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adityam/traffic-routing.ipynb

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traffic-routing.ipynb
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"Recall the best reponse functions for the 2 agent traffic routing example discussed earlier in class.\n",
"\n",
"$$B_1(q) = \\frac 14 - \\frac q2 \\quad B_2(p) = \\frac 14 - \\frac p2$$\n",
"\n",
"Let's check if these equations have a fixed point solution."
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"B1(q) = 0.25 - q/2; B2(p)= 0.25 - p/2;"
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"set_default_plot_size(8cm, 8cm);\n",
"\n",
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" layer(x=B2(A2),y=A2,Geom.line), \n",
" Guide.xlabel(\"q\"), Guide.ylabel(\"p\"))"
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" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" 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",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#fig-222749bf07da4cb8998687f464df0e25\");\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-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-222749bf07da4cb8998687f464df0e25-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-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-222749bf07da4cb8998687f464df0e25-element-15\")\n",
" .data(\"max_pos\", 59)\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-15\")\n",
" .data(\"min_pos\", 42)\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-16\")\n",
" .data(\"max_pos\", 59)\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-16\")\n",
" .data(\"min_pos\", 42)\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-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-222749bf07da4cb8998687f464df0e25-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-222749bf07da4cb8998687f464df0e25-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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"plt2 = plot(x=1:iterations, y=q, Guide.ylabel(\"q\"), properties...);\n",
"\n",
"set_default_plot_size(20cm, 8cm);\n",
"hstack(plt1, plt2)"
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" 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",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof define === \"function\" && define.amd) {\n",
" // Define as an anonymous module\n",
" define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
" return factory(Snap);\n",
" });\n",
" } else {\n",
" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key, 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\").replace(/px/g, \"mm\"))\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\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, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" 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",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\", \"Gadfly\"], function (Snap, Gadfly, Gadfly) {\n",
" factory(Snap, Gadfly, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly, Gadfly) {\n",
" var fig = Snap(\"#fig-98362dd5b99a4b428801166f74cae751\");\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-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-98362dd5b99a4b428801166f74cae751-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-12\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-12\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-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-98362dd5b99a4b428801166f74cae751-element-14\")\n",
" .data(\"max_pos\", 179)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-14\")\n",
" .data(\"min_pos\", 162)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-15\")\n",
" .data(\"max_pos\", 179)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-15\")\n",
" .data(\"min_pos\", 162)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-15\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-15\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-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-98362dd5b99a4b428801166f74cae751-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-16\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-23\")\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-98362dd5b99a4b428801166f74cae751-element-26\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-26\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-27\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-27\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-31\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-31\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-31\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-33\")\n",
" .data(\"max_pos\", 79)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-33\")\n",
" .data(\"min_pos\", 62)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-33\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-34\")\n",
" .data(\"max_pos\", 79)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-34\")\n",
" .data(\"min_pos\", 62)\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-34\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-34\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-34\")\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-98362dd5b99a4b428801166f74cae751-element-35\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-35\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-98362dd5b99a4b428801166f74cae751-element-35\")\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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" [] [Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Float64,Float64,Float64,Float64}(0.0,0.5,20.0,-0.5,Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation(0.0,Point(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(AdhocContainerPromise((anonymous function),0,false,false,nothing,nothing),ListNull{ComposeNode}()),0,false,false,false,false,21.84916666666667,9.611666666666666,0.0),Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Float64,Float64,Float64,Float64}(0.0,0.5,20.0,-0.5,Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation(0.0,Point(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(AdhocContainerPromise((anonymous function),0,false,false,nothing,nothing),ListNull{ComposeNode}()),0,false,false,false,false,9.611666666666666,21.84916666666667,3.0)] ,3:3,1:1,nothing,nothing,nothing,{},UnitBox{Float64,Float64,Float64,Float64}(0.0,0.5,20.0,-0.5,Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(2.0,MeasureNil(),MeasureNil(),0.0,0.0)),0,false,false),ListNull{ComposeNode}()))),0,false,false,false,false,nothing,nothing,0.0),ListNull{ComposeNode}()),0,false,false,false,false,nothing,nothing,0.0),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0)"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check the final values:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p[end],q[end]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"(0.1666661262512207,0.16666650772094727)"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"err = (p[end]-p[end-1], q[end]-q[end-1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"(-8.583068847600739e-7,-1.2397766113170228e-6)"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"20x1 Array{Float64,2}:\n",
" 0.25 \n",
" 0.025 \n",
" 0.1875 \n",
" 0.13125 \n",
" 0.171875\n",
" 0.157812\n",
" 0.167969\n",
" 0.164453\n",
" 0.166992\n",
" 0.166113\n",
" 0.166748\n",
" 0.166528\n",
" 0.166687\n",
" 0.166632\n",
" 0.166672\n",
" 0.166658\n",
" 0.166668\n",
" 0.166665\n",
" 0.166667\n",
" 0.166666"
]
}
],
"prompt_number": 18
}
],
"metadata": {}
}
]
}
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