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OAGr/test2

Created January 1, 2015 21:22
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test2
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{
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"language": "Julia",
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"input": [
"using Gadfly; using DataFrames;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 223
},
{
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"input": [
"Gthreshold = 20;\n",
"Fthreshold = 200;\n"
],
"language": "python",
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"outputs": [],
"prompt_number": 231
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{
"cell_type": "code",
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"input": [
"df = DataFrame(research = [\"AGI\", \"FAI\"], threshold = [Gthreshold, Fthreshold])\n",
"plot(df, x=df[:research], y=df[:threshold], Guide.xlabel(\"Research Type\"), Guide.ylabel(\"Research Rememaining (r)\"), Guide.title(\"Remaining AGI and FAI Research\"), Geom.bar)"
],
"language": "python",
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" // 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\"], 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-32dad49cf5354baaa1e313dd04c4471b\");\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-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-32dad49cf5354baaa1e313dd04c4471b-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-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-32dad49cf5354baaa1e313dd04c4471b-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-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-32dad49cf5354baaa1e313dd04c4471b-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-32dad49cf5354baaa1e313dd04c4471b-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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],
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" C1 = friendliness_ratio;\n",
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],
"language": "python",
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" 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-efd57231394b459aa56299ac2fae0b57\");\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-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-efd57231394b459aa56299ac2fae0b57-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-12\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-12\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-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-efd57231394b459aa56299ac2fae0b57-element-14\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-14\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-15\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-15\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-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-efd57231394b459aa56299ac2fae0b57-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-efd57231394b459aa56299ac2fae0b57-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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"text": [
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{
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"input": [
"function Gi_per_year(Fi, Ge, Fe, friendliness_ratio)\n",
" C1 = 1/friendliness_ratio\n",
" C0 = -(Ge + (Fe/friendliness_ratio))\n",
" C0 + C1*Fi\n",
"end;\n",
"plot(x -> Gi_per_year(x, Ge, Fe, friendliness_ratio), 0, 5000, Guide.xlabel(\"F\\'<sub>i</sub>\"), Guide.ylabel(\"G\\'<sub>i</sub> Maximum\"), Guide.title(\"Maximum AGI Research Rate vs. FAI Research Rate (Inverted)\"))"
],
"language": "python",
"metadata": {},
"outputs": [
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" 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-c1f5e8da1bb043a3b60723f3eee95dfe\");\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-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-c1f5e8da1bb043a3b60723f3eee95dfe-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-12\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-12\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-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-c1f5e8da1bb043a3b60723f3eee95dfe-element-14\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-14\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-15\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-15\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-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-c1f5e8da1bb043a3b60723f3eee95dfe-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-c1f5e8da1bb043a3b60723f3eee95dfe-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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"input": [
"function Gi_per_year(Fi, Ge, Fe, friendliness_ratio)\n",
" C1 = 1/friendliness_ratio\n",
" C0 = -(Ge + (Fe/friendliness_ratio))\n",
" Gi = C0 + C1*Fi\n",
" ratio = Gi/Fi\n",
"end;\n",
"plot(x -> Gi_per_year(x, Ge, Fe, friendliness_ratio), 0, 5000, Guide.xlabel(\"F\\'<sub>i</sub>\"), Guide.ylabel(\"G\\'<sub>i</sub> Maximum\"), Guide.title(\"Doom Chart\"))"
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" 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-ea408692ed4849c492eab13dda79f05c\");\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-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-ea408692ed4849c492eab13dda79f05c-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-12\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-12\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-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-ea408692ed4849c492eab13dda79f05c-element-14\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-14\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-15\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-15\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-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-ea408692ed4849c492eab13dda79f05c-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-ea408692ed4849c492eab13dda79f05c-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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