{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit 1\n",
    "Fit example.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Wim Lavrijsen  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Wednesday, March 03, 2021 at 09:39 AM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.23/01\n"
     ]
    }
   ],
   "source": [
    "from os import path\n",
    "from ROOT import TCanvas, TFile, TPaveText\n",
    "from ROOT import gROOT, gBenchmark\n",
    "\n",
    "c1 = TCanvas( 'c1', 'The Fit Canvas', 200, 10, 700, 500 )\n",
    "c1.SetGridx()\n",
    "c1.SetGridy()\n",
    "c1.GetFrame().SetFillColor( 21 )\n",
    "c1.GetFrame().SetBorderMode(-1 )\n",
    "c1.GetFrame().SetBorderSize( 5 )\n",
    "\n",
    "gBenchmark.Start( 'fit1' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We connect the ROOT file generated in a previous tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fill = TFile( 'py-fillrandom.root' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function \"ls()\" lists the directory contents of this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFile**\t\tpy-fillrandom.root\t\n",
      " TFile*\t\tpy-fillrandom.root\t\n",
      "  KEY: TFormula\tform1;1\tabs(sin(x)/x)\n",
      "  KEY: TF1\tsqroot;1\tx*gaus(0) + [3]*form1\n",
      "  KEY: TH1F\th1f;1\tTest random numbers\n"
     ]
    }
   ],
   "source": [
    "fill.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get object \"sqroot\" from the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Formula based function:     sqroot \n",
      "               sqroot : x*gaus(0) + [3]*form1 Ndim= 1, Npar= 4, Number= 0 \n",
      " Formula expression: \n",
      "\tx*[p0]*exp(-0.5*((x-[p1])/[p2])*((x-[p1])/[p2]))+[p3]*(abs(sin(x)/x)) \n"
     ]
    }
   ],
   "source": [
    "sqroot = gROOT.FindObject( 'sqroot' )\n",
    "sqroot.Print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fit histogram h1f with the function sqroot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.TFitResultPtr object at 0x85b0ad0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=198.935 FROM MIGRAD    STATUS=CONVERGED     148 CALLS         149 TOTAL\n",
      "                     EDM=2.98567e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           3.31658e+01   5.45703e-01   3.00376e-03  -1.11540e-03\n",
      "   2  p1           4.00667e+00   1.65304e-02   9.48491e-05  -3.06425e-02\n",
      "   3  p2           9.84663e-01   1.28238e-02   6.05976e-05  -3.04244e-02\n",
      "   4  p3           6.34464e+01   1.33233e+00   8.77483e-03  -3.96109e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n",
      "Error in <TFitResultPtr>: TFitResult is empty - use the fit option S\n"
     ]
    }
   ],
   "source": [
    "h1f = gROOT.FindObject( 'h1f' )\n",
    "h1f.SetFillColor( 45 )\n",
    "h1f.Fit( 'sqroot' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now annotate the picture by creating a PaveText object\n",
    "and displaying the list of commands in this macro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit1      : Real Time =   0.94 seconds Cpu Time =   0.52 seconds\n"
     ]
    }
   ],
   "source": [
    "fitlabel = TPaveText( 0.6, 0.3, 0.9, 0.80, 'NDC' )\n",
    "fitlabel.SetTextAlign( 12 )\n",
    "fitlabel.SetFillColor( 42 )\n",
    "fitlabel.ReadFile(path.join(str(gROOT.GetTutorialDir()), 'pyroot', 'fit1_py.py'))\n",
    "fitlabel.Draw()\n",
    "c1.Update()\n",
    "gBenchmark.Show( 'fit1' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}