doc/check_rf_conversion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18ce8a1e",
   "metadata": {},
   "source": [
    "# Convert a RooAbsPdf to a python callable\n",
    "\n",
    "This is a simple demonstration that the wrapper function `convert_rf_pdf` does what we expect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b258cd",
   "metadata": {},
   "source": [
    "## Make a dummy RooFit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3af5ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/02\n",
      "\n",
      "\u001b[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby\u001b[0m \n",
      "                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University\n",
      "                All rights reserved, please read http://roofit.sourceforge.net/license.txt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import ROOT as r\n",
    "from ROOT import RooFit as rf\n",
    "\n",
    "# observable\n",
    "mass = r.RooRealVar(\"m\", \"m\", 5000, 5800)\n",
    "\n",
    "# gaussian\n",
    "mean = r.RooRealVar(\"mu\", \"mu\", 5350, 5300, 5400)\n",
    "sigma = r.RooRealVar(\"sg\", \"sg\", 25, 0, 100)\n",
    "spdf = r.RooGaussian(\"sig\", \"sig\", mass, mean, sigma)\n",
    "\n",
    "# exponential\n",
    "slope = r.RooRealVar(\"lb\", \"lb\", -0.002, -0.01, 0)\n",
    "bpdf = r.RooExponential(\"bkg\", \"bkg\", mass, slope)\n",
    "\n",
    "# total\n",
    "sy = r.RooRealVar(\"sy\", \"sy\", 1000, 0, 10000)\n",
    "by = r.RooRealVar(\"by\", \"by\", 2000, 0, 10000)\n",
    "pdf = r.RooAddPdf(\"pdf\", \"pdf\", r.RooArgList(spdf, bpdf), r.RooArgList(sy, by))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4741cad",
   "metadata": {},
   "source": [
    "## Plot the dummy RooFit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13ca026c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(pdf) directly selected PDF components: (bkg)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(pdf) indirectly selected PDF components: ()\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(pdf) directly selected PDF components: (sig)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(pdf) indirectly selected PDF components: ()\n"
     ]
    },
    {
     "data": {
      "image/png": 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SaXBAPgENdqXU9hH2ZlbSqAcwOinRuU0tHOvzZtZXSdLBcYUPsJH06KyJw5EKaTLMTAJqJhadsf1jVJGk+5HWlcw1RRBrwtEZa2/XKyqkXUh4fbimCMKdSHTWUCEFcFRSovN4t//oVCFt7n8+qOUB+5MSnclu/0GStju33xfoR86U+HE49/Rvo3g2/rnVy0aZmVR70nN7zTEV5x6dsThGzy1JW36XZDOTak9E/ReSSWmwC7Szad/cOPW/9qmXH0iG6NxXM1Z29pNu/CmBhFefmd0JgYjO/k6sWiqwbMzuhFhS+jqzdWMXp48eXaUSflEJZajhwnnIJyU63bqxizOAWpIKD1MhL/m2ISmB+Y4zR4M9nX16SzduPF6uyY8k2uyQiegc0z69pRu3DxKm4bAbjyat1cxgEUQZoMFurdVaW2tr240xxpiwWpZlWZa1HQ5/9lOyZxtfHdzMj3+kPY8k3G6OxIRAh0antXY2m2mtZ7NZnJ5aa/WYqmG1qqqwPcuy29vbA5/95B0Sps1IbW4klYB+sgPHZLTWWmtjTFhYHTdbHdkv1FattcaYqqrijYf+KmesR2/gztfbN9jT1zpDR0H81HvWkXEOhMTFYCPscZu91nhfLBa11ThkvWyXocp5kpqVU6cy1/h2PKVW250SMKi/xcawFvCXAqwZYZhoPp83N0r4GDkpziml6q/p3uNQ2f8eoUhDYLAIQhxa6wyVzdCtGRaCjVmJEWyonbblkPskZt5pVPEUVkvGmTq01mmMCQ1qa60fNXLOlWXpN/pR9aIo/GpRFAeXGYPamJ4tM5O2RVfC2iB1T4xORIerkH5fBJvHiI4xGrX92TcUgPEiiIkLKRdiQo6tk+E3tvfbT+Jtc6ZaU7hlWJ9mO4SQcjXR8b6bCP10mJbUcn1+i74N/3Bppv+fMwWjkFLrPL3bf6BPLVWtX+q0paJa+8ZTaqBIT0p04ozsHan1OVJRjNZmrY49/o+zI6XBDiHGvOtHlJ4ulMRv3BSKPj2z9dmrG9OTZgwGR3RiAwl3/VizPfxcFJZZ4yKA1fbWjwOCFT0QnZi49bpqzT5NeIIVPRCdOGU7p07ttHMfsvU8SRkm4mYf2Oiofa/bxqv2mQjwVMLtU1cZvDphUqKTyUkSiLoz/Oj9re3B2ukkJV5PDw121I2eWVOxT3run4n77EmlQg4ptU5gG1F14a52Vl0Hr71Sh02D6MSKwIQ6k/rvPvHatb65f8iSs/3QYMeaM0mrKeo0ctVJ1/3pN1DUOjEJAmvEku1Zje2dgJ2qtKdaq5USnUxOGpfYbKIWfFT7h2zKqJ1E4EqJTiYnSSA5p8SG+1npGrWH/Clvj1QRZ6mU6MSIhKeS5EDHTj3SdhJ1J4aJsEJCQY6W9MyyTELFk1onJkN47Rhnheg8d5PII2rEkIbohFLTyaZJBD3OgZTolDA5ScKkqMRl2JZEEl4KtV6MEO6J01PgS3HOZZBDSnQyOSm9lq87l2ms9ASapEQnEptcbnqkJ4QYITo/f/78008/ff78eZ+dW9oIgz80bDEklGHjQ9nv2Sp3PvXJTRHvyCel4l9klDIIeSlO6+Q8sBgppZ7X+fnz53fv3t3c3Lx7904p9euvvyYuwNk6mWqa++iyT9FXuU2z+oypSx2d//zzz83Nzd9///3w8PDw8BCis+UPe7oPSSjDxod8ysQBNDnuo6v9XmFV7Msu6iEJZej9kAgurYuLi5ubG+fczc3NxcUFg0IAukqcWhuJuBDTkZ4AJiXdMJHWWmt9cXHx8PCglHp4eLi4uLDW+kettVrrbauDFyM+uDEmcTFqZTDGaK2NMSnLsK0YEt4RvzFlMWplqK2O9VL4J0p8YsRl8KeE54txbidnmzSV2+VyqZQqy7Isy/DUfnm5XIZHN64eqRj+yPGzpClGrQzxs/gto7wUZVnmeS7hHXGPTRA39jvSfINSvhSjnBjNMvjlPM/zPB/x5BwlLtr9T/hMO6q7uzul1IsXL7TWd3d31trr6+tQ3bu7u2tZDRWQYYvhD2utrarq+vr6xYsX19fXCYpRK8Pd3V38kd7+yhzvpfDPe319Pfo7YoypqsovjPWOxKtpytAsRnxOvnjxwr8aiV+KsPDbb7/9999/Y70U4ekSn5ztEvV1+oRSSt3e3vpPCU83mmktq4MXw2fW7e1tbbejFqNWhhCa8eqxy7CtGH4ynW/+jFKMsDCbzeLdUr4j/c7VwYuhlKqqyr8OyYrR/APxTxE3Fo9dhmYxjDHh+uyUJ2e7dNHpF4wxaeq5+xQjdefIljJorauqCmfqWMVQSjnndNS5lr4YVVXlee4LkKwY286KxOdq83nLsjSPRimDX62qKvFfSvOlyPPc/6Wk/Btpl2iYqPYHqdc7wttXj1eM5qMJilErg1/2mZWsDM1ihFX/jGMVoyzL+PijvCOdztWhytDyvCmL0fwDMcbkeZ6yDBuLEf46UhZjhzRdqnGLw2/ptHq8Yvjnqg1QHLUYtTKE81Ipled5mjI0i9H1DTpSMeKNyYoh9qVIX4yNZSjLMuww+snp/1TTFKNd5phTCQAdceckAOiM6ASAzohOAOiM6ASAzohOAOiM6AQgUbiIKJAzH14RnQDECjOd/dxSf3WmEEQnAKF0dLn6mOXYhOgEgM6ITgDojAsxAaAzap0A0BnRCQCdEZ0A0BnRCQCdEZ0A0BnRCQCdEZ0A0Nn/A77teG/QwE0SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = r.TCanvas('c','c',450,300)\n",
    "pl = mass.frame(rf.Bins(800))\n",
    "pdf.plotOn(\n",
    "    pl,\n",
    "    rf.Normalization(1, r.RooAbsReal.NumEvent),\n",
    "    rf.Precision(-1),\n",
    "    rf.Components(\"bkg\"),\n",
    "    rf.LineColor(r.kRed),\n",
    "    rf.LineStyle(2),\n",
    "    rf.LineWidth(2)\n",
    ")\n",
    "pdf.plotOn(\n",
    "    pl,\n",
    "    rf.Normalization(1, r.RooAbsReal.NumEvent),\n",
    "    rf.Precision(-1),\n",
    "    rf.Components(\"sig\"),\n",
    "    rf.LineColor(r.kGreen + 3),\n",
    "    rf.LineWidth(2)\n",
    ")\n",
    "pdf.plotOn(pl, rf.Normalization(1, r.RooAbsReal.NumEvent), rf.Precision(-1), rf.LineWidth(2))\n",
    "pl.Draw()\n",
    "c.Draw();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef6a560",
   "metadata": {},
   "source": [
    "## Convert the RooFit pdfs to python callables\n",
    "\n",
    "Making use of the `sweights` package wrapper `convert_rf_pdf`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cade52b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sweights import convert_rf_pdf\n",
    "\n",
    "_spdf = convert_rf_pdf(spdf, mass)\n",
    "_bpdf = convert_rf_pdf(bpdf, mass)\n",
    "_pdf = convert_rf_pdf(pdf, mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46200a4d",
   "metadata": {},
   "source": [
    "## Now draw the python function with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff9516a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fs = sy.getVal() / (sy.getVal() + by.getVal())\n",
    "fb = by.getVal() / (sy.getVal() + by.getVal())\n",
    "\n",
    "x = np.linspace(mass.getMin(), mass.getMax(), 400)\n",
    "plt.plot(x, fs * _spdf(x), \"g-\");\n",
    "plt.plot(x, fb * _bpdf(x), \"r--\");\n",
    "plt.plot(x, _pdf(x), \"b-\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b71e03",
   "metadata": {},
   "source": [
    "Looks ok!"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}