{
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"source": [
"# fitConvolution\n",
"Tutorial for convolution of two functions\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Jonas Rembser, Aurelie Flandi (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 08:24 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0ee6cec5",
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"execution": {
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"outputs": [],
"source": [
"import ROOT\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "eb3bac41",
"metadata": {},
"source": [
"Construction of histogram to fit."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a032196",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:24:50.150017Z",
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"source": [
"h_ExpGauss = ROOT.TH1F(\"h_ExpGauss\", \"Exponential convoluted by Gaussian\", 100, 0.0, 5.0)\n",
"h_ExpGauss.Fill(\n",
" np.array(\n",
" [\n",
" # Gives a alpha of -0.3 in the exp.\n",
" # Probability density function of the addition of two variables is the\n",
" # convolution of two density functions.\n",
" ROOT.gRandom.Exp(1.0 / 0.3) + ROOT.gRandom.Gaus(0.0, 3.0)\n",
" for _ in range(1000000)\n",
" ]\n",
" )\n",
")\n",
"f_conv = ROOT.TF1Convolution(\"expo\", \"gaus\", -1, 6, True)\n",
"f_conv.SetRange(-1.0, 6.0)\n",
"f_conv.SetNofPointsFFT(1000)\n",
"f = ROOT.TF1(\"f\", f_conv, 0.0, 5.0, f_conv.GetNpar())\n",
"f.SetParameters(1.0, -0.3, 0.0, 1.0)\n",
"\n",
"c1 = ROOT.TCanvas(\"c1\", \"c1\", 800, 1000)"
]
},
{
"cell_type": "markdown",
"id": "2ea6259f",
"metadata": {},
"source": [
"Fit and draw result of the fit"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3c747ab1",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:24:54.282997Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************\n",
"Minimizer is Minuit2 / Migrad\n",
"Chi2 = 298.12\n",
"NDf = 96\n",
"Edm = 1.60212e-06\n",
"NCalls = 448\n",
"p0 = 7.32861 +/- 0.0370515 \n",
"p1 = 0.0733017 +/- 0.0024398 \n",
"p2 = -2.26418 +/- 0.0491398 \n",
"p3 = 1.12807 +/- 0.0628225 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file fitConvolution.png has been created\n"
]
}
],
"source": [
"h_ExpGauss.Fit(\"f\")\n",
"\n",
"c1.SaveAs(\"fitConvolution.png\")"
]
},
{
"cell_type": "markdown",
"id": "b917914a",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "145623d7",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:24:54.286097Z",
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"shell.execute_reply": "2026-05-19T20:24:54.502257Z"
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"outputs": [
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"\n",
"\n"
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""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
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