wiso/MassFitGlobal.ipynb

## MassFitGlobal.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/02\n"
     ]
    }
   ],
   "source": [
    "import ROOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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": [
    "# very simple model\n",
    "# myy ~ N[mu=mH, sigma=a + (mH - 125) * b]\n",
    "\n",
    "ws = ROOT.RooWorkspace('ws')\n",
    "mH_var = ws.factory('mH[60, 120]')\n",
    "myy_var = ws.factory('myy[60, 120]')\n",
    "mu_var = ws.factory('expr:mu(\"@0\", mH)')\n",
    "sigma_var = ws.factory('expr:sigma(\"@1 + (@0 - 125) * @2\", mH, a[1.0, 0.7, 2.], b[-0.01, -0.1, 0.1])')\n",
    "model = ws.factory(\"RooGaussian(myy, mu, sigma)\")\n",
    "\n",
    "ws.saveSnapshot('truth', ws.allVars())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"\", \"\", 400, 300)\n",
    "frame = mH_var.frame()\n",
    "sigma_var.plotOn(frame)\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"\", \"\", 400, 300)\n",
    "frame = myy_var.frame()\n",
    "\n",
    "mH_var.setVal(70)\n",
    "model.plotOn(frame)\n",
    "\n",
    "mH_var.setVal(110)\n",
    "model.plotOn(frame)\n",
    "\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RooDataSet::data[myy,mH] = 4000 entries\n"
     ]
    }
   ],
   "source": [
    "mH_values = [70, 80, 90, 100]\n",
    "stat = 1000\n",
    "\n",
    "sample = ROOT.RooDataSet(\"data\", \"data\", ROOT.RooArgSet(myy_var, mH_var))\n",
    "\n",
    "for mH_value in mH_values:\n",
    "    mH_var.setVal(mH_value)\n",
    "    sample_mH = model.generate(ROOT.RooArgSet(myy_var), stat)\n",
    "    sample_mH.addColumn(mH_var)\n",
    "    sample.append(sample_mH)\n",
    "    \n",
    "sample.Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"\", \"\", 400, 300)\n",
    "frame = myy_var.frame()\n",
    "\n",
    "sample.plotOn(frame)\n",
    "\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:Minization -- createNLL: caching constraint set under name CONSTR_OF_PDF_gobj2_FOR_OBS_mH:myy with 0 entries\n",
      "[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization\n",
      "[#1] INFO:Minization --  The following expressions have been identified as constant and will be precalculated and cached: (mu)\n",
      " **********\n",
      " **    1 **SET PRINT           1\n",
      " **********\n",
      " **********\n",
      " **    2 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 a            1.00000e+00  1.30000e-01    7.00000e-01  2.00000e+00\n",
      "     2 b           -1.00000e-02  2.00000e-02   -1.00000e-01  1.00000e-01\n",
      " **********\n",
      " **    3 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **    4 **SET PRINT           1\n",
      " **********\n",
      " **********\n",
      " **    5 **SET STR           1\n",
      " **********\n",
      " NOW USING STRATEGY  1: TRY TO BALANCE SPEED AGAINST RELIABILITY\n",
      " **********\n",
      " **    6 **MIGRAD        1000           1\n",
      " **********\n",
      " FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.\n",
      " START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03\n",
      " FCN=6986.16 FROM MIGRAD    STATUS=INITIATE       10 CALLS          11 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       \n",
      "  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  a            1.00000e+00   1.30000e-01   2.42752e-01   2.21845e+01\n",
      "   2  b           -1.00000e-02   2.00000e-02   2.02430e-01  -4.79907e+01\n",
      "                               ERR DEF= 0.5\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " COVARIANCE MATRIX CALCULATED SUCCESSFULLY\n",
      " FCN=6984.87 FROM MIGRAD    STATUS=CONVERGED      46 CALLS          47 TOTAL\n",
      "                     EDM=1.15405e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  a            9.13321e-01   5.41558e-02   1.83406e-03   3.31240e-02\n",
      "   2  b           -1.20096e-02   1.37581e-03   2.26266e-04  -2.44487e-01\n",
      "                               ERR DEF= 0.5\n",
      " EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5\n",
      "  2.945e-03  7.161e-05 \n",
      "  7.161e-05  1.893e-06 \n",
      " PARAMETER  CORRELATION COEFFICIENTS  \n",
      "       NO.  GLOBAL      1      2\n",
      "        1  0.95910   1.000  0.959\n",
      "        2  0.95910   0.959  1.000\n",
      " **********\n",
      " **    7 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **    8 **SET PRINT           1\n",
      " **********\n",
      " **********\n",
      " **    9 **HESSE        1000\n",
      " **********\n",
      " COVARIANCE MATRIX CALCULATED SUCCESSFULLY\n",
      " FCN=6984.87 FROM HESSE     STATUS=OK             10 CALLS          57 TOTAL\n",
      "                     EDM=1.14871e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                INTERNAL      INTERNAL  \n",
      "  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   \n",
      "   1  a            9.13321e-01   5.37280e-02   3.66812e-04  -7.36655e-01\n",
      "   2  b           -1.20096e-02   1.36490e-03   4.52532e-05  -1.20387e-01\n",
      "                               ERR DEF= 0.5\n",
      " EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5\n",
      "  2.899e-03  7.043e-05 \n",
      "  7.043e-05  1.863e-06 \n",
      " PARAMETER  CORRELATION COEFFICIENTS  \n",
      "       NO.  GLOBAL      1      2\n",
      "        1  0.95843   1.000  0.958\n",
      "        2  0.95843   0.958  1.000\n",
      "[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization\n"
     ]
    }
   ],
   "source": [
    "global_fit_result = model.fitTo(sample,\n",
    "                                ROOT.RooFit.ConditionalObservables(ROOT.RooArgSet(mH_var)),\n",
    "                                ROOT.RooFit.Save())\n",
    "ws.saveSnapshot(\"global_fit\", ws.allVars())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"\", \"\", 400, 300)\n",
    "frame = mH_var.frame()\n",
    "\n",
    "ws.loadSnapshot('global_fit')\n",
    "\n",
    "sigma_var.plotOn(frame, ROOT.RooFit.VisualizeError(global_fit_result))\n",
    "sigma_var.plotOn(frame)\n",
    "\n",
    "ws.loadSnapshot('truth')\n",
    "\n",
    "sigma_var.plotOn(frame,\n",
    "                 ROOT.RooFit.LineColor(ROOT.kRed),\n",
    "                 ROOT.RooFit.LineWidth(1))\n",
    "\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[#1] INFO:Plotting -- RooAbsReal::plotOn(gobj2) plot on myy averages using data variables (mH)\n",
      "[#1] INFO:Plotting -- RooAbsReal::plotOn(gobj2) only the following components of the projection data will be used: (mH)\n",
      "[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(gobj2DataWgtAvg) constructing data weighted average of function gobj2_Norm[myy] over 100 data points of (mH) with a total weight of 4000\n",
      "[#1] INFO:Minization --  The following expressions have been identified as constant and will be precalculated and cached: (mu)\n",
      "..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"\", \"\", 400, 300)\n",
    "frame = myy_var.frame()\n",
    "\n",
    "sample.plotOn(frame)\n",
    "colors = [ROOT.kBlue, ROOT.kRed]\n",
    "\n",
    "model.plotOn(frame, ROOT.RooFit.ProjWData(sample,True), ROOT.RooFit.LineColor(colors[i % 2]))\n",
    "\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv3",
   "language": "python",
   "name": "venv3"
  },
  "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": 4
}