{
"cells": [
{
"cell_type": "markdown",
"id": "09a81d6e",
"metadata": {},
"source": [
"# fit2\n",
"Fitting a 2-D histogram\n",
"This tutorial illustrates :\n",
" - how to create a 2-d function\n",
" - fill a 2-d histogram randomly from this function\n",
" - fit the histogram\n",
" - display the fitted function on top of the histogram\n",
"\n",
"This example can be executed via the interpreter or ACLIC\n",
"\n",
"```cpp\n",
" root > .x fit2.C\n",
" root > .x fit2.C++\n",
"```\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Rene Brun \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:08 PM."
]
},
{
"cell_type": "markdown",
"id": "175bc88e",
"metadata": {},
"source": [
" Definition of a helper function: "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0a3bccb7",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:30.629411Z",
"iopub.status.busy": "2024-03-19T19:08:30.629058Z",
"iopub.status.idle": "2024-03-19T19:08:30.654299Z",
"shell.execute_reply": "2024-03-19T19:08:30.640281Z"
}
},
"outputs": [],
"source": [
"%%cpp -d\n",
"\n",
"#include \"TF2.h\"\n",
"#include \"TH2.h\"\n",
"#include \"TMath.h\"\n",
"\n",
"double g2(double *x, double *par) {\n",
" double r1 = double((x[0]-par[1])/par[2]);\n",
" double r2 = double((x[1]-par[3])/par[4]);\n",
" return par[0]*TMath::Exp(-0.5*(r1*r1+r2*r2));\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "38cda260",
"metadata": {},
"source": [
" Definition of a helper function: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "07a66424",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:30.680763Z",
"iopub.status.busy": "2024-03-19T19:08:30.680370Z",
"iopub.status.idle": "2024-03-19T19:08:30.686715Z",
"shell.execute_reply": "2024-03-19T19:08:30.685609Z"
}
},
"outputs": [],
"source": [
"%%cpp -d\n",
"double fun2(double *x, double *par) {\n",
" double *p1 = &par[0];\n",
" double *p2 = &par[5];\n",
" double *p3 = &par[10];\n",
" double result = g2(x,p1) + g2(x,p2) + g2(x,p3);\n",
" return result;\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5a98964e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:30.709035Z",
"iopub.status.busy": "2024-03-19T19:08:30.708660Z",
"iopub.status.idle": "2024-03-19T19:08:31.841545Z",
"shell.execute_reply": "2024-03-19T19:08:31.840275Z"
}
},
"outputs": [],
"source": [
"const int npar = 15;\n",
"double f2params[npar] =\n",
" {100,-3,3,-3,3,160,0,0.8,0,0.9,40,4,0.7,4,0.7};\n",
"TF2 *f2 = new TF2(\"f2\",fun2,-10,10,-10,10, npar);\n",
"f2->SetParameters(f2params);"
]
},
{
"cell_type": "markdown",
"id": "a9f5f129",
"metadata": {},
"source": [
"Create an histogram and fill it randomly with f2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7edd7f22",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:31.846994Z",
"iopub.status.busy": "2024-03-19T19:08:31.846470Z",
"iopub.status.idle": "2024-03-19T19:08:32.051737Z",
"shell.execute_reply": "2024-03-19T19:08:32.050565Z"
}
},
"outputs": [],
"source": [
"TH2F *h2 = new TH2F(\"h2\",\"from f2\",40,-10,10,40,-10,10);\n",
"int nentries = 100000;\n",
"h2->FillRandom(\"f2\",nentries);"
]
},
{
"cell_type": "markdown",
"id": "0fa70672",
"metadata": {},
"source": [
"Fit h2 with original function f2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5080260a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:32.057620Z",
"iopub.status.busy": "2024-03-19T19:08:32.057270Z",
"iopub.status.idle": "2024-03-19T19:08:32.960976Z",
"shell.execute_reply": "2024-03-19T19:08:32.959694Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************\n",
"Minimizer is Minuit2 / Migrad\n",
"Chi2 = 1048.29\n",
"NDf = 1139\n",
"Edm = 3.90056e-06\n",
"NCalls = 519\n",
"p0 = 392.558 +/- 2.07088 \n",
"p1 = -2.99838 +/- 0.0116072 \n",
"p2 = 2.98484 +/- 0.00840711 \n",
"p3 = -3.00201 +/- 0.0115172 \n",
"p4 = 2.97271 +/- 0.00841038 \n",
"p5 = 601.133 +/- 10.5562 \n",
"p6 = 0.00614073 +/- 0.0119548 \n",
"p7 = 0.81626 +/- 0.0107847 \n",
"p8 = -0.000781266 +/- 0.0134062 \n",
"p9 = 0.911288 +/- 0.0119899 \n",
"p10 = 146.899 +/- 5.12261 \n",
"p11 = 3.9882 +/- 0.0182639 \n",
"p12 = 0.727561 +/- 0.0142962 \n",
"p13 = 4.02637 +/- 0.0175896 \n",
"p14 = 0.703077 +/- 0.0140242 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"input_line_55:2:2: warning: 'ratio' shadows a declaration with the same name in the 'std' namespace; use '::ratio' to reference this declaration\n",
" float ratio = 4*nentries/100000;\n",
" ^\n",
"Info in : created default TCanvas with name c1\n"
]
}
],
"source": [
"float ratio = 4*nentries/100000;\n",
"f2params[ 0] *= ratio;\n",
"f2params[ 5] *= ratio;\n",
"f2params[10] *= ratio;\n",
"f2->SetParameters(f2params);\n",
"h2->Fit(\"f2\");"
]
},
{
"cell_type": "markdown",
"id": "4b06eb89",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "229c199b",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:08:32.966663Z",
"iopub.status.busy": "2024-03-19T19:08:32.966297Z",
"iopub.status.idle": "2024-03-19T19:08:33.521536Z",
"shell.execute_reply": "2024-03-19T19:08:33.519741Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gROOT->GetListOfCanvases()->Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ROOT C++",
"language": "c++",
"name": "root"
},
"language_info": {
"codemirror_mode": "text/x-c++src",
"file_extension": ".C",
"mimetype": " text/x-c++src",
"name": "c++"
}
},
"nbformat": 4,
"nbformat_minor": 5
}