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nezuQ/JuliaLang - Gadfly.jlのPlot要素一覧.ipynb

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JuliaLang - Gadfly.jlのPlot要素一覧
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JuliaLang - Gadfly.jlのPlot要素一覧.ipynb
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"JuliaLang - Gadfly.jl\u306ePlot\u8981\u7d20\u4e00\u89a7"
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{
"cell_type": "markdown",
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"source": [
"[@nezuq](https://twitter.com/nezuq)"
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{
"cell_type": "markdown",
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"\u53c2\u8003\uff1aGithub - Gadfly.jl / doc / index.md \n",
"https://github.com/dcjones/Gadfly.jl/blob/master/doc/index.md"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u3000 "
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{
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"input": [
"using Gadfly"
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"language": "python",
"metadata": {},
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{
"cell_type": "markdown",
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"\u3000 "
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"line"
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{
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{
"cell_type": "code",
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"input": [
"#\u5f15\u6570\u6307\u5b9a\u65b9\u6cd51 - \u914d\u5217\n",
"plot(x=1:100, y=randn(100), Guide.XLabel(\"\u6a2a\u8ef8\"), Guide.YLabel(\"\u7e26\u8ef8\"), Geom.line)"
],
"language": "python",
"metadata": {},
"outputs": []
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"cell_type": "code",
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"input": [
"#\u5f15\u6570\u6307\u5b9a\u65b9\u6cd52 - \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\n",
"plot(DataFrame(\u6a2a\u8ef8=1:100, \u7e26\u8ef8=randn(100)), x=:\u6a2a\u8ef8, y=:\u7e26\u8ef8, Geom.line)"
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"language": "python",
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"outputs": []
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"input": [
"#\u5f15\u6570\u6307\u5b9a\u65b9\u6cd53 - \u95a2\u6570\n",
"plot(x -> randn(), 1, 100, Guide.XLabel(\"\u6a2a\u8ef8\"), Guide.YLabel(\"\u7e26\u8ef8\"))"
],
"language": "python",
"metadata": {},
"outputs": [
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" .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\")\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",
" 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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".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",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".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",
" root.select(\".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",
" }\n",
" });\n",
"\n",
" root.select(\".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",
" }\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",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\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",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\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",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\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(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa\");\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-5\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\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",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-8\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-8\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-9\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-9\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-13\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-15\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-15\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-16\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-16\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-16\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f2a864b4ae8f42fa80614624be9cf1aa-element-17\")\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"
],
"metadata": {},
"output_type": "pyout",
"png": 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"xs = 0:0.1:20\n",
"get_df(nums,exp) = DataFrame(x=nums, y=eval(exp)(nums), ymin=eval(exp)(nums) .- 0.5, ymax=eval(exp)(nums) .+ 0.5, f=exp)\n",
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" 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",
" 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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".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",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".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",
" root.select(\".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",
" }\n",
" });\n",
"\n",
" root.select(\".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",
" }\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",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\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",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\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",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\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(\"#fig-21da761e789d436cb3f496c3a50f228e\");\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-7\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\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",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-10\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-10\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-11\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-11\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-16\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-18\")\n",
" .data(\"max_pos\", 85.91)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-18\")\n",
" .data(\"min_pos\", 68.91)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-18\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-19\")\n",
" .data(\"max_pos\", 85.91)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-19\")\n",
" .data(\"min_pos\", 68.91)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-19\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-19\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-19\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-20\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-20\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-21da761e789d436cb3f496c3a50f228e-element-20\")\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"
],
"metadata": {},
"output_type": "pyout",
"png": 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"<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",
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" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
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"\n",
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" Element.prototype[event_name] = function (fn, scope) {\n",
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" 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, val);\n",
" } else {\n",
" return this.node.setAttribute(key, val);\n",
" }\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",
" init_pan_zoom(root);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attr(\"stroke-dasharray\"))\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attr(\"stroke-dasharray\"))\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attr(\"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",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attr(\"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\")\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",
" 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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".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",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".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",
" root.select(\".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",
" }\n",
" });\n",
"\n",
" root.select(\".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",
" }\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",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\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",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\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",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\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(\"#fig-9a9d04f874c74ef7a28391daaf69287f\");\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-4\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\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",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-12\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-12\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-12\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-14\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-14\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-15\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-15\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-15\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-15\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-15\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9a9d04f874c74ef7a28391daaf69287f-element-16\")\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"
],
"metadata": {},
"output_type": "pyout",
"png": 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4zeIlpfxDa5hd2qbUCInor6PO0ClzdD8gBfyA/GabIdTJ5VFeGLhkuyxC1mVwIu1ytDQzQn5FK4NRpgHXMocnma0l+YNPmF3awhgpmRF1gg5xdVfq9Xp8XgL2RjmdOjmuOK4EUGJdFjXrMjhF0OVxspQ6OQfhaJS9cHgKV+9ltn4u6mgdUwRdGvMxrn4bVz/AVZ96vZF/q32l35hSM1N3wNVbC1fwWElKj406kjEfk043nR91hm2apuW4+vsPT+NWr6OjjtSWkuiyRFiXwSnaLuv1ZFL6Lq62ktJreEwHRB1pW8Ls0jalRkjEPyfqDFvl6lcYyHzyp6C6jxhDGCkzo47VlqLvsoRYl8Ep2i5Hyn0080XgAZT/pozXcPWYqGNtTZhd2sIYrV9EHaBNqkK9/hJ4CNgLOJ86OYVhsjziZFtTnF2WJusyOMXb5Sh5jzo5FeXbKP2Bmbh6btSxtqJ4uzQ93FwtI6V/L+yDaKBeB0cdyRjTzVw9gJQ2FA6/+lkpH9ZhSlxDQ9Pvo87wMU/q9qT0kcL+xJtL6ZRSRddlCbMug1NSXbrah5TeUPj3f2ux/fsPs0vblBohVb94zkbh6r60MgflcOAc6uR8DpeNUcdqr6LqssRZl8EpqS7zJyafgHAacDLNvICrB0cda7Mwu7SFMUIiZV+POgMArn4HSCNUAIdQJ3dEHamjiqbLHsC6DE5JdjlC7sXhMAQFnsHVn+FqPOpYJdmlKUGP60BcnVHYn/ggM3WHqCMZY4pE/pjH+wubVp9njn4+6khhsU+MEfK8xkcjG7xeR5FjAXAycA3vcRKjZU1keboo0i57GOsyOCXd5WhZwxxOIf9t0IPxeY56PSyqOGF2aQtjpKQlkmFd/QoO9wM7I5xHnVzGGZKLJEtgIuqyR7Iug1PiXU4Wnzr5Mco3gXKEh5ith0YTpsS7NEWsXk8npetxdY2dDsoY026ujsDV90lpMym9NOo4poeaP79pn9AGUxVcve7DU7vN0ZrQxg5BqF32cNZlcHpcl7P0QFzN4KpSrz8Nc+gwu7RNqRHyfX9qaIO5/AL4L+BVHA5nuKRDGzsEoXbZw1mXwelxXR4jLxPjywjzECbj6tiwhg6zS1sYo/VMKKO4+m2EHwLPkOMIhktjKOOGK5wuewfrMjg9r8thsgxlOPAwcDOuTgxp5NC6tNP+bMGkcROuVOVkYGU/Z+Mpv73lljYvwjvx3AlvIawEQOWnU/4+5f4wc26Tq98DfoPwIGs5mxNlQ9SRjDE9gKtxhLtRTgP+xkYmMFqao45luskl3xy/x6SxE54HZNLYCddMHHvRRVt67MSxE5Z0dpyGhsUndfa52zRTK6jXWwr7FO9nrpZ121hFoFu77GWsy+D0+C7zl6WbWXifeYDJ2m1bIcPs0jaltiFbVj5EVZ6cNGlSucJIRbZ8KiJFOzuOau6yzj53m/pxI8I4IEUFZ3KwtHbbWEWgW7vsZazL4PT4Ls+QFio4HUgBJzCCn3XXUGF2aZtS2zBh7ISzHGFfVc0qsknQ4VOm3nhqW4+dOHbCKpCFqL+upSJ79k033bRi831PP/1W3759cxMdR95NJCpvW7Bg8eeyWf80EK+mpvKxhobML3yfD1Tlwdraypc9r+kMVa2MxeSvQ4ZUrkqnGy8RkWwyWTVl3rzXB4qUjRXhzWSy6n/nzVuUEHGOU+XJ2tqqZxoaGr/i+/JFVX9a7erqrwJTBkpuSXV2Q/Ifle+uW7eu7GLQ5TU11VPT6cYqkDEiOj+ZrH4knc4cDhwRi8UeHjLkswsaGpq+5vta3dISv+Www/Ze2dDQdDEgiUTlH1566a2dW1py3wbJ1NRUTm9oWPwF3/ePB3m6pqbyKc9bdJyqk3Ac7kkkqhrT6cZzQXbbbrvW61evXu3HYrte4ji8n0hU/W3u3DcHxWKxM2MxXhwypOqhhobGL/m+HKXqP1pbu0+D52VOU+Vzqq1Ta2v3W55OZyaIaHkyWf37dLpxR5DzRXRxMll9dzrd+EOQrOM4zyYSg56YN6/pGBGt8f3YvUOHfvYNz2s6R1U/k8uV3zB06B6bPK/peyAf1NRU3tzQsGRv38+eDbxcU1P1YDq9+BDwRzgOsxOJqnnpdOPJIPuXl+vtBx1U/U5DQ+OFqtIvmay65tlnX9+hoqLsAuDtmpqqO+fPbzwgl5MTVfX52trqOZ6XqVPlYJHcfcnk515LpzPfAPYsL+/3p4MO2m1dOp25XETXJJPVf5k3b/GeIv43HMd5NZEYdP/8+U1Dczkdqeq4tbWD5jY0NJ3o+3qAqvOP2tpBSz2v8QJV2aGmpup3DQ3v9Pf9TReJyLJksvKOdPqt/SB3iuPwQiJRVT9vXuNwETk0FtN/DxlSvbChofFs35e9c7ncTQcf/LnVnpf5b1U21dRU3eh5TZNUtQ/o6zU11TM8L1OjyjEi/pxkcp/n0+nMaOAgx4n/M5HYe0n+ArK64+uvV167995Lyrc+5xcf5fv+lwKf87X7NHle5jxVdsnlVlw3YMAApxjm/MaNFV/P5fxdu2POz5vXdKCInlAMc355ruz0U1bv8fW16hy4f7zlrn9u9046JtkZQc75WEyPUfX7bmnOB7kGRH7+u6Lls6eK7CDIVaDDt/g4lXNbKloeKm8p/38VLWXjgN9tvmvAgDW5bHa7hSK51flbsuscJ7ZQxHkHQERvdxxnP4itAfB9fSsWk42qFS0AsZi86vvqA7S00Ny3ryxUzb1beOyqsjJZ6PvOCoBcTpfFYk7ZsA8qvwt8vy/adO32K/+n4s3dPlgS21523nnxwlxO1wCoZtfHYuULHcdfls+RWyESXyjS/EHhL/+W4zjN221XXthf4L8mIoUfoipaHGfjQvDfA3CcltVQthByK/KZecf3pQJa1+fHIhOLyfurV6/2Fy0a6g8evHihqq7Lv1bfDY7TujCXy+fIZuMr4nF/YS7H5hxLHCfWunEjzfmc/uuqTjz/961oKStrXZjLsSJ/X/w2Eb9WJLe80N07wMJ4vHVt/n7JiPBBefmqLOyhjiMLQdcDxOO6IZuVhaq6NP9n3vd9WQi6Kv93iL0di6m/cWNuY7575w3Jn1cW3+/T6jj+Qt/n/fzfIbYmFtOFudzma1fKe47DwtbW2OYcTSKs6ddvfTbfnyzM5fIna4/HdaOqLBTJ51DV9x1HForo+4XJ9rbjCCK6Mf9azhsi9APo129tdsOG8oWqrAIoL8+uzWadhaq8m38teS8Wk4XZrFOYa9LkOLJu48bylsL/p1c3H0CdzWZvKyuLD8vlnGWFHKscx1mYzZa9nx+XZSISi8d1Q/7voG+C9B8zBn355W3N+dxyx3EWBjnnW1v7FOaaLnIcZ/nrrw/1Bw5Ei2TO3zZ48OLR3THnHcdZLeIvLIY5v2ssN/9X262aNXHtLn9+LVt+1rfWfabi732X3R7knB8ypPK2hoamo7c05003mzhu4qiJ505Ye+F5F+5z0XkXHTlh7IS/bes5E86dcMbEsRdd15FxPK/xB51P2YaUnl/Y1t+Iq3sH+tpFLvAuezHrMji9rktXP1N4//GDPoFImF3aPsY2xFtb5iMs2X3Q7hkn5wxHmQcwfvz4soljJz4wcezEEQBjxoyJXfrtS3cGEPQQRRZ3cKjRgYV2dV+Ua4EVOBxHnXT6S0ElKrgujXUZnN7VZZ28g8NohFUof8fVXQN89dC6tIWxDdfdcdMy0DtXZJZ7iB7VP7bxNoCWlpYY6AkIewP079+/rDXXMnvi2AmvqCND/Rb/1o6M4/ux/w4kcP6SMLeR3zR+KsPl9UBet4QE1qWxLgPUK7scLq8ApwA7IWxza1t79couTRek9Ce46uPqWVFHMcYYAFI6hpTmqNcfRR3FlBDPy9zW5RdJ6RhczeJqt31NuhQE0qUBrMsg9fou8z+0Kymd1NWXCrNL25QaKa3q0tNd/QpwB/Aoc/h5EIlKVxe7NB9hXQanl3eZ4hfAAyh/IKXjuvZi4XVpC2OENmyIj+r0k+v1CIR/Ae+Q4xwmix9cstLTpS7Nx1iXwen1XU4WnzjfAhah3Ey9ntHZlwqzS1sYIzRgQEWsU0909QCEB4A4PmdwjKwMNlnp6XSX5lOsy+BYl8BRsgrldKAF4a/M0t078zJhdmkLY4RaW9fP7ORTrwZ2RLmCkfJckJlKVRe6NJ9gXQbHuiwYKQ3A5cD2xLkW1Q6fdS3MLm1hjJCIs6jDT8pvijgBeIQRXB94qBLVqS5Nm6zL4FiXHzGCG8nvb/w6qY6fUzXMLu1cqaWkXg9DcIENtDKE42Rp1JGMMabdHtHdKKcB2J0cSY6R+VFHaot9YoyQ5y06tENPcLge6At81xbFj+twl2aLrMvgWJefMEreA/LfTo3xk448NcwubWGMlFzd7oem9FiUQxFuo07u6cZQJaoDXZptsC6DY11+Sp08DFyPcBr1elj7nxhel7YwRkjVeaBdD5ym5Si/BJpo5eLuTVWa2t2l2SbrMjjW5Ras40coS3GY1t7zqVqX5uNcvQ5XlXo9LeooxhgTCFfPLZwV55Goo5gi4nmN2z4TRL2eVLiU1NOd+Ypzb9GuLk27WJfBsS63YrI6pPSFwg/9W77mbUGYXdqm1Ght+3+08IPCf7+PiHZ3oBJmb0DBsS6DY11uyWTxUb4HgMMV7XiGLYy9gar8ZqsPmK0jgS8j3M8IeSKcVKVpm12adrMug2NdbkOdpID7UEbj6gVbe2iYXdqmuWL1mA6gjPkoe5FjCMfIy1FHMsaYwNXrYIQFCGt5jz04Q1qijmSfGCPkeZktf/04ztkog4Cptihu21a7NB1iXQbHumyHkfIqcBvKzuzK8Vt6WJhd2sIYKd3aAatnAll8fhVWmtK21S5Nh1iXwbEu28XhF0ArDl/f8oPC69IWxgip8q0273hc90AYBtzF0fJmuKlK0xa7NB1mXQbHumyn4dJI/lPjiczUHdp6iHXZ29XrxaQ0xyw9MOooxhgTijlajast1OtFUUcxEUqnM5++jIqrcVx9hZT+K4JIJavNLk2nWJfBsS47yNXpuPoOT+r2n7wrzC5tU2qEROTT/SvnAQeg/DH8RKWrzS5Np1iXwbEuO8jnT8DuZAvHb39EmF3a4RrF5BHtTzmvI6xlOAfYAf3GmF5FVUjxElCFw2CGy1tRxLCfZiI0b96iyo/dUM6lwB4oN9ui2DGf6tJ0mnUZHOuyg/Lve38B+pLj8o/eFWaXtjBGyHHk9g//oCoIlwKtxPh7dKlK08e6NF1iXQbHuuyUfwCthW/mfyjMLm1hjJQ8/+FvH2dflJ2BWQyT5dFlKlUf6dJ0kXUZHOuyw+pkBfAo8AVm6f7/ucO67H1c/VnhKhpjo45ijDGRSunZhffDqVEMb58YI5ROZ0YDMFMrEC4GmoEZkYYqUR92abrMugyOddlJzdwPrAOO2XxTmF3awhghEc1/Jbkv+6HsjPAIdfJBxLFK0oddmi6zLoNjXXbSKFlP/kPCXjymAyDcLm1hjJRMzf+H/QBQpkUYpsQVujQBsC6DY112mnIXAHEOyd9gXfYurl5NSpvbOtuDMcb0So/pAFKaI6V3Rx3FhKihIXMZ07QcV98jpS9EnaeUNTRkLos6Q09hXQbHuuwiV1/E1RZc/UyYXdqm1Aip6knsyonAQGBu1HlKmaqeFHWGnsK6DI512UXCc0AZSiLMLm1hjJAIVwCDAfCxT4xdUOjSBMC6DI512WXPAiDsbV32Jq5OwVVlttZGHcUYY4rKLB1SOJ7xgTCHtU+MEUp7TbcCJwHNrOTFqPOUMi/fpQmAdRkc67KL3uclYD1wdJhd2sIYocZc7CDgs8CLnCEtUecpbf7nok7Qc1iXwbEuu+QMyQELgT4+hNalLYwR+vmGHc8HQHg14iglr0+f5hOiztBTWJfBsS4D8QrAw3756WENaAtjhD6gPP8b5bVok5S+ioqK1qgz9BTWZXCsywAILwM827J9aOuVLYwR+nnfFX8DQHk94vJJ//gAACAASURBVCglb/VqHo06Q09hXQbHugzEQoDvVqy6L6wBbWGM0GqN5S8v5djC2HWyOOoEPYd1GRzrsstyvA3wQmvf9VFHMWFI6bjCV5Groo5ijDFFaY5W46pSrz8Na0j7xBihPZ3s8QBs5P2Io5S8hoaMHQcaEOsyONZlAGKsANgxljsurCHjYQ1UaiaNm3ClKicDK/s5G0/57S23rO3K49rSF/0KkOV42v0c0zZVvQ4YFnWOnsC6DI51GYAjZS2ubsqqHBrWkKF/Yjzz+K9944zRYzaccsopO7Zx+6ZTTz11l7AzfdIl3xy/B8rxU6beUCPgbfD7nNOVx7VpmsYyuXhf4ANENKjsvZWIPBx1hp7CugyOdRkQYU2zih/WcKEvjGtYfw+wrryl/Fsfvd0XuQCRe+69996VYWf6pGxZ+RBVeXLSpEnlCiMVGdqVx7VpDH5OpAXYFFTu3iyRqPpl1Bl6CusyONZlQJTmVuStsIYLfWF86KGHmlG9BfSCzbedfuLpBwgcpTn9c9h52qKqO4mj7+va3KUKfxd05648rk0i2l90HZANKndv5nlN7f+0brbKugyOdRmY5pjoICZrKGtWJPsYHY3/2Zfc5WO+MubI6Q9PfzKWjV2A6IvTH57+ZBR52uSzp4rsIMhVoMM787i5c98cEI87/wZ5LZms+k46nTlcRH8NMiM5o/K6Vl8H7u5kmx/xGkclk9WPNDQ0/V7VHypS9vVEYu8lntf4KEhLMln11QULFn8ul8vdCjyTTFZ/v6Fh8UmquctEnL8mEpW3eV7jD4DRvh/779raQXM9L3M7aOWGDfFRAwZUxFpb188UcRYlEpVjPW/RoSBXqzoP1NRU/tbzGscB41TlNzU1VQ96XuZq0ENV+VZNTXUmnc7MFBEnmaz8yrx5iyodR24HeT6ZrLosnc6MFtEfgExNJqtuaWjIXKaqJ4lwRSJR/Wz+/Ib+58rK+o/u1299Nn9clyxOJqu+2dCQqVXV60Tk4USi6pf5NxH/fJHY1YnEoPs9r/HXwOG5XPy8oUM/+4bnNd4vIn0TiapjPe+tvSD7T1Xm1dRUXwJ6hec1ng/OHclk5U3pdOMlIpym6vywpqbyqXQ6c7OIDnYc5+SVKwet3WmnTD04S5PJyrMWLFg0JJeTKSCPJZNVP29oaDxblQtF5A+JRNW/GhoyV6nqMMdh/JAh1Qs9L3MvsGMyWVW3YMGi3XM5mS4iCxKJqgkNDZmRqjpZVe6qqam60fOaJoE/xvf5SW1t9RzPa/oT+Af5fvb02tr9lnte4+Mg7yWTVV/zvKaDwP8T4CaT1T9Lp5vGiPiTgBuSyeq70+nGySKMBOfCZLLypXQ6M11Ed08kqoY///zbO1dUtN4LvJJMVl8wf/6iYb4vV4EzPZms/GNDQ+OFqpwt4lyZSFTO9rzMH0ET2aycefDBVcs8L1MPrE4mq04V0Ys9r/F8VXmipqbqfzwvcyropary55qaqjs9L/Nj0ON8XyfV1u7T4HmZf4Lulc2uPBp26rfFOZ+sujadbjpfxD8H+EVvmPOtrRsuyM/L4Oe85y06DuTHvWHOH/aBsq/TuuFvJy2b3ueUxoFtzfkg3/4lyBfriDNGj3kAdNVaXf+d7WW7par8bPpD06dEleejJo6bOApf78nF/IQge4ov426YesO3O/u4Nj2i/SlnLcLrjJDBgf8lepl0uvHkmprq0A4A7smsy+BYlwFJ6Qv7x1refe2oitFhDBfd4Rq+3Ajyte1l+wuAClq5PbIsnxBvbZmPsGT3QbtnnJwzHGUewPjx48smjp34wMSxE0ds7XHtMkrWA00ouW75S/Qy9uYTHOsyONZlQJSW13LloX17P7KF8cAvHfiwwFLQ3wncNX3W9NVRZfmk6+64aRnonSsyyz1Ej+of23gbQEtLSwz0BIS9t/a49to/1pwDWxiDUNgEZQJgXQbHugzM9tWx1sOiDhGKMaPH/OCM0WP0rNGnHxx1ligc+uSGFaT03ahz9AT5/RYmCNZlcKzLgKR09d5zWteFNVykZ75xhL2AF+6a+a+5UeaIyoJsnzdRBvK09o06S6nL5eLnRZ2hp7Aug2NdBsDV7VB2eMePrQpryEi+lXrKKafsWLap7AuqnCei7fuySg+0UaUFEDYxCOyajF0xdOhn34g6Q09hXQbHugyAshcCWeS5sIaM5BNjeUvZs+LwCOhtdz94z91RZCgGJ1Ws+wwASmXEUUqe5zXeH3WGnsK6DI51GYi9AcaUr/1iWANG8olx2szpB0QxbrHZ12lpASBmC2NXiYhtjg6IdRkc6zIQewHgUBZxDhOKej2vcNmpq6KOYowxRSmlPyy8T4a2280uOxWhL5c1v1f47YGRBukB8mcFMUGwLoNjXQZibwT/ov7rXwhrQFsYI3TD9suuAN4GQtt23nNl/xl1gp7DugyOddllyj7A3PHly68Pa0hbGCPkQBrhPoR9eET7R52nxKWjDtCDWJfBsS67YprGgC8BD2Bd9iIpPRZXldka2kU4jTGmJMzRmsL7Y6gXe7ZPjBHyvEXHsYHHEXyEL0Wdp5TlrzRggmBdBse67CKf/IIoJMPs0hbGSMmPGS3NKMsQRkadprTJj6NO0HNYl8GxLrtEGAYoMf4dZpe2MEbKuQMAoQkYXtiebjql0KUJgHUZHOuy01QF5SiE5QyXRuuyt6nXOwvH6fTKk6kbY8ynbN6/mNKnwh7aPjFGKJ1uvAQA4eXCTaOiS1PaPuzSdJl1GRzrsguUrwLg8zcIt0tbGCMkwmkAxPgb0AycEmmgEvZhl6bLrMvgWJdd8lWE9xDuhHC7tIUxQqrODwEYJsuAemAoc/SzkYYqUR92abrMugyOddlJrn4GOBj4M3WyCazL3snVXxe2p0+KOooxxkTK1QtwtbWwQIbOPjFGKJ3O3PyRP04Fctjm1E75RJemC6zL4FiXnTYWYR518s7mG8Ls0hbGCIno4A//UCcLEf6BMoyHdecIY5Wkj3VpusS6DI512Qmz9EDgSyiPf/TmMLu0hTFCjuOc/LEbhKuBOH04N5pEpetTXZpOsy6DY112QoxxAMjHF8Ywu7SFMUIrVw5a+7EbhvEi8A5wCXPVLsrZAZ/q0nSadRkc67KDZmoFwrcQfGI8+dG7wuzSFsYI7bRTpv5jN4goQj3KINYwJqJYJelTXZpOsy6DY112UF/ORtkNn7s5SlZ99K4wu7SFMVLO0k/dpPwOyCFcFn6eUtZGl6aTrMvgWJcd9F/AJpTLP32Xddm7pfQvuKrU69FRRzHGmFC4OqJwaswHoo5inxgjtGDBoiFt3pHjHwAIdkBrO22xS9Nh1mVwrMsOEC4t/Pfvbd0dZpe2MEYol5Mpbd6xkqcADziaeh0eaqgStcUuTYdZl8GxLtvJ1X2BrwIPMkKmt/WQMLu0hTFS8libN58hOWAc0Irw81AjlawtdGk6wboMjnXZLsqPUDbicOGWH2RdGgBXJ+OqMlvtKuDGmJ5pth5KSnO4OjXqKJvZJ8YINTQ0nr3VB2zPLwAPh6tQlXBSlaZtdmnazboMjnW5DZPVweF6FAefW7b20DC7tIUxQqpb22wAHCytwKXAIcxhbBiZStU2uzTtZl0Gx7rchhGcCxwGPMVIntjaQ8Ps0hbGCInIH7b5oDpJAf+H8hue0J26PVSJaleXpl2sy+BYl1sxU3dA+RWwEoezEdGtPdy6NB/n6pmF4xpvjDqKMcYEIqXXFC61V3SHpdknxgh5XqZ93zhdzj1AI8J4XD24e1OVpnZ3abbJugyOdbkF9ToU5WKE98lyU3ueEmaXtjBGqp3HKOYP3/ghEEP4G9O0vFtjlSQ73jM41mVwrMtPmatlCH8DQDmTY2Rl+54YXpe2MEbIcRjf7gfXyd3Ar1GGsCs/7r5UpalDXZqtsi6DY122YS3fBxLA5dTJrPY+zbo0bZum5bi6CFdbmK21UccxxpgOmaNfxNVNxXTMYlvsE2OEPC9zb4eecIa0AD8Byohxq21S/Y8Od2m2yLoMjnX5EY9of3zuBl4BvtvRp4fZpS2M0dqxw8+ok38AV9sm1U/peJdmS6zL4FiXm5UzBTgA4SLqZFMnXsG6NFuhKrh6O6622EnGjTFFr16/VTg0o80rZxQb+8QYoQULFu3eqSeKKOu4AFiKcA+zdJ9gk5WeTndpPsW6DI51CczSAxFuRFiD8v3OvkyYXdrCGKFcru3Lq7TLibIBuATYlRj3M1N3CCxYCepSl+ZjrMvg9PouXd2ROPcC/fG5nDp5p7MvFWaXtjBGSEQWdOkF6mQGMAU4iL7cyTSNBRKsBHW5S/Mh6zI4vbrL/PvRP1H2By5npLTrQP4t6dVdmg5SFer1RlxVXP1d1HGMMQaAev1t4X3pn1FH6Si7lNEWTBo34UpVTgZW9nM2nvLbW25Z29bjJp474S2E/JkbVH465e9T7m/vGJ6XqUsmq9wuh52m5QzkeSCBckFXfzIrRYF1aazLAPXaLl39BnAH8DYt1DJK3uvqS4bZpW1KbcMl3xy/B8rxU6beUCPgbfD7nLPFBwsyZeoNySlTb0h2ZFHM0yu7GDXvDGnB4RxgJcKN1OvJgbxuSQmoS4N1GaRe2GX+fM43A2/icFQQi2JeeF3awtiGbFn5EFV5ctKkSeUKIxUZusUHK1u9VMrWqMpdnX3upwyXBeQYBixD+Cf1ekRgr10CAu2yl7Mug9PrunT1C8BMhEZiHMVwaQzqpcPsMh7WQKVEVXdyHN7Xtf6livxdtnbyWmG7iWMnPoP661oqsmffdNNNKzbf1dDwTn/VjT8Vcd5OJCqvf+GFzOdjMR2ryjM1NdUzRPRNz2v8jYjcnUhUzUunm84X8ffNZit+d/DBe67wvMzPRSSbSFReOXduZo94XC9R5ZWamuqpDQ2Nh6lymog8kkhU1afTTWNEMgffsGbHb97s73hThaOP/nHuO/88ou/Ki/r16+esXs2VwDvJZPXvPe/N/cH5tog8n0hU/SudbjpWxD/G92PTa2sHzW1oyHxbVfePxfTaL35xn3fT6cbJIuIkk1U/nTfv9YGOE79MRF5LJKr+Nm/e4oMdJzdG1ZlVU1P5mOdlTgM9TERvSST2eTWdbrxEhD02bIhPLi9/NxuP73KViCxPJKquXrBg8edyudx4cF5IJiuneV6mDvQrIvxvIlH9XDrdOFaEz2ezct3BB1ct87ymn4poeSJR9ePnnluyS0VF6xXgvJlMVt4kwjP5LnETieqH0+nGk0U43Pedv9fWVr7seU2TwN+7rKz/z196aeDG/ffP/FLVWVlTU/nbhoZMtap+V4R0IlF917x5jcMdh9Gqcl9NTdXTntd0DvhfECn7YyKx95J0OvM/IrJdMln5w3S6cUcRfihCJpGo/tO8eYsSjiNfV5U5NTVVMxsamk5U9Y8Uid2eSAx6MZ3OTBDRQZs2ZX/xpS/tt6aQ+YNEovpXc+e+OSgedyaoyvyamqp/eF7TkeCfKBJ7IJEY9EQ6nfm6iCZ8X2+srd2nyfMafwDslEhU/eDVV1/dbtOmih+DvJVMVk3xvKaDwP+WiPNkIlH573Q6M1pEh/u+3llbu0+D5zV+F6h2HOfXQ4ZUrkqnm34pohuSyaqrRGL35XM5LyUSlbfNm5f5suPoKao8VFNTnfK8xjOBWhH5cyJR1ZhON10h4u/y2mtVPxo8+N0+W5vzntc4ChgZ3Jz3D87l4jcPHfrZNzwv8z3Q3crKNvykWOZ8LvfuzYV/44HPec/L1ICeVSxzfvrG7Rp/0cyVcbT/Ldu/d28i3rJLApYFNedraqpu3NqcD3INsIURmDjuopmo7Jn/k5yjKPjsqSI7CHLVVs/qrnJuS0XLQ+Ut5f+voqVsHPDhF2Def3/35gEDmmaUleXWA5SVyTvZLDNiMTZ/Zfkw35cZvl++GECVp1TlxV12aV6Xv9v/dy4X9/O/L1/t+y0z4nFdlf9zrtH34zMcJ7YYIB6Xea2tsuSCfu+/fPPKHY+K9SE1Ye1up1Ss3/2aW97ltX33bZoB/ob8OM57qjIjHpd3ARwn9koup+tisZbFANksT4vIyxs2xNfkx4o9CFkB2LSpz7o+fXIzVPkgf19zk++XzYjFYm/lX0u8bJZlIrybz6X1ra1O/yVL9m4ZM2ZvTaebZojoRoD165uXl5eXzSgr27ypJfaa7+c2lZX5GYBYjGezWXkVylcD+L7MjMX8GMBuu7WuX7VKZgCrC//fRvk+M3w/93Yhx/xcjvficZbl/+ykslndbuPGxc1jxgz002mZ4Ti6CaCiYtOKDRv6zFCNLS/keN33/RkVFVr4aTf7nO/H3ojF4qvyueRhVb8MoLx8w4bm5v4zHEfWALS2li8pK8vOAH9pPrO/QFVWVFTI0kKOObmcv/3227dsKvydZsRiNAP07bv9+83NG2Y4Tm5l/v+Dvuk4MsP3831A7P98P9dYUdF/JYCIPJrLUSEiOnfu3E2O02dGLOaszc81Z1lzs84Q8ZcB5HK5Fx0ntqq1tWxJ/rWcJ3xfG/r3b9mQz+XfJ+JkC5P6rHyu3Pv5frSxuVlmgNOUf634CyK5xS0trYUc+pjvS58xY/BTqa3P+Vgs93Jra3xNkHNetWV5/rV0tqr0PfDAA7PTpyPFMOcHD/Yn5HLaLXM+my1/y3FaimLO36X90n/YtNP1wMD94tkJX5BN6Xg8Fuic97ymSSK6xTlvutnEcRNHTTx3wtoLz7twn4vOu+jICWMn/G1bz5lw7oQzJo696LqOjON5jY93PuU2uLpr4YTjS3H1gG4bp0h0a5e9jHUZnF7R5Szdv/A+k6VeL+6uYcLs0vYxtiHe2jIfYcnug3bPODlnOMo8gPHjx5dNHDvxgYljJ44AGDNmTOzSb1+6M4CghyiyuCPj+D4/CTz8ZnWyAjgJ2A5wmaOf77axikC3dtnLWJfB6fFdztH9iFEPDEQ4lZFyfXcNFWaXtjC24bo7bloGeueKzHIP0aP6xzbeBtDS0hIDPQFhb4D+/fuXteZaZk8cO+EVdWSo3+Lf2pFxamur53RH/g/VyYsI30DYDZ/6wo7xHqnbu+xFrMvg9Ogu6/UglBSwF8r3GCH/7s7henSX5j88r+lPoQzk6vcKJ/BdSUq/HMqYIQuty17AugxOj+3S1YNxdTmubqJeQ7mAcJhd2ifGSPkHhTJMnVwDnIVSjvIY9ToqlHFDFVKXvYJ1GZwe2OVsHYYwG9iEMjy8E4qE16UtjBHy/ezpoQ1WJ3fjMAwhh8P9uHpWaGOHINQuezjrMjg9rktXTyHGQyhpchzMSHkurKF7XJemiKS0jpSuJqU56vWiqOMYY0pEvV5ReN+4BVftUD/TPSL7Kvcs3QdXny2c4HcyqiV/ztxe8bX4kFiXwekRXU7T8sJiqNTrLUzWSLY02uEavUZQ5xDsoGNkEXAkcDXwM1LcxxO6UyRZAhNRlz2SdRmcEu9ylu7CQB5FGIdwG4/zHSaLH02YEu/SlJCU/qTwjdU3cTUZdRxjTJFwNVl4X1BcvaM3X+/VhCh/PssikNIf4qqPqxtwdWzUcTqjaLrsAazL4JRslyk9h5Sux9WNpPTyYlgUw+zSNqVGyi+OY5xGyK9Q6oClwK3U60242ifqWB1TJF32CNZlcEqsy7laRr1ej3Ibyns41DJCfscZkos6Wphd2sIYreK5gOlImUMZNYCLcD7CU9Tr4KhjdUDxdFn6rMvglE6Xj+serMVFmAS8iM+RDJdXoo71EaXTpelhZmoFrt5a2K+wjno9L+pIxphuNluHFU4EvglXJ5feFiPTY6TTTWOizrBFKa3D1VcKX8y5G1d3jDrS1hR1lyXGugxO0Xf5tPalXq8lpTlcfYJZun/UkbYkzC5tU2qERPxJUWfYohHispwEwo+ArwJp6vWIqGNtSVF3WWKsy+AUdZf1egQteAiXotzKco7mGHkt6lhbEmaXtjBG64aoA2zVGdJS+GLOQQgvI8zB1d/xtPaNOlobirvL0mJdBqf4unxa+5LSa3B4HGUQcBl18h3OkJaoo21D8XVpejlVoV6vwNVWXH2NlB4VdSRjTAfV6xGk9NXCdwjuwdWqqCMZ8zHpdOPkqDN0mKsjcHVZYZ/En4rljDkl2WWRsi6DUzRdurojrv6JlOZIaTOuTog6UkeF2aVtSo2QCCOjztBhdZKilaEoTwHfJcsrpPScqM+3WpJdFinrMjhF0WVKzwZeAb6LMpMsh1AnJbdZMswubWGMlHNh1Ak65ThZyhxGoJwJrEK5jRT1zNHPRxeqRLssStZlcCLs0tV9SekjKHcivIbPEdTJiRwj8yPL1CU2L02pmKtlpPQ3H26iSekv+Lf2izqWMb3Wk7o99fpLXN2Iq2lSenzUkYxpt3Q6Mz3qDIHJ73t8prBTfxGunhLm8D2qy4hZl8EJtctpGqNex+PqO4VF8fvFcI7ToITZpW1KjZCI7h51hsDUSYo6+TLKMUAGuBdXn6Fejw5j+B7VZcSsy+CE1mVKT2QgHsJfEJrIMZQ6+U1xnOM0GDYvewntARcI3iJXz8LV9wqfIOuZrYd353A9usuQWZfB6fYu63U4KX2q8O+sx31K/Cibl73Ec88t2SXqDN3qcR2Iq1NxtaXwD/cBZmttdwzV47sMkXUZnG7rcrbW4upDhVM2rial1/T0YxLDnJe2KTVCFRWt90adoVsNk+XUyVhgH+DXwAgc5uLq/wa9QPb4LkNkXQYn8C5n6f6k9G4c5gLHANewgc8yQr5HnWQCHavIhDkvbWGMVjFd0qX71MkS6uSHOHwe4V7gVBxeIKUPBriJtXd0GQ7rMjjBdOlqFa7eTIyXUE4DZgCHUCeXMVrWBDJG8QttXto2WxO+2XooMS4AzkLpB7j4XMXRUh91NGOKymw9HIdLgNOA91H+jHATdbIk6mjGdIv58xcNizpDpB7TAbg6EVcXFPaVPEW9ntaZLw/0+i4DZF0Gp1NduhrH1TNx9dnCvvlNpPQ3zNQduiFiyQhzXtqm1Aj5vlwVdYZIHSurqZMp1MkXUY4EPkD4FwN5A1e/15FrQPb6LgNkXQanQ13mz2d6GcKbwF3AYISbcPg8I+T7vWiTaZvCnJe2MEZIxLkn6gxFY6Q8xQg5obBAZoCrgbdwdQquHrCtp1uXwbEug9OuLudoDfV6E7AE+B3KWoSvA3swQi5guDR2d85SEOa8tH2Mpji5egxwFXAYoEAK+AvLubcErhtnzNa52gdhDMpFwJcAEObjcw0r+EdPOjDfmA5paGi0k+Jui6sHU6+/xdVMYX/LO7j6K+Zo9UcfZl0Gx7oMzqe6rNeDcPV3uLq8MJ8XktIroz0Bf2kIc17aptQIqXJ21BmKXp3MZaRcwRz2Ac5GeBf4AcobhQOcT2GalluXwbEug6PK2bi6K/V6Ma7ORXgROAv4K1BDnRzACPkZw8UOkdmGMOdlPKyBzKeJOFdGnaFkTBYfuIvJOo2j+BIOJwEnA/cykFUnrtn7eVJ6PNsxi4OlNeK0Jc3mZQCe1r608NXjVmdjwFKEMuD/gLOBe6iTbLQBS0+Y89L2MZrSVq8HIUxE+BZKP4T3Ue5FmY4w296ATGhmagV9GIXDmSgnAdsh+Cj3IfyeEfJE1BGNKXqel/lj1Bl6iofnvXUz9XpF4dJX2cL+mxXU602k9Fhcta0j7WTzsgNcjVOvo6jXW3B1VWHercXVe6jX8dNeePvWqCP2FGHOS3uziJQmok7QU3zGyQ5mpJwP/JYndCeyHA2MxeHbKOcjvE+9PohwP/AwdbIu4shFzOblVk3TcnbhSBzGAF9D2LXwybABuIUst3OsrAbY32v8ZrRhe5Lw5qUtjBHKZuXMqDP0FB/r8ihZBdwD3FO44sDpKMMRTgTOATbhqgvch8OjdpzYx9m8bIOr+wKjCr/qgO2AN4C7UVxgDnWy4pNPsy6DE2aXto/R9B7TNMbuDMFnODAcYRjKzsAbKI/h8BiKS518EHVUE7GZugN9qANG4TAKZZ/CPauAuxFuY4Q8E2FC041sYYyQ52Xqk8mqkVHn6Ak61aWrcYQ6lDOAU4FdgBzCC/jMAeaQ48nNm8V6i145L5/U7WnhKGAEwgigFogBWWAOwlPAs4UfnDa192V7ZZfdJMwubVNqhER697kPg9SpLvPfWH0MeAxXL0Q5GoczUE5AuBy4nDg5XPVQHgdSlPFEYVNtj9Ur5uUs3QWHwxCGASNoZShCvLCv8HVgOoqL8L9tbSJtr17RZUjC7NI+MRrTlkd1T+LU4DCs8NX7/Pla82+cCxDm4PMCMdL4vGKHhRSxuVrGWhLAYSiHIXwJ2I/81oGXUOahpBHmUUYDR8raiBObiNnCGKGGhkWDE4l9Xo06R0/Q7V3O0Wp8EigH4nAQyqHAvoV7m4EXgTTg8f/bu/fgqM77jOPfd2+vbqAL5mJuNigUExkMBuMEN+M4jbHBdk3brBI7dhHjKe2AYGwnnXrcxNPUmWl8ycSdCNIhm0Rp0mbGeJo2abDTaSxCW2xhiASOAjUBCSOMK2OhG7rsnj1v/zjCcWRjSWS1K8HzmTmD9spvn/3Nec9t9nU0EuPQRF3BTui+DAbBChzXY7ie4JDoUiB/8BmnMPwUx3+Q5j/5pHl7LMuZ0FmOM9nMUodSc8g5801Ac99lwJhnGVy52kwwc3rg524eaW7FsBq4CVgOBJubHj517hiGBhyHcBwGmjAcG+97lxOmL19wZcRYgGEphutxXE83iwE7uMk/QPCdvQjUAT/lFvPLbJY4YbKcALKZpQbGC9i6YdMq58w3HDTU1G6rutDztmzY/CXnuBt4uyDUt+7Jb397xHsJzumXMDIlJ1kGg+WOwQXqXBFp5hNmCY6bgY8B64DKd47NGJLUuf/FcBg4gs8xDMdJcZzV5o2sf4b3Ma768mduFlBOmHIc5UA5wZ56OVAKqnRPmwAACodJREFUnD+8fRpowfEvGPYBu/k5hwZ/SjBnxlWWE1w2s9Sh1PexqWrTjBA8Yxw/dsbceqGB8cH7Nl6ZjkT/7eu1227cUrX5aYc7VlO7fXuWy5XxzDnDfzEDn9nAbHzmYJiLYQ5wLY6FBFc/AvRjOI7jOI6TQCshTr7zN7SO5orIcS/YkJhGmJnA1TjmYpgLzMVwFY55/OYQ6HkpoAl4eXCpZxLH9Pu4kknaY3wf22u3vwl8prqq+uPBVIDvz4vGluDz31u2bIm5bv8TDjPiGecBGhtb/mjp0qt/+DuWK4zjLI1xwOnB5ZX3PL7X5dPH4sHDgctwLAVuxHAbEH1P++12bZwfJB1vYegEOvDpxNCJowNDJz5dODqIkiJED930stYMjKTki8pyl5uMpZQQpRhK8Qf/hVIMU3FMA6ZimArMwDEVyH9nkwDA0I3hLI6zONow/BJHw+CFMScYoI3bTfuo6sqxcduXE1A2s9TA+DtwzpWGQrS7bv8hh/muwd085HGzb9+psnB4wFuxorxz//790XR6xuS8vEj/ddfNOAd8rr6+dU9RUWd3RUVF8uWXj042Jj+6cuWss8YYv6npZFk67bslS646++yzLnzVVadKiot7ktdcc0330aNHbXt7flE67XpXrZrTd/Dgm4X9/V5eOPxm14oVK1INDc0lyWQ0fOONs98ero69e0/mh8OmoKysr2fBggUDR44cmdTZWRQ7cWJWR2WlSR86dKI0HA6Zioo57aOtY//+Y8XptI2sXDmr3Rjj6utbp8RiqfSyZfM66upcpKDgVPHF1OGcC+3bd6rUub7URz6yoAt4uL6+dU84HOpbsWJm78XUUVQUG6iomNbT3Nyc19YWLZw2LXVu3rx5/U1NbUU9PUk7kjqamppiPT3Fk87XsX//GwXptJ9/oe+4PnyqIFaWem3Zsnn7htbxwN7OsmN+/vwGLzSv04XL840/zUBhvwuFfCiOGJal4WrnKHvXodrA+Qnl/MElH6hzYOgCPOPoMpDyoQsggouA6fOg5/6u5Irobv8v0458H5M2uFAYQmlMnjNEjXMhA8bH+MBkoATeNcQ5fvtYVHA7iaM57FyLde5gOhQ6PODzWkWot215frizr8i27KwwyWF7vr51ykTq+YMHWx6qr2/dMxY9P7TXJkzPX0Qdvb2zOqHloYaG5rqh3/G71gEZo/kYgeoNm3ZVV21uDJbqxaN6sc9Mh7nWYF4Y+lB9/a8nWeslIpHwYwCh0LTrrPUSzvXdB+AcB631EslkwSqAvLzI56z1EgcOnJgOkEr5233f/T3AggUtc6z1En19eVsBzp0L32ytl8jP9+8M3qt/g7VeIhQq+zCAMeZxa71Ec3Nz3oEDp/Ot9RLhcDBtSyRyxbXWegnf718PUFjo3W2tl+jtjf0+QH9//kPWeolFi47NBPB99/VkMl0DsHDhqSut9RIDA/kPA/T2Rm+y1ksUFnp3B5+670+t9RLR6BWLAaLRyN9Y6yUOHfq/gqNHj1prvYQx5ssAU6a0LrLWS8DAA0Ed/h3Weolz5yIfB+jry9tqrZdYsKBlDkA67T+TSnnfAPjVr1qmWesl8vKinw9qZHeQdeqPg9sD9wa3y5YFnzn8xSDb45Pr6lwkqCP0dwDFxa//nrVeIpU692cAHR3cbq2X6OgwfxB8D32brfUS5eWt8wbzeDqVSu8AaGj49RRrvYS1sUeC5xatDLIeiAf/b/IzQV6Fywe/40et9RL79p0qBQg+v3kKoLT0+HxrvYTn9f4FwNbCjpueKXrjr/eUvjHALeYrL5WcPLW35OS0hrLmp7nFVB4oPXGgoeTEnhIoXRzqv/mJojP/88XC9meBW1dFer/0WdvddEN04HngieXRgZ/8oe05+qFQ6sf4fPWuWE/zn8S605ND6VeAxiWRgSvmh5NzgdITfsQrNm5JWcifAfRMD6X7F0a8K6eGvAHgyIfDqbfWxM6l5odT/wo89Snbvf+ztrspYnh0kvEffaSg/ZUnC9p+QJryz8c61v2o+PQL+ya3PMgt5pr9Ja9/b2/ZyakvF7c08Anzk38qabvzYfvmE3/Vd2LKpdjzzvGtser5gYGCj15qPd/VFbrVWi/R1RVeHbx3759b6yXKyprLnTP/EAqZJ4N1qDOvvvp6SdAP0S+QYTrH+AEGD6VWXegcY/WG6tvw3XPpsH+dwcw0vtmwrXbbA9mtUkREMkl7jKOwcePGaHVV9b8HAyZEUslDGFqnz53eEkqHbsbxi9G8X2NjS8a3dC5XyjJzlGXmKMvMyWaWGhhHIZlMhsHdgWE2wDPf33Ea3D+faXmrEeM+Vhju+8fRvaNbPRZ1Xp6UZeYoy8xRlpmjLC8LDQ3NS3Ndw6VCWWaOsswcZZk5ylJEREQuP42NLT/IdQ2XCmWZOcoyc5Rl5mQzS51jFBEREREREREREREREZHR0C/fZFE2prK6XIw0o+r1m09iCCajdeaxmu/W/CibdY5nI81Q/Tg89WPmjIf1pC6+yZJNVZtm+I6tOPfkBz3vwfs2XoljTU3ttmUGGnv9vPuzVeNEMaqMDKamdtvSmtptS7US+o2RZqh+HJ76MXPGy3pSs2tkSbamsrocjCoj9wFhX8ZGmqH6cXjqx8wZL+tJDYzjzHBTWckoMzIUVVdVv4Tze5LWu2fHjh1nsljquDXSDNWPw1M/Zt9Y96UGxjFSvWHTLpyZGdwy99fU1rw64hf7zHTGTDaYL6MV0XuydLiRZ+TM+qRNPh9Lxv7WJqMbgKeyU/UEMNIM1Y/DUz9m3xj2pQbGMVLzne1rL+Z1xpizzrn7/dA7U1lNqBnLx8LQLKs3VM8caUbnz+NsXr+5AeNWjXWtE8VI+0z9OLzRZKR+zIyx7ksNjDm2cePGaCwZ+yHwdE1tze5IKnnIi0Rbp8+d3nKm+cw9DjeqqawuBxfKaGiW8Xg8PHvy7OKvfetr7QZ3g8O8nuPSx42RZqh+HJ76cexluy91VWqOZX4qq0vfhTIammVhYWE0lU7+rLpq82EXMsv9pP+d3FY+fow0Q/Xj8NS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" 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",
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"\n",
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"\n",
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"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
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"\n",
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" x1: bbox.x + bbox.width,\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
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"});\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",
" init_pan_zoom(root);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
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"\n",
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"\n",
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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",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
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"\n",
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"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
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"\n",
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"};\n",
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"\n",
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"\n",
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"\n",
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"\n",
" tx = xscalable ? tx : 0.0;\n",
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"\n",
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"\n",
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" element.transform(t);\n",
" });\n",
"\n",
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"\n",
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"\n",
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" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
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" 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",
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" .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",
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"\n",
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"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
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" // 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",
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" 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",
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"\n",
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"};\n",
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" };\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",
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" }\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",
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"\n",
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" y0 = bounds.y0 - scale * bounds.y0;\n",
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" 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",
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" 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",
" 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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".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",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\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",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".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",
" root.select(\".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",
" }\n",
" });\n",
"\n",
" root.select(\".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",
" }\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",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\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",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\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",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_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) {\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;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\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(\"#fig-58ade121340a41d993297ff954450ff0\");\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-4\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\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",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-13\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-15\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-15\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-16\")\n",
" .data(\"max_pos\", 99)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-16\")\n",
" .data(\"min_pos\", 82)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-16\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-58ade121340a41d993297ff954450ff0-element-17\")\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"
],
"metadata": {},
"output_type": "pyout",
"png": 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