{
"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 ctypes\n",
"import numpy as np\n",
"from array import array\n",
"import root_numpy as rn\n",
"import matplotlib.pyplot as plt\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"l_flv = ['e','mu']\n",
"data_type = ['MC','small_data']\n",
"tree_name = 'Ds_OfflineTree/DecayTree'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"l_index = 1\n",
"data_index = None "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def find_file_path(l_index=l_index, data_index=data_index): \n",
" return \"/disk/lhcb_data/davide/Rphipi/\"+data_type[data_index]+\"/Ds_phipi_\"+l_flv[l_index]+l_flv[l_index]+\"/Ds_phipi_\"+l_flv[l_index]+l_flv[l_index]+\".root\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data = r.TFile(find_file_path(l_index=l_index, data_index=1))\n",
"MC = r.TFile(find_file_path(l_index=l_index, data_index=0))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ROOT.TTree object (\"DecayTree\") at 0x55f041fbe410>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_data = data.Get(\"Ds_OfflineTree/DecayTree\")\n",
"t_data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ROOT.TTree object (\"DecayTree\") at 0x55f041a503a0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_MC = MC.Get(\"Ds_OfflineTree/DecayTree\")\n",
"t_MC"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Switch on only the branches that you need\n",
"t_data.SetBranchStatus(\"*\",0)\n",
"t_MC.SetBranchStatus(\"*\",0)\n",
"\n",
"branches_needed = [\n",
" \"Ds_ENDVERTEX_CHI2\",\n",
" \"Ds_ENDVERTEX_NDOF\",\n",
" \"Ds_OWNPV_CHI2\",\n",
" \"Ds_OWNPV_NDOF\",\n",
" \"Ds_IPCHI2_OWNPV\",\n",
" \"Ds_IP_OWNPV\",\n",
" \"Ds_DIRA_OWNPV\",\n",
" \"Ds_ConsD_M\",\n",
" l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index],\n",
" l_flv[l_index]+\"_minus_MC15TuneV1_ProbNN\"+l_flv[l_index],\n",
" \"Ds_Hlt1TrackMVADecision_TOS\",\n",
" \"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\",\n",
" \"Ds_Hlt2Phys_TOS\",\n",
" \n",
" \"phi_ENDVERTEX_CHI2\",\n",
" \"phi_ENDVERTEX_NDOF\",\n",
" \"phi_OWNPV_CHI2\",\n",
" \"phi_OWNPV_NDOF\",\n",
" \"phi_IP_OWNPV\",\n",
" \"phi_IPCHI2_OWNPV\",\n",
" \"phi_DIRA_OWNPV\",\n",
" \"phi_M\",\n",
" ] \n",
"\n",
"for branch in branches_needed:\n",
" t_data.SetBranchStatus(branch, 1)\n",
" t_MC.SetBranchStatus(branch, 1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC event count 49002, data event count 233629\n"
]
}
],
"source": [
"#Count the events in the tuple\n",
"\n",
"MC_count=0\n",
"for event in enumerate(t_MC):\n",
" MC_count+=1\n",
" \n",
"data_count=0\n",
"for event in enumerate(t_data):\n",
" data_count+=1\n",
"print(\"MC event count {0}, data event count {1}\".format(MC_count,data_count))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Create a dictionary\n",
"\n",
"#dict ={'branch_name'=[branch_value[event]]}\n",
"\n",
"MC_tuple_dict = {}\n",
"\n",
"for branch in branches_needed:\n",
" \n",
" MC_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, 0),\n",
" treename = tree_name,\n",
" branches = branch,\n",
" start=0,\n",
" stop=MC_count,\n",
" )\n",
" \n",
"data_tuple_dict = {}\n",
"\n",
"for branch in branches_needed:\n",
" \n",
" data_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, 1),\n",
" treename = tree_name,\n",
" branches = branch,\n",
" start=0,\n",
" stop=data_count,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Check the Ds mass plot\n",
"\n",
"Ds_constrained_mass_MC = np.array([MC_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(MC_tuple_dict[\"Ds_ConsD_M\"]))])\n",
"Ds_constrained_mass_data = np.array([data_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))])\n",
"plt.subplot(1,2,1)\n",
"plt.hist(Ds_constrained_mass_MC,bins=70, range=(0,3000));\n",
"plt.subplot(1,2,2)\n",
"plt.hist(Ds_constrained_mass_data,bins=70, range=(0,3000));\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(16,6)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#HLT1 PRESELECTION\n",
"data_tuple_dict_presel_1={}\n",
"MC_tuple_dict_presel_1={}\n",
"\n",
"for label in branches_needed: \n",
"\n",
" data_tuple_dict_presel_1[label] = data_tuple_dict[label][data_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
" MC_tuple_dict_presel_1[label] = MC_tuple_dict[label][MC_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
"\n",
"#RareCharm D2pi l l HLT2 PRESELECTION\n",
"\n",
"data_tuple_dict_presel_2={}\n",
"MC_tuple_dict_presel_2={}\n",
"\n",
"for label in branches_needed: \n",
"\n",
" data_tuple_dict_presel_2[label] = data_tuple_dict_presel_1[label][data_tuple_dict_presel_1[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]]\n",
" MC_tuple_dict_presel_2[label] = MC_tuple_dict_presel_1[label][MC_tuple_dict_presel_1[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]]\n",
"\n",
"#PID preselection\n",
"\n",
"#MC_PID_indices=np.where(MC_tuple_dict_presel_2[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"\n",
"data_PID_indices_plus=np.where(data_tuple_dict_presel_2[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"data_PID_indices_minus=np.where(data_tuple_dict_presel_2[l_flv[l_index]+\"_minus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"\n",
"data_PID_indices = np.intersect1d(data_PID_indices_plus,data_PID_indices_minus)\n",
"\n",
"data_tuple_dict_presel_3={}\n",
"MC_tuple_dict_presel_3={}\n",
"\n",
"for label in branches_needed: \n",
"\n",
" data_tuple_dict_presel_3[label] = data_tuple_dict_presel_2[label][data_PID_indices]\n",
" MC_tuple_dict_presel_3[label] = MC_tuple_dict_presel_2[label]#[MC_PID_indices]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#np.float(MC_tuple_dict_presel_1[\"Ds_ConsD_M\"].shape[0])/np.float(MC_tuple_dict[\"Ds_ConsD_M\"].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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": [
"plt.hist([data_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))],alpha=0.2,bins=70,range=(1000,3000));\n",
"plt.hist([data_tuple_dict_presel_1[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_1[\"Ds_ConsD_M\"]))],alpha=0.3,bins=70,range=(1000,3000));\n",
"plt.hist([data_tuple_dict_presel_2[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_2[\"Ds_ConsD_M\"]))],alpha=0.4,bins=70,range=(1000,3000));\n",
"plt.hist([data_tuple_dict_presel_3[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_3[\"Ds_ConsD_M\"]))],alpha=0.6,bins=70,range=(1000,3000));\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(16,10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"data_tuple_dict=data_tuple_dict_presel_3\n",
"MC_tuple_dict=MC_tuple_dict_presel_3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"lower_Ds_mass = 1850\n",
"upper_Ds_mass = 2050\n",
"\n",
"lower_phi_mass = 900\n",
"upper_phi_mass = 1100\n",
"\n",
"#Retrieve mc signal and data bkg events\n",
"\n",
"data_bkg_indices=[]\n",
"MC_sig_indices=[]\n",
"\n",
"\n",
"for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"])):\n",
" #retrieving the Ds reconstructed mass\n",
" \n",
" if 0<data_tuple_dict[\"phi_M\"][i]<lower_phi_mass or upper_phi_mass<data_tuple_dict[\"phi_M\"][i]:\n",
" Ds_m = data_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the out of signal regions\n",
" if 0<Ds_m<lower_Ds_mass or upper_Ds_mass < Ds_m:\n",
" data_bkg_indices.append(i)\n",
" \n",
"for i in range(len(MC_tuple_dict[\"Ds_ConsD_M\"])):\n",
" \n",
" #retrieving the Ds reconstructed mass\n",
" if lower_phi_mass<MC_tuple_dict[\"phi_M\"][i]<lower_phi_mass:\n",
" Ds_m = MC_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the signal regions\n",
" if lower_Ds_mass<Ds_m<upper_Ds_mass:\n",
" MC_sig_indices.append(i)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Create the dict tuples with all MC signal and data bkg events\n",
"\n",
"data_tuple_bkg={}\n",
"MC_tuple_sig ={}\n",
"\n",
"for label in branches_needed: \n",
"\n",
" data_tuple_bkg[label] = data_tuple_dict[label][data_bkg_indices]\n",
" MC_tuple_sig[label] = MC_tuple_dict[label][MC_sig_indices]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6151,)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_tuple_sig[\"Ds_ConsD_M\"].shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def plot_sb_comparison(nbins=None, particle=None, variable=None, \n",
" MC_sig=None, data_bkg=None, \n",
" width_MC=None, width_data=None,):\n",
" #min_plt_range=None, max_plt_range=None):\n",
" \n",
" #Determine maximum between MC and data\n",
" if \"CHI2\" in variable:\n",
" if np.max(MC_sig)>np.max(data_bkg):\n",
" upper_limit=np.max(MC_sig)\n",
" else:\n",
" upper_limit=np.max(data_bkg)\n",
" \n",
" lower_limit=0\n",
" if \"DIRA\" in variable:\n",
" lower_limit=0.99980\n",
" upper_limit=1.\n",
" \n",
" if \"prob\" in variable:\n",
" lower_limit=0.\n",
" upper_limit=1.\n",
" \n",
" if \"Hlt\" in variable:\n",
" lower_limit=0.\n",
" upper_limit=2.\n",
" \n",
" #Create and fill MC Signal histogram\n",
"\n",
" h_mc= r.TH1F(particle+\" \"+variable+\" MC/data comparison\", particle+\" \"+variable+\" MC/data comparison\", nbins, lower_limit, upper_limit)\n",
" \n",
" for i in range(len(MC_sig)):\n",
" h_mc.Fill(MC_sig[i])\n",
" \n",
" n1=h_mc.Integral(\"width\")\n",
" h_mc.Scale(1/n1)\n",
" h_mc.Integral(\"width\");\n",
" \n",
" #Create and fill data bkg histogram\n",
" h_data= r.TH1F(particle+\" \"+variable+\" from data\", particle+\" \"+variable+\" from data\", nbins, lower_limit, upper_limit)\n",
" for i in range(len(data_bkg)):\n",
" h_data.Fill(data_bkg[i])\n",
" \n",
" n2=h_data.Integral(\"width\")\n",
" h_data.Scale(1/n2)\n",
" h_data.Integral(\"width\");\n",
" \n",
" a=[h_mc.GetBinContent(i) for i in range(nbins)]\n",
" b=[h_mc.GetBinCenter(i) for i in range(nbins)]\n",
" c=[h_data.GetBinContent(i) for i in range(nbins)]\n",
" d=[h_data.GetBinCenter(i) for i in range(nbins)]\n",
" plt.title(particle+\" \"+variable+\" Signal MC/ data comparison\", fontsize=20)\n",
" \n",
" plt.bar(b,a,width=width_MC,alpha=0.6, label=\"Signal MC\")\n",
" plt.bar(d,c,width=width_data, alpha=0.4, label=\"Background Data\")\n",
" plt.legend(fontsize=20)\n",
" fig = plt.gcf()\n",
" fig.set_size_inches(16,8)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"#Retrieve data from needed branch\n",
"MC_Ds_endvtx_chi2ratio=MC_tuple_dict[\"Ds_ENDVERTEX_CHI2\"]/MC_tuple_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"data_Ds_endvtx_chi2ratio=data_tuple_bkg[\"Ds_ENDVERTEX_CHI2\"]/data_tuple_bkg[\"Ds_ENDVERTEX_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Ds\", variable=\"END VTX CHI2\", \n",
" MC_sig=MC_Ds_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.05, width_data=0.06)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"#Retrieve data from needed branch\n",
"MC_Ds_endvtx_chi2ratio=MC_tuple_dict[\"phi_ENDVERTEX_CHI2\"]/MC_tuple_dict[\"phi_ENDVERTEX_NDOF\"]\n",
"data_Ds_endvtx_chi2ratio=data_tuple_bkg[\"phi_ENDVERTEX_CHI2\"]/data_tuple_bkg[\"phi_ENDVERTEX_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"phi\", variable=\"END VTX CHI2\", \n",
" MC_sig=MC_Ds_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.07, width_data=0.08)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"MC_Ds_ownpv_chi2ratio=MC_tuple_dict[\"Ds_OWNPV_CHI2\"]/MC_tuple_dict[\"Ds_OWNPV_NDOF\"]\n",
"data_Ds_ownpv_chi2ratio=data_tuple_bkg[\"Ds_OWNPV_CHI2\"]/data_tuple_bkg[\"Ds_OWNPV_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\", variable=\"Own PV CHI2\", \n",
" MC_sig=MC_Ds_ownpv_chi2ratio, data_bkg=data_Ds_ownpv_chi2ratio,\n",
" width_MC=0.01, width_data=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"MC_Ds_ownpv_chi2ratio=MC_tuple_dict[\"phi_OWNPV_CHI2\"]/MC_tuple_dict[\"phi_OWNPV_NDOF\"]\n",
"data_Ds_ownpv_chi2ratio=data_tuple_bkg[\"phi_OWNPV_CHI2\"]/data_tuple_bkg[\"phi_OWNPV_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"phi\", variable=\"Own PV CHI2\", \n",
" MC_sig=MC_Ds_ownpv_chi2ratio, data_bkg=data_Ds_ownpv_chi2ratio,\n",
" width_MC=0.009, width_data=0.008)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Ds_DIRA_ownpv=MC_tuple_sig[\"Ds_DIRA_OWNPV\"]\n",
"data_Ds_DIRA_ownpv=data_tuple_bkg[\"Ds_DIRA_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\", variable=\"DIRA Own PV\", \n",
" MC_sig=MC_Ds_DIRA_ownpv, data_bkg=data_Ds_DIRA_ownpv,\n",
" width_MC=0.000001, width_data=0.000001)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_probNNmu=MC_tuple_sig[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]\n",
"data_probNNmu=data_tuple_bkg[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\",variable=\"probNN mu\", \n",
" MC_sig=MC_probNNmu, data_bkg=data_probNNmu,\n",
" width_MC=0.01, width_data=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt1TrackMVA_TOS=MC_tuple_sig[\"Ds_Hlt1TrackMVADecision_TOS\"]\n",
"data_Hlt1TrackMVA_TOS=data_tuple_bkg[\"Ds_Hlt1TrackMVADecision_TOS\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt1 Track MVA TOS\", \n",
" MC_sig=MC_Hlt1TrackMVA_TOS, data_bkg=data_Hlt1TrackMVA_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt2RareCharm_TOS=MC_tuple_sig[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]\n",
"data_Hlt2RareCharm_TOS=data_tuple_bkg[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt2 RareCharm D2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\" TOS\", \n",
" MC_sig=MC_Hlt2RareCharm_TOS, data_bkg=data_Hlt2RareCharm_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt2Phys_TOS=MC_tuple_sig[\"Ds_Hlt2Phys_TOS\"]\n",
"data_Hlt2Phys_TOS=data_tuple_bkg[\"Ds_Hlt2Phys_TOS\"]\n",
"\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt2 Phys TOS\", \n",
" MC_sig=MC_Hlt2Phys_TOS, data_bkg=data_Hlt2Phys_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"#h_data_under.SetLineColor(38)\n",
"#h_mc_under.SetLineColor(46)\n",
"#\n",
"#c1=r.TCanvas(\"c1\",\"c1\",1200,700)\n",
"#r.gStyle.SetOptStat(0)\n",
"#h_mc_under.Draw()\n",
"#h_data_under.Draw(\"same\")\n",
"#\n",
"#legend = r.TLegend(0.9,0.8,0.6,0.9)\n",
"#legend.SetHeader(\"Ds End vertex chi2\")\n",
"#legend.AddEntry(h_mc_under,\"Signal MC\",\"L\")\n",
"#legend.AddEntry(h_data_under,\"data below Ds reco mass MC\",\"L\")\n",
"#legend.Draw()\n",
"#c1.Update()\n",
"#c1.SaveAs(\"/home/hep/davide/Rphipi/plt.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/MC_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_tuple_sig, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/data_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(data_tuple_bkg, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"with open('/disk/lhcb_data/davide/Rphipi/NN_for_selection/'+l_flv[l_index]+l_flv[l_index]+'/data_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(data_tuple_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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