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Python東海 第26回勉強会 発表内容（変換結果はこちら→ https://nbviewer.jupyter.org/gist/antimon2/c180a49222659c64a9b9 ）
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python_tokai_201501_antimon2_2.ipynb
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python_tokai_201501_antimon2_3.ipynb
{
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:5f8af4f0dca2679de9f2a94cddf752b2bb47e5dc936d53b838db47537a3d5f5a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Julia \u306e\u30b9\u30b9\u30e1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Julia \u3068\u306f\uff1f\n",
"- \u95a2\u6570\u578b\uff1f\u8a00\u8a9e\n",
"- [The Julia Language](http://julialang.org/)\n",
"- Python/Ruby/R \u7b49\u306e\u300c\u3044\u3044\u3068\u3053\u3069\u308a\u300d\u8a00\u8a9e\n",
"- \u52d5\u4f5c\u304c\u901f\u3044\uff01\uff08JIT \u30b3\u30f3\u30d1\u30a4\u30eb\uff09"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### IPython (Jupyter) \u306e Julia \u30e2\u30fc\u30c9\n",
"- IPython \u306f\u3001\u30d7\u30ed\u30d5\u30a1\u30a4\u30eb\u3092\u8ffd\u52a0\u3059\u308b\u3053\u3068\u3067\u4ed6\u8a00\u8a9e\u3082\u4f7f\u7528\u53ef\u80fd\u3002\n",
"- Julia \u306b\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u8ffd\u52a0\u3059\u308b\u3068 IPython \u306e\u30d7\u30ed\u30d5\u30a1\u30a4\u30eb\u304c\u8ffd\u52a0\u3055\u308c\u3001IPython Notebook \u3067 Julia \u304c\u52d5\u304f\uff01\n",
" - IPython 2.x \u3067\u306f\u3001IPython notebook \u8d77\u52d5\u6642\u306b `--profile Julia` \u6307\u5b9a\u3067\u3001Julia \u30e2\u30fc\u30c9\u3067\u8d77\u52d5\n",
" - Jupyter \u306f\u2026\uff08\u7814\u7a76\u4e2d\u3001\u305f\u3060\u3069\u3046\u3084\u3089 notebook \u4e0a\u3067 Kernel\uff08Python/Julia/etc.\uff09 \u3092\u9078\u3079\u308b\u6a21\u69d8\uff09\n",
"- \"Jupyter\" \u306e \"Ju\" \u306f\u3001\"Julia\" \u306e \"Ju\"\uff01\n",
"- \u30c7\u30e2\u2193"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i = 1:10\n",
" println(i)\n",
"end\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \u305b\u3063\u304b\u304f\u306a\u306e\u3067\u3001Julia + IPython Notebook \u306e\u30c7\u30e2\u3082\u3063\u3068\n",
"\u203b\u4ee5\u4e0b\u306e\u30c7\u30e2\u306f\u3001[Juno](http://junolab.org/)\uff08Julia \u306e\u958b\u767a\u74b0\u5883\uff09\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u3068\u304d\u306b\u914d\u7f6e\u3055\u308c\u308b tutorial.jl \u304b\u3089\u306e\u629c\u7c8b\u3067\u3059\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Pkg.add(\"Gadfly\")\n",
"using Gadfly\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A simple scatter plot:\n",
"plot(x = rand(10), y = rand(10))\n"
],
"language": "python",
"metadata": {},
"outputs": [
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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",
"(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-69994f3a3f594b37aea922da643c4554\");\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-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-69994f3a3f594b37aea922da643c4554-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-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-69994f3a3f594b37aea922da643c4554-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-69994f3a3f594b37aea922da643c4554-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-69994f3a3f594b37aea922da643c4554-element-17\")\n",
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" // AMD support\n",
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" 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",
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"};\n",
"\n",
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"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
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" }\n",
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" };\n",
"\n",
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" return {\n",
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" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
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"\n",
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" x: bbox.x + bbox.width / 2,\n",
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" };\n",
" };\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" 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",
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" } else {\n",
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" }\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",
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" 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",
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"\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",
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"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
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"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
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" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
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" 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",
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"\n",
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" 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",
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" .transform(xfixed_t)\n",
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" .forEach(function (element, i) {\n",
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" 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",
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" 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",
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" element.attribute(key, val * old_scale / scale);\n",
" }\n",
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"};\n",
"\n",
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"\n",
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" 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",
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" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" 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-49105567a20b4965b4f33cb5ff5578af\");\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-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-49105567a20b4965b4f33cb5ff5578af-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-14\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-14\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-14\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-16\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-17\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-17\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-17\")\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-49105567a20b4965b4f33cb5ff5578af-element-18\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-18\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-49105567a20b4965b4f33cb5ff5578af-element-18\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"svg": [
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
" xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
" version=\"1.2\"\n",
" width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
" stroke=\"none\"\n",
" fill=\"#000000\"\n",
" stroke-width=\"0.3\"\n",
" font-size=\"3.88\"\n",
">\n",
"<g class=\"plotroot xscalable yscalable\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-1\">\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-2\">\n",
" <text x=\"76.01\" y=\"92\" text-anchor=\"middle\">x</text>\n",
" </g>\n",
" <g class=\"guide xlabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-3\">\n",
" <text x=\"17.59\" y=\"86.39\" text-anchor=\"middle\">0</text>\n",
" <text x=\"76.01\" y=\"86.39\" text-anchor=\"middle\">50</text>\n",
" <text x=\"134.42\" y=\"86.39\" text-anchor=\"middle\">100</text>\n",
" </g>\n",
" <g clip-path=\"url(#fig-085b4c33b9084434a040b4c7a91113e4-element-5)\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-4\">\n",
" <g pointer-events=\"visible\" opacity=\"1\" fill=\"none\" stroke=\"none\" class=\"guide background\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-6\">\n",
" <rect x=\"15.59\" y=\"5\" width=\"120.83\" height=\"77.72\"/>\n",
" </g>\n",
" <g class=\"guide ygridlines xfixed\" stroke-dasharray=\"0.5,0.5\" stroke-width=\"0.2\" stroke=\"#D0D0E0\" id=\"fig-085b4c33b9084434a040b4c7a91113e4-element-7\">\n",
" <path fill=\"none\" d=\"M15.59,80.72 L 136.42 80.72\"/>\n",
" <path fill=\"none\" d=\"M15.59,68.43 L 136.42 68.43\"/>\n",
" <path fill=\"none\" d=\"M15.59,56.14 L 136.42 56.14\"/>\n",
" <path fill=\"none\" d=\"M15.59,43.86 L 136.42 43.86\"/>\n",
" <path fill=\"none\" d=\"M15.59,31.57 L 136.42 31.57\"/>\n",
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" 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-f8668992109b41afaa33d26f7c56c73e\");\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-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-f8668992109b41afaa33d26f7c56c73e-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-14\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-14\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-14\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-16\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-17\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-17\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-17\")\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-f8668992109b41afaa33d26f7c56c73e-element-18\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-18\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-f8668992109b41afaa33d26f7c56c73e-element-18\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
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" </g>\n",
" </g>\n",
" </g>\n",
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" </g>\n",
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" <text x=\"8.81\" y=\"43.86\" text-anchor=\"end\" dy=\"0.35em\">y</text>\n",
" </g>\n",
"</g>\n",
"<defs>\n",
"<clipPath id=\"fig-517444a869af4533b88ed6347fded759-element-5\">\n",
" <path d=\"M15.59,5 L 136.42 5 136.42 82.72 15.59 82.72\" />\n",
"</clipPath\n",
"></defs>\n",
"</svg>\n"
],
"text": [
"Plot(...)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Pkg.add(\"Images\")\n",
"using Images\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function foo(z)\n",
" c = (\u03c6-2)+(\u03c6-1)im\n",
" max = 80\n",
" for n = 1:max\n",
" abs(z) \u2265 2 && return n-1\n",
" z = z^2 + c\n",
" end\n",
" return max\n",
"end\n",
"\n",
"foo(x, y) = foo(x + y*im)\n",
"\n",
"foo_grid(n) =\n",
" broadcast(foo,\n",
" linspace(-0.5, 1, n)',\n",
" linspace(-1, 0.5, n))\n",
"\n",
"convert(Image, scale(foo_grid(500), 1/80))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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",
"prompt_number": 7,
"text": [
"Gray Image with:\n",
" data: 500x500 Array{Float64,2}\n",
" properties:\n",
" timedim: 0\n",
" colorspace: Gray\n",
" colordim: 0\n",
" spatialorder: y x\n",
" pixelspacing: 1.0 1.0"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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