{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ROOT as rt\n",
    "from rep import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means:   mu_x = -0.494    mu_y =  0.498\n",
      "Fitted mean of x:   mu_hat = -0.492 +- 0.008\n"
     ]
    }
   ],
   "source": [
    "from array import array\n",
    "import math\n",
    "\n",
    "rt.gStyle.SetOptStat(\"emr\")\n",
    "rt.gStyle.SetOptFit(1111)\n",
    "\n",
    "r = rt.TRandom3()                        # Random generator\n",
    "\n",
    "SavePlots = False\n",
    "\n",
    "verbose = False\n",
    "Nverbose = 10\n",
    "\n",
    "\n",
    "\n",
    "#----------------------------------------------------------------------------------\n",
    "# Histogram with fit (1D):\n",
    "#----------------------------------------------------------------------------------\n",
    "\n",
    "# Statistics and fitting results replaced in:\n",
    "rt.gStyle.SetStatX(0.92);    # Top right corner.\n",
    "rt.gStyle.SetStatY(0.92);\n",
    "\n",
    "# Define histograms (name, title, number of bins, minimum, maximum):\n",
    "if not 'Hist_x' in locals():\n",
    "    Hist_x = rt.TH1F(\"Hist_x\", \"Hist_x\", 100, -5.0, 5.0)\n",
    "\n",
    "if not 'Hist_y' in locals():\n",
    "    Hist_y = rt.TH1F(\"Hist_y\", \"Hist_y\", 100, -5.0, 5.0)\n",
    "\n",
    "# Loop to get some random values and fill them into histogram:\n",
    "Npoints = 10000                       # Number of random points produced\n",
    "for iexp in range( Npoints ) : \n",
    "    Hist_x.Fill(r.Gaus()*0.8-0.5)\n",
    "    Hist_y.Fill(r.Gaus()*1.3+0.5)\n",
    "\n",
    "# Plot result:\n",
    "# ------------\n",
    "canvas = plotting.canvas(name='sfd123ssfd', )\n",
    "#TCanvas( \"canvas\", \"canvas\", 50, 50, 1200, 600 )\n",
    "\n",
    "Hist_x.SetTitle(\"Distribution of Gaussian numbers\")\n",
    "Hist_x.GetXaxis().SetRangeUser(-5.0, 5.0)\n",
    "Hist_x.GetXaxis().SetTitle(\"Random numbers\")\n",
    "Hist_x.GetYaxis().SetTitle(\"Frequency\")\n",
    "Hist_x.SetLineColor(rt.kBlue)\n",
    "Hist_x.SetLineWidth(2)\n",
    "Hist_x.Draw(\"e\")                     # The option \"e\" shows errors (Poisson!)\n",
    "\n",
    "# Fitting histogram (with predefined function):\n",
    "fit_x = rt.TF1(\"fit_x\", \"gaus\", -2.0, 2.0)\n",
    "fit_x.SetLineColor(rt.kBlue-8)\n",
    "fit_x.SetLineWidth(3)\n",
    "Hist_x.Fit(\"fit_x\")\n",
    "\n",
    "# Drawing a second histogram:\n",
    "Hist_y.SetLineColor(rt.kRed)\n",
    "Hist_y.SetLineWidth(2)\n",
    "Hist_y.Draw(\"same\")                  # The option \"same\" makes it plot on top\n",
    "\n",
    "# Example of how to get e.g. means from a histogram and result of fit:\n",
    "print \"Means:   mu_x = %6.3f    mu_y = %6.3f\"%(Hist_x.GetMean(), Hist_y.GetMean())\n",
    "print \"Fitted mean of x:   mu_hat = %6.3f +- %5.3f\"%(fit_x.GetParameter(1), fit_x.GetParError(1))\n",
    "\n",
    "# Legend:\n",
    "leg = rt.TLegend( 0.15, 0.70, 0.38, 0.85 )\n",
    "leg.SetFillColor(rt.kWhite)\n",
    "leg.SetLineColor(rt.kWhite)\n",
    "leg.AddEntry(Hist_x, \" Gaussian (#mu = -0.5)\", \"L\")\n",
    "leg.AddEntry(fit_x, \" Fit with Gaussian to x\", \"L\")\n",
    "leg.AddEntry(Hist_y, \" Gaussian (#mu = +0.5)\", \"L\")\n",
    "leg.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "if (SavePlots):\n",
    "    canvas.SaveAs(\"Histogram.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<ROOT.TCanvas object (\"sfd123ssfd\") at 0x7f955b764c90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#----------------------------------------------------------------------------------\n",
    "# Graph with fit (1D):\n",
    "#----------------------------------------------------------------------------------\n",
    "\n",
    "# Statistics and fitting results replaced in:\n",
    "rt.gStyle.SetStatX(0.52);    # Top left corner.\n",
    "rt.gStyle.SetStatY(0.86);\n",
    "\n",
    "# Define a graph with errors: (name, title, number of bins, minimum, maximum):\n",
    "# Graphs in ROOT requires arrays, which is why these were included at the top!\n",
    "Ndata = 20\n",
    "x  = array( 'f', [0.0]*Ndata ) \n",
    "y  = array( 'f', [0.0]*Ndata )\n",
    "ex = array( 'f', [0.0]*Ndata )\n",
    "ey = array( 'f', [0.0]*Ndata )\n",
    "\n",
    "# Having defined four arrays filled with zeros, below we fill it with values:\n",
    "for i in range ( Ndata ) :\n",
    "    x[i] = 0.4 + 0.1*i\n",
    "    y[i] = (x[i]-1.0)*(x[i]-1.0) - 2.0 + r.Gaus(0.0, 0.2)    # Adding a Gaussian error of 0.2\n",
    "    ex[i] = 0.0                                              # Not really needed\n",
    "    ey[i] = 0.2                                              # Writing down the error\n",
    "\n",
    "# Define the graph:\n",
    "Graph_x = rt.TGraphErrors(Ndata, x, y, ex, ey)\n",
    "\n",
    "# Plot graph:\n",
    "# -----------\n",
    "canvas2 = plotting.canvas(name='grop')\n",
    "#rt.TCanvas( \"canvas2\", \"canvas2\", 100, 100, 1200, 600 )\n",
    "\n",
    "Graph_x.SetTitle(\"Fit of a graph\")\n",
    "Graph_x.GetXaxis().SetRangeUser(-5.0, 5.0)\n",
    "Graph_x.GetXaxis().SetTitle(\"Deciliters of alcohol\")\n",
    "Graph_x.GetYaxis().SetTitle(\"Spirit\")\n",
    "Graph_x.SetMarkerStyle(20)\n",
    "Graph_x.SetLineColor(rt.kBlue)\n",
    "Graph_x.SetLineWidth(1)\n",
    "Graph_x.Draw(\"AP\")                     # The option \"e\" shows (Poisson!) errors\n",
    "\n",
    "# Fit graph:\n",
    "# ----------\n",
    "# There are three ways of fitting!\n",
    "#   1: Predefined function (ROOT has gaus, expo, polX, etc.)\n",
    "#   2: Writing function explicitly (for simple functions)\n",
    "#   3: Defining external function (for advanced functions)\n",
    "\n",
    "# Predefined function:\n",
    "fit_x1 = rt.TF1(\"fit_x1\", \"pol2\", 0.3, 2.4)\n",
    "fit_x1.SetLineColor(rt.kRed)\n",
    "Graph_x.Fit(\"fit_x1\")\n",
    "\n",
    "# Writing function explicitely:\n",
    "fit_x2 = rt.TF1(\"fit_x2\", \"[0] + [1]*x + [2]*x*sqrt(x)\", 0.3, 2.4)\n",
    "fit_x2.SetParameters(-1.0, -1.0, 1.0)           # Remember to give good starting values!\n",
    "fit_x2.SetLineColor(rt.kRed-3)\n",
    "Graph_x.Fit(\"fit_x2\",\"+\")                       # Option \"+\" adds fit (not deleting old one!).\n",
    "\n",
    "# Defining external function:\n",
    "def func_advanced (x, p) :\n",
    "    if (x[0] < 1.25) :\n",
    "        return p[0] + p[1]*x[0] + p[2]*x[0]*x[0]\n",
    "    else :\n",
    "        return p[0] + p[1]*x[0] + 0.9*x[0]*x[0]\n",
    "\n",
    "fit_x3 = rt.TF1(\"fit_x3\", func_advanced, 0.3, 2.4, 3)   # Here you need to define number of variables\n",
    "fit_x3.SetParameters(-1.0, -1.0, 1.0)           # Remember to give good starting values!\n",
    "fit_x3.SetLineColor(rt.kRed+3) \n",
    "Graph_x.Fit(\"fit_x3\", \"+\")\n",
    "\n",
    "canvas2.Update()\n",
    "if (SavePlots):\n",
    "    canvas2.SaveAs(\"Graph.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "/w9kzWMK1WxEGgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<ROOT.TCanvas object (\"sfd123ssfd\") at 0x7f955b764c90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "BQAA4AChCgAAwAFCFQAAgAOEKgAAAAcIVQAAAA78/3fP5CSxeJgtAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<ROOT.TCanvas object (\"grop\") at 0x7f955bb3f240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canvas2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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