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mlubin/explicit support function.ipynb

Created July 1, 2015 22:13
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explicit support function.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ϕ (generic function with 1 method)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Distributions\n",
"Φ(x) = cdf(Normal(0,1),x)\n",
"Φinv(x) = quantile(Normal(0,1),x)\n",
"ϕ(x) = pdf(Normal(0,1),x)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using Optim"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"support_function (generic function with 1 method)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function support_function(ϵ,a,b) # support function of S_ϵ\n",
" # == max(a*x+b*y) for (x,y) in S_ϵ\n",
" @assert a > 0 && b < 0\n",
" solution = optimize(λ->-(a*Φinv(λ*ϵ) + b*Φinv(1-(1-λ)*ϵ)),1e-13,1-1e-13)\n",
" return solution.minimum \n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5000000149011627"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"support_function(0.05,1,-1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using Gadfly"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c = [-20:0.1:20;];"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = similar(c);"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in 1:length(c)\n",
" y[i] = support_function(0.05,exp(c[i]/2),-1)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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U5tXX22t8d5Cb/v66n/tuIDf19faahobC+H2Pj6ogiR8A2ADw8fIAMHt27shkcvDLvjvITZzzcXz/24JHyaTdWiqZF3x3kCt93ncBudIXkkm71XdFWRlcDYMWKI7aTV6XvstEtE8Vvb47yBXnIxGRf4/pfGS0Dxl9r+8UItoNrF69YUpnZy+f0FlhgiC30HcDuens7J2zevWG8XfnwfB1Fjm06ed8p4w3QZBrCMN8i+8OchPnfOQ1F2VUVxcPtdby3SIVx/DdIhXGWntBdXVx/L1bJIGbIOjB4/iq75TxRlUbAVziu4NcxTcfec1FGcmkealUsk/77iA3xuhDvhvIlT6dTJqXfFf8hTb9OwjOhGAurhXrO2e8SSSqNwNDq3x3kBvORyIiXx7RycjoRmT0475TiGg3k832NEZRbrHvDnIThrnLfTeQmyjKLc5mexp9d/yPdv0BMvqw74zxrKMjf0AUrT/Ldwe5iXM+8pqLMkTMNFWzwHcHuVE1vE6mwqiaBSJmmu8OAEBGF0JxNgyW+k4Zz4yx+6vak3x3kJs45yOvuShjcDD5XF2dvuG7g1xZvkypwhhj7u7vlxd9d+BhrYHgu1B8EQsk5ztnPBORAoDbfXeQK85HIqJ4tesXkNEujPdHkRNR5cpmC4dHUf4y3x3kJgzzX/fdQG6iKH9ZNls43GvEYzoNGX0DGV3otaNCdHYWZgZBnqeOKkyc85HXXJQhog2qMtt3B7lRlaN8N5AbVZktog1eIwQ3AHgEi6Tda0eFKBZLE0Uw/p5NQjsU53zk4b8yGho029eXeNZ3B7lRlfN9N5CbqipzY319sc9bQJseBcGHYXCEt4YKU1PT/0x//z7X++4gN5yPRERxyWgbMsqncBLR2AuC/PwwLNzgu4PcBEFhpe8GchOGhRuCID/fy8YzuhgZfQVP6D5etl+hstmeVBjmv+m7g9zEOR95zUUZIrYIoN93B7kR4e3DFaj/rf0tXqoCwZcA/BuOl1dj334FSySSJUA5HysM5yMR0Vhr07OR0Y14SOt9pxDRHqK7e9OEMOwdH08NpBGLoh5ewV5hwrB3Wnf3pgmxbvRaNWjXCBn9X7FudzexalVvXVfX2hm+O8hNnPORp0XKGBzsmwvYZb47yI21iVt8N5Aru2x4f4vRQpwNYF8A3411u7uJ2tqhQ0ql5NW+O8hNnPORi4syVGWLiK713UFuRHSN7wZyI6JrVWVLbBtUFQCfh+JGLJJtsW13N5JMJt5QxXO+O8gN5yMR0Vhp1zPRri/xWgsiil1XV8/UMOyZ57uD3ERR4UzfDeQmDHvmdXX1TI1tg+36G7TrF2Lb3m5o9eoNU4KgcJzvDnIT53zkaZEyikUzC0gs8d1BbqzFZ303kKvEkuH9LQbtejwUc6D4dizb201VVQ1OF9HzfHeQmzjnIxcXZYgkCtbaR3x3kCvlBXoVxlr7iEiiENPmPgvgFiyK8RqP3VAiYTdaKw/67iBXnI9ERKNrhR6Cdh3Ao3qQ7xSi3R2PXJTR1bV+VhjmeVqkwkRRnqdFKkwY5pd0da0f+9MiCXwawH04WdaP+bZ2c1G0YXoU5T7iu4PcxDkfubgoo1i0UwHhBZ0VxlrhBZ0VR+YN729jaPjdIR9DCf8xptvZQ5RKg1NUxc/7YGinxTkf+cr1MkqlUrdI9SbfHeRGVT7ju4HclErmVtXBl8d2I/gEgE6cKE+P6Xb2EDU1E9YVi1uX++4gN5yPRESj5Vo1yGgP2vV83ylEewqeFikjivKtQZDn420rTBjm7/HdQG6CIH91FOVbx2wDC3EGgDpsws/GbBt7mDVr8odFUeGLvjvITZzzkYuLMqxFrYhM9t1BblTlQN8N5EZEJluL2jHbgOIKAN/HuTI4ZtvYw4jYalWd4ruD3MQ5H3nNRRlbtjQ+XVOzIfDdQW7q6vo/4LuB3PT1mesHBqYPjcmHD992ehIsrhiTz99DrV3b/PuZMzd+3ncHuYlzPvLIBRHtvhL4BwCP4ESJ6yFdRAQuLspqaCjMq6+31/juIDf9/XU/991Aburr7TUNDYXRv+07o0kILgYQ22um9xSzZ+eOTCYHv+y7g9zEOR+5uCgjmbRbAX3Bdwe50ud9F5ArfWF4fxt1HwBg8TJ+OQafvUcT0T5V9PruIFecj0REuyajDyGj/+I7g2hPxCMXZXR0rJu0Zk3vbN8d5CabXX+07wZys2ZN7+yOjnWTRvVDf60HAjgVJdw+qp9LAIDu7k0ToqjnUN8d5CbO+cjFRRmJROKIRMJe5ruD3Ijo13w3kJtEwl6WSCSOGNUPrcLHAPwGJ0nPqH4uAQAGBrbOUjVLfXeQmzjnIxcXZSST5iVA+ajgCmOMPuS7gVzp08P726j6OJRHLcZKIlG9WURX+e4gN5yPREQ76zGdh4y+gV/pXr5TiPZUPHJRRjbb0xhFucW+O8hNGOYu991AbqIotzib7WkctQ80+BiA+3CqvDlqn0l/oaMjf0AUrT/Ldwe5iXM+cnFRhoiZpmoW+O4gN6rmAt8N5EbVLBAx00blw+7VagAfgeKHo/J59I6Msfur2pN8d5CbOOcjFxdlJJN2HVC633cHuTEGX/XdQK5K9w/vb6NgXywGMIDHkRmVz6N3NDRUvUFVfuK7g9xwPhIR7Yx2/QkyeqPvDKI9HY9clJHNFg6PojxvRa0wYZj/uu8GchNF+cuy2cLhu/xBT+pEKM4E8KNdr6Id6ewszAyCPG9FrTBxzkcuLsoQ0QZV4UO0KoyqHOW7gdyoymwRbdjlDxrCByHIYZGEo5BFO1AsliaKgA/RqjBxzke+cr2MhgbN9vUlnvXdQW5U5XzfDeSmqsrcWF9f7NvlDxKcD8U9o5BE76Kmpv+Z/v59rvfdQW44H4mIXKzQKWjXAazUg32nEBFPi5QVhj3zwrDAV65XmDDM85XrFSYMC9eEYc+uvXI9ib8D8HsskD+MThXtSBStPzIMc1/x3UFu4pyPPC1CRJVPcR4UvDWSiIiIRsHjuh8yOoTHdJbvFCIaxtMiZXR3b5oQhr2j89RAig1fA115wrB3Wnf3pgk7/QFFLAEQ4EQZnQdx0btataq3rqtr7QzfHeQmzvnIxUUZg4N9cwG7zHcHubE2cYvvBnJllw3vbztJcA4U941iEL2L2tqhQ0ql5NW+O8hNnPORi4syVGWLiK713UFuRHSN7wZyI6JrVWXLTv3yCp0CYAEUPxvdKtqRZDLxhiqe891BbjgfiYhGol0vQUazvjOI6C/xyEUZXV09U3f59jiKXRQVzvTdQG7CsGdeV1fP1J36ZcXZAI9axG316g1TgqBwnO8OchPnfOTiooxi0cwCEkt8d5Aba/FZ3w3kKrFkeH9z9KROBHAiDP5z9JtoR6qqBqeL6Hm+O8hNnPORi4syRBIFa+0jvjvIlX7XdwG5sdY+IpIoOP/iEM4AUMACeWb0q2hHEgm70Vp50HcHueJ8JCLaseHXq9/gO4OI/hqPXJTR1bV+VhjmeVqkwkRRnqdFKkwY5pd0da13Oy3ysNYAWAzFA2NTRTsSRRumR1HuI747yE2c85GLizKKRTsVEF7QWWGsFV7QWXFk3vD+5qAWJwLYikV4emyaaEdKpcEpqjLfdwe5iXM+8t0iZZRKpW6R6k2+O8iNqnzGdwO5KZXMraqDLzv9kuAsKB6EiI5RFu1ATc2EdcXi1uW+O8gN5yMRUTmqgoxuQJue6juFiN4ZT4uU0dXVMzcM83z8d4UJgvz3fDeQmzDML+vq6hn547/bcAyASdiG9jGLoh0KgnUHB0Gej/+uMHHORy4uyigWzQRA+OKyiiOH+C4gVzJteH8bIYOzAPwSp8vA2DXRjqhKvQj44rKKE9985DUXZWzZ0vh0Tc2GwHcHuamr6/+A7wZy09dnrh8YmD7k8CsfgOLrYxZE72rt2ubfz5y58fO+O8hNnPORRy6IqHI8qgcBOBKCh32nEFF5XFyU0dBQmFdfb6/x3UFu+vvrfu67gdzU19trGhoKI7vtO4kzAfwWi+RPY1tFOzJ7du7IZHLwy747yE2c85GLizKMwTZVfcV3B7kR0Rd9N5AbVX3FGGwb4Y+fAYALSM9UzaCIbPbdQW44H4mI3u5Xuhcy2o8VerjvFCLaMR65KKOjY92kNWt6Z/vuIDfZ7PqjfTeQmzVremd3dKyb9K4/WIWTALyEk+S/x76KdqS7e9OEKOo51HcHuYlzPnJxUUYikTgikbCX+e4gNyL6Nd8N5CaRsJclEokjRvCjZwB4aKx76N0NDGydpWqW+u4gN3HORy4uykgmzUuA8r0FFcYY5V8+FUefHt7fdvQjKhCcxrtExodEonqziK7y3UFuOB+JiP5cm6aQ0T6s0jrfKUT07njkooww7J2WzeYW+O4gN0GQv8B3A7nJZnMLwrB3x0/DFZwGoA3zpT+eKtqRKFq7fzZbOMl3B7mJcz5ycVGGaqnRGLPYdwe5kst9F5AbY8xi1VLjDn9oeHHxy3iK6N2USuYAY/Qs3x3kKr75yMVFGcmkXQeU7vfdQW6MwVd9N5Cr0v3D+1sZGW2AYj5KXFyMF0ND1RtU5Se+O8gN5yMR0XZtei4y+qzvDCIaOR65KCObLRweRXneilphwjDPF1pVmCjKX5bNFso/GIunRMadzs7CzCDI81bUChPnfOTiogwRbVAVPkSrwqjKUb4byI2qzBbRhnLfhOAUWC4uxpNisTRRBHyIVoWJcz7yletlVFfXdw4NDRR8d5AbY0qf9N1ArszN1dU1r73jt9qRgqABBo/HHEU7sG1b1fMTJw7c6LuD3HA+EhEBQEY/j3b9he8MInLD0yJlhGHPvDAs8JXrFSYM83xjZoUJw8I1YdhT7pXrpwJ4JM4eendRtP7IMMx9xXcHuYlzPnJxQUTj05M6EcBxKOJXvlOIiIhod9CmH0S7ln/+BRGNWzxyUUYul6vt7u6d7LuD3GSz6w/03UBuurt7J+dyudq/+oZgMSyPWoxH3d3d1R0dL+7ru4PcxDkfubgoY8sWaR0aslf77iA3InqP7wZyMzRkr96yRVr/6huCUwAuLsajgYG6w5LJQV6TVmHinI9cXJShKltEdK3vDnIjomt8N5AbEV2rKlv+4osr9WAoZqAabZ6yaAeSycQbqnjOdwe54Xwkoj1bRpcho+2+M4ho5/DIRRldXT1Td3B7HI1TUVQ403cDuQnDnnldXT1T3/blU6H4tZcgelerV2+YEgSF43x3kJs45yMXF2UUi2YWkFjiu4PcWIvP+m4gV4klw/vbW+7VagALwestxq2qqsHpInqe7w5yE+d85OKiDFX7gohd6buD3IjYu303kBsRu1LVvvA/X9gP8wH043EE/qpoR6w1m0TMCt8d5IbzkYj2XG36f9GmHIJEFYxHLsro6lo/KwzzPC1SYaIoz9MiFSYM80u6utb//6dFBKdAeL3FeBZFG6ZHUe4jvjvITZzzkYuLMopFOxUQXtBZYawVXtBZcWTe8P4GIKP7QtECgIfcx7FSaXCKqsz33UFu4pyPfOV6GaVSqVukepPvDnKjKp/x3UBuSiVzq+rgy2/915MgeAaLZIPXKNqhmpoJ64rFrct9d5Abzkci2jO16W1o06/7ziCiXcPTImV0dfXMDcP8Mt8d5CYI8t/z3UBuwjC/rKurZy4AQHASr7cY/4Jg3cFBkOfrESpMnPORi4syikUzAZBpvjvIlRziu4BcybRi0UzACj0cgqkYxBO+i2jHVKVeBDN8d5Cr+OYjr7koY8uWxqdrajbwPvsKU1fX/wHfDeSmr89cPzAwfQgJLAXwJE6VN3030Y6tXdv8+5kzN37edwe5iXM+8sgFEY0XJ/OR30S7By4uymhoKMyrr7d8pXCF6e+v+7nvBnJTX2+vGdprw3EAFsDiUd899O5mz84dmUwOftl3B7mJcz5ycVGGMdimqq/47iA3Ivqi7wZyo6qv3FyafAiAbXgCoe8eeneqZlBENvvuIDecj0S0Z2nXL9vkrjMAACAASURBVCGj9/jOIKLRUXEXdF558ZVpA7lDgUkCeV6tXrD8zuV/tYJeevHSZwWYBKA0/BX56M133Nw+0u10dKybJFK931FHzVg7Wu009rLZ9Ue3th7U4buDRm7Nmt7Zx7yGxRbgQ5kqRHf3pgnF4tZpqdTM53y30MjFOR/HfHFx3mkfvlBFvjdYPXTgAw88sOVtX791qKY47f777x/x4TUDuVUtrlt+5/L7l11y5VfEmOsBLH2nn5WkOeam79+0U0/6SyQSR4jYMwF8YWd+n/wQ0a8BWOC7g0buZSNL1SAN4fUWlWJgYOssY8xlAD7lu4VGLs75OObXXLyON+8DsLV6sPqiP/+6FbkcIve5LCyuuPSKmQCmL79z+QMAgKL5DqDnjGrwW5JJ8xKgT4/FZ9PYMUYf8t1Abr7bv3exSjWHBdLru4VGJpGo3iyiq3x3kJs45+OYH7n45S9/OXDuaR++DYLLAXwTAM4+8+z3SAnHa0mPd/mspGKGAhsAKAC8tO2l9VP32n/KVVddVXPTTTcNvP3ntWjbl128VAD9db3Z9rl/u+22N/78+2vW5A9LJOR4QH+XTjcFQZBbKGIOAcwv5syZsS4MC0eFYeGTr7560G3Tp69NvPlm9cdF8Eoq1XhfV9faGaVS1Wki8kwqddAT2ez6o43RVpHSylRq5nNBkD9DRKap2ntbWpq3hGHuElVoS0vzHd3dvZOHhuyHjdHeuXObftnZmXuPteZvjcGauXMb13R29vyttYn3iCQeTqWmbwiCwjki2Keqausd/f39mkzud4kqXm1pafxpFG2Yrlo63Vr7XGtr88ooyreqytGlkj5x1FFNz3R25k+zVmZUVZn7jjhixitBkPu4iCTS6abbgiDXIGLOVdUXWlqafhGG6w4BkgtVJWhpOeh3UbT+eFU9rFgsPnL00bPWR1H+bFWZMmmSvTOfbyrus8/6SwH7Wjrd/JNsdv2BxugHAP1DOt2UCcN8CyDHWIsnW1sb/zsMc6cCprFYrPrPo48+8E9RVLgI0KpUqunWp576w961tdUfEcHGVKrxoSBYd7BIchFQCtPpmU8HQeE4ERxhbelXra0zC2GY/ztA9u3rM3e9733TB6Jo/SdE7BupVPM9Q0P4URgWPimia1OpprZstidlTOJYEVmVSh30+zDsOQVINFk7eH9r68Evh2Hho6qoa2lp/F5396YJQ0P9F4jIH1Opgx7s6lo/q1TSE0VsZyrV/FQ2m3+fMTJHRB9NpZpyQZD7kIjZX6TmR6nU37wZhoVPqurWlpamu7u6eqaWSokPWoue1tbGFZ2dvXOste8zxvx27twZXdls4SRjMDORKP3XnDkzXwqC/AUiMiGdbrwliv64l+rAhap2U0tL8wNRlG9WlZOt1a7W1qbfRlHuvapmbiIhj82Zc9C6KFp/lqr+TVVV3d1HHLH/1iAo/L0I+tPpxh9G0dr9Vas+pIpcS0vjo1G0/khVnW9taXVr68woivInqMrsqir74BFHNP8xinLnq5qJqdRB3//tbzfU1NfbjwH6cjrddH8Q5JpEzCmq6G5pafxNGPbMAxJp1WKmpWXWH6KocKYqDti2bfDH733vwa9HUf4yQIZSqcY7Ozpe3DeZHPo7wBbS6eZfZbOFw43B+wH9XfqViZNmJgY7/zMsfNJa+Xlr60EvhmHuPMBMevXVg25rasonX3vNXCSim1Oppp91dKw7KJlMLt6+7wXB+mNEtAUotqfTs57/630vf6mqllpamn/w9n1v+wwQ0Y5UqimbzeYWGGMOBcwv0ukZL4Rh4VwADcXiy7fX1dXJ0NCEi98+A4wpPTt37szHOzsLR1mLo4yxj8+d2/xsEORPF5HpxuCnc+c2vhoEuYtFIOl08+2dnYV9rMU5qrqhpaXp4SjqOVQ1scBayba2HtSxfd9LJIZ+OWfO7N4oKnxYFZP32mvwBxs2zC4N73vYkk433huGvdMAe4aqfb6lpbl9+763fQZEUW6xqjloYCDxs2OPnb45igoXqWoynW66raNj3aRkMnkegBfT6cafb9/3ts+AMCy8H8Dhb9/39t576M7f/a7pp8P7mn09lWr+8dtnQBDk0iJmHoDfpNON3W+fAWFY+JiI1qRSTd9/9tlnJ27bVnf+9hmwZk3v7ETCniBio1SqeXUQ5OeLyJGq9tctLc354TdWy37FYtUPjzrqgP4oWv/32/e97u7c3wwNmbNEsC6Vanysq6tnbqmUeO/2fS8ICieLoFlk6IFUavamIMhfKCJ7pdONt2yfAar2pZaW5v/q7CzMtBYnbd/3ts8AY7Bi7tzGniDIfVDETN2+7701A95saWn60dv3ve0zIJEoPTVnzszOKCqcqIpZ2/e97TMglTroe2vWbKxLJoc++vYZoKq/b2lpWhVFuWNVTapUMm1HHTVj7fYZUFvbf8973vOeN6Io/wlVGUinG+/KZv+wnzHVS4BSPpVq+moYFo4AcJyqfbqlpTkMw/wiQA4uFvWho49u2rgzf8+/k1juFjGa/A6A95yz+Jz3A0CimLgcwO9/+shPnxyrbUpJTrv5juUHy5BpEZHJfbb231x+Pwx7p6naw8eqj8ZGIoElvhvITY3ome+tHnjedweN3Natpf1mzuw90XcHuQmC/AW+G0bduaef8/NzT//wXaeddlrNuaefs/mc085xfm/HFZdeMXPZxUs3ARAAWPaxZc1v/fcd+tRFVy5advFSp6dtBkF+fhgWbnBtJL+CoLDSdwM5WKkHm3Yt3fXUCyf7TqGRy2Z7UmGY/6bvDnIT53yM7zkXVr4FyIcnysTLAdRgCHe5fsS3b/t2D0Q3XHXxlUsAQBN6hSju2/79Ky++cvHSi5ZO+dyll0686hNXTQeAq666qqZk5CKF2/3zyaRdB5Tud20kv4zBV303kIMSTp4gNkrvNdDpO4VGbmioeoOq/MR3B7mJcz5KXBu69tprzTNPd/9BgekC3PWTh3/6iZ35nKsuuqpFjf0BFPtA8IyIufCm2296GQCWXbx0rTV6cVWxan3JFB8GsA8UVgUrqxPVn/rGrd/gQ7GIxpOM3g9BFgvlX3ynEFGFOuf0cz5/7unn6EdOP/to3y3vJop6Dt0tz0/t5sIwd53vBhqhjCaR0S2feerVa6Ko51DfOTRy2WxPYxjmL/XdQW7inI+xPv7bCKYBWPPjh3827h9yZK2ZIiJzfHeQG1Wz0HcDjZDFPAB6fu3WemvNFN855KQBQNp3BLmJcz7G8oTOD33oQw1V26qOVMWlInpZHNvcVdXV9Z1DQwMF3x3kxpjSJ3030AgZnALgsSRwU1V1/Wu+c2jktm2ren7ixIEbfXeQmzjnYyzXXJx7+jnPApgB6J33PnzflXjrORVEtAfL6JMA7sIi+a7vFCKiWIRhz7wwLPCV6xUmDPN85XoleFQnIaNDWKEzw7BwzfADuahSRNH6I8Mw9xXfHeQmzvnIV66XoWqSAOp8d5AbVZnou4FGoAonAMjjJOkBUPfW/kYVolQqJgDhfKwwnI9EtHtr02+hXfkWVKLdFI9clJHL5Wq7u3sn++4gN9ns+gN9N9AICE4GsAIAurt7J+dyuVrPReSgu7u7uqPjxX19d5CbOOcjFxdlbNkirUND9mrfHeRGRO/x3UDvYoXOBNCEIbQBwNCQvXrLFmn1XEUOBgbqDksmB3lNWoWJcz5ycVGGqmwR0bW+O8iNiK7x3UDvIoFTAKzGyfIaAIjoWlXZ4rmKHCSTiTdU8ZzvDnLD+UhEu692/RnalP/WS7Qb45GLMlav3jCls7OXT+isMEGQW+i7gXYgo0koToDBo9u/1NnZO2f16g18QmcFCYJcQxjmW3x3kJs45yMXF2VUVxcPtdby3SIVx/DdIuOZ4lgAik14evuXrLUXVFcX+W6RCqKqjQAu8d1BruKbj1xclKFqXxCxK313kBsRe7fvBtqBtx75jXOltP1LInalqn3BYxU5stZsEjErfHeQG85HIto9ZfQptOvf+84gorHFIxdldHWtnxWG+SW+O8hNFOU/67uBynhEJwM4GiX8+s+/HIb5JV1d62d5qqKdEEUbpkdR7iO+O8hNnPORi4syikU7FRC+76DCWCtn+m6gMmpxIoA/4ER529uGZd7w/kaVolQanKIq8313kJs45yOf51/G4GDyubo6fcN3B7myX/RdQGUoTgXwq7d/2Rhzd3+/vOihiHaSiBQA3O67g1xxPhLR7qZdC2jX03xnENHY42mRMrq6euaGYX6Z7w5yEwT57/luoHewQg+HYirewF/dgRWG+WVdXT1zfWTRzgmCdQcHQZ6vR6gwcc5HLi7KKBbNBECm+e4gV3KI7wJ6BwaLATyBM6Xvr78p04b3N6oUqlIvghm+O8hVfPOR11yUsWVL49M1NRsC3x3kpq6u/wO+G+gdCBYDeOSdvtXXZ64fGJg+FHMR7YK1a5t/P3Pmxs/77iA3nI9EtPtYpXXIaB/a9AjfKUQUD54WKSMI8vPDsHCD7w5yEwQFPlV1vNmGRQD+hBOk+52+HYaFG4Igz9saK0g225MKw/w3fXeQmzjnIxcXZRiDbar6iu8OciOivKVxvBGcDn3nUyIAoKqvGINtcSbRrlE1gyKy2XcHueF8JKLdR0bXol35tFuiPQiPXJTR0bFu0po1vbN9d5CbbHb90b4b6M+s0EMAHIQhtJX7kTVremd3dKybFGMV7aLu7k0ToqiHb7KtMHHORy4uykgkEkckEvYy3x3kRkS/5ruB/szwLai/wcnyWrkfSSTsZYlEghd7VpCBga2zVM1S3x3kJs75yMVFGcbYzara5buD3IjYdt8N9GcEp0Hw8I5+RFW7jLE8f19ZtgAIfUeQG85HIqp8vAWVaI/FIxdlhGHvtGw2t8B3B7kJgvwFvhvoLQM4ETu4BXW7bDa3IAx7+TTcChJFa/fPZgsn+e4gN3HORy4uylAtNRpjFvvuIFdyue8CeovidCh+/m4/ZoxZrFpqjCOJRkepZA4wRs/y3UGu4puPXFyUkUzadUDpft8d5MYYfNV3A71FcDqAX7z7D5buH97fqFIMDVVvUJWf+O4gN5yPRFTZVuhcZLQPD2m97xQiih+PXJQRRT2H8vx95QnD3HW+GwhAEmcAaHvnt6D+pSDIX8BnJlSWbLanMQzzl/ruIDdxzkcuLsqw1kwRkTm+O8iNqlnou4EAKM6A7vgW1O1EZI61ZspYJ9GoagCQ9h1BbuKcj3zlehnV1fWdQ0MDBd8d5MaY0id9N+zxHtf9UMJ7obhwZL9gbq6urin7kC0af7Ztq3p+4sSBG313kBvORyKqXBn9ODIa+M4gIn94WqSMMOyZF4aFa3x3kJswzL/rrY805j4AvPstqNuFYeGaMOyZN4Y9NMqiaP2RYZj7iu8OchPnfOTiogxVkwRQ57uD3KjKRN8Ne7SHtQbAqRA86PBbdW/tb1QhSqViAhDOxwrD+UhElSmji5HRF6EqvlOIyB8euSgjl8vVdnf3TvbdQW6y2fUH+m7YoynOAvAgRHSkv9Ld3Ts5l8vVjmEVjbLu7u7qjo4X9/XdQW7inI9cXJSxZYu0Dg3Zq313kBsRvcd3wx5LVSA4E4qHXH5taMhevWWLtI5VFo2+gYG6w5LJQV6TVmHinI9cXJShKltEdK3vDnIjomt8N+yx2nAMgAYIHnP5NRFdqypbxqiKxkAymXhDFc/57iA3nI9EVHna9UvI6L2+M4jIPx65KGP16g1TOjt7+YTOChMEuYW+G/ZYig8CTneJAAA6O3vnrF69gU/orCBBkGsIw3yL7w5yE+d85OKijOrq4qHWWr5bpOIYvlvEhxV6CAQHw+H5FttZay+ori7y3SIVRFUbAVziu4NcxTcfubgoQ9W+IGJX+u4gNyL2bt8Ne6QE/g6KNixyv3ZCxK5UtS+MRRaNDWvNJhGzwncHueF8JKLK0q6r0aZ8bwERAeCRi7Ky2Z7GKMot9t1BbsIwd7nvhj3OSp0BRSss/mtnfj2Kcouz2Z7G0c6isdPRkT8gitaf5buD3MQ5H7m4KEPETFM1C3x3kBtVw+tk4qb4MIAncZK8tFO/rmaBiJk2ylU0hoyx+6vak3x3kJs45yOf51/G4GDyubo6fcN3B7myX/RdsMdRnA3gxzv768aYu/v75cVRLKIxJiIFALf77iBX8c1HPv+fiHber/VAVKEAiyacKLwok4gA8LRIWV1dPXPDML/Mdwe5CYL893w37FGqcA6Ap3ZlYRGG+WVdXT1zR7GKxlgQrDs4CPJ8PUKFiXM+cnFRRrFoJgDC88AVRw7xXbBHEZwLwS4+lVOmDe9vVClUpV4EM3x3kKv45iOvuSijoUGzfX2JZ313kBtVOd93wx4jo9OhmIchnLMrH1NVZW6sry/2jVYWjb2amv5n+vv3ud53B7nhfCSi8a9N/xEZbfedQUTjD0+LlBEE+flhWLjBdwe5CYICn6oaF8H5AHb5Fc5hWLghCPLzR6GIYpLN9qTCMP9N3x3kJs75yMVFGcZgm6q+4ruD3Igob2mMw2M6C0AKwM929aNU9RVjsG3XoyguqmZQRDb77iA3nI9ENL616z8jow/7ziCi8YlHLsro7t40IQx7ebdIhYmiHr5dMw6KCwH8aDQ+Kgx7p3V3b+LdIhVk1areuq6utbxbpMLEOR+5uChjcLBvLmD5nIsKY23iFt8Nu72MHo3h2xB36l0if80uG97fqFLU1g4dUiol+ZyLChPnfOTiogxj7GZV7fLdQW5EbLvvhj3AhVDcj0WydTQ+TFW7jLE8f19ZtgAIfUeQG85HIhqfMppERjfiMT3FdwoRjV88clFGGPZOy2ZzfCtqhQmCPN+KOrYWQ2CxGY+N1gdms7kFvL6pskTR2v2z2QLfilph4pyPXFyUoVpqNMYs9t1BruRy3wW7uY/D4kc4V0qj9YHGmMWqpcbR+jwae6WSOcAYPct3B7mKbz7y8d9liCQK1hYf8d1BrvS7vgt2Wyt0CoAzYdE6mh9rrX3EmGRhND+TxlYiYTeWSokHfXeQK85HIhpv2vUqZPQp3xlENP7xtEgZUdRzKM/fV54wzF3nu2G3pbgMwO2j/bFBkL+AzyepLNlsT2MY5i/13UFu4pyPXFyUYa2ZIiJzfHeQG1Wz0HfDbqldjwEwG/27/i6RtxOROdaaKaP9uTSmGgCkfUeQmzjnI6+5KKO6ur5zaGiA54ErjDGlT/pu2C0NH7X4KU6X10f/w83N1dU1r43+59JY2bat6vmJEwdu9N1BbjgfiWj8eFInIqOv4zHlm0uJaER4WqSMKMq3BkGej7etMGGYH/XD9nu8IVwAQQ4nyqqx+PggyF8dRflRvQOFxtaaNfnDoqjwRd8d5CbO+cjFRRnWolZEJvvuIDeqcqDvht3Q5VCM2TsJRGSytagdq8+n0Sdiq1WV18lUGM5HIhof2vQ4ZPR1PKx7+04hosrBIxdl5HK52u7uXh65qDDZ7HquzEeTwZUQ3DU2F3IO6+7unZzL5XjkooJ0d3dXd3S8uK/vDnIT53zk4qKMLVukdWjI8pqLCiOivOZitDyuB0BxNopYPpabGRqyV2/ZIrzmooIMDNQdlkwOXuO7g9zEOR+5uCgjmbRbAX3Bdwe50ud9F+w2SrgSQDtOkv8e2w3pC8P7G1UKEe1TRa/vDnLF+UhEPmW0Fu36Etr0dN8pRFR5eOSijNWrN0zp7OzlEzorTBDkFvpu2C0oLoLiVTyOMX95X2dn75zVqzfwzoMKEgS5hjDMt/juIDdxzkcuLsqori4eaq3lu0UqjuG7RXaVqkDwaQi+hmvFjvXmrLUXVFcX+W6RCqKqjQAu8d1BruKbj1xclKFqXxCxK313kBsRe7fvhoqXwVkA9oHirjg2J2JXqlpe31RBrDWbRMwK3x3khvORiPxp198go5/3nUFElYtHLsrIZnsaoyi32HcHuQnD3OW+Gypauy6C4ggU8e24NhlFucXZbE9jXNujXdfRkT8gitaf5buD3MQ5H7m4KEPETFM1C3x3kBtVw+tkds0/AbgZJ0tsbylVNQtEzLS4tke7zhi7v6o9yXcHuYlzPvKV62UMDiafq6vTN3x3kCvLlyntrDY9DopjAZwX52aNMXf398uLcW6Tdo2IFADc7ruDXHE+ElHcMroCGb3BdwYR0W6rq6tnbhjml/nuIDdBkP+e74aK1KYLkNHXkdHY3xcRhvllXV09c+PeLu28IFh3cBDk+XqEChPnfOQ1F2UUi2YCIDwPXHHkEN8FFUnwJSj+HYvkTx42Pm14f6NKoSr1Ipjhu4NcxTcfec1FGQ0Nmu3rSzzru4PcqMr5vhsqTrueCcV7sA1eHvVdVWVurK8v9vnYNu2cmpr+Z/r797nedwe54Xwkonjcqwm0azfa9B99pxDR7oOnRcoIgvz8MCzw4rYKEwQFPlXVxb64DIo6bBvb16rvSBgWbgiC/Hxf2yd32WxPKgzz3/TdQW7inI88LVKGMdhmrb7iu4PciChvaRypR3USDP4FFlfhdBnwlaGqrxiDbb62T+5UzaCIbPbdQW44H4lo7GX0q8jo41AV3ylERHuE7u5NE8Kwl3eLVJgo6uHbNUdihR6OjPYjo2nfKWHYO627exPvFqkgq1b11nV1reXdIhUmzvnIay7KGBzsmwtYPueiwlibuMV3w7inKkhgOYDvY5GEvnMAu2x4f6NKUVs7dEiplORzLipMnPOR11yUYYzdbK3p8t1BbkRsu++GcW8lLgbwHgBLPJcAAFS1yxjL8/eVZQuAcbAwJRecj0Q0NlboVGT0T2jXc3ynEBHtccKwd1o2m+NbUStMEOT5VtQdyeh9yOh/+s74c9lsbgGvb6osUbR2/2y2wLeiVpg45yOvuShDtdRojFnsu4NcyeW+C8atdj0fwEIksNR3yp8zxixWLTX67qCRK5XMAcboWb47yFV885GLizJEEgVr7SO+O8iVftd3wbiU0ekAbgawFH8rG33n/Dlr7SM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" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#fig-9ea44b191cb8487194e11a302ec13c3a\");\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-5\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\n",
".mousewheel(Gadfly.guide_background_scroll)\n",
".drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-8\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-8\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-9\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-9\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-13\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-16\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-9ea44b191cb8487194e11a302ec13c3a-element-17\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(x=c,y=y,Geom.line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.3.9-pre",
"language": "julia",
"name": "julia-0.3"
},
"language_info": {
"name": "julia",
"version": "0.3.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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