{
"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_index = 0\n",
"data_index = None \n",
"mother_index = None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"mother_ID=['Ds','Dplus']\n",
"l_flv = ['e','mu']\n",
"data_type = ['MC','data']\n",
"\n",
"def find_file_path(l_index=l_index, mother_index=mother_index, data_index=data_index): \n",
" return \"/disk/lhcb_data/davide/Rphipi/\"+data_type[data_index]+\"/\"+mother_ID[mother_index]+\"_phipi_\"+l_flv[l_index]+l_flv[l_index]+\"/\"+mother_ID[mother_index]+\"_phipi_\"+l_flv[l_index]+l_flv[l_index]+\".root\"\n",
"\n",
"mother_index=0\n",
"\n",
"data = r.TFile(find_file_path(l_index=l_index, mother_index=mother_index, data_index=1))\n",
"MC_Ds = r.TFile(find_file_path(l_index=l_index, mother_index=mother_index, data_index=0))\n",
"\n",
"tree_name_Ds = mother_ID[mother_index]+'_OfflineTree/DecayTree'\n",
"t_data = data.Get(tree_name_Ds)\n",
"t_MC_Ds = MC_Ds.Get(tree_name_Ds)\n",
"\n",
"\n",
"mother_index=1\n",
"\n",
"MC_Dplus = r.TFile(find_file_path(l_index=l_index, mother_index=mother_index, data_index=0))\n",
"\n",
"tree_name_Dplus = mother_ID[mother_index]+'_OfflineTree/DecayTree'\n",
"t_MC_Dplus = MC_Dplus.Get(tree_name_Dplus)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ROOT.TTree object (\"DecayTree\") at 0x55d6c06bb800>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_MC_Dplus"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def return_branches(mother_index=None):\n",
"\n",
" branches_needed = [\n",
" #________________________________________\n",
" #D\n",
" #________________________________________\n",
" #D Geometric variables, pT and FD\n",
" \n",
" mother_ID[mother_index]+\"_ENDVERTEX_CHI2\",\n",
" mother_ID[mother_index]+\"_ENDVERTEX_NDOF\",\n",
" mother_ID[mother_index]+\"_IPCHI2_OWNPV\",\n",
"\n",
" mother_ID[mother_index]+\"_OWNPV_CHI2\",\n",
" mother_ID[mother_index]+\"_OWNPV_NDOF\",\n",
" mother_ID[mother_index]+\"_IP_OWNPV\",\n",
" mother_ID[mother_index]+\"_DIRA_OWNPV\",\n",
" \n",
" mother_ID[mother_index]+\"_PT\",\n",
" #mother_ID[mother_index]+\"_FD_OWNPV\",\n",
" mother_ID[mother_index]+\"_FDCHI2_OWNPV\",\n",
" \n",
" #D Reconstructed mass\n",
" mother_ID[mother_index]+\"_ConsD_M\",\n",
" \n",
" #D Trigger variables\n",
" mother_ID[mother_index]+\"_Hlt1TrackMVADecision_TOS\",\n",
" mother_ID[mother_index]+\"_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\",\n",
" mother_ID[mother_index]+\"_Hlt2Phys_TOS\",\n",
" \n",
" #________________________________________\n",
" #PHI\n",
" #________________________________________\n",
" #phi geometric variables, pT and FD\n",
" \n",
" \"phi_ENDVERTEX_CHI2\",\n",
" \"phi_ENDVERTEX_NDOF\",\n",
" \"phi_IPCHI2_OWNPV\",\n",
" \n",
" #\"phi_OWNPV_CHI2\",\n",
" #\"phi_OWNPV_NDOF\",\n",
" #\"phi_IP_OWNPV\",\n",
" #\"phi_DIRA_OWNPV\",\n",
" \n",
" \"phi_PT\",\n",
" \n",
" #phi Reconstructed mass\n",
" \n",
" \"phi_M\",\n",
" \n",
" #________________________________________\n",
" #PION\n",
" #________________________________________\n",
" #pi Geometric variables and pT\n",
" #\"pi_OWNPV_CHI2\",\n",
" #\"pi_OWNPV_NDOF\",\n",
" #'pi_IP_OWNPV',\n",
" \n",
" 'pi_PT',\n",
" \n",
" #pi PID variables\n",
" \n",
" \"pi_MC15TuneV1_ProbNNpi\",\n",
" \n",
" #________________________________________\n",
" #LEPTONS\n",
" #________________________________________\n",
" #leptons Geometric variables and pT\n",
" \n",
" #l_flv[l_index]+\"_plus_OWNPV_CHI2\",\n",
" #l_flv[l_index]+\"_plus_OWNPV_NDOF\",\n",
" #l_flv[l_index]+\"_minus_OWNPV_CHI2\",\n",
" #l_flv[l_index]+\"_minus_OWNPV_NDOF\",\n",
" #\n",
" #l_flv[l_index]+\"_plus_IP_OWNPV\",\n",
" #l_flv[l_index]+\"_minus_IP_OWNPV\",\n",
" \n",
" l_flv[l_index]+\"_plus_PT\",\n",
" l_flv[l_index]+\"_minus_PT\",\n",
" \n",
" #leptons PID variables\n",
" \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",
" \n",
" \n",
" ] \n",
" return branches_needed"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Switch on only the branches that you need\n",
"t_data.SetBranchStatus(\"*\",0)\n",
"t_MC_Dplus.SetBranchStatus(\"*\",0)\n",
"t_MC_Ds.SetBranchStatus(\"*\",0)\n",
"\n",
"\n",
"for branch in return_branches(mother_index=0):\n",
" t_data.SetBranchStatus(branch, 1)\n",
" t_MC_Ds.SetBranchStatus(branch, 1)\n",
" \n",
"for branch in return_branches(mother_index=1):\n",
" t_data.SetBranchStatus(branch, 1)\n",
" t_MC_Ds.SetBranchStatus(branch, 1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Create a dictionary\n",
"\n",
"#dict ={'branch_name'=[branch_value[event]]}\n",
"\n",
"MC_Ds_tuple_dict = {}\n",
"branches_needed=return_branches(mother_index=0)\n",
"for branch in branches_needed:\n",
" \n",
" MC_Ds_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, mother_index=0, data_index=0),\n",
" treename = tree_name_Ds,\n",
" branches = branch,\n",
" start=0,\n",
" stop=t_MC_Ds.GetEntries(),\n",
" )\n",
" \n",
"MC_Dplus_tuple_dict = {}\n",
"branches_needed=return_branches(mother_index=1)\n",
"for branch in branches_needed:\n",
" \n",
" MC_Dplus_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, mother_index=1, data_index=0),\n",
" treename = tree_name_Dplus,\n",
" branches = branch,\n",
" start=0,\n",
" stop=t_MC_Dplus.GetEntries(),\n",
" )\n",
" \n",
"data_tuple_dict = {}\n",
"\n",
"branches_needed=return_branches(mother_index=0)\n",
"for branch in branches_needed:\n",
" \n",
" data_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index,mother_index=0, data_index=1),\n",
" treename = tree_name_Ds,\n",
" branches = branch,\n",
" start=0,\n",
" stop=t_data.GetEntries(),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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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",
"Dplus_constrained_mass_MC = np.array([MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"][i][0] for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"]))])\n",
"Ds_constrained_mass_MC = np.array([MC_Ds_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(MC_Ds_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",
"\n",
"plt.subplot(1,2,1)\n",
"plt.hist(Ds_constrained_mass_MC,bins=70, range=(1750,2100), density=True);\n",
"plt.hist(Dplus_constrained_mass_MC,bins=70, range=(1750,2100), density=True);\n",
"plt.subplot(1,2,2)\n",
"plt.hist(Ds_constrained_mass_data,bins=70, range=(1100,2750), density=True);\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(16,6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Check the phi mass plot\n",
"if l_flv[l_index]=='e':\n",
" lower=700\n",
" upper=1375\n",
"if l_flv[l_index]=='mu':\n",
" lower=925\n",
" upper=1100\n",
"\n",
"phi_mass_MC_Ds = np.array([MC_Ds_tuple_dict[\"phi_M\"][i] for i in range(len(MC_Ds_tuple_dict[\"phi_M\"]))])\n",
"#phi_ConsD_mass_MC_Ds = np.array([MC_Ds_tuple_dict[\"Ds_ConsD_phi_1020_M\"][i][0] for i in range(len(MC_Ds_tuple_dict[\"phi_M\"]))])\n",
"\n",
"phi_mass_MC_Dplus = np.array([MC_Dplus_tuple_dict[\"phi_M\"][i] for i in range(len(MC_Dplus_tuple_dict[\"phi_M\"]))])\n",
"#phi_ConsD_mass_MC_Dplus = np.array([MC_Ds_tuple_dict[\"Ds_ConsD_phi_1020_M\"][i][0] for i in range(len(MC_Ds_tuple_dict[\"phi_M\"]))])\n",
"\n",
"\n",
"\n",
"plt.hist(phi_mass_MC_Ds,bins=70, alpha=0.5, range=(lower,upper),label='phi_M from Ds MC', density=True);\n",
"plt.hist(phi_mass_MC_Dplus,bins=70, alpha=0.7, range=(lower,upper),label='phi_M from Dplus MC', density=True);\n",
"plt.legend(fontsize='10')\n",
"plt.ylabel('# events a.u.', fontsize=15)\n",
"plt.xlabel('Reconstructed D_s Mass (GeV)', fontsize=15)\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(8,8)\n",
"plt.savefig('/home/hep/davide/Rphipi/'+l_flv[l_index]+'/phi_reco_m_'+l_flv[l_index]+'.png', format='png', dpi=100)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#HLT1 PRESELECTION\n",
"data_tuple_dict_presel_1={}\n",
"MC_Ds_tuple_dict_presel_1={}\n",
"MC_Dplus_tuple_dict_presel_1={}\n",
"\n",
"for label in return_branches(mother_index=0): \n",
"\n",
" data_tuple_dict_presel_1[label] = data_tuple_dict[label][data_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
" MC_Ds_tuple_dict_presel_1[label] = MC_Ds_tuple_dict[label][MC_Ds_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
"\n",
"for label in return_branches(mother_index=1): \n",
" MC_Dplus_tuple_dict_presel_1[label] = MC_Dplus_tuple_dict[label][MC_Dplus_tuple_dict[\"Dplus_Hlt1TrackMVADecision_TOS\"]]\n",
"\n",
"#RareCharm D2pi l l HLT2 PRESELECTION\n",
"\n",
"data_tuple_dict_presel_2={}\n",
"MC_Ds_tuple_dict_presel_2={}\n",
"MC_Dplus_tuple_dict_presel_2={}\n",
"\n",
"for label in return_branches(mother_index=0):\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_Ds_tuple_dict_presel_2[label] = MC_Ds_tuple_dict_presel_1[label][MC_Ds_tuple_dict_presel_1[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]]\n",
"\n",
"for label in return_branches(mother_index=1): \n",
" \n",
" MC_Dplus_tuple_dict_presel_2[label] = MC_Dplus_tuple_dict_presel_1[label][MC_Dplus_tuple_dict_presel_1[\"Dplus_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",
"data_PID_indices_pi=np.where(data_tuple_dict_presel_2[\"pi_MC15TuneV1_ProbNNpi\"]>0.4)\n",
"\n",
"data_PID_indices = np.intersect1d(data_PID_indices_plus,data_PID_indices_minus)\n",
"data_PID_indices = np.intersect1d(data_PID_indices,data_PID_indices_pi)\n",
"data_tuple_dict_presel_3={}\n",
"MC_Ds_tuple_dict_presel_3={}\n",
"MC_Dplus_tuple_dict_presel_3={}\n",
"\n",
"for label in return_branches(mother_index=0):\n",
"\n",
" data_tuple_dict_presel_3[label] = data_tuple_dict_presel_2[label][data_PID_indices]\n",
" MC_Ds_tuple_dict_presel_3[label] = MC_Ds_tuple_dict_presel_2[label]#[MC_PID_indices]\n",
" \n",
"for label in return_branches(mother_index=1):\n",
" MC_Dplus_tuple_dict_presel_3[label] = MC_Dplus_tuple_dict_presel_2[label]#[MC_PID_indices]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"if l_flv[l_index]=='mu':\n",
" lower_phi_mass = 980\n",
" upper_phi_mass = 1060\n",
" \n",
"if l_flv[l_index]=='e':\n",
" lower_phi_mass = 850\n",
" upper_phi_mass = 1100\n",
" \n",
"#Retrieve mc signal and data bkg events\n",
"\n",
"MC_Dplus_indices=[]\n",
"MC_Ds_indices=[]\n",
"data_indices=[]\n",
" \n",
"for i in range(len(MC_Ds_tuple_dict_presel_3[\"Ds_ConsD_M\"])):\n",
"\n",
" phi_m = MC_Ds_tuple_dict_presel_3[\"phi_M\"][i]\n",
" #fixing a window on the phi mass\n",
" if lower_phi_mass<phi_m<upper_phi_mass:\n",
" MC_Ds_indices.append(i)\n",
" \n",
"for i in range(len(MC_Dplus_tuple_dict_presel_3[\"Dplus_ConsD_M\"])):\n",
"\n",
" phi_m = MC_Dplus_tuple_dict_presel_3[\"phi_M\"][i]\n",
" #fixing a window on the phi mass\n",
" if lower_phi_mass<phi_m<upper_phi_mass:\n",
" MC_Dplus_indices.append(i)\n",
" \n",
"for i in range(len(data_tuple_dict_presel_3[\"Ds_ConsD_M\"])):\n",
"\n",
" phi_m = data_tuple_dict_presel_3[\"phi_M\"][i]\n",
" #fixing a window on the phi mass\n",
" if lower_phi_mass<phi_m<upper_phi_mass:\n",
" data_indices.append(i)\n",
"\n",
"MC_Ds_tuple_dict_presel_4 ={}\n",
"MC_Dplus_tuple_dict_presel_4 ={}\n",
"data_tuple_dict_presel_4={}\n",
"\n",
"\n",
"for label in return_branches(mother_index=0): \n",
" \n",
" data_tuple_dict_presel_4[label] = data_tuple_dict_presel_3[label][data_indices]\n",
" MC_Ds_tuple_dict_presel_4[label] = MC_Ds_tuple_dict_presel_3[label][MC_Ds_indices]\n",
"\n",
"for label in return_branches(mother_index=1): \n",
" MC_Dplus_tuple_dict_presel_4[label] = MC_Dplus_tuple_dict_presel_3[label][MC_Dplus_indices]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"if l_flv[l_index]=='mu':\n",
" lower_MC = 1.8\n",
" upper_MC = 2.1\n",
" lower_data = 1.0\n",
" upper_data = 3.0\n",
" \n",
"if l_flv[l_index]=='e':\n",
" lower_MC = 1.75\n",
" upper_MC = 2.15\n",
" lower_data = 1.0\n",
" upper_data = 3.5\n",
"\n",
"\n",
"#plt.suptitle('Reconstructed D_s mass', fontsize=15)\n",
"plt.subplot(1,2,1)\n",
"label_1=\"Ds_ConsD_M\"\n",
"label_2=\"Dplus_ConsD_M\"\n",
"#plt.title('MC', fontsize=12)\n",
"plt.hist([MC_Ds_tuple_dict[label_1][i][0]/1000 for i in range(len(MC_Ds_tuple_dict[\"Ds_ConsD_M\"]))] + [MC_Dplus_tuple_dict[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"]))],alpha=0.3,bins=70,range=(1.8,2.1), label='MC no presel');\n",
"plt.hist([MC_Ds_tuple_dict_presel_1[label_1][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_1[\"Ds_ConsD_M\"]))] + [MC_Dplus_tuple_dict_presel_1[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_1[\"Dplus_ConsD_M\"]))],alpha=0.4,bins=70,range=(lower_MC,upper_MC), label='MC after HLT1 TOS presel');\n",
"plt.hist([MC_Ds_tuple_dict_presel_2[label_1][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_2[\"Ds_ConsD_M\"]))] + [MC_Dplus_tuple_dict_presel_2[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_2[\"Dplus_ConsD_M\"]))],alpha=0.5,bins=70,range=(lower_MC,upper_MC), label='MC after HLT2 TOS presel');\n",
"plt.hist([MC_Ds_tuple_dict_presel_4[label_1][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_4[\"Ds_ConsD_M\"]))] + [MC_Dplus_tuple_dict_presel_4[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_4[\"Dplus_ConsD_M\"]))],alpha=0.7,bins=70,range=(lower_MC,upper_MC), label='MC after cutting on phi mass');\n",
"\n",
"#plt.hist([MC_Dplus_tuple_dict[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"]))],alpha=0.3,bins=70,range=(1.8,2.1), label='MC no presel');\n",
"#plt.hist([MC_Dplus_tuple_dict_presel_1[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_1[\"Dplus_ConsD_M\"]))],alpha=0.4,bins=70,range=(lower_MC,upper_MC), label='MC after HLT1 TOS presel');\n",
"#plt.hist([MC_Dplus_tuple_dict_presel_2[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_2[\"Dplus_ConsD_M\"]))],alpha=0.5,bins=70,range=(lower_MC,upper_MC), label='MC after HLT2 TOS presel');\n",
"#plt.hist([MC_Dplus_tuple_dict_presel_4[label_2][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_4[\"Dplus_ConsD_M\"]))],alpha=0.7,bins=70,range=(lower_MC,upper_MC), label='MC after cutting on phi mass');\n",
"#\n",
"plt.legend(fontsize='10')\n",
"plt.ylabel('# events', fontsize=15)\n",
"plt.xlabel('Reconstructed D_s Mass (GeV)', fontsize=15)\n",
"\n",
"label=\"Ds_ConsD_M\"\n",
"plt.subplot(1,2,2)\n",
"plt.title('Data', fontsize=12)\n",
"#plt.hist([data_tuple_dict[\"Ds_ConsD_M\"][i][0]/1000 for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))],alpha=0.3,bins=70,range=(1.0,3.0), label='Data');\n",
"plt.hist([data_tuple_dict_presel_1[label][i][0]/1000 for i in range(len(data_tuple_dict_presel_1[\"Ds_ConsD_M\"]))],alpha=0.4,bins=70,range=(lower_data,upper_data),label='Data after HLT1 TOS presel');\n",
"plt.hist([data_tuple_dict_presel_2[label][i][0]/1000 for i in range(len(data_tuple_dict_presel_2[\"Ds_ConsD_M\"]))],alpha=0.5,bins=70,range=(lower_data,upper_data), label='Data after HLT2 TOS presel');\n",
"plt.hist([data_tuple_dict_presel_3[label][i][0]/1000 for i in range(len(data_tuple_dict_presel_3[\"Ds_ConsD_M\"]))],alpha=0.7,bins=70,range=(lower_data,upper_data), label='Data after \\mu & \\pi PID presel');\n",
"plt.hist([data_tuple_dict_presel_4[label][i][0]/1000 for i in range(len(data_tuple_dict_presel_4[\"Ds_ConsD_M\"]))],alpha=0.7,bins=70,range=(lower_data,upper_data), label='Data after cutting on phi mass');\n",
"plt.legend(fontsize='10')\n",
"plt.xlabel('Reconstructed D_s Mass (GeV)', fontsize=15)\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(18,8)\n",
"plt.savefig('/home/hep/davide/Rphipi/'+l_flv[l_index]+'/reco_mass.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data_tuple_dict=data_tuple_dict_presel_4\n",
"MC_Ds_tuple_dict=MC_Ds_tuple_dict_presel_4\n",
"MC_Dplus_tuple_dict=MC_Dplus_tuple_dict_presel_4"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ggGXLlvGvf/2Lpk2b0q9fPyIip3FTTW677TZGjBhROVNizz33pFevXjl/RlUVFBTwr3/9i/fee48WLVpw+OGHf6Z+cmGyQpIaiaFDhzJ58mRuv/32Ty0Badu2LU2aNGHlypU591dSUsKpp55Khw4daNOmDWeddRalpaXbdf/SpUspLi6ufFx11VW8//77lW06deq0zX6qLqVo1aoV7dq1Y/ny5dX2UVJSwt/+9rctXvOee+6pHFDde++9PPzww3Tr1o0jjzyycnBUUlLCtddeu8V977zzzjY/r1WrVrFp0yY+//nP5/ahbEVBQQGTJ0/mxhtvpFOnTpx00kmVU2z1aRHRNSKejIiXI+K1iLgsWz4mIt6NiIXZx8Aq94yOiCXZe06qUn5IRLwQEYsjYnxEREO8J0naWaxcuZJ27doB8NRTT3HkkUfStm1biouLK2dd1mR7xxf//d//zRe+8AVat25NcXExq1ev3u7xyCmnnFL5+7137940b96cVatWVbbZ1nikXbt2fO5zn6u87tKlS41jkRUrVrBhwwYOOeSQytccMGAAa9euBeDiiy+mW7dunHDCCey9996MHTuWlFJO46aavPfee7UyFgG4+uqr2bhxI4ceeij77bcfv/vd72ql3+qYrJCkRmLvvfemR48ePPLII59a7tGqVSv69evH/fffn3N/l19+OYWFhbz++uusXbuWqVOnVq5Brc4n/77r2LEjPXv2ZM2aNZWPdevWMWvWrO16X++++27l8/Xr17N69Wr23HPPatt27NiRE044YYvXLC0trfxFe/jhhzNjxgxWrFjB6aefzhlnnFF535gxY7a478MPP+Scc87Zamy77747LVq04M033/xUXYsWLbb41qaiomKLAUd1fw8PHDiQOXPmsGzZMg488MBPTVPVFjYBo1JKXwQOAc6LiC9n665LKX05+3gEMgkJ4FvAl4ABwO8iYvMUmNuA81JK+wN7A6fU5xuRpJ3JkiVLeO211zjqqKMAGDx4MGeffTbLly9nzZo1jBo1qnK8UN3vuu0ZX8yePZubb76ZGTNmsHbtWtasWcPuu+9eY/vqXq9jx47Mnj17i9/xH330EV27ds35Pa9evXqLpaElJSU1jkV23313mjdvzmuvvVb5emvXrq0cE7Ru3Zrx48fz5ptvMnPmTMaPH8+jjz66Q+Omzp07VzsWgU+PR6omaar7vDp37sxtt93G0qVL+cMf/sAPfvADXnnllW3G8FmYrJCkRmTSpEnMmTOn2l2xx40bx29/+1tuueWWyl9aL7744qf2gNisrKyM3XbbjcLCQpYvX84111yz1ddu164db7/9duX1McccQ0VFBTfeeCMbN24kpcSrr75auX9Erh5++GEWLFhAeXk5v/jFLzjggAPYd999q217yimnsHDhQu677z7Ky8upqKjghRde4JVXXmHjxo3ce++9lJWV0axZM4qKiip/SZ933nnccsstPP/880Bmecdf/vIX1q1bt9XYWrZsyeDBg7noootYuXIlKSXmz5/Phg0b6N27N6Wlpfz5z3+moqKCcePGUVZWtsXn9c4771RuqrV8+XJmzpzJhg0baNGiReUmqKpeSmlZSuml7PN1wEvAXlu55WvAtJTSppRSCfB3oG9EdAOappQ2/8e8O9tWklTFpk2bmDNnDqeccgpDhgzhwAMPBDLjhcLCQlq0aMELL7zA5MmTK+8pLi4mInjrrbcqy7ZnfFFWVkaTJk0oLi6mvLyccePGbXUzznbt2vH+++/zwQcfVJaNGDGCn/zkJyxduhTILI3dvPlkrsrLyxk7dizl5eU8++yzPPTQQzVuPNmyZUuGDh3KxRdfXPklxbJlyyo3uZw1a1bl51FUVETTpk2JiB0aNw0bNoyJEyfyP//zP0BmTPHaa68BcNBBBzF16lQ+/vhjXnrppS2Wr1T37/Pggw+ybNkyILMfSZMmTepsPGKyQlLtmHxm9Q/llX322afGTRmPOOIIHn/8cR5++GE6d+5MYWEhw4YN47jjjqu2/ZgxY3jmmWcoKipiwIABlSd81GT48OEsWLCAoqIivvnNb9KsWTMeffRRHn/88cqpnkOGDNnqIKM6Z511FqNHj6a4uJjZs2dXbtpVnXbt2jFr1iwmTJhA27ZtadeuHT/84Q8rvw259dZb2WuvvWjVqhXjx4/nnnvuATKJlV//+td8+9vfprCwkG7dunHLLbfkFN/NN99Mly5d6NWrF23atOHCCy+koqKCtm3bcsMNNzB06FA6depEs2bNtljScvrpp7N+/XratGnDV77ylcqB0OZTRR577DEmTJiwXZ9VYxUR3YFDgaezRRdExCsRcU9E7J4t6wK8U+W2kmxZTeWS6sGgKYOqfSh/DBo0qPKkjx//+Md8//vf57bbbqusv+mmmxg9ejStW7fmpz/96RazO9u0acNFF11Enz59KC4u5plnntmu8cXAgQM5/vjj+fznP8/ee+8NsNUZEV/84hf5+te/TpcuXSguLmbp0qX8+Mc/5qijjuKwww6rPPHriSee2K7PoGPHjrRq1YrOnTvzH//xH1x77bWV+1xV56abbqJt27b07t2boqIijj766Mp9Nv7+97/Tv39/WrVqRZ8+ffjOd77DiSeeuEPjpuOOO47x48czbNgwCgsLOfzww3njjTcAuPLKK/n73/9OmzZtGD16dOWsUqj+3+epp57iy1/+Mq1ateLkk0/mV7/61Wfe/2JbYmtTdndGffr0SQsWLGjoMKTGp6bExNnVH3u5K1myZIkbHTaQYcOG0aVLF8aOHdvQoeSVmv5PRsRzKaVGdYRIRBQCc4GrUkr3R0R7YDWQgDHAPimlcyJiIjAnpTQ1e9/vsve9DfwspTQgW94PGJNSOqma1xoBjADo1q3bIVVnEkn6bGpKTEwfPL2eI2k4jjPy29y5cxkyZAglJSUNHUre2dHxiDMrJEnSLikimgN/AqaklO4HSCmtSCmVp5QqgAlkZlxAZsZE1a/jumTLair/lJTSxJRSn5RSn+05WUeSJH2ayQpJkrTLyZ7YMQlYklK6tkp5hyrNvgUszj5/BDgzIppHRBfgi8CzKaV/ARUR8ZVsu3OAmXX+BiRJauSa1fcLRsRbwDqgHPg4pdQnItoB04COwHvAmSml97PtRwPnZtv/V0rp0fqOWZKUn26//faGDkH560hgKLAoIhZmy64Azo6ILwEtgH8BwwFSSgsi4gEyG3FWACNTShuy9/0n8IeIaAHMITNbQ5Ikjj32WJeA1JF6T1ZkHZdSqno4/S+AmSml30TEj7LXP/jEMWJ7Ak9HRK8qgwdJkqRPSSk9DVS3PXmNW7ynlK4ErqymfAHw5U/fIUmS6kq+LAP5GnBX9nnVI8GqPUasAeKTJEmSpAaxqx2KoF3f5qPXd0RDJCsS8FhELIqI72fL2qeUVkBm4ytg83pSjwuTJEmS1Gi1bNmSVatWmbDQTiGlxMaNG3n33XcpKCjYob4aYhlIv5TSsuwGV7Mi4pUd7fATR4XtaHeSJEmSlBe6dOlCSUkJK1asaOhQpJw0a9aMNm3asMcee+xYP7UUT85SSsuyP/8dEX8kc2TYiohon1JakT3//N/Z5jkdF5ZSmghMBOjTp48pR0mSJEm7hObNm9OjR4+GDkOqd/W6DCQiCiKi1ebnwAAyR4Y9AgzJNhvC/z8SrNpjxOozZknSZ/Pyyy/Tu3dvCgoKGD9+fEOHU6e6d+/O7NmzGzoMSZKkXUZ9z6zYE3gwIhLQisxxpQ8BTwHTIuI7wHLgDNjmMWKSlL8mn1m3/Z89Leem3bt3Z+nSpSxdunSL6XgHH3wwCxcu5M0336R79+4APPvss4wZM4Z58+axadMm9t13X0aMGMH555+/3SGOGzeOAQMGcN111wEwbNgwunTpwtixY7e7r8ZozJgx/OMf/+Duu+9u6FAkSZLqXb3OrEgp/TOl9KWU0kEppX1TSj9JGatSSieklA7M/lxd5Z4rU0q9U0oHpJRmbq1/SVL1evTowZQpUyqvFy1axIcffrhFm3nz5nH88cczcOBASkpKWLduHb///e+ZO3fuZ3rNkpISDjjggB0Jewsff/xxg94vSZKk+pMvR5dKkurQ0KFDufPOOyuv77jjDs4999wt2lxyySV873vfY9SoURQUFBARHHLIIUybVv0sjjfeeIP+/fvTtm1bWrduzamnnsrq1Zlc8/HHH88TTzzBqFGjKCwsZOLEidxzzz2MGzeOwsJCBg0aBMBbb73FwIEDadOmDR07duTqq6+u7H/MmDGcdtppDBkyhOLiYm6//fZPxTBs2DBGjhzJiSeeSFFREX379uX111+vrI8Ibr75Znr16kXPnj0BWLhwIf3796eoqIhu3bpxxx13VLZ/8MEH+cIXvkBBQQGdOnXiV7/6VWXdtGnT6NWrF0VFRRx88ME8+2xuqxLLysoYOXIk7du3p6ioiH79+rF+/Xrmzp1Lly5bHnC1eTnJrFmzuOqqq5g2bRqFhYUcdNBBAEyYMIGuXbtSUFBAt27duOuuu6p7SUmSpJ2eyQpJagQOP/xwPvjgA5YsWUJ5eTlTp05lyJAhlfUffvgh8+bN49RTT92ufseOHcuqVat4++23Wbt2LaNHjwZgzpw59O/fn5tuuonS0lJGjBjBOeecw6WXXkppaSnTp0+nvLyck08+maOOOopVq1Yxf/58Jk6cyAMPPFDZ/4wZMzj77LNZs2bNFvFWNWXKFMaOHcuaNWs49thjOeuss7aonzlzJvPnz2fx4sWsWbOGk046iREjRrB27VpmzpzJRRddxHPPPQfA8OHDue222ygrK+PVV19lwIABADz99NOMGjWKqVOnsm7dOi6++GK+8Y1vsH79+m1+RhdccAFvvvkmixcv5oMPPuCGG26gSZOt//odMGAAV1xxBWeeeSalpaW8+OKLrFmzhksuuYTZs2dTVlbGc889R58+fbb5+pIkSTsjkxWS1Ehsnl3x2GOP0bt3b/baa6/Kuvfff5+Kigrat2+fc3/77LMPxxxzDE2aNKFt27b86Ec/4sknn8z5/qeffpqysjKuuOIKmjVrRteuXfnud7/LvffeW9nmyCOPZODAgUDmnPnqfP3rX6dv3740bdqUMWPGsGjRoi1mV1x22WW0bt2ali1b8tBDD9GzZ0+GDh1KkyZNOOCAAzjttNP44x//CEBBQQFLlixh3bp1tG7dunJGw6RJkxg5ciQHH3wwAOeccw6tW7fe5vv96KOPmDx5Mtdffz3t27cnIujbty+77bZbzp/TZs2bN6dp06YsXryY9evX0759e3r37r3d/UiSJO0MTFZIUiMxdOhQJk+ezO233/6pJSBt27alSZMmrFy5Muf+SkpKOPXUU+nQoQNt2rThrLPOorS0dLvuX7p0KcXFxZWPq666ivfff7+yTadOnbbZT9WlFK1ataJdu3YsX7682j5KSkr429/+tsVr3nPPPZXLV+69914efvhhunXrxpFHHlmZjCgpKeHaa6/d4r533nlnm5/XqlWr2LRpE5///Odz+1C2oqCggMmTJ3PjjTfSqVMnTjrpJF5++eUd7leSJCkf1fdpIJKkBrL33nvTo0cPHnnkESZNmrRFXatWrejXrx/3338/hx12WE79XX755RQWFvL666/Tpk0bZsyYwciRI2tsHxFbXHfs2JGePXuyePHi7X8zVbz77ruVz9evX8/q1avZc889q23bsWNHTjjhBB555JFq6w8//HBmzJjBxx9/zE033cQZZ5zBsmXL6NixI2PGjOHSSy/drth23313WrRowZtvvsl+++23RV2LFi222OS0oqJii0TNJz8vgIEDBzJw4EA++ugjfvKTn3DeeefxzDPPbFdMkpSPBk0Z1NAhSMozzqyQpEZk0qRJzJkzh4KCgk/VjRs3jt/+9rfccsstlX9Ev/jii5/aA2KzsrIydtttNwoLC1m+fDnXXHPNVl+7Xbt2vP3225XXxxxzDBUVFdx4441s3LiRlBKvvvpq5f4RuXr44YdZsGAB5eXl/OIXv+CAAw5g3333rbbtKaecwsKFC7nvvvsoLy+noqKCF154gVdeeYWNGzdy7733UlZWRrNmzSgqKqpMGJx33nkIK/AaAAAgAElEQVTccsstPP/880Bmecdf/vIX1q1bt9XYWrZsyeDBg7noootYuXIlKSXmz5/Phg0b6N27N6Wlpfz5z3+moqKCcePGUVZWtsXn9c4771BRUQHA8uXLmTlzJhs2bKBFixaVm6BKkiTtikxWSFIjss8++9S4KeMRRxzB448/zsMPP0znzp0pLCxk2LBhHHfccdW2HzNmDM888wxFRUUMGDCg8oSPmgwfPpwFCxZQVFTEN7/5TZo1a8ajjz7K448/XrmUZMiQIZVLMnJ11llnMXr0aIqLi5k9ezZTp06tsW27du2YNWsWEyZMoG3btrRr144f/vCHlRtl3nrrrey11160atWK8ePHc8899wCZxMqvf/1rvv3tb1NYWEi3bt245ZZbcorv5ptvpkuXLvTq1Ys2bdpw4YUXUlFRQdu2bbnhhhsYOnQonTp1olmzZlssaTn99NNZv349bdq04Stf+Qrl5eWMHTu28lSRxx57jAkTJmzXZyVJkrSziJRSQ8dQq/r06ZMWLFjQ0GFIjc/kM6svP7v6Yy93JUuWLHGjwwYybNgwunTpwtixYxs6lLxS0//JiHgupeQRIvXA8Yi0fbZ3Gcj0wdPrKBJJdS3X8YgzKyRJkiRJUl4xWSFJkiRJkvKKp4FIknZat99+e0OHIEmSpDrgzApJkiRJkpRXTFZIUi3YfLyk1ND8vyhJknYFJiskaQcVFBTw7rvvsnHjRna1E5a080gpsXHjRt59910KCgoaOhxJkqQd4p4VkrSDunTpwsqVK3n77bf5+OOPGzocNWLNmjWjTZs27LHHHg0diiRJ0g4xWSFJO6hJkyZ06NCBDh06NHQokiRJ0i7BZSCSJEmSJCmvmKyQJEmSJEl5xWUgkrbP5DMbOgJJkiRJuzhnVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSpF1ORHSNiCcj4uWIeC0iLsuWt4uIxyJiUUT8JSLaVrlndEQsyd5zUpXyQyLihYhYHBHjIyIa4j1JktSYmKyQJEm7ok3AqJTSF4FDgPMi4svAL4CZKaUDgZnZayLiEOBbwJeAAcDvImK3bF+3AeellPYH9gZOqdd3IklSI2SyQpIk7XJSSstSSi9ln68DXgL2Ar4G3JVtdnf2muzPaSmlTSmlEuDvQN+I6AY0TSk9V809kiSpjpiskCRJu7SI6A4cCjwNtE8prQDI/uyQbdYFeKfKbSXZsprKJUlSHTJZIUmSdlkRUQj8EbgwpbS2jl9rREQsiIgFK1asqMuXkiRpl2eyQpIk7ZIiojnwJ2BKSun+bPGKiGifrW8P/DtbXgJ0rXJ7l2xZTeWfklKamFLqk1Lq0759+9p7I5IkNUINkqyIiKbZXbVnZK+3e2duSZKkmmRP7JgELEkpXVul6hFgSPb5EDKbbG4uPzMimkdEF+CLwLMppX8BFRHxlWy7c6rcI0mS6khDzaz4IbCkyvVn2ZlbkiSpJkcCQ4HjI2Jh9jEQ+DnwtYhYRGajzJ8BpJQWAA+Q2YjzUWBkSmlDtq//BP4QEYvJzKr4U/2+FUmSGp9m9f2C2W8rvgZcCVyULf4acFj2+d3AM8APqLIzN1ASEX8H+gJP1WvQkiRpp5JSehqIGqpPqOGeK8mMTz5ZvgD4cu1FJ0mStqUhZlZcD1wKVFQp296dubfghlaSJEmSJO066jVZERH/Afy7ylnltcINrSRJkiRJ2nXU9zKQI4GvZ9eMtgRaR8TdZHfmTilt3qF7WztzS5IkSZKkXVS9zqxIKY1OKXVJKXUHzgLmpJSGsJ07c9dnzJIkSZIkqX7V+wabNfg5MC0ivgMsB86AzIZWEbF5Z+4KttyZW5IkSZIk7YIaLFmRUpoLzM0+X8V27swtSZIkSZJ2TQ1xGogkSZIkSVKNTFZIkiRJkqS8YrJCkiRJkiTlFZMVkiRJkiQpr5iskCRJkiRJecVkhSRJkiRJyisNdnSpJDUWw2+fX235pGGH1nMkkiRJ0s7BmRWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLxiskKSJEmSJOUVkxWSJEmSJCmvmKyQJEmSJEl5xWSFJEmSJEnKKyYrJEmSJElSXjFZIUmSJEmS8kqzhg5AkiRJUuMwaMqghg5B0k7CmRWSJEmSJCmvmKyQJEmSJEl5xWUgkiRJ0k6upuUV0wdPr+dIJKl2OLNCkiRJkiTlFZMVkiRJkiQpr5iskCRJkiRJecVkhSRJkiRJyismKyRJkiRJUl4xWSFJkiRJkvKKyQpJkiRJkpRXTFZIkiRJkqS8YrJCkiRJkiTlFZMVkiRJkiQpr5iskCRJkiRJecVkhSRJkiRJyismKyRJkiRJUl4xWSFJkiRJkvKKyQpJkiRJkpRXTFZIkiRJkqS8YrJCkiRJkiTlFZMVkiRJkiQprzRr6AAkSZIkaXsMmjKo2vLpg6fXcySS6oozKyRJkiRJUl4xWSFJkiRJkvKKyQpJkiRJkpRXTFZIkiRJkqS8YrJCkiRJkiTllZxPA4mI5sB+wJ7ZomXAqymlTXURmCRJkiRJapy2mqyIiGbAGcBQ4BhgNyCy1QnYEBFPAncB95q4kCRJkiRJO6rGZSAR8R3gn8B1wHvAhUB/oDewf/b5hcBS4Frgjew9kiRJkiRJn9nWZlacTyYZ8VBKqbyGNv8LTIyIpsA3gcuBP9RuiJIkSZIABk0Z1NAhSFK9qDFZkVI6NNdOssmMP2UfkiRJkiRJn5mngUiSJEmSpLyyw8mKiOgaEd1qIxhJkiRJkqScjy7din+SSXo0rYW+JEmSJElSI1cbyYrh/P/jTCVJkiRJknbIDicrUkp31kYgkiRJkiRJ4AabkiRJkiQpz+Q0syIi7t1Wm5TSGTsejiRJkqSd3aApgxo6BEk7uVyXgbSvpqwAOBBYDbxaaxFJkiRJkqRGLadkRUrpuOrKI2JP4AHgutoMSpIkSZIkNV47tGdFSmk58H+BcbUTjiRJkiRJauxqY4PNCqB7LfQjSZIkSZKU8wab+1dT3BToCVwJLKzNoCRJkiRJUuOV6wabLwOpmvIA/g4Mr7WIJEmSJElSo5ZrsqK6DTY/Bt5LKf2zFuORJEmSJEmNXK6ngTxR14FIkiRJkiRB7WywKUmSlHci4g8R8e+IeLlK2ZiIeDciFmYfA6vUjY6IJRHxckScVKX8kIh4ISIWR8T4iIj6fi+SJDU2O5ysiIjXI+KN2ghGkiSpFt0ODKim/LqU0pezj0cgk5AAvgV8KXvP7yJit2z724DzUkr7A3sDp9R55JIkNXK1MbPiyexDkiQpb6SUngRW59j8a8C0lNKmlFIJmQ3E+0ZEN6BpSum5bLu7s20lSVId2uFkRUppeErpP2sjGEmSpHpwQUS8EhH3RMTu2bIuwDtV2pRky2oq/5SIGBERCyJiwYoVK+oibkmSGo163bMiIlpmf4kvzC4fuT4y2kXEYxGxKCL+EhFtq9xT7fpRSZKkz+Bm4AvA/sAbwPja6jilNDGl1Cel1Kd9+/a11a0kSY1SrkeXAhARRwE9gZafrEsp/TaHLjYAx6SUyiKiOfA0mWNRTwFmppR+ExE/An4B/OAT60f3BJ6OiF4ppQ3bE7ckSRJASqlyykNETADmZi9LgK5VmnbJltVULu0UBk0ZVG359MHT6zkSSdo+OSUrsus1HyHzLUQCNu+Cnao022ayIqWUgLLsZXOgKfBvMms/D8uW3w08A/yAKutHgZKI+DvQF3gql7glSZKqiogOKaV/Zy+/BSzOPn8EmBAR15P5guSLwLMppQ0RURERX0kpPQ+cQ2asIkmS6lCuy0CuB1YCncgkKg4FOgOXAK+QmW2Rk4hoGhELySQp5qaUXgbab/6mI/uzQ7Z5TutEXSMqSZI+KSKmAPOAXhFREhHDgd9ExEsR8QowCPg+QEppAfAA8BLwKDCyykzO/wT+EBGLyYxF/lTPb0WSpEYn12UgxwPnApszAZFSWkbmF36QmVWR034SKaVy4MsRUQw8GhHHbWfM1fU5EZgI0KdPn7SN5pIkqRFIKQ2upnjSVtpfCVxZTfkC4Mu1GJokSdqGXGdWfA5YmVKqADYCe1SpWwAcsb0vnFJaA/wZOBxYERHtAbI/N0/PdJ2oJEmSJEmNTK7Jin8Ae2WfLwLOrFL3TeDDXDqJiD0ioij7/HPA/wFeJrNOdEi22RBgZvb5I8CZEdE8IrqQXT+aY8ySJEmSJGknlOsykFnAV4H7gKuBeyPi2GxdN2BMjv10Bu7MLh1pCUxJKU2PiP8FpkXEd4DlwBmQmXYZEZvXj1aw5fpRSZIkSZK0C8opWZFS+q8qz/+UTVScCrQAZqeUHsixn5eoZs1nSmkVcEIN91S7flSSJEmSJO2acp1ZsYWU0lN4fKgkSZIkSaoDNe5ZERFda6rbyj1771g4kiRJkiSpsdvaBpuLIuLWiDh0W51ExNERcSeZzTclSZIkSZI+s60tAzkY+DnwdESsAP4GLAZWAwHsDhwAHAa0BaYAX6rTaCVJkiRJ0i6vxmRFSulNYFhEXA4MJnPM6DBgz2yTZWRO6fi/wLSU0vK6DVWSJEmSJDUG29xgM6W0DLgu+5AkSZIkAAZNGdTQIUjaRW1tzwpJkiRJkqR6Z7JCkiRJkiTlFZMVkiRJkiQpr5iskCRJkiRJecVkhSRJkiRJyiufOVkREe0j4rCIKKjNgCRJkiRJUuOWU7IiIq6MiF9WuR4AvA38L/BWRBxUR/FJkiRJkqRGplmO7c4Gflrl+tfAX4FfAFcC44CTajc0Sdq1Db99frXlk4YdWs+RSJIkSfkl12UgewH/BIiIbsABwM9SSs8C1wOH1014kiRJkiSpsck1WfEh0Cb7/BhgTUrpuez1B8ButR2YJEmSJElqnHJdBvIkcHlEAFwM/LlK3X5k9q+QJEmSJEnaYbkmK74PTAUeAl4ARlepOxt4upbjkiRJklRHBk0ZVG359MHT6zkSSapeTsmKlNLbQL8aqr8BrK+1iCRJkiRJUqOW69GlcyJivxqqOwKP1l5IkiRJkiSpMct1g81jgdY11BWR2XRTkiRJkiRph+W6ZwVA+mRBRDQFjgZW11pEkvLD5DMbOgJJklTP3MtCUr6oMVkRET8Hfpa9TMAz2dNAqnNjLcclSZIkSZIaqa3NrHgEWAkEMB64FnjrE202Aq+klJ6qk+gkSZIkSVKjU2OyIqU0H5gPEBHrgD+nlFbWV2CSJEmSJKlxyvXo0jvqOhBJkiRJkiTIMVkRES2Ay4FvAJ2quy+l1KF2Q5MkSZIkSY1RrqeB/A4YAjwA/Bkor7OIJEmSJElSo5ZrsuJbwIUppZvrMhhJkiRJkqQmObb7CFhUl4FIkiRJkiRB7smKW4HBdRmIJEmSJEkS5L4MZDlwTkTMAmYDH36iPqWUbqnVyCRJkiRJUqOUa7Li+uzPbsCJ1dQnwGSFJEmSJEnaYTklK1JKuS4XkSRJkrSLGTRlUEOHIKmRyXVmhSR9NpPPrL787Gn1G4ckSZKknUbOMyYiontEXB8Rf4uIf0TEAdny70fEkXUXoiRJkiRJakxySlZERF/gJeAk4HmgB7BbtroDcFmdRCdJkiRJkhqdXGdWXAfMBPYHvg9Elbp5wGG1HJckSZIkSWqkck1WfAW4JaWUyJz8UdUaoLhWo5IkSZIkSY1WrsmKtcAeNdT1AJbVTjiSJEmSJKmxy/U0kIeBX0bEM8B72bIUEUXAxcD9dRGcJEmSJOWquiNWpw+e3gCRSNpRuc6suAz4CHgdmJ0tuwH4J1AO/Kz2Q5MkSZIkSY1RTsmKlNL7wOHAKDIzK2YDS4ErgCNTSuvqLEJJkiRJktSo5LoMhJTSRmBS9iFJkiRJklQncppZERFPRsT5EdG+rgOSJEmSJEmNW657ViwHrgHejYjHIuI7EdG2DuOSJEmSJEmNVK57VpwOdAC+DZQCNwPvRcSMiBiaPRVEkiRJkiRph+U6s4KUUllKaUpK6RQyiYsR2apbgWV1EZwkSZIkSWp8ck5WVJU9/eMN4E3gA+BztRmUJEmSJElqvLYrWRERfSPi2oj4F/AkcAxwA7BvXQQnSZIkSZIan5yOLo2IXwGnA3sDrwO3AdNSSovrMDZJkiSpURo0ZVBDhyBJDSqnZAWZRMW9wNSU0sI6jEeSJEmSJDVyOSUrUkqfr+tAJGlnN/z2+Q0dgiRJkrRLyHnPiohoHREXRsS0iJgdEftmy8+IiP3rLkRJkiRJktSY5LpnRU9gDplTP54FjgOKstWHAacCZ9VFgJIkSZIkqXHJdWbFeOBVMhtsDgKiSt2TwFG1HJckSZIkSWqkct1gsz/wjZRSaUQ0/UTdCqBD7YYlSZIkSZIaq1xnVnwEtKyhrhOwqnbCkSRJkiRJjV2uyYrHgCsioqBKWcrOshgFPFLrkUmSJEmSpEYp12UglwD/A7wBzAIScDnwRaAAGFwn0UmSJEnSDhg0ZVC15dMHT6/nSCRtj5xmVqSU3gEOAm4FepJJWuwNPAgcklJaVmcRSpIkSZKkRiXXmRWklN4Hfpp9SJIkSZIk1Ylc96yQJEmSJEmqFyYrJEmSJElSXjFZIUmSJEmS8orJCkmSJEmSlFdMVkiSJEmSpLySc7IiIs6NiOK6DEaSJEmSJGl7ZlbcBnQDiIyfRUTHuglLkiRJkiQ1Vs1qqoiImcBC4MXsI4CUrW4C/ByYASyr4xglSZIkSVIjsrWZFbOATsBoMsmKBNwUEWOAAWyZvJAkScorEfGHiPh3RLxcpaxdRDwWEYsi4i8R0bZK3eiIWBIRL0fESVXKD4mIFyJicUSMj4io7/ciSVJjU2OyIqV0Q0ppWErpIKCITHLieaAXMJ5MouKuiLgmIgbUS7SSJEm5u53MFyxV/QKYmVI6EJiZvSYiDgG+BXwpe8/vImK37D23AeellPYH9gZOqfvQJUlq3GpMVkTEDyKif0QUpZQ2ZItvSykNJpOwCGAKUAjclMuLRUTXiHgy+43FaxFxWbZ8u7/lkCRJ2pqU0pPA6k8Ufw24K/v87uz15vJpKaVNKaUS4O9A34joBjRNKT1XzT2SJKmO1LhnBfAfwI+BPSLibTIzKc6KiM8Bi7JtZqaUnt+O19sEjEopvRQRRcDzEfEoMDzb128i4kdkvuX4wSe+5dgTeDoielVJnkiSJG2P9imlFQAppRUR0SFb3gWYU6VdSbasHHinmvJPiYgRwAiAbt261XLY2pUNmjKooUOQpLyztWUgJ6aU9gT2Ar5HZibFCWT2slhNJnlxfkR8tco0ya1KKS1LKb2Ufb4OeCnb/3Z9y7F9b1GSJKnupZQmpvT/2rv3aEmuul7g3x+ZAIbwTIbgJZH4jhC5ARIREQhvEEdEkDABXGAQ8F5AWBdBkAvoIipXEAWWxmgwomYIbxgJ76ARBcmIIeENSjDRPCZAQCCEPPb9o+okPWe6Z/rMnNNdp8/ns1atPr2ruutXu3t67/nVrl3t2NbasZs3b553OACwru311qV9guE9/dOntNZum+TYdMmLI9JdD/q1le64qo5MclySD2fZWY4ko2c5pjqbAQAwhZ1VtTlJ+sfL+/KL0/Vrlhzel00qBwDW0F6TFRN8pn98YWvtiCT3WMmLq+rgJG9O8uzW2tf3MYbR93tqVe2oqh07d+7c37cDABbXWUme0P/9hHSTbC6Vn1BVB1bV4UmOTvKx1tp/JLm+qu7eb/f4kdcAAGtkT3NW7KK1NprYaEm+nOTqft1nxr5ojKo6MMlbkmxrrb21L95ZVZv7a0enOcuxPLZTk5yaJMcee6zbqQIAqaptSY5PN//WxUle0i9nVtUvJ7ksyWOTpLW2o6relu4S1euTPH1kjqwnJ3ldVd003bwWb5npgQDABjR1smJUa+36JN+/0tf19yU/LclnWmuvHFm1dJbjVdn9LMcpVfWH6SbYPDrJx/YlZgBgY+nvYDbOgyZsf3KSk8eU70hyzCqGBgzApIlNt2/dPuNIgHH2KVmxH+6d5IlJLqiq8/qyF2bfznIAAAAAC2imyYrW2ofTTcw5zorOcgAAAACLaV8n2AQAAABYE5IVAAAAwKBIVgAAAACDIlkBAAAADIpkBQAAADAokhUAAADAoEhWAAAAAIMiWQEAAAAMimQFAAAAMCib5h0AAABsBFu2bZl3CADrhpEVAAAAwKAYWQEAANCbNAJm+9btM44ENjYjKwAAAIBBkawAAAAABkWyAgAAABgUyQoAAABgUCQrAAAAgEGRrAAAAAAGRbICAAAAGBTJCgAAAGBQJCsAAACAQZGsAAAAAAZFsgIAAAAYlE3zDgAAABbJlm1b5h0CwLpnZAUAAAAwKJIVAAAAwKC4DAQAAGAvJl3es33r9hlHAhuDkRUAAADAoEhWAAAAAIMiWQEAAAAMimQFAAAAMCiSFQAAAMCgSFYAAAAAgyJZAQAAAAyKZAUAAAAwKJIVAAAAwKBsmncAAAAA69WWbVvGlm/fun3GkcBiMbICAAAAGBTJCgAAAGBQJCsAAACAQZGsAAAAAAbFBJsAALAPJk2sCMD+M7ICAAAAGBTJCgAAAGBQJCsAAACAQZGsAAAAAAbFBJuw0Z1xwrwjAAAA2IWRFQAAAMCgGFkBAACwyibd2nb71u0zjgTWJ8kKAADYg0n/6QRg7bgMBAAAABgUyQoAAABgUCQrAAAAgEGRrAAAAAAGRbICAAAAGBR3AwEAgLjrB8CQSFYAAADMyKSk2Pat22ccCQyby0AAAACAQTGyAmBgTjr93LHlpz3puBlHAgAA82FkBQAAADAokhUAAADAoLgMBGCFJl2mAQAArA4jKwAAAIBBMbICAABgztzSFHZlZAUAAAAwKJIVAAAAwKBIVgAAAACDIlkBAAAADIpkBQAAADAokhUAAADAoEhWAAAAAIMiWQEAbDhVdWFVXVBV51XVjr7sdlX1/r78fVV125HtX1BVn6mqT1bVQ+cXOQBsDJvmHQAAwJzcv7V2xcjz30ry7tbaH1TVc/rnz6qqeyR5dJK7JjksyYer6kdba1fPPmRgo9mybcvY8u1bt884EpitmY6sqKrXVdXlVfXJkTJnMQCAIXhEkr/q//7r/vlS+ZmttWtaaxcn+VSSn5hDfACwYcx6ZMXpSV6b5PUjZc5iAACz1pK8v6o2JTm1tfaaJJtbazuTpLW2s6pu3297eJKzR157cV/GOjXpTDUAwzHTkRWttXOSfHVZsbMYAMCs3au1drckD0zy5Kp68P6+YVU9tap2VNWOnTt37n+EALCBDWGCzV3OYiQZPYtx0ch2E89i6BwAACvRWru0f7w8yZuTHJdkZ1VtTpL+8fJ+84uTHDHy8sP7suXveWpr7djW2rGbN29ey/ABYOENIVmx33QOAIBpVdUtquqgpb+TPCzJp5OcleQJ/WZPSPLu/u+zkpxQVQdW1eFJjk7ysdlGDQAbyxDuBrKzqjb314au+CwGAMAKHZbk7VXVkhyU5Mwk70jyD0nOrKpfTnJZkscmSWttR1W9Lcn5Sa5P8nRzaAHA2hpCsmLpLMarsvtZjFOq6g/TdSqcxQAA9ltr7d/TTeC93FeSPGjCa05OcvJaxgUA3GimyYqq2pbk+CSHVtXFSV7SL85iAAAAAElmnKxorW2dsMpZDAAAACDJMC4DAQCAVbdl25Z5hwDAPlqIu4EAAAAAi8PICmA+zjhh97ITz5x9HAAAwOAYWQEAAAAMipEVAAAA68ykOVm2b90+40hgbRhZAQAAAAyKZAUAAAAwKC4DgY1i3ISWAAAAA2RkBQAAADAokhUAAADAoEhWAAAAAIMiWQEAAAAMimQFAAAAMCjuBgIwwUmnnzvvEAAAYEOSrAAAYF3bsm3LvEMAYJVJVgDDccYJ48tPPHO2cQAAAHNlzgoAAABgUCQrAAAAgEFxGQgAAMCCmDSHy/at22ccCewfIysAAACAQZGsAAAAAAZFsgIAAAAYFHNWAAAALDhzWbDeGFkBAAAADIqRFcCGd9Lp5847BAAAYIRkBcA6MSmpctqTjptxJAAAsLZcBgIAAAAMimQFAAAAMCguAwEAYF2YdDcDABaPkRUAAADAoEhWAAAAAIPiMhAAAIANatLlVdu3bp9xJLArIysAAACAQZGsAAAAAAbFZSAAAADsYtzlIS4NYZaMrAAAAAAGRbICAAAAGBTJCgAAAGBQzFkBi+aME+YdAQAAwH6RrAAAYFDGTewHwMYiWQEAAMBeTUokuksIa8GcFQAAAMCgSFYAAAAAg+IyEGDDOOn0c+cdwpoYd1ynPem4OUQCAACrw8gKAAAAYFAkKwAAAIBBkawAAAAABkWyAgAAACB1DpIAABZBSURBVBgUyQoAAABgUNwNBBi+M04YX37imbONAwAAmAnJCgAAAPbZlm1bxpZv37p9xpGwSFwGAgAAAAyKkRUAAMzFpLOxwGIw4oL9YWQFAAAAMCiSFQAAAMCgSFYAAAAAgyJZAQAAAAyKCTaBhXPS6efOOwQAAGA/SFYAAAAwM+4SwjQkK4D164wTJqx47kzDAABg/0liMEqyAli3zrvoyvErDpttHAAAwOqSrID1auKoAgAAgPXN3UAAAACAQTGyAlg4z7zsRWPLX3PYy2Ycyfys9I4opz3puDWKBGDydegA0zCXxcYkWQFsGJIYKzcp6SG5AQAMleTGYpCsAAZv4kSaAABsWEZtLTbJChg6E2kCAMB+M+JifTHBJgAAADAoRlYAg+FyDwAAIJGsAOZkPSQmTMgJAADzIVkBrKn1kJQAYHWY7A5Yj8xlMUySFQAAALDMSpMYkh6ra10kK6rqYUlekeSAJH/ZWvu9OYcEq2+d3PVjEUdKTLrcY7XeZz1cNnLS6eeuyvanPem4uWwPa01fZHdGUQAbld+/2Rh8sqKqbpbklCT3SXJpko9U1ftaax+fb2SwF5OSDyeeuaa7nZRMOOaI26zpfjeS1UpurKX1kjhZaZJkpe8jucFq0BcBYH+MS24YnbF3g09WJLlnkk+11i5Kkqo6M8kjkuggsCGsVvJhpSMiJDfmZx6JhtXa53kvf+j4FQNLksAK6YsAsKpWOjpjIyYx1kOy4vAkF408vzjJ8fMJhYWzhpdeTEwyuNyD3noYoTHJWsc+8f3PmJREe+6K3n8lIzomxXLM89+7ovc2ymNdG0RfZLU6qoYvAyyOtf5Nn2cypFprc9v5NKrqxCT3ba09vX++NcnxrbWnjWzz1CRP7Z/+aJLPrXIYhya5YpXfc5Gpr+mpq+mpq5VRX9PbSHV1p9ba5nkHsd5M0xfpy/VHVsbxDNsiHc8iHUvieIbO8ezdVP2R9TCy4uIkR4w8P7wvu0Fr7dQkp65VAFW1o7V27Fq9/6JRX9NTV9NTVyujvqanrpjCXvsiif7ISjmeYVuk41mkY0kcz9A5ntVzk3nsdIU+luToqjq8qg5MckKSd885JgBg49AXAYAZG/zIitbad6rqV5O8N11y5a9bazvmHBYAsEHoiwDA7A0+WZEkrbWzkpw1xxDWbEjnglJf01NX01NXK6O+pqeu2KsB9EWSxfuuOp5hW6TjWaRjSRzP0DmeVTL4CTYBAACAjWU9zFkBAAAAbCCSFb2qel1VXV5Vn9zDNsdX1blV9YmqOmeW8Q3N3uqrqn69qs7rl09W1XVVdbtZxzkEU9TVHarqg1X16ar6fFU9fdYxDskU9XVIVb27r6+PVdXRs45xKKrqiKo6p/839vmqev6YbaqqXt3X179W1d3nEeu8TVlXR1XVR6rq6qp67jzihEXrjyxaf2HR2vRFanMXrU1ctHZryuN5YlVd0G/zL1U12DtqTHk8j+yP5/x+u4fPI9ZpTHM8I9seV1XXVtVj1jyw1pqluxTmvknunuSTE9bfIcmnktyhf37ovGMecn0t23ZLkrPnHfNQ6yrJy5K8vP97c5Irk3zPvOMecH29JslL+r+PSvKRecc8x7q6Q5K79n/fMskXkhyzbJtHJ3lHkurr9RPzjnvAdXX7JMclOTnJc+cds2VjLovWH1m0/sKitemL1OYuWpu4aO3WlMdzzyS37v9+eJLz5h33fh7Pwblx2oW7JvmPece9P8fTrzsgydnp5nB6zFrHZWRFr7V2TpKv7mGTxyV5Y2vt0n77K2YS2EBNUV+jtibZtobhDNoUdXVxkltWVaX7UbsiydWziG2Ipqivo9L9SKa19tkkt6+qO84itqFprV3aWju///u/k5yfZHldPCLdnQtaa+3jSTZV1REzDnXupqmr1trlrbVzk1wzhxAhyeL1Rxatv7BobfoitbmL1iYuWrs15fH8c2vt6/3TDy9fPyRTHs83W/8//CS3SHLpbKOc3pT/fpLkmUnekuTyWcQlWTG9o5J8b1V9tB/O8yvzDmg9qKqDkjws3Zea8f4syZ2T/FeSC5L8Wmvt+vmGNGgXJPmFJKmqn0hypyTfN9eIBqCqjkx3duXDy1YdnuSikecX92Ub1h7qCtaDheyPLFB/YdHa9HXZ5i5am7ho7daUx/O0JO+cRTz7a0/HU1WPqqrPJnlPkmfNNrJ9M+l4+kTlo5L8yaxikayY3k2SHJPkgUnun+T5Q75ub0C2JPnH1tq0Z1U2oheky17+j3TfsddW1a3mG9Kg/VaSw6rq00men2RHkg19W6OqOjjJm5M8e+SMBGOoKxbAovZHFqW/sGht+rprcxftd34jHk9VHZ/kpCTPm2Fo+2Rvx9Nae1tr7ah0v3Gvr6pB//97L8fzh0meP8sE7KZZ7WgBXJTkktbat5J8q6r+Pt21RxMnwCJJN1x10EM6B+A+SV7WDxP7YlV9Kd1ZmY/ON6xh6n84T1x6XlX/nuTz84tovqrqwHRnIre11t46ZpOLkxyRG79Ph/dlG84UdQXrwaL2Rxalv7BQbfp6a3MXrU1ctHZrmuOpqrsmOS3Jw1trX5llfCu1ks+ntXZOVW1KcliSS2YR30pNcTzHJnlDd5VbDk3yM1V1bWvt7WsV06AzOwPzriQ/XVWb+qGK90ry2TnHNGhVdesk90s3kRGT/Vu6M2SpqsPSdWounGdAQ1ZVt+5/7FNVT0jyrwtwJm6f9NdEn5bkM621V07Y7Kwkj++3v3uS61trF03YdmFNWVewHixcf2TB+gsL1aavpzZ30drERWu3pjmeqvq+JG9N8sTW2mCTYsnUx/P9I3/fPcnNMqO5HlZqmuNprX1/a+3I1tqR6UZf/K+1TFQkRlbcoKq2JTk+yaFVdXGSlyQ5MElaa6e01j5eVe9JN7TvwCSn9RPzbEh7q69+s0cleV9/9mfDmqKufjvJX1fVZ9LNsPt/lyZO24imqK+7JDm9qr6T5IvphgluVPdO8sQkF1TVeX3ZC9NfT9zX11uS3L8fwvvdJE+eR6ADsNe6qqo7pBvifKsk11fVs5PcubX2jXkEzMa0aP2RResvLFqbvmBt7qK1iYvWbk3z+bw4ySFJ/rg/e39ta22oty+d5ngeV1WP79d9J8njWmvXzTzS6UxzPDO3dCsVAAAAgEFwGQgAAAAwKJIVAAAAwKBIVgAAAACDIlkBAAAADIpkBQAAADAokhUMUlW9tKrayPL1qnpPVf3Pece2P6rqpv2xHTPj/d6+3++Rq/ier6iqC/eyzejneH1Vfa2qzq2qk/vbba2pqrqw3/eLxqz76ZHYjlzrWKZRVS+oqveOKb9fVb2jqi6vqmv6x3dV1eOqaurf8araXlUX7GH9a6vqyqq6WVVtqap/q6qb7uvxAKx3+iOrvl/9kd3X6Y/svl5/hCSSFQzb15Pcq1+enOSIJB+sqtvONar9c9N09zCfaecgye37/R454/0mN36OP5XkcUnemhvv43yPGez/m/1+l9varxuEqrpNkucnOXlZ+bOTfCjJdUmemeSBSZ6R5BtJ/ibJ/Vewm21Jjq6qO4/Z/wFJHpPkra21q1tr25N8O8lTVn40AAtFf2T16I/sTn9k1/3oj3ADyQqG7NrW2kf75a1JnpDkkCQ/N+e4ZqKqvmfeMayS0c/xva21301y1ySXJHlD3yitpb9NcueqOnqpYKQhfOca73slnpLkktbaOUsFVXX3JK9I8tuttV9orZ3ZWjuntfbG1trWJD+d5IoV7OMd6Rr8rWPW3T/JYek6EEv+LMlzqqpWeCwAi0R/ZDHoj0xHf4TBkKxgPVkaLnbH0cKqul1VnVpVl1XVd6rqn6rqnsu2OaAf0vb5qvpuP2ztb5Zt84yq+kJVXV1VX6yq5yxb/9KquqKq7lZVH+339dmqesiy7R5bVef3+/lWVX28qpayzf/dP/7F6JC/fmlV9fiqen1VXZlke/9+raqeMS6WZWV3qqptfYzfrapPV9UT+yGFS3X3oaX9rrD+blNVZ1TVN6vqkqr6zd0/num11q5M8rwkP5TkwdO8pqqOqaoP9XX63f6z/LUpXvqfST6cXc9mPCDJwRnTOaiq36iqf62qq/rjfX9V3XXZNg+oqn/u6+uqqvpkVZ0wsn5P34FJnpjk7cvKnpnk8iQvG/eC1tpHWmufWBbbU6rqU/33+MtV9byR7b+V7nt1wvL3Slc/lyc5e6TsHek+o5/cS+wAG4n+yLJYlpXpj4ynP3Lj9vojTEWygvXkiP5x51JBVd0syQeS3DfJs5L8TJKLk3ygqn5g5LV/muTFSf4yyYOSPC3JaAP57CSvTnJmkof0272iqn5jWQwHJfnzfttH9LG8qapu3b/Pj6XLBL8rXaP3c0nekORW/esf0D++LDcOKb1k5P1/P90P9M9lQoMwTlXdPslHkvx4uiF5D0zy2nTDLS9J8vh+0/89st+V1N8b+vf81SS/1G8/bijjSvxdkmszRcNT3ZmHv003hPMX+lh+P8mBU+5rW3aNd2u6RvJbY7a9XZJXpfse/GK6oZkfGPmMb5euU3FBuu/Aw9N9v27Rr9/bd2Dc8R2W7uzOR5etum+Ss1tr105zkFX160n+uN/fg5P8QZLfHu0g9LH9cI0Mea2qA9PV6xtba9ctlbfWvpzk0iQPnWb/ABuE/sgE+iN7pT9yI/0R9q61ZrEMbkny0nTDyTb1yxHpGodvJTlsZLuTklyd5E4jZQck+WySV/fPj0rXEfiVCfs6oN/XKcvKX5WuMbr5SEwtyb1HtrlLX/bI/vnjk+zcw3Ed3G//pGXlR/blbxjzmpbkGePqZ+T57ya5MsnmCfs9un+f45eVT1N/d+tf+/Mj2xyU5LIkF07zOe5h/SVJ/mSK78Md+xjussLv0YXphi1uTnJNkuPSXaf7tSQ/n+Rn+/c9cg/fjZv32/9SX3bv/jW3mPCaPX4HJrzmQf17/vCy8quS/O6yshr5d7EpyU368lul68i8YNn2L0rylSSb+udLx//7I9ss1cNPjYnt/UnetpLjsVgslkVZoj8yuk5/RH9ktEx/xLLmi5EVDNkh6X7Qr0nyH0nul+QRrbXLRrZ5ULrs739W1aaq2pTux/Pv0mfr0137dl26yX/GOarf15uXlb8x3Q/uj4+Ufbu19o8jzz/bP35v//iJJIdU1V9U1YOr6uBpDnTEu1a4/ZIHJDmrtbZzr1vuapr6+6l0ZxzOWnpRa+3bSd63j7GOmvbaw8vSdySq6hf7zP/U+no5O93ZjIf1+3332ICqjq+qc6rq2+mO+6okt0nyI/0mn+vLzqhuhurlE6zty3dg6Xi+Oi78Zc8fnRv/XVyT5P/15fdKdzblTUufZf95np3u7MyPJklr7bvpJhV7bNUN136ekOTL6c6GLfeVkfgANiL9kenpj+yB/oj+CCsjWcGQfT1d5vkn0w2TvC7dkL9Rh6YbmnbNsuVp6Rr89I//3Tdo4yz9uC9vWJee326k7KrRDdqNQ9Q29c8/mW742lHpGp+vVtWbVtCYfW3K7ZY7JF0DulLT1N9tk3yzb1RGrWQipd1U1c0zZdytG3b40HT1c3qSS6u7TvcnVrDLNyR5bJITk7y9tXb1mJh+MMl70l3LuzXdd++4Psab97Fc0cdycLoO5RVV9b6q+uF+/f58B5Z3lv4ryeHLyj7Yx3Rcdh2ye2j/+IXs+lkudWYPGdl2W5LvS3Kv/nN4ZLqzaMs7IuNiAtho9Eempz+yd/ojN9IfYY82zTsA2INrW2s7+r//uaq+meRvquqM1toH+vKvpvvxe/aY1y/9+H8lyS2r6qAJHYSlBnnzsvKl5+OyyxO11t6e5O1Vdat01w++JskpSR61kvcZcV264X+jbrHs+b5mm6epvyuTHFxVN13WQTh0zGtW4v7pfoPGZc9301q7IMkj++sZ753k5Un+tqru2Fq7Zoq3eFu6z+EX013bOc6WdI3hY1prVyU3XJ+6y/WdrbV/SPLA6mZIv3+SP0p35utu/fqVfgcu7R8Pya6drnOSPKSqDljqiLbWvpZkRx/b6Oex9D19SMZ3Mj838veH0nV4HpfuLNwts+us26NuNxIfwEakP9LRH4n+iP4Is2RkBevJtiSfSnd/7iUfTPJjSb7QWtuxbFmacfrsdI3ruNsjJd3QySvSZZ9HPSbdvaMv2O0VU2itfaO1dmaStyRZuo/00o/5tBMxJV22emnIX/qhcg9cts0Hkzy8qg7JeJP2O039/VO6RvxnRmI4KF0jtE+qu4f3y5N8Md2EWlNrrV3TWvu7dJM1bc6UnZTWzfj98nSfx6R93jRd5+D6kbJHJhl727bW2lWttbPSTXK2273CJ3wHxlmq67ssK39Nuk7fC/fw2iUfSXem7Q5jPssdrbWlmd+XzsC9MV1H6cQkn2nLZvEecXSS86fYP8BGoT8S/RH9kYn0R1g1RlawbrTWWlX9TrqzGffps8mvT/L0JH9fVa9I8qV01/PdK92kQn/UWvtcVZ2a5DX9LNUfTpedfXRr7Zdaa9dV1clJXlnd7bc+kOQ+SX4tyYtaa9+ZNsaq+pUk90jy3nQdjh9J18l4W38M362qLyV5TFV9Kl2jvbcf3ncmeVJV/Ut/fE/J7g3iq9INSf1QVb0sXYfizkkObq29Mt01tlcleWJVfSPJdf1Zomnq7+NV9f4kf9pf73hZkuemG9I3jU1VtTTD9i37+vnVdJNiPWxk6OpE1d2q62Xphjl+KV2n4HnpsvNTZ9lbay/eyyYfTPJ76W7l9udJfjDJb6Q7m7MUyyPSNajvTDcs8ogkT03y9/36PX4HJsR1eVWdl+563LeOlH+8qp6b5A+q6ph0s8P/V7ozWfdOcod0k1iltXZlVb00ySnV3R7uH9J1dH4syf1aa8tvD7Yt3a3IHpVdO9w3qKo79ftYjeuBARaC/oj+SPRH9EeYjTaAWT4tluVLJszanO6MxOeTvHuk7Nbphr1dlK7B2plupu77LHvdC5P8e7/NZUn+atl7PzNdZv27/XbPmTKmG2bHTvfj/p4+hmvT/ZC/OslBI9s/Il2jdm3/2iNz4+zbPzvm/W+d5E1Jvp2uIXxRkt9aHkuSO6VrPL7WH8Onkjx+ZP2T001adF33T39F9XfbdNdYfquvuxenm9X6wik+x9Yv16drZHckOTldxn3a78Nh6RqzL/f19rV0je0P7OV1FyZ5xR7W7zb7drqG/j+TfCfdZF/3HH2fdNd+vr3/bK/t6+sv0898Ps13YEIs/yfJlyasOz5dZ2Rn/xldnm6CscclqWXbPiHJv6TrDH67r+/nT3jfL/XH/0MT1j8r3b+J2lPsFovFsqhL9Eey7Pj0R/RH9EcsM1uq/wIAMEf9UNQL051h++Ccw0mSVNX5SU5trb123rEAAGtPf4QhMWcFwAC0G69h/c15x5IkVbUl3QzjfzbvWACA2dAfYUiMrADmrqpukj0kT1t3qzAAgDWjPwLDYmQFMASvy+73Vr9h6SdoAgBYS/ojMCBGVgBz1zf+e7rl1/lt13uqAwCsKv0RGBbJCgAAAGBQXAYCAAAADIpkBQAAADAokhUAAADAoEhWAAAAAIMiWQEAAAAMyv8HTRjzpSyNCD8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1296x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"if l_flv[l_index]=='mu':\n",
" lower_MC = 1.8\n",
" upper_MC = 2.1\n",
" lower_data = 1.0\n",
" upper_data = 3.0\n",
" \n",
"if l_flv[l_index]=='e':\n",
" lower_MC = 1.6\n",
" upper_MC = 2.15\n",
" lower_data = 1.6\n",
" upper_data = 2.4\n",
"\n",
"#plt.suptitle('Reconstructed D_s mass', fontsize=15)\n",
"plt.subplot(1,2,1)\n",
"label=\"Ds_ConsD_M\"\n",
"plt.title('MC', fontsize=15)\n",
"left_h_Ds=[MC_Ds_tuple_dict[label][i][0]/1000 for i in range(len(MC_Ds_tuple_dict[\"Ds_ConsD_M\"]))]\n",
"label=\"Dplus_ConsD_M\"\n",
"left_h_Dplus=[MC_Dplus_tuple_dict[label][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"]))]\n",
"plt.hist(left_h_Ds,alpha=0.7,bins=70,range=(lower_MC,upper_MC), label='MC after presel cuts',density=False);\n",
"plt.hist(left_h_Dplus,alpha=0.7,bins=70,range=(lower_MC,upper_MC), label='MC after presel cuts',density=False);\n",
"plt.legend(fontsize='12')\n",
"plt.ylabel('# events (a.u.)', fontsize=15)\n",
"plt.xlabel('Reconstructed D_s Mass (GeV)', fontsize=15)\n",
"\n",
"label=\"Ds_ConsD_M\"\n",
"plt.subplot(1,2,2)\n",
"plt.title('Data', fontsize=15)\n",
"right_h=[data_tuple_dict[label][i][0]/1000 for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))]\n",
"plt.hist(right_h,alpha=0.7,bins=70,range=(lower_data,upper_data), color='green',label='Data after presel cuts',density=False);\n",
"plt.legend(fontsize='12')\n",
"plt.xlabel('Reconstructed D_s Mass (GeV)', fontsize=15)\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(18,8)\n",
"plt.savefig('/home/hep/davide/Rphipi/dataMC_after_presel.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#data_tuple_dict_presel={}\n",
"#\n",
"##data_tuple_for_NN=data_tuple_dict\n",
"#\n",
"#indices=[]\n",
"#for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"])):\n",
"# Ds_m = data_tuple_dict[\"Ds_ConsD_M\"][i]\n",
"# if 1890<Ds_m<2050:\n",
"# indices.append(i)\n",
"#\n",
"#for label in branches_needed: \n",
"#\n",
"# data_tuple_dict_presel[label] = data_tuple_dict[label][indices]\n",
"#\n",
"#data_tuple_dict=data_tuple_dict_presel"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"if l_flv[l_index]=='mu':\n",
" lower_Dplus_mass=1830\n",
" upper_Dplus_mass=1910\n",
"\n",
" lower_Ds_mass = 1930\n",
" upper_Ds_mass = 2010\n",
" \n",
"if l_flv[l_index]=='e':\n",
" lower_Dplus_mass=1810\n",
" upper_Dplus_mass=1900\n",
"\n",
" lower_Ds_mass = 1900\n",
" upper_Ds_mass = 2000\n",
"\n",
"#Retrieve mc signal and data bkg events\n",
"\n",
"data_bkg_indices=[]\n",
"MC_Ds_sig_indices=[]\n",
"MC_Dplus_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",
" m = data_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the out of signal regions\n",
" if 0<m<lower_Dplus_mass or upper_Dplus_mass < m < lower_Ds_mass or upper_Ds_mass < m:\n",
" #if 0<m<lower_Ds_mass or upper_Ds_mass < m:\n",
" data_bkg_indices.append(i)\n",
" \n",
"for i in range(len(MC_Ds_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",
" m = MC_Ds_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the signal regions\n",
" #if lower_Dplus_mass< m <upper_Dplus_mass or lower_Ds_mass< m <upper_Ds_mass:\n",
" if lower_Ds_mass< m <upper_Ds_mass:\n",
" MC_Ds_sig_indices.append(i) \n",
" \n",
"for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"])):\n",
" \n",
" #retrieving the Ds reconstructed mass\n",
" #if lower_phi_mass<MC_tuple_dict[\"phi_M\"][i]<lower_phi_mass:\n",
" m = MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"][i]\n",
" \n",
" #selecting the signal regions\n",
" #if lower_Dplus_mass< m <upper_Dplus_mass or lower_Ds_mass< m <upper_Ds_mass:\n",
" if lower_Dplus_mass< m <upper_Dplus_mass:\n",
" MC_Dplus_sig_indices.append(i)\n",
" \n",
"#Create the dict tuples with all MC signal and data bkg events\n",
"\n",
"data_tuple_bkg={}\n",
"MC_Ds_tuple_sig ={}\n",
"MC_Dplus_tuple_sig ={}\n",
"\n",
"for label in return_branches(mother_index=0): \n",
"\n",
" data_tuple_bkg[label] = data_tuple_dict[label][data_bkg_indices]\n",
" MC_Ds_tuple_sig[label] = MC_Ds_tuple_dict[label][MC_Ds_sig_indices]\n",
" \n",
"for label in return_branches(mother_index=1): \n",
" MC_Dplus_tuple_sig[label] = MC_Dplus_tuple_dict[label][MC_Dplus_sig_indices]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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 \"IP own pv\" or \"FD own pv\" or \"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=0.9998750\n",
" upper_limit=1.\n",
" \n",
" if \"PT\" in variable:\n",
" lower_limit=60.\n",
" upper_limit=0.\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",
" \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=15)\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",
" if \"CHI2\" in variable:\n",
" plt.xlabel(\"CHI2/NDOF\", fontsize=12)\n",
"\n",
" plt.legend(fontsize=15)\n",
" fig = plt.gcf()\n",
" fig.set_size_inches(16,8)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"plt.subplot(1,2,1)\n",
"MC_Ds_endvtx_chi2ratio=MC_Ds_tuple_dict[\"Ds_ENDVERTEX_CHI2\"]/MC_Ds_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)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Dplus_endvtx_chi2ratio=MC_Dplus_tuple_dict[\"Dplus_ENDVERTEX_CHI2\"]/MC_Dplus_tuple_dict[\"Dplus_ENDVERTEX_NDOF\"]\n",
"data_Dplus_endvtx_chi2ratio=data_tuple_bkg[\"Ds_ENDVERTEX_CHI2\"]/data_tuple_bkg[\"Ds_ENDVERTEX_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Dplus\", variable=\"END VTX CHI2\", \n",
" MC_sig=MC_Dplus_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.05, width_data=0.06)\n",
"\n",
"plt.savefig('/home/hep/davide/Rphipi/endvtx_chi2.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"plt.subplot(1,2,1)\n",
"MC_Ds_IP_ownpv=MC_Ds_tuple_dict[\"Ds_IP_OWNPV\"]\n",
"data_Ds_IP_ownpv=data_tuple_bkg[\"Ds_IP_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Ds\", variable=\"Ds IP own pv\", \n",
" MC_sig=MC_Ds_IP_ownpv, data_bkg=data_Ds_IP_ownpv, \n",
" width_MC=0.001, width_data=0.002)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Dplus_IP_ownpv=MC_Dplus_tuple_dict[\"Dplus_IP_OWNPV\"]\n",
"data_Ds_IP_ownpv=data_tuple_bkg[\"Ds_IP_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Dplus\", variable=\"Ds IP own pv\", \n",
" MC_sig=MC_Dplus_IP_ownpv, data_bkg=data_Ds_IP_ownpv, \n",
" width_MC=0.001, width_data=0.002)\n",
"\n",
"plt.savefig('/home/hep/davide/Rphipi/ip_ownpv.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"plt.subplot(1,2,1)\n",
"#Retrieve data from needed branch\n",
"MC_Ds_endvtx_chi2ratio=MC_Ds_tuple_dict[\"phi_ENDVERTEX_CHI2\"]/MC_Ds_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)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Dplus_endvtx_chi2ratio=MC_Dplus_tuple_dict[\"phi_ENDVERTEX_CHI2\"]/MC_Dplus_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_Dplus_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.07, width_data=0.08)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"plt.subplot(1,2,1)\n",
"MC_Ds_ownpv_chi2ratio=MC_Ds_tuple_dict[\"Ds_OWNPV_CHI2\"]/MC_Ds_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=0.008, width_data=0.008)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Dplus_ownpv_chi2ratio=MC_Dplus_tuple_dict[\"Dplus_OWNPV_CHI2\"]/MC_Dplus_tuple_dict[\"Dplus_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=\"Dplus\", variable=\"Own PV CHI2\", \n",
" MC_sig=MC_Dplus_ownpv_chi2ratio, data_bkg=data_Ds_ownpv_chi2ratio,\n",
" width_MC=0.01, width_data=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"##Retrieve data from needed branch\n",
"#plt.subplot(1,2,1)\n",
"#MC_Ds_ownpv_chi2ratio=MC_Ds_tuple_dict[\"phi_OWNPV_CHI2\"]/MC_Ds_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)\n",
"#\n",
"#plt.subplot(1,2,2)\n",
"#MC_Ds_ownpv_chi2ratio=MC_Dplus_tuple_dict[\"phi_OWNPV_CHI2\"]/MC_Dplus_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": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"plt.subplot(1,2,1)\n",
"\n",
"MC_Ds_pT=MC_Ds_tuple_sig[\"Ds_PT\"]/1000\n",
"data_Ds_pT=data_tuple_bkg[\"Ds_PT\"]/1000\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Dplus\", variable=\"PT\", \n",
" MC_sig=MC_Ds_pT, data_bkg=data_Ds_pT,\n",
" width_MC=.3, width_data=.3)\n",
"plt.subplot(1,2,2)\n",
"\n",
"MC_Dplus_pT=MC_Dplus_tuple_sig[\"Dplus_PT\"]/1000\n",
"data_Ds_pT=data_tuple_bkg[\"Ds_PT\"]/1000\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\", variable=\"PT\", \n",
" MC_sig=MC_Dplus_pT, data_bkg=data_Ds_pT,\n",
" width_MC=.3, width_data=.3)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"MC_Ds_DIRA_ownpv=MC_Ds_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=\"Dplus\", 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",
"plt.subplot(1,2,2)\n",
"MC_Dplus_DIRA_ownpv=MC_Dplus_tuple_sig[\"Dplus_DIRA_OWNPV\"]\n",
"data_Ds_DIRA_ownpv=data_tuple_bkg[\"Ds_DIRA_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Dplus\", variable=\"DIRA Own PV\", \n",
" MC_sig=MC_Dplus_DIRA_ownpv, data_bkg=data_Ds_DIRA_ownpv,\n",
" width_MC=0.000001, width_data=0.000001)\n",
"plt.savefig('/home/hep/davide/Rphipi/dira_ownpv.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(2,1,1)\n",
"MC_probNNmu=MC_Ds_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)\n",
"plt.subplot(2,1,2)\n",
"MC_probNNmu=MC_Dplus_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=\"Dplus\",variable=\"probNN mu\", \n",
" MC_sig=MC_probNNmu, data_bkg=data_probNNmu,\n",
" width_MC=0.01, width_data=0.01)\n",
"plt.savefig('/home/hep/davide/Rphipi/probnn_mu.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"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)\n",
"\n",
"#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)\n",
"\n",
"#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": 27,
"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": 28,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/MC_for_NN_Ds_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_Ds_tuple_sig, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
" \n",
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/MC_for_NN_Dplus_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_Dplus_tuple_sig, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
" \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",
" \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": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14066"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_tuple_bkg[\"Ds_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1974"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Dplus_tuple_sig[\"Dplus_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1466"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Ds_tuple_sig[\"Ds_ConsD_M\"].shape[0]"
]
},
{
"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