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Python東海 第27回勉強会 発表資料
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JupyterRISE.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Jupyter + RISE による<br>ライブデモ＆プレゼンテーション"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"text-align:center;font-size:150%;line-height:150%\">2015/05/30 Python東海第27回勉強会<br>後藤 俊介 ( @antimon2 )</p>"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 自己紹介"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"+ 名前：後藤 俊介\n",
"+ 所属：株式会社コスモルート クラウドR&Dグループ 数学班\n",
"+ 言語：Ruby, JavaScript, Python, Julia, Haxe, …\n",
"+ twitter: [@antimon2](https://twitter.com/antimon2 \"あんちもん2(@antimon2)さん | Twitter\")\n",
"+ Facebook: [antimon2](https://www.facebook.com/antimon2 \"後藤 俊介\")\n",
"+ GitHub: [antimon2](https://github.com/antimon2/ \"antimon2 (GOTOH Shunsuke)\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Jupyter とは？"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"+ [IPython](http://ipython.org/ \"Jupyter and the future of IPython &mdash; IPython\") Notebook \n",
"+ Webブラウザ（やその他のプラットフォーム）上で動く REPL。\n",
"+ Python の他に、[Julia](http://julialang.org/ \"The Julia Language\"), Ruby, Haskell, … 等、様々な「カーネル」を利用可能。\n",
"+ Markdown で文章（メモ）も記述可能。$\\TeX$ 記法で数式も記述可能。\n",
"+ reveal.js を利用したスライドにも変換可能。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"このスライドは、Jupyter + [IJulia](https://github.com/JuliaLang/IJulia.jl \"JuliaLang/IJulia.jl\") で作成しています。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## RISE とは？"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ [Jupyter](#Jupyter-とは？ \"Jupyter とは？\") でリアルタイムにスライドを表示できる拡張機能。\n",
"+ 参照：[https://github.com/damianavila/RISE](https://github.com/damianavila/RISE \"damianavila/RISE\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"このスライドは、Jupyter + RISE でリアルタイム表示しています。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## デモ"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Julia のコード実行サンプル"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, World!\n"
]
}
],
"source": [
"println(join(map(ucfirst, [\"hello\", \"world\"]), \", \"), '!')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2-element Array{Float64,1}:\n",
" 3.0\n",
" 5.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# solve x + y = 8, 2x + 4y = 26\n",
"A = [1 1; 2 4]\n",
"b = [8, 26]\n",
"A \\ b"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
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},
"outputs": [],
"source": [
"# Pkg.add(\"Gadfly\")\n",
"using Gadfly\n",
"# ↑ v0.4.0-dev だと ↓ に WARNING が出力されてしまう"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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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",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// 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-4221c6055e0345d98df6432aca118655\");\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-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-4221c6055e0345d98df6432aca118655-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-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-4221c6055e0345d98df6432aca118655-element-15\")\n",
" .data(\"max_pos\", 156.8)\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-15\")\n",
" .data(\"min_pos\", 139.8)\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-16\")\n",
" .data(\"max_pos\", 156.8)\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-16\")\n",
" .data(\"min_pos\", 139.8)\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-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-4221c6055e0345d98df6432aca118655-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-4221c6055e0345d98df6432aca118655-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"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set_default_plot_size(7inch, 7inch/golden);\n",
"plot(x=rand(10), y=rand(10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### $\\TeX$ 数式のサンプル"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"インライン： $\\sum_{n=1}^{\\infty} {1 \\over n} = 2$, $P(x, \\sigma, \\mu) = \\frac{1}{\\sigma \\sqrt{2 \\pi}} e^{-(x-\\mu)^2/2\\sigma^2}$\n",
"\n",
"ブロック：\n",
"$$\\sum\\limits_{n=1}^{\\infty} {1 \\over n} = 2\\\\\n",
"P(x, \\sigma, \\mu) = \\frac{1}{\\sigma \\sqrt{2 \\pi}} e^{-(x-\\mu)^2/2\\sigma^2}$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 環境構築"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 環境\n",
"\n",
"+ Mac OSX 10.8.5/10.9.5/10.10.x\n",
"+ Homebrew\n",
"+ Python 2.7.9 / 3.4.3\n",
"+ Julia 0.3.8 / 0.4.0-dev\n",
"\n",
"ここに IPython・IJulia・RISE をインストール。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"※ Python 2.7.x / 3.x は pyenv で共存インストール \n",
"※ Julia 0.3.x はバイナリパッケージを配置、0.4.0-dev は Homebrew でソースからインストールすることでパスを分けて共存"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## IPython のインストール\n",
"\n",
"Jupyter（＝IPython Notebook）を利用するために \n",
"依存関係含めてまとめてインストール\n",
"↓\n",
"\n",
"```shell\n",
"$ pip install 'ipython[notebook]'\n",
"```\n",
"\n",
"または（古いバージョンがインストール済の場合）\n",
"\n",
"```shell\n",
"$ pip install --upgrade 'ipython[notebook]'\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"※ Python 2.7.x/3.x 両方に同様にインストール"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"確認\n",
"\n",
"```shell\n",
"$ ipython --version\n",
"3.1.0\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Python カーネルの（追加）設定"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"デフォルトで使用する方（Jupyter の起動に使う方）じゃない方の Python のカーネル設定を追加。\n",
"\n",
"例：デフォルトを Python2、Python3 のカーネルを追加する場合。\n",
"\n",
"```shell\n",
"$ cd ~/.ipython/kernels/\n",
"$ mkdir -p python3 && cd python3\n",
"$ vi kernel.json\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"kernel.json（サンプル）：\n",
"```json\n",
"{\n",
" \"display_name\": \"IPython (Python 3)\",\n",
" \"language\": \"python\",\n",
" \"argv\": [\n",
" \"/path/to/user_home/.pyenv/versions/3.4.3/bin/python\",\n",
" \"-c\", \"from IPython.kernel.zmq.kernelapp import main; main()\",\n",
" \"-f\", \"{connection_file}\"\n",
" ],\n",
" \"codemirror_mode\": {\n",
" \"version\": 2,\n",
" \"name\": \"ipython\"\n",
" }\n",
"}\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## IJulia のインストール・設定\n",
"\n",
"各バージョンの Julia ごとに REPL で以下を実施：\n",
"\n",
"```julia\n",
"Julia> Pkg.add(\"IJulia\")\n",
"```\n",
"\n",
"※古いバージョンがインストール済なら↓\n",
"\n",
"```julia\n",
"Julia> Pkg.update()\n",
"```\n",
"\n",
"※最新バージョンがインストール済で IPython を新しくした場合は↓\n",
"\n",
"```julia\n",
"Julia> Pkg.build(\"IJulia\")\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"参考：パッケージの追加／更新と共に、ビルド・IPython へのカーネル設定まで自動で行われる。 \n",
"ログ（サンプル）：\n",
"```\n",
"Found IPython version 3.1.0 ... ok.\n",
"Creating julia profile in IPython...\n",
"Changing KernelManager.kernel_cmd to [\"/path/to/julia_home/bin/julia\",\"-i\",\"-F\",\"/path/to/user_home/.julia/v0.x/IJulia/src/kernel.jl\",\"{connection_file}\"] in ipython_config.py...\n",
" : 《中略》\n",
"Copying julia.js to Julia IPython profile.\n",
"Writing IJulia kernelspec to /path/to/user_home/.ipython/kernels/julia-0.x/kernel.json ...\n",
"Copying logo-32x32.png to Julia IPython profile.\n",
"Copying logo-64x64.png to Julia IPython profile.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"確認（Julia の REPL から）。\n",
"\n",
"```julia\n",
"Julia> Pkg.installed()[\"IJulia\"]\n",
"v\"0.2.5\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## RISE のインストール\n",
"\n",
"```shell\n",
"$ git clone https://github.com/damianavila/RISE.git\n",
"$ cd RISE\n",
"$ python setup.py install\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### CSSの調整\n",
"\n",
"※お好みに応じて。\n",
"\n",
"`vi ~/.ipython/nbextensions/livereveal/main.css`\n",
"\n",
"```css\n",
"/* …《中略、最後の方に以下を追記》 */\n",
".reveal .slides div.text_cell {\n",
" font-size: inherit;\n",
"}\n",
".reveal .slides div.text_cell pre {\n",
" line-height: 1.5em;\n",
"}\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 起動"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"```shell\n",
"$ ipython notebook\n",
"```\n",
"\n",
"これだけ。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"※ 実行した場所がワークディレクトリになるので注意。 \n",
"　ワークディレクトリを指定する場合は↓\n",
"\n",
"```shell\n",
"$ ipython notebook --notebook-dir /path/to/work_directory\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## スライド編集・表示"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 1. Cell toolbar の [Slideshow] を選択"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"RGBA Image with:\n",
" data: 302x136 Array{AlphaColorValue{RGB{UfixedBase{Uint8,8}},UfixedBase{Uint8,8}},2}\n",
" properties:\n",
" imagedescription: <suppressed>\n",
" spatialorder: x y\n",
" pixelspacing: 1 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Pkg.add(\"Images\")\n",
"using Images\n",
"imread(\"celltool_slideshow.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"※これは Jupyter に元からある機能。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 2. 各セル右上の [Slide Type] を設定"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"RGBA Image with:\n",
" data: 490x414 Array{AlphaColorValue{RGB{UfixedBase{Uint8,8}},UfixedBase{Uint8,8}},2}\n",
" properties:\n",
" imagedescription: <suppressed>\n",
" spatialorder: x y\n",
" pixelspacing: 1 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imread(\"slidetypes.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 3. ツールバーの [Enter/Exit Live Reveal Slideshow] ボタンクリック\n",
"\n",
"⇒RISE によるリアルタイムスライドビューに移行！"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"RGBA Image with:\n",
" data: 254x132 Array{AlphaColorValue{RGB{UfixedBase{Uint8,8}},UfixedBase{Uint8,8}},2}\n",
" properties:\n",
" imagedescription: <suppressed>\n",
" spatialorder: x y\n",
" pixelspacing: 1 1"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imread(\"enter_slideshow_btn.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"※これは RISE で追加された機能。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## スライド公開"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[nbviewer](https://nbviewer.jupyter.org/) を利用すると Jupyter で作成したスライドを簡単に公開可能。"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 1. `.ipynb` ファイルのアップロード\n",
"\n",
"Jupyter で保存した .ipynb ファイルを GitHub/Gist/その他ネット上 に公開する。\n",
"\n",
"《実演》\n",
"⇒ https://gist.github.com/antimon2/2be8a00524deadb8e0a9"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 2. [nbviewer](https://nbviewer.jupyter.org/) に登録\n",
"\n",
"GitHubのリポジトリ名/GistID/URL をテキストフィールドに入力して [GO!] ボタンクリック。\n",
"\n",
"《実演》\n",
"⇒ https://nbviewer.jupyter.org/gist/antimon2/2be8a00524deadb8e0a9"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 3. スライド変換結果を共有\n",
"\n",
"表示されたページの [View as Slides] ボタンでスライド表示。 \n",
"その時の URL（これがスライドの Permalink になる）を共有。\n",
"\n",
"《実演》\n",
"⇒ https://nbviewer.jupyter.org/format/slides/gist/antimon2/2be8a00524deadb8e0a9"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 参考：ファイルを更新した場合\n",
"\n",
"一度変換したら10分くらいはキャッシュされて再変換されない。 \n",
"URL の後ろに `?flush_cache=true` をつけて再表示すれば再変換される。\n",
"\n",
"ref: [FAQ / I want to remove/update a notebook from Notebook Viewer.](http://nbviewer.ipython.org/faq#i-want-to-removeupdate-a-notebook-from-notebook-viewer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 参考資料"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Jupyter 関連\n",
"\n",
"+ [Jupyter Project](http://jupyter.org/ \"Jupyter Project\")\n",
"+ [IPython](http://ipython.org/ \"Jupyter and the future of IPython &mdash; IPython\")\n",
"+ [nbviewer](https://nbviewer.jupyter.org/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Julia 関連\n",
"\n",
"+ [Julia](http://julialang.org/ \"The Julia Language\")\n",
"+ [IJulia](https://github.com/JuliaLang/IJulia.jl \"JuliaLang/IJulia.jl\")\n",
"+ [Gadfly](http://dcjones.github.io/Gadfly.jl/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### RISE / スライドショー 関連\n",
"\n",
"+ [RISE](https://github.com/damianavila/RISE \"damianavila/RISE\")\n",
"+ [IPython 3.0.0-dev、IPython Notebookで\"LIVE\"なHTMLプレゼンテーション](http://qiita.com/ssh0/items/a67b84110c5d9e0adeda \"IPython 3.0.0-dev、IPython Notebookで&quot;LIVE&quot;なHTMLプレゼンテーション - Qiita\")\n",
"+ [nbviewerでスライド表示もできるようになった！【Jupyter】](http://qiita.com/ssh0/items/de6cc6aacd004a2e57dd \"ipython_notebook - nbviewerでスライド表示もできるようになった！【Jupyter】 - Qiita\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## おしまい"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"ご清聴ありがとうございました。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Julia 0.3.8",
"language": "julia",
"name": "julia-0.3"
},
"language_info": {
"name": "julia",
"version": "0.3.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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