Multigraph_cpp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multigraph tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Canvas\n",
    "To begin with, we must create a canvas on which to put our multigraph (which we will create using the class TMultiGraph)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?1034h"
     ]
    }
   ],
   "source": [
    "TCanvas c1(\"c1\",\"multigraph\",700,500);\n",
    "c1.SetGrid();\n",
    "\n",
    "TMultiGraph mg(\"mg\",\"mg\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph 1\n",
    "We then create our first graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "const Int_t n1 = 10;\n",
    "Double_t px1[] = {-0.1, 0.05, 0.25, 0.35, 0.5, 0.61,0.7,0.85,0.89,0.95};\n",
    "Double_t py1[] = {-1,2.9,5.6,7.4,9,9.6,8.7,6.3,4.5,1};\n",
    "Double_t ex1[] = {.05,.1,.07,.07,.04,.05,.06,.07,.08,.05};\n",
    "Double_t ey1[] = {.8,.7,.6,.5,.4,.4,.5,.6,.7,.8};\n",
    "TGraphErrors gr1(n1,px1,py1,ex1,ey1);\n",
    "gr1.SetMarkerColor(kBlue);\n",
    "gr1.SetMarkerStyle(21);\n",
    "mg.Add(&gr1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph 2\n",
    "We then create our second one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "const Int_t n2 = 10;\n",
    "Float_t x2[]  = {-0.28, 0.005, 0.19, 0.29, 0.45, 0.56,0.65,0.80,0.90,1.01};\n",
    "Float_t y2[]  = {2.1,3.86,7,9,10,10.55,9.64,7.26,5.42,2};\n",
    "Float_t ex2[] = {.04,.12,.08,.06,.05,.04,.07,.06,.08,.04};\n",
    "Float_t ey2[] = {.6,.8,.7,.4,.3,.3,.4,.5,.6,.7};\n",
    "TGraphErrors gr2(n2,x2,y2,ex2,ey2);\n",
    "gr2.SetMarkerColor(kRed);\n",
    "gr2.SetMarkerStyle(20);\n",
    "mg.Add(&gr2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End Product\n",
    "We add the two graphs onto our canvas, and update it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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OgHmHS3Ewo3a+F/qZpxsY/9Ur+fnn75vvc3srP/0kr14NeLNhbUgy48Y6K+Tqtx2vYYen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\nQCcKBQAA0IlCAQAAdPr/w0Xf080zurkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mg.Draw(\"APL\");\n",
    "mg.GetXaxis()->SetTitle(\"X values\");\n",
    "mg.GetYaxis()->SetTitle(\"Y values\");\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT C++",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": "text/x-c++src",
   "file_extension": ".C",
   "mimetype": " text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}