{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hep/davide/miniconda3/envs/root_env/lib/ROOT.py:301: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
" return _orig_ihook( name, *args, **kwds )\n"
]
}
],
"source": [
"import ROOT as r\n",
"import root_numpy as rn\n",
"import pickle\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import array as array\n",
"from architectures.data_processing import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"l_index=1\n",
"l_flv=['e','mu']\n",
"MC_Dplus_sig_dict, MC_Ds_sig_dict, _ = load_datasets(l_index)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"Dplus_M=np.array([MC_Dplus_sig_dict[\"Dplus_ConsD_M\"][i][0] for i in range(len(MC_Dplus_sig_dict[\"Dplus_ConsD_M\"]))])\n",
"Ds_M=np.array([MC_Ds_sig_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(MC_Ds_sig_dict[\"Ds_ConsD_M\"]))])\n",
"\n",
"data=np.concatenate((Dplus_M,Ds_M),axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#cut on mass\n",
"cut_indices=[]\n",
"\n",
"lower_mass_cut=1930\n",
"upper_mass_cut=2030\n",
"\n",
"for i in range(len(data)):\n",
" if lower_mass_cut<data[i]<upper_mass_cut:\n",
" cut_indices.append(i)\n",
" \n",
"data_cut=data[cut_indices]/1000. \n",
"\n",
"lower_mass_cut/=1000.\n",
"upper_mass_cut/=1000."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data_array=np.array([data_cut[i] for i in range(len(data_cut))], dtype=[('D_sel_M', np.float32)])\n",
"\n",
"plt.hist(data_cut,range=(lower_mass_cut,upper_mass_cut),bins=100);\n",
"fig=plt.gcf();\n",
"\n",
"fig.set_size_inches(16,10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"rn.array2root(data_array,\n",
" filename='/disk/lhcb_data/davide/Rphipi/selected_data/'+l_flv[l_index]+l_flv[l_index]+'/sel_data_NN_'+l_flv[l_index]+l_flv[l_index]+'.root',\n",
" treename='D_M',\n",
" mode='recreate',\n",
" )\n",
"f=r.TFile('/disk/lhcb_data/davide/Rphipi/selected_data/'+l_flv[l_index]+l_flv[l_index]+'/sel_data_NN_'+l_flv[l_index]+l_flv[l_index]+'.root')\n",
"tree=f.Get(\"D_M\") "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"mass = np.array([0],dtype=np.float32)\n",
"branch = tree.GetBranch(\"D_sel_M\")\n",
"branch.SetAddress(mass)\n",
"\n",
"num_entries=tree.GetEntries()\n",
"m = r.RooRealVar(\"m\",\"m\",lower_mass_cut,upper_mass_cut)\n",
"l = r.RooArgSet(m)\n",
"data_hist = r.RooDataSet(\"data histogram\", \"data\", l)\n",
"\n",
"\n",
"for i in range(num_entries):\n",
" tree.GetEvent(i)\n",
" r.RooAbsRealLValue.__assign__(m, mass[0])\n",
" data_hist.add(l, 1.0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Creating the signal PDF\n",
"\n",
"\n",
"mean_dplus= r.RooRealVar(\"mean_D+\",\"mean_D+\",1.87,1.77,1.97)\n",
"sigma_dplus = r.RooRealVar(\"sigma_D+\",\"sigma_D+\",0.020,0.,0.2)\n",
"alpha_dplus = r.RooRealVar(\"alpha_D+\",\"alpha_D+\",0.020,-10.,10.)\n",
"n_dplus = r.RooRealVar(\"n_D+\",\"n_D+\",0.020,-50.,50.)\n",
"sig_dplus = r.RooCBShape(\"signal_D+\",\"signal_D+\", m, mean_dplus, sigma_dplus, alpha_dplus, n_dplus)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Creating the signal PDF\n",
"\n",
"mean_ds= r.RooRealVar(\"mean_Ds\",\"mean_Ds\",1.97,1.87,2.07)\n",
"sigma_ds = r.RooRealVar(\"sigma_Ds\",\"sigma_Ds\",0.020,0.,0.2)\n",
"alpha_ds = r.RooRealVar(\"alpha_Ds\",\"alpha_Ds\",0.020,-10.,10.)\n",
"n_ds = r.RooRealVar(\"n_Ds\",\"n_Ds\",0.020,-50.,50.)\n",
"sig_ds = r.RooCBShape(\"signal_Ds\",\"signal_Ds\", m, mean_ds, sigma_ds, alpha_ds, n_ds)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#coef0 = r.RooRealVar(\"c0\",\"coefficient #0\",1.0,-1.,1)\n",
"#coef1 = r.RooRealVar(\"c1\",\"coefficient #1\",0.1,-1.,1)\n",
"#coef2 = r.RooRealVar(\"c2\",\"coefficient #2\",-0.1,-1.,1)\n",
"#bkg = r.RooChebychev(\"bkg\",\"background p.d.f.\",m,r.RooArgList(coef0,coef1,coef2))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Add 2 sources of signal\n",
"\n",
"# first add the sig and the peak together with fraction fpeak\n",
"ns = r.RooRealVar(\"# of Ds sig events\",\"# of Ds sig events\",100.,0.,40000.)\n",
"#nplus = r.RooRealVar(\"# of Dplus sig events\",\"# of Dplus sig events\",100.,0.,40000.)\n",
"#nbkg = r.RooRealVar(\"# of bkg events\",\"# of bkg events\",100.,0.,40000.)\n",
"# sigpeak(x) = fpeak*sig(x) + (1-fpeak)*bkg(x)\n",
"model = r.RooAddPdf(\"model\",\"two mass peaks\",r.RooArgList(sig_ds),r.RooArgList(ns))\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"model.fitTo(data_hist,r.RooFit.Extended())\n",
"\n",
"\n",
"\n",
"xframe = m.frame(r.RooFit.Title(\"Fit to \"+l_flv[l_index]+\" data\"))\n",
"\n",
"#bkg_component = r.RooArgSet(bkg)\n",
"data_hist.plotOn(xframe)\n",
"#model.plotOn(xframe,r.RooFit.Components(bkg_component),r.RooFit.LineStyle(2))\n",
"model.plotOn(xframe)\n",
"#sigpeaks.plotOn(xframe)\n",
"xframe.Draw()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#NB=nbkg.getVal()\n",
"NS=ns.getVal()\n",
"Nplus=nplus.getVal()\n",
"\n",
"if l_index==0:\n",
" Nsig_from_MC=1466+1974\n",
"if l_index==1:\n",
" Nsig_from_MC=5881+9639\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15520.023435679977"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NS+Nplus"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15520"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Nsig_from_MC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini