{ "cells": [ { "cell_type": "markdown", "id": "ebd51f04", "metadata": {}, "source": [ "# rf203_ranges\n", "Addition and convolution: fitting and plotting in sub ranges\n", "\n", "\n", "\n", "\n", "**Author:** Clemens Lange, Wouter Verkerke (C++ version) \n", "This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:29 PM." ] }, { "cell_type": "code", "execution_count": 1, "id": "fe657f13", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:44.376872Z", "iopub.status.busy": "2026-05-19T20:29:44.376750Z", "iopub.status.idle": "2026-05-19T20:29:45.337759Z", "shell.execute_reply": "2026-05-19T20:29:45.337226Z" } }, "outputs": [], "source": [ "import ROOT" ] }, { "cell_type": "markdown", "id": "b1f64d1a", "metadata": {}, "source": [ "Set up model\n", "---------------------" ] }, { "cell_type": "markdown", "id": "9c75c764", "metadata": {}, "source": [ "Construct observables x" ] }, { "cell_type": "code", "execution_count": 2, "id": "a09b5af7", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:45.339941Z", "iopub.status.busy": "2026-05-19T20:29:45.339793Z", "iopub.status.idle": "2026-05-19T20:29:45.497519Z", "shell.execute_reply": "2026-05-19T20:29:45.496721Z" } }, "outputs": [], "source": [ "x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)" ] }, { "cell_type": "markdown", "id": "c419d6f9", "metadata": {}, "source": [ "Construct gaussx(x,mx,1)" ] }, { "cell_type": "code", "execution_count": 3, "id": "71c96863", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:45.499244Z", "iopub.status.busy": "2026-05-19T20:29:45.499120Z", "iopub.status.idle": "2026-05-19T20:29:45.629703Z", "shell.execute_reply": "2026-05-19T20:29:45.629018Z" } }, "outputs": [], "source": [ "mx = ROOT.RooRealVar(\"mx\", \"mx\", 0, -10, 10)\n", "gx = ROOT.RooGaussian(\"gx\", \"gx\", x, mx, 1.0)" ] }, { "cell_type": "markdown", "id": "c498be56", "metadata": {}, "source": [ "px = 1 (flat in x)" ] }, { "cell_type": "code", "execution_count": 4, "id": "d8c1900a", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:45.631723Z", "iopub.status.busy": "2026-05-19T20:29:45.631557Z", "iopub.status.idle": "2026-05-19T20:29:45.743395Z", "shell.execute_reply": "2026-05-19T20:29:45.742914Z" } }, "outputs": [], "source": [ "px = ROOT.RooPolynomial(\"px\", \"px\", x)" ] }, { "cell_type": "markdown", "id": "8942f7d6", "metadata": {}, "source": [ "model = f*gx + (1-f)px" ] }, { "cell_type": "code", "execution_count": 5, "id": "346e1287", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:45.747423Z", "iopub.status.busy": "2026-05-19T20:29:45.747298Z", "iopub.status.idle": "2026-05-19T20:29:45.921647Z", "shell.execute_reply": "2026-05-19T20:29:45.920877Z" } }, "outputs": [], "source": [ "f = ROOT.RooRealVar(\"f\", \"f\", 0.0, 1.0)\n", "model = ROOT.RooAddPdf(\"model\", \"model\", [gx, px], [f])" ] }, { "cell_type": "markdown", "id": "2c7a7b12", "metadata": {}, "source": [ "Generated 10000 events in (x,y) from pdf model" ] }, { "cell_type": "code", "execution_count": 6, "id": "3c72bb48", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:45.923258Z", "iopub.status.busy": "2026-05-19T20:29:45.923134Z", "iopub.status.idle": "2026-05-19T20:29:46.068932Z", "shell.execute_reply": "2026-05-19T20:29:46.068481Z" } }, "outputs": [], "source": [ "modelData = model.generate({x}, 10000)" ] }, { "cell_type": "markdown", "id": "8db97e5b", "metadata": {}, "source": [ "Fit full range\n", "---------------------------" ] }, { "cell_type": "markdown", "id": "66abfde8", "metadata": {}, "source": [ "Fit pdf to all data" ] }, { "cell_type": "code", "execution_count": 7, "id": "b6d9e1fa", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.081434Z", "iopub.status.busy": "2026-05-19T20:29:46.081299Z", "iopub.status.idle": "2026-05-19T20:29:46.274122Z", "shell.execute_reply": "2026-05-19T20:29:46.273415Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data\n", "[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations\n", "[#1] INFO:Fitting -- Creation of NLL object took 812.744 μs\n", "[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level\n", "[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n" ] } ], "source": [ "r_full = model.fitTo(modelData, Save=True, PrintLevel=-1)" ] }, { "cell_type": "markdown", "id": "f3dea233", "metadata": {}, "source": [ "Fit partial range\n", "----------------------------------" ] }, { "cell_type": "markdown", "id": "2fcf987b", "metadata": {}, "source": [ "Define \"signal\" range in x as [-3,3]" ] }, { "cell_type": "code", "execution_count": 8, "id": "9c53aa89", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.276070Z", "iopub.status.busy": "2026-05-19T20:29:46.275942Z", "iopub.status.idle": "2026-05-19T20:29:46.391032Z", "shell.execute_reply": "2026-05-19T20:29:46.384617Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'signal' created with bounds [-3,3]\n" ] } ], "source": [ "x.setRange(\"signal\", -3, 3)" ] }, { "cell_type": "markdown", "id": "3f433df4", "metadata": {}, "source": [ "Fit pdf only to data in \"signal\" range" ] }, { "cell_type": "code", "execution_count": 9, "id": "9fc9bdf7", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.392728Z", "iopub.status.busy": "2026-05-19T20:29:46.392583Z", "iopub.status.idle": "2026-05-19T20:29:46.506798Z", "shell.execute_reply": "2026-05-19T20:29:46.506011Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'fit_nll_model_modelData' created with bounds [-3,3]\n", "[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data\n", "[#1] INFO:Fitting -- Creation of NLL object took 493.991 μs\n", "[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level\n", "[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n" ] } ], "source": [ "r_sig = model.fitTo(modelData, Save=True, Range=\"signal\", PrintLevel=-1)" ] }, { "cell_type": "markdown", "id": "9043bb7e", "metadata": {}, "source": [ "Plot/print results\n", "---------------------------------------" ] }, { "cell_type": "markdown", "id": "add6370e", "metadata": {}, "source": [ "Make plot frame in x and add data and fitted model" ] }, { "cell_type": "code", "execution_count": 10, "id": "5e1a5fef", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.508737Z", "iopub.status.busy": "2026-05-19T20:29:46.508509Z", "iopub.status.idle": "2026-05-19T20:29:46.710303Z", "shell.execute_reply": "2026-05-19T20:29:46.709490Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n", "\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n", "\t- Explicitly specify the plotting range: Range(\"\").\n", "\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n", "\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n", "[#0] ERROR:Plotting -- Range 'Full' not defined for variable 'x'. Ignoring ...\n", "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'Full'\n", "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit Range() and NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n", "\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n", "\t- Explicitly specify the plotting range: Range(\"\").\n", "\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n", "\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n", "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n", "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n" ] } ], "source": [ "frame = x.frame(Title=\"Fitting a sub range\")\n", "modelData.plotOn(frame)\n", "model.plotOn(frame, Range=\"Full\", LineColor=\"r\", LineStyle=\"--\") # Add shape in full ranged dashed\n", "model.plotOn(frame) # By default only fitted range is shown" ] }, { "cell_type": "markdown", "id": "a9012ef5", "metadata": {}, "source": [ "Print fit results" ] }, { "cell_type": "code", "execution_count": 11, "id": "b0fa3c36", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.711949Z", "iopub.status.busy": "2026-05-19T20:29:46.711823Z", "iopub.status.idle": "2026-05-19T20:29:46.821257Z", "shell.execute_reply": "2026-05-19T20:29:46.820383Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "result of fit on all data \n", "result of fit in in signal region (note increased error on signal fraction)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " RooFitResult: minimized FCN value: 25939.4, estimated distance to minimum: 3.77183e-06\n", " covariance matrix quality: Full, accurate covariance matrix\n", " Status : MINIMIZE=0 HESSE=0 \n", "\n", " Floating Parameter FinalValue +/- Error \n", " -------------------- --------------------------\n", " f 5.0441e-01 +/- 6.32e-03\n", " mx -2.1605e-02 +/- 1.77e-02\n", "\n", "\n", " RooFitResult: minimized FCN value: 10339.5, estimated distance to minimum: 0.000279216\n", " covariance matrix quality: Full, accurate covariance matrix\n", " Status : MINIMIZE=0 HESSE=0 \n", "\n", " Floating Parameter FinalValue +/- Error \n", " -------------------- --------------------------\n", " f 4.8979e-01 +/- 1.62e-02\n", " mx -2.1518e-02 +/- 1.79e-02\n", "\n" ] } ], "source": [ "print(\"result of fit on all data \")\n", "r_full.Print()\n", "print(\"result of fit in in signal region (note increased error on signal fraction)\")\n", "r_sig.Print()" ] }, { "cell_type": "markdown", "id": "2cd533fb", "metadata": {}, "source": [ "Draw frame on canvas" ] }, { "cell_type": "code", "execution_count": 12, "id": "f1aeb073", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:46.823003Z", "iopub.status.busy": "2026-05-19T20:29:46.822877Z", "iopub.status.idle": "2026-05-19T20:29:47.034879Z", "shell.execute_reply": "2026-05-19T20:29:47.034063Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Info in : png file rf203_ranges.png has been created\n" ] } ], "source": [ "c = ROOT.TCanvas(\"rf203_ranges\", \"rf203_ranges\", 600, 600)\n", "ROOT.gPad.SetLeftMargin(0.15)\n", "frame.GetYaxis().SetTitleOffset(1.4)\n", "frame.Draw()\n", "\n", "c.SaveAs(\"rf203_ranges.png\")" ] }, { "cell_type": "markdown", "id": "2b849cb5", "metadata": {}, "source": [ "Draw all canvases " ] }, { "cell_type": "code", "execution_count": 13, "id": "82eff388", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:29:47.036622Z", "iopub.status.busy": "2026-05-19T20:29:47.036485Z", "iopub.status.idle": "2026-05-19T20:29:47.216430Z", "shell.execute_reply": "2026-05-19T20:29:47.215820Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "
\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ROOT import gROOT \n", "gROOT.GetListOfCanvases().Draw()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }