{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to write a C++ ROOTBook\n",
"
\n",
"In order to use a C++ [ROOT](root.cern.ch)book, just start the [ROOT kernel](https://github.com/root-mirror/root/tree/master/bindings/pyroot/JupyROOT/kernel). That's it. \n",
"We will see also how the user can mark cells containing C++ statements with the **%%cpp** magic."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From this point on...\n",
"... it's only C++ ...\n"
]
}
],
"source": [
"cout << \"From this point on...\\n\" \n",
" << \"... it's only C++ ...\" << endl;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now create a [ROOT histogram](https://root.cern.ch/doc/master/classTH1F.html) and fill it with [random numbers](https://root.cern.ch/doc/master/classTRandom.html) distributed according to a Gaussian."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TH1F h(\"gauss\",\"Example histogram\",64,-4,4);\n",
"h.FillRandom(\"gaus\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we create a [canvas](https://root.cern.ch/doc/master/classTCanvas.html), the entity which holds graphics primitives."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TCanvas c(\"myCanvasName\",\"The Canvas Title\",1600,1200);\n",
"h.Draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the histogram to be displayed in the notebook, we need to draw the canvas."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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rbduiKBKD17D/9LqzYRhCHDaOY9ywqqo44coxcNhJ5n65m/+WxfHkTPr139nJ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\nLv2zGsadO99Z2xB7LffP64X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HqClg3Zh/X7QCAAAAqiRahTOt+Duf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\nAITz/0TxjLQBURUnAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"c.Draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now fit the histogram."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ih/ipN2Mu7lLvRn6q592WM8fqlu6q6zrP88TgNew/ve6saZoQh7VtGzcsyzJO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\n838TIrwZXlQElQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" FCN=47.4997 FROM MIGRAD STATUS=CONVERGED 53 CALLS 54 TOTAL\n",
" EDM=8.44224e-09 STRATEGY= 1 ERROR MATRIX ACCURATE \n",
" EXT PARAMETER STEP FIRST \n",
" NO. NAME VALUE ERROR SIZE DERIVATIVE \n",
" 1 Constant 2.46469e+02 4.31494e+00 1.19094e-02 -2.44811e-05\n",
" 2 Mean 1.04782e-02 1.43576e-02 4.87656e-05 -6.34020e-03\n",
" 3 Sigma 1.00315e+00 1.03818e-02 9.45504e-06 -2.70309e-02\n"
]
}
],
"source": [
"h.Fit(\"gaus\", \"S\");\n",
"c.Draw();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before moving forward, let's save our histogram on a file. All ROOT objects can be written to [ROOT files](https://root.cern.ch/doc/master/classTFile.html) out-of-the-box."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TFile outputFile(\"output.root\",\"RECREATE\");\n",
"h.Write();\n",
"outputFile.Close();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check what is the content of the file with the [*rootls*](https://root.cern.ch/how/how-quickly-inspect-content-file) command line utility. A magic or the well known *.!* ROOT directive can be used."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1mTH1F \u001b[0mJan 08 16:36 gauss \"Example histogram\"\r\n",
"\n"
]
}
],
"source": [
"%%shell\n",
"rootls -l output.root"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## And if I make a mistake? Diagnostics\n",
"Clang diagnostics are offered by ROOT, for example:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1minput_line_113:3:11: \u001b[0m\u001b[0;1;31merror: \u001b[0m\u001b[1mmember reference type 'TH1F *' is a pointer; maybe you meant to use '->'?\u001b[0m\n",
"myHistoPtr.Dump();\n",
"\u001b[0;1;32m~~~~~~~~~~^\n",
"\u001b[0m\u001b[0;32m ->\n",
"\u001b[0m"
]
}
],
"source": [
"auto myHistoPtr = new TH1F();\n",
"myHistoPtr.Dump();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Magics\n",
"It is possible to declare functions or to use ACLiC with the **%%cpp** magics.\n",
"\n",
"### Declaration of functions\n",
"The **-d** option needs to be used when declaring functions: we must be explicit."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%cpp -d\n",
"void f() {\n",
" cout << \"This is function f\" << endl;\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The value printing capabilities of ROOT are outstanding: you can inspect the code of a JIT-ted function from its name."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(void (*)()) Function @0x7fed99a9c080\n",
" at :1:\n",
"void f() {\n",
" cout << \"This is function f\" << endl;\n",
"}\n",
"\n"
]
}
],
"source": [
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Advanced: ACliC\n",
"The **-a** allows to compile the cell code with ACLiC. This is not so relevant for performance since the ROOT interpreter just in time compiles the C++ code. Nevertheless, ACLiC is most useful when the automatic creation of dictionaries is required, for example in presence of I/O operations."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : creating shared library b8651c13_C.so\n"
]
}
],
"source": [
"%%cpp -a\n",
"class A{};"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's verify that the dictionary is there:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Bool_t) true\n"
]
}
],
"source": [
"TClass::GetClass(\"A\")->HasDictionary()"
]
}
],
"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++"
}
},
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}