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Created May 8, 2016 16:03
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Data science.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<center><img src=\"julia.svg\" width=\"300\"></center>\n",
"\n",
"# <center>Julia for Data Analysis</center>\n",
"\n",
"## <center> Features, interfaces and future directions</center>\n",
"\n",
"### <center>Simon Byrne - Julia Computing</center>\n",
"\n",
"### <center><img src=\"GitHub-Mark-64px.png\" width=\"20\" style=\"display: inline\"> <tt>simonbyrne</tt></center>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# What is Julia?\n",
"\n",
"Julia is a modern, high-performance, dynamic programming language for technical computing.\n",
"\n",
"- *modern*: based on the lessons of the past 60 years\n",
"- *high-performance*: as fast as traditional \"fast\" languages (Fortran/C/C++)\n",
"- *dynamic*: \"simple to use\" (R/Matlab/Python)\n",
"- *technical computing*: anything involving numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Why Julia?\n",
"* To write fast, efficient code in an easy, elegant dynamic language\n",
" - Avoid the \"two language problem\"\n",
"* It is easy to \"peek under the hood\"\n",
" - Most of Julia is written Julia\n",
" - Can inspect various stages of the compilation process\n",
"* It's free.\n",
"* It's fun.\n",
"* Play nicely with existing tools"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"logsumexp (generic function with 1 method)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function logsumexp(X)\n",
" u = maximum(X)\n",
" t = 0.0\n",
" for i = 1:length(X)\n",
" t += exp(X[i]-u)\n",
" end\n",
" u + log(t)\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Syntax heavily influenced by Python and Matlab\n",
"\n",
"Main syntactic differences from Python:\n",
"\n",
"* explicit `end` vs. significant whitespace\n",
"* 1-based vs. 0-based arrays"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Types\n",
"\n",
"Types are roughly analogous to Pythons classes; every object has one:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Float64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"typeof(1.0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Function"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"typeof(logsumexp)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DataType"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"typeof(Function)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"New types are declared with the `type` keyword:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"type Baz\n",
" a::Float64\n",
" b::Float64\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Baz(1.0,2.0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = Baz(1.0,2.0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Unlike many dynamic languages, user defined types are just as efficient as the builtin types (indeed, most \"builtin\" types are actually written in Julia)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Generic functions and multiple dispatch\n",
"\n",
"\n",
"Julia functions are *generic* in that different code paths can be called depending on the type arguments."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"f (generic function with 2 methods)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(x::Float64) = \"$x is a float\" # \"$\" does string substitution\n",
"f(x::Int) = \"$x is an integer\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `f(...) = ...` is the same as `function f(...) ... end`\n",
"* `::` is an optional *type specification*."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"1.0 is a float\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(1.0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"1 is an integer\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Unlike traditional object oriented languages (C++, Python, Matlab), functions don't \"belong\" to a type. This allows for *multiple dispatch* on any combination of arguments."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"f (generic function with 5 methods)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(x::Float64,y::Int) = \"$x is a float, but $y is an integer\"\n",
"f(x::Real,y::Real) = \"$x and $y are both some sort of real\" # Real is an abstract \"super\" type\n",
"f(x,y) = \"I don't know what $x and $y are\" # fallback"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"1.0 is a float, but 1 is an integer\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(1.0,1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"1 and 1 are both some sort of real\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(1,1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"I don't know what aaa and 2 are\""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(\"aaa\",2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Say we want to change how `Baz` is printed, this is handled by the `show` function:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baz(1.0,2.0)"
]
}
],
"source": [
"show(b)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"`show` is a generic function: it is made up of different methods for differnt *type signatures*:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"105 methods for generic function <b>show</b>:<ul><li> show(io::<b>IO</b>, r::<b>LinSpace{T<:AbstractFloat}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/range.jl#L238\" target=\"_blank\">range.jl:238</a><li> show(io::<b>IO</b>, r::<b>UnitRange{T<:Real}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/range.jl#L401\" target=\"_blank\">range.jl:401</a><li> show(io::<b>IO</b>, r::<b>Range{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/range.jl#L399\" target=\"_blank\">range.jl:399</a><li> show(io::<b>IO</b>, tv::<b>TypeVar</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/expr.jl#L49\" target=\"_blank\">expr.jl:49</a><li> show(io::<b>IO</b>, z::<b>Complex{T<:Real}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/complex.jl#L69\" target=\"_blank\">complex.jl:69</a><li> show(io::<b>IO</b>, x::<b>Rational{T<:Integer}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/rational.jl#L55\" target=\"_blank\">rational.jl:55</a><li> show(io::<b>IO</b>, s::<b>IntSet</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/intset.jl#L16\" target=\"_blank\">intset.jl:16</a><li> show(io::<b>IO</b>, ::<b>Base.EnvHash</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/env.jl#L148\" target=\"_blank\">env.jl:148</a><li> show<i>{K,V}</i>(io::<b>IO</b>, t::<b>Associative{K,V}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dict.jl#L26\" target=\"_blank\">dict.jl:26</a><li> show(io::<b>IO</b>, iter::<b>Union{Base.KeyIterator{T<:Associative{K,V}},Base.ValueIterator{T<:Associative{K,V}}}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dict.jl#L146\" target=\"_blank\">dict.jl:146</a><li> show(io::<b>IO</b>, s::<b>Set{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/set.jl#L16\" target=\"_blank\">set.jl:16</a><li> show(io::<b>IO</b>, c::<b>Char</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/char.jl#L49\" target=\"_blank\">char.jl:49</a><li> show(io::<b>IO</b>, b::<b>Base.AbstractIOBuffer{T<:AbstractArray{UInt8,1}}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/iobuffer.jl#L45\" target=\"_blank\">iobuffer.jl:45</a><li> show(io::<b>IO</b>, s::<b>Base.SubstitutionString{T<:AbstractString}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/regex.jl#L221\" target=\"_blank\">regex.jl:221</a><li> show(io::<b>IO</b>, s::<b>AbstractString</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/strings/io.jl#L48\" target=\"_blank\">strings/io.jl:48</a><li> show(io::<b>IO</b>, exc::<b>UnicodeError</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/unicode/UnicodeError.jl#L30\" target=\"_blank\">unicode/UnicodeError.jl:30</a><li> show<i>{S}</i>(io::<b>IO</b>, g::<b>Base.UTF8proc.GraphemeIterator{S}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/unicode/utf8proc.jl#L231\" target=\"_blank\">unicode/utf8proc.jl:231</a><li> show(io::<b>IO</b>, re::<b>Regex</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/regex.jl#L68\" target=\"_blank\">regex.jl:68</a><li> show(io::<b>IO</b>, m::<b>RegexMatch</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/regex.jl#L98\" target=\"_blank\">regex.jl:98</a><li> show(io::<b>IO</b>, s::<b>IOStream</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/iostream.jl#L28\" target=\"_blank\">iostream.jl:28</a><li> show<i>{T}</i>(io::<b>IO</b>, x::<b>Nullable{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/nullable.jl#L23\" target=\"_blank\">nullable.jl:23</a><li> show(io::<b>IO</b>, t::<b>Task</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/task.jl#L9\" target=\"_blank\">task.jl:9</a><li> show(io::<b>IO</b>, f::<b>Function</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L71\" target=\"_blank\">show.jl:71</a><li> show(io::<b>IO</b>, x::<b>IntrinsicFunction</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L85\" target=\"_blank\">show.jl:85</a><li> show(io::<b>IO</b>, x::<b>Union</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L89\" target=\"_blank\">show.jl:89</a><li> show(io::<b>IO</b>, x::<b>TypeConstructor</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L94\" target=\"_blank\">show.jl:94</a><li> show(io::<b>IO</b>, x::<b>DataType</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L105\" target=\"_blank\">show.jl:105</a><li> show(io::<b>IO</b>, tn::<b>TypeName</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L134\" target=\"_blank\">show.jl:134</a><li> show(io::<b>IO</b>, ::<b>Void</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L141\" target=\"_blank\">show.jl:141</a><li> show(io::<b>IO</b>, b::<b>Bool</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L142\" target=\"_blank\">show.jl:142</a><li> show(io::<b>IO</b>, n::<b>Signed</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L143\" target=\"_blank\">show.jl:143</a><li> show(io::<b>IO</b>, n::<b>Unsigned</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L144\" target=\"_blank\">show.jl:144</a><li> show<i>{T}</i>(io::<b>IO</b>, p::<b>Ptr{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L147\" target=\"_blank\">show.jl:147</a><li> show(io::<b>IO</b>, p::<b>Pair{A,B}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L150\" target=\"_blank\">show.jl:150</a><li> show(io::<b>IO</b>, m::<b>Module</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L165\" target=\"_blank\">show.jl:165</a><li> show(io::<b>IO</b>, l::<b>LambdaStaticData</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L173\" target=\"_blank\">show.jl:173</a><li> show(io::<b>IO</b>, t::<b>Tuple</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L256\" target=\"_blank\">show.jl:256</a><li> show(io::<b>IO</b>, v::<b>SimpleVector</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L257\" target=\"_blank\">show.jl:257</a><li> show(io::<b>IO</b>, s::<b>Symbol</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L259\" target=\"_blank\">show.jl:259</a><li> show(io::<b>IO</b>, ex::<b>Union{Expr,GlobalRef,GotoNode,LabelNode,LineNumberNode,QuoteNode,SymbolNode,TopNode}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L291\" target=\"_blank\">show.jl:291</a><li> show(io::<b>IO</b>, M::<b>Bidiagonal{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/linalg/bidiag.jl#L93\" target=\"_blank\">linalg/bidiag.jl:93</a><li> show(io::<b>IO</b>, v::<b>AbstractArray{T,1}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L1330\" target=\"_blank\">show.jl:1330</a><li> show(io::<b>IO</b>, X::<b>AbstractArray{T,N}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L1269\" target=\"_blank\">show.jl:1269</a><li> show(io::<b>IO</b>, stream::<b>Base.LibuvServer</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stream.jl#L241\" target=\"_blank\">stream.jl:241</a><li> show(io::<b>IO</b>, s::<b>BufferStream</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stream.jl#L1201\" target=\"_blank\">stream.jl:1201</a><li> show(io::<b>IO</b>, stream::<b>UDPSocket</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/socket.jl#L389\" target=\"_blank\">socket.jl:389</a><li> show(io::<b>IO</b>, stream::<b>Base.LibuvStream</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stream.jl#L242\" target=\"_blank\">stream.jl:242</a><li> show(io::<b>IO</b>, stream::<b>Pipe</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stream.jl#L569\" target=\"_blank\">stream.jl:569</a><li> show(io::<b>IO</b>, e::<b>Base.UVError</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stream.jl#L1046\" target=\"_blank\">stream.jl:1046</a><li> show(io::<b>IO</b>, ip::<b>IPv4</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/socket.jl#L37\" target=\"_blank\">socket.jl:37</a><li> show(io::<b>IO</b>, ip::<b>IPv6</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/socket.jl#L91\" target=\"_blank\">socket.jl:91</a><li> show(io::<b>IO</b>, st::<b>StatStruct</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/stat.jl#L35\" target=\"_blank\">stat.jl:35</a><li> show(io::<b>IO</b>, cmd::<b>Cmd</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/process.jl#L42\" target=\"_blank\">process.jl:42</a><li> show(io::<b>IO</b>, cmds::<b>Union{Base.ErrOrCmds,Base.OrCmds}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/process.jl#L60\" target=\"_blank\">process.jl:60</a><li> show(io::<b>IO</b>, cmds::<b>Base.AndCmds</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/process.jl#L69\" target=\"_blank\">process.jl:69</a><li> show(io::<b>IO</b>, cr::<b>Base.CmdRedirect</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/process.jl#L125\" target=\"_blank\">process.jl:125</a><li> show(io::<b>IO</b>, p::<b>Base.Process</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/process.jl#L658\" target=\"_blank\">process.jl:658</a><li> show<i>{mime}</i>(io::<b>IO</b>, ::<b>MIME{mime}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/multimedia.jl#L18\" target=\"_blank\">multimedia.jl:18</a><li> show(io::<b>IO</b>, b::<b>BigFloat</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/mpfr.jl#L843\" target=\"_blank\">mpfr.jl:843</a><li> show(io::<b>IO</b>, x::<b>AbstractFloat</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/grisu.jl#L119\" target=\"_blank\">grisu.jl:119</a><li> show(io::<b>IO</b>, m::<b>Method</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/methodshow.jl#L43\" target=\"_blank\">methodshow.jl:43</a><li> show(io::<b>IO</b>, mt::<b>MethodTable</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/methodshow.jl#L82\" target=\"_blank\">methodshow.jl:82</a><li> show(io::<b>IO</b>, v::<b>VersionNumber</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/version.jl#L69\" target=\"_blank\">version.jl:69</a><li> show(io::<b>IO</b>, x::<b>BigInt</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/gmp.jl#L509\" target=\"_blank\">gmp.jl:509</a><li> show(io::<b>IO</b>, u::<b>Base.Random.UUID</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/random.jl#L1271\" target=\"_blank\">random.jl:1271</a><li> show(io::<b>IO</b>, c::<b>Channel{T}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/channels.jl#L77\" target=\"_blank\">channels.jl:77</a><li> show(::<b>IO</b>, ::<b>Base.WorkerState</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/Enums.jl#L95\" target=\"_blank\">Enums.jl:95</a><li> show(io::<b>IO</b>, manager::<b>Base.SSHManager</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/managers.jl#L79\" target=\"_blank\">managers.jl:79</a><li> show(io::<b>IO</b>, manager::<b>Base.LocalManager</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/managers.jl#L183\" target=\"_blank\">managers.jl:183</a><li> show(io::<b>IO</b>, ex::<b>Base.PrecompilableError</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/loading.jl#L145\" target=\"_blank\">loading.jl:145</a><li> show(io::<b>IO</b>, s::<b>Base.LineEdit.MIState</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/LineEdit.jl#L33\" target=\"_blank\">LineEdit.jl:33</a><li> show(io::<b>IO</b>, x::<b>Base.LineEdit.Prompt</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/LineEdit.jl#L53\" target=\"_blank\">LineEdit.jl:53</a><li> show(io::<b>IO</b>, s::<b>Base.LineEdit.PrefixSearchState</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/LineEdit.jl#L1069\" target=\"_blank\">LineEdit.jl:1069</a><li> show<i>{T,S<:AbstractArray{T,2}}</i>(io::<b>IO</b>, C::<b>Base.LinAlg.Cholesky{T,S<:AbstractArray{T,2}}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/linalg/cholesky.jl#L187\" target=\"_blank\">linalg/cholesky.jl:187</a><li> show(io::<b>IO</b>, J::<b>UniformScaling{T<:Number}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/linalg/uniformscaling.jl#L16\" target=\"_blank\">linalg/uniformscaling.jl:16</a><li> show(io::<b>IO</b>, f::<b>Base.SparseMatrix.UMFPACK.UmfpackLU{Tv<:Union{Complex{Float64},Float64},Ti<:Union{Int32,Int64}}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/sparse/umfpack.jl#L119\" target=\"_blank\">sparse/umfpack.jl:119</a><li> show(io::<b>IO</b>, F::<b>Base.SparseMatrix.CHOLMOD.Factor{Tv}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/sparse/cholmod.jl#L1059\" target=\"_blank\">sparse/cholmod.jl:1059</a><li> show<i>{sym}</i>(io::<b>IO</b>, x::<b>Irrational{sym}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/irrationals.jl#L7\" target=\"_blank\">irrationals.jl:7</a><li> show(io::<b>IO</b>, p::<b>Base.DFT.ScaledPlan{T,P,N}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dft.jl#L259\" target=\"_blank\">dft.jl:259</a><li> show<i>{T,K,inplace}</i>(io::<b>IO</b>, p::<b>Base.DFT.FFTW.cFFTWPlan{T,K,inplace,N}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/fft/FFTW.jl#L289\" target=\"_blank\">fft/FFTW.jl:289</a><li> show<i>{T,K,inplace}</i>(io::<b>IO</b>, p::<b>Base.DFT.FFTW.rFFTWPlan{T,K,inplace,N}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/fft/FFTW.jl#L296\" target=\"_blank\">fft/FFTW.jl:296</a><li> show<i>{T,K,inplace}</i>(io::<b>IO</b>, p::<b>Base.DFT.FFTW.r2rFFTWPlan{T,K,inplace,N}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/fft/FFTW.jl#L304\" target=\"_blank\">fft/FFTW.jl:304</a><li> show<i>{T,K,inplace}</i>(io::<b>IO</b>, p::<b>Base.DFT.FFTW.DCTPlan{T,K,inplace}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/fft/dct.jl#L24\" target=\"_blank\">fft/dct.jl:24</a><li> show(io::<b>IO</b>, info::<b>Base.Sys.CPUinfo</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/sysinfo.jl#L56\" target=\"_blank\">sysinfo.jl:56</a><li> show(io::<b>IO</b>, info::<b>Base.Sys.CPUinfo</b>, header::<b>Bool</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/sysinfo.jl#L56\" target=\"_blank\">sysinfo.jl:56</a><li> show(io::<b>IO</b>, info::<b>Base.Sys.CPUinfo</b>, header::<b>Bool</b>, prefix::<b>AbstractString</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/sysinfo.jl#L56\" target=\"_blank\">sysinfo.jl:56</a><li> show(io::<b>IO</b>, i::<b>Base.Pkg.Types.VersionInterval</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/pkg/types.jl#L15\" target=\"_blank\">pkg/types.jl:15</a><li> show(io::<b>IO</b>, s::<b>Base.Pkg.Types.VersionSet</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/pkg/types.jl#L39\" target=\"_blank\">pkg/types.jl:39</a><li> show(io::<b>IO</b>, a::<b>Base.Pkg.Types.Available</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/pkg/types.jl#L73\" target=\"_blank\">pkg/types.jl:73</a><li> show(io::<b>IO</b>, f::<b>Base.Pkg.Types.Fixed</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/pkg/types.jl#L86\" target=\"_blank\">pkg/types.jl:86</a><li> show(io::<b>IO</b>, x::<b>Base.Dates.Period</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dates/periods.jl#L26\" target=\"_blank\">dates/periods.jl:26</a><li> show(io::<b>IO</b>, x::<b>Base.Dates.CompoundPeriod</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dates/periods.jl#L187\" target=\"_blank\">dates/periods.jl:187</a><li> show(io::<b>IO</b>, df::<b>Base.Dates.DateFunction</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dates/adjusters.jl#L76\" target=\"_blank\">dates/adjusters.jl:76</a><li> show(io::<b>IO</b>, x::<b>DateTime</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dates/io.jl#L16\" target=\"_blank\">dates/io.jl:16</a><li> show(io::<b>IO</b>, x::<b>Date</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/dates/io.jl#L24\" target=\"_blank\">dates/io.jl:24</a><li> show(io::<b>IO</b>, x::<b>Base.Docs.Binding</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/docs/bindings.jl#L26\" target=\"_blank\">docs/bindings.jl:26</a><li> show(io::<b>IO</b>, x::<b>Nettle.HashType</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/hash.jl#L51\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/hash.jl:51</a><li> show(io::<b>IO</b>, x::<b>Nettle.Hasher</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/hash.jl#L56\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/hash.jl:56</a><li> show(io::<b>IO</b>, x::<b>Nettle.HMACState</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/hmac.jl#L53\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/hmac.jl:53</a><li> show(io::<b>IO</b>, x::<b>Nettle.CipherType</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/cipher.jl#L287\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/cipher.jl:287</a><li> show(io::<b>IO</b>, x::<b>Nettle.Encryptor</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/cipher.jl#L292\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/cipher.jl:292</a><li> show(io::<b>IO</b>, x::<b>Nettle.Decryptor</b>) at <a href=\"https://github.com/staticfloat/Nettle.jl/tree/37e1c9a59741d791d1678e6767ca5a6a11a284de/src/cipher.jl#L293\" target=\"_blank\">/Users/simon/.julia/v0.4/Nettle/src/cipher.jl:293</a><li> show(io::<b>IO</b>, msg::<b>IJulia.Msg</b>) at <a href=\"https://github.com/JuliaLang/IJulia.jl/tree/a675ac5c5b08bf75a2f4d2f3b80035252a4af3c1/src/msg.jl#L42\" target=\"_blank\">/Users/simon/.julia/v0.4/IJulia/src/msg.jl:42</a><li> show(io::<b>IO</b>, x::<b>ANY</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L7\" target=\"_blank\">show.jl:7</a><li> show(x) at <a href=\"https://github.com/JuliaLang/julia/tree/2ac304dfba75fad148d4070ef4f8a2e400c305bb/base/show.jl#L3\" target=\"_blank\">show.jl:3</a></ul>"
],
"text/plain": [
"# 105 methods for generic function \"show\":\n",
"show(io::IO, r::LinSpace{T<:AbstractFloat}) at range.jl:238\n",
"show(io::IO, r::UnitRange{T<:Real}) at range.jl:401\n",
"show(io::IO, r::Range{T}) at range.jl:399\n",
"show(io::IO, tv::TypeVar) at expr.jl:49\n",
"show(io::IO, z::Complex{T<:Real}) at complex.jl:69\n",
"show(io::IO, x::Rational{T<:Integer}) at rational.jl:55\n",
"show(io::IO, s::IntSet) at intset.jl:16\n",
"show(io::IO, ::Base.EnvHash) at env.jl:148\n",
"show{K,V}(io::IO, t::Associative{K,V}) at dict.jl:26\n",
"show(io::IO, iter::Union{Base.KeyIterator{T<:Associative{K,V}},Base.ValueIterator{T<:Associative{K,V}}}) at dict.jl:146\n",
"show(io::IO, s::Set{T}) at set.jl:16\n",
"show(io::IO, c::Char) at char.jl:49\n",
"show(io::IO, b::Base.AbstractIOBuffer{T<:AbstractArray{UInt8,1}}) at iobuffer.jl:45\n",
"show(io::IO, s::Base.SubstitutionString{T<:AbstractString}) at regex.jl:221\n",
"show(io::IO, s::AbstractString) at strings/io.jl:48\n",
"show(io::IO, exc::UnicodeError) at unicode/UnicodeError.jl:30\n",
"show{S}(io::IO, g::Base.UTF8proc.GraphemeIterator{S}) at unicode/utf8proc.jl:231\n",
"show(io::IO, re::Regex) at regex.jl:68\n",
"show(io::IO, m::RegexMatch) at regex.jl:98\n",
"show(io::IO, s::IOStream) at iostream.jl:28\n",
"show{T}(io::IO, x::Nullable{T}) at nullable.jl:23\n",
"show(io::IO, t::Task) at task.jl:9\n",
"show(io::IO, f::Function) at show.jl:71\n",
"show(io::IO, x::IntrinsicFunction) at show.jl:85\n",
"show(io::IO, x::Union) at show.jl:89\n",
"show(io::IO, x::TypeConstructor) at show.jl:94\n",
"show(io::IO, x::DataType) at show.jl:105\n",
"show(io::IO, tn::TypeName) at show.jl:134\n",
"show(io::IO, ::Void) at show.jl:141\n",
"show(io::IO, b::Bool) at show.jl:142\n",
"show(io::IO, n::Signed) at show.jl:143\n",
"show(io::IO, n::Unsigned) at show.jl:144\n",
"show{T}(io::IO, p::Ptr{T}) at show.jl:147\n",
"show(io::IO, p::Pair{A,B}) at show.jl:150\n",
"show(io::IO, m::Module) at show.jl:165\n",
"show(io::IO, l::LambdaStaticData) at show.jl:173\n",
"show(io::IO, t::Tuple) at show.jl:256\n",
"show(io::IO, v::SimpleVector) at show.jl:257\n",
"show(io::IO, s::Symbol) at show.jl:259\n",
"show(io::IO, ex::Union{Expr,GlobalRef,GotoNode,LabelNode,LineNumberNode,QuoteNode,SymbolNode,TopNode}) at show.jl:291\n",
"show(io::IO, M::Bidiagonal{T}) at linalg/bidiag.jl:93\n",
"show(io::IO, v::AbstractArray{T,1}) at show.jl:1330\n",
"show(io::IO, X::AbstractArray{T,N}) at show.jl:1269\n",
"show(io::IO, stream::Base.LibuvServer) at stream.jl:241\n",
"show(io::IO, s::BufferStream) at stream.jl:1201\n",
"show(io::IO, stream::UDPSocket) at socket.jl:389\n",
"show(io::IO, stream::Base.LibuvStream) at stream.jl:242\n",
"show(io::IO, stream::Pipe) at stream.jl:569\n",
"show(io::IO, e::Base.UVError) at stream.jl:1046\n",
"show(io::IO, ip::IPv4) at socket.jl:37\n",
"show(io::IO, ip::IPv6) at socket.jl:91\n",
"show(io::IO, st::StatStruct) at stat.jl:35\n",
"show(io::IO, cmd::Cmd) at process.jl:42\n",
"show(io::IO, cmds::Union{Base.ErrOrCmds,Base.OrCmds}) at process.jl:60\n",
"show(io::IO, cmds::Base.AndCmds) at process.jl:69\n",
"show(io::IO, cr::Base.CmdRedirect) at process.jl:125\n",
"show(io::IO, p::Base.Process) at process.jl:658\n",
"show{mime}(io::IO, ::MIME{mime}) at multimedia.jl:18\n",
"show(io::IO, b::BigFloat) at mpfr.jl:843\n",
"show(io::IO, x::AbstractFloat) at grisu.jl:119\n",
"show(io::IO, m::Method) at methodshow.jl:43\n",
"show(io::IO, mt::MethodTable) at methodshow.jl:82\n",
"show(io::IO, v::VersionNumber) at version.jl:69\n",
"show(io::IO, x::BigInt) at gmp.jl:509\n",
"show(io::IO, u::Base.Random.UUID) at random.jl:1271\n",
"show(io::IO, c::Channel{T}) at channels.jl:77\n",
"show(::IO, ::Base.WorkerState) at Enums.jl:95\n",
"show(io::IO, manager::Base.SSHManager) at managers.jl:79\n",
"show(io::IO, manager::Base.LocalManager) at managers.jl:183\n",
"show(io::IO, ex::Base.PrecompilableError) at loading.jl:145\n",
"show(io::IO, s::Base.LineEdit.MIState) at LineEdit.jl:33\n",
"show(io::IO, x::Base.LineEdit.Prompt) at LineEdit.jl:53\n",
"show(io::IO, s::Base.LineEdit.PrefixSearchState) at LineEdit.jl:1069\n",
"show{T,S<:AbstractArray{T,2}}(io::IO, C::Base.LinAlg.Cholesky{T,S<:AbstractArray{T,2}}) at linalg/cholesky.jl:187\n",
"show(io::IO, J::UniformScaling{T<:Number}) at linalg/uniformscaling.jl:16\n",
"show(io::IO, f::Base.SparseMatrix.UMFPACK.UmfpackLU{Tv<:Union{Complex{Float64},Float64},Ti<:Union{Int32,Int64}}) at sparse/umfpack.jl:119\n",
"show(io::IO, F::Base.SparseMatrix.CHOLMOD.Factor{Tv}) at sparse/cholmod.jl:1059\n",
"show{sym}(io::IO, x::Irrational{sym}) at irrationals.jl:7\n",
"show(io::IO, p::Base.DFT.ScaledPlan{T,P,N}) at dft.jl:259\n",
"show{T,K,inplace}(io::IO, p::Base.DFT.FFTW.cFFTWPlan{T,K,inplace,N}) at fft/FFTW.jl:289\n",
"show{T,K,inplace}(io::IO, p::Base.DFT.FFTW.rFFTWPlan{T,K,inplace,N}) at fft/FFTW.jl:296\n",
"show{T,K,inplace}(io::IO, p::Base.DFT.FFTW.r2rFFTWPlan{T,K,inplace,N}) at fft/FFTW.jl:304\n",
"show{T,K,inplace}(io::IO, p::Base.DFT.FFTW.DCTPlan{T,K,inplace}) at fft/dct.jl:24\n",
"show(io::IO, info::Base.Sys.CPUinfo) at sysinfo.jl:56\n",
"show(io::IO, info::Base.Sys.CPUinfo, header::Bool) at sysinfo.jl:56\n",
"show(io::IO, info::Base.Sys.CPUinfo, header::Bool, prefix::AbstractString) at sysinfo.jl:56\n",
"show(io::IO, i::Base.Pkg.Types.VersionInterval) at pkg/types.jl:15\n",
"show(io::IO, s::Base.Pkg.Types.VersionSet) at pkg/types.jl:39\n",
"show(io::IO, a::Base.Pkg.Types.Available) at pkg/types.jl:73\n",
"show(io::IO, f::Base.Pkg.Types.Fixed) at pkg/types.jl:86\n",
"show(io::IO, x::Base.Dates.Period) at dates/periods.jl:26\n",
"show(io::IO, x::Base.Dates.CompoundPeriod) at dates/periods.jl:187\n",
"show(io::IO, df::Base.Dates.DateFunction) at dates/adjusters.jl:76\n",
"show(io::IO, x::DateTime) at dates/io.jl:16\n",
"show(io::IO, x::Date) at dates/io.jl:24\n",
"show(io::IO, x::Base.Docs.Binding) at docs/bindings.jl:26\n",
"show(io::IO, x::Nettle.HashType) at /Users/simon/.julia/v0.4/Nettle/src/hash.jl:51\n",
"show(io::IO, x::Nettle.Hasher) at /Users/simon/.julia/v0.4/Nettle/src/hash.jl:56\n",
"show(io::IO, x::Nettle.HMACState) at /Users/simon/.julia/v0.4/Nettle/src/hmac.jl:53\n",
"show(io::IO, x::Nettle.CipherType) at /Users/simon/.julia/v0.4/Nettle/src/cipher.jl:287\n",
"show(io::IO, x::Nettle.Encryptor) at /Users/simon/.julia/v0.4/Nettle/src/cipher.jl:292\n",
"show(io::IO, x::Nettle.Decryptor) at /Users/simon/.julia/v0.4/Nettle/src/cipher.jl:293\n",
"show(io::IO, msg::IJulia.Msg) at /Users/simon/.julia/v0.4/IJulia/src/msg.jl:42\n",
"show(io::IO, x::ANY) at show.jl:7\n",
"show(x) at show.jl:3"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"methods(show)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Most `show` methods take an `IO` object as a first argument\n",
"* this allows writing to different places (`STDOUT`, buffers, files, etc.)\n",
"\n",
"There is also a generic single argument method\n",
"\n",
"```julia\n",
"show(x) = show(STDOUT, x)\n",
"```\n",
"\n",
"which prints to `STDOUT` by default."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"show (generic function with 106 methods)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import Base.show # we need to import to extend\n",
"\n",
"show(io::IO, b::Baz) = print(io, \"This is a Baz object, with a=$(b.a) and b=$(b.b).\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"This is a Baz object, with a=1.0 and b=2.0."
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is incredibly powerful: for example, we can define different matrix multiplication methods for different combinations of arguments (e.g. symmetric, triangular, sparse, etc.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Just-in-time (JIT) compilation\n",
"\n",
"Julia uses JIT compilation, using the LLVM backend (used by Clang, Rust, Swift).\n",
"\n",
"Functions are the unit at which JIT compilation occurs.\n",
"> **Perf tip 1**: Put code inside a function.\n",
"\n",
"Compilation occurs for each type signature.\n",
"\n",
"One of the main tricks used is *type inference*: try to figure out the type of each expression.\n",
"> **Perf tip 2**: Try to write *type-stable* functions: for a given combination of input types, variables should not change type."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"e.g. this is why:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "LoadError",
"evalue": "LoadError: DomainError:\nsqrt will only return a complex result if called with a complex argument. Try sqrt(complex(x)).\nwhile loading In[18], in expression starting on line 1",
"output_type": "error",
"traceback": [
"LoadError: DomainError:\nsqrt will only return a complex result if called with a complex argument. Try sqrt(complex(x)).\nwhile loading In[18], in expression starting on line 1",
"",
" in sqrt at /Applications/Julia-0.4.5.app/Contents/Resources/julia/lib/julia/sys.dylib"
]
}
],
"source": [
"sqrt(-1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So `sqrt(x::Float64)` is known to return values of type `Float64`.\n",
"\n",
"To get a `Complex` number you need to explicitly convert it beforehand:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0 + 1.0im"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqrt(complex(-1.0))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Metaprogramming\n",
"\n",
"Julia has extensive support of *metaprogramming*: writing code that generates other code."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
":(sin(x) + 2)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# \":\" quotes an expression, which is itself a Julia objects\n",
"ex = :(sin(x)+2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Expr"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"typeof(ex)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"**Macros** transform expressions, and are prefixed with `@`:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.048858 seconds (54.23 k allocations: 2.373 MB)\n"
]
},
{
"data": {
"text/plain": [
"5.1844271675557465"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time logsumexp(rand(100)) # prints time to run a function"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000010 seconds (7 allocations: 1.063 KB)\n"
]
},
{
"data": {
"text/plain": [
"5.119954465795896"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time logsumexp(rand(100)) # first run is slower due to JIT compilation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"quote # util.jl, line 153:\n",
" local #68#stats = Base.gc_num() # util.jl, line 154:\n",
" local #70#elapsedtime = Base.time_ns() # util.jl, line 155:\n",
" local #69#val = logsumexp(rand(100)) # util.jl, line 156:\n",
" #70#elapsedtime = Base.-(Base.time_ns(),#70#elapsedtime) # util.jl, line 157:\n",
" local #71#diff = Base.GC_Diff(Base.gc_num(),#68#stats) # util.jl, line 158:\n",
" Base.time_print(#70#elapsedtime,#71#diff.allocd,#71#diff.total_time,Base.gc_alloc_count(#71#diff)) # util.jl, line 160:\n",
" #69#val\n",
"end"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"macroexpand(:(@time logsumexp(rand(100))))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Various macros provide the ability to peek inside the compilation process"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1-element Array{Any,1}:\n",
" :($(Expr(:lambda, Any[:X], Any[Any[Any[:X,Array{Float64,1},0],Any[:u,Float64,18],Any[:t,Float64,2],Any[symbol(\"#s52\"),Int64,2],Any[:i,Int64,18]],Any[],Any[UnitRange{Int64},Tuple{Int64,Int64},Int64,Float64,Float64,Float64,Float64,Float64,Int64,Int64],Any[]], :(begin # In[1], line 2:\n",
" u = (Base._mapreduce)($(Expr(:new, :((top(getfield))(Base,:IdFun)::Type{Base.IdFun}))),$(Expr(:new, :((top(getfield))(Base,:MaxFun)::Type{Base.MaxFun}))),$(Expr(:new, :((top(getfield))(Base,:LinearFast)::Type{Base.LinearFast}))),X::Array{Float64,1})::Float64 # In[1], line 3:\n",
" t = 0.0 # In[1], line 4:\n",
" GenSym(2) = (Base.arraylen)(X::Array{Float64,1})::Int64\n",
" GenSym(0) = $(Expr(:new, UnitRange{Int64}, 1, :(((top(getfield))(Base.Intrinsics,:select_value)::I)((Base.sle_int)(1,GenSym(2))::Bool,GenSym(2),(Base.box)(Int64,(Base.sub_int)(1,1)))::Int64)))\n",
" #s52 = (top(getfield))(GenSym(0),:start)::Int64\n",
" unless (Base.box)(Base.Bool,(Base.not_int)(#s52::Int64 === (Base.box)(Base.Int,(Base.add_int)((top(getfield))(GenSym(0),:stop)::Int64,1))::Bool)) goto 1\n",
" 2: \n",
" GenSym(8) = #s52::Int64\n",
" GenSym(9) = (Base.box)(Base.Int,(Base.add_int)(#s52::Int64,1))\n",
" i = GenSym(8)\n",
" #s52 = GenSym(9) # In[1], line 5:\n",
" GenSym(3) = (Base.box)(Base.Float64,(Base.sub_float)((Base.arrayref)(X::Array{Float64,1},i::Int64)::Float64,u::Float64))\n",
" GenSym(5) = (top(ccall))((top(tuple))(\"exp\",Base.Math.libm)::Tuple{ASCIIString,ASCIIString},Base.Math.Float64,(top(svec))(Base.Math.Float64)::SimpleVector,GenSym(3),0)::Float64\n",
" t = (Base.box)(Base.Float64,(Base.add_float)(t::Float64,GenSym(5)))\n",
" 3: \n",
" unless (Base.box)(Base.Bool,(Base.not_int)((Base.box)(Base.Bool,(Base.not_int)(#s52::Int64 === (Base.box)(Base.Int,(Base.add_int)((top(getfield))(GenSym(0),:stop)::Int64,1))::Bool)))) goto 2\n",
" 1: \n",
" 0: # In[1], line 7:\n",
" GenSym(7) = (top(ccall))((top(tuple))(\"log\",Base.Math.libm)::Tuple{ASCIIString,ASCIIString},Base.Math.Float64,(top(svec))(Base.Math.Float64)::SimpleVector,t::Float64,0)::Float64\n",
" return (Base.box)(Base.Float64,(Base.add_float)(u::Float64,(Base.Math.nan_dom_err)(GenSym(7),t::Float64)::Float64))\n",
" end::Float64))))"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@code_typed logsumexp(rand(100)) # type inference"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"define double @julia_logsumexp_22055(%jl_value_t*) {\n",
"top:\n",
" %1 = call double @julia__mapreduce_21898(%jl_value_t* %0)\n",
" %2 = getelementptr inbounds %jl_value_t* %0, i64 1\n",
" %3 = bitcast %jl_value_t* %2 to i64*\n",
" %4 = load i64* %3, align 8\n",
" %5 = icmp sgt i64 %4, 0\n",
" %6 = select i1 %5, i64 %4, i64 0\n",
" %7 = icmp eq i64 %6, 0\n",
" br i1 %7, label %L3, label %L.preheader\n",
"\n",
"L.preheader: ; preds = %top\n",
" %8 = bitcast %jl_value_t* %0 to i8**\n",
" br label %L\n",
"\n",
"L: ; preds = %idxend, %L.preheader\n",
" %t.0 = phi double [ %20, %idxend ], [ 0.000000e+00, %L.preheader ]\n",
" %\"#s52.0\" = phi i64 [ %13, %idxend ], [ 1, %L.preheader ]\n",
" %9 = add i64 %\"#s52.0\", -1\n",
" %10 = load i64* %3, align 8\n",
" %11 = icmp ult i64 %9, %10\n",
" br i1 %11, label %idxend, label %oob\n",
"\n",
"oob: ; preds = %L\n",
" %12 = alloca i64, align 8\n",
" store i64 %\"#s52.0\", i64* %12, align 8\n",
" call void @jl_bounds_error_ints(%jl_value_t* %0, i64* %12, i64 1)\n",
" unreachable\n",
"\n",
"idxend: ; preds = %L\n",
" %13 = add i64 %\"#s52.0\", 1\n",
" %14 = load i8** %8, align 8\n",
" %15 = bitcast i8* %14 to double*\n",
" %16 = getelementptr double* %15, i64 %9\n",
" %17 = load double* %16, align 8\n",
" %18 = fsub double %17, %1\n",
" %19 = call double inttoptr (i64 13136387440 to double (double)*)(double inreg %18)\n",
" %20 = fadd double %t.0, %19\n",
" %21 = icmp eq i64 %\"#s52.0\", %6\n",
" br i1 %21, label %L3, label %L\n",
"\n",
"L3: ; preds = %idxend, %top\n",
" %t.1 = phi double [ 0.000000e+00, %top ], [ %20, %idxend ]\n",
" %22 = call double inttoptr (i64 13136405952 to double (double)*)(double inreg %t.1)\n",
" %23 = fcmp ord double %22, 0.000000e+00\n",
" %24 = fcmp uno double %t.1, 0.000000e+00\n",
" %25 = or i1 %23, %24\n",
" br i1 %25, label %pass, label %fail\n",
"\n",
"fail: ; preds = %L3\n",
" %26 = load %jl_value_t** @jl_domain_exception, align 8\n",
" call void @jl_throw(%jl_value_t* %26)\n",
" unreachable\n",
"\n",
"pass: ; preds = %L3\n",
" %27 = fadd double %1, %22\n",
" ret double %27\n",
"}\n"
]
}
],
"source": [
"@code_llvm logsumexp(rand(100)) # LLVM intermediate representation (IR)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t.section\t__TEXT,__text,regular,pure_instructions\n",
"Filename: In[1]\n",
"Source line: 2\n",
"\tpushq\t%rbp\n",
"\tmovq\t%rsp, %rbp\n",
"\tpushq\t%r15\n",
"\tpushq\t%r14\n",
"\tpushq\t%r13\n",
"\tpushq\t%r12\n",
"\tpushq\t%rbx\n",
"\tsubq\t$24, %rsp\n",
"\tmovq\t%rdi, %r15\n",
"Source line: 2\n",
"\tmovabsq\t$13120883104, %rax ## imm = 0x30E10C9A0\n",
"\tcallq\t*%rax\n",
"\tmovsd\t%xmm0, -56(%rbp)\n",
"Source line: 4\n",
"\tmovq\t8(%r15), %r14\n",
"\txorl\t%r12d, %r12d\n",
"\ttestq\t%r14, %r14\n",
"\tmovl\t$0, %eax\n",
"\tcmovnsq\t%r14, %rax\n",
"\txorpd\t%xmm0, %xmm0\n",
"\ttestq\t%rax, %rax\n",
"\tje\tL165\n",
"Source line: 5\n",
"\ttestq\t%r14, %r14\n",
"\tcmovsq\t%r12, %r14\n",
"\tnegq\t%r14\n",
"\txorpd\t%xmm0, %xmm0\n",
"\tmovabsq\t$exp, %r13\n",
"\txorl\t%ebx, %ebx\n",
"L95:\tmovsd\t%xmm0, -48(%rbp)\n",
"\tcmpq\t8(%r15), %r12\n",
"\tjae\tL219\n",
"\tmovq\t(%r15), %rax\n",
"\tleaq\t(,%rbx,8), %rcx\n",
"\tnegq\t%rcx\n",
"\tmovsd\t(%rax,%rcx), %xmm0\n",
"\tsubsd\t-56(%rbp), %xmm0\n",
"\tcallq\t*%r13\n",
"\tmovsd\t-48(%rbp), %xmm1\n",
"\taddsd\t%xmm0, %xmm1\n",
"\tmovapd\t%xmm1, %xmm0\n",
"\tincq\t%r12\n",
"\tdecq\t%rbx\n",
"\tcmpq\t%rbx, %r14\n",
"\tjne\tL95\n",
"L165:\tmovsd\t%xmm0, -48(%rbp)\n",
"\tmovabsq\t$log, %rax\n",
"Source line: 7\n",
"\tcallq\t*%rax\n",
"\tucomisd\t%xmm0, %xmm0\n",
"\tjp\tL261\n",
"L192:\tmovsd\t-56(%rbp), %xmm1\n",
"\taddsd\t%xmm0, %xmm1\n",
"\tmovaps\t%xmm1, %xmm0\n",
"\tleaq\t-40(%rbp), %rsp\n",
"\tpopq\t%rbx\n",
"\tpopq\t%r12\n",
"\tpopq\t%r13\n",
"\tpopq\t%r14\n",
"\tpopq\t%r15\n",
"\tpopq\t%rbp\n",
"\tret\n",
"L219:\tmovl\t$1, %eax\n",
"Source line: 5\n",
"\tsubq\t%rbx, %rax\n",
"\tmovq\t%rsp, %rcx\n",
"\tleaq\t-16(%rcx), %rsi\n",
"\tmovq\t%rsi, %rsp\n",
"\tmovq\t%rax, -16(%rcx)\n",
"\tmovabsq\t$jl_bounds_error_ints, %rax\n",
"\tmovq\t%r15, %rdi\n",
"\tmovl\t$1, %edx\n",
"\tcallq\t*%rax\n",
"L261:\tmovsd\t-48(%rbp), %xmm1\n",
"\tucomisd\t%xmm1, %xmm1\n",
"\tjp\tL192\n",
"Source line: 7\n",
"\tmovabsq\t$jl_domain_exception, %rax\n",
"\tmovq\t(%rax), %rdi\n",
"\tmovabsq\t$jl_throw, %rax\n",
"\tcallq\t*%rax\n"
]
}
],
"source": [
"@code_native logsumexp(rand(100)) # System assembly"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Macros can be quite powerful: \n",
"* Used to embed a domain specific language (DSL) inside Julia (e.g. the JuMP.jl package for convex optimisation).\n",
"* Mark various optimisations:\n",
" * `@inbounds`: disable array bounds checking\n",
" * `@simd`: allow reassociation of floating point operations to exploid SIMD operations"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Packages\n",
"\n",
"Julia has an extensive and growing collection of 3rd party packages. See http://pkg.julialang.org/.\n",
"\n",
"Packages are installed with\n",
"\n",
"```julia\n",
"Pkg.add(\"PackageName\")\n",
"```\n",
"\n",
"Typically suffixed with .jl for ease of searching (e.g. DataStructures.jl)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Julia packages for Data Science"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Distributions.jl\n",
"\n",
"As the name would suggest, **Distributions.jl** provides functionality for various probability distributions."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using Distributions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Each class of distributions is its own type:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Distributions.Gamma(α=3.0, θ=2.0)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = Gamma(3,2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Various methods can be applied directly:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6.0"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean(d)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var(d)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"13-element Array{Float64,1}:\n",
" 0.0 \n",
" 0.0143877\n",
" 0.0803014\n",
" 0.191153 \n",
" 0.323324 \n",
" 0.456187 \n",
" 0.57681 \n",
" 0.679153 \n",
" 0.761897 \n",
" 0.826422 \n",
" 0.875348 \n",
" 0.911624 \n",
" 0.938031 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf(d,0:1:12)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"100-element Array{Float64,1}:\n",
" 5.39953\n",
" 3.61173\n",
" 6.2011 \n",
" 3.11482\n",
" 3.41657\n",
" 4.96106\n",
" 5.10545\n",
" 8.31128\n",
" 6.675 \n",
" 5.55154\n",
" 4.469 \n",
" 7.4219 \n",
" 6.36242\n",
" ⋮ \n",
" 5.90721\n",
" 4.04813\n",
" 1.64912\n",
" 1.77183\n",
" 2.8467 \n",
" 8.42509\n",
" 6.27939\n",
" 9.22847\n",
" 1.34706\n",
" 11.7556 \n",
" 3.15932\n",
" 4.03302"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = rand(d,100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"There is also some basic estimation functionality"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Distributions.Normal{Float64}(μ=6.055299705901676, σ=2.9761607894821847)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fit_mle(Normal, X)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Distributions are defined as `immutable` types\n",
"* Allows many optimisations (e.g. stack allocation)\n",
"* Essentially no overhead to creating and destroying Distribution objects\n",
" * can be used efficiently inside loops (e.g. for MCMC)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Multivariate distributions\n",
"\n",
"Efficient methods for multivariate distributions:\n",
"* Pre-compute Cholesky factor\n",
"* Exploit known structure (e.g. diagonal)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ZeroMeanDiagNormal(\n",
"dim: 10\n",
"μ: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]\n",
"Σ: 10x10 Array{Float64,2}:\n",
" 0.0531498 0.0 0.0 0.0 … 0.0 0.0 0.0 \n",
" 0.0 0.974371 0.0 0.0 0.0 0.0 0.0 \n",
" 0.0 0.0 0.562228 0.0 0.0 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0989702 0.0 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0 0.00158649 0.0 0.0 \n",
" 0.0 0.0 0.0 0.0 0.0 0.261165 0.0 \n",
" 0.0 0.0 0.0 0.0 0.0 0.0 0.0124902\n",
")\n"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Multivariate normal with zero mean, diagonal covariance\n",
"d = MvNormal(rand(10))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10-element Array{Float64,1}:\n",
" 0.0361213\n",
" -0.581391 \n",
" -0.657611 \n",
" -0.140955 \n",
" -0.230415 \n",
" -0.281859 \n",
" -0.595367 \n",
" -0.046335 \n",
" 0.0539264\n",
" 0.0660403"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rand(d)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## DataFrames.jl\n",
"\n",
"**DataFrames.jl** provides tabular data functionality for hetergeneous columns of labelled data (similar to `pandas` in Python, or `data.frame` in R)."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `readtable` for loading data from CSV or other delimited files\n",
"* `read_rda` for loading R rda format\n",
"* Additional packages for importing from databases.\n",
"\n",
"Can also be constructed directly from vectors:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>col1</th><th>col2</th><th>col3</th></tr><tr><th>1</th><td>0.46919036094201694</td><td>1</td><td>aa</td></tr><tr><th>2</th><td>0.2858318329324889</td><td>2</td><td>aa</td></tr><tr><th>3</th><td>0.9941098483097439</td><td>3</td><td>aa</td></tr><tr><th>4</th><td>0.5294312910527554</td><td>4</td><td>aa</td></tr><tr><th>5</th><td>0.5307409618482002</td><td>5</td><td>aa</td></tr><tr><th>6</th><td>0.9321771878849086</td><td>6</td><td>aa</td></tr><tr><th>7</th><td>0.781353954411222</td><td>7</td><td>aa</td></tr><tr><th>8</th><td>0.3935982887961047</td><td>8</td><td>aa</td></tr><tr><th>9</th><td>0.9273399882480897</td><td>9</td><td>aa</td></tr><tr><th>10</th><td>0.75054535655697</td><td>10</td><td>aa</td></tr></table>"
],
"text/plain": [
"10x3 DataFrames.DataFrame\n",
"│ Row │ col1 │ col2 │ col3 │\n",
"┝━━━━━┿━━━━━━━━━━┿━━━━━━┿━━━━━━┥\n",
"│ 1 │ 0.46919 │ 1 │ \"aa\" │\n",
"│ 2 │ 0.285832 │ 2 │ \"aa\" │\n",
"│ 3 │ 0.99411 │ 3 │ \"aa\" │\n",
"│ 4 │ 0.529431 │ 4 │ \"aa\" │\n",
"│ 5 │ 0.530741 │ 5 │ \"aa\" │\n",
"│ 6 │ 0.932177 │ 6 │ \"aa\" │\n",
"│ 7 │ 0.781354 │ 7 │ \"aa\" │\n",
"│ 8 │ 0.393598 │ 8 │ \"aa\" │\n",
"│ 9 │ 0.92734 │ 9 │ \"aa\" │\n",
"│ 10 │ 0.750545 │ 10 │ \"aa\" │"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DataFrame(col1=rand(10),col2=1:10,col3=rep(\"aa\",10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Each column is a `DataVector`, a type of `AbstractVector` that supports `NA` values. These can be created using the `@data` macro:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>col1</th><th>col2</th></tr><tr><th>1</th><td>1</td><td>Distributions.Normal{Int64}(μ=1, σ=1)</td></tr><tr><th>2</th><td>2</td><td>Distributions.Normal{Int64}(μ=2, σ=1)</td></tr><tr><th>3</th><td>NA</td><td>Distributions.Normal{Int64}(μ=3, σ=1)</td></tr></table>"
],
"text/plain": [
"3x2 DataFrames.DataFrame\n",
"│ Row │ col1 │ col2 │\n",
"┝━━━━━┿━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥\n",
"│ 1 │ 1 │ Distributions.Normal{Int64}(μ=1, σ=1) │\n",
"│ 2 │ 2 │ Distributions.Normal{Int64}(μ=2, σ=1) │\n",
"│ 3 │ NA │ Distributions.Normal{Int64}(μ=3, σ=1) │"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DataFrame(col1=@data([1,2,NA]),col2=[Normal(x,1) for x in 1:3])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>SepalLength</th><th>SepalWidth</th><th>PetalLength</th><th>PetalWidth</th><th>Species</th></tr><tr><th>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr><tr><th>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr><tr><th>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>8</th><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>9</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>10</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr><tr><th>11</th><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>13</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>setosa</td></tr><tr><th>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr><tr><th>15</th><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>setosa</td></tr><tr><th>16</th><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>17</th><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>setosa</td></tr><tr><th>18</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>19</th><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>setosa</td></tr><tr><th>20</th><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>setosa</td></tr><tr><th>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>setosa</td></tr><tr><th>22</th><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>23</th><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>setosa</td></tr><tr><th>24</th><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>setosa</td></tr><tr><th>25</th><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>setosa</td></tr><tr><th>26</th><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>27</th><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>setosa</td></tr><tr><th>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>29</th><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>30</th><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>"
],
"text/plain": [
"150x5 DataFrames.DataFrame\n",
"│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │\n",
"┝━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━━━━┥\n",
"│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ \"setosa\" │\n",
"│ 2 │ 4.9 │ 3.0 │ 1.4 │ 0.2 │ \"setosa\" │\n",
"│ 3 │ 4.7 │ 3.2 │ 1.3 │ 0.2 │ \"setosa\" │\n",
"│ 4 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ \"setosa\" │\n",
"│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ \"setosa\" │\n",
"│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ \"setosa\" │\n",
"│ 7 │ 4.6 │ 3.4 │ 1.4 │ 0.3 │ \"setosa\" │\n",
"│ 8 │ 5.0 │ 3.4 │ 1.5 │ 0.2 │ \"setosa\" │\n",
"│ 9 │ 4.4 │ 2.9 │ 1.4 │ 0.2 │ \"setosa\" │\n",
"│ 10 │ 4.9 │ 3.1 │ 1.5 │ 0.1 │ \"setosa\" │\n",
"│ 11 │ 5.4 │ 3.7 │ 1.5 │ 0.2 │ \"setosa\" │\n",
"⋮\n",
"│ 139 │ 6.0 │ 3.0 │ 4.8 │ 1.8 │ \"virginica\" │\n",
"│ 140 │ 6.9 │ 3.1 │ 5.4 │ 2.1 │ \"virginica\" │\n",
"│ 141 │ 6.7 │ 3.1 │ 5.6 │ 2.4 │ \"virginica\" │\n",
"│ 142 │ 6.9 │ 3.1 │ 5.1 │ 2.3 │ \"virginica\" │\n",
"│ 143 │ 5.8 │ 2.7 │ 5.1 │ 1.9 │ \"virginica\" │\n",
"│ 144 │ 6.8 │ 3.2 │ 5.9 │ 2.3 │ \"virginica\" │\n",
"│ 145 │ 6.7 │ 3.3 │ 5.7 │ 2.5 │ \"virginica\" │\n",
"│ 146 │ 6.7 │ 3.0 │ 5.2 │ 2.3 │ \"virginica\" │\n",
"│ 147 │ 6.3 │ 2.5 │ 5.0 │ 1.9 │ \"virginica\" │\n",
"│ 148 │ 6.5 │ 3.0 │ 5.2 │ 2.0 │ \"virginica\" │\n",
"│ 149 │ 6.2 │ 3.4 │ 5.4 │ 2.3 │ \"virginica\" │\n",
"│ 150 │ 5.9 │ 3.0 │ 5.1 │ 1.8 │ \"virginica\" │"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using RDatasets # sample datasets included with R\n",
"iris = dataset(\"datasets\", \"iris\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"150-element DataArrays.DataArray{Float64,1}:\n",
" 5.1\n",
" 4.9\n",
" 4.7\n",
" 4.6\n",
" 5.0\n",
" 5.4\n",
" 4.6\n",
" 5.0\n",
" 4.4\n",
" 4.9\n",
" 5.4\n",
" 4.8\n",
" 4.8\n",
" ⋮ \n",
" 6.0\n",
" 6.9\n",
" 6.7\n",
" 6.9\n",
" 5.8\n",
" 6.8\n",
" 6.7\n",
" 6.7\n",
" 6.3\n",
" 6.5\n",
" 6.2\n",
" 5.9"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris[:SepalLength] # indexing by a symbol gives column"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>SepalLength</th><th>SepalWidth</th><th>PetalLength</th><th>PetalWidth</th><th>Species</th></tr><tr><th>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr></table>"
],
"text/plain": [
"1x5 DataFrames.DataFrame\n",
"│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │\n",
"┝━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━┥\n",
"│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ \"setosa\" │"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris[1,:] # usual row,column indexing"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>SepalLength</th><th>SepalWidth</th><th>PetalLength</th><th>PetalWidth</th><th>Species</th></tr><tr><th>1</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>2</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr><tr><th>3</th><td>4.4</td><td>3.0</td><td>1.3</td><td>0.2</td><td>setosa</td></tr><tr><th>4</th><td>4.4</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr></table>"
],
"text/plain": [
"4x5 DataFrames.DataFrame\n",
"│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │\n",
"┝━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━┥\n",
"│ 1 │ 4.4 │ 2.9 │ 1.4 │ 0.2 │ \"setosa\" │\n",
"│ 2 │ 4.3 │ 3.0 │ 1.1 │ 0.1 │ \"setosa\" │\n",
"│ 3 │ 4.4 │ 3.0 │ 1.3 │ 0.2 │ \"setosa\" │\n",
"│ 4 │ 4.4 │ 3.2 │ 1.3 │ 0.2 │ \"setosa\" │"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris[iris[:SepalLength] .< 4.5,:] # can subset by indexing"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Supports the \"split-apply-combine\" (*c.f.* R's `dplyr`)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>Species</th><th>x1</th></tr><tr><th>1</th><td>setosa</td><td>1.462</td></tr><tr><th>2</th><td>versicolor</td><td>4.260000000000001</td></tr><tr><th>3</th><td>virginica</td><td>5.5520000000000005</td></tr></table>"
],
"text/plain": [
"3x2 DataFrames.DataFrame\n",
"│ Row │ Species │ x1 │\n",
"┝━━━━━┿━━━━━━━━━━━━━━┿━━━━━━━┥\n",
"│ 1 │ \"setosa\" │ 1.462 │\n",
"│ 2 │ \"versicolor\" │ 4.26 │\n",
"│ 3 │ \"virginica\" │ 5.552 │"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by(iris, :Species, df -> mean(df[:PetalLength]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Gadfly.jl\n",
"\n",
"**Gadfly.jl** is a very elegant plotting library."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using Gadfly"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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M5iMAAADAeLNhg8Q3bhxodN0Bdh577LFJrhvAzsaNA40bNgg2l0fI7bdLrLNzW5PrDrBTy/mIZZEKmpvzS5uazNWuO8BOodB4j+sGsNPUZK5ubs7jOT4RMn9+7qR4vPgZ1x1gp5bzEScXFcTjZj+RbHXdAbbkCdcFYEu2HjzeICqYZViEBlx3gC3MRwAAAIDxZdOmwZbu7gHcoTNiPC+33HUD2OnuHli0adMgrjyIEM/LNWsdpF13gJ1azkcsi1SQSJQXGGPwbJHIUXi2SMQYY1YlEmU8WyRCRKSViC5x3QG2ajcfcXJRQTyudhDJZtcdYEcpWe+6AWzJ5oPHG0RFLJbYzSwbXXeAHcxHAAAAgPEmk+lr9f3cCtcdYEfr3GWuG8CO7+dWZDJ9ra47oHqdncEM3+8/x3UH2KnlfMSySAXMaqaIWua6A+yIKOyTiRgRtYxZzXTdAdVTykwTMWe47gA7tZyPuCteBcVi/PHGRtnnugNsGTxMKWKUUrcUCrzNdQdUj5nzRHST6w6whfkIAAAAML5kMvkTfT9Y7boD7GgdfNF1A9jx/WB1JpM/0XUHVK+7Oz/X84K1rjvATi3nI/ZcVMAszSI833UH2BHhJa4bwI4Iz2eWZtcdUL1yOZzETLg3ScTUcj5iz0UFzc2SGR6OPea6Y7wREUVExxLRdCLaS0SPM/PI2L0/XzhW7wW1UVenrmtqKg+77oDq1dcXHi0UplzjugPs1HI+cq0+CEBEZh4o09oS0eLBEZo0rZ5GmxQF9TH6Wh1RlplD140AAAAvG88LTtM6f63rjvFCROKFUP77rl2y8509UjrDk9Jbs1L6xjY5sKckPxeRY8biczwv/8BYvA/Ujtb5az0vOM11B1Qvk+lLah18xXUH2KnlfMSySAXMpkwUK7juGEcW7g/pTV8dpJbfP0NcNAe/NfvjMMVOnUSvaWikVxPRnS/1Q5hx+XAEFQ4ebxAVsVg8FAkxHyMG8xHGHRFZ6e2TrUc+KCFtEPnLPz/aIcN7S/JvrhsBAGBs4GqRCnp6dk7UegB3DRw7xaYYyd/a5NOoiGKKRsfiQ3y/DzvYI0brgZk9PTsnuu6A6m3cONCYzfbOdt0Bdmo5H3FyUUGxOLyYyKxz3TGO6KPraPgtLSSNiuTQL5dNJjpxAklM0Zg8gdaY2I1j8T5QS2bdweMNoqKhoXRcGMavdN0Bdmo5H7HnogIRHlJKel13jCODjYq+vHYGXXXqJDoqKJA6MkGmYyKVp9fR1xuIusfiQ5ilayzeB2qHWXqN4SHXHVC9eDy2LwzlcdcdYKeW8xGXokLNiMiU4ZBeOyq09KkizZhUR0OTFOkJMbqfiLYys3HdCAAA8LLJZvuma9231HUH2PH9/NmuG8CO1n1Ls9m+6a47oHqbNg22eF7+dNcdYKeW8xF7Liool9U8oti5rjvAjjH0YdcNYCt27sHjDaKirq44i1nOd90Bdmo5H3FyUQFzLG+Mudd1B9iSb7guADvGmHuZY3nXHVC9WMxsN4bvdt0BtjAfAQAAAMaXbLZ/ntYBlkUixvcDLItEjNbBudlsP5ZFIsT3B2f5fu4C1x1gp5bzEcsiFZTLZjoRY0NnxBjD2NAZObz04PEGURGGxRYRxvNgIqaW8xH3uaggDMMe5sRO1x1gR4Q/5LoB7ISh+rZIcZfrDqheff3ELeXy/htcd4AdzEcAAACA8cb3gw7PC3B724jROrjVdQPY8bzgSt8POlx3QPW6uoITfD//CdcdYKeW8xF7LiowhhqYearrDrAjwse4bgA7zDzVGGpw3QHVYzYJEWlx3QF2ajkfseeigqGh1s319YOe6w6w09hYOMt1A9gZHlbXjI7OKrnugOr19rY/Mnfu9qtcd4CdWs5HfHMBAAAAYwonFxU0N+eXNjWZq113gJ1CofEe1w1gp6nJXN3cnMdl3xEyf37upHi8+BnXHWCnlvMRJxcVxONmP5Fsdd0BtuQJ1wVgS7YePN4gKphlWIQGXHeALcxHAAAAgPGls3PL5K6ugfmuO8BOJtN/susGsNPVNTC/s3PLZNcdUL2enp0Tfb9vgesOsFPL+YhlkQpisdjCWMysdt0BdpjlC64bwE4sZlbHYrGFrjugeqOj++eJqLWuO8BOLecjTi4qiMfVDiLZ7LoD7Cgl6103gC3ZfPB4g6iIxRK7mWWj6w6wg/kIAAAAMN5kMn2tvp9b4boD7Gidu8x1A9jx/dyKTKav1XUHVK+zM5jh+/3nuO4AO7Wcj1gWqYBZzRRRy1x3gB0Rtcp1A9gRUcuY1UzXHVA9pcw0EXOG6w6wU8v5iJOLCuJxs4UovMt1B9hRij7vugFshXcdPN4gKkqlxKAI3+a6A+xgPgIAAACMN5lM/kTfD3ApasRoHXzRdQPY8f1gdSaTP9F1B1Svuzs/1/MCXIoaMbWcj1gWqYBZmkUYN9GKGBFe4roB7IjwfGZpdt0B1SuXw0nMhJtoRUwt5yMeuV5Bc7Nkhodjj7nuADsifKHrBrBTV6eua2oqD7vugOrV1xceLRSmXOO6A+xgPgIAAACMN1r3LdU6j0euR4zWAR65HjFa56/Wug+PXI8Q3+8/SevcZ113gJ1azkfsuQAAAAAAAAAAiJyenp0TtR7AXQMjBo+Bjh6tB2b29Oyc6LoDqrdx40BjNts723UH2KnlfMSySAXF4vBiIrPOdQfYMSZ2o+sGsGXWHTzeICoaGkrHhWH8StcdYKeW8xEnFxWI8BCz9LruADvM0uW6AewwS68ID7nugOrF47F9IvS46w6wg/kIAAAAMN5ks33TcXlc9Ph+/mzXDWBH676l2WzfdNcdUL1NmwZbPC9/uusOsFPL+YhlkQrKZTWPKHau6w6wYwx92HUD2Iqde/B4g6ioqyvOYpbzXXeAnVrOR5xcVMAcyxtj7nXdAbbkG64LwI4x5l7mWN51B1QvFjPbjeG7XXeALcxHAAAAgPElm+2fp3WAZZGI8f0AyyIRo3Vwbjbbj2WRCPH9wVm+n7vAdQfYqeV8xLJIBeWymU7E2NAZMcYwNnRGDi89eLxBVIRhsUWET3PdAXZqOR/xyPUKwjDsYU7sdN0BdkT4Q64bwE4Yqm+LFHe57oDq1ddP3FIu77/BdQfYwXwEAAAAGG+y2b7FWge4/XfEeF7wTdcNYEfrYF0224fbf0eI52051vMC3P47Ymo5H7HnooJyWU0kYjy4LHL4ONcFYItnHjzeICpEuImZ8OCyyKndfMSeiwqGhlo319cPeq47wE5jY+Es1w1gZ3hYXTM6OqvkugOq19vb/sjcuduvct0Bdmo5H/HNBQAAAIwpnFxU0NycX9rUZK523QF2CoXGe1w3gJ2mJnN1c3Mel31HyPz5uZPi8eJnXHeAnVrOR5xcVKAUjYjIHtcdYIdZtrluADsiskcpGnHdAdUTUUVm3u26A+xgPgIAAACMN52dWyZ3dQ3Md90BdjKZ/pNdN4Cdrq6B+Z2dWya77oDq9fTsnOj7fQtcd4CdWs5HLItUEIvFFsZiZrXrDrDDLF9w3eCCiCRGRBbsL8tFO0flP7aPyGf3FeXyosgSEWly3fd8YjGzOhaLLXTdAdUbHd0/T0Stdd0Bdmo5H3EpagXxuNoRhmaz6w6wo5Ssd93gQpHo+D8V6YNPHKC3PTlMTUVD1N5IpQVN9OD8Jvq8iPyGmY3rzr9NNsfjaofrCqheLJbYTVTa6LoD7LxS5yMAvEjPlOTKn+yS/ad0ieENIrRBZP5DYr46KCNDZfmWiExz3QgArxxYFqlA64GZmUxumesOsON5wSrXDS7sF5rXuY/qNj1DLM/+rrdA/MAQqe2jNIuIDtv9Q5lMbpnWA7gbboT4fu+0TCZ/husOsFPL+YiTiwpEwlal1ArXHWCLL3Nd4EIhpPqCIXnu70cM8f6QEkRU7yCrKkqpFSJhq+sOqF4YqhlKyTmuO8BW7eYj9lxUEI+bLWFId7nuADtK0efH6r1ERO0jmtJEdCoRTYsRbSGih4lohJn/6h/kLrXE6U/zGkmmJ0h2FImJiI6IEc1vJJlZT08R0XbHic8jvCsep7zrCqheqZQYTCTC21x3gJ2xnI8A8CKISMNoWd76dFHy+YIMPzQk+7eNyr5nSvKQiHS47nuuUZG3PrxXvI9ukZHXe1J6XUZKlz0mxV/skb6RUK4QkcP2mwsAgFeMTCZ/ou8HuBQ1YrQOvjgW77N/VBb3F+SpD/ZKGH92g+RRD4q5bkBKBSMPi8gRY/E5Y0VEmofLcuHTJbnzjwfkwcwz8oedo3LPsycWM1z3PR/fD1ZnMvkTXXdA9bq783M9L8ClqBEzVvOxGlgWqYBZmkX4sN0EB3+bCC8Zi/fhOJ22ZS9N+K8BUofWP3YVib+2ldTbWqh5fiMtI6LD5rIuZh4SkdsaiX7bHKfj6OCxnSOigJnLjvOelwjPZ5ZHXXdA9crlcJJSCjfRipixmo/VwMlFBYlEU3epNIp14IhRKlwzFu8zXKbp+VEyz91YUTRETwxT/fxGOnosPmcsPXsfi63P/okQdX0iUb/XdQVUb2Sk7olJk0avc90BdsZqPgLAi3QglMt/87QMx+4XQ88ui9AGkXkPSfjosDwpIme7bgQAOFzhUtQKtO5bqnUej1yPGK2DMXmkcF1IG+c30PBlx5AkFEmcD16JcenRJHMTNERED4zF5wCR1vmrte7DI9cjxPf7T9I691nXHWBnrOZjNbAsAvA31NXR48fU0ZrPtNNXrphJk/KjxMc1UfnIOG1JxOgDzPyM60YAgMMVuw4AOFyJCBNRCxG9noimEdGTRPRbZh5xGgYAANGUy+UaenoGprruADuZTP8xrhvATk/PwNRcLtfgugOq19PTk+js3Hak6w6wU8v5iD0XFQwNcUepZK503QF2mOVW1w1gp1QyVw4N8WF3YzKobHS08YR4vIg9aRFTy/mIk4sKRHiIWXpdd4AdZuly3QB2mKVXhIdcd0D14vHYPhF63HUH2MF8BAAAABhvstm+6bg8Lnp8P4/7T0SM1n1Ls9m+6a47oHqbNg22eF7+dNcdYKeW8xHLIhWUy2oeUexc1x1gxxj6sOsGsBU79+DxBlFRV1ecxSznu+4AO7Wcjzi5qEDEbGU2uFFSxDCbW1w3gB1m84CIidgty1/ZjFE7mdWvXHeAHcxHAAAAgPEmm+2fp3WAZZGI8f0AyyIRo3Vwbjbbj2WRCPH9wVm+n7vAdQfYqeV8xLJIBeWymU7E2NAZMcYwNnRGDi89eLxBVIRhsUWET3PdAXZqOR/xbJEKwjDsYU7sdN0BdkT4Q64bwE4Yqm+LFHe57oDq1ddP3FIu77/BdQfYwXwEAAAAGG+y2b7FWgfrXHeAHc8Lvum6AexoHazLZvsWu+6A6nnelmM9L8DjESKmlvMRey4qKJfVRCKe6boDbPFxrgvAFs88eLxBVIhwEzPNdt0Btmo3H7HnooKhodbN9fWDnusOsNPYWDjLdQPYGR5W14yOziq57oDq9fa2PzJ37varXHeAnVrOR3xzAQAAAGMKJxcVNDfnlzY1GTxSOGIKhcZ7XDeAnaYmc3Vzcx6XfUfI/Pm5k+Lx4mdcd4CdWs5HnFxUoBSNiMge1x1gh1m2uW4AOyKyRykacd0B1RNRRWbe7boD7NRyPnKtPmisfeTSSycdCBvPZOZi3YG6X3zpji8VnvuaD1z8gRWK1KRDPxcTxZ/ceOONWNsFAAB4GdXkm4vzzjzv38578zs+/pe/u+Csdyw/783vuG3lypWNtu+3Zs2aumHT+DsiWklEbyw1jd79t17HxF8nktce+lMsFmPVfkZn55bJXV0D823bwK1Mpv9k1w1gp6trYH5n55bJrjugej09Oyf6ft8C1x1gp5bzsSZXi7AyQyL8mbPPPvuL69evHyYiMobWEPGRd9xxx1994/BCEqXE+cSSv+HmG1YSEa27eK237t3r2q///vW5Q69Ze9HaFmLKXX/TDf/0YppjsdhCZnM2EX30xfznwfvRr2cAACAASURBVA1m+QIRLXPdAdWLxcxqkdh6ItrougWqMzq6f55SajURvaj5Cm7Ucj7W5JuLYim8jYgSjWHjmUREFy+/uIGYz2aSH7yoNxRaSML+n3/mRzlulvx/r4nRfBaasu7itfetvXjtD9a+Z+3rbD4iHlc7iGTzi+oDZ5SS9a4bwJZsPni8QVTEYondzIKTwYip5XysyTcXd913187z3rLyPjL0DiK6c7hxeAUJxeNSf+eLekOR6cz0+0M/Mst2Ijr6L1/ChuPM8qAo/k8K5QRmumPNmjULb7zxxqcOvaarKzghFuPXEsnDqVSb53m55czqOCL100WLZm/ROr9E6/yap5+e851Zs3pjBw4k3sNMe5LJ1h9ns72zw7DuTGZ+NJmc87tMpv9kpaSDOXwgmZz7uOcFb2HmmSLm9nS6fUjr3CUiJOl0+809PQNTSyXzDqVkYPHitp93d+eON0a9TinqWry4tau7u+91xsSOZ479LJmcNeh5+ZXMNKWubv/NhUJB4vGjLhGhp9Pp1jt8f3CWSPhmY8zjHR3tD/h+0CHCJ4eh/G7JkrZHu7uDM43h2XV16scLF87e43m59zBzLJVq+47n5ZqZ1XkisjWdbvup1luOI4ovF2EvnZ7zsO/3v1ZETiiXy/eefPK8ft8P3i7CLZMnm+8FQVt5ypT+S4nM3lSq/bZMpv8YpeQsInkylWrboHWQJuJXGUO/7+ho/aPWub8nUq3lct3/nHzyMU/5fv4iIqlLJtu+/dBDTx7R0JC4gJm2J5Ot6z1vy7HM8dcThTqVmrvZ8/KnM9NCY8JfdHTMzWsd/AMRHzk8rL7/6lfPGvX9/vcym33JZPutpRL9UOv8GmbpTSbbfpPJ9CWVip3CzBuTyTmPaN33JqJYmzHFuzo6jt2ldf5dItSYTrd+s6dn58RSqbCKmf+UTM65O5vtnxeG8gZm051Mtj+UyQSvVooXMcsvk8m2nOfl3saspjHX/zCZPPqA1vk1IrI/nW67JZvtmx6GsbcaQ30dHa2/6u4eWGSMebVS6g+LF8/OZjL5M5SiubFY+L+LFs3d4XnBKmaemEq13uj7f5ogMvpOEbMznW7/ie8H7SL8RmMk29HR9gffz50qohbHYvzrRYvmbPH9/nNE5Oi6usZbFi6ctt/z8u9jpkIq1foD3++dJlL3NhHKpdOtv/T9/pNE5DRjwk0dHXN93w/+ToTn19WZuxcubP+T7+cuFFGTksk53/rDHwbrm5rMu4lkVyrVdpfn5dqY1ZtEqCedbn1Q676lRLGUSHlDOj3vSd/Pny1CM0ZGij869dRjn/H9YDURl5LJ1u91dm47Mh4v/QORyadS7b/IZPInKkWvefbYu0vr4PVa599gDN/T0TFnm9a584nU5KefnvOdtrYgvnevuohZdieTbXd2dm6ZE4/HVxw69jyv/1XMkiYq359KzXvir4+94FIRCdPp9u8+99g7NAOYpTOZbMtkMrllSqkFROqnqdTsrVrnzyOi5nJ5102NjY1cKk28+LkzQKnwscWL5/62uzu/xBhaopT57eLF7Y95XvBmZp6lFN2xeHHr056Xu5iZOJVqv6m7Oz/FGFopIoPpdNvPfL9vgUhsmTGc6eiY03no2IvFSj9ftGj+gO/n3yFCUydMKH53cHB+ePDYo6FUqvV2rQdmEpm3iJgn0un2+w8de4dmgO/nVoioOaOjsTtPOWXWbt/PXyQi8VSq7TudnVsmx+Px84loWyrVes+hY+/QDNA6/xoiOvG5x94RR5S+9/DDbXccPNbMM8lk+4+eOwM8L5diVkuJ6MFUqrXnuTNA6/y7maU+mWz71mOPPTZpZKTxwkMzoKtrYH4sZv6O2fjJZPsmzwtOY+aTRMx96XR7cPCJ1XxUuVz3gyVLZhR8v/99h469np7c0aWSOoeZtiSTrb/OZvsWh2Hs1EPHnufl38hM7cylnyST83d6XvBOZp6QSrXeeGgGiJgd6XT7/3Z35+caQ2ccOvYOzQCl6FeLF7f2eV7urcxq+qFj79kZcCCdbvvhc4+9QzMgFgsfWrRobrfv598gQvMOHXuHZkAyOeebXV3bG+Px0rueOwNE5JF0um2j7+dOEVHJMFS/WbJkdu+hGdDQULj1+OOP3+f7wXtFeDSVav1+JvPkUUolziUKg2Sy7fNa5xcS0ekiZnM63a61Dl5PxMeWy7L+5JPbtr+ofyb/DbW7iZbwD0jJjRcvv7ihwAdWEtH//vDnP3zmxb0VdRPJ/7t7piGaGgvp/7vE5vrvXv8gET347I/b/vHitb+oL9adQ0TfqeYztB6YKVI+kVnh6oMIicXoXCIqu+6A6mUyuWUifCRHdnv5K8/+/eFRc+cOnOC6A+x4XrCKiPwXfOEYqNnhfPbZZzc1moYdTPI+Eb5RmC6846d3/PTFvNe696w7nVg+ff3NN7z+8lWXT1EJlSFDJ+8s7Bw6ctKRrV//ztf71r1n3WpmOemrN99wxZo1a+oSxbouFnXuV7/71S3VfMazZ8pnp1Kt2HMRIZ6XfyCdbsWeiwjROn+tiKxPp9vwNXtEHPxGUK1Opdqw5yJCajkfa3afi/Xr1w+T8P+I8HVEVNh1YNcvXux77Rje8RCR7PjHi9dujiXUo0roSzd874bdLQ0tLTGjthARNZiGHwnTKesu+cDPEsVEnoh/V+2JBRFRPG62EIV3vdhGcEMp+rzrBrAV3nXweIOoKJUSgyJ8m+sOsFPL+VjTLyJXvnnlG5noPhL5/O0///FLfqLeukvXHdNEw/s+953v7Kv0mg++a80MijcVvnzzl4de6ucBAADAYebtZ7392PPevNK8/ay3H+u65YX4ft+CZ9enIEK0zn3KdQPY8bxgFe6ZEC2ZTF+r1sGlrjvATi3nY002dC5fvjx+1FFH1an99GFR9PM777nzyVp87kthjGph5kWuO8COiFruugHsMPMiY1TgugOsNBNRynUE2KnlfKzJycVRDUfN4wP0mDBtZlKra/GZL1Ui0dRdKo3mXXeAHaXCNa4bwJa6PpGo3+u6Aqo3MlL3xKRJo9e57gA7424+fvKTn1QXnHPBMa47AAAAAJzRum+p1nk8cj1itA7wyPWI0Tp/9cEbckFU+H7/SVrnPuu6A+zUcj7ikesViKg4EVk/VA3cEuFJL/wqOMw0Pnu8QUSEYTlGxJiPEYP5CAAAADDe5HK5hp6egamuO8BOJtOPvT0R09MzMDWXyzW47oDq9fT0JDo7tx3pugPs1HI+YlmkgqEh7iiVzEu+0RfUFrPc6roB7JRK5sqhIe5w3QHVGx1tPCEeL2JPWsTUcj7i5KICER5ill7XHWCHWbpcN4AdZukVYdxBN0Li8dg+EXrcdQfYwXwEAAAAGG82bRps6e4ewB06I8bzcstdN4Cd7u6BRZs2Dba47oDqeV6uWesg7boD7NRyPmJZpIJEorzAGINni0SOwrNFIsYYsyqRKOPZIhEiIq1EdInrDrBVu/mIk4sKRMxWZvOA6w6ww2xucd0AdpjNAyJmq+sOqJ4xaiez+pXrDrCD+QgAAAAw3mSz/fO0Ds513QF2fD/4sOsGsKN1cG422z/PdQdUz/cHZ/l+7gLXHWCnlvMRyyIVlMtmOhHjeQcRYwyf7boBbPHSg8cbREUYFltE+DTXHWCnlvMR9/OvoFiMP97YKPtcd4At8wnXBWBHKXVLocDbXHdA9Zg5T0Q3ue4AW5iPAAAAAONLNtu3WOtgnesOsON5wTddN4AdrYN12WzfYtcdUD3P23Ks5wV4PELE1HI+Ys9FBeWymkjEM113gC0+znUB2OKZB483iAoRbmKm2a47wFbt5iP2XFQwNNS6ub5+0HPdAXYaGwtnuW4AO8PD6prR0Vkl1x1Qvd7e9kfmzt1+lesOsIP5CAAAADDeeF5wmtb5a113gB3Py+OuqhGjdf5azwtwWWOEZDJ9Sa2Dr7juADu1nI/Yc1GBUjQiIntcd4AdZsEljREjInuUohHXHVA9EVVk5t2uO8AO5iMAAADAeNPZuWVyV9fAfNcdYCeT6T/ZdQPY6eoamN/ZuWWy6w6oXk/Pzom+34cn2UZMLecjlkUqiMViC2Mxs9p1B9hhli+4bgA7sZhZHYvFFrrugOqNju6fJ6LWuu4AO7Wcjzi5qEAps1tEsq47wA6zud91A9gRkaxSBuv30TJERNp1BNjBfAQAAAAYb7QemJnJ5Ja57gA7nhesct0AdjKZ3DKtB3A33Ajx/d5pmUz+DNcdYKeW8xHLIhWIhK1KqRWuO8AWX+a6AOwopVaIhK2uO6B6YahmKCXnuO4AW7Wbjzi5qCAeN1uIwrtcd4AdpejzrhvAVnjXweMNoqJUSgyK8G2uO8AO5iMAAADAeOP7fQuwfh89Wuc+5boB7HhesAr3TIiWTKavVevgUtcdYKeW8xHLIhUYo1qYeZHrDrAjopa7bgA7zLzIGNXiugOsNBNRynUE2KnlfMQj1ytIJJq6S6XRvOsOsKNUuMZ1A9hS1ycS9XtdV0D1Rkbqnpg0afS6sXo/EWEimk5E84goRkR9RLSNmc1YfQbUdj5yrT4IAADguUSkeSSkc4pEK/eUaFooxEfW0Z4GRffUK7qNmXe5bgQYM1r3LdU6f7XrDrCjdXCP6wawo3X+aq37lrrugOr5fv9JWuc+OxbvNVyWCzc9I498uFdGTuuS8qldUn7/EzK6YUh6iyIfEJG6sfgcqO18xLJIBSIqzkyNrjvAjghPct0A1hpFFGZRhIRhOaaUGpP5OCp0ym+fpvk3/4kST5UOfpv+6DCp5hjNPr6RTj06Qb8ioifG4rNe6TAfAQDgFeGJYbnl8sdllDaI/OWflY9IsXOf3Csir3HdCPZwtUgFuVyuoadnYKrrDrCTyfQf47oB7PT0DEzN5XINrjugej09PYnOzm1HjsV7TYxRsTH217+vV0QTYzRKRMWx+Byo7XzEyUUFQ0PcUSqZK113gB1mudV1A9gplcyVQ0Pc4boDqjc62nhCPF4ckz1pk+OUWzqJwtREkkO/O7aR6LWTSWYkqJ8OXjkCY6CW8xHrnBWI8JBS0uu6A+wwS5frBrDDLL3G8JDrDqhePB7bF4by+Fi8V52i9accQQs+P5fOeqxAiVBIjm2k8LhGeqApRncQ0Z6x+Byo7XzEpagAAOCMiCRGidrLIZ2yL6QTDZGawPREYx1tShD1MnPBdWOUiUiMiCaHRKcXDbUmFG0tED04kWgPM5dd973ibNo02NLdPYA7dEaM5+WWu24AO93dA4s2bRrEHTojxPNyzVoHadcd8MJKIqc/VZQne4el8IunTOHxYRneNSpbSyJ/LyIv29YI7LmoIJEoLzDG4NkikaPwbJGIMcasSiTKeLZIhIhIKxFd4roDnp+ITCwauuNr26j9xIep/u+z3HDCJmr8dD/NGDV0GxEd9XJ9Nk4uKhAxW5nNA647wA6zucV1A9hhNg+ImK2uO6B6xqidzOpXrjvgBS1/ukST/j0gVTQHt0EYIvqvQeK+AsVDojMd9wEAAECUiMj7fj8ke9X9Yp57H5Ef75ThUij/+nJ9Nr65qCCT6Wv1/dwK1x1gR+vcZa4bwI7v51ZkMn2trjugep2dwQzf7z/HdQe8oMG5TURNioj4z5f61jPRgiYycUUv28M5cXJRAbOaKaKWue4AOyIK+2QiRkQtY1YzXXdA9ZQy00TMGa474AU9MDVOu65qpXBqjGhSjGhynGTdLDLzG2kfEf3i5fpg3OeigmIx/nhjo+xz3QG2zCdcF4AdpdQthQJvc90B1WPmPBHd5LoDnh8zD4vI+VfMou++Zxod1TMsDcc1ceHoBO1vULSamZ962T775XpjAAAAcE9EGojoDUTUSkSDRPRrZj7gtuoVKpvtW6x1sM51B9jxvOCbrhvAjtbBumy2b7HrDqie52051vMCPB4hYmo5H7HnooJyWU0kYqwDRw4f57oAbPHMg8cbRIUINzHTbNcdYKt28xF7LipobpbM8HDsMdcdYEeEL3TdAHbq6tR1TU3lYdcdUL36+sKjhcKUa1x3gB3MRwAAAIDxxvOC07TOX+u6A+x4Xh53VY0YrfPXel5wmusOqF4m05fUOviK6w6wU8v5iD0XFShFIyKCR/1GDLPgksaIEZE9StGI6w6onogqMvNu1x1gB/MRAAAAYLzp6dk5UesBXC0SMb7fh6drRozWAzN7enbiapEI2bhxoDGb7cXVIhFTy/mIZZEKisXhxUQG97mIGGNiN7puAFtm3cHjDaKioaF0XBjGcZ+LiKnlfMTJRQVKmd0iknXdAXaYzf2uG8COiGSVMli/j5YhItKuI8AO5iMAAADAeKP1wMxMJoenokaM5wV4KmrEZDK5ZdjfNPZEpEFE5ovIYhGZJSJj9k217/dOy2TyeCpqxNRyPmJZpAKRsFUptcJ1B9jiy1wXgB2l1AqRsNV1x3ghIvFRkRP2lemKXSX62JYC/eveEn10OKSLRWTaWHxGGKoZSsk5Y/FeUEu1m4+4/XcFzLG8MeV7XXeALfmG6wKwY4y5V6l43nXHeDFCNHNPkT7y0DP0tgeHaMIzIcnsepLXNNOK1zXTUSLyRWYuvZTPiMXM9jCM3T1WzVArmI8AAPAi7CvLP2zeK8+c0iWGN4jQBpFJvxNz0aNS2lmUh0XkRNeNMP5hWaQC3+9bgPX76NE69ynXDWDH84JVuD/J2CkJLegtUN2mZ4jl2d/tKxM/9AypXIGOIKKFL/UzMpm+Vq2DS1/q+0Bt1XI+4uSiAmNUCzMvct0BdkTUctcNYIeZFxmjWlx3jBeGSBGTPPf3cvDviIh4DD6mmYhSY/A+UEO1nI/Yc1FBItHUXSqNYh04YpQK17huAFvq+kSifq/rivGinumJuQ0UpieSePsPnkhMUERLJpHMbaT9RPToS/2MkZG6JyZNGr3uJcdCTdVyPo7FGSwAABwmRkZk7i6hT/9uL53z+72UeCYkmt1AZtlk2vX6ZroxoehzzDzquhPgFcn3gw7PC3B724jROrjVdQPY8bzgSt8POlx3jBciUjcqsnhfST45MCq3+Pvkf3cW5ZsHQrlcRI4Zi8/o6gpO8P38J8bivaB2ajkfsSxSgTHUwMxTXXeAHREek+EJtcPMU42RBtcd48Wzl5l2i8iWiUTtsxLURERPEVE/M5fH5jNMQgT7ZKIG8xEAAABgvMnlcg09PQP45iJiMpl+nJlHTE/PwNRcLodvLiKkp6cn0dm57UjXHWCnlvMRl6JWMDTEHaWSwZ6LiGEW7LmImFLJXDk0xNhzESGjo40nxOPFq113gJ1azkfsuaggHjf7w1Btdd3xQkSEiehoIppBRGUi6ieivcz8V9e5vzLIE64LwJZsjcfNftcVUD1mGRahAdcdYKt28xGXokaYiEw5ENJbDNGyXUWanlAUHhGnwfoY/W890R+Y+YDrRgAAAHjWpk2DLd3dA4ftHTpFRBXK8t4Hh0R/IieFC3uk+K4/SvHLg3Lgj/tlQ0nkFfm4eM/LLXfdAHa6uwcWbdo0iCsPIsTzcs1aB2nXHWCnlvMRey4qSCTKC4wxh/OzRWaNCP39+t10wte3Uf2tO6nuBzuo7r8GqTGzn055pkyvcx3ohsKzRSLGGLMqkSjj2SIRIiKtRHSJ6w6wVbv5iJOLCkTMVmbzgOuO5zF7R4la/P2kdhb/vLyVKxD3HKDYniK1u4xzhdnc4roB7DCbB0TMYb+/Cf7MGLWTWf3KdQfYwXyEFyQip/UOy6/f7EuJnn2s8qE/H+uTkS0F+ZbrRgAAeGXCNxcVZDJ9rb6fW+G643nkjkzQrlOOIHNM/Z+fgHh8I1FyIsmUOPW6jHNF69xlrhvAju/nVmQyfa2uO6B6nZ3BDN/vP8d1B9ip5XzEpagVMKuZIryMiO513VLBjkai/3lLC7UfVUeLnihQrI6JFk+kcmoC/XpinDa4DnRBRK0iom+47oDqiahlzPIMEeEpxBGhlJkmQmcQ0d2uW6B6tZyPOLmooFiMP97YKPtcd1TCzEZEfnFCE+2f20in7yrRnARTaUqcepvi9Os6oqzrRjcMHqYUMUqpWwoF3ua6A6rHzHkiusl1B9jCfAQAAAAYX7LZvsVaB+tcd4Adzwu+6boB7GgdrMtm+xa77oDqed6WYz0vwOMRIqaW8xEbOisol9VEIp7pugNs8XGuC8AWzzx4vEFUiHATM8123QG2ajcfseeiguZmyQwPxx5z3QF2RPhC1w1gp65OXdfUVB523QHVq68vPFooTLnGdQfYwXwEAAAAGG88LzhN6/y1rjvAjuflD+e7qsLfoHX+Ws8LTnPdAdXLZPqSWgdfcd0Bdmo5H7HnogKlaERE9rjuADvMgksaI0ZE9ihFI647oHoiqsjMu113gB3MRwAAAIDxpqdn50StB3C1SMT4fh+erhkxWg/M7OnZiatFImTjxoHGbLYXV4tETC3nI5ZFKigWhxcTGdznImKMid3ougFsmXUHjzeIioaG0nFhGMd9LiKmlvMRJxcVKGV2i8gr9Bba0cVs7nfdAHZEJKuUwfp9tAwRkXYdAXYwHwEAAADGG60HZmYyuWWuO8CO5wWrXDeAnUwmtwz7m6LF93unZTL5M1x3gJ1azkcsi1QgErYqpVa47gBbfJnrArCjlFohEra67oDqhaGaoZSc47oDbNVuPuLkogLmWN4Yc6/rDrAl33BdAHaMMfcyx/KuO6B6sZjZbgzf7boDbGE+AgAAAIwvvt+3AOv30aN17lOuG8CO5wWrcH+SaMlk+lq1Di513QF2ajkfsSxSgTGqhZkXue4AOyJquesGsMPMi4xRLa47wEozEaVcR4CdWs5HPHK9gjAMe5gTO113gB0R/pDrBrAThurbIsVdrjugevX1E7eUy/tvcN0BdjAfAQAAAMYb3w86PC/A7W0jRuvgVtcNYMfzgit9P+hw3QHV6+oKTvD9/Cdcd4CdWs5H7LmowBhqYOaprjvAjggf47oB7DDzVGO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" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\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",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\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",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
" 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",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\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",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\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",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_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, event) {\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 = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\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(\"#img-8f617f61\");\n",
"fig.select(\"#img-8f617f61-5\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-8f617f61-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-8f617f61-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-8f617f61-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-8f617f61-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-8f617f61-14\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-8f617f61-14\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-8f617f61-14\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-8f617f61-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-8f617f61-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-8f617f61-16\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-8f617f61-17\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-8f617f61-17\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-8f617f61-17\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-8f617f61-17\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-8f617f61-17\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-8f617f61-18\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-8f617f61-18\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-8f617f61-18\")\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"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(x=1:10, y=rand(10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"It is inspired by Leland Wilkinson's *Grammar of Graphics* (the motivation of R's `ggplot2`).\n",
"\n",
"* *Coordinates* (`x`, `y`, `color`, etc.) are provided as keyword arguments.\n",
"\n",
"* Plays nicely with DataFrames:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\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",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\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",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
" 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",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\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",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\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",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_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, event) {\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 = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\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(\"#img-ddc3aa5d\");\n",
"fig.select(\"#img-ddc3aa5d-4\")\n",
" .drag(function() {}, function() {}, function() {});\n",
"fig.select(\"#img-ddc3aa5d-6\")\n",
" .data(\"color_class\", \"color_setosa\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-7\")\n",
" .data(\"color_class\", \"color_versicolor\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-8\")\n",
" .data(\"color_class\", \"color_virginica\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-10\")\n",
" .data(\"color_class\", \"color_setosa\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-11\")\n",
" .data(\"color_class\", \"color_versicolor\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-12\")\n",
" .data(\"color_class\", \"color_virginica\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-16\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-ddc3aa5d-19\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-19\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-125\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-125\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-419\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-419\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-419\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-423\")\n",
" .data(\"max_pos\", 104.83)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-423\")\n",
" .data(\"min_pos\", 87.83)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-423\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-ddc3aa5d-425\")\n",
" .data(\"max_pos\", 104.83)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-425\")\n",
" .data(\"min_pos\", 87.83)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-425\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-425\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-425\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-ddc3aa5d-427\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-427\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-ddc3aa5d-427\")\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": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(iris, x=:SepalWidth, y=:SepalLength, color=:Species)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"The *Geometry* specifies the type of plot: these are passed as extra arguments:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
"d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
"amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
"{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
"\n",
"]]> </script>\n",
"<script> <![CDATA[\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof define === \"function\" && define.amd) {\n",
" // Define as an anonymous module\n",
" define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
" return factory(Snap);\n",
" });\n",
" } else {\n",
" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\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",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\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",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"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",
" 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",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\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",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\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",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_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, event) {\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 = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\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(\"#img-518bc361\");\n",
"fig.select(\"#img-518bc361-5\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-518bc361-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-518bc361-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-518bc361-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-518bc361-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-518bc361-12\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-518bc361-12\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-518bc361-12\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-518bc361-14\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-518bc361-14\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-518bc361-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-518bc361-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#img-518bc361-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#img-518bc361-15\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-518bc361-15\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-518bc361-15\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-518bc361-16\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-518bc361-16\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-518bc361-16\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(x=1:10, y=rand(10), Geom.line)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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