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Visualización-G3-Mapas.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
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<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Grupo G3 - Los panteras" />
<meta name="date" content="2020-06-03" />
<title>Visualización parte 2</title>
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jNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ/hbrd7GuYBwhgAIYtbTx/3+d4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB/cXYlcQshhyMsZrI6PYLWc3lOG/vlA4rHr/3uTFD3r38/r+3fMKOke9W4oJ9G566u7au84CpOz/ct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY+0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq+BLL299am8j+zv774zj995/dgTOZsOfWr3rnTWPj2h8qGbo1/M//kYYvmxfms7TtPrM54E7ns4vwBw0rFy/aNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk+hMQ5oJDqhrC/l//FxmAUlGYeK5Z6Jl5MDec2yJQdc+l5ViNduL1avoZ805eGll04jy6COKheT8S+U6kQwdw+lW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M+WgUwldSkjbL1HPLrCf51d8MHbv66zu/mcGl5Kz0YNZ0+mcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn/9i8n08OU3L/760UX2E369YuvqVUPrI9VryFR8CXc5V/rYefbW7svv/YNdxUHv/OnFVQ1V8yse2Dde0UcAIY/zU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF+mpaKOYunHhldNAlZhEyFGpz4R20C+c47Vmu+6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf+vWjNvnr22b+bsfDJR7+e+cL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg/48+8C5U0euLuu/f8ozr1xteHTRssdGru8V3kwfeHTMsN937/zksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0/Iwe1eO++6/RVHpg2mv/uPbBuguPMtfKLU+tuXfjkIFraEVzg2tlMuZg6O57/vXBP1C3kZ3H9od2PPV81RMVE/aNAy3HEcaokRS34Ta+LAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL+Y3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj/T6FDccpXsmn6Rjlxv+knyrTFMR8+U/cF9+DiRwh/UCiChwdeXD58cDhSwsRjeikNNcTo83/0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up+g5GIBDhGEr2BkRNVlMZTa/P3HKVyrMMKrF3H/KPYUAWjlGsXaRnXrxTIhrJwqp/bMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ+aq/ctSSXgtmD6gf2emV91/9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj+zqPXn0w4On3e9nZ+NJLYFZ1yqkQ2ITFEM5zzwyA+1KLJ1kVwpAjsvSTgx3S+rQQeiisxv5Ky+9kGbnqUmllmSFEhOP6/G4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH/udKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt/DeaWvZt3+8wA7swe6Y/5cvjv3I1rHJn+AyhLM44ODVn14/7bBUDpq/hpxb8c388XfdM+rU3veu+Tws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz+5W8H0OIlBsz/tURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9/gxBMbluwe4SAjxpj/mcgN0ef3cCt2IAhVVLsR/7+TIjjZjU9PTeY1ew4I9/Ovhn8cCeI/Nf9BnK2Pk3/kZ7TF00+6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi/HK2DL+4emRymUNIE3+Ro3WokKfbtNP37Cs0/7rxjQ0X2Cvs2Rex/NNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA+ZuAPZTBx2Az2Uo2CY/hIHysic/1z59PI/dU5CtWz+aJB9gi9gKmYebVKZgHgMq89Bc+r1GJWSSDAQXQoWAyS/reEUlCQsTeEUKRr3B03DZmUZBwxy/6S/MZmh+dTYZHt5OF4oH1LKc+eilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4+8/HaphRyV9+rq5lT1xe9nfPc0a2IrDuKQL//9bve3DrL/so/Qj0kbVrGXCYuWZWXjUhzzD7xn/+D6GvYau8Q+Ze8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG/Qbydi5f/DYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15+JJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC+UjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4+jCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0+0aio0pV/bIp9V+KIgpPrUZJOFCUev/JSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo+bh4oGrVQLPOume7Uev/BCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj+WvNn+tsW4/RVB2xkGeEk582NR/nE3ZMwaxy2guAqFp99FZ5bu+IXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9+v1/fvn5h/9Eeb77LHuYa+94HIt1bArbxs6yU1iIuRjEAnYqZp+E8erqdUBRONnA+c75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW/mlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp+Zp8MeAIEa+OqmZtns6x0xC7KTL2yZM+MtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf/DnN+WMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7/VXTzl2vfEZGRLeJB94/zf4+LjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D+o5jgx+6jmBbwy4BEI+9d3rHnZ0I/GN+7usnL1ey+xM389WLx/1+INHRbWXfoDLjz+6Z07su+YN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij/2F/cJfFxrfee3ld8QDzf2vsC8wo5nuaa44+Mabh+ghQAAA4XW1/pMcNqJgMuooCJQqiPLlrxWvQhjgF8//SgXTwej3O6M/NmF1x8zWHdVaFh/5uU3bnwXkmg1yXz6aT6km+QwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u+nL9p6UYpSa0Nne7yy+1EQ/7PaW6/dbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj/PIY0oTXmKdjuAkfHg/60QWROeQZnI4+gq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE+qKL+GCJdcNEhlrSb+Q6T8+R887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82/0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B+dkS6svyKX6HWC0A+i1c2Kd5c2XRy3h0mgYbo/4spg/KNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA+KF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO+G5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU/LQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ+s1ZoxwDW1OMStBHU83G1fm5MZ0+4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz+Rw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o/98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc/VveH0F3HR77xCrNs/mPDWy89tOWB3js3Y1+b1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0+D1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv/3+k0l/7GwKzGzQ6Wa811i/qXFjfb0wlJ1jP/DXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9+2mlNGRBlVqGU88fB+dM97E+VvGCx2CV7ht/htgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi+JOiG7ST//n2W+/++TCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA/rh0V+/mfny+4Gv3r54+i+fxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632++JsUuivNbh53Kb+x/2JYp/e/+7qFl8eecf/zBk65bfb7WQLstc2AZl1GMH9v3fJxx/p2pttp/+c/eGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE+zrxreeANH3lObN6LH8KHopW83l9G3+3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn+Qzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm/JekTY2T7teEU9KnHUa+zj/8pXd+rzbxD1uragaVBdAqDC+jaAUkrJv/OXKcGMXmJOnbhQXF/F3QsHJVnf87VhB3sSqoa/te5X9jf3r7FdPzMgtC/ccNOnTtwb3ZPb6ZWdOPLzh7amPD50/4z8/1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY+2PS66ypEhs2ZFOn5IO08/ZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm/+GZhgadoWD/jBMnyJuLfn/kk+jrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04/3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv/eNGZnLt9g16B6h+aqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW/XTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz+gdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv/zx9HjQmV99dDDg8e8+heuMZq2cnxdUBBOApeiri69x23S22xcWW02g/V2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ+q8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf/wOTZcvobX5lZ3PPffii88/10Cy2I/swyeR/AFNmMfeZ1f/8rfzH545p1j5vdyW1apU+6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y+gRHrThpU8Niry7c+PBf/+f7yzvryabGFc8+6xowcMRg1kUqqh9azT5h/1GcNr14+GTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz/+Y2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf/fJ57P0SQsajObpM/d9mHXp3YunT59birloRDO2a6z/9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk/fTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq/rj/5t8pdueG5kbsG25Hfpq50+j/e/+tjA/bXzF82+dmN88r/evSPL3Z6ftEjj7Yds+J13jSzsaHnpjbt7h4Uvrdr2aAH+yzaXLm4R1W3O7p2KO71FCCkX/uG7BQrwKPWJlwu3jPioEKS1+C0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9/83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS/4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau+HAQ0ms6hHK6jhiJZl+NX0NFTicIYQt7ER+76ptuiMte/tYyP4oI/8o0cx9iPtrx6K5UpSgI/Winsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY+3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR/dK5QyUaz6j5l+4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m/JktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z/sVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS/tYTsWfl/StKu2xq3cXzuCVn9wf+pn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2/5cz0oeZ/ksHR0+TL6D5y31Q6eN685sPxrixetlPl5/YlJxu9AFbZRbmnpqlpTq09K3F7TdV/bpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58/49MClCxdM44aRZaRxE+aPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN+tlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx+Z/9jjp45OnvHwVFIePIvB49LSPRvZ+yPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS/XAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK/sMJSfuLFn/UKvsVinhsvqH/RkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1/Xt0PNKk/Gszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg+07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY+tHv7My8sX/FdifTO+xlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI+dGD1OMS3dlORL6h/R+3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2+99f/EEzUL+/1uGTs397MxS+7YtDz/xwtzsfO+U4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4+OLx8+eOq7t/UVXVgmF14+YuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD/CHvfj7JZN+N2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW/YmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg+33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd+ARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p/YebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK/n1/mCviN+tt/WK6OGGznh/s4t9I39VVFmLztSUlwuwZdCiRC2l/Kk33lG0dHD/qprTbw5/ZmTxqMV9Z8yYvelw/cCqjf/+6K9P9H9t4KLl7R+cvmJR99W/f6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH+klDl+fctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI/FxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t/2dq9X3n575qfMjIXZI/Q7b/u6brOGD0zj0rT+wD/+wB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0/edPztWvHjM+btnB+HauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI+CBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1/dk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc+CdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS+hp0Bqm51Fnr+L2UjHz5YPXLwfRNx36B+l3eeXrwWxYbNVy/8n+pGrtwd7tNtSfXsNFaLo9jTdPZ89ub/pXB47YrkEiRpzW3r+oJ09UfBJLnmAoG5dBi5LJ5U83Z/2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c/fPn39g4Oqa+fun195VPX3qwLunC2vmH9i/oGZlTdOCgdOm3l0zdZoiv/GASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz+0JMt+sjy645vIQH91edGont0XbPj9msiaPXiIVI2/NHhk35IePbMLh0yeP6V6/ZPPA4KflKlzBqAsnGkVRaCONIPUOstxn/MhJ+nrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd/2PpPpA3vsPbRzw4w1sz/8snbmA6Or7+w+pUPP8mXDl2wVvqx+wJu//YmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6+LeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz+Z/0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa/8RJkG7MortqVTEvILI6Z9PL1rzacn//ov0pY1S3t/raYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz+7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog/71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu+bHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2/hFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V/3qF3eu3/B8lN07fsKwYRMeGCZM3nHw8LPP7T+w/TH+b/YjjwCBau4hdsY9BF+ZRr1AgMrEoJdu5R/4fBhELEUxdqM72c5aTGef1+IQVnvjPTGxCb3wfhzek01IufGW24c+AOIZzq8gnCYLACAbHrsGKMNHNDV6EPR/osTBA8ziYuCw7Tjs+ThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w+9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9+dbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB/yIz70jFvcU/eGRvmVKrdoPJ0bltbq9R1v/YaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA/fAdPM+wIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y/SAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG/AvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC/hhXgUA+gFip9id54Z5wod3t1glmAKcgCUk+rogS11erXC6/JJ+WL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf+bn/07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS+3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0+0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa/3fbaLyfPTsaR+CIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0+a32f2945YHTpRoDazQHnjnES1lrm3+Fq4+YgL/ygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu/9zy/NwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO/frHjvH9+pMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM+zzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq/B6ei/V+5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj+vm0xpaZCBL2vZIQjWCo6Q2/2lnOTKUqE/1UYJv5ZAOKb36Lxv32p+OTCrfUnn27ofnjujZq094yVz2TcPf/v7+58IPi6dX3OnPyC0L3b917LZdPTcF8w/0mVQxcHZN+cTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t+7FP1eajFc/Gz689fe+UW3xc/vP58whegruiOKsCNGRZehzj+cwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF+WlJTe3ghi5y4pbYNtKyK+AqGgV6RD66BdECyZQU+xzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy/+qHSkOv7bLFExMz5TiAMaaVIb/wg7NmPnUc0VVb4+a/3xO8a6Hj/0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY/+yddrEUqp/ie+4eMYP/9+yRWGwjyVpav5k5sXH9/5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU+JFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4/9rD7PafGcS1R7PsEQk1d7TaLX/gqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0+9LqSttY3QmLJw6/3b430QyITiIlAqxdlBMcj/lHpUk+6gRVqnV4kwil39+e/sK5T/9sUYXdkp9n3vr4YN77ll3OW+pzc8v7NpC3vppe0vPUtC7Ev2FzR/cQmlWcInr25+cGHXgtrefZ6cNHMlm8b+taaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk/1HquySb/e8drD6PPN2z4+p45Ngi+d8fu35a9/f4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ/QQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP/q3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M+b2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt+Dw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs+5ZLXnE/yxtZarrfrYDqw6wr2xGWIjpKsAWu+I2t+VyXex0jOkFJfNZpfsrQMOsKeYPHqqT+NdjB7q5euvRZPnb3oYUWsXUUomXo/W9JUVbx7J4HugOKR748Sz333/yd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce/ZMxo1nF6Nmgn7Y/TmxejV+puEyuv9TaJArLfsb+Iw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj+Vwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8+Rfs09fmXXEH+ecs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs/Nzup6pu8B+ROnrBae6djz2+InL0aAOq4Y/e8+QDVf9G154buPm5xvWCb3mrjKRjN+7vp4xEwtQh3q8Y+a0KbPYz19MYDO5tw1mkLIPz3985rOPP/10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE/Igzx1QzhIE0DR3nLfB89CcmUMWLuFF2u+WPJGTu3C+t3TBoiIAgpP5iG2lhdp+kEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn+k78TN1N5wPn+Szg2gC/nKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB/TOLAxZRpmn39tucP/KjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff/FEyCCePMvngykw+K/eMIh5f8VUtYgffQ49lB7+R0HUNTpQenhP6WBBkscHEs5y+QZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk/bGKDPGARzmLjqmfcouq/e4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1+x3pUgbyrhA/vjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe/ei9JQJ4ELUkjOsxJ7m6+QYbnXvbTY2Ow6D6FHh/7lTTBZZSVLOtqB8g4iCCHzeZK+dC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD/URrIP3Wr6u+OqQ9OmDF94qRp5JtZj/9u9sx5C/icym8TiHvgB8gGOwAEwU4c/M4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ+16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4+ZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb/+ILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT/dUDsCHUKOz4AWfRHQvA065Z1snHLxtW7/oddaNewgZANO4LY+n9OPN+rQSxmD80rC7ed1/Rm9/puaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y+EuJtrjkUwGEVlI650ylKvE+5ABA/HNTwuf9lc+BgItUcf0/AgZwQedwuks0ypTyaYjSqY+iqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO+0ESk9LSjDup6913x/KzVKdeX9THFGzb1v5TDDfpQ45bECoJ9+43cBcf0nCXXr/F8/43notvxJ6rVEnqc1TWG05X9cp+AAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK/V+GK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua/dUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n/qjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786+2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d/YbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS/ohgnt9rG+ooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc/4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8/sMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW+jOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX+V/3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr+Pdo7x4dIHTKe5DFn+O/j+W2VnE3ooW6isf0LIUENvZs1gf/LHojJwdpplCP5gn/5gi26FoYa19ZVFOJ6Sxuoz/q2Ti20IKVJdnqvYJwnhfPH/2f6YHoQF30aZaK9J8T026RxH5fA/WPW/8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM+hdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe/uYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H/DXNOTeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58cY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JtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh+BQ8XJOgmziYWsLhqD+1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL+CQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf//uZLf5xv0ulUfvGjOONEDrXMYEgzK+CtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc/0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF+vs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd+ThplmiAy+BMMx2M6XcOLuERnVGvx+3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd/XVrfR7t8jpaBvqQuejTSZZXeCR145+8+1PDivZbnPyN+hT3SphMXhgNARhQWRMoMKEHQ6/X19RkWu3V+Xr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk+Ltyhf7dl5pPfHLSgb4QUvT7nsi2+R+bhTt2fL+U90tDx99FwN5Pu4fbWMBnC3/ZprdiD9/ciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h/FXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU+dFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy+67Pqa0tKaiorSGvGlCzavv+iCDZu7ykKhsrKqKkDwa+HPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET+/fAk7F+uray7vKyjOr+KHAr4bGHqI3IN7+G5S+AS7SU0nbeih999Xlbp/qtQllG7Sj/p4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L+p1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl/+gzwRczUIGsiggSSZNFlkHRtI0Z+oT8E+bOoWSnwxY/oUzVPdILhSZyRP8ezp2Vz+E4SGJn/ndpNDXwrMFMaMYjsRi+qN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6/avnXvvku37cGf9hzou3r/LbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v+Aps89kZzPacGPqPmctKUf+VkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W/VN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN//IKMjge2AeK7BW0S+6EYdkQaJaL3+XI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO/CtQOhvro/H5DgKdA420DrVfS4oWELdb/7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG+e6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT//Q4+XsWgfzjp+0n6MTYDbdHRriMbs/Sh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn+8DhpfWI1CfA+zWrZnHP5+zefKBrTh0zXKHkmuzliH39q3rwfXHT/UN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL/EV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6/wCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo/buCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv/bnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna/oRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN+U5LpOW1pswUpmU0hZj+TGOmWnQ2lPNyV2rEoO/A+mUw0CwATG8cNjkwyXzEYZrG9Of5NUyy+XBY7Q4Hm9a8tgCH/WU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D+zHRXUbqzi96bmrEQNEeim4V965jWnB+ho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts/Gdwsk5fIecd011vh9O/OaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe+yRgh/6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui/GeGFqDOZLiPj7iXnpkDsKJ5+TwO3LmEe8JYecb2fcazoXMC/Ed4z0J7EFS3MdH3EuPJJX07gom+ff4/DMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK/S1jzBBfUZbwUv5X/7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk/own8RLYvLrQAAeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc/bdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6+7P+rH/qtf6+2Z3u2Z3u2Z3u2Z3u2Z3s+O66jKoYBGASA/iUFeLO2tqfgvhIgVkOshvj/8f/jF8VqiL8dqyG+d4klllhiiSWWWGKJJY444ogjjjjiiCOO+Pua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae/p45np2e356gbeH94HP8Q3x3feH/X38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH/0UqxTrEVsbux/PHe8b3xh/lgglzESJRJfE6MS6ZChZJzkj+RouCA9GJKQuMhI5hsZRHR2A7kZ/YZWxldhtPDPeFd+IPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ+8BX5Lvxe/qOACmWEnsJ60SsyYjqxiLhE3CoeE6+LL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr+dXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10/piU3YZu6+91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE+AmeAW+p8R/A5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL/aDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8+XI+qI1COZ/kliIXhPkiyDo3vCnG2CaEn0+2lH+gmfIvotowZa3769ULZST4K+cujqTb/j36S4w/QmgDF0tWvalemNWLX+KSMBvYkhQSLG2FZR+afmERIsqPpn7+yvxjfMlsTjlihz3OuZE38bTtlAAa/TAFAHgBbMEDjJYBAADQ9/3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw/3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn/wKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm+Oyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36+Gappx57oq+PPpurv34GGGSgwTYYYpihhhthlJFGG+ODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG+BtFBTBAbxAXxQYJC7rvjrnv/xpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp+aVFxaUFqUWZ+UVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW+Qwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT/fEXNRJwqmlaemnQMbN8E1OE7Mzb/P/8xzKZrEMA2hl3rQATa0Uxs2bN+2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq/19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6/enoi05EcG62tsXhkPcdk87BHVC+ZXleUPrOsUHaUI2tb4y/8OwbsTEAJAA==) 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<h1 class="title toc-ignore">Visualización parte 2</h1>
<h3 class="author">Grupo G3 - Los panteras</h3>
<h3 class="date">2020-06-03</h3>
</header>
<hr>
<div id="mapa-mundi" class="section level1">
<h1>Mapa Mundi</h1>
<p>Para ejecutar todos estos pasos, primero debemos de tener los mapas en nuestro directorio y luego instalar el paquete:</p>
<p>devtools::install_github(“rOpenSpain/Siane”)</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(devtools)</span></code></pre></div>
<pre><code>## Loading required package: usethis</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">library</span>(Siane)</span>
<span id="cb3-2"><a href="#cb3-2"></a></span>
<span id="cb3-3"><a href="#cb3-3"></a><span class="kw">library</span>(RColorBrewer)</span>
<span id="cb3-4"><a href="#cb3-4"></a><span class="kw">library</span>(classInt)</span>
<span id="cb3-5"><a href="#cb3-5"></a></span>
<span id="cb3-6"><a href="#cb3-6"></a><span class="co"># Directorio de los mapas</span></span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="co"># Hay que especificarle el directorio completo y descomprimir los zip del repositorio</span></span>
<span id="cb3-8"><a href="#cb3-8"></a><span class="co"># Ignacio:</span></span>
<span id="cb3-9"><a href="#cb3-9"></a></span>
<span id="cb3-10"><a href="#cb3-10"></a>obj <-<span class="st"> </span><span class="kw">register_siane</span>(<span class="st">"C:/Users/ignac/OneDrive/Documentos/COVID19_LAB_R"</span>)</span>
<span id="cb3-11"><a href="#cb3-11"></a></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># Andres:</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#obj <- register_siane("Z:/R_Workspace/COVID19_LAB_R/")</span></span>
<span id="cb3-14"><a href="#cb3-14"></a></span>
<span id="cb3-15"><a href="#cb3-15"></a><span class="co"># Mapa españa por provincias</span></span>
<span id="cb3-16"><a href="#cb3-16"></a>shp <-<span class="st"> </span><span class="kw">siane_map</span>(<span class="dt">obj =</span> obj, <span class="dt">level =</span> <span class="st">"Provincias"</span>, <span class="dt">canarias =</span> <span class="ot">FALSE</span>, <span class="dt">peninsula =</span> <span class="st">"close"</span>)</span></code></pre></div>
<pre><code>## Using default year as the latest year</code></pre>
<pre><code>## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS =
## dumpSRS, : Discarded datum European_Terrestrial_Reference_System_1989 in CRS
## definition: +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs</code></pre>
<pre><code>## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\ignac\OneDrive\Documentos\COVID19_LAB_R\SIANE_CARTO_BASE_S_6M5\anual\20190101", layer: "se89_6m5_admin_prov_a_x"
## with 50 features
## It has 17 fields</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>raster<span class="op">::</span><span class="kw">plot</span>(shp)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="preprocesamiento-de-datos" class="section level1">
<h1>Preprocesamiento de datos</h1>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>covid_datos <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"Datasets/covid_datos.txt"</span>, <span class="dt">sep=</span><span class="st">";"</span>, <span class="dt">encoding =</span> <span class="st">"UTF-8"</span>)</span>
<span id="cb8-2"><a href="#cb8-2"></a></span>
<span id="cb8-3"><a href="#cb8-3"></a><span class="co"># Se genera una ultima columna al finalizar cada linea con ';' por lo que la eliminamos</span></span>
<span id="cb8-4"><a href="#cb8-4"></a>covid_datos <-<span class="st"> </span>covid_datos[, <span class="dv">1</span><span class="op">:</span><span class="dv">4</span>]</span>
<span id="cb8-5"><a href="#cb8-5"></a></span>
<span id="cb8-6"><a href="#cb8-6"></a><span class="co"># Para evitar problemas con los acentos en las columnas cambiamos el nombre de la primera</span></span>
<span id="cb8-7"><a href="#cb8-7"></a><span class="kw">names</span>(covid_datos)[<span class="dv">1</span>] <-<span class="st"> "Fecha"</span></span>
<span id="cb8-8"><a href="#cb8-8"></a></span>
<span id="cb8-9"><a href="#cb8-9"></a>covid_datos<span class="op">$</span>Fecha <-<span class="st"> </span><span class="kw">as.Date</span>(covid_datos<span class="op">$</span>Fecha, <span class="dt">format=</span><span class="st">'%d/%m/%Y'</span>)</span>
<span id="cb8-10"><a href="#cb8-10"></a></span>
<span id="cb8-11"><a href="#cb8-11"></a><span class="co"># Los valores NA los convertimos a 0</span></span>
<span id="cb8-12"><a href="#cb8-12"></a>indices <-<span class="st"> </span><span class="kw">which</span>(<span class="kw">is.na</span>(covid_datos<span class="op">$</span>Valor))</span>
<span id="cb8-13"><a href="#cb8-13"></a>covid_datos<span class="op">$</span>Valor[indices] =<span class="st"> </span><span class="dv">0</span></span>
<span id="cb8-14"><a href="#cb8-14"></a></span>
<span id="cb8-15"><a href="#cb8-15"></a></span>
<span id="cb8-16"><a href="#cb8-16"></a>c1 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "Curados"</span>, ]</span>
<span id="cb8-17"><a href="#cb8-17"></a>c1<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-18"><a href="#cb8-18"></a><span class="kw">names</span>(c1)[<span class="dv">3</span>] =<span class="st"> "Curados"</span></span>
<span id="cb8-19"><a href="#cb8-19"></a></span>
<span id="cb8-20"><a href="#cb8-20"></a>c2 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "Confirmados PCR"</span>, ]</span>
<span id="cb8-21"><a href="#cb8-21"></a>c2<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-22"><a href="#cb8-22"></a><span class="kw">names</span>(c2)[<span class="dv">3</span>] =<span class="st"> "Confirmados PCR"</span></span>
<span id="cb8-23"><a href="#cb8-23"></a></span>
<span id="cb8-24"><a href="#cb8-24"></a>c3 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "UCI"</span>, ]</span>
<span id="cb8-25"><a href="#cb8-25"></a>c3<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-26"><a href="#cb8-26"></a><span class="kw">names</span>(c3)[<span class="dv">3</span>] =<span class="st"> "UCI"</span></span>
<span id="cb8-27"><a href="#cb8-27"></a></span>
<span id="cb8-28"><a href="#cb8-28"></a>c4 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "Hospitalizados"</span>, ]</span>
<span id="cb8-29"><a href="#cb8-29"></a>c4<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-30"><a href="#cb8-30"></a><span class="kw">names</span>(c4)[<span class="dv">3</span>] =<span class="st"> "Hospitalizados"</span></span>
<span id="cb8-31"><a href="#cb8-31"></a></span>
<span id="cb8-32"><a href="#cb8-32"></a>c5 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "Defunciones"</span>, ]</span>
<span id="cb8-33"><a href="#cb8-33"></a>c5<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-34"><a href="#cb8-34"></a><span class="kw">names</span>(c5)[<span class="dv">3</span>] =<span class="st"> "Defunciones"</span></span>
<span id="cb8-35"><a href="#cb8-35"></a></span>
<span id="cb8-36"><a href="#cb8-36"></a>c6 <-<span class="st"> </span>covid_datos[covid_datos<span class="op">$</span>Medida <span class="op">==</span><span class="st"> "Total confirmados (PCR+test)"</span>, ]</span>
<span id="cb8-37"><a href="#cb8-37"></a>c6<span class="op">$</span>Medida <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb8-38"><a href="#cb8-38"></a><span class="kw">names</span>(c6)[<span class="dv">3</span>] =<span class="st"> "Total confirmados (PCR+test)"</span></span>
<span id="cb8-39"><a href="#cb8-39"></a></span>
<span id="cb8-40"><a href="#cb8-40"></a>df <-<span class="st"> </span><span class="kw">Reduce</span>(merge, <span class="kw">list</span>(c1, c2, c3, c4, c5, c6))</span>
<span id="cb8-41"><a href="#cb8-41"></a></span>
<span id="cb8-42"><a href="#cb8-42"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Andalucía"</span>] <-<span class="st"> "1"</span></span>
<span id="cb8-43"><a href="#cb8-43"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Almería"</span>] <-<span class="st"> "04"</span></span>
<span id="cb8-44"><a href="#cb8-44"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Cádiz"</span>] <-<span class="st"> "11"</span></span>
<span id="cb8-45"><a href="#cb8-45"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Córdoba"</span>] <-<span class="st"> "14"</span></span>
<span id="cb8-46"><a href="#cb8-46"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Granada"</span>] <-<span class="st"> "18"</span></span>
<span id="cb8-47"><a href="#cb8-47"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Huelva"</span>] <-<span class="st"> "21"</span></span>
<span id="cb8-48"><a href="#cb8-48"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Jaén"</span>] <-<span class="st"> "23"</span></span>
<span id="cb8-49"><a href="#cb8-49"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Málaga"</span>] <-<span class="st"> "29"</span></span>
<span id="cb8-50"><a href="#cb8-50"></a>df<span class="op">$</span>codes[df<span class="op">$</span>Territorio <span class="op">==</span><span class="st"> "Sevilla"</span>] <-<span class="st"> "41"</span></span>
<span id="cb8-51"><a href="#cb8-51"></a></span>
<span id="cb8-52"><a href="#cb8-52"></a>df<span class="op">$</span>codes <-<span class="st"> </span><span class="kw">as.factor</span>(df<span class="op">$</span>codes)</span>
<span id="cb8-53"><a href="#cb8-53"></a></span>
<span id="cb8-54"><a href="#cb8-54"></a><span class="kw">levels</span>(df<span class="op">$</span>codes)</span></code></pre></div>
<pre><code>## [1] "04" "1" "11" "14" "18" "21" "23" "29" "41"</code></pre>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>p <-<span class="st"> </span>df</span>
<span id="cb10-2"><a href="#cb10-2"></a>p<span class="op">$</span>Curados <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb10-3"><a href="#cb10-3"></a>p<span class="op">$</span><span class="st">`</span><span class="dt">Confirmados PCR</span><span class="st">`</span> <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb10-4"><a href="#cb10-4"></a>p<span class="op">$</span>UCI <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb10-5"><a href="#cb10-5"></a>p<span class="op">$</span>Hospitalizados <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb10-6"><a href="#cb10-6"></a>p<span class="op">$</span>Defunciones <-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb10-7"><a href="#cb10-7"></a></span>
<span id="cb10-8"><a href="#cb10-8"></a><span class="co">#Inicio Confinamiento</span></span>
<span id="cb10-9"><a href="#cb10-9"></a>p1 <-<span class="st"> </span>p[p<span class="op">$</span>Fecha <span class="op">==</span><span class="st"> </span><span class="kw">as.Date</span>(<span class="st">"2020-03-16"</span>), ]</span></code></pre></div>
<p>Nuestro objetivo es realizar una escala de colores dependiendo del cuántos casos postivos se confirmaron el día que empezó el confinamiento:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>level <-<span class="st"> "Provincias"</span></span>
<span id="cb11-2"><a href="#cb11-2"></a>canarias <-<span class="st"> </span><span class="ot">FALSE</span></span>
<span id="cb11-3"><a href="#cb11-3"></a>scale <-<span class="st"> "6m"</span> <span class="co"># "3m" also accepted</span></span>
<span id="cb11-4"><a href="#cb11-4"></a></span>
<span id="cb11-5"><a href="#cb11-5"></a>shp <-<span class="st"> </span><span class="kw">siane_map</span>(<span class="dt">obj =</span> obj, <span class="dt">canarias =</span> canarias, <span class="dt">year =</span> <span class="dv">2016</span>, <span class="dt">level =</span> level, <span class="dt">scale =</span> scale)</span></code></pre></div>
<pre><code>## Using maps from 2016</code></pre>
<pre><code>## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS =
## dumpSRS, : Discarded datum European_Terrestrial_Reference_System_1989 in CRS
## definition: +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs</code></pre>
<pre><code>## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\ignac\OneDrive\Documentos\COVID19_LAB_R\SIANE_CARTO_BASE_S_6M5\anual\20160101", layer: "se89_6m5_admin_prov_a_x"
## with 50 features
## It has 17 fields</code></pre>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a>value <-<span class="st"> "Total confirmados (PCR+test)"</span></span>
<span id="cb15-2"><a href="#cb15-2"></a>by <-<span class="st"> "codes"</span></span>
<span id="cb15-3"><a href="#cb15-3"></a></span>
<span id="cb15-4"><a href="#cb15-4"></a>shp_merged <-<span class="st"> </span><span class="kw">siane_merge</span>(<span class="dt">shp =</span> shp, <span class="dt">df =</span> p1, <span class="dt">by =</span> by, <span class="dt">value =</span> value)</span></code></pre></div>
<pre><code>## Joining by: id_prov</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a><span class="co">#Plot the map</span></span>
<span id="cb17-2"><a href="#cb17-2"></a></span>
<span id="cb17-3"><a href="#cb17-3"></a>pallete_colour <-<span class="st"> "YlOrRd"</span> <span class="co"># Escala de colores</span></span>
<span id="cb17-4"><a href="#cb17-4"></a>n <-<span class="st"> </span><span class="dv">5</span></span>
<span id="cb17-5"><a href="#cb17-5"></a>style <-<span class="st"> "kmeans"</span></span>
<span id="cb17-6"><a href="#cb17-6"></a></span>
<span id="cb17-7"><a href="#cb17-7"></a>values_ine <-<span class="st"> </span><span class="kw">as.numeric</span>(shp_merged<span class="op">@</span>data[[value]])</span>
<span id="cb17-8"><a href="#cb17-8"></a></span>
<span id="cb17-9"><a href="#cb17-9"></a>colors <-<span class="st"> </span><span class="kw">brewer.pal</span>(n, pallete_colour) <span class="co"># A pallete from RColorBrewer</span></span>
<span id="cb17-10"><a href="#cb17-10"></a></span>
<span id="cb17-11"><a href="#cb17-11"></a>brks <-<span class="st"> </span><span class="kw">classIntervals</span>(values_ine, <span class="dt">n =</span> n, <span class="dt">style =</span> style)</span>
<span id="cb17-12"><a href="#cb17-12"></a></span>
<span id="cb17-13"><a href="#cb17-13"></a>my_pallete <-<span class="st"> </span>brks<span class="op">$</span>brks</span>
<span id="cb17-14"><a href="#cb17-14"></a></span>
<span id="cb17-15"><a href="#cb17-15"></a></span>
<span id="cb17-16"><a href="#cb17-16"></a>col <-<span class="st"> </span>colors[<span class="kw">findInterval</span>(values_ine, my_pallete,</span>
<span id="cb17-17"><a href="#cb17-17"></a> <span class="dt">all.inside=</span><span class="ot">TRUE</span>)] <span class="co"># Setting the final colors</span></span>
<span id="cb17-18"><a href="#cb17-18"></a></span>
<span id="cb17-19"><a href="#cb17-19"></a></span>
<span id="cb17-20"><a href="#cb17-20"></a><span class="co"># Todo junto</span></span>
<span id="cb17-21"><a href="#cb17-21"></a>raster<span class="op">::</span><span class="kw">plot</span>(shp_merged,<span class="dt">col =</span> col) <span class="co"># Plot the map</span></span>
<span id="cb17-22"><a href="#cb17-22"></a></span>
<span id="cb17-23"><a href="#cb17-23"></a>title_plot <-<span class="st"> "Casos confirmados (PCR+tests) por provincias el 2020-03-16</span><span class="ch">\n</span><span class="st"> (Inicio Confinamiento)"</span></span>
<span id="cb17-24"><a href="#cb17-24"></a></span>
<span id="cb17-25"><a href="#cb17-25"></a><span class="kw">title</span>(<span class="dt">main =</span> title_plot, <span class="dt">sub=</span><span class="st">"Ignacio Pascual"</span>)</span>
<span id="cb17-26"><a href="#cb17-26"></a><span class="kw">legend</span>(<span class="dt">legend =</span> <span class="kw">c</span>(<span class="st">"Menos que 12"</span>, <span class="st">"12-22"</span>, <span class="st">"23-27"</span>, <span class="st">"28-38"</span>, <span class="st">"Más que 39"</span>), <span class="dt">fill =</span> colors,<span class="dt">x =</span> <span class="st">"bottomright"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>A continuación, mostramos la misma gráfica pero con la fecha de inicio de la fase 0:</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a><span class="co"># Inicio fase 0</span></span>
<span id="cb18-2"><a href="#cb18-2"></a>p2 <-<span class="st"> </span>p[p<span class="op">$</span>Fecha <span class="op">==</span><span class="st"> </span><span class="kw">as.Date</span>(<span class="st">"2020-05-04"</span>), ]</span>
<span id="cb18-3"><a href="#cb18-3"></a></span>
<span id="cb18-4"><a href="#cb18-4"></a><span class="co"># Inicio fase 1</span></span>
<span id="cb18-5"><a href="#cb18-5"></a><span class="co">#p2 <- p[p$Fecha == as.Date("2020-05-11"), ]</span></span>
<span id="cb18-6"><a href="#cb18-6"></a></span>
<span id="cb18-7"><a href="#cb18-7"></a>shp_merged2 <-<span class="st"> </span><span class="kw">siane_merge</span>(<span class="dt">shp =</span> shp, <span class="dt">df =</span> p2, <span class="dt">by =</span> by, <span class="dt">value =</span> value)</span></code></pre></div>
<pre><code>## Joining by: id_prov</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a><span class="co">#Plot the map</span></span>
<span id="cb20-2"><a href="#cb20-2"></a></span>
<span id="cb20-3"><a href="#cb20-3"></a>values_ine2 <-<span class="st"> </span><span class="kw">as.numeric</span>(shp_merged2<span class="op">@</span>data[[value]]) <span class="co"># Values we want to plot are stored in the shape@data data frame</span></span>
<span id="cb20-4"><a href="#cb20-4"></a></span>
<span id="cb20-5"><a href="#cb20-5"></a>pallete_colour2 <-<span class="st"> "PuBuGn"</span> <span class="co"># Scale of oranges and reds</span></span>
<span id="cb20-6"><a href="#cb20-6"></a>colors2 <-<span class="st"> </span><span class="kw">brewer.pal</span>(n, pallete_colour2) <span class="co"># A pallete from RColorBrewer</span></span>
<span id="cb20-7"><a href="#cb20-7"></a></span>
<span id="cb20-8"><a href="#cb20-8"></a>brks2 <-<span class="st"> </span><span class="kw">classIntervals</span>(values_ine2, <span class="dt">n =</span> n, <span class="dt">style =</span> style)</span>
<span id="cb20-9"><a href="#cb20-9"></a></span>
<span id="cb20-10"><a href="#cb20-10"></a>my_pallete2 <-<span class="st"> </span>brks2<span class="op">$</span>brks</span>
<span id="cb20-11"><a href="#cb20-11"></a></span>
<span id="cb20-12"><a href="#cb20-12"></a></span>
<span id="cb20-13"><a href="#cb20-13"></a>col2 <-<span class="st"> </span>colors2[<span class="kw">findInterval</span>(values_ine2, my_pallete2,</span>
<span id="cb20-14"><a href="#cb20-14"></a> <span class="dt">all.inside=</span><span class="ot">TRUE</span>)] <span class="co"># Setting the final colors</span></span>
<span id="cb20-15"><a href="#cb20-15"></a></span>
<span id="cb20-16"><a href="#cb20-16"></a><span class="co"># Todo junto</span></span>
<span id="cb20-17"><a href="#cb20-17"></a></span>
<span id="cb20-18"><a href="#cb20-18"></a>raster<span class="op">::</span><span class="kw">plot</span>(shp_merged2,<span class="dt">col =</span> col2) <span class="co"># Plot the map</span></span>
<span id="cb20-19"><a href="#cb20-19"></a></span>
<span id="cb20-20"><a href="#cb20-20"></a>title_plot <-<span class="st"> "Casos confirmados (PCR+tests) por provincias el 2020-05-04</span><span class="ch">\n</span><span class="st"> (Inicio Fase 0)"</span></span>
<span id="cb20-21"><a href="#cb20-21"></a></span>
<span id="cb20-22"><a href="#cb20-22"></a><span class="kw">title</span>(<span class="dt">main =</span> title_plot, <span class="dt">sub=</span><span class="st">"Ignacio Pascual Gutiérrez"</span>)</span>
<span id="cb20-23"><a href="#cb20-23"></a><span class="kw">legend</span>(<span class="dt">legend =</span> <span class="kw">c</span>(<span class="st">"Menos que 8"</span>, <span class="st">"8-14"</span>, <span class="st">"15-20"</span>, <span class="st">"21-60"</span>, <span class="st">"Más que 60"</span>), <span class="dt">fill =</span> colors2,<span class="dt">x =</span> <span class="st">"bottomright"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" 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