{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph fit example within pyROOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.20/08\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import ROOT\n",
    "from ROOT import TF1, TVirtualFitter, TGraphErrors, TCanvas, TMinuit, TFitResult, gROOT, TGraph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "numDat = 10 \n",
    "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='d')\n",
    "dx = np.array([0.1,0.1,0.5,0.1,0.5,0.1,0.5,0.1,0.5,0.1], dtype='d')\n",
    "y = np.array([1.1 ,2.3 ,2.7 ,3.2 ,3.1 ,2.4 ,1.7 ,1.5 ,1.5  ,1.7 ], dtype='d')\n",
    "dy = np.array([0.15,0.22,0.29,0.39,0.31,0.21,0.13,0.15,0.19,0.13], dtype='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define fit function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myNFunc(x, p):\n",
    "    return p[0] + x[0]*p[1] + p[2]*x[0]**2 + p[3]*x[0]**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create ROOT function and set start parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "func = TF1('func',myNFunc, 0.5 ,10.5, 4)\n",
    "func.SetParameters(-1.3, 2.6 , -0.24 , 0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Graph with error bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Graph = TGraphErrors(numDat, x, y, dx, dy)\n",
    "Graph.SetTitle( 'Fit Graph' )\n",
    "Graph.SetMarkerColor( 4 )\n",
    "Graph.SetMarkerStyle( 21 )\n",
    "Graph.SetMaximum(4.)\n",
    "Graph.SetMinimum(-1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start fitter and return fit result for later processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=2.15982 FROM MINOS     STATUS=SUCCESSFUL     90 CALLS        2039 TOTAL\n",
      "                     EDM=4.56889e-09    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0          -1.03327e+00   4.63548e-01  -1.99485e-03   1.64067e-03\n",
      "   2  p1           2.54886e+00   3.59144e-01   2.70551e-03  -5.29683e-02\n",
      "   3  p2          -4.84278e-01   7.01863e-02  -2.72411e-04  -7.70245e-01\n",
      "   4  p3           2.56882e-02   3.97082e-03   3.97082e-03  -1.30073e+00\n"
     ]
    }
   ],
   "source": [
    "result = Graph.Fit(func, \"M E S\",\"\",0.5,10.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confidence plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = result.GetConfidenceIntervals(0.66, False)\n",
    "interval = TGraphErrors(Graph.GetN())\n",
    "for i in range(Graph.GetN()):\n",
    "    interval.SetPoint(i, Graph.GetX()[i], func.Eval(Graph.GetX()[i] ))\n",
    "    interval.SetPointError(i, 0, values[i] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Canvas for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = TCanvas( 'c', 'A Simple Fit',700, 500)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw canvas and graph and 1 sigma confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Graph.Draw(\"AP\")\n",
    "interval.SetFillColor(21)\n",
    "interval.Draw(\"3\")\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create graph for confidence interval\n",
    "\n",
    "Compute the confidence intervals at the x points of the created graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "GraphConf = TGraphErrors(Graph.GetN())\n",
    "GraphConf.SetTitle(\"Fit Polynomial with .95 confidence;x;f(x)\")\n",
    "for i in range(Graph.GetN()):\n",
    "    GraphConf.SetPoint(i, Graph.GetX()[i],0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "  \n",
    "TVirtualFitter.GetFitter().GetConfidenceIntervals(GraphConf,0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the \"GraphConf\" graph contains function values as its y-coordinates\n",
    "and confidence intervals as the errors on these coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "myC = TCanvas(\"myC\", \"Confidence Intervall\",800, 800);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "GraphConf.SetLineColor(4)\n",
    "GraphConf.Draw(\"AP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Graph.SetLineColor(2)\n",
    "Graph.SetMarkerStyle(21)\n",
    "Graph.Draw(\"P SAME\")\n",
    "func.SetLineColor(4)\n",
    "func.Draw(\"SAME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myC.Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}