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@chrisburr
Created February 3, 2017 15:29
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import base64\n",
"import tempfile\n",
"\n",
"from IPython.display import Image, display\n",
"from ROOT import RooWorkspace, RooArgSet, RooFit, TCanvas"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fit_data(w, roodata):\n",
" w.pdf('model').fitTo(\n",
" roodata,\n",
" RooFit.Minimizer(\"Minuit\", \"Migrad\"),\n",
" RooFit.Extended(True),\n",
" RooFit.Range('lower_sideband,middle_sideband,upper_sideband'),\n",
" RooFit.NumCPU(64)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_fit(w, data):\n",
" c = TCanvas()\n",
" frame = w.var('mass').frame()\n",
" roodata.plotOn(frame)\n",
" w.pdf('model').plotOn(frame, RooFit.Range(\"full\"))\n",
" frame.Draw()\n",
" # Quick hack to make the image display in the notebook\n",
" with tempfile.NamedTemporaryFile(suffix='.png') as f:\n",
" c.SaveAs(f.name)\n",
" encoded = base64.b64encode(open(f.name, \"rb\").read()).decode('utf-8')\n",
" display(Image(url=\"data:image/png;base64,\"+encoded))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make a model with 1st order Chebychev polynomial and generate a toy dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = RooWorkspace(\"w\")\n",
"# Construct our model\n",
"w.factory(\"Chebychev:combinatorial_pdf(mass[1780, 2050], {bkg_a[-0.2, -0.5, 0.5]})\")\n",
"w.factory(\"SUM:model(n_combinatorial[380000, 0, 1000000]*combinatorial_pdf)\")\n",
"\n",
"# Define some ranges which will be used for the fit\n",
"w.var('mass').setRange('lower_sideband', 1780, 1844)\n",
"w.var('mass').setRange('middle_sideband', 1896, 1942)\n",
"w.var('mass').setRange('upper_sideband', 1994, 2050)\n",
"\n",
"# Make a toy dataset\n",
"roodata = w.pdf('model').generate(RooArgSet(w.var('mass')), 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### For the first fit everything looks good"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit_data(w, roodata)\n",
"plot_fit(w, roodata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The second has the normalisation of the model of by a factor of two"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img 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\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit_data(w, roodata)\n",
"plot_fit(w, roodata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The third has the normalisation of the model of by a factor of three"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<img 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\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit_data(w, roodata)\n",
"plot_fit(w, roodata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:analysis]",
"language": "python",
"name": "conda-env-analysis-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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