poc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e85d94dd-1a99-4395-b937-ebe84dbe97a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"ns-client-bavo-protocol-manual-lhc-mellinbright-catmetrics/to/somewhere/far/far/away/foo.bar\"\n",
    "b = \"ns-client-bavo-task-script-lhc-plate-reader-echo-catmetrics/to/somewhere/far/far/away/notfoo.bar\"\n",
    "c = \"ns-task-script-hello-world/Lab1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b4e8f26-7e6c-4c1a-a6db-d30803a0dc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "asplit = a.split(\"/\")\n",
    "bsplit = b.split(\"/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "459e20c3-b0cd-4aab-b91a-15ac1f72c9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_mask = []\n",
    "for i, j in zip(a, b):\n",
    "    match_mask.append(i == j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a78a400f-0d8f-4c78-ba25-ecbccc5d4205",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_string = \"\"\n",
    "for m, i in zip(match_mask, a):\n",
    "    if m:\n",
    "        final_string += \".\"\n",
    "    else:\n",
    "        final_string += i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6c04ad9-682b-426b-87af-09c312d1ddb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.................f.le/to/somewhere/far/fa./aw.y..oo.bar'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc703a4a-ad5d-4557-88fd-4da9ac216ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range\n",
    "for i in range[tuple(data[i:i+3]) for i in range(len(data) - 2)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "66b3401e-5a75-4453-93f8-c7dfe40343d4",
   "metadata": {},
   "source": [
    "final_string = \"\"\n",
    "for i in range(0, len(a) - 2, 2):\n",
    "    if a[i: i+3] == b[i: i+3]:\n",
    "        final_string += \".\"\n",
    "    else:\n",
    "        final_string += a[i: i+3]\n",
    "    # print(a[i:i+3])\n",
    "    print(a[i: i+3], b[i: i+3], final_string)\n",
    "final_string    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d13bad94-ea20-486c-b15f-9705ef4f91e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verylongpathwithafile/.../.../.../.../.../foo.bar\n",
      "verylongpathwithadifferentpath/.../.../.../.../.../notfoo.bar\n"
     ]
    }
   ],
   "source": [
    "a_final = []\n",
    "b_final = []\n",
    "for ai, bi in zip(asplit[:-1], bsplit[:-1]):\n",
    "    if ai == bi:\n",
    "        a_final.append(\"...\")\n",
    "        b_final.append(\"...\")\n",
    "    else:\n",
    "        a_final.append(ai)\n",
    "        b_final.append(bi)\n",
    "\n",
    "a_final.append(asplit[-1])\n",
    "b_final.append(bsplit[-1])\n",
    "\n",
    "a_final = \"/\".join(a_final)\n",
    "b_final = \"/\".join(b_final)\n",
    "\n",
    "print(a_final)\n",
    "print(b_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1229c2dd-0f23-4124-ac94-ff3e839018fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1\n",
      "2 2\n",
      "3 3\n"
     ]
    }
   ],
   "source": [
    "for i, j in zip([1,2,3], [1,2,3,4,5]):\n",
    "    print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f95c38c1-1cce-4b5b-8ae3-6b6b34aa75a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dafsa import DAFSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60b03dcb-7614-4f14-a198-04ff942a70bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ??DAFSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6e0e3b47-beef-4205-ad0d-4b3eed208eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verylongpathwithafile/to/somewhere/far/far/away/foo.bar\n",
      "verylongpathwithadifferentpath/to/somewhere/far/far/away/notfoo.bar\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d6ac927a-3f2b-4ade-a59d-35ee6d8a354f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dobj = DAFSA([a, b], delimiter=\"/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "31ec8ff6-2fad-4e60-b5cb-64b6aebcde36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0(#1/2:<v>/2)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dobj.nodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "96ce698e-3624-4275-8979-ddf9c6d79edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.................d................................................................................'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_str = \"\"\n",
    "for i in sorted(dobj.nodes):\n",
    "    if \";\" not in str(dobj.nodes[i]):\n",
    "        common_str += \".\"\n",
    "    else:\n",
    "        common_str += str(dobj.nodes[i])[0]\n",
    "    # print(dobj.nodes[i])a\n",
    "common_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "02a5753c-3777-4224-8e1f-0b40fc91d625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 v|1\n",
      "67 \n",
      "66 r|67\n",
      "65 a|66\n",
      "64 b|65\n",
      "63 .|64\n",
      "62 o|63\n",
      "61 o|62\n",
      "60 f|61\n",
      "59 t|60\n",
      "58 o|59\n",
      "57 n|58\n",
      "56 /|57\n",
      "55 y|56\n",
      "54 a|55\n",
      "53 w|54\n",
      "52 a|53\n",
      "51 /|52\n",
      "50 r|51\n",
      "49 a|50\n",
      "48 f|49\n",
      "47 /|48\n",
      "46 r|47\n",
      "45 a|46\n",
      "44 f|45\n",
      "43 /|44\n",
      "42 e|43\n",
      "41 r|42\n",
      "40 e|41\n",
      "39 h|40\n",
      "38 w|39\n",
      "37 e|38\n",
      "36 m|37\n",
      "35 o|36\n",
      "34 s|35\n",
      "33 /|34\n",
      "32 o|33\n",
      "31 t|32\n",
      "30 /|31\n",
      "29 h|30\n",
      "28 t|29\n",
      "27 a|28\n",
      "26 p|27\n",
      "25 t|26\n",
      "24 n|25\n",
      "23 e|24\n",
      "22 r|23\n",
      "21 e|22\n",
      "20 f|21\n",
      "19 f|20\n",
      "18 i|19\n",
      "97 /|60\n",
      "96 y|97\n",
      "95 a|96\n",
      "94 w|95\n",
      "93 a|94\n",
      "92 /|93\n",
      "91 r|92\n",
      "90 a|91\n",
      "89 f|90\n",
      "88 /|89\n",
      "87 r|88\n",
      "86 a|87\n",
      "85 f|86\n",
      "84 /|85\n",
      "83 e|84\n",
      "82 r|83\n",
      "81 e|82\n",
      "80 h|81\n",
      "79 w|80\n",
      "78 e|79\n",
      "77 m|78\n",
      "76 o|77\n",
      "75 s|76\n",
      "74 /|75\n",
      "73 o|74\n",
      "72 t|73\n",
      "71 /|72\n",
      "70 e|71\n",
      "69 l|70\n",
      "68 i|69\n",
      "17 d|18;f|68\n",
      "16 a|17\n",
      "15 h|16\n",
      "14 t|15\n",
      "13 i|14\n",
      "12 w|13\n",
      "11 h|12\n",
      "10 t|11\n",
      "9 a|10\n",
      "8 p|9\n",
      "7 g|8\n",
      "6 n|7\n",
      "5 o|6\n",
      "4 l|5\n",
      "3 y|4\n",
      "2 r|3\n",
      "1 e|2\n"
     ]
    }
   ],
   "source": [
    "for k, v in dobj.nodes.items():\n",
    "    print(k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1c2e4344-4b2f-439a-a030-611360f26967",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[94], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcount_sequences\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpritn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "for i in dobj.nodes:\n",
    "    pritn(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "697077ae-8c1b-4e0a-a0b5-9d84af621944",
   "metadata": {},
   "outputs": [],
   "source": [
    "dobj.lookup(\"very\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fdf9f4e1-9fbf-43ee-a8f0-e5c891d9d169",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = dobj.to_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "095567fd-6ac4-4dca-aa2d-098e7a4758c8",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Graph' object has no attribute 'draw'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'draw'"
     ]
    }
   ],
   "source": [
    "G.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f92e79f9-7f8c-4a2f-afc7-0e507714468e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Graph' object has no attribute 'labels'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[88], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabels\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'labels'"
     ]
    }
   ],
   "source": [
    "G.labels()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e3d6c5-3856-4b53-841d-02f8d10eb10a",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "621457dc-ec86-4017-bcbb-a66194890402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PDpYUUt0tKV1HiL5KAkohRL+WqIc0JO5AYyocBjo93lP7sY2fHd6uBXx4az7DOi56yjReD+l0OdvT0D1BZ4PYzvysE/E3nQKCBd0jdefPWoieTAJKIUS/Fq+HNLTfgUaXYSNj+FScO9eRM2tpuHSQY8daNG8rtjYBZdse0t3hbE5D91ad+VmrHheK3ohiOLNeVtM0mjf/Awh2y4l0Nn7WQvREctcLIfq9tj2kof0ONAC5c5ZxauX3qH7mB2RO/Rz+lnpaPniRjBHnYxk5LXxcvA403e1sTUP3Rqn+rL2nDlD38i+xTpiDMW8Qms+Da/8WPMd2kTn1SswDzpRCOhc/ayF6ClnoIYTo926aWRqz9rCjPtLmAaMpWfoTFIOJxneewLH9DTKnXEHRtT+IOi5eBxpx7qT6s9bnFGMeOhHXvi00vvskTZueRvN7yf/cneR/7s6oY+VnLfozRdO09PW3EkKIXmrZkxVRHWjSIdSBRvo79yxn+2ctSxBEfyABpRBCAEcbXMx/ZD2eNJZ8MRt0rLl7blqyqUX6nI2fdThJam8NVQ1xkqTyrZSXFXPTzFLGlPTuJCkhQAJKIYQIW7W1Kq0daB5aMrlTdR5F90v3z/pniydx44xhHG1wpVzGafbowrSVcRLiXJGAUgghTnN6/Dz0xm7+tqWqy+f63oKypHtXi3Pj0bX709JtqGn937hhci6Xfe0e7nt1V6cLzd+/aCJL5QFE9FISUAoh+rX2piZT1dUONOLsW7W1qsvdhlp3vsu3/+8Ncucs6/L7+e6CsdxVPqbL5xHibJOAUgjRL6UyNRksha0k3CtTl71bV6epZamEEBJQCiH6oc6OSrXV1zrQ9Hed6TYUSvA5/tKvcO54J+G5B9+5AkNWsF2jFvBhr3gRx4538TdXozPbMA8cTf7n7sKQXSjJXKJXkoBSCNGvpGvd3LKLhrH8ynFS/qWPSrbUT6gEkevoLnyNp9rs1Wh48w8YckoYdNsfg1sCfmqeuw/P8d1knvc5TEXDUd0OPCf3kXPJjZiKhkm5KdEryW9CIUS/sWprVVqCSYCVW44waVC2TE32Ucl0G9pf3cLGA3UAmAePxzx4fNR+99GdaD4PtgmXhbfZt76Eu2oHA25+CPOgsrjnDagaGw/UcaCmRUa9Ra8hAaUQol842uDi3pd3AuA5dYDmTc/gObYLze/DkFtC5tQryb5wERAcRWp+/1mcle/gd9RjyCzANuUKci6+HkWnD5/zRy/vZNaoQpma7Keerqhqd82lc9d6QME2YS4AmqbS8uHLWMdejHlQGZoaQAv4YvqHQ3Ct5lNbqrhv0cTu/AhCpI0ElEKIfuGeFyvxqxqtn22j5vkHMJWMImfWUhRTBv6mUwRa6sLH1r3yK1x7NpE55QpMA0fjOb6X5o1PEbDXUnDVN8PH+VWNe16slKnJfmrt3pqEwaQW8OPaswnzkPEYcksA8NVVEXA0YCoeTv3rv8ex4x0I+DEWDSd//u1kDJsSfn1A1Vi7r4b7kIBS9A4SUAoh+rzQ1KTqcVH36q+xjJpO0eIfoCi6mGM9J/fh2rORnFlLyZ1zMwBZ538evTUb+wcvkTXtakzFIwCZmuzPHB4/VQ2uhPtbP9uG2mqPmu72N5wAwL71n+gyMin43F0ANL//LNXP/oiBX34kfG8BVNW7cHr8sk5X9Aqxv02FEKKPCU1NOnetQ3U2kTfnFhRFh+p1o2nR7fc8R4PT4tYJc6K2W8fPATScuzdGbQ9NTYr+5Ui9s92apc5d60FnwDr+0vA21ecO/tfrouTGn5I5ZT6ZU+ZTsvQnoIG94oWoc2jA4XpnN7x7IdJPHnuEEH1eaGrSfXg7itmK31FPzeqf4G84jmLMwDapnPx5X0cxmNACPgAUgynqHIrRDID31IGo7TI12T952+kDrnpbad2/BcuI89FbssPbQ/eUefAEDNlF4e2GnGLMQybgObY7pesI0ZPICKUQok+LnJr0NZwANUDtCz/GMuICihbfQ+aUK3B8/Dp1//oNAMb8IQAxX+6hkcuAoz7mGqGpSdF/mAyJvz5d+7YEs7snXha1XZ9ZEPyvLTfmNXpbDqrbkdJ1hOhJZIRSCNGnRU5Naj43ms9D5vlXkX/FHQBYy2ahBXw4tr+Bb/ZNWEZdiD67mMa1T6IzmjENGI3nxF6aNqwEnR7N5425RmhqsqMyM6LvGF5gQ4G4097OXetQTBYsY6KTtUxFw0BnINAS+1ASaGlAZ42+f5TT1xGiN5BHHyFEnxY5ZRiacrSNnxt1TChxwnN8D4rBRPH196K3ZFP74oMc/9PXqHv11+RcshRdRhaKKbbES9vriL7PZjZQGqdcVMDVjPvwdqxjLoopB6QzW7GMuhDP8d346o+Gt/vqjuI5vpuMEedHHV9aYJWEHNFryJ0qhOjTIqcM9ZkF+OqqYqYc9bbgyFBoytFUNIyBt/4BX10VqtuBsbAUxWCi8Z0nyCid1OF1RP9QXlbMyoojUaWDnLs3gBqIme4OyZ17C+4jn1D99/8ma9pCAFo+egWdJYuci68PH6fXKZSPLe7W9y9EOslvQCFEnxaamgQwDRgFgL/NlKO/pQEAfcSUo6IomIqGkTF0InpLFp6qT0FTyRg+NeYaMjXZP900szSmDqVz5zp01ty49wmAqbCUki/9DGNhKc2b/0Hz+89iGjiWATf/ItzrG4LJXjdfJF2YRO8hI5RCiD4tNDV5pMGFbdxs7Fuex/HpW1iGnxc+xvHpW6DTYy6dHPccqs9D04an0GfmYxs/J2a/TE32T2NKspg9upDNh+rDgeXAW37V4evMA0YHSwUlEOrlLbVNRW8ivwGFEH1eaGrSNGAUtilX4Pz0bWpVlYzSSbirKnHt2UT2xddjyApm4da+9HP0mfkYC0tRPS6cn76Nr+kUxdffi84cvW5Opib7twcXT2b+I+sTdszpDINO4cHF8R9uhOipFE3T0vevQAgheqD91S1c8ZsNwJk+3Y5P1xBwNGDIKSLrgqvJnn5N+PjmLc/jqFxDoLkGxWDCPGQiubNvwlQyMu7519w9R0aT+rFVW6tYvroybed7aMlkbpgu092id5GAUgjRLyx7siJqajIdQlOT0stbPLp2Pw+/ta/L5/negjLuLB+dhnckxNklSTlCiH7hwcWTMeiUjg9MgUxNipC7ysfw8yWTMRt06FO8z7SAH5MuODIpwaTorSSgFEL0C0Pzrdy/KL3tER9YNJGhcWoRiv5p6fRS1tw9l1kjT3fE6SCwDO031B8i9/1HuX7akG5/j0J0F5nyFkL0KzI1Kc6G/dUtPF1Rxdp9NVTVu6I66igEKwOUjy3m5otKObbrI8rLy1mxYgVf/vKXo87j9Pg5XO/E61cxGXQML7BJRQHRI0lAKYTod1ZtreLel3fiV7WU1lTqdQoGncIDiyZK0oRIWjJB4Y033si7777Lvn37qHHrgsHo3hqqGuIEo/lWysuKuWlmKWNKJBlM9AwSUAoh+qWjDS7uebGSjQfq0OuUdgPL0P7Zowt5cPFkmeYWaXfs2DEmzJhN2c33UasvlHtS9DoSUAoh+rXIqcnDdU4U5cy6t7ZTk1IaSHSXVVur+O/Vn+BXVRRd8lPaoVHz+xdNZKmMmotzSAJKIUS/5/T4OVhjZ8Yll/GN//wvvrDwSnIsRlmvJs6KdK3r/e6CsdxVPiYN70iI1ElAKYTol8Ijk7JOTZxDUhRd9BUSUAoh+hVZOyl6iqMNLuY/sp7jL/0K5453Eh43+M4VEAhw/LFbEx6Ted4CCq76FmaDjjV3z5V7VZx1ElAKIfqNrmZ3yzo1kU6h7k2uo7vwNZ5qs1ej4c0/YMgpYdBtf0T1unHtez/mHO7PPsK5cx2F1y7HNu5S6d4kzhlZHCSE6Be6sk4tcDoAXb66kjqHR9apiS7bX93CxgN1AJgHj8c8eHzUfvfRnWg+D7YJlwGgM2WQOak85jzOyjUoZivW0TOA4L268UAdB2paJIlMnFXSKUcI0eet2lqVlqQHgIff2sc/tlal5Vyi/3q6oqrdTjrOXesBBduEuQmP8TsacFdVYh07C8VgCm/X6xSe2iL3qDi7ZIRSCNGnHW1wce/LOwHwnDpA86Zn8Bzbheb3YcgtIXPqlWRfuAgATVNxbH+Dlo9fx994EsWYgWnAKHJmLSVjyJkRpB+9vJNZowplnZrotLV7axIuu9ACflx7NmEeMh5DbknCc7h2bQBNxTbxsqjtAVVj7b4a7iO9rUaFaI+MUAoh+rR7XqzEr2q0fraNUyu/S8DVTM6speTN/zqW0TMItNSFj2189880vPlHTEXDyZt3G9kzrsXfcJzqZ5bjObE3fJxf1bjnxfRl5or+xeHxU9XgSri/9bNtqK328HR3Is5d69Bn5pMxbErMvqp6F06Pv6tvVYikyQilEKLPCq1TUz0u6l79NZZR0yla/AMUJfZZWlMDOD5+HWvZJRQu/E54u3XcpZx47DacO9dhHlQGyDo10TVH6p20lxLm3LUedAas4y9NeIyv4TjeUwfImn5N/PsZOFzvZOKgnK6/YSGSICOUQog+K7ROzblrHaqzibw5t6AoOlSvG01To47VAn40vwe9LTdqu96aC4oOxWiO3i7r1EQnef1qwn2qt5XW/VuwjDgfvSU74XHOnesAsE2MTdRJ5jpCpJuMUAoh+qzQOjX34e0oZit+Rz01q3+Cv+E4ijED26Ry8ud9HcVgQmc0YxpUhqPyHUyDx5ExZCKqx0nze6vQZWSSOfXKqHPLOjXRWSZD4rEc174twezuNusi23LuWochfwjmAaM7dR0h0k0CSiFEnxS5Ts3XcALUALUv/JjMKQvImPtl3FWVtHz0CqrbSdE1/wVA4cLvUPfSQ9S/8qvweQy5Axhw8y8w5g6IuUZonZq0ZxSpGF5gQ4G4097OXetQTBYsYxLXkfSc2Iu/8SQ5s29KeIxy+jpCnC3y+CKE6JMi16lpPndw1GfS5eRfcQfWslnkX3EHmVOvxLV7A76G4wDoTBaMhaVkXfBvFC2+h/wF30BTA9Ss/gkBV3PMNULr1IRIhc1soDROhYCAqxn34e1Yx1yEzpiR8PXh6e52knZKC6zyoCPOKgkohRB9UuT6sVCNPtv46Jp+oS9kz/E9aGqA6lU/RGe2kb/g37GWzSLrgs9TsvQn+BtPYa9Y3eF1hEhWeVlxTB1K5+4NoAbane7W1ADOPRsxDSrDmDcw7jF6nUL52OJ0vl0hOiQBpRCiT4pcP6bPLAj+t23CjS2YAau6HbirduCrPRIz1WjMH4yxYAie47s7vI4QybppZmlMHUrnznXorLlkDJ+a8HXuw9tRnU3tjk4GVI2bL5IWoeLskvFwIUSfFLlOzTRgFO7DH+NvqcdYMCR8jL+lAQC9NQfV1RTcqMUZcVQDaGogZrOsUxOdNaYki9mjC9l8qD4cWA685VcdvAosI6cxbPmrCfeHenlLOStxtsmjtRCiT4pcp2YbNxsAx6dvRR3j+PQt0Okxl07GkD8YAOeuDVHHeE4dwNdwHFPJyJhryDo10RUPLp6MoZ32i51h0Ck8uHhyWs8pRDLkN6EQos8qLytmZcURTANGYZtyBc5P36ZWVckonYS7qhLXnk1kX3w9hqwCDFkFZAw/H+eOd1C9LiwjzifgaKTlo1dQDCayL7wm6tyyTk101dB8K/cvmsjy1enruvTAoonSElScE4qmae0V7BdCiF5rf3ULV/wmOOKoBfw0v/8sjk/XEHA0YMgpIuuCq8mefiZQVH0e7B+sxrV7I/6matAbyBgygdw5y+KOUK65e45MLYoue3Ttfh5+a1+Xz/O9BWXcWZ64LqUQ3UkCSiFEn7bsyYqodWrpEFqntvLWxLUChUjFqq1V3PvyTvyqltK9qtcpGHQKDyyayA3TJRFHnDsSUAoh+rSjDS7mP7IeTxrL+5gNOtbcPVemFkVaHW1wcc+LlWw8UIdep7QbWGpqAEWnZ/boQh5cPFnuRXHOSVKOEKJPC61TSydZpya6w9B8Kytvncnb357DspnDGFZgpW3KjgIMyTHj+Ph1bh14gpW3zpR7UfQIMkIphOgXZJ2a6I2cHj+H6514/Somg47hBTZsZgMLFy6kpqaGioqKc/0WhQAkoBRC9COyTk30FS+88AJf+MIX2LlzJxMmTDjXb0cICSiFEP1LKuvUQvtlnZroaTweD4MGDeK2227joYceAhKPZgpxNkhAKYTol/ZXt/B0RRVr99VQVe8i8hehQrBoefnYYm6+qFRKA4ke6Zvf/CYvrNnMbT9fwfp9dVQ1xLmP862UlxVz08xSxpTIfSy6jwSUQoh+z+nx843v38vWbdt5btUzMrIjeryjDS7u+ut7fFLjRQe0V8NARtrF2SBZ3kKIfs9mNlBs9BKoPsDEQTkSTIoebdXWKuY/sp4ddT6g/WASCC/r2HyonvmPrGfV1qpufoeiP5LfmkIIAZjNZjwez7l+G0K0qyvVCgKnk9GWr66kzuHhrvIxaX53oj+TEUohhEACStHzrdpalZbSVwAPv7WPf8hIpUgjGaEUQghAZ7Lgyyzh46pGyZAVPc7RBhd33HYr9k/XJDxm8J0rMGQVomkqju1v0PLx6/gbT6IYMzANGEXOrKVkDBkfPv5HL+9k1qhCWVMp0kKScoQQ/VY403tvDUcanBDRl0QyZPu3nlaCZ9mTFby7YROehpNt9mg0vPkHDDklDLrtjwA0vPMELVtfwjaxHPPQiahuB47tb+C31zLg5l9gHlQGSE96kV7y+C2E6Hfi16KMbnKnAUcaXKysOMKK9w9Lhmw/EPmA0ZNK8OyvbmHjgTqMg8ZhHDQuap/76E40nwfbhMuAYI9vx8evYy27hMKF3wkfZx13KSceuw3nznXhgDKgamw8UMeBmhYpjSW6TNZQCiH6lVCG7OZD9QAddsyRDNm+72iDi2VPVnDFbzawsuIIR9oEkxD9gHHFbzaw7MkKjja4zsr7e7qiCr2ubVfvIOeu9YCCbcLc4PsM+NH8HvS23Kjj9NZcUHQoRnP0dp3CU1vknhZdJwGlEKLfeHTtfpavrsTjV1NqvQjBwNLjV1m+upJH1+7vpncozrae9IDh9PjZeaKZj6sa2XmiGafHD8DavTVx35cW8OPaswnzkPEYcksA0BnNmAaV4ah8B8fOtfiba/DWfEb9vx5Bl5FJ5tQrYz7P2n01afsMov+SKW8hRL+Q7gzZokyz9PU+S7prPWNPKMHT0TT7kDwLRxtb47629bNtqK328HR3SOHC71D30kPUv/Kr8DZD7gAG3PwLjLkDYs5TVe/C6fFLEproErl7hBB93tEGF/e+vBPV24q9YjWeE3vxntyH6nZQ8PlvkzllftTxR35+dcJzZQyfSsnSn0iGbDfr7vWM5/oBI5me8hokDCbh9HS3zoB1/KVR23UmC8bCUsyDx5Ex7DwCziaatzxHzeqfMOCmh9Bbc2Kuc7jeycRB0duFSIUElEKIPu+eFyvxqxqqy07ze39Hn12EsXgEnqrKuMcXXP2dmG3eU/tp+fBlMkacD4Bf1bjnxUrJkE2zZAOtriRMhR4wvLVHaN70DN5TBwg4m1CMZowFQ8meuQTrmDM/17pXH8G5452Y8xjyhzD49seA1ErwrNpaxb0v78R/+rOluvwCQPW20rp/C5YR56O3ZIe3a2qA6lU/JGPoZPIX/L/w9ozh53HiiTuxV6wmr/yrMefz+jvqtyNE+ySgFEL0aaEMWQB9Zj5D7lqJPjMPz8n9nPrr3XFfkzmpPGZbfVUloGAbH0x+kAzZ9Es10Gq7nvH+RRNZmsQoYegBI2CvQfW2Yps8D31mPprPg2vvZmpf+DH5V95FVuR6Q72Rgqu+FXUenflM8JjsA0ZXptkjufZtCWZ3T7wsaru7age+2iPkXX5b1HZj/mCMBUPwHN8d93wmg6RUiK6RgFII0SckWmcXypANqBqKwYg+My/lc2t+H66972EunYQhuzC8PZQhe9+iien8KP3S2VrPGPmAYRk1Hcuo6VH7s6ZdzckV38b+wUtRAaWi08d90Ih8Dx09YKRzmt25ax2KyYJlTHQAq7qagv+jxRlxVANoaiBmswIML7Cl5X2J/ksCSiFEr5XMOrsGp7dTU4qRWg9uRfU4Y0aDQhmy9yEBZVeczfWMkQ8Y8Sg6PYasQjynYjP5NTWA5vNEjUxGau8BI9Vp9qjrBvyc/PM38dUfJbf8a2ROnof78HZs4+egM2ZEHWvIHwyAc9cGLCOnhbd7Th3A13CczKmfizl/aYFVEnJEl8kdJITodVJZZ5cOzl3rQG/EVnZJzD7JkO2aow0ulj/5GrXrnkoqyNI0FcfHb9Cy/XX8DcdRDGaMxSPIn3cbppKRQPvrGeOV4FG9bjS/B9XjonV/Ba2HPsI6fnb0dX0ejj7yxWBAmZGJdcJc8i77CjqTJXxMew8YnZpmP63lo1fw22vDf3bu3gBqIOYBB8A8YDQZw8/HueMdVK8Ly4jzCTgaafnoFRSDiewLr4k6Xq9TKB9bHHMeIVIlvwGFEL1KOhIaUqF6XLQe/BDLqAvRZWTG7JcM2a6558VKPI3VSQdZ9f/6Lc5d67BNupysaVejed14qw8RcDWHj0m0ntHh8VMV5yGj8d0ncGx/I/gHRYd17MXkL/j38H59Zh7ZF12HqWQUaCqth7bh2PYvfDWfUfKln6Ho9OFj4z1gdHaaHSDgbKLpvVVkX3QdzRufBsC5cx06ay4Zw6fG/Tstuu6H2D9YjWv3RhoPbQO9gYwhE8idswxjwZDo86saSy4YHPc8QqRCAkohRK+RroSGVLj2vofm98bU+oskGbKdEwq0zCMvpGTkhVH74gVZzt0bce54h6LF92Atm5XwvInWMx6pd8Z0wAHInn4N1nGXEmipx7VnE5qmQsAX3p932VeijrdNmIsxfzBNG/6Ga8+mcJcaiP+A0ZVp9sZ1KzDmD8Y2sTwcUA685Vcxx0XSGc3kXnIjuZfc2O5xIdf84T3pWy+6TNK6hBC9QjrX2aXCuXMditmGdfSMhMf0xwzZRF1dUtFeS8FQkKV6HOFt9q0vYRo4FmvZLDRNRfW6E547XkvBRIG/sWAoluFTyZw8j+Lr70Xzuql5/gE0LfHod9b0a0DR4T78Scy+t9a8y6ZNm9i3bx+NjY28m2CaPeBqxtd4EvsHL9F66CMyhp0XdYznxF6cO94lf/7XUYj/95QO57KtpOg7ZIRSCNHjhRIagKSLk0P76+2S4Xc04K6qxDZ5HorBGPeY/pQhm+5i423XM7a3llH1uPCe2EfWBZ+ncf1fafnoVTRvK4acEnIv+wq2NmseA6rGKx8dxFj5EocPH+bw4cN81uiDK77X4fuyjruEhjcexd9wPGaKOERnNKOzZBFwt8Ts+/73voOv5rPg34vJwtC7n0VRogPCjqbZNU2j4e3HsY6fjXnwePxN1R2+767qbBkmIUACSiFELxBKaACSLk4O7a+3i7cesi3Xrg2gqe1Od/eHDNnuKDYebz1je0GWv/EkoOHcvRF0OvIu+yo6sxX7hy9T989foDNbo7KaAeo8Oh55/I8MHzKQ4cOHc/V5o3gJDToY7dN8HgBUjzPhMarHheqyx3SdUYCD27fgaKqnpqaGjw/X8ps9sdfraJrdWbkGX+0Rihb/oN332h3S1VZS9C99+7egEKLXi0xogOSLkydab2f/6BU8J/YScDQA0HrgA/wtwfNnT1uILuPMaKNz1zr0mflkDJsc9xr9IUO2u4qNx1vP2F6QpfqCLQjVVjsDbvkV5kFlAFjGzOT4n26lefM/YgJKRVFYv21X1HrGj3+5Npz9H3A2obflRr1GC/hx7ng3OJpdWIrm96IF/DGlgpo3rwI0LCMuiNpeWmBl6MBiGFjM+PHjyR7RyG/2bI75/MaCoRgLhgKQOXke1av+h5rnH2DALb9G87bSuP6vZM9cgiG7KOa1Z5P0rRfJkoBSCNGjtU1oSLY4edv1dprPi86Ugb3iRQL2mvBxrn2bYV/wCz9zYnk4oPTVH8N76gBZ069FUeKvkQyoGjdf1He/aLuz2Hi89YztBVmKwQyAIackHExCsG+1ZfQMnDvXoamBqIzreNcpLytmZcURAqpG/RuPonldmIdOQp9VQMDRiHPXOvz1x8i7/FZ0Jgv+pmpO/uVbWCfMDU9/uz/bRuvBD8kYOQ3L2IvC5473gJHs+trIaXbnznUQ8GMdPzs81R166FHdDvxN1eiz8lH08ZdhJFvrsmX7Gzh3rsNXfwzV40CfWUBG6WRyL7kRQ25J+DjpWy+SIQGlEKJHi1c3sCPtrbfLK/9qzHq7eIwFQxi2/NWE+/U6hVkjC/ps28XuLjaeTKAVGWTpM/MB0LUZUQSCo4yqH83rRsmIXs/a9jo3zSxlxfuHAbCNn43j07dp+fg11NYWdCYLpgGjybvsq+HAS5dhwzJ6Bu7DH+Pc8Q6aqmLMG0ju3FvInrEk6mEj3gPG8AIbCsTNLo8UOc3ut9eiuh2cfOIbMcfZ338W+/vPMvCrvwvX3YTg+tWjp9e1Jlvr0lt9CENOCdbRM9BlZOJvrqblkzdpPfABA7/2ewxZBYD0rRfJkYBSCNFjJaob2JHOrLdLlUGn8ODi+FPhvV0qxcaP/PzqhOfJGD6VkqU/AWJHuZIJtCKDLHPBEPS2vPBShUiBlnoUgwnFbInaHi9hakxJFrNHF7L5UD22CXOjSv7Eo8vIpHDhd9o9BhI/YNjMBkrzrSlNs2dduBBrxMgnQMDVTMMbj2KbPB/rmJkYcs6MIPoaTqDf8gxfunopT9cNSbrWZcHnYgNWy9iLObXi2zh3vEvOxdcHry1960USJKAUQvQI8XpxJ6ob2JHOrLdL1QOLJvbZKcBUio0XXB0bbHlP7aflw5fJGHF+eFtolOtvX5vBJ598wt///ndU53iUrKKkgiwA6/jZtHz4Mq2ffYzl9LkDrmZcByrIGDYlZmlCooSpBxdPZv4j69NaFL+9B4xUp9nNA0bDgNFR5whNfRsLS7GOvTi8Xa/A9BE5OI9aePyt7dimDoyZ9of2a11GfY6c4JR924Qk6VsvOiIBpRDinOmoDE1JtrlT5+3sertkfW9BWZ9NUki12HjmpPKYc9RXVQIKtvFnRv9Co1zjZlzGvg83UFhYyIRl93JUKaImiSALIOfi63Ht2UTtiw+SPf1adGYbLdtfh0CA3Dm3RL2H9hKmhuZbuX/RRJavTlwhIFXtPWCkOs2eioAGP/vaVYxe/kUufegdjjWdqc2ZTEtJgECrHVQVv72W5vf+DhBTE1P61ouOSEAphDjrki1Dc8ru6dT5O7vert1z6hQMOoUHFk3ss8EktN/VJZlRLs3vw7X3PcylkzBkF0bvUwMUXHQtr//4B8ybN4/DDW6u+M2GpIMsvS2Pkpt/QeO7T2L/8J8QCGAeXEbhwu9ErSeEjhOmlk4vpc7hScs60Y4eMFKdZo/HkFsSs6Y3cprd4fFzvCm60HtHtS5Djj365XA2vc6STd78O8IjwJGkb71oj9wVQoiz6mz04jZkFaS83i4hNQA6PbNGFrRbV7GvSKXYeDytB7eiepzYJl4Ws0/R6TEOm8qVVwZHNceUGIOBlu6ypIMsY+4Aipf8d7vHJJswdVf5GAozzeH7MZV7MdUHjO6eZk+1DFOkki/ej+b34qs/Ghy998XvQCR960V7+l+/MCHEOfPo2v0sX12Jx692SyAZyTp+NgF7La2ffRzeFm+9XVaGgWEF1phS1wowrMDKeTY71X++i59fOaTPB5OJio0f+91NnHj86zSu/XPCUa4Q5651oDdiK7sk7v7QKFfIg4snY0jQfrGzUkmYWjq9lDV3z2XWyGBGc6JWkCGh/bNGFrDm7rlJj1aHptnTKXKaPVEZpmRaSmYMm4Jl1IVkz1hM4bXLaX7v79g/eiXuNaVvvUhERiiFEGdFOsvQ2D96BdXtbLc4eTLr7fQ6hevOH8J9iybGTQqymQ3Y7XaGPPwN/vCHP/Dggw8C8ROI+sI0YFdGuSBYrqn14IdYRl2YsBNR21Gus72eMZ6h+VZW3jrzzJrefTVU1cdpLVlgpXxsMTdfVNqpbOfunGZPtQxTopaSxryBmEpG4ty5juxpC2P298e+9SI5vf83oBCix+tML+66Vx/BueOdmHMZ8oeg+b0dFidPZr1d5Do7m9kQdyovOzub2267jf9d9TLKhV9k08HGtPSx7olSLjbepj+1a+97aH5vu60q413nbK5nbM+YkizuWzSR+0j8gNFV3TXNnmoZpvaoPm/ch4b+1LdepE4CSiFEt+tsL270Rgqu+lbUJp3ZmnQ2bHvr7ZJdZ3e0wUXVyEVk3jCPZ7YeQ4vTBzrVPtY9VVdHuZw716GYbVhHz0j5OmdzPWMyEj1gpMPS6aVcMqqww8S0kND+9tbxRta7TKqlpBpA9baibzOS7DmxF1/t4bhrWvtD33rReXJnCCG6VWd7cUMwiSNeWZp0SGadXdsEonjBZKRk+1j3VF0Z5fI7GnBXVWKbPA/FEL8lILQ/ytUdgVZP1R3T7BcOy+NIgyupWpeq28HxP3wF6/jZmApLUYwZ+GoP46hcg85sI+eSpVHn7g9960XXSEAphOhWne3FHaKpATSfB505vcFCR+vsurOP9dmS6rRtqqNckVy7NoCmdjjd3dEo19laz9hTpGuafdXWKl759CSQXK1LxWgm87wFuI98Glyq4POiz8zHNn4uObNuiOrlDX2/b73oOgkohRDdqjO9uEM0n4ejj3wxGFBmZGKdMJe8y74SLnTdWR2ts+vuPtbdqaNi8R2t9Qx1dUm22HiIc9c69Jn5ZAxLPOqbyijX2VjP2NN0dpq97cNPMrUuFb2R/Pm3J32NDIOOp7ZU9eo1wqJ79b1/kUKIHqOzvbgB9Jl5ZF90HaaSUaCptB7ahmPbv/DVfEbJl36WcpebZNfZdSaBCMC5eyP2rS/hqz+GougwFg0je+Z1WEdPj+lj3R2SLRbf0VrPUFeXVDq6+OqP4T11gKzp18a0P4zU2VGu7lzP2Nul8+GnPW6/2uvXCIvuJQGlEKLbdLYXN0DeZV+J+rNtwlyM+YNp2vA3XHs2JV0IO9V1dp1JILJ/+AqNax7HMmo6mZd9Gc3vw1G5htrn76do8T3ox1/CPS9WsvLW1FvrJSPVYvHtrfUcWWhliKGFo+MuTb7YeMGQmC4ubSWbBCWSd7TBxR233Yr90zUJjxl85woMWYVoAT/N7z+Ls/Id/I56DJkF2KZcQc7F1yf9cNbb1wiL7iUBpRAibdpOTTa3xq9X2FlZ06+haeNTuA9/EhXsDMg2U233dHmdXWcTiFo+egXTwDEUfeFH4VI6mVOu4Ngfvoyj8h2sZbPYeKCOAzUtaQ+o0rnW89+GG7jllluo2HGA0jseJ5DG95lKsXGRnHterCRz6pUYS89rs0ej4c0/YMgpwZAVbH9Z98qvcO3ZROaUKzANHI3n+F6aNz5FwF5LwVXfTOm6PWWNsOhZJKAUQnRJe2v20k1nNKOzZBFwt0Rtf/LL0xleYOvyOrvOJhCpXhfG/MFRdRl1Zis6YwaK0QQER+ie2lLFfWnslpLutZ73vfM4WbXHeOef/+CEZfg5LTYu2hd6+DEOGodx0Liofe6jO9F8nnCClOfkPlx7NpIzaym5c24GIOv8z6O3ZmP/4CWypl2NqXhEp97H2V4jLHouCSiFEJ2SzJq9dFM9LlSXHb31zHq6UBmadKyz62wCUUbpZFx73sP+4StYR89AC3ixf/QqqsdF9oWLgOCoztp9NdxHegLKow0ulj/5GrXrnsJ76gABZxOK0YyxYCjZM5fErHNsb40ngKZpZF9+G69+4yImlAYTZ3pCsXERX9uHn0jOXesBJTyK7zkaXBNsnTAnfIy39gie6oOAxsm/fBtdhi3m3tE0FWflu7j2bcZbfQjV3YIhpwTr+DnkzFyCYgg+LJ2NNcKi55MeSkKIlK3aWsX8R9az+VA90PGavVRpfi+qJzaZp3nzKkDDMuKC8LZ0FVvuSgJR/vw7yCidTOOaxzn+2K2c+L9/x7V7IyU3/gTz4PHh49r2se6Ke16sxNNYjeptxTZ5Hnnzv07OrBsAqH3hx7RsfyN8rP3DV6j750PoLdnkXfZlci5ZiupxUvv8/bj2BjsMKYqCojfws7c/C7/urvIx/HzJZMwGXYc9rtvS6xTMBh0PLZnMneWj0/CJRaREDz9awI9rzybMQ8aHS/9op7vehAJAgIC9Bs0f3G4sGBz33tF8Hupf+w0BVzNZ519F3ryvYxo4luZNz1D97L3hnuB+VeOeF9M3mi16JxmhFEKkpCtr9kI66sWtuh2c/Mu3sE6YG+7G4v5sG60HPyRj5DQsYy8C0ltsuSsJRIrRjKFgCLasQiyjp6N5W7Fv/Se1qx+k5OaHMOYNAmL7WHdWaLrTPPJCSkZeGLUva9rVnFzx7eBU5tQrgeTWeELwwaDtWs/+VGy8t2jv4af1s22orfaoeqDG/OC/Ic+x3RhzBwBgGTUdv72OhuO7QVHInn5NzL2j6A2U3PxLMoaceSjKmnolhpwSmjc9jfvIJ1iGT41734j+RwJKIUTS0rVmz17xYoe9uC2jZ+A+/DHOHe+gqSrGvIHkzr2F7BlLwqVp0llsOV4f62TVvvRzFEVH8fX3hrdZxlzEicdvp2n9Soqu/X5arhPS3nSnotNjyCrEc2p/eFsyazxD4q317G/Fxnu69h5+nLvWg86Adfyl4W2WUReizy6mce2T6IxmTANG4zmxl6YNK0GnR/N5gdh7R9Ebo4LJEOvYi2ne9DS+uqNYhk8FumeNsOhdJKAUQiQlVJ8xldqMIVrAz8k/fxNf/VFyy7/GkG/8ucPrFS78Trv7012GJpk+1vH4mk7hPvQR+VfeFbVdb8nCPGQCnuO70nKdSG2nO1WvG83vQfW4aN1fQeuhj7COnx3en8waz5D21nr2x2LjPVGihxLV20rr/i1YRpyP3pId3q4YTBRffy91/3yI2hcfDG7UG8kr/yrNm/+BYjDiazwZ996JJ+BsDJ7CeuYa6V4jLHof+dcvhEhKqD5jsrUZI7V89Ap+e21a30+6y9Ak08c6HvX0lyta7Je8pvrR1DPFd9rrY52seNOdje8+gSO0ZlLRYR17MfkL/j28P3/+HaguO41rHqdxzeMA6CzZMWs8Q0JrPTtq0yjFxs+NRA8lrn1bgtndEy+LfU3RMAbe+gd8dVWobgfGwlIUg4nGNf+L6mrmxONfj3vvxGOveAHFbMUyclrU9mTuG9F3yU9dCNGhyPqMydZmDAk4m2h6bxXZF11H88an0/ae0l2GJrKPdSoMeYNA0eHcvZHMqVeFp5X99jo8x3ZhHjIhfGw6EojiTXdmT78G67hLCbTU49qzCU1TIXCmBmiyazxD0rXWU3SPRA8/zl3rUEwWLGPiF9BXFAVT0bDwn1sPbgUg68JrMJWMjHvvtNW8+Vnch7eTv+Ab6DIyo/bJfdO/SZa3EKJDoTV7kHxtxpDGdSsw5g/GNrE8be+nu8rQlJcVx2Qz2z96hab3VuH49G0gmEDU9N4qmt5bhep2orfmkDllPp6qSqr//t/YP3qF5vef5dTK76L5PORcdD2QvgSieNOdxoKhWIZPJXPyPIqvvxfN66bm+QfCWbi1L/2cQHMNhVffjW3cpWROuYKSL/0MLeCnaf3KpK8jeobQw0+kgKsZ9+HtWMdchM6Y0eE5VJ+Hpg1Poc/MJ3f2TQnvnUjO3Rto2rCSzCkLyLrg83HPK/dN/yUjlEKIDnW2PqPnxF6cO95lwM0PoZBa2Zm2ku3F3RWhPtaRkkkgyv/cnRiLR+D45G2a1v8NANOAMRRe/Z9klE4C0pdAlMwaTOu4S2h441H8DcdBb0hpjWcq1xHnTnlZMSsrjoT/XTp3bwA1EHe6G4IPFfrMfIyFpageF85P38bXdIri6+9FZz4TnEbeO6EKCwCtn31M3au/xjLqQvKvvDPh+5L7pv+SgFII0a7O1mfUNI2Gtx/HOn425sHj8TdVd+r6Z7MMzZiSLGaPLmTzofrwF3UyCUSKTk/2tIVkT1sYd386E4iSWeup+TwAqB7nmbWdSazxDEnHWk/Rvdo+/Dh3rkNnzSXjdNZ1W6YBo3FUrsGx/Q0UgwnzkIkULvoeppKRUcdF3TuneU7spXb1TzEPGEPhtcsT9v6W+6Z/k4BSCNGuztZndFauwVd7hKLFP+jUdc9VGZoHF09m/iPr01qsPZ0JRJFrPQPOJvS23Kj9WsCPc8e7KAYzxsJSNL836TWeIekqFi+6T9uHn4G3/Krd43Mu+gI5F30h/Odk7h0AX91Rap67H0NOMUXX34vOaE54Dblv+jf5yQsh2tWZNVGqx0Xj+r+SPXMJhuyipF7z99tmkm0xnvMyNEPzrdy/aGKP7mMdmu6seeNRNK8L89BJ6LMKCDgace5ah7/+GHmX34rOZAGThcwp83F88hbVf/9vrGUXo3lbadn2WtQaz5B0FosX3asrDz/1Sdw7qsdF9bM/QnU7yJ65hNYDW6POYcwbEK4SIPeNkIBSCNGuzqyJsleshoAf6/jZ4anuUCcc1e3A31SNPisfRW8MvybbYuwx2aFLp5f26D7WoelO2/jZOD59m5aPX0NtbUFnsmAaMJq8y74a1cs7mTWeIeksFi+6V1cefpK5d9TWFgKny301rVsRe45J88IBpdw3QgJKIUSUtgWrizLNKddn9Ntrg+0Tn/hGzD77+89if/9ZBn71d+H1Wz1x7dVd5WMozDRz78s78ataSqNA3Z1AFJ7u1F2GbcLcDo/vaI1nSLqLxYvu19mHH9uEuR3eO4bcEoYtf7XDc8l9I0ACSiEEnGmpt7eGqobYlnp6nYI/hYAq68KFWE/32w4JuJppeONRbJPnYx0zE0NOSXhfT1171ZP7WPf0tZ7i7OnKw086yH0jQAJKIfq1ow2uDoMlDWKCSftHr6C6nQQcDUCwNmNoSjt72kLMA0bDgNFRrwlNfRsLS7GOvTi8vaevvUqmj7WmaQwvtKUlgSjZloZD86388Kqx/M8rezp9rbbSvdZTnD1tH3460/Wps+S+ESABpRD91qqtVeERDSClUY1kajMmq7esvUrUx/rtN19n+Z23sqO+hqyszgWSHY0Ql+ZbKS8r5qaZpYwpCV4jEAjw0q/+C0dtBpmXfKnLn6+7isWLHk7TQOl8jVi5b0SIosUrhy+E6NMeXbs/LQknXRVae7Xy1vit4nqDrVu3MmPGDD788EOmTZvW8QsiJDNCHBLaP3t0IT+9dhI/vec/efLJJ3nxxRdxDjivR671FGdP5ANiKveApmnhUlLJkvtGxCMjlEL0M6u2VvWIYBL6xtqrsrIyAD7dtZeMgaOTLnuU6ghxaP/mQ/WUP/wuNVsO83//938sXBhMtOmpaz1F9+vKA2JkMKkowQHLROS+Ee2REUoh+pGjDS7mP7KeVpcTe8VqPCf24j25D9XtoODz3yZzyvyo41u2v4Fz5zp89cdQPQ70mQVklE4m95IbMeSWJLhK8h5aMrlXj3CEpqr//OYH6LKLIKK9ZKKpauj6CHFoVOm7C8ZyV/mYuO8p3lrPc1UsXnSfVVur0lozNd9motHplftGpEwCSiH6kWVPVrD5UD2ehlMcf+xW9NlFGHIH4KmqjBtQ1r/5RzSfB1PRMHQZmfibq2n55E1QVQZ+7fcYsgo6/V6+t6CMO8tHd3xgD9TZqeoHF0/mvYN1aQ0A2gvKk03wEb3T0QYXs+/5G7XrnsJ76gABZxOK0YyxYCjZM5dE1SL1nNiLo/IdvCf24q09DGogbkkgs0HHy3degl/V5L4RKZGAUoh+Yn91C1f8ZgMAmt+H6nagz8zDc3I/p/56d9yAMh7PqQOcWvFtcud+mZyLr+/w+Eh9Ye1VZ9eq6XUKep2Cu/ow9Rue7jAAgGA2fcu2f+FvOoXeko11/GxyZy9DZ8oIH2M26Fhz91yZfuyHlj1ZwZq33qBp68uYB49Dn5mP5vPg2rsZz7Gd5F95F1lTrwSgaePTNL//HKbi4ajeVvwNx+MGlH1hXbM4N+SRQ4h+4umKqvBomWIwos/M69R5DDnBEj+qx5n0a/rK2quuTFUHTgegnqZqVG8rtsnzogKA2hd+HBUANK79C/aKF7CWXUL2hYvw1R2l5aNX8dVVUXLDj8Pn9asa97xYKQFAP7O/uoWNB+owj7yQkpEXRu3LmnY1J1d8G/sHL4Xvp6wLPk/2RV9AZzTT8NafaGk4Hve8AVVj44E6DtS0yNS2SIkElEL0E2v31nS64HGg1Q6qit9eS/N7fwcgY9h5UccYTgeNfXXtVbqSmSyjpmMZNT1qW9sAwO9owL71JWwTyylc+J3wcYb8QTS+/Tiu/RXh0UwJAPqnyAfEthSdHkNWIZ5T+8Pb9LbkHyD1OoWntlRx36KJaXmvon+QgFKIfsDh8VPV4Or06489+mUI+ADQWbLJm38HlhHnRx0TUDUqfjCPWoenz629Otrg4t6Xd6J6W5NKZoLkpqtD2gYA3uN7QA1gmzAn6jjb+Dk0vv04zt0boqbHJQDof9o+IKpeN5rfg+px0bq/gtZDH2EdP7tT5w6oGmv31XAfcj+J5PX+3/RCiA4dqXd2qWtGyRfvR/N78dUfxblzHZrPHXOMBtQ6PEwclNOFK/VM97xYiV/VUF12mt/7O/rsIozFI/BUxU+uSWa6ur0AQDsdvCsGc9R5FWPwz95TB6O2SwDQv8R7QGx89wkc298I/kHRYR17MfkL/r3T16iqd+H0+PvEA6E4O+ROEaIf8PrVLr0+Y9gUACyjLsQy5iJOPnkniimD7GkL03qdnii0Vg1An5nPkLtWRiUztZXsdHV7AYAhfzAA7mO7wn/3AJ6jOwEIOOpjrisBQP8R7wExe/o1WMddSqClHteeTWiaGp5V6AwNOFzv7JMPiKJ76M71GxBCdD+TIX3/1I15AzGVjMS5c123XqenCK1VA5JKZmpvuhrAuTuYaZ89/RqKl/6Egn+7G8vIaVEBgHnAaEyDyrBXvIDj07fxN1XTevBD6t/4A+gMaD5PzHVDAYDo++I9uBkLhmIZPpXMyfMovv5eNK+bmucfoCuFXPriA6LoPvIoK0Qf1Lb+YFGmGQW6NO0dSfV5Y0Y/FGB4QfI9vHuLVJOZkp2uNhYMxVgwFIDMyfOoXvU/1Dz/AANu+TWKolC0+AfU/fMX1L/229Mn0JE941rcVTvwJcjQlQCgf0jmwc067hIa3ngUf8NxjAVDuu06QoRIQClEHxHukLK3hqqG2A4pep0SbvOXDE0NoHpb0WdkRm33nNiLr/Ywtglzo7aXFlj73HRrZ5KZOjNdDbEBgCGrkAE3/wJfw3ECzkaMeYPRZ+Zx7NFbMOYPinsOCQD6h+EFtg4fEEOj2KmU94p3HSGS1bd++wvRDyXTtUWDmGDS/tErqG4nAUcDAK0HPsDfElwrGFwbqXH8D1/BOn42psJSFGMGvtrDOCrXoDPbyLlkafhcep1C+djibvuM50pnkpkip6sNWQVklE7BV3+U+jf/mHC6GhIHAMb8wRhPB6neuioCjgZsk+fFvL6vjhCLWDazgdJ8K0caXAScTehtueF9vobjNG1YiWvf+wDUvfIrbBPLyZ65GJ0xusLAqaf+C++pgyhmC7Zxs8mdews6kwUI3k8NTm+fe0gU3UfuFCF6sciuLUBKU7P2ihcJ2GvCf3bt2wz7NgOQObEcfVY+mectwH3kU1x730PzedFn5mMbP5ecWTdE9fIOqBo3X9Q7O9+0p7NTyO1OV9cfizleC/hx7ngXxWDGWBj/71HTVJrW/gXFaCZr6lUx+/viCLFIrLysmJUVR6h541E0rwvz0ElgMNG86RlQA6AGsJZdgmKy0LzpaTzHdgaPAVqP7gDA13gS8/DzUFBo2f4GvsYTlHzx/vA1pGC+SIX89hGil+pK1xaAId/4c4fH5M+/vcNjQq3a+mJR7c5OIbc3XY2iUP33ezAPnYQ+q4CAoxHnrnX464+Rd/mt4RGihrcfRwv4MBWPRFP9OHetx3tiHwVX3x3uVhTSV0eIRWI3zSxlxfuHsY2fjePTt2n5+DVUVzNoGqZBZeRc/MUztUo1FeeOd3Ef3h51DtXZiPvAB5iHTiJ/wf+j4fXf0/rZNiwjLkADKZgvUiIBpRC9ULq6tqSDQafw4OLJ5/ptdItk1qq1J950tWXMTDSfJxgAtLagM1kwDRhN3mVfjSpWbioZhf3Dfwaz6RUF88CxlNz406h1mSF9dYS4P2ibQJdsM4AxJVnMHl3IRuaG1zM3rluBfcvzFH/hR+itZ8r96DPzQdEx9O5nQVM5+tsbyZ5+DXnlXwsfowV8NL7zBK7dm7CMuCD4OimYL1IgAaUQvUwqXVs0TcVZ+S6ufZvxVh9CdbdgyCnBOn4OOTOXoBhMXX4/Dyya2Gt7c3ckcq1aV0ROV+fPvyNmhDGezCnz43bgaasvjxD3VR0l0JXmWykvK+ammaWMKYn/c9U0jbHNH7FBG4qiBEfSM0onY9/yPPWv/Y7c2Tehs2ThOb6blo9fI2vaQnSmDNzHdoEawDRgTNT5FL0RU/EIvNVniuZLwXyRCgkohehlUunaovk81L/2G0yDysg6/yp01hw8x/fQvOkZ3Ec+oeTGB1EUpdPv5XsLyrhhet8eGQutVQutT+0omUmXYUtpurqr+vIIcV+TbALdkQYXKyuOsOL9w8weXciDiydHPbS1trZyxx138NQ/nqf07mfD2y0jp5Ez+2bs7z/HyQMV4e3Zs24gb84ygPB9G6+eqj4zP1yNIEQK5otkyR0iRC+SatcWRW+g5OZfkjFkfHhb1tQrMeSU0LzpadxHPsEyfGpK70GvUzDoFB5YNLHPB5NwZq1aSEfJTLoMW0rT1V3Vl0eI+5JUE+hC+zcfqmf+I+u5f9FElk4v5fDhwyxZsoQ9e/bw0B/+zB8ORj8QGnJKMA+diLVsFnpLNq6DW7Fvfha9LZfsaQvR/F4gOCLZlqI3op7eHyIdc0SyJKAUohcJdW0JqFpSXVsUvTEqmAyxjr2Y5k1P46s7mnRAGbrurJEFMSMmfVlordrmQ/UEVC2pZKZkp6u7qj+MEPcFXUmgC6gaAVVj+epKKj7ZzcrlN5GVlcXmzZvR8ofxhz9tDh/r3LWehjceZdDtj2PILgTAWjYLNI2mdSuwTZgbXuaixWnLqAV86OIsg5GC+SIZUgVXiF4k1a4tiQScjQDordlR2w06hbYT4AowrMDKspnDWHP3HFbeOrPfBJMhDy6ejEHX+aUB8Rh0Cia9Em7rmCy9TsFs0PHQksncWT46re9JpF86E+hePOhn1BXL+PDDD5k6dWpMFYKWba9hKhkZDiZDrKNnoPk8eKsPBRN0gICjMeb8AUdDeH8kKZgvkiEjlEL0Ep3p2pKIveIFFLMVy8hpUdsDqkbFD+ZR6/CknHXalw3Nt3L/ooksXx27TrWzfnrtJGaNKuxwTV1Ifx0h7s1CCXTe2iM0b3oG76kDBJxNKEYzxoKhZM9cEpXZD+CrO0rDO/+H59guFL0By6jp5M27LZi1rWk0jV6AS7FQQGwVgoCrCV2bzlYQ7HoFBJNxCoeBTo/31H5s42efOSbgw1vzGdZxl0a9Vgrmi2TJY4cQvURnurbE07z5WdyHt5M39ysxXz4aUOvwMHFQDueX5jFxUE6/DyZDlk4v5bsLxqblXKGp6qH5Vn509QSuPW8QVqM+4fFZGQYWThnYb0eIe6tQAl3AXoPqbcU2eR55879OzqwbAKh94ce0bH8jfLzfXsepp7+Pv/EkuXNvIXvGEloPbqV61Q+DU9RKsH3qPS8GH2xCVQhCjHmD8FYfjOn17ty1HhQdxqLh6DJsZAyfinPnOlTPmQdUx461aN5WbG0CSimYL5Ild4kQvUQ61jE5d2+gacNKMqcsIOuCz3fbdfqCePUB7yofQ2GmmR+++Ck+fwBFn/yv0LbJTMlk/Ia4vAFe2n6CeodXRid7icgEOsuo6VhGTY/anzXtak6u+Db2D14ia+qVADS//yyaz0PJV34TrgZgGjSWmlU/xFH5DllTrySgalEFxyOrEGTPvI7WQx9x6qnvkzXt34JJOQc+wH3oIzLPW4AhqwCA3DnLOLXye1Q/8wMyp34Of0s9LR+8SMaI86NmLaRgvkhFvw8oUykq29kCtEJ0Rtv7ratrJ1s/+5i6V3+NZdSF5F95Z8Lj+vN6qWTqA15WVoRh8xOYJ12JM2top6aq05XxK3quyAS6eBSdHkNWIZ5T+8PbXHs3Yxk9Paq0lGX4VAz5g3Ht3hgOPCMLjkdWIcgoncSAZb+kadMzOLa9RqC1BUNuCblzbiH7ouvC5zQPGE3J0p/QtG4Fje88gWKykDnlCnLnfjnqPUrBfJGKfhkNpVJUFuhyAVohktXRvdlZnhN7qV39U8wDxlB47XIUXfzp1f66Xiql+oDvH0a9+OtMKtTzXwun8e6eGtbuq6GqPs7vhwIr5WOLufmi0nDh8XRl/NY5PNxVPqbjF4lzIl4Cnep1o/k9qB4XrfsraD30EdbT6xj9LXWoriZMA2ITrcwDx9J68MPwnyMLjretQmAeVBbVjzuRjKETGbDslwn3S8F8kap+FVCm8qXxty2Hw099egUCcR4ykylAK0Qykr03O8NXd5Sa5+7HkFNM0fX3ojOaEx7bH9dLpTpaqJ4O7Xc3qHx95Yfcv2gi9y2amNQMRjozfh9+ax9FmWYpG9QDJUqga3z3CRyhNZOKDuvYi8lf8O/AmazreFnW+sw8VHcLmt+HYgjWj4wsOP7g4snMf2R9WipAhEjBfJGqfvPNkfKXRsTueMFkpLbTUfd8fhwXDsuXqXGRlFTvzUgddW1BUah+9keobgfZM5fQemBr1OuNeQMwDw7WqeyP66XSPVrYXvHnUMZvW82b/0HThpUYC0sZdNsfo/a5j+2mad1f8J46iGK2YBs3m9y5t6AzWQD40cs7mTWqUB5ie5hECXTZ06/BOu5SAi31uPZsQtNUOF0PUvN7gEQFx03hY0IBZWTB8e6oQiAF80Wq+kWU05UvjVSEvmDufXlX1HaZGheJdPXe7KhrC0DAXgtA07oVMa+3TZoXDij723qpsz1aGMr4jeS319H8/rMoxoyY473Vh6hZ9d8YC4aSN+9W/C312CtW42s8EZ7SDGX8rrx1ZszrxbmTKLHNWDAUY8FQADInz6N61f9Q8/wDDLjl1yiG4MxB/ILjp7vbGKJnFyKvs3R6KXUOT1ruaSmYLzqjzweU6fzS6CyZGhfxpOPeTKZry7Dlr3Z4TH9bLxUaLfSc3Iez8h3cVZX4m6vRWbIxDyojd84yjPmDo17Tbn1A2h8tjMz4jdS49knMg8rQVBW11R69b/1f0WVkUvKln6EzB89pyCmm4fXf0/rZNiwjLojJ+BU9Q7KJbdZxl9DwxqP4G46Hu16FZhsiBRyN6DKywqOTia4TqkIQmvFIZbajv7VUFenXpwPKRFNMAJrfR9PGp3DuXIvqdmAsGk7unGVYRpwf93jV24q9YjWeE3vxntyH6nZQ8Plvp9ReTTI1RUjo3kz2vvKc2Iuj8h28J/birT0MaiCpQDFZ/W29VGi00L7leTzHdmMddynG4uEEHI20bHuVk3/5Dwbc8jCmouHAmfqAOrON3Lm3oHnd2D9Yjbf2MAO//GsUvbHd0cJ4Gb/uqh249rzHwK/+joa3H4s6XvW4cB/eTvb0a8LBJEDmpMtpfOcJXLs3YRlxARCd8St6hrYFxxPRfMFpbtXjxFwwBJ01B++pAzHHeU7uw1QyImpbogS6pdNLuUQK5otzoE8HlPGmmELq/vUIrr3vkX3hNRjyB+GsXEPNc/dRcuODZAyN/cWsuuw0v/d39NlFGItH4Knq/FqV9jI1pTRR/xC6N5O9r1oPfojjk7cwFQ/HkDsAf5vCxV3Vn9ZLRY4WZk1fTOGi70WtW7ONn82JJ+/CvuV5Chd+F+hcfcBIbTN+NTVAw9uPkXneAkzFw2PeY+ihwTQgOotb0RsxFY/AW30wvC0y41d0XTp+B4cKjh85nZgTcDaht+VGHaMF/Dh3vItiMGMsDA4sWMtm4ax8F7+9FkN2EQCth7fjbzhO9vRrol7fXgLd0HwrK2+deaZqRApVCITorD4bqSSaYoLgaI9r9wZyy79GzswlQPDJ/8QTd9K07i8MWPZwzGv0mfkMuWsl+sw8PCf3c+qvd6flfT781j5UVaPB6ZPSRP1E5L2Z7H2VdcHnyb7oC+iMZhre+hMtaQwo+9t6qcjRwowh42P2G/MHYyosxVd3NLytM/UBQ+Jl/Do+fh2/vZaSG38a9z2Gpj1D06CR9Jn5eI5Gz7xEZvyK1KVSSi7Z38GRBcfr33gUzevCPHQS+qwCAo5GnLvW4a8/Rt7lt4aTrHIu/iKuPe9R/cw9ZF24CM0XnMEwFg0nc/IV4XMnm0A3piSL+xZN5D6CVQhGnjeTG29axtdv/aoMVoi067N3U3tFZV173wNFF/4SAFAMJjLPu4Km9X+Lejo8s98Y95d7pFTWY0X69Zr96JTozPIQWX/Z90Tem8ncVwB6W8fHpKI/r5eKVx8wkqZpBFxN4VGjztYHDGmb8RtotdO08WlyZ90QXn8Z8x78p5Mw4mb8GlFP7w8fz5mMX5G8lOqPpvg7+NpJBeHSc7bxs3F8+jYtH7+G2tqCzmTBNGA0eZd9NaqXtyG7iJIv/YzGd5+gaf0KFJ0By+jp5F1+a9T6yc4k0NnMBtwnDzDUGpD7RHSLPhtQtvel4a0+hDF/cNTaJADTwLHh/W0DymQkux4rno7WTsv6y76jo4CmO/X39VKJ6gNGcu5cR6ClntxLbwK6Vh8QYjN+mzasRGfJJOvChQnfg2I4XSYmbsavD93p/ZGkZWZquqtbkaqqPPXUU3z/+9+H8m+SMWwKtglzsU2Ym9T7MhUNo+SGHyfc35UEOo/Hg9mcuA6tEF3RJwPKjr40Ao6GhFNJof2dkex6rK6QThm9WzIBTXeQ9VJBieoDhvjqj9Lw9p8wDx6HbfI8oGv1ASE6E9fXcBzH9jfJm/d1Ai1nfs9oAR+aGsDfVI1itkb8LmqMuWbw91dscNufW2amqru6FW3dupVvfetbbNmyhS9+8Yvcfc8X+fI/9uNJY7Df2QQ6TdPwer2YTLEPI0KkQ58MKDv60tD8Xoj35RAaFWgznZSsZNdjpUui2neS2HPuJfoZdHRvpsvzd1yMxaSXe6CN9kbxAo5Gap67H53ZRuG1Pwi3p+xqfcDIjN9ASz1oKo1rHqdxzeMx5zv+2K1kXbgoODqq0+M9tR/b6dZ8offgrfkM67hLo17XX1tmdkZ31B+9rNTMPffcw1/+8hcmTZrE2rVrueyyywC4v9XUIwqO+/1+NE2TEUrRbfrkN0xHUz+KwRTuThApvG4pznRSZ7Vdj5WIt/YIzZuewXvqAAFnE4rRjLFgKNkzl0StsWkrVPvO7QtIz/FzLJmF/ZMGZ5+V96LXKbJOKo5Eo3iq20n1s/eiup2U3PwQhqyC8L6u1geMzPg1Fg2jaMl/x5ynacNKVG8r+fNvx5A7EF2GjYzhU3HuXEfOrKXh5TmOHWvRvK3Y2gSU/bFlZmd0pv6o/aNXaNn2L/xNp9BbsrGOn03u7GXoTMFi9Pes/oSGlXejuBp49NFHuf322zEYzvwsekrBcY8nONIuAaXoLn3yN1BHUz/6zPzgSEEbZzIrY6eTOqvteqxEAvYaVG8rtsnz0Gfmo/k8uPZupvaFH5N/5V1RCUSR/AGVhY9uoqnVl/ZF5SI5qSzsP9p4dqa7Zfozvnj1ATW/l5rnH8DfeJySpT/B1Obhz5BV2OX6gKGMX6w5WMdeHHMe+9Z/AkTty52zjFMrv0f1Mz8gc+rn8LfU0/LBi2SMOB/LyGnh4/pjy8zOSrX+aOPav2CveAFr2SVkX7gIX91RWj56FV9dVXidoz+gMurGH/Lm8oUUFBTEve65Ljju9PipPNqIaeBY6vwZUhFAdIs+eUd1VFTWVDwS+5FPUT2uqMQc74ngE6SpZGRa3ke89ViJWEZNxzJqetS2rGlXc3LFt7F/8FLCgDKgQVNrcLQ1XYvKRfK60iO+u8j0Z2Jt6wNqaoDalx7Cc2IPxdf9MNyGsq2u1ge8aWZpOOM3WeYBoylZ+hOa1q2g8Z0nUEwWMqdcQe7cL0cd199aZnZWqvVH/Y4G7FtfwjaxnMKF3wkfZ8gfROPbj+PaX4F1zEwUvYE6imgMmIgfTgad7YLj8WZMBn751zy8E361802ZtRJp1ycDyrZfGm1Zx12C/YPVtGx/I1yHUvP7cFS+jWlQWacyvNtKtB4rFYpOjyGrEM+p/V1+P1HvTRJ70uJs9YhPlUx/ti+yPmDju0/SeqACy+gZBFodOHasjTo2c1KwH3pX6wOOKcli9uhCNh+qjxtEDLjp53Hfa8bQiQxY9suEn6W/tczsilTrj3qP7wE1gG3CnKjjbOPn0Pj24zh3bwgvR0q2W9HZKDjenaWQhGhPn/3WifzSaMs8qAzruEtpWv9XVFcThrxBOCvfwd9cQ8lV/5HwnPaPXkF1O8NT460HPsDfEnzizZ62EF1GcFSovfVYHVG9bjS/B9XjonV/Ba2HPsIasSg/3RIl9oj2pXNhfzL3lb+5BseOdwHwnJ56bXpvFRDs75w56XJApj+TETla6K0+BAT/zlsPfBBzbCigTEd9wAcXT2b+I+vTWjKqv7XM7IpU64+GkrDaJlwpxuCfvac6362obcHxdCVRdlcpJCGS0WcDyo6mmAqv/k+aNjyFc8daAm4HpuLhFH/hR2SUTkr4GnvFiwTsNeE/u/Zthn2bAcicWI4uw9bheqyONL77BI7tbwT/oOiwjr2Y/AX/ntI5IjVv/gdNG1ZiLCxl0G1/jHtMKLFHnk6Tk2ofbgBf3VEa3vk/PMd2oegNWEZNJ2/ebeitOUndV/6mUzRvfCrqnKE/m4dOCgeUMv3ZscjRwkQjg/F0tT7g0Hwr9y+a2CMyfvubztQfNZxOznEf20XGsCnh40JdigKO6HX4ne1WZDMb0pJA112lkIRIVp8NKDuaYlIMJvIu/xp5l38t6XMO+caf292f7Hqs9mRPvwbruEsJtNTj2rMJTVPjZqQnw2+vo/n9Z1GMGe0fp2rc82IlK29NnE0uzki1D7ffXsepp7+Pzmwjd+4taF439g9W4609zMAv/7rD+wogY9gUhi1/td1jZPozeedqtLCnZPz2N52pP2oeMBrToDLsFS9gyCogo3QKvvqj1L/5R9AZ0HyeqHOcy25F3VEKSe4tkao+G1BC93xptCfZ9VjtMRYMxVgwNHj85HlUr/ofap5/gAG3/BpFUVJ7P2ufxDyoDE1VUVvtCY8LqBobD9RxoKZFgpEOdKYPd/P7z6L5PJR85TfhXtCmQWOpWfVDHJXvJEy4SpVMf8YXb0rxXI4WnuuM3/6oM/VHAYoW/4C6f/6C+td+G9yg6MiecS3uqh34Go6ndJ3uEpoxgWDh/KaNT+E5tgu11YE+uwjbhLlkz1yM7vTAQutn23Du3oj3xF589cfQZxXGPNTKrJXojD4dUHbHl0Z7kl2PlQrruEtoeONR/A3HMRYMSfp17qoduPa8x8Cv/o6Gtx/r8HidAr99Zz8/XzJFEjra0Zk+3K69m7GMnh4OJgEsw6diyB+Ma/fGtAWUMv15RjI1QcvLivnKrOGs2HwYTdNSfmCLlOpoYcoZv0qwosOUwTncMWckQ/OtUvolBZ2pPwrBklEDbv4FvobjBJyNGPMGo8/M49ijt2DMH5T0dbpTaMbEb6/l1F//E8VsI+uCq9FZsvAc30PzpqfxnjpA8Rf+BwDnzvW49mzEVDIqYYk8mbUSndHnfxulc4qpI6msx0pWaFpF9TiTf40aoOHtx8g8bwGm4uFJvUbV4JVPT/Lqpyf7bTmJZBbHp9qH299Sh+pqwjRgdMw+88CxtB78sMvvG2T6MyTVDNeAqmF2N+A2ZKE3GgmkMJnR1dHCZDJ+IRj0mCwZBNDz8dEm/t/T2wBpWJCKztQfjWTMHxwueO6tqyLgaIgpBXcuynVFzpg4d6xF9TgZePMvMBUNAwg+rGoqzh3vEnA70Gdkkjv3Fgqu+iaK3kDNc/fjrT0Sc16ZtRKd0ecDSujaFNPZEnA2obflRm3TAn6cO95FMZg77LQTyfHx6/jttZTc+NOU30d/KyeR7EjWTTNLGZhrSbkPd6gXc7yRAH1mHqq7Bc3vi+m0kgyZ/ozW2QxXtzEbo0HP8KJMDtQ4ur0+YFvxMn6PNrh4fMMhPj7ahM5swavFjnz15X+r6W4f29n6o21pmkrT2r+gGM1kTb0qat+5KNcVOWOieoOfre33iD4zHxQdii743pKtOpJsKSQhQvpFQAmpTzHplDNFqEPTTd2p/o1H0bwuzEMnoc8qIOBoxLlrHf76Y+Rdfis6kyWp8wRa7TRtfJrcWTegt3Z+cXhfLyfRmVpt5w/NTbkPt+YPjjAr8XrH603hYyIDysh7L550BzR9QVcyXBW9Ab8GB2ocfGXWMNCUbqkPmAyb2UDl8eaowBil/WnUvvJvNZWHu86MxpaXFbNyy2ECWvLr3Rvefhwt4MNUPBJN9ePctR7viX0UXH131BKWc1WuK3LGJKN0MvYtz1P/2u/InX3T6Snv3bR8/BpZ0xaGW0UmK9VSSEL0m4ASOldUVtNodzoqXWzjZ+P49G1aPn4NtbUFncmCacBo8i77aru9vNtq2rASnSWTrAsXpuV99cVyEp0dyfrkWFPK1wrVsNPi9Y4PeKOOCblq4kB2nGw+JwFNb5TODNcVm4/w0JLJ3LcovfUBk9UfS7+cjULcTU1NNH34CgEtmLSW7Hp3U8ko7B/+E+fOdaAomAeOpeTGn0aVEYJzU66rbSkky8hp5My+Gfv7z3HyQEV4e/asG8ibs6xT1+hsKSTRP/XLuyTVorKJjv3wcCMPvr47LdPotglzsU2Y26Vz+BqO49j+Jnnzvk6gpSG8XQv40NQA/qZqFLMVvaVzgUhfKCfRlS/szvyIQ0k7oaLlkQKORnQZWTHT3d8oH8XEQTnnJKDpbY42uPj+Yy/SuP1t3FWV+Jur0VmyMQ8qI3fOsvC6NwDPib04Kt/Be2Iv3trDoAbilmKKzHA9myVg+mPpl+4uxN3c3Mxvf/tbfv3rX+PxeJj8rcep1xckvd49c8r8uHVlI52rcl3xSiEZckowD52ItWwWeks2roNbsW9+Fr0tl+xpqQ8ynMtSSKL36fffTqkUlW177MRBOVw+rjjlTM3uEmipB02lcc3jNK55PGb/8cduJevCReTPvx33kU+p/vs9cc8zYNnDmAePi7uvN5eTSOcXdrIMWYXorDl4T3e3ieQ5uQ9TyYiobZEL+9NV8Lgvu+fFSurfew73sV1Yx12KsXg4AUcjLdte5eRf/oMBtzyMqWg4AK0HP8TxyVuYiodjyB2AP07ZFzg3Ga6RpV8iJWpM0BdKv3TnaKzdbg8Hkq2trfy///f/+P73v4/fnBMsJZfG8j7nqlxX2xJFzl3raXjjUQbd/jiG7EIg2IMeTaNp3QpsE+ait2R3+TpCJNLvA8quSnUaPd9m5Ndr0tubO8RYNIyiJf8ds71pw0pUbyv582/HkDswal/WtIWYBo6N2mbIiz4mUk8pJ5Hq6F2iL+wQz6kDNK3/G57juwEwDxpHXvlXMZWM7PJ7tZbNwln5Ln57bbhPfOvh7fgbjpM9/ZqoY6UPd/JCGa6Z06+lYNF3o9ap2sbP5sSTd2Hf8jyFC78LQNYFnyf7oi+gM5ppeOtPtCQIKM9Fhmuo9Euk9hoT9PbSL901GtvS0sLvfvc7fvWrX+Fyubj99ttZvnw5gwadKfHTV7oVtS1R1LLtNUwlI8PBZIh19AyclWvwVh/CMnxql68jRCLyzZUmqUyj63RKt4yU6a05WMdeHLPdvvWfAHH3mYdOxDbu0qSvcS7LSXRl0X68L+wQz6kDVD/1X+izCsm95EY0NFq2vcapZ5Yz8JZft1v/M5k+3DkXfxHXnveofuYesi5chOYLtmw0Fg0nc/IV4XNJH+7UhDJcM4bEZuga8wdjKizFV3c0vE1v67hmaPjYs5jhGln6JVJ7jQl6c+mX0MOd5+Q+nJXvdLhUAcC5eyP2rS/hqz+GougwFg0je+Z1WEdPB4Kjsdvfepb//fXPcDgc4UBy8ODBMdfvK92K2pZCCria0GVkxhynqYHg/4T+m4JzUQpJ9F7y6NENQlOV55fmMXFQTsyI013lY/j5ksmYDTr0us4XU04X1eM680snSd/8+8fsr27ppncU7WiDi2VPVnDFbzawsuIIRxpik6MiF+1f8ZsNLHuygqOnF6yHvrATLUdo3vgUisHEgFseJnvmEnJmXseAZb8MThWt/1u7781e8SLNG5/C8fFrQLAPd/PGp2je+BSq2wGAIbuIki/9DEPeAJrWr8C+5QUsoy6kZOmPo9ZPSh/u1LRXE1TTtOAXrDX1KT44k+F6NoQC40ihxgR5826P+xpDVgGKvuPxgFBg3JOEHu7sW57HtXczGcPOI2/+7WSe9zncR3dw8i//EVzjepr9w1eo++dD6C3Z5F32ZXIuWYrqcVL7/P249gZ73ru9Pv6yw8PSpUs5ePAgv//97+MGkyFd+R2s1ymYDToeWjKZO8tj68ueLaFSSCHGvEF4qw/GdPBx7loPig7j6aUfqZAZE5EKuVPOkZQ7ZZzen2sxYnf7UkoQaW8Bev1rv0XztoKiwzx0InnlX8M8sOPs0N2nWrjiNxuSzrbsbIJJOhbt7znZ0u7fr/voTiwjp0WtLzJk5pMxdBKugx+gelsTlm1Kpg83gKloGCU3/DjhfunDnZq2Ga5tOXeuI9BST+6lN3X6Gmcrw7VtYNyZxgSJ9LTSL5GjsVnTF1O46HsdLlVo+egVTAPHUPSFH4W7GWVOuYJjf/gyjsp3sJbNQtHpyRg+lbvv/hZDkvw31NnfwT2pXFd5WXG4QH/2zOtoPfQRp576PlnT/i2YlHPgA9yHPiLzvAXh+pPems9w7Q9mgfsaT6J5nDS9twoAU/GIcFURmTERqZKA8hzqTBkjs0HP/EfW4+nqQmm9EWvZLCwjL0RnzcFXV4X9gxepfvr7DLj5l5gGjErqNO1lW3a1rly6Fu3nWoztflFoAR+KwRSzXTGaIeDHV3skYZJSukgf7tTEy3AN8dUfpeHtP2EePC6mm0kqzkaGa7zAuCuNCeLpSaVfIgtxJ7tUQfW6MOYPjmqNqTNb0RkzUIxn/t12ZplCZ34H96SHvptmlrLi/cMAZJROYsCyX9K06Rkc214j0NqCIbeE3Dm3kH3RdeHXeE8dpHnjU1HnCf3ZNmleOKCUGRORqnP/G0akXMYoHYvKM4aMj/6FPmYm1nGXcPLJb9K4/q+U3PBAUueJl22Zjrpy6Vy039QaWwMykjF/CJ4Te9HUAIpOH3x/AR+eE3sB8LfUY27vBGkgfbhTkyjzNOBopOa5+9GZbRRe+4PwzzPd10mXtoFxuhoTROpJpV86al0aWqoQ2Rkso3Qyrj3vYf/wFayjZ6AFvNg/ehXV4yL7wkXh47oyGpvq7+CeYkxJFrNHF7L5UH2wjeigMkq+eH+7r+nJpZBE79Yz/5X0Y8mUiumu/uTGvEFYxszEtW9zVHCVrIff2sdndU5e/fRkl6aoLxlV2G5Gtq/hOE0bn8JzbBdqqwN9dhG2CXPJnrkYXZyM2I5kXfB5Gt78I/Wv/S74JK+pNG/+R7htoub3pnzOVEgf7tTFyzxV3U6qn70X1e2k5OaHkm4xl+p10qltwJruxgSJrnMudLRMAeIvVciffweqyx5VDk1nyabkxp/EtExMx2hsbyvX9eDiycFSSGlsKSwzJqIzJKDspbqrP7khuxACfjSfB8VsxXPqAM2bnsFzbBea34cht4TMqVdGjQxEemFb/FIsHYkc6RyWb02Yke2313Lqr/+JYraRdcHVp9uL7aF509N4Tx2g+Av/k/K1s87/PH57HfaK1Th3vAOAacAYsi+6DvvmfyTVsqyjdoltSR/urmmb4ar5vdQ8/wD+xuOULP0JpsKu/52ejQzXyIC1OxsT9ITSL+0tU4DESxUUoxlDwRBsWYVYRk9H87Zi3/pPalc/SMnND2HMO1MSqCeNxp4tQ/OtfaYUkujdJKDsxVJdVJ4Mf9MpFIMJxZRB62fbqHn+AUwlo8iZtRTFlIG/6RSBltgSJ+l0pL1kix1rUT1OBt78C0xFwwDImnolaCrOHe8ScDvQxymd0ZG8ubeQPXMJvtoj6Mw2TMXDaVz/VwAM+YmzRUPOG5LLx0ebeuXC/t4olOF6pCFYoaD2pYfwnNhD8XU/jBm16qyzkeEaGRin0pggFT2l9Et7o6TtLVWofennKIqO4uvvDW+zjLmIE4/fTtP6lRRd+/2kr9NX9ZVSSKJ3k4Cyl0tmUXk8AVdzzBotb/UhXPs/wDJyGprXTd2rv8YyajpFi3+AonRuhKPu1UfCo37xDL5zBYaswoT721K9wWBTb8uN2q7PzAdFh6Lr/C2tz8hEP/TM+iv34e3oswrbrUMJwS/sp26dyYmm1l65sL8na289WyjDtXbNk7QeqMAyegaBVgeOHWujzhHqy+xvrsGx410gWHsUCGe3GnKKyZx0efg1ZyvDNTIw7kxjgmT0lNIviUZJ21uq4Gs6hfvQR+RfeVfUa/SWLMxDJuA5vivp6/R1XZm1khkTkQ7n/reMSIu2i8qXr/6Uf1WeTDgNW/vSQ+iMJsyDx5/O8j6K45M3UIxm8i77Cs5d61CdTeTNuQVF0aF63ShGU8qBZdb5V5IR051Bo+HNP2DIKUkpmITgAn37luepf+135M6+6fSU925aPn6NrGkLk5qeToZz9wa8J/eTV/61Dj9z6Au7ty7s72mSrQ5w6egCVrx/GG/1ISBYVL71wAcx5wsHlE2nEma3modOigooz2aGaygwJoXGBN1d+qU77t+2yxSg46UKqrPx9IGxo46a6o+pn9tTRmPPlb5QCkn0XvIN1wfZzAa+dfkYXvn0ZMJjrGMvwrlzHfYPXkL1uk532ZlFzqU3YswbRNPGlShmK35HPTWrf4K/4TiKMQPbpHLy5309bpmdeMyDx8dMQbqP7kTzebBNuCzlz2YZOY2c2Tdjf/85Th6oCG/PnnUDeXOWpXw+CBaRbn7v72SMOB+dJRvviT04Pl1DxshpZLVpjdhWoi/s3rawvyfoTHWAXIsR/c0/J5DEYEzGsCkMW/5qh8ed7QzXyNIvyeqO0i9dLfPVkcjRWCCppQqGvEGg6HDu3kjm1KvCpYP89jo8x3ZhHjIh6vieMhp7LkXOWj3+7m7+vu4TjPkDCf4Ug2TGRHSH/v0vrw9rW06irewLFyVMrAHwNZwANUDtCz8mc8oCMuZ+GXdVJS2nWw0WXfNfnX5vzl3rAQXbhLmder0hpwTz0IlYy2YFi/ce3Ip987PobblkT0s9O1afVQA6HfaK1aje1tO125aRPePaDjPdpVZbenS2gP2ZIv8akV+YXXG2M1w7+rcarzFBOku/pKPMVzICgQBD9M0cUXWg09P4bsdLFfTWHDKnzMfxyVtU//2/sZZdjOZtpWXba2g+DzkXXR/1eaUQ9xljSrK4JOM4v/rf29l36Ahec47MmIhuJXdUH9aVchKaz43m85B5/lXkX3EHANayWWgBH47tb+CbfVNMv92kzhvw49qzCfOQ8RhyS1J+vXPXehreeJRBtz8ezEg//b7QNJrWrcA2YW5Ux5tkGPMGttvFJhGp1ZYeXSlgn+5gEs5Nhuu5Kv2Sjk5UbRsatOV2u1mxYgUPP/wwVU1eBn39TwBJL1XI/9ydGItH4Pjk7XArVNOAMRRe/Z9klE6Kem/ycBetoqKCwYMHM2aE/L2I7icBZR/WlXISoSlt2/joUUTbhMtwbH8Dz/E9nQooWz/bhtpq79R0N0DLttcwlYwMB5Mh1tEzcFauwVt9CEvMms3uIbXaui49BezTF0yeqwzXc1H6JV2dqEINDdpqamrij3/8I7/97W+pra3lC1/4Aqv+67/47ScBNh+qb7clbCRFpyd72sJ2Zx/k4S6+LVu2MHPmzHP9NkQ/IQFlH9fZchL6zAJ8dVWx2dS24LpA1e3o1Ptx7loPOgPW8Zd26vUBVxO6OGWBwovz2yzS705Sq61rjja4uPflnXhO7sNZ+Q7uqkr8zdXoLNmYB5WRO2dZ+KFF01Scle/i2rcZb/UhVHcLhpwSrOPnkDNzSdSaXr1CUmsqw8f3kAzXs1n6JZ2dqB5+ax9Fmebw9Y4fP84jjzzC448/js/n4ytf+Qrf/e53GT16NAAPjnRJIe6zIBAI8OGHH3Lvvfd2fLAQaSABZT/QmXISpgGjcB/+GH9LfVTZHP/pgsudaQuneltp3b8Fy4jzU56WDjHmDaL18Mf4Go5HjZA6d60HRYexaHi7r8+1GDtsxZgMqdXWdfe8WIlf1bBveR7Psd1Yx12KsXg4AUcjLdte5eRf/oMBtzyMqWg4ms9D/Wu/wTSojKzzr0JnzTld0P4Z3Ec+oeTGB1EUBZ0CWRnBn3FvzHDtUukXBQx6XYeBcSqBPMCRn1+d8FwZw6dSsvQn/OjlnZTQzMrHfsPKlSuxWq1885vf5Fvf+hYDBgyIeo0U4j47du7cidPplBFKcdZIQNlPtC0n0RHbuNnYtzyP49O3sAw/L7zd8elboNNjLk19NMC1b0swu3viZSm/NiR75nW0HvqIU099n6xp/xZMyjnwAe5DH5F53oJ22+3pdQrXTh3MuIFZUqvtHNtf3RK+D7OmL6Zw0fdQ9Mbwftv42Zx48i7sW56ncOF3UfQGSm7+ZVT/+aypV2LIKaF509O4j3yCZfhUVC3Yu/3JW6axcX99r6wJmnLpl9MjsoMMTp759tXhwCpR6Z9UAnmAgqu/E3NN76n9tHz4MhkjzgfA4/Nzw6/+iX7D6/z0pz/ljjvuIDs78UOjFOLuflu2bEGv1zNt2rRz/VZEPyEBZT8SWU7iW6s+ZveploTHmgaMwjblCpyfvk2tqpJROgl3VSWuPZvIvvj6TvVJdu5ah2KyYBnT+SfmjNJJDFj2S5o2PYNj22sEWltOZ2XfEuzD3Y7Qov3RxVlSq+0ce7qiKvz3GhkkhhjzB2MqLMVXdxQARW+Me5x17MU0b3oaX93R8NpZvU5h4/76Xl0TNJmGBZGB8Z5X/491L6+i6bbLeXLTZwlL/wzIyeBksxtILpCHM8kxkeqrKgHlzBprRYdlxPm89vAOJgzJT+ozSiHu9Gp7n7/3wYdMnjwZm63/1uUUZ1fP/q0qusWYkix+t/R8rvjNhnaPK/jcnRiyi3B8ugbXvvcx5BSRN+/rZHdQmzGegKsZ9+Ht2MbPQWfsWvFx86AySr54f0qvabtoP9Uv7J44ktWbrd1b024AoWkaAVcTxg56cgdOF77WW8+MhgVUjbX7ariPYNej3lwTNNli+ZtK72CdvZCFf6pot/RPKJgEkgrk49H8Plx738NcOikqOU6vU3h220nuSzKgBCnE3VXt1Q6lcBGWK+Zz38s7O107VIhUSEDZT3VU+w5A0RvIvfRL5F76pS5fz7l7A6iBLk13d0WiRfvS3ebsc3j8VLXTrx3AuXMdgZZ6ci+9qd3j7BUvoJitWEZGT+tV1btwevx96ueXKDAOlv45TMbppSldSXZJJpBvPbgV1eOM+bfcNpBPljzcpS6Z2qEoCq16W5dqhwqRir7z21akrDtq3yXi3LkOnTU3ThvGsyOZRfu9eSSrNzlS72y317yv/igNb/8J8+Bx2CbPS3hc8+ZncR/eTv6Cb8Rk/mvA4Xpnn/95RpX+SbEtajzJBPLOXetAb8RWdknMvq4E8vJwl5yzUTtUiM6Qf6H9WHdkWyYy8JZfpXS8XqcwJM/Ckfr2R7KSIYv2exavP7Yvc0jA0UjNc/ejM9sovPYHCTsVOXdvoGnDSjKnLCDrgs+nfJ2+IJ2lfyC5QF71uGg9+CGWURfGL99FegJ5ebiLr7trhwrRFRJQ9nPpzLZMJ4NO4amvzeS9g3WyaL+PMRnij6SpbifVz96L6nZScvNDCRO/Wj/7mLpXf41l1IXkX3lnytfpC0Klf9pq3vwPmjasxFhYyqDb/giA6nPj/HQNrv0V+GoPo/rcGHIHkjX1SjKnfg5Fp086kHftfQ/N7223MUFfD+TPle6sHSpEOkhAKdKSbXn1lIG8sO142t5TaIp6ab4s2u9rhhfYUCBq2lvze6l5/gH8jccpWfoTTAnW8HlO7KV29U8xDxhD4bXLEwY+yunr9DbJTvWGSv9E8tvraH7/WZQ2SW/+plM0vP04GcPPI2vGtehMVlo/20bDW3/Ec2IP+fPvSCqQh+CUuGK2YR09I+ExfTmQP1ciHyB8Dcdp2vgUnmO7UFsd6LOLsE2YS/bMxeiMGUk9QAD86OWdzBpVKL8jRdpIQCmA9GRbjii0dUtdOVm037fYzAZK860cOZ2Yo6kBal96CM+JPRRf90PMg2OzjwF8dUepee5+DDnFFF1/LzqjOeE1SgusvWbNXXuZugpQmm+lvKw4nKkbWcMzUuPaJzEPKkNTVdRWe3i73pbHwFsfxVQ0LLwt6/yrqPvXb3BWrsFXd7TDQB7A72jAXVWJbfI8FIMx7jG9NZDv6UIPEH57Laf++p8oZhtZF1yNzpJ1usD/03hPHaD4C//T4QNE4dX/CYBf1bjnxUpW3iqFz0V69I7fuOKs6Grg1t115WTRfu8XurcanN7wtsZ3n6T1QAWW0TMItDpw7Fgb9ZrMSeWoHhfVz/4I1e0ge+YSWg9sjTrGmDcgHIjqdQrlY4u7/8N0UTKZuhpwpMEVlalbnGWOOd5dtQPXnvcY+NXf0fD2Y1Hn0Ftz4na2soyZibNyDd7qgxR/4X8SBvIhrl0bQFPbne7uTYF8bxH5AOHcsRbV42Tgzb8IPyBkTb0SNBXnjncJuB0dPkDkXLIUY94gAqrGxgN1HKhpkQdwkRbyL1/E6Ergdrbqysmi/d6lveDJW30IgNYDH9B64IOY12ZOKkdtbSFgrwWgad2KmGNsk+aFA6JQAfuerCuZuqqqRS8XUAM0vP0YmectwFQ8POn30PLRqwCYB49LGMhHcu5ahz4zn4xh8btk9ZZAvreJbAKgeoOj+npbbtQx+sx8UHQoOgO6jMy4DxDWsReHR6SNeYOCr9MpPLWlivsWpVbqSYh4JKAU7epM4CZT1CJSR8HTgJt+3uE5DLklDFv+aofHtS1g3xN1NVO3LcfHr+O311Jy40+TPo8W8OE9vgcAz7FdeI7tijkmMqD01R/De+oAWdOvRUlQnqg3BPK9UWQTgIzSydi3PE/9a78jd/ZNp6e8d9Py8WtkTVuIzpS4aUQyTQCE6AoJKEW3kSlq0ZXgqTMSFbDvKdJd6ifQaqdp49Pkzroh7qhUIg1vPYbm91B8/b1YRk3v8HhjwZB2A/reEMj3Rm2bAFhGTiNn9s3Y33+OkwcqwtuzZ91A3pxlCc+jBXy0fPgyhpwSTAPHRu3ri00AxLkhd5A4K2SKuv9Jd/CUjGQK2J8roUxdz8l9OCvfwV1Vib+5Gp0lG/OgMnLnLMOYPzjqNZqm4vj4DVq2v46/4TiKwYyxeAT5827DVDKSpg0r0VkyybpwYdLvo7niBRyfvEnO7JuTCiaT0dMD+d4qXhMAQ04J5qETsZbNQm/JxnVwK/bNz6K35ZI9Lf590PDWY/jqqii+/t6Yygj9pQmA6H4SUAoh0i7V4Kll+xs4d67DV38M1eNAn1lARulkci+5EUNuSVLX7OkF7EOZuvYtz+M5thvruEsxFg8n4GikZdurnPzLfzDglocxFQ0Pv6b+X7/FuWsdtkmXkzXtajSvG2/1IQKuZnwNx3Fsf5O8eV8n0NIQfo0W8KGpAfxN1ShmK3rLmVFDx6draFq7gszzryL3kqVp+2w9OZDvzdrW9HTuWk/DG48y6PbHw33UrWWzQNNoWrcC24S56C3ZUa9J5gFCaoeKdJCAUgiRdqkGT97qQxhySrCOnoEuIxN/czUtn7xJ64EPGPi13yesjdhbCthHZupmTV9M4aLvoejPlN6xjZ/NiSfvwr7leQoXfhcA5+6NOHe8Q9Hie4JBQxvuI5+CptK45nEa1zwes//4Y7eSdeEi8uffDoBr3xbqX/8d1rKLyV/w72n7bD09kO/N2tb0bNn2GqaSkeFgMsQ6esbpjP1DWCLa2yb7ACG1Q0U6SEAphEirzgRPBZ/7Rsx5LGMv5tSKb+Pc8S45F18fta+3FbCPzNTNGBJbnseYPxhTYSm+uqPhbfatL2EaOBZr2Sw0TUXzeaOSLoxFwyha8t8x52rasBLV20r+/Nsx5A4EgmWF6l7+Beahkyhc+L2EiTXJ6i2BfG/XtglAwNUUv+WlGgj+T+i/JP8AIbVDRbpIQCmESKvOBE/xGHKCJWhUjzNqe1aGgevOH9Kt1QHSnUQWmakbj6ZpBFxNGE8XFlc9Lrwn9pF1wedpXP9XWj56Fc3biiGnhNzLvoJt/Gz01hysYy+OOZd96z8Bwvv8zTXUvPBjQME27hKcezZFHW8qHo6peERSn6O3BfK9XdsmAMa8QbQe/hhfw/GoJSPOXetB0WE8PeKfygOE1A4V6SJ3kRAirVINniIFWu2gqvjttTS/93cAMoadF3VMvs3ULXXzUu1Yk6y2mbrxOHeuI9BST+6lNwHgbzwJaDh3bwSdjrzLvorObMX+4cvU/fMX6MxWLCOnJXV9f9MptNNBecNbf4rZn3PJjVEB5aDcDE42uaXMVw9RXlbMyoojBFSN7JnX0XroI0499X2ypv1bMCnnwAe4D31E5nkLMGQVpPQAIbVDRTpJQCmESJvOBE+Rjj36ZQj4ANBZssmbfweWEedHHXOk3sXmg3XkWIxpKT/V2Y41yY7QxcvUjeSrP0rD23/CPHgctsnzAFB9rcH/ttoZcMuvMA8qA4LdbY7/6VaaN/8jYUDZtq5nxrApKdXwXHnrTCnz1YPcNLOUFe8fBiCjdBIDlv2Spk3P4Nj2GoHWFgy5JeTOuYXsi64DUnuAkNqhIp3kN4QQIm06EzxFKvni/Wh+L776ozh3rkPzueOe50tPBGvwdWXkELrWsWb+I+u5f9FElnawhrC9DNqAo5Ga5+5HZ7ZReO0PwiVdFEOwT7khpyQcTALoTBYso2cE/27UQEwJmK6ILP0jZb56jjElWcweXcjmQ/UEVA3zoDJKvnh/wuNTfYCQ0WaRLpLaJYRIm84ET5Eyhk3BMupCsmcspvDa5TS/93fsH72S8JyRI4dX/GYDy56s4GgHI6Qhj67dz/LVlXj8akp95yEYWHr8KstXV/Lo2v3tHpsog1Z1O6l+9l5Ut5PiL94flcmuz8wHQNemxR6cbrun+tG88YPtzpLSPz3Xg4snY9ApaT2n1A4V6SYBpRAibToTPCVizBuIqWQkzp3rOjy27cjhqq1V7R6fzqLrD7+1j3+0c71Qpm4kze+l5vkH8Dcep/j6H2Fqs57UkFWA3pZHwNFAW4GWehSDCcVsScfbB6JL/zg9fnaeaObjqkZ2nmjG6fGn7Tqic4bmW7k/zeuG5QFCpJtMeQsh0qZtmROIDp5Klv4kJnhqj+rzhtdUJiOgagRUjeWrK6lzeLirfEzMMakWXa979RGcO96JOY8hfwiDb38MgB+9vJNZowqjvqA1TePTTz/lueeeQ3OMgcxg7UBNDVD70kN4Tuyh+LofYh4cmwkPYB0/m5YPX6b1s4/D60gDrmZcByrIGDYlJnM30frPRLSAH6NBz08XT+GC0jzue3ln2hOSRPosnV5KncOTlgchqR0quoMElEKItGlb5iSZ4ElTA6jeVvRt6ut5TuzFV3sY24S5nXovD7+1j6JMc8wXZ2c61qA3UnDVt6LOozOfCR79qsY9L1byt6/N4JNPPuG5557jueeeY//+/eTm5jJh2b2cpBAVaHz3SVoPVGAZPYNAqwPHjrVR582cVA5AzsXX49qzidoXHyR7+rXozDZatr8OgQC5c24589Z0CtdOHUSN3dNuYlHk8QFVI9t9irp1K3mx9Od8f3VltyQkifS6q3wMhZnm8LrfVB4gpHao6G6KpmmpLR4SQoh23PfyznCZk4Y1/0vLhy9jGT0D67jZMcdmTipHdTs49oevYB0/G1NhKQFXM60Ht+KrrQI0dLY8MoZOjNvrOkQL+Dn552/iqz9KbvnXyJm5BACzQceau+eGg5/91S1c8ZsNALiP7cY8cHRU0XVfw3FOPHkXtnGXhIuu1736CK6971H6nec7/OyGN3/GwY/fIy8vj2uvvZbrr7+eefPmcaTRE77uqaeX4zm6I+E5IhMqfE2naHz3SdxHPoFAAPPgMnIv+wrmgWOjXrPm7jmMLs46U/poXw1V9XFGGiNK/6z55DN+9tZBdHoDWgqFzkOBSTIJSaJ7RFYm6Cg5K/SgIA8CortJQCmESKvIoC2Z4EkL+Ghc+xfcRz7Fb69B87SComDMH4J1UjmoAVq2vYrmdceOHJ5m/+BFmjY+jeZzRwWUkaVwIDrYTeTkX/4DgIFf/S1wJqAcevc/0HyeqJHJSJoaYKj7M354VRnz5s3DaDRG7V/2ZEU4Uzdd2n6+SO2V/nl07f7TU6caxKzwTN53F4yNu6xAnB0Lb/o6+7QSBlwwr8MHCMnmFt1NpryFEGkVWeakbU3EeBS9MdxvGuKPHMZr1xgScDbR9N4qsi+6juaNT0fvUzU2HqjjQE0Lo4uzOl10XfN5OPrIF4MBZUYm1glzybvsK+hMZxJjFJ0e/dApXHlledxzP7h4MvMfWZ/WgLK9TN1EpX+iE5K6ljmcaFmB6H4tLS2sWf0U999/P//13XKpHSrOObnbhBBp15XgKdV2jY3rVmDMH4xtYnlMQAnBUbyntlTx3c+Vdarouj4zj+yLrsNUMgo0ldZD23Bs+xe+ms8o+dLPoqYbq+pdOD3+uF/koUzd5asr230PqUg1UzeUkNRW8+Z/0LRhJcbCUgbd9seI7c/iOlCBv/EkqrcVQ3YhllHTyZl1A3prMFiNl5Akut8///lP3G43N9xwAyC1Q8W5J2WDhBBpl+4yJ6GRQ501O2q758RenDveJX/+11ESjLYFVI21+2o6XXQ977KvkHe6f7ZtwlwKr76b3Dm34Dm2C1ebtnYacLjeSSJLp5fy3QVjE+5PRWcydUMJSZH89jqa338WxZgRc7z31AFMxSPImXUD+Qv+HeuYi3BUruHUyu+inq6DGUpIEmfXqlWrmDVrFsOGDTvXb0UIQAJKIUQ3SWfwFBo5tEUk9miaRsPbj2MdPzth6Z2QI3VO7rjr7oT7kym6Hilr+jWg6HAf/iRmX3vF3SGYqfvzJZMxG3ToUyxWrdcpmA06HloymTvLR6f02v3VLWw8UBczaty49knMg8owDYg9X9GSeyi48i6yp19D1nkLyLv8Vgqu+g/8jSdpPRDsVhS5rECcHfX19bz55pvceOON5/qtCBEmAaUQott0JXgKSTRy6Kxcg6/2CHmXfaXjkygKpqz8uLs6U3RdZzSjs2QRcMcGUYmKu0daOr2UNXfPZdbI4LU6+rsJ7Z81soA1d8/t1JrFpyuqYq7jrtqBa8975M27PcGrYhlySwBQPWdGYkPLCsTZsXr1alRV5frrrz/Xb0WIMAkohRDdKtXgKVKikUPV46Jx/V/JnrkEQ3ZRUuf66Y+Wp9yxJhHV40J12cPrCEMUgsXdkzE038rKW2fy9rfnsGzmMIYVWGPenwIMK7CybOYw1tw9h5W3zuz0WsW2CUmaGqDh7cfIPG8BpuLhCV8XXG7QTMDRiPvoDhrffhwUHRmlZ5KBQssKxNnx97//ncsvv5ySkpJz/VaECJOkHCFEtwsFT+3VSWwrcuSw5OaHokYO7RWrIeDHOn42/qZqAPwtdadf58DfVI0+Kz8qUzzbYky96Lrfixbwx5QKat68CtCwjLggantpgTXlzNoxJVnct2gi9zGx2zJ1HR5/TEKS4+PX8dtrKbnxp+2+VnU2cezRZeE/67MKKVz0PYwFQ6OOay8hSXROvPuhub6GdevW8cQTT5zrtydEFPmXL4Q4a+IFT/ZWHzc+URF1XEftGv32WlS3g5NPfCPmGvb3n8X+/rMM/OrvMJWMBM6MHJaXFYfrUCbTsSbgaOTkX76FdcJcjAVDAHB/to3Wgx+SMXIalrEXhY/X6xTKxxZ36e+nuzJ12yYkBVrtNG18mtyIbO1EdJZMipf+BM3vxVt9ENfe99G8rTHHhRKSJNO4a8IPXQnaYGbrveRfcQdTZn/uXL1FIeKSgFIIcU5EBk/DUhw5zLpwIdaIYA6Cfa4b3ngU2+T5WMfMxJBzZjowNHJ408xSVrx/GABv9SEAWg98QOuBD2KukTmpHF2GDcvoGbgPf4xzxztoqooxbyC5c28he8aSqH7aAVXj5ot6Zj3GtolCTRtWorNkknXhwg5fq+iNWIZPBcA6egYZw6ZS/dT30NlysY6e0e51RPIiu9+01wazOWAi8/zP84U/b2f26GPS/Ub0GBJQCiHOuVRHDs0DRkObrOTQ1LexsBTr2IvD2yNHDlMtuq7LyKRw4Xc6PC7UsaandiOJTBTyNRzHsf1N8uZ9nUBLQ3i7FvChqQH8TdUoZit6S/zPkjFkPPrMfJw718UElMkkJIlYq7ZWhftzAx3Xbz39ILP5UD3zH1kvbTBFjyABpRDinEt15DAVbUcOz3bHmp5geIENheAIV6ClHjSVxjWP07jm8Zhjjz92K1kXLorqXtSW5vdGZXlDaglJ4owzbTBTF1A1AqrG8tWV1Dk80gZTnFMSUAohzrlURw7jMeSWMGz5q1Hb4o0c9oSONWebzWwIJyQZi4ZRtOS/Y45p2rAS1dtK/vzbMeQODBYuV0DXpuC5c897qG4H5gHRwUtnEpL6u+g2mF0jbTDFuSb/+oUQPcLZHDlcOr2UOocnLV/mnelYcy6ElhVgzYlaEhBi3/pPgPA+b/Uhqlf9EOv42Rjzh6AoCp5TB3DuXIs+p4Ss6YvCr01HQlJ/E9kG09dwnKaNT+E5tgu11YE+uwjbhLlkz1wcDuilDabo6WTBixCiR0h3u0Zof+TwXHWsOVdumlmaUrCuzyrAWjYL95FPaFr/VxrefQLPsV1kXXA1A7/8a/SWM20we3JCUk8VaoPpt9dy6q//ief4XrIuuJq8+V/HPHgczZuepu6fvwwfL20wRU8nI5RCiB7jbI8cLp1eyiWjCjvMrg0J7Z81sqDXZddGLiuI9xnbLjXQW3MouPKuDs/b0xOSeqJQG0wA5461qB4nA2/+BaaiYF/urKlXgqbi3PEuAbcDfUYmRUvuiTmPadA46l76Ga0HKrBNmBvVBlN+HuJsk4BSCNGj3FU+hsJMczjrNaVRNZ2CQafwwKKJSU9DJ1N0XSG4RrB8bDE3X1Taa7+su3NZQXcVZe+LQm0wA6qG6g2Wy9LbcqOO0Wfmg6JD0SX+O2yvDeZ9aR7tF6IjiqZp6fvNIoQQaZJMXb6Q0P7ZowvTMnLYl4OjVVur0pqQNGtkAcebWuMW4S7Nt1JeVsxNM0sZU9I7g/DuMPeXa8N1V1sPfUTNs/diGT2T3Nk3obNk4Tm+m/o3HiVz8hXkz/96+HWapqG22kFV8TUep2ndX/Gc2Mug2/4Q1bloWIGV9d9NrRqCEF0lAaUQokfrDyOHZ1tXStW0dbaD/d7O4fEz+b43o+7jpvdWYX//OTS/J7wte9YN5M1ZFvXagKMxpg1m3uW3Yhs/O+o4Bdhx3+f6zEOQ6B0koBRC9Bp9eeTwbIsspp3SFLimoqGgU5R2e7G3FVqO0N+LcO880cy//X5T1DbHjrU4d67FWjYLvSUb18GtOD9dQ94Vt5M97Uw3Iy3gw310Z1QbzOxpV5N53oKY6/zrm5dKG0xxVslvYiFEr9Fdva77o84mJKHoQNNSCiZBinCHtG1P6dy1noY3HmXQ7Y9jyC4EwFo2CzSNpnUrsE2YG86olzaYoieTskFCCNFPhRKS3v72HJbNHMawAittCygpBNfkzRyRf2abklqZpbYefmsf/9ha1aVz9FZt21O2bHsNU8nIcDAZYh09A83nCXeOiieyDWZH1xGiu8kIpRBC9HNjSrK4b9FE7mNi3GUFDU4v8x9ZH/O65s3/oGnDSoyFpQy67Y9xz626HRz/3ztQXc0UXrsc27hLgf5bhDuyDSb8//buPD7K+lr8+OeZPZOZ7BsBQliDBAQEREDAqEVrK26tYgVal2r70972trXl2k1tr63V6q21Lr16qwVcK1hrURFlUxSoG/sSEIIJCSSTbWaS2Z7n98dkhpnMJJkkAyTkvF+vvgozzzzPTGZwTs73e86BgLsBncUWc5ymBoJ/CP1/B2QMpugr5FcYIYQQYaFtBZOLMiktTCfVbAg34Y7kb6ql8YOXUNqNZmyvYeNyNJ8n5vaB2oQ7NAYzxJhZiLfmAD5HZdRxrl3rQdFhzC1G9bai+lpjziVjMEVfIp84IYQQHYpswh2pfu3TmAtL0FQ12MomDu/xQzR/sor0WQto3Lg86r6B3IQ7NAYzoGqkTb+GloMfUb3sp9infCVYlFO+hdaDH2GbOA+DPVvGYIp+QTKUQgghOhRqwh2ptWIH7j3vk3nRrZ0+tn7NX7COmYFlSPwm26Em3ANN5BhMS9F4ChY9gKlgJM6PV+FY87/4G6rJmLOYrEtuB2QMpugfJEMphBCiQ2v3Houq/tbUAI63n8A2cR6mvOIOH+fa8x6eyj0U3vI4/saauMcEVI21+45xNwNrqkv7MZjmwhLyr72nw+NlDKboDyRDKYQQIi6nx09F20SX8G2fvIG/6TgZ7ZpuR1J9HurffRr7tCvC4wE7UlHnxuXxJ+X59if3XTUBg6531fLthcZgCnE6SEAphBAirsN1rqh+k4GWJho2Lidj5nXorR33A2368O+gBkifcW2X19CAQ3WuLo870wzNsnJPkudt3zu/dMBVzYu+QwJKIYQQcbVvjt2wYSm6FBv2qZd38AjwN9TQtHkFGXMWoTOl9Og6A8WCaUX8eN6YpJzrznklXDeAJxCJ00/2UAohhIgrsjm2z1GJ89O3yLzo2wSaHeHbtYAPTQ3gb6hBMVtp2LgMvT0Lc9EE/A3BvZMBVz0AqrsJf0MN+vRcFEUX9zoDzR1lo8mxmXs0BjM0zvLe+aUSTIrTTmZ5CyGEiMvl8TP+7rfQgNbD26h5/q5Oj7dPnY+35iCeIzs6PW7oD14IN/NWgB13XzLg+yYecbhZ8spnvH/QgaKpaErHQXZoDObsUTncd9UEWeYWfcLA/hcshBCiQ6Em3Icdboy5w8i9+mcxxzRsWIrqbSHr4lsxZAxC9bhi+lJ6jx+mceMy0qZfg3nw2Khm6NKEO2holpWrMit5+X9/yG0PLmNbbYCKOnfUHlaF4M+rbEweC88rkmpu0afIv2IhhBAdCjXhxpqOdcyMmPubtv4DIO59ITpzKo2AadDoqOOkCXe0p556iqljhvCnb80BiDsGU4Jv0VfJJ1MIIUSHbphexDMfHDop55Ym3CccPnyY1atX89RTT4VvC43BFKI/kD2UQgghOrXo6c3hJtzJotcpTC/O4mdfOUsycMDdd9/NH/7wB44ePYrNZjvdT0eIbpOAUgghRKeOONxc/PB6PEls76MA7b98FKAoy0pZSR43TC9idP7A2CMYCAQYPnw4l156KX/5y19O99MRokckoBRCCNGlF7ZWsGTF9lNyrYFWxfzGG29w2WWXsWXLFqZNm3a6n44QPSIBpRBCiIQ8unY/D67ed8quF+qzeM/8UhacwX0Wr7nmGsrLy/n0009RlOSOYxTiVBmYm1WEEEJ0W2+acGua1u1gKdB2jSUrtlPr9HBH2ejuPuWk603ldbzHOhvqeO2113jooYckmBT9mmQohRBCdMsRh5u7Vm5nY3lteHn6VLj/6gmnZSLM/ppmlm+uYO3eY1Q44vSG7GTfZ1ePteu8HP3obd545C6mjhlyCl6NECeHBJRCCCF6JBws7TsWtwl3vC+Xxk0v0rBhKcacIgpveSx8e/XyJXEn7FiGn0P+dfcCYDboWPOfc0/ZnsruBM7t930CiQfdmgqKrld7RqVnpTjdJKAUQgjRa+0Dmv/+1242H3JEBVL+plqq/vc2QMGQnhcTUPobqsmY+82o8+ptWaQUTwz+Wacwc0Q2S2+eftJfzwtbK3o8Xzu0cK1Bj2ZzJ7pntDeZUyGSTX59EUII0WuRTbj31zSz6WBdzDH1a5/GXFiCpqox4xkBdGYrtvFlHV4joGpsLK+l/FjzSR072Jvio94s/ye6ZzSRzKkGHHa4Wbr5MM98cGjAVMyL06fj6fNCCCFEDyzfXIFeF11g0lqxA/ee98m86NZOH6upAVRvS4f363UKyz6sSMrzjOeFrRWntJK9Iw+u3seLW2Nf5wtbK7j44fXhgL2rADZ0/6aDdVz88HpeiHNOIZJBMpRCCCGSau3eY1GBjqYGcLz9BLaJ8zDlFXf4OJ+jioo/XAMBP7rUDOwTLyF91vUo+hNfVQFV45VPvjgpy7hHHG5+9dpOPNXlNKz/G57K3QCYC8eSWXYjpvwRMY/RAj6aNq/EueNd/I016MypGLOHoDNb8dYcQG1xok/LJXXcXNKmX4XOaIk5h9rqpPIvt6G6G8m5cgmpY88H4Jev7WTmyJxwVrG3mdO+VjHfE7JXtO+Sd0EIIUTSOD1+Khzu6Ns+eQN/03Hyr//vDh9nyByEZdjZmHKLUX2tuPe8T+OmF/E5qsi98qdRxza3+vnS/2xI+jLuXSu346raT82yn6C355Ax63o0NJo/XkX1c0sYtPghjNknKrG1gJ9jL9+Dp3I3tomXYMotxt9QTePmV9Bb07FPuRxdih1P5R4a31uOt7qcvK/9Iua6DRuXo/k8Mbf7VY27Vm5n6c3Tk5o5fXD1PnJt5tNSMd8Tsle0f5CAUgghRNIcrnNFfeEHWppo2LicjJnXobemd/i4nMu+H/V32/gLqXvjTzg/ewtP5RWYB4+NeUxoGTcZjc/31zSzsbwWx/qlKAYTBYsfRJ+SBkBqaRlVf7mNhvV/I/fqu8KPadr6Kq0VOyhYeD/mwhIAGje9BGqAvOt+jSl3GAD2SZeCpuLa8S6BVid6y4lZ3d7jh2j+ZBXpsxbQuHF51HMK7RnduP84v3ptJ0BC2VN/Qw2VT9zc4Wu1TZzHLw0/iMp+9kWnaq+oZD2TQ35iQgghksbbbt53w4al6FJs2Kde3u1zpZ17Fc7P3qLl0KdxA8pkLuOG9n22HtlJyogp4WASwGDLwjJ0PO4DW1C9LehMKWiaSvO/X8M6ZkZboVEALeBD9Qazs/rUjKjz621ZoOhQdNFfu/Vr/oJ1zAwsQ0ppjPO89DqFn76yDb+q4akuTyh7qrOmk/3VH8Wcq/Xzj3DtXIdl+DlR2c++KLLKHrq/V7SrXzIk65l8ElAKIYRIGpPhRK2nz1GJ89O3yLzo2wSaHeHbtYAPTQ3gb6hBMVvRp8T/wtan5QCgtjZ3ed3eLuOG9n1qAR+KwRRzv2I0Q8CP7/hhzIPH4qutIOB0YMorDmZSd7wDAT/69DwA6lY9QsbsG9qWvHfT/Mmq4BK46cQeStee9/BU7qHwlsfxN9bEfV4BVaOqsRWAxo3LEsqe6kyWuNXyru1rUMxWrKPOPWUV8z1xMveKSoX8ySMBpRBCiKQpzk4NNzUPNNeBplK/5knq1zwZc2zlEzdjPWsOaqsz7hKuv6EaAL01PaHG5+2LWBIVue/TmDUET9VeNDWAotMDwQDYU7UXAH9zHWbA76gCoGnrP9BZbGRfcgcAjR+8BIqOls8/pqV8c/gaaTOvI3POovDfVZ+H+nefxj7tCgwZ+R0GlJESzZ7G43c6aK3YTur4C8MBc6hi/u75pYn+qE66k7lX9GRnPQc6CSiFEEIkTarZQFGWlcMON8bcYeRe/bOYYxo2LEX1tmCfcjkNG5ZisOeQPuM60CnhJdyCRX+gcdOLQDBobDn4cXCpN07j85CeLuNG7vu0n3MZjrceo27VI6Sddw1oKo2bXiTgrAdA83sBUH3BrKHqdTPoxj9iSMsNPtdhZ1P5xC3oU2ykn38D+pQ03Ae20rTpJfSpGaRNCS79N334d1ADpM+4NuHnmWj2NB73rg2gqaSWXhC+LaBqrN13jLtJXkDZm/2IoSp7CO4VbXzvOTxf7ELz+zBk5GObdClpU+cD0PL5x7h2b8RbtRdf3Rfo7TkM+X//F3PO0C8Z//iscsBXyJ9sElAKIYRIqrKSPJZuPgzWdKxjZsTc37T1HwB4KrahM5rJvPBmHG/9Geu4OdgnXULDe89Rs/wnqC3N2CZdirlgFHByGp97vV72lR8M/90++TL8TbU0bV6Ba8c7AJgKRpN23jU0bXoxvGQdCuzMg8eFg0mgLdOqgaIPFuMA1pKZoGk0rHuG1HFz0TwtNG1eQda873SYUYwn0expPK5d69DbsrAMOzvq9oo6Ny6Pv1dFKMnaj3jXyu34VY2Wzz/m2N/vxZQ/kvSZC1BMFvwN1QSaa0+8np3rce/ZiCl/ZNQvFe35VY0bn91K+TFnj19fpP5WIX8qSUAphBAiqW6YXsQzHxzq8rjQEq4xrxjz0FLc+z5EddWDqqK2NJN50bext2WkQjQ1gOb3dhiIdbSM63A42LNnT/h/e/fuZc+ePRw4cABddhGFN/0pfGzm3MWkTb8a3/HD6MypmPKKqV//LACGrMHB69iyg//frvim+eNV6Cx2tHbN2a2jzsW1fQ3emoO4tr+D3p6FuWgC/obgUnfAFcyAqu4m/A016NNzUZTo2SOJZk/b8zkq8VaXY592Rcw5NeBQnSs85ag7krkfMVRlr3rc1L7+ECkjp5F71X/FPN+QjLmLyf7y91D0Bo69fA/e44fjHhdQtahgsrPMp+prxbVtDe79m/EdP4Tqa8WQMQj7pEuxTbokHMSHsp5ZqSapDo8wcF+5EEKIk2J0vp3Zo3LYdLAubpBRcMPvADj8wJUoBhPGjAJyr1wSvv/4q7/Dvec9zIUlKMqJiTuJNj7/1yeHSCt/KyqAPH78OACKolBcXMzYsWP5yle+wtixYykeVcJtbzujMmt6iw390BNBaeuhT9Hbc8KV1KbcYaAzBPeJRgi4G9ACPvSpmVG3a2og+Ac1gL/pOP76o1Q9cUvMz8axOjjffOgPXkCJaC8EiWdP23PtXAcEC3jiaV+Zn4hk70cMVdk371qH6mogc85iFEWH6m1FMZpiAkuDPbvbz7mrzKe/oRrH209iKZ6I/dwr0ZmstHz+MY7Vj+Gp2kPOV38IBH9eX3p4PR6/KtXhESSgFEIIkXT3XTWBix9e32mg0Z0l3O40Pj/WovGLh39DyYhg4HjxxRczduxYxo4dy+jRo0lJic1uFn20lsPtGrKHuHZvwHt0P5llN4UDG53ZSsrIqbSUb8FXdwRj9lAA9KlZ+B2VmEadG32OXetB0WHMLSZjzqKYWebe44dp3LiMtOnXYB48FiXORB1ILHsa8/x3rcOQNSS8daC9yMr8RJyMKuxQlX3roU9RzFb8zjqOrfgNfkclitFC6vgysi76dtw9pIlIJPOpT81k0M2PhvuHAtgnf5naf/0Pru1rSJ+1AGNmIRrQGicIH+jV4RJQCiGESLqhWVbumV/KkhXbOzymO0u43Wl8rigKm3ccZPyQjISfb2jfp+vQdhrffx7L8MnoUtLwVu3BuW0NlhFTsE+7IuoxGXMX03r4M2qe/xn2tmIbX+0hAFo//4SG958PFuWUb6H14EfYJs7DYM+Om13TmVNpBEyDRsfddxqpq+xpJE/VXvz1R0mffUPccykEK/MTdTKqsL9ydmG4yt7nqAI1wPFXfo3t7HlY5n6T1ortNH/0T9RWF7lX/KRH13IlkPnUW9PjNt+3jpmBa/safLVHMGYWdnmtgVodLgGlEEKIk2LBtCJqnZ4OA5CeLuGGdNb43NfFEmx7oX2fens26HQ0bV6B6m3BkJFPxpxFpJ17ZTiLGmLKKSL/G7+lYd0zwYp0RcEybCKppRfg3PY2zo9XEWhpbjvH4mDQnGTxsqdR94eWu8ddEPfxavMxfnDHd5k3bx4XXXQRWVkdF7h0pwoboPWL3TSs+yve6gMo5hRSx84mY+7iqP2vv3xtJzk2c3jpWPO1ovk82CZ/mawv3QYEi5q0gA/np2/im30Dxg4ysZ3pTeYztL9Vb03r8Ji4jxtg1eESUAohhDgpjjjcbD7o6PSYnizhhnTW+Ly7y7gn9n0q5F/364QfZy4YRf6C38Tcnjr2/G5d3zLsbIYteT3qNr1OId9upqbZE1wOrtiRcPYUgvs2XXs2YioswZg5KOZ+HVCc0sr7777PU089haIoTJs2jXnz5jFv3jzOO+88jEZj+PjuVGF7aw5y7IWfYcweSuZFN+NvrqNp8wp89VXkX3tP+Di/qvHIu/vDfw8FdqlnzY3+eY67AOenb+Kp3NOjgLKnmU8t4KP5369hSM/HNGhMt68bMhCqwyWgFEIIEaO3843bF210pjtLuJEiG59H6u4ybkgi+z5PJYNO4XdXT2DxX7cCdCt7CsGfo+pqIHXGdXHPrwJ/uXMhox74LkeOHOHtt99m9erVPPbYY/zmN7/BbrdTVlbGvHnzKJk2t1tV2PXrn0VnsZH/jd+iMwf3EBrS83C88SdaPv+YlOHnAMEs3mdfnBg6qbdl46utiB1dmRp8j9XWnrX/6Wnm07H6CXy1FWRefCvHV/x3hxnZxk0v4S7fjL/+aPC9ScshZeQ00iNm2Pe08X5/IQGlEEIIIHn9BHtTtBFvCVf1uFH0RhTDiWyZpmlRjc8jFWVbe9S+JZF9n6fSvfNLmTMmL1wxT+agbmVPU0ZMicl6huh1CjNHZIf7dQ4dOpSbbrqJm266iUAgwCeffMLq1atZvXo1P/jBD7BfcBNp53wlob2IqsdN66FPSZt2RTiYhOC+1/p3nsK9+71wQAmApqKhoCgKpoKRtB76BH9zXdQvFP620Z3x9jgmoieZz8bNr+D87C1Sx19I/dr/6zwjW12OKW84qWfNQTGl4K87QvNnb9FyYCuDbvwTOpOlz89P7y0JKIUQYoBLZj/B7hRtJLqE660up/a1B7COmxOssvV5cO//EM8Xu6Ian0MwUCobk9ezHwRd7/s8Ve6cVxJeHj0ZmVODTuG+qybEvU+v1zN16lSmTp3KXXfdRXNzM2UPrae2VUloL6L3+CFQA5gKovcMKnojprzheGsORF9Q0aFrCypTx86m6cO/49y2mpTiieFDnNtWg06PuSj+c+5KdzOfzm1raFj7DKlnf4mWA1u7zMiG5qhHMhWOpfbV39JSvpnUcXP79Pz0ZJCAUgghBrBk9hOMLNqI1LjpRRo2LMWYU0ThLY+Fb9fbs0FRaHz/eTSfB6CtXY5GwFmPoW2PpD49L7rxuaJgzB5K1iW3Y2ubRhP5/Bae17t9aneUjSbHZg7/XLoTyOl1CsHOmRo+nz+qR2YijzXoFO6dXxq11+5kZE7vnV+a8NKrYkqhrjX4qhLZixhwtmUTbZkx59LbsvAcif2MaIoOnQKmgpGknv0lXNve5riqYikaT2vFdtx73iNtxtfDFfLeY5/j3h+cle6rP4rmcdHw/gsAmPKGYx0dnQXsTubTve9D6t54BGvJDEwFo3Bte7vLvpjxGDLyAVA9rhOvvw/OT08WCSiFEGKASnY/wVDRRiR/Uy2NH7wUt69i5MhC+9T5wR6TrU48R/e1fQkHA8r2jc870n4ZtzcWTCti1sicLjO3kdcOqBozR2Rz31UTWLJkCRtah6AfXNrtx8YL9JKZOY3MfiYictZ5InsRQ+2eFL0x5lyK3ojawUSf0I8o+5LbMaTl4ty2Bve+DzCk55J50bdJi8paH6Bx47Kox4f+njr+opiAMtHMZ2vFDmpf+z3moePJufxOav/5QMLV4ZqmBfuLqiq++koa1j0Lig5LRFb1ZMxP7yskoBRCiAEo2f0E1bblvPbq1z6NubAETVVjmnk3bX2V1oodFCy8H3NhSa+fR2fLuD0xNMvK0punn9hbuu8YFXVx9pZmWykbk8fC84oYlWfn8OHDvPTXx/nd737H/IVzuvXYzvQ2cxov+5mIyEk6iexFVIzBdvRawBdzLi3gQ9dBi56JQ9LZUdVEAAMZ53+DjPO/0eFzsp19MbazL074NSSS+fQ3HuPYK78GFFLHzsK15z08VfvQ/F6OvXw31jGzyOykOlx1NfDFo4vCf9fbc8iZf2e46X1IMuan90Vn1qsRQgjRpfb9BBvW/w1P5W4AzIVjySy7EVP+iA4fr7Y6qfzLbajuRnKuXELq2PP54zvl6AhWDoe0VuzAved9Bt34CI63n4g6h6apNP/7NaxjZrQFnIFgsNHBhJhEdGcZtztG59u5e34pd1OaUPX7/fffT3p6Ot/5zndITU3t1mO70tvMaU9+PpEtmBLZi2hqa1EUalAfKeB0oLfF73X5/QtH893nPj5pVfZdZT79DdVobcvTjtWPxzzemFWItWRmh9XhuhQbeQt+g+b34q05gHvvBzEz3aF389P7MgkohRBigAktTXuqy6lZ9hP09hwyZl2Phkbzx6uofm4JgxY/1GHbnoaNy8N7HkMCqgoRc7c1NYDj7SewTZyHKa845hy+2goCTgemvOLgxJsd70DAjzG3mKyLb8Uy7OxuvabuLuP2VKrZ0GkgUFVVxdNPP82vfvUrUlOjWxd19dhE9TRz2lPF2akoBAOhRPYimnKGgU6Pt3o/qWfNDh+jBXx4j32ONU6PTgWYPiI76XtFR+fZOFjrIqBqKPrOM5/xeoFWPfX/8NVWkP+N32EpGh++PV51uKI3klI8CQDrqHOxDJtEzbI70aVmYG03irMn89P7OgkohRBiANlf0xxemm7cuAzFYKJg8YPoU4JTQFJLy6j6y200rP9b3MpV7/FDNH+yivRZC2jcuPzEHRHBJIDzkzfwNx0n//r/jvs8/I4qAJq2/gOdxUb2JXcEn9MHL1Hz0i8Z9M2HMeUN7/S19GYZ92R54IEHsFqt3HHHHSf9Wt3NnPZUqtlAUZaVww53QnsRdZZULMWTcO1cR/rMBeHWQc4da9G8LXGbvodaPSV7r+j8iYW9qpDvTV9My5Cz0NuycO1cFxNQdrfxfn8gAaUQQgwgyzdXhJdBW4/sJGXElHAwCWCwZWEZOh73gS2o3paoMXkA9Wv+gnXMDCxDSmlsf/I2gZYmGjYuJyOiqXN7qq81+P9eN4Nu/GO4QMcy7Gwqn7yVps2vkHP5j+M+NhnLuCfDsWPHePLJJ/nJT35CWlr3xvT1VrKynx0JzTpPtAo7Y84iqpfeSc1z/4Vt0iX4m+to3rISy/DJpIyYEnXu9q2ekr1XtDdZz972xdT83qgqb+h54/2+TgJKIYQYQNbuPRb+gtYCvrgzjBWjGQJ+fMcPR83Idu15D0/lHgpveRx/Y02H12jYsBRdig371Ms7PCZ0XfPgcVHV3ob0PMxDxuH5Ynfcxw1L0jLuyfDQQw9hMBj4j//4j9P9VJIuNOscEqvCDo2kbFj3DPXvPIViSsF29pfImPvNmHPHa/WUzL2i3c16Rs4pV9u2djSs+ysp3/qf8DGhjKwpfwQVf/wGWktTeD9xiGvP+6itTszt+nH2tPF+X3fmvSIhhBBxOT1+Khzu8N+NWUPwVO1FUwPh0X1awIenai8A/uY6zG3Hqj4P9e8+jX3aFRgy8jsMKH2OSpyfvkXmRd8m0HxijrcW8KGpAfwNNShmK3pbMJPVfikxeFs6vvbNr4G/3zaDqcXxCzpOlY6Wluvq6vjzn//MHXfcQVbW6X2OJ8OJWed1CVVhA1iGllKw6IFOj+ms1VMy94ommvWMN6e8+aPX8VaXc/zV+2Myss2fvIHWlm13fraagLMeRVHwVJfj2rkWfXo+9mnzo15vbxrv92USUAohxAAR2U8QwH7OZTjeeoy6VY+Qdt41oKk0bnoxXJ2rRfQLbPrw76AGSJ9xbafXCDTXgaZSv+ZJ6tc8GXN/5RM3Y586n4zZC0FnCB4fcw4Hujjzuc8adGqXkUMSGUmZ0nAQJWMQP/zhD0/LczwVTvXEnpBk7RXtKuvZ0Zxy24SLafzgpZiMrKV4Ikf/+n3s515J84ev4Dt+ODjrW/VjSMvDfs5XSZ95bdSWkmQ03u+rJKAUQogBon1lqX3yZfibamnavALXjncAMBWMJu28a2ja9CI6U7CFj7+hhqbNK8ia952YPZXtGXOHkXv1z2Jub9iwFNXbQtbFt2LIGITObCVl5FRayrfgqzsS7tXnqz2Cp3I3tslfjnr86Vgm7M5ISk3NJWvhw/zwtYPcd1Vqn9nXmUyne2IP9H6vaPus5yuffEFzqx+g4znlHVSH1zx/F9YxM7AOn0Lzh6+QefG34xYchSSz8X5fJAGlEEIMEPEqSzPnLiZt+tX4jh9GZ07FlFdM/fpnATC0tUNp2LgMvT0Lc9EE/A3Bpe6AK5jFVN1N+Btq0Kfnoig69NZ0rGNmxFynaes/AKLuy5i7mNbDn1Hz/M+wTwnut2z+6J/oUuykz/h6+LjTsUzY3ZGUoS0D8UZSnklO58SeZAplPdfuPRYOKBOZUx6S6H7iSMluvN/XSEAphBADRGQ/wUh6iw390BOj4FoPfYrenhOuavU3Hcdff5SqJ26JOadjdXA299AfvIBisXXr+Zhyisj/xm9pWPcMjZteBEXBMmwimWU3YrDnhI871cuEyR5JeaY5XRN7kq39nuJE5pRD4vuJ2ztZjff7CgkohRBigIjsJ9gR1+4NeI/uJ7PspvAesow5i2LGJnqPH6Zx4zLSpl+DefDYuLO6IxXc8Lu4t4eqgTtyqpcJkz2SMtdmPu2B08lwOib2JFv7PcWJzCk3Zg1OeD9xpNOZjT1VJKAUQogBJNRPMKBqtFbsoPH957EMn4wuJQ1v1R6c29ZgGTEFe0QLGEtE9jJEZ06lETANGh13iTtZTuUyYWgkpc9RScPGZcG2MS1O9Gm5pI6bS9r0q8KjIauXL8FzZEfMOSzDzyH/unvDf//lazuZOTKnTwRQyRa5H/F/1+1h+bufYswaRLBUKSiZE3uSrf2e4oTmlOsMCe8nDrn9gpHcXjYqOU+6D5OAUgghBpDIfoJ6ezbodDRtXoHqbcGQkU/GnEWknXtleE/g6XYqlwnvWrmd1oZjVD/7QxRzKvZzvoouxY6ncg+N7y3HW11O3td+ET5eb8+J6avYfk61X9W4a+V2lt48/ZS8hlPN5fHjDaicleLk+Ku/5d1/vUpm4bCTMrEnmY443Nz7+q6o2xKZitOd/cQhl00YdBJfSd/R995lIYQQnepN65TR+XbOLbKz5XAjxsxB5F/3625du3HTizRsWIoxpyg899jfUEPlEzd3+BjbxHlkf7n7zb5P5TJhaCRl07Z3UT0uBi38PabcYQDYJ10Kmoprx7sEWp3o2/aK6sxWbOPLOj1vQNXYWF5L+bHmPpWd642O2igV3vQnFr1yhKKsOspK8rhhelGfDCZDBVe+QHSGMpGpON3dT3ymTsWJp++900IIIWIk0gsx9CU+Oj9+4OLy+Hll9Qbe++sDGC+4DUXfvXnC/qZaGj94KWa/pM6aTvZXfxRzfOvnH+HauQ7L8HMSvsbpKtoIjaRUvcH9pTFZKlsWKDoUXfTXpqYG0PzeTpc/9TqFZR9WcPf82K0D/Ul32igt3XyYZz44xOxROX1mzyR0XnCVyJxyfVput/YTn6lTceIZGK9SCCH6qd5+iYcC0Xf3HqPC4QIUTBfd0aPnUr/2acyFJWiqGvWlqjNZ4mbqXNvXoJitWEed2+W5T3fRRmgkpaVoAk0f/p26VY+QMfuGtiXv3TR/sgr7lMvDvTkhWBVc8YdrIOBHl5qBfeIlpM+6HkUf/dUaUDXW7jvG3fTfgLK7bZRC9/elNkpdFVwlMqc8NKs8Ukf7ic/kqTjxSEAphBB9VG++xC96eD3DMq3sP+5Er0BAg8hiie5qrdiBe8/7DLrxERxvP9Hl8X6ng9aK7aSOvzDuvPCQvlC0Edk+JmXEFNJnL6Tpg5c5Wr45fEzazOvInLMo/HdD5iAsw87GlFuM6mvFved9Gje9iM9RRe6VP425RkWdG5fH3y+zVWdCG6VQwVVI5Lxuze/DkJGPbdKl4TnljR+8jHvPxvDxTR+8TNMHL8cUXXXmTJ6KE0//+2QLIcQAkIwv8f3HncG/93JSnqYGcLz9BLaJ8zDlFSf0GPeuDaCppJZeEHPfoHQLj33jnD5TtNG+fYwhPR/z0FKsJTPRp6ThPrCVpk0voU/NIK2tAXvOZd+POodt/IXUvfEnnJ+9hafyCsyDx0bdrwGH6ly9mvJyOpwpbZTuWrk9/ItZvHnd/oZqAs214ak4rYe34W+o7rLoCsAy7OzwfuLwcWf4VJx4JKAUQog+Jplf4sng/OQN/E3Hyb/+vxN+jGvXOvS2LCzDzo65r7qxlTH59tMeSIZEto9x7VqP481HKbz1SQxpwebq1pKZoGk0rHuG1HFzo2YzR0o79yqcn71Fy6FPYwLK9tfpD4443Nz5yPN8sWxJ3PsLFj0Yfp1awE/jBy/h2v4OfmcdBls2qWd/ifQZX4/qGHA62iiFCq6g43nd8SRSdNWRM30qTjx941+zEEIIIHppzlNdTsP6v+Gp3A2AuXAsmWU3YsofEfWYxk0v4S7fjL/+aLD9T1oOKSOnkT7zOvTW3mXEAi1NNGxcTkY3zuVzVOKtLsc+7Yq4X9h9LVsXOZKy+eNVmPJHhIPJEOuoc3FtX4O35iApxZPinkff9hi1tbnL6/QHd63cTkALZvXsUy7HNGhM1P2GzBPtcGr/+Qfce97DdvaXMA0ahadyL40blxFoOk72l78XPu50tFEKFVwFVK3jed0dBJaJFF3Fc6ZPxYlHAkohhOhDQktznupyapb9JNjrcNb1aGg0f7yK6ueWMGjxQ1FtTbzV5ZjyhpN61hwUUwr+uiM0f/YWLQe2kjpuLo3vv4Axp4jCWx4LP0bTVJyfvknzJ2/grz+KYrRgKgguAVqGnBU+rmHDUnQpNuxTL0/4Nbh2rgMgtbTj7E5fytZFjqQMuBvQxRkhqamB4B9C/x+Hv6EaIG7g3d/ax4SyemrbMrF5aCmpY8+Pe6zn6D7cezaSPnMBGXMWAmCffBl6axpNW17FPuWrmPKGA6enjVKo4Aq6N6870aKr9gbCVJx4JKAUQojTLNRX8sAxZ3hprnHjMhSDiYLFD4aXWFNLy6j6y200rP8buVffFX585J9DTIVjqX31tzR++Pe4YxHr3/0/mre+SmppGfZzvoLa6sT56ZvUPLeEgoW/x1xYgs9RifPTt8i86NsE2nrxAWgBH5oawN9Qg2K2ok+JDgxcu9ZhyBqCuaDj6SB9KVsXOZLSmFlIy6FP8DkqMWYNDh/j2rUeFB3G3GJUjxtFb0QxGMP3a5oWnEcOcdsk9bf2MaGsXiTV40YxmmOa3nuOBDPq1nFzom63njWHpi0rce3eGA4o4dS2UerpvO7uFl31pRnlp0v/+XQLIcQZpLO+kgCtR3aSMmJK1H49gy0Ly9DxuA9sQfW2dLoMZ8jID/5/ej761MyoNj+aGsD5yRtYS2aRc/mJ/pHWsedT9cQtuHauw1xYQqC5DjSV+jVPUr/myZhrVD5xM/ap88m6+NbwbZ6qvfjrj5I++4YOn1tfzNaFRlKmTb+GloMfUb3sp9infCVYlFO+hdaDH2GbOA+DPZvWw9uofe0BrOPmYMwsRPN5cO//EM8Xu7BNujQmkO6P7WMis3oAdav+iOZtAUWHeWgpmWU3YR4UrNjWAj6AmGp+xWgGghn0SKeyjVJP53V3t+jq7MHpPLJg8oBb5o4kAaUQQpxCifSVhOCXdLx2O4rRDAE/vuOHo77UNE0LBo2qiq++EsdbjwOQecGNNG19td25/Wh+T2zzbmtGsHl3WyBgzB1G7tU/i3kODRuWonpbyLr4VgwZ0WPlwsvd4y7o8GfQF7N1oZGUlqLxFCx6gIb3nsP58SoCLc1tIykXk3beNQDo0/MwDy3Fve9DVFc9KArG7KFkXXI7tkmXxpy7v7WPicrq6Y1YS2aSMmIqOms6vtoKmraspGb5TylY+ACmgpEYs4LbLzxf7MaYURA+TyhzGXDWxVzjVLVR6sm87sjMdKTOiq5++dVxAzqYBAkohRDilOlOX0lj1hA8VXvR1EB4iVEL+PBU7QXA31yHOeJ41dXAF4+e6JOITo952ESsY86LCSh1RjOmwhKc29/BNHgsliGlqB4Xje+/gM5iCwdFemt6VKPmkKat/wCIuU9TA7j2bMRUWIIxM/784r6arRudb2f2qBw2HazDXFhC/rX3dHisMaOA3CvjVz631x/bx0Rm9SxDzoraU8vo6VjHzuLo09+jfv2z5F93Lykjp6JPy6N+7dPBz1bBKDxVe2nYsBR0ejSfN+Yap6owq/3WikTmdXeks6KrvrSF43SRn4AQQpwCj67dz5IV2/H41S4blAPYz7kMv6OSulWP4K2twHv8ELWvP0TAWQ+A5o/+ktal2Mhb8Btyv/ZLUkZOA03rdEJNzuU/wpg1mLp//oHKx2/i6P99D2/NAQoW/j4qy9QdrYc+RXU1dJqd7MvZuvuumoBB1/Pm7/H0x/YxXRVMGTMLSRk9ndaKbcFfeAwm8r7+K/QpaRxfeR+Vj99E7esPkT5rATqLHcUUu4c3keskQ6jgKsRUMBII/kIWKXJed0c6Krrqi1s4TgfJUAohxEnWk76S9smX4W+qpWnzClw73gHAVDCatPOuoWnTi1EjAAEUvZGU4kkEWpqoe/1h7JO/TP07/xvV2iWSzpSCMacI8+CxWIZNJOBqoPHDlzm24jcU3HB/p1+sBTf8Lu7tKSOmxDR4jtTXs3VDs6zcM7+UJSu2J+2c/bF9TCLZNkNaDgT8aD4PitmKKXcYg27+M77aCtRWJ8acIhSDifp3nsJSNL7H1+mtyIIrSGxed3eLrvriFo7TQX4CQghxErUf+RapcdOLNGxYGtvSp61JtHvXekBDn5pJSslMsi6+lYaNywAwdLDPK9TmJ/OiW3Dv+yC8pzGSpgaoeeHnWIZOIGved8K3W4onUvXU7TRtXkFm2Y09f9Ed6A/ZugXTiqh1enhw9T40TUNRep6x7K/tYyLbKHXE31CNYjBFZR8VRcGUOyz895YDW0FTscTp23kqs3qDM1PCAWUi87q7U3TVV7dwnA4SUAohxEkUOfItkr+plsYPXorb0idek2jnx/+CgB/vsYPo7TlRfShD2rf5UX2tBJz1MW1+vDUH8R0/TOaFt0Q93pg1GGP2kHAj9WTrL9m62y8YxbKnnuBo4fnojaZuja48E9rHRGb1Au7GmGy1t+Yg7v1bSBkxpcOG4KrPQ8OGZehtWaSeNSfm/lOV1TvicLP10ImWV57qcgKuehSDCfeejbj3bESXkkbmRd8mbdoVQHC8oi4ljeaPXg/3HVXMVjLmfpO0874Wdf6+vIXjVJOAUgghTpLIkW/t1a99GnNhCZqqRrX06bxJ9EoAMstuCn+Rq95WUEBntMRt8+M5cmL5NtTmx1xYErxBi7OHTQ2caOKdRP0pW/f3v/+dzc89xP+9OJd3m3O6rMgHwvfPHJHNfVdN6BeBc2dCbZSqX70fndGEefBZbVXeR3B+9iaK0UzmBd8KH3/81d+ht2VhzClC9bhxbXsbX0M1eV//FTpz9M/iVGb17lq5ndDbFjnDO2PO4vAMbzQ1HExqAT+Ot5/A31iN/ZyvBPtQtjrxHN1HyqhzozLWfX0Lx6kmAaUQQpwkkSPfIrVW7MC9530G3fgIjrefiLov1GpFnzmImud/hmX4ZHQpafjqjwZvzyjA3vblB+Cvr6LmhZ9jPWs2Bns2tomX4Ks/iufIDnQpdjLLbqJp8ytRbX401Q+Aa9cGUkZMOXHt6nJ8jkpsky6Jek5ZqSZcHj9+VUuooCikP2brGhsb+f73v8+VV17JjdfO50YieobuO0ZFXXTPUIVgtq1sTB4Lzys6Y4KLUBsl65jzcO1cR9OWV1G97rbK/5mkn389xszC8PGmglE4t6/B+embKAYT5iGl5My/M2ZMKJy6rF5PZng3bX2V1oodFCy8/8QvXh3QK31/C8epJAGlEEKcJO2bQ0Nw/6Lj7SewTZyHKa845jGhJtGGtFzQ6WjavALV24LelhW8PaMgalKJ3p6NtWQmrYc/I9BUi6b6MaTlYZ9yOekzr0VvTce57W0gus2PpXgyrh3voHrdpAyfTMBZT/NH/0QxmEibeiJg1esU5p9dyM3nD0+of2boMf01W/fzn/+cpqYmHnnkkfBto/Pt3D2/lLspDU818vpVTAYdxdmpZ2RBRriNku4K0qbO7/L49PO+Rnq75eB4TmVWr7szvDVNpfnfr2EdM6Nt9SCAFvChi7MtBWDqsMx+9dk+2c68fwVCCHESJRpQtB/5Fr79kzfwNx0n//r/jnv+UJPoQHMd+df9Onx78ydv4Hjrz8FG2hH01nSyL72j268j95qf07RlBe7dG6k/+DHoDViGjCNjzqKo/ZmhbNLQLCtLb57er7N1Xb13//73v/nzn//Mgw8+yNChQ+OeI9VsOOm9E/uK+66awMUPr+9WVrorp7Iwq7szvH21FQScDkx5xcGpODvegYAfY24xWRffimXY2VHnr2xsOSWvo7+QgFIIIbrQ2ZhEBSjKslJWkscN04sYnR8MotqPfAMItDTRsHE5GTOv67AtT0+aRHclXpsfndFMxqzryZh1fYePi5dN6m/ZukTfu+umDua2225j4sSJ/Md//Mfperp9Sn9uo9STGd5+RxUQbNyvs9jIviT4i1rjBy9R89IvGfTNh6Nmkp+qaT/9hfwUhBCiA4mMSdSAww43Szcf5pkPDjF7VA73XTUhbtPmUEsf+9TLO7xmqEl07T/u5/jK+4I36o1klt1I46aXOmwSfTJ0lU3qy9m6nrx3LaOv4qlbL8RgkK/GkMg2Sr11KguzejLDW/W1AqB63Qy68Y/BbSeAZdjZVD55K02bXyHn8h+fOCenZtpPfyH/aoQQIo7ujEmMvH/TwToufng9t82JLkZo39InpH1LH32KvUdNok+G/tLmp72evnfW4on8eG0jLnsFC/pJEdGpcEfZaHJsZn7x6na8Pj+KPvHQ4XQVZvVkhndohr158LhwMAlgSM/DPGQcni9i22mdimk//YUElEII0c6ja/f3OCMTaKuEfuTd8ujb47T0iRRq6ZN18a1A95pEnwz9qc1PpN68d5qiw+NXWbJiO7VOD3eUjU7ys+u/Fkwr4sl7f0RF/iwCuaP7fGFWT2Z4m9qq1tsfEzrOV3Ogy+sMZBJQCiFEhJ6MSUyEMXcYuVf/LOb2hg1Lo1r6xNNVk+iQ0Jf4qDwbFXUuAlrX2bn2j+9vbX4iJfO9e3D1PnJt5n75c+ipzvbEbtmyhXWv/50XX/w6k+fO6fOFWe2n/ZgKRtJ66BP8zXVRRWeRM7xNucNAZwj+8tdOoNmBTmZ4d0oCSiGEaHPE4ebOR57ni2VL4t5fsOhBzIPHhv/e+sVuGtb9FW/1ARRzCqljZ5MxdzE6U0rMY4P9+2bE3N609R9AdEuf7jSJhvhf4onsIQw/t37c5ickNOLS56ikYeMyPF/sQm1xok/LJXXcXNKmXxXV/kUL+GjavBLnjnfxN9agM6diHjSKrEvuCM6pBn752k5mjszplz+PRCVatPTB0j9TUlLCNddcg16v7/OFWZ3N8NZZUml877ngZ8QTvN9Xf5RUsxVz0Xg8hz7l8O++Gve8dW88QvaXg0VbgzIsfeK19hXykxBCiDZ3rdxOQAt+pdqnXI5p0Jio+w2ZJzKI3pqDHHvhZxizh5J50c34m+to2rwCX30V+dfe06vnkWiT6D8tmMSIXFvcL/Ezoc1Pd9y1cjutDceofvaHKOZU7Od8FV2KHU/lHhrfW463upy8r/0CCE5DOfbyPXgqd2ObeEnUNBTV4wKCAaVf1bhr5XaW3jz9NL6yk6M7RUt/+/AQ6qhrGTnxSqoaPVEBdl8uzApN+wmoWtQMb/fuDejTctGn5aIe+xxTYQmaL9gCKGPuN6k5shPFYCJl+GQAWj7/BM3vhYAPy/BzTlwged2UzggSUAohBCemaqhtX6zmoaWkjj2/w+Pr1z+LzmIj/xu/DWcNDel5ON74Ey2ff0xK5BdPJ+K19OmqSXSonc/lEwd3ef7+1uanJ0LvXdO2d1E9LgYt/H14/6l90qWgqbh2vEug1YneYkt4GkpA1dhYXkv5seZ+HWy3192ipdDdh1rMXPzweu6ZX9ovipZC035CMi+4EffujWiaRqCpDiVDHzXDG8AyaDQFix6gYd0ztBz4NygKlmETCbgb8NVWYB11bvjYqsbWM+6z0Rv9/78kQgiRBKGpGpFUjxvFaI6aTBO6vfXQp6RNuyJqCdo2/kLq33kK9+73Eg4oe6KnzaH7cjapN0LvneoNLl/GFF7YskDRoegM3Z6GotcpLPuwgrvnl57sl3FKJKPgrL8ULY3Ot1OYbqGqMdgOyL33PTRfK4W3PI4xZ2h4Wk575oJR5C/4TfjvfqeDyj9/i9TxF4arxeHM+2z0lgSUQghB7JjEulV/RPO2gKLDPLSUzLKbMA8KfoF6jx8CNYCpIPoLVdEbMeUNxxunGjSZ+ms7n5Ml9N5ZiibQ9OHfqVv1CBmzb2hb8t5N8yersE+5HJ3Jgvf4oW5NQwmoGmv3HeNu+n/QMCCLliJ+R0xkWk487l0bQFNJLb0g6vYz6bORDBJQCiEGvKipGnoj1pKZpIyYis6ajq+2gqYtK6lZ/lMKFj6AqWAkAWdbZagtM+ZcelsWniM7Y273VJfTsP5veCqDvezMhWPJLLsxal+k6mvFtW0N7v2b8R0/hOprxZAxCPukS7FNugRFp8egU5g5Muck/BT6p8j3LmXEFNJnL6Tpg5c5Wr45fEzazOvInLMIoNvTUODMmIgSKlpqPbyNmufvintMZNGZpqk4P32T5k/ewF9/FMVowVQwkvSZC7AMOQvo+0VLTo+fow2t4b8nMi0nHteudehtWTG/bMCZ8dlIFvkJCCEGvMipGpYhZ4W/MAEYPR3r2Fkcffp71K9/lvzr7g1u0CeYkWxP0RtR/dHjET3V5dQs+wl6ew4Zs65HQ6P541VUP7eEQYsfCrcx8TdU43j7SSzFE7GfeyU6k5WWzz/GsfoxPFV7yPnqD9HgjC0U6Yn2E1EM6fmYh5ZiLZmJPiUN94GtNG16CX1qBmlTLu/2NBQ4Myai3LVye3jPJHRddFb/7v/RvPVVUkvLsJ/zFdRWJ85P36TmuSUULPw95sKSPl+01JNpOcas6H3JPkcl3upy7NOuQFFie06eCZ+NZJGAUggx4HU17cKYWUjK6Om4921CUwPhpTEt4Is5Vgv40LVbOmvcuAzFYKJg8YPoU9IASC0to+ovt9Gw/m/kXh3MGOlTMxl086NRDc3tk79M7b/+B9f2NaTPWgCZhWdkoUhPRb53rl3rcbz5KIW3Phlu/WMtmQmaRsO6Z0gdNzf83nVnGkr76/Q3oaKlSJ0VnWlqAOcnb2AtmUXO5T8K324dez5VT9yCa+c6zIUlfb5oqSfTctoHlK6d64LHlJYlfJ2BSlq8CyEGvESmXRjSciDgR/N5gkUeQMBZH3NcwOkI3x/SemQnluJJ4WASwGDLwjJ0PO4DW1C9wZYl4ebK7YR6VPpqjwSPaysGENHvXfPHqzDljwgHkyHWUeei+Tx4aw6it2UDHU9DUVudXV6nv4lXcAbB4jJNDcTcrgX8aH5PbHGTNSNY3NQ2ohD69mcx3rQciFO0FTEtpz3XrnUYsoZgLhiV8HUGKvkpCCEGvNBUjc74G6pRDCYUkwVTzjDQ6fFW7486Rgv48B77HGO7fpFawBd3w79iNEPAj+/44U6vHXAFA1e9NRiQhooBRPR7F3A3oGmx2aJw0KQGuj0NBfr/RJT2BWcQLDo78vC1VDxwFdXP/Reeoyc+yzqjGVNhCc7t7+DcuRZ/4zG8xz6n7l8Po7PYsE26NHxsX/4stv93bSoYCYC/3XsfOS0nkqdqL/76o6SWRmc0I/X3z0YySUAphBjwQlM1AALuxpj7vTUHce/fgqV4MoqiQ2dJxVI8CdfOdeFJGwDOHWvRvC0xS4nGrCF4qvZGZYO0gA9P1V4g9gsukhbw0fzv1zCk50fteQsVAwx0ke+dMbMQb80BfI7KqGNcu9aDosOYW4zObCVl5FQ8lbvx1R0JH+OrPYKncjeWtmbWkYqyrf226CKq4AzCRWdZF32b3Gt+QcacRfiOH6Zm+U/xVp/oTpBz+Y8wZg2m7p9/oPLxmzj6f9/DW3OAgoW/x5hREHWNvvpZjPxsQHBaDoBz2+qo45zbVoNOj7kouhVXeLl73AUdXqM/fzaSTX4KQgjBiaka1a/ej85owjz4rLYq7yM4P3sTxWgm84JvhY/PmLOI6qV3UvPcf2GbdAn+5jqat6zEMnwyKSOmRJ3bfs5lON56jLpVj5B23jWgqTRuejG8ZK61K+KJ5Fj9BL7aCvK+/quofphSDHBC6L1Lm34NLQc/onrZT7FP+UqwKKd8C60HP8I2cR4Ge3DJM2PuYloPf0bN8z/DPuVyAJo/+ie6FDvpM74edW69TqFsTN4pf03J0r4wJZGiMwCdKQVjThHmwWODjb1dDTR++DLHVvyGghvuj8rm9eXPYkfTco6rKpai8bRWbMe95z3SZnw9/PmAYFbbtWcjpsISjBHFSpH6+2cj2SSgFEIITkzVsI45D9fOdTRteRXV626bwT2T9POvx5hZGD4+1Py4Yd0z1L/zFIopBdvZXyJj7jdjzm2ffBn+plqaNq/AteMdAEwFo0k77xqaNr2IzhS/qXbj5ldwfvYW6bMXkjJyWsz9UgwQFHrvLEXjg1NO3nsO58erCLQ0Y8jIJ2PO4mAg38aUU0T+N35Lw7pnaNz0YngaSmbZjRjs0fsvA6rGwvP6eK/FTiTyGWlfdAZQ88LPsQydQNa874SPsxRPpOqp22navILMshu7fZ3Tof20nOxLbseQlotz2xrc+z7AkJ4bMy0Hgj0rVVcDqTOu6/Dc/f2zkWwSUAohBMGpGrNH5bBJdwVpU+cn9BjL0FIKFj2Q0LGZcxeTNv1qfMcPozOnYsorpn79swAYsmJHKDq3raFh7TPYJn+ZjFkL4p5TigGCwu/dwTrMhSUJzVJvPw0lntCIy75YwZyoRD8jkUVnnqP78R0/TOaFt0QdY8wajDF7SLiXak+uc6pFfjYCqoaiN5Bx/jfIOP8bnT4uZcQUhi15vcP7z4TPRrL1zU+AEEKcBvddNQFDnGrYZNFbbFiGlmLKKwaCWRC9PSfchzLEve9D6t54BGvJDLLmfTfuuaQYINrJeO96OuKyL0mk4Ayii85Ud0PwxjgFTqiBmMrwvv5ZlM/GqSEBpRBCtBmaZeWeUzSX17V7A96j+0mbOj+qYXJrxQ5qX/s95qHjybn8zrjNlEGKAdo7Ge/dmTDisn1hSiJFZ6GMuWvXhqjjPNXl+ByVUdOdoO9/FuWzcWr03U+AEEKcBgumFVHr9CRl5rGigKYFg8TG95/HMnwyupQ0vFV7cG5bg2XEFOwRe7f8jcc49sqvAYXUsbNw7Xkv6nymvGJMecOlGKADX5tcyO8feQLHkFm9Pted80r6/pzqBEUWphxPoOjMXDAKS/FkXDveQfW6SRk+mYCznuaP/oliMJE29cRntr98FpP57/pM+mwkkwSUQgjRzo0zh+P1qzyx/iB+VaVdC79O6XUKBp3C7ReM5KE1wd5+ens26HQ0bV6B6m1pKxRZRNq5V0ZVbvsbqtE8LgAcqx+POXf6rOsx5Q2XYoAO/OhHP2L7C4/x87+W8dxeP35Vi+m/2JnQe3fv/NIzKmCILExJtOgs95qf07RlBe7dG6k/+DHoDViGjCNjzqKoLRr96bN4R9locmxmfvXaTvlsnASKpmnd+E+lEEKcmfbXNLN8cwVr9x6jwuGm/X8YNU1DUTreh6XXKQRUjdmjcrjvqgkMzbKy6OnN4WKAZAkVA/TV+cnJ5PL4OVTnwutXMRl0FGendri0+tRTT/Htb3+bxx57jO9+97sccbi5a+V2NpbXht+bjsR778408lk8QT4bJ4cElEKIAa07Xy7xKAT3kJWNyWPheUVRVZ9HHG4ufng9niS2VDEbdKz5z7ln7BdbZ4G9AhRlWSkryeOG6UWMzg/+rN977z0uvPBCbr75Zh5//PH459t3jIq6OOfr4L0708hnMZZ8NpJLAkohxID1wtaKHi1/6ZRg5uK7c0dy25yRnRYkvLC1giUrtifj6QJw/9UTzsglt55mjW6fns0VF83irLPOYvXq1ZhMsSMuQ7qT8ewPuvt6ln/4OT/7x66kXf9M+iyeaZ+N00ECSiHEgPTo2v1J2aD/43ljuKNs9Cm51p3zSri9bFSvz9PX9DSw1ysQ8Pvgo5fY+sIfyc3NPYnPsm/oSQYXwOfzcf3117O2xoT9/Bt6/TzO1M+i6DkJKIUQA86pzhrur2nmV//cyaYDHc/s7siZXgzQ22A7tLc1kcC+P+vNvr9BaSZuuOEGVq5cySuvvIKrYKIUpoikk4BSCDGgHHG4mfn9P/HFsiVx7y9Y9CDmwWMBaPn8Y1y7N+Kt2ouv7gv09hyG/L//i3lMR3vJerM/cyAUA8h2gMT0OIPbFgAOqfmADX/9LS+//DJXXnklIIUpIvlkg4AQYkC5a+V2Am2/R9unXI5p0Jio+w2Zg8J/du1cj3vPRkz5I9Hbsjo8p1/VuGvl9qhq18ggAOhWIKAA04uzuPeK0jO2GOCIw82vXtsJgM9RScPGZXi+2IXa4kSflkvquLmkTb8KndGCv6GGyidu7vBctonzyP7yf/DL13Yyc2TOGRXw9CaDG1A1AgGVA9nTufmhE8EkBJt9L715uhSmiKSRgFIIMWDsr2lmY3ktaltwZx5aSurY8zs8PmPuYrK//D0UvYFjL9+D9/jhuMcFVI2N5bWUH2tmVJ6998u4wKaDdby5s5o7ztAv8btWbsevavibjlP97A9RzKnYz/kquhQ7nso9NL63HG91OXlf+wU6azrZX/1RzDlaP/8I1851WIafA8QP7PuzF7ZW9H7vbVurqzerjLy4tSImgzs6387d80u5m1IpTBG9Ip8UIcSAsXxzBfp2M31VjxvFaI5qMB5isGcnfG69TmHZhxWMHWRPSgEOwIOr95FrM59xy7ihwB7AtWMtqsfFoIW/x5Q7DAD7pEtBU3HteJdAqxO9xYZtfFnMeVzb16CYrVhHnQvEBvb92RGHmzsfeb7LrRmJZm+BLjO4qWYDpYXpvX/yYkCSgFIIMWCs3Xssaum5btUf0bwtoOgwDy0ls+wmzIN6VtgRUDVW767m+a0VeKrLaVj/NzyVuwEwF44ls+zGmBnIiezRPBOXcUOBfUDVUL1uAPSpGVHH6G1ZoOhQdPG/pvxOB60V20kdfyGK4USroFBgf/cpmsl+siS6NSPR7C2ceRlc0bdIQCmEGBCcHj8VjmDwgt6ItWQmKSOmts0zrqBpy0pqlv+UgoUPYCoY2aNrVDW04j92gJplP0FvzyFj1vVoaDR/vIrq55YwaPFDUWPrEtmjeSYGAZGBvaVoAk0f/p26VY+QMfuGtiXv3TR/sgr7lMvRmSxxz+HetQE0ldTSC6JuD6gaa/cd4276b0DZna0ZOpMloewtnFkZXNH3SEAphBgQDte5wgUHliFnYRly1ok7R0/HOnYWR5/+HvXrnyX/unt7fB3H+qUoBhMFix9En5IGQGppGVV/uY2G9X8j9+q7wscmskfzTAsCogJ7IGXEFNJnL6Tpg5c5Wr45fHvazOvInLOow/O4dq1Db8vCMuzsmPsq6ty4PP5+u/+vu1sz2usoewtnTgZX9D3981+bEEJ0k7eLkXPGzEJSRk/HvW8TmhpI6Is7ntYjO0kZMSUcTAIYbFlYho7HfWALqrcFnSkleHuCezTPpCAgMrAPMaTnYx5airVkJvqUNNwHttK06SX0qRmkTbk85hw+RyXe6nLs065AUXQx92vAoTpXv90P2NutGR1lb+HMyOCKvkkCSiHEgGAyxAYe7RnSciDgR/N5UMw927OoBXwxWSEAxWiGgB/f8cPhPpeJOpOCgPaBvWvXehxvPkrhrU8Gf/6AtWQmaBoN654hddzcqOAcwLVzHRDM/CZ6nf4iGVszOsveQv/P4Iq+qev/wgohxBmgODsVpYtj/A3VKAYTSgf79hJhzBqCp2ovmhoI36YFfHiq9gav0dz9aTlwIgjo79oH9s0fr8KUPyIcTIZYR52L5vPgrTkYcw7XrnUYsoZgLuh49F8iv0D0Re23ZuRedRe2ifOwjp5O+oyvU7D4QUChfv2zcR8fyt5az5odN3sLJzK4QiRT//wXJ4QQ3ZRqNlDUVikdcDfG3O+tOYh7/xYsxZM7/CJOhP2cy/A7Kqlb9Qje2gq8xw9R+/pDBJz1AGh+b4/Oe6YEAe0D+4C7AU2LzSaGA/KIwBzAU7UXf/1RUkvndngNpe06/VGiWzNaK7ZF/dISkkj2NpHrCNFdku8WQgwYZSV5LN18mOpX70dnNGEefFbbUuIRnJ+9iWI0k3nBt8LHe499jnt/sFDEV38UzeOi4f0XADDlDcc6Orby2j75MvxNtTRtXoFrxzvBYwtGk3beNTRterHDquVEnAlBQCiwP9y2rGvMLKTl0Cf4HJUYswaHj3PtWg+KDmNucdTjwwHTuAs6vEZRtrXfLuf2dmtGItnbRK8jRHf0z39xQgjRAzdML+KZDw5hHXMerp3raNryKqrXjd6ajnXMTNLPvx5jZmH4eG/1ARo3Los6R+jvqeMvihtQAmTOXUza9KvxHT+MzpyKKa84vERpiAiauutMCQJCgX1A1Uibfg0tBz+ietlPsU/5SrAop3wLrQc/wjZxXlThkqYGcO3ZiKmwBGPEiMxIep1C2Zi8U/VSki6Uwe1sUGdHWzNC2dv02Td0eo3+nMEVfZcElEKIAWN0vp3Zo3LYpLuCtKnzuzzedvbF2M6+uMvjdAq0H9Wtt9jQDz1RRNN66FP09pyoPpTd0deDgO6M7QsF9gCWovEULHqAhveew/nxKgItzRgy8smYs5i0866JelzroU9RXQ2kzriuw+cRUDUWnnfqJwsla2xhZAY34G5Eb42uVA9tzUgZMSVma0Yi2Vvo3xlc0XfJJ0oIMaDcd9UELn54fVRblt4y6nVk20xUNbTGvd+1ewPeo/vJLLupx/sz+2IQsL+mmeWbK1i79xgVDndUVk0BirKslJXkccP0Ikbnn+ihGQ7sD9QS0MBcWEL+tfd0eb2UEVMYtuT1Du/X6xRmjsg+Zf06e/r6u9LdrRmQWPYW+n8GV/Rdfeu/TkIIcZINzbJyz/xSlqzYnrRz3jZnBAeOO6lurMZ9eAeN7z+PZfhkdClpeKv24Ny2BsuIKdinXRH1uET3aPa1IOCIw81dK7ezsbw2PEKxPQ047HCzdPNhnvngELNH5XDfVRPCIyRvmWRj496jaDoDitJV/X1iDDqF+66akJRzdSYZr78z5+X6eUbVEt6aAYllb+H0ZXDFmU/RNC15v6YLIUQ/8eja/Ty4el/Sz+urP4pj9WN4qw+gelswZORjG38RaedeiaI3Rh3r3LaGulX/E/c8qeMvIuer/xn++5r/nNMnJuW8sLWCX722E7+qdSvLq9cpGHQK98wvJbthL9deey3Z587Hf861SXtu9189geumndxgKRmvf0EHz7Guro67776bxx9/nCGLfoeuYCxql82uEhfK4J5JYzxF3yEBpRBiwOppcHAq6RSYNTKnTwQByQrCGzcu4zx7A88//zzLPjmelHPeOa+E28s6r2zurWS9/h/PG8MdZScm3Xi9Xv785z9z7733EggE+PnPf87Vi77NVx77EE8SK/vNBh1r/nNuQllSIbrrzCgZFEKIHlgwrYg1/zmXmSOClcTt5yfHOvVBp6rBgVond7+2k/01zaf8+iEvbK1IWkY3ffZCFt/9BBkZGdxRNprfXT0Bs0GXwM8/ml6nYDbouP/qCSc9mEzm639w9T5e3FqBpmn84x//YPz48fz4xz/muuuuo7y8nJ/85CeMGpTJPUketXnv/FIJJsVJIxlKIYQgosBi3zEq6tynIXTsXGivXnf24iXLEYebix9ej/PYERo2LsPzxS7UFif6tFxSx80lbfpV6IzBFjaapuL89E2aP3kDf/1RFKMFU8FI0mcuwDLkrPA522fLEtmXGHKqfxah19944FNqnr8r7jEFix6MGqmpBXw0bV6Jc8e7+Btr0JlTMQ8aRdYld2BIy8Gkh/yP/pf33vwHX/rSl/jDH/7AhAmx+z+TlRU9FRlcMbBJQCmEEO1EtoBZu/cYj7xbfrqfUlgie/GSbdHTm9nw6R6++N/bUcyp2Cd/GV2KHU/lHlzb15Ayajp5X/sFAI53nqJ566uklpZhHlqK2urE+emb+JuOU7Dw95gLS8KvI95+vs4Ce4VgtXvZmDwWnld0yvaULnp6M5sO1uH6/DNqnr8L+5TLMQ0aE3VMyohzwi1+tICfYy/fjadyN7aJl2DKLUZtdeI5uo/0Wddjyh2GFvCjqz3Ao9eUcNlll3VamNTbfZv3zi896XtLhZAqbyGEaCfVbKC0MJ0jDjcP/+1Vvli2JO5xoayU6mvFtW0N7v2b8R0/hOprxZAxCPukS7FNugRFp0/acwu0BRVLVmyn1umJ2ot3MuyvaWZjeS1N295F9bgYtPD3mHKHAWCfdCloKq4d7xJodaIzpeD85A2sJbPIufxH4XNYx55P1RO34Nq5LhxQBlSNjeW1lB9rjgoMR+fbuXt+KXdTmrTejsl4/ZHMQ0tJHXt+h49p2voqrRU7KFh4f/j1tqfoDWj5JZRMm9NllfuCaUXMGpnT7QzuzBHZpzybLQYuCSiFEKIDd63cTqBtESdeVsrQ1u/P31CN4+0nsRRPxH7ulehMVlo+/xjH6sfwVO0h56s/PCnP78HV+8i1mRPKPvU0OFu+uQK9TkH1Bkcl6lMzou7X27JA0aHoDGgBP5rfE3uMNSN4jNEcfbtOYdmHFdzdwV7BUGB/OoVef/sATvW4UYzmmF8WNE2l+d+vYR0zA3NhCZoaQAv4wlsCInX1+iMNzbKy9ObpfTKDKwRIQCmEEHGFMlNqWyDRWVZKn5rJoJsfDWfuAOyTv0ztv/4H1/Y1pM9aENU30FNdTsP6v+Gp3B08d+FYMstuxJQ/IubcrV/spmHdX/FWH0Axp5A6djYZcxejM6UA8MvXdjJzZE7cLFQyGm+v3XuMgKphKZpA04d/p27VI2TMvqFtyXs3zZ+swj7l8vCMclNhCc7t72AaPBbLkFJUj4vG919AZ7Fhm3Rp1LkDqsbafce4m+QWnyRT6PVHqlv1RzRvCyg6zENLySy7CfOgYKbYV1tBwOnAlFdM3Rt/wrnjHQj4MeYWk3XxrViGnR0+T09ef1/L4AoRIp88IYSII5SZitRRVkpvTY8ZkQdgHTMD1/Y1+GqPhANKT3U5Nct+gt6eQ8as69HQaP54FdXPLWHQ4oeiRjN6aw5y7IWfYcweSuZFN+NvrqNp8wp89VXhyTJ+VeOulduj9iImq/G20+OnwhHMTKaMmEL67IU0ffAyR8s3h49Jm3kdmXMWhf+ec/mPqH31fur++YfwbYaMAgoW/h5jRkHM86ioc+Py+PtkIBT5+gHQG7GWzCRlxNS2yTUVNG1ZSc3yn1Kw8AFMBSPxO6oAaNr6D3QWG9mX3AFA4wcvUfPSLxn0zYcx5Q0Pn7I3r78vZHCFCOl7/4KFEKIPaJ+Z6iwr1ZGAqx4AvTUtfFvjxmUoBhMFix9EnxK8PbW0jKq/3EbD+r+Re/WJKuL69c+is9jI/8Zv0ZmDgZ4hPQ/HG3+i5fOPSRl+TsxexMgCDqDLIo7Q/ZsO1nHxw+ujin0O17mispqG9HzMQ0uxlsxEn5KG+8BWmja9hD41g7QplwOgM6VgzCnCPHgslmETCbgaaPzwZY6t+A0FN9wfE3hrwKE6V68Co5OVqWv/+i1DzoqqVGf0dKxjZ3H06e9Rv/5Z8q+7F9UXHL+pet0MuvGPGNJyg48ddjaVT95K0+ZXyLn8x+FTJOP1C9EXSEAphBDtRGWmEshKxaMFfDT/+zUM6flRey9bj+wkZcSUcDAJYLBlYRk6HveBLajeFnSmFFSPm9ZDn5I27YpwMAlgG38h9e88hXv3e6QMPyf4FNv24uXYTT1uMROv2Mcb0VTbtWs9jjcfpfDWJzGk5QBgLZkJmkbDumdIHTcXnTmVmhd+jmXoBLLmfSf8WEvxRKqeup2mzSvILLsx5treHjTvPllztLv7vIyZhaSMno573yY0NYBiMAFgHjwuHExC8BcB85BxeL7Y3aPrCNHXSUAphBDtRGamEslKxeNY/QS+2gryvv6rqCVyLeALBx2RFKMZAn58xw9jHjwW7/FDoAYwFURnQRW9EVPecLw1B8K3BVSN17ZV4XB5e/yaI4WKfcYPPpE1a/54Fab8EeFgMsQ66lxc29fgrTkIKPiOHybzwluijjFmDcaYPSS8Z7Q9kyHxGRsne452T56XIS0HAn40nwe9ra1JfrvCpOBt6fgi3rfuXkeIvkwCSiGEaKerjFFMVqrdnsrGza/g/Owt0mcvJGXktOjHZg3BU7U36nFawIenai8A/uY6zEDA6QBAb8uMub7eloXnyM6o2xwuLz5HZdeNxwN+Gj94Cdf2d/A76zDYskk9+0ukz/h61Ov45Ws7ee32WSgEA7SAuwGdxRbzXDQ1EPyDGkBtdYZujP2hqYETx0ZQgOLs1Njj40jmcn4iirNTw6+/M/6GahSDCcVkCRZm6QwEmutin0+zA127Jf/uvH4h+jL5tUgIIdpJJGMUmZWK5Ny2hoa1z2Cb/GUyZi2IeZz9nMvwOyqpW/UI3toKvMcPUfv6QwScwf2Wmt8b9f+K3hhzDkVvRPVHZyP9TcepfvaHeCr3Yj/nq2Re/G3Mg8fS+N5yav/xQPi42n/+gcb3nscybCJZF9+Keeh4Gjcuw/HWY9HnUzX+45n1GD2NQDCI9tYcwOeojDrOtWs9KDqMucUYsga33bYh6hhPdTk+R2XcKvaibGtC+x0fXbufJSu24/Gr3Z67HlA1PH6VJSu28+ja/Qk/TvW2kGk6EQQH3I0xx3hrDuLevwVL8WQURYfObCVl5FQ8lbvx1R0JH+erPYKncjeW4ZOjHp/o6xeir5NPsRBCtJNIZioyKxXi3vchdW88grVkBlnzvhv3cfbJl+FvqqVp8wpcO94BwFQwmrTzrqFp04vh9juhZXEt4Is5hxbwoWu3bO7asbbLxuP++ircezaSPnMBGXMWhp+P3ppG05ZXsU/5argCOaBq7G3UYXYcQhl0NmnTr6Hl4EdUL/sp9ilfCRbllG+h9eBH2CbOw2DPxmDPxlI8GdeOd1C9blKGTybgrKf5o3+iGEykTb0i6jnrdQplY/I6+SkHJXuOdme9O10uF6+//jovvfQSq1atIuX8xaSd8xXQ6Tn+6v3ojCbMg89q2097BOdnb6IYzWRe8K3wOTLmLqb18GfUPP8z7G3FSs0f/RNdip30GV8PH5fo6xeiP5CAUggh2kk1GyjKsnLY4SbgboypTA5lpVJGTEFRgtnM1ood1L72e8xDx5Nz+Z3h2+PJnLuYtOlX4zt+GJ05FVNeMfXrnwUIZ/n0tiyAcOYyUsDpCN8fkkjj8dAyuXXcnKhjrGfNoWnLSly7N0a1tNEp8OVLv8yrn1VhKRpPwaIHaHjvOZwfryLQ0owhI5+MOYtJO++a8GNyr/k5TVtW4N69kfqDH4PegGXIODLmLIpqiQTBoHXheZ0vQR9xuPnVaztpPbwtoTna1cuX4DmyI+YYy/Bzwvtd2/fubGlp4Y033uDFF1/k9ddfx+12M3XqVH79618z7aLL+eZLwdGb1jHn4dq5jqYtr6J63eit6VjHzCT9/Ouj+oyacorI/8ZvaVj3DI2bXgRFwTJsIpllN2Kwn9iDmsjrF6K/kIBSCCHiKCvJY+nmw1QnkJXyNx7j2Cu/BhRSx87Ctee9qHOZ8oqjAjUAvcWGfuiJhtathz5Fb88JB12mnGGg0+Ot3k/qWbPDx2kBH95jn2Nt12Q9kcbjoWxn+6Kg0AQbb3X0zHJVg0++aGDasEy2Hq7HXFgS7n/ZEZ3RTMas68mYdX2nx4VmeXc1zeWuldvDeyah84lF4XPbc8iY+83o2yICcL+qseSVz7gu/zgvvvgir732Gk6nk0mTJvGLX/yCr3/964wceaJ6f/bHDWw6WEfa1PmkTZ3f6fMNMReMIn/Bbzq8P9HXL0R/IQGlEELEccP0Ip754FBCWSl/QzWaxwWAY/XjMedKn3V9TEAZybV7A96j+8ksuymc2dRZUrEUT8K1cx3pMxeEWwc5d6xF87bETO1JpPG4MSsYrHq+2B3VZDyUuQw4YwtJDte5qap3x9zeWwadwn1XTej0mJ7M0QbQma3Yxpd1eH9A1Xj/oIOX/ut2xg7K4Cc/+QnXXXcdY8aMiXv8fVdN4OKH13d772ZnEnn9QvQnElAKIUQco/PtzB6VwybdFV1mpSzDzmbYktcTOm9rxQ4a338ey/DJ6FLS8FbtwbltDZYRU7BPuyLu0u6Rh68FIHX8Rbj3bMQyfDLe6gM0vPcc/vqjqN4WDGk5GDIKMBWWkDpuTtzG4ykjp6JPy6N+7dPojGZMBaPwVO2lYcNS0OnRfPHbDvlOQpvEe+eXdtnGp7tztCNpagDN7w2PqGxPQeM7Dy7nT9+aE/f+SEOzrNwzv5QlK7Z3eWyiEnn9QvQnElAKIUQHTkZmSm/PBp2Ops0rgoFgRj4ZcxaRdu6VUQFSaGnX5/gC9+6N+BuP0XJgK7azv0TG3G9S96//wZQ3nNSz5qCYUmjZ9wEtB7aiT88jddwF6EyWmMbj+pQ08r7+K2r/cT/HV97X9oSMZJbdSOOml6IKjE6mO+eVdFgUE6m7c7RDfI4qKv5wDQT86FIzsE+8hPRZ16PoT3zlaShsq41tY9SRBdOKqHV6klIclOjrF6I/kYBSCCE6cDIyU+asQeRf9+uuj4tY2o2clR0SOaIRwLX9HQxZg/E7Kmkp30zquLlAdOPxlOJJmHKHMejmP+OrrUBtdWLMKUIxmKh/5yksReOjzploIUwktdVJ5V9uQ3U3knPlkqjlaZNBx6/nlyYUTPVkjjYE91Nahp2NKbcY1deKe8/7NG56EZ+jitwrfxp1je7O0b6jbDQ5NnO4F2Z3ftHQ6xQMOoV7E3z9QvQ3ElAKIUQnkpmZ+vb5w/nbh4fxJDhqL5Gl3ZCAuwFFZ2h7nCt8e2Tj8RBFUcKthQBaDmwFTcVSPCnuuRMphAlp2Lg8pjdnyMTB6QkHUz2Zow2Qc9n3o85jG38hdW/8Cednb+GpvCIqCO7JHO0F04qYNTKny2k9IaH7Z47I7tG0HiH6CwkohRCiC8nMTI3MsyWU8UxkaVfTNNSWJlBVdCl2vJV7AQVL0Ylij8jG4/GoPg8NG5aht2WRelb8/YSJFMIAeI8fovmTVaTPWkDjxuUx9289XE/5seaEKpsdjc1dHtPVxKKQtHOvwvnZW7Qc+jQmq9qTOdpDs6wsvXn6iXni+45RURdnnni2lbIxeSw8r0iqucUZTwJKIYRIQLIyU11mPBNc2gVQXQ188Wj0crhituLa817cxuMAx1/9HXpbFsacIlSPG9e2t/E1VJP39V+FK8njSSRbWr/mL1jHzMAypJTYmTLBn8myDyu4e/6Jdkn19fXs2rWLXbt2sXPnzvCfj/lMFN70pw6vFRI5sUjp4Pnr2+aPq62xQWpv5miPzrdz9/xS7qYUl8fPoToXXr+KyaCjODtVJuCIAUU+7UIIkaBkZaYiM56+gEpkXJro0i6ALsVG3oLfoPm9eGsO4NyxFp3B2GnjcVPBKJzb1+D89E0UgwnzkFJy5t8ZdyxiSCLZUtee9/BU7qHwlsfxN9bEPU9A1fjH1v041jwZDh6rq6uDr0WnY9SoUZSWlvKtb32LkSXjuGdXh08pLN7EonjHADEN6pM5RzvVbOjW0rkQZxoJKIUQopuSkZkKZTzveP5jPvsiXj7vhI6WdhW9kZS2fY/WUediGTaJmmV3kvu1X2IddW7cc6Wf9zXSz/taYi80wWyp6vNQ/+7T2KddgSEjv8OAEsDhM/D22g2Ulozi1ltvZdy4cYwbN44xY8ZgNpujjn3mgbUcbivMSWRikepxo+iNKIYT8881TQtOqyE4LSeSzNEWInnkX5IQQvRCbzJTQ7Os3HfVBL7yp/e6PDaRpV3LkLPQ27Jw7VzXYUDZHYlmS5s+/DuoAdJnXNvlORVF4e9vrU/oZxaaVhRQtYTmaHury6l97QGs4+ZgzCxE83lw7/8Qzxe7sE26FHPBqPC5ZY62EMklAaUQQpxGxdmpKEBXZT6JLO0CaH5vVJV3srXPlgaaamnavIKsed/psIl4e4kWwoSmFUFic7T16XmYh5bi3vchqqseFAVj9lCyLrkd26RLo84tc7SFSC4JKIUQ4jRKNRsoyrJ2b2nX2woK6IzRwaVrz/uorU7MBdH7G5MtMlvasHEZensW5qIJ+BuCS90BVz0AqrsJf0MN+vTc8EhJSLwQJjytKME52saMAnKvXNLleWWOthDJJwGlEEKcZt1d2vXXV1Hzws+xnjUbY9YQFEXBU12Oa+da9On52Kd1HnjFE2pvlEiPzMhsqb/pOP76o1Q9cUvMcY7VjwEw9AcvoFhsQPcLYWSOthD9gwSUQghxmnV7adeejbVkJq2HP8O1/R001Y8hLQ/7OV8lfea16FPSEr52+/ZGC5/e3K1sacacRcFemJHHHD9M48ZlpE2/BvPgsSgRmdTuFsLIHG0h+gcJKIUQ4jTr7tKu3ppO9qV3dHlenQIFaRaMBl3C7Y26my21DC2Nva45lUbANGg01jEzTjzvHhbCyBxtIfo+CSiFEKIPOBlLu0a9jhdvncHQLGvC7Y26my3tjt4UwsgcbSH6NkXTtOT910sIIUSPvbC1IqlLu/dfPaFHAdSipzez6WBdUoPbUCHM0pun9+o8Rxzubk8rmj0qR+ZoC3GSSUAphBB9yKNr9ydtaff2slFdHxjHEYebix9en1CBTqLMBh1r/nNu0oI6maMtRN8iAaUQQvQxL2ytOO1Lu30lW5oImaMtxOknAaUQQvRBfWFpty9kS4UQ/YMElEII0Yed7qXdvpAtFUL0fRJQCiFEP3G6lnb7QrZUCNG3SUAphBAiIac7WyqE6LskoBRCCNFtUggjhIgkAaUQQgghhOgV3el+AkIIIYQQon+TgFIIIYQQQvSKBJRCCCGEEKJXJKAUQgghhBC9IgGlEEIIIYToFQkohRBCCCFEr0hAKYQQQgghekUCSiGEEEII0SsSUAohhBBCiF6RgFIIIYQQQvSKBJRCCCGEEKJXJKAUQgghhBC9IgGlEEIIIYToFQkohRBCCCFEr0hAKYQQQgghekUCSiGEEEII0SsSUAohhBBCiF6RgFIIIYQQQvSKBJRCCCGEEKJXJKAUQgghhBC9IgGlEEIIIYToFQkohRBCCCFEr0hAKYQQQgghekUCSiGEEEII0SsSUAohhBBCiF6RgFIIIYQQQvSKBJRCCCGEEKJXJKAUQgghhBC9IgGlEEIIIYToFQkohRBCCCFEr0hAKYQQQgghekUCSiGEEEII0SsSUAohhBBCiF6RgFIIIYQQQvSKBJRCCCGEEKJXJKAUQgghhBC9IgGlEEIIIYTolf8PH9ZZmlspLtUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "nx.draw(G, with_labels=True)\n",
    "# dobj.count_sequences()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b4366f3b-4ee4-4816-9584-f21a8c38e4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dobj.count_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "281798e1-18b2-475f-bc24-764e166c15dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verylongpathwithafile,to,somewhere,far,far,away,foo.bar,"
     ]
    }
   ],
   "source": [
    "for i in a.split(\"/\"):\n",
    "    print(f\"{i},\", end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8077291-90dc-4bb0-ba51-179fdc9d55a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "a0_ord = [ord(i) for i in a.split(\"/\")[0]]\n",
    "b0_ord = [ord(i) for i in b.split(\"/\")[0]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "599c80f2-d0b1-41e8-94f7-ab2848715271",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab0_diff = []\n",
    "for i, j in zip(a0_ord, b0_ord):\n",
    "    ab0_diff.append(i-j)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5df2fbd4-3743-4365-9ed9-7aa4d3fd38b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " -4,\n",
       " 17,\n",
       " -4,\n",
       " 9,\n",
       " 66,\n",
       " -16,\n",
       " 12,\n",
       " -6,\n",
       " -60,\n",
       " -3,\n",
       " -19,\n",
       " 65,\n",
       " 13,\n",
       " -19,\n",
       " -7,\n",
       " 0,\n",
       " -8,\n",
       " 8,\n",
       " 18,\n",
       " -71,\n",
       " -2,\n",
       " 56,\n",
       " -6,\n",
       " 7,\n",
       " 8,\n",
       " 10,\n",
       " 14,\n",
       " -9,\n",
       " 58,\n",
       " 3,\n",
       " 17,\n",
       " -59,\n",
       " -2,\n",
       " 66,\n",
       " 6,\n",
       " -14,\n",
       " -10,\n",
       " 2,\n",
       " -60,\n",
       " 1,\n",
       " 66,\n",
       " 16,\n",
       " -2,\n",
       " -19,\n",
       " 7,\n",
       " 8,\n",
       " -17,\n",
       " 10]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab0_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0440f0d7-7d29-44ea-9811-41dc605f80e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts-c ts-c [0, 0, 0, 0]\n",
      "lien lien [0, 0, 0, 0]\n",
      "t-va t-va [0, 0, 0, 0]\n",
      "lo-p lo-t [0, 0, 0, -4]\n",
      "roto ask- [17, -4, 9, 66]\n",
      "col- scri [-16, 12, -6, -60]\n",
      "manu pt-h [-3, -19, 65, 13]\n",
      "al-h ts-p [-19, -7, 0, -8]\n",
      "ts-c late [8, 18, -71, -2]\n",
      "elli -rea [56, -6, 7, 8]\n",
      "nsig der- [10, 14, -9, 58]\n",
      "ht-m echo [3, 17, -59, -2]\n",
      "osai -mos [66, 6, -14, -10]\n",
      "c-do aic- [2, -60, 1, 66]\n",
      "tmat dotm [16, -2, -19, 7]\n",
      ".........lo-protocol-manual-hts-cellinsight-mosaic-dotmat\n",
      ".........lo-task-script-hts-plate-reader-echo-mosaic-dotm\n"
     ]
    }
   ],
   "source": [
    "a_edited = \"\"\n",
    "b_edited = \"\"\n",
    "window_size = 4\n",
    "for i in range(0, len(ab0_diff) - window_size - 1, window_size):\n",
    "    print(asplit[0][i: i+window_size], bsplit[0][i: i+window_size], ab0_diff[i: i+window_size])\n",
    "    # print(ab0_diff[i: i+window_size] == [0]*window_size)\n",
    "    if ab0_diff[i: i+window_size] == [0]*window_size:\n",
    "        a_edited += \"...\"\n",
    "        b_edited += \"...\"\n",
    "    else:\n",
    "        a_edited += asplit[0][i: i+window_size]\n",
    "        b_edited += bsplit[0][i: i+window_size]\n",
    "\n",
    "print(a_edited)\n",
    "print(b_edited)\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7785d1e1-0448-49af-ab6e-f3887ce214a8",
   "metadata": {},
   "source": [
    "from itertools import zip_longest\n",
    "for i, j, k in zip_longest(asplit[0], bsplit[0], ab0_diff, fillvalue=-1):\n",
    "    print(i, j, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "a4419a1b-c9e3-4fff-bf45-04c7da651733",
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = a.split(\"/\"), b.split(\"/\")#, c.split(\"/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "95633d83-b7e8-4291-88b6-df3ab70e68f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts-client-valo-protocol-manual-hts-cellinsight-mosaic-dotmatics/to/somewhere/far/far/away/foo.bar ts-client-valo-task-script-hts-plate-reader-echo-mosaic-dotmatics/to/somewhere/far/far/away/notfoo.bar\n",
      "['...o-protocol-manual-hts-cellinsight-mosaic-dotmatics/to/somewhere/far/far/awayfoo.bar', '...o-task-script-hts-plate-reader-echo-mosaic-dotmatics/to/somewhere/far/far/awaynotfoo.bar']\n"
     ]
    }
   ],
   "source": [
    "final_slices = []\n",
    "for i in range(len(slice_list)):\n",
    "    final_slices.append(\"\")\n",
    "\n",
    "print(a, b)\n",
    "\n",
    "for i_slice in range(max(map(len, splits))):\n",
    "    dots = False\n",
    "    slice_list = []\n",
    "    for s in splits:\n",
    "        try:\n",
    "            slice_list.append(s[i_slice])\n",
    "        except IndexError as e:\n",
    "            continue\n",
    "            \n",
    "    max_in_slice = max(map(len, slice_list))\n",
    "\n",
    "\n",
    "    if max_in_slice < 10:\n",
    "        for sli, sl in enumerate(slice_list):\n",
    "            final_slices[sli] += \"/\" + sl\n",
    "        continue\n",
    "\n",
    "    slice_list = [i.ljust(max_in_slice, chr(0)) for i in slice_list]\n",
    "    # print(slice_list)\n",
    "\n",
    "    mask = []\n",
    "    for vals in zip(*slice_list):\n",
    "        if len(set(vals)) != 1:\n",
    "            mask.append(True)\n",
    "        else:\n",
    "            mask.append(False)\n",
    "\n",
    "    surround_size = 2\n",
    "    \n",
    "    mask_shifted = mask.copy()\n",
    "    mask_shifted += [False] * surround_size\n",
    "    mask_shifted = mask_shifted[surround_size:]\n",
    "    final_mask = [a or b for a, b in zip(mask, mask_shifted)]\n",
    "\n",
    "    for vals in zip(*slice_list, final_mask):\n",
    "        if len(set(vals[:-1])) == 1 and not vals[-1]:\n",
    "            for i in range(len(vals[:-1])):\n",
    "                if not final_slices[i].startswith(\"...\"):\n",
    "                    final_slices[i] += \"...\"\n",
    "        else:\n",
    "            for i in range(len(vals[:-1])):\n",
    "                final_slices[i] += vals[i].strip(chr(0))\n",
    "\n",
    "\n",
    "print(final_slices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13cc83e0-a0ed-4dd3-ac74-0aeb65996b1c",
   "metadata": {},
   "source": [
    "# Function below doesn't work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "8d77c847-9f51-40e2-beff-74445456ef73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abc', 'abc']"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [\"abcjgf\", \"abczxc\"]\n",
    "qwe = list(map(lambda x: x[0:3], l))\n",
    "map(lambda x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8269cb08-a802-4646-9f14-1bcfc56dd23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'qwe'"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\".......qwe.........\".strip(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "cd3d0ba5-9cd1-4ddf-b662-9d2c7e42b27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 34]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qwe = [1, 2, 34]\n",
    "result = qwe.pop(1)\n",
    "# qwe.insert(1, result)\n",
    "qwe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5c1b9f2e-1bc5-439f-a156-fb0b3e6f96ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(116, 116, 116)\n",
      "(115, 115, 115)\n",
      "(45, 45, 45)\n",
      "(45, 45, 45)\n"
     ]
    }
   ],
   "source": [
    "for vals in zip(*slice_list):\n",
    "    if len(set(vals)) == 1:\n",
    "        print(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e3f63c3f-b8b3-49e6-8b34-fc268ba0959f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[102, 111, 111, 46, 98, 97, 114, 97, 97, 97],\n",
       " [110, 111, 116, 102, 111, 111, 46, 98, 97, 114]]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice_list\n",
    "\n",
    "[list(map(ord, i)) for i in slice_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "db8e27a7-7d50-4a15-8156-6c6c8fd2baaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "81477ff1-5419-4d05-a454-3275509f85c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[97, 97]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(ord, \"aa\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b49d613a-e204-42fb-8721-1342cbf44d21",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "0 is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msplits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mValueError\u001b[0m: 0 is not in list"
     ]
    }
   ],
   "source": [
    "splits[2].index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cb50adb1-a669-4ae7-87a0-435801278331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lab1//////'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits[2][1].ljust(10, \"/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "834058e5-1525-47ca-886d-0ce12a05f138",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = [0, 1, 3]\n",
    "x = [0, 4, 3]\n",
    "c = [0, 1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "10680683-6c15-4349-9128-87b87f26bd6a",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "sub expected 2 arguments, got 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[77], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: sub expected 2 arguments, got 3"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "list(map(operator.sub, *[z, x, c]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8ca9f7dd-4c07-43ac-a4e6-a1ef5e0b308e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "-4\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "diff_list = []\n",
    "for vals in zip(*[z, x, c]):\n",
    "    tmp_diff = vals[0]\n",
    "    for i in vals[1:]:\n",
    "        tmp_diff -= i\n",
    "    print(tmp_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "461d32aa-f71c-41eb-bcb1-cae13b14f241",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'decode'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[86], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mchr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'decode'"
     ]
    }
   ],
   "source": [
    "chr(0).decode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c8817110-f066-452f-84da-0610593f2b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1, -3]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[z[0] - i for i in z[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6021fb8e-a1a6-43ae-9e84-d657d9633918",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my_env",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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 },
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