learn-pca-fft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets learn some pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "X: [ 1  2  4  6 12 15 25 45 68 67 65 98]\nS: 0      1\n1      2\n2      4\n3      6\n4     12\n5     15\n6     25\n7     45\n8     68\n9     67\n10    65\n11    98\ndtype: int64\nMean(S): 34.0\nStd(S): 33.22923138114721\n"
    }
   ],
   "source": [
    "dataframe = {\n",
    "    \"X\" : [1, 2, 4, 6, 12, 15, 25, 45, 68, 67, 65, 98]\n",
    "} \n",
    "\n",
    "X = np.array([1, 2, 4, 6, 12, 15, 25, 45, 68, 67, 65, 98])\n",
    "S = pd.Series([1, 2, 4, 6, 12, 15, 25, 45, 68, 67, 65, 98])\n",
    "\n",
    "print(\"X:\", X)\n",
    "print (\"S:\", S)\n",
    "print(\"Mean(S):\", S.mean())\n",
    "\n",
    "print (\"Std(S):\", S.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets learn some statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Std(X1): 8.32666399786453\nStd(X2): 1.8257418583505538\nVar(X1): 69.33333333333333\nVar(X2): 3.3333333333333335\nCov(study):             Hours       Marks\nHours   47.719697  122.946970\nMarks  122.946970  370.083333\nVar(study(Hours)): 47.71969696969697\nVar(study(Marks)): 370.08333333333337\n"
    }
   ],
   "source": [
    "X1 = pd.Series([0, 8, 12, 20])\n",
    "X2 = pd.Series([8, 9, 11, 12])\n",
    "\n",
    "print (\"Std(X1):\", X1.std())\n",
    "print (\"Std(X2):\", X2.std())\n",
    "print (\"Var(X1):\", X1.var())\n",
    "print (\"Var(X2):\", X2.var())\n",
    "\n",
    "studyFrame = {\n",
    "    \"Hours\": [9, 15, 25, 14, 10, 18, 0, 16, 5, 19, 16, 20],\n",
    "    \"Marks\": [39, 56, 93, 61, 50, 75, 32, 85, 42, 70, 66, 80]\n",
    "}\n",
    "\n",
    "study = pd.DataFrame(studyFrame)\n",
    "print (\"Cov(study):\", study.cov())\n",
    "print (\"Var(study(Hours)):\", study[\"Hours\"].var())\n",
    "print (\"Var(study(Marks)):\", study[\"Marks\"].var())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets learn some PCA\n",
    "* We took the two vectors `x` and `y`.\n",
    "* We standardized the data using `StandardScaler`. Note that the library returns a numpy multi-dimensional array and not a pandas data frame.\n",
    "* We than apply PCA to transform vectors and retrieve two principal components of these vectors. Note that the maximum number of components we can derive are equal to the number of features we had in the original data (2 in this case).\n",
    "* Finally, we look at the variation present in each of the components to understand which are the dominant components using `pca.explained_variance_ratio_`. In this particular example turns out almost 96% of the variation is captured by the first component and so we actually don't have to worry about the remainder of the components.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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Kf/wBt255O4rwXbx5kb6r+1I+R3kq5qzo9HG9y/amTr46fLD8AxbsX+DGCJVS3qYTE0oppZRSESshIn+IyGIRye/tYJQKz5QpUKgQpE0LzZvD0qVw7563o/pX/zX9uXTrEoPKD0JEnD4utFNH4fSFaTizITvPhJt1pZSKA9w2MRFe3qaITA2Tp3lURMJtvOrYtsOx32Z3xaiUUkopFYmtQFZjTEHga2BOeDuJSGsR2Swim8+ePevRAJW6cQM++ACefRZq14Y5c6BSJciQAdq1g3Xr4MED78V3+OJhvv79a1oUakHBdAWjfXySwCTMbTCXoARBVJ1clbPX9f+YUnGRO1dMjAcqhX3BGFPfGFPIGFMImAnMiuT4co59i7kxRqWUUkqpcBljrhhjrjn+vggIFJFU4ew30hhTzBhTLHVqbW2oPGvQIDh+HL79FsaOhdOnYdYsKFsWxoyBl1+G7NmhWzfYvh08XUeyx8oeBPoH0rdc38c+R2injtPXTlNrWi1u37vtwgiVUr7AbRMTkeVtil3DVQ+Y7K7rK6WUUkrFhIikc4xZEJEXsOOm896NSql/hYTAwIFQty6UKmVfS5QIataEadPgzBmYMAHy54chQ6BwYfv3fv3g4EH3x7c+ZD3Tdk2jS4kuZEyeMUbnej7j84yvPp61x9bSdmFb7dShVBzjrRoTpYC/jTEHIthugGUiskVEWnswLqWUUsrnGGN4+eWXWbx48T+vTZ8+nUqVKkVylIqKiEwG1gN5ROS4iLQUkTYi0saxSx1gp4j8AXwFNDB6N6R8SI8eNk3jiy/C3x4UBMHBsGiRXUnx3XeQOjV88gnkygXPPw9Dh8LJk66PzRhD52WdSZcsHR+U/MAl56z/bH0+Kf0J47aPY8j6IS45p1LKee4cj3irXWhDIl8t8bIx5oSIpAGWi8hexwqM/3BMXLQGyJIli+sjVUoppbxMRBgxYgR169alXLly3Lt3j549e7JkyRJvhxarGWMaRrF9OLadqFI+Z8MGmDgRPvwQsmWLev9UqaBNG/sREgJTp8LkyfD++9C5M5QpAw0b2joVTz0V8/hm7pnJ+uPrGVV1FMkSJIv5CR16le3FnnN76Lq8K3lT5eWN3NrNVylPced4RNw58S8i2YAFxphnw7wWAJwAihpjjjtxjt7ANWPM4Kj2LVasmNm8WWtlKqWUcq09e/bwzDPPeDsMunbtStKkSbl+/TpBQUF8/PHH/9knvFhFZIvWbPIMHYsoT3jwAEqUsBMM+/dDshjc9+/bZ7t6TJpkzxUQYItnNmwI1ao93rlv37tNvm/zkSQwCdvf3o6/n//jBxiOG3dvUHpcafad38dvb/5GgbQFXHp+pXxVXB6PeGPFxGvA3ogmJUQkKeBnjLnq+HsF4PGr5SillFJxRK9evShSpAgJEiRAb36Vir8mToTff4fx42M2KQGQJw/06mXTO7Zts6sopkyBBQsgSRKoWtVOUlSqBAkTOnfObzd9y+GLh1nSeEnkkxLGwKpVtqVI2rROxxzaqeP5Uc9TbUo1NrbaSJqkaZw+XikVM+4Yj7htYsKRt1kWSCUix4FexpgxQAMeSeMQkQzAaGNMFSAtMNtRayoAmGSM0bWqSimlfEPHjra0vSsVKgTDhkW5W9KkSalfvz7JkiUjobN3CEqpOOXaNeje3daHaNLEdecVgSJF7MfAgbB2rZ2kmD7dpn088YRN82jY0Hb88I9gvuHCzQv0W9OPCjkrUPHpipFfdOBAWygjMBDq1IF33oGSJW0wUciYPCPzGs6j1LhS1Jpai5VNV5IwQH8vqngkjo1H3NmVo6ExJr0xJtAYk8kxKYExprkxZsQj+550TEpgjDlsjCno+MhvjBngrhiVUkqp2MbPzw8/P2/VrlZKedvAgbZY5bBh4K5fBX5+ULq0LZZ56pQtnlm1qp2geO01yJTJ3hNt3Pjf9qP91/Tn0q1LDCo/KPKLjB9vJyXq1IF337UXKVXK3hiNHAnXr0cZZ7EMxfihxg+sC1lHm4VttFOHUh7k6vGIt4pfKqWUUrGTE08SlFLKHf76CwYPtqsWXnrJM9cMDITKle3HjRuwcKFdSfHdd/Dll5AjBzRoAI0aQaL0hxj++3DeLPwmz6V9LuKTLl4MrVrZWY6JEyFBAujf3574m2/g7bfhgw+geXO7iiJPnghPVS9/Pfac3UPv1b3JlyqfyzqAKOXz4th4RB+5KKWUUkopFQt07WqzHAYO9M71kySBunVh1iw4cwbGjYOcOeHzz22ZiKKFA2BtD1plj2TB8++/21USzz0HM2faSQmApEntZMXWrbBuHbzxhp39yJsXypeH2bPh3r1wT/lJmU+ol78e3VZ0Y/6++W74ypVS7ubWrhyeppWwlVJKuYOvVMF2hnbl8C4diyh3+fVXm17Rqxf07u3taB72998wcORhho46BSElAXjxRbuyo149SJfOseP+/baGRFAQ/PZbmA0ROHMGRo+GESNsC5LMme1qilat/lMs88bdG5QZX4a95/Zqpw4VZ8Xl8YiumFBKKaWUUsqHPXhgazpkymRXTfiaNGkM6zM0Jv17ddm17waffWbTPt57DzJmtAsexv7vEpdeq2OXfCxdGvWkhD0x9OwJhw/bFRN58sBHH9kJisaN7eSG4yFraKeO5AmTU3VyVc5cP+Pmr1op5Uo6MaGUUkoppRTApk02xWDtWm9H8pDx422Gw8CBNp3C18zYPYMNxzfQr1w/8uVOQvfu8McfsHOnrW95+OB9WnZ+grQhm6iZbx/TtuXixo1oXCAgAGrUgOXLYe9eW3di4UK7+qJIERg1Cq5fJ0NQBuY2mMuZ62eoObUmt+/ddtvXrJRyLZ2YUEoppZRSCuD772HHDqhYEX7+2dvRAHDlil00UKKETY3wNbfv3abbim4USFOA5oWaP7Qtf37o//FtDmavwEb/l3inxik27n+S+vVtJkZwsG3GEa3M8jx5bNG/Eyfsv9eDB9C6tV2a0akTxa4l54caP/BbyG+0XtBaO3XEEdevw5073o5CuZNOTCillFJKKXXvHsyZYyclsmeHKlVg2TJvR8Vnn9kaDl9+abMgfM03m77hyKUjDK4wGH8//4c3PngAzZohP6/ihXFtGTo7GyEhsHKl7eSxcCG8/jqMGfMYF06a1E5IbN9uV7hUqWI7euTJQ91Oo5gc0JCJW39k0G9RtC1VsUKlSnZxTLRW2qhYRScmlFJKKaWUWr0azp+3N7s//2yfzFetau+eveTwYfjf/6BpU3j+ea+FEaELNy/Qb00/KuasSIWcFR7eaAy8/z5MnWpzUJo0AcDfH155xWZf/P03FCjwmBMToURsSsekSXDsGPTrB3v20OCjyZz6LgmXP+nGkt9+jMEFlLdt2WLnnnbtgs6dvR2NchedmFBKKaWUUmrGDFvAoVIlSJ0aVq2y9SZq1rSFF73ggw9seYVPP/XK5aPUb3U/rty+wqDy4axKGDzYLvN47z37hYQjQQI7X7FhAxw86IKA0qWzxTGPHIFZs0j5XHEGrIJypZtxsc7rDxXLVLHHqFGQKJGdMxwxwi5sUnGPTkwopZRSscDs2bMpVKjQQx9+fn4sXrzY26EpFfvdvw+zZtm8gtDqkilTwooVULQo1K0L06Z5NKRffrEh9ehhyyf4moMXDvLNpm94s9Cb/23NOWGCbR9Sv75d8hFJDkrDhnbzxIkuDC4gAGrWxH/lKs5sXs1PLyXFf+Hif4tljh6tOQGxxLVr9mejfn34+mv7z9eypS0xorzDXeMRnZhQSimlYoGaNWuyffv2fz7eeecdSpUqRcWKFb0dmlKx37p1cOYM1K798OspUtg6Ey+9ZO+gf/rJI+Hcv2/bg2bN6rtL13us7EGgfyB9y/V9eMOSJfDmmzZf44cfwC/y241MmaBsWfutdcdihjRFS1NoympyfpCQL5rk4MG9e/DWW/8Uy+TAAddfVLnMlCl2cqJ1a7vCZtIkuHULmjWzJUyU57lrPKITE0oppVQss3//fvr27cuECRPwi2LQr5RywowZdq14lSr/3RYUBIsX27vnpk1h7Fi3hzNmjG23+cUXkDix2y8XbeuOrWPG7hl0fakr6YPS/7th0yaoU8e245g9GxImdOp8wcE2leP3390Tb9EMRfmuwQS65TxM8z6FMKtX25Sd4cMhd25b8HTePDsjpHzKyJH2x6lECft5njw2Q2jlShgyxLuxKdeOR3Q0o5RSSsUid+/epVGjRgwZMoQsWbJ4OxylYr8HD2DmTHujGhQU/j5Jk8KCBVChgl1HPmKE28K5fNmWSShVymaQ+BpjDJ2XdSZ9svR0eanLvxsOHLCpMKlT24mc5MmdPmft2nYOw6XpHI+ok68Ofcv2ZcKOn/jCbz1MngwhIdC3r62qWL065Mhh26CcPeu+QJTTtm2zc12tWz+cDdSypf2Z6dkTNm/2XnzxnavHIwEuiEkppZSKNzp2tN3pXKlQIRg2zLl9P/74Y/Lnz0/9+vVdG0Q8JCJjgTeAM8aYZ8PZLsCXQBXgBtDcGLPVs1Eqt9u4EU6e/G8ax6MSJ7ZV9+rWhbZt4c4d6NDB5eH06wfnztnfCb7YHnT67ulsPLGRMdXGkDRBUvvi6dN21YExsHQppE8f+UkekSIFVKtml+0PGQKBgW4IHPio9EfsPrebHit7kDdVXqrnrQ4ff2wLecybZ9uN9uwJvXtDvXrw7rtQvLhv/kPEA6FFL4ODH35dxK6k2LgRGjWCrVshWTLvxOhNcW08oismlFJKqVjil19+YebMmQwfPtzbocQV44FKkWyvDORyfLQGvvNATMrTZsywd8JVq0a9b6JEdnVFrVq228SgcLpRxMCBA/DVV9CihS3y52tu37tN9xXdeS7tczQr2My+ePWqTYH5+2/bWjV37sc6d+PGdqHC8uUuDPgRIsLYamMplqEYjWc15o/Tf9gNAQH233TlSti92z6inzvX5g8ULWpza7RYpkddv27rjtSta+vQPiplSltj9eBBe4OuPMsd4xFdMaGUUkpFg7NPElzt4sWLtGjRgkmTJhEU0XJzFS3GmDUiki2SXaoDPxpjDLBBRJ4QkfTGmFMeCVC5nzF2oqFCBfvY3hkJEthH+02b2s4Tt2/b3AsX6NzZzn0MGOCS07nc8N+Hc+TSEZYFL8Pfz9+uGqlVC/78E+bPhxdeeOxzV65sbzZ/+in8Uh+ukjgwMXMbzOX5Uc9TdXJVNr21ibTJ0v67wzPP2PYPn35qg/nmG2jVyrY8bdHCrpZ5+mn3BagAmDrVznm1bh3xPmXL2sUun35qF+z4YuqTO8W18YjbVkyIyFgROSMiO8O81ltETojIdsdHuL92RKSSiOwTkYMi0t1dMSqllFKxxYgRIzhz5gxt27Z9qEXX1KlTvR1aXJYRCAnz+XHHayqu2LIF/vor6jSORwUG2pvWJk1sKsAnn8S4pcTy5fbe/sMPIV26GJ3KLc7fOE//X/tT6elKlM9Z3tbmaN7ctlQdM8bOLMRAggQ2e2LOHHtD6k7pg9Izr+E8zt04R82pNbl179Z/dwoKspMQO3bA6tVQvrxdzpIrl61HMn++toVwo5Ej7RxRyZKR79e7t50Pa90ajh3zSGjxntvGI8YYt3wApYEiwM4wr/UGukRxnD9wCMgBJAD+API5c82iRYsapZRSytV2797t7RCcFl6swGbjpvf72P4BZAs7Vnlk2wLg5TCfrwSKhbNfa2AzsDlLlixR/yMp39GtmzEBAcacP/94x9+7Z0zLlsaAMV27GvPgwWOd5u5dY/LnNyZHDmNu3Xq8UNztvcXvGb8+fmbH3zvsC++/b7/uzz5z2TXWrrWn/OEHl50yUjN2zTD0xgTPCjYPnPm3O3nSmD59jMmQwQb63nvuDzIe2r7dfnuHDnVu/wMHjEmWzJjSpe1/ybgsLo9H3LZiwhizBrjwGIe+ABw0xhw2xtwBpmCXUiqllFJKedIJIHOYzzM5XnuIMWakMaaYMaZY6tSpPRaciiFjbH2JV14JP4ndGf7+9tFu27a2t2enTo+1cmLkSNsYYvBgpztsetSB8wf4ZtM3tCzckmfTPGsrVP7vf9C+PXTr5rLrvPQSZMvm3u4cYdXOVwDI1K0AACAASURBVJt+5frx058/MXDdwKgPSJ/ero45ehTeftv2rVy71u1xxjejRtn/B02bOrf/00/bzq9r1sDnn7s3NuU+3ih+2U5E/nSkejwZznZdNqmUUkopXzAPaCrWi8Bl4+H6Ejfv3mTrKW0E4hZ//AGHDkGdOjE7j5+frUPQsaO9UX3nnWgt8b940d7rli0LNWrELBR36bGyBwn9E9K3XF87a9Cli827cHHrEBHbgWHFCjjlof9pH5b6kIbPNqTHyh7M2TvHuYMCA+0sUrZstnflzZtujTE+uXHDFrWMqOhlRJo2hQYNoFcv2LDBffEp9/H0xMR3QE6gEHAKGBLTE4pIaxHZLCKbz2rPYaWUUko5SUQmA+uBPCJyXERaikgbEWnj2GURcBg4CIwC3vF0jO8uepfyE8pz9/5dT1867ps5004quGI2QMSuIOjWDUaMgLfegvv3nTq0Tx87OeGr7UHXHlvLzD0z6VayG+l++9PWlShbFn780X7/XKxxYzuvM2WKy08dLhFhTLUxvJDxBYJnBbP9tJP9F5Mlg9GjYf9+W+hAucS0aXDlSuRFL8MjAt99B5ky2Z+hK1fcE59yH49OTBhj/jbG3DfGPMC+wYdXutepZZNhzqnLJ5VSSrmdeYzl2Z4WG2L0JcaYhsaY9MaYQGNMJmPMGGPMCGPMCMd2Y4x51xiT0xhTwBiz2dMx1shbgws3L7DqyCpPXzpuMwamT4cyZcBV40cR+Owz+8h27Fh7A3/vXqSH7N37b9OHggVdE4YrGWPovKwzGYIy0CVhOVskNF8+W6HSTTknefPaDp0//eSW04crcWBi5tSfw5OJn6Ta5GqcvnbauQNffdX+4w0eDJs2uTfIeGLkSPsz8PLL0T/2iSfsgp6jR6FdO5eH5jNiw3v948To0YkJEUkf5tOawM5wdtsE5BKR7CKSAGiAXUqplFJKeUWiRIk4f/68Tw8GjDGcP3+eRIkSeTsU5UIVclYgKEEQ03ZN83Yoccvu3bBvX8zTOB4lYp+eDxhg76wbN4a7Ea92ef99SJIE+vVzbRiuMm3XNH4/8Ttf5mxP4mq14KmnYPFi51urPqbgYNi6FfbscetlHpI+KD3zGszj/M3zEXfqCM/gwbb2RMuWtn2qemw7dsD69Xa1xOOuHipZ0jbKmTABJk1ybXy+IC6PRwLcFE/o8siyQCoROQ70AsqKSCHAAEeBtx37ZgBGG2OqGGPuiUg7YCm2Q8dYY8wud8WplFJKRSVTpkwcP34cX08ZTJQoEZkyZfJ2GMqFEgUkonre6szZN4cR90cQ6B/o7ZDihpkz7Z1PzZruOX/PnnZFQZcudmJiyhTbDzOMxYvtx+DBkCaNe8KIiVv3btF9ZXfKJc5H7c6jbX7F0qWQIYPbr92gAXTubJ9+9+/v9sv9o3D6wkyoOYHa02rTal4rJtScgER1h5wihU3fqVr13xUz6rGMGmX/mzhb9DIiH31k2++2bQslSkD27K6JzxfE5fGI+PJsS3QVK1bMbN7s8VWWSimllE8TkS3GmGLejiM+cMdYZN6+eVSfUp0ljZdQ8emKLj13vPXcc/aG8tdf3Xudr7+GDh3g9ddtBxDHE8S7d20I9+/Dzp3/mbPwCYN/G0yfBR9wYn4ukh86DqtWwYsveuz6lSrZRS2HDrmllEWkBqwZwEc/f8Snr3xKj1I9nDuocWObHrRlCxQo4N4A46AbNyBjRqhSxTVdWY4etelR+fPbbh0Bbnscr6IjsvGIN7pyKKWUUkopJ1XIWYHkCZNrOoer7N9v14y7Oo0jPO3bw/ffw8KFUL26vfsCvv3W1pcYMsQ3JyXO3zjPwFX9+GVBKpLvOWxvuD04KQE2nePoUfjtN49eFoCepXrSqEAjeq7qyaw9s5w76MsvbZGDN9+MsraI+q/p0+HSpegXvYxItmx2Icv69Z5ddaMen05MKKWUUkr5sEQBiaiWpxqz987W7hyuMHOm/bNWLc9cr3VrWwxz+XJ44w3O/XWd3r2hfHl44w3PhBBd/X7uw7DpVyi645xdX//66x6PoUYNW3/Dk0UwQ4kIo6uOpnjG4jSZ3YRtp7ZFfVCqVDB8OGzeDEOHuj/IOGbkSMidG0qXdt05Gza0aSH9+sHata47r3IPnZhQSimllPJxdfPV5eKti6w8stLbocR+M2ZA8eKQOXPU+7pKixa2Gt/q1fQqsZSrVw1Dh/pme9AD5w+Q+bPhNP4TW8SzRQuvxJEsmZ2cmDbNOzUlEwcmZk6DOaRMnJJqU5zs1FG3rq1b8skndmWOcsrOnXZlTEyKXkZk+HC7eqJxY7siQ/kunZhQSimllPJxoekc03dN93Yosdvhw7bdgyfSOB7VuDE7v1jEiFPVaZNqBvkz+uZd0rpOtem8znC9dQvo4WR9BTcJDoaLF22RUG9Ilywd8xrM48LNC9SYUiPqTh0itv9rokS2S8eDB54JNJYLLXrZrJnrzx0UZLtznDgBbdrYTsHKN+nEhFJKKaWUj9N0DhcJTeOoXdvjlzYGOi2pSIpk9+lzvj289hpcuODxOCKz96teNJ+wg91l8pP021FeX9JRvjykTu2ddI5QoZ06Np7YSMt5LaNu05g+vU3lWLsWvvvOM0HGYjdvwo8/2syqVKncc43ixaFvX5g61V5L+SadmFBKKaWUigXq5aun6RwxNXMmFCnilf6BCxbAihXQe0ACnpo71q5fL1cOfKTt34Ply8j5fj9+y5mAbPPXgL+/t0MiIMDWCZg/37vL8Gs9U4sBrwxg0o5JfPrrp1Ef0KwZVKwI3brZCp4qQjNmuLboZUS6dYMyZeDdd+HgQfdeSz0enZhQSimllIoFNJ0jho4dg40bvZLGcecOdO4MefNC27bYnojz58OBA1C2LJx2on6BO23bxv0a1dnzlOHouKEkCUrp3XjCaNwYbt/+d7GLt/R4uQeNCzTmo58/irpTh4it5ihi77g1fyBCI0fC00/b/wbu5O9vy7wEBkKjRrZlr/ItOjGhlFJKKRULJAxIqOkcMTHLcTPphTSOr7+2cxD/+5+9MQJsnsKiRfDXX/ZR7okTHo8LgMOHMZUr83fCu3TplI/6Jd/2ThwReP55yJXLu+kc4OjUUS0anTqyZIEvvrDdWMaN80yQsczu3TbjxR1FL8OTObOtZ7FpE/Tq5f7rqejRiQmllFJKqVhC0zliYOZMKFDA9iT0oDNnbH575cr24yFly8LSpXDqlJ2cOHbMo7Fx5gxUrMitm1cp3+g+3ep9hb+f91M4whKxRTB/+QVCQrwbS6KARMxpMIenEj9FtSnVOHLxSOQHvP227X/5/vtw8qRngoxFRo2yE3XuKHoZkTp1bF3Szz+3P1PKd+jEhFJKKaVULBGazjFt1zRvhxK7nDwJ69Z5JY3j44/hxg27WiJcJUvap+rnztmb2MOHPRPYtWvw+uuYEyeo1kjI8VIVXs3xqmeuHU2NG9s/J0/2bhzg6NTRcB4Xb14k9/DcNJndhO2nt4e/s58fjB5tc1HattWUjjBu3YIffrDdVdOk8ey1hw2zq3CCg32u/my8phMTSimllFK+7M6df/6aMCAh1fNUZ87eOZrOER2zZ9ubQg9PTPzxh70vffddW18iQsWLw6pVcPWqXTmxf797A7t7134vtm1jVNdXWZX+JoPKD3LvNWMgZ04oUcL76RyhCqUrxK53dtHu+XbM2TuHwt8X5tUfX2XRgUU8MI+0CM2VC/r3h3nzbFsIBdgFTBcvur/oZXiSJbMtRM+cgbfe0vkiX6ETE0oppZRSvurjj6Fo0YdGznXz1dV0juiaOdPODOTL57FLGgMdO8KTTzqZz16kCPz8s326XqaMTcB3V2AtW8LSpZwe0od3/ZfwVpG3yJfac9+bxxEcDDt2wJ9/ejsSK+sTWRlaaSghnUIY+NpA9p3bx+uTXufZb59lzNYx3Lp369+dO3aEF16A9u19pguLt40caSecypXzzvWLFoUBA2zpmTFjvBODephOTCillFJK+arcuW1byZX/TkJoOkc0nTkDq1d7fLXE7Nk2h71vXzs54ZTnnvs38b1sWffchffoYdsT9OtH23SbSRSQiD5l+7j+Oi5Wr55tH+orqyZCPZHoCbqW7Mrh9w4zoeYEEgYkpNX8VmQdlpV+q/tx7sY52xJi7Fi4fBk6dPB2yF63dy+sWWNXK/h58W60c2d49VV47z0bk/IunZhQSimllPJV9epB6tQwfPg/L4VN57hz/04kBysA5syBBw88OjFx6xZ06QLPPvsYS9Xz5bMTKQkS2MfJW7e6LrAvv4SBA6FtW9YEl2LO3jl0L9mdtMnSuu4abpIqFVSqZJfg37/v7Wj+K4F/AoKfC2Zr662sbLqSYhmK8ckvn5BlaBbaLmjL/nSBdgXUlCk2rSMeGzXKTjI1b+7dOPz84McfIXFi20L09m3vxhPf6cSEUkoppeIlEakkIvtE5KCIdA9ne3MROSsi2x0frTweZMKE9s52/nw4evSfl/9J5zis6RxRmjkTnn7arkbwkGHD4MgRGDrU3oBFW+7cdnIiWTL7SPf332Me1NSp0KkT1KrFg6++pMvyD8gYlJFOJTrF/NweEhxsu6quWePtSCImIryS/RUWNlrIrnd20ahAI8ZuH0ve4Xmplf13rj3zNKZNG7h0yduhekVo0csaNSCtD8yHZchgUzm2bYOPPvJ2NPGb2yYmRGSsiJwRkZ1hXhskIntF5E8RmS0iT0Rw7FER2eEYBGx2V4xKKaWUip9ExB/4BqgM5AMaikh4SfZTjTGFHB+jPRpkqDZtbM/E777756XQdI7pu6d7JaRY4/x5mwZTu7b9Hkbh1r1bTN4xmYs3Lz72JU+dsrnr1arBa6899mlsAv6aNZAypT3RunWPf65Vq6BJE3j5ZZg4kSl7prPp5CYGvDKAJIFJYhCkZ1WtCkFBvpfOEZF8qfMxutpojnU8xkelP2LNyfWULnWQ+3+f4vCbNbn34J63Q/S42bPtf0tvFL2MSPXqtmnK4MG2QY7yDneumBgPVHrkteXAs8aY54D9QI9Iji/nGAQUc1N8SimllIq/XgAOGmMOG2PuAFOA6l6OKXyZMtmeeqNHw82bgKZzOG3ePLvu38k0jmEbhtFoViMyD81Mh8UdOHwx+q07P/rILgkfPDjah/5X1qx25UT69FCx4r/1J6Jj+3b7eDpPHpg3j1sB0GNlDwqlK0STgk1cEKTnJEli55hmzPjnv0KskDZZWvqW68uxTsdo/dZ3jHnlSXLM/oVm72Zg6PqhXLl9xdsheszIkZA9u10I5EsGD4ZnnoGmTbU+qbe4bWLCGLMGuPDIa8uMMaFTgxuATO66vlJKKaVUJDICIWE+P+547VG1HSs9Z4hIZs+EFo527eDCBZuf7qDpHE6YMcPe3BctGuWuxhhGbx1NkfRFqJ2vNiM2jyDX17moO70uG45vcOpyW7fCuHG2vmGuXDEN3iFTJjshkTUrVKkCK1Y4f+yRI1C5MjzxBCxeDE88wVcbv+LY5WMMqTAEP4l9Wd3BwXDlCixY4O1Ioi9JYBLaFGvDW/OOczV7RgZPv8LH898n89DMfLDsA0Iuh0R9klhs/377o+ztopfhSZIEJk+2v2ZbttQWot7gzR+JN4HFEWwzwDIR2SIiPrTQRymllFLxyHwgm2Ol53Lgh/B2EpHWIrJZRDafddejttKlbSXFr7/+Z8Ss6RxRuHzZrst2Mo3jl6O/cOjiIToW78gPNX7gyHtH6PpSV1YcXkGJMSUoObYks/bM4v6D8CsvGmOr+6dKZWsculT69LaV6NNPwxtvwKJFUR9z9qxdZXH7NixZApkycfb6WQb8OoDXc73OK9lfcXGQnlG2rP12xJZ0jvD4JU5C0E/TSH/hDodO16NKrioM3TCUHF/loPGsxmw95cKCpz4ktOhlixbejiR8BQvCF1/Ykj5hMueUh3hlYkJEPgTuARMj2OVlY0wRbN7nuyJSOpJzuX8woJRSSqm45gQQdgVEJsdr/zDGnDfGhNZpHw2E+9jdGDPSGFPMGFMsderUbgkWEbtqYts2WL8e+DedY/be2ZrOEZ758+HuXafTOEZvG02KhCmok8/unzF5Rj577TNCOoXwVaWvOHX1FLWn1SbP8DwM/3041+9cf+j46dNh7Vro3x9SpHD5VwNp0tjJifz5bWrG3LkR73v9up3ACAmx34d8tnxK39V9uX7nOoPKD3JDgJ7h7287KCxaZGsVxFovvQQdOpB23DQmp3mHQx0O0f6F9szfN5+iI4tS7odyLNi/gAfmgbcjdYnbt2H8eFt7JV06b0cTsQ4dbPeXzp1h1y5vRxO/eHxiQkSaA28AjY0Jf5GMMeaE488zwGxsHmi4PDIYUEoppVRcswnIJSLZRSQB0AB4qIefiKQP82k1YI8H4/uv4GB7xxumdWi9/PW4dOuSpnOEZ8YMyJgRihePctcLNy8wc/dMgp8LJnFg4oe2JUuQjPbF23Og/QGm151O6qSpab+4PZmHZqbnyp6cunqKmzfhgw/sE9eWLd31BQFPPWWLeRYubCdcpoezWubuXahbFzZvtqk/JUsCsO/cPkZsGcFbRd7imdTPuDFI9wsOhnv3wv/yY5UBA2zBhZYtyZowDf+r+D9COoUwqPwgDl44SNXJVcn/bX5GbhnJzbuxqKhGOObMgXPnfKvoZXhE7ARK8uTQsKHtIqI8w6MTEyJSCegKVDPG3Ihgn6QiEhT6d6ACsDO8fZVSSimlHoej5lU7YCl2wmGaMWaXiPQVkWqO3TqIyC4R+QPoADT3TrQOSZPCm2/au7FTpwAon6O8pnOE5+pVm75Qq5ZTyew//fkTt+/f5q0ib0W4j7+fP3Xy1WF9y/Wse3Md5bKX4/O1n5N1WFZeajWTY8dsm1B/f1d+IeF44gmbolK8ODRoAJMm/bvNGJvAv3gxjBhh2w04dFvRjUQBiehdtrebA3S/ggXtwpHYnM4B2P/To0bBgQPQuzcAKRKloMtLXTjc4TATa00kSWAS3l7wNlmHZaXPL304ez12rhAfOdKWSSlf3tuRRC1tWlsrZscO6NbN29HEH+5sFzoZWA/kEZHjItISGA4EAcsdrUBHOPbNICKhyXJpgbWOQcDvwEJjzBJ3xamUUkqp+MkYs8gYk9sYk9MYM8Dx2ifGmHmOv/cwxuQ3xhQ0xpQzxuz1bsTAO+/YLhMjRwKazhGhRYvs2nEn0jiMMYzaOopiGYpRMF1Bp07/UuaXmFlvJgfaH6Bx1q5sn14JnpnJZ8crsuzQMiJYFOw6yZPbiZfSpe3ygfHj7esffgg//AB9+tgJCofVR1czd99cerzcg7TJ0ro3Ng8QsV/2unVwOPqNU3zLq6/af6vBg2HTpn9eDvQPpFGBRmx+azM/N/uZ4pmK03t1b7IMy8Lb899m37l9Xgw6eg4csB1rfbHoZUSqVLFpHV995VxJFxVz4vZfnB5UrFgxs3nzZm+HoZRSSvkUEdmi7bc9wyNjkddft+0f/voLEiRgwf4FVJ1clUWNFlE5V2X3Xju2qFsXfv0VTpyIcgnDxuMbeXHMi3z/xve0Lhr9deZNm8LUqYYOP45g4vF+nLp2igJpCvB+ifdp+GxDEgYkfNyvImo3bth6E8uX20mYGTPg7bdt5T5Hwc8H5gHFRxfn9LXT7Gu3jySBSdwXjwf99Rdkywb9+tkWrbHa5ct2CUjKlDYFJ0GCcHfbe24vQ9cP5Yc/fuD2/du8kfsNOpfoTJmsZRAnCrx6S7duMGQIHDsGGTJ4Oxrn3boFL7wAf/8Nf/5pV1KomIlsPBJL5qyUUkoppRRgi2CePg2zZgH/pnNM2z3Ny4H5iBs37CPOWrWcyqsYvXU0SQKT0ODZBtG+1MaNMGECvP++MKh+W468d4Tx1ccD0GJuC7J/mZ1Pf/2UCzcvRPvcTkmSBObNs493Z8ywkxTffPNQF5LJOyaz+eRmPn3l0zgzKQE2LaB0aZvOEeufs6ZIYVNvduyAzz6LcLe8qfLyfdXvOdbpGL3K9GLD8Q2U+6EcxUYVY9KOSdy9f9eDQTvnzh2bFlG1auyalABIlMi2EL1yBZo3hwdxow6pz9KJCaWUUkqp2KRiRds20lEEM2FAQmrkrcGcvXM0nQNsisONG7ZNaBSu3r7K5J2TaZC/AckTJo/WZYyBjh1th4GePe1rCQMS0qxQM/5o8wdLg5dSIG0BPlz1IZmHZqbdonYcunDocb6iyCVKZCepZsyw9SbCTMbcvHuTnqt6UjhdYRo/19j11/ay4GDYt88uIIr13ngDGje2bV127Ih01zRJ09C7bG+OdTzG9298z/U712k8qzE5v8rJkN+GcPnWZQ8FHbW5c23nWl8vehmR/Pntao8lS2y3ZuU+OjGhlFJKKRWb+PnBu+/aBPtt2wCom6+uducINWOG7V5RpkyUu07dNZXrd6/TqkiraF9m0iTYsAE+/RSCgh7eJiJUyFmBpcFL+bPNn9TLX4+RW0aS6+tc1J5Wm99Cfov29SKVMKGdiEn8cEeRrzZ+xbHLxxhSYQh+EveG/XXq2KyHWF8EM9SwYfDkk7bI7b17Ue6eODAxrYu2Zve7u5nfcD45U+aky/IuZB6amc5LO3Ps8jEPBB25kSMhSxaoUMHbkTy+tm3tio+uXeGPP7wdTdwV935DKaWUUkrFdc2b22X8jlUTms7hcOsWzJ8PNWtCQECUu4/aOor8qfPzYqYXo3WZ69dt3nzRotCsWeT7FkhbgHHVx/FXx7/o8XIPfj7yMyXHlqTEmBLM2D2D+w/uR+vazjp7/Syfrv2UqrmrUi57Obdcw9uefNIuNJg82an7eN+XKpVNxdm8Gf73P6cP8xM/3sj9Bj83+5nNb22map6qfLnxS3J8mYOGMxuy+aR3avAdOgQrVkCrVh7oVuNGIjBmjC0B0rChXZClXE8nJpRSSimlYpsnnoAmTexj+/PnNZ0j1PLlcO2aU2kcf/79J7+f+J1WRVpFu3DgoEG2ruawYc53GUgflJ4Brw4gpFMIwysP5+z1s9SdXpdcX+fiq41fce3OtWjFEJU+q/tw/c51Br420KXn9TXBwbY44cq4slioTh07sdarF+zfH+3Di2YoysRaEzny3hE6vdiJRQcW8fyo5ykzvgzz983ngfFcoYTRo+3/jzff9Ngl3SZ1avjxR9izB7p08XY0nnHlimevpxMTSimllFKxUbt2doXA2LHAv+kcKw6v8HJgXjRjhp20eeWVKHcdvXU0CfwT0OS5JtG6REgIfPEF1KsHL78c/RCTJkjKuy+8y752+5hVbxbpg9Lz3pL3yDw0Mz1W9ODk1ZPRP+kj9p3bx4jNI2hdtDXPpH4mxufzZVWq2H/yOJPOIWJXTSRODC1bPnbFxcwpMjOowiBCOoUwpMIQjl46SrUp1Xjmm2c88jvizh37q+mNNyBjRrdfziPKl7eTEt99Z2tnxEV//21rabz0EuTO7dmVSDoxoZRSSikVGz37LJQtC99+C/fvUz5HeVIkTMH03dO9HZl33Llj7xaqV4+w3WKom3dvMuHPCdR6phZPJXkqWpfp1s0Wvvzii5gEC/5+/tR8pibr3lzH+pbreS3Ha3zx2xdkG5aNZnOa8cfpx09m77qiK0kCk9C7bO+YBRkLJExou8POnm1TbOKE9Olh6FBYu9b+/46B5AmT836J9znU4RCTa08GoP6M+py+dtoVkUZo/nw4cyb2Fr2MyIABULiwnTM6GfM5RJ9w6ZLtnFK+vO2c0qGDXXj23nv216qn6MSEUkoppVRs1a4dHD0KCxeSMCAh1fNWj7/pHKtWweXLTqVxzNozi0u3LvFWkbeidYnffrP1DLp0se0qXeXFTC8yve50DrY/SNtibZm5eyaFvi9E+QnlWXJwCSYa/TB/OfoL8/bNo8fLPUiTNI3rgvRhwcF2UiJOPcVu2hQqVYLu3e3/8RgK8AugwbMNmNtgLtfvXKfNgjbR+rmKrpEjIVMm+yXEJQkS2N8BN2/af6LY2kL0xg2YNs1mDaVNa9NtDh+GHj1g507480/79yQe7DCsExNKKaWUUrFV9ep29O8oghmv0zlmzLDtMcqXj3LX0dtGk+PJHJTNVtbp0z94YJ8gZshgV024Q/Yns/Nl5S8J6RTC569+zu6zu6k8sTIFvivA2G1juX3vduQxmgd0WdaFzMkz0/HFju4J0ge9/LLt/BBn0jnApnR8/73986237DIdF8ibKi/9X+nP3H1zmbxzskvO+agjR2DZsthf9DIiefLAl1/auiZDhng7GufdvQuLFtnyRGnTQv36trNQ27awcSMcPGi71ebP7534dGJCKaWUUiq2Cgiwo8rly2Hv3vibznHvHsyZY3v6JUoU6a4Hzh/gl6O/0Kpwq2i10JwwwTZL+PxzSJYspgFH7snET9Lt5W4cee8IP9b4kQC/AFrOa0nWYVnpv6Y/52+cD/e4STsmseXUFj599VMSByYOd5+4yM8PGje2N8N//+3taFwoSxabM7RihV1r7yKdXuzEi5lepP3i9m5J6YhLRS8j0rIl1KoFH34IW7Z4O5qIPXgAa9ZAmzY2Q+j112HBAmjQwE6sHD9ui/i+8IKdA/MmnZhQSimllIrNWrWy64u/+Sb+pnOsXg3nz9uOBlEYs20M/uJPs0JR9PkM49o1u6z5hRfsDbCnJPBPQJOCTdj29jZWNFlB4fSF+fjnj8k8NDPvLnyXgxcO/rPvzbs36bmyJ0XSF6FRgUaeC9JHBAfD/fswdaq3I3Gxt9+GMmXg/fddVtTA38+fcdXHuSWl4+5dW/SyShXInNllp/U5IjBqFKRJY1uIXnNtU50YMcZOloSmnJUpYydWy5e36U6nT9vYX3nFt1a0ODUxISIvisgmEbkmIndE5L6IeLiBiFJKKaXiIx2HRCFNGrsmd/x4uHKFevnqxb90jhkz0B4WZgAAIABJREFUbDJ0xYqR7nb3/l3Gbx/P67lfJ0NQBqdP/9lncOqUXb7tbHtQVxIRXs3xKosbL2Zn2500fLYho7eNJvfXuak5tSZrj61l2IZhhFyxHRiisxIkrsiXDwoVimPpHGB/4EaPtlUI27Z1S0rHpB2TXHJOsE/jT5+Oe0Uvw5Mypf15O3gQOvpA5tS+fdC7N+TNC8WKwVdf2UKdkybZQqSTJ0O1arZgrC9y9rfWcKAhcABIDLQCvnFXUEoppZRSYeg4JCrt29tHdhMmUD5nPEvnuH8fZs2ya5SjqNS2YP8C/r7+d7SKXh45YvPIGzeGF1+MabAxlz9NfsZUH8NfHf/iw1IfsuavNZQaV4oPV31ItTzVolU3I64JDoZNm2D/fm9H4mJPPw39+sG8eS5dEtLpxU6UyFSC9ovbc+rqKZecc+RI2x60cmWXnM7nlS1r65OOGWPnRz0tJAQGDYIiReyERN++9vs/cqSdIJo3z67oSJrU87FFl9PTqcaYg4C/Mea+MWYcEMdqrCqllFLKV+k4JArPP2/zDIYPJ4FfYPxK51i71j4OdCKNY9TWUWQIykClp53/8ena1S53/vzzmATpeumSpaPfK/0I6RTCt1W+pULOCgypEIsq8blBw4Z2if3Eid6OxA06drT/x9u3h7NnXXLK0JSOm/du0mZhzFM6jh6FpUtt/YWAAJeEGCv06WN/Bb/1lp0ocLdz5+C776B0aVuGpGtX+/0eOtTWjFi1ysaSMqX7Y3ElZycmbohIAmC7iHwhIp2icaxSSimlVEzoOMQZ7drB3r2wcmX8SueYOdMWvKxSJdLdQi6HsOTgEt4s9CYBfs7dNa1ebZ+Cdutmm5/4oiSBSWj7fFuWBC/h6ZRPezscr8qQAV591S6vd2MnTO/w97fFGy5fhg4dXHbaPKny0L9cf+btmxfjlI4xY+yfLVu6ILBYJDDQpkvcu/dvrRNXu3rV1omoUgXSpYN33rETFP36wYED8Pvvdu4qg/MZaj7H2Tf1Jo592wHXgcxALXcFpZRSSikVhlvGISJSSUT2ichBEekezvaEIjLVsX2jiGSL6TXdql49SJ0ahg//J51j2q5p3o7KvR48sBMTlSpF2Spj7LaxGAxvFnauVcD9+3agnzmzLSKnYofgYDh82LZBjHPy54dPPoEpU2wVQxfp+GLHGKd03LtnJyYqV7ZP8eObp5+2XZvXrIGBA11zzlu3YPZsqFvXlhJq2hR277a/j7Zvh1274KOP7LXjAmcnJmoYY24ZY64YY/oYY94H3ojqIBEZKyJnRGRnmNdSishyETng+PPJCI5t5tjngIg4XzZZKaWUUnHNY41DIiMi/tg6FZWBfEBDEcn3yG4tgYvGmKeBoYCLhptukjChrTg3fz4JQk5SI28N5u6bG7fTOTZssJ0KokjjuP/gPmO3j6V8jvJkfzK7U6ceP94O/gcOjLJ0hfIhNWvaBTRxrghmqG7doGBBWwjz0iWXnNIVKR0LF9oCsfGh6GVEmja1bTg/+QQ2bny8c9y7Z9vetmgBadPalqRr1tjmS+vW2Zo3n39ufwS83d7T1ZydmAhvYqC5E8eN5785oN2BlcaYXMBKx+cPEZGUQC+gOPAC0CuiCQyl1P/ZO++wqK6nj38PXZQiioq9YYnYEew19sQWWxQTo4K9a6LG+Jpi1Kg/o1Fj7yVREBsqlsSosUREUbBg74qFpvTdef8YQFQ6u3t3l/N5nvvscu/Zc2eX3XvPmTPfGYlEIjF6cjsOyQw3ADeJ6DYRJQD4A0DX99p0BbAh+bk3gDZC6PlQcNgwHq3+/jt6fdTL+OUcPj4cR/1J5n6qI7eP4H7kfQypNyRb3UZFAdOmAY0b80RDYjjY2gJdu3KOyMREpa3RAubmLOkICwMmTtRYt2klHVsu5zxJx8qVgJMT56DNryRfelG6NNCvH19HsgMRcOoUpw8pVYqLC+3cyU4Jf3/g0SPgt9/4eqTnd6A8kaljQgjxuRBiL4AKQog9aba/AbzKqnMiOp5Ou7Q3+Q0AuqXz0vYADhPRKyIKB3AYMsmVRCKRSCT5iryOQ7KgFIC0acoeJu9Ltw0RJQGIBFAkj+fVLqVL85Lx6tVoW7Kpccs5iDgBRLt2gJ1dpk1XBa5CUeui6Fr1fd9T+syaxfO+X3817omAseLhAbx8yZM6o6RePc54uHYtL69riHENx6FxmcYYc2BMjiQd9+8DBw7kv6SX6WFvz9E6d++yoyEjiIBLl4CpU4EKFYAmTbgqbPPm7JR49gxYt44vb/nlM83qbZ4C8ARAUQBp0/xGA7iUy3MWJ6KUb/pTAMXTaZOdwYJEojjPn3P+IQsLwN2dkyXXqsV/SyQSiSTPaGMconGEEF4AvACgrD6Iq0eNAry9YbHdB92qdUutzmFhamQ3p4AAnhF9/32mzcLehGH39d0Y4zYGlmaWWXZ76xY7JL78kjPtSwyP9u2BIkV4gphFMI3hMmMGJyDw8gIuXwZsbPLcpamJKdZ2WYs6K+pgmN8w7OqzC9kJEsuvSS8zomlT4Lvv+NLUoQNXi0nh1i1g2zberlzhnKbt2nESy65dOeInv5KpY4KI7gG4B6CRNk5ORCSEyFPOXL0bDEjyDZGRfLG5coUXajZu5P2WluzIdnd/u5UvL1dcJBKJJKdoeRzyCJxEM4XSyfvSa/NQCGEGwA7Ay3TsXAlgJQC4uroqXwugeXPAxQX47Tf0+uNHbAjagMO3DqNzFSOLsfbx4aXELl0ybbbh4gYkqZOyLeOYNImj5X/+WRNGSpTA3JwlOGvWcDi9UU72rKz4DTZtysvuS5ZopNsUScekw5Ow5fIWeNTyyLR9StLL9u15vCthpk8HDh9mdV2FCpwOZ9s2rp4BAM2aseyjZ0+gaFFlbdUXspVjQgjRUAhxTgjxWgiRIIRQCSGyqZr5gGdCCKfkfp0AhKXTJjuDBQA8GCAiVyJydXR0zKVJEknOiI3lcdClSzwuevKEQ7b+/BMYORIwMQGWL2cPacWKnLzm00/ZG3rokMZyFUkkEkm+QMPjkBTOAXAWQlRILkXaF8Ce99rswdv8Fj0B/EW5yQqna4TgGOKLF9EuzAZ2lnbYcWWH0lZplhQZR+vWgINDJs0Iqy+sRpMyTVDdsXqW3R46BOzaxfklDLnsnoTlHHFxHBZvtDRuzKG7S5cCJ05orNucSDoOHOAcCPk56WV6mJm9TcDaqBEwfjw7cebN40Cv48fZaSGdEm8R2bm/CiECwDfsHQBcAXwBoAoRTc3Ga8sD2EdELsl/zwPwkojmJJfmciCir997jQOA8wDqJe8KBFCfiDLVk7q6ulJAQECW70ciyQuJiSzf3b+fPZ99+mTcLjiYs/KmbFevvj1eteq7URW1arGHXyKRSDSNEOI8EbkqbUduycs4JIt+OwH4FYApgLVENEsI8QOAACLaI4SwArAJQF1wTou+RHQ7sz71Zizy5g1nUevYEQP7WGLXtV0ImxxmPHKOixeBunU5456nZ4bNjt87jhbrW2B91/X4sk7mRd4iIoCaNbnq6IULvCAtMVyIAGdnXsU/YsT5X/HmDX9xzcyAoCCgQAGNdBv6MhS1l9dG24ptsbvv7gwlHZ9++lZVJcexH/LXX8Dp0xwZUbWq0tYoT2bjkexW5QAR3QRgSkQqIlqHbCSjFEJsA3AaQFUhxEMhxGAAcwC0FULcAPBx8t8QQrgKIVYnn+sVgB/BqxnnAPyQlVNCItEFKhVrTv38OPwqI6cEwBfnunXZG7puHUs+IiI4rOunn/ji5O/PERaurhxm2Lgxe1T/+IPLARnAupxEIpHohNyMQ7LR534iqkJElYhoVvK+GUS0J/l5HBH1IqLKROSWlVNCryhYEBg0CPD2xgDHjxEZH4nDtw4rbZXm8PHh8MRu6eVQf8vqwNWwtbRFz48yLycKAGPHcgTkxo3SKWEMCAH0788Tw8ePlbZGixQsCKxaBdy4Afzf/2ms2ypFqmBW61nYG7o3wyodDx7wQt2gQdIpkRGtWwPffiudEtkhu46JmOQwx4tCiF+EEOOz81oi+pyInIjInIhKE9EaInpJRG2IyJmIPk5xOBBRABENSfPatckDgcrJAxCJRFGIOJ/Ytm3A7NnA0KE578PODvj4Y75A7d79rgRkxIiMJSA//SQlIBKJJF+Tq3FIvmfECEClQotDobC3sjceOQcRsGMH0KIFkImMNzw2HDuu7ED/mv1R0KJgpl3u2sUOiWnTZMJLY6J/f/66bNumtCVapk0bjhxasAA4d05j3Y51H5uppGPtWkCtBoZkL32LRJIp2ZVylAPwDIAFgPHg5E/Lklcv9Aa9CZ+UGCXTprFD4uuvgblztXeexEROrnz2LCfIkRIQiUSSV4xAymEQ4xBAD8cinTsDgYEYvPhj+Nzai2eTnmWrMoVeExLCyT2XLmXnSwYs/W8pRh0YhfNe51HPqV6G7cLCuLvSpTlBnaysZVy4uwMJCSzPMWoiI4EaNYDChYHz5zX2Rc5I0qFSsUzmo4+MuCyrROPkWcqRnBXbBoAlEX1PRBP0cTAgkWiLefPYKeHlBcyZo91zmZtzVY/hw9OXgFSpAhw8KCUgEokk/yDHIXlg1Cjg6VOMelgSkfGROHLbCMT23t4cp9+9e4ZNiAirAlehnlO9TJ0SRCy5jIzkiAnplDA+PDw4JUlwsNKWaBk7O2DFCn6jGiwpk1bSsfnS5tT9Bw8CDx/KpJcSzZGpY0IwM4UQLwBcBxAqhHguhJihG/MkEuVZvZqjJPr0AZYtU6bsZ1oJyJ49wNOn2ZeAHD4sJSASicQwkeMQDdC+PVC5Mmp7nzQeOYePD9CkCeDklGGT80/OI+hZEIbUzTzGfPNmwNeX75cuLpo2VKIP9OkDmJoCW9JPk2BcdO7MnphZs7h0nIYY6z4WTco0wZiDY/A4mhN2rFzJ480sqvVKJNkmq4iJ8QCaAGhARA5EVBiAO4AmyfpOicSo2bGDPcEdO/JKiqmp0hYxQgDlygG9e7Oc8ORJrtN9/jw7Tzp1Am7eBL77DmjXjqP6qlXjxJ3LlnG7xESl34VEIpFkiRyH5BUTE2DkSJicOoUxFs2w69ouxCfFK21V7rl+nfWOPTNPZrnq/CoUMCuAfjX7ZdjmwQOuqtqkCTBhgqYNlegLxYrxWGjLFs6HYPT8+iuX0B00iOtTagBTE1Os7boWcUlxGLpvKB4+JOzbJ5NeSjRLVo6JAQA+J6I7KTuSM1J7gEt1SSRGy8GDnDSpSROOGtX38M60EpD16zkvRVYSkDVrlLZaou/Mmwd06MBROhKJAshxiCYYOBCwtsaQU3GGL+fw8eHHHj0ybPI64TW2Bm9F7xq9YWdll24bImDwYHbSr1+vPwsPEu3g4cGOqJMnlbZEBxQpAixZwqtQ//ufxrqtUqQKfm79M/aF7sPY2UEy6aVE42TlmDAnohfv7ySi5wCkf0xitPz7L495atQA9u4FrK2Vtih3pCcBuXOHc1HUrct5KeSEU5IRAQHAlCmc1MrdPR/ocyX6iByHaAJ7e2DAAJTedwIVVLbYfmW70hblHh8foGFDoEyZDJtsD9mO1wmvMaRexrOm339nx/38+UDlytowVKJPdO3KVTU3b866rVHQsycPZGfM4CgjDTHGfQwal2oG361F0LxVHCpW1FjXEkmWjomEXB6TSAyWixdZolemDE/I7O2VtkhzCMEZlPv0ATZsAOLigKlTlbZKoo8kJvJqYvHiwNGj/HfjxjLztkTnyHGIphg1CiIuDnPuOWP3td2GKee4fRsIDAQ++yzTZqsDV6Na0WpoUqZJusdv3gQmT+bw/mHDtGGoRN8oWJDn6du389jH6BGCq9ZYW3NYg4Y0LKYmphhosxUUUQavay1Edqo7SiTZJSvHRG0hRFQ6WzSAmrowUCLRJaGhnCfMxoZXUooVU9oi7eHszBET69dzWVKJJC2//MJ5s5YtA1q35rK1FSqw027FCqWtk+Qj5DhEU7i4AC1b4pOjDxAda6ByjhQZRyaOiZCwEJx+eBpD6g5JLWuYFpWKlS3m5ixnVCKhtUQZPDy4+sr+/UpboiNKlOB8EydP8s1cQ+z/ozQKFX6DQJv/w6ZLmzTWr0SSqWOCiEyJyDadzYaIZAilxKh48ABo25adyocPA2XLKm2R9pk+ne9bo0fnk4RQkmxx9Srwww9Ar15At268r0wZHtu0b88rjJMmye+MRPvIcYiGGTUK1o/C0OduQcOUc/j4APXrs5c0A1YHroa5iTm+qJ1+CpIFC1iuuWQJULq0tgyV6COtW3MUYL6RcwDAgAGcKGrKFC7nlkceP2aJ83DPAmhSwQ1jD45NrdIhkeSVrCImJJJ8wfPn7JSIiOBQ9WrVlLZIN9jYAHPncsTEJun0lgCpyawKFgR+++3dYzY2wO7dnEB1wQKWsMbEKGOnRCLJBV27AqVLY1qQneHJOe7f59CtTKIl4pPisfHSRnSr1g2OBR0/OH75Mler6tGDk1tL8hdmZlxW3c8PCA9X2hodIQSHOQoBeHpy1tc8sG4dRx15eZpgXdd1qVU6pKRDogmkY0KS74mKYmfyvXvsBa5XT2mLdIuHByc2nDKFPwtJ/mbZMuDUKY7+LF78w+NmZuyw+PVXYNcuoGVLmUBVIjEYzMyA4cPhEvQYJR5G4vDtw0pblH127uTHTBwTvtd88Sr2FTzreX5wLCEB+OILzhu1fLmUcORXPDz4u+DtrbQlOqRsWS6xdeQIsHZtrrtRq4FVq4A2bThhrHMR59QqHVLSIdEE0jEhydfExgKffspaeh8foHlzpS3SPSYmwOLFPLn86SelrZEoyb177KBq356jPzNCCGDsWHZMhITIih0SibZJ0GSazyFDQBYWmBBogR1XdmiwYy3j7Q3UqsW1rzNgdeBqlLcvjzYV23xw7McfObn1ypWA44fBFJJ8Qr16HBWbr+QcAODlBbRoAUycyHqMXHD4MI8TvLze7hvjPgZNyjSRkg6JRpCOCUm+JTGRNfQnTrCMoVMnpS1SDjc34KuveBU8NFRpayRKQAQMHcrPU6I+s6JLF+D4cVmxQyLRJnPn8nxCY7KpYsUg+vbFgAtqHL3oaxhyjsePOZQrk2iJW69u4eidoxhcdzBMxLvD2//+A2bPBr78ktUskvyLEBw1cfw4T7LzDSYmwOrV7OUcNixXko4Up15K7imAq3Ss67oO8Unx8NrrJSUdkjwhHROSfIlazVm5/fy4lnnfvkpbpDw//wxYWQETJihtiUQJNm1ix8KcOUC5ctl/Xf36smKHRKJNnJ35N9anD5CUpKFOR41CgbgkdPsv2jDkHL6+PJHq2TPDJmsvrIWJMMHAOgPf2R8byxIOJyd2vksk/frx49atytqhcypX5tDYvXtzfLN+8gTYs4fHzhYW7x5zLuKMn9v8DL8bftgYtFFz9hoLPj6sH5NOmyyRjglJvoMIGDWKb0izZ79dJc7vlCgBzJjBzpp8U0pLAgB49gwYN46jHkaMyPnrUyp2tGvHCzGTJ8uKHRKJpujRA1i6FNi3L9cLnR/SoAHUDRpgTIAJdoQYQHUOb2+genXgo4/SPZykTsK6i+vQybkTStu+W2pj6lTg+nVO2mdvrwtjJfpOhQpAkyYs58h3c8WxYzlJxPDhrNmMiMjWy9avZ8fokCHpHx/jPgZNyzbF2INj8SjqkebsNWTCwznbas+e/Hl//XU+/MLlDOmYkOQ7pk/nKImvv2Y9veQtY8awfHf8eA1rmiV6zejRwJs3HOVpksu7go0Nr6aMHAnMny8rdkgkmmT4cODbb4E1a4CZMzXTp8mYMajyXI0IPx/9lnOEhXHcfSYyjv039uPJ6ycYUvfdWdPffwOLFvF16eOPtW2oxJDw8ACuXAGCgpS2RMeYmgIHDwLffw9s28Z5W/76K9OXpCS9bNUq4xQvJsIEa7usRYIqQVbpADghR82aIG9vrOxeFuubFATmzweNHSudE5mgc8eEEKKqEOJimi1KCDHuvTYthRCRadrM0LWdEuNk/nyWLHh6csi65F0sLN7mmVi8WGlrJLrA1xfYsYOjZapXz1tfsmKHxFAQQjgIIQ4LIW4kPxbOoJ0qzVhkj67tTMuPPwKDBgE//KAhyVSvXoh3sMOgkzH6LefYtYtnRpnIOFYFroJTISd0rtI5dV9UFOdOqlyZ83RIJGnp1QswN8+HSTABvlnPmAGcPg0UKMARFBMmAHFx6TY/ehS4c+fdpJfpISUdYO3Y2LFAu3aIthRoM9waExu8wsL+FbGgESB++w2vBvaVYaUZQUSKbQBMATwFUO69/S0B7Mtpf/Xr1yeJJCNWrSICiHr3JkpKUtoa/aZzZyIbG6InT5S2RKJNwsOJnJyIatUiSkjQbN+7dxNZWxOVLUt0+bJm+5bkHAABpOD9Xt82AL8AmJL8fAqAuRm0e53TvrU5FklM5OuziQmRr2/e+0uaOoVUAjR+efe8d6Yt2rYlqlyZSK1O9/DDyIdk8r0JTT0y9Z39gwfz53TqlC6MlBgiXbvyPTBfjwnfvCEaOZIHyDVqEF248EGTzz4jKlKEKC4u6+5UahU1XduU7Gbb0cPIh1owWI8JCCCqVo0IoBPd6pHVt6A6y+tQ6ItQSlIl0bKzS2lBSysigM62c6GoN+FKW6wImY1HlJZytAFwi4jyU15ciQLs2MGe3g4dOMmfqanSFuk3Cxey43zqVKUtkWiTyZM5v8TatbxypEnSVuxo0gQ4dEiz/UskeaQrgA3JzzcA6JZJW73BzAz480/A1ZWlyydP5q0/0xEjQUKg7FY//ZRzvHzJYeY9e2ZYKmjdxXVQkxqD6w5O3efnx7KXr78GGjXSlbESQ8PDg5M6/v230pYoiLU1sGQJcOAA8OoVl2mbOxdQqQBw1OPu3Zz00tIy6+5MhAnWdV2HBFUCvPblkyodSUnArFlAw4ZIiorA2PHV0axOIL5qNBynB5+GcxFnmJqYYrjbCAzYew+7P68Lt0PBONy0JP68uCV/fEbZRGnHRF8A2zI41kgIESSEOCCEqKFLoyTGhb8/0L8/J/bz8fkwm7DkQ5ydOc/E+vVcZk1ifBw9yjklJk3iyhraIKViR/nyXI535UrtnEciyQXFiehJ8vOnAIpn0M5KCBEghDgjhNAL50XBgjzxLlsW+PRT1snnmtKlEdauCQacS8DRK/s0ZqPG2LOHJ0gZ5JdQkxprLqxB6wqtUcmhEgD2ZQwZwtJ5TeXjkBgnn3wC2NrmUznH+3ToAFy+zPV0p0xhLebt26lJLz09s99VZYfKmN1mNvbf2G/8ko6bN4HmzYHp0/GofWM4D4nFOseH+OOzP7Cs8zJYmVm909yxUDF03RqI+98MQ48LsRD9PdBubSuEhIUo9Ab0jIxCKbS9AbAA8AI8OHj/mC2AQsnPOwG4kUk/XgACAASULVtWw8EmEkPn3385nLxOHQ5bl2SfqCiiEiWI3N2JVCqlrZFokteviSpW5OjomBjtny8qiqhjR44UnTRJfp+UAPlQygHgCIDgdLauACLeaxueQR+lkh8rArgLoFIG7XQ+Frl9m6/RZcoQPXiQ+34Sjh4mAmjVyEaaM05TdOpEVK5chjKOQzcPEWaCtl3elrqvd28ic3Oiixd1ZKPEoBk0iKWrb94obYmeoFYTbdpEZGtLqoI2VNExklq0SP/3lxkqtYqarW1mvJIOtZpo5UqiggVJbW9Pm6d0Jsx8K93IDqr584gA2veROVnPMKWJ/hMpKi5Ky4YrT2bjESUjJjoCCCSiZ+8fIKIoInqd/Hw/AHMhRNH0OiGilUTkSkSujo6O2rVYYlAEBfEqbalSnIBYlgnLGTY2nCD07FmWv0iMhxkzgNu3OWKiQAHtny+lYseIEbJih0R3ENHHROSSzrYbwDMhhBMAJD+GZdDHo+TH2wCOAaibQTudj0UqVODSzhERQMeO2a769wHmrdrgYVl7uO08i/jE9JPfKUJEBGe2z0TGsfrCajgUcEC3ahzM8scfwPbtHClRu7YObZUYLB4eQHQ0sHev0pboCULwh3L5Mv6q7IXbz23h9XohV8fJASbCBGu7rjVOScezZ6xX9fJCnGsddJtWAR5WfhjhOiJVupEdTCZOAn77DZ2vJOK0X0ksOb4A1ZZWw7bL24zr88oBSjomPkcGMg4hRAkh+C4khHAD2/lSh7ZJDJwbN4B27XhCdOQIUDyjIF1JpgwYALi7c1RfdLTS1kg0wdmzXDVj2DCgRQvdndfMjGWsCxfKih0SvWAPgC+Tn38JYPf7DYQQhYUQlsnPiwJoAiAvwgmNU7cuV9a5fp0jsDNIqp85QiBiyADUeqLGOe9FGrcx1+zbx0lqMpBxPH/zHL5XffFFrS9gZWaFx4/Z+enuzrklJJLs0KIFULq0lHN8QNmyWOk8Dw7WsehxaSZQs2aOvTdpJR0bgjZk/QJDYNcuwMUFOHIEwdOGoFT7EPyddBN/9vwTSzsv/UC6kSWjRgErVqBW4EM8O94A5S2Ko9/Ofmi9sXW+lHco4pgQQhQE0BbAzjT7hgkhhiX/2RNAsBAiCMBiAH0pv7qOJDnm4UOuV65W82JL2bJKW2S4mJhw2dCnT4GfflLaGkleSUgABg8GSpZUpnyeEMC4cXxfDwnhCURwsO7tkEgAzAHQVghxA8DHyX9DCOEqhFid3KY6gIDkscjfAOYQkV45JgCu9LdhAyeb9fBIzVmXI6qM/R6RVgKmy5Zr3sDc4u3NIY/u7uke3nRpExLViRhSbwiIOK9EXBx/FmZmOrZVYrCYmAD9+nFk7fPnSlujPzx7BvjuEvhyaAFYBZ4CnJxSowTw+nW2+xntPhrNyjbDuIPj8CjqkRYt1jJRUVyvuXt3UJnSmLe4L2parEY5hwoIHBqI3jV6575vLy9g7VrYnQzASR9brG71K4KeBqHOijpZZ/G4AAAgAElEQVSYdGgSouPz0cpgRhoPQ9xkuVBJWBhX6rGxITp/XmlrjIeBA1mze/260pZI8sLMmUQA0d69SlvCVbWcnIhsbYn8/ZW2xvhBPswxodSm1FhkwQL+fY8cmWFKhkw52KUGJZiA4u7f0bhtOSYqisjSkmjMmHQPq9VqqrakGjVazXkxUsqBL1qkSyMlxsKlS/z9WbJEaUv0h7lz+TO5ciV5R1wc0TffEAnBSar+/Tfbfd14eYMK/FSAOm3pROrcXJyU5vhxovLliUxMKGLiKGq63I0wEzRi3wiKTYzV3Hm2bCEyNSVq0oRePLlNnns8ScwU5DTfibZe2mqYn106ZDYeUboqh0SiMaKiWGd79y5HgNarp7RFxsPs2YCVFTBhgtKWSHJLcDBXs/r8c85ErjSyYodEolkmTAAmTgSWLuX8QDnFavR4mKqBu3P1oE70/v1AfHyGMo5TD07h2otrGFJvCO7c4SpSrVpxVLREklNq1uQqLlu2KG2JfqBWA6tWAc2aAdWrJ++0tOQLyz//cINmzYDp0zkUMwsqO1TGnI/nGJ6kIz6etcwtWgAmJji5eTYqOG5B0Kur2N5ze+6kG5nRrx8nyjl7FkW69sXKpnNxZsgZlLIthX47+6HVBuOv3iEdExKjIDaWy6YFBXH0Z/PmSltkXJQowQkT/fx4vCgxLFQqlnDY2gKL9EhCXqYMcPIk54MZOhSYPJnHOxKJJHf88guPbadN43LPOaFRqwE4UtUMxbfsztZkQ6t4e3NyqCZN0j28KnAVClkUQs/qvfHVVywTW7eOw/IlktzQvz9w+jRw65bSlijPsWNcBdPLK52DzZrxYHvgQF7taNQoWzWLR7mNMixJR3Awy8jmzoVq8FeYuqATmoV+g/L25RE4NBC9avTSznl79gR8fICLF4E2beBmVQlnBp/B8s7LcTnsMmovr42J/hMRFR+lnfMrjLyESwyexESgVy/gxAlg40agc2elLTJOxowBqlThlSmlx6ySnLF4MfDff/yob8WL3q/Y0auXrNghkeQWExOeoH/8MedcOHAg+6+1MLVASO9WsI+IReKOP7VnZFbExLAHvEcPwNT0g8ORcZHYHrId/Vz6Yc3vhfDPP+xwLVdOAVslRsPnn7ODS0ZNcARj4cIZBizxKseaNcDOncD9+xwCuXhxpisLaat0eO71BEf06yFqNfC///F7evIEYVtXo4l7COYELcHIBiNxavApVHaorF0bunQBdu9mh0+rVjB98RJDXYfi+qjrGFR3EBaeWYhqS6ph6+Wt+vs55hLpmJAYNGo1O239/IBly/jGItEOFhZczSE0lO8/EsPg9m2OtuzcWX9/H2krdvj6yoodEklesLDgBbdatXjx7b//sv/a6h7jcMMBiF7ws/YMzIqDB9k50bNnuoe3BW9DbFIs2tiMwtSpHC05cKBuTZQYH2XK8L1n82bAyOZ6OeL5c/Y3fPFFNsqJd+8OXL7MGXjHjgXat+cM9BmQIuk4cPMA1l9cr1G7NcL9++zVnTgR6NABB33nodr9ybjy/Aq299yOJZ2WaFa6kRkdOvDk5uZN/mI+eYKi1kWx8tOVqfKO/jv7o9WGVggOM54s4tIxITFYiIDRo4GtW4Gff+byhxLt0rEjT3B/+EFOHA0BIg7FNDUFfv+dV4P0lZSKHb6+smKHRJJXbG056KB4cb5m37iRvde1qdQW6xoXgMOFa0BgoHaNzAhvb6BIkQw1masCV6FW0fqYN9kFhQrx6q4+X9skhoOHB/9Wzp1T2hLl2LCBI5E9PbP5ghIluIzoihWshalZE9i2LcPmo9xGoXm55hjvPx4PozJ2YugUIvZI1awJnDuHpFUrMHF4JXQ8/CUqFK6gXelGZrRpw2Fv9+9znotkp49bKTecGXwGKz5Zgcthl1FneR1M8J9gFPIO6ZiQGCzffcdREpMnc24aiW5YuJBLsk3Vg/xoksxZtw44epR152XKKG1N9ujalcseJiayvPzwYaUtkkgMkxIlOPgA4IXM7DiTzU3NEdm3O96YA6rfFAiNi4vjSU737unW/Ax8EojAJ4EoHbQUAQECy5fz+5RINMFnn3GOx82blbZEGYjY0dekCVCjRg5eKASvgly8CFSrxoluPv8cCA//oKmJMMHaLizp8NrrpbwU4eVLoE8fYMAAoGZNPDqxH02xFv87uxCjGozCqUE6kG5kRosWwKFDXL+1RQvg3j0AgKmJKbzqeyF0VCgG1x2MX8/8iqpLqmLLpS3Kf6Z5QDomJAbJggWcc8fTE5g7V66W6BJnZ84zsX59zkKEJbrl8WPO0t+8eQYJrPSYlIod5cpxlI6s2CGR5I4qVTga+Nkzrn4THZ31az51H4BNtcCrni9fat3Gdzh0CHj9OkMZx+rA1bB41giH1rnh888zbCaR5Ao7O5YG/fEHO8fzG//8wxEjuR4zVK7MCd9++okjn2rWBI4c+aBZJYdK+iHp8PdnG3ftAmbPxu4VE+Di3wVXX1zFjl478Fun32BpZqmcfSk0bsyf46tXPKhLk6G1iHURrPh0Bc4OOYvStqXh4euBlhta4vKzywoanHukY0JicKxZA0yaxEny9D083ViZPp1XqcaMkVUU9BEiYORIrnS1apVhZqpPqdjRti1X7Pj6a/ldk0hyg5sbsGMHcOkS55PMKnlxmwptsKmZLUzjE/iGq0t8fAB7e679+R4xiTHYHOiNAvu2w9FRYMkS3ZomyR94eHCehXTm00bPypX88+uVF9WCmRnw7bfAmTOc3bptW84/ERv7TrMUScc4/3G6l3TExHBt4Q4dgMKFkXjqJCbUDUM3789QsXBFBHoFoudHeub1bNAA+Osv4M0bdk5cv/7u4VINUuUdwWHBqLuiLsYfHI/IuEiFDM4dBjhcleRnvL3Zk9u+PYfapZOwW6IDbGy4nPXZs8CmTUpbI3kfHx9eAJg5k1dMDRVbW47qHjECmDcvf1TsCAvjFW4DjsSU6CGdOgGrV/Nka9CgzJ185qbmqNayJ45XNAUtW8r1hnVBQgJnou/alTN4vseOkB2IPjgJkQ9KY80awMFBN2ZJ8hcdO/J3K7/JOV684LHDgAHZSHqZHerX5zw1Y8ZwxvR69YDz51MPp0g6ktRJuq3S8d9/QN26wNKlwIQJuHd0J5peHI2FZ95KNyo5VNKNLTmlbl3g77+BpCSWdYSEvHP4fXnHorOLUG1pNWy+tNlg5B3SMSExGPz9WbbWqBFfPNMZt0h0yIABvBI3ZUr2woMluuHVK46WqFePE0sbOsZcsSM2Fjh1it9b375AhQqcrPCTT4A7d5S2TmJsDBzIEsgtW4Bvvsm8ba8avbDIVQVx7z6wb59O7MPRo0BkZIb6jAV/ngVOT4KnJ6FjR92YJMl/WFgAvXvz/SY/jW02bmTfYLaTXmaHAgW4lu+hQ0BUFNCwIV+EkpIAJEs62szBwZsHtS/pSErizO2NG/MKx9Gj2O3ZHHU2NMS1F9f0S7qRGTVrAseOcShsy5ZAUNAHTdLKO8rYlsEA3wFosb6FYcg7iMhotvr165PEOPn3XyJra6LatYnCw5W2RpLC2bNEANHXXyttiSSFL78kMjUlunBBaUs0z65dfB0oV44oOFhpa3KGSkV09SrR+vVEw4cT1atHZGbGvx+AqGxZol69iObPJzp+nCguTrPnBxBAenCfzg+bPo9F1GqiESP4O/e//2XcLiEpgYrOsqcXRayJPv5YN8YNHkxkY5Pul//c7auEwjfJwSmcoqJ0Y44k/3LyJP9GNm5U2hLdoFYTVa1K1KiRFk/y8iVRnz78wTZqRHTzJhERqdQqar6uOdnOtqUHkQ+0c+7r14nc3Pjc/ftT/ItnNP7geMJMUP0V9enmy5vaOa82CQ0lKl2aqHBhooCADJup1CpaGbCSHOY6kOn3pjTuwDiKiI3QoaEfktl4RPEbuCY3fR4MSHLPxYtEdnZEzs5ET58qbY3kfQYOJDI352ukRFkOHuSr+rRpSluiPQICiJyciGxtiQ4dUtqajHn2jGjPHqJvvyVq25avYSlOCBsbojZtiKZOZWfLkyfat0c6JuRYJIWkJKIePfi7uG1bxu0G7RpE37ez5IZXrmjXqIQEIgcHon790j1cu/O/BKGiXQdfatcOiYR4ol6+PFH79kpbohv++Yd/5uvW6eBkW7fyDbFgQaKVK4nUarr58iZZz7KmDps7kFqt1ty51GqiZct4RaNwYaI//6Q74XfIbZUbYSZo9P7RFJeo4VUAXXL7Nq/U2NkRnT6dadMXb17Q0L1DScwUVHxecdoUtEmzn3UOkI4JicESGkpUrBg7Be/eVdoaSXo8ecITrc6dlbYkfxMdzavuVasSxcYqbY12uX+fqGZNjgxZuVJpa4hiYjiq63//4wWh8uUp1QlhakpUpw7R0KFEa9cShYRw9ISukY4JORZJS2wsUfPm7FQ+ciT9NgduHCDHSaAkczOikSO1a9Dhw/yD2bnzg0N7/eIJIHL+ZLd2bZBI0vDtt0QmJrpxHCtN//48t33zRkcnvH+fvfMA0SefED19SovPLCbMBK0JXKOZczx+TNSxI5+jXTuihw/J96ov2c+xJ9vZtuQd4q2Z8yjNvXtElSoRFSpEdOJEls3PPTpHDVY2IMwENVvbjIKeBunAyHeRjgmJQfLgATsCixblEGiJ/jJvHl9N9u9X2pL8y5gxREJwCGp+IDKSqEMH/t5Nnqy7yX5OJBknTuhwoJcF0jEhxyLv8+oVUY0a7FhOT/qVkJRAhecUpuMtK/KgNzJSe8YMG8YrqDExH9hYuFgMwTGYdgf7a+/8Esl7XLnC1/SFC5W2RLu8eEFkaal93+MHqFREv/7KJy9alFQ7fajFuhZkO9uW7kfcz1vf3t5ERYoQWVkRLVlC8YlxNO7AuFTpxq1XtzTzHvSFhw95Vcramuivv7JsrlKraNX5VVRkbhEy/d6Uxh4Yq1N5h3RMSAyO58+JqlfnAVMm0imJnhAfT1SlCm/x8Upbk//49192SowapbQluiUxkZ0DANFnn2nHCaBvkozcIh0TciySHg8ecERiiRIcFfw+g3YNopYjCvIX/rfftGNEUhKHRvbq9cGh/v2JhGkilZjQiZJUSdo5v0SSAfXr82bMLFzIP++LFxUyICSEqG5dIoAi+/ei4t8VoPab2udOZhARQfTFF/yGXF2Jrl6l269up0YIGLx0IzOePCH66CN2xvhnz4n74s0LGrZ3WKq8Y+PFjTqRd+ilYwLAXQCXAVxMz0AAAsBiADcBXAJQL6s+DWkwIMmYyEi+EVhZse5NYhj4+fEVZd48pS3JX8TGElWrxiv1+TEpnFrNEgohOLdVXpwDMTEccaLPkozcIh0TutsMbSwSHExkb8+O5efP3z124MYBwkzQq1rOvCKnjUHrsWP8I/vzz3d2e3sn//5azqCZf8/U/HklkixImbRrO8WKUqjVvAjo7q6wIfHxnBzLxIQinRyo6Ve5kHQcO8YDIVNTohkziBISUqUbdrPtyOeKj3Zs1yfCwrhKgIUF0b592X7ZuUfnUvNuNF3bVOvyDn12TBTN5HgnAAeSHRQNAZzNqk9DGwxIPiQmhqhFCw6NzsFvSqIndO7Mq8j6vHJsbEyfzlfyAweUtkRZclqxw1AlGblFOiZ0txniWOTECV4McHcnev367f4UOceysY35h6GNjLOjR/PJo6NTdz19ypHYTlUeEr4zz3tot0SSC5484TwT336rtCXa4cQJ/lmv0VBahzxz8iSpK1YklQAtaGFB98OyUS0jLo5o0iRenahcmej0aYpPiqexB8YSZoJcV7oan3QjM16+5GgRc/N0c/ZkxPvyjjH7x2hN3mGojokVAD5P8/d1AE6Z9WmIgwHJWxISOAeOEERbtihtjSQ3hIbytfCrr5S2JH9w8SJPpr/4QmlL9IOAAA5JT69ih7FIMnKLdEx8MMboBSAEgBqAaybtOiSPP24CmJKdvg11LLJzJ0/COndmmVQKg3YNoiI/FCK1oyNRly6aPalKRVSyJFG3bqm71Go+jaWlmopNbkkdN3fU7DklkhzQvj1HzylUwECrDBjA97+0zkjFiYqiyAG9iQC6WdaG1JcuZdw2KIgzYQOcp+b163ekG2P2jzFe6UZmREQQNWzI0SPvRaJlxcuYl+/IOzZc3KBxeYe+OibuAAgEcB6AVzrH9wFomubvo5kNHsiABwMSlpj278/fyGXLlLZGkhcmT+b/49mzSlti3CQmsuTJ0ZGTV0mYe/feVuyYMMH4JBm5RTomPhhjVAdQFcCxjMYWAEwB3AJQEYAFgCAAH2XVtyGPRX7/nX8ngwa9nYgdvHGQMBN0fVgvXjm4c0dzJ/z3Xz7h5s2pu9avT7bhmxDCTNDOK9lf9ZNINM3Gjfx9NLbE0i9fct7J4cOVtiR99s7zomfWoEQLM6IFC969USclEf3yC0sWihdnLTER7byyk+xm2+Uf6UZmREURNWvG3uZNm3L88hR5h8NcB3oZo9kyzfrqmCiV/Fgs+Wbf/L3j2XJMAPACEAAgoGzZshr94CS6ITaWqGdP/jbOmqW0NZK8EhnJq9bu7vljwqcUv/xC6cmyJcTfwZQqYcYmycgt0jGR4VgkM8dEIwD+af6eCmBqVn0asmOC6K087Lvv+O+EpARymOtAo1d2Z8/e5MmaO9n48Ty5iOCQ4Xv3OOKpWTOiLlu6U7F5xSghKUFz55NIckh0NMsEhw1T2hLNsmgR/87Tq8ijD6jUKuq2uDHtq56ssWzVii8Qd+5wrWOAqHt3oufP87d0IzNevyZq3ZodyrnQ66jUKrr6XPNlETMbj5hAIYjoUfJjGABfAG7vNXkEoEyav0sn73u/n5VE5EpEro6OjtoyV6IlIiKADh0Ab29gwQJg2jSlLZLkFVtbYM4c4OxZYPNmpa0xTm7cAGbMALp2BXr1Utoa/cPWFvDz4+vLvXvA9u3AxIlA06aAtbXS1kkMiFIAHqT5+2HyPqPmhx+AQYOAH38Efv8dMDc1R7eq3bD+xRGounUBVq8GYmLyfiIiwMcHaNsWsLODWs3nVamAX5aEwe/mHgysPRDmpuZ5P5dEkksKFQK6dQP+/BNISFDaGs1ABKxcCTRoANSpo7Q16WMiTLDAYxP69LfAwsE1QP/9B9SqxduFC8D69YCPD+6YRqPp2qZYdHYRxrqPxcmvTqJi4YpKm68fFCwI7NsHtGsHDB4MLF+eo5ebCBNUK1pNS8ZlcE6dni0ZIURBIYRNynMA7QAEv9dsD4AvBNMQQCQRPdGxqRIt8vgx0Lw5cOoUsGULMGGC0hZJNMWAAYCbG/DNN0B0tNLWGBdqNeDpCVhaAsuWAUIobZF+IgRgZ6e0FRIlEUIcEUIEp7N11cK5vIQQAUKIgOfPn2u6e50iBLBiBfDJJ8DIkYCvL9C7Rm9EJ0TjTFdXIDwc+OOPvJ8oIAC4fx/o2RMAX8+OHgX+9z/g78g1UJEKQ+oNyft5JJI84uHBX/sDB5S2RDOcPg2EhABeXkpbkjkVC1fE3La/YEKZEOzY8i1Qrx7g7g5cugR8+SV8r+1C3RV1EfoyFDt778SvHX6FpZml0mbrFwUKALt28QV9+HBg0SKlLcoUpSImigM4KYQIAvAfAD8iOiiEGCaEGJbcZj+A2+CEU6sAjFDGVIk2uHYNaNQIuHOHVzb79VPaIokmMTEBfvsNePoU+Oknpa0xLlatAv75B5g/HyhZUmlrJBL9hYg+JiKXdLbd2ewiW5GbyecyquhNMzP2Pbi5AZ9/Dlg+agOHAg743ToEcHHhCzzLW3KPtzefqEsXhIYCX38NdOwIDB6ixpoLa9CiXAs4F3HWzBuSSPJA27aAoyMvohk6d+8Cs2dzJEjfvkpbkzXDGwxHy/It4Xl1Dh74bgAOH0Z8aSeMPTAWPbb3QJUiVXBh6AV0r95daVP1Fysrjk7r0QMYNw745RelLcoQRRwTRHSbiGonbzWIaFby/uVEtDz5ORHRSCKqREQ1iShACVslmufMGQ6pjosDjh3jC77E+HBzAwYOBBYuZOmBJO88fMiD91atOCpPIpFolXMAnIUQFYQQFgD6gqM58wUpUcDlygHdu5mhpfVI7Andi8QRw4CLFzncMbekyDjatEGSrQO+/JLHzqtXA//cO4Zb4bfgWc9Tc29GIskDZmY8id+zB4iMVNqanBETA+zfD4wdC1SrBlSowL/r0aPZOaHvmAgTrOmyBiq1Cp57PXE7/DaarmuKxf8txjj3cTg56CQqFK6gtJn6j4UFe5v79uVw5h9/VNqidFEsx4Qkf+LnB7RuDdjb85imfn2lLZJok9mzebA5frzSlhg+RByFl5jIURNSwiGR5B4hRHchxENwgks/IYR/8v6SQoj9AEBESQBGAfAHcBXAdiIKUcpmJShaFPD35+v48VlTEf3cFocaOrJOasmS3HccFATcugV89hnmzeMFi6VLOQpsdeBq2FvZo0f1Hpp7IxJJHvHwAOLj2Z+mzxABwcGct61tW8DBAejcmXNKVKgA/PorcPUqMGuW0pZmn4qFK2Lux3Phf8sfHy39CDde3oBvH18s7LAQFqYWSptnOJibc/K3AQM4Udl33+U98k3DmCltgCT/sHYt69nq1GHvbbFiSlsk0TYlSvB17+uvWZvZsaPSFhkuf/7JqxwLFgCVKiltjURi2BCRLzjx9vv7HwPolObv/WBpab6lfHm+fjdvbgXTrYexvs5CdB40iOUcCxbkTlPm7Q2YmCCo8mf4v5GcZqJvX+BlzEv4XPXB0PpDUcC8gMbfi0SSWxo0AJydeV43aJDS1rzLq1fAkSPsRPT3Bx4lC85q1OA8MR06AM2asYPRUBneYDj8b/kjPC4cG7ttlFESucXUFFi3jiMofvqJvW1z5+rNapcgPfOU5AVXV1cKCJCKD32DCPj5Z2D6dE4M6+0N2NgobZVEVyQkADVr8vPLl/laKMkZL14A1asDFStypJGpqdIWSQwNIcR5InJV2o78gLGORf76C2jbPgmi9Fm83FUYdnVdeNVt5sycdUQEVK+OBKdyaPDKH8+e8Qpv0aLAojOLMM5/HIKGBaFW8VpaeR8SSW754Qf+ut+7B5Qpk2VzraFSAefOAQcPsiPiv/84Mba9PUdJtG/PW+nSytko0XPUamDUKC69NHYs66515JzIbDwipRwSraJSsY5t+nSgf39g717plMhvWFjw9S40lBfYJDln3DjWta5ZI50SEolEGVq3Br6eFwLV3SboMNkeqg6duXxHTmsoXrkCXL+O7y1/xqVLLE0rWhQgIqwKXAW3Um7SKSHRS/r3Z7/atm26P/ejRxx53Ls3J+Js1IjTBBBxZOqpU8Dz51wee/Bg6ZSQZIGJCevnxo3jSh0jRrCzQmmzlDZAX7l3jxcC4uOVtsRwiYvj0MylS4FJk4CNG+VqeX6lUyfevv+eK3VIso+fH2cCnzqVk+FLJBKJUvww+iNYf/ItzhwuibGmS0BPn+ZcdO/tjTNoiDmH6+Grr4BPP+XdZx+dRcjzEAypK0uESvSTSpXYIaCL6hxxccDhwzx+dnFhR8PgwcC//wLdurG88/lzzs8ycybbZSYF+pKcIATXZ/7mG2D5cq5Fr1IpapJ0TGTArl3siXRz43K5kpwRGcmaNm9vlqDOm8fOOUn+ZeFCvtFOm6a0JYZDVBQwbBjw0Ufyc5NIJMpjbmqOzz2fwbzZIizdVw6zHebnOAlmzPZ9+LLAnyhdWuDXX9/uX3V+FQqaF0RfFwOoYSjJt/Tvz/MCTc8NiIDr13nxulMnTlrZrh1Hmjo58Tj68mWuzpUSOeHgoFkbJPkQIThT/YwZ/MUaOBBISlLMHDlVzICxY1l28OwZ4OoKzJmjuBPJYHj8GGjenMPKtmwBJkxQ2iKJPlClCkeMrVvHekhJ1kyZwuGba9YAlpZKWyORSCRAr496IbHVeLT49AG+fTUR605VAQIDs/fi69cx5coAhMaWxfr1gK0t746Kj8IfIX+gr0tf2FhKvadEf+ndmyMTNBE1ERkJ+PoCQ4dyxYxq1XicdOsWL177+XFiy7SRE3qSo1BiTAjBIc2zZnF21/79uQScAkjHRCZ88gknZOralcOomzcHbt5U2ir95to1Die7fZsvqP36KW2RRJ+YPh0oXhwYM0YvpGx6zfHjb3MSNWyotDUSiUTCtK7QGg4FC8Op/3S0bZUIT6zC/m/+ydZrj/5yHr9hDMZ8FY1Wrd7u/yP4D8QkxsCznqeWrJZINIOjI0cEb9mS83GMWg0EBPD8r3lzoEgRoEcPzllRty5H09+5827kRMGC2nkfEskHTJsGzJ/PiUr69Ml5/iANIB0TWVC0KP9/tmzhfE21awPLluld2Ve94MwZoGlTDtc/dowzA0skabG15apEZ8+yU1aSPrGxwJAhXKbvp5+UtkYikUjeYm5qju7VusPvti+2+KhQu+hj9Drihf8OR2b6ushI4KtNrVDF6h5mL3k3KmJ14Gq4FHOBWyk3bZoukWgEDw+OZvwnG/64p085x1r//lxCvUEDXqSJiWFp//HjwMuXbyMnypfXuvkSScZMnAgsXsxfyB49eFKnQ6RjIhsIwSv/wcE88U6pCfzwodKW6Q9+fpyx296eJRz16yttkURfGTCAc7d88w0QHa20NfrJDz8AN25wtnq5WiKRSPSN3jV6IzohGqee+cNvRwyK4xk6dzdHaGjGrxk3OBqPEotho+dJWFu/3R/0NAjnHp+DZz1PCBmnLjEAPv2UK8ylJ+dISODFualTOQrCyQn48kvgyBGeO2zezDLxlMiJZs0Ac3OdvwWJJGNGj+aKS35+LBuIidHZqaVjIgeUKsU1g5ctA06eBGrW5ItSfo+eWLeOv7cffcROiUqVlLZIos+YmLAz9ulTGQ2QHoGBnORq0CDg44+VtkYikUg+pHbqCWwAACAASURBVFX5VnAo4IDtV7ajRMtq8HebAcTGon17Srfy0p49wHofG0zBHLiPb/zOsdWBq2FpagmPWh46sl4iyRvW1sBnnwE7dvCC8q1bPDfo2pXlGa1acUS8nR3nFQwMBJ48eRs5UayY0u9AIskCLy9OhhkXp1PttXRM5BAhgOHDgaAgoHp1Dufq3Rt48UJpy3QPEfDzzzyBat0a+PtvebGVZA93d078u3AhRwZImMRELgfm6MiDGolEItFHUuQce6/vRVxSHJy/6QE/dUeEPVGhUyeuKJTCixecyK+2dSj+r+5ezvKXTGxiLDZf3ozPPvoMDgVkiQGJ4dC/P3/Py5cHKlfmaOrLlzkqdPduTlp57Bgnsa5bV1amkxggX33Fk7tChXR2SvkzySWVKwMnTrAndPduzpS7b5/SVukOlYojfb79li/O+/ZxWJtEkl1mzwasrGTVlrTMnw9cvMgrL4ULK22NRCKRZEyKnMP/pj/QpQvcSj+Bd/UZuHSJpckJCbyAMXw4EB5O2BjTExa9u73Th89VH0TERWBI3SEKvQuJJHe0asXSjAYNuKRnaOjbyIkuXeSYWGIk6NijJh0TecDUlD2hAQFcaeDTTzlhXdqVAmMkLg7o2xdYupRzpGzcCFhYKG2VJLtEx0fjwI0DmHZ0GmYdn4Xd13bj1qtbUJNuy2SUKAF89x07tQ4c0Omp9ZJr17haU8+eQPfuSlsjkUgkmZNWzgEzM2D4cHS8OBurf3iMo0d5sW3rVsDbG/ih3b+ohcsc/56GVYGrUNmhMlqWb6nMm5BIcompKY9d9u4FRo0CnJ1lKU+JJK8IMqIECa6urhQQEKDIuePjeVIxdy5QpgywYQPQooUipmiVyEigWzcOT5s/nx0TEv0mNjEWpx+exl93/sLZq4cRd/E8qj5TweWFABEh3AqIsAJiClnAtnhZFC1ZGSXL1kC5crVRpbI7ShavDKElj2lCAudqATgEMr86uNRqLh125QpvJUoobZHE2BBCnCciV6XtyA8oORbRNZ57PPFHyB8ImxSGAuHRPADy9MTsUkswbRovtrm7AydEc5i+jmQdbDKhL0NRdUlVzG4zG1OaTlHwXUgkEolEV2Q2HjHTtTHGiqUl51v45BPOvtuqFTB+PGfctbJS2jrN8Pgx0LEjcPUqJ/3s10+DnQcGcqpiBwegalWgShV2P6dN3S3JFomqRJy/fRKX/9mBF/8dg/nVUFR/poJnGPBTmmpuZF0AMDWFSC2NkQDgZvJ2MLVdginwxtocCTbWoML2sHBwhHWx0rAqWpz1Bvb2vKU8T/toZ5dpumkLC84z0bkzh0LmV0fX778D//7LiWSlU0IikRgKvWr0wuoLq+F/yx/dqnXjcMoNGzDl4c8IC7PFhg3Ahl+ewbTZCS43lIY1gWtgKkwxsM5AZYyXSCQSiV6h84gJIUQZABsBFAdAAFYS0aL32rQEsBvAneRdO4no3TtaOujLKsWbN8DXX7POrHp1ljq4Gvg61fXrQPv2XGt5506gbVsNdErERaBnzwYOHeJZakLCu23KlGEnRYqzIuWxXDmOo8vvJCZCdf0a7p30w9OzR0DBwXC8G4ZKLwmmyT/tJHNTvKlUFta1XWFeuy4nRHFx4c/QxARISuJQmIgIIDwciIhA9NP7ePLwKl48uomoZ/cQ9/wJVOEvYf0mEYVjAfs4oEi8gH0sYK7K4hpSqNC7Dot0nBid1/XEiRvFEbolACUqFXx7vFAho4+NvHeP/x2NGgH+/kb/diUKISMmdIe+jEV0QaIqEU4LnNC+cnts6bEFOHeO60EvXgyMHo3ERMB85VKOdQ8J4fJdABJUCSizsAwal2kM3z6+Cr8LiUQikegKfYuYSAIwkYgChRA2AM4LIQ4T0ZX32p0gok8UsC/PFCzI+Re6duWKFQ0bspZ+2jTDrFV85gxHgpiasoSjfv08dqhWsyhv9mzg7FlO0DFnDjBsGOtUb9zgLELXr7993Lz53eQdFhacgTStsyLledGixje7U6uBO3eAkBDQ5cuIPP8vki4Fwe7uE5irCBUBlBPAvWIWiKxaEaG166N04w6wqd8IZpUrw84sk5+6mRnXtypSJHWXTfJWJU0zIsLT108RHBaM02HBCA4LRnDYZdx+FALz6BgUjmOHRRUTR9QwL4nKoijKwQ4lkwqgSLwJzKJes+PjwQPg0iV2hERyCMdCLIMLgjGtx1WsxeC3JzU1Td+Z4ewMNG0KNG7MURkGChF/7YmAlSuN72srkUiMm5TqHH+E/IHYxFgUaNCAHRNLlgAjR8Lc3ISTTFSvnuqUAIB9ofsQ9iZMJr2USCQSSSo6d0wQ0RMAT5KfRwshrgIoBeB9x4TB064d6+ZHjwZmzuQkfxs38v3ZUPDzA3r1AkqW5NXcSpXy0FliIrBtGyfiuHKFS4YtW8Z1IwsUeNuuTh3e0kIEPH/+rrMi5dHPj/tOwd7+wwgLQ5GGELFmJjg4daPgYNCVEJjExAIABIBweyC4GPCopR3MatVBqUbtUadlX1R0rJB5/3lACAEnGyc42TihbaW3ITNqUuNexL1kR0Uwgp8HY1NYMK69OIEEFUfAmAgTVCpcCS7FXOBSrBlcirmghmMNVLGvBPM3sagSHo5x34dj3sZBGD6nPBoUuf1OBEfqY0QEOzZ27mTtlBBArVrspGjWjB9LldLaZ6BpNm8GDh4EFi3ikmMSiURiaHwg5xg9mmsmHj0K1K4NHD/OJbzSsCpwFUrZlEKHyh0UsloikUgk+oaiyS+FEOUBHAfgQkRRafa3BOAD4CGAxwAmEVFIVv1pMnzy3/v/Ysm5JRjtNhqNSjeCyONSprc3r4y+ecOBAmPG6H9N43XruPZ4nTo89y9ePJcdxcQAa9dytsx79zjb4ZQpQO/evFqfV5KSuN/3HRahocDDh++2LVPmwwgLpaQhL168dUCEhLx9HhGR2iSicAFcdlQjwCEewcWAZ+WLwsm9DRpXb49WFVqhvH153dqcA5LUSbj56uZbh0XyduPVjdQKIOYm5qhatCpcirmgsnU9LPliNMqXB86dsYCZaSY/kNevOdrm5Emu23v6NH/PAHZ4NW361llRrZpehiKEhbGTsmpVfgtSmSTRJlLK8S5CiF4AZgKoDsCNiNIdPAgh7gKIBqACkJSdzzA/STmAdOQc8fF8r23UiBMIDR3KSS9r1QIA3I+8j/K/lsf05tPxQ6ssVboSiUQiMSIyG48o5pgQQhQC8A+AWUS0871jtgDURPRaCNEJwCIics6gHy8AXgBQtmzZ+vfu3dOIfYdWT8PrxfOx1iURz5vVw8jGY9GnRh9Ymlnmus+nTwEvL1YxtGzJE399XCUlYufJt99yLgkfn1zWY46IYE3LokUc7dCkCTB1KtCpk+4mim/efCgNSXkemSYTZIo0JD2nRV6lIVFRHCGSJgoCwcHAs2epTdT2dnhR0QlXipvgWMEw/F3oBUIcAeFYFK3Kt0LrCq3RukJrODs459lJpjRxSXG49uLaO86KkOchuBtxF7jwJbB7PSx6eqJWu4uo4VgjOcqCt1I2pdJ//4mJPPA9cYKdFSdP8swfYIlKkyZvIyrq1dOL8h99+wK+vsCFC+9EOEskWkE6Jt5FCFEdgBrACvDiR2aOCVciepHdvvObYwJ4rzqHeQFg+nSOaqtRg2uMh4am3kdnHpuJH/75AbfH3tZr57pEIpFINI/eOSaEEOYA9gHwJ6L/ZaP9XWRjYKDRwcDmzaBxYyFevsLLQqbYXEMFP/fCcO8yAsPdRqCkTclcdUvEDolx4/jvRYtYyaAvc02Vim1bsgTo358DHXI8h3vyhEstLF8OREezI2LKFJ4Y6gvvS0PSOi5u3nxXGlK48IfOiqpV2ZGRVhoSGwtcu/au8yEkhKM5UrC2BmrUQHw1Z4SWtMAJm3DsEFdxLCEUEICtpS1alm+J1uVbo1WFVnAp5gIToeehNRoiOj4awc+uoH+nCnj22ApuczxwLfocnr5+mtrGztIOLsVcUNauLOyt7FHYqjA/FuDH1H2Wdijy6BVs/7sEk1On2GFx8yZ3UqAA169Liapo1AiwtdXpe929m8vu/vgjj98lEm0jHRPpI4Q4BumYyDOHbx1Gu83t4NvHl+UcDx/yyotKxff/2bMBACq1ChUWVUB1x+rw9/BX1miJRCKR6By9ckwIXu7cAOAVEY3LoE0JAM+IiIQQbgC8AZSjLIzV+GAgIQE4eBC0aRNoz26YJCTiSlFgSx0TRH7WGf07T0XD0g1ztYJ99y47JP75B/j0U2DVqjxIJTREXBzLQr29uWzjL7/kUG5y6xYwbx6wfj1P7Hv35gFJ7dqZvuxNwhv4XvOF3w0/mJmYwd7y7UTz/Ylnyt82ljbam7DnVBpSsSI7Y27e5CSVAGc5rV49tQJGbNVKOGv3GgeSruLovb8R+CQQBIK1uTWalm2K1uU5IqKuU12YmeTvKr5nz3LC2G++4ZyoL2JeICQsJDWyIjgsGE9eP0F4bDgi4iKgIlWm/dla2sLeyh6V4wuhyQOgwe04uIRGotydVzBRE9QmAuFVyiKqQS3ENWoAk6bNUKicM+yt7GFtbq3xCJWICF5ELFqUE9jrQfCGJB8gHRPpkw3HxB0A4eAqYiuIaGVWfeZHx0SSOgkl5pd4K+cAOEGVtzcQEJCaNfvAjQPotLUTdvTagZ4f9VTQYolEIpEogb45JpoCOAHgMjiMEgCmASgLAES0XAgxCsBwcAWPWAATiOhUVn1rdTAQHg7s2IHYdatQ4EwA1AL4pxxwvHk5VPGaih7uA3Ms81CruaLWlCkslVi+HPjsM+2YnxWRkbyCe+wYp4KYODEHLw4K4hnk9u2cM+Krr4DJkzPNlKlSq/DXnb+w6dIm7Ly6E28S38CpkBOszKwQHheOyLhIEDL+bpoIE9hZ2n24Up6JMyNtWyszqxy8wTSkJw25fRsoUeJtGU4XF8SVL43TTwPw152/8Nfdv/Dfo/+QpE6ChakFGpVulCrNcCvlBgtTOTN9n4EDga1bOeDEOV0RF0NEeJ3wGhFxEQiPY0dFRFxEqtMiZV/qY5r9SZHhcLn9Bk3vA03vAw0fAgWTA2VuFgZOlgVOlzfFJWdbvCpbFPYFCmf7O1bYqjDsrOzSdTJ5eQFr1rADxtDLCEsMh/zomBBCHAFQIp1D39L/t3fn0VnWZ/7H31cSSMgKhLAlIItlk0VqsW5THAqi44zKVOYnSGitR1HbcfRnR2rn/GY8nTNtXWa6WK3TBSS4dOryq4IUlapoXRCQLRATCWsSQiACISFk/c4f3+yETcl9J08+r3Ny8uTJ/Ty5cp+HcN3X872ur3MvNxzzNqcuTKQ75wrNrD/wBvCPzrl32jmuQ9pKu5IT2jny832/2n33NS0L/cYfvsG7u9+l4P8W6P8+EZFuqFMVJjpSYO9S7NhB1ZJFVC7+Nb33HqAyBlaOj+XoP8xi+h0PMbjP0LN6um3bYP58WL8e5s2Dxx7zG0sEpagIrrkGcnJ8m8nNN5/hA9991xckVqyAxES48064914YNOikD9myfwtLNy/lmS3PUHS0iJTYFGaPm838SfO5fOjlTasg6l09ZVVl7V5knsmFZ2Vt5SlDj4uJO/UFZlyLgkeb76XEpZywWqOmroa1RWt5c+ebvLXrLd7b8x5VdVVEWzRT0qc0zYm4bMhlxPfo5DuDdAL79vlumalT/UyWjlJTV8ORqiP+dVV2gLqP1xH74VpS1m5hwIZPiT9SAUBZUixbRqWwdkQsfxkK7/er5EDNYWrra0/5/Ik9E1u9xnruuZpV//YA8xbs43ePpyoxl8B0x8LEmThdYaLNsQ8C5c65R091XHdcMQHttHO0sb98Pxk/zeCer97DI1c9EkKEIiISNhUmOopzuA8/pOCJH5Pyx5Ukl9dQnAgfTx1Fxl0PMOHq+dgZ9kLU1Pg5Uf/+7/7N98WL/eDJjpabCzNnQmmp34HxtD/TOV+I+PGP4b33/Hr0e+6Bu+7ysxjaUVxezLNbnmXp5qVsLN5ITFQMV59/NfMnzufvRv/d51+9cAp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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataframe = {\n",
    "    \"x\": [2.5, .5, 2.2, 1.9, 3.1, 2.3, 2,   1,   1.5, 1.1], #2.5, .5, 2.2, 1.9, 3.1, 2.3  , 2,   1,   1.5, 1.1,],\n",
    "    \"y\": [2.4, 0.7,2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9], #2.4, 0.7,2.9, 2.2, 3.0, 2.7  , 1.6, 1.1, 1.6, 0.9,],\n",
    "    \"z\": [4.0, 0.8, 5,   10,  9,   1,  1.8, 9,   5,   6] #4.0, 0.8, 5,   10,  9,   1,  1.8, 9,   5,   6,]\n",
    "}\n",
    "data = pd.DataFrame(dataframe)\n",
    "fig = plt.figure(figsize=(18,8))\n",
    "subplot = fig.add_subplot(221)\n",
    "subplot.plot(data.index.values, data[\"x\"], 'g', label=\"X\")\n",
    "subplot.plot(data.index.values, data[\"y\"], 'r', label=\"Y\")\n",
    "subplot.plot(data.index.values, data[\"z\"], 'b', label=\"Z\")\n",
    "subplot.set_ylim(0, 20)\n",
    "subplot.set_title(\"Raw Data\")\n",
    "\n",
    "subplot.set_xlabel(\"Time\")\n",
    "subplot.set_ylabel(\"Data\")\n",
    "subplot.legend()\n",
    "\n",
    "\n",
    "\n",
    "# Normalize the dataset.\n",
    "standardizedFrame = StandardScaler().fit_transform(data)\n",
    "\n",
    "# `StandardScaler` returns a `numpy` array so need to re-create  the data-frame from the standardized data for pretty printing. \n",
    "# We technically don't need the standardized data to run PCA.\n",
    "standardized = pd.DataFrame(standardizedFrame, columns=[\"x\", \"y\", \"z\"])\n",
    "#print (\"Standardized:\", standardized)\n",
    "\n",
    "\n",
    "subplot = fig.add_subplot(222)\n",
    "subplot.plot(standardized.index.values, standardized[\"x\"], 'g', label=\"X\")\n",
    "subplot.plot(standardized.index.values, standardized[\"y\"], 'r', label=\"Y\")\n",
    "subplot.plot(standardized.index.values, standardized[\"z\"], 'b', label=\"Z\")\n",
    "#subplot.set_ylim(0, 2)\n",
    "subplot.set_title(\"Standardized Data\")\n",
    "\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Data\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduce some anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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tJiIiJUnWWCRplRciIpKdCgv9lbz16zdMZGST0lZIJJpzju7du9O/f3+aN2/Oddddx7XXXsvLL78cJB4RCevpp/2KIq++WrbnbbWVnw/jssvg0099NcbDD/tJP484wldjnHgiqCtNJL2FGI8kcyyiygsREUmowkJo0cJ/ZWvyIpQnn3yS5s2b/688s0ePHvz000+MGjUqcGQikmqrVsE990DHjnDooeU7hpl/7muvwbRp0Lcv/PornHaa/xt/661+u4hIXDLHIkpeiIhIQhUU+EFtbq6SF6nWrVs38vLy/vd9lSpVGDduHIeW95OLiFRazz8P06f7uS7MKn687baDG2/0yYt33oF27aBPH/+3vmtXeP99X3EnItktmWMRJS9ERCShCgv9YFbJCxGRMNasgbvugv32g0TP2VulChx/PAwb5hMZ118PX34JnTr5FUz69fMThIqIJJqSFyIikjDLlvlBa7zyYu5cv01ERFLnlVd88jhRVReb0rKlT5JMm+bP2bixT2Y0bQrnnuuTGs4l7/wikl2UvBARkYSJry4Sr7wouk1ERJJv3Tq4805o29ZXSKRCjRpw1ll+cs8ffoCLLoK334aDDvJxPPYY/PFHamIRkcyl5IWIiCRMPFERr7wAtY6IiKRSfj788gvcfHNyqy42ZffdYeBAv9zq449DTo5fnaRJE+jRwyc3RETKQ8kLERFJGCUvRETCWb/erwjSpg2cdFLYWOrUgW7dYNw4GD0aTj4Znn0W9tzTr4Dy8st+RRQRkdJS8kJERBKmoACqVYNGjfzM9DVqKHkhIpIqb74JP/4IN93kKx7SgRl06ADPPQczZsD998OcOXDOOX5ujH/+E377LXSUIlIZpMmfNRERyQSFhdC8uR805+T4+0pepIZzjo4dOzJ8+PD/bRs0aBCdOnUKGJWIpIpzfunSnXeG008PHU3x6tWDnj3h559hxAg4+GCfzNhpJ+jcGYYM8XN2iEjllczxiJIXIiKSMIWFvmUkTsulpo6Z8dhjj9GzZ09WrlzJ0qVLufHGG3n44YdDhyYiKTBsGIwfDzfe6JczTWc5OX4J1zfe8O8bt94K338PJ57oVzDp0wdmzw4dpYiURzLHI0peiIhIwhQU/DnXBfj7Wm0kdXbffXdOOOEE7rnnHm6//XbOO+88dtxxx9BhiUiSOQd33OH/5p59duhoyqZJE7jtNv/+8cYbsOuufonXZs18BcnHH2u5VZHKJlnjkaoJiE1ERIRVq2DWrL9WXsyZA8uXwxZbBAstq/Tu3Zu9996b6tWrM3bs2NDhiEgKjBwJX3/tV/eoVi10NOVTrZqfZPSkk2DyZP9ann0WBg3yCY233/YtMSKb8sQTftLaSy4JHYlAcsYjSl6IiEhCTJ3qbzdOXoCvvthtt5SHFM5VV/n67URq2xb69y9xt9q1a3PGGWdQp04datSokdgYRCTtxKsumjaF888PHU1itGoF993nX9egQf7D6MCB8NBDoSOTdLVqFVx/PSxdCgcdBHvsETqiNJFh4xG1jYiISELE20M2bhsBzXuRajk5OeSky1IDIpJUo0bB55/7VTsyLV9Zqxacd56fH2PIELWPyKaNGAGLF/vVbbp39xUYEl6ixyOqvBARkYSIJy+Kq7zIuuRFKa5IiIgkwh13wPbbw4UXho4keWIxn7z44QfYc8/Q0Ug6ysuDbbaBu+7ylTovvAB//3voqNJAho1HdFlGREQSoqDAzyDfpMmf27bfHqpXz8LkhYhICnz5JXz0EVx3na9SyFRduvgr6kOGhI5E0tGKFX5OlJNPhosvhgMP9L8TCxaEjkwSTZUXIiKSEIWFvue66GRxOTm+EkPJi9S67bbbQocgIinQpw80aAD/93+hI0mu7baDDh188uLmm0NHI+nmvff8XBdnnOHHHY8+Cnvv7ZcNfuyx0NFlt0SPR1R5ISIiCVFYuGHLSFxurpIXIiKJNnYsDB8OPXtC7dqho0m+WAy++QZmzgwdiaSbvDxo2BAOP9x/v+eecOWVfvWRMWPCxiaJpeSFiIgkREGBkhciIqnSp4/v8b/00tCRpEYs5m/feSdsHJJeli2DoUPhlFOgapGegttug0aN/OSd69YFC08STMkLERGpsLVrYcaMDVcaiWvRAubMgeXLUx6WiEhG+v573+N/5ZVQt27oaFKjdWvYYQf/ukXi3n3Xjy9OP33D7Vtu6eeq/O4730YimUHJCxERqbAZM/yVjU1VXsCfq5GIiEjF9O3rP5xdcUXoSFLHzFdffPihn99ABHzLyPbbwyGH/PWxU0+FY46Bm26C2bNTH5sknpIXIiJSYfG2kM0lL9Q6IiJScT/9BIMGwWWX+baRbBKLwapVMHJk6EgkHfzxBwwb5pMUVar89XEzGDgQVq6Ea69NfXySeEpeiIhIhcWrKoprG1HlhYhI4tx5p18W9eqrQ0eSeh07wtZba8lU8d55xycmNm4ZKapVK+jVC15+GT7+OHWxSXIoeSEiIhUWT0w0a/bXxxo18sunqvIi+d58803atm27wVdOTg7Dhw8PHZqIJMCUKfDKK34SwoYNQ0eTetWqwXHH+Q+tmoRR8vKgSRM46KDN79erl58vpUcPWL06NbFlu2SNR5S8EBGRCiso8EmKmjX/+lhOjm8nUfIi+U466STGjx//v68ePXpw8MEHc+yxx4YOTUQS4K67oHr17C6Bj8Vg3jwYPTp0JBLS4sV+qeDTTvPjjM2pVcu3j/z8MzzwQGriy3bJGo8ESV6Y2dVm9qOZTTCzV82sppm1NLMxZjbFzPLMrHq0b43o+ynR47lFjnNDtH2SmWlkJiISSGFh8fNdxGm51NT75ZdfuP3223nxxRfJKWlkJyJpr6AAXngBLr7YT1CYrTp18ktiqnUkuw0Z4qsoNtcyUlTnznDyyXD77WpjTbVEjkdSPpoxsybAFUB759zuQBXgTOAe4EHn3E7AQuDC6CkXAguj7Q9G+2FmraPntQE6AY+YWTFTtYiISLIpeZFe1qxZw9lnn839999P8+bNQ4cjIglw771+AsLrrw8dSVhbbQWHHabkRbbLy4PmzWH//Uv/nP79fZXGlVcmLy7ZUKLHI1UTEFN5z1vLzNYAWwCzgCOAs6PHnwduAx4FTozuAwwGBpqZRdtfc86tAv5rZlOA/YCvUvQaREQEWL8epk6FU07Z9D65ufD777BihS/fzHRXXQXjxyf2mG3b+oFXadxyyy20adOGM844I7FByCaZ2dXARYADfgAucM6tDBuVZIoZM+Dpp+GCC6Bp09DRhBeL+WVif/kFdt45dDSSagsXwogRPglhVvrnNWsGvXv7BODQoXDCCcmLMV1k2ngk5ZUXzrkZwH3AVHzSYjHwLbDIObc22m060CS63wSYFj13bbR//aLbi3mOiIikyOzZvnSzpMoLUKlmKnzyySe8/vrrDBw4MHQoWWMzVaUiCdGvn5+gslev0JGkh/iHzqFDw8YhYbz1FqxZU/qWkaKuugpat/bJr+XLEx+b/CkZ45GUV16Y2Tb4qomWwCJgEL7tI5nn7AZ0A1Q+KyKSYPGERGmSFwUFsOuuyY4ovNJekUi0hQsXcsEFF/DKK6+w5ZZbhgkie21cVTozcDySIX7/HR5/HM49F1q2DB1NesjNhT339K0j11wTOhpJtbw8v3pI+/Zlf261avDoo3DooX7Z4T59Eh9fOsm08UiIGbyOAv7rnJvrnFsDvAEcBGxtZvFkSlNgRnR/BtAMIHp8K2B+0e3FPGcDzrknnHPtnXPtG2bjulIiIkkUn8sinqAoTtHkhSTPY489xpw5c+jevfsGy5Pl5eWFDi2jFVdV6pwbETYqyRT33++r2268MXQk6SUWg88/h/nzQ0ciqTRvHnzwga+6KEvLSFGHHALnnefnkZk0KbHxiZes8UiI5MVUYH8z2yKau+JIYCLwMXBqtM/5wNvR/SHR90SPf+Scc9H2M6PVSFoCrYCvU/QaREQkUprKi0aN/NUOJS+S64YbbmDZsmUbLE82fvx4zX2RZBtVlTYGapvZORvt083MxprZ2Llz54YIUyqhefPgkUfgzDOhVavQ0aSXWMzPufTuu6EjkVR6803fQlWelpGi7r0XateGSy8F5xITm/wpWeOREHNejMFPvDkOP6FVDvAE8E+gZzTxZn3g6egpTwP1o+09gV7RcX4E8vGJj/eAS51z61L4UkREBJ+QqF/fDwI2JSfHzwqu5IVkqOKqSg8suoOqQKU8BgyAZcvgpptCR5J+9tnHJ8a16kh2ycvziby2bSt2nO22820jH37ojymVQ5DVRpxzvYHeG23+Db9ayMb7rgRO28Rx+gJ9Ex6giIiUWmHh5ltG4rRcqmSw/1WVAivwVaVjw4Ykld2iRfDQQ34lp9atQ0eTfnJy/MSdr7wCq1ZBjRqhI5Jk+/13+Phj30JV3paRorp1g2eegauvhs6d/TK8kt5CtI2IiEgGKSzcfMtIXG6uVhuRzLSZqlKRcvv3v2HJErj55tCRpK9YDJYuhU8+CR2JpMIbb/hWoYq2jMRVqeIn7/z9d7j11sQcU5JLyQsRESk353w1RWmTF7Nnw4oVyY4qHFcJGmcrQ4yVkXOut3NuV+fc7s65c51zq0LHJJXXH3/4VQJOOKHi5fGZ7IgjYIst1DqSLfLyYLfdYPfdE3fM9u2he3cYOBC++y5xxw2tMrzXlydGJS9ERKTc5s3zyYjSto0ATJ2azIjCqVmzJvPnz0/rAYNzjvnz51OzZs3QoYjIZjzyCCxYALfcEjqS9FarFhxzjE9epPGfXkmAmTPh00/hjDMS0zJSVJ8+0KCBT2KsX5/YY4eQyeORIHNeiIhIZijNSiNxRZdL3WWXZEUUTtOmTZk+fTrpvpJEzZo1adq0aegwRGQTli3zy6Meeyzsu2/oaNJfLAZvvQXjx0O7dqGjkWQZPNgnqBLVMlLUNtvAfff55VOffhouvjjx50ilTB6PKHkhIiLlFp+As6zJi0xUrVo1WrZsGToMEanknnwS5s7VXBeldfzx/kr8kCFKXmSy/HzYYw/fNpIM55wDTz0F//wndO0KlXlRqEwej6htREREyi1eeVGatpFGjaBatcxNXoiIVNTKlXDvvXDYYdCxY+hoKodtt4UDDtC8F5ls2jT44gvfMpIsZr5d648/oFev5J1HKkbJCxERKbfCQqhbF7beuuR9q1SB5s2VvBAR2ZRnnoFZszTXRVnFYjBuHEyfHjoSSYZBg/xtMlpGimrTBnr29L+HX3yR3HNJ+Sh5ISIi5VbalUbicnOVvBARKc7q1XD33XDggXD44aGjqVxiMX87dGjYOCQ58vN9S1CrVsk/1y23QLNmfvLOtWuTfz4pGyUvRESk3AoLS9cyEqfkhYhI8V54wZfH33JL4ldTyHS77go77aTWkUxUUABjxiS3ZaSoOnVgwAD44Qf4979Tc04pPSUvRESk3AoLy1Z50aIFzJ7tl1cVERFv7Vq46y5o396vMiJlY+arLz76yM9ZIJkjP9/fJrtlpKiuXeG44+DWW2HGjNSdV0qm5IWIiJTLokWweHHZKy8Apk5NRkQiIpXTq6/Cb7/5FUZUdVE+sZhvvRkxInQkkkj5+X7J4FQunmHmqy7WrvVzYEj6UPJCRETKJb7SSFnnvAC1joiIxK1bB337wp57/jl3g5TdQQfBNtuodSSTTJkC336bupaRonbYAW680SdPlBBLH0peiIhIuVQkeRF/rohIths8GCZNUtVFRVWtCscfD8OGaaLFTBFvGTnttDDnv+46P0nopZf6ZYwlPCUvRESkXOLVE2VpG2nc2A8wVXkhIgLr10OfPrDbbnDKKaGjqfxiMZg/H776KnQkkgj5+XDAAX6Z9RBq1oSBA30FSL9+YWKQDSl5ISIi5VJYCLVqQYMGpX9OlSp+EKLkhYgIvP02TJgAN90EORqVV9ixx0K1amodyQSTJsF//hOmZaSoY47xk4X27Qu//ho2FlHyQkREyim+0khZy5y1XKqICDjnqy522in8B7RMUbcuHH64kheZIC/Pjy9OPTV0JPDAAz4pdsUV/vdWwlHyQkREyqWgoGwtI3FKXoiIwPDhMG4c3HCDb6eTxIjF4Jdf/BJEdtoAACAASURBVJV7qbzy86FjR2jSJHQkPoY77oB334W33godTXZT8kJERMolXnlRVrm5MGuWJr8SkezlnP8w1KIFnHtu6Ggyywkn+FtVX1ReP/7ov9KpIumyy/yKQFdeCUuXho4meyl5ISIiZbZsGcybV/7kBcDUqQkNSUSk0vjwQxg9Gnr18uXokjjNm0PbtkpeVGZ5eX4OmHSaxLZqVXj0UZg2zSceJQwlL0REpMziS52Wt20E1DoiItnrjjt8KfoFF4SOJDPFYvDllzB3buhIpKyc8y0jhx4K228fOpoNHXggXHihnwPjxx9DR5OdlLwQEZEyiycvKlJ5oeSFiGSjTz/1X9dfDzVqhI4mM8Vifhnad98NHYmU1fff+/lK0qllpKi77/YTw/boock7Q1DyQkREyiyeeChP8qJxY19+qeSFiGSjPn1g223hootCR5K59t7bv9eodaTyycvzy6qffHLoSIrXoAHcc49PQL70Uuhoso+SFyIiUmaFhb5Pu1Gjsj+3ShVo1kzJCxHJPmPGwMiRcO21sMUWoaPJXGa++uL99zU5dGXinE9eHHEENGwYOppN+8c/YP/9/e/xwoWho8kuSl6IiEiZFRb6SdFyyvkuouVSRSQb3XEH1K8P3buHjiTzxWJ+cumPPw4diZTWuHHw22/p2zISl5PjJ++cNw9uvjl0NNlFyQsRESmzgoLytYzE5eb+OW+GiEg2GDcOhg2Dq6+GOnVCR5P5Dj8catdW60hlkpfn20pPOil0JCVr2xYuv9wnMcaODR1N9lDyQkREyqywsHwrjcTl5sLMmbBqVaIiEhFJb336wNZbw2WXhY4kO9SsCcce65MXmlgx/cVXGTn6aKhXL3Q0pXP77X5FlO7dYd260NFkByUvRESkTFatglmzKl55ATB1akJCEhFJaz/8AG++CVdcAVttFTqa7BGL+UT5uHGhI5GSfP21vzCS7i0jRdWt65dNHTsWnngidDTZIUjywsy2NrPBZvazmf1kZgeYWT0zG2lmk6PbbaJ9zcweMrMpZva9me1d5DjnR/tPNrPzQ7wWEZFsE084VLTyAjTvhYhkhzvv9K0iV14ZOpLsctxxfn4CtY6kv7w8qF4dunYNHUnZnHEGHHkk3HAD/P576GgyX6jKiwHAe865XYG9gJ+AXsCHzrlWwIfR9wCdgVbRVzfgUQAzqwf0BjoA+wG94wkPERFJnvhcFYmovFDyQkQy3aRJ/oPZpZdWnnL4TNGwIRx4oJIX6W79et8y0qlT5atMMoOHH4bly+H660NHk/lSnrwws62AQ4CnAZxzq51zi4ATgeej3Z4H4nm3E4EXnDca2NrMGgHHAiOdcwuccwuBkUCnFL4UEZGslIjkRePGflIuJS9EJNPdeaeff6Fnz9CRZKdYDMaPV5tiOvvqK5gxA04/PXQk5bPLLj5x8cILMGpU6GgyW4jKi5bAXOBZM/vOzJ4ys9rAds65WdE+s4HtovtNgGlFnj892rap7SIikkQFBVClCjRtWv5jVK0KzZopeSEime233+Dll+GSS2DbbUNHk51iMX87dGjYOGTT8vJ8gi/+f1UZ3Xijryrt0QPWrAkdTeYKkbyoCuwNPOqcawcs488WEQCccw5I2LzAZtbNzMaa2di5c+cm6rAiIlmpsBCaNPEJiIrIzVXyQjJHcfN5hY5JwrvrLv+38rrrQkeSvXbZBXbeWa0j6WrdOhg0yM9PsuWWoaMpvy22gH//GyZOhP79Q0eTuUIkL6YD051zY6LvB+OTGb9H7SBEt3Oix2cAzYo8v2m0bVPb/8I594Rzrr1zrn3Dhg0T9kJERLJRYWHFWkbiWrRQ8kIySnHzeUkWmzoVnn8eLroIGjUKHU12i8Xg449hyZLQkcjGPv8cZs+uvC0jRXXpAieeCLfdBtOmlbi7lEPKkxfOudnANDPbJdp0JDARGALEVww5H3g7uj8EOC9adWR/YHHUXvI+cIyZbRNN1HlMtE1ERJKooKBiK43E5eb6JexWrar4sURC2sx8XpLF7r3X32oSv/BiMV/K/74+KaSdvDxftdClS+hIEmPAAHAOrroqdCSpsSjF73ShVhu5HHjZzL4H2gJ3AncDR5vZZOCo6HuAd4HfgCnAk0APAOfcAuAO4Jvo6/Zom4iIJMnatX5SrURUXsQTIJpETTLApubzkiw1axY89RScfz40bx46GjngAKhfX60j6WbtWhg82CcuamfIX8wWLeDWW+GNN+Ddd0NHkxxr1vhWnyOOgNatUzvHR5DkhXNufNTGsadzrqtzbqFzbr5z7kjnXCvn3FHxRES0ysilzrkdnXN7OOfGFjnOM865naKvZ0O8FhGRbDJjhu9PTWTyQq0jkgFKnM9L829ll379/AezG24IHYmAn3ekSxcYNsz/v0h6GDUK5s7NjJaRonr2hF13hcsvhxUrQkeTONOmwS23+ITs6af7CYkvuwxWr05dDKEqL0REpBKKJxoS1TYCfy69KlKJbWo+r//R/FvZY84ceOwx+NvfYIcdQkcjcbEYLFwIX3wROhKJy8uDOnX8ZJ2ZpHp1eOQR/+H+7rtL3j+drV8P773n5/LIzYW+fWHvveGdd+DXX/0qK6msmlHyQkRESi2eaEhE5UWTJn7JVVVeSGW3mfm8JAs98ACsXOkH9ZI+jjnGf6hU60h6WLMGXn/dJ5Vq1QodTeIdfrhPYN59N0yeHDqasps3z8/b06oVdO4MX33l5+/59VdfwXT88X4Ml2pKXoiISKnFkxfNmm1+v9KoWtUfR8kLyRDFzeclWWbBAnj4YV9SvcsuJe8vqVOnDhx5JLz9tp9QUcL66CP/+5JpLSNF3Xcf1KzpWysqw8+cc74y6Zxz/AWmf/7T377yim8ZuesuaNkybIxKXoiISKkVFPgl/2rWTMzxcnOVvJDMUNx8XqFjktQbMACWLoWbbgodiRQnFvNXjn/+OXQkkpcHdetCp06hI0me7bf3bRYjRviJSdPVH3/Ao4/CXntBx46+Ounii+GHH+DTT+Gss6BGjdBReqVKXpjZ/mb2jZktNbPVZrbOzLRSsohIliksTEzLSJySFxKKxjaSaIsX++TFSSfBHnuEjkaKE1+OU60jYa1eDW++CV27ps+H4mTp3h3atfNLp/7xR+hoNvT99z6+xo2hRw/fBvL4434Z+4EDYffdQ0f4V6WtvBgInAVMBmoBFwEPJysoERFJTwUFiU9ezJwJq1Yl7pgipaSxjSTUwIE+gXHzzaEjkU1p2hT22UfJi9BGjoRFizK7ZSSuShVf1TBrFtx2W+ho/Hw8L70EBx3kKy2eew5OOQVGj4Zx46BbN99ila5K3TbinJsCVHHOrYuWJc3gIh8REdnY+vW+5zERK43E5eb6Hstp0xJ3TJHS0thGEmXpUnjwQT+J3d57l7y/hBOL+ckH58wJHUn2ysuDbbaBo48OHUlqdOjgkwIDBvhqhxB+/dVPuNmsGZx7rl+i9v77YcYMn8Do0AHMwsRWFqVNXiw3s+rAeDO718yuLsNzRUQkA8ye7Us9E115AWodkSA0tpGEefRRmD9fVReVQSzmk+bDhoWOJDutXAlvveXbq6pXDx1N6tx5p0/YdO/uLwalwtq1foLaTp1gp538SkgHH+zn4Pj5Z+jZE+rVS00siVLaN+lzo30vA5YBzYCTkxWUiIikn3iCIZHJi/ixlLyQADS2kYRYscKvKnDUUbD//qGjkZLstZe/+qzWkTDee8/P/ZANLSNF1asH/frBl1/C888n91yzZ0OfPrDDDn5ekR9+gN69/bxlb7zhK15yKmmqvrRhd3XOrXTOLXHO/cs51xPokszAREQkvcSXSU1k20jTpr4fVMkLCUBjG0mIJ5/0LQi33BI6EikNM199MWKETzxJauXnQ/36cMQRoSNJvfPO86t5XHedr9RKJOfg4499UqhZM//3aNddfbKioMDPt9GkSWLPGUJpkxfnF7Pt7wmMQ0RE0lw8eZHIyouqVX0CQ8kLCUBjG6mwVavg3nvhkEP8l1QOsRgsXw4ffRQ6kuyyfLmveDnlFKhWLXQ0qZeTA4884icrvfHGxBxz4ULo3x92280nhD78EK68En75xSfoTjops/6tq27uQTM7CzgbaGlmRYurtgQWJDMwERFJLwUF0KAB1K6d2ONquVRJJY1tJJGeffbPCe+k8jj0UNhyS/9B+vjjQ0eTPd59F5Yty76WkaL22MMvm/rAA3DBBeVvNfvmGz/Xzmuv+Qqi/ff37SinnQa1aiU25nSy2eQF8CUwC2gA3F9k+x9AoLlSRUQkhMLCxFZdxOXm+isFIimisY0kxJo1cPfd/kPDkUeGjkbKokYNP4nh0KH+A2Bl7f+vbPLzYdttffIom912m0869OgBX3/tq1BLY/lyePVV/zP77bf+YtK558Ill0C7dkkNOW1s9p/KOVcIFAIHpCYcERFJV4WFviwx0XJz/ZXL1auza+ZxCUNjG0mUF1/0fxcfeaRyLDEoG4rFYNAg/yFw331DR5P5li6Fd97x1Qal/bCeqerU8cumnnqqT0Rcfvnm9//5Z7/f88/D4sXQujUMHAjnnANbbZWamNNFqfKMZra/mX1jZkvNbLWZrTOzJckOTkRE0oNzvrUjkZN1xuXm+uNPm5b4Y4tsisY2UhFr18Jdd8Hee0PnzqGjkfI47jg/YbRWHUmNd97x7Q1nnBE6kvRw8sm++ufmm2HWrL8+vmaNT64dfri/cPToo/5n9tNPYcIEuPTS7EtcQOkn7BwInAVMBmoBFwEPJysoERFJL/Pm+UFHstpGQPNeSMppbCPllpcHU6b4Dx6quqic6tXzKz8oeZEa+fnQqBEcdFDoSNKDGfz7337S32uv/XP7tGl+pZDmzf3cIAUFPlE6fTq88gocfHB2/80pdYeXc24KUMU5t8459yzQKXlhiYhIOknGSiNxSl5IKBrbSHmsXw99+8Luu8OJJ4aORioiFoPvv9f7T7ItWeIn6zztNF/tIt5OO8ENN/ikxH33+b8nubn+70v79jBsmE+S9url5wqR0icvlptZdWC8md1rZleX4bkiIlLJxQd2yWgbadrUD2Y0eJQU09hGyuW11+Cnn3zVhSZ6rNxOOMHfDh0aNo5MN2SIrzBQy8hf/fOfsOOOcN11MHq0T1T897/+ZzLe2iR/Ku2f3HOjfS8DlgHNgFOSFZSIiKSXZFZeVK3qExhKXkiKaWwjZbZsmf+w0a6dn2xPKrdWrfx8AmodSa78fP8+X95lQTNZzZq+wuLNN33LSN++yRlrZYpSzfXqnCs0s4bR/X8lNyQREUk3hYVQty5svXVyjp+bq+SFpJbGNlIe99zje89ffVVXRDNFLAb33+9XccjGCRCTbdEieO89v6KGKpWKt8su/ktKttkfIfNuM7N5wCTgFzOba2a3piY8ERFJB8laaSSuRQslLyQ1NLaR8ioogH794Kyz/ESPkhliMb96zHvvhY4kM731ll85Qy0jkggl5b+uBg4C9nXO1XPObQN0AA6KekNFRCQLFBYmt4wxNxdmzIDVq5N3DpGIxjZSLtdd52f5v+ee0JFIInXoAA0bqnUkWfLz/Xv8vvuGjkQyQUnJi3OBs5xz/41vcM79BpwDnJfMwEREJH2kInnhnO/3FEkyjW2kzD7+GAYP9isDNGsWOhpJpCpVoEsXvxrGmjWho8ks8+fDyJF+yc9sXt5TEqek5EU159y8jTc65+YC1ZITkoiIpJNFi3wvcDLbRrRcqqSQxjZSJmvXwlVX+QTutdeGjkaSIRbz73Wffx46kszy5pv+90ctI5IoJSUvNlfAq+JeEZEskMyVRuLiyYv4uUSSSGMbKZMnn4Tvv4f77oNatUJHI8lw9NFQo4ZaRxItP98vA9quXehIJFOUlLzYy8yWFPP1B7BHKgIUEZGwUpG8aNrUz0KuygtJAY1tpNQWLIBbboHDDoNTtJBuxqpdG446Ct5+27cwSsXNnQsffeSrLtQyIomy2aVSnXNaBEpEJMvFEwrJbBupVs0nMJS8kGTT2EbK4rbbYOFCGDBAH8AyXSwGw4bBxInQpk3oaCq/11+HdevUMiKJFWy1XTOrYmbfmdk70fctzWyMmU0xszwzqx5trxF9PyV6PLfIMW6Itk8ys2PDvBIRkcxWWOhLpRs0SO55cnOVvBCR9DFhAjzyCPzf/8Gee4aORpKtSxd/q9aRxMjLg112gT1UzyYJFCx5AVwJ/FTk+3uAB51zOwELgQuj7RcCC6PtD0b7YWatgTOBNkAn4BEz09UUEZEEKyjwLSPJvuqo5IWIpAvn/CSddevCHXeEjkZSoXFjv5ynkhcVN3s2jBqllhFJvCDJCzNrChwPPBV9b8ARwOBol+eBrtH9E6PviR4/Mtr/ROA159yqaLmzKcB+qXkFIiLZo7AwuS0jcbm5MGMGrNaUiSIS2Ntvw4cfwr/+BfXrh45GUiUWgzFj/IdvKb/Bg30C8PTTQ0cimSZU5UV/4HpgffR9fWCRc25t9P10oEl0vwkwDSB6fHG0//+2F/McERFJkMLC5E7WGZebC+vXw/TpyT+XiMimrFwJ11zj5z3o3j10NJJKsZj/0D1sWOhIKre8PP/7o7lDJNFSnrwwsy7AHOfctyk8ZzczG2tmY+fOnZuq04qIVHrLlsG8eamrvAC1johIWA8+CL/9Bv37Q9XNTm0vmWaPPXyyXq0j5TdjBnz+uSbqlOQIUXlxEBAzswLgNXy7yABgazOLv0U0BWZE92cAzQCix7cC5hfdXsxzNuCce8I51945175hw4aJfTUiIhksFcukxsXPoeSFiIQyYwb07Qtdu/qlMyW7mPnqi5EjYfny0NFUToMG+VslLyQZUp68cM7d4Jxr6pzLxU+4+ZFz7m/Ax8Cp0W7nA29H94dE3xM9/pFzzkXbz4xWI2kJtAK+TtHLEBHJCqlMXjRtCjk5Sl5I5bXxSmpS+dxwA6xZA/ffHzoSCSUWgxUr/JwnUnZ5edC2Ley8c+hIJBOFXG1kY/8EeprZFPycFk9H258G6kfbewK9AJxzPwL5wETgPeBS59y6lEctIpLB4omEVLSNVK8OTZooeSGV2sYrqUklMno0vPiin+9ihx1CRyOhHHKIX2VGrSNlV1jof480UackS9BOPufcJ8An0f3fKGa1EOfcSuC0TTy/L9A3eRGKiGS3wkKfVNh++9ScT8ulSmVVZCW1vviLLSmzahVMmgR77pnKs2aW9evhiiugUSO48cbQ0UhI1atD584wdKj/uchJp0u9aU4tI5Js+nUUEZFNKiyEZs1SN3jLzf2zVUWkktl4JbWUueQSOPxwmDo11WfOHC+8AN98A/fcA3XqhI5GQovF4Pff/c+ElF5eHrRvr8olSR4lL0REZJMKClLTMhKXm+uXSl2zJnXnFKmo0qyklsyVz2680f/OnH46rF6d0ENnhSVLoFcv2H9/+NvfQkcj6aBzZ6hSRa0jZfHbbzB2rFpGJLmUvBARkU0qLEzNZJ1xubm+THf69NSdUyQB/rKSmpm9VHSHZK581qoVPPssjBkD112X0ENnhb59/VX2hx5Si4B422zj575Q8qL08vP9rZIXkkz6Ey0iIsVatQpmzUp98gI074VULptYSe2cVMZwyilw5ZX+A3i871xKNnkyPPgg/P3vsO++oaORdBKLwYQJvqJASpaX56uXUjlmkOyj5IWIiBQr3j+f6rYRUPJCpDzuvdd/eLjwQvjll9DRVA7XXAM1asBdd4WORNLNCSf426FDw8ZRGfzyC4wfr6oLST4lL0REpFjxiTNTeRWlaVNftq3khVRWzrlPnHNdQpy7enV/9bNaNTjtNFixIkQUlcf77/sPprfckroVlaTy2HFHaNNGrSOlEW8ZOa3Y9SFFEkfJCxERKVaI5EX16tCkiZIXIuXVvDm89BJ8/z1cdlnoaNLXmjVw1VWw006+3UakOLEYjBoFCxeGjiS95eVBx47+AoRIMil5ISIixSoo8LOtp3ow0qKFkhciFdG5M9x0EzzzDDz3XOho0tPDD8PPP/v5LmrUCB2NpKtYDNatg/feCx1J+po40c8NopYRSQUlL0REpFiFhb4KomrV1J43N1fJC5GK+te/4PDDoUcP+OGH0NGkl7lz4bbb4Nhj4fjjQ0cj6Wy//WDbbdU6sjn5+WAGp54aOhLJBkpeiIhIsVK9TGpcbq5fKnXNmtSfWyRTVKkCr7wCW23lP1QsWRI6ovRx882wbJmvujALHY2ks5wcP3Hn8OGwenXoaNKPc75l5NBDoVGj0NFINlDyQkREilVQkNqVRuJyc2H9ep/AEJHy2357eO01mDIFLr7Yf9DIdt99B08+6ecD2W230NFIZRCLweLF8NlnoSNJPxMm+PYrtYxIqih5ISIif7F2LcyYEa7yAtQ6IpIIhx4Kffv60u6HHw4dTVjOwRVXQIMG0Lt36GiksjjqKKhZU60jxcnL89Upp5wSOhLJFkpeiIjIX0yf7icpC5m8iK92IiIVc/310KUL9OwJX38dOppw8vPh8899MmfrrUNHI5XFFlvA0Uf75IWql/4Ubxk54gg/L4hIKih5ISIifxFPHIRoG2nWzPehq/JCJDFycuD556FxY1/evWBB6IhSb/lyuO46aNcO/vGP0NFIZROL+fekCRNCR5I+vvvOt6SpZURSSckLERH5i3jyIkTlRfXqfpUTJS9EEqdePV95MHMmnHeen1cmm9x7L0ybBgMG+MlMRcqiSxd/q9aRP+Xn+9XITj45dCSSTZS8EBGRv4gnDpo3D3N+LZcqknj77QcPPADDhvkP89misBDuuQfOOAMOPjh0NFIZbb89dOig5EVcvGXkqKOgfv3Q0Ug2UfJCRET+orDQL3tWo0aY8yt5IZIcl17qy7xvuglGjQodTWpcd51vRevXL3QkUl7LVi/DBZ5wIhbzc8bMmhU0jLTwzTf+PVotI5JqSl6IiMhfFBaGaRmJy831k4auXRsuBpFMZAZPPQU77QRnngmzZ4eOKLlGjYJBg6BXLz+fjlQuP839iXPeOIe6d9el5YCWXDviWkZPH816l/q+p1jM377zTspPnXby86FaNejaNXQkkm2UvBARkb8oKAgzWWdcbq5f7WT69HAxiGSqLbeEwYNh8WI4+2z/u5aJ1q2DK6/07W/XXhs6GimLCXMmcObgM2nzSBvemvgGtzc4nSPZgUe+HMABTx9Ai/4tuOq9q/h86ucpS2S0aQMtW6p1ZP16n7w49ljYZpvQ0Ui2qRo6ABERSS/r1/uJ7U49NVwM8aqP0EkUkUy1xx7wyCNwwQXQuzf06RM6osR76in4z3/8B60ttggdjZTG+Nnj6fNpH9748XWOml2Lz+btw/5jZlJlxmsAPJWTw/Jt61Ow9Qr+U+PffLzVAN7abita7HUo+x14Gu33P5kqtZLzn23mqy8efxyWLYPatZNymrQ3erQfI9x5Z+hIJBspeSEiIhuYPRtWrw7fNgKa90Ikmf7+d/jsM+jbFw46CDp3Dh1R4ixc6Of1OPTQJCRiV62CMWNg9939Mi5SYd/O/Ja+n9zOghFDOHtSNZ6ZXIe685dCjR+gUye440RwDisooHZhIW0KCtitoAB+mEbO+sUwaAgwBDiXRfW2gBa51N15D3JatvRvZrm5/qt58wplsmIxv2LNBx/AiScm5rVXNvn5fj6seBuNSCopeSEiIhuIJwxCVjw0a+avcil5IZJcAwfC2LFwzjnw3XfhVhhKtNtu8wmMAQP835KEWbzYN/p/8gnk5MABB/isT+fO0Lat3yalNqbgC4Y8cQ3NRozhsUnGtkvB1aqKHXeszzodf7zvcypGDviJkWbMYMXkn/jhm2EUjP+EFb/+TNMFE2n5wc80XwxV127UVrLttn8mNIq73cT5wK9Ws9VWvnUkG5MX69f7OWQ6d4a6dUNHI9lIyQsREdlAYaG/DVl5UaMGNG6s5IVIstWq5ee/2Gcfv3LAp59C9eqho6qYiRPh4YehWzfYa68EHnjmTP+pbeJEv+bsggUwfDjcfLP/2n77PxMZRx8NW2+dwJNnkNWrmZg3kKlPP0D7r2fQdwWsrlUd69IFTj8L69y59D0ZVatCixbUatGC/Y7qxH7AijUreP/X97l14mDe+eltai9Yyh7L69C1VjsOI5dWf1SnytRp8P33PguxatWGx6xXb5PJjWotWnDccVszdKixbh1UqZLgf5s09/nn/tfgjDNCRyLZSskLERHZQDokL0DLpYqkSqtW8Oyz/kL39ddD//6hIyo/5+Cqq/zF8zvuSOCBf/kFjjkG5s2DYcP8ffAnmT0b3n8f3n0X3nzT/2NWqQIHHgjHHeeTGXvumeASkEpm1SoYOZLZzz1M7eEf0Hr5WprVMKYe2o7aF11HrS5dfSYtAWpVq0XXXbvSddeurFq7ipG/jWTwxMHcMOltFq38jC0bbkmsc4xTW3fn2JZHU2vhH/6Nr6Bgw9tJk2DECD/BRRGxWhfw6opnGHPo9RzYbsVfkxwNGmTs/3V+vv9v6tIldCSSrZS8EBGRDRQU+LFX6MnIcnPhiy/CxiCSLU45xa/MMWAAdOwYdsLeihg6FEaO9K+jQYMEHfTrr337gplvF2nffsPHt98ezj/ff61d62c0HD7cJzNuuMF/NWni52447jg46qjsqLlfsQLefx83eDDr3n6LqkuXUaMmvNumJtXPPJ9j/u9e2myZ3DlDalStQZedu9Bl5y6sXreaj/77EYMnDubNn9/k5R9epna12nTZuQuntj6Vzid1oXb1jd74nIP58zdIanSa9DtVn1zLkF925cAJPX0rUVFbbLHhPBvHH+8TWJW8pWjdOl+ldfzxUKdO6GgkW5lzLnQMKdW+fXs3duzY0GGIiKStzp1h7lzfBx/SzTfD3XfDypW+MliSy8y+dc61L3lPqah0HYusXu0nuPzxYj9HfwAAIABJREFUR/j2W1+RUZmsWuWXs6xRA8aPh2rVEnDQ4cN9Jme77Xx1RVn/UWbOhPfe88cZMQKWLPF/0Dp29H9sjzvOB50pV+qXLfOvdfBg3DvvYMuWsbh2VQbtvJaP29fjgPNu4sIO3alVLTFVFuW1Zt0aRhWOYvDEwbzx0xvMXT6XWlVrcVyr4zi19akc3+p4tqyx6bkvjjrK/9dOnAgsWlR85UZBAfz6q09u7LYb9OzpJ5epWTNFrzKxPvoIjjzSz3lRWZObUjlsbjyi5IWIiGygdWs/znr99bBxPPUUXHyxH/+FbmHJBkpepE46j0WmToV27aBpU19AkKBK/pS4+25f5DBihJ9yosJeeAEuvNCvKjJ8uK+wqIg1a+Crr3xFxvDhfs4F8DMUx+fKOPLIzU4YmZb++MO30gwe7F/bihWsqrcVw3avwSMt5vDrnk247tAb+Ue7f1Czavp9cF+3fh2fTf2MwRMH8/pPrzN76WxqVKlBp506cWrrUzlh5xPYquZWGzznoYd8pdIvv5SQz1qzxvda3Hefz6htuy1cdhl0757A0qDUuOQSeOklmDNHSw9Lcm1uPJLy+iUza2ZmH5vZRDP70cyujLbXM7ORZjY5ut0m2m5m9pCZTTGz781s7yLHOj/af7KZnZ/q1yIikmmc88mCkCuNxGm5VJHSW+/Wl7xTKTRv7j+gfP89XH55Qg6ZEjNnQp8+fgWICicunIN+/XwbyCGHwKhRFU9cgC8FOeQQn2X5z39g2jR48knfhvLqq3DSSVC/vk9g3H+/v6yfrhcZFy/2Pyhdu0LDhnDWWbgvvuC/Jx3O//XcmS0uW0zPk2px+pWP8/NVv9Jj3x5pmbgAqJJThcNyD2PgcQOZ0XMGn13wGZe0v4SxM8dy7pvn0rBfQ7q80oXnxj/HghULADjhBP/coUNLOHi1avC3v8G4cfDhh35m3Ftv9b9oPXrA5MnJfXEJsnatv6BxwglKXEhYIZqv1gLXOOdaA/sDl5pZa6AX8KFzrhXwYfQ9QGegVfTVDXgUfLID6A10APYDescTHiIiUj7z5vk25XSodFDyQqR0nvj2CY57+TgWrVyUkON17gw33QRPPw3PP5+QQybdDTf4i9z331/BA61fD9dc42cuPf10X0mQrPkpmjaFiy6CN97wf3w/+sjPNvr773Dttb6dpGVLf5V+6NC/TByZcgsWwHPP+dkat90Wzj0Xxo7F/d//MerFPuxz63bssPO7fNBiLU92fZrJl0+m2z7dqFG1Rti4yyDHcujYvCP9O/Vn6tVT+erCr7iiwxVMmDOBC96+gO3u245OL3Xiw4VP0brNWoYMKeWBzeCII/zP04QJcNZZ/hdsl1180uqLL9I3UYX/0Zw3T6uMSHgpT14452Y558ZF9/8AfgKaACcC8bfI54Gu0f0TgRecNxrY2swaAccCI51zC5xzC4GRQKcUvhQRkYyTLiuNgK+kNlPyQqQkVdcbn03+kA5PdWDSvEkJOea//gWHH+4/N//wQ0IOmTRjxvgOj549YccdK3Cg1av9nAQPPghXXOGrIWqk6IN39er+H/zee/2H28JCeOwxaNsWXnwRYjG/hOcxx/jlYCZNSs2H3XnzfA9fp05+3o8LLvDxXX456774nPzh97Hnrh9x2K83s3Ttcp7v+jyTLpvEP9r9g2pVEjHpSDg5lsP+TffnvmPu479X/pdvLv6Gaw64hskLJnPx0Iv5qf69fPrZOu774Fl+X/p76Q/cpo1PXBQWwo03+vWJO3aEAw7wrTfr1iXvRZVTfr7vZuqkT1oSWNBpb80sF2gHjAG2c87Nih6aDWwX3W8CTCvytOnRtk1tFxGRcoonCtKhbaRGDWjcWMkLkZL844N5zH5zR+oXzKHDUx34//buOzyqamvg8G8nEDqh9yogXUooAoJ4RRFQQMWLXK5gAUTs2FA/xOsVBVRUREEExI6KCIgQqhQVKaF5KQJC6C20QHoy+/tjTcikTBrJlLDe55lnJuecObNPmck+6+y9dui+0CteZ2AgfP01BAfDPfdIWgNf5HBInKFqVbkOzLWLF2UYhW++gTfflACBN0eHqFULHn4Y5s2T0S6WL5dcCUeOwNNPQ6NGUL++TPv5Z4iOzrvPPnlSAifdukl3maFDpXvDM8/Axo0k/b2Prwe3pvmWofSfO4AkRxJf3fUVux7dxaAWgygUUPAyLBtjaFOtDeO6jWPf4/vY8vAWBvcvi3UE8txHK6j6TlW6zurK5A2TOXbxWPZWWqWK9HU6dAgmT5ZA0T33SBKNDz6AS5fyd6OyKT5eGgf16eO3uUZVAeK1X2VjTEngB+Apa22k6zwrWUTzLJxsjBlmjNlkjNl0+vTpvFqtUkoVOL7U8gKkHBq8UCoLjRtT6lgEv34YwwtbS9Drq5688/s7XGlS9ipVYPZsuW4dOtQ3W7V/8YWMZDpu3BXkuTx5Ulo9/PILfPopjBrlW6N/FCmSOg/GgQPw0UeSXfnTT6UbR7ly0t9n0iTYty/nn3HsmFxAd+0qUeNHHpGcHKNGwZYtsG8fiW+8zueFd9JkSlMGzh1IYEAg3/b7lj8f+ZN/Nf8XgQGBeb7pvsgYQ8sqLZkx/BGqVLHcmvQhr9z4ChHRETy++HGqT6zODTNv4L0/3uPwhcNZr7BECXj0UWlN88MPEol74gkJYL30Ehw/nvU68tHy5XDunHYZUb7BK6ONGGMKAwuBJdbaic5pfwFdrbXHnd1CVllrGxpjPna+/sZ1ueSHtfZh5/RUy7njyxm+lVLK2x5/XC4GzudN1/krNnAg/P671NVV/tLRRjwnX+oiJ07Agw/C4sVsva4SPbud4tbOg5l6+9QrTpSYPIrH5MlyjeUrLl6Ea6+Va7x163LZUOLvv6F7d7l4//57aX3hT2JjpdvB4sWST2HPHplev74Mw9qjh4x/m9GwMYcPyy31OXNSci40bSrjYPbrd3kI14SkBL7Y/gVj145l/7n9tKjcgldufIW+jfoSYLzaiNvrhg2TAN/p0xJj2nV6Fz/s+oE5O+ew7eQ2AKqUrEKdMnWoHVw79XOZ2tQOrk2JoBLpV7xunQSr5s6VoXUHDpSWL82aeXgLJW/t/PkS4/NULyp1dfOpoVKNMQbJaXHWWvuUy/S3gDPW2nHGmFFAOWvt88aYXsBjQE8kOecka207Z8LOMCB59JHNQIi19mxmn6/BC6WUcq93b2l9sW2bt0siXn4Zxo+X+nmhgtcS2ado8MJz8q0uYi18/DF25EhiC1kG3RbLke7XM/efc6laqmquV+twSJPxJUvkGrdt2zws8xUYNUp+H/74A9q3z8UKNm+Wi/vEROl6cf312X7rpfhLlChcAuNLLTRAgjGLF8tj5Ur58SxWTFqW9OwpO2r1aglY/PGHvKdFCwlW3H23jJPtFJ8Uz6yts3jz1zcJPx9OSNUQXrnxFe649g7f224vWbhQRuBYskTSkbjae2Yv83bPY3fEbg5eOEj4+XAOXThEgiMh1XIVildwG9yoe8ZBqSkzYOZM6RrUvbsEMbp180jroLg4yc16113SyEcpT/C14MUNwFrgTyB5XK+XkLwX3wG1gIPAP621Z53BjslIMs5o4AFr7Sbnuh50vhdgrLU2y6+VBi+UUsq9Fi2kq0a2M6jns08+kTtb4eG+05WloNLghefke11kzx5JPLlxI9+0DOS1fpX44oEFtKmW+8N79iy0dt4u2rxZeil407590jBgwAAZACPHli+XUR7KlZMrz0aNsvW2yLhIngx9kllbZ1GsUDFqBddKd8GZ/HfVUlW92zIhJkYCFYsWSTDDtTtJSEhKwKJBg1Rvi02MZeaWmYz7dRyHIw/Tvnp7XrnxFXrU76FBizRiYmR02wcflJZJWXFYB8cvHr8czDh43vl8IeU5NjE21XvKFC1Di0I1GLIhkd7LDlH6XDQXGtbm3IgHKT14GGVLV86347JggQQuFy/WZJ3Kc3wqeOFtGrxQSin3goOlieikSd4uiVi2TO5mrVolLZ9V/tHgRe4ZY2oCnyPJxi0wzVr7vrvlPVIXSUiAsWOxr7/O0dLwwF2BPPjkLAY0H5DrVW7YIIMidO8uzci9mc+yTx9pWLBnj6QIyJHZs2HQIBmmMjQUqmcv3/vag2u578f7OBx5mBFtRlCkUJFUF6Gno1PnVQsKDKJm6ZpugxvVS1f3bHLLvXth40YZ1aJu3XSzYxJi+GTzJ4z/bTzHLh6jU81OjLlxDN2u6aZBi0z07SsBvYMHr7wxhLWWU1Gn3AY3jkUcoG9YNM+sg6an4Wgp+LhjEMtvqUe5qtdkeK5VLF4x18dv4ED5ipw4AYX9e/AY5Ucyq49oI1yllFKA5LmIjPStFg7Jo56Eh2vwQvm0ROAZa+1mY0wpIMwYs8xau9NrJSpcGF59FdOjB1UGDmDJzAO8s+tfjP6/LfzntnG5ahHQrh1MnCi5cd56C154IR/KnQ1Ll8od4XHjchG4eP99eOop6NJFIjBlymT5lrjEOMasGsOE3yZwTdlr+PWBX+lQs0O65aLiozh04VCqi83k14v2LeLEpROplg80gdQoXSPlQjM4dXCjZnBNggKDcriBmWjQIF0ri+Ryfxz2MRN+m8DJqJPcWPtGvrzzS7rW6apBi2zo3VtOpW3bZGTbK2GMoXLJylQuWZl21dulm2+t5WzMWQ6eD+e3hfOo+cm3vLZkLy+u3sPcDqcZ33YNfxZPPTRQsULFLufXyCi4UaVklQx/D2Ji5Ht2770auFC+Q4MXSimlAN8baQQkER+klE0pX+Qc6v248/VFY8wuZPh27wUvkrVvT6Ftf5I08imemzad7fe/xZMjNzD2qQWULlI6x6t79FFYu1by0XToIDEAT0pIkNhDvXrynG3WStbR8eOlu8jXX2dr3Mcdp3YwcO5Atp3cxtDWQ5nYfSIlg0pmuGyJoBI0rtiYxhUbZzg/NjGWQxcOZdhVYOWBlRyNPIp1GWzPYKhWqprb4Eat4FoUK5xBIs5suhR/iY82fsTbv7/N6ejT3Fz3Zr7t8i031tFIcU706iUtLhYsuPLgRVaMMZQvXp7yxcvDsBAY9l/YupVi77zDwNmzGbjaQfydd3JwSD921S2Z7lwLOx5GRHREqnUGBQZRK7hWuuBG1PbuXLpUSUcZUT5Fu40opZQC5M5R377SqriND3UeqF5dmqnPnOntkhRs2m0kbxhj6gBrgGauQ8EbY4YBwwBq1aoVctALETm7cCExg/9F4IWLvN+7MndPW0u9CunvxGfl4kX5jbh4UUbRrFw5HwrrRnLDifnz5Y53tiQkwJAh8Pnn8PDD8OGHEJj5sJ4O62DS+kmMWj6K0kVKM733dHo3zO4H5k58UjxHIo9kGNwIPx/O4QuHSbJJqd5TuURl6pSp47ZrSkYjWUTGRTJ5w2QmrpvImZgzdK/XndFdRtOpVqd83b6CrGNHiI8Hr15iHDkifT4//liaUXbpIsk9b789VR+vqPioTHNuXG4h9P1sgg5357t1v3F7o9uumqFwlfdpzgsXGrxQSqmMTZoETz4Jp05BxYreLk2KTp1keLaVK71dkoJNgxdXzhhTEliNJBGf6245r9ZFTp/m9L/vpOLS31h7TSECZn1Gp87/yvFq/vxTBq7o0EG6cWQRC8gTp09Lr4f27aUffrZ6NERFwT33SMbB//wHRo/O8o1HIo9w/7z7WXFgBXdcewfTe0+nUolKebMRVyDRkcixi8fcBjcOXThEfFJ8qvdUKF6B2sG1L7fcCAwIZPrm6ZyLPUevBr0Y3WU07WvkZqgW5Sp5OOHDh6FGDS8XJjISZsyA996DQ4dkPOGRIyXPS0ZD5qYRmxjL/44coGPjehRq9Q0x3e+ndnBthoUM46FWD1G5pAejleqqpMELFxq8UEqpjD3zDEyZInV9X+rmPHCgDHm/f7+3S1KwafDiyhhjCgMLgSXW2omZLev1uoi1nJo8gRLPvUQiDta/OIhbRn+KyWEGzlmz4IEH4P/+D/773/wpqqvhw2H6dNi+HZo0ycYbIiLkrvPGjfLjNmxYlm/55s9vGLFoBAlJCbx323s81Oohv8n74LAOTlw64Ta4cfD8QWISY+jTsA+ju4wmpFqIt4tcYOzcKaPfTJki56lPSEyUIXHffhvCwqBCBen3NWKEjH+aie++g/79YdnyRM5XnceUTVNYeWAlhQMKc1fju3ikzSN0qd3Fb74byr9o8MKF1ysMSinlo+6+G3btkkqYL3n5ZZgwQZKHFdJMTflGgxe55xzW/TPgrLU2y0wMvlIXubhrG4f73kSTPefY1Kku1/34O0EVq+RoHQ89JF268nsoxa1bZajWJ56QG8pZCg+XAoWHy+gifftmuvi5mHOMWDSC2f+bTYcaHfjizi+oV65eXhTdZ1hriU6IzrAriboy1koDhwYNZGRan2ItrFkjQYyFCyXXy6BB0hqjYcN0ix88KEHJXbukJ0pyq6q/Iv5i6qapzNo2i/Ox52lSsQnDQ4YzqMUggosGe3ijVEGWWX3Ei4NcKaWU8iUHD/pWss5kderIDaRjx7xdEqXc6gTcB/zDGLPV+ejp7UJlpVTjFjTacZIlD3alxboDXLi2Nud++j5H65g8Ga67Dv79b2mhnh+slS5t5crBmDHZeMP27ZKE4ORJWL48y8DFiv0raD6lOXN2zuH1m15nzQNrClzgAiTZowYu8ocxkoNlxQq4dMnbpUnDGBmu66efJCJx333w2WfQqJEUevVq9v9tmTBBRhSqUwd++UVGFXLtDtawQkPeve1djo48yszeMylRuARPhD5BtYnVGLpgKJuPb/baJqqrhwYvlFJKAb4bvEguU3i4V4uhlFvW2l+ttcZae521tqXz4Wv3XzMUUKgw3Wf8woqvxxJROIGyvf/J6SEDpKlTNhQrJi3T4+Phn/+U57z2/fdy43jsWChbNouFV6+WRIUBATIsyg03uF00JiGGp0OfptsX3SgZVJJ1D63j5S4vUyhAm3ipnOvdW87/pUu9XZJMNGoE06bBoUPsefR93ljRntZdS1GvvuGFF8A6LOPGwd698NJLGa+ieOHiPNDqATYM3cCmoZsY0GwAX/35FSHTQmg/vT2zts4iJiF7vx9K5ZQGL5RSShEVJd3D69TxdknSSy6TBi+Uyj+39X+J6HVrmNG5BBVnzCayWQPpJ58NDRpI15H16+H55/O2XNHR8Nxz0KKFDBiSqR9+kKGJqlaF33+HZs3cLrr1xFbafNKG99a/x2NtH2Pzw5tpU017Tanc69RJgmsLFni7JO7t3AmvvQbXdatEww+f4OXolylStxpvVxjHAeqwMaIuLxR5j/qVL2ZrfSHVQpjeezrHnjnG+7e9T2RcJA/Mf4DqE6szcslI9pzZk89bpK42GrxQSilF8qiNvtjyolYtedbghVL5K6TeDfT8eS9PPN2Ii6ePktS+HY7XX5d+W1no10/yUbz/vrTEyCtvvSXdUSZNymJEkylTZFSR1q3h119TfjjSSHIkMf7X8bT7pB3nYs4ROjCUD3p+QPHCxfOu0OqqVKgQ9OolaSWSkrJe3hOslZGBxoyRJLdNm8Krr0KZMvJdPXwY1u2vwjMnn6fOvPfle/P001CzpnQvmTRJMmZn0RKrTNEyPNH+CXaO2Mkvg3/hlnq38MGGD2g4uSHdPu/GDzt/ICEpwTMb7S+ioyXIOmmS7OsBAyQL8okT3i6ZT9OEnUoppVi8GHr2hN9+k67ivqZaNcm9N3Omt0tScGnCTs/x9bpIbGIsz3zzAF3Gz6b/Dkjq0J7AL76CepnngYiPlx4bO3dKo40GDa6sHIcOSSv3O+6Ab791s5C1cmX23//KyCLffgvFMw5EhJ8PZ9CPg1h7aC39mvRjaq+plC9e/soKqZSL77+X7lNZ9FjKV9bCli0SRJwzR7qABATId7NfP7jrLmmc5NaGDRLZWLky5UI6MFBaMrVpA23bynPz5hAU5HY1Jy6dYMbmGUzbPI1DFw5RrVQ1hrQawtCQodQo7e3xZD0sPl6iSBs3wqZN8rxjR0qUq1o1OXDHj8vfrVtDjx5SMWvf3jNjUfsQHW3Eha9XGJRSyhumTJHR044elf+hvqZjR0mQvnKlt0tScGnwwnP8oS5ireW9de+yeeKzfLjIUDKwGAHvvQ8PPpjpWMqHDkGrVlCjBvzxh+TEyK1774X582H3bjetwhIT5Yfrk0+kXB9/nOGQRNZaPtv2GU8sfgJjDJN7TObf1/1bh3lUeS4yUkYkfeopGSXLU6yV6+HkgMWBA3K9e9NNErDo2xcqV87FSo8dS7nYTn4+e1bmBwVJf67kYEbbttC4cboL7SRHEov2LmLKpimE7gslwARwR8M7GNFmBDdfczMBpoB1BEhMlMSorvtt27aUhEDly2PbtuV80/rsqFOMVRUusSJuF5fiLnLrpSrc8lcizTcfptyWvzBJSdIXqXt3CWbcdluWw9wWBBq8cOEPFQallHIVFyd9VJcuhY8+kvpBXhs1Ct59V1qGBvhgPeJf/5ILof37vV2S7Fu3Dj78UJrSt2vn7dJkTYMXnuNPdZEl+5bwzPR7mDInls5/J0CfPpLwL5MKdHJLrocegunTc/e5a9bIAAljxkgz93RiYiS6sWCBjKf83/9mGFSJiI7g4YUPM3fXXLrU7sLnfT+ndhkf7B+nCozu3aUr5u7d+fs5Dof8X5wzR9K9HDoksbtu3SRg0aePBFLylLXSh9M1mBEWBhedOTKKF5dWA64tNOrXv1yx2H9uP9PCpjFjywwioiOoX64+w0OGc3/L+/2zFZTDAfv2pd4fW7ZIlxCA0qUhJISL1zViV91SrKkYxfKkvWw6HsaZmDMABAUG0bJKS4KLBLM7YjeHIw8DEBwDPcML0f9QKW7cFUOZ87EAxLRsRtDtfQjsdbvs4wLYKkODFy78qcKglFLr16eMt162rCTWnDhRbjbm5U3DAQPkf+6+fXm3zrz00kvS9z021vf/T0dGSnk/+kjqeYUKwZtvwsiRvhkYSqbBC8/xt7rInjN76PPVHdy+aB/jVgYQWLYczJgh3TTcePlleOMN6cI9eHDOPi8pSa55zpyRC8B0vUDOnpWhHZL7iz/2WIbrWbx3MQ8ueJCzMWcZ+4+xPH390wQG+PgPiPJ7H34op+Tu3dCwYd6uOylJuncmByyOHZMGELfeKgGL3r2zMSJPXnM4YM+elIv35Av4WLnYJjgYQkJStdCIq1aZH3bPZcqmKfx66FeKBBahf7P+PNLmEdpXb++braKslaiUa4uKsDC4cEHmFysGrVsT3aIpe68J5rdKcSwJ2M/GE2EcvyTdQQJNIM0qNaNttba0qdaGttXb0qxSM4ICU7rfRMZFsuv0Lnae3imPiJ3sOrmDMrsP0mMf9NwL1x+BQAuRpYIIb9eQ6FtuJLj3P7mmQTuKFCrijb2TpzR44cLfKgxKqatTTAyMHi2tIapXl1bRbdvCoEHw88/Qv79MK1Uqbz6vQwcoUQKWL8+b9eW1adPg4YflzlLNmt4ujXvz5kml9dgxePxxeOYZaT78449yN+6zz3LRdNdDNHjhOf5YFzkfe54BPwzgyG+hLA4tT40DZ2DYMHjnHShZMt3yiYlwyy0SgF2/XrrHZ1fy9332bPmtS+XwYWk6vW8ffPGFJBhIIyo+iueWPceUTVNoVqkZX931FddVvi6HW6xU7hw6JN2c3noLnn32yteXmCg5NL7/HubOhZMnpRvlbbdJwOL22yU+4FMSEyWng+uF/vbtkOBM2lmxogQy2rQhvEFFPiaMD4/M5WL8RVpWacnwkOEMvG4gJYPS/7Z4zPHjqVtUbNokw7IBFC4MLVoQ17oF+68py+9VElla5BDrT4Rx8IJkQDcYGlVoJEEKZ7CiZZWWFCucu750UfFR7I7Yzc7TOznwdxjFV/3KNev3cMOOi1SKAgewsQZsaF6ewzc0p0i7jjSu3JQmFZvQsHzDXH+uN2jwwoU/VhiUUleXtWulufXevTB8OIwfLy0PQW5wTJggdzXr15e7Lzm5KHCnWjXpTjljxpWvKz8sXSoX/2vWQOfO3i5NekePSrDixx/leHzyieTYArlZM3WqJHAvUwa+/FKa9foaDV54jr/WRZIcSYxaPopJa9/ms7Ca9A89gqlXT4II11+fbvkTJyT/RXCw1P2zE2w9f14SfTZuDKtXp2lhtnOn/BBcuCDJMG66Kd37NxzdwH0/3sfeM3sZ2WEkr//jdYoWKnoFW61UzrVqJef7mjW5e39CAqxaJf/jf/wRTp+WFkg9e8qgOj17Zhgz9G1xcRLAcA0G7NghFRvAUb0aB+tX5OfgkywofYI9dUrS6/pBPNL2EZpVcj/scZ6IiJBWFK7BimPHZF5gIDRtSkLrlhxqUIn1VZNYWvw4605vTjUU7DVlr0lpUVGtLa2qtqJ0kdL5W24gJi6Kw7/MI/aneZT9ZR3Vdx8lwMKpEhBaDxY3gGX1oEz1ejSp2CTVo1GFRt4NELmhwQsX/lphUEoVfJcuSXeDyZOhTh3pK/6Pf2S87OrV0t37wgXpnnD//bn/3Lg4uYvz2mvS2sMX7dkjzW8//1xGFPMVDofkCBw1SnJxjRkjrS0KF06/7Pbtcsx274YXXpD9ndFy3qLBC8/x97rI59s+Z+hPQ7nzZDk+n2cIOnZSIqqjR6c7qVetgptvlguub77Jurvb00/LQAdhYXIBeNnvv8st5iJFJKlGy5ap3pfoSOSNtW/w2urXqFaqGp/1/Yyb6qYPbijlCWPGwOuvSyuJ7OadiI+HFSskYDFvnvSOKllSTvt+/aSlRYkS+Vtuj4uKgq1bUwcN9qQEBPaXhY3VIKJpHRp2H0jnvk9SpFzFK/vMyEj5gXENohw4kDK/YUOSQlpz9NqqbKphWFrqFL+f2cqO0ztwWAm01ChdI1WLijbV2lC15S0PAAAad0lEQVSuWLkrK1deiYiAJUtIWvQzNnQxhc6exxFg2Fu/HEsbBvJl9bNsrJSIdXZjrR1cO11Qo3GFxgQX9V5zHg1euPD3CoNSqmBauRKGDJE8WI8/Ln3Fs6qknDghiSx/+UUS7U+enLvM/nv3wrXXSpeGQYNyVfx8Fxsr2+ZLAZYdO6TV/O+/y8XZ1KnSGiYzUVHw5JPSwuX66+Virk4djxQ3Sxq88JyCUBf548gf3PntnQREXuSPbW2pOX+VNAP/8st0Hf3ffDMlMPvoo+7XuWsXXHddysAhly1YIP1HataEJUugbt1U79t7Zi/3/Xgf64+uZ2DzgUzuOZkyRcvk3cYqlUNhYfJ1yOr/amwsLFsmAYv58+WGROnSkruiXz/JZXElI/b4pfPnYfNm2LiRuPW/EbvuV4JPnAPAYSCiRjmKdbyRUh27yk5u2dLt8MhER0v+jU2bUoIVf/2VMr9uXRwhIZxsXJMt1QNZVuYMay9sZ/vJ7SQ4pItLxeIVaVu9LW2qSo6KNtXaUKVklXzeCXkkKUm2e9EiCfpu3AhAYsUKHO3UnLAWlVh8TRKbYvaxO2I3sYmxl99avVT1dEGNJhWbeCRIo8ELFwWhwqCUKjgiI+G556SP97XXykVtTsaGT0qSTPyvvy6V/u+/l/XkxPLl0jd91SrJ7u+rfKVrS2wsjB2b0p1n4kRpDZKT/GLffiuBD2Oki8k99+RfebNLgxeeU1DqIkcjj9L3276EHQtjjunPne8vxcTEwNtvwyOPXP5SOBxyMbZ0qSQbzGjEJGvlzvL69RJQrZh8c3X6dEmAERIiCX8qVnR5j2Va2DRGLh1JkcAiTOk1hf7N0ibJUMrzrJXhgjt0kMCEq5gYCA2V6T/9JAN1lCkjw5n26yfdCov4f87FPOU4dZItC6fzV+iXlNy+mzZHodolmWcDAzFNm6YkBIWUxKEu3VKoVg3btg0RjeuwvVYRVpQ9z+roHWw5voWYxBgAgosEp2pR0bZ6W2qWrumbCURz49QpCQAvWiTP585Jt5gOHXDc1p2jN7Rga2XLzohd7IzYeTlpaHRC9OVVVC5ROV1Ao3OtznmaDFmDFy4KSoVBKeX/QkNh6FDpVvnMM/Cf/+T+DktoKPz739LsdMaMnF0MT58u5QgPlyRjvqpDB7m5smKF98qwerUEHfbskYDFO++kupbKkf37ZZSXDRtkne++6/7mkSdo8MJzClJdJCYhhiE/DeHrP79meNXeTJ4TReCyFRKJmDkTqlYFpAl869byns2boVyam3c//SQBjnfflSS3WCtRwtGjJc/FnDmpOvmfvHSSIT8NYeGehXS7phuz+syieunqHtpqpbL2yCOSDiYiQvJXLl4sp/HPP0srvPLl4c47JWBx000yaojK2pHII3wS9gkLfplKrb2nuPlMML0uVKLu3ggCzkkLDcqXx7Zty/lmDdhRuyirKlxiRdwuwo6FcTFehnUtUbgErau2ThWsqFeuHgHGh4cFy0uJiVIBWbxYghmbN8v0qlXlTlGPHnDLLThKl+LwhcMpo584R0DZeXonkXGRFC1UlEsvXtLgRX4pSBUGpZR/OndOhs2cNQuaNJH6fXJyxytx+LAk3v/jD+l68vbb2asMjR4tzbpjY2VYT1+VfKH/99+e/+yzZ+H55yUwVLeudBG59dYrX298vOz/CROgaVNpkdG06ZWvNzc0eOE5Ba0uYq1lwm8TeHHFi7Sq0pLlF++i7Oix0vdt2jS46y5Avr833CCxiPnzU4YOjouDZs3k92f7digckCT9qz78UKKyM2ak+jGbv3s+Q34awqX4S0zoNoFH2z169VxwKL+xeLEk1uzUSa4LY2KgUiX5OvTrJy0dffl/rq9LSEpgwV8LmLJpCisOrKCwKcTD5btTu2wdliftZdPxMM7EnAEgKDCIllVapkqo2ahCIx062dWJE3InbPFiaZVx4YKcoB07yonco4dkJHe2QrHWcuziMQ5eOEjHmh3ztCiZ1kestVfVIyQkxCqllLfMn29t1arWBgZa+/LL1sbG5u364+Ksfeopa8Hadu2sDQ/P+j333WdtrVp5W478MGqUtYUKWZuY6LnPdDisnT3b2kqV5Jg9/7y1UVF5/zmhofIZRYtaO3WqfK6nAZusD/yfvhoeBbUu8tNfP9lSb5Syld+qbDev/NrakBD5MRo82Nrz56211n7wgUwaNy7lfePHy7TQUGttTIy1/frJhGeftTYp6fJykbGR9qH5D1lexbaa2sruOLXDsxuoVA7ExFhbubL8z3/sMWtXrfLs/6+rye7Tu+3ToU/bsuPK2sD/BNqWU1vaIfOH2I83fWzDjoXZuMQ4bxfRvyQkWLt2rbUvvmhty5byewzWVq9u7ZAh1s6da+2FC/n28ZnVR7TlhVJKeUBEBDzxhCRobNECPv00TSb9PPbDD5L0LjBQmq326uV+2S5dJJC+enX+lScvfPyxDB176JDk7ctvBw/CiBHSmrJNG8lNkWaAgzx14oQkdlu2TO7KffKJ9IP2FG154TkFuS6y8/ROen/Tm8ORh5nW/UMGLzgoGYhr1oTPP8d27sK998pv1MqVMizqtddKs/kFX1yAPn3kx+idd6SJmtNvh35j0LxBhJ8PZ1SnUYzpOoagQG1nr3xbbKw0GgrQhkEeEZ8Uj8M6dHjkvHbsmLTKWLRIKimRkdIqo3NnaZHx2GN5mlk2s/qIfpWUUiofWQvffSfdQ+bMkdEyNmzI38AFwN13S7bzWrVkmLWXXpLujRk5eNC3c10kSx6VIzw8fz8nMVH63TdpItdQ774rXXHyM3ABUKWK1A3GjZNh8lq2hHXr8vczlcprTSo2YcPQDXSu1Zn7Fw1l5A1RJK5ZJRXdrl0xo15g+odx1KsnQwePGCHdRia+cFIiqb/9JiOWOAMX8UnxvLziZbrM6oK1ljX3r2HszWM1cKH8QtGiGrjwpKDAIA1c5Idq1eSO2Jw5cjdu1SpJ1hYRIf1ePZiwRb9OSimVT06ckDvo/ftLcCAsTPIbeOo3vn59ufgdOlRyWnTrBsePp14mMRGOHtXgRbItW2QI05Ej5U7wjh2SPDDQQ91iAwLghRdg7VppDdO5sxy75GTpSvmDcsXKsXjgYh5v9zjv/vEuvQ68zrl1v8BDD8GECZTq1p45b+7l/HkJ1D09+Az1B7aXhDY//wwDBwKw6/QuOszowBu/vsH9Le5n2/BtdKrVyctbp5RSV7HChSVhy7hxkqRo3z7PVZIATROjlFJ5zFr46ivJNxcVJb/vzzzjncRcxYpJvrzOnaXLRatW0nXlpptk/pEjMtxqcmDAl9WqJc/z5knQJShIHkWKpLxO+7e7eWn/z0ZFyZCz774LFSpI4sx77snZ8Kd56frrJZDy8MPSamblSun+U8VPhpZXqnBgYSb1mETzSs15dNGjXP9tNxa8uYCGd9wBQ4bQ/F/NmdX/B6bv7sT/zQ2BwGi5m9emDQ7rYPKGybyw/AVKBpXkx/4/0rdRX29vklJKqbSCgz36cZrzQimlMmGtNGmOioLo6NTPGU2LjpZWz6GhMrTnzJnQqJG3t0Ls2CEtQfbske4rL74od/i7dpUujN26ebuEWWveHP73vytfT0BA6sBGXJx04Rw6FMaPh7Jlr/wz8oK1MtDCE0/IKJGffy4jUOYHzXnhOVdbXWTtwbXc/d3dxCfF883d39CjdGsYMgQWLpQF6taV7PYNGnA08igPzH+AZfuX0atBL6b3nk6Vkhq1U0qpq0Vm9RG/b3lhjLkNeB8IBKZba8d5uUhKKQ9yOCRgkFEQISfP7uZFR+e8yX7p0jBxolxwerAlXZaaNoWNG+Vu/v/9H/z6K9xyi8zzh24jAJs2ybCl8fEScIiPz/x1dpdLSpIRGbt08fYWpmaMXON17Cjdj3r0gGefhbFjPdrFVKkr0rl2ZzYO3Uif2X24/ZvbmdBtAiPnz8fMnClBiw8+gCpV+G7HdwxfOJy4pDim9prKsJBhGG81f1JKKeVz/LrlhTEmENgD3AIcATYCA6y1O92952q726GUr0tIkIvRiAg4c0aeXR/J0y5ezDjgEBub888sWhSKF5dHiRLySH7t7jk7yyQ/Fy6c9/spL1krI3c8+aRcuIPsxyJFvFsulbmYGMnFMXUqtG0rebOSu9LkBW154TlXa10kKj6KwfMG88OuHxjUYhAf3/4xRQsV5XzseR5f/Dhfbv+S9tXb88WdX9CgfANvF1cppZQXFOSWF+2Afdba/QDGmNlAH8Bt8OJqkjIorzySpzkc6efl9pHZHem0N0uy+jujaQEBMi0gwP3rtNOSH77EdX/l9DlZ2m1z/Tuz6Zktm7aMmf2dnWWshUuXMg9CpP37/Hn3+61ECShfXh6lS0PlyjkLImQUeChWzLdaQ3iDMZL/om1byetQpIgGLvxBsWIwZYp073ntNelGopQ/KRFUgu/u+Y7X17zOmFVj+CviL57t+Cwjl4zk2MVj/Kfrf3ip80sUCvD36qlSSqn84O//HaoDh13+PgK099SHP9ntUyateODy3waH89lisFf82l7+K+Xhbnrq+TqIjMGBwRKAg4AMXruTvA9zMz/5GDgISPN8lV8pOxUnivJEUM6coTwR1CKC8iaC8gHyd3kTQQWbMr8CZygWFwvHkIcL1yNojftpaac7DESlmQZcPqrGup/m7m930zKTVXs3m4Pgm3GzsoxWkXbZ64CttijRFCch8Gz2PzSb0m5H2qK6O05ZvTcn+yc/2hbmdWw0szJmtK23ADdbQ/TxdVDOY//ylMoTASaAV258hWaVmnHfj/dxz/f30KBcA35/6HfaVW/n7eIppZTyYf4evMgWY8wwYBhArTxsY9vuljIMPjEdC9jLV1Apl8bWmlR3ppOXsRjna5t+eVyuxIwzmGGcU428lqpz8nTnvAB5vrw2Q+r3gfO9ruu0znnJ73W4LGdTf3bazzMSDDDG2aTAbVOHlG12ZTHpLgAcJvW05P1hbYBzHwfgsFLSJBtweT9aApwtG5yhCSsPBwFgDQ6XZRwEYK1xH8CwKfvp8nFIt0XW7cwAHGC4vG8uh5WMgwCT8tpY5zMQYByp969xBltcjkPK0XXuT0vKNNfzBlz2y+XL7cvnaPJ7kvevSXfl6zxHkk8jY12mOqelaW6Reh2WooGxBBc5T3CQ81EkktJB5ylaKC71eeLy2rq8jjVFOUoNjlIjg/PKuhQm+bXFWLDJM2xGx8/dspd3x+XPSi6Lcb42rqsJSNmnycsmFzH5nLZZNPtJu/9yJIPvWkafmfxX8jngOtua9N+9TD8yq/LkZh5p9kO6JjyZLJvJdze7Zchwaqa/YxnIowhGZj83WZ0rLcpWyptCKOUFdzW+iwblGhC6L5QRbUdQIqiEt4uklFLKx/l78OIoUNPl7xrOaalYa6cB00D6mebVhw984U4GvpBXa1NKKaWUuno0r9yc5pWbe7sYSiml/IS/9y/YCDQwxtQ1xgQB9wILvFwmpZRSSimllFJK5SG/bnlhrU00xjwGLEGGSp1prd3h5WIppZRSSimllFIqD/l18ALAWrsIWOTtciillFJKKaWUUip/+Hu3EaWUUkoppZRSShVwGrxQSimllFJKKaWUT9PghVJKKaWUUkoppXyaBi+UUkoppZRSSinl04y11ttl8ChjzGngYB6usgIQkYfr8yW6bf6noG4XFNxtK6jbBbpt/qa2tbaitwtxNciHuggUzHPSl+n+9jzd556l+9uzdH+ncFsfueqCF3nNGLPJWtvG2+XID7pt/qegbhcU3G0rqNsFum1KeZKek56l+9vzdJ97lu5vz9L9nT3abUQppZRSSimllFI+TYMXSimllFJKKaWU8mkavLhy07xdgHyk2+Z/Cup2QcHdtoK6XaDbppQn6TnpWbq/PU/3uWfp/vYs3d/ZoDkvlFJKKaWUUkop5dO05YVSSimllFJKKaV8mgYvsskYc5sx5i9jzD5jzKgM5hcxxnzrnL/eGFPH86XMOWNMTWPML8aYncaYHcaYJzNYpqsx5oIxZqvz8Yo3yppTxphwY8yfzjJvymC+McZMch6z7caY1t4oZ04ZYxq6HIutxphIY8xTaZbxm2NmjJlpjDlljPmfy7Ryxphlxpi9zueybt472LnMXmPMYM+VOmtutustY8xu5/n2ozGmjJv3ZnruepubbXvVGHPU5Zzr6ea9mf6WepubbfvWZbvCjTFb3bzXp4+bKrh8/XtVkGSn3qTynjEm0BizxRiz0NtlKeiMMWWMMXOc9ZVdxpgO3i5TQWeMedr5e/I/Y8w3xpii3i6Tr9JuI9lgjAkE9gC3AEeAjcAAa+1Ol2VGANdZa4cbY+4F7rTW9vdKgXPAGFMVqGqt3WyMKQWEAX3TbFtX4Flr7e1eKmauGGPCgTbW2gzHTHZeXD0O9ATaA+9ba9t7roRXznluHgXaW2sPukzvip8cM2NMF+AS8Lm1tplz2gTgrLV2nLMiXtZa+0Ka95UDNgFtAIucuyHW2nMe3QA33GzXrcBKa22iMWY8QNrtci4XTibnrre52bZXgUvW2rczeV+Wv6XeltG2pZn/DnDBWvtaBvPC8eHjpgomf/heFSTZqTepvGeMGYn8vy/tD3Ubf2aM+QxYa62dbowJAopba897u1wFlTGmOvAr0MRaG2OM+Q5YZK2d5d2S+SZteZE97YB91tr91tp4YDbQJ80yfYDPnK/nADcbY4wHy5gr1trj1trNztcXgV1Ade+WymP6IBco1lr7B1DGWSnxJzcDf7sGLvyNtXYNcDbNZNfv02dA3wze2h1YZq096wxYLANuy7eC5lBG22WtXWqtTXT++QdQw+MFywNujll2ZOe31Ksy2zbnb/o/gW88WiilMufz36uC5CqvN3mFMaYG0AuY7u2yFHTGmGCgCzADwFobr4ELjygEFDPGFAKKA8e8XB6fpcGL7KkOHHb5+wjp/1FdXsZ5cXIBKO+R0uURI11dWgHrM5jdwRi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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "an_dataframe = {\n",
    "    \"x\": [2.5, .5, 2.2, 1.9, 3.1, 2.3, 2,   1,   1.5, 1.1, 2.5, .5, 2.2, 1.9, 3.1, 2.3  , 2,   1,   1.5, 1.1,],\n",
    "    \"y\": [2.4, 0.7,2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9, 2.4, 0.7,2.9, 2.2, 3.0, 2.7  , 1.6, 1.1, 1.6, 0.9,],\n",
    "    \"z\": [4.0, 0.8, 5,   10,  9,   1,  1.8, 9,   5,   6,   100, 150,200, 1000, 75, 10000, 250, 220, 990, 100]\n",
    "}\n",
    "\n",
    "an_data = pd.DataFrame(an_dataframe)\n",
    "\n",
    "an_fig = plt.figure(figsize=(18,8))\n",
    "an_subplot = an_fig.add_subplot(221)\n",
    "an_subplot.plot(an_data.index.values, an_data[\"x\"], 'g', label=\"X\")\n",
    "an_subplot.plot(an_data.index.values, an_data[\"y\"], 'r', label=\"Y\")\n",
    "an_subplot.plot(an_data.index.values, an_data[\"z\"], 'b', label=\"Z\")\n",
    "#subplot.set_ylim(0, 20)\n",
    "an_subplot.set_title(\"Anomolous Raw Data\")\n",
    "\n",
    "an_subplot.set_xlabel(\"Time\")\n",
    "an_subplot.set_ylabel(\"Data\")\n",
    "an_subplot.legend()\n",
    "\n",
    "# Plot the normal data\n",
    "an_subplot = an_fig.add_subplot(222)\n",
    "an_subplot.plot(data.index.values, data[\"x\"], 'g', label=\"X\")\n",
    "an_subplot.plot(data.index.values, data[\"y\"], 'r', label=\"Y\")\n",
    "an_subplot.plot(data.index.values, data[\"z\"], 'b', label=\"Z\")\n",
    "#subplot.set_ylim(0, 20)\n",
    "an_subplot.set_title(\"Normal Raw Data\")\n",
    "\n",
    "an_subplot.set_xlabel(\"Time\")\n",
    "an_subplot.set_ylabel(\"Data\")\n",
    "an_subplot.legend()\n",
    "\n",
    "# Normalize the dataset.\n",
    "an_standardizedFrame = StandardScaler().fit_transform(an_data)\n",
    "# `StandardScaler` returns a `numpy` array so need to re-create  the data-frame from the standardized data for pretty printing. \n",
    "# We technically don't need the standardized data to run PCA.\n",
    "an_standardized = pd.DataFrame(an_standardizedFrame, columns=[\"x\", \"y\", \"z\"])\n",
    "an_subplot = fig.add_subplot(222)\n",
    "an_subplot.plot(an_standardized.index.values, an_standardized[\"x\"], 'g', label=\"X\")\n",
    "an_subplot.plot(an_standardized.index.values, an_standardized[\"y\"], 'r', label=\"Y\")\n",
    "an_subplot.plot(an_standardized.index.values, an_standardized[\"z\"], 'b', label=\"Z\")\n",
    "#subplot.set_ylim(0, 2)\n",
    "an_subplot.set_title(\"Standardized Data\")\n",
    "\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Data\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Run the PCA\n",
    "pca = PCA(n_components=3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect PCA for original and anomolous data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Variation (Raw Data):  [0.65926364 0.31606027 0.02467609]\nVariation (Anomolous):  [0.66399376 0.31251942 0.02348682]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Run the PCA\n",
    "raw_pca = PCA(n_components=3)\n",
    "\n",
    "raw_principalComponents = raw_pca.fit_transform(standardizedFrame)\n",
    "\n",
    "# Retrieve the two components.\n",
    "raw_principalDataFrame = pd.DataFrame(data = raw_principalComponents, columns = ['PC1', 'PC2', 'PC3'])\n",
    "\n",
    "\n",
    "# Check the variation.\n",
    "print (\"Variation (Raw Data): \", raw_pca.explained_variance_ratio_)\n",
    "\n",
    "# Run PCA on anomolous\n",
    "an_pca = PCA(n_components=3)\n",
    "an_principalComponents = an_pca.fit_transform(an_standardizedFrame)\n",
    "\n",
    "# Retrieve the two components.\n",
    "an_principalDataFrame = pd.DataFrame(data = an_principalComponents, columns = ['PC1', 'PC2', 'PC3'])\n",
    "\n",
    "# Check the variation.\n",
    "print (\"Variation (Anomolous): \", an_pca.explained_variance_ratio_)\n",
    "\n",
    "raw_percent_variance = np.round(raw_pca.explained_variance_ratio_* 100, decimals =2)\n",
    "columns = ['PC1', 'PC2', 'PC3']\n",
    "raw_pca_fig = plt.figure(figsize=(18,8))\n",
    "raw_pca_subplot = raw_pca_fig.add_subplot(221)\n",
    "raw_pca_subplot.bar(x= range(1,4), height=raw_percent_variance, tick_label=columns)\n",
    "raw_pca_subplot.set_ylabel('Percentage of Variance (Raw Data)')\n",
    "raw_pca_subplot.set_xlabel('Principal Component')\n",
    "raw_pca_subplot.set_title('PCA (Raw Data)')\n",
    "\n",
    "\n",
    "an_percent_variance = np.round(an_pca.explained_variance_ratio_* 100, decimals =2)\n",
    "columns = ['PC1', 'PC2', 'PC3']\n",
    "an_pca_subplot = raw_pca_fig.add_subplot(222)\n",
    "an_pca_subplot.bar(x= range(1,4), height=an_percent_variance, tick_label=columns)\n",
    "an_pca_subplot.set_ylabel('Percentage of Variance (Anomolous)')\n",
    "an_pca_subplot.set_xlabel('Principal Component')\n",
    "an_pca_subplot.set_title('PCA (Anomolous Data)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting FFT of the raw data.\n",
    "* Instead of running PCA on the raw data we will run PCA on the energy associated with different frequencies on the raw data.\n",
    "* We apply FFT to the raw data and get energy levels at 100 different frequency intervals ranging [0, F), where is the sampling frequency of the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import scipy as sp\n",
    "import scipy.fftpack\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "fft_data = pd.DataFrame(dataframe)\n",
    "fft_fig = plt.figure(figsize=(18,8))\n",
    "fft_subplot = fft_fig.add_subplot(221)\n",
    "fft_subplot.plot(fft_data.index.values, fft_data[\"x\"], 'g', label=\"X\")\n",
    "fft_subplot.plot(fft_data.index.values, fft_data[\"y\"], 'r', label=\"Y\")\n",
    "fft_subplot.plot(fft_data.index.values, fft_data[\"z\"], 'b', label=\"Z\")\n",
    "fft_subplot.set_title(\"Raw Data\")\n",
    "\n",
    "fft_subplot.set_xlabel(\"Time\")\n",
    "fft_subplot.set_ylabel(\"Data\")\n",
    "fft_subplot.legend()\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fft_x = sp.fftpack.fft(fft_data[\"x\"].values.tolist(), 100)\n",
    "fft_x_abs = np.absolute(fft_x)\n",
    "fft_y = sp.fftpack.fft(fft_data[\"y\"].values.tolist(), 100)\n",
    "fft_y_abs = np.absolute(fft_y)\n",
    "fft_z = sp.fftpack.fft(fft_data[\"z\"].values.tolist(), 100)\n",
    "fft_z_abs = np.absolute(fft_z)\n",
    "\n",
    "\n",
    "# Build a new data-frame with the fft\n",
    "fftEnergyDataFrame = {\n",
    "    \"x\": fft_x_abs,\n",
    "    \"y\": fft_y_abs,\n",
    "    \"z\": fft_z_abs\n",
    "}\n",
    "\n",
    "fftEnergyData = pd.DataFrame(fftEnergyDataFrame)\n",
    "fft_energy_fig = plt.figure(figsize=(18,8))\n",
    "fft_energy_subplot = fft_energy_fig.add_subplot(221)\n",
    "fft_energy_subplot.plot(fftEnergyData.index.values, fftEnergyData[\"x\"], 'g', label=\"X\")\n",
    "fft_energy_subplot.plot(fftEnergyData.index.values, fftEnergyData[\"y\"], 'r', label=\"Y\")\n",
    "fft_energy_subplot.plot(fftEnergyData.index.values, fftEnergyData[\"z\"], 'b', label=\"Z\")\n",
    "fft_energy_subplot.set_title(\"Frequency Energy Data\")\n",
    "\n",
    "fft_energy_subplot.set_xlabel(\"Frequency\")\n",
    "fft_energy_subplot.set_ylabel(\"Energy\")\n",
    "fft_energy_subplot.legend()\n",
    "\n",
    "\n",
    "# Normalize the dataset.\n",
    "fft_standardizedFrame = StandardScaler().fit_transform(fftEnergyData)\n",
    "\n",
    "# `StandardScaler` returns a `numpy` array so need to re-create  the data-frame from the standardized data for pretty printing. \n",
    "# We technically don't need the standardized data to run PCA.\n",
    "fft_standardized = pd.DataFrame(fft_standardizedFrame, columns=[\"x\", \"y\", \"z\"])\n",
    "fft_energy_subplot = fft_energy_fig.add_subplot(222)\n",
    "fft_energy_subplot.plot(fft_standardized.index.values, fft_standardized[\"x\"], 'g', label=\"X\")\n",
    "fft_energy_subplot.plot(fft_standardized.index.values, fft_standardized[\"y\"], 'r', label=\"Y\")\n",
    "fft_energy_subplot.plot(fft_standardized.index.values, fft_standardized[\"z\"], 'b', label=\"Z\")\n",
    "\n",
    "fft_energy_subplot.set_title(\"Normalized Frequency Energy Data\")\n",
    "\n",
    "fft_energy_subplot.set_xlabel(\"Frequency\")\n",
    "fft_energy_subplot.set_ylabel(\"Energy\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similar to the raw data, lets analyze the FFT of the anomolous data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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CFWa2n+D3+qcEn8Op4fAlEEwGfzzB1Zh/5sjflWuAr1kw/Mjl4T7KCXqjb4rJJzJl2eHDMomIyFgysx8TTML3Nc85/hW4yDn3Tz5zDMbMniGY4OV/Yp77OlDsnLtg4C1FREREZCpTm1pGK7yCcyOQETumuJntJBgW5O/ewokkCfWUFhFJMuHlap8B7vSdJZaZnW5m+eGlrR8Bjiamt094SerHmWC5RURERCT5TNQ2tQzMzN4TDrcxh6CX9R/7FKTfRzDW88O+MookExWlRUSSSDh+XgPBGGa/9BynrxKCS0GbCS5xfX/vjNdm9kmC4TUecM497i+iiIiIiCS7Cd6mloF9CqgHdhLMZ/PKsCJm9ihwB3BJOJ64iIwxDd8hIiIiIiIiIiIiIuNGPaVFREREREREREREZNyoKC0iIiIiIiIiIiIi4ybVd4DhyMnJcYsXL/YdQ0REREQS7IUXXmh0zuX6ziHjT218ERERkalpsDb+pCpKL168mOeff953DBERERFJMDMr951B/FAbX0RERGRqGqyNr+E7RERERERERERERGTcqCgtIiIiIiIiIiIiIuNGRWkRERERERERERERGTcqSouIiIiIiIiIiIjIuFFRWkRERERERERERETGjYrSIiIiIiIiIiIiIjJuVJQWERERERERERERkXGjovQQnHO+I4iIiIiISII459TGFxEREfFMRekB7G3fy1tuP4UfP36r7ygiIiIiIpIAFfsqOOfbR/HAut/4jiIiIiKS1FSUHsDsdsdvLn+O9muvVk8KEREREZEpoLDhIPd/fRPbr7lcbXwRERERj1SUHoDNnUvz617L+x9r5OHNf/EdR0RERERERil1eTF1x5dw7gMVPL71Qd9xRERERJKWitKDyPvaNcw/ABtv/qrvKCIiIiIikgDzr7mFglZYf82lvqOIiIiIJC0VpQdhp7+F0sWLeeP9L7GtcavvOCIiIiIiMkrpb3wz1Uct4l2/38pzZU/6jiMiIiKSlFSUHkBzMyxeYvzsuJ9zVAM8+L0v+Y4kIiIiIiKjZcacq69n0T54+trP+k4jIiIikpRUlB7A7NlQUgL/88Kp7MnOovinf6H5YLPvWCIiIiIiMkpZ73wfNSvyOfuetbxcs8F3HBEREZGko6L0IC65BMorjD+ceT1v3t7N7+77lu9IIiIiIiIyWmbM+MZ/sWIPPPqdz/hOIyIiIpJ0VJQexLveBUVF8Ms9H6cjLULGf3+Prp4u37FERERERGSUZp73YWoXzuP0XzxBadMO33FEREREkoqK0oNITYWLL4aHHkvn/868kPc838oDT/3MdywRERERERmtSISM/3cVR9XDgzdqbGkRERGR8aSi9BA++UlIT4ffz7uWzC6ou+EbviOJiIiIiEgCzLnwYuoLZvG6Hz9ITctu33FEREREkoaK0kOYPx/OPRd+8sccXnrd8ZzzYDkv7nrKdywRERERERmt1FTsK1/lhN2OP3/3877TiIiIiCQNFaXj8NnPwv798PcT7iK/DZ6/6XLfkUREREREJiwzSzGztWb2J99ZhpJ78RdozMniNXf+jj0HmnzHEREREUm4Xz3y33z9L1/yHeMw3ovSk6HBeuKJsGYN/OCxY6heOJeT7n1Sl/eJiIjIlPRY2WPc+sytdHZ3+o4ik9ulwGbfIeKSns7+z1/MKRU9rP3TD32nEREREUm4Od/8Dpd+4CbfMQ7jvSjNJGiwmgW9pTdvNp54+00cUwsv3HuL71giIiIiCddy5Vd4+9v/jdRu5zuKTFJmVgS8HZg0Fd788z8JwMGn/+E5iYiIiEjizS+to3bhPN8xDuO1KD2ZGqznnQfz5sGvKs8Lnnj6ab+BRERERMbA9G27yCQVS0/3HUUmr5uBK4CegVYws4vM7Hkze76hoWH8kg0gc1kJrRlGyqYJ3VdGREREZNjaDu5neU0HB4qX+I5yGN89pYdssE4U06bBJz4Bv/9LButmLCF9xy7fkUREREQSLrdyD40Lc3zHkEnKzM4B6p1zLwy2nnPuTufcGufcmtzc3HFKNwgzqhbOZt7OGt9JRERERBJqx7qHmXkIUo893neUw3grSsfbYJ1IvSguvjj49/ZpnyOnzH+PDhEREZFE2t++j2V1nRxcPrF6UcikchrwTjMrA34FnGlmP/cbKT77ixeztPoAHZ0HfUcRERERSZiGpx8GIOd1p3tOcjifPaXjarBOpF4UixfDO94B97ZcSH6tU4NVREREppRdLz3Ovq58GqOvx2lIaRkB59xXnXNFzrnFwAeAh51zF3iOFZfIa49mXjuUbn7SdxQRERGRhOlY/yIAhSe/yXOSw3krSk/WBusll0DzoTk8c/Ct7Nr6jO84IiIiIgnT8ML/cSuf513f/Rbd3b7TiIyvOWteD0DtUw95TiIiIiKSOJlbd1IzN52UOXN9RzmM7zGlJ53TTgv+3chR1L3wmN8wIiIiIgnU8dJatlLC0sXdpKb6TiOTnXPuUefcOb5zxKvotLMBaF/3nOckIiIiIolTUNZA3ZIJMIdHHxOiKD2ZGqxZWbCgsJOtlHBgw6DDYYuIiIhMKqnbdrLJSlj5GlWkJfmk50dpnJlCxuZtvqOIiIiIJMTelnqW1XXRUbLCd5QjTIii9GRTsjqVzVZCZOtW31FEREREEiZ7VwOlLKekxHxHEfFi9+IcckvrfccQERERSYjSZ/9Geg9kHHeC7yhHUFF6BEpKjG0UM7O0xncUERERkYTo6unCanLpdBmUlPhOI+LHgZKlLK/poO3gft9RREREREZtz7PB0MN5J73Rc5IjqSg9AiUl0OqyyajKwmlqehEREZkCKkrX0tQeVKNVlJZklXbMsWR1QekLf/cdRURERGTUul9aT1cE8tec4TvKEVSUHoHeE7XW/cXU1Gz3G0ZEREQkAWqee4StBI2c4mLPYUQ8yT3pTAAann3EcxIRERGR0Zu+rYyq+ZlYZqbvKEdQUXoEeovSWymh6rn/9RtGREREJAFa1z/HVkrIntVF7sSbnFtkXERPehMAHetf9JxEREREZHScc0TL99C4NN93lH6pKD0CCxbAtIxutlHMvvXP+I4jIiIiMmq2ZQtbrJiVK1MwzXMoSSplVjZVOelkbdnpO4qIiIjIqNTX72JxUw+dqyfm2HwqSo9AJAIrVhhbKKF700bfcURERERGbUZpNZsjKylZqYq0JLf6JfPJL2v0HUNERERkVMqfeoAIMP34k3xH6ZeK0iNUsjLCyykrmb6zwncUERERkVHLruqirjuqSQ4l6R1cuYKlDV0076vzHUVERERkxPa98AQAhSe/2XOS/qkoPUIlJVDZvYTZFa2+o4iIiIiMSmNTJe17lwOa5FBk2nFrSOuBXU//1XcUERERkRFzL22kPRXmvfZE31H6paL0CJWUQDepuIaF7G/b6zuOiIiIyIhVPPt3dhBUo9VTWpJd/klnAbDnucc9JxEREREZudnbK6iIzsBSU31H6ZeK0iPU24uozBVT9uLDfsOIiIiIjMLedU+xlRLMHMuX+04j4lfBCafTGYHuDet8RxEREREZEeccCypb2Lu8yHeUAakoPUK9vYi2UkLTi//wG0ZERERkFLpefoktlLBooSMz03caEb8sI4OKgkxmbiv3HUVERERkRHaXvUTBfod7zWrfUQakovQIzZ4N83N72EoJHRvX+44jIiIiMmKZO8rZmLKSkpVqGooANC4toLBCQ/SJiIjI5FT1ZDA3xswTTvWcZGA68xiFkpURNqasJH17qe8oIiIiIiOWU9FEqSvWJIcioa7VK1m0t4eGWrXzRUREZPLZ/8JTABSderbnJANTUXoUiothGyXMK6v3HUVERERkRA52tDGtbh4HemZokkOR0PTjTwKg/Mm/eE4iIiIiMnyRTZtozjRmL9PwHVNSSQns7c4luyaDru5O33FEREREhq1s/eOU9wTVaBWlRQLRU94CwL7nn/CcRERERGT45u6opmpBNpj5jjIgFaVHoffErfZQMZVbnvUbRkRERGQEGp5/nK2oKC0SK2f1GlrTgY0bfUcRERERGZbu7i4WV7exr3iR7yiDUlF6FHpP3LZSQu3zj3rNIiIiIjISBzeuZSslZGX2EI36TiMyMVhKChVFM8neXuk7ioiIiMiwVGx6itkHIXLUa31HGZSK0qOwdCmkpDi2UkLbhud9xxEREREZttRtO3gpZSUriiNE1DIUeUXzsiiLKltwzvmOIiIiIhK3mqceBGD2mn/ynGRwOvUYhbS0oDD9cqSYyJatvuOIiIiIDNucslq2RUo0dIdIH+6oo8htc+zeuc53FBEREZG4HVj7HAALTnur5ySDU1F6lEpKjM2pq5i1a7fvKCIiIiLD0tPTTV51J9Wdi1SUFulj1gmnAlD15F89JxERERGJX9rmrdRmpzAjf6HvKINSUXqUSkqgrGs5BVX7fUcRERERGZaa0g3sPbiMHlJUlBbpo+jUswFoe/Fpz0lERERE4pdbWsvuRXN9xxiSitKjVFICh3qm0bl/AU21u3zHEREREYnb7uceZitBNbq42HMYkQlmzuKVNE2PkPLyZt9RREREROJy6FA7S2sO0layxHeUIakoPUq9J3BbKaHimQf9hhEREREZhv3rn32lKK2e0iJ9mFG5MJs5OzVMn4iIiEwO5S88zLQuSH3tsb6jDElF6VHqPYHbRjH71j3jN4yIiIjIMLgtW9hkxeTnO2bN8p1GZOLZv2IRS6rb6O7q9B1FREREZEj1T/8vAPNOPMNvkDioKD1KeXkwa5Zjk5XQvWmj7zgiIiIicZu5s4qNaasoKTHfUUQmJFu9mpmHoGbrC76jiIiIiAzp0MsbAFh48ps9JxmaitKjZAYlJcZLaavI2lnhO46IiIhI3Aqr9lHaU6KhO0QGkLViNQBNW170nERERERkaJHKKhqnR5iWPc93lCGpKJ0AJSWwg5XMr9zjO4qIiIhIXPY17SazOZt9XXM1yaHIAGaXHANA6/aXPScRERERGdr03Y3U52T6jhEXFaUToLgY6g5FyalP4+CBFt9xRERERIZU+dzfNcmhyBDyX3MSAId27fCcRERERGRocxr205I/23eMuKgonQC9J3JlLKdi7aNes4iIiIjEo3n9M2wj6CKtorQkiplNM7NnzWy9mb1sZt/wnWk0smbn0pRlRCoqfUcRERERGZTr6aFgzyE6ovm+o8RFRekE6D2R20oJ+7ZrskMRERGZ+A6VlbKVEtLSHEuW+E4jU0gHcKZz7hjgWOBsMzvZc6ZRqc/JJKumwXcMERERkUE1VmwhqxNYtNh3lLioKJ0AK1YE/26lhANl2/2GEREREYlDpLqajZESli0zUlN9p5GpwgVaw4dp4c15jDRqLXnZzKnTEH0iIiIysdVveg6AzGWTY8IYFaUTICsLFi7oYSsl9JSX+Y4jIiIiMqRpNQ1siazUJIeScGaWYmbrgHrgIefcM/2sc5GZPW9mzzc0TOxeyAcL88jfcwjX0+M7ioiIiMiAWrYFozdkFx/tOUl8VJROkOKSCC9HSojsrvEdRURERGRIWfWtlHcv03jSknDOuW7n3LFAEXCimR3Vzzp3OufWOOfW5Obmjn/I4Vi0iBmHYG/1Tt9JRERERAbUUboNgLzVJ3pOEh9vRempNglKSQnscMVMq2nyHUVERERkSAeb8uh0GSpKy5hxzjUDjwBn+84yGhnhJbD1m5/3nERERERkYFZRwf50yC5Y7DtKXHz2lJ5Sk6CUlECry6arPst3FBEREZFBHWpvpaEtmBRDRWlJJDPLNbPZ4f1M4E3AFr+pRqf3Eth92zZ4TiIiIiIysIzdddTOy8Aik2NgDG/T2jjnHDBlJkFZujT498DeQr9BRERERIZQv30dFSwGXm3DiCRIAfATM0sh6ABzj3PuT54zjUruqjUAHNy51XMSERERkYFl1zbTnDfLd4y4eZ1rPWysvgAsB24baBIU4CKAhQsXjm/AYSgqCv7ddyhK655aZszN9xtIREREZAB7tm+giiJSIj3k5U2OnhQyOTjnNgDH+c6RSPMWFNOWBpSX+44iIiIiMqC8poPUH7XEd4y4eT0LmUqToESjwb/VRKnfttZvGBEREfmLnYMAACAASURBVJFBtJZuoZooufM6SEnxnUZkYrNIhNq56WRU1/qOIiIiItKvtj11zD3gcAsX+I4StwnRNWYqTIIybx6kp3VRTZTm7Rt9xxEREREZ0KHyUqqJUrRgQjQFRSa8vfNnkl3X7DuGiIiISL9qXw4Gn0hbstxzkvh5OxOZapOgmEHe/E6qiXJg1zbfcUREREQGZFXVVFqUhYszfEcRmRQOFOaS29juO4aIiIhIv/ZuXQfAzBVHDEIxYfnsHlMAPGJmG4DngIcm+yQoCxelUU2UropdvqOIiIiIDCijpp5qoq8MPyYig+teUEROm6N9X5PvKCIiIiJHaA8nZM5ZdYLnJPHzVpR2zm1wzh3nnDvaOXeUc+4/fWVJlAULUym3IiLVNb6jiIiIiAwoUtdNq8t+ZaJmERlc76WwtZuf9ZxERERE5Eg9Zbs4lAJ5K471HSVuGkgwgaJRqCHKtNpG31FEREREBtTROBdAPaVF4jR9xWoA9m5e5zmJiIiIyJHSqnZTMzuVlNQ031HipqJ0AkWj0OEycfWaxl5EREQmpq6Odg62FQAqSovEq/dS2AM7N3tOIiIiInKkmbV7acqd7jvGsKgonUC9J3ade3L8BhEREREZQMOODdQQNFpUlBaJT/6K4+iKQHdZqe8oIiIiIkfIaWijrWCe7xjDoqJ0AvWOy7j/YJSDLXv8hhERERHpR9P29VSrKC0yLGkZmdRkp5BWqbljREREZGLpOniAvJZuOosKfUcZFhWlE6j3xK6aKPVb1/oNIyIiItKP1tItVFFE9sxDZGX5TiMyeTTmTmd6bZPvGCIiIiKHqdv6IhEHKYuX+o4yLCpKJ1BBMDwj1UTZu+Mlv2FERERE+tFRXko1UQrVS1pkWFrz55LT0OY7hoiIiMhh9mwJOsZmLV/lOcnwqCidQOnpMG9OB9VEaSvd6juOiIiIyJGqqqi0KAsXTp6ZuUUmgs4FheTt66Kn85DvKCIiIiKv2L99IwBzVx7nOcnwqCidYAsWRqiiiM7yXb6jiIiIiBwhY3cdVRYlWmS+o4h48b//C62tw98usmgJqT1omD4RERGZULp27QSgYPWJnpMMj4rSCbZgYRoVFiVSXe07ioiIiMgRMuraaOjJf2WCZpFksns3nHUWzJsHb3kL3Hor7NwZ37ZZK4JLYhu3vDCGCUVERESGJ6WyitqZEbJmzPEdZVhUlE6waBSqLUpGXaPvKCIiIiJH6GqahSPyygTNIskkNxceeQQ+9zmoqIBLL4Xly2HVKvjyl2Hv3oG3nV18NACt218ep7QiIiIiQ8va3UhDTuag6zz+ONx00zgFipOK0gkWjUJzTw4ZdR2+o4iIiIgcpqfzEB378wFUlJaklJYGZ5wB118PmzfDjh1wyy2wYAHccAOcckrwXH/yw0tiD+2Ks2u1iIiIyDiY07CflrzZAy7/n/8JrhT7wQ+gvX0cgw1BRekE670UtrNprt8gIiIiIn00lm6kzgXVaBWlRWDZMvj85+HBB+Hhh6GhAU46CR577Mh1Z83Oo36GEamoHP+gIiIiIv1w3d3k7zlER1H+Ect6eoIrwT72seBL+SefhMzBO1SPKxWlE6z3BO9AeyGH2lr8hhERERGJ0bRtPdWoKC3Snze8AZ59FubPhze9KehV1Ff9vGlk7W4Y/3AiIiIi/Wiu2Ma0LmDRosOeb2uD970PrrsOPv1p+POfYfbAnam9UFE6wXpP8KqJ0rBtnd8wIiIiIjH2l26miiLS0rrJyfGdRmTiWbYMnnoq6E30sY8FvYt6el5dvi9vNnPq1fFEREREJob6Tc8BMG1ZySvPVVXB618Pf/hDMKnzbbcFQ5hNNCpKJ1hsUXrP9g1+w4iIiIjE6CjbSTVRCvK7MfOdRmRimj076E306U8HvYve/37o7AyWHYzmkbenA5zzG1JEREQEaNn2EgDZxccAsHEjnHhiMEfGn/4UTO48Udv9Kkon2KxZMD2ri2qitJVu9R1HRERE5BWuqpJKi7Jw0QTsKiEygaSlBb2KbroJfvc7+MpXwgWLFpHVCS2VmuxQRERE/Oso3QZA3qo17N0L73pX8PyTT8Jb3+oxWBxSfQeYasygsNBRtaOIzvK/+44jIiIi8or03XVURKKcHJ2g3SVEJhAzuOwy2LkTbrwRTj4ZCpauAKBu07PMWrjcc0IRERFJeuXlNE+DOXlLec97oLIymLD5qKN8BxuaekqPgQULUymPRKG62ncUERERkVdk1TVT21NEUZHvJCKTxw03BAXpj30M9mT+EwD7tr/kOZWIiIgITNtdT+3cDK691vjjH4Mv0k85xXeq+KgoPQaiUaPaisio0czcIiIiMnFEGjPocJmvzIEhIkNLT4d774XMTLj82rNpZToHd2qYPhEREfEvu66ZBzPP4f/9Pzj/fLjkEt+J4qei9BiIRqGuu4DpmplbREREJgjX1cWhljwAFaVFhqmoCO6+G3aWpvORyA9xZRW+I4mIiIhwqCGHr1feyapV8IMfTNxJDfujovQYKCqCbtJwjTN9RxEREREBYG/5FupdUI1WUVpk+N74Rrj6auO3PR/g/hff7juOiIiIJLl9u+v4eMc9dLoM7rsPpk/3nWh4VJQeA70neh0HCuhqb/MbRkRERARo3LaOalSUFhmNL38ZXj/rAW6t/BpPPOE7jYiIiCSzz3/2AM9wMl/+wN2UlPhOM3wqSo+B3hO9aqI0bt/gN4yIiIgI0LJzE1UEMxwWFnoOIzJJRSJw+eu+zgIr5wMfgP37fScSERGRZPT738NPf7eEL3I957yn03ecEVFRegzEFqX3qCgtIiIiE8DBsh1UEyVnXifp6b7TiExemcuy+aW7gOpqx9e/7juNiIiIJJvWVvjsZ2FZbgXX8FVyV6/xHWlEVJQeA3l5kJLSQxVFtO7c7DuOiIiICFRWUmlRFixM9Z1EpjAzW2Bmj5jZJjN72cwu9Z0p0dKWLOdknuFD76rk1lth7VrfiURERCSZXHUVVFXB5UdfTXdqF/lLj/YdaURUlB4DKSmQl9dDNVEOVZT6jiMiIiJCak0d5ZEo0egkmpJbJqMu4IvOudXAycAlZrbac6aEmr78NQBcdOZ95OTApz4F3d2eQ4mIiEhSWL8ebr4ZLroIjjv4ELvnpJKWluE71oioKD1GFixIoTwSDb66EBEREfFset1eaiiiqMh3EpnKnHM1zrkXw/v7gc3AlJpaM2fV8QCk1azjppvguefg+9/3HEpERESmvJ6e4MvwuXPhmmtgZs0emnKn+441YipKj5Fo1KiMFJFe0+A7ioiIiAiZjV00d+e8MveFyFgzs8XAccAzfpMkVsGK4+hIge6yUs4/H846C776Vaip8Z1MREREprI774RnnoEbbwwK0zmNbbQWzPMda8RUlB4jRUVQ01PEjIZm31FEREQkybnubrqacwFUlJZxYWYzgPuAy5xzLf0sv8jMnjez5xsaJlcnjmnpWeyenUJq1W7M4PbboaMDvvAF38lERERkqqqrg698Bc48Ez70IehpP8D8lm66iiZv415F6TESjcKBnplMa0zxHUVERESS3P7KnTT0BA1WFaVlrJlZGkFB+hfOud/2t45z7k7n3Brn3Jrc3NzxDZgATTlZzKhtAmDFiqCn9K9+BQ8+6DmYiIiITElf/CK0twdfhptBw9ZgpuWURUs8Jxs5FaXHSO8J38H9+fR0HPQbRkRERJJaw7a1VKOitIw9MzPgLmCzc+5G33nGyv6Cecyrb3vl8Ve+AsXF8JnPBCeMIiIiIony97/DL34RtDdKSoLnmra8CEDWilUek42OitJjpPeEr4YoTTs3+g0jIiIiSa1l5yaqCGY41ESHMsZOAz4MnGlm68Lb23yHSrTOwjxyW7pwnZ0AZGTAHXfAzp3BxEMiIiIiidDREXzpvXx5cGVWr9bSLQDMWXG0p2Sjp6L0GOk94asmStP29X7DiIiISFJrL9tBNVGysrqZNct3GpnKnHP/cM6Zc+5o59yx4e0vvnMlWqRoISkOmsu2vPLcmWfC+efD9ddDdbXHcCIiIjJl3HEHbN8O3/0uTJv26vOd5bsAmF98nKdkoxdXUdrMbjCz1yTyhc1sgZk9YmabzOxlM7s0kfv3rbendBVFtO7Y7DeMiIiIJDVXUU6lRYlGDTPfaWSiGIs2frLIWLwM4IjOJ9/6FnR3w1VXeQglIiIiU8q+fXD11XDWWXD22Ycvs+pqGrJg9ux8P+ESIN6e0puBO83sGTO72MyyE/DaXcAXnXOrgZOBS8xsdQL2OyFkZsLs2d1UE6WjfKfvOCIiIpLEUnfXUpZSRFGRLpKTw4xFGz8pzFoajN/YUnp455MlS4JLbH/0I9i0yUcyERERmSquuw6amuDaa49cll7bSOOcDGwS9ziJ68zEOfdD59xpwL8Ci4ENZvZLM/vnkb6wc67GOfdieH8/QaN4Sk29U1QUoTwShaoq31FEREQkiWXV72E3UU1yKIcZizZ+sphXfAwAB8uO7HzyH/8BM2YcPu6jiIiIyHBUV8NNN8EHPwjHH3/k8pkN+2jOmTH+wRIo7u4yZpYCrAxvjcB64Atm9qvRhjCzxcBxwDP9LLvIzJ43s+cbGhpG+1LjqqjIKI8UkVZT7zuKiIiIJLFZDW3UdxdqkkM5wli28aeyvAWraE8FV1lxxLKcHPjyl+EPf4B//MNDOBEREZn0rroKurqC4Tv6M29PO+15c8c1U6LFO6b0TcBW4G3At51zJzjnrnXOvYOgmDxiZjYDuA+4zDnX0ne5c+5O59wa59ya3Nzc0bzUuItGoYYiZtTv9R1FREREkpVz2N7ZdLs09ZSWw4xlG3+qS0tNp2Z2Cqk1tf0uv+wyKCyEL30JnBvncCIiIjKpbdoUDAX2mc8EQ4P11dN+gJzWHroLC8Y/XALF21N6A3CMc+5Tzrln+yw7caQvbmZpBAXpXzjnfjvS/UxU0Sjs6ZrPzMZDvqOIiIhIkmqrLqOxJ6hGqygtfYxJGz9Z7JmbxfS6/jufZGXBN74BTz8N998/zsFERERkUvv3fw+GAvva1/pf3rTjJQAiCxeOY6rEi7covR4oMbPjY27LzCzVObdvJC9swUjcdwGbnXM3jmQfE100Co4IPS25uM5O33FEREQkCTVsW0c1KkpLvxLexk8mrfOzmd3UNuDyCy+EVauCsaW7usYvl4iIiExeTzwBv/99MBRYTk7/6+zZvgGAzEUrxjFZ4sVblL4deBq4E/gB8BRwL7DVzN48wtc+DfgwcKaZrQtvbxvhviak3hO/GqLsq9juN4yIiIgkpZZdW6giGExaY0pLH2PRxk8anfnzyW3uhJ6efpenpsI118DWrXDXXeMcTkRERCYd54KhvwoK4NJLB15v/64tAGQvWz1OycZGvEXp3cBx4djOJxCMMVcKvAm4biQv7Jz7h3POnHNHO+eODW9/Gcm+JqreE78qimgq3eg3jIiIiCSlA5U7qSZKSkoPeXm+08gEk/A2flIpKiKjG1qrywZc5Z3vhNNOCyYrahu4U7WIiIgI998PTz0VDAE2ffrA6x0qKwUgt3hyTwESb1G62Dn3cu8D59wmYKVzrnRsYk0NvT2lq4myv2yr3zAiIiKSlLqrKqkmSl6eIyXFdxqZYNTGH4X0RUsBaNi2dsB1zOC666C2Fm6+ebySiYiIyGTT3R2MJb1yJXz0o4Ov66oq2ZcBOfn9zII4icRblN5kZneY2enh7fbwuQxAgyUPYN48SE/voZooByt2+Y4jIiIiSchqa9kViVJUFG+zT5KI2vijMGPpSgBadm4adL1TT4VzzoEbboCWlvFIJiIiIpPNr38NW7bAN78ZDAE2mPTaBupmpxGxyd2+jzf9R4AdwGXhrRS4kKCx+s9jkmwKMIPCQkcVUbqqK3zHERERkSSUXtdEVaSIaNR8R5GJR238UZi74mgA2suGnjvmyith71647baxTiUiIiKTTXc3XH01HHUUvPe9Q68/o34ve3MGGd9jkhii9g5mlgL8xTn3z8AN/azSmvBUU8iCBSnsqigiUlvnO4qIiIgkoelNLdT2FPE2TXIoMdTGH738pUfTGYHuivIh112zBt72tqC39Oc+BzNmjENAERERmRR+8xvYvDnoLR2Jo/vw3KYD1B6/cOyDjbEh36pzrhvoMbPsccgz5USjUG1FZNQ1+Y4iIiIiSSi9KUJbz6xX5roQAbXxEyEzYzq1syKk1NTGtf7Xvw5NTeotLSIiIq/q6QmG7Fi1Ct73vqHXd52d5LR001Uw+WcwH7KndKgVeMnMHgJemTfaOff5MUk1hUSjUNdTSFbjft9RREREJNn09HBwf9BgVVFa+qE2/ig1zZ1GZpydT046Cd7yFrj+evjsZ2H65L/qVkREREbpvvvg5Zfhl78krknJm8u2MMeBFS0Y+3BjLN6i9G/DmwxTYSEccplE9mb4jiIiIiJJprVqF/WuEAjaJCJ9qI0/Si252Sws2xP3+ldeGUx8eMcdcPnlYxhMREREJryeHvjP/4SVK+Hcc+Pbpmn7euYAGYuXjWm28RBXUdo59xMzywQWOue2jnGmKaWgIPj30P75uK4ubKgpNEVEREQSpKl0IzUEjZHeNolIL7XxR68jP4fcF2vAuWCW8yGccgq86U3wne/AZz4DWVnjEFJEREQmpN/9DjZuhJ//PL5e0gD7d24GYNay1WOYbHzEMXw2mNk7gHXAX8PHx5rZH8Yy2FTRewLYQD77K3f6DSMiIiJJpWXXFhWlZUBq44+eK4oyvRMONsY3rjQEvaXr6+F73xvDYCIiIjKh9faSLi6GD3wg/u0Olu0AIKf42DFKNn7iKkoDVwEnAs0Azrl1wNIxyjSl9J4A1lBAU+lGv2FEREQkqRyo2Ekt+WRkdJOt6ezkSFehNv6opC1YDEDDtnVxb3PaaXDmmXDdddDePkbBREREZEL7wx9gwwb4j/+Iv5c0QE9lBe2pML+oZOzCjZN4i9Kdzrl9fZ7rSXSYqSi2KN2ya4vfMCIiIpJUuqorqaGA/HwXz8gCknzUxh+l6UuDE8J924fX+eTKK6GuDu68cyxSiYiIyETmXNBLevly+OAHh7dtWk0dNbNTSEtNH5tw4yjeovTLZvZBIMXMVpjZd4EnxzDXlDFzJmRO66aGAtord/mOIyIiIknEamqoiBQQjQ6j+4UkE7XxR2n28qMAaCvbPqzt3vAGOOMMuPZaOHhwDIKJiIjIhPXHP8LatUEv6eFOPZdVv5c9c6fGpBTxFqU/B7wG6ADuBlqAy8Yq1FRiBvmFQU/p7qoK33FEREQkiaTXNbE7UkhBgbpJS7/Uxh+l/BXH0QN0VQy/88mVV0JNDfzoR4nPJSIiIhOTc3D11bBkCXzoQ8PffnZTG63zZyc+mAdx1eOdcweA/whvMkyFBREqygqw2vgnQBEREREZrelN+6jvKdAkh9IvtfFHb9aMedTMNCLVu4e97emnw8knww03wEUXDb+nlIiIiEw+jz0Gzz0Hd9wBaWnD3Linh/nNnWwqyB2TbOMtrqaPmRUDlwOLY7dxzp05NrGmloIC44VIIel1Tb6jiIiISBLJaHLs75mtorT0S238xGiYm8G0usZhb2cGX/4yvOc9cN99cN55YxBOREREJpTrroP58+EjHxn+tm3VZUzvBooWJDyXD/F+H38v8D3gh0D32MWZmgoKoL6ngBlNLb6jiIiISLLo6aGrJQdARWkZiNr4CdCSM5OCmv0j2vad74Ti4uAE9dxz0YSkIiIiU9iGDfDAA8HwHZmZw9++YdtapgMZC5ckPJsP8Ralu5xzd4xpkimsoADaemaRscd3EhEREUkWbbvLaXRBNVpFaRmA2vgJcDBvHjkvD7+nNEAkAl/6Enzyk/Dww/DGNyY4nIiIiEwY3/kOTJ8On/70yLbft+NlAGYsXZnAVP7EO9HhH83sM2ZWYGZze29jmmwK6T0RPLR/PnSrE4qIiIiMvabSjdQQNELy8z2HkYlKbfwE6CksJPugo6uleUTbX3BB8Dt67bUJDiYiIiITRnk53H138EX03BG2ttrLdwAwd8XRCUzmT7xF6Y8AXwKeBF4Ib8+PVaipprco3eAKaK0u85pFREREksO+XVteKUqrp7QMQG38BEhZuAiAxu3rR7T9tGlw2WXw0EOwdm0ik4mIiMhEcfPNwTBd//ZvI99Hd0U5nRHIX5pERWnn3JJ+bkvHOtxU0XsiWEMBjTs2+A0jIiIiSaG9Yic1FJCS0kPu1JigWxJMbfzEyFpSDMDe7S+NeB+f+hTMnBlc1isiIiJTy5498IMfwPnnw8KFI99P6u5aarIjZGZMT1w4jwYtSpvZFTH3/6XPsm+PVaipJrYo3VK+1W8YERERSQqd1ZXUUEDufEck3mvjJCmojZ9Y2ctWA9BaOvJ2/uzZQWH6nntg165EJRMREZGJ4Pbboa0tmEdiNDLrmtgzZ1piQk0AQ52ifCDm/lf7LDs7wVmmrHnzIDW1hxoKaC8v9R1HREREkoDt3k1FpIBooSrScoQxbeOb2Y/MrN7MNo52X5PB/OLjAOisGF07/7LLgokPb7wxEalERERkImhvh1tvhbe9DV772tHtK7uxlZbc7MQEmwCGOkuxAe7391gGEInA/DxHDQV0VVf4jiMiIiJJIK2+iepIIQUFarLJEca6jf9jkqgDy7y5RTRmgVVVj2o/0Wgw6eFdd0FjY4LCiYiIiFc/+Qk0NMAVVwy97qCcI3dvBx35OQnJNREMVZR2A9zv77EMIloYoTxSgNXU+o4iIiIiSWB64z7qXIEmOZT+jGkb3zn3OLBntPuZLMyM+jnppNeNvpJ8+eVBj6rbbktAMBEREfGquxuuvx5OPBHe8IbR7aujsY6sTnBF0cSEmwCGKkofY2YtZrYfODq83/t4lJ3Ok0tBgbE7Ukh6vbo9iIiIyNib0dRBc3eOitLSH7XxE6w5ZwYz65tHvZ/Vq+Ed74DvfjcYe1JEREQmr9/+FnbuDHpJ2yivRWvYthaAtIVLEpBsYhi0KO2cS3HOzXLOzXTOpYb3ex+njVfIqaCgAOpdAdMbW3xHERERkanOObpb5uGIqCgtR5gIbXwzu8jMnjez5xsaGsbjJcdUe95c5u5pT8i+rrgCmprgZz9LyO5ERETEkxtvhOXL4d3vHv2+mrcHU3VkLSke/c4mCM18M04KCqC5O4esPV2+o4iIiMgU115bRVNPUI1WUVomIufcnc65Nc65Nbm5ub7jjFp3fj45rT30tB8Y9b5OOw3WrIFbboGengSEExERkXH39NPB7dJLISVl9Ps7sGsbAHOWHzX6nU0QKkqPk94Twu6WeWpdioiIyJhq2vkSNagoLTJeIgsXArBn58ZR78sMLrsMtmyBv/1t1LsTERERD26+GbKz4cILE7O/rsoyeoD8FcclZocTgIrS46T3hLDRFdC2u9xvGBEREZnSmndtVlFavDGzu4GngBIzqzKzj/vONNamLV4OwJ7tGxKyv3/5FygsDE5oX9HWBtu2JWT/IiIikkBdXbDh1TZARQX85jfwyU/CjBmJeYlI9W7qZhqzZsxLzA4nABWlx0nvCWEt+TTuSExjVURERKQ/7eU7XylK5+V5DiNJxzl3vnOuwDmX5pwrcs7d5TvTWJu1bDUArTu3JGR/6elwySXw4IPw8svA+vVw3HGwahU88URCXkNEREQS5NJL4Zhjgm7RbW3cdhs4B5/9bOJeYlptIw1zMxK3wwlARelx0luUrqGAlrKtfsOIiIjIlNZZXUENBcyb1016uu80IlNfbnFwKW1HRWnC9nnRRTBtmuOWizfDSScFPaUXLIAPfhCamxP2OiIiIjIK998Pt98OJ58MP/0pbWtO587vdfPe98KiRYl7mZmN+2nJmZm4HU4AKkqPk7w8MHPUUEB7xU7fcURERGQq211DRaSAwkI19UTGw/z8ZbRkgKuqTNg+c6a18q9FD/Ozfyym8ZR3wNq1cM89sHt3ULF2LmGvJSIiIiNQWQkf+xiccAI89hg8+CA/qXojzS0p/Nuqvyb0pXL2HORg3tQZugNUlB43qamQk9NDDQV0JrCxKiIiItJXWn0jVZFCCgrMdxSRpJASSaFudirpNfWJ2eFLL8GaNVy681IOksn33/hrunPm0XLMSvjWt+Dee+GuKT8qioiIyMTV3Q0XXACdnXD33eztbqPnzLO4Zf63eN3MzZzyzbfCxz8OBw6M+qW69u0l+6Cjp7AwAcEnDq9FaTP7kZnVm9nop6meBAoLI5RHCrCaGt9RREREZAqb3rCPOgo0yaHIONozbzrT6xMwrMaGDXDiibBvH6sf/m/OelMP37mlnZW3HE3RjUWUfeL9cNZZwfiVmzeP/vVERERk+L79bXj8cbjtNm5u+jNzr5vLaV+9+v+zd97hURVdGH83jSRA6JCE3nsTRFBp0lRApAqCgAgKKkVU8JO2gDQp0kGK0nvvvXcSIPQWCCWNFJJA+u6+3x+HUCQJKdsS5vc8+0B2586cu+XemXdOwc07dhgwqww0Q4YA//4LNGmS7uimkGeFlG2LGDEfiBVgaU/pRQA+trANZsPVVQNfW3c4PAq2tCkKhUKhUCgyMdlDoxGiL6BEaYXCjETmz4XcIen3hsLKlYBOh+hTxzDd6SIuFOuG8OCsiL/0OQw0oO/u/uDixYCzM9CpExATk/4xFQqFQqFQpJzjxwGtFujcGfc/q4+hB4aiUv5K8NjwIZDdFwt1zXHwm0bghAnAiRPAjfTVlgt9Jko7FS9tBOOtB4uK0iSPAAi1pA3mxM0NCKQbsgZHWNoUhUKhUCgUmRUSmnAX6GmvRGmFwozEuxVA3ggdGB+frn64cyfuVyqMomvqoP+u/ij33n0ULvEUeS/+AW39kdh2cxs2R5wBFi8GvLyAwYONdAYKhUKhUCjeSFiYFB0uVgyYXlnStwAAIABJREFUPRv9dw8AQUyqshu62w3w8ZfeuBJyAR8t+QhtDSvlmF3pyy8deUdE7ZylKqXTeOvC0p7SbxVubkCoPj9cQqMtbYpCoVAoFIpMSswjP4QaRI1WorRCYT40hQvDlkDEvZtp78TPDxovL8zOcxfvuL2DI92P4Og3hzFkUDZ4empQU9cfVQpUQd+dffG0cT1gwABg+nRg2zbjnYhCoVAoFIrEIaXYsJ8fsHIltvgfwqbrmzCi/gis/dcdTk7AsjH14DPAB7M/nQ3PLCG4nk8D3fb03afjHtwFAOQvU90YZ2E1WL0ordFovtVoNB4ajcYjKCjI0uakCzc3QE97MCInYDBY2hyFQqFQKBSZkBDvy/CHEqUVCnOTpWhxAEDITa+0d7J7NwDgfLUC2Nl5J+oWrQsA+OorIFcuYOYMO8xtPhcPIx5Ce0gLjB8PVK0KfP+9FFxSKBQKhUJhOg4ckGLDo0fjabUK+HHHj6iYryK6lPwJy5YBXbsCefIAjnaO6PNuHyxqtQg7ShKaI0fSVfBQ4+uLYGcgT66CRjwZy2P1ojTJeSRrkqyZL18+S5uTLhIWhiEGN0QFPLCsMQqFQqFQKDIlYXevKlFaobAALiUqAAAivK+muQ/dju3wzw6UbtgWGo3m+fPOzsB33wEbNwJu+jr49p1vMfXUVHg9vg4MHw48eJDu0GCFQqFQKBRv4O+/RXX+6SeMOjwKDyIeYG6LufhngT1iY4F+/V5t/mGRD3GsQlbYxsUDhw6ledgs/kF4lMvhlblBZsDqRenMRMLC0B9uCPG+bFljFAqFQqFQZEqi7t1RorRCYQHylKkKAIi5dzttHeh0MOzZhZ0lgc/KtXrt5e+/BzQaYPZsYFzjccjtlBu9t/eGoUVzwNVVFsoKhUKhUChMQ2Cg7A53745LYTcx5eQUfFP9G7zn9iFmzwaaNgUqVHj1EHtbe7g0aYFIe8CwY0eah84WFI6wvNnSeQLWh0VFaY1GsxLASQBlNRrNQ41G840l7TE1L4vS4XevW9YYhUKhUCgUmZL4hz7whxuyZzfA2dnS1igUbw+uRSog2g4wPLiftg5On4ZDRCQOlXdE/aL1X3u5cGGgTRtg/nwgiyE3JjedjFMPT2HBpcVAjx7A9u3Aw4fpPAuFQqFQKBSJsmiRbCD3/Aa9t/dGTsecmNB4AtavB/z9X/eSTuCTSq1xsBgQu31zmofOExKN6AK503y8tWJRUZpkJ5JuJO1JFiK50JL2mJqXReno+96mG+jIEaBv33Tlq8mMrFolYY9qrq5QKBSm5/x5yal24oSlLbEy7t+Xm9HtNHpSpgR/fzywcYW7e+YK71MorB0Huyzwz2kLe//ANB3PnTuh1wC2TZohi12WRNv06weEhQHLlwNdqnRBg2INMHjfYAR/+bkUX1qYqZdTCkWmITgqGDNOz8Cm65ugN6h88AqF1WMwAPPmAfXr45+o4zjx4AQmNZ2EPM55MH06ULIk8MkniR/6camPsbeMDZx8HqZpDcDoaOSJNEDn5prOk7A+VPoOM+LkBLi4GOAPN8Q/vGeaQf7+G2jUCJg5Exg61DRjZDDi4kSj79RJriHlywPTpqlaMAqFQmEKnj4FBg4EatYEli4F6tcHZswQreStx2AAuneXm1GtWsD+/SYZxj4wGA9s3eHmpkRphcLchOZ2gnNAaJqOjdyyHicLAY1qtEuyzQcfANWrA9OnA4AGc5rPQWRcJH65NUvihhcsUJNchcKKuRBwAT0290ChKYXQb1c/tF7dGiWnl8SkE5PwOPqxpc1TKBRJceAAcOcOnn7dGYP2DkK9ovXQrWo3nD0LnDwpmpNNEgprDscceNygtvyRhvoPobcuAgBsChdOq/VWixKlzYy7uwb3bNwA/wDjdhwfD/z4I9C7N9CkiSx6p04Fjh837jgZjIcPgQYNRKP/+Wfg5k2gbl1gwADRAzw8LG2hQqFQZB42b5Y8an/9BXz7LXD3LvDpp+LZ17kzEBlpaQstzLx5wMGDwLBhgLs70KyZSRR75+BwBMJd5ZNWKCzAE9dcyB30NPUHPnqEbJeuY09pDT4t/WmSzTQauaZeuSKXk3J5y+GHd3/A8kvLEd71C5n8qoKHCoVVoTPosP7qetRfVB/V/66O1VdWo0f1HrjY+yI2dNiA4rmK49e9v6LQX4XQe1tvXHl0xdImKxSK/zJvHpAnDxYXj8DjmMeY2mwqNBoNZswAsmUTCS45atXrhFu5gaeb16V66ODr5wAATiXLpcFw60aJ0mbGzU2Dh7bucAgMMl6nISGysJ01C/jlF2DrVnGfKFJE8stFRxtvrAzEwYNAjRrApUvA2rXApElA6dKSbm/NGsDPD3jvPaB/fyAiwtLWKhQKRcblwQPg88/lkTOnpOyYMwcoVkxqgYwdC6xeDdSuLZuDbyU+PsCvvwKNGwMjR8qblKDYf/edhPUYiRwhkQjWuypRWqGwAHEF3ZA/XAem9je9ezcAILDeO8jtlHzOyI4dgbx5E7ylgW/e+QY6gw5LijxWBQ8VCisjVheLhosbot3adngQ/gCTm07Gw58eYnbz2ahcoDJal2+Ng90Owqu3F76s9CUWey1GpTmVMPPMTEubrlAoEkgocNitGxZeW44abjVQ3a06AgIkTWz37kCOHMl30bJMS+wsBWQ5egKIiUnV8E9uyUZVrrLV0ngC1osSpc2MmxsQCHc4BxtJBb1yRVx+jx8HFi8GJk6EwUaDIJsYySl38yYwfLhxxsogkMDEibLuz5MHOHMGaPdSFKRGA7RvD1y/DvTpI05qTZqo0HKFQqFIC2FhsgG4Zw/w55+ApydQp86L121sgP/9T/SWgADg3XdlTvdWQQI9e8r/FyxAYOQjwMUF2LQJ+P13qVrWuDEQZIQNaxIO4Y6IMTgrUVqhsACaokVhSyD8ztVUHfd081oEZgXKNe70xraOjrKXtWWLRKRUyl8JNd1r4p8rS1XBQ4XCyui7sy+O3T+GeS3m4VbfWxhYZyByOeV6rV2VAlUw/7P5ePjTQ7Qo0wIDdg3AIZ9D5jdYoVC8zrMCh9db18P5gPPoXq07AHGeTkha8CaK5iyKm+8Wh31svNSBSwXxdyUPtWu5mqk03PpRorSZcXMDgvWuyBFihCKE9+/Lyj8qCjh8GPzqK2y+vhnV5lZDwSkFcaFiHpmxTpkiSW7eEhYuBAYNAtq2BU6flhzSiZEjh6T1mDdPhOtNm8xrp0KhUGQG/vpLtNQjR8QR2N4+8XaNG4tgXbYs0KaNXHffGubPl/zRkyZhRcRxuE52RZOlTXDG3wMYMwZYsQI4e1aSxcbHp2uo2KAAPNaLGq1EaYXC/CSE1j66looccXo9bPcdwO6SwGflP0/RIX36ALa2EigJAN2rdseFgAu41uoDVfBQYTFI4MkT2Sw5exbYuRNYskSWo5Mmybpr5Upg2zaZN1y4kLkjVud5zsP8c/Px+4e/o1eNXrC1sX3jMXmc82B5m+UolbsUOqztgPvh981gqfkhRc44c0amSBs3yndl1ixgwgRg9myJbj5wALh4EfD3N2pQmUKRcl4qcDgv8jDsbezRqVInxMVJZOjHH8v6JiUUaP4FYuyA6C0bUmWC5sED+GfXIHfOzDe51zADuYfWrFmTHhk8CfDkyZJhI9AmJ/LrHovbblqZNQv48Ufw0iXsdw7AkANDcMb3DErnLo2Q6BBUc62Gfa02QFOlilRZPH9e/s3ExMcDZcoA+fMDp06l7O3V6SQHasJblFRyeoVCoVC8SmgoULy4RJusS2F6tCdPJK3HBx+Il1+m5949oFIloFYtRO3cgjIzyyKLXRZExEYgOCoYrcq2wuiGo1F530WgSxeJfHr//TQP53dyL26+PwYNcQj79wMffWTEczExGo3Gk2TmcwFRvJHMMMdP4NKRdahcvz08x/dHjcFTU3bQ6dNA7dr49euCmPhPyj2cO3US0e/hQyDWJgRuk93wY60fMeWvqxJN6eMjyrVCYSL8/GTD2cNDHp6eEuWeWkqUkAKe1arJo3p1oGBB49trTk4+OIn6i+qjUYlG2NZpW4oE6Ze5HnwdtebXQpk8ZXD066Nwss+463idDrh2TdbaFy68eDxOZV1HjUYczmrWlCi9mjXl++LsbBq7FQoAwL59QJMm0C1bAne/n1G3aF2s77AeK1ZIzZydO0WYTgkefh4IrvcuahvckPOOX4pN8KqcH3j6FFXvGsG51QIkN8e3M7cxbzsJXkshBje4BPrC0bVQ2jvbvx8xhd3wiWdfHPI5hMIuhbGg5QJ0q9YNcz3mou/OvtgacBifLVgg1bi1Wtl2zMSsWCHz7xkzUq7329kBI0aIFrBhw6upPhQKhUKRNFOmiMis1ab8mOzZpdjs8OGyIKmW+VKjvYAEevV67rU46eRk+D7xxZHuR1DNtRqmnZ6GSScmoercquhVrA3majTQ7N+fLlE6/O41+EN5SisUlqJAeVlzxT4LtU0J0Vs3IosGcGneNlVj9esnuSyXLQN6986Dz8p+hmUXl+HPnjNh1/4LKXjYvHmq+lQokiM6Gti7V9ZMe/aI9yogTj3ly4swU768OAjlyye5z/Plk5SKtrYyZ4iIkH+fPJEUYNevvxAp169/MVbFii/qVdSokT5fLnPj98QPbde0RZEcRbCizYpUC9KAFDFd1mYZWq1qhT7b++DfVv9Ck4HehMhISd22aZN4xicI0E5OQJUqQIcOMgcsXFgymmXP/uKRNascHxQEBAe/+NfXV74ne/aIVzUg370qVYCWLSVSukqVjPVdUWQAnhU43F3ZGUG3g9C9ancAUtehTBmR2lLKO27vYGQlF3y82V+Eq2LFUnRczkcRuFfs9bQ/mQKSGeZRo0YNZnQOHCAB8gAa8OGxnWnvSKdjfE4XLqwO5p+Yn9NOTWNMfMzzl+N0cSw3sxxLTy/NWF0s2asXaWNDnjplhLOwTnQ6skwZslo10mBI/bHlypEVK5J6vWnsUygUisxEUBCZLRvZoUPqj338mHRxIdu1M75dVsX8+XLTnzWLD8Mf0nmMM9utefWkQ6JC+Nve3+g8xpkX3G0YX69uuoY8O64vJ+MnAmRoaLq6MjsAPGgF8031UHP89GAwGBiYFTzxSeUUHxNUuSRPFgRP3D+RyrHImjXJ8uXl/1tvbCW04JZL60lXV7Jly9Sar1C8Rng4uXIl2b49mTWr3NZy5iQ7dSKnTSOPHSOfPjXOWBER0t+UKWTDhqStrYxXsCD5/ffk3r3Wv1aL1cWyzoI6zDomKy8FXkp3fyMOjiC04IzTM4xgnWkJDSUXLpRLj6OjfHa5cpFdu5LLlpFXr5Lx8cYZy9eX3LyZHD6crFdPpA6ALFGC/OUX8sQJ6/+uKDIAAQGknR05cCDbrG7D/BPzM04Xx1On5Ps2Iw0/y+FzviABxs2cnrIDDAZG24G7W1dJ/WBWQnJzfItPQlPzyAwT1mvX5F1fjk68snRK2jvy9CQBdm4DBj4NTLTJthvbCC049eRUmU0ULkxWrpx6xTaDsHKlvLfr1qXv+FWrjGuXQqFQZEYGDyY1GvLKlbQdP2RI+o63ekJCRHlv0IDU69ltYzc6jHagd6h3os33ee/jxDqgzt6OjIxM87Anvv+Mv+BPZsmiz3C3eyVKv72PzDDHf5lLhbPwfNUCKWscFES9BvyziTP1htQrKEuWyPx1zx5xSikwsQBbr2pN/v67qDQPHqS6T4WCJC9eJL/+msySRb5jBQqQvXs/+67FmceG4GBy8WKydWvSyUnsKFaMHDtWtCJrpPfW3oQWXHN5TdKN4uPljfz+e3LUKPLGjSSb6g16tlzRknaj7HjY57AJLE4fBgN58iTZvfsLIbpoUbJ/f/LgQeOJ0G8iMFB8AT75hLS3FzuKFCEnThRnCIUiTYwfTwIMPX+S9qPsOXDXQJJk585k9uyykZZadtzYzjs5wYBGtVPU/ul9bxLg7v4tUj+YlZDcHF9lzzUzCaG0/nBD1D3vtHe0fz8A4HGd6sifNX+iTT4t/SmalGiCkYdHItReB4wcCVy6JHnrMhkGA/DHH5IbunXrtPXRvr2EiWm1gF5vVPMUCoUiU/HokRSK7dRJrrtpYcAAyQE4ZoxxbbMaVq+WGOUpU+AZcB6LvRaj/3v9USJXiUSb1y1aFyfKOMI2XgecOJHmYenvj/s2bnBz06jwVYXCQjwukAMuj8JT1Fa3eydsCMQ1aQQbTeqXZh06SKqEadMAe1t7dKnSBdtubkNo5zYyQVYFDxWpwGAAduyQWhFVqsit7OuvgWPHJHXCnDnyWlJFjY1NnjxA166SLiQ4WNLVFC8O/P47UKgQ8MUXwMGDkiXLGljitQRzPedi8AeD0b5i+1dfNBikuuP33wPu7hLzv2iR5DMrW1ZylEycKLUoXsJGY4OlrZeiZK6SaL+2PR5FPjLfCSXDkyfA3LmS/7tOHakt0r27FLi8exeYOhVo0EBSZZqD/PmBnj3l+xsUJGmNSpSQItyFCwP9+wN37pjHFkUmwWCQYuX162NZ7FnEG+LRrVo3BARIEc4ePSTdTGppWOIj7CtjhxzHPVJUvTOhcLJDidKpHywDoERpM+PiAjg5GeAPN8Q/vPfmA5Igbu9uXM0H1KzRMsk2Go0Gk5tORnhsOEYdHiXJkp2cXiRgykRs2iT1XIYMSXuhQltbEaSvX5cJj0KhUCgSZ+JEySs5fHja+8ibF+jTR663t24ZzzarYckSoHJlsGpVDNwzEPmc82FI3SFJNnewdYB9g48QbwPw2cZzWrAPDMIDW3e4uSlFWqGwFDFu+eEaHJMipezRuiUIdgIqN/86TWNlyQL07i1CzK1bQLeq3RBviMfyJydE9PrnH+VtoXgj8fGivVSsKGnIr14Fxo0DHjwQIfqDDyxfM9PZWUToAwdkvdavn9Qf++gjyWM9bx4QE2M5+6Ljo/Hbvt9Qp1AdjPnoPzvuU6eKMlq/vgjRDRuK0h4SIm/ylCmi3g4aJDlm338fOHfu+eE5HHNgfYf1CIkKwbij48x6Xv/Fx0cEXnd3mcdpNCJO+/nJd6VmTcvndM6RQwrQHTwob2Pr1sDs2UCpUkCbNsCZM5a1T5FBOHQI8PYGevXCIq9FqO5aHVUKVMGcOVK884cf0tato50jghu8C8cYHXj06Bvbh93wAgDkKF05bQNaOUqUNjMaDeDmpsE9G7cXlSFSS1wcbI4dw/7iwMelki/zWblAZfSs3hOzzs7CjVg/uSKvWgXExqZtbCuEFC/p0qVlopIe2rQRr4BRo+RCo1AoFIpXCQgAZs2SyX7Zsunr6+efAQcHWfhmKm7eBE6dArp2xcYbm3Dk3hGMajgKORxzJHtYg4otcLogELtnR5qHdg4OQ4DGXRU5VCgsCIsUhnM8EB3om3xDgwHZDh3H/tI2aFy6WZrH69NHPFenT5e5/ztu72CR1yJxG7x/X5Q7hSIRSGD7dln/fPutCL/Llomn62+/AblzW9rCxClbFpg8Wby3lywBsmUDvvsOKFoUGD1atF5zM8djDvyf+mN84/GvFjbUaoGffhLlfOVKCTdbvVrW5Y6O4vL9008SzeztDYwdK7/bxo1fEaYr5q+IblW7YY7HHDyMeGj28/PwADp2BEqWFPG5TRsx+dw5ee/T4jFqDqpXl+/IvXvA//4HHD4MvPce8OWX8jYrFEmyYAGQKxcuf1gW5/zPoXu17oiJke9/ixaiP6WVIq26IdYWCFy/+I1to+/cBADkf1ZIObOhRGkL4OamwQNbd9gHBqetgzNnYBcdi9NlnPFuwXff2HxUw1FwsnPCoH2DJP7p8WOZfWQSduwAzp+XMK707uDb2Mi84eZNYMUKo5inUCgUmYoJEyTSbNiw9Pfl6iqL4KVLxfMm07B0KWBjg9gObTFo7yBUzFcRPd/p+cbDmpVqhgPFAYcLl4DwlIX+/5ccIVEI0rsqUVqhsCAOJUoBeBFymxS8fh0uYdEIqF0ZzvbOaR7P1VXSKf37LxAWBnSv2h3n/M/h8nslJCxlwYI0963IvFy+DDRrJuKKwQBs3izCY+fOsmGcEXB0BL76SlJGHDwoXrrDh4tT8o8/isZrDp7GPcW4Y+PQpEQT1Cta78ULWq2k0Pz6a2DPHlF1s2VLuqMSJUQ5PXZMQqz/I0wPqz8MBhow5oh5cp8lbFo0bAi8+y6wcyfwyy+yabF4MVCrluW9olOKu7ukjPPxAYYOBTZulM2NoUOBp08tbZ3C6ggJAdavB7p0waIbq2BvY48vK3+JFSskPcxPP6Wv+6ZV2+BEYUB34M3Rkbx/D08cgAKFy6VvUCtFidIWwNUVCNS4wzk4LE3Hc/9+GDSA/UdNYGfz5iRNBbIVwO91f8eWG1twsISNJLZeujRNY1sbpOyGFysmEyhj8PnnsqOqvKUV1oCnnye6beqGfjv7wSfMx9LmKN5y/PwkRLNr1/R5B7zMr7/KhuCECcbpz+IYDOJm1rgxZj7cAO/H3pjcdHKK7tclcpXAzaoFYWOguPKkFhLOYTZ4qs+pRGmFwoJkL1URAPD4xoVk2wWcOQAAKPThp+kes39/IDJSUkh3qtwJ9jb2WHR9pVywN28W70yFAvJV6N0bqFpVROipU6Xs0GefZRyB8b9oNJK/ePt2Eds7dpR0HmXKyIbNheR/iulm+unpCI4KxuiGo188+bIgvWBB6nJMFismqQP+I0wXy1kMvd7phQXnF+Du47vGPIVXiI+XqUyVKrJpcfs2MGmSZBqZMAEoWNBkQ5uc7NlFP7hxQ7y9x4yROa3KdKR4heXLgbg4xH/dDcsuLkOLMi2Qxykvpk6V30WDBunrPl/WfAgu6Ypc9wLfmOory0N/+Oeyh52tmZL5mxklSlsANzcgSO+KnKFRaTo+ctdWnHMFPqyadD7p/zKg9gAUzVEUPx8YDHbqJHfs4DR6alsR+/dL2NBvvxmv4IZGI3MIb+9Mo90rMhgkceDuATRZ2gQ159fEpuubMNdjLkpNL4UuG7rg8qPLljZR8ZYyfrxs1g0darw+CxWS9do//0gYbobn2DHAxwdRHdth9JHR+KTUJ2hWKuVh+fkafYZoO0C3b2+qh44LDkS43hUAlCitUFiQfBUkxDba+0ay7R5fOAkAKPZe2lN3JFC9uqSrnTEDyOmQFy3LtsSyi8sQ372rKExqUvvWQ8rXoGxZ2bz48UcRG/v3zzie0SmhYkWZU/j4iFfv9u3y+/j4Y9MURQyLCcPEExPRskxLvFfoPXkyPYJ0AkkI00PqDYGdjR1GHRllrFN4TmSkXENKlRIPdFJSX9y5IynXXFyMPqTFKFJEdMeTJ6V45jffAB9+KGK14i2HlCT7776L3Vn9ERgZiO7VuuPgQdnAGzDAOBt4hjJlkDVGD75hAZQ9MAyh+ZOJsMjgKFHaAri5AZH6HMj22FY8qlJDVBScPC7gQHGkapHraOeIwR8MxvmA87jVoo5MTlevTqXl1sfo0bJT2727cftt2RKoVEmuRQqFuTDQgI3XNqL2wtpotKQRLj+6jAmNJ+DBTw9wt/9d9H+vPzZd34TKcyqj5cqWOPHghKVNVrxFxMbKwqRjR4kuNSaDB4t3ysSJxu3XIjxLbrmxHBEeG45h9VKX56Rx+eY4VgSI2b0t1UMH37gAf4garURphcJyuBWrjGg7wHDPJ9l2hqtXcN8FKFP0HaOMO2CA5E3dvFlSeARGBmJ3lgdAnToijBlbjVNkGAICJBq0a1cRbS9dAqZNs96c0cbA3V28eu/flzTNFy5IUcTatSUq31hesVNOTkFYTBhGNXwmEhtDkE4gEWHaPbs7vq/5PZZ4LcGNYOMoqMHBYnLRolJAskgRYOtW4OJFEaeN5fxljdSuDRw/LtO3GzeAatWAv/5SXtNvNWfPSshFz55YdGER8jnnwyelPsHUqUC+fBJ9YQyyVKoGAAjxPJZsu3zBUYh0y2ucQa0QJUpbgISFYjDdEPXQJ3UHHz8OW50et6sXRSGXQqk6tF2FdrDV2GKR4ZzEa2Vwj4lz54AjRyT0O0sW4/at0YjwcvIk8ND8dSQUbyEk0X5te7RZ0wbBUcGY23wu7va/i0EfDIJLFhcUdCmIyc0m4/5P9zGywUicfHASH/zzAUYdNr6XhEKRGPv3S5pjY03EXqZ4caBLFwm1zdB5/aKjgbVrgbZtsezORhTLWQy1C9VOVRcNijXAoZK2yHbTBwgMTNWxj70vK1FaobAC7O0c4JfLDg4Pky9q7nznAXxcsyB7FuNUCGvZUq6nU6dKMfT8WfNj0YVFUvDw+nXghNrMftsgpU5OxYqS0njyZMkOVS5zpiZNlJw5JU2zj4+kIAsJAdq1E4/xWbPEOzitBEcF469Tf6F9hfao5lpNfnzGEqQT+K8w7e2NwR8OhpOdE7SHtenq+tYt4PvvRYTWamX/6uhRebRoYRzzMwIajYjvV64ATZoAAwdK5MmtW5a2TGERFiwAnJ0R+llTbLmxBZ0rd4bPHXts2yaFhR0djTNMvhp1AQDB55MWpXVPwpE70gBDoQycM+cNvCWXGesiYaHoDzc8unku+cb/IW7PLsTbAHmbtkr1uPmy5kOjEo2w6vIqsEsXyXuRgeNT1q4F7OzkBmIK2raVfzduNE3/CsXLzDwzExuubcDIBiNx48cb+K7md3C0e/2Ol9spN4bXH457A+6hU6VO0B7S4pDPIfMbrHjrWL9e1kONGpmm/x49RNPdscM0/ZuFLVuAiAiEd2iFvd578UXFL6BJZXxfVoesCKtTXf44dChVx0bdvalEaYXCSgjJlw3ZAkOTbkDC9WEYQosVMNqYtrbi5XjsGOB13h6dK3fG1ptbEdKikRRXUwUP3yoCA2U907mz5Fa+cEHEtvQWhs+oODoC330ny99168Tj8ccfRZAdNizV+8AAgD+P/4nIuEhoG2hFwfztN0lzODLNAAAgAElEQVTObSxBOoFixST3iF4PfPst8jvnQ7/3+mHV5VW4FHgp1d2dOCH5lBNSuXz5pQiyW7dKCou3FTc3iTRZvFjej6pVJaIgtcHtigzM06fAypVAhw5YeX874g3x6F6tO6ZPl4iBPn2MN1SpCh8iLAsQd9kryTaPrnsCAGyLlzTewFaGEqUtwMuidPjtq6k6Nmr3VpwqBDSs2CJNY3es2BF3w+7C66OKcqPMoN7SpEwmGjY0XdhZuXLiVbBunWn6VygSuBBwAb/s/QXNSzfHsHrDUlQQLatDVsxrOQ+l85RG5w2dERyV8XPEK6yX+Hhg0yZZZxk7MiWBDz4A8ufP4NfcJUuAwoWxOl8g9NSjY6WOaeqm+EdtEZ4FiNy5JVXH6e77wBdusLUl8uVL09AKhVHQaDQfazSaGxqN5rZGo/nN0vZYgki33MgblHT9GMOD+3CONSC+tHEXmj16SCGvadOA7tW6I04fh1U+2yTMZc0aICLCqONZMyRh4NupJu3dK8W4duwA/vxTNirKlrW0VdaBra2I9SdPStqG+vWl2F2RIpIS0tMzZf0EPA3AzDMz0blKZ1TIUw7o1UuU77lzTeNiXLy4fJgHDgCLFuGX93+BSxYXjDg0IkWHx8ZKDuXatWXOdfgwMGSIpPxZsACoUMH4JmdENBpJc3P5smgNAwYAn3zy9taKfeuuoWvXijDdsycWeS1CNddqKOpYFf/+K7dRV1fjDZU/WwHczm8Lh1t3kmwTcu08ACBryfLGG9jKUKK0BXhZlI7xSUVMSFgYXK7cxpGSdqhbtG6axv683Oewt7HH0qB9QNOmIkpnwK2/ixelMEe7dqYdp21bCV9Ky865QpESIuMi0XFdR+RxyoN/W/2bKq/KbA7ZsKrtKgRHBaPH5h6gyhWpMBGHDwOhoS8iSEyBra147WzfDkSlrQ6wZQkMBHbvBjp3xqqra1AmTxlULVA1TV01K9cch4oB+v37UnWcxtcXd+3cUaDA2xNyq7A+NBqNLYBZAD4BUAFAJ41G89bJHbpCBZHviR76qMRzAzzyPAIAcKpsnHzSCbi4SMGu1auBvPoqqOZaDYu8FkkKj6goYNUqo45nrZz3P48Ksysg+7jseH/h+/h++/eY7zkfHn4eiNHFWNo8k6HTidDYrBmQNy/g4SGpDt9W7+g38f77wIYN4j3ds6dsjNesKWksVqwA4uKSPnbc0XGI08dhRP0RUlnx8GEpjmHKUKVevYC6dYGff0buiHj8XOdnbLy+EZ5+SSvpvr7iCV6kiKRKe/xYihnevy/1mYwpsmUmChYEtm2TPYbDh8Vr+sABS1tlWoIig7DHew8mHJuAjus6otzMcnAY7YCvN3+NyLh05LnJSCxYAJQrhyulcsDDzwPdqnbDwoWS5qd/f+MOpdFoEFgkD/LeT9q5LNJbnFjzlK9u3MGtCLVksQB58wJ2dsRDuEH/4F7KDzx8GDYGIvyDGomG9aeEXE658HGpj7H6ymoYunSWu9HRo2nqy5KsXy8L7s8/N+047dqJV7ZK4aEwFX139sXNkJtY3mY58mVNvWtjdbfq+LPxn9h6cytmnJlhAgsVClmkZc0qi1xT0q6daCa7d5t2HJOwciWg1yOozcc45HMIHSt2THXqjgQq5a8Ez3IucHn4SFyYUohjQDAe2rnDzc0IJcEVirRTC8BtkndIxgFYBSD1eecyOLbFigMAgm6cT/T1kHPHAbzIKWlM+vYVcXL2bCl46OHngStFnYHKlTN9Cg+SmHN2DuosrIOI2Aj0qNYD9rb2WH5pOb7d9i3enf8uso/LjjFHxljaVKPz4AHQoIEU9evRQ2p1VapkaatSQVCQqIDDhkloVufOwC+/AFOmyD320KFU3RNTQ+nSkl/a11eiDEJCZPgiRYDhwyUf9cs8CH+AuZ5z8XW1r1EqNqvYWb++7AiZEhsbKcDxTCEbUHsAcjvlxtCDQ19pZjBIto8OHaR44ZgxwHvvyfzq2jVJW5I1qwnsi48HvLyAnTtFqP/jD+CHH8TroHNneXNPnRK37QyARiMpX86ckdzkjRsDI0ZkviKI4THheH/h+8g/KT+aLWuG3/b/hlMPT6F8vvLoWrUrFl9YjHfnv4vLjy5b2lTTcvWq5Lbp2ROLLy6BnY0dOpT/EjNmyM+7ugl04ZiSRZE3LE4K9ySCzucO9BrAvWxN4w9uLZDMMI8aNWows1CwINnO4V+ealA6xceEfduNkXbgjKOTSZJ+fmS9euTAgaSnJ2kwpKyf5ReXE1rw+PW9ZLZsZI8eaTkFi1K+PNmwoenHMRjIMmXIxo1NP5bi7WPFxRWEFhyyf0i6+jEYDGyxogUdRjvwnN85I1mnUAg6HZk/P9mhg+nHio8n8+Qhv/zS9GMZnerVyZo1Of3UdEILXnl0JUWH6XTkvn1yK/7sMzIuTp4fOu1zEqB+wYIUm+Dt5sRSTpfYokVaTsDyAPCgFcw31SN9DwDtACx46e+vAMxMpN23ADwAeBQpUoSZjZNLxpIAL6+Ylujr51vXYagjGPw0yCTjf/65XE/vBT2i3Sg7/rrnV3LqVBIgL1wwyZiWJiw6jB3WdiC04MfLPuajp4+ev6Y36Okd6s11V9ax9arWhBZceG6hBa01Llu2kLlzy9Ju+XJLW5NC4uLIBQvITp3I4sXluwmQtrZkhQrynKPji+cTHk2ayI0zpYvfNKDXk7t2kS1akBqNDPvRR+SSJWRkJDlw10DajbKjz2Mfsm1bMksW8sYNk9nzGqNHi1FbtnDc0XGEFrzgf4F37pAjRpDFisnLuXKRv/xCenub2J6nT+X6UqTI659XrlzyeRYs+OI5BweyVi2yb1/y5EkTG2ccnjwhu3UT8+vXJx8+tLRFxiFWF8tGixvRbpQdxx4ZywN3DjA0KvSVNnu997LAxAJ0+sOJC88tpMGEvz2LMnAgaW/PeH9fuk5yZauVrbh2rXzmGzeaZshNE3qQAEMP7kz09RMNS/NBThvTDG5GkpvjW3zimppHZhKla9Yk6zjtoVeFPCk+JqSEG3eVBK8HXSdJjh8vn6C9vfxbtiw5ciR582by/UTERNDxD0f+sP0H8uuvyezZ5e6aQbhyRc531izzjPe//8ncKMg0awbFW8rtkNvMPjY7P1j4AeP18ck3josT5SoZgiKD6D7ZnWVmlOGT2CdGtFTxtnPokFxz16wxz3g9e8ptKTraPOMZhUuX5E2aPp3vL3yflWdXTra5wUCeOUMOGEC6ur5YnwHk7t3SZtXFlQx0Bh+1bpZiM8IcNczpEMRevdJzMpZDidKZ45FSUfrlR2aa4ydw4/ROEuCpUd8l+vq1Sq48W8TeZOMnXLvnzSNbrWxF10mujH8UIBebvn1NNq6l8PTzZMlpJWk70pbjj46n3qBPsm2cLo5Nlzal3Sg77rm9x4xWGp+4OPLnn+WzrlbtzetAq2HPHvEyAkSsbNuW/PNP8vBhETgTMBjIx49lAbhvHzlmDFmggBxXsya5du0b58jpxceHHDWKLFFChs2e3UCHmov50agR1K3dIE+OHWtSG14jNpasVIksVIh3b/rQvk1PulW6TkBE9CZNyBUryKgoE9sRFCQqeO7c8j58+CG5dCl5/Dh59+7rk7mHD8n168lBg8S7ztlZjvvqK9LX18TGGodFi8TsvHnla5yRMRgM7LaxG6EFF51flGxb/yf+/GjxR4QW7LKhS+Zbb8bEyIfarh2339xOaMGN1zbygw/kt2+qy8yRPQtIgNcn/S/R173K5eK5UtlMM7gZUaK0FdKyJVnC+Qrv5XNI2QEBASTAcS1yPd+Zeucd2WAMCZEJZ4MGr+7kJncTaremHfNPzE/dvr1ywMqVRjgr8zBypJynn595xvP0lLcoFc5qCkWyxOpiWXNeTeYcn1M8LBLDYJCJ+ddfi9tLgQKy6rh0Kcl+D949SI1Ww+6bupvIcsXbyI8/iqPSEzPNPXeKjsMtW8wznlEYNIi0s+OD2+cILTjmyJgkm/r4kOXK8bkQ3bq1rKlDQ0WM/+YbaRccGcxVlcCIPNlS5A0WHxbKONhRAz2HDzfWiZkXJUpnjgeAOgB2v/T3/wD8L7ljMtMcP4GIiGDqAR79ulGirwflsOeOuq4mG99gkACOcuXI9Vc2Elpw+83tZMeOZM6cZlCrzMfKSyvpMNqBhaYU4rF7x1J0THhMOKvMqcLsY7PTK8DLxBaaBl9f0QABsk+fDLKZ6+0tbvyAKD1btqTe4zk6mvz7b7JUKemndGly8WLT2PsSer1Mzd9veYOwf0KAzG/ziD1ybeDm9fFm9fG6d4+c8fNdNsFu2tvEixid5xaHjIjivXtmMCA0lOzXj3Ryks/gs89EiE4tT56Qv/8uE6KsWcXrLibG+PYamWvXZE9Ao5ENC33Se2BWjfagltCC2oPaFLXX6XUceWgkbUbasOyMsnwYnkncxUnxvgHIXbvYYW0H5pmQh4ePxhGQIABTcS/Ym7E24LmuTRN9/UEeex7+oLDpDDATSpS2Qvr0IbPbP2aUHVK22Fy2hAQ4ZkobkuStW/LpTZr0arsHD8ihQ+W1FSuS7m/dlXWEFtx3a4+E2Xz8cXpOx6xUqSITMHNhMEgI1CefmG9MReZmzJExhBZcf3X96y/evSs7LwnuGNmyiTD9+eeknZ08V6MGOWMGGRz82uHDDgx7sfBUKNKJXk+6u4twai5iY0Uv6dbNfGOmC51O3qQWLTjx+ERCC94OuZ1k819/lZ/yggXi/PUyXbpIlGtCCo+xXz0LZ7527Y1m+J89yHso/NwzMiOiROnM8QBgB+AOgOIAHAB4AaiY3DGZaY7/Mr4uGh5vXO615w2hoSTADd3rmHT8VavkErJmfRzzTMjD9mvai7cpIHkIMgGXAi/R8Q9H1v2nLoMjX58XJceD8AcsOLkgC04umOHElUOHxF/B2TmDpOt4+pQcMkTSXDg7i2dxelV0nU52dWvUkO/04MEmTelBimdp1TlVWfGv97j8o/nsiJV0ySqisKOjOJ7Nnk16eBhXWw0Olkiq4cPFIz4hC0bZXAH8FX9y1fhtxAhwyokpxhs0KQICZEFuZ0d27y5e7Onl9m2yVSs5qVKlyK1bTf5ZppenT2XeBohOkMiyzKpZdH4RoQW7beyW6nQcB+4cYNYxWVnv33rU6U0bqWA2GjcmixRh6JMgOox2YL8d/diqlQQBmNIxx2Aw8Gp+G3rVLvH6a/HxjLMB93zxrukMMBNKlLZCxoyRdz8STowOePMkyPeL5gx1BDdeXvfK8YnthOr1ojMnJ6JGxUUx29hs7Lm5J/nbb5KfIjAwradjNm7coMl3qxLjl18kTcp/BQSFIrU8jX3KPBPysPny5q++YDCQP/zwYpaZkLju5RDGR4/ky58wG3VweC2PTZwujsWmFuP7C9/PvPm+FGbj+HH5qi1bZt5xu3UTYTo21rzjpokEgWf1atb4uwZrzquZZNME/bply8Rf37pVutqxQ/6euqwvCfDplAlvNOPKimk8jjqvHJ/RUKJ05nkA+BTATQDeAIa8qX1mmuO/jFdxZ56v+Hqqvkf7tpAAt01KPLWHsYiPJ0uWfJa6dXs/Oox2YOjTYLJiRRF+EnbAMiiRcZGsMKsCC0wswIAnAWnq44L/BWYfm51V51RleEy4kS00PgYDOWGCLN3KliUvX7a0RSkgIOBFqo4vvxQvKmOi15O9e/O5y7gJ3VaP3jtKaMHNCwbJeAMHMjZWpgL9+pFFi/L5VN7eXvTy774j588XL+tLlyTa97+CtcFAhoeLb4qHh9zHx40j27V7kR8aIG1syLp1yYkTyevXKWpZkSJk+fKs93cdlpxWMtnUNenm3j3xTHd2Nk3uil275IsNkD/9ZPXCtMEgmxD29vLZnz1raYtSxj7vfbQbZcePFn/EWF3aJtuLLyxOlZe1VZOQ72rCBM4+M5vQgusOXSUg2WlMzb53cvGem/Nrz4fcukgC3PdrO9MbYWKUKG2FLF4s7/5NlOL9w1vf2D7YLSc3ltc8nyxVrUrWSca54n//k5uWv3/SbTqv78zcE3Izzus8E3JhWjtjpWYM798377gnT9IqnUrOnJG0XIsXkzNnSsTT0KFy8VQ5sK2TaaemEVq8Hl46ePCLybSPz5s7On9edp6A11xkZp6eSWjBwz6HjWi5wlhcvy7X6OHDJX3inDmSfm/jxmcLDCti4EDZ+wgLM++4CeLszsRrflgXz2oz3H54idCCk45PSrLpnj1MNj93TAyZI4c4HpHkiXvHeTcH+KBxrTeaceaP77ka7QmQFy+m5UQsjxKl395HZprjv8zR2u70SSRV3+UJv5AAT+w3fcqBuXMpERTrbhJacPaZ2eS2bfLkzJkmH9+U9Nzckxqthnu996arn123dtF2pC2bLW3GOJ31CvVhYS8yX7RrR0ZEWNqiFBAUJHkOTCBi+kX4canXUnbd2JUtl7fgrZ5t5M3p0kV2ZEzAF2u/YM5xOah7v47sMr/sPEIRKe/cEQfuwYPJRo3kvv7f2n+AvCWFCkkx6YRgyP8+SpSQQtN//kkeOJDEfGzTJhLgaW0vQgvuuGminekbN8jChWlwyc4ls75jo8WN+Nve37jPex+j442YOyYuTnLHmcn73RicPi17Aw4Ocs21ZpMvBV6iyzgXVpxVkY+j0+dx99WGr2gz0oaH7h4yknUWwGCQndtChcioKNaaX4uVZ1dmt24GOjubR1PZ3K4y423wmjfO9c3/kACPzxlieiNMjBKlrZADB+TdP4AGvPjP+OQbP3pEApzZUVz6r1+XY//6K+lDrl6VNlOSieDZemPrizD/qlXJ995Lw5mYl3fesYyZer1cpz77zPxjJ0Z8vOzGJzZ5sbGR/Faurhm/+EJmI1YXy8JTCvPDf/6Tf2bSJD4XpFMzi4mOlvLPdnavqHeRcZHM92c+frJM5ZyxJgwGmag6OcnvNLHfr62teFxYAwaDeH00b/7GpkYnJubV/MpWS1QU6eJCdu/OPw7/QWjB+2FJ75p+9ZUsTpOLWO7WTdrExpLx+niuqu7A0Nyve0/8l2PffcrJ+IlAxo3qUaL02/vITHP8l9nb9h1G2+E1z82zXRoxxhb0f2xkj9FEiI6WNA/NmhlYeXZl1ppfSy7w9euT+fJlEGXzdVZeWklowd/3/Z50o/BwUYt27pSQn+nTxXNjwADxjHuJ+Z7zCS04/ugb1mUWwstLnNvt7GQNaM2i13NCQiS6z9GR3L8/3d3F6+O5/eZ2Dtg5gBVnVSS0ILRgngl56D7ZXdJXtMhLAtR91tLouYl9I3xpN8qOf//RmgRSPGEzGCT15t695OrV4owwZoxs/HfvLp7U//ufeD8vXCga89Gj8valeID33qOhcGEWGV+Any7/NO0nmRReXtTly8MnLo6s84MjoQXLzyxPu1F2hBZ0/MORTZY04YRjE3g50Aju+wbDC+/3DFIoIyiIbNZMTO7WjWbNMZ5S4vXxrDS7El0nufJe2Esh93FxEo3788/iBTh3ruys7N8v7v1JRB88iX3C0tNL032yO4MiM6hHXEIu6X/+4dVHVwktOHTDPNrZkf37m8eELcO+IAFGnD/9yvOnJw0kAV4+sNo8hpgQJUpbITdvyru/BF14aliPZNs+2b+LBLhifBeSkkwfeHPkU40aUuAkKWJ1scw5Pie/2vCV3AUBqy7ZfOeOmDhxomXG799f0qBZeu7++DHZtKm8FwMGyCTV21uyr0RGyj3cy4usUIEJUWUZoV7EW0FC7q5X8j0nhE106JC2sr5hYTLhd3YWl/5nJOStPu9/3giWK9JLUNCLVHlNmkjopl4vUZf+/nLp9fAQARiQTC6Wjqo+e1Zs+fdfy4z/5Zdknjwmc3YyDgkT2b17WWl2pdc3nF7i6VOp4dOzZ/Jdbt8uXW59FkS1/IuK8sR/vLH+y/HmVfmd3WRmzWrIGGJFIihR+u19ZKY5/svs+0ncWsPuvhoGc+G9YrxawMZsabbGjaNEw/+7hNCCVx9dFbEWIIcNM4sNxuR2yG1mH5ud7y98P2nP5n37xAU1sR3gLFnEg+P331+52bZY0YIu41ysTlxZulQ2tN3cRKzMEISFkTVriuvorl3p6ipeH8/FFxaz5LSSrwmgnn6e1Bv0jNPFcfnF5Xzn73f4wyfyOd+uUYJBj3yMdELkiIMjqBkBRletJLv21pRjbO9eEuD2fp9Qo9XwVsgto3V9ZfNCPslqzwcuYMW+tvxqw1e84H+BJBkRE8FtN7ax/87+r2wUfL7q8+dt0oxeT/boIb/ZP/4wwpmYHp1ONHRAfP5uJ11ixCIkbL6tu7LuxZN370oIPiAbSIldM5s1S9Jl+Lz/eTqMdmDz5c0zXurI2FjJcVWpEqnTcfDewbQdacte3z+lnV3iqXJNweF1U0iA1+eNe+X5o9+3IAEGB9w1jyEmRInSVkhk5LPrK37jsW4fJdvWe8owEuCenZI7tlKllBX6mzZNxkgu11iPTT2YfWx2RvvclsmZVpua0zArCbr5nTuWGf/IERl/5UrLjE9K1FTZspK3asGC5NtGRb2IfKpa1Tg1KBRpR2/Qs/zM8qwyp8qLG/bWreIa27hx+nYOAgLkhpo79/MP+nH0Y2Yfm51frP3CCNYr0sOePbKQdHCQ6JXkUh3qdJLDHpCQzxR7yZiAwYPFI8tSNmzYIO/Dvn2WGT9FtGpFurnxsp8XoQVnnJ6RZNOlS+V8jhxJvsvYWCl2+NVX8vea4e1IgDEep5M9zqOGO5s5rWXZsqk9CetBidJv7yMzzfFf5uhMyTt7Y8fSV56/X8CJ+2u8nmvaVISFSVBHq7bRtB1py8F7B8sLHTrIprafn9lsSS+xuljWnFeTOcfnpM9jn9cb6PXk6NESklShguTGOnlSJtFBQXKjffpUQnEACcF8tri4+ugqbUfasu+OvmY+q8SJiSG//17MrF8/+bSMVkVEBFm7tixYtr45TWVS6PQ6LvNaxjIzyhBasNrcalx/dX2yqSIMBgMP3j3IKb2rUqcBj5V2lDzq6SRWF0vXSa4cNeAdJnhVWhXPoh90BfIz+1BbDtw10Cjd7ts6nU/sQe/cNhy/tPcbC4L6Rvhy5KGRzDEuB6EF265uy0uBl9JugE4nEyJLeqalge3bZS6XIwe5ZYulrRGexD6h6yTXV+sOrVolNwcXlxciR1QU6esrueAOHZKI3ixZJGz8xIlE+55xegahNVOhTWMyY4Z8t7Zvp06vo/tkdzad+yWdncmuXc1nxu17F0iAHj+0eeX5oy2qMsQJGU/sTwQlSlspuXOT3exn8UST8sm2u9jzM8ZrwCu+F3jlClOc/jkwUPSuwYOTbrPn9h5CC264uoFs2FDiwqz0S//ee+L9bSl0OkmJ0c5Ceeb37pXCX3nzSqGMlLJtm0RnOjpKJI7CMmy4uoHQgisurpAnjh6VD6VmTeO433t7yxe0UKHn27qD9gyizUgbo3pLKFKOTidRcIDU9zmfCqf1f/+VtVypUuS1ayYzMUkMBhm7aVPzj51AVJR4FvfubTkbkiUkRD6kgQM57MAw2oy0of+TpBWDpk2lWFFK6i/16CHrg+hocsdqqWx8f0HyE/0bhbOykvNpNmqU2hOxHpQo/fY+MtscP4GLe2Q36uzUlybjMTGMtwG3dqhmVlsGDxadtuGUXnSf7E6dXidufPb25LffmtWW9DBw10BCC66/uv71F4ODX9Tb6Nz5jREmXL1aVCMXF3KFzM96b+1Nu1F2vBF8wwTWp5z792XtA8hmtVVHDb3M06fiPWVrK7vLaSA4MpjLvJax3MxyhBasMqcKN1zdkGph5sZ42RRa2KViukWdVZdWUTMcjChTVAr9WeMH8syDaslXVZhzfE5GxqUvf8SdwBs8727D0Gx2fOKduqInj6Mfc/iB4cw+Njs1Wg07rO3AE/dPpK2oXnw8+YWkN+C0aak/3kLcuSOpRwFyyJC0BcQakxEHRxBa8OSDk/I7TfBCr137zV5/np5k8eLirTJ16mt6kcFgYKuVrWg/yp5nfTNItcfwcBFWGjYkDQbuurWL0ILtv7/yRsdOY6PT63g/B+jRqMIrz599x5VXC2YxnyEmRInSVkqVKmQDp608Xzlfsu0u1S/Pm7nBmPgYjhghDs2+vikbo3lz0aiSugjG6+OZ78987LC2gySxAiScz8q4f19MGzvWsnb06SMOJebOETV3rsztKlZMm6d4QMCLHFfpcFhQpBGDwcBa82uxxLQSjNfHizdzzpxkmTKSM95YeHnJ4qpsWTIkhH4RfswyOgu/3ZJxFpuZiT/+4PNU4Wm5Zhw7JhtKOXKY31v4gmzY8++/zTvuf2nfXqKvLT2RT5Rn1cMMHh4sPb00Gy1OWg329RUxaOjQlHW9S7J2cdMm8tyNwyTAywM6J3tMcFYb5nf0ZbduqTgHK0OJ0m/vI7PN8RMIvC+FYI72+/z5cyFnRDjark3+N21s/PzE2a1JB+9Xi6H17SuTzKtXzWpPWthxcwehBX/Y/sPrL546RRYunPpKYy+HrnfvzoDAO8w2Nhtbr2ptVNtTw759opVkzy4FzVOL3qDnuivrWH1udZaZUYbDDgzjtSAz7HAbDFKJ0cZGBP8UoDfoeTnwMud7zufXm75m2Rlln6eAqDirItdeWUu9IQW7uUnYc7tuJUbbgv8u/zVtfTzjw38+ZL9uz9LBPNvAsEqaNWNcrhzM9j9wvuf8NHcTEx/Dv1u4kQADFqe92ElIVAiH7B/CbGOzPU+9Uvefuhy0ZxA3XdvEwKeBKesoLo5s/SyXdxo3O1JDnC6O229uZ+f1nZl/Yn52Xt+Z14NSX408OlrStgESGGuOonmJ4RvhS+cxzmy/pr1M8suUEVFpyJCU5wt8/PhFLsK2bUXUfYmQqBAWnlKYJaeVZERMBqhVMHSonMtZEdE7ruvIXKMKM7NCI9sAACAASURBVHdug0XqiB0vn403SuR45bmbBZ14snryWmFGQYnSVsqnn5JlnC/Q280x2XZ3iuXkwfJONBjE265+/ZSPsWoV3xj+3GdbHzqPcebTR74yW+3XL+UDmImpU+U8bljWaYH794sdaZkgphVPT7lnfPLJa9f+VBETIxshBQpY7ob4trL/zn5CC849O1fcJGvXFrXRx8f4gx0+LIvLZ+6l3239jg6jHegXkXFCczMD586JM8EX6cye4uMjm1G5c4sDmLkYNkzWlIEpXCuYitWr5ZqbmugQs1G3Llm+PM/5ehJacJ7HvCSbJqSfSuk9LC5O8ml/+SUZHhNOv2yg16dJz4H0UZHUwYY2mngOycAFupUo/fY+MtscPwG9XsfwLOCRz995/tyVOVIc5vimpNP9mIrvviMdHAzMO6wy6/1bT7xHHz0S9bNVK7PbkxpCokLoNsmNlWZXej19w6JF4vFdrJgUaEgt8fFy49NoyG7dnheuPeLzhnxLRsZgIMePf5F55HoqNbAEMbry7MqEFiw7oywbLW5Em5E2hBasPrc6Jx6fyAfhJiqwOX8+CUi4/zOi46O55vIafrbyM1aYVYElp5VkwckFmffPvMw+NvvzQnkJBQtbrmjJcUfH8bDP4bSL0S9h8PdnWHYHerqBp+6kLSH3ef/ztB0GhhTJJ3k0UxLyZCmeFQSZ2bIAq86pmmYP8YlTOzBeA977rL5RzAqJCuHaK2s5cNdA1l5Qm/aj7J9/7o5/ODLHuBwsMLEAi/xVhKWnl2bVOVXZf2d/evp5vjiH2FgJm86dO+UeeqlAb9Dz2L1j7LOtD/NMyENowVzjc7H1qtZ0HuNMm5E27LKhS5rE6YULRWYpUuS5BmpWvtn8De1H2fPu1ZMiBri7kwcOpL4jg4H8809Za5Yu/Zq33NF7R6nRavjd1u+MZLmJ8PWVRP0dO5IkbwbfpO1IW9b7dgOBJLOUmJQdn5bhkyyaVzZUwxw13N+8QjJHZRyUKG2lfPstmdMhmOGOmqQbGQyMzGLDjU2L8tIl+cRmzUr5GFFREpGWnOfUYZ/DhBZceWml5KbIn9/qQpI+/FAEVUsTHy9ei998Y57x9Hpx3sifXzYn04uXl8zZ27a12iwtmZImS5rQdZKrLKISChuasnpcv36ysDp3jrdDbtNmpA1/3ZM+DxFFyomOlsWkm5tx8jF7eb2yz2AWatQgP/jAfOMlxZMnkuWmr3Wk93yBjw8TCu8M2jOIdqPsGByZ9K5BlSpkrVqpG6JXLzJbNrmPnyxuzxvlCyTZ9tHFU/SFGwFyzpzUjWNNKFH67X1ktjn+y9xwc+CZd92f/326d0sS4H1f8+dmunVLBM/GXT0ILbjz1k55ISG0x4qr6HVa14l2o+x4zu/cqy+sWycnZYxCDCNGkABjFsxlwckFWWt+LbPl8gwPJ9u0kY/hiy/k/pdSEhOjl19cLilaSPpF+HHqyamsNb8WoQU1Wg1/2/ubcU/g5k0JJ23UiAadjkfvHeW3W759nlu44OSCbLO6DTuv78wem3qwz7Y+/GnXT/xt729cdH4RbwTfMNl7/XTlEhLglKYuyd6rk6Ln5p7s1cZePpyNG01goZH5/HPGZnVkrkHg0Xup/02v91zGq3nB0LzZjLMATYTo+Ggeu3eME49P5K97fmW/Hf343dbv2G1jN3Zc15HNljajw2gHQgtWmFWB44+Ol82Ua9dETGza1KibA0GRQaw6pyqhBZ3+cOIXa7/g5uubn6cbCXwayF/3/PqKOJ3aFD8eHlIfM0uWN9eGMiYXAy5So9Vw4Pb+5Ecfyft3KR15vklJFZMrl+T6Cwh45aVf9/xKaMFdt9JX4NSk9Oolooi3N0nxknYamYMFC+lYr55lTNr+kxQ1jPSW71VkkB8JcF/vZpYxyMhYrSgN4GMANwDcBvDbm9pntgnrKHGUYDSyMDY0cddVg68vCXBtn/ocOlTmXP/53b+Rb76RhW1S4eN6g57uk935+arP5UYLkDt3pvJsTEdQkGlrMJ56cIrzPObR088z6QreL9G6texymmOO+u+/NLp+OX689Ll06ZvbKtKPh68s/CYcmyArDldXSRJoSi+Lx48l7vPDD0mDgR3XdWS2sdkYGhVqujEVz0nII23My2jCPoOnp/H6TIrgYBlr5Mg3t42Mi+TRe0c55+wck+Uub9XKfNfcFDN2LAnQ4O3Non8V5SfLPkmyqZeXfB9mzkzdEHv38nlkzra6BRjsYp9k22vr/+ZpvJvhUzQpUfrtfWS2Of7LnKqah9eLOD//+2zDsryXU2OxwkVffEG6uBhYZFxlVp9bXbxRIyPFc652bSu72AprLq8htOCoQ6NefWH/fknXUafOm/NHpwSdTvKLOjtz47oxhBZcdWlV+vt9A1euSOY1W1spiJyajyBOF8e2q9sSWrDMjDJc5rVMxGidjpw3j/zpJwk53bhRnBVunWHXDV8RWnDyicnGOYG4ONl5zZWLm/bOZIlpJQgtmHVMVnbd2JX7vPc9F8gtRXCbTxhvA/Yf/UGqPLBDo0LponVkUP5ssmNvhb+P17h4kQaNhhMbZJEUnangdshtzvjAgQQYt3O7iQxMGaFRoZx7di7fX/j+882UpkubMnjSaKa4yFYKiIyLZO0Ften4hyMXnpnHJzeviOi6dKls2PXvT545Q/JVcdp2pC3XXF6TqrGCg//P3lmHR3F1YfzdeEIMQnAJ7gWKS5HipVAKLa7Fi30tXigsLsGtUFzaosVdU9xdgksIMUJcd+f9/jgkEKK72WQD5Pc88+TJ7sy9Z2Z2Zu6ce857yEaNxPxevSSQJa1puqGpaIyPHSUdr1xpmIZPnxYHd8WKUk33LeHR4Sy9uDTzzs6bMd89b98Wp9qQISQlEwJq8NsRWwiQ+/YZx6wTq9UkwPv/SATqY7edJEC3mQONY5CBSWqMbwYjoVKpTAEsBtAIgAeAiyqVahfJO7q0Ex0dDQ8PD0RERKSFmQbDysoK+fLlg7m5eexn+fLJX0/kgeX9a8hbrWG87V7fOIfsAMxLlMHmJUC9ekDOnLr13aULsHIlsGMH0LFj/O9NVCZoW7otllxagsBBy+CQNSvw119A06a6dZRGHD4MkECzZoZtlyTmn5+PoYeGQqECALA0tUTF3BVRJU8VVMlTBS1LtISDlUOc7Ro1ArZvBx48AIoXN6xN7xMQAIwcCdSoAXTtarh2hw0Ddu8GBg4E6tYF8uc3XNvpjqIAJibGtiJJpp+eDgdLB/Sr3A/4fTLg5QXs2pW2djs6AtOmAb17Axs3YlT9Udh4ayOWXFyCMXXGpF2/qUWGShn+nCaFmxswZw7Qv79hb6ETJgAbN8p1e+pU2h6io0flNDRuHP87jyAP7L2/Fxc9L+Ki50Xc9rkNLbUAAHtLe/zT5h98U+wbg9rTrBmwcyfg7g6ULGnQpvWDBDZsAGrVwnkLHzwLfIaJ9Scmuvr69YCZGdCunW7d1KsHODsDmzcDP7oUgNNJbyAoCLC3j7duyON78IAMKmLGFplk8rHzMY/x3ycstzOKPXCP/d/+iSde5rFDAZUqvUyMw8iRwKZNKjT0WIt/I7/E1jtb0bZMW3nQ9O4NTJkCjB1rFNsSwjvEG/339kflPJUxqvaod19cugR8950MxvfsAbJkSX1npqbyDlShAr4b/zeq9/0Co46OQquSrWBpZpn69hNgyxagRw8x/+hRGZunFK2iRdcdXbHt7jZMbzAdw2oOg6mJKXDlCtC3rxwjCwsgKip2myIA1tjZ4fv6xdFaGYo8dnnQvmz71O3ElCnAhQs4Mr0PWp0eiMp5KkNdV43vS30PWwvb1LVtIJxW/o2Qk4XQd/5pzKs5Db9+nbLx8Jpra9DhYgSy+wBYOxkw0nWrE+XKQdWuHQbt2IZCVbfBq6kXctnmSnazCE0Epk9uiuWnoxDUqyvsmxp2PKcrWa2zom/lvuhbuS8e+j/EhhsbMP/8fFQ0v4O7jesjy4gRwNdfA2XK6N2HRtGg/db2eHn7HDxOlICT+mdAo4m7koUFsGABMHAgckyejJmNZmJYzWFos7kNOv7bERamFviu5Hcp6s/JCdi/Hxg/Xi6ba9eAbduAAgX03oUkOfToEA48PIDNTv1hM3oG0Lmz3HAMQc2aYnzLlnIvPnAAsLKClZkV1rVah2orqmHIgSFY9/06w/RnCN68Abp3B2xtY59zY46NgaN5drjv+B5ffGE8F1iuyvUAAAHXzgPtf8ab+9dRCIBd0dLGMSg9ScxbndYLgBoADr73/2gAo5PaJqEoisePH9PX19do0QYpQVEU+vr68vEHmjuHDokHxg1f8eaGOQlue3f6MBLg6uW7CEjdDl3RaiXKrGnTxNc59+IcoQbXXF0juiJZshgm4sAAdO8u0lGGLHQVER3B7ju6E2rw+43f87bPbW68uZFDDw7lV6u+os0UG0INVlhaIV7l4ocPqVfUm64MGZJ2kZEPH8opbtAgY8uixcPPj9y1ixw+XKJizM1FB7FkSake0a2bFGxYvjzOjK2xcPdzp0qt4m9HfhNhQHNzqXScHmg0EtGRNy8ZHMxmG5ox+8zsDIsKS5/+k+L5c4luGD5cRHPr1CGLFBGdBltbCSGYMEEioDLIfSglBAZKWl7RomljdlpkTiREr14iU/ShitOzgGfMPjM7oQazzcjGphua8vdjv3PXvV284nmFFZdWpEqt4vST0w36TH78mBmr4PrVq2LQkiUcsn8ILSZZMCA84fuNRiPBh/oWTOnXT7KhN43rSQIMOnMiwfVODfyO8zCYwMddMwCZkdKf7fKpjfHf50jvBiTAiIDXpFbLUAsVD3xbKh2tjE+LFqSDg8ISM2uw+MLiUoRZoyE7dyYB8rffMkREqKIobPlPS1pOsuRtn9vvvrh3TzLCXFzSRFuWhw6RKhVf/tiUUIOzTs9KfpskOPLoCEssLMGCcwuy967e3Hp7K/1C3nD0aDncNWqQHh66talVtLHvMtNPTpcPg4MlMtrERHRj//lHzqOPjwjZbt1Kzp5Ntm1LAjxeyYn248x57LEe2rIxnDlDmpjw7jfVCDX4zV/fxNf8ziAoBw+SAOfWUKVI1kKraFlmdhF6Z7UQTbMMcE2kmHv3qJiYcHZ1cJLbpBRt8uvmXnziAAYXzJ1hx99XX12l0wwnllfnYnT2bGSFClI4SQ8URWHvXb35RT8w2NlBNE9HjZIq3wcOiFRIaKi8Uw4cKC/mefJICpuiMDAikNWWV6PFJIt3Ukg6sGOHdJk9O3nihEJ3P3cuOLeAzf9qTsfpjmy7pS29gnVMkX8PjVbDL/74glUmFaCSK5ekY+iiC5RS/vpLbmStWsV5eRh/fDyhBv+9k/aFKVOEj4/8XiwsyJ07SYoGNtTgD6P2EpBbpLGIjI6gvxV4roXUoDg5siMJ8MWd88YzyoAkNcY32uATwA8AVrz3fxcAixJYrw+ASwAuFShQIN7O3blzJ0MPVmNQFIV3PqhqfeeOnIG/0IHnJiYsBn+1WxNGmoA/D/GnqalcS/rw228yPnn1KnH7XOa5SAqym9tbw/7SrzMDoihy72+rW+ZRkngGebL6iuqEGhx/fHyCKVwarYabb22mSq1ip22d4v3GChVK23owN26kvYbsn3/SkJlPaUdQEDlypIj0xsTSmpuTNWuKTsKQISKSXb06mS+fHDhAdK6mTJHtjcTQg0NpNtGMXkGvZFbI3l53/Z3UcPp07MtlTLHF9deNqNvy8qUM6iwkLZAWFmThwlIwrkMHctgw8uefyfLlZeAHSLXAqlUl9TSDad1/SI8ecp89ezZt2o+pkWkojfmEUBSZxPz++7ifh0WFsdKySrSfZs+LLy8m+NwNjQpluy3tCDXYcVtHg06AFCsmxYEzBMOGkWZm1Ph4M/es3CJ9lQgxk89btujX1bFjsv244XtIgA+XTElwvVOtKnGQ6UxaWSkf1Tvzh2Q6pT/fJSGn9Mc8xn8ft6l9SYBPz+xn4P1bJMD9QxO/b6QHN27IY/b7nu6EGlx+ebl8odVKcAog4ysjH/+119bGdwq/eEHmzy8Pw/v3067zMWNIgK79ytNxuiNfh+muVx0QHsDeu3rTfhS4rGFWHvzahcNaWLFWB3taFNlDgKzc4hKfvU7kBS0RFEVh3919CTWoPq6WD3fskOMCyAtEcgOFOXOoqFS8UtiaBcfa8obXDZ33j0FBZOHCDMidjXajwJb/tGREtH4OwvQiom8valVgz1+KJHt/OfzoMAc2gxxTfYrCGZvu3RlhbsIv1Xlk4ikJrr66yuUVQa2JyjhV3nTghtcNOs90ZpcejnJuRozQq52JJyayYRcw3MZC3iFvJHMNnD8v7ygA+e235NOn9A/zZ8WlFWk12YpHHx/V2Ya/jl2kY95XhEkUs9frx5ZtwXnNsvG/Oi7s/KMps03PyvXX1+v1LFx1ZRVV48BXNb6QwJ/k9i81LFggx6Vnz9jnRpQmihWXVqTzTGd6hxi5cvrLl2SpUiI3ckC0rhVFYe1VtZlrekHmL6Bl5cpGf+TxqosVb5TJTpJ0a1+DkaZgdHSkcY0yEB+1U/r9JbEB68fCh7YGBckZmIHhPNk7YQHz67WK8a6TikWKKGzUSP++796VvmYnIR028vBIKdYU7CNeiWaJa2SmFzHFHQ1VDOCCxwXmnZ2XNlNsuPV28lNhk9wmEWpw3tl5cT7v00f8i1HJS1DrjKJI4KiTk2GKpCXVT/Pm8oy6m/61dlLG7t0ywFapyCZNxMns5iaVvxJDoxHdrxZSSIhOTuSMGek+4x+piaTzTGe23tRa9gMQocD0pksX0sKC2gf3WXh+YdZbUy/9bfDyksgdKytxMvfpI+H6ST3537wRUa/Ro8UpDUjk95UriW9jRHbsEBPHjEnbfi5flsvhrQyawXF3Z0wQcCyKorDr9q6EGtx1b1eS2yuKwqn/TaVKrWKlZZWkKI0BGDBAIob1DIYxHDGhzy1a8MSTE++KBCdC584Sda6vZqBGI8FujZv5kwCvDfghwfXOV8vP5lYbWbSofv1kFDKd0p/v8qmN8d/n8qb5JMCra6bzzoZ5JMBT6xKeYEpPOnUirawUVnRtznxz8r2LblUU8n//Y6zoqSFTFXXgecBzOkxzYO1Vtd/pEfv6imPB3j7txwPR0WSdOtTYWLPkAOhcGHC3+27mnZWHP7RTMTBbFiomJmS2bLyH4iyBuzRDFGda/8x9RcHWfbPyyKMjKWpXURQO2T+EUItNilYrE/oAWbasBCSklM2bqVha8qGzGav/loPPAp7ptI/86SdqTVSs1QNsval1bFG4DE1ICAPzOfOmM7j7zo4kV/1xfUu+tDehtnYGqPysDw8eUDEx4cya4I67Se/rqKlfkwDDfx2cTsaljts+t5nTNSfXVLeiolKRx4/rtP3KKyvZtRUYbaqi8sUXMtmVEqKjxaFiYyNpx0eO0DfUl2WXlKXNFJsUF5aM1kZz9IHhHFsfPJbXkU1NZJKqD5YyEhby/grwbBkHugwBm//VXKcxdUR0BAvMLcAl3+WVe8Off6Z4W735/XfGThK8fce76X2TFpMs2HpTa+NNMj95IkFQtrbiR3jLvvv7JEr615MEJJDE2BypV5De9qYkyTNfufCpk6mRLTIcGdUpbRD5DmMPWJ8/f04XFxe+fus99Pf3p4uLC588eRJv3YRsdXBQ2MtsAU99UzbB9p/kt+OWEsUMkrpcubLo0CfGFc8rhBr889Kfkrpiakp6G3dWa/Zs+ZU+f576tvbe30vLSZZ0mefC617XU7SNVtGy1cZWNJ1gyuNPjsd+vmWL2HXqVOrt+pC//5a2ly0zfNsf8uqVPPNq1DD+zGAcvLykEg9Alimj/4z9+fMyuQJIRM2CBem2o9vubJPK9rd2iDRFqVJpM4uRHC9fykO4ZUtOdptMqMGHrx+mT99hYTIwsbaW+0mPHrFVjnVCUcjNm8U7Z2oqkfNJTUykM0FB8vOqWJGMTIf3sX795DCkRcDDokVyuTx4r2bhwvML40ZjpYBd93bRbqodc7rm5B2f1D+nd+1ixghUOnpUDNm4kf339KfNFBuGRCY84RUSIu8svXunrssePcjs2RW+sAevNP4iwXVuF7ZjeZszrFcvdX0Zm0yn9Oe7fIpj/Bie3jhJAjw1pivPDu9AAnx8N41SanTg4UOZJ27R8QWhBueceW/iXFFiI4XZsWO6ZyopisJG6xoxy5Qs78Ysd+7IeMrSkjxxIn0M8fAgs2fn8wIOzKa2TlEqvV+oHztt68R8v4DHvrCTY1ihAnnxIvftVehgr2V2h0i69f+H7NqVkflyU6MCf2kCqo+NT7IgoKIoHHFoBKEG/7f/f+KQHjBA+vj1V/3GmSdPMtrRnt62KrYdUSjlhcnezsZP+gpst6VdiorFZxSiN8rLlrpHoUQdZS8CX7Bvi7dZexnBW6Un2o4dGGKh4o9L6ie6zm3vWzzqAgY72hg1w1RX7vneY5Gpufgwuwkj8+QSHb0UsNd9D8fVl3Or/fpr/SQfnz6VSSBra/L4cXoFe7HEwhK0m2rHcy/OJbmpR6AHmy2oxoOFQQLU1KpJzcAhHP3NVQJkrRoaer3UkAsXUrG1ZZSlOX9ras6sk+247NKyFDl3F51fxNo9IJNhHTumz/uvopD9+8s106NHbCTJjFMzCDW44fqGtLfhQ+7flwA3R0fy3LvzolW0rLC0Al1mlKOzs8L69TOGL+RAb5kcivTz5o2i9rxU0sHYJhmMjOqUNgPwGEAhABYArgMok9Q2GXHASpIzZsxg77dvnX369OHUqVMTXC8hW8uUIRtabeelL3PF30CrZZi5ipOqdSZAHknZBHqiuLrKGU9sIlBRFBZfWJxfr/2avCXphcYW8WzcWHx5qcU7xJvZZ2ZnhaUV6Buqm+BmYEQgSy4qSeeZzrERBK9fS7Ti+PGpt+19goIkCK9SpfQLTImR8XibyWJcFIVctUqkNywsyIkTDePlO31aBLQBidhNh6dO87+aM8/sPNRMnWL8Ae2MGSRAn23raDLBhGOOpnE4LykDkSZN5ELp3NkwKbb+/pIWBsiL6VHd0+TSgmnTxKS3hbnTHD8/0dmvU8fwP+XvvhN5oph23Z660WyiGVv83UKnavWkRLFkn5mdlZZVSvXLalCQOE9GjkxVM6mnRw/Szo7RwYF0numcZFX7mASJw4dT1+WsWW/v0YXy0L24U4LrvHIwZS6r5+zcOXV9GZtMp/Tnu3yKY/wYIiNCGa0C3Tp/xTPNy/O1NajRZAw5qv795d5ay7Urs8/MzqCIDxxSU6fKDej779PVWfX7sd8JNbjkwtu0nQMHJDo6Rw7dIoENwb59JMDptVUcsj/pNKVobTSrLa3M/31jwghrCyo2NqSrK5WoaM6YIUOiChXEnxVLUBCjW35LAlxTHmy6vF485/fzgOd0Pe3KiksrEmqw3+5+4pCOiWgfOjR1A4K7dxmWLxdDzEH15IbJrx8YyGBnB17NCXbd1CFZaYgMh1ZLn1IF+NQBPHx7T4KrqA+N4WNHMPzL8hnDW6Uvt29TUcnkwYPXDxJcZdrv4ggLdjV+BoeuPHj9gC2G5KBWBQb16Z7s+q+CX3FJLUsSYFSnDql7z/T2FnnJLFnIkyfpEejBwvML03KSJdtsasOtt7fG01c/8OAAa//qyEfZVNSYm8VLB9+4Ufzc+fK9rSv1/LkMzgHeL2DLyr3jZ3B/SGhUKAtNzUkPZysqRYqk70SDVkuOGyf3pdq1SR8farQa1lxZk7ZTbXnFMx0zXi9dInPlIp2dyWvX4ny18eZGQg22+fkagbSTXtSVEwuGkgAf7f2LLx1N6VavsLFNMhgZ0iktduEbAPcBPAIwJrn1M+qANSoqiuXKlePcuXNZunRpRiUyS52QrU2akKVsLtM9n02876KfPSEB9qm3kIDuBTA+5MYNOeMrVya+zrhj42gywYSvgl+R1apJARFjRHdSAiGtrFKfpq4oCltvak2LSRZxi6TowD3fe7SbasdKyyrF6qRWqSKyxoZkxAg5R+eSnmA1KJGRotZi9GhpjSa28Apr15aIGEOiKOTgwdL+6NFpurMegR40mWDCaRsHyUCllXG1IxkRIaK8xYuzxZomzDs7b5KROKkmMvKdfIqhtHfe59gxqSYIyMSFEQkOlgIl6a13vGyZ7P4/iStH6ExUlLzz9+kj/78IfMEcrjlYfGHxRAv5JcfW21sJdcqL7CRF3bpJZ/ukOS9eyGRZ37489PBQssVbBg40jOTIHpGTpuuXrehvaxbveyUyktFQ0cwkiqN0yy7PcGQ6pT/f5VMc47/Pi6ymPF23CK+XysarheOP+Y2Fp6c4QJq29ks8I2b+fPGmFiiQ+lm2FLDkwhJCDf604ydxvM6bJwUbypf/wJubjvToQY2JilX6m/F5QOLpm1NPTOa/JSE37aZNycePGRX1bj69bdtE1OS0Wirjx5MAL+ZVscLvztx1bxeXXlzKOqvrEGoQarDq8qpcfGExtVqNFIs2pPb3q1f0y5uNz+zB/ef/TnJV/16dqVWBgydUT9vxZBoSeWAvCXBB+/hOnyhNFIe0c5Dju3u3EawzLGHfNWeAJTh228B43933ucfLucHXOe0zgEaaftz3u88/qptRqwK155L2Ls4YWp0E+LpXZ4NdNyxRQrJSz5yhR6AHB+8bzJyuOQk1aD/Nnt22d+PBhwf525Hf2KodGGppwugczolmAl+5IrdbGxvJFCRJ/vsvlTx5qFGBHdtb8L5f4sE+s07P4syab+9D6ZVV8iEbN4ojx8WFvHmTHoEeLDC3AHO65uQjfz2yZnUhIoIcO1bSSvPmjedTiNJEsdiCYiw1szbt7ZU0rROmK7dPSwbK+fG9qFGBRzt/pNJBCZBhndK6LskNWIfsH8K6q+sadEluRjyGAwcOEAAPQp4X5wAAIABJREFUJRENmdCAtWdP0snSm69tTOJ957F9HQmwZb2TtLVN/X0zpmjgjz8mvs5tn9uEGlx4fiG5Vx7WaeJUSgFvCyRz377UtfP3jb8JNTjj1IxUtbPz3k5CDXbb3o2KovC33+Rep0/GT0KEhIhDqEMHw7SnC3/8QYNE4+uNorzTw5syRWZZ9SQ4mJw+XRxCHTuKekflKtHM7RLI/KVe8m7bt1ElEyYYcAfiMuW/KYQafNO3m4Qg6SNZYWh27pSXnckDCDW4734qL6zEiI6WwpMAd/RbzhwuPsxfJJg1amr47bdk167y/rR0aapOs8xadenCdNO6SYSYDJT0nmHXaCQ4o0oVw7V56hRji/KFR4ez6vKqtJ1qm2r5jfZb29N8ojmvvbqW/MpJMOVt0kF61gqNw4ABcj0/ecKfdvxEu6l28SJg3qdoUdHtTy0PH8p+D/xKIhaVD4oNvHa/Ri/kICDyKx8zmU7pz3f5FMf473O1mB2vlXCkj50pj31dKEX9phcjR4rPuaHrL7SbasfH/o/jr3T6NFm8OAmIJlEK0+R1ZcvtLVSpVWzxdwtGR4S9K7rYqpUM8IyFvz81ObLzcm6w/46ENZluet/k7w3eFtueOZNUFAYEkA0bykdjx6Zg3LN9OzVZbOhjb8oaP4kjuuSikpzkNuldlKuiSAV7QMbOBgyyiDpzitEm4PYK1nyTiIxH9JlT1KrAZTUt6RnkabC+jcHzysXpYwOeuhl3TLzlxka6ZwMDShf+uKOkY7h2jQQ4tZF1vCLUS4fWIwG+Wb7QSMYZhtUn5vOFHehTLG+iAXV7jv9JP2vQs0Rew+rtvXwpgz57e5GOpGRNHH50mD129KDDNAeqxoHj64IEqKlSOdloQ09PkV41MRH1SZJkQAAjq1ZisAXYafKXCWYwBkUEscEgB2pUeBdlYiwuXCBz5xaH/e7dvOt7l9lmZGOR+UVSJIWkFxcviqwKIC+dCRTomnNmDqEGv//pAVUqqWGWUQgLC2KEKXihViES4PGxXYxtksFIaoxvgkwMwv79+5E7d27cunVLp+3y5QP8I7PDNswUmrCQON/53zgPAPAJK4aSJQGVKnU2qlRAkybAkSOARpPwOqWdS6NcjnLYeGsj0KwZUKUKMGUKEB2dus714NAhwMICqFNH/zZeBb/CgH0DUD1fdQytMTT5DUggJAR4+hS4fBkIDY39qmWJlhhfdzzWXl+L9TfWo3FjQKsFTpzQ37732boVCAoC+vc3THu60KMHkDcvMHFi+vcNAJg+HViyBBgxAvjtN8BEv1tTZCTQujUwahSwboMGB068wcm7d3HJ/xheZdmHF49tUPp4bxypVxUYPx6YMcPAOwIoVLDq6iq0zFYTjuu3AB07AoULG7wfnWnRAvjiC1TacATOVk5YeXWl4fvQaoFu3YBt2zCj1XdotaY1fAKC8cJyP857/Yf/bj3EnsNBWLFSQb9+wC+/yCWnF9bWwMqVwDffyEWze7dBdyUlhIUBrq5Ao0ZA9erp27epKdC3L3DxInDjhmHaPHxYnhNffw38dvQ3XHh5AetarUMp51LvVvL1Ba5dAzw8gIiIFLW7qNkiZLPOhm47uiFKG6W3fU2ayN8jR/RuQn9evgSWLwd69EBU/jz4996/aFWyFazMrBJc/fFj4OHDdzanBhcXeRa+Mv0SAOB3/Wyc7/0eXIcH8gGQMUUmmWRiWPQd479PSK5sKPA8EM7BWmiKFzWgdaln5EjA3h7QHpkIgij3RzksPL8QWkX7bqWaNeXeP3y4PHvLlgUOHjSoHSceH8OoVR0xILgUtgQ0hlmjJsCffwKjRwPbtgG2tgbtTyeyZoXp4j/w5SvAfvFKPH7zOM7XGkWDpVNbQ31Mi4i2rYFhw/DsuQq1asl7wqpVwKRJKRjetmoF0/MX4JS9IE6tNUHYluK4c6oCxh7Xoujhy8D164BaDUydCvTpAyxcmPoXxPcwr1ELXkP7otW1cGwd0zr+CtHReN3lB3jaAk6z/0Buu9wG69sYOC9cCecw4PHvA+J87r5kIor7A7YTZhj0+BqN8uXh27AG+p4Mx44L62I/fur7AA1Wn8BLFyc49jDCC6gB6VZnEFb/VBHOD17Cd+rYeN/7h/ohS5+BsNGaIPu/B2RgZSjy5AGOHQOcnGTgd+IEzE6fRcMjj7HKzRGv3aojZLkz1G4AunWD6X8n5cU7CXLnlntHixbA4MHAkCGA1tYBFv/uhKmtPcbMu4I/js2Mt93C03Mwe3MgtDmcgZnxv09XqlSRF5USJYCWLVFy8lKcsxmC3Hc90G55YwRHBhuur8hIYMwYeSHz9wf27AHWrgWyZYtdxTvEG223tMWvh35Fbcd22P93EXTuLI+zjIK1tR2eOZuj6NVnAIAsRUoa2aJ0IjFvdUZcMmpq39WrV1m6dGk+e/aM+fPnp6dnwrPGCdm6fLlM5DxFAXpejVut9WKHegw3BfPl1xhMJ3LjRukvqbpxMcXQngc8f5c3nJTmRxpRtqzIAOuLoihs8XcLWk224j3fewmvpNGIMHSFCiLeZGUl+xuzFCoUR4NIq2hZ+c/KdJnnwuCwSGbJIsFzhuCrryQIxVgT8gsXyi7rWLw49axZIx136pSq0FmNhvzhBy0BMnvHX2NTHSssrcAxR8fwzPMzXLP9GU3MoqlyOcb1ZeRc+08dZ8CdIU88OUGowWt9Wsl+3dZPMiZNeFtFc9XvLWg+0Zw+IT6Ga1ur5et2LUmAA2vnoCrrY9o4BvPUNU8eeHCAg/YNYuH5heW8jAedv97wNmA9lT/4kBAJF7a2Tvdw5Tlz5BSfTFmhbYPj5ydqEoMNVCi9Zk05lM8DntNikgV77uwZd4WdOyUK5P17ZJYskppXpYpcy4mw4+4OQg2OO6b/9abVilRKF2MEDQwcGBslvdt9N6EG997fm+jqS5bI4XF3N0z3ZcuStas/JQHenvtbnO8uzR/FHWhJQOTzPmaQGSn92S6f4hj/fY62l5RxAjy1OOPp7MRkomw78IpNNzQl1GCNFTUSlr07e5YsWVI26NlTd73SsDDJT1+/Xgqrt2zJ8GKFGW4Gxnm+2NnJOhkFRWH4t00ZZgYOXxxXlm3R5uH0tQYDiuUnQ0J48aJImTo46Fn+4vVrOTbffCPvIipV3GPz00+pTDdLAo2Gj8vkZYAleMotbmEyj7FDSICzhtdOm76NwL2vv2CwOXjpijzT73rf5i1n0LtQzrQ7xkZAOX+eBLi4df7Yz/7++SsSoO/mNUa0zHB4Br7kvtLmDLMwYfSjuPrZG36qTAJ8PiMN6+o8fUoWLBj3WrW2Fj9Dhw4yTtbxRV+jkXJIANmypbz2KEePUqMCt5Ux4X3fdwNN/zB/TmgsetncscPAO5cKQkPlPR9x7/GBWcyprVpVBvbTp4tUzqNHul93ly9L+iggtV/evInztaIoXH11NbNOz0rLSZac8t8U9uqtobk5+TiBxCBjc7Jq7thj9OBMwpr3HyNJjfGNPgjVZcmIA1ZFUVi9evXYlL4FCxawY8eOCa6bkK3798tZOIWavLV5cZzvrlVz4WVnWwLk5MmGsdfPT9JAkirQ9+D1A0INzjo9S26clSvLgCgdtaVfvpTjMiMVihtrr62NX038fYKC3une1qsnN7Hhw6XTlSvJdetEh8jamtzwblC27/4+Qg0uu7SM33wjUr2p5d691O9vagkPlwyb+okXZzY8+/eLBkqDBqlKo9JqFTZp+1Tu303+x1ora3H55eX0CIyfGrVhgxzronXOckspGeT/O7hJiioZp4Qu/3ZhHrUdlaxZja8l/SHR0WThwgytUJYYn8S1oSPhUWE82KgwCXB8vazMXcKDNjZKvMJ/iqLwnu89zj4zm8XmlyDKryFAjpzyLHUGeHtL4UMnJ7mY0oGwMHnhTNfrJQHat5e6oOGJq0ikiIAAuRTHjCH77e5H84nmfPrmqXz5ftGSSpXIzZulQuqUKTJS7tJFBtyAFFtKpEprl3+70HSCKS+91N9z2qEDmTNnOk/eeXiI979XL5Jkp22dmHV6VkZqEr9ntWwZt2BkamnThixcJJJagJd6fxvnu1O//MBF+Nm40iYGItMp/fkun+IY/32Oj2rPmJfM++fSSD4rFYSEyL21bl0ZU627to7ZZmSjxSQLTjgxIf79Ljz8ne5HkSLJF0N59Ijs3l3Wfd/Bam7OyJLFuLesJZfUt+XredPEi/vsWfpV/NaFly8ZlsWSx1zAO97isL/17BIv5lUx1NqMdHfnzp3y6uDiYsC4hLAw8vp1iS7asCHNj024+x0GW5rwfGFLBoeJTmHE/bsMM1dxfxlLvg6LnxL/sRJy8wqjTcBdjQuSJFf99g0J8M3qpcY1LA14Uq0kfWzAaw9P86XnfXragvdL5/o0JEresvPwIgZZgA+qvYv0+m/3YoabgndqpkP0l4eHaBTu3y9OagNNbCxaJD6cL78UGeuASWNJgAvbFYqV8Zi7ph/DTcE3LRobpE+DExZG3rpF7tjBC7+045LK4M1yOankzcs4DmsbG3nfcHVN+gVHoxFntrm5aNQmoPn6yP8RG65rSKjB2qtq857vPd6/L+88hgosNDSHO9aIPRYhAb7GNsdgZDql05Bly5axbdu2sf9rNBpWrFiRJxIQlU/I1ps35SxsRFuemxE33O1pHhsuL14nVuPTUFSrRlavnvQ6lf+szCp/vhUrNUK09OrV0uU1PSVIXwS+oMM0B3616qsE9Zb45AlZrpzckZIS4fTyIuvUYWwhkagoeUlZUZ355+Sn66xoiXR/qp+dMYwYIaa8epW6dlLL3Lmyq//9lw6dXbwoUZYVKqRKm/Cy52UWaP4XATJb4yXccXdHsg7mmGLyA//3mlcq5WGUCbhn6zS9bYghIDyA1pOtueWntw+Tt7piGYqlS0mAA34tyTKLyxjEGb9udHMS4LG2tdmkWSRNTN4rzJEIUZoozj+9hBal9hPQstYvi/gsIBXO6YcPpbqyi0u6XEgLFtA4mQUfcPiw2PF30jWJkmWH1NXgP3s8aT7RnP339Jcv/P0lUgsQh0JYWMINREdLNDEgQsoJXNP+Yf7MMzsPyywuw4ho/YrppPbZoBcxUdKPHzMsKoy2U23Za2evRFePjBT5vH79DGfCmDGkqanC+w7mvFy/ZJzv/vuxGoepptLcXPnog7oyndKf7/IpjvHf5/yfahJghCkYGZnIfdTIxGTMxdR18w7xZvut7Qk1WHh+Ybbe1JqD9g3itJPTuO7aOh59fJRe+7ZIZKCpqdTqiI6O26ifH/m//4nTwMZGCtuo1eSWLXx8ei9dT0ylyzwXZp2elbe8b6X7PutD0MLZJMClfSoxWhvNbV9lJwEG/LOGy5eL46hKlY9/kvDenDEkwN3dpBL63cqFGGQBHnNba2zTDM7lFpUZaQJe/G8jb+Q24cs8dhlzUiSVBB7dRwLc9FM17ulWkwT4cr8BnQwZhDU9KpIAHy+dzsAAb97NZUZfO1NGeL4wtmmpYs8eeXUuVIh8cF/h0waVGa0CNy8ZSO+gVzxd0ITBWSw+mpvP9JPTCTXYbEMzbjm9gsHHD4mMwC+/kLVqyQOpYEGZiPtwgPvsmcyiAtS2acObd9y42303l15cynHHxrHnzp5stqEZbabY0G6qHZdcWBLrE2rTRh5Hxva7JMaJqVJLwSeLytimGJRMp3QGISFb37yRszALv/Lkz+9FP2k0jDAFh1caRsCwAuzjxsmAKQHd91hcT7sSavDh64fvoqULF063aOmYaDh9XrAVRWHTDU1pM8VG7P+QU6fEeeXgQCZRtCaWqChxSAPioPby4qGHhwg1OOafvwnI/VNfoqLIHDmMF1TrGeTJW963qCgKQ0PFlkaN0rjTR4+ko4IFpZKDjjwPeM7119ez7Za2RKOhBMha399kZHTKfp+K8q52zh+ugXxja8ZzBUz53P+pzra8zx8X/6DFWDAypzP59depaivNCA8nc+Xii6olCTV47kUy0U3JcPL6br7KAj4u4sz+/UQ+ZfHi5LeL4ZV/APN/8ZgwiaJZlxb8ec/P3H53O/1C/XQ3Jmaio2JF3dOJdSA8XCbkv/pKfktaRctLLy8xMCJtCj8lhVYrfvjUSB2RUispSxayx9a+tJhkwReBL8gbNySqzdxc9ChSMoGxeLE4J8qWlcm/D4jJNBl9ZLRedsZk0cycqdfmuuPhQVpaxkZJb729lVCDhx8dTnSTEyfExu3bDWfG+vXS5t+FKtG9sGOc787WdmEryw10cTFcf8Yi0yn9+S6f4hj/fdzdtpMA3XNbppNFuhMZKRnQBQtK5HQMu913s/H6xiy1qBTtp9nHyqPFLFVnleClBpI6ra1RQ8Z4YWGS/ufgIC8dvXpR6/GCZ56f4cjDI1liYYnY7b9c9iXPvkjnasGpQVH4qHxBvrEEN3WSLKE7P30XK4HStGnc4/fRoii8VKcoo0zAc53qkQA39a5pbKvShMCHdxhmBt53lkKV92bpN0b5GLhTLje9bMEAS/BylfzJb/AR8jrYh9fymdPXzoxujaRA6911hskONTbnz0tiaI4c5KUTQXyROwu9bMFNbUqRAD0XTDW2iSlGURRO+W8Kc7jmINSg+URzNlrXiIvOL5L3kCNH5J0OkBDxGC2kf/6h1sGeUTZW/GNANWad5hjnmaRSq5hrVi5WWlaJ3bZ3E0nat+zdK81NmmSknU4BN/evJQHeKmhjbFMMSqZTOoOQkK2KQtraKuxvOof/fVcx9vNQ99skwA7V/6GJSepTs9/nzBk585s2Jb7Os4BnhBqceGKifLB7t2y0apXhDEmE1OqGrr66mlCDi84nEAG9bp2kYRctSt69q1vD69dLPl7evFRu3mTtVbWZe1Ye5s6j8L1AGp3ZLu8psZEp6UVAeABHHh5Jq8lWhBos/0d5Lji3gBOmhhJIWnc8VWg0EqqfNWu8c6AoCiM1kQyMCKR3iDefBTyju587r766ytVXV7P7ju4sNK9Q7EPH+gdJWf++TaTOAQ3R0RIAampKbui/lQQ4p3uJhCPrU0iVP6tQ3fFtCtLhxJ1WRmfmTBJg7f6W7L0r4SryKSEwIpAbathSowInD3xCQKL+dSUggCzzRSRNLSJo0ade7Pktt6QcB+4dyC23t/CG1w3e97vP5wHP6RPiw6CIIEZpEpiE2LdPXoD79tV7v5IjRi94w3Yvjj8+ngXmFiDUYPaZ2bng3IIkZR3SgokTxZ5Hj/Rvo1gxsl6jUJpNNOOgfYMkHMPGRjRKTp/WrbHDh0lHR5n8S0Bwu+fOnjSZYKK3jEdq6w3oxKBBsVHSJNl6U2vmcM3BaG10opuMGiWbpCIBJB4XL8o5Hlu+HwOsTeJMENwo7sBK1m786ivD9WcsMp3Sn+/yKY7x3yfQ14MEeKp63nSySD/++4/JPsuDI4Pp7ufOY4+PcfaZ2Wy4riHNJ5qzXRvwjRUYam3GN852JMALFXOwnbosC80rRJspNoQaNJtoxobrGnLh+YWpy5AyIoG3LjPsrQb2tTI5OXigTMp36pSuaodpTtCrZ/RwFEft9fzvpDw+Rf5rL5HDz53MqaRCUjCjc2+TDGI1KvDJqU9Hr/ZDTm1fQI1KrtH/WlYwtjkG5d49skABkd3fuvgKQ8xlP2+Wz/NRSrFotBqefn6aIw6NYPGFxWPfA51mOLH0gpKc1LsEfbLbkABfFZTMlDP5wMKDwRyuOdhtezf+deMvnntxji8CXyT8fkiRtnZxIUuVIiP0S9hMFwJfe8o+Vs1tbFMMSqZTOoOQmK0lS5JNrLbyfNV3A9WHG/8gAdarcoNFihjWjuho8Rf89FPS6zVe35g5XXMyLCpMbnCVKqVLtPSlS/LL1Ke2iW+oL51mOLHWylrxnYuzJd2O9esnHSaeFFevSgh32bI8fveARIh8c5vZsumf5dW8uURdfpjxmFZEaiI57+w8Os1wItRg5387c8G5Bfxy2ZcySzk2Ky3sAlmlrm+qHLSJEqN7sG5d7Ee+ob50Pe0a50GU0OI0w4nfb/ye887O44b992hqqrBxY/3lqIODZQI2a1aF9yp+yTeW4Io9+k2d3vC6QZNx4Jt82SWzICMPCoKCSEdHXqxegHZT7RgSqV9Iz5TpIttxtM0wqlRk27b6S6d5e4tjNHt2hbsun+Nkt8lstK5R7EtsYkv1FdW56soqhkaFvmvs11/lN5ZAinVqCQwNp1OuEDoUvUWMV1GlVrHRukZcdmkZ66+pT6jBoguKcsvtLQbTKU+O58/FDz92rH7bP30qh6vqT3/TarIVX929KNFtFSvqlclAUkbMxYrJJOAHGm8B4QGxEQware43zqFDpdnQ0OTXTRUxUdI9peDjfb/7NJlgwhGHkp55qVhRkmoMSVCQnKOuVVeQAKO83p2XF9nMmNfqCTt0MGyfxiDTKf35Lp/qGP99zpZ24FF193SwJnX07CkTazdupHyboIggbr+7nSNXduCJ4pY8l0/FH/tnZ8WlFdl4fWN2/rczfznwCzdc38A34W+Sb/Aj4MSoDryc346tWwQQkIzzj11CKSEu/TOH93KZ8eq+1cY2JU3xf/mI93KZ023Gz8Y2JW1RFJ6qnIOHvytnbEvSnH09vuKFEnYMDdQj+zKD4+FBlikj4+FJXVfzbm4LPr9ywthmGYS7vnfpetqV/ff05w+bf2Cd1XVYfm4JjvvGhk8dwGUt8nLCkd958eVFnXwVI0eSAOnmlobGG4hTFZ14ZMSPxjbDoGQ6pTMIidnasCFZ1voCbxeyjf3s8tieJMDixYLYvLnhbfnhB3GEJuUzOfHkBKEGF55fKB/s2sUPo6WDgqTmVceOkvJ3ywBycDHpb/rIIXXf0Z1mE83i69Jt3y6FVX74IfVO9Zi8jxEjWG9NPTp0lGjdixd1b8rDQ5xJv/2WOpNSgqIo3HhzIwvPL0yowQZrG/CK55U461x9dZWD9g2iVZMJIokx8X96OY0S5elT0Qho0oSKVsvjT46z/db2tJhkQajBWitrcZLbJM4+M5uLLyzmyisr+deNv7jtzjbe8LoR++AJD5ffW758EmWbGt4Wo6brCG9GmptwczkT3vXVMYqe5JD9Q9jxR4km4bZtqTMqPRg7lopKxRIDwDVX1+i8+c7rW3jTGfTPYc/O7aOYJYv+cz0x3L4tg6s2bd7dm6I0UTz74iy33t7KDdc3cMXlFVx0fhFnnZ7FsUfHsuQikSFxmObAgXsH8obXDcmbLVRInKKJaSDrgX+YP3N1+J0AmaNPV6qPq98VA6RcY3vv72WZxWUINVhteTX+9zQ9BNrJZs2kLqs+k2PLl8vP1mRAWf6y/39k48YSJf3gQfIbJ8Xr16IZb2cXT4Nq061NhBqcf26+zs0ePCj2JlDPRGd27SJLlCD795d240xwxURJvw1B/2nHT7SabEWv4MQfTl5eYtuUKam37UPy5iUbVZEb1vP9kuqkaDSMMAHNTSI4fLjh+0xvMp3Sn+/yqY7xP0b8/CRjsXp1/Z2s6TUpa0yCg8nGjRUCUmvrU97lz+F8ZvJp8in/dl+/JmvWFBfH4sWf7n6+j77n88YNGdInF5SZSdqR6ZTOICRma/fupLOlJ73sTWM/O/djTQaZqWhlpXDoUMPbskKCrZLUqlYUhbVX1Wa+OfkkHV1RyC+/pHfBKly+VMPmzSWIDJDBq4ODBIimNuK3bl3xY+jK8SfHE9YqvXJFHCxVqxrOQdW7N6lS8erWRcSwHASkeJ6uTJ4sx+9hAtLXhkRRFPbZ1SdWEuHAgwNyU1cUcutWctgwKfi4bx957x69X76htV0YUfJfDj9kIE+HoojQXpYsdHNbF6sn6DjdkYP3DdapwE3MTOeBA4Yx7euvZZLGd+QoEuDgQcUSTf1JiIjoCDpNz8YnBR3Ew/UxhMr4+FCxtubWanb8apVuef/eId6c1DQLCdB92QGamkpwsiGYPl3O7T//pGx9RVHo9tSNnbZ1ouUkS0IN1lxZk0+3vL3JjRplELs0Wg0br21GZH3IImX9qQkIIK9flwmv2bPlALyVudBoNVx5ZSXzzM5DqMHFF3QQ2daTbdtkd/fu1X3bH38krbO9ptUkawbOfXsCdBEGT4oXL0QCxMVFwuHfEqP9bzvVVnTjdCAsjLSyktpZqcHHRxRGcueWRwQgz7GOHcnNS18z2CJbbJT00zdP30mbJMG6ddLO5cupsy0hGjQgy5byJQFenfELSfLNk3v0hRMBcr7u/v0MR6ZT+vNdPtUx/sfKWpG05NKlxrYkY/L6tRSONzFJ1zrwmWSSSSZxCA0lv/2WsTrJn7APXm+0WrJGDfFX+X16QfMfDZlO6QxCYraOHUuaqKIZCVNqIkQ8+mqlvDzkXDTVRfQS48ULOfuurkmvt//BfkINrri8giT5YOkR2iKIgPgYfvlF9Oc0GtGojokW0JegIJnFGjlSt+0ioiNYYmEJFppXKG4av6enhNPmz2/YEqtBQRKJWaQImy+rS7Pct1innm4hilqtqKHUr284sxJj4omJhBoccWjEu8hnHx+JHAfkoAPvFpWKY+zmUwUtMagw11/XQ0vlQ95W6nowbhCtJlux1KJSXHttrcjD6MDZs/IS0Ft/KeR4HDoku718SRQDC+XlY0dwyr6Uh69vub2FTTu9PXbpoLtuMAYNosbUhPn/Bz54nbKoWEVR2G9eI4aZgQHfNuKAAVIH74WBClpHR8uLXrZsul+yfqF+nHNmDnO45mCe2XkY1PEHEQ2/ejXVdg07OIwWbVoRILfY94h7vcRcQyqVCHG+LQIQGhXKb//+liq1itvvGrDqXQJERoqD9fvvddtOoyEdsmqICms4bVVP8c42amTYUe2FC+JFrlUrjojbI/9HtJpsxdabWuvcZOPGogmXGtq3l9/uzZvi6N61SyIosmeXyLfCeMjQW6IlPWCeMyDFAAAgAElEQVTvAJpPNI9TLCUhOnWS85AW81I//0za22sZDfBsd6lGe//gRl5F+Y8mQSM5Mp3Sn+/yqY7xP1YURcanDg6GHT5/Crx8+S5t3pAFbTPJJJNM9CE6WmpxARIjk+mYjsuyZXJs1qwxtiWfN5lO6QxCYrYuXSpn4gXy0uv2BZLk05xWnFmsLYEE60QZhDJlRDokKRRFYaVllVhkfhFGa6PZvr1CG9MIXkQlKnPmfrAu2bq1DNL0PS0xCiExxVVTSozTdf+D/e8+DAsjq1QRuYhr1/QzKCnc3EiVip5dvidquNLUPFonjdOjR2Vf//pL/vcN9WXX7V3ZcF1Dzj07l4/8U1G17D1WXVlFqMGu27u+S3nZuVPK9lpYyCxCdLS8dZw+Lc7jCRPo2aAzzRHJb/Mup9UEC158qYc+SQw+PqSTE4O/LEeHybYsvbg0/UJ1n6oMC5NA5AIFDFtELEYyvVgxUnPMjQQ4s5Yqxfvc/K/mPFvYgkq+fPoLXBuDZ8+omJlxfjVw7NGUCRKvubKaBwuDETaW9LrqSSur2GBSg3H3rmRhfPedfgOrm943mXV6Vlac5kJNDmep2JyKFI5119Yxy2iwss1ZFsQTRnfpIdfN5s0igu/vLxNVffvKRV2mTGyobEhkCKsur0qryVY88zz11UMVReEVzytUH1ez9qraHHFoBCOixdE7bJj4xnWRPoopoGf1Qw9G1agqHghDzTC8z+bN0lGXLnFO6rST0wg1uOveLp2aiykR8DxpH3Gi/PsvE66+rdVS07UHN0KevzNmkJ5BnrScZMleO3sl2aZWKw7pzp31syk5YuT4zzvm5sWvpNjEpSW/czeay+fn06bf9CTTKf35Lp/qGP9j5t49GSZ+Cnr1huLhQ4lJsbXV/V0lk0wyySSt0GpFdQ4ge/RIv1pVGR0vL6mlVq9eprPe2GQ6pdMQRVFYq1Yt7ntP3HLz5s1s0qRJvHUTs3XPHjkTZ1GNt3euIKOjGWUC9invSoD09U0b23/9VQabIcnUOPv3zr+EGpyyeS8BcvRIrYi+AuSff8ZZ18tLIhyrV9dP23TAAAnU06Ui6n2/+7ScZMm2W9q++1CrlaprKpU4YNOKtwXV2v44QCLVdqXcK92xo9wkw8LIw48OM/es3Kzez4yDe+ZhkUGgahxYZnEZjjo8imeen9Gr6OD+B/tpOsGUjdY1EgmWgACyWzc5d+XLJ13FRlHYpcxl2iKIayvkZoGZeegZpGfRsw4dqJibseYvDnSZ50KPQA+9mhk6VEw/fFg/M5JiyxZpe9MmMqJbZ0abgB3Hl01Wu8ozyJM1eqlk43nzDG9YWtOtG8MtTFhuUp5k9cODI4PZq73IdmgXLuDo0XKJubsb3qxZs+SQbtig3/bnXpxjlilZ+Euv/NLQzJl6tXPe4zydx1hwY746BMjZnZLRZdi/X7RgzMzICRPIqCj6hPiw6IKidJrhRHc/3Q9WRHQE9z/Yz/57+jPfnHw0/R0s1x8c0SM/Cw8Gy/9Rnrd9bvPuXd139X9jvOQ4d2rNDwuQGpyJE/mh4HKUJoplFpdhgbkFdCq4efOmNLVihe5m+PlJvdqKFT8oMaAoEo4MkGo1mzYls2YlB24bS5MJJslmE6SmSG9KiMnoWOzSnO4F7UiSJ0d25B/oS0Ci9z52Mp3Sn+/yqY7xP3bUarnvHDxobEuMz40bokaVLdunMQmYSSaZfFooCjl+vNyzW7fWzZ/yqdKxo/i77t0ztiWZZDql05ibN2+yZMmSDA8PZ3BwMIsWLcqHCYgEJ2brtWtyJragDc/NGUb/mxK69k3FQ8yWLe3sjikWlZwGqVbRssziMrQtc4KOjgr9/SnRoN98Ix6pD97A//pL2p01S3ebihaVgl0pRVEUNljbgPbT7OM6TMeNY2ocUSkmPJwsVYpvchYgTCNYt8OFFG3m7y+RoP1/1nDYwWG0GgOurp+N78sBRGSx4rUSjpxXXcUurcDv/qjL12EpryR36eUlZpmShRWWVmBgRKCERObPL3IGY8emKKL38iVJY5+FX7mlnClrL60WG5WZYt7OusxqbMdcs3Lx4Wv9BLRPn5afW79+em2eLBoNWby4OKoUv9cMzWrL4wXBU89OJbndzFMzuak0qHGwl6o37xEaKvI7s2ZJxOWUKeKn/P13caalxYxtaKj0N26c+ACnTJG+Z88mN25MQFbgrXdvVAPw8KOkvf1/uM2mpy0YVKE0A15raG8vesRpgUYjxTuyZhUVHn048ugILSaa83iFrFSsrHQu3OcZ5MkyE3Pxen4LdlL9RVurqJQV1vT3Fx0HQIT2PTz48PVDOs90ZqF5hfgqOOW50O5+7qw3qQj7fAuuqGLG+0UcGW3xTm5HY2XBkd9lofVESy65sIS1aiksXjzlv61c5W7SOvt1KhYWov2RlmEEiiKjQ0C07N9y6tkpQg0OOzhMp6by5NHv99epk8wZxEmgURRy+HCxbfhwUlF45Yr8a15vBjtt65RsuzFFet+TzjYoz59L+4PLjGOQpYpUFLp1rMVRmERTU0WvieCMRqZT+vNdPtUx/sdORISMjQoXjjfE+aw4e1bGI3nySFHmTDLJJJOMyrx5Ml5s0ODzvm8fOCDHYfx4Y1uSCclMp3R6MHz4cKrVag4fPpwTJ05McJ3EbPXzkzMxF0P435DveWfdHBJgxVLPWLNm2tkcHk5aW5ODBye/7rjVhwmQnX99rzJiWJgIzpmaxhGzVBRJu7ey0i2C8tEj6hxsuuH6hviFxBYtYmzuSnrkaVy8SJqa8gv7UzTLczNFTtuFC8XEEmPa8cs+4MsCWeWDAQNEf3XFConYq1GDirU1CfC+E/j17wV5xyf53/xj/8fM6ZqTBeYW4MuglzI96OQkQuA6hnfUqUMWcAxkNEy5owTYe0vXZKOHYwkKoiZfHt7PZc6ckx15wyuJyOwkCA0VaQ0XF1FJSCtWrmRsAcWI2TNJgGPH10l0fUVR2GBCEWpUiCeErigSrP+h9PD7y9Chhv2JRkRILcmk+kyoIKemUUN62qnYZWO7RNvWaDWc+L2TNOLmFluQMC0KusXg7i73kRYt9D9O2+9uZ/6hJgyxNqW2bp0UNxQeHc5ms7/kzZwqPrdwoZmplkOG6Nj5li2knR1Ztiz55g0veFygzRQbVlpWicGRyY8SDz48yG/62PKNtUTiK46OUpVz6FCZ/Tt/XiYHAV4u48R8v4AV+s5PsezTnZdPCNNw9nL4Q3QnfHx03EE9CA+XVBprawn9fUuvnb1oOsGU116lXGqpe3dxEujijN25M5EB6oQJ8sXPP8f5jZSuf4MwD6HbreRDLOrUEaWYtEKrFTWqVuV3kABDnj/i6XpF+IPFGubPn3b9pieZTunPd/lUx/ifAidOSFBAx46fZ/rzgQOSxVmkCPnkibGtySSTTDJJnjVrxEVTtWraZd1nZDw85NWmdOnYUj+ZGJnPxyk9ZAhZt65hlxR6IUJCQli8eHGWLVuWEYnkSiQ2YFUU0spK4WATV7q1qcxzIyTCLrtTJH/6KUXd603TpqLRmxSKQtaspdDU3osVFtaM65AMDpZypubm5HvpjZ6e4iyoVSvlDoM//pBf5N27KVvfO8SbzjOdWXV5VZEdUBQJQQXIli3TV9t33DhOxm8SVXx4bbKrl6niS5OcNzi1sQ21ZqZk3ryJ50ZqNOThw4xytKePrYp1f7bh3vsJh7crisLTz0+zxMISdJzuKA5sT0/x5ubIIWJ4OrJ9uxzSzT1luvFAEXDVqUUp2jb8fwOpVYH1+1rx7IuzOvcdQ4xG1rFjejeRIiIj5VTUrUsyLIxBWbPwUGEkGt193uM851QHtaYm8XR4Y+Qnpk4V/evQUGlfq5Wfasw+TZhgGNujo9+p6ixfLn1oNOKoDgkR5Zb27eXFMt5P7e1U8k9tzBkQnnAo8M5rm+hhB/pULsWwMJE/aNzYMLYnxdy5THX9yLXX1rL3tyABalMoTzF6TVfedQKjrS05usMTqlQycaYzhw/L/bFuXTI8nHvc99BkggkbrWvEu74J3+wUReG8s/PY7kcVI81UjCxRlLx1K2FvgKKQy5ZRyZKFEbZW7NDKnjALY6feyTuYW0yeJ9kyaJa+1Zq8vckvvpDj8s8/JMnXYa9j7+eRmpTdu//5R34b586lrFt/fzJ3buk6zuMh5mLt3j1OKkFAeABth1akykTDgQOTbjswUKKvR49OmS368uWXZNXS7iTABztW82qprKxmfYw1aqRtv+lFplP6418A/AjgNgAFQOWUbvepjvE/FSZPltvk4sXJr/spsX693NvLl88s+JhJJpl8XOzYIcE9JUqQT58a25r0IypKsm1tbVPuV8ok7UlqjG+CTAxClixZ0K5dO3Tp0gWWlpY6batSAfnyqfDYPD8sXvlAe/8eXpg7wO+1BUqWTCOD39KkCeDuDjx9mvg6+/YBZ06r0GHAI1x7fQYHHx1896WtraxQrhzQujWwZg1AInduYP584PRpYNGilNly6BBQoABQokTy60ZqItFmcxsERwXjz2//hCkB9OsHTJoE9OwJbNsGWFikrGNDMGYMGhR8CACYu+4iNIom0VUfvfTH7UuO6BmxH6MPhcGkbTvg5k2gceOENzA1BRo2hPm5C8jqlA/7V0Rgibo5XE+7xrwAwivEC66nXVF6SWnUWlULr0JeYVf7XShlkQdo1gzw9ZXzVKSIzrvWogVQuDAw904TKCtXoNFjwHLIr/AL80t6wwcPYLZoCdZVUOG3EbtRPV91nfsGgJkzgYULgSFDgPr19WoixVhYAEOHAm5uwNlr1lCG/opGj4Gda0cnuP4/p5ai1xVA8+MPQL58sZ8fOwaMGAG0aQOMGgXY2wM2NtK+iYlc8/PmAd27A+PHA3Pnps5uRXn3s587F+jVS/owNQUsLYEsWQAHB2DFCqBMGaBDB+DJk/caaNwYYSUKY9CpaGy6tTHBPh7M/g15g4FsU+Zi7VrA2xsYnfBhMSiDBwP16gF9+wJ79+rXRtfyXVFm9Bxcyg2EDR0EhIYmuf7FZ2fRevQ6uIRZIGrXMSw76IJWreQ60JmGDYG1a+VH1aULmhdpij+//RPHnhxDqcWlUGtVLay8shLBkcEA5N7We3dvPBv/P2zcQphWrwGLM+flxKlU8dtXqYA+faC6fh2WX3yJv3cEobL1CWz7NwIarTZRs7xCvHByVyTMEI06rZyAVq302Dk9yZFDjkeNGvJjXLAA2ayzYfE3i3Hh5QUM2jco9t6WFA0byu4fPJjsqgCAX34BfHyA1avfPh6io4ExY4Bhw4C2beUCMXk3LFpycQlC7K6iVYc3WLbsg2vmA44dAzQaeaamJSVLAh7+cq95c/MCHF+H4hXzv3/7ySQTY3MLQGsA/xnbEEORmjH+p8Lo0UDz5sD//gecP29sa9KH2bOBLl2A2rXlkZUrl7EtyiSTTDJJOd99J/4Vb2+gZk1xN3wOjBgBnDkDrFyJNPelZWIgEvNWZ8QlI6f2keT48ePp6uqa6PdJ2Vq/Plne+gyvF7PnlQo5+bdzbQLkrl1pYek7YgpjLV2a8PdarUQHFClChoRHMv+c/Ky1slZ8+QZfXylrCkg4ZsD/27vv8Ciqtg3g90kCBGIoinQpCgKCgq+IiuhrQUBEEEVRPwsooCIivqiAisaCvTdsYKUpiiiIggh2pUlRmihdpAcSElJ27++PZ1PoKVuyk/t3XXvBtpkzmZ3dZ5455znJ9PvJiy6yIW/btx+6HZmZZMWKZO/eh2+z3+9nr097EUnguMXjbExGt2627nvuidjYwqyp01kRyWxR63WOXTz2oK9rN2AEAfKHhPOsyG9hbNzI7P+cTJ8De18MXv7h5ewytgtjH4wlksAzR57JkfNHcteeXdZF9rzzrItHMWeoeeEF5vZI3HJ7HxLgs0kdD/meLe3acFdZcPiHtxV5vc9aJRtedVXRJs4sipQUm8SmSxe7syuxHKceH8sd6Tv2el16Vjrv7RTPfWtYrF1LVq1KNm16+FIj2dlWE/cAc4YWWP652Q4yqngvf/5JVqpEtmxpvbdzl/PWWyTA/oNO2O89c/7+kasrgRua1WNWpp8NGpCnnRa+Q23HDivNXLasdeouCr/fz0HDTiMB7hw88KCv8/l9fOGyOiTA3aPfyR3B8d13RWx8jmeesQX170/6/dyYspFP/vAkm7zchEgCE4YnsOenPdn2zTZ89nSQAP3dLyvcmLPsbPKxx/hyTD8C5LBxow/60run3c3j4mezbcwP5Jo1xdy4IkpPJy+5xP4u995L+v0cMn0IkQS+9OtLBVpEq1Y2Iudwfv01bzUkbQz2GWfYgzfcsM+Mh2RqRiqrPlmVF35wIdets54m11138OXffLNVagn1AJ0HHySd8zPZlecP/3c2U+LA+Njd/N//QrvecIF6SnvmBmAWgtlTOsKKE+N7xfbtNvCuTp3wVHuKFJ+PvPNO+3no3l2ThYlIdFu82OrhV6pEfvttpFsTWuPH23d3oUsuSsgdKsaPeNBamJuXA9ZrryVrlFvHdUfGcc3RZTn0uAEEyBUrQtHSPH4/WbeuJdBmzNg/yTRmjH1KRgdyGy//+jKRBI6af4Cx9NnZNtNTbKxFrT//zNmzWaCh999/b6/76KPDt/nZn54lksD7ZtxnNQn++1978wsvFGibQ6lrzV9YH3/xgkebHrDu8k9rf+IpR47m0dhE35fTDrCEAkhJoT9QOPihs8G6j9fg4OmDuWxLvpqnPh/Zo4f9XQpYruBQdu2yiwY9epBMT+emOlX4d2VwzvKZB3x9xpdTSIBPdD6SaZlpRVpnTmnwyy6z0hThlDNz8eLF5IYh/UmA7761d/H18b+N5pqK4LbTWuQ+lp5uSbLExILP8puRYRdvnMs7zgrK77dS1vnmZiuQwNyTvPbafO9JT2dqlSP4eSPs/Vki+fotrUmAuz/9KHci008/LVxbi2vbNkukx8eTX39dtGX8vf1vfnhiLPeUiTloInbcxEeYFgeuOfcU+rL9bNyYPOWUICXgBw2yP95jj+U+5Pf7+fO6n9nnsz6s+cAR/LhZTF40td+slAWz5h678NWm+dADTqq4I30HL7imFh18TDp3VpE3JyiysuxqJED27k1fZgYvHnMxYx+MPezEm6QlmWNjedgJKG+7zSaX3bmTdjGwYkW7HeDCYLYvmwOnDiSSwB/X/kjSEhTO2XfCXq/NtuP2qKNsLoVQywm2P6/yHy5oXpU7UImAXcDzAiWlvXMrSFIaQF8AcwHMrVu3LvfllRjfS+bNs+/SCy4IX2eBcMrMJK+5hrnTvHhxG0Wk9Fm9mmzSxL6/P/kk0q0JjSVLrGRHmzbhreIqBaOkdJgUJ2AdOpSMdVnc4xyzYsAeTd9hmTLhScZNnGh1NgE7iL/4whIwmZnWQ/qkk/JyI2mZaTxz5JlEEnjNJ9ccuP7sTz9ZUjo2lv7hj/LYY/2HrT07bBgZE3P4HtXT533E0/s4vtDvFPqHDLFsepkylj0vAV5K2mqTNTZqsF/d5yxfFvvd3JCVsZ3XNS16fWWStnNuuIEE6I+Lsyr+V1xBPvyw7dBbb7Ud+uSTxVtPPoMGWfJnzRpy94yvSIBjzq9m9bzzy8ripvpH86/K4PQ/Pi/Sul5/3Zrftet+HRjDYutWm1DsuutI7tzJXRXiOLV5PDOz8xrzaL+TSIC+SZad9ftzd0mhy/OmpdlAg9jYwiV7c2o83nJL4ZOmSUn23pfzlQffde9dJMBn3sobsrB2699cWQVc26g6/T4/W7Swj1sR86XFsmWLzRlYvrxNvFQUr04YwrQ4cF2ns/Z7blvqFv5wbBx3lY+lf/16TrFrK/zgg2I2PIfPZzNFAVYse8wYG93RpQt57LHMnY2yuBlGn48nJixnG/ct73y5y35PPzrjQT5XsZuN2JhRArqA+f3kfffZtp91FvcMu4eDbjiGbQck8s+1Cw/51u++s7flm2t3P9nZVgP90i5ZeQfp6aeTf/+932tX7VjFtqPaEkngjZNuzH1861bLYecknjMzybfftglYAbJZM3L+/KJsfOEsXGjre7jujUyLAxehudX8/zD06w4HJaWj4wbga1iZjn1vXfO9Rj2lPerNN+17aNiwSLckuHbuJDt0sG175JHSOamjiHjX1q0W/sbEeG9+gJQUSwsdfbRNciglj5LSJcSh2vrKK7Y3NqI6CfDMJnN5wv6j6EMmPZ189VXymGOsHa1akTfdZP//fJ+8YpYviw/OepCxD8ay7nN1+e3qA4wDSU7O7al7T70PGBvr56ZNB19/69bce6KmtDRyzhxy5EhywADynHOYdXRV5iZtACtLccIJxS5NEUxLlljT3kBv9r+7+V7PvT75QX5a7iwC5ISxQci0+v2WwTxQUiunp2UQI+rVq+1H7K677P7yHu3oAzjx7cF7vW7j45Zcevau/ZN+BTFqlDW/U6fIDpns18+uJm/bRi7vfxUJ8IsJj5Mk1yev4+xa4JbaR+ZmZ3MS6bnlAQpp1y4riVGmTMGS2jkJ6WuvLVqC2OcjO3e2w+iHHwIPbt7MjDIxfP/0CrkXG8be3YkEuGn0G/zpJxar1EgwbNpkQUdCQr52F0JmdiZf61SNBLj7m71HK3xw69kkwHXPPECSbNfOhrsF9Wp7RoYtOOc4jY3Nu6j00ENF26gDuHfATsYgm19Xr8JZy/O2c3fmbt57cQJvwSs8Ij4rIhd9DurVV8l69axLcr7vMt8xdWxW3sGDrVvy77/nXrHNzLSRCTfddPDFzphhi/qw1gBb9r337ne1y+/3870F7zHx0URWfKwi31/4/n6jXR56iLlVourXt/+ffLIlxMN1kSYtzTbhusYWNHyBjgTsWrAXKCntnZvXktKHE01tLS6/n+zVy74DJ0+OdGuCY+VK+ymOjbWku4iIF6Wm2vlfzmiQEnUeUER+v1WPjYmxmF9KJiWlS4hDtXXSJNsbs9GKBFi/9jZeemkYGxeQkUG+9VZefvPMMw+e1/x53c887oXj6JIch349lBnZ+2Ru/H5y1CguKteKAPnq//484HK2brWT7AfuTiMHDrSxJTGBIewAmZDArNat+OHpiUy6KIGbRr9ptRFK4Leo30/Wqulnt/hPuLAa+P1fM0mS63es5VfHx/KOmKdYJs532FrDRZaSQs6eTU6fHpIsyeWXWz2qlBTSn5zMf48sx6XVYrl1+waSpG/rFiYnxPG7Y+O4KeXfAi/X7yf/+MN67zpHtm9fuFK6ofDbb8ytCuPbtpUp8TGc1qqKJa9e7ksC3PzUgyTJv/6yescdOhRvqGdysl3Bjos7eM9Hv5+8/37mJqSLs74dO2w0RO3aeX/vv7u3Y1oc+PUvY7lr9w4uPzqGq+tVIv1+9uplw6JSUoq+zmD45x/y+OMtGfnaa1bHuzBmL5/JdYngmobVco+ThXO/YHI5cPlJdUi/n4sW2d/40UdDsAFpaeSUKeSiRSG78vLLL9b+D3A137jgqNzv5zenPMLkcuBx5dfwootKaDewtDRy4UL+8fIDvP+8GM5oU5P+FifZFZuc34Vy5Swj/Pjj7HpxNuvXP/hvVZ9zVjABKdxd41jym2/2e35b2jZe8dEVRBJ41qizuGrHqgMuJyXFemAAdgFp8uTI9KRr0IA8t4nVvHoDvQkU/hgoqZSU9s5NSWlvS0uzOWcqVrSa/dFsxgyyShWbT0QJDRHxuuzsvLr5555ruZho5fdbZ5GQnbNJ0CgpXUIcqq3z5tne+ASXMBNxjIvzc+jQMDZuH1lZ1lvzcPNfpWSksPek3kQSeOKrJ3LAFwP41I9Pcdzicfxx7Y9cm7yWmb/N5wllV/BsfGvD0vc5ix8/1mc9vSp1tGR0586WdZswgXuW/sHPlnzK0986nWUfLptb37Mku+46smpiOn1wHHGddXcf1aslCbBRtc284IIIN7AYcnrK5pTvXjXaeut9fpUdm79fdT6zHfjJ2AcOu6zkZOth2KdPXg/9nB7SaUUrQx10rVpZuQi/n5zfsyN9AGd//S6/OukIJifE5c4U2L+/5cuCMVxo1y6ybVs7FPYtG+H3k0OG2N/pxhuDU+tw2jRb3siRdj9j8QIS4PgezfnFw9eTAFeMGM7kZJu0tE+f4q8zGNavt32T87lp1sxKzEyfXrALGu8Mst7Kfz17P32+bH7XojJ3lwF3/mE1GHr1sjIh27aFeENCxOcjq1UjuxzzDX0AR7/Yh5nZmRzfOoErY+rmVhAp6d6c9yaRBHb/sDt/WDmTvoUL7MC46y7ybOvZ/srRDxAgly/f58179jCjz608Elt5dbXp3LNuNVduW8mZq2byvQXvcfh3w3nL5FtY+5najHsojo99/9j+5Yj2MX8+OXNmZId1X3gheXzdjSTAYUhiTIy/JF6jLRIlpaP/BqAbgPUAMgBsAvBVQd7n5Rjfq9assU4sFStG52gNv99KmOUMWFq5MtItEhEJn3fftU5Vxx5rgxCjTf65lXr3jkxpSSk4JaVLiEO1ddMm2xsvoj9nlz2egH1RRIuJSyfypBEn8YhHjyCSsNctYXgCW3T7gA4+rkNtm8p6505749y5vOHoSayM7cw687/kwoXMzM7k1D+nsuenPVnpsUpEEljl8Socs6hk1I0+nHfftX35edPO3B4PjnuhL9NjwWkt29k+fjHSLSyetm1t5vWcDp5zzm3CjBhw/gtDmRUDTvpvDfr8B/9VWLrUKrvExtrfKTGR7NbNyl+sXh2mjSigN96wNv78M7l7w2qmlgF/aVSePoC/3XgRSUtaVqhA9uwZvPWmptqVa+esbi1pP7x33MHcGtLB+uH1+623U9Omecv8/dR6/DcBXFajDP+uWZ70+fjqq7bu2bODs95g8PstiHr6aauIUbastbFCBRt08e8hOuvvTNvBeXXLcHPFOA7vH3wAABodSURBVM6671oS4Jz/XUmSXLXKeqvfdlt4tiNUevUiK1f2c1WNylxX0fGDJ2z2psfPfpmAddSOBg/MfIDxj8QTSWDtZ2rzji/v4C/rfrHyGlOncmW98wiQL7V4M+9K6urVZKtWnIILCZAtb7+XcQ/F7ff7dOQTR7LtqLacu2FuZDeyEO64g4yPz6YPjleVGclatSLdouBRUrr03rwc43vZ2rVkw4YWywWp+lRYZGSQfW3QGy++OO+0RESkNPn5Z7JGDRsJ+9lnkW5Nwfn9eb29b75ZCelooKR0CXGotvp8ZNmyfg5yj/Olo7sTiM7hcH6/nzvSd3DRv4s4ZcUUvjbnNd70+U2sOriNDRFpfAezYxx31K3OTVd1oQ+OtWLW84zGP7H/5Ft56fhLedQTRxFJYKXHKvH6idfzixVf7DXBXEm3fr0dWQ/fsZqZMWC2AzcnxvLJpO0EDji3VlSZPt2279VX7f7OdX9xa4LVgE0uBy7/4/sDvu+vv8jrr7cewAkJllj59tsSWYUl165d1tZevez+95efTgLMiAVTVq0gSQ4fbn+PxYuDu+7du62MCWAlKnLmrgxyqXCS5Pvv27KnBObmXDF+BHO6IP/6xACSVimhZcuSPfFPaqqVVLj2WvucVahgPcsP1tv5m7GP5W7nHw2OoD/wYezTxxLc0T5Rxscf2+Z9/NxPud9FmyrF8erLM1mtWsnel/vatWcXRy8azS5ju7Dsw2WJJLDec/V45YQrOWhSf9aquIEXxUxmVvly/OfGHkyrWIEp5WNZp967RPx2Nny2Ge/86k6Omj+K0/+azmVbljE1IzXSm1Ukr71m+3VFzDFsEz+drVtHukXBo6R06b15Ocb3uvXrraRWQoJNPlvSrV5tHSwAm2g+GKPORESi1bp15CmnWGeo++8v2efmpJ2/DBzI3LrY0XQ+U5opKV1CHK6tDRqQnePHsG/DJwhYeQOv8Pl9bNw8hTUar+FVt9XixgRLkAw+sanlhC7uzSqPV2GTl5vw6o+v5qRlk7gnK4Kz3BVT06Y2L9esy1uTAOeOfITnnWclBqKd30+2aWMlN3J6S3/3eD8S4NRb9q9Nsm6dTUIWF0fGx5P/+x8POellSdO7tyU3d+4kN61cyNQy4LfnNSRp21+9utWSDoX0dPKii5hbouKuu0Lzw5uZaXWlzz3X7vt9Pi45pjz/PjqO2Rl7OHeurT+aZmpetoy86ioLsCpWtHrl+/aE8vv9nHFWHWbEgktnjCdpJ6txcRbkRLtduyy5PmgQOeumjiTAnx7tz+rVyauvjnTrii45PZnvLniXncd0ZsMXG9oInVav0JVJ4bjGVnd6QXWw87CTWa7CHl569fb9Ji2MZrNm2fH4+tGdWb/C8ojMPxEqSkqX3pvXY3yv++cfmxKmQgUrcVQS+f02+iwx0XoFjomOAZgiIiGXlmYlSAErX7l0aaRbdGB+v5XNDFVHLQkdJaVLiMO19ayzyNatknlVj52sWTNMjQqjJ5+0T9zKlX7+vuRbfj3tdQ58YI3VAl0ZvQnoA+nf3wLz1NQMLv11CpOTLdE1eHCkWxYcX37J3B68pCX3Fv/0KbOzs/Z63Wef2XxkZcpYkm/Dhgg0tphmz7ZtHTHC7i/7ZQp3bLUutCNH2nPTp4du/RkZNizp8cdD+8Obc3zOm2f31/85n2uXWa2Ovn2tvnI0XihbvJi89FLbtqpVbULN/NJ27+TyudNy7990k31evTJxXPv21oPN7/Pxj58mcdEi/141xL1i3EfpVh5p3GLOmPQ8V/+zNLen+LRph39/NPn3X9uupHtWMzExmwMGRLpFwaOkdOm9eT3GLw02brTazOXLl7zv3c2brVQcYOdb0T5qUUQkFD76yCZ9jY8nX3qpZJXFyMqyEpaAdXJTQjq6HCrGj4EUy8SJE9GyZcu9bjExMZg6dWqhl3XMMcDW7ZWwem1FNGkSgsZG2BVX2L8ffujQrOnZOP+Cvlj2a100bgwcf1y5yDYuyNq1A9LSgLlzy6JJ60746isgOxvo3DnSLQuO9u2B004DHn0UyMwEnHNofkZXxMbG5b5m1SrguuuAE04AVqwAXn4ZqFUrgo0uolatgBYtgDfftPuNT+uEykfVBgk884w9d/75oVt/2bLAiBHA4MGAc6FbT9++QGKibRMA1G54Mo5pfCpSU4ExY4AePYBKlUK3/lBp3hz4+GNg7lwgJga4/HJg9+6858tXqIjjT7kAALB2LTBqFHDjjfZ97AWdO9vx9+fKGJxwRhfMmGEfonbtItywILuwfTzi4oB/FjTHeV1uR72aTTBuHFCtGnDuuZFuXXBVqwZUrgz8uaYeUlJiPfNZFSlpghnjlwY1agAzZwINGwIdOgBDhgAZGZFuFTB5MnDiicCUKcCTT1obGzSIdKtEREqe7t2B33+32Pm224COHYENGyLdKuDPP4GzzrJz4rvvBp5+OrTnxRJeSkoXU7du3bBgwYLcW79+/XDWWWehQ4cOhV5WnTrA+vXA0qXwZFK6Xj2gTRtg3Di7v2cP8O23Frh6zTnnWAJsxgy7P3kycOSRwBlnRLRZQeMc8MADlsR79939n8/MtCQmCUyYANSvH/YmBo1zQJ8+wPz5wLx5eY9/+SWwZAlw553e+FGsVMm2c/x42685xo8HUlPtuWh2yinA6NH2/dq//4Ff8/jj9u/QoeFrV6jlXAibMsX+nTEDaNQIqFs3cm0KhYoV7fv1q6/sfmqqfe9efjkQF3fo90Yb5yxGyPl9qVMnsu0R8apgxvilRbVqwI8/Ar17A088AbRuDSxaFJm2rFkDXH89cPHFQPXqwJw5wF13AbGxkWmPiEg0qFnTzhtGjLDv8+bNrXNZZmb420JaO1q2BJYtA8aOtd8WL5x7Sx4lpYNoxYoVeOihh/D+++8jJqbwf9o6dexgT072ZlIaAK680oLTJUuA778H0tO9mZSuVAk49VTg668Bnw/44gugUydvBcIdO9o2PvookJW193N3323B/9tvA8ceG5n2BdP//R9Qvnxeb2nArtDWrm3Jd6+4/Xb798UX8x574w3r7e6FCyrt2gHDhgHvvGO3/NavB0aOBHr18lbCtkEDoFkz4PPP7TidNSu0PfsjqUMH4LffgE2bbHvT0+03x4uaNAH+/df+r6S0SOgVN8YvTRITLXaYPNm+j1u1siSCzxee9W/ebPHM8cfbhfWhQ4HZs4GTTgrP+kVEop1zwM03AwsWWEL4ttuAxo2tM1q4vss3bAAuvBDo1w9o29Z6cHs1ri/tPNV/aOBAO3CCqWVL4PnnD/+6rKwsXH311XjmmWdQt4gZjfwnll5NSl9+ue2n8eOtvEXZssB//xvpVoVGu3bW83LaNGDbNuup4SU5vaU7dwbee89KHgDAJ58AL7xgJwTdukW2jcFSubKVnxkzxpLRK1YA33xjw0DLlIl064Knbl3bzjfesOTtmjV2Ivfcc965In3//XZBrF8/u6jSrJk9/sQTgN/vrV7SOS6+2D6306dbD2Kvle7I0aEDcN99djHwww/tN7VNm0i3KjTyxwhKSktpEO0xfml00UWWRLj5Zivl8fnn1uPtxBNDs77kZPute/55G43Zq5f95qvEkYhI0TRqZOe806YB99wD9Oxp50yPPGLn+aE4P8zKsh7RAwfad/krrwC33OKdc1HZny71B8mwYcPQrFkz9ChGt8n8J5aNGwehUSVQjRpW2mLcOBtm3bYtkJAQ6VaFRrt2diVx8GAbPu7FHuGdOlkPmOHD7Qfk77+BG26wZN+TT0a6dcHVpw+QkmIXVJ55xnoC9e0b6VYF36BBtp1vvmm3cuWAa6+NdKuCJzbWLi5UrGgXyVJTgX/+sW3t2TO6S80cTOfOVtN+yBAL6LxWYznHyScDRx1lx+jUqTaKwasdGvMnpaOxVr9INAlGjF9aVa0KfPQR8P77lqA+6SSrCzp6tCUbiou0OSMGD7aRecOH22/ekiX2u66EtIhI8ThneYy5c60sJwlcdpmd748YkTdyr7jWrbMLifXqWeml44+3i9H9+ikh7XWe6ildkN4OoTBr1ix8/PHHmD9/frGWk5OULl/e20HUlVfmJfOeeCKybQmlM86wfbl4MXDeedE5SdzhOGc/Hl26WOmDt96yx8aPt17wXtKmjZWxeOopYOVK6wnuxX16yimWtHz+eUvYXnaZJfq8pEYNOyG+4AILdKpUsQtI99wT6ZaFxumn2z5cvNj275FHRrpFoREba/s0Z94CLw/xy7lwXb26975rRQ4k2mP80sw54JprbBj2O+8Ar71m92+/3Xoz33STTY5YUH4/8OuvNonxhAk2qisuznpmJyVZD3gREQku5+y8sGtX4IMPLI/Trx9w6612sbF7d+DSS628ZUH5fNYL+7XXrOQTaZ3ebr7ZfjO8VPpUDs5TSelI2LFjB3r16oUxY8YgMTGxWMuqVs2CqsaNvdu7C7Avq379rOde+/aRbk3olCtnX9DTpuVNNuZFnTsD//mP/SD5/cDEid6c1TxnwsM77rAfyJz6y1505512cgdE/wSHB3P++VZ+JinJvm979vTm5xawz2unTtZTzaulO3J06GBJ6eOOswS8Vx13nMULKt0hEjrBjPHFLo4OGmRx1MyZloR4/nkruVGzpg0Tb9gw71anDrB9u9UVzbn984/NTbNhg5VPa9/efse7dPHuBVcRkZIkLs7Om66/3kalTJhgtwED7HbyyXZOVbt23q1WLTsf+esv69z155/278qV1gmqenUb0dm3r/WUltIlIklp59zlAJIANAXQmuTcSLQjGF577TVs3rwZt9xyy16PDx06tNDD/GJj7USzRYtgtrDkOeoomyTvt9+8P+lIx45Wx9Vr9aTzy6kt3bWrnWhcckmkWxQ6115rNYcvvdRbk+Htq2NHq7Wcne3dmu+A1R/+/nvg22+920s6xyWXWFLai2WE8mvf3n5Lr77a20P9ypSxkRuF6V0oIoUTzBhf8sTE2IXh888HNm60klq//27JiS++OPBQcOes807t2jZyrUsXi629OGJNRCQaOGfni82aWS5g2TIbwTJrliWrv/4a2LVr//fFxVnSulEj4OyzrZxr164a+VeaOZLhX6lzTQH4AbwO4M6CJqVbtWrFuXP3funSpUvRtGnT4DcyBArS1lWrrNap14bL72vrVmDnTkvCe1lmJvDHH3bF0OsWL7YkideH2SxZYidFXj8R2rDBhlR5OfkOAOnpwNq13q3jn4ME5swBWreOdEtCb+FC25/x8ZFuSWitXWvbWK1apFsSPM65eSRbRbodEn6lIcaXgklNtQT1+vVWk7p2bSu75aWJpUVESoOUFBvhsmGDdXZq2NDOLeNUr6HUOVSMH5GPA8mlAOC83I2piLw6fHxfVavazevKli0dCWkgdLOplzQnnBDpFoRHYeqBRbPy5b2fkAasN0NpSEgD3h9tlMPrF4xEpHQ64girC63a0CIi0S0x0c6zSsO5lhSdhysXi4iIiIiIiIiIiEhJE7Ke0s65rwHUOMBT95KcVIjl9AXQFwDqqluQiIiIiIiIiIiISFQLWVKaZLsgLecNAG8AVm/uIK8p8aVAIlG7W0REREQkWinGFxEREfGuqC/fER8fj23btpXogJAktm3bhnivz7okIiIiIhIEivFFREREvC0iEx0657oBeAnA0QCmOOcWkOxQlGXVqVMH69evx5YtW4LaxmCLj49HnTp1It0MEREREZESTzG+iIiIiLdFJClNciKAicFYVpkyZdCgQYNgLEpEREREREoAxfgiIiIi3hb15TtEREREREREREREJHooKS0iIiIiIiIiIiIiYaOktIiIiIiIiIiIiIiEjSvJM1rvyzm3BcCaMK+2KoCtYV6nhI72p/don3qL9qf3aJ96Syj3Zz2SR4do2VKCKcaXIND+9B7tU2/R/vQW7U/viUiMH1VJ6Uhwzs0l2SrS7ZDg0P70Hu1Tb9H+9B7tU2/R/hSv0GfZW7Q/vUf71Fu0P71F+9N7IrVPVb5DRERERERERERERMJGSWkRERERERERERERCRslpQ/vjUg3QIJK+9N7tE+9RfvTe7RPvUX7U7xCn2Vv0f70Hu1Tb9H+9BbtT++JyD5VTWkRERERERERERERCRv1lBYRERERERERERGRsFFS+iCccx2dc8udcyudc0Mi3R4pPOfcMc65mc65Jc65P5xztwceP9I5N90592fg3yqRbqsUnHMu1jn3m3NucuB+A+fcr4Fjdbxzrmyk2ygF55yr7Jyb4Jxb5pxb6pw7Q8do9HLO3RH4vv3dOTfWORevYzS6OOdGOec2O+d+z/fYAY9JZ14M7NtFzrn/RK7lIgWjGD/6Kcb3JsX43qH43nsU40e/khrjKyl9AM65WACvALgQwAkArnLOnRDZVkkRZAMYRPIEAKcDuDWwH4cAmEGyEYAZgfsSPW4HsDTf/ScAPEeyIYAdAG6MSKukqF4A8CXJJgBawPatjtEo5JyrDWAAgFYkmwOIBXAldIxGm3cAdNznsYMdkxcCaBS49QUwIkxtFCkSxfieoRjfmxTje4fiew9RjO8Z76AExvhKSh9YawArSf5NMhPAOABdI9wmKSSSG0nOD/w/BfZjWBu2L98NvOxdAJdEpoVSWM65OgAuAvBW4L4DcB6ACYGXaH9GEedcJQBnAxgJACQzSSZDx2g0iwNQ3jkXB6ACgI3QMRpVSH4HYPs+Dx/smOwK4D2aXwBUds7VDE9LRYpEMb4HKMb3HsX43qH43rMU40e5khrjKyl9YLUBrMt3f33gMYlSzrn6AE4G8CuA6iQ3Bp76F0D1CDVLCu95AHcD8AfuHwUgmWR24L6O1ejSAMAWAG8Hhmu+5ZxLgI7RqERyA4CnAayFBao7AcyDjlEvONgxqXhJoo0+sx6jGN8zFON7h+J7j1GM72kRj/GVlBbPc84dAeBjAANJ7sr/HEkCYEQaJoXinOsMYDPJeZFuiwRNHID/ABhB8mQAu7HPUD4do9EjUIOsK+xkpBaABOw/REyinI5JESkpFON7g2J8z1F87zGK8UuHSB2XSkof2AYAx+S7XyfwmEQZ51wZWLA6muQngYc35Qw9CPy7OVLtk0I5E0AX59xq2HDb82D1yioHhhEBOlajzXoA60n+Grg/ARbE6hiNTu0ArCK5hWQWgE9gx62O0eh3sGNS8ZJEG31mPUIxvqcoxvcWxffeoxjfuyIe4yspfWBzADQKzCZaFlbE/bMIt0kKKVCLbCSApSSfzffUZwCuD/z/egCTwt02KTySQ0nWIVkfdkx+Q/L/AMwE0D3wMu3PKELyXwDrnHONAw+dD2AJdIxGq7UATnfOVQh8/+bsTx2j0e9gx+RnAK4LzNB9OoCd+YYAipREivE9QDG+tyjG9xbF956kGN+7Ih7jO+uhLftyznWC1baKBTCK5PAIN0kKyTnXFsD3ABYjrz7ZPbCacx8CqAtgDYArSO5b8F1KMOfcOQDuJNnZOXcsrFfFkQB+A3ANyYxItk8KzjnXEjapTVkAfwPoBbtgqmM0CjnnHgTQA0A27HjsDas/pmM0SjjnxgI4B0BVAJsAPADgUxzgmAycmLwMG8KZBqAXybmRaLdIQSnGj36K8b1LMb43KL73HsX40a+kxvhKSouIiIiIiIiIiIhI2Kh8h4iIiIiIiIiIiIiEjZLSIiIiIiIiIiIiIhI2SkqLiIiIiIiIiIiISNgoKS0iIiIiIiIiIiIiYaOktIiIiIiIiIiIiIiETVykGyAiEo2ccz4Ai/M9dAnJ1RFqjoiIiIiIFJNifBGR8HEkI90GEZGo45xLJXnEQZ5zsO9Xf5ibJSIiIiIiRaQYX0QkfFS+Q0QkCJxz9Z1zy51z7wH4HcAxzrm7nHNznHOLnHMP5nvtvc65Fc65H5xzY51zdwYen+WcaxX4f1Xn3OrA/2Odc0/lW9ZNgcfPCbxngnNumXNudCBYhnPuVOfcT865hc652c65ROfcd865lvna8YNzrkXY/kgiIiIiIlFEMb6ISOiofIeISNGUd84tCPx/FYA7ADQCcD3JX5xz7QP3WwNwAD5zzp0NYDeAKwG0hH0Hzwcw7zDruhHATpKnOufKAfjROTct8NzJAJoB+AfAjwDOdM7NBjAeQA+Sc5xzFQGkAxgJoCeAgc654wHEk1xY3D+EiIiIiIhHKMYXEQkTJaVFRIomnWT+Hgn1Aawh+UvgofaB22+B+0fAAthEABNJpgXe91kB1tUewEnOue6B+5UCy8oEMJvk+sCyFgCoD2AngI0k5wAAyV2B5z8CMMw5dxeAGwC8U9iNFhERERHxMMX4IiJhoqS0iEjw7M73fwfgMZKv53+Bc27gId6fjbyySvH7LOs2kl/ts6xzAGTke8iHQ3yvk0xzzk0H0BXAFQBOOURbREREREREMb6ISEioprSISGh8BeAG59wRAOCcq+2cqwbgOwCXOOfKO+cSAVyc7z2rkRdEdt9nWbc458oElnW8cy7hEOteDqCmc+7UwOsTnXM5gexbAF4EMIfkjmJtoYiIiIhI6aIYX0QkSNRTWkQkBEhOc841BfBzYF6SVADXkJzvnBsPYCGAzQDm5Hvb0wA+dM71BTAl3+NvwYbszQ9McrIFwCWHWHemc64HgJecc+VhtebaAUglOc85twvA20HaVBERERGRUkExvohI8DiSkW6DiEip5ZxLggWST4dpfbUAzALQhKQ/HOsUERERESlNFOOLiByeyneIiJQSzrnrAPwK4F4FqyIiIiIi0U8xvohEK/WUFhEREREREREREZGwUU9pEREREREREREREQkbJaVFREREREREREREJGyUlBYRERERERERERGRsFFSWkRERERERERERETCRklpEREREREREREREQkbJaVFREREREREREREJGz+H8uTozc4G1tkAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import scipy as sp\n",
    "import scipy.fftpack\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "an_data = pd.DataFrame(an_dataframe)\n",
    "\n",
    "an_fft_x = sp.fftpack.fft(an_data[\"x\"].values.tolist(), 100)\n",
    "an_fft_x_abs = np.absolute(an_fft_x)\n",
    "an_fft_y = sp.fftpack.fft(an_data[\"y\"].values.tolist(), 100)\n",
    "an_fft_y_abs = np.absolute(an_fft_y)\n",
    "an_fft_z = sp.fftpack.fft(an_data[\"z\"].values.tolist(), 100)\n",
    "an_fft_z_abs = np.absolute(an_fft_z)\n",
    "\n",
    "# Build a new data-frame with the fft\n",
    "an_fftDataFrame = {\n",
    "    \"x\": an_fft_x_abs,\n",
    "    \"y\": an_fft_y_abs,\n",
    "    \"z\": an_fft_z_abs\n",
    "}\n",
    "\n",
    "an_fftData = pd.DataFrame(an_fftDataFrame)\n",
    "\n",
    "fig = plt.figure(figsize=(25,10))\n",
    "\n",
    "# Normalize the dataset.\n",
    "fft_an_standardizedFrame = StandardScaler().fit_transform(an_fftData)\n",
    "\n",
    "# We technically don't need the standardized data to run PCA.\n",
    "fft_an_standardized = pd.DataFrame(fft_an_standardizedFrame, columns=[\"x\", \"y\", \"z\"])\n",
    "an_subplot = fig.add_subplot(221)\n",
    "an_subplot.plot(fft_standardized.index.values, fft_standardized[\"x\"], 'g', label=\"X\")\n",
    "an_subplot.plot(fft_standardized.index.values, fft_standardized[\"y\"], 'r', label=\"Y\")\n",
    "an_subplot.plot(fft_standardized.index.values, fft_standardized[\"z\"], 'b', label=\"Z\")\n",
    "\n",
    "an_subplot.set_title(\"Normalized Frequency Energy Data\")\n",
    "\n",
    "an_subplot.set_xlabel(\"Frequency\")\n",
    "an_subplot.set_ylabel(\"Energy\")\n",
    "an_subplot.legend()\n",
    "\n",
    "\n",
    "\n",
    "# Plot the non-anomolous data.\n",
    "an_subplot = fig.add_subplot(222)\n",
    "an_subplot.plot(fft_an_standardized.index.values, fft_an_standardized[\"x\"], 'g', label=\"X\")\n",
    "an_subplot.plot(fft_an_standardized.index.values, fft_an_standardized[\"y\"], 'r', label=\"Y\")\n",
    "an_subplot.plot(fft_an_standardized.index.values, fft_an_standardized[\"z\"], 'b', label=\"Z\")\n",
    "\n",
    "an_subplot.set_title(\"Normalized Frequency Anomolous Energy Data\")\n",
    "\n",
    "an_subplot.set_xlabel(\"Frequency\")\n",
    "an_subplot.set_ylabel(\"Energy\")\n",
    "an_subplot.legend()\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figures above show the \"normalized\" frequency spectrum of the raw data and the anomolous data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Variation (FFT Energy):  [0.94355479 0.05429187 0.00215334]\nVariation (FFT Anomolous Energy):  [0.87115586 0.12663701 0.00220713]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Run the PCA\n",
    "fft_pca = PCA(n_components=3)\n",
    "\n",
    "fft_principalComponents = fft_pca.fit_transform(fft_standardizedFrame)\n",
    "\n",
    "fft_an_pca = PCA(n_components=3)\n",
    "\n",
    "fft_an_principalComponents = fft_an_pca.fit_transform(fft_an_standardizedFrame)\n",
    "\n",
    "\n",
    "# Check the variation.\n",
    "print (\"Variation (FFT Energy): \", fft_pca.explained_variance_ratio_)\n",
    "print (\"Variation (FFT Anomolous Energy): \", fft_an_pca.explained_variance_ratio_)\n",
    "\n",
    "# Plot the PCA components for normal and anomolous PCA.\n",
    "fft_percent_variance = np.round(fft_pca.explained_variance_ratio_* 100, decimals =2)\n",
    "columns = ['PC1', 'PC2', 'PC3']\n",
    "fig = plt.figure(figsize=(25,10))\n",
    "an_subplot = fig.add_subplot(221)\n",
    "an_subplot.bar(x= range(1,4), height=fft_percent_variance, tick_label=columns, color='g')\n",
    "\n",
    "an_subplot.set_ylabel('Percentage of Variance (FFT)')\n",
    "an_subplot.set_xlabel('Principal Component')\n",
    "an_subplot.set_title('PCA FFT Normal Energy')\n",
    "\n",
    "\n",
    "fft_an_percent_variance = np.round(fft_an_pca.explained_variance_ratio_* 100, decimals =2)\n",
    "an_subplot = fig.add_subplot(222)\n",
    "an_subplot.bar(x= range(1,4), height=fft_an_percent_variance, tick_label=columns, color='c')\n",
    "an_subplot.set_ylabel('Percentage of Variance (FFT Anomolous)')\n",
    "an_subplot.set_xlabel('Principal Component')\n",
    "an_subplot.set_title('PCA FFT Anomolous energy')\n",
    "\n",
    "an_subplot = fig.add_subplot(223)\n",
    "an_subplot.bar(x= range(1,4), height=raw_percent_variance, tick_label=columns, color='r')\n",
    "an_subplot.set_ylabel('Percentage of Variance ')\n",
    "an_subplot.set_xlabel('Principal Component')\n",
    "an_subplot.set_title('PCA Raw Data')\n",
    "\n",
    "an_subplot = fig.add_subplot(224)\n",
    "an_subplot.bar(x= range(1,4), height=an_percent_variance, tick_label=columns, color='b')\n",
    "an_subplot.set_ylabel('Percentage of Variance (Anomolous)')\n",
    "an_subplot.set_xlabel('Principal Component')\n",
    "an_subplot.set_title('PCA Anomolous')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above compares the PCA obtained by analyzing PCA components of the energy specturm of the raw and anomolous data, and the PCA components of the raw and anomouls data themsevles. As can be seen the PCA components of the raw and anomolous data looks almost identical, but the PCA components of the energy spectrum start showing differences."
   ]
  }
 ],
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