{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\tdemo(plotmath)\n",
      "\t---- ~~~~~~~~\n",
      "\n",
      "> #  Copyright (C) 2002-2016 The R Core Team\n",
      "> \n",
      "> require(datasets)\n",
      "\n",
      "> require(grDevices); require(graphics)\n",
      "\n",
      "> ## --- \"math annotation\" in plots :\n",
      "> \n",
      "> ######\n",
      "> # create tables of mathematical annotation functionality\n",
      "> ######\n",
      "> make.table <- function(nr, nc) {\n",
      "+     savepar <- par(mar=rep(0, 4), pty=\"s\")\n",
      "+     plot(c(0, nc*2 + 1), c(0, -(nr + 1)),\n",
      "+          type=\"n\", xlab=\"\", ylab=\"\", axes=FALSE)\n",
      "+     savepar\n",
      "+ }\n",
      "\n",
      "> get.r <- function(i, nr) {\n",
      "+     i %% nr + 1\n",
      "+ }\n",
      "\n",
      "> get.c <- function(i, nr) {\n",
      "+     i %/% nr + 1\n",
      "+ }\n",
      "\n",
      "> draw.title.cell <- function(title, i, nr) {\n",
      "+     r <- get.r(i, nr)\n",
      "+     c <- get.c(i, nr)\n",
      "+     text(2*c - .5, -r, title)\n",
      "+     rect((2*(c - 1) + .5), -(r - .5), (2*c + .5), -(r + .5))\n",
      "+ }\n",
      "\n",
      "> draw.plotmath.cell <- function(expr, i, nr, string = NULL) {\n",
      "+     r <- get.r(i, nr)\n",
      "+     c <- get.c(i, nr)\n",
      "+     if (is.null(string)) {\n",
      "+         string <- deparse(expr)\n",
      "+         string <- substr(string, 12, nchar(string) - 1)\n",
      "+     }\n",
      "+     text((2*(c - 1) + 1), -r, string, col=\"grey50\")\n",
      "+     text((2*c), -r, expr, adj=c(.5,.5))\n",
      "+     rect((2*(c - 1) + .5), -(r - .5), (2*c + .5), -(r + .5), border=\"grey\")\n",
      "+ }\n",
      "\n",
      "> nr <- 20\n",
      "\n",
      "> nc <- 2\n",
      "\n",
      "> oldpar <- make.table(nr, nc)\n",
      "\n",
      "> i <- 0\n",
      "\n",
      "> draw.title.cell(\"Arithmetic Operators\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x + y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x - y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x * y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x / y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %+-% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %/% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %*% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %.% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(-x), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(+x), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Sub/Superscripts\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x[i]), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x^2), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Juxtaposition\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x * y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(paste(x, y, z)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Radicals\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(sqrt(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(sqrt(x, y)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Lists\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(list(x, y, z)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Relations\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x == y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x != y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x < y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x <= y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x > y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x >= y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %~~% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %=~% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %==% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %prop% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %~% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Typeface\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(plain(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(italic(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(bold(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(bolditalic(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(underline(x)), i, nr); i <- i + 1\n",
      "\n",
      "> # Need fewer, wider columns for ellipsis ...\n",
      "> nr <- 20\n",
      "\n",
      "> nc <- 2\n",
      "\n",
      "> make.table(nr, nc)\n"
     ]
    },
    {
     "data": {
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BnzCGds2Tk25uAuZoPbvVZB8mFiznyMSO5Ru26QQimZWyPSUaovkTEPc86WnWFj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\n+H/0PDs3yK0vxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$mar\n",
      "[1] 0 0 0 0\n",
      "\n",
      "$pty\n",
      "[1] \"s\"\n",
      "\n",
      "\n",
      "> i <- 0\n",
      "\n",
      "> draw.title.cell(\"Ellipsis\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(list(x[1], ..., x[n])), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x[1] + ... + x[n]), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(list(x[1], cdots, x[n])), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x[1] + ldots + x[n]), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Set Relations\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %subset% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %subseteq% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %supset% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %supseteq% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %notsubset% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %in% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %notin% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Accents\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(hat(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(tilde(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(ring(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(bar(xy)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(widehat(xy)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(widetilde(xy)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Arrows\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %<->% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %->% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %<-% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %up% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %down% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %<=>% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %=>% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %<=% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %dblup% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x %dbldown% y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Symbolic Names\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(Alpha - Omega), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(alpha - omega), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(phi1 + sigma1), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(Upsilon1), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(infinity), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(32 * degree), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(60 * minute), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(30 * second), i, nr); i <- i + 1\n",
      "\n",
      "> # Need even fewer, wider columns for typeface and style ...\n",
      "> nr <- 20\n",
      "\n",
      "> nc <- 1\n",
      "\n",
      "> make.table(nr, nc)\n",
      "$mar\n",
      "[1] 0 0 0 0\n",
      "\n",
      "$pty\n",
      "[1] \"s\"\n",
      "\n",
      "\n",
      "> i <- 0\n",
      "\n",
      "> draw.title.cell(\"Style\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(displaystyle(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(textstyle(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(scriptstyle(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(scriptscriptstyle(x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Spacing\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x ~~ y), i, nr); i <- i + 1\n",
      "\n",
      "> # Need fewer, taller rows for fractions ...\n",
      "> # cheat a bit to save pages\n",
      "> par(new = TRUE)\n",
      "\n",
      "> nr <- 10\n",
      "\n",
      "> nc <- 1\n",
      "\n",
      "> make.table(nr, nc)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+kWE6Rmn/CvGon8S08ZnVSQzvJ2bD6d2obF7tIhUprhgdXCdXR0z1U3CAcjsyOT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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$mar\n",
      "[1] 0 0 0 0\n",
      "\n",
      "$pty\n",
      "[1] \"s\"\n",
      "\n",
      "\n",
      "> i <- 4\n",
      "\n",
      "> draw.plotmath.cell(expression(x + phantom(0) + y), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x + over(1, phantom(0))), i, nr); i <- i + 1\n",
      "\n",
      "> draw.title.cell(\"Fractions\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(frac(x, y)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(over(x, y)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(atop(x, y)), i, nr); i <- i + 1\n",
      "\n",
      "> # Need fewer, taller rows and fewer, wider columns for big operators ...\n",
      "> nr <- 10\n",
      "\n",
      "> nc <- 1\n",
      "\n",
      "> make.table(nr, nc)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KsrHj4QSQAUUBS/ciK0ZX8c8TGggKK4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\nCCASQAT+B7yQ22B+KxsJAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$mar\n",
      "[1] 0 0 0 0\n",
      "\n",
      "$pty\n",
      "[1] \"s\"\n",
      "\n",
      "\n",
      "> i <- 0\n",
      "\n",
      "> draw.title.cell(\"Big Operators\", i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(sum(x[i], i=1, n)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(prod(plain(P)(X == x), x)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(integral(f(x) * dx, a, b)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(union(A[i], i==1, n)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(intersect(A[i], i==1, n)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(lim(f(x), x %->% 0)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(min(g(x), x >= 0)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(inf(S)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(sup(S)), i, nr); i <- i + 1\n",
      "\n",
      "> nr <- 11\n",
      "\n",
      "> make.table(nr, nc)\n"
     ]
    },
    {
     "data": {
      "image/png": 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EQC8DGgSEQC8DGgSEQC8DHAF4kQw9A5fAa+SEMXgCAAHwOKRCQAHwOKRCQAHwOK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\n5AuU8ye6AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$mar\n",
      "[1] 0 0 0 0\n",
      "\n",
      "$pty\n",
      "[1] \"s\"\n",
      "\n",
      "\n",
      "> i <- 0\n",
      "\n",
      "> draw.title.cell(\"Grouping\", i, nr); i <- i + 1\n",
      "\n",
      "> # Those involving '{ . }' have to be done \"by hand\"\n",
      "> draw.plotmath.cell(expression({}(x , y)), i, nr, string=\"{}(x, y)\"); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression((x + y)*z), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x^y + z),   i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x^(y + z)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(x^{y + z}), i, nr, string=\"x^{y + z}\"); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(group(\"(\", list(a, b), \"]\")), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(bgroup(\"(\", atop(x, y), \")\")), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(group(lceil, x, rceil)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(group(lfloor, x, rfloor)), i, nr); i <- i + 1\n",
      "\n",
      "> draw.plotmath.cell(expression(group(\"|\", x, \"|\")), i, nr); i <- i + 1\n",
      "\n",
      "> par(oldpar)\n"
     ]
    },
    {
     "data": {
      "image/png": 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J0Sb87MJvOsVdoE4yVEGhBEaksvd3Dt\njFnZTWaO+7B1jZNjEczg1UizsHEf1RsGRHoGQfeqnn7uYGzXdu7fjr59il2rdHKll56dtXUt\nUPzOHsnKBiK1pZ87eLTh8yPfRPmZh2K7VqR39Ghsft3rr/lMKtkobZicPuuFSG3p6Q4u7bK8\nefKjplX4bLbatYuz/t97sTMj9zoaHRJEegpESqm2SE6sbTKbBceEIh2jUL13ITbTZGq+vS5B\neTUon2bcX6UQqT393MG5GyMF/bSDjQ+VNQ6BSO/qke/cxWZyXWyqR/XqESK1ppc7uHFWrOy6\nXDSzUWXVXSDS23rkOnfG7K6Lw6D07REitaaPO3iK0udIgThba2dNxyel2YcXV20AvsxXWGAu\n5CXjdDKiTxCpLX3cwUW+sqHcuTvdXr36viLdb5F2o6pqrweR2jLUHdz+1d9TTNwYqabfdgnK\nEB4hUmuGuoNxOGT6M3w7S6bm+gHZJShDeIRIrRnmDro+2/s9bH2Ewyh9jnTdubsEpTpg6gVE\nasswdzDyC4b+Ip/5yoarzh0i3QaRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAf\nA0QCDYiPASKBBsTHAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8B\nIoEGxMcAkUAD4mOASKAB8TFAJNCA+BggEmhAfAwQCTQgPgbyRQJwDJ3De8gXaegKgATExwCR\nQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAfA0QCDYiPASKBBsTHAJFAA+JjgEig\nAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TFAJNCA\n+BggEmhAfAwQCZppSkf/qREfA0SCRprD0XtsxMcAkaCJW9noOzfiY4BIOUdrj9mn7fypE+fb\nF9RGBIj0BIiUs7J2lX442tNTJ54KAd8Nc365pMSkBEX9gEht6esORrNZlH6Il0+euYy7r40E\nKiKZoByRKiBSxt6uVnbvPmwaGyRr68tPdvOqWg1KxSETFCNShb8tUpy6s7eLZGGPR/eWJLPY\nqzFLdxfvGaFINsdfpXzU+4BIz/C3RTpa352LolPilZlZL9XalcxTwVzrtCod3CjSOjv63Tgb\nY4qxUbmHh0ghf1sk58AqWbmuWaqM/7C0B1e8TRsn30yVjq3p2i2sHx8d7LPDKhWURUquWiJE\nCvnjIrnO3drO/dvRt0+x+5AOkWbp26VnZ22pBSqdnM4znOxbTjfUilSZcugNRGrLi+/g0dq6\nZse3VH4C4lJcI1JcCNQ0DaGbukER099N/HWRXF8u6JflTpz84GkVPiGq6hJn/b+aPW/CrWkF\nHshW+OsiNbRIzq9tMgtn40JdjtHFwHcSaWx+3euv+USkp/jrIs3dGKk8wsnHSMnBxodgzq6i\nS9mjtxoj7czIvY5Gfs4lnaUT4REitea1d3DjdFilM945y2Iqe2ajytqfQKSSR24s9U6zdrGZ\nJlPznX7mZxQP87dFOkXpc6SSMecJhq21t56zlicfVu/1HGlsYjMZuhJVEKktL72Di3xlQ6lr\nNss/n2ylZxdSFunNVjbsjDG7oStRBZHa0vcd3ObN09Y+uqr7aN/shxRf5mvoKlyBSG3p/Q7m\nq7/j8sjpJu+2+psW6b+ASFXS3yO5Ptujerzd75Embow0HroSVRCpLf3fwe3Czz88/DPZxZt1\n7L5dx25qpLWyiNQW8XfwzTiM0udI0jp34mOASBDwma9sENa5Ex8DRAINiI8BIoEGxMcAkUAD\n4mOASKAB8TFAJNCA+BggEmhAfAwQCTQgPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHx\nMUAk0ID4GCASaEB8DBAJNCA+BogEGhAfA0QCDYiPASKBBsTHAJFAA+JjIF8kAMfQObyHfJGG\nrgBIQHwMEAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TFAJNCA+BggEmhAfAwQ\nCTQgPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogE\nGhAfA0QCDYiPASKBBsTHAJFAA+JjgEigAfExQKSco7XH9MPC2qdOtHbxivoIwZRe/YeUAeqB\nSG3p6w6urF3597l9WqR3NukikgmLegaR2tLXHYxms8i/W7t/8szDk+bp4uyQKRX0DyK1pac7\nuLerVapQsxWNexDp9SBSW156B+PUnb3rmy3s8Zh20bwVJztLdxfvGaEvNudqx7thfEbMeXA0\nUGAQqS0vvYNH67tzUXRKvDKzsxXzrH+3yYZNOX9ZpOScFESq52+LlKydKiu7yZTxH06pWtts\n/mCRT+Rl1PiysLF7jezppZUcllCkofKCSG158R2M7drO/dvRt09xsswaoVnqxqVnZ22pBSqd\n7D1yAi5fW8lhKTdFTH838ddFOlpbbnbyOfC0pfITEJfyGpFyj9Kp89fWclBERASR2vLqO7gM\nmpNCpLSHtwp6dlddu/j8/Oj9RBqbX/f6az4TIRFBpLb02yK5Uc86fV/abTKbBYeGthyjs4Gr\n93siuzMj9zoaHZJBOnLXIFJbXnwH526MFJe2j/nWwcaHYM6uIlLJoyQKW663IDbTZGq+h67G\nGURqy2vv4MbpsMoboYxCl5mt+hGIVPLoPae/xyY2k6ErcQGR2vLSO3iK0udI4XRD9r61dlZ7\nTnHYmz9H2hljdkNX4gIiteWld3CRr2wode4KK0620rMLeXuRki/zNXQVSiBSW/q+g6UW6dGR\nz1uKRIv0HIhUIcpXf8fByOkW+7cUaeLGSOOhK3EBkdrS9x1cpt019xLfPzbFHfqGCxu+Xcdu\nah69B68HkdrS+x1cepEiv27oMd7yd32HUfocSU7nDpHaIv4OviWf+coGMZ078TFAJNCA+Bgg\nEmhAfAwQCTQgPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJ\nNCA+BogEGhAfA0QCDYiPASKBBsTHAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQa\nEB8DRAINiI+BfJEAHEPn8B7yRRq6AiAB8TFAJNCA+BggEmhAfAwQCTQgPgaIBBoQHwNEAg2I\njwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAfA0QCDYiPASKBBsTH\nAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8BIkGVw/T+Mcbz+qqc\nER+DvyjSdp4k1vp/rrfO+O1K2bZ0fgOlM+bbur3ZZfOL+7eawwZm94BHHkQq8wdFOtpTSZ1w\n68K1SDNbOr+B0hkne6zZeyVSzWHDshs37grVQaQyf1CkeJmU1Am3LlS3LyXpGQ2Uz1rGNXuz\nA0oi1Rw2LOPfxl2I1MzfE2mTNijzuf+nulWiUaTNjQYpOOtkN5W9569J+3T5xvVhg/L90bwP\nkZpRL9Iyssv8/+FPM+uTuZ7Z2drvCv7ff2mjrCWZpS3Acun/qW6VOHfttrG18TbrkNnzGW6g\nZIsrhmedvyiJZ9W6Fl/jL3TeuDpsUD6+mvchUjPaRYp9vBdZ6l20l3mJ9Wkvi7QqCvd2XTo9\n3CpTiLROL+cOy0XKz1hlxVWT0kpkX+RP3d+4+Jnmwwbg1/xkH34mxowqUjl1vi6FiFRGuUhb\nGx2SQ5SlPvZ9rk1esglFOhcu7aF0frhVphAp8kds7Ky4XH6G9dfaXPX/Sl+UJIcrz8oXP9N8\n2ABMzb/8PSU0yZiJLxwXWz3WC5HacvsOzq2fPd5mqd+XS+JQpKxw7hus8hAnbhzwFCJlpxYl\nlTNqRDp/kR/9NM0jBOc1HzYAkzwQxny78VLFFddG/Uv+jfyuBJFClIuUJ7I0V31VUtkXZv96\nSqG8Jx3KuN7a4XA59nzGcbuKa0SqeWus9t1K9M/NYZBJ+30/ZlK387UgUluGFilZuY6jjY5V\nkbKh2BuLtPuZjq9EqnnrB0Rqy+AiuZ7acnYZI+VlCztbb49vLNLYXK8CQqRmlIsUjJGCknle\nsr+Mn7Z28fwYqbqVn5Fu1Im0T1SPkQo7Ps1H/LNDpIdRLlIwa5eWlGbtZnadnOLyrN3Wj3nK\nk83LmzPU/n+zbHoubZGOlzO8MYe6MdL5i7zDD83aNR82ABOTTWOmllyL5Fc9MEaqQ7lIxVCl\n1HxcniOlj4Dm+dR4+jnxsV2VTg+3ygEvLrnJrr/3StnofMbSnsvDsxbFF/lnTZW9dd9THCaE\nYvrbO/PveoyUzdr95Fs91guR2vLAyoZ4H/TD1lG+ssHPEywKI+ZF4SzoSIVbNSJlKxt81Pcz\nL1JxxsKXZn248KyljXI5syULd0UStbLhN5/b/sqGSL4JmhTPaJ06n75sUmz1WC9EastDd/De\nKKMc3G2w3DrcSh4Y9m9rVnXXHnjMn0DdEeloRf2Qolgi5JQZ/6a9uLEZ5fuylQ3Ty1Z/IFJb\n7s3auRHMaX5vlBG2AMHBwdZm8UCNrlZ/N5yVL+uu21uuj7DV3z+j67KaogSRQpSLlC95i+5c\nJBAp/D1RsNX8i73G8xvPKn5odL03eP4k7vdI459qyfd37YGIVEa5SMnajWBmd2e9wnHKdtG8\n9QiPnbFo7LEFIjUfNhC76u8o4oYfKCFSGe0iQefsJo8dh0hlEAmu4D9+8jyIBBoQHwNEAg2I\njwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAfA0QCDYiPASKBBsTH\nAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mMg\nXyQAx9A5vId8kYauAEhAfAwQCTQgPgaIBBoQHwNEAg2IjwEiwS3+feb/5e/fz39D1kN8DBAJ\nbjAdXz5PHvgvGb8M8TFAJGjm66t5q1/ExwCRoJFp5e9SDNgmiY8BIkET/6p/KSkZNfyppNcj\nPgaIBE18pNqM/R9kTn7Np3+9UqsvxMcAkaCBn6xjt0v/FvNodPAbk6u/i9kT4mOASNBA8cdk\nYzNNpib7O7I/4xsnvBLxMUAkaOAcjbGJTTHtUP8Hzl+P+BggEtTzc56y2xljdvnnz4GmG8TH\nAJGgnml8/vhlzk+QvgeaARcfA0SCeibnJUHlFunfg3/yvGvExwCRoJ6PQ/Fp4sZIxSTDYaAJ\ncPExQCSo55yMb9exm5q4Wtwv4mOASFBPkYzDKH2OVHTuEKkeRIJ6imR85isbxmFxz4iPASJB\nPQ3JQKR6EAnqQaSnQCSoB5GeQqFI1ra74naeXiO/TLhVf8Ldy91hfn2F/CvzPeGWDBDpKf6e\nSEd7KqkTbtUyu/l96QXucLLHalH+lfmecEsGiPQUf0+keJmU1Am3arn9fekF7rGMqyXFV2Z7\nwi0ZINJT/DmRNmkLMp/7f6pb9dz8vs0DDZJvajaVkuIrsz3hlgwQ6Sl0irS0Ud4QLCO7TKNu\n7WlmfRjXMztb58flr+UzZun/6S+X/p/qVpnt3GanWE9SuXB4udju3eveLkrnn+ys9B7PKpc/\nf2W6J9ySASI9hUqR5j7bqQKx/7TIZHGly7wk3VkWaVUU7u26dKlwq0x6QnrBQqTyhSuXO9rI\nvUZR0DjNU7tck7Vyr+tso4ZwT/NxvYNIT6FSpOiQHCLfC9rmH1NZYp/jzWVnWaRz4dIeSpcK\nt8Iv2fiL2aS4TnDh6uXWzpZVpVu2zRqoRTqBcLBNQ6lwT/NxvYNIT6FSJD9LvPX9uHn+MZUl\n/f/yoiQORTqfEQdjmvjOCKckUnDhq8vFdm2ro6xZui/r4Z1s0zRCuKf5uN5BpKdQKdL5rf5j\nUFJ/WHipWo7bVVwS6ea3us6dvZq69s2U6/ut7nzT4zXqF0R6CkRqIBsSPSqS6+Nd9clOfuS0\nyv1CpHYgUlsGEmlhZ+vtsVWL5OTaJrPZvW9CpEdApLbUieRHQ9djpHTn/DJ+SUv2l/FTOvp/\ndIyUnlwWKbjw1eXmbox0Nbo52PiQ9+wYI7UEkdrSPGu3rczapTtLk2szu05OcXnWbutbifL8\n8rJxttnLcijGSMekdtbucrmN69itrqfSZzbK26l942xcuKf5uN5BpKdQKdLCj17SSbJ8JFPq\nZF0e96zTo/Kp8eKMYvCfEW6V+1XL/MJ7r0P6mKj8HKlyuVOUPkfy0kEeZXgAAA91SURBVARd\nM9dU5j27VSpcXb9tFbi84jlSPYjUlqaVDbkBy8jG+2Dws47yBQjJKnK9r3zfvCicBX2ncKsc\ndCdrvE97cvtZKtLlwleXW+QrG+KKKydbiJqtWKgTKVzLwMqGBhCpLY/cwXsDi3KAt8GkQLiV\nPDbSb75c5RLbYgbiaLcN1y/21G0NCyI9hW6R0vUHp/m9gUXQEoTLtYOtTXmx3H+6XHiJuBg2\n5au6a64frvdm9XcTiNSWm3cwXxEX3blGkPzwB0TB1v2f6N27XPkSxXrAy++Mrq8f/gKJ3yM1\ngkhtuX0H127YP7s70RWOTbaL5q2HuHW5EtF50dCiscMW7mk+bgAQ6SmUiwQvA5GeApGgHkR6\nCkSCerJkHMxH8I5IDSAS1JMnY5L+h1aTbzMNivtGfAwQCerJk/GT/hXm5JP/9vdtEAnqKZLx\nYfyPgIueHSI1gEhQT5EM/7eYk9+iZ4dIDSAS1FP8obGDGSXJtOjZ8YfGGkAkqOf8py+/zE/y\nUfjDn75sAJGgnul3/uGfGf879+xi/hhzPYgE9fx8Fp8+zPnv9SWfP8PURnwMEAkaGBUffsx5\nzm6wvIiPASJBA+Oi8TmYc8/uZ9x09IsRHwNEggZ+inkF1yIVPbvJQD07+TFAJGjiI7dmbIrf\nG/4ONPmtIAaIBE38pqMkY8y5P/fxO1RdxMcAkaCRqe/cjcz50dFkoLnvREEMEAma+fpq3uoX\n8TFAJLjBtDRLNx6uPVIQA0SCW/z7zIdFv5//bh/5WsTHAJFAA+JjgEigAfExkC8SgGPoHN5D\nvkhDVwAkID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TFAJNCA+BggEmhAfAwQCTQg\nPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAf\nA0QCDYiPASKBBsTHAJGgDlNQUzZEfcTHAJGgjhu5QKQ6EAnqQKQnQSTPdp4k1jbs9Dsadz5K\ncZH5tuWFegKRngSRHEd76kukkz22vFI/INKTIJIjXiZ3XGkt0vkiy/jucRJApCdBpCTZ+Aap\nL5FOdtPBpV4OIj3JG4q0jOwy70qdZtaNfpL1zM7WflfmQ97NWtpomZ4wiy/73Mlx1vlyJ0Xr\n0uFlYrt3r3u7CErP33fzIvHs6X+jAUCkJ3k/kWLrWGTBnVvnVFZivS1lkVZF4d5eJEsPjXwD\nNQ9Oqoh0tJF7jdIDLwTf13iRdSqhdBDpSd5OpK2NDskhyoIb+zRv8pJNKNK5cGkPRenGn7Hw\nMmz9p1Nst/WTDWu7SlbVTtr5+25e5GCX/+lG9AsiPcnbiTT3sXURToO7L5fEoUhZ4dw3IKei\ndO7POPnmZp4Wnuy8YdYutuu0F1fm/H03L3KyGqYbEOlJ3k6kPPKl9F+VVPZdf0y3chpEOrpd\n1Yns8PsaL9LJxMWrQaQnQaT/JpLrEF710N5TpLHxf0X213xe7+oRRGrLYCIFV/yvLVLdRZSJ\ntDMj9zoaHa539QgitaXVGCkoKZYB7S/jp62fwC6NkeLS8Oa8lqdWpLkbI1XHOvlBdy6ibowU\nm2kyNd91u/oDkdrSatYuLSnN2s3s2s+ilWbttr6Tls4R+NK1n2ZLO23pScm6abJh445ZZdPm\nF/KD7lxkr27WbmxiM6nf1RuI1Jb/9hzJltN/eY609h/m+dR4+jnxyV75oyrPkbKTomPJgYtN\npyh9jmSPV9235N5FnH/aniPtjDG7+l29gUht+U8rG+J90Iyso3xlQ7KKXGcu3zcvCoOVDU6u\n86IEuzgmtSIt8pUNca1INy+icWXDl/lq2tUXiNSW/3gH7w1Eyn217aMrsmtnCZ6cOjhaFT+k\noEV6krcTyfqx0Gl+byASDHrix0Ytm8WjhTdQuPp74sZI4/pdvYFIbXn2Dq6yEVJ057BApKM9\nNR5YorqSobmwGYW/R/p2Hbupiet29QciteXpO7h2I/zZ3SYmnIbbPtms/HcWKjp25VwcRulz\npEvnDpHqeD+RoAsuufjMVzaMr3f1iPgYIBLUwVq7J0EkqAORngSRQAPiY4BIoAHxMUAk0ID4\nGCASaEB8DBAJNCA+BogEGhAfA0QCDYiPASKBBsTHAJFAA+JjgEigAfExQCTQgPgYIBJoQHwM\nEAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TGQLxKAY+gc3kO+SENXACQgPgaI\nBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJNCA+BogEGhAfA0QC\nDYiPASKBBsTHAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8BIoEG\nxMcAkUAD4mOASFDHjVwMEhnxMUAkqAORngSRoA5EepK/LNJ2niTWug+nhbXL9FMnPHa5+bar\n73sFiPQkf1ikoz3lIs2ttavuRHrscid77OoLXwAiPckfFileJrlI1me6O5EevNwy7uoLXwAi\nPcnfFWnjG6RCpKRbkR663MluuvrG7kGkJ3kbkZaRXabptfY0s270k6xndrb2u7JMZ/uSpY2W\n6QmzOC+1KflR55OC8/Mrnont3r3u7SKoQnbc45eLZ8/ciH5BpCd5F5Fin95FJovL8jIvsd6W\nskironBvC8nKyb+cFJyfXfHC0UbuNYpOQR2y4x6/3DrVUSaI9CRvItLWRofkEGWyxD7fm7xk\nE4p0LlzaQ1F6eSmdFJwfnyrft7arZFXtmhXHPXq5Q2inKBDpSd5EpLn1k8nbTJZ9uSQORcoK\n576FCEOfvpROCs6/bjliuw57e8nluEcvd7JypxsQ6UneRKR8aJ+Pg2pLKvvKh51f6vfXzRsc\nrb2avi6Oe/hy3c1vdA4iPQkiJf9JJNc1vOqXvadIY/PrXn/N5/WuHkGktggV6e+0SDszcq+j\n0eF6V48gUlv+wxgpKCmWAe0v46etn7a+OUaaX59/9YXrqxHOlUh3LqdkjBSbaTI133W7+gOR\n2vIfZu3SktI02cyuk1NcnrXb+q5ZOuR/bNbu6vs2rmO3suuw8EqkO5fbK5m1G5vYTOp39QYi\nteWZ50i23HO6PLhZ+w/zfGo8/Zz4FK/8UaFITQ9+sitehDpF6XOkylKgK5FuX86ZqOM50s4Y\ns6vf1RuI1JYnVjbE+2AIso6KVQWryHXm8n3zovC8sqH8Ujrp8vFapEW+siG+I9LNy+lZ2fBl\nvpp29QUiteWpO3hv0FHupG2fX31dOzfwnycMjlbwDylokZ7kTUSyfvBxmt8bdASjnfjZEcpm\n8WjhQ6hZ/T1xY6Rx/a7eQKS2PHYHV9kIKbpzWCDS0VYX/tyhupKhufAR1Pwe6dt17KYmrtvV\nH4jUlgfv4NoN5md3m5hw/m37nxuTLlgI7tiVc3EYpc+RLp07RKrjXUSCbrnk4jNf2TC+3tUj\n4mOASFAHa+2eBJGgDlNQUzZEfcTHAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQa\nEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TFAJNCA+BggEmhAfAwQCTQgPgaIBBoQHwNEAg2I\njwEigQbExwCRQAPiY4BIoAHxMZAvEoBj6BzeQ75IQ1cAJCA+BogEGhAfA0QCDYiPASKBBsTH\nAJFAA+JjgEigAfExQCTQgPgYIBJoQHwMEAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOA\nSKAB8TFAJNCA+BggEmhAfAwQCTQgPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk\nuIG5sdUr4mOASHADRHoURLpmO08Sa/0/yfklfb9H3Unz7Uvr+mIQ6VEQ6YqjPXUo0skeX1rb\n14JIj4JIV8TLpEORkmX8wrq+GkR6FESqsvENUjKf+39yJ4qNe9SedLKbF9b2xSDSo7y9SMvI\nLtNkW3uaWZ/s9czO1n5X1spk+5KljZbpCbO0BVku/T/5McXGhdju3eveLsLvqj0pnrX8VxgQ\nF4+x+XUffs0nIt3i3UWKrWORyTK3zqmsxHpbyiKtisK9XZfPb+jSHW3kXqPoVLs3PGmdSqcT\nF4+dGbkPo9EBkW7x5iJtbXRIDlEmS+xjv8lLNqFI58KlPZQv0DQ2WttVsmrqtIUnHeyy/jAF\n+HjEZppMzXeCSLd4c5Hm1s8+bzNZ9uWSOBQpK5z7BitoZRonGWK7tk3jpvCkk9U73ZDGY2xi\nMzlvDQMitaXdHcwjnY+Daksq+yrmNIp0dF3BpontR68hnzQeO2PM7rw1DIjUFqkiuT5gY4/t\nzURKvsxXaWsQEKktUkWiReoVRGpLh2OkoGSel+wv46etn81+dIw0d2OkprHPm42RJm6MND5v\nDQMitaXDWbu0pDRrN7Pr5BSXZ+22vscWTFY3ibRxHbtVOFPedNJe+azdt+vYTU2cINIt3lyk\n/KlRuc92eY609h/m+dR4+jnxsV+Vzw+cuGycovQ5ku/c1agWFq10P0c6jNLnSL5zh0jNvLtI\nfmVDvA8GP+soX9mQrCLXmcv3zYvCWdARaxBpka9siB8QSfnKhs98ZcMYkW7x9iKl3BullJO/\nDeYQQidq+3l3RDpaxT+kYK3do7y5SNaPhU7ze6OUIPnxsmnPJlxa11gYnMTq7y5ApLa0u4Or\nbIQU3Tms0oScGvbUrmSoKSyfxO+ROgGR2tLyDq5ja2d3Z83CDtx20bTnQconLRR37BDpcd5d\nJGgFIj0KIsENEOlREAk0ID4GiAQaEB8DRAINiI8BIoEGxMcAkUAD4mOASKAB8TFAJNCA+Bgg\nEmhAfAwQCTQgPgaIBBoQHwNEAg2IjwEigQbExwCRQAPiY4BIoAHxMUAk0ID4GCASaEB8DBAJ\nNCA+BogEGhAfA0QCDYiPgXyRABxD5/Ae4kUC0AAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAi\nAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0\nACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAi\nAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0\nACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAiAXQAIgF0ACIBdAAi\nAXQAIgF0wP8BZBVzQOEJewUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo(plotmath)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}