{"url":"https://api.github.com/gists/1177827","forks_url":"https://api.github.com/gists/1177827/forks","commits_url":"https://api.github.com/gists/1177827/commits","id":"1177827","node_id":"MDQ6R2lzdDExNzc4Mjc=","git_pull_url":"https://gist.github.com/1177827.git","git_push_url":"https://gist.github.com/1177827.git","html_url":"https://gist.github.com/mbostock/1177827","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/1177827/raw/703d310b399098a243a76a50bc209167e924cfd2/.block","size":17,"truncated":false,"content":"license: gpl-3.0\n","encoding":"utf-8"},"index.html":{"filename":"index.html","type":"text/html","language":"HTML","raw_url":"https://gist.githubusercontent.com/mbostock/1177827/raw/3fcd29ceba2996c46a7daf0926411e8d05273def/index.html","size":1756,"truncated":false,"content":"\n\n\n\n\n \n
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_url(x) : x;\n return image;\n };\n\n image.zoom = function(x) {\n if (!arguments.length) return zoom;\n zoom = typeof x === \"function\" ? x : function() { return x; };\n return image;\n };\n\n image.render = function(canvas, callback) {\n var context = canvas.getContext(\"2d\"),\n viewSize = view.size(),\n viewAngle = view.angle(),\n viewCenter = view.center(),\n viewZoom = viewCenter[2],\n coordinateSize = view.coordinateSize();\n\n // compute the zoom offset and scale\n var dz = viewZoom - (viewZoom = zoom(viewZoom)),\n kz = Math.pow(2, -dz);\n\n // compute the coordinates of the four corners\n var c0 = view.coordinate([0, 0]),\n c1 = view.coordinate([viewSize[0], 0]),\n c2 = view.coordinate(viewSize),\n c3 = view.coordinate([0, viewSize[1]]);\n\n // apply the zoom offset to our coordinates\n c0[0] *= kz; c1[0] *= kz; c2[0] *= kz; c3[0] *= kz;\n c0[1] *= kz; c1[1] *= kz; c2[1] *= kz; c3[1] *= kz;\n c0[2] = c1[2] = c2[2] = c3[2] -= dz;\n\n // compute the bounding box\n var x0 = Math.floor(Math.min(c0[0], c1[0], c2[0], c3[0])),\n x1 = Math.ceil(Math.max(c0[0], c1[0], c2[0], c3[0])),\n y0 = Math.floor(Math.min(c0[1], c1[1], c2[1], c3[1])),\n y1 = Math.ceil(Math.max(c0[1], c1[1], c2[1], c3[1])),\n dx = coordinateSize[0],\n dy = coordinateSize[1];\n\n // compute the set of visible tiles using scan conversion\n var tiles = [], z = c0[2], remaining = 0;\n scanTriangle(c0, c1, c2, push);\n scanTriangle(c2, c3, c0, push);\n function push(x, y) { remaining = tiles.push([x, y, z]); }\n\n // set the canvas size and transform\n var tx = viewSize[0] / 2 + dx * (x0 - viewCenter[0] * kz) | 0,\n ty = viewSize[1] / 2 + dy * (y0 - viewCenter[1] * kz) | 0;\n canvas.style.webkitTransform = \"matrix3d(1,0,0,0,0,1,0,0,0,0,1,0,\" + tx + \",\" + ty + \",0,1)\";\n canvas.width = (x1 - x0) * dx;\n canvas.height = (y1 - y0) * dy;\n\n // load each tile (hopefully from the cache) and draw it to the canvas\n tiles.forEach(function(tile) {\n var key = url(tile);\n\n // If there's something to show for this tile, show it.\n return key == null ? done() : pixymaps_cache(key, function(image) {\n context.drawImage(image, dx * (tile[0] - x0), dy * (tile[1] - y0));\n done();\n });\n\n // if that was the last tile, callback!\n function done() {\n if (!--remaining && callback) {\n callback();\n }\n }\n });\n\n return image;\n };\n\n return image;\n};\n\n// scan-line conversion\nfunction edge(a, b) {\n if (a[1] > b[1]) { var t = a; a = b; b = t; }\n return {\n x0: a[0],\n y0: a[1],\n x1: b[0],\n y1: b[1],\n dx: b[0] - a[0],\n dy: b[1] - a[1]\n };\n}\n\n// scan-line conversion\nfunction scanSpans(e0, e1, load) {\n var y0 = Math.floor(e1.y0),\n y1 = Math.ceil(e1.y1);\n\n // sort edges by x-coordinate\n if ((e0.x0 == e1.x0 && e0.y0 == e1.y0)\n ? (e0.x0 + e1.dy / e0.dy * e0.dx < e1.x1)\n : (e0.x1 - e1.dy / e0.dy * e0.dx < e1.x0)) {\n var t = e0; e0 = e1; e1 = t;\n }\n\n // scan lines!\n var m0 = e0.dx / e0.dy,\n m1 = e1.dx / e1.dy,\n d0 = e0.dx > 0, // use y + 1 to compute x0\n d1 = e1.dx < 0; // use y + 1 to compute x1\n for (var y = y0; y < y1; y++) {\n var x0 = Math.ceil(m0 * Math.max(0, Math.min(e0.dy, y + d0 - e0.y0)) + e0.x0),\n x1 = Math.floor(m1 * Math.max(0, Math.min(e1.dy, y + d1 - e1.y0)) + e1.x0);\n for (var x = x1; x < x0; x++) {\n load(x, y);\n }\n }\n}\n\n// scan-line conversion\nfunction scanTriangle(a, b, c, load) {\n var ab = edge(a, b),\n bc = edge(b, c),\n ca = edge(c, a);\n\n // sort edges by y-length\n if (ab.dy > bc.dy) { var t = ab; ab = bc; bc = t; }\n if (ab.dy > ca.dy) { var t = ab; ab = ca; ca = t; }\n if (bc.dy > ca.dy) { var t = bc; bc = ca; ca = t; }\n\n // scan span! scan span!\n if (ab.dy) scanSpans(ca, ab, load);\n if (bc.dy) scanSpans(ca, bc, load);\n}\nvar hosts = {},\n hostRe = /^(?:([^:\\/?\\#]+):)?(?:\\/\\/([^\\/?\\#]*))?([^?\\#]*)(?:\\?([^\\#]*))?(?:\\#(.*))?/,\n maxActive = 4, // per host\n maxAttempts = 4; // per uri\n\nfunction pixymaps_queue(uri, callback) {\n var hostname = (hostRe.lastIndex = 0, hostRe).exec(uri)[2] || \"\";\n\n // Retrieve the host-specific queue.\n var host = hosts[hostname] || (hosts[hostname] = {\n active: 0,\n queued: []\n });\n\n // Process the host's queue, perhaps immediately starting our request.\n load.attempt = 0;\n host.queued.push(load);\n process(host);\n\n // Issue the HTTP request.\n function load() {\n var image = new Image();\n image.onload = end;\n image.onerror = error;\n image.src = uri;\n }\n\n // Handle the HTTP response.\n // Hooray, callback our available data!\n function end() {\n host.active--;\n callback(this);\n process(host);\n }\n\n // Boo, an error occurred. We should retry, maybe.\n function error(error) {\n host.active--;\n if (++load.attempt < maxAttempts) {\n host.queued.push(load);\n } else {\n callback(null);\n }\n process(host);\n }\n};\n\nfunction process(host) {\n if (host.active >= maxActive || !host.queued.length) return;\n host.active++;\n host.queued.pop()();\n}\npixymaps.url = function(template) {\n var hosts = [],\n repeat = \"repeat-x\"; // repeat, repeat-y, no-repeat\n\n function format(c) {\n var x = c[0], y = c[1], z = c[2], max = 1 << z;\n\n // Repeat-x and repeat-y.\n if (/^repeat(-x)?$/.test(repeat) && (x = x % max) < 0) x += max;\n if (/^repeat(-y)?$/.test(repeat) && (y = y % max) < 0) y += max;\n if (z < 0 || x < 0 || x >= max || y < 0 || y >= max) return null;\n\n return template.replace(/{(.)}/g, function(s, v) {\n switch (v) {\n case \"X\": return x;\n case \"Y\": return y;\n case \"Z\": return z;\n case \"S\": return hosts[Math.abs(x + y + z) % hosts.length];\n }\n return v;\n });\n }\n\n format.template = function(x) {\n if (!arguments.length) return template;\n template = x;\n return format;\n };\n\n format.hosts = function(x) {\n if (!arguments.length) return hosts;\n hosts = x;\n return format;\n };\n\n format.repeat = function(x) {\n if (!arguments.length) return repeat;\n repeat = x;\n return format;\n };\n\n return format;\n};\npixymaps.view = function() {\n var view = {},\n size = [0, 0],\n coordinateSize = [256, 256],\n center = [.5, .5, 0],\n angle = 0,\n angleCos = 1, // Math.cos(angle)\n angleSin = 0, // Math.sin(angle)\n angleCosi = 1, // Math.cos(-angle)\n angleSini = 0; // Math.sin(-angle)\n\n view.point = function(coordinate) {\n var kc = Math.pow(2, center[2] - (coordinate.length < 3 ? 0 : coordinate[2])),\n dx = (coordinate[0] * kc - center[0]) * coordinateSize[0],\n dy = (coordinate[1] * kc - center[1]) * coordinateSize[1];\n return [\n size[0] / 2 + angleCos * dx - angleSin * dy,\n size[1] / 2 + angleSin * dx + angleCos * dy\n ];\n };\n\n view.coordinate = function(point) {\n var dx = (point[0] - size[0] / 2);\n dy = (point[1] - size[1] / 2);\n return [\n center[0] + (angleCosi * dx - angleSini * dy) / coordinateSize[0],\n center[1] + (angleSini * dx + angleCosi * dy) / coordinateSize[1],\n center[2]\n ];\n };\n\n // The number of points in a coordinate at zoom level 0.\n view.coordinateSize = function(x) {\n if (!arguments.length) return coordinateSize;\n coordinateSize = x;\n return view;\n };\n\n view.size = function(x) {\n if (!arguments.length) return size;\n size = x;\n return view;\n };\n\n view.center = function(x) {\n if (!arguments.length) return center;\n center = x;\n if (center.length < 3) center[2] = 0;\n return view;\n };\n\n view.zoom = function(x) {\n if (!arguments.length) return center[2];\n return zoomBy(x - center[2]);\n };\n\n view.angle = function(x) {\n if (!arguments.length) return angle;\n angle = x;\n angleCos = Math.cos(angle);\n angleSin = Math.sin(angle);\n angleCosi = Math.cos(-angle);\n angleSini = Math.sin(-angle);\n return view;\n };\n\n view.panBy = function(x) {\n return view.center([\n center[0] - (angleSini * x[1] + angleCosi * x[0]) / coordinateSize[0],\n center[1] - (angleCosi * x[1] - angleSini * x[0]) / coordinateSize[1],\n center[2]\n ]);\n };\n\n function zoomBy(x) {\n var k = Math.pow(2, x);\n return view.center([\n center[0] * k,\n center[1] * k,\n center[2] + x\n ]);\n }\n\n view.zoomBy = function(x, point, coordinate) {\n if (arguments.length < 2) return zoomBy(x);\n\n // compute the coordinate of the center point\n if (arguments.length < 3) coordinate = view.coordinate(point);\n\n // compute the new point of the coordinate\n var point2 = zoomBy(x).point(coordinate);\n\n // pan so that the point and coordinate match after zoom\n return view.panBy([point[0] - point2[0], point[1] - point2[1]]);\n };\n\n view.rotateBy = function(x) {\n return view.angle(angle + x);\n };\n\n return view;\n};\n})()\n","encoding":"utf-8"},"thumbnail.png":{"filename":"thumbnail.png","type":"image/png","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/1177827/raw/3a150024bc946e6127437260a6ab8b5a62051758/thumbnail.png","size":34523,"truncated":false,"content":"iVBORw0KGgoAAAANSUhEUgAAAOYAAAB4CAYAAADmBo6IAAAAGXRFWHRTb2Z0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\nIbKs8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BUa4rok5C2uE2Hn\nsLGPQCK9vN/ci1/Svki9+6vwywVeBwVza0ctl7RjsflT6HQeFVR09jyikggq\nlXC+jPjOu8jMn0FqfBptWviaJxb9AF5pDLY0ctMWJD6qvGaJB8QDwt9mX4OW\n1otZ3sYOgg4VxNYdopaLABWQtFMNqSSlDnSTSmP9z/6ttsR0c/S9cxkigSYG\nd2+rYHozcwg+uI3h+r0vBPNJ0T6xgu7kdNKp4MhMcBO6yP7J/0D0sY9hq47U\nxKwc+6N861GQ4kQE9yh4kaSEhGg/FMEsZJSp5jZ8DAYbGlSRRDdGZHBtd+hx\nUwdo/uPrmi4p/cG34R/uo3//PlKlkkK6yM3BJlTFvEhjGjGtui1tMZZOI772\nNlLS6Y++TLtJq5UrI+Rj7vwinKuvoHR4gO7a3U8mHxolvVPlfoXqNi6QlAJI\n+C/KR8kZ0qyrwYO7TusqlpDQFImrcEQuH/nQ4h9qCQgtZXHc+HYSzVIYzjVP\n29qBb7D3QBWWohBd+ggereFgg1ZLoH1lDP6503D2D2ATNkfKnaaSoHKzpErp\npZeJXAzbBxNzGK7d5kdPIsW/B/TT44VFhK+8hmhvF/aO9PlJawRVsljnp/JY\nnDmF2w8e4r23fo4XL66oD/zeWoyrZ08hS+F+79E+5io5DM9exE6zjRn0ceni\nEn5ycxNjgxgvrkxi98DGgzs38bWZcQyLabzt4AvBfGL+khpX+s4+Vtcp1ofC\nYbcPNK3R3/4l/Hd+jswr3zT9WjSQa5vInUBHCT7wYAgcFUETixhQgHu0DKqh\nCXHc6QUTUKKFyPDACOQZilYOTMpDo32xlEqJv0mr82gL3Qe3JVlITdvnASHE\nGyuqQFo8wJE8VbjHEnnMmgMswQtbK+MszdspE2p7Wxt71ZZp6cenaG1ypnQr\nGPz2QqqtMvmzUAYmZ41vKMXK8pj4ciKA2+vmp+R8Rw2vRtHVX8tVNumfoLoF\npzzF34eGqSPpjn7fpKiU5OEe5S17N68jTQQjdDp3h8JVLCMq5GkZaaFmTZRb\nop5Omf6/CGe9boI/8h+vP+Z19aXVTZs+/O4+FSfXrLxORbeM6MIFomgftjTj\n2t3G8vQKHm1u4vTyIta3q7h66Txq1SreW93Aiy8Q5TTreOPdG5heeQkt7tXW\njXt47vllXLl8EW/d24TPPXvxhSt4uHOAh3t1TS3JPs3Nzn1OCAafpy8pQp5f\n0mqXI/qdNPaqNdC7exe5738dMbWxM7mEkPDE4mH2hIAgNZKEawJbpARLIqZ2\nfwCXljbKZDDY2kSwt2NKtHi4spdehU0/L2x3FCqXpJcRvwZBeFQt4mRy6ptJ\nV0ApNZKonVoS6WHDA+2GA/CIIei1tV5QaF92u22se4HCMTWjJI1u3xcSmF6r\nw8NglWiBeGikWt87/Ryi7U0Tqh9VaXwcf1NL9Lg+C8vAldeAM4RuonAIjbF6\nC7h7A1i7B/Ce0Ukgqm0f0SRhfcxqGMuUZ0mKI2gcIBIrK9ZNc6AfKsHT2j9f\n/daI1kiESC2l8F/FGxfoKH6toJMKoasU4ct9cH8sVRhcdyqYmL9HEX0/6XJH\nxSDtP+zVuxrg88+cpT8/hZC+oCjZ5elJXL/7SBVyo1bFA+5RUJzAxr27qBOl\nhNzrAS37RCaFA4G83Lebd+5RCF20iYYOpVk3r29vYw3TfK8Bz87te/d7Xwjm\nE75yz7+gCX0c9QuyEdY76v+JLxnsHWpBbmy5KjAeBUie2qc/MnzrdQSpDP2S\nCnw+J6JgSId3IQykFxY0OCFNff3aLhwRkO7A8C25IUOxypHpjyvZjVNL5zB7\n+QK6ixP8rIife6DJbo/X40hhb59Cnsppv1Jnalx9OdTrmuwHNXo8Oam+rEtr\nJUGLlBST0+oPpTRO/E4qhYhWxKWFs/qhFgtr64xfJzAaqAgMTH3190y7DrGI\n19+iQNKaS+Bk2D/yQ48F8XchiDyhvO3k+0mEc5RblvSQ5HS9xGqLAEsAijDW\noaAIdJaqEWXyCKz1TGtQqSYR1CEKTt9bEIAUe/cbsFYuwqFyi/l6S7rdP1zV\nAFt7fA4P+TmDqXn4QQ/V1gA2FfL3XruCg1YXD3a47hkK8IUr2F19AKe+g0Zn\ngFachu9kNaDURAZ7UtxNNLO5toEdKnAKee8LKPshGCuUt9TC0jGMlZjNMFSh\nsEsZhIRH8WCI9PIpWBS4ociutCdZe0gLRmglkcBWFV36kvmJSeQoYTUKpUI3\nWsNsqaxC55SLiO7fptBTsLIVbZas500CFJ7padvYvofG1gdotqQ5k41iZUrr\nG/1uU9k1towj0MQ3Nb6kW/JlwwySwD+FQ2BaRM3uSqU+33xAazOQZlNKLaGF\nb/DAXn8f9pe/zkNTxXCvm/jF0UepfVHS4Gp6HpilMFYmKYS0hnevGWsovp/7\nGZBBpMCAPqzsmdTWioWDUzhBCLKO8uIWLZMrrUVoRcPxcUSE+yKksaALiZz3\ne1oQrRaT+yP3aEl3BAmkCQFC+gPRwmlq695tOJ5YYAr33AKilUsarfa217B3\nWMdXLp+jP/oI0pi1SsQTLBBR9HluIukxxH2JuI+i2LNF6oYUBsn4CydTVOv7\nhcX8kDXIXn4Bqbn548CPWMvDBqHoOry5aQyoLWWjHcKiqFih70LNurlGv66L\n1MochTaE7cfa04igCR1udkirpcweQqHu3i7yzz+P7NnzVOaEoQ/vwAm6OvfC\nC/vwIsLOoQw92kOPAtmnL2VJ0W1pUgtqh5HFDU3BKs/AS5lZGIHk4yTCS+iq\nllK6pEsqRASccDWWFpbiA2tzbamUySdNsfmY9MGRyp7zF+DsPEJA2PlYD1qx\nQiJ4cp+vfINm/JwRyGtv8rM2EoL6Z0wIkVal0zOaz3VEeXX8Jw80GgXkpDF4\nswmH7oldp0UUocxntQ+QBN3ctU39m6CheMQY5DmIub5Rhc95+SvA2LgKMAY9\n2AJRH97Xz+zOLWI7tLGxtYsUEchX6HcuR33sUlE2aWHjVEHdm17jUGMSUhIm\naZ9sVjjG80iVKMRB8AWUPXkAJYiQu/oytVb2xAgCG/5ujdDFk5kSmmeTMjKx\nmhJltCl4WVpPibBI1/f0whkK9oLmHIWYLIn0IBGKSDYxlVYhEuphSKELfcIu\nBbdU2gJz3SwCfodeTmEqMgVCVAkMWUnXbkvfMy3MFPF/pNETYVEsSWyBrhLx\njUypkTNe0ZaL2N83hATbdHiQUL+d5P+E+2lR8DyBpvSts4RV8lnaqTxM/ELx\nH1/+mibl8e4bFMhN87hyST3TaR2fYfcEQRHbGzqywteWk70kuu2ZIUuW/TiF\naiSgUjAgQrq/p/ciQSKJqsa5tEFKwjLKJzlOQSjVKqxeH/bGI+1CEC0uAOOT\n2mBLSA02r8GmRY3oBvUWz6BLaNx68AHPSQ/PT4/Rz3TR93ieJmbxwle+gVaz\nh35o6R664icRNqNzKGSJLwTz2E2JtHoj9/zVD+27g3C3qgJquZYWbUuLSwky\nuGfOoU+V6kgH9Jk5kSx4ExOEhLv6HGHEqP8nqQ1aTOkCL7lLCQBJ13jZ7JAw\nWcheIoyBRE4jQ88zgQ3bCI82sYqMlRNrzc11rYQJ1KlSME2RLWbnYQvZoH5o\n0qaSiqhM8EAdHlkW9dF4aIU4PYpoxm6sOTdrcYnXFGO49dCQ4eepcL70LRPU\noe+MtfsmheGcKCqIP+v6VEuhvIk7m9SUjrTr0L8T3rD08EmUriqRJxD8JUor\nXewjKjZtKelYGt22elS8O7twt7bhbm7D2T8k1D1UK2tLUI8+qHf7nkJccG9D\nWT++1nlwD87aI/rtA1SpcrdvX0Ofzzk3OY5FKviD9hDPf/t72GzXMSZDhKwY\ng1SGx4Frmh9Dmhf9hY95IsqopHFJ/I9IAtSoYbOjeTJvaYYCZgIKlkufplzC\nYJzQpt1BsLOD4fY2raGXzPU85HsE9HsWlCsrneFlYpkjlSoaUYS2PZGIqRIA\nkoZWR2VJ5sTr93FLWychpscU5gi9VKxBIDmObiYpAyMsksMiwRc5WKKphaES\nzUyrbyTV8UdzXeS9ZRiQljQRBdDKpNcJ00+fh1ffx2BqGvYUlc2NtwjVHxqu\n7cmRECcrNj47bcp15NGeWFSrGUqdp3SiE6qhQvAhouYeff4DE8mm/yZtPmz6\ndeoTJ0UGOs9EaJNUZPbGbSUT2APfRH/D6OgsHPmrdCfCMAlwSQqNvqN9/4HO\nJwmnpxF9eQ6WwNu9fWRaDaTnzmGdr1m/dgMVL48aBfMv//f/DdHENCYuvYgz\ntJZ3Xv9r9Lpt5ItlKlrnCx/zSOtz0QuvfNmMURiNYKB1GT7YgDsp8z1Txspx\nI4fC3qFAhgJpufCZc0uwCznCqF0KSGiKactlHoKcCquImsxUccoFDLZ3lA0i\nPqhDvyIahrr5Sqq2rFFnxaRw1z4SUutIwZsBTXbSrc8e8Jo8Ql+JtGbSiGdn\nYY1NIj5H+Ek/FoRV8dIKLEnsV/f1vSW4JEXBEiCy5cDyGofCZun7hGdL8M9f\ngiVC+oufAPXD48jqY36d/cm3ovztN05rPJ1sSVtnCjndkVmdmaIGt0TgYm3M\nbHLD4oqIFZXZnXHCEzbzRF2Tx+QeuQ2ugzSATtyBoxSPlrcNtblXKM2t2xT2\nxVMIM0RUPB8R3Rdr9R6weoMKmJ8/v8zH5uETDwkpBBT0uDCBgXCVfboyu3eo\nKC0MOl3s0crmaTlnXriEsaGPg07nX7DFHNU8HtmgE+MVRkRxZeqYsL6Ub0lh\ndJzMltSfjiGJD7bX4PZKWic5oGX05mk9F6l53Zwm0QvfvExNPUTm1XMItmrw\nb28Q0k6q9Yw6ppA6oN8q+coM/dHgYB+5CxcNlzXeoW/aNuF7SfDrZGjPwE4J\n6gik5LdE/OykFaLAUKl09woVJVoH0oFN8ncy/WzlNA8l/UaNPu5rNYU9PqEV\nGlL9ECede4UvGhemERZKSnyYnp9DL19Eh7+HPLS58Sn4PLBBbFpxf3R9w8/B\nJlsqKGL5lRmUuB6WVI+ksiOoQZ+zq0ookvSSb8rDgjotWmNXn2elcqaqg68N\nhMOr7VWTXKu4EbL+Moh3RMIYffbf/rkRWskhS++goWFuRdd+Tih7H3GW73Xq\nNC78yQ8wRr/+5//hL+DJe0ox9bTYSfqWwvG938bl50/Dmq3gBmHwUKLH/xKD\nODpqT9IEY+PHnQEkFC2OvAiJTAQbDAx0FTI4/UEZnns0jp6LLCPnvcqUad41\nbOvE6MLVF9WHFKFLvbAMHl9ayZ6pEpD84vIMvJU57mUKw9uPTP0mr8OxikhN\n8poWptX6BnfXuZGmbMuScHvUOCK5m+4FkSm+HY1pEI0eR4lll2xAAl0lfycb\nLT1rROgkuS9d2N97m68fGj4r398apwBSOENa0YgCKgfQSbnqQ8WEYdLK//v/\n5r/H+++/j/f+n79CenIG/pUXkeF7dv7uLwnXUp9rtPP4DJv4KPAjkXKzphkV\nQCl5MwUCPS2U1m8ZSz8wBISj2lfr8fyprvuouBsncqgjuuVRIb0RZKFW2s1d\nFIZZ9N/aRLPbQmfuFOJXvoSh8GSprC26OzI5ADv3Md6rYzb2kTuoIjs3iTea\n/9IEMzIHN3f1JWSfu5L0lDWemvGt7CNtL8J7NEYv+f0oGCCHmm8kkFVrGHm4\nJYIatJoKdeUwR50BZYfCRmtqLZ9XCCt+pVg+7wp9nk5AGPxQaXw6BLdS0m7p\nw1v3EdSa8Ol/yPONhU40M4wS8aYnkL10Hv2H6xg+3NREtxbt8jO8uSlC7qvo\n3rgFf6uKWIRXGhzLrEphD0lFPK1/JOTvPB+jAArLRN7D5ueVqnuo2BE2d+sI\n2k2i4AKCV1/DX//4xxi++y7SsoDij8oBfvlVZO+8j+7Woyf3/P08yCVwFCh7\n3OeNNYF/JLijQVEC/2khpUt7XJ41gSJpDi0VNrLv8bGvfzxt3DgTGlySIJvA\nYyGDcM096ZCAEyM85DjRugYHD+nqR8iWxrC7Suu5dYgUz06QshFMzcI+f047\n5AXhHLLlSbzXjdFuE0kFXZybG/8XJJhJYW3x699CWkYuBMFRtT/whILjIwHE\nUVrkWBOa2jt/b48LP4X0uRVDnaOFMwqTENN30b/zENmp4vGBsBK/sJyGd2YK\nwfWbPPxt+AcHtLgthFX6N92BUb5HHd3jE/vqGJ6tWnUeFqkT5N/TVoSQPqGM\nt/NrDTR//iZ8KR8SOh5h1Nj5CygTKu0KY7fegNPrYSnvobW5jwuTReRcW5FB\neamEEq3tbrWOjUdbWODrmhevoF07ROM/vaVwz5XWGITKHq87lK72X/59pH78\n5zQKA2UdfUoz/X4nhGSUmnUiZRM/FqSyjmbffIRPBNAVcIqGuCHBI4GqEsnV\nEfXJ6918GROVMho9n7CfSpowWOCtO7FEAS8k08BGBjzU4FJuksqZAt8UbjQR\nlJOXxl99VYSux73b2UI/pKU9fQWPckU+h6s7OaEzXq62/oXUYxqOZ4TCq19B\n5sxZtUwfyXU9abT7CaqYTxgRNpMWmp6N9OIy/J0a3OlJzQHKnEcRVKHXxfQB\nhvU6QtdC5g9e0lFtVkqmgAUI9uhjUjsObj7EYGcPSAJCRwdEfJnE0xsFeeKj\nto4WIWYKIS1qZ/dAWSz5YkFbIPZ5jzleF/+Ky5N5OLNlpFMeirTGRUL0nZ0d\nRZyr+9uYn5nmQSriXtDRlEllbAIP9qqo8hpqtNiNdo9auoI/+B//J1x75028\n97dvmmbEFHKFvPubcFpVuOtcl+VFpF75PYTvvUkfq5H0ZLU+XwdA2nE47onc\ns/sEu/pE7awBHSECZMYKaFgykYz7lB+DNzOOsNHR4nPZB2m4JaPrJX1l0z9U\nWyDBJBn9IL6/xgh8857DjhII8nQhhNUzrO9p+gYi+NE4Uc84OlLqFURI+Y7G\nD0KP63/psp6Ma/nppzvt65OGMCbAE2lvnuz5iwpf48fa8CfNoqQQWcLdShmL\nj/rvSLoiSny77o072uBKNsGlb5mZW6KV24ddTCN78ZJpDExLpGR0Cme/3UD2\nK19D5ZWzChN7795G/9o9uovUtqGtA2QszwRwMEp8hAa26vAaHdUwSAJU1lEy\nXIt+JUAhUJLw+tsvnFaCe6fTxiQ16rU791Gi31oulSjzu3oWD/e2lcr1/PkV\n1KlcAr7usNXDXr2l0KtPCB5cugqb0DW6+4HmJd3Xvoqi58J/9y36yiZfKtfo\np6jxI4dwN4WOz2OiVRnT8N9+Qw/hE+l6n/Y+/xr/0iIklZEHOvUtsVjKekpl\nfjP01sBQj+ifVnF8DL5Q+gRq7tdM71ta4rBVS9IiWVUAnlS5aFCwz+e3TBqF\n7+HmK0rckP1O+y0MOy0EUrOr3QzbZkhtGBzNbB21PRWrq8OJGnuwSzQCuYrM\n0Kw+uxZTbo4+Yv7FV5E5fda02QiCD+1bQGHqwyoYhkrc7uqoOOGpWvkMhem6\nDr6RuRJS8S+L7GTHKL8ZDXFbxSz/5qB/n34krZMliWchBIgBlDaSnT7af/WG\narmA1tE/rGoeVNg9ktMaJfU1WZ/UHKqgRknXNQlKJAOURCgVInMDbRHSdEoP\n5V36kYNgX583eHig1SfRIa2pVZV6EcPvFQXj+dg4pOCZzjTKUPKkOJrCL0XS\nEYVSWEDqd776JYBKpvHWL/Vaxa/SIm/ev9ugmBYraHiuvpOMlYfAL+nLKnC6\nvvVUOxZavwHGamcBYS8lPF31jYVUYH28N1dlRMGwJQ7ANdc5J0fReuNmiD86\nSptIp3kRfpdrtjA1B4cIqF7dRSfpMyyRby+2kZuZ12qTAd/bI6wN6ztEIutJ\nHvxY7LRJ2mu/x4dPwWrQfcjnEC3MP5tQdpR6L3zpq8hKpFHKrE4KpWXoVgEP\nsDWeQ/6PX0H7P77FVaAPdX6Wjj4taIFWopzFcP2RCt1xoTMFT7iqwpqpH5jA\nEaxkMExoop8Oj39ziOFu01DXks8TPix09PngqHxKGjWZ/s1JA63IlEtpzk1a\nPXabKhxW0obESiBZnKW27Q2wVR+odhWL5vA76B9q86tUJoM+IZIjAs33C6Tb\nG5WTCGOUVIDEkcl32pKzrB1g/tQS2t/5I3Tp53r3VhEWynqgRywgDfXnaEVr\nG8qCsfNTSEknBQ2OBYg/h2fBGk3TSqzjkVvwW5wm6Q3cJxIRtCRwVJWkKHqx\nyKIkpdpHHnfMeD8RXklHaicE7XqY13pZXT9IayG6NC0KXGZM+wwFtW31Rd3Z\nJbopu4aRdTKye/MdxOcuI1heIHSjFf7l3z2jPqbvI332PC3leS2YfSzpLRqz\n34PfqKuwpJ9fQesvf4pwp4vU+UVkXj5nquYpKMEjWqDbD7k4wYkSL88wdNSC\n2Mn6WSq8yo9NwukyNFfHhlsJKyQ5vI/1OVXqmq2DkUZhfGkzKdE4aeehFSDi\n10kpUugnBjYp/G00pRwaVufA9CyNTEFyRg5BRF9TDAStYmdvDcoJypXV8gr8\n1cih5N54PTKXUeBUhq/9/R/8V7j59tt45+//HdILS1QMExhsridcUMK0dA59\nQteou6vV/1af1zk2pV3jgvXrGoj6fHV/t45TSh/fzn7kubY0fZaot3QmiNsJ\nKd86qjnViH1kejopMUHXOcDm1pppoynMIUmTqOUtQ6tfaUXDEiHy2Jy6JhEF\nzlKi+oTyeY86+MuZJezFuz831EcJSIbRs2sxU7MLH5XXvV21ntLLM0OrGOQy\nyPzV6/DdNPI/+CZveIDe23eRWp6GM1VJRq/5Grg56lYn8FfaHCpeTfxYsXwn\ncpx6BSLcJc8wRGz78WqGUf9W4WBGw6ODojNQlA1EmCoHX2SXFk6S5DItSvrk\n+K26zr6QMi2PvnNQySPspDCUthxZQvDCmJZ8xWnpwEeYlCqZYawycj1JCaiG\np/CKNQ2ExSK0wNOn8Bd/9u8Q376j7Sd71RpS+TSc2Vn019eNAGvrkx1AEvTZ\nCURS4jRJJSAkBh17MPgs5/V+9EuslXcyjfPPaGwtVk569+hg4oQ/PKrMkb9J\nlFX5yrYZDCZkBkFH0qWB/rysraATMRDu5Dxyl89RJ9JX3VvXkQkjGCyCKVPt\nXLpR0ktYSAbKRmod4oh/LGdKzhIPwLMnmAnZXCo4juCrVHBUDzF8cB/W1BS8\ndgsFJ4fTtHA+faVb0jrCCSmQs3BKeQy3qwi3DxF3ewaGCAtGSMkiZAJdlEUy\n0E5mGiiS9IUIAw+urayQUMvBTFQ1PgqTW0lrCE0rqN+YSp5vpmGr5fOlmLaj\nFQX9g3140vDZMsGfUJpJlcfRl14+NgVhbonQewHdv32LyqOom+cTJsl0KimK\ntv2kK7qX1bEDnlTfJ0T4L52dpdFtodkdokkBXLIHmCIEux60KNBFLI/naSGF\nsE39cuUUDhsd7NaamBsbQ73Tg+sEKJ2dwwPegy9kfEnFiM8V+5+TqKySC5Px\nBab9ion+GPfhY6daEv9+RO6wRn1+E+guexgn+UwrYWMJmvK4P5df/bZSGvc2\n7tJ6rsKbL+vIvrRMrxufggz36le3jELT4F5Ki7i9ZQpvW2amZHR8hHT4i4Xj\nK9Y/Gcb0zHFlRQCkeXLm/HOPLbBPRzvUNAkdb2F5dNs6Dm631UG7VkdAf81f\n20X/wQbigya6P3sX/d1Dup3SbgMYk1mabogBtVbEg521I4V/ssUpasixXBYz\nExUUPEF5hKG0vh4XUoqjbR5Yx5cR3RHSFFrP4ms9bgCtYC4liX0qAL+rljHQ\nKceRSZvIa8eXtMRrYNEqCm2WQgb5XdpV1moItqrGT5SyJJmPIX1khc8rTZDF\nMifdAnRjqZ2FJzqgdp7mhV5YWUI+42HcCrBM4ZT6TalyKGZcLE8UsL59gCKF\ns5RLY26ygtnxAi2sj5W5KZxdpBKjBVntBbhy/hT+1//5v1Of64N3rn923fE/\nErmJE6jpa2RU6XbD/mPCdjLx/5EUGkxxvE1EoP6jKDvZF09at6QTUodJvSmy\nElYWP8PSBt1CI97C4e46ur22uhDB1o7Wyg7pgEo3/HRlCt3avlHQhMvqz/d6\nStPUYmue05SUGEoRggy9nTunYx3iYe8Z5co+1lYipnD14PDmegc1ONkQTZh2\n+GHC2vC4af5P3kCPAjddymJ+ahxBkTDOKyCzMseFpdBSEF554SVUGy0e3gFy\nxPsT42XsHVS1WHl2ZgadbgfVehrb+wcUkqHp0q2zLPgxmZSZESn+h2RGxM/g\nZg20XQHhaCTJ5SymFs5TfkrY3VzFZJ4b5kR4VG1T4G2U8w62eB2lfA7TRQcH\njTb93DEVTKn+mM0RLbhZbDYHOG21tcHTQUy4RGW1ODeD7T6VQtDARHqIzY1N\n3AwcjBHiTvFCf3TnEfI8q/3ItJ20Bm1sHXawuXeAlcmCRnPX9hu0mEXU6Z+v\n7VKLTy+iW5nEV0/N4z/7/u/j7r1H+GH0w8/VOYia+/TH9/AYlc7LwC5Nmzkj\nWhhgKTI6eW6UXCKBOq5DVNs0frljYKxa4KN+QpYpHRMG1cCMhhfjHPKxIHk/\ntbKS30znFOpKoEjra7VQoZgwz2y1mnZx/CgAKGfI1nSdKUkTzq5dtDWC+wwK\n5knuoplxGO7UEVKgMvPL8KXJFLWSwo+k25zALzclRbGEimNlVCiY04S8vV4f\nXQpPihbj/v0WbtKa+oFpHzmodvA+f+8NDAPk2sNtdAeBicqWppTV0Y+TQT5R\nmJQHhUelWaNOcNmLpxBSYURVH+XxaczOr1DAWyhRm54bixW+Ptrew+XzF3D1\nufP4P/79f0QxN4b/4ve+gb/92T/ibrOvPt6QAvulL1/F1tYe1vfW8M3XvoR9\nvu9f/PQ9nJobx3e++iJ++Po15DI2/vNvfB33H27ib9YOMZtNY43v3+e1P7ey\niP16BwP63xfPn8W9nV9gYXEBM5US/tPNexrpnZ8ZV3dhaj6NrfMvIFu2sd+s\nYX+viq3tHYXwn6/jYH8UWYtwVDeOhYv7ofMxx+bU79dBtTr/xMQIQhmy++Hq\nGdlDCrg3d0EL2IVkIpbZplsUdvumC0I2pxH1UPvM5hReazM2mT0iU6vFF+Xj\n8WhG5ihSr9flav+nvqZ2bKMQZBTD/iN1edxnz1iaRL9CARnXp0niIdeph/TU\nPH/6hK1DQ5OSRZAC1CiFUKANF3u9PsBBewPdaw/Ud5qlrzo7v4DtWgtrBw1d\nJJewQzWfJK1H7KCk36s2G4Zv5itKGxDp+2J9KB54YnJVVG8rI0iCOYcHu9jd\n2zTpFNn3hovXXuLhFwYRoe/m9jYmCxl0mg1cu3kTBanZ3NrWGSTy+Rtr67i7\n24abztJ6rRJ28/riISb4mrUNmZWxj2HXxfUbtzA1OYmYVjM4PY3psRJq/QYG\nA19HvckwIIHE03PzaPCx07zFyVIO+62hdjNI0Tp0uL6FipSRefjbv/gJfvHD\nf4/1zW1Dun9WEJWWfCWoilZISsSQTOzSOIIKY/zR4N0J/1Ojrtp1XVrD5LRJ\noearBe6KezA5jeB20/j6SOaXyg/pEySdICLbpGFCw8FVIkTCi9Ygt7yPXItY\n0sCUqFkU6meS+SM3Wfr97yG1sGhumM5Z9/2bSJ9ZxmB7E4ONbeMHxUkz5qS2\nTvYoQz+rSEi5Kx3TuAjzUzO4dO55/NObf28aVVmm8FiERw7vcbopMvnMJI0S\nNbdgV+ZhhTiqShlVJyTlzUpxk7pHaRDsjY+juDQPZ/MQ2UKZmx1h4B+g9egR\nOoS6tkBvEXohMcjeSo5schxpWtIh4eaQQik+qTBdpHpB2ji60lVBOoJLJwTp\nqMD7Cs9cgCVMIOnOJxqfyiNLP7LlSwsLvl7GMMgwIvq/PWlEWxhDpm2gYI/w\nz5seQ1qadtWke7iUud03SXfCeOmBY1kWnskv2b/8ONyJheM5MjKo9mCNUHbr\nCYIZKdvHW3iO9982Pmviz0oEVmtisxmFyJI6U2s4av1JQcvFA3VnfNvMOtUq\nFlgJycR0pNBOhcWCfo5wsfWlhL5hu/ZsMn/kRodb60gtLmsO0sqmCDlmMdzd\nJ0wJzfDXozHj8YmhpTGtRh9DKRBO8M/h4S7e7zYVwhoBjE1x7ahVBexE6I5h\nkczM1BymlmPFGJnMKNGaVrLohkEiwaUYi5kYY6kYlTPzSNMi5elbbu718As/\nQmpsEsHQBI9CKQVLuZqQtiSyJ42gI9N+BLlxWH5X/RenNKEcXfmMwPIwT6F/\n/l//N/jpB6uICHcloORIpQkyaMq1BF3jZ0melpC/u7vO380B7cgAS6lM+eo3\nMJAeR7TE7lv/BKu2j3FeQy+ekDw5Ifk64n7T+GPPmoCKYu7W4dMF0dyyFhE4\nJnD0RGtJSymT0wLf5KctO+k0Lw0fMiZqGxB61g6Ppra5UuGT+Jv0HUyaTSmW\nQy3mVjKKtuB09PV2EsUvZm1VhH2iFBnSJCbkmRRMuVmplxQa3ah+0Snm0f3g\nhvJhj8q5RjTxkZOeVHJEKkT0O7g5Qx7avVbbVLCPnpM0vXpsPrMm1k+wSgYC\nl4VbaQpyxdotTZaRoQntclPOL8+jRr93LJ/FdrWGskRCUzY29ui3UjgeNrs4\n7PSRp5WTMeDBYKAqwIx/I8yNBsgd7mERffRoXXMXV5Cj5cvwDH3wYB07bUJO\nHcLa1QnOnXOXsbtzCPfBXfqQZuakRhxdV/NpIqBxr6ENw6jWNMcZhrS89Ekd\nWtno8mXE7QbS77+HgNeMcxc1yp2h0gpW76InvFnJo0owY9BR/ufnjsz+cb4S\nLrUI1THsfTyYqBOjy9NKZtc0mERqpRh6OCIZDI7H2Cs7KGNojYEJ/EhZmVTo\nSDM1U/ziGCGUnLXSCDOGkil0QKK0ST42dnYRBboJjw7qWN3axzMblRXCufTR\n8ZICZ8M/jE24fBQqj8Jk1IChwsWOoa+51FoTORfNfqjd7SJavEImpeyeOi2Y\ntHzQjdDiWSOUkvzX+skR5Us0WykDJ5U3CWZJsXBhK/TVllOOWmCPymKHn7lf\nLOOAsPH9N2/roNdRsy2XljerNpkaMl/ChfE0UpJc5vufmp/G+3fu48LilFrr\nmrRbpB/aIOSRseKmi8FQ+Z3xiy+jQYt3/f/+U7jS0InwViCRamuJJAqHk5Bp\nkOPn7ndpCRuIJVjB+7EvvYjw3AXE2+twpIt4bYcWeQL2+28je+kK6lyr0Dfz\nJiUC7RQnCN3oo6rlsZ7Js/Ob2EGyP9lCCfPTFXVntg5a2slBdfyo5cioguXI\nAsbaeE3hrqT0pFm06AHlKqdUUKU5m/xdHjM5b3O21vbb6OTFxUrj6so8ZoqZ\nZ1cwzYTnfgIvR5ZUqtZleE4RPi3ay6enaD0GPE8ZzI2XsNtoo0C/YGv3AJdW\nFhQ25Pm7Rysl1ujm2hbeORiqoAxprZRqRX/Qt/qIZPQAnX3x6TSAMP8Ccqcu\nmDFrMvS008Z2Podt7qAwjwKZ8tT3tXzIyvoIq4TLfoiM8FNHvWTE4vltA2s8\nS/Ofab53ZYyCTIt60Ozj9Rur6A58tLt9s9Fi6QnfZ+aW0W43MTx/zqQDpDNb\nn35ncVpnMUqIXyliCqUIk50KPAEME9TiwlKRtXrhMnxpwXj3A3i83+j+HZ2r\nEkdpwtme1pFGMhCIFiGuPzLKjQJppQvPoFCO3JmPIbh0ZYbNKpoZk9e0x3i/\nnSGVk6WdJ2RKt1I/B53jksJkoNSouklcHEcTIbGeJ7GaJhJhH2E53X/JwVNg\ntxoDza/v1Zq4tDz7LNdjxjpgNikR0DC+k81rq0htnVEZ09yiSw21NDNB4ZIB\nVAW0JVDEJVrd3qVhddHiQgjX1KfV7NFK5s8vwZXO3oc1wr4G/MahNnDKvPoN\nbf8vQSWdDk0hjKsHcChE0iUgpuX1lk6ZwbKtJuxe0qGudkAIOVDBFEogpVsj\nxhJV1qAAfUGBP0LZut2LtYKlc3tdgzwaEOJ7KXMoMtrYVQaRsfrRy1/mtdXg\nXHsL9vwi7OkFbeOf4RoMO6IICEXp91i0xsPDR8iOzWongmHA911Y5mdbajVd\nCqFT30UkxAyBb0I7E4K9cHntEq+PP3NlM0JeGll1m8+eYI5iDlH4GwVU1ntY\n38F240C7FBT/8GvwdtoIdy0lluj7SLd0EbZktqh2eJA0jHTgE6aYkMiiNirz\n8+jJGEDpnOBKEXpoXAsJWtLKSif/QBSfFWtKph56eOPO5jMsmDFOtJKIlShu\n5wjhJkr679TsPNa6HQwbKdy5cRvpq6/AKdBKCJWOh2/3zV/CnZlF+tSKPl+E\n3JagEWHGUKYNc/FCWjObcNRqV+E/uqctCYVx5NKXCNZ2deqXT4spflxqclrJ\n85EMDRIUTcia/dpVCnMXwSY3WISOkManv2mUbPzYxGhRr86l57Tdf/r2be3+\nDt4PijlT7RCb7u6RhNdpdfdWTiPeWUNqY0t5sTKEdjA9j0zzlqZ7+oT6Tjw0\nKRtuesh/D6gQ3MFQ00DR7hrstaEZ2beyRChLv6YYK50wsj1a175+nsJgNw2P\nB24olDIRTn/w7J2XUcQ06dH7m1pvam5RUm7tQ7R/+Fc63dorjaNYmaDxDDX/\nLGMtNP1BhSaRXbUPUkzAzwrSk1SAfaRffpF7TDdif8cgDm60I+UJthny6w5a\nSEVdw/6KbU1niVJ8pjsYxNGJynQRKjm0PMgy2yN1qkiLOAVv6ioa/7ZLAdqH\nvbCoI+hE6DLLy0i/eBnumTljee9sc6EHScNfQtFmQxt0SQtKee/ew3vI8OBL\ncXVPqs9FsGQ6sXTklhmUMmyoWtWhQ9KeUmZdRNu0Zm5WrasfHZjAQRQeDbsZ\n9RmS5LKMlZfPs/MFuC+9BP/hOuLZBYxRKGW47EAI27zfzHgFvVIFFrWw8+4b\nyJYmlVvrSw8jEXzC5UhG3vEApjIVUyTR68MrzOpwnUgikbS+UvvHJ8CWicgS\ncLxyFdYHN+DWqUToRwYW0YbQxbgeJa6pTK6StIGJWD6rByY2lURJgh8nWoI8\n5nYeN/NNCqalaIFKfq+JulhIyWG7GS0qUDjaoBsgReRU+ELlTOWzCJcWlHMS\nSrbOjXRyuBTUm3HxgflsyXV6We5hAQ73uMBz6WfSup/Pbl9Zaqw0oaMrI/Ck\naFosQ60D7+tXdKCrVaKWnyvR0lEQOzysjaZGcaUjekR/MPevfg/ehXkDO9Z3\n4d99aHgBjnM0mi1o1HRcQiB5Jm6I8Bqllk6q3qVje3pxEe7UlBIdZBR77rtf\nUqsd0lcQ7m60U0Wwm0zaWppTWD3YPzDF0UIXu3BZpwrLUBrrwnmE3Jg+ofVw\nc1d7nGZ3NzHPzxvs7KD74AGmeFD+8L/8N9j82U8wuHEdaSdGT5rri7UvVpRo\n4cF01JOazYwdaqBBYJWnM0Ykp9pJ+iPZmm9N8bCU6ZP26hTm558HqJAcgVtj\nFaRC+sYT0/A6XU0z6eEL/c9Z6dc/U0BHEmk/gTlkJwI5auCmMQE3GSjsawMv\npeBRsY36IMloRWHsxFK5Q/dnsHaPKEo6Kk5rmV4gNM5eU62wNpgekRx43nxZ\nZ+6cVA1Ju5hU2H+GRyTQ8sgcS0/a2ieCGRxU4UyX4dE/G7x3D/0bj2C3Y7VE\nalGF1UIhMEyQFgVyD/137iDYq8Mq5DWFMHj4QGvi0lKiQ8EU5zySag8Kqjc2\nqfMlUzOL8MYnjYXtB7SiTfqCbcJXH/1rt+GvEda0uqY7+1geue9/iUJJZXFr\nFT2pfqByiC9cALSlPje9S9jZkgE3h4jurRJWbsDutFCkoG7UanRdqHByBfiX\nLmNzj0J664bmO6foM6fok7baHQPjpZsehUhHvPOA+PQRpeNBloojT6XiyzDd\nZNCsVP7bYqnF7ZKD2mrBFmu7IlPM6rymvjaRCnNpdLe2tAJC2zw+60L5RCsa\nP+Gx6Phvj/1uUI5TmlEBDQ83ksZdZtq1xhjadXVppHDeocKU1qdxL0gyBKES\n1tVAKyl+oMwwTbnx78rmSmWf4YbPAgUHg8fC3hKYie7tog9fIagMm5W5lJLi\nGK7TH6M/1V9dRcCDnKZg5ymAs+NFHQcuLQxLXLTywhTu7+zj8vw07PkJDGmN\nZJjom9duoeaNUaPl6K+1MHi0YXq3KO0qVEs6fLBurkbaimi+lBaNh3zw4/d1\nlma8uKJ+rCplGXzz05+Ylpe8Dj+hj3nLp5B/7SVe7yYOea0SVXVlgO3LL2NI\n4anKYBvRpBTIc4uz2qd0t9bCpeUlzF18Dj/+v/5MCRahJMDD2AwRmp2Au7wA\n+/oD44/rBGqzZpIGCITfKZHdnU0d7hqePgNQQTj0NdPpLAZc10AJ3DGeXRz7\nyUZ3Y7+neU6pbdW8rmQIROgorNopMZ3B8NFDwtsyHOnCj7QG1zQazjMk5y2k\nspMBTtLJwoqNcZEuiQOJmj+7S2NaOxznSgRUhBjeumU0WtGkJfzaPkIZGOP3\nEdJHkEpz6R5gF4qYny2jkk1pRDSkAKaI7yUt0aD/dUunivDbipEnvOgU6K+l\niggOa6azgSiGkw7KxQs6qi2S9pNSQkTBE22ZluARLWdHJ0V1zbhzyYeO2CRa\noSLBHcc04JIUCDdUuqdL3x+J3NkvXIUv6Zp334J97rzmE4dhE+VyGTfuPVTY\ntVOtoSHj43ggUlakPmeQRBj7PhVTraXzOWXStPCKY19Kk7pIv3yBa5FH7x/f\n1ftytjbhX76EuMjDcrAHZ2XREFuCIb74OiabyAxUHcGXK8GuzKkVjOo7x9Fq\nOY/8e1jdQ35yjq5CW5lhtmvpXrvlaUUgi1kLe80+esKndp2j9jXus6y51GLG\nx+HwwKblqVd1lqRPQTgOEgmuz6lGExaH1ySM7Ua4v2ej/WBbmTauWpmEr0pB\n2aafaOIcsVq5UKiSJdf0lnWS0LsEZPRzKainTsO+e4uHva9Cl+KLT7l5ZIMB\n9vf20d7bgi25aekJIgfdPm6/b6YgJ3nYTp9+JqHP7iFe/erXkLp4Ea8/JLS9\ndR320gogDaBkVJ7kPvke5SItPqo6RlzyM8J5EigbjVqlCEFgn/7u3iGyKW68\nW9LubnZxnspjgOGddVMgLDBYAsQSoHAJuSRQ9vAeITMtQ6nCA1g2nRKSSokv\nhJPf0ix6lMt0vI+kkCSaHmysoS0JOcvUeDpjRUVDknvOpdJYmC7ipefGcf3u\nA2wc1JEi2up12s+wYKpG6h/7B0J1Eo0T9HmAGkelNiP4pWFtyxxaichJxUYk\nA2Bp1WQ5g2JJZyW6gX8k6HFCdhbO47gXIT02AX9moD175DGHfp2UjZ33O1hb\nvYXe9iYKVqi1jeMTUxjTwF8bTZl/KX1lxEp6tjZoGhX6jrqEC5zRKV3S2nJ2\nDtHsEvozyzrXRNrp2/kS/RQzeWzyzAo6RAK79KmFMIGEG2zlC7CrDQQB702U\nkESB4/Bo3J7j0MdsHyqZWwgRavmD2PSxkWuRXButqKVhWjPDRQJhocDpiSX4\n4l9JmkCsZzj8XHZmf9pn8LiS5Vd0dtDG4Zs6kUymh/ubu0mz8QF6RDJ7Le5L\npoer58+gkNvGWLlA995/lgUTx31ZE/aFjkhfOo341HkTEOp3jIBGZghpnNRy\nSluPOJU5jo0TWlrJeHUtvem2zaGWFIS06pDSqJkylmiRu66xwBPSgqPRxBt3\nHmHy3CJOlzMYlJbpvNO/paAUCBkDv4Wdwyq6fD+PQuxKW5O9qoHANoVVo7mx\nEqmtKi1XqQz/wvOa6XIPdnDzz6/rXBQpbVbBk4T/9Dw6X3kF/vQ0tvle56Rd\nim2KcBVcy/2NcnS2czyVS9pfEib7bgw3NaZcV8tLH69BUicoKRlJCaFURG68\nhMnWLt/fxSN5fq6iebpwf9VUS9hfWM6P5lo+SmyQ+EOws0qUMqFNo0fTvCPu\n7GojxMZAJq3tyFxMTeXNT0486xbTtK1UnqwcvkyOB3c6mWEYm8CKwF0p35Iy\nL7ECMtSl1zBTogrTKtymG1qi/UedxiWlIYl0sRa0gLcIe+9k+C/fBFZKhT0K\nXFdGlOD62h5MzMdFtd5AThpuWbs6mi8QzqzwX8t5mMbOQs3yTdf1iQqirR7S\nA0KiUxcwWD4HrK8hom+niX2BzEm5kW6wsEukKbV2I8ugknb4WXkTjNC2i675\nDLGQyiwJjnJySmgYNQ6TNWrWPtocO/FJtficn9Vv9/DK1fNaq3rfycBNm4ZV\ndnHKzKH84uvjW1bugeQ74TbUL5XRDILc3KTH7MD30edZfnd1Hx+s7T+7gqkj\nXvyhEUytvTRVJ+HP/56WadawNo6q260TYwhspcbJ4Bd4ZdPlwLaOn2fZSYW5\ndWKQjoUeIWiYIuwYGjpdu9pS/1Ma/u7UOyYYFAX6WN/NK83Oy8RK77PiJgZb\n27y+1HHoila3/+A+HLmeuWUMaemVr0qIGmnPmlEf25M5NWMJhYMrQlejJV7W\n2FEKQ1kHCcfbpnJCLaftHBf8SkCsUEFqooQgx/vgdUmFzBEbZlSyJj64pvIs\nTZDv1xroD0LTHkMEXiryj4b4fIYj3p9Fizoa6dc8UIUvrUQi2zRicwSxSfqE\nqCi07WfbYurgIBl/IKkRWsgCH5s7dwYbzUA5iKNir+OqgFGJT6S+l610NRx1\nRjPtEGnRJK90YjCrvir21ArZySi8KImeyZck8zVlQv/WHavoKHc3n6fhlsTy\nHh8XLE0rJMXG0uBJWlRIKZHAxqkJDOZPIfXO61qYHIkmpVU0Ud+kQDuZWKZd\nFAIJcNUIN9vUurYWTUvBtPBf1TaqPym1fl4yGTsZCSFBqn4PM+ULqBGqtmvb\ncLMV0wlCDkapZOpcuz189+oVZcj8iIek2ulr8MuVJHh3YFpoHFHavhDKf7Zf\nKlUovSb1a1/rPoWGJ8N3pTEXpBv/Mw3ppcIkGZknVmacNzlHP24869IQJtFO\nHZlmmh+NfgbSK3Q0wXkUHRXomKUfODdtyrySgIkW1GrhK/27pNmvpDXSy4s6\nEVrbRiSfIRbRSuWRPrWsoxmE6mdKrwLTwjAhQkgrCQmlY+WsjlaP7942FRx7\nG7DaNePv2s5RxwTTyDpJr4gP3Ka/VxpDrVbH/dVHSBMqpyRodLKBVBg9FrrX\nx6T95cM7mB2bgSd9ebWXTVp96dTKIjKXLnBdXNy6cxcf3HugDc5Eucj7C+Ff\nI48ja/lxqzW++PrVX7KvUsBQ30Z4sIbgYN2Q4t30MyyYydwO7cRumSS/my/g\n7NlzqErPzmR4i/ZZlTmE0v2ctxuqATHkcfGxRICFCiXd0CWBP9jc5WPDZABM\nlPSVjQzUE9jrmN6i/s6uNmEaTdFTAeahdQpZpObnNK0Stug7auOm2PSklZyX\nOP+0jDKGPS5XYN/+ALa00U8V4RaFwJB6vHxoFMRxzGyTWIJS0kGhdqi9eS5d\nPI/Ts+OmA4P0H5J0TGSI8UcEeVVQJkgWE/YXM0UUHFNypH/nOg55vUINk0ZW\n68021qo1tbAlKp+ZiQldN/WTenUTONJRDl/I1idiQRPkJQUC0eE64k71GR/D\nF5uuZKZJgaXk30a/r+mKc/OmB+ztjQOMS2mWpJ0ofEIwmCqP42Cjj6npAs/7\nGA4JC0vFPHr03QIezPL8FDarddTafdOMWU6lcFGtnsJS0x1AnLbUcWAlye0J\n6UFI606ljJAWTRuGjQb3iLXL5BCuLMMinLRuXlcII36bJVOOeS9hccZElBN/\n2IwYDI7yh7GEmOWeKTR9N9bgUpWCJBS9qH4AR5LYibbQAt6ETmYlPW76zRoe\n3Hgb3V6Vn1s2fVNpEaN7awbxyvVOTcPqNhFnc9g/qKIopGzplJCT4usIg9Ub\nRjC/iMp+8haU5ySqPfPzMXmSJJeZ+Do+T1aOGr6QdrTs6dXzF7FAHy4jjJ5O\nF/lclv/OotProbm9SkPi49Urz6Ervh//O6w3+PcMfApwtdVGVdvl2xp00YMo\nBcJdOfTJaPUT8Ng4qxb83QP0M3eQv/o8fAq38GmHOzuUCfpm2TSC5XlYW4Ss\nB4fq7Gv7fBloOmwhdGX6pSk5UhSb+CPWaKKmlZAqZAiRwFJaLkc7wluqcL73\nB3+IR2//Enffv2Gsaxg+Vkg+qq6X3jLByF+OkxKnxE8Ohmb6GbK8tpzHj5KJ\n2FQyz13SDhDONg/N9BmdlSk9dExQw/rC3/xEg0TWsy6YlloKHevNA9ilYG7s\n7SNPATho9vD69ft6cLv9IXzCU+kWJ4Wqec/C/OIS2sMYP/zJP6GQyyjsbfN5\nHqGeMPzlb/ao3WAcHw20lcEzOklLco+jKcbJiDYpOUOrh2DrEAGFMpR2E4Sc\nlh/AOfUcwqU5WLevA8JKyhsrLpUq8lqxtMK9FLOlzYX5miwRjrRmCk4k8kdz\nLGNh6PgWrzetjaeFu9k6pFKQGY9CFaMC0pSGCF3KDEnSCgn1nc24BoGtolzE\nT9cO4PyvUsijzfsbFqVkLEWl1Uc6DpCWRlF5QvBSCY5PoS5N6NwN7S7eqStx\n4QtG0Cf35T7rykXaZGjOUaAqD90BhStLYewQlnaHpt7OSojjhQLh7URJe7fO\n5AP86M2bNIAemk0/acvoquWzrAwhruQvXW12dUTeHkHK2AyhlfHpNoU66hqf\nNKbFNXnEFF+ZQ4ZQulei0BULCBZmYW9uAPW6zh0xuUnXBJYkN6ihc++oWZRc\njx8llR+S/rCScYByndm8pjXoVfM+B9of6O79R3j97/5Gxz1IplS7EHAd7PFx\n2I0OUv8fe1/2JGl+XXW+Jfe9srL2qt6q12mNZkYbDmNGGtkIRxCBIeAFvxEE\nfwER/Bs88cATLwQvhrAJApC8yoPHkkazdlfvXftemVlVueeX38I99/dl9Yxk\nGEnYKFzKVHRMq6srKzP7d3/33HvPPUculQGfj85f9+5h+IM9ldZUJzLanasM\nptTbXPqVn5/ZfikB10ZHXsTLg2NgaVWVGML6oTKDGJAUAWN9pJdWuz5JmpPA\nHAemrYJcWrtxHMBxydwcrEIfz49bpqsaG8KQO1pcmke2Oo3k+Sn6XPqVbORw\nbPIZkyjLijTLIpYW/KyerH49K/Wg39PDSAPcNOlx9PNQQxhPO7TBMDBGuVyi\nrU4hXFyEu7WNSOArSQI6/tC61DRlUhRRPjtAlCoo35W+mAwYtbWR/5+ZL8Nt\n9yQZx7Qvjog485Lnf/Jy0zQPJLC+NmUy/ydNR2E6Iaqtf69npHX5eVRraD/5\nsUDyFvLTS0jAdHLtQCB3OJDX5SLIyIWzfoh0OJTATOCTSC6NzU0kThratcVP\nepc47iSSJoH52biUwJQg04Xm6oyubVXkkG11u+bgx7NAxMG1OWwjE+YxO2jj\n/lQZ2VwWAznwryBYHMRUgIPp9Br19Xhup7Q3GsEOBCpmEbR66K09N/qxo+6F\n85Qv9e0tdku//dv43v/8b3CeC6Smqh9rylFsUJuI6XJWoHbilptW17HEdFnJ\nBb4EAUamRiT8VoU1tWIIjPgw7d/OLOw15b22pIaNbNTKRSOP+OipkvJVvnK3\no7UqCQjJfAn9vFwUm+uSSecEycrFQHjKCw5GdsXJVgyrKlnAIGmsyZ1CRbKl\nXBwCXbm6pFqsY/J9FHNtJzB2EpifbTVzDEFfTLc2p1D2qNPGTKWEpLOPkQST\nHevqMIgHzzZgHTeQvHcTvj+QMx7EjRb7J3yKxiazxuZbDyKZNUjETs0xRY5/\nx4tiqUsDb42Nn4u6HObgwSewt7YQOSkEbONyhpkpGhfpVEpnnUnJStFILofy\nrJIWqEKQW70hQd+WC6Klwe4dn+menm7Qc3ZLAoJliPm2ZMpAIHGhOo+8XDQP\n1wUu01BHmT9Unhe4S91cwdWsisPGmUJtfR3pmgRnrFtDkequ/L1SBRaXvp9+\nCtLxmd3txNcRVuTC+LSpr8OUDkYx7kII2Zrg2L/OacMlwCCROVCU7ZCa7+Ss\njhTncpR5HPsexp0umgqR2dKWg5cqZ5Ci9mcweEX6voj3VyJZyavTCk8H48Xo\neHB/odaeMors2gFVbZgBnKUrqHd6OPnRD+FaUWyjHmoApyRTDvtN2B0PruMg\nkJqW3FP6itgQSCsXhre9qwFq9EqZlTxjJR5LK6r1nhuTCeRnc2NmtVZBo3mK\nl9v78JTda2zjh2pRnlaYrZbuyrdtwyZ7KGtfIIKQ9gskG/B5BSo7lMGkSdI1\nqS0p35KSjP6Nb71yxaKRzv6u1saKEg6eGRNWx50E6S98lENDLMlVLkNgGsg2\nNo9JZTPQ9gUzm23HTZ3YwIXQ1wuw/+ljrF+pmgaOugVbnx/BMJQVBlPgS7JM\nLya2s8kRRjHbx0BKFVQ2ctsG8krGieZmYa+tmdkl4a+upAlM9fvovVhDsbaA\n20t38bIXotk+1+4o+b7Gns3HcGvbsGy0fmMXNRNfFqHRiYms2I6ciXyE2VIW\nr68u49H6DoYe5RGjWFkvvmRiG3l9l6yf/SESAklHybTp3I7ZRczqksmpzhdc\nWVZ6GOew2YN9uTj68GZn4ZyeweZ8Vt2PXR31EFXYxZqa9oTdU93on0Dbn3Ps\np934NOzSrJ5A9xLEZewsHeksr8sRBXydXQ796ML+HOPFYZApy/9ZqBZzODo9\nMup1VkyM12F8LMgcGvIA9XRUZYCbHEoqaKkIkx2E5lKgPqxvmi0R5TBPThB1\n269I8JKp3CHVy1tw529hKP8AL8/OcTqSmi6VVcEsTer5AvJL83AOD3BG3R1m\nH84pPbONwszOejHJcQYZRPzHJN8yGeHx9gGeb+7rKMSV+tmtTWN0cKQbOKoW\nzrspk0Noebr7SR8TI9ef1WC6UIRzkwilfnaT8nXNinvwlxZh7R3A3dlBMCUX\nT6mIxElTtyMiv282TnJlIF9VXVV//+nnyPOTxxc9pMQp18wyerxOdzky5sjI\nXmizY9BHuVbW5seA0hzjDqIO7E23lVyAxdkZbNkC3bI1ROSecpvDdWL1OOuC\nxO7lc4bidu0GrCnKi8hzHkkwFyQ71Buwnj/VzEwiOxYW4NFPZeO58m7JtOH3\nOl4XTjDEsLho6H9BF0MuLsvrSRSqSK3M6uuj4h6lSwZsBOm9EBrWEYOfdawS\negSOcyGczSPJYFanjns3lrB10kJ36MPNZ5GSbMdgTl1d1stiuLuHEZtJ1SlE\nZ/Ka6TotX48sX+eXFql86rshn2MmDactr3dv33xWjiWBn1QhMyJ1bG4gqs3A\nm6mpU5V9eBg7WUVGhlOem7e+OmhZziTmvuhBplZ1UYKybGbaMISPS5Ax5fB0\nuypOzC2LRnEKf7F3qIY49re+ZTLOwYHUTcOYpRMpL/Zc6sxD1lpWyqRR+ftW\nzzd7jRfQOESw7V8cuqieNE2gU4Gv0xWEt+8KdC2pG9c7X/26Zts//Hf/1owp\nsnm48jXv5WOBsEMEpTntripBQDVFpeZjN/SsieTMFNyFacluHkbHdW1m0YrB\nIr2ASmu5nARsU4MtIuyW96tO1lMzuDpfRbmYxPvPdsz7kJ/Re/DIcHNTxnBJ\nN3ByBXnfLWQSGQneKnpygalKklwSlLxQMru8piAhwXXWMnujtFaQy8LhknWv\nrTugVCZ3evI6NnYwqpZg3bgG++ULNSSi8DG3T9ypRUH9gzgTTyDtz1aOjWLn\nMVsTjfu3Py4tVUDnwjMdfsPaLLYbAiU7crgOdhG15L/7+688JqJYMeCNmwI5\n5c9PO3HDAq9MhDD+vRWvaVpmFdLOaJYBieLtnhzIdYV7KBTwVH5O6uHHSIUj\nOdBy6PfPEOyFUqbJ4V28LRkuqY0fn4Y+bUN+j8hRFVg6eLGJJFfN0jaStHM4\nOjQEAQms5Pys8U1h9rVjSCtB6sjz5BbncW+uggPJhu2ep9L7uvhtm04zA10v\nCcnA4ewCsP4hUsV59FQTwbhNJXhxBfRyTBtXq3wJkWRFenqyFkWhiKHUmDoW\n6bVUT8mIh7lI1M/UPjC8dQt2nRKNHV2/sznGnb5i6m/fm0DaL3qopIzhXWvn\nnbrAlyFjaueSa14Zox1LJXSVg2QHlTDM+iyfkxsmphlzbXEW+3RRjjc6LvY3\ntc7ERUMp3ow0PyOIYbNkBGt/R5/fvnMHB3/0B3C2NwSaTgn8qyhs9uXnh9NV\n2MUsHEJaCWAuKPcePpLaLqFK8r4cZlXHe/4CiYU5CYykdl6NTRvQf/TMZHGp\nV6kFyyxGIvvwxi1UpV5My/tarxvPDMsf6CUTfaaJZRK/PN/RNqyWXBb5OQlL\nucSGZ5oJeTHYU0vwudbGrZdeV1BzTrJ7Q2l9qppAaCsZ3Z2qYrj2TKVZHIGs\nuuy7u6MTFPv+GyoNit1tvRD18w78SVB+YXkpF2jvVD9T+sOY5X4HlwJnqB6O\nE9tnc9dSAhOVKZVqHC+ljtXH2QWhJCVHC+e9mCs67OvmOH/R3i91bwWZ33hL\nhZ6j2KyUQa4sGg7kuXdJ3ikJA/fuI7H9DMmjYwTlJfSlbvUiB55a9kngEWb3\nBxcNKGY01doZm6VSxoPaRKaFqn/fzEKNlKE2o+gwxiCiaQ19SKTedQUOv1nN\noSuv8bTTg8vGFOtpy361NjbeKuFnxO0YgaN+QwKpV4dDZ+qC1LSSQT1vFHfr\nR6bz3D1XO3nONG1eWPKag0ZLlcfdpQWl6gWxhwk7iRSLzgy7yAuaSPgS2LUp\nOFMVqWXTE8Gun3FuGalrd0v3MflvfgkCc7wQGfsNspjm6EEOcMTgVNuDpJru\nsBPJzEMRJHJQh+zmcklYIGHyilFF14MmcZRwskjXKnDLRe1yJuampdZLm84t\n6wEGM4WR23XY21vw6D6cjEcU6pkYB8jAx0gONSGsd1LHYH1TRyIqicKZlWQz\nR+Aus6EaIyVdk2kCP3YtHpoAY3OJmXV+Sb8vLbXkdLmEw+Y5hgKjk8vT8hpn\nTKMKiJemnXjE4sI52dP6OFGsYVSYxyhTRZgqmrFMPJNlNubn4Z83jCN2TjJ0\n61xVEqjs1/90Db7AWo5QWEuGXk+JCgizaP3Zn+pzZF77MpxSGWH73GzCTDLm\nz5w5aXFoU9GA5k+XonCO1e2sGIZShydqS82zsSa1jwRO50SgXl5uI1s5pDz0\n1O9MpRMx37arep/Z1+4q1Y7aryOpS518Tmq8eSQqFVUwGAg8858+RoosHSm1\nRvksko/XERTn9HntMDa2jc1NVYuIASJZyD8lD/XsYsVqrEPEuo5elpHaNkg9\nnE+PN6+VhM6GjFLeQvl7V68bZg6zeN/Hp49e4vi8o5fNaHPXwF9mdcIihbGG\nmZOAL899ZIjxDBT+zAFdtDOvDFQ50y3N6FIAv9dnncyMubkON51XzxMdvWip\nXTTZWzvFgX5P0DxC+93di4xtlCEmjZ+fu0kr/y78fC8H+ziIl5DHglqSzRLV\nKoqSqcJI3uLUtNzuXYFfDfipvLpztQYj1IoSBJQO7EgA0CEr39Rs0K8fKv9W\nlcnX88jPzEkg9OGd1c3MLlXA6Etfgd0S+BG66kWq1um9ju5KXqgfjGFKTLKn\nbyfG7tZ+zLTh3+FWByVPTiRY6rZ2SZX1w7dGqzfKa965L5fHNCyuaDWOMao3\n8TFrXoE+qcUFWPTEfPESZmAZKiJwmIX7Z+gfPkP2H/4LuC+fYbD1KdLXyhhx\noZyW8E5MsePal9SUnPtSuWBleRZp7ww7lLxkfcz60xvGIl+2CmiHMZHALqXh\nDMsSnPVXhjyTxy/WL2G3/vz4EkBZpamFseWAWYvyJRAytXmpq2roOXl0K3MY\nfP1tuKu35RDKzX/8TBKZhYpkp2zYg1/fQtRpIjxrYnC4J1DCQrJUk7+TlGDr\no7V3iF5nKAfUUX9gb3YJ0cpV2NzzZF3KLi8bIenMBQtIM6E2cfxYTcA22TIO\nVn3pDEDH6PuMZ6wkOziFvKKAQILiN77z9/GNf/mvEC4saaPG2pHXun+gGdZ1\nrLjZHCK7el0gcVGNHUgaSGXzcFoH8I/XYX37n2IoNbUnrzeSnxkcPoc7P6X+\nGheyFnSxkqAOm8far50TCP9lubhCdl3lIqGNuYHF8pnQDoBEfxIr+gKj52LV\nBfm5TnVl3DXDKyu7CZz9ORomiqAuRcY0Gw7+K2hLdbpmE0GqprCPEI8yH52r\ndzRzWpUFHDTaaO5sSDZdRiA1FShkzJZ17ObE5wyTganZJBuOWK+5RiDZn11E\ngovUPJjUvwmS2hhRka1YOUBrQ5WPjAMxjKUeGaC0ynPMTqbWmWwG8fvYfeX6\nWrtlOsrVaXSnFySbyvOsP0Z0dKDqf26xpOY03KyxYwvAYaOBqCC/twWmS2b3\nm3vqom3d/iqiwyZwWhc4n1OXKv/oBcrfeAudv/xYfn9iutLMvuz80hxXLgcq\ngh+ftzGg43Q6BScaqVmrk5I62s7JhdU2y+FSuw+eb8n3lgT6z1+ISAeN7Vfi\nXWOj20mA/szJ5nJAWc0u7YvsSRZO52gfyVpGaXp2ty9QdIDR1mNYvYGUWEWB\nsl2BsALFKiWtP7U8pb9haFyVx4Nxy7WN2WkUdzfLU7DpkXmwg0iyEnEsA9Fv\nNXRITyuEwDyZNnSieI7HTHahwzMWlI5rQPUz4UWgUpqWasOGxax2fD/YfAF8\n8gPYQ8mWdJWSi8A/4kJzWt5Lx7gzyvN3th+ZTC2Bqp3fyrxcNlNwBwFuz9o4\nsko4Zy9mdlkg+S5av//f5aJJmzqWmZgXB02R5P3n0knkJRifrm9rM4yMoMXa\nFCIpAXY7Ulv2wovPnT9PH6T20TKeFuhSq5JsgFiJMKAECQkH5OXy8xjbrk8e\n/8fHpdlwZRfQ/CaQ2ioJa5FdVjksZw1VlQuyCdjFGSRoIrpzcFHrsZ7LSPDZ\nZKkk0hglMvBt0wFlBnRm5ZB5EgiSkUJb4OntN2CtbyCkL+Lb/0B1XvHskQZD\nSDlIf2TqA0LUwI/9SWC2WFTIOWkmjITfql4wkF/xYjZHFXNzCPNZRAsrWi6S\nn2oFtto9EFYmclJnSo1LKOmWlzCiERLrvHQWaXqkpHOS/ATKw8NSJYfluRpm\nJaj+6EcPUW+14PZdCZw5ff323G1cbImTb+e6WnfeXqyqFEu9OzKTXwmk7ZNT\nKQ/ilpK8LycWfo7GRrbcTqHgtmRSh/qo/KfgrJeGRdxAIbdXqYVJI8+irsyJ\nSRa91IGpt3I3HhVExq03n6efkmbIEYytQPT8BSyp1dyZKR2ppM8zGLZONMMm\nXn9LDpGLzIHUZTSwnZ2HvbGHkgQy2TtbB9sm0408tfjD1hrwwY/UKwW370gg\nRBju7kstNjQNFb6S2KKAdSbH/r5mVyM2rQc+hrMKY7OS6WZm1cxW68S1T+W1\nSyCeHehIJcpPy+uSvyMXx2I2woLUzczInf4QZQnklgTdVCGnq2SnrTaW5q9i\n56iBZ7t1/PC4hQ59GAkrqXaXLiNwTuTucOAzE3NmynGJvK+sfAYFeZ7T7kBl\n+22Y1bDICtCtn5o9T20YxZsuTspImJSTGJE2SHNc9eCMZ5iUAtUaVrIpNyc4\nFkg5ysHF3oZR/JvQ9i5vxozi5oS283lLszN5fgzHlZChng07hvUjDHgw8wUk\nV64h88bXED59hO7zJ7DJ2plbhEuO6/EuElMltfXLu5KBaLemviPGtsA73IbD\njRFfasm1j+G8/Y5afUfPnpshv0rhG4FmLkjXyhXcv/dVnAv0bDSOJXlUMOx3\n0GyeoNuRelKyMmZqyt7Ru+W9P9caM1WgQ9gAUfWaEbDmexK4OJQgmbm1guPG\nOVWJ0O500Op7ODrrqMM0yQ9rO48ksELdS6ULmj1uxigxIqWdU4poOcUFAzE5\n+igUUZif5xWDx1v7GPlSFpBvy+YW9zC5ai3fG8prJVXPardpf6Qu1jTj1AYS\nu80C5215L+qWzFEPkYQTj1aWVrQrDpI/msdAtx3bGk4elzJjGv1Vk31sORwJ\nukdv/wC49RWQMZBi1rlxC97uFvzThrEuIGvMkqz0jTcRNlvw9uVrJckaxSqi\njz5EefE20tPLSPWayObyaElWph06Yv/JgG7NJLevrWH0+puwrt8CDvcNe8Ya\n+6FESjg/qu+h22oilSujkM0hk83C87ro5uTgC3x1z84ki6Vh72xqs8SW4EXv\nFHZlCZ7OBaXy1TW2EHU56//1zz+UrJNUiY9o3O0dG+qy9pPD7srPSnGJmYaq\nbFyNpfkZaJJ56eFiKWlaAoh2fvLfnNwp5UxKlwLUS8OPidVU8WMwc+2LxA2+\nz3QavlxgJPTbpO1xfsplAEJXVUWAER7jryBmPW1swJ2ekfcqz3l++ooBNXlc\nzozJrQoNTMIyz0N2bgnB/dcxIslAakY7X0Ti7uvadPDWn8Ou1lD4R99Ecrlm\nAnvgXQzoh//+95B/8y35nhKCro90toR5yabe3haGUq/q/c4DzuzDholkYuuj\nD3Q1LJqdA46kht3dhDWKt0ik5tt5sYZO6xRX7r6FhOXibCWNbpb0tgD5pWnY\nC7Nob+4Z4r0EQkJtD4roC1RkwPASMLtgvnZ+LdoX6DaCISsg3jslu0nHL/z7\nsTyKdWGqGsWKCPLbTAlRv45krmDgp1xIdDd7PWej3euj0RoIesiaz5Z8ztVb\n5vOtVGA9eSLZ0myTuOeCJtJJBNNVgfS3tdFmN08Fcksuv7kqX0vp5WHJRcgV\ntpAeKfwAd9dNt3Yi5HV5A1PZK6xlQmPWqhZ3AhtLN++hJQfZf/QQ/ZfP1fQ1\n/eU35MauIvv2V+GMYr1YlSC3tAPLcUEkGTU9dw2j5arUehIod+4jOX0Ld9YH\n+KR3JMknrQaz2sjgoSONr9uF9eBjSTeS6WoS7G9+3RDdm03JbGkU5IDa1O2R\nzDHoCQTd62Hw/CFsEgck8XXYu9reNJmfVgvDDjzWnpYfiz3DUOcUUsb6QrF6\nA98zO7u6UB0ZKZNQL4QhXMm8DFb1StEsbggIXGwOznapWquByxlsJBHzl1KP\npiSoLIH6kIuIEpfhFH0xs0jV5AKRrOiXC2rTZ0yVXO0Y27tHEoSnCAQBBLWS\nWeKun8CS74ls+ZwXFqScaMJaf6Qq9NF5/UKFYfK4zFBWApOUMVfFjQMdIfBW\nT6+sosfE0fkhhk/W4CzPIfvOr8lh6sPdPMKomoeTi/0+WBPK4Rmdt1D2s3Ak\nEK0yrdVHSLeHaOfLcqB2JFNJthR46odx0Gi30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