{
"cells": [
{
"cell_type": "markdown",
"id": "0d4ed41f",
"metadata": {},
"source": [
"# rf707_kernelestimation\n",
"Special pdf's: using non-parametric (multi-dimensional) kernel estimation pdfs\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, March 19, 2024 at 07:17 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "42b1c59f",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:36.659081Z",
"iopub.status.busy": "2024-03-19T19:17:36.656084Z",
"iopub.status.idle": "2024-03-19T19:17:40.088475Z",
"shell.execute_reply": "2024-03-19T19:17:40.087432Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "f2dc5ddd",
"metadata": {},
"source": [
"Create low stats 1D dataset\n",
"-------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "e3216e63",
"metadata": {},
"source": [
"Create a toy pdf for sampling"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0d157d7a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:40.094354Z",
"iopub.status.busy": "2024-03-19T19:17:40.093763Z",
"iopub.status.idle": "2024-03-19T19:17:40.776625Z",
"shell.execute_reply": "2024-03-19T19:17:40.775323Z"
}
},
"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", 0, 20)\n",
"p = ROOT.RooPolynomial(\"p\", \"p\", x, [0.01, -0.01, 0.0004])"
]
},
{
"cell_type": "markdown",
"id": "abd00f5c",
"metadata": {},
"source": [
"Sample 500 events from p"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "da1f56a6",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:40.781904Z",
"iopub.status.busy": "2024-03-19T19:17:40.781567Z",
"iopub.status.idle": "2024-03-19T19:17:41.069655Z",
"shell.execute_reply": "2024-03-19T19:17:41.049965Z"
}
},
"outputs": [],
"source": [
"data1 = p.generate({x}, 200)"
]
},
{
"cell_type": "markdown",
"id": "8deb05ea",
"metadata": {},
"source": [
"Create 1D kernel estimation pdf\n",
"---------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "fd37251b",
"metadata": {},
"source": [
"Create adaptive kernel estimation pdf. In self configuration the input data\n",
"is mirrored over the boundaries to minimize edge effects in distribution\n",
"that do not fall to zero towards the edges"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c3022c34",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.094918Z",
"iopub.status.busy": "2024-03-19T19:17:41.094457Z",
"iopub.status.idle": "2024-03-19T19:17:41.259503Z",
"shell.execute_reply": "2024-03-19T19:17:41.248774Z"
}
},
"outputs": [],
"source": [
"kest1 = ROOT.RooKeysPdf(\"kest1\", \"kest1\", x, data1, ROOT.RooKeysPdf.MirrorBoth)"
]
},
{
"cell_type": "markdown",
"id": "351e3ab9",
"metadata": {},
"source": [
"An adaptive kernel estimation pdf on the same data without mirroring option\n",
"for comparison"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f7c60751",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.267447Z",
"iopub.status.busy": "2024-03-19T19:17:41.267052Z",
"iopub.status.idle": "2024-03-19T19:17:41.386476Z",
"shell.execute_reply": "2024-03-19T19:17:41.377507Z"
}
},
"outputs": [],
"source": [
"kest2 = ROOT.RooKeysPdf(\"kest2\", \"kest2\", x, data1, ROOT.RooKeysPdf.NoMirror)"
]
},
{
"cell_type": "markdown",
"id": "c64a6c4a",
"metadata": {},
"source": [
"Adaptive kernel estimation pdf with increased bandwidth scale factor\n",
"(promotes smoothness over detail preservation)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "302ae45d",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.395332Z",
"iopub.status.busy": "2024-03-19T19:17:41.394934Z",
"iopub.status.idle": "2024-03-19T19:17:41.518738Z",
"shell.execute_reply": "2024-03-19T19:17:41.516608Z"
}
},
"outputs": [],
"source": [
"kest3 = ROOT.RooKeysPdf(\"kest1\", \"kest1\", x, data1, ROOT.RooKeysPdf.MirrorBoth, 2)"
]
},
{
"cell_type": "markdown",
"id": "b3796821",
"metadata": {},
"source": [
"Plot kernel estimation pdfs with and without mirroring over data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cd6ecc80",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.528240Z",
"iopub.status.busy": "2024-03-19T19:17:41.527875Z",
"iopub.status.idle": "2024-03-19T19:17:41.950155Z",
"shell.execute_reply": "2024-03-19T19:17:41.948773Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = x.frame(Title=\"Adaptive kernel estimation pdf with and w/o mirroring\", Bins=20)\n",
"data1.plotOn(frame)\n",
"kest1.plotOn(frame)\n",
"kest2.plotOn(frame, LineStyle=\"--\", LineColor=\"r\")"
]
},
{
"cell_type": "markdown",
"id": "c4380a17",
"metadata": {},
"source": [
"Plot kernel estimation pdfs with regular and increased bandwidth"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1bf78270",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:41.966022Z",
"iopub.status.busy": "2024-03-19T19:17:41.965635Z",
"iopub.status.idle": "2024-03-19T19:17:42.109911Z",
"shell.execute_reply": "2024-03-19T19:17:42.104852Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame2 = x.frame(Title=\"Adaptive kernel estimation pdf with regular, bandwidth\")\n",
"kest1.plotOn(frame2)\n",
"kest3.plotOn(frame2, LineColor=\"m\")"
]
},
{
"cell_type": "markdown",
"id": "aaaa198d",
"metadata": {},
"source": [
"Create low status 2D dataset\n",
"-------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "33e57ec2",
"metadata": {},
"source": [
"Construct a 2D toy pdf for sampleing"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d59200c5",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.115190Z",
"iopub.status.busy": "2024-03-19T19:17:42.114804Z",
"iopub.status.idle": "2024-03-19T19:17:42.422789Z",
"shell.execute_reply": "2024-03-19T19:17:42.421781Z"
}
},
"outputs": [],
"source": [
"y = ROOT.RooRealVar(\"y\", \"y\", 0, 20)\n",
"py = ROOT.RooPolynomial(\n",
" \"py\",\n",
" \"py\",\n",
" y,\n",
" [0.01, 0.01, -0.0004],\n",
")\n",
"pxy = ROOT.RooProdPdf(\"pxy\", \"pxy\", [p, py])\n",
"data2 = pxy.generate({x, y}, 1000)"
]
},
{
"cell_type": "markdown",
"id": "7b1fa55e",
"metadata": {},
"source": [
"Create 2D kernel estimation pdf\n",
"---------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "8062fd11",
"metadata": {},
"source": [
"Create 2D adaptive kernel estimation pdf with mirroring"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8218d67e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.478381Z",
"iopub.status.busy": "2024-03-19T19:17:42.477971Z",
"iopub.status.idle": "2024-03-19T19:17:42.962155Z",
"shell.execute_reply": "2024-03-19T19:17:42.947678Z"
}
},
"outputs": [],
"source": [
"kest4 = ROOT.RooNDKeysPdf(\"kest4\", \"kest4\", [x, y], data2, \"am\")"
]
},
{
"cell_type": "markdown",
"id": "d91cf819",
"metadata": {},
"source": [
"Create 2D adaptive kernel estimation pdf with mirroring and double\n",
"bandwidth"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fb0275fe",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:42.985710Z",
"iopub.status.busy": "2024-03-19T19:17:42.985125Z",
"iopub.status.idle": "2024-03-19T19:17:43.604897Z",
"shell.execute_reply": "2024-03-19T19:17:43.598648Z"
}
},
"outputs": [],
"source": [
"kest5 = ROOT.RooNDKeysPdf(\"kest5\", \"kest5\", [x, y], data2, \"am\", 2)"
]
},
{
"cell_type": "markdown",
"id": "9c3688b4",
"metadata": {},
"source": [
"Create a histogram of the data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "dfa75e21",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:43.648012Z",
"iopub.status.busy": "2024-03-19T19:17:43.647607Z",
"iopub.status.idle": "2024-03-19T19:17:43.998976Z",
"shell.execute_reply": "2024-03-19T19:17:43.958615Z"
}
},
"outputs": [],
"source": [
"hh_data = data2.createHistogram(\"hh_data\", x, Binning=10, YVar=dict(var=y, Binning=10))"
]
},
{
"cell_type": "markdown",
"id": "86f530dc",
"metadata": {},
"source": [
"Create histogram of the 2d kernel estimation pdfs"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6f21defd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:44.034476Z",
"iopub.status.busy": "2024-03-19T19:17:44.034068Z",
"iopub.status.idle": "2024-03-19T19:17:45.118468Z",
"shell.execute_reply": "2024-03-19T19:17:45.096485Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rf707_kernelestimation.png has been created\n"
]
}
],
"source": [
"hh_pdf = kest4.createHistogram(\"hh_pdf\", x, Binning=25, YVar=dict(var=y, Binning=25))\n",
"hh_pdf2 = kest5.createHistogram(\"hh_pdf2\", x, Binning=25, YVar=dict(var=y, Binning=25))\n",
"hh_pdf.SetLineColor(ROOT.kBlue)\n",
"hh_pdf2.SetLineColor(ROOT.kMagenta)\n",
"\n",
"c = ROOT.TCanvas(\"rf707_kernelestimation\", \"rf707_kernelestimation\", 800, 800)\n",
"c.Divide(2, 2)\n",
"c.cd(1)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame.GetYaxis().SetTitleOffset(1.4)\n",
"frame.Draw()\n",
"c.cd(2)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame2.GetYaxis().SetTitleOffset(1.8)\n",
"frame2.Draw()\n",
"c.cd(3)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"hh_data.GetZaxis().SetTitleOffset(1.4)\n",
"hh_data.Draw(\"lego\")\n",
"c.cd(4)\n",
"ROOT.gPad.SetLeftMargin(0.20)\n",
"hh_pdf.GetZaxis().SetTitleOffset(2.4)\n",
"hh_pdf.Draw(\"surf\")\n",
"hh_pdf2.Draw(\"surfsame\")\n",
"\n",
"c.SaveAs(\"rf707_kernelestimation.png\")"
]
},
{
"cell_type": "markdown",
"id": "59ff1c94",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "73589425",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:17:45.140417Z",
"iopub.status.busy": "2024-03-19T19:17:45.140015Z",
"iopub.status.idle": "2024-03-19T19:17:45.497123Z",
"shell.execute_reply": "2024-03-19T19:17:45.495232Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}