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sdwfrost/kitp.ipynb

Created February 19, 2016 20:11
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Julia overview for KITP
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kitp.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Julia Overview for Kavli Institute of Theoretical Physics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Matrices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrices can be multiplied etc. just like in Matlab."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2x2 Array{Float64,2}:\n",
" 19.0 22.0\n",
" 43.0 50.0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = Float64[1 2; 3 4]\n",
"b = Float64[5 6; 7 8]\n",
"a*b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, one can use elementwise multiplication using ```.*```."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2x2 Array{Float64,2}:\n",
" 5.0 12.0\n",
" 21.0 32.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.*b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List comprehensions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like Python, Julia has list comprehensions, which are handy for constructing arrays."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10-element Array{Int64,1}:\n",
" 1\n",
" 4\n",
" 9\n",
" 16\n",
" 25\n",
" 36\n",
" 49\n",
" 64\n",
" 81\n",
" 100"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = [i^2 for i in 1:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Julia is typed, allowing one to overload functions based on the type signature."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Container 1 result = 5"
]
}
],
"source": [
"type Container1\n",
" a::Float64\n",
" b::Float64\n",
"end\n",
"\n",
"type Container2\n",
" a::Float64\n",
" b::Float64\n",
"end\n",
"\n",
"function do_stuff(c::Container1)\n",
" c.a+c.b\n",
"end\n",
"\n",
"function do_stuff(c::Container2)\n",
" c.a*c.b\n",
"end\n",
"\n",
"c1 = Container1(2,3)\n",
"c2 = Container2(2,3)\n",
"\n",
"@printf \"Container 1 result = %s\\n\" do_stuff(c1)\n",
"@printf \"Container 2 result = %s\\n\" do_stuff(c2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random numbers and distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Julia has a nice interface for distributions and drawing random numbers from distributions that makes good use of types."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.2302390197173243"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".0\n",
"Container 2 result = 6.0\n"
]
}
],
"source": [
"using Distributions\n",
"θ=3\n",
"N=100\n",
"x=[rand(Exponential(θ)) for i in 1:N]\n",
"mean(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Optimisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a dedicated interest group in optimisation, and there are optimisers written in Julia, as well as wrappers around libraries such as NLopt. Note the use of Greek symbols below, obtained by ```\\theta``` followed by a TAB."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"function ll(θ,x)\n",
" sum(logpdf(Exponential(θ[1]),x))\n",
"end\n",
"\n",
"f = (θ,grad) -> ll(θ,x);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // reveal zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 1.0}, 250);\n",
"};\n",
"\n",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
" },\n",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_scale);\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#img-423985b8\");\n",
"fig.select(\"#img-423985b8-5\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-423985b8-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-423985b8-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-423985b8-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-423985b8-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-423985b8-14\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-423985b8-14\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-423985b8-14\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-423985b8-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-423985b8-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-423985b8-16\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-423985b8-17\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-423985b8-17\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-423985b8-17\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-423985b8-17\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-423985b8-17\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-423985b8-18\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-423985b8-18\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-423985b8-18\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"theta = collect(1:0.1:5)\n",
"y=[f(th,1) for th in theta]\n",
"using Gadfly\n",
"Gadfly.plot(x=theta,y=y)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using NLopt\n",
"\n",
"opt = Opt(:LN_NELDERMEAD, 1)\n",
"lower_bounds!(opt, [0.])\n",
"xtol_rel!(opt,1e-6)\n",
"max_objective!(opt, f);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"got -217.25561344091918 at [3.230238914489746] (returned XTOL_REACHED)\n"
]
}
],
"source": [
"(minf,minx,ret) = optimize(opt, [1.0])\n",
"println(\"got $minf at $minx (returned $ret)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataframes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like R, Julia has dataframes."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>x</th><th>y</th></tr><tr><th>1</th><td>0.0</td><td>0.0</td></tr><tr><th>2</th><td>0.01</td><td>0.009999833334166664</td></tr><tr><th>3</th><td>0.02</td><td>0.01999866669333308</td></tr><tr><th>4</th><td>0.03</td><td>0.02999550020249566</td></tr><tr><th>5</th><td>0.04</td><td>0.03998933418663416</td></tr><tr><th>6</th><td>0.05</td><td>0.04997916927067833</td></tr><tr><th>7</th><td>0.06</td><td>0.059964006479444595</td></tr><tr><th>8</th><td>0.07</td><td>0.06994284733753277</td></tr><tr><th>9</th><td>0.08</td><td>0.0799146939691727</td></tr><tr><th>10</th><td>0.09</td><td>0.08987854919801104</td></tr><tr><th>11</th><td>0.1</td><td>0.09983341664682815</td></tr><tr><th>12</th><td>0.11</td><td>0.10977830083717481</td></tr><tr><th>13</th><td>0.12</td><td>0.11971220728891936</td></tr><tr><th>14</th><td>0.13</td><td>0.12963414261969486</td></tr><tr><th>15</th><td>0.14</td><td>0.1395431146442365</td></tr><tr><th>16</th><td>0.15</td><td>0.14943813247359922</td></tr><tr><th>17</th><td>0.16</td><td>0.15931820661424598</td></tr><tr><th>18</th><td>0.17</td><td>0.16918234906699603</td></tr><tr><th>19</th><td>0.18</td><td>0.17902957342582418</td></tr><tr><th>20</th><td>0.19</td><td>0.18885889497650057</td></tr><tr><th>21</th><td>0.2</td><td>0.19866933079506122</td></tr><tr><th>22</th><td>0.21</td><td>0.20845989984609956</td></tr><tr><th>23</th><td>0.22</td><td>0.21822962308086932</td></tr><tr><th>24</th><td>0.23</td><td>0.2279775235351884</td></tr><tr><th>25</th><td>0.24</td><td>0.23770262642713458</td></tr><tr><th>26</th><td>0.25</td><td>0.24740395925452294</td></tr><tr><th>27</th><td>0.26</td><td>0.2570805518921551</td></tr><tr><th>28</th><td>0.27</td><td>0.26673143668883115</td></tr><tr><th>29</th><td>0.28</td><td>0.27635564856411376</td></tr><tr><th>30</th><td>0.29</td><td>0.28595222510483553</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></table>"
],
"text/plain": [
"101x2 DataFrames.DataFrame\n",
"| Row | x | y |\n",
"|-----|------|------------|\n",
"| 1 | 0.0 | 0.0 |\n",
"| 2 | 0.01 | 0.00999983 |\n",
"| 3 | 0.02 | 0.0199987 |\n",
"| 4 | 0.03 | 0.0299955 |\n",
"| 5 | 0.04 | 0.0399893 |\n",
"| 6 | 0.05 | 0.0499792 |\n",
"| 7 | 0.06 | 0.059964 |\n",
"| 8 | 0.07 | 0.0699428 |\n",
"| 9 | 0.08 | 0.0799147 |\n",
"| 10 | 0.09 | 0.0898785 |\n",
"| 11 | 0.1 | 0.0998334 |\n",
"⋮\n",
"| 90 | 0.89 | 0.777072 |\n",
"| 91 | 0.9 | 0.783327 |\n",
"| 92 | 0.91 | 0.789504 |\n",
"| 93 | 0.92 | 0.795602 |\n",
"| 94 | 0.93 | 0.80162 |\n",
"| 95 | 0.94 | 0.807558 |\n",
"| 96 | 0.95 | 0.813416 |\n",
"| 97 | 0.96 | 0.819192 |\n",
"| 98 | 0.97 | 0.824886 |\n",
"| 99 | 0.98 | 0.830497 |\n",
"| 100 | 0.99 | 0.836026 |\n",
"| 101 | 1.0 | 0.841471 |"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using DataFrames\n",
"x = collect(0:0.01:1)\n",
"y = sin(x);\n",
"df = DataFrame(x=x,y=y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns within the dataframe are referred to by the symbol syntax, ```:```."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"101-element DataArrays.DataArray{Float64,1}:\n",
" 0.0 \n",
" 0.01\n",
" 0.02\n",
" 0.03\n",
" 0.04\n",
" 0.05\n",
" 0.06\n",
" 0.07\n",
" 0.08\n",
" 0.09\n",
" 0.1 \n",
" 0.11\n",
" 0.12\n",
" ⋮ \n",
" 0.89\n",
" 0.9 \n",
" 0.91\n",
" 0.92\n",
" 0.93\n",
" 0.94\n",
" 0.95\n",
" 0.96\n",
" 0.97\n",
" 0.98\n",
" 0.99\n",
" 1.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:x]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Help"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Help can be obtained as in R using ```?```."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"search: "
]
},
{
"data": {
"text/latex": [
"An AbstractDataFrame that stores a set of named columns\n",
"The columns are normally AbstractVectors stored in memory, particularly a Vector, DataVector, or PooledDataVector.\n",
"\\subsubsection{Constructors}\n",
"\\begin{verbatim}\n",
"DataFrame(columns::Vector{Any}, names::Vector{Symbol})\n",
"DataFrame(kwargs...)\n",
"DataFrame() # an empty DataFrame\n",
"DataFrame(t::Type, nrows::Integer, ncols::Integer) # an empty DataFrame of arbitrary size\n",
"DataFrame(column_eltypes::Vector, names::Vector, nrows::Integer)\n",
"DataFrame(ds::Vector{Associative})\n",
"\\end{verbatim}\n",
"\\subsubsection{Arguments}\n",
"\\begin{itemize}\n",
"\\item \\texttt{columns} : a Vector\\{Any\\} with each column as contents\n",
"\\item \\texttt{names} : the column names\n",
"\\item \\texttt{kwargs} : the key gives the column names, and the value is the column contents\n",
"\\item \\texttt{t} : elemental type of all columns\n",
"\\item \\texttt{nrows}, \\texttt{ncols} : number of rows and columns\n",
"\\item \\texttt{column_eltypes} : elemental type of each column\n",
"\\item \\texttt{ds} : a vector of Associatives\n",
"\\end{itemize}\n",
"Each column in \\texttt{columns} should be the same length.\n",
"\\subsubsection{Notes}\n",
"Most of the default constructors convert columns to \\texttt{DataArrays}. The base constructor, \\texttt{DataFrame(columns::Vector{Any}, names::Vector{Symbol})} does not convert to \\texttt{DataArrays}.\n",
"A \\texttt{DataFrame} is a lightweight object. As long as columns are not manipulated, creation of a DataFrame from existing AbstractVectors is inexpensive. For example, indexing on columns is inexpensive, but indexing by rows is expensive because copies are made of each column.\n",
"Because column types can vary, a DataFrame is not type stable. For performance-critical code, do not index into a DataFrame inside of loops.\n",
"\\subsubsection{Examples}\n",
"\\begin{verbatim}\n",
"df = DataFrame()\n",
"v = [\"x\",\"y\",\"z\"][rand(1:3, 10)]\n",
"df1 = DataFrame(Any[[1:10], v, rand(10)], [:A, :B, :C]) # columns are Arrays\n",
"df2 = DataFrame(A = 1:10, B = v, C = rand(10)) # columns are DataArrays\n",
"dump(df1)\n",
"dump(df2)\n",
"describe(df2)\n",
"head(df1)\n",
"df1[:A] + df2[:C]\n",
"df1[1:4, 1:2]\n",
"df1[[:A,:C]]\n",
"df1[1:2, [:A,:C]]\n",
"df1[:, [:A,:C]]\n",
"df1[:, [1,3]]\n",
"df1[1:4, :]\n",
"df1[1:4, :C]\n",
"df1[1:4, :C] = 40. * df1[1:4, :C]\n",
"[df1; df2] # vcat\n",
"[df1 df2] # hcat\n",
"size(df1)\n",
"\\end{verbatim}\n"
],
"text/markdown": [
"An AbstractDataFrame that stores a set of named columns\n",
"\n",
"The columns are normally AbstractVectors stored in memory, particularly a Vector, DataVector, or PooledDataVector.\n",
"\n",
"### Constructors\n",
"\n",
"```julia\n",
"DataFrame(columns::Vector{Any}, names::Vector{Symbol})\n",
"DataFrame(kwargs...)\n",
"DataFrame() # an empty DataFrame\n",
"DataFrame(t::Type, nrows::Integer, ncols::Integer) # an empty DataFrame of arbitrary size\n",
"DataFrame(column_eltypes::Vector, names::Vector, nrows::Integer)\n",
"DataFrame(ds::Vector{Associative})\n",
"```\n",
"\n",
"### Arguments\n",
"\n",
" * `columns` : a Vector{Any} with each column as contents\n",
" * `names` : the column names\n",
" * `kwargs` : the key gives the column names, and the value is the column contents\n",
" * `t` : elemental type of all columns\n",
" * `nrows`, `ncols` : number of rows and columns\n",
" * `column_eltypes` : elemental type of each column\n",
" * `ds` : a vector of Associatives\n",
"\n",
"Each column in `columns` should be the same length.\n",
"\n",
"### Notes\n",
"\n",
"Most of the default constructors convert columns to `DataArrays`. The base constructor, `DataFrame(columns::Vector{Any}, names::Vector{Symbol})` does not convert to `DataArrays`.\n",
"\n",
"A `DataFrame` is a lightweight object. As long as columns are not manipulated, creation of a DataFrame from existing AbstractVectors is inexpensive. For example, indexing on columns is inexpensive, but indexing by rows is expensive because copies are made of each column.\n",
"\n",
"Because column types can vary, a DataFrame is not type stable. For performance-critical code, do not index into a DataFrame inside of loops.\n",
"\n",
"### Examples\n",
"\n",
"```julia\n",
"df = DataFrame()\n",
"v = [\"x\",\"y\",\"z\"][rand(1:3, 10)]\n",
"df1 = DataFrame(Any[[1:10], v, rand(10)], [:A, :B, :C]) # columns are Arrays\n",
"df2 = DataFrame(A = 1:10, B = v, C = rand(10)) # columns are DataArrays\n",
"dump(df1)\n",
"dump(df2)\n",
"describe(df2)\n",
"head(df1)\n",
"df1[:A] + df2[:C]\n",
"df1[1:4, 1:2]\n",
"df1[[:A,:C]]\n",
"df1[1:2, [:A,:C]]\n",
"df1[:, [:A,:C]]\n",
"df1[:, [1,3]]\n",
"df1[1:4, :]\n",
"df1[1:4, :C]\n",
"df1[1:4, :C] = 40. * df1[1:4, :C]\n",
"[df1; df2] # vcat\n",
"[df1 df2] # hcat\n",
"size(df1)\n",
"```\n"
],
"text/plain": [
"An AbstractDataFrame that stores a set of named columns\n",
"\n",
"The columns are normally AbstractVectors stored in memory, particularly a Vector, DataVector, or PooledDataVector.\n",
"\n",
"### Constructors\n",
"\n",
"```julia\n",
"DataFrame(columns::Vector{Any}, names::Vector{Symbol})\n",
"DataFrame(kwargs...)\n",
"DataFrame() # an empty DataFrame\n",
"DataFrame(t::Type, nrows::Integer, ncols::Integer) # an empty DataFrame of arbitrary size\n",
"DataFrame(column_eltypes::Vector, names::Vector, nrows::Integer)\n",
"DataFrame(ds::Vector{Associative})\n",
"```\n",
"\n",
"### Arguments\n",
"\n",
" * `columns` : a Vector{Any} with each column as contents\n",
" * `names` : the column names\n",
" * `kwargs` : the key gives the column names, and the value is the column contents\n",
" * `t` : elemental type of all columns\n",
" * `nrows`, `ncols` : number of rows and columns\n",
" * `column_eltypes` : elemental type of each column\n",
" * `ds` : a vector of Associatives\n",
"\n",
"Each column in `columns` should be the same length.\n",
"\n",
"### Notes\n",
"\n",
"Most of the default constructors convert columns to `DataArrays`. The base constructor, `DataFrame(columns::Vector{Any}, names::Vector{Symbol})` does not convert to `DataArrays`.\n",
"\n",
"A `DataFrame` is a lightweight object. As long as columns are not manipulated, creation of a DataFrame from existing AbstractVectors is inexpensive. For example, indexing on columns is inexpensive, but indexing by rows is expensive because copies are made of each column.\n",
"\n",
"Because column types can vary, a DataFrame is not type stable. For performance-critical code, do not index into a DataFrame inside of loops.\n",
"\n",
"### Examples\n",
"\n",
"```julia\n",
"df = DataFrame()\n",
"v = [\"x\",\"y\",\"z\"][rand(1:3, 10)]\n",
"df1 = DataFrame(Any[[1:10], v, rand(10)], [:A, :B, :C]) # columns are Arrays\n",
"df2 = DataFrame(A = 1:10, B = v, C = rand(10)) # columns are DataArrays\n",
"dump(df1)\n",
"dump(df2)\n",
"describe(df2)\n",
"head(df1)\n",
"df1[:A] + df2[:C]\n",
"df1[1:4, 1:2]\n",
"df1[[:A,:C]]\n",
"df1[1:2, [:A,:C]]\n",
"df1[:, [:A,:C]]\n",
"df1[:, [1,3]]\n",
"df1[1:4, :]\n",
"df1[1:4, :C]\n",
"df1[1:4, :C] = 40. * df1[1:4, :C]\n",
"[df1; df2] # vcat\n",
"[df1 df2] # hcat\n",
"size(df1)\n",
"```\n"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataFrame DataFrames DataFrameRow SubDataFrame GroupedDataFrame\n",
"\n"
]
}
],
"source": [
"?DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several packages for graphical output; a few such packages for plotting are shown below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gadfly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gadfly is similar to ```ggplot``` in R."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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vZw6vqQJvR57VsUr+Ut7TEx1RXvsj4AljTN/+bI/uxIMQjYb/o9gZlCpFsVjD48XOoFShiIgLRIFJecthqzKc9lwXzS/3EHrHMBwH3NvWgwB5ARf//wFW9EEqA+8eBpuykOxFMhbpyMDpI+g9ppYXuzza60KsrTIsdBwWjBw5ZEeh2qadi0FIJtvPjkQaHip2DqVKTTy+erLrOu6MGY3Lip1Fqf1JRA7r87i+3xJb10/ZmHKqn9lJ5Q87IZXB1Ab9sxK7OxPJHqgNwKnD4cGt/pmKX66H7zbBnVOxa/vZFjRkJpTRU+1ye5nDXWOM2bznZxaydmnnYhCs5TpAOxdK7WOOEzhOREKAdi5UyRi4fXREn8fbckKkz2N4Rjjjvi2Ef7CW0IgA5rKxmMU90Jbx39PtQaQagg7krD+e4q6NcPloOLYaae/HGxHEGxUkV+vy4KwangB2Ai1AuzHm/9yBWsjapZ2LQZFfFDuBUqXJtDiOp1Ouq4OeiBhgJBAGhu7I866VfVy+rJehZQ4cO4TgD9diXulDIlWYfgveHk94aemCi0fC7NFwz2bwQJ7aCe8ahvfRsXSuz/HiUJcttUGWGIfHgI5//yjvwtUufUKnUkoptQ+JSG3a4zQP3rctx9igMKwHpn1xDYEHtmAuHAUXj4JrV/rPohgT8m8XzQrMX+f/rNqFy8bAlxuQ7jz9G3JkDiunf0SIZJnh88aYZLHb+a/omYtBSCZT10Ui4euLnUOpUpNMth0r4gSi0YZni51FqTdDRAJADJiStUx7Jc3p/72Vw1/sInh6HU5TBc79W/wxFDvyUBcEd+DP+y05eHoXnDMC/mMCLOnDE8FOqsDbnmPJlAr+cXglbfiXC583xvQPJmMha5d2LgbBWnM2oJ0LpfY553BjJARo50IdNERkcq/HTzKWt7dncCeWUfbcLtxfrocNWczUShgWeHVw5vNd4ACzx8BP18H2HDyxE95WDVeMI3+25VkrbBsZZG1FgL8F4FljzK63mrOQtUs7F4MgYj5b7AxKlaJcTh4PBFwdc6EOWANjKUZ0e5wgQmSXx4i05T33bSX8jTYCqQzmR4fB8j7YkPXfs67fPytRXw4dGejKw1fa4HtNcN4I7PI+cmNDeI2V9A8L8L3RDv8EeoF1wNZ9NS16IWuXjrlQSiml/oWBDsUw/MGZtb2W97zUyxVLuhkWcuC4IQQvXurf7QHwk8mwsg9uWuf/e1IFfL/Jn0Tska1Ir0XGhbAXj6LvfXUkcoZ/OLDR5nmmO0Rq3H5+wFUh6JmLQUgkUndHo+GLi51DqVKTSKTOAxOMRhvuLXYWpcB/cmY/nJT1OH9XnvEiDDUO0R90EHxgC+acERByoG+POz22ZiFWA0dVwUu9/gDNuzfBV8PYC0eyeX2GzRPL2TE8wNIyl5+UG7OiEG0pZO3SzsUgiJhxxc6gVCkyxtSAhIqdQx3aBp5LcSRweB6mruzjfY9s46gFOwm9cyhOcy3OnwYGZ27Lw8gQlBswxp959PEdcEkZfKMRlqfJ5y3elAq8kMvCCWU8VF/GGmAt8JIxJlu4dhWudmnnYhAqKtI65bpS+0Eg0HNPKBTSy7WqaESkoc/jq1nLuzqzlI8KUr24i8qfrYO1Gcy4cphU+ergzBe6oN+D2WPhhg5Ym0Vae/1Hc59TR+7YWv6cF9bVOKyuCvKc43coeovRtkLWLu1cKKWUOmSJSEUWDrMex/QIE9J5Tn1kB5EfrqViWR/mqw2YTTl/ICZAWxrGl8HkSn9cRcbCF9bALw5Hnp+JLE+THRkkPyFEf6XDl4IO9xhjuorbysLTzsUgpNMVD6NTriu1z+Xz1R/M50WnXFcFISJu1uOszhw3dGQY1ZuDo2oI3rEB58VuxBNMv4Czx7m0lh5YnYbPjIeHtyPdeWxdAFmZIX10LYvGhngQ2Aw8CWzcV3d67AuFrF3auRgUKcjgG6UONdbKNpBgsXOo0iQioSxMyVlO6fWYvCHL0EqXc3/cScXPOjHvGQ4fHpjO3BP/bspNOTi62p/nI9njv3brBvhOI/Z9I9jS0c/6iWVsGx5kRcDwU2PM8mK38/UVrnbptU2llFIla+A20vHABA/q12U4f3EP70n2UDm5AmdmDYEjX/DHUBw/BL5QDz/ogGcGHlnVXAOXjYaJIXglg5f2yE+uxJtVS3JqBQ848DKwAVhSyMGZB7qD6szFNbOviTqYOwWGGMwKsfKh+XfN37bnMtd+9NoJkrfP7/GjKgMdN945PwIwZ/acZQaGAAMzxpmP3HTnTU++mRwtLR2zmpvrF72lxiil/o9Fi1JjAwHXiUYnrit2FnXwE5G6PsuFOY+Tt+QZMcRl3Ct9HPbddtxF3ZiPjYPRe01rnvbggpHQ2e/PUPpyL7zSC2eOIHfcUP6RtrQPDZCqgAUOtBxM4ykKWbsOqs6Fg7lNLF+ff9f8++defs33jON8A5iz5zI33npjJzBh97/nzJ5zB2JW7bmMCThHDyw3KMbIDeiYC6X2uWDQnAqejrlQgyYiLjATaMhZ3v5iN6c9uIXwK324F4/G2ZzFebHbXzaVgYkVMDoEm7LQ68H31sLXG+DRGciKNLkhLvnGCrLDXG6oDvBLY8ymYrbvrShk7TpoOhdXX3F1E5YJ8++a/wAAeefnuLKQvToXe7rukuuqMqTPs46dvi+zOI48tC/Xp5Taza4QcQ6a45I6sIjI4b0eP+63HLY+S/nYEMMf3ErlnZtgew5zbC0E9ni4/IJdsLUfrpsId66HPkHyFrs6jX1PHR0Ty7nTha2Ow4IgrAIO6idnFrJ2HTQ7cUCYKNDJwBmsTZlNHaOrRtVde+21ZTfeeONrzhDX7/Sdj/Diz+782do9fy55++Tc2XMMyF8rncznv3/77d17vn7vveJOndrRYK3TH41OXLdo0frKUCg3JpsNds2aNW5rIODe3tra3uS6FZunTRvVs2hRamwoZCqy2S1rZ82alUsmU42eZ21zc1P70qVLQ55XPcF1bd+0aY0bn39+ZW1lZWiE59ntsVjjzpaWlSMDgVBNT4+z4fjjJ6aXLFk1USQYnD69vg0wS5Z0hEXcbCQyoXPvHK2t7cOAYaFQesvUqVO7984Rj7eFAWKxxtTuHNmspGfNCm9YtmxZTTZbMRLYMWNGw47dObLZ4MZZs8b1JZOdE4zxQtOn16cAlizpCBuTy02fftja555bW1Fdbcfm89nu5ubJW3bn6OvLbj322MldS5e2jfE8p9J1ezqnTZuWjcfbwo5jTCQSblu0aFEwFBo5ce8cruvsnDZt4vZFi9aPCIVytXvnWLasvv3CC7FLlnQ07p1DJNcTiRy2OR5vG+q6zvC9c1RUZNdNnjy5v6VlTYPrOs6eOfJ5k2lurl+/dOnmas9Lj9o7h0jZpkhkTG8isXa849iyPXNks/n8rFmTOtra2sq7u51xe+fIZvPbZs2atGvJkjWjRdyqPXMEAq47Y0bDmieekEBdXUf93jnSaXfXMcdM2LZwYWddRYU3ZO8c27bVd5x8ssm3trY37Z3DGK93+vSmTYsWrR4SCgXq9s5RU2PXNzY2ZhYtWl0fCgUCe+bY/Z1PJjdWGdM/eu8cu7/zLS0d4wIBKd8zRz7vec3NTe0rV64sS6dD4/fOsfs7n0yuGmVMsHrvHNOn17fddx8vTp3a0ZBIrB3/Wvve0qVrh3ueHbp3jtfa93bneL19b3eO19r37rsPZ+rUjobX2/d253i9fS+ZTDVaK7Lnvrd3jr33vdfI8T/73u4cr7fv7c7xWvve7mPA6+17u3O83r43kENea9/bneP19r3dOV5r39ud4/X2vd05Xmvf251j48aNVfl8cMbQoTWHl1cFA5k8/3H3Fpp+1EnoiEr4wEicpT3+xGAAW/JwfK3/OO7Vaejx/NtIf3Y4cvEo+lb20RWuoGtMkPW5vvy316xZ37bXvjf63nul/aKLjPda+97uY8Dr7Xu7jwGvte/tPga83r63O8dr7Xu7c7zevrc7B+Tvam1tb3qtY8C994oLcNFFxntr1dpX0hMEWcfMNnDnnj8znjn9pjvnTzY5J2aMGd5ny7+/9/tmzFheaa2dB97lAGVlmcOstfNcN/segFwu/21r7bx8vvcogECAi62184LBocMARPis4zifAsjnh4yy1s7L5cwFAJWVbtRftzkewJjAmdbaeWVlXhjA8wIfs9bOW7VqVWjx4g3l1tp5It4VAMFgvslaOy8QyJ3mf4450Vo7L52umOG/bi7y1z1ihL9uPgN8GqC/v2aEn9FcBJDJVET8dXMCgOOEThtYd6O/FbwrrbXzFizoLEulUiG/ve6V/vbwwv72CJ3uZ5bjrbXzKirKIv72cS7021w70l+38ykRPuO/d/hw/3PkgwB9feVHWWvnZbP5EwFcN/tea+28UMib5LcxP9taO2/KlE3lixcvDlhr53le4CqAqqpcvd8G90z/veY4/72BGEA2a95vrZ23a5czeiDHtcB1/v8PGWqtnec49mL/99Q7zc/sneRvy/53W2vnWZue7G9L7zJr7bz6+lVV992H47ff/TjA9u2BCf52D5w9sN2P8be1O9PfPuY8a+28np7AWD+nM8daPud/H9YNsdbOM0Y+4mfOHGGtnVde7p08sK1P8dedmeL/XvKXWGvnDR2aqhYR429L5xqA7m5nnL993Pf5bQwc7bcxcLSfw32fn8Md739vnWv87Sdm6NBUtf85+Uv8z8lMGfhengJQXu6d7P+eMkf4bbQfttbOq6xcNwTAWj7nus4cgJ6ewFh/25n3+9vSnem3kWP8dQfOttbO27nTTvS3h/txa+28++7DmTJl7fHWyvUi3my/DZnJ/rbsf7f/3fJOGtj3pvmv24v9Ng4ZOrD7XjfweyaTMaP8zOZ8gFAoEPN/b+Y4/7vlnmmtnVdVlasf2PeustbOW7x4cWDKlE0D+17+cv+93qSBY8B7/d9T/kRr7by+vvKj/DbIB/ztNXy4v24+s/sYkMvVjvS/W86FABUVZRH/9yTH++8Nne7/zm2j/110r7TWzkulUqEFCzrLBo5FV/q/B9votzc0cAzgBGvtvEymIuK/17nIWjuvv79mxMD2+PTAcQAYMcJvg3wAIJ2umOG30Zzofx9yp/nbOt/kr9u7wt8eG8pXrVoVGtj3PgZQXZ1v8H+ngTP9dZvj/e+DG/XbbC7wvwNlu/e9T4nwWf/7MHSY/71l9763+xjwDn97ZN/jtzEzeSDHbGvtvBkzllc++SSuv97Ax0WkNlQ55JPlI4c+vsEJ3rKyj5u6LEd8M0X5K32Y9f04ub1uI13U5T+X4pPj4cOjse8fiT1uiHhb8rQNJ/+XI7K7lld27by73OWMVas6R/jbJ3O4/53PX2qtndfUtKZaRAaOAYFP+N95d/zAMeAcvw2Bt/nbMjDL/26Zc/3vS3DcwL43x1o+D1BX11Hjt1cu8T+nf+rAMfBdA9+9d/nbun+q/7p8xFo7r66uo2Zg3/t8IODve319wYFjgDnX39aBWQM53+ZvS+cGa+287dsDEwZyfmLgGOA0Na2pnjw5VcOh5uorrm6aO3vOZgbucJl7ydzGgX+/pjlXzmmYO3vOjs9c+JmK11vmk5dec/Lc2XPibzZLPN7+1Jt9j1Lq30sm2y9NJlMfLXYOdeARkWCfyMTevJy1LSef7c7KzS/3SfeVy8RznxCZ/YrI37aLlD8lwhMio/4pcscGkW+2iUx8zv9Z5dMiVywTeblX8qmMLE32yGOb+uXXGU+uFpExxW7j/lbI2nXQXBa5+fab18y9/JrOa2dfc96Nd/7sT+LK1Y7wh92vXzP7mtOMNS/uvnvEeHKpEfOHH933o/TuZT5/xRU1aadqyI233th57bXXlnnd9lIg8WazOA7X75NGKaX+F2vzC1zXcYudQx2QjtyW5fObMpzRnqF8UiWBV3oJ3L7Bv1a+Mw8jQq8+X2FrDv60FS4cAd9thJX95EPgTasiXxPgr2ND/KmhjA6vZqwAACAASURBVDZgPbDWGJMvXtMKo5C166DpXAAYz71SHPuruZfN+QnwCo7z4d2vOZibbEBmA88CIOZSwcze8/39+Zph4uT/PHf2nGHSZS2Gp0Ju6JNvNkck0qADOpXaD2KxSSuLnUEdGESkpjfPMTjM3NlPfa/HzGd2Ev1BB6FED+YrDVAdePU20md3QbUDV46DW9ZBVuCJHdBcBZ+eSLYrz/2OYUttkGXlDs+4sMIYkylqIwtMa9cBLpFI/bDYGZQqRfF4+6mtranTi51DFZeIONm8zE50y/Jfb5D0bRsk09Il3qVL/csbPCHy2VUid20UcZ/0/22eEDlniUiiS+SpHeL9cp30/3W79Lb1yY5sXq4Q0dl2C1m7DqozFwcKETOz2BmUKkWOw1gRDvkicKgRkUA/NOYtZ6Q9pnb2M6LG5V23bmTIrzbiHFWJuWwMdO8xS0eiG84fAR8ZDb/fDBZkZRqW9OFdMJJXjqzkL+UB2kMOTwdhtT49s7C1SzsXgyBiLi52BqVKUSBQ8SfX3a7TEhwCBh7LPRwYn4cxO7J86MVuznlhF9X1FZjjanH/uBnTnYdteQgYGFsGVa7/sKsXe+CujfCdRmRePbnNWTKHVZKrc1lc7vC5ijLTWuw2HmgKWbu0czEIzc3164udQalSNG3aqJ5iZ1D7n4jU9HmcZIVTt+WYUOEydkOWmd/vIPjcLswFo6Ch/H/maKAtDcv7/GdUbMlBohsBpM8i67Nsbq7iv4+oJAG8Aiw0xrzms48OdYWsXdq5GIREIvVwNBo+q9g5lCo1iUTbB8AJRaMNvy52FrVviYgDhIFxOZi5PM1FT+4g2tpL8JThOAFwnx2YLGx1GuoCcHiFfxdI1sJvN8OoEPx4EnZTjs0OdE0oY1dtkN858DtjzOs+mkD5Clm7tHOhlFJqvxKRMRnL7H7L0VvzjBzmUP/sLib8ZB1ORwZTF4TpVa8uv6QXWnrgirEwJoRsyCKVDjZgsFUuyUiQ2xzHn4kU6DBm3zxVUu07em1TKaXUPici5cBUYGTG4wNP7OK9D2xhdNDBOXU4zsNbMLdu9Je9YBRcPRY+tQpe6vV/dmQlfKcJOXYIPe0Z2kcF2TYyyLoyl5sC8OKh8FyKg5meuRiEZHLNlEikaXmxcyhVahYu7KxzXcfMmjVua7GzqMERkUAOjur1+FTa0tiVZ8jQIJPu2EjVI9swh1VgjqwC2WPyiXg3rB4Gn5kAz3Vhd+WxE8qwQcO24S5/Hl3DQ8AWoNUYo+NyBqmQtUs7F4NgrXsLOuW6Uvtcebl3JuR1yvWDzMCdH6OAwzwYuzPHtQ9sZeafNlMeqYXTh+MkujF9Hmzoh+48TK+EpgpYk4a1/fDAFvjPMN57h5PamqNjbBnralziQYd7jTGdxW5jKShk7dLOxSAYI4uLnUGpUmQtG1xXj0sHExEZkvE4Oyt8cFuO0SGXoVtzNP6gA2dVGmMMnDL01eW35eGJnXDhSPjCRHg5jWct3pFVeDUBWsaWcc/4MpLARqBNL3/sO4WsXTrmQiml1JsycOfHZKA+D8cku/nQ7zfT9OwuAuePwjSW4Zy/1F92YhncdYT/oKu7N8OuPFS7cOkYuG4iOQML8sLauiCpSniqLMBCY0xXMdun3jr9C2EQksn2s/UZ7Urte/H46smu67gzZjQuK3YW9dpEZHxvnk9mhbdvyFI9qowx/+xi5B0bMVtzmBnVMHWPuajXZ+E3G+GcOohVI2vSeEMCeDNryNW6/GGI4beBABvxz1RsN8bY1/ts9dYUsnZp52IQrOU6QDsXSu1jjhM4bmAOCO1cHEAGficzgHEZj4v/vot33rmBEW0ZzKcn4qzuw2zN+cu29sInxsIZdfDoNvAE/rwdplUjl41hx/YcT1Q7rK8O8lK1wz+AlF76KIxC1i7tXAyK/KLYCZQqTabFcTzn3y+nCmHg8seRPR5fz1gat+Wprgsw7lcbqXx0B2Q8zLYchNxX37OsF+7ZAp+bAOfUYTdl8erLyc+oZsvQAN8dHuBW7UwUS+Fql465UEop9T8G7vyow39GxYhdeb5092aOumMjZbNq4Mw6zFfWYOIDN4R+rh5iVfDDTljUDY6BY6rhyw14xw+ldVeeZG2ANVUBXiiDF4wxO4rYPFUgeuZiEJLJ1HWRSPj6YudQqtQkk23HijiBaLTh2WJnORSJSE3a4/Q8zN6RZ4xrqM1awt/twFmbwVQ5/uUOZ48/S/+yHcaF4L+aYHWGfL8lP7kcb3IVz9TA94aV87SOozgwFLJ2aediEKw1ZwPauVBqn3MON0ZCgHYuCmSPOT8a8hBblebK321m0t93EDh3JKa5Cqcj4y+7pBeswJl1sK4fNmZhWR+80AVn1JGNVPHnrLCyymFFRZCFAVilHYsDRyFrl3YuBkHEfLbYGZQqRbmcPB4IuDrmokBEZHTGcmW/5eSNWYbWBRj7Yjdjb9uA2ZLDTK2Cd+75jIoc3LwOrhoHd0xFVqXJ17rkp1aRGx7klqoAPx1pzNritUj9K4WsXTrmQimlDiEDZyqmAeP6Lec+uZMz7t7E2GV9OFeNw1mTwXy73V92SiU8PB3+s81/ToUAtS7MGY98MUxXX457XcO6SpcW49JSARt1EjEFeuZiUOLx1C9jsfDHip1DqVITj6fOBAnGYo0PFDtLKRKRcJ/lC1nL4ZuyDBsZov6+LQz941ZMj4c5OwuVe5w3as/A9zvgPybABSOx7f3kG8rwjqxiV6XDV2rKuFM7EwePQtYu7VwMijm82AmUKkWOY+qAULFzlBIRqQUagZp+4SsPbeP432ygclIlnDoMZ1Ufpmege9DRD2cOh7cPgX/ugoyFBbvg5GHkzxjG0h7LwmqXlSFY4MIS7VgcbApXu7RzMQgVFemzip1BqVIUCPTcEwqF9HLtPiAi5Xl4W5fHxb15GjyhriJA7JspAi/3Yo734IQhENjjTMUTOyFcDt9thDUZvF6P3KQKvMMqeWFIkO8MgX/oAM2DVyFrl3YulFKqRAw8o2IkMCbrccTaHB9/dBtve2onZScMxRxbg7u01192eR9kLZw8FFb2+Wct2tKwYCecNozcOSP5S7/H0iqXlWUuL6B3fqg3QTsXg5BOVzyMTrmu1D6Xz1d/MJ8XnXJ9EERkSNrj1JzlpB2WMdUuTct6mf6TTgKr0xjXwMlDXl1+ex7u2gSXjYabJsOqDPlKh/wRleTGhLh7mMsNJmBWFK9Fal8rZO3SzsUgGCPri51BqVIkIt1ggsXOcbARESfjcXZrL596aifTlvUROL0Ot7MfZ1XaX2ZhFwwLwgUj4Y9b/OdVPLMLJpfDNxvJzqrlHgc6q1yWBP0zFXpLaYkpZO3Sa5tKKXUQEpG6Xo8zPSG2I8fEIUGm/3QdTT9fj7uhH/OVBhgRhE+v8pd3DcwZ53culvUh7f3kJ4TwmmvJNtfwXwG43hiTK26rVKnQMxeD0NLSMau5uX5RsXMoVWoWLUqNDQRcJxqduK7YWQ5kIuJm4WMtPcz5505GlTuYWbW48S6cDf3+Mh39cGwtHF0LL3b5s5M+uh0iNXgXjia1I8eiWoc1lS4vBuAZ7ViUvkLWLu1cDIIxcgM65kKpfS4YNKeCp2Mu9iIiLjCux+P4rDBtS5bxQZfzfraeIfdvwcSqMcOCYPc4F72wC2ZUwZcmwstpvK48+aZybKSKlVVww9Byfqezkx5aClm7tHMxCI4jDxU7g1Klya4QcfS4tJd0mrHdLnOXpbnilR5qR5djJpUReGwbpt/6ZylyAtEqWNztz/vRloHHt8NxjeSPHUpLj8dLQx06a0I8H4TntWNx6Clk7dIxF0opdYARkWAaxohHJCtMcQ3TVvTygS+nKH98O86Fo2DOeDirFbo8fzzF+SPhjOHQlYflGTxH8GZU4729lmcmV/GtoDHPFLtd6tChfyEMQjye+lAsFv5dsXMoVWqSyY6jHCfvTJ/e1FrsLMWUhSO2Z/nsxn7OTqWpaKwksCKD+9ftGAFe7vXv/JhVC8/s9M9a/GU7jAzAF8Pk0pYFDqwdHqCtOsDjAWgpdptU8RWydmnnYlDMxwHtXCi1z0mztU4IOKQ6FwMPv6rIw9vEMi3vccqzOzntB2spb+nGzKuH+nJ/4jDwpzl/fBtcNRamVCAbs9haF3vsEPJDHf42PsQNwDZgHbDTGCOv++HqEFK42qWdi0FwHK4vdgalSpG1+QWu67jFzlEE07s8bu7JM2NTDndkkNA/duAu7vZf3JSD42uhodyfTCwnMH89/L8G7H+G2bkpx8oRAdbVBVld4fAr4BV9mqbaWyFrl465UEqpIhCRaqAZGNdn+crt6zn8mx0EwuWY2WPg6Z1wz2Z/2Ug1fLEe2vvh7zuQjIedUI59/wh2nDuSx1z4DbAZWG6MyRSvVUqpQUskUj8sdgalSlE83n5qa2vq9GLn2J9EpCydk1O68/LchoykVvfJpg39km1cIJYnRA57XuTmdSKfWSlS/bQIT4hUPCVyelLkhV3iremTDYu6JLE+I4+lPfm2iDQWu03q4FDI2qWXRQZBxMwsdgalSpHjMFak9KZcHxhTMQpo8qBxq8c3bt/AxNs3EjijDnPGcEx2YFREKuOPqTimFjZlYXEPIiAjgtisZcvECn7TCE8DKfwzFfrwK/WGFLJ2aediEBzHu6rYGZQqRZmM+4jrOiV1uVZEhmQ8zsrBedtzjK5wGJ3KEP5hJ86uPGZ5H1w6GsodMAbyAr/fDJUGvtOE3Zlnm8D2iWXsqna5LwC/McZsLHa71MGnkLWrpHZipZQ6EAycqRgL1PVbTmnt5Yr7tzB5YRfBC0ZhKh2c2cv8ZSsceGwGvNgDP+qArXnEAB8ZBd+eRPcIl685DquBpUC7PvxKqRKVSKQeLnYGpUpRItH2gUSi/ZJi53grRGR4Oi8f6c7JT1b1yd3bsvLy9R2SG/VPf0zF1StEHtzqj6XY/d+MF0UW7BTp7Jf837dL77Je2d6dl868yEUiUl7sNqnSUMjapZdFlFJqHxERt99yyeJern5qJw0dGZxzRxBYncbZnPWXaenyn0/xwVH+5Q8BOjLw8Hbsf4ZpGz+MW4HVwJPANn1GhToY6WURpZQapIHLH8N688yyEOsXGgMOp31hNRN+vxlnZx7z08mwIg03dfrvqQ3ARSPgMxNgUx5vXT/5w8rJN1SQGhXicy48ph0KdbDTMxeD0NLSMa65uX59sXMoVWqWLt1c7brbzdSpU7uLneWN2AJVZZZLV2eYt7yX4eUuNJYT/OdOzM6BkRFbs9BcDdOrYEkvdOdhYQ+8lCZ3xnD+2VzNC2WGJbj804V12rFQ+0sha5d2LgbBGLkbnXJdqX0un0+/P58vP2CnXBcRk4KycRAGolgmb8zxuR+spereTTjvGQ5zJ/zvqc8f2wGXhuA7jbAig5e35KdUkp9WzdPlLt8PwNP6NE1VCIWsXdq5GARjZHGxMyhViqxlg+se0MelUcPzXLXdY86mHLVVLu6WHMF7NvkTiq1MQ5mBmTXQmfFnLG3tgcdD8O1GcrNq+UdeWFYbZEWFw/MBWKYdC1UohaxdOuZCKaX+BRFxgMlAY7/l3Yu6uer7a6l6eCvONePh3cPg3Jf8ZQMGPj0BZlbDy2kklcFWOHjH1+KdOozH6uCL5eVmeTHbo1QhHMh/IRywksn2syORhoeKnUOpUhOPr57suo47Y0bjsmJnARCR8WnLp/stJ63vp7ouyOind1H10FYcwX+a5viyVycUywv8dhPUuMjVY+nb6dFa5dAxLMDqcuH+YJC2YrdJHboKWbu0czEI1nIdoJ0LpfYxxwkcJyIhoGidCxFxgUZgRMbyicd3cNqt66lbmcb5j4mYTVn+Z8Tl4m5Y3++frXhgC9JjkeEB7NgyMiNCLBhr+DawHf9R3T06WFMVUyFrl3YuBsEY+7tiZ1CqNMlL1hZnyvWB20on9Fku7fc4sstjXFWAI3+9kRGP78BkLGZbDkYEIWj8ac+35ODb7XD9YdjzRtK1PsOW+nI21wVZGTT8wBjzcjHaotRrKWTt0jEXSikFrF8vlTWj+OGTu7josa3UTCyHWbW4X1qNeWHgxtjLx8AFI+HWDfC3HeCBHFUJn68n/b4R/DlguA9oBxLGmP5itkcpdZBJJlPXFTuDUqUomWw7NpFoP6EQnyUigXRa6rvyct72nHx5Z15u3dAvfSe0iHWeEDmxReTBLSJvX/zqY7pHPivy+VUinRmxyR5JP7dLtm3sl/UZT24VkcMKkVupwSpk7dLLIoNgrTkbuL7YOZQqPc7hxkgIeHZ/f1J/Pw07XOa09XLJqj5q6itwKgzBZ3f5r7/UC1mBM0ZARz+s7fcvg7zYDZuzZGfUcJsLi4BW4CVjTHZ/Z1bqrShk7dLOxSCImM8WO4NSpSiXk8cDAdfZX+sfuK30CGBav+WE5V1c/s0UVU/uxFwyGj42/tVld+Zh/nqYOxZ+fSSyso98jUv+8ApyDRX8Igc3BYzp2F9ZldrXClm7dMyFUuqQICL1fR5fywlndWYoHxqk7E9bCX5ypX8cjFXD74+Eq1bAUzv9CcUqHLhwFPLDSfQC92JYXQ6LCZCsgi3GGK+4rVLqwKRnLgYhHk/9MhYLf6zYOZQqNfF46kyQYCzW+MC+WN8eZyrG9wtXPbaD077bTmWiF74Wxtg9bgxdnoY/bfUnFDu+FtmYxRtdhveOIWRqXW4LOnxJB2mqg1kha5d2LgbFHF7sBEqVIscxdUBoX6xr4EzF57LC1K1ZRg4NMvG3m6hY3AMimJ05OKIKRodgUxb6PLhtA3ypHvvJCWzYmmPZiBBrhrosCTrcrx0LdfArXO3SzsUgVFSkzyp2BqVKUSDQc08oFHrLl2tFJJj2+P4j2zntdxupbqqEE4fg7MhhZOBsRbwHjqmBK8bAUzuRrMVOqkCGB9k6PMCDo0P8GtgCpIwx+beaSaliK2Tt0s6FUuqgJyKB/n4a8gFOyHgcsT1HgxjO+l4HlfEeaM5ijq2Fqj0ez/VCFwxx4Wth7OVj2LrLsm5iGbuGBXkmCLcZY9qL1yKlDm7auRiEdLriYXTKdaX2uXy++oP5vLzpKdf7+6nfAp9e3s3FS7qpaarAGV+O+1KvP65iSa9/98d5I6AtA0t7oduDXg+yQu6Icn4VcHgKaANWGmNy+6WBShVRIWuXdi4GwRhZX+wMSpUiEekGE3yDywaBKZ7H9Kzh2FQ3l38rReXTOzHnj4QvNLy6bL+F69fC/6uHxyJIW5pcpUOuoZxctcuPAg6/MMZs2k/NUuqAUMjapbeiKqUOOiJS32P5Qtrjog39VAwJEPz7DgJXLvePaeFy+NsM+HI7/HGzP1spwNnD4Y4jyAwL8kPxWOW6PAes0TMVSu1bB9WZi2tmXxN1MHcKDDGYFWLlQ/Pvmr9t7+XmzJ6zzMAQYOAedPORm+686ck3s45/paWlY1Zzc/2it94ipdSeFi1KjQ0EXCcanbhu79cGbis9HJiYsVzyxA7e/912Khd2Yb7UAHV7nO9Y2w8/XgefGAvvHoqkMuTHhvCaq8nVuFzvwA9MwKQL1zKliq+QtWu/dy4+cPoFHxZjfpkN5cY98MADO/f6+W25svz4+++//w0Vdwdzm1i+Pv+u+ffPvfya7xnH+QYw57WWNQHn6BtvvbHzrazj9RgjN6BjLpTa54JBcyp4/2fMhYhMSFs+mckT2e4xsjbAxD9uoWJBl3+mYnMOotX+GYtUBjyB+7dAfTnepaNZ3+uxeEiQZZUBFufgmTKjHQt16Clk7drvnYsuev9QQ/WPQtnQpcBPd//cGvNxY8wf3mjH4uorrm7CMmH+XfP9h+vknZ/jykLeRMdgX6wDwHHkoTezvFLqjbIrRJz/dVwSkUDG8rXHtvG+ezYzdHQIc9JQnJ25Vy/rtnTDO4bANePh6Z1I2uKFy5DDK9gyLMBdo0N8yxiTKXx7lDpwFLJ27ffOxaOPPtp/0ekX3I7h4wx0Ls4/+/ypxuNE8eTEN7qegDBRoBP/qbxsymzqGF01qu7aa68tu/HGG//Pw20kb5+cO3uOAflrpZP5/Pdvv737ja6jra2tfNcu51KQrdFo+E8tLWsaHMd9L/ByNNrwrLXOU4lE+1Ui+SdisUkrE4n2s4Bx+Xz+97NmTdqVSKSuMMbkI5GGuxYu7KwrK/PON8Z2RCKNjy1enDrCdc2JIC9Go+F4PN72TmOcw8F5JBqduC6RaL8IGLpjR/3tEyascnt7Q5cZw/ZIpOEPS5asmuh5wdONMa9EIvXPtLR0zHIcaTbGeyoSaVoej6fOMMZMELH3xmKNOxOJtstFkFis8c7W1vZh1nKhiHTGYuE/t7a2TbXWeYfjsHjGjIbFra1r3mGtO9V1c49On37Y2mSy/QIRhgeDPXem02kJBEZeDuyMRhvuTSTWjgd7prV2eXNz41PJZKpZxMzyPHlm5szwK62tqdOtNRODQecP06ZN3B6Pt11mjHGj0fDt8XjbUGOci0RkXSwWfiSRWH04BN4pYuKxWP2LyWTHiSJyRD6ff2zWrEkdyWTqfBFTN2SIvSuVCueHDeu4AuyuaLTx9y0tHeMcR84CWRmNhp9IJFIxMEdby7PNzQ0vJxJt7wWnIZ8P/mnWrHFbk8n2S0GCkUj4tuefX1lbXh76oDFsiEQaHorHV082JnAyeIlotOmFeLz97cYwTcT+NRZrTCUSqfPAjOzrc3593HET+pPJjo8aY7sjkca7ly5tG5PLOecYI6sikfA/WlrWRBzHPcYY81wkUv9SPN5+qjE0GpN7IBI5bHMi0f4RESpisYZfLl26uTqXS3/IGLMxEql/cMmSjkmeJ6cYY1sjkcbnW1pSxzmOmW6MPB6JhNvi8bZzjXFGGVP220hkTK//PZSeWCz8uyVL1oz2PPd91rKmubnhb62ta6dba49zHGfBjBkTl7S0tL/bcWhyXe+/p09v2hSPpz5kjKmORhtuSSY3Von0f1jEbo7FGh9IJlONIuZUa2VJc3N4QTLZdqyIM8N1zd+nT69fnUx2nCMiY4LBit9NmzaqJx5v/5gxpKPRht+0tKwc6Tih88BLRaNNf00mO44SkeOt9RY2Nzclk8nUu0TMYfm8PDRrVnhDMtl2sYhTE4nU37pgQWeyslIuW7lm49TxjaN39KbzJ2zoyh4XrAgd8ZNOahZ0w5QKzMxaGBIEx4AVeLkP7tgA3wiLXFDHru2eeWl8iJ5AOt2/rr17eVPT6ExLS/uRjsMJu/e9RCJ1MpjJ1pqHm5vr1ycSbR8AZ8iOHfW3h8OpwK5dzqXGyLZIJPzHRYtW1wcCgdN273vxeMfRxkgM8k9Go5NWxOOpM40x41/d91JXiIgXizX+aunStcNzOXuB48jaGTPCj+4+BhgjiyKRcEtLS9tJjuNMMcb9cyQyoXP3MSCf33JHRUWFyeWqZ4uwIxZruC+Z7Jwg4p3hON6yGTOanm5tbZ9pLTMdxz49Y0bjstfY92Ybg4lGG+/Ye99LJtdMEXFPsta0NDfXL9q97+19DKiqyv6qs/Mwz9/3/vcxQMSuiMUan9y97+0+BiSTbaeJOPX9/e4fjzlmwrZksv1Skf/P3p3HR1Wdjx//nHtnyWQh7MiaCSBuwMwAaq1at9q61NraYl2qpdpqZbGLdWmtVm31K+rXHStaV1q1at21alUEcUEhMxNAWZNJAgkEAllnvfc+vz8mKfz69futRDJDwnm/XvxhZnueMfc+J/ec+xxxBYP+h5ct21Dqcrl+ANQHg2WvdB17XeeAzl1xD3Yc+40pU8bWRCKxM0ANLi11Hv/kE39mwoTaC5VyWgOB8qey02jqtK5zQDhcHVTKOAx4PxgsWxWJVH0DTL/jpJ+fMmX/rZFIzXlKiTcQ8P959erVJcmk7+yuY2/58rrxpukcr5QTDQTKl4bDsa8qpSb++7FnWe6/TJ06PBGN1v6069jbeQ5gQyBQ9vaKFVWTbdv8Stex9+/ngHA4dq5SqigYLHug6xwg4mwJhcpfrKysGes4fL3r2Os6BxgGb02eXFYVDlefrpQxrOvY6zwHdIRC/r9Go+uHiri/I0J1KFT2z65zgGnaH02aNLYSVDgSqbnI7XZeOuSQ8s1d54BAYMyDy5c3+ACmTRsR706t/3c9tkHQ//ch4rofOHD6SdOPAjAt82Jg5TOvP9MjOx8qW51876Pz9lcZI6SUGhh3Cm7Zo++vnG/uyffTNC2ruNg82OM1JhQM6H9SRSt/eqHZNWtZxjMtlqTk0wQq7aDWJGBzCk4aCIeVgEdBxkGUQhxFZoiTXjq1hOs9HYkb6mu3vdHaGm/Md16atneQ4/MdwR535inTXznzlO8vOPnkk71nnjK9afrJ02fvzusvueCSsbNnzGqk8w6X2efNLu/87//TpefPPG72jFnhL/Me/y4crlm0u6/RNO1/JyJKRMY2Nrbdsb0l/lK0TVpPrRTbWChy+gqR91tEhr4vwsLsP/+HIvM2idSnxFnaKsllrdKyLSONKVtuFJER+c5H0/ZGuaxdOblyAYCj7gP1/RJVcjHgJcOC3Xn5nx7+UxVKNs6ZMfO7AGLKJUp4tuvxmTNmnjTr/FmDrrjggpI5P5kzCmDOnDle21DnC0S+yHt8UYbBbbv7Gk3TPp+IDIs7XN+S4e0dhUUzOry+r69PUPxaE4ZDdj2Fz4BTB0JhZ4fNWBJe3Aq2kJpWxF1TS5gzyMXXPAa/V0rpPjSa9jlyWbtydivqwV85+PXPPl5VL8itChY8/dYzLbv7Hso2LxTDeWz2j2bdBXyGYZzb9ZiButdxyYyUVVIrhvXa7BmzBkir46BY5DE9l36R9/iiAoEyvaBT074EEVHAGGBYyuGsj1qZcUcd/RY1K+PSkaixvs6FUUB9Gu6rh7OHwtGlSHUSa4gHO1hMptRknmFwi1JqRx7T0bResAwKFwAAIABJREFUoc/WrumnTL/qzFOmy1mnfG9avmP5MiKR6uvzHYOm9VYiMiRuy+wWSx6NJeTtbWmJ/Wq92K53xWGhyA8/FfnHNpGyD3dOgwxbInJ1ldhbUtJQk5QXtqRkbtySs0RkZGf/C03T/oNc1q6cNtEyFCNFWP7Ua3/v1Q2oRIxj8x2DpvVGImImHS75sJUfv97ECK+BcWg/jMY0RlcXzYp2OG0QzB4Jb+5AEjbOSC/OxCJaSl08P9TgMqX7VGjabstl7crJ4OI73/lOf3fSPVGEC5SSC3PxmT3JMOyL8h2DpvUGIqK2QlFhhnHKYFqbw4G28P2HGhj90jaMMQWoUQVQvMtupVUJmFcPN5XjnDGYpu0WW0YW0DDQJOLN7gGiBxaa1g25rF05GVx40u6PMBgN8vjfXn32b7n4zJ4UCIxdk+8YNK032LqVooKBnLnZ5oZNcQYZgtHfjfvjVlS7DRsS0JCC4/pDpB0+aoWkA0kbWmysr3h5eayPl4Eq4DO9B4imdZ+uXXu5SCT2Sr5j0LS9VedtpfuJyNGWyE9jSdlx4WqxzYUip1aKvNkkMv6jnespxn4kcmedyIa4OO/sEOud7U4qlpTmVkvuF5Hx+c5H0/qKXNauXrVx2d5CRJXkOwZN2xuJSP+kzelpOGt7mqEFBkNqkvR7uAFDgM/i4DXhyH6wJQ1tNlQn4NlG+MZAUlPcmaWZTHpjqbfoXjesBDrynZOm9RW5rF16y3VN0/aYuCVnrYxzzVONjFvSjOvMYaiRHoyzP80+biqYNRKOKYUNSSSWwC42cQ7rh3V0Kc/283CtT6ma/GahadqXpa9cdENFRe2IKVPG6EY92j5PRIqSELIdDm3OMCopHLmkmf0f24yrKYMa74NDR8IAN+zIZHcrfWoLeBTyq9G0tmWIFpqsL3WxusTFq7FYc8vq1atLDjzwwLZ856ZpfU0ua5ceXHSDUvIkest1bR8nIkba5qT1Sa78tINDapKYR5Tirk5iNHUuu/y4FS4aDnNGwJONSFKQQS4Y7SU1xE3lcA9XAduBOqVUPLu5XMH/2HJd07QvL5e1Sw8uukEpWZ7vGDQtX0TECxwGHJiCi57eyuQH63FvTqNuHw/eXVpa1abhhlq4Yxzyo/2Ib0rRWu6jZYiHdS7FNUqp6K7v7Tg0mKY+L2laT8hl7dJrLjRN+0JExMzA1LTN3XGbUTssCjwm/aavxLWsLXsuuWIMBIvg9o2wrHNiY4IPriojc/Zg3ixw8SrZhZofK6VS+ctG07SepP9C6IZwuPrYUKj83XzHoWm51AGDUxaP/7mesXdvxHVUfzh3GKqrsybAP7bDcDfMHQtVSeyMQ2b/QqyxPha7XMwF3ldKOf/bZ1RUVJWZpmEEAv7qHKSkafuUXNYuPbjoFuN69JoLrY8TkYJ4hom2yQnxDONSFuMa04y9sRZXq4Va2QFuBeN8sC4BHTasicPTW+HI/qS/O4R30w4VxSZrlM0nLtjwfw0sAEzTPAZEr7nQtB6Ru9qlBxfdoJTzRL5j0LSeloGJ9RZXLG/mG9E2fFNLML0GZquVfXxtHMJt2X1AigyIpXA8CufY/jiDXIT7ubjao9Ru7iMkKx3HMP/z8zRN2125rF16zYWmaf/SucPoRODguM2Jb+zgrOtj+CrbUecOg1mj4IhdloSN8sLPRsD5+5Helqa22EXDUBcNRW4edsF7Sql4vnLRNE3rVaLR2K/zHYOm7Wki4m+35IkdGWla0SZttUlJ31iT3QadhSJjPxRZFxf56WoR1fmzgndF5qwTJ2FLg4icLSJHi8hAEenWHy7RaPVXIpGao/Z0bpqm5bZ26WmRbnAcdRpwW77j0LQvq/NKhR8YkhB+82oT37ixloJVHfDHclRylxUSG9Pwm2r4YxlcsB9OdZLMOB9WWQFNBQazgVeUUvL5n/RFGROUEg+w5Mu9j6Zp/y6XtUsPLrrF+X2+I9C0L0tEhnc4/NAWDtmRoazQ5KCHN+Ot7AAEtS0DE4uyCzY3JCDtwPvN8FwJ1syRVE4sYolLUYHJu0DDlx9YgG3bi0zTMP7zMzVN2325q116zYWm7YNExJ2wuG1xGz98Yxv9RhTApGLMP1Sj3m/NPufoUpixH7TbUNGOk3Sw9/fhfH0gVV8t4W63wcNKqXR+M9E0TesjwuHYg/mOQdN2l4i4RaTMsuR7GZGrt2Wk7biwOMZCkcOXi7ywTeTrkZ1boRctFjk+IvJhi1gb4rJ+RYe80piWhxKWzBCRoT0RYzgcOzUcrv5OT7y3pu3rclm79LRIt6gJ+Y5A03aHiAyIO5wTd/htU5oBLoXZYeNe2Jy9ehlth7YMnDkU6lLZfhUdNvgU+AysMV4echksBOqBTUopuyfiNAw1CPD0xHtrmpa72qUHF93g8yW+le8YNO0/6bxjYyiwX8rh2LVxrr+tjpKntmCcPQwuHbXzuUkH/lgLf/TDOwFkQ5JMiYvMGC/pfi5udsHDSqltPR2zy9X+lMfj0dO1mtYDclm79EGsaX2QiPRPwIkZm2O2pxlebHLQi9uZ8NPVmAJMKoKXJsGM1bC4GbpWYn6tP/LcRBIDDOYZBivJ3rVRq5Sy8peNpmm9jb5y0Q3hcM2iUKhMt//W9koiYiRtvl3ZwaULmzl4dQeu04Zg1qcwugYRaxLwbCP8ciQc3g/ZnMYZ7MI+tj+ZfgaPGQbXKqWSuY49u+W6eAIBv27/rWl7WC5rlx5cdINSUp/vGDRtVyJibIOi/nCEBVPSwimvbWfSA51boY/0wWgPuBRYkr2t9K5NcNUYnF+MZmtTivWDvawrNfnUbfB0PgYWnXm0gXLn47M1ra/TtUvTtN2SFpm2PSMrapPSUdEm8XUdYp++YuedH99eIfLudpGzPxUpXCTiXSTOwR+L8+d6SaYceUVEjhGRA0REF3ZN07R8qKionZbvGDRNRAo7BwRfidtSPbdOMsWLxTliucjjDSLfrtw5uBjwnsgla0TqkuKs7JDUe82yoz4pjUlHnhORQ/KdS5dly2LDI5G6kfmOQ9P6olzWLj0t0g1KyX+jt1zX8kREPBk4uN3hu3GL8SjGWMJ+N8Uw221UTQrSAsFiWNYO9SnYYcE7O+Cbg8h8cwAvFBTyT+BTYLlSKpXvnLq43epEsPWW65rWA3JZu/TgohuUct7NdwzavisF5VuSXLWklZPeb6ZwagnGWB+GLdm7v7ak4c3tcMoguGo0rE1im4IdKMY+0Mcil8M9ylR75d4dStkxEUOflzStB+SydulbUTWtFxARE9gfmJRwOPqTVi78xXp8kXbUNwbCbePg2Ahst0AESl1wTCncsT8ZBRER1gx0U1UAbxW4qFBKdeQ7J03TNG0X4XDsnHzHoO07RGRke0bu2p6RravapaM2KelHGnZuhe5bJBJpF7kxJjL4PZGCReIULhZn9lpxWjKyXUS+KSKHiMiA7m6FnivRaO3EFSuqJuc7Dk3ri3JZu/Tlx25RFwNP5DsKre8TEVfSYdbiFi78r1oK329BXT4mu1Npl4QDF6yGxw5AfjgMZ02CZHkByREe4l64GHirp9p173kyxXEMD1CZ70g0re/JXe3Sg4tuMAxuy3cMWt8lIoM6bL5lw9RNKcZ6DQ5+fhu+91qy05g1STiuP4SKIdyefc36OMyrx5k7ljUnDuDvQBh4WynVmr9Mdp/jWB+apmHmOw5N64tyWbv26kukmravEREz4XDZsjZ+9l4zI7wGxiGFuP7cgPp7584eE3wwexQMdGW3Qm+1sMu8OEf2Z9PR/ZjnMrhXb4WuaZrWy0Qi1dfnOwatbxGRfiJygohc2mzJhvM+lUzRYnEmfizycIPIj1eLqHezayw8i0QmfyLy8jbJ1KVk/aft8kZjWu5NWPIjERmW71y+jMrKqq9Fo7Hj8x2HpvVFuaxdelqkG0SMY/Mdg9Y3iIg3ZXN8u8MtbRn2S4E7bVO8sBmjw0atTUBDCk4aAJ91wMdtiCUwwAVFBtZQF8+P8vB3engr9FwRMf0gHuCdfMeiaX1NLmuXHlx0g2HYF+U7Bq1vSMOERov5921i+Px6zG8OhItG7JyuTDvwQANcPgr+PhHZlCJjQNrvI11s8LDH4C6l1MZ85rAnJZPmq6Zp6OlaTesBuaxd+iDWtBwSkYIMHJJyOLLDZqxXMSnawdEnRXElHdSBhfDogTC3Fl7fnr0TBLLdNp88mOT+hTxswifAx8BavRW6pml7I33lohsikdgrwaD/W/mOQ+t9LJhaneCyFe2csCZOwdQSzPoUZrJzELE+kR1UnDUUxvuQujROiYF9dCn2EA+vpOHmQqXq8ptFz4lEqn8AhicYLFuQ71g0ra/JZe3Sg4tuEFEl+Y5B6z1ExAAOBvwdNj9Y2spJt9RSsKoD9bORcMrAnc+1BObXw89GIJeOpq05w7pSkw2DPKx32zzthoZ85ZELShnezjUXmqbtYbp2aVofISJlHbY8sD0t0WibrK1LScuctTu7ax66TCTSJnL+ZyKq82cD3xO5rlrspCMrOrtrThaRwnznommapvWgioraEfmOQdv7iYg7bsmCpxpl+3EVYo39SOxHNotz8ZqdW6EXLxY57zOR9R0iy9rEemGrJFd2SNv2tEREZJ/beXfVqsbi1atX67+uNK0H5LJ26WmRblBKnkRvua79m859O/q3WwQFpjRnOMRSHDd/E6VL2lAZB9WYhvICGO6BhjS02/D2djismMz5+/HRIYW8bxh8nIYlwPZ855RrlpU4w7IK9JbrmtYDclm79OCiG5SS5fmOQdv7bIUir8MPa1L8Zm2cgUUmqr8Ld30KlelcsPlxK3x7EPx0OEQ6cGwHZ1IxTrCYdT4Xf3bDk0qpTH4zyR/HocE09XlJ03pCLmuXvhVV076EzqsVg4CxNhy0KcVdv62i5KktGMcOgN+WwaXrYFXnBucDXHDiAPhdGbbXoDYjVA/3sKXQ4M0Ck1eUUtvymI6madoeof9C6IZwuPrYUKj83XzHoeWXiJQkbE604fs7MozwGIzYmKL4iS0YQnZTMRE4YzBsTUNjBnZY0GaBxyAzvpB5RrZfRQyo7+3dNfeEioqqMtM0jEDAX53vWDStr8ll7dKDi24xrkevudjnpWyOWZPkqpe2MfGDZjynDEaN8WYHFgA7MjC3Dn49Ek4YgGxIYg00sQ4oJD3cw20GPK6U2prXJPYypmke03krql5zoWl7XO5qlx5cdINSzhP5jkHLPRExgdKkRcgx2D8tnLy0hcnz6/HUp1AFJlw9BkZ5YWMKBHi/GQpNnPkT2DG5iNcNRcTrYmkBrAKa85zSXkhWOo7ecl3TekIua5dec6FpX1BaJNBiceOONEfXpnCN8uJ9uAHzls5+mSM98MAB8Gkcnt+KdDjIQBfO9KGkLhzO2x7FOUqpjvxmoWmapu2VIpHqi/Mdg5YbImKKSLmIfKXDkTce2CTJ8R+JoxaK3FIr8tsNO3tWuN4VOXyZyHvNYlclpGVJs2yoS8hHCVsWiMgh+c6lN4hGY1PC4dpD8x2HpvVFuaxdelqkG0SMc4D5+Y5D6zmdd4GM6HD4QcbmkA4HvwmTHt+Ce30ie8VvcwqmlUCoOLt40xLYnIZIO3aoiA/KC3gG+BQIK6VSeU2o11ATDcPxkN2cTdO0PSiXtUsPLrrF+X2+I9B6nK/d4vIlrZz3zg5KRnpRhxRjJpydU4kLm2FMAVzvh3VJ7KSNtb8Pe2IRHxe4uBt4U98Bsnts215kmoaR7zg0rW/KXe3Say40rVPn1YqhwGQHJm1Jc925n1K8qBk1tQSuL4fba+HdluxVCq8BB/ngrglk9vdRYQmflBhUFbn50A0rlVLt+c5J0zRN6yXC4diD+Y5B27NEpDRhyQWtGYnWJGTjxrRs/7hl5wZj3kUij9SLzN8kMm2ZyID3xBm0RJzzPxN7RZs0JjNyUr5z6AvC4dip4XD1d/Idh6b1RbmsXXpapFvUhHxHoO1ZCZvj1ya57k8bGf5iE+aZQ2H6kJ1X9lIO3FQLvy+D5yfi1CZpL3XRMcpLe6HiVrfJh/mMv68wDDUI0Fuua1qPyF3t0oOLbhBRZ+c7Bu3LERFfBxygbKa12ZSnhKOWtrLfgkbMuI36sBVmjoSvlsIHLdnXVCXgoS3ISYNp/WopdwOVwEdAg1LKyWM6fYbL5XvONLfr6VpN6wG5rF36INb2OSKiLDi+Ks6V6xN8JZbEPbkE95tNmH+oyT6nyIQby7MNsd5rQRrT2MM8OMf1J3nyQBa4DX6tlErmNxNN0zStzwiHaxblOwZt94mIISKHiMi32y157c46SYzr7FlxQ0zktrqdPSvUQhH/hyIPbhJ7Y1I2V7bJ+/UpeTxuy5UiUpbvXPqqaLTm/Gg09pN8x6FpfVEua5eeFukGpaQ+3zFou0dExsQdfp12OHB7mmEek9Fv7sCzobNnxbo4nLcffHMgvLk927rbBCxwBnv4ZKSXu4CNwIZ9eUv0niYibaDc+Y5D0/qiXNauXjUtMnPGzKCBelSgVKHWiiPnzHt8XtOuz/nZeT8b6jJdD4NMAywF76Q8mZ898MADcYBZM2atVlAKdPYfUD+899F7381xKloOiYg7YbPgle2c9ORmisf44MQBGHfUod7u3N1jpBdm7AfnDIWGFHarTXq8D2t4AUsGu7gO+EQpJf/X52iapmlZvapZjYF6SByun/fovHKUE1GGuuF/PMc0DFFy/2D/kFGqxBgnilJP2nPVrs9RLuPQex+dNyr7b/cHFtFo1QFfIg0tB0TELSJllsj3HPhd3OHkm2vo9+J2jNe3Y7bZKL8PSjq3yGpIwxNbYH2S9LRS/vn1AVw3wcc5vhQXABV6YJEbS5duHLRsWf3gfMehaX1RLmtXr5kWueSCS8biMGre4/NeAMAy7seUpcCsXZ9336P3bQZe6fxPa9aMWYtAJu3JWBzHfAC95fpeS0T6xx1+0GFxbZPFAJfCbLdxr+xAOZK966OyHY4uBduBzxI4LpCvluKUeVlbAHcXuNU/8p3HvqigwD4VLL3luqb1gFzWrl4zuHAJoyU75y0AW5JbaocVDR00Z84c7z333PO5+zbMmDGjQMEMhVy968/Fct6dPWOWAnmz0EheccvDD7ftTixKOe92Nw+t58VtjtyQ4ObbNtLvr5sxvjcUrhpDdhJQICPw2BY4byhcX47dYrPJVGwe4aWx0OSvHvgg3znsq5SyYyJGrzkvaVpvksva1WcP4uuuu861rWbrEzjyxj2P3fdy18+VrU6+d8G9sTnnzinB4zwYdwpuAS7Z9bWrV68uSSa9t4GqCwb9f1yxomqybatZImpRKOR/QoRwJFI9XykeCQTKP4pEYr8COUAp+5pAYHxjJBK7F0gHg/5fRaMbR4lkrgG1Ihj031tZWfU1x1HniqgXQyH/a+Fw9QylOMK21Z1Tp/o/i0RiN4AMKyqyLm1pKTJdrtQdYGwKBstuiEZrJ4rYc8B4Lxgs+0s0WvttEftUEePxUKjs/Wi05ucizsGWpa6bNs3fEIlU3yWChELlv6ioqB1hGPbvlTI+DQTK7opEao4C5zyljFcCgbKXI5Ga88A5Coy7g8GyVdFoze9FnBHxuOsXw4db0tLCXSI0hELl1y1fHjvINOUXShnvBwJlj4fDsVOVkm8rZf4lEBjzXiRSMwecieC6IRgcvSkarblDRMxg0H/pihVVw2xb3QCsDgbL76ioiB1hGDJDhNdCofIXw+HYuUrJ1wzDde/kyaNXRCKx34GMVsr3q0xmY9rlGnQvqMZg0H9NNFp1gIj6ldtthsdMGL0mEU/OaM9waEdGVLRDFS/YjCHAx61Q6oLTB8PzW7Otu+tTUNkBRYY4YwrULU1NrWpLXdvhyaSVDgb9LdFo9VkiHCfCn0Kh8kg0GvutiJQlk9bltbXjOyZMiN0noraFQv6rly+vG2+a1uVKqY8DAf9DkUj1N4EzlFJ/CwT870Qi1T8DQiL8VyhUHotGY7eIqJJgsOySpUs3DvJ6MzeJGBtCobJbwuHaQ5Wyf6KU8c9AoOzZSKTmTHBOUErNDwT8FZFI9VVAuQhXZuOM3Q/sCAbLr6qsrBnrOM6VYCwPBsseqKio+bphONMdx3hmypSytyKRmovAmWqa5i2TJo3ZEIlU3wwMCAT8P4tEYqVKMReoDgbLb45GY1NE5GIw3g4Gy56ORmu+L+KcKGL+ORQa80k4HLtcKRmfSrl/e/jho5oikZo/KSVtgYD/inC42q8Uv1GKSCBQ/qdoNHa8iPwAeC4YLH8jGo1dKCKHiTi3hULj1oXDsRuVksFr1/pnxuPrIwUF6tZwOHZEKOS/saKiKmAYaqZSLAwEyp+KRGLfBTlJKR4OBMqXRiKxy0AmOI71uylT9t8ajcbmiZAIBv2/XrFi/WjbNn8noipDIf+8iorqYwyDcwxDvTB5sv8fkUj1j4GvKCW3BwJj10QisT+ADLWsptlu9yiPSOJ2UBuDQf8fKivrJjmONVtELQ6F/H8Nh6tPV4pTHEc9OmWK/8NwuPoXSnGQacq1kyaN3RKJxO5WStmBQNkvI5G6kWBdC8bKYLDsnmi09mgR+4ci6qVQyP9qNFpzvohzpOMYd02ZUvZpOFx9nVIMLy3l5w0NLlVYaN2plFEfCJRdX1FRc7BhOD8HY0kwWLYgGq05TcT5FhgLgsGyJdFozaUiziGOY14/ZcqY+nC4+k6lUMFg+c+XLYsNd7nkOhE+C4XK7wyHa45UyjlfKfPVQGDMS5FIzQ/BOVop855AYMzKSKTmWnBGWpb3l6WlHXZHh+tuUFuCQf+1lZXVBzoOvxThw1Co/NFwOHaKUnK6YchfJ08euzgSic0GmWSa9h8nTRpfF4nEbgc8waB/djS6fqiI+QdQa4JB/+3RaPVXRPixCP8IhcpfCIdj5yglxziO3DdlythoOBy7WikZU1CQ+nVDwwGJAQNi85RSWwMB/+8ikQ0TwLgM1NJg0P9wNFp9kgjfBfVUMOhfGI1WXyJC0HHkpilTxtaEw7FbgaJQyD9z2bL6wS5X6kYRtT4U8t8aiVQdBupCUG8Eg/7nIpHqHwDHg7o/GPSHo9Hq34jgtyzniqlTx7ZFo7E/iRhNoVDZb1esqB1n2/YVSqlPAgH/n8PhmhOVcr6vlPF0IFD2dudupFOUUjcHAv7qSKR6LhilwWDZzyorawY4jnMzGFXBYNncysqaqY7jXCRivBUKlT0TDtdMV8r5umEYD0yeXLY8HK65QilnnGEYV02eXLYjEqm5H5yWYLD8ymg0Vi4iVwEVwWD5/Gi05gQR50wR49lQqOyfSqm6SKR6vm27bp06dfT6cLjmJqWcQYGA/5Lly6tKTNNQoVB5856owb1mcGEp6kxhNJ1/fw4rGDYGpOnzrlpMnz7d3BZr/IugGuc9dt/luz5274J7qwHu+es9rZeeP/N+MdTt//76ysoD4gceWDvXcYwUQCpVsN7jycxNp92tAEox1DCMuabpawSwLJ70eAxfOt20o/Px/7ZtxwFwuVoabbt4rmk6cYB43I4UFno22razHUDEetU0PYvjcaMBwDStB0Xc7vHjx6cBtWJF7VwRMw2QybiqPB7ZJQ55TyljpceT2Jp9XJ72eAwfbN2WfW/u6MrJ623bZtvFc9NpSQAUFCSi6bSvHtgB4Djp110uz/vptHtz9hXmQ4aB54gjRqUAVqyonatUJpP9PsxYcbEz17LSbdmY1QegPovHU9sA3G7nGds2XjXN1q3Z93buMgylsq/dvt3jGfKvOAoLkyvTad9c0zSaAWzb84bHk/kwnTY3Z3N0PaqU7Vm9elhy+vRhzq5xdHS4a0tLubVs/NDDN6S4JhwvOGxtHM/RCmNTCrNrkURdCu7aCDNHwAn9kZoU1ggPdqgIu0DJ/aAe2rIl5bJt+zW322zOfpfetzyezMci3i3Z79J8zDAcb23t+I7p03FWrDDmptOWBTBwoLWxrc2YK5Jp7/zel5qmsS6dtpqy3488L2K+6fOlG7I5OvNcLtPM/j6MbPH5audaVrZnhsdT8JltJ+YmEmZL53f9ts/Hsq44wLXAMBxvU9OYdqWUVFbW/CuOkhKnvq3NmKuU3ZH93q1PXC5XlWV1xWG/KGK+VVxs12d/b537PB6XSyklCxdK+6BBO3/noWCNYaT+FUcyaS70+agwzYLGbI7GX01TCuLxkS0AhsGtluXYAMXFVkMi4flXHJmMvdzjccW6fufBetkw3AtLSqjPfh/2fI/H5Zo+HWflygK/iP2i45jRbA4F61yuncee220usm0n2nXsOY7xpMslBZbV0nVCvM1xssdeQYE0JhLGv469dNoKFxZ66rriUMp+VSn3ovb2fx17D4i43VOnTrWeeQbnwAN3HnvptLnB43H+FYfH43rPtp0VBQXZY8+21d88HuVLpbZ2vjd3OI4j2Zhbt+56DkgkUtHCQs+mrmPPttP/cLk8S7rOAS6X/ZCI2+33j0n7/dljryuOZNKoLi7mX8eeUixRyljVdQ5wuZynbdsodLvbtnV+H3fKv1YLbdtmGDuPPZ8vUZlO++aC7Mj+Prhf93gyH6TTrs5jz3xYKTxTpw5PArLrsdfe7qrZ9RwA8oFhGKvj8XTnOUCetW3jNcOIb8n+f3Lu6tqQLpNp3rHrOcDlKlpp24ldzwFvejyZjyzL3RXHo4bheCsrD4h3HXtdcZSWGrW7HnuOw0emaaztisMw5DkR842iovTm7O+tc29XHG1tw5sHDdp57LndxZ/uGkcq5X7L57M/2XkOcD1uGI63qsrfPm2acnY99oqL7U27xmHb1scej2vDLueAF0TMf/p86a5jb57Hk71C19Q0pm3XOES8q3c99tJp8x2fj+U741B/MU2joKlpTFvnsXdLOu1YAIWFmfr//9izlnk8ruquOETEZRjG3NJSq/PYs+7vPAc4Tz+PawJrAAAgAElEQVQt++5eSLN/PLNizoyZZwDMmjHrljk/mnVf12MzZ8w8adb5swYBavaPZz82e8asR/m3u2GuuOCCkjk/mTMKYM6cOd5ZM2Y9MmvGrEd2Nw7d5yL/OntWBETknNaMvHxbrSTKP8z2rLhyvcjDDSLud3f2rRj4nshvqsSqT8nG6oS8tjUlc+OWnCkiw/Kdi7aT7nOhaT1H97n4XyjbvFAM57HZP5p1F/AZhnFu12MG6l7HJTPmnD8nLeKcD6ph9oxZdQCILLz3sfvOS1klA8SwXps9Y9YAaXUcFIs8pufS3Y9E5u+pnLTdJyLlcYdrUzaTmiz6ewyGLG3FW53MDiYj7XDqYDh7GPx9K9iCDPXAGC/WIDcfeRQz9I6leytVYRh2r7qLTdN6j9zVrl7V50LTRMSdEv72960c/+Amikd4UWcMQT2xBfVc54XgUhd8fzBc60faHTJNGTrGFZAZ5GGpV/FbpdTK/GahaZrWt+m/ELohEqm+Pt8x7CtERIlIcUpkcrslP0443NpqcfRttZS814qxpBVju4U6oAiGdu6l2WJlu2wuaSFT7uO1o0u5coSXM7yK6XpgsXerrKz6WjQaOz7fcWhaX5TL2tWrpkX2FiLGsfmOYR9S2prhnCabq+tTDCw0UbbgqU2ibIFNqeyupccNgIuHw2dxHAX2lGKcgwuJeBQPKKV7VvQWIqYfxAO8k+9YNK2vyWXt0oOLbjAM+6J8x7CvaLcI1Wf44021lD6xGeOUQfA7PxidE3q2wMtN2dtLr/FjCWxAqBrmZlOhwSum7lnRqyST5qumaejpWk3rAbmsXfog1vYqIlKcsjgiDcftsBhdaHLQ+60EvrsClwDDPPBGAP5cD49sho7OHWK+PRj+tD+JYV4uNWEFEAO2KqWc/GWjaZqmaV9QJBJ7Mt8x9EUiYiQtOTXSJu/ct0naZ6+R9N+3inX/pp23lBrvikxfJfJes8jLW8W5q07Sf9si8cp2aW635XIRKcl3Hlr3RSKx72Ybh2matqflsnbpaZFuEFEj8h1DXyIiBjAJmJyB0xc3c8Sdm/BWJ1AZ4JSB2TtAWixwBN5ogmID56ZyNh/Wj/fdBmGfi48KIAroW0x7MaVUSeeaC03T9rBc1i49LaLlVWfPiutSNlO3ZejnNRhwQw1FDzVkfzenlcAtY+HVJnh2G5IWZIQHLtyP5IUjeMOjOF/3rNA0TdN6vYqKWn3lYg8QEXfSkWf+ukWaj1wu1rcqxV6wWZwZn+2cBilcLHJ6pcin7eKsjkvHe82yeWNK1sQteVZEJuY7B23PWrWqsXj16tV6akvTekAua5eeFukGpeRJ9Jbru01EFFAczzABk6ntDoGkw1fvqaN4WTvGYDfq2P5wUCGM9mb3BInbUNEOS1rJnD2Utw7w8TawDFiulPrc3XC13suyEmdYVoHecl3TekAua5ceXHSLrM13BL3RNih2Zzi70eL3te0MKjBQHgN3QwZlCTRm4L1mOH0I/HIUrIrjOII9sQh7ajFhNzwEvKaUsvKdi9YzHEeaQNz5jkPT+qbc1S695kLrcSLSHxietpm0Kc38G2rpt6AB44SBcI0fZnwGGxLZ55a64KhSuKkcq9jNGtth9SAXG4vcvOWF95RSLfnMRdM0TfvP9JWLbgiHq48NhcrfzXccezsRKUpaHN5mcWKbwxiPYkIsRfFjDRgCLGsDJXDmUJi/CbZb2TtCDKDERWaMl7tMWA5sBLbpnhV9X0VFVZlpGkYg4K/Odyya1tfksnbpwUW3GNej11z8RymLI9anuOL9Fo6ItuM9pj+GgCmdj+/IwB9q4MrRcNIAZH0Sa4gLa4KP9FAPN5rwrFJqR16T0HLKNM1jOm9F1WsuNG2Py13t0oOLblDKeSLfMeytOntWHASM7bA58+NWjv7vOrzrE6gWCy4aCYPdsC0DQnaNRYGBc/8BbJtUxFsuxXKXiw998Bmgp0D2ObLScQwz31FoWl+Uy9ql11xoe4yIjInbXJ1y+OqmFL5+HobeWUfxHXXZ37P9fTB/Aixqhr9tRZIOMsSNnL8fqYtGsMijOFP3rNA0TdP2SZFI9cX5jmFvIyLuhCWPPL1Vmo4Li+X/UOw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"amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
"{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
"\n",
"]]> </script>\n",
"<script> <![CDATA[\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof define === \"function\" && define.amd) {\n",
" // Define as an anonymous module\n",
" define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
" return factory(Snap);\n",
" });\n",
" } else {\n",
" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\n",
" .mousewheel(Gadfly.guide_background_scroll)\n",
" .drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend);\n",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\n",
" };\n",
"});\n",
"\n",
"\n",
"// When the plot is moused over, emphasize the grid lines.\n",
"Gadfly.plot_mouseover = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // reveal zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 1.0}, 250);\n",
"};\n",
"\n",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
" },\n",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_scale);\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#img-dd5d25c2\");\n",
"fig.select(\"#img-dd5d25c2-5\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-dd5d25c2-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-14\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-14\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-14\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-dd5d25c2-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-dd5d25c2-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-dd5d25c2-16\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-dd5d25c2-17\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-dd5d25c2-17\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-dd5d25c2-17\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-17\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-17\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-dd5d25c2-18\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-18\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-dd5d25c2-18\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Gadfly\n",
"Gadfly.plot(df,x=x,y=y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GR is a cross-platform framework for visualization."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <clipPath id=\"clip00\">\n",
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],
"text/plain": [
"GR.SVG(UInt8[0x3c,0x3f,0x78,0x6d,0x6c,0x20,0x76,0x65,0x72,0x73 … 0x2f,0x3e,0x0a,0x3c,0x2f,0x73,0x76,0x67,0x3e,0x0a])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using GR\n",
"inline()\n",
"GR.plot(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PyPlot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By exploiting the ability to call Python code from Julia, PyPlot exposes matplotlib functionality."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7ff7279a2710>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1-element Array{Any,1}:\n",
" PyObject <matplotlib.lines.Line2D object at 0x7ff727942d50>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyPlot\n",
"PyPlot.plot(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Winston"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Winston is a 2D plotting library for Julia."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"WARNING: deprecated syntax \"AbstractVecOrMat{T} (\" at /home/simon/.julia/v0.4/Winston/src/Winston.jl:96.\n",
"Use \"AbstractVecOrMat{T}(\" instead.\n",
"WARNING: requiring \"Dates\" in module \"Winston\" did not define a corresponding module.\n",
"WARNING: module Winston should explicitly import * from Base\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"Winston.FramedPlot(...)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Winston\n",
"Winston.plot(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interfacing with other languages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3x4 Array{Float64,2}:\n",
" 0.986564 0.353065 0.290205 0.00459599\n",
" 0.822947 0.235458 0.749866 0.746264 \n",
" 0.902983 0.552081 0.472788 0.36482 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyCall\n",
"@pyimport numpy.random as nr\n",
"nr.rand(3,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### R"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"RCall.RObject{RCall.NilSxp}\n",
"NULL\n"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using RCall\n",
"@rput(x)\n",
"@rput(y)\n",
"reval(\"plot(x,y)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### C"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.772453850905516"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ccall((:gsl_sf_gamma,:libgsl),Float64,(Float64,),0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shell and pipelines"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gks.svg\n",
"kitp.ipynb\n",
"sir.jl\n"
]
}
],
"source": [
"ls = `ls`\n",
"@printf \"%s\" readall(ls)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"kitp.ipynb\n"
]
}
],
"source": [
"run(pipeline(ls,`grep ipynb`))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stochastic simulation using the Gillespie algorithm"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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52.97 70.32 52.97 70.32 52.98 70.32 52.98 70.32 52.99 70.32 52.99 70.32 52.99 70.32 52.99 70.32 53 70.32 53 70.32 53 70.32 53 70.32 53.05 70.32 53.05 70.32 53.05 70.32 53.05 70.32 53.06 70.32 53.06 70.32 53.07 70.32 53.07 70.32 53.07 70.32 53.07 70.32 53.09 70.32 53.09 70.32 53.09 70.32 53.09 70.25 53.1 70.25 53.1 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.13 70.25 53.13 70.25 53.14 70.25 53.14 70.25 53.14 70.25 53.14 70.19 53.15 70.19 53.15 70.19 53.16 70.19 53.16 70.19 53.16 70.19 53.16 70.19 53.17 70.19 53.17 70.19 53.17 70.19 53.17 70.19 53.18 70.19 53.18 70.19 53.22 70.19 53.22 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.06 53.24 70.06 53.24 70.06 53.25 70.06 53.25 70.06 53.26 70.06 53.26 70.06 53.27 70.06 53.27 70.06 53.28 70.06 53.28 70.06 53.29 70.06 53.29 70 53.3 70 53.3 69.93 53.3 69.93 53.3 69.87 53.3 69.87 53.3 69.8 53.3 69.8 53.3 69.8 53.31 69.8 53.31 69.8 53.35 69.8 53.35 69.8 53.36 69.8 53.36 69.8 53.38 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67.95 54.66 67.88 54.68 67.88 54.68 67.88 54.7 67.88 54.7 67.88 54.71 67.88 54.71 67.88 54.71 67.88 54.71 67.82 54.72 67.82 54.72 67.75 54.76 67.75 54.76 67.75 54.79 67.75 54.79 67.69 54.79 67.69 54.79 67.69 54.81 67.69 54.81 67.69 54.82 67.69 54.82 67.69 54.82 67.69 54.82 67.69 54.83 67.69 54.83 67.69 54.84 67.69 54.84 67.69 54.85 67.69 54.85 67.69 54.86 67.69 54.86 67.63 54.88 67.63 54.88 67.63 54.88 67.63 54.88 67.63 54.89 67.63 54.89 67.56 54.89 67.56 54.89 67.56 54.93 67.56 54.93 67.5 54.94 67.5 54.94 67.5 54.96 67.5 54.96 67.5 54.96 67.5 54.96 67.43 54.98 67.43 54.98 67.43 54.99 67.43 54.99 67.43 54.99 67.43 54.99 67.43 55.03 67.43 55.03 67.37 55.05 67.37 55.05 67.3 55.08 67.3 55.08 67.3 55.08 67.3 55.08 67.24 55.12 67.24 55.12 67.24 55.14 67.24 55.14 67.24 55.16 67.24 55.16 67.18 55.16 67.18 55.16 67.11 55.17 67.11 55.17 67.05 55.19 67.05 55.19 67.05 55.2 67.05 55.2 67.05 55.25 67.05 55.25 66.98 55.35 66.98 55.35 66.92 55.39 66.92 55.39 66.92 55.42 66.92 55.42 66.86 55.42 66.86 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65.06 56.44 65.06 56.44 65 56.44 65 56.44 65 56.44 65 56.44 64.93 56.45 64.93 56.45 64.87 56.48 64.87 56.48 64.87 56.5 64.87 56.5 64.87 56.54 64.87 56.54 64.8 56.54 64.8 56.54 64.8 56.56 64.8 56.56 64.74 56.57 64.74 56.57 64.68 56.6 64.68 56.6 64.68 56.61 64.68 56.61 64.68 56.62 64.68 56.62 64.61 56.67 64.61 56.67 64.61 56.7 64.61 56.7 64.61 56.71 64.61 56.71 64.55 56.74 64.55 56.74 64.55 56.77 64.55 56.77 64.48 56.83 64.48 56.83 64.42 56.85 64.42 56.85 64.36 56.85 64.36 56.85 64.36 56.85 64.36 56.85 64.36 56.86 64.36 56.86 64.36 56.88 64.36 56.88 64.29 56.88 64.29 56.88 64.23 56.92 64.23 56.92 64.16 56.98 64.16 56.98 64.16 56.98 64.16 56.98 64.16 57.03 64.16 57.03 64.16 57.08 64.16 57.08 64.16 57.11 64.16 57.11 64.1 57.12 64.1 57.12 64.04 57.12 64.04 57.12 63.97 57.13 63.97 57.13 63.91 57.18 63.91 57.18 63.84 57.2 63.84 57.2 63.78 57.25 63.78 57.25 63.71 57.26 63.71 57.26 63.71 57.26 63.71 57.26 63.71 57.33 63.71 57.33 63.71 57.37 63.71 57.37 63.65 57.38 63.65 57.38 63.59 57.4 63.59 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50.18 45.89 50.19 45.89 50.19 45.83 50.19 45.83 50.19 45.77 50.22 45.77 50.22 45.7 50.23 45.7 50.23 45.64 50.23 45.64 50.23 45.57 50.29 45.57 50.29 45.51 50.3 45.51 50.3 45.57 50.31 45.57 50.31 45.51 50.33 45.51 50.33 45.45 50.37 45.45 50.37 45.38 50.37 45.38 50.37 45.32 50.37 45.32 50.37 45.25 50.38 45.25 50.38 45.32 50.39 45.32 50.39 45.25 50.4 45.25 50.4 45.32 50.41 45.32 50.41 45.25 50.42 45.25 50.42 45.19 50.43 45.19 50.43 45.12 50.52 45.12 50.52 45.19 50.52 45.19 50.52 45.12 50.53 45.12 50.53 45.06 50.54 45.06 50.54 45 50.54 45 50.54 45.06 50.56 45.06 50.56 45 50.6 45 50.6 44.93 50.6 44.93 50.6 44.87 50.61 44.87 50.61 44.8 50.64 44.8 50.64 44.87 50.64 44.87 50.64 44.8 50.64 44.8 50.64 44.74 50.65 44.74 50.65 44.68 50.67 44.68 50.67 44.74 50.67 44.74 50.67 44.68 50.67 44.68 50.67 44.61 50.67 44.61 50.67 44.55 50.67 44.55 50.67 44.48 50.68 44.48 50.68 44.42 50.68 44.42 50.68 44.36 50.69 44.36 50.69 44.29 50.69 44.29 50.69 44.23 50.7 44.23 50.7 44.29 50.73 44.29 50.73 44.36 50.76 44.36 50.76 44.29 50.76 44.29 50.76 44.23 50.76 44.23 50.76 44.16 50.79 44.16 50.79 44.1 50.82 44.1 50.82 44.04 50.82 44.04 50.82 43.97 50.84 43.97 50.84 43.91 50.84 43.91 50.84 43.84 50.84 43.84 50.84 43.78 50.87 43.78 50.87 43.71 50.88 43.71 50.88 43.65 50.89 43.65 50.89 43.59 50.89 43.59 50.89 43.52 50.9 43.52 50.9 43.46 50.9 43.46 50.9 43.52 50.9 43.52 50.9 43.46 50.91 43.46 50.91 43.39 50.92 43.39 50.92 43.33 50.92 43.33 50.92 43.27 50.94 43.27 50.94 43.2 50.94 43.2 50.94 43.14 50.94 43.14 50.94 43.07 50.96 43.07 50.96 43.01 50.99 43.01 50.99 43.07 50.99 43.07 50.99 43.01 50.99 43.01 50.99 42.95 51 42.95 51 42.88 51 42.88 51 42.82 51.01 42.82 51.01 42.88 51.01 42.88 51.01 42.82 51.02 42.82 51.02 42.75 51.05 42.75 51.05 42.69 51.09 42.69 51.09 42.62 51.09 42.62 51.09 42.56 51.09 42.56 51.09 42.62 51.1 42.62 51.1 42.56 51.11 42.56 51.11 42.5 51.12 42.5 51.12 42.43 51.13 42.43 51.13 42.37 51.14 42.37 51.14 42.3 51.15 42.3 51.15 42.37 51.16 42.37 51.16 42.3 51.16 42.3 51.16 42.24 51.17 42.24 51.17 42.3 51.18 42.3 51.18 42.24 51.18 42.24 51.18 42.18 51.19 42.18 51.19 42.24 51.23 42.24 51.23 42.18 51.25 42.18 51.25 42.11 51.26 42.11 51.26 42.05 51.27 42.05 51.27 42.11 51.27 42.11 51.27 42.18 51.28 42.18 51.28 42.11 51.32 42.11 51.32 42.05 51.32 42.05 51.32 41.98 51.36 41.98 51.36 41.92 51.36 41.92 51.36 41.86 51.36 41.86 51.36 41.79 51.38 41.79 51.38 41.73 51.39 41.73 51.39 41.79 51.41 41.79 51.41 41.73 51.42 41.73 51.42 41.79 51.46 41.79 51.46 41.73 51.48 41.73 51.48 41.66 51.48 41.66 51.48 41.73 51.49 41.73 51.49 41.79 51.49 41.79 51.49 41.73 51.49 41.73 51.49 41.66 51.51 41.66 51.51 41.6 51.52 41.6 51.52 41.66 51.52 41.66 51.52 41.73 51.53 41.73 51.53 41.79 51.55 41.79 51.55 41.73 51.56 41.73 51.56 41.79 51.58 41.79 51.58 41.73 51.59 41.73 51.59 41.66 51.6 41.66 51.6 41.6 51.6 41.6 51.6 41.54 51.62 41.54 51.62 41.47 51.63 41.47 51.63 41.54 51.63 41.54 51.63 41.6 51.7 41.6 51.7 41.54 51.7 41.54 51.7 41.6 51.73 41.6 51.73 41.54 51.74 41.54 51.74 41.47 51.75 41.47 51.75 41.41 51.76 41.41 51.76 41.34 51.77 41.34 51.77 41.28 51.78 41.28 51.78 41.21 51.78 41.21 51.78 41.28 51.79 41.28 51.79 41.21 51.81 41.21 51.81 41.15 51.88 41.15 51.88 41.09 51.9 41.09 51.9 41.02 51.92 41.02 51.92 40.96 51.93 40.96 51.93 41.02 51.96 41.02 51.96 41.09 51.97 41.09 51.97 41.15 52.01 41.15 52.01 41.21 52.03 41.21 52.03 41.15 52.04 41.15 52.04 41.09 52.06 41.09 52.06 41.15 52.07 41.15 52.07 41.09 52.12 41.09 52.12 41.15 52.12 41.15 52.12 41.09 52.14 41.09 52.14 41.02 52.16 41.02 52.16 40.96 52.16 40.96 52.16 40.89 52.17 40.89 52.17 40.83 52.19 40.83 52.19 40.89 52.19 40.89 52.19 40.96 52.2 40.96 52.2 40.89 52.2 40.89 52.2 40.96 52.21 40.96 52.21 40.89 52.24 40.89 52.24 40.83 52.24 40.83 52.24 40.77 52.26 40.77 52.26 40.7 52.26 40.7 52.26 40.64 52.27 40.64 52.27 40.7 52.28 40.7 52.28 40.64 52.29 40.64 52.29 40.57 52.3 40.57 52.3 40.51 52.33 40.51 52.33 40.57 52.33 40.57 52.33 40.51 52.36 40.51 52.36 40.45 52.4 40.45 52.4 40.38 52.41 40.38 52.41 40.32 52.47 40.32 52.47 40.38 52.48 40.38 52.48 40.32 52.49 40.32 52.49 40.25 52.49 40.25 52.49 40.19 52.51 40.19 52.51 40.12 52.52 40.12 52.52 40.06 52.53 40.06 52.53 40 52.53 40 52.53 39.93 52.53 39.93 52.53 40 52.54 40 52.54 39.93 52.54 39.93 52.54 39.87 52.54 39.87 52.54 39.8 52.57 39.8 52.57 39.74 52.57 39.74 52.57 39.68 52.57 39.68 52.57 39.61 52.6 39.61 52.6 39.55 52.63 39.55 52.63 39.48 52.64 39.48 52.64 39.42 52.64 39.42 52.64 39.36 52.67 39.36 52.67 39.29 52.68 39.29 52.68 39.23 52.69 39.23 52.69 39.16 52.69 39.16 52.69 39.1 52.7 39.1 52.7 39.04 52.73 39.04 52.73 39.1 52.73 39.1 52.73 39.16 52.75 39.16 52.75 39.1 52.77 39.1 52.77 39.16 52.77 39.16 52.77 39.1 52.84 39.1 52.84 39.04 52.85 39.04 52.85 39.1 52.86 39.1 52.86 39.04 52.87 39.04 52.87 38.97 52.88 38.97 52.88 38.91 52.88 38.91 52.88 38.84 52.89 38.84 52.89 38.78 52.89 38.78 52.89 38.71 52.89 38.71 52.89 38.65 52.92 38.65 52.92 38.71 52.97 38.71 52.97 38.65 52.98 38.65 52.98 38.59 52.99 38.59 52.99 38.52 52.99 38.52 52.99 38.46 53 38.46 53 38.39 53 38.39 53 38.33 53.05 38.33 53.05 38.27 53.05 38.27 53.05 38.2 53.06 38.2 53.06 38.14 53.07 38.14 53.07 38.07 53.07 38.07 53.07 38.01 53.09 38.01 53.09 37.95 53.09 37.95 53.09 38.01 53.1 38.01 53.1 37.95 53.12 37.95 53.12 37.88 53.12 37.88 53.12 37.82 53.12 37.82 53.12 37.75 53.13 37.75 53.13 37.69 53.14 37.69 53.14 37.62 53.14 37.62 53.14 37.69 53.15 37.69 53.15 37.62 53.16 37.62 53.16 37.56 53.16 37.56 53.16 37.5 53.17 37.5 53.17 37.43 53.17 37.43 53.17 37.37 53.18 37.37 53.18 37.3 53.22 37.3 53.22 37.37 53.23 37.37 53.23 37.3 53.23 37.3 53.23 37.24 53.23 37.24 53.23 37.3 53.24 37.3 53.24 37.24 53.25 37.24 53.25 37.18 53.26 37.18 53.26 37.11 53.27 37.11 53.27 37.05 53.28 37.05 53.28 36.98 53.29 36.98 53.29 37.05 53.3 37.05 53.3 37.11 53.3 37.11 53.3 37.18 53.3 37.18 53.3 37.24 53.3 37.24 53.3 37.18 53.31 37.18 53.31 37.11 53.35 37.11 53.35 37.05 53.36 37.05 53.36 36.98 53.38 36.98 53.38 36.92 53.4 36.92 53.4 36.86 53.41 36.86 53.41 36.79 53.41 36.79 53.41 36.73 53.42 36.73 53.42 36.66 53.43 36.66 53.43 36.73 53.44 36.73 53.44 36.66 53.45 36.66 53.45 36.6 53.46 36.6 53.46 36.66 53.47 36.66 53.47 36.73 53.47 36.73 53.47 36.79 53.47 36.79 53.47 36.73 53.48 36.73 53.48 36.79 53.51 36.79 53.51 36.73 53.51 36.73 53.51 36.66 53.52 36.66 53.52 36.6 53.55 36.6 53.55 36.54 53.56 36.54 53.56 36.6 53.57 36.6 53.57 36.66 53.58 36.66 53.58 36.6 53.59 36.6 53.59 36.66 53.6 36.66 53.6 36.6 53.62 36.6 53.62 36.54 53.62 36.54 53.62 36.47 53.67 36.47 53.67 36.41 53.71 36.41 53.71 36.34 53.72 36.34 53.72 36.28 53.77 36.28 53.77 36.21 53.8 36.21 53.8 36.15 53.83 36.15 53.83 36.09 53.84 36.09 53.84 36.15 53.86 36.15 53.86 36.21 53.86 36.21 53.86 36.28 53.87 36.28 53.87 36.34 53.88 36.34 53.88 36.41 53.88 36.41 53.88 36.34 53.9 36.34 53.9 36.28 53.91 36.28 53.91 36.21 53.95 36.21 53.95 36.15 53.96 36.15 53.96 36.09 53.99 36.09 53.99 36.02 53.99 36.02 53.99 35.96 54 35.96 54 35.89 54 35.89 54 35.83 54.02 35.83 54.02 35.77 54.05 35.77 54.05 35.83 54.06 35.83 54.06 35.89 54.12 35.89 54.12 35.83 54.13 35.83 54.13 35.77 54.15 35.77 54.15 35.83 54.2 35.83 54.2 35.89 54.2 35.89 54.2 35.96 54.22 35.96 54.22 35.89 54.22 35.89 54.22 35.83 54.24 35.83 54.24 35.89 54.24 35.89 54.24 35.83 54.25 35.83 54.25 35.77 54.27 35.77 54.27 35.7 54.3 35.7 54.3 35.64 54.31 35.64 54.31 35.57 54.32 35.57 54.32 35.51 54.32 35.51 54.32 35.57 54.32 35.57 54.32 35.51 54.33 35.51 54.33 35.45 54.36 35.45 54.36 35.38 54.36 35.38 54.36 35.45 54.37 35.45 54.37 35.38 54.38 35.38 54.38 35.32 54.38 35.32 54.38 35.25 54.4 35.25 54.4 35.32 54.41 35.32 54.41 35.25 54.41 35.25 54.41 35.32 54.42 35.32 54.42 35.25 54.43 35.25 54.43 35.19 54.45 35.19 54.45 35.25 54.46 35.25 54.46 35.19 54.47 35.19 54.47 35.12 54.52 35.12 54.52 35.19 54.53 35.19 54.53 35.25 54.55 35.25 54.55 35.32 54.56 35.32 54.56 35.38 54.57 35.38 54.57 35.32 54.58 35.32 54.58 35.25 54.6 35.25 54.6 35.19 54.64 35.19 54.64 35.25 54.66 35.25 54.66 35.32 54.68 35.32 54.68 35.25 54.7 35.25 54.7 35.19 54.71 35.19 54.71 35.12 54.71 35.12 54.71 35.19 54.72 35.19 54.72 35.25 54.76 35.25 54.76 35.19 54.79 35.19 54.79 35.25 54.79 35.25 54.79 35.19 54.81 35.19 54.81 35.12 54.82 35.12 54.82 35.06 54.82 35.06 54.82 35 54.83 35 54.83 34.93 54.84 34.93 54.84 34.87 54.85 34.87 54.85 34.8 54.86 34.8 54.86 34.87 54.88 34.87 54.88 34.8 54.88 34.8 54.88 34.74 54.89 34.74 54.89 34.8 54.89 34.8 54.89 34.74 54.93 34.74 54.93 34.8 54.94 34.8 54.94 34.74 54.96 34.74 54.96 34.68 54.96 34.68 54.96 34.74 54.98 34.74 54.98 34.68 54.99 34.68 54.99 34.61 54.99 34.61 54.99 34.55 55.03 34.55 55.03 34.61 55.05 34.61 55.05 34.68 55.08 34.68 55.08 34.61 55.08 34.61 55.08 34.68 55.12 34.68 55.12 34.61 55.14 34.61 55.14 34.55 55.16 34.55 55.16 34.61 55.16 34.61 55.16 34.68 55.17 34.68 55.17 34.74 55.19 34.74 55.19 34.68 55.2 34.68 55.2 34.61 55.25 34.61 55.25 34.68 55.35 34.68 55.35 34.74 55.39 34.74 55.39 34.68 55.42 34.68 55.42 34.74 55.42 34.74 55.42 34.8 55.43 34.8 55.43 34.87 55.47 34.87 55.47 34.93 55.5 34.93 55.5 34.87 55.52 34.87 55.52 34.93 55.52 34.93 55.52 34.87 55.67 34.87 55.67 34.93 55.7 34.93 55.7 35 55.73 35 55.73 35.06 55.73 35.06 55.73 35.12 55.74 35.12 55.74 35.19 55.75 35.19 55.75 35.12 55.77 35.12 55.77 35.06 55.79 35.06 55.79 35.12 55.81 35.12 55.81 35.06 55.81 35.06 55.81 35.12 55.83 35.12 55.83 35.06 55.83 35.06 55.83 35 55.84 35 55.84 34.93 55.84 34.93 55.84 35 55.85 35 55.85 34.93 55.87 34.93 55.87 35 55.94 35 55.94 35.06 55.95 35.06 55.95 35 55.95 35 55.95 35.06 56.01 35.06 56.01 35.12 56.03 35.12 56.03 35.06 56.06 35.06 56.06 35.12 56.06 35.12 56.06 35.19 56.12 35.19 56.12 35.12 56.15 35.12 56.15 35.19 56.22 35.19 56.22 35.25 56.28 35.25 56.28 35.19 56.29 35.19 56.29 35.25 56.3 35.25 56.3 35.19 56.3 35.19 56.3 35.25 56.32 35.25 56.32 35.19 56.34 35.19 56.34 35.25 56.36 35.25 56.36 35.32 56.37 35.32 56.37 35.38 56.4 35.38 56.4 35.45 56.42 35.45 56.42 35.51 56.43 35.51 56.43 35.57 56.44 35.57 56.44 35.64 56.44 35.64 56.44 35.57 56.44 35.57 56.44 35.64 56.45 35.64 56.45 35.7 56.48 35.7 56.48 35.64 56.5 35.64 56.5 35.57 56.54 35.57 56.54 35.64 56.54 35.64 56.54 35.57 56.56 35.57 56.56 35.64 56.57 35.64 56.57 35.7 56.6 35.7 56.6 35.64 56.61 35.64 56.61 35.57 56.62 35.57 56.62 35.64 56.67 35.64 56.67 35.57 56.7 35.57 56.7 35.51 56.71 35.51 56.71 35.57 56.74 35.57 56.74 35.51 56.77 35.51 56.77 35.57 56.83 35.57 56.83 35.64 56.85 35.64 56.85 35.7 56.85 35.7 56.85 35.64 56.85 35.64 56.85 35.57 56.86 35.57 56.86 35.51 56.88 35.51 56.88 35.57 56.88 35.57 56.88 35.64 56.92 35.64 56.92 35.7 56.98 35.7 56.98 35.64 56.98 35.64 56.98 35.57 57.03 35.57 57.03 35.51 57.08 35.51 57.08 35.45 57.11 35.45 57.11 35.51 57.12 35.51 57.12 35.57 57.12 35.57 57.12 35.64 57.13 35.64 57.13 35.7 57.18 35.7 57.18 35.77 57.2 35.77 57.2 35.83 57.25 35.83 57.25 35.89 57.26 35.89 57.26 35.83 57.26 35.83 57.26 35.77 57.33 35.77 57.33 35.7 57.37 35.7 57.37 35.77 57.38 35.77 57.38 35.83 57.4 35.83 57.4 35.89 57.42 35.89 57.42 35.83 57.44 35.83 57.44 35.77 57.47 35.77 57.47 35.83 57.48 35.83 57.48 35.89 57.49 35.89 57.49 35.83 57.51 35.83 57.51 35.89 57.52 35.89 57.52 35.83 57.55 35.83 57.55 35.77 57.56 35.77 57.56 35.7 57.58 35.7 57.58 35.77 57.59 35.77 57.59 35.7 57.6 35.7 57.6 35.77 57.61 35.77 57.61 35.83 57.61 35.83 57.61 35.77 57.71 35.77 57.71 35.7 57.73 35.7 57.73 35.77 57.73 35.77 57.73 35.83 57.75 35.83 57.75 35.89 57.75 35.89 57.75 35.83 57.81 35.83 57.81 35.89 57.85 35.89 57.85 35.83 57.87 35.83 57.87 35.89 57.9 35.89 57.9 35.83 57.95 35.83 57.95 35.89 58 35.89 58 35.96 58.04 35.96 58.04 36.02 58.06 36.02 58.06 35.96 58.06 35.96 58.06 36.02 58.1 36.02 58.1 35.96 58.12 35.96 58.12 35.89 58.14 35.89 58.14 35.96 58.17 35.96 58.17 35.89 58.24 35.89 58.24 35.83 58.25 35.83 58.25 35.89 58.31 35.89 58.31 35.96 58.33 35.96 58.33 36.02 58.35 36.02 58.35 36.09 58.35 36.09 58.35 36.15 58.36 36.15 58.36 36.21 58.36 36.21 58.36 36.28 58.37 36.28 58.37 36.21 58.38 36.21 58.38 36.15 58.38 36.15 58.38 36.21 58.42 36.21 58.42 36.28 58.42 36.28 58.42 36.34 58.44 36.34 58.44 36.28 58.5 36.28 58.5 36.34 58.5 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50.18 52.75 50.19 52.75 50.19 52.82 50.19 52.82 50.19 52.88 50.22 52.88 50.22 52.95 50.23 52.95 50.23 53.01 50.23 53.01 50.23 53.07 50.29 53.07 50.29 53.14 50.3 53.14 50.3 53.14 50.31 53.14 50.31 53.2 50.33 53.2 50.33 53.27 50.37 53.27 50.37 53.33 50.37 53.33 50.37 53.39 50.37 53.39 50.37 53.46 50.38 53.46 50.38 53.46 50.39 53.46 50.39 53.52 50.4 53.52 50.4 53.52 50.41 53.52 50.41 53.59 50.42 53.59 50.42 53.65 50.43 53.65 50.43 53.71 50.52 53.71 50.52 53.71 50.52 53.71 50.52 53.78 50.53 53.78 50.53 53.84 50.54 53.84 50.54 53.91 50.54 53.91 50.54 53.91 50.56 53.91 50.56 53.97 50.6 53.97 50.6 54.04 50.6 54.04 50.6 54.1 50.61 54.1 50.61 54.16 50.64 54.16 50.64 54.16 50.64 54.16 50.64 54.23 50.64 54.23 50.64 54.29 50.65 54.29 50.65 54.36 50.67 54.36 50.67 54.36 50.67 54.36 50.67 54.42 50.67 54.42 50.67 54.48 50.67 54.48 50.67 54.55 50.67 54.55 50.67 54.61 50.68 54.61 50.68 54.68 50.68 54.68 50.68 54.74 50.69 54.74 50.69 54.8 50.69 54.8 50.69 54.87 50.7 54.87 50.7 54.87 50.73 54.87 50.73 54.87 50.76 54.87 50.76 54.93 50.76 54.93 50.76 55 50.76 55 50.76 55.06 50.79 55.06 50.79 55.12 50.82 55.12 50.82 55.19 50.82 55.19 50.82 55.25 50.84 55.25 50.84 55.32 50.84 55.32 50.84 55.38 50.84 55.38 50.84 55.45 50.87 55.45 50.87 55.51 50.88 55.51 50.88 55.57 50.89 55.57 50.89 55.64 50.89 55.64 50.89 55.7 50.9 55.7 50.9 55.77 50.9 55.77 50.9 55.77 50.9 55.77 50.9 55.83 50.91 55.83 50.91 55.89 50.92 55.89 50.92 55.96 50.92 55.96 50.92 56.02 50.94 56.02 50.94 56.09 50.94 56.09 50.94 56.15 50.94 56.15 50.94 56.21 50.96 56.21 50.96 56.28 50.99 56.28 50.99 56.28 50.99 56.28 50.99 56.34 50.99 56.34 50.99 56.41 51 56.41 51 56.47 51 56.47 51 56.54 51.01 56.54 51.01 56.54 51.01 56.54 51.01 56.6 51.02 56.6 51.02 56.66 51.05 56.66 51.05 56.73 51.09 56.73 51.09 56.79 51.09 56.79 51.09 56.86 51.09 56.86 51.09 56.86 51.1 56.86 51.1 56.92 51.11 56.92 51.11 56.98 51.12 56.98 51.12 57.05 51.13 57.05 51.13 57.11 51.14 57.11 51.14 57.18 51.15 57.18 51.15 57.18 51.16 57.18 51.16 57.24 51.16 57.24 51.16 57.3 51.17 57.3 51.17 57.3 51.18 57.3 51.18 57.37 51.18 57.37 51.18 57.43 51.19 57.43 51.19 57.43 51.23 57.43 51.23 57.5 51.25 57.5 51.25 57.56 51.26 57.56 51.26 57.63 51.27 57.63 51.27 57.63 51.27 57.63 51.27 57.63 51.28 57.63 51.28 57.69 51.32 57.69 51.32 57.75 51.32 57.75 51.32 57.82 51.36 57.82 51.36 57.88 51.36 57.88 51.36 57.95 51.36 57.95 51.36 58.01 51.38 58.01 51.38 58.07 51.39 58.07 51.39 58.07 51.41 58.07 51.41 58.14 51.42 58.14 51.42 58.14 51.46 58.14 51.46 58.2 51.48 58.2 51.48 58.27 51.48 58.27 51.48 58.27 51.49 58.27 51.49 58.27 51.49 58.27 51.49 58.33 51.49 58.33 51.49 58.39 51.51 58.39 51.51 58.46 51.52 58.46 51.52 58.46 51.52 58.46 51.52 58.46 51.53 58.46 51.53 58.46 51.55 58.46 51.55 58.52 51.56 58.52 51.56 58.52 51.58 58.52 51.58 58.59 51.59 58.59 51.59 58.65 51.6 58.65 51.6 58.71 51.6 58.71 51.6 58.78 51.62 58.78 51.62 58.84 51.63 58.84 51.63 58.84 51.63 58.84 51.63 58.84 51.7 58.84 51.7 58.91 51.7 58.91 51.7 58.91 51.73 58.91 51.73 58.97 51.74 58.97 51.74 59.04 51.75 59.04 51.75 59.1 51.76 59.1 51.76 59.16 51.77 59.16 51.77 59.23 51.78 59.23 51.78 59.29 51.78 59.29 51.78 59.29 51.79 59.29 51.79 59.36 51.81 59.36 51.81 59.42 51.88 59.42 51.88 59.48 51.9 59.48 51.9 59.55 51.92 59.55 51.92 59.61 51.93 59.61 51.93 59.61 51.96 59.61 51.96 59.61 51.97 59.61 51.97 59.61 52.01 59.61 52.01 59.61 52.03 59.61 52.03 59.68 52.04 59.68 52.04 59.74 52.06 59.74 52.06 59.74 52.07 59.74 52.07 59.8 52.12 59.8 52.12 59.8 52.12 59.8 52.12 59.87 52.14 59.87 52.14 59.93 52.16 59.93 52.16 60 52.16 60 52.16 60.06 52.17 60.06 52.17 60.13 52.19 60.13 52.19 60.13 52.19 60.13 52.19 60.13 52.2 60.13 52.2 60.19 52.2 60.19 52.2 60.19 52.21 60.19 52.21 60.25 52.24 60.25 52.24 60.32 52.24 60.32 52.24 60.38 52.26 60.38 52.26 60.45 52.26 60.45 52.26 60.51 52.27 60.51 52.27 60.51 52.28 60.51 52.28 60.57 52.29 60.57 52.29 60.64 52.3 60.64 52.3 60.7 52.33 60.7 52.33 60.7 52.33 60.7 52.33 60.77 52.36 60.77 52.36 60.83 52.4 60.83 52.4 60.89 52.41 60.89 52.41 60.96 52.47 60.96 52.47 60.96 52.48 60.96 52.48 61.02 52.49 61.02 52.49 61.09 52.49 61.09 52.49 61.15 52.51 61.15 52.51 61.21 52.52 61.21 52.52 61.28 52.53 61.28 52.53 61.34 52.53 61.34 52.53 61.41 52.53 61.41 52.53 61.41 52.54 61.41 52.54 61.47 52.54 61.47 52.54 61.54 52.54 61.54 52.54 61.6 52.57 61.6 52.57 61.66 52.57 61.66 52.57 61.73 52.57 61.73 52.57 61.79 52.6 61.79 52.6 61.86 52.63 61.86 52.63 61.92 52.64 61.92 52.64 61.98 52.64 61.98 52.64 62.05 52.67 62.05 52.67 62.11 52.68 62.11 52.68 62.18 52.69 62.18 52.69 62.24 52.69 62.24 52.69 62.3 52.7 62.3 52.7 62.37 52.73 62.37 52.73 62.37 52.73 62.37 52.73 62.37 52.75 62.37 52.75 62.43 52.77 62.43 52.77 62.43 52.77 62.43 52.77 62.5 52.84 62.5 52.84 62.56 52.85 62.56 52.85 62.56 52.86 62.56 52.86 62.63 52.87 62.63 52.87 62.69 52.88 62.69 52.88 62.75 52.88 62.75 52.88 62.82 52.89 62.82 52.89 62.88 52.89 62.88 52.89 62.95 52.89 62.95 52.89 63.01 52.92 63.01 52.92 63.01 52.97 63.01 52.97 63.07 52.98 63.07 52.98 63.14 52.99 63.14 52.99 63.2 52.99 63.2 52.99 63.27 53 63.27 53 63.33 53 63.33 53 63.39 53.05 63.39 53.05 63.46 53.05 63.46 53.05 63.52 53.06 63.52 53.06 63.59 53.07 63.59 53.07 63.65 53.07 63.65 53.07 63.71 53.09 63.71 53.09 63.78 53.09 63.78 53.09 63.78 53.1 63.78 53.1 63.84 53.12 63.84 53.12 63.91 53.12 63.91 53.12 63.97 53.12 63.97 53.12 64.04 53.13 64.04 53.13 64.1 53.14 64.1 53.14 64.16 53.14 64.16 53.14 64.16 53.15 64.16 53.15 64.23 53.16 64.23 53.16 64.29 53.16 64.29 53.16 64.36 53.17 64.36 53.17 64.42 53.17 64.42 53.17 64.48 53.18 64.48 53.18 64.55 53.22 64.55 53.22 64.55 53.23 64.55 53.23 64.61 53.23 64.61 53.23 64.68 53.23 64.68 53.23 64.68 53.24 64.68 53.24 64.74 53.25 64.74 53.25 64.8 53.26 64.8 53.26 64.87 53.27 64.87 53.27 64.93 53.28 64.93 53.28 65 53.29 65 53.29 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65.06 53.31 65.06 53.31 65.13 53.35 65.13 53.35 65.19 53.36 65.19 53.36 65.25 53.38 65.25 53.38 65.32 53.4 65.32 53.4 65.38 53.41 65.38 53.41 65.45 53.41 65.45 53.41 65.51 53.42 65.51 53.42 65.57 53.43 65.57 53.43 65.57 53.44 65.57 53.44 65.64 53.45 65.64 53.45 65.7 53.46 65.7 53.46 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.77 53.48 65.77 53.48 65.77 53.51 65.77 53.51 65.83 53.51 65.83 53.51 65.89 53.52 65.89 53.52 65.96 53.55 65.96 53.55 66.02 53.56 66.02 53.56 66.02 53.57 66.02 53.57 66.02 53.58 66.02 53.58 66.09 53.59 66.09 53.59 66.09 53.6 66.09 53.6 66.15 53.62 66.15 53.62 66.21 53.62 66.21 53.62 66.28 53.67 66.28 53.67 66.34 53.71 66.34 53.71 66.41 53.72 66.41 53.72 66.47 53.77 66.47 53.77 66.54 53.8 66.54 53.8 66.6 53.83 66.6 53.83 66.66 53.84 66.66 53.84 66.66 53.86 66.66 53.86 66.66 53.86 66.66 53.86 66.66 53.87 66.66 53.87 66.66 53.88 66.66 53.88 66.66 53.88 66.66 53.88 66.73 53.9 66.73 53.9 66.79 53.91 66.79 53.91 66.86 53.95 66.86 53.95 66.92 53.96 66.92 53.96 66.98 53.99 66.98 53.99 67.05 53.99 67.05 53.99 67.11 54 67.11 54 67.18 54 67.18 54 67.24 54.02 67.24 54.02 67.3 54.05 67.3 54.05 67.3 54.06 67.3 54.06 67.3 54.12 67.3 54.12 67.37 54.13 67.37 54.13 67.43 54.15 67.43 54.15 67.43 54.2 67.43 54.2 67.43 54.2 67.43 54.2 67.43 54.22 67.43 54.22 67.5 54.22 67.5 54.22 67.56 54.24 67.56 54.24 67.56 54.24 67.56 54.24 67.63 54.25 67.63 54.25 67.69 54.27 67.69 54.27 67.75 54.3 67.75 54.3 67.82 54.31 67.82 54.31 67.88 54.32 67.88 54.32 67.95 54.32 67.95 54.32 67.95 54.32 67.95 54.32 68.01 54.33 68.01 54.33 68.07 54.36 68.07 54.36 68.14 54.36 68.14 54.36 68.14 54.37 68.14 54.37 68.2 54.38 68.2 54.38 68.27 54.38 68.27 54.38 68.33 54.4 68.33 54.4 68.33 54.41 68.33 54.41 68.39 54.41 68.39 54.41 68.39 54.42 68.39 54.42 68.46 54.43 68.46 54.43 68.52 54.45 68.52 54.45 68.52 54.46 68.52 54.46 68.59 54.47 68.59 54.47 68.65 54.52 68.65 54.52 68.65 54.53 68.65 54.53 68.65 54.55 68.65 54.55 68.65 54.56 68.65 54.56 68.65 54.57 68.65 54.57 68.71 54.58 68.71 54.58 68.78 54.6 68.78 54.6 68.84 54.64 68.84 54.64 68.84 54.66 68.84 54.66 68.84 54.68 68.84 54.68 68.91 54.7 68.91 54.7 68.97 54.71 68.97 54.71 69.04 54.71 69.04 54.71 69.04 54.72 69.04 54.72 69.04 54.76 69.04 54.76 69.1 54.79 69.1 54.79 69.1 54.79 69.1 54.79 69.16 54.81 69.16 54.81 69.23 54.82 69.23 54.82 69.29 54.82 69.29 54.82 69.36 54.83 69.36 54.83 69.42 54.84 69.42 54.84 69.48 54.85 69.48 54.85 69.55 54.86 69.55 54.86 69.55 54.88 69.55 54.88 69.61 54.88 69.61 54.88 69.68 54.89 69.68 54.89 69.68 54.89 69.68 54.89 69.74 54.93 69.74 54.93 69.74 54.94 69.74 54.94 69.8 54.96 69.8 54.96 69.87 54.96 69.87 54.96 69.87 54.98 69.87 54.98 69.93 54.99 69.93 54.99 70 54.99 70 54.99 70.06 55.03 70.06 55.03 70.06 55.05 70.06 55.05 70.06 55.08 70.06 55.08 70.13 55.08 70.13 55.08 70.13 55.12 70.13 55.12 70.19 55.14 70.19 55.14 70.25 55.16 70.25 55.16 70.25 55.16 70.25 55.16 70.25 55.17 70.25 55.17 70.25 55.19 70.25 55.19 70.32 55.2 70.32 55.2 70.38 55.25 70.38 55.25 70.38 55.35 70.38 55.35 70.38 55.39 70.38 55.39 70.45 55.42 70.45 55.42 70.45 55.42 70.45 55.42 70.45 55.43 70.45 55.43 70.45 55.47 70.45 55.47 70.45 55.5 70.45 55.5 70.51 55.52 70.51 55.52 70.51 55.52 70.51 55.52 70.57 55.67 70.57 55.67 70.57 55.7 70.57 55.7 70.57 55.73 70.57 55.73 70.57 55.73 70.57 55.73 70.57 55.74 70.57 55.74 70.57 55.75 70.57 55.75 70.64 55.77 70.64 55.77 70.7 55.79 70.7 55.79 70.7 55.81 70.7 55.81 70.77 55.81 70.77 55.81 70.77 55.83 70.77 55.83 70.83 55.83 70.83 55.83 70.89 55.84 70.89 55.84 70.96 55.84 70.96 55.84 70.96 55.85 70.96 55.85 71.02 55.87 71.02 55.87 71.02 55.94 71.02 55.94 71.02 55.95 71.02 55.95 71.09 55.95 71.09 55.95 71.09 56.01 71.09 56.01 71.09 56.03 71.09 56.03 71.15 56.06 71.15 56.06 71.15 56.06 71.15 56.06 71.15 56.12 71.15 56.12 71.21 56.15 71.21 56.15 71.21 56.22 71.21 56.22 71.21 56.28 71.21 56.28 71.28 56.29 71.28 56.29 71.28 56.3 71.28 56.3 71.34 56.3 71.34 56.3 71.34 56.32 71.34 56.32 71.41 56.34 71.41 56.34 71.41 56.36 71.41 56.36 71.41 56.37 71.41 56.37 71.41 56.4 71.41 56.4 71.41 56.42 71.41 56.42 71.41 56.43 71.41 56.43 71.41 56.44 71.41 56.44 71.41 56.44 71.41 56.44 71.47 56.44 71.47 56.44 71.47 56.45 71.47 56.45 71.47 56.48 71.47 56.48 71.54 56.5 71.54 56.5 71.6 56.54 71.6 56.54 71.6 56.54 71.6 56.54 71.66 56.56 71.66 56.56 71.66 56.57 71.66 56.57 71.66 56.6 71.66 56.6 71.73 56.61 71.73 56.61 71.79 56.62 71.79 56.62 71.79 56.67 71.79 56.67 71.86 56.7 71.86 56.7 71.92 56.71 71.92 56.71 71.92 56.74 71.92 56.74 71.98 56.77 71.98 56.77 71.98 56.83 71.98 56.83 71.98 56.85 71.98 56.85 71.98 56.85 71.98 56.85 72.05 56.85 72.05 56.85 72.11 56.86 72.11 56.86 72.18 56.88 72.18 56.88 72.18 56.88 72.18 56.88 72.18 56.92 72.18 56.92 72.18 56.98 72.18 56.98 72.24 56.98 72.24 56.98 72.3 57.03 72.3 57.03 72.37 57.08 72.37 57.08 72.43 57.11 72.43 57.11 72.43 57.12 72.43 57.12 72.43 57.12 72.43 57.12 72.43 57.13 72.43 57.13 72.43 57.18 72.43 57.18 72.43 57.2 72.43 57.2 72.43 57.25 72.43 57.25 72.43 57.26 72.43 57.26 72.5 57.26 72.5 57.26 72.56 57.33 72.56 57.33 72.63 57.37 72.63 57.37 72.63 57.38 72.63 57.38 72.63 57.4 72.63 57.4 72.63 57.42 72.63 57.42 72.69 57.44 72.69 57.44 72.75 57.47 72.75 57.47 72.75 57.48 72.75 57.48 72.75 57.49 72.75 57.49 72.82 57.51 72.82 57.51 72.82 57.52 72.82 57.52 72.88 57.55 72.88 57.55 72.95 57.56 72.95 57.56 73.01 57.58 73.01 57.58 73.01 57.59 73.01 57.59 73.07 57.6 73.07 57.6 73.07 57.61 73.07 57.61 73.07 57.61 73.07 57.61 73.14 57.71 73.14 57.71 73.2 57.73 73.2 57.73 73.2 57.73 73.2 57.73 73.2 57.75 73.2 57.75 73.2 57.75 73.2 57.75 73.27 57.81 73.27 57.81 73.27 57.85 73.27 57.85 73.33 57.87 73.33 57.87 73.33 57.9 73.33 57.9 73.39 57.95 73.39 57.95 73.39 58 73.39 58 73.39 58.04 73.39 58.04 73.39 58.06 73.39 58.06 73.46 58.06 73.46 58.06 73.46 58.1 73.46 58.1 73.52 58.12 73.52 58.12 73.59 58.14 73.59 58.14 73.59 58.17 73.59 58.17 73.65 58.24 73.65 58.24 73.71 58.25 73.71 58.25 73.71 58.31 73.71 58.31 73.71 58.33 73.71 58.33 73.71 58.35 73.71 58.35 73.71 58.35 73.71 58.35 73.71 58.36 73.71 58.36 73.71 58.36 73.71 58.36 73.71 58.37 73.71 58.37 73.78 58.38 73.78 58.38 73.84 58.38 73.84 58.38 73.84 58.42 73.84 58.42 73.84 58.42 73.84 58.42 73.84 58.44 73.84 58.44 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52.97 70.32 52.97 70.32 52.98 70.32 52.98 70.32 52.99 70.32 52.99 70.32 52.99 70.32 52.99 70.32 53 70.32 53 70.32 53 70.32 53 70.32 53.05 70.32 53.05 70.32 53.05 70.32 53.05 70.32 53.06 70.32 53.06 70.32 53.07 70.32 53.07 70.32 53.07 70.32 53.07 70.32 53.09 70.32 53.09 70.32 53.09 70.32 53.09 70.25 53.1 70.25 53.1 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.12 70.25 53.13 70.25 53.13 70.25 53.14 70.25 53.14 70.25 53.14 70.25 53.14 70.19 53.15 70.19 53.15 70.19 53.16 70.19 53.16 70.19 53.16 70.19 53.16 70.19 53.17 70.19 53.17 70.19 53.17 70.19 53.17 70.19 53.18 70.19 53.18 70.19 53.22 70.19 53.22 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.13 53.23 70.06 53.24 70.06 53.24 70.06 53.25 70.06 53.25 70.06 53.26 70.06 53.26 70.06 53.27 70.06 53.27 70.06 53.28 70.06 53.28 70.06 53.29 70.06 53.29 70 53.3 70 53.3 69.93 53.3 69.93 53.3 69.87 53.3 69.87 53.3 69.8 53.3 69.8 53.3 69.8 53.31 69.8 53.31 69.8 53.35 69.8 53.35 69.8 53.36 69.8 53.36 69.8 53.38 69.8 53.38 69.8 53.4 69.8 53.4 69.8 53.41 69.8 53.41 69.8 53.41 69.8 53.41 69.8 53.42 69.8 53.42 69.8 53.43 69.8 53.43 69.74 53.44 69.74 53.44 69.74 53.45 69.74 53.45 69.74 53.46 69.74 53.46 69.68 53.47 69.68 53.47 69.61 53.47 69.61 53.47 69.55 53.47 69.55 53.47 69.55 53.48 69.55 53.48 69.48 53.51 69.48 53.51 69.48 53.51 69.48 53.51 69.48 53.52 69.48 53.52 69.48 53.55 69.48 53.55 69.48 53.56 69.48 53.56 69.42 53.57 69.42 53.57 69.36 53.58 69.36 53.58 69.36 53.59 69.36 53.59 69.29 53.6 69.29 53.6 69.29 53.62 69.29 53.62 69.29 53.62 69.29 53.62 69.29 53.67 69.29 53.67 69.29 53.71 69.29 53.71 69.29 53.72 69.29 53.72 69.29 53.77 69.29 53.77 69.29 53.8 69.29 53.8 69.29 53.83 69.29 53.83 69.29 53.84 69.29 53.84 69.23 53.86 69.23 53.86 69.16 53.86 69.16 53.86 69.1 53.87 69.1 53.87 69.04 53.88 69.04 53.88 68.97 53.88 68.97 53.88 68.97 53.9 68.97 53.9 68.97 53.91 68.97 53.91 68.97 53.95 68.97 53.95 68.97 53.96 68.97 53.96 68.97 53.99 68.97 53.99 68.97 53.99 68.97 53.99 68.97 54 68.97 54 68.97 54 68.97 54 68.97 54.02 68.97 54.02 68.97 54.05 68.97 54.05 68.91 54.06 68.91 54.06 68.84 54.12 68.84 54.12 68.84 54.13 68.84 54.13 68.84 54.15 68.84 54.15 68.78 54.2 68.78 54.2 68.71 54.2 68.71 54.2 68.65 54.22 68.65 54.22 68.65 54.22 68.65 54.22 68.65 54.24 68.65 54.24 68.59 54.24 68.59 54.24 68.59 54.25 68.59 54.25 68.59 54.27 68.59 54.27 68.59 54.3 68.59 54.3 68.59 54.31 68.59 54.31 68.59 54.32 68.59 54.32 68.59 54.32 68.59 54.32 68.52 54.32 68.52 54.32 68.52 54.33 68.52 54.33 68.52 54.36 68.52 54.36 68.52 54.36 68.52 54.36 68.46 54.37 68.46 54.37 68.46 54.38 68.46 54.38 68.46 54.38 68.46 54.38 68.46 54.4 68.46 54.4 68.39 54.41 68.39 54.41 68.39 54.41 68.39 54.41 68.33 54.42 68.33 54.42 68.33 54.43 68.33 54.43 68.33 54.45 68.33 54.45 68.27 54.46 68.27 54.46 68.27 54.47 68.27 54.47 68.27 54.52 68.27 54.52 68.2 54.53 68.2 54.53 68.14 54.55 68.14 54.55 68.07 54.56 68.07 54.56 68.01 54.57 68.01 54.57 68.01 54.58 68.01 54.58 68.01 54.6 68.01 54.6 68.01 54.64 68.01 54.64 67.95 54.66 67.95 54.66 67.88 54.68 67.88 54.68 67.88 54.7 67.88 54.7 67.88 54.71 67.88 54.71 67.88 54.71 67.88 54.71 67.82 54.72 67.82 54.72 67.75 54.76 67.75 54.76 67.75 54.79 67.75 54.79 67.69 54.79 67.69 54.79 67.69 54.81 67.69 54.81 67.69 54.82 67.69 54.82 67.69 54.82 67.69 54.82 67.69 54.83 67.69 54.83 67.69 54.84 67.69 54.84 67.69 54.85 67.69 54.85 67.69 54.86 67.69 54.86 67.63 54.88 67.63 54.88 67.63 54.88 67.63 54.88 67.63 54.89 67.63 54.89 67.56 54.89 67.56 54.89 67.56 54.93 67.56 54.93 67.5 54.94 67.5 54.94 67.5 54.96 67.5 54.96 67.5 54.96 67.5 54.96 67.43 54.98 67.43 54.98 67.43 54.99 67.43 54.99 67.43 54.99 67.43 54.99 67.43 55.03 67.43 55.03 67.37 55.05 67.37 55.05 67.3 55.08 67.3 55.08 67.3 55.08 67.3 55.08 67.24 55.12 67.24 55.12 67.24 55.14 67.24 55.14 67.24 55.16 67.24 55.16 67.18 55.16 67.18 55.16 67.11 55.17 67.11 55.17 67.05 55.19 67.05 55.19 67.05 55.2 67.05 55.2 67.05 55.25 67.05 55.25 66.98 55.35 66.98 55.35 66.92 55.39 66.92 55.39 66.92 55.42 66.92 55.42 66.86 55.42 66.86 55.42 66.79 55.43 66.79 55.43 66.73 55.47 66.73 55.47 66.66 55.5 66.66 55.5 66.66 55.52 66.66 55.52 66.6 55.52 66.6 55.52 66.6 55.67 66.6 55.67 66.54 55.7 66.54 55.7 66.47 55.73 66.47 55.73 66.41 55.73 66.41 55.73 66.34 55.74 66.34 55.74 66.28 55.75 66.28 55.75 66.28 55.77 66.28 55.77 66.28 55.79 66.28 55.79 66.21 55.81 66.21 55.81 66.21 55.81 66.21 55.81 66.15 55.83 66.15 55.83 66.15 55.83 66.15 55.83 66.15 55.84 66.15 55.84 66.15 55.84 66.15 55.84 66.09 55.85 66.09 55.85 66.09 55.87 66.09 55.87 66.02 55.94 66.02 55.94 65.96 55.95 65.96 55.95 65.96 55.95 65.96 55.95 65.89 56.01 65.89 56.01 65.83 56.03 65.83 56.03 65.83 56.06 65.83 56.06 65.77 56.06 65.77 56.06 65.7 56.12 65.7 56.12 65.7 56.15 65.7 56.15 65.64 56.22 65.64 56.22 65.57 56.28 65.57 56.28 65.57 56.29 65.57 56.29 65.51 56.3 65.51 56.3 65.51 56.3 65.51 56.3 65.45 56.32 65.45 56.32 65.45 56.34 65.45 56.34 65.38 56.36 65.38 56.36 65.32 56.37 65.32 56.37 65.25 56.4 65.25 56.4 65.19 56.42 65.19 56.42 65.13 56.43 65.13 56.43 65.06 56.44 65.06 56.44 65 56.44 65 56.44 65 56.44 65 56.44 64.93 56.45 64.93 56.45 64.87 56.48 64.87 56.48 64.87 56.5 64.87 56.5 64.87 56.54 64.87 56.54 64.8 56.54 64.8 56.54 64.8 56.56 64.8 56.56 64.74 56.57 64.74 56.57 64.68 56.6 64.68 56.6 64.68 56.61 64.68 56.61 64.68 56.62 64.68 56.62 64.61 56.67 64.61 56.67 64.61 56.7 64.61 56.7 64.61 56.71 64.61 56.71 64.55 56.74 64.55 56.74 64.55 56.77 64.55 56.77 64.48 56.83 64.48 56.83 64.42 56.85 64.42 56.85 64.36 56.85 64.36 56.85 64.36 56.85 64.36 56.85 64.36 56.86 64.36 56.86 64.36 56.88 64.36 56.88 64.29 56.88 64.29 56.88 64.23 56.92 64.23 56.92 64.16 56.98 64.16 56.98 64.16 56.98 64.16 56.98 64.16 57.03 64.16 57.03 64.16 57.08 64.16 57.08 64.16 57.11 64.16 57.11 64.1 57.12 64.1 57.12 64.04 57.12 64.04 57.12 63.97 57.13 63.97 57.13 63.91 57.18 63.91 57.18 63.84 57.2 63.84 57.2 63.78 57.25 63.78 57.25 63.71 57.26 63.71 57.26 63.71 57.26 63.71 57.26 63.71 57.33 63.71 57.33 63.71 57.37 63.71 57.37 63.65 57.38 63.65 57.38 63.59 57.4 63.59 57.4 63.52 57.42 63.52 57.42 63.52 57.44 63.52 57.44 63.52 57.47 63.52 57.47 63.46 57.48 63.46 57.48 63.39 57.49 63.39 57.49 63.39 57.51 63.39 57.51 63.33 57.52 63.33 57.52 63.33 57.55 63.33 57.55 63.33 57.56 63.33 57.56 63.33 57.58 63.33 57.58 63.27 57.59 63.27 57.59 63.27 57.6 63.27 57.6 63.2 57.61 63.2 57.61 63.14 57.61 63.14 57.61 63.14 57.71 63.14 57.71 63.14 57.73 63.14 57.73 63.07 57.73 63.07 57.73 63.01 57.75 63.01 57.75 62.95 57.75 62.95 57.75 62.95 57.81 62.95 57.81 62.88 57.85 62.88 57.85 62.88 57.87 62.88 57.87 62.82 57.9 62.82 57.9 62.82 57.95 62.82 57.95 62.75 58 62.75 58 62.69 58.04 62.69 58.04 62.63 58.06 62.63 58.06 62.63 58.06 62.63 58.06 62.56 58.1 62.56 58.1 62.56 58.12 62.56 58.12 62.56 58.14 62.56 58.14 62.5 58.17 62.5 58.17 62.5 58.24 62.5 58.24 62.5 58.25 62.5 58.25 62.43 58.31 62.43 58.31 62.37 58.33 62.37 58.33 62.3 58.35 62.3 58.35 62.24 58.35 62.24 58.35 62.18 58.36 62.18 58.36 62.11 58.36 62.11 58.36 62.05 58.37 62.05 58.37 62.05 58.38 62.05 58.38 62.05 58.38 62.05 58.38 61.98 58.42 61.98 58.42 61.92 58.42 61.92 58.42 61.86 58.44 61.86 58.44 61.86 58.5 61.86 58.5 61.79 58.5 61.79 58.5 61.73 58.56 61.73 58.56 61.66 58.6 61.66 58.6 61.6 58.65 61.6 58.65 61.6 58.71 61.6 58.71 61.54 58.73 61.54 58.73 61.54 58.75 61.54 58.75 61.54 58.76 61.54 58.76 61.47 58.77 61.47 58.77 61.47 58.77 61.47 58.77 61.47 58.79 61.47 58.79 61.41 58.87 61.41 58.87 61.34 58.88 61.34 58.88 61.34 58.89 61.34 58.89 61.28 58.92 61.28 58.92 61.28 59 61.28 59 61.28 59 61.28 59 61.21 59.01 61.21 59.01 61.15 59.1 61.15 59.1 61.15 59.16 61.15 59.16 61.15 59.19 61.15 59.19 61.15 59.21 61.15 59.21 61.09 59.22 61.09 59.22 61.02 59.23 61.02 59.23 61.02 59.36 61.02 59.36 61.02 59.44 61.02 59.44 60.96 59.48 60.96 59.48 60.89 59.5 60.89 59.5 60.83 59.52 60.83 59.52 60.77 59.52 60.77 59.52 60.7 59.54 60.7 59.54 60.64 59.62 60.64 59.62 60.57 59.63 60.57 59.63 60.51 59.71 60.51 59.71 60.51 59.74 60.51 59.74 60.51 59.75 60.51 59.75 60.51 59.75 60.51 59.75 60.45 59.79 60.45 59.79 60.38 59.8 60.38 59.8 60.38 59.81 60.38 59.81 60.32 59.84 60.32 59.84 60.25 59.84 60.25 59.84 60.19 59.86 60.19 59.86 60.19 59.89 60.19 59.89 60.13 59.91 60.13 59.91 60.06 59.95 60.06 59.95 60 59.96 60 59.96 60 60.02 60 60.02 59.93 60.04 59.93 60.04 59.87 60.09 59.87 60.09 59.87 60.12 59.87 60.12 59.8 60.16 59.8 60.16 59.74 60.18 59.74 60.18 59.74 60.21 59.74 60.21 59.68 60.29 59.68 60.29 59.68 60.31 59.68 60.31 59.68 60.31 59.68 60.31 59.61 60.35 59.61 60.35 59.55 60.4 59.55 60.4 59.48 60.41 59.48 60.41 59.48 60.42 59.48 60.42 59.42 60.43 59.42 60.43 59.36 60.44 59.36 60.44 59.29 60.45 59.29 60.45 59.29 60.48 59.29 60.48 59.23 60.52 59.23 60.52 59.16 60.53 59.16 60.53 59.1 60.54 59.1 60.54 59.1 60.57 59.1 60.57 59.1 60.58 59.1 60.58 59.1 60.58 59.1 60.58 59.1 60.6 59.1 60.6 59.04 60.65 59.04 60.65 58.97 60.66 58.97 60.66 58.91 60.7 58.91 60.7 58.84 60.71 58.84 60.71 58.78 60.74 58.78 60.74 58.71 60.75 58.71 60.75 58.65 60.78 58.65 60.78 58.59 60.79 58.59 60.79 58.52 60.81 58.52 60.81 58.52 60.82 58.52 60.82 58.46 60.87 58.46 60.87 58.39 60.92 58.39 60.92 58.33 60.98 58.33 60.98 58.27 61.03 58.27 61.03 58.2 61.04 58.2 61.04 58.14 61.06 58.14 61.06 58.07 61.06 58.07 61.06 58.07 61.08 58.07 61.08 58.01 61.08 58.01 61.08 58.01 61.09 58.01 61.09 58.01 61.09 58.01 61.09 58.01 61.09 58.01 61.09 57.95 61.1 57.95 61.1 57.88 61.12 57.88 61.12 57.88 61.14 57.88 61.14 57.82 61.17 57.82 61.17 57.75 61.21 57.75 61.21 57.75 61.23 57.75 61.23 57.69 61.3 57.69 61.3 57.69 61.32 57.69 61.32 57.69 61.33 57.69 61.33 57.63 61.35 57.63 61.35 57.63 61.36 57.63 61.36 57.56 61.4 57.56 61.4 57.5 61.42 57.5 61.42 57.43 61.44 57.43 61.44 57.43 61.44 57.43 61.44 57.43 61.45 57.43 61.45 57.43 61.46 57.43 61.46 57.37 61.53 57.37 61.53 57.37 61.56 57.37 61.56 57.37 61.58 57.37 61.58 57.3 61.6 57.3 61.6 57.24 61.62 57.24 61.62 57.18 61.63 57.18 61.63 57.18 61.65 57.18 61.65 57.11 61.66 57.11 61.66 57.05 61.72 57.05 61.72 56.98 61.77 56.98 61.77 56.92 61.78 56.92 61.78 56.86 61.83 56.86 61.83 56.79 61.88 56.79 61.88 56.79 61.89 56.79 61.89 56.73 61.93 56.73 61.93 56.66 62 56.66 62 56.6 62.06 56.6 62.06 56.54 62.08 56.54 62.08 56.54 62.3 56.54 62.3 56.47 62.35 56.47 62.35 56.41 62.38 56.41 62.38 56.34 62.42 56.34 62.42 56.34 62.5 56.34 62.5 56.28 62.52 56.28 62.52 56.21 62.55 56.21 62.55 56.15 62.57 56.15 62.57 56.09 62.61 56.09 62.61 56.02 62.63 56.02 62.63 56.02 62.71 56.02 62.71 56.02 62.8 56.02 62.8 55.96 62.87 55.96 62.87 55.96 62.88 55.96 62.88 55.96 62.9 55.96 62.9 55.89 62.91 55.89 62.91 55.83 62.97 55.83 62.97 55.77 62.98 55.77 62.98 55.7 63 55.7 63 55.64 63.01 55.64 63.01 55.57 63.07 55.57 63.07 55.51 63.08 55.51 63.08 55.45 63.24 55.45 63.24 55.45 63.24 55.45 63.24 55.38 63.24 55.38 63.24 55.32 63.31 55.32 63.31 55.32 63.32 55.32 63.32 55.25 63.39 55.25 63.39 55.19 63.41 55.19 63.41 55.12 63.46 55.12 63.46 55.06 63.55 55.06 63.55 55 63.7 55 63.7 54.93 63.74 54.93 63.74 54.87 63.84 54.87 63.84 54.8 63.87 54.8 63.87 54.74 63.93 54.74 63.93 54.68 63.93 54.68 63.93 54.61 64 54.61 64 54.55 64.07 54.55 64.07 54.48 64.07 54.48 64.07 54.42 64.07 54.42 64.07 54.36 64.09 54.36 64.09 54.36 64.13 54.36 64.13 54.29 64.17 54.29 64.17 54.23 64.2 54.23 64.2 54.16 64.3 54.16 64.3 54.1 64.34 54.1 64.34 54.04 64.35 54.04 64.35 53.97 64.46 53.97 64.46 53.91 64.47 53.91 64.47 53.84 64.56 53.84 64.56 53.78 64.61 53.78 64.61 53.71 64.64 53.71 64.64 53.65 64.75 53.65 64.75 53.65 64.77 53.65 64.77 53.59 64.81 53.59 64.81 53.52 64.86 53.52 64.86 53.46 64.87 53.46 64.87 53.39 64.93 53.39 64.93 53.33 64.94 53.33 64.94 53.33 64.94 53.33 64.94 53.27 64.96 53.27 64.96 53.2 65.07 53.2 65.07 53.14 65.14 53.14 65.14 53.07 65.14 53.07 65.14 53.01 65.14 53.01 65.14 52.95 65.16 52.95 65.16 52.88 65.26 52.88 65.26 52.88 65.26 52.88 65.26 52.82 65.28 52.82 65.28 52.75 65.31 52.75 65.31 52.75 65.52 52.75 65.52 52.69 65.54 52.69 65.54 52.62 65.6 52.62 65.6 52.56 65.66 52.56 65.66 52.5 65.67 52.5 65.67 52.43 65.73 52.43 65.73 52.37 65.74 52.37 65.74 52.3 65.75 52.3 65.75 52.3 65.75 52.3 65.75 52.24 65.8 52.24 65.8 52.18 65.84 52.18 65.84 52.11 65.93 52.11 65.93 52.05 65.94 52.05 65.94 51.98 65.96 51.98 65.96 51.92 66.06 51.92 66.06 51.86 66.06 51.86 66.06 51.79 66.06 51.79 66.06 51.73 66.11 51.73 66.11 51.66 66.14 51.66 66.14 51.6 66.18 51.6 66.18 51.54 66.19 51.54 66.19 51.47 66.2 51.47 66.2 51.47 66.2 51.47 66.2 51.41 66.3 51.41 66.3 51.34 66.31 51.34 66.31 51.28 66.35 51.28 66.35 51.21 66.4 51.21 66.4 51.21 66.49 51.21 66.49 51.15 66.51 51.15 66.51 51.09 66.52 51.09 66.52 51.02 66.6 51.02 66.6 50.96 66.69 50.96 66.69 50.89 66.7 50.89 66.7 50.83 66.72 50.83 66.72 50.77 66.79 50.77 66.79 50.7 66.8 50.7 66.8 50.7 66.83 50.7 66.83 50.64 66.83 50.64 66.83 50.57 67.05 50.57 67.05 50.51 67.07 50.51 67.07 50.45 67.07 50.45 67.07 50.38 67.11 50.38 67.11 50.32 67.17 50.32 67.17 50.25 67.32 50.25 67.32 50.19 67.32 50.19 67.32 50.12 67.34 50.12 67.34 50.06 67.38 50.06 67.38 50 67.43 50 67.43 49.93 67.46 49.93 67.46 49.87 67.46 49.87 67.46 49.8 67.5 49.8 67.5 49.74 67.66 49.74 67.66 49.68 67.66 49.68 67.66 49.68 67.75 49.68 67.75 49.61 67.76 49.61 67.76 49.55 67.83 49.55 67.83 49.48 67.84 49.48 67.84 49.42 67.95 49.42 67.95 49.36 67.96 49.36 67.96 49.29 67.98 49.29 67.98 49.29 67.99 49.29 67.99 49.23 68.05 49.23 68.05 49.23 68.09 49.23 68.09 49.16 68.12 49.16 68.12 49.1 68.16 49.1 68.16 49.04 68.23 49.04 68.23 48.97 68.25 48.97 68.25 48.91 68.29 48.91 68.29 48.84 68.48 48.84 68.48 48.78 68.49 48.78 68.49 48.71 68.6 48.71 68.6 48.65 68.64 48.65 68.64 48.59 68.67 48.59 68.67 48.52 68.71 48.52 68.71 48.46 68.84 48.46 68.84 48.39 68.96 48.39 68.96 48.33 68.99 48.33 68.99 48.27 69.08 48.27 69.08 48.2 69.08 48.2 69.08 48.14 69.17 48.14 69.17 48.07 69.17 48.07 69.17 48.01 69.2 48.01 69.2 48.01 69.31 48.01 69.31 48.01 69.35 48.01 69.35 47.95 69.49 47.95 69.49 47.88 69.7 47.88 69.7 47.82 69.79 47.82 69.79 47.75 69.89 47.75 69.89 47.69 69.93 47.69 69.93 47.62 69.94 47.62 69.94 47.56 69.96 47.56 69.96 47.5 70 47.5 70 47.43 70.02 47.43 70.02 47.37 70.04 47.37 70.04 47.3 70.18 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45.84 61.6 45.87 61.6 45.87 61.54 45.89 61.54 45.89 61.47 45.9 61.47 45.9 61.41 45.9 61.41 45.9 61.34 45.91 61.34 45.91 61.41 45.91 61.41 45.91 61.34 45.91 61.34 45.91 61.28 45.93 61.28 45.93 61.21 45.95 61.21 45.95 61.15 45.96 61.15 45.96 61.09 46.02 61.09 46.02 61.02 46.02 61.02 46.02 60.96 46.05 60.96 46.05 60.89 46.05 60.89 46.05 60.83 46.05 60.83 46.05 60.77 46.06 60.77 46.06 60.7 46.08 60.7 46.08 60.64 46.1 60.64 46.1 60.57 46.14 60.57 46.14 60.51 46.16 60.51 46.16 60.45 46.17 60.45 46.17 60.38 46.18 60.38 46.18 60.32 46.18 60.32 46.18 60.25 46.18 60.25 46.18 60.19 46.21 60.19 46.21 60.13 46.21 60.13 46.21 60.06 46.25 60.06 46.25 60 46.27 60 46.27 59.93 46.27 59.93 46.27 59.87 46.28 59.87 46.28 59.93 46.3 59.93 46.3 59.87 46.33 59.87 46.33 59.8 46.33 59.8 46.33 59.74 46.35 59.74 46.35 59.68 46.37 59.68 46.37 59.61 46.38 59.61 46.38 59.55 46.41 59.55 46.41 59.48 46.42 59.48 46.42 59.42 46.43 59.42 46.43 59.36 46.43 59.36 46.43 59.29 46.44 59.29 46.44 59.23 46.44 59.23 46.44 59.16 46.47 59.16 46.47 59.1 46.48 59.1 46.48 59.04 46.5 59.04 46.5 58.97 46.53 58.97 46.53 59.04 46.56 59.04 46.56 59.1 46.56 59.1 46.56 59.04 46.56 59.04 46.56 58.97 46.56 58.97 46.56 58.91 46.57 58.91 46.57 58.84 46.58 58.84 46.58 58.91 46.59 58.91 46.59 58.84 46.61 58.84 46.61 58.78 46.61 58.78 46.61 58.71 46.62 58.71 46.62 58.78 46.63 58.78 46.63 58.71 46.67 58.71 46.67 58.65 46.67 58.65 46.67 58.59 46.68 58.59 46.68 58.52 46.71 58.52 46.71 58.46 46.71 58.46 46.71 58.39 46.72 58.39 46.72 58.33 46.75 58.33 46.75 58.27 46.8 58.27 46.8 58.2 46.83 58.2 46.83 58.14 46.84 58.14 46.84 58.07 46.88 58.07 46.88 58.01 46.88 58.01 46.88 57.95 46.91 57.95 46.91 57.88 46.91 57.88 46.91 57.82 46.92 57.82 46.92 57.75 46.98 57.75 46.98 57.82 46.99 57.82 46.99 57.88 47 57.88 47 57.82 47.02 57.82 47.02 57.75 47.02 57.75 47.02 57.69 47.03 57.69 47.03 57.63 47.06 57.63 47.06 57.56 47.07 57.56 47.07 57.5 47.07 57.5 47.07 57.43 47.11 57.43 47.11 57.37 47.11 57.37 47.11 57.3 47.11 57.3 47.11 57.24 47.11 57.24 47.11 57.3 47.13 57.3 47.13 57.24 47.14 57.24 47.14 57.18 47.15 57.18 47.15 57.11 47.19 57.11 47.19 57.05 47.25 57.05 47.25 56.98 47.26 56.98 47.26 56.92 47.27 56.92 47.27 56.86 47.28 56.86 47.28 56.79 47.29 56.79 47.29 56.73 47.29 56.73 47.29 56.66 47.3 56.66 47.3 56.6 47.3 56.6 47.3 56.54 47.31 56.54 47.31 56.47 47.31 56.47 47.31 56.54 47.31 56.54 47.31 56.47 47.31 56.47 47.31 56.41 47.37 56.41 47.37 56.34 47.4 56.34 47.4 56.41 47.41 56.41 47.41 56.34 47.41 56.34 47.41 56.28 47.42 56.28 47.42 56.21 47.45 56.21 47.45 56.15 47.5 56.15 47.5 56.09 47.51 56.09 47.51 56.02 47.53 56.02 47.53 55.96 47.53 55.96 47.53 56.02 47.57 56.02 47.57 55.96 47.59 55.96 47.59 55.89 47.61 55.89 47.61 55.83 47.65 55.83 47.65 55.77 47.65 55.77 47.65 55.83 47.67 55.83 47.67 55.77 47.68 55.77 47.68 55.7 47.68 55.7 47.68 55.64 47.68 55.64 47.68 55.57 47.68 55.57 47.68 55.51 47.7 55.51 47.7 55.45 47.71 55.45 47.71 55.38 47.76 55.38 47.76 55.32 47.77 55.32 47.77 55.38 47.77 55.38 47.77 55.32 47.8 55.32 47.8 55.38 47.8 55.38 47.8 55.32 47.82 55.32 47.82 55.25 47.83 55.25 47.83 55.19 47.83 55.19 47.83 55.12 47.84 55.12 47.84 55.19 47.84 55.19 47.84 55.25 47.86 55.25 47.86 55.19 47.86 55.19 47.86 55.12 47.86 55.12 47.86 55.06 47.87 55.06 47.87 55 47.9 55 47.9 54.93 47.92 54.93 47.92 54.87 47.93 54.87 47.93 54.8 47.94 54.8 47.94 54.74 47.96 54.74 47.96 54.68 47.98 54.68 47.98 54.61 47.99 54.61 47.99 54.68 48.03 54.68 48.03 54.61 48.08 54.61 48.08 54.55 48.08 54.55 48.08 54.61 48.08 54.61 48.08 54.55 48.09 54.55 48.09 54.61 48.09 54.61 48.09 54.55 48.1 54.55 48.1 54.48 48.1 54.48 48.1 54.55 48.14 54.55 48.14 54.48 48.16 54.48 48.16 54.42 48.18 54.42 48.18 54.36 48.19 54.36 48.19 54.29 48.19 54.29 48.19 54.23 48.21 54.23 48.21 54.16 48.22 54.16 48.22 54.1 48.23 54.1 48.23 54.04 48.25 54.04 48.25 53.97 48.26 53.97 48.26 53.91 48.26 53.91 48.26 53.84 48.27 53.84 48.27 53.78 48.27 53.78 48.27 53.71 48.29 53.71 48.29 53.65 48.29 53.65 48.29 53.59 48.3 53.59 48.3 53.52 48.31 53.52 48.31 53.46 48.31 53.46 48.31 53.39 48.33 53.39 48.33 53.33 48.33 53.33 48.33 53.27 48.37 53.27 48.37 53.2 48.38 53.2 48.38 53.27 48.39 53.27 48.39 53.2 48.4 53.2 48.4 53.27 48.41 53.27 48.41 53.2 48.42 53.2 48.42 53.14 48.43 53.14 48.43 53.07 48.43 53.07 48.43 53.01 48.44 53.01 48.44 52.95 48.47 52.95 48.47 52.88 48.49 52.88 48.49 52.82 48.49 52.82 48.49 52.75 48.49 52.75 48.49 52.69 48.5 52.69 48.5 52.62 48.52 52.62 48.52 52.69 48.53 52.69 48.53 52.62 48.54 52.62 48.54 52.56 48.54 52.56 48.54 52.5 48.55 52.5 48.55 52.43 48.56 52.43 48.56 52.37 48.56 52.37 48.56 52.3 48.56 52.3 48.56 52.24 48.57 52.24 48.57 52.18 48.58 52.18 48.58 52.11 48.59 52.11 48.59 52.05 48.59 52.05 48.59 52.11 48.6 52.11 48.6 52.05 48.6 52.05 48.6 51.98 48.64 51.98 48.64 52.05 48.64 52.05 48.64 51.98 48.64 51.98 48.64 51.92 48.64 51.92 48.64 51.86 48.65 51.86 48.65 51.79 48.67 51.79 48.67 51.86 48.68 51.86 48.68 51.92 48.69 51.92 48.69 51.86 48.7 51.86 48.7 51.79 48.72 51.79 48.72 51.73 48.74 51.73 48.74 51.66 48.8 51.66 48.8 51.6 48.82 51.6 48.82 51.54 48.82 51.54 48.82 51.47 48.83 51.47 48.83 51.41 48.83 51.41 48.83 51.34 48.84 51.34 48.84 51.28 48.84 51.28 48.84 51.21 48.84 51.21 48.84 51.15 48.84 51.15 48.84 51.09 48.85 51.09 48.85 51.02 48.85 51.02 48.85 50.96 48.87 50.96 48.87 50.89 48.88 50.89 48.88 50.83 48.88 50.83 48.88 50.77 48.89 50.77 48.89 50.7 48.9 50.7 48.9 50.64 48.9 50.64 48.9 50.57 48.94 50.57 48.94 50.64 48.95 50.64 48.95 50.57 48.96 50.57 48.96 50.51 48.99 50.51 48.99 50.45 49.01 50.45 49.01 50.38 49.02 50.38 49.02 50.32 49.03 50.32 49.03 50.25 49.04 50.25 49.04 50.19 49.04 50.19 49.04 50.12 49.05 50.12 49.05 50.06 49.08 50.06 49.08 50 49.11 50 49.11 50.06 49.12 50.06 49.12 50 49.14 50 49.14 49.93 49.16 49.93 49.16 49.87 49.17 49.87 49.17 49.8 49.21 49.8 49.21 49.74 49.22 49.74 49.22 49.68 49.22 49.68 49.22 49.61 49.23 49.61 49.23 49.55 49.23 49.55 49.23 49.48 49.24 49.48 49.24 49.55 49.24 49.55 49.24 49.48 49.26 49.48 49.26 49.55 49.26 49.55 49.26 49.48 49.28 49.48 49.28 49.42 49.28 49.42 49.28 49.36 49.28 49.36 49.28 49.29 49.29 49.29 49.29 49.23 49.29 49.23 49.29 49.16 49.33 49.16 49.33 49.23 49.37 49.23 49.37 49.16 49.39 49.16 49.39 49.1 49.39 49.1 49.39 49.04 49.41 49.04 49.41 49.1 49.41 49.1 49.41 49.04 49.42 49.04 49.42 48.97 49.43 48.97 49.43 48.91 49.44 48.91 49.44 48.84 49.45 48.84 49.45 48.78 49.45 48.78 49.45 48.84 49.47 48.84 49.47 48.78 49.47 48.78 49.47 48.84 49.48 48.84 49.48 48.78 49.48 48.78 49.48 48.71 49.49 48.71 49.49 48.65 49.49 48.65 49.49 48.59 49.49 48.59 49.49 48.52 49.49 48.52 49.49 48.46 49.5 48.46 49.5 48.39 49.52 48.39 49.52 48.33 49.53 48.33 49.53 48.39 49.57 48.39 49.57 48.33 49.57 48.33 49.57 48.27 49.57 48.27 49.57 48.2 49.59 48.2 49.59 48.14 49.6 48.14 49.6 48.07 49.61 48.07 49.61 48.01 49.66 48.01 49.66 48.07 49.68 48.07 49.68 48.01 49.68 48.01 49.68 47.95 49.69 47.95 49.69 47.88 49.7 47.88 49.7 47.82 49.71 47.82 49.71 47.75 49.72 47.75 49.72 47.69 49.73 47.69 49.73 47.62 49.75 47.62 49.75 47.56 49.78 47.56 49.78 47.5 49.78 47.5 49.78 47.43 49.81 47.43 49.81 47.37 49.82 47.37 49.82 47.3 49.83 47.3 49.83 47.24 49.83 47.24 49.83 47.18 49.83 47.18 49.83 47.11 49.84 47.11 49.84 47.05 49.85 47.05 49.85 46.98 49.85 46.98 49.85 46.92 49.85 46.92 49.85 46.98 49.86 46.98 49.86 46.92 49.87 46.92 49.87 46.86 49.87 46.86 49.87 46.79 49.91 46.79 49.91 46.73 49.92 46.73 49.92 46.66 49.93 46.66 49.93 46.73 49.94 46.73 49.94 46.66 49.94 46.66 49.94 46.6 49.94 46.6 49.94 46.54 49.96 46.54 49.96 46.6 49.98 46.6 49.98 46.54 49.99 46.54 49.99 46.47 50.01 46.47 50.01 46.54 50.01 46.54 50.01 46.6 50.02 46.6 50.02 46.54 50.02 46.54 50.02 46.47 50.03 46.47 50.03 46.41 50.03 46.41 50.03 46.34 50.03 46.34 50.03 46.28 50.03 46.28 50.03 46.21 50.05 46.21 50.05 46.28 50.06 46.28 50.06 46.21 50.07 46.21 50.07 46.28 50.08 46.28 50.08 46.21 50.09 46.21 50.09 46.15 50.1 46.15 50.1 46.09 50.11 46.09 50.11 46.02 50.11 46.02 50.11 45.96 50.14 45.96 50.14 45.89 50.14 45.89 50.14 45.83 50.16 45.83 50.16 45.89 50.17 45.89 50.17 45.83 50.18 45.83 50.18 45.89 50.19 45.89 50.19 45.83 50.19 45.83 50.19 45.77 50.22 45.77 50.22 45.7 50.23 45.7 50.23 45.64 50.23 45.64 50.23 45.57 50.29 45.57 50.29 45.51 50.3 45.51 50.3 45.57 50.31 45.57 50.31 45.51 50.33 45.51 50.33 45.45 50.37 45.45 50.37 45.38 50.37 45.38 50.37 45.32 50.37 45.32 50.37 45.25 50.38 45.25 50.38 45.32 50.39 45.32 50.39 45.25 50.4 45.25 50.4 45.32 50.41 45.32 50.41 45.25 50.42 45.25 50.42 45.19 50.43 45.19 50.43 45.12 50.52 45.12 50.52 45.19 50.52 45.19 50.52 45.12 50.53 45.12 50.53 45.06 50.54 45.06 50.54 45 50.54 45 50.54 45.06 50.56 45.06 50.56 45 50.6 45 50.6 44.93 50.6 44.93 50.6 44.87 50.61 44.87 50.61 44.8 50.64 44.8 50.64 44.87 50.64 44.87 50.64 44.8 50.64 44.8 50.64 44.74 50.65 44.74 50.65 44.68 50.67 44.68 50.67 44.74 50.67 44.74 50.67 44.68 50.67 44.68 50.67 44.61 50.67 44.61 50.67 44.55 50.67 44.55 50.67 44.48 50.68 44.48 50.68 44.42 50.68 44.42 50.68 44.36 50.69 44.36 50.69 44.29 50.69 44.29 50.69 44.23 50.7 44.23 50.7 44.29 50.73 44.29 50.73 44.36 50.76 44.36 50.76 44.29 50.76 44.29 50.76 44.23 50.76 44.23 50.76 44.16 50.79 44.16 50.79 44.1 50.82 44.1 50.82 44.04 50.82 44.04 50.82 43.97 50.84 43.97 50.84 43.91 50.84 43.91 50.84 43.84 50.84 43.84 50.84 43.78 50.87 43.78 50.87 43.71 50.88 43.71 50.88 43.65 50.89 43.65 50.89 43.59 50.89 43.59 50.89 43.52 50.9 43.52 50.9 43.46 50.9 43.46 50.9 43.52 50.9 43.52 50.9 43.46 50.91 43.46 50.91 43.39 50.92 43.39 50.92 43.33 50.92 43.33 50.92 43.27 50.94 43.27 50.94 43.2 50.94 43.2 50.94 43.14 50.94 43.14 50.94 43.07 50.96 43.07 50.96 43.01 50.99 43.01 50.99 43.07 50.99 43.07 50.99 43.01 50.99 43.01 50.99 42.95 51 42.95 51 42.88 51 42.88 51 42.82 51.01 42.82 51.01 42.88 51.01 42.88 51.01 42.82 51.02 42.82 51.02 42.75 51.05 42.75 51.05 42.69 51.09 42.69 51.09 42.62 51.09 42.62 51.09 42.56 51.09 42.56 51.09 42.62 51.1 42.62 51.1 42.56 51.11 42.56 51.11 42.5 51.12 42.5 51.12 42.43 51.13 42.43 51.13 42.37 51.14 42.37 51.14 42.3 51.15 42.3 51.15 42.37 51.16 42.37 51.16 42.3 51.16 42.3 51.16 42.24 51.17 42.24 51.17 42.3 51.18 42.3 51.18 42.24 51.18 42.24 51.18 42.18 51.19 42.18 51.19 42.24 51.23 42.24 51.23 42.18 51.25 42.18 51.25 42.11 51.26 42.11 51.26 42.05 51.27 42.05 51.27 42.11 51.27 42.11 51.27 42.18 51.28 42.18 51.28 42.11 51.32 42.11 51.32 42.05 51.32 42.05 51.32 41.98 51.36 41.98 51.36 41.92 51.36 41.92 51.36 41.86 51.36 41.86 51.36 41.79 51.38 41.79 51.38 41.73 51.39 41.73 51.39 41.79 51.41 41.79 51.41 41.73 51.42 41.73 51.42 41.79 51.46 41.79 51.46 41.73 51.48 41.73 51.48 41.66 51.48 41.66 51.48 41.73 51.49 41.73 51.49 41.79 51.49 41.79 51.49 41.73 51.49 41.73 51.49 41.66 51.51 41.66 51.51 41.6 51.52 41.6 51.52 41.66 51.52 41.66 51.52 41.73 51.53 41.73 51.53 41.79 51.55 41.79 51.55 41.73 51.56 41.73 51.56 41.79 51.58 41.79 51.58 41.73 51.59 41.73 51.59 41.66 51.6 41.66 51.6 41.6 51.6 41.6 51.6 41.54 51.62 41.54 51.62 41.47 51.63 41.47 51.63 41.54 51.63 41.54 51.63 41.6 51.7 41.6 51.7 41.54 51.7 41.54 51.7 41.6 51.73 41.6 51.73 41.54 51.74 41.54 51.74 41.47 51.75 41.47 51.75 41.41 51.76 41.41 51.76 41.34 51.77 41.34 51.77 41.28 51.78 41.28 51.78 41.21 51.78 41.21 51.78 41.28 51.79 41.28 51.79 41.21 51.81 41.21 51.81 41.15 51.88 41.15 51.88 41.09 51.9 41.09 51.9 41.02 51.92 41.02 51.92 40.96 51.93 40.96 51.93 41.02 51.96 41.02 51.96 41.09 51.97 41.09 51.97 41.15 52.01 41.15 52.01 41.21 52.03 41.21 52.03 41.15 52.04 41.15 52.04 41.09 52.06 41.09 52.06 41.15 52.07 41.15 52.07 41.09 52.12 41.09 52.12 41.15 52.12 41.15 52.12 41.09 52.14 41.09 52.14 41.02 52.16 41.02 52.16 40.96 52.16 40.96 52.16 40.89 52.17 40.89 52.17 40.83 52.19 40.83 52.19 40.89 52.19 40.89 52.19 40.96 52.2 40.96 52.2 40.89 52.2 40.89 52.2 40.96 52.21 40.96 52.21 40.89 52.24 40.89 52.24 40.83 52.24 40.83 52.24 40.77 52.26 40.77 52.26 40.7 52.26 40.7 52.26 40.64 52.27 40.64 52.27 40.7 52.28 40.7 52.28 40.64 52.29 40.64 52.29 40.57 52.3 40.57 52.3 40.51 52.33 40.51 52.33 40.57 52.33 40.57 52.33 40.51 52.36 40.51 52.36 40.45 52.4 40.45 52.4 40.38 52.41 40.38 52.41 40.32 52.47 40.32 52.47 40.38 52.48 40.38 52.48 40.32 52.49 40.32 52.49 40.25 52.49 40.25 52.49 40.19 52.51 40.19 52.51 40.12 52.52 40.12 52.52 40.06 52.53 40.06 52.53 40 52.53 40 52.53 39.93 52.53 39.93 52.53 40 52.54 40 52.54 39.93 52.54 39.93 52.54 39.87 52.54 39.87 52.54 39.8 52.57 39.8 52.57 39.74 52.57 39.74 52.57 39.68 52.57 39.68 52.57 39.61 52.6 39.61 52.6 39.55 52.63 39.55 52.63 39.48 52.64 39.48 52.64 39.42 52.64 39.42 52.64 39.36 52.67 39.36 52.67 39.29 52.68 39.29 52.68 39.23 52.69 39.23 52.69 39.16 52.69 39.16 52.69 39.1 52.7 39.1 52.7 39.04 52.73 39.04 52.73 39.1 52.73 39.1 52.73 39.16 52.75 39.16 52.75 39.1 52.77 39.1 52.77 39.16 52.77 39.16 52.77 39.1 52.84 39.1 52.84 39.04 52.85 39.04 52.85 39.1 52.86 39.1 52.86 39.04 52.87 39.04 52.87 38.97 52.88 38.97 52.88 38.91 52.88 38.91 52.88 38.84 52.89 38.84 52.89 38.78 52.89 38.78 52.89 38.71 52.89 38.71 52.89 38.65 52.92 38.65 52.92 38.71 52.97 38.71 52.97 38.65 52.98 38.65 52.98 38.59 52.99 38.59 52.99 38.52 52.99 38.52 52.99 38.46 53 38.46 53 38.39 53 38.39 53 38.33 53.05 38.33 53.05 38.27 53.05 38.27 53.05 38.2 53.06 38.2 53.06 38.14 53.07 38.14 53.07 38.07 53.07 38.07 53.07 38.01 53.09 38.01 53.09 37.95 53.09 37.95 53.09 38.01 53.1 38.01 53.1 37.95 53.12 37.95 53.12 37.88 53.12 37.88 53.12 37.82 53.12 37.82 53.12 37.75 53.13 37.75 53.13 37.69 53.14 37.69 53.14 37.62 53.14 37.62 53.14 37.69 53.15 37.69 53.15 37.62 53.16 37.62 53.16 37.56 53.16 37.56 53.16 37.5 53.17 37.5 53.17 37.43 53.17 37.43 53.17 37.37 53.18 37.37 53.18 37.3 53.22 37.3 53.22 37.37 53.23 37.37 53.23 37.3 53.23 37.3 53.23 37.24 53.23 37.24 53.23 37.3 53.24 37.3 53.24 37.24 53.25 37.24 53.25 37.18 53.26 37.18 53.26 37.11 53.27 37.11 53.27 37.05 53.28 37.05 53.28 36.98 53.29 36.98 53.29 37.05 53.3 37.05 53.3 37.11 53.3 37.11 53.3 37.18 53.3 37.18 53.3 37.24 53.3 37.24 53.3 37.18 53.31 37.18 53.31 37.11 53.35 37.11 53.35 37.05 53.36 37.05 53.36 36.98 53.38 36.98 53.38 36.92 53.4 36.92 53.4 36.86 53.41 36.86 53.41 36.79 53.41 36.79 53.41 36.73 53.42 36.73 53.42 36.66 53.43 36.66 53.43 36.73 53.44 36.73 53.44 36.66 53.45 36.66 53.45 36.6 53.46 36.6 53.46 36.66 53.47 36.66 53.47 36.73 53.47 36.73 53.47 36.79 53.47 36.79 53.47 36.73 53.48 36.73 53.48 36.79 53.51 36.79 53.51 36.73 53.51 36.73 53.51 36.66 53.52 36.66 53.52 36.6 53.55 36.6 53.55 36.54 53.56 36.54 53.56 36.6 53.57 36.6 53.57 36.66 53.58 36.66 53.58 36.6 53.59 36.6 53.59 36.66 53.6 36.66 53.6 36.6 53.62 36.6 53.62 36.54 53.62 36.54 53.62 36.47 53.67 36.47 53.67 36.41 53.71 36.41 53.71 36.34 53.72 36.34 53.72 36.28 53.77 36.28 53.77 36.21 53.8 36.21 53.8 36.15 53.83 36.15 53.83 36.09 53.84 36.09 53.84 36.15 53.86 36.15 53.86 36.21 53.86 36.21 53.86 36.28 53.87 36.28 53.87 36.34 53.88 36.34 53.88 36.41 53.88 36.41 53.88 36.34 53.9 36.34 53.9 36.28 53.91 36.28 53.91 36.21 53.95 36.21 53.95 36.15 53.96 36.15 53.96 36.09 53.99 36.09 53.99 36.02 53.99 36.02 53.99 35.96 54 35.96 54 35.89 54 35.89 54 35.83 54.02 35.83 54.02 35.77 54.05 35.77 54.05 35.83 54.06 35.83 54.06 35.89 54.12 35.89 54.12 35.83 54.13 35.83 54.13 35.77 54.15 35.77 54.15 35.83 54.2 35.83 54.2 35.89 54.2 35.89 54.2 35.96 54.22 35.96 54.22 35.89 54.22 35.89 54.22 35.83 54.24 35.83 54.24 35.89 54.24 35.89 54.24 35.83 54.25 35.83 54.25 35.77 54.27 35.77 54.27 35.7 54.3 35.7 54.3 35.64 54.31 35.64 54.31 35.57 54.32 35.57 54.32 35.51 54.32 35.51 54.32 35.57 54.32 35.57 54.32 35.51 54.33 35.51 54.33 35.45 54.36 35.45 54.36 35.38 54.36 35.38 54.36 35.45 54.37 35.45 54.37 35.38 54.38 35.38 54.38 35.32 54.38 35.32 54.38 35.25 54.4 35.25 54.4 35.32 54.41 35.32 54.41 35.25 54.41 35.25 54.41 35.32 54.42 35.32 54.42 35.25 54.43 35.25 54.43 35.19 54.45 35.19 54.45 35.25 54.46 35.25 54.46 35.19 54.47 35.19 54.47 35.12 54.52 35.12 54.52 35.19 54.53 35.19 54.53 35.25 54.55 35.25 54.55 35.32 54.56 35.32 54.56 35.38 54.57 35.38 54.57 35.32 54.58 35.32 54.58 35.25 54.6 35.25 54.6 35.19 54.64 35.19 54.64 35.25 54.66 35.25 54.66 35.32 54.68 35.32 54.68 35.25 54.7 35.25 54.7 35.19 54.71 35.19 54.71 35.12 54.71 35.12 54.71 35.19 54.72 35.19 54.72 35.25 54.76 35.25 54.76 35.19 54.79 35.19 54.79 35.25 54.79 35.25 54.79 35.19 54.81 35.19 54.81 35.12 54.82 35.12 54.82 35.06 54.82 35.06 54.82 35 54.83 35 54.83 34.93 54.84 34.93 54.84 34.87 54.85 34.87 54.85 34.8 54.86 34.8 54.86 34.87 54.88 34.87 54.88 34.8 54.88 34.8 54.88 34.74 54.89 34.74 54.89 34.8 54.89 34.8 54.89 34.74 54.93 34.74 54.93 34.8 54.94 34.8 54.94 34.74 54.96 34.74 54.96 34.68 54.96 34.68 54.96 34.74 54.98 34.74 54.98 34.68 54.99 34.68 54.99 34.61 54.99 34.61 54.99 34.55 55.03 34.55 55.03 34.61 55.05 34.61 55.05 34.68 55.08 34.68 55.08 34.61 55.08 34.61 55.08 34.68 55.12 34.68 55.12 34.61 55.14 34.61 55.14 34.55 55.16 34.55 55.16 34.61 55.16 34.61 55.16 34.68 55.17 34.68 55.17 34.74 55.19 34.74 55.19 34.68 55.2 34.68 55.2 34.61 55.25 34.61 55.25 34.68 55.35 34.68 55.35 34.74 55.39 34.74 55.39 34.68 55.42 34.68 55.42 34.74 55.42 34.74 55.42 34.8 55.43 34.8 55.43 34.87 55.47 34.87 55.47 34.93 55.5 34.93 55.5 34.87 55.52 34.87 55.52 34.93 55.52 34.93 55.52 34.87 55.67 34.87 55.67 34.93 55.7 34.93 55.7 35 55.73 35 55.73 35.06 55.73 35.06 55.73 35.12 55.74 35.12 55.74 35.19 55.75 35.19 55.75 35.12 55.77 35.12 55.77 35.06 55.79 35.06 55.79 35.12 55.81 35.12 55.81 35.06 55.81 35.06 55.81 35.12 55.83 35.12 55.83 35.06 55.83 35.06 55.83 35 55.84 35 55.84 34.93 55.84 34.93 55.84 35 55.85 35 55.85 34.93 55.87 34.93 55.87 35 55.94 35 55.94 35.06 55.95 35.06 55.95 35 55.95 35 55.95 35.06 56.01 35.06 56.01 35.12 56.03 35.12 56.03 35.06 56.06 35.06 56.06 35.12 56.06 35.12 56.06 35.19 56.12 35.19 56.12 35.12 56.15 35.12 56.15 35.19 56.22 35.19 56.22 35.25 56.28 35.25 56.28 35.19 56.29 35.19 56.29 35.25 56.3 35.25 56.3 35.19 56.3 35.19 56.3 35.25 56.32 35.25 56.32 35.19 56.34 35.19 56.34 35.25 56.36 35.25 56.36 35.32 56.37 35.32 56.37 35.38 56.4 35.38 56.4 35.45 56.42 35.45 56.42 35.51 56.43 35.51 56.43 35.57 56.44 35.57 56.44 35.64 56.44 35.64 56.44 35.57 56.44 35.57 56.44 35.64 56.45 35.64 56.45 35.7 56.48 35.7 56.48 35.64 56.5 35.64 56.5 35.57 56.54 35.57 56.54 35.64 56.54 35.64 56.54 35.57 56.56 35.57 56.56 35.64 56.57 35.64 56.57 35.7 56.6 35.7 56.6 35.64 56.61 35.64 56.61 35.57 56.62 35.57 56.62 35.64 56.67 35.64 56.67 35.57 56.7 35.57 56.7 35.51 56.71 35.51 56.71 35.57 56.74 35.57 56.74 35.51 56.77 35.51 56.77 35.57 56.83 35.57 56.83 35.64 56.85 35.64 56.85 35.7 56.85 35.7 56.85 35.64 56.85 35.64 56.85 35.57 56.86 35.57 56.86 35.51 56.88 35.51 56.88 35.57 56.88 35.57 56.88 35.64 56.92 35.64 56.92 35.7 56.98 35.7 56.98 35.64 56.98 35.64 56.98 35.57 57.03 35.57 57.03 35.51 57.08 35.51 57.08 35.45 57.11 35.45 57.11 35.51 57.12 35.51 57.12 35.57 57.12 35.57 57.12 35.64 57.13 35.64 57.13 35.7 57.18 35.7 57.18 35.77 57.2 35.77 57.2 35.83 57.25 35.83 57.25 35.89 57.26 35.89 57.26 35.83 57.26 35.83 57.26 35.77 57.33 35.77 57.33 35.7 57.37 35.7 57.37 35.77 57.38 35.77 57.38 35.83 57.4 35.83 57.4 35.89 57.42 35.89 57.42 35.83 57.44 35.83 57.44 35.77 57.47 35.77 57.47 35.83 57.48 35.83 57.48 35.89 57.49 35.89 57.49 35.83 57.51 35.83 57.51 35.89 57.52 35.89 57.52 35.83 57.55 35.83 57.55 35.77 57.56 35.77 57.56 35.7 57.58 35.7 57.58 35.77 57.59 35.77 57.59 35.7 57.6 35.7 57.6 35.77 57.61 35.77 57.61 35.83 57.61 35.83 57.61 35.77 57.71 35.77 57.71 35.7 57.73 35.7 57.73 35.77 57.73 35.77 57.73 35.83 57.75 35.83 57.75 35.89 57.75 35.89 57.75 35.83 57.81 35.83 57.81 35.89 57.85 35.89 57.85 35.83 57.87 35.83 57.87 35.89 57.9 35.89 57.9 35.83 57.95 35.83 57.95 35.89 58 35.89 58 35.96 58.04 35.96 58.04 36.02 58.06 36.02 58.06 35.96 58.06 35.96 58.06 36.02 58.1 36.02 58.1 35.96 58.12 35.96 58.12 35.89 58.14 35.89 58.14 35.96 58.17 35.96 58.17 35.89 58.24 35.89 58.24 35.83 58.25 35.83 58.25 35.89 58.31 35.89 58.31 35.96 58.33 35.96 58.33 36.02 58.35 36.02 58.35 36.09 58.35 36.09 58.35 36.15 58.36 36.15 58.36 36.21 58.36 36.21 58.36 36.28 58.37 36.28 58.37 36.21 58.38 36.21 58.38 36.15 58.38 36.15 58.38 36.21 58.42 36.21 58.42 36.28 58.42 36.28 58.42 36.34 58.44 36.34 58.44 36.28 58.5 36.28 58.5 36.34 58.5 36.34 58.5 36.41 58.56 36.41 58.56 36.47 58.6 36.47 58.6 36.54 58.65 36.54 58.65 36.47 58.71 36.47 58.71 36.54 58.73 36.54 58.73 36.47 58.75 36.47 58.75 36.41 58.76 36.41 58.76 36.47 58.77 36.47 58.77 36.41 58.77 36.41 58.77 36.34 58.79 36.34 58.79 36.41 58.87 36.41 58.87 36.47 58.88 36.47 58.88 36.41 58.89 36.41 58.89 36.47 58.92 36.47 58.92 36.41 59 36.41 59 36.34 59 36.34 59 36.41 59.01 36.41 59.01 36.47 59.1 36.47 59.1 36.41 59.16 36.41 59.16 36.34 59.19 36.34 59.19 36.28 59.21 36.28 59.21 36.34 59.22 36.34 59.22 36.41 59.23 36.41 59.23 36.34 59.36 36.34 59.36 36.28 59.44 36.28 59.44 36.34 59.48 36.34 59.48 36.41 59.5 36.41 59.5 36.47 59.52 36.47 59.52 36.54 59.52 36.54 59.52 36.6 59.54 36.6 59.54 36.66 59.62 36.66 59.62 36.73 59.63 36.73 59.63 36.79 59.71 36.79 59.71 36.73 59.74 36.73 59.74 36.66 59.75 36.66 59.75 36.6 59.75 36.6 59.75 36.66 59.79 36.66 59.79 36.73 59.8 36.73 59.8 36.66 59.81 36.66 59.81 36.73 59.84 36.73 59.84 36.79 59.84 36.79 59.84 36.86 59.86 36.86 59.86 36.79 59.89 36.79 59.89 36.86 59.91 36.86 59.91 36.92 59.95 36.92 59.95 36.98 59.96 36.98 59.96 36.92 60.02 36.92 60.02 36.98 60.04 36.98 60.04 37.05 60.09 37.05 60.09 36.98 60.12 36.98 60.12 37.05 60.16 37.05 60.16 37.11 60.18 37.11 60.18 37.05 60.21 37.05 60.21 37.11 60.29 37.11 60.29 37.05 60.31 37.05 60.31 36.98 60.31 36.98 60.31 37.05 60.35 37.05 60.35 37.11 60.4 37.11 60.4 37.18 60.41 37.18 60.41 37.11 60.42 37.11 60.42 37.18 60.43 37.18 60.43 37.24 60.44 37.24 60.44 37.3 60.45 37.3 60.45 37.24 60.48 37.24 60.48 37.3 60.52 37.3 60.52 37.37 60.53 37.37 60.53 37.43 60.54 37.43 60.54 37.37 60.57 37.37 60.57 37.3 60.58 37.3 60.58 37.24 60.58 37.24 60.58 37.18 60.6 37.18 60.6 37.24 60.65 37.24 60.65 37.3 60.66 37.3 60.66 37.37 60.7 37.37 60.7 37.43 60.71 37.43 60.71 37.5 60.74 37.5 60.74 37.56 60.75 37.56 60.75 37.62 60.78 37.62 60.78 37.69 60.79 37.69 60.79 37.75 60.81 37.75 60.81 37.69 60.82 37.69 60.82 37.75 60.87 37.75 60.87 37.82 60.92 37.82 60.92 37.88 60.98 37.88 60.98 37.95 61.03 37.95 61.03 38.01 61.04 38.01 61.04 38.07 61.06 38.07 61.06 38.14 61.06 38.14 61.06 38.07 61.08 38.07 61.08 38.14 61.08 38.14 61.08 38.07 61.09 38.07 61.09 38.01 61.09 38.01 61.09 37.95 61.09 37.95 61.09 38.01 61.1 38.01 61.1 38.07 61.12 38.07 61.12 38.01 61.14 38.01 61.14 38.07 61.17 38.07 61.17 38.14 61.21 38.14 61.21 38.07 61.23 38.07 61.23 38.14 61.3 38.14 61.3 38.07 61.32 38.07 61.32 38.01 61.33 38.01 61.33 38.07 61.35 38.07 61.35 38.01 61.36 38.01 61.36 38.07 61.4 38.07 61.4 38.14 61.42 38.14 61.42 38.2 61.44 38.2 61.44 38.14 61.44 38.14 61.44 38.07 61.45 38.07 61.45 38.01 61.46 38.01 61.46 38.07 61.53 38.07 61.53 38.01 61.56 38.01 61.56 37.95 61.58 37.95 61.58 38.01 61.6 38.01 61.6 38.07 61.62 38.07 61.62 38.14 61.63 38.14 61.63 38.07 61.65 38.07 61.65 38.14 61.66 38.14 61.66 38.2 61.72 38.2 61.72 38.27 61.77 38.27 61.77 38.33 61.78 38.33 61.78 38.39 61.83 38.39 61.83 38.46 61.88 38.46 61.88 38.39 61.89 38.39 61.89 38.46 61.93 38.46 61.93 38.52 62 38.52 62 38.59 62.06 38.59 62.06 38.65 62.08 38.65 62.08 38.59 62.3 38.59 62.3 38.65 62.35 38.65 62.35 38.71 62.38 38.71 62.38 38.78 62.42 38.78 62.42 38.71 62.5 38.71 62.5 38.78 62.52 38.78 62.52 38.84 62.55 38.84 62.55 38.91 62.57 38.91 62.57 38.97 62.61 38.97 62.61 39.04 62.63 39.04 62.63 38.97 62.71 38.97 62.71 38.91 62.8 38.91 62.8 38.97 62.87 38.97 62.87 38.91 62.88 38.91 62.88 38.84 62.9 38.84 62.9 38.91 62.91 38.91 62.91 38.97 62.97 38.97 62.97 39.04 62.98 39.04 62.98 39.1 63 39.1 63 39.16 63.01 39.16 63.01 39.23 63.07 39.23 63.07 39.29 63.08 39.29 63.08 39.36 63.24 39.36 63.24 39.29 63.24 39.29 63.24 39.36 63.24 39.36 63.24 39.42 63.31 39.42 63.31 39.36 63.32 39.36 63.32 39.42 63.39 39.42 63.39 39.48 63.41 39.48 63.41 39.55 63.46 39.55 63.46 39.61 63.55 39.61 63.55 39.68 63.7 39.68 63.7 39.74 63.74 39.74 63.74 39.8 63.84 39.8 63.84 39.87 63.87 39.87 63.87 39.93 63.93 39.93 63.93 40 63.93 40 63.93 40.06 64 40.06 64 40.12 64.07 40.12 64.07 40.19 64.07 40.19 64.07 40.25 64.07 40.25 64.07 40.32 64.09 40.32 64.09 40.25 64.13 40.25 64.13 40.32 64.17 40.32 64.17 40.38 64.2 40.38 64.2 40.45 64.3 40.45 64.3 40.51 64.34 40.51 64.34 40.57 64.35 40.57 64.35 40.64 64.46 40.64 64.46 40.7 64.47 40.7 64.47 40.77 64.56 40.77 64.56 40.83 64.61 40.83 64.61 40.89 64.64 40.89 64.64 40.96 64.75 40.96 64.75 40.89 64.77 40.89 64.77 40.96 64.81 40.96 64.81 41.02 64.86 41.02 64.86 41.09 64.87 41.09 64.87 41.15 64.93 41.15 64.93 41.21 64.94 41.21 64.94 41.15 64.94 41.15 64.94 41.21 64.96 41.21 64.96 41.28 65.07 41.28 65.07 41.34 65.14 41.34 65.14 41.41 65.14 41.41 65.14 41.47 65.14 41.47 65.14 41.54 65.16 41.54 65.16 41.6 65.26 41.6 65.26 41.54 65.26 41.54 65.26 41.6 65.28 41.6 65.28 41.66 65.31 41.66 65.31 41.6 65.52 41.6 65.52 41.66 65.54 41.66 65.54 41.73 65.6 41.73 65.6 41.79 65.66 41.79 65.66 41.86 65.67 41.86 65.67 41.92 65.73 41.92 65.73 41.98 65.74 41.98 65.74 42.05 65.75 42.05 65.75 41.98 65.75 41.98 65.75 42.05 65.8 42.05 65.8 42.11 65.84 42.11 65.84 42.18 65.93 42.18 65.93 42.24 65.94 42.24 65.94 42.3 65.96 42.3 65.96 42.37 66.06 42.37 66.06 42.43 66.06 42.43 66.06 42.5 66.06 42.5 66.06 42.56 66.11 42.56 66.11 42.62 66.14 42.62 66.14 42.69 66.18 42.69 66.18 42.75 66.19 42.75 66.19 42.82 66.2 42.82 66.2 42.75 66.2 42.75 66.2 42.82 66.3 42.82 66.3 42.88 66.31 42.88 66.31 42.95 66.35 42.95 66.35 43.01 66.4 43.01 66.4 42.95 66.49 42.95 66.49 43.01 66.51 43.01 66.51 43.07 66.52 43.07 66.52 43.14 66.6 43.14 66.6 43.2 66.69 43.2 66.69 43.27 66.7 43.27 66.7 43.33 66.72 43.33 66.72 43.39 66.79 43.39 66.79 43.46 66.8 43.46 66.8 43.39 66.83 43.39 66.83 43.46 66.83 43.46 66.83 43.52 67.05 43.52 67.05 43.59 67.07 43.59 67.07 43.65 67.07 43.65 67.07 43.71 67.11 43.71 67.11 43.78 67.17 43.78 67.17 43.84 67.32 43.84 67.32 43.91 67.32 43.91 67.32 43.97 67.34 43.97 67.34 44.04 67.38 44.04 67.38 44.1 67.43 44.1 67.43 44.16 67.46 44.16 67.46 44.23 67.46 44.23 67.46 44.29 67.5 44.29 67.5 44.36 67.66 44.36 67.66 44.42 67.66 44.42 67.66 44.36 67.75 44.36 67.75 44.42 67.76 44.42 67.76 44.48 67.83 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31.53 45.21 31.6 45.23 31.6 45.23 31.66 45.26 31.66 45.26 31.73 45.26 31.73 45.26 31.79 45.27 31.79 45.27 31.86 45.28 31.86 45.28 31.92 45.28 31.92 45.28 31.98 45.29 31.98 45.29 32.05 45.31 32.05 45.31 32.11 45.31 32.11 45.31 32.18 45.32 32.18 45.32 32.24 45.33 32.24 45.33 32.3 45.33 32.3 45.33 32.37 45.41 32.37 45.41 32.43 45.42 32.43 45.42 32.5 45.42 32.5 45.42 32.56 45.48 32.56 45.48 32.56 45.5 32.56 45.5 32.62 45.5 32.62 45.5 32.69 45.53 32.69 45.53 32.75 45.53 32.75 45.53 32.75 45.59 32.75 45.59 32.82 45.61 32.82 45.61 32.88 45.63 32.88 45.63 32.95 45.65 32.95 45.65 33.01 45.65 33.01 45.65 33.07 45.65 33.07 45.65 33.14 45.66 33.14 45.66 33.2 45.66 33.2 45.66 33.2 45.72 33.2 45.72 33.27 45.76 33.27 45.76 33.33 45.76 33.33 45.76 33.39 45.78 33.39 45.78 33.39 45.79 33.39 45.79 33.46 45.81 33.46 45.81 33.52 45.81 33.52 45.81 33.59 45.82 33.59 45.82 33.65 45.83 33.65 45.83 33.71 45.83 33.71 45.83 33.78 45.84 33.78 45.84 33.84 45.84 33.84 45.84 33.91 45.84 33.91 45.84 33.97 45.84 33.97 45.84 34.03 45.87 34.03 45.87 34.1 45.89 34.1 45.89 34.16 45.9 34.16 45.9 34.23 45.9 34.23 45.9 34.29 45.91 34.29 45.91 34.29 45.91 34.29 45.91 34.36 45.91 34.36 45.91 34.42 45.93 34.42 45.93 34.48 45.95 34.48 45.95 34.55 45.96 34.55 45.96 34.61 46.02 34.61 46.02 34.68 46.02 34.68 46.02 34.74 46.05 34.74 46.05 34.8 46.05 34.8 46.05 34.87 46.05 34.87 46.05 34.93 46.06 34.93 46.06 35 46.08 35 46.08 35.06 46.1 35.06 46.1 35.12 46.14 35.12 46.14 35.19 46.16 35.19 46.16 35.25 46.17 35.25 46.17 35.32 46.18 35.32 46.18 35.38 46.18 35.38 46.18 35.45 46.18 35.45 46.18 35.51 46.21 35.51 46.21 35.57 46.21 35.57 46.21 35.64 46.25 35.64 46.25 35.7 46.27 35.7 46.27 35.77 46.27 35.77 46.27 35.83 46.28 35.83 46.28 35.83 46.3 35.83 46.3 35.89 46.33 35.89 46.33 35.96 46.33 35.96 46.33 36.02 46.35 36.02 46.35 36.09 46.37 36.09 46.37 36.15 46.38 36.15 46.38 36.21 46.41 36.21 46.41 36.28 46.42 36.28 46.42 36.34 46.43 36.34 46.43 36.41 46.43 36.41 46.43 36.47 46.44 36.47 46.44 36.54 46.44 36.54 46.44 36.6 46.47 36.6 46.47 36.66 46.48 36.66 46.48 36.73 46.5 36.73 46.5 36.79 46.53 36.79 46.53 36.79 46.56 36.79 46.56 36.79 46.56 36.79 46.56 36.86 46.56 36.86 46.56 36.92 46.56 36.92 46.56 36.98 46.57 36.98 46.57 37.05 46.58 37.05 46.58 37.05 46.59 37.05 46.59 37.11 46.61 37.11 46.61 37.18 46.61 37.18 46.61 37.24 46.62 37.24 46.62 37.24 46.63 37.24 46.63 37.3 46.67 37.3 46.67 37.37 46.67 37.37 46.67 37.43 46.68 37.43 46.68 37.5 46.71 37.5 46.71 37.56 46.71 37.56 46.71 37.62 46.72 37.62 46.72 37.69 46.75 37.69 46.75 37.75 46.8 37.75 46.8 37.82 46.83 37.82 46.83 37.88 46.84 37.88 46.84 37.95 46.88 37.95 46.88 38.01 46.88 38.01 46.88 38.07 46.91 38.07 46.91 38.14 46.91 38.14 46.91 38.2 46.92 38.2 46.92 38.27 46.98 38.27 46.98 38.27 46.99 38.27 46.99 38.27 47 38.27 47 38.33 47.02 38.33 47.02 38.39 47.02 38.39 47.02 38.46 47.03 38.46 47.03 38.52 47.06 38.52 47.06 38.59 47.07 38.59 47.07 38.65 47.07 38.65 47.07 38.71 47.11 38.71 47.11 38.78 47.11 38.78 47.11 38.84 47.11 38.84 47.11 38.91 47.11 38.91 47.11 38.91 47.13 38.91 47.13 38.97 47.14 38.97 47.14 39.04 47.15 39.04 47.15 39.1 47.19 39.1 47.19 39.16 47.25 39.16 47.25 39.23 47.26 39.23 47.26 39.29 47.27 39.29 47.27 39.36 47.28 39.36 47.28 39.42 47.29 39.42 47.29 39.48 47.29 39.48 47.29 39.55 47.3 39.55 47.3 39.61 47.3 39.61 47.3 39.68 47.31 39.68 47.31 39.74 47.31 39.74 47.31 39.74 47.31 39.74 47.31 39.8 47.31 39.8 47.31 39.87 47.37 39.87 47.37 39.93 47.4 39.93 47.4 39.93 47.41 39.93 47.41 40 47.41 40 47.41 40.06 47.42 40.06 47.42 40.12 47.45 40.12 47.45 40.19 47.5 40.19 47.5 40.25 47.51 40.25 47.51 40.32 47.53 40.32 47.53 40.38 47.53 40.38 47.53 40.38 47.57 40.38 47.57 40.45 47.59 40.45 47.59 40.51 47.61 40.51 47.61 40.57 47.65 40.57 47.65 40.64 47.65 40.64 47.65 40.64 47.67 40.64 47.67 40.7 47.68 40.7 47.68 40.77 47.68 40.77 47.68 40.83 47.68 40.83 47.68 40.89 47.68 40.89 47.68 40.96 47.7 40.96 47.7 41.02 47.71 41.02 47.71 41.09 47.76 41.09 47.76 41.15 47.77 41.15 47.77 41.15 47.77 41.15 47.77 41.21 47.8 41.21 47.8 41.21 47.8 41.21 47.8 41.28 47.82 41.28 47.82 41.34 47.83 41.34 47.83 41.41 47.83 41.41 47.83 41.47 47.84 41.47 47.84 41.47 47.84 41.47 47.84 41.47 47.86 41.47 47.86 41.54 47.86 41.54 47.86 41.6 47.86 41.6 47.86 41.66 47.87 41.66 47.87 41.73 47.9 41.73 47.9 41.79 47.92 41.79 47.92 41.86 47.93 41.86 47.93 41.92 47.94 41.92 47.94 41.98 47.96 41.98 47.96 42.05 47.98 42.05 47.98 42.11 47.99 42.11 47.99 42.11 48.03 42.11 48.03 42.18 48.08 42.18 48.08 42.24 48.08 42.24 48.08 42.24 48.08 42.24 48.08 42.3 48.09 42.3 48.09 42.3 48.09 42.3 48.09 42.37 48.1 42.37 48.1 42.43 48.1 42.43 48.1 42.43 48.14 42.43 48.14 42.5 48.16 42.5 48.16 42.56 48.18 42.56 48.18 42.62 48.19 42.62 48.19 42.69 48.19 42.69 48.19 42.75 48.21 42.75 48.21 42.82 48.22 42.82 48.22 42.88 48.23 42.88 48.23 42.95 48.25 42.95 48.25 43.01 48.26 43.01 48.26 43.07 48.26 43.07 48.26 43.14 48.27 43.14 48.27 43.2 48.27 43.2 48.27 43.27 48.29 43.27 48.29 43.33 48.29 43.33 48.29 43.39 48.3 43.39 48.3 43.46 48.31 43.46 48.31 43.52 48.31 43.52 48.31 43.59 48.33 43.59 48.33 43.65 48.33 43.65 48.33 43.71 48.37 43.71 48.37 43.78 48.38 43.78 48.38 43.78 48.39 43.78 48.39 43.84 48.4 43.84 48.4 43.84 48.41 43.84 48.41 43.91 48.42 43.91 48.42 43.97 48.43 43.97 48.43 44.04 48.43 44.04 48.43 44.1 48.44 44.1 48.44 44.16 48.47 44.16 48.47 44.23 48.49 44.23 48.49 44.29 48.49 44.29 48.49 44.36 48.49 44.36 48.49 44.42 48.5 44.42 48.5 44.48 48.52 44.48 48.52 44.48 48.53 44.48 48.53 44.55 48.54 44.55 48.54 44.61 48.54 44.61 48.54 44.68 48.55 44.68 48.55 44.74 48.56 44.74 48.56 44.8 48.56 44.8 48.56 44.87 48.56 44.87 48.56 44.93 48.57 44.93 48.57 45 48.58 45 48.58 45.06 48.59 45.06 48.59 45.12 48.59 45.12 48.59 45.12 48.6 45.12 48.6 45.19 48.6 45.19 48.6 45.25 48.64 45.25 48.64 45.25 48.64 45.25 48.64 45.32 48.64 45.32 48.64 45.38 48.64 45.38 48.64 45.45 48.65 45.45 48.65 45.51 48.67 45.51 48.67 45.51 48.68 45.51 48.68 45.51 48.69 45.51 48.69 45.57 48.7 45.57 48.7 45.64 48.72 45.64 48.72 45.7 48.74 45.7 48.74 45.77 48.8 45.77 48.8 45.83 48.82 45.83 48.82 45.89 48.82 45.89 48.82 45.96 48.83 45.96 48.83 46.02 48.83 46.02 48.83 46.09 48.84 46.09 48.84 46.15 48.84 46.15 48.84 46.21 48.84 46.21 48.84 46.28 48.84 46.28 48.84 46.34 48.85 46.34 48.85 46.41 48.85 46.41 48.85 46.47 48.87 46.47 48.87 46.54 48.88 46.54 48.88 46.6 48.88 46.6 48.88 46.66 48.89 46.66 48.89 46.73 48.9 46.73 48.9 46.79 48.9 46.79 48.9 46.86 48.94 46.86 48.94 46.86 48.95 46.86 48.95 46.92 48.96 46.92 48.96 46.98 48.99 46.98 48.99 47.05 49.01 47.05 49.01 47.11 49.02 47.11 49.02 47.18 49.03 47.18 49.03 47.24 49.04 47.24 49.04 47.3 49.04 47.3 49.04 47.37 49.05 47.37 49.05 47.43 49.08 47.43 49.08 47.5 49.11 47.5 49.11 47.5 49.12 47.5 49.12 47.56 49.14 47.56 49.14 47.62 49.16 47.62 49.16 47.69 49.17 47.69 49.17 47.75 49.21 47.75 49.21 47.82 49.22 47.82 49.22 47.88 49.22 47.88 49.22 47.95 49.23 47.95 49.23 48.01 49.23 48.01 49.23 48.07 49.24 48.07 49.24 48.07 49.24 48.07 49.24 48.14 49.26 48.14 49.26 48.14 49.26 48.14 49.26 48.2 49.28 48.2 49.28 48.27 49.28 48.27 49.28 48.33 49.28 48.33 49.28 48.39 49.29 48.39 49.29 48.46 49.29 48.46 49.29 48.52 49.33 48.52 49.33 48.52 49.37 48.52 49.37 48.59 49.39 48.59 49.39 48.65 49.39 48.65 49.39 48.71 49.41 48.71 49.41 48.71 49.41 48.71 49.41 48.78 49.42 48.78 49.42 48.84 49.43 48.84 49.43 48.91 49.44 48.91 49.44 48.97 49.45 48.97 49.45 49.04 49.45 49.04 49.45 49.04 49.47 49.04 49.47 49.1 49.47 49.1 49.47 49.1 49.48 49.1 49.48 49.16 49.48 49.16 49.48 49.23 49.49 49.23 49.49 49.29 49.49 49.29 49.49 49.36 49.49 49.36 49.49 49.42 49.49 49.42 49.49 49.48 49.5 49.48 49.5 49.55 49.52 49.55 49.52 49.61 49.53 49.61 49.53 49.61 49.57 49.61 49.57 49.68 49.57 49.68 49.57 49.74 49.57 49.74 49.57 49.8 49.59 49.8 49.59 49.87 49.6 49.87 49.6 49.93 49.61 49.93 49.61 50 49.66 50 49.66 50 49.68 50 49.68 50.06 49.68 50.06 49.68 50.12 49.69 50.12 49.69 50.19 49.7 50.19 49.7 50.25 49.71 50.25 49.71 50.32 49.72 50.32 49.72 50.38 49.73 50.38 49.73 50.45 49.75 50.45 49.75 50.51 49.78 50.51 49.78 50.57 49.78 50.57 49.78 50.64 49.81 50.64 49.81 50.7 49.82 50.7 49.82 50.77 49.83 50.77 49.83 50.83 49.83 50.83 49.83 50.89 49.83 50.89 49.83 50.96 49.84 50.96 49.84 51.02 49.85 51.02 49.85 51.09 49.85 51.09 49.85 51.15 49.85 51.15 49.85 51.15 49.86 51.15 49.86 51.21 49.87 51.21 49.87 51.28 49.87 51.28 49.87 51.34 49.91 51.34 49.91 51.41 49.92 51.41 49.92 51.47 49.93 51.47 49.93 51.47 49.94 51.47 49.94 51.54 49.94 51.54 49.94 51.6 49.94 51.6 49.94 51.66 49.96 51.66 49.96 51.66 49.98 51.66 49.98 51.73 49.99 51.73 49.99 51.79 50.01 51.79 50.01 51.79 50.01 51.79 50.01 51.79 50.02 51.79 50.02 51.86 50.02 51.86 50.02 51.92 50.03 51.92 50.03 51.98 50.03 51.98 50.03 52.05 50.03 52.05 50.03 52.11 50.03 52.11 50.03 52.18 50.05 52.18 50.05 52.18 50.06 52.18 50.06 52.24 50.07 52.24 50.07 52.24 50.08 52.24 50.08 52.3 50.09 52.3 50.09 52.37 50.1 52.37 50.1 52.43 50.11 52.43 50.11 52.5 50.11 52.5 50.11 52.56 50.14 52.56 50.14 52.62 50.14 52.62 50.14 52.69 50.16 52.69 50.16 52.69 50.17 52.69 50.17 52.75 50.18 52.75 50.18 52.75 50.19 52.75 50.19 52.82 50.19 52.82 50.19 52.88 50.22 52.88 50.22 52.95 50.23 52.95 50.23 53.01 50.23 53.01 50.23 53.07 50.29 53.07 50.29 53.14 50.3 53.14 50.3 53.14 50.31 53.14 50.31 53.2 50.33 53.2 50.33 53.27 50.37 53.27 50.37 53.33 50.37 53.33 50.37 53.39 50.37 53.39 50.37 53.46 50.38 53.46 50.38 53.46 50.39 53.46 50.39 53.52 50.4 53.52 50.4 53.52 50.41 53.52 50.41 53.59 50.42 53.59 50.42 53.65 50.43 53.65 50.43 53.71 50.52 53.71 50.52 53.71 50.52 53.71 50.52 53.78 50.53 53.78 50.53 53.84 50.54 53.84 50.54 53.91 50.54 53.91 50.54 53.91 50.56 53.91 50.56 53.97 50.6 53.97 50.6 54.04 50.6 54.04 50.6 54.1 50.61 54.1 50.61 54.16 50.64 54.16 50.64 54.16 50.64 54.16 50.64 54.23 50.64 54.23 50.64 54.29 50.65 54.29 50.65 54.36 50.67 54.36 50.67 54.36 50.67 54.36 50.67 54.42 50.67 54.42 50.67 54.48 50.67 54.48 50.67 54.55 50.67 54.55 50.67 54.61 50.68 54.61 50.68 54.68 50.68 54.68 50.68 54.74 50.69 54.74 50.69 54.8 50.69 54.8 50.69 54.87 50.7 54.87 50.7 54.87 50.73 54.87 50.73 54.87 50.76 54.87 50.76 54.93 50.76 54.93 50.76 55 50.76 55 50.76 55.06 50.79 55.06 50.79 55.12 50.82 55.12 50.82 55.19 50.82 55.19 50.82 55.25 50.84 55.25 50.84 55.32 50.84 55.32 50.84 55.38 50.84 55.38 50.84 55.45 50.87 55.45 50.87 55.51 50.88 55.51 50.88 55.57 50.89 55.57 50.89 55.64 50.89 55.64 50.89 55.7 50.9 55.7 50.9 55.77 50.9 55.77 50.9 55.77 50.9 55.77 50.9 55.83 50.91 55.83 50.91 55.89 50.92 55.89 50.92 55.96 50.92 55.96 50.92 56.02 50.94 56.02 50.94 56.09 50.94 56.09 50.94 56.15 50.94 56.15 50.94 56.21 50.96 56.21 50.96 56.28 50.99 56.28 50.99 56.28 50.99 56.28 50.99 56.34 50.99 56.34 50.99 56.41 51 56.41 51 56.47 51 56.47 51 56.54 51.01 56.54 51.01 56.54 51.01 56.54 51.01 56.6 51.02 56.6 51.02 56.66 51.05 56.66 51.05 56.73 51.09 56.73 51.09 56.79 51.09 56.79 51.09 56.86 51.09 56.86 51.09 56.86 51.1 56.86 51.1 56.92 51.11 56.92 51.11 56.98 51.12 56.98 51.12 57.05 51.13 57.05 51.13 57.11 51.14 57.11 51.14 57.18 51.15 57.18 51.15 57.18 51.16 57.18 51.16 57.24 51.16 57.24 51.16 57.3 51.17 57.3 51.17 57.3 51.18 57.3 51.18 57.37 51.18 57.37 51.18 57.43 51.19 57.43 51.19 57.43 51.23 57.43 51.23 57.5 51.25 57.5 51.25 57.56 51.26 57.56 51.26 57.63 51.27 57.63 51.27 57.63 51.27 57.63 51.27 57.63 51.28 57.63 51.28 57.69 51.32 57.69 51.32 57.75 51.32 57.75 51.32 57.82 51.36 57.82 51.36 57.88 51.36 57.88 51.36 57.95 51.36 57.95 51.36 58.01 51.38 58.01 51.38 58.07 51.39 58.07 51.39 58.07 51.41 58.07 51.41 58.14 51.42 58.14 51.42 58.14 51.46 58.14 51.46 58.2 51.48 58.2 51.48 58.27 51.48 58.27 51.48 58.27 51.49 58.27 51.49 58.27 51.49 58.27 51.49 58.33 51.49 58.33 51.49 58.39 51.51 58.39 51.51 58.46 51.52 58.46 51.52 58.46 51.52 58.46 51.52 58.46 51.53 58.46 51.53 58.46 51.55 58.46 51.55 58.52 51.56 58.52 51.56 58.52 51.58 58.52 51.58 58.59 51.59 58.59 51.59 58.65 51.6 58.65 51.6 58.71 51.6 58.71 51.6 58.78 51.62 58.78 51.62 58.84 51.63 58.84 51.63 58.84 51.63 58.84 51.63 58.84 51.7 58.84 51.7 58.91 51.7 58.91 51.7 58.91 51.73 58.91 51.73 58.97 51.74 58.97 51.74 59.04 51.75 59.04 51.75 59.1 51.76 59.1 51.76 59.16 51.77 59.16 51.77 59.23 51.78 59.23 51.78 59.29 51.78 59.29 51.78 59.29 51.79 59.29 51.79 59.36 51.81 59.36 51.81 59.42 51.88 59.42 51.88 59.48 51.9 59.48 51.9 59.55 51.92 59.55 51.92 59.61 51.93 59.61 51.93 59.61 51.96 59.61 51.96 59.61 51.97 59.61 51.97 59.61 52.01 59.61 52.01 59.61 52.03 59.61 52.03 59.68 52.04 59.68 52.04 59.74 52.06 59.74 52.06 59.74 52.07 59.74 52.07 59.8 52.12 59.8 52.12 59.8 52.12 59.8 52.12 59.87 52.14 59.87 52.14 59.93 52.16 59.93 52.16 60 52.16 60 52.16 60.06 52.17 60.06 52.17 60.13 52.19 60.13 52.19 60.13 52.19 60.13 52.19 60.13 52.2 60.13 52.2 60.19 52.2 60.19 52.2 60.19 52.21 60.19 52.21 60.25 52.24 60.25 52.24 60.32 52.24 60.32 52.24 60.38 52.26 60.38 52.26 60.45 52.26 60.45 52.26 60.51 52.27 60.51 52.27 60.51 52.28 60.51 52.28 60.57 52.29 60.57 52.29 60.64 52.3 60.64 52.3 60.7 52.33 60.7 52.33 60.7 52.33 60.7 52.33 60.77 52.36 60.77 52.36 60.83 52.4 60.83 52.4 60.89 52.41 60.89 52.41 60.96 52.47 60.96 52.47 60.96 52.48 60.96 52.48 61.02 52.49 61.02 52.49 61.09 52.49 61.09 52.49 61.15 52.51 61.15 52.51 61.21 52.52 61.21 52.52 61.28 52.53 61.28 52.53 61.34 52.53 61.34 52.53 61.41 52.53 61.41 52.53 61.41 52.54 61.41 52.54 61.47 52.54 61.47 52.54 61.54 52.54 61.54 52.54 61.6 52.57 61.6 52.57 61.66 52.57 61.66 52.57 61.73 52.57 61.73 52.57 61.79 52.6 61.79 52.6 61.86 52.63 61.86 52.63 61.92 52.64 61.92 52.64 61.98 52.64 61.98 52.64 62.05 52.67 62.05 52.67 62.11 52.68 62.11 52.68 62.18 52.69 62.18 52.69 62.24 52.69 62.24 52.69 62.3 52.7 62.3 52.7 62.37 52.73 62.37 52.73 62.37 52.73 62.37 52.73 62.37 52.75 62.37 52.75 62.43 52.77 62.43 52.77 62.43 52.77 62.43 52.77 62.5 52.84 62.5 52.84 62.56 52.85 62.56 52.85 62.56 52.86 62.56 52.86 62.63 52.87 62.63 52.87 62.69 52.88 62.69 52.88 62.75 52.88 62.75 52.88 62.82 52.89 62.82 52.89 62.88 52.89 62.88 52.89 62.95 52.89 62.95 52.89 63.01 52.92 63.01 52.92 63.01 52.97 63.01 52.97 63.07 52.98 63.07 52.98 63.14 52.99 63.14 52.99 63.2 52.99 63.2 52.99 63.27 53 63.27 53 63.33 53 63.33 53 63.39 53.05 63.39 53.05 63.46 53.05 63.46 53.05 63.52 53.06 63.52 53.06 63.59 53.07 63.59 53.07 63.65 53.07 63.65 53.07 63.71 53.09 63.71 53.09 63.78 53.09 63.78 53.09 63.78 53.1 63.78 53.1 63.84 53.12 63.84 53.12 63.91 53.12 63.91 53.12 63.97 53.12 63.97 53.12 64.04 53.13 64.04 53.13 64.1 53.14 64.1 53.14 64.16 53.14 64.16 53.14 64.16 53.15 64.16 53.15 64.23 53.16 64.23 53.16 64.29 53.16 64.29 53.16 64.36 53.17 64.36 53.17 64.42 53.17 64.42 53.17 64.48 53.18 64.48 53.18 64.55 53.22 64.55 53.22 64.55 53.23 64.55 53.23 64.61 53.23 64.61 53.23 64.68 53.23 64.68 53.23 64.68 53.24 64.68 53.24 64.74 53.25 64.74 53.25 64.8 53.26 64.8 53.26 64.87 53.27 64.87 53.27 64.93 53.28 64.93 53.28 65 53.29 65 53.29 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65 53.3 65.06 53.31 65.06 53.31 65.13 53.35 65.13 53.35 65.19 53.36 65.19 53.36 65.25 53.38 65.25 53.38 65.32 53.4 65.32 53.4 65.38 53.41 65.38 53.41 65.45 53.41 65.45 53.41 65.51 53.42 65.51 53.42 65.57 53.43 65.57 53.43 65.57 53.44 65.57 53.44 65.64 53.45 65.64 53.45 65.7 53.46 65.7 53.46 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.7 53.47 65.77 53.48 65.77 53.48 65.77 53.51 65.77 53.51 65.83 53.51 65.83 53.51 65.89 53.52 65.89 53.52 65.96 53.55 65.96 53.55 66.02 53.56 66.02 53.56 66.02 53.57 66.02 53.57 66.02 53.58 66.02 53.58 66.09 53.59 66.09 53.59 66.09 53.6 66.09 53.6 66.15 53.62 66.15 53.62 66.21 53.62 66.21 53.62 66.28 53.67 66.28 53.67 66.34 53.71 66.34 53.71 66.41 53.72 66.41 53.72 66.47 53.77 66.47 53.77 66.54 53.8 66.54 53.8 66.6 53.83 66.6 53.83 66.66 53.84 66.66 53.84 66.66 53.86 66.66 53.86 66.66 53.86 66.66 53.86 66.66 53.87 66.66 53.87 66.66 53.88 66.66 53.88 66.66 53.88 66.66 53.88 66.73 53.9 66.73 53.9 66.79 53.91 66.79 53.91 66.86 53.95 66.86 53.95 66.92 53.96 66.92 53.96 66.98 53.99 66.98 53.99 67.05 53.99 67.05 53.99 67.11 54 67.11 54 67.18 54 67.18 54 67.24 54.02 67.24 54.02 67.3 54.05 67.3 54.05 67.3 54.06 67.3 54.06 67.3 54.12 67.3 54.12 67.37 54.13 67.37 54.13 67.43 54.15 67.43 54.15 67.43 54.2 67.43 54.2 67.43 54.2 67.43 54.2 67.43 54.22 67.43 54.22 67.5 54.22 67.5 54.22 67.56 54.24 67.56 54.24 67.56 54.24 67.56 54.24 67.63 54.25 67.63 54.25 67.69 54.27 67.69 54.27 67.75 54.3 67.75 54.3 67.82 54.31 67.82 54.31 67.88 54.32 67.88 54.32 67.95 54.32 67.95 54.32 67.95 54.32 67.95 54.32 68.01 54.33 68.01 54.33 68.07 54.36 68.07 54.36 68.14 54.36 68.14 54.36 68.14 54.37 68.14 54.37 68.2 54.38 68.2 54.38 68.27 54.38 68.27 54.38 68.33 54.4 68.33 54.4 68.33 54.41 68.33 54.41 68.39 54.41 68.39 54.41 68.39 54.42 68.39 54.42 68.46 54.43 68.46 54.43 6
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sir.jl
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