UA1f5Z+J5UrVYrOezp6QlPT0/JsZKSEhw6dAjz5s0Tj7m4uCAiIgKZmZlGi8/MzER8fLzkWFRUFNLS0qpfd3JeXDhFSuI3TtkEg1SSquwNYOx2ALIsha+tv7+/5OeEhAQkJiZKjl26dAllZWXw9fWVHPf19cWJEyeMlpufn280f35+fvUrTc6rFMBtpStBTqtU6Qo4BwapdFdV0/z6j5P9sODv5Ny5c5Lpo4qjqER2hSOppCRz2x7/jsrCIFUfp/uNv/6Kx5z9GlmL3Otq4el+tVp9z3ucGjVqBFdXVxQUFEiOFxQUwM/Pz+hz/Pz8ZOUnMkkpOJpFyjGn7XG6Xzau7idDVQWqTvYGISkPDw+EhoYiIyNDPKbT6ZCRkYHw8HCjzwkPD5fkB4D09PRK8xOZpJSJSeFEVseRVH0cSb2rfKq/su2pSHkWHkk1VXx8POLi4tC1a1d0794dK1asQHFxMSZMmAAAGDduHJo1a4akpCQAwMyZM9GvXz8sX74c0dHR2LRpEw4ePIi1a9eKZRYWFiI3Nxd5eXkAgJMnTwK4MwpbPuKan5+P/Px8nD59GgBw9OhR1KtXDy1atEDDhg2rdQnIAZWBgQIphwunbIJBKklVti8qA1XrU3i631SxsbG4ePEiFi5ciPz8fISEhGDnzp3i4qjc3Fy4uNydpOnZsyc2btyIBQsWYP78+WjXrh3S0tLQqVMnMc+2bdvEIBcARo0aBUC6eCs5ORmLFi0S8/Tt2xcAsG7dOowfP17eiyDHx9EsUpI5bY9BqmzcJxV6e5id5z6pAAxHURmg2oRWC2haytgntSWgtsANO1odoPnTefbdI8cmtv8vAHUdpWtDzkpbDGiGyNwn1QJ9trP11xxJJanKvkWDgar1OchIKpFd4EgqKYkjqTbBIFUfv+rsrnt9ZSoRkZJ4Tyopiduf2QSDVDINA1Xr40gqkek4kkpK4kiqTTBIJUPGRlEBp3tz2D0GqeTMuJk/KYmr+22CQSpJVbZHKkdSiciecCSVlMS2ZxMMUumuil+LyntS7RtHUsmZMUglJXG63yYYpOrjZv6VXwNeG+vjPalEpmOfREoyp+0xSJWNQao+dnp3cTTV9nhtiUzn+k8iUgLbnk0wSCWpqgJ1BlH2hSOp5MwYpJKSzGl7HEmVjUGqPo6k3sWRVNvjdD+R6RikkpIYpNoEg1SSMrb1lP5jRET2gEEqKYltzyYYpOrjSKrhvqgcSbUdjqQSmY5BKimJI6k2wSCV7jJ1Cyone5PYDINUItO5gEEqKcfFjOcwSJWNQao+jqRyCyoicgwcSSUlse3ZBINUfQzE7uLCKdvjSCqR6RikkpI43W8TDFLpLk73K4tBKpHpGKSSkhik2gSD1IqcrAEYqGqPVGe/NkRkP1xg3n2BRJbAtmcTDFLprqpGUssf0ylQL2ch99pyJJWcmRv4F4yUY07b40iqbHyL6+NoYdXXgAGqfWGQSs6Mq/tJSVzdbxMMUvUxSK18NJUBKhHZE96TSkpi27MJBql0lykBqrMH8dbEhVNEpmOQSkriwimbYJCqjyOppgWozn6NrIXXlch0DFJJSWx7NsEgVR+DVK7udzT8nZCz4sIpUpK5bY99tix8i+tjIHZHxVFUbuZvG+ZM9ytxXiJ7wC2oSEnmLpyqLifrr2vMW3z16tVo1aoVvLy8EBYWhv379ytdJcdjbJq/su2omKyTiJxEtfvs8tX9TExKpBoTPdm3GjGSunnzZsTHxyM5ORlhYWFYsWIFoqKicPLkSfj4+JheEAOFql+/s18be8ORVHJQFumzy4MFIiWY0/Y4kiqbShAEh3/JYWFh6NatG1atWgUA0Ol08Pf3x4wZMzB37lyD/Ldu3cKtW7fEn7VaLfz9/VF0AlDXs1m17VdVo6hkNdqrgCYYKCoqglqtrjyfVguNRoMiNaBWWeC8AqDR3vu8RJYip8+utL/+lf01KUd7FdB0NK3ftGSf7Wz9tcOPpJaUlODQoUOYN2+eeMzFxQURERHIzMw0+pykpCQsWrTI4Lj2mtWq6RgqruQ3FqAyULWa8vZXAz43ElVKbp9daX99AxxJJcVob9z5l/21dTl8kHrp0iWUlZXB19dXctzX1xcnTpww+px58+YhPj5e/Pmvv/5Chw4d4N/VqlUlMsnVq1eh0WjunZHT/eSA5PbZ7K/JnpncXwOc7jeDwwep5vD09ISnp6f4c926dXHu3DnUq1cPKtWdsfjyKaVz585ZZUjdmuWzbMcsWxAEXL16FU2bNjXxCWafyjrlEFmBKf014BjvcVuXbe3ynbls2f01wCDVDA4fpDZq1Aiurq4oKCiQHC8oKICfn59JZbi4uKB58+ZGH1Or1Va978Oa5bNsxyvb5E/kRA6qun12Vf01YP/vcSXKtnb5zlo2+2vrc/hNFDw8PBAaGoqMjAzxmE6nQ0ZGBsLDwxWsGZGVcesrckDss8lpsb+WzeFHUgEgPj4ecXFx6Nq1K7p3744VK1aguLgYEyZMULpqRNbD6X5yUOyzySlxul82hx9JBYDY2FgsW7YMCxcuREhICLKzs7Fz506DG/Pl8PT0REJCguReKEuyZvksu+aUba/kbsSekpKCwMBAeHl5ISgoCDt27JA8npqaisjISHh7e0OlUiE7O9ugjJs3b2LatGnw9vZG3bp1MXz4cIMpY3IMjtZnO2rZ1i6fZZO11Yh9UomcibjnXi0L7pN6w/R99zZv3oxx48ZJNmJPSUmpdCP2vXv3om/fvkhKSsLgwYOxceNGLFmyBIcPH0anTp0AAB9//DFycnLQtGlTTJo0CVlZWQgJCZGUM3XqVHz55Zf48MMPodFoMH36dLi4uOCnn36q/kUgIrISS/bZcvtrR8cglcjBiB2elwWD1Jumd3pyvzwjNjYWxcXF2L59u3isR48eCAkJQXJysiTv2bNnERAQYBCkFhUVoXHjxti4cSMef/xxAMCJEyfQvn17ZGZmokePHua8dCIiq7Nkny23v3Z0NWK6n4iqT6vVSpL+t/yUK9+IPSIiQjx2ry/PyMzMlOQHgKioqErzG3Po0CHcvn1bUk5gYCBatGghqxwiInIcDFKJHJWFV/f7+/tDo9GIKSkpyeCUVW3Enp+fb7Sa+fn5svJXVoaHhwfq169frXKIiBTD1f2y1YjV/UROycKr+ytubM1FBUREFsTV/bJxJLUSclcvmyIpKQndunVDvXr14OPjg5iYGJw8edICtTX02muvQaVSYdasWRYp76+//sK//vUveHt7o1atWggKCsLBgwctUnZZWRleeuklBAQEoFatWmjTpg0WL15s1nci79mzB0OGDEHTpk2hUqmQlpYmeVwQBCxcuBBNmjRBrVq1EBERgVOnTlW77Nu3b+OFF15AUFAQ6tSpg6ZNm2LcuHHIy8uT/RqUUr6xdXkyFqSasxG7n59ftb5so7yMkpISXLlypVrlUM3FPlvKWn22JftrgH02VY1BqhGbN29GfHw8EhIScPjwYQQHByMqKgoXLlyoVrnff/89pk2bhp9//hnp6em4ffs2IiMjUVxcbKGa33HgwAG8++676Ny5s0XKu3z5Mnr16gV3d3d89dVXOH78OJYvX44GDRpYpPwlS5ZgzZo1WLVqFX777TcsWbIES5cuxcqVK2WXVVxcjODgYKxevdro40uXLsXbb7+N5ORk7Nu3D3Xq1EFUVBRu3rxZrbKvX7+Ow4cP46WXXsLhw4eRmpqKkydPYujQobJfg8kU2MzfnI3Yw8PDJfkBID09XdbG7aGhoXB3d5eUc/LkSeTm5nIDeGKfXYE1+2xL9tcA+2xO99+DQAa6d+8uTJs2Tfy5rKxMaNq0qZCUlGTR81y4cEEAIHz//fcWK/Pq1atCu3bthPT0dKFfv37CzJkzq13mCy+8IPTu3bv6latEdHS08OSTT0qODRs2TBgzZky1ygUgbN26VfxZp9MJfn5+wuuvvy4eu3LliuDp6Sl8+umn1SrbmP379wsAhD///FNW2fdSVFQkABCK3CAI7tVPRW53ur6ioiKTzr9p0ybB09NT+PDDD4Xjx48LkydPFurXry/k5+cLgiAIY8eOFebOnSvm/+mnnwQ3Nzdh2bJlwm+//SYkJCQI7u7uwtGjR8U8f//9t5CVlSV8+eWXAgBh06ZNQlZWlnD+/Hkxz9NPPy20aNFC2LVrl3Dw4EEhPDxcCA8Pt9BVJUfGPlvKmn22tfprQWCfbY3+2tFxJLUCc1Yvm6uoqAgA0LBhQ4uVOW3aNERHRxuspq6Obdu2oWvXrhgxYgR8fHzQpUsXvPfeexYrv2fPnsjIyMDvv/8OAPjll1/w448/YtCgQRY7BwDk5OQgPz9fcm00Gg3CwsKsskK8qKgIKpXKYLGPo7vXRuy5ubk4f/68mL9nz57YuHEj1q5di+DgYGzZsgVpaWniHqnAnTbWpUsXREdHAwBGjRqFLl26SLaoevPNNzF48GAMHz4cffv2hZ+fH1JTU230qslesc82ZM0+21b9NcA+m7hwykBVq5dPnDhhsfPodDrMmjULvXr1kvyxro5Nmzbh8OHDOHDggEXKK/fHH39gzZo1iI+Px/z583HgwAE888wz8PDwQFxcXLXLnzt3LrRaLQIDA+Hq6oqysjK8+uqrGDNmjAVqf1f5KvDqrjQ3xc2bN/HCCy9g9OjR1tvLTsGvRZ0+fTqmT59u9LHdu3cbHBsxYgRGjBhRaXnjx4/H+PHjqzynl5cXVq9eXem0IDkn9tmGrNln26q/BthnW60MB8IgVSHTpk3DsWPH8OOPP1qkvHPnzmHmzJlIT0+Hl5eXRcosp9Pp0LVrV/znP/8BAHTp0gXHjh1DcnKyRYLUzz77DBs2bMDGjRvRsWNHZGdnY9asWWjatKlFyre127dvY+TIkRAEAWvWrFG6OkRkAeyz76hp/TXAPtuecbq/AnNWL8s1ffp0bN++Hd999x2aN29ukTIPHTqECxcu4IEHHoCbmxvc3Nzw/fff4+2334abmxvKysrMLrtJkybo0KGD5Fj79u2Rm5tb3WoDAObMmYO5c+di1KhRCAoKwtixYzF79myj+3RWR/nvz5q/2/LO7s8//0R6erp1vxFEgYVTRPaGfbYha/bZtuqvAfbZ7K8ZpBowZ/WyqQRBwPTp07F161bs2rULAQEB1a2uaMCAATh69Ciys7PF1LVrV4wZMwbZ2dlwdXU1u+xevXoZbLvy+++/o2XLltWtNoA7qyxdXKRN0dXVFTqdziLllwsICICfn5/kd6vVarFv3z6LrBAv7+xOnTqFb7/9Ft7e3tUus0oMUonYZxthzT7bVv01wD7bkv213C3aUlJSEBgYCC8vLwQFBWHHjh2Sx1NTUxEZGQlvb2+oVCpkZ2cblHHz5k1MmzYN3t7eqFu3LoYPH27wgeOelF23ZZ/utXrZXFOnThU0Go2we/du4fz582K6fv26hWouZamVovv37xfc3NyEV199VTh16pSwYcMGoXbt2sInn3xS/UoKghAXFyc0a9ZM2L59u5CTkyOkpqYKjRo1Ep5//nnZZV29elXIysoSsrKyBADCG2+8IWRlZYmrNV977TWhfv36wn//+1/hyJEjwqOPPioEBAQIN27cqFbZJSUlwtChQ4XmzZsL2dnZkt/vrVu3ZL+OqogrRVUQBJfqpyKVc60WpZqHfbaUNftsS/bXgsA+2xb99aZNmwQPDw/hgw8+EH799Vdh0qRJQv369YWCggKj+X/66SfB1dVVWLp0qXD8+HFhwYIFBjuyfPTRR8KiRYuE9957TwAgZGVlGZTz9NNPC/7+/kJGRoZw8OBBoUePHkLPnj1lXTsGqZVYuXKl0KJFC8HDw0Po3r278PPPP1e7TFTyuWjdunXVr7ARlurwBEEQvvjiC6FTp06Cp6enEBgYKKxdu9Yi5QqCIGi1WmHmzJlCixYtBC8vL6F169bCiy++aFZH8d133xm9xnFxcYIg3NnS5KWXXhJ8fX0FT09PYcCAAcLJkyerXXZOTk6lv9/vvvtO9uuoitjhAYKgqn4qAoNUcnzss6Ws1Wdbsr8WBPbZtuiv5W7RNnLkSCE6OlpyLCwsTJgyZYpB3vLrWDFIvXLliuDu7i6kpKSIx3777TcBgJCZmWly3VWCYObXRBCRIrRaLTQaDYoAWOLuKS0ADe5sv2LV+7GIiJyQJfvs8v7a2NdYG/uWwJKSEtSuXRtbtmxBTEyMeDwuLg5XrlzBf//7X4PntGjRAvHx8ZJvP0tISEBaWhp++eUXSd6zZ88iICAAWVlZCAkJEY/v2rULAwYMwOXLlyVberVs2RKzZs3C7NmzTXq9XN1P5KC0dlYOERFVzhJ9bXkZ/v7+kuMJCQlITEw0yG/OFm35+fnV3vYrPz8fHh4eBnvOyi2HQSqRg/Hw8ICfnx/8LbhPoJ+fHzw8PCxWHhER3WHpPtvPzw+//PKLZOsyY6OoNQGDVCIH4+XlhZycHJSUlFisTA8PD4vv1UhERJbvs+X01+Zs0ebn51ftbb/8/PxQUlKCK1euSEZT5ZbDIJXIAXl5eTGoJCJyEEr12fpbtJXfk1q+RVtl3xoYHh6OjIwMyT2p6enpsrb9Cg0Nhbu7OzIyMjB8+HAAwMmTJ5GbmyurHAapRERERDVUfHw84uLi0LVrV3Tv3h0rVqxAcXExJkyYAAAYN24cmjVrJn4hw8yZM9GvXz8sX74c0dHR2LRpEw4ePIi1a9eKZRYWFiI3Nxd5eXkAIO7L6+fnBz8/P2g0GkycOBHx8fFo2LAh1Go1ZsyYgfDwcPTo0cPkujNIJSIiIqqhYmNj/7+9Ow2J6nvjAP6dlrGxqdTRck1txSlDWpC0TchU0rLElMol08goBMMXLWYrWGQTIVIZOYItRqZotBDhENhIe0hITvrCIi1Te1GW6Xh+L6L77/7USvOv86vvBy54zzlzznMFH54z49yLpqYm7NmzB42NjfDx8cGNGzekL0fV19fLHtDg5+eH8+fPY/fu3di5cyemTp2KkpISzJw5UxpTWloqFbkAEB0dDUD+BS6dTodhw4YhIiIC7e3tCAoKQk5OTp9i5y2oiIiIiMji8LGoRERERGRxWKTSf4bBYIBCocD79+9/ax4PDw8cP358QGIiIqKeMWfT72KROsRevnyJhIQEODs7Q6lUwt3dHSkpKWhubpaNW7JkCRQKRbejs7OzW7+VlRVcXFwQFhaGK1eudFvz27jKykpZe3t7OzQaDRQKBQwGQ68xNzU1ITk5GRMnToSVlRUcHR0RFBSEioqK3/+FEBFZMOZsosHDInUI1dXVYe7cuTCZTLhw4QJevHiBkydP4vbt25g/fz5aWlpk45OSktDQ0CA7RowY0a2/trYWRUVF0Gq1iI6OxqZNm7qt7ebmhry8PFlbcXEx1Gr1T+OOiIjA48ePkZ+fj5qaGpSWlmLJkiXdkjQR0Z+EOZtokAkaMsHBwcLV1VW0tbXJ2hsaGoS1tbXYvHmz1LZ48WKRkpLS61y99Z89e1YAELdu3ZLaAIjdu3eLsWPHytYODAwU6enpAoAoLy/vcZ3W1lYBQBgMhl5j2bBhg1i+fLms7cuXL8LBwUGcOXNGinfr1q0iJSVF2NjYiPHjx4vTp0+LDx8+iPj4eKFWq8XkyZPFtWvXpDnKy8sFAHH16lXh7e0trKyshK+vr6iqqpKtdfnyZaHVaoVSqRTu7u7i6NGjsn53d3eh0+mEEEJ0dXWJjIwM4ebmJpRKpXBychLbtm3r9dqI6O/FnM2cTYOL76QOkZaWFty8eRNbtmyBSqWS9Tk6OmLdunUoLCyE+M2bL8TFxcHW1rbbR0hz5syBh4cHioqKAHy9BcWdO3cQExPzw/nUajXUajVKSkrQ3t7e45jExETcuHEDDQ0NUtvVq1fR1taGqKgoqS0/Px/29va4d+8etm3bhuTkZERGRsLPzw+PHj3CsmXLEBMTg7a2Ntn8aWlpyMrKwv379+Hg4ICwsDB0dHQAAB4+fIg1a9YgOjoaVVVV2Lt3L9LT06HX63uMtaioCDqdDqdOnYLJZEJJSQm8vb1/+Dsgor8PczZzNg2Boa6S/1aVlZUCgCguLu6x/9ixYwKAePPmjRDi6y525MiRYvTo0dKRmpoqjf/Rrt3X11eEhIRI59/WPX78uAgICBBCCLFv3z6xatUqadfd265ciK+7XltbWzFq1Cjh5+cnduzYIZ4+fSobo9VqxeHDh6XzsLAwER8fL4t3wYIF0nlnZ6cYPXq0iImJkdoaGhoEAGE0GoUQ/9uVX7x4URrT3NwsVCqVKCwsFEIIsXbtWhEYGCiLJS0tTWi1Wun8+115VlaWmDZtmvjy5Uuv10tExJzNnE2Dj++kDjHRh133unXr8OTJE+nYsWPHL6+hUCi6ta9fvx5GoxF1dXXQ6/VISEj4pfkiIiLw+vVrlJaWIjg4GAaDAbNnz5btfBMTE6X/n3rz5g2uX7/ebf5Zs2ZJPw8fPhwajUa2I/52o+G3b9/KXvf9I9Xs7Owwffp0VFdXAwCqq6vh7+8vG+/v7w+TyQSz2dztWiIjI/Hp0ydMmjQJSUlJKC4ulr7YQET0b8zZXzFn02BgkTpEpkyZAoVCIf2h/lt1dTVsbW3h4OAgtY0bNw5TpkyRDnt7+5+uYzabYTKZ4Onp2a1Po9EgNDQUGzduxOfPnxESEvLL8Y8aNQqBgYFIT0/H3bt3ER8fj4yMDKk/NjYWdXV1MBqNKCgogKenJxYuXCibY+TIkbJzhUIha/uWpLu6un45rr5yc3PD8+fPkZOTA5VKhS1btmDRokXSR1FERABzNsCcTYOPReoQ0Wg0CAwMRE5ODj59+iTra2xsxLlz5xAVFdXjbrov8vPz0draioiIiB77ExISYDAYEBsbi+HDh/d7Ha1Wi48fP0rnGo0G4eHhyMvLg16vlz0+7Xd9fxuW1tZW1NTUwMvLCwDg5eXV7bYqFRUVmDZtWq/Xp1KpEBYWhhMnTsBgMMBoNKKqqmrA4iWi/z7m7P5jzqb+GvHzIfT/kp2dDT8/PwQFBeHgwYPw9PTEs2fPkJaWBhcXFxw6dKhP87W1taGxsRGdnZ149eoViouLodPpkJycjICAgB5fExwcjKamJowdO/aX1mhubkZkZCQSEhIwa9YsjBkzBg8ePMCRI0ewcuVK2djExESEhobCbDYjLi6uT9fyI/v374dGo8GECROwa9cu2NvbIzw8HACwfft2zJs3DwcOHEBUVBSMRiOys7N7fV6wXq+H2WyGr68vrK2tUVBQAJVKBXd39wGLl4j+DMzZ/cOcTf3FInUITZ06FQ8ePEBGRgbWrFmDlpYWODo6Ijw8HBkZGbCzs+vTfLm5ucjNzYVSqYRGo8GcOXNQWFiIVatW9foahULxSx9BfaNWq+Hr6wudTofa2lp0dHTAzc0NSUlJ2Llzp2zs0qVL4eTkhBkzZsDZ2blP1/IjmZmZSElJgclkgo+PD8rKyqBUKgEAs2fPxqVLl7Bnzx4cOHAATk5O2L9/P+Lj43ucy8bGBpmZmUhNTYXZbIa3tzfKysqg0WgGLF4i+jMwZ/cPczb1l0L05b/Aifrgw4cPcHFxQV5eHlavXj3U4RAR0Q8wZ5Ol4TupNOC6urrw7t07ZGVlwcbGBitWrBjqkIiIqBfM2WSpWKTSgKuvr4enpydcXV2h1+tljwEkIiLLwpxNloof9xMRERGRxeEtqIiIiIjI4rBIJSIiIiKLwyKViIiIiCwOi1QiIiIisjgsUomIiIjI4rBIJSIiIiKLwyKViIiIiCwOi1QiIiIisjj/ABx6+P5uTzgJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.suptitle('Channel magnitude for the first pair of antenna')\n",
    "\n",
    "hm1 = ax1.imshow(np.abs(hActual[:,:,0,0].T), cmap='autumn', aspect=.1, origin='lower')\n",
    "ax1.set_title(\"Actual Channel\")\n",
    "ax1.set_xlabel(\"OFDM Symbols\")\n",
    "ax1.set_ylabel(\"Subcarriers\")\n",
    "ax1.set_xticks(range(0,14,2))\n",
    "fig.colorbar(hm1, ax=ax1)\n",
    "\n",
    "hm2 = ax2.imshow(np.abs(hEst[:,:,0,0].T), cmap='autumn', aspect=.1, origin='lower')\n",
    "ax2.set_title(\"Estimated Channel\")\n",
    "ax2.set_xlabel(\"OFDM Symbols\")\n",
    "ax2.set_ylabel(\"Subcarriers\")\n",
    "ax2.set_xticks(range(0,14,2))\n",
    "fig.colorbar(hm2, ax=ax2)\n",
    "\n",
    "fig.subplots_adjust(top=.9, wspace=.8)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "985fca0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}